Discriminating volcanic rock samples' tectonic settings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminating volcanic rock samples' tectonic settings using machine learning\n",
    "\n",
    "A take at making classification diagrams better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User : CloudyOverhead\n",
      "Python version : 3.6.1 |Continuum Analytics, Inc.| (default, May 11 2017, 13:09:58) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "2017-08-01T09:31:00-04:00\n",
      "\n",
      "CPython 3.6.1\n",
      "IPython 5.3.0\n",
      "\n",
      "pandas 0.20.2\n",
      "numpy 1.12.1\n",
      "matplotlib 2.0.2\n",
      "sklearn 0.18.2\n",
      "\n",
      "compiler   : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n",
      "system     : Linux\n",
      "release    : 3.10.0-514.21.1.el7.x86_64\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 32\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import getpass\n",
    "print(\"User :\", getpass.getuser())\n",
    "print(\"Python version :\", sys.version)\n",
    "%load_ext watermark\n",
    "%watermark -i -v -m -p pandas,numpy,matplotlib,sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "import datetime\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random as rd\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "\n",
    "from itertools import count, cycle, combinations\n",
    "\n",
    "from scipy.stats.mstats import gmean\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import (f1_score, make_scorer, confusion_matrix, mean_absolute_error, \n",
    "                             median_absolute_error, mean_squared_error)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, LeavePGroupsOut, cross_val_predict\n",
    "\n",
    "from tensorflow.contrib import learn\n",
    "from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib\n",
    "\n",
    "from matplotlib.colors import Normalize\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "pd.set_option('display.max_columns', 150)\n",
    "pd.set_option('display.max_rows', 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate Georoc database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenation\n",
    "\n",
    "Concatenation of the *.csv* files contained in */Georoc_files* into a single *master.csv* file at source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Files from /Georoc_files are opened one by one and stored as DataFrames in the *files* list.\n",
    "\n",
    "dir_ = os.curdir + '/Georoc_files'\n",
    "files = os.listdir(dir_)\n",
    "\n",
    "nb_data = 0\n",
    "\n",
    "for file, file_no in zip(files, count()):\n",
    "\n",
    "    file = pd.read_csv(dir_ + '/' + file, encoding='iso-8859-1', dtype=object)\n",
    "\n",
    "    index = np.where(file.CITATIONS.values == 'References:')[0][0]\n",
    "    file = file[:index]\n",
    "    file.insert(0, 'FILENAME', files[file_no])\n",
    "\n",
    "    nb_data += file.shape[0]\n",
    "    files[file_no] = file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Files are regrouped in *master_file*. *nb_data* was needed because of the *.append* method being resources-expensive.\n",
    "\n",
    "master_file = pd.DataFrame(index=np.arange(0, nb_data), columns=files[0].axes[1])\n",
    "current_row = 0\n",
    "\n",
    "row_no = 0\n",
    "for file in files:\n",
    "    for _, row in file.iterrows():\n",
    "        master_file.loc[row_no] = row\n",
    "        row_no += 1\n",
    "\n",
    "del files, file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Non-volcanic rocks, duplicates and unnecessary columns are removed.\n",
    "master_file = master_file[master_file['ROCK TYPE'] == 'VOLCANIC ROCK']\n",
    "master_file.drop_duplicates(subset='UNIQUE_ID', inplace=True)\n",
    "master_file.drop('Unnamed: 171', axis=1, inplace=True)\n",
    "\n",
    "# Empty columns are deleted.\n",
    "a = master_file.iloc[:, 28:-1].count(axis=0)\n",
    "for index, value in a.iteritems():\n",
    "    if value < 1000:\n",
    "        master_file.drop(index, axis=1, inplace=True)\n",
    "\n",
    "# Tectonics settings are formatted identically.\n",
    "master_file['TECTONIC SETTING'] = master_file['TECTONIC SETTING'].str.upper()\n",
    "\n",
    "# *SEAMOUNT* and *SUBMARINE RIDGE* tectonic settings are removed, as those are topography types and not formation processes,\n",
    "# and thus geochemistry can provide no informative insight. *COMPLEX VOLCANIC SETTINGS* is removed as this tectonic setting\n",
    "# is an amalgam of settings.\n",
    "master_file = master_file[~ master_file['TECTONIC SETTING'].isin(['SEAMOUNT', 'SUBMARINE RIDGE', 'COMPLEX VOLCANIC SETTINGS'])]\n",
    "\n",
    "# *OCEAN PLATEAU*, *CONTINENTAL FLOOD BASALT* and *OCEAN-BASIN FLOOD BASALT* tectonic settings are regrouped under the label\n",
    "# *FLOOD BASALT*, as their formation processes are the same.\n",
    "master_file = master_file.replace(['OCEANIC PLATEAU', 'CONTINENTAL FLOOD BASALT', 'OCEAN-BASIN FLOOD BASALT'], 'FLOOD BASALT')\n",
    "\n",
    "# *ARCHEAN CRATON (INCLUDING GREENSTONE BELTS)* is formatted into *ARCHEAN CRATON* for convenience.\n",
    "master_file = master_file.replace('ARCHEAN CRATON (INCLUDING GREENSTONE BELTS)', 'ARCHEAN CRATON')\n",
    "\n",
    "# Removing data messes with the indexing\n",
    "master_file = master_file.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data under limit of detection is formatted to half the limit of detection.\n",
    "\n",
    "for row_no, row in master_file.iloc[:, 28:171].iterrows():\n",
    "    for col_no, data in zip(count(), row):\n",
    "        data = str(data).strip()\n",
    "\n",
    "        if data[0] == '-':  # Data under limit of detection has format *-data*. *<data* is not used\n",
    "            master_file.iloc[row_no, col_no + 28] = -float(data) / 2\n",
    "\n",
    "#         elif data[0] == '<':\n",
    "#             print(master_file.iloc[row_no, col_no + 28])\n",
    "#             master_file.iloc[row_no, col_no + 28] = float(data.lstrip(['<', '='])) / 2\n",
    "#             print(master_file.iloc[row_no, col_no + 28])\n",
    "#         else:\n",
    "#             data = float(data)  # failsafe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# WT% are converted to PPM, leaving only PPM units.\n",
    "\n",
    "for index, column in enumerate(master_file.axes[1].tolist()[28:-1]):\n",
    "\n",
    "    if column[-5:] == '(WT%)':\n",
    "\n",
    "        master_file[column] = master_file[column].astype(float).apply(lambda x: x * 10**4)\n",
    "\n",
    "master_file.columns = [col.replace('(WT%)', '').replace('(PPM)', '') for col in master_file.axes[1].tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data is saved in *master.csv* at source, allowing it to be reloaded for future uses.\n",
    "\n",
    "master_file.to_csv(os.curdir + '/master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Should be used on reloads to produce faster results.\n",
    "\n",
    "master_file = pd.read_csv(os.curdir + '/master.csv', encoding='iso-8859-1', dtype=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset after transformation:(263526, 126)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILENAME</th>\n",
       "      <th>CITATIONS</th>\n",
       "      <th>TECTONIC SETTING</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LOCATION COMMENT</th>\n",
       "      <th>LATITUDE MIN</th>\n",
       "      <th>LATITUDE MAX</th>\n",
       "      <th>LONGITUDE MIN</th>\n",
       "      <th>LONGITUDE MAX</th>\n",
       "      <th>LAND OR SEA</th>\n",
       "      <th>ELEVATION MIN</th>\n",
       "      <th>ELEVATION MAX</th>\n",
       "      <th>SAMPLE NAME</th>\n",
       "      <th>ROCK NAME</th>\n",
       "      <th>MIN. AGE (YRS.)</th>\n",
       "      <th>MAX. AGE (YRS.)</th>\n",
       "      <th>GEOL.</th>\n",
       "      <th>AGE</th>\n",
       "      <th>ERUPTION DAY</th>\n",
       "      <th>ERUPTION MONTH</th>\n",
       "      <th>ERUPTION YEAR</th>\n",
       "      <th>ROCK TEXTURE</th>\n",
       "      <th>ROCK TYPE</th>\n",
       "      <th>DRILL DEPTH MIN</th>\n",
       "      <th>DRILL DEPTHAX</th>\n",
       "      <th>ALTERATION</th>\n",
       "      <th>MINERAL</th>\n",
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       "      <th>FE2O3</th>\n",
       "      <th>FEO</th>\n",
       "      <th>FEOT</th>\n",
       "      <th>CAO</th>\n",
       "      <th>MGO</th>\n",
       "      <th>MNO</th>\n",
       "      <th>NIO</th>\n",
       "      <th>K2O</th>\n",
       "      <th>NA2O</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>H2O</th>\n",
       "      <th>H2OP</th>\n",
       "      <th>H2OM</th>\n",
       "      <th>CO2</th>\n",
       "      <th>F</th>\n",
       "      <th>CL</th>\n",
       "      <th>SO3</th>\n",
       "      <th>S</th>\n",
       "      <th>LOI</th>\n",
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       "      <th>BE</th>\n",
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       "      <th>F.1</th>\n",
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       "      <th>S.1</th>\n",
       "      <th>CL.1</th>\n",
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       "      <th>SC</th>\n",
       "      <th>TI</th>\n",
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       "      <th>ZN</th>\n",
       "      <th>GA</th>\n",
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       "      <th>AS</th>\n",
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       "      <th>RB</th>\n",
       "      <th>SR</th>\n",
       "      <th>Y</th>\n",
       "      <th>ZR</th>\n",
       "      <th>NB</th>\n",
       "      <th>MO</th>\n",
       "      <th>RU</th>\n",
       "      <th>RH</th>\n",
       "      <th>PD</th>\n",
       "      <th>AG</th>\n",
       "      <th>CD</th>\n",
       "      <th>IN</th>\n",
       "      <th>SN</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BA</th>\n",
       "      <th>LA</th>\n",
       "      <th>CE</th>\n",
       "      <th>PR</th>\n",
       "      <th>ND</th>\n",
       "      <th>SM</th>\n",
       "      <th>EU</th>\n",
       "      <th>GD</th>\n",
       "      <th>TB</th>\n",
       "      <th>DY</th>\n",
       "      <th>HO</th>\n",
       "      <th>ER</th>\n",
       "      <th>TM</th>\n",
       "      <th>YB</th>\n",
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       "      <th>SR87_SR86_INI</th>\n",
       "      <th>PB206_PB204</th>\n",
       "      <th>PB207_PB204</th>\n",
       "      <th>PB208_PB204</th>\n",
       "      <th>OS187_OS188</th>\n",
       "      <th>RE187_OS188</th>\n",
       "      <th>HF176_HF177</th>\n",
       "      <th>HE3_HE4(R/R(A))</th>\n",
       "      <th>UNIQUE_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC.csv</td>\n",
       "      <td>[19915]</td>\n",
       "      <td>INTRAPLATE VOLCANICS</td>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...</td>\n",
       "      <td>SOUTHERN INTRA-CALDERA</td>\n",
       "      <td>46.5</td>\n",
       "      <td>46.7</td>\n",
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       "      <td>VOLCANIC ROCK</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>WHOLE_ROCK [19915]</td>\n",
       "      <td>741200.0</td>\n",
       "      <td>2900.0</td>\n",
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       "      <td>4.43</td>\n",
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       "      <td>224.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC.csv</td>\n",
       "      <td>[19915]</td>\n",
       "      <td>INTRAPLATE VOLCANICS</td>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...</td>\n",
       "      <td>NORTHERN INTRA-CALDERA</td>\n",
       "      <td>46.5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.35</td>\n",
       "      <td>SUBAERIAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>s NIC [19915]</td>\n",
       "      <td>RHYOLITE [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VOLCANIC ROCK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WHOLE_ROCK [19915]</td>\n",
       "      <td>748400.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>140600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>6899.999999999999</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>700.0000000000001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>21400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.83</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259.96</td>\n",
       "      <td>59.23</td>\n",
       "      <td>46.11</td>\n",
       "      <td>194.48</td>\n",
       "      <td>12.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413.22</td>\n",
       "      <td>36.85</td>\n",
       "      <td>74.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC.csv</td>\n",
       "      <td>[19915]</td>\n",
       "      <td>INTRAPLATE VOLCANICS</td>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...</td>\n",
       "      <td>NORTHERN EXTRA-CALDERA</td>\n",
       "      <td>46.5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.35</td>\n",
       "      <td>SUBAERIAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>s NEC [19915]</td>\n",
       "      <td>RHYOLITE [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VOLCANIC ROCK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WHOLE_ROCK [19915]</td>\n",
       "      <td>767300.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>133500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57100.0</td>\n",
       "      <td>19900.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.78</td>\n",
       "      <td>23.8</td>\n",
       "      <td>53.78</td>\n",
       "      <td>110.33</td>\n",
       "      <td>14.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.11</td>\n",
       "      <td>23.33</td>\n",
       "      <td>51.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC.csv</td>\n",
       "      <td>[19915]</td>\n",
       "      <td>INTRAPLATE VOLCANICS</td>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...</td>\n",
       "      <td>SOUTHERN INTRA-CALDERA</td>\n",
       "      <td>46.5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.35</td>\n",
       "      <td>SUBAERIAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>s 14 LOWER [19915]</td>\n",
       "      <td>RHYOLITE [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VOLCANIC ROCK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WHOLE_ROCK [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "      <td>149.9</td>\n",
       "      <td>10.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.2</td>\n",
       "      <td>60.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC.csv</td>\n",
       "      <td>[19915]</td>\n",
       "      <td>INTRAPLATE VOLCANICS</td>\n",
       "      <td>ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...</td>\n",
       "      <td>SOUTHERN INTRA-CALDERA</td>\n",
       "      <td>46.5</td>\n",
       "      <td>46.7</td>\n",
       "      <td>11.15</td>\n",
       "      <td>11.35</td>\n",
       "      <td>SUBAERIAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>s 14 UPPER [19915]</td>\n",
       "      <td>RHYOLITE [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VOLCANIC ROCK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WHOLE_ROCK [19915]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.3</td>\n",
       "      <td>180.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.8</td>\n",
       "      <td>64.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>24.9</td>\n",
       "      <td>5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              FILENAME CITATIONS      TECTONIC SETTING  \\\n",
       "0  ALPS_SARDINIA_CORSICA_PALEOZOIC.csv   [19915]  INTRAPLATE VOLCANICS   \n",
       "1  ALPS_SARDINIA_CORSICA_PALEOZOIC.csv   [19915]  INTRAPLATE VOLCANICS   \n",
       "2  ALPS_SARDINIA_CORSICA_PALEOZOIC.csv   [19915]  INTRAPLATE VOLCANICS   \n",
       "3  ALPS_SARDINIA_CORSICA_PALEOZOIC.csv   [19915]  INTRAPLATE VOLCANICS   \n",
       "4  ALPS_SARDINIA_CORSICA_PALEOZOIC.csv   [19915]  INTRAPLATE VOLCANICS   \n",
       "\n",
       "                                            LOCATION        LOCATION COMMENT  \\\n",
       "0  ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...  SOUTHERN INTRA-CALDERA   \n",
       "1  ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...  NORTHERN INTRA-CALDERA   \n",
       "2  ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...  NORTHERN EXTRA-CALDERA   \n",
       "3  ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...  SOUTHERN INTRA-CALDERA   \n",
       "4  ALPS_SARDINIA_CORSICA_PALEOZOIC / SOUTHERN ALP...  SOUTHERN INTRA-CALDERA   \n",
       "\n",
       "  LATITUDE MIN LATITUDE MAX LONGITUDE MIN LONGITUDE MAX LAND OR SEA  \\\n",
       "0         46.5         46.7         11.15         11.35   SUBAERIAL   \n",
       "1         46.5         46.7         11.15         11.35   SUBAERIAL   \n",
       "2         46.5         46.7         11.15         11.35   SUBAERIAL   \n",
       "3         46.5         46.7         11.15         11.35   SUBAERIAL   \n",
       "4         46.5         46.7         11.15         11.35   SUBAERIAL   \n",
       "\n",
       "  ELEVATION MIN ELEVATION MAX         SAMPLE NAME         ROCK NAME  \\\n",
       "0           NaN           NaN       s SIC [19915]  RHYOLITE [19915]   \n",
       "1           NaN           NaN       s NIC [19915]  RHYOLITE [19915]   \n",
       "2           NaN           NaN       s NEC [19915]  RHYOLITE [19915]   \n",
       "3           NaN           NaN  s 14 LOWER [19915]  RHYOLITE [19915]   \n",
       "4           NaN           NaN  s 14 UPPER [19915]  RHYOLITE [19915]   \n",
       "\n",
       "  MIN. AGE (YRS.) MAX. AGE (YRS.) GEOL.  AGE ERUPTION DAY ERUPTION MONTH  \\\n",
       "0             NaN             NaN   NaN  NaN          NaN            NaN   \n",
       "1             NaN             NaN   NaN  NaN          NaN            NaN   \n",
       "2             NaN             NaN   NaN  NaN          NaN            NaN   \n",
       "3             NaN             NaN   NaN  NaN          NaN            NaN   \n",
       "4             NaN             NaN   NaN  NaN          NaN            NaN   \n",
       "\n",
       "  ERUPTION YEAR ROCK TEXTURE      ROCK TYPE DRILL DEPTH MIN DRILL DEPTHAX  \\\n",
       "0           NaN          NaN  VOLCANIC ROCK             NaN           NaN   \n",
       "1           NaN          NaN  VOLCANIC ROCK             NaN           NaN   \n",
       "2           NaN          NaN  VOLCANIC ROCK             NaN           NaN   \n",
       "3           NaN          NaN  VOLCANIC ROCK             NaN           NaN   \n",
       "4           NaN          NaN  VOLCANIC ROCK             NaN           NaN   \n",
       "\n",
       "  ALTERATION MINERAL             MATERIAL      SIO2    TIO2     AL2O3 CR2O3  \\\n",
       "0        NaN     NaN  WHOLE_ROCK [19915]   741200.0  2900.0  136000.0   NaN   \n",
       "1        NaN     NaN  WHOLE_ROCK [19915]   748400.0  2300.0  140600.0   NaN   \n",
       "2        NaN     NaN  WHOLE_ROCK [19915]   767300.0  1000.0  133500.0   NaN   \n",
       "3        NaN     NaN  WHOLE_ROCK [19915]        NaN  2000.0       NaN   NaN   \n",
       "4        NaN     NaN  WHOLE_ROCK [19915]        NaN  3000.0       NaN   NaN   \n",
       "\n",
       "  FE2O3  FEO     FEOT                CAO     MGO                MNO  NIO  \\\n",
       "0   NaN  NaN  21100.0             6000.0  7800.0              800.0  NaN   \n",
       "1   NaN  NaN  14800.0  6899.999999999999  3800.0  700.0000000000001  NaN   \n",
       "2   NaN  NaN  12700.0             3800.0  2500.0              600.0  NaN   \n",
       "3   NaN  NaN      NaN                NaN     NaN                NaN  NaN   \n",
       "4   NaN  NaN      NaN                NaN     NaN                NaN  NaN   \n",
       "\n",
       "       K2O     NA2O               P2O5  H2O H2OP H2OM  CO2    F   CL  SO3  \\\n",
       "0  45900.0  35100.0  700.0000000000001  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  59000.0  21400.0              400.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  57100.0  19900.0              100.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3      NaN      NaN                NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4      NaN      NaN                NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "     S      LOI   LI   BE    B  F.1    P  S.1 CL.1    K    SC   TI      V  \\\n",
       "0  NaN  15100.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   5.2  NaN   20.4   \n",
       "1  NaN  18200.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   5.7  NaN  13.83   \n",
       "2  NaN  15000.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  4.22  NaN    NaN   \n",
       "3  NaN      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN  NaN    NaN   \n",
       "4  NaN      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN  NaN    NaN   \n",
       "\n",
       "     CR   MN    CO    NI   CU     ZN   GA   GE   AS   SE      RB     SR  \\\n",
       "0  6.67  NaN  4.43  4.78  NaN   44.7  NaN  NaN  NaN  NaN   224.1   91.4   \n",
       "1   5.8  NaN     4  4.08  NaN     35  NaN  NaN  NaN  NaN  259.96  59.23   \n",
       "2   NaN  NaN   NaN   4.5  NaN  32.44  NaN  NaN  NaN  NaN  283.78   23.8   \n",
       "3   NaN  NaN   NaN   NaN  NaN    NaN  NaN  NaN  NaN  NaN     NaN    NaN   \n",
       "4   NaN  NaN   NaN   NaN  NaN    NaN  NaN  NaN  NaN  NaN     NaN    NaN   \n",
       "\n",
       "       Y      ZR     NB   MO   RU   RH   PD   AG   CD   IN   SN   SB   CS  \\\n",
       "0   34.9   193.9   12.3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  46.11  194.48  12.48  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  53.78  110.33  14.11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3   24.9   149.9   10.9  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4   24.3   180.2   11.8  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "       BA     LA     CE   PR    ND   SM   EU   GD   TB   DY   HO   ER   TM  \\\n",
       "0   322.7   26.8     57  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  413.22  36.85  74.15  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  102.11  23.33   51.9  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3     NaN   21.2   60.7  4.5  17.5  3.9  0.4  3.7  0.6  3.9  0.8  2.4  0.4   \n",
       "4     NaN   30.8   64.4  6.4  24.9    5  0.7  4.4  0.7  4.2  0.8  2.2  0.4   \n",
       "\n",
       "    YB   LU   HF   TA    W   RE   OS   IR   PT   AU   TL     PB   BI     TH  \\\n",
       "0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN     25  NaN   21.8   \n",
       "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  24.48  NaN  20.15   \n",
       "2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   23.6  NaN     21   \n",
       "3  2.7  0.4  4.3  1.6  NaN  NaN  NaN  NaN  NaN  NaN  NaN    NaN  NaN   19.7   \n",
       "4  2.4  0.4  4.7  1.4  NaN  NaN  NaN  NaN  NaN  NaN  NaN    NaN  NaN   17.9   \n",
       "\n",
       "     U ND143_ND144 SR87_SR86 SR87_SR86_INI PB206_PB204 PB207_PB204  \\\n",
       "0  NaN         NaN       NaN           NaN         NaN         NaN   \n",
       "1  NaN         NaN       NaN           NaN         NaN         NaN   \n",
       "2  NaN         NaN       NaN           NaN         NaN         NaN   \n",
       "3  3.8         NaN       NaN           NaN         NaN         NaN   \n",
       "4  4.1         NaN       NaN           NaN         NaN         NaN   \n",
       "\n",
       "  PB208_PB204 OS187_OS188 RE187_OS188 HF176_HF177 HE3_HE4(R/R(A)) UNIQUE_ID  \n",
       "0         NaN         NaN         NaN         NaN             NaN   1014271  \n",
       "1         NaN         NaN         NaN         NaN             NaN   1014272  \n",
       "2         NaN         NaN         NaN         NaN             NaN   1014273  \n",
       "3         NaN         NaN         NaN         NaN             NaN   1014274  \n",
       "4         NaN         NaN         NaN         NaN             NaN   1014275  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('Shape of the dataset after transformation:' + str(master_file.shape))\n",
    "master_file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              FILENAME\n",
       "1             CITATIONS\n",
       "2      TECTONIC SETTING\n",
       "3              LOCATION\n",
       "4      LOCATION COMMENT\n",
       "5          LATITUDE MIN\n",
       "6          LATITUDE MAX\n",
       "7         LONGITUDE MIN\n",
       "8         LONGITUDE MAX\n",
       "9           LAND OR SEA\n",
       "10        ELEVATION MIN\n",
       "11        ELEVATION MAX\n",
       "12          SAMPLE NAME\n",
       "13            ROCK NAME\n",
       "14      MIN. AGE (YRS.)\n",
       "15      MAX. AGE (YRS.)\n",
       "16                GEOL.\n",
       "17                  AGE\n",
       "18         ERUPTION DAY\n",
       "19       ERUPTION MONTH\n",
       "20        ERUPTION YEAR\n",
       "21         ROCK TEXTURE\n",
       "22            ROCK TYPE\n",
       "23      DRILL DEPTH MIN\n",
       "24        DRILL DEPTHAX\n",
       "25           ALTERATION\n",
       "26              MINERAL\n",
       "27             MATERIAL\n",
       "28                 SIO2\n",
       "29                 TIO2\n",
       "30                AL2O3\n",
       "31                CR2O3\n",
       "32                FE2O3\n",
       "33                  FEO\n",
       "34                 FEOT\n",
       "35                  CAO\n",
       "36                  MGO\n",
       "37                  MNO\n",
       "38                  NIO\n",
       "39                  K2O\n",
       "40                 NA2O\n",
       "41                 P2O5\n",
       "42                  H2O\n",
       "43                 H2OP\n",
       "44                 H2OM\n",
       "45                  CO2\n",
       "46                    F\n",
       "47                   CL\n",
       "48                  SO3\n",
       "49                    S\n",
       "50                  LOI\n",
       "51                   LI\n",
       "52                   BE\n",
       "53                    B\n",
       "54                  F.1\n",
       "55                    P\n",
       "56                  S.1\n",
       "57                 CL.1\n",
       "58                    K\n",
       "59                   SC\n",
       "60                   TI\n",
       "61                    V\n",
       "62                   CR\n",
       "63                   MN\n",
       "64                   CO\n",
       "65                   NI\n",
       "66                   CU\n",
       "67                   ZN\n",
       "68                   GA\n",
       "69                   GE\n",
       "70                   AS\n",
       "71                   SE\n",
       "72                   RB\n",
       "73                   SR\n",
       "74                    Y\n",
       "75                   ZR\n",
       "76                   NB\n",
       "77                   MO\n",
       "78                   RU\n",
       "79                   RH\n",
       "80                   PD\n",
       "81                   AG\n",
       "82                   CD\n",
       "83                   IN\n",
       "84                   SN\n",
       "85                   SB\n",
       "86                   CS\n",
       "87                   BA\n",
       "88                   LA\n",
       "89                   CE\n",
       "90                   PR\n",
       "91                   ND\n",
       "92                   SM\n",
       "93                   EU\n",
       "94                   GD\n",
       "95                   TB\n",
       "96                   DY\n",
       "97                   HO\n",
       "98                   ER\n",
       "99                   TM\n",
       "100                  YB\n",
       "101                  LU\n",
       "102                  HF\n",
       "103                  TA\n",
       "104                   W\n",
       "105                  RE\n",
       "106                  OS\n",
       "107                  IR\n",
       "108                  PT\n",
       "109                  AU\n",
       "110                  TL\n",
       "111                  PB\n",
       "112                  BI\n",
       "113                  TH\n",
       "114                   U\n",
       "115         ND143_ND144\n",
       "116           SR87_SR86\n",
       "117       SR87_SR86_INI\n",
       "118         PB206_PB204\n",
       "119         PB207_PB204\n",
       "120         PB208_PB204\n",
       "121         OS187_OS188\n",
       "122         RE187_OS188\n",
       "123         HF176_HF177\n",
       "124     HE3_HE4(R/R(A))\n",
       "125           UNIQUE_ID\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column names\n",
    "\n",
    "pd.Series(master_file.axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ICELAND.csv                                              9163\n",
       "HAWAIIAN_ISLANDS_1.csv                                   8058\n",
       "HONSHU_ARC_1.csv                                         7657\n",
       "YELLOWSTONE-SNAKE_RIVER_PLAIN_VOLCANIC_PROVINCE.csv      7093\n",
       "ANATOLIA-IRAN_BELT_-_CENOZOIC_QUATERNARY.csv             6572\n",
       "ANDEAN_ARC_1.csv                                         6412\n",
       "ANDEAN_ARC_2.csv                                         6303\n",
       "EAST_AFRICAN_RIFT.csv                                    6183\n",
       "IZU-BONIN_ARC.csv                                        5658\n",
       "APENNINIC-MAGHREBIDES_CHAIN_1.csv                        5418\n",
       "HONSHU_ARC_2.csv                                         5375\n",
       "APENNINIC-MAGHREBIDES_CHAIN.csv                          5170\n",
       "CENTRAL_ASIAN_FOLDBELT_-_CENOZOIC_QUATERNARY_2.csv       5119\n",
       "MEXICAN_VOLCANIC_BELTS.csv                               5069\n",
       "BASIN_AND_RANGE_-_GREAT_BASIN.csv                        4925\n",
       "NORTH_ATLANTIC_IGNEOUS_PROVINCE_OR_NAIP.csv              4621\n",
       "NEW_ZEALAND.csv                                          4455\n",
       "CASCADES.csv                                             4346\n",
       "EUROPEAN_OROGENIC_BELT_-_CENOZOIC_QUATERNARY.csv         4284\n",
       "KAMCHATKA_ARC.csv                                        4281\n",
       "CENTRAL_ASIAN_FOLDBELT_-_MESOZOIC.csv                    4259\n",
       "HAWAIIAN_ISLANDS_2.csv                                   3922\n",
       "SUPERIOR_PROVINCE_ARCHEAN.csv                            3754\n",
       "CANARY_ISLANDS.csv                                       3743\n",
       "CAPE_VERDE_ISLANDS.csv                                   3743\n",
       "CENTRAL_AMERICAN_VOLCANIC_ARC.csv                        3678\n",
       "CENTRAL_ASIAN_FOLDBELT_-_PALEOZOIC.csv                   3413\n",
       "SIBERIAN_TRAPS.csv                                       3390\n",
       "AUSTRALIA_001.csv                                        3381\n",
       "DECCAN.csv                                               3051\n",
       "ALEUTIAN_ARC.csv                                         2975\n",
       "TONGA_ARC.csv                                            2819\n",
       "KAROO_AND_FERRAR_PROVINCES.csv                           2740\n",
       "LESSER_ANTILLES.csv                                      2659\n",
       "WEST_AUSTRALIAN_CRATON.csv                               2506\n",
       "KURILE_ARC.csv                                           2458\n",
       "CARPATHIAN_BELT_AND_PANNONIAN_BASIN.csv                  2426\n",
       "EUROPEAN_OROGENIC_BELT_-_PALEOZOIC.csv                   2396\n",
       "RYUKYU_ARC.csv                                           2269\n",
       "SUNDA_ARC.csv                                            2242\n",
       "KAAPVAAL_CRATON_ARCHEAN.csv                              2172\n",
       "CENTRAL_ASIAN_FOLDBELT_-_CENOZOIC_QUATERNARY_1.csv       2172\n",
       "AEOLIAN_ARC.csv                                          2163\n",
       "BALTIC_SHIELD_-_PROTEROZOIC.csv                          2059\n",
       "NEW_ZEALAND_001.csv                                      1989\n",
       "AEGEAN_ARC.csv                                           1919\n",
       "APPALACHIANS.csv                                         1909\n",
       "CENTRAL_ATLANTIC_MAGMATIC_PROVINCE_-_CAMP.csv            1888\n",
       "MARIANA_ARC.csv                                          1877\n",
       "BALTIC_SHIELD_-_ARCHEAN.csv                              1835\n",
       "PARANA.csv                                               1707\n",
       "LUZON_ARC.csv                                            1613\n",
       "CENTRAL_ASIAN_FOLDBELT_-_PROTEROZOIC.csv                 1584\n",
       "NORTH_AMERICAN_CORDILLERA_-_CENOZOIC-QUATERNARY.csv      1503\n",
       "MASCARENE_ISLANDS_INCL_REUNION.csv                       1481\n",
       "ARABIAN-NUBIAN_SHIELD_-_CENOZOIC_QUATERNARY.csv          1479\n",
       "ARABIAN-NUBIAN_SHIELD_-_PROTEROZOIC.csv                  1457\n",
       "NEW_HEBRIDES_ARC_-_VANUATU_ARCHIPELAGO.csv               1410\n",
       "DHARWAR_CRATON_ARCHEAN.csv                               1378\n",
       "SCOTIA_ARC.csv                                           1325\n",
       "GREATER_ANTILLES.csv                                     1316\n",
       "AZORES.csv                                               1300\n",
       "BISMARCK_ARC_-_NEW_BRITAIN_ARC.csv                       1282\n",
       "LIGURIA-SARDO-TYRRHENIAN_ARC.csv                         1280\n",
       "MEXICAN_BASIN_AND_RANGE.csv                              1215\n",
       "ANTARCTICA.csv                                           1205\n",
       "GALAPAGOS_ISLANDS.csv                                    1106\n",
       "MID-AFRICAN_RIFT_SYSTEM.csv                              1090\n",
       "EMEISHAN.csv                                             1079\n",
       "ROSS_ISLAND.csv                                          1039\n",
       "ETHIOPIAN_PLATEAU.csv                                     916\n",
       "AUSTRAL-COOK_ISLANDS.csv                                  837\n",
       "CARIBBEAN-COLOMBIAN_PLATEAU.csv                           801\n",
       "MARQUESAS.csv                                             770\n",
       "MIDCONTINENT_RIFT_SYSTEM_-_KEWEENAWAN.csv                 750\n",
       "                                                         ... \n",
       "ROSS_OROGEN-TRANSANTARCTIC_MOUNTAINS_-_CAMBRIAN.csv        33\n",
       "AHU_VOLCANIC_FIELD.csv                                     32\n",
       "ARGO_ABYSSAL_PLAIN;INDIAN_OCEAN.csv                        30\n",
       "SARMATIAN_CRATON_PROTEROZOIC.csv                           30\n",
       "PETER_I_ISLAND.csv                                         29\n",
       "NORTH_AMERICAN_CORDILLERA_-_PROTEROZOIC.csv                29\n",
       "DESVENTURADOS_ISLANDS.csv                                  27\n",
       "WESTERN_ALPS.csv                                           27\n",
       "WEST-AFRICAN_COASTAL_BELTS_-_PAN-AFRICAN.csv               27\n",
       "VERKHOYANSK-CHUKOTKA_COLLISION_ZONE.csv                    25\n",
       "EUROPEAN_OROGENIC_BELT_-_MESOZOIC.csv                      25\n",
       "NATURALISTE_PLATEAU.csv                                    24\n",
       "NANDALING_-_YANSHAN_BELT.csv                               23\n",
       "CHAGAI-RASKOH_ARC.csv                                      23\n",
       "WEST_AFRICAN_CRATON_ARCHEAN.csv                            23\n",
       "GREAT_METEOR_OR_NEW_ENGLAND_HOTSPOT_TRACK_OR_CONT.csv      23\n",
       "HIKURANGI_PLATEAU.csv                                      23\n",
       "MARATHON_LARGE_IGNEOUS_PROVINCE.csv                        22\n",
       "ANGOLA_SHIELD_PALEOZOIC.csv                                21\n",
       "KATANGAN_BASIN.csv                                         21\n",
       "KAAPVAAL_CRATON_PALEOZOIC.csv                              20\n",
       "TIBESTI_VOLCANIC_PROVINCE.csv                              20\n",
       "GULF_OF_SUEZ_RIFT.csv                                      19\n",
       "CHAD_RIFT_BASINS.csv                                       18\n",
       "CENTRAL_INDIAN_TECTONIC_ZONE.csv                           17\n",
       "EUROPEAN_OROGENIC_BELT_-_CENOZOIC.csv                      17\n",
       "SAO_LUIS_CRATON_CRATON_PROTEROZOIC.csv                     17\n",
       "UMU_VOLCANIC_FIELD.csv                                     16\n",
       "AUSTRALIA_002.csv                                          16\n",
       "MASCARENE_PLATEAU.csv                                      16\n",
       "TANZANIA_CRATON_PROTEROZOIC.csv                            15\n",
       "MAKKOVIK-KETILIDIAN_OROGENIC_BELT.csv                      15\n",
       "BALLENY_ISLANDS.csv                                        15\n",
       "TALKEETNA_ARC.csv                                          15\n",
       "GAWLER_CRATON.csv                                          14\n",
       "KYUSHU-PALAU_RIDGE.csv                                     14\n",
       "SIBERIAN_CRATON_PROTEROZOIC.csv                            14\n",
       "VILYUI_RIFT.csv                                            14\n",
       "SIBERIAN_CRATON_PALEOZOIC.csv                              14\n",
       "SOUTH_TETHYAN_SUTURE_ZONE_-_PAKISTAN.csv                   13\n",
       "UMKONDO_LARGE_IGNEOUS_PROVINCE.csv                         13\n",
       "HIMALAYA.csv                                               12\n",
       "UKRAINIAN_SHIELD_ARCHEAN.csv                               11\n",
       "ANGOLA_SHIELD_MESOZOIC.csv                                 11\n",
       "SARMATIAN_CRATON_ARCHEAN.csv                               11\n",
       "ANGOLA_SHIELD_PROTEROZOIC.csv                              10\n",
       "MAUD_RISE.csv                                               9\n",
       "APENNINIC-MAGHREBIDES_CHAIN_2.csv                           9\n",
       "ANTARCTICA_-_PALEOZOIC.csv                                  9\n",
       "PARNAIBA_SEDIMENTARY_BASIN_PALEOZOIC.csv                    9\n",
       "AVANAVERA_LARGE_IGNEOUS_PROVINCE.csv                        9\n",
       "CONGO_CRATON.csv                                            8\n",
       "NAMAQUALAND_NATAL_BELT_CENOZOIC.csv                         7\n",
       "UBENDIAN_BELT.csv                                           7\n",
       "TANZANIA_CRATON_CENOZOIC_QUATERNARY.csv                     7\n",
       "CARIBBEAN_LARGE_IGNEOUS_PROVINCE.csv                        6\n",
       "NAMAQUALAND_NATAL_BELT_PALEOZOIC.csv                        6\n",
       "NORTH_ATLANTIC_CRATON_MESOZOIC.csv                          5\n",
       "MCDONALD_ISLANDS.csv                                        5\n",
       "ZIMBABWE_CRATON.csv                                         5\n",
       "SCOTT_ISLAND.csv                                            5\n",
       "BRAZIL_BASIN.csv                                            4\n",
       "TUPA_VOLCANIC_FIELD.csv                                     4\n",
       "MAGELLAN_RISE.csv                                           3\n",
       "CAPE_VERDE_RISE.csv                                         3\n",
       "DHARWAR_CRATON_MESOZOIC.csv                                 3\n",
       "CUVIER_OR_WALLABY_PLATEAU.csv                               2\n",
       "UKRAINIAN_SHIELD_PROTEROZOIC.csv                            2\n",
       "MADAGASCAR_SEYCHELLES_PROTEROZOIC.csv                       2\n",
       "PENOKEAN_OROGEN.csv                                         2\n",
       "SAO_PAULO_PLATEAU.csv                                       1\n",
       "MATSUMAE_PLATEAU.csv                                        1\n",
       "TUAMOTU_ISLANDS.csv                                         1\n",
       "BAIKAL_RIFT_ZONE_CENOZOIC_QUATERNARY.csv                    1\n",
       "SIBERIAN_CRATON_CENOZOIC_QUATERNARY.csv                     1\n",
       "Name: FILENAME, Length: 305, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Samples contained per file\n",
    "\n",
    "master_file['FILENAME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT GIVEN                                                                    52853\n",
      "BASALT                                                                       51809\n",
      "ANDESITE                                                                     19243\n",
      "RHYOLITE                                                                     17189\n",
      "THOLEIITE                                                                    11461\n",
      "ANDESITE, BASALTIC                                                           10745\n",
      "DACITE                                                                        9912\n",
      "BASALT, ALKALINE                                                              6757\n",
      "TRACHYTE                                                                      4952\n",
      "BASANITE                                                                      4623\n",
      "KIMBERLITE                                                                    4437\n",
      "KOMATIITE                                                                     3492\n",
      "BASALT, THOLEIITIC                                                            3400\n",
      "DOLERITE                                                                      3085\n",
      "PHONOLITE                                                                     2860\n",
      "TRACHYBASALT                                                                  2132\n",
      "TRACHYANDESITE                                                                2058\n",
      "BASALT, OLIVINE                                                               1915\n",
      "PICRITE                                                                       1914\n",
      "HAWAIITE                                                                      1820\n",
      "RHYODACITE                                                                    1592\n",
      "DIABASE                                                                       1493\n",
      "TRACHYANDESITE, BASALTIC                                                      1317\n",
      "THOLEIITE, OLIVINE                                                            1245\n",
      "BASALT, ALKALINE, OLIVINE                                                     1232\n",
      "SHOSHONITE                                                                    1021\n",
      "BASALT, TRANSITIONAL                                                           960\n",
      "NEPHELINITE                                                                    949\n",
      "ADAKITE                                                                        886\n",
      "LATITE                                                                         788\n",
      "BASALT, KOMATIITIC                                                             778\n",
      "TEPHRITE                                                                       760\n",
      "MUGEARITE                                                                      734\n",
      "BONINITE                                                                       688\n",
      "ANDESITE, AUGITE-HYPERSTHENE                                                   665\n",
      "LAMPROITE                                                                      573\n",
      "ANKARAMITE                                                                     567\n",
      "BASALT / NOT GIVEN                                                             566\n",
      "BASALT, CALC-ALKALINE                                                          504\n",
      "THOLEIITE, QUARTZ                                                              501\n",
      "PHONOTEPHRITE                                                                  499\n",
      "BASALT, ANDESITIC                                                              445\n",
      "BENMOREITE                                                                     433\n",
      "PANTELLERITE                                                                   429\n",
      "ANDESITE, PYROXENE                                                             423\n",
      "PHONOLITE, TEPHRI                                                              416\n",
      "THOLEIITE / BASALT                                                             404\n",
      "BASALT, SUBALKALINE                                                            402\n",
      "COMENDITE                                                                      394\n",
      "NEPHELINITE, OLIVINE                                                           382\n",
      "TRACHYDACITE                                                                   366\n",
      "ANDESITE, THOLEIITIC                                                           356\n",
      "TRACHYTE, ALKALINE                                                             316\n",
      "ANDESITE / NOT GIVEN                                                           314\n",
      "ANDESITE, HORNBLENDE                                                           311\n",
      "BASALT, PICRITIC                                                               295\n",
      "KOMATIITE, PERIDOTITIC                                                         291\n",
      "LEUCITITE                                                                      270\n",
      "ANDESITE, HYPERSTHENE-AUGITE                                                   270\n",
      "CARBONATITE                                                                    251\n",
      "NOT GIVEN / BASALT                                                             234\n",
      "FOIDITE                                                                        225\n",
      "PICRITE, FERRO                                                                 216\n",
      "BASALT / THOLEIITE                                                             216\n",
      "OCEANITE                                                                       207\n",
      "ANDESITE, 2-PYROXENE                                                           200\n",
      "LATITE, QUARTZ                                                                 197\n",
      "ANDESITE, BASALTIC / NOT GIVEN                                                 190\n",
      "ANDESITE, CALC-ALKALINE                                                        188\n",
      "MELILITITE, OLIVINE                                                            177\n",
      "MELILITITE                                                                     174\n",
      "BASALT, ALKALINE / BASALT                                                      171\n",
      "TEPHRITE, LEUCITE                                                              169\n",
      "BASALT, OLIVINE-AUGITE                                                         168\n",
      "RHYOLITE, PERALKALINE                                                          167\n",
      "                                                                             ...  \n",
      "TRACHYANDESITE, CLINOPYROXENE-PHLOGOPITE-HORNBLENDE-PLAGIOCLASE-SANIDINE         1\n",
      "DACITE, HORNBLENDE / ANDESITE, HORNBLENDE                                        1\n",
      "DACITE, HORNBLENDE-AUGITE-HYPERSTHENE / DACITE                                   1\n",
      "DACITE, HORNBLENDE-BIOTITE-AUGITE                                                1\n",
      "DACITE, BIOTITE-CORDIERITE-LABRADORITE                                           1\n",
      "DACITE, BIOTITE-AUGITE-HYPERSTHENE                                               1\n",
      "TRACHYANDESITE, FELDSPAR                                                         1\n",
      "DACITE, AUGITE-HYPERSTHENE, BIOTITE-HORNBLENDE-BEARING                           1\n",
      "DACITE / ANDESITE / NOT GIVEN                                                    1\n",
      "DACITE / BASALT / ICELANDITE                                                     1\n",
      "DACITE / BASALT, ALKALINE / BASALT                                               1\n",
      "DACITE / BASALT, HYPERSTHENE                                                     1\n",
      "DACITE / BONINITE                                                                1\n",
      "DACITE / DACITE, THOLEIITIC                                                      1\n",
      "DACITE / LATITE / NOT GIVEN                                                      1\n",
      "TRACHYANDESITE, HYPERSTHENE-CLINOPYROXENE-PHLOGOPITE-PLAGIOCLASE-SANIDINE        1\n",
      "DACITE / NOT GIVEN / DACITE, THOLEIITIC                                          1\n",
      "DACITE / NOT GIVEN / RHYOLITE                                                    1\n",
      "DACITE / PORPHYRY, QUARTZ-FELDSPAR                                               1\n",
      "DACITE / RHYODACITE                                                              1\n",
      "DACITE / RHYODACITE / NOT GIVEN                                                  1\n",
      "TRACHYANDESITE, HYPERSTHENE-AUGITE / TRACHYANDESITE                              1\n",
      "DACITE, 2-PYROXENE-AMPHIBOLE-PLAGIOCLASE / NOT GIVEN                             1\n",
      "DACITE, 2-PYROXENE-BIOTITE-HORNBLENDE / DACITE                                   1\n",
      "DACITE, 2-PYROXENE-QUARTZ                                                        1\n",
      "DACITE, AUGITE, QUARTZ                                                           1\n",
      "DACITE, AUGITE-HORNBLENDE-HYPERSTHENE-QUARTZ-PLAGIOCLASE                         1\n",
      "DACITE, HORNBLENDE-OLIGOCLASE                                                    1\n",
      "DACITE, HORNBLENDE-ORTHOPYROXENE-CLINOPYROXENE                                   1\n",
      "VULSINITE, BIOTITE                                                               1\n",
      "TRACHYANDESITE, BASALTIC / SHOSHONITE                                            1\n",
      "DACITE, QUARTZ-HORNBLENDE-HYPERSTHENE                                            1\n",
      "DACITE, QUARTZ-PLAGIOCLASE                                                       1\n",
      "DACITE, SANIDINE-BIOTITE-PLAGIOCLASE                                             1\n",
      "DACITE, THOLEIITIC / NOT GIVEN / DACITE                                          1\n",
      "TRACHYANDESITE, BASALTIC / TRACHYANDESITE / TRACHYTE                             1\n",
      "DACITE, TRACHYTIC / DACITE                                                       1\n",
      "DACITE, TRACHYTIC / RHYODACITE, ALKALINE                                         1\n",
      "DELLENITE, BIOTITE-OLIGOCLASE                                                    1\n",
      "DIABASE / BASALT                                                                 1\n",
      "TRACHYANDESITE, BASALTIC / NOT GIVEN / TEPHRIPHONOLITE                           1\n",
      "TRACHYANDESITE, BIOTITE-HORNBLENDE                                               1\n",
      "DIABASE, HORNBLENDE-AUGITE                                                       1\n",
      "DIORITE, HORNBLENDE                                                              1\n",
      "TRACHYANDESITE, AUGITE-OLIVINE                                                   1\n",
      "DOLERITE / BASALT, ALKALINE                                                      1\n",
      "DOLERITE / DOLERITE, CLINOPYROXENE                                               1\n",
      "DOLERITE / DOLERITE, OLIVINE                                                     1\n",
      "DOLERITE / DOLERITE, OLIVINE-CLINOPYROXENE                                       1\n",
      "DOLERITE / THOLEIITE, OLIVINE / BASALT                                           1\n",
      "DOLERITE, ALIVINE-ANALCITE                                                       1\n",
      "DACITE, QUARTZ-HORNBLENDE-2-PYROXENE                                             1\n",
      "DACITE, QUARTZ-HORNBLENDE                                                        1\n",
      "DACITE, QUARTZ-CLINOPYROXENE-ORTHOPYROXENE-HORNBLENDE                            1\n",
      "DACITE, QUARTZ-BIOTITE-PLAGIOCLASE                                               1\n",
      "DACITE, HYPERSTHENE / BONINITE                                                   1\n",
      "DACITE, HYPERSTHENE / DACITE                                                     1\n",
      "DACITE, HYPERSTHENE-BIOTITE                                                      1\n",
      "DACITE, HYPERSTHENE-BIOTITE-HORNBLENDE                                           1\n",
      "TRACHYANDESITE, BIOTITE                                                          1\n",
      "DACITE, HYPERSTHENE-HORNBLENDE-QUARTZ / DACITE, HYPERSTHENE-QUARTZ               1\n",
      "DACITE, LABRADORITE / ANDESITE, LABRADORITE                                      1\n",
      "DACITE, LABRADORITE-AUGITE                                                       1\n",
      "DACITE, OLIVINE                                                                  1\n",
      "DACITE, OLIVINE-AUGITE                                                           1\n",
      "DACITE, OLIVINE-HYPERSTHENE-AUGITE                                               1\n",
      "DACITE, ORTHOPYROXENE-CLINOPYROXENE-HORNBLENDE                                   1\n",
      "TRACHYANDESITE, BASALTIC / TRACHYTE                                              1\n",
      "DACITE, PYROXENE-OLIGOCLASE                                                      1\n",
      "DACITE, PYROXENE-OLIVINE                                                         1\n",
      "DACITE, PYROXENE-QUARTZ-BIOTITE                                                  1\n",
      "DACITE, QUARTZ-AUGITE-FAYALITE                                                   1\n",
      "DACITE, QUARTZ-AUGITE-HYPERSTHENE                                                1\n",
      "DACITE, QUARTZ-AUGITE-HYPERSTHENE-BIOTITE-HORNBLENDE / DACITE                    1\n",
      "DOLERITE, ALKALINE / BASALT, ALKALINE                                            1\n",
      "Name: ROCK NAME, Length: 2606, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Samples obtained per rock name\n",
    "\n",
    "temp = master_file['ROCK NAME'].value_counts()\n",
    "filter_ = r'\\[(\\d)*\\]'\n",
    "\n",
    "# Citations are removed from the index so that identical rock names have identical indexes.\n",
    "temp.index = temp.index.astype(str).str.replace(filter_, '').str.strip().str.replace('  ', ' ')\n",
    "\n",
    "# Samples with identical rock names are regrouped\n",
    "temp = temp.groupby(temp.index).sum().sort_values(ascending=False)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CONVERGENT MARGIN       96893\n",
       "INTRAPLATE VOLCANICS    56862\n",
       "OCEAN ISLAND            41105\n",
       "FLOOD BASALT            35321\n",
       "RIFT VOLCANICS          19080\n",
       "ARCHEAN CRATON          14265\n",
       "Name: TECTONIC SETTING, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Samples obtained per tectonic setting\n",
    "\n",
    "master_file['TECTONIC SETTING'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QA/QC\n",
    "\n",
    "Elements columns that doesn't contain enough data are dropped. Next, samples with missing observations in the remaining columns are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Number of missing observations per element.\n",
    "\n",
    "a = master_file.iloc[:, 28:-1].count(axis=0)\n",
    "a = len(master_file.index) - a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Elements with quantity of missing observations over *value* are dropped, with the exception of elements \n",
    "# in *index_keep*, and with the addition of elements in *index_drop*.\n",
    "\n",
    "# PARAMETERS #\n",
    "value = 110000  # Deletion threshold  (52: 152000) (41: 110000 (Important ones))\n",
    "index_drop = []  # ['MNO', 'ND', 'RB']  # Deleted elements\n",
    "index_keep = ['TA', 'HF', 'SC', 'TH', 'CE', 'ZR', 'NB', 'Y']  # Preserved elements\n",
    "##############\n",
    "\n",
    "index_ = (a > value) & ~ a.index.isin(index_keep) | a.index.isin(index_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A file with deleted data is created. The original is unaltered.\n",
    "\n",
    "filtered_file = master_file.copy();\n",
    "\n",
    "# Removes columns with too much missing data\n",
    "filtered_file.drop(a[index_].index.values, axis=1, inplace=True)\n",
    "\n",
    "# Removes samples with missing data\n",
    "filtered_file.dropna(subset=a[~ index_].index.values, inplace=True)\n",
    "\n",
    "filtered_file = filtered_file.reset_index(drop=True)  # Removing data messes with the indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset after transformation: (47715, 49) Out of: (263526, 126) \n",
      "\n",
      "Index(['SIO2', 'TIO2', 'AL2O3', 'CAO', 'MGO', 'MNO', 'K2O', 'NA2O', 'P2O5',\n",
      "       'SC', 'RB', 'SR', 'Y', 'ZR', 'NB', 'BA', 'CE', 'HF', 'TA', 'TH'],\n",
      "      dtype='object') \n",
      "\n",
      "INTRAPLATE VOLCANICS    17241\n",
      "CONVERGENT MARGIN       12883\n",
      "FLOOD BASALT             7119\n",
      "OCEAN ISLAND             4407\n",
      "ARCHEAN CRATON           3100\n",
      "RIFT VOLCANICS           2965\n",
      "Name: TECTONIC SETTING, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Shape of the dataset after transformation:', str(filtered_file.shape), 'Out of:', str(master_file.shape), '\\n')\n",
    "print(filtered_file.iloc[:, 28:-1].axes[1], '\\n')\n",
    "print(filtered_file['TECTONIC SETTING'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Utility functions and classes\n",
    "\n",
    "f1_scorer = make_scorer(f1_score, average='weighted')\n",
    "\n",
    "\n",
    "class MidpointNormalize(Normalize):\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        Normalize.__init__(self, vmin, vmax, clip)\n",
    "\n",
    "    def __call__(self, value, clip=None):\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y))\n",
    "\n",
    "    \n",
    "def file_split_gen(file, n_out=1, n_splits=1, seed=None):\n",
    "\n",
    "    if n_out * n_splits > len(file.unique()):\n",
    "        raise IndexError\n",
    "\n",
    "    if seed is not None:\n",
    "        rd.seed(seed)\n",
    "\n",
    "    groups = list(file.unique())\n",
    "    rd.shuffle(groups)\n",
    "    current = 0\n",
    "\n",
    "    for _ in range(n_splits):\n",
    "        train_set = file.copy()\n",
    "        test_set = file.copy()\n",
    "\n",
    "        group = groups[current:current + n_out]\n",
    "        train_set = train_set[~ train_set.isin(group)]\n",
    "        test_set = test_set[test_set.isin(group)]\n",
    "        current += n_out\n",
    "\n",
    "        yield train_set.index, test_set.index\n",
    "\n",
    "\n",
    "def to_int(x):\n",
    "    return x.replace({'ARCHEAN CRATON': 0,\n",
    "                      'CONVERGENT MARGIN': 1,\n",
    "                      'FLOOD BASALT': 2,\n",
    "                      'INTRAPLATE VOLCANICS': 3,\n",
    "                      'OCEAN ISLAND': 4,\n",
    "                      'RIFT VOLCANICS': 5})\n",
    "\n",
    "\n",
    "def make_CLR(dataframe):\n",
    "    temp_df = pd.DataFrame(index=dataframe.axes[0], columns=dataframe.axes[1])\n",
    "    for index, Series in dataframe.iterrows():\n",
    "        if index % 1000 == 0:\n",
    "            print(index)\n",
    "        Series = Series.apply(lambda x: x / Series.apply(lambda y: (y - x)**2).sum())\n",
    "        temp_df.iloc[index] = Series\n",
    "    return temp_df\n",
    "\n",
    "\n",
    "def do_grid_search(classifier, parameters, file_splitting, n_out=20, n_splits=2, seed=43, verbose=True):\n",
    "    pipeline = make_pipeline(StandardScaler(), classifier)\n",
    "    # print(pipeline.get_params().keys())\n",
    "\n",
    "    if verbose:\n",
    "        start = datetime.datetime.now()\n",
    "        print(\"Start time :\", start)\n",
    "        verbose_ = 2\n",
    "    else:\n",
    "        verbose_ = 0\n",
    "\n",
    "    # Regular grid search.\n",
    "    if file_splitting: # Grid search using file splitting.\n",
    "        cv = file_split_gen(filenames_train, n_out=n_out, n_splits=n_splits, seed=seed)\n",
    "        grid = GridSearchCV(pipeline, parameters, cv=cv, scoring=f1_scorer, n_jobs=-1, verbose=verbose_)\n",
    "    else:\n",
    "        grid = GridSearchCV(pipeline, parameters, cv=2, scoring=f1_scorer, n_jobs=-1, verbose=verbose_)\n",
    "\n",
    "    # Data fitting.\n",
    "    clf = grid.fit(X_train, y_train)  # Will take quite some time. View the progress on the console.\n",
    "\n",
    "    if verbose:\n",
    "        end = datetime.datetime.now()\n",
    "        print(\"Start time:\", end)\n",
    "        print(\"Time elapsed:\", end - start, '\\n')\n",
    "\n",
    "        print(\"Best parameters set found on development set:\")\n",
    "        print(clf.cv_results_['params'][clf.best_index_])\n",
    "        print(\"Score: %0.03f\" % clf.best_score_)\n",
    "\n",
    "    return clf\n",
    "\n",
    "\n",
    "def do_visualize_score(clf, range_x, range_y, label_x='', label_y='', vmin=0.4, midpoint=0.83):\n",
    "    scores = clf.cv_results_['mean_test_score'].reshape((len(range_y), len(range_x)))\n",
    "    print(\"Mean scores:\", '\\n', scores)\n",
    "\n",
    "    stddev = clf.cv_results_['std_test_score'].reshape((len(range_y), len(range_x)))\n",
    "    print(\"Standard deviation:\", '\\n', stddev)\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.subplots_adjust(left=0.3, right=0.8, bottom=0.3, top=0.8)\n",
    "    plt.imshow(scores, interpolation='nearest', cmap=plt.cm.hot, norm=MidpointNormalize(vmin=vmin, midpoint=midpoint))\n",
    "    plt.xlabel(label_x)\n",
    "    plt.ylabel(label_y)\n",
    "    plt.colorbar()\n",
    "    plt.xticks(np.arange(len(range_x)), range_x, rotation=45)\n",
    "    plt.yticks(np.arange(len(range_y)), range_y)\n",
    "    plt.title('Validation accuracy')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def do_validation(classifier, verbose=True):\n",
    "    # Estimating success scores on test data.\n",
    "    pipeline = make_pipeline(StandardScaler(), classifier)\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    y_pred = pipeline.predict(X_test)\n",
    "    \n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "\n",
    "    if verbose:\n",
    "        # Viewing validation results\n",
    "        print('f1:', str(f1))\n",
    "        print('Train data:', y_train.shape[0], 'Test data:', y_test.shape[0])\n",
    "\n",
    "        conf = confusion_matrix(y_test, y_pred)\n",
    "        print_confusion_matrix(conf)\n",
    "\n",
    "    else:\n",
    "        return f1\n",
    "\n",
    "\n",
    "def print_confusion_matrix(conf):\n",
    "    conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]  # Normalizes\n",
    "    plt.imshow(conf, interpolation='nearest', cmap=plt.cm.gray, norm=Normalize(1, 0))\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Data')\n",
    "    plt.colorbar()\n",
    "    labels = sorted(y_test.unique())\n",
    "    range_ = np.arange(len(labels))\n",
    "    plt.xticks(range_, labels, rotation=90)\n",
    "    plt.yticks(range_, labels)\n",
    "    plt.title('Validation accuracy')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def do_adjust_TIO2(series):\n",
    "    pass  # SINCLAS, Dialnet p. 26\n",
    "\n",
    "\n",
    "def do_to_DF_coords(dataframe, c_1, c_2):  # c_ -> coefficients_: tuple of length 5\n",
    "    \"\"\"\n",
    "    Formats data into coordinates for a single graph from [1].\n",
    "    \"\"\"\n",
    "    coords = pd.Series(index=np.arange(0, dataframe.shape[0]))\n",
    "\n",
    "    assert (len(coefs_1) == 5 and len(coefs_2) == 5)\n",
    "\n",
    "    for index, series in dataframe.iterrow():\n",
    "        adjusted_TIO2 = do_adjust_TIO2(series)\n",
    "        series = series.apply(lambda x: np.log(x / adjusted_TIO2))\n",
    "        x = c_1[0] * series['NB'] + c_1[1] * series['V'] + c_1[2] * series['Y'] + c_1[3] * series['ZR'] + c_1[4]\n",
    "        y = c_2[0] * series['NB'] + c_2[1] * series['V'] + c_2[2] * series['Y'] + c_2[3] * series['ZR'] + c_2[4]\n",
    "        coords[index] = (x, y)\n",
    "\n",
    "    return coords\n",
    "\n",
    "\n",
    "def do_DF_classification(DF, center, A, B, C):  # A, B, C have formats {0: x, 1: y, 's': (setting_1, setting_2)}\n",
    "    \"\"\"\n",
    "    Classifies data in DF according to the center and outer points from the corresponding graph of [1].\n",
    "    Tectonic settings the lines split should be specified in ['s'].\n",
    "    \"\"\"\n",
    "    classification = pd.Series(index=np.arange(0, DF.shape[0]))\n",
    "    quadrants = [None, None, None, None] # Refers to each of the 4 quadrants of the graph, centered on the intersection, and\n",
    "                                         # organized as: [[0, 1]\n",
    "                                         #                [2, 3]]\n",
    "\n",
    "    for pt in (A, B, C):\n",
    "        index = 0\n",
    "        if pt[0] > center[0]:\n",
    "            index += 1\n",
    "        if pt[1] < center[1]:\n",
    "            index += 2\n",
    "        quadrants[index] = pt\n",
    "\n",
    "    neighbors = {0:(1, 2), 1:(0, 3), 2:(3, 0), 3:(2, 1)}  # Neighbors of each quadrant\n",
    "\n",
    "    for pos, splitter in enumerate(quadrants):\n",
    "        if splitter is None:\n",
    "            for setting in quadrants[neighbors[pos][0]]['s']:\n",
    "                if setting in quadrants[neighbors[pos][1]]['s']:\n",
    "                    # Empty quadrant is filled with a dummy point.\n",
    "                    quadrants[pos] = {0:0, 1:0, 's':(setting,) * 2}\n",
    "\n",
    "    for pos, splitter in enumerate(quadrants):\n",
    "        if splitter['s'][0] in quadrants[neighbors[pos][0]]['s']:\n",
    "            # Tectonic settings are reorganized correctly.\n",
    "            quadrants[pos]['s'] = quadrants[pos]['s'][::-1]\n",
    "\n",
    "    quadrants[1]['s'] = quadrants[1]['s'][::-1]\n",
    "    quadrants[3]['s'] = quadrants[3]['s'][::-1]\n",
    "\n",
    "    # At this point, tectonic settings to the left of the line should be in position 0 of the tuple, and vice versa.\n",
    "\n",
    "    # print([i['s'] for i in quadrants])\n",
    "\n",
    "    for pos, pt in enumerate(quadrants):\n",
    "        # Linear equations x(y) for each of the lines are computed.\n",
    "        quadrants[pos]['e'] = lambda y: (y - center[1]) * (pt[0] - center[0]) / (pt[1] - center[1]) + center[0]\n",
    "\n",
    "    for i, coords in DF.iteritems():\n",
    "\n",
    "        # Determines in which quadrant the point is in order to select the right equation.\n",
    "        index = 0\n",
    "        if coords[0] > center[0]:\n",
    "            index += 1\n",
    "        if coords[1] < center[1]:\n",
    "            index += 2\n",
    "\n",
    "        # Classifies the point accordingly.\n",
    "        if quadrants[index]['e'](coords[1]) < coords[0]:\n",
    "            classification[i] = quadrants[index]['s'][0]\n",
    "        else:  # If equal, will choose the setting to the right\n",
    "            classification[i] = quadrants[index]['s'][1]\n",
    "\n",
    "    return classification\n",
    "\n",
    "\n",
    "def dry_basis(dataframe):  # Accepts a whole dataframe, slices it accordingly and returns the normalized sliced dataframe.\n",
    "    dataframe = dataframe.loc[:, 'SIO2':'P2O5'].astype(np.float32)\n",
    "    total = dataframe.sum(axis=1)\n",
    "\n",
    "    for i, series in dataframe.iterrows():\n",
    "        series = series.apply(lambda x: x * 1000000 / total[i])\n",
    "        dataframe.iloc[i] = series\n",
    "\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>SIO2</th>\n",
       "      <th>TIO2</th>\n",
       "      <th>AL2O3</th>\n",
       "      <th>CR2O3</th>\n",
       "      <th>FE2O3</th>\n",
       "      <th>FEO</th>\n",
       "      <th>FEOT</th>\n",
       "      <th>CAO</th>\n",
       "      <th>MGO</th>\n",
       "      <th>MNO</th>\n",
       "      <th>NIO</th>\n",
       "      <th>K2O</th>\n",
       "      <th>NA2O</th>\n",
       "      <th>P2O5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>743057.6250</td>\n",
       "      <td>2907.268066</td>\n",
       "      <td>136340.859375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21152.882812</td>\n",
       "      <td>6015.037598</td>\n",
       "      <td>7819.548828</td>\n",
       "      <td>802.005005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46015.039062</td>\n",
       "      <td>35187.968750</td>\n",
       "      <td>701.754395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>749674.4375</td>\n",
       "      <td>2303.916748</td>\n",
       "      <td>140839.421875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14825.203125</td>\n",
       "      <td>6911.750000</td>\n",
       "      <td>3806.470947</td>\n",
       "      <td>701.192017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59100.472656</td>\n",
       "      <td>21436.441406</td>\n",
       "      <td>400.681152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>768452.6875</td>\n",
       "      <td>1001.502258</td>\n",
       "      <td>133700.546875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12719.079102</td>\n",
       "      <td>3805.708496</td>\n",
       "      <td>2503.755615</td>\n",
       "      <td>600.901367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57185.777344</td>\n",
       "      <td>19929.894531</td>\n",
       "      <td>100.150223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SIO2            TIO2          AL2O3  CR2O3  FE2O3  FEO  \\\n",
       "0  743057.6250     2907.268066  136340.859375    NaN    NaN  NaN   \n",
       "1  749674.4375     2303.916748  140839.421875    NaN    NaN  NaN   \n",
       "2  768452.6875     1001.502258  133700.546875    NaN    NaN  NaN   \n",
       "3          NaN  1000000.000000            NaN    NaN    NaN  NaN   \n",
       "4          NaN  1000000.000000            NaN    NaN    NaN  NaN   \n",
       "\n",
       "           FEOT          CAO          MGO         MNO  NIO           K2O  \\\n",
       "0  21152.882812  6015.037598  7819.548828  802.005005  NaN  46015.039062   \n",
       "1  14825.203125  6911.750000  3806.470947  701.192017  NaN  59100.472656   \n",
       "2  12719.079102  3805.708496  2503.755615  600.901367  NaN  57185.777344   \n",
       "3           NaN          NaN          NaN         NaN  NaN           NaN   \n",
       "4           NaN          NaN          NaN         NaN  NaN           NaN   \n",
       "\n",
       "           NA2O        P2O5  \n",
       "0  35187.968750  701.754395  \n",
       "1  21436.441406  400.681152  \n",
       "2  19929.894531  100.150223  \n",
       "3           NaN         NaN  \n",
       "4           NaN         NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_basis(master_file).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lazy creation of training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = filtered_file.iloc[:, 28:-1].astype(float)\n",
    "y = filtered_file['TECTONIC SETTING']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.55, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Custom creation of training and testing sets:\n",
    "Allows splitting data by filenames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reinitializes the generator.\n",
    "\n",
    "generator = file_split_gen(filtered_file['FILENAME'], n_out=30, n_splits=4, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INTRAPLATE VOLCANICS    4421\n",
      "CONVERGENT MARGIN       2118\n",
      "FLOOD BASALT             912\n",
      "RIFT VOLCANICS            76\n",
      "ARCHEAN CRATON            38\n",
      "OCEAN ISLAND              16\n",
      "Name: TECTONIC SETTING, dtype: int64 \n",
      "\n",
      "6 settings out of 6.\n",
      "Test data: 7581 Out of: 47715\n",
      "30 test files out of 226\n"
     ]
    }
   ],
   "source": [
    "# Splits the data. Run this cell again to progress through the splits.\n",
    "\n",
    "train_ind, test_ind = next(generator)\n",
    "X = filtered_file.iloc[:, 28:-1].astype(float)\n",
    "y = filtered_file['TECTONIC SETTING']\n",
    "\n",
    "X_train, X_test = X.iloc[train_ind].reset_index(drop=True), X.iloc[test_ind].reset_index(drop=True)\n",
    "y_train, y_test = y.iloc[train_ind].reset_index(drop=True), y.iloc[test_ind].reset_index(drop=True)\n",
    "filenames_train = filtered_file['FILENAME'].iloc[train_ind].reset_index(drop=True)\n",
    "\n",
    "count_ = y_test.value_counts()\n",
    "print(count_, '\\n')\n",
    "print(len(count_), 'settings out of 6.')\n",
    "print(\"Test data:\", y_test.shape[0], \"Out of:\", filtered_file.shape[0])\n",
    "print(len(filtered_file['FILENAME'][test_ind].value_counts()), 'test files out of', len(filtered_file['FILENAME'].value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time : 2017-07-25 10:53:23.075978\n",
      "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n",
      "[CV] svc__C=10.0, svc__gamma=0.001 ...................................\n",
      "[CV] svc__C=10.0, svc__gamma=0.001 ...................................\n",
      "[CV] svc__C=10.0, svc__gamma=0.01 ....................................\n",
      "[CV] svc__C=10.0, svc__gamma=0.01 ....................................\n",
      "[CV] svc__C=10.0, svc__gamma=0.1 .....................................\n",
      "[CV] svc__C=10.0, svc__gamma=0.1 .....................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.001 ..................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.001 ..................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.01 ...................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.01 ...................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.1 ....................................\n",
      "[CV] svc__C=100.0, svc__gamma=0.1 ....................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.001 .................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.001 .................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.01 ..................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.01 ..................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.1 ...................................\n",
      "[CV] svc__C=1000.0, svc__gamma=0.1 ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...................... svc__C=10.0, svc__gamma=0.1, total= 1.1min\n",
      "[CV] ...................... svc__C=10.0, svc__gamma=0.1, total= 1.1min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... svc__C=100.0, svc__gamma=0.1, total= 1.4min\n",
      "[CV] ..................... svc__C=100.0, svc__gamma=0.1, total= 1.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..................... svc__C=10.0, svc__gamma=0.01, total= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of  18 | elapsed:  2.1min remaining:  5.6min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... svc__C=100.0, svc__gamma=0.01, total= 1.4min\n",
      "[CV] ..................... svc__C=10.0, svc__gamma=0.01, total= 1.4min\n",
      "[CV] .................... svc__C=100.0, svc__gamma=0.01, total= 1.5min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... svc__C=100.0, svc__gamma=0.001, total= 1.6min\n",
      "[CV] ................... svc__C=100.0, svc__gamma=0.001, total= 1.7min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... svc__C=10.0, svc__gamma=0.001, total= 1.8min\n",
      "[CV] .................... svc__C=10.0, svc__gamma=0.001, total= 1.8min\n",
      "[CV] .................. svc__C=1000.0, svc__gamma=0.001, total= 2.0min\n",
      "[CV] .................. svc__C=1000.0, svc__gamma=0.001, total= 2.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... svc__C=1000.0, svc__gamma=0.01, total= 2.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  18 | elapsed:  3.1min remaining:   36.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... svc__C=1000.0, svc__gamma=0.01, total= 2.5min\n",
      "[CV] .................... svc__C=1000.0, svc__gamma=0.1, total= 2.7min\n",
      "[CV] .................... svc__C=1000.0, svc__gamma=0.1, total= 2.9min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:  3.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2017-07-25 10:58:17.819453\n",
      "Time elapsed: 0:04:54.743475 \n",
      "\n",
      "Best parameters set found on development set:\n",
      "{'svc__C': 100.0, 'svc__gamma': 0.01}\n",
      "Score: 0.398\n"
     ]
    }
   ],
   "source": [
    "C_range = 10.0 ** (np.arange(1, 4))\n",
    "gamma_range= 10.0 ** (np.arange(-3, 0))\n",
    "param_grid = dict(svc__C=C_range, svc__gamma=gamma_range)\n",
    "\n",
    "clf = do_grid_search(SVC(cache_size=2000, class_weight='balanced'), param_grid, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean scores: \n",
      " [[ 0.38044451  0.39623428  0.39408699]\n",
      " [ 0.39397007  0.3982029   0.3913141 ]\n",
      " [ 0.39572079  0.39203481  0.37319255]]\n",
      "Standard deviation: \n",
      " [[ 0.09384678  0.07225311  0.03224043]\n",
      " [ 0.08117955  0.06095616  0.01341036]\n",
      " [ 0.07691411  0.04272331  0.03967854]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+TFV6D0mLJf2skes5qJpZt5XWs7sWGA8cAZyV1r3LWwC8OSKOBs4HrqtKvxx4sNFrOqia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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4447c4fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_visualize_score(clf, gamma_range, C_range, 'Gamma', 'C', vmin=0.3, midpoint=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: 0.64833883841\n",
      "Train data: 40332 Test data: 6358\n"
     ]
    },
    {
     "data": {
      "image/png": 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FvbMkS/pIoWx0LjuyULaopMfbHL8g7+m2Ugx7F479paSdu/6LDiE02mBNdTQUaklQOdlc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\nnZvlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f445c7e2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_C     = 10 ** (2)\n",
    "best_gamma = 10 ** (-2)\n",
    "\n",
    "do_validation(SVC(C=best_C, gamma=best_gamma, cache_size=2000, class_weight='balanced'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time : 2017-07-25 10:51:45.649544\n",
      "Fitting 2 folds for each of 12 candidates, totalling 24 fits\n",
      "[CV] kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=uniform \n",
      "[CV] kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=distance \n",
      "[CV] kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=distance \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=distance, total=   1.5s\n",
      "[CV]  kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=uniform, total=   1.6s\n",
      "[CV]  kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=uniform, total=   1.8s\n",
      "[CV]  kneighborsclassifier__n_neighbors=2, kneighborsclassifier__weights=distance, total=   2.0s\n",
      "[CV]  kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform, total=   1.8s\n",
      "[CV]  kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance, total=   1.8s\n",
      "[CV]  kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=uniform, total=   2.3s\n",
      "[CV]  kneighborsclassifier__n_neighbors=3, kneighborsclassifier__weights=distance, total=   2.3s\n",
      "[CV]  kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform, total=   2.1s\n",
      "[CV]  kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance, total=   1.9s\n",
      "[CV]  kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=uniform, total=   2.8s\n",
      "[CV]  kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=uniform, total=   2.0s\n",
      "[CV]  kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance, total=   2.4s\n",
      "[CV]  kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform, total=   2.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  13 out of  24 | elapsed:   21.9s remaining:   18.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=distance, total=   2.0s\n",
      "[CV]  kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=distance, total=   2.5s\n",
      "[CV]  kneighborsclassifier__n_neighbors=4, kneighborsclassifier__weights=distance, total=   2.4s\n",
      "[CV]  kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=distance, total=   2.7s\n",
      "[CV]  kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=uniform, total=   1.3s\n",
      "[CV]  kneighborsclassifier__n_neighbors=6, kneighborsclassifier__weights=uniform, total=   2.7s\n",
      "[CV]  kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=distance, total=   2.0s\n",
      "[CV]  kneighborsclassifier__n_neighbors=5, kneighborsclassifier__weights=uniform, total=   2.8s\n",
      "[CV]  kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=uniform, total=   2.9s\n",
      "[CV]  kneighborsclassifier__n_neighbors=7, kneighborsclassifier__weights=distance, total=   2.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:   24.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2017-07-25 10:52:11.889168\n",
      "Time elapsed: 0:00:26.239624 \n",
      "\n",
      "Best parameters set found on development set:\n",
      "{'kneighborsclassifier__n_neighbors': 5, 'kneighborsclassifier__weights': 'distance'}\n",
      "Score: 0.407\n"
     ]
    }
   ],
   "source": [
    "neighbors_range = np.arange(2, 8)\n",
    "weights = ['uniform', 'distance']\n",
    "param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range, kneighborsclassifier__weights=weights)\n",
    "\n",
    "clf = do_grid_search(KNeighborsClassifier(), param_grid, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean scores: \n",
      " [[ 0.3416898   0.39383381]\n",
      " [ 0.38536984  0.40127217]\n",
      " [ 0.37936503  0.4052755 ]\n",
      " [ 0.3900355   0.4074609 ]\n",
      " [ 0.39150702  0.40437888]\n",
      " [ 0.39682197  0.40678319]]\n",
      "Standard deviation: \n",
      " [[ 0.00383283  0.0043159 ]\n",
      " [ 0.00653686  0.00329274]\n",
      " [ 0.00064665  0.00903251]\n",
      " [ 0.00267556  0.00922092]\n",
      " [ 0.00403365  0.00756986]\n",
      " [ 0.00111492  0.00561754]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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HLZ0kSa8FrpC0f/5n/xx4jLSH/U+k3ZfdUoLpwJ9Jiz62IHU1dgPWsP39rJxH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\na0JBg1oTChrUmlDQoNaEgga15v8DNWTOMwaOJqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44726c76a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_visualize_score(clf, weights, neighbors_range, 'Weights', 'Quantity of neighbors', vmin=0.3, midpoint=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: 0.645679338034\n",
      "Train data: 40332 Test data: 6358\n"
     ]
    },
    {
     "data": {
      "image/png": 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66OGHEKoyUAlK0m6S7pI0S1LLHiFJb5P0iqR9+6uz0+U2Bsq3gIuB33e4/1G2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\nRESVkqAiIqJKSVAREVGl/wG76z+X694xCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4447a76f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_n      = 5\n",
    "best_weight = 'distance'\n",
    "\n",
    "do_validation(KNeighborsClassifier(n_neighbors=best_n, weights=best_weight))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3) Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time : 2017-07-25 11:01:27.295418\n",
      "Fitting 2 folds for each of 16 candidates, totalling 32 fits\n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=1 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=2 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=3 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=4 \n",
      "[CV] extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=4 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=3, total=   3.8s\n",
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=4, total=   3.9s\n",
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=4, total=   3.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  32 | elapsed:    5.2s remaining:   50.5s\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=3, total=   4.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=2, total=   4.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=1, total=   4.9s\n",
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=2, total=   5.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=3, total=   5.4s\n",
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=4, total=   5.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.2, extratreesclassifier__min_samples_leaf=1, total=   5.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=3, total=   5.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=4, total=   5.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=2, total=   6.0s\n",
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=2, total=   6.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=3, total=   6.6s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=4, total=   6.3s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=3, total=   6.7s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=4, total=   6.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=1, total=   6.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.4, extratreesclassifier__min_samples_leaf=1, total=   7.0s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=2, total=   7.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  20 out of  32 | elapsed:    8.5s remaining:    5.1s\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=2, total=   7.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=4, total=   7.9s\n",
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=4, total=   7.7s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=1, total=   8.2s\n",
      "[CV]  extratreesclassifier__max_features=0.6, extratreesclassifier__min_samples_leaf=1, total=   8.0s\n",
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=3, total=   8.4s\n",
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=3, total=   8.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=2, total=   9.0s\n",
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=2, total=   8.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/CloudyOverhead/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1115: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=1, total=   9.8s\n",
      "[CV]  extratreesclassifier__max_features=0.8, extratreesclassifier__min_samples_leaf=1, total=  10.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  32 out of  32 | elapsed:   11.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2017-07-25 11:01:46.039451\n",
      "Time elapsed: 0:00:18.744033 \n",
      "\n",
      "Best parameters set found on development set:\n",
      "{'extratreesclassifier__max_features': 0.40000000000000002, 'extratreesclassifier__min_samples_leaf': 1}\n",
      "Score: 0.423\n"
     ]
    }
   ],
   "source": [
    "range_1 = np.arange(1, 5)\n",
    "range_2 = np.arange(0.2, 0.9, 0.2)\n",
    "# estimators_range = np.arange(10, 101, 20)\n",
    "param_grid = dict(extratreesclassifier__min_samples_leaf=range_1,\n",
    "                  extratreesclassifier__max_features=range_2)\n",
    "\n",
    "clf = do_grid_search(ExtraTreesClassifier(class_weight='balanced', max_depth=40, n_estimators=90), param_grid, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean scores: \n",
      " [[ 0.40888856  0.40458103  0.3991417   0.39885587]\n",
      " [ 0.42291301  0.41227652  0.40208592  0.3980537 ]\n",
      " [ 0.41907632  0.40947509  0.40201184  0.40309837]\n",
      " [ 0.42174028  0.41193926  0.40232769  0.40505723]]\n",
      "Standard deviation: \n",
      " [[ 0.04344613  0.00332679  0.02376276  0.03661499]\n",
      " [ 0.0486602   0.00669279  0.0083807   0.02767407]\n",
      " [ 0.05277017  0.01081559  0.01090271  0.03098989]\n",
      " [ 0.04789464  0.01051342  0.00908903  0.01687439]]\n"
     ]
    },
    {
     "data": {
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ZrdGOr8hsBGye9tkit/0VwPMDm1lb6DEJpi9Ffinp+oh4uh9jMjPrN0U6Rv4m6ZvAbuQm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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4447e613c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_visualize_score(clf, range_1, range_2, 'Min samples leaf', 'Max features', vmin=0.3, midpoint=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: 0.670819194818\n",
      "Train data: 40332 Test data: 6358\n"
     ]
    },
    {
     "data": {
      "image/png": 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b5TYGyneA84E/d7n/obbPzwMsTiN9UUL6Qn6zpA/n16tKWs/2Pfn1Dq1uMG5h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\nSklQERFRpf8BvnwWNqJhOf8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44526fa278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_depth      = 50\n",
    "best_estimators = 100\n",
    "best_leaf       = 1\n",
    "best_features   = 0.4\n",
    "\n",
    "clf = ExtraTreesClassifier(max_depth=best_depth, \n",
    "                           n_estimators=best_estimators, \n",
    "                           min_samples_leaf=best_leaf, \n",
    "                           max_features=best_features,  \n",
    "                           class_weight='balanced')\n",
    "\n",
    "do_validation(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q3c3s+aIqj4DqNAR1UyIzm0GYno0ljEolkdQ3PhM9K+mEdlzHja1Orqi3nWgK8CtWnspB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44404a4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importance of each element regarding classification.\n",
    "\n",
    "feature_importance = clf.feature_importances_\n",
    "feature_importance = 100 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(pos, filtered_file.iloc[:, 28:-1].axes[1].values[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n",
      "12000\n",
      "13000\n",
      "14000\n",
      "15000\n",
      "16000\n",
      "17000\n",
      "18000\n",
      "19000\n",
      "20000\n",
      "21000\n",
      "22000\n",
      "23000\n",
      "24000\n",
      "25000\n",
      "26000\n",
      "27000\n",
      "28000\n",
      "29000\n",
      "30000\n",
      "31000\n",
      "32000\n",
      "33000\n",
      "34000\n",
      "35000\n",
      "36000\n",
      "37000\n",
      "38000\n",
      "39000\n",
      "40000\n",
      "41000\n",
      "42000\n",
      "43000\n",
      "44000\n",
      "45000\n",
      "46000\n"
     ]
    }
   ],
   "source": [
    "# Formatting data. Aims at reducing data closure (each element's weight depend on the others', as weights are in percentage).\n",
    "\n",
    "_X = make_CLR(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualizing decision boundaries  # DEPRECATED\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# PARAMETERS #\n",
    "best_depth      = 50\n",
    "best_estimators = 100\n",
    "best_leaf       = 3\n",
    "best_features   = 0.8\n",
    "\n",
    "h = 0.2\n",
    "p_samples = 0.2\n",
    "##############\n",
    "\n",
    "n = 1\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "for pair in combinations([0, 1, 2], 2):\n",
    "    # Taking the two corresponding features\n",
    "    X_ = _X.iloc[:, list(pair)]\n",
    "    y_ = to_int(y)\n",
    "\n",
    "    x_min, x_max = X_.iloc[:, 0].min() - h, X_.iloc[:, 0].max() + h\n",
    "    y_min, y_max = X_.iloc[:, 1].min() - h, X_.iloc[:, 1].max() + h\n",
    "\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    # Train\n",
    "    clf = ExtraTreesClassifier(max_depth=best_depth,\n",
    "                           n_estimators=best_estimators,\n",
    "                           min_samples_leaf=best_leaf,\n",
    "                           max_features=best_features,\n",
    "                           class_weight='balanced')\n",
    "    clf = clf.fit(X_, y_)\n",
    "\n",
    "    # Plot the decision boundary\n",
    "    fig.add_subplot(3, 3, n)\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "    plt.xlabel(filtered_file.iloc[:, 28:-1].axes[1].tolist()[pair[0]])\n",
    "    plt.ylabel(filtered_file.iloc[:, 28:-1].axes[1].tolist()[pair[1]])\n",
    "    plt.axis(\"tight\")\n",
    "\n",
    "    # Plot the training points\n",
    "    labels = filtered_file.iloc[:, 28:-1].axes[1].tolist()\n",
    "    for k, color in zip(range(len(y_test.value_counts())), \"rgbycm\"):\n",
    "        kdx = np.where(y_ == k)\n",
    "        size_ = int(len(X_) * p_samples)\n",
    "        np.random.seed(42)\n",
    "        filt_X = np.random.choice(X_.as_matrix()[kdx, 0].reshape([-1]), size_)\n",
    "        np.random.seed(42)\n",
    "        filt_y = np.random.choice(X_.as_matrix()[kdx, 1].reshape([-1]), size_)\n",
    "        plt.scatter(filt_X, filt_y, c=color, label=labels[k], cmap=plt.cm.Paired)\n",
    "\n",
    "    n += 1\n",
    "\n",
    "r_ = mpatches.Patch(color='red',     label='ARCHEAN CRATON')\n",
    "g_ = mpatches.Patch(color='green',   label='CONVERGENT MARGIN')\n",
    "b_ = mpatches.Patch(color='blue',    label='FLOOD BASALT')\n",
    "y_ = mpatches.Patch(color='yellow',  label='INTRAPLATE VOLCANICS')\n",
    "c_ = mpatches.Patch(color='cyan',    label='OCEAN ISLAND')\n",
    "m_ = mpatches.Patch(color='magenta', label='RIFT VOLCANICS')\n",
    "plt.legend(handles=[r_, g_, b_, y_, c_, m_]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computing 151 nearest neighbors...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 7000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 8000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 9000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 10000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 11000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 12000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 13000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 14000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 15000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 16000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 17000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 18000 / 18676\n",
      "[t-SNE] Computed conditional probabilities for sample 18676 / 18676\n",
      "[t-SNE] Mean sigma: 4604.497150\n",
      "[t-SNE] KL divergence after 100 iterations with early exaggeration: 0.873133\n",
      "[t-SNE] Error after 250 iterations: 0.873133\n"
     ]
    },
    {
     "data": {
      "image/png": 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UUkoppZTaNDTgKKWUUkoppTYNDThKKaWUUkqpTUMDjlJKKaWUUmrT0ICjlFJKKaWU2jQ0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OGsNHT6lmxXWjM2ybxPG9f1hsdChLPZhRyfmOQ4lbmmEy0p4gFRQBV35bDTwghHVcgySNLoqogq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CuE\nI2Uwlk7UryxqhGRsGGjwsJkjlEalCOwEj73xMirpFJTUuY9pqzM9eOiDFMqRNprfWftKIeE6GCIM\nOXHoTlfn0gpVhZDAwgjSTOJR4Vf5E//Tp3/H52Wday+ghCKTK8lqK8MXluUHLr6BOz+nFxLCqVYu\nWsjMTqLISs9l/qx8Icvgvn1lqzujv5GBZAnM5nxV8B4AutNlgZyxcifFgBK0x07oMdHXrAQdEcOk\nsJVKkVmdbP1OHY19+wlVIY8TenpisWWi5cdBRPzNIidK4LfONhqHOMhEjS+asfPHduZDH/rQL/r4\nRvjQ36UkQvTG19b4fa3sxxzsT2J4FIiNTmbRwMWIcKxceDp/R4mKCPR2h4iZm9HnSbuqvKdNFB6h\nPMLQMLJNTKhr4JrIVDRmmraiPKXMA+cgCD84p0DN6PKJQmJlDuGGZUKEpIlB4uv2ypf1C6Yzlftb\n74QdxHvxQpxI48Awhg4OUU6WEUmk8nna07ig/Rfrkhw7ixbS+oSIIONGXb7Hbm8MOmrCMgZCJtmZ\nTnAm8D4g7L1wqjiGjhvJjFBFtWBaeBZFZZ1ZObXzTXaSr5wFFGdEJ3Kj58u0spy+owjTzjbGzCdq\nLxRZCWtYKAQcftB6AzayCouf2dqd4RnKgmkmkTi5oFsiqXJaMj02IhLhQmknKMoQEA8Wc1JaWYDX\n+xuIMKLh0RgBa4LikxQ3+pjEuaj0kch9zMDIAUkGSkcjyHVFRqH7eVpyJDhKRd4zbCIGoQ3kzK6Z\n75eVH85PfHcqBJmLXhAUd6U2wTqcSvwr64uIcNLES+tA4myJSzrx0u6oBELwGAOPwIGWT5xa4D4Y\nUVgsoakwxFFRsJU/1orlylhXYm/c6oNOcNGFbImUz4g/yGnOXE0uGTjB5jtJEkupjNrYH984+qDV\nG/ex0/SC2QWNjZCCtp0iT4gudDPUK07FEWQ39mEgnYgC8f6bPNBdaKvSrNMMXB6YFvbhhBk1HO+V\nw6eFasgLb6MSGKdkjLTQG6RQAgffGVE4fGFJxueiiCsz7nFgwyipYgKrQoyZXXNCOPodt5Wyruz9\nRIqgpufZLVJltBuhmZQSpwqvMsNZFx20UWmR5jhGpHdYxGBROPWEsTA8SAJEI9ToS3AwkNHgfMFX\n5qa9LXi7sqhQCdx92k//fOsMFk0YnY6TBTycQVBjJ9kg4/QcbP3g0M5ihrVgSMNUOQSaDI48cDmA\nC8UThw2QgeCoVC6i/Kfn/5Ef8wU0EWrgypPOjscQqMnoMWits+YrxmBUY+NGlg51khcbC80GjRvX\nqvzz68+k9Yq2Bz4qMRoHcPaKaEZtRf70XewN6QK905ioeCmdTersZCcjJDF8m7CRcJpXymgUFvb2\nSvdKRUii6LjBNqmWJKh0Tn+rg9Mq+7Hxte3UoyMOpMR9yZwuB2dZObP+W29tH/rQh/5/po8C50N/\nlyIG+/Yz//n1j7y0g6NXcnVMFoLEiTI95gEuC5wXTpfvyOVMaX8kxjFvmCkjTYk0OyXdHzwP5TaM\nYwhJjOyDLSoeGymgpPO0qMnM8K524qGCeSAKRcBNuOQr+fxrwhtvbZ8zFuFAsEjHVHn0g3urPJWF\nT3aiemKPuSEVINtCsowfdxIZEEwLomXmUdgKDPhTgTMGuKMifF4+83PdaOnES4e6NKxXsiQSg8xB\nUuUh0Mfb3IDZLDbUMhqG6zNCcMQgMEzPrHrmROHVMt/cOWpHR2dIItwnDW1Z6Tyo5cKSjGNsf/rg\n5qYznCxOqHFVpfYbO51EpXGiaKZHcIqGx0rpiWUtLJLRfaKiFzoSGZHGuSSSgJTPvBwbNR8UKWBn\nBs59DH72BykK7gP3g30Abaf5GT2Aen5/fxUHqgU6BiMqhQeKk4/g6BVnWiQ7TkTn2oOWdRYEMubs\njTmWnNN54ZIreJlrTvQ9D2dunkbA3oRTib9Y3/Ox7sYSwuHOMYJi8GO58LU/ePwZsKHcxqBwYVmv\nbHVu1v8sK7gt1HB27ywDNJ/w6Aw6gXMXeM4rBchj4BgmQbzTCY93i9zD92kFfbyy+wve1vfcoY3o\nZ+DgM4lVHVWnEyhPHA4mDbEgPEEfQIcwDuZcDeE0FLqx9SCLcwu4LM5qD3I+M8LZjld2faHLMjfw\nNPZQksvcPLPShzDcKQQh4D7LGQ8lXEgDhgTyjjOvQ7imOb908jPSgq6DLE73jbKcuaaVux+kDKsU\nmm/0UCwWvM+g15Nl/q/x4DFuqApnE9yD6EHSxlkL0g31RtI71TspBEFJuSAKasoog10bV1aoC1ll\nXp+yc5WMt0ZzI1unjUQRYVXhm1dcdrpWXIzubdLHXObz9I2flxuHNDQJe3ROkcg6wQF7VpaRGQSH\nVJzAZTqyVHaeLfhff/U/8x+//AeWPnhKZ37uB9N0Oovysp7R8D8X7IOKc7DmJ0Y/2JYbux7c6sJZ\nO6LKmUz2xtv+QEk8ScHov7jAROg+OOXCKV3mb/PBaDu/P27U9sAJTpb5vT7QDGvdyTwIFfa44Xlw\n2oM2Npo/6H4QMd+fkMRIiaivtHRDRFH7G0VKa7Rt49YbD6uMVeg9qHEnxFg88313ssz39EMf+tCH\nPr4JPvT/Wu6d/fZPvDz+mbf9hfuAFsGrOusQSgghUCQzxHhaQa8n1nJF84ra91g8kJgbRHkvVFTh\n8vQD4yj0Y0e1Y/Ug+Yb3A5NBWi64BN6ds8JjOG6QxWiSWdOJIgdVg+f1Oy7rJx77z7idsVwQf5C8\nsvzFBnQflScW1nThKUA9yHYCJklr9AfXcp34WDP0PejRx45bQS1DSkSrxKhIdFQS2RZOtvJHF473\nORl1JYsjOaMqjL4jtqL5GVJAzAyV8IGlMxuC9wPhQPOZfv4Ne35i9Iz0Tk4QuoArdcAI4WlZoGQi\nDkZSVjVGLGzaGL3h4nR3ksKTZq4i3NW4e0VDyBxoCMWMp3wl/ExCKKEcOjMpTqqclwySaAACns6k\n9QtjC0rJrKrsVXAXjj7ofqKPDQvm62Oenpe9oa1S2YmA3oTkFyRVaq64PzgwksPVExFtlgUCg3mC\nfbaDyzDeNAjJk8Zkxq9KJjFYYtBG55KX9w2fcO8b4YOkie5MPPj7utg7f6anmSrnvwhSbd4425mi\ngx6OIUgEtb2xA5au73DsGcJoy+eZ/eSzCPaYhTv5gvkxh+GBFs6SFn5VfuR39ZV4R1zPfzPXoodz\nasEejZd+5+GVb2NnOEQ4QqXiPLnzIwcirwSfqbqADLoYj77OwXu5Y/0zlwMWB8Q4dFB1Y7ET5sFb\nKGsUIk+KoXPwsE6ToPoNZ645i8bWbB5WyBWN2cVQEVJdKYexkFhxpAVlSdQuFE80eYDDcigmkLSQ\nwnj2Qi6Diwkpf+bnbeEYA6/Bt97ABw5ENiyvJGk8HGrvmM7i0IZQIhM54cuFNSXi4YwxLV9iO4dP\nFIUMwdKgh3BY4Xa8F37vyrqyRueUMqzG637HeZBs52KJfbwRvnFLD2rLqDtIp7/T9vo7WbDZJKJt\n2rHTho0FCMSNJEFYBpQ84NAdkaCg/Hb9jv/lh//AP5x/pIny9PQ9RY3n4yCNzmQnKDU6/b0yOdvK\n17a/h2gWDhE0hDoylZUeiZNnugQjjDUq4oOWV1ZTogWLOKbKUENEuaYn3sbGt9p4PV45euWlgzG4\n+IH7xl3gYf83e++zJMmWlHn+VPWcY2buHhGZeetWVzOzmFkgbBHhARBEWPEKiCBseTYEFgg8AA/A\nEyCDjMzQ1J97b2ZGhLub2fmj2guLukUBQ/f0jBQ0ld8mJCPcPSzdzcKOHtXv9105y43eX982HgxV\nJ7sw/H6MLvo4NmS00bWT4szon4H3uM68tuMeUfQIM9ZW2dyp3qld2Yaz/twftw62UCRuKMrX07v/\nj3e6L/qiL/qPoC8Fzhf9d6tuH9m2b3npN4ZXLBT3Yye350b0RO0ZF7hMnZqV99E55cKSCh5vdKio\nDN+JBJpOEJ1UHmFZePn4U6boqFYmWznLjll/yxxZCAoSQTFhRMdZaBizKZ4mJJ1Zp3ec88xjDERW\nigqzLfTeCedtZOXI1YmAnBe+QpE+qBy0oTEqFpndDxrTP90U9LEjkuja8bgjviN9PYzx0xPfTQnf\nKoQS6XKkt8fgJvBBCue5IGkhmR4EsEhgnd7WY749lCkVcvkh5CfMCpsrV3Ee9KAh8ZbhEppoqsci\nu2S2XlEaF7Fjvl6U3eA5lHuHSQOzAFGepkd0H3yTblgcc/wndS6idALByCSKJWQ2tCtZHdUZ02AD\n5uWCKswmpDfkskoixQnXO5MaXY2FIwepxg6bUsaVe+xUJnooSNAksQynaadLsOLMLiRVQo5Ohmk6\numskAsX8xnsLbPoal+Dsdz7I4KsIHvrMHoV5dhQ/whi9sY+dOQqLTbi/WcYC+viXUU0Rwa0Hcw40\ngglF1DDLGBeG1yP6VTI7M5WE1Q75GDPSf/SyqplT+cBrfwUCteXIagL+8/QV13HnlE+cpgd+Kj9D\nxbh0Zx+VNj4DO6914z4mIuTow4RQ6dy1s4XwTq9cFOZYeJWdIUJLDm7MfeEHWzsCFAecovDDpjRL\nfOMwbKb3yrY4/c0TFMO4UtE4Oib4dBSqNmiy0rQwj58wlw/IUKbN0BC6BeceJBcKwnI2njXRQsgR\nyDoYyxv+1xxaQq3wzhYoM8KFUhupJZ7bndwN54RLo4/GVo/x1j4lJB6RHU7EAbDoRkmg/sDr8plC\ng8tMvwt3EZIWhMSIwa07vRhdC0sESQo9BoqSmHlXTiS58ux3xmTIKpxTMJlwj8bNd5gKnkBbkDEW\nU15Go4lzW4Lsb2N+OPe0cR4TGfAC5oKJMMfCZoO8nHgoJx7LxA+mxI/Ov0FKJ4bqkR1jhokQ+xXG\njo+gjUqPjqUJoXCyQkhHXJjsxBqv7D6D6OGp4TjO+4AmmR8Q5LTwocx0gzG242+MZjQtmCrqMxKd\nHMEsGU1KRKGHMHmn+XZ4fNyZMUSVoKOzc+rGWIMlQG0BM0beCXUiGiOclt4dlL23sdM2YOudxzEY\nAfsQ+hA2r3gIww13ARJXYEkb77x/6eJ80Rd90ZcC54v+++TutP0jMXZqr7hmujsbSlWhRzuM3yfl\nyYz3SyanhKaF1TsWAg5qJxab0PE2hiAJ35WXvtM18Dnz3Fa6OiMPxHcCZ47gmULSHVVltonqgz42\nJJ3oKKGFPD0QotyHs+QFaRsSjo6ONujtTrNGjIMqpfl05KR4IOnMrQ5eWsc9KDhPWTkJvPTBQ7Lv\nTeERg95eCW9QykFXUz1Ib3HnOjqrGiMpeEbzE+GNHs5nr8fuNJVkZ6Ac2Tu5IARra5wlY+WRZflA\n3W5Ee6UzUdPxfuuoyHQm0sKOswv8lE7ar+SAYsaVO2PcGWnixU/se0UsIxGMvvE6fcAmpfnK+wGq\nCXNhD2OLYHPjQQtdMjZfiEmJtTEETIOzzZh3LgZLMrYZBhAIE0bIjnrjUYy7OA8i1Mi4b0d4XwTi\nzpCOkxiAyHZ4rOLOqkbnxM6x41s1QxFcIEVn4ig20JmkO2c7UOKznrjIxA8SfNATz/0B3YIuO7sF\nvTt17PToZDFE7O0c/1fOf6C1HfxG82OEKFnGdEJFCZ1oYjyPoPpbIIcIqTsiTlZY8i9G4YplTpzZ\nvJLtF4GOSZX/Nf+QqUx8ePjAtx9/wgsb277Rxs6LB3sY4Yp7OXw1LnhMR4KQNj6mI4xzSMVjkLhx\n5e29Q3i3nhDdsHBSZE41EOkIcHLnqo3SYOQgitKp3GWnx0RIJYeQxBEGRfIx9mjBV3aj2omTn5iT\nk0VQF+QGpIYp2Dhjluh0pjEjcWOTTpXAUjCSY1boDDROqFSydLZRsT3A3kIvRzrGGMvOLplMHLjr\n+p6hDbMOMhgh1GbUbUYm59RujAnkuh0dVZ5wzbyqcM+J1I4i9rVBG4oQLKnybi6cyiP4jiejnhe2\ndifa80EGmy/sOM2ca6qo65ExkwbD4KTvyLvzXD+THjLDNQAAIABJREFUwujqfMw3PvgjRTqud2Z5\nYEmZp/mgNL5fHjHZj0wmjm6QcvjtwDDfiLF9f+6YCLgz2grJmUxRmcAmLmp8rEqtr1QPFGESxURp\nFK7M6Agex2CNwjR9IPmOjwPGIXoEiGJnsBuTJCrBoqApc55mtHxmqRurOK07KoNHgUmVBx1Y6tA+\nkz0Tk7GmgVg6RnfLI10fONvp+7DPn2sA97cRXA+jsjNCaf0XcJAQoXWjVmXN7UuB80Vf9EVfCpwv\n+m8rwvE3L0dEwwgqQjVjDaUz2N2pGGkkHobhPmPF8Ml4YWWbjeIryTLTcianidWDZ2u8EGgUdN+5\n76/svHJLz2TZmaTSh7NL52QTT5rATjQ5KFol5TfKj5LyI28MtCP1w84UecX2KzdvR2dDHxCZCDrn\nsjDandsQvvOC6xFkOJvRSWgcRvL2RvS5decx/5ygdaCvgQMX3So+HKkbLQZ1HDv+8EZdS0rsEH2n\nDidk8EFhhEFKR+aFb2CJef5PPGgh1YZxLMBF9GgxtMEoSjo9sFjiEwdZ6qV3mjsamTw6273z3hrn\nyXFTSnXEhJzeIRJEDG5j47auvPp2eA36SqoTj5o5ZyhshF/o8cjcgjENInXMM6Kd6h3Cib7ieiKS\nklFid3q7QjSSr2h/gb7SWRlh3D0omphtZm1CI7FLpREUNjrO7huuxoiNPRaCEx79oNxJ0OX4jFNM\naG5HcRYFjWDOF36QTzztCelQEFrv1LUyLhms03zQfKAE7/U98K/nbHhf2fuVPe5vHUCo3hA2LJ/Y\nXYBOj1+8yKQTHsI+ArPK5MYi+v342fI2yrhYwSNIYhRN6NvCs1hm0pnmK2KQRKnhPNfgypFZA4WQ\nAYyjixOJwWeMRJF6UMYQJr8gFHRsqHRU7gzdmDkWlM06IyllZGSqkG5MUQ6Eb96gZXZX8EL3xqKJ\nJytM3pBemGwwz8bwjcSZPAnSg5MJp9PguU6EzHgsiB9jgN46s81Yq+xjY8uN35gTPge7Gi6NtDmf\nCT6FEsxorWQ/8q8sMld1dmm0YfR5ZpIZRmOKG7NW6qiITczjCX++cLeVMs9klNeAdFFsEnrsbG6Y\nw2gnRJwxBhGD4Z3/8nLjWjpXXnCCnIOWClttrF65esd15tGMnUzzzj4OjPNsxjVuiCWW6ULyzjV2\nahr8ZHzmQ5z5YXqPTpWSMo/l6chTmive5KDDRUBUFsu4H92OMTYuybj2Y2QvS0KlUftAYrCpks3J\nKogW8vKBDyPhe2V4JetCl4LI4S1s6sxmtAhady6RSCNxTkYpFzydkF6PkNtcYOy/uEdwwBUedOIk\nG+F3rN+YemdSPbpNBEOdSMf1cI7EGIHmwmxnWnpArBxUyn8sEaoIk0MmER4HWe/7nx8epKxgZPYe\n8K8Fgn7RF33Rr4V+7Qqc3jt/9Vd/xd/93d+xrivv37/n93//9/nN3/zNf+tD+3en8EHvN8Ibrd3Z\n1m+p+zMz+TCW+wEn3dzZyUhk3rfMkjLXttNHMNfEactUKUyloGPntR6FUQv4WDt7NHIO9vozqjwT\n8onRboQe6eyKI+EswEO+HCbX+UeILcT8/i2QDsIrzRMqiQDW4fzQHng2Ye+fMJ3RlFmW93Qq6+a8\naPCx3blbIMmpEZxyIpnRmrMNSBKYD7o38hCSZfLPg+h6o68bawQ9AtfM563z0js1n+jDQUEdTEBs\nwkR5sJWzBhGdLZRdg5ImihpjPjP1gugKIscoFJkydlpA9ozmCxsryW9vi/9g9A5SaXmmi7B357HC\nZFeSX5jyfHQbIljH4Cf9zojgtDyiEnhd2Ri0/so7EuYLeYKUNsIPulXYSu3fcN+FbGfCMnsSWm60\nm9P6Rts2rDe0ds57ozucxomVgucD7SrqbK5svHUA4Psww/vY+ZT7USCh9FR4lo2lJ1I4QaFHISKR\nQxmS+EoSjygRA2uCrndW6cynM5aMz/1OSMH2jLw1TETAdWP3dmBm9fDi+D/J2YhwWr8y/Iao/9IO\nc+D4qEzpQg1n0oo2EDI+OjcbmAkpKa+908I4m5L1SJh/SKfDl/OmEc6n9sK97fz4u2d+vD6DNzY6\nH6XRCKofQaRVHeFORDo6JZERT2/emztVxuFV8sNHMyfloZ+Yk+FWiea0rAw7xqOMjKpj+UYItBhs\n/caKktcLc2SaJEIG0hKFnZxAA76yR6aaqFKRsTIILCuvGFudwcAF9nHnJiBilAG5nYmTIbExMRhl\nQCmkrnxqOy/3IJqwjY65woAaSg4hmNhip5I5jYW8Kg0Y6ohknjQfZLNqTPeFVYyVJzQSPTpJnLkO\ndIaeFK2NbUyYCjHujDgw24ayDWNfX7FyJ6eCalAATUIaxvO4kyTRcAgOyAoHWnrzgY4dCee9ztyo\ntHHkXFkqZJt4Wi5Injjpwmz5jZw2WMy4iHDiTu6V3jpdZ9wmwiuSJt7lherOcFjrzH00YkDVxL0N\nphz8cFnYSsZneIo7Vz9zG8InP65HYzDZiaLCum3YrXMT5euipDiRmKhH9idJjpwwdfBW366RA5oR\nCR714Q1usrETrCNo9MNLk84kBnMEamCiGHp0bcsjpfzzgE+AyIWJSlHhJAvX+EXnKixRxCg6oSK4\n27/4Gl/0RV/066VfuwLH3Xl8fOSP//iPeXp64m//9m/50z/9U/7kT/6E9+/f/1sf3r8bhQ9aezm+\n1k+0/Rnvd9b9mRSZi5+4M6MxAGFRuFTjXRem2iFWNnVkNmadsftEU0FN+fH1OxoTeSS0GpMrLoPb\n2ujjldRemLiTvDFFkKRj6NsW+4JZIavB/IHnPt7GjZyPntA6eDo9oCIYnU8Dui1YqgyCJU18tTzy\n/PpT/svesVLYR2NERvrgRnDdB2cL8J21X0EblidyWnAtmGbG2BDA18rV/Xvq0BqBW0Gik/pMkkKL\nOx47KS1M2TiPO+INNUHNUHHmN0+MpYmuiUHgwYGt3VdEoMRBqFJ/xWelZ45d/GhHxo74gXMVIdxp\n3ngJ59IHD9kRhD4qtz74PFZ+HmE5RqNME329MMlnhlcahcmUzT+xDWWLmTLuQKeOBubcZaNzhnHm\n/t2NevuWyTulZ9IeTK2S2DmlxPOYwB1rnWneuQsQQifeNluPlL7G4DUrXTuZjWOoqlP1RLDw1Bfy\nPmM6U7Nz0xkTBYElVrx+Pizx28xUJoZ8YkuvpHQipOPM5J4ZxUnmJDHq+spsJyIZkyW2rt9/nu6d\nvn9m2z+x2J21g2tCdf4+pyO8skii1MrsdizmGbz2yjRgaCZCSXqkufdQHm1ieSPy/VzDO/+wfcu3\n7coWTvKdv18/8t39IxIb55IZu2EcvptACQZExskoCWImhtAliD5jcjzSIlP8yAt5VKNTWP0Tk8Pk\nCU+wu7PKziAdfizfiS0f5v9uWDgzmS6QPOEqhGSyNk7J0N14Pz+QctBGUHtlhPPSApWV2RaEzEAY\nN8GbMsLobVAM9pEQF67cyQg/6QnfE30MmhitHz/HhVsUctrI1RDPWCrUfdAH7Dm4T41twFISyz6x\n93IUcjgaSnVjiCKt4L3BlOkNWjSe285JNzIHlGDn6LJKVJbomDqQkTcfVusNRZkkaCSuvdLDiJjo\nAfvYKCF0FpyOSubJZkiQtfBVeeLr6T1VjayJSRIhMGlhbo0i9UA3izLn81GY99vhIuxBB4otfKoA\nmQdLtBgs6RiBVFf27pxSps2PR+f0vnP1ShMDCVQbEne+3TLvdwGMHbiS6d1Jrz+FW+JeGhI3PCrZ\nFjabj86MgchOQVB7oNsLKVeirtzf4B5ujxTJiAk9nzl7HDll5RHLH9DyiP4/jZaZofnMOyrEwubQ\nOEYxk2Yy6Sh+e3CxL+2bL/qiL/o1LHBKKfze7/3e9//+rd/6Ld69e8ePf/xj3r9/z8vLC9fr9Zee\nU2vlfD7/qg+VlNIvff1VqredlIy6fibGnZQyzI+Yd/p6pejg5I0mg0UzFhNfbZlFlZkdR49Qx825\ny0qSCZ0O8+8rC3M7ES4o9TCi9h3bB82DTicRSK8kNorwVlwoS8nY6TdIy9ecI95Mu8bp7caYdTCL\nMyej9cE1AiI45SMrQszYWuXuEKKEKMmEoUrz4HW7003YWqOYQCTEnUvfsGSkcsHziX102vqJGpmk\nRhZhRBBDuPRMkLgOsGmh9UzrV5g7j0l5l7/iwX7ExE5vNyQ6ngQrZyQtuCzcemeUI5i0eZAVVJRJ\nKz1PWB+gzj6cdTjX/QXTQomBRSOioXF0GO4ePOkrIxrVnW1/5eY3hhRymokeoDuDTrH25iUSulTu\njONzHJWbNW5+LPo+5GDrd6o3fK3c8sJ0V0wCiyvpFjyZoJbZ6RBKIUhhTDHx0wL3ZYcQtB8OnIVB\ntY3VFPNM0o7okRV0qoEzM3SmqlBwFiY0n0ATL33nwT6jKHSho9x8sPSNwRFMKlNBS2Mpgs4Ke+UB\nxXSQCeidok5ZTlRX+nB6XUnT0RXokSkRb2NfDStnsgiTCoGTVdneLtV1OCnBOiqtVWw6o5bABFUj\np5ny8+t6DKg7L/ePvOzfHoTAkrm3Ozff2SV4qZUHE/aSWduOecLp2FhQv6ChaExHUCwdixnsBKxk\njoDcpDO9QBsJXxumP+BsD0w+MfYgZPDx1HDPiDheg571KJqYEWk4zmW7HNAAM6YYJBrX207RxHKb\nuOWd6IPVG6+68RrOZSzMY8MiqFHIa9AcwiBfAy+GxgFlqGPm/7hUXuPE1oWx6bEZkO7kdhRiWzhz\nTyCJIhPPgMRBRctjwttgLkIaTq8JjwRZsbSz+kEe8xDu7vjeeVyOqaZb7HhvJIOpKBpOopO042mw\nTAegQ98+O5WJ6Duqickmep2IdvjYLBUeVdnEefUdG+CSUBpJJ54sYQLvygPvp0ckzXxXn1GzA+rh\nHfXBJsY0BtWU7gPSREoLo69YygjjAI0IWDpK86ILKZ15jGD3ysbKY3JqFEYk6hRMaoy+vT1eqDGo\n+4r6BUoiED66s203FhmcBCZObJNxGkHnhmLsHCOVX6WFMpTYB+/0HXsxVisUAgFSeYeMnZMZKWVy\nmjnZjKiRp3c0OyOm/8yDAzCbUVIizwtsQayPfN4rw+UNXgMpBbMdBblokP6FRo6ZkfO//wLo3/Le\n/0Vf9B9Fv/ZXz/V65bvvvuPrr78G4G/+5m/467/+6196zO/+7u/+UlH0q9a/RWdpuwvDJ676TJze\nHcF+Y6Zl4RsS2hqnyIg8crGZ8qqkPFgETiMho7HFQCSTQkjc2PTMNcB0OgINvWN++EFibExZGTcD\nE0QciZ0k7VgQlokPlwfK+/+Fx3f/Oy7KvXUmP2bQI5y1Hj6hlBqbds7nhbYJRtARHnPGA57XjVEm\nzkuiTIWB8d1Qvnv9TFhjG04VuOhEEVAz2pT5wTIhaeDZKadH1IK6dnY1TJXZBHsBPRvvn058s69o\nSYDj6wk5wX9+PJHrnaW/YCzo+R1aJloByRPPbZBt5jStbPvKsyy0bMzlwnsbeJwAp9kgaKyxsFYn\ntsTmleaGxMqszjwtLAEyKidb2aRz7TtqQRLB/Y70Z0o+wiWDAIVZDPFEsxMphIFgbMzW6ZLYgCad\nGwtjZJI4y+qUSGQVLlGZ1GkqlDQToog5I5RTPnIvTiejm3CXj/TWQFZaCMNnzihIBWYsIKNMkfgc\nhlB4UCFbIdmCakbzUcR6OlMFuipZM6Gd+2SUlHENluWEXE5My4lpNk7ThSUVimae8j/awEgJOV+o\n+ytjHCN93147u/dfukZKuRy/XwupZbzDczsyRKw3rn3Hh3ESeLiceTqdvvfflLLw9emJGANuV8Zc\n+GxX1BYuHryOldo3LGfUF8pb9k/ShWWZQBUbd7atoOJoFJAB0hBVkhSwhJI4pTPzpBQm9th4TRvn\nfuIiZ86nd8Q6joLHEksWugwsCqMYXePwT3hD5Bg3/Y17QlC6Cbk2Rils44JK4r5c2UvQk5BCKGNG\nxsSLHZCR2cBc6SWQAOuQ8sRIHVDWONGG0McgcuZuQbFy+NCmwpBPDO1owKCQdaIlo0VH8oF6T164\nZGEpnTFgQ5ElMwqU6UAWz5OwV1jrxubG1ndKSiy90YqjNsAyT9koOsjWuUvicXng5oO7wK1dkXBM\nFpYqUB84YfR25qR2bKAkZZGFKU6M+A7TIGsg4iRTTgoP0wNPDz9gEGSfeUgnsgTrdkPn4Kt8powD\nNR4RjJT46vxE+AV529jpXHi2YzyuqFGmJ1SN6h3pKzMTH+bGsm/8/cfPaElcspKGkgWSchRPW2IH\nHnNml4S3FU3H6OlKMKcHfvThgRFPTGMjYnBZnrAuVD9j4Xzadtr2zDm9R2xlS46HMi/vgcYDO3Mu\nJCu8z0do73L5EZbPrLIQ8svLkqLKY/lFt/PrCN7dnJ89O9e90gbkBJNlDOW0COez8Hj654XS/2z6\nMlXyRV/0P65f6wJnjMGf/dmf8du//dvfFzi/8zu/w2/91m/90uNqrXzzzTe/8uNLKfH+/Xs+ffpE\n7/2//YT/H1W3j7T9lfX+M3w0xrhTR+X/vm38Qz3M89UyTZSxDx7GYG6dU3UkBj4aRjoWCmPwHJWR\nO1WU1IIb+5HJEQ1vV/r2EYvO6DvWK0N3lmgc/CAjWsXbjq47Xm7U0bn1QLRgdefb9YXdB8kKfXxi\nHc65FIZngkJ4hWi8mwofUuLTuhO28K5mriPz03rnc9249cbdgzlDrxt5rFgxxu7Y/TNNJiI9k8oj\neyRe75VJhSzBKRTdJk55RvXKpIaRqH1gkdFrpW0/RqNxHa9EBKoJ0UQ8fmCVO7feQAxi4K9X+jhI\nY7f2wmDHYmcw2C3x7Jm77ny33bnuwb3v7KNRFM5J8BqEwof2zL1/pkuhkqmi+NgYVHLcWPsDauUI\nztsbRY8xqNUa3Y/ATfM7PU289pmVme/c2d0Z4WQffB2NA0K2cW0rrQ2QK24Zz2diBkZw7wqq7BEH\npjiE5ishGx4PZCqBIXGAbA3B6kKnkWlM0jmWMYB3ppFZZefqO//nvnOSwfltRGodGy/9SkmFlGYK\nr/S1Ut8n1k+Vj3GQp35QZmq5o//YW3O+UtsrEW+BlNV53j8DMAgEoaQdTRMpP/ChJe590MbgPga3\n4XxqNyA4mdLDea7HUKBHcFWFZSXtO9I7Iwbfrt/xcb9RvfHiG1tyvtucW9+5j51wp0aj94qPM9YX\nFiuEHAG1dXRU21FwaSZCmHRGHKwJxQLRnVPPjGzkEHZtfLSd5zlIDmkYvQTPqdLGRvRCl4ZHYW7B\n43pC/Ai+DYUWRq5B2Tq3pSMefPLX41wdiTQ6PuDlBJ35QARHkF3I20QlUB302rglR3oij0BE+VZ2\nbq2QvVHGxmSAKXvhMPrv0LTRxgHuEFU2BpOtxA67JlT6UWhXCDpZhMvpwm2/8XLd2VxZ00CfOxDU\nChfdifJMLcYehSBTR2aw8fe3Z6omflbv2GZIDB4A3SdusTEk0VPGDaiDLQWlOA/pxK4dxgvSD2+P\ntiNnxqzw+fUI/M1iWJkZbWOSQtuuvOoVaRu78xbrCZ8+3/kgMOvES3th15lPsRA6HWO0soEY97Gi\n0TFWXrJTR+N+vXIdg1kMsyPgePPAfWDbIEVipI3uQm+v9H5HfWdSofaN1z7z7vyeFDumxqByKr/B\n8/0j3jfWAaMrWxvcvFBPjpaJeq94v9MUPpwyFhs5GyldaLEDO6W8oyFUd4SjuEGVb//J/al1qHeh\nvVlxGtBlJSfoG1yf4YeP8c/AIdM0se87/971P3rv//k65ou+6It+jQscd+fP//zPMTP+4A/+4Pvv\nPz4+8vj4+EuP/Yd/+Adaa7/qQ/xevfdf6e+PcLbtlX39hu32U1r9CCifxsy9Cyk6XTIlBloWXred\nl62TW0NDmcadGoJRmbtyRWmncaRXO+x9MGvHEfbesX6nO4gPzgQeA42VmY6miWwzli6s9kQPpe4d\nGRuqE94rXl+Yo6IEfb+xt5WtB9e7gky4LoAdo0TDmR8uKM7aVz5bQekYA/FBf0uQt4CT7GSp0IN7\n2/m/wo+keKDriTj9iMEj3Y2LKiKZHsF1v1HkMEs/1k7WDGkiv36kTC+M6EdHzHeIQKcTeruyT2fG\ncESOY9hSRoDRdlr/TAVONggZfB7QNDPGDWk3tl3Z3el+JMgnh9qu6PRweAd0UOtnTqGkUC7sfIxK\ns0Jvd2R0IhrZlMkntrixUxi+I7ERcWMdmedh1LijkfDmJIfcMkFGeMXTRpeAMC4xQDupB1kWgp0+\nCtjMaTQqkHwnySs9LhQg4sDWGgWNDQ+leKHIHaOjMchiZFUmC5Lv9LExWBnsdBk4lX2C0g4vQ0XJ\nktEh3Lvh7c6+39nkKHDWXnlshcesnHRGRYi90kf7vsBJUoDCp/oJf/ve5PBgFy7MRHSWOLodWRQT\n5y5xFL8Em1fGVtlHICSecuYnfuWyV06qCCDd6dvGS7tSpTOmjHkwRuPWdoiEitBwzF5QLlDlIAeq\nUVRJMqOyM9lEisSszqLGU8qYOk/9xDIu7KnQRuYayhYnRh686o6q8DEloHPqifaWh4QEp6p0YPPD\niyMeh48qnMUzw25s4oRMlDCgUXF6rJTNECu0N/O8oagLYRWwg6zWQaMfQat7ZiR5I64FQ+GujmYl\nGWgMpGdGXOkoQwQROyARaeMewotsnC0QF55GonVByoTawuv+fEAJs+N0JEDG0VWq7UIdGe5wf7uu\nk0JNlRed+YlveD1w5ggkN6Zh9KEICt6IEhiJkxwwlRSJFI9oSmQdGDsOnPPM+/Sem28kTcwk5g57\nG1AS7keByziM/NUH+9h5vl35WBZm23hImawnZINXGwz699jv0Xe2+hmPyn+aBx3B+8B749U7OS8U\ny4hDONzZqGPihFNHZbQ77g2JykBZ4wb7IOMHbKEn7tcdeRhscWbvlXU00hDWcEIS1jNRpuMcj53w\nwdbgIRVETyATvTVEM30chc334PRxbBr8Uw0HdyXbcdzwZtH0A/DAgG33fzamllL6N72X/7/Vr/re\n/0Vf9B9Jv5YFTkTwF3/xF9xuN/7wD/8Qsy/UlX+sVl/wvuKjMvqG90oL4dYr2IXJlBzQxMErRXYu\nwxCrx40HYTLlJB1HuZnQi2ARCIFIhSF0SQjHCNIxTrPzFZ3JbjgbHaWJscqZrgvLGLSh9KGQ3rOP\nhow7PQbGYLSNdf9MHcHWgxpKsTudF0p+R7MHbibU0wPvSaQR3H3QCRZRJjsS1x8MHjUo4Thw3e4H\nSS4q72RgurDbTuYj5bzQ+6BGpqhwjsbqjiWYYqPXY4tRLLPIir/twYoopjONxoid5AXvguP4CPbo\n3FFq7Nz6C0kys024NmSsuChbv/Jcb1w7VBlUyXRgCkH6SmhjHZ+OuM6Asw0eWuOK4qI8MXh15daN\nJo71Fx60kiLziY3v3FFZCBIuH6hNuVMJB+nGY014xIG4HpAoTHWnZ5iTI0MO74vClJQRidDgvnQm\n6zzWirECwjfM1JiQEM4x6DLjFCrKQwjKiR+JMMUD5oW+D3QyclEQx0NwaagGHsaQnT1/YsSEIWy2\n4Ga8LjMTRquNeZ5RNVoMrr2R1Bhx5ymdQQS1wujH2OM6dtQyj9NX7F5xnHn6gIjg7ZV7vSL7jqUT\nD+mBByvcvfN5v/Ot78Dy5tfKLKnTjqXwQb+K4MGDxz3RWtBHIhRqd9a9URlEGB7OiI768dzBDWyw\n9wnDyWKcU+ZsD8fCWyuzdIpdOE2Jd3KCa6Oocm3Op664Qg9ITaAEYAR6jIZmR6rDWwBsciUcmhoW\ndoRAxgAJAiG6sM9H3lQVxewoHrruNC08+kxPIBL0EuQ9U/LxP1GFrIo7bHLk/Ig7gbOLoaIogTks\n+S0LJWXUG2ijjUQikS2R40BMI6+oLDwsC2Rjuw/SgGtUqmbauR80tbQhGBYT6h3H+boWigq9zQTQ\nZQc7MRbHPLD9A24fUQWv00GaO1iHzF25l05KSh+HH6cFvLOJRykkqdQozKrkvNC8HjS+3sl6FEY+\ndqJ2envlY+vkKLyMjTWcPgaTFOoY+OicCC7cSJrYNmhlwiIY/YaNSu87SV64rp1sE5mZEZ3VN6Qd\no10XgzvBNBVSGuzrRoQdFMe3s2Jox/0V2wq1Lri8p9oTsyWkQqkzwRNuN16GktIBRRmS8bcA4CV/\nTZbBIDhPF8zK938PU/7v97maAnEUNdVhbUL40a6ZsnOZfg5z/6Iv+qJfV/1aFjh/+Zd/yTfffMMf\n/dEf/U9hOPxVavSdtn2ktysiStAY3mihDFc01iPsLRoRBR0vvEuF85RZInNKDcYgvyFWwwJNwqMt\nhykeYS2gOxQ6GhvnnFATUn2lTIXJJu79K5o7lYkoj7heeJZ3TL1SRJjmD/T9iuLc4xWLQdQrr114\ndXiNA2HNGBQClYapk6cPLBacTzNxfWGMzuaQ9MDoepqYcIijWLvWTvOKjf0oKoB9OBGDpwy13xj6\nwKaCSceSUYCZRuFAaRcCra/0eCblB3jb5R31FYED6RrPqDpbvfHSnhEr7K48j6CPHbPCohnRwto3\nmm88V+djD2oEpoWZYPfOwGms7DJ49IU1Zv7BhcsIzlwJ2bhHYYwZpLCQKe0FlcwtMp802Ol4vHL3\nysiPTJK492NsbIuJH9QTjBnFCQMkGC6oZKY+sPmKZcc4I2Um28TQwi0DBXK7ctLGgw02D5I3PsfD\n8f5KwSTTgILSU+JH/sT/ZmdEJq7NaV5Qz3R25OlYylxlJsYdGw2TRg6nMdjmE0ueIV9QmdhG8Nkz\nc3eKCUkEZdBHwtSpDLIZGvNR5Htn82MXNauRdUHTjGhiv/+U14CLTaCDsX9Hq880PaPblbVfqUlp\nmxMoZXkg64kW7VidmdFb52fXneozJU40D+5N6K5ssaIhTCw4TvVBcYGAcMEkI+kYDZtkxlXYI1hM\neTcnZjtTxiBio+93bi34oGdSTAycvQ48Et3rxxh5AAAgAElEQVQN7RO7NZzOLtBSR/uBzXbA5Vgc\nuwov1kieWEQQUarAbVKKjCPQFjkwyxIkK6zTRoyVbBnSdixQ7UTaT7gPhiop+5G104/Vq2tnDo4u\nDoZKI9EQcRacOK3YfnQRF6kIjQc1us9c8ytZB3tyflCEKp18hk0rXDKtH/WcRuXe/yt777JcSbJc\nWS5Ve7j7OQAiMm/yUZQe9BfwT/hP/AD+GoctPatRFYv3lREAzjnubmaq2gNDXtZlsTkrYTcTWyQm\nEQgRwAG4mZrtvbbgXghThidyc1wSHhdECqdnNAomDe5vmCbyqGA/Egysb3ieA7JLkMPJnnBTXIJ9\nOF/rlTwcLY1LZBYf/GHcwP6IZ+E+7hSgpStSXqgI93bjdtw4LdEleB877YO3XeuFDtxx8IF6Z1W4\nFOVkcIyD6A+2AoUHw43mMLhRwigIO8F9HNAqlzrhFC95YaSTmztrc1IIpcwi564PLp4hBrVByyfh\nO5G+/mn90A4lK2uujLTxpIkbd9DCklcuuRABqwqXusx+H8mQFuDfKaL6N7Tk4I934XH+edZm77Og\n9YeLTTjjpz71qV+lfnUDzvfv3/nHf/xHUkr8wz/8w5/+/u/+7u/427/92//Az+w/XhFBb9/p/ZXw\nQW+vuHXcjLCd0YOh0zbTyLT+hmjF6xO9ZH6SjacxF+BjBJJkkn/WjZac0MStP3DPrAFLS6zuXJdJ\n/zll5S07Ej9xsxuSDEtfecp/DaZ4WxgsE5+McK1P3Ns3qjce/eDm00ePGysdAU6UnYUYCaXzg944\n24OuGa3C5o1Np4e75Mx3X+hk+gj2ML7371xDSaosQEhFItFCuI/BOjq5GltSLnKypx2539lykFGS\ng2rB1Rjs8GhkVkYYB7NIVOxBLSuprOz9lYgBkRnWkVBcHPWDNmApX5B85Q/HK+/j4GcXzKdFBxRX\no4SSIhFyZf8onjx95xGFwpUf/DsqxhuKxsHmsz/n0AtG5hYZl0Yl8ZDyQZtb8FLpw1lFSSGoCsiF\nEvaxGVaUhRLzNsHXypkOZBOkZI6cuKaNBScZWKpEmlal7Cd7dHYWshSKZjaB0JVVNp5PSLHwUhJV\nOrspoYIX4WV9pi8H0e50n/jxZMLFE2n5G8ryIyUtnHZhPISHGqdVWheexegSjDC+lJktO2uh8HGq\nXF/o7e1Pp8EiCc0Lqgvn43c0O3n4vJUrSYmoPM6dR79xzwUvSlEwN4Yb+/nGJS9s6cLpHU2Z373u\nPI5Ockcs8TTmT28PZxkLRQbuyvBB8soqlf5x8+jSuepGSQOVg5wKQx6ornQuHGPgfrCeje9H4TDn\nTZwXzYgKxIJYmna2IbyuY6Kn3YkkPLaO9iBHodfB0/Bpq0RpKdgzEMqZjb0ImxQ0wRILE1sxOHJC\nPXHmwXMEORfWlDhjp2alu3FWR0jIEYgJe3Fwp+i0eGoEaAEGewsuClEbj/QgtLNqpjLx2LfYcd05\nFUSVfw5nkYNLXsiycCmdy9OJH4PRCiMcM2fQiEi89AWvieaKICSfaOvZ53KB8sBCEV9ZBqxNEVYi\ng5VOXTIhgofSZLCVTPgAVQqZmx0cbnQGXyPT+4HZieHcEX4Xnf8iFbNGCyFL4oEgaYMQiq7kXBki\ndH/lHJ2Kg26UVFiTkeSO5M4Tme+jEzZo5+BikO3gykqtlW/auLc3Sgx+WrdpZfWgVTjTXBNyZHwo\ndcwS5WUExZx3EgzD9cbXMWaZbm8QwvXpiU6evTXrC54dKVdUdKKwNZFyZrdZEh0fOZNFlS2lP8E4\n/j3VHPPQTQL/eM+lD3paCBwD6uf55ac+9avVr27A+fr1K3//93//H/1p/H9S1u+M+x8Zt9/Rzze6\nnxBtUpqGk6Ry7406duq40odiSYk6KOvAlsytVnIbkA5uOslH6geuztso+PmEms1Nu+1IE+KEcRXe\ntxVNwe9HcOSKU1n6j9xMqJrJWsjxAnfhlJ8hBZVG1sHbaFzHjZcGX8x5C/ielCNVmiQySnF4Pw7+\nr5/f+D8y/CYFOSV+s14nkteMF+m8snCkC+dQsmSGBjEGm98xwKXiFO5ulFzJZWONDLbT/M66HTQX\ntDVcE5oLQ3bCOuPxzrfYeI2G+zwZR4NK5uXcyTRclG5j5pP8INsgk7hnGL5yH4PWdx4BFokmzvBG\nI7GRCDXcL6DzlF7jRPJKsplpeI8fWekTfUsQcdBCeI/Cu1w5ojLkmLa4vNACXApVEsHGmgKtSsHA\nHI0nSB3TdyIGNQZbdd4URu2MKKTyjEegMQgJzvSVPr6T5IWmwXfgncbpgWij8kLJG50Lq8xhOeos\nS90Uuh9YzVxrIeXCrWSepHDaSQV+XAoXUx5RSfnC9Xgm9sbenaTBGpWhM7e05sYQwUomtoqkfzkR\n/sU6U0SIiD+RnDyMt/aNHoYAd2t8G3AYnGfn3Q6URNGFVQwNn00/AeJOEsFibhJ/1wNpJyWEdx/s\nwMbKF4Xvqvyu30gCSVY0OoXAxDARUhhFdn6QSveG5ndukvCsnNGQU8me6L2Qe2bxxGs9sHpnP59I\nHzQvS529CFqEKgHspDxRzz0H0eB7bTx1YWud2gs5FO/GnuEPV3gsndqCJIomm883F0Y5WK2wpitl\nDV6qzh6o0klJySjPDqMn/sjBXRsjDbQnIpQqnTTvkPAYpLhzmuGnY8KkLm4PIhmO8eOZeKRGyEp8\ndAbdvNPCec6d//Pywq7wh55wgjVV7makCLoIki+cDGpksqw4GXFoY6WRaKosZmzntGD23LiMRNhg\niwo1odhkF9fOj2EsTenq/L7vJDq/9W+sJcjWKdKpM0GGD7hJ5kjOqomXvPL77twdGgmTzBIBMRju\nEJ2h4J6QAPOG6AXdB9WdqoN03jh3wSxxV+Mwp6gRfeFHfeFYDzKOhhNpxUV4IXiPk+GK+86qlVpX\nsB3tyk2/MPQJIzNM+B/nnX6c6Ah8KJpOtmzIpZCqkkhkUfTDkramxPsYf8o8/qLTnR7Bl5z/rCPq\nzzTGLHtuQtYVLQL/syFN5u3OOZSPN+ynPvWpX6F+dQPOp/5tRQTH9//Kfv/G9/t3Hv1OWMP7jW4H\nkpywxOnPtLGQJOgoSSranVor+7gzwhA6JXaeJGNmWEncLSHD+OLOez+wU3kPaCK8IcijzmbvsjI4\neVil+JV3S9S08JCVa97maeZxI5tjdfAix2xON6WcF8boSByIL6hd0Kp4nhuCZk5Dkb7zWxPQYKsr\nXxa4lgtFd2iN2wheUqXkzrE94ed3knQaiUSjxgMnY5ZJeyIpSOyMMHKcc3HFqUvHY2ZsCENY+d52\n9v7GLRoPcQZCksrmiddYqLXyJDtqB8t4gB8UN4YrTaY1pfiDwBlaMVHUg5QENXANUs74uJBzooiT\nQ2neSb4gemHIyQNDZZLsdvvOjZVTKgM4mTjtE0FGJ9JCJhOeCAFUUEmATGKXCC+XlR/8nTUeEMo/\nb1/pIrjcWVLm7HeGrjynzLuuIG1CEWJn9y8MNdQPQpWB0OPkIokF6FR6JG7jThZjSUFJkGUjMXs2\nXjTxvF2JsQADFcO8kyLxcq6M0/k+HrwNkJQZOEvPqGZsE5YieJ2Zgyp/fuybJf0vm619POgfsAFF\n+WOH04OHwW3snA4Ik5ZFomoGmRSs0wYRQZjx/Rg0d7a60H2QojJG4xyBjUk1K6lOK6BPV5vYScmK\neZC8sEjHtc2uGCnzxqEHvk+bFLKAd76L8cUS9RS+pZ1eFMnHhDtQOBYjaWERpUcg+g7RUVY2EpTB\n23qj+jNBw0JxgvsWSElIhm+l8WJBqYNIoJFYELYSVDmwOtCnjCxl9qr4SeHCoxv3dqMdRtQDdSXE\nibbhHJjOh3dlUFJjxMdtMk64k5kZrCQnshn02TsU0TjduLlQJNP1pNWd/5IuvN5OzmJ4N3JOdBOy\nLEgJ3Cv2QfFzVSQEi4qjjGGUGEgILvvM4VTjSmLLSogROjir8pSg2EGLhrOSMKrceR3f+T4OHtr4\nqgu1FqpkQPEwsgpf84XDlOeqDIMHTndmx1Nv5JRYNOPRSSIkFYoX/HsnN6e6s0rwo6882klm0D1Y\nRMkCHZ3vB1dGDg47ac3JKdOp/FgywzvEHbE7zyHcXTjyF1aZiPaHB3sEWYJH7Cw68099nFzVeOkQ\n+wXJmYig8sylrgj8L8PNL/KIefDwr7Ox7sj+mLY2QJqydmOkykiT7DctkaDypw/71Kc+9SvV54Dz\nKdwHx/t/4/H233gzZ+8P2vHP4Eofxt2FMeZJ/9K+4CUYrBiF4sGDwv99u9FV0BRczsFzOL+RO5Y6\nr/IMYWyWED+oXTkHLGKcMr39yRUOJbXO21bZZeUnewYNTAs1LXx3OI47z2nQ++DRfqaVlewZbUqo\nIwmSnagEKrA1xQNIlWBBYiFFBXvQovMT8PPD+avtiSKFqw6+Fp1t9eNfNvSiGYuBuJGYp/EvOFdu\npBYgT2RNPOskFFkIb95pERBOkguPNhijEyLsBmcwN25J0H6itvNgg5LJvjN8YLKAzFN9sZPb+Y5J\n4iRAgxLBECWLk32AzDLBtV54WlZexNjPCrZDHvS+AM8fiOpvuAbfbaFmI2lGLQGJLJ1OYaCIroRW\nVBcufpC9E1nJTUjRWFPww/aFdAgpXti10cmccbJoQVIi4iSnCw9Wuj0hNFIWGoqZoXagrLgMqnYk\nTZqSa8GZtLUkDhr0AV9KoYjSc0Ex1rKicVCr0APehrEsC8kLv//tH7GRyJEoOEMK+wj26PwggXr6\nU0dS0Uz5V23qKsqqld3+BS/bZo88guMfIfIecEawxyx9rWQO77yNYEsxN1witPOgtZ9ZJPE4HVqC\nlHkP43Shhc4OKQcCihY8GlXLDOHHiXjiKQY9gtXnc8mS6aFc2Kgt4y5YdG7+IDsMHJPO1SdYgOhz\nsGGw1wemxmGZS0poeVASbJKIE8BZk7Kq0Z8G3QFzskBNiVSUlJ37cnKTznNMEht/uqU8uG2DtQiv\nWqku/JATIUYSx/VAyo57hzYIU0wN0wOzBR9XlggkK49xpSQnxfx5T1LJdtLr4LmCcWEQnL4ToewI\nEfNAY5fKzzb4SVf+4go5Om9utHBWr8CBSUEnW4EehnpiRKaIMUQ+fk8G99XZKCSFNSfqWoniZIW6\nKjkHHietG4c1EvO5dzHEByJOi8HvYyKji8akA0p8WDgDj+Ai0PO8EOrACQSZHwElTWBJFDZWNi/0\n/o6bkwkII58X/sqF7/KNW1QkZ9DCGkaMxkgXFMPCSRHU+ds5LXW5kGIn2DlNQTeaZmhGohMuuCYe\nR6PjmJ5QD3JyXOEYjZd4ENuVpSws7CQzmlz+3TWp/RsDjhz7n00tS3JElOKNnAX+VSlmSf/ysc07\nuzcerXH2g6qFTZc/Q8N/6lOf+s+lzwHnV66IYPR32vv/4NYf9Ji4ZtyJ0Xi4cpLoUUmmFHeKd1Jy\nOsG7C/cI2gfx7C4npQYLhXc1zITUjkmeOjYKCTsLHsIATBKFTqLTNHFQGT438HcfZA82NcY4MTK7\nd55JnN5B4QjHRya5YGkjUJp1EoYKaGw8W3AmR8RZknFRwc/EXmaYtkaDsbNK46ZP4AUL0JzxXoBC\nlRWjIRjPdK7ANS8UdZSB+IMqmUUr/dzpNqhaZ4bJO7f+Tg/hCAE7OKzT9JdGF+cMuITC+eCPkXnW\ngjCwCI5hvMqVJzaSDw5VWq4MU7IIaCJ7h8rM/OQLl5RYS6KPTmgi65i9FjgXQIHECyMO0nKZ4fxw\nsspEZqc56Ei6clm+ksmcHh+h/Eypg4uu5DYb7QvOtb4Q1eaJez8xLxiNfTRGdELnZsptkEWIlHg0\nY1jjRQSSUz2jYohC84HmxpaDsia+urCoIlpACjngEn8ECqO/IgR3WXnHEF1BCvd9Z3eQlFBV1Aoq\nzqKGJ52lj1o4w1hY8eF8az9TZdKlNK/ox2YorHEfO2fArTuuF56icTcBM05zhs+MBASalOqFdzt5\nuPCksI+BqPBjMUbAozfGcPZe59cM02oEZBJZHc2QTiEhXGVj6LQ/7TwIGVQRTibVjHCusbAE/Own\nDSeArkpYcCaDdHC0BDnjYuxqkAL3QdaDPQaVTNFpJTJJiEABNAlhB0lXiiYWgZ4GZzLW1ImtMDR4\nb50vTLLZux/c68E6+c647axpY28gPvMq9+7czAgH74nKQXHH7QvdE4GjobRYGEm5eSPCSQlqOvmi\njZqUkirvZowkNDHevU07nARZNxKFoz3YVVFr/FCNTe+87zd6VEZs3LtzyIVrlI8CTAXyZK8X5SLB\n5oLrRD9c88ZSlW3LpGiUYeSHIOuJDeUbszupItzjQQ1hDcUYPGLijp9QzjhYJWOR+UMLmnXkmD05\nz8lRGRiGy0aumfRRppo1M1KmHon1uFHb67T3utHswYiKekAVUgyWUsgp8bAgvFBCiZRxH6wouH0c\ngFxQca6Xn7C2cnRDm+P95PDGOi7UuOIS8z13djzdudagWLD3O6cY1EIt6QNFPd+3TYIHgse0NFYt\npH9j2Bgxh0MfjdQbixaKzMGnJFhScJogZsT/NOAIcK0fuH1rvI07ZzSqLnRrDDe6Gi/58jnkfOpT\n/0n1OeD8ymXjoO1/5Dx+x304t9EZfSB6IeuN02eOwWlkINMYHU5ZGJo4InGjc0chhDsTcyooNxOe\nMbYYZJkLzc0TNZQ1nE4BTTSfOOY+3nGZaNGUhBFB+EkZTsrGNV1YywLRKHSGdlJkxCsuzqsnNBZO\n+cqu4JGBlRKDkzaDq5JpMclPMRK9H1Au/LHNAadp5d0cJOF+UjTzAJCFmq+UgJ/kwSXgrBs9MpKU\nwkFxxcbEzPIBV0UG4R1ibnFC4PSOp4qEAIIyLVONYEkXjtZo+SDsFWHlbSiHzpuompROIqGUlDGf\n/T8qxhlKKYW/qCu/KYG5cXanhjHKQjsGVSqaBosYEhVLUFyInMl2xxi8xM4ZKyNXcrnwZb1QRGax\np8DwRsIoq+FrJqUFeVrpGDUJtju7HTQGbfSZuSHxZELEyXBHBYYdDN/p9uBNEqYzuyHSMPd5e8YD\n2Z4QXyltoX5gkcJPJF5BDNZnEsHdhZs/SOsPiCiYMb35jrkRkhgkSkoUhFIySy5casJciNHYzQgf\n7ARFMy/lRFMlYvYkxQgUZw1Hl5/o+x/Yj/s8KPhgXBQtLPXCCKN/WJuqgoWzqvAsmaUZFz8ZR8MC\nDpzFM3sKPOssOE1K8c7RHbGGx4KKsKXEsyfGsFmaWjJvakhOXFGqO0vaeNNJNDs/8gkighAcenJU\nRUvijAw6SARCR0hoBMJBFyHpQi0zXJ6jk9OKaPAscxCL1JFsLDU4dQdxxCdU49TKe3Qe6mx6IUI5\n4yQF3PoDzhe+ZGFkMBduLSFn5qXBZol7NMRh8UGyjPFEzwHeMBJJKi06FsqzZK66YNpxU97TCZqJ\n0SYR7ZcbiTRhCtLvFD9xlNYPxJSSBzleifrGmV7ossD+lSKVoQdDxixKLcq1FzarKMElO+tQ6u5c\nSHQdjNVY1DhCuA5l5I3HR0lviHEVuIXDxzvhYIBkklZ+SBe0XYmRyUAKJY+MeuJex7ytRNHkvOSZ\nD7Pzne+379wsWEZmiytvI7CuuDeqbKwrdA8sLZRUyNYxEUpqiCUOjAihDXjY7EN72b4iKVHWSvdX\nvH9D3PASvFpgXBnWqEfH9cG1GLk13pIx6GgErQXX7YWlv/OiP/Dddnrc0PIEzFupwxvXtFJ12kOL\nKoc17rZ/LFSdEZ3TOhuZS8yty5fqvPZCc/9TCicLPK1OLfMA7+f+xsM+GkFdOa1x0LjmjeqZS1r/\nt6ytn/rUp/5j9Tng/JrVTsbbP9Fv/50/PHZ+NwRDGaEMNlpAYJw4KhdMTjJCJtHdedfMG/BOnRsI\nCbpk2kfXi6GsHKjP0O0jD6RdQI1iSicodpKAnjJHaqgXuhqJwNVYHRgdceepAg7VD1zfkdiJcfLe\nrySvVEncJRFpJVNwS2AynQuquAoDRWWl6qCiCIbZgRHctRDjjeEFZC56axJ0vUA7WWPhLzn4Kb9Q\n0sIYDw6CMxfcBpIuE1cNPLRwkMhyRWnUNHsrhBPHMB+EZCDoLJgWtCY8bXT/xjkqwQtdF5acMRJE\nMNwZubIzCxYhsYlTBBYRntaNp5x5LpVswc+tMiKxxYXD3xkfmN1KUFLnL/OF14B3Pyl14Yskemyc\nuvGuK+vyzE9LJqfMb+O3vD8CPE2KVKk85ULNma/PhddHJvqNvR9063S9k91BlFNnI99LDDQgR5Bs\nJ9qNEU6OgYuhqVKSsZPpAT+WzHOurNno40R7nRYr/xmvAyuDlJ6xEAbCiJM0HmS9IOc5T/nLIDcl\nLHEVIWuZZZLhyGJ0URYMG7Ms9BcbzAm82Xy+qVx46x1DEMmskjnanVVeGEvhvZ+EHXSH5Eq12cOh\nJrzkhUstiBjLcVLJHyHxWTxYz4EwN10iCf3okfoxHRzAYsJFV17Myf1gXyCL8wVDxPleL3SMjpJF\n2UShZi6j8BbOitABVwh1XCppNSRBtY/vJcbKtGI6J6ROfBRc5rWwhZKTssjg4huLJEIazokvD1JZ\nGCFkhBRCkKlaOH1QZCNJx+SkeedsnTe/cpXvPLFyDmXvSrJCdOfZElB4CgUL1Av4Ot85A44kzIvL\nTJHMxkLps3D2d23wlmGIYuakKDiBoIxdkN1xccImXjpK5/BBptLDsDDWelJtEMdfI2G4nJg0lM7a\nO2UET+dKHSC5sC3wskA+jS9A/2Hi8SWCK5Aic3SnL0ZJHxY33aD3j7DIwC1IbIh/obcLR1q5bM+s\n3iegwmYu5qtnohpnHFyT8pSE0xtj7LgtnH4STdnQ2bmVNo4I+lD0XMmr8RiDMOPJlSEZHX9AI8hp\n5RUIzVyzsOSMxMnDVyrwpVx51MDGSYhAXmEM3pNTmc+ZEA7vhJ24CD1dqKzsbePRlbM3sAfFG7oA\ntUKZ+cu7HWRJH3/g7aODCvho8Zxr1jHuVN0oksjAb1KmXy60xT8OgeAXd9vdjn8Zbv6VHnZQ5XPA\n+dSn/rPqc8D5lUqOHW8H4/zOz+3BH0x4nDuNjJNoFCwrzW36uHNiG4MjpbmYhRB0DjYgzRub3LBf\niiwJAqNbZ0mdaa44keQ0fyJ8myeTOAjcU5tN16mTwxkCnudmokZCO4DTzCCd2ObkASbGTW482ZWU\nCps5Ekakmc/YUawyrSYIw2FlUEgsuU7righbvXBrO8MGTynzvb9jMbdGKVWWuvAjhb9gUMQRKYg8\n2HJhlY6XSq0b+xg8vDMCNCumFwaOHu8fdzQnKTsyGibKQxTXxJITTTceumHSZnmmPLPJSXhgPlvt\nhsyMyEU33scrELh3HiIUSXQf1JAZlq8LL/3gtTt7N9YcWCiZDTSheuGSHph0ekDKC6ErHokkz1yB\n57qSk/Jt7CQOrsW4WQEvdB4wTp7yCy06pwu34zu388bpOybTFlcEkmSOvtN056sDYZwhnKE4Sscn\npMEeWF4wlKpCkoWmK3c+TmRrRziQfrJpQC7ztkZX8B0xw/srUhWxTjk7u+8UeSaJIi4s1jlk5jmW\nJ1hywfs7wx4sOu0qIybCe4w75juWnrlLITG/npwKxZ1mJyWtuAX1AzldSYhNm9/C4MmFmhaICT8g\nAmy2yj+poAXkPFCCKwvNp+1se3rh8T64qFCDGVCvmUoQyfmeZg7sRYwulYc0Fpl2wfd8olVYCEYE\nwtxIh1SkZo66wwhcT4Zn8I0ld2oaJG2QLzQSz3mbmGpVoikF4cpACNDBXR7U4kBjyBwMTTrPyTnj\nRtDZJOYz1J1JmW6svnLqgfPKGFdGwKaCutM8sbiy9IqazYMIU968o2NwiFPzIKmwAUtfiBI8RkHs\nNzxb5yzv3M4dsUTEQAbkkbinwft+YJF5yc6XWDlt4bfSJ9ZdoUQmBixysqdXHj4IH4QJxZQVIa0d\n2sEX3fjxI09SpKCSeLadS4W9Z6wudE4sOpsUbjLoSXCtiFwQD15iY8TKQlBiJcULjyFcfWLXNQro\nOt9V7hxN8WRoEb71HWHn8KDH4IlChPBujaSCe9Aj8SiNqsHiSkpMnHMSlvwzyqCkwqCTzaFs1PJE\nTxe6nYTO7+sqk544An7bGs07D8vsUlkX4RLtozhZeKFMS2wkIl9ox4KmwvF+4ykbIVC8sbSTXgr+\n9AIReBjPdQ5qf6aUiNaQPvujzuh/sqphAwnHXOgG58fcuNTg8Pb/ugZGBOe/8++f+tSn/v+tzwHn\n1ygzGIPe3ujjwT/tr9zGncAYnLRQbiTwTCrzbNc0gVY0d07PLEOYjTFGF9iz0dNAcBKGuQCdJM4z\nD9ycIcqeT94EXBs/WqbpSssL4sbQ4GkZiDq7JGo0zjp7/5Yz+FYSUk5MOhJOl4WwHfFGWxKrPVGy\nUJi3KEIQq1BkZxPh50gkYOMgGDRVNsn85fWJlDei3QlVsh1ICKcrLolkxlY2nqqyiKDeCYKkz7jM\n3p2lvkC6ctqNmhRbrqhuNIeHHNzzFenzhmvVO09655te6Vq4iJPStLqpGWeaxDlzI6fMKU7V4DGC\nQcL1Skorz9LxOCmSsJg9LM86ez1eT+Mt3uj9YNjCIsKaN95bRqSCKDllvsUsJF1SsEtgdiJS6HRq\neUEQ3j5C83jiHk6RwZcFqsxul9NOCn+DSmA5I2mAdGqAaZk/Je4YYxacfuQahjurLHNjHEEgvImx\nRMalErLxmr6QdeEigcngbh2zk5dx/2AuvyCRyKkgMe2MTT6KMM1Zjwct4NSdUmYmZimDYJB+fOa6\nrFgofZyUmIPNw39JrsDb8Z1LDC7LStdCJzgEVtuxx2CEsMtJ6ztfES5p5fTAW+fSd5Jm0oCLTZT3\nNz8ZSXAzTJxnhGqGpeBpOJeiPLxAkQnH0MTZG2aNv5LEF0ksEfyTdIYmsmbu5mh2VikkybSyIyUj\nGb60Sh2BhHCzRK+DVp0LL9x1n108Fkt0340AACAASURBVOQMizoXjGt54ZJe+G7H/B3yRqTBfnFY\nFi6u1KZICVZXAuV9hVsfVMn8VBZUB6V9wxkcfqICF6kcvs+bpg/C2j4GV6YNzbuRIyMdSgcTR2JB\nZUf15EmDLM6jvFPFqZL5qhcs6bSuAZ1BcufreSG8cIsDRbnavGHwgN0MHP7aK8MS6dzYSuEHUbLP\nm853U3LqPEXnMCdrkHqZnUm2Im2w1EZZjZwXnIxqoupCaULSB80FL0Erzot9Bd25o/h4JRYh64I4\ndL9gIqx5RTQR5vNwqE1KmiHkj5hId6eZM8i8D+Xd3xnD8CFYNuSR6K4stA+E+6T49QVsNTwlSA1N\nJ8V3fkpByRVIfHe4zDtKoiw81yu96bQl2oH6nRw3XuXgJsJDCkUGP2WQGLzHSdg5pwtd0Ki4brjr\nhCGczjGUTKLUoHnmazE2G4ze0MuFS1aSzGzOL2o+OL1h7IifH1TDdQYJAU+Z1zNxOvNm6Zf/Z0LX\n9O/2hyqf+ZtPfeo/qz4HnF+jbJaqtfbK/f7P3NqOjYNgnpbeZCXCps1DF55pJDquk2pVAClzQyjc\naVqQD6QqgLrhIdRkfMWoBE0FZOGMQpYg1oOfyRSElGao+jka16xc5GBBeVfnlEK4wbagaVDMOGVh\npQNCk0otCZfCzRtP8iMaRtAgdbYwvuiFwxsvAHXFUuLhQVVly5MclHGyLjzOnW+uGB9OJU1IWvG0\ncKzPPL/8gLWfCR8c7cb9fKdZx4dh/Z3wPgvqbefWDnYLHuOcG2jgUjI5ViqB0XhKC5TEHcPbK5Iu\n5DhoYyDRP068KyGKp4Wqy0QpR2cLwfUriwQiBxGwacVMOe2dIwIxRwh8FCwSqoVhTpApqZPThYgH\nl2XlpQwOWzFrbAKtZnJe8XhML32+IGOnR9BNMIXTB0kSG8YZO1/8zgGEXDjFqKHzxD5lshslCxN1\nMIhY6Tm4+Ip345ELORyTJyJd0PqEUvg2OjWcUoLTTsY4uXhj15WLFDjvFAtKZEgbHo70E8zImvnB\nKz0a+J2jGFEXssBzeiY8+DY6zYwljFdrVIUkwmlGd2eIcXgn6UTYHn3wz/tJtoNanpCA1YzVB4s4\nfxOC9F9u6By8s+WVG43hHTc4oyNJkWkaJAXUGkTpiCt3gm47ajtxdnJMOAG6foT+ha/BbLSXDDmj\nZcWAWzSeqyK20Mq8NRouXMRp7nwZ04ZaRLjFgalTirKmyjOZp5TJohAFaw31DrXSw3ln530Z5PXK\nNTLZlFcRTjIXyXwtG5IGzRViYcE5OVFAJKY9MhzBKKIcHnhr8+R9GD/pwpcQrqmAKzvOAC4yOHE8\nwbNWWB482wllw8zptiOhqDlOJwy6/JLAEjQmljhEZujjEbw/ZbIreV95uTssQaqKno2tV1Jy9nBq\neiesUItRSGh0LuYs4aRImDjLgCVnwuEeCfWFqg06HLpwSuJRFtZYGEfhiEbSg+QN5woI++mIDsQa\nJa0cNkgjca2DTTfuw3hOCU/BUOGP58EdZoFpzuz9jT+kg+e0gq0gB0OMMzXqIlxSIWnnrm+I7xQN\nvoeytAerFDwSLpBOWNevZIPsL6hn7uc3PHXkA3m/5ZUwg2jkMBYRTA7OVIh4sHthS4VMnt3LXhh9\nEFHoI+PVUILhSk1OPXb8eiV9ZOySKBFw74P33iBAvU5aWgweDJ4Vcl7Yo3KeQqj9GUnNAvqoExYj\n499cCq+f9rRPfeo/rT4HnF+jYgZI98c/8b7/Fh9KMBPSQlAZhCxI+UIK4YsEojPj0PpOUeWJwP3O\nKYH5lQezSwMSq4JycAVWDGdCCc7JUGOLE/eKaEGkc5VOEWUVoSbhq1QkKyMJQSH3QPNKJvHwN5IL\nKGQKkRYsVRKDq2aiKg6soQwLijglLbQOW31iqZVWK0l/ZsFowDcXvthAyzO/P2ffRPO5yc2yUWQ+\ntIcF75744fo3nP2Bl7+krg/6639FGRwOQxaW8U6m0/tBt2mLc7shSVlVUKnkZeMpCl2feUQQ40FS\nZXBg44aPAwVUM2qdkTYkFVyFzZ0eJ/iO24mVyrPMjZefd24WNIQ9ytyoDuNKI6xwVZtBfk46F5I6\nZVmmxS6dLGmAzPxV8YGI4D1IISS5sOXMOd7pcSIWNBcsLbODx53kQaCYNUIcV+GNxNU7q3SqXhh2\nAAPTzulO0c5eC52FnQxyZdMLPsDihoryexzOO9UfbOWJmv8S1+DOB3J3HFwjc0sLP/jK8EYPR8vG\nao0fu/w/7L1LcyVJkqX3qaqZuft9AJGZVdXsGQr//y/ibhYjMmxhPTIjAsC97m4PVS4Mld3zIPes\nChWBSAgWQMRFXLipnXO+g2I8+pO9TIJZP945l5nH2MyofUzc84CrBc2dojYBESJ4zD6Vs/VZ8koQ\nfQIPrn1aObV9Z42J2RjrlXJWNASxk5aN15b5q87X7h4dB6rCncQ3c8borLJiVvmC8H2c9Bxca9BC\nedadkhIlKytBskIJnT0gogSO5MJL3hhpkIYQMqldNQZ6wJ/KK4dDtcGFL/QxD//JCteSCSo+dl6o\nHNZICUwqH954DydcWEslAv48OvvIhM5l8tQPNk1cRrCK8G4rtW8gO2sI3QdJgtZ3qiX6AK/GiER3\n4UHnZ/G5zJHZvH0S1J4kuUAI2YMDo6KUfhDDWWKqBK+WeJdZvHqEUrOxaWW1jDXny1F4kczhzi6D\nNJSLrIS+U8+VpglLRj8qtSb69iTkycIg6x0bxiqwJqd1QZuyeaakBTKME5qe0CvFnOOA1DpDnZKd\nQw3SlYUXug9yrfRwejr5GM6wYInO1TsyEudoswtKOogyVChp4fi8/JgChLKWxtddOA00Dcgzy/Uc\nY4IVEpQEH0yccwnon7CVGh0XJclGyKQ67o+DFBXJRs4LR91YxjlBLQjDK6NXGJ0PHli+kxVyxOwf\nGsIlK21snHrl787MIhsuxt4rr7n9ez1nOCKz0Bmm3e9ojY96EMxSmzGERqYk4V6uPFV5scz+/FRh\n/hfFoJlMxMbQnRb/vuQIwjXNbrUf82N+zD/m/Fhw/hnHjFrfOY+/cNav2FBOn4H3YcZQo3Hj1A1N\nKw9bsP6G1jdyvHEdA0W4iPKLKRYnHzFvcYOFjcpgtmFvBA+2qYj4vFU/0obL7DexmLSzPybjRSvX\nNFDgr104I3EV0FRQzfg4yR4kjEvv9E1RhSUnhq6kMegCQwqCchOhpBtFJ6FrLU6VzmUp9LpMrHAU\nvnXn/x7KTcZE7ooSzBvAwnQ4iArijff6zkVX9uEEM0Bv+Tppb5+EtI/0hd4+OIfTRpAJNktkZjkg\nqaD5p/nQJmNjJyuIKGOcRDsxTlYGFoU1jAhDhoC9kBjcLQOdZ3T8fOLs3FLiL9V482AwA9CrbZhN\n5OwQZ9NgkYk/xgTU0ZwZ3qn+QUkFCWYXy3A2czbLnOOguVKks+SNEfNg0OqTv7dVKFC1sHDwIUp3\nQVRxgXdZuIpyMSNE+T6Y3S40RGaAenfjRFF3GB2LBg4GJD15xoM6OuYH9/IzKSYK/IhOCiX1weu6\nMSLomlALkh8UDXRVmlSSNH4qmbez8+38yrqs3Evmo3UOSRCzfKb6/PcUzYguqCW6D4xEbZ2Bk61g\nHQgjcodjLjlPOv8SKzEaIpOe9y5wUeWajIuutAuM1ugoryFoDJIk/uqNasFVdaI7xAlJaEn0cTJi\n0HG+IzxScIlKSCYvr1SFIMjtpNBQlIvA0EJJF9r55JHSp4HTOWuhN+McRu4rmxqWjHt6cPiT/kkC\nFB+IDgaNTRKKI9J4itOsz++ZEhaBiHKMxuonospNMqcIqyQsnGcchMyC370dWJ+qVh4boQnNyrll\nbmfBcyZFY6jhkSlLJetBSgehxvCVZe8sOjMtRYUYQfRCVUAeDAly2rjUzNaEtS4ssiB01o+pfD3M\nOfPP3ElEGtA6eblzPjOdT7JJOrDasA4hwZFOUltZQtl8RTp0g/Oy0/uD2hsfH51REyGFs1SOJrRI\nuCh/2QahmfA00ex14ZqfbHQkddwrF7tMWpoffN8TUl4Zi6FRoB+sOKIHqwbnmNCPoEEPalRWZq6u\nywDfp9V4ES5xAxe67xjBQDldESm4z490GrLOvq/aDzQOHGH0wkgVHTvDg6DhkkFhSGACryTewqhR\nGBgeimkiSbAIjAiyCIShNABElattvyOb+9C5nAgQUKOxx8HwRhkJkw9e84W7bowQUAU1xucipX//\nlBhZNyRNimFOmdSVoon7D0T0j/kx/9DzY8H5Z5yU2OuvnOev+PhAR2ZE+XzQwXcR9iLsI/hDViqF\n2pU8OtforOJ0XRjjnfvzbwy9ky2zu5E0Y+TZFSHCw5W5DiUOGrteiRBWCRqGk3BXKjDCCJSdoDPp\nVrusqCipn6wowcrwkzOcZexIP1nzIJU/cY4rIeUTdmt8sRXxk/dxsklleMFNuKWMlI2/9UmNkj59\n3R8Oh6xYPFij07VQx85CApbPdmzhrfXpixdo52/0CJzZ1H14w4HRKk0WdhVA2Wjc4h1dNkw38nKl\ndkcGuAeDzwNifzL5c8oXnkg4xTYOS2yyEjKbzL+70TwRvaHjwKRTolO5EgQiCfGDHBslrRwBgzLt\nLeWCS5lZoV75qAcSlSIfDLlAKI8hZL1CBDdbOXXHONkdFoXLp7U9I7zQ6fHEe6URJIISFccwEgZY\nDFIupFy46R/56H/FYzCbNxK7LJyuExUtSnhjAM92IP3JRRqPNAP1+xC+79+45p+5awID0yuYo96x\nUdm0oMkYrTPinNjpZeHwYAx4DzgVor6Tt5+554XqlWYX4lNlWMuF1nbEEo4SHlzDOcNpKBdb2cyw\nIew+ZvBeDPcxg8/HQAO+2MSmnzhSMsSBO7xjeDII6GKUEF5YqDF7n2qCqac0fjNlVQMxvsm0Br17\npwWMsnL1imkmnZ2CoTYPblcxSls4zkAfRu1QxXiQaSOhPjA3ighLv2Cnsp+d3RfeKiQZs6MogoXO\nqg6W6R64QMkbSXRm1/rAvNFG5aM++AkhqfCfOHENZnUsfPRBKOSY2RxT5RThTuGL3ThK4pTgVTpe\ngl0SIY3OjtmBbo2bwhLORW+IBmoTL/wcUzG7j4xdphr94pnVMz+3jdwU0YRZwGGUKvz1pZJVGSVj\nshBLmt1WF8N8YeidOozinRKB3zpqAVWoIQwObFmRxenjN76lip1KPw48lA8OfqtK9zxVpVjoPqib\nU0TYhzPCuMiNnN5Yc8bMyNY/y5YvvMtCWRZ6L0RMY2Nmm3bh2HGc10U5RXnn4FoXihSKdIZ/ghrW\naRUNjNErKXaCweDK8OX3wtL3vgEb0oPCtykSnb9S/aCdH4z0RlInS5oWYlGwglFZxKH8zGtZeY5E\nVp2KtV1Y9YEyO6hezdAwLjYX+3z9gn0iogG6TzXnZhfe+4OnHwxTkhsBvPWD3U9e9UbWRJXC2eQ/\n9oCiAiUF96WQbHanlbywjNlX9r/q3fkxP+bH/OPMjwXnn2wi5m3+0995hrO7EhEU3/nKle+ycU54\nMpc46F3Z/ST1EyHNkH3KXCMIf9DGwc9+QGQEZ9ULC8YzCm9xw9Md6YPGStWMMy1kB5BxHOWIIFrl\nXBZGPbhnIeUXsl6IgD5muDaRaNJwTrIZTkc0o+MgtW8UKxCX6de2xEVWan/j52h8j6D7To4MVdhH\nodgVUUXJiCR6/0Bk58lCzhfS39OpyXi2E7GFV6DXyvCD1h+0821apNKFx+i0cDwKQ5QRMzDb/95y\nT2brjUhCP95pdsFjWkhSdB6jo71xZSobSTNbSiyy0MrGox58xRAxBlNdOukMUTIwZGGEkqXPUPTo\n1NGofSORIeAYHUFYzXgx5RjC2Z5UIIvjnPOWOQSJc5KdGNzsSjWwcdBhFp1KUN35zkl7G5xn8Dac\n4bOw8IWpnJkViEZWQVUJWyBfcYwyHEdmh2JSimSSJZ6jof1E/EmOSm7BazMuEazSOPuOXj7o6co9\nCmd7kpZCtgwtGH7MpcQE7w2S8hyDZ0AJISwTqjTvvNfKy7JwX27Uz9JC8cHVnV2vc3GrB5tntCzc\nkuO9ch8gBuTCGhlFOI+dtZ6zXwTlnhdEFtZWcfssZU2FZxI8lFgWOE8C5RnOeZ7QnUetLGmlJ+Ho\nB90Su8DZD/7kwUveeKaFc5kB+ad3vpzOT/kLpsY+2rwYGIV4Jt7PnbNmlrGg3Wgd9qUTCKt2kgVO\nYW8Le1dcFjYbJNIkcdFpvpDZJ8hDEosmTAqHKIhS/MCiz54aDbZwLr6z8cB95d+yUWQehJ+upICb\nJoo4OU6yLCjKap2UhcWFw1aiHmgMqgc9TVXLRNg0c1mU1jtK8DGc5tAIHtkxX0hJWZ5X0sdCDZ1l\nvFawKkgZ9DRYnkaNg+d4cGphKSsRSkqOpwnEkHzB451hndVurGVj3RZyJLrtLCK4zvd/SALvgDMQ\n/ubBewSNCXvwKXfgvSPZWGywj5jKo19YpJPDWexBZOMdI9PxdhC24A7DDWcn+kDTZ++WVlx2biW4\nlMpGxZnW4tMUiUqQEWBdMhrGs1Vqc5I1Ul5YzMC3uSiMRuYkafDe3hjtOe1ifTBy4+p/t9GuvG7/\nyiGB98SSYDEnTidFJtKGR0LWK+v5dSLR++AmirZGXm7Ydv0fnlNgYpgI1SuKoWZgiTiOidgm+Ka/\n8Xr9P/j6TP/dcgPgMT/yp5JzMWNLG7v9f1AHfsyP+TH/MPNjwfknmmMMjuGcbeev+3e+252Rnyzn\nNw5d0YDFnaUL4k7W4OxPTnlOkhEVvHPWyqqVZRyMeBJAGgshgo6BkzjtFx664mOQcAYTVRtMO9JM\n5jQSnUzjxuCQjUxi9Yb4E1vuSLlRa2W3g+4nRZfP0OjEl17U0fZBZ2DlBdXO7itCp3pj6IJ7Z4ST\n4gSEo+4cfZDsynamieYNSH4hovGhTxxIaqgYIjOhJAg+TjQq7/sH34fTGqz9K6t+cH7CGU4qd4IQ\nR23FW52HDEmsZgQB6UKWwqrOR2soO8t4R9i50MgIr974YldKGvQE/2dVSAWX2W/ZgRyCEpwk7uoU\nhOFKjMHggvqgMzUhE0ix8poXbsk4PQhRwoWvUekuLDGmrcqV7175g0BPF4JMkUxZNwqDP8bOs5+c\nMVj3jasaXjvXapzjhSU7t3IlxcKO8KYzpN87HLGwpBdeEVwb76OAGLkPHq5UBjZO8EpE8FozKYIh\nA2WqdzyVnQdjGfyUDNGVPBL5UrhGoOdBEEQ2BokelcfouAg9OeqGxKxZ9Ric7mxpI0ToUinnk9VW\nQqG6snzWYNKd2/qCnDvujVobNc3ceqZgFiz3F77tlXsdWIcbjTVNy59vRsmJZ5yE6URRWyHVztEO\n2vOd79H5kMFQ4cMOPsaD61BcM02efGN6cO7lJxaBFeOnsvJTg39dXkAXfmszc6APnaCKPaHnZ9C/\nBaUZL8fK42XCOjKg0ekkCONiGyP2GbIXw92pPt+7v3yi03eUrGlaf8y4yMZ5nIQqAiTNrO2DrXbe\n9eSLrhy6QlpZ+rT8JQolZWIUls/FLKnxNCVnwWzlEhtH/c6eD0QnJS27semKL0HTRkQiQhgaHAwE\nuKdM0wu5FxznIScUWEtBdFphg8E6oAeMoRza8OYUfSFUSOmkyJgdRb5OJPaSWFIiG6gsVFWWT/Vx\n7J1WOzIeaAi/GXwbmUFihEHM91ufBle6wM2MLSegcZPgrp27HbyUzPe+UMxZVXm0QaJTNZG80rwy\nFHQMCKXHAx1vrLZxLcYixtcxi2Tv6wsWs29pjUqXlTP9BDi9B+5GGTdse4HW6fUAmWq88YGOxvuY\nr6zYgzpOnEwJWGRh9AOVxN0uU9XMG3cx3sfKEPC2fy6okOLJ1irVLhxL4ViV7fmN7fIKaao4prP8\n1VCqz+xM4HBUdAxUnTUVHuPkvj+5aGInTXrnJOqTLLguQXcoP8SaH/Nj/unmx4LzTzLnGDzH7Kgh\nBidGDXhyheScXaFnrj1RCdJoKO9EXfCAw3bWFIgaySvlfJuBZkmoJkQzPo55K6krkVY0bRhKGR0Z\nC52CM28wZVbvUTjZcFwSPupce0awZsdkYnurVwJnNWdJShqN62j0UVF2RDO2vKCa2R2OtJBV6f2N\noRtNCiMaWZyVhOUL+mxcu3JZrux1h/YBo7KMzkvKtCKEKIMEGlzVuanzPD8ofnD2naNWvB8T3RwQ\n1vGYlpH+WSgpttK90zxzSuFU4Q9LYbv8iY/zg2dUtFVKPOkMqgQtBiuJagvVXriWnzjPIFip3fjm\nO/P+Mkgol0/AwSWMRWc/xRELKo7JpBQlN4omcs70gL+2OoO9Vj/Rz5kWwT7mcjGYt+J/fXbY4HX9\nAzoeqD9J0Tlip5475WnQf2NdFsQXrGXusc2S1qVgyw1tQho7nScWBePCFzJLUk6rPDpIGIs0eg9O\n7/gQMo3XfnLrgppDKDvtU/krSFsgBx7KwSSSxXB03biUDK3ho+EenJFg2YBOSCBjJ+uGSwJRmn/m\nXuo7/fkr0XeeCmu684UXrKy8jYETLGXlJS/818dvvNWdZ2+YJrIaF820Z+O9wTeMV5xXP/mXkbj3\nxIdD9uCWLzw1qC2I6pznwR6Dp8D+2TPzXg/eR6V+Qi5sPFBxXOGhndzfEVYsOiOuvEfmF38iZK62\nEL3QvbLXwS994Zt16jnw4URkkifKIRyXCp8Ai/CYuQRVIlaInSQZtzRBCyjZjNfLC2ebv1M2U+4p\nc9R3LGyqQgJ3nCTKV1s5geZCzplf8sKB8M0PxAUTIRSImF0vCGsSomS0HyzqRN64e0ckkQwkhL43\nLGeuOfF9PBCgSWYl2EdmWTLLEUBHPruAlErvwlEbJQo1wRBICK7BdRg2FC3wPTVW2XixQo6dbhXT\nYLPOUoRLuqISxIBhwZsMjoBFKyNBy/DVG6PbXMDcYHxaEhOcOCbz6iRbw32gfBD9z2SMLn9ENLHk\nlVWD3YNHq+xywGgIhcv4jsrJGUIVZRFlZRC+8846lbZkEJ2RCq0G7zHYYuEl7tS3jvbA07QM/1Yr\n38c7Pi5oErIoWzi9VwzBaUgZLCSURGPlstz4smTa0ThJ1PSFTeE06OHU8c7pU17JeiKRiFumJ+WB\nssTgfewse0ZvL7PDy4LdhUVnHfPTT3rd0f2gKLyURHYBb3jsXEomr1e6Kx5gGmTjk3z478/BiOAc\n80IDIImw2FSKfsyP+TH/WPNjwfknmX3473+OcFxf+B4LjUZEAz9Y+sKJkci4LsC0Pql3ugekxgjB\nw3gnI2IU6RTLWE70E0IMSRMbHVJIaQUOZCgi0/o09QQoOPmzhyB6pQhTnUjGki40qXQpLPMOnqu/\ns7XGyolJZUhm9Em6ktYhhGd3XH9DTHmvXxkk1PI82qhyzcqXLJ/I5IT3HfEHmaCY0Dx47XAsnaYF\nVeMlz+XtaAcXabjMChb1AwdCyu/V2cWEPuDQlVte8KiYGZGnratY4uf1lUiZU1ZkP1AtVF1wNTTB\nR5uHFOPGh/xCPROHZ7RcObzz3WdMXHtjU+E9grDMh8NdTgQha5plobqBCqHBM5QjMmMMUt6ngWU0\nvkuBz0yKeycYbJ+amyO0sRP1L/zsicf5DfoHpyrnw8ldWWUWqVrvZFE+AtwLrTmHCtI6hmLJSLaw\npoSPQT062EBYWSQRMlhloAwKlYaynUoZlUs4H5b5iMxPJhxp5WoLxTa0JM5klOjoaJyW2FKe2Zmz\no/YFHQ2xgowGOIiwmuKmdE0giXb8isTgFwYqToyB+Fe0v3HZ/ne2nPg2Go+2807ntEFaCxeUkgvn\nCP72cfBvzUFnz9DqnT+mjY8E/znmDbercg0FWfi38WD0k7905+mV0xudjnjlrSXqudNyIolQSSRt\naBIKwbdxspCIlNG2c03K6c7TH9xtQzyI0Uht5i7Ulc4JPojeaVHoXbCYVj7DcBxLRnzCPe4i2OgM\n76y6ctleKWvjOYIXSRjOhYXFg7/UNotfTfiDCnt/cobQbSHFoJkQMml1K8FdgmoJ9ZiXKtq5WefF\nVtYkuBxEe7JGgCx8WMHYkQBvsEhmGSu2Ni6ycI4dRlDVeEriuwcvY/BulUsYq8z8xq8DdP7mAxL7\nTWgCLy70UIYES1SWE0YPjmRUNtYyu2pedLDgaEyoxJILviys4432BY7WkOVEYyEfCSPoMTABl6DK\nINKgidMAlQrRyTEwfydi56sMUiuYTJT1x7Gw1juMIDFLi11OLmlwM+P0zrd+sttOl0yXDQVyKmwG\np5+0I1Hkxn5uxA56jqlgqiDnwbnv/LoWcgRuldwnevltzBLfF3FKfqCpIB4Mu6CR2NZX7vf/NHu3\nzgK28VUNdaHk4GwdVZ04fa8YyiZGk5NHPbjazDcOd345E2W9zp6k0fg4n7yNJ0SQzsqqyiad3k66\nBqtN5PvojRyDXP7nReXvn4kI3lrj8feLPiY98HDnnmZe6Mf8mB/zjzM/Fpx/ghkRvxcXRkzc8UMu\nDFnoPBmSML1TpuOeXaY/Hv3stbFE6QXvT2qeh6VHFDwMpLCQsLjR0kmXMoOvtmI4jcRpN8jB6s4y\nGk+UhLAAWVaMzk1OROAksemCirEZdB80TlYTri7k8Y6PB90rICT/INUX7GOg6co2nKbBkYKmFdeG\njROxgrHSxknrAV14yoOSEhcBTcYZcB2VHmAESxLui4IW9gHn8aQBCyDMQ85iSohxTULSFWzjLaCg\nlHxleMX0yaLCosoiRklfZnlgKPd84SHwMRyRHfdMlkaXxNArXQq/NSHyjb/ZyuDBKnD4oMrfw+4X\nigw0Bk2NqznHgCPdCZ25lCRgkjkJdlUumnDvnHqhtorptEsJnRDDUYYHFvCsD8bxnX9ZXrnIIHFw\nxkqvTgqnpo2nXUihoJksSpXC0VZUE9aFMlYkLqyiEJVHHMCKIqwa1N5oFB5U0giUk1eCn/IkgT1Y\nEQ/uCFsIPb3Q8i94SiTdeQZ4vR32WQAAIABJREFUFJYx0D4zCYsEyYx1+xdsVOr5QDRTSiZkNqVf\nlzspvxD9yer+eSNcf8fWArTx5Pv5Z1q68GjTIvdbDDxgCSGVFbTwvT34lcHIwRpK8oP33tjN+N7B\nrXJ7Cmu5IQKrdW5t8N+OB1/7N/ao1FEp/mCI8VWEkIWCs0sBcU4plNh5REAIixrjs4h1IHTJeMxy\nxESjRXBU5zgHaRTKOft9+lDODI2VaA7nIC9Kkk7JRpWFYsGmhmfHRmexxMsqLBaM0bmNipWfeXrw\nkM4qQfaDP4mxm4Ik0E4dcC8XRAuRjL/VnREgtpCkgDvWnSSVokpYp9nPeMgspvTGVQ5ecVwXlp6p\nEbN3pT+JdwPZWPKNq5z8N4K3DCngDFjViRXiLNRRMFO6TNBBvTppmVarjxioNCQK0ToyYDUYq0zS\nXwYuJ0fveB9sKoQFlhqaNsSN1Ra6X6lpUO8dOVakZebL4bT4TmWla2EL5YtCXkCPkz/24Jd1QSkc\n3ZH9nSiJUZW9XWjRGGN21eQ+UA0kT5KiSCNZJ4ljEvRwQg8WybzXnaiFiywUDfyZwIOnDzZJmHRc\nhR633ws0k3WGvmGaqH7ScnDGd4oeJLljljkoaMoMMbbrH6m9YNsrTa+M8yQVJeoHphNt5gwkZraw\nA3xim8cATdAZfPQHd19wgoMnIz15LcZbe2LyJPo0gyLCOZw/+cpK5Uma1t//MBHx7z1WbeY4l/+X\nJebRB19++Nh+zI/5h5ofC84/2RzuuCZEhVRe6O2d4UrxoH2ynlIkEooP5wzBVDGdtJzUG4cuoEKT\n+ZDb7c4tOj3KJ5Z2oejGRYWnCg8XXtRJttJMWT7LIcMzeZy8cJCTsirc08ZmU1EAw2LnwsGXvJOP\nSmtfGeP8VEY2Us/kUUnyhto0beEZWkFTo7szTJHRQZwthBHOH5eVD29UMgNYVMgkflozb63hSZDk\nJAFkcIRjtkGcoFDM2K0gCEWETGDJuCV49oLqtGcVBOmz8FB65yVt5FCeo8I4yShL2iibok3xs9GT\ns6uSbWVNK7UnhhT288HAUV0gTkyNwbwFf9WKRKNokNR4Xf70qcMIixREZ05loouFQ4RtXeijE/Gd\nMSoSfb7sPlCZ3RgtZt9FE8eHIJZ56MaIxBDh9Ib4Qq0LMZQZtd9oQ7ElYy2Taqao0NzmIeMUsiq7\nKU/JnN7ZCVSFiy1Ue9B7Z0H4RYTFhAR8MCgeGEaVO9kWNHVUje6D91CqXXmxBXSqAqNc2GxlSRtX\nFs5xknMGLqBpHkxxSjxQmIuz6WyeZR6S/kZjP//Ct1NoIyh55RlO6UbtTm0nezx4htJGo3uQaXg4\nnUHpjQ9R/kurfDk/iPLENVOK8Ws7+b+Or3zzg6DTebAjPMPorYF0kitFA5KhMWlswwdfJGOfB7ZN\nlHV54SgLclYOb5ytccjA68oYSj0EGYUcjRA4w2d3EZkiM9xvWbAifIxAUvnM1e1giSUJtwS7B4sH\nJop440u+I974QDiB39rB6TLLc2UhmRO6crv8RPLOOYSH7CS9svogqSI5cTNj0UGoUv2dLBs3A1Xl\nlpyE8n4qFk92P3hIZbGFNG6szXmvB3sapE9EcMN5ykkO4X3tPOVk3RduKlCcpzRcEuVYIRomQcmd\nw212zIhRRBETNm3gxt4auQxOUdbL4GqGy4XhBxqZtQbWlEcTiEa7Cd+lI6S5PHn6VBqD7Ik/DeH6\n7Lxo53odXHLm2TZyVIzEsTcaCU0D84M+bhMvH51FTsYI2nBu0mmus4NGE0pmiFB7J72v5H7lXq6I\nn5y74zGBCB9xssYHhPGIBTmNvCXCK9tWEdkhnTzqk33AVcBa5WV9xfSCqlLWL6R8IRdIsVDriXud\nKnkqaHKMIAl4Xz6xz4Py+1NpsqCTKCHKcxw4zgjHGdzLSvWd6m3S51S4snCRwotsJO9kTs7/sLxE\nBI8+EHOKBCPgvXVOayT3/0mtceYy9EPF+TE/5h9nfiw4/wRjIqgIHpN6FQjm5+xfyHd8DMgdmnH4\n7AbInCgFG508OlJAbGNo4WSQbdLQZvdN4o1M1zI7MexGE+VimZeUGTgVYwmZ3ShuuCX8aCQqiwZX\n4C47Cqzpj7itNAZFKpEgudF1ECkBjo+OMlhjY0RlYEQ9f8fFBkKMGy2fnyWN4G68krC8oOuF11pB\nM3UEF4VbUpwNovNVTkY78FEp60/ccqYzsxWgrOvPnKPT+sxL7CEUMmjmPy0LXTNl+xlh4GPF9+9c\n80pWoahwJbF35Yzg1/bOLobpNn9WGTJK18KHXPhQYa87w3ewBY2Yh/FodJwcM5t7TVdutpAi8+ee\n2X168g8PRj3JSVhF2Ay+B5zD2btjuszAsz+RGKgKIgWlkyKwcBTl7J0Ria8sZIEtDx6tUFueh12B\nNApnLIgYIsJ6TqUhECI68TxwdTaDNwt+1cY7SgthhHBRoZQNcYdItPyOnnmS3GKQdUXLnVXBUjBs\n/l1PKlte4DMTlnLB0ob3k8Odixn3spA8Qy60/o73g6yZzYzWPnj6icXALIEOaI2/1jf+beycYTxG\nI0viGZX9OFmikMvK+1l5DOfDC/sQRIX6aSMsPq1/tTc8MX8e7clfrSB95st+884pwkEQsRDMRTNF\ncDLVmItc2HT9VMAWNm1cLXG1lWtauCxXUlpADdk2fjsqkWGIIIuTPoJ2zgC2RkFkWtGuOhibki24\nlE7aFkSFxeDXw3l2x6Lxv23GPc131tFPlnYSNh8fx+h8Pd9wSWi6Ee2NNoIqcNNgSzc035G8EfXJ\nvdz4o1wp+ULzE4nGOeadhohTGLyPg9QmBW4bCl2ocrD2Qm8PWuyoGOqZHs47D54dvjF46EbpypkG\nB50/q3PpK4sMtOxIWXE3GifJlNYqMNAwcl04h1CXQGrw8EqqQco6FaqW2S4La1p4XTPJMvup8BTk\neyfpnUynyMoiD37qjX1x3tRp7mxHYhXIxbnpyR/KB5fhpBHYpy10qHIOUIJ+KlJOBisdg+QzTxQn\nA6E8HbfORwwOT6z8RCrGuAZyXliOjFUl6cpCwXuQfKdXQBpd5rJrVjGMEU/QQbGGpUxtg/conLJw\nXQKkMIDv46CkV37Z/oX75V9JaUXlYIRj8kaSQYrySWysiKyEbDzlwOuJ4rOvRqe6pQhZMqTEc5wU\nTRMM0Xd6DFYt5LTR+xMJ4YttrH83OPfGRTraH7SW8JQ4zbA0SPb7YxCf/315jok+l/8hd+P/vQD0\nY37Mj/n/+fxYcP5J5mLGe2szBC+QtLCJYi3T+gtjDJYwUux86DlD3NL5SU/wxndVTCYWQO3CJvDw\nxuEZzDhmSphNOpsl5JOuBIMXE5q9Uj0+aT9Af1D1YOiK0Dh1cIuTn1PGZGfb/jM1vuPufK+dM6aF\nbJARE1TgRga70XA8GiGFrMqThSoLyRZEn/SUqWOQLHGkO6deuWxKijsiCWMSvgTH2xNZgi9lpQ7H\n0oUkQSmJkww4MU4I48v2yvvxxjEc0YWSNtQWVhXy9gdIadr4LFMGhFciBospKRV+80at35ktKY6q\nE+nzll1sdl+YoVLpTFKX+Umms9JQBjc1Vh2sklgtwacdChk4g9pg+Dw4RXd+znA14e5O84PMYBPo\najRZ6dHZTDEJvHVMhHMmfnjDeGtQRblb0Mvg2BcijOZwiFH6RtYFT43VA4/GUQe/KWzqrHTcGqcn\nNIyW5kE4ycS59pgqDtkZ5853MZY8uPbEnRXLhpuScuNj2Ul2pSdlKTdSWsi6spWM5pX2SV5rfedi\nc+G6mJEsYDwxW8jhPHvwYKG2JzFO1mJcQhij81/bQY+gW6KOSssLuR8c8WRncB8QI/gIYYyJPy+u\nDIRw6ApPgls0doI/o5zhPENo2nmXxmlCDOcAgkTFAGXRIIt+Bq2vLNxhNBLwpyX4Zb2hlgDBdPt9\nuUdALRNS8P0DLYOPteHnyunz62cgteD71hGcezIiF0qa6tojlNd1zDLH4QwJPlpljEr1wdd2sjbl\nZpkjJXo7qbUTJLb0ExeFg5Nuiety43W7EenKQSLVnUuGos63qLQJPMaj0kdjBOjHypP3aXXVzNlP\nqJVyQotZjvpoyjd7o+l17qMI734wkvKLlt9paqcYR2q8OmgKftsO0giWvpIkUOWzywr2z66raI5H\nwqMxQlldWWqjt0q9rFwXQyKIEZQD6lPpI9GHghQEQ5Pyp+WAdpJWQSqoVVTgJ8tcVEiWWNoDYzDc\n+LV3CBixEQPqSMgYNIWcFooENoyPvtLOB8/ujKUisZPEgI3REvd3JXIin1ApECu9GxEXUoeq79hp\nsAWIg0DWadsbunDqRmvKPnZIkPONRQ/WMsHTiOA2i6BLuiDxWUFwzhzblphFnRGYJJ7nAXIh+pXa\nM/BOcuOylv+HvXfJkSRLzjU/ETnnqKqZuUdEZharihz0AriVAmrCGSdcA9fAhRDcDzloEOgpcXHZ\nrEdmxsPdTFXPQ6QHx6uKJLobF92N2wAzZBKDABzmpqquR0T+//sRgqstyLrNl9NbnaPisy1BZEql\nvUwP1yMaQfBozjUK8vSOrEGWDnRGF1j+tCOCmWsMs5FpEZT/1OB8pUd/ra/1X6u+Njg/kSqqPOfM\now3qGVx6IvZ3OINuiTEOKCeXU/nuDH5IJ6dlsIVUGkMHIhsPMiUSv6fj9p5ImTZOSp4ksVOEm7Y3\n2MCK2gWNwGRnNeXLSIRm9j6TcJ6k8p0OhJk5UvXCc1pJCpEunMedReChBc83fHzBI1C9MXTlHIUk\nQcwxJJoKHyicvtFcOG1DUiYprKmgtnDqBTdDzJAj0Fhp/eAlDnrudBtEQEmFnOZL0vvBkld+LgOV\nTPXg0CfQjSecRTOXnN+2B+/QfEH/8AJ1R9RAN65mLKp87K98s9zoHFjvLKrcR2PQCBWqLhPTmhZu\n2jlk0GLgMmlLjmIy2DRY1MgiKMajO59GY0uJ7ge7AySyLhSrDFX2rphXblnoahjwIS08G/xwf4W3\nhuYuxkUHGaMCB8KwTAqhjQe7O2N9cI0LiWdqFx5FybEjKdGHsBG86AsvJrxvRqiT5eT0g08kTkuY\nKiqBqJKlEA5rnlkteShujWqBoVxL5sgZWYLFKkhmibmdLBSuOTEk89L/YCSezLVPrfOUZi4R+511\nTFDEl7EzgCQTWO69Uo/GvVW+SOOLBVnWiduwghJUlEMEYmfrcESlRaDcsLe5stOIcM7hVJ+m+kEm\ncF6l82ncMVW6QgznRBkEgU+CFsIQo6hw03dY3DC9cSvGGpX3eUFg4o513tci67zdAm5pZso0G+x+\n0HPmUZRxKu5BSGKogSs1EhXh3mBYB51yzpwUVOhD+WF/kGTnpk62CfM4RmcPh/aF4Z0aQQaaCG6J\nFIlhjmlQLKOmvMsFSYksnU/1EzXmdSpU9mh88cbTuGKe0RGsEdTc+RJ3nnbI55WxLZg67QjSqRy5\nIWn6ckiAOErnJkbNBYlXKs6Z4aKDRyrcurJYwpaVbkAvDE4agnRD+6SAqRqXLtgIXCeWIF4FTRVP\nnagJGU+MmnEyXZSzV5oP2imka+J/WRIXC17biScoAlmCnOBnW+Fx1hke25UdpzVl0YGxEpJp44lu\neXrKBKo4rg4t4WVCBLy/p/p8jjRtSL+QzLnI4Bzz+WjaQBMWCZGCqJNUKAGlJ25ufLTpN4xcMHeO\nuYfkXRqs2uh+sOQ7ljayriy20fvOvn/PcXxk9E4hSPFg6AseH0ASQxdqVbwH7/PCS0zZWlEn2xOy\nlj92GJsWmndaDBYt7OPEgYc5jETKiVOE9xSqCyMn3pXyH5qjmX1lf4S/ACyq/AG1E/8pNCeJkL7K\n077W1/ovVV8bnJ9Q5a78WV946YNoG9YO2rFxl4FLMHDu9kqKSkuF1xS8lMFNlSILsGAEr15JGCXP\nnIQWiiiID0yEFsElX0nlgoTQDN7bzG05YoCfHN4pBt/5jpHnC9cWXr2y9BlEWbZvGD6ofudqjkXw\nGiDDGePO7yRzIbFFoyikckNkUuA2MUbuXJaFkJjY27QxdOW2XjmPk3XdkAW+7APGQhp3AkUlc+r2\n5s6ZdY4BAqtdGX4S0Tjd0PVbljeZUzGbxDaZL9WnlGjuuEwa2Qjn+1rZfbDHyZMZ7/IHVPLEzhIM\nqfy+J45QCsIlKbKtXEbFw6jRCZ0EOnTKyN4r/MVy5UUSdQRLKqgmLqrsaYAoMFBJnB5cpJM12BBu\ny8zV0RkcwRg3Wm/c/cHylgM01pXiThLhEVNCIiPQt8DMjyGUxflegRjkSCCD3YVrP+kzQYYRc0Pz\n2eGOsnvl6DNZPotSNGO2wDiwmFuGK43CTkfpOXgxWNTBC7kHl9y4lXe8OsS48/n1jpUPlHIhvx1a\nclowFo7ReQekyAxbqSP+eIV1wNa+4dMJX14/4iE8QihpZWyKU1ExiODuJzU6sMNodK9cRUk+eNaB\nc0Fi8InOePODFIWQwV2CM2KGSL7xnUyNIUp4RT24EpwiiK0QweHGIpXaJur3z2/f8S7fiHAinYQB\npLfNbOJD3vj9OQ3cS76xjylN6ua0BfpIFAwQumfChaMPvDvRnJwhv70ZVCa4IGInaUAMdDhFoOJ8\n8iC3O9dy5SpvxCoTVJSQYCNxyxuWnzAzvi3vGOH8uP8O0YR6Zfjc9GbLFISnl4z2hPkgDWc/KoNB\nS1eaZWAG954xt1drc1qCaoNkA8N5jMpImVtaSQwKDXxwtUG3QiVRfYXTkTwbfquOjE45lEOCqju9\nK2UoJc2QzmoNuqP3xHeaWT042knWTMXoofO6iZLfiJIujXelYmfQKYxR6XRKglMKKT0x9sydxqMF\nhwSEcS0JSoZRuDwCSW/gCwf1YIhDEg5ZCVYg8DQzinRkxjixyLzHOQmGdzSckZXRO5YSOQXvEfw8\neNWBZkf8RjxW7mVwxBOLA6XS9KSPKxHfcBEwKnF+wo8f2OPK68vvGQOWtJLzQoxKkx+5+4URV1SF\ndVtIGrxnY9DBBGdwDmWzQEV4Shc+ttf5bIiRxHilEqqwFJbIFBKlZcKgm3Iq/Pt9jQl4H/+xwbG5\n/T3gP2ChVYRr+noU+lpf679afX2qfyrVQY7pAfjSK9KDRROLd9bWiVSRcadhkBZGSkRuMz2aRKaT\nedBYeVDeskROsgy6JCTsTUMOO4nOyqMGnYMPeeMjwYWDi0CyjZCTEo3IGyoZEUVEiAgOEZ5znmCD\n9c84zt/wEsHnUKpkTF5YybgGexlINWCQAswSxxAQQUphy5khimkBu7CUlZQ27C0PxgHKjas6XgPE\nUDWW0antlazwGJPKtamgljlG4h6dblduqUzTPuAI17fmJiLwcVIYHGPwo1c+1/n9eDgtnHMEt2Qs\n5Yosy7xOJdiOnd/vjepwtUIk+LOLIGPjcXYQZWGwqpBk4akUrut7mgc/K5lPQ9++U4c4aT4peqsJ\nmxnf6aCI8azC81bmNXPnYz1IlgkGZSw0Bi5GC+ETndUVj4oOuIx3WBReIqFyZUjCdcAIzjghEmaJ\n3jrOIIfOCbcFp2TcT5oGgmI+cIXmk2LVwmkuPKeNIa+kmMbkh0/50Aghtztb2ijjRBgYymcPqgep\nfSFsbiXUGx/6TldBJeEtkDiQftBlZpK4AafyOKGysJZvOYeTvJLbnfAHnhWXzjFOPvqDxsk1Bg+U\n8MGHXGYI4/iMmfBqCZWKEzyhmCR6OLvPxmmRwGkMd44kk3IoHbeZNVIwshViJFqAuJA5UFeMheEP\nUvlASTdKGm/eB+E5L2QRPtXxJtGDSBtmnVycOGYXohITAZ4MpXNG42idU3ayBM+m3CIzfPB6NM4j\ncHa6Ok8Fkgo5J6JPadeTCpdlo1FpYrQYqATfJeUX735Jyk9z+OGd//288wUgXxESeCWlgtaTay1I\nb2ivCMapEKIs+wprYWRn6RdezpOqwuqZxAwppex0Cu9UcK90c0RXzObhXw2qZrIF2wiWerJl470Y\n7ZE5x8znGr6THUp0PtSFEuDW6clIlw23yr04fq+814JG4BKcdJrfiZhwE1chxYotiZoH+TLYdsXF\nGDHAM+01c8s3cn/mx3hB60lKg5EqaldEM5wCOn15SQ1i0AliU0YuJO9UNdSCLoINON+w1IcUrqps\n1riH42/wh3wxUGfVgi6N8VRoZ2a0zMJG8J6BcLSTFx+cfSGnwrpUhODTvnPglLRQUuM8Fu53Rf0T\nsZzUCg8ya1q4pBsD51XmYOcYUyY7IujdKQYyOlmmn8zEuGjht6NyeAXAfUDKfIjCzRPa+2ym3aE2\neq6UuPxxi7Oqzu/oP70GLymhZQ6kIoKkSvm6uflaX+u/ZH1tcH4iJW3+6+MgWbCa8vCT1B8s44B4\n8CWCXRZMjYcmPplzjZNVhBOnknlnYOGAkmVnsMypbr5Ru2LutACLiuGEGBpgHuxMedamoOsNry+T\n7mVTh02AJYiygV3poXwayos+030nsmKSqdHpKc9E7iXwxfDxRLgilpH1xl3hnjdynr6MkjIgpKTT\njF3HHzGit2QklFOmT8bDEM0kS2QZZFEQEDX8LTch2UpFqeEsb01N9SC7kyUY7U7Y9M38Zj9nSKQP\nRmTOGLyMzipKE+HdeqXI/DyIcNmufGuVcyjX5ULtBZfKt+PkIheOcfBehE2drVywvM08CA6whLpj\nKCqwmVLk7RCalJ/nhW+10aOz5Iy8pc5vBL8brxh9bjW8gSufR+N0pTOQnCjn4BrPPGvhc3f60EnL\n2o2Ldg6ck06Ec5XBvZysZ6AOpgcnCyMa99xp4lgMEis6lHQaEW0a4MuU8CzpwmPM8M+Cz3BZgss4\n2dqU0p28IvnG1XRKiLzj/YDesHpAUsKMNh7cX3/Eyka0TpSV4QdxBq1u1N7oGiS9smjC6kmocwZU\n/8yw4JTOSaP3nW80ofa28fNGHoPC4BlY9cJFTjqD1Re+J/NKwQMGzgvzUGfJKXnDx4GGk0bCJKHp\nwoe1cOxOoFw0cxXjYnMjqDIgHizy7i2l3VjM/nhY+2Yp/P44OQzGYqQupL0zbDYnDaMZVINNB0MG\nJQIEgspLC852sAyjj46EI5oZMajD2HKaMIroLKuSLs94O1Bf6f6YjSiG28IpShZ4jMb39ZWjd6rP\nK1nSBSNRjs8UN/qokIKnuNAjeMQgpTSfv+6konxZA+LCSzwY4SQGPQVX20jAoglZGkuGY+wsOdE8\nGJHQqGxHZujJbVm5aSc/FtKYIJKP9krfGvk0fvm6cbpT88FVKkU3EpmoC61WksLdBqVvnOyM2t+G\nNPP5eZwHTT6y5gW7LOS8IHqSXgb9XpAvs3Hf08kPa/CaV7BECcUlaFsh3ydDWRbYJUAaDz050wM9\njR4B6UJ3Z3nzfZWcMKYUsLrhI5MkeFYIdXYdfFjfc+iJ9UGoUCTRY6WJgG+gK1oTPRKHv+BnQnAq\nBU9fIBwliIDfnivJE5+OZ85TuZ4Hl2UQecIilvqFiuLrz994aVAjWHX+3cxiPOvKqoXTGx4nr21n\niFNj0ghDYMkr4UqcQbEyNzrDwQxxR86TWKdMs6iy5sL+n96DJsK7nL8Ge36tr/UTqK8Nzk+l/oC9\n9ZPW7/TxA+z/xsIrr0wj6qsuhE8p0atMCc1driQ6nQ4knMLNFHRBxSmi3ES401mobBpU2yhMGZdR\nGB50s3kYl4UiM2jxjEB8JxiIZrIKahdcPnCMhYjC94fTxQkDm7F4JL8BgggsNnXkHUdMaaL8SKY7\nqBihFyrQh7OpktX4s3Xj9axcBfYxGOOgjQMCYtQ59ZMD0ZXzPNEBmjLENIgLRkor2YMewfLvvubm\njvhOxlExPrdOi5h5JAL3MRHZronDZhjko088NtbxcG5p430WPrdGLgtfHi8cozMiMFU+yJQh7TiD\nwaMetCkYoUuQmMAHFZn+o95QhFtKfEiJIkG4k1L+0+0xdva6s7c+iXsivPrMr4iYCfHu481HA2E6\nGxEgJCgcPPUHLuPtegpdlC0d9NSwWnAXPqpxpmnwdUk82cqlbqwN+hDCKxcp6OrsW+UHV6JNeVfD\nWQMukshqvGI8i6Gj0v1k2TZQqH2g+2cWDPPOeTqZk4ggvDP8zcy+f8HzDQ2l9064I2kBHPVGFrjp\nyj46I4Q+dswHK1P2liSwmEG5r+NBJfGOQo+TQwUXQBLeleKwhfPQgokQUXErWJ6bpWwFMFZTuixI\nWnleNkp9RQNWHbh37sfBp1Qo64Wl78TyRHvzE7TeJ5lKQETYTIkluCdh3QxPgubEl0OJqYKiLIHp\noKhwWRzyYOicfL8eFUmFVaevBiCrzfuhCaXERDjjXMoT53Gw1zsaA1VjKyvX7R0d58f6yn04w50k\nxuoD3u6vdzlBKJssvNBJ5twaExIQA7pgacVi8KkPHuosCnkRznxMfHnKLL6wmXNPwjfLjUOdJi8k\nSXgvlFGxnrhK4ZCDVztY48La4ByzudOAbMYi0NOABns5SeGM1smRiE1or8ohoEtF/OQRlZBGOzpj\nBIcHlTYzprpyOd/xJJXkJ/l4Qtvc6FUZvEbjPBNd4V4mAiJLRobyPASLTPdg2IDU35pMxdLEIDcM\nMeHsE7V8oHBpvOvGWIx9dMSV7jOrq68neoG1JmScnLXzpVUeTUFWBMH7oIdDgiQbzkBl0N3hdJKu\nhGd+05RzBKUpta64O69n4XUoloScGg8yFSdFIZuTklNdKOkN5x2VPQQfjRHO5/bK7+qP1DGAeV08\noEfF2+CSn1jSMxGBnAdEUCTB6HOjowqqbMtCeSOHwltzUwr7vwv6/Fpf62v9162vDc5PpXTSnmq7\n4/vveT2/nxS0PvHAn8k8VFBAMJDKFoYw2MXmoZ4F14WhsMjcYojMzcBFOlWY9LW3l9JiF5TG4ZUh\nKyoFUeXZgnPAtn7LGgfNG6hiUjj1maflmdODz33Q5M30GoCemBjgmGQE59kqfezMTw67D5rMgMKr\ndHZx/O3Aj8y8mzUZkRMYskHaAAAgAElEQVQX4OgnPo75FVmBcHycyAnpdbDZM1UAT+BgWUlLApkB\nlaf/x5el+9xKXJJOqlAEHsHenRefTKCwFSVxeiX7RGSfHnzIc4p5sZXhA/yVKoPHeZ8epyEslllD\nuNc7w+HulWzOoolrXjjaCxYLj36waSK0QCm8F+Ev0oKpsixXFpmI1fvxkeP+G15f/zvHUDw90TWz\nj4NMmrhrwCWRxlvA6dtWq0SmMDAPTIPkziWfbOrzkMXCpQhr7zyWwqs7g2lEzwrFCpdjoZyGWyeJ\nYiljw6EqGsFvSqdL4RLODZ9SLVVGWUiSeA3j4oFr5zhPTk30NhBx+mSDM8Yx0+nbyTtTwFFJrFQq\nAxeFULIW+tsGRIZzBb4HnnUlq5IGmBWuA2Q0HnJQa2VY0AzO6DwH/E4bB5UShdXe8ZoK9IWf+crp\nwhnGIlde7GDIYMmGyIIzWHRlrx00kUfQaODBHo5r4CG8HJ9RqSTdSNszN5st9qN3Tg9Upv+rmJE1\n8A9CHIZ+gd/sEEk435oWpyI4V5vUhXclOFD21jGZI40nMz5bQnyg4hzh3GtjtXkt1J55Gp2KELYh\nEiQg+5+M2x/ryT4GiqDnQR5O+8Og4zy5ODPw0wP1ximONJ33VlmwpOjjQc9gQzAPnlvh1IWehMcG\n3YI1wbel8BfXb/hcT6IO1AYawtknunhR8LTjlng4jNxnKqg4ETEbSpRmnVyNotB6ojpkGtqVIcKX\nerIf8F1KWBt8jINhg9obZwr2NZB8stUgh/MxTr79Unj0gySdRy50oL7l7qT7gCvAYMSgefAYyqbK\nxQoPndu3V2RuxsXx7eRLMzKT9li1UZfCzxdjoPgYjBacQ/gigiVYBHScvE+JElfOFmRNUw4XysDo\n7lQPhgiKUkxYdcF7UPvPGT7YxZEwjpFZuzPag0UnTuP1uNG4TsmvFKCzlAVVxTTYymBPkKyhVnlP\nkLnjUvnt+SOPsVO0sGhGEHoManS2gESankERoiys3UnYH/4AQ87EMjc5JlOW+7W+1tf66dXXBucn\nUpGB5hzHZx5t53e98T2vhA90CE13CsqeEs0eXB0sbTxIc/2vRk5GqJDoJAYmhVRueDLe54J55eyN\nH7tgMvNMfOx474hmPDqKUSzz5ys4ykttWC5YyrRRyCgfvbP6W2PSX6G+0n3QbFLDQBh+slnmIj59\nBjKzYVwK7/I8jITaxBlLmhkHMqUL2YyU0jTaeuUYzogAb2Q6dij5SyJr5lKeUAzG7HHCB204keaU\n/H0pJBHa25TwosKNP/lwJIJ9dL6Mg8eYeTJYwSSRNGMol7SwauadbRiCHDupdz6EcG933uEsy5WX\netB74ww4hvHadkIzPy8bF+8sfVCGzvDNWCh0vtGKLk8seUMsk1Lmulwo4+DLx/8V+fzf8P235Ppg\nlcJLu3PYgkbCUVK+YFrmoTkWVDr0CyMy1wJ+VsLBIujDOHJiU2cJx+TOxYyLJcZY6MAaK93nAf5i\nmS3yhDckgZhZGCGV1hprW9ncqQSfMB6y8zOFuyaWyHwIGL0yZNBFqK1xRkLSDDhlBNkHSwRuM1fk\nRY2CEtJRKbyzTE9P+FCaJFJ0RBzNyqmJUU8e+oBwbssNH59pHNzlnDKdMO5jejs2CSSc7DP3RyPo\nJGQspJo4dWCWeVZjGwULZXByzY6akfTK6M6qbYaailDF2bnTh73lLF1YRHBv3C14+INrFJo7R58Y\n8hDjEOHyZpxe1saPJ3w0Zd/gbDFR7TgulQh4dOOizunCNRsWg/uYuPDNErd15TGCz2Nis5MKl5TZ\nbCGFcZgTEiymUxo4nEtJ6OPOyxh8CaV6I52N89jp4QgnloXFCtHn9P46BGlCpWFl5twkdo6088OT\n8BqF3oLXfrJq4mqZH8pOj4INeM6NP5fOO06uaWBhmGUu1viEkE14znDZV37sO+eoLEAWUFGeLdF7\nJWR6XvzSyDbe6GqDgVE7hDl7dPQ0PsaJ5MYRB4d0PtuOqGImQOWsd25zLcxrBdUBMd6oeRc6ToxG\nskTqRiQY0hhDODA2NVI+uMmGy+CWpqy0y+BgkFMlZ1jTDbeVVTdSMnwc9FJpCe7VEDcKYGFIJEbZ\n2c8gxYG6E34QLIx4ontwqBMehAq4oj04W8cHVB8IwmyBM0cPRn9mjz69dvGBFoaJkKTNDJwzIyps\npXHUzkcJwpWuwfNDuOQb6+ULrQxGBHt9oJp5n26ELYSsFB9EvG1kUNa8sC6FGAPCiesN8p8201/r\na32tn259bXB+KpWhycljOI/zwePxA7XuuA32svNZMlnON+LWgpMw0clNk5WNwUWNbFDU3gywwpIT\nOd/4Zrvy6fEjMBgCXTPuUHRjTSclKYnOpgvvyoLYez7XT+hIqKWZDeOTQvYgAzOQzn2f2Q8qpIAx\n5vYgq3PVgQqYBjed4Z6SrjxE36aZM3h0McMJsirfloVvloWPr6/s7pw+pV9Hu4MPahdujzkPXOko\nDZPEiEBPyFfFqtPTlAItqqjIHxOwn03x9ifl9xknZzRUgiDoOGc7MRkkTWSUlxYsIry0zqWeLG9y\nIMFZgOdIbGKcKdH7Z45I9HjgmlG9TDpdb9gILihJE5tNkppEo7QvrJYRNS6jYh0e9//G/vKvjON7\n9HzB+p0ihSIHe98YciHlJyI6OZRVE6sJq7ybO5gx6UabTiO7A5KeUFNcDzaBTYU1KZb/jA9cSK0x\nJPHSO90DITEawPQxZREqQidPyZobYiendnIsbKLUa6eHTJ/XcL61jsnC7x12n5kma8yD4YjBMqac\n6abMcNqAo5S3Jmfmf6w5kT4kXurC7ax89sousyEa1jnSwTUME+UeFbdASdBWWsAZTNlVdIYOHCd1\npdsy/68XmhRKmllFq66oGjdvRE88qfO0bihC90rdPlDj5JoL/3Yowzpq05ukCTbbCFEsr4xwznFw\n1p3+77aJ+zhY5MYeOvHNK+QmWFMSwpac7o1zOG00UoI1g0jm3jtPySgDvs2Fb8pGbx3rTuiEZFgy\n3i8ri2ZydUIrZEHboKTEssznorvzsu8EQnXnPA8Ob2/mb2OrgxEvZJxvueJ9oF645DxBIRH82A4+\n+uD1olSEx6FUy4QEeXE2X6hhnP1kT8FdO0trlJTY/EDsws+y8VKVnJ3VhAedFSWV4HImbiK4CjUq\nfi68RnDDqGWwl0Y/B0t6AjJ367S1c0mFrJmzwxd/ZZXKZ3GGdpJ2uhitv2C98amBxQKHsSQhktG8\nUmNup4cIhE3IhFZqDgTYc+WbtEDK9LFTGTSv6H6n6iB35XnLPG8LKsoXhGwQ4jQ7uddOIKTcaG2S\nF4sA3nltJ4v/gB4H5omFlUoi4gttuQLCFs5A0YB7a4w+h0k1ZN6vI6MYYon61jhHbASZQHAJMKW7\noppRgXEMoMxtmQaShN6dl1zRlwt5/Y5l/VfMgsNOnBUdY2Lk08J39sz7dPuP7zczZkLy1+bmp163\n243X19f/y///l3/5F371q1/xz//8z//DP/Nv/uZv+NWvfsVf/dVf/X/xEb/W/6T62uD8hGovnTPt\nfK7f03mg9kKVSteKsJKHksfG0GdSWZC+wrqxCLwfOzNQUHlKICnzMoTTjbCVf+3OHgsaJzdzXkkM\n4MS4bT/nWYFo/OL6nqaGA1UX1usvQQRlsKWNGkLyRPedRRLqg8OnJ2BNUx9vEQQr36TORQ3rDVVF\nbSPLSomgpY1i+S1LJiME4RXzV9rxA8fxI1/OIBA2GSg+CUR9ziVXnfhQH5W1XDkj6O5oF27ZuIuz\nJvtj1o0At5RIqrSRCW+c0TAJis7GZx/BPRQiGHQWDFUhwnl453117r3TVLn9kfIzf36tlUsydk1Y\ngItQBIrNGWoFUquUvLGhFNtYFXIMzmFwdpBCx3h8/h0v3/9vWP03Rv1E6w+IwerzM11sJxKcXcii\nRLqQaKjCrsEiM9zzNUDjgqpwinDaziaJazLeWyVE2dJC1eD9skC68rl1ngzGmCQ/NHhOQaERYRw+\nyE1pEewBdxZUBegERrTGKIF22EW55Pd4SngfNEtsqQBKkoQRZMnY6MyM2QQRdCls25VhFUGQvJBT\n5vqUub8qcodNMvd+5xhQ4kILoY1OisIpneYXTI1sQaZRtJDdKJqBnV0T65u0prwltKssJDJJ1ymX\nBBbLLNbY/YQIrgLb8syIlTWvfN4rn31gVFYNrgZfpGM8kWLh6J0jvhDxn2U4zqO+0vWKh064lE4E\ntNjgsXeG25zCy6D7iYfONHkSS0p8qwebTEqgpY3oO1ebMN5tWSkm+JsEMPlEeg/fEYSIjRfv/Pbc\nOUN5aSCa2TnI8qfnprVg+MElC/96vpJCJt2rNW450aj01TAZXGWla+CL4Bq8RCB90EIhTjZ7wOg8\nBtS28su8saaV5pWntfDzYnweTregDyfJioixb43l9UoHlvRE0xcQY19OjuIQFyTXOQyR4Ngg5ZnL\nVEM4vXOPRNMHqNN7x0cjc1JGZTlXDEjaUe+kc8VH55Pe2caN7pA101XpMhjZ6EtQlsySBVkTnx6N\nox+8N+H5ePDFHI9OtGAEjOjIRVhjx/XgPgbNlSCjbwOfZVFqDw4/WRHWJmg09tI4qhJS6bLQtTFG\nhVFQDUwG+RzEKdQBbpUiiSEXYFA9YSo0LjiJICExN9xE0IYxAkyUGDBYAQcPos2fe2Cg897MdmG9\nBu/e/RbPg7B44z5P3+Ca1v/T99sfZGlf62t9ra8FXxucn1SNUM7+kdPu7OXgx6i8onQKuS/UcSFI\nXIElLmy84/BMKveZWj0aFxoEvLSDlt5x3d4xYh5YVjEOu7Im4z3Cw2XmseSFSy68N8BWekwDPok3\nT82skIyGgA98NL7EnL6fPj0qhw96DH6Rg+9K4pIupPKM9/ezw9DEGKAoDxd6n9GK0h6M/iAneFqf\nATjGoI95qIxRySpkZiaJCNThWJK3TJs2fTEahAaxwLdXpfqUtilCUZkvdCDlK71+oXqnqHC1mTny\njSipZwIw4BqJrSeyCkWU1hsLUN1pIiQxtA/KeXDvTlRI0bi8ZT9kcdLb1zeGc/TO4g9yMsjfUjip\nfXD2jqrwaD9y9jtj/x11fwEKIxLuYD7IcfCOyuk3RAurHtQoQCJpoJbIayJ1Z4yFVRZCAPbZdIwp\n9bnogmjilk40ZUQuSL7wLsebCRvWZcVsoGNwOyfVa0RioSMDuieqVsIctUkWywrmKzkOXHz6BoCI\nhEjmkgtLMqy1KRHSgroQOmg0iiqSN2R9BplSnXAnUoJSJvjglhjbM00Kx+E89aDud477g9obhQvm\nwQOfB3kVEjLvY1XOMIYsFFHIxppXnr2wa8bdGGRUlkmQE9AcoNMzpJrIWkgkki0EikhhS+8ZXjF1\nwgzRhef1iVMSdTSOmDLAP8AGgLkNcwcqIuvbtD3wcB61URuU4aRQki6ENIQTD2HRzKrKzy7fErXR\nRqCasbRBNEpWllJAFImDJkrtjjSnuxDeZnPVEt6NlBNbj/lMaaKVzmKJ8MHoDxTn6FeGGNcEo3fu\nSfh9SkjuWGRKzA3fF1OGw5BO+GD3GdKoJhRNdALXzPDExx78It04/RPhg3cZcpw0eUA6yeczSy+s\nsXF/Unoz7nvlYOPjZUfLoAQsNVF6Yi8HL/ZgJOMmmXsM9l5ZLYGt3PuOWePEGNF5IvFt3fCRySHk\npES502vCz8HIlXvZ2UbGJHNuB8dyUNPKRQqLKc82oR6vZXCkxrV2cukwoPgcdkQAx8nhD9KaWYry\nfduRUDSUZApSZtPlD3w43ge1vTLIPEZC7STJZ4Yr6MZFKjsPlBvLnskIww4EQSXoPulvw2ZD7CGI\nziDc3qf3JeZYCUKRKb7FHSL6bIBCCBL9LZNJhs/ntTseP8dUePf0I6MkUjhFM9/KRr48E30go89f\n3ozIZcIFvtbXeqvX11d+/etf8/HjR1pr/N3f/R2//vWvAei989d//df80z/9E3/5l3/JP/zDP3C5\nXPjHf/xH/vZv/5bX11e+++47/v7v/55f/vKX/z//Jl/r/2l9bXD+B2pZpjnyf3aJCI/Hg5zzRKX+\nv6iIgR0n0V+598bHofyGJxoQnng3FgZBpcyQOaAkZQljxIbIC8WCd9enibrtSpGGSUfX79jKxlhO\n1vaCeOMXa8I047qypsS3SyFs4x5/+j16fcH/faJ0wBaFoY3T08RLh/DBjGROdyfHyYfk3CwQbyxc\nWW6/ZGgHEa4RvPTB1hpNnDY67Xxl4eA7u7H1zv7ygpqxvGXPdGn8IfLRe0e8QU+UtCA6KMlI+Y2V\ntgFPAouw/d9939uVdnxPG5Wfrc7Vla3DWhvjCNrhWBhLzvxcV9JQ1IQlveFLVdnC8SZ8WApVB65G\n7iDjgUflKpNtd4yA8UB80AQ2Dzb/CBKMXuhn4+X877TkvPYXPp0nZzu5SHCTQN8obzWUi1R+Li8c\nLG/BeoasmXDH0wwf3MP43CrRChTDEiQPngWSdLIObgyel0TKF7D3pJR4DfjlUyZiJlSYCOMXncvv\nOnokcGE/OrLDbp1WGhqZEYKWmeshI0i5IDp4UlAzJBfKCHxb+VAyXiuvbwGiRlBiZfOdbd2QslBU\ncZN5r1ue2zQLlryR7IK58qk1spz0/SRvVzY17ucntL6SRuL9uPMjTN8EzhEnLkIj8RBlTcFWMohT\nykFmoemFrCtP65U1Jao0btlIz8q7nFnzynl/cK8Hn3tjQfhm2UgJ7j0RJqzrwpYSt+sz2YPkE4t8\nyZnoTsAbZMB46QPEyHlhnDPD5Mf/g7132bFsS8o1P7NxmXOu5ZeInbkzT54qTlGqBhQIiQYNEHSR\n6MALQIseDS5teAsk6IHogISEhHgCJDq06NEAVCUVlBBk7kuEu6+15pxjDDOrxvS9M88hq3SgOEVx\nMixa4RHhl3Bfc5gN+//vXwe5J3Q/tjrpdUA768J/yjPlpJR8z33N1Cmx1UGyjkTwcErUnEnpeBaO\nfuEqThdF98bdtLDhfGI7T9vKZIrJxJQqJ8+IBK6ZHOWAdYzBuRrvrCJaSTXhktjaICJRy8KLTmTp\n3EZHC7yPlWc3UGf3K8/hlFSZtLKHMKFIZE4MrN14OM28rceAfk6ZF52xyNyXxnsXsk2EKaPBiwif\nP75mM3FinTYOMaJzbc88jytFlFkTITvP7qyhmCyktOBy5r4GpW/cbCJFYumDbAbpuAiZasbY2QiK\nF24KaR748oRNjtiBe35bJr5SBudyYpeZHINHL+QeaIZJwawf/kggNJjdKBiTH5k/uPPiAp4JgiUf\nlzdmShnChrLFhOogYidQzhr4uNGycr7P3FlmyMDM2UdQc4AHL60whWLTgL5jdmwls+qXQccSSqAQ\ngvmBhxYBQfFXD0+8wmGOpK4jlyrCEcvcLt/gUe+R/I66PHMOyOp4CebTPUn++WeyqrIs/09P7v9/\n1L/m2f+9WvM888d//Mc8PDzw6aef8uM//uP83M/9HAB//dd/ze/8zu/wkz/5k/ziL/4iv/3bv82v\n/uqv8su//Mv8yZ/8CR9//DF/+Id/yG/8xm/wu7/7u//GX8mH+pfWh1fOf0Xt+/5v8nFLKbx584br\n9Urv/V/8fiKC0d5j2zO35qxhrG4MZhqFyQuditKZGRTZWMi8jTOKc+uDvXTuJrBx3JpuA3LO9Mt7\n7uWRpZ7xyJDu6XFjeDo8IO547zQt9BE8jxvBccOMB/srweyLmrIh/cZtfeYs9UiQjxujbwSDSYJb\nD6a1U3JlbZ/Rrlem+etYEUjKTKK3Db98huwvTL6So7Nfv0Usj3z98Udwsy+/r+4CkRn9hbBGtkKu\nQjeDccMGlEmRnIgFwuGfBCx8l/KR6KaAoua8XDvb5pRVcRdKhpqN67qx5ExxYz+9DloIte9MdcbH\nykMYI4QXG4ztPXMYUTO37UYbB3xA5Eh/Tyy823Zulxdk3HPzlTBjfQUerLrjY/Ac4EkIS6RQnJlb\nCMRM8RM6IPVB1IFLQd0hXrB2RzHlqW0MFRZRNA38NUCS0ZGsjK2j7R2nKfOfylu+2cdBgCoTyCtJ\nrSjXxbh1YxnKA8pT7ni2A06AoT0OE3g+GvZWKguDSidpes1ZcdgbtzhG1SEJN4MxDmrU6Su4grdO\nFOVbl8+J6UTK40DyYpT9Ckl4Mj9CMrszbrcDwBfComeEhgdMeqK7knLFY8NHYDgSgefMRTrZBvcB\nV96RrFKksuiJqRsnP2RD6aTI0MObNTZezBn74GI7928emPpgsc5TNEYI4gDKuXfOOiERjD5YzSmv\nsIuSlGbjNbgzc+eZfTO21dm3QboF7tBNEHWm0qjDeXoS7lNnnxvijc83//azSIVJE+/6yikd/pq2\nX3juA49gyRPvrxesrfR2JW+K5syUK2vfcesIivrrbX8fFN/JwwjLWB60MPZ2RJvgG7s1tnoi03gR\n2IYcm8dboqyZc7vnTGCzcZ0vXNOMVHhpg9YHVYTqgxdW3miQtXGaK80fWd1JvbA/OzC4mWCuSIa4\nFx7mhYLSRmPbbiyjgWU6QivQJegMkgRdE6kJtSg3Eud0YrBxumaSFGq+scVgjoSbs0qj3xlbwHou\n3LLxMIKyBR+nQpkT96Vx1sToK3VOFDles3NUzDaWCM44O68kxeEUGiMrFoOSJxKVaDdu3vE80TwR\nJkzszKnS+o1kK1k4ICttYdnuiMjkauh2JsmE635s5qLjuiExHTxtV3QMct7RIrAbFhwSTA6J7fDX\nYed1lDF3go5zQGYCPfx7JOL1lyJICN5n3j8ZeQw++orxuCTstvHMJ7wsNx7LHfrPHHKWZWFd/yse\n3v/G9S89+9++ffvf8LP691URwa//+q/zZ3/2Z6gqf//3f883v/lNAL7v+76Pn/zJnwTgF37hF/jN\n3/xNfuZnfoa//Mu/5Kd/+qeBw+/7YXvz77s+DDjfA+W+E+FHJgjCLoVnqUcjSKG+/hgYBReoSag4\nJ1WmvDD5O/YpsZREUcdIpFxRoALqOzczcgQimVoeKFMivR5dpzLTNfHSO9fxbSN0FSFLZsQAXjXb\nMrjLK1+blev2OQPHzIlx4+w3rFQYCss9SQ/NtduO+06xEzGdGRbE9e+Z2zOix+HtQBDY/sL+/h+p\n9fHL0DnVmYh+yOamBVSYTPEw6ImUFEsb+vYM303m/UpQ+06JhIUzYvBJe8Hc6aMe8p+RMfphrvXE\npe2MksgmnEoF2yAFOb7Qnhz46izGIs6ZnTy9odvGSziWgtIHWeGUD7xtQmi3Rh3By9gJM24irAZt\nFDTVAxPscDXDozKzIjQ07qn6EZmFWWBhodpELQvDnbwlonVyLJw3pVoimpPOC7fYefJEnk7s4SS7\nAcFZLrzbOzXfUwdMJMp0pqjit51cOuO+UyZIZ3izF+aNIyujCGs0qnE0wblRgfu68DUVPprv2F04\nqfCEcIkga2WuE7sZOWApmaF6DEyiPNsLLQz6jSYTF29AoaTMIzciZm7DSF0p44TsjVfxIZWdi3Q0\n3/EgmWuCmUopd6gPRsoYzyjK13JFgA2Iu0LdEg97MNdEl45np6ydZjt7OXxVg0wvcvSP3gkVptMD\nb+Oei3dSMhaZeJPvmPNMBXp7YVLllA66VnPneRwDz5DCpQ+uTXja/dgUiuOvAiIRhzhQwM3h/U14\nnHltQb9d3Y+MlUknmncmDXYz1mEIiWLOp/1CGy+YrWRdMM2HD8xhE+hxBONmrWhWPJxLQJFMlIa8\nho8KBppoSSg50dPMjmObUduE7gMdgtjCSYS8DdJI7PeNUozdb6xe+CgZwzYur0HCp+7kVLjPSkuV\nfctkFYzAmiImfKVPbBIMMU67kBVuw8EzXy8rqc18s8Gl3siS2TkGvKTOImCSSDlTpDKlRJ464kE0\n4+KDqoWkGbXENhuVxNImTiKvl0GCiHJ3FublIMZJMs5FUSYegV0q67ZztL1BiiBHJ8RRKSyajiFR\njwuU0RzR43nfyfRxbKDqNNO9U2OwtJm8LZgXGor2BbdDghp54qYrqytCsOiVWhQzpacrUhOlwhaF\n5o6zgVfcT2TxY5MjoKkfkAHzg0jn5ZCwHT+Jh8r4da/jIwPOze940sL/9mmlfPyOh/sEEXjb2dPE\nkiY+1If6bvX7v//7fPLJJ/zFX/wFpRS+//u/n207LlS/kJN/UUdAb/DDP/zD/Pmf//m/xaf7of4b\n1IcB53ugwo8BYpcjKtNQmhQ2Dj9IiDFxSAa0zHQNpMxEPZGnM8SNvCwsBBffSQKVxqDSdCK5M8nR\nWE0pMSWlpsOc/EVewWpGViXJccsH0AKmNDFJoftgTsrsG3UMbnZFY5Bsx/cXvH/OxWHYzJt0R4wN\nTwWVg5TmvrPbwsvzzh5Gv25UKrM6RRK8DlEAbf0MtDLjrK6kcqJ1YevH7fKSYM8d3W6kKWH5CvPM\nkv8D6TuFaaMjrX054LgI1AnPiW/t7/m0XbmOxrUbrQ8klDqUKoWTTGzumClNOleFr6ZHvMwgjfr6\nPrf2nsv2j5g6MTbO7R173GHlq0TKyPZNrgGzBBoNTwvYSmoTuzVulnBVesgRlhlCGyeuR64kIRPa\nhAuHnPCufERNmaaChaMxY2RkFHQ3VjuAENIHc0DKTgw7UpJG4WM6iJEdIpVjQ8CAKGh05lTY2hWp\nE5AplphU+EZVdA3cBgNj1oRE4mSdloWuwkWDqBv3wDdwvn7+H7DX772qcDG4NMChSmMqwkcl8ZVa\nKCIsIrx/DZYFwIxw5+YBOGdRhg8eirCb4BsUP9FzRmww2wY+cV++TivOCBiqaCpkd6xfaTrIHKby\nJU3HVitlbnbQp/blikzTgRq3jdQKdLieHRHnuT3jAbXeoXOl7zdCCpMr4pm3deY+LzzWb9OiUpqZ\nZXxp3HfkCEdMFZXC1oNrC1ZTahPOLejdGQLNOTDrPo7voUykZES8kvH4djOwu/OQM8k7i3We7QBb\nyL7xPhlP+wvmK/MlockAACAASURBVNvYOO2DKSXOFKas9JTxKBSDSRKnWlCOpsLMeDRlxJFdJLkc\nYbV6IMOjzpyA/XpDzJlHxSPh6WiLw2F2ZdkX9unCi0LxQkSi54nkyiaOSOfsCyVNyNiBimDULY6m\nOwYjCnt3ylNQZxiLv3pdjIgZK4O8g5vS1DEVRrQDUewzk0HOgvfGFs7kmXNM1JJoNngx+Eq9I2Kl\n0rmLhEiQOGiSKcGjvOGBN6AHRrwgfL3es2UjxspOog9BmlK8U1Uo7qTJDjojBZNEA5JC0UHDCNUv\nZV1Xc56Hon5iWTdyP57XXYON17BMDDNniDIwNDs9YNPOY3kGeUaWRtYT85z4ZL/CmA5vlt4QMcIL\nVSqneeNmRhuF3pS+N6zf2P0eoyIHlxAlCDLxek4pwT4y77Yzf/V54X9dLtyXQMY4cnH4MOB8qO9e\nT09PfO1rX6OUwp/+6Z/yt3/7t1/+2d/93d/x53/+5/zET/wEf/AHf8BP/dRP8QM/8AN88sknX769\n987f/M3f8MM//MP/hl/Fh/p/Ux8GnO+JeiUWoWxSWJlIGJUdQ2gKncosTpJgkMhlQeeFno68BW1X\nLvkwhGoq3IvwNA69tOmE8qrnF+ExZ0SESZUlJZ77t4eLc848t86rIpvmzpyPkMq7nFhvT4zxTPc4\nDkrvQCP88G0MmxmshDutBbU8IJq4NecdztBODyNGsIvQInGvC8dxH5jvtP5Cb3dkSZwRtnECFuYa\nJAyxndv6HtXOJDvigu4zuZyIcHI5Q+9HijZH47e6H36i1rhk51t2AYSimZkJo/PiOx6Zc5qQVyjD\n7k7SRCa42gYVTnN+lVjd6OMdQ4+GPHAMIceNU/8/Cf0Ijw2ic/POGIM36lSpKAstnK4diXqI3+No\n5N2F5oVctqOxqgVXBd6yyx1LfqWWSeWCcJfvufNOQXgSh1CcxDINePVUlVB6StxxoqZByU/sbGwh\nfD6UR4NlOJoEE7j0jTkvuAlnFxYzPh9G2497adfBrhlk8FidXoRyyhSv/McCNVeKGGt6YKMeDd6m\n1DYYAaLCgxeSdPYU3NXE5g2P4Dsv70bwiiwOug88EsnhjSeGgqQ4Ag77egQnSuKtQinCJ2ngfaPv\nKy0rpM4SK65OStMxeKfCKU+cRoKUsEhEKLMq21Cu5jSHxwkibSQOcEX34JxnWp7ZW2PKE0MzWRMe\nQTMjxcBsJ0VD1LFIpFRpKLtkVCsTB/n9vcN9E2QN0k0JP0C+cw6sGJoGqQ68Bv+4NlKSAzUuShaY\nU6IFjOt7hu88JGFERnrn0l+4to2rOS++MzBuCR7smSITVSdWCxZVpjJxmh5Y7jISO1mEz+2ZNo67\nfARkdHJZ8JRJ7Piq3LfKvhViF8wLQ8ApSABqYAWPHW0LUSaqJJbsiENrCiQaE2naKFF4ujYuvVPX\nwkTF5BUj7sf2NI1APUimLDpR2I7HqBsmnXDB1UjiiArBPS+jM/VClIkHW1iscVUlRqXEjemVSNct\nCFmYPKPHdROzpAOyoGeKFmx1Hs560PnSmaXMdLvyD+uVeuuYKcmFs57AGpfS6XPjZDeSD2T6mKwz\nhmMJhh6QA4nESyjVjLvmxBBmv0eaYLohJShyQpIgybm0HXOllIyWA1pQQpAEMt0oVbivDvrM3V1j\n6jNBIkcl65VSC+flCmXn/aWw906riV4a0mFtT1z3B4hBleP1YV+eWJBed42NyvNI/N3nwf/ytStz\n+faG+0N9qO9WP//zP8/P/uzP8iM/8iP82I/9GD/4gz/45Z/9wA/8AL/1W7/FL/7iL/JDP/RD/NIv\n/RK1Vv7oj/6IX/mVX+Hp6YkxBr/2a7/2YcD5d1wfBpzvgdJUaWOlk4+QRYwcrxub19veS+kUC06S\neVtOqChJnJgH93KH7Rt7HE5R50AFv01wy0fuiovytlT+x2VmSunLm1+Pwz4KhyY2bGNhp5sdjWiq\nLPWBczl+FL1faAFVYW0bt35lbRtt2PFvs3GXYGemlkO3r/mBf7BGk2dc7xip0mOiDOOsRi6Zh3zC\nxjNBh/z46nZN5HQmDTipE7kQLmzbZ3js+DhyGkoaGNC2TxFRUp7RtmMRPPfOxY9QxqKJCPjm5R3X\npOR0COM2y7QYdIxQ494rc3LmNPFYCq5GsNEjONcD9vASg+af8WD92BDFEUS4k3DfMBn0lhgMQhzr\nOxvOLQphBe0J54SnhEpDpIIPMuASiN4wgUzCUgYqkR7J6QRZqGGHxyCfGExkgTfFj6HsVlhLoaRD\nshKv5LqiAJ3mKzdpPA9HQ7E1MWWnKZCDSRJWD6x2sNE80aOQpSEiR5CkZGYJbmVQdPDg8HFWprKA\nBwhcx5UuZ5okuk+oN5b07c3GEFBTbqtxSso5g2riFf0GKf1nQiyzTtpA687dJqxj0L1RtaKhpLQw\npQmNw9P0GA3rl0OmJAmjM+Z7bv2FU53wlDiLonJo6lerGJWqBbedKsIeiSLG1o1JjEkhcWTubOZk\nUUiZHsaUK9fRMIW1b1R3HnRnVvAQ4JA3dWZCMhHOHp3rcHornK7KbXTcMhEJU4MB6Ra0u85ImffW\n6HSSB1t03uaFQeazvZGicfZnhGBzoY/OtTeuo3Ez49N+PXJPVNgS0Fc+u7xnyUGVhTwJX58eqfcL\nJOFhCHsZ1OkJSxkLIQ1nDMeARxViy6yiKMoDQu8L3jPX3PBIiEGOiSI3miVoB4BkKgnbNi4m5CwU\nGYy+c1kLd2LoZ51lFNIqZJSahX1uaE4sOVBx5glOubBKoljFhtFHJwI0OaoHjuAudXrfye1EVrgv\nMFCGzUyaGGcj9q8gbpiutDSIklnQY3MmECTmdKamQvhO9oyPYB47xZ395QCDTLeM3Bcem9NuGxaG\nTUJmZnhwKjeIDbNGLXd8JT9wq5nn2NkJrq1TmpAsk6cHHKF0Y4giccek4MLxuWYlK1wERgTmg6rK\nUs5IWalz52FKwAWdA9GGt6dj2xuFU52ok9D0kdUTKXVqBKlcebxTZB/0XXi+dvb+yOgTEZlXdR1V\nBqFHgOxh2RTWkXlaC7kYs35oXz7UP60vMnC++tWv/t/Kzf7qr/7qu779R3/0R/mzP/uzf/L23/u9\n3/tX+/w+1P939eEJ8T1QqgXRyq19kwzMbCySGRjrayDb0GAtG2+T8jAbd2dY7nZSmYFC5K8h2zPv\n+6CNI5dA88L99MipTjzME2+A5b8gvnyn0nX0CxEDBaakr+KCgfqViMdjIHIwtyMjYVxx2xljxV6b\nnAjhYs5de49555xmXqhsnmkWWGnUPEE909uVmynZjHPK7FR6ntDlYyy/YYrDWAwQbogWRr/S+8sR\nQifQPZH18A24bYxxI7WF2xi8ayufjXYkkgeUqJy08rwHLzqoxZmzc4n1kDM5HD2Ys4iwRyPHjLGh\nsrPWY5jJR1oKfdy42U7trxsvDwghxGljw9yRKOS+0W1jskdyzKypchrOiTumKPSpcbMXXHdybMcg\nkguK4q/f/46i1tEMHuPIKYpMyImdRI2Ze+kUPWhq6kcTXlVpCXyCMOOpr/TNuNHYozHvE0MbUeFh\nOjO6saREXIIRO9l2kgu3LrzNlTtXBKUH3OwF8YYDZTo2aTYSogUtJ1oIrR445f3WSKq4fXtkaR5c\nAN2dT2tj0kQVsDRD24hUSF9stsyZRlDnMxFO7yvZnRnI3ujjemTByARU8BunEEIWPonrkf2hBW2K\n6pnNglMuqBwDvmiiuHDOC/bqOShaOGlGBG56o8jxe5LgOVFVideBb/eVkM6UEhoDt53db5gcMrSj\nAhvXQ/Yl82EuDyNcuXtSxm0Q1okMU08HaCCDiyPduGTomsgqFIU2nHfsvM2J3TtlvIAGkwqE0W+O\nvg/iekbVeSOZVo2L3mi6881UCRU+QilaeZ8yuQrfSMf/+d4HXWCqj5zyhu4Z60EL8B5UD66a8UgM\nDPd8yL7CuNsKJgaRSAjOiTycbVlJ0qA1NssEyuzGtHfoBTwzZGVphbCgpUHzRGXCzfi8dKZciLlT\nJHNWYR6Z3t7SrDNaYorBysqYdpIK57RStp3hzpwDhtJ9YFU5j4JZYZTEc6yoO3McuTClT0iqeJqQ\nZASw2UDIZAuSG7F3LuOZYBC24D2O7bFmznNh3T+l6MIcwr7PNDmhWihxx1366Hj2C5xUie1zbtE5\nj4aYUFKFaSHtV/bqpF2YWqPMwsgzz6ZY7tyKs02D2owhxrVulCykCL4SypsEaap4u6HVKdWAjXNN\nZGau1ukpozlIqSN+kP7mR+Hkg/uHC+mqvOydT65K80KI4X5saTyC2xBaQFLldK1MC3wk9V/5pPxQ\nH+pD/fdUHwac75GqeQE3ehwxa7MYHcisr0bP4G0YX02Dr919jTfnEylP1HKmChRZ+FTvYF9RN1Je\nSOUeNLHZ4C6COf2XYYPHFqeIsttOfIcP5otKcOjbvR3NWHTUB3tb2cdKGxshkBGod7hkLtG4jgv3\nyQmZ6RSG7RgTeKPtT6TpDrNGE2dHeS9GlEKdzkyP3+D5srG2lWwBkVAtoAtXcy4Gux3m4yklHkpm\nDmWOwMaNW37LS1+5xDHceEAfmR2n2Y19NHpJ9HbIypCBUCjihwdqOrTuaQi3uCHpQtRD937tRzZP\nJVH2G5KFXA+zugTQDMaF7odEaZHDOC9xTx4TAyNhkJ3qhSILMSamuvB5/yZNOlE69xRSSgfWdXSu\nMrGmV98NiaEVkcIag9SeWVPhaWyIbaifqA4hymAm0gmfJ9JW6LfgSV8lW5ZJUTF3PtkPmtVDOXGV\nRGqDRZQlG/OkmAWbgEyBdmf3wKTT1bimFZPBvRWKGHjDxUmnr2OqMCA8qKKEBsPjkCkCSQsTStXA\nSIyAuSzsy5GlktwwC+J645TOxD4wfyFc0SgM6QwB5spqnV4KicyiM6kZ96mwpDPvauY5grCVpNBs\nha7sMogq6CTMnMlpRgVIFTVjP5SalFoo+Jc+mrlUvrrMtH7jf28rFo0sd/Rwtn6hxuFUeD8a95pI\nXwAuwpnE+Gxcj62ODfQinLvyiXEE2YYRKow4tnktGzfpVA/utDLhLCPo+wHL6JOhbKQYZGnsDNY1\nsT1X4gWi7aQs3MdMG4NWO9eUuRZD1Fknp1ShpsSnY6PsyldrZYuByHEBw3o0tDUbJ5Sw4LIK5/mR\noS98y64EFa0zd1tFhxxbHI9jM50E8466kDTQNgjq4UVqlW00qs9oBBcCnzK3HcZQNB3eFbIzO3Qx\n3hJMBtoDtkQeE5AI6WySmCVDnKn5W5x7J8eEJaVoYQpjsEGa2TOICUM7OpTZF+7rsTXXdoAs9iE0\nsWNQ00xOlajCOoRL67yVQcSgOPRDZcrenoicKGlmkOi+YCTOEtR0xjWxdafmwsiOj5VIZ6K/Q1QR\nVVwU6TuZibmvTMNYAvoe7FOnSvBCIHVl0oHNgcnAZOdmia/KwubOO+mcLXEZgxqDG8KU7siWeT+O\nj3PSCSvw0jqhhmG4OPMsLKfK23Ow9530rY1/vMDugqqQAnYLBsJmivkBDCEV7ifl44cPMrUP9aE+\n1HevDwPO90qFowimFUOYbGU/IgpxnDMX7hmvpuDBY06czm/J+Yx7x23FJPFQz6wemJZD7sMh+1lS\non6XAQfglBPbd0FdCnBKh6fgZX1PRxhyIvKOd2dIfQUSCCoZQUEyWYyL3vO2nHA6iEM5bmcB8EEk\nJ5+/RrQr3TaipsO3cfeGlGfG+BQfjT2EGpXNOrsJIZmW7unRQKAnx+RoRIfBfXJGJLbvGNbGUMwd\nvPE0NjZ5Ye0ZlYQJiO6vifeVKvA4ZbKAzobpC4iweWemEh6MgNZvFK2YXVliIr8GkBbAdCE02NMd\nTWd67ORtRwIycSCfs6LluEl2qyS54/woLCTGGNzcuM+CqmH5hHgClCRC0RlRoUsiSVDVKXrDJifv\nMy062VaW+sCGQA7KktAtQV6QYhT1Q+uSnCRK98znw6iz4u5kEpfrzlZW/kMqTKJEEt53Y6s7I3Wk\nd3qsx1ATwhXlMQ51IWNjzhOrCC5BScruzqSJJIfMaRZlScqcC/WkiHRYd7zt3PX+ShMsLO6onnGd\neB47Gp2zCOKDSHq47WuFaDiOENx08DYdwImUlCkV3objzKTR6Pmep1ePRopEn2Y+6cY8Ns5kZjrR\nn+jm+HkCraQvejU9tlRJghdbaT5IuX7pzenDuPjOoyqTJlYf3Olxmx0RzOHYemF3YRBoy+TXHKb6\nCp1QghTC5sGqB/0tRuajJLzZp2PYSIPw4LwNkg28CmtcaX2g6yOLKZfBAbAwQ1WJlsETJx3sSUiR\nUTfUOqYJG4kXX/nIBr7vrO3KfZ9gzHg0bmwkCSRmdq0s3Zlq5lEnXjLH+O6Zs0/UVrmknb368fpe\nHIqyMziVjR5nnEppSiAkD1QVF+EiiXZqROv4yEw240NoU0PqEVo5WcFboXsjGODCku55OQ1Oyan2\nwohEGW+YY+aFRLi8bnQTqBMZWnL2yTh5oCkoOLNWyikxbodMuFmgJVPLI6cpY4vTx8o2OrusSAdG\nkG4HOj95ZqQNnaB7ZcRELQLTQXKT6QSlMtJgbe/Yxo2UhKzjGCg5NrXVVmo6QdUjVJdCSY5aZp3g\nedog7yiJjcYeK7fhZHHeaYM4UctBQzOHForpRGJikLgZJE4QmSlVVllpIxA1NlM+mgtv0x0zV6o6\nX3+zsQe8WycihGaCSQHk2KTnRNbEJ1enfuKcJ+E0/csy6jzioL7FkclVVf8JXetDfagP9e+3Pgw4\n3yPlIlQxZhl8MjruxkSnk1EcwZj9xkzG93/gqUzcP/zPIIJjbOsLqRuOUjXh6cTgwHo+TguzCq1f\nX2+gE0MOEs4XB8dDTqwhND8kBxaBivAyBjcziu+UeibScmxtdGB6OjTr1jAXDJi94xoMCqtmci5I\nHmjEgWP9DlFcYOTTR5wLzIuQuKLqjHEjXillIke6+xYJleNWOyQjOoAD/7o53CUOrHZ6oAsoldx2\n+nBeLHBbj5T4aORcOItyiZ3RjMidkhpvygMflzd8VBOug9Eb0So+giRKU+OidtDnujPkTNDZ3Lnj\nGB5ncW4RjPRVdj3yZHKtjEgMXmgoj94oMrNzYY9KPZ2gBpor2Ik7bpxf0a3j1UT+9WnmDccQcBCY\nKic2VGeqGpNfsLwQyTh3IdfOzOEHqefKR+c79svnfCpGlcoeR8bNWQ7kcB/+arB2bkNwNm4y2PrO\ntnfelAnJhdav1OGoOpYbLoXdOl4OqU0LWFIml3tKLpxIPPlgqYlrP9xeDsxJmVJmSke2yew3fL8y\nc9yyw+B922l9P4YbBu+GURjcySHD6mUjZEFMWAk2DcyfqFOiOIhlaq4MzThxBNOOAalwS0GUmUTQ\nIjj1RLqbuO0r++35CJCsCY8b23giW0bPHyGa0TQjIgfCWYTQRItEM39tyoQ2ghadtyX4Sjka1rUP\ntm1HNbD1mc6rnyEmTOVo9pMzOGSAKQmTOCuJrez8xyVTu0AcsBC8svjGm1HYe+ayNjYNvBW8XSj7\nBY1Bjs57z6g7HUF7pk2GeCKLIAjJHNjJ0SGdeLHOeXfKNvCWWXNmzjN5FJoPPsuFs6UjPNWCmpT7\ngFsOxtx5roMLOxZGJOUyV6ReyXHDR9BopHhPijM1TWQJugVNjg3ljSM4NhKMNJBm5DqgGioTe1f8\ndGLz4Jqc4UZJmaTOXRJeyg4ts9tCk0a1RkHJIZhCTsFgoQdIMrBMjUSNnZQ4IC850BPsPXOX78lV\nyNPAliNDpvcb7jsXy5xXZYiTvdP9kPLWLkTAyEA0NAXERBTFE3y+PWPJ6fael7jhLZiBlB7Iligi\npKY0BiyOjkFPE62Aq9HSlVMVWmy4ZIZfaTgZqOIQRsPpbeJO7zmNwkvrlOoMcboHYY9oqURAzRPf\nyIV3+xWL4FyVx3Q8d2+9svaD8va4HLCP92tmMyVCUYWUAmSwt6ArfPPF+Orng//p40zK/7zwzt2c\nq/3nioKbGXc5U/4NQr0/1If6UP/69WHA+R6p8dogn9ozxXeuCCsJZVAY1GhU7QgHIMBicN0+Y5Z7\ndHPquKPuKymMdxL0+07Smbme6da4be+oBHsEN/PDwF/OiBwH2FkLp9SYNXgexhcoq+swdhvsodx5\nUDRRprfMkeg7pKmTx0rvToqOiRKSqbVylcRJnIJxlzovodg4krolduR1W1IUwhohgUV/pToHx18e\nZHaUR/DAIiFpJhOI3FAJ7NX+Uus9qh+RL0Lvhc2c0oCtseadpsEqwRLCkuERZXfo6hRVSnIeJuVN\nnWHA1jYmrTzJhc/aM5dhmAZtGVR3Fqncn/4T2/rCHTuCMdUzD+ktqyc2N9wbzTs9CTomXApX0hHS\nGRsug+p31DyRtDLriafYcHe6OT0HkmfyfOY0LmjS43aVRh8rmgNiHJyDOPwa1M6jdnLZeR4ze0n4\nHTyshfx0PFQ0DrlZjYJ4MNQpWnnqznU4uzWec+euC5/HIEfj/xiJx5QowdEoRkFTsEyPjFRYSiJr\nZiozmisRzke1IMDzMnhrmec2wAQjkzzTEMrkPF1vJIwiwhsUp2I+iL5C7ogkEoG5sBPcXrNNrOy8\nc+WaDh+RaGF4RraN/ZR5a4nVVvbROLed0Y33aeJ5LqjHIR9UmItSwhF/Yq0BFJ7HhUxniJFw9nbh\nNL9FfHCSTFIlp4WpBM97o0ewDcPNGNYx6+CZr6mTs7JvOyJK805NGQvjYoP7pvQipPPOuBbwYyNI\nSgwSXhOa7shRmBxaHIOitmBqCctB22FaBcod+7oyxCm3jGcoOjEBm++ETpgoQyo5nApUMuodWicD\nJYQ7KczCQcobx3b3RiXLwhDosYEG3RJmid0v3Ex4Gv2AcGtm04ZbI1OofaenGz11Bi8MBg8eZApq\ngxbPeJrpPjPFQmB0A2TGQ9gmwWtASeQ5eKTQCHooc5nYdcPCiVDyGMyys+3HU3TkFWHhG3miu/B5\nJHaHmuAb80dsy8HcDt+xIZTYoHdu7Xj9dnHm8jHcZ4wg6USML9DTBXo64jBVsTJYXgyzTMkzdQv0\npIzZ8fL6rMmFz/cLuyg2BrcYIIcs7NkaSeGr8pbSwSURDncijFy5TnDYEoUUCw9Jcb3xuV/JrNwf\nuz+CgukxII0IXtJMioFIIg+hWiGmGTjTXkOhxysx88105jY2hndexg4SDEswhFMEU2y8LR1MWfs9\nLkEuAeHHpkuh4ly68A8X4esPV2YRUvpuIWXf5Sz0fzrcvJ4IXMbgTSkfNjkf6kP9d1AfBpzvkTIb\nbA4XBgUjv5rqD5Kao8CQiY0F0QnRgu0NMT+2BJqp5czzCO4040NIjw9EGDZ2mk+0CNZwIoLdOn08\no/kOBdakfEWUm307QtAj6BGI6JGV4wcSdHe4yIm9OHOaCXnPrBtEHAePKqcMJQu7Kw8x8DQQy3QG\nlAWRwazwxp7ZLNCcX8PkBLOO7A16R7VQUyF7J2yQU+V8d4ePIFzBDUSpZUb1RN8Mc+fFVlwyPinS\nnWwzt7qRI7Gz8X+x966xtqRVvfdvPLeqmnOutfbuvZuGI+cNrREJKDZIkHsAQ4IQLgZFo9H+4gfj\nSxQTBCLEaJAgYowYicFPkBh3iwQvaY8mGkC7o0JoQ2J73kRUGhWhb3uvy5yzqp7LGOdDrd7S0ijq\nOe/B0OPTXrVrVT1zrjmraoz/bcBzIfQoDY1LtkSzwmndk2rk4LTnIhsqijWY9HziqSCzMAcjWWMd\nVxg9fVzRCdQ6spt2dKVyue05a42xVeginfVQAqZCqcpeMuYcx2VPVzMH3QrnV/hmTDIiDjaDw8We\npjPmEwljFR3WFkFv5xqtLUiNEwMd6dzy8IV6vFSQLUVH9lZoTdlpJjmPiSP7EZsBhRIWs+5JC9d0\nQb989ay94wwhtXlJgV8NBB/pdcLCKU6EJoJza/qwNMwWE4jDiXApRg5DIPfGY2fj6lllXwrqC8Ur\ntVVahuQXGs5xruxz5f7aQCHWmb7b4FtDXGDWzLYph97YVWPXKSZLMKVaYBaPix2Tg63uCSg1z3x+\nzqhzTL5RVJBs7EVYpY6dKqu8J8nSqE/ljGKFIToueE8uI7WMWIr04QAVmNpMMQgSyW3HWamoZbC6\nID0IiczxaBy3a0x0+HhIodCZMGolOqg6EhSm0NNcwpeAmUNR5gPoD3pCAq0L1UxlMZmIcwAqTSsr\nTex0ZKoLakJLmK/4mqghE/0yaVcJFF8R1ygBjnzkSJVmjjg11q3nYnDcoH7Rka06SmfMTRldTxAh\neM+AQ5OiWcgers0LYtAE9t7hrGFViMVIslzP9taooSIH4H0jqbCeJ9pYwRkTRvaOLnV0ZV4Q5QCd\ndbQoWJewTUC9I2lgp0ofFl1atoqZ4Fm+D95mjqKSZEuohU0UhA5coNdGIaIGJ7Jl3R3S5j3mJ7Rl\nHsgza4GeiveNDYlt3hOnRPRGq4Vx3pN8YGpCbJ7Z9SRdAk0nGs4vrmtd57BYmGNj7zPNJqhbjnXx\nInOu4sQtf0/ncdqY9BrWgSPSBY+EBG1HESPRsOYQ3+Fcx+zPqPTEWEjiqCrMuqAcyXncHMg+UAFz\ny0CrEsGEqJ5mmaoOlQ5fFsQMzo0DdMZ0BjxNO1bFKHVCmyc64WJfOa2ZSTsMKFoBIbuGmtGFRT16\nbYabwvRlNzjzQ8HMj1DGP+e5PVqP1qP1X7sexWK/SqrlY6bWMBVOpTvnSYOnYkRMIrNbLGGbgbiE\ny4K6DvVrLKyX8MG0xoWOoAlrFW0ZWFzRTs+nYvumS4ilLuJYBfa1cGKJJv/CZU0CKR3hxLOvjdPa\naGZU4KgbIK7I8Qh/TtthWS2mA74+hrwdKFvHxey4JJWjfuAwBm6Ijo1UkjOOfIW2ZMk4F/Bt0YCE\nsEGlY8Iztsa+ZnLZ0fB03Q2k7gZCdwOSDplizzwLJc9MdcdZGbk6n7KtEyFW1t6xUeEwslg/my56\nDVfPAxMdZ6DxegAAIABJREFUzRp5mtkdj0x5BhOaNqR4wjggRMBDTajrGXxH0YpfojVR8exJnPjI\nGXVxJ2pG0oK4Std6Uk2sTfB5ps2es1w4lmsUy4gXtj5S+guUeERYH+GHQxCPcwnvI13oWDnHgReS\n75kMzmpjJLCtI6VOuFaRVjmtDdGRUeF4ez/NGjfGSDSPtYIvJzjZErsJoqI2kduekZEcCqmx0Apr\nQ1tlBqJv9CGxjp6+X+HjeZCfGQ4jOmFOia0a26mwOz2hbrekcU9XZ7JXtJso3WJVrTS0TpS6YyqQ\nmzGpsddz83LnqAojQguJhmPXAqcKOw08EMKCMJY9+zpxdc6c5S0zMKJkDyF17EPkWurZpiXcU/U8\nZ742zBQ1mLXSyp5Sdwg7EjNS99R8hlplasrn55HP7I/5+93E53cTn772WT579o+c7O/nbLrKLp9S\nWkMF+ugZGPj7PPE/R+Wf5sZ908hpqRznQp2EOiqnTWlzoM+CdzD3jrMEU4LmFKfK5S6yjitqCzgJ\npBKIfglhbUXZtwxBcGzBnSE6UUPGXCSQlmGCC6xodKlxGAvRxyVDiiXI87/VxE1euOgEUWFWZd43\n9grWAk4DWMI0AT1VOnaHkWurmdELWRxjqOyHPcnAN1CXST7jaHg5w8cdQw0c5YEL+QCvIy3M+NoR\nZIX0iWltiO8YLFGItOgQH/Bhg7aOIB6cp5qyjRMjE80EilDHxr5lrppHk+OQA4bW4wuc2CmTO8V8\nwflK7c5odpV7d/fyYHuAJuMSyBoa1Z+RfcFJj1lPEc+DZyO7E6Neq6SzCX+652BSYjUysC0DNa/A\nr4irDW7To32kxYHaPGYRF9eYG1BgpxNNCn0UxHtUC4ZRtFHIyNDgAKQ/YUcjW6KpLCRfa5jPrFJg\nHXuCi0QXAEFlsV3fWIdaQjCieGKI4FYgS+ZUno0uOcRHihV2tZC1MreMeGUdAwdhxUodfsxMdUKX\n6FbMDImJFBRUmZui59RKVU9tntKErJXT7BhLxexLNy4Puxf+i/ycosrUGvO5Hqc+mq/zX75++7d/\nGxF5mB30PffcwzAM3HLLLTz5yU/mB37gByhfoA3++Mc/zgte8AK+4Ru+gac97Wn84A/+IPv9nve9\n73287nWve9jxX/jCF/KJT3wCgCc84Ql80zd9E7fccgu33HILP/IjP3J9v1orN954I29+85u/6Pef\n8YxnXP/5E5/4BC984Qsf8bX89V//NS972cv4+q//ep7+9Kfz2te+lnvvvZePfvSjHB0dccstt/Ck\nJz2JN7zhDV/0u69+9at51rOedf3nt7/97dfX6b2//u9f+qVfAuBXf/VXedKTnsSTnvQknvnMZ3Ln\nnXf+h9b8lVKPIjhfBWXWyHlL3t/Httr5RKygKG1pF4i0BeGIG8R3VFXGElC/aGU4tzlOIhRbJmfS\nDEQZvCOJcO1cfDqrEs55zK2OYIahbKsQfaSLqwW1YZnAmgEuketiISpumaT1zrFKPeYSOMGXRZCv\nNdG1gJphLjHh6e0iF8046hrqlKaKl0VED4JZY88SQFornNWCIueoyERolcnAaWSaT6FbI84zWmE/\nb9FpIh/PdCKkEDjRzFUZ8c5zMazwkrngE9ULk2U8juAaEzvm8/C6iGfaZ4KNxCbMmunOKTLBPF0N\ntLio6Ks3jrWRWuawwSk7dr6jxh5rE1WWtqnicCQOipBTpdiedV1yOqCCa3RSMRuZ2pq1j4ziWA2X\nWPWL/W7WU5zCJnZ0bskQP/SeMxK1zov9sY9Y2eKAvS1Wy5MKipDLxICBNVI/cCNwOs1ErXiJxMEz\nAKcVzuaZfS5ApMqiCShAbMZg02Iha8v02TxIOqTl7YIU9gOfQ2jV6FohSU92SkaRosy1cm0ujD5Q\nTbk2zbSWGSRD3uNkpMqKo5TOwwQNCx7XFjvmPQYhIsHTe/DJIW3HWd4BC6KV20SzRHOFvSna91R1\nzCkxsYjxgzmyzgyuMjgPrWEhUNxDr43rIYVBINeRPYnmElMRoggrb3z+eMfZ2Z4dM02UASiaERoH\ncU1qcG9TtjYgvmKyhPnmSamzLdQ4deRitHxuwY7igeKEGh1CwUy5ljOrCNU3YoFejWyVa1qo855Q\nCyEozndEGlX2FHqKy9A8nSSigPgzrPP0qwMOvWBmrBAeS8+NLuPVqNqYW4aqbKlM0jMHI6oibTG2\nmBPEFCgus5s8GiPBZrw2AovhAaXHtCfj0FRpfWPwjVVV1nIJCzC2wM4XYhdZuQG1RMVx7VBBIrlF\nXHMcVsHvjaH39JI4DafMuTKGCfWOOK2Q7BdXNWdULTjxdOEQb4K1QqzQVoHSO4orrCI0ycx10YGd\nyhZfhYsI0jxj8aTW4ZKjY170Ui4ymrLyPSksdFtyRImo9eRuxSY0Bt9jKI6Z4ieCgErHLB17Xaz0\nq2SiFA58opBQyThJBEl0bUPQDU0rUgohpCXA1wJqisiI6wOrsCa4jlwVpNG7wqYp5jxCWshqpnit\nJF3WUKn06RAnnhAjPhhTVaLAaDPr5MmtnpuFLN8ZD1SE5pQYOrw69tWIohgFsbggt0BushiupMZZ\nKQz7yGGAzUr4cphlSwu3oEjXcmbbFkQonOtFb+p61o8+Gf2XritXrvC85z2PK1eu8NM//dPXt3/d\n130dn/zkJ2mt8ZKXvIQPfOADfN/3fR/33nsv3/Vd38Vtt93Gs5/9bAA++MEPcnZ29mWd7yMf+QiX\nL1/+ou1/+Id/yBOf+ER+8zd/k3e84x0Poz7ed999/P7v/z7f/u3f/iWPO00TL3/5y/mFX/gFXvGK\nVwDw0Y9+lPvvvx+A5z//+dx+++2M48jTnvY0vuM7voPnPve5ABwfH3PXXXex2Wz4u7/7O772a7+W\nt7zlLbzlLW8BYLPZ8MlPfvL6uW6//Xbe+973cuedd3L58mX+4i/+gle/+tV8/OMf57GPfeyXveav\npHr0a/xVUNoqtc2UcpXGCBI4T+egnvuozfQMIdDiBkXQxb6M1qalmWGx3604DkJcUJ7YYSjZGldz\n4azWRRRthlejp+Jc+AK3tcXKtVUjhA2+BNZTZa6Qc0BbR+4zc1TGqqxj4jAmcjthZKbzS07EOgvu\nPG/HiaPrLtCcZ6qZrnjC2i/WCW26/h4sHG3HmRnOdSRZHtRPyx6PshYQ56mSGMQ4m08Ya+Xa9hjZ\nFnZ1h5uUEITQRTo34FSZ+pFCZPAB32W6EGla6J0SxHAWF4qIKXIu4DZRJi0MznHSdiR1XBSBYkyh\nYS5RpkJXE5vNEVEdE8oxjcQONaURqGmNs4a4NbGM7MsOCZXmGxnI6hATjI6dgZaRFFcgYP2A6zp6\nXSIUc9mx0wUZWPWCl8ZNpbGPK2Y7QusZWKK2hoqnnjvamR+ARaezPMCAD4EhLMnxVYQbQuKCS2CN\n+wxUK95HfFSCeqIZToxewLNw8Ff05C7gnKOlDUfBYy6xb0pucFoqK1cJ4rA8c5IzGEw5450/D09s\nqDVqVTZmSN7SdGaqe6AjANlHGDaYGftaWTlPHwLrYaAfBjZ74bSMZM2EttAJvSzUNjBOqmGtLK5j\n4qg0Sq1L69kmOh8YzaGOJV8mDEjZUYAkSx5INsOskt0F1BwmxlzrYoeNo6NjsoqXQiIRxDE0Y9+M\ns5ZJ4hfdhAEV2qScWqFHKc3RtzX4JYQ2d4YVoDZs9syi5JqZJ2E2pUpl5ZRSjb5BDMZYZ0TBO2Xt\nBlybIDZad8qcV6x1Q/CNM87wzjO4RCqR4Co3xMrjWHMAXCyw18gJgnpFsjAKtNLYJkdxjbULdN6Q\nw8TMuZhdI5WJLjRucAXF47XHellCTEPDDY3erUEyF+oaZcV8TtVrthiUjBi9c8TYUUyJNXJUIr0z\nVr2SZmGYl4DheaicrDI5Q1Ahh0yvEVwGl3nsnEgG624R5O9kT8aQGtDg8LEhBmdmVBsXtElnTkKh\nVVjnSDVlDKccSKIrcLmsGVeKk4Jqz740TopgCil3RIkE72hEzlpi7YU5RJqHzit0DY3KBd8x+sLY\nJjKw18U6vDmPz4lDWREYkOZILSO22DG7sKCl5o2pcxAre0tEd8DFPmBZaHqCc8rg2qJzZM0QOi51\nA/u6p7NM83G5q8TlM4M6Vn0kilBkYh0i3i1IicwVBDpfqDmgqjirlKZECzymDwTZc60mtpOnmMc5\nxTultsbx5HAGR13iUhWGLyMap/OOXBqfmyaOc0EfAmwEOudIUlgF/4ixB4/Wl1fTj/+//8fP0b/r\nPY+4fbvdcuedd/KRj3yEV7ziFQ9rcB4q7z3PfOYz+exnPwvAe97zHm699dbrzQ3Ad37nd/6n13jl\nyhV+9Ed/lF/5lV/hz/7sz3jOc55z/f9+/Md/nLe//e3/arPw67/+6zz72c++3twA11GTj370o9e3\nPYRMPfR6AD70oQ/xile8gptuuonbbruNn/iJn/hX1/rOd76Td73rXdcbtac//enceuutvOc97+Ft\nb3vbl73mr6R6tMH5KijVTJmuspszzio9jRPpUDN65sVC1UcGWaZyKV6iTwf4sKLNE7XsCGGFB8YG\nsxW6/gBJiZN55p/2e3wIiBqzKVnPXagsc6FP5LYkk3sgeVmoBNuMk0CnnrpvtFlZk0htIA+KpcZs\nlQemHY12HqQIWhe6grVTvE9sfMS7QExHGBNRAvjzsMovaHBUHCe1oSJ4PEl6xmYIfpkAE1iHSA+0\n4HlgvMrVk6tYFbQ2tu0UZ8JqGti3mVVXaSpc05FdMm5MF/Ctcdl1XAw9ykh1HmcBbwU1BeM8mV6o\nrlF1plbFSWDlO25wA1tfOWsThzbgnF8e1g22WYkIU9ozOLgQHCdV2Zmjl0gIjYNyjUojeRhtcbJb\nO0F9Yu+XgM4cE71P4DxzscXK2QLYMlkamzDXnsFgqDAALnbkfmAMwknOmBnmDCOg6jFrTKXxYAHM\nGEtmnBsBI4ixnxvBTRyGyI0eJg/toYf7VInZ4cW44CMbhN4JFiai74HAhRQZukPOamNqBdMMrXJW\nCqaNUpTZHL0TRBolK9kv6GJohokseSE1U3PmzEecjvi0oppjhxGcZ/LQ+8B6PdD3PR6FNtK0UZc/\nH0EW1MqsIdZQFQpwVpVARdDlg26BRkdyhiUI0XPgAquw4tQ+j2OmO7car+YR19FkBUCUjlwzIRmL\nL6DjguuZCURbKBWlTUzNL5lHGEmUag6yUq0RXaBJIRPx3pEDTOqos1C0LYnx0ugtkFqjSOOsnZJ8\nxy5V5q5hW8eJVqZNJe2Nri10o+DXSNdYR2PwQh+EYzNOd0aQSFQlkblhbBx0gSbGXIUyXETmTKqN\nPcrVCLRIxWHOIRIIKVA6z8ovtui1RULs8TbTp0InHTo5DMUTCdGxGcAnA9mgcwANiBj+3M1PdWnG\nBQ+zUrpCAjbFc3Tu8tbPE0kdoygajLUIWeBUHTsPXTMsFIIZB6Vj7YRUF9MJVVmssEXxYkQ9RbUw\nyYXl4bnNiMSFClr3MAt7VxG3oiOC6yhzptNKXz07Hxmlw1nFyUhziQwUAV/gsD8AzVjXqGmh9+1q\nww0jIUQaWzYhsisAlewiyRkbEwaJ3JB6EjORFVkMDYajMveK8z0uBJxVzmyPx0CM5JULOHCBB1um\naeHAOSRVLvnIhbgiBMXUGM2WjKyOxcLZHKpuCU8OQgqL1frUGnUyUCXpBBjaNmhTgi52+FE8F4eK\nNUeh4cpy/RT8oocrjhOBa9PADaPQR/s3UZzkHFNrnJaHmpvzDscWZKeYclLKow3Of9H6nd/5HV76\n0pfyxCc+kUuXLnHXXXfxLd/yLQ/bZ5omPvaxj/Hud78bgLvvvptbb731Sx7zN37jNx5G1/qbv/mb\nh/3/i170Ivz55+XWW2/lx37sx5imiT/6oz/ive99L8fHx1y5cuVhDc6zn/1sfuu3fouPfOQjHBwc\nPOJ577777i9a+yPVtWvX+NSnPsULXvCC69uuXLnCT/7kT3LTTTfxmte85t9scP7qr/7qi871jGc8\ng/e///3/rjV/JdWjDc5XQbVW2ZeRJoI/v6GLCJgjs6Enc8E3NqHncf2G6gcEIYctblLQRmueEHp6\nd27znGCslfvKkoPhRVCMbVEqyr4qg3PkudJ7YfDunBYkaAbLBSKESXAmXIiJet4EuLmgLXMct5Ra\nGTz0LnFSHdr2bM6n1V5AvOd0/yBNjV5WiAiG4VxAXMC0MqmydXA8zst70fdMo5JbWZCsGMk4XFNG\n5zmtZ3z+7EG25YRuHjiezzjVHYIw6sQwdpzZjPdLNsnOT7iwBXXU5vim9UVwKyatiM5YNTIVCUJq\ny4ixdI04VnpZLTGrZsTkCJ2SJgFtdClxFDpmna/TKlwG6xYtyuXkOWxgJmz6HtOBU2aUJWsGqYjr\nQCqdwFEQemlUa1gZmW2Fl7KEAKoSKAwGnNryUNSF5aFMOkKueHcJ569x1jICFISmmVnbYsGrgraK\nlca5XOqcQgdZDV8KG++5MQ4UFrRHbcZ3jQviCOK58WjN4XrJ7olTIMsBEiPmFkrfYmqxp9WRbTOs\nZMzgRIVLoWNNQ9UxOnDnDa0BuTaMgRyNIfWIgUsdRY2c95SUWIWOo6HH+3ObXquUtvzdzWA+p5c1\nMzoXGJxDxDOL4WpeptcsOUbORzQliIEb1xuOYmDwDjM4TI/nOA/k8SpZFJUVarKgJOKJoWcuS3Pu\nCODAO9h4R26BqpVoRkdmSTldTAlnAd8q1SDQceQHYoxoOWDKEXJki4FXQoVYHGqeXpSDVeXBOBE7\nR6UwY2wHI20bWQtnolwiEhX2fcK7QGkV14x/KHuKecStwZQqDu89XbxA6npq59mPE4ZndB7nhQcp\n7JPQxOOqo5N+GVRIoJgtjlteEA+HKdLcakHlNsYeR6uZoVYOw4qDKKgbGLWHqEg1BjXOWgOD6Izc\nVuALk8HUCtkqXVuRQsCViY0teUpF53OLcceqwVEJPJCW97g5I5gQ8Phzl7tYG94CiKOI4NTRFeNa\nbFSbqNXIzpGC4Z2xiQN+KlQMf26d7P1CH8M7YjNm1xNFljBYGkgDB9shEKfE4SC40KG+kWiMVikt\nUxFqyWgbQRsHXjhpDnSxLr/o1hwNPWKZDYmpPZT7IueNWSF7WT5z5jAcIo7oG63s8WJcShsGm3E2\ncBQd2QMoapnOOSQ2drnShsi6C0QnUDJiSopgSQgOvEUqdUGcT0aYZroyc8EXRpQhrNjS2HuPieBN\n6RLktmQpIYvtvMfRaWBfHA9slUtrIf4bTzXNbKEDAmOrjLp8TnrniBLJqozntDX35XDeHq2vqHoI\nNQH4nu/5Hq5cuXL9wf1v//ZvueWWW/j0pz/Ny1/+cp761Kd+Wcf87u/+bn75l3/5+s//UnvySBS1\n22+/nRe96EUMw8BrXvMa3va2t/GLv/iL1xshgLe+9a38zM/8DO985zv/Iy+VO+64g2/+5m/mU5/6\nFK9//euvU8nuvfdePvWpT/G85z1vCVuPkbvvvptv/MZv/A+d5wvrP7vm/z/r0Qbnq6Bqy4gLRKuM\nrCiuUlvE5Fz/Ip7RRXrXMfjEWGeaRMQV2gp89lhbpntd54mDYyeVZotgZp02pOg4zo0hCHsLeBHG\nmnGmrAkktzheRRG8OaLAWgNNGqvo8Hi2pVHajtIKOOjbcjNMVgmuklJga4GuNiIJcYFswtgcbd6z\nC4AX1urovSOENft8xi7GxRp0mnDeM8QBknK6W9zAesuErqeJcCrK5/dnXNtP7HXE15m9zsxW8OLZ\nxpm5VHITNt4hSSl9xacVgnLcrvFAPeCJm8dxVndIgT0Tve8IwWFNkeYwGn4VEG3U4jiVkXld6CSx\nckvDuIkrhuDYzws/vJnhNSwezDoDkLyn6+GIwNQPi0ZER5oCZuzryN4VUhYs9oDRzjnz3iaKFsQF\nxDlgYKjn+iozxCXCeb6Ec4lQC2cEdlYZxPBAdpHZPEa4TuPqXFgCU7XiCFxtRgCCLNqsm/qBpjC1\nEbOJwSmHXnhM2rDeDMQcoTli11MlgYJMYNXYsSe3yr4sRhYrNZJbEJVJK8lFYiiICVJnDMeuQasF\nXKWFgTxXNlHwGAfecQBoCnTOGIIQxbGvDWMieMdBUJILZFWaGQHjwAeQyCQBV0ZW0UOGBBRxKB4r\nBdYrLoTFKW5xCzQgsI5H7LRRdEKsUnWxtu78gIij8x4TI6YebTOCcRDCgsJVoddKqB5pgau1MobG\nzrZ0nDtaWcMkcOQucF/ryBZYt+U73dXFRc1EUJfZ+0w3C0P0qMzkcwZe00zuGtkyeKNzurgpethq\n4jgKae7oWySq47Tucc64GNISFGuG5cY2eqQXpqmydY6mkFLHYNAmz34wXC+MrREoMMOM4AahD40j\n6dhVxXwD75E5IJuZuK8cuYEhgOKp7givga6bSWXJkTGrmHlUEoqjhoZ6pa8BjzGVicPScOGcHumU\neD6siU5YOyOrw7llMLQOPb4ZbVRW6tEETSrWHdI7JVukyERTR3OZoRm5wR7PmSgbNTZdpFSHQ+jO\nfX6kT8TZ8NLhnOAFEEOdw/tEWPXM4pDOgxgOwbuIcx2da5ywZV8Lzk6xc4OLQQIXu0N86DnysMGT\nLBKr4s4dCDFhbBOmFbMTMFBuRPwBhyHhhgAy087RRgEuph6vmcEvmT/qC6tDY/SwzTOlH2kIxyN0\ntXGQAkMEb0afI9o59mLQlHz2IHJ6Dd+EwwYb9gxhBc4TvdF1PSGtOd5lfD6fbDnBiUdQvHhaE7az\nsuviovX5Mhqc2pYGuJ2j6g1lNuVqKVSDoxQfwnUerf9CdfXqVT784Q/zl3/5l4gIrS25cu9617uA\nf9bgPPDAAzz3uc/ld3/3d3nlK1/JU57yFO666y5e9apX/W9by5UrV7jzzjt5whOeAMCDDz7Ihz/8\nYV7ykpdc3+fFL34xb33rW/nzP//zRzzGU57yFP74j//4S57jIQ3Opz/9aZ71rGfx2te+lltuuYUP\nfOADXLt2jZtvvhmA09NTrly5wtvf/vYveawnP/nJ3HXXXbz4xS++vu2uu+7iKU95ysP2+7fW/JVU\nj7qofRVU8xGVSAkrBM7zNZYwv8FVBlEuiOBF2ZctnfM4t2glcFBSJg8jc7elDjPRV4KDwQlr74ne\nL5PYsCLEDZu4YhU6vCzIjcryQVse8paJmLpANCE5f568A71THEo512E7dXgRbug6ggiRTHSNlhaE\nYDahnKMEJ82oVrjaG/+IcOwctRs4W11m9iv21TCJ+LDChwGNEetWZOc4w3GixlURTvLM5+ZTjsvE\nWYXjtiAEy/240VBG19immZM0sR0K625g7btzLnlHViWIp3cdm7jiUrpwPTwu9w0Jwsolknh2umcb\nd+RNQwNMlqksTcIqLO+XwDIJXaJLURzmukWo3iq9ZjrOiFoZgpB8XPJa2oTaSEPJNXBWItM40mll\n8EKkkWymx1h5Rx88qh0xDPRxhfuCW7z3CVVHaY4urIhhRfArDroVFzePIXhPcMpKFmSt0DOTOEU4\na8pskHEU15FCx3/vE1/X9XxdF3l86rgQB1r0nE2VXbYlwHEeCXlG8kzLhTw1ci4UEcx7jMVtr9ry\n/nirqIvMEdROGe2UM53pbM8RMysJHGkFlLMxM+9O0GmLzCOrXDhwwtyWh8NsCxLiXM/ghAvBcRQC\nF2JkExPOG0fdmotxxUHakLo1R5sNB6tDjoYDLg4DX7MeeGyKrIJfps1xgxlsy8i9u/uw+zP95wI3\nnA5c6i5z43AZXETF0ac1Q3+BiNA5T0DYN2NXl8/WkfsaHh8ei7MVaxmoW4URdjaRtdKoOBHun7fs\nyhljU+boEOsQjTTzFFNGN3JVRk4005lbAkyBUApWCq5NeNcwP2Fp4tRd5Wr9R47ZclZm8ljxW+Wg\nJi6XgU3ekLQjSqKace3cmcq6jryB2nfM/RKE2sRo60qRylzHRYdkiogRq1G08vl94ywrpiucXYB6\ngT4O3NAfsDo8wK0D2iVa1+NCjwsRDjbQdfQ+0VlHam5xIhNhu0qYHDC2xJk5shlFM15HfDC6sOQh\nVWnsBFxY1FpNI7VGhBWzv0D0lxB3REsDLg7EdEByKwRHCYFBO85KZGy2fPerUmeYmjGpZ5Pgsd3A\n0PcMQ8/B+gJhtYbQceCF3hvRNUyXZqSFkYNYkRCoK6gD0Btz2hO6Y8RO0TJed+9TWJr8MtNJ4mI6\n4CAe4GRgq3DWlH2bOG4j2ZRsykhmbxOmx3T6IBc2B1zqLzFIopfAIJED19O7RHTC4ISNFy4l4cZV\nwseK840jf4GVC3Q2Y66ybTNFF6e1levwc8ZaxfYTbSpMdabWHaqF1hpxOoVW8F0k9IFN79hExc7D\nlx0GGqh1IOeBcYqcjkIpUL443uaLSliynmpTslbG2sjNyM3Y18a1Mi+apP/UXffR+r9RH/zgB/n+\n7/9+PvOZz3DPPffwD//wD9x8883ccccdD9vv8uXL/OzP/izveMc7AHjd617H+9//fj72sY9d3+dD\nH/oQ9957739oHaenp9xxxx38/d//Pffccw/33HMP73nPe7hy5coX7fvWt76Vn/u5n3vE43zv934v\nf/qnf8rv/d7vXd/2J3/yJ9x9990P2+/mm2/mzW9+83VU5cqVK/zBH/zB9XPfdddd3Hbbbf/qmt/4\nxjfypje9iQcffBCAT37yk7zvfe/jh3/4h/9da/5KqkcRnK+Ccn7F3q3J1tNcw65nnLN4cAXHxguN\nZep6OawQB1M+Q0RQrSieXRkZ6hEaF+eiblgRnaO2RvkCa00nQu8DZh0H3ghuaXT8eXNjHoQOPc9a\nkbbQyrBF5D07xbtGCB0+VjrnmEIH846eRguNItBqj0ki4xicwaCob3jf8UAzzljWpXj2RSjq8OfL\nzKqcaWFWZdIZXyJxF8h1QjMM5QKznTFaxTfBa6b47cIjB/COSSaaGEcysG07EMGTODcgpneJao21\n7xFuLnAoAAAgAElEQVSEanVJvE+OzhIn+wchwSomECjqsGCIGOIaUZZbbHKOsVbUwHWJIrqEfI6F\noY1s/MQDZUbM4ZsQxaP6IJOrqCiHzWFUuvzAkleijTIUprLQ5YRMoKP3DjXDC0QPnYcsjmJKbTDR\nM4Q1EipOACeIdHjnOFqt2OviLlWpdF1iLh2tzexaZTKjc4nkBrJlohVukAlzM811EBN9cFAqVY3t\nOCGhQTBOijHZYitMhF1QvHd0JJxONDMuCZgIu2B4TXTS0ZwRtRGjIzjBzFFcJdZCKII6xzqAs4JY\nIhRH9oUTa2QrRLdoci76Da3NnJXCXlmQGN8xpDU3mOfeVujCimCAKREICCsveG04kSW3xkUmjbRP\nbHnCX38NvnhK1xgvFspl49rjZ4YLG0INrCSRXETDxNk8gezIJbMOwlE9XJyk3JqY4IH9VSoe0RW4\nmT55kgU+nw1XHLtZ8U3JobARz94VmkB1oAEMw6QxqbGpno1kcitMraC6UEQHdVjLzHUmmzLVzIYD\nJo3EBnOxJQwX0CngpJGDEb2y8R0TsoTLBuGUSnKgzdGJsYqenUGUGSGg5hnwbLNj3yYsFg5jxVug\nVmGe6/I+B8feB5oznIf5rLKJB3SuMJdMEUHEL2ihwEm3IIoEh3dCiXta7picY3aBqMqmc4yy4wTI\nJoQEvXX4FrGW6KQnhoRfyXLdkoCLO5qbKAJ7Zwv6YGsCAXORi9ktDZ8T1BpdnbnoMweDowXlrFZU\nI0cHN+AChGJoOUOc4IOjJkGCR0VwtqUL6yUbqu7ppNLqyEDDUKpWsizXGxVhrxUrE3OMzN7o7RBX\nZkrdcdpOKKok34Mzsl9kKGMY+G/DisqSU+YlMYQVczmjWMOJY+0jcN5NiAMfKVapmvChZ1MK1XUU\nW7SKNUf62JOtMFpGJ8UVWOVK1Y4iyrEYDUcypdM9PtxAcIUziwTvWaXKflamKdF0yYFSWUKsx9lx\nbQ9Fz7mx/0oJD+WONU7K+XVVluusP6duV9NHwz7/E/WlDAD+T9eVK1d405ve9LBtr3nNax5x+6tf\n/Wp+6qd+ijvuuIPnP//53HbbbbzhDW/gvvvuwznHC17wAl760pd+Wef9Qg3OU5/6VL7t276NF7/4\nxXRdd32fV73qVbzxjW9knueH/e7LXvYybrzxxkc87jAM3H777bz+9a/n9a9/PTFGnvrUp/Lud7+b\nBx544GH7/tAP/RA///M/zz333MNnPvOZh9lD33zzzRwdHfGxj32Mb/3Wb33Ec73yla/ks5/9LM95\nznMQEQ4ODvi1X/s1Hve4x33Rvv/amr+SSsweNX3/t+qf/umf/q+cN8bIjTfeyP333/8wv/Z/b12b\n9vx/D9zD1ZNPc7q/ylnNzBizBbwoF5m4HCJ9t+EgBL5mfUTnAtv5GoW4hHP6NUPoGboN4Nn7DV3Y\n4LpDdqWyE5jnf17jOjiC86yl0lpm7SGeaxa8Hwhj5IJG3CzMNTNqQ+uMSmXX1WWyulIGV/CaGVWZ\n1cj5jEEniuuY5YCpLTesg25xqApxA75fHLFCYD/uGOfC2TTi1EheuHThgP958jk+lzNFK/t95UgD\nqxCYamEqmTgF1jNcDTuyH4nF0xlImNh1W07XlTkWNmHFhXiId57oEhvf84T1TXzj4X8HFm1NtiX/\nIUpiVzPCQtU7zvcTz+NHH+KEOxFcdsS24WK6QNcnTnd7jovifMCtAjst7PenlLxlLcK6bqm1EWxH\naHvmtuPe+ZirbaAiBMt0onTOE7sbyN3/w6V1z7YqpRmIJ4QOEc+NOvC40LNKSvAB/CHb3MhaOc4T\n21XBRDCpiFsuHSlGVgeHbE9OONk36jyza42TVjgpDTPwTjhyDvVQ6jUOBDa+EXUmSuGGkBjSQJg7\nwujBCikO+LBir8rVnJm0sA2N087RxHMhRnJVXB45CIvw/CoRq8olGlIaU9kSzrVdtDVFACrmHeYb\nfRep3mPO0adACpGj9WWyVYSZaoZrlYAgplSDhtD5yKXVY3C5MM0ZA6Y6YjoTZbFTFxFSjPg+4OKG\nthU2/wPSP4IVUGc0r9SonF2aaI+H08c3wmrDUdpwcHSRa8f3c63cTw078B4vPd1OmPMJ+1aZJHC1\n7HlgmjluZdEoDSNGj45g5SKtbciuY10Sl8pipKFSaMGBKxAmdGikzqGc4sKE0milMWtmIC1ucHVL\ntcaxVvY4jvIRzQJdNQ70oZwU5THSsemF6ahweX1IWK24VoxWe3KZyWUiAWEfaA4k1HMkVvASWIeO\nYD2nsXHmdrRmeHGLBmc8Y1ZZHNoY8D7hRPDBE6eA1RXd1NBayDkzmi3NuRW0F3YrY7M5oIw7xAIt\nN7qcuTErG1+x6DiJwtY1pv/F3ruFyrZe952/Mb7LnLNqXfa56RY5diJsQVCQsOwHOzQIBwsiHIPR\ni/UkqxEkwQ95CMZ+N0KB5NFY2E4sY/AtCXQgL24UWsIvgTRpHEMCtnG3CHFLOpd9WWtVzTm/yxj9\n8NU50rEU2u1O3G5lD1hsqMusWVVz1/eNy//3pxDyguXEtGduy4I4xJi4no4sVXm677R8otg9uzbO\nHWLsnL1xDo10EqI6h1DREDDbyb5x40a8vWa6mFMmScTDgUeP3omTefzwlJiEdd8oPrqUo/ggzDFD\nX7E2RhdDe0ozQ2LntX2lSSKEBDBGXfM1IV/xQopom9mfvsGz8oxtvyedO7kd6FOgpEg/CCyDYHmd\nrmnzyzSvPGyP0X4iSScJXKkO6qF15uNL3L7jL/GfXn2dtc1jfGzf6M0pTS6YDOGFNGN6IsTKqezc\n3T9jvXvMuVT6pWPyEpFFA0d25uuXefbo3VjObNX4Tw9nvvrsBZ48u6VbGB0qGd3bqMJLR+f7v7Pz\nV975X/bDWZaFr93d8Uf39/wfp5W71qiXLmNQ4SpG3jlN/JXjge88Hoem0zpmDdGA6p9PTfjPuva/\n5z3v+W94Vs/jefz/K553cL7Nw62zlXumvDDnl1nLirSKcsHdEmhM7AgvijL1E7ZXLN9ySAu17TQE\n+pivd19QUeYwUbyT65lFIxIibZB6WYLyYs5EEarHMScehSCKXJDR6UqxdcfWnWBGMmeVQsudrGkg\nY5OSdMEskdvG1jeidaJ0zAvB7hGZiDJoYO4ZUWU3o+O88fDAVnZ2dzYBrJNceHz/Fe5roXrnrpyY\n6swpdB5vK0sdm2xvzuqNqzqRtkyPxjpVYnJevBKSVNZYuIpHsii4U63iYeImzoO8hdG9c+E0sYQj\ny0VfeG47JlesvmOtvPV9RRUOy4RY5DocWOLMHpx3ZGFNxoM3TqVx3h8QGg/dOW8bwYypV45WuW/P\neNIjTzxyRjGcpRWuqLzUHhP6FeRXuE6VkzXQmzEWJJ0Qz8xx0NuMzOunnb03gkJPzuqF4MISZuYw\nOjkpRQ450kImLfDYOuc+kBLqYxRJcYoGaPdgjSIRQgCEWTMV49CUVDJxlTFi1yIskKQxc7m4KKAT\nZ6u0snIMiRbhyfaE1Sto4iiBLgb5yBwPUIy9J7IHghubBoro8ABhJHhIZetjjLG3lasYeWoDoCEa\nyRppvY0NY0hc50cXep9y486DGUs40nuk2w5uuHd2FYLO6J1x+F8g/ucA+0VL0AXtQjTh+MbMq8tG\nDMb23hPaO4dZ6fUE1RE9QFb6XrkrBes7b9TKQ+88bjtP+8UA0Z3HLdFMQGYSkSwzQuRZGH5B128K\n+C/duhIVYuO8nbgLz7hxJQTnqnemEhDrBO8sHliz0MOO75H7NubZzgq7FiYTZlEep0q/gEU050FA\nsIDGieBO7nDoglgFc05muBbECqYTTTqtb2xSySzcmdGDs9cNb5XJExX4qqxceSGa4mbccmDywH2v\nqHeSK0sRTmUlunFSp85Kk0ZB6X5iDZDnQI0rqQpzOFBkZpLGIVyx5k6XjC2ZfttJXbjyjsrGWQ94\nylQapZ9p7aJpZOCap5qZoiPWCJJJNGYZY5SnOFFiZ9HOQSPMgZCVIsZC4ToZTTO7jEQiqRLC6G7e\nP9zRTBCJAz3SAlfhGXsXZoXulSAJEYjhQm8DTt2o7VV8Noor4Vmg7MK9bhx6Imkm74kHb7y6NAjG\nK0F5o1VimjFxMoVFG+bGJpEXr97L4fgCy/wCIRX0YqjcUfb29tqpXUaKawvcOWytchJljZFSDPfI\nRuQVF6YkY9TvesEFrpaJEiZefzqPdMlHYSuIIgKqztaF/3zfeeHFRhZhDuGbIAHdbOCpRYiqXMcB\ntXgzebxJkRyUFAK1V87P/pDt/McITsw3zMf3Mi/veiuBfB7P43n8xY3nCc63efS+Yu5MvTD1x6go\nLRypfXh1jC3m8MLxvhG10DpgxpJv2Juze6X7QO5q60zzkewbtTtGZZaFRKfIGNV5IachkgZOrZFC\nIH4DOSSKMEmlTWd4EaRAbMI1ib2t6CxcTZlT7xfzu4h654UU8XBDKZD6ztqeYd1JywsEOQJObxud\nzNacu+1EsEKxRjVYDSp16Dn6ilmhG3QxrFRkNV66n0k9Y9KIwHUR7uf7oSGaZ+py4hgXkj3D9QqP\ngguXcQnlhXQgBuX1+oysYxHcuyFiJEkD0Xz5zE/dgMSSJmBsDFwim8OUHA6CToqhA7pA4GFvNFsR\nRlLUy07rnW6Vswcqyn825yt+pCC4FcSdbsruzh3GbbmjnjLhaiYFYZ5GsivAIQldz1h9mcdb4r5U\nnIBFp6dIJFPZ2fpOjgmRQRxKXUgS6VQeLQun3tnriVXr8F/BONdOtEKzxiTjuIsERDqHEhANSEzD\noh7FiegGTQtCwEXZVRCb8PZA14apsLYzMSiTTgidwJnanV4d80dkmbCoNO1EAksw7uqZRTvRQPuJ\n1Qd97UDAWsBECb2zu1Nl6I+EmWINa8a7ZGMOIxmPwCKCqlJ1AZ8Rb9xLoeSFvcLtH8L01QgXY9s3\na8xigrTx3OUxrLMh1Vl95dmeeHV9hrQV84ldO72fsRbJolQX7lvlviulG0Kgo7R+4L5lcp9wS6hH\nsICJ8frc6ZvwkjjzJPikxOyU3mh9I1oi1KH78d6Z/UynsnuAFphMCSlxJnPlQnBnF+WkTosGUTmo\no9oJKVL6Cm5EfURx51gU7ZGtnkGcqQRmi5ynEyg0q9zvzhmjWmQOMOmE9T4ocyR2AzNDtFP6RhZI\nOuFaead3uqzUXeh7wjBKMKoleq34Q+eNuCIEqkXMRomnxYktNJo4KdZBe5sdwVF3AkJSZU4Qg9Ca\nsekZKROLT0i4hX5PSjtbiMQJpEUkFCRMhKAc4/CcOZuwyijeMAU2HVdt705oFY2BY6qIVnJ5GBoZ\nlKd7Ym+Ks5LEEEDEQTtbLSCN4pna+6C8xYUoiSVmugTuamE2AwXtA3Gwh5XdImpC646UYaSpQVmX\nyLlXJhGmmPGQUW8sLtAElQnxmSCJHKdxLpdokjHbqV0HpMESPSgWFAuVpzEDxsmhVKF7Ri/ubM9I\nTPHAcbnhOh9QGSa1V8mJRLIIRRW/oO27QbwsL1sdna7NnWLOdYpvjUbDGE2DQUyLIoQQmL9hvXwT\nhOO98rWv/M+07WvgjshAue+nV+mPvofrF95/Mat+Hs/jefxFjecJzrd5uFWibdTta6jdk8qZ2SI7\nsJvhmkd11zu7TSBC7xDKAw9yxakrnYzFzCaRUw9cbyceTcZVmBENHKMyT5kXcEzBQsTdECu8rBV3\npzUlhIkpLWRV6v4wTlAdT42h6lSSHrELISyJUGz4GixiRA1ozHh+mW2/I/GMFIYSWNMRDQlwajtx\n2goP+z3RK7uN2XL3wKu9UC0QemfVzlaN66eJ1GaO20xqM4HATU/M3ckIqQirrExrpCWlp86708Ks\nyrN8opmTNXKbjsz5mvv2wE04vvUdiEQmiax9I1xwyLgQu1DahuswHvMQIEB3SDoTRMdw+Deso/dt\np12SIQB6I0lguAUZXgMPZIp09qaoRV6sQnYfh+qGW6TnM70IHG7Z9Uik8oJCRmg90FLkcdnZErRL\nuTQ1RXRmjorJSA6Lw6yJdy7XvP7shJfCG3Ul2OhGRBlYYzfjoXWyD0fzAHivFIGTObYGJJx5dLgi\nxzrMIwmYN9qeYemcQqMhSJyIbaeY8KzunHrkJoDaHcXONAxFqc1QH6z+aJVmna6dSOS27VynMUYz\n2YZoYw4LU8p0g7U4jkKPSDRCCMPEcxBleaMUXkydTQtXaaa1ztQLA57t3Iuyxml4fLwqyKsCG2CC\nmKACdtkQigvaIe/KuTrtVDhJo8SNUzkTRZh7x6xwZzsiRrPI6pGdPNDU1qAnkMSyR8yhy0RTo2kl\n9kBAcek8m4w5GBYqws6272grrNJJCIhxswfcI2tg6FuG2y8WhLnPXHnkqsyAMEmkR+hTAIWbKfHi\nMZOnTtMwOpn7A1f7kWkP0BtRhD05HgxdjasSeJpWHoA9bhSd8B5pVjkmwVXRPhDcm2+kYMSw07xz\nb4UDEY0vsnFF74pUeLA2cMcW2XUUaUpp1PPM2RtR89jMEjjHFZXEvRrBKqLCtXUCO4qzSebchnaH\nXqi1or2ieSJgrF3xmxu6FpTOIazI1qiSEFFyUGKCZ6XzgA94gDlnh2iAGsGUzZzYG6ncMc8zpmOM\ntPVGqYZ1p/VCw1nEcNuxvuM9jG6RRoIuaJhQhm9RSgeMhJczAHVfiZtQ0ZEQ+8VYOVTUM1SFnugk\nzr0wX5IHcQhrIoQ4sPV9mMqKQHhZOcbEmY1uwlqVh7pQ9/G7s6hylo55YhOle+EhHXjadmAGKoqQ\nGGOLbVmo6fYtfUwzYYrKnEbBR2WM9UYZDUJxGYlO+vp4ml28do7xm7c5U4y8NCWe1DqIk4zXmVW4\niYH71/5XptPXTROdjlijeuPh7n9nOryLaX7hT70OP4/n8Tz+/ON5gvPfQXh5TGh39LLy2HVU9ml0\nh+CFGDJIpsjEgySWlBFfWXthc+FkkOloCATgSdkRydzmwlU+cgiBrMNGXYAYDvS2Df8GABEyDr4R\nTHCmYfbZO7KtbztXEWGNmRYX0NHpuN/u2Coc1MbG0CG6Mk03vOTGUwtoSAgjF8j9zJO6oexsFy/L\nDlQ6sxYkCGuvpE25ejLz8sPCXBPX6zXRIyUZS1cSgobGFDKJxDFkksF9vcOnjlhgJpJT5RCHN8gU\npgEWiAtZE1kSdxe0T3fncdnIktnWM7EFtn7xlzNDzHAz5unIdTy+9XlMqpx7p5mxtjpGqkKEshNE\nxqaUAL2yuqAcyOwUF67qAZWO6OgcdKm4ROK+M11dEeOEpogS6XthiUprwsO28WxfaURIY7yr2EBD\nX5GIMTCFSIrCMU9MvTH1xlPrrH2ABo7ibNao1qkYYpXilUkF8U6QTgrC1mcmSbgmDKdEodUTL2i4\nYHcdm41kcJLAVjtnj0ySMCscFPZ+JomPkR2vFM8U60h9DeVFWhcWjVjfsFYIUhER1AS8oeUeCyt9\nmolkrDf2VllbQTThy7iG+uUnc687q0EKyhtyYooHRDcO0qlmvFZWtAWu/ZbpjQlOI8s2E4Jx8aES\nTOwCIQALje3aEDe8O2kL4I2zdYIIJ2ucurNr4cUObkLgAKak0ok9scVItEo2IfZET5GeKh52Yg8s\nRTiaIFTuj0/YYkEvxDWzevE6HKaMESf3zINu9DDheqBZJe2RFDuWjbhFQoArmYndSVrJm1BzIa2J\nm5SZRHiogbQbQSOYcDCle+AhFEo8U/s9T9hYEawITQEJuER6KxeKn7MxOq4HMdwq3Ssug6Zn0ngc\nT4gFgnf2njDpmBa6OK0Ear5G90RnA22EKBQaqxtRCjd1ItVIUseujS02qg44iPWMuCJs0Bn6OWl4\nMJbkbObkfIsAUzpQ2xPkJENPFoYkvwKz9kG1DJ3gSkPZDYIGkMDezlyHCXdn7f7mTyhmSqn3IIK1\nFaMwXX5zG8NsNMdImm4IOsx8p+UVTBNqRtLAw/qEbTtzKJlugbNPqBScjlkfRpwy4XGhEnnL0AoI\nuyAumA8zzEFGE7I1rl+tXJeFrRVeXTulQPWE5kgyYfM+0P0hQT3j1ikE1nAkWSWKEDXSwoSniMaZ\nkBasjwKP+/hLQcgqNAPVMabWHZo50o0qO0+LcRUj8TKu/PVS05gegNGpeTFPJFFK7UgdnZtDjFCf\nodsf8yfD3UbXv95R11efJzjP43n8BY/nCc63e2hC1ldhe4217ew2UemYy9BtSCdZp+oVBwlUVc69\nMTEIRK6R3nfMOrCjOuFWObWNOUVu2x1reQM53A6vEcB6QcP0LSk0vW1ISmND/yeSG4DSjb2eifkG\nJHC6iEABnjUjyFicrDlHdUSUl6KjMfDQOnvvnMs9yZyKYhhjdK2z98asRo3KYXVunryEnuD6dOTQ\nE8GFbLBsSiLQU0c1EkTJMpOS01m5sokH2eg4pQt7j2xWeGlZ2Hth80Kow+fiGBdgHoagbeCtszSw\nTpTIdRjUJ6TSzDha4CZcE+TrI33mzsOFotYY1VZNM1Mt5BjwtXDcVobrS+QgjzhzxyPvTCoMF5qd\nIBBlIkRYyfSqhFZYYmPqhrphpmzWeaPvNCq1G45RQ2DSBKYYwhICSwp06az9xJNT4dxXuhdERjKX\ng3LTG93sgicXSjeyGUEaswZcAhpmqB1JC6EHYoG5QxBHLgLzrVd2IMYJHA4qzCpUi5zaHbttiHTU\nK61Vdp2Hw7xX9rDxKFyR08Rh30CM15qy1RPJnBRWqnUeMG72xxzDGJtb60Y3Y7LGvj+l6Uw1B5wp\nTVQXojmn/kAt90whc8iJ4g4Ou+3cfGUlvi5IydRoLJJwH1ZG4xsegzkE5/7Rzn7diAJZDG+V2BW0\n8ZTKZs59rwQxXs9DV/SsCQ+mBDJrchKRIGl8lxJYaqDEcZ4vngOpN9CG2UZWQzpYAKTjvSAqmEQw\n554hEN+60qJjMY7kwEHYWdWIs7NYYAqQipBInI6V0J1DB9GZY4loFEw7pp1dOopzrpXTXlilsrqy\n9AOTVU4XR/lA5RwdSYlgxqxg9QHVob8SrZgnTDMhDwT3vRQkRg5z4kxH3AjecVdOEeaayZaw1And\n8Fw5zUaokUebEc0xaUO8/mwYg54fFfJsmM48Kx3VSrbAwRuuE+GS+B/7AyUcKBKw5oQp8xKGtFFQ\nkhB4+XDgfj/zmJXaFTElamKJBw6acW9EOiEuNPm6uDwA3TZ6fQY4Zs7uDXwlESFORFVyvoL8CC7i\n+Fruh6+SB3jY8IfK1COhBZIJB3U2yyQN5HTgMN3SJHPKIJfXubMz5s5SIotEzoxrGIe8B4quPNEn\ntPKUpSjvaIFzC7AFJClMAxDRyk6wlaV3ntod1GdEJmqeOPjMLCMBifOC5ytyjBxjoNJAnIdtjKWl\nPGzAnEty404MxpQLIRpPa+Ohd941jc/kGyPr+F1WxgTDtCqHk7x5JzcaYdupzb6xT/5WuHWsF7qX\nb3Hv83gez+MvUjxPcP47iL0+oZQTp6YYjTOJ6kLy8RP+wIwyE3zAo0u/I6RbAonubXRn+ugAtHYi\nhAWNC0kD9DMO1PYwRP4i9D4M9mK6Ai6LgrchtteEe0f6t4b3Ffchnm3OHgxzEM2sfSxciziHqARR\nqneywB2Z0DqhdXJ94OiV6154o0KvbfjsSCTSwZ2rsyBvzBzvE4/OV8w9k+qAIJh2og+fH40CQQka\nmFRpsTIlZdID977xVHe21ggSkCDcN2fzM6/khe5GdWcv96jsJK5wBtVIeh/jd/gFBX3RosSx0J7r\nAzkJRz9ybo3qw+RxN+MmRsyc5kpcrgnrM1qrOI0jRmKhOGNkRxzRdVQ+eTPJyGSvLPEG8QNnV07r\niUcEXkqR4saDj43tpEI3w/qgju2tcKgzWoZB5qo7+6jzYjHQvI/NoVeci1lpVEI98USEzSvQydKZ\nMc6tUwxirjyKC7MnrnogxoCkQOvj+269oF1Zg+FtQ90vbkAHppA579vAlKvRO2CKm1BqAg84whoq\n3hVkIgbF6ol7F1ISjuEG+gPmnbt94xBfJ6Qj6sYkgUkCqzesbyM5QVHSwHq7Ub0TrOAqNIvc186p\nQz0bLzw2qhQkR9pR6KUTTHHToaEAWob/9N6nfPW9d9xaQqwz6xjZoVa2qVGnOIw/GSNBQqTlxOaV\nWsd4p5HwC5Uvi9AZ/2ennrguQ//kaogXJDeyN47b8MZ5fDwTZQAp7rQP5LUkEobGa0KMVI8kz2wR\nakqj2ADswJptgD6CE3DOBnfVuPUGJFLtPLGN2o3iFdyo3Tib8Vp0bmqEPJLhZkqTjQ2YmyGL4xTc\nGge9p/bx3nJXZpwHj5xr4lwSOQYsd6TF8V5N6BIoAst+JKoRU2PSlQnYm3DchNwY3Twq6mkk9wYQ\nuVorPu9MqdPL/aWQMyNxmEF2A0FZgPN5o8+PcLPxmKlxnIyDRJ6WwhPbeBord96x7kxBwDvbxW9L\nRTnIMIWN6RpNBtZp9cRBKkUzW33AmpBqxC3TQySakKeFKS14jMNbpu8oC4tGrjdofSDBe4AUnbkE\nrEHoyiEt3KZbwjaxTcLZO+4NvYzUltYwc1apAzihM3EzpK30qPQN7tc71pIpfsXsgVNQMOd86phV\nsnWaCl06bsqBSDRnUyWHSEBJaWIKGRHlOgamoBxC4o1itCYEVa4nOCgUg3MzgnTm3DkuxpTHmtbM\nuauVV+ZvVNiMzqkK9O7kJxCfDGCBAjc5ju5qCNzbLV1O8C1J0UZKz7s3z+N5/EWP5yq5b/OwtlIs\n89SguTBRmXBUhU1n7i+3VIl0hyqBGm7J+YpOx62hONcpcMyJJSSWlHgxzwT/Blc1HyMjiA5BuFWs\nF2p9oNU7rJ3p7UQrz+h9Jer0zefqY8zDJI9F3Z3mxsmV+9apfWXtGw915dR29macTbnzhbu9UOrO\nXavc140DQqSwW6H6xtbPVOvstXNzN/GXH7/IO0633JSJqQYEIbpQxdijU+IOcSPMneuUyDfK1ZYN\nzpMAACAASURBVBJY4oE1dlYZnh6BOASoFin7iXf1mboWTlvh1IxTc05t5/Vyz2o71Rpb24fvgigq\nffjMOGANWiG2NrpkfWW7UIlURtfkXcsVr8yJl3Jkmg4cyRw0MYXEjWRe7p3bNhzU0QPCQtKJqIkQ\nZiYRJCwkPQ5yWi/MZaNdNpsncUKcEJwsmTk4UTvT7tw+Uaa1cV2N63MgvuZQ3hwfuSCjce7qSvRC\ntjMLhatQueGBWz3znrDzojaiJgh54LUl0Q4TtQjO8JkJ8YikBY3KfD1zkMQiTrRK7Ge8b9T6FKGQ\nZZh8dmsUEza5odvQ0EQxzDfOvbH2SqHzrCv3bpiAq1AksHrA3YjWmeuZpT7wjgCLRpBAkIB6g74T\nrHBfC8Xh3DunauO67YXHtXDf1+Gjcw6c68bZK3Uenj7bVWc/VHqu7EvhyTvu+aMPv8GTv7zjM1S5\nx1OlKyCO586eC56dHI40PbCy0C+O98eYiGZ0U5QhOo8KOQgHhagQayPWN3dqhZAah+Zc1ZmlRV6s\nEy/sE4tl1Ib/yyk7LmAKk2TMMpMbeKXFPuh5CikMXPfsyoKQpbD4Dm3jVHfuysapd+5a4z7sPPiZ\nszUkCFdhR9WQbpxxdt/YqBgbyQuJE0kqR1eSFbbyBO2Va19J7Yw0I/RE6gFrO1CIIkgWenJ8KbTD\nA216RpBOxsnqhLxzpZBjw9WRpswVJq8k9gsevFFsFDmuutPrY077PWYbpT2w2us8s41z29l7odaN\n+6IIjUkc74naJvZVOZfGJjuvpjOvhWesuiMxoiFy6sLawti8t8JNzBzigJMkFYIm0IBIYEljMx9s\nJu+R2DvJGnPfSF7RdoW4kMQ5UrmNyk3OzFUxrxxjJKeZHBI+CbtA8MBVGvos1o42WFLgGmFenVSE\nY5i5zTPXOQJjRC0YzP1yTTnQOtXhvijWtnE/ndB2OJ3ZHwp9L7ivBO1kFxYTQn1gqRvZGpHGbTdu\nW+M2RWIIpDBGMdUjYokgyqSQI+TkHCbnajGWyZinzpS/Xjg7NWPWr3fCAbY+1pVHe+Kda+Y2RK5C\n5Bgj6nBdJ+Z6TZ5eRvztyREwtIj5RfLyrv+nS/Hz+HOKr371q/z4j/8473vf+/jwhz/Mxz72Mf7g\nD/4AgP/wH/4DP/RDP8T73/9+vvu7v5uf/dmffWvt+pVf+RVUld/7vd9761gf+MAH+PKXv8ynPvUp\nfuEXfuFtr/Mv/+W/5G/9rb8FDA3thz70obf+/uE//IcAfOQjH+H9738/H/zgB/n+7/9+fvd3f/et\n5z88PPD3/t7f433vex/f+73fy4c//GF+6Zd+CYAvf/nLLMvytmP+6q/+KgDf9V3fxcc//vG3jvMv\n/sW/4Cd+4if4/Oc//9Zjc8789b/+1/nQhz7Ez/zMz7ztvL/0pS8hIvyTf/JP3rrtd3/3dxER/vE/\n/sdv3dZa45VXXvmm5/+/eU8f+MAH3nYO/+pf/au3nvsjP/IjfOlLX/ovfKt/tnjewfk2j1pOrMgQ\nkxJQOpFG6MYuGddE1cyBSApKk4kShrGatQo4EUFx+mXcYZ4fkWXgT78xzBopLJhXvBdqvUMl/Ikz\nGhtB5EBMV/S+Yb1yNqcQqDrRfGhOXMa/vZ4J8YiaoVTAaB44i3EMV7go1H0YT1rH9chuZ64oNK14\nN0KIrM1Z10C+z+R2JJgiLkwe6TKE6QuJh9RAOjOB62km50jWQNthnQuPbwo1Z2710qFqDfNnJBJd\nlNYianVoamJgLRun9sB7l5eZgtDVaFZ5MUw8dkO7Ifs+9EMKcwzItrKmziEd3/bpzZp4lBbOslNr\nYU4R8cJVX6E2VoNszg2Bx14wzzSFJomjG3PoUDsP6lR1Dsxo6AQD9xkJinoFiex9yH4nM67WgEQ4\nJnhxnjE3khnx9Y3yjkathb08odbOQRrnbvS+sprRrSJ+Jjtci4LOeO9ovGKVhepwJ33QrKrzbhYO\nWlBp+LRx9kTRheCdSmFjZlIQW/F64hgDURXtjcckVIScJxYarQ1dQ9L6lofN2Rv4U4440TJBMgXH\n3clhwjHUOjONLoKF29EF6itmncc2MTkcArgkcovMdRiRajbmqBTtHLaJQ1HiGvDg9CvHksM0cLXr\nvHL3np20wCM3/pjKs0uim+KEqrJ5RvuEPXRqumPygPcBfugudBVyzrR2SXBwwuVaRiFYxE2Zm2Oh\nIwRyM2aTMeoZnOCNWRxvCQk+iF9JiS2Re+eshnjCo8Kxc91HRzPGjFvlgHEdI8cMNTiJAXSY2Lgj\nEtuKhMA+Oc2M5Aqa8DixhJ3rJmy6Ia4UTcMkqINHpfaNtkGKlRuFRTreGplI84ghzOIMvppeaFcd\njs6xBKQUVguoZTQ2ikaiLSyudNnI0nAJ3LiR5SlJlZM0qghNAtGNKJ2oo1tTJXLqhuqE+EqIgqHs\nfUYpIyk1SFKhVcrlPE9r5cFWolQszohD2RJXJRJduAmBa1V0V9o84/1EBiYVzm0YmwqdKUG+77hA\npgGCeCWVxhZX5nJDT41iTpUJqpELzMNKmZs0s7edFSEcA2lS9iykHeIMe+5MGpg65ByZ2xi3u54n\nwjJ+X4HhweP5zZ90CEaxADK6RNELaStsHaw73jvFDOKZiUCUgrWNK7tn545er/AYqGI8smuuY2BO\nAzBz2uFuU0oTAnLxBBojrDm18bgAx0MlXEq2b/rjTOHtNdy9d2igmxNR0jcvT0Q7skzvoek9a/s/\nR+HODRBCfsQL7/4fiN8CXPA8/r8Pd+fHfuzH+OQnP8lv/uZvAvDv//2/52tf+xrf8R3fwY/+6I/y\nuc99jo9+9KOcz2c+/vGP8/M///P85E/+JADvfe97+cxnPsNv/dZvve24n/jEJ/jsZz/L3/k7f+et\n237zN3+TT3ziE8DwV/rGjf43xq/92q/xfd/3fXz+85/np37qp/jCF74AwKc//Wn+6l/9q/zhH/4h\nqsprr73GL//yL7/1vPe9733/xWP+u3/37/iP//E/8tf+2l9767ZPfepTfOpTnwJGEvTFL36Rl19+\n+Vs+/wMf+AD/7J/9Mz796U8DwyD1gx/84Nse84UvfIHv+Z7v4Z//83/OZz/72bdJDv6s7+kb483P\n+m//7b/9Le//rxHP/5d+m8feHlCNtPiIs3U2MzplEKI0oWSQRFaG3ibeEKJzrnfcBGMLmUQHDNUx\nOpDZCfGGyQ3rZ9wa7pGQrtCQwQKtbaMjEd6+gojEcT7eyJpRzTxIxczGuJYZD93wGDn3Rm0FMCQo\nMV+ziCGXxVpDGiL5DlH6SL7ikVNzignZO7f2MDapklirEM8z+TyjLmSLRFeQRvBAD6AeyTJRryGk\nSIjK1SJIEFQTT6YzdMiWhhGmO0pll7G4P7RG0sRBA62uF3yrcyCSTVlqgZzxvfPUT0RRlta41sv4\nmgb8UsGVsmO90i4KW5WxKT3GmSkkmp+IDnssHFqhWWEngEOqyisOASPFA1vaOYed0iozwrm9AWti\n67dcL1domAdC1oSn1mihYRZxC8xlmCleZ7jKiWNMbO0Z2lcWF/oe6Qen1TOlG9GEQ9/o3ujtgc1H\nR+foRjLhTKPpSNxmgbOPUROhcQ4dCU525T3JWG3HfKP1RJOO6DCG3aQy+R1XCa7TzK1eIXpFXDeq\nK80DW9hJ3nA3nIzq8FsJ9YEiZWySeqPWEwWhxiO7BbwY78zDib554rElTBrVlFNvJG1Da1Ir1y1w\n8AOJlTucbB1bjReeJA5PEosFjmVCa2KaAhyNHsb1zm1FslOCs5d00cMEQpwu+Nobqt+hW6F3wTSR\nZgb2uBtdJqIGJFdi4XItD0JXD06oE5MZVRpHUcQiEDi40GKje4VunNVocWfxmVudmQ+RY1DueuGU\ndrYu7N7ZA2SUF/bIFaM91Fui9pUlge4rV6mBQzCorbLzwNQhaaS5sSdDp2WYK4ZAnSNxF9JpwEdo\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LuGBYkORGW4p5lNt9496QOfZK6UC3ATmLXO/OeK5c4yHp8Q1kNtIxUi1BWLEJgZvB8wjw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9TIxaaLNxa+iEtvmmUdJtqozk8895qUY7I643eYFsK3IUghcOHhg1MOKE0D0ap+GAaCLYwoVv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ohAmR6/nMvpDUF/M/sy9vrdd/TpO0/gu971rnf9Aeq9wHnXH5z07Uf1KgLyFSD0euXUrrgk\nIgqqHY8EUZjKSo1HgsKcjNdY2JgwXVjoLNp55GDPHc+NtdzTojHZhQ9l4U/nmeNm4EHSjFkFglPJ\n7OkOIaN5JaWR7JLymQ/zPeukhB00vxHegMDU2EmIXaDeiL6NxQeg+YmbdybveG/4y8FeL1Tpb74d\nw+qG+84N4VwPJCl7rSQzStIReBnx69Sit39OqlxjdLF+k+aykMVJ5X64Pb6EpUKzgqYHZHqi9ETE\nNrDBgBQ4lTumPdNnZzJl3UeIaHJlfQ1czvQ4QBuLBFkHCEDyAnaFgIsHKiAWLP1gy4XdhFYrH04r\nWYTnm7EK+ATHVnHJrJ6ZAJ0dv8UIWQV6AlS4LsHxFfSzk3MeviGMlJQpK7oD4m/hpTA5FIbv4GBF\np8YyL2zTCDbtEUypMI8hPZwV8Q5ZeZwyWZWK0XUaeU2/pFMR9h54BOec8Xgz2QOTCndT4rUfzEnf\nslMyt+q89GOMkiXh6BXxjPpMMQWc8uaXOSRYAiYLTgYhQc1gAmQj1OnZqbNDbrRSOTo87Ym9NKae\nWawSJoQJaDA7FJdB3grh9nacZgQmxa1TrTIzfDQfb0ZoYkPJKjwEdDJ7NDZZkd45WSP1QphgKqS7\nzN2SmW1jS0ZTRWvic4OHeYR1hlbMYJuH307MOafM1owjQU/GvQhbFsqu9BBeWsJeDyxgU8GTUkzo\nJm9dQUFk+KBUE/Nyz7rH6AblAQ649k7zAVb41CqHxch6EsUCdnPOycjZqd3fTPqGxkbOwRfjXQSb\nGtJHXk+NRs3Bfgrq1JEQ9uLMS3C3Nr46feBzK3QfxzFCOSLj3Tla55VMtY39DVF/NmHPIxMqxXh+\nTZwgkRM8MFEYeVQjFNRJ7tRaaSqUPJFtJ8ULmxn3JE7qJIW6Xen9ytPX/z2afrE0KKo8qmIxzul3\natq73vWuH4LeC5x3/UEqv/2o9pSIaeXHd3/C9fnf8s3Hf83z9edYfabLhqSRLZLljtydIjPzG7q3\niaIoMwclCbNWFHgqK3tvPMUr5yQkPZHymV5upFSQfkUi4XnmPmUsPUGeEUlkcda8cioJcBAhsmCA\n6ozwAFbhuGC+43YlpztUZ1w6rVakfyDrTK8XuhcSipcgpjH+IsdBL4VvXHkwo/p3mF1Yv+zW/0IR\nQdeEvC0+Zk3033RMEdaUCW+IpBEMGIG7svexQEul8FgKlh952RIpngkaiYBeScCjziy+8Jxu3GyQ\nuC7d6L1jnnk8OSk7ipJroGWiU6lRaUAy46G18bcMRHbqB4mFrMq8zATO8xzI0ZDaCVNYAs2J495Z\nt0L3Rk/Oy9K5njrfng2ZFDFBumAYPQUhiQ9lImFE9GHGj4q9jQ+Zj+VpD0e0cp9PJBFOWggpWKsc\n7oSs5OHEQSzIkcmRx4jg98Z6lIfZsLfWosoIPM0S/Ggdx/m7d/GUElediWi0N9pYM+NGkLoNqEBv\nrH3ivg/61qbBKUBtgAYSEAmSOKodKU7OwZ6HJ2SPzqkl5gZPLTOZslinZ0E7yCFEgZScLDBPQmHi\n0IDUmedjGOZvQfPOyzHG3qaS2UuBmqmpE/uOpREwOe/Dg5SKYTmRSJx6x4+NDePwxNGFec+DTBfG\nJGdmXdj9oMvOfU6kaUYW5VJfwZXkCd8LPSt7EaJPA3XcEjo7VYzb5qyaAaWHQRJKCTyMogOLLSgq\noMmZ3oJ5d2/sYWRRTIxFE3hgNfDslJ7xG5wrbCnYIwgKPRRJnZSCmw9IQySo8Ta2GdCLcEmJRQux\nwjELLJ1UZ251nBG5ByLKgpMRPh9OM9g86Dg9VWoqtAbyVuHkEM6aqAKWE01jwB1EwR11Zetwnp3E\nDffOap+548pJBelO0KiiI3+MYLv9Nae7P0Pk+x2aJPI+kvaud73rB6P3AucfoHme0f8EZkoR4Xa7\nUUoZu9J/wFJV1vXXwyh/l3q4/x94/PAX/Pybf8PL/onWbtjxyiaJUzRojd0mpshkd5SE0wdtS4Kk\ngYpRjo+cysqiiZwyKpWkwTKfEUm0No+OjIBhzHpBYienOxCYknDOo2mi8z1t/0xoH+dIwJrOdPs5\nxYMod+SyUso9AHmfSGqUkiBPTH368voCI4pxLAtXC6QkFBnJ4+nt2OaZaVm+LDQ2MzZVYh5dhCTC\n090DSzY2GzQ1ZYyUrEkJn7m0jV3ziPuM4OgwaRujTv6KhPPBX1l5pamTNTjajndnLZk5EmlemDDU\njB5Cr8aKkNcTaTpzf1eYygNcLwB4uuc4XilmYB3mQgqQPJF04lIS4cbT3R1n4OelkFbjlZ3Ti5On\nzFIzroqshf2+cDh0O7A1+OaxIln5qi188OBwOJH5KKB3ylcseOq0rMTtwEqwReVWDkIMsOHpSoms\nxsM8YyRWK/TvMlMsuIuOSiZlZbmrnDWxhMP8fZLdssA5DeqceTAnZcn6Zec7e+G5XYgITqKkLpR6\n5Xm7UkVAnRbw2EenJtnoWGoYkwspYCcoAZqCHEGaKsydOQevU2BLxaNzdqE0ZekQJb+NNXVmGwWu\nBvTZ0TQIbi07p1k4KdgMhHCkHTLIq1DLgUtgkzLHh4FVl84hjbUUsoNr4nCQKbiXiSyJJsYDEy9+\nIc3CJJnYjWqvtJZGLo88MOcTTydl3SpRAi0FqYnnunM9Mi1WVin0cyJrZqKw1Uy3oOSR+XMgPM3K\nZsphikrFCuw98fPnnbNMgJA0OJ8KOcG1Z+YeTBaIQWzOtR94d86fM/c6KIB6GKUFDznx+SSIFsJg\nKp1G5bHApjYu0xkRkHDwRF+cqcC0Bst8R8gdqRtZ0giOZRSuj8l5NeXyBnIQBC3CtRuC8CMrTDo6\nOeuaWLUwO/RlhyI0BsVRoqEk1qzM2hB/xvwZFSe5sXtn1hHkGX6DvCJxoeSgTP+w7/P336nfvX5I\nx/Rd7/pD1fsn5x+g4zj+kzxuKYWnpyeu1+sffEL0uq5s2/Z7fxzRBx7u/wlzvqPaCP58qTdcM8zp\nPxSGAAAgAElEQVTG5Ib0wNxQ65jP2CGEVY4oeC4ssjC3TphR1tPYk/fLyMqIgXLu1pjC2KXSGfk3\nEWmkhPvBHne4fUcosxFmaDvROq8Btb2ObBMfO/nGgnpAVfa4ksoj4tB6G0YKIFoQWnEvNGmIKD5N\nHL3z3Z7/JJmXfWdVZXPnpgmKwr5/OUZX4CyJxX5xGW4cHS4WmJ7xeuDeqfXGUYWIzurb8A7USms3\nWn1FNDFNfVDgjkJthqaZTTa6Cbgxl4mHIsh1o/VO34NNn/DlQGqFNl6jRWY/dqJ3JIJNCqQJXAgP\n+tH4fLnQRbi0TrWgzs5pSuQjsfSEZGjhuDfKPMIt+0mYSuKrm7InBwke0xv+96hEVl7zhdUSF9lI\nZ8eOgyN2ujoTzguNkoyZM0s/WKfC6QxfH9Ba4N6gbVQb44FT7ky3kQtztAOOg1hPXw53FiFKocBw\nO/U3Q/gvn8sWvLaNrTYynRaNwxzzQuvK1EE9OJIzd+e7XJvJhbmDq3MtDRc4CRiOuOPhzAnWHvwH\ndUpPzJERUSJG1/HIQWrB2TqqMGGEz1CClhqrZswXttro8kKo0XJwXRspbUjPzC0ReozwUFfyDV6P\nnZRX1A1z53Y4XXee0oRoo9cLrTc2ccwvJEvMlwltgeuFOjs+T4gGWwSuHYnOZM5jWpBpontm6YVv\nW3A1I8U4f7tBCYe3Tth+DBz8t7USvREEpRjzNHE5DmYRlqR8+2KQGnuH7Qj0AF6G16YRlM3oR2PP\nioniRZk14VW4D7iejSSC78KajTkpmuHTkxMvHbXh42vemcRxbKCrN+XFJ251eiO2VdyVZiOgt9A5\ni6FJuHojVNmXjYhEbh1NM7OMovWrfOKTOOucOcuAG0Q43jNb7FjbyLGRpEEcBJ1mG+6daxxEvwxv\nXl7Ztiufbxem8z9lmp8oKTGr/taxtPffqd+9/r8e0w8fPvwen9W73vXD0nuB864fnHK5QxBmNzj9\nCVI3jnahHa9IvzGr062RknKpgcnChiMezL5RIqNppeUfc2sHs2yIOt77oI9FBw9CnYWKuJLKA0kb\nJZ/RfCJpph0vHNvPsXZlt0ZIQWVG0hmPTtKFmzeaCNI2spx5lKDGzhpw0kLLCm0UIhIjoyVJGt6N\npUAp4MEkiUUn0rTSNDHnxO3v+OE7SNxND3jfx+JcBgEsciJJIqWZVl/RNCNJ6MeVJpDcsbZz3Z7Z\n+oVqB6dpZNqEzZwssPQVve1jDEYUWiPrRk/DwCwkfN+h1jdzT+MmsAFHDULP7BKkPHbg0zSRopIE\nWje+9bFA/VivfHsc3HnhwTKTnSkezBlyVjKZD3lmWoI4hOc5WKeRs9J1gtZZkuMe1JPyMVfq8Uop\nUNsLcgTiwT4fbOu4z2Mk7mQhJHMumR/dTzS98vOXjYiKFyNk4+jC3XTHVylTeyduVygT5EwW4e4f\nsOt6SgvuwguvdDM2GVlEsQ8a1xRClYrnYG6Z4hVikOCyJw5tPM9wMEYYE477IIAtc7Cmib07lwZZ\njDq9FdJRiMjU3FkCluycBOqyM09wLoKnhZYC5xnhNsDIfaGYE6JkOpaC6M5kmbQJrSlzFvQEVQfl\ncJYCAVsPTll5DmOfhMMb0Z18KHkzQoWSOpN14tV5IYMGL087IjDnAT+oO0xMA45AAxdeesMskd68\nUyULKvDcKt2cLd6IYTERzLwchreNh5zoMVM70ILa4fbSserkA2oP9OLkLRHF6D2YERpCWhVkQtzf\nsruCu6yYLLh2JlUaws/uD/rRscNIKCkL8yQscaLIidvRMU/gmWUKPBmHB59bp2qjZKerMZnSvCPJ\nsTWok6I5MUXmwxz42YgEfyEL2Tsf60EjQ9o4h5EjeCqJRZ2zPNJuf0W2g7CN7i8QoyCttfCXLxfq\nt/8PZd04nR/408ef8NW08mHOpFyIafpet/L3rv8F9F9DOoYvz/458N/9x3v4d73rXT9MvRc47/rB\nKaUZ79uYQwfu5gT5hJARLdxr4XV/Jtord0XoPZHnH6He+ZCDu/mO1hOSztT+yiTGcGEI4RWJgVSO\nMDzG7mxKb/k7BG5XwjN1+4Zj/5aNiW4OsYHsZDdcT3SdMJ3GksidXDpZZw5dkfmOiM5pOg9Dd69v\n2Rs6MmqWO+6WmSUtHGkfNLOUCBmLt/73JIz3CEQKebr/ctlmhnyHpgYinJQy3m/AGIlaauV2VK77\nCx51LCJbw3Jhi0r0G+ZC0j8nla8YLqeGLM72vNERvD4zHbCgyPrIzYNdhTwvaOuYGzVNeJk4lYmi\niXtmiMpzOB5Gba/87e3KzSu9n7h/uePiN045I+ZMeSVieHMWX/hxdpanGymfsVrg6CQLbhQuWlmX\nwudy45M0HqaJPQmt7HxD40hOkkRhZwrBemNqC3PvbMeFae18nYLrrWOtog4nhYWDWWdOZrSj4ZdX\n0v09eV6+58n5TYoIzDZy3zhFp/dOcSNJIfLO4eAy0dNBRPDt6rAlTpZxcao6l3wgAueqSIZMoKZc\n6TQ1fIISiWnuVGlMR6GihDhuStbEXUowG202zufgq0U4TQJccG78tX/i+TiYa8a6gwvmyl1LLCE8\nh4+ROZRunUVn7GK0yUlrxR1EEkUDEaiR6TkI20lHJrfENgnahFQzJrBkATOustNenGOqfMrOkoQU\nyppmyjJzkuDz3jAGQABXkgrNg5vtw2tlO42OkKhaoTfOvhIhbLGxW8YDNJR66/RwbvsoSOZDyaaE\nBYFQxSGCsgtHNnJRikwUd/ZkZJQlF6o6PQrajFOf+IRzKwcnnzgSiE9MaeHSEm7Gbh0jczXQ7Mwp\nmFIjSsNTp7fR0ZFwVGUQ3cSJHEgJ6hKcZOen+ZGvSybaxoep82wHzYzWA1XlKReKJEQSdix4fWX2\nDbed8OB2PPJN/8At7qBv9PaZ2/O3vH7+K/7iw39OevqaD/OE9Da6lf8RKGr6P0H597/0byD/z1D/\nCuJ//L0//Lve9a4fsN4LnHf94CSSRjBmuwIxwvvcaZqY5seRB5ImXq8jdO9aFiaxEWxZZiSt2PFM\n9BuazlhOUG80men5R7gfzCmTI96AqyNrBRT3YVQ3f8XslUMnnIy64NS328m4X5o50xAZ2GSdC8pM\nWX5CTydCoXhwNxW2nGjZkFK4F6W+kYpGZulABMc0fDbT72D3NGKk3Ksw/AkA5nR3qneOqFSHLI5b\nY/LGlAtNK8hfsuQH/HBSVpjgmwYv9RtuVLrCZ6s8oDzRYfmamBZkmjmnzHU/8KmAKBVYRTilQovM\nLp1ujW/7DrpTAiRtRM9sOoEEkoWNxGOa8dzp3rk+fMLjoFwS5oZYgGTmgNYnclfyqqPpJBspNXa/\n8kgMMplViitLT5xr8LhfUfnIHja8UidlnTJhI0hx4LONY79yH0qSUTBGa2BGLAvk8luPv/Urbgcq\nsKZBqMqhqA8vV0lOSzsg9HBM4NO60urBako1aNr5UAsuQUdQD1JWfBZqOOThX9HD8cmQEKQNPwcJ\nHMeWQFJwTZ3DncWV3IVJOlNxHtcRopptwA+WWLn1ziIj+PJDOJMYO4IVJ6WO1YS6kVrCU5BCES1Y\nUkQr+fRC24LioBG42hg968MDZcVI2REzWjtoFrS10805S6a6gGayKPHd5y0Z3pSIgSE/+vAX7QGB\nQihFB157ZkeycLiyH8GUJmoD6U73oLrTDkdMaNFRz6wBPTs9nMWd7JnehSrK1RbIjZ3EY1ZWKxw3\nWOvG1JRiMzWCNhdQ4bDCFImKIRhOJ1SogHfourPORtLGpwjcnd4hRUbEmVLhx3nmp8tKUeVBhJ+e\nMoeMAsZl5k4MqzK6bSWIAE0zuBNhrNMD5fg59IPujdbOXA+4RAZpYHCwj3P8gH93+7fYyz/jn//0\nv2KZngaO/nz3j/4e+jv1v3+/uPmigOn/guP/BP7L3+9TeNe73vXD1XuB864fpDTNFM24HUQ4Z1Em\neaLGMNeuVrn3VzxX/jYqx/aJIFHtCty4XT8hZJbzTwn5wI0Di4xHJ2TGpLAqLPJmnAjHo+HhAyVd\nL4MaJssIzVRH+wQBLjrGQ0J4FEGlQ5pI+Uw6/ZhFvh6Us5TwaSa5cp8cP+sYSYvgduwctQ7ccZnG\n5SLMqhQdCznht8Ggoch3i/Bfvkz4bvp8XCfsdlDTzlYPlu5U4IpzREbkhktjw6kxsbZKUWORyml5\n5ZhekLpw8YmkiY/Lhu037mK89hecVj9TljOPsgKBLgv3SekWdB0JNXdpPNctZ2gHHpUXu1Ctc3Nn\n7omfTcrj5vg0ktkv4qTlIK1KlRvX9VvkNRN9oltHakW10NPEmoLtGnhTahReps6UnLMGuzlzVJJX\nPsTKh1snSUL8xvbtv6eVGZnvOfcJmyqJ+EXIqhlhHfJEfFfMvHXWZN+Jc/6NnZxww+0Xvr5TUr6e\nMp+PDDROSbg1sOJkq4RkmhpaCkfqLH0AC+Y+MNlLCAQYApPzeFZCZp57UObMsShaJ+qdUY7A3oIb\nU8r0VOnZoRiqws0HGvoWG3cpcM4st4TbIKwtyTHPzMdMKJzyQkwCRbEdEMOxUexYohyJvAgiifAF\ny8HJDMnXQblDmMmoQ09BHlFQJA1SbhQNEtAi8RwGeqHqlbk25nzPmoNbG0TAMnWmruTmTNXYLajy\nBiqQGPRAV352XJmj0HAylScdgZiHCeIKGSZRjKAkJRXAhMQYGfzUHL0ZsmbqJLyqk6yQMzy4sDTj\n59cX9trAg3vesoXUeV0nzBNXcyzGB3hRZ87t7XM5yG45OVUSVMhkilbCOmoLnWDNFXXlIc+c80xE\n5s/OmUtzqk+oJs5SqX1jzhNzOIbg5kgEJytcxakove7s28TFFlwMaL/0zfL2PVErf/Pyr/g6PvHn\nf/YvKVeB0/nv7VT+Y5T+zd9xZYD8K4j3Audd73rXb9F7gfOuH6xEEikPY7fZDuFv8ZWJzsGhGdNC\nxjkIPHYkxi5pWCeicxxXhIyHEdGIgFAlrHKxHV0fWXRAByIcif72d8fciSyoFFQToLy2K2ZgWRGZ\noF05q1GWiVwekSzsvDDFjMlKmhIxFaLwJcsGEU7raYx0TRN121BGqvh33RsRYVHhUvdR5OGoTqS8\nIiKs6de7PFmVIkoLJyK44jSvoDMlX5n9xmtXPCeK3AgaHiMTyNi4SeJRhJwKYc+onnjJB1c/8Fi4\n9s4kiV3TGxBBqUVp3jjZQWqGUYno5FzYDdCJXRKehctx4fP+La9+ULtxtU71zosJ5ok6HeyWuabE\n1+mOmBbcHSlGk8xXe0Gq0zrcwrikg3NTPFbkvvD4Vqu2lhG/Yy87k24YO/cR/MSEEsqSCrPY8PLU\nK10K1zSxugIHI5EHpBs5zZDTlwL0e96E3oYv51f0yyGs3YMawTkl/slp5vMBtu+sU6YloUYjt0A9\n06OTFD6enOSVP93OHALixgml58BmiK6ECuLOnjL7BBaK+whHpXWSDjT0syZkFZIKS1ZuVCwb9+Jc\nTVi6kvojz3tDcGISzic4MRFeRh5QCR5n4WiNzQNTQa3QFJBgasGp7fipkGoh2Ym1dHqu5CmxtsSU\nMoqyeWXWCQ+jqcPq1AiqdGq+QkxklBYbxMJMwUMxSZyPxOLwYpXX2nETFilcLejFRoETlVxmPCpN\njY/NCZz7olRXJDKpNLQY4mMz4VgC3Z2ITmOnSsZbxifhRWHvha/mTAvh48XQfcdaG/4uD4oIEoLv\nlVScIxL2Rhav4UwjFZQeQQZqBK+bcad33O+d1BuJzC2Gv8pldPxWhZM0Zh85TRdzUoYlnKyJp+XH\nfLw6n/uFTyYkr5xT4l4ykjLoTD0+Eb5hfh4juPKL4sYYI2gCeDRau/D59W+4//xjPnz13yCtwfTr\n5/fvSrr//dfb332Td73rXf8/1nuB864/CqmWLz92EWO3NqWZehgznU1GEYOM6zWNcS/xSu8XUpoI\nSaRSCGSkg0dQLVhSIedEeBm+HB+Es6QJohO+Q7rnKtAClGBOCweB5hN7hpIXIjruB87BVM70yUjz\niVx+8yjTpDqwu/3XU23cO9pfyO2VrVf8bcO15IWn84/JOo2RqXBE3rLuI7hLys3hc9tAMpomEnBe\n7gjfUXdaNspyh/mG2HfBpyNMEDpO5mKBUFGdSdE52ghmTJIwVW7DvcO9CkTn1i+cZCbyTAf2nKhh\n3LxybR1pxtFu/PVROcz4WI3PPTgiSKnT9crWlS0HiycOOjdO9Dj4ygvZT3yTnVOt9IBmSjPjYxZ6\naawbFGusxXlIwtUnzO8hGV/JwZyMJRJRElk6iYwAc0C3/ja+l8jLhHtBHLQE83wm3rwIkcv3d7R/\nq09q3ObSjfp2m1kVUeHP15nDBFPnmzCOadDSpBs3v4IqzzN8OAqtG02MkMKBcS4D3HCYggWvudFy\nID7Rswz/C0FLzmMCVUG2xBSCnBvkoLlxnwoaglWhtoyhnOeZw4wiGXpifljgpnhzmhi1H8wZ9ua4\nJVyHX6RZZ+oNkSBfMnZyEkqwYLJyJMMtDUiAj1G7KU+gRi9Oj1EkXLThAXsYM8qkQTNhUkfmg8lP\npCo8N2czoUYMuhqJqRufFQglkdkP5W4+WN9GAbvuuEzUIpQqZMlc1k5u9jbaZdweKsSO9sCi83yu\npJNwdOV2KLOcuDTjvMPUDw6EfVh2MBWqJyIcOxqXNJDssyoe0HJGJHNYJzRYoqCmuCuHDXreSmL1\nmVsxksLWBaVxTitHBBLy5o0LRITdBjTgSqJHQvWeJEaXxmGvNEmEnr9sqqQ4EGYkOk0m/HsZOIJp\nQv3K0T7y8eV/Y1ofOcVPUH5/BY49gn76O65/+r099Lve9a4/Ar0XOO/6o5BIQtP8NvrzFrCYZkQz\nSYyzwiYLAeSScRYmAnGjBojM5MS4r6Sx8E8LorDe/SkqivUb1jfMbrSayFOj9I5JpkXQwtBpIelM\nmR8oklA5Exh775RyIzGNRHtpaHqi11fMKjmv4/nKP8xfY+0Vq69MdKaseHwXKlnx4yPVzwM/HY7U\nAzfBdQFR5lJYk5M1o+UeMMJOWD6zv35m4QZrQfszYRWvn4le0SRMCv2NEicOkZWWVgJHVGkhFO8j\nSV2UxTt3fnBrlTyvqDuXPA8PkMC9tvHcdeIbd8B5MWH3RI9KC+HmmbZu/PSyoKE0Dq6287fHM7jx\nlyv818c/46TB3xToqXGY0FowhZN65Uoj5uAOhmHbKn+SHulFsWJ06VxcWTzh4ZgMU3fWmTvNHDgt\nlJwHgSyH8mG6Q+NthZjLr+9m/xavlGrhav6luAGYk/J1ydS5cmqZW4V7jOSVHv5WyDgeifDMZXY+\n985iM52Onhw/KXeiNHdaGJ/vg9NU4ZixMJxMmoK8C+IgJJg6szkPFkTtvETD1ekx09zABFIgXXBJ\nzGlGJWEyYXPllg9eY3iJSHWQ00LoJBQjxRtuHbgHZu/87WFcSaQyI8uJQ4NkwXXLRAiRG9OD4k24\n1aARWKrMCkUMk8K1Fu6S4DJGHKfDuaZOe5vbVJRA8D7hDtjBNikrFcFJSXhaBnbbMKoFaQ2MClXI\nRYg7p3nH1HnVK6sfmCeeRTgmJzp0qxyRuVYni3B/CD+KTlOoAXsEdyZcfNQSe3ei7GS7o/XMfZlx\nh2sYc1Zyhh975jlXLj2o5iQV8neePIMjGYcLP6/COSfmaQVvwPLlfHptnZdeAYV8RqzSUZxEyYlF\nbnQxLD1yEedIhe6FoHPoPYSQ6CiBU8myU+SKs1P7M7t9prRXSvkCQv+dy/9b4C/5jXO4kYB/8Xt5\n2He9611/JHovcN71R6OUz4CMIkcSgjFPX2HLDbe/olgn8sS6nKjSSJq51RsqCUkJIRHeSWWFCDRl\nVJWUZiI6IkIuJ1QLuTxQ5g/o7Wdc3dhi4JdBWc5/wTStnGnUGPQy71eIAUQ4pUTRCbdtFCG2IwSt\nPqNaEC2o5i9dpl+VW8VsjHp9p+8yKiKC1/0j0Rsprci+cWudLQKRnTnfI8eVV3/lvMwseULLQson\nUj4x+8yjfERiprZ7tu2vSXZBBNYEa7qjTU9gG907QSblZUxooXivtN4oZSJC2FGyBLPAhcoeE9Fv\nnEIpTEBHgSPgcOUamUkaWRKrCt07ncQhwv/98MJ5VzgCk8pr7mSDcig/6/8H/5n+lLQrp+zgmaSJ\npZfhr5JOCyBOhDurOyLGZSpMckfEFUZOK5Mouxib7zwkZS4LD2WlLc6pCMv8FUs5Ia0S2z5oUr9a\nzIhA+s1fr6KJKgUYPhyP4NaNz92YUuK/eMictsp/6IlFgkuqmHc2Cxqd3hpLPXOUxsuknHAeRLEW\nfMqD5tXP0CLz8iqoVg4Two21CxnnuTcmgTlV2snpKLk7yMSBIQI9RjGekqMeCAVRJenM0YPL1Hid\nd2rrpF6I1sGFsgZPNeFiyCWYJWBWUGdvwkXBCLzXMcZ5Vj534TkFM8HdAlNkGitVDJsbswhJlDVl\ncirceuHeg1UVcbg5TCqsKQiDVRb2GO9J0CmROFxRm8GFHE6YMZWOIWP0U4KPS0NnoAftNEY5FxLL\ndXjR9tTYpYzOjdeR7+SV2RRPE5dkpCM4RSPrACm4ZAilEWw4D2klJOE+Rs1UZIyOlcSUjFRH97b6\nKLTdQeUAcxTFcidrxkhULQMNjnCnMSAL5nxbK4dVljTG1cjrl++Hwy5gz7xIIfTETQ1bGlIdemP2\nC01OOEKOg1x+zgf/GVkaPSacoEvH48D6BvPpN57n/2j9U6j/Aqb/FUYq8VBkqP8S+NHv52Hf9a53\n/XHovcB51x+NRgFyJvKKponebqR8h9ORcFr7jKDcnT9wjSvWOw/nP+PTcUHTuI+KIjoNjw6du+k0\niEH+i19Y0UyZHpiAef0JD9Z46c5VBHpnLieKCmEwC2R1yImvlgfW5eEXiGAdH7/wTm8XwgfyNpV7\nQjNmB77M7GY0d5IIp5zfRs9+ffo8InhplcMaRRrNM5/2yuHBKY1RseftWwYTrrED58k5WWWZTmha\nkLlwbQtrFObpx6ScuD/9v+y9z5MbSZK297h7RGQmgKoiu3u2Z79dM5lkJukg00knXfX/H3SVZCbJ\nZPq0u9O/SFYByMyIcNchQLLZw+7Znen5ZmYXjxmNZCUKSACVqHjD3d/3BP2KbRdmDZ4Ren4iJAgB\nzQunx0fO/V/o0RGdEUkQhpQFK4WSIdgAo4uidPAr3TfMZmo4uwOSMRNO3vFYx468JaoIEZ1vy5VL\n7nhsCMFjP9JF+adeedu/57Ue+LrOnCQo3Vm78tY6ew5Sm8himBxQXXjpQdVgmV7TyFAb5278XQeR\nPvzz0pGmwqUIr48nDqWQy+N4wS0RmpD9J0HAIsS8/OwAdsRoXTSEvW+cW2PtDiQsTUwhvF5GW+F/\n9kZ45upQaUQPji4k2+nZeMngTSgUpjDCgvPBsUWpbTjKeUD3oNGIKgid7rDRcQJtcJHKnJyUg31W\nph1yXhB16jUoB8G7YjFDwKqVl9NOPfoQAy3Yr4nYduarYLbTqnGiMatAV9a9UtMYYi+5kexIlcpz\nz6TI2BKQKg9W2D1YdaZPlVAjh3GUcQ323Yg18U11jgYg9H1cHyKCeB5W62psOOqGaGPWTjGYFXrf\n2N3RljhOzikZ72rnpVUe5kLJo81vdeFK0LaNZ7mSAK+dS1eqB0kEacrcEg854VLpvRPeyUUJgRdv\nQOaSOyHBuVWKKUsSTPpNHAfXcL5KxgkDhYdeuValx4ZEUOlIKAeBSdtoK+sH0IkAenRemnP1ztqd\nLZy9NSbVD0GdKsLuo3XPdGazA+gVA3RaMXVSP5OHdzaZNzzECybDRh9bSOUEafhFho/ZxD8X8T/D\n9t8D/yvIC8QD8D8Bj3+2h7xz586/E+4C586/O0R0hIFKovcrZXqi15fbwjIxza/Z9owmp0yveKWJ\nK7fZG5tRTYRmMs5pfkLzA237HgREC4LyfksxpYm0fMksibd7Z1v/BSEQTbzXICojHyO/30X1NgTA\njdHu9XEx7H1DNVF74/968w3X/rH1KenOb2ycwU9ZHao33jsfbbWxxTDSPbdOve02l7SMeZre6ZG4\ndMfqBQ+jN5gR1BJuSnDCq3OaHjkujXU/k5lRnO6VLoYWY5onvjx+yduARaGYUSkccuYxF2odzfTB\nyBzafdj7undEgoazdlj7cCnLemSxxqaOdyCM1bebWUSMGQ46q8LiAMZG8CIdKRtrGIhxzTsqhkXh\n4DD1QGXnGoZPgrQjhyVT/MDF3/JtaYTPfOHCohNMC31JtKWiOt+qhD+iFCIlaBWJINQgfd497ePP\npzB020LzhOUO4h/e00RwSolNlC9LRnehVphM6NF4kERKAmF4cq4Fvu2VxwSHnGlLp3bFbsYCKrDf\nqm29j0yYooFEcPYdbY0JQUIhBW8uzpI7r9KMzoVLalCDYw8kRgjpjkNV0jXzzgtmE8+ihE/IbmTd\nMRNayrRu0Feyd66lggTNnZSueJ6IbmANkUZtjWrOXAyrwSIHSMMQxGJD3Gj7xLWP3JtVhOZKa4l5\nh0t2Vh8/T91t3LcEboY4eB+ho7s0mu7QjMcyD5MRKl/Oo2r7vI+K7WNWvFf+SWB3BZQejd4TGp2p\nK9oVpGElKBJcS2VvDVOlitE18NKIgF0VjU7gdG1YgldJR36UjFmnEsFrEeZSWPedSw0WgYqBbswZ\njik4iCKxkjSTp4m3tZFU6R48t8qbuuNeUeCxZB6SMZuBFQ5yRMsjl2mj72fUzyOvKAstNQ7tHdCw\ncIZlyKhk53Jievh7xCZU/zytab/HF8D/8vOOkXfu3LnzOe4C586/WyzNqE2YLaT0wHr5Z4KVUh6Y\nDxMiC2oLx8f/hsv5n7m2/bb8FuY0scxPt++fSWlmv36LqtHb9YMTltoyFgZpYZFG91eEb3ATS+Er\nOS0cLGF2c98S+cni4NPFcEQnIviXrUEWIioi4/bNg39x52t9b7v28df++5kO1QyaqO7EbfpjN4AA\nACAASURBVA7g2oPuO4kRhFhEh0yJsQBe90Zf31Bk4VFPtH5h79DV6KkwsxNquD1hdWXvjpaFPD2w\n2UJOmcfjV/xmesRi7I6vPXg1TYhvSEtENLK8b8wSTEbi/TmGm9QWjorRfFQWJnu4te/BkjOX/oZw\nHTvHKEpmTY1pG/8TAiTYw3lHxqdg1Z3HdUFl7MTPEUzBmBEiWK6JRMYxpCsPstPmxHM2VhNkNp6W\nEUgq5YjqZz4yVaFM/6YF2KTKpffhaibGGGgax5Iqr8pEEmO/XtlKcHHHBV515ZQzKuC3TBOTCRVF\nbeTfnHvDq2LiuAc9lGjBtAtly2QXaurUBDllDOFBIe8G3lET3hYQW3mUwlwS9Ma0KC9b0BXaEpgX\n1u8zfdqxo5CiEF3w5KSYUdvReae9vNBdyLKjLSg2coyedcXrMx4nWgRZR6CuB7zUxkvvtICvyszV\nJ77dC3IO9stwG9MQaldaDHvkq4Nujman12FVHupcVVHRsVAPwW8ZSRe7YPlKw9k0cZVKC4MeTElR\nRrZRa47JEyWu9H5TSTQkoDRDtGLakAiMQpJK5IZPxhtvGIUpOWY6DBrUyDqyk4opS05jhglBVanW\nUVe+KIV/OAjfXx2JUTFpxXmYNoop/1CMnFYoX9DERiJQBN/tG9+sG+da2fqKS+fNXvj6MHMy51Ux\nIr0mSiWyw1zZ9QdS7JSoEAvYgd5XNK4QjWwzVk68Pr2mLF+Q0gHRjIj9q+cG79y5c+e/JHeBc+ff\nNSKCpXnMicxfoNJ59epEfP8DHomUxmCupYV5fyaiI8iYg7F8qwQZKR/J6YFt/QZQogsmGVFDbOxg\nP9pEkuGe1NoG0ZhEOZSJZDO9b6hmgsDbZZyfZjR+es7KS+/DQhZuTmgfjzvCRQ8cUxuzPe+/HqBp\nCC50glTZt53q8SFzAx2DJnsEkTJPecK90fcLx3RkuVWZTJTkGx6NZjPOzEbnrV8ISUSeqZpIUjhk\nw1TwfuaQj7gH6kFJganSe2A20/vKZAn34TY3m/LCI70PQ4LflMQPdcwM5Fvq/WsmzrGi0ljTwku8\nkN3pkXECF3hbnKfqqAhZBUd5J40owqKJq4H0ygFltyBPSpeRaJ8ts/fhrocljEyoock45QWNTNZM\nSQk+Wzf741jM2H7U9phUaX2Eyi5mJFX+8XRkqhu/0yuvi/H/vuxc6cyuiASrVmYTLHZ8d669Y5G5\nRhBxJSXBRSjXRGkJOnQvTFWYWjDTcFOKB6Y7e1ZyCU5F8C40Uy5ReR0TT2miOjwcnL5Aqpm3XUko\nhx2ebaM3hy68qNOBXDuFxvflyqvdEByq4K60UKrPtB4YwoIjAcRKjZlrd2IY//HdeUd05mVXdE/0\nHqhAEkYIaHU2HxUTzR2boEmhN2HXhHYhqRISiMAxQUhGJPGYlN03NvfRihqd5/2ZdLsGejdoTqKQ\nVSHOlKic1HA3Jh2ipEtl945YZzYlmYLCPI1ZsB463AoBQ4dDoiUeLLOkBD1oPiqoqzrWO752vrLO\nwzzeixerVEs8pcwXJpxSQtOCloV33bGA361XfnfdWNvKZb/QYthgN9kgVg4Pr6kxMaUD9vSIltdE\nOrBfv2GvL2MeMR1pDm1/h/iKy8qkzuN04vTw9+R8Yjr89vbZuvxq18SdO3fu/JrcBc6d/zCoJnJe\nOBx/w/kCtdYPxywdbi5sI/dlDPp/2oKhqTAf/xPeN3o709oF1fKjXX3h8fhbnmRUK4gxUP5+hzPC\n8b7hfad6vxkJFCrPI5znw3kWtv5R9Xxuh3QP5cvD17T9Hb1diOhMmsEWeq9AR1QJuVV5hLEAvJ2r\nyHDFyjpa1Vwzk35shVMbAi/CEYOLV0IqGhUiYYx2PaRx9AtfpolnaxxSBu+kcN51x+HDLm+enlCb\nOXkDGflBx9pQM4ooj8l5yBvvWuBiiAf/UAp7nxAqX2bj/PyOi2y4B+KNRMc1eDc5bpAkgyuzF06e\nmVC6KaoVt4yrQ1IOdSZZYUkTlZ2iGQ+jtsbjVpg1c9Jh8rC+DeaTkx9/vY9LEeFVzuwebN5JYmMO\nQvSDYcRjKaR55kGUN6XS6Xxfg0dTNOCljfPxvmNmNBqrBogiXUYblQepy+ioDKUrbCo87XBoxm6N\nZMI1FUI601V5loqURK9XzqWxayeVjJlxUMi+881uxKVR+oapsHrmWneSODMZCWP3xqqCSuG5xLBC\nLitdhKSG1YW3NFpuGI1ZhNo7z6uwoxQVDpG4uLO505sxVaFVJ2flre/QDZUAdbpAZEhFmej0qmSB\n1vxW9QuS3azMxbB+xHtl9bccYwGF1jcsNhwjULZulHAOBJonnombhbjQUZR9DONrQ0IQnNM04wid\nximPx93rqPbMUQgvqBjcsocMIUlmSsKj7fi2wgwpT1SbKO0Nxyy8zguXDl8kGb5lvZEimGSjiPIs\nwu5B8x3aBaQjMZ57RsgR5HrluLyi+rAUL8uXPOjCevyvmHrnpa18lYQQo7aN0l5QUR5T5aus2O06\nzvMXWD7+rBHKnTt37vyluQucO3dujODQX96R/KQiFK/wvt8qLMNa+r0Yed9S9un3KpYWLC1omuh1\nVF9UC34TOKIFtQK9fTgn+YwNq9zuL0+vyNMIhNDeOfeO2kxvV7I5eZpoW2WOQGTB+3D9yrkw23Bx\nSh4cNOGfab8az8dIecZi5cwbdkZFKOrzcLbShaAzR6X4SipP9H7hwMZLdyRNqPBhMWRWeExGjQDJ\nHOTIe3n3GEe+cmdrjTe1DiHVA0e4mnL2v+f/OP9nXnynyRgwUR+zB4ekPHpm8kIh8UjiJBPuyjkf\nqLqPx8wTr3Qiy0TWUbEpOvGOyrwmXtkB/VHboAO+KVPPv2YRBxHhKScufTzWMSXW3tluoUaLGdM0\n8ZucceDrZeJ/e37mXW7IFRqdtu1MfaJ7cBbhWhxrAjqMBVLNqOitciek5kweqCg1KeYFYqeHoAQX\nD2xL7Oq83J79+XhlSgsPfUZ2p2NEV+iN1gNxBx/PBxLLizFXZQtDY0HahGZhO254rrg3EsLE+FuS\nscbOs3ey3Wy6EV52eO6QFOSiLCjmibQLUfsYrNdRRYoYFZ3dKg/SKKWR9EDtiXNUJKCG3VzhbtlD\nkujRqDXhrszi9NaAYGuV7pmtwsFmHswoDiszFUW1gSnSE1kzOe2cUuE3szHnAy+tsekVNQdXvoox\n93YVJRQOTVlIfHHL3yrZSNaxvrPcrg1RIVJC9cQi7zDpoz2RILYNlUQuJ7IoJ4Ef1ivXvbK1dWxI\nAKKgAaaBibN6JXxHbOalteHipgWS89wumM3MUyGLcjh9hSC0dsZ8R0vhNB1J5WG4Pf7CnNmdO3fu\n/KW5C5w7d/5I/jWC6OcwG05j3ldEE130Zk09qignU56bkvKBff/9oM9T+v1LdzKjRbC5k/KBB5u5\ncqVMQVHjUhtrn5monNKoFMjt+15NR972+JCn82N2YM6ZwGhboXul9guE08J521a+Bookgk5EJZdH\nUnZK76we7F7p7UIhWOzm6ISQ8kx3od0W9UlGe89BjWxG8o1LhYsri2T+x+M/8mgz/+f5/+Pb9j1d\nDYsDSyp8oUd+Uye8GLOe+LvpcfTC4WhtvEtgprw+PPF6F3DFtRNiRMAhMo/5OF4X/ehSN+nIf2EL\nyL/uom62MTtx7R29ueQtERymiX88nfh2Xak3offbeUaAb6edba58/a5wrWfOPXgnnVk64UYz6CI4\nThYhK+BCDWHxodEkAg8orkBBA/oUuHS677zbKlOBhHBR+KEFe99xmWk1cVmD3RPdhc0q2x6oKnnL\n9D3YohLWkQ50IzvUw05WG46GsSGupD4z50bdQaPgfWScCKO9sYbTrsrkilBp/UpTRXcl9UzNAmmM\nwa85yBJECFmD09SHjbGCR0d6IlQpVpCoYEHrirRHujaqOHXbObcLNWXQjnmAzTSfkT7xRYKX6FRP\n6ARHEaDTLPiyGA+50FAOqeDTCOqd1sQV5YdwAmVSoUvFNXi+wOlh5otpmJo892DW25wQo3XxVF5R\nMKKvPGVFa4XyQCpPFMso8LZ1OqMq/a6uH0JGRW6aPMDxm6HJTouZoxl7DCts0wWTzOaVp3nG5GNo\nbbm18aapMP1MKPGdO3fu/LVxFzh37vwBIsaC7do3Nq8IcNCZY5rH7vgfierYnQXI5Qn3NswLwjnm\nxCsNmhjwqcBZTD8rcGBUAeYIdndCla+XAwLUCL6cJtwPVHdabGSE19PCoglZrzxYcO6d9iORowin\nUggR3tWVY37gZfv+1tt/G11ByJJG+J4Y3jcsLYgoJb3POi9EHHDfRvaPKFknamuEOKt/+hxFhEdT\nqnd+O2V+t1XOHZIl/ofTb/nvjl/wtv7AW2+s4RRTypY45MyDHVBmgkxNjnfnIVVMlcPpxH/98Eha\nO1Y3Ko5KYe8KYSyRRybKFLgEhvJQMop8GPT+tVnMmHTYDgNkFaY8dsjdK62+3F4T47fzxFMpvHjj\nm3lFZWJpkG9D+Q8hXGTjhYyrYFxI24Im4dAyZsNVL4cwd6MD6Tb7I1fhRWBPTi+NEOWqO2uFkmB9\nNr7dnUWvJJzaYSfzLAkczrVi12Azp5mj0kEFA66yMe3C87yDVIhMNkXMedkqograwMdr3RjVhWgZ\nbYHUH/BodBe8FzZNeM9oL+wz6Bwk66RQesAiQrYrIVee1DjvBReniWLW2VsjmjElw2Tky6z7Cxc/\nU8g8NEfsTMhMAq4u5J4JnTmmwGmgsKZGWo2JhSSNzTNnGltZqXRO/UDyiV0UwWk4LYRZDFWhNae5\n86ZVDjQmUxaEozmWMytOsuCUn5jsS3J5Qs8vn1RPXvpwSDQRFmDtnQjYemcWxwyqQ3RlLkJmBIeK\nCJMYADWca+sUKzQMu1dn7ty58zfOXeDcufMHeOlXXtqVS79+WOA+c+HYZ74sT5RfyS51CJ6Pl+TX\nC+xm/HPdaREY8JASr0v5xfYQE2GxsXDJqjy3Rrndfmc4MmWZecqZkm+WxilhrfGYEtUDZ4QGekpc\nVDm3nd0bZhNFZmq/jDYahDIlWjhJM7Weh8uUCGoTqtOHcxURzOZPzvVkxnMEBzMu/WPVJKtwlMwe\nhgj8/Sy8aZ2tBwL8XTry3x4fqDZRaSwmlItRyBTJbN35f64vfL/thBlzTnw5zTw9JF4fjU0r+Trx\nj/kVJpnmwTeXjeRKzBAKBiy3GYms+lOzu18VFWFyQXZGT1wJtvKOur3D+8dZMWRlsROnNPN4NH6n\nwnnd6QR7BGs1JhH2vLF3xYsxBRwC9r5CDCOM01UJhD13ckDW4dw3e+IciZ2dYOfsDX3JXBDi6tja\naZ7JRVnLxpobxIyqkq6Gd6HqDrx/L51ZnBwZqYmtdVQz8z4xr5lahLYlLpFIOdOmRsLIsbOHIL3R\namPtZ3IU6EKSivhEz45JI80n0lzYfDh+JVGygdo3nMzp8UhVRwgu1tkis7ZEsmCKDjiTbGwxZlrw\nxEZlVljSmA/y6FQRVJSUHzBpKCsA9XhF1LjkSrIL/9RWCGEhM1nw3De6C6/ykbetoxG4CC+tcUyJ\nt22n6HBSazGqbsdkTNlIbiy5YyqIGuE73ldc7BYgOjYz+i3gs5hyzJm36xkF1gDpcEjwaM6TNZbp\niNqnGzMZIZuNa/4zVdyiQr6Lnjt37vwNcRc4d+78AqvvrH37RNy85+IbuZ15nR9vQXi/Pk+lUA4H\nIuKP6nnPqjymxOrOm72y3xZQsxmd4G2tnFKizAvUirRKFmcHXkQJM7buvNl3XlolqRPut8pSJwRq\n77ytjYd9Be+IJMIb3RuuOyk//Oy5J1WecmY25+BGdSfdBFoWqCSuvfOMjDR7u1nr3qx/H6cHZhNa\nOOpCboaKckiwWOG7stPcKWakg9JzJ1lGM5ScOcg0RIVBfjLe5R3XIdyyynDU49ZKVj77FH4dVsZ5\n3PC608+jqkLio7gKp20vaLxiSYnHeYbeIYJaFdHARXnRSlbhlKCnjf1ibDoygdiMqxqdW5uWOkUa\noRWXYNdOp/NON9gmXtWJzZ3ah0nBosGxKeLCZE5LF1SmYR8tOxVgLyABcquguVIVDvVIapl5TzTr\ndHY6zpaGe560zHe+YtYIN/b+HUvdxrxNn8mMANkGTNGxNJN0p/eRT9VcaKmTxJFwrvUw2g/TsB5H\nwLMg4uQoZMAjuLYrJoLJzBojCPPqRnQh2RUjMydYktOtE3Rah6RBIpPUWYryQ7uQxHEyi82oLlw3\n5dqFRr9lPBnRnUpjT06WzhadJImMUaNTPZiAbI7dgkklGr3vXPvK3hoiRujC2YO9b6QIVvfhjNg2\nqjd6wEHhKcMXCV7NB74qiaqZ7UfV2mLG16b8UBs/rVO+d/abb5smd+7cufO3wF3g3LnzC+xe2W4D\nuz8lIti9sfnO4SeViV+bP2WgN6mSIzgk48DvL1JeWuN1zkjORM54BM+10iN4aQ0PyGo0D767vHBI\nMGuiiDHrcIrbru8oN9eyHzsrhVfct9+r3PwYvQma5TMLKNfM1hpZhXyrbnkEKsLrMlNuMwEJYBq7\n1e/JZhzft/IJ9DyqZHOakBZMqpAgbqdWUJ7mwvbS6R/axZTZDMvy5xM4/VNxA+BtA8rH7sRPioRO\neEWt8JAycjySdUei89D6sIkuT0gfbmLVgjrDpWXy1jGBNY/Be5Mdt84l7dRp45ycc7rysrzDwzhu\njyOipyubC6U6OQIXR1pm1xWV4Lu58046Twip53HCDpvsYPBWO0cJDnpg0c6zwWrvs38ycyu8SCN1\no6ZgLxsllNbeMcsLswcrT3QvH1ohrzwxaaP3DtGp5iRRJhIvdedFM3OM7JsizrVnNFdOi3O5dDQM\nbw3pQW5Gb52NwmFXoieSgc+dTWTMI+VAqSxyIenERSA7XPeGiCMUcoZXAd2FoytrNXYRcOHqwTHl\nEfIZ4GFcqSyaUBtVz2wHah32znMOOvHBgEQl8dw61RLSGhGd6/VfeFOvXHpnRlEtnESxeWHzleid\nVxZ8mRMPhwN/d/yCWYMQIX2mzVUZP3ItggjIIjzkxENK97a1O3fu/E1xFzh37vwCEUEP/9njY3D3\n54//tfDjzJXPsbsz3QTGfrvttfWbq5fjAd19WF27MOeFSW7zNFEpOrFvO8s8HN36h5yfgvRfFji/\nxCrTWP3HR+WiIiBGs4VPNEeCmEBGkigmQlFlD8dnPnFAe1+V+Sm5KPmVQv0oliKPP38KETFai/o2\nspbERohsWn46YjVuz8f3S6oQ+UcS2yBuJ5dVecyFhGEmPPbAi/DF6liHd1tlvzrrrrTLSmjwqI3e\nnKqg7lx54ZIqtjiuhnkwVaV5oVQFhDVA947UiU2FNRxBWDehZ8GuGS/G28l5/TLd3iMofdiUy9S5\nzCs2g14dDxnPMSYcUDdcOis+hM2W6XEZ82R2oURG2EnaEQlQZeuF73Sht2DRxiEb2ZTeRhCsifHc\nYBFFVHGg1Qm6YNKo3tANSoB5YalC2XXYWyew1ElkSlV6KRRRWt9ZRionEUqvQhFBRZibI7NgIkMg\nifDOQRZBItBVPwTVYk7TwLNwMLt1iQYlB6SJ8EbV4dpnUolotIjhAmhGlIn2/Dva+s90FiKMsyWE\nnakPm/djeSCrMKuwJGW2zMkmTA0X/exnwlMpHFOiu4+NBNW7sLlz587fJHeBc+fOL2BiY7H2M9Pl\nSexPMhr4L8UfkmA/Pt4jRvWmN9Y+jngEEkqgrN6pGAc7EN6Z1DmWE5d6xb1CjNmECAeuhFdSfvw3\nV6GqO4GQyyOtbWz9SsdRjIxwXt+SSx5ZRDaP+58gEkOgBCxTAu2s8eP5HqX80o60Mu7n33S2v0yv\nL8NA4kZEH/lF3kjx8Hu3/4Pp8IvBj4TcIaURlHkze2sBTaDsMvKQmvAYxjWCt6nzuA4hsecgesHd\nKWdHBU7TxEmCvSYO28Qaw35ZuuHubK50FHBaBu+Fap3anE2M89x4XY3jzbjhmoRrUV714PvLzrJ3\nVG4zPzFCMLf3GTVV6K3STHAvtJhQXTjb9yiCRcY1sVvwkoYVOhLsNFI4WYYgS16pK2w4Lo1FJhod\nVchSoGRsq6QS+C5cXeh1gmjUEDSudFUsjGSJJ12YEhTplDRx7TGc59gBYdJC8w7dyRmaGIsJ7sIW\nhX1yNoXdoYfiBboGX+cJE2HrzqzCu9oQEWbLdM283c+0/XsmEVwSLgkRoUtwjQtaJg4k1sg0oDDe\n+xxtZOvoEPkny3yZx+yeWuGoiXTbvOgRmAiT6odNDlP9TK33zp07d/52uAucO3d+gdkKpQ/71J9i\nYmRJI2Dzr5yRI//zS/YfL/ZVhO7OdhM3W+/sPobYG4q6cK7wm5xZpiNJoGniZX9D04xEI8dGkffi\nqJHqA7k8/ZvO+dYlRvXGOXZCFI+g12cgWKzw5DJmffz9rM/NHcA+CpQFY46xgy/AMWeu7TNlkz8T\n3vdPxM0nx3zHpWI/yTr6JEDxJytNEUVK/ijkRiQLYtANeAtJhN7GItxdyAFFjV47NQLPHe8y3L28\nMHlj8eBqQZPGyWTMushKaRPV86gqoBCOR6az89KCsBU2xXUiCjRTfmeN5NOo4iCAo01JfUI2YYmg\naBCJ0frmQvUhrC/FmUJJoqgXTGYsFt6UN7hMqBaukfFwRJSIYfFMQKsVrQeiLRCGqeOaqSiulYKg\nqSA9OMlGK41dhbhlEWHBlANVo+VOssSrcmRJRl4arIWiMykMSw2PgkhFhWGz3Dtn7Tzdqjx4kGLD\nCV7neWwQIIhkiik5KuftSjblEpnNJg45URB+OH/Pt9czW91ZCIJgDeVQjqhf2byRUZachnteDzrC\nyYIWwWJOTgceknJKNuzZbfoQYDyZfRA0d+7cufPvjbvAuXPnF0hivMoPtOhc+/bh6ybGyWYONmPy\n179ImE2p7fN1HGXs8r5nuv07GO1quzubd7ZQ1uH8S2vwZVMOSTiHkPdKEnCv9H5lZ1QQDiYIyr5+\nh6XDh8XVZ3nv3nQTWyajtevcr8TtmLcL76XL7pUWnSQ2TA36SkqHz961iPzFdqR/Ttx8OC4bpvmT\nMpppIaUhED5pTxPF8mlUq34i5CaH6zZE3KTKMDMWdh3Oc7hRBOiN3RxHCGs3++tgFcFV2F34XYW6\nr+RwXq+Zx5YIF5zOOUO3jT2dwSYcQSON8+ij2uni9OgYBtpIIagLKQQTw5ozidP2gBI0AdmN3Sqr\nGwgcEIwT3o2miZ231NjBnWqJFCd2h5d+oaljvTEhmGcshhlF8cBxiiYyCy0quxq1dTIVJ+hakVnI\nktk9E2FkO/E0Z7Ah/mdLnJaFXXd0D4hGIfjCEntvVB9OY8fyxEu8Q4BWd4TMYolDFCYtXN05qbGU\nib2+UFIlqZAEeuzjwkoPvOwX3t42FUISW9+oAWdvdLnyIGO7YgcI5cGEowm7Q40hoOb5gVkTk418\nqUM5kvIfl9t1586dO39r3AXOnTt/gEkz/2n6ipd+Ze07IjDrxKT5w+D7XztZlcWM64+smOGWc5M+\nXfqrCKecyfvOuTlbOJsPG9olT9Q2VtHf7hu9njkU5XF54hKNfXsGzagaWwizzuM1CqfVl1HFiQDR\njy1rrSH7Bu9nAlIiykRSpUf/KG6i39reBpMKm1fSbRc6+gY/I3D+onzGdvcTBOIAcuETkZOnE+np\ngd7OQHyY2/m59jVTOBbYMngIx6LMc8Jq5ZxggptnWScI9tSJLGxAz06lc7MZgwhSb6Q6IbFz2BUJ\nJbnysDnfHK6cixDqCA1zeCejJctHlCgWILHRE/x9z1AC3YIHT6g46pUcju+dl7mx9wKiQ4RJsIeS\nJd0MLh5BnFaEECFItAZbNFYJ3J3eGlfJTL5i7CyRMBOyLYgEWY00CavudHVq7yQZr+shK6krdMEx\ngkS3MloZFb54OKDJqfWCxs7WnGtAJxGhZHEes6Gl8Civ2fqFGpmDzqjNKJke8BiBaCdYacV4tRQU\nGW2fPkQ9feN3+0opo4onmln7sKvWvvGunrEM+IZJoXogJnyZZLSWOjQpPCyvSTeTjIPdWm3v3Llz\n5z8Ifxurszt3/sKICA/pwMNf4wL6X8liNobubwPE6TaE/7nZmKMZvymFc61cY8zgTGaYJGZVUrtg\n/cw7r7wqR45lYs0L7CvgiMyYTfTbYjwiqNtbwisiRg1wLZgn5lY/nYdpDemdWA4sCc5dbtkcn4qb\novKJAUT8ISHxF0I0w89UcSKgeaIL6AJpjJQQBeTJ0D2T5PivfyyD5QAmwZ5BPTEl5dvrhTAjTKnh\npNYIcwil6c5uTu7gGErDxJhbobgzOXiuXK1jPZCAgyfqFjxrp1rnu/lMi47ETKfx3te6ROOhGbM3\niEYX6JJAg4tVVhpXEb5NO6kLixhVFYsC3smWSFFolpjKSk9C9QM7cBWlywsdJVqDSHg4u6xM4rhn\nuixEr1RbSCIccjDbxGEWvr1W6I2D6ZB2eeTBTGJ0TYgFD9k4WsIm8HbmN5PxrifO63YLZt0xy+Q8\nc8nKAWXKiadi9J647kN8N4fWwVQo5tSoSE6fGF28N5ZofWXz+GCgISIgid6vmJ/xAGHG9EDETokr\nhRmVPMwOAF1+g6oiwH4Lck2iFJU/yZHxzp07d/5WuAucO3f+A/HjENBfQkR4zJkvSqHHTo+guSPR\noK1MKROtghpv3XiqldZ31G59U8SHSkOE072SRHAPzt5pBETFtsaWDiy3CtMHIoj1DLKz+MttPkMw\n4pY+PxZpPzZ4EP3rbBVUK/R+HY5zP6I7bC2jeUbiFoYKzDnI+Y9fhMYEpYM1WBfgpfHqYVRLSu0c\nBa44L1pJ4lzzaBELghaJJoG5MMWExoYBW+q4XunZ6V3RPiNqdHG+LZ0qILFiroiMlrhUK4duaOqI\nQ2mZU1dMgxY3i2gHqMxV2bNSq7B3RcJIWhA6rhNzvlIOX7EEfLfNrG0jvHH1K0pFRWf5PgAAIABJ\nREFUFaJPWCRgJWxDVUBf44wWsoNliMTBCpMLx1MZOUPdgaDn4KiFJynsc3BMieNUkEmYUyP5EAfT\nlHg4GpdtQwhEjTQfQYyq8Hoyclw4pD7mglzIBmkOVMB7h+aEfhTkRYXNhz3z5lB7jBm4CIQxx9MI\nLM0YwqvlgYlK216Go15UYMwmyfI1eX7C3XnZVnptqMBDLsOUIGcm++s3Rrlz586dP4W7wLlz585n\nWczo4fzz9czb2omAGjsTcGiVCSerYM3Z3Dl7JvlG1iBurmVJBMfHjIkY55AhbgDcx5C9T1wZrXHv\n53/cG21/Ji8HBMgKmeDaV7Y2oWoUFSb5+BH2x1pR/7kRUXJ+pLUX4pZpEgFrL1g6frKjHsBahWn6\nmTu74TfTC7m5an2CQRxhv3b26PjDTq+Vfty5vDnj2VkOsLedNy6jHQ3Yc1BpdAwL6CHkyOzqNPUx\nXhPKroJLY83QtNO0QhRMlLALXSG5kyTjNgI/XRJl72iC0zrCOM8IEpkUwtQ2XmRl1+X2fAKRYAvB\n3ZD5wIMlHqxi0fBIdG8kd9AVsY5EQ72g5kM3qaN6vgltx6xRCnx5mHn3dmdv0KfK5MYciRSJPCmX\n1CkoqShlMh5zItpGiFDdudQrk7/j3J0W4B70fmVavmRKBgxLZu+dQ3HW9qmYEFEOWdktfvQ1oYjy\nrlUcYUqJ3Z3WnZN0JODaKw5kzVgEZhM6JUKVhzIxlye6zIgYuHN5eaHf2j6dYN3PHC5nLvOCHY6k\n8tdvjnLnzp07fyx3gXPnzp3f44ftwv/+8sw/PX/L//3ywrkFivAqGakc+K42XqtzNKUFIxhTlJpO\nGBsWjlliyhPhI8fGtXxITv8x4aMStPZGkTxscPuF8E60K8Ubz75x8Uww4b1i6JiTCCUjWJo/dR77\nK0PUyOWJ2nf23rg2pUmmIPx0mRlA/RmTt95XvF0/ziKJYrZg6VNxV3EuuUOGvTnfXHeutXOeX7hk\nYQ7BVufpKkBDknEWZzUle6bvjnRl905Y4LETrrf2KEVuwZRSOqHQZYeA/eao/tiDyDvdK6EFKPgG\nWndIMeyafQiPVR0Ekne+ebhgKHPNwwlOjL0oBz2SZKe5klQ46k6XoEobIlqcbvVmUOGo+DAXkJvA\nTEJJG6dpZMOEVB5S4orRrZMLSDghM8mG0C4mHC0wgcv/z967LMuSZGWa31qqamZ+2/tcIzMrSUqo\nagEphO4BOekZwhRhxgMgjOBtmCHMeArGKUxgBBNEWkpauqGg6LxEZJyzL+5uF1Vdqwfq57JPnIjM\nysy4ZIR9k5BwP9vdTN22b/1trfX/tXK/zEzzDR8fP0LxFnhaM2JQ7QWmL3F9xmZ4RogbrM7E4Gyl\nkk2oJohASoEYtkxl4XwRIO7O4u33KYYdBGW0iZMVjksmWAvcRSK72DG54OYMGhhiYkhbZnOKjagm\nbCmUVzNtbjDPZFqGDuOJWSBZj/fDa1OPlZWVla8Tq8BZWVl5wFgW/q/7G+7PH3E/3rMFJmkZHi+W\nkadW6YY9Cw4OSeA2F7I5vfQQBjYBrvtds5zOtbltSbq0A124VGuqz9S8IG5sPCISKfmMseDW0wFS\nhWATJh1d2rNJB5IEZoQhDYTw1b8bnc24rwCR7NKCG2slmbF7J1Xe3jNOZLWZOjzA7WJCwAORM721\nzjfFGasDzhAr1Z2lGhad7gDX2qGmjBlCisw4x1TJ3cROYDsnoneYtWtAHHLvkGY0tvYr9YujF6BS\nCS6ogsSZEITMRN4FDudAVMghYLXiVNCWV3PuCy6BGgIlGUV2RFECilHpNIEaQmXIwoTxlI6RyiKK\naQUd23lKZa8dV3GPas9+JzzqAluNjHWh2qUFTbZIHOmD4t7ihQZRSh6JVO7GCXOY84jYTM13qDQ7\n67zcMkghhY6Qdqid2PgOX17iMRG7AzUfUTX611VNIXXXiAZE7gklM5txttbWt0mbZt1sC6hya8bZ\nIErHvgvsROli+71ZzNlJRed77o7/RgkbZumJYUvJ4GFPQSnThJsRRThcwl1LKU3saIbuQVzuysrK\nyteCVeCsrKw84IfziaXMzPM9SwuN5+BwcqjAbCM76yAkJjcOlzBBBEzaHeVn+6evbwyHuKGW8XW7\nzGtEKFLQCqqxjaW7U8vINH1E2D5C3MkOQmBzmbFRhD4Mr2duZhO6r+b4zQPu84xZy26Rt1rrsjtz\nrQ8ySd53U72W8VNf2+r4QODUSxtgNmMshiBUy4CikjGrLOLcVWGnEFV4niLHlLgrldAnaqpM7gwG\nJUMVZQlGCWBJsS2ETaWrMJsTiqFeqcFxjSDSBvBDQVHiYORSuK+whMjLvtLRclXN2hyPe0EEihoi\nC0m3KIIJOMIuKkEG9sfIlQujjUxFuYvGXRhxuWOjC4818nzY8l+2RtcnutRzdG1hn24EgckqSSPP\n0xakUt1wz9RSSZJ45Td4Vys5T/Q+QV3oBE55BjcWFzo1krS5s2AL2EIpZ7r+MchAnW6xfMIloMOG\nku9Q7YhpT4jGYIW6ZGYVljoz5jvO1dkPPbvYWhDVFq7TQLCJXp3ZFpYy8vH4kn55wSa0sNuZjqIb\nmAN3OiK6azcVQkCAu2rsVBkulVQpGV8FzsrKyteQb6zA+fjjj/mrv/orfvd3f5c/+ZM/+bIPZ2Xl\nK4G7cywzOR+BV15YEKXd3TYXojvqCynuGNo+tmWvxA0uCYu7Jlguu3SXgNWZTkGrvfZCc6sQI6aR\ncVlIIrzMBan3uEyEckvwnkzE6d/Mmnh9YChQ3Jhqbe5wtLmfXpWkX41BarfKebljyW+c1JyA+6HN\nSwBTzUSbMC8IIN2A25M3/97r67mm976HG2YFvdiWt+YvGJcT+VV2kARGIFszGhCEGiPbYCgFui37\nFDiYM8fAixJYUuG4F8qtYefCZIUlCaWvWG8EcZRCdagK0nJAOQWjt8iWJqqqVEjCMSq1wjkWAq3q\nY1LQXriqHZ4qEiHRzAi2/UwnrdJy3Uc6c7YlIP3Cb/bP+PH8EUcznkniqEdycAaPXPeR726f8a2r\nRyS5opjhbFgEYlBmkXbNBkckso+tvfE4Hx9Yv5s75WKtPnmgF6EXmL2ZapsEXJp7W/RCX0/U0SBX\n9HzGrVDKEbPSrNG9IN0O68CXQuyumUUvJg8LZjOLORXnXI3qThcC6o7XkXNdOPkJ9wxu+PISVcFV\n2bpTy8KxFkpRzqocYmzmCG70XY/jnMw4hMs5vnvTYWVlZeVrwjdW4Pzt3/4t3/3ud7/sw1hZ+coR\nLsnzQVr7WfY3IiZIYC+BINCJsImJ1A1cHZ5z5kSez0i55zQtbLoDIXSIKDFdUcqJXXCOteIIqFJ9\nw4gRhg0BZyknajWqRfZlAU0Um6lUYtyxmOMa8FJIqgQRxmq83dG1uLOYsXvbFpu26e++YNHjbuR8\nR60Ph2qEitYjFq5wL1g+Y6mJnRgMfGKeXjYR+HMivCn79KrM8xmxhVdRoC4BT3s6OrY5k1GGWkma\nEI3ULhEE9iHgqvQOgnOkcncoLNuZpToZI4gSHUotGLCNMLmQRCEoXcr0i6PerpNejYzjvVNSRjUR\n3KhRqJLpEEosbFKzNu6CsCGiCI+HyHe6DTFAOhf2FwEUxZGwYWHBOWB+YNyeCJ653l7xfPst9mnD\nXOC2btlqYiOCh0iNAcTpk1HMMFdUhFIrfXj3z+Kl2qEJYY/qwnbI9ItQUAYyvR1J7mjuMArl9AIN\n1wQdsFjaL5AbpZzbWYWAB6hlYrQA5cxyfkG1hdmVSiDFSHbQOtFhmFdmz6g5apmpzCABF0Nq5s6F\nvUF0YZQt2MzRCo89sTNHrVVyNMXmMAev20RXVlZWvm58IwXOP//zPzMMA8+fP+fFixcPnru7u+N4\nPD54bFkWdrufP4viV0W89OXH+NX/mEIIpPTVn4P4dVpT+HLW9TvbAx9Nd3g5swdybgH1XJy1HnVC\n6K652j1mnxLbrkNrZjz/CKkzS0jMnkh+QoendP0jINH1Axuv7MyYDeZy5mY8so+JTsCme6QuKFA8\ncZMzG6nU2HEsjkghpC1BlKmemC7zLId+997P82RNTC3mmHsLaAV6kS9sTWsZ8RjoVVjKQ7ESI6CV\necloF+hTJAUjBiWGgF+y6lPaAAnxAfuUKo5IoOvftKillJjrPZDozSnuVIzQJYY+IdPEUAKHTtjG\nyFkVR+hDQEQJMeBL5i7D4rDvnDEbKqX1KSJ0wcmmlIuYHBBiiKQgWBWOUkgoexUeR+UsmeUK0jLQ\nFQNtTmmaO4jw+ErIFjjnZv/dERhC5Lf2VzwaIvN0BmAbAyEpQ53YpYF7nKlM4Mbjrmd/2LPdXLON\nm+ZYFnrmc6ZUQUNEU6B0AVVHLxU0iaGF4XYb4jtBqgPC7DMO9P0jyHcgRqXSmbGjErQjFEEsU8kE\nAlbvcBuJDC2c6EKgogKkxFImas54PTHIzMkzVgUJA7WcCQhRK9Ez1R2JHUECORdKuWPwCXTgZIpZ\npvfIVhWPARw23YCYsqHdoIjDwLDZtO+VEJrJwC/5u/Dr9J26/p1aWfnm8I377ZmmiR/84Af86Z/+\nKf/0T//0ief/8R//kb/7u7978Ngf/MEf8Id/+Idf1CF+gsePH39p7/11ZV3TT+e6PmIZev7fH80M\nFIZi3OWWz3HQyPf2PR88+a9cbw6ICGV6ycu7H9P1QpANABYD3X7PNlYOhx2x+2RA6pJnfnL/EQA+\nTWRZsF6ZqjHNdwza8ajvIUXEhJNFhigM3Z5wucs+lcquD1ztHqFvta2ZOz8+j+DOLj4c0Dmq8r1n\nzx6Gi35OzNMNZm1N4jRzLq2VDiCJsIk9Kh2HFB/M4Lzi6rCh3zy7OMtds8y3732frrsixDcucjaN\n7DgyzguDKh9Wp4SIiaAC51yIi/M09XQx8EyUIShXMXETEy+WmW8BPzyP3C0L93lhmRfGunDMS5up\nUaOUhak6Q1BcWwtUUgVxyryQonK1izweBmxxNhou4tZ4qonBI6ea8S7wnV3P4yFyXgqdDAQiQXq+\ne9gjPlJ3QojCdRfZDJDkAMtCIOKiLBj6ZGB79ehBPhJAmCbMHTPHcXYbyPamqnaVEo/6HcfxzJzP\nD352b8bLscfqzLPdU3KZuL3/Cfe3Qr/cEum5qgkJ3tonicQ0IKnDMTQIabtDU7tmBSFtD5Sk3Gch\nTkbggNREXCZiqcxlIqUNCdgHGDOQDkRVap3JS2abDjBXUujoPOFWcFOG0BPCFtKex4//M2TjUc2E\nENGrKwAOMTIMG2T7qwsuXr9Tf/Wsa7qy8ovzjRM4P/jBD/j93/99rq+v3/v897//fX7nd37nwWPL\nsvDRRx99EYf3gBgjjx8/5uXLl8315itM3/fM8/xlH8bP5NdpTeHLW9dveyDvvs2/v/gXUq18W5Rn\nEb61CTy5/g0kDtyfz9Qycj9+DLJwPJ4o5gRAIkzHI9fDgftjZrP99ifeI5txez5idUHGkZJPmGfu\nqlOr48yUacFQjl2PWuY+bvFeL9k4Qq7Gj88n5tOJmPZQC5TCKRc+Kpk+DQzpoXBIqaPeH7n6AnJA\n8nKHWbv7/nIp3Jd6qcw0RAJPoiJd5O26cQyB60ePuLm5QY/2OjTV6kItI+bt2lUJhLhFw92bH54n\nbJq4mV6wXOzYhmoccUZNTDi9CNu4Z1wy4zQhDpuUGPueIhO1VgZVdiVzM44sSyYobMUZQmATYM61\nzYqIUyW0DTaO1baJ36lyrYHOIiXDshSKZ86ltirdVtn3PXUyllwoZ2dcErvUI1KBivrCcl6Y5nsA\n9qaUWTm+GWe65AwdcGlC+Tz/9BPZQKdSmGolxsj19TV+XyjLTPFKECUMwul8z1QWbsfbNzbcrzDH\nXfl4ueFuqfjYE+u3KHXPTT4yTi94VG8J/QGNA0u+AREsJTR16OJIt8FswmtlHI5MtlAdPiwBk0Cn\nEOuZvJwYzbneOrkszOpgBQuVE4nCRM0T7pU8gUvGKCQ3FhPu1OmiIvJt7u8vrnshEC3j9/eoKgwD\nx2pweseV7xfg1+k79ev+d+r58+ef41GtrPx68bUSOH/zN3/Dv/3bv733ue9973v80R/9Ef/yL//C\nn//5n3/qa1xdXXF1ucv1ih/+8IfknH+lx/q/QinlS33/n4cY41f+GN/m12FN4XNe1wKS238R8ATN\nk7m1s/zm7hm/sX3CPN9AXQiaCN0jNASolVCN43Tkdh6xCOfiuBU2CndZGGolSCRKIKZPnoO5t1yb\nsuAlYyYspVBcKTK02R+rFK+M2RBJHDwSakVFUAc3I5szu8FsyMUe+X5eGEvBdKIMG2JKRFHMjSLG\nUhai7x8MlH8emCklZ46lYu5s8TZE/qplLm2pZaTIe3yhaQZYVtpmvyGgW+StHJxqXBzSADPkfOYm\nZ24XY64Zxwki7FTwWumikkKEEDh5hdBmXYoK9zmTVDmoMFdjq8rzrqNX5VwKSRMaK8Uy207p20QO\nP6nCmBfMoQ9KFKFLA6Vmgjkdzl7gJ6VwLpWrGHEXxlyIoiQNdBKbM0JtVtYA277HbUKtzTEtLryY\nWtWrVyUKWDkxyku8z2CBEDYXh7LN63WM7litvNoqllLAnIiyD5FaapvzQhjCjtNyuoSpOkJgSAOH\nbstH40xfZoJGJOzBOkoN5FqY6DnUgqu2T2vJiARMnbHkNutjC1V7xNr80miRebrnTE8fAjtxxI3O\n2zW6B2otqCaiVw4Id1gzN/CExi2YMZmwoeAYaMdu812kv2K8tEU6kIcBCZFtjBSn9Z7+Cvl1+E5d\n/06trHxz+FoJnD/7sz/7zOf/4R/+gZubG/7yL/8SaJUZd+ev//qv+Yu/+Isv4hBXVr4aZJC3XYcd\nZAYK+JbX9mkqymZ48p4XgG1UsoIRSGIMzSkaFcGB0aCr9sAS+W1UhCEE5m6PZVgkM5pwrMZc4Soo\nIQRwJ8SB7MaHuYBAUL2YHggKSK3gzamqeJu9OddKdsXPZ7QfiOokrfTaE6xwV05ECRzi9hMtTb8q\nRDuQwHKpuIgIfWiL26yCe0quVC/vbZkLYXhvJo582vGWwlwrL0uhat/cu7zN4JTqCIXUbUhxg4ty\nuGgKRVjciMAhBqJqE5KAdQkX2IVAUkFFWGpFVNmJU8uZZTHSxR9vG4SoPYsHrlPPTitB4Cp5qxpE\n5VG/BYkUd7oAT3RLoWIYSZVOlSiRPjglH5E6tSBRBGJB8sy5Dng+sZMT/dUBokOtlJoZS27D/aFH\nHIIKvQhBlSCKXay53VubY1JhCJEhKLvUMcTEYoZ7a7lLqky1Yh5JFFABd0Cp3myvPRt7K4g5IQxU\nJvDAyRKZroW0SuQcdyRpzoD/Y86cCxQytQq9V77X9QwJHg879iEh7lg5crKW0TNZpKihsiWkAxgk\nnK0H+m7gsT5iE9vMaCeCIPSbgdj1dKqfqG6trKysfB35Wgmcn8X3v/99fu/3fu/1///93/89Nzc3\n/PEf//GXeFQrK18w/lDcmDtihpYMtbap8k3AU9fCAD8FEcVE2aUtMZ4wFao/3DxlQmsd+xS2IVDd\n+diNUjOigXmZAWHxhBhobAGGYznRaduQt9QRKO5sVdFy2XTS2pFMmpgwb05mYx5ZqFzHSN/19Bf3\nqOKVYx25ip+PiYiIoOmKUO+pdQEMQdHQv86taZWGGfzNnVoBYtoSItj/0h1c575WzEFQYtxhdX7d\n0jZ7YB/3POqH9rlfjhFgulRO4mVt+hDoQ2BrgScpUczYpoQCY63cl0KnAUsdc5zh1ARxDIkogWcx\nEFWIGtgF4Vgy/znBWAy/fFaDCEkh2Ime5o72JCT2Q08XEyXf4pJ4WRLipdmYY5yk4j5CGNnFyAdi\nbC6q/FQrSzkRiRiR8VLVSyo81oB5c92b3rIsH63lEWUP7GOkU2UTAm4tjLRU45grx8XorJJEyOZM\nxTB6FumZyfzEAgeDLnR0qec+DZSUCJsrVJQSOoI7xeHf58zkQiFQrEJoxhgfZfjtrdB1OzrtCRqZ\nx4zVGVHhSdwRLFIlEUOi764JF8EbJDCkHV4KVGuCbRhWt7SVlZVvHN8ogdN1Hd1boWZd1xFj/FIc\n0lZWvjQufTpLrUzLjJ+OMM3EGBk2A1ESngpSCr7ZfqbIiXFAzemHJ5zuH7oPiib6bk+Mnz7ILCKI\nV7pYCKVlwDyNPUerjOZMumHfbanVWFCCGcWcdNmvtcwb4XHo8BBZzFERHsdIMeeutoyWU664CMUL\nzy/ViFdkK1SvBPl80kKjKiluCXGDu3/iDrqI0KcDgmGW2xB6vyWlHXB+/4t+GiGy+FszPighbHh9\nZqrY5f31neMIIp94DHhdwdhcbLcBHrtzkzOzO+bK/7bd8Sj2jGbgzj5GHnUd7i2nyLw5lu2CMppz\nlzMOuBVqPVOAbYo8jcIhFaj3mGxxK80cIAxgR26rcV/a+S35SDG4tcRM5lHXhNRiAM79PDHWCIRm\nAW2VboF6PnGbM+9M2TDWVj06l0pK3mbC6sK5Gjelclecu2WD1sLgkBC0Fkou1Bwp9Jy1soTElSaW\n2JF3e7p+i26uKeWImyO1cpsLC0o2oRCp0kS7SmT0zEel53HcECS2Suf22/TcUjUimnjMgckNCZvX\nobBXqeNR2rWKX7hU4dZqzcrKyjeUb5TAeZcv0xltZeVLw9vd+vnmJXK6h9OR8Tyx2IKkDdvvfIvQ\n9QxBSfOIbj+9AhPDhl2shG6gG57h0x1GQSXSD8/Ydz3nfMLMCJro00DQh0JiWu4JIRA2GyiFjWTO\nC20jHpVM23w/6/etzYfaZjxUSCJI6JGQ6NyBdndeEDpVUg1AbW1tl3adtvF7eB7F7XMTONDmUsZa\n39se1KtehEVoLXl8RgvazyIEYghteOc9xK57r1sbwBAC4lD4ZE+cIm8dJyDCLsZW8QOGriPnzFAN\nlebSpSK4O49CT3V7LbzSpXJ0LJm5Tu0xEZ6lyC68Om+nlFNLZHIQDVjYcVxGoEBZWDK4B7DC6JVO\neooH3Nvn/9IKUR0nY2XijLGUkW9vlJvxjkPctGTSt5hqxX3mo/NLslXuKrzIUKVdT1OpLD6Qp5la\nF67MSRUKwlY3EB0NkdJ1WN8zSc/18OgSTCtEBa1wrpU2T9UhUol0iEYCEMWYYo+GAb3YgosoV/sP\nGEm4N9OJLUr2gnkToc/7DSsrKysrjW+0wFlZ+Sbi4kzHe/Tuhnp7x4ubF9ScoRSyCeNPfkT/W7/B\n+YOnpM1ICpV9d3jdBvM2QwhUvWa33zEdZyQMqEaSdIgdOc9H5qIXC+cZyWeuhmuGkHBbqGUilwnR\niKhC1yHShvC3oiDG1pzulaWv9hy6yFVsswQaIkJoQT1lej3/ky9Vg61DKRFiQKPQxdb+tZjRqWJe\n8LpQrZA1EUKPhv4T5/nLsgmhzX28kxzfqbL9jArZL0K/3TH7kSW/5b4kgncdGgIf9P3rMNR3j0No\n7WezvQlPTaJsY/hEdacP7bGxVgTYh8h1bJWhV+eqIozWWt9OpbCLkSDCoy6xD8JMAAnsVNm/Y+eN\nt0BYlRZhOhtoSEgxsneghuDgBuYs80LQntkFc4PQNvy1jLwK6zzXZvZQLTPnSt+9ZSjjxjIfuZvP\nfDSfuL8IkcmMPgxsu45BlFwjJ2DJmUhFxYihkMgscYtIZIg7+mHLFCJZLvM/YUOtZzZBUZptRBcE\n9wjS4yhBpZlCBGlhnLWJGw09XdwitTLWN1lInSSEJihXVlZWVt6wfiuurHzDyMGR6Yifz9ze31Cn\nCUrBSsS8cqwz+n//d873z5FvfUD1zGY/8rQ7sAvDg0qHirANSh97rjcHdqXgbszjx5yXewZVzMA0\noGGDKtyML7lOiYhT64zWkVLaQL57Rs2IXsnZkFrZbBOjQcXZUNAM0u+b0cACcb4Edy6VrhY6lJe1\nUE4gpsTUMdRKWQpdp+h1m5+IPjMtR4Iosetwy+S6QFjo0v4zh7FfbeCrO0oTCPFnzDnsYmRwJ7/K\nwfmcWog2MZK3O/RijewA2sJGDynRhUDHKxe7N8YQr9jGyMYde89z7/K6fa2/tKddXvc25wc/96r9\n7ZgLVyk2cSqQQjOL2LwrbmgW2iIR90In0sJmLy1wGSfGLdUWki2IQHUnVeOI4trss602J7RXRFGW\ni/DCK+4ZkQRu1PMNx/HEv45HxlopGLfmLB64SkeyDYwIT0JlrlB8wTB2KsggFA2IZbJE1CshRlKI\nVG+CndARRZA68TwVxmqYJra8aiuUy7oo1/2GGg6k+LCa96pNcLEm4OOlsrQaB6ysrKw8ZBU4Kyvf\nMNwd48wyn6nzArWgWVgoFFlwKj8VQ1/+lB4jlqfcfPuAbyO5qzxO+weuY0MIPO57jhf76FrORBsZ\n3t7wW8X8BHGL5RMjew6pR6RtvHOp5PljYtwiKBtVoi9tw+cTV3FPdkMRxA1dFkQHdHJiLSzzhLiT\ngKcW+clc2sawa61BHYKoIQbLfeVjTkx5xN24Dj2nPNIFp9QCcs/QZ667AztAawUcV4XUkd25fyeb\nYjJjUGX7M+6kB5HXbWifF0mV/aVFbEjptZlAp+FBleSzhIuI8Ise5dvVn7fZhcBIJZvRhYBIIIqy\nVXmv0BMJxO5AyfdsQzOTOFkzBhCJBB1IGkkEvM70YnRUJAx0/SOQ+LoFDppY20XFHbZB8VzxvEAQ\nmCfOy8hP5pFTLZhNWC1UixQVbvOAlglTWAboMNxmIhCDNjvwixtbDoWQEpgRysQxJuaLFV4QYQhb\n/tN+g8eZj0vlLmeE9hlFVQ4xsovNk24xp3/ngwgibD7na2hlZWXl151V4KysfMNIqkwCcxjJMmJa\nCBKYZQaBDMwi9MVhWZBxQm+cGnruVdiGzOadNq542VSnulCqc/uJEW7Am6WwAPVyt1810sVEXyYq\njtnScky8EnEO3YYgTlTDJDJVR8TRWtDs+DSR3XjlMya0fJzv1I4fD1D94kjYQXXwAAAgAElEQVSm\nAbeBY8388H7mQxmJamw1covx4+mWaIXfSG19zjmTw5mqG65TamYIgM8zx9hB+GS1ZjIjWRtU/7Lp\nX9/pd8C/0IHz8j5fa5po2sZIutiDmzuzbxjLGa/OoEr31rqGuGkhnt01Vhee6wwV7hcjeGuDCzLg\ndBC3PE2Brht4ljatwgEsIri3GaJNkMt1Guhq4bxM3LthdUIs87KMLJZRF07VcJeWs+RKLSNnSYQY\nsHlpXXGAaQusnWthI8pGnK7OBA2M1ZjNeByhBmWxSs5nFiqHFPlPyfhwmjkVxxEGVQZRng89Km3m\nacr5U2emVlZWVlY+nVXgrKx8w1AR0v4AQJBCCaX1+194NaaPXvbxtSBZoRRqqYzxkwLn9c9eAidV\nLsP9NJtm84xbodSC6AaRAzkKUVur0SD3xNDa0DQIe4TJm3lAe92CayQobEWJBsuciZfAy+pOtnb0\nuRj7GnkigSUq7g7u3HgTcmOpbFJHZWEy51xmbJnpVfgY5ds9+HjG88ix27Dt92gXKSGQ3ZsF73YH\n7xEMU/1qCBx4mLnzxb4vvFvCaSLAWpuhBhy/BLQGxhrJtYUw7YPyrO9JcftgFkpDxyZ0PJOezfGe\nsRovc7ND6DTyOA5sguJdzyG2a6aYMbBhXkbssgxJhO+KcSPOIsrjOOA+kzww5okbgzuD2RXcEK+I\nN8OLBSNKvAh1Yydw7WB5QotjXUIX4RAril+qekJyY64jp/EnmC2c3Hk5dwTZ8jwpScC0R1CGqCzV\neJQSUZXJ3y8WV1ZWVlY+m1XgrKx8A9k+e8784Y/g5QuyA2JEh1iNOSmokjQSJWAiSNM7UDLvb0Bq\nvNpO9xo5LkfMClYm0ICTmHJEVehDZlo6RANDFEJ/heSRpEJMG5SODec2DO+QHRynF8FFuKuV03RC\nKVRrd8A7acc7u1NrZXBwCVSBYynMNTdLaISJZjTmXjnPM1txeoT7anx7KehSQXrm+czHKvQGaOCc\nOmZzhqwM3Sftr+tnrM03hVczIq+Yan2dRTN7ZqkTP5oyhtBrQCVRdEO1wmyADXznLXFTL4YI7k6M\nkcfDwDZnrmJksuaoF0QwVYoEkl8ElRt93DBgOM0I4VFQemB22MSBGBNYBWnZNgveMpRQnGZi0IlT\nEDrt2GogIDxVowJSFrRUdhjdAt4J27gllMK2jxjCj8cbbnyk1jOTQXG4rYUhzDxKV3QhEILg0kwe\ntm9VsT6rjXBlZWVl5dNZBc7KyjeRGLn6r79DOB3Z/cf/ZKSwrx3nCEkDIcCmDxAUj8Imdc1S151e\n0sPXqpXz/Qtub37MvIzUck9Qp/PKi/v/oNpMCAOm3yH2h2Y/jVPqRNIdU1H6kNCQCXGHhpZVJSkx\nlEJ0Z9T+9bB1deNGC1UrZamEi9NW9oVBE0GVl5LZVCEm57RMvJzPuBn3pbJ0gl1mHgyo1ZhU2Gkb\nui+lcG+B4pXFM89j5APtUKtorbgoY5kIsSPpW1+h7oSckWVuKffhEpb6FXG4KmZMlxwhpKXcD+GT\n7mi/LJ0qUYTiTjF7LW4mmzHPIPLaVe0sBWdhGwZUIhV4uWSGEHjW95xy4bbM5EsIapLEJiauQmRf\nMnhsAZ0hcg6BdDmXFBR1afM+acdAIZJJVlAJmG6Ir+bItM2O7eMGL2d2BqMbxQAPDBfh8Vtu/KaD\nxZ4ZZTofmakoygFh0wc6AnsCSZvRxeTwH9OJo2duSrvmemlyqxPjWEaCbXmqhZjadV/csUvlpv+K\nVANXVlZWft34avzlXVlZ+cIJux277/+fTLs9/b//D+w0c6iRxyrc9x1LPyDbDf22Q7oNfkmp38e3\n2tNq5f70Atc91QoGzOXMeP4hdfqIVM5UYLQel4ln/e+zTfs2/F0zkEmAFyHEDtU34sm7HnEn1/DA\nSWpWp6KMXWEZK4MIQ2hfZXPNCAm/ckZzJC9YnVBx7qsxuHMsFc+V0EU0ysVa2jFRqJn/kK45v1ll\nxonVoBjPotJbYYodpspkyxuB445MI4PIm9T4WpE64n0P6U3A8JdBNntojOAwubOYc0jxVzOf47TB\nfZx9CMw4NxdLY/PKYplehGJOcafizNWbyYQXemlrZDjHUuhU+Xg5Yv7GFrlQmG3B05Ynuz1cnMTO\nOTNfzu+VO93bhg7bNLTWTKv0w4EQZsqr9YgRSqEPA8915sMAg2yY8kjxSijGU4Hf1sq3lsJthZvs\nhGVmG4QgAQlCnyup61lCj+VM1sj/M4/81Ctjqa1lToVZoBOnBmexzC7A7MareqBxaSP9OZz5VlZW\nVlbezypwVla+wYSuY/d//D72X34b/Z//Snx5gyzOtkvcbgJLL1g/4JsNvUSe7x4/sKRdxvvXd9cB\nzBZqyUynH1K9sBVncCMjZMv8fx//M5un32eIiVRmyjISNfGk79noDltmahJcQUJCD3s009LibcLE\n+Oly5LZU7kyYYmWbA48d9iGwBKNGZ993nIKzzLcEc3AQy4ziRBOm2diMldBtQCrWFyZ3Nmxbm5lW\nqlWCCBnlVI1e4HGMbDVwDK3C82YhMj2tevEuMs94TO+d2fmiOJX63scNZ6q1hXb+EtRxpry4be1e\nAKp0Q8ssyp451tZiNl2qOlNtFRJzxzwzLWdC2LQg1jjgJD6aj7ya43pwzF45lYlDTEQR7nPmxbJQ\nL92BYzV6lQeOdtmsDetfHksivJZ7ItD3aM18EHs6rVSPFCDkG/ZUrly5miudJDQvrXVNOwpCloiH\ngKjzRCKmgbuc+Q91PsKZDO6rUq1yNmcfoA9QDEKAoELUxBCUYkYngcep4yolxnfc+lZWVlZWfj5W\ngbOysoLu9/Df/nfKPMPdLbvzkaSBMSWs6+hCpN/umv3tK8xY6nIZzrngzjT9GDSgJkw2UyWxmDPX\npQ2a3/4LdfsBvcFVdwBNTCgHEUIYCC54/2aIX71wt9wy15Fsxo/HO9y8hTuKokPHC4ncScteSaFn\nFuNYFnYyEXuY7zP3DkrkygbCDOAEMz4YDqSaOfeZrIWlZFya89pBAyLC7K0itBclbjdciZC9hWCq\nQI+RPsvtqhaI6dOf/xzJZp85NzWbsfslXt/mTJ3zG3EDYIafR06zcIqBqTZ76FfzMngTV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TgeyJHqpWDqZsfWRyjuIdLniSOBYpeFtn7RQChcCejDNPLMKE4aUySEEkEBaHF2HsMyEI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\nbOM/tJznQi4ybQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4440663ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363291160167620)>\n"
     ]
    }
   ],
   "source": [
    "# Dimensions reduction. Shows how well data is splitted in the 20-something dimensions space.\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.manifold import TSNE\n",
    "from ggplot import *\n",
    "\n",
    "temp_X, _, temp_y, _ = train_test_split(X, y, train_size=0.4)\n",
    "\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=50, n_iter=1000)\n",
    "tsne_results = tsne.fit_transform(temp_X.values)\n",
    "\n",
    "df_tsne = temp_X.copy()\n",
    "df_tsne['x-tsne'] = tsne_results[:, 0]\n",
    "df_tsne['y-tsne'] = tsne_results[:, 1]\n",
    "df_tsne['label'] = temp_y\n",
    "\n",
    "chart = (ggplot(df_tsne, aes(x='x-tsne', y='y-tsne', color='label')) +\n",
    "         geom_point(size=70,alpha=0.1) +\n",
    "         ggtitle(\"Dimension reduction of the features\"))\n",
    "print(chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Deep learning - sklearn's Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time : 2017-07-31 09:30:01.916980\n",
      "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV] mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65) \n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65), total=  28.1s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65), total=  38.4s\n",
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65), total=  39.9s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65), total=  39.4s\n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65), total=  39.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of  18 | elapsed:   43.4s remaining:  1.9min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65), total=  39.2s\n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  48.3s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  48.0s\n",
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  48.7s\n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  49.5s\n",
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  50.3s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65), total=  50.9s\n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  52.1s\n",
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  52.6s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  53.8s\n",
      "[CV]  mlpclassifier__alpha=0.0001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  54.0s\n",
      "[CV]  mlpclassifier__alpha=0.001, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  54.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  18 | elapsed:   55.3s remaining:   11.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  mlpclassifier__alpha=1e-05, mlpclassifier__hidden_layer_sizes=(65, 65, 65, 65), total=  54.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:   55.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2017-07-31 09:31:01.358195\n",
      "Time elapsed: 0:00:59.441215 \n",
      "\n",
      "Best parameters set found on development set:\n",
      "{'mlpclassifier__alpha': 0.0001, 'mlpclassifier__hidden_layer_sizes': (65, 65)}\n",
      "Score: 0.524\n"
     ]
    }
   ],
   "source": [
    "range_1 = [(65,) * 2, (65,) * 3, (65,) * 4]\n",
    "range_2 = 10.0 ** np.arange(-5, -2)\n",
    "param_grid = dict(mlpclassifier__hidden_layer_sizes=range_1,\n",
    "                  mlpclassifier__alpha=range_2)\n",
    "\n",
    "clf = do_grid_search(MLPClassifier(tol=5 * 10e-3), param_grid, True, n_out=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean scores: \n",
      " [[ 0.523053    0.50985389  0.50413132]\n",
      " [ 0.52380521  0.51248074  0.50816358]\n",
      " [ 0.52315549  0.51436162  0.50516513]]\n",
      "Standard deviation: \n",
      " [[ 0.03488287  0.03670536  0.0388454 ]\n",
      " [ 0.03522423  0.03742059  0.03708917]\n",
      " [ 0.03320223  0.03726003  0.03460021]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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xYiXkwxtnJL1R0su63Y7xRtL2wAmStgbW7nZ7xhNJr5K0tu1+WxasoxJsxxFJ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\n6IAE24iIDkiwjYjogP8PHnVpLStVa3sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc768701fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "do_visualize_score(clf, range_1, range_2, 'Layer size', 'Alpha', vmin=0.3, midpoint=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: 0.556487075998\n",
      "Train data: 42501 Test data: 4189\n"
     ]
    },
    {
     "data": {
      "image/png": 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wM+CPHe5/pO2f5QEWZ5O+KCF9Ib9B0gH59eqSRtu+N7/esdkNxk3sC6wIzEyX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc660ef8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_layers = (65,) * 2\n",
    "best_alpha  = 0.0001\n",
    "\n",
    "do_validation(MLPClassifier(hidden_layer_sizes=best_layers, alpha=best_alpha, tol=5 * 10e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reliability test. Takes one validation file at a time and computes score across the whole database.\n",
    "\n",
    "# PARAMETERS #\n",
    "n_files = 234  # / 234\n",
    "n_out   = 100  # / 233\n",
    "##############\n",
    "\n",
    "import warnings\n",
    "\n",
    "X = filtered_file.iloc[:, 28:-1].astype(float)\n",
    "y = filtered_file['TECTONIC SETTING']\n",
    "counts = filtered_file['TECTONIC SETTING'].value_counts()\n",
    "f_counts = filtered_file.groupby('FILENAME').first()['TECTONIC SETTING'].value_counts()\n",
    "\n",
    "score = 0\n",
    "s_score = 0\n",
    "f_score = 0\n",
    "w_score = 0\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "for no, (train_ind, test_ind) in enumerate(file_split_gen(filtered_file['FILENAME'], n_out=1, n_splits=n_files)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_ind].reset_index(drop=True), X.iloc[test_ind].reset_index(drop=True)\n",
    "    y_train, y_test = y.iloc[train_ind].reset_index(drop=True), y.iloc[test_ind].reset_index(drop=True)\n",
    "    filenames_train = filtered_file['FILENAME'].iloc[train_ind].reset_index(drop=True)\n",
    "\n",
    "    range_1 = [(65,) * 2, (65,) * 3, (65,) * 4]\n",
    "    range_2 = 10.0 ** np.arange(-5, -2)\n",
    "    param_grid = dict(mlpclassifier__hidden_layer_sizes=range_1,\n",
    "                          mlpclassifier__alpha=range_2)\n",
    "    clf = do_grid_search(MLPClassifier(tol=5 * 10e-3), param_grid, True, n_out=n_out, verbose=False)\n",
    "\n",
    "    best_layers = clf.cv_results_['params'][clf.best_index_]['mlpclassifier__hidden_layer_sizes']\n",
    "    best_alpha  = clf.cv_results_['params'][clf.best_index_]['mlpclassifier__alpha']\n",
    "    f1 = do_validation(MLPClassifier(hidden_layer_sizes=best_layers, alpha=best_alpha, tol=5 * 10e-3), verbose=False)\n",
    "\n",
    "    score += f1\n",
    "    s_score += f1 * len(y_test) / filtered_file.shape[0]\n",
    "\n",
    "    count_ = y_test.value_counts()\n",
    "\n",
    "    for row in count_.iteritems():\n",
    "        print(no + 1, row, f1, (best_layers, best_alpha))\n",
    "        f_score += f1 / f_counts[row[0]] / 6\n",
    "        w_score += row[1] / counts[row[0]] * f1 / 6 \n",
    "\n",
    "print()\n",
    "print('Average score across files is:   ', score / n_files)\n",
    "print('Average score across samples is: ', s_score)\n",
    "print('Weighted score across files is:  ', f_score)\n",
    "print('Weighted score across samples is:', w_score)\n",
    "\n",
    "warnings.filterwarnings('always')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Deep learning - TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regular convolutional neural networks have generated good results on the database, even though the current problem isn't fit for such neural networks. This suggests exotic types of convolutional neural networks (types which would be more appropriate) would achieve much better accuracies and convergence times than typical machine learning algorithms. Following code is adaptated from examples from the TensorFlow website."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Periodic table convolution\n",
    "\n",
    "As periodic table neighbors share the strongest relationships, operating a convolutional neural network on data arranged in a 3D tensor (rather than a table) may give better results and converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rearrange data in a 4D tensor of shape (nb_samples, height, width, chromaticity_dimensions). The latter is 1, as data is\n",
    "# essentially a monochromatic *image*.\n",
    "\n",
    "periodic_table = [['NA2O', 'MGO', 'AL2O3', 'SIO2', 'P2O5'],\n",
    "                  ['K2O',  'CAO', 'SC',    'TIO2', 'MNO' ],\n",
    "                  ['RB',   'SR',  'Y',     'ZR',   'NB'  ],\n",
    "                  ['TH',   'BA',  'CE',    'HF',   'TA'  ]]\n",
    "periodic_table_sizes = (len(periodic_table), len(periodic_table[0]))\n",
    "\n",
    "def tensor_4D(matrix):\n",
    "    tensor = np.ndarray(shape=(matrix.shape[0], *periodic_table_sizes, 1))\n",
    "\n",
    "    for row_no, row in enumerate(periodic_table):\n",
    "        for col_no, item in enumerate(row):\n",
    "            tensor[:, row_no, col_no, 0] = matrix[item].as_matrix()\n",
    "\n",
    "    return tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convolutional_model(features, labels, mode):\n",
    "    # Input Layer\n",
    "    input_layer = tf.reshape(features, [-1, *periodic_table_sizes, 1])\n",
    "\n",
    "    # Convolutional Layer #1 and Pooling Layer #1\n",
    "    conv1 = tf.layers.conv2d(\n",
    "        inputs=input_layer,\n",
    "        filters=64,\n",
    "        kernel_size=[4, 3],\n",
    "        padding=\"same\",\n",
    "        activation=tf.nn.relu)\n",
    "    # pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[1, 1], strides=1)\n",
    "\n",
    "    # Convolutional Layer #2 and Pooling Layer #2\n",
    "    conv2 = tf.layers.conv2d(\n",
    "        inputs=conv1,\n",
    "        filters=64,\n",
    "        kernel_size=[4, 3],\n",
    "        padding=\"same\",\n",
    "        activation=tf.nn.relu)\n",
    "    # pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[1, 1], strides=1)\n",
    "    shape_ = conv2.get_shape().as_list()\n",
    "\n",
    "    # Dense Layer\n",
    "    pool2_flat = tf.reshape(conv2, [-1, shape_[1] * shape_[2] * shape_[3]])\n",
    "    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)\n",
    "    dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)\n",
    "\n",
    "    # Logits Layer\n",
    "    logits = tf.layers.dense(inputs=dropout, units=6)\n",
    "\n",
    "    loss = None\n",
    "    train_op = None\n",
    "\n",
    "    # Calculate Loss (for both TRAIN and EVAL modes)\n",
    "    if mode != learn.ModeKeys.INFER:\n",
    "        onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=6)\n",
    "        loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)\n",
    "\n",
    "    # Configure the Training Op (for TRAIN mode)\n",
    "    if mode == learn.ModeKeys.TRAIN:\n",
    "        train_op = tf.contrib.layers.optimize_loss(\n",
    "          loss=loss,\n",
    "          global_step=tf.contrib.framework.get_global_step(),\n",
    "          learning_rate=0.00001,\n",
    "          optimizer=\"Adam\")\n",
    "\n",
    "    # Generate Predictions\n",
    "    predictions = {\"classes\": tf.argmax(input=logits, axis=1), \n",
    "                   \"probabilities\": tf.nn.softmax(logits, name=\"softmax_tensor\")}\n",
    "\n",
    "    # Return a ModelFnOps object\n",
    "    return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions, loss=loss, train_op=train_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fc66687e0f0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': None}\n",
      "(5927, 4, 5, 1) (5927,)\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/model4/model.ckpt.\n",
      "INFO:tensorflow:loss = 9104.15, step = 1\n",
      "INFO:tensorflow:global_step/sec: 53.619\n",
      "INFO:tensorflow:loss = 4502.38, step = 101 (1.867 sec)\n",
      "INFO:tensorflow:global_step/sec: 55.0443\n",
      "INFO:tensorflow:loss = 2760.41, step = 201 (1.817 sec)\n",
      "INFO:tensorflow:global_step/sec: 55.0006\n",
      "INFO:tensorflow:loss = 1850.11, step = 301 (1.818 sec)\n",
      "INFO:tensorflow:global_step/sec: 50.8588\n",
      "INFO:tensorflow:loss = 1424.31, step = 401 (1.969 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.6395\n",
      "INFO:tensorflow:loss = 845.179, step = 501 (1.829 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.7285\n",
      "INFO:tensorflow:loss = 514.664, step = 601 (1.827 sec)\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 1.53506, step = 24601 (1.860 sec)\n",
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      "INFO:tensorflow:Saving checkpoints for 32291 into /tmp/model4/model.ckpt.\n",
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      "INFO:tensorflow:Saving checkpoints for 97003 into /tmp/model4/model.ckpt.\n",
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      "INFO:tensorflow:global_step/sec: 54.212\n",
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      "INFO:tensorflow:loss = 1.19247, step = 112301 (1.843 sec)\n",
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      "INFO:tensorflow:loss = 1.19314, step = 112601 (1.840 sec)\n",
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      "INFO:tensorflow:loss = 1.27534, step = 112901 (1.848 sec)\n",
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      "INFO:tensorflow:loss = 1.2628, step = 114001 (1.861 sec)\n",
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      "INFO:tensorflow:loss = 1.25981, step = 114101 (1.848 sec)\n",
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      "INFO:tensorflow:loss = 1.27264, step = 114201 (1.848 sec)\n",
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      "INFO:tensorflow:loss = 1.12034, step = 114301 (1.864 sec)\n",
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      "INFO:tensorflow:loss = 1.27396, step = 114801 (1.914 sec)\n",
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      "INFO:tensorflow:loss = 1.25079, step = 115101 (1.856 sec)\n",
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      "INFO:tensorflow:loss = 1.22288, step = 115201 (1.854 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.741\n",
      "INFO:tensorflow:loss = 1.24605, step = 115301 (1.861 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2545\n",
      "INFO:tensorflow:loss = 1.2543, step = 115401 (1.843 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.0279\n",
      "INFO:tensorflow:loss = 1.26746, step = 115501 (1.851 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2123\n",
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      "INFO:tensorflow:global_step/sec: 54.4819\n",
      "INFO:tensorflow:loss = 1.20507, step = 115901 (1.835 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.7375\n",
      "INFO:tensorflow:loss = 1.29121, step = 116001 (1.861 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.6685\n",
      "INFO:tensorflow:loss = 1.24742, step = 116101 (1.829 sec)\n",
      "INFO:tensorflow:global_step/sec: 52.7888\n",
      "INFO:tensorflow:loss = 1.3625, step = 116201 (1.894 sec)\n",
      "INFO:tensorflow:global_step/sec: 52.4817\n",
      "INFO:tensorflow:loss = 1.24371, step = 116301 (1.905 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.1716\n",
      "INFO:tensorflow:loss = 1.31627, step = 116401 (1.846 sec)\n",
      "INFO:tensorflow:global_step/sec: 52.7898\n",
      "INFO:tensorflow:loss = 1.30172, step = 116501 (1.894 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.9699\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 1.2536, step = 116601 (1.853 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.5953\n",
      "INFO:tensorflow:loss = 1.22885, step = 116701 (1.832 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.9453\n",
      "INFO:tensorflow:loss = 1.29662, step = 116801 (1.854 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.3146\n",
      "INFO:tensorflow:loss = 1.35537, step = 116901 (1.876 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.0158\n",
      "INFO:tensorflow:loss = 1.2176, step = 117001 (1.851 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.9768\n",
      "INFO:tensorflow:loss = 1.34038, step = 117101 (1.853 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.1972\n",
      "INFO:tensorflow:loss = 1.25605, step = 117201 (1.845 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.8101\n",
      "INFO:tensorflow:loss = 1.13566, step = 117301 (1.858 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.202\n",
      "INFO:tensorflow:loss = 1.24782, step = 117401 (1.845 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.1041\n",
      "INFO:tensorflow:loss = 1.22768, step = 117501 (1.848 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2171\n",
      "INFO:tensorflow:loss = 1.26608, step = 117601 (1.844 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.9885\n",
      "INFO:tensorflow:loss = 1.17111, step = 117701 (1.852 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.0255\n",
      "INFO:tensorflow:loss = 1.25534, step = 117801 (1.851 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2905\n",
      "INFO:tensorflow:loss = 1.19913, step = 117901 (1.842 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.6297\n",
      "INFO:tensorflow:loss = 1.32044, step = 118001 (1.865 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.4327\n",
      "INFO:tensorflow:loss = 1.30119, step = 118101 (1.837 sec)\n",
      "INFO:tensorflow:global_step/sec: 52.892\n",
      "INFO:tensorflow:loss = 1.40517, step = 118201 (1.891 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.3414\n",
      "INFO:tensorflow:loss = 1.22318, step = 118301 (1.875 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.8578\n",
      "INFO:tensorflow:loss = 1.34149, step = 118401 (1.857 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.6483\n",
      "INFO:tensorflow:loss = 1.28369, step = 118501 (1.864 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.7374\n",
      "INFO:tensorflow:loss = 1.17471, step = 118601 (1.827 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.483\n",
      "INFO:tensorflow:loss = 1.3057, step = 118701 (1.835 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.1937\n",
      "INFO:tensorflow:loss = 1.27719, step = 118801 (1.845 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2275\n",
      "INFO:tensorflow:loss = 1.34585, step = 118901 (1.844 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.6332\n",
      "INFO:tensorflow:loss = 1.17677, step = 119001 (1.865 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.4192\n",
      "INFO:tensorflow:loss = 1.32145, step = 119101 (1.872 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.5553\n",
      "INFO:tensorflow:loss = 1.22965, step = 119201 (1.867 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.259\n",
      "INFO:tensorflow:loss = 1.26537, step = 119301 (1.843 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.3386\n",
      "INFO:tensorflow:loss = 1.29861, step = 119401 (1.875 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.2198\n",
      "INFO:tensorflow:loss = 1.25637, step = 119501 (1.844 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.7511\n",
      "INFO:tensorflow:loss = 1.31395, step = 119601 (1.860 sec)\n",
      "INFO:tensorflow:global_step/sec: 53.8567\n",
      "INFO:tensorflow:loss = 1.24849, step = 119701 (1.857 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.1075\n",
      "INFO:tensorflow:loss = 1.22706, step = 119801 (1.848 sec)\n",
      "INFO:tensorflow:global_step/sec: 54.118\n",
      "INFO:tensorflow:loss = 1.31255, step = 119901 (1.848 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 120000 into /tmp/model4/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 1.23632.\n",
      "INFO:tensorflow:Restoring parameters from /tmp/model4/model.ckpt-120000\n",
      "\n",
      " 0.365851051209\n"
     ]
    },
    {
     "data": {
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RolBLgsrJ5kLgStvr2x4LfBFYTdIypNEf37K9MbAl8GbgPwtVzASObFP1OaTRI0UTcjnA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6754ec1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the Estimator\n",
    "classifier = learn.SKCompat(learn.Estimator(model_fn=convolutional_model, model_dir=\"/tmp/model4\"))\n",
    "\n",
    "# Format data\n",
    "with tf.Session() as session:\n",
    "    X_tf_train, X_tf_test = tensor_4D(X_train), tensor_4D(X_test)\n",
    "    X_tf_train, X_tf_test = session.run([tf.cast(X_tf_train, tf.float32), tf.cast(X_tf_test, tf.float32)])\n",
    "    y_tf_train, y_tf_test = to_int(y_train).as_matrix(), to_int(y_test).as_matrix()  # to_one_hot(to_int(y_train)), to_one_hot(to_int(y_test))\n",
    "    y_tf_train, y_tf_test = session.run([tf.cast(y_tf_train, tf.float32), tf.cast(y_tf_test, tf.float32)])\n",
    "\n",
    "print(X_tf_test.shape, y_tf_test.shape)\n",
    "# Train the model\n",
    "classifier.fit(x=X_tf_train, y=y_tf_train, batch_size=200, steps=120000)\n",
    "\n",
    "# Evaluate the model and print results\n",
    "eval_results = classifier.predict(x=X_tf_test)['classes']\n",
    "\n",
    "with tf.Session() as session:\n",
    "    conf = tf.confusion_matrix(y_tf_test, eval_results).eval()\n",
    "\n",
    "print('\\n', f1_score(y_tf_test, eval_results, average='weighted'))\n",
    "print_confusion_matrix(conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Comparison with other methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepares the sets associated with each graph from [1]. Elements needed: NB, V, Y, ZR, TIO2\n",
    "\n",
    "set_ = filtered_file[filtered_file['TECTONIC SETTING'] in (,)]\n",
    "set_y = set_['TECTONIC SETTING'].str.replace({'':'M', '':'O', '':'C', '':'I'})\n",
    "set_X = set_['NB', 'V', 'Y', 'ZR', 'TIO2']\n",
    "\n",
    "setA = (set_X, set_y)\n",
    "setB = (set_X[set_y != 'M'], set_y[set_y != 'M'])\n",
    "setC = (set_X[set_y != 'O'], set_y[set_y != 'O'])\n",
    "setD = (set_X[set_y != 'C'], set_y[set_y != 'C'])\n",
    "setE = (set_X[set_y != 'I'], set_y[set_y != 'I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Formats current data into coordinates for the appropriate graph.\n",
    "\n",
    "DFA = do_to_DF_coords(setA[0], (-0.6611, 2.2926, 1.6774, 1.0916, 21.3603), (0.4702, 3.7649, -3.911, 2.2697, 4.8487))\n",
    "DFB = do_to_DF_coords(setB[0], (-0.6146, 2.3510, 1.6828, 1.1911, 22.7253), (1.3765, -0.9452, 4.0461, 2.0789, 22.2450))\n",
    "DFC = do_to_DF_coords(setC[0], (-0.6624, 2.4498, 1.2867, 1.0920, 18.7466), (0.4938, 3.4741, -3.8053, 2.0070, 3.3163))\n",
    "DFD = do_to_DF_coords(setD[0], (-0.2646, 2.0491, 3.4565, 0.8573, 32.9472), (0.01874, 4.0937, -4.8550, 2.9900, 0.1995))\n",
    "DFE = do_to_DF_coords(setE[0], (-0.7829, 0.3379, 3.3239, 0.51232, 16.0941), (1.7478, -0.0421, 3.5301, 1.4503, 28.3592))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Classifies each point according to the separation lines from each graph.\n",
    "\n",
    "params = {\n",
    "    'A': {'center': (0.64849, 0.87812),     1: {0: 0.02820,  1: 8,       's': ('CO', 'I')}, 2: {0: -3.2318,  1: -8,       's': ('CO', 'M')}, 3: {0: 8,        1: -4.5532,  's': ('M', 'I')}},\n",
    "    'B': {'center': (0.883172, -0.667465),  1: {0: -8,       1: 1.66740, 's': ('C', 'O')},  2: {0: 2.27820,  1: 8,        's': ('C', 'I')},  3: {0: 1.87600,  1: -8,       's': ('O', 'I')}},\n",
    "    'C': {'center': (-0.016496, 0.972583),  1: {0: -4.19440, 1: -8,      's': ('C', 'M')},  2: {0: 8,        1: -5.79920, 's': ('M', 'I')},  3: {0: -0.43580, 1: 8,        's': ('C', 'I')}},\n",
    "    'D': {'center': (-0.322489, 1.040295),  1: {0: -3.72100, 1: -8,      's': ('O', 'M')},  2: {0: -0.81840, 1: 8,        's': ('O', 'I')},  3: {0: 8,        1: -4.36500, 's': ('M', 'I')}},\n",
    "    'E': {'center': (1.129586, -0.4194316), 1: {0: -8,       1: 1.48500, 's': ('O', 'C')},  2: {0: 2.43000,  1: -8,       's': ('O', 'M')},  3: {0: 3.32100,  1: 8,        's': ('C', 'M')}}\n",
    "         }\n",
    "\n",
    "classA = do_DF_classification(DFA, params['A']['center'], params['A'][1], params['A'][2], params['A'][3])\n",
    "classB = do_DF_classification(DFB, params['B']['center'], params['B'][1], params['B'][2], params['B'][3])\n",
    "classC = do_DF_classification(DFC, params['C']['center'], params['C'][1], params['C'][2], params['C'][3])\n",
    "classD = do_DF_classification(DFD, params['D']['center'], params['D'][1], params['D'][2], params['D'][3])\n",
    "classE = do_DF_classification(DFE, params['E']['center'], params['E'][1], params['E'][2], params['E'][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Computes global scores for each graph.\n",
    "\n",
    "f1A = f1_score(setA[1].str.replace(['C', 'O'], 'CO'), classA, average='weighted')\n",
    "f1B = f1_score(setB[1], classA, average='weighted')\n",
    "f1C = f1_score(setC[1], classA, average='weighted')\n",
    "f1D = f1_score(setD[1], classA, average='weighted')\n",
    "f1E = f1_score(setE[1], classA, average='weighted')\n",
    "\n",
    "print('f1 scores:', 'A:', f1A, 'B:', f1B, 'C:', f1C, 'D:', f1D, 'E:', f1E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualizing graphs with data from the current database.\n",
    "\n",
    "# from matplotlib.colors import ListedColormap\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "ref = {0:'A', 1:'B', 2:'C', 3:'D', 4:'E'}\n",
    "\n",
    "for n, scatter in enumerate(DFA, DFB, DFC, DFD, DFE):\n",
    "    fig.add_subplot(3, 3, n + 1)\n",
    "    m = ref[n]\n",
    "\n",
    "    plt.xlabel('DF1')\n",
    "    plt.ylabel('DF2')\n",
    "\n",
    "    # Plots the lines.\n",
    "    for line in range(1, 4):\n",
    "        plt.plot([params[m]['center'][0], params[m][line][0]], [params[m]['center'][1], params[m][line][1]], color='k', linestyle='-', linewidth=2)\n",
    "\n",
    "    # Plots the training points.\n",
    "    labels = filtered_file.iloc[:, 28:-1].axes[1].tolist()\n",
    "    plt.scatter(scatter.apply(lambda x: x[0]), scatter.apply(lambda x: x[1]), c=color, label=labels[k], cmap=plt.cm.Paired)\n",
    "\n",
    "    plt.axis('tight')\n",
    "\n",
    "    plt.legend(handles=[]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "\n",
    "[1] Verma, S.P., Agrawal, S., 2010, *New tectonic discrimination diagrams for basic and ultrabasic volcanic rocks through log-transformed ratios of high field strength elements and implications for petrogenetic processes*, Revista Mexicana de Ciencias Geológicas, v. 28, núm. 1, 2011, p. 24-44."
   ]
  }
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