{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Some stuff that may or may not be necessary need to clean this up later  \n",
    "import sys\n",
    "sys.path.append('../')\n",
    "\n",
    "# Set options to allow for better display in the iPython notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "% matplotlib inline\n",
    "plt.rcParams.update({'figure.figsize':[12,10]})\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "pd.options.display.max_rows = 10\n",
    "pd.options.display.max_columns = 10\n",
    "pd.options.display.max_colwidth = 50\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**PESTools - A Python toolkit for processing PEST-related information**</center>  <br>\n",
    "\n",
    "<center>Evan G. Christianson<sup>1</sup>, Andrew T. Leaf<sup>2</sup></center>\n",
    "<center><sup>1</sup>Barr Engineering, echristianson@barr.com, Minneapolis, MN, USA</center>\n",
    "<center><sup>2</sup>USGS – Wisconsin Water Science Center, aleaf@usgs.gov, Madison, WI, USA</center>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**ABSTRACT**</center>\n",
    "\n",
    "PESTools is an open-source Python package for processing and visualizing information associated with the parameter estimation software PEST and PEST++.  While PEST output can be reformatted for post-processing in spreadsheets or other menu-driven software packages, that approach can be error-prone and time-consuming. Managing information from highly parameterized models with thousands of parameters and observations presents additional challenges. PESTools consists of a set of Python object classes to facilitate efficient processing and visualization of PEST-related information. Processing and visualization of observation residuals, objective function contributions, parameter and observation sensitivities, parameter correlation and identifiability, and other common PEST outputs have been implemented. PESTools is integrated with the pyemu software package for linear-based computer model uncertainty analyses, allowing for efficient computations using the Jacobian Matrix without any external utilities or files. The use of dataframe objects (pandas Python package) facilitates rapid subsetting and querying of large datasets, as well as the incorporation of ancillary information such as observation locations, times, measurement types, and other associated information. PESTools’ object methods can be easily scripted with concise code, or alternatively, the use of IPython notebooks allows for live interaction with the information. PESTools is designed to streamline workflows and provide deeper insight into model behavior, enhance troubleshooting, and improve transparency in the calibration process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**INTRODUCTION**</center>\n",
    "\n",
    "In recent years the PEST software suite (Doherty, 2010a: Doherty, 2010b) has become the industry standard for calibrating groundwater flow models and evaluating uncertainty in their predictions. However, many challenges remain in using PEST, especially for highly parameterized models. Calibration of highly-parameterized models typically requires managing large volumes of information spread across numerous input and output files. This information may provide valuable insight, but can be difficult or impossible to effectively visualize without custom programming. PESTools aims to provide a central platform for managing and visualizing information from PEST and PEST++ (Welter et al, 2012), which minimizes the number of intermediate files and custom code required for parameter estimation workflows.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**DEMONSTRATION**</center>\n",
    "\n",
    "PESTools is designed with flexibility in mind. Data are manipulated using Python object classes, allowing interactive exploration or through simple, customizable scripting. Reading and writing of the PEST control and Jacobian files and linear algebra computations (e.g. parameter identifiability) are performed using the pyemu Python package (White, 2015). PESTools stores and/or returns information in Python dictionaries, pandas dataframes (http://pandas.pydata.org/), and Matplotlib plots (http://matplotlib.org/), allowing for easy interrogation of the information and customized visualizations.\n",
    "\n",
    "Some examples of PESTools’ capabilities are presented below, including the code (indicated by courier font; user-supplied arguments are in red type) used to generate the tables and figures. In most instances only one or two lines of code are needed to quickly generate useful output. Additional documentation, example datasets, and interactive tutorials using IPython notebooks (http://ipython.org/notebook.html) are available on the project webpage at https://github.com/PESTools.\n",
    "\n",
    "\n",
    "\n",
    "At the top level of PESTools is the Pest class.  A Pest class instance is created by providing the path and base name of a PEST run.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pestools\n",
    "example = pestools.Pest('../cc/Columbia')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Pest class the user has access to numerous PEST related inputs and outputs, most commonly returned in the form of a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parnme</th>\n",
       "      <th>partrans</th>\n",
       "      <th>parchglim</th>\n",
       "      <th>parval1</th>\n",
       "      <th>parlbnd</th>\n",
       "      <th>parubnd</th>\n",
       "      <th>pargp</th>\n",
       "      <th>scale</th>\n",
       "      <th>offset</th>\n",
       "      <th>dercom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kpkpx_tc2</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.300000e-01</td>\n",
       "      <td>1.354000e+01</td>\n",
       "      <td>kp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kpkpx_tc3</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.300000e-01</td>\n",
       "      <td>1.354000e+01</td>\n",
       "      <td>kp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kpkpx_tc4</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.300000e-01</td>\n",
       "      <td>1.354000e+01</td>\n",
       "      <td>kp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kpkpx_tc7</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.300000e-01</td>\n",
       "      <td>1.354000e+01</td>\n",
       "      <td>kp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kpkpx_tc8</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.300000e-01</td>\n",
       "      <td>1.354000e+01</td>\n",
       "      <td>kp</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>r_cr</td>\n",
       "      <td>none</td>\n",
       "      <td>factor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>rech</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>r_gg</td>\n",
       "      <td>none</td>\n",
       "      <td>factor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>rech</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>r_lc</td>\n",
       "      <td>none</td>\n",
       "      <td>factor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>rech</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>sfrc</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>sfr_cond</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>wr_drnc</td>\n",
       "      <td>log</td>\n",
       "      <td>factor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>sfr_cond</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>597 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        parnme partrans parchglim  parval1       parlbnd       parubnd  \\\n",
       "0    kpkpx_tc2      log    factor     2.42  4.300000e-01  1.354000e+01   \n",
       "1    kpkpx_tc3      log    factor     2.42  4.300000e-01  1.354000e+01   \n",
       "2    kpkpx_tc4      log    factor     2.42  4.300000e-01  1.354000e+01   \n",
       "3    kpkpx_tc7      log    factor     2.42  4.300000e-01  1.354000e+01   \n",
       "4    kpkpx_tc8      log    factor     2.42  4.300000e-01  1.354000e+01   \n",
       "..         ...      ...       ...      ...           ...           ...   \n",
       "592       r_cr     none    factor     1.00  1.000000e-01  1.000000e+01   \n",
       "593       r_gg     none    factor     1.00  1.000000e-01  1.000000e+01   \n",
       "594       r_lc     none    factor     1.00  1.000000e-01  1.000000e+01   \n",
       "595       sfrc      log    factor     1.00  1.000000e-10  1.000000e+10   \n",
       "596    wr_drnc      log    factor     1.00  1.000000e-10  1.000000e+10   \n",
       "\n",
       "        pargp  scale  offset  dercom  \n",
       "0          kp      1       0       1  \n",
       "1          kp      1       0       1  \n",
       "2          kp      1       0       1  \n",
       "3          kp      1       0       1  \n",
       "4          kp      1       0       1  \n",
       "..        ...    ...     ...     ...  \n",
       "592      rech      1       0       1  \n",
       "593      rech      1       0       1  \n",
       "594      rech      1       0       1  \n",
       "595  sfr_cond      1       0       1  \n",
       "596  sfr_cond      1       0       1  \n",
       "\n",
       "[597 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example.parameter_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Table 1. Parameter data section of a PEST control file presented as a pandas dataframe.  Per pandas conventions the middle section of the dataframe are excluded from view but are accessible using standard pandas methods._**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obsnme</th>\n",
       "      <th>obsval</th>\n",
       "      <th>weight</th>\n",
       "      <th>obgnme</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obsnme</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7089222501_b</th>\n",
       "      <td>7089222501_b</td>\n",
       "      <td>858.13</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>head_best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089192101_b</th>\n",
       "      <td>1089192101_b</td>\n",
       "      <td>893.24</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>head_best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089103101_b</th>\n",
       "      <td>2089103101_b</td>\n",
       "      <td>924.60</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>head_best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089103201_b</th>\n",
       "      <td>3089103201_b</td>\n",
       "      <td>911.44</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>head_best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089475301_b</th>\n",
       "      <td>2089475301_b</td>\n",
       "      <td>729.25</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>head_best</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5406328_marshcr</th>\n",
       "      <td>5406328_marshcr</td>\n",
       "      <td>98200.00</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>headwaters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5405820_wiscons</th>\n",
       "      <td>5405820_wiscons</td>\n",
       "      <td>75700.00</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>headwaters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ccsg12_beavercr</th>\n",
       "      <td>ccsg12_beavercr</td>\n",
       "      <td>55500.00</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>cc_streams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5406060_blum'sc</th>\n",
       "      <td>5406060_blum'sc</td>\n",
       "      <td>40100.00</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>headwaters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5425926_beaverd</th>\n",
       "      <td>5425926_beaverd</td>\n",
       "      <td>38400.00</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>headwaters</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4599 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          obsnme    obsval    weight      obgnme\n",
       "obsnme                                                          \n",
       "7089222501_b        7089222501_b    858.13  0.200000   head_best\n",
       "1089192101_b        1089192101_b    893.24  0.200000   head_best\n",
       "2089103101_b        2089103101_b    924.60  0.200000   head_best\n",
       "3089103201_b        3089103201_b    911.44  0.200000   head_best\n",
       "2089475301_b        2089475301_b    729.25  0.200000   head_best\n",
       "...                          ...       ...       ...         ...\n",
       "5406328_marshcr  5406328_marshcr  98200.00  0.000020  headwaters\n",
       "5405820_wiscons  5405820_wiscons  75700.00  0.000026  headwaters\n",
       "ccsg12_beavercr  ccsg12_beavercr  55500.00  0.000018  cc_streams\n",
       "5406060_blum'sc  5406060_blum'sc  40100.00  0.000050  headwaters\n",
       "5425926_beaverd  5425926_beaverd  38400.00  0.000052  headwaters\n",
       "\n",
       "[4599 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example.observation_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Table 2. Observation data section of a PEST control file presented as a pandas dataframe._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following a PEST run, residual information can be read in, from a single .res file, or set of .rei files containing residual information by iteration. Ancillary observation information such as spatial coordinates, date/time, or measurement type can be read in from an observation information file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "residuals = example.res('../cc/columbia_svda.rei.16', obs_info_file = '../cc/observation_locations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerous plotting options are available for residual information, which can be visualized categorically (by group as defined in the PEST control file or by other classifications in the observation information file). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x10ae5ced0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x10b724f50>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xyKCT9/JjsWKNzv1yYy8/5uTOzMyszrLE7kOkMlXnRsS8nEOqCSd3+XC3rJmZWR1lBev3\nBJYjtdjVrX5e0xkEM1trwbNlzczM6kRSK6kA/RhSi50TuyFC0lZlK1TNkDRd0q+yllokHSHp9mzV\nqj1qFYtb7szMzOpAUhuwL6mqwh8iosHXORgC6pTlSDoGOJhU8gzgx8CxETFd0qnAnpJuA75IWhFq\nJDBD0l8iYkG143HLnZmZWY1lqxQdAHQCFzqxq5O2Gj2W9ihpOdKucXjviYjp2e9XkWZEbwHcEhEL\nI2JWdswmVbvXEm65MzOzpiEVW4Doa9arVGyNKHRU55oaBnwceAP4U0R0VuO8Vj/T5qVHTyLiUkmT\nSjaVTrZ4gzS+chzwejfbq87JnZmZDXlZUncwaZ3YV6XiZyIKN3Sz3wjS0pHfloq3A5+LKNzf/+tq\nBHAgaS3yK5zY1VmVspwpY9KjS3FWn4eU/juPA14DZgFjS7aPZcl136vG3bJmZjakScVW4C7gl6RV\ngtYBLpOKZ5XttyppiclvkpYZ2xb4u1T8av+uq5HAIaS1xS93YtdU7pa0Y/b77sB04O/A9pKGZ+vM\nb0AqjF11brkzM7OhbiSwIUv+zRsNvL9sv3WAEaR1ZSE1gIwkFRr+0bJcUNJoUmL3OPCXGKxFZQe7\n+pdC6fp3/ipwWtYl/yBwcUREtsrVzaT/to6txWQKcHJnZmbNoW7JlaSxwCeAfwI3OrFrDhHxJKm1\nl4h4BJjSzT6nA6fXOhYnd2ZmNtQtIo2B6mTxcKSFpDFQpd4gdceWms8yjIvKuts+AdxTMlvS8tKk\nWY7H3JmZ2ZAWUZgHbAXcDswG5gJ/AnYu2+9eYC/gqZL9fgF8upLrSBoPHAbc4cTO8uS1Zc3MrClI\nRQHvA16OKNzZy35twN7A7RGFJys7t1YktdjNiIjbqxDukJD72rIb1+jc9zX22rJO7szMzAZA0sqk\nxO6GiLg773gaiZO7fDRpb7SZmdnASVqVVD/v2oi4N+94rEz9Z8s2BCd3ZmZm/SDpbaQCxVdGxIN5\nx2PWxcmdmZnZMpI0EdgfuCwiHs47HutBk2Y5TXrbZmZm/SNpbWBf4NKIeCzveKwXTZrlNOltm5mZ\nLTtJ65Jm0l6UFa01azhO7szMzCogaX3gw8D5EfFM3vFYBTyhwszMzLojaUPSAvDnRsRzecdj1hsn\nd2ZmZr2Q9C5gV+DsiJiZdzy2DJo0y2nS2zYzM+ubpM2AHYGzIuLFvOMxq4STOzMzs25I2hJ4LzA1\nIl7JOx7rhybNcpr0ts3MzHom6b3A5sAZEfFa3vGYLQsnd2ZmZhlJAnYANiYldrNyDskGokmznCa9\nbTMzsyVlid3OwDtIXbFv5hySWb84uTMzs6aXJXa7AWuRErs5OYdk1eA6d2ZmZs0nS+z2AFYlzYqd\nm3NIZgPi5M7MzJqWpBbgI8B4Uh27+TmHZNXUpFlOk962mZk1O0mtpHViR5FWnliQc0hWbU2a5TTp\nbZuZWTOT1AZ8lPR38LyIWJRzSGZV4+TOzMyaSpbY7Q8sAs6PiI6cQ7Ja8YQKMzOzoU3SMOAAYA7w\nRyd2NhQ5uTMzs6YgaThwIPAqcFlEdOYcktVak2Y5TXrbZmY2WEjFFuAg4PvADODoiMKzy3YOjQAO\nBp4HroyIqHqgZg2iJe8AzMzMeiIVxwP3Ab8CVgf2Ah6RiodXfg6NAg4F/o0Tu+bSVqNHg3NyZ2Zm\njWx9YE1gTPZ8GDAS+EQlB0saAxwGPApc48TOmsEgyD/NzKzJ9WtsnKRxpCTwPmC6E7sm5NmyZmZm\nDWc+qbWu1CJgdm8HSVqe1BV7R0TcUqPYzBpSzbplJW0l6cbs900lTZd0o6SrJa2SbT9C0u2SbpW0\nR7ZtpKRLsv2vlLRSrWI0M7OGdzfwedIM17mkEiaXAf/V0wGSVgAOB25zYtfkmnTMnWrRSi3pGNKs\npDcjYltJ04AvRcS9kj4DvAM4GfgLsBlp/MQMYHPgC8CYiPiupP2BbSLiK91cIyJCVQ/ezMwajlQc\nA3wKuDWi8Pee99PKwCHATRFxZ73is+7l+bdaUsRna3TuX0Mj5yC1yj8fBfYBzs6eHxARz2e/t5O+\nfW0J3BIRC4GFkh4FNgHeC5yU7Xs18J0axWhmZoNEROFN4Ge97SNpAqlh4bqIuKcugZk1oJokdxFx\nqaRJJc+fB5C0LXAksD3wAeD1ksPeAJYDxgGzyrZ1S9LxJU+nRcS0AQdvZmbLRCqKtOrDpsDJEYWX\n6x+DVicVKL4qIh6o9/X7Syq+Dfhv4DrgyojCoJ70IWkKMCXnMBYbBF2otVC32866WI8FPhgRL0ua\nBYwt2WUs8BopsRtbtq1bEXF8baI1M7NKSMX3AGcCa5P+phwpFY8DflKvREXSmqTk8vKIeKge1xwo\nqdgKHA98lfS+fRq4VyoeHlF4OM/YBiJrZJnW9VxSIbdgmlhd6txJOpjUYjclIp7MNv8d2F7ScEnL\nARsA9wO3AB/M9tkdmF6PGM3MrF9OAzYERgPDs5/fA9apx8WzXqIDSOvEDorELrM1cDRpzHk76X3b\nGvhBnkENOa01ejS4WrfchaQW0jiJp4BLJUHqQi1KOgW4mZRkHhsR8yWdCpwp6WbSFPgDaxyjmZn1\nXxtQPrB8AXX4EyhpHdL47osj4olaX6/KWoGFZdvE0mVfzJZZzZK7rIVu2+zpij3sczpwetm2ucB+\ntYrLzMxqruZDfiRNBvYELoiIp2t9vRrpLgEe1GPuGk6Tjrnz8mNmZjYQJwFvAvNIickc4Frg8Vpd\nUNI7SYndeYM4sbsbuJPFxZjnkmr5/TS3iGzIqEmdu3pwnTszs8YgFVcA/hd4N3B0ROHW2l1LGwO7\nAeeUlNgatKTi+0gJ8lXAiVnJlyEj9zp3X6/RuU9q7Dp3Tu7MzGxQkPRuYGfg7Ih4Ie94rG9O7vLR\npL3RZmbWX1JxeeCdpNUiItu2CrBmRKEmq0JI2gLYDpgaES9n1xSpbupdQ63Fy6pkEMxsrQW33JmZ\nWUWkYhvwWVKpkzbgPuCLwPtIdUxFKmd1ZEThX9W7rrYhrWp0VkS8msWyDfBbUsmVeaSyImdFFDqr\ndV0buNxb7o6t0bm/55Y7MzMbGn4NfBwYlT3fHLiVVPpkRLZtJ+Aeqbh2RGHAY+Ik7QC8i9Ri93ra\nVnwvaW3yEaSEciTwS2AyKck0S5o0y2nS2zYzs35Yg8WJHaSKC50sTuwgdYTNIS0d2e/kTqko6k6k\nAvdTI+KNkpdXJtWIG1mybRTwtv5ez4aoJs1ymvS2zcysUWWJ3fuAt5MSu9l9HGJmJZzcmZlZpe4g\nTWDoar2bT2q9m1eybS6wCHixPxfIErsPAqsDZ2aF7cs9Tvr7tYC0okNk1727P9e0IaxJsxwXMTYz\ns4pEFL4NfAR4jJTAnUPqCj0Q+A8p2ToVmBRReGVZz58tV/kRYAKp3El3iR0RhXtJ4+v+BHQADwC7\nRhRcANgMz5Y1M7NlJBVbgRUjCi+UbBsGjI0ovNy/c6oF2BsYA/whIhZUGMsE4IWukizWWHKfLXtS\njc799caeLevkzszMciWpFfgoqYv1gohYmHNIViVO7vLRpL3RZmbWCCS1AfuRZt2eHxGLcg7JhpIm\nzXKa9LbNzCxvktqBA0gTMi6NiI6cQzIbEpzcmZlZ3UkaTiqI/Drw54jwyhJWfU2a5TTpbZuZWV4k\njQAOAl4ArojBOvjbrISku0hfViCV6/kGcDqwPGkllU9ExJP1iMXJnZlZE5OKk4CfkCYzfCWi8Eht\nr6dRwMHAM8DVTuysplrrc5nsCwsRsVPJtqmkkj4XS5oCbAQ8WY94XOfOzKxJScUC8CCwB/B+0pqw\nJ9fuehoNHAo8gRM7G1reBYySdI2k6yVtDWwLrCnpL6SW6hvqFYxb7szMmtcxLLk+axtwdLa9qiSN\nJSV29wM3ObGzuqhSljPtYZj2r153mQ38ICJ+J2k94GpgbeCViHifpO8AXwcK1Ymod07uzMyspiQt\nR0rs7oqIGXnHY02kSlnOlA3To0vxyqV2+RfwKEBEPCLpZWAicFn2+uXACdWJpm/uljUza17dzVCt\n6qxVSSsAhwN/d2JnQ9jhwI8AJK0OjAX+SBryALAjqdW6LtxyZ2bWvPYnzeYbR5rNNxv4r2qdXNJK\nwCeA6RFxR7XOa1axOk2oAH4HnCFpevb8cOA54HRJnwNeI63BXBdefszMrIlla8IeSZot+7OIwrzq\nnFerAIcA10fEP6pxTht8cl9+7Iwanfvwxl5+zMmdmZlVlaTVSLMDr46IunVFWePJPbk7u0bnPqSx\nkzt3y5qZWdVIWoO08sQVEfHPvOMxa0ZO7szMrCokrQXsB/wpImpaDNmsIk2a5TTpbZuZWTVJejvw\nUeCSiHg873jMmpmTOzMzG5CsaOtewIUR8VTe8Zi9pUmzHNe5M7OmIBXfKxXXyzuOapOK20vFdfK7\nvjYgJXZ/cGJn1hg8W9bMhjSpuAFwKrAFqZbbGcB3Igqv5BrYAEnFjUj39R7SF/XTgOMiCq/VLwZt\nBHwAODci/lOv69rgkfts2UtrdO59PFvWzCwXUnEscBfQzuJypp8CtiElRYOSVBwP3EG6r64emM8A\nWwJb1ycGbQrsApwVES/U45pmVhknd2Y2lI3IfpbWqR8OrJhDLNU0Cugg3UuX4cBK9bi4pM2BHYAz\nI+KlelzTrF+aNMtp0ts2M2tOUnEcMCqi8Hz/jtfWpNbBqRFRUde2VBSwLvBYRKGqa9ea9apJsxxP\nqDCzoWwWMJO0ZmqX2cAt+YRTNa8Cr7D0fd3c0wFSsU0qfh54BnhCKv5EKi63LBeVtB2p6/eMZUjs\nNgZmAA8A90rF9y7LNc1s2Tm5M7MhK6IwH5gMHEdKfp4hFdk9KM+4BiqiMIfUEvZdYC7wFPDRiMLh\nvRx2DXAyMI7UXf1Z4HGpOKKXYwBQMgXYlJTYvV5JnFJxV+BvpJa+duCdwF+k4qGVHG82YK01ejS4\nJm2wNLNmEVFYAPxYKp4KLIwoLMo7pmrIEteTpeLPqey+3gGMLnk+gjR7eBQwr6eDJAnYlZRMnhER\ns3vatxtrA8HihgQBI4EhV5LGrJE4uTOzphBRmJt3DLVQy/vKErsPAGuSJk/MqdW1zGqiSbMcd8ua\nmTWHp4HS5Gx+9ug2OcwSuw8Bq5PKnfQnsfs3qbWutKDqnCwWM6sRJ3dmZs1hZ+B/SWMP5wHnAet1\n1/InqYW06sSKwDkR0WO3bW8iCldl172PlEg+CewTUfhtf85ntszaavRocF6hwsysiUjFlYFxEYXH\nun9drcA+pDF550fEwipcU6Si0fcMlTGPVpncV6i4qUbn3rGxV6hwcmdmZgBIagP2JXWlXhQRTsRs\nQHJP7mbU6NzbNXZyNwgaF82snFR8H/AT4F7gaxGF53IOyQY5Se3A/sAC4JKI6Mg5JDPrJ7fcmQ0i\nUnE4cBWpkOxo0h/iDuCoiMJv8ozNBi9Jw4CPA28Af4oIryJhVZF7y91tNTr31o3dcucJFWaDy0Rg\nKxbXKxtGqhv2hdwiskFN0gjgYNKqF07szIYAd8uaDT7uLrOqkDSSlNg9B/xfDNauHLOeNGmW06S3\nbTZoLSS11pUKUpkJs4pJGg0cAjwO/MWJnQ1JTZrluFvWbBCJKDwJfA54jVR8djZp7U6v1WkVkzQW\nOAz4F07szIYcT6gwG4Sk4hjgSOAB4MqIwuD8H9nqTtJywCeAeyJiet7x2NCW+4SK+2p07o0be0KF\nkzszsyYhaTwpsft7RNyadzw29Dm5y0eT9kabmTUXSSuSErsZEXF73vGY1UWTZjlNettmZs1D0sqk\nxO6GiLg773jMrLac3JmZDWGSViWVO7k2Iu7NOx6zumrSLKdJb9vMbOiT9DbgQODKiHgw73jMrD5q\nWgpF0laSbix5vrekc0ueby3pNkkzJB1Xsr0g6W+SbpG0RS1jNDMbiiRNJCV2lw0ksZOKq0rFA6Xi\niJJtk6TiflLRDQTW2Fpr9GhwNZstK+kYUlfAmxGxraSfAe8H7o6IA7N97gb2iYgnJF0JfIuUcP4g\nInaRtCZpAestuzm/Z8uamXVD0trAvsClEfFY/85RHAF8DfgmqVD2bODrwEbA54FO4GXgsxGFq6oR\ntw09uc9HOAWrAAAgAElEQVSWfbxG5357Y8+WrWXL3aPAPkDXzd9CKr4qAEnjgOER8UT2+jXArsB7\ngWsBIuIZoC2b5WVmZn2QtC4psbuov4ldpkj6wj2KtJbxKsDpwJdI6xmPJq11fKlUfPeAgjarlbYa\nPRpczZK7iLgUWFTy/MKyXcYBs0qevwEsl21/vZvtZmbWC0nrA3sD50fEkwM83fLAiLJtAbSXbZsP\njB3gtcxqo0mTuzxDnMWSHwjjSEsqLSjbPjbbvhRJx5c8nRYR06obopnZ4CBpQ2B34NyIeK6Ol24h\nteSZIWkKMCXnMJpebsldRMyStEDS24EnSOPxjgc6gJMl/RBYE2iJiFd6OMfxdQrXzKxhSXoXaVjL\n2RExs0qnvQE4hNRS10ZayxjSZ3Q7KanrIH0Bv0AqHg2cGVHoqNL1bRDKGlmmdT2XVMgtGBgUrWy1\nUNPZspko+730+WeBc0kLn98VEbdHxF3AzcCtwMWkgbtmZtYNSZsBuwBnVTGxI6JwAbAhaTz0IuBX\nwKrAZsCdXZfPfi4HnEIak2dmOfPasmZmg5SkLUmT0M7sqYejOtcptkUUFpU835I08a18PPTNEYUd\nahWHDT55z5btfLk2525ZsXlny5qZDQlSseEmDEh6L7ANcEYtEzuA0sTOzBpfk/ZGm5n1TSquCvwA\nOFAqTgc+H1H4Z74xScAOwMakxG5WH4fUwvOkvx8LgGHZttnAAznEYtajjibNctxyZ2bWDam4Oale\n536kz8rtgTul4oH5xSQBO5PGwk3NKbEjovA0MBm4hJTgPQ8cisdImzWEJs1pzcz6tAFpAlhXy1Qr\nqeTHlsB59Q4mS+x2A9YiJXZz6h1DqYjCc6QWzbWA5yMK8/OMx6w7zdpy16S3bWY2eGSJ3R6k2apn\nRcTcPg6pm4jCU3nHYGZLcnJnZlZGKq5PWld1dNlLc4GX6hjHaOg8Fj72Rfj3jTD+0IgrGyaxM2t0\ni1prNfqss0bnrQ6PuTMzKyEVjwfuIo1rE6lrtoO0LOJXgBPrFMcW0Pk0PPhVWDgWdtkVtnzU67ia\nWV/ccmdmtqRDWXI5LZG+pm8WUXisfmEs3BUeXh46W9LE2NZRpM/sKcDd9YvDbPDqaKtVmrOgRuet\nDid3ZmZ9WwS8Wa+LSWqDbTeB1QI2wp0sZv3T0dqadwi5cHJnZk0p6948GbgNODGi0DX7dC6ppa40\no2oDFtYnLg0DDoCWebDhQmgp/evUCcyrRxxmNnj566CZNRWp2C4VzwNuIa3JejTwjFTcPdtlH2AG\nqSjvbODfwN4RhZquApFi03DgIGAWbPIFaPkpKdmcl/38ITC11nGYDRUdtNbk0ei8tqyZNRWpuDGp\ntW5U2Uu3RxS2LNnv/cBE4MyIQs1b7SSNAA4mFQS+MrIPZ6k4ETgAODei8Gyt4zCrprzXln0xxtTk\n3CvrzYZeW9bdsmbWjPpcKzWicG09AgGQNAo4BHgKuCZKvnVnq0GcXK9YzIaSRYOgla0WnNyZWbPp\nBNq72d5R70AAJI0BPgE8DNwQg7U7xcwahpM7M2s2/wR+AhxF+gxcADwHfLnegUgaR0rs7gOmO7Ez\nq66OJk1zPObOzJqSVFwTOA64lTSurq4td5KWJ9XUuyMibqnntc3qJe8xd/+OFWty7jX08hJj7iS1\nAqcBk0mFzz8bEQ9kr/0EeCgiflOTYLrRnCmtmTW9iMIzwBF5XFvSCqTE7q8R8bc8YjBrBnWc2foh\noDMitpO0I3CCpE8DZwPrkXoM6sbJnZlZHUlamTR54qaIuDPveMxs4CLiz5KuyJ5OAl4FxgAFYHfS\nSjd14+TOzKxOJE0glTu5LiLuyTses6GunjXpIqJD0lRgb2DfiHgSeFLS7r0eWANO7szM6kDS6sCB\nwFVdY3HMbHC4bdp8bpvW93qyEXGYpK8Df5O0QUTMrX10S3NyZ2ZWY5LWJBUivjwiHso7HrNmUa2W\nuy2mjGKLKYvrnp9SXHKpaUmHAGtExIksXsKwsyoX7wcnd2ZmNSRpEvAx4I8R8eji7UUB2wH3RBRm\n5RNd9WX3tT1wd0ThjbzjseZWxyLGFwNTJd1EqqP55YiYX/J6XUuTuBSKmVmNSFqHtFbtxRHxxOLt\nxS2B35DKJiwEjgF+V+9yLNUmFbcGfgusQ6of+DVg6mC/L+u/vEuh/DPWqsm5N9BTXn7MzKzZSJoM\n7AlcEBFPL95e3Ay4CRjO4hl0PwE2JIdCytWSJXY3ACNI9zUKOAVYH/jvHEOzJtasRYxb8g7AzGyo\nkfROUmJ3Xmlil1kZmM+SpRFGAW+rU3i1sjKpta78vlbPJxyz5tWcKa2ZWY1I2hjYDTg7Ip7vZpdW\n6lzzyqxZ1bMUSiNxcmdmViWS3g3sDJwVES8s+VpRwIdJY9LGAR3w1l+eOcDtdQy1Fh4nDSRfAAwj\nDSCfC9yVZ1BmzcjdsmZmVSBpC2AKMLU8scucCpwHTCjb/giwW0ThpNpGWFsRhQeAdUmzBjuAB4Fd\nIwo/yjUwa2odtNbk0ejccmdmNkCStgG2JCV2r/aw22bA6JLnrcAbwEcjCvfVOMS6iCj8BzhIKh4F\nvBhRGJzlGMwGOSd3ZmYDIGkH4F2kxO71ZTw8qHP9q3qIKHTXcmlWd3Wsc9dQ3C1rZtYPSnYGNqay\nxO5Z0ti6Lh2ksWk9tfSZmfWLW+7MzJaRJAHvB9YmJXazKzhsf+BLQIH0xfqvwJERhWdrFqhZk2vW\nOndeocLMbBlkid0HSfXbzlnWhcGl4krApIjCHbWIz6yR5L1Cxa2xaU3OvY3+4RUqzMyGAkktpHIm\nK5LKnczv45ClRBReAl6qdmxmtrTBMLO1FpzcmZlVIEvs9gbGkFrsFuQckpn1wcmdmZl1S1Ir8FHS\nBIjzImJhziGZmfXIyZ2ZWS8ktQH7AZ3A+RGxKOeQzKxCbrkzM7MlSGoHDgDmAZdGREfOIZmZ9cnJ\nnZlVlVQcCXwVWB44IaLQbR03qbgycBzwFPDziEK3kxOk4mTg28D/ARf0tOqBVNwOOBI4NaIwfeD3\noeHAx4HXgT9HROdAz2lm9dWsRYxdCsXMqkYq7gWcBowi1XJbCPx3ROE3ZfsdDXyX9AVzEWkZrsMi\nCteU7DMM+ClwGGms21zgCeCg0uW6pOIqwBmkdV1HZvtNBw6PKDzfv/vQCOAg4AXgihisH5RmOcu7\nFMq1sV1Nzv1+zWjoUiheocLMqul0YCVScjcCGAv8Siq2d+0gFVcATiKtszo8+7kq8Nuyc70XOJSU\nsLWSZqluTEoKSx0E7JpdU9nPXYFD+nMDkkYBnwCew4md2aDWQVtNHo3OyZ2ZVVMlnykitdaVK+8/\nEanlr1z5J2tP356X+fNN0mhSQvkEcLUTOzMbjBo//TSzwa48+Vqb1M1arrtEqrvPqO72K79Gn90l\nUnEU8Cng7ojCDEljSYnd/cBNTuzMBj/PljUzG7hvAj8ifba0klrefhRRWCgVlwNOAD7J4gStE5hP\nasn7Rtm5/gZcT+piHQnMIU1uOKlsv0uBg4HJpC7eN4HHgIt6ClIqHgicQurCDemYGbDCrfDKdREx\nox/3bWbWMDyhwsyqKls79QRgPGkyxVPZ9u8CX2fJVrsO4DrggIjCaz2cb1vge8DlpFm1S60MIRVF\nKjJ8FPAz4KJeZtVOBu4ljfcj5Yz3dMIq90Sc9Z5lvV8z61neEyr+HO+vybn31LUNPaHCLXdmVlXZ\n2qn/1c1LI1i6O3YecGFPiV12vr+SZsL2ds0ALs4efRlOai0cDrOBe4C1WuBtLk5sZkOCkzszy1Ne\n33xbUu/tvaQhgKsttYNUXA9YNaJwc51jM7MqadY6d07uzKxergQ+Q2q9G0lqNpsF3FLnOB6H156A\n+yfDesNhwkLSmL+p8Faplv8l1dcLqXgn8NmIwoN1jtPMBmgwlC2pBZdCMbO6iCjcBEwEfgW8CBwP\nTIooPFzfSI4fDz89G8Z+AyY8A1wBrB9R+FW2w7WkSR8jSRMu3gvcKRXH1TdOM7P+ac6U1sxyEVGY\nBXwte9SdpLWA/YA/RVz6CGkFjHIr8tZkCyB9CQ5Ssjer5kGaWdW4FIqZ2RAmbb41DN8V5p8XEY/n\nHY+ZWa04uTOzIU0qjodnToHxB8Bn58L4l6XiaRGFnmbHzgBWJtXMgzQ28CVSjT0zG0SateXOY+7M\nbMhKq1C88AQ8dQBs3AbjxwI/AP6vl8M+AXwMeJqU2B0HTI4ozKt9xGZmA+eWOzMbws7ZDCaNhne1\nwdiujaOBd/R0RFYz7yqpuA7Q5qTObPAa7KVQJK0BLEea0f914JSI+Edfx9Ws5U7SVpJuzH5fV9IM\nSdMl/UqSsu1HSLpd0q2S9si2jZR0SbbvlZJWqlWMZlYbUrFFKub6qSppU/jPTrDpopLErmIRhUVO\n7MwsZ+cBq5BW6fkL8JNKDqpJcifpGOA0Fs84+zFwbETsQCpauqekVYEvAtsCuwEnShoGfA64J9v3\nLODbtYjRzGpDKu4OPAHMlIoHS8W6D/+QtDmwM0yaCmMWkFak6NIBrCkVT5CKo7s9gZkNCR201eRR\nR53AzcByEfGH7HmfavWh+yiwD4urz78nIqZnv19FWgh8C+CWiFgYEbOyYzYh1ZS6Otv36mxfMxsE\npOLFwEWkenYrAqcCt9czwZO0NbAdMDXi/qeBycD5wMKuXbLHV4CnpeKkesVmZraM2oGTgOmSdmLp\nJRy71WP6Kem+7NdxpP7ef5I+JJ+PiA17O2lEXCppUunpSn5/IzvfOJacfVa6fVbZNjMbHHZm8SxT\ngDHAu4FWKvzGORCStgPeA5wREa8DRBRmAodJxVVJvQRdieYoUsL3DuDJWsdmZvU3BGbLHk5q5Pod\nsCdwaCUH9ZjcRcTGAJIuAj4XES9JGg/8vh/BlX6ojwNeIyVwpQNhxnazvWtbtyQdX/J0WkRM60ds\nZlZbUesLZON4pwAbkhK7N7rZbXat4zBrdpKmkP5fbAhDILn7UkR8Ifv9QklnkWb096qSjuM1I+Il\ngIh4VdLq/Qjubkk7RsRNwO7A9cDfgRMkDQdGABsA95PWmfwgcHu27/TuTwkRcXw/YjHLTdY9eQhQ\nBC4Gvput2lDNa6xBGnS7AfDliML1Azzfbtn5/gF8FXge2Bc4kTTA91ukVvhDSV/IOlncOhbQ0gKf\n+po088fw61bSjK/DgO+TxuaOJr0fHwG+A/wholBxK1+W2O0KrEtK7JZK4qTiDsCOZbFB6uLwqhNm\nVZI1skzrei6pkFswg5ikL5A+W1eQ9NGuzUBFa1wrovcv1ZJ+S+q+uJ00+eE/EfGVCgKbBJwXEdtK\nWo/0IT4sC+yIiAhJnyYtJN4CnBARf5Q0EjgTWI00CPrAiHihm/NHRKh8u1mjkoqrAzcAbyN1V84F\nFgD7RhSuq9I1vgCcTPri1k5qrforsEdEYWFvx3ZzrhGkca+bkxKwBaTp+C8DK2Tb5pEmKHS1uI/J\nnrdCW8BKnbBDK6wwG/4zHy5rgRhG+kyZDbyZnaeVtLzXm8BTwM4RhaX+v186Rgn4ALAmcE5EzCm7\nh1bgj8Au2TWz2FgAvECawHVlVv7EzKosz7/VkuIX8amanPsL+h31uC9J34qIE5b1uEpa7j5L6udd\nn5Ss/bmSE0fEk6RkkIh4hG6aaSPidOD0sm1zSWs/mg01W7E4sYOUzIwEDgaqktwBR2bn7DKaNLng\nbSz7uLK1SBOfRmXPh2WPESxu/RpR8rOr/6MV1gHWD1iza9toeHIULPFhOLrsOEjvzURSQtlboeGu\nxO5DpDIBZ0VEd2VLViCNs+sahNx1rVnA25c14TUzq7OfS9qfkvWuI+Ksvg6qJLkbA2xG+uPwkKR1\nI+LRfodp1txqPqmgytfs6GZbd61cZdtGAatX0hrW3T59xiuphfSlczlSi938Xnbv7nxznNiZDX2D\nvYgx8GfgWeCZZTmokuTu96Rv0FNI3TG/B3ZYxuDMLM3MLP9/bhFL1mAbqPksPa6sncVlQJBmvo80\ndu5XwOURE5ZKsLKxgR9hyZmvZOcu74pYctuwYbDdRsGI5Vq4aQ7M7oQWwXKbQqwWvD5NxDwYMRY2\n3LWFua8HD98sOt4KsSze4qbA/wBXAKfD8cAGx8LEfeHdJ8OIBb28H4tY+j0PUrfsgEnFCVlsbwLF\niMKA1p+VijsC3yB9zl48kO5iqTgc+DKp9fXYiMIjA4xtbVIh1QeAH0UU5g7kfGZWEUXEwf05qPcd\npBsjYqeSnzdHxPb9DrNKPObOBhup2E76w/0NUsLRQUpYvhRReL5K19iINGV+Q1KCN5c0qeIcaeaq\npHpvXWPoZpMmMe0fMeGpknO8C7gQWD3bT1ms84B7SAU1jyQlYR3ATcC/gM+w4Sbt7LZ7G23tHdAi\nOmnh6vmdvDqsBakDOiEWtbLik51MensLau0kOoOOha3cfdkinntoPnACaf3XdtKwjb1J3bezoeMl\nuGseDF8bNhwOrbOBx4H9IgoP9fCe7Af8IruXyGL9VETh7gG+10eTEruuMi8LgS9GFPrsMunmXCsB\n5wDbk5o93yTV/tyvP0mZVNwlO99Y0nu3gPTfxZeXZbJKdi6RxnF2/ZsvIP23c1hEodeuc7O8x9z9\nKD5fk3N/Vb+q15i7U0irVNxN1tMREX1+Oa2ksGhIWj+7yBqkb8JmtowiCgsjCv9DGpD2fWCHiMJ+\n1UrssmvcD2wNfBwoABMjCudkL+8LbMPi1rjRpFadQ8pOcwyppuUYFrfIdQI/BLaLKHwDmERqxflA\nROGDEYWvAOuy+4cWMXwEtLa20qoW2gWvD2+hpQWkVtTaSvtwePvkFlrboaWlhda2VoaNhPfsOYc0\nDu7EiMKiLNY9SWMIBR1j4P5JMHIybDQ8Gz43GtiYlHj09J5cSBo/+G3SjN7NBprYZX5ESsSGZzGO\nA37ez3N9iNQj0jW+cQywKfBf/TzfD4BVWXKyyqfoZU3dXqxJWk1oJOlLyShgZdLKQ2ZWW1OAPwAP\nAQ9njz5V0i37ZWAqqazCJaTZZWbWT1kyV7PyAFlX3uXZo1x5q0133zy72zYHuLOrmzCi8DLw3bLr\nPqdjmcviSRY9ny609Ob2EW90M0N20eIf95PmRazfmcqr9BlzaWxzqXBNxhx116JWzZaBgXwxX0TJ\ngO6Me06s4Q32OncRsUl/juszuYuI+0gtAWaWM6m4NamV55cRhf90v89MAXuRZrj/PGLCm6Uvl+0e\nLD2pobuxGqOALaTiFRGF0ERWAr4E3BBPp5pWOpY1WHKmbi830u3W8uRm83TdhcB9WQiTgZbuPq3f\nOlYqDgOOIHUjT40odDcppFaWeO+y+nrvA35eQWmX8oQ16P9kmO7+Ddt62F6J7v5WuHyMWY1JurFs\nU0TEzn0d19vyY8/Tw6y4iOhPIWMz66esRt4vSWU9WoGjpOIPgBNKZ31KMzchja3agJQs/Lc086iI\nCWeSiibvReru7Kozdy9wdtnlTiIt4bUGi8fctQBHg96n1V68hfaVP0v6/Piq1uYW9uRfjOKTpDFZ\nEHRkqUkrE+hkJi1AJyIQrbRkkz6CTlISNo+ULCIVJ5PG2m0GC1tSiGMD1guQSJ9LXT9nk8am/TI7\n9oOkmprLZa8fKxU/GVG4aSDvfw+OAv43ex+6xtx9IYtjIvAbUldrG+nf6wTg5B6SzcuBj5G6YLr+\nbR7KztEfXwPOJXUVd425+y1pvOGyegb4Kenfp500aWc2aW1es4Y22FvuWNxbKtLn8rsrOajPCRWN\nyhMqrJlIxdNJqzqUflLNIQ24v3LxfjP/AWzCkm1jC4C3R0x4NttnF+Bo0mzZ/+tltuxXSGO3Frco\nDVsjWOXQQG2Lt61JJ9sRtJbE9jLBq8A/EF3V51YgWBl4F2IYKRV6kUUs4grmcFBcypzs2tcDU2BB\nS5q/MZ40TFFdSV2XTlIR9N93dRdLxUVl7xHAaxGF8eX3WA1ScWXSpIo3gP/pWm1EKl4AfJSl/70+\n2FuiKRW3Y/Fs2T8OcLbsMFJCtjnwrYjCY/09V3a+tUiTXR4EfhxR6K6uoNkS8p5QcWLfay70yzf1\n07pMqCjXNbm1r/16a7n7Qw8vRUQc2O/IzKw/hrN00rKIrpayJfcr/8BZWLpfxITrSUsA9iii0CkV\nryTVHVm8BrRaRXR0opKPjhZaiLLxXC8i7qSDKIl5LWDDktjagdWZD1we36N0ZYkRMD9L7FYC1gZU\nXt4FUmvfVWUJUHeTxCoZW9wvEYUXSYXey3X377XEv0MP55tB6navRmwLSJNgqiKi8BSp4LbZoDHY\n69xJKp1UtRpLl6fqVm8feqXdAYOzec9saKtktntF+0nFFUlrOf8xolC6NuuSn4yjxsL4FlFazW08\nMIYWSttx3gRiySRz1EZvEGuKuU+PKd3cRlqRIotjZgu8baVUWnMV0qTcFmBtwczsxG9pB1YEnuvr\n/mpJKu5MKop8Wx+7Du6/MmaDUEftvtvVy2oszsEqXsGrx7vOFv9F0jhSaYTVSeNC7htIlGbWL1NJ\nLTpdZTe61mAtTyhOIZXoaCVNLZ0DTCdVOF9KSdfd8V3HS8WjSOPwnszOvxXtw0ezwc4dTHp3K7SJ\nRQQvIrahg3fSSitiIZ08Rgu30MF/aCWrjzds3Xmtq534dMeITea00gpznhrT8dwlE1s7Zg3rzGL8\nuo5lA8555UJ45fvw7KTUAz2uA55thW0i7Sbgvk64oyVbOKMFuC0by9bVTfg74KDsfQpSa9kv+/eW\n904qbgj8mjQGRlLxRlKduydIYwZ3YvFybbOBR0i1qszMKhIRx0vag1S79OFsadc+VfLN//ekIqGT\nSStU/K6/QZpZ/0QUrie1cP0ceIxU/2yT8hp5ERNOBdYFLiXNRNgjYsLuERN6WmqrqxDv6OwxnjQW\n72MRhfkRhV2AvdjyY7NYezNobYdWieGI/YGNEe1AC2IY4hrgWZTN8RTQsvblDzNyi9lqGRm0DAtG\nrDFbHbPbul4XMILXO/bm3zMvgovWha3a0jK87xDsAowWtCt9F+2aJwGkBHYU8B1SDTsiCkeQJoz8\nFbgB2DSry1cLd5DWzx6dxbEbMCOL4wpSR/SvSRM+DiPV13u5RrGYWTc6aK3Jo14kfR/4JGns9KGS\nflTJcZW0V64YEb+XdEhETM/WdDSzOosovAF8PXv0st+E50hFjCvRtYJBd9u7rnud9uEe0uoJiw2j\nk5aSL4hC2UJqpZ8R0ohOVLpfqEWCKB0bOOuFdnRBENuRahJDVsuugyW6MztaQOVLio0Ali+Jd+lY\na6P8fWsjFR/uiuM10ozao+oQi5kNTTtExLYAkn4G/K2SgypJ7rxChVkDk4pjgEnZ6hS97bcqMDLr\nNuwyrqKLdDCyql9WS0fxvvk8PHI2tOwIne8s37GbasfdVskbu8QOmrkWsDBLdAckW37rPcC9pWVn\netAqFTWQWa5mVj1DoBRKm6TWiOiArKRUBSpphetaoeI9pBUqvtrfCM2seqRiq1T8JKkO2e1S8Wqp\nuG43+42SikVSd+6DUvG3UnFtqfgzUrFfWPyBMY/UWnY3gCYyURO5lP+wKZ1AvJWWdTCTFjpKtrXQ\nybrA8JLUbUTE3AdH0Rl6a5s6OqN1xEJYRCdvPAv3nw3rfhDaN+pawxaISN8jF7XAoq66cJ1p/kRr\nK7SUbAPgAKl4qnTl2tLMXwD/BB6VZn5fmrnEDI5lIRW3AO4kdbc+IRU/nCV7kMYjls7wCNJ4yDuk\n4ub9vaaZDU6StuoqOizpfEk3Zo8nJZ3Xz9NeANwi6aekz6ELKorFde7MBiep+DtgfxZPjV9EmkCw\nUUTh8ZL97iaNme1at3Q+aaD/fBZ3LXZ1f/4S+E5E4VVNZBXgCdKs1HZGAmvTyUhaslF1LSwPbE4w\nGjGJYFXEncBPI5gXaj92Dq27L2B0+xxW4BXi9Rae/+aavHnjOFjt6WD4hWKNDwcPry9mBrwxJ3j9\ndcErnfw/e2ceJ1dV5u/nPbeqek+6s3USQsiObIGwbwooMjAgKo4oiyPiKOIy4+iMOurPokRHxwXc\nGXFfEWSRAVQ22RdRIGENIQkhnZB09vRay73n/f1xbnUt3Uk6nXS6A+fh059Qp+4999xbXbff+y7f\nl/sMbAEOs3BE/MQqxi37bxE8E1AZts3Ce2ugNgdSPK9eoE21dad7qsZVsLfG16h4r+kBPq+avjI2\n8t6Jy0uuL9tG3Vo4SzX9l509rsfzamKkde4+rZcNy9z/I5dV6NyJyKdwUkFdxTBqPN4M3AOcrqrt\nQzmWiByM6zi0RFWfGsw+29O5K1bFFqvu1uNEpzap6jFDWaDH49mtzKJS8yiBq8qchCuCKrIvJcMO\nSj1Cy3PGAiCrmv5o2VgLznhy+/YCKzHMJeoTLN4CrEN4ByE18f3kSDA35CTZ2BuZgACEnrCBLd8c\nr9HPawUVCF+CxdcLdW8PeWmO208ExjQIPddEFFaXxVL+bmCehTGmtPwTA9c/u1Aec6mN7bDy86rD\nVfoPhX3i8y//w1SPK5Qo9vC9TiRzAnF3jZiiB9J38vF4XjssBc6hf8efLwLf2QXD7oPAPFX9DxH5\ns4j8VlV/uaP9tieFckg88c+Ar6rqCyIyG8gMZYEej2e3M9jipgGempM4J98OZg+qNpN41/KsD9F4\nJWWHsfQrhdBCPB4uhd6boP6dUDODONy73dUOzLA7A3Zr8VicG9mjmh5qv1iPx7OT7CkRY1W9UURm\nlI+JyCTgjbj0tqFyKXB0/P9vAR4Ahm7clTFbVV8AUNVl1Yv3eDx7FpFMHU578gRKfVbBlcongDXx\ndpOArwLjIA6j0gicEMGMAFZF8EAAHeDCjSv6jvFZFvBufgI0sZyIJwiYAVyMMpGARVj+psYcEJJ8\nX7fKWE2EXbU27Ko1rBDsX2vI2Zog8YZeDQ7NSvRrq/ZbHUL2Cai9D1ouhIOnK4eSII/yGMIqAEKS\nE/b1sN4AACAASURBVCG/uhfndYvPa0sATT0gRQ9kFpqSsDFHySvZAz01UJ8r266bSi/mYK6v4DQF\nv467YOXdMXpwPV/LWRyPF49ZNGtXxPM1Ap/Dyc68JJK5ZJh63Xo8nmGi7d6XaLv3pR1vWMk/Ab/R\nXct/CykVsobsxoKKDSJyuYi8VUS+hhNO9Xg8I4BIZizOaPgU9AkFK86guAGYp5p+WSRzCM6oKbYK\nFCd0/m5gv7gCdR9xYuf7ZXFad4cDyGf5KPAQhsNcc4j4aP8BTEYIgEPR5Jd6SH2kS02ziggkGrMq\n94XwoCpZgbwQ3lunuUMs4WcipXcx6B2QP9Pytn3gUJQEUI9wIsrrscCfaDlzLvBeXEuKPHAt3L8v\nyL/iAsFZ4Mdw3FScxl0XLmh8BTw7GeQb8etunDjzUTt5mX8DXANMjF8Xb8wvAKerpv+3fGPV9FW4\n7h6LcTff+4AFqukH496zK3Bh2xSwP/BHkczXdnJNHo9nCEQkdsvP1JPncsxlp/X9DJI3AX/axVO4\nGXhARK7A5e7932B2Gozn7kJc78R/xDWM/n9DXaHH49llJuE8ROU5dAIsU02X93zeH2fwNZS2GQdI\nBCaOUxjjYqqnXKG631fL9j2ektcMAgImEJEqi28EBGZsaKWm9IAoQqBrAoikFC8NxbA2An3WuEYZ\n74TUJEODgUTZw2UCYV/adSVnu9tSeoVI5hZgomq6Ld7qJyLt1wJNqq1roBU44gqRzE+BZNznFSAt\n0v5dIFJt3TyYi1rFcVTmMga4Ko6Tq0Wji6im7487VsxUTS8re2sfXCC7/POqZ8/o8Hk8nj1PuZdu\nf3YyctBvMtUvichtuKK4X6jqosHsNxjjLo+7sRWlB/bO8lqP59XDQN/B/NCmEqC2d1cWs0N0EfAI\nrrB0wra3M333GLebayfWVjnW2kVVg9lYLJiq7TYMeb0DU50Z2I84l27Z9rbxeDx7lj2pcxe3Bju+\n7PXBu2neJ9nJ1oWDCcteDcwG7sAFaH6880vzeDzbQiRTJ5L5fyKZDSKZr4tkmrazeScuvFeed2GB\nuSKZfxbJGJH2uXDaZyConEcKikjVnS4EHv2gSOZot5b201nYcwahVuZ15AnQsmMqqgUxGpXGVFGp\nU5CyfVc/agkeBd5l+wy70EaogrXlx8gBu9sgGyobIO614ShWzA7FCN5Kqc9tkV5g3ZBX5/F4Bs3e\n3n5sqAzGuJurqp9Q1T+o6seBucO9KI/ntUKcG7cS11JsPPARoC2W1+hHHBY8BniMSm9dDfB9OGot\n6FMw6zA4XaBRkYSSSMHBs+HIZqgRhUhd6tp9Agungdwr8twrwPXc3tnMPZ1C3oKoUhfnxhWz+yxQ\nQPMPNBEur1GNQCOwXYHVo4DJohhg9UOw5VH4zkfg+MlKUoEQCkvhpz+BlS8rhQKo5nAPjW8chks8\nFE7DPdT24gol7sfl0HXs7ERxN5ATcU/d3fGcX2fw7eE8Ho9np9mhiLGIPAacoqrdIlIP3DMadO68\niLHn1YBI5lLgCvr3Kb1MNb1N2aG4ovNxYEHlO++20Fz20GbhyA5o3Q9S8SEihf/7JbCKUhFWClfD\nUPZEOisBmXHKFJG+etwssBVLb1mu3fgIabWR7Um6nVXhsfth+VPK2e8VGuIOZ195QLnrSYGyNLh9\npmWpq8/oi+eV5/yNCkQys4FW1fTDu2EuAU4BFqumd7klmseztzDSIsYf1G8Ny9xXy8fZE+clIqfh\n+lMX/0aoqu7wQXgwOXffBhaKyLPAAbjqM4/Hs/vY6TxW1bSKZLbueEsDk+ZCqszeCwRkJegOKurz\nCs3qxIXLyVauV3sDtCtw3jpVeOJuWLsE3naRpa6pZC229FJh2AGsXhUxSkOUcWHEbsmhiwWPfbcK\nj8ezs1yJ08lbtTM7ba9Dxc8oaWgtjrd9EXgzTibA4/HsAiKZ8TgtklTVWxZ4q0jmRzvw8nThXG/x\n97gVqDfOwipaZG3w8C0w/RBl5lGCxB29Jl0MnX+FnmIjmjkKptKK2xIpn94iXNwIC1IuCHwrlmcI\neDOwD0jS0rhgs02MLwQdL7YQ3XcPvPwybHqv5UcNhvNwgiKdgB4KR7Qqz9wp5LqBscDxtVD3bpH2\nm1RbN4u0H4YLWz4GfEW1tUuk/UTgv3GtwL6t2poTyZyBq9z/BfBj1XRFMUZ8fQPg4vjny8BtsZHl\n2QEimZnAN4BNwGfLKpE9nr2KaFA+rFHNy6p6187utM2wrIg8hSvZ/w1QEZZQ1duHssLdiQ/LevZm\nRDIfwD2RJXBSGXHv1L5/Q1wi/2Wq6QE10UQyU4FvQ+JMeHMK9gkgiLPicgb+rLBeIBJMwlLXKkx+\nr1JICopg85bcVtiwVaBWIWlwD3TFhzoFDDUos+sU22TISUSIIYHUvr/Ljv3PDUYCjVTU5G65R7qu\n6dHwsX8RbF2EIiQwHI8liyFSi7UQhYb7X7R0tBgwFiQPEgJPA4fhwg+9uKKGZcCBuHtRN/R0w7Vr\nITcbJ1fSjdPDO0c1vajs2hwM/AGYXLbdU8DbVdNDagP0WkEk8xWcpyCJ+x0Mgf+o1vfzeAbDSIdl\n36/fG5a5fyIf3VNh2Z/jEmIWEt+fVfXqHe23zYIKVZ0PvB13o/00rrx32Wgw7DyeVwFfwhkdNZS+\nh+X/pnBac1/c1gSq6VdU0++E4/8VpkXOThQBCWC19Bl2ADY0yFQhZ5xhB2BShlytgSaJDTsodbuQ\nvvXkEDbUCt1S7OUjhNB0/mYxKQWxQf7/7hTdtJHw6fcLYR1YAhRDAegqipyIwQQGm4TOicat15i4\nF2wjcGx8zoIz5lpwwspFjbgGWD0JCgdR0qFrwPXY/Zeqy3MRrsq/fLvDgNO3dT09IJJJ4e73dbgP\nqAZ37b48kuvyeIbKq6BadgWu61Ar7mF1ymB22q6/UlWfxn3REZE3AF8VkWmqeuwuLdXj8exGDlyM\n83RVhXclov933MIO70wDufP77yeoRpFkb7od7clSd8Fb6fhmzeASCIt+wR0zwFYy0J7VT9ADPVH3\nC916BqS8pZ3H4xkBRGRfVW1jiGlwOwxGi8gY4Bxc36IG4NdDOZDHs7PEeT//Adysmr5jpNezuxBp\nnw3JRigMZvOESOZg1fQz8b5vBt4GfFO1tah8bnEhtCpslRGnLsy6Y/pvo/3HNBea7PV/gjCk7vyz\nkURix2YjbM+wG8igkAFeVh8l1liJt5D2etj3KGirNlICyvQB4967nwb+Blzr8/G2y6D6WXo8o429\nQZNuG3wCVyX7Q/rfMU/Z0c7bvNGLyLtE5CZcL7OpwKWq+nrVYQpgezwxIpkakcw3gWeBS4AbRTL3\nimRmjfDSdgmR9lqR9m8DT8OpNVCnEES43LJO4Ce4ctJeSl4mAzwm8r3rRNbcD9yEuybPirRfKdJe\niys+iHXZNIKChSYLieIfZIWE0tNu6e212DitToAp9RHjkxEmvnkYYGLSMibhAqvEGneTcpYmtQRq\nRawmUt2Mv/JqrddubTz3dItJWFsQUpf0WupVCbBInMsssWko8ViDgdMblRRgsJS6PxTXa3E5ckuB\n68uuh8J0C7NsbN9pvN0DwLfia3wusBLecCRMsXEOosZz3AjcLJIRkcx/4doCfQT4EbBQJHPYLn/I\nezmq6Tyu3WQnLs+nB1fN/L6RXJfH81pDVf89/vdkVT0F17/6jPj/d8j2Ciosrkq2uo+Zqur5A+yy\nR/EFFa9e4krI66nsx2mB36qm3zMyq9p1RNrPxrnY4/OKgKcU1j8Dy09STW8WyTTiqkJfT8XD1ykR\nzDNVuiQ9wLtVW2+J558JSx+Hl1oqFTxmAq8DpgMCqQS8bhxMEij2sFiXg5ezMLcempPOybc+B1ty\ncEIDjE+AQmvHap2TfJb9Zn5VJuzTw4K378t1m8/TR9afSMeT48VmA+eQvAdlM3AIQio+1R6UemAO\nQgB0WfjvjUq7hUoPWwh8D/gP1dZIpH0OTtNvTGmTjcCTFjafr/rRa8uucVR53dqAxQVYdrRqeqHb\nJnNgPF+5tqACj6qmj8eDSKYZ+BzuQn8rbgXn8ew0I11Qcb7+ZFjm/q28f1gLKkTkMOByXMHY74Br\ncfepT6jqL3e0//bCskWRvKL1J1WvPZ7hIqB/zNIwYOhxryKgLHzoXi4Q4Ilig3vVdJdI5j7gpMpd\nzQCCc4SUhShVW18S+d92XCFCGSdRYcfkQ2fvlfvtJ9W4nyIiMK0WTqxVTPzdFxh70DqZE6RtSyty\n+Nn7YIwyu2OF3P3YmaGNAnc/SQJHI2wg6ltfAMwAWsqMuEYD05IR7bnq+1A3cKdqaxSf11KR9g1U\nGHfjgVOzOK9dOVXXaF9g35zqhQvLBgOcsEu5cScMTvfzNUHcr/c/R3odHs9rmKuALwDjgJtxgvXr\ngNuBoRt3qnrv7lmfxzMkBkqUeDU8WGw3ASTuZDBAi7+GgVIoanAVoW7fc6hl1tFjWb8cOuM2rZKC\n+kbIWojiqGeDOEOrnb4OqolxeZqO3srWB1qw3e62MK51AzOPXsbTTx1GPl8DYZbup2+k9/R95ZSz\nFRPXNWysaSE1s9fkl6Wg+CDrGqOVDC3FOROb4xWX3hnoybfiGolk6uCEJphGld1avd1E1+lrNq7Y\nc7uMuCEnkmkALgDuUU2/ONLr8XhejYR7b85dTlXvBBCRf1PVJfH/dw5m5xG/wXk8A/AITvNsPiWN\nsh7ck8zezIO4PMKilEc3Toj4aoA45+tq4GD69OYmGzjZQqPERYxFb5jifGSXi7QfxxvH3c3Y5GUc\nclozaqHtmYjl7YbGkwWSFowh2xNxlBXe0mBIYDlQjaxUO2HBWh131roAwU58zytm6/UtesSEJ+yC\nN/49wMCpZ/yZP996sq5f+GedeGat2XrGu/VGKXAAz8ljHB0tnTo3aGzdLHULOnXLnROl8ExtRC7O\nsgPLRgyPYulAMCitWN5CQCtdvL6unSdyLWif5EZRj+6R2NA9D/g2PDzGuRrnRnCsgdosLodurUim\nBpd4/Hl4KIJHE7DAwmERBCHwlarPYQlwN/AmXIi8F+cp3iMt0OLzuhCXJ1gLiEjmZ8DnVdObt7uz\nx+PZKfZiEeNyZ0au7P8HZa3utWftefWimt4okjkBl0D6JVz+3RV7e96Paut6kfZjgbNw+nXXAleq\ntha/uPfj9N5ib1YAvAUIyr125Xp0AtTRnHgrjYm3AwYTuP3GzhfGiqCmtM/x9YYzgaT0jY1761oZ\nt6BdTKI4t/Lm8//MTFaYIHC5cDbXg2n/KZNPnsP4U+ejIiZLgt9zLr3UCmKQBJJoComWJCCrUhFC\nvgOI4vVGwCoMPyPiM7yfQ2p+j1ILfBJXlf854M+qrSrCscCPgTp3n4uAFwS618OZp6u2Pgkgwvtw\n4Yu6UuHs40D4LBxztmprW+XnkM4BZ4tkjge+CTyKE4seRDu33cLrcRVw5e7Ff4lfX7yH1uDxeEY3\nB4nIb3H3+QNFpCiJcuBgdvbGnWdUEstS/DH+edWg2qrALfFPNbVUhCnNQCHLopBI6b1AEli1BGWb\na2CoaE0GpERiOeG+Jz+T0qJhV9osKNggllHJduRY9MtnaDr4aFtz0vxAymy2iMB5BMvPLy+AlI9J\nXPcrFWNZcnox1wFwcWsvzoj/UtW51uMCvGVGkBpoe6xo2JVtV/U0ay08+TvVs9vYBqrph4HjtvX+\nMDLAeZGiIqfQ4/HsDvZiKZRzKd3rf1g2PqhOMd6483h2IyKZI4HVquk1O7mfEw3p/84AW6sMSmNW\nkP5ZitX7bjuNsXdLlkW/eIapR05m6wkH0Fl9TEWGLHWr1ohcHhR7wopkxuA6SDxQpTc32CMMRsR4\nNDHa1+fxeEaQXa17GIygqcfj2QEimXkimTtxodWlIpnLXCHAoPY9HafpVi3vq0iHlkw0VeeMy6nT\nsou36QyhoIbQlnTiGoEUAYnYbxbYyKwLCRKRERNFAEbCKL+uFmMjhHiMKFpLa9CzqZeFP31apx07\nlWkn7Bs1s9nEaYAWIFJj67UbVVB167N50dTMXgWFeIwQZXxfv9p4dQXofS4JvCCSebNI5hJgJfBn\nnN7cMfGWzwNbcXl44PLiinp15TwUj/fGr4v6bNWVtKOFZ3C5ltXn9YcRW5HH8yrlVdB+bEhsU+du\ntON17jyjBZHMBJygWpJSeLAXeEg1/eYd7PsOXFl7SdNPapSgHlreItTMgN4cbFoP2qFwn8B6YK7C\nSeJEesV50GbUKYc3CeOxHCKGJPAwNvhraKacuio69AuPB0FLyPNPHmJXLJ5tFsz5W3T6EX8MpMby\nOIdHK5gZzGWJnbH+b+bZXz3F9DdMj6YcOTVYx0S7kQkmIqBLG22ogVmRm6FrC1PEGEud9Fqj1nTc\nOp7sUw2AQB6lgPAYsDo+L5tVoi7YfIuQW1k82wJOw7Cow6I44+xk1fRjIpkkTmg4A/wF+DfVdN/O\nZddxLHAZ8AFcnt5lsZzHqCTu4foxII3LSvy4anrVyK7K49n9jLTO3Vv0umGZ+xY5d1h17nYVb9x5\nPLtI3DljEa4YopznVNMH7WDfS3FJ/SUv35iTYWyVzN3GG0J6nqlKozg7hKmVY18eb5mcqPDIn3X+\n76mpzVdsdgR/7xeczZOiq72Hp379LLNOnUHLodNYypyKZL6N4Tj+3nNEFJHse3QNn09SuK0hJJTS\nWhYDjxOhZY+4W+5QOh8ZSKuvOj1kK3C+avpVlW/p8bwWGWnj7gy9YVjm/pO8Y1Qbdz4s6/HsBHHr\nqilD3LdGJDO+ari/MHMQDPKbmafUpSymh35dQLOdtVQ/w3VTXxX/hc2v9LDoV88w5/SZTD50Eq7z\nWKWWtI0E7ai8n2k+hN6eygOE6moaKjYMGSS7TVB4Vz4vj8fj2VvxnjuPZ5CIZA7FVSodhQsRflQ1\nvSQOCS7DhVaLHrhu4AbV9HtjXbN3AN/FqfB+H9dW5u3Ad3AeP4sYw6yjlQPfKEgCVouyHkEJ6XxY\n2XKXBU3hjJ8IJHAFtq8HMxuamyIaawPGorxFhOnABiJ6NGiauNUecc6jpn56N8uYHXXSFEyiPXoT\nfwka6WLpyhr7wrVPm9lnzYvmHJAIUuRJEFpBzWaao5U6PVj15Awev+oYzW6pk+CEfJR4Z09g71hC\n4ZP3QUcejjjacvwbDIutcneXECo0NViaag1df7VsvcfExqiWzqFf8kpxbB3wQeD/qgosdubzOhJX\nZXYoLp/vY6rpl4Yyl8fjGRoj7bk7TW8elrnvkLeOas+dN+48nkEgknkn8AtcbpjBGSF54GzV9F0i\nmSbg88C/AZuAS4kNE5HMr3CGXEM8XS/OY5crG4t406UBDS0RQRzytFiyGJ7jNuBjtGVagZ/gdI7K\nWnuNUyZ/SJBEhIgbm4jyZgSDdetVrd2/W1ouXAcB1smVqE5llcxYcT8rrntC9zvnMBkzZ6Idx0Yz\ngY3l/QbtLT95m3nszuM1yjuRPIxGPPb7gPUvKtkwHgsi9OyAoNUS9vkeI7gmcI1ko+KYLbuGwXbG\nuoFrVNMfGMLndRHwA0ryMiEuv+/NqumHdnY+j8czNLxxNzJ4KRSPZ3AcQKUuWdEAmQfcpZruBD4t\nkvkq0K2aLk9yW0DJiCOeR6vGAhrHgwlKniyDoY68ruQsN5B+SSTzNuAJyvP7ggYBa/sMO3cEwRJh\niusUMS0WRSKhbGzZUl6+8QlmvPNwaZo5AcDUkCvbBgTM6qX7RlG+lGeHlYBNay3ZsBRAtlEAzZQZ\ndvF16oBYTbnvzCqvYXFMq8YagMMZGodQ+XklcIUac3DVtR6P5zXA3lDZOhx4487j2Y3s9vZRAyjV\nMdgeu7r97cIly9lw88Mc8a4jaZw+biir83g8nlGNN+48nl1EJHMKLscpBXwY+NNQ86V2NyKZ2cD3\ngBNw8hPfU00Xtr9XBe24coX6sjGNx7d1zCQuPLs/pbAjbCssme81pBoiTJ8HLgJScg5fBy7npswY\n4AqgqWI+k7OYoOj5cmGCKLIkTMKNxW0lelFBAxAKz71I7rZ7mH7eqdo4bXNFaKFAMogwGmAlPkkd\nO3FLYJaH1hbiSlxFqR1j6O4oC7eKhV4DNeXnGkFNAPmIuOsFzoNWgwuVJqvGqkO18+OK4h+ppgdd\nkQGsof/nZdjO5+XxeDyvFny1rGe3IJK5FrgVmAvsB1yHS2IfcUQyHwKeBk7FGUaXA8tFMq07Mc3V\nwAU4o6EXWA68TTU9YJ29SGYiTpj4v+n/EFU0eMt6yM5S7l4PK3shcsrAZdt8hJVPrYnn+4e+OQKB\nmiScd5DrrDoLlRqLaQiZcckyOegDT1I3sUdNIiRVl+WIeX+Tw+VxzFOLNPzjXUy+8BQ9b9r9cjL3\nUEuvOjmUpD7JAp7iEPIkiDB006Bz/vV55ly4WIKaEKxCF5bmC2DMCSAJd4q1s5XWidBYVzLo62qg\n9VKoPzi+DJLD5Q3Ox7VgK4oPXwEcAdwLFSW6CeBrOMHjlh1/TH18E3gfrjCjF3gB+EfV9Kj4nfR4\nPHuGkGBYfkY7vqDCs1sQyXRRmUMGYFXTI/4tEMncDpxWNdwBnKWa3qkuBrH47HHAg8XWWdvY7njg\nT/TvF1pVIToXmB/BxNLYIY0wp+pS/u2GkFVVOnefOCvk+NclaIm3VZhb94xtPmizSYx1Ti61MObl\nTibts45EKmTjk2288pcXOPc9CY6ctLKvgUUX9VzJv9NNI0WbM0GeVtq1g7F9LcvW3dPKU/9+hLWb\nyrT0ok6IuiJSU0rnEEagakmWbVfYEBF1XaHrZnyq7DodAmwuF/AVySzEVbiW04kTNn6CnUAkUwsc\njfu87I6293g8u5eRLqg4Ue8YlrkflNN8QYXHMwoZ0lNNXChx3y4co2osCVTluxUGubR9J9Bn2AEI\nNBzWRaK+FL0UA60z1xJgWf/YCtY9tIy5Fx3L0eOvx5QtJcCSpY7ylqcRCbbSXCF2nKgLkVzVE2HQ\nBEFT5Vgi6H+uyQmQnFCRk6iafnqAM+seYGxIhplqOotrCefxeF6DRK9RM8eHZT27i14q/wArTupj\nNNCBky0pJ8XARsQ2EckkRTIfFck8J5K5SCRjRNprRdo/L9L+jEj7OSLtRVuoh1JLLZwix0kW3h5A\nHA0OmmDcMZaJ4w3JshvQyl7loc1KV1mK2etOCjjqHUptXCS7YBocMymgWW3xWzx73BJOrrnHzNPF\nNiAElEY6MVjaH1pm1z+ynFnvPY6VuSP0vY9eww1t/6ShDYgwPMECrSUbixYrUY+w8guT7MOHHCLr\n/tCCKhTyCdrW72ejCxMBMysuTUi5VRhGsGGLsm6ToRCfQ2EDrPu1sOb754lkZsbXc4FI5j6RzK+r\nhIY7gOp8yJr4mno8Ho9nB/iwrGe3IJI5ACfwe0Q89BxwiWr6yZFblSPuCvHfwD/jwqKdwEeBGwdb\n8BGHD2/DudkagC7YZxOc2QimJh7rxjXe+kf43/XA24Dvw/7j4MQaMBEYA5HQFFrGTDVIYEEERdiw\n2ZIrGIpGssHwhnGWlqRBVVFVJDKcs9lyaIuhJqGATUk+uGDyz+yUhtWSCgoSIVGBZLCKfdVgWXf/\nEtm0cJVtfeeb5d4V76QnV6+RTZoak7WTm1fLYfP/RsEkbUgyULAb7x3DU++Ya6JuE2nOBKY+0poz\nRPNnTzDWBpFaE5BHWY3ltxQo8HPc5/1FtnbV09mdQrE4g09I/NUSPm0gtPG5FYAlOBmZ2vh1CHxa\nNf09kcwkXJ7dufH2W4BLVdO3DPmXwOPxjAgjHZY9WgcbaNk5HpOTfFjW8+pHNf08cJJI5lScl+WP\no6VSVjW9EbhEJHMlcCxOGHdnvYpvB/Yte90I8+rAVOuyvQ44UTV9I3CTSOaPcNwySO5DX65dApom\nm3hX53cTiA270pgRGBvnrIkIIsJ4A4dPNDgtYQGCybVrmNK4WlKmIAABGmRJENi8rr33RbPl+bXM\nfd9xZunW/enKNlrVwADkbK1JNufoNfUU1yZgVl81yYabEn1jtieQ3nktSpjsGyOFMJ2QM7lQv8MN\nADKdn9PRvRbnFS1FBcKFJo7QmvgnQWVOXSr++QyuinkdcJFI5hs4jcDf7WRls8fj8bym2WPGnYik\ngB/jREQLwL/iPB0/xz2dPwN8RFVVRD6Aaz0UAl9S1dv21Do9u4Zq+q6RXsO2UE0vxnnWdtuUA4xV\nFFmopnMi7VuAfSo3k5Jsyc5i+++rairGVJVX7nheO1dsYs5Fx5FsqIGt21xzJQPp4w00FpDlMFb2\nbbKSThFyVMqPDBnV9DO4+4LH4/EMCa9zN/x8AOhR1eNFZB7wO6AN+Kyq3i8iVwFvFZFHgY/hwnt1\nwIMicqeqVudMeTx7kgj3sFH2nbHiNEukouaAMgPP6eudPxWaqrYbhDt/oJQJBQKVctvOghrpa+2F\nqrL8tiXa9YqaOe89lkRdqmxCqcizVRVVRcpXJkntf16hmv6nSiNwmvyUx/XivnzLndGiq2a7+8aV\nyh/BlRh/UTW9dheO5fF4PK9a9mRBxYHEumequgTnyXijqhYr2f6E0yE7CnhIVQuq2oHT9pq/B9fp\n8QzE1cCNuMKRyP379O/B/gWX6B/G710F3B4XX3wHeAbuGAObFUKLYAkExuaVlCqiFquWyEIqqTjz\nzZLC0mKU+aFSCwRqJRXZVKJXW2WVGgkRIpuUvBZsUvNRyoISRWqfv3mp9rZ3MPefj9ZEXQpVNLQB\ndU09NqgPYyNMVYxlc3ac7bF1kUVQi0ZZQ8PRVqlP2rg7hiLAH0JlDZY8GufTgbMwPwM8JT/tC7Oe\nB6zEeeV7gI1w0DdxenPd8c8rOF27LfE168J9zy/Y1sUXybwJeAmnUfh+YJlI5rMimVGb8+LxeEae\niGBYfkY7e9JztxA4C/iDiBwLTKQyRNQJjMXpgm0dYNzjGTFU0+uBd4lkjgAuAq5S/dhzACLtBJ/+\n+QAAIABJREFUJwHnAFeqtq5wYxyHM0JqYQNwHTS/HiYcoezfBCnjTKfnc7Amr3T2FM0oOCEJJ6Tg\nPQ1CSiAHLU+up3nyJtP6pjVIAPtGK5BNIrNrlsv8pidFBCKrPHHTGunsTsm095yMSbnEvCVbXyfL\nO+fR3js1oAVohObEBmmeupG6pt5gExMIugqEN9TKyp/Nomd5Y8B4IJuH3iw0NUAuMFwNnAicQjHz\nzrjZOAj4MnCWauvdIu2zcZ76FuBbqu/oEXnm8ziPfA74X9V0XiRzGfAJnDH4y+3pBuKKdaaWvU4B\nlwE/wwlLezwejydmTxp3PwUOEJEHcI27XwAmlL0/Bvck34HrIlCkCRiwX6eIXFb28l5VvXc3rtfj\n6Ydq+nHg8cqx1vvor30nVMt51K2HA+qiuC2Y26I2Ejp7bMVjzuEp4f2Npdc1MP5t6+3EMev6PO3J\nIOSEiQ9G43GVDzayPHvDUjpy45h+/rGYZOnJ8okNx5KztaX5kjB57qqKCGs+quXFLx6MFsqc+bUp\nqE1V5ve1ASGWoN+ja1lYuDXEeTDLrlE6C3y9aqwTyDA4BooyFBhq3qLH4xkWRORk4OQRXkYfe0M3\nieFgTxp3RwN/UdVPiMiRwDHAEhE5SVXvA84A7gYeA74sIjU4mYQD2EZStapetkdW7tlpRDIzcCG6\nX5V3HxgNiGQacZ6lR1TTj+7yfNM5GDgT+LGuZGPZW5Xfr1mTYGYQsJpY7ESpOSmLOaogvT9LQCQg\nyrTD26CxllVdMwDBEDE9+ZKpJcdmWvrGasgagCi0PH3dEkJJkj/nAjabDsazCYACCcY2b2BL13jy\n+ToAahK9jGErPTT0CXzmV6bQcBB2Ukcb/GWV4ZQjnPE32GskmQD3+5ADrh9CJfVA2+/V1f4imWbc\n7+GdqumFI70ej2d3EDtZ7i2+FpH0iC2G166I8R7TuRORccC1OLmILO6mZoAf4UIszwEfiKtl/wVX\nLWuAL6vqTQPM53XuRiEimQacN+bDuM/PAt8CLldN947w2gQXUr0CJ9eiwJ3Ax1TTbTs933QmAN/A\n6bEJLhcvDXybtkwC+BVwJpNaannvWywzpxkSCSUUSWzOydh3bbCJfQqCVbUbRMyVPXLoJS/Yuv2y\nQo3QU2jQtVunmuNaHtS6oEdFVHqkXjtpMvuzhCQFa/OhLLrmBW1LzpXFZ3xGwqDGCpjWxJpoem2b\nrDOtxqqxVo3p7BwbNUQ9Mr35JSNiLYLZ2jk2WvrVA9hy3YRA82JRKX5migu+WsAQblU232bJvRQQ\nqCWVNLzrNOWEw3oQ6QDO1Yt5cBvX/Q24Svmp8bwvAR9QTf91Jz67d1K6V6RwotQ/jT+7USG5M1hE\nMgb3/fhvXIsSBW4CPqGabh/JtXk8u5uR1rk7QHeqY+GgeV4OH9U6d17E2LNbEcn8O/AVKrozkMX9\n4bpq4L32DCKZw4BHcB7hIiFwl2r6jJ2ebzrfxz2kJMuGe4BzdSW3xcc8iss/fBdTJjSV16ROOncl\npiFSkVJY8Rh9lEBKTT4Ey3R9uSJ8KkQ6ji0iQJgLWXTNC3Q3TtEHTv0fiUzpktcluuykug1SPn+9\ndmkLWzBS+t4svXKeLv/uPAhN+XepeFMoja37tZJbTkWVbxBYPnjOtzjywP/Si/t1ACmtWTJZKn8f\nFNiimh63jV22Nc8Y4P8B+wP/qZp+YWf2Hy2IZE4BbqVSMibE6S/+88isyuMZHkbauJuni4Zl7iVy\n6Kg27l6b/krPcFJD/98rQ+Uf95GiBhcWLDfuEgxdl62eSsMO3B/pvnNVTf9NfkobruigRKAVhh2A\nyAAPWtLvpQBayIay8DeLaZxUz4TT58vDvdioLC9NEKNIKGjfZ2FERdAISkkoNm+EsF86my3fxp1I\nzoJWjkVRJ1f9/ndxv93tUX2NhCH8PqimO4D/3Nn9RiE19G+vtiu/hx6Px1OBN+48ez1xDt2pwO2D\nCP3uUP4nbqU2TjX90BCWUzG/nIPhH2jqZ97sQiFAvqcgi369mLH7NjLv9Bm0RYJWCdABoJh+R1Eq\nTcqBHPf99ewYYAB8b+pdwV87j2cPsDfIlgwH/gbj2d3cjZOyKTZ5745f37O7DySSMSKZi3BSGr8G\nXhbJvHs72meLgZdxumrg8rZ6cV1SEMlMFMn8BFcNe4dI5m6RzP7bWcLv47my8etuYCPwdwA5hxOB\np3mRyUSAjfXhLNq7uFG14LTlAASrm2nWONlNFdQi9FCvlqJLTzXXneeJnz+vLTOadN7pM1CMbTab\nCZz+ryvTsES5fA1RPjA2dHFemyfq6amj0Jk0UdZEAFHORC1HbwJjoSi8bAsW22uwBdCoNFY3t3hN\ni3HjbmAFrkfsjrgW9/tQ1PHrxeUjvlZZCLTjriE4b3I38NsRW5HH43lV4XPuPLuduKji08CHgO8A\n3xyOYgqRzIdx8hrl4awe4BLV9K+3sY8B3hXv9wDwSdX0K/F7zwKzKYUMI1z4bNy21i/TGQd8Cadz\n90Xgal1JKOdwIM5IdCHgOuBQLGMxrHerTEzKM/6CNZqakJXxsok6siTJ08xmNShZasUS0EAnM3lJ\n6djK8l/+VfY5eCwzT5rGFh3HK9FU+8fsP5qt2gygmhOJ2hNRYVF9QF5oed16nTCvXTpua47Wf31K\nQFbY74NL7X4Xv2RWXbOffem7c03UmwCNLDZr6HzY0vlXg0nB2DdF1O0fsPUepftJid18xU4dFwHX\nDraYQSRzHPADnEF9qWp6eLKc9xLi6uH34USZbwP+K9ZS9HheVYx0zt1++vywzP2yHDCqc+68cefZ\naxHJfA5nUJV7oLPAp1TT3x3CfGuAyVXDeWBinO81+LnO4Xjgj5QLcDcCUwkxpXSI1mNWM+PMFyOT\nKOWzpchSU5WS9botj4Rtv7w/se/hE5h1olviD7s/GK6IZlWkVmT/PMZqV1Dpkf8fnK9se9gsrP4G\nVa1xwVl01d+zRarpw3Ywo8fj8XjjboTwOXee1yQynZnAGl3ZF1KF6iKCeNOKFy7kOw9YUvRaiWQS\nwEzV9IvF7cz4MCkJNVF7KdnOpCISYwvkO0v1HPWJbprooLtMt1twXco0tlkLmzpZ+ItFZv5xk5l1\nbEn324qh2vbSjfFZlH+zLZWoQrgREuO3kUq3dyCSmQcs20FnC4/H8xrmtapz99o8a8+rhSdx+UpJ\n3O9yHmfKPLWtHWQ603A6d28Ftsh0Pkrb5U+C/R6uY0q5tdSNa23VCyCSOR7XY3Ye8IRI5kPAJOCH\nwD4imXtJmY9MyV146KQfchURTdnH66OOn44Lxs9frzPet0xMTZTYsKg1WnXHfsHZ8/+g7z/th5II\nwuCvHBPdyalBkoJtossA5EnZrg1Z88ov7rEtJ73RLDtyFgEv6ATWy32cHHXWjUmMYSvZbJ1mV9ZK\n4Sv1EfeZgFpVzhRhEnArETmCvvPKr4FNt1gK7YbEeMu4sw0100CS3QT1AVFnhNOiLJ5/AufOK4a+\ne+jfjWOPEhe8XAUcj8uz/KBqerfndHo8Hs/eig/LevZqRDIzgW/jOkRcj9PTWz3gttPZDyeWnaQo\nz5F/pZf2nyRj91bxYUdxhuIngatV0wWRzAU4Ed1anPFnIS6TKMvRG/O9o7X+Q/sXJJA6AI2wE1ln\n6qKsDVLWANiI6FK5KpgarbE1ybwBCDHRr3hP0MHYSGIPYk/71mjZr/8WjHvjodGYBbMC3MHsYg4w\nFomUIAAovJSwG/5hH0OuT4QYAhSLoLEIMUDvEmXD7wXCogGrSEJoObOThsMuYf2vbyC77BKcuO4W\n4FJcgcwngc/hClcuUU3fu/Of1O5BJHMCTng6RcnT2gN8ZiiheI/HM7yMdFh2ii4flrnXyCwflvV4\nhgvV9EvA2SKZBtV09w42n4wrkCgVYESddUgQobY8JCvAjarp75eNzcaVRRQpdnIo12sLEgc1h0XD\nDkACTErzNghsXx6cCQgm6gYtGnYACWzQTSNFw653zWbafvNwMP70Y2k6eEbf2gqkjCJaNOwAdH1g\nJMBq0bADiPo6ZpTOK9wiYMvHBA2VTbd8Qzcedg1cCPBdkcyPgFA1HcbbfUkk800gp5quDvLuafbD\nFXSUfxb1OGFjj8fj8eCNO88oR6Q9qdpaLfjaj0EYdqAWJwM8qIetapf20J/QBtpzO7P1rNpI2zWP\nMPmsBZgD9uu/sIFKHAZmkG55W2Gwqaaz1VuMdOs4j8fjGQpe587jGUWItDeJtH8N6BZpv0ukfZc8\nMyKZ+ay96gdoOBYtM2ak1qJRMSetiALniGQuFskEIpl3AP9eNaWCJDBBxX7hi50JWyifS21I0mhF\nWYO1WxgrFukrBIiQqJ4eulesi9queYSpbzuSxgOmRQC2vIQ1qxFWRMPSfNIUWc1jUC030nophY8d\npqHYL7Z8zVlcuHVvYTXuobT8HHqA4Ym9eDyevZrIBsPyM9rxxp1n1CHSPgdncHwUlxt3MrBQpP0D\nQ5sv80ngUcINh7H2B5BdpmgIUS90PyGx/VM0gop+sVqcRt9W4BeUJE0UMdA0UXnD++CofxJqG5VE\nAAfsYztkPh0vTsAWBFSpJatJ8nHbMMUQMoOXdQMT2Mg4iTCEGJYyl+zy1ay57mGzzzuOpn7uVNYy\nRRazP1towUZC1BOw8tuzZd1J08jeXq+aBdsBPd8XYdVGyObVyR/rZlzP2yOAv6KRJexUep4qnmtR\nTHgpcKZq+pdDua4jgWr6PuAEnFB0DmgD3glcOZLr8ng8ntGEL6jwjDpE2s/CdZwYW/XWtaqt7975\n+TK3A6dVDCYmWqItBq2I+O444GkCeP37oGVqKbzbEMHxG5R5k6Q41ti8hbnzn7O1iVzfA9Q0VnI4\nj4fj2dKXDrGRFl5harT6xVyw8g8LmXHuEbTvdwQvM5MCqb7D9qQbw56bxyQKm0opfkHT+tCuihLa\nUbbkZKIT1bdoYcJ9ADIdYe0PN1FY11yliRIBY1TTPeyFxJI0hwLPqqZ3GLb3eDwjw0gXVIzNrRmW\nubfWTPEFFR7PqCMcYjMAEWieXJm3FwQwq7XSLLRCgrBiV4Myhs6KsQIpVj3XRdttzzLzvKNomNbC\nGoIKww4gt6mOcsMOIFoaQL6qvqEQWpxHCwBdiYqs3ZYO3EgXRwyZWGNw4Uivw+PxeIqIyDHAV1X1\nFBGZg2ttaYFngI/oHvSmeePOMxrpgCrrxuWGbRrifJvi/WP14BnAcQbWWHjUQBbq5ytjTxF6F1u2\n3us8eo1HKk3HC91PWjofMgTAMSdY5hrDJpTNCElgAZYEfYrCiWSeqdNX2iCI+rx2KXK0sMm2sW+i\nlXU00EOeJCue7tL1tz8bzLvweFJTWshSg2J0Mq+wkQlSIEX++RpyG+oC5qqyUoQcMAZ43dgEWwqW\npzsNWQt1NdDcOAaRT8h0PkxbZjPwXpwHtCSJ4v6/QJzLJ9Jej2sXdzGun8X/qrZWWqbbQCQzHRcS\n3R/4N9X03UP8jDwej2e3E4V7xswRkU/hJAeKvcuvAD6rqveLyFU4bdU/7JHF4MOynlGISLsAZ+GE\naptxPrGvAV9Tbd3pqk2RTBPweUh+DM6ugWaBpEAUYQiY1KgEtYpJGTSK0DBAQ0VSikkatBDREgW8\nUSGViEgkAyyWZoRDERJEBASAHTepXeYcsFhEbGScOaitrGEGK8RgrYARrAaEumbhevPyXS/pAe85\njPrWRnmRuXYjE2KpE8Fakee/dqjtXDjWkBdFUVQMBZQEECBYtRQwPG0VK2CMAFnCrZa1P9iK5sfg\nRImLEihZXL7ah1TTz4q0HwvcipMWqccJF28E3qTaunQH1/XjOE28BC43sgd4CJfH50OlHo9nxMOy\njd3D07K5q2FiRVhWRM7BCej/SlWPE5FVqjotfu9s4DRV/eiwLGYAvOfOM+pQbVXgFpH223FPOw+o\ntq4d+nzpTuDTIn99CMZfCyb24AUBqRoIGhVjnFdLggAJQFOKxLpxkgzYNwl1CiKuTMpgmIalBqGk\nG2cm7/OKDQJbPiZTWKOBi4AaAMXIqr+vk1X3v8yBFy2Q+gmuIYQz7AwUg745Q+ffxxqcRJ7E/0Eq\n/hfAiCEPGENZrLiW/GpQW1O2juK/f1FNn1l2ed4CjC973RCv82RcwcX2+DD99eZej9MTbNvBvh6P\nxzPsROGeqWxV1RtFZEbZULlB20X/HPJhxRt3nlGLamse+P3um3FGOy4frbbqjf7ua5EBXNr9Hj7L\nQ53bG6so1Hjl0TbWPNKmB120QOrG1bM9RAZYnAxU+CHbGqtmMNnFu+LO931ePR7Pq47ogQexDz64\nM7uU5zQ34br+7DG8cefZq4mrJs/GeZGuUE3fLtJugPOBC4DLVVsfjjcv0C+XTxVkIGOscsyisUkl\nZWMBipaPWSuBKiplY65zhIIIqx5Ywbon13DwxQukZmxthUEmqlQEL4xTNqlabryKsg0lXlsFxoKt\ntkZDyootYgrxeNm9IKqBRW8Veex3qukutk2O/sZsKp7T4/F4Rpzd5rk77iTMcSeVXn/1azva40kR\nOUlV7wPOwLVy3GN4nTvPXotIZj/gUeA3OKmTG0R+8Dewi3H5ev8A3CnS/n8i7c3Ak8CngE5c71gl\nm7d0dClWQdXG1pSl5L1SAiCPjfu0ujEDZItjimA1MCFBj9UaclbUEmjB1msPR/c+rtPCNvvKX5bo\n+oVr9JD3HcqEsXlNECqqGmpge2ydduTGkLNJtVZs1GtsoT2lddIDBZTIWsK80rFOWXyfku+FKIxQ\n7aaBR6jhazjXfw7ooW72HQQN38HlweVxosbXA1+suozfAX4J2utak4XA0wKPvwloE8n803Y+gvOB\nx3F5ej24XL2LVNNDDqF7PB7PXk7xb8cngYyIPIx7eL5+Ty7CF1R49lpEMp/DGStlDyknWTig+qGl\nB7hItfX3br/2ZuB5XG6YwxiYMj7OXSvjQGAWrqwDnJ9tOjCNvmyz2lQ3+01ZzpxpS6itcZ27Jobr\nWJBfpO/K3SBNtos774U/rprN2IvPsC1NBWNiZ9tT4XyeKxyozxQOERs3jxj7QAfRPSnWXz/FOd8E\nqF9k6XzRsPpZd9AgCbOPyVPb+DVmH/MFvRGV6YwDPgLcpyu5P75GU4BLgT+opp/Y9rVceCyseQie\nM6ViLwDWqKanbnu/Ps/pXOAHe6tunsfjGR5GuqDCrN1e8GHo2MmNXufO49mDDPS0UiHrodq6RaR9\nPeXGnbWlkGc5h0JFa0IBZlNhTgbG8roZz2oiiPr2bdEtXJi9Vms1K3++G9pWwwcuKHBfY7cNSZri\nVL22jmcKB1tLEBQPsH5RK9F1ZXUKCqxcaul5tnTUqABLHswCj+uiYxRAV7IJuLzyXNNrgC8McE2q\nuHlxfJ2qJWi2S6w3d/PO7OPxeDye4cUbd55dRiQzA7gEuFY1vUvCsrEn6CycWfUd1XSHSMYA7wL2\nAb6vmu4VySSAIyv3NkCLDNBooqIXqUj7OGBSxa4NBhaI8AqwIR5rwdWRFgObwKRJa5k/5wkeX3MM\nm3tdkemMxuUcbJ6WFcwkG7vzZocvkdCc3Ho7rNsA73kXLGqagMEGxfVZFdrCfbGYUn6eBdsawMmq\n3C+CBWwB8hsGSqGo7rG6K/TPMyyNezwez16JjV6bZo4Py3qGjEimDucp+jDO0AiBm4CPq6Z3WlxI\nJHMg8FPgYJy/LAf8ABf22w9nfPTEY/+MM9Bq3PgMgTco1KjbrK/ioBf4E06gtxP4OC6UWwMkMFhO\naBCOaTAEKBZhnUTUA4cS4DRMTG3YE51xzC0cePDTgQkiazUwz689KJpZ8xIHtTwTJExBVUQ2RBPs\nad1/0Vn55eZPfyxoRwfmH84dq4+OO0G3BGMlIlAVMS+Gs/Xa3vN0i46VkKSCSLQ1IfkV9UrBKDmg\nS4QvPyc8fRvYnIVIKFmuWeAW4P07KHrYmev/eeCz8bUP42O8XzW9x4Q3PR7Pq4uRDsvy8jDVd+2X\nHNVhWW/ceYaMSOZC4Goqtc5C4Guq6c8NYb5HgaOpdLsVf0G381kb4F8YwPGUA96g2vqYm7/9cODB\nivXOSinnNEOyrPy0CWUiYErHPHH+PXrK4XcQmNLv3ARdr2PYiimrjD2p8yFtza7lpluRbBbefQ7c\nOv4f2RiMU6R0jC90ZtiqzZTT+3gzFeWyvXk4+6tg+31He4HXq6Yf3/Y1GRoimanAl4BVwP+oprt3\n9zE8Hs9rB2/cjQyvTX+lZ3eRpH9/0gQ7mbdVRi0DG3FD/QJ1FQ27mCQuwFoy7owIqhFIKbPOGWvF\njg4ABCYSUyUbZySKTPV3qFCIrr+ZhCqc9w5IJCCUhO0TRI4JCQbQpatCFYwMZNx1DIdh5w6ZfgXn\n5fR4PJ69nz0kYjza8MbdbkYkMxeYj6tOfC0Iuo4SOZ0B7aQamU6triQbv56CC8fueGcZhEGp5b49\nCAuWm67PB5MS8I630FcigQ4w10BjA9HfsOu/VMnMBg4HblJND6onrMfj8XhevYySP8x7PyKZcSKZ\n7wOLgF8AL4hkTh3hZQ039wKv4HTOwOXDrQduGOJ85bpsxPMuw4UhQ1yIthdYEv8buTELvGjjTZSi\nN7GpPgWslCnh+0TarwR+i/MqxhZTFPFKD2ztEAp5t48hogAkEIybJxnko9Xt+6pYcFpwkNCCDQkk\n0BDRKArzlod+06Ybx0zVs89OQCARQJ7k/2fvzsPkKqvEj3/PvVW9pzt7h4QkBBJCWEIgYd8SQBYB\nF0TBkcVlVFAZXEYU/TlFjaOOC+oIKqAjGAVh2JFdlmyEECJ7EpaQkEBCOnt3eq+69/z+eG91Vy/Z\nu7q6U+fzPPWk+/bte997qyp96n3Pe14d2foBoQqqbvWG1qYinbT2jVCbQQNCVVVtUqS2OaRFIVQl\nDBUR2L+6vbaeu8Cm6D4hkhwokvwf4DXgFuBtkeSZu3nvjTFm75OW3Dz6OMu56yEiyYeB0+jYM9QC\nHKaaeDs/rco9kaQPfA64BhdgXKeaaNqD41UDPwOOAL6pmngyKlb8K2A0cKVqYoFI8iDgflx9tehD\nylDwz1ViA5TBlR6xqGN6w5aAppYQNywb2QqsCmGhBy1w8KFw4cfgAC9kinjEgLXokJr1cuqB/wjP\nPvTvXjyWolYrw1aKvam6SCfraxLi8WLLQeHtf4t5DBvK6HMPpYwWpjfOC8em3/PuKzuP1+MHE5M0\nA9dtCdkk3p2Ji3nzuYORUQHxq+tDqtKS+nYg+qLCWB/+TRStg+sfEVZmpu4S4oowX6CaeDe6V/cA\n59DxNdcMTFNNLN7d58AYY3pK3nPu3sxRjDNR+nTOnQV3PUQkOR84rtPmWmCGauKlPDRpr9dtEeNB\n5wVUHNkxyWLdpjQtqU4pCHeGsLljz/Wz31KGVnR4Td0y/tPEvfaEXF/TnBU+hhd1/jU1KbfeVszi\nUafp/udMlMyciRQxtjAw1KyqyMsfG8+TV52VTjcVtbelvhE2b+2Q3wcLgS71hhtxwe5NWdf/DDC9\n0361wFmqiQWdD2CMMb0t78Hd4hzFOIf07eDOhmVzq88+8f2NSM1wkZrxO9yxpKhrJmnYzRtw1BC3\nb5ZYWQqkfX6IEOJlRn6ztimCAg0Nyu0zQ0aMiTPmw5Oyqq9Aa7qIDfXDOhy/dauPtnRa7zWsx8Vj\nO7SzryV7zRljTIGzCRU95xFgCm1112jAjf2tzGej+juRmlLcGn3XAJ5Izf8BV6tW1+DWlW0GYhSV\nFnHIhwJGT3JFgmsk4P3AZ+PWgFS6fWrDcF+4ZEDAhIt8Uin4vyc0VrdSqm46JowfutkLqdO6dUNk\nTHoVl1ffEBZLiyeqmpaYDGYTh7BY015M6uuC8O6ZKS9+0HheOO3z2igVEicVDtJN3tI1h+nj75wj\n6TDuVVeuCU4+4Cl/1R+rdeF/HC5BU61PcXPAwBKfxvkhdc95oD4cEMBxPpS2QrW6GE0FlyPYjMu3\n69yd9zBwDB1fcw3A8tw+K8YY008U6BQzG5btQSLJw4GbgMNwSz5dr5po3f5vme0RqXkON/u4LNrU\nEj1GqFY3iSRHIP4v+PC3PkOsKMCL5qg2hwFPrPcJXBFiAIb5If85xCNGiOdKk8SrGsIh59V4xFDx\nXNfbpHCxfl5ukTgplWjW7AjW6IG8LT4hdbXKnTNTNE0+irUnf0pTUhS9DlXnLz1ZltccqOkwHm0L\nQ763xfNeaQ7DZj/TUx7C3R5sDiGItkkAcR8uvQNi34QbBbgO+CTuNfUD1cSmrvcneShwIzAVuBb4\ntWqipSfuvTHG7Km8D8u+kqMY5/C+PSxrPXc9SDXxikjyOEBUE53rv5ndM4b2wA7aJw+UAE2qibVy\nPl9A9SIkq1ZdqJmv21MPKsQj0JCi9jw4rxJPxQs8r21/Bnq14mkYiLTnwZXQIh4hWza7wO7Io32W\nHXtQ+j3Jyp9DpLZpYDodxrO2eR5rwuzALmpTvbYHduB671rT8Md/dYWDEwCfFklevL2SOqqJ10WS\nJwFegZTeMcaYnVegPXcW3PWwaCH1/tkd2otEkuVAS3ZdNpGaAUCDavWOAuPcf1rq5gybNip3zUxx\nzIk+Rxzls6znz9rlddM5YIvW3q1UTdRm7aO4sjDGGGOMTagwvUskWRLNct0ALBdJflhk5QCRmp8D\nG4HFIjUnZf3KG7TX0QMXxBQB94jUTBRJjuf+Hz5AS71PkG4PcIokxBOiGRFOQxgSE4/sQKgJ9bzQ\nh7AtoKxjgHqEflQzD1Bdsz7G325Nc9wpvh5xlE8IOoz1MclaoCMMCAeWbI6Rysp1CMKQsZ6HpLMD\ntwAGCsSyg9gmYBPtNf66u3dTcHmGG0WSfxBJDt3WvsYYY4BUjh59nAV3ptdEvXXv4CZHlACjofQu\n8DcAX8HVoTsIeEyk5ifRr52Jm1DReY3Tk+HdV0AWo+FpPHE9LFsghAGIwkhP+MpQmFwlXPMkAAAg\nAElEQVTqioyUCZxb3jbQKhJSWtqgxx8+V0705jKUjeoRUM1a/TR3yFhWUkJzCErtB4369z/XMe5D\n45hwZLkGeKxgXDifE2ignCD0NEwJa+aO1ve/NAZ+J8LmUEkFMGeJsuQW0Nm0xW3lJbDPF2Dg6YIU\nAZLCFSYer5ro9r8NkeRXgPnANNwVXQKsEEkevOfPjDHG7KWCHD36OBuWNb1pEDCQDjl0FWWgIR3X\noy0DTgRQrU4DN4nU+MDPs37Xh42Z3jUhSMGSpzz2qwyYONnHR8CDsyt9LihPM9iLUdxWq8Q//YyH\nwxEjPvB833WeTeVF/1we0LG8J37UG7cvq/3Fqwfw/O3rvGPPGczIg8t4nWrv73yE9Qz3M2O3696r\nkgWfP0Vr3x7kQsdVwAurhfDBkJoNUTj5tlASh4HnpYmXuPfdgGOE8ikQNt6rawZ9dwf37jiy18R1\nuYctwIHAkh38rjHGmAJiwZ3pL1rYqVzGdIdywABU+V1WlC0rayQT2GVUsw4/a5j1g1UpZt9Zw3Ef\nGcLoiZmYUtjAMDom5Qn1qys6niAA1td13CYh+J06y71i8IqbMcYY0/NsQoUxOdeASwVoL0/C5gBu\n9+FohYME6oG5IaybKtLyKeAuuPwQ3NBsWcfDHawwDpgDrIVJ4+BDkzziqqwXoVUpPriRqo9sjKW3\nxsPa54Z6YZ3PlPGL9IjKF71WLQrXyXAvxGMSS9QnlBSxIEbaX7UiYOZdVVr8ibOl5oC6YASv+imN\nc3vLvwRPtJ7hjy16NzygeLnnS0isPN2iKa+ow3V5JS1oEMcFpC4SDJtbEK/ze64RWLcT924dLi8v\nu/duAHCNSPI11cQ7nX9BJBkHvgZ8H7gXuEY1sXEnzrXTRJITgd8C44GvAw9EEzyMMcbkidW5M70q\nyhH7Pe3Fd6OAyA8hFq3IHGSCpHo4sQEOqcIlp3m0996JG5IVgTR8vUw5sAKK44KqIsiQs9dobEgK\nr0hFQ8KisNX7ZPourfTrtCie8hTCIlq8KbxMMa1hjMADDV5fVuz/733DGXPBtKBsXLUvBMHm9GB/\nduPJpIkHaeK+RxD4EvjHlD3fXOlvnTn3X07967p5+/w3rpC1ADfwwQ2Pkd74K+CAaNuPGfnvL+KX\nXw+MiK79GuBmXbX9FN0oULsc+BFQHh1PcJ9LU0BCNfHzrP33A+bihsLLcYWQA+BC1cTDu/8MdmjT\n/wO+hxtS93HB+6vAdKvvaIyBPlDn7qkcxTinWZ07Y9qoJpYAp2StxRv14AVeFNRJ+zYqYFx5h3W9\nOoyHRttjMThkoOBJZrP45Sniw1IiMffGFg9vsLeJSr+OIkl50YG8SrZSTEsYI/QA3n2jxX/i78KY\nC48Jy8YM9QEU318djKKZUsDV0gvx/VC91pebpvx0XcWYa7kdZAwn4nIFV+kqVsLXMjNcTwVeV03U\nAMgYJgJnAM/pKrbs5H1LAddH9+1Z2geaY9HjUlxOYsY0oAoX2IGbwALwcdzKFj3hUjr2JJbjgttq\n4L0eOocxxphdZMGdyZfNPXq0bj6cqXZTrk6yhknbNrnfXr64iece3coxn5nIkpGDdMdVqCWoDwe0\nDanqKhTXW5bVhoQCT3XYtooAeHSHh+9eHW7abfGOdiQ/9RateLcxpu8o0Jw7K4ViOhBJFoskrxZJ\nzo1WPkAkWSGS/LFI8kmR5BE9cI5DgCPpPhDoNMk8Le315jrQbr5q3xSi4nX8QYCnXtRDlxEiIeC/\n/UoTCx7bytmXDGLoyGKl03vDIwxAO7RNW9LFqZ8+f+Ge1psTqRGRmk+I1CwQqblMpGZ778sWugZ2\nAS53L5tbc7ejFF1LyuyJRroWBYiznVp9xhhjcs+CO9MmCubexa2LeyLwmEjyBdwQ21W44cVnRZK3\nRzlgu3p8TyT5B+AFYDjtYVkrsAX4LvAY0AReAKXqOqokBBQPpdRTJldAZUzxUYpQhvsha9MAKoRh\nzG/VEeVr9ZjG5ymlUX3SoU+KYlrCRkqjSFFDCGmmRBcsiukLT9Vz5iWDw6rqYsqpD4ewQV2BYg1A\nm8bFV/zWJ7xeVZs0FQTamCL9i0Wkf7HoGOBdkeSXdu+e14yO7sefcXmIN+AKOU/sbn/VxCrgImAt\nLrhqBJ6ItmV7DJcPV48L9JqBP+HWn+0pFwCzojY0AGuACzLDz8YYk3fpHD36OJtQYdqIJB8APtJp\nc9bM1jaNwNGqicW7ePx9gWV07XlaD+yvmqh3+z10NZT+JxxU3Nb5NDwOI0tCxpZ6eFFnnrbACJRp\nxW35dkeMX6gH7bdEDhz9BiIQ4PEmE8OBbPFG8kF0OmUIG7ScRln1fA1vz9/IKZftS9ngYt3IUGmM\n0tTS+NRSGTZSfvhv+dbrACVPLpkYPPjO0vSdbwjrOnSWrVdNDN+V++GuteYK4Ffd3JNrVauT27mX\nxcBlwFLVxNzt7DcY+CzwsGrizV1t384QSc7ATRqZaRMpjDHZ8j6h4uEcxTjn2IQK039090Lt7p2x\nJ59buqvtXZsJ7JyjluB6mtoDnlIfxpRq1qQJmFACE+nQ6tEjVoUTx7zRVunOJ+RglnYKUIUyGvXd\nZz+Q5Ys2c/LnxlI2sIjVjCTMKpIXI2AIm9ND2Lwqs63lQ3e9F11DT753djlPTTXRAty8E/ttAn65\nO43ahbY8AzyTy3MYY8xu6Qe9bLlgw7ImW3dvg+4CvnLghO0dSCR5lEjypmi2aEZI90HRUJHkaPd7\nNR5wMtk17TzglCLleLy2dSx8YBxKVXsLi2ItjBq8SjxNayYmLU018aEVT/uH17zalrpXnq7Df2iu\n1D6/kpM/O5bygUWsqxvOvc9+Ut5a3T4auvbNEdz7nU/Fr5t+zVFtG/f5+jQGnesTz+6kGwWcUiFS\nMz66dhFJni+S/I1IctT27hPd94yG9PLEBJHkCdHzdUhvnjfXRJIniSRvFElOyndbjDF5YMOy/YsN\ny/a8qAbdX3FLWpXgcuEeBKYC++DKXmSKEDcBLwGfzS6gG00u+D1wDu1LZN2HK6a7Bfg2LqcvUxtN\ncD1hKRh3B5xxLMiY6Pw+k+PKd6pgoChxgUA8lqJMAIoI8RAU74gBL+h5R9wr8Vgq9L0ABDlizSvM\nWDlPYhqEgXhsLaqQlfvuq+ueXuIteyfUiz/p0VQxRL/07k08vuwcLx3ENOan2af8/bDimXreemKS\nn26JKUgTrlKyAtNRLUHTQv2bAbVpYKgHMQVpgdX3wt8n4foUi3D/DfwSV4euS6+lSM1g4Ebg3Oia\nM+f6vGr1B53372kiyX1wPYCnRudvAe4Avq6aqNve7/ZlIsmRwB+BU3Cvw1bgNuAbHXuJjTG5lPdh\n2XtyFON8om8Py1rPnWkT1aCbisvlmgkcppq4CDgIF4BkepM8XO/d8cB/djrM54CP0h4IlgKfAC5W\nTahq4mfA/sD7tBfijbn9jrwsOlc5mUXErqkSRvhCiefhi+u5m4xQhhDDx8PzY2kuOOZvUlrcTMwP\nPBG8ypatcvq7s6U4TOFr6BWFaW9wsEVqHn/dW/luyOc/hQwsC+WfNUd5T759pqSCIhRPUkGRrJo1\nzlvy6KFeuiVOVEuvDFeX7kygFBHBi0NqX4ERPsQFJLrWly4CjoiuIR5d/zeA9t6/Dve8epNq9adw\nvZW3AR9WrT67NwK7yBXA2bhrzDxfF+Ges/7sKtxzVoZ7LZUCnwE+ls9GGWN6WSpHjz7Ocu5MB1Fd\ntnuiR2ZbIJJ8CvhXXGHcDA8XwGTz6fqhQcha8VU1sU4kuRS3dlinw0nHT0J+17p03Q4Udz6hKqF4\nblIsbkT24XnwQSt62QVISVTSN1QfnzDMbh+hCCoBHVep7eaDkIRRUJcl6Npe919B5xVvO1CtXgRc\nsuMr63E+Xdum3Wzrb2J0vYbOz6kxxuyV+nXPnUiyz3aJ9mciyYNEkl8SSXZay3X7fxhlDELxmAPZ\nQfglUhODASO7/qTbLu5utnXtZu/uF70o5SAM4YG5sH4LXHI+ZAK7jBCvu/fBTry2tLuAr7tjldAl\nkM0tkeSxIsmLRZK78wFub3hfdTcWY4GdMYUmyNGjj+vXwR0wXyQ5Od+N2FuIJIeIJG8GXsSV51gl\nkvxMFEQvAF6mvQhuI66EyQ0AMobDgGcZdO6F+AMFYpkh3HpgOfB3d46aDwFvwUkHuVQoL3qb+Aor\nMhMhAkApAV5qCBHF1ZsLNe61sF/58jDutSCEoUcQxqWVhtqy0CcNGoYShEGTFlO7aUAYNMM9s9Da\nesLPzICSVjTzxmwlHkwe9AoDBtWq+EEUCRKwH8oAJOo1BCEkhhJHkWhoOmwNKWnFpcilM291hcNx\nqXZe1jZiwM0iyVtFkrtcLmVXiCTHRCVtnsINpb8tkjxjO7/yf8Aq2p/XBuBt4PFctrMX3IYb+s++\nrjeBJ/PWImOM6SX9ekIFXBsCW1QTQ/Ldnr2BSPKvwCehbU4quOjlNNXEc9E+Z+MWr78XuE410QQg\nY1gPDAY8NISGl5WtCyC9/rPAX1UToUjNMNwf3Oj4KeClEFZ5cCywr9s8klBGihT91BdvmoemhKrV\n9eGQ9Cb5/IF/kDEVK1nfNIz/e/tftEhb+NGE78q4shVsoYrnVx6jFcuaOPV3c6R0QwP3pCF1HHrh\nyWh8Cx4hMBCWTRvHrPKT9MaqL0mDlFH/fiXvPzoOtkhIrbj93sWFuUNRhke9WTWBsnyDsPVZaHwN\n9/loaghHeu0dXilgbghvdf7wlAIeUU3kLO8rKjo9hY4pF63AWNXE2m38Tgz4MvAl4CfAndHwfL8W\nFdq+AvgC8F/A3XvDdRnTn+R9QsWfc/SWv6xvT6jotZw7EfFws9cOxCXmfxHXQ3Nr9P3rwFdVVUXk\ni7g/NGngv1R1WwudZxLATc+opGNgBy4waBueVU08SvfromYmULiRyYojhYojQ13FzKx9inHPaXSO\nOHC0B0d3KAfiHSZe0Z9igVSIDyBxZcqERd6lJTODuKR9gGGl6/nZ5G/IOFa0vbsGUssn7ngwLHk0\n7acV7lwHMYGLBhLENmW91rfAN8p/Fr4z6AAPXEg2YHQd/oZAg/p4dA24gdRWQLOGKYeGwoKb23L5\n3Ev3DQ8mq5tYkbmugz1YEUIqO8CLAwO6uXc9qYLulx0r6WZfAFQTaeC30WOvoZpIAb+JHsYYUzB6\nc1j2DKBcVU/EzbD8MXAd8D1VPRn35/SjIjICuBI3E/NM4Cci0jngyObtZl5RvyeSHC6SPDbXp+l0\nThFJniKSrMza5tO83CfMmkKkaWhenuk9iawshff97HQoGagUneRGYdtU0SU7qliaae00d6NkUwv+\nho7l4ASltQRur4FiDy4YBk2jyqWltP13VWHT+0NIN7afREPQ5Slo2UF5uUBBR3a+LdvQZzqJ+uyn\nS2OMyakCrXPXm0FRE1AlIoL7890KHKOqc6KfP4oLAAPgWVVNASkRWQZMBhZt47gx4C2R5JdVE//I\n6RX0ESLJEuCbwPej718ELt/V5cC6cQ9wOu6exnF5SrXAkug8x+Jqoh0ApEWS38Hl0/2e9bfH8Yqg\n6izwYrD5ESVsVmCFSPIrwCDgV+DHoVyJTZeyrw7TAT9MCT5e+g0Ja79a7HFKLIx9w/cowQeCIlr8\n6cWzgmlFi/y1jPDLaApGNa72j5z1WjBm6WrfI5SWA/2g6ei4X/Z8KlAv8G+rhEH7E54xtMhbfNW4\noO6wAT6eMmrx2mDTC8P9K1bfGL68fIqX9nyGnfaBlmitrL28KgyXb/WQephRoRxeKnjSQCnNNFKK\nahlrWuDlOoWzBJoVZgu8p9DYDC1NEC/C9ZyFUNUKfjOk47iyKGlcD9pde/gc7cidwNVECY3Rc/gO\nbi1aY4wxBaDXcu5EJIZLZt4HGAKcB9ytqqOin88APo9b8PwwVf1utP3PwExVfarT8bTTGuiNwKdV\nEw/m+FLyTiR5P/Ah2odLQ1ywPDFaWH5Pjj0GN5niNFyx4d+rJlIiyaOA2bjhvUxPUAuujy3rQ4IX\nlQLp0AOWwnVjtfXAVt58PGUXH6BS6kqfaIjWN5RLuiUWSlHbjNPwi+U3e8O8dWFMwmibhl/53R+9\nisbG0AvdbFUVAnz8phTh7f/EG1EJZxzuBQsvP9IPfQnx3PHerxkVfO6Gv/mtQVzJrGO2piXk9i0e\nKdW2Miw+IVNKGzij8hLgQd7gFN5uuIMl9cMJs3vB0gpz34U3L4LLFwGfxhUsfhO4Am58E5d+8CNg\nPnClamLFrj8ru0YkeSiukPQEXI29OyzXzBiTD3nPubspR//1fdly7jKuxvXIfV9E9sWtRZk9zlaJ\nW8Ggjo55SQOAzd0fMns5y7Ee7D+sJxvch+1D9vJcroemFRiIm/m426LgsLsCtkOjc2TnOGZy6LKE\nAl1q03l0GmiNjS5NS6m0vf7EQ1S87MAOwKuQem0P7Nyepc0tZAI7AFH8xhb4y4t4YwfBmROhpSTm\nhn+99jInda0Dfd9PhwTF7cdrUA8IQNrbF+Dxz6YHdVHlA9GWWSL1vwOSHa811gwzfqF60cJow23R\nI5IAF2T9nl6kmngdOKk3z2mMMQAiMh2YnudmFLzeDO7KcYEbuGAtBrwkIqeo6mxclfyngIXAj0Sk\nGNdLNAk32aIbM7K/6QeVZ/ZKPfjJZfc+YdW3wMyXYOJwOHVClzLIuym9vTxPsw3Rsl8fWE+hMYVJ\nVWcBszLfi0gib42BfpEflwu9OaHi58CxIjIXF8Rdg1tvNCki83HB3t2qWoOb3ZbZ73uq2rqNY2bG\n/tK4a3kzh+3vSxbSXr8LXD5jCORyyaoVuOeoJfo+88fbp/15yGyTrG3Zqz9EdeMkbH1hY0w7LOGi\nQcxPe0LYFhT4pIJ1wTAhK3fAa0oHGysHEra649c1wy0voIdUw4wDCDKBnaYl1FAIkLagv7KiVkN8\nD8n6IFAZU1R82j4cqLqX08KPiSSTWYWcX6f9PkP7fxlvbO+mFQqR5Niovt4q4JVemOhjjDE7VqAT\nKvp7nbvngGOAh4B/U02szHOzeo1I8ixckdp9gN8B16omanN8zmrgZ7hlspT2DweZZZ3Cbra1L/kk\nhIh4nHBQwFfP8osmxBk4ab36JWmpkPpgoG72G9IVrGkeqYH6clrRk8HHiu/3A4mxMTUYTcGE/14U\nTvj5C96Qw0MtPhWZ+SpMK0dPLEcYQhjs43ktxcU8MP1sfeXgQ2SCLAv3C1d6dakq/eWb35F5H5wM\nyyTgfXxSKGsRNgXw6taA1U0+bA5htgfrwOVx1gL7qSZaRWoOju75Cbjc0CtVq5fn8p73ByLJabh8\nzCLcBwDFBcL/rpro1SFpY0zfkvecu//JUYxzVd/OuevvwZ0HDFVNrM93e/IhKgFTqZrY1MvnXQ10\ns3zYDgypgOu/ANUD2zaVD6hl4hGvhcWx9npwY3UFM3gmGCS1bXlwVbe+z/BvLwmKN7T44JIz/zEK\nzpiCHjusfWj4nkvPCV44caqfjrencz786kfTyzYeFEtrVorn7YRs6tRz/fD1Suumzm/WVmCYaiKT\nUoBIzXDV6nW7fP17KZHkhcAf6FrD7ybVxOV5aJIxpo/Ie3B3XY5inG/17eCuX9eHi/J6CjKwg7bi\ns70a2EV2UAxuG0SgqvNytYLf6XCeKOU0dtwWhMS3utH5TcCDwOmlcMzQjkdL+3GyAzuAxqCcDoEd\ndJ/eJy3dbOzKAjtjjDF9Wb8O7kxHUbmSm3DreH0d+FvnxPZondjzcKsRNABfVk3M3rnj10T19f51\nJLwfwDwfmoFpIRzqwfIAnvPxgKrTAson+9S/FFD7tM/gOFz54YDieNtQ7aDSjZw3/t5wjP+u9w4H\n6ErGyrj17/L/nvxpuP+md/1XT5qkKw4dI+kXtvD+D9/S1S3qV+FqiswYADNGoXyAMAi0FBYeOY2H\nj/qwn9KYDpENUhy2sHjWZF39v2NiDNCQGeIxSOGBRuUvDR5VsZAplR5lCm/ND2lp8MgeRna9dq20\n5xl2d8/3xZWOOQc3ZP3TzJJsBaSG9uHYzCfZRmB13lpkjDHgCnEVoH49LNuXu0R7m0jy17gl2zJ1\n6DILwB+jmmiN9hHgaeAo3OxlcH+E71dNfGb7x68Zg5vIUeF+Nwwh9KLUuwB8H8KAIt9n2CAQL0B8\nH00HHKE+VwjEvBDf84Bg6sgF/lkTHsKXIPQ89QK88KTn5svJC5+TeNCqHkg65oVz7/O8d54PoSnU\nLSALQb80AU4dBZ7vAonQE/3Br38gKw4Yq63FxW5bi/D0t8+iYd2AMN0a9wAlHQqzN0JzGNKKB4RI\nnYd3H2hrQBhk5w22AvcD31RNdBukiCQ/hVs+L1P0uTG678epJt7ZtWewfxNJHo8rcL0/7h78G1Zf\nz5iCl/dh2f/O0X9B37VhWdM7PkbHGnTluDIyA4lmB0Smd/q9MuDcnTj+IdHxo6DQ81wMpFk14jyf\n4lKQmCLRNon5TNaQYvFon2zhHzRscRD3035mm0/oHfb2krA4aBWi3p9YOvTW/DNUmpBNIC8Ak0Gm\nu8CuTeD78tbE8ajvtb3RmmrLqF9XSdAay5xTqAugMVTSbe3w0E0QpEII/LZtzhOqiQt3cE/OpuM9\nL8P1/B2OWxWiYKgm5oskJ+OWDfxnAfZeGmP6ogItkmbB3d5t93Ljtq2bj0Cy449F0nUf6eZYso06\ndxuAfwJHAMO3dQq0uwN2t6dCl09b3Z14d2ceF2xPlWoiBOblux3GGFPoLLjLA5Hkfrg8rQrgKtXE\nkm3sNw34Na7G3LdVE9tbH3QrrvpO9nPq44YXs7XihhAzAU5Ax5p53bSjZjDwZTrPhnTRmN8xnFHt\nElS1hp6EqHrtPWutqSKfUBWvveRwc7zYC0PU89qPsDGGLAKdChItP6Lp0A34SuYaQlUJVfDblxDz\nYoGG6U7ljH3ShNphpQyIhdB5GyEwQyQ5RTXx8nZuTR0uH684+4Ds4H52JpL0gMtwq7j8nmjJt272\nK432+SRuabj7cCu7JHAVva8ulPWVjTFmp/SDmnS50JtFjA0gkvw+sASXgD8DWCSSvCHKh8vs44kk\nbwfm4Ia5PgW8I5K8YjuHPhd4hPa8rzeA01UTWzI7RPlPxwEvRvs04CqJz+h8sPa21FwErATOJPN6\n8VBiwJElIaeWQ5FALNp2mKdMRokpXlFa42WtnDjhGaaOWaAxL0UxTTqQzZz+9iyOXPtKGAtS+E2p\nsKSmQdc+WELzK4RhK6Ra0UVvo/OByRAOBXwfrapC6weAFBOGHpoqjun68UP16HULKaVJCVTTTT51\niweGxRtboBklIERpoip+HwNiXwDW0DZJYpTCyeris7YeRgVGAPNFkrdkPzed/D9cwe2m6L5vAq4A\ndjrAEknuDyyNjnMQ8GNgeTTEmb3fybgCwVfjhshn4op2vwdcDkwB7hdJPi2SLMcYY0zBsgkVvUwk\nWYvrbenMyySfiySH4/5od14Ca6VqYr8dHP9YYBRwXzRM1t0+ggsuG1QTz3S3T/u+NYuAqR02zqhQ\nJhYLg6JOwpYQljbD+GKoiDrBWkNOP+JRDjxnKaUDm922Opjw0io+w+1UiOvc+uCDQbzx5Cg9YOZS\n8VtdcsSGkTC7kvChN/EOU3ezDh0Nh48nPGD/9r6+xYcdyOvHTtIVR+0niOtGfOSWc8M3Hj7U27Cw\nur29g2khRkIX8dPomoqAZcDo9p1SwEyFVOfXVBoYkl3nrpv7ORY4HTeBYFd77a7A9eIWd/rRtaqJ\nZNZ+fwEu7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AT4F2AD8JAFdsYYY8zey4K7vZyIlAIXA2uAR7S/To82xhhjdlWBZpVbcLcX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\nngssktQO/AXMjIifShC/nSJXq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae5ced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals.plot_hexbin(['head_fair','wcrs1'],print_stats=['Mean','MAE','RMSE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 1. Plot of measured vs modelled values in the form of a hexbin plot.  Hexbin plots are a 2D histogram; the color of each hexagon indicates the number of observations that fall within the space occupied by the hexagon.  Warmer colors indicate more observations.  Hexbin plots are useful when observations number in the thousands._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting methods in PESTools provide default plotting behavior; additional customization can be obtained by supplying keyword arguments to the underlying matplotlib methods (e.g. color='k')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x105197dd0>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10ccc3710>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x10cdd7d50>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x10d367ad0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x10d3d0110>]], dtype=object))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkiRJ0nQY5A7Od1TVZ+gNWXo/8KNjrkmSJEnSFBgkLHw7yXHA3kmeCxw05pok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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105197dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals.plot_hist(groupinfo=['head_best', 'head_good', 'head_fair', 'head_poor'], color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 2.  Histograms of residuals. Each histogram represents an individual observation group._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PESTools uses the binary Jacobian matrix writen by PEST and the weights recorded in the residuals file to calculate parameter sensitivities.  Sensitivities can be recalculated with specific observations, or observation groups, removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x11288f250>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x11288f5d0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JMhwQ3gZ8PckNwCpgfpLtgbvo9Eh8BLimqi5I8gzgdDrzL9a5JuAbwIIkK+j0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\nrfs/ttkZxSdqblwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11288f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parsen = example.ParSen(drop_regul=True)\n",
    "# Note: in the official manuscript the above line was: example.ParSen(r'../cc/Columbia', drop_regul=True)\n",
    "# that was incorrect, the base name is already known and part of the Pest class (example)\n",
    "parsen.plot(n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 3.  Plot of the 20 most sensitive parameters.  Each color indicates a separate parameter group as defined in the PEST control file._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cor class of PESTools handles calculation and plotting of parameter correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x10b1f3e50>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x10b1e7090>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rV3fJpsCNA/vLkizq1w++P916vs8F/nz+olw5kzxJktSUUU682P1xD2f3xz38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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1f3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = example.cor\n",
    "cor.plot_heatmap(par_list=['kpkpx_tc2', 'kpkpx_tc34', 'kpkpx_tc21', 'kv_w', 'wr_drnc', 'kzkpz_tc47', 'kv_lo', 'sfrc', 'r_col'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 4.  Heatmap of parameter correlation values for a select number of parameters._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Identpar class produces visualizations of parameter identifiability (Doherty and Hunt, 2009), a qualitative indicator that describes the extent to which individual parameters are constrained by the observation data. In comparison to parameter sensitivity, identifiability is advantageous in that it accounts for both parameter sensitivity and correlation between parameters. Values of identifiability range from 0 (not informed by the observation data) to 1 (fully informed by the observation data). In sparsely parameterized models (or those that have a relatively small soluton space), identifiabilities can be visualized in decreasing order using a stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-06-01 15:43:50.030040 starting: pre-loading base components\n",
      "2015-06-01 15:43:50.030124 starting: loading jco: ../cc/Columbia.jco\n",
      "2015-06-01 15:43:50.075100 finished: loading jco: ../cc/Columbia.jco took: 0:00:00.044976\n",
      "2015-06-01 15:43:50.075162 starting: loading pst: ../cc/Columbia.pst\n",
      "2015-06-01 15:43:50.112987 finished: loading pst: ../cc/Columbia.pst took: 0:00:00.037825\n",
      "2015-06-01 15:43:50.113362 starting: loading parcov\n",
      "2015-06-01 15:43:50.194096 finished: loading parcov took: 0:00:00.080734\n",
      "2015-06-01 15:43:50.194486 starting: loading obscov\n",
      "2015-06-01 15:43:50.494349 finished: loading obscov took: 0:00:00.299863\n",
      "2015-06-01 15:43:50.494720 finished: pre-loading base components took: 0:00:00.464680\n",
      "2015-06-01 15:43:50.496291 starting: qhalf\n",
      "2015-06-01 15:43:50.502599 finished: qhalf took: 0:00:00.006308\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x10b8f3550>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x10b8f3990>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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srdZxaOvYoP2tssW1jkd2K7oxFBERKcPSSR+AzBNFN4bKGNaoo4xhFGUM+3hl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\nZfr+tGkewxiax3B6/LD6Kt9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vPr77qpuz1NE8hnHSzGN4beDHhwPXq0fzGMYp7f9qoX6r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\nIiIiIsqwMCQiIiIiACwMiYiIiCjz/6ElY9M1uBszAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8f3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ip = example.IdentPar(par_info_file='../cc/parinfo.csv')\n",
    "# Line as in official paper, works but redundant\n",
    "#ip = example.IdentPar('../cc/columbia.jco',par_info_file='../cc/parinfo.csv') \n",
    "ip.plot_bar(nsingular=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 5. Stacked bar chart showing the 20 most identifiable parameters from a highly parameterized model, based on a solution space of 50 singular values. The colors indicate identifiability that is gained by the addition of singular values to the solution space. For example, the recharge parameter on the far left (r_col) is almost fully identifiable with only one singular value, therefore it is well-constrained by the observation data._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In highly parameterized models, however, the solution space may contain hundreds of parameters, which in groundwater modeling are typically distributed spatially. In this case, identifiability may be better visualized on a map. PESTools can plot total identifiabilities (equivalent to the total height of the bars in Figure 5) spatially for a selected group of parameters. The location coordinates of each parameter are supplied to PESTools in csv format via a parameter information file (as shown above). Figure 6 shows the total identifiabilities for pilot points in zone 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pestools.plots.SpatialPlot at 0x111f9fd90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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87ne/633qqaf+fc6cOX8t5vOnjTMQCCPSOk6QZyRkGQt5xhIlzw0bNlQ2zDy0ZseOHV1e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\nSZKm9v8BVjTV72zmO8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d7e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ip.plot_spatial(nsingular=50, group_col='zone', groupinfo=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Figure 6. Map of pilot point total identifiability values for a given zone in a highly parameterized model. The large circles indicate identifiabilities close to 1; the smallest points indicate identifiabilities close to 0._**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**SUMMARY AND FUTURE WORK**</center>\n",
    "\n",
    "PESTools is an open-source package aimed at leveraging the data analysis and visualization capabilities of Python to streamline parameter estimation workflows, allowing for deeper insight into model behavior, enhanced troubleshooting, and improved transparency in the calibration process. PESTools is a work in progress. It can be downloaded at https://github.com/PESTools. Additional features are being developed, including adjustment of observation weights and group assignments, support for the inclusion of GIS data in map figures, and expanded integration with the linear analysis capabilities of pyemu. Many additional capabilities are possible. We encourage contributions to the project via GitHub."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**ACKNOWLEDGMENTS**</center>\n",
    "\n",
    "Jeremey White, Mike Fienen, Jonathon Carter, and Dave Dahlstrom have all contributed to this project through code development or testing.  Their participation is greatly appreciated. The use of trade names is for identification purposes and does not constitute endorsement by the U.S. Geological Survey."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>**REFERENCES**</center>\n",
    "\n",
    "Doherty, J., 2010a, PEST, Model-independent parameter estimation—User manual (5th ed., with slight additions): Brisbane, Australia, Watermark Numerical Computing, accessed November 13, 2014 at http://www.pesthomepage.org/.\n",
    "\n",
    "Doherty, J., 2010b, PEST, Model Independent Parameter Estimation. Addendum to User Manual (5th Edition): Brisbane, Australia, Watermark Numerical Computing, accessed November 13, 2014 at http://www.pesthomepage.org/.\n",
    "\n",
    "Doherty, J., and Hunt, R.J., 2009. Two statistics for evaluating parameter identifiability and error reduction, Journal of Hydrology 366 (2009), 119–127. doi:10.1016/j.jhydrol.2008.12.018.\n",
    "\n",
    "Welter, D.E., Doherty, J.E., Hunt, R.J., Muffels, C.T., Tonkin, M.J., and Schreüder, W.A., 2012, Approaches in highly parameterized inversion: PEST++, a Parameter ESTimation code optimized for large environmental models. USGS Techniques and Methods, book 7, section C5, 47 p.\n",
    "\n",
    "White, J.T., 2015. Pyemu: linear-based computer model uncertainty analyses. Accessed April 6, 2015, at https://github.com/jtwhite79/pyemu. \n"
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