RHESSI_Solar_Flare_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solar Flare Energy Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initial Data processing steps "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial steps taken in the preprocessing are as follows - \n",
    "1. The dataset was taken from NASA RHESSI Data [repository](https://hesperia.gsfc.nasa.gov/rhessi_data_search/rhessi_data_search_vme.html) accessed through a web form.  \n",
    "2. The initial format of files was FITS. This was converted to CSV by using tools provided [here](https://fits.gsfc.nasa.gov/fits_viewer.html).\n",
    "3. The many csv files generated from the FITS files were concatenated to make the current dataset in the [repository](https://github.com/Byte7/Solar-Flares-RHESSI-Mission/tree/master/data). [This](https://github.com/Byte7/Solar-Flares-RHESSI-Mission/tree/master/Join_script) script was used for the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some background into the RHESSI Solar Flare Dataset\n",
    "\n",
    "The Sun is the most important start to the planet Earth. Not just because it is the nearest star, but because it is the essence of all the life on planet Earth.  \n",
    "\n",
    "The Sun has a distinct activity that can affect life on earth on a very massive scale, called ** Solar Flare**. As per wikipedia and the internet, a solar flare is a sudden flash of increased Sun's brightness, usually observed near its surface. Flares are often, but not always, accompanied by a coronal mass ejection. \n",
    "\n",
    "Solar flares produce high energy particles and radiation that are dangerous to living organisms. However, at the surface of the Earth we are well protected from the effects of solar flares and other solar activity by the Earth's magnetic field and atmosphere.  \n",
    "\n",
    "There are many organizations working on monitoring the Sun activities( one of them the flares activity ). These organizations have an amazing resources. One of them is the Reuven Ramaty High Energy Solar Spectroscopic Imager which is NASA solar flare observatory (RHESSI) and they are trying to predict the Sun flares and the active region using their amazing computing machines and of course extraordinary staff ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Metadata and General Description "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Flare - Numeric - An ID number, ymmddnn, e.g., 2042101 is the first flare found for 21-Apr-2002. \n",
    "        These numbers are not time ordered.\n",
    "Start.date - DateTime - The date when the flare occurred\n",
    "Start.time - DateTime - Flare start time\n",
    "Peak -DateTime - Flare peak time\n",
    "End - String - Flare end time\n",
    "Dur[s] - Numeric - Duration of flare in seconds\n",
    "Peak[c/s] - Numeric - Peak count rate in corrected counts, peak counts/second\n",
    "Total Counts - Numeric -  Total of counts in corrected counts, counts in energy range\n",
    "Energy [keV] - String - The highest energy band in which the flare was observed.\n",
    "X pos [asec] -  Numeric -  Flare position in arcsec from sun center\n",
    "Y pos [asec] - Numeric -  Flare position in arcsec from sun center\n",
    "Radial [asec] - Radial distance in arcsec from sun center\n",
    "active.region.ar - String - No Description\n",
    "flag.1 - String - No Description\n",
    "flag.2 - String - No Description\n",
    "flag.3 - String - No Description\n",
    "flag.4 -  String - No Description\n",
    "flag.5 -  String - No Description\n",
    "\n",
    "###################### Flags - Quality Codes ######################\n",
    "    Flare Flag Codes:\n",
    "    List item\n",
    "        a0 - In attenuator state 0 (None) sometime during flare\n",
    "        a1 - In attenuator state 1 (Thin) sometime during flare\n",
    "        a2 - In attenuator state 2 (Thick) sometime during flare\n",
    "        a3 - In attenuator state 3 (Both) sometime during flare\n",
    "        An - Attenuator state (0=None, 1=Thin, 2=Thick, 3=Both) at peak of flare\n",
    "        DF - Front segment counts were decimated sometime during flare\n",
    "        DR - Rear segment counts were decimated sometime during flare\n",
    "        ED - Spacecraft eclipse (night) sometime during flare\n",
    "        EE - Flare ended in spacecraft eclipse (night)\n",
    "        ES - Flare started in spacecraft eclipse (night)\n",
    "        FE - Flare ongoing at end of file\n",
    "        FR - In Fast Rate Mode\n",
    "        FS - Flare ongoing at start of file\n",
    "        GD - Data gap during flare\n",
    "        GE - Flare ended in data gap\n",
    "        GS - Flare started in data gap\n",
    "        MR - Spacecraft in high-latitude zone during flare\n",
    "        NS - Non-solar event\n",
    "        PE - Particle event: Particles are present\n",
    "        PS - Possible Solar Flare; in front detectors, but no position\n",
    "        Pn - Position Quality: P0 = Position is NOT valid, P1 = Position is valid\n",
    "        Qn - Data Quality: Q0 = Highest Quality, Q11 = Lowest Quality\n",
    "        SD - Spacecraft was in SAA sometime during flare\n",
    "        SE - Flare ended when spacecraft was in SAA\n",
    "        SS - Flare started when spacecraft was in SAA\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To display plots and figures inline \n",
    "%matplotlib inline          \n",
    "\n",
    "# To ensure python2 and python3 compatability\n",
    "from __future__ import division, print_function        \n",
    "\n",
    "# Importing required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "\n",
    "# Setting plot display parameters \n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "plt.rcParams['figure.figsize'] = (12, 9)\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "# Suppress warnings display\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>flare</th>\n",
       "      <th>start.date</th>\n",
       "      <th>start.time</th>\n",
       "      <th>peak</th>\n",
       "      <th>end</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>energy.kev</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "      <th>flag.1</th>\n",
       "      <th>flag.2</th>\n",
       "      <th>flag.3</th>\n",
       "      <th>flag.4</th>\n",
       "      <th>flag.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021213</td>\n",
       "      <td>2002-02-12</td>\n",
       "      <td>21:29:56</td>\n",
       "      <td>21:33:38</td>\n",
       "      <td>21:41:48</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021228</td>\n",
       "      <td>2002-02-12</td>\n",
       "      <td>21:44:08</td>\n",
       "      <td>21:45:06</td>\n",
       "      <td>21:48:56</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021332</td>\n",
       "      <td>2002-02-13</td>\n",
       "      <td>00:53:24</td>\n",
       "      <td>00:54:54</td>\n",
       "      <td>00:57:00</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     flare  start.date start.time      peak       end  duration.s  peak.c/s  \\\n",
       "0  2021213  2002-02-12   21:29:56  21:33:38  21:41:48         712       136   \n",
       "1  2021228  2002-02-12   21:44:08  21:45:06  21:48:56         288         7   \n",
       "2  2021332  2002-02-13   00:53:24  00:54:54  00:57:00         216        15   \n",
       "\n",
       "   total.counts energy.kev  x.pos.asec  y.pos.asec  radial  active.region.ar  \\\n",
       "0        167304      12-25         592        -358     692                 0   \n",
       "1          9504       6-12         604        -341     694              9811   \n",
       "2         11448       6-12        -310         375     487              9825   \n",
       "\n",
       "  flag.1 flag.2 flag.3 flag.4 flag.5  \n",
       "0     A1     P1    NaN    NaN    NaN  \n",
       "1     A1     P1     PE     Q1    NaN  \n",
       "2     A1     P1    NaN    NaN    NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset and displaying a few records\n",
    "df = pd.read_csv('data/hessi.solar.flare.2002to2016.csv',sep=\",\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113942 entries, 0 to 113941\n",
      "Data columns (total 18 columns):\n",
      "flare               113942 non-null int64\n",
      "start.date          113942 non-null object\n",
      "start.time          113942 non-null object\n",
      "peak                113942 non-null object\n",
      "end                 113942 non-null object\n",
      "duration.s          113942 non-null int64\n",
      "peak.c/s            113942 non-null int64\n",
      "total.counts        113942 non-null int64\n",
      "energy.kev          113942 non-null object\n",
      "x.pos.asec          113942 non-null int64\n",
      "y.pos.asec          113942 non-null int64\n",
      "radial              113942 non-null int64\n",
      "active.region.ar    113942 non-null int64\n",
      "flag.1              113942 non-null object\n",
      "flag.2              113942 non-null object\n",
      "flag.3              94035 non-null object\n",
      "flag.4              93740 non-null object\n",
      "flag.5              52991 non-null object\n",
      "dtypes: int64(8), object(10)\n",
      "memory usage: 15.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Dataframe information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>flare</th>\n",
       "      <th>duration.s</th>\n",
       "      <th>peak.c/s</th>\n",
       "      <th>total.counts</th>\n",
       "      <th>x.pos.asec</th>\n",
       "      <th>y.pos.asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active.region.ar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.139420e+05</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>1.139420e+05</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "      <td>113942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.086459e+07</td>\n",
       "      <td>493.051728</td>\n",
       "      <td>212.321532</td>\n",
       "      <td>3.772013e+05</td>\n",
       "      <td>-9.752181</td>\n",
       "      <td>-43.129812</td>\n",
       "      <td>689.528892</td>\n",
       "      <td>979.922926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.832864e+06</td>\n",
       "      <td>433.389295</td>\n",
       "      <td>833.776871</td>\n",
       "      <td>3.071203e+06</td>\n",
       "      <td>757.017401</td>\n",
       "      <td>404.547666</td>\n",
       "      <td>513.203089</td>\n",
       "      <td>1350.484625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.021213e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>-10012.000000</td>\n",
       "      <td>-10005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.110734e+06</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.292000e+04</td>\n",
       "      <td>-704.000000</td>\n",
       "      <td>-249.000000</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.112022e+07</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>5.857400e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-75.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>687.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.403013e+07</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>1.794420e+05</td>\n",
       "      <td>706.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>946.000000</td>\n",
       "      <td>1543.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.412161e+08</td>\n",
       "      <td>4444.000000</td>\n",
       "      <td>113156.000000</td>\n",
       "      <td>4.355501e+08</td>\n",
       "      <td>1002.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>14154.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              flare     duration.s       peak.c/s  total.counts  \\\n",
       "count  1.139420e+05  113942.000000  113942.000000  1.139420e+05   \n",
       "mean   1.086459e+07     493.051728     212.321532  3.772013e+05   \n",
       "std    9.832864e+06     433.389295     833.776871  3.071203e+06   \n",
       "min    2.021213e+06       8.000000       0.000000  8.000000e+00   \n",
       "25%    4.110734e+06     212.000000      28.000000  2.292000e+04   \n",
       "50%    1.112022e+07     364.000000      53.000000  5.857400e+04   \n",
       "75%    1.403013e+07     628.000000     144.000000  1.794420e+05   \n",
       "max    1.412161e+08    4444.000000  113156.000000  4.355501e+08   \n",
       "\n",
       "          x.pos.asec     y.pos.asec         radial  active.region.ar  \n",
       "count  113942.000000  113942.000000  113942.000000     113942.000000  \n",
       "mean       -9.752181     -43.129812     689.528892        979.922926  \n",
       "std       757.017401     404.547666     513.203089       1350.484625  \n",
       "min    -10012.000000  -10005.000000       0.000000          0.000000  \n",
       "25%      -704.000000    -249.000000     469.000000          0.000000  \n",
       "50%         0.000000     -75.000000     759.000000        687.000000  \n",
       "75%       706.000000     198.000000     946.000000       1543.000000  \n",
       "max      1002.000000    1012.000000   14154.000000       9999.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flare               False\n",
       "start.date          False\n",
       "start.time          False\n",
       "peak                False\n",
       "end                 False\n",
       "duration.s          False\n",
       "peak.c/s            False\n",
       "total.counts        False\n",
       "energy.kev          False\n",
       "x.pos.asec          False\n",
       "y.pos.asec          False\n",
       "radial              False\n",
       "active.region.ar    False\n",
       "flag.1              False\n",
       "flag.2              False\n",
       "flag.3               True\n",
       "flag.4               True\n",
       "flag.5               True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flare                   0\n",
       "start.date              0\n",
       "start.time              0\n",
       "peak                    0\n",
       "end                     0\n",
       "duration.s              0\n",
       "peak.c/s                0\n",
       "total.counts            0\n",
       "energy.kev              0\n",
       "x.pos.asec              0\n",
       "y.pos.asec              0\n",
       "radial                  0\n",
       "active.region.ar        0\n",
       "flag.1                  0\n",
       "flag.2                  0\n",
       "flag.3              19907\n",
       "flag.4              20202\n",
       "flag.5              60951\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Making flare ID as index and parsing dates\n",
    "new_df = pd.read_csv('data/hessi.solar.flare.2002to2016.csv',sep=\",\", index_col=0)\n",
    "\n",
    "# process date / time columns\n",
    "def parse_date(sdatex,stimex):\n",
    "    datex = datetime.strptime(sdatex, '%Y-%m-%d')\n",
    "    timex = datetime.strptime(stimex, '%H:%M:%S')\n",
    "    return datetime(datex.year,datex.month,datex.day,timex.hour,timex.minute,timex.second)\n",
    "\n",
    "# Adding year, month, day, start date, peak date, end date and dropping earlier columns\n",
    "new_df['dt.start'] = new_df[['start.date','start.time']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "new_df['dt.peak'] = new_df[['start.date','peak']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "new_df['dt.end'] = new_df[['start.date','end']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "\n",
    "# clean columns\n",
    "new_df.drop(['start.date','start.time','peak','end'], axis=1, inplace=True)\n",
    "\n",
    "# add new columns\n",
    "new_df['year'] = new_df['dt.start'].apply(lambda col: col.year)\n",
    "new_df['month'] = new_df['dt.start'].apply(lambda col: col.month)\n",
    "new_df['day'] = new_df['dt.start'].apply(lambda col: col.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration.s', 'peak.c/s', 'total.counts', 'energy.kev', 'x.pos.asec',\n",
       "       'y.pos.asec', 'radial', 'active.region.ar', 'flag.1', 'flag.2',\n",
       "       'flag.3', 'flag.4', 'flag.5', 'dt.start', 'dt.peak', 'dt.end', 'year',\n",
       "       'month', 'day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying current columns of dataframe\n",
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns with better names \n",
    "new_df = new_df.rename(columns = {'duration.s':'duration_s', 'peak.c/s':'peak_c_s', 'total.counts':'total_counts', 'energy.kev':'energy_kev', 'x.pos.asec':'x_pos_asec',\n",
    "       'y.pos.asec':'y_pos_asec', 'active.region.ar':'active_region_ar', 'flag.1':'flag_1', 'flag.2':'flag_2',\n",
    "       'flag.3':'flag_3', 'flag.4':'flag_4', 'flag.5':'_flag_5', 'dt.start':'date_start', 'dt.peak':'date_peak', 'dt.end':'date_end'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_s</th>\n",
       "      <th>peak_c_s</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>energy_kev</th>\n",
       "      <th>x_pos_asec</th>\n",
       "      <th>y_pos_asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active_region_ar</th>\n",
       "      <th>flag_1</th>\n",
       "      <th>flag_2</th>\n",
       "      <th>flag_3</th>\n",
       "      <th>flag_4</th>\n",
       "      <th>_flag_5</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_peak</th>\n",
       "      <th>date_end</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flare</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021213</th>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002-02-12 21:29:56</td>\n",
       "      <td>2002-02-12 21:33:38</td>\n",
       "      <td>2002-02-12 21:41:48</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021228</th>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>PE</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002-02-12 21:44:08</td>\n",
       "      <td>2002-02-12 21:45:06</td>\n",
       "      <td>2002-02-12 21:48:56</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021332</th>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>P1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002-02-13 00:53:24</td>\n",
       "      <td>2002-02-13 00:54:54</td>\n",
       "      <td>2002-02-13 00:57:00</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_s  peak_c_s  total_counts energy_kev  x_pos_asec  \\\n",
       "flare                                                                \n",
       "2021213         712       136        167304      12-25         592   \n",
       "2021228         288         7          9504       6-12         604   \n",
       "2021332         216        15         11448       6-12        -310   \n",
       "\n",
       "         y_pos_asec  radial  active_region_ar flag_1 flag_2 flag_3 flag_4  \\\n",
       "flare                                                                       \n",
       "2021213        -358     692                 0     A1     P1    NaN    NaN   \n",
       "2021228        -341     694              9811     A1     P1     PE     Q1   \n",
       "2021332         375     487              9825     A1     P1    NaN    NaN   \n",
       "\n",
       "        _flag_5          date_start           date_peak            date_end  \\\n",
       "flare                                                                         \n",
       "2021213     NaN 2002-02-12 21:29:56 2002-02-12 21:33:38 2002-02-12 21:41:48   \n",
       "2021228     NaN 2002-02-12 21:44:08 2002-02-12 21:45:06 2002-02-12 21:48:56   \n",
       "2021332     NaN 2002-02-13 00:53:24 2002-02-13 00:54:54 2002-02-13 00:57:00   \n",
       "\n",
       "         year  month  day  \n",
       "flare                      \n",
       "2021213  2002      2   12  \n",
       "2021228  2002      2   12  \n",
       "2021332  2002      2   13  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display new processed dataframe\n",
    "new_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping Data wrt. to year, month and as a function of energy values\n",
    "DATA_by_yr = new_df.groupby(['year'])['total_counts'].sum()\n",
    "DATA_by_month = new_df.groupby(['month'])['total_counts'].sum().sort_values(ascending=False)\n",
    "DATA_vs_energy = new_df.groupby(['energy_kev'])['energy_kev'].count().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Selecting 1000 rows and 4 coulmns from new dataframe for plots\n",
    "    and finding value distribution and relation'''\n",
    "data_part = new_df.iloc[1:1000, 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_s', 'peak_c_s', 'total_counts', 'energy_kev'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display column names for data part\n",
    "data_part.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relation between Flare duration, Peak count rate and Total count with respect to Energy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the flares are of the energy range 6-12 KeV. Another small majority of flares are of range 12-25 KeV. Flares having energies greater than range 25-50 KeV are very sparse and rare in occurrence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x19641c10048>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MivJKsSaEiJDhSyFaCL8/hEWHxT/tg8NqBmhQKQLV6oA9m2I37tkU2e46WZrA5LDj/mxL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\ng5WRZ9i5dVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19641c10400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=data_part, hue='energy_kev', vars=['duration_s', 'peak_c_s', 'total_counts'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density plot to visualize the distribution of the Flare wrt. Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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12FgltxGcllKlWi3WKknq8s3gnm1e7VrjUWOtqZwCQ2NuydKpk50x9xFA7zpZ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\npn1mkm4su0evBz8up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aqIEUYAAACog1+npL3++uuaM2eOLr744sq2zZs3y2Kx\n6JprrtG0adP0/PPPy+12y+Vyac2aNbriiiskSf369VPXrl21bt06ffnllxo0aJC6du0qSbr66qv1\n/vvvyzRNffrpp5o9e7ZsNpsiIiI0ZcoUrVixwp+XCQBA86utsLWHotcAAAConV9HGD3wwAOSpK++\n+qqyze1267zzztN//dd/yeVy6ZZbblFoaKimTJkij8ejqKioyn1jY2OVmZmpkpISrylscXFxKigo\nUGFhoTIyMhQfH++1bffu3dX6Ehpql81mbVD/rVaLIiNDGnQMKnDvTg/3r/G4d82voc/b1vSetei+\nljhU5CMXMmS22H636Ht6CvraNOhr4zXm8+2Z0NLuQ1NpC9fJNQaOtnCdbeEam5vfaxid6ucRRD+7\n4YYb9I9//EOXXHKJDMO79oJpmrJarfJ4PNW2SZLFYpFpml7bTNOUxVJ9IFVBQWmD+xoZGaLc3KIG\nHwfu3eni/jUe965m0dFhfvtZDX3etqb3rCX31ZZf7HOb6fa02H635Ht6KvraNAKpr/581kqN+3x7\nJrSm9+x0tIXr5BoDR1u4zpOv0d/P27ai2VdJe++997Rr167K16ZpymazqUOHDjJNU7m5uZXbsrOz\nFRsbq/j4eGVnZ1e2Z2VlKSIiQiEhIdW2ZWdne41GAgCgzfO4m7sHAAAAaOGaPTDau3ev/vSnP8nt\ndqukpESvv/66Lr30UtlsNo0bN05vvfWWJGnXrl3av3+/Ro4cqTFjxmjLli06ePCgJGnp0qWaMGGC\nJGnChAlatmyZXC6X8vLy9MEHH2jixInNdXkAADQT02cZI5MaRgAAAKhDs09J+/3vf69HHnlE06ZN\nk8vl0sUXX6w5c+ZIkh588EHdf//9mjp1qgzD0JNPPqmwsIqhZgsXLtQdd9yh8vJyde7cWYsWLZJU\nUQA7JSVFM2bMUHl5ua688kqdc845zXZ9AAA0i9qKXrNKGgAAAOrQLIHRE088UfnfTqdTCxcurHG/\njh076oUXXqhx29ixYzV27Nhq7TabTQsWLDgzHQUAoNUyJbN6vT9JrJIGAACAOjX7lDQAANAEahth\nRA0jAAAA1IHACACAgOS7hlGtYRIAAAAgAiMAANoeDzWMAAAAUDsCIwAAAlEdo4hMRhkBAAC0Gqmp\nqTrrrLP8+jMJjAAACEimVFsmxCgjAAAA1ILACACAQEVgBAAAUKe3335bs2fP1syZM3XTTTcpJSVF\n8+bN03//93/r2muv1aRJkzRv3jy53RULh2zatEnXXHONZs2apTlz5uirr76SJCUnJ+vqq6/WrFmz\ndNNNN8ntduuJJ57QpEmTNHv2bD300EO69tprlZ6ermHDhqmgoECS5P7/7N15eFTl+f/x95mZZMgC\nRJawJCBgi1pttRS6KFQFXFo3ZEfF0roUqrg1qJFdiEFFFFHcaEtBEBBwwx1w16pfUayI1R+KkCAJ\nGCCSTGYyc87vjyEDsyWTbTIJn9d19cI8c86Z5wx05s4993M/Ph9nnnkm27dvj2m+X331FQMHDuTN\nN9+MOv/S0lL69u1LSUlJ4LwLLriA//u//4v5dXHEfGQIj8fDl19+Sc+ePWndunVdLyMiIiKNwbIA\nI/rjShiJiIiI8NFHH/H888+zYsUKkpOTefXVV5k0aRI9e/Zk69atLF68GMuyuOCCC3jnnXc49dRT\nmTx5MosXL6ZTp04UFBQwZswYnn32WQC++eYbNmzYQHp6OitXruTzzz9n3bp1GIbBhAkTAOjatSv9\n+vVj3bp1jB49mrfeeosePXrQo0ePGuf75ZdfMnHiRO6++2769u0bdf4rV67krLPO4oUXXmDs2LF8\n9tlneL1e+vbtG/NrE3PC6NtvvyU3N5dbbrmF3r17M3r0aP7f//t/pKWl8eijj9bqSUVERKSxWdUW\nGGmnNBERERF4/fXX+eabbxgxYgTg7/O4b98+srOz6d+/P8nJyQD07NmTkpISPv30U4qKirjmmmsC\n17DZbGzbtg2A3r17k56eDsAbb7zBxRdfjNPpBGD06NEsWbIEgEsvvZT777+f0aNHs3LlSkaPHl3j\nXD0eD+PGjeO0004L5GCizd/j8TBixAjmzJnD2LFjefrppxk+fHitXpuYE0azZs0iMzOTHj16sGbN\nGkpKSnjzzTdZvXo1c+bMYfXq1bV6YhEREWlEVvU9jCzTV139kYiIiMhRwefzMWTIEHJycgDwer38\n8MMP3HfffYFED4BhGFiWhdfr5Sc/+QmrVq0KPFZUVET79u3ZuXMnqampgXGHIzjlYhiHo68BAwYw\na9Ys3n33XbZu3cqCBQtimu+DDz5ITk4Or7/+OmeddVbU+ScnJ/PrX/+asrIytm7dyquvvspzzz1X\nq9cm5h5GmzZtYtKkSbRr144NGzYwaNAgOnXqxJAhQ/j6669r9aQiIiLSuIzq64vAVIWRiIiISP/+\n/XnhhRfYu3cvAEuWLOFvf/tb1ON/+ctfsn37dj7++GMAPvvsM8477zxKS0vDjj3jjDNYt24dHo8H\nr9fL008/HXjMMAxGjx7NlClTGDJkCElJSTXONTk5mb59+zJr1iymTp1KSUlJjfMfNmwY06ZNo0+f\nPrRv3z62F+WQmCuMUlNTKS0tpaSkhE2bNnHFFVcAsH37djIyMmr1pCIiItLYatolzRe3mYiIiIgk\nqgEDBnDllVcybtw4DMMgIyOD++67j4ULF0Y8vl27djzwwAPk5+fj8XgwDIMHHniAdu3ahR07dOhQ\nvv32Wy655BJSUlLIzs4mJSUl8PiQIUO49957GTlyZK3n/Pvf/54ZM2bwwAMPRJz/kXN44IEHuO66\n62r1HACGZcXWxGD69Om8//77OJ1OysrKePnll1m/fj15eXkMGzaMm2++udZP3pT27Pmx1udkZKSy\nf395I8ym5dNrVz96/epOr11kHTvGb7OC2r7fNqe/s0Sea1Lh+xyYOJ7yPc6Ij7d77hVsxxwT51nV\nLJFf01Caa+NoSXON53st1C2+bQjN6e+sPo6G+9Q9thxHw30eeY/xfr+N1VtvvcW+ffu4+OKLAZg9\nezZOp5NJkyZhmibPPPMMGzZs4KGHHmrimUYWc4XR1KlTeeKJJygsLOTyyy8nOTkZj8fDhAkTuOyy\nyxpzjiIiIlIH1X4lpAojERERkUZ1/PHHc9ttt7Fo0SJM0+SEE05gxowZAFx++eUcOHAgKFl05513\n8sEHH0S81uzZs/n5z38ej2kHxJwwcjgcjBs3LmhsyJAhYcedeeaZLFu2jKysrHpPTkREROqq+gJi\nyzTjNA8RERGRo1OnTp3417/+FfGx5cuXh43dfvvtjT2lWom56XWsDhw4gKkgVEREpGlZFlS3D5o+\nq0VERESkGg2eMBIREZEEUV2RkU9L0kREREQkOiWMREREWiLLqn5RmiqMRERERKQaShiJiIi0SFa1\nFUbqYSQiIiIi1VHCSEREpEWqvum1KoxERERE4uvRRx9l1KhRDB06lKeeeiriMZs3b2bs2LGBn7du\n3cqll17K2LFjufLKK9m7d2+8ptvwCSPDqKbBpoiIiMSHVX2FkRJGIiIiItEtew16jALbWf4/l71W\nv+t98MEHfPLJJzz55JMsXbqU3bt3hx3z+OOPM2XKFNxud2AsLy+PqVOnsnTpUs4++2wef/zx+k2k\nFho8YWRZNXyjKSIiInFQU4WRml6LiIiIRLLsNbhmLnxX5P8O7rsi/8/1SRq988479O7dm2uvvZbx\n48dz5plnhh3TvXt3FixYEDQ2b948TjzxRAB8Ph9Op7Puk6ilWiWM1q1bx4YNGwI/5+bm8vLLLwcd\ns2TJEjp37twwsxMREZG6qanptU8VRiIiIiKRTF4E5e7gsXK3f7yu9u3bx+eff878+fOZOXMmOTk5\nYQU35557Lg6HI2gsMzMTgE2bNvHEE08wbty4uk+ilmJOGD366KPccccduFyuwFiXLl2YNm0aS5Ys\nCYz9/Oc/JykpqWFnKSIiIrViYIEVfZm4ml6LiIiIRLajuHbjscjIyKB///4kJyfTq1cvfD4f559/\nPmPHjuXhhx+u9twXX3yR6dOn89hjj9GuXbu6T6KWHDUf4vfkk09y3333cfrppwfGrr/+ek455RRm\nzpzJFVdc0SgTFBERkUagJWkiIiIiEXXP9C9DizReV7/61a9YsmQJf/7znykuLsYwDJ5//nnsdnu1\n5z377LOsXLmSpUuXkpGRUfcJ1EHMCaPS0tKIS82ys7MpKSlp0EmJiIhIPdXU9FpL0kREREQiyrvK\n37PoyGVpqU7/eF2dddZZfPTRRwwfPhzLspg2bVqNySKfz0deXh5dunRh4sSJAPTr14/rr7++7hOp\nhZgTRv369WP+/Pnk5+eTlpYGQFlZGQ899BC/+tWvGm2CIiIi0ghUYSQiIiIS0WVn+/+cvMi/DK17\npj9ZVDVeV7fcckuNx2RnZ7Nq1SoA7HY7H374Yf2etB5iThhNnTqVK6+8kv79+3PssccCsGPHDrp0\n6cLChQsbbYIiIiJSFxbVbVyqHkYiIiIi0V12dv0TRM1dzAmj7Oxsnn/+ed577z22bdtGUlISxx57\nLAMGDMBmO9w7++uvv6Znz55hnb1FREQkjqrLFgEoYSQiIiIi1ahVVic5OZkzzzyTM888M+oxo0aN\n4tlnn6Vbt271nZuIiIjUWQ0JI5+WpImIiIhIdLaaD6kdq6ZvNEVERKTx1dT0WhVGIiIiIlKNBk8Y\niYiISCKwACP6w0oYiYiIiEg1lDASERFpkdT0WkRERETqTp2pRUREWiI1vRYRERFJOJs3b2bu3Lks\nXbqUrVu3MmvWLOx2O8nJydx111106NAh6Phox8yePZtNmzaRlpYGwMKFC2ndunWDzlUVRiIiIi1V\ntT2M1PRaREREJJrl7lfptX8oSSX96bV/KMvdr9b7mo8//jhTpkzB7XYDkJeXx9SpU1m6dClnn302\njz/+eNg50Y7ZsmULixYtYunSpSxdurTBk0XQCAkjw6imX4KIiIjESU27pKnCSERERCSS5e5XGV82\nhx1mERYWO8wixpfNqXfSqHv37ixYsCDw87x58zjxxBMB8Pl8OJ3OsHMiHWOaJt999x3Tpk1j9OjR\nrF69ul7ziqZBEkZerzfw38nJyUoaiYiINLUadklTDyMRERGRyKa4HqEcd9BYOW6muB6p13XPPfdc\nHI7DnYEyMzMB2LRpE0888QTjxo0LOyfSMeXl5Vx++eXcc889LFq0iOXLl/Pll1/Wa26RxJwwWrZs\nWcTx//znP1x88cVBP2dnZ9d/ZiIiIlIv1dYYaUmaiIiISEQ7zeJajdfHiy++yPTp03nsscdo164d\nL7/8MmPHjmXs2LF8/vnnEY9JSUnhiiuuICUlhfT0dH772982SsIo5qbX9957L6WlpUyYMAGAPXv2\nMGfOHF588UUuuuiiBp+YiIiI1J1hWWBVU/GrJWkiIiIiEXWzZbLDLIo43pCeffZZVq5cydKlS8nI\nyADgvPPO47zzzqv2mO3bt3PTTTfx9NNPY5ommzZt4pJLLmnQuUEtEkZLlixh/PjxHDhwgC5duvDA\nAw/Qs2dPnnzySU499dQGn5iIiIjUR027pKnCSERERCSS2SnjGV82J2hZWipOZqeMb7Dn8Pl85OXl\n0aVLFyZOnAhAv379uP7662M65sILL2TkyJEkJSVx8cUX89Of/rTB5lYl5oTRySefzJNPPslVV13F\nzp07mTFjBiNGjFC/IhERkYRUfcJIPYxEREREIrvUeQ7g72W00yymmy2T2SnjA+P1kZ2dzapVqwD4\n8MMPqz3WbrdHPebqq6/m6quvrvd8qlNtwihSp+1hw4bx4IMP8vrrr2OzHW6BNHz48IafnYiIiNSN\nZWFVlzNSwkhEREQkqkud5zRIgqg5qzZhtHDhwojjHTp04H//+x//+9//ADAMQwkjERGRhFLDkjSf\nlqSJiIiISHTVJow2btwYr3mIiIhIQ7Ks6nNG1ZYfiYiIiMjRLuYeRuDfGe3bb7/Fd8S3kh6Phy++\n+CKwe5qIiIg0A6owEhEREZFqxJwwevLJJ5k9ezY+nw/DMLAOfTNpGAannHKKEkYiIiIJpfoKIzW9\nFhEREZHq2Go+xO/xxx9nwoQJfPbZZ7Rv357XX3+ddevWceKJJzJo0KDGnKOIiIjUmgVUs5OpEkYi\nIiIiUo2YE0bFxcUMGTKE5ORkTjrpJD755BN+8pOfMHnyZJ566qnGnKOIiIjUVg0tjDC1JE1EREQk\nXiorK5k0aRKXXnopw4cPZ8OGDWzZsoUBAwYwduxYxo4dy4svvhh23quvvsrgwYMDx3z44YeYpsm0\nadMYNWoUY8eO5bvvvmuUOce8JK19+/aUlJSQnZ1Nr1692Lp1K3/84x/JzMykuLi4USYnIiIidVVD\nxsinCiMRERGRaFzLVnJw8gzMHQXYumeTnjeDlMtG1fl6zz33HBkZGdxzzz3s27ePSy65hGuvvZY/\n//nP/OUvf4l63pYtW5g0aRLnnntuYOzVV1/F4/GwcuVKPv30U+bMmcPDDz9c57lFE3PC6I9//CO3\n3nors2fPZsCAAeTk5HDiiSfy+uuvc+yxxzb4xERERKQeatgFTT2MRERERCJzLVtJ6TXXQbkLAPO7\nnf6foc5Jo/POOy8o6WO32/n888/59ttv2bBhA8ceeyy333476enpQedt2bKFrVu38u9//5tf/OIX\n5OTk8PHHHzNgwAAATj31VD7//PM6zakmMS9J+/vf/85FF13E/v37Oe200xgxYgR33HEH//3vf5kx\nY0ajTE5ERETqqoYKIy1JExEREYno4OQZgWRRQLnLP15HaWlppKenc/DgQa6//npuvPFGfvGLX3DL\nLbewbNkyunXrxkMPPRR23umnn87UqVNZtmwZ5eXlrFixgoMHDwYllux2O16vt85ziybmCiOHwxG0\nE9pNN93ETTfd1OATEpH4WlPhIM/lpNA0yLJZTE5xM6xVw7/ZiEh8GTVUGGlJmoi0FIplRKShmTsK\najUeq++//55rr72WSy+9lAsvvJDS0lLatGkDwNlnn82sWbN44okneOWVVwCYO3cuw4YNCxwzaNAg\nXnnlFVq3bk1ZWdnheZkmDkfM6Z2YxVxhBPDGG28wbtw4Bg4cSGFhIffffz8rV65s8EmJSHysqXBw\nc1krCkwbFgYFpo2by1qxpqLh32xEJN4sTFUYiUgLp1hGRBqDrXt2rcZjsXfvXv7yl78wadIkhg8f\nDsCVV17JZ599BsD777/PSSedxOWXX87SpUtZunQpmZmZXHTRRezevTvomD59+vDWW28B8Omnn9K7\nd+86z6s6Mb+TPvvss+Tl5XHFFVfwySefYJomHTt2ZM6cObhcLsaNGxfTdSzL4rbbbqN3795ceeWV\n+Hw+5syZw9tvv43P5+Mvf/kLY8aMAWD79u1MnjyZffv2kZqayl133cVxxx0HwOrVq/nnP/+J1+vl\nd7/7HVOmTCEpKQmXy8WUKVP44osvME2TSZMmMXjw4Nq/MiJHgTyXE1fIttsuDPJcTn0zJ9LsWVgY\nIf8PP+JR9TASkRZAsYyINIb0vBlBPYwASE0hPW9Gna/5yCOPUFpaysKFC1m4cCEAt912G3feeSdJ\nSUl06NCBWbNmBZ1jGAazZ8/muuuuo1WrVhx33HGMHDkSu93Ou+++y+jRo7EsizvvvLPO86pOzAmj\nRYsWMXPmTP7whz/wj3/8A4DLLruM9u3bc/fdd8eUMNq2bRszZ87ks88+C2TAVqxYwfbt21m3bh1l\nZWWMGjWKk046KdDM6U9/+hMXXnghb775JjfccAPPP/88X3/9NQsWLODpp58mIyODnJwcFi9ezNVX\nX82CBQtITU3lpZdeYteuXYwaNYqTTz6Zzp071+0VEmnBCs3Iv0pGGxeRZsSysCBqwggljESkBVAs\nIyKNoaqxdUPukjZlyhSmTJkSNr5ixYpqz+vfvz/9+/cPG7/jjjvqPJdYxbwkbceOHZx88slh4yee\neCJ79+6N6RrLli1jxIgRnHfeeYGx9evXM3ToUBwOB23btuX888/nueeeo6ioiG+++Ybzzz8fgDPO\nOIPy8nK++OILNmzYwMCBA2nXrh02m41Ro0bx3HPPBa43YsQIALp27crpp5/OSy+9FOttihxVsmyR\n16tEGxeRFkQJIxFpARTLiEhjSblsFB23b6WT+SMdt2+tV7KouYo5YdS7d2/efPPNsPE1a9Zw/PHH\nx3SNadOmceGFFwaNff/993Tp0iXwc+fOndm9ezfff/89mZmZ2GyHp9ipU6fAY6HnFBUVRbxe1Tki\nEm5yipuUkG2UUvA3ixSR5q6mXdKUMBKR5k+xjIhI44l5Sdqtt97KX//6V95//30qKytZuHAh3377\nLVu3buXRRx+t8wQsy8IwjKCfbTYbpmkGjVc9ZrfbsUJ2fqk6J9KP3UBeAAAgAElEQVT1gKCkU5X0\ndCcOh71Wc7XbbWRkpNbqHPHTa1c/jfX6XQmk/mgxtQR2eqGbA2a1sxjTOhlIbvDnawr6t9f0avt+\n25z+zhJ5rraUZIxqEkYOu5WQc0/k1zSU5to4NNe6q0t82xCa8nWIZyyTaH/fjUH32HIcDfd5NNxj\nU4s5YdS3b19eeeUVli1bRnl5OT/88AP9+vVj3rx5dO3atc4T6NKlC8XFxYGfi4uL6dy5M127dmXP\nnj1BCaCqx6Kdc+T1OnToEHjshBNOCHvegwdr/61DRkYq+/eX1/o80WtXX435+v0B+EPbIwZ8sH9/\nozxVk9C/vcg6dmwdt+eq7fttc/o7S+S5tiqv/nUvLy9PyLkn8msaSnNtHC1prvF8r4W6xbcNoan/\nzuIVyzT1fcaD7rHlOBru88h7jPf77dEi5iVpPp+Pf//73yxfvpz333+ft956i6effjrQO6iuBg0a\nxJo1a/B6vZSWlvLCCy8wePBgOnfuTPfu3XnxxRcBePvtt7HZbPTu3ZuBAweyceNGfvjhByzLYuXK\nlYGd0AYNGsTKlSsB2L17N2+//TZnnXVWveYoIiLS/FS/JM3nq4zfVERERESk2Ym5wujOO+9k/fr1\n3HrrrZx88smYpsl///tfFixYQGVlJRMnTqzTBMaMGcOOHTu4+OKLqaysZNSoUfz6178GYN68eUyd\nOpWHH36Y5ORk5s+fj81m44QTTuDaa6/lT3/6E5WVlZxyyilcffXVAEycOJEZM2Zw/vnn4/P5mDRp\nEt27d6/T3ERERJoty8KIvkcaPq8SRiIiIiISXcwJo+eee46FCxfSr1+/wNgJJ5xAdnY2f//732uV\nMJozZ87hCTgcTJ48OeJxPXr0YOnSpREfGzZsGMOGDQsbT0tL45577ol5LiIiIi1T9TsEqcJIRERE\nJH58Ph9Tpkzh22+/xW63k5+fH1bc8uijj7Jx40YqKysZM2ZMYAf4phJzwig1NRW7PbyJXuvWrSM2\nlRYREZGmY9WwC5oSRiIiIiLRfbGujLfnH6B0t482ne0MuKEtP7sgrc7Xe/311wFYsWIFH3zwAfn5\n+Tz88MOBxz/44AM++eQTnnzySVwuF//85z/rfQ/1FXPCKCcnh8mTJ5OTk8Mvf/lL7HY7W7duJT8/\nnyuuuIKdO3cGju3WrVujTFZERERiZFWfMDK1JE1EREQkoi/WlfHKjH14K/wV26Xf+3hlxj6AOieN\nBg8ezJlnngnArl27Aht1VXnnnXfo3bs31157LQcPHuSWW26p+w00kJgTRpMmTQLg2muvDexaVrW9\n/f/+9z/uu+++wI5mW7dubYSpioiISKwMs/olaaYqjEREREQienv+gUCyqIq3wuLt+QfqVWXkcDi4\n9dZbee2113jggQeCHtu3bx+7du3ikUceoaCggAkTJvDyyy8H8i9NIeaE0YYNGxpzHiIiItKgakgY\nmd44zUNERESkeSnd7avVeG3cdddd5OTkcMEFF5CdnU1qaiqnnXYaGRkZ9OrVi+TkZHr16oXT6aSk\npIT27dvX+znrKuaEUVZWVmPOQ0RERBqSVUPCyKuEkYiIiEgkbTrbKf0+PDnUpnN4X+dYPfPMMxQV\nFfHXv/6VlJQU0tLSWL58OU6nE/D3OFqyZAl//vOfKS4uxuVykZGRUefnawgxJ4xERESkGakxYaQl\naSIiIiKRDLihbVAPIwBHK4MBN7St8zXPOecccnNzueyyy/B6vdx+++2BZBHAWWedxUcffcTw4cOx\nLItp06ZF3HgsnpQwEhERaYGsGnoYWb76l1SLiIiItERVfYoacpe01NRU5s+fX+0xidDo+khKGImI\niLRINSWMtCRNREREJJqfXZBWrwRRS6CEkSSUbevdbFpUQVmxRVqmQZ+rWnHcYGfNJ4qISDDTrP5x\nJYxEpAko1hMRaT6UMJKEsW29m/fmuvC5/T+XFVm8N9cFoEBCRKS2auhhpCVpIhJvivVERJoXW1NP\nQKTKpkUVgQCiis/tHxcRkVqyaqowUsJIROJLsZ6ISPOihJEkjLLiyN+GRxsXEZFq1FBhpISRiMSb\nYj0RkeZFCSNJGGmZRq3GRUSkGjVUGBm+GiqQREQamGI9EZHmRQkjSRh9rmqFPWT5ut3pHxcRkdqp\nqcDIqKkptohIA1OsJyJHuyFDhjB27FjGjh1Lbm4un376KSNGjGD06NE8+OCDUc9zuVyMHj2abdu2\nAWCaJtOmTWPUqFGMHTuW7777DiDm68VKTa8lYVQ1O2wJO2doBxARaXI19jBSwkhE4qumWE/xk4gk\nlOJlsGMyuHeAszt0z4PMy+p8Obfb38Rt6dKlgbGLL76YBQsW0K1bN6655hq2bNnCSSedFHTef//7\nX6ZPn05RUVFgbP369Xg8HlauXMmnn37KnDlzePjhh5k+fXqN16sNJYwkoRw32NnsAwPtACIiCaGG\nCiObEkYi0gSixXqKn0QkoRQvg23XgFnu/9n9nf9nqHPS6Msvv8TlcvGXv/wFr9fLxIkT8Xg8dO/e\nHYD+/fvz/vvvhyV4PB4PDz30ELfccktg7OOPP2bAgAEAnHrqqXz++eccPHgwpuvVhhJGIg2suh1A\nFPCISLz4amhqbZhqMisiiUPxk4gklB2TDyeLqpjl/vE6JoxatWrFlVdeyYgRI9i+fTtXX301bdq0\nCTyelpbGzp07w8771a9+FTZ28OBB0tPTAz/b7fawsWjXqw0ljEQamHYAEZFE4LW81T5uMy0sy8Iw\n1GxWRJqe4icRSSjuHbUbj0HPnj059thjMQyDnj170rp1a/bv3x94vKysjDZt2nDfffexadMmABYv\nXozdbg+7Vnp6OmVlZYGfTdMMG6u6Xn2o6bVIA9MOICKSCDxmcMLIG/KJbzOh0qyM44xERKJT/CQi\nCcXZvXbjMVi9ejVz5swBoKioCJfLRWpqKjt27MCyLN555x369u3LTTfdxNKlS1m6dGnEZBFAnz59\neOuttwB/o+vevXuTnp5OUlJS2PXqQxVGIg2sz1Wtgtbgg3YAEZH4qwxZkua1g+OItkV2Eyp8FSTb\nk+M8MxGRcIqfRCShdM8L7mEEYEv1j9fR8OHDyc3NZcyYMRiGwZ133onNZiMnJwefz0f//v055ZRT\nYrrW2Wefzbvvvsvo0aOxLIs777wTgJkzZ9bpetEoYSTSwFrSbm8i0nxVhixJ89mBIwqK7Ca4zZCG\nISIiTUTxk4gklKo+RQ24S1pycjL33ntv2PiqVatiOv/I3dVsNht33HFH2DGnnnpqzNeLhRJGctRa\nU+Egz+Wk0DTIsllMTnEzrFX1PT9i1RJ2exOR5s3r83HkQg7TZsERIzYTPKEdZkVEmkAgJvtlOlkP\nN2xMJiJSZ5mX1StB1BIoYSRHpTUVDm4ua4Xr0C9PBabBzWWtgAoFKCLSIngsL0emrX0hS+DtJhz0\nVcR1TiIioRSTiYgkLjW9lqNSnssZCEyquDDIc6kqSERahtAeRmbIJ77dAo8SRiLSxBSTiYgkLiWM\n5KhUaEbecSN03Fm6inbfnESHr9rS7puTcJY23HpQEZHG5LXM4AEDTCN4e2p3pSuOMxIRCRdrTAaK\ny0RE4k0JIzkqZdmsGsedpatoXTQRu3cnBhZ2705aF01UcCIizUKlGVxhhBFeZeTxKGEkIk0rlpgM\nFJeJiDQFJYzkqDQ5xU0KwYFICv4mi1XS9s7EsIJ/mTIsF2l7Z8ZljiIi9eHFDBuzQj713ZXlYceI\niMRTLDEZKC4TEWkKShjJUWlYKy/z0irItpkYWGTbTOalBTdXtHkLIp4bbVxEJJFUmiHNYg0LK2SF\nR6WWpIlIE4slJgPFZSLScmzevJmxY8cC8N133zFmzBguvfRSpk+fjmn6v/B78MEHGT58OKNHj+az\nzz4Lu4bP5yM3N5fRo0dz2WWXsWPHjnpdLxoljOSoNayVl03HlFHU/iCbjikLC0xMR3bE86KNi4gk\nEq8Z3sMotMLIU6mm1yLS9GqKyUBxmYjE37ID0OP/ge1L/5/LDtT/mo8//jhTpkzB7fZXUebn53Pj\njTeyfPlyLMtiw4YNbNmyhQ8//JCnnnqKefPmMXNmeCXl66+/DsCKFSu4/vrryc/Pr9f1olHCSCSK\nsg7TsYyUoDHLSKGsw/QmmpGISOy8IT2MDIhQYaSEkYg0D4rLRCSelh2Aa3bDd16w8P95ze76J426\nd+/OggULAj9v2bKFX//61wD8/ve/57333uPjjz+mf//+GIZB165d8fl8lJSUBF1n8ODBzJo1C4Bd\nu3bRoUOHel0vGiWM5KhmrlmL2acfZqeu/j/XrA085m4zkh87LcDn6IaFgc/RjR87LcDdZmQTzlhE\nJDZeK7zpNSFNZD1akiYicVBdvBUrxWUiEk+T90B5SE/+css/Xh/nnnsuDocj8LNlWRiG/xu9tLQ0\nfvzxRw4ePEh6enrgmKrxUA6Hg1tvvZVZs2Zx7rnn1vt6kThqPkSkZTLXrIWbc8B16BemggK4OQcT\nsA0bCviDEwUiItIchSaMDCO8wshbGdxUVkSkocUSb8VKcZmIxMuO8JWx1Y7Xlc12uIanrKyMNm3a\nkJ6eTllZWdC40+kM9D067bTTmDBhAgB33XUXOTk5jBw5khdeeCHm67Vu3Tq2+dXr7kSas7z8w8FL\nFZfLPy4i0syF9TDCCvvUr/RqSZqINDLFWyLSDHWPUloTbbyufvazn/HBBx8A8NZbb9G3b1/69OnD\nO++8g2ma7Nq1C9M06dy5M0uXLmXp0qVMmDCBZ555hkcffRSAlJQUDMPAbrfHfL127drFND9VGEmz\n5yxdRdremdi8BZiObMo6TI/t26fCwtqNi4g0I5VWcMLIMMAwgmurVWEkIo3JWbqKtPu/wpZpYRYb\nHHwkiYr1Sf4HFW+JSALL6+jvWXTksrRUwz/ekG699VamTp3KvHnz6NWrF+eeey52u52+ffsyatQo\nTNNk2rRpYeedc8455Obmctlll+H1ern99ttxOp11vl40ShhJs+YsXUXrookYlv+bK7t3J62LJgLU\nnDTKyvKXRUcaFxFp5nwhFUYGhFcYqem1iDSSQIzW2f/blr2zRetbPQD+pJHiLRFJYJe19f85eY9/\nGVp3hz9ZVDVeH9nZ2axatQqAnj178sQTT4QdM3HiRCZOnBj1GqmpqcyfPz9svK7Xi0ZL0qRZS9s7\nM5AsqmJYLtL2xrBV4ORcSAnebYOUFP+4iEgzF7okzV9hFHKMKoxEpJFEitFsKZA+vlLxlog0C5e1\nhe0/AfME/58NkSxqbpQwkiZXn50zbN4IFULVjAcdM2wozJsL2dn+36Kys2He3Fo3YIzFWmMj/WxX\n0NX2B/rZrmCtsbHBn0NE5EheQiuMrPAlaV4ljESkYYTGc7bKnRGPs2VaDR5vKc4SEWkcWpImTaq+\nO2eYjmzs3vCAxHRkx/T8tmFDoRESREdaa2wkx5iPy/D/YlZAMTn4yweHWgMb9blF5OgVtiTNACPk\nayKf1xPHGYlIS+V+clVYPGcW27B3Cm2+D2ZytwZPFinOEhFpHKowkqZVz50zyjpMxzKCl5VZRgpl\nHaY31AzrLd9YHAhiqrgMN/nG4qaZkIgcFbyhTa8BW0iFUaUqjESkAbim3hEWzx182IHpDl4H2xgx\nmuIsEZHGo4SRNK167lTmbjOSHzstwOfohoWBz9GNHzstiG2XtDgpZE+txkVEGkJ4hZEV1sPIVIWR\niDQAc2d4K4CK9Un8OCe50WM0xVkiIo1HS9KkaTXATmXuNiPjkiBaa2wk31hMIXvIoiO51riYSp2z\n6EgBxRHHRUQaS2iFkY3wCiMtSRORhmDrlo25I7xFQMXWHnh6fVTtuXWNr6oozhIRaTyqMJKm1Ux2\nKqtaH19gFGMZFgVGMTnG/JiaKuZa40ixnEFjKZaTXGtcI81WRCRyDyO7EkYi0ghSZk2rUzxXn/iq\niuIsEZHGo4SRNKl47lRWH/VZHz/UGshc6wayrUwMyyDbymSudYMaMYpIo4q0S5otdElaZWUcZyQi\nLZVzzMg6xXMN0X9IcZaISOPRkjRpcvHYqay+Yl0fb65Z62/YXVjoX1Y3ORfbsKEMtQYqcBGRuDJD\nKoxsBthtFkemiKxKVRiJSMOoSzxX3/iqiuIsEZHGoQojkRhEWwd/5Li5Zq1/S9mCArAs/5835/jH\nRUTizGcFLz+zAQ5bSNWRKoxEpAkpvhIRSWxKGInEIKb18Xn5YVvK4nL5x0VE4iw0YWRgYbeFJJEq\nffgsXzynJSISoPhKRCSxaUmaNBv13UWjPqqeJ59qnr+wMPLJ0cYbybb1bjYtqqCs2CIt06DPVa04\nbrCz5hNFpEXxhe6SZoDNHnxMshc8PjcpjtQ4zkxEWrJY47Vt6934Fv2KacWLKc3cx0tXL2Pv2V8n\nbHwlInI0UsJImoWqXTSqGiMWUEwO8wHimjSq9rmysvxl0pHG42TbejfvzXXhO9Q/sqzI4r25/m/l\nlDQSObp4Q5ekGRaGPXgsyQtuJYxEpIHEGq8FxysGbYracek9EznNSgmPVxIgvhIROVppSZo0Cw2x\ni0ajm5xbpy1lG9KmRRWBZFEVn9s/LiJHF9MM72EUMWFkhrxpiIjUUazxWq3ilQSIr0REjlZKGEmz\nEG0XjQKK4zyT6GzDhtZpS9mGVFZs1WpcRFqusCVpWBghPYySvRbu0N/aRETqKFpcFhrH1SZeSYT4\nSkTkaKUladIsZNExYhBi4C9/TpStVOuypWxDSss0KCsKD7bSMo0mmI2INCUzZEmaPUIPI4cP3D5V\nIIpI/a01NmIAkVJBobuh1TZeaer4SkTkaKUKI2kWcq1xGBEiEMsgsZalNbE+V7XCHrL03+70j4vI\n0cOyrLCEkY3wHkbJXnCbnnhOTURaqHxjMVaEfI9hEbzrGYpXRESaCyWMpFkYag2M+I0VRF+u1lw5\nS1fR7puT6PBVW9p9cxLO0lUxn3vcYCen5aSQ1skAA9I6GZyWE6GBpIi0aJVmZViS3TAIW5KW5PPv\nkiYiUl/R4jGL4IbXztJV9OvVh5vmH8tf7/odJ/z6acUrIiIJKmGWpM2ZM4eXX36Ztm3bAtCzZ0/u\nv/9+Hn30UZ5++ml8Ph8XXXQR1113HYZhUFJSwi233MKuXbuw2Wzccccd9OnTB4A33niDe++9F4/H\nw/HHH8+dd95Jenp6U96eNIBsMiMuSwstc27OnKWraF00EcPy72xm9+6kddFEfKnJ4BgS0zWOG+xU\nwCVylIuUMIIoTa+VMBKRBhCtfUA2mYH/DopzDGiTUcgFV93Gj53ScLcZGc/piohIDBKmwuiTTz5h\n3rx5PPvsszz77LPcf//9vPnmm7z00kusXbuWdevW8cEHH/DSSy8BMHPmTPr27cuLL77IPffcww03\n3IDL5aKkpITc3FwWLFjAK6+8Qrdu3Zg7d24T3500hFxrHClWcCIkxXKGlTk3Z2l7ZwaSRVUMy4W9\nYEoTzUhEmiOP6cYWocLIFnFJmnoYiUj9xRKnRYtz0vbOjMcURUSklhIiYeTxePjiiy9YtGgRF154\nIRMnTmTXrl289tprXHDBBaSmpuJ0Ohk6dCjPPfccXq+XN954g5Ej/d9EnHjiifTo0YO3336bd955\nh5///Of06NEDgDFjxvD8889jWdolqrkbag1krnUD2VYmhmWQbWUy17ohYRpeNwSbtyDyA56d8Z2I\niDRrHtNDpNaxqjASkcYSS5wWLc6JGv+IiEiTSoglaUVFRfz2t7/lxhtv5Kc//Sn/+Mc/+Nvf/kb7\n9u353e9+Fziuc+fOFBUVsW/fPkzTpF27doHHOnXqxO7du6moqKBz585B5xw8eJCysjItS2sBhloD\nW1SCKJTpyMbujZAcSu4W/8mISLPl8XnCl6QZYIR8TZTkhTIljESkgdQUp0WLc0xHdmNOS0RE6igh\nEkbdunXj8ccfD/x85ZVXsnDhQo455hgM4/B3pJZlYbPZME0zaLzqMbvdHvExAJstOEpOT3ficNjD\njquO3W4jIyO1VueIn1672Fjd87C2j8cwyw+P2VKxut+p16+O9G+v6dX2/bY5/Z0l6lyLLVuEhFH4\nLmlJPgub00yoe0jU1zQSzbVxaK51V5f4tiHE+jpEj3PyEup1jCbR/r4bg+6x5Tga7vNouMemlhAJ\noy+//JIvv/ySIUMON/W1LIuuXbtSXHy4eV5xcTGdO3emffv2WJbF/v37ycjICDzWqVMn0tPT2bx5\nc+CcoqIi2rZtS2pq8D+kgwdr/41qRkYq+/eX13yghNFrFyPHEJyZHtL2zsTmLcB0ZFPWYTop7Ubr\n9asj/duLrGPH1nF7rtq+3zanv7NEnesPB0rDd0kjvIdRkhcOHDyYUPeQqK9pJJpr42hJc43ney3U\nLb5tCDH/nUWJc9yOIdAM/s6b07/NutI9thxHw30eeY/xfr89WiREDyObzUZeXh47d/pLVJcvX87x\nxx/PoEGDeO655ygvL8fj8bB27VoGDx6Mw+HgzDPPZNUq/3bjX375Jdu2beM3v/kN/fv3Z/PmzWzf\nvh2AFStWMGjQoKa6NZFac7cZSUmvLeztfYCSXlu0a4iI1Fqkptf+JWmhFUZQ4VPTaxGJH8U5IiLN\nR0JUGPXu3ZspU6YwYcIEfD4fnTt3Zt68eXTt2pWvvvqKESNGUFlZyaBBgwJVSNOnT2fKlClccMEF\nGIbB3XffTevW/qxifn4+119/PZWVlXTv3p277rqrKW9PREQkrip9lZF7GEXYJc1jqoeRiIiIiIRL\niIQRwMUXX8zFF18cNj5+/HjGjx8fNt6hQwceeeSRiNc644wzOOOMMxp8jiIiIs2Bxwxvem0YYAtp\nbaJd0kREREQkmoRYkibSWNYaG+lnu4Kutj/Qz3YFa42Nmk8Damn3I9JSeExP+JI0wpteJythJCKN\nKFKcEEvsoPhCRCQxJEyFkUhDW2tsJMeYj8vw/zJUQDE5zAeodsvXo2U+9dXS7kekJfGY7piWpDkr\nwa0laSLSCCLFCTcyD7CoNHyBsdDYQfGFiEjiUIWRtFj5xuJAsFHFZbjJNxZrPg2gpd2PSEvi8Xkw\nQsYMwOYIThi1qgRPpZpei0jDixQnVBreQLKoSmjsoPhCRCRxKGEkcRevMuNC9tRqvLEl2nzqq6Xd\nj0hLEnFJmuHvY2SFJI2s8rL4TUxEWrQjY7wCimM+78jYQfGFiEjiUMJI4qqqzLjAKMYyLAqMYnKM\n+Y2SNMqiY63GG1uizae+Wtr9iLQkHtMDERJGACSZQcOmEkYi0gBCY7ywMsdqHBk7KL4QEUkcShhJ\nXMWzzDjXGkeK5QwaS7Gc5FrjGvy5muN86qul3Y9IS1LpC68wMg5lkIyk4AeUMBKRhhApxguVZDlI\nsoK3awyNHRRfiIgkDjW9lriKZ5lxVWPEfBZTyB6y6EiuNa7JGiYm2nzqq6Xdj0hLEq3pNYDdYXFk\nBxGz7GC8piUiLVjUWM4CAyMQJ0D1sYPiCxGRxKGEkcRVFh0jrmlvrDLjodbAhAowEm0+9dXS7kek\npfCYnqgJI4fDDEoYUa6m1yJSf9FivGwy+chcEjRWU+yg+EJEJDFoSZrElcqMRUQanyfCkrQqSSE9\njGwVFfgsX+SDRURipBhPRKTlUcJI4mqoNZC51g1kW5kYlkG2lclc64a4fIsUr93ZmlpLu8+Wdj8i\n8VBpVoZVGBmHKoxsIbukpbrB5S2P08xEpKWKFuMBUT/Ha/qMVwwgItK0tCRN4q4pyoyrdu6oasZY\nQDE5zA/Mp6VoaffZ0u5HJF48pps0Kzgx5MHfaNYW0vS6lQcOeg+SntQ6bvMTkZYpNMar7nMcqPYz\nXjGAiEjTU4WRHBXiuTtbU2pp99nS7kckXjxm+JK0SsP/HZHdEbwkLcUDZZXaKU1EGl51n+M1fcYr\nBhARaXqqMJKjQjx3Z2tKLe0+W9r9iMSLx+ep6nEdUHnoIz+0wijFY1HuVcJIRBpeXT7Hqx5TDCAi\n0vRUYSRHhWi7sDXW7mxNpaXdZ0u7H5F4ibRLmqcqYeQIX5JW5j0Yr6mJyFGkus/xmj7jFQOIiDQ9\nJYzkqBDvnTuaqkljS9uhpKXdj0i8REoYVS1JM0ISRsleKFOFkYg0sLXGRspwQch7UdXneE2f8YoB\nRESanpakyVGhqjliPospZA9ZdCTXGtcoTRObskljPO8zHlra/YjEi8cXqcIoCQCbPUKFkXoYiUgD\nCo2FALDgGNow2xof9Dke7TNeMYCISNNTwkiarbXGRvKN2IOIQOBx6Jx8YzEf8gUbjA8bNBCJ2qSR\nxXEJcppiF7rG1NLuRyQePKY7rOm1x4i8JC3ZC/u1JE1E6mGF5zWm2h4NxFMlHAiLhTAgzWoV9Jle\n02e8YgARkaalJWnSLFV9c1VgFGMZFgVGMTnG/GqXfkU659/GulpdIxbRmjH2W/0/zD79MDt19f+5\nZm29nkdEJJrqehiFLklzVqKm1yJSZ2uNjVzrvpu+a77kP6du4f2Or/H6qZ8wZHVJ2LEFFDfBDEVE\npK6UMJJmqS5brUY6J3QboYbYrjVSM8Yhq0u456YCKCgAy/L/eXOOkkYi0igiJoyMyEvSnJUWB5Uw\nEpE6yjcWc87q77nnxgKyCyqxWZBdUMk9NxaEJY3s+tVDRKRZ0bu2NLm6NIiuzzatdb12rCI1abx9\nVhEpLjP4QJcL8vLr9VwiIpFUmpXhS9Kqehg5gt+LVGEkIvVRyB5yZ+0m1RX8ppPqssidtTtozEdI\nLCQiIglNCSNpUnVZWgaQQetajUPs27DWd7vWodZA5lo3kG1lYlgG2VYmXQs9kQ8uLKzXc4mIRBKp\n6bX7UMLIiNj0Wj2MRKRusuhI18LKiI+FjmeTGY8piYhIA1HCSJpUXZaW+Vm1HI9c+RNtq9f6GmoN\n5CNzCbvMl/jIXIKRlR35wKysej+XiEgoj+mOviQtQtPrcnBLmg4AACAASURBVG95vKYmIi1MrjWO\n77OSIz62Kysp8N9Jlr1BYiwREYkfJYykSdVlaRnAPn6MOL6f6N+SR6r8+ZN1QdDPc60bGmc3jsm5\neJ0pQUNeZwpMzm345xKRo5rP9OKzfGEf8G4iJ4xaVcJB7ZImInU01BrIf6ZfQHlKcGPI8hSD/Kmd\n/T9YcL/1d4ZaA1nztoM+E9LoNDKdPhPSWPO2Nm0WEUlUeoeWJpVFx4g7ZlS3LGytsRGDyLVENS0n\nC92eddt6N79aNJayYou0TINTrmoFg2OdfeyezhzJGz9tRc7XM8hyF1DozGbuT2dwZuZFDMPb8E8o\nIkctj+lfAhK+JM3/kR/a9Dq5EsqUMBKROlprbOS2oQVsNLPJnbWbroWV7MpK4rGRY+i98iruXNCe\nHzP3c8pVWaxxOrj5kVa4PP7kUsFeg5sfaQVUMGyA4iERkUSjCiNpUpGWidW0LCzfWIxlRHjAgjJc\nMTXNBn+y6L25LsqKLP+5RRbvzXWxbb275pNrKW+5k5XtR3FnzxkUOrPJcheQ8/UM/m/Osw3+XCJy\ndPOY/p5pYQkj49CSEVvwgw4T3G41vRaRusk3FlNOcOy0O+kP9NxwE8cUdcSwbLQpasd7c1089bA3\nkCyq4vIY5C0PaRkgIiIJQQkjaVKRlolVLQuLVrIcdbmaAfuMH2Nqmg2waVEFvpDckM/tH49VrDu8\nFf5gcEnRSu796jq6uXdiw6KbeyeTP56IuWZtzM8XL2sqHPTZl0anH9Lpsy+NNRUqRhRpLmpKGBkG\nGCHL0rzlB7Gs6D3gRESiKWQPQ1aXcM+NBWQX+Hdo/M5xPXZvq6DjfG74TVFIS4F+QB4UzDIUb4iI\nJCC9K0uTC10mBrDm7egly1lnRF7GVsVluMlncY29iMqKI/9yFG08VNUOb1VNuwsoJof5gXs6UlZ7\ni9vfn0Gq6QoaTzVdkJcPw4bG9JzxsKbCwc1lrXBx6LU3DW4uO1Qu3krl4iKJznMoEx6aMKrgcPNZ\nm93Cd8TmRXaPl0rTQ7Jd3/KLSO1k0ZHcWVtIdR1+0ylL6hLx2AzTd/iHfsDlgBPAULwhIpKAVGEk\nCSlvuTNqyXLE3c5C1NQ0GyAtM9K6Nv94LJVDtdnhbfKlbrLcBVEmW1jjXOMpz+UMJIuquDDIc+kX\nSZHmoKrCyBbWw+iIhFFIhZGzEg56tSxNRGov1xpH18LKoLG0yu8jHmtrayMl+dD7zxAOJYsOU7wh\nIpJYlDCShFT4Q+RkTuEPRmAZ2zFW66DO10NWl/DBKV+ws8NmPjx1a41Lvfpc1YrQL9PtTvBd9Q05\nxnwKjGIsw6LAKI64zK02O7wNG+ClokNWxOPLs9pXO894KzSjvPZRxkUksVRGWZLmNexUWId2SrOH\nJ4zKlTASkToqzEoO+vmXRfdhD6mq9jjdvDLxX4yZ9gLZHUxoF7miW/GGiEjiUMJIElJW+8hBxFmO\ngzw1+gClZ/Vh/Mi7OGX96QBha+e7Frjh5pxqk0bHDXZyWk4KaZ0MMCCtk8FpOSnce859NVYOrTU2\nYiNyQBNtp7bUWbl4U4IDqvIUg8lT2sTcqDseskLLEmoYF5HEEm2XNNOw4Tr0dX5oD6PkSiir1E5p\nIlJ7+cZi5kztxFftzmfNT9ez5Gdb+KTTTRy3by2plYVYmOzrtIe1tzzG6+e8xKoT5zHlkefJtive\nEBFJdEoYSUKafKn7cMnyIb/2HuS8vfsDu5q1KWrH0Luv4ZTXTid31u6gtfMAuA71B6rGcYOdjFjR\nlnEbMxixoi3HDXbWWDlU1bvIZ5jhB1n+XkaRlrHZhg1l5n29KchOwjSgIDuJSfdns2pEaybu+3eg\nqXdTm5ziJoXg1zIFi8kpDb97nIg0vKoeRqG/c5kYVOBPWodWGCV7LcpUYSQidVDIHr5teyEfdZpF\nWXIWGDbKkrPYdsxQ+uy+j0EHTuXupyay+ex3gcNfwineEBFJfEoYSUIaNsDLvPEVZHcwMQyL7A4m\nI6wDELxEnmS3k3MfGx22dr6KWVhQ7e5lkUSrEPrzai9mn35clHkZb5z6CUNWl4QfZPj/F20Z27+G\nO/jN5p/Rbe8p/Gbzz3hmeDsAfMcUc/MjrRIiaTSslZd5aRVk20wMLLJtJvPS1IBSpLkI7JIWMu7D\njss6tFNahCVpShiJSF1k0JpL7h+Jz5YSNO6zpfBJp5sixmiF7FG8ISLSDChhJAlr2AAvmx4uo2jV\nQTY9XIZVGqGiB8gobs+urKSIj+3KSqLAKOZvnvmc8K93Y0rIRGqqPfKpH5l+01dQUIDNguyCSu65\nsSBy0uiQSA2woyWj2JsZaOqdCIa18rLpmDKK2h9k0zFlDGvljakRuIg0PU+UHkamYVBxaElaWNNr\nL5R5tSRNRGrvj6uLcJZGjm/KkrpEjNEsLPrZrsBIeTUs3hARkcShhJE0G9F2Nduf+QP5UztTnhL8\neHmKQf7UzgAYrdzsv+ixmKp4qppqZ1uZGJZBtpVJ3uxSHC5P0HGpLovcWburvVbo8rZIySirwom1\nbLz/+CjNvpta1TK8mhqBi0jTq/RF3iXNh40KIje9Tq6Eg+phJCJ1cP3sb6Puipbq/T4QiwWpphpb\nREQShxJG0mxE2tXM43TzyjUreGZ4Oybdnx3WH6hqyRcAHYqrreI5soIm31hMrjWOXeZLfGQuIbXw\nh4jnRFsKVyW0oqgqGWX7oROWaWAVd8J6+Dast8/xHx+l2XdTyzcW19gIXEQSQ1WFUUhrECzDhutQ\nwjp0SVqyF/ZWRO7fJiJSna6FlRF3RbObLj67YMnhWCxCiKNYQkQksTV9wxSRGB032P+LzjuL9mMW\nJ7M/cy+vXLMi0ETxmeHtghNEofZmApGreKoqaKqSIgUUk8N8wJ/kISsLCgrCzou2FA4gxXKSa40L\nXD/fWEwhe8iiI2OLruTJa8/H5Tk8l5Rki8mXJmajx5oagYtI4vCYkZtee7Hhitr0GvZUFMVlfiLS\nslRkdaBXwQsAfNLpJsqSupBW+T3Heh/glsnfBY6zCO+tBoolREQSmRJGkpC2rXezaVEFZcUWaZkG\nfa5qxXGDnRw32MnocyZRYBRHPjFKNHLksq9IVTxRK2hY7E8YTc6Fm3P8O68dUp5i8MCUnhxjtWE/\nP5JBOmCwnx/JoiO51jiGWgMjJqNWnTiPMdPg1fv/SOEPBlnt/cmiYQMSc+1+Fh0pIPw1j9qTSUSa\njCfKkjR/D6NDTa9DexhVwvaKKO+rIiLVSJ08ky+nvsSWY24MJItO2nc/C/I2A/4v8iwLjIpWkFIR\ndr5iCRGRxKWEkSScbevdvDfXxaGdoSkrstgwdz9TjRUMH/zz6N9EWZCKE8OyceGBHeTt2UE3r5ud\n9lbcvukGlr99TtQqnpoqaGzDhmIC5XnTaVW4l11ZSeRP7cwzw1NIsdw8aE3yJ5YiiJaM2nDiP9j0\n8BmxvShNLNcaRw7zg+7jyAoqEUkcVUvSbCH7BFQa9sNNr8MqjCz2uJQwEpHaW9W2J2VZszjply/x\n+0vupk27XZSWdOF4fgnsA8AwIM2ejGlZiiVERJoRJYwk4WxaVBFIFlVJdjvp9/gfyTk7hxSSKSfC\n0i0DynEzev8eHt29jTTL/9vSsb4KHjv1Hmz9N5BcOp1hx4cnabLoSPvXfsq5j40mo7g9+zN/4JVr\nVnB6v42023MSNm8B5i+zuenNTBZmZAWdG1SJFMGRyaghq0vInbWbroWV7MpKwpy8FtuwobV8heKv\n6t7yObysrqqCSkQSS1XCyOELHvfaknBZhyqM7OEVRnvde/FZPuyGPS7zFJGWYfeidIYOm0Wfs57A\nOLQ9Y9v2u5jhLiFntMk3W1PJn9qZZ4cZPGhNUiwhItKMKGEkCcVcs5ay3WeCEd6PPaO4vf9bqRr6\nQuft2RFIFlVJs0xmu/7LySfO49eW/7eoI3sKnfbqIE6aex7Jbv+378cUdWTKhs78oetn2G3+xtZ2\n707ydxdSYhzHirbB5dPVrb/PoDX7KGXI6hLuubGAVJf/BrILKvHefBMzjYf513BHwgdOQ62BCTs3\nETms8lDCyG4Gv1l6bA7KaAWAzRG+S5pp+Sip+IGOKZnxmaiItAi/7f5OULKoSpKzgjZ/NcgeXsk9\nNxaQZqUydKhiCRGR5kS7pEnCMNeshZtzom7NimVwy4gFnLL+9Gqv060ycuPobpVuXIabKcYjYVvE\n91h0WiBZVGXgBfNoZQveBS3NMskr3hF27erW37sPVUPlztodSBZVcbg8/HX2/7RVvYg0mGgVRpWG\nnVIrFYi8SxpAsRpfi0gtmGvW8vtL7g5LFlWxZfrHU10WN87eHseZiYhIQ1DCSBJHXj64XBG3ZgUw\nMDimqCND776GU16LnjQ6cKBzteP7KA3rKZRR3D7s+DbtdkW8TnePm50dNvPBKV8wZHVJtevv1xob\nA8vnuhZWRjzmyPGWur3smrcd9JmQRqeR6fSZkMaat1XcKNJYqppe20N6GHltDkpJA8J7GDkPvQ3t\nUeNrEamFb+7+T9R4CcAsPrwTSedCt+IBEZFmRgkjSRhWoX/b+l6lL/C7wqmkeQr922qESHY7Ofex\n0WHjputsfHvX0L7XLo5t+x3LkscEHqt0p/DOmtuiPvf+zB/CxkpLukY81iw2sFn+JWVzbyzkyaf6\nVtvwumrXtl1ZSRGPCR1vadvLrnnbwc2PtKJgrw3LMijYa+PmR1opSBRpJNEqjNw2B6WWP2EUrcKo\nyKUKIxGJ3SeOP/GPyr/RI+NbbO189Mj4NhB/WZbBwUcOxzi7spIUD4iINDNKGEnC2J3VKvDfvUpf\nYNjXg4nWsOjIiqAky4HpOhvrx1vB7IxlGOxwdOfqtEUsS7qUAz9k8fLSOWzeejofnPIFOzserg6q\n8so1K/A4g6uONq67GZcZHMSYLoKCnxSXSXbeP6MuIzsy+ZM/tTPlKUbQ4+UpBvlTgyui6ru9bKJ9\ne5e33InLE3zfLo9B3nJnlDNEpD48ZuQKo0ojiQNVFUYRehgB7NGSNBGphbfPOo7rOtzHd/YeWIaN\n7+w9uCb9cZYlXcpnG4dTsd4fM5WnGORP6ax4QESkmVHCSBJG3pTMsIRKqjdyP6NARZAFlXixysYD\nKUHHuGypXFfxbx67/X2+/uA8zvhqLtkFlYHqoHtuLAgkjTYPfpe1tzzGvk57sAyT/Z32kD+wmKuz\ne/Kdw4kJeHcb/HhXciD4qdKpsCJq76Ejkz/PDG/HpPuzKchOwjSgPLsDk+/ryTPD2wWOqe/2solY\nzVP4g1GrcRGpH8+hbSbDehjZ7PTu5q+cDF2Sluz1/1zs0pI0EYndy5e3wZ0UHBeVG2ncZD1M+b0G\nFlCQncSk+7N5+owTI15D8YCISOJSwkgSxhsXHh+UUCnITuKzC5aEVf5YWGz93cf+H4xD/zMj7+qz\nv4OdZG8JDtPFfzrfxZqfruebNucD/gaMubN2B67z2eB3uXvVRBa+cSPdV+7kysG/4/mU4zjuJ78i\n+cTT2Hxj27BkEfhLrKP1Hsq1xpFiHf7m7Jnh7Tjz01/yXPEy0jf9lzOGzSLbysSwDLKtTOZaN9Rr\n95BErObJah+5SizauIjUT4WvAojQw8hwUGFvDYQvSXOqwkhE6uBA+8jL7fe0bk2v0hcozE7iN5t/\nxtMXdMFaNj7isYoHREQSlxJGkhBu+9+b/H/27jy+iur+//h77po9N3tIAoRFFBdArUt/SqlSxeWL\nVtFW7ZevtlbRttpqrYKodUFx32ptS7G1Wiy1AiKudcOVWkREFEVlEUjIAuFmz91mfn+EXBgSCEm4\nN9zk9Xw88sjNzGTmc+bi3PGdc85sC7ZowaRsHbPiYA3cMlrHrDhYHx6dJtNhytppaJohQ9966QT7\nxNeOjv8qXlC5RRFHsgKubMlwqNFTrCXFt0VDo+JNoejwNMuQflYb0Jqvl+nS1WfpglVXaGLllugc\nRJ0NKdt17qF577g047KJanzwOjm2FkgdhEJnWydqqfmEys2XtNR8osePmt0fe/NMvyCgZI/9ZjDZ\nY2n6BR0/zQ5Az9SFaiW172EUdjhV586V1H5IWltgRA8jAF2RX9l+DkhJyquv15qM0zXzxkJZlmT9\nYaqsd07WrlMNcD8AAPs3ZplD3M033tBM43FtUpWccujQV7+tCX8+T3dU/lFt6YzpMLXm8E91mvdL\nnXjjicrILlddTZHeXnCtvlh6VnTi6yG1izTttgr9Z8w0/ebWR9ScnBo9TnJzo06Z41fEYR+qFnEk\n672SO/Wu7lZqaLMOr3xA9/zqRR2fUatLBlXKabX+WT7duVF/aiyTaodpbmZedOjYtNsqVFQWUnmx\nWzNvLIwu33n4WdvQsOagIb0zQaF3JijZY2nqZS06e2w4Zue2OMfSpi3tw6He/OvdpLFhSS26/Smv\nyrYaKs5pvTmcFMPzAPRndcE6Se17GEUcToVdqfpX+Dv6vvNd2zpPWDJMS6M+3KL6stlKOf1MOXN7\nNp8agL7vlKf8+ucvM9Ti2tGTOcVq1L3mlao9a4yePWeTFHFsD4uk7HRLKV6L+wEASBAERoir+cYb\nusZ4KPpY+0Nf/bbOvudSeQL2IVNO06kzHGt1ynk3yO1tliRl5pTplMmtTzr7YulZyqrI0e8u2yiH\nJZ2z6V8yLGnmVbeorKhEA0MbdEfoev3PGW/r7UhryLQzy2j9p9/W4+jbZdLlSf+Sd5ensqVapm6v\n2qC5ma3/4/TsOdm2OYfaJFtePfKvA2TefpRUVqZjk0p0yuCbtaDgh9Ft2oaGxfLGaPoFgR1BVVtt\n+8Ff7yaNDXNDCMRJXag1MHK1G5LmlNOQbgz/WKYMjdJX0XWesHTe26bOWmIpoFkKv/GGfI89IcPF\nbQKA3ZsUfkknNf1H01Pv0AbHIA0yN+j2puv1I/Mfqj27WJY1WNYnR0hqvR+5/ccERACQSLgTRFzN\nNB5XsxHQ6FeP04RZ58lXmStDHQ+X+s5Zd0fDojZub7O+c9bdrb2MIn4tGP6aGt0DWnsKvfOAPg0c\noPTrgop2KsqRLWTqSMSRrOUFV+nYnKc7XD8wvIewxZJSlKSn/nWkjrr6r1Jza70Dmjfqvi9/oRwj\nV3mukfKZEfkdTr3UkqEjLk/t9l/U5r3j0vSv35L/jFky8qqUFcjTDM9F0aFs9OYB+rdgJKiWSOt1\nyNluSJpDToehFnl1o/UTLdS06Lq0FumsJTsC88jarxX65GN5jvhWXOoGkJjG//BmpYS26Uf+f7Rb\nl5FdLsMYLBVvVHGuyf0IACQgAiPEVZmqNfrV43T23e17Fe0qI7t8t8sNM6iwI01BV2tvn7aeQodf\n/rwcyUHb9juHTLvT6B6gYLVT3oJIu3UtW1L0/WdqOuxZJENqslpUNO3paFjUpiLtRB2kUplm6z6z\nzIj+t65SBy6+T39fPU665QyNCTTqo9ktaqyylJpvqORYlzb9Jxz9OXx8qu75LFNlWw35UlNVf+Sr\nMqfcLSOpNcTallSln1t3679apTutX0ja/3rztA1BLFO1ipWnadZFPZ6rCUDH2uYvkjqYw8hwKbk+\nouOaXcqNuPXBUTeoJan1umZYETnDLUptqlBqY7kya9fJvXQZgRGAPUpO27bbdXU1rU9lNPIq9dEf\nGuNVEgBgHyIwQlx999VTNP6O/9UhRz6n75x1d7u5iXZWV1OkzJyydvuoqymS0woq7EyzLY84kuXN\nb263vbQ9fGobbma079GUEt6sR4P5usysVrJjR9gSCiTrzQW36ooXlkpasdvQaEDtlnaLlw64XqbD\nY1tmOjxamztFM1d9Tw/flqMmx4Ha/gRsNVZaWr0wFN22sdJScH6jclM92pSUqm0Nhhzn/ykaFu18\n/Cf0vI7WwftdELPrEMRNqtI1ekiS9rtagb6gfvtwNFmW2h6E1uL1aVPxCbqkIUXpyxtUrNanGjWm\nDrD9btiVokBStmqyD9bGgdKqjWEV/v0rDR6To+KRWXI4efQ1gL1jWdK7838pHf/sbnuSAwD2fzwl\nDTG35rWAHj6hUn89wa+TZvyfDjnyOZ0yeaoyc8pkGFZ0bqKDjlpg+723F1yrUMA+YXUokKy3F1yr\nsCNVHWn7a9au6rcWtL7oICySZak2PU2hGUv02l/uU+3WYlmWodqtxXr5yTu16sMf6LOsX2nabRW7\nbWN5cfvHygacWR1uG3BmKcVs1pAmXzQs2h2PZen8hhpNqynXmJZGKbfjJxhZRutwv70x7x2Xjrg8\nVQU/SNMRl6dq3juxy43bhiDurNkI7HWtALpm5wmvm5LztOqgyVpyzK3aMOh7Sre69pFvGi6Vr/Zr\nyT/X6IX7V2jVW+UKtuw/vRcB9L5gS3KHy8Mht9b8p3Wia0u99+ANAEDP9NkeRosXL9Z9992nYDCo\nAw88UHfccYfS0tI6/0XsU2teC2jx7c1yqu35Z0ancxO1aXvdYU+k3fyx6u0F1+qUydfJ7W2JLgsF\nkvXWs9M6Dovajt+SGT1mR0PXGt1FKvo61G55m1dPTtdFf6np0t/QAq69ewKRodbhbOc0bJNj/qla\ncc6LHW5XpuoOl897xxWd0ygrzVJ9k6FQpLXSTVsMXf3HJEktMRnGtruadrccQM/Uhmrlinh01IaT\n9MFRJ8hytA+zu6O5LqRPXyvT6ncrdOBxhTrg2wVye537ZN8AEtMvH/5Es49LktS+d3cokKaA0xf/\nogAA+1SfDIxqamo0bdo0/eMf/1Bpaanuuece3Xvvvbr55pt7u7R+57U7WrTrTEV7mptoV7sLcHan\nbdtx379T6TkVux3uZrOHICm6iSId9iJqc9K/69uFRZ7ItugcS7sulyRvuFoBd0Gnx47+nixN+PN5\n+njSix2WXKz2AdS8d1y2p6bV1Lf/xVg+va1Yedqk9r2iOqoVQM/517XovI+nKy3oU0cdikyn9JUj\norWuiC796G8q3fqVDCssy3Ao4PGpMa1Idemlqs4dpZbk3Ha/H2qJ6NPXy/TlkkoddHyhhh+TL5eH\n4Ajojw6fV6zkCf4O1yWn+ZUS2ixJylJ6PMsCAOxDfXJI2rvvvqvDDjtMpaWlkqTzzz9fixYtkmXR\nJTbePB2c890NG9vd8q76YulZ+tP1/9G9l32jWdcv6VLgtDuWHJp5Y+Fu1xeVte99dPTmO2SY9gm4\nDTOoozffoSZHslY6axXs4rh+X7NHnte+L2uXx2UnW15Nsy5qt/3tT3mjYdGelG2NzfwC06yLlGzZ\nI8Pd1Qqg+8yIqU9e3aTmF7OVFmz/V/3k5kq9nBxUzQk5ei41qE+9ETlaquQON8oVCcgdblZa02YV\nVC3TAWvm6dsf/FZHfThTpUMMOd3tbxWCTWF98u9NevGBlfr6gypFwma7bQD0cYZjj/d0DSP/LIfp\n1Azr8jgXBgDYV/pkD6OKigoVFu74n/vCwkI1NDSosbExOiwtLc0rl6trfxV1Oh3y+VL2aa19X/u/\nPLUOG5tqG5bWNjfR/iqQUd3xhNfblRe7VbLJHhoNrXtBkrS84Co1ugcoNbRZh1c+oEF1L+uKg2Zp\nQf54jWlp1JmNfqVY5l5FR06fU3VnX625wcN0U3CWNllVKjHydav3Up3nOand9nsbBA3MU0z+bf9E\n/6OUoHevao0V/rvtfV293ibSe7Y/1Fq/tUVv/W21KtfVtZtc1hPwa/jaZ1VY+1/desp9Gpe2I8AN\nOHffa9KQlN6wScMO8GvcZafqs7fL9ckbmxRstvdEbGkI6aPnv9FXSyp15GmlGv6tfDkcPQug94dz\nureoNTaotfu6c3/bfdv2eE93/x8X67Hk6+P6mR9r+9v7HQu0se/oD+3sD23sbX0yMDJNU0YHY3Yc\njh1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SGGcs3u8YXQdnAlyKTFtZcZqIiIjIFnCUNKqRItSO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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196403e8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g =  sns.PairGrid(data_part, hue='energy_kev', palette='bright', size=5)\n",
    "\n",
    "def f(x, **kwargs):\n",
    "    kwargs.pop(\"color\")\n",
    "    col = next(plt.gca()._get_lines.prop_cycler)['color']\n",
    "    sns.kdeplot(x, color=col, **kwargs, linewidth=4)\n",
    "\n",
    "g.map_diag(f)\n",
    "g.map_offdiag(plt.scatter)\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Countplot to Visualize distribution of flares in the different energy ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The countplot visualizes the distribution of flares from the data_part dataframe(having 1000 rows of the entire dataset). We see that the 6-12 KeV energy range dominates all the other value range. We assume the same trend continues for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1964383f2b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkCHq06ePChQooM8//1xRUVEOCQsAAAA4K6vF3c/PT4sWLVLRokXVpUsXHT16VPv27VNY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1963f7862b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='energy_kev', data=data_part, hue='energy_kev')\n",
    "plt.xlabel('Count', fontsize=16)\n",
    "plt.ylabel('Energy Density Range (KeV)', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countplot on the entire dataset to verify assumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1964459a6d8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T1pxTp05qypRZ1r2xeMBQmEhKSlLWrFkf+lliYqKe9qfLVqlSRb169VLnzp3l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\n+pZabrFYlJSUVCkFAgAAAKiZ3AoTDRs21MCBAyu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98sorqlu3rvLz8zV79myl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19644271eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='energy_kev', data=new_df, hue='energy_kev')\n",
    "plt.legend(title='Energy Range(KeV)', loc='right', prop={'size': 10})\n",
    "plt.xlabel('Count', fontsize=16)\n",
    "plt.ylabel('Energy Density Range (KeV)', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hence confirm our assumption by plotting a countplot on the entire dataset. A majority of flares appear in the energy range of 6-12 KeV, followed by the 12-25 KeV range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relation between Energy density and Duration(in seconds)\n",
    "\n",
    "The plot uses only thousand rows(data_part dataframe). We infer that short duration pulses are having high energy density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1964459d5f8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R/FpzaI8Ml0YnuWbZsw5Lv73C2JoAAEDw8Sq0x8TEaOzYsf6uBSGq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\nd4mWl11pcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19644349358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_part['duration_s'], kde_kws=dict(color='green', lw=3, shade=True),\n",
    "             hist_kws=dict(alpha=1, color= 'gold', edgecolor='red', lw=3))\n",
    "plt.xlabel('Duration in seconds', fontsize=16)\n",
    "plt.ylabel('Energy Density', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram and Density Curve of Flare Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x196448f8d68>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KrTLdunVr3XXXXZo7d67mz5+vMWPG6KOPPtLYsWN122236aOPPlJZWZm3swIA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\nraUDCVKM2WSA3+LKNAD4qdM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INW1LpK5J3SRJVTVVWr5/qeFEgOf5dJmHJI0aNUqjRo2q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\nk16VlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1963aed8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['duration_s_log'] = np.log(df['duration.s'])\n",
    "sns.distplot(df['duration_s_log'], bins=80, kde_kws=dict(color='green', lw=3, shade=True),\n",
    "             hist_kws=dict(alpha=1, color= 'gold',edgecolor='red', lw=3))\n",
    "plt.xlabel('Duration [log(duration.s)]', fontsize=16)\n",
    "plt.ylabel('Energy Density', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relation between Radial values and the total count of flares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x196448b5978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JdnZ2f2ruJNYw8YbglRonjSELKxAmf309aTt9tIxxUzM9h0pseIJOLskLKHxE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\nAQAAAIBB/taz2FUSAbASDwAr8QCwCmyRbQuJ8KP7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196448b5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "sns.jointplot(x=new_df['radial'], y=new_df['total_counts'], color='m', kind='scatter', s=100, edgecolor=\"skyblue\", linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relation between Duration and log(count of flare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x19645cb7518>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Nw+yOfFdAc35ZwBgeI4ktDjidrtht9thtVrDClhutxtmsxl33nkn7rzzTvzm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\nQghhJQmehRBCCCGEsNL/AQuiY7PcFL7zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1963aed8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['flare_log'] = np.log(df['duration.s'])\n",
    "sns.violinplot(x=df['energy.kev'], y=df['flare_log'], width=3, linewidth=2, hue=df['energy.kev'])\n",
    "plt.ylabel('Count of Flares(log)', fontsize=16)\n",
    "plt.xlabel('Energy (KeV)', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of flares over time duration (in seconds) wrt. Energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19643d41c18>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qUqtW3lQ//fQTe/fuBWDPnj2cPn2aTp06uf89PDycU6dOMXv2bJKTkwGtA+Ip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\n8OhSeMLm4eHh4dGl8ITNw8PDw6NL8X9TiRzggNHtNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19646210da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "sns.lmplot(x='duration_s', y='total_counts', hue='energy_kev', fit_reg=False, legend=True, data=new_df, palette='bright')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solar Flares trend over the Years (2002-2016)\n",
    "\n",
    "The solar flares activity per year is very important to see the flares trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x19646ec8630>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8GIXk119/XZI0duxYSZLjOMe/FhMTI8dxVCRUW6cBftqwwT1ee81dE/njj6Xe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19648198860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "plt.plot(DATA_by_yr, 'yellow', linewidth=10)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Solar Flare per Year', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x19646ec1dd8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nc2XD4MGDY/ny5QXZdkent7mjt7mjt7mjt7mjt7lTTL3t2XNhDBhwbnTt+nab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\nMgAApAjJAACQ8v8A2R9Kis6t1eEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196464b0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('dark_background')\n",
    "plt.plot(DATA_by_yr, '8', color='yellow', markersize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Solar Flare per Year', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sunspots - Mapping Flares on the Sun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering data - removing flares having attributes null or NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Filtering data based on the energy range 3-6 KeV, flags and radial values. Filtering possible values \n",
    "    which may be wrong or without any solar event '''\n",
    "\n",
    "def filter_data(DATA,lwrong):\n",
    "    ## filter 1: 3-6 kev\n",
    "    lenght1 = len(DATA)\n",
    "    DATA = DATA[DATA['energy.kev']!='3-6']\n",
    "    lenght2 = len(DATA)\n",
    "    if lenght1!=lenght2:\n",
    "        print('filtering 3-6 kev energy range',lenght1,lenght2)\n",
    "\n",
    "    ## filter 2: radial\n",
    "    radial = DATA['radial'].values\n",
    "    lenght1 = len(DATA)\n",
    "    DATA = DATA[DATA['radial']<=np.percentile(radial,99)]\n",
    "    lenght2 = len(DATA)\n",
    "    if lenght1!=lenght2: \n",
    "        print('filtering radial',lenght1,lenght2)\n",
    "\n",
    "    # filter possible wrong values or without solar event\n",
    "    #lwrong = ['NS','SD','SS','DF','DR','ED','ES','FE','FR','FS','GD','GE','GS','MR','P0','PS','PE']\n",
    "    for icod in lwrong: \n",
    "        ## filter\n",
    "        lenght1 = len(DATA)\n",
    "        DATA = DATA[DATA['flag.1']!=icod]\n",
    "        lenght2 = len(DATA)\n",
    "        if lenght1!=lenght2: \n",
    "            print('filtering %s'%icod,lenght1,lenght2)\n",
    "\n",
    "        lenght1 = len(DATA)\n",
    "        DATA = DATA[DATA['flag.2']!=icod]\n",
    "        lenght2 = len(DATA)\n",
    "        if lenght1!=lenght2: \n",
    "            print('filtering %s'%icod,lenght1,lenght2)\n",
    "\n",
    "        lenght1 = len(DATA)\n",
    "        DATA = DATA[DATA['flag.3']!=icod]\n",
    "        lenght2 = len(DATA)\n",
    "        if lenght1!=lenght2: \n",
    "            print('filtering %s'%icod,lenght1,lenght2)\n",
    "\n",
    "        lenght1 = len(DATA)\n",
    "        DATA = DATA[DATA['flag.4']!=icod]\n",
    "        lenght2 = len(DATA)\n",
    "        if lenght1!=lenght2: \n",
    "            print('filtering %s'%icod,lenght1,lenght2)\n",
    "\n",
    "        lenght1 = len(DATA)\n",
    "        values = DATA['flag.5'].values\n",
    "        lfilter = [i for i in list(values[pd.notnull(values)]) if icod in i]\n",
    "        DATA = DATA[~DATA['flag.5'].isin(lfilter)]\n",
    "        lenght2 = len(DATA)\n",
    "        if lenght1!=lenght2: \n",
    "            print('filtering %s'%icod,lenght1,lenght2)\n",
    "            \n",
    "    return DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filtering 3-6 kev energy range 113942 107606\n",
      "filtering radial 107606 106537\n",
      "filtering SD 106537 106501\n",
      "filtering SD 106501 106408\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Reading dataset and filtering values based on above parameters of the function\"\"\"\n",
    "data = pd.read_csv('data/hessi.solar.flare.2002to2016.csv', sep=',')\n",
    "# filtering basic\n",
    "lwrong = ['NS','SD']\n",
    "filtered_data = filter_data(data,lwrong)\n",
    "\n",
    "# include energy bounday ranges\n",
    "filtered_data['energy.kev.i'] = filtered_data['energy.kev'].apply(lambda col: int(col.split('-')[0]))\n",
    "filtered_data['energy.kev.f'] = filtered_data['energy.kev'].apply(lambda col: int(col.split('-')[1]))\n",
    "CENERGY = filtered_data[['energy.kev','energy.kev.i','energy.kev.f']].drop_duplicates(inplace=False).sort_values(['energy.kev.i'], ascending=[1], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x9LY740wTfGZjQcpjEtF4zJNI1cZUXSAP4q8oxAzrMxVNtZj9yTrIPlLSv7yU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IAAAg\nAElEQVR1kLsV7HHfb5PIc3S9Q81w2sleAwycjfGxcMOnoPg5SL3VbASmug2bGZD+4bz7uHzjKMmN\nuOhqiudNCzX8jwO+bfj+xY2DRtjzioiMSBf7khlpF2Uh8SzuwManYUwJSRm1aTQDIY4GUxxvWeiO\nzQlR+M+v240ts+HqzZKcGYB7Db+wazsB74XfG2R16b0EHmPTsHikXi4hHIah4mSTVdKQP4OAvIhr\nOLUX4cNJh8ibhpcdBivmBkPCJq9V43GG8EjRV1MOSB1ef/jYhUm5XufpmNkm1zDVGrQaEVmQnyMC\numvAHlTCcmcrhkwPVA6P306dd9bYd/PQ9ACk32AOkwTO59yvPaHn5NUDK0MgpKleL7IChR9uAgrV\nPjpv8S7WX/J1OhYMMrSrkw1XrKfvqQl9n1GzICONPs17z66JaYAVHc/wcebfpBYwtKsZ3SQ1tNvH\n4VRD2ZUpmP6jpjyXhhNpRCdSu3cnHu+Y9Rsn8/AosKfFuDM+ozxYR8egcPj6nW4DDNB5BFiXiyEX\nlhw0fitk3whWi/c7v4c3jh4chGfZVqbF22g11uYz3WYZ4nnT1DqvWkRwqF/EOGiEPa9YQ6QBBnqD\nJm5s3u6Hlgn4aC90Kov7UVNwfRm2OQN/I9BT8YYkdw8KB6F2MsCKp8EUx1sWRsq2MsJVUSep4iY4\n4RCY4wtbzLXg7avhhh2aCzYj9RRbkO5dAasI2deDdXhtEQDvxFS423nuGj0dT61Mw6KuKuOXgWRL\ntBH9tqymnIomJGwK85rCIy6Hy9NnOuGO42D77ehRkT9Wu+Zau5y0+r8Vg9J+Ekq/hqa/9xlmRZng\nc2sl+0kHexKRC/FXNiiLUenvGwlDyMok8eD3zrpcE08SyCiklkEyhuyErl/bDk/Ij3rJzDqEJSx4\n+oFbWiwbXIztMeYt3sVVd15I9/Jauv+hr/4dF5/0D/TtKHo9fv4sSOM1x6D4A7DLmt/FCXGWJKwK\n4rUxIdnGhs9+ikOP/ke6VwxWP9751Bw2XGngXoJ4wzy8ouoXhnsxGZutVC0QqxWyn4KGlznaYQ5c\ngwr051Tn8Oo47IThs+CbN+sToHQbYICxZmj+P2JklXeYDXR7FAp3UTU6rVZoOh3SviiIKYTrh3Gj\nvlekKhJZmHH6YVcePtwNc5Ux2YPYsnG8aWUDB7GKCA71ixgHjbDnFTF3IP4JrZ7YfHkE3pF2DDAF\n2Qb4MHClc8wQ8MUEHD8MzSOG7MgCkDd7YjyZUzG8ZabCsC4CnrNRaN6DdsFr99ea3OSEr3weL22Y\n1Ckgre4I04fI8bZmgVKNzVghzH3cabb7DImwHbXuveRONngQe+FKCXdmljbQc8UiMt0NlHZOse3S\nHZS2Nxqu9RmwPweFT3nbOZ5zwoiuXIKinl4elr7oJ/kXNwVvvCpKHKJCH1Ap10tQhIrRukhk43Mt\ndf3a1AdMSur1wJrrcJh872gXbPvsayg9t1B/nNoniptYf/GQxwAD6F7ex/p/+k+uOf8oMYbLuzRh\nMrUtdSTkGLNJR8RoVeeMptPxZHBr0PdcLxef+kXWX3wDHXMeY2hnkg1Xvp++PkO2ZbXNvrmzq1U2\nNR3tMDYH7svW5sz0IdRKLhkScIyJRJ3izfNLdrjPR6VSeMZhOyxbp0+A0umBuVmtVkYSGcZvC1ZS\nUPmqqpGeWmDY8GajdeoAfoZQstR90O4R2FjxelMr03DShNcAw7lnk8PY7zRNL632ez10G+6XBg4a\nYc8nrCVekT7dwE8dGkytjhubr0xJSK/3DP31M75jhoAbR6SItLHNrUGPkb8MTXaNGB5RBgyYybjV\n732T6JDB6zA+FzLKczItDtowqWZmsLLUCt+6PBOERKzCNXwWHgdrF0FbI4ymZpc1FCckHBXm1fG6\n7DF9n5mbhnedRebBflbd8yy5FTWPRsvRzWx522JKfbprnQ2FmwkQY+1HoDAjxnDa946S7SI5MvNs\neH+PE3qvlIMGTqJBPDJqWZUoMdrqNWMaYCZulGr8xpWXCJx7RMjKuhJH2TVk5tzHqgcO84hftvz1\nk2xZ265X6/f1iY65j2kv2zHf6aRWI0yPh4c9o/qef9Evj2qMq0pwzkjFCy31bV/ANR/4Zyg97Fzv\nYcivisgiVcZOVytctM5r1CxHqeWollxaIlm77n3HKeWUWmTO1nWTb3Tj0JQA5eqBvft90NYUjHhY\nWbOkB9RHYUkkqRYqT7QG31sHklSpTteTZbh5D4z4NkuJtGyKdTDZeS34PPUW3HkCPPsNzY9LCH3h\npYmDRtjzBWupIwyqxJ/8hkP6VZJR5ifPhsXmVSQaRN04jP/gP8aYIdgK2bVBbZz5L4O3bIe2JIwU\n4T/nw4gyUdklMVxsw+4yiuRddZU7oVCtWnRZdrQqkp2QPwvGb/YZibNYIK0GqsrcDYfJO1PfUwfw\nsbkwXzl3nKwhv0fnnmFYasE85aXsHvGGhOOEeSG4ILYadNLa2+n5lNcAA8j1Zun5VJEnPqi7Vit6\n3Z77gd6QNma9kiM6xCEJV0po78VKSn+z+zTFgWfx3lW4/BvQV5pwjd/8+vqMsMq06IgV74fc2zGV\n6+q5wqs+DpBbPknPJVvN6v2K12PoOf3qN7Rb2YBEPaOwvqcznnVhrmR7kKeny9YOQ2oF5N8rofDK\npJmPqFINQAwdP9HdRCxPtkH+HKASHhJWYUpySLaJRAmY84JM/K+BUbhpCpKG5A+TXI/773pgZWF6\nRGo6+t+lznhsTMIbuoVq60e9VNMJNNGdXvjCx2qCyaDMa+eK17J4E9jf5qWUKXnQCHte0CaieUkf\nAci/q2w6QT+wTR1c93myDZ4ahVfFOCbA61K8Wv5MsQ6ExvPyjGjLuDgEr/FhZWB6WJFl8C1eUdIb\nCbwGRWEMPvcAnHEytGWd3WFSa+xklmXoufgYMnO3U3quICG2PXVk9pngD5NGTe6mRWLqj8Bk7Zk8\nuxm+eCqcRI3rd0/Fe29xwry6BXHEwMkaxVxsd76JxzWGOUPJjtfGAKYQjbAIQ6A8DDO7IWmQc7Ey\nMDOhETCNQRIPQ2VaFlK/qr1/4xQnc9REejdxy+wxMgv1tSmN7wg8ocwN/3Qkh/71//g4Vd1eTpW/\nDwXGa8h71RnPJi+336iZ2WF+n9rjmxSjJMQzZfd7N2AmQ8e0qY0IkXpQHpTNpi7JIdEsc2gHkmzp\nIJMr0LNqK5nGEqWVCbbd9U5KT5e8BPrG48Ay1J01Idkm3uLZwJVf8dAaitCyFND0wVbDvL0R+AuC\nVaVMaCIYkpyTgJNa4eazYewmkXDxzGsrILUSxleA/UVge8yLvbBx0Ag74HAKXut2vFBbhBpPMHNK\n6i1A+929ooDR5ZtUBp1j1N24Uf9KgY5f5EK3s0x2QfMH9G5zexTK/WSWdkpR5AUlSruUEhdWU7Ad\nxUPglvAwZmZxgVV3bia3fBIZ4U0SYnvzQ5QG/UkFI8iONyZ3C2Sicz0iXboHAbRMQun3UPqNKJJ7\n5AeGoXifd5HIrRUvomdX7vMcxEmK0C2I92WgpwRzlXfg9JnSMfqJtPTcnmBtQs+1lohHt1qfLg3F\n38RrYwCOp8a00Fdm5LuJO2QyTs03vy83vDWbrOKKLX8HajW2B0M/EDTIi5vk+qa2hZHeQ55bqe+V\nSC1WL6oFkXVwQpkUN9E3uI6LT52S7Mj5Qwzt7mDDlefSt31B7TqlzdIHdZuuKI5kPdpxCZ8xVLw/\n+D79pYNmg0SzV2TZRHSfRYJwFaqyO+gTY2xHeuREquG8TK7AqjdtJtfsvNP50HJvgS1rV1N6eqFo\n0jW9oz5D0EWiGcqzNEjc8edukF1DbKSE1gjba4gvDgGPIYZYFPYgSae6fVIrEiptOlXK8GnD4cdB\nYQj4Mi8Fj9hBI+yAYw1SQsa0qyxC7nRIrzCfwlOAlmjl4j3D8JnvwZnHwUonM+Zp4HvOMYk04Msi\nCgsL6VzTKvzzhjWXgFJ2slN2eRO3k1nUz6o7n3IMJkHLq0ecCakluHOO4pEBPZds9ZwPnBDbP7fy\nxFlhoqRdsiBEkaqtucIbAdhr+M3A1pou0fjN0ZlYcUKNcZIidOcZAj7fL4kX7c0w1grf74Ah2Hbl\nSlpePeJ5XoU/Jtl22Z6I+phzIf8TSC6rXSd1hHBpotoYgDOZlzbLgu9/x4mULGTN62HqaQjT06sk\nxZgw8bPsCeFfmd6x6XOTt1atqFDaTLAklCFEGmiX+m6zckzpIci+jm1XvZyWv/yd9x35CyJr29xc\nHct92+Ga8y/1fu9uwEo/D69d6eFIaigGdZW9GtfctzI+orhXcVBxNlXuedKHwH8OwSHT3lqO/Tbc\nW8FMVtK1f1o4XjM7vBtXcIynU5WSR47AL4s982LPqq01A8yBJ7ycXw/WLLNrEzkgLVxWXfUJEzz1\neX0bmO8Dy23JRHexB9gYYijfgXj+dGtFEXiOmiPgRISf54f7aBON5vkx1QPWPLDXOBd9ceOgEXbA\n4XQk/663AzihBG29MJqOJna7BWijUB4FGmHqXLgxAUzpB3eAU9RslsGI2pypa0zFDhpgLtK9YLXS\nc+mT5JZ7T1qdkM7r1R+rgxL2M4bYun2TUiItOykr6+zym6INMLvkNRJ0nklX3iMqkcFz3phhvCr5\n3iDuazrPnuEa+ddKOZzETkoTObbcupqek53QyOMZtv3TSkqDrwJuMZO1G4/3GmAgBrNjXNetbWW1\niiEQZmQn0pCJMDwSGa+Apx/lfqTouMaoSlhkFhfEK7u4RCmdYdtPV1J6Jucdk56xkYeNeflODa1X\nf5OB4Wb45h2y+FQ5ZQZZiWoodRfkL4RkL6UdsGXtamnX/FKwILI2YxRIziFQZ9Rzv84GLLM6vsfQ\npRio71erHVfUv0t7WPOZct/59bMzwuyZ2lzj96JZWRjthi/hm9MsqURUDyxXg81HJ3H7b7XtjfL/\nibvk2YzWnk2mMYICMFsDDOT9ZA6Lz7WzJ2H6aUUUeQxIe9/lEPAlq76SRaqzoNM5bi+OTqXv2Mjo\nTlrPMQTZaOXXwfhOmRJf5DhohB1QKMVY1V3vnHb48FxvqGg25SDAqcuFTEIzu2UCjlq0ILhwtxfN\nMhhhDg1/WDTMoHEMoMyiXQgnyIvM/FItPBRor49nVR6Uya55vRSP3WUIse1iH4Rvx2H6KbDaveFk\nd7I5YQzyA5KgcC9QOh2a5+HJ2AursVl3GM/wbOOcxy5JOxadABf0UpqT44lHHYL3HoQom8wZ6jC6\n97LE8PkshRTjKndHwYowoitFsAdrnh0F3jC2oOVlI2x5cDWlnpyQkN+EiKqr9nx1vCoGmF+KYPmH\n4Is2DBs8av6+kT0LkrVNSGl7zkzCT1h6o8cvDKxDsg2sOsVm/Zs2nYe2tDnoXYsMS1OfV616jIF3\n6UfczWscxKqe0SkG7vgtcMfJ0LMM5iQoTRrmp7Dwsoo4OpFxw7kzTwelNXScstk8u7jHREV3rKRI\nbPg1/lwkOyH7Lih8lRd7SPKgEXbA4HLBlgXDNMevgbm+hUvlVtVTNNVqgunfQ/HnDg8pBqdANzG+\nDbMMhm7XMoUIn3/P+f/6mO1t6KXUNwXsCnxV2p0Rlz8anoWnjp8z4WdWO1ISaX2I7cki267ond1C\nr3J5cmsJEKiHgK9tk+foGnm60eTP2nQL56YW1QznmX7HM6eEStWMPPdek+2QyItx6AkN+RbESlG0\nDd1drvq7k7uCNUPVvhdGlDfttBM50X5yeTJxw5L7msEYFxWcNgSNMG0Yu3mSnlVbeaKwCj6IN03f\nhZ8LaZIEeRvmRcm/qJuSCUyaTna/ZLelVpiz6XTw16b0Qzf/7GkjoOOkk0fRhaU7gNPWaopSO4ij\nvefC5WQl8mae7X5E1Uvqclf/uYnSo86Xpv6bbJNns30DfPZVcMYJbOtbScuZI+Tm1Blehn2r4ejn\n2pVHZTwENO6U/rU/inbHQZTBFpWlmjoErDPA/sr+bNXzjoNG2AHDGrCW68UOO7T1dqTT1zvgEpZk\n6KQPDd8J2eO1eoOVghgCiRQk58txnQZvQivhuxZTWaEvWYb2GgympxrZ9pl5UPyWfJB9bS2EUw3b\nKGnLvuda2p5TwjfjlLbvYNslT1EaXKVVNwiFPR1U7teFXtK90HB4tOhhsl3a6/JH1NBLQ69wh8Zv\nqhlp/j7jXqM6ObbD6GK4ZzE8e4fXOJ3cFLxGanHNCGwzFNJ1bXd7zJApN2rOanP7YLKbQIp/mCdw\nNh4QP+IQupNN4jnRwBjGdsNHYXTEVsO/Tb/RwcP/M8wL9l59uM7lnLUYMg3LE8IR8qill/QGmNu3\nuhBbVf3JkcDOdthzPnzrNug3CGf6+01ps+jp/X2vl5fVu9ArVupuIvJnRYcl3QoIlfHw3+0rOiCz\nvsCqD20m16lyVzNsWdNA6Zkpc/91s1QBCsfABosSObbcFBJeDsO+1HAMhGnz4Rv1WRh8AUP1+pWU\nXpk78EZcsgnyV8H4D8B+5gBc4PnBQSPsgKHZ7K7eazB4Rpn9gIt0RTdI6nOyFWgPvnkT2XzUUSY3\n7Vq0HgALjh+BW5q8C4DjWvYaTMqE9Kyz8NmjUDClfmIMY5WeLvPEOQkobq4t+rlZLPQzTwZ3/NUd\nfjtVdfh6kOwMGkfV79ogf67UXUyk9btU7eTYDte9B4bVrDaN/EeyXYzu4v2w1zDkRxHv39Tj5sxW\nXVab5zqayV1Xz9FFmAckbrZcpQQ0RITBWyGlN5SNYWxD+MiDUcO/VQQTHL1QF/Lijx0BUNULPAAT\nd5vDfNk1IeWSpmBmyNl4jYnR5g+tg9O3Ksxbupv1q75OR+MgQ5OdbNhyLn2FBWKQLQeWZ6HnTLjy\nhqDau2nzcGrSXJrrK6qMTVHuyVSU3nOt5poYqg77mmnpjLWek7Z6DDCA3PISPZ89hifet0Ta7C8e\nDrUsVQhsFI3h5TDM1sDXIZEEK8TjWOf6ow3nv3mELT9ZTangGJizpdrEQbIDGv8vTJx6AE7+/OCg\nEXbAMGZ2V98zLt4iVWtqEFG0N/HSZzPgVKgCpDqYiJJ39UFpwMliag+6iE3tat4rpEn19xW7mkim\nnZCiOEnVewkJY/nDX8VN+uy7apv87vqRWlhNhRt6yZ0eVIePC1MdRBCyafJIMVR1ME2Ob8t6J0cT\nTya1SJ7txpTmPVfgzidh4iEpjRImyzB+MzT/XTzuUbVNyvvye0vcMLMrcOmGWt0QbHp5+LViGcNK\ncWsftF7ZsUa2bVkppHrT6f1cyI3ACoLinIuRRV23AAV4ez5j3/VaZ1ZD4QHIHed8VoGyq3xvGAv+\nbMGyLaKc2TUEQusnwrylu7nqTRfS3azUmux6nIsfvFYMMRdzM3q1d93GKJEMKc3VNnuuZrJbvPgm\nJBISwq2UxKCrt6anM9aMZPruRkj3yH9M43V/hNrd5AuTga/7PFYosWLm1NVp8GnD+W1OOP8Xzvyu\nM+LcdkYR+OMg9do6D3hh4aARdsCwCWwDYXDPMFz9PTj9ROhaDpOWzIthm6R90baJA1PIcbixtlO1\ndEKyhhp+Y80aqYlMNKE2vVL4UO4irOMWhRXRzq/ziWmOgr3HzB+pTDt/bH0Kuqf9rV6h2npRmQIM\n4UAXpgVjX3fDiYQ8S917vmsX7LhPwkEmo0ZdVEwLjwlh9RzDwpWFHU7qvs8Iq5ezYmWNnjKPV9af\nHfkzpHSLnwv5GJIZr15zCClv57cpOoETbdigXF/VmtKVAKt6WzOI13qJeJU6k8p9r4R7zoE+b33I\nKvyeINeQ1umatcL6VV/3GGAA3c07Wb/q61zzC5/MRefyID/MZHSY5q2x5tllRFYq8Qj5iaRwFWcj\nYuqMqVhketN4rRTrGyd+3p9dBHtIEig2ZvUb5Ht92dJxQ4mVSSj8EPKnBcdFPQYfMcL5LtR5StfO\nLsTbOiuvWQvCwX5xEvQPGmH7HW2INlgzFG+WCdTDJSqJgVEEblssmV3rMZe3ANG22RiRAaZDvYuV\nP+RYKXt30zrc8RQs6fB69XYPwr0ltOrW5X6YGZewmW5htBpFbsDPtUotVLwm7WZjThf+Kg+bM/g8\nHkJ/3MSH0NCPA7sEWIaU84Q520eFf0KulGHUwDuLa5zPbK8tCv73XBp1jKOQ9+xyxaJ+50dUPcew\ncKV7XRWzISlHSJCEhonq0eczec2adte8ybOtodmZ1Nx3K/xrP4z4BHZNfcxVRy/v9r7DUehoHAz+\nHuho1NzsWF6z2dFsjDqQoeUUR6hi9yDcUyBU/d6EesOMJr6myoFrQd6tq2Hl3NK2LStp6Rrx6Htp\nyfS6552cL0LDcRMOyrudcKxCd3CzeQdK8IUynJR22joFdz4Lux6G/Dtq9xgnlFipiEc+d5x+XNQp\nDB47nF+klrzVCXT5KghMZsT7vBR6vrWVzKRPxDsMlSn01TxeHDhohO1XOBmRrkVlI4ZD/j01D4OV\ngeb3ApXaIAjzZvSPwGfvF/mDeiagOItVmJEWNwV8OCUk29PW1LKfvrcZSu/RKyq4mkFR2lz+yTPZ\nCc3nRB/n/tafXRhnMowyCEJCDPMW72L9xTfQMfcxhvqPYMNV59XUyav3lBahzExEyZaEUxPRFcgs\nbYY7joFlPoKzbnK0ixKGUflZaohVJ2WRIPzZ2KUa/yjWgjIsXuD2omQHtp0s/eIXXfB6gv3N9X7q\njBM/b6xezqTJIIkrbOk3WMPGhckgnpgLqUZZlMsGUnvUszXd9ynz4Utf94Z4E43QoMm6c3WX/GHx\njTB0fifMDx4yNO6TsnD7nLZygE8H8YKKNxN3qgIP/xFu2QiFNZCJCOurOmD7EyZPzApkjrxF/i7N\nybHlwdViKKRKlH7r6On5jQK7EORDJttrUhWxEg5yUPiajAM/3cHKiJPH7YflvTB+v7MpVOZJ0zry\nsgpckHDGXAKGGjF65OsUBteG8/c64XwXZaSCi/IIAhUEgNY5Q0CCxnyN71cV8Q4zxGa2oy3/9SLB\nQSNsv2INAZdWZnUwxJNI4FHZDtXhGoa+J6DpSf3EaoJxsXJCI5FGWlzhvzEh6aokW+tUSOpEG0v1\nlzxR4TfATEakNU8WBBeqFy29UrxtJoRxOQxh0HmLd3HVnR+ne3mf88lPOHT1H7h47bVeQ6xScsjt\nc6ONGb9A5vbb4crWmrE73gX3NWsM6hSMlOHeUXkvftV2nYRA2PuwizB+m5O5GYPnUh6WXX1XHj62\n3KuFd7TtnXGq/c3xfupCk/7MuaiwbHkUyjvl+ifloS0DezViyNN/lNBymFcywBccFkOqGiYves+h\n8yIUgZ+56v9nS008nSEW9WxN992SCEpFNJ2u/617K/5+PAQb3n8uh373cbrn1EKSO5/qZsP6NbB6\nDNqagwvynHY4XpGe+N5dMO48m+Pb5HsVDQkoFqRfWhGyFOVBMSYPxBIVVgFkDvAaqoZIqTXHEz9Z\nZTZE7CJU9qJ9QdW6jDdH898qY7VjouAawP7fmtaR1kSteXHCfbokLMNc64bz1/zrf3Lm39xGNj3J\nWLKFaxovZoC5+kQP9BUEGvPB0GZk0XrbmVPjrlcvQBw0wvYrNLsd/0DRdeaNSBq4LpwxPhdaPipq\n3yaVbB2Mi5VzfKRHIYbXzQ01xa3ZV95FrJInccKooUakpmRSZrVDrF8brq4e1rbiJie87OW6rb/4\nBsUAE3Qv38n6S77uKxvjPFN/sdz0ErA0Ok/+vjMwWiNEq/fheRYp+bMc+IINz9wRre1kyjQrD9ek\nLcD8bOwyMO1kKqZEruIUgtHdlK/v+j1YJk+kupCNGvqYe4v2Xmi8Hz6yDuYr41FdfMrDzsRNuFdS\nNcDsYs2TpUqlpBbU+sMQ4kVRtcWyiHP8i8CQUxNPW9IqYkyYFtghTc1AUwKI+3lxE6SWerw3fY8s\n4OJjr/XWmvzMqfQ99kPYkg+OmQ4cwWnFA6FKTzSvRzsfdileT/84SKQl+7ZSkazOhmWGm95HRPEo\nXVmeOKKjdj/YE4bvFLmXSgHaUvC2HLSlg/NaeaR2TBwk22rHuNBtAvyIK22hImLD3tu9lQ+87gbS\naeHf5ZqKXH3sxxktddCVq4W51UQPU9KDDsai9eoGEZsXKy/soBG2X6HJ4lIHVVhn/jeCwpB7gPua\ntLz3SEQRLKM8CvYIVFK+0JXj5fCXXcmtjRemsoeASrg0QVzOT71hKdegCSu47HqNjO0fFT2vxuPk\nHJUKzOygY+5j2p93zPdZjsnWmpGhGhomw7BS9IZVVY+WWnPR9CxOaoevGsKrKgk8NU9z7bIYHf5M\nU917s5JIJ1U6b9yEAf/vTIKlrkfsnnOEC2XirFh58RbO97XR0zcseYdWHmOpHT+sLGReB6kup07g\nlHhU/fIEryG4mVKvnWjUJye4pW48/FElHKdbYPun4d6UeL48Y9JkLCufawy1vu0LapsGuwTjt1It\n7Oxv24llr5cTatITN9wpVSR02OvzelaFap2NnJuEoasBur8QxaP0f29PAyV9lq49IfyvwOclSHZB\n8/nSd8LmtT3KvGOqpeqHNRcKP4LUYjLLMjWdrqE02x5dTKmtFZosSGsWj3oz7Q3zS2Z9gZ5Dt3Lx\n2y6vGmAu0smKxwADb6KHyhlrYYTX8VPyjDNOnod4LXupbXS1VQXKozB+ozI/tQJnATfzYjPEDhph\n+xWTBHZ/pc01krlpsXw78FXgKsweoHpJ9j8j6F3rL8NGZ1BGGWnJLpjeFlRz12WxxXGh20UoPgQ4\n4ZOJuyB/ZpBfE9e4MhqRpvqKikFj8vKV95gzI6vnGZU6iQBdrfCeNQy1r0RYvV4M7daUhklrMst0\ni1xl2ilUq7xAd+ECb83FMIPa0nlnY3guE0nIHAOF22uf1SOqGTdhwP+75Lzg81Gv/8yN8IWzxMDU\njYXKmITHdHCfU7LVy+Gxp5xsuojdTkOv4h2LEL01fp7QJyc0nQ7lPiAlhl2lKEhaS+QAACAASURB\nVFlsLjnbw9WxYdSCjWkYX+klvacWipyFP7GlUobmx+GstZKRvTcTUYkjI97j4pgY63YBsBy+4Rxo\nNTyr9mZ5f79YCEcR3FS6xrJbScIewVO78PlQa7+3DD1JvcdIx7OccTJZ82d7veDlEUd9XjfGMnjK\nZIXNazc3ybkrBdmExOEqWlnIHkNmSYlVdz7s1el6/QhbfvJXNZ0uP0xjU6d5Bto+nckVeP3H7ufN\nLQ/QWofR4yZ6bPvtSjoX9tPZMMg7+Tbtyjm62cm3OIO9tIVUFaho5oh24ATgttjteSHgoBG239CG\nzCA+ZFbXJkPTBH0YNS0hnRdnOUEvWVhsvwMJgai/n7Th378PA2+WCSIqCyaRdNTcB80SAi7iuNDt\nfu85Mqv3TafG1JyBp6DcHvTgqWEjE9LLzAaAH12tcNE6mN/JhqfncOiKZz0p/juf6mbDlecGj7Py\nsju29zhhsU01o7Tp7WC1SMhZp2/khuvcf7sIM6iTc4P3FJdg39ALRd+x9ijY49FGmK5/zdjekKRu\nwbOSEdmSo/DMzXCjwRBMtMPYjP5Y03OyGsSjkYgo/RMnMSbsXVRsTDUsRSdONR4bwfYtotX5IYSS\nkOx0Mt98RlJnEj78DpirfB7FD0ofEhT/jdKuGh6DhSfAWS3e+aeIhGrVa6mZ15XpfSvPEweVGcCG\n4YaaQavLjvTIOUwpuoF+3lFF7x3TIWxeS6SduSlifvIj3UvPZY9pdLpKXp0uFSGZjsKB1HyueddH\nHfULzm7Z4DGe4mCorwMGoNSVozCaZ+2cuwLnaGeEowu/ZMOPztEnQoBspptOl1ficRKs4MUWljxo\nhO03HIeW1KV6iUwTVwPiDSuhLwukq18XFnrT7boaLXjDUrilXzSz4mbBRGUMQrQgKsgip4bWTIt4\nlIfOhTY8MwW3b4RxvFyTOAYYyGSYP1tCjolm4e8ksuKVmLhDvCzued/VVuUc9RUWcPGD1woJdXqI\nof/tYMOV5wazI11YWehaDCcuhtbDYOAZ+M9OGInRRp3XMcygtjKilq96tOIKSSbS0PIheYaqhlqc\nhUftXy0F8dz8V1bCda3AqCO7oltco9pnj8r7aF4X3Lknm+H7wPKy1+AIW3yASGJvXD6m6V3ca8P0\ns/WJk1qZYLg0jhq8pVnxT8T7PCCaH6Qbz+4zMIVHv7MJ3v3B4PyTRd79U4ZrJdLaeSuztEDPxq1k\nGkqUhjJs+8RKSo8YPDzLERmExgpMJuS+nkLeXaUkhl6yRX475NzDicA0Mr/cW4Zhn1aXyznKrQ3O\nWfVItcSd1+pBIh1fp2sCeJRo76cOmnd9YufGUANsxk4wU06TTU9VP9u5t5sN3z9XsiW7YHI8R36O\nvvxUqzVqNsDAcRL4+JzVxJ4Xl1zFQSNsv6ANo9S9rXAjNgJ/gV64/gi8G1x3B3giAQPs8M4tXHjM\n1TStHWfiY3mu/T8X8eiCVTVjyjTPtzc7EhEOmTYu+dS/KOpqC47fpg8vgrjtk75yN7aBM3JvCZZO\nwTzlmroFVGdE3jNVK6dSuFMJuznGTZxQR7INmj/oeKKU0FPz2TKJuwuTb/7tKywQUcvfO+0Kg2fH\nn4aX9cIhxNvxJ+foRSB3IhUXKsA2vGKifgmIemo2JtKQTEPyMEh1w8xOJ2stBoaAG4elSLlLgK8u\nwpbZoNAlCnj6XFHaoguduNf9UlIygVtsMfjuawh/tjPbxWvo4WRNOSEakNJIMSUt/P3y3jIMVWZH\nNLf7YboEDcudkGkMb5zuN3E9zHGhHX+TMv7aDMZqHEK8g0yuwIqjHqdzwSDJlC0fLoeWu0fYcvLq\noCG2HPgIzoqWgCbn/9cBT1lOn1X6rdbrloTrpqC/T+ZJlX5h2hgkGsNlS1zUqb8VF7F1uh6lPjK+\nCv+7XgD5hLl25+R0hvGpHHOahqufFaYa+ddffJK+pQvAcVRv27KSkYWtLNUMq8lsjlc98EuGf9wZ\nv8Zm1WEwwIvJG3bQCNsvWIO2LIrVKouFiyFEbfsvNKfwz1vuLtU3cR3euYWrj/0E6aQQIZuXTXD1\nNz7BRQ/8C48OOu5nU1m14bFwUrwJ6qJtUj2fuEs4LKihi2nRxYJgFpqV1U9ewxn4EnDCBLTnYDRR\nM5b83oiAhpOv3WrYrZ5Qh46T4ecM7cvOdl8K8rpeKFfQVXdfC/3HpLxk6Nn0AQhyqaLgZleaJDBM\nBoXfKRU3+1bFEI5KvSXPyh41t708WAs5uYZepRjcOMTFoA03FYCEY8w3xEo21rdtQniEUXy1KMTt\nr36h4DAExJ0b5V0Nj6MNrcUkxOs0pFzk2ibp+cpWnvjmKu9Gaj3B1SzlfP5pzbWMZcAa4KvDXs+/\n1QqWwUvtbsrsooSaE3mwcgSKpvf1w7+Mw9pl0Na43/huWp2uEZ9O134w9jzvej3sHFjEysVPan86\nOe01wAByDZMcf8i9PPqLVfJeKlAq5Lj+xx/js397IR1W7ffDtPEQryUzZ4r5p++i5dUjjLx3Dpe9\n9iq6259j5/BCLr39Mzyzpyd4casZWYvXIDvRFz4OGmH7Bc5EbbUy75AjWf+pH9GxYIih3RU2XFWg\nb7sz+Xcgk7FfQdoEd4ep4MJjrq4aYC7SyTIXHnM159z7LfkgS7DuXf+UhAo89emahQgalomkylBk\n1whZ3P/7anFq30TlppwbayZW9GGe4QzcqtnhRYWDZnzp+urudV8MHx3i7mzLTvq0r0yMFrrP7ZJw\nWZI+vlIiKQvmiRqCsem+kp210KROHiC9PL4Eih8Blf9pyaSF+jxvEAyDLTwOTu00ezCjPJyJJDLr\nP6zcL8EsX6gtvrrwk4J5i3eJnMOCQYZ2dXrDzwkLphyXnybrNfRYFa6QbpiXpTwO5e2QWkao/l3c\n/mrvDTc8w4y0RFr61Le/B8vPrjMcXGtjz0lBDSkVmbYSvMz5j7uRaqygtXRNjyRsDKrJM3GrRFhZ\noMvHnyuLMT/zrBj546Nw00cgGdOTHAOeslvzS5R2Z9h2/UpKr8wExkNmcaGWRRlXjV6HjXDjkeex\net0vyaWDO/5EQh/a91RfcF7V9sHl3LD7A5zSfVc1O/K3rPJkSz4x/xAu+9Q1LJl+rnr40St/ybFX\n/ihoiFXnmjr5dX9CHDTC9gvaxAB7+Zu56u4r6V7uErT/wKGrt4po58SCoMfCkMhXhTuAlCzHpga9\nG7ipwadVsxMpiFoNiSSgcBzkdfISBt2s8kgtGy8ym85gaIVNXrGKL8dEpQLFn3s/Uxf/5yMcYyqW\nWxX4bJeQ12hMxXU3DTt3MiQ1u76QAsmZpQV6vqyZcFWyvZ/nlzs9Ws3fD3sSprc6tT6PqYmXJtLC\n2UjOFS9p+nBHyiLOOZX31tUKf9/r1RxTPZhxPZyJTHRheBUR1RGuuvNCZZzDoa9+3CvOazXLNeMe\ne8rn6HumGWb6xHMZR0gXEAO/W3stD+L01/KoU1PwHT6D2nY0/vZCJQsNS8yGmNUM/Tvg6pvgneug\nrSG+18dpY+at4RpSnlDbHEQuY2IMmjQLr8mWC/MMWnlo/oBUrLDy8b2hfmO5MwknNgnvc3Ql3N0v\nSQH7CUvnbOOK0y8T79AjC7n0Msc7VLHhN896wt+ZxQVW3bnZm0X5xhG2vP3VlH4XkZACogXojt8h\n6LtqAf809ln++fyLyGVq59w51s3TI8s4ZvHPA6cYmtQkrgFD5Q428jZpEyOBbMlTn7zHY4AB9M7/\nI1ecfhnrrr+19mGlLPMQAPN4sYQkDxph+wU5yK5h/WV3eCZXUEQ7f35p0GOhGGCBOlq/WknpZzmZ\nmBQNsYmpPM2ZoDjgxJRvIA3g84SkvQts+hCYuFsWS7e0i+pRGJmCb98PM2ucnWEEGbtSJLDtfD7S\nzV0kEuKNUxXNK+Xazv1AEGMHK7BB2X3rvATJNmlXZVxCFeVBuKcJluVilCAacjhcpviyvv2ZXIFV\nZ24mN0eZcNXyH24hZz+vL66av/cGHQ+acy+6jM7MarD79FmBfqi1Jrta4ZKzvc8JvJ6+uB7OypRZ\nc02HEO/d+ku+bh7nVZ0tR4Q37rGX3ugca0tiiNu2qALULtE8DgLhQ78XugINrwr24YTleMjiVHpo\nk6Lr42PwtT/Wb9QPQel/M8KR1KAw5gu1AbRMww3fk76SUtpexuzljvIMWhlJYJotApuDFPR0BzYH\nsbxTGu7k0jnbeOCSv6V3/h+rn3m8Q6n5HsmJnku2BrMo50zS8+9P8sSJq2LMy2U8gpVD8Ojlq/jg\nf3xdvLqLhxhKd7Dhp+fCGCw762m6lyrZ4mPdbNiiyRYHMpma0f06fhog/LeU9GOxu91XvD6RdES5\ndyC0mBcHQf+gEbZfkACrmY4FhiK484dCPS46DkRL1whbJlZTuionZGZHQ+zawkVcffYnSKdqk/N0\nOcm1P7+odsI4rn8rC/nTapNwYNJogJ53CME5aoBWyrKDzh0bzcG6rgLDsyXIRMAtDaPbpR8IYmyA\n02SjVdZNtlMNWSfbZXN23bDwT9qazAZqexHWrYWuJbBXfjOvyRfKuv5c+noWeO6r59CtHgMMfOU/\nrHY9r2/8FiVM2e5odik7d61R7RRAbzhMFnUdrGYoD3j5kSqq57VhYBhuRy5y0TqYawgrtPr+Nn0P\nNUNGDQ3qSiSp0PHmnHqToeMcaokDGs+f8di/GHJkatpq2cjJRZD2eUD3dWPjOd7yHp9sM3sA0xEl\nnkCMBVV2ojwivEBPQk40iV3HcypPJxh6dg5PPnJoUP9qZFLG1F68KkF7CTpCKg4FYihZX3F2iKi1\n68tijbE5yPxNgVW3bCbXZtgsudBwJ684/TKPAQY+75AvymDMomwrxaNkGN69R+BXwcXfu7ZmnM3t\nYMMjtZJFVcwAKUg31jIo8wQjPcWMvr/sHNbMJ57+q4kevABx0AjbX7DHGNql3yUO7e4I9bjo6mjl\nmifpeeNWnnh4lQwQZxf7KKu46Hv/wif//TO0zR2lYif43a+PZOC/58pmpZ6JWd0F6yaNucmYA9RJ\nF1YFWI3E10To+faZt2AKk8QOH+4DohYpFSPtcPOUORzd9v/Ze/Mwucoy7/9TVd19uqv36nR6SZqk\nknRI2JrFgPCKDsRxDbK5L42Iw6gILoCOMb4yCsGfQVEQh5nXjNKoo46AmDhuhCAozhC2FjANHVKE\nJL0k6equXqr7dHVVvX8851Sd5XnOOdVJkPf65XtdXpKqrqqzPc/zfe77e3/vFFyzHBYWSVDLKYNs\nXHudbYe56jU7WX/5LQyf1lY4Ly2umHDN9h9misW2qDTBvW8WPSqtLuZ1VxWjpE5SfQoi7T2O0HhU\nhuXX1Yw8OT2nYghrlhMw6lrCRqXoh2HPoNv13oqU4/9V7+dyQivorEyMNAnX/HymGB3Td1j6Qk4U\n+42GG8U1M6wfPMc5GAugPNWu/Gx5TFzf24FDK0Vq2CnIV21s7sZi/YH6uQ6SuvXSe0ngOV4jDTC7\nU1THli0WZGJuSFx7jxSfVOd041L0dD1cDS1LBos9CUdj9NzayvB7L4OY49gbcc9foVDxHINWh4OP\n433Krcfz2xwsg/i3+20EDCS9EiXEr6V6kNe+1p3uA0l0yIBnFaXsWF2/GypprrSRs88gmqM7sR+o\nhsyMBrUi2j8p6TrTvyJONDVD4/R44bVdB5az4WdfdX+nLYKt8f9CSvIYCTsiyMDMg/Tc+GFWvWan\nLd1QMO2cQmJeCZRJPF0MaFW6fYAYA+PQkoXMVWpUaKLW94xzn2DjouvcDaNLweFqpsKNIu1mLrLz\n+D6pbkG2M5wvSpl05wsztRsEzirMGLAui3bcGPG37UVrmBWp6d5O9HSU7vM22wgYGKmsq+w9KvVz\nNDjb/XP6kGZEAubki8rSFXDrR2B8kXj/gjzUzQmSpeEm1ZUIawAnrAt7PivK+KOX2q+L7PdNNDdA\npUexiDWC6ZtWCkO5s1zUQPnx9o1IxUn2f5tVv9UX2UhDz01XqMe5D6SfNVM1hUhJpTyNp9rY+Bk5\nmwvq8bjH32EUpwQar2VLIFRWtPeoWCHGiI/fmb43Km3a3HLvIBt/fl2x0XgrrPppC+vvX8cwkrlv\nvppPJwmpQH7t1+cgW2f3JQP/zUE3aFGfzZJkjJgbsarUFAy5PyuNDmFEF88bs0XIC6ld57EeadPc\nEeQkbBRaQoNcUnkPzRwsiPLbGbClJFPROm449XOc8+hTNGeSDOTa2ZD6KnuyjkiXVcoAiJ3hqz8l\neYyEHRHMQC7F8O4w6y92NMG1Vj6ZkZgWhI2AcfVdni5YXjcHiGVgdJ+9uTgJGZA3jC4Bh6uZCtfb\n9Snz+D6pbsG5M3y1Y/YlqFgSzFPKCuP+akt0utY+605Nb1tDrMonDWZAWrZutv8IhSAcU0Q+y+Hi\nDkFgCpOwETWYJThsvRIjUCFpO+L4fZcm8vFO9IyEeB/CvhgEiXCqSLGzEtT57+Ym+PAHoUGzfe/w\n3jbvce6Bwmd7NhNrSZKcjtHTa0nVeJEG1XteRs4x4Oo8NHtIAOZJVAKN17DlHhbMVMuFYP4+oIuS\nItPdV8nmvmG6z93Mphckc1+KYCncfFa0aQtHoXkhXF1mHx+q57/SeGZsvmTGb8Tt171lZpDu8zYT\nu3SEZGsTf8pIdkoYm6WY8X2OVqrmRqw/HachNU71dFEvumt2ORt+J4kOYUQX37mG+L/2ozXoxc1d\nMip+o1scs1adJr65H22xbtsA0gxcAnxPcR2ssIr4Qb5RGoGWEwbZeOl1tk4j7QywnXN5W+63VIbF\nRdfIcFrkGT41fXtxnDijnPk50WLPBbcu89WGYyTscBE+GSqvEDu+cDXDe0NqImS6NH8REQEzFp6q\nmjSZ2QjlFUWdV3qiisT2zuKO3rJoBV2MS4I0opAXYWg/ZEfdLt3z0GApdQsLk8Edy48mbIULOmyZ\ngTHLeeczohl2qQQMCvdXmZru6ic57ZMGMyBN51jTRCG8I5UyglbqKZVozOnSRDaN0fvrNehZyyJ+\nEPluPEiEs9Tnp7DpsWyQLNEAlRbG8/uMZ2c41camH28QBMQJr01PKUUk5vVdhzcBA2ilsAiXEulQ\njtdWyes2M1UEafkodleJANGWlqXydFsskxTPh5M41QCfwm5gLa2eNWxMwk1wQdn8nn+rL1kSuD1U\nuOctkUE2XmaXEqyefI6fTL6b2ZpiKjM9VkXijk7x7El62Ztz/0y0ih1ndNG5K0GlPstgvpV1u/+L\nPWfHoV9+ePozUSHCX2dcj0UUo9nLQDvFGIdL3BtAPR0VsgGzvZ4XsQ0b9jmExJhLgnZvmvg3+tFi\nOvqYRuKZTrpfv9lGwEC0LDov93CBgJlojQ4Xmn8XYJ1jQmUiely20KH1rOLVnpI8RsIOB+GToWa7\numLIfFCt/ckaAU2+8MxlIkztr2Y6VU3i/k70nmjxwbY8cEEX45IgdcBOw1i1/Vysg+5QFjLPGa7q\njf7f5zPJK3UL+/bB7PNikKkGfz5viGO1o0PWXCF6DZZMw7f7YXxp0ZYhUoIuzArj/nqlpnv+6wpW\nhXfaq44UaTBVOkfAo/dfCjWBCupvZ35PwPelxLNumviZ/fRt7xLaEVlfv1JQ6jNxuL5yVtInS++M\nGP+zDmW/QpGtiLSO9TMzs1ApuSnm9Q0S5aoHzqTklJNyvA5JXv8w7tXGyQ19rm9LxyDx1S9J30vu\njYljfw+wChHArUAQBydUv1NuNGg/nOffKg2zbA6673RLCdpqhjh971/4xcGLRAQ4rZH4VCf6aVF5\nmh773D8TreKZU8QJPphYy55dcf/7bR5TN64UYfy8fhsBg+IGsO/PXeLc1+GIlJsfxq0tbMzABSG0\nJdPuau3OMRbMHJQeYlVYXg1u8xkDMQ5MUmjC1WYvzKvduPUYCTscVH7Hm4A5H1TLzka28JSVZ5k+\nVE3fm7qKZKMJMbAs81pP7xWsWrAzWMPoUuCMKOgvi51Fc5Oi0hGhSwgp7CtK1GBJ02gvlpHYsA/0\nWWhdCVdHFIM/dERNEF2QLcqtDXBhHn44D+LlJJPGvKNMTQ9oDG9sY/2355cGsyEU8o5UrlN87q+I\nhWgVYlOhQmBjTpGu8dREViLIypHS8gWtLpyPRjI3I9oMZccgZHiygfzZaQKeBl4McCxemJyGibTQ\n0ZmwXn8VGZ7NQ4WDBZWoD5OO14NGJMeKGMG9Mz2ub/cXN0tNXNMTVcW5bznSAuVAvxMKiWNV1YPs\npNjfdwny2guFL5mqKraeFH0PdInPmZqy09WHLZ37rfYP5v3OZ0W/XpWFieT8Pceh9XNBNigx4Opy\nEd0/W1KtXTvNrKzLDDCDRiXuY3H5jC2gWMxiHTfONm0Ssf+rCcdI2OEgrNiuQOFBdWldjBy78oFv\n00WYvgPlZFBoGL1qM7HRJMm9JSzGQReh7IjwjQK4/BpoduplgAsi0HOKv5dRQMirotrR9z4LNReJ\n3zuSrvelQLU4NMzDcNYjMpLo7aRuwZhtsUlPVJHY2inSYMkS02AqeEUqVQTN7EcZAzYgjwqkCBZN\nSQK3ZeCCCvQzffrfeRGfUtrslCI4DqppzM1Cdr/beyxcD+VGZanq+KtQa2xk49TclFmxoB527ITn\nX4YFnTBeZR/Tqns5EZIXVZSgD9P3Rum9XKIzuiQqopfmMazD25TaCo8IqorIvLQzLua+blRFqcF+\nx3w+JGlADgL3UDwnZ3oVRKGVYh5SVsX+OQafD3BsBgpzf9dmYlUOTaGVfIciQA5yk5APiXSdtTJZ\n8hue2mTr54JsUCxETbXWbZtey5LKl2guL97XURrYylt5y8TvaK4tvj4w0c5PXng/q87uta+lzVG4\nFtGz13zuw1XCjqZ8pehpnP2L4oBfHThGwg4HubT6vXp5ynHB4gP0Pni6+oGf1eS9Ja1IwfBjbWy6\nbkNhUmjpGOT6O29Ut0Ix7QBWY188ZYtQbrKYVw/X+w+6UMS32ikoXGm07IQILzc3FVuVOGFMmi0n\nD9L9jc3EYiMkk030XHsFw8/Ms1rUCdXEOCYxqfWDR2REL4vSu22Nm7hnj0B1qBPWSKU1feaXSk4i\nogKybOfzBIvm5LPCPbwHErFO6j4jIZ6mKafq2mdTMPXzoq1E2WLvytRSUoxBNI35rGhJIyOBuZTo\nm6qtLr1IRUUWVf6xNR1w2/cEAXG2SVLdy3XISViJ5sX6aVH6nnQ8COY1NX/nREVLISd8IqgqIjP0\nUoCihiC/I3s+QJCru7E/17sRmYBuxPC3RrIkUFbU3ni5+49lz57FK3U43VbURh1CbOBkG2prG7ls\nCvLh4viQ/EZieyd14TFbStI6DrWZNPHz+tFW6+jlFtG+CeuzY7kXqrVuaKqdLz71da4671s0VKSY\npIaHOZeRiSZuuPorXHrxvcROTpIkxk/630/bOXulBUs6UXk6PVwpLJMmvu3uK/wqwjESNm80wMzD\nULZM7nmTUqccTznvSf6y/XR5xMPpBi3DEMUJTiH6FK1QNjG8r93HDgD3IpTZXdzRV54vTB1lsA66\nI0DApJgbgpZl4vhVk2w7tPyvQTbefR3tDcY1WAar7t/J+gtvOTJETDYxDo3Bz34H+XcGj8aAd2Tk\nedDro0KDYcXhOPsHgUnATDLtl0q+B2inNPPbQnQnD6lIYdHQe6L0Ll4jJnhHxNjzO7MHILvPcMgG\n6j7rrckrJcXoRUTzcyLVA8WUIxQtLQodG2YgOw5b60orUlGRRcUQ08rDxH98DlrLAPqBHImbVtot\nXWT3ciuwNA8LLV86H/NiVequFcucE2Bu8Imgah1p/hQ9m67pp2iyaINsMgzVGJlBbA4qmV8Kugzh\nw+YkWLuRNweXQFlRu6fOnT41n71LEJq2CuQpVlWhigyRenvGwmwR1Z0mfqEx7nZq7PzKSSz6wj60\nGss4HIuihdJ0XbSDaEwh2ncWcVnuhTK6b4zxm+77Mueue5Dzqx/ibfyaydoafnfjG7nzTx9nUWgf\nWpVO2zkvE621a8VsejUQmafuNPFVDt+6Xf8M6UsCXKS/DY6RsHnjfNHKY/IuqPmIO/e+FbRr5WHY\n8oosi1buK0Y8KnX0GcnOQoVpbKSq+2yFf9Td/86m72+QezxZcTxFgWM+B5QLt25tjcivH0m3+VIc\nv7MpKGuGd9R4H38ldG/aXCRgBtobBuj+xmY2vWke6TvZcd6WF2azhcKFPEyeAhUlEDDwjowcDWd/\nL5Riyuj8W6dJ6B+d34Xdq8q5KJ+CaMm1G/SNUWFMbGogx/Nil+/1fJQvLT6n4VpA3ji4gFIjUjLy\nkh0TbYUqz3dHnSJNUPNhe3l+Piu+525sURPtgTTxjQqT03pF5CiFOEWrtcdMmq7LrBVtQ9S9JuXv\nrTeShW++DJfES9ekZdMi5eMlYl9I8NQgeEZQTT+yimUZfsa7eD2PUDWdZu+DS/n++iuLEX/Z2Jmh\n8Iz5wmujY55nqR0L8nkgB6GIu6I2pwOZ4r+d3x1CkfLPw/M52Go8Z90YG5uQ9/E4NopatdHezNT0\nrTT6ST64xr4OVRi6rphEtL+in77HuoTs6poc9IQtFh1AM+hpRXQ/HYVDcPKqp/lg9Y9t3mDtiwco\nf+csU5HiA1bHmK2x98Ocy2hVUSemRdN0XbWDaJPDt+6C1eh/7QD2Ki7M3xbHSNh8EF4ClZ+D8EKh\nBckOu0lYEvSDmtB2SSCqYUTEoyU7SPfrNhN77QipTAMP8zqmymvtD6uJg4jBaZlolJYVLUkRpvXz\neKrHInAMg7YabXkH8Q0vorU9IxaJO4yqncMREQfV5JhNoU1xc4A0Q6xOcQ1iJR5kDPsO1HacTrf/\nRiibhx2FF9F6JZz9TSzD3+jThN+983tfFt2pNH5/I47UaB7yun+D91A51FwmGl4Hgey6mxVvzior\nGXJTMHWPIGDlioi1s1VRKCK++0MU0uZas0+7mpQicjQC3IXt2ZBWtAXx7h1HkAAAIABJREFU1stN\nwtCzcNcSe+VoPid6Dnpd+3C58MPT4sLIV6ahUqV/0nkIh+zPnM8mw+pHNk6DaPZcBUNTbXbJRRL3\nRsk5dpyyiXyWlkXP0v2le4gdlyLZ3EzPs5IWO6bfWKkmpqEQtjBWbg5yI0LmEa6kUHEl+26V1HYo\nBD0R+cYmaIVrDOKbJT5vdfbokqlpbmqXVzJqTUbxDCGoCsGngX2IbI1lk6a3RulLS57HEVjXtdXV\nL7KRMc6LPOzZ2LudAb6T+WTh3/GufhsBA2MsfGmYvvddAnzb56L8bXCMhJWK8BKoeQAiljREPiP9\n08QfOllw8gFi5SMuBj80LQZ5S3SQjedeR3tTMYpzCk/zE97DOA3Uxcbo/d0aYar3IkIc/QH77ygt\nK8xqkiA8wZKWFDvPJ468c31QTU6mX4ic664S/w6QikuON4n0mPP1ZAm2HaWmbQEUFT42OCf+AJor\nbbulHcw5RqRkT1g8awtq7AvNlgD9OHNZyA6IBsuRWnGuTgJmnqdpOmkeT24W3uaowDP/NmgjbS+j\nUWlrmYBhlKAEDIrX3aqNrEBoMBdRXLxU/Q3nXjbc80tpcI7r2sS7fNrV+JF0y7XSLi3Bq8uKSD1E\n3yY3rM2LZ0nZkihUDqEZUbxzqEmuLUsDsqni2ZBNSiGNmBbey8LWSGl+ZCNz0OMYk4XvzMDIfvh1\nK4yK56vluANsvO9m2pcNF/58VfNO1m+/pUjErFXDh1sYFC6DfIWb5Mq+WxVgN+fD+R6PaQy92Lsa\nUqZpdv1tTZqu8x+zBwyOQ/zvFIr2MtPItc4pqInIxY7WPpKyxt6NjPF3kT/wKGvFsWiK82kZpWTd\n7iuIYySsVFTeaidgoBQD60NRBh5dxMdf/11ioeID1Job5E7t43Sd/xjvrP6Z1LDu9TzCVi4gWj9N\n/DXGzsTsvuIgJb5lyyDSGH7SDGOhPGrO9aqF2Lqm5fOih1/l+Z4iUhsOQs/1V7Dq7p22lOTAWDs9\n15Zg26ES5pqQHn8GX5Yr08t5aK6U7WDeXoM+FYVrVjt24yHv3W9Oh8kfCv1U/bXitXWo00XW0u+R\nrCjSqD0fqc9AveP/Ve8HSfW8EkgibAact8xcvH4wazSjf5OdiOUzQv/p03xaCsf5KSujTVIRJBpq\nVIWW5NXlhKTBuHhdQ1s8Rdd9j6s3YuFqoX/b8kGIa27CeDci+heASBbg2gRFIA56soRzDDtaY10K\nrDbtOMqBpdBJYbx0f3GzjYABtNcP0L14M5u2bLBf+/nYlsgg22DMp6hgvsezDrQlabRqeWGZVi2I\nlVaddumwrFg4McRHE3fRpCcZ0WLc8b+u5Dd/urCYualEWIYsRxCxUYRPpokR0J5KM10lJ0jWPpKy\nxt4ALY2Dhf/WdcVzMtyIuPevTtPWo0bCPvCBD3D99deTz+dJp9Ncc801PPHEEzz++ONUVVUxOyty\nZD/60Y+45ZZbqKqq4nvf+x6nnXYa4XCYz3/+89x///1H6/DmiQ4InxHsT2PAYrj0uHttBAygKTzK\nVe3fIUUDTRySftz60BUmbWvVkYWUDKfbWP+ft9DdtJnY6iTJckcrFBAELIkYxE3IUwjGQhlo55nP\niGob1UQug2ohXkQxHRQKgXaOfafoXJRmEKSyisIkOZxsY/2FtxjVkUmSyRg9n/0Iw8+0BNIFA/6T\nl8ueQIe5Qbs424lSNSRA/IsvyEnwhkP0Pfam0na/JgHLTwjvHJN5+Z1rM7AuC3cY1YeTsgeG4I20\ntyJ2xjLid7SLDpyRSK/FK1wBHW+HC8KOe1Ye/DlywnF+yspoK6nwK4ww9D2eLaoOA/ENu7w3YuGF\nUHkOjGpqwqh6PTcjT3cqIjuJ3k7qTi7xHGWpOst3muNFZXsRSw/DbY4uCyo+ouYp8uN6e1jwAes1\nCTIGnMUL82w1py0xI1zuA89lIVo740m+MnoZC8YP8s2+9SyaLpKgE1LPc9nqOrY98Xb3h5rAZf1V\nB2f8+5+prJ4hQ4RyS/51lAYe5tzCv2WNvQGm8sXXpQUAu6tIfLUVEVZ8dZq2HhUStnLlSjZt2sTp\np5/O0NAQb33rW7n33ntZvXo1y5cvp7m5mbm5OdtnbrjhBiYnJznhhBPo6Ojgz3/+M48//jj79+8/\nGoc4T1xqCIADwPD0Uem1GhinNT1stJ3QmdE0+lfEmYmKXYH1obNN2k1IJ7jhrW1sSm7w9nCqgZb/\nHKT7s5uJnT5Ccq5J6jHju7vO6SI1U3YcwZwRDagWYmdKqmKFKO+3IoDx6/AzbW4Rvjn/BrHQ8JpM\nZbqVsCZSU9kxezm4iXk2wtVa5Ls1raMVnldUqtbnkJoxhTWxWEYW2tNoQSb9eqD6QrFg/gqRdlIV\nDPgVFCQRAmlnCvRIFx3kZgVBsYqQnffda/GKAVeHS75nnnBcm0RvJ3WNY7aUZCDilJ8Tfk8W2Lz1\nWkbRh6pJ3HQ8+r7DszTx3YiFK6HMELyqxqbs9XxGeDdpa6B8lb2JvYIc61mfNlwyBIxqK/27hha6\nU7Wqug+fepACCn08Ja2w/KL94C5ekH3mQLYo2Fcgfq67ah8gMxumvMLfy2FkoJkvHLzFRsAAFk0P\n8pnU7Wzj7XJ/TEd+uqN1N1fF7yDGaPEYKCPBUraxlnFL5P1hznU19h6lga0jRWfpQgFAvB8tZXlO\nXjLXkqCOwa8sjgoJ03Wdj370owwNiTbvjz/+OK2trbzuda9jcnKS3/zmNyxcuJAHHniA9evXMzMz\nw8UXX8z73/9+APbu3cvvf/973v3ud3PrrbcejUOcJ2ohP4k9puqAGfk4CTqadrOkSV6WU5meZs0T\nvbYGrA2pcXac0cVgtK2wC5ierLTbVixCLIavo9gOqYxihMzDw6mlYZCN919nS9mtqtvJ+jtuYbin\nrTDAE3d0UvcmxyIxZnHCDmvypsx+SAID+JtEhsqhrE30pLTafwTt/+eMPv0ReJ2HWNf8zGLJd2UQ\nTvFWo0YrIo2g90Ooyp2qmo9mI59FH6pFxgj1oRpIKYbs2BzkMiJN5ET5SreFRpBJPxUpRjqDeIfd\nNmdo1SLy6zyGWEjiiIUrMQf3lh3ZooNwAAGkX7cA2T1z6uRKgePa6akovbeuIX5VCaQCUIXiCt56\nmQRM9kD4T7D0Gkk0z/FBj41JoDRnPij7sGB2V9FWpOYjEBZETutIE79A0jgacfzebbgkCBjVVvp3\n3fRR92dUsqKgciNZH0/rfGA+I9a+jiZkmxXXmMwZ+lDvw9AyCr3gTBh8SFh6XFhLLGiSBxea9BF5\nL1iLnYUWTbP8jJ1cvujfiYXtB1vOHBkqbAQMREHGf+TfwxtCRW317ybfyBNP2Jug6+kofd/vshT5\nZIW8Bfj/FQnbs2cPe/bsKfz7m9/8Jr/85S/RNI3t27fzqU99inQ6zY9+9CNuvvlmPvOZz9DR0cHe\nvcUS0n379rF4sWxVhB07dkhfP1pYvXo1v/jFH7joou2CGJQpSh5jFJrFdjTt5ua/v47G8Lj0Tzt3\nJWwEDKB6eobm/lFu7fq05SF0THRmRZl1gC5A5N3NXdU9uE1Zge4uiY1D04BojLpuQ2HC1iui9D4k\nKSk+LQrPyE+9cP5+qbdDBDOJjDSIAWRdKEJh/4iWLPp0OvYn3RLZaOkYFN49p46QLG9yp3GfQ+1q\nbqJiqVwXqFoI6jJIBf15Ucqe2LiautdO2k0T9xiRkikN4jlYaCGjB4FfeZAPmYeZOYFfqzjOWeST\nvlc08tAE/PskhNvQlszaLRjuMJzUrfelbQpyOTw3NSCiJ14mrKXCi1Cq7pmqRUoQ5PMwkhfl+wZ0\nDFKRyxiFCAGmYj8vurwxn5gRl4WW96RNq0NiPstPiPSiJUUYKM05tw9YGLxQITsqeGRNt6gqzwnJ\nhVQDaS7aiSxs0Si5g7xXpPfALNz7ImQqGX4pxvrLHTKGayVG1+sAld1g0HS6n4bLOr6CyhhsYzJM\nkNYE+l4Nzna/nkmWUV4353o9PVGJPh5FH9ZIfLkT/YQoL567nLMqHnf97e6x5cRPlPSCrZ2ma+1j\nZKYraGvcz/nlD7GcF6XHZ0pxnJYUD+XP5Ze5d5DPw9hwIy9sPQm9POpNVkMREXVN7wPquPPO37B6\n9epXnEN44agK86PRKD/4wQ/o6OjgLW95C6lUii1bthTe37hxI/feey+f+cxnCIfD5C07q1AoRDYr\nr9Fds2bN0TxsF3bs2MFFF30XWC2q9soWySeeSyiIzK8857s0hdVbkkpdvhvRZytFVaT5ANZMcnDt\nr/jOtmuL5EAlqLbuqnbhal6rtLI4PQlTxX+3lA8abTFGSE4LYqKno967y6CpN1UU4o8YfjdYJh3J\nouOXUpRFMpxPuXGdWrYPsvG+62y74FULdrJ+m6UyKsguV0UQlGmvGaQkzIjy6VNGWN1qXrq9U4jy\nk8B3wvIJOkgUyIokIjJ1puS9v1Ia2cjnjKhlo3xRPW+M3j85/Ida6uGtO+EHeyHcKFKmYc29AP0y\nLYS9Eb/wRgmwWWJYIqxeC+p8W2SFQkijWKZer/oSueFzqcjXQN018IFaWOgYO6pjz41B+n6oerPY\nXObzQBZ9b4N3CtDa1qzyfCiLi6pbGXK6IIjhGoisLr6eHYPsKPEvvuzWn9VOE1/cT9+1q0RnhVKh\nsiN5bgZ6fiQ2DFVvhgXVDF9Sz6YXLDKGSyi2XfKqmAbhpn8ywrTVwzEfKE3D5dzwxHDPjyCJ+ONL\n3FQEe+dVJ7H6jmddr7uq4k+A9ZM3c1bDYywPJwovv5iLs/7pm2k4UW5nEa2doa52yGU14cQkNXJL\nivBAwTWgsS3JSRc8TiSfIzOrMT0eJXF/J3pP1H3OFgnRxz72a844o+kV5xB5j6jxUSNhHR0dbNmy\nhZ07d3LeeecxMzPDunXrSKVSPPLII4AgWpmMsHd4+eWXaW9v58CBAwC0t7fz9NNPH63Dmwcs0a+5\nAxCqtS16tWeNsHrDM5Rrc2Rmy2jU5ITHxIwmD/ePaDHXA7ik9mU2rr3OTg5UqEcM2Bb3W0ori7mi\njUNLdJCNa6+zVVoWiEnK8dvWxVIm9pdN/LIoxB9xV1LNV4cTdJ2uNyujHJHBWiMyaLYFORzRuFSz\nocN9Q4CHtnAd6JUO13yrbi6APs4F1c7aq0+k32etsKSJpdW1zQ53axNNS4GXxERpEjAXma+H23Tf\nNIsTSosFJ0Jho+GxDluj3mnauhnQEyJdLtMBloLsiGF5ISFgFpuMQrRW1ZLMRMVibwNV2ev5Oaj9\nmF0onx2FbAp9b708BZgdFRWzIAhYuNZncxSRE+hIA8zuRGuRD3ItrQvh/3xgzDMt3YN0X2hsKJ+r\noOeeOoYPVQtT3UgDXIC3ZEClLZsAqhEraBlis2amrCVETOswWv6crqPPBewIYULm57cctNo08TdY\nNmrndqLrlufbYoZshbRHrzE2Amnv4rAnF2ft2DZujH6J9vAAA7l2NqS/yp7j4lSNybM/ILeasCJF\nLQ9zrtKSwnQNCIehtt2s7pyhnhR1tWP03mPYOVkRbhS+bLkU4qa9unBUSFhNTQ0PPfQQd911F1/5\nylcKry9evJgvfelLvOENb2B2dpbPfvaz/PSnPwXg/vvv58orr+QTn/gEixYt4i1veQs33njj0Ti8\neUITN7LmQ64oWO1ZI5z2yx2F6uhybY7pvLe+o39FnIbUuC0lOVpZx4EVjbyP/6AB+4NcIAfbN3g7\nUaeQN/pFYWUxYrey6O7a7LLMaK8d4FN//3V6tA+TiBl9DGcQGiq/TIRJCp2LuJVEdHPkGnMHJU0p\nj8oosy3K4YrGC4QzD/Uh49w1mFhqmJEqFpggi2guG7wy1S9KadWi1CMWGFNjiM9nJVCKumXWDONV\nor+iCZUm6wKtpGdBafOh8roLRcT9OKTD7ZpYUGUFoSMJIAMhH3IQRL8YrpITsOwoTN0Ltd20HHfI\nHa19zU7WX3yLm4iZRChoxCU7DuXL3GnOSCPoOyF7EMrj9vezI0UCJpkLXcjnvb3cQpXoe/chYzqB\nbDZAXOvctKtJdUv1IBs/aLl2K2HVyZWsv+B6hocNAu031lTva7gzf2WIuewG49/GvKctMZzpmy3P\nYt0YvXeskUZubJuHpEZixt07Vuvw1l0BYp34DGI870ZsrIzfUmnsAmnvjKDOnlycD03+0P5eOSQe\n7qRu+RjRerf4X2U1YWKYFsZpUP6d1+eVNkqRBvGsTt4NuRJ3cq8AjgoJ++QnP8mSJUu4+OKLufji\niwuvr127lmXLlvHkk09SVlbG9u3bCyTty1/+Mv/yL//Cs88+SyQS4frrr2f37iC9Jl4pRMSuzzLp\nmDvUpRe8yHR5lIc5t6DleiR0Lp35frSQ3Mh1JlrFjjO66NyVIKdHmNMi7FmxiKao+iGJZZJiZ+OM\nGpkw03oSTSkYVhbbbqF71WZio0mSe2P09F3B8IriZK5MWVYnaX3rIHWvcwx0PzhaLAHuRfxI+e+A\nIA7L8SaHI+LvkucoIoPDMXiMI+NUnwR6HFGCkGF3YHg9uaDyRrS+Xopdgl+BgBkRuxpBPBYgdt9x\nRCFFiQRZKeoec7wuI7mlPgu5KeHwnp8w/hsIVxL/giTF5ed1Z1ZVHtLhmyPw6SZYaDnmoTFh9KkF\nSB3m5yDkkUbLjkA+jVQPlxsT4vXMLrq/eI87WrtsgO4vbra3wLFCFd3c4jALDNeoiWK40hD51xej\nXY0zInLUcCFMNMB/NfqPDz/5QLiBxJez1J1dRnR5UY8U2GYjnxfnEDEiHJYxJY10r5ih+4Y/senj\n54sX/Air6n0VvzblC5aNT/zsfhsBA4g2TRNf1U9f0v4sSjcPE+45V9aX2NVLEcTtrkMYpXYgTOOD\nzmnSAid85x59QZTe36xh+Tk7aWobIVJWFPurrCZMaEZ7F9Xf1TPGB7jb0IlVk6LRtu4qTYojTeI5\nTt/L/v1yf7S/FY4KCfva177G1772Nel7n/vc5/jc5z7nen1qaoru7u6jcThHCCFbbrmlw60naqeY\nsx6ngQP5ZjpCA7IvAwQRe/iUczjIAlayS/l3JpJPx8SOxnT9XobYkc0gXn8QuAwx6GRIwfBjbWy6\nboOip586ZWkOCulAV2EEV4slwF1tFmTnXopQdR/eJGyf+DtlZVT3Fa42Y4FTW6VAJbRWTXLW149E\nw3Dr6yqipgpGeBBkpaj7HzvhNLzvodez4HSyN6MyOfeHtJaTkKUefJ3kQfzGwUNw08/gXedDYy2M\nTsDWcphc7f95gLlhKF/saJGTh7lDkBsU2tLK84UOy/ls3zsDk8D0b4m1Dkm/PtbquHDWyJss5b8l\nA6MOHaJXpC5nuJjnUuJYF70ZPrsCFlq+YznyiKjESkOJSCN68kP0XqKXZkFROAfnBiciInyEiLUe\nkH4k1mbZ6Pq1slIR2ojxvhPmI28ZT0pz3tfoInJmGQfSVL5kzlV+p+J1QMyJQbMLQQqcVAgLW5Hc\nXJmNgIHcasIKc52R/V2WkLGyiixRA+MsZtC27mbJS7TF5nHVAiG++c1nA5zEK4djjvkB8MQTRnTI\nnJiQ77KsOWuAVKiBDtQkLJML85Pwe3gbv/Y9BlE2bXF/b6coK6pAlDSvRU0+skAC98JnTthG+kWW\nsnQa53kOdCv24e3KvgFhp7ENb48p2YSw3PL9zsHm1/HGeH94bxvrL75F6G1akySHYlK9TcmpLdOp\nu2DFgC0V4PpbJ7lUHb/feeUzkEu79TdBSK6KVKn0pB5pX09tiVd1LagXvV+OwuS9xWbduQlBDgwC\npi2pIH7jYrT2CvRByEy2AW5tSuAUV/kySD4I/3Jf8bWabnmXKmfFbnZUiNRdBCEkIkzhDqj9RxHB\naxiHq+vs57t0OXzrI3BwlOTeXunhJYdi9mdndAZ+VQmjFiJmXWyzMxAJWGGaz4pobWSxME6uWAGX\nlNurLUEdEQ3SncOKUKR0CwrvL4TJzSQH5JHI5GjMvlDfjZg7Va2srIR2Wnw9jQjHAyuPnaN4LSzj\nSWnOG9JEUYyZGQC0U+Vza1X1lO3felrxnYrfKkDVHN6JIAVOPpCtE+M00DPzQc5K7uC0hU+hlRUb\nG1vXmXEa+AnvKVRH1jPmkuiYsK67Naemxbpgwpp1MdbvZ5892u7QpeEYCQuAf/qnJ4GQURl5HEQa\nlHoia876kdC5LPJg/elsNYTVodcx6kjRQHI4xrcv/lyRHJQatQCIgHZOmvhH+tFGdfS9loUxiYha\nLbCkLLs2s7h1D5mqClu4F+wDvSXqrqS0VXF6Pe8VCD+zdmzNXl2kSna+TRQIpxZNE/+Y5bz63MaA\nNliOaXhvmzqtY6CkNk4Wm5ICVKkAlVZL5U/sN3fM7hKLvJOE+Rmpen33PsRzVaLB6rwX1STw7RS8\n9SDEWmE8D/ftg/2/FYQrvc/+9+F6tJXn0vWrMaLLiqn/6ZdnmX5Zo+q44kJQkpN8uMaiIzEujmUT\nZoezdZVH78twjYWcVcE7cu5ne2EFXNQBPR30fO16Vp11Pe0riimUgd3t9NxxhePZicKyHHwHSWQq\nA3ND6upFJ0IRqFht95crJU08384CBgJHnVXR8XAUaj9Oz81JVp1pz1YM7GmnZ+QKe0WwmXZXtbLq\nMf4nG69ziMhZGnt1pGU8SZ3cJ6qK/o/NiIrMRaCXyyfx6sYptGhapCRnIPHLTuqu8PhOFVTN4Z0I\nIAXRomnip/SjRSXebqgJ4UuDK/jNny8trB3Htb+ErmmudabQrB14r0QnbYW57kY0h6NCM/C/gbEc\n3LkHdkI6Pev6/N8Sx0hYAIyNGTctlxJtaiINSqdlK6Ey2fxatrGM3ZRhD83Wl0/wvrn/YEvZ26Vu\nwGaIdegPbfbojEfUQkWKXAZ6ZzuiOVZikm5j0583iM/8L7ug1DrQO8p3c9NbPkdzZZGQ2iwezInR\nzxS0GUHAVGFyjwlBel57DA1FpWTi9iMQEkF1kDZOhYXjVB293D0hSVMBKjI9YBynF2mSoXwFZF6S\nvzeAIFPOyJy5kC1ApLWt3OFQBlrDUGlJfc4gCPPhaOVUWjgQKcY9d8N3A+xWjUKZ+D/vsxEwgKrj\nZjj4q4Wk/jtSeorLRKQJai4TGq3cBMzuhPLj7SlRWQueSIN4jmRwRsfqVR0QxP8N7z+B9RdcS/cN\nDxFrmyxGa89rk5C3sDwyldkDIbf/ky+s96gUe4V8DnDc4yAdKygh6lxwn7d8ZyHqIbR90ki3QwML\neG5gtSVTxO/cJQhhlUZin2NclwFP4r7mFm1qwcl9VT9apY6el8wPcaBeELamRQcor7CTibLyLPF4\nvzAi/SPolxnfafVyfKQTfSKqtt0ztLCB4NOKyc+QFTzI5yOd0A/D7W1sGtzAqrN6aT190PUbVvhp\nyZo4xDq28LvMG91vlgELwvBP6+DmNDN9fYhd8V733/4NcIyElQpjwu256QpWnb+T9gXqtB3AyGwT\n91VcysXcI9V9NZSNs3riBf5t+kreWP17GrVRpsuKIv/0nioSfZ1iB2aSGsWE2DI2yMb3ORzxDVLU\n2HXALeS0RnMkZEnf43D2HjUmj2wULZLmqs9+y0bAwFLF+csN9h5yqmozE147L8f5WonmXHWEHbWv\nse2gokumiTf107e9q1jxN44wivUT20t0Mn7u4Z5mk9aJ1nmOqnOuxL+BswzhcpE6ys0W7VNku3ez\nEbzsvRlEJG4E4Z59qiOFVYkgzIdTM5OfE0TM6muWz4hI3sxvxXG96+KiFus/H4RDkofeKJTR2uSm\nj2WxOZ770OnFF2ReS37XNNJYrGCsONlOJHJzKMM+TiKviu4EIDfD+1ew6SMd4t6av19KZCrSZHT6\nUCA3C8yJCJIKQaKpJkJhN8kOQMCghKjzuhw0O8aqMz0ag+Hz2tj03AZ41DjWDyh+WJJ216Jpuj74\nuL2q8fgA41oCPR2lb1uXEO7LgqRVxb+bnqiivMl9v7SULs6tmwK5s2lzn0VsrsxK50ZE+jxHceMF\nwcaATzMEZWHASf30PdZVOBcbUZzQSPyH28sr0dFJ3QNjruIFK56ki9X8VVkPUUOaE/krTeEReqOv\nlVs5lYXg4++CL03CVAyy13if5CuEYySsVOTEFmF4qo0btn+Vqy78Fg0VKSapcYVT0+NV7PzzSSxa\nuY9Ie06526orS/HEU2cytLKdquopyqtmycxoTO+Nkni6E32FY8d0N+4JcQa6T5U44huk6HdVb5L+\ndiGao3AR15PytFL8zn6aahSVlJmkXbCbRIjwvUwPvQIflgVA5mO2nBcLUcPCeTXqpdtbKODnHh5U\nTOs6R68FeD5eYGC4r1cUo01elZFI3qtEELAe4OoSLTSCIqwJCwQybn3Xgnr4woeg1RJpXnk8fOsA\nHBwVfwfCaLN8JeBBkhu1YmP4efbxtMFJJMKmSZQ3PKM7Ml8yJ7kJt0OZ5XfyeXVaSfZMRRohK3m9\n8P0VkA9Q6LGfYsruJdStvErpJ+uAb9TZjKj5RBCl93tpHgYV1y2BCI5YHrv4KklVY5BxDXKbIFWE\nKkfhumrRNNE6ORkp6BlV468K73mjlDGgMqlOgTaTplHRtkhDLxY24CCKj8mPTd8bpfeHa4hfJMja\nXKaMmsYUVTXFZ+EsdgToBQCt0WG7z6MTVWGI1EHtJ2DivyH74wDfenRxjIT5oqFYph1ZAGHDBXUd\nVL9mgt9UvM31CX2inNHnF7C/ZzGLPr0PrUpndLZRScIyVRWc/qYdtjk+TYi/PtdlS6lp0TTxd/Sj\nvVVHf1EjMW7xj6mE2Elq3yulONQqVC5h4W9dOkAzcmfk5NMx96A2Sd4lCCd/y6QTX9WPFtfRz1Gk\njCwEsftyt4+ZsyACQK/XXJVH84WfiWEgXyxZKmBLDpaGYKHlxh+phtahiPASq1csiF5EynxPRRLH\nnHlLCXI5IE3LcQfo3vBjt9FoRafwdprbVyRgCxfD+g/aGxyDsIl7+GzmAAAgAElEQVQwNFJUrDa8\np4pRtMRNndSdN+ZOm/d1wroM9JTPr4/nfGFNaccgvtknuuMV9ZT5bIVCpUWmQLQICkXV3nRe1bay\nxbtd/ee+US8PDzXPqHOQzgbm67L7vTCkTvWbleUWaNUBxrXqmpeyUbFcinhXP2XlbsacmYwU9Yxe\n574MMe9VIYoIrDq1UsaA4je0g8LzTKuT66r0iCbvMOUzr+l77FE9ke58jGitCHq0exS4ObG4do8g\ntrJHzByGoQhUfxfGj5GwVz/CH5SbEtarqwTTz9aS+H0nXV/eQWvTIK/nEeoZYyZXRmXYrs1IUceT\ndHFBaEuhR9bDnAu1ED+rn77HxYPpysGvdPvHJOcUvlfTMXl+3ipUDmoBYWDtwm1ouD3QZjIV9ipO\n24Eg+jCqTAydOjUHtJY0HZ17XK+DvSAiPVFFItFprzw6AkTMFRHM6UAOfUDRBHlcE9cygZjkC9c3\nD1tDQNjQahkVSy/hEV2YB8IR9YTdhLodn/mZrcBSR5/KAxn4xV7Qy4yemcVzd6bcpr5fyw13bKB9\nWVHvYTUa1ZZGiH9xFq31TPSREInRU9ArfaJvpseaBc6dtE0kXDclLAvqFWXDR7ATUgEWAsbVoC1W\nLOYduj019CMknQgUhMavoboTuVGYeRRq3uVvImtFPg/rQvNoRO/R79Pj9z2jztbP+ZFQr2iR7LpJ\nIlf6nIIQ7tNEuy+vaz7PAjzlmjJeI9qWgfrcXwQ+nYew8cxUY3fxV1wTbUma+J2OVLkiQhvvcm8o\nCscoKwyYQFTAez2bMUSAwhFB6912Jmeue4RIWWmN4pc1vkhLzSDDU232ucJavQqi682rAMdImB+q\n/tFOwCzNXOcy8sunN2rEP9RPa9Ogvf9VGGayZcxRAREYoJ3/YQ3r+LVNlL+Cfn7GOxkNF41ogpjz\n9fRewaq6nbQ3WaqBxtoLvR8L4lBndeQ8UjUVBzKw1P36gWwzYz4rW0v1IN3nbKbjDXuYbbZXXxYi\nBOu70LrTxC/sR6vWyYQi1LZMoGpEkNpby+h0zC2KVy0WJZJOKYyoQmLjidSt+R+iyxyVeO/qFNpP\n1/UNCdEuGBO/MVN4RRdKhZfgPotan2eN2En7VJbDRCeUjYo0YkSY0slSbm9562baa+2CW9No9Lab\nPisxpXxcbQTss6g5d9K2z2UPQKoOqb/E0axWNyIP+rTmakb8MOcy1NjmrtIrZcMQNHKdHS36kpVC\nwHJzIgo3HzPlzC7Il0HF8pJ+M3DrHD8S6rivWjQttEmv0dFPlHyn5FwSvZ3U1Y0RbXIQwis63Zpu\nmbGpk8SYiQoPD0NVxmJaj9rnMWehzYPANRYCZsLq4i951oXuzeHmb26Eb4+6zkn7saLn8XS5fOxq\n+BMw59qTBV4EfS5K8rRmmpccYID2QF6aAGWRPLe++RM8NXQGPY9dwfB4G+jAEPB2is+Kd2fBVwzH\nSJgXwksKuhPA1sNLi6apaXQ/1dMTlST6OjnhnKd5e/pXvH7Xo1TqOjOaRv+KOESreGLqZB6oFhqt\ndWxxWVhoZHg3P+fre5cXFtAg5nzD6TbW32E44pvVQHdcwfBpbVAPeipK33Vd7gExj1TNoZeapY2f\nJyrriX+ln77L5fYEfia3ICIE2vo0Xe/a4SKeKkz+pZ7ex86E4yVvOifYI6EPskAsHGcR/8IOtIWH\n0A92kNh4UnGSV1lsOHGk0mNegvtG5CaTKqgW+kij0THiNmJtI8x1RNix1F4g0VArZzixU5PEvxJQ\nRwfBUrTKyEiZSGGWmr47EjCeu6kXann3cT+jKVI0CW2dG+QLe26x//3RSo+GaqDyzcIeIyhy2dLb\nIBU+OyskHOFYgYD52k7EEFKFOOihKH1PdBWjwqqCCi8Sarnf0iyCM+IuORc9HaX3DmPjKiOE1o1O\nO/aNjqndddruAFyH0lA70dtJXdOYTRdWiDDVoy60WYsQnstg6rskY0Cqe7Omyi1FDlytttAYHVog\n3zxVIDwT/4/kQzEKBVsFkmxGsis60Z+MsuupVVTHJthWu5YWhqlHZRNjR2PVGOfHt7GqbifrL7mF\n4UvahAymcOLAbXN4tLF8xXCMhHmh8uvF1iMxbE1U4139NuGgifREFD0d5bjQHt7zxC+ony6myRpS\n4+w4o4tYZXGlV/XC0sjw+vgfeXRwLeBh+Gd9fRaGe9rYlHSIEv0MMlU72uMpCpsd6LnpCroueoqm\niuKbZnWotlpt5hrE5FYv14if5478QbGthRPR2ungi8VR0Afpe6P0/cNxMPkQhPdCVa3wlKPC0GUF\njAYETY952TzIzs8U3HtZhUBgV20ZmXYWSKjKypPlMbTTAzp+zxHMEkMVGTEbQJeavjsSMJ679678\nsY2AATSVjfLelT92C4iPRno0XC76c+a81PnOz1ieLWkj+lnYEkIaXQxXiGICA762EzJvvS5Eb9q7\ncLdpC7Jhstzv+OWSLIKz4lJB0vWeqKu1ECAnQ1aobHdioJjCIAX6Y1F6+9YQf48ktW72BZ6HR2Sh\nut5BDLW4v/0OFH/X1/NMhtW41xHz+i3wsLvIrClkcMa7+rmz+uOsrdrGCdHniEZ8PDQMtDcN0H3r\nZjb1OcZZM3BBSHg3/o1xjIR5IexoLGzZ6agiUw0LR9Giaa586Qc2AgZQPT1D564ET5xyRuE1XSnM\ngfrKInsI9PD/FbugN58X/ih+C4+KvNQjBopkwhve28Z3d32CS06415ZiGafB07XZz+Q2vVv4yKx6\nn7y1hHJhr4+JXdcI9sn8SPQnDIrmGFz+KWiohPEyEYXxasskQ9C/9SJhXucXRF4R4DoEIdMPcy4L\nJ4Zpri3e84EJkR5v7JK3lHE9O2UEt8TwS8/Nt+J0vjAWdmU/1ioJizDv/5FIlzsRjgT267LBRmDz\nMJaBX1XAqM/nDPjaTsgqCTFe+yTeJqp+x31XHu0Sea9AG9GQkfQ/or4HMjLkhCoCL5MCHKQwz+qx\nKH1VXfKobQkWG4XXKylmCMxOIwb0AwEKtqBwLno6ys5HT2L1Oc9QXjFHZraMnY+e5N1LuAIR8RrB\nrsEzzk8ptTm7n77/6SpUWCainQx1tXNZ279zauQv6t9zINaWhD7JG/WH6Sp8hHCMhHnB6pDtGFAq\nohEpyxM/pZ+m/fIZM6LnCl5idYzRwrDy55PTxbxRQdMV70dL6+iNhjO8+fDPIHQBJkIhaAoFS7t5\nGapaJzzHwvDE/Wcz11HmJob3q3dFKpPb5HCMoT+0iXD/eVHl9ZX1FBuYaKcncYVwph8BepG3MzKR\nkoS/ezvRU4fRCzIGXF0vv9ay6yvTh5SSHgurybtnRHAacZ28EIAIBukYMbS7jS9e9XXe+7UfE2tJ\nkpyOFcyDx3rr3Xob1Y76aESHXgkkgbsh+XbFMz/myAub9/8Ip8ttKJWAmUgCd+VElWVE1ZxWDl/b\nCcf9lY5N5yIf9JkIhdAHFdpdJ9GwknS/exDk94NE4EH4FzotfVSEsFXxWwlEWtL6/bIKQUunEYDE\nTCd1IxLdm7OzhHEuWjTN6nOeLVQtlmtzrD7nWbWW08QCiuQzjq2jmCqg0Vo7wAVnbyFWNUIq08CT\njacwW1PFnzmbOC9R73DQVxVEJmcU+otRD++8VxDHSJgXZnqg7HghzHcMqERvJ80dw64GpQBaVOfF\n8eWcVf64670+7fhCukZUTcpz3IOTrfT02qsM9XSUvh91iQpDiz4NjP//EPbBHDTtZg76a5FPLgot\ngj4SpfeeNcTfYJkwtxtmfAqommbb2jJthcQp7sjf9LjGwdRK/k27krVV26hIZziUbra3SmpCVAnJ\nNAgGtKfSdN2wg2iDJfzdOEbvrWu82x15we9aS1Jh2qI08W/0o8V09KRG4l870ZOH2RQc/PVPDj8k\nGwISQRWZPvhSM6Mvx2z6mU3f34D2d2JhbX3tXhqnD5Do7bTrbeo1EgnJYgtHVzx/tPE66Hn2Cla1\n2PuxDky00/OLK4RY2Blp6eaVs9MoBaFwMALm2KzpSZ9oi+X+BnFid35GaBM3F21QLDpYUpC4vYW6\nNWmlz5/0+GXm0tZ74PdMlhKBH0Fu6eNFCJ2/ZRqxWueYBYh1wgEnyd35uPCy1Kp09DGNxD9aCras\nPTNHIH62f4GYL5qxVS3KNtx1jPHuxp/QtLB4oU/lCX7M+xmngR/zPtayrWBdoSpyk62jABzMw0/v\nCXa8RxnHSJgXcv8Fk6tEVdH9TbC0rVCur6ejjAw0sfA4t1eWPq2xfuJmzmp4jOXhROH1/VVt/OuK\njxb+rdKDjc408IUHvil3/TUf3tfhtmqyTBItHYN0X76ZWIukp6NsMkgidlOyKIlKi9AEem+Uvqe7\nAqdNAjXNToK+MUrvvjWiOrJKR9+pkfhmJ/ppGtTDQ6k3iVTrMsnO3meXGr+q30bAAKIN08Q/8Tx9\nH10AkYVqPyUV/FKcjlSY1pGm6/sWncwyqPu+R1PwUuCnf/pWFi6I2BsSOyOHPmkrFZn+zsXXuhug\ne5Devm8ZE3c30kIPZji64vmjjXp7P9ZYlSUiON0mJ1VHK13+SkBCGBIjndTtGSO6xEKCrJ1AZhCp\nzcZgVeDWCl6ZNnHVm3ay/qFbCvOdvnQRvd1Zlv9Tkvo1KSDP1E6FPYFMn2aFeQ9kGx1rt4lSZB9+\nhE4y92rRNPGTjTZIwxqJezvRn4nan6duXCQsEMk9zTgPJ/EbAS0bUMvphzQFvzaZ1OYNs9tpqrBf\nmHomWMs27uNSxmngPi4Vx2/IIF7PnzjIAg6yAI1ZJqnhd7yxWK2fQmx6Uln4yc/hwLG2Rf8PYI3R\nNPg+OHAxfGdRcWFrghefXE1N46Tt4cnMRqiqScNZcOELP+fm2g205A6QrGrgrlUfZChajCer9E1P\nDZ4hJ2AANYjBdaLikOsVE5O1p2MTNqGk1pEm/pV+tNN19DlH+H8GeyjZiUpEFdM6aFkySPdPNhPL\njJDc2+QmVwakTbMlGhj9W1GxSOeyQAby0/Cn/4CsIWr41JfkJMxnUlOnRyZg8peisqv2ars42Q8l\nTrAlNQWfD7z0T4fS8P25YjseKQzvMgUCkWkDStJ7lXGuuRnYWilf1L7L0RXPH20Y99/sxyp7T/WZ\nwK8fTRjaw8BNtSWEQa+M0nuXJeo5qpEYcXQCGQGeA+31csG1apGXaRPbGwbsrukLI/CmZmpOeBmt\nRajim99+gOrVE+5NzyV4WkgU7sF8Cj3mW6HrIN+1TSOcct6Txf6Si6Du/jF6L1wjiJjH7wUiufWo\nN91jPt5pre7jlSKK8C9bB/qJ7j6YbW1yzajTtLWOMbsNFPa+y9RQPLfnEXPi7F6Y6iOYQPbo4xgJ\n84TBPBbUw/vj0EDRVPEye2+squopqhunKK/IUt+cor45Re44+Gak2J8ql7XnrB/KnUtLdogF5cWR\nawqXlVhE0WNKhpRiYqq1TEwLELu9b4NW7dP3sBJpSLuAGeBqQcBs7YTOthhzvtwqCFSoUu4Z5KW/\nGMkbZCgCVELNB2Hyh5CfgC35YJNa4PRIlSBguZRxYtUeJ+5AiRNskKbgNuRmIVRWms+TCpFa0Yx6\nbi+ULYeI5DzzGQh5RwNtZNrDBV1rVSysrbowvJ26H7gIbtfmJ0bPZUQFoOffpNXP39HEVqATd8ua\nOYTOR/WZV9JOQxX1zI4AEbSlFcGaamdHob4emTJHz1rMjmVRT0NGoD9aCe92H4otZWWp4FVpE51F\nD/ELX1Rvev7xlOL5x6VfJ+C8B6UWeiSB23RYNwt1YZioEL57fs+4M117/pMuV/1owzTxb/TT9ybL\nBs5JFKdBOy9AJGsaJZFKPNJJXUxipmt6p6ki2rJzMq9fN+hnOnz+Lg7wHQhJj9PiyVkgpFXp9ntX\nfpwxzx8K9iNHGcdImBINwEJLLztL+DpOoT2aWbmx6uxe6hfahYLOQEo4AunxSvR01BCcvp4dvMGd\nplBFwfy6xRgPWuzSABNTE3ApxM8I5tdkbZpdSG/uaRObiWbo7nK3E2pfNkD3Vzaz6fINMLtHNGeu\nvdLdKNhTT+Xs16dBzQdgLoFWoxNf2o+22tCj3e9uDhs4PbK7isTGE6G2C+bkrvwFyBatEnfGfk3B\n7b+X9Rbim8jphqN8AKJRFofMC5A9KCdhmQGoWBLsu3KzMHUf1LzTXrGZzwMZ9CH5Q6sPaeJ+Rt8I\n5OdfvZifAos/mQtZkYLybFB9tJAE9uAmYV5Vn65nKQdbsuLeejVfnm9FpVmAFIqK5zqvizEw8yjU\nvFcdte3pp+/7XYbxZQ6m7oVDb4DjV7h/wxrFq1dIqOsVjvmygg2DJCgLfabtYmylz2Krbh/LquDv\nLKUXRljmCRFJfAGteQ/6/nESG/ahH3w7aKe4P+dh/KpqawSgxXTITkEoD/mQ2LQlNduY0s/TQLK8\n2EhuCGXUVd/jY6brVeRlhXWJknxm56ETOKfjUdfHElPLSE3VUV41S1lZlpoquaTHKvXR92n2excK\nC4lR+m6fg3xlcIyEKXE+hJvgfZdBq2MGbQannj5oTlyfiNL73TOFOLoWhkNRd5riEOIhnUGQnCo8\nhZZMAc9RmHSDTkwsDdb3UNY0e1XNTtbfegvDfydGtLIM//SkmFRGzIlZ8sgF0cDYJqZKtCfb6fqB\nY3deO0bvPWvs4nZVeuTBNcRPt+jN/rcxkSwA1nV6L2ShkCA8Tt2YF4nI5SBcXHj8moKTHYXcGIQb\nfNKGBvJZEdmqkCyAMoQrxQKQN0Lyzon/F2UwMgTlQWz8cxB9s9syIxQCKvzPNdJktICaB7JjIiqq\nImG5jFiMwiVENY80VM2Qrc93bhaYLRqq2p6lMMTC3tV6ftV82QnRp7OsDSKOaxWps1uehKIQaYPq\nd0K4Wj1HtOii4OJj/WhlOvrUGSS21qIfmoAFlk2rNQqR0+HQbjh+tfsLUxLH/KhGYp+kYMMgCVJt\n4pgjm3AQ9J0arMQF16YngfAoc2InpafF87OQm0ZbkqPrvl6jo4YGNFP35jZ63/ok+our7e2dVPfR\n8PfSXqseJ3pSE+PaOg5zM5A7IJ6vssUk/qmTuhMcG1AnyTUlJoporJ6UtHAzIYm+uQqBZBHF24H1\nFIIM//bEJ1ne+CItNUX3gOHJFr7xwHoRpNBBa0zzjrfdzxLJ/tSU+qRTVSSu7XTfu3At7rYHfxsc\nI2EqhFeInpGNigXQMXa9vLFsfzersWRlghvLv0R7ZD8DFYvYoH+VPTkjDj6LEEXKegj+g+JLd2Fb\n/KUTkyLNaY3IWFurHKxuZijawXC6TR7lah6g+6rNbHpUEMjktIL4zcWMiJZhGCmDnwZGMjHFP/aS\nfHe+uZ++LV1FAqUgeLoWpe85YyI5iCCysglwOWKsmkTYJGW5YcjXBiNI4IqcebZnyY7A5N0iLVrT\n7f8buSyk/wuq3yJ/3yNVSCgkP++lLXDbdDBH6XAlXiHaQK1osiMQavFuJC3D3CCQgbKO4ms2Qlke\nLOVzNBFE4xUKCeKtcrX3q771ez93CNI/E2mYmsvcz5TzuluImipqm8lE3CLvZSl6f346+lNpiEYl\nG5k8bAFWpKDFMjgtC7OtT6vs2bT8rVSb6KiOZCskqjupO9ljI2DiHoRJrHU6GzFeLxVhDfTniX9+\nJ9Fl9oko2jxH/O6T6FuXt/utqe6jYfyqn1Mh3Yhn9AiJzy5338dwpeifelErNFaip6D3k2uIb+pH\na1BYgJipwvmaGzs3o0EitEmE/vMaoExoKD//wK3yLNEhoFJkoe7Y/mluOv9zNJcXgwAHM01sHV3H\n0FSbOLfTom7D8twY8NsAJ3P0cYyEqVD5Lqk1RQEV2LTLsgqPXNaekkxPVJH7SzkPNP89K8peLLz+\n2vB/88bx37MnEhff24Vog+EMf3sZ8llQmJi+spnY6iRJYvT8VZLmTEDiThGlaF1m73O5pPZlNv7d\ndax/6BZ1lOvUJHwdOMPoW7lAUobfe4W/UFOqgTGbXCOvDmpQ7M4X60VjwjiC0OLjPWQuVuZ/W+Hw\n1eEUxGSxU4eyEsrV8jnXBOlqCp6bhMxfRJ+/nPHg5QI4Q+dnoHrd/D2gZBP/wgp4R4U7sudlEusB\naQN0K3KHYOLXUH0JhKoAo1G332+FKyH9W6g4Ufzt0fTYmi+CaLxC5aK9kAp+0WK/98MNgtDnJiA/\niTs/qoYqRRgiJBd5/91u+npPEtrZdQiD0cLiWwmTq+E7WbgA/wVeRga25GG0+KxLC30ci66eLKEn\n5bfF72lL0sTP7UfLzKBvrLT/fU4X4y7iMweEa9EWNyOzydcac3CBY4x53cfsCIkbKqh7TZXtXszN\nRPhL98noz0qenxjwyeMLVf0A+hlR+p5UjEXrc3mkzI2Dfs9uhFi/G2hQFLOAeAaMx3fvyDKu/9Xt\n3pIeZ3wgOyKkMa+GnkUcI2FqmOkLrxy31esk7a7w2P/C4qL/irHwfy98pY2AAayoepEbQ1/iQ5M/\nLL4o8wVSpTUkrw9PtbHppQ0iwiPDCHBvcXJ6x6820LjY/lC2NwzQfdxmkuNNUpPAZHlMeJO9CMMr\nFWX46TY5kXXujsx2Gk3GaxM5WBdRRrMCtXFqBg6BNpOm6wKfsuygfKoSsVv7Vgu8pPBMcmrG8nlE\nV1oPQmGNfpkI14v0kR9kmi4r/HRdQS0RsqNC96OtgYoTRJqvVOQzgpBaU7nZkSLxnLxLRKAjXiVq\nFuQmxOdmd4PWGdwbLzsmomjhSkF0K1bY00LS33LsqgrnJCGm1mcgCdyWF21SFiD6Bo4bx2QlH7lJ\nICc/d79omtf7+ayIfJnRrxJSv6YH14JDB5kNl7Mtt5ahUDuJ3k5WvVbe1UKr0qEpAlfnodkyDqxk\neDQSfIFPAj/IAbOiwCc/DWWld7v33QhYfk97cJyuXzxJdJmxCTrbUYwQ1kDfDXMHvJuU5yaMSKKb\nhOnTmnuMqe7jmA6Td6M/D70Xvpf4l4aLZPKrLegvZaQdpFiHjYAB6mkohWTjb+zw57vBCwrrerAb\nsd4cp/jbemznoCRrJjoQxG4rcHDMmGeD9SR+JXCMhKlgVk6YO7HrAYW1jAlTpG9F35/tE2p73X7p\nZ9vDA+4Xgw5Q2esqd+YUolTXsfNsbBqV/DHEmpN864nrWdWmiHI1AwPACAwjGQwHEcJSq6D4j8j7\nwd1tvL4AWBARYfe48f0OJHo7qasZszWflQp4l0O8JUBZdinl/2XAVTVwM0U9ji3cHhJCZXNiDoWK\nPUityE4ak1u5qNyrejNM/7ZIxCrPd+t3jjTyOUgpFhDnNcmNCXuQ9D4h7I/4DAgTpr6tcUZEPxpr\nYLwGfjkJB0ftkb/K84MTsOyoIE413YJo5PPqViTmWMpNQma3/TcBQu8Wjb5VyOfkBEymDQTI7BFR\nklCliJi8/Du49xT47ApYUC6e8eXYiUm4pkhyw412v7qtwNKssFswYY1aeL3vSlEFk07IrG7a9wyy\nftst6JXqrhYFcrHAcS8Ox3A2lIex/0/8d/TieZGwUhBf/1SRgBlwWciEa0Sxh5KA5WDmQRL/fC51\na+fkc5VzjKmipvcfEM9rZDH6y1X0XXmiuCaZPTD9EzF3yFCKt9zzOAiYh4zhSEIWvTY80F0FYY95\nFK6pUIGo2IwD35qC8VeX+/MxEiZFA8z8AcrXiJ1yEtDxJWFeMNNh0wc1aRR0ICeZVIIOUFnpumrw\nDeGaBONf7Ge6Sl45lpyLqc0mzcFQSSGEX4hkjSNy9zLCdQpyo9kPIwak8/X9FIz9TOh7ovTeuob4\nVT4C3grQlvsUH8yKv2NbDuJh/8oegKpwMZIhTX+F/dNf4WrLDrNCkIBIG0z+wJhwg6eM5o2msDh3\n8xqYkD1X1jZec/sg4kFaCp/Rxa6zOQbXRGGhZbu+NAQ33yMKUKIXC7FsJMDFz8+J6FWo2k2cvDYq\n1mhjuL74m7kJUQlYtkxNUJSLkUIjULYAJr5nJ3rrVtvPH+zEJNII1RfZj7HyfHGMQzNwayVcvFg0\nhE8BWy3P2EgWvhM5/H6TloVXanWzZIDupu+xafuXSAx3UvehMaILHORie6cY/zJvQT9SoLLLyFsq\nAmcehLJFwcl6qcjpaC2HkFnU2CxkwjU+YzQDuRT6C4/Qe8l7iP+fA3Yd1p6oe4wd0uVWLQcnofoD\nUL7cfn3Kl0PofYYuU4KgfMMc7/m8qI4NVQQnYLk5YNZIqftEk2WQBQzCioKwsp2sT98CUdzV+n7k\nrBm4sBVurzc0Ya8OHCNhUrxZpB5yByFsCH7N6sR5wOpS/MOW93LqE39h0fRg4f1dc8vZkP6q/UPm\nomjtPu8nlrRGZIz5ybWT2HUFw44aZa1Nl/ZkHMnECmJ+z5Cv1fPFCVkLFpWGWxX0qcrD7SG3mWvS\nIeDdgLvZL6Drih37lPF6BaJTwKKwiMatBVbLv8sGlalhM6IFlCTiaINssYk0QO3HIXsAwgt9DuAw\nIduBziIqwX5u192IxbEOIouh8hwoO86dhsvpguiEo8YkblhQhNvhHaAtTRPv+qtdl/fudfDDxtIW\n1FAZRBT6KdlG5YBp8VAFNVcYfmFl9utf1jHPXb9i0QnXiLTqpFEGX3k+tErK80AYL5vpkmQT1P6D\nUVk3CblRmHlMkLMJTaLRy0BuSjw3R0LDY7kGSg+uhmEhEidK7w/WCKNnp03MOuSV3CYpcD47+ZyR\nIsrJI6yZ/UXSnJ+BbNLwsTPaPZRirGz+fuZliMTsuq7cDEz+CH1/BTISVqimzI5APo2nti5vNA7P\npdAf/Sl9b7sc3lHvnrvNSHFuAvQdkL8IeizjITsqr2oF8QyXt6iPwc8ywpkZCYUArbT0Y7hMrJfZ\ngxBuEfdiGeKZrkIEMAbzUBaSd+hQEHNpQVjHAFeeczvLF+2ipabYrebE5r/w+Qe+7U/EFkRE1HDq\np4g85d++QvIYCZPCSEZnR4tVVyPITVKTiPx0NcqraXUpHjxN4VYAACAASURBVIq2ct0ZG/nIrruo\nTU2ya/R4NqQt1ZGmhU6BGOBu7iqbaCULaosm2UlctpP199xiczbXBzXGDZdhszpykhru3XmJ/0Pt\nZyIZIBzu27A3FfJfYJLAX5G2XUo83knd0jFXo/H9uxaz6uxe++++LiomjQC2XMx4nF89xRB4qaLw\nsFYk/zLM1w/KCRmBrECc/6hjEg6FoGIplH9Yon/KQGYX6H8RVhXhOheh0ZYo2qXsPhXdT9xcClwb\nlSxsjcBo2NvQdb5Rx3BEnZKMNEHN5UAemhtE1Z0M1TielWrxYqQR6ICKkzyqW+cZffCCEY1SWt0M\nFcPV+t4ofZcbGyGrZk6WHj2gwy8OgD4qyIa2RhCL8EIRyTG1jS6CloHyRWqvvOwYZP8ve+8eH8dZ\n3n1/d0arlVaHlbSSD7IVW7ac2KGgFDCEtNDipElLnIMJGChECU05lYQeHnj7kEOftsQJfTk8IQFC\nnz4hwQk0hJxjoIFg+hKaUlxaFA422LHi2NbB1mFlSasdrXb2/eOe2bln5r5nZ2Un5A//Ph8OXu3u\nzM7Mfd+/+7p+1+8q++9hMJVWmgaj0ZMFJExhzTH3oJP+dSOiIk09dGM9ree2kO7zdoz550yG/q5B\nPOeF3YJY12nGabkkUssu7Gk4dDfcHdA7qrSgs/d6EVB7RtzfqFR5FNzx8Fbg7DLUS+P6OJ4W11c8\nsQT9l7vZAui14SOGtx42AR2a75SKp4LQFYSdfcYvaGv0e3Iubz7O+19zB594+pbo82wFkn2iUMV+\nO/DZ6Pe/BDhNwpRwJrXCbjHIzHb1jmICOIJIU6bQXs3G5rzv36PpFdzyqr9marSDwd0Be+HgXBtX\nR6FYUAderdhJrBlm4Ia7fNVEbvUT66i4DOcPNjJ43WYxeIO/WWXZoEOVcHjVXmYLxHcKfxhBWgPp\nWmtGXTSx6byfh487tBkrF9PUs1z999GFEPJPcHKEycWprP6LK8iXoapWTCSBurBZq4TeN2p0eW8a\nYp+yYelJwNf8+BSk6KqiLHyYVCTBJZhbiTZaBv1YrxahS1T74hrh2GXs/LsL2fian9K93lsMhw80\nsPPvL9N8zqaimJ4Ebp+FPxqGtgaYmoFv7IZxacDkjzj6roACO2FC6YRDmhySGcULzDZY2O981tHg\n5b8jiItMZILpczMrCFj+Ef/rRgbr+BYGL0vRe9MoqWXjWMN5YbB6SDIQVaVFyzbYJ2DuIeFf50t5\n7/YTrHJBzCHpy7y/AzRcBHWrHePcgkhLLwWlaSgNw1gDfGEGmvfA234POteLja1OmxtnLonaCF5l\nxGcWrqY4IDcBve1RY1Itqt+Y3Vf9eNOIZ6FhC+RPN/B+GaMI1IvdydzDwrRwvAluN0WFUwZSDXl6\nt0V4rQAsQqo1z8rMEbbwr5UI0w94Iydoq/ThqkSCGiysguK74oxBxXu01hIr/CNM6+M0lxYDJIWY\nLIYQRKeWAVpAtB7KqmfRqr3M9muOpzNLVaVrt4aLJja+YVB93DfuZ999MSqoQJDvr1DdIVruvSlP\nckuxe4hb/QfVI2a1FHpUQ93ayN+SKmp0eQt55eunBC+VXYVOjyMj7jq6lPW2lmcobhTVzjH2y29z\n/fl9DOxopGNVI5OjWXb+3R8w9kI65JMoziMQkctl4J5Dwp8sCFfvltwQ/hs4eskafldyrXT8Rkhf\nKCpt3QhT84DmOIHUp5GpVOdaI7Dvz86SolWBCkd7Ohy1cgs+pO+poH6TiBjPO/5Uwb/XnQEYIkIn\nf6Zsx78O8v3NNcD9z8Axp9duHrgnDylnLlZJReJs+quNK10VP5qsR0Pam7fPojIGdLZHTclZUnXF\n8JcnYvSCdJdEo4VoZv/S4TQJU2IE6BMDqelybzc7BewULSj6v7fHJ0gNWR7kgKPwmvf/O++p/5pP\na9XNMPct/DFDH3sTqc15+j+8h3Q24rsqOgqNaFV+jwStgepoUP2uKN9WDbRV6kP7oPrcBPBTxEAN\nLDLadiLu6yvw6+JclAtO6jblHVdeXL4qfUYRxUzVaY5btOK33nCvh2IC0aIL2GrDPSVYPOqkWtZE\ntyWSCWfc6FUcAlJrj8KoBbyKhsQ6nII3KF4fGhGL3Ishsq6FsMZF1BiMQlxiWysBrqWCrRZS6hRh\njB1ewaf+7GMBsjAd3gSVi+q0aJDkdGZg+0XQ2ecY6SqODbVvToLHNtuh+WqvyKWs8duTi01AXZ1r\nZkV0iqI/gmU0+IlXte9x04rmMmFtETqORhQb9/6uA/4MKeKagt73wI47vQikfD9qiYQH9cZBfbQ8\nruZRtt3VZj2edbqc7EQ8n87x3YKw97/mDjZm95FIlHk+t5aUafGa7p+Evv+ovZr+LT8W5O7pDVjT\nab1Tfxz/xZcIp0mYEs5l0ZTL9/79fh8BA00n+jbY2rVL2WD0d0v/xrcueDsb1w76CFjou+RGv6rJ\n3x0cyxHvle7ozu9fw0ZjL91rpJ3EwW52fuGa6B50sPSIizRAfUUBR7Ls/B/XMPbWlf4qx2p+X1Kz\nXh/MjBAkk4q3uAQielZTSugyg8c9nFK33thIOJ3UIJ3bTtTnoULWgKsNyKyF3AJ8/RGYv0BNREpT\nUPgJpLeIyThu9CrO/avFFTvqGo9bwrspwk5Dafj5XB1DN42IaELL+6JbCy2F/Cwl3RoFtzNBsg/q\nz1IvjrrzVBHewHiN3aS7XAbK4vi1FBPEHdPlotBspbeprUiCGj7bgsUXoF4R1ZJJTqUPr/Scv5hG\numabiDbNPQqmwuiwlPNSgC6CpNFFlI9c3SqFx19EKb2ZhcRJ6iDLTnZBzjicRXiOWpaCt2+BOx8R\nQQXDG6PLzREG3qCoMAzOJXHnNfcn7aTifC8jMuvh9opSzG9r2w7RkRYWSuf1PMPYbBfj+SydaS/T\nk7Nb+WHT79DeJB6kViPH4MBm4Zav3DSeYh3lSeA0CVPCCXFoBlJqU4xO9M483JxQNxhtYg66YnxX\nHaJaT9XoN2pwFGDsiyu5/nOfZuD6O+g4I89kspOd/34NY1et9A9W1wVePsbJRFxQlBevgI137+X6\n937aaylSgKFDG2h9f85HREN+X7pzccP0UYuLykJiFQx9tkobk2AhwF+hrvaSzy1IalQ7Ruf4XpFH\nPfRug1vvhdnzwpN9oh7S53sLe9zoVaaIsnIveC3jVtRtLUFXIDrhXuN73MqoFm0EQ5vynvo9aHxe\n2E5EoRYC5qZ5tYRV00BaB3sOis95EY9kRGPzRELssuUUZWladIC4vQG2JkQD6xMN8MOEEEWrFgm7\npO8YYE8vzT8u7pheeN7JAMSMThopYDEc0XRNeF28fYufgEF0ZDJ4HZcCMyu6MKgKLxZHwhGsYGTM\nhW7R7gC2ZqH1T0VPTFf3pvseF8ZJLr3ZRDxiBJBdB81/4vOdW94zwi1XfdS3Qd/YuZfrv/FpxnYF\nirF0npNBuJfSdb6/Fl+Bkzbr0S69HpjfVBWSy5uP88zhN/DsWJqOxkkWmwz2tLyWE1J5fXrNPL2f\n2c++d/arCX7dCsQu6DeP0yRMCWfCVwykntcf5F1r7qONaZ++C9RRneHx1WzoORB6fWR8tfYzAKmm\nvBdaTW7AeigdL1rlogHYAmMLK/nUr2+GuQQ0JMItHNz3/hmwQ7Il0C1gwSiu5hyU5cXrnH6TUlGA\nRZrB+53F+dUWlqnQxOnMjRdHIbsIGzWVbREWEtZvB0lBPUM7zgy3MXExTnTJPYjd6WRCEoUTnigL\nKHar9XDFZviKIqUTjA7FiV6VbRg/BGcpmnkv1aewtYjSavss4M/TML0BnrD9ffACsF5oCDuWJ1BH\nUE4GCadicVcqTFhHc/DPD4G12UstmSuqVEeWvWq4OF5m9jEo5hydUMGzF5hCVKPZFhjOOFNtrnTG\nsCCiN/aMmoTZ88K/LblOTd7iRFFLE5BYrD09nGjQ66NctGuiQypyWJoQESy3ctHsqvTVXNM1xM3b\nb6K7/SjDU6u48YFPcOh4b/S5qaAieIXdULemejsiCIzvZuBV0LdKbKgmdwuNVy1kWacRVUVX4xIj\ngJlmSPotXQZuuMtHwAC6W4YZyN7FpyYDVkRxgnbBjeBBhARFqjvTZj3kJury/JaFjt9X65rTyUKl\nErJ/y49pbwkzrdRqS9wfVaS1XPmv3zhOkzAl5oCMGJDJsyq7h57XH+TWxz9KNumtNN0Mcz/vYHRm\nZditHbj7J+/nzPZ9rGwerbw2MruCu3/yfgCGHttAa7ff+d0uQbqlQLpFMJ7WzhyDezdj3RYgCNUG\nxytwgiExdv0NwNsS8E/Ov3chojXBuXg1fo2W5hziFgWApEd7H9Af/oxWP9mZhGsTouxYhWn9+ZEp\nYx1Ksu/9fSKVUngGUoa/ZF6GKgJVQviJvQ94sATj837/KhVh6kRN5tpb4gdnqkWvEgY8UYT1E/7I\nw4JToq7S2FXDCc1UkcG7xr1GdGqpltRZB7DVccBXEM1UT14Q6JUW1oiqIfgxYXB6+3q4xPCu/+MJ\nGJ8BW6qIq4jEz1ZHKMqQOttpFbPyONbICXXfwcqxc5DeLSI/netEdwD5/KMc620bjIjrFJWyLe4X\nlX7maqcHp+uH5hD7alHUsh3dRDwKbporH7iucnVgrqT+7DTiuKVRcc4ygcs7ovL0Nki9ijVdQzx1\nwx/Qt0LqvbvhR1yw47t6IlYuoFSL66JVRoSyXIaKCK3IwvatcE8x+l75zsPZ2WqjflKTYhdxs5kF\nlClurQdcu2Lw6sj7ONFV34HnbWhwA63tOdJtmuxDUHM6C5OWRtc8L9mkzOoDGak1eaytaUXFcb14\nPmuoeXixcJqEKfEvwHan+uU+IfA0TN5/5xd9BAyEvuu8hWf4X9/7ZLg6EiEu/PhTn1W7zS+AtTPN\nYMtmei8XFSOppnyFfLlIt8yz/m17+eVtr/F/ebWoRq1p7014C/QkwooiOAaCGi3NOUzmNP0mFUUB\nFejmIN3r21axfN04A/2fD+sa3MVlq+az0wnHNyoJZrcoJ3cXEHM1NL/bPym6hOoK4GzxMUxEhWQ/\nsNqETx2HmUb/bjZImAZQk7BcAWxFxc9SMdUgduRXboVXrRfkqz6h9p6Lg1111YsVTlb07qISYVD3\nHUz15Ol/ZI8vlezr6weiSXXdOuEP5jufjNceKhi1IQmpsB9T6ow8/Y/9Kvp4Lso2dHXCX3xI6HEU\n569FuYSozI5IwSWSYqMQjJrIqb/SEThxu/j/DnkBqkdRE4ZIh9sashQFs90zp9VVB/5LCc4kYKRb\nhIcPwOyT4dSgDMcO4ubtN/kIGEDfiue4eftNXPmF+8KfcyNqwfRqKQe4La8kob3RhrLFmAo6ItS5\nXlQgxvbzM6M97FSbF8WlClUe/ngd1hdblMeM4wFXgY68V3ueK89bETJJrOlAl5PRFENf2ID15rTY\nnHbjf/QX1BWSx60se8qvYeMrBhl6bAND/7iB1rv90hIQgYz+8/cI2yEC49Sod2wqIs7/JcJpEqbE\nJio7j/KMmNDJ0NU+pnz3OvMgbUwzRjrc/oUIt/m9wKRoweMK+vu3/DhEwgCyGyZIvTKP9TPpYdqF\nWFRjzhlVUY9/EW3UlBRnAuegGKA7P3ANG+/e62t7Mnywm507rtEfv0bLhOVrjnPL+R/zm9F27uX6\nRz/N2B0rxSQQR0NlZgTRtqec3XFSvSt121ep5ssssK0TvnCPiEIYrerUgu58vt0NHNNXmdUKe0Zo\nUxazfoNGWBpZCi7gK1FHIE+F76pO4/cR4HbRZis04cp9/UoTzqKqiTjVnaGxDzga1iGVi/TeeDT6\neDISBlzeDcFmB0G/uCcKgii7sAuQ/y406XYNBBZ1Ex6fhuMnROSqnBeLSjAFGPSyiqMBNMzqVZeK\nZubL1y4w8PEjdCw7wOSxPnbeusCYbEg+ZYZJ4MMH4LDCwiJ0PGEH0Z35lfLP3e3OHFC2nS4CE1Aa\nlxrDB/y5zBVKwq3DGnOIm9tuorvuKMOLq7gx9wkOTfsjb23HhtjyjZtoeeEoM8VV7H7XJ8htkt7T\nC9xehKmg5GAJ430X0Os1SE+l8/T//h5flKk1Mclg8fVhAgLs3HENG18bmJ+Pd7Pz6J9I3RucP9RS\nwBPEJHAPlSi/RaDLSZSurR5fy7zO5uOUM2WeSZ0H6xKsYITWlhyDD21mcNtm+nf/mHQ2HMDofaMk\n/JdRa5eFFwmnSZgSknahYQuYGZb3jLA8fUz9bnOOW87/KNd/79OMHVopIg3VruxxwPWKk1J/ury5\nmbTp/cf97Nva7x8ce1Gn8KpBEeEGfItoyowoKXYHtmaAjk2u5Pptn2bghrvoWDHJ5GgHO3dc43Pq\nD6FGy4SBN345rDtrGWbg9Xfxqbfe6LXHOIqojmxBEIc8YkF0+1vuAibbPP1GOSIiFUUyWhNeFKLp\n3SKqEIRuQstlIFUjgykXhYi6rsuvPZGjIm2aqEqtZCm4qy+gfu50RLoWl39thAG4DlI9GoHvCktE\nOGbvFQaYwb+7KcwVBazRBoZ2NHqRrERSdAQIIpEk1a1+Hip9BIO/TSenkv3i1tbD7dOCKLtVes1X\n6QsQQvYDwNo03G5Drh1oF8bSqkq9xWNAvWN+GnPKTxgiRUiduo1QkICFmn3/hI2v+yjXb/N35wiR\nwGID+skoAHua4ePqez881S3OV/YGC3zWc7lXVHxGYI05xFPLL6Av6Qn4zk39iAu++V0O9fZClyBg\nV976B2SPeVG6Vf/nR9z78e+SW+YQsS7g4nn4yqxTxFLj8lu2nUuVgHEb/uH7sG0VZPvovXS/j4AB\npNda6o0CMHZYzM/vveX/sPJ1R5hLN7OrsJVcb5uotA9GbpfaEqts60lmHF3bAowhghgb3zDIiq4R\n35/lzZD16zTpN4QDGDqPQuyltCk79ThNwpSQtAJOheTADXfRkFzQvN9Z/Pvv4nbrr3jNef/O1uwu\nmhOzDI+v5u6fvF9MREfw98xSLEJDgxvo6hnFrAuLBlNtVjiC8RAijBtXpOliGnWvRmn+0pUUrz93\nL79ESo2qBmi5zNjhlT4RPuAIi6d8Qlvf99xegIsL0NYsdEgRi3VHUaNraJ0Mty9y3ypXLLr6rOCE\nExWJikoBZxdFGf74tKhE0uFkevzZltA72VN+Y0idIDon9DChiOaBDcodsg8uuVClCiac/+h8eILf\nU4tpatQ17gIrGSHwdXU4Ab1PrBSmBtZIxPF0xRfVsMwQfQTvBpgTpNFoUZNVCBMwEM3AL233P0tm\n1kmzPKJOq9cCOwf5x8JRQwWUzb7XhbtzhI8xQ4iARTzPN37lzZy7aZi+bm/sHxhdz43//FdqAuZ+\nl9kORteSrsXNmet9BAygL/kcN1s3ceUd98FW2PK9m3wEDCB77Dm2fOMmHv6wlCZtbwazxsVffiaO\n/9yrwJxGVLUbGVLnv8r3kVZyvImnabiwwKEv9YoN8PONQKJSdJAjw+5Nv1/RI9c3Felv2yM8KrvS\n8aLluk4RUD2aGmcj+EtEdikDqddGbL6I8CJMpsQYldfd0hQUvh/jBF58nCZhSuxGhLOyFZM/nZBR\nRmf6OL938ZNc3byz4g22oecAZ2b28fFLPsvYzzRRIKcSBISz++RIF1094aibNZ9SWwzUYhbqYgIh\nPwkuHp1UwtGpDvVDn/3dCVI9+ejFS7Wjl8W3umjT6K/hDsfTxte+w/Y7SQOTh7PKQSeLNr2T1p9q\nTek5XcECwLI24YN0671QjNn6KA5Kc8INulwW1W+FgH7Gng63XnHx9YdJ9W+n/5L/8kc0ByYYfPD1\nWEc04uFqqYIsovrpOTyfojKBHnTOe2s1Ta1iljv09AZaOzT2Iomkp/mSqtOqpjAjoPQ4c4+n+m0N\nqKtgg8ggFkS3Ek9HVo/a0KBZzFTj3WxzNFnviS4CqAZ7JpzKMzqVUSSt0FtRiFNB0MIC1FoyKbp3\naKyDC/7nu7n56h/S3WkynFvJjV/9AIde+IGagDVfvTQ7D6mpdnfiP5Vv6TaH4RiwE1pOqBsgNuf8\nxJTpKgQsSFx8z4QBZ70K+s6A26bgxCrxXnsW68j3cB1SW8nxTr4u1qA2WL/9IBtfu5frL9/B2OHu\nylcrx4TsURlnLUkkhAVLIulo6STvumqFONU2K8fxdWixXpESmsIA3OpK5TjNNTJ0ZINYGwHWluGz\nz8PRx8T9fRng5RGPe9khh6gln6tUseqEjDIWGpNc2PxUyJx1ZesoAx++S//BwMN+4Ccbyc/4K3Qq\n3lmqKMEk8JUyfAYv4lMNEwjy9mPEQuouGusQZcXX6atOzLRN7w37o7+/A0HmrgMGitC8H0gI7U2y\nV+xMywEBsDspG454Oum4qRYPw9w3xN8l7NxxDcMHu32vDc90s3MwQnemQ1zyOgl8DkFAVIHRFVlR\nFVdLJWA1JOpE1NBsETqW5iv9/eTcKrTmAacaTfrbsSP0Zr4TjmiuXaD33gPiHqlqJeKkChoRJOqr\niEjZOYjJznl+Kt8b15/KxSRi+GlsfKxDwl5k9IGVTP2gg9EHVvojWkknDTx7DyzsBXuW1Aq1z0kl\npRgB1+NMeTzdbxhGjK1fAZoUWmgsX4GarPZGPEuq+SDR4pCmkyBg5aJHkFySP7sTFoeUb9cKvUc0\npculmXDaFCIc67dU/nlorIMr/+FSzv/YxVy549Ucen6POgXZeNHSCJhdEO3q8o8BMFz+LeXbhkve\n3DNjqtuJzLZJ89MEwjD6OtTjzi6E5w1lBWYbvLVXRKASdWC2MfQPF5I/KFj/m3g6tAZ1rxtm4Mb7\nfEUHqZVVPCrj2NkkkmITkXBS3bXMezqHiBNF+HEpFCkf2rGB/MHAuihVV4bG6X+uZPBfN/sL5pYl\nYJt7T14GqnxOR8KqoFgJX99/9x/Tv+2/QtWRLoZz3Xxv4Xze2Xq/8u++HWEw5RDgIlbeaTj9W/tJ\nNUuC+ENpvaN2IiEe2NvKwmpirfP6UUSOX57XCggX/klYvvsoA1++m47VgerCLhg6sYGuRU1q1F28\ngh42HYjFZBNSwUAS1q6Dz5v+9FPWFCagrg3B43WQXwnpwORpbhLeR/aE09i3GRJGRdcwcMNddJwz\nyWRSqjytFT6vpBxQ9ntHyYL5SeD/IibTswij3a1eq8UQNCx0Fq8rDCvNLKy6SFQddbTDTBd8s8G7\ntgFdUKpDzWZSyy1BmFSpwTikdLoI5TphQBoV6aqx4AIQJqaq2ckpubcm0/oIVqVB7yMwJ0Tf1vDv\nK9/q8yiKQKitl4uo8v2diOegdQT+8gyRgnQRTN12IMZMLSjYsEvxjNmz0Y7tcWBb4hpaezyvLntG\n/DvYtBqN0Pvgcnb+7fnq73fJXLDBtaHxa1vK76lTtMSIA6NBbHQSdZA1uPH8z3Luz39GX95LNx4o\nrufG3Ccq/96d+ltWLf6IrO29Z8Jczu4z/1YQ8XlEhw75EXLH3YTttFaaB8O/qYy7gbGOtDC47XX0\n3rCfhgsLSqlJqGewLs0+n4rfvcFFXDNleT7TOYEcPgJfrgvdP63hs5SR8Y1T3fzcloLGi2Huk/HO\n+UXGaRKmRBtwtfhfR1vS9N4ZHkhu5008TTOzWC7DGIMjh9ew88g1tPcfYxa1x06l9DemhsTKp9n3\nlX4hT6ulImVK8vpyERT1NgBXwvKHR7jl7o/Rvc4TO27s3CsKDPIrsUoRqVHV4hWVwlpm+tNPlfdK\nxKM3A7e/XdgKBGGkwhMUeLqztmn4SLM4jgoTiLivao6fQOopZokICvh1KW7fNxm6BXh2We0VT4kE\nWHsFwTSaRVVuKedoWQKLSQdwbZ/QBLlYh0ekZF0QYA2rtYyVIpBgatC2ILeIsgGci9EJ+Oq9MNMC\nmQEi3fl3AWtL/ntTbZLPaFzth4lXlVXX6/gAiZs0dP3PaH3dK0iv83b/Po+iIOL2ZKxWTJIwYWYt\nfJ7owoSt6Kucnyes+5y34fZDMKnwxirPem16qhVElIuCtAXNas1mMF8F9a/wV/kmz4S5x8RYMFqE\nzsps9m+IfIU4c2AqnPStPeG0Y/IsvR7TaBORXpcIysRQ1kDKerKTKRs36itz1KGuXi7o/y43//Am\nuqeGGX6hmxuPfYJDpV4R0VscImft4d7k7Wwp/SPN9gizdonddZ8g95gTlR0gLGGojDtDXH9bwUpq\n2MC4BOTQl3pZvz3sAhy0n1Cm7yYaGbp3g5gLQv1Ei0ur5CxNORuDZkE0yYr5XPfbZtrAHlX+yTqc\nZt/1/d4z/Wb062LUtUuuAyN92ifs5YstVLYSTol3aqXFCdrYxSW+d079qoPB770OzoLcYIbvdF1A\nd/OwLxw88sIKz5pBpyFRweDkPZdARBWCx+iCgc/c5SNg4BUYuJYaB36ykaa2GV86K39cWrzkHVC1\nFJa8e9PphC4J+jrFxPFJuPVBeOe7RUXgPF51pLv4XIGahB1BGsS2N6EHjSfrev1RKdUCfMwSUala\nkTCAIsx+2f96elt4R78VPwGDMJFKbhCfLexm6G+maT1vtZ+ARLWGMlLCQmFNTqQ+XBRKcLgAx16A\nB550mgJPw/G9QqsShDsJjs/CrV+Hd1whooRxNhRT0yhv1njEZ2SYLZ5vFWCNvYnBS9P03jRFamUJ\na7Qx2nC1+CswV2qc6RecXpmZ+OX7cjGGXRRa09IomJ1iAdZFPBbwqqh9xzCEGW2I4OS8DgBxCiIW\nDgiNYcMWZ2EKbCKDNitGg/DcmrlTjJPWj1T+pCzESRZhZmeYNKnSjrr0adkWv8clikFi6EZ+IVYR\nQWxIc9Shtl6u3OoI7H+Mdy/LU5V5Ilc6wsO82vt8Utq0xYloGQ1h/7caK8ZBF5UM2wO5kaX1O/aS\n2TwNlJnbkxbPm2psLj4PZkdt17dcEqndpsud+yeNad1v+1a78GAs5cLjr5Yin6hrlzChYTvkPx//\nt7xIOE3ClJBC344w1Rr+HeU7rVGP0Vv5NP/fUxcxPf9CsgAAIABJREFU+5pWtmZ30bQ4y8jTq7n7\n+vd7Zdq1WAPEycmrELNkvqNDI6Zt9J7mSmrUrazLpRj6gGbxqvbb5N9Tq06oGow24YT+xceg+W1q\nj644ZrDlCI3Q4mFIrvciJMEFeGoKHp+FE4o0SHmxekm6KuUS9HmCmBN6ozDprFuFdewYg9vWiDD+\nORZWUtEaKvis5TJw2364ZASyfXAiCbtMmGwCex3MtngfemgPnHmW35xUXiiKB2H6CHx5BOpjaHRK\nE/DAU7DubfGiZ7oeg2bWKe5YBmYWaxj2fQgxuS8ehzobDEXEqzTtWAFoSEFp3F8kUmu1a+mI0FeB\nF7nJ9eKbd1z8Em9xCbl+K1oFyYaz1QoiShNekUf+ERFpiuOWb6S8SGt5FvXOxj3HpCBgwcKRWtKL\nwfsTHNuyZqwaQSiXnMifNFh0UoA44ywR8TvKUoojbkRrcUwQHfd5VpH8J0rCc00DfVQyINNwNLnN\nZ8+SWi6i5V1bJ2g6e4+6athcJiqzaXDsTmJEihcOivuvui9RGxizHawhx29ROk4tRT7VNkjGKSLr\nJ4nTJEyJQH7Qnmbo+l/QurlXXSE1R4VxW/k0zzx9Ps8cP1/NznWDcRH/3ag1J++ihpL5yVz1lhAA\n1gtp9v13f/UIRhRpDP6epeiEomC2i0qoRIO2iXSsYy4eD/9dVbHlQl6Ai05fv5SChNkzVfoTom6j\nIlenuZGKWq6dmYVEg6eVUD0fumdtuhu+8gLUB80lU8L6YOZL4t+Fy+HzKfVkJ1fAVVvgy2WxQM49\nCoubhYawWoTJtmD2q9D8DvX3J3vCr5ttolXV4kHPTV5GoiHayNPMnnzVYeX/OwToaxlYc6W/zZRb\nHRb1PcHK2OYB7//rSETrPFj7w8au1ZpOy3BJVGmquvZKRbhqOVYt51MNCRNKw7B4yDNuLdcJj7hg\nKjTOOLNn9ceSpbRxI1r2OOQf8I/3EMk3RRrUaNZqsZRRydD5lem9fm/8qmG5khecQgJTn0Iul8D6\nATRuUf8dlBuY5T0jDoEcZ3K0008gl1Lko9sg2XHD6i8uTpMwJQKXxchgjV/C4HsbWX/HXjK901Au\nM3fAGfi1OAqrBmOQgBUg9VSe3lsUvfHsOfFmeRGwLbHYmVnYmopXMj8BO5+9mo0b/C0hQtWFcdpT\nRP22BcRuXio1plyGXYn4YXZdY9sgqlVCxZkIVb0DVakTFdxUSzBypWudIkOuDF11MWxbA20GTM3C\n1x+CY1KkotYUhdzAuJZn1WgEQ2E4C2KnLkcfgpOdo5XxLfSJKiQskRBpxKbLhQN8tQiTXRLRycYt\nItKo/E5NujFq0Q4SLF9keVE0BlddL7sA9iQkmhwzTkPx+RI83AiFjN/jrdACt03C5aYQDk8b8E3N\ncUBt7wB+cqMtGtgP+Xucf0irl+rZ1Wnj3ChPIUajahXhUh3rZFALqUs0iIINlYVFuQiURRVhnHFm\ni0KtNclJbl75fbqTMwwXW7hx5M0cUrU9c+2IMgi971bCG5Y4kclEQ3wxfNl2flMwtVxHqlvdoipO\n1TBGg9Cx1q1QbzATpoiC1XBvwqa/CHsN1/T3VG3eSxNQuLbGD704OE3ClAg0PWzYAl1ZeG+e5t5Z\nUi1O6PbCYzSdNcPgpU7oNk5KIrgIyuahDlIdefq/vMfX1Lv1tTkGLz8H6xd3e+cUFKYaGWj9U1AV\nBwwj9DTuwltfZOyc1ZWWEKG+lvK5VshTFbFytQW+bIM9D5RgsrWGVhiByca2gHLtxotxCIjR5hN0\ni9di7LJLU+oWKfL9cV/vaodLW6AtCdM2PHIEjj4pvmfNe+HPM9LE3wbrroZb74HZmfi/Q0Zw8g2S\nG53gNmEQ6WITdV1crUxnBt6+TTQon62Hb0acpwszC6UYqQ6312G196hgz+gJmoxQ5LBOrUGxF0RE\nrjwDLR/0EzDf503o3QC3fxjGDonf6i5gs4Dc/jC4+bALYB/zHPZVtgwyuVGRiNEJ+Ma/AA86L7wV\naGDZsjaOHTsceHZPwMI+aN4W7qdorvDGSem4noRpyaIzHpqvUbvXB6uufX+L6JsZh9gZy4ReMpEO\nn3ciCQuO/c74Wrjd9BrAB73wHp+GQ7tZk5zkqb576Ut5lfPnNh3hgqMXcQipcMJto3YdXveEdcDa\nInz2gJgD4kYmaxHIJwyHXIY3spFGxHFgNAi/LV2U32gRVh+hjemU5BlZEP17zUx101+lDncBnqij\nquNWaVpskuwZKNwH9s/i/cYXGQn0bh0vS5TLZRJxdwBLxkfxEZnmAfizXjZ+cJAVvSOhd48+sLKq\n4aMWijLajW/QHOf+Jva9SzGhyfjQNvhdRYpFFpMCXDsHGyOq30CUVt/h/P+oSTEKtiXCvkbWT5qC\nhM62HMO/mLYO1QihqipsImbFm10QC2rpiPi33ATZdw5F8V6VgSqgdP7uQBi6ymmn0Qlh8JrfAu97\nlbCNCOLHRfi/BzzR9alEtbSCDtaz4n+V16YErYfhL7thmbSAx42sFodFROpURUp851aEE1+A5vf6\n0ysqDKC5H4Q3XaUJ0SJITmXW8nkVKqahJ6D5P+GKzYLQTs14zulByM9dewEuQRSr+D6zH+GZIG9k\nSqgWatLb1elZ61nxTLd+WP3s2HNQfMFZqAObxYr5a5v6ebbnqHQ/8L2+AHOPeNWZqupIHbELQhdh\nt+ccYiA9e6o0/rEi/O/nubfuL3lP6zdDX3Pf1O9w5fTT/nlT9zz88Fm4M6iby4hWVqd6vEtQdZLI\nH2yM1UkCiJ4D3L8XdvuNtxcPCzNl3z0TY/HWx/6Sc97409DX/PQHv83HL/+s+Ic8t+dm4Gt3wfR6\n0XdVt0aVToiip8ocvRf4evXfd4oQxVtOR8KUGAGkyjF7BnolE7sAtKFb1zOrF0F1h4CHFmBKWpQU\nc6j2OMvUvSt9+MZu6FsV1pbIIfTSBORipPeWqs8CZ6CUoPkYnGiGbzX4F94gAVs8XD2qIUNFpuwF\nMQizSU0FjRFe/FXk0miAlqthcVikGxb2ivJ5OU1VmlCbTVa+Q6Ejq1sF24/57w14Bq/3RVTJZRyL\njNK0Xki8VBgN4nurERIZcvQheG1ALG7b1qobWcfpTmBmYfY+SJ0nzFeXUhqvw8IBcd+C0R0VatGg\nmFl8Rrm1fl4FOydE/J0Z+OsAee9bJch7kIjJOrFZRGubEPrwRZijWl/pUmJGu/hMkIBVFslGmN7k\nRWnrekRRQ3Kt/zOhyNaU/9x8x6yH9AX6sWdPizS4qSEFMrQShySYAQKoEoQvS8K2DXQ/Oaf8lu6G\nOjgR+B26+96u0s1Ni1ZMvs4hifC5uSjbToQ3pc4SKOa6ON5bWpSLQBKsZ6BuTXj+sC0xdwbnwWBL\nN6c/M0SY/sr2GnIkf+EIcBE0nRkdJEgEr8cKhAPCb941/zQJUyKgL7H2QPmV2uba6bNm2filQf/D\n2wH8Of7KxHOA1YiWEzlnd6MIr1q5VCgjCmCNLFK12e34NPzDo44dQKvQlriTYLnolaQ/fgWsb9Nb\nSsjErdYomM8DzBHtrkcfATFSQlewVNhzgOlNPLVU0JRL6qrFrAlbeyDTA9O/JSwB3HO3F0UVZcMW\nfVpI5/yd1aRQO88URKOa5qEWoiSjml+UfQJhUBtIzwQXSPkZ6kCQx05EM/Tgd54MATFSkNoCda2n\nloC5FYHg6Jp0jpEOatWgBJ8l3fuyiOsXFRHsAN7SBi0D0NUGywIRkRVZ2H4R3FNUk6dIBAhYRKsg\nPQlrJjRXBtvsgJS+bVdHdRKm5CXV6gjOI+65mRURIl2z7pPVm6k2eBHP8vCi2i1/uLQ6/KLu1kxp\nUo/2tGch4kYOdf59CUPMD7YmKKCZw0NGxOVS5BLjfZ9Tiaubu42U0HcGN2hu1TIIcml4Y/D+u/+Y\nV/7RT+lq8Sr3VfYagN+OpRqMep93oqjo3UJ05ctLg9MkTAlpoTQy0PRWGEowNLiB1s5cqAVMavkC\nK7aP+BsCS/0gfeish0uKcI8z6YzMwue6RTNfZ4Ec+tIaWu+ZIL3OM9nMH2xk6JOvBGMkepI1MqJa\n7b6A2LR4wB8CPj4Fd/R4C7Ord2gowuQYfHsFTDmPh1aboRF81torECCbEmmTOBon1XnIu8NaFn+V\nED+UejD8OiCjDoxuqOsOudN736tJh5zQkLATDWLNWoIvUFXE8daxnecxpJExkfvoVRb5zgx8/Gq/\nj1jwO09WRFu/psY2KEVByBNJdSqrNOW/V/nvQPP26A2GUoNShF0xiaGuD2Yn4p7oNiYdwLUlh3hF\nLDLZPkhJ56J9HiMiXboNQ8uHRDRSZ0NRnhGpbBlLGfvudxlN8TcZZrvnAxf8rRX95UVQf2a8oh4Z\nqk1ZxLN8Y+4TnJv6EX1JvaN+BVqdnkZmoiLI1QqVjJTetqUK1mT3cfP2/4fu7AzDU6u48YFPcOi4\nwhBYhkuClBs9jbasvi9EtFM9eVZ+4TAPtrytYoqem8nwhQ//hd9ew86L9Uy2Y4mD0Jwcw47lJcBp\nEqaEsyupDIB2eBisHs8zq33FOKlGfxNqX2lv1FzS1uSQBhvyD8Eh4J+2wLIsXLYc681tDN53rvDm\napdCxEfTot1CeV6/81VNqIkk1K2D9BVisS3sFv85vgp2Bqr4XGFuSrGLC6JcUkcpao2AdADXJeMZ\n8MmfqQz4Bj9p006YGhf2IGpZSALu9BXoRLW7TFhr6T21ahXdx4Hu97wVUb2aKcJ4Er6ZEREtF+41\nbk3D+GGxUOSdi7v9Ij8Bc79TvkYnSyhr1QcmkoJE6prD2zn/WKnfVD3C696Pt0xBS05ELHYlYTbm\n5O9+/iOECnB818ueh+LzIgKRaBARsGDkS4UTgfGneh7N1eFm3r5Il05UnRJp+eJh9d/tBagLeE8t\nNfqZaKld+6Qbe+CkZB8A80+W3r5IRsSzfKjUywVj3+XmtpvoNocZLnVzY85x1A9CHt+tRZg4IBkf\nK6Ccz00R7YqySbGPCcuc5AZfpCkKa7K/4qkb3kRft2fTc+6GH3HBju96REwXUa/FRBWUkU63objP\nFL1FdKvhP6Q3lh0Zu1kjiQrNyRH2Ii8hTpMwJXYDr/APgElY/p0RBj51Fx2pCco2/IjXcyLQpKui\nD4va7VdSS9Ikkt4Nf/4hWCZuiUWafc/3w//A/xAn+/wLR3Dnq4vAGClE+5se7zNzj4oon1uhMveo\nMynH9dzRLGA6sqO7JrXunkMDPukf8LoKmvsfAcsR9Jpd+jRLrQuJzmS1flN4spkEbjsmFvXODXCi\n0T+RyRPcV4GJJRZExDnvs3E6uyThrE2woSTa64Qm1RTwKug7Q1Rpjk9DVrOwyceKQyjtRXU0Mi5U\nBRq6VFZwEtY953KfUIDjE/AFeYxloHlZ/HTXJOI+diruo3u9ygV/cUfLAJERMIDjjtVLEMl1/hY/\nze/WpIScuSfKvy1hholW5Tu6wpGrpUQ/SxMiurEUAXq1uSqOj5n2szOiqKhcgoleuEPvW3eo1MuV\nE6K0NdWTp/fT++nf9GPR9/exDVg7095zX9E0JcEC2ALNmk219vdV6bdjdIh0XfH5eNEiu8DNV1zl\nI2AAfSue4+btN3HlF+6LJlpRc/g9gahccHw50DYUD2quzSah+QtGYWUE54VQlW4ZiKGxfglwmoQp\nkQPGwGgXA+qG/axYP8yfvPIuuuq9XPVanud+3uEjYtaUM9ntQuiggvO03KcQvEH29i3+6AiIh/gK\n/L0ggwtycDcYx5PFzAp/HF+T6kZougIWR8AIbtk10DnAP/wcrD1DH+0JQnc4HXm4gmjSFlz8cxZ8\n7U5nt7lPvF9X8Qi1LyQ6k9WFA+oJ8PiUqISSz0E7wSVOLgoG+vMO6tLl/p6qSXVFm4iAffEBmNYU\nVQePVc3rK3GSxdm6aFlwonf/LduP6MbKwgHA0VmVC2K+Tl/mXyTnHhW6pGoE0iXWK3QbFud/g+k1\nnUbo2BQcz4n+et9q12gsmz1ipSqacJFcJ65HVdd7TQGD6vVaop9lW7SHmnc0TyyBLMn30E25drXD\npc3QOit89r4taXBBkBPf3KfB4pCzQd4myGjUs+zoZlM9efof30N6jWQvtC7H4OrNWLekw/crmJYL\nbqq183mVKLHRLOaW0nT1qBmA0UB3p9ozrLvdsYyIIlq6/UhHUVSay22rVH14WYJdhtEgIshypK+9\nCBfPQWtebHAfnxXzbUgrmUA0nvyh5sRfOrzsSNhb3vIWbr31VlKpFM8++yzXXHMNMzM1GPGdMoyT\nWltXKd/dyi668Lf5aSfHm3i6Ejqdn015/fgmgc8hCMNa5wND+E1LQQgTm7ZDh9+lvoJNVBfwGi3e\nBGS2x9MDqHx9zDa9349dQJSwN0RX5pUm4Og34dYWr49jVEqtA9GcWIXggl62IWuIa6JCMAqz0/n+\nrSZ86E9h4rAI/U8CJLU7ssiFJMqjSEZnBrYDnUWYTqod5GUB8VK1NHGg+j1OYVMImcD/BtHhLJSP\nHIF1m2IuthEtm2q1xaicB9ERtuIBQZ7cRc6d+M1l3iLnXP/laxeEQ/fKCSaHW9j5d3/A2OHVjk1B\nQPjrLpKpzfEIWJBYS0gV8vS+eT+pKyRD5gPOhkpV5examYxPi0hXMo4gOWLxNZoF8StViQhoNaFz\nhAob4qbTgzYwhd2iOXgtOiZfNwZHOtKVla65c33W5+C2vTAlWWWA0Iwle4RfWKh1VU767hiZgUQC\nSlP0fvyXPgIGkG6Zp/fN+9n3bL+i9VSVdHLzHnjnmf55dKJUnVRVvi8wkMslce0VFZbDOfVEPDzl\nvB6VIdD9rXVO3OP8EfcEHFuS7tC5KRuKH0x5fYpVKI3D4qyIdnYA19XDsjYqvZ/XpeE24LiqiKoG\nvemLiJcVCevs7OTuu+/md37ndzhw4ACf/OQn+eQnP8mHP/zh38DZ7Kb3xoOVB6JZkz/2v57wt3+b\nxB/FUi4czuIwq4kI1FN9ITY6wl491Xy04sKeFb3/rD2O47umMseeh6LUCuXYNHz+S0LcGzVhbEXd\n07GAf0F3r91GwhEcF0HSVlkE6xAizE3Q1w132P6dcbD6M2ohKRfAttV+Ny46M2EvsJAhY1kSEG+B\n1g0oK/VUk5udh0Rj/DTleAHuaPB+zzziOqr4z7TjFaWLnp1wntOjT8LnVsCl7dXbCi0eVO58l4xq\n+pPShBddiVrk7GmWZ7/ELQ//N93rvQ3Wxs3POQ7dCk2P+/moKIr7POka2k9D6nie/qsCEZPX5hi8\nZAXWLxFE69Z7RYRc5Qt2qtr+mFnhbeZ23IiL0gGYuxaa7gYzkK6sFjEqHhK61sYt/uji7FfD2jXt\n8aUiC9lPSxfB3foC3PmA95qRET1F5VRsuSTG9+IL/nEd91rbOVLLjqAatKlGKxzx120CXdLXmYG/\nvhxWSBPk2pJo5xUaZwuI6G0V78eEiRCCht9341ffz7l9T9PXPVV57cDoem58wCkwiMoQJFFnNHKS\nyLRSHNKusIyQ7DI+vofUslGs4QWG/mYaayyrbi4PIoprOvfxUsKWOMtScHkP7OxRFK1USem+RHhZ\nkbALL7yQPXv2cODAAQDuvPNOBgcHf0MkLEdqxRTuJZrVVFLIrzc2F+h943720R9+Y7WFY1dCWFio\nCEaUqLVcVlcUnQwB85nhleFruwGFQNR3HgVFL7ppIRA1ItIMut92FG+iqVy7iN+0QDgKo5qQl2fE\nYJUXCRWZ0S0k8iRXdyY0OemtMlRMKbcnw15gy5KwtQh3utfHOabr6TS+DVCkR+WJz/Veay0L/6E4\ngn034lA8D+4+Q5RqXwU0KCb/eQvufxTsy0VrHlU08JEj3nkf+oooKDHanYkwsHjallNdN+NoGU+R\n1YQuanjxDNx1xEsfBv2IXEiRjYG/f9ZHwCDg0K2C2QaGztsFx8/JhozmeR2F3lfsD0dM1s3Te9Mo\n+97lvDA+HTbwdKGyYdBFwKtFxg25EXin+M5EncbIdB6KP4fCTrBfKQwwm/8UzOWK9wbSYOWSkDsk\nV/mfhfpNInJZeEYQ9oqGq05/3m6RhVw8BRE+XAGGoBO8LzwXFvrHtbywZ7DCHtsAwt6oG39Ww55T\nZx5c0vf2LYp5xNRsyhdEAUUc/Vd5Bkp2wMF+gkMv7OWC//lObr76P+juSjOcWyOqI0u9MOBkIYLt\n79zo91aE+38Qxxwfrqj+uxKs5+fZ9+7/DmxsH1F/vjQhxrr7WjUtry/KWAa+FXkuLxVeViSsp6eH\nw4e9SpwjR46QyWRoaWnxpST37NnzkpzPpxumGEPkyX/AG+lmmHbJ3G2KNn7AG32fSRUlEWG5CNTp\nd8VBHdMvEUQsiChR66nuHhAiiy2isfDteX/lXBBmO7ReB8UhmN/l2Ri8q0VkBXSREt1vk9dFXURB\nxi+scDn0yfhUxYFhCquKYD61U1OdpzJkdPHQHjjzFWKSdSGl91KvzNP7pV+Tal8QYt/BDVi96egK\nUre5demIqBRz0TQAKKI8h4/B8RFoLohrGYwGPmF57ZXAI5DpbSKtE0TxoJduKh44ddEw3f1rnhHE\nq2rbmraKcL1j1c+Ub+lYEcFujeXVozUJQ2gRUbxvOkKE/Ns2XD8Q7YgP/iiqq7WpRKsDC9XcoyJ9\nWterdpJXNQLXaSaL+yH/LSphD3sRZv9ZFBIYkqRCPq7RIohg3UpIKqqu3VRxsgY7CZeoBMmUbj6Z\nW+7XA+pSjGab+O26lmNmGxjLwuSwlANrD0Of/ENaf/eAj2DnZxqFTKUBP4FKJB2vK4mIySlW3Xyh\nijgZzeL6lqaqa91KOSg8rLAsmWO0oYcdv/UGUqvSWGOdjHaV4G0L/q4XBbw2eO6cXs16I07/3dKM\nuM4QfQ/k19OXeZ+Po+V17ntDg8nTT38p+nxeIrysSJhhGJTL4bRcqeQXDG7evPklOJs2Ur1/Q/9T\n55BeZ3GCNu7nHZxXeIbEVIKF5iR7Wl4bqo60Dgcaa5vOji8OIXgYWEX8kv4orc1SoQznZ+FSw9/X\nToWEKTQ45tXQ/SR8dBs0SoNXVbIcR8hbjTSNTsDOR8E6z+8LtJRKrSDKRSiNQKI9XisUEBowFXRi\na4DZzSLNEEyBjlukVg3T/+AL/l6inTkGv7cZa6vUs9RegOJzhNrExD2P47Okzn47vTc9H24cD1A8\npv4+bUWutFDNP6knSCqhtF1wtFziXroFMqmVFlZjiqEjG7DyAVfv2U4wq7jgl21xHOdYk8fOBp4O\nvc3n0B36XTH1OI8dgzVpZfcK6zyNCLm+A17hEOS+M+C2Eb+WKRhpDkZtVBXPriZHF01QaRp1jeit\nPeoFcmYnNLwZjFb/664WKL1Nrzd1EZeA+bRggWdPO5+Y8QqYjGWiIbkLOYXlbjjkv7tYHIHUZqyj\nqxi8tJ3e+/aT6rK8DZP7rMpzmdEkSNPCXnG/XCLtEo0ZzfVaaUOHovuH2Sa+a/GwV1QS1DQGm4RL\nSK2pp/+pV5Duc8fQPK3nP8/gv3Vj5aVx1YAgYHI0LlQMtQBfk7o5xNHVLQ6J/40yDg5ZAUkVkrEa\nrov7XigssnnzH/BSOeareI2LlxUJe+GFF3j9619f+feqVauYnJwkn8//Bs5mC9bY+Qxu66u0dBgd\nXcmTOy7FmkuTuj5P/9v3+Ixb84ca/SJCOUhVjRCUSzBpVhe1lsuCfC0+DyyeWq0N6AlPywzYjfGE\ns11t8LG3QUNgUlUJzX2D1/Y7/LvQXbvcDPx8yIsYpIv+iVw3KJ+o7hdWWfSXT2AdPsrQjrOxxl4T\n+RnfcdcWRQrSRZQhI4hJSpUCLR2j92/GfQQMHLFv/372/axf9KPLlGD8hWjPIRcq0fcxi9TPltH/\n2M99wlifAbHtaEU6M36t0q6C2nJHboYuG2gme0RrFXtB6G9cB/vgLrfxYqjfoOxv19qXY/BfN3uL\n23HiGagG0vQ7b/kQGzcfoHudl0fSOnRHQaX3HJ2FWx8S16rrt3zPtlKE7EZMXKxog3e1ebrSkB1N\nRhMJkyueL/c+E9VcPgh7WrRLajg/OtLmWyAfRdt/Mq7tjQo+eYSTMj/h6rUCNgVRek75HKw94cpR\n2wrPb0GhvC7KZGYEAcVxoL+rX90jMnipzXZBmuYeINQr8lvAOTY0BOaqRgMuXoB7FRuORIP4LhdG\nxt/2KKIIo3fHOomACaS7FsU88+8BiY1qnZDnL+s5z1MQquvqSlNi09Xyp2Htl8oPzv1dcqu7akUh\nvg2HwWnHfAW+853v8JnPfIa+vj4OHDjABz/4QR577LHf0Nm0gNESbungwLolzeCRzfRetp9Uo4W1\nN8XQ30hRg1JORFBckqT0rrLhiXlYGPE8d3RapNKM2CkEm9We6obO04soH4upHNiJaH2Xi7cSJmAu\nVIN3vCS8ZMol6GoNDyLltSvBrQ/AsSPea8GJXjcoy7Mw0KoluuFFv4vWc4cZfOuZWEdiLCaTCBH+\n1mL1ZssuVJNUB3DxMlKb1UKTVKMFq8qwPgGYcFYfrL9S3U9Qhk/03Qazy+CbDfTess9HCkAyIH7f\navHsqYoO+nLw+YANAKhdzWUxtJmkoqRV7XId/Z1r4ug7r7Z5elfvZ98T/d49nKo9NT92eCXXb/uM\nqI5cMcnkaAc7d1zjd+iuBp3e8zN1cNjRdWX8lX+hnn2ZFENDiuieXB0tt+uBcMRAVV0YsrBRXGcZ\nFWLXIIpy5PkmvU3trO9bIDXjfqmFBKFrm4I1l3vPuOqW6+ZQ9xyMTLidjl0Ae9KRFwQgzysJXQun\n5f6q8VqsOtzvb7jIP5dPAkcNYXUUROsCSgGx6jrXrfSi+PWbRCHF3EN+24jCblKruxDiWj+UvYxz\nC1A86kTbApWOpSlvY+XC2hN+Pu2C0AyX5sQ5RgUU5HugMh92odzIKtZO4LRjvgLHjx/nve99Lw8+\n+CD19fU899xzDAwM/IbOZiZ64pgE67Y0+274Z+V2AAAgAElEQVRTiPABMTvUeRUwwYjP+EF4YJcT\nwdlWvV2HPa4IxU5DafTUkrCH98MZy8Kl8d/YDaUt8YwPo7pcqLiBYQJN0cULITJlihQeEglTmfcF\nB2XbFFxngKwjltOktkXv9T8NL/p9SXpvPKgk5CGUi0I7dWcEEQoimP7pAK4rQ1cKK6FOXRULJjQE\nViG3GbhO0O3CFX2nt0FKpFf0Zonz0GHBVVfBxnrR8cF3zDb4o70iXR18FuVFWtceR+V6bq4GY0X0\neeUtcd9OEmOHV+pF+HGg03tuWwNfHvBSqwH4NngDqCMnwepos11UQtv58Jyhi1LrHPFD71OkK+vW\neCRaGwWqkmaEpfVzLOXgLYjIugz5GVdU2am/S4qCqJ5DowHKGmd5eR2wZ9XXIWjbU0vnCzdiXKfQ\ny02gJmGTh6EUTDVOhVPLjReF74/ZJnwi5XOuW4U18mtUJMzKBeaf48Ajz8GsE3GLaonl/r3p8gAB\nk/SqcVLV9oz4nsaLatQOFkRksHRE8cfTjvlKfPvb3+bb3/72b/o0gN1QeOXSG8GaGf8kWbbh+Dz8\n0wt+Z2yIF6rXEcK4k1Ac2I7w+lbUpfHGbqGHqKrt0LxeIrplTbXiheAOx11cVKFpF2VbHNhNff0R\nsDyw45KPYaRIrVTn70POzXYpPPmCEKHX0rsPwB6F2b+Fxqsg2Q9bk9AlLuTQ4AYyXZM0NvuP39I2\nQyqdD0dPOvsg8z9E6nrxSPh5852X9+xpzRKnGuEvuqOLI9oaRMWacoFqCR1Ldw7i3xlnp2tEn5fO\nxLEWxDGyrAbd/qmtHpJV+u652AWcU4b6CFd9F4lk9U2bDHOZX5SuQzWSrIsCJWKmGhePifkqkdYX\nFNkWYIsIS/470Hax+n2uaD1OhM0VfFcT5ZcXws9DUDNnTxHbVLaaUbELN2KsqmjXCd4ffAZhQinD\nuabyXKMidhCet8wsQzvW0vq6Xwd8uhoZ+sAG+G08Mvn4lLpARwcl6U2JSFz+SPX1z9UixqiuDMFo\n8KfkK5hAdMb5zeNlR8JePsiB/TmYrYfmD518tClhQOkFf5Wai6r5co14NuqzpSkx4cXsG1axMrCn\nYTIDX0GkzW1ENQyAPU0qex+9t/ST6jaxRuoZ+tsWrNE+/85kCFQuHfyKaEsF3bqic9Q3l0Pz+8T/\n6gxkE4b4IWYSjDMhq3aFlo9tjTZT6R+K/HrQgmEMyAbK8IuOXYVcidUaLTYFIAn256H8eki81n8+\n+TSzUxkam/16jsa2glqvcSLtSd7MTaLR+NyDofSD0Al5v1NtlujolBT81oepGbS2O2aX2O3KkUpZ\n5zPVBv+c8VKoDVt811R7XlEmjlGwS2K9Ki+Khb7xPP+9CfrGVYOO22SpbrRcLoln5vg8/KIBflsx\nXmspJFFtDIyUvsei731VSLIuCmRXiSjEtCegbEv3vRGa3wYnNON6xokexYqwLXrn0XARmKvUbzO7\n/ETIllq5uajFGqQWmFmnQCUAOaLWMgsTB50erlsgpYhwNV4Ur0JYAetQkcHL+ui9aVykyEelwpyf\nEU7rxYmANWwR/StVMKoQafl4caordQhYU2ze3MGePbfxUonyq+E0CdOiDdgidj66HX6tSPapd6TK\ngW0JEaXbcDtKjKuqYqo04ta05nFRLoo2LW60RJmSEIQh1TNP/3dWk+6bq/yp9bVTDL61x9PCATwE\nrHZ8ZVxMAPcHjh3cdeoWm6C/jgujXq3h0CFh6id16dhi0R8nvc6zmlAu+kZHOIqSSAqvnjrJmb3h\n/OgIg5FxKsv+CMxzQucDUJdcVJ52qi4QnZNtLeSKwuFXM3RLn3efXBJY2C1SzGZ7WKc0mmRox1lY\nlwcibUGM5oQo3mhWR5YqLVRyYnPQ1R5IO7fDWknLFiAD4fMKVG3WCpekJExoegvMPij0KEa70Leo\nCH2U+bEqWgFi83AdehuR0pTQd7lj++vbYXXMLgQqlCagbFXXNemgWwzd13VRILdgQ4c4C2i5rOgB\nauq1Vd+S9Ia+go/GcKrKbHfatCWiI4jB4xsNXrTGRVxrkKXAvc7BTIMbUSseh1mHSDdr7mddT1jY\nHvv4CawXmtj3gZXqZ12WxEQ1hQcnbVjFG7DsbMp0lbhxopdxUfl8giNHCrxcCBicJmEatAFXUmmI\ndarcqRNJ9Y40btWSjiD5/Hikz6oqgEo5pz+kpuw9IiXRe/Mg6T7/ji+9viSE27JWahK4rQR/+CvI\nboATderGzXOPQvPbvQG/C+FXGtxUBv11TgYxBLPW4TSDF7fRe8MvSXXXY43UMfQP52IdkRZ9VSWV\nDDMrFgaK0TtBXZQgcJ7WvCYl918p+DEh3YmyonDzCa/S0RV5l2dFekj67fs+cLYg5gmgPh1dnfqL\nI/Dtbpjd5Dnw25b43aEqpzaRFt5qh413ZZ2PYrxV9FPlBVgcB9NEW+DqEqZqrY1ALNjpC+HE7Xq/\nM/CeUVWUzI1WfIRw1FbXeipIwEDdheDxaZhKVze6tS3JD0xBwuLMYdYe8azK0fPSuBeFt/ZA/Sv8\nJKdcEq+7UG0SkypRUwC6yGOUtsrdyBR2h93vg4iQUCzvGfHaVo1k/YUZqlS5ap6evddf2bgU2FMw\n/x296NynTdPczwgrhKpIro6OALuRbCOjbgrflYWrrob2Jn+rNh3Mbi8oIa9/qn6tZfUmNDak63Xi\nRFj39pvEaRKmxBZ8HUlVTL3WlIULHaOvllcHPUFKbVZUlmkqgOYe0ogUq5yf0U6qZxXK6pmgVgpg\nKgn3APWGRiPm2GvIO65JhFO+as6uQQITiTiC2XIZ63CGfVc5jrHNV/kncdsSO7VqUbhgc94g7Bl9\nlCBwnkMHNtB6Vc5vAnmwkaEbu8U1C0BZUehWOrqE2WxH2bg5kRRtQkoT4req3PPdPob5iyAVFIin\nBMlWwaiPcDV3nr2oFFOiHtqn4ZI8dK4LW5qUpiH/L9Dzh3BtCyyTnq/esrohutHqiINjLKBRZGEC\ndeo8Q7jnqGpQ+LoQyFYdF1W3ojFS0HCeWLyC7XDsgvht6W16SwojI/zFZAJmF2DuMe/9qc3hKFPC\nlLQ9ig1FkLQtBVHaKqPlpFJVy3tGuOWRj9K9brjy2sbX7nXaVq30k52ILIFo6v4wtFy9tN9rF7yI\nmoqABSUpuujR4jEhQVgKqq1ndSvxVc/KqBRVSfOkyhdShumkh/MPeOuf7hore6zFRODazc2VEBHd\nw9qPvJQ4TcKUCBARe9qJ2kg7lKU61St3MGX0anYHRka/o1QRJ10FUDC8Huv8AHMZ1tgkELZL0Aqk\n61ZHtE8yhIN3ELpqoFp0MdVQTTCbSDims1fC4mR4cTZSUK7Sow2qhOJL4n5G7Vwnga8IQ16LNIMP\nKVJyz0+CGdYBNa5Ve+spCbMKRsojmeMFuC3nRGjKkJuH7z4F2y+CzrM8IgsSuW3U74R199I1ka1E\nFq4hZJDbAVzb5/dg8/WNHIbFffCHm2BZIBXflVBHpRKmSJeqqmtrgTZiaAlfNBlmm+irah/zqtpc\nL6/ghmr+STFWqumOgumfso1omNwARo9IVYW0iA6C9gjgzBfneTrWapoxXSugpSJEXBWwZ04qVTVw\nw10+AgZS26r3fchPfKoVLqhIalzYx8TnVWTStiD/VDgCp8qegIgK+gIGp6iPsNnmHQ/8keYs8aPA\nMoKFA7prXNZoeXUozYlrqvXCezfwJV4OacnTJEwJBRFJbY5fRVW2YXECzNboSpsKNATM3XWY7YhW\nGZrjq4hT3Eo0+ThuKDjY+sIRng7t2EDm3Ekaz/AW8vkXIrrcRw38RJ2IigSxC1gbaJOh0sUsNRIZ\nRNT3mFlItKr/lkhGC3J1zXnd4yVMSMQgcvZM5V4oPevqwo28Uz150pvUYuklVRQaDTDVDvc6n+1o\ngGvf5m+x5BLnyvxp6HfCqpRwvgDL2uFD27xq3MUhMANEait+Agb+yd6tFta1fImKqBoNIl2qei7j\nQJnqLsOuRZTti4xUdXIExG5uHXzeVONPZwmiq6KTX6+mGTtZ3Q6IucfOeanMqHZLZdt7T9XvnRbp\nygBJ6lg5oXx7x4oxT5QfV2B+Mr+/lIuYs1OiSEE+d/d5UWVPqrVYOhm4a4TKTkiFahmM4NyrvYaa\nOVonOUjUQf4xcR3ktGZlfDVw2qz1ZY3diP5BEiOvZYAlDKjrkAS/VdypVYhbUWRb4nuDWgXdrj44\nkSpbmeTA2uvpxsx2PJPW4GBw/h3cbZVyQiSrg46gHZ+A/z0D29ZW0fIknAl7QVQ1GUvc6VUjcrqd\nbSJAfuwFWDwkBr89I8hb/drajyejXIbFHEJQrNG0KL6v94b9JFvCO8fFGXPpFYXy4r8VPwED31Cp\nQLcTllOt2bIYaukGOLNH/KdvlUh1Tiq8087SOLK7k737fOtaM1UbfomEaP1iZMQC1lkXz+sp+Lsq\n70/AVAyyrSNHLkpHYOZO/eIaJ2rkwmgh5Gyvey6NRi+NqUuBuRvLk9XOBsXYIKL2zQNqEpYwRAp1\n7uGw9tWFPSt6mJJUNreeHFazhMnR5SI16LaBikpVVwoXqkRSdRs+e8GrANQhVGwQ8bxUa7EE8fSS\nKtgzQDJWP99UOi/MzC9QtEBzsSilBI0MGLp5zgivMVG+khP1wk9P3pgkz/S8yQDQ6wRfSpwmYUrk\ngEeB91DZwdY6wciCXx8ULXNUYs+4OodSzvPIkh84txJN1TdMPmZynVpAvfgC5J90zkucR+8N+2k8\nwz/RNJ5R8OuMykXhk1VGOelFolwUk17iHfFE+OU8mJ1LJ2CxzqmkJoyhHVw9lOdhztE1tHzo5I+d\nSECqV1wXnSeZAjpz07lfNtVWUaibqGvR5+ne66aEBxLh9LMs0ncXwc5GuDYJmYjq1lLOe76/sTuy\nIXoIld+ahOk+eKIkCLVuko8iYkstIHELNXQFOnKq0lztiKMdIlZLGqxcQMxBkgxi8bBad5aoE6la\nN/ISVUCk1M7GSSnOi8bg2hZKEXOv2S6Rpcv8xFQmdc1q0++df/8WNr7mP+ju8+a1StsqMwtNb4uu\nqJTn1Kj9VbXrkL5MnYWIgjZy5s7tZ6r/HkVeooiYu+FPX1Z1Dkil8/Sf72/r52uBBmK8zjt+Y8s3\nwjuvgEydGMs/BH4X/9wzNio2BUaLeC51vpIfBfYlRMW2/HuMBhFNnrnTec5OQeT2FOA0CdNiM5CS\nJsV2UZmVqCFVkWhUN7uVoRMilmP2y6xbBonl4deDzVzl4xsZUbIdZbqaPDO0u9Q7qkuvJ5KCNCwl\nNJ9Iigm1mj7OhbEcjBf5ES7PE1sUWtcrPS+nwEjURSLp+FoFdtIarYfO3HT+UIyIjIuoiboWfZ77\nXl3qVivSb4P0H0P9OrF4XUKlw1EILrlaHPHG1/g03DYGl3erd/v2nBPNNBS/NQm9SRhGPcmfqkrd\nIMqFGH5yDlKbl55mKkNojM0/CeZK/ZwgR14qtiqKVI+cCku0iPGTSIv/6NK8xf3RRUmF3fpIl3tu\n9Ztg5ksRBFZN5Maeb+D6C/oZ+GSCjm4r3LZKJxmwF6H4S/8xdGa2pTlHfqAhYUY9GI4+tpSLnxJX\nylBiZFCqmWJrsegdt8oc0Nu/30fAQCoM+sDZUDzA8mUPM/CFZ+lYm2Aya7LzV8cZyzvX/dX42Ukv\n8JlpOPxPULcRmrdDRrNOtCK6T6iIpc8z7xS5HpwkTpMwLSLsA+IiUe/36VJNqDohYilmdCcqvRVs\n5upC1coiCMUEH9u53J0ElwIzK/Qb1XCyTudxw/GJeu9Y1T5jtkDLB0/dsUPnErjX5UUgEdICLcnc\n1F4QhCThTAlRE7VK+zQBUIasdI4uOSpNOPYJ5wmNkdHoHUd3q4srICUtRDqyNo030brPrNtgvLMV\nTgDfROyq3+28/wkbxouefkz3W3WP16mq1JVRyglyFLetk9b1vQiY0XrMZA8hv0J7Gmbv8bRPKpPn\nivYpE7ZjqN/k+Q0WdjvzplR9W8pBaT4cVQq22nFb09T1+Ds+zN7nj/yFftNZ4jP2LCFvRYhMpY4d\nauRT12xTeypqp1dLcU90HQXqor7ID7MN7DzKvpAydPriOBkU3fNb7bk2msTmfe4heLwHetu1KUll\nv0mcDXsiKapSn/ihFIEcY+PqvVz/vU8LIhZkJl3AJUn4IpD+QzEXVlsmdMSyMnZeHlYVp0mYFhH2\nASqoohLBfyu7wessK+YhkTm5VJvbkyzoYpysZn+uRuzF3d2FRu1eo2DPiR2oLspVmhKTbZxm4kGU\nFyFbQ5rJnfTbbdFzstpnqkUnIiJMqSbJXFWnofCdWz2Ubb8pq/O5ms1NE3Xi2rhrRdRErbP5ICE8\nwFoXYboMj03Acclnqm5ZWNuzC5GODA6znnq/Qa9uwpW7MBjtsGw1/PXl/t6noV21AXe0eZ/T/VZd\n4eqprNR1sXhc/+zI5MeN9Gi1MzGitkZzuLE6SFqiyyHVH/6cu7FSVVLKJsWlYwoy6UTmS8NhgiUb\nUQcj9OYmqFshPNWiihOMFJByzqtHWGOULacV0YwgZo1PwfYLRYutXAG+/iiccI6tajBdmhLzkIo/\nlYuK12ZRWr7UPAdq5j27BCyIiGmwaMDd+MbpE6p7fuM812YbNG2HY0fgM+OwbZW4nvM29CQgK6J9\nWl9DZ8M+8L+e8qWAAbpbhhnov4tP/bumj2vnOmh6tzcedCbJMlRju6Ldq1ZV8NLgNAnTYjcY/2/8\ntyeMeBV7QdJlz6gjI+PZk9c6uT3J3MnW3cHqJuoqpcyxnMvlqsA4XlrK885GE7C5h6Hpitq/17aE\nSHfrJk2Ux4admt9/iXFyqSnb2XVtrVd+T2ogT/97AuaqQQ2FAqkzCmFTVudzsZqNu0gY/lR7tYla\np33aaYBti9SQvMCnt+n90A4TJmFZ/Nc2hskuZhu8892wIkBmVLtq+bvnUWMIUTTQ5YiM+/eTqrOw\nmlIMff8kHPtVSK50BOQKuM2Lg1F5V2e0lMiqayYcaqNWhsL3RcRSJ8DXVVK636sjiEYGZv5J/1k3\njRn6znavHU9cQpMwRQqUtPjOjh64VtYINsK6AfjsARh+Rt1gGkOfFqxUhEokSJeODKJsO+2q5kUF\nfQhFlJEwwxTnTaMgo4sjkFzln8/tGBY0ukh2PWKDWO0ZMlsEOZ6ehp0pKq3hOhBzaNMoQ3sXaX3v\nAul13u+QN+wd3Wq5TUdjxIM7bfj7A8ubwbNQEy7VPFa5Xkusgj7FOE3CtMjFr2R0EafyzR28bspk\neado8dMoLf7nlGFvSrT/CT6TuglX97ocfVPtYH3nX530CUf1VwGKNiMgHvD6TUJfElUdGQVlldMC\nFJ8D6xlBwKqlU4Mo26L0nTrhdaXa3p6FvtffUkP45UWxc10cEcfMrFceu/eyGOaqCmhNWe/az74n\n+gXBSCAqsuMs0O5zlEX0DJX5TNwWOkaDU5ggNWJWecK50D0m8rWNY7ILYlceB+53d6DuxzxBpXo9\nNZCn/8N7SGed69wDra+sTpBrQqJJkAZdMY3Og6ttGq5N633TXMjzQ+WZ2Ajj74YHdnl9O0n4tV0q\nfdUSKoqFK/2X6ejay+TRMjtv7GXsUODGR81NdT3Q2by0ND6oK3qXJeGtm+CeDeFNnxtZ08Fsh/R2\n0Z1ATrHG2YiXLaGDU7U7KhfVUbYgjJS6+lo5dxYcXanz+4NjqQCsBs6RPhNHqB9MLU8iNmGlRqwT\nuxi8t57eHWeS6lmFNdrobdhti8lRdUPgyfkO9bEK+EmiK9zvBFohVc7T+7r9pJotrPkUQ4MbsA6l\n1fNV3Qrn/5xOR778Yf0b1PfrBZW17kDdbvA92+Gv+sJ+Ry7qE6IBdjf+gaBLZd2L6LKkS5W50beo\nHWwtSCSoqnGolSRVPaYjTk+dV/271wEDiMV9HhHxOGhA3XLxHx23zhj6Xn9LDeEn6sQOuX5D5Psj\nNRQR0BZLrLYEqQyiF7i9DFOK+6d6vhy/T4qIguG4i15FQ9cIme0wnajdvDX4epzKw7j7Jvd9LuEM\n4giVc+3duN8jYA7iEOSaTDJdg+CgPUylmEYjW7jEjPZNgwhPpwSc1QcbPgy352H8hN84Nr1bbBTb\nW2Bqi+TfNuI9zzGgdKU/9wTXX9DP2OEVYnNYdwYYEYS24/9n793D5KrKtO9fVXX17qo+nw/pkFSS\nDgmHdDi0I4yoHEZAAxIUdDw0QvzUV1DREXUCjgxC9BtQR/A4Y0QacfAEAvHVeRVQUfAlwEsjmJYO\naaCTdDpJV5+re3d3VX1/rL2r9mGtvXdVd4D3u3Jfl5ekumrX3rvWXutez3M/9xMurqLPhNcmqpji\nnnCFvPo7yEY8HMtXnE7/QnQ6MI12Q1ExvxViOeKH7Jw71W19lrpxPwOLKUAxtJb6YIy+j77ZTjIz\nOpCm56YtrDt1l21M7D/URs+ft8iPGcFOEi0SA2klZsUYvV/rQk/KxlSpIdN5sfBrOwI4SsK8oG30\nJmDOSWEDooXMCO4FJz1hlFFfBBfXqyu9rF+/IkXi7n60GaH12TfUzrIL96LFLGy/MS4eIi+34nCN\nKNFeStM+J4oVmwdFKCQibNmM9/tWIXr4mSO7HLgasageMM7LS0ugmnyCpMOC3APFcfRdGkgqyv3M\nVZXFEgpNhhC4huSTq0ygbnKICHA5cCugyJrZYHs+jIWpEPPWQ4jdbjeFjSnZsRawz3TW3021OFse\nlUBVwTIU41Ju2sNMOdKEqkKXGgVxsV6XrFenFU1RuLAaeqrzxrFlv3Br60z/tpnC+vhJXenXzNJ9\n0z5u/uhng1kyXFi2ODnAYnRQRwpmyzmZsXMoItL6oazYBAW0p5Ei7FMVrSSoATq5yGA25pZFbxs0\n2JRmuLqVrT+8he7O7dTVJklNlEEoxNWn/BvJaD09vVvylZLgLlC3PM/SSszGGRJXKjZJ4VKhPZz6\ntthkvso4SsK8EPZoKWMsWDmtiJUYpeLuBaehEi67BGqroEV9WBMydt+UHrI9i1UNY/Q+2IUe85iI\ns2kxyS2msawfZIR0I7ALeUp1MfBb2Lpxj+owcIzxP/N3uQ3hJyOTZMgmJWsI3wiBM0G+WrAG+Cj2\n9J2MdCiOM/DXDqreNEa8xlL0MBZj4JveEYeBb3ZQ9RbH5yZjDPR6fK7Y1GoJ4v7+S4AS+kLK4GWp\nxj/iHd1VwXqsqnkYn4M/lbs9h/wE/5bXg1YFiwKJ59Fa54IVVqggi/RKPbgyQicjQ84aRIfqADoq\n628fqYd//AC0OBZ+07/tzsI2c0pX+mWx4PNSteI6g1aqysj5LOIZ7MZf1nGkEK5EuQybmuBFNwbx\nOYDyGfD5XHpCEDxnQ/fU/xIaUGeHgdw6Id4/TCs3v3gdzV8dYtvtjkhpg6VScg5P+VZRWYRIDcQu\ngumt3tf4CuAoCfNCRj55AFAtJ0oN7QeZHi1nZrqcgfEO9H+PG4MvBI2KFjgSyNi9czMUr5wh0dlP\n3+/dbL85PkT3BdupO2eE5FC93fvGC85JSGaa55yUZAtuKfKU6pGGnwzNSgT6EH4yTqgmpSTi+j+G\nmLwbEJV9GxA7Nedm1Yt0/JE8aWtA9Ib8XZeb0J8Uh2cUOpM60C9WfC7lsfgvJioQy0L6MNDsvTsv\nlOg5U43dFB/5yB0rCukpiJSro3cBIpxeVcFmZWpspWgVZe1UEKSwQoqQpBoyM2nYfHSJCriSNrH4\n+Z7/AowHIGHO315liltbuP1MckheYZ4cLqBoRzU261HrOG1fhpBtdANxxPxUhoicryKYrONIIFwL\n4eDrQsEIkhIPEuXPZgz/R+tYyoiuMPG3iBSkqf+MnyMvwlFszLq/IomUVu6nO7Gdm2+/TvxWG1FC\nWYk5arZZQ06sl0qes0gcJWEqhFdAyUnqv4/LiVJJNE110wTVTFB15Ri9P+9CPzPuXw2rIzYsBuNX\nsXsntJju2iU0x4fY9uZP01Zj2Vmcuoutm2/xJmKyiJbMNM85KXntRo+kuaUMM4gUpBfM8w0y+Tgh\nm0i8AgOye1OHO2oG6Kk4fY85CHU1ap2JcS7Sz6ngdX1BSr5nQhA1Fk8vEfJi0z/FRuucCJUKnZVK\nRxhA8O+sCp6fiBAKhTjuP5+mfP00JZIWURBQNyZDZkpt4mwK5kPLA55/1v93lY0J1e80OimPynlA\nqv/Z00bPtg8H+jygvoYG4AtACkG0VZH3OgTBkuvBxXH9ZB1LjWxWPS5lxAHJa77k0CObY8I2hrJy\n/WYojLQJfel6e1cYVRU0KJ/dujpFpHQ8Ke57Hbkq5RwsEoOB3g6qGsZsa3F6IUw4sYB2Ygr94vz6\n2xwfovsj26kbHSH5cjU9n9UYfinYWnukcJSEqVB2I0RWqv/e60+U4vXGJPycYhIeR+iUJA+ZvlYL\nlLZ07QLGoXvtdhsBA2hbtZ/ua7dz80cUHizmdzsnOWl5v8PKwW9hPRLmlir0YNeEyWC1WQhScWdF\nodciuzeb8CZufp834bUG6ggd3CTBqyOt96MZWJ6FsIVkLWBfjLxEyMUQXCu8SFwhKaNwuais9YoI\nBBD8m83TteUplyWIF2IrpsV/pCcMGwP/3JLWfpDEF05CW/ECenQvA4+YlV71sHCWW6Tvdf4Lg3Co\nCW6rl1RHUph28cCIEOeb1ZNOw1YFhgdb2br5Frqv3U5dS9LtSh8E5tj8OG6iFEF0oOlEVPl9XXI9\nAXodKlsJHqn5SzUWZJths7WX9ZkPFKULmMvMjSF5xF3rNvpAxnT0XRoD/2Kk2ktW2b0olb6XszAu\nSxdAMlkvopHO1w/U5c9NJlc4GzhBbEJ7H+xizSl91LUeIlKSJVKSoWnDISp+NkXvn7rQU3ERnDj7\n07RVGmvjabDulE62ntP7qhKxoyRMhbBH49N3A+s8xM8WaC06PKr4499wT57Gvwd+10HVfXatj7N9\noFT7cwDqZhQ7ixafbVPQycapz/DbabKuJBUAACAASURBVC9W/JrNAJm8y7oX9iDE49a0g0qUDYX3\n+ivkWmaBB6Yhk7W7aQe9z369Dpd5fHYX8L2A32OF9X4sH4ItLcI+JVdl6ni/LBqWzUIypDB0JZjY\n3kusX2iVnEq/VoT+J3GD2xLEC+XHTaO1j6A/dyeEW4W9iqwazzgXbcUUne+bIN6oYxqYVR1raD8T\ncfhaLRwaDfbl1t58C2fB981WQikI1zt8sTJ2X0Lnwjemw4/uzFtZZMaFgapfRxHjuoarW7n50evk\n9zi7EOzZTiKKnlTRLHD7y5kI8sypTmEpxftBxpyMMMpucZAoXTHFIVbUgbY1ReclFtnNWqjaOEbv\nhV3ogxVQcTlM/8xIlat+nFLYEZY+0z3f3cK62yWR0psslZKyeXoP8ClglSBi6YUIkRJ75C/eaEh2\nHuuku3N7noAZaFsTp/vGBDe/vy/4PVliHCVhKsiqJuqAT5B7IGRhUCf0UU0QAae4cNY4jlUUav3c\nSW6tz77n21lWvxctpaPHNQb2SrQ/4x4aDHNnoUKh5f25AyMm7IuB47BfZyHRDxWyc4j2PAGH6x7g\neuO/g0x66XGjlY7lxLOGN4OzOjZIyg4gjWixceAFw+HcQsJU93kWQcyDRK38omlFWrTlkNHh5Ua4\n3mcSl+3mzdecE2chjYNVUUq/dkpBSVUxTYzrQDu5sB1zSWWaxNa/0rfFiGAt9EPE4S1lOZfEaS8Y\nBCyPnPYz1QkXVsA3fy4R6c/B3D4oaRD3f2FQEDAzQuFqsePQm0Ua3MbK1t9v/iBMOQauGRGLnZu3\nWLAi6D3OTAYX6AeZo4KadgbBUsxfJoLej0Iib0c6y7AJEmdKqg9XWFLtkWp1JwMT4bDymR5OLiJS\nOpH/T6VA33i9LqYITrQtYZ/fInCUhKkwez+UHG+f6ByeQmYYNNHZT6wiRbx6gmhpnonP7NUYGOmA\njZYqyjIdfVpj4NkO9LK4CDOfBEwjIrUZhFN3lVzr0/dovRjIsgfamDB6HlZoMKw7Cxmk5f1ZKJH0\nA3QiiYi8HInqomKsNcwIjVekKzMjTBNDUeEAbUUo7DbOBPtEUg+sQP4UPQu8kBa+cFqX/W+qSq1v\nEcz+AfwnX9mik5kSi3PJMd5l65mFYL0yC/HBgsIbBx+ehR7Hb6+67noKI1XFNDHeBPpC4RO21hax\n9yV02qxYzsVvIaFyMk9+lp0Lm9tFI+ORQfjJf1tMVxFEK75ZbrhqtigyEd8sxP4qZBzRN2cLJVkX\nDsc91uIpEhf2o52voz9tVI++HDU6YFwUTGMWSLeIO9oadPNkxThLK8oPMuayaRjP4PZkUOBIW2xU\nB6w+DPuI4E0o5uPhaSNSan5uOuD5WR5HpUB/TLyenFEEJ/Yf1YS9NpEZyYtgIzUQWS7t2m4SJS2e\nYuM5jxMttfTDiofE7hmJmVyzkWJIxcXzZtUjbEQsyg6sCA9w48rP03bdPvaPLuO6H32Rl05LuAZ8\n0TsL104lC38M+VdHOo9xJEX4gRtv+2gh6oC3zkNlJUw1iCbPzuOoRN3Wa1yFW2R/CCEQDkVE+ikz\naW84nkQYpl4QKp6sek2+KqKczYroSOxcIahVIVwSbNdeaKqjELF9dl70FiQL0ZX5RV5ptEtwUXUd\nciNbr3M0/hYk+u2EfsBBeJ33zfKdyoXEfH10TPx/HXB1E7SYGpz1sLpJ+HgdHvcW9jt7yZadJar0\nMgrrEWezaNmxZbBcl6uSfK1RPfq2WvSxvfm5tmQVRDza/5hz1DvEJbusC0YRHRBk2im/FjdOWPuS\nBoHf3BRk/Gem4YESSETdbYXAfl1LGaVTYdyn+tDZicF57728M00UE5U2P7c6/0/Zs5naE2Pgwx1w\nEvQMb2Hd+3bR1mgJTuxO0XPdgMeXHHmECFQ+8dpBNpslFMSVeNH4Z3I0O75Z7GS7kVsaAOtO66Ul\nMeR6/cBAK+GSBZqWH5L+zbOqzdI2ZkV4gN9W/ANrSl/I/Xn3gdWcc9NveOmQR0uYxeKV9s3xOxdZ\n9K/Q3Wohx0mPQvqAiBxFlsnNe4Peo8wsZA4KXY4s/RLEJTubFhN1Y5X7Guay8Nc0/DwEo4rjpEdg\nIQmaj+O5aqw/TvEku5hjpsctepNKqJ2Fq1uhxUKMDyEKECTiXv6G+F1NKN3jA5yLcf5mVFtEv6eI\nluarI126zcMxer/VhZ6Oq8eG5b7IbG9SkzGxYdszkydZV18KXRIi/cdn4Nv35ucsJ9Kjon1XZlLe\nNseJTBoyw8KWJOekrzi2E5brUs6Pd43T9z6LHidcDVVXBmtEXoeQQJjT34uI52OjZOxbf1fZGJAZ\n+hYyrwSZUwoZ/0tWHblI1IF2XYrOd0jG5L1d6ONx7wIhK1T3NMh9sXZBMbs6leMKGOYyTvOWaKvF\nIka0zzKCE/vD9HzuG6+IKN+LtxyNhClh+XWtXdtXIx10qpBtrCJFRa3cV8fXhsLYQWgrUtx20pWs\nmXvB9uc1LS9w46Wf5/3f/KHro7nB1lqgT5gVfjsUc6IwTUfH8d7xANQuIgJUTAppsceJ1IoU3uTt\nUH6pPIIUNPoXLoP5MQin5SQsSJuSkJGzluorQpA0HmlVqjBSD/VlcCHev8FSWURYUUzFZKQaKv4R\n5ndD6j6hS/p/2+FdF0NNOUyUCMHvJuQkzBk586qS8zuXHcAG4elmbp6cZs37nm9n2VpLV4snOtDX\nWHSbsh2+5b7kJA7r+tEmdfSQxsAflqPv3p1vG9RQDRssjYwNaPEUiQ9Nor1zHfrBSQa+lHJ7lFmN\nm6PH+jfEDkeEVqykLR9JU1XAye6XcV3KlNapjdAwZE+jBo0LmBIIMOaieTg+g6wCz7MP6Qxiv91u\n/L8O7HcewAdB5pRCxr85p5hz7HtZGuJV6KY6CfruvOzG5kVYUgABA/Ucq5pTjkWsPxmgg0BsRU/F\n6Xu0EwYRzgOOtObwYGveIWB+D0x9JejZHzEcJWFBYJoTJhHlz5JQuCpkGy3TiZTIeyP4VleOgPaw\nKIlfNrJP2m+0rdY9W0h7tVl9wowHsXnFEN1nbKdufoTkoISo+YmgnQTNNC9NALfOw2g0Z2aptero\nSaGR08t8FiUVVG00CiUGqvdXObfDBsKVEGkXTcmd5CY7D7UpuCAKNTG7x45swjvckW/rUSxMkb8X\n+QuF5ZG1OuBjcf/QfyE+X5l5YA7tmAyJ6/aI31rmGF+MJQiInoLWXnuzF8EPHSQ26AKn+u3nEGad\nXkL+Tbj4gVS3+ZjHyiRbiKz3pQH0qjh9f+nMb2iGnsk38a6ohH+sAc0eArBH0KqBSapet9PbLNaP\ngDkRqRfnENSs1XJd+vFyyx29tB7++f35CF/ZWfYCmSDIbRY9omeyYqIexKbw4yH7uCnGaDropmUf\n+XXjRbw7ihSbplOh2OO1F+hF6IVCiiaqFe/3Qwh3hxRpZqIw0+EjhaMkTAmL6+fsQxA9DhpK8lVp\nuxCVgMZzL81HT8aYn4lCpXvRTS+EvFvLGBNwYpsoiR+Zklc27h/Ni2mb2/fTfd332fimp6hrtgtp\n21bt58v3fZLPXfY1hi9upXmF2zPl7y94hGcfOwF9pox4VYpkVT09A44eXiDIlsyvx0QjsHkWrWqe\nzit32pofV02OsevRE+zRArOzAEhIjlkZGVW30ShUnKp6/9gkIEsTLkDlBxztObIiOlPxFHz8Imiy\nLGie7tsxSMa8jU79ELRK1Hq+Jok4Fre2UUYMCopaZdBWhOi89ymbfYPUMX4xmsFIPZRfLI8iBiV4\nqt++FPF7ySZsvxSm+f3VSIMwLlQjJ+iybgyJLPx7M4x+JF+gIrl8af+8VTMktvfT9+5O1zXZNkce\nLZZc77vxGPQXDri1ks48rPW+9MDAwx1U3evoOmBa7LTERTukb9+rjLJp7VMkrtstbwnl5wG2gLA2\nkeGCkPqzS9mbUjaG/BoGFBKxD1JEswl3D9Eg17iUgiWnlLIOKAUtmiJxagFdP4JCdX1OneOriKMk\nTAnHQlWfdT9Es+RImLVS0jqQ1pzSh62O1sDYwVr1IJsgZzhoNg/+/prLOG78byybyesqRkuqGexq\nonn5EGTTbPvFZ13tH6xoWTnMfzzUTf9cB5lMhLZy+3u12BynnPWU7bV1qyw9vEy04W82ekIFiTc9\nYyNgIMrtO896ipKopb2L2VngpbC7EtK6Ky40nZWZh+w0ZFIiehSuEARPdpyD8/CLw5AO2ReX9Kio\nJnRGlEIhiDTCu95uJ2Bgc9929RY1Cecroms0EIREOHecKlIDEp8vjcS1vS7/rKId473glQqzEjxV\nOtarSk41Yfst8qbW5QPIU6JOzCCPSOyTfE9jCN7ebD8nyYKvTPe16+K7LORSZjgrI8ye73tRh7ld\nol1NZhIam+GiZv+uAz39aM2ShbbW+F0z7g2rOI8n1edbPY9nNWEJorhIVnnsF2kJGonxm5uKkVIE\nja55RbhG5sWzENb8036qjcsA8rZBCwgtpnVTMIIwiV6FMNB1wjrtGeetrZAUrjVYCtcWi2pEVmB+\nwGiMblYKjy3+2EuAoyRMihpsLKPsLJFukrWr0cnp92Uh26xiG5FOe2yZ+8g9CGbz4APxFj59yjY+\nvPt7rNP/RloL078mwUnx/8NX/uEq9o+00dbuL2QojSxwfGwX84G27EYPr87t3PyYkUe3FAt4f1FI\nuTBYCRhYOgt82Efs6xXtkO4ELWWnU98X/19xGSRrJceJwlQHMGpZXGahpFWdtglXQI3iEYrJRdY5\nwmmNOhzp4odNWbGYe2E8YyyAJfkKuQJ8vszNghOeTXSLQdAoYGZG9Ix0whxDnyZ483bV4jUNPEf+\n9zqMPwk7hFiIZAuyKgvn/H7Jgq+PKdJ9M5rLRy1xudtwNr5qhsQ/P0nfB1ugZCWEoySuVbzPJNYL\ngzD9E6FRu/qj0GR5oyQNpA/G6bu9Uy7CHpuF+KVQ6ta6eZ7H/9MOhw/CsR7VvqDWLvlF0YNG2f0i\nscVoLINKAlQE7+PArdH8OQRJ+8nSd/fgrnoEwR4ywNMIwbzpZP8GQFU4bV07jPOWRnEr8yari8Y4\nIvMw+3vITor1PP52g4zdAZmXFv8di8BREibFWdhGUbhS/bDsBaaAlYhURDz/US2eorphVPqxqEFE\nXJGSpzrQ/5hfoK3Ngw/EW3hyQyezjl1ffSyJ1l6YziiKvN+dDHXzSVFlNo54EFf7fMBAkI4CJrQW\nPVh0SJbO8tM6RGqELcP0TyA7BdSq02KmEH/6J1B+KdrKUhLX9irSNmH1xDajmFzqjRTRA535Sev9\nYjeYGwcf0Rj4cAf6X5ZgFwhSaxUbDiHE7WGf7/PYzZubBSf0A6+CEWJG9+7WkqSw5u2q3/g5/FO4\nIwiRsLlI7UCIrAuByhzZsuAPfLeDqtsV6T4Q6U3jGdGaFYS5aT9MPwQV3RBO+BNrMyp56bnQ5GCQ\nqiiPtB3SGPyqBTS5YavyPJpGYOph+FklrF3nvdFYhiDITrsdr8ioNZLltGKQtX3ySrUXorE04dU1\nwhqNVslCzN/cnAeDeKWpNItfRy5BqQdeIO8R6Rdxt16vQer8vPGa40N0d26nLjZCcqaenl4hkXGt\nnbIUpvkbhkuEoWx2xp7pKPktTJ3zqhKxoyRMCkccNTPpvYu4AzFQHaXmG8/532ixBenH9BlNHilp\nGKP3ua6cRkofjNN70alCl9E1R2nr3OLd0A3MU0IU+flZkZyog7vIX6OMhM0ixM2W6IJMJzc/FSFa\n4SaAi1qsg4T6S44xjCsDNOQMV0K4Gm1VmzodMxWDC8JiUrJEB7V4SlS2TenEm6akh9fa9bxP1QbQ\n6hzjoAWq7huj9+1dS0PEvBaAvxE88uaxm7duFkyk9sQY+GKBFblBkZ4WETurlYHZ5SCsYXNxlMEv\nfWTVOAVNgy9Wl/YiItVfSPWcAR0j3be9H61dsihVkVtAlb5P+43KH0Ow7EusTWFz/XL59cjGi+0e\nZWFsHh4ohTH1OFefR0RENGYb4LaQt4FyGW4/P6d/WANQlYGxORgJwS+1fJTdi1wEEbd7jSFVJFw2\nnoxNm0sWo4J1HjSP56XnBfXvpmoZZb7/YrwJ2Cx5bZ6l7ZqXN56r3yOwrmEX1z/6RVpPH1SnMOcQ\num1r4YNsXoisEX2iU+/3OPEjiwj5Bi//V+D666/nX//1X4/wt6xGdDA2kJmC4U44MZzT6ucQB04A\n/gJ0kRukHaf+ldpmec55fi7CX/+4kURnP7UWAX0VY7xV+xXnn/xLTlj3DC/0djA9UUl6ZJTD98Hw\n/ImccMIzJGrd5nIvsoIwGWKeT6QdL7CKfVOt1ITHKQnLI2P7J9v4au/nmD62UlzjQeM6reHmBeAb\nQCu2hzQ9H+Xw3iai2hzzU6WM/66G3Z89lprTR4nW5slfak+MvitPJD3hoeuoAy4B3gisQ0QXzOfv\njcgnhymE1wwAUShpCWYDAVB6Kh23DFD7BnskM1q7QLRtjsPHtsBahB6iBJgFbSxF51t3UrtilFjb\nDCWl8ns6fqiGw3sNMlhijhXH95QtED1tjsP3trjFrIViEDFGrWP3EPDvwJ8Jfvx1yPtV7oH0I1EO\n/6qJaP0c8yOljP/vGvquPBH9JYQuzy/KViiy0zC9A6Jr879pKCTXgWUzoorVWi06gxjP5cBUBvaE\n4IeICTs9Atmk8KxyvVdcb+69TswAvYhx14v83qp+jx8Yn8t9TwZ+GPInyNl5CEVIT0Q5/MsWhsuX\ncTjZQno+mj/2BOIZAiaTVdQvO0hUszyDu+fo+8ALpMfTkB6C6LFM/qWJ+nMPyp/V0QlhGRIqg42n\nwTLJc7XHuAfKexSCZyIw6+0JNtlbJT+Pq7pITzWJxdU85p8RBVOyoJqTmJUDJyOI2w7gIeB3Ifhz\nCTxTAqmMGFOXIJ51FcqN/8mu1XrNsjEEguCtRcxhy8ivJzO4x9MF4r1aPEXHqX9l2dqXqW0/zORQ\nFems4j5a58EZ4ES8SRjGe5zzrMfzzyDCNcBrei1BdIfZDZyDIMsoxuN4jL7fn8iHTvkWG5qfsR2m\nUpuktXkfeyvt/Z2j2gJRbU7Mrc8C/0mwuS17GOaOpMO4N285GgmT4iHEk2DmFbuE+aWxi9COkYRB\nN8VtO1wvD7CqzARfeMu11GijpCjnD5wBwLv5MbWMQSV0XLo7byvxcgvQIFoSbdjCuoZdtp3BKDU8\nyNkAdE0+QWxmhkTtAPGopfl3FsKWaP0oNfxy6q38/rfnUsM43cdtp64qSWq+DAgRj86QnKnLhX5z\nOypwjxpT+CrZ4eupuEi93UpuMend3EXihn609cb9+1UH+rlxe8rGuvD4pRuDhPqDCuGzeQ8vZRpk\nve5uq1EGiQ39xBu9n3pZ03VlOL5GVwt3s/PBDC0BRjJwW7gALZ3iOD4RIX0wnhfh545bJvQ+O3Sh\n0VtsQ2ETkVphNKpqzm1FfRg2lUJ1Rp4+WhgSxMva3qfsLCixRHgWU9VpRXZe3AeviFnue4zWWdlJ\nCDfLtYlm31Preap6bhoRbFsR0fw0+s5BBq7dgz4Yg/jbxL0NxdEHNfGsXtuP1qKjD0UZuKEGfa8u\nmoCXnSs0kzuihfu/FYCcqN88jwPqak4gmDbPRDUieyGLZoXCIiJaHWDjFkTAb6YEzd9mE0IHWIhg\nv1qhNa0eo/e+LnRNck+c86NXCnSBfHUu2O+L1/NvXosfzIjkvvxLqqI2PR1X9nusKZUHOHJzaSGd\n7jKFmsItLY6SMCnGENsHI7dmah+SoKUVKcSBLvRvxvOmi4oQaxVjvLvMIFsG2tjPIRpsr4Gwlei+\ndrthLhcRLYm2tbJ17y1cfsl/sHz1i8yUxvkDZzBBDanJGDc+eAN6Km7JoydJztTxP5+/gAuO+wWt\ndXuZpoIdyU08+eRp6Kk4w4fiom+XrALGCp+WLtyF/CF1CnSn4/S92JknMqaniwnnhOhINzbHh+i+\ncDt154+QfLqenm9uYTjRuvhFwFFmr0yDKH5brU5OpvSZKKnxSqVuQZ/y+B6VncHhkNClOP92/xQc\nTkKoWkSfQgbxKUZLh8T095tbGD6p1Zu02Y4bBuJiUVzKPnwQzOfKdS64W6kcrhYkLHVfvqWPvhNK\njw8eOQ2K+d3Cby5ZE4zUme72Jce4/5YeE1ErZ99T2W/tWED1VJy++zfAV3bDvseBmLQVkY1Yg+hY\ncHkZVNfaf38VqUxPiBZwkSbvfqU+cJ2HF2Rkwa+gSEV8MvtgtBZ5qZ8FQQT8sudN4v0I5Oda5/M9\nq9Ca1s2QqOtndKSJ7jMt+qmHtzC8wyEJkN2fOcSS53RCkqUzZb9zIX5eZe73q3zIVP0ex+ZqpIqD\n3NwsSwhls5CZEAbQJtK7Yfa6YOd9hHCUhCkxTI6EWUzdEmcoKjnO6Kfvi51ikF4t9FDVjUliFfaF\n+Y084iJbtYwRVTyNdS3uVWs42cqX7/4CWiRFYlM/WptbAzKcas1XNALMw3MPdYocOcitB5bhndOv\nR73bGUetYZDtygsRh1qeGZdGYK1hRHv5Lf7kwA8OnyOlzum+DnAXcaEnNekOfPRAg7rK5xAMfLKD\nqjvHiNdIRNUqO4Nbs4KEOf+2Mgo33QOps/xbyyi1dBnoCfub/hZ8XOyLnMpbaikhO5cyRFTINBa+\nrQKSG4SD/NQPIb1XRL+XmoClR0TvToCKD7h7ksoQaRQERobshEgHKr9vVJC4cAMkK+UdFqY6xLkQ\nsi9OMtQBHytTk3YZqcyMwFRP8FZHfggSuQ2qpXJCdvmZafhlkxgrxXZaMCEbi17zqYy0jYBWIt/s\ntbTs55qN/0bbCsvzWrmLrXfcwnDS8ryqyNRluEkY2KsiVb9zoV6NGmIT5OO439Przvzsn2zj9qeu\noO30fbZKe1uWoR1xLbbIZgiIWKrfJ2D27UerI1+7sNwafSeUngihENq8InVkvp4E/h30L8R5+rd/\nR8fr/kJt4zihcIZsNkRFiVysnUmHpfn0ZLwO7cQUiSv70Zbr6LUaA32CbOnE6ftTp9AZyObiOcQi\nPoAoM7YOSNmDZD6Y9QhCZj2mGaaWQSUwlU18CYS3jB+sE6LlAe/u3G57IMGIGF65Pd+OYomgTINM\nx6V94qQVamOO9KPpo2OprNKTcXrf3kXiu/1oNRZC/VJcbWfwNl3klxsdj3CTBu/ZAncESL8qhfYi\nYtR97XaX75w9OmvA6cnl5UdknRjT+4Gmwp3bC4HfDt1KDsMaVLwXpu6CaNB8VgBkZmC+P997EWD6\n56Jay7dtkEcz61AlpAcV3zkLU3fkez1GNnhUBAcgg1Cc11WkCao+LqrSMrNuH8BCUIjju+xa/Rp4\nt6ShO2KJ4o0Io9LRMnebI1l1pB9UY3EOOxmzpvic97se9EOasIxw4OzmB20EDKCtYT9XPfAVbnzv\nDeh/8jFNLsSXTGY07IyujSICiDKWUYm47iQiAteEdA0bTrWy9cFbbFmdu59/D62nD9oI2PxchF2P\nnpDPMtSjaENXAeG1MPUzWPgr8OoSMDhKwjxgMIVwNRzzTrhQ9DvUo4rU0aDl9SRCrNgJ8cpZIlGz\nbVGWKeST6rP7T2RlzUsuxn/3yHvovG+nLUpStcxSBVKPqAJJYB/EZhoQgvUesz5YI8B95Mu565ET\nMGt1HbgnyFNwE8tGvO0DrMc2YXnAVRoBWcRwKaBMg0h2knpSQtq+2YF+UplvhE7/S5y+TZ3uyU1l\nZ1BTpvbMqqlU+/RY4aOlq2sNeK+zKQhZxrVXJbG1ZD5jFCOEFdV1S4EgO3TrIhMu8yZHQZqsuz4z\nKyQNZWfliZjWtfjvCJWKDWLJMnsaMaPD9H3i+8KV4vsXS4BAaOpkA8uL6IZN1Xpt/tzSI+J8CyXf\nxZBAK0zioap2rI4IfdjKebg1BYdTUNJu/6wHfLsQqMbiLoTfZMAU38DvO6iqcWz2XopRmp2Xvr+x\n6hCdP9xJ74U+1dYTyOd5jbyZq2pjba0yrUakA9vxZhiliN8ijugw0ml8/0psw8yZ1Vl3Wq8rGxUt\nTbNs7V57yzDVuAxFoOIS0Q84uFPTEcNREqbEQxA+DVZcBp+ozg26gb0dVK1xpI72xBj4Zgd0W/ox\n1o2w0BhhZ+WpTJDfaf6BM2hjvy0lOTTVwn88+TEAG+Pv6d1CbedB23eBxMguDGxDlAgnECRnP8Kn\nVPXAFCJ8N1upOHGY/MTUjXtSU60j44gdpld43+mFcyfwBkgeXy81pUwekLd1OmJQTMpS0vaXRRxT\nNXFPeDy6QVMDPkL75JA8V+C61wtDEF2R726wA1iZhibJADAXzdtH8wJ42bZ+qRDEG6keh2O4ghwU\nkz7NZuxNs4M0wQ5K8sLlojhh+hcGqTMKC/Sd4nUrMUuPQya0uKjjqEUna4VzvHmRyLAG1Itzjp/j\n0qB5ohjDU1l7MGs6ThYVa4rChdXQY/mDTxpU2l3gzDF632khPqrnzWqjYD1fxXOsR+L0Xt6Vz45o\nGgMvdPCmUx+Rvn+KCuLVMyS+0k/fWzx0daqChkpsljquiJWVCFvXg6A/bRlwBWKenMR3A+nnK5aD\n1zwYCosWaBMfDHiSRw5HSZgSY2KBuLDW7kyditP7uy4S6/vRKnT0aY2B+zvQ3xkXGhqHp8lqXuBu\n3pUjYhPU0DP7Pt6Y+SPloSmGDrdz+5MfyrUFsum4gJaYPN1gG3DmYFtGfkLpRDw4Xg+MCb8dpmow\nLyOfXipEmDmC8Faz7pqy2F2XFeSx5+EtrLt3ly1Ntn9PGz03bSngBBYBRTucoL34CsYDM7AybG+N\ndCgr9DywuKo0H1+rnpu2sO5Un3udmYPoSnul5uFZ+PJ98C8XQI3kHlROi1QZiM85Kz1VLYdyf1f0\n3XSSpGxWjDXTQ6oBaMtCmeWzXtVgThRDwMzryC3i9TB6GfzigEjDOFFo94RIPZSdLsyFTZRf6iY3\nkWqhhQGIrgleWWvF/eOwMgMt1e0/dAAAIABJREFUlvSlc7ylx0Q1Z4kHsQ5rUPFOkRIqXW9E69Li\nM14ksRjD01BInFOoBLIREQ0MhfIbno/h3yUhQBpU6urfOEPiu/0iwq3y/XL+vtk0EFa3VwPR5P3i\nOH3/1mmbJ2X6qVFqctX3qsKhHIJsWFTBVOc9LGQ9ALFh30igyJSXr1gOZtGNF7z0lK8gjpIwLyic\n8vVUnL4nLTsKoxGrTK9Uyxhv5BF2cEHutReH1vBw7/m5ktzazoOM9VZL+2T5Djgv/UCQByaTyWmA\nlO/bgXwHVIY/UXPCPF+v8L4sqmaQwuGeVrZuvkVU7LUkSR6oo+emLd5C8WKQNVLI9U5rhzCM2Hf6\nWvs0nfc+4d+8uhiMAF8bgs3HiN/JFFSbE3cQc1AvePwOw4M+9zqzILeICJfB5Ab4Wxj+TnZNL4j/\nr7jcLgbPZoGsv41Fdg5w2F1ks26SZBK1EVFoAEBdKH+/ZGn2QtJbfrASMNsiXgsrQ/CNURizGFr5\nLfaZeQhH8+/N/e5r4b/a4eBeIZ+ISqpGQCw6Uz3iPdZUZaTF3hRdlbo8NApf+jlcugkaEjAesY+3\n9DhM/UB0p/C9NxGIv0WQ8di5wYihNJKUhQcyeBpUZUbFdQNUXW0fc0GIXYA0qNLOxmkz45fWDFuW\nZC9z1UZy/WlNmPqpq87+Co2Vh5iiIlc5D0bhkBecJLGF4GTK6cxTqFDfRIB9jswE3GX9U0q+VZdq\nPswucUu1InGUhHkhcwDGO/zfZ0ClV6qwbHlTkzH2Pd8euGHpQG8HVbWO9OehGAO/6BCawmJKhK0P\nSDisdlw2X08i0puyULWVqHntogpxZ/dJOwwPti5OhB8k2hAKeyyKERjJR2MS1+0uvHl1UN1POAaT\nK/OT/fIUiW2OiFvPEhmh1iFS2qvI9UUd3tPKzVuvU0SGPKaPknb4ZZk4lvX+HdThpw9B7G25xbDg\nKKIsWuLlA2cla9ZFUJVmL3QX7wfZIt5SA+fvgjsO5AmI72KvA1HJuIzAyvfBTd8WVbEqMpOZtBMw\n0xMN7K/N7TI82Cz3OT0i3jsL/LAWItaIYwbm98DMDqF3k7fLdSMkt8VQwhVJyohN0ajPc2Spbid9\nyE7CPFLyuXF5qo4ektjLWBpf+9rMBIT0WRiJy8eppHPKcKqVGx+8wbW+pMZiDPxTgLXM+nxYOsDY\nIJMGLsdedBMgqhao7ZAESl8x62crjXPfiNs530T6gO93vRI4SsK8MHsl3P97SNR6h2gNqDxNDk02\nMjpdlxsshTQs1YeMyrkrfYwKVTsPpz+OLF2lmjStr6v0AuMWL6nlIyS1enpe2MLwvCUyJfEK80Qx\naYegKKTCynNRzC/6RTWvthKwgCkobXmKzvt3El9hIe9/NyYEt8VG3MzvbkBEdK1jpRSR1m5H9I5T\n/X5SH7OwPP1y32E4PA7VK/PXpGoN5bymQlJ1Qd672HFmCqH9IjiqRbimDKYtkamqDqQrq/n5hUFB\n2DZF3eOySYNLzoI7FVqzbFqQKyfpMTVqqXvFv8PV4j1WApZNQ3rSMGdth4jjO0JhUZxhVn4GLgAI\nFaYJA0ckKUD1SXoiTzTD1aJrhvN4kmiyVi4Zl86NcjW532ZgpoOqSUV0JmBHCuWz8MMudCTPt1yH\nL0jKz7tIbDCqrZMaA/9URC9aVQZEdtvrERG7EfLPm3lfWxBzi4VtKFv2SQIRMqh8xVww57A2JGa8\nrwFVPkdJmDcyf4GXvwG3fgYu0LwrBVF7mnzjwX/Kab6gAGEhwN+Myjk/o0LVjs4QtHsuRqpelNbX\nFcdv/j8SL6l6w5sm3VpcisxHMF4UzEV5HW5tsSoFpVofHK8vqnl1AaQwcUO/jYABxFfMkLihn77L\nA0xIQb5bBlW5t+oYiSzcWipK1J3pl5oK+B+bhbXGOCTOlGhpZFHEOuAT2O/9auTkMOg9Xew4y0zZ\n03gqqEjdqBGhyYwLEnR4M2LVk3ze9BjLAtXr3e8BqF8FYcWOyqwIc6Z6I/XCJ2xhSJCncI37mkIR\nKF2puAjzODVGb9ZKcQw/ZNPCQyzc5v/exSB9UNzfcLW4zoYKOTl3jO3ENsm4VGyUAfRZj+iMM0qk\ngFRXtsr4zpc73fORat5Ogv7vcfqSRcwJjuMoMyAyWLWVG4zPHkY09wZLf05InBU8EAHFR81ykPoU\nTgT//BHEURLmB+3NMKrlfzzZYmBA5mlitv1ZER7gxvjnaQvvY3ogxo+qLuVA3L4rc+m/lkpkvcfn\ns5JFQounSFzQj3aOJSx+W9x1/O5tEi+pFfvpXrcI364gAtZC4FiUpQ90teSBDliNpTR1vSlA+L+A\nsnttvUcbpWLgZ5prhepeSM8/BBdE5Q79V1VCU55oaCcrrmmjbq9YvBj3M1dvvP49x+tB7+lixll6\nRNgtBCFhMrJ3YESkZa346UOwZhm0WC704Dzcsxum/luQidn/hjGnH42ByQpvTY1KaxepCe4VpkK4\nye7qL0u3Z9LAvPAMm75HVHSWHGESFl0FFVdAuAoaqwNveJTR7azu9vUyoIzO+G1ijPGnbVR8Z60O\nj+Ie/6o93iGWrjNFIS2grCgzPreK/D22XL92UvBAxGKjZjlY57D0CMz+JvhnjyCOkjA/hB0jP4nY\nfb+DvJ9Jufh/LZ6itvMg/yv2FhtbXxEe4LdV/8CaEkOQPAknPfk015yyLUfEcqHrWUSFiI54mPz8\nvZznVoyg2LFIaPEUnW92eJOZKSKH/iiwl1ShKPZaZLAsysoH+hlJyF/lm+PYQBXc286KAsrulUUa\nepG2A8XqCK1QGfjKXt8ENNmJgL6guKaoli+LV3CO3N+cKMTKoNBxls3C3Iswex9UXBbsM1ayV7UA\n4zNw70H383x4HL50p0gr1laKnpsPIMxCrR5jd98Fifc5KmZZsl6NBSObcacgZXrHcAT054F5iJ0F\n2QWoTcMFkaXZbMkQCkPUqNQsYMOjjG4ntWAefE7Ixp5jc6j0oDyg+XZNsmEpZBsmdiB0VUH6Qqog\nuceFtIQrRL7jiXHERmD+OeNZevWNWuEoCfNH5rD7tSSiQ7vxEGnNKdac0kdd6yEiJfl0gMnWbwx/\nPk/ADLTPDHHF7jv44nH/zMhQPS88uR59Kp5fbMqxt5DwKp1fLBwRgcQF/W5vMmeKyNjBJasCekm9\nmrBMgJ4PNI4HWrULlAyJgnrbWVGALmngvg6qVkl0J3/tCJTuCPzdDmhzKRJn9qO9QyKcl9hG2V63\narMkBawDvR1U1Y8Rr/KodGpE3WNPBtV1tQLdWXt1qRN+WrJQCKJtEH47hNwLhrLIIEf2ShAr6nqo\naBXVhBnLCR8eh2/fm9dmRerBlJyZ+q2De4UI/5KzoLYGppfBjkj+PAu1uVgsCmnKXro2T9AK0Wcu\nBQog59Lo9kSMgb0dxa2asjHpIIXSqj/Tg/IDAb8niDVDIUgC/cDxkr8tIPRuGv4kzSHFk95f63Nv\nEf/HKlLSQ6pel8LcpKSH8/pHR/vAVwtHSZgfZq+C0qflE80m0Fa4IysmzMW97bl97s8C9XqSSEmG\nzEKJCKt6zWWyHVt2TghPI/Xe1WF+cEzaytZMptB8FfBRoAx6BrawbpVDB/dK+nYFgWUCVOrxaiWv\nF6oZKmbxK+A79J44vcd2kXidI5Wajhdnq6BqdBwhl9bSMsb4Xq4Qzo+j7qag0Jw508G7HjuBZfWD\naKk59GqNgQGJ3kNHPtG/aPnvTEakbLSwPGVUBbwupF7oZee7Efgr9rZfYQ3C7hBcQUUGIFKAlR8W\nNhHZGVEtmJ2BULnoQOC03IjUi4hY6t48WYtvBi2fBtROTLnbXyXiR47YFAprhKxQB/zFkssCNjy5\n6PYN/Wgn6+gLAXVICwjDUWuWWjVnOMhfruov0Y82bomonxdXpx6PNOqQbp6A/H4iCJYD1yBaMu9w\nZA826uhRx/21rIXRMvmcrXrdhmngOfJjJRdUyQAPqT71iuIoCfND5i+ixFnW3LZaHlmxIlY+zXR9\nTNovcUQT0SIVMXCh2flCidgxL5aAORYevdYjLF5HjoCBQweXTpJ8qo67b38PtdcepKV10NtyIOik\nutjJ10I2lCk9mYi+EM1Qsbv6Qr4jCXp/nL60JOJWrK3CfsQEn0X0tGy3Hyvx9/02AgZGVHR7P33v\n7hQ779WS444gXWSV6eD/PAH9izVwWUReFv8CYiK3Bl5HyDekB2gIu3+DNPLWWbKFXtVgeSOioONb\neOorlcJqL6uScAyhsDZXbYfGzDn2718Fh7rz9hLh/Pu15Sk6f7aTeKNEO7MpvnTpfRUKfU5VY7YB\ne7cMVVu0QqNmf8Rd7eexqdIH4/Q93CkWcgO+AvEShHVQP/73QUb+UnH6bu+0/1aylLsKXvqzQmHO\naYUkNeYR98C5JIWAFcb/EsCdoL8hTt9zxhziMX/Nz5RCpdtHaX4mQI70OfL3IpsW3SQAsUs7Ggn7\nvwfTP4PKD7h1DuP+BKq8dpofdV7KyU8+zbKZodzr+2KtfH+N0JSoiAFgE/TvX7+M6zq+yEv9xlMZ\nCqtTAUEnRMnCM9BnhMUb8pN5eiZEpDyN1p1CL7MTqlxvrznQbk/RebukfccPu0RDavM8gpIWyfu0\nM1Ki/LpMR39WY+BzPvorC9EZGO6g6n1jtoUqlYwxMN0Bn0KkGq33KqhmyGtXf4eRolaR5UJ0SR5W\nITmoPMisIuCyFInNjoiJFndpr5SRw3ZDOH8n6kiepO+lMh38xr30herVkcF7jP82os+JM/rR5nX0\nbRaSL/sNVEJ12aTvRWTLEJuPbSgXfW254l4t192kIghxkD4jFXBnhVHxvB7GsvBLcbzEtf22cQ2W\nVPtQZz5lnckIf8ClRDGbEFVkqg37GE8gNgt+UTOvOa8O4S5vjG8tniJxQj/amI7e6rFRtER5AwvE\nY7iLRWQIGgUP6rtmopANmdc9K6Rwx0SQRgyN5DbyTlI7/Xwl7177I+piIyRn6unp3cLMdDnVTiEu\nMDNd7v09znsZiohikNRe1OaYrzyOkrAAKAntY2HyB1D+TlGCbRKfHaBfpSZQmQyURNMciLbw6VO2\nccXuO6jXk4xodXx/zWUciLcwM1Vm179Y4BL0l8LrP/dnzvncb3jpkMf2qJAJUSYAN1ozra7aRf0b\nRojEM0RiWRrfdpDy10/S+4iiKqUUEl9RtO+4yBBRmuexyeiz2bnd9sANb2r1bKnkmgiXQ9UJY/Ru\n8vHKMoiOTpzeH0jSDFrcXc1TSLTNS2+ymEgl2Nv0KF3Ds2KyzkxB6lcQP88eva0DbavQdsUqUsSr\np4iW5n1yVNVGnh0bGhFkQBXJk1XdqkidmQL3iQxqDztSfqdZUn6yClcVZATATyNn7RDhRJ1HBLlW\ns0f3go4vFbHPRaKNFW+VOJ6yoi+mi3t5VQa++hIkQxA9pjAtlx8KSS2aC389bh9D57/N46imWXOI\n+815XsU5p3qkjS2ax8AC8aDC+CRiE9ONIG4zxr+d42IAEY11QtFPPfD3+92zpTYttqLM/TtUMcal\nx/yE+sho7m3HNz7LwMRKmhaGmSmJ5zoAuHSjVmQQfShlBq25nq2vDXsKOErCAuGkk2rYuXMvTN0u\nKqLMsvQkDPyig6oP28WU5ppp3WweiLewbcNnXceeHK1U6gxujLsF/WvKX+Arl/8T7/y3e6SfAQqb\nEFVNYufiZJpKiMQzttfj9d5VKar+ZLnF1ziP5hXuPpvrGnaxdeAWhq0iBMdEIJ0Il89wyk2PcTYP\nsq6rjxBZdj2+mv+49mqG97a7r02SZrChmNY1R9JgNhSyC76TphYsBuNZwzU8JEL+kSpBwKZ/BuWX\niH8DWneKzkvk2kVQVxv5tgipJh/Jc/Z0lBBGfUyTNmDXBy2rrHk8c7G2VAh7pvweDVgYoUpBBemd\n52HVMdDXQdUyx70aizHQ51gsgo4v1Xcp+sH6Vpw1heHiFfkWTkuJoKJ32cI/S95Tqh55elsVDTKf\nL785z684R5U2tmgeA/k7ziLSnkFgRufMaFu58W8nQb8Hdyo+jUiN1zlenzX+3Y1/xNXvnqnmLlmK\nvwg4f4c38oiNgAE0VwzTXDGc+3fLwgG+c/DDTKcrOe7ve4mW6czPlDIzXZ5PDQ8hCudkyJgRsNdG\nKhKOkrBAuPbajVx00YNCFOvwBdIn7SZ9WnmKuCR/rUI0qnbtbQvLBf2n/f2faP77IYb/oTW/g+oh\nr1dRTYjH4q6i2wGcgvShUjm+a5o6BasnNWm6zBZRqYbuM9x9Ntsq99N9xnZu5rq8C//GEZLR+pzf\nmmwirGKMd5/9X9TG8rub0y94ktWdn+SzF9wq7yvpt8srdBd4JAxmDUgF3yeO0XvRqeh7JSH5SLUI\nu099H6o+BqEIibd7axdBvsjoqTi9v+0isVGhg7FO1M6InySqNfDdDqpuD+CpZin+yCHhkfJr0f1J\nlFOk64R5vu8ATkC+0KgWpmpFO5U/d6BnC/CgC/Jdiu/3rTgDdZ/YxUJ1rs4hJ1v4yxAErAdBHmQk\nbABYhvr58iOBQYpzZPOdRfOojApbWxaVISdS4E79aQT3s/s6whPvOIROMQJ0GOf3tHFss+PFauN/\nfhFXn3um/Z8UiY/kJQvTz1fy7hU/oi41wnhDDX8IvYHpaKUY5093oM8EjEQvACXu36FC2tHejvqS\nJO9o/in3Rd6Rf7Fylmom8tF8zeM8clPUa0OUD0eIhL33ve/lmmuuIZvNkkql+PjHP86TTz4JwBNP\nPEEsFmNuTtSc33XXXdxyyy3EYjG+973vcdJJJxEOh/nsZz/LfffddyROr2AsWxYHpiyhTAvG7SZ9\nnWc9XhAJ89KD7c8sk76erYbur2/n5j8bZqjlwNWI3oz/hedCwcewP5hJIIW0ykV1blplis6zHncv\nyLMw8FgHVSscmivnQjAOdfMKf7H5JM3LJS78DbvY+uAt0nN6I4/YCJiJ5mMO032twzQ2mxUVaOMa\nntu5QiNYizH+zOjyfogGlNGf63Z7CL4rjT5+OoTigYo/9DENkhmosyzUC6DPKEwog5BMh95NJ4Cn\nmqP4I4dGHy8l8zf4eEaI9J2winS9zvc/kZNAr+s1xovLsPMw6upRP6iqV2WeaeMBKs6Cfm8x2IFY\n+J2ONU63eD+yFEATKH2+PG1JENGpYopzLOcjjQofijHwbIBIpywCqLJdUfnZySp+6xEFNU24V3O/\niKvqnrWAdnWKzsvyLdKqGOPS5T+hviQfqdrA09zNu5igRhCg/9klKrW9kMmfp/N3mKLC+7MGVkde\npI2XOZleKpjKNSqnUkTX+vo8IuKhMrq6atm58//HkbC1a9dy8803c/LJJ3PgwAHOP/987rnnHlas\nWEE8Hmf16tU0NjaysLBg+9z111/P1NQUxx13HMuXL+exxx7jiSeeYN8+eTTolcdPIdPtftlRcaN6\nwOdmSgiXZCmxRL4889rAdakv8raqHdTO5Z+W6VgZ/WsS1E04VvcwsB7h5n8H8gkRxIP5Duzh2heQ\nag5kk04mDfHK2RzRrGoYo/f+LnRdiLr1sji9f+oisa4fbVJHr9QY6LMsBMZCljxT4S92sI7uayUu\n/JX76d6wnVt7P+U6p9h8SikIdZnGhkJQH4fSrNL5Wpv18MXyQtEGsxFheKnQ6BTVmzLXtFikk/Vd\nGqxVvz11KMbAhztgcFq06KopzadiZIUAE4gUkp+ZcDYDC4eEHYNBNPXBOH0fPkEIZWXC4E0ozVkH\nHumgqs4jkpYEbpVUSRYaldyDEOH7kerMHIRKREpY1Tbs/Y7XD87B/TP4hsNkxP6PkuNZri3nVydb\n9I+koWsSQQZkXQ2sRMAvbe+3mVE9X6ooaBVCj2dU5PEGRXGOLBprjs1JIAT6eJzeZ4xIZ62xgdjX\ngd4cINKpqryVIaAxcnPc0NS+aYTkXD5bYINXa07VPauGxHn2Fmlv5BEbAQOoZYw38gg7uEDIGY7r\np+/uTlFJ7NjfyqpKnevLHziD1ux+6kLeBCnKApfyMzRL88w29nM372I0VicIqco3MTNuZLY8v+IV\nxZKTMF3X+eAHP8iBA6JD+RNPPEFLSwvRaJTXve51TE1N8etf/5qmpiZ++9vfsnXrVmZnZ9m8eTPv\nec97ABgcHOQ3v/kNl156KV/72teW+hSLxCDMPmw0sDVGtqPiBtQamt4HuwAK6n/1UibBd1qvoFu/\nizJ9jlmtlP41CWbjMcZHFBN4PXCuiFYlTlN813HYB6lMc4A7vSJLtcYrZ0h09dP3RKftc31PdcLj\n5BfVamiOGJPG7SOkquMMTzXSXHEo97n9k2309F7B1WfeLL20unhSmvIZnF3JqvUvSj/jMo3NLU6W\n1Nk8YqIdM3yxLnM0yfbyeloKhC2PoVNXRRG9Ka2l2AuDULqegX/poGrjmO26FqYiTD9XzsxL5Xmi\nmZ6CO+YgbKwQ3chJmNkY14Qq9REKQ2YYUv8l0vnhSkEQww3Q3CYXBkvsXHLX/FKASNpStL3KZiAZ\nDkaq51+G5Er1d9pez8C9+yBZAuEQhCu8BfIyYh/k2pbiHhQKVVcD61QlW/jNzZA5J3loAkmSlyq0\njpAcqqfnpi1CcmBe77G4SZBZRGItzvGLxjrHZhb02xw9GbuxWQfliNGpIySPt5ybim87N4JeRNlS\nJNAcV2hqH7zFTsRqUVfmeqTfg6YKra/HKqfFXJpxHMujqtQ6lx+YaeXb2ke5qO0XVDCFTimr2EMJ\ndl0yYCNg4jIFIfzezHp1AU12DmZ/wLJl75Ney6uFoknY+eefz/333+96/YorruDOO+/M/furX/0q\n999/P/Pz81RWVvLwww/ziU98glQqxV133cWXvvQlPvnJT7J8+XIGBwdzn9u7dy/t7W5R9auKzJhw\nrDYXk7c2QKM9jyfVhVgIUN9jnbldwbrXP+tLxr6+6xpazhmiteJA7rV5IjS1DLG8fg+DI+4VUjsl\nRec/eJRSR7EPUlNz8C+4RkSQVKtWqYjUrEiR2CbE5OXjk3xww/doPSZ/HcNTzTw6eBrx6Gy+z2a6\nleSQIko2U+c6J4Dbdzex7gPP0XzMIdv7h19udpvGynakUWA30AOJ70iaZPt5PRWCII7sDhTcmzIU\ngYr3wtRdoulzpBV9sIbeC82FZxr9QJyBm9a6iWUoBAsvQ6nRJFq2aC6gFIdLSYuZGp19KP/shGNq\nYbCqmHRWnI+eDNCdoNiopEmCg1YOhkshOw/pcUhWy7/Tdi5hIJGP3KZHIX0YIi2i9U8oQI1/0GtL\nIuxRZNW5KhuTxSBIccpIBm4Li4V/PYKAyLzYFJV7zfcMse12h1Th1F1s3XyLIDs9xud8LEh8O1wE\nLW6yPB8uYrTWcm7jCsfTKfKE5UXkFX0mLEUC3Z0KTW3ndmEXZKISe2XuBux+d0mEEbJjKARNFVpf\nL2+bRjs95VrL/KpKrXO5Fk8xd3Zp7v2b+Tlr2S39bifKM1OESxbQ4il5pfTCMGRG3a+/yiiahP3q\nV78iGlVPGPF4nB/84AcsX76c8847D4AHHniABx54IPeebdu2cc899/DJT36ScDhMNpsvgQmFQqTT\nctH6zp07pa8fKaxfv56dO3fS1fVL+2JSu0L6fmUjVwpvRjqcauV7ox/kXRV3k+BFoiwQJc366N+4\n6azPcM0vb3OFoBMnBSil3giciFjwRhACfx8nBaWWYlLSviWeovOKncSrxXls4gFaOWB7T3PFMM8d\nOoEvPrIt/+I49Ny0hXVn7qKt0eLCP9lGT+8WtybmEAz3tPLZn3+dD910G+u6+oAsfU8cx39svYrh\nl+oRej5jsvAToxaT+guKoNYh2Xm0Y+Zt7W92XXkCyy7fG7w3ZbgMKt4Hk98WrXHKzkLfU0nfFeUQ\nPUG9AIeiMPsolKwWBMMaUalHiKODRDysyM5CpF2cj1X7Vp1FOujGEdVwTi3Ut1iaSI4k2phDMXY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aj9zcyOx/hA9Q9sQn1buXMJomrzVgQh8RO0g1iEtiJC96qquyTwV/xD+1EEofJa/GXnpCEC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owbvvvgvA2rVr6d27N/Xq1QtZtm7dusB2vv76awYPHgyInpDnzp1j8ODBYW8H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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1964819e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Plotting all the Flares from the filtered dataframe'''\n",
    "\n",
    "colors = plt.cm.jet(np.linspace(0,1,len(CENERGY['energy.kev.i'].values)))\n",
    "\n",
    "# build figure object\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "# loop over energy ranges\n",
    "for i,irange in enumerate(CENERGY['energy.kev'].values):\n",
    "    # collect filtered data in temporary dataset\n",
    "    AUX_data = filtered_data[filtered_data['energy.kev']==irange][['x.pos.asec','y.pos.asec']]\n",
    "    # scatter plot to plot flare\n",
    "    plt.scatter(AUX_data['x.pos.asec'].values,AUX_data['y.pos.asec'].values,color=colors[i],label='%s Kev'%irange)\n",
    "    ax.legend(loc='best',fontsize=9,shadow=True)\n",
    "    # delete auxillary/temporary dataset\n",
    "    del(AUX_data)\n",
    "# set title to plot\n",
    "plt.title('SUNSPOTS per Energy')\n",
    "# plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that most flares are in range 6-12 and 12-25 KeV. The flares having high energy are very little and happen very scarcely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flare position in the Sun arcsec from the center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IwP79+9vc565du7BhwwZ8/fXX0Gq10Ov1WLBgQce0mIiIiMhGWP2R26tNfMWI6cRHu22r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\nfwvKvd5hIQm5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19648eaccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Y position distriution arounf the center of the sun'''\n",
    "fig, ax = plt.subplots(figsize=(10,2))\n",
    "# hist\n",
    "y = filtered_data['y.pos.asec'].values\n",
    "plt.hist(y, bins=np.linspace(np.min(y),np.max(y),100), normed=True, label=\"label var y\", color='magenta')\n",
    "# set limits\n",
    "ax.set_xlim([np.min(y),np.max(y)])\n",
    "# title\n",
    "plt.title('Y Position Distribution')\n",
    "# plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that there is a uniform distribution of flares around the center of the Sun. No occurence of flares from a particular region or area of sun."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yearly analysis of Flares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19648e038d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ej/feew9nzpzB+fPnERoa2ljvj4iIiEiTbnufsN69eyMsLAxDhgwx3v+lZ8+eKCoqwsCB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19647f8f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Number of events per year \"\"\"\n",
    "plt.style.use('dark_background')\n",
    "new_df.groupby(['year'])['total_counts'].count().plot(kind='bar',figsize=(10,2),title='YEARLY NUMBER OF EVENTS', color='magenta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping solar flares intensity over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R6enpkaamppgNTZs2pZSUFDp69Ch16NCBhg4dSmlpaeTs7FyufTo6OhQTE0Mr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lRVVcmPP/4oeXl5Eh4erjw/PdQlUOZcLWvSF/4+32pdF53NueqLq4eIjY2VlpYWGTdunACQlJQU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1964913ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Number of events per year and intensity ranges \"\"\"\n",
    "\n",
    "# calculate limits of intensity ranges\n",
    "intensity = new_df['peak_c_s'].values\n",
    "p10 = np.percentile(intensity,10)\n",
    "p50 = np.percentile(intensity,50)\n",
    "p90 = np.percentile(intensity,90)\n",
    "\n",
    "# plot average of events intensity per year\n",
    "PI0 = new_df[(new_df['peak_c_s']<=p10)].groupby(['year'])['peak_c_s'].count()\n",
    "PI1 = new_df[(new_df['peak_c_s']>p10) & (new_df['peak_c_s']<=p50)].groupby(['year'])['peak_c_s'].count()\n",
    "PI2 = new_df[(new_df['peak_c_s']>p50) & (new_df['peak_c_s']<=p90)].groupby(['year'])['peak_c_s'].count()\n",
    "PI3 = new_df[(new_df['peak_c_s']>p90)].groupby(['year'])['peak_c_s'].count()\n",
    "PI = pd.DataFrame({'year':PI0.index.values,'very low':PI0.values,'low':PI1.values,'high':PI2.values,'very high':PI3.values})\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "# build figure object\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "# collect data\n",
    "ind = PI0.index.values\n",
    "y0 = PI0.values\n",
    "y1 = PI1.values\n",
    "y2 = PI2.values\n",
    "y3 = PI3.values\n",
    "# plot\n",
    "ax.stackplot(ind, y0, y1, y2, y3, colors=['blue','green','orange','red'])\n",
    "# set limits\n",
    "ax.set_xlim([ind[0]-1,ind[-1]+1])\n",
    "# set legend\n",
    "ax.legend([mpatches.Patch(color='blue'),  \n",
    "            mpatches.Patch(color='green'),\n",
    "            mpatches.Patch(color='orange'),\n",
    "            mpatches.Patch(color='red')], \n",
    "           ['very low','low','high','very high'])\n",
    "\n",
    "# set label\n",
    "ax.set_xlabel('Years')\n",
    "# set title\n",
    "ax.set_title('YEARLY NUMBER OF EVENTS per INTENSITY (c/s)\\n (Limits Classification = %s c/s, %s c/s, %s c/s)'%(p10,p50,p90))\n",
    "# plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of solar flares per year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the [wikipedia](https://en.wikipedia.org/wiki/Solar_cycle),  \n",
    "The solar cycle or solar magnetic activity cycle is the nearly periodic 11-year change in the Sun's activity (including changes in the levels of solar radiation and ejection of solar material) and appearance (changes in the number and size of sunspots, flares, and other manifestations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19647f3cc50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qAACAWRNqAACAWRNqAACAWRNqAACAWRNqAACAWRNqAACAWRNqAACAWRNqAACA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\nQw0AAACAXfsHVOLbtFc3uXMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19647f8fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "new_df.groupby(['year'])['energy_kev'].value_counts().unstack().plot(kind='bar')\n",
    "plt.ylabel('Number of Flares', fontsize=16)\n",
    "plt.legend(title='Energy Range(KeV)', loc='best', prop={'size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11 Year cycle confirmed for all energies from the above barplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting solar flare energy range from the given dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicting_df = df.copy(deep=True)\n",
    "\n",
    "predicting_df['dt.start'] = predicting_df[['start.date','start.time']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "predicting_df['dt.peak'] = predicting_df[['start.date','peak']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "predicting_df['dt.end'] = predicting_df[['start.date','end']].apply(lambda x: parse_date(x[0],x[1]), axis=1)\n",
    "# clean columns\n",
    "predicting_df.drop(['start.date','start.time','peak','end'], axis=1, inplace=True)\n",
    "# add new columns\n",
    "predicting_df['year'] = predicting_df['dt.start'].apply(lambda col: col.year)\n",
    "predicting_df['month'] = predicting_df['dt.start'].apply(lambda col: col.month)\n",
    "predicting_df['day'] = predicting_df['dt.start'].apply(lambda col: col.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['flare', 'duration.s', 'peak.c/s', 'total.counts', 'energy.kev',\n",
       "       'x.pos.asec', 'y.pos.asec', 'radial', 'active.region.ar', 'flag.1',\n",
       "       'flag.2', 'flag.3', 'flag.4', 'flag.5', 'duration_s_log', 'flare_log',\n",
       "       'dt.start', 'dt.peak', 'dt.end', 'year', 'month', 'day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display columns\n",
    "predicting_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration_s</th>\n",
       "      <th>peak_c_s</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>energy_kev</th>\n",
       "      <th>x_pos_asec</th>\n",
       "      <th>y_pos_asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active_region_ar</th>\n",
       "      <th>flag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>flag_4</th>\n",
       "      <th>flag_5</th>\n",
       "      <th>duration_s_log</th>\n",
       "      <th>flare_log</th>\n",
       "      <th>date_start</th>\n",
       "      <th>date_peak</th>\n",
       "      <th>date_end</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021213</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304</td>\n",
       "      <td>12-25</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.568078</td>\n",
       "      <td>6.568078</td>\n",
       "      <td>2002-02-12 21:29:56</td>\n",
       "      <td>2002-02-12 21:33:38</td>\n",
       "      <td>2002-02-12 21:41:48</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021228</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504</td>\n",
       "      <td>6-12</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>Q1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>2002-02-12 21:44:08</td>\n",
       "      <td>2002-02-12 21:45:06</td>\n",
       "      <td>2002-02-12 21:48:56</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021332</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448</td>\n",
       "      <td>6-12</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.375278</td>\n",
       "      <td>5.375278</td>\n",
       "      <td>2002-02-13 00:53:24</td>\n",
       "      <td>2002-02-13 00:54:54</td>\n",
       "      <td>2002-02-13 00:57:00</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     flare  duration_s  peak_c_s  total_counts energy_kev  x_pos_asec  \\\n",
       "0  2021213         712       136        167304      12-25         592   \n",
       "1  2021228         288         7          9504       6-12         604   \n",
       "2  2021332         216        15         11448       6-12        -310   \n",
       "\n",
       "   y_pos_asec  radial  active_region_ar flag_1 ...  flag_4 flag_5  \\\n",
       "0        -358     692                 0     A1 ...     NaN    NaN   \n",
       "1        -341     694              9811     A1 ...      Q1    NaN   \n",
       "2         375     487              9825     A1 ...     NaN    NaN   \n",
       "\n",
       "  duration_s_log flare_log          date_start           date_peak  \\\n",
       "0       6.568078  6.568078 2002-02-12 21:29:56 2002-02-12 21:33:38   \n",
       "1       5.662960  5.662960 2002-02-12 21:44:08 2002-02-12 21:45:06   \n",
       "2       5.375278  5.375278 2002-02-13 00:53:24 2002-02-13 00:54:54   \n",
       "\n",
       "             date_end  year month  day  \n",
       "0 2002-02-12 21:41:48  2002     2   12  \n",
       "1 2002-02-12 21:48:56  2002     2   12  \n",
       "2 2002-02-13 00:57:00  2002     2   13  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns and display few rows\n",
    "predicting_df = predicting_df.rename(columns = {'duration.s':'duration_s', 'peak.c/s':'peak_c_s', 'total.counts':'total_counts', 'energy.kev':'energy_kev', 'x.pos.asec':'x_pos_asec',\n",
    "       'y.pos.asec':'y_pos_asec', 'active.region.ar':'active_region_ar', 'flag.1':'flag_1', 'flag.2':'flag_2',\n",
    "       'flag.3':'flag_3', 'flag.4':'flag_4', 'flag.5':'flag_5', 'dt.start':'date_start', 'dt.peak':'date_peak', 'dt.end':'date_end'})\n",
    "predicting_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns\n",
    "predicting_df = predicting_df.drop(['flag_1', 'flag_2', 'flag_3', 'flag_4', 'flag_5', 'date_start', 'date_peak', 'date_end'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flare               False\n",
       "duration_s          False\n",
       "peak_c_s            False\n",
       "total_counts        False\n",
       "energy_kev          False\n",
       "x_pos_asec          False\n",
       "y_pos_asec          False\n",
       "radial              False\n",
       "active_region_ar    False\n",
       "duration_s_log      False\n",
       "flare_log           False\n",
       "year                False\n",
       "month               False\n",
       "day                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "predicting_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113942 entries, 0 to 113941\n",
      "Data columns (total 14 columns):\n",
      "flare               113942 non-null int64\n",
      "duration_s          113942 non-null int64\n",
      "peak_c_s            113942 non-null int64\n",
      "total_counts        113942 non-null int64\n",
      "energy_kev          113942 non-null object\n",
      "x_pos_asec          113942 non-null int64\n",
      "y_pos_asec          113942 non-null int64\n",
      "radial              113942 non-null int64\n",
      "active_region_ar    113942 non-null int64\n",
      "duration_s_log      113942 non-null float64\n",
      "flare_log           113942 non-null float64\n",
      "year                113942 non-null int64\n",
      "month               113942 non-null int64\n",
      "day                 113942 non-null int64\n",
      "dtypes: float64(2), int64(11), object(1)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Viewing dataframe object details\n",
    "predicting_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: '100-300',\n",
       " 1: '12-25',\n",
       " 2: '25-50',\n",
       " 3: '3-6',\n",
       " 4: '300-800',\n",
       " 5: '50-100',\n",
       " 6: '6-12',\n",
       " 7: '7000-20000',\n",
       " 8: '800-7000'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enumerating energy range values from str to category\n",
    "dt = predicting_df['energy_kev'].astype('category')\n",
    "dict(enumerate((dt.cat.categories)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning the enumerated codes to ernegy column\n",
    "predicting_df['energy_kev'] = dt.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flare</th>\n",
       "      <th>duration_s</th>\n",
       "      <th>peak_c_s</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>energy_kev</th>\n",
       "      <th>x_pos_asec</th>\n",
       "      <th>y_pos_asec</th>\n",
       "      <th>radial</th>\n",
       "      <th>active_region_ar</th>\n",
       "      <th>duration_s_log</th>\n",
       "      <th>flare_log</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021213</td>\n",
       "      <td>712</td>\n",
       "      <td>136</td>\n",
       "      <td>167304</td>\n",
       "      <td>1</td>\n",
       "      <td>592</td>\n",
       "      <td>-358</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>6.568078</td>\n",
       "      <td>6.568078</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021228</td>\n",
       "      <td>288</td>\n",
       "      <td>7</td>\n",
       "      <td>9504</td>\n",
       "      <td>6</td>\n",
       "      <td>604</td>\n",
       "      <td>-341</td>\n",
       "      <td>694</td>\n",
       "      <td>9811</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>5.662960</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021332</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>11448</td>\n",
       "      <td>6</td>\n",
       "      <td>-310</td>\n",
       "      <td>375</td>\n",
       "      <td>487</td>\n",
       "      <td>9825</td>\n",
       "      <td>5.375278</td>\n",
       "      <td>5.375278</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021308</td>\n",
       "      <td>244</td>\n",
       "      <td>20</td>\n",
       "      <td>17400</td>\n",
       "      <td>1</td>\n",
       "      <td>-277</td>\n",
       "      <td>378</td>\n",
       "      <td>469</td>\n",
       "      <td>9822</td>\n",
       "      <td>5.497168</td>\n",
       "      <td>5.497168</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021310</td>\n",
       "      <td>236</td>\n",
       "      <td>336</td>\n",
       "      <td>313392</td>\n",
       "      <td>2</td>\n",
       "      <td>-272</td>\n",
       "      <td>390</td>\n",
       "      <td>476</td>\n",
       "      <td>9825</td>\n",
       "      <td>5.463832</td>\n",
       "      <td>5.463832</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     flare  duration_s  peak_c_s  total_counts  energy_kev  x_pos_asec  \\\n",
       "0  2021213         712       136        167304           1         592   \n",
       "1  2021228         288         7          9504           6         604   \n",
       "2  2021332         216        15         11448           6        -310   \n",
       "3  2021308         244        20         17400           1        -277   \n",
       "4  2021310         236       336        313392           2        -272   \n",
       "\n",
       "   y_pos_asec  radial  active_region_ar  duration_s_log  flare_log  year  \\\n",
       "0        -358     692                 0        6.568078   6.568078  2002   \n",
       "1        -341     694              9811        5.662960   5.662960  2002   \n",
       "2         375     487              9825        5.375278   5.375278  2002   \n",
       "3         378     469              9822        5.497168   5.497168  2002   \n",
       "4         390     476              9825        5.463832   5.463832  2002   \n",
       "\n",
       "   month  day  \n",
       "0      2   12  \n",
       "1      2   12  \n",
       "2      2   13  \n",
       "3      2   13  \n",
       "4      2   13  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display dataset - Focus on the energy_kev column, the ranges have been converted to categorial values\n",
    "predicting_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating train and test set for models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test = train_test_split(predicting_df, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((91153, 14), (22789, 14))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = X_train['energy_kev']\n",
    "X_train = X_train.drop(['energy_kev'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test['energy_kev']\n",
    "X_test = X_test.drop(['energy_kev'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions using Various models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.18748428621582058, 0.19628770702289033)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_regression = LinearRegression().fit(X_train, y_train)\n",
    "linear_regression_predictions = linear_regression.predict(X_test)\n",
    "linear_regression_score = linear_regression.score(X_test, y_test)\n",
    "linear_regression_score_train = linear_regression.score(X_train, y_train)\n",
    "linear_regression_score, linear_regression_score_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 19 % for the test set** - Performs very poorly in predicting the energy range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.78590548071437971, 0.788454576371595)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_regression = LogisticRegression().fit(X_train, y_train)\n",
    "logistic_regression_predictions = logistic_regression.predict(X_test)\n",
    "logistic_regression_score = logistic_regression.score(X_test, y_test)\n",
    "logistic_regression_score_train = logistic_regression.score(X_train, y_train)\n",
    "logistic_regression_score, logistic_regression_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        32\n",
      "          1       0.64      0.17      0.26      3904\n",
      "          2       0.00      0.00      0.00       393\n",
      "          3       0.90      0.26      0.41      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.00      0.00      0.00        84\n",
      "          6       0.79      0.99      0.88     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.75      0.79      0.73     22789\n",
      "\n",
      "(0.78590548071437971, 0.78590548071437971, 0.78590548071437971, None)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, logistic_regression_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, logistic_regression_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 78.6 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.81236561498968796, 1.0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "decision_tree_classifier = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "decision_tree_predictions = decision_tree_classifier.predict(X_test)\n",
    "decision_tree_score = decision_tree_classifier.score(X_test, y_test)\n",
    "decision_tree_score_train = decision_tree_classifier.score(X_train, y_train)\n",
    "decision_tree_score, decision_tree_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.14      0.19      0.16        32\n",
      "          1       0.50      0.51      0.51      3904\n",
      "          2       0.11      0.14      0.12       393\n",
      "          3       1.00      1.00      1.00      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.12      0.14      0.13        84\n",
      "          6       0.90      0.89      0.89     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.82      0.81      0.81     22789\n",
      "\n",
      "(0.81236561498968796, 0.81236561498968796, 0.81236561498968796, None)\n"
     ]
    }
   ],
   "source": [
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, decision_tree_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, decision_tree_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 82 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Random Forests Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.86107332484970822, 0.99312145513587047)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random_forest_classifier = RandomForestClassifier(n_jobs=-1).fit(X_train, y_train)\n",
    "random_forest_predictions = random_forest_classifier.predict(X_test)\n",
    "random_forest_score = random_forest_classifier.score(X_test, y_test)\n",
    "random_forest_score_train = random_forest_classifier.score(X_train, y_train)\n",
    "random_forest_score, random_forest_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.17      0.06      0.09        32\n",
      "          1       0.63      0.58      0.60      3904\n",
      "          2       0.21      0.04      0.06       393\n",
      "          3       1.00      1.00      1.00      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.36      0.05      0.08        84\n",
      "          6       0.90      0.94      0.92     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.84      0.86      0.85     22789\n",
      "\n",
      "(0.86094168239062707, 0.86094168239062707, 0.86094168239062707, None)\n"
     ]
    }
   ],
   "source": [
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, random_forest_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, random_forest_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 86 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.76141998332528849, 0.82539247199763033)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "kneighbors_classifier = KNeighborsClassifier().fit(X_train, y_train)\n",
    "kneighbors_predictions = kneighbors_classifier.predict(X_test)\n",
    "kneighbors_score = kneighbors_classifier.score(X_test, y_test)\n",
    "kneighbors_score_train = kneighbors_classifier.score(X_train, y_train)\n",
    "kneighbors_score, kneighbors_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.06      0.06      0.06        32\n",
      "          1       0.45      0.42      0.43      3904\n",
      "          2       0.12      0.03      0.05       393\n",
      "          3       0.47      0.25      0.32      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.13      0.02      0.04        84\n",
      "          6       0.84      0.90      0.87     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.74      0.76      0.75     22789\n",
      "\n",
      "(0.76141998332528849, 0.76141998332528849, 0.76141998332528837, None)\n"
     ]
    }
   ],
   "source": [
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, kneighbors_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, kneighbors_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 76 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.86787485190223357, 0.87591192829638076)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gradient_classifier = GradientBoostingClassifier().fit(X_train, y_train)\n",
    "gradient_classifier_predictions = gradient_classifier.predict(X_test)\n",
    "gradient_classifier_score = gradient_classifier.score(X_test, y_test)\n",
    "gradient_classifier_score_train = gradient_classifier.score(X_train, y_train)\n",
    "gradient_classifier_score, gradient_classifier_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.22      0.06      0.10        32\n",
      "          1       0.68      0.52      0.59      3904\n",
      "          2       0.33      0.03      0.06       393\n",
      "          3       1.00      1.00      1.00      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.28      0.06      0.10        84\n",
      "          6       0.89      0.96      0.93     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.85      0.87      0.85     22789\n",
      "\n",
      "(0.86787485190223357, 0.86787485190223357, 0.86787485190223357, None)\n"
     ]
    }
   ],
   "source": [
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, gradient_classifier_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, gradient_classifier_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 87 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Neural Network (scikit-learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.75137127561542849, 0.75227364979759304)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp_classifier = MLPClassifier(hidden_layer_sizes=(100,10)).fit(X_train, y_train)\n",
    "mlp_predictions = mlp_classifier.predict(X_test)\n",
    "mlp_score = mlp_classifier.score(X_test, y_test)\n",
    "mlp_score_train = mlp_classifier.score(X_train, y_train)\n",
    "mlp_score, mlp_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Report on data set:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        32\n",
      "          1       0.00      0.00      0.00      3904\n",
      "          2       0.00      0.00      0.00       393\n",
      "          3       0.00      0.00      0.00      1248\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.00      0.00      0.00        84\n",
      "          6       0.75      1.00      0.86     17123\n",
      "          7       0.00      0.00      0.00         1\n",
      "          8       0.00      0.00      0.00         1\n",
      "\n",
      "avg / total       0.56      0.75      0.64     22789\n",
      "\n",
      "(0.75137127561542849, 0.75137127561542849, 0.75137127561542849, None)\n"
     ]
    }
   ],
   "source": [
    "print ('\\nReport on data set:')\n",
    "print(metrics.classification_report(y_test, mlp_predictions))\n",
    "print(metrics.precision_recall_fscore_support(y_test, mlp_predictions, average='micro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Achieves an accuracy of 75 % for the test set**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Take away from the analysis\n",
    "\n",
    "We analysed the trend of solar flare over the years and over the various attributes such as duration, position, radial values, intensity, etc.  \n",
    "\n",
    "Our Top 3 models' results for prediction of a energy range for a solar flare based on attributes of the flare are - \n",
    "\n",
    "1. Gradient Boosting Classifier - 87 % accuracy\n",
    "2. Random Forests Classifier - 86 % accuracy\n",
    "3. Decision Tree Classifier - 82 % accuracy"
   ]
  }
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