output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Soil Moisture & Rainfall Data Analysis (SENTINEL-PAGASA BUCAF AWS-Project SARAi)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Import dependencies**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**show bar graph values function**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for showing values for each bar in bar plots\n",
    "def show_values_on_bars(axs, orient='v',space=0, num=2):\n",
    "    def _show_on_single_plot(ax):  \n",
    "        if orient == 'v':\n",
    "            for p in ax.patches:\n",
    "                if num == 4:\n",
    "                    _x = p.get_x() + p.get_width() / 2\n",
    "                    _y = p.get_y() + p.get_height() + space\n",
    "                    value = '{:.4f}'.format(p.get_height())\n",
    "                    ax.text(_x, _y, value, ha=\"center\")\n",
    "                if num == 2:\n",
    "                    _x = p.get_x() + p.get_width() / 2\n",
    "                    _y = p.get_y() + p.get_height() + space\n",
    "                    value = '{:.2f}'.format(p.get_height())\n",
    "                    ax.text(_x, _y, value, ha=\"center\")\n",
    "                else:\n",
    "                    _x = p.get_x() + p.get_width() / 2\n",
    "                    _y = p.get_y() + p.get_height() + space\n",
    "                    value = '{:.0f}'.format(p.get_height())\n",
    "                    ax.text(_x, _y, value, ha=\"center\")\n",
    "        if orient == 'h':\n",
    "            for p in ax.patches:\n",
    "                if num == 4:\n",
    "                    _x = p.get_x() + p.get_width() + float(space)\n",
    "                    _y = p.get_y() + p.get_height() / 2\n",
    "                    value = '{:.4f}'.format(p.get_width())\n",
    "                    ax.text(_x, _y, value, ha=\"left\")\n",
    "                else:\n",
    "                    _x = p.get_x() + p.get_width() + float(space)\n",
    "                    _y = p.get_y() + p.get_height() / 2\n",
    "                    value = '{:.2f}'.format(p.get_width())\n",
    "                    ax.text(_x, _y, value, ha=\"left\")\n",
    "\n",
    "    if isinstance(axs, np.ndarray):\n",
    "        for idx, ax in np.ndenumerate(axs):\n",
    "            _show_on_single_plot(ax)\n",
    "    else:\n",
    "        _show_on_single_plot(axs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _**SENTINEL (Experimental dataset)**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Yearly Average / Total**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Soil_Moisture</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Brgy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2016</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.480053</td>\n",
       "      <td>4.320474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.483753</td>\n",
       "      <td>4.353780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.549886</td>\n",
       "      <td>4.948977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.501046</td>\n",
       "      <td>4.509412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.539041</td>\n",
       "      <td>4.851368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.552781</td>\n",
       "      <td>4.975027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.522425</td>\n",
       "      <td>4.701826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2017</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.509811</td>\n",
       "      <td>6.117738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.512896</td>\n",
       "      <td>6.154749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.521941</td>\n",
       "      <td>6.263290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.505594</td>\n",
       "      <td>6.067128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.510194</td>\n",
       "      <td>6.122326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.507118</td>\n",
       "      <td>6.085415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.472174</td>\n",
       "      <td>5.666085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2018</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.513405</td>\n",
       "      <td>6.160864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.541549</td>\n",
       "      <td>6.498592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.529628</td>\n",
       "      <td>6.355532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.446757</td>\n",
       "      <td>5.361080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.525120</td>\n",
       "      <td>6.301446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.508888</td>\n",
       "      <td>6.106659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.508456</td>\n",
       "      <td>6.101470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2019</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.517548</td>\n",
       "      <td>3.105290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.525540</td>\n",
       "      <td>3.153243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.524570</td>\n",
       "      <td>3.147418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.444123</td>\n",
       "      <td>2.664740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.518255</td>\n",
       "      <td>3.109530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.513674</td>\n",
       "      <td>3.082041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.507112</td>\n",
       "      <td>3.042675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2020</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.518832</td>\n",
       "      <td>6.225985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.524550</td>\n",
       "      <td>6.294604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.519228</td>\n",
       "      <td>6.230740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.484644</td>\n",
       "      <td>5.815732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.529497</td>\n",
       "      <td>6.353964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.511992</td>\n",
       "      <td>6.143905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.475696</td>\n",
       "      <td>5.708348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Soil_Moisture          \n",
       "                               mean       sum\n",
       "Year Brgy                                    \n",
       "2016 Agpay                 0.480053  4.320474\n",
       "     Binogsacan Lower      0.483753  4.353780\n",
       "     Binogsacan Upper      0.549886  4.948977\n",
       "     Calzada               0.501046  4.509412\n",
       "     Catomag               0.539041  4.851368\n",
       "     Malabnig              0.552781  4.975027\n",
       "     Morera                0.522425  4.701826\n",
       "2017 Agpay                 0.509811  6.117738\n",
       "     Binogsacan Lower      0.512896  6.154749\n",
       "     Binogsacan Upper      0.521941  6.263290\n",
       "     Calzada               0.505594  6.067128\n",
       "     Catomag               0.510194  6.122326\n",
       "     Malabnig              0.507118  6.085415\n",
       "     Morera                0.472174  5.666085\n",
       "2018 Agpay                 0.513405  6.160864\n",
       "     Binogsacan Lower      0.541549  6.498592\n",
       "     Binogsacan Upper      0.529628  6.355532\n",
       "     Calzada               0.446757  5.361080\n",
       "     Catomag               0.525120  6.301446\n",
       "     Malabnig              0.508888  6.106659\n",
       "     Morera                0.508456  6.101470\n",
       "2019 Agpay                 0.517548  3.105290\n",
       "     Binogsacan Lower      0.525540  3.153243\n",
       "     Binogsacan Upper      0.524570  3.147418\n",
       "     Calzada               0.444123  2.664740\n",
       "     Catomag               0.518255  3.109530\n",
       "     Malabnig              0.513674  3.082041\n",
       "     Morera                0.507112  3.042675\n",
       "2020 Agpay                 0.518832  6.225985\n",
       "     Binogsacan Lower      0.524550  6.294604\n",
       "     Binogsacan Upper      0.519228  6.230740\n",
       "     Calzada               0.484644  5.815732\n",
       "     Catomag               0.529497  6.353964\n",
       "     Malabnig              0.511992  6.143905\n",
       "     Morera                0.475696  5.708348"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel = pd.read_csv('data/SENTINEL.csv')\n",
    "sentinel.Date = pd.to_datetime(sentinel.Date, yearfirst=True)\n",
    "sentinel_yearly = sentinel.groupby([sentinel.Year, sentinel.Brgy]).agg(['mean','sum'])\n",
    "sentinel_yearly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Monthly Average/Total**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Soil_Moisture</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Brgy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Jan</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.531168</td>\n",
       "      <td>2.124674</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.557662</td>\n",
       "      <td>2.230647</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.546078</td>\n",
       "      <td>2.184312</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.531233</td>\n",
       "      <td>2.124933</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.535628</td>\n",
       "      <td>2.142511</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Dec</th>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.548965</td>\n",
       "      <td>2.744825</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.478323</td>\n",
       "      <td>2.391613</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.538790</td>\n",
       "      <td>2.693949</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.546018</td>\n",
       "      <td>2.730088</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.514544</td>\n",
       "      <td>2.572722</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Soil_Moisture              id\n",
       "                               mean       sum  mean\n",
       "Date Brgy                                          \n",
       "Jan  Agpay                 0.531168  2.124674   1.0\n",
       "     Binogsacan Lower      0.557662  2.230647   1.0\n",
       "     Binogsacan Upper      0.546078  2.184312   1.0\n",
       "     Calzada               0.531233  2.124933   1.0\n",
       "     Catomag               0.535628  2.142511   1.0\n",
       "...                             ...       ...   ...\n",
       "Dec  Binogsacan Upper      0.548965  2.744825  12.0\n",
       "     Calzada               0.478323  2.391613  12.0\n",
       "     Catomag               0.538790  2.693949  12.0\n",
       "     Malabnig              0.546018  2.730088  12.0\n",
       "     Morera                0.514544  2.572722  12.0\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel['id'] = sentinel.Date.dt.month\n",
    "sentinel_monthly = sentinel.groupby([sentinel.Date.dt.strftime('%b'),sentinel.Brgy]).agg(['mean','sum']).drop(labels='Year', axis=1)\n",
    "sentinel_monthly = sentinel_monthly.sort_values(by=[('id','mean'),'Brgy'])\n",
    "sentinel_monthly = sentinel_monthly.drop(columns=[('id','sum')],axis=1)\n",
    "sentinel_monthly\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Overall Average per Brgy**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brgy</th>\n",
       "      <th>Soil_Moisture</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agpay</td>\n",
       "      <td>0.508438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Binogsacan Lower</td>\n",
       "      <td>0.518725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Binogsacan Upper</td>\n",
       "      <td>0.528352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calzada</td>\n",
       "      <td>0.478786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Catomag</td>\n",
       "      <td>0.524287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Malabnig</td>\n",
       "      <td>0.517511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Morera</td>\n",
       "      <td>0.494518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Brgy  Soil_Moisture\n",
       "0             Agpay       0.508438\n",
       "1  Binogsacan Lower       0.518725\n",
       "2  Binogsacan Upper       0.528352\n",
       "3           Calzada       0.478786\n",
       "4           Catomag       0.524287\n",
       "5          Malabnig       0.517511\n",
       "6            Morera       0.494518"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_avg_brgy = sentinel.drop(labels=['Year','id'], axis=1).groupby(sentinel.Brgy).mean().reset_index()\n",
    "sentinel_avg_brgy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Monthly average of Brgy per year**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Soil_Moisture</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th>Brgy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Apr</th>\n",
       "      <th>Agpay</th>\n",
       "      <td>0.534529</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <td>0.402420</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.551414</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.542972</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sep</th>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <td>0.501257</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calzada</th>\n",
       "      <td>0.461235</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catomag</th>\n",
       "      <td>0.525270</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malabnig</th>\n",
       "      <td>0.536276</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morera</th>\n",
       "      <td>0.495253</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Soil_Moisture   id\n",
       "Date Date Brgy                                \n",
       "2016 Apr  Agpay                  0.534529  4.0\n",
       "          Binogsacan Lower       0.402420  4.0\n",
       "          Binogsacan Upper       0.551414  4.0\n",
       "          Calzada                0.545455  4.0\n",
       "          Catomag                0.542972  4.0\n",
       "...                                   ...  ...\n",
       "2020 Sep  Binogsacan Upper       0.501257  9.0\n",
       "          Calzada                0.461235  9.0\n",
       "          Catomag                0.525270  9.0\n",
       "          Malabnig               0.536276  9.0\n",
       "          Morera                 0.495253  9.0\n",
       "\n",
       "[357 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_avg_brgy_monthly_year = sentinel.groupby([sentinel.Date.dt.year, sentinel.Date.dt.strftime('%b'), sentinel.Brgy]).mean().drop(labels='Year',axis=1)\n",
    "#avg_brgy_monthly_year = sentinel_avg_brgy_monthly_year.sort_values(by=['Year', 'id'])\n",
    "#avg_brgy_monthly_year = avg_brgy_monthly_year.rename_axis(index={'Date':'year', 'Date':'month'})\n",
    "#avg_brgy_monthly_year.to_csv('SENTINEL_brgy_monthly_year.csv')\n",
    "sentinel_avg_brgy_monthly_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _**PAGASA BUCAF AWS (Rainfall dataset)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>date</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>81.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1827 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  rain\n",
       "0    2016-01-01  11.3\n",
       "1    2016-01-02   7.0\n",
       "2    2016-01-03   0.0\n",
       "3    2016-01-04   0.0\n",
       "4    2016-01-05  19.2\n",
       "...         ...   ...\n",
       "1822 2020-12-27   NaN\n",
       "1823 2020-12-28   NaN\n",
       "1824 2020-12-29   NaN\n",
       "1825 2020-12-30   NaN\n",
       "1826 2020-12-31  81.8\n",
       "\n",
       "[1827 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aws = pd.read_csv('data/AWS.csv')\n",
    "aws.rename(\n",
    "    columns={'DATE':'date','RAINFALL':'rain'},\n",
    "    inplace=True\n",
    ")\n",
    "aws.rain = aws.rain.str.replace('T','0.025')\n",
    "aws.rain = pd.to_numeric(aws.rain, errors='coerce')\n",
    "aws.date = pd.to_datetime(aws.date, format='%d/%m/%Y')\n",
    "aws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Monthly Average+Total**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "aws_monthly_avg = aws.groupby([aws.date.dt.month]).mean().reset_index()\n",
    "aws_monthly_avg.date = pd.Series(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
    "aws_monthly_total = aws.groupby([aws.date.dt.month]).sum().reset_index()\n",
    "aws_monthly_total.date = pd.Series(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>date</th>\n",
       "      <th>avg</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>6.751774</td>\n",
       "      <td>1046.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>3.576585</td>\n",
       "      <td>507.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>2.334150</td>\n",
       "      <td>357.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>1.365833</td>\n",
       "      <td>204.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>3.967742</td>\n",
       "      <td>615.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>5.768151</td>\n",
       "      <td>842.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>9.105444</td>\n",
       "      <td>1129.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>8.643167</td>\n",
       "      <td>1296.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>8.364833</td>\n",
       "      <td>1254.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>13.544355</td>\n",
       "      <td>2099.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>9.602333</td>\n",
       "      <td>1440.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>18.921600</td>\n",
       "      <td>2365.200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date        avg     total\n",
       "0   Jan   6.751774  1046.525\n",
       "1   Feb   3.576585   507.875\n",
       "2   Mar   2.334150   357.125\n",
       "3   Apr   1.365833   204.875\n",
       "4   May   3.967742   615.000\n",
       "5   Jun   5.768151   842.150\n",
       "6   Jul   9.105444  1129.075\n",
       "7   Aug   8.643167  1296.475\n",
       "8   Sep   8.364833  1254.725\n",
       "9   Oct  13.544355  2099.375\n",
       "10  Nov   9.602333  1440.350\n",
       "11  Dec  18.921600  2365.200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aws_monthly = pd.merge(aws_monthly_avg, aws_monthly_total,on=\"date\")\n",
    "aws_monthly.rename(\n",
    "    columns={'rain_x':'avg','rain_y':'total'},inplace=True\n",
    ")\n",
    "aws_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(ncols=2,figsize=(22,7))\n",
    "\n",
    "# avg rain\n",
    "ax1 = sns.lineplot(x='date', y='avg', data=aws_monthly, color='#8BC34A', linewidth=2, ax=axs[0])\n",
    "axs[0].set_title('avg rain (2016-2020)', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].set_yticks(np.arange(0,22.5,2.5))\n",
    "axs[0].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "# total rain\n",
    "ax2 = sns.barplot(x='date', y='total', data=aws_monthly, color='#F44336', linewidth=2, ax=axs[1])\n",
    "axs[1].set_title('total rain (2016-2020)', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].set_yticks(np.arange(0,3000,500))\n",
    "axs[1].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1], orient='v', num=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Yearly Average+Total**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aws_yearly_avg = aws.groupby([aws.date.dt.year]).mean().reset_index()\n",
    "aws_yearly_total = aws.groupby([aws.date.dt.year]).sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>avg</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>6.892418</td>\n",
       "      <td>2522.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.736849</td>\n",
       "      <td>2823.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>7.869692</td>\n",
       "      <td>2557.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>6.125890</td>\n",
       "      <td>2235.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>9.037650</td>\n",
       "      <td>3018.575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date       avg     total\n",
       "0  2016  6.892418  2522.625\n",
       "1  2017  7.736849  2823.950\n",
       "2  2018  7.869692  2557.650\n",
       "3  2019  6.125890  2235.950\n",
       "4  2020  9.037650  3018.575"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aws_yearly = pd.merge(aws_yearly_avg,aws_yearly_total, on='date')\n",
    "aws_yearly.rename(\n",
    "    columns={'rain_x':'avg','rain_y':'total'},inplace=True\n",
    ")\n",
    "aws_yearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yP/PWruObz7zIyk2bGNSxPZePPpjmRUW8smwFX3vqBVZv2kzzoiZ0adGCB447nI0VFZzw4OMAtGnalF+MGMqgju3zemxb9Lv57p3u+0HjTucklCRJUmOwEfhUSmltRDQFnoqIfwLfBn6XUrolIv5ILvy7OntckVLaNyLGAZcBp0fEIGAcMBjoCfwrIvZLKVXk46AkSdKeGdChPfccc9j72vu0ac0dR33ife1DO3fk6bHvv0KweVERDx73qdoosd7wdmNJkiQ1eClnbfayafaTgE8Bt2ftNwAnZs/HZq/Jth8REZG135JS2phSehOYA4ys/SOQJEnKL0NCSZIkNQoRURQRU4ElwERgLrAypVSedSkFtsxK3guYD5BtX0XuluSq9u28p/pnjY+IyRExuaysrBaORpIkqW4ZEkqSJKlRSClVpJSGASXkrv4bUIufdU1KaURKaUTXrl1r62MkSZLqjCGhJEmSGpWU0krgUWAM0CEitszDXQIsyJ4vAHoDZNvbk1vApKp9O++RJElqtAwJJUmS1OBFRNeI6JA9bwkcCcwkFxaemnU7G9iy/N+E7DXZ9kdSSilrHxcRzbOVkfsDk+rkICRJkvLI1Y0lSZLUGPQAboiIInJfhN+WUro3ImYAt0TEfwJTgGuz/tcCf42IOcBycisak1KaHhG3ATOAcuA8VzaWJEmFwJBQkiRJDV5K6RXgoO20v8F2VidOKW0APreDff0S+GVN1yhJklSfebuxJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkNRDz58/n8MMPZ9CgQQwePJgrr7wSgKlTpzJ69GiGDRvGiBEjmDRpEgA33ngjQ4cO5YADDuDQQw/l5Zdf3mp/FRUVHHTQQRx//PF1fiySpPqlON8FSJIkSZJ2TnFxMZdffjkHH3wwa9asYfjw4Rx55JF873vf46KLLuLYY4/l/vvv53vf+x6PPfYYe++9N48//jgdO3bkn//8J+PHj+f555+v2t+VV17JwIEDWb16dR6PSpJUH3gloSRJkiQ1ED169ODggw8GoG3btgwcOJAFCxYQEVVB36pVq+jZsycAhx56KB07dgRg9OjRlJaWVu2rtLSU++67j3PPPbeOj0KSVB95JaEkSZIkNUBvvfUWU6ZMYdSoUVxxxRUcffTRfOc736GyspJnnnnmff2vvfZajj322KrX3/zmN/n1r3/NmjVr6rJsSVI95ZWEkiRJktTArF27llNOOYUrrriCdu3acfXVV/O73/2O+fPn87vf/Y5zzjlnq/6PPvoo1157LZdddhkA9957L926dWP48OH5KF+SVA8ZEkqSJElSA7J582ZOOeUUzjrrLE4++WQAbrjhhqrnn/vc56oWLgF45ZVXOPfcc7n77rvp3LkzAE8//TQTJkygb9++jBs3jkceeYTPf/7zdX8wkqR6w5BQkiRJkhqIlBLnnHMOAwcO5Nvf/nZVe8+ePXn88ccBeOSRR+jfvz8A8+bN4+STT+avf/0r++23X1X/Sy65hNLSUt566y1uueUWPvWpT/G3v/2tbg9GklSvOCehJEmSJO2EuWeMzXcJTC5bxl8ffpr927dl0A1/BuCCoQO5uFdnvn7yiVSkRPMmTfjZiKHMPWMsP5g0lbL5Czn3M7m5CIsiuOvoT261zwWLl7L+tbn14vj63Xx3vkuQpIJlSChJkiRJDcSIrp2ZM+6E7W67e5vwD+CSkcO4ZOSwD9zn6O5dGN29S02UJ0lqwLzdWJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJH2AisryfJcgSZIk1bp6ERJGxPkRMS0ipkfEN7ezPSLi9xExJyJeiYiD81CmJEkqIJWpgsfevJY7ZlxMZarIdzmSJElSrSrOdwERMQT4CjAS2AQ8EBH3ppTmVOt2LNA/+xkFXJ09SpIk1biN5eu577X/4o0Vk2gSRbyzehYl7QfnuyxJkiSp1tSHKwkHAs+nlNanlMqBx4GTt+kzFvhLynkO6BARPeq6UEmS1Pit3LCIm175Dm+smESL4jacOvgXBoSSJElq9OpDSDgN+HhEdI6IVsBxQO9t+vQC5ld7XZq1bSUixkfE5IiYXFZWVmsFS5Kkxql01TRunPotlq1/m04tSzjrwN/Rp8OB+S5LkiRJqnV5v904pTQzIi4DHgLWAVOB3Zr4J6V0DXANwIgRI1JN1ShJkhq/Vxc9xMS5f6AyldO3w8F8dsD3aV7cOt9lSZIkSXUi7yEhQErpWuBagIj4FbkrBatbwNZXF5ZkbZIkSXukMlXw+Jt/5sV37gTg4J5jOWzvc2gSRXmuTJIkSao79SIkjIhuKaUlEdGH3HyEo7fpMgH4ekTcQm7BklUppYV1XackSWpcNpav597XLuPNFZNpEkV8ut//Y+hex+S7LEmSJKnO1YuQEPhHRHQGNgPnpZRWRsRXAVJKfwTuJzdX4RxgPfClvFUqSZIahZUbFnLnjJ+zbP08WhS35YQBP6RPh6H5LkuSJEnKi3oREqaUPr6dtj9We56A8+q0KEmS1GjNX/UqE2b+infLV9OpZW9OHnQRHVr2yHdZkiRJUt7Ui5BQkiSprryy6EH+NfcPVKYK9u44guP3/54LlEiSJKngGRJKkqSCkFug5FpefOduAIb3PJFP7v1lFyiRJEmSMCSUJEkFYGP5Ou557TLeWvEiTaKYI/v9Pw7Y6+h8lyVJkiTVG4aEkiSpUVv57kLumPEzlr87n5bF7Thh4I/o3X5IvsuSJEmS6hVDQkmS1GjNW/kKE2b9ig3la+jcqg8nDfopHVq4QIkkSZK0LUNCSZLUKL286J88PPfqaguUXEjz4lb5LkuSJEmql5rkuwBJkqSaVJkqeGTu/zJxzlVUpgpG9DqJkwb91IBQkpRX8+fP5/DDD2fQoEEMHjyYK6+8EoCLL76YXr16MWzYMIYNG8b9998PwFtvvUXLli2r2r/61a9W7evmm2/mgAMOYOjQoRxzzDEsXbo0L8ckqXHxSkJJktRobChfy72zLuOtlS/lFijZ9+sc0P3IfJclSRLFxcVcfvnlHHzwwaxZs4bhw4dz5JG5v6O+9a1v8Z3vfOd97+nXrx9Tp07dqq28vJzzzz+fGTNm0KVLF773ve9x1VVXcfHFF9fBUUhqzAwJJUlSo7Di3Xe4c8bPWP5uKS2L2zF24I8ocYESSVI90aNHD3r0yM2L27ZtWwYOHMiCBQt2eT8pJVJKrFu3js6dO7N69Wr23Xffmi5XUgHydmNJktTgzVv5Mje+/C2Wv1tKl1Yf4axhvzMglCTVW2+99RZTpkxh1KhRAFx11VUMHTqUL3/5y6xYsaKq35tvvslBBx3EJz/5SZ588kkAmjZtytVXX80BBxxAz549mTFjBuecc05ejkNS42JIKEmSGrSpC+/n9uk/YUP5WvbpOJIzh/6GDi32yndZUr21q/OiTZw4keHDh3PAAQcwfPhwHnnkkap9HXPMMRx44IEMHjyYr371q1RUVOTlmKSGZO3atZxyyilcccUVtGvXjq997WvMnTuXqVOn0qNHDy644AIgd+XhvHnzmDJlCr/97W8588wzWb16NZs3b+bqq69mypQpvPPOOwwdOpRLLrkkz0clqTHwdmNJktQgVaYKHn3jGqYsvBeAQ3qdwsf7nk2TKMpzZVL9tqvzonXp0oV77rmHnj17Mm3aNI4++uiqWyRvu+022rVrR0qJU089lb///e+MGzeuzo9Jaig2b97MKaecwllnncXJJ58MQPfu3au2f+UrX+H4448HoHnz5jRv3hyA4cOH069fP15//XVSSkBuvkKA0047jUsvvbQuD0NSI2VIKEmSGpwN5Wu4Z9ZlvL1yCkXZAiVDXKBE2im7Oi/aQQcdVPV88ODBvPvuu2zcuJHmzZvTrl07ILeQwqZNm4iI2i1easBSSpxzzjkMHDiQb3/721XtCxcurPqdvPPOOxkyJDddRllZGZ06daKoqIg33niD2bNns88++7BhwwZmzJhBWVkZXbt2ZeLEiQwcODAvxySpcTEklCRJDcrydxdw54yfseLdBbRs2p4TB/6YXu0G5bssqUGqPi/a008/zVVXXcVf/vIXRowYweWXX07Hjh236v+Pf/yDgw8+uOrqJoCjjz6aSZMmceyxx3LqqafW9SFIO2XuGWPzXQKTy5bx14efZv/2bRl0w58BuGDoQO55ewEzV64igF6tW/GfhxzI3DPG8sD8d7ji1ddo2iSICC4asj8rzvsSAP+vpBtj9t2H4gh6tm7Fr0cdVC+Osd/Nd+e7BEl7wJBQkiQ1GG+vnMqEmb9iY8U6urTqy0mDfkr7Ft0//I2S3md786L95Cc/ISL4yU9+wgUXXMB1111X1X/69OlceOGFPPTQQ1vt58EHH2TDhg2cddZZPPLII1W3Lkva2oiunZkz7oT3tR/Wc/t/jx3TuyfH9O653W1n7tuXM/ftW5PlSZILl0iSpIZhysJ7uX3aT9hYsY5+nUZx5tD/MiCUdtOO5kUrKiqiSZMmfOUrX2HSpElV/UtLSznppJP4y1/+UjUPWnUtWrRg7Nix3H23VxFJktRQGRJKkqR6raKynH/N/R8enns1iUpGlpzKiQN/TLPiVvkuTWqQPmhetC2qz4u2cuVKPvOZz3DppZfy0Y9+tKrP2rVrq95TXl7Offfdx4ABA+roKCRJUk3zdmNJklRvbShfw4SZlzBv1csURTFH7fsNBnc/It9lqR6KiN7AX4DuQAKuSSldGREXA18ByrKuP0wp3Z+95wfAOUAF8I2U0oNZ+zHAlUAR8KeUUo0tG1of5gzb1XnR/jD9dWbPmM2Pz/kSPz4nNx/a9YeNIZH4yhOT2FRRQSUwulsXjmQ9c5+emL+DwznRJEnaXYaEkiSpXlq+vjS3QMmGd2jVtANjB/7IBUr0QcqBC1JKL0VEW+DFiNiSVv0upfSb6p0jYhAwDhgM9AT+FRH7ZZv/ABwJlAIvRMSElNKMOjmKOrCr86KdN3g/zhu833a33XnUJ2q0NkmSlD+GhJIkqd55a8UU7pl1CRsr1tG11d6cOOintG/RLd9lqR5LKS0EFmbP10TETKDXB7xlLHBLSmkj8GZEzAFGZtvmpJTeAIiIW7K+jSYklCRJ2h7nJJQkSfXKlHfu5R/Tf8rGinXs22kMZxz4XwaE2iUR0Rc4CHg+a/p6RLwSEddFRMesrRcwv9rbSrO2HbVv+xnjI2JyREwuKyvbdrMkSVKDY0goSZLqhYrKcibO+QMPv5FboGRUyWmMHfhDmhW1zHdpakAiog3wD+CbKaXVwNVAP2AYuSsNL6+Jz0kpXZNSGpFSGtG1a9ea2KUkSVJeebuxJEnKu3c3r+GeWVsWKGnK0f2/waBun8p3WWpgIqIpuYDwxpTSHQAppcXVtv8fcG/2cgHQu9rbS7I2PqBdkiSp0fJKQkmSlFfL1s/nxpe/zbxVL9OqaQdOP+BSA0LtsogI4FpgZkrpt9Xae1TrdhIwLXs+ARgXEc0jYm+gPzAJeAHoHxF7R0QzcoubTKiLY5AkSconrySUJEl589aKl7hn1qVsrFhHt9b7cOLAn9DO+Qe1ez4K/BvwakRMzdp+CJwREcOABLwF/DtASml6RNxGbkGScuC8lFIFQER8HXgQKAKuSylNr7vDkCRJyg9DQkmSVOdSSkxZeA+PvvF/JCrp33kMx+73HZoVtch3aWqgUkpPAbGdTfd/wHt+CfxyO+33f9D7JEmSGiNDQkmSVKcqKst5+I2reWXRAwCM7n06H+3zeSKcBUWSJEnKF0NCSZJUZ97dvJoJs37F/FWvUhRNOab/NxnY7bB8lyVJkiQVPENCSZJUJ5atn8edM37Oyg0Lad20IycO+jE92g7Id1mSJEmSMCSUJEl14M0Vk7ln1mVsqlhPt9b9OGnQT2nbvEu+y5IkSZKUMSSUJEm1JqXES+9M4LE3/5QtUPJRjt3v2y5QIkmSJNUzhoSSJKlWVFRu5uG5V/PK4gcBGN17HB/tc5YLlEiSJEn1kCGhJEmqce9uXs3dM39F6epXKW7SjGP6f5MBXT+Z77IkSZIk7YAhoaSCl1KiMpWzuXIj5RUbKa/clHteuYHN1V9XbCQi6NdpFM2LW+e7bKneWrp+HnfO+BmrNiyidbNOnDjwx/Rou3++y5IkSZL0AQwJJdVbKVVSXrlpq5CuvHJjFuBtfF+At71t7z1/b/tW27L3Jip3uq7mRa0Z3utEDu55Ai2K29TifwGp4Xlj+WTufS23QEn3Nvty4sCfuECJJEmS1AAYEkraZZWpgvKKjWzeJmir/jwX1m0J6TZUuzqveki3YbuBXfW+daVJFFPcpBlNmzSnuKh57jH7aVqUPTZpxuqNSyhdPZ1n5t3IiwvuMiyUMiklXnznLh5/8zoSlezX5WMc2/9bNHWBEkmSJKlBMCSUGolduWU293zDDq+42+7VeNWCwIpUXmfHVdykWRbQNc8930GAV9ykWbUwb0fbWuwgCGxGUZOd/+Nw/qppPDvvJuaterkqLDy451iG9xprWKiCVFG5mX/N/R9eXfwQAGN6n8mhfc5wgRJJkiSpATEklGpZSmm7QduWIK68Ihfe1fUts3sm3he0NW3SjOKiFjsI85rRtKjFe8+3CvCabxPuVX9vs3oZMvRuP4TeB/yK0lXTeCYLC5+dfxMvvnMXw6vCwrb5LlOqE+s3r2LCzF9RunpatkDJtxnQ9eP5LkuSJEnSLjIklHbB2o3LKF09nYVrXmNj+br3XalXH26Z/bAr7nLhXrMP3La9q/He29aCoigmIursuOqrkvZDOO2AX1G6ajrPzr+Jt1dO5dn5N/PiO3dzcM8TGN7zRFo2NSxU47V03du5BUo2LqZNs86cOPAn7NW2f77LkiRJkrQbDAmlHUgpsWrjIkpXTaN01XRKV09j5YaFu7WvrW6ZLXrvarr3hXTZtg8L6bYNArfst0kU1fB/Be2MkvaD+Vz7X24VFj43/xZeeufu3G3IhoVqhOYun8R9r/2aTRXv0r1Nf04c+GMXKJEkSZIaMENCKZNSJcvWz6d09bRcMLh6Oms3LduqT7OilvRsN4iSdoNo3bTTNuFe8x0EgfXzllnVvC1h4YLVM3hm3k28vXJKtbDwBIb3PMmwUA1eSonJC+7k8beuAxL7d/kEx/Q/3wVKJEmSpAbOkFAFqzJVsGTtG1uFghvK12zVp2VxO0raD6ak3RB6tR9Mt9b7eLWePlSvdoP43JD/ZMHqGTw772beWvkSz82/lZfemcBBPT7LiF4n0bJpu3yXKe2y8srN/GvOH5i2ZCIAh/Y5izG9z3D6AUmSJKkRMCRUwSiv3MSiNa9noeB0FqyZyeaKd7fq06ZZZ3q3P6AqFOzcsrf/+NVu69VuEKcO+QXvrJ7JM/Nu4q2VL/F86W28tPAeDjYsVAOzfvMq7p75Sxasnk5xk+Ycu9+32L+LC5RIkiRJjYUhoRqtTeXreWfNrKorBReueZ2KtHmrPh1b9KSk/ZCqULB98+6GgqpxPdsNzMLCWTwz/ybeWvHiVmHh8F4n0qpp+3yXKe1Q2bq3uHPGz1m9ZYGSQT9hrzYuUCJJkiQ1JoaEajTe3byaBaunMz9bZGTJ2rkkKqv1CLq22puS9oPp1W4IJe0H06ZZp7zVq8LTs90ATh38cxaumcUz827mzRWTc2HhOxM4qGfuykLDQtU3c5dP4t7Xfs3minfZq81+nDjoJ/7ZKUmSCs78+fP5whe+wOLFi4kIxo8fz/nnn893v/td7rnnHpo1a0a/fv3485//TIcOHZg0aRLjx48HcnM6X3zxxZx00kkArFy5knPPPZdp06YREVx33XWMGTMmn4cnAfUgJIyI/YFbqzXtA/w0pXRFtT6HAXcDb2ZNd6SUfl5HJaqeWrNxaRYKTmPB6uksXf/2VtuDJvRouz+92g2md7sh9Gw3yEUjVC/0aDuAUwb/jIVrXuOZeTfx5orJTCr9O1PeuYeDeh7PiF4nGxYq71JKvLDgDp54689AYkCXT3B0/2/StKh5vkuTJEmqc8XFxVx++eUcfPDBrFmzhuHDh3PkkUdy5JFHcskll1BcXMyFF17IJZdcwmWXXcaQIUOYPHkyxcXFLFy4kAMPPJDPfvazFBcXc/7553PMMcdw++23s2nTJtavX5/vw5OAehASppReA4YBREQRsAC4cztdn0wpHV+HpakeSSmxasMi5me3Di9YPZ2VGxZu1ae4SbP3QsH2Q+jRdgDNilrmqWLpw/Vou/92wsLbmfLOvQzr8RkOKTnFsFB5UV65mYlz/pvpSx4G4KN9/o3RvU93OgZJklSwevToQY8ePQBo27YtAwcOZMGCBRx11FFVfUaPHs3tt98OQKtWraraN2zYUDWOWrVqFU888QTXX389AM2aNaNZs2Z1dBTSB8t7SLiNI4C5KaW3P7SnGrWUKlm6fh6lq6exYNV0SldPZ+2mZVv1aVbUkp7tBtG73RBK2g+he5v+FDdpmqeKpd33Xlj4Os/Ou4k3VrzACwv+wZSF93JQj9yVha2bdch3mSoQ6zatZMKsX7Jg9QyKmzTnuP0uYL8uH813WZIkSfXGW2+9xZQpUxg1atRW7ddddx2nn3561evnn3+eL3/5y7z99tv89a9/pbi4mDfffJOuXbvypS99iZdffpnhw4dz5ZVX0rp167o+DOl96ltIOA64eQfbxkTEy8A7wHdSStO37RAR44HxAH369Km1IlXzKlMFi9fOrbpKsHT1dDaUr9mqT8vidpS0H0xJFgp2bb03TaIoTxVLNa9H2/04efDFLFozm2fm3cQbKyZVhYXDenyGQ3qdYlioWlW27s1sgZIltG3WhRMH/ZTubfrluyxJkqR6Y+3atZxyyilcccUVtGvXrqr9l7/8JcXFxZx11llVbaNGjWL69OnMnDmTs88+m2OPPZby8nJeeukl/vu//5tRo0Zx/vnnc+mll/KLX/wiH4cjbaXehIQR0Qw4AfjBdja/BHwkpbQ2Io4D7gLet6xiSuka4BqAESNGpNqrVnuqvHITC9e8zoLV0yhdNZ0Fa2ayueLdrfq0bdalauXhkvaD6dSyt7e6qSDs1bY/Jw++iEVrZvPs/JuYu3wSkxfcwdSF9xkWqtbMWfYc9732X2yu3ECPtvszduCPXaBEkiSpms2bN3PKKadw1llncfLJJ1e1X3/99dx77708/PDD2/0368CBA2nTpg3Tpk2jpKSEkpKSqqsQTz31VC699NI6Owbpg9SbkBA4FngppbR42w0ppdXVnt8fEf8TEV1SSkvrtELttk3l61mwZmbuKsFV01i45jUqUvlWfTq26FktFBxCu+bdDAVV0PZq25+TBl3EorWzeXbezcxd/ny1sPC4LCzsmO8y1cCllJi04HaefOsGIDGw62Ec3f98ips4N44kSdIWKSXOOeccBg4cyLe//e2q9gceeIBf//rXPP7441vNQ/jmm2/Su3dviouLefvtt5k1axZ9+/alS5cu9O7dm9dee43999+fhx9+mEGDBuXjkKT3qU8h4Rns4FbjiNgLWJxSShExEmgCLNteX9UP6zevYsHqGZSumk7p6mksWTuXRGW1HkHXVnvnbh9uP4SSdoNp7RUr0nbt1aY/Jw36KYvXzuHZeTczZ/lzTF5wJ1MX3s+Bex3HyJKT/f3Rbimv3MRDc65iRrZAycc+8gVGlZzmFzSSJKlemXvG2HyXwOSyZfz14afZv31bBt3wZwAuGDqQn7/0KpsqKvnkfvsCMKxzR35xyIHc+eZ8/nfmHJo2CSKCnw7ej1X/cQ6rgO+1LuLUMaPYXFlJ7zatuWzUsLwfY7+b787r56t+qBchYUS0Bo4E/r1a21cBUkp/BE4FvhYR5cC7wLiUkrcT1yNrNi5lwerpzF81jdLV01m2fuu1Z5pEEXu12b/qKsFe7QbSorhtnqqVGqbubfblxEE/YfHauVlY+CwvvnMnLy+6LwsLTzEs1E5bt2kFd8/8T95ZM4viJs35zH7foX+XQ/NdliRJUr00omtn5ow74X3th/Xsvt3+J+3dm5P27r3dbYM6tueuoz9Zo/VJNaFehIQppXVA523a/ljt+VXAVXVdl7YvpcTKDQspzW4dLl09jVUbFm3Vp7hJM3q0fS8U7NF2AM2KWuSpYqlx6d6mHycO+vE2YeFdvLzofg7c61gOKTnVueT0gZasfYM7Z/6cNRvLaNu8KycN/AndXKBEkiRJKmj1IiRU/ZZSJUvXz6M0W2SkdPU01m1avlWfZkUt6dVuMCXtcrcPd2/Tn+ImTfNUsVQYtoSFS9bO5dn5NzN72bO8+M7dvLzonwzd61hGGhZqO2Yve5b7X/tNtkDJAE4c+COvQJUkSZJkSKj3q0wVLF47t+oqwQWrp7OhfO1WfVoWt6uaS7Ck/RC6tt6bJlGUp4qlwtatTT/GDvwxS9a+kYWFz/DSO3fzyqJ/MrT7MbmwsHnnD9+RGrWUEpNK/86Tb98AwKCuh3NU/2+4QIkkSZIkwJBQ5CauX7jm9apQ8J3VM9lcuWGrPm2bdam28vBgOrXs7cT2Uj3Trc0+jB34I8rWvckz825m9rKneWnhBF5e9E8O3HJloWFhQSqv3MRDs3/PjLJHgeDjH/kCI0s+55/jkiRJkqoYEhagTeXrWbBmZhYKTmfRmteoSOVb9enYslcuEMyuFGzXvJv/mJQaiK6t92bswB9Stu5Nnp13M69XCwuH7pW7srBt8y75LlN1ZN2m5dw185csXDOLpk1a8Jn9v8O+ncfkuyxJkiRJ9YwhYQFYv3kVC1bPqLpScMnaN0hUVusRdG29d1UgWNJusPNTSY1A19Z7c8LAH1K27i2enX8zry99iikL7+GVRQ8wdK+jGVnyOcPCRm7J2rncOfMX1RYo+Snd2uyT77IkSZIk1UOGhI3Qmo1Lt1pkZNn6eVttbxJF7NXmvZWHe7UbSIvitnmqVlJt69q6LycM+AFl697iufm38NrSp5iy8F5eWfQAB+x1DKMMCxul15c+zf2vX0555UZ6th3I2IE/onWzjvkuS5IkSVI9ZUjYwKWUWLlhYdVVgqWrprFq4+Kt+hQ3aUaPtu+Fgj3aDqBZUYs8VSwpX7q27stnB3yf0dXCwqkL7+XVRQ9wQHZlYbvmXfNdpvZQSonnS2/lqbf/CsCgbkdw1L7/4YrzkiRJkj6QIWEDk1IlS9fP2yoUXLd5xVZ9mhW1pFe7wVW3D3dv099/HEqqsiUsHLPubZ6dfwuvLX2SqQvv49VFDzKk+1GM6n2aYWEDtbliIw/N+T0zyx4Dgk/0/SKH9DrFOWUlSZIkfShDwnquorKcJevmVoWCC1bPYEP52q36tCxuV23l4SF0bd2XJlGUp4olNRRdWn+Ezw64kDHrz+C5eTcza+mTvLzofl5d/BAHdD+KUSWfo12LbvkuUztp7abl3DXjFyxa+zpNi1rymf2+w76dR+e7LEmSJEkNhCFhPbO5YiOL1r5etfLwO6tnsrlyw1Z92jbvWm2RkSF0alniVSKSdluXVn04fsCFjF5/Bs/Nv4VZZU9UCwuPZGTJabQ3LKzXFq+dw10zfsGaTUtp17wbJw26iK6t++a7LEmSJEkNiCFhnm0sX887a2Zki4xMZ9Ga16hI5Vv16diyV9VVgiXthviPdUm1okurPhy///cY03scz82/lZllj/Pyon/y6uKJhoX1WPUFSnq1G8QJA35E62Yd8l2WJEmSpAbGkLCOrd+8igVZIFi6ehpL1r5BorJaj6Br672zUDA3r2DrZp3yVq+kwtO5VR8+s/93Gd17XLUrC3Nh4ZDun2ZUyemGhfVASonn5t/C0/P+BsDgbp/myH2/7hy0kiRJknaLIWEtW7NxadWtw6Wrp7Fs/byttjeJIvZqM6Dq9uFe7QbSorhtnqqVpPd0btWbz+z/Xcb0zt2GPLPscV5Z9ADTFk9kSLdPM6r36bRv0T3fZRakzRUbeXD2Fcxa+gQQfLLvlxjR62SnnpAkSZK02wwJa1BKiZUb3nkvFFw1jVUbF2/Vp7hJM3q0HVB1pWCPtgNoVtQiTxVL0ofr1KqE4/b/TtWVhTPLHueVxQ8ybcm/GNzt04zqfRodWuyV7zILxtqNy7hr5n9WLVBy/P7fo1+nkfkuS5IkSVIDZ0i4B1KqZOn6t5m/ahoLslBw3eYVW/VpVtSKXu0GVV0puFeb/hR5K5ikBqgqLOxzBs/Nu4WZZY/x6uIHmb7kXwzudgSjep9uWFjLFq2dzV0zfsHaTcto17w7Jw36qQuUSJIkSaoRhoS7af6qadw98xdsKF+7VXvLpu1zgWC20EjX1n1pEkV5qlKSal6nlr04bv8LGNNnHM/Ov5WZSx7l1cUPMX3Jwwzq9ilG9z6dDi165LvMRue1pU/yz9d/R3nlRkraDeaEgT+iVdP2+S5LkiRJUiNhSLibOrTYiw3la2nbvGvVVYIl7YbQqWWJc0JJKggdW/biuP2+zZjep/Pc/FuZseRRpi2eyPTFDzO42xG5sLClYeGeSinx7PybeWbejQAM6X4kR/Y7z6vSJUmSJNUoQ8Ld1LZ5F8Yfcj3tmnfNdymSlFcdW/bi2P2+nc1ZeCszljzCtCUTmb7EsHBPba7YwAOzr+C1pU8SNOGTe3+Z4T1P9MsoSZIkSTXOkHAPGBBK0ns6tuzJsft9izFZWDh9ycNVYeGW25A7tuyZ7zIbjDUbl3LXzP9k8drZNCtqyfH7X8g+nQ7Jd1mSJEmSGilDQklSjerQsgfH7PdNRvc+nedKb2X64oeZvuRfzFjyCIO6Hc7o3uMMCz/EojWzuXPmz1m3aTntm3fnpEEX0aX1R/JdliRJkqRGzJBQklQrOrTswTH9s7Bw/paw8GFmLHmUgd0OZ0zv0+nYsle+y6x3ZpU9wQOzf0d55SZK2g3hhIE/dIESSZIkSbXOkFCSVKs6tHgvLHx+/m1ZUPgwM5c8ysCuhzG6zzg6GRaSUiXPzLuJZ+ffDMAB3Y/m0/2+5gIlkiRJkuqEIaEkqU50aNGDo/ufz6iqsPBfzCh7hJlljzGw6ycZ3XscnVqV5LvMvNhcsYF/vv5bXl/2NEETDtv7HA7uOdYFSiRJkiTVmSb5LkCSVFg6tNiLo/t/g3OG/x9Dux9NRDCj7FH+/NLXuO+1/2L5+tJ8l1in1mxcyi2vfI/Xlz1Ns6JWnDToIob3cgVjaVdFRO+IeDQiZkTE9Ig4P2vvFBETI2J29tgxa4+I+H1EzImIVyLi4Gr7OjvrPzsizs7XMUmSJNUlQ0JJUl60b9Gdo7aEhXsdQ0Qws+yxqrBw2fr5+S6x1i1c8xp/e/lbLF43l/Yt9uKsAy9nn04j8l2W1FCVAxeklAYBo4HzImIQ8H3g4ZRSf+Dh7DXAsUD/7Gc8cDXkQkXgImAUMBK4aEuwKEmS1JgZEkqS8qp9i+4cte9/cM7wP3HgXscS0aQgwsJZZY9z66vfZ92m5fRufwCfP/B3dG7VJ99lSQ1WSmlhSuml7PkaYCbQCxgL3JB1uwE4MXs+FvhLynkO6BARPYCjgYkppeUppRXAROCYujsSSZKk/HBOQklSvdC+RTeO3PfrjCw5jUmlt/Hq4onMLHuMmWWPM6DrJxjTe1yjCNFSquTpeTfy3PxbABi61zEcsc9XXaBEqkER0Rc4CHge6J5SWphtWgR0z573Aqp/C1Gate2ofdvPGE/uCkT69Gn4fzZJkiQZEkqS6pUtYeGoktN4vvTvvLr4IWaVPc6ssicY0OXjjO5zBl0aaFi4KVugZHa2QMnh+3yFg3p81vkHpRoUEW2AfwDfTCmtrv77lVJKEZFq4nNSStcA1wCMGDGiRvYpSZKUT4aEkqR6qV2Lbhy573mMKvkcz5f+nWmLH2LW0ieYtfRJ9u/yccb0HkeX1h/Jd5k7bfXGMu6a8QuWrJtL86LWHD/gQvbuODzfZUmNSkQ0JRcQ3phSuiNrXhwRPVJKC7PbiZdk7QuA3tXeXpK1LQAO26b9sdqsW5IkqT5wTkJJUr22JSw8Z8SfGNbjMxRFEa8tfYLrp5zHPbMuZem6t/Nd4odauGYWN079FkvWzaVDi56ceeDlBoRSDYvcJYPXAjNTSr+ttmkCsGWF4rOBu6u1fyFb5Xg0sCq7LflB4KiI6JgtWHJU1iZJktSoeSWhJKlBaNe8K5/u9//euw150QO8tvRJXlv6FPt1+Shjep9B19Z9813m+8xc8hgPzL6CirSZ3u2HcsKAH9Kyadt8lyU1Rh8F/g14NSKmZm0/BC4FbouIc4C3gdOybfcDxwFzgPXAlwBSSssj4hfAC1m/n6eUltfJEUiSJOWRIaEkqUFp27wLn+73NUaVfI5JpX/nlUUP8PrSp3h96VPs1+Vj9SYsTKmSp97+K8+X3gbAgXsdx6f2+XeKmvhXr1QbUkpPATua4POI7fRPwHk72Nd1wHU1V50kSVL9579UJEkNUtvmXTii39cYWfI5JpXevnVY2PmjjOlzZt7Cwk0V7/LP1y9n9rJnswVKxnNQj+NdoESSJElSvWVIKElq0HJh4VcZWXLqe2Hhsqd5fdnT9O/8UQ7tcwZdW+9dZ/Ws3rCEO2f+nLJ1b9K8qDWfHfAD+nY8qM4+X5IkSZJ2hyGhJKlR2BIWjsquLHx50T+ZvexpZi97mv6dD2VM7zPo1mafWq3hndUzuWvmf7J+80o6tujJSYMuolOrklr9TEmSJEmqCYaEkqRGpU3zznyq37/nrixc8A9eWfRPZi97htnLnqF/5zFZWNivxj93xpJHeHD276lIm+nT/kA+O+AHLlAiSZIkqcEwJJQkNUptmnfmU/uMZ2TJqbxQdWXhs8xe9iz7dhrDoX1qJixMqZIn3/4Lk0r/DsCwHp/h8L3Hu0CJJEmSpAbFf8FIkhq1Ns06cfg+4zmk5FReKP0HLy+6nznLn2XO8mfZt9NoxvQ5k+67GRZuqniX+1/7DXOWP0fQhE/1+3cO6nF8DR+BJEmSJNU+Q0JJUkHIhYVfYWTJKUwqvSMLC59jzvLndissXLVhCXfN+Dll63MLlJww8Ad8pIMLlEiSJElqmAwJJUkFpXWzThy+z7mMLDmFFxb8g6kL3wsL+3UaxaF9zqR7m30/cB9bLVDSsldugZKWveroCCRJkiSp5hkSSpIKUutmHTls73M5pNd7YeHc5c8zd/nz9Os0kjF9zmSvNv3f977pSx7hodlXUpHK+UiHYXx2wPdpUewCJZIkSZIaNkNCSVJB2zosvIOpC+9j7vJJzF0+iX06juTQPmeyV9v+VKYKnnr7L0wqvR2Ag3ocz+H7jKdJFOX5CCRJkiRpzxkSSpLElrDwHA7pdQqTF9zBlIX38saKSbyxYhL7dDwEgDdWvEDQhCP6fY1hPY7Lc8WSJEmSVHMMCSVJqqZ1sw58cu8vc0jJKbxQ+o8sLHwBgBbFbThhwA/p0+HAPFcpSZIkSTXLkFCSpO1o1bR9VVg4ecGdLFv/NoftfS4dXaBEkiRJUiNUL0LCiOgA/AkYAiTgyymlZ6ttD+BK4DhgPfDFlNJLeShVklRgWjVtzyf6fjHfZUiSJElSraoXISG5APCBlNKpEdEMaLXN9mOB/tnPKODq7FGSJEmSJEnSHmqS7wIioj3wCeBagJTSppTSym26jQX+knKeAzpERI+6rVSSJEmSJElqnPIeEgJ7A2XAnyNiSkT8KSJab9OnFzC/2uvSrG0rETE+IiZHxOSysrLaq1iSJEmSJElqROpDSFgMHAxcnVI6CFgHfH93dpRSuialNCKlNKJr1641WaMkSZIkSZLUaNWHkLAUKE0pPZ+9vp1caFjdAqB3tdclWZskSZIkSZKkPZT3kDCltAiYHxH7Z01HADO26TYB+ELkjAZWpZQW1mWdkiRJkiRJUmNVX1Y3/g/gxmxl4zeAL0XEVwFSSn8E7geOA+YA64Ev5atQSZIkSZIkqbGpFyFhSmkqMGKb5j9W256A8+qyJkmSJEmSJKlQ5P12Y0mSJEmSJEn5ZUgoSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnDFu/qGiOgMNN+2PaX0To1UJEmSpILlWFOSJCk/djokjIiTgP8FOm+7CUhAUQ3WJUmSpALiWFOSJCm/duVKwiuBCcBVwLu1U44kSZIKlGNNSZKkPNqVkLAt8OuU0uu1VYwkSZIKlmNNSZKkPNqVhUv+B/hmRDStrWIkSZJUsBxrSpIk5dGuXEl4K/AY8KWIWAxUVt+YUtqnBuuSJElSYXGsKUmSlEe7EhLeCMwEbsZ5YiRJklSzHGtKkiTl0a6EhPsAQ1NKc2urGEmSJBUsx5qSJEl5tCtzEk4AjqqtQiRJklTQHGtKkiTl0a5cSbgA+G1EnA28AZRX35hS+kJNFiZJkqSC4lhTkiQpj3YlJOwM3LKDbakGapEkSVLhcqwpSZKUR7tyu/F5wGRgLbB5m5/yD3ifJEmS9GEca0qSJOXRrlxJeBPwSeBx3r/inN/uSpIkaU841pQkScqjXQkJPw0ck1J6qraKkSRJUsFyrClJkpRHu3K78Sx2LVSUJEmSdpZjTUmSpDzalYHYF4G/R8TNwDygsvrGlNJfarAuSZIkFZYv4lhTkiQpb3Y1JNwf+AbbnyfGgZskSZJ21xfZg7FmRFwHHA8sSSkNydouBr4ClGXdfphSuj/b9gPgHKAC+EZK6cGs/RjgSqAI+FNK6dI9PTBJkqSGYFdCwn8HPp9Suqm2ipEkSVLB2tOx5vXAVbw/TPxdSuk31RsiYhAwDhgM9AT+FRH7ZZv/ABwJlAIvRMSElNKM3axJkiSpwdiVkHAp8EptFSJJkqSCtkdjzZTSExHRdye7jwVuSSltBN6MiDnAyGzbnJTSGwARcUvW15BQkiQ1eruycMk3gD9GxNERsV9E7FP9p7YKlCRJUkGorbHm1yPilYi4LiI6Zm29gPnV+pRmbTtqf5+IGB8RkyNicllZ2fa6SJIkNSi7ciXh3dnjP7PHlD1G9ryopoqSJElSwamNsebVwC+y9/8CuBz48h7UWCWldA1wDcCIESPSh3SXJEmq93YlJNy71qqQJElSoavxsWZKafGW5xHxf8C92csFQO9qXUuyNj6gXZIkqVHb6ZAwpfR2bRYiSZKkwlUbY82I6JFSWpi9PAmYlj2fANwUEb8lt3BJf2ASuasW+0fE3uTCwXHAmTVdlyRJUn20K1cSSpIkSfVSRNwMHAZ0iYhS4CLgsIgYRu5247fIraBMSml6RNxGbkGScuC8lFJFtp+vAw+Su735upTS9Lo9EkmSpPwwJJQkSVKDl1I6YzvN135A/18Cv9xO+/3A/TVYmiRJUoOwK6sbS5IkSZIkSWqEDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKXL0JCSOiKCKmRMS929n2xYgoi4ip2c+5+ahRkiRJkiRJaoyK811ANecDM4F2O9h+a0rp63VYjyRJkiRJklQQ6sWVhBFRAnwG+FO+a5EkSZIkSZIKTb0ICYErgO8BlR/Q55SIeCUibo+I3tvrEBHjI2JyREwuKyurjTolSZIkSZKkRifvIWFEHA8sSSm9+AHd7gH6ppSGAhOBG7bXKaV0TUppREppRNeuXWuhWkmSJEmSJKnxyXtICHwUOCEi3gJuAT4VEX+r3iGltCyltDF7+SdgeN2WKEmSJEmSJDVeeQ8JU0o/SCmVpJT6AuOAR1JKn6/eJyJ6VHt5ArkFTiRJkiRJkiTVgPq0uvFWIuLnwOSU0gTgGxFxAlAOLAe+mM/aJEmSJEmSpMakXoWEKaXHgMey5z+t1v4D4Af5qUqSJEmSJElq3PJ+u7EkSZIkSZKk/DIklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkiRJklTgDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKnCGhJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkhq8iLguIpZExLRqbZ0iYmJEzM4eO2btERG/j4g5EfFKRBxc7T1nZ/1nR8TZ+TgWSZKkfDAklCRJUmNwPXDMNm3fBx5OKfUHHs5eAxwL9M9+xgNXQy5UBC4CRgEjgYu2BIuSJEmNnSGhJEmSGryU0hPA8m2axwI3ZM9vAE6s1v6XlPMc0CEiegBHAxNTSstTSiuAibw/eJQkSWqUDAklSZLUWHVPKS3Mni8CumfPewHzq/Urzdp21P4+ETE+IiZHxOSysrKarVqSJCkPDAklSZLU6KWUEpBqcH/XpJRGpJRGdO3ataZ2K0mSlDeGhJIkSWqsFme3EZM9LsnaFwC9q/Urydp21C5JktToGRJKkiSpsZoAbFmh+Gzg7mrtX8hWOR4NrMpuS34QOCoiOmYLlhyVtUmSJDV6xfkuQJIkSdpTEXEzcBjQJSJKya1SfClwW0ScA7wNnJZ1vx84DpgDrAe+BJBSWh4RvwBeyPr9PKW07WIokiRJjZIhoSRJkhq8lNIZO9h0xHb6JuC8HeznOuC6GixNkiSpQfB2Y0mSJEmSJKnAGRJKkiRJkiRJBS7vIWFEtIiISRHxckRMj4ifbadP84i4NSLmRMTzEdE3D6VKkiRJkiRJjVLeQ0JgI/CplNKBwDDgmGyVuerOAVaklPYFfgdcVrclSpIkSZIkSY1X3kPClLM2e9k0+0nbdBsL3JA9vx04IiKijkqUJEmSJEmSGrW8h4QAEVEUEVOBJcDElNLz23TpBcwHSCmVA6uAztvZz/iImBwRk8vKymq5akmSJEmSJKlxqBchYUqpIqU0DCgBRkbEkN3czzUppREppRFdu3at0RolSZIkSZKkxqpehIRbpJRWAo8Cx2yzaQHQGyAiioH2wLI6LU6SJEmSJElqpPIeEkZE14jokD1vCRwJzNqm2wTg7Oz5qcAjKaVt5y2UJEmSJEmStBuK810A0AO4ISKKyIWWt6WU7o2InwOTU0oTgGuBv0bEHGA5MC5/5UqSJEmSJEmNS95DwpTSK8BB22n/abXnG4DP1WVdkiRJkiRJUqHI++3GkiRJkiRJkvLLkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKnCGhJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkiRJklTgDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKnCGhJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJKlRi4i3IuLViJgaEZOztk4RMTEiZmePHbP2iIjfR8SciHglIg7Ob/WSJEl1w5BQkiRJheDwlNKwlNKI7PX3gYdTSv2Bh7PXAMcC/bOf8cDVdV6pJElSHhgSSpIkqRCNBW7Int8AnFit/S8p5zmgQ0T0yEN9kiRJdcqQUJIkSY1dAh6KiBcjYnzW1j2ltDB7vgjonj3vBcyv9t7SrG0rETE+IiZHxOSysrLaqluSJKnOFOe7AEmSJKmWfSyltCAiugETI2JW9Y0ppRQRaVd2mFK6BrgGYMSIEbv0XkmSpPqoXlxJGBHXRcSSiJi2g+2HRcSqbLLpqRHx07quUZIkSQ1TSmlB9rgEuBMYCSzechtx9rgk674A6F3t7SVZmyRJUqNWL0JC4HrgmA/p82Q22fSwlNLP66AmSZIkNXAR0Toi2m55DhwFTAMmAGdn3c4G7s6eTwC+kK1yPBpYVe22ZEmSpEarXtxunFJ6IiL65rsOSZIkNTrdgTsjAnJj35tSSg9ExAvAbRFxDvA2cFrW/37gOGAOsB74Ut2XLEmSVPfqRUi4k8ZExMvAO8B3UkrTt+2QTUQ9HqBPnz51XJ4kSZLqm5TSG8CB22lfBhyxnfYEnFcHpUmSJNUr9eV24w/zEvCRlNKBwH8Dd22vU0rpmpTSiJTSiK5du9ZlfZIkSZIkSVKD1SBCwpTS6pTS2uz5/UDTiOiS57IkSZIkSZKkRqFBhIQRsVdkE8lExEhydS/Lb1WSJEmSJElS41Av5iSMiJuBw4AuEVEKXAQ0BUgp/RE4FfhaRJQD7wLjsvliJEmSJEmSJO2hehESppTO+JDtVwFX1VE5kiRJkiRJUkFpELcbS5IkSZIkSao9hoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkiRJklTgDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKnCGhJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFThDQkmSJEmSJKnAGRJKkiRJkiRJBc6QUJIkSZIkSSpwhoSSJEmSJElSgTMklCRJkiRJkgqcIaEkSZIkSZJU4AwJJUmSJEmSpAJnSChJkiRJkiQVOENCSZIkSZIkqcAZEkqSJEmSJEkFzpBQkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkiRJklTgDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoEzJJQkSZIkSZIKnCGhJEmSJEmSVOAMCSVJkiRJkqQCZ0goSZIkSZIkFbh6ERJGxDER8VpEzImI729ne/OIuDXb/nxE9M1DmZIkSSoAHzY2lSRJaozyHhJGRBHwB+BYYBBwRkQM2qbbOcCKlNK+wO+Ay+q2SkmSJBWCnRybSpIkNTp5DwmBkcCclNIbKaVNwC3A2G36jAVuyJ7fDhwREVGHNUqSJKkw7MzYVJIkqdEpzncBQC9gfrXXpcCoHfVJKZVHxCqgM7C0eqeIGA+Mz16ujYjXaqXirXXZtg4VDM99YfK8Fy7PfeGqi3P/kVrev3bezoxN8zXuzJeG9effLV5LUE3DOnfg+dua569ha1jnz3NXXcM6d7Cr52+H4876EBLWmJTSNcA1dfmZETE5pTSiLj9T9YPnvjB53guX575wee61PfkYd+aLvwMNl+euYfP8NWyev4arkM9dfbjdeAHQu9rrkqxtu30iohhoDyyrk+okSZJUSHZmbCpJktTo1IeQ8AWgf0TsHRHNgHHAhG36TADOzp6fCjySUkp1WKMkSZIKw86MTSVJkhqdvN9unM0x+HXgQaAIuC6lND0ifg5MTilNAK4F/hoRc4Dl5AZr9UVB3Gai7fLcFybPe+Hy3Bcuz30B2dHYNM9l5Zu/Aw2X565h8/w1bJ6/hqtgz114QZ4kSZIkSZJU2OrD7caSJEmSJEmS8siQUJIkSZIkSSpwhoTbiIjeEfFoRMyIiOkRcX7W3ikiJkbE7OyxY9Y+ICKejYiNEfGdbfbVISJuj4hZETEzIsbk45i0c2rq3EfE/hExtdrP6oj4Zp4OSzuhhn/vv5XtY1pE3BwRLfJxTPpwNXzez8/O+XR/3+u/3Tj3Z0XEKxHxakQ8ExEHVtvXMRHxWkTMiYjv5+uYpF3heLdhc8zacDnmbNgcOzZcjv12nnMSbiMiegA9UkovRURb4EXgROCLwPKU0qXZ/wgdU0oXRkQ34CNZnxUppd9U29cNwJMppT9FbnW8VimllXV6QNppNXnuq+2zCFgAjEopvV03R6JdVVPnPiJ6AU8Bg1JK70bEbcD9KaXr6/qY9OFq8LwPAW4BRgKbgAeAr6aU5tTxIWkn7ca5PxSYmVJaERHHAhenlEZlf8a/DhwJlJJbFfeMlNKMPByWtNMc7zZsjlkbLsecDZtjx4bLsd/O80rCbaSUFqaUXsqerwFmAr2AscANWbcbyP0PRUppSUrpBWBz9f1ERHvgE+RWZialtMkBU/1WU+d+G0cAcx1s1W81fO6LgZYRUQy0At6p3eq1u2rwvA8Enk8prU8plQOPAyfX/hFod+3GuX8mpbQia38OKMmejwTmpJTeSCltIjfgH1snByHtAce7DZtj1obLMWfD5tix4XLst/MMCT9ARPQFDgKeB7qnlBZmmxYB3T/k7XsDZcCfI2JKRPwpIlrXWrGqUXt47qsbB9xcs9WpNu3JuU8pLQB+A8wDFgKrUkoP1V61qil7+Ds/Dfh4RHSOiFbAcUDv2qpVNWs3zv05wD+z572A+dW2lWZtUoPheLdhc8zacDnmbNgcOzZcjv0+mCHhDkREG+AfwDdTSqurb0spJeDD7tMuBg4Grk4pHQSsAxrd/eqNUQ2c+y37aQacAPy9xotUrdjTc5/NYTGW3D+aegKtI+LztVSuasienveU0kzgMuAhcreLTAUqaqVY1ahdPfcRcTi5geKFdVakVIsc7zZsjlkbLsecDZtjx4bLsd+HMyTcjohoSu5/nBtTSndkzYuz+9i33M++5EN2UwqUppSez17fTm4QpXqshs79FscCL6WUFtd8pappNXTuPw28mVIqSyltBu4ADq2tmrXnaup3PqV0bUppeErpE8AKcnOVqB7b1XMfEUOBPwFjU0rLsuYFbP3Nf0nWJtV7jncbNsesDZdjzobNsWPD5dhv5xgSbiMigty8KjNTSr+ttmkCcHb2/Gzg7g/aT0ppETA/IvbPmo4AGs1klo1RTZ37as7A2zYahBo89/OA0RHRKtvnEeTmu1A9VJO/89nE1EREH3JzytxUs9WqJu3quc/O6x3Av6WUqg/iXwD6R8Te2ZU447J9SPWa492GzTFrw+WYs2Fz7NhwOfbbea5uvI2I+BjwJPAqUJk1/5Dc/eq3AX2At4HTUkrLI2IvYDLQLuu/ltwqU6sjYhi55LkZ8AbwpWqTX6qeqeFz35rcX977pJRW1e2RaFfV8Ln/GXA6UA5MAc5NKW2sy+PRzqnh8/4k0JncxNTfTik9XKcHo12yG+f+T8ApWRtAeUppRLav44ArgCLgupTSL+vqOKTd5Xi3YXPM2nA55mzYHDs2XI79dp4hoSRJkiRJklTgvN1YkiRJkiRJKnCGhJIkSZIkSVKBMySUJEmSJEmSCpwhoSRJkiRJklTgDAklSZIkSZKkAmdIKEmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFBSwYqIqyPi/m3aLomIeyKiJCLuioh1ETEvIi6NiGbV+n0pImZGxKaIWJrtqzjbdn1E3BARU7JtB9T1sUmSJKn+cNwpqSEozncBkpRHNwP/ioiOKaUVWdtpwI+BO4FpwHCgK3A10BS4ICI+BvwPcBbwInAI8DfgUeC2bD+fB04FSoHpdXI0kiRJqq8cd0qq9wwJJRWyJ4FFwInAnyPiEGAvYDWwDzA6pVQBzIqI84CHIuJC4F3gnJTSHdl+3o6IC4DB1fY9JaV0Zx0dhyRJkuo3x52S6j1DQkkFK6WUIuIWct/i/hk4HZgA7A10AFZFxJbuATQDPpJSejEi3o2In5EboB0A9Acerrb7t+riGCRJklT/Oe6U1BA4J6GkQncTcEREdAQ+R+5WkGJgNjCs2s+B5AZk8yPiaOAloAfwALnbO57eZr8bar90SZIkNSCOOyXVa4aEkgpaSmkqMBe4AGhHbvD1GtAbWJZSmpNSmkPudpBLyP25+RXghpTS+JTSn4CZQD9y3/pKkiRJ7+O4U1J9Z0goSblvdS8A/pFS2gQ8BLwJ3BgRB0bEGOBPQGVKaQOwDBgTEUMjYjBwPblvd5vnpXpJkiQ1FI47JdVbhoSSBLcALcjd8kE2afRngQrgGeAecpNNn5v1vxhYCDwL/AvYBPwBOKgui5YkSVKD47hTUr0VKaV81yBJeRURnyA3YCtJKVXmux5JkiQ1To47JdVnrm4sqWBFRHfg48D3gOscqEmSJKk2OO6U1BB4JaGkghUR/cmtFvci8NmU0po8lyRJkqRGyHGnpIbAkFCSJEmSJEkqcC5cIkmSJEmSJBU4Q0JJkiRJkiSpwBkSSpIkSZIkSQXOkFCSJEmSJEkqcIaEkiRJkiRJUoH7/0Rhm3rXg1v4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(ncols=2,figsize=(22,7))\n",
    "\n",
    "# avg rain\n",
    "ax3 = sns.lineplot(x='date', y='avg', data=aws_yearly, color='#8BC34A', linewidth=2, ax=axs[0])\n",
    "axs[0].set_title('avg rain', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].set_xticks(np.arange(2016,2021,1))\n",
    "axs[0].set_yticks(np.arange(0,11,1.5))\n",
    "axs[0].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "# total rain\n",
    "ax4 = sns.barplot(x='date', y='total', data=aws_yearly, color='#F44336', linewidth=2, ax=axs[1])\n",
    "axs[1].set_title('total rain', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1].set_yticks(np.arange(0,3000,500))\n",
    "axs[1].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1], orient='v', num=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _**Project SARAi (Comparison dataset)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zyrvs\\Desktop\\Project DE\\Projects\\data analysis and visualisation for flood hazard mapping\\output\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: DtypeWarning: Columns (34,35,36,37) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Ignore DtypeWarning error: --- string values are cleaned and converted to null.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list columns for use\n",
    "columns = ['Date', 'Time', 'Rain', 'Rain Rate', 'Soil 1 Moist', 'Soil 2 Moist', 'Soil 3 Moist', 'Soil 4 Moist']\n",
    "\n",
    "# read data & remove column name with trailing spaces/replace with _\n",
    "sarai = pd.read_csv('data/SARAI_BUCAF_2016-2020_as_of_12162020.csv', usecols=columns)\n",
    "sarai.columns = sarai.columns.str.rstrip().str.replace(' ', '_')\n",
    "sarai.rename(\n",
    "    columns={'Rain':'Rain_Rate','Rain_Rate':'Rain'},\n",
    "    inplace=True\n",
    ")\n",
    "sarai.head()\n",
    "\n",
    "'''Ignore DtypeWarning error: --- string values are cleaned and converted to null.'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change date column to datetime object\n",
    "sarai['Date'] = pd.to_datetime(sarai['Date'], yearfirst=True)\n",
    "\n",
    "# change soil object to the proper data type, fill parsing errors with NaN values\n",
    "sarai['Soil_1_Moist'] = pd.to_numeric(sarai.Soil_1_Moist, errors='coerce')\n",
    "sarai['Soil_2_Moist'] = pd.to_numeric(sarai.Soil_2_Moist, errors='coerce')\n",
    "sarai['Soil_3_Moist'] = pd.to_numeric(sarai.Soil_3_Moist, errors='coerce')\n",
    "sarai['Soil_4_Moist'] = pd.to_numeric(sarai.Soil_4_Moist, errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**per day DataFrame**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Rain_Rate</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Soil_1_Moist</th>\n",
       "      <th>Soil_2_Moist</th>\n",
       "      <th>Soil_3_Moist</th>\n",
       "      <th>Soil_4_Moist</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-21</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-22</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.023810</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>2020-12-13</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.850000</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>0.013229</td>\n",
       "      <td>0.153125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.878049</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.657143</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>0.016889</td>\n",
       "      <td>0.202222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.588235</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1405 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Rain_Rate      Rain  Soil_1_Moist  Soil_2_Moist  \\\n",
       "0    2016-04-21   0.004808  0.000000           NaN           NaN   \n",
       "1    2016-04-22   0.000000  0.000000           NaN           NaN   \n",
       "2    2016-04-23   0.000000  0.000000           NaN           NaN   \n",
       "3    2016-04-24   0.000000  0.000000           NaN           NaN   \n",
       "4    2016-04-25   0.000000  0.000000           NaN           NaN   \n",
       "...         ...        ...       ...           ...           ...   \n",
       "1400 2020-12-12   0.000000  0.000000           0.0           0.0   \n",
       "1401 2020-12-13   0.002604  0.000000           0.0           0.0   \n",
       "1402 2020-12-14   0.013229  0.153125           0.0           0.0   \n",
       "1403 2020-12-15   0.005208  0.000000           0.0           0.0   \n",
       "1404 2020-12-16   0.016889  0.202222           0.0           0.0   \n",
       "\n",
       "      Soil_3_Moist  Soil_4_Moist  Year Month  \n",
       "0              NaN           NaN  2016   Apr  \n",
       "1              NaN           NaN  2016   Apr  \n",
       "2              NaN           NaN  2016   Apr  \n",
       "3              NaN           NaN  2016   Apr  \n",
       "4              NaN           NaN  2016   Apr  \n",
       "...            ...           ...   ...   ...  \n",
       "1400           0.0     33.023810  2020   Dec  \n",
       "1401           0.0     36.850000  2020   Dec  \n",
       "1402           0.0     41.878049  2020   Dec  \n",
       "1403           0.0     46.657143  2020   Dec  \n",
       "1404           0.0     52.588235  2020   Dec  \n",
       "\n",
       "[1405 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_per_day = sarai.groupby(sarai.Date.dt.strftime('%m-%d-%Y')).mean().reset_index()\n",
    "sarai_per_day.Date = pd.to_datetime(sarai_per_day.Date, yearfirst=True)\n",
    "sarai_per_day = sarai_per_day.sort_values(by='Date').reset_index()\n",
    "sarai_per_day.drop('index', axis=1, inplace=True)\n",
    "sarai_per_day['Year'] = sarai_per_day.Date.dt.year\n",
    "sarai_per_day['Month'] = sarai_per_day.Date.dt.strftime('%b')\n",
    "sarai_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupby month and aggregate by the mean/reset index to make the date as a column\n",
    "sarai_monthly_avg = sarai.groupby(sarai.Date.dt.month).mean().reset_index()\n",
    "sarai_monthly_avg.Date = pd.Series(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
    "sarai_yearly_avg = sarai.groupby(sarai.Date.dt.year).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupby month and aggregate by the sum/reset index to make the date as a column\n",
    "sarai_monthly_total = sarai.groupby(sarai.Date.dt.month).sum().reset_index()\n",
    "sarai_monthly_total.Date = pd.Series(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
    "sarai_yearly_total = sarai.groupby(sarai.Date.dt.year).sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **_SARAI monthly avg-total_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>rain_rate_avg</th>\n",
       "      <th>rain_avg</th>\n",
       "      <th>soil1_moist_avg</th>\n",
       "      <th>soil2_moist_avg</th>\n",
       "      <th>soil3_moist_avg</th>\n",
       "      <th>soil4_moist_avg</th>\n",
       "      <th>rain_rate_total</th>\n",
       "      <th>rain_total</th>\n",
       "      <th>soil1_moist_total</th>\n",
       "      <th>soil2_moist_total</th>\n",
       "      <th>soil3_moist_total</th>\n",
       "      <th>soil4_moist_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>608.0</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.33</td>\n",
       "      <td>295.0</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>482600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63.84</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>64.56</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>548600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.16</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4701.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>326708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10.33</td>\n",
       "      <td>565.0</td>\n",
       "      <td>6052.0</td>\n",
       "      <td>33686.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>56400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.39</td>\n",
       "      <td>805.0</td>\n",
       "      <td>10231.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>16.32</td>\n",
       "      <td>861.0</td>\n",
       "      <td>9227.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>97360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.03</td>\n",
       "      <td>787.0</td>\n",
       "      <td>9266.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.46</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>23412.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>0.49</td>\n",
       "      <td>8.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>5616.0</td>\n",
       "      <td>102061.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>20743.0</td>\n",
       "      <td>8480.0</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>20691.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  rain_rate_avg  rain_avg  soil1_moist_avg  soil2_moist_avg  \\\n",
       "0   Jan           0.05      0.49             0.03             0.00   \n",
       "1   Feb           0.03      0.29             0.00             0.00   \n",
       "2   Mar           0.03      0.22             0.00             0.00   \n",
       "3   Apr           0.01      0.21             0.00             0.00   \n",
       "4   May           0.03      0.40             0.15             0.02   \n",
       "5   Jun           0.05      0.54             6.16             0.01   \n",
       "6   Jul           0.08      1.02             0.00             0.00   \n",
       "7   Aug           0.07      0.80             0.00             0.00   \n",
       "8   Sep           0.08      0.93             0.00             0.00   \n",
       "9   Oct           0.14      1.97             0.00             0.00   \n",
       "10  Nov           0.49      8.87             0.00             0.00   \n",
       "11  Dec           0.13      1.63             0.73             0.71   \n",
       "\n",
       "    soil3_moist_avg  soil4_moist_avg  rain_rate_total  rain_total  \\\n",
       "0              0.00             0.00            608.0      5798.0   \n",
       "1              0.00            45.33            295.0      3151.0   \n",
       "2              0.00            63.84            270.0      1935.0   \n",
       "3              0.00            64.56            128.0      2027.0   \n",
       "4              0.00            43.16            402.0      4701.0   \n",
       "5              0.01            10.33            565.0      6052.0   \n",
       "6              0.00             5.39            805.0     10231.0   \n",
       "7              0.03            16.32            861.0      9227.0   \n",
       "8              0.00            20.03            787.0      9266.0   \n",
       "9              0.00            10.46           1698.0     23412.0   \n",
       "10             0.00             4.19           5616.0    102061.0   \n",
       "11             0.73             1.79           1687.0     20743.0   \n",
       "\n",
       "    soil1_moist_total  soil2_moist_total  soil3_moist_total  soil4_moist_total  \n",
       "0               334.0                0.0                0.0                0.0  \n",
       "1                46.0                0.0                0.0           482600.0  \n",
       "2                 0.0                0.0                0.0           554908.0  \n",
       "3                 0.0                0.0                0.0           548600.0  \n",
       "4              1124.0              133.0                0.0           326708.0  \n",
       "5             33686.0               80.0               39.0            56400.0  \n",
       "6                 0.0                0.0                0.0            28200.0  \n",
       "7                28.0               10.0              175.0            97360.0  \n",
       "8                 0.0                0.0                0.0           143054.0  \n",
       "9                 0.0                0.0                0.0           101339.0  \n",
       "10                0.0                0.0                0.0            39755.0  \n",
       "11             8480.0             8200.0             8400.0            20691.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_month = pd.merge(sarai_monthly_avg, sarai_monthly_total,on=\"Date\")\n",
    "sarai_month.rename(\n",
    "    columns={'Rain_Rate_x':'rain_rate_avg', 'Rain_x':'rain_avg', 'Soil_1_Moist_x':'soil1_moist_avg',\n",
    "    'Soil_2_Moist_x':'soil2_moist_avg', 'Soil_3_Moist_x':'soil3_moist_avg', 'Soil_4_Moist_x':'soil4_moist_avg',\n",
    "    'Rain_Rate_y':'rain_rate_total', 'Rain_y':'rain_total', 'Soil_1_Moist_y':'soil1_moist_total',\n",
    "    'Soil_2_Moist_y':'soil2_moist_total', 'Soil_3_Moist_y':'soil3_moist_total', 'Soil_4_Moist_y':'soil4_moist_total'\n",
    "    },inplace=True\n",
    ")\n",
    "sarai_month = sarai_month.round({\"rain_rate_avg\":2, \"rain_avg\":2, \"soil1_moist_avg\":2, \"soil2_moist_avg\":2, \"soil3_moist_avg\":2,\n",
    "\"soil4_moist_avg\":2, \"rain_rate_total\":0, \"rain_total\":0, \"soil1_moist_total\":0, \"soil2_moist_total\":0, \"soil3_moist_total\":0,\n",
    "\"soil4_moist_total\":0})\n",
    "sarai_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **_SARAI yearly avg-total_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Date</th>\n",
       "      <th>rain_rate_avg</th>\n",
       "      <th>rain_avg</th>\n",
       "      <th>soil1_moist_avg</th>\n",
       "      <th>soil2_moist_avg</th>\n",
       "      <th>soil3_moist_avg</th>\n",
       "      <th>soil4_moist_avg</th>\n",
       "      <th>rain_rate_total</th>\n",
       "      <th>rain_total</th>\n",
       "      <th>soil1_moist_total</th>\n",
       "      <th>soil2_moist_total</th>\n",
       "      <th>soil3_moist_total</th>\n",
       "      <th>soil4_moist_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2177.0</td>\n",
       "      <td>26442.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2577.0</td>\n",
       "      <td>30245.0</td>\n",
       "      <td>34810.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>23772.0</td>\n",
       "      <td>8480.0</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4308.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>139.74</td>\n",
       "      <td>6695.0</td>\n",
       "      <td>113838.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>2391415.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date  rain_rate_avg  rain_avg  soil1_moist_avg  soil2_moist_avg  \\\n",
       "0  2016           0.09      1.08             0.00             0.00   \n",
       "1  2017           0.07      0.86             1.01             0.00   \n",
       "2  2018           0.07      0.94             0.34             0.33   \n",
       "3  2019           0.03      0.29             0.03             0.00   \n",
       "4  2020           0.21      3.55             0.00             0.01   \n",
       "\n",
       "   soil3_moist_avg  soil4_moist_avg  rain_rate_total  rain_total  \\\n",
       "0             0.00             0.00           2177.0     26442.0   \n",
       "1             0.00             0.00           2577.0     30245.0   \n",
       "2             0.34             0.33           1879.0     23772.0   \n",
       "3             0.00             0.00            394.0      4308.0   \n",
       "4             0.01           139.74           6695.0    113838.0   \n",
       "\n",
       "   soil1_moist_total  soil2_moist_total  soil3_moist_total  soil4_moist_total  \n",
       "0                0.0                0.0                0.0                0.0  \n",
       "1            34810.0               80.0                0.0                0.0  \n",
       "2             8480.0             8200.0             8400.0             8200.0  \n",
       "3              380.0                0.0                0.0                0.0  \n",
       "4               28.0              143.0              214.0          2391415.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_year = pd.merge(sarai_yearly_avg, sarai_yearly_total, on='Date')\n",
    "sarai_year.rename(\n",
    "    columns={'Rain_Rate_x':'rain_rate_avg', 'Rain_x':'rain_avg', 'Soil_1_Moist_x':'soil1_moist_avg',\n",
    "    'Soil_2_Moist_x':'soil2_moist_avg', 'Soil_3_Moist_x':'soil3_moist_avg', 'Soil_4_Moist_x':'soil4_moist_avg',\n",
    "    'Rain_Rate_y':'rain_rate_total', 'Rain_y':'rain_total', 'Soil_1_Moist_y':'soil1_moist_total',\n",
    "    'Soil_2_Moist_y':'soil2_moist_total', 'Soil_3_Moist_y':'soil3_moist_total', 'Soil_4_Moist_y':'soil4_moist_total'\n",
    "    },inplace=True\n",
    ")\n",
    "sarai_year = sarai_year.round({\"rain_rate_avg\":2, \"rain_avg\":2, \"soil1_moist_avg\":2, \"soil2_moist_avg\":2, \"soil3_moist_avg\":2,\n",
    "\"soil4_moist_avg\":2, \"rain_rate_total\":0, \"rain_total\":0, \"soil1_moist_total\":0, \"soil2_moist_total\":0, \"soil3_moist_total\":0,\n",
    "\"soil4_moist_total\":0})\n",
    "sarai_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add year and month column for hue parameter in regression plots\n",
    "sarai['Year'] = sarai.Date.dt.year\n",
    "sarai['Month'] = sarai.Date.dt.strftime('%b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Visualización**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Avg rain vs rain rate (month)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LpJqkvKtTvr874uiKB8k/AAAAAAAAFJxcv7+w15/FYoo1M/Rjqkj+AQAAAAAAoOAc0e8vNJz8Y+vvZJH8AwAAAAAAQMHJbA0SfLG1ecm/7MTfbST/JovkHwAAAAAAAAqKp1PK7NgmSYo1r88dz37Ptt/JK4jkn5m9x8weNbPHzOzqqOMBAAAAAABAdHxnqzQ0JFt2nGzevNzx3Lbflq3ydDqq8IpK5Mk/MztV0p9JOlPScyS92szWj38VAAAAAAAAStXwsI/jjzhu8xfIGpdIAwPy3buiCK3oRJ78k3SypHvdvdfdU5LukPTaiGMCAAAAAABAREYb9pGV2/pL379JKYTk36OSXmJm9WaWlPQHklZFHBMAAAAAAAAiMlbln0Tfv6lKRB2Auz9hZp+WdIukHkkPSTpq07aZXSXpKklavXr1XIYIAAAAAACAOZTZHk76Ha3yLzvxdzuVf5NRCJV/cvf/cvfnu/u5krolPT3KOde5+0Z339jY2Dj3QQIAAAAAAGDW+f5ueWeHVJOULVt+1PO5oR8k/yalIJJ/ZrYk/LpaQb+/70QbEQAAAAAAAKKQ2/LbvE4WOzp1ZavWSImE/Nld8r6+uQ6v6BRE8k/Sj8zscUk/l/ROd98fcTwAAGAS2nta9OuW/1YqMxh1KAAAACgRuWEfo/T7kySrqJCtapLcldmxbQ4jK06R9/yTJHd/SdQxAACAqfvNjuu1tete1VUv12nLLow6HAAAAJSAXOXfKP3+smJr1yu9fYsy27YoftIpcxVaUSqUyj8AAFCE9vUEn7R29LZEGwjKhpldZGZPmdkWM7tmnPNeZ2ZuZhvDn5vMrM/MHgofX527qAEAwFSMN+wji75/k1cQlX8AAKD49KcO69BAuySpo3dHxNGgHJhZXNKXJL1CUpuk+83sBnd/fMR58yW9R9K9I26x1d3PmItYAQDA9PjQkDI7tksaTvCNJjfxdxvJv4lQ+QcAAKalo6dl+Pve1ugCQTk5U9IWd9/m7oOSNkm6dJTzPi7p05L65zI4AABw7Hxni5RKyZavlNUkxzxvuPJvq9x9jqIrTiT/AADAtLTnJf96BrvUN3QwumBQLlZI2pn3c1t4LMfMnidplbv/zyjXN5vZg2Z2h5mN2nPazK4ys81mtrm9vX3GAgcAAJOTzvb7Cyv7xmINS6T5C6SDB+SdHXMRWtEi+QcAAKalvXf7ET9T/YeomVlM0uckvW+Up3dLWu3uz5X0XknfMbMFI09y9+vcfaO7b2xsbJzdgAEAwFFyk37H6fcnSWZ2RPUfxkbyDwAATEu28m9B1VJJDP3AnNglaVXezyvDY1nzJZ0q6XYza5H0Qkk3mNlGdx9w905JcvcHJG2VdMKcRA0AACYtszUc9rF2/OSfJMWas33/npnVmIodyT8AADBl7plcz7+TGs+VJHX0MPQDs+5+ScebWbOZVUq6QtIN2Sfd/YC7N7h7k7s3SfqtpEvcfbOZNYYDQ2RmayUdL2nb3L8FAAAwnslW/knDff+cyr9xkfwDAABTdqB/r4Yy/ZpXWa9VC0+TJHWy7RezzN1Tkt4l6WZJT0j6vrs/ZmbXmtklE1x+rqSHzewhST+U9A5375rVgAEAwJRkujql/d1Ssla29LgJz89N/N3OxN/xJKIOAAAAFJ/2nqDfX0NtkxqSayRJHb075O4ysyhDQ4lz95sk3TTi2IfHOPf8vO9/JOlHsxocAAA4Jpm8YR+TWVPGmtYG17W2yFMpWYI012io/AMAAFPWHvb3a0w2aV5lvaritepPHVLPUHe0gQEAAKBoTWXLryRZslZ23AppaEjexi6UsZD8AwAAU5at/GusbZaZqaG2SRJ9/wAAADB9w5V/k0v+Scqb+MvW37GQ/AMAAFOWnfTbWNssSWpIrpbExF8AAABMX2ZbOOl3kpV/Ul7fv20k/8ZC8g8AAEzJYLpP+/t3K2YJLa5ZIUmqD/v+MfQDAAAA0+GDg/KdLZKZYmvWTvq6WHN26AcTf8dC8g8AAExJZ+8OSa76mlWKxyok6YihHwAAAMBUZXZsl9Jp2YpVspqaSV9H5d/ESP4BAIApaQ/7+jWGff4kqaE2m/xrlXsmirAAAABQxHLDPqbQ70+SbPlKqbJKvm+P/PDh2Qit6JH8AwAAU5Id9tEQ9vuTpGTFQiUrFmko3aeDA+1RhQYAAIAilRv2MYV+f5Jk8bhiTcG6lKEfoyP5BwAApmR42EfTEcfZ+gsAAIDpyibuppr8k/In/tL3bzQk/wAAwKS5uzp6g8q/xrzKPykv+RcmBwEAAIDJcPfhyr9wgMdUxJqDhCF9/0ZH8g8AAEza4cFO9acOqyaxQLUVdUc8l+37x8RfAAAATIV3tksHD0jz5suWLJ3y9cOVfyT/RkPyDwAATFq2319jbbPM7Ijn2PYLAACA6chV/a09/qg15mTkJv5u3yp3n9HYSgHJPwAAMGnDyb+mo56rT2Yr/3Yq4+m5DAsAAABFbHjYx9S3/EqS1S2WFtVJvT3yfXtmMrSSQPIPAABMWntvi6QjJ/1mVSWSml/VqLQPaX/f7jmODAAAAMUqN+xj7dSHfWRlr6Xv39FI/gEAgEkba9JvFlt/AQAAMFX5236ni4m/YyP5BwAAJiWVGVJX706ZYqqvWT3qOST/AAAAMBU+0C9va5ViMcWajt5dMlm5vn/bnpmp0EoGyT8AADApXb2tcmVUV7NcFfGqUc8h+QcAAICpyLRslzIZ2crVsqrqad+Hyr+xkfwDAACTMrzld+xPZBtqw+RfD8k/AAAATCxbqRdbN/0tv5IUW9MsxWLyna3ywcGZCK1kkPwDAACT0t479qTfrMU1qySZuvt2KZUZmpvAAAAAULRyyb9j6PcnSVZVLVuxSsqklWltmYHISgfJPwAAMCm5yr/k2JV/FfEqLao+Tq6Muvva5igyAAAAFKvM1nDS7zFW/klSrDnb94+Jv/lI/gEAgElp7wkq/xrG2fYr0fcPAAAAk+PueZV/64/5frG1Qd8/307yL19BJP/M7K/N7DEze9TMvmtm0+/wCAAAZlzPYLd6h/arMp7UgqrGcc+l7x8AAAAmw/ftlQ4fkhYslNWPv8acDCr/Rhd58s/MVkh6t6SN7n6qpLikK6KNCgAA5OvobZEU9Pszs3HPpfIPAAAAk5E/7GOiNeZk5JJ/VP4dIfLkXyghqcbMEpKSkp6NOB4AAJAn2++vIdk04bkk/wAAADAZ2Qq9Yx32kWXLjpNqkvKuTvn+7hm5ZymIPPnn7rsk/YukVkm7JR1w91tGnmdmV5nZZjPb3N7ePtdhAgBQ1rL9/hon6PcnSXU1yxWzuA7079Fgun+2QwMAAECRymwdrvybCRaLKda0Nrj39q0zcs9SEHnyz8zqJF0qqVnSckm1Zvbmkee5+3XuvtHdNzY2Hvs+cAAAMHm5Sb+1TROeG49VqK5mpSSpq3fnLEYFAACAYpbb9tt87MM+srKDQ9j6Oyzy5J+kCyRtd/d2dx+S9GNJZ0ccEwAACGU8rc5wC+9ktv0G52W3/rbMUlQAAAAoZt7XJ9+1U4rHFVsz8e6SyWLox9EKIfnXKumFZpa0oLvjyyU9EXFMAAAg1NW3S2lPaWHVUlUlkpO6hr5/AAAAGE+mZavkLlvVJKusnLH7Dlf+se03K/Lkn7vfK+mHkn4n6REFMV0XaVAAACCnYwr9/rIaasPkXw/JP8wsM7vIzJ4ysy1mds04573OzNzMNuYd+0B43VNm9sq5iRgAAIwmN+xjhvr9ZcWa1wX3b9kqT6dn9N7FKvLknyS5+0fc/SR3P9Xd3+LuA1HHBAAAArlJv5Po95fVkFwtico/zCwzi0v6kqSLJW2Q9EYz2zDKefMlvUfSvXnHNki6QtIpki6S9OXwfgAAIAK5fn9rZ67fnyTZ/AWyxiXSwIB8964ZvXexKojkHwAAKFxTmfSbtbB6mRKxSh0e7FR/6vBshYbyc6akLe6+zd0HJW1SMDhupI9L+rSk/HHTl0ra5O4D7r5d0pbwfgAAIAK5Sb9rZ7byT8qr/qPvnySSfwAAYALt4dCOqST/YhZXfc0qSVJnb+tshIXytEJS/gjptvBYjpk9T9Iqd/+fqV4bXn+VmW02s83t7e0zEzUAADiCu8/atl9JijUH96TvX4DkHwAAGFN/6pAODbQrEavSouplU7q2PtwmTN8/zBUzi0n6nKT3Tfce7n6du290942NjY0zFxwAAMjxPbul3h5pUZ1ii+tn/P6xtWHl33Yq/yQpEXUAAACgcLWHibuG5BrFptgebXjib8tMh4XytUvSqryfV4bHsuZLOlXS7WYmScsk3WBml0ziWgAAMEdy/f5moepPkmLN2Ym/JP8kKv8AAMA4hvv9NU35WoZ+YBbcL+l4M2s2s0oFAzxuyD7p7gfcvcHdm9y9SdJvJV3i7pvD864wsyoza5Z0vKT75v4tAACAbPIvPgv9/iTJVq2R4nH5s7vkfX2z8hrFhOQfAAAYU8c0hn1k5Sr/2PaLGeLuKUnvknSzpCckfd/dHzOza8PqvvGufUzS9yU9LumXkt7p7unZjhkAABwtO+zDZnjSb5ZVVMhWN0vuyuzYNiuvUUzY9gsAAMaUHfbRkGya8rXzqxpVGa9RX+qgegb3q7Zy0YzGhvLk7jdJumnEsQ+Pce75I37+pKRPzlpwAABgUrLDPuKztO1XCib+prdvUWbbFsVPOmXWXqcYUPkHAABG5Z5Re0+LpOlt+zWzXPVfJ1t/AQAAIMl7e+TPtkmJhGxV06y9Tmwtff+ySP4BAIBR7e/fo1RmQPMq61VTsWBa9xge+kHyDwAAAFKmJdiGG1vTLKuomLXXiTWHE3+3kfwj+QcAAEbVkav6m3q/v6yGsGIwey8AAACUt2y/v9gsDfvIGq782yp3n9XXKnQk/wAAwKiy/f6ms+U3q56JvwAAAMgznPybnWEfWdawRJo3Xzp4QN7ZMauvVehI/gEAgFG1Zyf9Jo+h8i+37be17D9xBQAAgJTZNjeVf2Z2RPVfOSP5BwAARpUd9tFwDJV/yYpFqkks0GC6V4cGy/sTVwAAgHLnmUyuB19sFif9Zg33/Xtm1l+rkJH8AwAARxlM92l//27FLKHFNSunfR8zU0NtWP3Xw9ZfAACAcuZ7npX6+2T1DbJFdbP+erHmoPLPqfwDAAA4UmfvDkmu+uQqxWOJY7oXE38BAAAgzd2wj6zhbb/lPfGX5B8AADhKdsvvsfT7y6on+QcAAADl9/ub3WEfWbGmtcHrtrbIU6k5ec1CRPIPAAAcJTfs4xj6/WVlK/86Sf4BAACUtbmu/LNkrWzZcmloSN7WOievWYhI/gEAgKPkKv9qj73yr6F2tSSps3enMp4+5vsBAACgOOUq/+Zg2EcWW39J/gEAgBHcfUYr/6oT8zWvsl6pzIAO9O895vsBAACg+Pjhw/I9u6WKStmq1XP2usMTf0n+AQAASJIODXZoIN2jmoqFSlbMzBQ2hn4AAACUt2zlXaypWRY/toFyU5HdYpwp44m/JP8AAMARclV/ySaZ2YzcMzf0o4fkHwAAQDma62EfWVT+kfwDAAAjdOT6/TXN2D0bahn6AQAAUM7methHlq1YKVVWyfftkR8+PKevXShI/gEAgCNkK/8aZmDYRxbbfgEAAMpbtvJuLod9SJLFE4qtaQpiKNOhHyT/AADAEdp7WyTNbOVffTJo6tzV16Z0ZmjG7gsAAIDC5+m0Mi1Bz725rvwLXjM78bc8+/6R/AMAADmpzKC6ettkiqm+ZuamsFXGq7WwepkynlZ337Mzdl8AAAAUPt+9S+rvlzUukS1YOOevH2sOk39l2veP5B8AAMjp7N0pV0Z1NctVEa+a0Xs3hNV/bP0FAAAoL1H1+8vKJf/Y9gsAAMpdbtLvDPb7y2pINkki+QcAAFBuhpN/czvpNyu2Npz4u32r3D2SGKJE8g8AAOS0z8Kk36zs0A8m/gIAAJSXzLZoK/+srl5aVCf19sj37YkkhihFnvwzsxPN7KG8x0EzuzrquAAAKEcdvWHlX3IWKv9qw4m/PST/AAAAyklUk37zlXPfv8iTf+7+lLuf4e5nSHq+pF5JP4k2KgAAylO28q9hFrb91tWslCmm7v7dGkoPzPj9AQAAUHj80MGg2q6qSrZiVWRxlPPE38iTfyO8XNJWd6ckAACAOdYz2K3eof2qjCe1oKpxxu+fiFWormaFJFdX384Zvz8AAAAKT67qr2mdLB6PLI5Yc9j3L9yCXE4KLfl3haTvjvaEmV1lZpvNbHN7e/schwUAQOkb7vfXLDObldfITfxl6y8AAEBZGO73F82wjywq/wqAmVVKukTSD0Z73t2vc/eN7r6xsXHmqxEAACh3w5N+m2btNRrCezPxFwAAoDwMT/qNrt+fJMXWNEuxmHxnq3xwMNJY5lrBJP8kXSzpd+6+N+pAAAAoR+29LZJmZ9hHVnbiL8k/AACA8lAIwz4kyaqqg56DmbQyrS2RxjLXCin590aNseUXAADMvo65qPwj+YdjZGYXmdlTZrbFzK4Z5fl3mNkjZvaQmf3GzDaEx5vMrC88/pCZfXXuowcAoLx4OqVMyzZJ0W/7lfL7/pXXxN+CSP6ZWa2kV0j6cdSxAABQjtKZlDp7WyUNJ+hmw6Ka4xS3Ch0aaNdAqnfWXgelyczikr6kYMfIBklvzCb38nzH3U9z9zMkfUbS5/Ke2+ruZ4SPd8xJ0AAAlDFv2ykNDsiWHiebNz/qcHIJSN9O8m/OuXuPu9e7+4GoYwEAoBx19+1S2lNaWL1MlYnkrL1OzOJanFwpSeqk+g9Td6akLe6+zd0HJW2SdGn+Ce5+MO/HWkk+h/EBAIA8hTLsIyvWHA79oPIPAACUm/becMvvLPb7yxre+ts666+FkrNC0s68n9vCY0cws3ea2VYFlX/vznuq2cweNLM7zOwlo72AmV1lZpvNbHN7e/tMxg4AQNkZHvZRYMm/lvKa+EvyDwAAqL2nRdLs9vvLou8fZpu7f8nd10l6v6QPhod3S1rt7s+V9F5J3zGzBaNce527b3T3jY2NjXMXNAAAJahQhn1k2bLjpOoaeWeH/MD+qMOZMyT/AACA2nPDPuag8i9MMHaECUdgCnZJWpX388rw2Fg2SbpMktx9wN07w+8fkLRV0gmzEyYAAJDyt/0WSPIvFhse+lFGff9I/gEAgFwijso/FLj7JR1vZs1mVinpCkk35J9gZvn/dfEqSc+ExxvDgSEys7WSjpe0bU6iBgCgDPmBA/KOdqm6WrZ8ZdTh5JTjxN9E1AEAAIBo9acO6dBghxKxKi2sXjbrr7egqlEVsWr1Du1X79ABJSsWzvprojS4e8rM3iXpZklxSV9398fM7FpJm939BknvMrMLJA1J6pZ0ZXj5uZKuNbMhSRlJ73D3rrl/FwAAlIdc1V/TOlmscGrPslWIme3l0/eP5B8AAGWuvSeowGtIrlEsKIyaVWYx1SdXa8/hp9XZ26rkwtNm/TVROtz9Jkk3jTj24bzv3zPGdT+S9KPZjQ4AAGTlhn0USL+/rHKs/Cuc1CsAAIjEXPb7y2qoDbf+9rD1FwAAoBRlthdWv7+sXPJvxzZ5Oh1xNHOD5B8AAGWuI5f8a5qz16TvHwAAQGnLbC2sSb9ZtmChrKFR6u+X7x5vbljpIPkHAECZa+9tkTTHlX/JJkkk/wAAAEqRp1LK7AjmamUr7QpJbO16SeXT94/kHwAAZcw9o/Zw0m82ITcXGpKrJQVTht19zl4XAAAAs8937pCGhmTLlstq50UdzlFizWHyr0z6/pH8AwCgjO3v36NUZkDzKxtUUzF/zl63tnKxqhPzNJDu0eHBzjl7XQAAAMy+3KTfAtvymzVc+UfyDwAAlLjssI+GOez3J0lmluv719nbOqevDQAAgNmVragrtGEfWbmhHyT/AABAqctu+Z3LYR9Z9Qz9AAAAKEmZrYVd+WermqR4XP7sLnlfX9ThzDqSfwAAlLGO3nDSb3Luhn1kNdSGyb8ekn8AAAClJLftt0Ar/6yiIkgAuucGk5Qykn8AAJSx3LCPCCr/mPgLAABQery7S97VKdUkZcuOizqcMZXTxF+SfwAAlKnBdJ/29+9WzBJaXLNyzl8/N/G3d4fcM3P++gAAAJh5w1V/62Wxwk075fr+hfGWssL9UwAAALMqu922PrlK8Vhizl+/pmKBaivqlMoM6MDAvjl/fQAAAMy8Qh/2kUXlHwAAKHntEfb7y8r2/euk7x8AAEBJGB72sT7iSMYXaw6Tf9u2yN0jjmZ2kfwDAKBMRTnpN6uBib8AAAAlpdCHfWRZ4xJp3nzp4IGgR2EJI/kHAECZ6ugJK/9qI6z8I/kHAABQMnxoSJnWFsks11OvUFlejNmtyqWK5B8AAGXI3Qui8q8+m/xj2y8AAEDRy7Rul1Ip2fIVsppk1OFMKNv3z7eT/AMAACXm0EC7BtI9qqlYqGRFXWRx1IcTf7v6dirj6cjiAAAAwLErlmEfWVT+AQCAktXe2yJJakw2ycwii6MqkdSCqiVKe0rdfc9GFgcAAACOXa7f37oiSf6FScpSn/hL8g8AgDLUXgD9/rLo+wcAAFAaMluLrPKvaa2kYLuyp1IRRzN7SP4BAFCGCqHfX1ZDLX3/AAAAip27F13lnyVrZcuWS0ND8l2tUYcza0j+AQBQhgqp8q+eyj8AAICi512d0v5uqXaebMmyqMOZtHLo+0fyDwCAMpPKDKq7b5dMsdzAjShlt/12kvwDAAAoWsPDPtZH2lN6qrITf0u57x/JPwAAykxnb6tcGdXVrFAiVhl1OFpcs1KmmLr7nlUqMxh1OAAAAJiG3JbfIun3lxVrDpN/VP7NLjNbZGY/NLMnzewJM3tR1DEBAFCqCmnLryRVxKu0qOY4uTLq6m2LOhwAAABMQ2ZrcfX7y6Lyb+78q6RfuvtJkp4j6YmI4wEAoGQV0rCPLCb+AgAAFLdirfyzFSulikr53t3yw4ejDmdWRJ78M7OFks6V9F+S5O6D7r4/0qAAAChhhVb5J5H8AwAAKGY+OCBv3SHFYoo1rY06nCmxeEKxpmBdnGkpzeq/yJN/kpoltUv6f2b2oJn9p5nVjjzJzK4ys81mtrm9vX3uowQAoAS4+3DyL9kUbTB56hn6AQAAULQyO1qkTFq2YpWsujrqcKas1Pv+FULyLyHpeZK+4u7PldQj6ZqRJ7n7de6+0d03NjY2znWMAACUhN6hbvWlDqoqXqv5VYXz79OG2rDyr4fkHwAAQLEp1i2/WaXe968Qkn9tktrc/d7w5x8qSAYCAIAZlt/vz8yiDSZPXfVyxSyhAwN7NZjuizocFDAzu8jMnjKzLWZ21AfGZvYOM3vEzB4ys9+Y2Ya85z4QXveUmb1ybiMHAKB0Feuwj6xY8zpJw0nMUhN58s/d90jaaWYnhodeLunxCEMCAKBkZbf8NhRQvz9JiscSWlyzUpLU2dsacTQoVGYWl/QlSRdL2iDpjfnJvdB33P00dz9D0mckfS68doOkKySdIukiSV8O7wcAAI7RcOXf+ogjmZ78yj93jziamRd58i/0V5K+bWYPSzpD0j9GGw4AAKWpECf9ZrH1F5NwpqQt7r7N3QclbZJ0af4J7n4w78daSdkV/KWSNrn7gLtvl7QlvB8AADgG7j5c+Vek236trl5aVCf19sj37Yk6nBmXiDoASXL3hyRtjDoOAABKXUdviySpMVlYlX+S1JBcLYmJvxjXCkk7835uk3TWyJPM7J2S3iupUtLL8q797YhrV8xOmAAAlA/vbJcOHZTmL5A1Lok6nGmLNa9X5sH7ldm+VbGlx0UdzowqlMo/AAAwy9KZVG5LbbbKrpA05Cb+su0Xx8bdv+Tu6yS9X9IHp3KtmV1lZpvNbHN7e/vsBAgAQAkZrvpbX1A9padquO9f6U38JfkHAECZ6O7bpbSntKj6OFXGa6IO5ygNySZJVP5hXLskrcr7eWV4bCybJF02lWvd/Tp33+juGxsbC2ciNgAAharYh31kDff9I/kHAACKVHtvMOyjEPv9SdLC6qVKxKp0eLBTfUOHog4Hhel+ScebWbOZVSoY4HFD/glmlv9fHq+SlB3bd4OkK8ysysyaJR0v6b45iBkAgJI2POyjyJN/zWHyj8o/AABQrLLDPrIVdoXGLKb6sO9fJ9V/GIW7pyS9S9LNkp6Q9H13f8zMrjWzS8LT3mVmj5nZQwr6/l0ZXvuYpO9LelzSLyW9093Tc/0eAAAoNdlkWbFO+s2KrWmWYjH5zlb54GDU4cyoghj4AQAAZl97T7byr/CGfWQ1JFdr7+Fn1NG7QysXnhp1OChA7n6TpJtGHPtw3vfvGefaT0r65OxFBwBAefGBfnlbqxSLK9a0NupwjolVV8uWr5S3tSrT2qL4+hOiDmnGUPkHAECZ6Agr/wp12680PPSjg6EfAAAABS/Tsl3KZGSrVssqq6IO55iVat8/kn8AAJSBvqFDOjTYoUSsSgurl0UdzpiyU4g7etj2CwAAUOhKpd9fVnbir5dY3z+SfwAAlIGO3hZJQWVdzOLRBjOObD/Czt4dcvdogwEAAMC4SmXSb1Y2iZnZvjXiSGYWyT8AAMpAMfT7k6R5lfWqiteqL3VQvUPdUYcDAACAcQxX/hX3sI+sbOUf234BAEDRaS+Cfn+SZGa5ib8dTPwFAAAoWO4+XPlXItt+bdlyqbpG3tkhP7A/6nBmDMk/AADKQLFU/kn5ff8Y+gEAAFCofN9eqeewtHCRrL4h6nBmhMViuanFpVT9R/IPAIASl/F0roou21OvkA1P/KXyDwAAoFDlD/sws4ijmTm5ib8lNPSD5B8AACXuQP8epTIDml/ZoJqK+VGHMyGSfwAAAIWv1IZ9ZMWaw+RfCQ39IPkHAECJy/b7ayjwfn9Z2Tg7mPgLAABQsEpt2EcWlX8AAKDoFFO/P0lKVixUsmKRhtJ9OjjQHnU4AAAAGEWpDfvIyk383bFNnk5HHM3MIPkHAECJK5ZJv/myE3872foLAABQcLyvT/5smxSPK7a6KepwZpQtWChraJT6++W7d0Udzowg+QcAQIlr7w0r/5LFUfkn0fcPAACgkGVatkrustXNssrKqMOZcaXW94/kHwAAJWww1asD/XsUt4TqalZEHc6kNdSGyb8ekn8AAACFZnjYR2n1+8sqtb5/JP8AAChh2cq5+uRqxWOJiKOZPCr/SpOZXWhmtVHHAQAAjk122Ee8xPr9ZeX6/m0n+QcAAArc8KTf4tnyKw0n/zp7W5Xx0mi0DEnSdyUV119GAABwlGzln5XYpN+sXOUfyT8AAFDohvv9NUUbyBRVJWo1v7JBaR/S/v49UYeDmfN7SS+JOggAADB97p5LisXXlWbln61qkuJx+bO75H19UYdzzEj+AQBQwoYn/RZfsVV92Pevk75/peSQpC+a2T4zu8/Mfp3/iDo4AAAwMd+zW+rtldUtltXVRx3OrLCKiiAB6K7Mjm1Rh3PMiqf5DwAAmBJ3V3tPWPlX2xRtMNPQkFyjlu4H1NG7Q8fr7KjDwcz4XfgAAABFKtvvL1ai/f6yYs3rlG7Zqsz2rYqfdErU4RwTkn8AAJSogwPtGkz3KlmxSLWVdVGHM2UM/Sg97v6xqGMAAADHZnjSb4kn/9auV/q2W3LJzmJG8g8AgBLVUcRVf5LUUEvyr9SYWaWkt0t6jqRqSZb/vLu/NYq4AADA5A1X/pXmsI+s4Ym/WyOO5NjR8w8AgBKVHfbRkCy+fn+SVF+zSpKpu2+X0pmhqMPBzPiapE9LapSUkZQe8QAAAAUusy0Y9lHy237D95fZtkXuHnE0x4bKPwAAStTwsI+mSOOYrop4tRZVL9P+/t3q6ttVtO8DR3idpEvc/VdRBwIAAKbOe3vkz7ZJFRWy1U1RhzOrrHGJVDtPOnhA3tUpq2+IOqRpm3Lln5nVm9nykY/ZCA4AAEzf8LCP4qz8k4b7/nWy9bdUdEvaE3UQAABgerJbYGOrm2WJ0q4nM7Pc1uZstWOxmnTyz8z+0Mz2SdonaWfeoy38Om1m1mJmj5jZQ2a2+VjuBQAApKH0gLr7npUppvrkqqjDmTb6/hU/M4tlH5I+KenfzexEM6vIfy58HgAAFLByGfaRle3759uLO/k3lTTtv0q6QdIXJfXNQiwvdfeOWbgvAABlp7OvVa6M6pOrlYhVRh3OtOUm/vaQ/CtiKUn5jXJM0uNjnBuf/XAAAMB0lcuwj6xSqfybSvJvvqTPuPvTsxUMAACYGbl+f8mmSOM4VvVJKv9KwEujDgAAAMyMchn2kRVrDpN/RT7xdyrJvy9LutrM3uPuMz1yzyXdYmYu6T/c/bqRJ5jZVZKukqTVq1fP8MsDAFBaOsJ+fw1FPiRjcc0KxSyu/f17NJTuV0W8OuqQMHXXSrpV0v9JutfdMxHHAwAApsEzmeHkX7ls+21aK0nKtG6Xp1JF2+dwKr1VvifpCkkHwx592/IfxxjHi939eZIulvROMzt35Anufp27b3T3jY2Njcf4cgAAlLbhSb/FO+xDkuKxCtXVrJDk6uw7phbDiM41Crb6/pOkPWZ2g5m9x8xOnc7NzOwiM3vKzLaY2TWjPP9eM3vczB42s/8zszV5z6XDHtMPmdkN035HAACUId+9S+rvk9U3yhYuijqcOWG182TLjpOGhuS7WqMOZ9qmkrL8tqQnJH1XM9zzz913hV/3mdlPJJ0p6dcz+RoAAJQLdx+e9Fvk236loO9fZ2+rOnp2aNm88viUuZS4+z2S7pH0cTNLSjpX0sslfdPMlkq6TUFV4P+5+7j7u80sLulLkl6hYOjc/WZ2g7vn9xB8UNJGd+81s7+Q9BlJl4fP9bn7GTP37gAAKB/Dwz7Ko99fVqx5vdJ7diuzbYtia9ZGHc60TCX5t1bS6e4+oxudzaxWUszdD4XfX6hgewgAAJiG3qFu9aUOqipeq/lVxV8t35Bco6d0J33/SoC790r6ZfiQmTVIepmkCyT9vaSJ/mviTElb3H1beP0mSZcqb4CIu9+Wd/5vJb15puIHAKCc5YZ9NJfXh7Gx5vVK33Nn0PevSDsZT2Xb7w0KEnMzbamk35jZ7yXdJ+l/3P2Xs/A6AACUheEtv00ys2iDmQENDP0oOWZWb2bLJVVK+o2kjyqoCJzICkn5+7/bwmNj+VNJv8j7udrMNpvZb83ssikFDQBAmRvu91dmlX8lMPF3KpV/uyR9zsyulLRNUir/SXd/63QCCD+5fc50rgUAAEdrzw37KO5+f1n1tUHyr7O3ePusIGBmfyjpPyTVj3xKwQC4+Ay+1pslbZR0Xt7hNe6+y8zWSvqVmT0yclcLQ+YAABhdrvKvTIZ9ZMWa10kq7om/U0n+1UvaNMZzPgOxAACAGZBf+VcKFlUvU9wqdGigXQOpHlUlaqMOCdP3rwp2k3xR0+shvUvSqryfV4bHjmBmF0j6B0nnuftA9nhen+ltZna7pOdKOmIl7+7XSbpOkjZu3MgaFwAASX74sHzPbqmiUrayvD4cs5WrpIpK+d7d8sOHZfPmRR3SlE1l2+87JW2WdFjS0IhHapzrAADAHGrvzQ77KI3Kv5jFVZ8MFpls/S168yV9xt0fcvenRj4mcf39ko43s2Yzq5R0hYJkYo6ZPVdBdeEl7r4v73idmVWF3zdIOkd5vQIBAMDYMtuz/f7WyuJTqSMrfhZPKLYmWFdnWoqz+m8qyb/vSPqEgk9b5494FF/aEwCAEpTOpNTZG7REawi3y5YC+v6VjC9LutrMKqZzsbunJL1L0s2SnpD0fXd/zMyuNbNLwtP+WcHa9Adm9pCZZZODJ0vaHPaZvk3Sp0ZMCQYAAGPIbA37/TWXV7+/rGLv+zeVdO0Fki5y99/MVjAAAODYdPW1KeMpLao+TpXxmqjDmTENtWukdqmjh+RfkfuepNsl/YmZ7ZWUyX/S3ddOdAN3v0nSTSOOfTjv+wvGuO5uSadNPWQAAFCu/f6yir3v31SSf09O8XwAADDHOkqs319WdtsvQz+K3rcVVOx9V9Pr+QcAACKQm/S7tkyTf7nKv2cijmR6ppLMe5uC7RPfldSqoz+pvX4G4wIAANOQ7ffXUCL9/rIakk2S2PZbAtZKOn3khF0AAFC4PJ1WZnuY/CvTyj8Ltztntm+Vu8vMIo5oaqaa/DtR0rt19Ce1LonkHwAAESu1Sb9ZC6oaVRGvUe/QfvUM7ldt5aKoQ8L03CDpQklfiToQAAAwOf5smzQwIGtcKpu/IOpwImF1i6VFddL+bvm+PbKlx0Ud0pRMJfn355Le7O7fma1gAADAsWnvCSf91pZW5Z+ZqSG5RrsPPanO3h0k/4rXLkmfM7MrJW2TlMp/0t3fGklUAABgTJmtYb+/teU57EMK1qKx5nXKPLhZme1bFSuy5N9Upv12SHp4tgIBAADHpm/ooA4PdioRq9Ki6mVRhzPjmPhbEuolbVLQ929AUjrvkRrnOgAAEJFcv78y3fKblZ10XIwTf6dS+fduSV81s49L2q6jP6ndNpOBAQCAqcnf8ms2lc/3ikNDOPSD5F9Re6ekP5F0kqSqiGMBAACTkJv0W6bDPrKGJ/6WdvLvZ+HXX4RfPfxq4ffxmQoKAABMXUdviySpMRyOUWoaaoPKPyb+FrXvSDpP0h0avYc0AAAoMLltv+Ve+RcmP0u98q+0mgcBAFBisv3+Gkqs319Wbttvz46inLIGSdIFki5y999EHQgAAJiYHzwgb98rVVXJlq+MOpxIxdY0S2byna3ywUFZZWXUIU3apJN/7s4eGwAACtjwtt/STP4lK+pUk1igvlTQ23B+VUPUIWHqntTUPnwGAAARyvX7a1oni5f3hk+rrpatWCVva1WmtUXx9SdEHdKksfgCAKAEZDyd64XXGG6PLTVmpvrkGrUdfEQdvS0k/4rT2yT9wMy+K6lVUib/SXe/PoqgAADA6LL97cq9319WrHmd0m2tymzfQvIPAADMrf39e5TKDGh+VaOqE/OjDmfWNNSuDpJ/PTvUXLcx6nAwdW+TdKKCQXKj9fwj+QcAQAGh39+RYmvXK33nbfIi6/tH8g8AgBKQ7fdXqsM+snJ9/xj6Uaz+XNKb3f07UQcCAAAmlpv0u259xJEUhlhz8HvIbN8acSRTE4s6AAAAcOxyyb8S7feXNZz8oxVxkeqQ9HDUQQAAgIl5OqXM9m2SpFgzlX9SUPknDW+HLhZU/gEAUAI6csM+miKNY7bVh8m/zt5WuWdkxueYRebdkr5qZh+XtF1SKv9Jd98WSVQAAOAo3tYqDQ3Klh0nmzcv6nAKgi1bLlXXyDs75Af2yxYuijqkSWHFDABACchW/jWUeOVfTcV8zausVyozoAP9e6MOB1P3M0lnS/qFgsm/z4SPLeFXAABQIHKTfpvZ8ptlsZhiTWslFVf1H8k/AACK3ECqVwcG9ipuCS2uWRF1OLNueOtvS7SBYDqaRzzWho/s9wAAoEAw7GN0xdj3j22/AAAUuWwSrD65RjGLRxvMHKhPrlbL/t+po7dV6+tfFHU4mAJ3p1kjAABFIjfsYy3Jv3y5vn9FNPGXyj8AAIpce5n0+8ti6AcAAMDsy2wNt/1S+XeEWPM6ScVV+UfyDwCAIpfr95dsijaQOdJQGyb/ekj+AQAAzAY/sF/e2S5V18iOK/22MlOR2/bbslWeyUQczeSQ/AMAoMhlt/02lviwj6z65GpJUldfm9KZ1ARnAwAAYKqGh32sk8VIHeWzhQtl9Y1Sf798966ow5kU/gQBAChi7p6r/CuXbb+V8RotrFqqjKfU3f9s1OEAAACUHIZ9jK/Y+v6R/AMAoIgdHNinwXSfkhWLVFtZF3U4c6Y+7PvXSd8/AACAGcewj/EVW98/kn8AABSxcqv6y6LvHwAAwOyh8m98w5V/z0QcyeSQ/AMAoIhl+/01JMuj318WE38BAABmh6dSyrQGHzBnK9xwJKPyDwAAzJX2nhZJZVj5R/IPAABgVvjOHdLQkOy4FbJkbdThFKTYqiYpHpc/2ybv64s6nAkVTPLPzOJm9qCZ3Rh1LAAAFIvhbb/lVfm3OLlSppj29+1WKjMYdTgAAAAlI9fvjy2/Y7LKStmqNZK7Mju2Rx3OhAom+SfpPZKeiDoIAACKxVB6QN19z8oUU31yVdThzKlErFJ1Ncvlyqird2fU4QAAAJSMXL8/hn2MK9Yc9v3bXvgTfwsi+WdmKyW9StJ/Rh0LAADForOvVa6MFidXKhGrjDqcOVfP1t+yZGYXmdlTZrbFzK4Z5fn3mtnjZvawmf2fma3Je+5KM3smfFw5t5EDAFAcGPYxOcMTf0n+TdYXJP2dpMxYJ5jZVWa22cw2t7e3z1lgAAAUqly/v2RTpHFEhb5/5cfM4pK+JOliSRskvdHMNow47UFJG939dEk/lPSZ8NrFkj4i6SxJZ0r6iJnVzVXsAAAUi9y2Xyr/xjU88Zfk34TM7NWS9rn7A+Od5+7XuftGd9/Y2Ng4R9EBAFC4Osq0319WQ22Y/Osh+VdGzpS0xd23ufugpE2SLs0/wd1vc/fe8MffSloZfv9KSbe6e5e7d0u6VdJFcxQ3AABFwbs75d1dUjIpW7os6nAKWm7b77YtcveIoxlf5Mk/SedIusTMWhQs4F5mZt+KNiQAAApftvKvocwm/WZR+VeWVkjKb/LYFh4by59K+sU0rwUAoOxkq9hizetlsUJIGRUuW7JUqp0nHTwg7+qMOpxxRf4n6e4fcPeV7t4k6QpJv3L3N0ccFgAABc3dy3bSb1ZdzXLFLaGDA/s0mOqd+AKUFTN7s6SNkv55itfRagYAULbo9zd5ZlY0ff8iT/4BAICp6xnqVl/qoKritZpf2RB1OJGIWVyLa4Ipx519rRFHgzmyS1L+aOuV4bEjmNkFkv5B0iXuPjCVa2k1AwAoZ2n6/U1Jtu+fF3jfv4JK/rn77e7+6qjjAACg0OVX/ZlZxNFEh75/Zed+ScebWbOZVSrYNXJD/glm9lxJ/6Eg8bcv76mbJV1oZnXhoI8Lw2MAACCU2Rpu+6Xyb1Jyff+2b404kvElog4AAABMXW7Sb5n2+8ui7195cfeUmb1LQdIuLunr7v6YmV0rabO736Bgm+88ST8IE+Ot7n6Ju3eZ2ccVJBAl6Vp374rgbQAAUJB8aEjeul0yU6xpXdThFIVimfhL8g8AgCJU7pN+s0j+lR93v0nSTSOOfTjv+wvGufbrkr4+e9EBAFC8Mq3bpXRatmKVrKYm6nCKQqxpraTgd+fplCxemGm2gtr2CwAAJofKv0A9234BAABmBFt+p85q58mWHScNDcnbCrcHNck/AACKTDozpM6+nZJM9WHlW7laWLVEiVhVMABl6GDU4QAAABStDMM+piXX929b4fb9I/kHAECR6erbpYyntKj6OFXGy3tLhlksb+tv4X7aCgAAUOgyW8PkH5V/UzI89KNw+/6R/AMAoMgMT/ptijaQAjGc/GuJNhAAAIAi5e55lX/rI46muMSag+EohTzxl+QfAABFpp1hH0dooO8fAADAMfGuTunAfmnefNmSZVGHU1SKYeIvyT8AAIpMe1jh1phsijSOQpGt/Otk4i8AAMC05Lb8rl0vM4s4muJiK1dJFZXyvbvlhw9HHc6oSP4BAFBkOnKTfqn8k6T65GpJQc8/d484GgAAgOLDsI/ps3hCsTXBujzTUphbf0n+AQBQRHqHDujwYKcqYtVaWL006nAKwrzKelXFa9WfOqSeoe6owwEAACg6DPs4NoXe94/kHwAARSRb9ddQ2yQz/jUuSWamhnD4CX3/AAAApi47qZbKv+kp9L5//FcDAABFpD235bcp0jgKDRN/AQAApscHB+StO6RYTLEm2spMh+Uq/0j+AQCAY9TeG076TbIwyzec/KPyDwAAYCoyO7ZLmbRs5WpZVXXU4RSlbMVkZvvWguxBTfIPAIAi0p637RfDskM/OntbI44EAACguORP+sX0WN1iaeEiqeewfN/eqMM5Csk/AACKRMbT6gwr2xpr10QcTWFpqM1W/rXKPRNxNAAAAMWDSb/HzsyG+/4V4NZfkn8AABSJ/X27lcoMan5Vo6oT86MOp6AkKxYqWbFIQ+k+HRxojzocAACAopEdUsGk32OTm/hbgEM/SP4BAFAkhvv9NUUbSIGi7x8AAMDUuDvbfmdIrJnKPwAAcIyGJ/0y7GM02T6IHeHvCQAAAOPzjn3SoYPS/AWyhiVRh1PUhpN/WyOO5Ggk/wAAKBLtPWHlH8M+RkXlHwAAwNTkqv7WHS8zizia4hZrWiuZyVt3yAcHow7nCCT/AAAoEsOTfqn8G00DE38BAACmJNfvj2Efx8yqq2XLV0qZtDI7W6IO5wgk/wAAKAIDqR4dHNiruCW0uGZF1OEUpPqw8q+zd6cyno44GgAAgMKXm/TLsI8ZkZv4W2BDP0j+AQBQBLJbWeuTaxSzeMTRFKaqRFLzqxqV9iHt79sddTgAAAAFb7jyj2EfMyHb988LrO8fyT8AAIrA8LCPpkjjKHT0/QMAAJgcH+iXt7VKsbhia2grMxOo/AMAANM2POyDhdl4SP4BAABMTmb7NimTka1eI6usijqckhBrXidJymwn+QcAAKaoo7dFEpV/EyH5BwAAMDm5fn8M+5gxdtwKqbpa3tkhP7A/6nBySP4BAFDg3D1X+deQpPJvPA21YfKvh+QfAADAeHL9/hj2MWMsFlOsqfCq/0j+AQBQ4A4O7NNguk/JikWqrVwUdTgFbXHNKkmm7r5dSmWGog4HAACgYOUq/5oZ9jGTsr/PTAEN/SD5BwBAgaPf3+RVxKtUV32cXBl197VFHQ4AAEBBcndltobJPyr/ZlRsbVj5V0BDPyJP/plZtZndZ2a/N7PHzOxjUccEAEAhYdLv1LD1FwAAYHy+b4/Uc1haVCdbXB91OCWFyr/RDUh6mbs/R9IZki4ysxdGGxIAAIWjvTes/KPf36TUM/QDAABgXLmqv7XHy8wijqa05JJ/LVvlmUzE0QQiT/554HD4Y0X48AhDAgCgoHRQ+TclTPwtbWZ2kZk9ZWZbzOyaUZ4/18x+Z2YpM3v9iOfSZvZQ+Lhh7qIGAKCwDA/7oN/fTLOFC2X1jVJ/v3z3rqjDkVQAyT9JMrO4mT0kaZ+kW9393lHOucrMNpvZ5vb29jmPEQCAKAyl+9Xd96xMMS1Oro46nKJA8q90mVlc0pckXSxpg6Q3mtmGEae1SnqbpO+Mcos+dz8jfFwyq8ECAFDAhod90O9vNsSaC6vvX0Ek/9w97e5nSFop6UwzO3WUc65z943uvrGxsXHOYwQAIAqdvTvlymhxcpUSsYqowykKdTXLFbOEDvTv0WC6P+pwMLPOlLTF3be5+6CkTZIuzT/B3Vvc/WFJhbHPBgCAAjQ87IPKv9kQW1tYff8KIvmX5e77Jd0m6aKIQwEAoCAMT/ptijaQIhKPVWhxzQpJUmdva8TRYIatkLQz7+e28NhkVYc7SX5rZpeNdgK7TQAApc77+uTPtkmJhGKr6Sk9GyxX+fdMxJEEIk/+mVmjmS0Kv6+R9ApJT0YaFAAABaK9t0WS1JhsijSOYpMd+tHJ1l8caY27b5T0JklfMLN1I09gtwkAoNRltm+R3BVb3SSrYGfJbKDy72jHSbrNzB6WdL+Cnn83RhwTAAAFYbjyj09lp4K+fyVrl6RVeT+vDI9NirvvCr9uk3S7pOfOZHAAABSD3LCPtfT7my2xVU1SLC5/tk3e1xd1ONEn/9z9YXd/rruf7u6nuvu1UccEAEAhcPe8Sb8k/6aioTZM/vWQ/Csx90s63syazaxS0hWSJjW118zqzKwq/L5B0jmSHp+1SAEAKFC5YR9r6fc3W6yyUrZ6jeSuzI7tUYcTffIPAACMrmewS32pg6pOzNO8yvqowykqVP6VJndPSXqXpJslPSHp++7+mJlda2aXSJKZvcDM2iT9kaT/MLPHwstPlrTZzH6voMf0p9yd5B8AoOwMD/ug8m825Sb+bo9+4m8i6gAAAMDo2nuDTwkbks0ys4ijKS4Lq5cqEavS4cFO9acOqzoxL+qQMEPc/SZJN4049uG87+9XsB145HV3Szpt1gMEAKCAeSaTS0ax7Xd2xZrXK33brQWR/KPyDwCAAtWe2/LbFGkcxShmcdUng9ZwTPwFAAAIpO/4P6m3V7a4Xla3OOpwSlpu6Mc2kn8AAGAMDPs4NtmJv/T9AwAAkIZ++XMN/FNQLJ+45PURR1P6Ys3DyT93jzaWSF8dAACMabjyb020gRSp4b5/LdEGAgAAELGhH2/S4L98QspkVHHln6nij/8k6pBKni1ZKtXOkw4ekHd1RhoLyT8AAApQOjOkrr6dkixXwYapYegHAAAod+6uwev/U4Nf/rwkqfIv/1qVb3k7/aTngJkVzNAPkn8AABSgrr42ZTytRdXHqTJeE3U4Ramhdnjbb9RbLQAAAOaaZzIa/MoXNHT916RYTJV/+yFVvPaKqMMqK9mtvx5x3z+m/QIAUICG+/01RRtIEZtf2aDKeFJ9qYPqHdqv2sq6qEMCAACYE55OafBz/6TUzTdKiYSq/uETSrzkpVGHVXZyQz+2b402jkhfHQAAjGq43x/DPqbLzNSQXC2Jib8AAKB8+OCgBj7xwSDxV12tqk98lsRfRHLbfiOu/CP5BwBAAcpV/iWbog2kyNH3DwAAlBPv69PAh/5G6Ttvk2rnqfrT/67ExhdGHVbZijWFyb/W7fJ0Kro4IntlAAAwpvZwQi2Vf8emIdw23RFWUgIAAJQqP3xI/de8W+kH7pUW1an6s19R/JTTow6rrNm8ebKlx0nxhHzfvsjioOcfAAAFpnfogHoGu1QRq9bC6qVRh1PUqPwDAADlwLu71H/Ne5TZ+rSscamq//mLiq1cHXVYkFTzxf+SFtbJYtHV35H8AwCgwGSr1Bpqm2RGkf6xqM8l/1rl7jKziCMCAACYWZm9e9T//r+St7XKVq5W9af/XbGly6IOCyGrq486BLb9AgBQaJj0O3NqKxepJrFAg+leHRrsiDocAACAGZXZuUP9V18lb2tVbN3xqvn8V0n84Sgk/wAAKDC5Sb9J+v3NhIbasPqvh62/AACgdKS3PK2+v/5zeftexU45XdWf/UpBVJmh8JD8AwCgwLT3Uvk3k+j7BwAASk360d+r/31/Ie3vVvz5Z6n6U/8mmzc/6rBQoEj+AQBQQDKeVmdvq6ThSbU4Ng3JJkkk/wAAQGlIbf6t+q95t9RzWPGXvFRVH/8XWU1N1GGhgDHwAwCAArK/b7dSmUHNr2pUdWJe1OGUhOy2385wOzUAAECxSv36Vxr4xw9JqZQSr3y1Kt/7AVmc1A7Gx98QAAAKSG7LL/3+Zkx9crUkqbOvTRlPK2bxiCMCAACYuqFf/lyDn/tHKZNR4rVXqPId75HF2NCJifG3BACAApIb9sGW3xlTnZineZX1SmUGdKB/b9ThAAAATNnQjzdp8F8+IWUyqrjyz1T5F1eT+MOk8TcFAIAC0t6THfZB5d9MYugHAAAoRu6uwev/U4Nf/rwkqfIv/1qVb3m7zCziyFBMSP4BAFBAqPybHdm+fx09JP8AAEBx8ExGg1/5goau/5oUi6nybz+kitdeEXVYKEL0/AMAoEAMpHp0cGCv4lahupoVUYdTUqj8AwAAxcTTKQ1+7p+UuvlGKZFQ1T98QomXvDTqsFCkSP4BAFAgsomphuRqhlLMsIZkkySpk+QfAAAocD44qIF/+rDSd94mVVer6qOfVmLjC6MOC0WM5B8AAAUi2++vgX5/M25xcpUkqauvTenMkOKxiogjAgAAOJr39Wngo+9X+oF7pdp5qv7Hzyt+yulRh4UiR88/AAAKBP3+Zk9lvFoLq5cp42l19z0bdTgAAABH8cOH1H/Nu4PE36I6VX/2KyT+MCNI/gEAUCCY9Du76PsHAAAKlXd3qf99f6nMYw/LGpeq5gvXKb7+hKjDQokg+QcAQAFwz6ijt0USyb/ZQvIPAAAUoszePer76z9XZuvTspWrVf2F6xRbuTrqsFBCIk/+mdkqM7vNzB43s8fM7D1RxwQAwFw7MLBPg+k+1VbUKVmxMOpwSlJDbZj86yH5BwAACkOmrVX9V18lb2tVbN3xqvn8VxVbuizqsFBiIk/+SUpJep+7b5D0QknvNLMNEccEAMCc6mDL76zLVv4x8be4mdlFZvaUmW0xs2tGef5cM/udmaXM7PUjnrvSzJ4JH1fOXdQAABwtveVp9V19lbx9r2KnnK7qz35FVlcfdVgoQZEn/9x9t7v/Lvz+kKQnJK2INioAAOZWdthHA8M+Zk1dzUqZYuru362h9EDU4WAazCwu6UuSLpa0QdIbR/nQuFXS2yR9Z8S1iyV9RNJZks6U9BEzq5vtmAEAGE36sYfV/76/kPZ3K/78s1T9qX+TzZsfdVgoUZEn//KZWZOk50q6d5TnrjKzzWa2ub29fc5jAwBgtrhn1HbwMUlU/s2mRKxCdTUrJLm6+nZGHQ6m50xJW9x9m7sPStok6dL8E9y9xd0flpQZce0rJd3q7l3u3i3pVkkXzUXQAADkSz9wr/rf/1dSz2HFX/JSVX38X2Q1NVGHhRJWMMk/M5sn6UeSrnb3gyOfd/fr3H2ju29sbGyc+wABAJgFg+l+3fDkP2rH/gcVs4RWLjgl6pBKGn3/it4KSfmZ2zZNfsfIpK7lA2cAwGxK3Xmb+j/4Pqm/X4lXvkpVH/yErLIy6rBQ4goi+WdmFQoSf9929x9HHQ8AAHPh0ECHNj38d3qm8x5VxWv1ulM+qoXVS6MOq6Qx8RcT4QNnAMBsGbr5Rg18/O+loSElXnu5Kt/3QVk8EXVYKAOR/y0zM5P0X5KecPfPRR0PAABzYfehp/XTJz6unsEuLapertdu+IgWJ1dGHVbJI/lX9HZJWpX388rw2GSvPX/EtbfPSFQAAExg6MebNPjlz0uSKt76Z6p4y58qSIcAs68QKv/OkfQWSS8zs4fCxx9EHRQAALPlqY479b1H3q+ewS6tWnia/vg5nyXxN0dI/hW9+yUdb2bNZlYp6QpJN0zy2pslXWhmdeGgjwvDYwAAzBp31+A3/zOX+Kv8i6tV+da3k/jDnIq88s/dfyOJv/UAgJLn7vrtzk26q/VbkqTTlr5SF6z7C8VjFRFHVj4W1RynuFXo0EC7BlK9qkokow4JU+DuKTN7l4KkXVzS1939MTO7VtJmd7/BzF4g6SeS6iS9xsw+5u6nuHuXmX1cQQJRkq51965I3ggAoCy4uwa/+q9K/ei7Uiymyvf+vSouek3UYaEMRZ78AwCgHKQyg7r5mX/VE+23SzKd3/ynev7yy/jUd47FLK765Crt69mmzt4dWr7g5KhDwhS5+02Sbhpx7MN539+vYEvvaNd+XdLXZzVAAAAkeTqtwc//k1K//LmUSKjq7z+uxLkvizoslCmSfwAAzLKewS799IlPaPehp1QRr9GrT/w7rVt8ZtRhla2G5Brt69mmDpJ/AABgFvjgoAY+9RGlf/0rqapKVR/9jBIveGHUYaGMkfwDAGAWtfds148f/5gODbRrflWjXrvhI2qsbY46rLJWn1wtSerobY04EgAAUGq8r08DH7tG6c2/lWrnqfqTn1P81OdEHRbKHMk/AABmydbOe3XjU5/RUKZfy+efpEtP/qBqK+uiDqvsNdQ2SZI6eloijQMAAJQWP3xI/f/wXmUee1haVKfqT/2b4utPiDosgOQfAAAzzd21edePdUfL/5PkOrnxfL3y+PcoEauMOjSIib8AAGDmeXeX+q95jzJbn5Y1LlX1Z/5dsVVrog4LkETyDwCAGZXODOnWrV/So3tvlSS9eM1bdNbKyxnsUUAWVDWqIl6j3qH96h06oGTFwqhDAgAARSyzb6/6/+5d8rZW2YpVqv7MFxVbuizqsICcWNQBAABQKvqGDuoHj35Ij+69VYlYlV5z0gf0wlVXkPgrMGYxNWT7/vVQ/QcAAKYv09aq/quvkre1KrbueNV84T9I/KHgkPwDAGAGdPbu1Ld//161HXxE8yrrdcVpn9aJDS+OOiyMIbv1t5OhHwAAYJrSW59W39VXyfftUWzDaar+ly/L6uqjDgs4Ctt+AQA4Ri3dD+rnT/6TBtI9Wlq7Tpdt+LDmVzVEHRbG0Vi7VvXJ1YrHWAoBAIpf5tldGvrBt5W++9eyxYtlq5oUW71GsVVNiq1aI1u5SlZZFXWYRcvTKfme3crs3CFvbVFm5w5lWluU2fKUNDCg2PPPVPVHPyOrqYk6VGBUrHgBADgGD+6+Ub/a+h9yZXR8/dm6+IT3qTJeHXVYmMDzlr9Gz1v+mqjDAADgmKS3Pq2hTd9U+o7/lTIZSZJ3tkvPPKV0/olmsmXLg0Tg6iAhmE0OauEiWpSEvOewMjtbldnZIg+/ZnbukO/aKQ0NjXpN/PwLVPV3H5FVMtgNhYvkHwAA05DxtG7bdp0e3H2jJOmslW/Qi9e8RWZ01AAAALPH3ZV5+EENbbpe6fvvCQ7G40q88tVKXPZH0tCQMq0t8mx1WmuLfPez8t27lN69S7rv7iNvOH+BYmFC0FatGf7+uOWyeOmlDNxd3rFP3rojrODbHlb07QgSp2OwJcvC5GlYUbl6TVBhWc9uDxS+0vt/MgAAs2wg1aOfP/kptez/neKW0CuPf482LHlZ1GEBAIAS5pmM0vfcqaFN1yvzxKPBweoaJV51mSpe90bFlizNnRvfcNqR1w4Oyne3KRMmvHzncOJLhw4q89jDyjz28JEvmEjIVqwKqwSbgsTgqibFVq2W1c6b7bd7zHxwQN62M3ifO1uUad0hb9uhTOsOqb9v9IsqKoP3l9s2HSb4Vq5mSy+KGsk/AACmYH/fbv348Y+pq2+naioW6rKTP6gVCzZEHRYAAChRnkop9aubNbTpenlrS3BwwUJV/OEbVHHJH8kWLpzwHlZZKVuzVrE1a4+8t7u8q1MeJscyO1tyFXG+b498x3ald2w/cguxJKtvDCvg1ii2ujlXEWcNS+Z0C7G7Swf255J7+T35fM+zkvvoFy6qy1U4xlY15ar5bMlSWTw+Z/EDc4XkHwAAk7TzwKO64YlPqi91UA3JNfrDDR/RwuqlE18IAAAwRd7Xp9QvfqahH3xH3r5XkmSNS1Xxhj9W4qJLZqQSzcxk9Q1SfYPiZ2wc8fq9yrS1hsnAvORaW6u8s13e2a7Mg5uPvGF1zfB22Ly+grZi5TENHPF0Sr772dygjeHKxRbp0MHRL4rFZStWHFm5uDqs4lswccIUKCUk/wAAmIRH9t6qW7d8URlPaW3dC/SqE/9OVYlk1GEBAIAS4wcOaOhnP9DQT78vHTwgSbI1zaq4/K1KvOxCWWJu/jPeapKKH3+SdPxJR8aXTsv37gmHYoR9BcNknPZ3K/P0k9LTTx5ZLRiL5Q0cGZ5CHFvdJFu4aPjeRwzcGL6379oppVKjB5pMDlcf5vcsXL5SVlEx478XoBiR/AMAYBwZT+vOlm/o/l0/kiQ9f/llOq/5/1PM2BICAABmTmbfXg398DtK3fRTqb9fkhQ7+VRVvPFKxV/4YlmsMIaKWTwuW75CseUrpLPOOeI5P3BAmbajq/N897PyZ9uUfrZNuveuI2+4YKFiy5bLOzumNXDDFtczrRiYAMk/AADGMJju0/889c/a2nWvYhbXBev+UqcvuyjqsAAAQAnJ7Niuoe9/U6n//aWUDurl4me+SBVXXKnYaWcUVWLLFi5UfOHpip9y+hHHjxo4kqsWbJEOHlAmrHBURaVs5eojtwyvXhMO3GDHBTBdJP8AABjFwf59+skT16q9Z7uqE/N0yUl/r9WLnhN1WAAAoESkn3hUQ9/7ptJ33REMpojFFH/phaq4/C2Krz8h6vBm1LgDRzo75HuelS1ukC1dxsANYBaQ/AMAYITdh57UTx7/uHqH9quuZoVeu+EjqqtZEXVYAACgyLm70g/cq6FN1yvz0APBwYpKJS56jSr+6E2KLV8ZbYBzzMxkDY1SQ2PUoQAljeQfAAB5nmy/Q794+vNK+5BWL3yOLjn5A6pOzI86LAAAUMQ8nVb6ztuCpN+Wp4KDyVpVXPI6JV57hWKL66MNEEBJI/kHAICCT+Lvbv227tn5XUnSc5ZdrJetfYfiMf5VCQAApscHB5S65SYNff9b8mfbJElWt1iJ171RFa9+rWzevIgjBFAO+C8aAEDZG0oP6JfPfF5PddwpU0znN79dz1t+SVE12AYAAIXDDx/W0I0/VurHm+RdnZIkO26FKi5/sxIXvkpWWRVxhADKCck/AEBZOzzYpZ8+/nHtOfy0KuM1evWJ12jt4o1RhwUAAIpQpqtTqR9v0tANP5J6eyRJsXUnqOKKtyp+7ktlcf4THMDc4588AICytffwVv308Wt1aLBDC6qW6rUbPqKG2jVRhwUAAIpM5tk2DX3/20rdfKM0NChJij3neaq44krFN57FbgIAkSL5BwAoS8903qP/eeqflcoMaMWCDbr05A8qWbEw6rAAAEARSW99WkObvqn0Hf8rZTKSpPg556ni8rcovuG0iKMDgADJPwBAWXF33bfrh7qz5RuSXBuWvFwXrv8rJWIVUYcGAACKgLsr88hDGtr0DaXvuyc4GI8r8cpXqeINb1FsTXO0AQLACCT/AABlI5UZ0q1b/l2P7fs/SdJL1lypM1f+EVtxAADAhDyTUfq3v9HQpuuVefyR4GB1tRJ/cJkqXv8mxZYsjTZAABgDyT8AQFnoHTqgnz3xSe06+JgSsSq96oS/0fENZ0cdFgAAKHCeSin1q1s09L3r5Tu2BwfnL1DFH16uikv/SLaQtiEACltBJP/M7OuSXi1pn7ufGnU8AIDS0tGzQz95/GM6MLBX8yrr9YcbPqKl89ZFHRaAaTCziyT9q6S4pP9090+NeL5K0vWSni+pU9Ll7t5iZk2SnpD0VHjqb939HXMWOICi4319Sv3yBg19/9vy9r2SJGtcooo/+mMlLr5UVlMTcYQAMDkFkfyT9N+SvqhgoQYAJWMw1astXb9V2tNqrG1Sfc1qVcSrog6rrGzv3qyfP/lpDaZ7tXTe8frDkz+keVX1UYcFYBrMLC7pS5JeIalN0v1mdoO7P5532p9K6nb39WZ2haRPS7o8fG6ru58xlzGPxzMZpX76fcVf8CLFVjFpHCgUmfa9Sv3y5xr6yfelgwckSba6SRVXvFWJl14oq6BPMIDiUhDJP3f/dfhpLAAUPXfXs4ee0MN7btZTHXcqlRnIPWeKqa5muRqSa9RY26yG2iY11jZpYdVSmcUijLr0uLse3P1z3bbta3JldGLDS3TR8VerIl4ddWgApu9MSVvcfZskmdkmSZdKyk/+XSrpo+H3P5T0RSvQxp6Zp5/U4Jc/L+nzstVNSpx9ruLnnKfYiRtkMf6dAMwVd5e3blfqrl8rfdcdyjw1/I+U2EmnqOKNVyr+opfw/0sARasgkn+TYWZXSbpKklavXh1xNABwtJ7Bbj2271d6dO8t6upryx1fueBU1VYuVnvPdnX37VJXX5u6+tr0dOdduXMqYtVBIjC5JkwINqsh2aSaivlRvJWil86k9Ktt/6Hf77lJkvSiVW/U2avfRIIVKH4rJO3M+7lN0lljnePuKTM7IClb7ttsZg9KOijpg+5+58gXmMs1p1VVKXHBxUrde5e8tUVDrS0a2nS9rL5B8RcFicD4Gc+nygiYBZ5OK/PkY0rfdYdSd90h35X3j5aqKsVf8CJVXPYGxZ7zPAaDASh6RZP8c/frJF0nSRs3bvSIwwEASVLG09re/YAe3XuLtnbdp4ynJUm1FXU6ZekFOm3pK1RXsyJ3fiozqM7eneroaVF773Z19OxQe8929Qx1a/ehJ7X70JNH3H9eZX2YFAwqBBtrm7W4ZqXiMf5DcDTurt6h/brp6X/Rjv0PKW4VuuiEq3Vy4/lRhwYgerslrXb3TjN7vqSfmtkp7n4w/6S5XHPGmtep6pqPqjKVUuaRh5S66w6l77pD3r5XqRt/rNSNP5aStYqfdbYSZ5+n+JkvktXOm82QgJLmgwNKP7hZ6bvuUPqeO+XdXcNPLlioxIteEiTdn3emrJqdAgBKR9Ek/wCgkOzv261H9t6ix/b9nw4PdkoKtvSuW3yWTl/6SjUv3qiYxY+6LhGr1NJ5644aNtE7dCBICPa0qKM3+3WHDg926vBgp1q6H8idG7O46mpWBsnAZFMuOTi/qrGkP5lOZ1LqGezS4cFOHRrsVM9glw4NdOR+R4cHOnV4sEtDmX5JUrJikS47+UNavuCkiCMHMIN2SVqV9/PK8Nho57SZWULSQkmd7u6SBiTJ3R8ws62STpC0edajnoAlEoo/d6Piz90of+d7ldnyVFiN9Gv59i1K33ar0rfdKiUSij/3BYqfc67iLzpXsfqGqEMHCp4fPqT0vXcHyfX775H6enPP2bLlip9znhLnnKfYKafJ4vznMYDSZME6KHphz78bJzPtd+PGjb55c+TrNABlZig9oGc679Ije2/RzgOP5I7XVS/Xqcsu1ClLXq55lYtn7PUyntaB/j1qH5EU3N+/W9LR/+yuitfmegg2JIe/ViWSMxbTbHB3DaQP69BAfhKv84ik3qHBTvUOHdBo73ukili1jpt/oi46/motqF4y+28AmICZPeDuG6OOoxSEybynJb1cQZLvfklvcvfH8s55p6TT3P0d4cCP17r7G8ysUVKXu6fNbK2kO8Pzuo5+pUAhrDkzz7YpffevlbrrDmUe/b2Ut3aPnXzqcOKCgSFATqZ9r9J336nU3Xco89ADUjqdey62/kTFzzlXiXPOkzWvL+kPTgGUl/HWnAWR/DOz70o6X1KDpL2SPuLu/zXW+YWwEANQPvYe3qJH9t6iJ/bdroF0jyQpEavSCQ0v1mlLL9TKBafM6cJxMN2vzt4d4dbhYNtwR0+L+lIHRz1/QdVSNdauCROCwZCRxTUrRq1MnGnpzJAOh9V6+Ym8I5N8XUcMRRmLKaZk5SLNq6zX/Mp6zauq17zKes2rXKx5lQ2aVxUcr4wnWcijoJD8m1lm9geSviApLunr7v5JM7tW0mZ3v8HMqiV9U9JzJXVJusLdt5nZ6yRdK2lIUkbBevPn471Woa05vbtLqd/+Rum7f6305nulocHccwwMQTkbb2CHYnHFTj9DiXPOU/zscxVbelx0gQLALCr45N9UFdpCDEDp6U8d0hP77tAje2/Wvp5tuePL5p2g05ZeqJMaz1VVojbCCI/k7uoZ6g63Dm9Xe+8OdfRsV2dvq9KeOur8uFWoPrlKDbXNasybPFxbUTepxJm7qz91KNiCm1+pN9CRS/YdGuxU39CBScVfGa8JE3nhI0zs5Sf5aivr5iRhCcw0kn/Fq5DXnN7Xq/Tme4PtwffeJR0a/gCIgSEoB57JKPPEo+MO7IiffZ4SZ50jW7gwukABYI6Q/AOASXDPaOeBR/TI3lv0dMddSvuQJKk6MV8blrxMpy29UI21TdEGOUXpTErd/c/mkoLB1uEdOjiwd9TzaxILjhgwUhGvGTPBl/39jMcUU21lXZDIq6ofM8FXWeBbk4FjQfKveBXLmtNHGRiSk6xV/KxzgqqnF7yQgSEoagzsAICxkfwDgHEcGujQo3v/V4/uu1UH+veER01rFp2h05ZeqPX1L1KixKbrDqR68waLhF97WnLbmiejKl4bJvAWH5HQm19ZH2zDrVysZOUiqvVQ9kj+Fa9iXHO6+1EDQ3IqKoLBImeH2x8X10cXKDBJ4w/sOE7xc85nYAcAiOQfABwlnRnS1q779MjeW9TS/Tu5MpKk+VWNOnXJBTp16Su0sHppxFHOLXfXoYF2tfe2hP0EW5TOpDS/sl61VYvDpF695lUFib3KeE3UIQNFgeRf8SqFNeeYA0PMjhwYsnJ1tIECeTId+5S+69cM7ACAKSD5BwChzt5WPbL3Fj2271e5fnQxS2h9/Qt12tILtWbRGVSqAZhRJP+KV6mtOSccGHLOecHAkBNOZmAI5hQDOwDg2I235qQuGkDJG0z36an2O/XI3lv07KEncscbkmt02tILdfKSlypZQSNoAEBps7rFqrj4ElVcfMlRA0O8tUVDrS0a+u43ZPWNip99ruLnnKv4cxgYgtnBwA4AmDsk/wCUJHfX7kNP6pG9t+jJjjs1lO6TJFXEa3Ryw7k6bdmFWjbvRLaKAADKktUklXjJS5V4yUtVOcrAkNTPf6TUz3/EwBDMKAZ2AEA0SP4BKCm9Qwf0+L5f6ZG9t6iztzV3fMWCDTpt6YU6oeElqoyzmAQAIMsSiWAQyHM3yt/53qMGhqRvu0Xp225hYAimhYEdABA9/ukKoOhlPK2W7gf1yN5btLXrXmU8JUlKVizSKUterlOXvkL1yVURRwkAQOEzM8WPP0nx409S5dv+/KiBIen77lH6vnukf/20tHBR1OFOS6xxieIvCrY1x9Yezy6AWZDp2Kf03XcGFX4PbWZgBwBEjOQfgKK1v3+PHt17qx7b+786NNghSTLFtLbuTJ227EKtrXuB4jH+MQcAwHTFlq9U7PVvUsXr33T0wJD93VGHNy2Z/d3KPPOUhq7/WlB5dnY48fjU06k8m6ZgYEdLUN1396+VefKx4SdjccXOeH6wdfxFL1Fs2fLoAgWAMsW/3QAUlVRmUM903q1H9tyi1gO/zx1fVH2cTl36Cp2y5OWaX9UQYYQAAJSmIwaGDPRLvT1RhzRl7spta07f/Wv5nt1K/XiTUj/eFPace3Gwrfn5Z9FzbgKeySjz5GPDAzvahtutqKpK8Y0vVPyc85Q468UM7ACAiJH8A1CQ0pkhHR7s0qGBdh0a6NDBwXbt73tWz3Terf7UYUlSIlapE+rP0alLL9SqhafKLBZx1AAAlAerqpaqii85ZpJiZ56txJlny9/z/uHk1W9ul+/aqdTN/6PUzf9D8moMPjio9EObh5OnRw3sIHkKAIWI5B+AOZfxtHoGu3VooF0HBzqCBN9gRy7Rd2igXT1D+yX5qNcvrV2nU5ddqJMbz1d1gqmDAABg6iwWU3zDaYpvOE0Vb3/nUdtW0+H048FYXLHTnhNsWz373LLbtuqHDyt9311K3fVrpe+7++iBHWybBoCCxz+dR3HLM/+mQ4Odqk+uVn3NKtUnV2lxchVJBmAS3DPqHdo/nNQb6AgTe8NJvsMDnXJlxr2PKabaysWaX9Wo+VUNml/ZqAVVDVq18DQtmbdujt4NAAAoB2YmW9OsyjXN0pvedtTAiszvf6fB3/9O+vLnFVt3guLnnFfSA0OOeP+/f0BKpXLPlcP7B4BSQ/JvFDv2P6QDA3u1vXvzEcdrKxcfkQwMvl+tZMUi/qWHsuDu6k8dOmIr7qGBIyv2Dg92Ku2pCe+VrFik+VVBQm9+ZZjgqxr+Oq9ysWIWn4N3BQAAcKRYwxLFLnmdKi55nfzwIaXvvTuoCrz/HmW2Pq3M1qdLamAIAzsAoLSZ++jb6grZxo0bffPmzROfOE2dva3q7N0ZPPpa1dW7U119bUplBkc9vzoxT4vDRGB9clXu+wVVDfQgQ1EZSPXoYK5a7+jE3qHBTqUyAxPepyaxIEzihQm9yvzEXoPmVTYoEauYg3cEANEzswfcfWPUcWDqZnvNieLjgwNKPxj0vEvd/esjJx4XWc87BnYAQGkZb81J8m+SMp7WwYF96uzdqa5cYjD4fiA9+qSzRKxK9TUrVZ9cfUSl4KKa46hoQiT6hg6qq69N3X27hpN8ef32BtN9E96jKl4bJPCqGrSg8shqvSDRV6+KeGEvdgFgLpH8K14k/zAeT6ePTJ7t2jn8ZIEmzxjYAQCli+TfLHJ39Qx2qbMvTAj2tqor/L53aP+o18Qsobqa5aqvCbcPJ1epvma16mpWqCJeNbdvACUnnUnpQP8edfW15R7dfbvU1dumvtTBca9NxKq0IK/H3situAsqG1SZSM7ROwGA0kDyr3gV0poThW3CbbMRDgxhYAcAlAeSfxHpGzqUSwTmbyE+OLBvjCtMC6uXqr5mda6vYENylRbXrFYVCReM0Dt0QF29berua1NX365cou9A/x5lPD3qNRXxGi2uWaG6mhVaWLU0b5hGkNyrTsyjfyUAzDCSf8WrWNacKDwjB4YoPbw2i60/UfFzzlXinPNkzetnZe3FwA4AKD8k/wrMYLpfXX1524d7d6qrb6e6+54dcwLqvMr6vH6CwfbhxTWrlKxYyL+wS1g6M6T9/buHk3t5yb7+1KExrjItqFqixTUrtLhmpRYnV2pxzUrV1azQvMp6/r4AwBwj+Ve8in3NicIwcmDIkZV3y4Otweecp9gpp0278m78ysOYYqedEVnlIQBgbpD8KxLpzJC6+57N9RIMthK3qrtv1zjDRuYHg0ZqVmpxcpXqalaoOjFPlfGkquJJVSaSqozX0GOwgLm7eocOhEm98NG7S919bdrfv2fMhHBlvEZ1NStVX7NSdTUrtTgZJPsWVS9n+zgAFBCSf8WrVNeciM64A0MWLlLihS8OqvKef6asavyee5Mf2HGObOGi2XlDAICCQfKvyOUPG8kNHOkLJhIPpnsnvoGC7Z5V8WSQFEzU5hKDwbFaVeW+zx6vVVWiJngunlRlolYVsSqqxo5BKlvF17sz6MGXS/S1jTk0RjItrFqixclV4XbdlblqvtqKOv48AKAIkPwrXuW25sTcOnJgyO3yXW3DT1ZXB8m7s7PJu2BgyBEDO+65U97VOXwNAzsAoKyR/CtRw8NGWnOJwQMDezWY6tFAui/82htOcD32P2dTTFWJMIEYrx1OHobJwsp4Tfh8kDDMnZuoPeJrIlZx7G++QAVVfN1523R3hVu6d+lA/94xq/iq4rWqy23TXaHFNUEVZ13NciVilXP8LgAAM4nkX/FizYm5Emzb3R4M5bjrDmWeenz4yVhcsdPPkC1YpPTme6ReBnYAAI423pqTfzsUMTPTvKp6zauq15pFzx3zPPeMBtP9Gkj3aDDVG3xN92og1afBdI8Gcsf6NJDKPhf+nLumV6nMgPpTh9WfOnxMccetYri6MF4zYmtyTGYmy33N/z7vmMWk8GssPK4Rz4/+NbiPwq+xvOeV93zu62jH8l7LPR1W8+3KTdUdq4rPFNOi6uPCJN+q3DbdxTUrlKSKDwAAoGyZmWzNWlWuWSu96W3KtO9V+u47lbr7DmUeekCZhx7IncvADgDAVJH8KwNmQcVeVSIpHUMruHQmdVRCcDhRmJ8w7M1VHQ6kwnPyrkn7kPqGDqhv6MDMvckCUhWvzQ3ZyA7aWFyzUotqlpd01SMAAABmRqxxqWKXvl4Vl74+GBhy393ynh7FX/BCBnYAAKaM5B8mLR5LqCY2XzUV86d9D3dX2oeGE4bpXg2GCUJ3V/C/TPh9ZviYZ4LHKM8r+/y0vrqUd7+MZ0bcb8TrjTgmSQurl+Ql+lYygRkAAAAzxubNV+Jlr4w6DABAESP5hzllZkpYpRKVlapVXdThAAAAAAAAlLRY1AEAAAAAAAAAmB0k/wAAAFA0zOwiM3vKzLaY2TWjPF9lZt8Ln7/XzJrynvtAePwpM2MfJQAAKAsFkfybaBEHAAAAmFlc0pckXSxpg6Q3mtmGEaf9qaRud18v6fOSPh1eu0HSFZJOkXSRpC+H9wMAAChpkSf/JrmIAwAAAM6UtMXdt7n7oKRNki4dcc6lkr4Rfv9DSS+3YBLXpZI2ufuAu2+XtCW8HwAAQEmLPPmnyS3iAAAAgBWSdub93BYeG/Ucd09JOiCpfpLXysyuMrPNZra5vb19BkMHAACIRiEk/1iIAQAAoCC4+3XuvtHdNzY2NkYdDgAAwDErhOTfpLAQAwAAKHv/f3v3HmRJWZ9x/PuwqKsQ1yISo1FZTICNF0CIEKo0LomJZUJFMSRCrCRAQlQEohWtXLwkGrUSw0ZBJAoqAklxSRBckRIpYGMoCALKgoviBRZvqRI0gou7yC6//NE97GHcmT2zzHSfc+b7qZqa05cz85ye7vf85u1++3wHeMbA9NPbedtcJ8nOwDLg+0M+V5IkaeKMQuefhZgkSZKGcQOwV5I9kzyW5gM8Vk9bZzXwJ+3jI4Crqqra+Ue2nwa8J7AX8PmOckuSJPVm574DMFDE0XT6HQn8Yb+RJEmSNGqqanOSE4DLgSXAx6pqXZJ3AjdW1Wrgo8C5Sb4O/ICmtqRd70LgNmAz8Pqq2tLLC5EkSepQ751/MxVxPceSJEnSCKqqy4DLps17+8DjTcDvz/DcdwPvXtCAkiRJI6b3zj/YdhEnSZIkSZIk6dEZhXv+SZIkSZIkSVoAdv5JkiRJkiRJE8rOP0mSJEmSJGlCpar6zjBnSe4G7lrgX/Nk4J4F/h0LwdzdG9fs5u6Wubtl7m6Ze3Z7VNXuHfwezbOOak7wGOqaubtl7m6Na24Y3+zm7pa5ZzZjzTmWnX9dSHJjVf1K3znmytzdG9fs5u6Wubtl7m6ZW3p0xnVfNHe3zN0tc3dvXLObu1vm3jEO+5UkSZIkSZImlJ1/kiRJkiRJ0oSy829mZ/QdYAeZu3vjmt3c3TJ3t8zdLXNLj8647ovm7pa5u2Xu7o1rdnN3y9w7wHv+SZIkSZIkSRPKK/8kSZIkSZKkCWXnnyRJkiRJkjShFn3nX5INfWeYiyRbktw88LV8lnXXJBmJj8BOUkn+bWB65yR3J7m0z1zDSvKK9jWs6DvL9oz7tobxOy6n217+ETs2x2bfHpTkLUnWJbmlbQsP7jvTsJI8Pcknk3wtyTeSnJLksbOs/4YkT+gy4zYyVJJVA9NvSvL3PUYaysB75roka5P8ZZJFX/uoP+P2/mbd2Y9xem8e920N43dcTmfdufCsO7tjzblwRiqMhrKxqvYf+Frfd6Ah3Q88N8nj2+nfBL4zlx+QZOd5TzW8o4Br2u9DS7JkYeLM6lFvay0qO7Rv9ynJIcBhwAFVtS/wEuBb/aYaTpIAnwAuqaq9gL2BXYF3z/K0NwC9dv4BDwCvTPLknnPM1dR75nNo2sKXAX/XcyZpnFh39sO6U5PKurNDY1p3WnMuEDv/gCS7JrkyyReS3Jrk5e385Um+nOTMtgf3swNvbCMjyYFJ/ivJTUkuT/LUgcV/1PZAfynJQb2FbFwG/E77+CjgvKkFSQ5Kcl2SLya5Nsk+7fyjk6xOchVwZfeRm/0DeCHwp8CR7byVST6X5NNJbk/yoame/SQbkqxKshY4pI/M7Ni2/lyS/QfWuybJfl2GHtRu40sHpk9LcnT7eH2SdwwcsyN39nC2/KNiln17pu3+20m+0rY1p/Z4Vv+pwD1V9QBAVd1TVd+dqS1sz3ifMiJt4a8Dm6rqrDb7FuCNwLFJdklycpvxliQnJjkJeBpwdZKre8y9meYTyt44fUH7XnlVm/nKJM9MsizJXQPt4i5JvpXkMV0Hn1JV3wP+HDghjSVJ/jnJDW321wy8pr9q25a1Sf6xr8yaTNadnbHu7I51Z8+sOxeUdWe3rDkXiJ1/jU3A4VV1AHAosCpJ2mV7AR9se3B/CPxePxEf9vhsHXpxcbtTfwA4oqoOBD7GI3vyn1BV+wPHt8v6dD5wZJKlwL7A9QPLvgK8qKqeD7wdeM/AsgNoXt+LO0v6SC8HPlNVXwW+n+TAdv5BwInAs4FfBF7Zzt8FuL6q9quqazpP29iRbf1R4GiAJHsDS6tqbWeJ5+6e9pj9V+BNfYcZUzPt2z+l3Zc+DLysbWt27yjjtnwWeEaSryY5PcmLx6gtfA5w0+CMqroP+CbwZ8ByYP/2zPK/V9WpwHeBQ6vq0I6zTvdB4NVJlk2b/wHg7KnMwKlVdS9wMzDVbh8GXF5VD3YVdluq6g5gCfBzNP983FtVLwBeAByXZM8kL6M5Ng6uqv2A9/YWWJPKurMb1p3dse7UMKw7uzeudac15wLo83L2URLgPUl+DXgI+AXgKe2yO6vq5vbxTTQHSJ82tg0JAEmeCzwXuKKtG5cA/zuw/nkAVfW5JE9M8qSq+mF3cbeqqlvS3CvmKJozhIOWAWcn2QsoYLCn/oqq+kE3KbfpKOCU9vH57fSlwOfbg5ok59GcyfpPYAtwUQ85H7aD2/o/gLcleTNwLPDxbtLusE+0329iawGsuZlp396WFcAdVXVnO30ezRmtzlXVhrZgfBHNP84XAO9iTNrCWawETq+qzQA9t3s/paruS3IOcBKwcWDRIWw9Bs9la+FyAfAq4GqaM/yndxR1WL8F7JvkiHZ6GU3Hy0uAs6rqxzB6fwdNBOvODlh3dse6U0Oy7hwtKxnRutOac2HY+dd4Nc3ZhAOr6sEk64Gl7bIHBtbbAoza8IsA66pqpsv8azvTXVsNnEzT2PzswPx/AK6uqsPb4mHNwLL7uwo3XZLdaC6Xfl6SomnYC/g0M2/bTe0l1X2b07auqh8nuYLm7MMfADOejevIZh55dfLSacunjs0tjGZbtr38vZpl3/4kI5x7SnuMrQHWJLkVeD3j0RbeBhwxOCPJE4FnAuv7CDRH7we+AJw1xLqraTo4dqNpT65awFxDSfIsmjbjezTvnydW1eXT1nlpH9m0qFh3dse6szvWnf2y7lxA1p29eD/WnPPKYb+NZcD32gLsUGCPvgPNwe3A7mluREqSxyR5zsDyV7XzX0hzqem9PWQc9DHgHVV167T5y9h6c+CjO000uyOAc6tqj6paXlXPAO6kOfNzUHu57k4027mvoRYz2ZFt/RHgVOCGqvq/hY23XXcBz07yuCRPAn6j5zxzNer5Z9q3d2LbuW8HnpWtn/T4qq4DT0myT3sFwZT9gS8zHm3hlcATkvxxm2cJsIrmiofLgdekvcl8W8AA/Aj4me6j/rT2jOSFNMMXplxLe+8emk6N/27X3QDcQHOW/9K+/zlNsjvwIeC0qiqa7f26dugOSfZOsgtwBXBM2k+6G/g7SPPFurM71p3dse7s16jnt+7sx9jWndac828Uz1p0pt3RH6AZL/6pthf/Rpp7U4yFqvpJe/noqWnGxO9M00u+rl1lU5Iv0lxif2w/Kbeqqm/TvMlP916aIQFvpTm7OSqOAv5p2ryLgNfRNDCnAb9Ec4nxxd1Gm92ObOuquinJfQx3hmVBTB2XVfWtJBcCX6IpDr7YV6a5GKP8M+3bR9K80T4id1VtTHI88Jkk99Ps/33ZFfhAWyRuBr5OMxTkDEa8LayqSnI4cHqSt9EUvZcBf0tzdnBv4JYkDwJn0rQxZ9Bs9+/2fP+VKauAEwamTwTOaodu3Q0cM7DsApqhXSs7S/dIj09yM83ffTPNEJF/aZd9hGZI5RfSjNm5G3hFVX0mzU3ob0zyE7b+faRHxbqze9ad3bHu7McY5bfu7MEE1J3WnPMoTUfk4pTmE6XOrKq+P41MYybJSuBNVXVYz1HmVZKn0VzSvqKqHuopw1gfl+OefzZJdm3vexKaG/F+rare13eu7UmyhuZ4vbHvLJIWr0l+f9DCsu5c0AxjfVyOe/7ZWHdK82vRDvtN8lqaG3G+te8s0ihoLwe/HnhLjwXYWB+X455/CMe1Z7TW0Qzj+XC/cSRpPCyC9wdpTqw7H71xzz8E605pHi3qK/8kSZIkSZKkSbZor/yTJEmSJEmSJp2df5IkSZIkSdKEsvNPkiRJkiRJmlB2/knSkJLsn+RF7eOVSSrJzn3nkiRJ0mSx7pQ0n+z8k6ThXQzs03cISZIkTTzrTknzxs4/SRpe+g4gSZKkRcG6U9K8sfNP0lhLsrwdBvG7SdYn2ZBkVZLnJbkpyf1JVifZpV3/6CS3JdnYLl858LPWJzkhybVJNiVZm+QF7bI1wB7AmUk+PhDhuCTfbn/POUmWdvbiJUmS1BnrTknjKlXVdwZJ2mFJlgN3AtcBrwX2Bc4FvgocDzwIfAp4K/Aj4IPA64D/AY4B/gJYUVXfTLIe2AU4DrgNOBNYWlUHJ9kNWAu8D/go8HzgamANcBLwFOAS4K+r6rSFfdWSJEnqmnWnpHHlDUMlTYp3VdUtwC1JTgHOr6or4eGzpyuAQ4DTquqc9jl/056BPRF4czvvnKq6pH3eKpr7rVBVP0iyBbivqu5NHh6J8fqqug24NckVwH4L+iolSZLUN+tOSWPFYb+SJsUdA483AndNm34c8MvA9dOed107f8o3Bh7fB+yUZMksv3dw/XsBh19IkiRNNutOSWPFzj9Jk2LztOmHtrHOxm3MW9J+TfnJNtaZ7YbLW+awriRJksafdaeksWLnn6TF5CvAwdPm/Spw+5DP9yapkiRJGoZ1p6SR4T3/JC0mq4Czk6xj642X9wOOHfL5G4AV7U2YJUmSpJlYd0oaGXb+SVo0quqiJE8F3gn8PHAz8NKqWjfkjzgNOBl4FnDqgoSUJEnS2LPulDRKUuXVxJIkSZIkSdIk8p5/kiRJkiRJ0oSy80+SJEmSJEmaUHb+SZIkSZIkSRPKzj9JkiRJkiRpQtn5J0mSJEmSJE0oO/8kSZIkSZKkCWXnnyRJkiRJkjSh7PyTJEmSJEmSJtT/A0M+o8p7a2niAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(ncols=2,figsize=(22,7))\n",
    "\n",
    "# avg rain\n",
    "ax1 = sns.lineplot(x='Date', y='rain_avg', data=sarai_month, color='#8BC34A', linewidth=2, ax=axs[0])\n",
    "axs[0].set_title('rain avg (2016-2020)', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].set_yticks(np.arange(0,11,1))\n",
    "axs[0].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "# avg rain rate\n",
    "ax2 = sns.lineplot(x='Date', y='rain_rate_avg', data=sarai_month, color='#F44336', linewidth=2, ax=axs[1])\n",
    "axs[1].set_title('rain rate avg (2016-2020)', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].set_yticks(np.arange(0,0.55,0.05))\n",
    "axs[1].set_ylabel('mm/hr', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Total rain (month)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(figsize=(15,7))\n",
    "\n",
    "# total rain\n",
    "ax = sns.barplot(x='Date', y='rain_total', data=sarai_month, color='#8BC34A', ax=axs)\n",
    "ax.set_title('rain total', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "ax.set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "ax.set_yticks(np.arange(0,120000,10000))\n",
    "ax.set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs, orient='v', num=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Avg rain vs rain rate (year)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(ncols=2,figsize=(22,7))\n",
    "\n",
    "# avg rain\n",
    "ax1 = sns.lineplot(x='Date', y='rain_avg', data=sarai_year, color='#8BC34A', linewidth=2, ax=axs[0])\n",
    "axs[0].set_title('rain avg', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].set_xticks(np.arange(2016,2021,1))\n",
    "axs[0].set_yticks(np.arange(0,4.5,0.5))\n",
    "axs[0].set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "# avg rain rate\n",
    "ax2 = sns.lineplot(x='Date', y='rain_rate_avg', data=sarai_year, color='#F44336', linewidth=2, ax=axs[1])\n",
    "axs[1].set_title('rain rate avg', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].set_xticks(np.arange(2016,2021,1))\n",
    "axs[1].set_yticks(np.arange(0,0.3,0.05))\n",
    "axs[1].set_ylabel('mm/hr', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1].tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Total rain (year)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set a figure and 2 axes for plotting\n",
    "fig, axs = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# total rain\n",
    "ax = sns.barplot(x='Date', y='rain_total', data=sarai_year, color='#8BC34A', ax=axs)\n",
    "ax.set_title('rain total', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "ax.set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "ax.set_yticks(np.arange(0,140000,20000))\n",
    "ax.set_ylabel('mm', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs, orient='v',num=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Avg soil 1-4 moisture (month)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,figsize=(18,10))\n",
    "\n",
    "# soil_1\n",
    "axs1 = sns.barplot(x='Date', y='soil1_moist_avg', data=sarai_month, color='#2196F3', ax=axs[0,0])\n",
    "axs[0,0].set_title('soil 1', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,0].set_yticks(np.arange(0,8,1))\n",
    "#axs[0,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,0].tick_params(axis='both', labelsize=10)\n",
    "#show_values(ax1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[0,0], num=2)\n",
    "\n",
    "# soil_2\n",
    "axs2 = sns.barplot(x='Date', y='soil2_moist_avg', data=sarai_month, color='#4DD0E1', ax=axs[0,1])\n",
    "axs[0,1].set_title('soil 2', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,1].set_yticks(np.arange(0,0.8,0.1))\n",
    "#axs[0,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,1].tick_params(axis='both', labelsize=10)\n",
    "#show_values(axs1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[0,1], num=2)\n",
    "\n",
    "# soil_3\n",
    "axs3 = sns.barplot(x='Date', y='soil3_moist_avg', data=sarai_month, color='#66BB6A', ax=axs[1,0])\n",
    "axs[1,0].set_title('soil 3', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,0].set_yticks(np.arange(0,0.1,0.025))\n",
    "#axs[1,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,0].tick_params(axis='both', labelsize=10)\n",
    "#show_values(ax1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[1,0], num=2)\n",
    "\n",
    "# soil_4\n",
    "axs4 = sns.barplot(x='Date', y='soil4_moist_avg', data=sarai_month, color='#26A69A', ax=axs[1,1])\n",
    "axs[1,1].set_title('soil 4', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].set_yticks(np.arange(0,80,10))\n",
    "#axs[1,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,1], num=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Total soil 1-4 moisture (month)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,figsize=(18,10))\n",
    "\n",
    "# soil_1\n",
    "axs1 = sns.barplot(x='Date', y='soil1_moist_total', data=sarai_month, color='#2196F3', ax=axs[0,0])\n",
    "axs[0,0].set_title('soil 1', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,0].set_yticks(np.arange(0,8,1))\n",
    "#axs[0,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,0].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[0,0], num=0)\n",
    "\n",
    "# soil_2\n",
    "axs2 = sns.barplot(x='Date', y='soil2_moist_total', data=sarai_month, color='#4DD0E1', ax=axs[0,1])\n",
    "axs[0,1].set_title('soil 2', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,1].set_yticks(np.arange(0,0.8,0.1))\n",
    "#axs[0,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[0,1], num=0)\n",
    "\n",
    "# soil_3\n",
    "axs3 = sns.barplot(x='Date', y='soil3_moist_total', data=sarai_month, color='#66BB6A', ax=axs[1,0])\n",
    "axs[1,0].set_title('soil 3', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,0].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,0].set_yticks(np.arange(0,0.1,0.025))\n",
    "#axs[1,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,0].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,0], num=0)\n",
    "\n",
    "# soil_4\n",
    "axs4 = sns.barplot(x='Date', y='soil4_moist_total', data=sarai_month, color='#26A69A', ax=axs[1,1])\n",
    "axs[1,1].set_title('soil 4', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,1].set_xlabel('month', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].set_yticks(np.arange(0,700000,100000))\n",
    "#axs[1,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,1], num=0)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Avg soil 1-4 moisture (year)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,figsize=(18,10))\n",
    "\n",
    "# soil_1\n",
    "axs1 = sns.barplot(x='Date', y='soil1_moist_avg', data=sarai_year, color='#2196F3', ax=axs[0,0])\n",
    "axs[0,0].set_title('soil 1', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,0].set_yticks(np.arange(0,8,1))\n",
    "#axs[0,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,0].tick_params(axis='both', labelsize=10)\n",
    "#show_values(ax1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[0,0], num=2)\n",
    "\n",
    "# soil_2\n",
    "axs2 = sns.barplot(x='Date', y='soil2_moist_avg', data=sarai_year, color='#4DD0E1', ax=axs[0,1])\n",
    "axs[0,1].set_title('soil 2', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,1].set_yticks(np.arange(0,0.8,0.1))\n",
    "#axs[0,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,1].tick_params(axis='both', labelsize=10)\n",
    "#show_values(axs1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[0,1], num=2)\n",
    "\n",
    "# soil_3\n",
    "axs3 = sns.barplot(x='Date', y='soil3_moist_avg', data=sarai_year, color='#66BB6A', ax=axs[1,0])\n",
    "axs[1,0].set_title('soil 3', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,0].set_yticks(np.arange(0,0.1,0.025))\n",
    "#axs[1,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,0].tick_params(axis='both', labelsize=10)\n",
    "#show_values(ax1, orient='v', space=.02)\n",
    "show_values_on_bars(axs[1,0], num=2)\n",
    "\n",
    "# soil_4\n",
    "axs4 = sns.barplot(x='Date', y='soil4_moist_avg', data=sarai_year, color='#26A69A', ax=axs[1,1])\n",
    "axs[1,1].set_title('soil 4', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,1].set_yticks(np.arange(0,80,10))\n",
    "#axs[1,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,1], num=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Total soil 1-4 moisture (year)**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,figsize=(18,10))\n",
    "\n",
    "# soil_1\n",
    "axs1 = sns.barplot(x='Date', y='soil1_moist_total', data=sarai_year, color='#2196F3', ax=axs[0,0])\n",
    "axs[0,0].set_title('soil 1', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,0].set_yticks(np.arange(0,8,1))\n",
    "#axs[0,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,0].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[0,0], num=0)\n",
    "\n",
    "# soil_2\n",
    "axs2 = sns.barplot(x='Date', y='soil2_moist_total', data=sarai_year, color='#4DD0E1', ax=axs[0,1])\n",
    "axs[0,1].set_title('soil 2', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[0,1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[0,1].set_yticks(np.arange(0,0.8,0.1))\n",
    "#axs[0,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[0,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[0,1], num=0)\n",
    "\n",
    "# soil_3\n",
    "axs3 = sns.barplot(x='Date', y='soil3_moist_total', data=sarai_year, color='#66BB6A', ax=axs[1,0])\n",
    "axs[1,0].set_title('soil 3', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,0].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,0].set_yticks(np.arange(0,0.1,0.025))\n",
    "#axs[1,0].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,0].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,0], num=0)\n",
    "\n",
    "# soil_4\n",
    "axs4 = sns.barplot(x='Date', y='soil4_moist_total', data=sarai_year, color='#26A69A', ax=axs[1,1])\n",
    "axs[1,1].set_title('soil 4', fontdict={'fontsize':20,'fontname':'Arial'})\n",
    "axs[1,1].set_xlabel('year', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "#axs[1,1].set_yticks(np.arange(0,700000,100000))\n",
    "#axs[1,1].set_ylabel('cm3/cm3', fontdict={'fontsize':14,'fontname':'Arial'})\n",
    "axs[1,1].tick_params(axis='both', labelsize=10)\n",
    "show_values_on_bars(axs[1,1], num=0)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _**Regression**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**SENTINEL-AWS**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_sent_aws = sentinel.groupby([sentinel.Date.dt.strftime('%Y-%m-%d')]).mean().reset_index()\n",
    "reg_sent_aws.rename(\n",
    "    columns={'Date':'date'},inplace=True\n",
    ")\n",
    "reg_sent_aws.date = pd.to_datetime(reg_sent_aws.date, format='%Y-%m-%d')\n",
    "sm_aws = pd.merge(reg_sent_aws, aws, how='outer', on=['date']).dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge16 = sm_aws[sm_aws.Year == 2016].drop(labels=['id'],axis=1)\n",
    "merge17 = sm_aws[sm_aws.Year == 2017].drop(labels=['id'],axis=1)\n",
    "merge18 = sm_aws[sm_aws.Year == 2018].drop(labels=['id'],axis=1)\n",
    "merge19 = sm_aws[sm_aws.Year == 2019].drop(labels=['id'],axis=1)\n",
    "merge20 = sm_aws[sm_aws.Year == 2020].drop(labels=['id'],axis=1)\n",
    "\n",
    "merge16['id'] = merge16.date.dt.strftime('%b')\n",
    "merge17['id'] = merge17.date.dt.strftime('%b')\n",
    "merge18['id'] = merge18.date.dt.strftime('%b')\n",
    "merge19['id'] = merge19.date.dt.strftime('%b')\n",
    "merge20['id'] = merge20.date.dt.strftime('%b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016:  0.39112593997594014\n",
      "2017:  0.26458261160352703\n",
      "2018:  0.25807455950557534\n",
      "2019:  0.3392543657040291\n",
      "2020:  0.23495413511966784\n"
     ]
    }
   ],
   "source": [
    "print('2016: ',np.corrcoef(x=merge16.Soil_Moisture, y=merge16.rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=merge17.Soil_Moisture, y=merge17.rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=merge18.Soil_Moisture, y=merge18.rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=merge19.Soil_Moisture, y=merge19.rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=merge20.Soil_Moisture, y=merge20.rain)[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=2,figsize=(15,10))\n",
    "\n",
    "axs1 = sns.regplot(data=merge16, x='Soil_Moisture', y='rain',ax=axs[0,0])\n",
    "axs1.set_title('2016', fontdict={'fontsize':14})\n",
    "axs1.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs1.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs1.set_yticks(np.arange(0,600,100))\n",
    "axs1.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs2 = sns.regplot(data=merge17, x='Soil_Moisture', y='rain',ax=axs[0,1])\n",
    "axs2.set_title('2017', fontdict={'fontsize':14})\n",
    "axs2.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs2.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs2.set_yticks(np.arange(0,600,100))\n",
    "axs2.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs3 = sns.regplot(data=merge18, x='Soil_Moisture', y='rain',ax=axs[1,0])\n",
    "axs3.set_title('2018', fontdict={'fontsize':14})\n",
    "axs3.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs3.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs3.set_yticks(np.arange(0,600,100))\n",
    "axs3.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs4 = sns.regplot(data=merge19, x='Soil_Moisture', y='rain',ax=axs[1,1])\n",
    "axs4.set_title('2019', fontdict={'fontsize':12})\n",
    "axs4.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs4.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "axs4.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.regplot(data=merge20, x='Soil_Moisture', y='rain',ax=ax)\n",
    "ax.set_title('2020', fontdict={'fontsize':14})\n",
    "ax.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "ax.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**SENTINEL-SARAi**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfsent= sentinel.groupby([sentinel.Date.dt.strftime('%Y-%m-%d')]).mean().reset_index().drop(labels=['id'],axis=1)\n",
    "dfsent.rename(\n",
    "    columns={'date':'Date'},inplace=True\n",
    ")\n",
    "dfsent.Date = pd.to_datetime(dfsent.Date, format='%Y-%m-%d')\n",
    "dfsarai= sarai_per_day.drop(labels=['Soil_1_Moist','Soil_2_Moist','Soil_3_Moist','Soil_4_Moist','Year','Month'],axis=1)\n",
    "sent_sarai = pd.merge(dfsent,dfsarai, how='outer',on=['Date']).dropna(axis=0,how='any')\n",
    "sent_sarai['Month'] = sent_sarai.Date.dt.strftime('%b')\n",
    "#merge_sm_aws = pd.merge(soil_reg, df_aws, how='outer', on=['date']).dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Corrcoef**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rain\n",
      "2016:  -0.04505863597578825\n",
      "2017:  0.26981516842504444\n",
      "2018:  -0.1570984859376146\n",
      "2019:  -0.97907427537451\n",
      "2020:  0.26016795345904314\n",
      "\n",
      "\n",
      "Rain-Rate\n",
      "2016:  0.2684109913848446\n",
      "2017:  0.2825133039262718\n",
      "2018:  0.1273179268552755\n",
      "2019:  -0.9711069278468126\n",
      "2020:  0.18550868911729745\n"
     ]
    }
   ],
   "source": [
    "sent_sarai16 = sent_sarai[sent_sarai.Year == 2016.0]\n",
    "sent_sarai17 = sent_sarai[sent_sarai.Year == 2017.0]\n",
    "sent_sarai18 = sent_sarai[sent_sarai.Year == 2018.0]\n",
    "sent_sarai19 = sent_sarai[sent_sarai.Year == 2019.0]\n",
    "sent_sarai20 = sent_sarai[sent_sarai.Year == 2020.0]\n",
    "print('Rain')\n",
    "print('2016: ',np.corrcoef(x=sent_sarai16.Soil_Moisture, y=sent_sarai16.Rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sent_sarai17.Soil_Moisture, y=sent_sarai17.Rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sent_sarai18.Soil_Moisture, y=sent_sarai18.Rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sent_sarai19.Soil_Moisture, y=sent_sarai19.Rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sent_sarai20.Soil_Moisture, y=sent_sarai20.Rain)[0][1])\n",
    "print('\\n')\n",
    "print('Rain-Rate')\n",
    "print('2016: ',np.corrcoef(x=sent_sarai16.Soil_Moisture, y=sent_sarai16.Rain_Rate)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sent_sarai17.Soil_Moisture, y=sent_sarai17.Rain_Rate)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sent_sarai18.Soil_Moisture, y=sent_sarai18.Rain_Rate)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sent_sarai19.Soil_Moisture, y=sent_sarai19.Rain_Rate)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sent_sarai20.Soil_Moisture, y=sent_sarai20.Rain_Rate)[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Soil Moisture-Rain**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=2,figsize=(15,10))\n",
    "\n",
    "axs1 = sns.regplot(data=sent_sarai16, x='Soil_Moisture', y='Rain',ax=axs[0,0])\n",
    "axs1.set_title('2016', fontdict={'fontsize':12})\n",
    "axs1.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs1.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs1.set_yticks(np.arange(0,600,100))\n",
    "axs1.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs2 = sns.regplot(data=sent_sarai17, x='Soil_Moisture', y='Rain',ax=axs[0,1])\n",
    "axs2.set_title('2017', fontdict={'fontsize':12})\n",
    "axs2.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs2.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs2.set_yticks(np.arange(0,600,100))\n",
    "axs2.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs3 = sns.regplot(data=sent_sarai18, x='Soil_Moisture', y='Rain',ax=axs[1,0])\n",
    "axs3.set_title('2018', fontdict={'fontsize':12})\n",
    "axs3.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs3.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs3.set_yticks(np.arange(0,600,100))\n",
    "axs3.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs4 = sns.scatterplot(data=sent_sarai19, x='Soil_Moisture', y='Rain',ax=axs[1,1])\n",
    "axs4.set_title('2019', fontdict={'fontsize':12})\n",
    "axs4.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs4.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "axs4.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nd38yvY+xdQDYYPUo1vR8pOlqXXOMlgHB6TSkvVLSw2b2HSVz0hrX7rygi9c+TcmVDBr2S3pFF88HAFhFHLuOVes6NkcwA0LXaUi7oqet6JKZ7ZS0U5J27NiRcWsAICxx7JqpRZqer2umGlEiAxgQHYU0d3+qB6/9tKTtTbdPT/etmbvfKulWSZqamuK3D4CRF8Wu6WpdM/PJqkyCGTB4Oh1J64UHJZ1jZmcpCWdXS3pbhu0BgIHVWJE5k9YuY0UmMPgyC2nuXjez6yXdo6QEx+3uvsfMbpS02913mdlFkj4paZuknzaz97v7+Vm1GQBCUq3HC7XL5mqsyASGTZYjaXL3u5VcF7R53w1N2w8qOQ0KACOvUVR2Jg1lFJUFhlumIQ0AsLz5eqT5eqy5WqT5WkwoA0YMIQ0AAtBc5X+unoQyTl8Co42QBgAZqNbjhZGyZDtmBSaAJQhpANAHc+kE/9lapGqdUTIAqyOkAUAPNArIzlTrmqvGqsfMJwOwNoQ0ANgg8/Vo4QLlnL4E0C1CGgCsUz1K65TVIkbLAGw4QhoAdChK65TNUqcMQB8Q0gBgGVHsyYT/NJgRyoDh5578MXZouqZnj83rpadv1Vgxn0lbCGkAkIpj11x96SpMAIPP3TVdjXRouqqDM1Udmq7p0Ew1+WqzPd/0s/+5X3+Nzn3hlkzaTUgDMLIaoWyuFnNRcmDAuLuOztXTgFXToenqku2DLftr0foW8hw8Vt3glneOkAZgZLj7QiCbq7ECEwhN7K4js7Ul4epgmwB2aKaqwzM11eP1/fxuKhe0rVLUtomStlVKx21Pptsv3b5VE+XsohIhDcBQm0sDWRLMCGVAv0Wx6/nZ9qNbrduHZ6paZ+7SlrHCcaFrsk0A21opqVTIdfScWc1FayCkARgajetfzqWBbK4WUdkf6IF6FOvwbCNgpXO6ltl+fra2ruBlkk4YbwSsYhKyJtLvlVI62pXcv3W8qEK+s+A1SAhpAAZSPYpVjWLV6q75KFItctW43BKwbtV6rMMzxwetg20C2JG5+rpeI2fS1kpz6Dp+ezLdPmG8qHzONvhdDhZCGoBg1aM4CV9xrFo9Vj121dJ9nLYEVjdfixbC1cFVRr2Oza8veOVztmzommzZv2W8qJyNdvBaC0IagEzVolj1liBWTb8TxIDjJTW8li8f0TzXa7q6vhXLxbwdH7qa5nZNNu3fPFaQEbx6gpAGoK/m0sKwM2kdMoIYRp27a6YaLRu6Wut6zdXWV79vrJBre3qx3fZEKU/wCgAhDUBPNS6lNFOta7YaKVrv0i1ggLi7js3Xl4Srg8uMeh2eqS0pnroWlVK+bQmJdqscx0vZrlTE2hHSAGyoxiVVqNqPYRO76+hsPS0jsXLV+sOz6y+eOlFuBK/F1YyTzdtpANtaKWZeIgK9RUgD0JVqPdZ8PVoofcGkfgyShRpeK5SQaA5gXdXwaikhsdx2pzW8MPwIaQA60qhBVo1iVeuLX5S8QGii2FcJXb2v4dW8vbVSVHEIa3ih9whpAJZwd9UiT2uQLYayWsRpS2SnFsU63FpKorlqfWN/lzW8FoPXYvianKCGF7JBSANGWK0pgFXTQMbpSvRLtR43FUptPq14fAA7us7glc+ZtjbC1iqT67eMEbwQFkIaMAJaq/MnwYwwho03V4vSka4+1PBaclqxfQDbPFageCoGFiENGCLuvnTOWLpN2QusV2O17nI1uw5N1xZDWRc1vEqF3NLTi8vM8ZqslDRRpoYXRgMhDRhQcZwEsvlazOgY1sTdNT0fdVRK4tBMdd01vMaKuSUha3KFVY0ViqcCxyGkAQOgdd4YE/nRyt11ZK7ecSmJddfwKuVbLhW0zKrGiZLGqeEFdIWQBgSiHi1eQLxxLUtGx0Zb7K4js7UlE+kPNk+qn05vp1Xr13tae1O5kKxcXDjN2KasxERJ28aLKhO8gL4hpAF91jqJvxa5atQbGxkLxVOn0/ldrSsZm7YPd1s8tc2lgVon2G+leCoQLEIa0CNR7MednqT463CqR/HiKcVl53glAez52ZrW8wkwaaGUxGrXadw6XlSB4qnAwCOkAV1qTOBvLf7KisrBVq3HOjxz/Nyu5pWMjQDWTfHUTktJUDwVGD2ENKBDUTpfrJYWfG2MjjGBf3DM16Kl1elXuGTQsfn1Ba9Czjq6RuNkpaTN49TwArA8QhrQpDmINSbv1yJXPWJkLFSz1aipav3K12mcWWfx1OYaXq2ha7IlgG0qFyglAWBDENIwkppPUXKx8LC4u6ar0bKT6Vu356jhBWBIEdIwEqr1WPP1SHO15Ht1nf+xY33cXUcbNbzaBK3WVY7rreFVKeU7mlg/WSlpvEQpCQBhI6RhqCxU4W9aTVnjVGVPrFrDq2n78ExN9S5reC0XuiabtqnhBWCYENIwkBphbDGIJRP56zEjZN1YqOHVXCiVGl4AkAlCGoJWT1dS1uLF8ha1uhPG1qAexTo8W+toYv3zs7V1BS+TdMJ4sX2l+tbtCjW8AKAThDRkxt1Vj11RnH6PkvBVX1hhyeWQlrNsDa82AaybGl5bK82h6/jtyXSbGl4AsPEyDWlmdoWkj0jKS7rN3T/Qcn9Z0l9J+glJz0l6q7s/2e92Yu2S4BUrjqXI0yCWzg2rxZS0aKdajxdCVq9qeOVztmzommzZv2W8SA0vAMhQZiHNzPKSbpb0Okn7JT1oZrvc/RtNh71D0iF3f5GZXS3pg5Le2v/WLrrvsQO65f692ndoRtu3VXTdJWfr0nNfkGWTMtMIYs2FXZN5YcM1Avavew/qzgf36Zkjszp1y7iuvmi7Lj57sqPHztaaSkm0vVTQ4lyv6XXW8CrmbdWq9Y3J9ZvH1l7Dq5v3D4RukD/fg9z20DT68snnjiVTbKJI9VhylzaPFfTOV5+lX7v8xX1vl2X1n6mZvUrS77j7G9Lb75Ekd//9pmPuSY/5spkVJH1f0sm+QqOnpqZ89+7dPWnzfY8d0A279qiYN40X85qtJRfHvvHK84cyqLl7+mFdLOxab5ySjHwkaor9696D+sgXH1chZxor5jRbjVSLXb/4ijO0fbJyXOg62BLA5mrrmztXLuQ6rlo/Ue5dDa/W9z9XS0L4uy47h/8MMPAG+fM9yG0PTaMva/VIh2draq3QlEyhNb3rshf1JKiZ2UPuPtXuvixPd54maV/T7f2SXrHcMe5eN7PnJZ0o6Yd9aWGLW+7fuxDQYpfKhbyiuK6bv/SEXnbGtiyatDF88ZRknM4Ti9N5YaPA3XVsvr4kXB1Mtz/79e9rplqXXAvz51zShz//b2t+nfFifsXQ1VxMdbwYRvHUOx/cp0Iu+cxLWvjj5M4H9/EfQUbyOVPOTLmcKW+mXE4yZf9ZGUSfeGi/SoXcwud7opzTbC3SJx7ar9eed0rGrVvZILc9NI2+fH6mqpxM0tIBCPckqN32z9/p+2jaUCwcMLOdknZK0o4dO3r2OvsOzWjreFGuZMWclJxq2ndoRs8dm+/Z62Lt4rR46sGmU42HZ1vndy2GsvUWT50o5ZfO7WoXviZK2lopLfwyHSTPHJnVlrGlvybGijl9/8hsRi0aPWZJSK6U85ooFVigsYF+cHROW8eLS/4gKuRMB47O6eTN5QxbtrpBbntoGn35vdiTn6+m+dJmSUjLmdY9JaUbWYa0pyVtb7p9erqv3TH709OdJyhZQLCEu98q6VYpOd3Zk9ZK2r6togNH5zTW9J/tXC3WC7eM9+ol0SSKXUfmVi4lcTDdPjxTW/fChM1jhYW5XE89N6N6HKtcyKuQM+VzpnrsOmmirJve+tKhr+F16pZxPTc9vyRg8pnvvWI+p3Ihp/FSEsxyBLOeaPxOr5QW/yucrUU6fVslw1Z1ZpDbHppGX5byOdVb/2D3JKjFnvxR3m9ZhrQHJZ1jZmcpCWNXS3pbyzG7JF0j6cuSflbSF1eaj9Zr111ytm7YtUfudeVztjAH4OqLtq/+YLQVxX5cKYnjCqim+7up4bVlvHh81frmUhLp/q2VoopNNbzazftwSW9/1RlDH9Ak6eqLtusjX3xcs7VoybwXPvMbp5DLqVxMQlm5kFepkGO0rE8av9NnqvUl84yvu+TsrJu2qkFue2gafbl5rKDnpqtL7nMlo2ixS+989Vl9b1tmCwckycz+g6Q/VlKC43Z3/19mdqOk3e6+y8zGJH1M0sskHZR0tbvvXek5e7lwQEoWD/zJP35bTz03rReymqatehQvhqtlVzXWFoLXej6BOWsuntqmlERL1fpu/tNrrPr5/pHZkfw3H/X3v5HMkrBfLuQXvhPIstVYsb//0IxOH7AV+4Pc9tA0+vLxHxxRNXJV65FqcTJneVO5t6s7V1o4kGlI64VehzRJmq9HevrQaM3JqdbjVULX4vZ6i6fmc6atlcaqxZUvkL1ljOKpGAyFXHLaslLKa7yY59QlgCVCXd2JjM3VonQl4wqjXem+6fne1fDa1lTDi+KpGHSN0bLxYl5j6RcArAchbcjMVqMllwZaWN3YZtRrtra+4FUq5NquZmzM6+pXDS8gBGamUiGnsXSifyjlUwAMPkJa4Nxd0/Np8GoTtA63VK2fa63C16GxYm7Zml2tYaxS4j8hjK6cWTpCtji3jJ8HAL1ASMuApzW8GuFqsZZXUjriYFPo6qaGV6WUX/ESQQsBbGIwa3gB/WBmSTmMYl7jpbzKBUIZgP4gpG2Q2F1HZmtLwtVxpSTS7cMzNdXXWcNrU7mw7JyuJQGsUlSZ4AWsSzG/ONl/rMBkfwDZIKSt0Z7vPa+/+8p+7Ts4uySAHZ6prquGlyRtGSssM9K19PYJ48WRqM0FZGGiXFhYgVnI83MGIHuEtDX67nMzuv2fn1zxmIUaXiusZGxsbx0v8h8CkBEz06ZygT+AAASJkLZG2ycrevWLTlSltPxpxxPGqeEFhCyfM20ZK2oLP6sAAkZIW6OXnHaC/vzai0aumC0wDMaKeW0ZL2qCFcoABgAhDcBQy+eSU5qbxzilCWCwENIADI2cmcrFnEr5nMrFvEr5HMEMwMAipAEYSPmcqVzIq1RIgli5kFORRTgAhgghDUDwCrlc0whZ8p1V0QCGHSENQJDKxbwmSnlVSgVOWQIYSYQ0AEEwM40Vc6oUC5ooU1AWAAhpADLBNTEBYGWENAB9U8jlNFbKqVIqqFLkmpgAsBJCGoCeKuZz2lQuqFLOq1zIZ90cABgYhDQAG66Qy2minNdEuaCxIsEMANaDkAZgQ+TMNFEuaFO5oPESwQwAukVIA7BuZqaJUjJiVuF6mACwoQhpANZsvJTXpnJBE6UCk/8BoEcIaQA6Ui4mwWxTuaA8wQwAeo6QBmBZjZWZm8YKXBcTAPqMkAZgicbKzE1jBUpmAECGCGkAlM+ZKiVWZgJASAhpwIhiZSYAhI2QBowQM9N4MZ8UmmVlJgAEjZAGjICxYn6h0CwrMwFgMBDSgCFVzOe0eaygiTIrMwFgEBHSgCFSyOW0aaygCS5mDgADj5AGDLjGyszNY1zMHACGCSENGEA5M1VKSS2z8SIrMwFgGBHSgAHRWJm5aaygSjHPykwAGHKENCBwrMwEgNFESAMCVCrkFi5mXmBlJgCMJEIaEIhiPrcwYlYqEMwAYNQR0oAM5XO2EMxYmQkAaEZIA/osZ6ZKOZ9czJyVmQCAZRDSgD6wtGTGRLmgCS5mDgDoQCYTX8xs0sw+b2aPp9+3LXPc58zssJn9n363EdgI46W8Ttpc1o7Jik7ZMqZN5QIBDQDQkaxmJ79b0r3ufo6ke9Pb7fyBpF/sW6uADVAu5nXiRBLMTj1hXFvGipTOAACsWVYh7SpJd6Tbd0h6c7uD3P1eSUf71CZg3Yr5nCYnSto+WdFpW8d1QqVI6QwAQFeympN2irs/k25/X9IpGbUDWLdGyQwuZg4A6IWehTQz+4KkF7a5673NN9zdzcy7fK2dknZK0o4dO7p5KmBFlMwAAPRLz0Kau1++3H1m9gMzO9XdnzGzUyUd6PK1bpV0qyRNTU11FfiAVjlbDGbjJYIZAKA/sjrduUvSNZI+kH7/VEbtANoyM02kJTMqlMwAAGQgq5D2AUkfN7N3SHpK0s9JkplNSfpld39nevufJJ0raZOZ7Zf0Dne/J6M2Y8iZmcaLeW0aK6hSzCvHikwAQIYyCWnu/pyk17bZv1vSO5tuv6af7cJoGl8oMlugVAYAIBhccQAjqVzMa1MpWZlJqQwAQIgIaRgJjVOZ46W8JkoEMwBA+AhpGEpmplIhp3Ihp7FinjlmAICBQ0jDUCjkcioXcxor5FUuJuGMFZkAgEFGSMPAMTOV01GycjGvsUKO05cAgKFDSEPwivnFQNYIZ4ySAQCGHSENwWmEsTFGyQAAI4yQhiCMFfMLl16iVhkAAIQ0ZKgRzCiJAQDA8Qhp6CuKyAIA0BlCGnqOYAYAwNoR0tATjWBWKedVJJgBALBmhDRsmHIxueTSRLlAMAMAoEuENHSlVMhpU7lAMAMAYIMR0rBmBDMAAHqPkIaONIJZpVRQqUAwAwCg1whpaMvMNFbMqVKijhkAAFkgpEGSlDNTuZhTuZDXWPqdyv8AAGSHkDaiCrmcxkqN62PmOYUJAEBgCGkjwsxUKeU1XsprvEjtMgAAQkdIG2KNYDZRLqhSzCvH6UsAAAYGIW3INIJZpZTXRKlAMAMAYEAR0oaAmWm8mNdEmWAGAMCwIKQNKIIZAADDjZA2QAhmAACMDkJa4BrBrJIGM2qXAQAwGghpAWpU+58oFwhmAACMKEJaQMbTchkEMwAAQEjLGMEMAAC0Q0jLAMEMAACshpDWJ+OlvCqlgjaVCWYAAGB1hLQeIpgBAID1IqRtMIIZAADYCIS0DUAwAwAAG42Qtk5M/gcAAL1ESFuHciGvU08Yz7oZAABgiOWybgAAAACOR0gDAAAIECENAAAgQIQ0AACAAGUS0sxs0sw+b2aPp9+3tTnmQjP7spntMbNHzOytWbQVAAAgC1mNpL1b0r3ufo6ke9PbrWYkvd3dz5d0haQ/NrOt/WsiAABAdrIKaVdJuiPdvkPSm1sPcPd/c/fH0+3vSTog6eR+NRAAACBLWYW0U9z9mXT7+5JOWelgM7tYUknSt3vdMAAAgBD0rJitmX1B0gvb3PXe5hvu7mbmKzzPqZI+Jukad4+XOWanpJ2StGPHjnW3GQAAIBQ9C2nufvly95nZD8zsVHd/Jg1hB5Y5boukz0h6r7s/sMJr3SrpVkmamppaNvABAAAMiqxOd+6SdE26fY2kT7UeYGYlSZ+U9Ffuflcf2wYAAJC5rELaByS9zswel3R5eltmNmVmt6XH/JykSyRda2YPp18XZtJaAACAPjP34To7ODU15bt37866GQAAAKsys4fcfardfVxxAAAAIEBDN5JmZs9KeqrLpzlJ0g83oDnDiv5ZGf2zMvpnZfTPyuifldE/Kwuxf85w97Z1YIcupG0EM9u93NAj6J/V0D8ro39WRv+sjP5ZGf2zskHrH053AgAABIiQBgAAECBCWnu3Zt2AwNE/K6N/Vkb/rIz+WRn9szL6Z2UD1T/MSQMAAAgQI2kAAAABGrmQZmZXmNm3zOwJM3v3Cse9xczczKaa9r0nfdy3zOwN/Wlxf623f8zsdWb2kJk9mn6/rH+t7p9uPj/p/h1mdszMfqv3re2/Ln++LjCzL5vZnvRzNNafVvdPFz9fRTO7I+2Xb5rZe/rX6v5ZrX/M7Foze7bpKjTvbLrvGjN7PP26pvWxw2C9/WNmFzb9bD1iZm/tf+t7r5vPT3r/FjPbb2Yf7V+rV+HuI/MlKS/p25LOllSS9DVJ57U5brOk+yU9IGkq3XdeenxZ0lnp8+Szfk8B9c/LJP1Iuv0SSU9n/X5C6p+m++6S9AlJv5X1+wmpfyQVJD0i6aXp7RP5+VrSP2+TdGe6XZH0pKQzs35P/e4fSddK+mibx05K2pt+35Zub8v6PQXUPy+WdE66/SOSnpG0Nev3FEr/NN3/EUl/u9Ix/f4atZG0iyU94e573b0q6U5JV7U57nclfVDSXNO+q5T8kpx39+9IeiJ9vmGy7v5x96+6+/fSm3skjZtZudcN7rNuPj8yszdL+o6S/hlG3fTP6yU94u5fkyR3f87do143uM+66R+XNGFmBUnjkqqSjvS4vf3Waf+08wZJn3f3g+5+SNLnJV3Ro3ZmZd394+7/5u6Pp9vfk3RAUtviqQOsm8+PzOwnJJ0i6R961L51GbWQdpqkfU2396f7FpjZyyVtd/fPrPWxQ6Cb/mn2Fklfcff5jW9iptbdP2a2SdJvS3p/rxuZoW4+Py+W5GZ2j5l9xcz+W2+bmolu+ucuSdNKRkC+K+nD7n6wh23NQqe/Y9+SnrK7y8y2r/Gxg6yb/llgZhcrGWn6dm+amZl194+Z5ST9oaTgpqGMWkhbUfoPdZOk38y6LSHqpH/M7HwlowDX9atdoVilf35H0h+5+7G+Niogq/RPQdKrJf3n9PvPmNlr+9i8zK3SPxdLipScqjpL0m+a2dl9bF4oPq3kNO8FSkbL7si4PaFZsX/M7FRJH5P0S+4eZ9C+rC3XP78i6W53359Zy5ZRyLoBffa0pOa/LE5P9zVsVjKf6j4zk6QXStplZld28NhhsO7+cffdZna6pE9Keru7D9tfaVJ3n59XSPpZM/uQpK2SYjObc/dwJqh2r5v+2S/pfnf/oSSZ2d2SXi7p3j60u1+66Z+3Sfqcu9ckHTCz/ytpSsncq2Gx6u9Yd3+u6eZtkj7U9NhLWx5734a3MFvd9I/MbIukz0h6r7s/0MN2ZqWb/nmVpNeY2a9I2iSpZGbH3H3ZxT19k/WkuH5+KQmle5X8JdqYWHj+Csffp8WJu+dr6cKBvRq+ic3d9M/W9Pj/mPX7CLF/Wvb/joZz4UA3n59tkr6iZFJ8QdIXJL0p6/cUUP/8tqS/SLcnJH1D0gVZv6d+94+kU5u2f0bSA+n2pJL5ntvSr+9Imsz6PQXUPyUlf/D8etbvI8T+aTnmWgW0cGCkRtLcvW5m10u6R8lKkNvdfY+Z3Shpt7vvWuGxe8zs40p+OdYl/aoP2cTmbvpH0vWSXiTpBjO7Id33enc/0NtW90+X/TP0uvz5OmRmN0l6UMkk+bt95XmPA6fLz8/Nkv7CzPZIMiWB7ZHet7p/OuyfX0tHFuuSDir5D1XuftDMflfJ50eSbvQhm7PXTf9I+jlJl0g60cwa+65194f7+BZ6qsv+CRZXHAAAAAgQCwcAAAACREgDAAAIECENAAAgQIQ0AACAABHSAAAAAkRIA4CUmX3OzL5mZnvM7E/NLN903yvN7M+6fP4z0stePZy+xi9332oAw4oSHACQMrMt7n7EkpL/d0n6hLvfmd73fiUXgf+7Lp6/pOT37nx6PdevS/pJTy56DQBLMJIGYOiY2dvTiyh/zcw+ZmZ/aWZ/YmYPmNleM7vUzG43s2+a2V82HufuR9LNgpKq5c1/xb5W0hfMLG9mHzazr6ev8V/S13zSzH4/HSXbbWYvTy8Y/+3GiJm7V919Pn2+svgdDGAF/IIAMFTM7HxJ75N0mbu/VNK70ru2KblG33+VtEvSHym53NuPm9mFTY+/R9IBSUeVjKbJzE6SVHP35yXtlHSmpAs9uVDz3zS9/Hfd/UJJ/yTpLyX9rKRXSnp/0/NvN7NHJO2T9EFG0QAsh5AGYNhcpuQ05Q+l5JJB6f5PezK/41FJP3D3R909lrRHSehSevwbJJ2qZKTrsnT36yX9Q7p9uaRb3L3e8vxSEv6Uvsa/uPtRd39W0ryZbU2P35eGuxdJusbMTtmwdw5gqBDSAIyKxmnGuGm7cXvJdYzdfU7SpyRdle56o6TPbfBrfE/JnLTXdPC8AEYQIQ3AsPmipP9kZidKkplNdvIgM9tkZqem2wVJb5L0WLqI4AJJD6eHfl7SdekxHT9/euzpZjaebm+T9GpJ3+r08QBGS2H1QwBgcLj7HjP7X5L+0cwiSV/t8KETknaZWWNC/5ck/amkn5D0VV9cCn+bpBdLesTMapL+TNJHO3yNH5P0h2bmkkzSh9390Q4fC2DEUIIDAFZgZu+T9ESjFAcA9AshDQAAIEDMSQMAAAgQIQ0AACBAhDQAAIAAEdIAAAACREgDAAAIECENAAAgQIQ0AACAAP1/6KyANfud0REAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.regplot(data=sent_sarai20, x='Soil_Moisture', y='Rain',ax=ax)\n",
    "ax.set_title('2020', fontdict={'fontsize':12})\n",
    "ax.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "ax.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Soil Moisture-Rain Rate**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=2,figsize=(15,10))\n",
    "\n",
    "axs1 = sns.regplot(data=sent_sarai16, x='Soil_Moisture', y='Rain_Rate',ax=axs[0,0])\n",
    "axs1.set_title('2016', fontdict={'fontsize':12})\n",
    "axs1.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs1.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs1.set_yticks(np.arange(0,600,100))\n",
    "axs1.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs2 = sns.regplot(data=sent_sarai17, x='Soil_Moisture', y='Rain_Rate',ax=axs[0,1])\n",
    "axs2.set_title('2017', fontdict={'fontsize':12})\n",
    "axs2.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs2.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs2.set_yticks(np.arange(0,600,100))\n",
    "axs2.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs3 = sns.regplot(data=sent_sarai18, x='Soil_Moisture', y='Rain_Rate',ax=axs[1,0])\n",
    "axs3.set_title('2018', fontdict={'fontsize':12})\n",
    "axs3.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs3.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs3.set_yticks(np.arange(0,600,100))\n",
    "axs3.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs4 = sns.regplot(data=sent_sarai19, x='Soil_Moisture', y='Rain_Rate',ax=axs[1,1])\n",
    "axs4.set_title('2019', fontdict={'fontsize':12})\n",
    "axs4.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs4.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "axs4.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XJ7klyR3dz9f1WackSVIregtnSVYAHwbOAk4F3jLHLaDOBx6qqpOBDwKXdu0PAD9UVd8BnAdc1VedkiRJLelzy9npwPaq2lFVTwBXA5tn9dkMXNlNXwecmSRVdWtVfaNr3wY8J8nhPdYqSZLUhD7D2XHA3TOe39O1zdmnqvYBDwNrZ/V5E/D5qnp89hskuSDJRJKJXbt2HbLCJUmShqXpEwKSbGCwq/Mdc82vqsuraryqxtetW7e0xUmSJPWgz3B2L3DCjOfHd21z9kmyEngusLt7fjzwceBtVXVXj3VKkiQ1o89wdjNwSpITkxwGnAtsmdVnC4MD/gHOBm6sqkryPOCTwMVV9fc91ihJktSU3sJZdwzZhcANwJeAa6tqW5JLkvxw1+0KYG2S7cBFwPTlNi4ETgbem2Rr9zi6r1olSZJakaoadg2HxPj4eE1MTAy7DEmSpINKcktVjc81r+kTAiRJkkaN4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSG9BrOkmxKcmeS7UkunmP+4Umu6ebflGR91742yd8keSzJh/qsUZIkqSW9hbMkK4APA2cBpwJvSXLqrG7nAw9V1cnAB4FLu/ZJ4FeAd/dVnyRJUov63HJ2OrC9qnZU1RPA1cDmWX02A1d209cBZyZJVe2pqv/NIKRJkiSNjD7D2XHA3TOe39O1zdmnqvYBDwNrF/oGSS5IMpFkYteuXYssV5Ikafie1ScEVNXlVTVeVePr1q0bdjmSJEmL1mc4uxc4Ycbz47u2OfskWQk8F9jdY02SJElN6zOc3QyckuTEJIcB5wJbZvXZApzXTZ8N3FhV1WNNkiRJTVvZ1wtX1b4kFwI3ACuAj1XVtiSXABNVtQW4ArgqyXbgQQYBDoAkO4E1wGFJfgR4Q1V9sa96JUmSWtBbOAOoqk8Bn5rV9t4Z05PAj3+LZdf3WZskSVKLntUnBEiSJC03hjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhqyctgFSJIktWBqqti5ew/3PTLJMWtWs37tEYyNZcnrMJxJkqSRNzVVXL/tm1x07VYm906xetUYl52zkU0bjl3ygOZuTUmSNPJ27t6zP5gBTO6d4qJrt7Jz954lr8VwJkmSRt59j0zuD2bTJvdOcf+jk0tei+FMkiSNvGPWrGb1qgNj0epVYxx91Oolr8VwJkmSRt76tUdw2Tkb9we06WPO1q89Yslr8YQASZI08sbGwqYNx/Lyd53B/Y9OcvRRnq0pSZI0VGNj4aR1R3LSuiOHW8dQ312SJEkHMJxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkN6vQhtkk3A7wArgI9W1X+ZNf9w4L8DrwZ2A2+uqp3dvPcA5wNPAu+qqhv6rPVgpqaKnbv3cN8jkxyzZnhXDdbSGfXf+aivv9Qiv5eH1vR47t7zOIetGOOJJ6c4bMUY//zEk0Md397CWZIVwIeB1wP3ADcn2VJVX5zR7Xzgoao6Ocm5wKXAm5OcCpwLbABeDHwmycuq6sm+6p3P1FRx/bZvctG1W5ncO7X/flubNhzrl2KZGvXf+aivv9Qiv5eH1vR4Xnr9l3jz+Eu4ZuLrvHn8JfzujV8Z+vj2uVvzdGB7Ve2oqieAq4HNs/psBq7spq8DzkySrv3qqnq8qr4KbO9ebyh27t6z/8sAMLl3iouu3crO3XuGVZJ6Nuq/81Fff6lFfi8Prenx/PenHcfv3viV/T9bGN8+w9lxwN0znt/Ttc3Zp6r2AQ8Daxe4LEkuSDKRZGLXrl2HsPQD3ffI5P5f1rTJvVPc/+hkb++p4Rr13/mor7/UIr+Xh9b0eCYc8HOmYY3vs/qEgKq6vKrGq2p83bp1vb3PMWtWs3rVgUO1etUYRx+1urf31HCN+u981NdfapHfy0Nr5njO/jltWOPbZzi7FzhhxvPju7Y5+yRZCTyXwYkBC1l2yaxfewSXnbPxgF/eZedsZP3aI4ZVkno26r/zUV9/qUV+Lw+t6fH8xG338q7XnbL/Zwvjm6rq54UHYesfgTMZBKubgbdW1bYZfX4G+I6qemd3QsCPVdU5STYAf8rgOLMXA38NnDLfCQHj4+M1MTHRy7rAU2d03P/oJEcf5Rkyo2DUf+ejvv5Si/xeHlrT4/ngnsdZtcRnaya5parG55zXVzjr3vjfAf+NwaU0PlZVv57kEmCiqrYkWQ1cBbwKeBA4t6p2dMv+EvAfgH3Az1fVp+d7r77DmSRJ0qEytHC2lAxnkiTp2WK+cPasPiFAkiRpuTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNWTZ3CEiyC/jaIl/mhcADh6Cc5crxmZ/jMz/HZ36Oz/wcn/k5PvNrcXxeWlXr5pqxbMLZoZBk4lvdSkGOz8E4PvNzfObn+MzP8Zmf4zO/Z9v4uFtTkiSpIYYzSZKkhhjODnT5sAtonOMzP8dnfo7P/Byf+Tk+83N85vesGh+POZMkSWqIW84kSZIaMhLhLMmmJHcm2Z7k4nn6vSlJJRmf0faebrk7k7xxaSpeWs90fJK8PsktSe7ofr5u6apeWov5DHXtL0nyWJJ391/t0lvkd+y0JP+QZFv3WVq9NFUvnUV8x1YlubIbly8lec/SVb10DjY+Sd6eZFeSrd3jp2bMOy/JV7rHeUtb+dJ4puOTZOOM79btSd689NX3bzGfn27+miT3JPnQ0lV9EFW1rB/ACuAu4CTgMOA24NQ5+h0F/B3wOWC8azu16384cGL3OiuGvU4Njc+rgBd3068A7h32+rQ2RjPmXQf8GfDuYa9PS+MDrARuB17ZPV/rd+yA8XkrcHU3/a+AncD6Ya/TUo8P8HbgQ3Ms+wJgR/fz+d3084e9Tg2Nz8uAU7rpFwP/BDxv2OvUyvjMmP87wJ/O12epH6Ow5ex0YHtV7aiqJ4Crgc1z9Ps14FJgckbbZgZ/GB+vqq8C27vXW06e8fhU1a1V9Y3u6TbgOUkO77vgIVjMZ4gkPwJ8lcEYLUeLGZ83ALdX1W0AVbW7qp7su+AltpjxKeCIJCuB5wBPAI/0XO9SW+j4zOWNwF9V1YNV9RDwV8Cmnuoclmc8PlX1j1X1lW76G8D9wJwXPX0WW8znhySvBo4B/rKn+p6RUQhnxwF3z3h+T9e2X5LvBE6oqk8+3WWXgcWMz0xvAj5fVY8f+hKH7hmPUZIjgV8E3t93kUO0mM/Qy4BKckOSzyf5T/2WOhSLGZ/rgD0Mtnh8HfhAVT3YY63DsNC/s2/qds1dl+SEp7nss9lixme/JKcz2LJ0Vz9lDs0zHp8kY8BvA80dbjIK4Wxe3S/nMuAXhl1LixYyPkk2MPgf/zuWqq6WHGSM3gd8sKoeW9KiGnKQ8VkJfC/wE93PH01y5hKWN3QHGZ/TgScZ7JI6EfiFJCctYXmt+ASD3bmnMdg6duWQ62nNvOOT5EXAVcBPVtXUEOobtm81Pj8NfKqq7hlaZd/CymEXsATuBWb+L+L4rm3aUQyOl/psEoBjgS1JfngByy4Hz3h8qmoiyfHAx4G3VdVy+x/ZtMV8hl4LnJ3kt4DnAVNJJquqnQNPF28x43MP8HdV9QBAkk8B3wn89RLUvVQWMz5vBa6vqr3A/Un+HhhncGzVcnHQv7NVtXvG048CvzVj2e+ftexnD3mFw7WY8SHJGuCTwC9V1ed6rHNYFjM+3w2ckeSngSOBw5I8VlXf8qSdJTPsg976fjAIoDsY/K9z+mDBDfP0/yxPHYy7gQNPCNjB8jtYeTHj87yu/48Nez1aHaNZ7e9jeZ4QsJjP0POBzzM42H0l8BngB4e9Tg2Nzy8Cf9hNHwF8ETht2Ou01OMDvGjG9I8Cn+umX8DgeM7nd4+vAi8Y9jo1ND6HMfiPzs8Pez1aHJ9Zfd5OQycELPstZ1W1L8mFwA0Mzur4WFVtS3IJMFFVW+ZZdluSaxn8QdwH/Ewts4OVFzM+wIXAycB7k7y3a3tDVd3fb9VLa5FjtOwt8jv2UJLLgJsZHPz+qZr/2MZnnUV+fj4M/GGSbUAYBLXb+6966SxwfN7VbUncBzzI4B9SqurBJL/G4PMDcEkts2PyFjM+wDnA9wFrk0y3vb2qti7hKvRqkePTLO8QIEmS1JCRPyFAkiSpJYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0kjL8n1SW5Lsi3JR5KsmDHvu5L8wSJf/6Xd7am2du/xzsVXLWm58lIakkZekjVV9UgGl+i/Dvizqrq6m/d+Bjdn/x+LeP3DGPy9fby73+oXgO+pwc2oJekAbjmTtGwkeVt3c+PbklyV5I+S/F6SzyXZkeT7k3wsyZeS/NH0clX1SDe5ksFVxmf+r/VM4DNJViT5QJIvdO/xs9177kzym91WsYkk39ndyP2u6S1kVfVEVT3evd7h+LdX0jz8AyFpWUiyAfhl4HVV9Urg57pZz2dwD73/CGwBPsjg1mzfkWTjjOVvAO4HHmWw9YwkLwT2VtXDwAXAemBjDW6g/Ccz3v7rVbUR+F/AHwFnA98FvH/G65+Q5HbgbuBSt5pJ+lYMZ5KWi9cx2B35AAxu7dO1f6IGx2/cAdxXVXdU1RSwjUHYouv/RuBFDLZsva5rfgPwl930DwC/X1X7Zr0+DEIf3XvcVFWPVtUu4PEkz+v6392FupOB85Icc8jWXNKyYjiTtNxN706cmjE9/fyA+wtX1STwF8Dmruks4PpD/B7fYHDM2RkLeF1JI8hwJmm5uBH48SRrAZK8YCELJTkyyYu66ZXADwJf7k4OOA3Y2nX9K+AdXZ8Fv37X9/gkz+mmnw98L3DnQpeXNFpWHryLJLWvqrYl+XXgb5M8Cdy6wEWPALYkmT5Q/2+AjwCvBm6tp05p/yjwMuD2JHuBPwA+tMD3+Hbgt5MUEOADVXXHApeVNGK8lIYkzSHJLwPbpy+pIUlLxXAmSZLUEI85kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSG/D/FH8gM5m1ypwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.scatterplot(data=sent_sarai20, x='Soil_Moisture', y='Rain_Rate',ax=ax)\n",
    "ax.set_title('2020', fontdict={'fontsize':12})\n",
    "ax.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "ax.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**SARAi**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Corrcoef**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rain-Soil_1_Moist\n",
      "2016:  nan\n",
      "2017:  -0.015526947287402032\n",
      "2018:  -0.00523657677575045\n",
      "2019:  -0.012251788709343403\n",
      "2020:  -0.001651145860099815\n",
      "Rain-Soil_2_Moist\n",
      "2016:  nan\n",
      "2017:  0.07817590831278916\n",
      "2018:  -0.005205999381199102\n",
      "2019:  nan\n",
      "2020:  -0.0017444866544279853\n",
      "Rain-Soil_3_Moist\n",
      "2016:  nan\n",
      "2017:  nan\n",
      "2018:  -0.005205999381199102\n",
      "2019:  nan\n",
      "2020:  -0.0034905845534833686\n",
      "Rain-Soil_4_Moist\n",
      "2016:  nan\n",
      "2017:  nan\n",
      "2018:  -0.005205999381199102\n",
      "2019:  nan\n",
      "2020:  -0.045045137454876746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\zyrvs\\Desktop\\Project DE\\Projects\\data analysis and visualisation for flood hazard mapping\\output\\venv\\lib\\site-packages\\numpy\\lib\\function_base.py:2691: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "c:\\Users\\zyrvs\\Desktop\\Project DE\\Projects\\data analysis and visualisation for flood hazard mapping\\output\\venv\\lib\\site-packages\\numpy\\lib\\function_base.py:2692: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Ignore RuntimeWarning error for true divide. This just shows that some of the values\\nfrom the corresponding year are invalid to even generate a correlation coefficient.'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Ignore RuntimeWarning error for true divide. This just shows that some of the values\n",
    "from the corresponding year are invalid to even generate a correlation coefficient.'''\n",
    "\n",
    "sarai16 = sarai[sarai.Date.dt.year == 2016].dropna(axis=0, how='any')\n",
    "sarai17 = sarai[sarai.Date.dt.year == 2017].dropna(axis=0, how='any')\n",
    "sarai18 = sarai[sarai.Date.dt.year == 2018].dropna(axis=0, how='any')\n",
    "sarai19 = sarai[sarai.Date.dt.year == 2019].dropna(axis=0, how='any')\n",
    "sarai20 = sarai[sarai.Date.dt.year == 2020].dropna(axis=0, how='any')\n",
    "\n",
    "print('Rain-Soil_1_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_1_Moist, y=sarai16.Rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_1_Moist, y=sarai17.Rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_1_Moist, y=sarai18.Rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_1_Moist, y=sarai19.Rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_1_Moist, y=sarai20.Rain)[0][1])\n",
    "print('Rain-Soil_2_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_2_Moist, y=sarai16.Rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_2_Moist, y=sarai17.Rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_2_Moist, y=sarai18.Rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_2_Moist, y=sarai19.Rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_2_Moist, y=sarai20.Rain)[0][1])\n",
    "print('Rain-Soil_3_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_3_Moist, y=sarai16.Rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_3_Moist, y=sarai17.Rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_3_Moist, y=sarai18.Rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_3_Moist, y=sarai19.Rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_3_Moist, y=sarai20.Rain)[0][1])\n",
    "print('Rain-Soil_4_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_4_Moist, y=sarai16.Rain)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_4_Moist, y=sarai17.Rain)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_4_Moist, y=sarai18.Rain)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_4_Moist, y=sarai19.Rain)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_4_Moist, y=sarai20.Rain)[0][1])\n",
    "\n",
    "'''Ignore RuntimeWarning error for true divide. This just shows that some of the values\n",
    "from the corresponding year are invalid to even generate a correlation coefficient.'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rain_Rate-Soil_1_Moist\n",
      "2016:  nan\n",
      "2017:  -0.017624505191583255\n",
      "2018:  -0.005571900677590096\n",
      "2019:  -0.011788128539186342\n",
      "2020:  -0.0021373547054842432\n",
      "Rain_Rate-Soil_2_Moist\n",
      "2016:  nan\n",
      "2017:  0.08484347137734317\n",
      "2018:  -0.005531444537652557\n",
      "2019:  nan\n",
      "2020:  0.0018884540870094115\n",
      "Rain_Rate-Soil_3_Moist\n",
      "2016:  nan\n",
      "2017:  nan\n",
      "2018:  -0.005531444537652557\n",
      "2019:  nan\n",
      "2020:  -0.003642741177385935\n",
      "Rain_Rate-Soil_4_Moist\n",
      "2016:  nan\n",
      "2017:  nan\n",
      "2018:  -0.005531444537652557\n",
      "2019:  nan\n",
      "2020:  -0.04912261474111671\n"
     ]
    }
   ],
   "source": [
    "print('Rain_Rate-Soil_1_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_1_Moist, y=sarai16.Rain_Rate)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_1_Moist, y=sarai17.Rain_Rate)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_1_Moist, y=sarai18.Rain_Rate)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_1_Moist, y=sarai19.Rain_Rate)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_1_Moist, y=sarai20.Rain_Rate)[0][1])\n",
    "print('Rain_Rate-Soil_2_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_2_Moist, y=sarai16.Rain_Rate)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_2_Moist, y=sarai17.Rain_Rate)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_2_Moist, y=sarai18.Rain_Rate)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_2_Moist, y=sarai19.Rain_Rate)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_2_Moist, y=sarai20.Rain_Rate)[0][1])\n",
    "print('Rain_Rate-Soil_3_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_3_Moist, y=sarai16.Rain_Rate)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_3_Moist, y=sarai17.Rain_Rate)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_3_Moist, y=sarai18.Rain_Rate)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_3_Moist, y=sarai19.Rain_Rate)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_3_Moist, y=sarai20.Rain_Rate)[0][1])\n",
    "print('Rain_Rate-Soil_4_Moist')\n",
    "print('2016: ',np.corrcoef(x=sarai16.Soil_4_Moist, y=sarai16.Rain_Rate)[0][1])\n",
    "print('2017: ',np.corrcoef(x=sarai17.Soil_4_Moist, y=sarai17.Rain_Rate)[0][1])\n",
    "print('2018: ',np.corrcoef(x=sarai18.Soil_4_Moist, y=sarai18.Rain_Rate)[0][1])\n",
    "print('2019: ',np.corrcoef(x=sarai19.Soil_4_Moist, y=sarai19.Rain_Rate)[0][1])\n",
    "print('2020: ',np.corrcoef(x=sarai20.Soil_4_Moist, y=sarai20.Rain_Rate)[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Soil (1-4)-Rain**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=2,figsize=(20,12))\n",
    "\n",
    "axs1 = sns.regplot(data=sarai, x='Soil_1_Moist', y='Rain',ax=axs[0,0])\n",
    "axs1.set_title('soil 1-rain', fontdict={'fontsize':12})\n",
    "axs1.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs1.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs1.set_yticks(np.arange(0,600,100))\n",
    "axs1.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs2 = sns.regplot(data=sarai, x='Soil_2_Moist', y='Rain',ax=axs[0,1])\n",
    "axs2.set_title('soil 2-rain', fontdict={'fontsize':12})\n",
    "axs2.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs2.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs2.set_yticks(np.arange(0,600,100))\n",
    "axs2.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs3 = sns.regplot(data=sarai, x='Soil_3_Moist', y='Rain',ax=axs[1,0])\n",
    "axs3.set_title('soil 3-rain', fontdict={'fontsize':12})\n",
    "axs3.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs3.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs3.set_yticks(np.arange(0,600,100))\n",
    "axs3.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs4 = sns.regplot(data=sarai, x='Soil_4_Moist', y='Rain',ax=axs[1,1])\n",
    "axs4.set_title('soil 4-rain', fontdict={'fontsize':12})\n",
    "axs4.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs4.set_ylabel('mm', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "axs4.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Soil-Rain Rate**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=2,figsize=(20,12))\n",
    "\n",
    "axs1 = sns.regplot(data=sarai, x='Soil_1_Moist', y='Rain_Rate', ax=axs[0,0])\n",
    "axs1.set_title('soil 1-rain_rate', fontdict={'fontsize':12})\n",
    "axs1.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs1.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs1.set_yticks(np.arange(0,600,100))\n",
    "axs1.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs2 = sns.regplot(data=sarai, x='Soil_2_Moist', y='Rain_Rate',ax=axs[0,1])\n",
    "axs2.set_title('soil 2-rain_rate', fontdict={'fontsize':12})\n",
    "axs2.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs2.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs2.set_yticks(np.arange(0,600,100))\n",
    "axs2.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs3 = sns.regplot(data=sarai, x='Soil_3_Moist', y='Rain_Rate',ax=axs[1,0])\n",
    "axs3.set_title('soil 3-rain_rate', fontdict={'fontsize':12})\n",
    "axs3.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs3.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs3.set_yticks(np.arange(0,600,100))\n",
    "axs3.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "axs4 = sns.regplot(data=sarai, x='Soil_4_Moist', y='Rain_Rate',ax=axs[1,1])\n",
    "axs4.set_title('soil 4-rain_rate', fontdict={'fontsize':12})\n",
    "axs4.set_xlabel('cm3/cm3', fontdict={'fontsize':10})\n",
    "axs4.set_ylabel('mm/hr', fontdict={'fontsize':10})\n",
    "#axs4.set_yticks(np.arange(0,600,100))\n",
    "axs4.tick_params(axis='both', labelsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Value ranges-SENTINEL**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>date_min</th>\n",
       "      <th>min</th>\n",
       "      <th>date_max</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brgy</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Agpay</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.286696</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.540522</td>\n",
       "      <td>0.253826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.416594</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.535412</td>\n",
       "      <td>0.118818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.363069</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.544692</td>\n",
       "      <td>0.181623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.451482</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.535687</td>\n",
       "      <td>0.084205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.493770</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.534501</td>\n",
       "      <td>0.040731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Binogsacan Lower</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.398691</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.557887</td>\n",
       "      <td>0.159196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.323048</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.562272</td>\n",
       "      <td>0.239224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.438723</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.566287</td>\n",
       "      <td>0.127564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.354678</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.563117</td>\n",
       "      <td>0.208439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.338517</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.563734</td>\n",
       "      <td>0.225217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Binogsacan Upper</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.509204</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.558167</td>\n",
       "      <td>0.048963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.334986</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.560883</td>\n",
       "      <td>0.225897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.461235</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.563681</td>\n",
       "      <td>0.102446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.400453</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.560883</td>\n",
       "      <td>0.160430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.391857</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.560097</td>\n",
       "      <td>0.168240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Calzada</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.261724</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.283731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.326946</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.542904</td>\n",
       "      <td>0.215958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.091957</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.536648</td>\n",
       "      <td>0.444691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.282640</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.542904</td>\n",
       "      <td>0.260264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.286309</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.541076</td>\n",
       "      <td>0.254767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Catomag</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.498398</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.549345</td>\n",
       "      <td>0.050947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.316228</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.544402</td>\n",
       "      <td>0.228174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.408068</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.544692</td>\n",
       "      <td>0.136624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.432220</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.544402</td>\n",
       "      <td>0.112182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.508481</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.537146</td>\n",
       "      <td>0.028665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Malabnig</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.526422</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.559361</td>\n",
       "      <td>0.032939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.265557</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.551893</td>\n",
       "      <td>0.286336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.363598</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.549441</td>\n",
       "      <td>0.185843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.371187</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.551893</td>\n",
       "      <td>0.180706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.399311</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.549116</td>\n",
       "      <td>0.149805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Morera</th>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-17</td>\n",
       "      <td>0.435429</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>0.566180</td>\n",
       "      <td>0.130751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>0.290405</td>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>0.560811</td>\n",
       "      <td>0.270406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.316160</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>0.563219</td>\n",
       "      <td>0.247059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>0.325413</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>0.560384</td>\n",
       "      <td>0.234971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.129850</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>0.569293</td>\n",
       "      <td>0.439443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date_min       min   date_max       max     range\n",
       "Brgy             Year                                                    \n",
       "Agpay            2016 2016-04-17  0.286696 2016-12-02  0.540522  0.253826\n",
       "                 2017 2017-01-06  0.416594 2017-12-08  0.535412  0.118818\n",
       "                 2018 2018-01-01  0.363069 2018-12-06  0.544692  0.181623\n",
       "                 2019 2019-01-09  0.451482 2019-12-06  0.535687  0.084205\n",
       "                 2020 2020-01-28  0.493770 2020-12-29  0.534501  0.040731\n",
       "Binogsacan Lower 2016 2016-04-17  0.398691 2016-12-02  0.557887  0.159196\n",
       "                 2017 2017-01-06  0.323048 2017-12-08  0.562272  0.239224\n",
       "                 2018 2018-01-01  0.438723 2018-12-06  0.566287  0.127564\n",
       "                 2019 2019-01-09  0.354678 2019-12-06  0.563117  0.208439\n",
       "                 2020 2020-01-28  0.338517 2020-12-29  0.563734  0.225217\n",
       "Binogsacan Upper 2016 2016-04-17  0.509204 2016-12-02  0.558167  0.048963\n",
       "                 2017 2017-01-06  0.334986 2017-12-08  0.560883  0.225897\n",
       "                 2018 2018-01-01  0.461235 2018-12-06  0.563681  0.102446\n",
       "                 2019 2019-01-09  0.400453 2019-12-06  0.560883  0.160430\n",
       "                 2020 2020-01-28  0.391857 2020-12-29  0.560097  0.168240\n",
       "Calzada          2016 2016-04-17  0.261724 2016-12-02  0.545455  0.283731\n",
       "                 2017 2017-01-06  0.326946 2017-12-08  0.542904  0.215958\n",
       "                 2018 2018-01-01  0.091957 2018-12-06  0.536648  0.444691\n",
       "                 2019 2019-01-09  0.282640 2019-12-06  0.542904  0.260264\n",
       "                 2020 2020-01-28  0.286309 2020-12-29  0.541076  0.254767\n",
       "Catomag          2016 2016-04-17  0.498398 2016-12-02  0.549345  0.050947\n",
       "                 2017 2017-01-06  0.316228 2017-12-08  0.544402  0.228174\n",
       "                 2018 2018-01-01  0.408068 2018-12-06  0.544692  0.136624\n",
       "                 2019 2019-01-09  0.432220 2019-12-06  0.544402  0.112182\n",
       "                 2020 2020-01-28  0.508481 2020-12-29  0.537146  0.028665\n",
       "Malabnig         2016 2016-04-17  0.526422 2016-12-02  0.559361  0.032939\n",
       "                 2017 2017-01-06  0.265557 2017-12-08  0.551893  0.286336\n",
       "                 2018 2018-01-01  0.363598 2018-12-06  0.549441  0.185843\n",
       "                 2019 2019-01-09  0.371187 2019-12-06  0.551893  0.180706\n",
       "                 2020 2020-01-28  0.399311 2020-12-29  0.549116  0.149805\n",
       "Morera           2016 2016-04-17  0.435429 2016-12-02  0.566180  0.130751\n",
       "                 2017 2017-01-06  0.290405 2017-12-08  0.560811  0.270406\n",
       "                 2018 2018-01-01  0.316160 2018-12-06  0.563219  0.247059\n",
       "                 2019 2019-01-09  0.325413 2019-12-06  0.560384  0.234971\n",
       "                 2020 2020-01-28  0.129850 2020-12-29  0.569293  0.439443"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_range = sentinel.groupby([sentinel.Brgy, sentinel.Year]).min().drop(labels=['id'],axis=1)\n",
    "sentinel_max = sentinel.groupby([sentinel.Brgy, sentinel.Year]).max()\n",
    "sentinel_range.rename(\n",
    "    columns={'Date':'date_min','Soil_Moisture':'min'},inplace=True\n",
    ")\n",
    "sentinel_range['date_max']=sentinel_max.Date\n",
    "sentinel_range['max'] = sentinel_max.Soil_Moisture\n",
    "sentinel_range['range'] = sentinel_range['max'] - sentinel_range['min']\n",
    "sentinel_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Value ranges-SARAi**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_min</th>\n",
       "      <th>time_min</th>\n",
       "      <th>rain_rate_min</th>\n",
       "      <th>rain_min</th>\n",
       "      <th>soil_1_min</th>\n",
       "      <th>soil_2_min</th>\n",
       "      <th>soil_3_min</th>\n",
       "      <th>soil_4_min</th>\n",
       "      <th>date_max</th>\n",
       "      <th>time_max</th>\n",
       "      <th>rain_rate_max</th>\n",
       "      <th>rain_max</th>\n",
       "      <th>soil_1_max</th>\n",
       "      <th>soil_2_max</th>\n",
       "      <th>soil_3_max</th>\n",
       "      <th>soil_4_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2016-04-21</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>9:45 PM</td>\n",
       "      <td>41.40</td>\n",
       "      <td>304.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>9:45 PM</td>\n",
       "      <td>23.62</td>\n",
       "      <td>218.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>9:45 PM</td>\n",
       "      <td>17.02</td>\n",
       "      <td>2090.2</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>9:45 PM</td>\n",
       "      <td>19.30</td>\n",
       "      <td>457.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>10:00 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>9:45 PM</td>\n",
       "      <td>91.44</td>\n",
       "      <td>2090.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_min  time_min  rain_rate_min  rain_min  soil_1_min  soil_2_min  \\\n",
       "Date                                                                         \n",
       "2016 2016-04-21  10:00 AM            0.0       0.0         0.0         0.0   \n",
       "2017 2017-01-01  10:00 AM            0.0       0.0         0.0         0.0   \n",
       "2018 2018-01-01  10:00 AM            0.0       0.0         0.0         0.0   \n",
       "2019 2019-01-01  10:00 AM            0.0       0.0         0.0         0.0   \n",
       "2020 2020-01-01  10:00 AM            0.0       0.0         0.0         0.0   \n",
       "\n",
       "      soil_3_min  soil_4_min   date_max time_max  rain_rate_max  rain_max  \\\n",
       "Date                                                                        \n",
       "2016         0.0         0.0 2016-12-31  9:45 PM          41.40     304.8   \n",
       "2017         0.0         0.0 2017-12-31  9:45 PM          23.62     218.4   \n",
       "2018         0.0         0.0 2018-12-31  9:45 PM          17.02    2090.2   \n",
       "2019         0.0         0.0 2019-12-31  9:45 PM          19.30     457.2   \n",
       "2020         0.0         0.0 2020-12-16  9:45 PM          91.44    2090.2   \n",
       "\n",
       "      soil_1_max  soil_2_max  soil_3_max  soil_4_max  \n",
       "Date                                                  \n",
       "2016         0.0         0.0         0.0         0.0  \n",
       "2017        27.0         1.0         0.0         0.0  \n",
       "2018       200.0       200.0       200.0       200.0  \n",
       "2019         1.0         0.0         0.0         0.0  \n",
       "2020         1.0         1.0         1.0       200.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_range = sarai.groupby([sarai.Date.dt.year]).min().drop(labels=['Year','Month'],axis=1)\n",
    "sarai_range.rename(\n",
    "    columns={'Date':'date_min','Time':'time_min','Rain_Rate':'rain_rate_min','Rain':'rain_min','Soil_1_Moist':'soil_1_min',\n",
    "    'Soil_2_Moist':'soil_2_min','Soil_3_Moist':'soil_3_min','Soil_4_Moist':'soil_4_min'},inplace=True\n",
    ")\n",
    "sarai_range['date_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Date\n",
    "sarai_range['time_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Time\n",
    "sarai_range['rain_rate_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Rain_Rate\n",
    "sarai_range['rain_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Rain\n",
    "sarai_range['soil_1_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Soil_1_Moist\n",
    "sarai_range['soil_2_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Soil_2_Moist\n",
    "sarai_range['soil_3_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Soil_3_Moist\n",
    "sarai_range['soil_4_max']= sarai.groupby([sarai.Date.dt.year]).max().drop(labels=['Year','Month'],axis=1).Soil_4_Moist\n",
    "sarai_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Standard Deviation-AWS**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain</th>\n",
       "      <th>avg</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>2016</th>\n",
       "      <td>18.725444</td>\n",
       "      <td>6.892418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15.822003</td>\n",
       "      <td>7.736849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>22.378843</td>\n",
       "      <td>7.869692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>18.936538</td>\n",
       "      <td>6.125890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>25.663601</td>\n",
       "      <td>9.037650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rain       avg\n",
       "date                     \n",
       "2016  18.725444  6.892418\n",
       "2017  15.822003  7.736849\n",
       "2018  22.378843  7.869692\n",
       "2019  18.936538  6.125890\n",
       "2020  25.663601  9.037650"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aws_sd = aws.groupby([aws.date.dt.year]).std()\n",
    "aws_sd['avg'] = aws.groupby([aws.date.dt.year]).mean().rain\n",
    "aws_sd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Standard Deviation-SARAi**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Rain_Rate</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Soil_1_Moist</th>\n",
       "      <th>Soil_2_Moist</th>\n",
       "      <th>Soil_3_Moist</th>\n",
       "      <th>Soil_4_Moist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.766256</td>\n",
       "      <td>9.241387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.578880</td>\n",
       "      <td>7.097624</td>\n",
       "      <td>3.936422</td>\n",
       "      <td>0.048227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.546969</td>\n",
       "      <td>14.761358</td>\n",
       "      <td>8.132192</td>\n",
       "      <td>8.131965</td>\n",
       "      <td>8.230537</td>\n",
       "      <td>8.132457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.310032</td>\n",
       "      <td>4.961279</td>\n",
       "      <td>0.159472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3.068577</td>\n",
       "      <td>61.580270</td>\n",
       "      <td>0.039839</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>0.109553</td>\n",
       "      <td>86.843113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rain_Rate       Rain  Soil_1_Moist  Soil_2_Moist  Soil_3_Moist  \\\n",
       "Date                                                                   \n",
       "2016   0.766256   9.241387      0.000000      0.000000      0.000000   \n",
       "2017   0.578880   7.097624      3.936422      0.048227      0.000000   \n",
       "2018   0.546969  14.761358      8.132192      8.131965      8.230537   \n",
       "2019   0.310032   4.961279      0.159472      0.000000      0.000000   \n",
       "2020   3.068577  61.580270      0.039839      0.089736      0.109553   \n",
       "\n",
       "      Soil_4_Moist  \n",
       "Date                \n",
       "2016      0.000000  \n",
       "2017      0.000000  \n",
       "2018      8.132457  \n",
       "2019      0.000000  \n",
       "2020     86.843113  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_sd = sarai.groupby([sarai.Date.dt.year]).std(ddof=0).drop(labels=['Year'],axis=1)\n",
    "sarai_sd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Average-SARAi**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Rain_Rate</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Soil_1_Moist</th>\n",
       "      <th>Soil_2_Moist</th>\n",
       "      <th>Soil_3_Moist</th>\n",
       "      <th>Soil_4_Moist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.089088</td>\n",
       "      <td>1.082257</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.073563</td>\n",
       "      <td>0.863233</td>\n",
       "      <td>1.014396</td>\n",
       "      <td>0.002331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.074382</td>\n",
       "      <td>0.940799</td>\n",
       "      <td>0.342502</td>\n",
       "      <td>0.331193</td>\n",
       "      <td>0.339284</td>\n",
       "      <td>0.331233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.026172</td>\n",
       "      <td>0.286150</td>\n",
       "      <td>0.026113</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.208615</td>\n",
       "      <td>3.547242</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>0.008119</td>\n",
       "      <td>0.012149</td>\n",
       "      <td>139.742593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rain_Rate      Rain  Soil_1_Moist  Soil_2_Moist  Soil_3_Moist  \\\n",
       "Date                                                                  \n",
       "2016   0.089088  1.082257      0.000000      0.000000      0.000000   \n",
       "2017   0.073563  0.863233      1.014396      0.002331      0.000000   \n",
       "2018   0.074382  0.940799      0.342502      0.331193      0.339284   \n",
       "2019   0.026172  0.286150      0.026113      0.000000      0.000000   \n",
       "2020   0.208615  3.547242      0.001590      0.008119      0.012149   \n",
       "\n",
       "      Soil_4_Moist  \n",
       "Date                \n",
       "2016      0.000000  \n",
       "2017      0.000000  \n",
       "2018      0.331233  \n",
       "2019      0.000000  \n",
       "2020    139.742593  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarai_avg = sarai.groupby([sarai.Date.dt.year]).mean().drop(labels=['Year'],axis=1)\n",
    "sarai_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _**Hazard Map (parametric conversion)**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Water-content**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Brgy</th>\n",
       "      <th>Soil_Moisture</th>\n",
       "      <th>mm_50mm_5cm</th>\n",
       "      <th>mm_500mm_50cm</th>\n",
       "      <th>mm_550mm_55cm</th>\n",
       "      <th>mm_600mm_60cm</th>\n",
       "      <th>mm_1200mm_120cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agpay</td>\n",
       "      <td>0.508438</td>\n",
       "      <td>25.421913</td>\n",
       "      <td>254.219127</td>\n",
       "      <td>279.641040</td>\n",
       "      <td>305.062953</td>\n",
       "      <td>610.125906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Binogsacan Lower</td>\n",
       "      <td>0.518725</td>\n",
       "      <td>25.936243</td>\n",
       "      <td>259.362431</td>\n",
       "      <td>285.298675</td>\n",
       "      <td>311.234918</td>\n",
       "      <td>622.469835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Binogsacan Upper</td>\n",
       "      <td>0.528352</td>\n",
       "      <td>26.417605</td>\n",
       "      <td>264.176049</td>\n",
       "      <td>290.593654</td>\n",
       "      <td>317.011259</td>\n",
       "      <td>634.022518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calzada</td>\n",
       "      <td>0.478786</td>\n",
       "      <td>23.939306</td>\n",
       "      <td>239.393059</td>\n",
       "      <td>263.332365</td>\n",
       "      <td>287.271671</td>\n",
       "      <td>574.543341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Catomag</td>\n",
       "      <td>0.524287</td>\n",
       "      <td>26.214347</td>\n",
       "      <td>262.143471</td>\n",
       "      <td>288.357818</td>\n",
       "      <td>314.572165</td>\n",
       "      <td>629.144329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Malabnig</td>\n",
       "      <td>0.517511</td>\n",
       "      <td>25.875536</td>\n",
       "      <td>258.755363</td>\n",
       "      <td>284.630899</td>\n",
       "      <td>310.506435</td>\n",
       "      <td>621.012871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Morera</td>\n",
       "      <td>0.494518</td>\n",
       "      <td>24.725886</td>\n",
       "      <td>247.258863</td>\n",
       "      <td>271.984749</td>\n",
       "      <td>296.710635</td>\n",
       "      <td>593.421271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Brgy  Soil_Moisture  mm_50mm_5cm  mm_500mm_50cm  mm_550mm_55cm  \\\n",
       "0             Agpay       0.508438    25.421913     254.219127     279.641040   \n",
       "1  Binogsacan Lower       0.518725    25.936243     259.362431     285.298675   \n",
       "2  Binogsacan Upper       0.528352    26.417605     264.176049     290.593654   \n",
       "3           Calzada       0.478786    23.939306     239.393059     263.332365   \n",
       "4           Catomag       0.524287    26.214347     262.143471     288.357818   \n",
       "5          Malabnig       0.517511    25.875536     258.755363     284.630899   \n",
       "6            Morera       0.494518    24.725886     247.258863     271.984749   \n",
       "\n",
       "   mm_600mm_60cm  mm_1200mm_120cm  \n",
       "0     305.062953       610.125906  \n",
       "1     311.234918       622.469835  \n",
       "2     317.011259       634.022518  \n",
       "3     287.271671       574.543341  \n",
       "4     314.572165       629.144329  \n",
       "5     310.506435       621.012871  \n",
       "6     296.710635       593.421271  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_avg_brgy\n",
    "sentinel_avg_brgy['mm_50mm_5cm'] = sentinel_avg_brgy.Soil_Moisture * 50\n",
    "sentinel_avg_brgy['mm_500mm_50cm'] = sentinel_avg_brgy.mm_50mm_5cm * 10\n",
    "sentinel_avg_brgy['mm_550mm_55cm'] = sentinel_avg_brgy.mm_50mm_5cm * 11\n",
    "sentinel_avg_brgy['mm_600mm_60cm'] = sentinel_avg_brgy.mm_50mm_5cm * 12\n",
    "sentinel_avg_brgy['mm_1200mm_120cm'] = sentinel_avg_brgy.mm_50mm_5cm * 24\n",
    "sentinel_avg_brgy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Soil+Aggregates**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Brgy</th>\n",
       "      <th>Soil_Moisture</th>\n",
       "      <th>mm_50mm_5cm</th>\n",
       "      <th>mm_500mm_50cm</th>\n",
       "      <th>mm_550mm_55cm</th>\n",
       "      <th>mm_600mm_60cm</th>\n",
       "      <th>mm_1200mm_120cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agpay</td>\n",
       "      <td>0.508438</td>\n",
       "      <td>24.578087</td>\n",
       "      <td>245.780873</td>\n",
       "      <td>270.358960</td>\n",
       "      <td>294.937047</td>\n",
       "      <td>589.874094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Binogsacan Lower</td>\n",
       "      <td>0.518725</td>\n",
       "      <td>24.063757</td>\n",
       "      <td>240.637569</td>\n",
       "      <td>264.701325</td>\n",
       "      <td>288.765082</td>\n",
       "      <td>577.530165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Binogsacan Upper</td>\n",
       "      <td>0.528352</td>\n",
       "      <td>23.582395</td>\n",
       "      <td>235.823951</td>\n",
       "      <td>259.406346</td>\n",
       "      <td>282.988741</td>\n",
       "      <td>565.977482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calzada</td>\n",
       "      <td>0.478786</td>\n",
       "      <td>26.060694</td>\n",
       "      <td>260.606941</td>\n",
       "      <td>286.667635</td>\n",
       "      <td>312.728329</td>\n",
       "      <td>625.456659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Catomag</td>\n",
       "      <td>0.524287</td>\n",
       "      <td>23.785653</td>\n",
       "      <td>237.856529</td>\n",
       "      <td>261.642182</td>\n",
       "      <td>285.427835</td>\n",
       "      <td>570.855671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Malabnig</td>\n",
       "      <td>0.517511</td>\n",
       "      <td>24.124464</td>\n",
       "      <td>241.244637</td>\n",
       "      <td>265.369101</td>\n",
       "      <td>289.493565</td>\n",
       "      <td>578.987129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Morera</td>\n",
       "      <td>0.494518</td>\n",
       "      <td>25.274114</td>\n",
       "      <td>252.741137</td>\n",
       "      <td>278.015251</td>\n",
       "      <td>303.289365</td>\n",
       "      <td>606.578729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Brgy  Soil_Moisture  mm_50mm_5cm  mm_500mm_50cm  mm_550mm_55cm  \\\n",
       "0             Agpay       0.508438    24.578087     245.780873     270.358960   \n",
       "1  Binogsacan Lower       0.518725    24.063757     240.637569     264.701325   \n",
       "2  Binogsacan Upper       0.528352    23.582395     235.823951     259.406346   \n",
       "3           Calzada       0.478786    26.060694     260.606941     286.667635   \n",
       "4           Catomag       0.524287    23.785653     237.856529     261.642182   \n",
       "5          Malabnig       0.517511    24.124464     241.244637     265.369101   \n",
       "6            Morera       0.494518    25.274114     252.741137     278.015251   \n",
       "\n",
       "   mm_600mm_60cm  mm_1200mm_120cm  \n",
       "0     294.937047       589.874094  \n",
       "1     288.765082       577.530165  \n",
       "2     282.988741       565.977482  \n",
       "3     312.728329       625.456659  \n",
       "4     285.427835       570.855671  \n",
       "5     289.493565       578.987129  \n",
       "6     303.289365       606.578729  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_agg = sentinel_avg_brgy.copy()\n",
    "sentinel_agg.mm_50mm_5cm = 50 - sentinel_agg.mm_50mm_5cm\n",
    "sentinel_agg.mm_500mm_50cm = 500 - sentinel_agg.mm_500mm_50cm\n",
    "sentinel_agg.mm_550mm_55cm = 550 - sentinel_agg.mm_550mm_55cm\n",
    "sentinel_agg.mm_600mm_60cm = 600 - sentinel_agg.mm_600mm_60cm\n",
    "sentinel_agg.mm_1200mm_120cm = 1200 - sentinel_agg.mm_1200mm_120cm\n",
    "sentinel_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Permeability metrics**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1175.04\n",
      "2496.96\n",
      "3403.0\n",
      "11.164698162729659\n"
     ]
    }
   ],
   "source": [
    "permeabilityFC = (5.1 * 24 * 30) * 0.32\n",
    "permeabilityR = (5.1 * 24 * 30) * 0.68\n",
    "avg_perm = int(sentinel_agg.mm_550mm_55cm.mean())\n",
    "print(permeabilityFC)\n",
    "print(permeabilityR)\n",
    "print((permeabilityFC - avg_perm)+permeabilityR)\n",
    "print((permeabilityFC - avg_perm + permeabilityR) / 10 / 2.54 / 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Permeability w/ Field Capacity factor**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R\n",
       "0   Jan  1046.525  334.888   711.637\n",
       "1   Feb   507.875  162.520   345.355\n",
       "2   Mar   357.125  114.280   242.845\n",
       "3   Apr   204.875   65.560   139.315\n",
       "4   May   615.000  196.800   418.200\n",
       "5   Jun   842.150  269.488   572.662\n",
       "6   Jul  1129.075  361.304   767.771\n",
       "7   Aug  1296.475  414.872   881.603\n",
       "8   Sep  1254.725  401.512   853.213\n",
       "9   Oct  2099.375  671.800  1427.575\n",
       "10  Nov  1440.350  460.912   979.438\n",
       "11  Dec  2365.200  756.864  1608.336"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permeabilityAWS = aws_monthly_total.copy()\n",
    "permeabilityAWS['FC'] = permeabilityAWS.rain * 0.32\n",
    "permeabilityAWS['R'] = permeabilityAWS.rain * 0.68\n",
    "permeabilityAWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Agpay**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>64.52904</td>\n",
       "      <td>-254.986094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-107.83896</td>\n",
       "      <td>-427.354094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-156.07896</td>\n",
       "      <td>-475.594094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-204.79896</td>\n",
       "      <td>-524.314094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-73.55896</td>\n",
       "      <td>-393.074094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>-0.87096</td>\n",
       "      <td>-320.386094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>90.94504</td>\n",
       "      <td>-228.570094</td>\n",
       "      <td>858.71604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>144.51304</td>\n",
       "      <td>-175.002094</td>\n",
       "      <td>1026.11604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>131.15304</td>\n",
       "      <td>-188.362094</td>\n",
       "      <td>984.36604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>401.44104</td>\n",
       "      <td>81.925906</td>\n",
       "      <td>1829.01604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>190.55304</td>\n",
       "      <td>-128.962094</td>\n",
       "      <td>1169.99104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.84</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>486.50504</td>\n",
       "      <td>166.989906</td>\n",
       "      <td>2094.84104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R    exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   64.52904 -254.986094            NaN   \n",
       "1   Feb   507.875  162.520   345.355 -107.83896 -427.354094            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -156.07896 -475.594094            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -204.79896 -524.314094            NaN   \n",
       "4   May   615.000  196.800   418.200  -73.55896 -393.074094            NaN   \n",
       "5   Jun   842.150  269.488   572.662   -0.87096 -320.386094            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   90.94504 -228.570094      858.71604   \n",
       "7   Aug  1296.475  414.872   881.603  144.51304 -175.002094     1026.11604   \n",
       "8   Sep  1254.725  401.512   853.213  131.15304 -188.362094      984.36604   \n",
       "9   Oct  2099.375  671.800  1427.575  401.44104   81.925906     1829.01604   \n",
       "10  Nov  1440.350  460.912   979.438  190.55304 -128.962094     1169.99104   \n",
       "11  Dec  2365.200  756.864  1608.336  486.50504  166.989906     2094.84104   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.82     NaN  \n",
       "7             NaN    3.37     NaN  \n",
       "8             NaN    3.23     NaN  \n",
       "9             NaN    6.00     NaN  \n",
       "10            NaN    3.84     NaN  \n",
       "11            NaN    6.87     NaN  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agpay_wet = float(sentinel_agg[sentinel_agg.Brgy=='Agpay'].mm_550mm_55cm)\n",
    "agpay_dry = float(sentinel_agg[sentinel_agg.Brgy=='Agpay'].mm_1200mm_120cm)\n",
    "agpay = permeabilityAWS.copy()\n",
    "agpay['exc_wet'] = agpay['FC'] - agpay_wet\n",
    "agpay['exc_dry'] = agpay['FC'] - agpay_dry\n",
    "agpay['total_exc_wet'] = np.where(agpay.exc_wet < 0, agpay.R, agpay.R+agpay.exc_wet)\n",
    "agpay['total_exc_wet'] = np.where(agpay.rain < 1087.8, np.nan, agpay.total_exc_wet)\n",
    "agpay['total_exc_dry'] = np.where(agpay.exc_dry < 0, agpay.R, agpay.R+agpay.exc_dry)\n",
    "agpay['total_exc_dry'] = np.where(agpay.rain > 1087.8, np.nan, agpay.total_exc_dry)\n",
    "agpay['wet_ft'] = round(((agpay.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "agpay['dry_ft'] = round(((agpay.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "agpay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Binogsacan Upper**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>75.481654</td>\n",
       "      <td>-231.089482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-96.886346</td>\n",
       "      <td>-403.457482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-145.126346</td>\n",
       "      <td>-451.697482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-193.846346</td>\n",
       "      <td>-500.417482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-62.606346</td>\n",
       "      <td>-369.177482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>10.081654</td>\n",
       "      <td>-296.489482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>101.897654</td>\n",
       "      <td>-204.673482</td>\n",
       "      <td>869.668654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>155.465654</td>\n",
       "      <td>-151.105482</td>\n",
       "      <td>1037.068654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>142.105654</td>\n",
       "      <td>-164.465482</td>\n",
       "      <td>995.318654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>412.393654</td>\n",
       "      <td>105.822518</td>\n",
       "      <td>1839.968654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>201.505654</td>\n",
       "      <td>-105.065482</td>\n",
       "      <td>1180.943654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>497.457654</td>\n",
       "      <td>190.886518</td>\n",
       "      <td>2105.793654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   75.481654 -231.089482            NaN   \n",
       "1   Feb   507.875  162.520   345.355  -96.886346 -403.457482            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -145.126346 -451.697482            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -193.846346 -500.417482            NaN   \n",
       "4   May   615.000  196.800   418.200  -62.606346 -369.177482            NaN   \n",
       "5   Jun   842.150  269.488   572.662   10.081654 -296.489482            NaN   \n",
       "6   Jul  1129.075  361.304   767.771  101.897654 -204.673482     869.668654   \n",
       "7   Aug  1296.475  414.872   881.603  155.465654 -151.105482    1037.068654   \n",
       "8   Sep  1254.725  401.512   853.213  142.105654 -164.465482     995.318654   \n",
       "9   Oct  2099.375  671.800  1427.575  412.393654  105.822518    1839.968654   \n",
       "10  Nov  1440.350  460.912   979.438  201.505654 -105.065482    1180.943654   \n",
       "11  Dec  2365.200  756.864  1608.336  497.457654  190.886518    2105.793654   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.85     NaN  \n",
       "7             NaN    3.40     NaN  \n",
       "8             NaN    3.27     NaN  \n",
       "9             NaN    6.04     NaN  \n",
       "10            NaN    3.87     NaN  \n",
       "11            NaN    6.91     NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binogup_wet = float(sentinel_agg[sentinel_agg.Brgy=='Binogsacan Upper'].mm_550mm_55cm)\n",
    "binogup_dry = float(sentinel_agg[sentinel_agg.Brgy=='Binogsacan Upper'].mm_1200mm_120cm)\n",
    "binogup = permeabilityAWS.copy()\n",
    "binogup['exc_wet'] = binogup['FC'] - binogup_wet\n",
    "binogup['exc_dry'] = binogup['FC'] - binogup_dry\n",
    "binogup['total_exc_wet'] = np.where(binogup.exc_wet < 0, binogup.R, binogup.R+binogup.exc_wet)\n",
    "binogup['total_exc_wet'] = np.where(binogup.rain < 1087.8, np.nan, binogup.total_exc_wet)\n",
    "binogup['total_exc_dry'] = np.where(binogup.exc_dry < 0, binogup.R, binogup.R+binogup.exc_dry)\n",
    "binogup['total_exc_dry'] = np.where(binogup.rain > 1087.8, np.nan, binogup.total_exc_dry)\n",
    "binogup['wet_ft'] = round(((binogup.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "binogup['dry_ft'] = round(((binogup.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "binogup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Binogsacan Lower**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>70.186675</td>\n",
       "      <td>-242.642165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-102.181325</td>\n",
       "      <td>-415.010165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-150.421325</td>\n",
       "      <td>-463.250165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-199.141325</td>\n",
       "      <td>-511.970165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-67.901325</td>\n",
       "      <td>-380.730165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>4.786675</td>\n",
       "      <td>-308.042165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>96.602675</td>\n",
       "      <td>-216.226165</td>\n",
       "      <td>864.373675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.84</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>150.170675</td>\n",
       "      <td>-162.658165</td>\n",
       "      <td>1031.773675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>136.810675</td>\n",
       "      <td>-176.018165</td>\n",
       "      <td>990.023675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>407.098675</td>\n",
       "      <td>94.269835</td>\n",
       "      <td>1834.673675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>196.210675</td>\n",
       "      <td>-116.618165</td>\n",
       "      <td>1175.648675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.86</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>492.162675</td>\n",
       "      <td>179.333835</td>\n",
       "      <td>2100.498675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.89</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   70.186675 -242.642165            NaN   \n",
       "1   Feb   507.875  162.520   345.355 -102.181325 -415.010165            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -150.421325 -463.250165            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -199.141325 -511.970165            NaN   \n",
       "4   May   615.000  196.800   418.200  -67.901325 -380.730165            NaN   \n",
       "5   Jun   842.150  269.488   572.662    4.786675 -308.042165            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   96.602675 -216.226165     864.373675   \n",
       "7   Aug  1296.475  414.872   881.603  150.170675 -162.658165    1031.773675   \n",
       "8   Sep  1254.725  401.512   853.213  136.810675 -176.018165     990.023675   \n",
       "9   Oct  2099.375  671.800  1427.575  407.098675   94.269835    1834.673675   \n",
       "10  Nov  1440.350  460.912   979.438  196.210675 -116.618165    1175.648675   \n",
       "11  Dec  2365.200  756.864  1608.336  492.162675  179.333835    2100.498675   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.84     NaN  \n",
       "7             NaN    3.39     NaN  \n",
       "8             NaN    3.25     NaN  \n",
       "9             NaN    6.02     NaN  \n",
       "10            NaN    3.86     NaN  \n",
       "11            NaN    6.89     NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binoglo_wet = float(sentinel_agg[sentinel_agg.Brgy=='Binogsacan Lower'].mm_550mm_55cm)\n",
    "binoglo_dry = float(sentinel_agg[sentinel_agg.Brgy=='Binogsacan Lower'].mm_1200mm_120cm)\n",
    "binoglo = permeabilityAWS.copy()\n",
    "binoglo['exc_wet'] = binoglo['FC'] - binoglo_wet\n",
    "binoglo['exc_dry'] = binoglo['FC'] - binoglo_dry\n",
    "binoglo['total_exc_wet'] = np.where(binoglo.exc_wet < 0, binoglo.R, binoglo.R+binoglo.exc_wet)\n",
    "binoglo['total_exc_wet'] = np.where(binoglo.rain < 1087.8, np.nan, binoglo.total_exc_wet)\n",
    "binoglo['total_exc_dry'] = np.where(binoglo.exc_dry < 0, binoglo.R, binoglo.R+binoglo.exc_dry)\n",
    "binoglo['total_exc_dry'] = np.where(binoglo.rain > 1087.8, np.nan, binoglo.total_exc_dry)\n",
    "binoglo['wet_ft'] = round(((binoglo.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "binoglo['dry_ft'] = round(((binoglo.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "binoglo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Calzada**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>48.220365</td>\n",
       "      <td>-290.568659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-124.147635</td>\n",
       "      <td>-462.936659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-172.387635</td>\n",
       "      <td>-511.176659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-221.107635</td>\n",
       "      <td>-559.896659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-89.867635</td>\n",
       "      <td>-428.656659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>-17.179635</td>\n",
       "      <td>-355.968659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>74.636365</td>\n",
       "      <td>-264.152659</td>\n",
       "      <td>842.407365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>128.204365</td>\n",
       "      <td>-210.584659</td>\n",
       "      <td>1009.807365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>114.844365</td>\n",
       "      <td>-223.944659</td>\n",
       "      <td>968.057365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>385.132365</td>\n",
       "      <td>46.343341</td>\n",
       "      <td>1812.707365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>174.244365</td>\n",
       "      <td>-164.544659</td>\n",
       "      <td>1153.682365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>470.196365</td>\n",
       "      <td>131.407341</td>\n",
       "      <td>2078.532365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   48.220365 -290.568659            NaN   \n",
       "1   Feb   507.875  162.520   345.355 -124.147635 -462.936659            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -172.387635 -511.176659            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -221.107635 -559.896659            NaN   \n",
       "4   May   615.000  196.800   418.200  -89.867635 -428.656659            NaN   \n",
       "5   Jun   842.150  269.488   572.662  -17.179635 -355.968659            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   74.636365 -264.152659     842.407365   \n",
       "7   Aug  1296.475  414.872   881.603  128.204365 -210.584659    1009.807365   \n",
       "8   Sep  1254.725  401.512   853.213  114.844365 -223.944659     968.057365   \n",
       "9   Oct  2099.375  671.800  1427.575  385.132365   46.343341    1812.707365   \n",
       "10  Nov  1440.350  460.912   979.438  174.244365 -164.544659    1153.682365   \n",
       "11  Dec  2365.200  756.864  1608.336  470.196365  131.407341    2078.532365   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.76     NaN  \n",
       "7             NaN    3.31     NaN  \n",
       "8             NaN    3.18     NaN  \n",
       "9             NaN    5.95     NaN  \n",
       "10            NaN    3.79     NaN  \n",
       "11            NaN    6.82     NaN  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calzada_wet = float(sentinel_agg[sentinel_agg.Brgy=='Calzada'].mm_550mm_55cm)\n",
    "calzada_dry = float(sentinel_agg[sentinel_agg.Brgy=='Calzada'].mm_1200mm_120cm)\n",
    "calzada = permeabilityAWS.copy()\n",
    "calzada['exc_wet'] = calzada['FC'] - calzada_wet\n",
    "calzada['exc_dry'] = calzada['FC'] - calzada_dry\n",
    "calzada['total_exc_wet'] = np.where(calzada.exc_wet < 0, calzada.R, calzada.R+calzada.exc_wet)\n",
    "calzada['total_exc_wet'] = np.where(calzada.rain < 1087.8, np.nan, calzada.total_exc_wet)\n",
    "calzada['total_exc_dry'] = np.where(calzada.exc_dry < 0, calzada.R, calzada.R+calzada.exc_dry)\n",
    "calzada['total_exc_dry'] = np.where(calzada.rain > 1087.8, np.nan, calzada.total_exc_dry)\n",
    "calzada['wet_ft'] = round(((calzada.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "calzada['dry_ft'] = round(((calzada.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "calzada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Catomag**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>73.245818</td>\n",
       "      <td>-235.967671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-99.122182</td>\n",
       "      <td>-408.335671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-147.362182</td>\n",
       "      <td>-456.575671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-196.082182</td>\n",
       "      <td>-505.295671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-64.842182</td>\n",
       "      <td>-374.055671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>7.845818</td>\n",
       "      <td>-301.367671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>99.661818</td>\n",
       "      <td>-209.551671</td>\n",
       "      <td>867.432818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>153.229818</td>\n",
       "      <td>-155.983671</td>\n",
       "      <td>1034.832818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>139.869818</td>\n",
       "      <td>-169.343671</td>\n",
       "      <td>993.082818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>410.157818</td>\n",
       "      <td>100.944329</td>\n",
       "      <td>1837.732818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>199.269818</td>\n",
       "      <td>-109.943671</td>\n",
       "      <td>1178.707818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>495.221818</td>\n",
       "      <td>186.008329</td>\n",
       "      <td>2103.557818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   73.245818 -235.967671            NaN   \n",
       "1   Feb   507.875  162.520   345.355  -99.122182 -408.335671            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -147.362182 -456.575671            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -196.082182 -505.295671            NaN   \n",
       "4   May   615.000  196.800   418.200  -64.842182 -374.055671            NaN   \n",
       "5   Jun   842.150  269.488   572.662    7.845818 -301.367671            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   99.661818 -209.551671     867.432818   \n",
       "7   Aug  1296.475  414.872   881.603  153.229818 -155.983671    1034.832818   \n",
       "8   Sep  1254.725  401.512   853.213  139.869818 -169.343671     993.082818   \n",
       "9   Oct  2099.375  671.800  1427.575  410.157818  100.944329    1837.732818   \n",
       "10  Nov  1440.350  460.912   979.438  199.269818 -109.943671    1178.707818   \n",
       "11  Dec  2365.200  756.864  1608.336  495.221818  186.008329    2103.557818   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.85     NaN  \n",
       "7             NaN    3.40     NaN  \n",
       "8             NaN    3.26     NaN  \n",
       "9             NaN    6.03     NaN  \n",
       "10            NaN    3.87     NaN  \n",
       "11            NaN    6.90     NaN  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catomag_wet = float(sentinel_agg[sentinel_agg.Brgy=='Catomag'].mm_550mm_55cm)\n",
    "catomag_dry = float(sentinel_agg[sentinel_agg.Brgy=='Catomag'].mm_1200mm_120cm)\n",
    "catomag = permeabilityAWS.copy()\n",
    "catomag['exc_wet'] = catomag['FC'] - catomag_wet\n",
    "catomag['exc_dry'] = catomag['FC'] - catomag_dry\n",
    "catomag['total_exc_wet'] = np.where(catomag.exc_wet < 0, catomag.R, catomag.R+catomag.exc_wet)\n",
    "catomag['total_exc_wet'] = np.where(catomag.rain < 1087.8, np.nan, catomag.total_exc_wet)\n",
    "catomag['total_exc_dry'] = np.where(catomag.exc_dry < 0, catomag.R, catomag.R+catomag.exc_dry)\n",
    "catomag['total_exc_dry'] = np.where(catomag.rain > 1087.8, np.nan, catomag.total_exc_dry)\n",
    "catomag['wet_ft'] = round(((catomag.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "catomag['dry_ft'] = round(((catomag.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "catomag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Malabnig**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>69.518899</td>\n",
       "      <td>-244.099129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-102.849101</td>\n",
       "      <td>-416.467129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-151.089101</td>\n",
       "      <td>-464.707129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-199.809101</td>\n",
       "      <td>-513.427129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-68.569101</td>\n",
       "      <td>-382.187129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>4.118899</td>\n",
       "      <td>-309.499129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>95.934899</td>\n",
       "      <td>-217.683129</td>\n",
       "      <td>863.705899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>149.502899</td>\n",
       "      <td>-164.115129</td>\n",
       "      <td>1031.105899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>136.142899</td>\n",
       "      <td>-177.475129</td>\n",
       "      <td>989.355899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>406.430899</td>\n",
       "      <td>92.812871</td>\n",
       "      <td>1834.005899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>195.542899</td>\n",
       "      <td>-118.075129</td>\n",
       "      <td>1174.980899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>491.494899</td>\n",
       "      <td>177.876871</td>\n",
       "      <td>2099.830899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.89</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   69.518899 -244.099129            NaN   \n",
       "1   Feb   507.875  162.520   345.355 -102.849101 -416.467129            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -151.089101 -464.707129            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -199.809101 -513.427129            NaN   \n",
       "4   May   615.000  196.800   418.200  -68.569101 -382.187129            NaN   \n",
       "5   Jun   842.150  269.488   572.662    4.118899 -309.499129            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   95.934899 -217.683129     863.705899   \n",
       "7   Aug  1296.475  414.872   881.603  149.502899 -164.115129    1031.105899   \n",
       "8   Sep  1254.725  401.512   853.213  136.142899 -177.475129     989.355899   \n",
       "9   Oct  2099.375  671.800  1427.575  406.430899   92.812871    1834.005899   \n",
       "10  Nov  1440.350  460.912   979.438  195.542899 -118.075129    1174.980899   \n",
       "11  Dec  2365.200  756.864  1608.336  491.494899  177.876871    2099.830899   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.83     NaN  \n",
       "7             NaN    3.38     NaN  \n",
       "8             NaN    3.25     NaN  \n",
       "9             NaN    6.02     NaN  \n",
       "10            NaN    3.85     NaN  \n",
       "11            NaN    6.89     NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malab_wet = float(sentinel_agg[sentinel_agg.Brgy=='Malabnig'].mm_550mm_55cm)\n",
    "malab_dry = float(sentinel_agg[sentinel_agg.Brgy=='Malabnig'].mm_1200mm_120cm)\n",
    "malabnig = permeabilityAWS.copy()\n",
    "malabnig['exc_wet'] = malabnig['FC'] - malab_wet\n",
    "malabnig['exc_dry'] = malabnig['FC'] - malab_dry\n",
    "malabnig['total_exc_wet'] = np.where(malabnig.exc_wet < 0, malabnig.R, malabnig.R+malabnig.exc_wet)\n",
    "malabnig['total_exc_wet'] = np.where(malabnig.rain < 1087.8, np.nan, malabnig.total_exc_wet)\n",
    "malabnig['total_exc_dry'] = np.where(malabnig.exc_dry < 0, malabnig.R, malabnig.R+malabnig.exc_dry)\n",
    "malabnig['total_exc_dry'] = np.where(malabnig.rain > 1087.8, np.nan, malabnig.total_exc_dry)\n",
    "malabnig['wet_ft'] = round(((malabnig.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "malabnig['dry_ft'] = round(((malabnig.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "malabnig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Morera**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "      <th>FC</th>\n",
       "      <th>R</th>\n",
       "      <th>exc_wet</th>\n",
       "      <th>exc_dry</th>\n",
       "      <th>total_exc_wet</th>\n",
       "      <th>total_exc_dry</th>\n",
       "      <th>wet_ft</th>\n",
       "      <th>dry_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1046.525</td>\n",
       "      <td>334.888</td>\n",
       "      <td>711.637</td>\n",
       "      <td>56.872749</td>\n",
       "      <td>-271.690729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711.637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>507.875</td>\n",
       "      <td>162.520</td>\n",
       "      <td>345.355</td>\n",
       "      <td>-115.495251</td>\n",
       "      <td>-444.058729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>357.125</td>\n",
       "      <td>114.280</td>\n",
       "      <td>242.845</td>\n",
       "      <td>-163.735251</td>\n",
       "      <td>-492.298729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242.845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>204.875</td>\n",
       "      <td>65.560</td>\n",
       "      <td>139.315</td>\n",
       "      <td>-212.455251</td>\n",
       "      <td>-541.018729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>615.000</td>\n",
       "      <td>196.800</td>\n",
       "      <td>418.200</td>\n",
       "      <td>-81.215251</td>\n",
       "      <td>-409.778729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>842.150</td>\n",
       "      <td>269.488</td>\n",
       "      <td>572.662</td>\n",
       "      <td>-8.527251</td>\n",
       "      <td>-337.090729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>1129.075</td>\n",
       "      <td>361.304</td>\n",
       "      <td>767.771</td>\n",
       "      <td>83.288749</td>\n",
       "      <td>-245.274729</td>\n",
       "      <td>851.059749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>1296.475</td>\n",
       "      <td>414.872</td>\n",
       "      <td>881.603</td>\n",
       "      <td>136.856749</td>\n",
       "      <td>-191.706729</td>\n",
       "      <td>1018.459749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>1254.725</td>\n",
       "      <td>401.512</td>\n",
       "      <td>853.213</td>\n",
       "      <td>123.496749</td>\n",
       "      <td>-205.066729</td>\n",
       "      <td>976.709749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>2099.375</td>\n",
       "      <td>671.800</td>\n",
       "      <td>1427.575</td>\n",
       "      <td>393.784749</td>\n",
       "      <td>65.221271</td>\n",
       "      <td>1821.359749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.98</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>1440.350</td>\n",
       "      <td>460.912</td>\n",
       "      <td>979.438</td>\n",
       "      <td>182.896749</td>\n",
       "      <td>-145.666729</td>\n",
       "      <td>1162.334749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>2365.200</td>\n",
       "      <td>756.864</td>\n",
       "      <td>1608.336</td>\n",
       "      <td>478.848749</td>\n",
       "      <td>150.285271</td>\n",
       "      <td>2087.184749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date      rain       FC         R     exc_wet     exc_dry  total_exc_wet  \\\n",
       "0   Jan  1046.525  334.888   711.637   56.872749 -271.690729            NaN   \n",
       "1   Feb   507.875  162.520   345.355 -115.495251 -444.058729            NaN   \n",
       "2   Mar   357.125  114.280   242.845 -163.735251 -492.298729            NaN   \n",
       "3   Apr   204.875   65.560   139.315 -212.455251 -541.018729            NaN   \n",
       "4   May   615.000  196.800   418.200  -81.215251 -409.778729            NaN   \n",
       "5   Jun   842.150  269.488   572.662   -8.527251 -337.090729            NaN   \n",
       "6   Jul  1129.075  361.304   767.771   83.288749 -245.274729     851.059749   \n",
       "7   Aug  1296.475  414.872   881.603  136.856749 -191.706729    1018.459749   \n",
       "8   Sep  1254.725  401.512   853.213  123.496749 -205.066729     976.709749   \n",
       "9   Oct  2099.375  671.800  1427.575  393.784749   65.221271    1821.359749   \n",
       "10  Nov  1440.350  460.912   979.438  182.896749 -145.666729    1162.334749   \n",
       "11  Dec  2365.200  756.864  1608.336  478.848749  150.285271    2087.184749   \n",
       "\n",
       "    total_exc_dry  wet_ft  dry_ft  \n",
       "0         711.637     NaN    2.33  \n",
       "1         345.355     NaN    1.13  \n",
       "2         242.845     NaN    0.80  \n",
       "3         139.315     NaN    0.46  \n",
       "4         418.200     NaN    1.37  \n",
       "5         572.662     NaN    1.88  \n",
       "6             NaN    2.79     NaN  \n",
       "7             NaN    3.34     NaN  \n",
       "8             NaN    3.20     NaN  \n",
       "9             NaN    5.98     NaN  \n",
       "10            NaN    3.81     NaN  \n",
       "11            NaN    6.85     NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morer_wet = float(sentinel_agg[sentinel_agg.Brgy=='Morera'].mm_550mm_55cm)\n",
    "morer_dry = float(sentinel_agg[sentinel_agg.Brgy=='Morera'].mm_1200mm_120cm)\n",
    "morera = permeabilityAWS.copy()\n",
    "morera['exc_wet'] = morera['FC'] - morer_wet\n",
    "morera['exc_dry'] = morera['FC'] - morer_dry\n",
    "morera['total_exc_wet'] = np.where(morera.exc_wet < 0, morera.R, morera.R+morera.exc_wet)\n",
    "morera['total_exc_wet'] = np.where(morera.rain < 1087.8, np.nan, morera.total_exc_wet)\n",
    "morera['total_exc_dry'] = np.where(morera.exc_dry < 0, morera.R, morera.R+morera.exc_dry)\n",
    "morera['total_exc_dry'] = np.where(morera.rain > 1087.8, np.nan, morera.total_exc_dry)\n",
    "morera['wet_ft'] = round(((morera.total_exc_wet / 10) / 2.54) / 12,ndigits=2)\n",
    "morera['dry_ft'] = round(((morera.total_exc_dry / 10) / 2.54) / 12,ndigits=2)\n",
    "morera"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Excess contribution in feet for rainfall >median**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Month</th>\n",
       "      <th>Agpay</th>\n",
       "      <th>Binogsacan Upper</th>\n",
       "      <th>Binogsacan Lower</th>\n",
       "      <th>Calzada</th>\n",
       "      <th>Catomag</th>\n",
       "      <th>Malabnig</th>\n",
       "      <th>Morera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>3.37</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.38</td>\n",
       "      <td>3.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>5.95</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.02</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>3.84</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>6.87</td>\n",
       "      <td>6.91</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.82</td>\n",
       "      <td>6.90</td>\n",
       "      <td>6.89</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Agpay  Binogsacan Upper  Binogsacan Lower  Calzada  Catomag  \\\n",
       "6    Jul   2.82              2.85              2.84     2.76     2.85   \n",
       "7    Aug   3.37              3.40              3.39     3.31     3.40   \n",
       "8    Sep   3.23              3.27              3.25     3.18     3.26   \n",
       "9    Oct   6.00              6.04              6.02     5.95     6.03   \n",
       "10   Nov   3.84              3.87              3.86     3.79     3.87   \n",
       "11   Dec   6.87              6.91              6.89     6.82     6.90   \n",
       "\n",
       "    Malabnig  Morera  \n",
       "6       2.83    2.79  \n",
       "7       3.38    3.34  \n",
       "8       3.25    3.20  \n",
       "9       6.02    5.98  \n",
       "10      3.85    3.81  \n",
       "11      6.89    6.85  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_exc = agpay.iloc[6:].date\n",
    "agpay_exc = agpay.iloc[6:].wet_ft\n",
    "bu_exc = binogup.iloc[6:].wet_ft\n",
    "bl_exc = binoglo.iloc[6:].wet_ft\n",
    "calz_exc = calzada.iloc[6:].wet_ft\n",
    "cat_exc = catomag.iloc[6:].wet_ft\n",
    "malab_exc = malabnig.iloc[6:].wet_ft\n",
    "morer_exc = morera.iloc[6:].wet_ft\n",
    "\n",
    "wet_ft = pd.DataFrame({'Month':month_exc,'Agpay':agpay_exc, 'Binogsacan Upper':bu_exc,'Binogsacan Lower':bl_exc,'Calzada':calz_exc,\n",
    "'Catomag':cat_exc,'Malabnig':malab_exc,'Morera':morer_exc})\n",
    "wet_ft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### _**Excess contribution in feet if rainfall >~3000mm**_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brgy</th>\n",
       "      <th>excess</th>\n",
       "      <th>total_excess</th>\n",
       "      <th>total_ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agpay</td>\n",
       "      <td>904.681040</td>\n",
       "      <td>3401.641040</td>\n",
       "      <td>11.160240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Binogsacan Lower</td>\n",
       "      <td>915.633654</td>\n",
       "      <td>3412.593654</td>\n",
       "      <td>11.196173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Binogsacan Upper</td>\n",
       "      <td>910.338675</td>\n",
       "      <td>3407.298675</td>\n",
       "      <td>11.178801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Calzada</td>\n",
       "      <td>888.372365</td>\n",
       "      <td>3385.332365</td>\n",
       "      <td>11.106733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Catomag</td>\n",
       "      <td>913.397818</td>\n",
       "      <td>3410.357818</td>\n",
       "      <td>11.188838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Malabnig</td>\n",
       "      <td>909.670899</td>\n",
       "      <td>3406.630899</td>\n",
       "      <td>11.176611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Morera</td>\n",
       "      <td>897.024749</td>\n",
       "      <td>3393.984749</td>\n",
       "      <td>11.135121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Brgy      excess  total_excess   total_ft\n",
       "0             Agpay  904.681040   3401.641040  11.160240\n",
       "1  Binogsacan Lower  915.633654   3412.593654  11.196173\n",
       "2  Binogsacan Upper  910.338675   3407.298675  11.178801\n",
       "3           Calzada  888.372365   3385.332365  11.106733\n",
       "4           Catomag  913.397818   3410.357818  11.188838\n",
       "5          Malabnig  909.670899   3406.630899  11.176611\n",
       "6            Morera  897.024749   3393.984749  11.135121"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agpmax = 1175.04 - agpay_wet\n",
    "bumax = 1175.04 - binogup_wet\n",
    "blomax = 1175.04 - binoglo_wet\n",
    "calmax = 1175.04 - calzada_wet\n",
    "catmax = 1175.04 - catomag_wet\n",
    "malabmax = 1175.04 - malab_wet\n",
    "moremax = 1175.04 - morer_wet\n",
    "seriesmax = pd.Series([agpmax,bumax,blomax,calmax,catmax,malabmax,moremax])\n",
    "seriestotalmax = seriesmax + permeabilityR\n",
    "series_ft = seriestotalmax / 10 / 2.54 / 12\n",
    "max_excess = pd.DataFrame({'Brgy':sentinel_agg.Brgy, 'excess':seriesmax,'total_excess':seriestotalmax,'total_ft':series_ft})\n",
    "max_excess"
   ]
  }
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