{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Example usage and output\n",
    "************************"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Imports\n",
    "======="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:56.777660Z",
     "start_time": "2018-04-27T14:50:56.004017Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#import mumpce_py as mumpce\n",
    "import mumpce\n",
    "import numpy as np\n",
    "import mumpce.toy"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Models\n",
    "======"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Creating a model directly\n",
    "+++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:56.784764Z",
     "start_time": "2018-04-27T14:50:56.779359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([{'parameter_number': 0, 'parameter_name': 'Parameter 1'},\n",
       "       {'parameter_number': 1, 'parameter_name': 'Parameter 2'},\n",
       "       {'parameter_number': 2, 'parameter_name': 'Parameter 3'},\n",
       "       {'parameter_number': 3, 'parameter_name': 'Parameter 4'},\n",
       "       {'parameter_number': 4, 'parameter_name': 'Parameter 5'},\n",
       "       {'parameter_number': 5, 'parameter_name': 'Parameter 6'},\n",
       "       {'parameter_number': 6, 'parameter_name': 'Parameter 7'}], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_number = 0\n",
    "my_model = mumpce.toy.toy_model(experiment_number)\n",
    "my_model.get_model_parameter_info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Evaluation and sensitivity analysis\n",
    "+++++++++++++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:56.859480Z",
     "start_time": "2018-04-27T14:50:56.786181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2214027581601699"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:56.932196Z",
     "start_time": "2018-04-27T14:50:56.860539Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value =  1.22140275816\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.22140e+00   1.22140e+00   1.0000e+00\n",
      "     1  1.22140e+00   1.22140e+00   6.0000e-01\n",
      "     2  1.22140e+00   1.22140e+00   4.0000e-01\n",
      "     3  1.22140e+00   1.22140e+00   1.0000e-01\n",
      "     4  1.22140e+00   1.22140e+00   3.0000e-02\n",
      "     5  1.22140e+00   1.22140e+00   1.0000e-02\n",
      "     6  1.22140e+00   1.22140e+00   2.0000e-02\n"
     ]
    }
   ],
   "source": [
    "V,S = my_model.sensitivity(0.0000001)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Measurements\n",
    "============"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true,
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Initializing measurements\n",
    "+++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.031205Z",
     "start_time": "2018-04-27T14:50:56.933278Z"
    }
   },
   "outputs": [],
   "source": [
    "toy_measurements = mumpce.toy.toy_initialize('mumpce_toy_experiments.xlsx',mumpce.toy.toy_model)\n",
    "toy_apps = mumpce.toy.toy_initialize('mumpce_toy_apps.xlsx',mumpce.toy.toy_app)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Sensitivity analysis with Measurements\n",
    "++++++++++++++++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.087935Z",
     "start_time": "2018-04-27T14:50:57.033094Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1\n",
      "Value =  1.22140275816\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.22140e+00   1.22140e+00   1.0000e+00\n",
      "     1  1.22140e+00   1.22140e+00   6.0000e-01\n",
      "     2  1.22140e+00   1.22140e+00   4.0000e-01\n",
      "     3  1.22140e+00   1.22140e+00   1.0000e-01\n",
      "     4  1.22140e+00   1.22140e+00   3.0000e-02\n",
      "     5  1.22140e+00   1.22140e+00   1.0001e-02\n",
      "     6  1.22140e+00   1.22140e+00   2.0000e-02\n",
      "Experiment 2\n",
      "Value =  0.548811636094\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  5.48812e-01   5.48812e-01   2.0000e-02\n",
      "     1  5.48812e-01   5.48812e-01   6.0000e-01\n",
      "     2  5.48812e-01   5.48812e-01   1.0000e+00\n",
      "     3  5.48812e-01   5.48812e-01   2.9999e-02\n",
      "     4  5.48812e-01   5.48812e-01   1.0000e-01\n",
      "     5  5.48812e-01   5.48812e-01   1.0000e-02\n",
      "     6  5.48812e-01   5.48812e-01   4.0000e-01\n",
      "Experiment 3\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.64872e+00   1.64872e+00   1.0000e+00\n",
      "     1  1.64872e+00   1.64872e+00   4.0000e-01\n",
      "     2  1.64872e+00   1.64872e+00   6.0000e-01\n",
      "     3  1.64872e+00   1.64872e+00   1.0000e-01\n",
      "     4  1.64872e+00   1.64872e+00   2.0000e-02\n",
      "     5  1.64872e+00   1.64872e+00   3.0001e-02\n",
      "     6  1.64872e+00   1.64872e+00   9.9998e-03\n",
      "Experiment 4\n",
      "Value =  2.22554092849\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  2.22554e+00   2.22554e+00   4.0000e-01\n",
      "     1  2.22554e+00   2.22554e+00   1.0000e+00\n",
      "     2  2.22554e+00   2.22554e+00   1.0000e-02\n",
      "     3  2.22554e+00   2.22554e+00   1.9999e-02\n",
      "     4  2.22554e+00   2.22554e+00   6.0000e-01\n",
      "     5  2.22554e+00   2.22554e+00   3.0000e-02\n",
      "     6  2.22554e+00   2.22554e+00   1.0000e-01\n",
      "Experiment 5\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.64872e+00   1.64872e+00   3.0001e-02\n",
      "     1  1.64872e+00   1.64872e+00   1.0000e-01\n",
      "     2  1.64872e+00   1.64872e+00   1.0000e+00\n",
      "     3  1.64872e+00   1.64872e+00   2.0000e-02\n",
      "     4  1.64872e+00   1.64872e+00   6.0000e-01\n",
      "     5  1.64872e+00   1.64872e+00   9.9998e-03\n",
      "     6  1.64872e+00   1.64872e+00   4.0000e-01\n",
      "Application 1\n",
      "Value =  1.0\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.00000e+00   1.00000e+00   1.0000e+00\n",
      "     1  1.00000e+00   1.00000e+00   2.0000e-01\n",
      "     2  1.00000e+00   1.00000e+00   1.0000e-02\n",
      "     3  1.00000e+00   1.00000e+00   1.0000e-02\n",
      "     4  1.00000e+00   1.00000e+00   1.0000e-02\n",
      "     5  1.00000e+00   1.00000e+00   1.0000e-02\n",
      "     6  1.00000e+00   1.00000e+00   1.0000e-02\n"
     ]
    }
   ],
   "source": [
    "for meas in toy_measurements:\n",
    "    print(meas.name)\n",
    "    meas.evaluate_sensitivity(perturbation=0.0000000001)\n",
    "for meas in toy_apps:\n",
    "    print (meas.name)\n",
    "    meas.evaluate_sensitivity(perturbation=0.0000000001)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Projects\n",
    "========"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Create the Project\n",
    "++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.170079Z",
     "start_time": "2018-04-27T14:50:57.089227Z"
    }
   },
   "outputs": [],
   "source": [
    "my_project = mumpce.Project(measurement_list=toy_measurements,\n",
    "                             application_list=toy_apps,\n",
    "                             model=mumpce.toy.toy_model,\n",
    "                             parameter_uncertainties=mumpce.toy.parameter_uncertainties)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Initializing Measurements within Projects\n",
    "+++++++++++++++++++++++++++++++++++++++++\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.244707Z",
     "start_time": "2018-04-27T14:50:57.171164Z"
    }
   },
   "outputs": [],
   "source": [
    "my_project_with_init = mumpce.Project(parameter_uncertainties=mumpce.toy.parameter_uncertainties,\n",
    "                                      initialize_function=mumpce.toy.toy_initialize)\n",
    "my_project_with_init.measurement_initialize('mumpce_toy_experiments.xlsx')\n",
    "my_project_with_init.application_initialize('mumpce_toy_apps.xlsx')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. note:: This is exactly equivalent to creating the :py:class:`.Project` with the measurement and application lists already created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.306096Z",
     "start_time": "2018-04-27T14:50:57.246163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1 (Active): 0\n",
      "Experiment 1 (Active): 0\n"
     ]
    }
   ],
   "source": [
    "print(my_project[0])\n",
    "print(my_project['Experiment 1'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Sensitivity analysis with Projects\n",
    "++++++++++++++++++++++++++++++++++\n",
    "\n",
    "Do sensitivity over all measurements in the measurement and application lists\n",
    "\n",
    ".. note:: Calling :py:func:`.Project.find_sensitivity` is the same as calling :py:func:`.Measurement.evaluate_sensitivity` for each measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.368534Z",
     "start_time": "2018-04-27T14:50:57.307245Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1\n",
      "Value =  1.22140275816\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.28247e+00   1.16324e+00   9.7619e-01\n",
      "     1  1.25769e+00   1.18617e+00   5.8557e-01\n",
      "     2  1.24547e+00   1.19780e+00   3.9035e-01\n",
      "     3  1.22738e+00   1.21546e+00   9.7581e-02\n",
      "     4  1.22319e+00   1.21962e+00   2.9274e-02\n",
      "     5  1.22200e+00   1.22081e+00   9.7580e-03\n",
      "     6  1.22260e+00   1.22021e+00   1.9516e-02\n",
      "Experiment 2\n",
      "Value =  0.548811636094\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  5.49347e-01   5.48276e-01   1.9516e-02\n",
      "     1  5.65115e-01   5.32979e-01   5.8557e-01\n",
      "     2  5.76252e-01   5.22678e-01   9.7619e-01\n",
      "     3  5.49616e-01   5.48009e-01   2.9274e-02\n",
      "     4  5.51496e-01   5.46140e-01   9.7581e-02\n",
      "     5  5.49079e-01   5.48544e-01   9.7580e-03\n",
      "     6  5.59627e-01   5.38205e-01   3.9035e-01\n",
      "Experiment 3\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.73116e+00   1.57021e+00   9.7619e-01\n",
      "     1  1.68121e+00   1.61686e+00   3.9035e-01\n",
      "     2  1.69770e+00   1.60116e+00   5.8557e-01\n",
      "     3  1.65679e+00   1.64070e+00   9.7581e-02\n",
      "     4  1.65033e+00   1.64711e+00   1.9516e-02\n",
      "     5  1.65114e+00   1.64631e+00   2.9274e-02\n",
      "     6  1.64953e+00   1.64792e+00   9.7580e-03\n",
      "Experiment 4\n",
      "Value =  2.22554092849\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  2.26940e+00   2.18253e+00   3.9035e-01\n",
      "     1  2.33682e+00   2.11956e+00   9.7619e-01\n",
      "     2  2.22663e+00   2.22446e+00   9.7580e-03\n",
      "     3  2.22771e+00   2.22337e+00   1.9516e-02\n",
      "     4  2.29165e+00   2.16133e+00   5.8557e-01\n",
      "     5  2.22880e+00   2.22229e+00   2.9274e-02\n",
      "     6  2.23643e+00   2.21471e+00   9.7581e-02\n",
      "Experiment 5\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.65114e+00   1.64631e+00   2.9274e-02\n",
      "     1  1.65679e+00   1.64070e+00   9.7581e-02\n",
      "     2  1.73116e+00   1.57021e+00   9.7619e-01\n",
      "     3  1.65033e+00   1.64711e+00   1.9516e-02\n",
      "     4  1.69770e+00   1.60116e+00   5.8557e-01\n",
      "     5  1.64953e+00   1.64792e+00   9.7580e-03\n",
      "     6  1.68121e+00   1.61686e+00   3.9035e-01\n",
      "Application 1\n",
      "Value =  1.0\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.05000e+00   9.52381e-01   9.7619e-01\n",
      "     1  1.00981e+00   9.90289e-01   1.9516e-01\n",
      "     2  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     3  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     4  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     5  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     6  1.00049e+00   9.99512e-01   9.7580e-03\n"
     ]
    }
   ],
   "source": [
    "my_project.find_sensitivity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.423597Z",
     "start_time": "2018-04-27T14:50:57.369838Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value =  1.22140275816\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.28247e+00   1.16324e+00   9.7619e-01\n",
      "     1  1.25769e+00   1.18617e+00   5.8557e-01\n",
      "     2  1.24547e+00   1.19780e+00   3.9035e-01\n",
      "     3  1.22738e+00   1.21546e+00   9.7581e-02\n",
      "     4  1.22319e+00   1.21962e+00   2.9274e-02\n",
      "     5  1.22200e+00   1.22081e+00   9.7580e-03\n",
      "     6  1.22260e+00   1.22021e+00   1.9516e-02\n",
      "Value =  0.548811636094\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  5.49347e-01   5.48276e-01   1.9516e-02\n",
      "     1  5.65115e-01   5.32979e-01   5.8557e-01\n",
      "     2  5.76252e-01   5.22678e-01   9.7619e-01\n",
      "     3  5.49616e-01   5.48009e-01   2.9274e-02\n",
      "     4  5.51496e-01   5.46140e-01   9.7581e-02\n",
      "     5  5.49079e-01   5.48544e-01   9.7580e-03\n",
      "     6  5.59627e-01   5.38205e-01   3.9035e-01\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.73116e+00   1.57021e+00   9.7619e-01\n",
      "     1  1.68121e+00   1.61686e+00   3.9035e-01\n",
      "     2  1.69770e+00   1.60116e+00   5.8557e-01\n",
      "     3  1.65679e+00   1.64070e+00   9.7581e-02\n",
      "     4  1.65033e+00   1.64711e+00   1.9516e-02\n",
      "     5  1.65114e+00   1.64631e+00   2.9274e-02\n",
      "     6  1.64953e+00   1.64792e+00   9.7580e-03\n",
      "Value =  2.22554092849\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  2.26940e+00   2.18253e+00   3.9035e-01\n",
      "     1  2.33682e+00   2.11956e+00   9.7619e-01\n",
      "     2  2.22663e+00   2.22446e+00   9.7580e-03\n",
      "     3  2.22771e+00   2.22337e+00   1.9516e-02\n",
      "     4  2.29165e+00   2.16133e+00   5.8557e-01\n",
      "     5  2.22880e+00   2.22229e+00   2.9274e-02\n",
      "     6  2.23643e+00   2.21471e+00   9.7581e-02\n",
      "Value =  1.6487212707\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.65114e+00   1.64631e+00   2.9274e-02\n",
      "     1  1.65679e+00   1.64070e+00   9.7581e-02\n",
      "     2  1.73116e+00   1.57021e+00   9.7619e-01\n",
      "     3  1.65033e+00   1.64711e+00   1.9516e-02\n",
      "     4  1.69770e+00   1.60116e+00   5.8557e-01\n",
      "     5  1.64953e+00   1.64792e+00   9.7580e-03\n",
      "     6  1.68121e+00   1.61686e+00   3.9035e-01\n",
      "Value =  1.0\n",
      "Param  Value+       Value-           Sensitivity\n",
      "     0  1.05000e+00   9.52381e-01   9.7619e-01\n",
      "     1  1.00981e+00   9.90289e-01   1.9516e-01\n",
      "     2  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     3  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     4  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     5  1.00049e+00   9.99512e-01   9.7580e-03\n",
      "     6  1.00049e+00   9.99512e-01   9.7580e-03\n"
     ]
    }
   ],
   "source": [
    "for meas in my_project: \n",
    "    meas.evaluate_sensitivity()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. note:: The :py:class:`.Project` acts as an iterable over its measurements and applications"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Response surface analysis\n",
    "+++++++++++++++++++++++++"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Active parameters\n",
    "-----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.485464Z",
     "start_time": "2018-04-27T14:50:57.424667Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1\n",
      "Experiment 2\n",
      "Experiment 3\n",
      "Experiment 4\n",
      "Experiment 5\n"
     ]
    }
   ],
   "source": [
    "my_project.find_active_parameters(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.581735Z",
     "start_time": "2018-04-27T14:50:57.486549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 4 6]\n",
      "[ 2.71828183  2.71828183  2.71828183  2.71828183  2.71828183]\n"
     ]
    }
   ],
   "source": [
    "print(my_project.active_parameters)\n",
    "print(my_project.active_parameter_uncertainties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.655568Z",
     "start_time": "2018-04-27T14:50:57.582896Z"
    }
   },
   "outputs": [],
   "source": [
    "my_project.set_active_parameters()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Response surface evaluation\n",
    "---------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.775211Z",
     "start_time": "2018-04-27T14:50:57.657096Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a1a255794324953b34e3ecc0bb4de5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "311b39cbd261413295c3f2a20839a743",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce45efdd309040e3b9f86186f409bb01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62c77ddf9349467d831b3469077a08af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fdac8a9513d4a9788aa410a712f712c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d2d208c19034a8bb540db07b24fe5dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for meas in my_project:\n",
    "    meas.model.loglevel=False # Suppress output in sensitivity analysis\n",
    "my_project.make_response()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:57.803644Z",
     "start_time": "2018-04-27T14:50:57.777449Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    0.6   0.4   0.03  0.02]]\n",
      "[[ 0.02  0.6   1.    0.1   0.4 ]]\n",
      "[[ 1.    0.4   0.6   0.02  0.01]]\n",
      "[[ 0.4   1.    0.01  0.6   0.1 ]]\n",
      "[[ 0.03  0.1   1.    0.6   0.4 ]]\n",
      "[[ 1.    0.2   0.01  0.01  0.01]]\n"
     ]
    }
   ],
   "source": [
    "for meas in my_project:\n",
    "    print (np.array([meas.response.a]))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Solving the Project\n",
    "+++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:58.015087Z",
     "start_time": "2018-04-27T14:50:57.805782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([-0.58888728,  1.12003551,  0.00704031, -1.62385897,  0.8043988 ]),\n",
       " array([[ 0.01488721, -0.01894579, -0.0104734 ,  0.01325883,  0.04734255],\n",
       "        [-0.01894579,  0.04284732, -0.00131388, -0.04801795, -0.04545704],\n",
       "        [-0.0104734 , -0.00131388,  0.02343655,  0.01588293, -0.05618227],\n",
       "        [ 0.01325883, -0.04801795,  0.01588293,  0.08171971,  0.00875019],\n",
       "        [ 0.04734255, -0.04545704, -0.05618227,  0.00875019,  0.2066927 ]]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_project.run_optimization()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Parameter uncertainty analysis\n",
    "++++++++++++++++++++++++++++++\n",
    "\n",
    "Show the uncertainty in the parameters and in the associated experimental and application targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:58.023465Z",
     "start_time": "2018-04-27T14:50:58.017552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Name                  Value  Unc    OptVal OptUnc MdlVal MdlUnc\n",
      "Experiment 1        :   0.40   0.05   0.25   0.04   0.20   0.62 \n",
      "Experiment 2        :   0.25   0.05   0.23   0.05  -0.60   0.62 \n",
      "Experiment 3        :   0.20   0.05   0.34   0.04   0.50   0.62 \n",
      "Experiment 4        :   0.70   0.08   0.79   0.08   0.80   0.62 \n",
      "Experiment 5        :   0.40   0.80  -0.05   0.24   0.50   0.62 \n",
      "Application 1       :   0.00   0.00  -0.37   0.10   0.00   0.51 \n"
     ]
    }
   ],
   "source": [
    "print(my_project.validate_solution())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:58.105388Z",
     "start_time": "2018-04-27T14:50:58.025749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.59    0.24    0.55    1.28\n",
      "Parameter 2                    :       1    2.72    1.12    0.41     3.1    1.51\n",
      "Parameter 3                    :       1    2.72    0.01    0.31       1    1.36\n",
      "Parameter 5                    :       1    2.72   -1.62    0.57     0.2    1.77\n",
      "Parameter 7                    :       1    2.72    0.80    0.91     2.2    2.48\n"
     ]
    }
   ],
   "source": [
    "print(my_project.interpret_model())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "This information can also be accessed by the :func:`__str__()` method of :py:class:`Project`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:50:58.175433Z",
     "start_time": "2018-04-27T14:50:58.107847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "project\n",
      "6 measurements\n",
      "Experiment 1 (Active): 0\n",
      "Experiment 2 (Active): 1\n",
      "Experiment 3 (Active): 2\n",
      "Experiment 4 (Active): 3\n",
      "Experiment 5 (Active): 4\n",
      "Application 1 (Application): 0\n",
      "\n",
      "Name                  Value  Unc    OptVal OptUnc MdlVal MdlUnc\n",
      "Experiment 1        :   0.40   0.05   0.25   0.04   0.20   0.62 \n",
      "Experiment 2        :   0.25   0.05   0.23   0.05  -0.60   0.62 \n",
      "Experiment 3        :   0.20   0.05   0.34   0.04   0.50   0.62 \n",
      "Experiment 4        :   0.70   0.08   0.79   0.08   0.80   0.62 \n",
      "Experiment 5        :   0.40   0.80  -0.05   0.24   0.50   0.62 \n",
      "Application 1       :   0.00   0.00  -0.37   0.10   0.00   0.51 \n",
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.59    0.24    0.55    1.28\n",
      "Parameter 2                    :       1    2.72    1.12    0.41     3.1    1.51\n",
      "Parameter 3                    :       1    2.72    0.01    0.31       1    1.36\n",
      "Parameter 5                    :       1    2.72   -1.62    0.57     0.2    1.77\n",
      "Parameter 7                    :       1    2.72    0.80    0.91     2.2    2.48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(my_project)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Here we show the joint probability density functions (PDFs) of several pairs of parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:00.929878Z",
     "start_time": "2018-04-27T14:50:58.177993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa95ae5c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors_list = [[0,1],\n",
    "                [1,2],\n",
    "                [2,3],\n",
    "                [3,4],\n",
    "               ]\n",
    "fig = my_project.plot_pdfs(factors_list)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Because there is an outlier, the parameter values estimated for the constrained model are preposterous. Consistency analysis will fix this."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Consistency analysis\n",
    "++++++++++++++++++++\n",
    "\n",
    "Remove the inconsistent measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:00.941945Z",
     "start_time": "2018-04-27T14:51:00.931868Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      "Experiment 1 \n",
      "    Uncertainty Ratio:   0.86\n",
      "    Normalized Score:  -1.47\n",
      "    Weighted Consistency   1.08\n",
      "(5,)\n",
      "Both actual and predicted relative reductions in the sum of squares\n",
      "  are at most 0.000000\n",
      "No inconsistent measurements\n"
     ]
    }
   ],
   "source": [
    "my_project.remove_inconsistent_measurements()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "The parameter values are much more reasonable with the outlier removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:01.079907Z",
     "start_time": "2018-04-27T14:51:00.943236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "project\n",
      "6 measurements\n",
      "Experiment 2 (Active): 1\n",
      "Experiment 3 (Active): 2\n",
      "Experiment 4 (Active): 3\n",
      "Experiment 5 (Active): 4\n",
      "Application 1 (Application): 0\n",
      "Experiment 1 (Inconsistent): 0\n",
      "\n",
      "Name                  Value  Unc    OptVal OptUnc MdlVal MdlUnc\n",
      "Experiment 2        :   0.25   0.05   0.24   0.05  -0.60   0.62 \n",
      "Experiment 3        :   0.20   0.05   0.21   0.05   0.50   0.62 \n",
      "Experiment 4        :   0.70   0.08   0.70   0.08   0.80   0.62 \n",
      "Experiment 5        :   0.40   0.80   1.11   0.31   0.50   0.62 \n",
      "Application 1       :   0.00   0.00  -0.68   0.11   0.00   0.51 \n",
      "Experiment 1        :   0.40   0.05  -0.15   0.08   0.20   0.62 \n",
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.74    0.25    0.48    1.28\n",
      "Parameter 2                    :       1    2.72    0.26    0.51     1.3    1.67\n",
      "Parameter 3                    :       1    2.72    0.57    0.36     1.8    1.44\n",
      "Parameter 5                    :       1    2.72   -0.17    0.77    0.84    2.15\n",
      "Parameter 7                    :       1    2.72    0.36    0.92     1.4    2.52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(my_project)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. note:: Application 1 doesn't actually have a value, so the 0.00 value and 0.00 uncertainty are placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:03.645696Z",
     "start_time": "2018-04-27T14:51:01.080983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PP/+cVlZWTE5OJkkeOHCAmzdvpqIoel2nMDwA3jRy7Pnvj8SiwktRFJ44cYLnzp0jmRZrzM3N+fXXX5MkdTod33//fR45ckTdvnPnDpOSkvJ8vsjISPXzV69e5YQJE/j48WOS5MqVKwmAd+/eJUmeP3+ea9asYXx8vF7XKQxPYpEwtIsXL6qxhiSrVq3KQYMGqdufffYZt2/frm7fuXMnz7FBURQ+ffqUcXFxJMn79+9zypQpvHbtGsm0fhAAnjhxgiR548YNLl++nBEREXk6n8g/EosKh1mzZhEAt23bli/HDwoKYseOHQmAr732Gn18fPJ8rGvXrnHPnj3qdqdOndiqVSt1e8aMGfz555/V7eDgYMbGxub42L169SIA1q1blydOnOCzZ88YExNDkoyJieHUqVN5+vRpkmRAQAABcP369STJiIgIzp8/n8HBwVme59ixY9RqtXznnXfkfq6A5DQWSYASOaYoCv39/dWkUHx8PK2srDhp0iT1/d9//50PHz40SvuePHnCf/75R93u378/69Wrp27v3LmTp06dMkbTxH9IZ0no686dOzxz5oy6XaNGDb7zzjvq9p9//snbt28bo2mMiYnhyZMn1Q7PF198QTs7OzXBfeTIEe7du1c6RCZAYpHQ16NHj3j48GF1u0uXLmzatKm6ffjwYfr7+xujaYyPj+fZs2fVJNMvv/xCAHzw4AFJ0svLi9u3b89zQl0YjsQi0+fr60sLCwsOHDgwX46/efNm2tvbs2TJkly7dm2u+whRUVHcu3evuv3hhx+yVKlS6sN2T09Penl5GbTNBw4cYM2aNQmAH3/8MZ89e5bpfsnJyfT29lYfth08eJAA6OnpSZK8efMm169fryahnrdo0SIC4MKFCw3adpG5QpFEArANwCMAKQBCAIzMav/iEKBMUfoTK0VR6OzszB49eqjveXt7q50TU5Oamqp2lEiyTp067N27t7rt7e2d51FRQj+m1lmSWFQ4PP/0vFu3bqxTp4667evry6ioKGM0K1uKovD+/fvqdo8ePdikSRN128fHR0YpGYnEIpEXz8eiMWPG0M7OjomJiSTJ69evMywszFhNy9bzsWjs2LEsVaqUmuC+du0anz59aqymFWsSi0ybTqejq6srHR0dGR4ebtBjJyUlccyYMeroo/RRzDkRFRVFnU5Hkpw3bx4BqH/jt2/fznakjyHExcVx4sSJ1Gg0rFOnDv/9998cfS40NFSNPQsWLKBGo2FoaCjJtLanx1FFUdi3b1+am5vz/Pnz+XMRQlUokki5/SnqAcoUjR07ljVq1FCz4efOncvQASlMnj17pgbTyMhIWlhY8KuvvjJyq4onU+ss5fZHYlHBmz9/Pu3s7NSh1j4+Prx582a+nzd9usjdu3d55coVenl58dy5c7xw4QJ9fX15+/ZtRkREqJ24nEhMTGRQUBDJtI5pxYoV8+3JpsiaxCKRW9u3b6dWq1X/hgMDA+nj41MoRxbqdLoMcbR169Z89dVXjdii4ktikWn76aefCIC//fabQY/75MkTvvbaawTAL7/8MlcPtz09PWllZcWjR4+STCsj8s8//+SqP2JIx48fZ6VKlWhlZcV169bl6rOKojAgIEDdHjx4MF955RX1WqKioli9enXWqFFDEt35LKexSL+1AEWRc+3aNSxcuBA//fQT7O3t0atXL9SvXx86nQ4WFhZo3bq1sZuYZ3Z2drCzs1P//44dO9CgQQMAaYVyR40ahTVr1qBRo0bGbKYQAmkFIGfPno0vv/wSNWrUQJcuXaDT6aAoCgCgadOmBj0fSdy5cwdeXl7w9vaGv78/bt26hZCQECQmJmb7eTMzMzg6OsLJyQk1a9ZE3bp10bhxYzRq1Ah16tSBVqtV97WyskLNmjUBABqNBps2bULJkiUBAJGRkXBzc8OPP/6ITp06GfQahRC5Fx0djblz56J3795o164d2rZtiy+++AI2NjYAAGdnZyO3MO+0Wi1q166tbi9dulQtYKvT6dCpUyeMGzcOAwYMMFYThTC6x48f45tvvkG3bt0waNAggx333r176N69O+7evYtt27Zle+zk5GQsWLAA9evXR79+/eDi4oKPP/4YVatWBQBUrFgRFStWNFj7cqtz587w8fHB4MGDMXLkSPj4+GDx4sUwN88+3aDRaFC3bl11e+rUqXj33XfVvtPw4cPRp08fLF++HOPHj1cXShHGI0kkAZ1Oh+TkZNja2iIqKgq7d+/G6NGj0aZNG/Ts2dPYzcsXlpaW6NOnj7odHh6O+Ph4VKhQAQBw+/ZtODg4GHz5TCHEy5HEs2fPUKJECZDE1q1b0a1bN9SoUQMtW7ZEy5YtDXq+5ORkHD16FHv27MHff/+NBw8eAEhL8tSvXx8uLi7o3bs3KlSogNKlS6NkyZKwsbGBVqtFamoqkpKSEBsbi6dPn+LJkycIDQ1FcHAwLl26hJ07d6oJL1tbW7Ro0QKvvfYaXnvtNbRr1w4lSpQAkJZ86tq1q9qmR48eQaPRoEyZMgDSVkfR6XSoUqWKQa9dCJG12NhYlChRApaWltiwYQMqVaqEdu3awcnJCXPnzjV28/JFq1at1P8fHh4OKysrdZXK2NhYPHjwAPXq1TNW84QwiunTpyM+Ph7Lly+HRqMxyDFv3bqFLl26IDY2FkePHkX79u1fum96LLKwsMD27dvh5uaGfv36wcbGRl39zFQ4OjriwIED+OKLL7BkyRIEBQVhx44d6kP8nGrYsCEaNmwIAEhMTIRGo0HNmjXx9ddfY86cOWjatCkmTJiQH5cgcionw5VM5aeoD5U0hri4ODo7O3PatGkk04YTvqwoWnHSq1cvVqtWzWhDQos6yLBt8R+KorB9+/Z877331NfyKxYFBQVx8uTJLFu2LAGwRIkS7NevH1euXMl///1XnaOvj4SEBF66dIkbNmzg+PHj6erqSq1WSwA0Nzdnly5duHDhQt64cSPL44wbN462traMjo7Wu03iRRKLRGaGDh3KNm3aqFPUpF9ELl26lBqNJsOUE2E4EotMk7+/P83MzDhu3DiDHTMoKIiVK1dmuXLlsl197dtvv2XVqlXV4veFKRatWrWKZmZmbNWqlcFWhExKSmL16tUJgB4eHgY5psgop7HIzIj5K2Ekqamp8Pb2BpD2hHzgwIFo06YNgLThhLnNFhdFc+fOxfLly6HVakESK1asQGRkpLGbJUSRQhJeXl4A0mJP37590b17d/V9Q8ei27dvY+jQoahduzaWLVuGTp06Ye/evQgPD8fOnTsxduxYNG/eHBYWFnqfy9raGi4uLhg2bBiWLl0KLy8vPH36FEeOHMGkSZMQFhaGKVOmoG7dumjatClmz56Nu3fvvnCciRMnwt3dHQ4ODgCAX3/9Fffv39e7fUKIjC5evKiOHnRzc8OAAQPUbekXAYMHD8aaNWvUKSc7d+7E9evXjdwqIfLX1KlTYW9vj2+//dYgxwsNDUXXrl2RkJCA48ePZzo138/PD3FxcQCADh06YMSIEUhJSQFQuGLRmDFj4OHhgcuXL6NLly548uSJ3se0tLTExo0bodFocOjQIQDA4cOHcf78eb2PLXIpJ5kmU/kpqlnugvbVV1/Rysoqw8pl4uWuXr1KjUbDFStWGLspRQbkiZsguXr1agKgt7d3vp4nMTGR06dPp6WlJW1sbDh58mSTiH/37t3jkiVL2K5dOwIgAHbo0IFbtmxhQkLCC/tHRESo7ReGIbFIkOThw4cJgH/88Yexm1IopKSksEqVKuzbt6+xm1JkSCwyPRcvXiQAzpw50yDHi42NpYuLC+3s7HjhwoVM97lx4wbNzMy4YMECg5zTFBw6dIjW1tZs1qwZIyMjDXLMiRMnEgDPnTvHtm3bsnnz5oVycQNTlNNYZPSgk5ufohigCsrNmzcZEhJCMu3G5ffff5c/tly4evWqOpT0+PHj3LNnj/z+9CCdpeLr0aNH6nSI2NhYuru7G2T62MsEBASwSZMmBMDBgwercdDU3L17l7Nnz2bt2rUJgGXKlOHXX3/9wmqYd+/eVYeF+/n5cd26dUxNTTVGk4sEiUXFV3R0tLoUtU6n4y+//MK4uDgjt6rwCA8PV1e8DQsL448//qj2k0TuSSwyPb169WKZMmUMshpYamoq33nnHZqZmXH//v0Z3ktMTOS5c+fU7XXr1hls+pepOHjwIC0tLdm6dWuDTMmLiYlhpUqV6OLiwujoaLU0QEJCAmfPns2YmBi9z1FcSRJJqGJiYujg4MBhw4YZuylFQp8+fVinTp18vfEt6qSzVDylpqaydu3a7NChQ4Gc78iRIyxZsiTLlSvHvXv3Fsg59ZWamsojR46wb9++NDMzo7m5OYcPH87r16+/sO/EiRNZpkyZItfZLEgSi4qvrl27snr16rlaUltkbvny5bSwsJB6SXqQWGRafH19CYAzZswwyPFmz55NAFyyZMkL740ePZr29vZF/rvcw8ODZmZmfOuttwxSc/a3334jALq7u6uv/fXXX9RoNDx27Jjexy+uchqLNGn7Fg4tW7Zkei0fkb3bt2+ry0j/9ddfePXVV9XVx0TepaSkICQkBDVq1IBOp8PWrVsxZMiQDEt4i6xpNJpLJA271FYBkliUO7dv30aNGjWg0Whw/PhxODk5ZVhWOj/s3bsX/fr1Q7169bBv3z51Cdy8SEpKgo+PD3x9fREYGIjg4GA8efIEMTExSExMBElYWFjAzs4OpUuXxiuvvAInJyfUqlULDRo0QIMGDdQVjnLjzp07WLJkCdauXYvExET873//w3fffYc6deoASHsIdOvWLdSuXRsksXnzZgwYMCBP5yquJBYVLyEhIXB0dISVlRUuXboEkgZf9bG4unnzphqbduzYATc3N5QqVcrIrSo8JBaZlqFDh2LXrl0IDg5WV0vNq1OnTqFLly4YNGgQtmzZAo1Gg8jISGg0GpQuXRp37txBYGAg3NzcDNR607Vy5Up88sknGD9+PJYuXarXsUiiffv2uH37NgIDA2Fvbw8gYyzav38/mjdvjkqVKund9uIip7FIkkhF1O7du9G/f3+cOnUqy2UjhX5+//13DBo0CIcOHSoWwd9QpLNUfHh7e6Ndu3ZYt24dhgwZUiDn9PT0RNeuXdGkSRMcPnw4TzcyDx8+xO+//469e/fi3LlzSExMBJBWMNvJyQnly5dHyZIl1YRNSkoK4uLiEBkZibCwMISFhSH9+9Xc3BwNGjRA69at0alTJ7z++usoX758jtsSHh6ORYsW4aeffkJSUhJGjRqFGTNmZDiGl5cXWrVqhVWrVmHMmDG5vt7iSmJR8fHgwQM0aNAAn3/+ucGK5IoXPXjwADVq1MCkSZMwb948Yzen0JBYZDoePXqEatWq4aOPPsLy5cv1OlZ0dDQaN24MGxsb/Pvvv7C3t0d8fDzq1auHrl27Yv369QZqdUZJSUnw8/PDtWvXcPfuXYSHhyM2NhaKosDKygplypRB5cqV4ezsjCZNmqBy5cr50o7MTJw4EUuXLsW6devwwQcf6HWs8+fPo02bNpgxYwamT5+e4b2EhARUq1YNnTp1wo4dO/Q6T3GS41iUk+FKpvJT1IZK5of02hjPnj3j9OnTGRsba+QWFW2KovD48ePq9r1796RWUg5Ahm0XeemxKDU1ld999x0fP35cIOd9+PAhy5cvz9q1a/PJkye5/vyZM2f49ttv08zMjADYqFEjTpw4kR4eHrxz506O6w8lJiby2rVr3LFjB6dOncru3bvTwcFBLaDt4uLCmTNn0t/fP8dtCw0N5SeffEJzc3OWKFGCixYtyjCt9tSpU+oQ8fv37xtkuHhRJ7Go6Hv+b3bBggUMCgoyYmuKh4sXL6o1SUJDQzNdKEBkJLHIdHz//fcEwMDAQL2PNXToUGq1Wnp5eWWIRatXr+alS5f0Pv7zAgMDOW/ePHbo0IGWlpZqfwMAS5UqxSpVqrBatWp85ZVXXni/SpUqHDp0KHfs2GGQmkVZSUlJYdeuXWllZWWQhVXeeecdlihRItM+382bN9XaktHR0YyKitL7fEVdTmOR0YNObn6KUoDKD+vXr2enTp2kVo+RPHjwgKVKlTLY/OmiTDpLRdt1NvMKAAAgAElEQVSsWbMIgNHR0QV6XkVR2KtXL9rY2OQqOUOSt27d4ptvvkkALFeuHKdOnaoWajQUnU5HLy8vzpkzh23btqVGoyEANmnShIsXL85xoi0gIIA9e/YkADZs2JD//PNPhvfj4uJYs2ZNDh8+3KDtL4okFhVt27ZtIwDevXvX2E0plhRFYbt27dixY0d5wJYNiUWmISUlhZUqVWKPHj30PtbBgwcJgNOmTWNQUBCbN29OLy8vA7Ty/6SkpHD79u1s3769mhBq3rw5P//8c+7YsYPXr1/PNImrKArDwsJ46tQpLlu2jAMGDGDZsmUJgLa2thw+fDgvXrxo0LY+Lzw8nFWqVGGtWrX0Llzu5+dHjUbDr776Ksv93nvvPTo7O0tSOxuSRCqGduzYwR49ehT4jZtIo9PpOHfuXN66dcvYTTF50lkq2oYNG0YA6so9BWX//v0EwIULF+bqc+7u7rSxsWGJEiU4f/78Aluh6eHDh/zpp5/o6upKALSysuLQoUPVFaOyoigK9+zZQycnJ2o0Go4fP15tt6IodHd359mzZ/P7Ego9iUVF208//UQAvHLlirGbUmzt3buXHh4exm6GyZNYZBr27dtHANy1a5dex0lISGCtWrVYp04dJiYmMjIykq6urhlmL+gjNTWVW7dupbOzMwGwVq1anD9/Pu/du5fnY+p0Op48eZKjRo2ivb09AbBz5848c+aMQdr8X2fOnKGZmRmHDh2q97EGDhxIe3v7LEegnzt3jqtXr9b7XEWdJJFMVGpqKi9fvswtW7ZwzZo13LNnzwtLOOfGnTt3eOTIEXVbnvSYji+//JK7d+82djNMknSWip4nT55kuFEo6FikKApbtmzJmjVr5niZ6dTUVE6YMIEA2K1bN4aEhORzK1/Oz8+PH3/8sdpxc3Nzy1HHLSYmhp988gkBsE6dOpkODV+2bBlXrlyZH80u9CQWFT3x8fHcvHmzui39ItPh4eHBqVOn5nhacHEiscg09O3bl+XLl9d7VsecOXMIgFOmTFH/vRsqFvn5+bFt27YEwKZNm3LXrl0G/5t6+vQpFy1axAoVKhAA+/Tpwzt37hj0HCT57bffGiRpd/XqVQLgd999l6P9z58/z1GjRsmopExIEsnEJCcnc8mSJaxSpUqGOajpP40bN+by5ctz/QS8R48erFq1ao5vmkTBiIuLo6urKydNmmTsppgk6SwVPWPHjqWdnR3Dw8ONcv7z588TAH/55Zcc7a8oCsePH08AHDdunMnUD4qOjuYPP/zA8uXLEwB79OhBX1/fbD937NgxVqlShRYWFly8eLHaWVUUhb179+Y777wjN9OZkFhU9CxcuJAajYZXr141dlPEf4wfP56tWrWSG7dMSCwyvqioKFpaWnL8+PF6HScsLIz29vZ89dVXCYD79u0zSPtSU1P5448/0tLSkmXLluX69evzPSEbFxfH2bNn09bWlra2tly2bJlBz5mcnMzmzZvzlVdeYUREhF7Hevvtt1mmTJkc1XRasmQJa9WqlafamUWdJJFMyIMHD9TpCp07d+amTZt4/fp1BgcH8/z581y0aBFbtWpFAKxYsSK3bt2abWc//f0HDx4YvG5HfkpNTVXbHhAQwPXr16vZ/tOnT/Obb75hYmIiybTCjKtXr1YTZLGxsep7hUFCQoJ6Y/ro0SOpVfUc6SwVHel/zzExMfk6fz4748aNo7W1dY7n1ru7uxMAx48fz+DgYG7cuFHtwFy9epXffPMNHz16RDKtMOMvv/yivh8fH68m/J88ecIzZ87wt99+488//8xFixZx6dKldHd35+7du+nt7Z2n+f5xcXFcsGABS5cuTY1Gw9GjR2eboHvy5An79OlDAOzbt69a2Fan0zE+Pp5kWic5/XUhsagoSY9FKSkp+Tb9Ij8oiqLelEVERHDz5s3qE/+QkBBOmzaN165dI5lWpHrFihVqjaekpKRC9fesKIp6g5eYmMiwsDAjt8h0SCwyvvXr1xMAL1y4oNdxxo0bR61Wy+vXr/PEiRMGaVtMTIz6/d6nT598W6wkPRYlJibyt99+U6cC37hxQ5065+bmxnv37vGnn35Sk/WpqamMjIzM08Oqy5cvU6vV8sMPP9Sr7WfOnCEArlixIkf7p8ciRVH0mhVU1EgSyUQ8evSItWrVop2dHXfs2JHlvqdOnVKTTf369XtpBfnFixdz1KhRJv9UOSIignv27FFvtjZs2EArKyuGhoaS/L9aBemBcNGiRdRqteqKcjNmzCAApqSkkEwb8mhubq4mlY4dO8YNGzaY/LDohIQE1qtXj4MGDTJ2U0yGdJaKhh07dvDtt982iQSps7Mz33zzzUzfe/bsGQ8cOKDGnvRiuy1btqROp1NrKZ0/f54kuWvXLmq1WrVztHnzZnWlFkVR+NVXXxEAq1atmunI0sx+qlevzgEDBvDnn3/OVeI/MjKSEydOpFarZZkyZbhp06YsY7+iKPzxxx+p1WrZuHHjDMPPFUXh66+/zjZt2ph83CwoEouKhtOnT7Njx44mXxMyKSmJx48fV2snBgYGsmTJkty6dStJ8tq1awTAbdu2kUy7uTIzM+Off/5JkvT09CQAHj58mCR58uRJAlDLGgQFBfGnn34qsNUw9fHhhx+yRo0asorw/yexyPjeeOMNVq9eXa/7q1OnTlGj0XDAgAEGa9fDhw/ZtGlTarVaLl261CD3f4qi8OzZs+pI54SEBJYrV46zZ89WtwFwzpw5JNNGSadfl7W1NStVqkQAXLVqFUkyODiYALhmzRqSaQ+sFi1axNu3b+eoPZMnT87QD8vrNbm6urJu3bq5+h3NmTOHZcqU0aueVFEiSSQTkJKSwvbt29PW1pbnzp3L0Wd0Oh0XLFhAc3NzOjs7MyAg4IV9pk2bxn79+pnEjdvzoqOjuWnTJrWuyJ49ewhALe566dIlTpkyRX3yFBkZydu3b790Gkl8fHyGGiWenp5cvHixuj106FA6OTmp26tXr+bPP/9s8OsyBHd3d54+fdrYzTAZ0lkqGtatW8f27dsb/cYtIiKCADh//nySaZ2fP/74g35+fiRJX19fAuCWLVtIku+++y61Wq1a4DI2NpaBgYEvHemYlJTEkJAQenh4sHHjxgRACwsLvvnmm5w/fz4HDx5MrVbLO3fuMDo6mhs3buTnn3/O8+fP08PDg3PnzuW7776bYTpzvXr1OH369BwX4r969apaA6FXr17qKKmXOXz4MB0cHFi+fPkMq8Hs27eP27dvz9E5iwOJRUXDoUOH2Lhx4wIv5p8dnU7Hffv2qf2g9BuxmTNnkkyLVZ988ol645SUlMQbN268tLRBSkoKHz16pE4Hu3PnDufPn68myDds2JBhafLTp09z6tSpJrmstZeXF5cvX27sZpgMiUXG9fTpU1paWnLixIl6HSd9YRFDTWELDg5WByMcPHhQr2OdPHmSBw4cIJmWcClXrhxHjBihvj9lyhT1fTJtxkhmI6kvXbpEJycn2tjYcOfOnSTTRkIvWrSI169fJ5kWewDw0KFDJNNGMk2aNOmlMTomJoYVK1bkq6++qtdDro0bNxIAjx49muPP3Lp1i99//73JD84oKJJEMgE//vgjAWQo8JhTnp6edHR0ZNmyZXnx4kUqiqJOZVAUxWTqd9y+fVvN3KY/QVu/fj3JtM7S2bNn823uu06ny5Bkevvtt9mtWzd1293dXe8hqflh3759xX4OrnSWCrfnp1WZQiw6d+5chnpIT58+pbm5OadNm0YyrY3Hjx9nTEwMQ0NDaW5uzs8++yzHx4+Ojma/fv0IgHXr1uX69eszzLn/71Do8ePHs06dOur29u3bM4wUWL58OTt37kyNRkMA7Nq1K/fv359tByY1NZWLFy+mtbU1y5cvn2FRhcxcv36d1atXp52dndqRe96ZM2cYFBSUo99BUSWxqHAztVhEpj2BT3+6rygKK1asyPfee099/+TJk3rX/niZ9FiUHksWLVrEkiVLqgnygwcPcufOnSY3EtHf39/gS58XNhKLjOuPP/4gAJ46dSpPnw8PD+fTp09ZsmRJDhw40CBtCg0NpbOzM0uWLJnjwQjPi4uLy1BmoG3btmzTpo26fe7cOT58+DBPbXv06BFdXFxobm7+0pk2YWFh6j3gzp07aWVlpd4zXrhwgRs3bszw8C49Cf7bb7/lqU1kWmK+bNmyfPfdd/P0+YcPH2baXypOJIlkZOHh4SxRogTffPPNPGc2AwMDWb16dTo4OPCDDz5g9erVTSr5EBsbSysrK06YMIFkWufl8uXLRu2cpAer5ORklilThp9++qn6njFXXkoXHh5OOzs7jhkzxthNMSrpLBVea9asYenSpU2qFtvOnTsJgB07dlRfu3LlijoV9nkrVqwgAHWUUnbCw8PZtGlTmpubc968eZkeMzPPJ8+bNm2aYapdeiwKCQnhrFmzWLlyZXWVlT///DPb74yrV6+yQYMG1Gg0/OGHH7Lc/9GjR2zatCktLS35119/qa8nJSWxWrVqGRLvxZHEosLr4MGDtLe3N7n6Rx07dmTjxo3VbX9/f6MWkn7+3H369GGDBg3UmBESEmL0p++KorBdu3asW7euySQCjUFikXF98MEHLFWqVI6/45/n4+PDkiVLqqOQDFEfMi4uji1btqSNjY06kjG3hg8fztKlS6tlQG7evGnQGmpPnz5l+/btqdVqc7S62vOxaOLEiXRwcFCTSA8fPmRycjKbNGnCmjVr6jXbZsKECbSwsMjTtN4hQ4awdOnSRh9hb0ySRDKyadOmUaPR0N/fX6/j3Lt3j9WqVWOpUqU4duxYo3/ZT5s2LcMTNQ8Pj3xZ8tEQYmJi1Klz165do0ajUesOGNPZs2eLfQ0A6SwVXrdv3+Znn31m9Om0K1euZJcuXagoijp8+flh2C/Tv39/VqtWLUexVKfTsUOHDrS2ttbryVRiYqI6UikqKorW1tacO3eu+n5ycjI3btzI2rVrq8mw7FaWevbsGQcOHEgAHDJkSJYrdEZGRtLV1ZUWFhbcv3+/+rqvr2+20+KKOolFhVdERATHjBlj9MLSu3btYvPmzdUbJE9PT164cMHo/bXMpKSkqEW5dTodq1SponcxW0O4d++eOg2muJJYZDyKorBKlSrs169fnj6fkJDAsWPHsnbt2nz11VcN0p7BgwdTo9Go9dBywtPTk02aNFEfVPn6+vLYsWP5+nA/JiaGrVu3pqWlJU+ePJnjzymKkmEkdLt27fj6669z7969BEB3d/c8t+nq1asEwKVLl+b6s1FRUSY5i6UgSRLJiJKTk1m+fHn26tVL72Ndu3aNN2/eZLly5VirVq18GwKdleef2M+cOZMjRowwuaHQ2Xn8+DFnzJihDn0/f/48N23alKcnDoaSlJTEmTNnqqsmFSfSWSp80lcHMqaAgAD1b/bXX39lnz59GBMToxa+zsnoqDp16rBv3745Ot/y5csJgBs3btSr3c+LjY3l4sWL1QcM6dPb4uPjmZyczJUrV7Js2bI0Nzfnt99+m2VySFEUzpo1S50Sl1VyOioqii4uLrS2tn6hPpuiKJw/f36hKMZraBKLCp+AgACj90Hu37+vPqk+duwYu3TpUuiKsiYnJ3Pt2rXqjd/Tp085Z84co9dPWrVqFW/evGnUNhiDxCLjuXHjRoYi0Tl1+/ZtNXl89uxZAuDatWv1bs+qVasIQC1ynZWIiAh1StqtW7f46quv8t9//9W7DbkRERHB+vXrs3Tp0nn621UUhTt27KCHhwcVRWHLli1ZunRpvWrcNW/enC1btszz58m0gRJ5HQVWmEkSyYj+/PNPAuDevXv1Os6ePXuo0Wh4+PBhnj17lpaWlnRzcyvQ4b7pc4RNbbi4vj7++GOWL18+Q12Tgnbs2DGamZnRw8PDaG0wFuksFS7e3t40NzfX68mQvry8vKjRaLhp06YX3kt/cpWTVT3s7e3VKbhZSUlJYcWKFdm5c+c8jyhITExkTExMlsnqhQsX0tLSMkNNpSdPnnDo0KHqCnLZ1S3asGEDtVot27Vrl+WojPDwcNatW5elSpXK8NQ/ICCA1tbW/PHHH3NxdUWDxKLC5f79+yxZsiS//vpro7Xh4cOHtLKy4owZM4zWhvyQPi1Yn9WR9BUREcFy5crx448/NlobjEVikfGsWbOGADJdzOhl4uLiWLVqVXXl5Y8++og2NjZ6j4y8efMmbWxs6Obmlm2yPCEhga+88gqHDRum1zkN4fbt2yxbtiwbNGig92yLhQsXEkCGkiS5tWjRohw/XMxMcnIy69Wrx549e+a5DYWVJJGMaPjw4SxVqpTe0z3i4+M5Z84c9TjpQW769OmGaOZLhYSE8MqVKyTTguSPP/5o9OHihpaamqremCmKwtGjRxslUWZKdWUKknSWChedTsd58+ZlukpHfoqMjFRvaBRF4aJFizIdLXP58mUCeGlxx+dZWFjwq6++yna/EydOEIC68khOJCYmcu3atXzjjTfo6OiorsQGgJUqVWL37t05a9Ys+vj4ZEhMPb9C29dff809e/aQTHsKVqpUKZYuXTrblUb++OMParVaduzYMcvRjXfu3GH58uXp7OycYWTrjRs3THL6TX6TWFS4KIrCn3/+OUPStSDEx8dnKLi7cuVKk53Kr4/nY9HixYvp7u5e4HHh1q1bRh0lbiwSi4xn2LBhdHR0zPW/9W3btvHy5ctMTk5m2bJl1YRSXimKwk6dOtHBweGldVxTUlIyLKqxefNm9Z7N2I4cOUKNRqN3Uis1NZU1a9ZkixYtqCgKf/vtN/7www+5uq++f/9+jkdzZXWMl62UWZRJEslIFEVhpUqVOGDAgCz3CwgI4KhRo9Qln0uUKMFOnTrx559/pp+fX6ZLTSuKwmHDhlGj0fDEiRP50v7U1FTWr1+frVq1ypfjm6L79+/TycmJa9asMVobrl69yl9//dVo5y9o0lkqHEJDQ406teGNN95g5cqVs+04xMbGUqPR5GhkgIODQ46ebs2bN48Acnz9586dY40aNQiAzs7O/OCDDzhr1iwuXLiQ06dP59ChQ9m4cWM1qdSkSROuXbs2w3S1+Ph4Nm7cOEOSKygoiA0bNqS5uXm2K5Zs3bqVGo2G/fr1y/IJ5tmzZ2lhYcEePXq8sN/9+/e5ePHiHF1zUSCxqHCIjY016uIYn3zyCW1sbIrNlE9FUditWzeDrTKVF1FRUZw9e7bRpy4WFIlFxlO7dm327t07R/vqdLoMyVaSPHr0KAHkqLh0VrZu3UoAXL169Uv3SR9hYyqJo//69ttvCYB//PGHXsf55ZdfCIBnz57l6NGj2apVq1zHgtatW7N58+Z6tYNMe0BYnMqPSBLJSAICAgggy4TEmjVraGlpSVtbWw4YMIDTpk3jJ598wkaNGhEAzczM2LJly0wz4rGxsXR2dma1atUMOjro2rVr6h9ncVz2OS4uTp0muG/fPq5du7ZAn769//77dHJyMur0uoIknSXTpygK27dvz1dffbVAO/FBQUHqF/WVK1d4+fLlHH2uXr16fOONN7Ldr2HDhjmqVzdu3DiWLFkyR+f29fWlnZ0da9asycOHD2cZO8LCwrhy5Uo2a9aMAFijRo0Mq6alpKSo1//vv/9yzpw5fPLkCTt27EiNRsPNmzdn2ZbFixcTAL/99tss91u5ciUBcN68eRlenz59OkuUKKFXLYLCRGJR4TBkyJAC/44MCwtT6yjev3+fx48fL7BzmwJFUdRpKSEhIZwyZUqB/v43bNhACwsLenl5Fdg5jUlikXFERkYSQIbFLrKS/h35/GjITz/9lDY2NnqNWklISGDVqlXp4uLyQp8rNjZWrbkWGxvLXbt2mezI4eTkZLZs2ZKOjo56rSgeGxvLkiVLcsiQIep2+v+OGzdOjc1ZWbBgAQHoXa/uxIkTxar8iCSRjGTTpk1ZLh+9du1aAmD37t0ZGhr6wvv//PMP69atSwDs06dPptNHzp49SzMzM4MtE3/x4kVqtVqj1jsxJYMHD2aTJk2yLGhraMZ+ylrQpLNUOJw4cSJXK4Po68GDB7Szs8tTvZOxY8fSzs4u26W0Bw4cSCcnp2yPN378eJYoUSJH5+7cuTPLly+vFrfMCUVRuH//fjZs2JAAOGzYsBc6oNOmTWOFChUYERHBuLg4du7cmVqtNstV6BRF4YgRIwiAf//9d5b79e/fnxYWFvTx8VFfT0lJeeEpa1EmsahwuHr1KtevX19g54uPj2flypX1np5SVLi7u9POzo6BgYEFdk5FUYrVlH+JRcZx/PhxAuDBgwdztH9wcPALo3Vr1qzJt956S692pC/k8d+p64qi0NXVla1btzbZxNF/XblyhVqtliNHjtTrOB9//DGtra0zjAg/ePAgraysXlggJDPpAztWrlypVzvI4lV+RJJIRjJlyhRaWlpmOp/b399fLY6dWYLi9OnTjI+PZ2pqKhctWkStVsvGjRtnemMyadIkAqCnp2ee25qe6VYUhQsXLmRkZGSej1WUpKamMiwsjGTaCmr5NXUwM4qicMuWLdneCBd20lkybQU9he35p24rVqzIU72Tv//+mwAyLGGfmWXLlhEAb9++neV+8+fPz9F0toiICALIc5HdpKQkfvvtt9RoNGzZsuULT9cePXpEMi027Nmzh82aNWPJkiWzvJlLnxbn6OiY5fSbJ0+e8JVXXqGLi0um31l79uzJ0dO+wkxikWnz9fUt0FXPno9FW7ZsUVdRFP8Xi8i02icFOUL19OnTRX7FNolFxrF06VICyPDvOzMv6wsEBQURAH/66ac8tyEpKYmVK1dmhw4d1Nee//vav39/oVvgaPLkydRoNPT29s7zMS5evJjp9L7n/1udPHky0xIwZFq/qWbNmnzzzTfz3Ib/8vPz47lz5wx2PFMkSSQjefPNN9m4ceNM3+vevTtLly6tJiied+TIEQLIsCLF4cOHaW9vzzp16rwQ3J49e0YnJyc2btw4TwW8jx49yqZNmxb5GwR9LViwgBqN5qUjywztwoULBsuamzLpLJkuPz8/Ojg4cPfu3QVyPh8fHzZq1EjvpzyJiYksXbp0tjU8AgMDCSDblcgOHz6c7WgeMu33BYDbtm3LdZuf9+eff9La2potWrTIdGWT9JWT1q9fzzJlyrBly5ZZxv4rV67QwsKC77//fpbn3bFjR6Yd4JCQEFpZWfHzzz/P2wUVEhKLTNejR4/UGmIF4d69e3RxcclQQFu8yNvbW++b5pdJSUnhgQMH+Nlnn9HV1ZWOjo4sUaIEzczMWKlSJW7cuLHIPmSTWGQcI0eOpKOjY5b7xMXFsUGDBpw0adIL76XPMNEn4bxly5YM/Y2oqCh26dIl2+nrOREaGsrVq1fzvffeY7NmzVipUiWWL1+etWvXZvfu3Tlt2jR6enoaPCkcHR3NcuXKsUuXLnk+hqIorF+/fobk2vPSV8qcOHHiS4/x8ccf087OziCzSxRFYbNmzeji4lJoRoXlhSSRjKROnTrs16/fC6+nZ1MXLFiQ6efu3LnDd99994XpDJ6enrS1tWWzZs1euLHYtWtXnr/IL126xNatWxfJ1UUMKSEhgb///ru6XRBB4/jx40W+kKR0lkxXeHg4R4wYke1TOUMJDg5my5Yt+e+//+p9rPHjx9PCwiLTqcLPc3V1ZaNGjbL8e46Pj6ednR0//PDDLI/1+PFjAuDChQvz1Obn7du3j2ZmZpmubKLT6bh161YqiqImfrJLhH3zzTdqYcqXURSFXbp0YZkyZV540nr69OkCndZrDBKLTFdcXBw//PDDDNMt89PTp0/Ztm1bHj58uEDOV1gpisLff/9dTeYYol+UvhJw+mIztra27NKlC8eMGcPx48fTzc2NTk5OBMDKlSvrXcDYFEksMo62bduyY8eOWe6TkpLC6dOnZxobhg0bxnLlyun1d9C2bVvWqVNH7fsnJyeze/fu3LRpU56Pee3aNQ4cOJBarVZdIbZnz5784IMPOHr0aL777rts1qyZ+n7NmjW5bNkygyZplyxZQgB6zeiYMWMGNRoNHzx4kOn7+/btU2svZfbfwMPDgwAMNpLL398/V6ULCiNJIhmBoii0trbm5MmTX3hv9OjRtLOzy9MS2QcOHKBWq2Xv3r0zJBey6vxnJikpiYcOHcrweZFzAQEBbNu2bbbTYAwlKiqqyI4Uk85S8aYoCvft26fGIEPFooCAAGo0Gk6dOjXL/datW0cA2d4sjhgxgnZ2dtkWh2zUqBFdXV0Nch3Tpk0jgCzn+4eGhrJUqVK0t7fPMvY/e/aMFSpUyLaDfPnyZQLg9OnTM30/ISGhyBballgk/v77b3VhDekX5U5cXBxff/31LOu0ZSU9IZWePOrcuTN37dqV6Y2soig8dOiQWkdu6tSpReq/l8Qi4yhXrhxHjRqV58/Xq1dPr3pIN27cUAcZHD9+XC1en9d/20lJSfz666+p1WpZokQJTp48mVeuXHnp8aKjo7l582a2a9dOXewjp/WhshMfH88KFSqwa9eueT6Gv78/AXDFihVZ7peamsp33333hdp5T548IQDOnDkzz23IjKIoBVojriBJEskIoqOjM306rNPpWK5cOQ4ePPiFz2zcuJEffPBBtssGptfx+O/qAZcvX6ZGo+GXX36ZbftmzpxJMzMzXr9+PQdXI/7L09OT9erVK5AkUnJyMp2dndm3b998P5cxSGfJ9Jw8eZJvv/22Xqtp5FT60O3nk9qG0r9/f5YsWTLL60hMTGTlypXZtm3bLDtqV69eJQB+8cUXWZ5z1apVBlnSlkzrdJUvX57vvPPOS/cJCAhgjRo1Ml1d7b/Svzuyq5/3zjvv0MHB4YVVPxVFYefOnTNdMaYokFhkem7cuMEuXboUyHft2bNnCYCrVq3K93MVRaGhoWzRokWepj9HRESwX79+BIcRph0AACAASURBVEAXF5ccFcodOXIkK1WqxOHDhxMAp02blpdmmySJRQUvJiaGAPjDDz9k+n76yqgvGykdExNDjUaT55qIJPn9999To9HQy8uLFhYWObqfe5nw8HC2b9+eADhixIhcP4g+cuQI69evTwCcNGlSprUSc2vevHkEkOcRpYqisHbt2uzRo0eW+z179ozdunXjsmXLXnivcePGdHNzy9P5X2b27Nm0s7Mr0Jp9BUWSSEaQXlzt119/zfB6ep2b33777YXPzJ49m506dcq2c64oCgcNGkStVsvz589neG/IkCG0sbHJdnhdfHx8hqWkRe6lP60kmafiv7mxadOmIru0rXSWTM+vv/7Kxo0bF8gSzqmpqdy5c2e+PEX28/OjmZkZx48fn+V+a9asIQBu3749y/2GDx9OCwuLLOuiJScns0WLFixdurRBnkyNHTuWtra2WX4v6HQ6dujQgXXr1s1ylNCzZ8/o4ODA9957L8tznj9//qVP+/bv35/nkQamTmKR6Tlw4ACdnZ1fOn3B0Dw8PAxys1RcPd8vyumIRW9vb1atWpUWFhacN29ehmNk5eLFi9y4cSNTU1M5cuRIAigyU9skFhW87GoaXrp0ibVq1XppAsTT05MA9Lq3aty4MV977TWSaVOz8toHCw8PZ8OGDWltba1XjcaEhAR+/PHHBMC3335b7+ltkZGRtLGx0Wu014QJE2hlZfVCyZf/+u89Wnofc8yYMXRwcDDog7B79+5x0aJFOY5dhYkkkYwgfUqAh4dHhtcXLVqUZeX/nP4DjI6OppOTE+vWrZvhjzowMJBarZbjxo174TMREREcN25ctiOdRO6kL3d79erVAjlfURqyTUpnyVTl55dhQkICx48fXyAjnUaPHk1zc3NeuXLlpfvodDo2b96cFSpUyHJlysePH7NcuXJs2rRplnE0KCiIZcuWZdWqVfUuEp6+Wkx2v6uff/6ZAGhhYZHlqK6PPvqINjY2WXZOFUWhi4sLmzZtmuU5JRaZ1o/EotxLTU3lV199xVu3buXbOYqjq1ev0tbWlmvXrs1yPw8PD1pbW7Nq1ap6PShLTEyki4sLHR0di8TqwhKLCl76qq5ZjdTNKhatXr2aAPJcXzY4OJgA+Nlnn+Xp8+kSExPZpk0bWltb89ixY3odK116/+Ktt97SO8meXhogs0VDcuLgwYMEkONpdqGhoSxfvrw6UjG9hMG1a9fydP7sFNd+kRmEwSQkJAAAbGxsMrzu4+ODypUro0KFCuprnp6e8PHxUbf//PNPTJw4EePGjcPGjRsRGxv7wvEdHBywbt063LhxA7Nnz1Zfd3Z2xvvvv481a9YgNDQ0w2dOnToFd3d3+Pr6GuQaRZo333wT48aNQ7169fL9XDNnzsSnn36a7+cRxZO/vz9OnjwJANBqtfl2Hl9fX6xZs0Y9V36aO3cuHBwcMGrUKKSmpma6j1arxbp16/DkyROMHTsWad+bL3J0dMTGjRvh6+uLDz/88KX71axZE0eOHEF8fDxat26Nv/76K8/tv3//PiwsLFCiRIks93vttdcAAG5ubmjXrt1L9+vfvz8SEhJw4sSJl+6j0WgwdOhQ+Pr64ubNm5nus3btWvTr1++lvwMh9BEWFoadO3eCZL7GouDgYLi7u2P37t35do7iqG7duhg3bhzeeuutl+6zevVq9O/fH82bN8fFixfh6uqap3Pt3bsX3bt3x6pVq/DkyRPMnTs3r80WxdjDhw8BAJUrV87wekJCAn799VcoipJlLLpx4wasra1RtWrVPJ1/3759AIDHjx/n6fPpvvrqK5w7dw6bN29Gly5d9DpWuk8++QQrV67Evn37MHHiRL2O9cEHHyAuLi7PMbd9+/YwNzfPcf+xfPnyGDduHIYMGQIAaNWqFQDA29s7T+fPyvnz59GmTRtEREQY/NgmLyeZJlP5MfUs96lTpwiAR48ezfC6q6vrC0XFWrduzUaNGjEsLEydv2pra0t7e3sCYOnSpbly5cpMs5tDhgyhpaUlb968qb4WGBhIMzMzfvXVVy/sb8pV5J9fojooKIgXLlxQtyMjI1+oz2GKnj59qvfIg6xMmTKFQ4cOLVL1SCBP3EzG4MGDWa5cuWyHCRtCQcairVu3ZlpH7r/mzp2rrnKZHm9DQkLo6empPoF8+vSpWvB64sSJWT51CgoKYrNmzQiAgwYN4t27d3PV7rCwMDo6OvKNN97Idt/Y2FgC4Pz5/4+98w6L4vre+Lu7ICBFFFCx915i7y12jb3EEg0aC0nsGo0BE3v0Z8cWS8ResLfYuwYVG6hRVBQQC6ggvezuvL8/yM6Xlba7zFLMfp5nnse5M3PuDO6+e++Zc89ZSDJZT9MKu4+Pj9cp10JQUFCGleZWr17NTp065Qld1hWTFuUe3NzcaG5uni1J3F+/fp1r3x6rVCrx9/7du3e8du2aGEUYGxvL9+/f59p71yAIAm/duqXVpomw/Oqrr7IcHX/ixAk2bNiQL1++5NChQ2lpacmwsLAs2cxpTFqU/cybN48AUn0eNZEr3t7eGV7fs2dPVqtWzeD+hw0bxoIFC2rNhfRFk9dtzJgxBttID7VazQkTJhAAt27dymvXrokR0omJiQwNDdUpalQQBJYuXVqncU16NG7cmM2aNTPo2hs3btDKyooTJkwwuP/0uH//PqtXr05fX1/JbecUumpRjouOPltuF6iLFy8SAM+fP6/V7uDgQFdXV6228PBw3rlzh82aNaOlpSU3bdpElUpFQRD4999/s23btgTAvn37phK3N2/e0NbWlt26ddNq79evH+3s7BgeHs5Ro0ZpOWRyA48fP9bKFzVp0iQWKlRI3Hd1daWTk5O4P2LECBYpUkTc9/T0zLSkdU7QrVs3li5dmgkJCUaxn9sHi4ZgGizlHuLi4ujj42M0+7/88guPHj1qNPvpIQiCWN7201D14OBgbtiwgYmJiVSr1axWrZpWXoOFCxcSgDhx0ww0v//+ezFPgLu7OxMTE9PsOyEhgb/99hstLS1pbm7OIUOG8PLly5k6gu/evSvmNNAlCWV8fDwBcN68eSTJcePG0dbWlqGhoanOrVOnTqaJKUmyatWq7Ny5c5rHBEH47PTIpEW5B6VSyb///tto9letWpXrEmi/e/eOW7duFb+zR44coUKh4N27d0mSe/fuJQBxae7OnTsJQMzR5u3tzWnTpuW6Sq7Lli2jQqEQJ1Zr1qwhAPbu3Ttd3dQXjRb9888/Or0wyO2YtCj7mThxIq2trVO1C4KgU0n4OnXqpPt7mRGHDx+mu7s7q1evniXHiiAIbNSoEYsXL27wUjEN0dHR3LNnjxig4OvrS3Nzc+7Zs4cNGjRggQIFCICHDx8m+b88iprx3bNnzzhx4sR0lwlPnDiR+fLlM/g+p0yZwnz58umtH4cPHyYAVqxYkW3btjWo78z4r46Lclx09Nlyu0ClFYmUkJCgVVowMTFR/LBp1pvu2LEjlS1BELho0SLKZDJ26NAhlYPi999/JwBevHhRbLt586bYV7ly5dLMUJ+dhIWFcf369eJaWs0bf83a9RMnTnDOnDlaA4GUz3P+/Hlu3rxZ3B80aBCbNm0q7ru7u+f4M5Kkn59ftiSdDQwMzLTCUl7BNFjKeZRKpdGj22JjY1m/fv1Mk1wbi48fP7J8+fJ0dnamh4eHONHatWsXAYgVV86dO0cnJyfa2dnx7t27fPHiBU+dOiW+Ybt16xY9PDwoCIL4Vi5ljqEVK1bQzc0t1UAiODiYY8eOFSNMCxcuzD59+nD+/Pncvn07Dx48yO3bt3P27Nls3bo1AdDR0TFVNGt6PH/+nAC4YcMGkskRVNu3b0/z3N69e+v0xnTYsGFazvy0ePfunVEq6+UEJi3KeQRByNKbeF376NatG3v06JGjA/74+Hju379fTMCvKbyiyaX57Nkz/vLLL2LFn7dv3/LkyZPixMvf35/Lly8XteePP/6gpaWleHz37t0cM2ZMjufBjIqK4tq1aykIguj46tatm+T/zzExMTx8+DBbtWrFChUq5OnJnEmLsh8XFxeWKFFCq00fJ4WTkxNHjRqld79jxoxhvXr1KJfLOWPGDL2v13Dq1CmtMYA+qFQqnjx5UowYDA0N1YpEjoiI4LRp0+jn58f79+9ToVDwq6++EiP+Xr9+zZUrVzIkJIRkcn4pKysrPnz4kGTynHj48OFiPuDz588TAA8dOmTQs3p5eRFAqgjHzFAqlfTw8OCQIUNYrFgxg/rWhaSkJO7duzdPa5AGkxMpB/D29iYALYfC69evCYBr1qwhSY4dO5YdOnRgYmIiS5cuzWbNmmX4gdu0aRMBcNCgQVrnxcXFpVmiunHjxixXrhwjIyON8ISZk5CQICb91rxB0zg+QkNDGRQUlKUvWMoBSKdOnbTE29PTM8eX7j1+/NhoAtK4cWNWrVr1PyVQuXXL7VqkC0uXLmWdOnUYERFh1H5iY2NzpHqFSqVidHQ0fX19aWlpSQBiwlfNEtSUTrSgoCCWKFGCTk5OGVZiS+ngr1WrFh89esTRo0drRfl4eXlpvY2Ljo7mjh07OHjwYJYrV44AUm3Vq1fn3Llz+eHDB52fccuWLQTA27dvpzr2/PlzrQIMLi4uLFmyZKY2NYUgMopsGDRoEO3t7bNlCaSxMWlRznPo0CGWL1/e6ImulUpljjhXBEEQx2RhYWGUy+ViSfCkpCT6+fllSSNTjovmzp3L6tWri+OE48ePG1xaWwouXLhAc3NzNmnSJMtVntLCzc2NcrlcjB5NSwvzCiYtyn769Omj9XLl7t27dHZ21ikiUqVSUSaT8ddff9W7X0EQxDljZhViM6Jbt24sUqSIXo6vjx8/kkzWQwcHB7q4uIjHfH1907X1ww8/0MzMLMMk4kqlUtQeT09POjs7i+OEc+fO0dLSkj/++KPO95qSp0+fGuwwI/8XYW6sgkgbN27MNEl7XsHkRMoBNNXZUpYb9ff314o2WrNmDX/55Rdevnw5w7KSKdEspVi6dKlWuyY8+MyZMySTI5G6d+9OADx9+rSET6YbL1++pJOTE9etW0cyefLo5+dnVKeHZhL48uXLHA9nfvDgAS0sLIwWHXXv3r1syRWRHZgGSznPvn37OGLECKPYfvnyJSdNmmSUSYMuxMXFsXz58mIOoP3791Mmk7F79+4ZVhnx9/ens7MzHR0dM33bdeLECTo4ONDKyorLly8XJ3IJCQm0s7PLsJxtVFQUHz9+zDt37vDRo0cGhXcLgsCmTZuyTJkyqSLK3r17x4IFC2qt/x80aBDLli2bqd0DBw5kOhkLCAgwWpWT7MakRTnPxYsX2bdv3yxXAEqLqKgojhkzxujO8vQQBIEtWrRg//79xbZbt24Z5Vk1aPRAEARWqVLFaEs4MuPJkycsWLAgzc3NDVryowvv3r2jt7c3w8LCKJPJxKj/vIhJi7KfTp06sWHDhuK+r68vu3fvrpNehIWFEQA9PDx06kupVHL8+PHiOH7//v0GRdZoeP/+Pc3MzDh16lSdrxkyZAgbNGgg7vv6+uo8Tnv58iXNzMz0iixPOTbp3LkzraysWKtWLZ2v/9SWtbW1wZXsDh48KC5rM8aLzcTERJ4+ffo/9aI/x0VHny23C5TGS7p161axzc/PjwC4d+9erXPd3d2pUCh0ihgSBIE9evSgubm5VsnqhIQEFitWjK1btyZJ8Y22k5MTu3fvLtFTZUxERITodRUEgRMnTjRqToOMePLkifgW/8KFC+zRo4cYRpkdCILA//u//8t1uQlyI6bB0ufN+vXraWNjo5X839jEx8drlbadNWsWjx8/Lu6vXLmSAPjtt99muIzv6dOnLF26NG1sbDJdpvr69Wt27tyZAFi/fn1R+0JCQvjy5UuSyctTvvzySz569Cgrj5eK7du3a0W5fsratWv5/Plzcb9169Zs3LhxpnbTKxDxuWLSos+bEydO0NLSkpcvX862PjXLRDSsWrWK27Zty7b+U/LhwwdRhz9+/MjWrVvz2rVrRu83KiqK1apVo4ODA5cvX54tEUL16tVj8+bNjd6PsTBpUfbTqlUrtmzZ0qBr05rzZcS9e/doY2MjasGyZcsIQExUrS+aMcDNmzfTPUcQBJ48eVJ0muzZs4fLli0zOJXBwIEDWaBAAYNeEMbExNDV1ZVyuZwRERHs1KmTmIdSVxo0aGCwU1wzH58+fbpB1/+XMDmRcoB3796l8krfu3ePALhu3ToeOXJE9FB26NCBX3zxhc62w8LCWLhwYTZo0EDLg6pZeuDj48OQkBDeu3ePU6dOpUKh4Nu3b6V7uHTo3bs3nZycjJZU2lC2bdvG6tWr51heAEEQjBKFERcXxwEDBnDt2rWS285OTIOlnCM6Oprbt2836ptwktmiPymZMmUKzc3NM3Qcz549mwA4fPjwDN9EvXr1irVr16ZcLufSpUszfLMkCAJ37NhBZ2dnAmDPnj3FXEskeebMGZYvX17SpbZXrlxh/vz52axZM53+H2NjY1mwYEGdIs/+/vvvVMuy00IQBI4ZM4a//PKLzvedGzFpUc6hVqvp6elp9N/p7Nai1atXZynCwFj4+vqyUqVKWvpkDARBYL9+/SiXy1M5o40VnTp//nxWr16d+fLly3XjUV0xaVH207RpU9EpsX37dr0iFjWrTzT5zHQhpRZNmzaN+fLlMzhyZeTIkbS3t8/QIfTXX3+lGchgKJocTClX3OjDkSNHCIAHDx5k3bp19bbzzTff6LQsPy2ioqIIgAsWLCBpPC3y9PRkx44d83REkq5aJIcJybC3t4dMJsP79+/FNoVCAQA4ceIE+vTpg9evXwMA/P39Ub16dZ1tOzk5Yfny5fDx8cHGjRvF9u+++w758+fHvHnzULx4cdSuXRsuLi5Qq9XYunWrRE+mzaNHjxATEwMAmDNnDk6ePAkLCwuj9GUo33zzDfz8/GBlZQW1Wo1u3brh4MGD2dI3SfTp0wcjRoyQ3LalpSUiIyMRFxcnuW0T/w12796Nb775Bnfu3JHctq+vLx49egQAKFKkiOT2PyUkJETU1EmTJuHkyZMoWrRouue7u7tjxowZ2LRpE4YOHQqlUpnmecWKFcPVq1fRvXt3TJo0CV9//TUiIyPTPFcmk2HQoEF48uQJZs2ahQsXLqBu3bpo3749Dh8+jNatW+PJkydwdnYGkKzZa9euNeh5SWLjxo1o3749SpQogf3798PMzCzDayZNmoSmTZsiIiICrVq1yrQPjbbkz58/w/NkMhni4+ORkJCg+wOYMJGCCxcuYNiwYThy5IjktoODg3Ht2jUA2aNFERERePr0KQBg2LBhOHDgAOrWrWv0fvWhVq1aePToEerUqQMAmDFjBmbMmIHkOYN0eHh4YO/evViwYAHatm0rti9fvhxffPEFYmNjJe0PAJKSklCgQAEkJSXh/v37kts38XkiCAIUCgUCAgLwzTff6PXbnJiYCCB5XJ4RUVFROH78OABtLfrw4QMKFSoEmUxmwJ0Dt27dQoMGDSCXa0/lExISxO9Ax44dsWfPHvTs2dOgPj6lTZs2sLe3x7Fjxwy6vlatWgCAt2/fwsfHR7yvDRs2YPTo0ZmOJ8qVK4eQkBAkJSXp3betrS3s7OwQEhKCffv2oUKFCuL4UUo0evrx40fJbec6dPE05ZYtL3i5HR0dOXr0aHFfE+74559/iksd1Go1zczM9H6Dq1lbX7hwYTGHhlKppK2tLWUymZgxn0xOwmzoutOMeP36Na2srDhlyhTJbRuL0NBQNmzYMM0qeMbi999/57Jly4ziic7L3m0NML1xyzHUarVOpWsNoUWLFqxQoUK2JNKOj49nsWLF2KdPH72v1VS37NixI6OiotI9T61Wc+HChVQoFCxdujQvXbqUqe2IiAjOnz+fxYoVEyuyjRkzhufOnWNUVBQ7d+7MOXPm6HW/arWaJ06cYNOmTQmAX375pZbeZ8S2bdv4xRdf0NLSUqe3rJs3byYA+vv7Z3quSYtyfsvLWiQIAq9du2aUqMhvv/2WdnZ22VJkRBAE1q9fn3Xr1s0z3wlBEDhixAgOHz5cUrt37tyhubk5u3XrlupvcfHiRQ4fPjxNzY2KiuK5c+e4adMmenp66v25EASBT5480SqgkNcwaVH207hxY7Zv355kcqSePpFIV65c0SkH7cyZMymXy1MVDhgwYAArVqyo/00z+fNubW2dZn6ifv360dnZ2WgRnj179mT58uUNulatVtPKyooTJ07Uap89ezbbt2+f6TK7P//8kwC0lurrQ5UqVdinTx/6+/uzb9++Rkl5kld+AzJCVy3KcdHRZ8sLAlW7dm1+9dVX4v7bt28JgKtXrxbbIiIiCIBLlixJ00ZMTAwfPHiQpgBcv349VQLpkydPaoXokaSHhwcB0NfXV4rH0vpSbNmyJdvDw7OKSqUSn+HkyZNGD+fODvJyYlvTYOnz5O3btxmuz5eClFp04MABsVS2vmzcuJEKhYK1atViYGBghud6e3uzfPnyBMAff/xRp4lpUlISDx06xN69e4vV4aytrdmxY0e6u7tz37593L17Nw8dOpRq0BEXF8eHDx9y165d/OGHH1iyZEkCYLFixbhhwwa98hkEBAQwX758Opchnjp1Ks3NzfUqxf306VO9qsPkJkxa9HkSFRXFixcvGrWPlN/b8+fP08fHx6j9GQONwz8wMDDLL9piY2NZpUoVFitWTOc8L5cuXRJzfuKTipVFixbl8uXLdX4poVaraWlpyR9++CErj5FjmLQo+2nWrBm//PJLg67V1YmkSbj8Kb169WKNGjUM6vvjx48EwMWLF4ttGj3y9fXVyskmNQsWLCAAhoeHG3R9tWrV2KtXr1Ttmu/5x48fuWbNmjSdMSdOnMhSBbRWrVplW9608PDwHK8YbigmJ1IO0b17dy1RiI2NJQAOGTJEbHv16hUBpJnXZs+ePbS1tSUAFixYkCtXrkz1RerSpQsdHBy0Ejg3b96clStXFs8NCwujQqGQJF9FQEAA69evn6NlYqVCrVazRo0abN68ebZ4i0+fPp2uszAreHl5EQCvX78uue3swDRYyhkGDhzIzZs3S243u3KPvX//nq1atco0X4+unDp1igUKFKCjo2OmiaRjYmI4fvx4ymQyOjs7c8uWLTo7c2JiYnjo0CH+8MMPrFmzJuVyudZkycLCgoUKFaKjoyPz58+vdcza2prdunXjzp079XbUqNVqtmvXjtbW1jx+/Djd3NwyvaZFixasX7++zn3cuHGDMplMp0qjuRGTFuUMEydO5MKFCyW3Gx8fb3DSWH2Ii4tjnz59xGq0eZ1x48bRzs6OoaGhBtsYM2aMVsXg9Hjy5AmHDRsmFiVwcnLipEmTePLkST5//pxPnz6ll5cX27ZtK0aMxsTEZNr/69evCYBVqlQx+BlyEpMWZT9t2rRhmTJlOHHiRL3nBN7e3gTAv/76K83jSqUyw5cxPXv2ZM2aNfXqU0NQUJAYdadWqzl27Fj+/PPPBtnSl2PHjhGAwQn6O3TooFUh7lOWLVtGMzMz3r9/P9WxO3fuZCknU79+/Vi5cmVx/82bNxwzZkyGEemGkJCQQEdHR6NVQDY2umqRKSeSxJQpUwaBgYHi/vv37yGXy7XyJAmCAOB/+ZI0PHv2DEOHDkX16tWxbds2NGjQAGPHjsWUKVOQ/H+azPTp0/HhwwfUrVsXbm5uAAAXFxf4+/vj+vXrAJJzKLVs2RL79+/XutYQLC0toVQqER0dnSU7uQG5XI5z585h165dkMlkWf7bZIaXlxc2btxo0PrdjOjUqROWLVuGSpUqSWrXxOdLbGwsXr16hQ8fPkhue+DAgRg8eLDkdj/FwsICSUlJYk62rNKhQwfcuHEDhQsXRocOHTBv3jxRnz/F2toay5cvx40bN1CyZEl8++23aNiwIc6cOZOpjlhbW6NHjx5YvXo1/Pz8EB0djVu3bsHT0xNTpkzBuHHjMHDgQPTt2xeurq6YN28etm3bhjt37iAiIgJHjhzBwIEDkS9fPr2ez83NDWfPnsXSpUvh6+uLjRs3IiwsLN3z3717h7///hsdOnTQuY/69etj8eLFaNmypV73ZuK/C0kEBwcbJR/FL7/8gtatW6eb70wqzM3NkZiY+NnkBFu8eDGuXLmCwoULA4DeY6OLFy9i1apVGDduHNq1a5fhucuXL4enpycuXbqERYsWISgoCEuWLEHHjh1RtmxZVKhQAf369cOZM2ewbt06nDlzBv37909XmzU4OzujZs2aRh/Xmfh8sLKyQlxcHIKCgvTOTaTJhaTJjfQpa9euxRdffJHumMvc3BwqlUq/G/4Xjb6Zm5tDJpMhKSkJSUlJ2fLZL1OmDIDkvHOGULhwYbx79y7d4+PHj8fNmzdRo0YNANpa5OjoCABac2p9KFSoEMLDw8X9wMBAbNiwQcyfJxUWFhZYtmwZfvjhB0nt5jp08TTlli0veLk11dI0pebJ5NC9nj17ivthYWEEwJUrV2pd+/PPP9PMzEwMf1Or1eKbnfXr14vnCYLAunXr0sHBgV5eXiTJyMhIWlhYcNy4ceJ5f/zxBwHQz8/PoGd58eKF6JnPjjd72Y0gCBw5ciRnzpxptKikyMjIPFspxJjA9MYtx5D6sy4IAhcuXMgVK1ZIajclwcHBYqizMbQoOjqaAwcOJAC2adOGwcHBGZ6vVqu5detWli5dmgDYrFkz/vXXX1n6286fP5/Dhg2TJJ+UIAj89ddfCYCjR4+mIAhMSEjIdBne0qVLs/SbkRcxaVHOYYzf3c2bNxu1hHNoaKgYFfM5jotIcseOHezUqRNjY2N1Oj8mJoZly5Zl+fLlM7xGpVJx/PjxBMAmTZowKChIJ/tr1qzJMAVESlxdXeng4KCT3dyGSYuyn379+rFKlSoGaZG/vz8BcNu2pXTLEQAAIABJREFUbWkeP378OEeOHJmu7aFDh7JUqVJ690uSL1++1FrOlp1aFBoaSgBctWqVQdePHTuWBQoU0OncCxcusEmTJuLKG02FtUWLFhnUt6YiXkp0zS/5X0JXLTJFIklMuXLlAAABAQGIj4+HUqlEqVKlEBQUJJ5ja2sLIDljf0rOnj2LZs2aiVV85HI5VqxYgfbt22P8+PFihJNMJsOYMWPw4cMHsRKRnZ0dOnfuDC8vL6jVagBA9+7dAcCgLPr+/v6oUaMGVqxYId7L54YgCFAqlZJHCaXEzs4OFhYWUKvVCA0NldQ2SRw9ehSnT5+W1K6Jzw+lUon4+HgAMLgSSHrIZDJMnToV48aNk9Suhg8fPqBRo0aYNGkSAONokY2NDXbs2IE///xTfAO2efNmJP+WpkYul2PIkCHw9/fHqlWrEBQUhC5duqBmzZpYt26dQVGbiYmJkmhRREQE+vfvj9mzZ2PYsGFYvXo1ZDIZLCwsYGdnB5JpRoAkJiZi+fLlaNasGWrWrKl3v5cvX8bevXuzfP8mPm9IimMfqbUIAL799lvMnz9fcrtAso62a9cOAwYMAPB5jouAZC1QKpXp6t+nzJw5Ey9evMCff/6ZblXHxMREfP3111ixYgUmTJiAy5cvo1SpUjpFo7m6uuKrr77Cb7/9lmEkJZAc5RAeHo4NGzbodO8m/ttYWVkhMjLSIC0qUKAAAKRbubVLly5Yv359urbt7e0NruBVsGBBAMkRhCqVKlu1SFONO70IrMzInz+/zhWmk5KSoFKpxChEjb4YGo1uY2ODpKQkrUhVJycnADBKZOyzZ8/EefRnSXreJQB2AH4HsA3AoE+OrdHFQyX1lhe83P/88w8BcMuWLVyyZAmdnJzo4uLCQoUKaZ2XP39+rez0GWXaDw4OZv78+dmvXz+SyetRL126RBsbG3733Xfiebt37yYArcpL9erVY6NGjfR+DrVazVmzZuXZpGC6olarxbcExkwM27p1a7Zp00ZSm4IgsGrVquzataukdrMD6PHGDUBRAGsBrAbgAGAmgPsAvAA462pHyi0vaFFKDhw4QFtb2zTXmGeFq1ev8vTp00bPL7ZixQo+fPjQqH1oCAgIYIsWLQiA7dq10ylxd2JiIrds2cLatWsTAG1sbDh8+HCeO3dOr8gizdtEQ7RIpVJx8+bNLFKkCM3MzLhw4cI0/1+GDx/O8uXLp6p8tHDhQgLgqVOn9O6bJLt27cpq1arlucokempRpxT/LgDgTwB+AHYCKKKrHSm3vKZF9+7do4WFhWR5zTT8888/9PLyMvob+V27dvHChQtG7SM3oPk7KpXKDDXs7t27VCgUGeb+SExMZNeuXQmAy5YtE9vnzJlDOzs7nRL0+vv7Uy6XZ5r3Zfny5QRAOzs7xsfHZ2o3N6GPFiWfjjsA3AGU1+c6Y215TYvevXtHMzMzmpmZGfS7lZSURACcNWuWVntYWBjXrVuX6e/4nDlzCMDg1Qp2dnZs27atQddmhQ8fPhAAly9fbtD1M2bMYLL7QTc0WiQIAhMTE2lubm5w/qfFixcTQKqo7K1bt9LMzEynqrT6sHTpUioUCr58+VJSu8ZGVy3KSJz2A1gAoCeAI//uW/x77I4uxqXe8oJAKZVKWlhYcPLkybxw4QKnT5/ORYsWpcpkX6FCBX799dfifkJCAgFw3rx5adrVfOn8/PxYq1YttmjRgkOHDmWBAgVEAfr48SPNzMw4depU8brZs2dTJpPpHK4XGhpqcMb9vMyLFy9YqVIlHj161Cj2d+3axb1790o+wQoICNCrilJuQc+J20kAYwH8/O+EbRqAUv+2HdbVjpRbXtCilNy9e5djx46VvJR2jx49WKZMGaN8BmNiYnLsh1etVnP16tW0s7OjhYUF3dzcdErsKggCvb296eLiQhsbGwJgkSJF+N133/HQoUM6VXULDw9ngwYNdA4VDw8P58qVK1mxYkUCYMOGDTOsPnn27Flu2LBB6//s0aNHtLS0ZI8ePXTqMy2Cg4OzLcG6lOipRXdS/HsjgLkASgOYCOCQrnak3PKaFgUEBHDcuHE6V+/SlQkTJtDW1lYrlYBUqFQqg6tA5mWSkpLYtWtXTp48Oc3jarWaTZo0oZOTU7p/d5VKxb59+6ZZTMbPz4+LFi3SSVvJ5ETEjo6OGU7ON23aJGll4uzEACfSCwCLAQQDuPmvDhXTx4aUW17TotDQULZp04YAGB0dbZCNggULpqoG6OHhQZlMxkePHmV4reaz+uzZM7361Dg6mjZtmm2VxlISEBBAAPT09DToend3d8pkMr2uEQSBrq6u7NOnD62srDhlyhSD+tZULk9ZmIpM/iy4u7tL/rsUERGR56qZk7prUUbidO+TfTcA1/6NBDA5kTKgfv36Wt7hI0eOpMpk/+WXX7Jx48bifnx8PAFw/vz5adp8//498+fPz+HDhzMqKooBAQE8fvw4AfD48ePieW3btmXVqlXFfU31AE3upIwQBIGtW7dm3bp1P9u1/ukRExPDrl278u+//87pW/lPoOfE7W6Kfwd/cuyernak3PKKFhmbhISETAdKhjJs2DAWK1bM4MGdFLx+/ZqDBw8mADo7O3Pjxo06O+Li4uK4Z88e9u/fn3Z2dgRAhULBevXqcfz48dy+fTv9/PxSvYVUqVQcNGgQDx06lKbdhIQE+vj4cNmyZezUqZNYGrt+/frct2+f3tr98eNHVqtWjY6Ojp995GlaZMGJ9OkYyaRFOYhKpZI80lLDnDlzaGNjw8DAQKPYz81MmDCBa9asSfOYp6dnppPJsWPHpipFbiiasXR61bDI/1WuNdZnwZgYEomU4t8tAKwB8BbABQCj9LElxZYXtWjz5s0EYLCTuEaNGuzevbtWmyAIOjkxz58/r1M1w5Rs376dZmZm9Pb25tixY5k/f/5sf5F86dKlLEUt//TTT7SwsND7uqVLl9LNzS1LTiRNfrU3b94YdP1/BSmcSI8AyD9p+xbAQwBBuhiXessrAuXi4kIHBwfxQxoYGEgAWj/Eo0aN0kr+JwgCzczMMgzR++6772htbS1OqhISEmhjY8NRo0aJ52gSo2oSFiqVStrb23P48OE63fvly5e5f/9+3R/WhM7ExcVx7dq1fPLkiaR2t2/fzpEjR0pq09joOXHzTfHvuZ8c89PVjpRbXtEiMrmc8osXL3L6NvTmwYMH3LBhQ07fBkny2rVrbNy4MQGwUqVK3Llzp17L1BITE3n+/Hm6u7uzVatWtLKyIgBxK126NFu2bMn+/fvT1dWVkydP5vTp0zlt2jSOGzeOQ4cOZYcOHVixYkUqFArxuooVK3LKlCm8ffu2Xs+jVCq5c+dOnjt3ji1atKCZmRnPnTun758lFSdPnmSfPn3y1EsIPbUoBMAkAJMBPAcgS3HMpEWZEBISwocPH+a5JY8vX76UxAnyOREZGcnChQuzSZMm6X7fV69eTQBaqRs+RRAEHj9+nMeOHcu0z4SEBFpbW6eK/EiJxtG0ZcsWdu/enREREZk/TC4hK06kFG0KAJ0AeOpjS4otL2lRREQEfXx8eOrUKQLgxYsXDbLTpUsXfvHFFwZdGxISoneC6oiICM6ZM4cqlYr79+8nAF66dMmg/g1FU7Tp+fPnBl3//fff09HR0eD+s7KcLTMn0tWrV7l9+3aD7y0tnj17xq+//jrbUjJIgRROpP8D0C6N9k4AnupiXOotrwiUZk12jRo1SCb/SBYqVEhrzbjmnJRhbqVKleLgwYMztZuyClLv3r1ZokQJcVDm6+ub6s1Qnz59WLp06Qzv2VRB7H8sX76cEyZMkNxuaGgo8+XLl+6SRUOZO3cu69evn6f+D/WcuM0GYJNGewUA+3S1I+WWV7SIJIcMGUIHBwdJqn5pCAgIYL169fR2XuhCbv0cC4LAAwcOsHr16qIzacOGDQbdr1KppJ+fH3fs2MGZM2dy0KBBbNGiBStWrEhHR0daWVmJuRrMzc1ZqlQpNmjQgH379qWbmxu9vLwyrSCXEQkJCXRycmLhwoUpl8u5e/dug22lZPv27axatWqeWv+vpxb99snm9G97UQBbdbUj5ZaXtOjXX3+lXC6XdMlAVFQU69aty5MnT0pmU0Nu1aKc4MKFC+zYsaP4N5k2bRoB0MfHJ83zr1y5QjMzM3bt2jXD3x5BEFivXj2dc7t07txZK9r+U06cOEEAXLduHUuXLs1bt27pZDc3YIATabc+5xt7y0tapIlA0jhi0quwlhk//vgjCxQoQEEQqFar2bJlS27dulWnawVBoL29vVYgQHokJiamctZGRkYyX758GTppjcF3333HggULGvwyoH///qxUqZJB16rVagJgmTJlDPodWblyJQGkm+Klb9++LFeunKQvwl69esWiRYsaLV2KMciyEyk3bnlFoP7++28C4Jw5c8S2tm3bMuX9X7x4MVVYbrt27Vi/fv107R44cIBmZmbs37+/2KbxCGvWyKrVahYuXFjLGaVxPoWEhKRpNzIykhUqVDB4fevnxqRJk9ijRw/J88eQyR5pY5RYz2voO1jKbVte0SIyORIpo/B/Q7h27Rrr1q0rubNAqVSyYcOGnD17tqR2pUStVnPv3r2sU6eOmPNoxowZfPXqleR9rVy5kk2bNpU8T93Vq1fp7OxMS0tLHjhwQDK7Ji0yaVFGhISESB7p7O/vz+bNm9Pb21tSu2TyS7qUxUv+yxw7dozVqlXj8+fPGRgYyHz58nHo0KFpnhsWFkZnZ2dWrFiRHz9+zNR2YGCgzktyZs+enWZiXA2ayJIrV67kOT0yaVH28f79e+7cuZMxMTEEwLlz5xpkRzO/Cg0N5bt379i5c2fu2rVL5+u//PJL6vJ3mzx5Mjt06JDqe9KrVy8WLlzYqIWBUiIIAsuUKZNqCZ8+tGzZki1atDDo2qioKAJgoUKFDHIQa1brpBeh+OrVK8bGxhp0bxnxuWrR51mfNIepV68eLC0tERERIbY1aNAAvr6+YpntunXrQi6X48aNG+I5tWrVwv3799Mt89yrVy/0798fly5dQvL/MdCqVSsAyeWVgeSSsy1btsSVK1fE65o1a6Z1zqckJSWhXr16qFq1qqGP/FmxcOFCHDx4EGZmZpLbLl++vFFKrJswkR4VK1ZE586dJbXZtGlT3L59GyVKlJDUblJSEho2bIgqVapIaldK5HI5+vbti9u3b+PMmTOoV68e5s6di1KlSqFHjx44evRouhquLz/88AMuX74slvPNKpGRkRg/fjxatGgBKysrXL16Fb169ZLENmDSIhMZU7x4cfTu3VtSm5UqVcKVK1fQuHFjSe0KgoBatWqhevXqktrNq3Tt2hW+vr4oW7Ys3NzcIJfLMXfu3FTnkYSLiwvCw8Oxd+9esQx6RpQuXRrm5uY63Ue9evUAAL6+vmke15QCVygUJj0ykS4ODg4YOHAgrK2tUaRIETx//twgO5p508OHD+Ho6Ii//voLAwYM0Pn6Ro0awdfXF7GxsRmeV6FCBdSqVSvV92TkyJEICwuDl5eX/jdvAHfu3EFgYCC6detmsI3g4GCULFnSoGsjIyMBAPPmzRO1QB8SEhIAAJaWlmkeL1asGPLnz2/QvWXE56pFJieSEThx4gTKly+v5SBq3rw5VCoVrl+/DgCwtbVFzZo1ce3aNfGcJk2aIDExEbdv305l8927dxAEAc2bN0doaCiCgoIAAJUrV4ajoyP+/vtvLTvBwcEIDQ0FANSpUwe2trZajqWUODo6Yvfu3WjUqFHWH14Pzp8/jy1btoj7vXv3Rtu2bcV9d3d3/Pbbb9l6TwBgZmYGmUyGt2/f4tixY5LbX7ZsGYYNGyapzXXr1qF27driAMqECSD5O3bq1ClJbYaHh0OlUklqU0P+/PmxcuVK9OvXzyj208PHxwcrV64U9ydOnIgKFSqI+ytXrsT3338vOu+B5EFBu3btcPz4cTx58gSTJ0/GjRs30L17dxQtWhSurq44e/ZslhxKcrkcCoUCUVFR2Lt3r8F2oqOjsXjxYpQvX158lnv37sHf3x9dunTReq6scuTIEVSsWFHrJYoJE7du3cLBgwehVColsxkVFSW+mJMauVyO3377DRMnTjSK/fR4/PgxFi5cCLVaDQBYvnw57O3txUnm3r17MXToUERHR2frfQHJYyMfHx/s3LkT48ePT3MiuHbtWvz1119YtGgRateurbPtM2fOoEWLFoiLi8vwvGrVqgEA/P390zyu+XyZm5vj5s2bqFatWroOJxP/TZ49e4adO3ciJiYGQPKLtqdPnxpkq1atWgCA27dvIyoqSu/rW7ZsCZVKpTWHSwtXV1csWrQoVXvHjh1Ro0YNzJs3T/Jx2cuXL7Fs2TK8f/8eAHDgwAE0bdoU+fLlQ+/evXHlyhUMGDAAgYGBOttMTExEcHAwypcvb9A9vXv3DgBQuHBhqNVqbN++Xa8xVmxsLORyOSwsLNI95+7du2jVqpU4z5aCoKAg1KtXD4cPH5bMZm4gQyeSTCaTy2Syptl1M58L06ZNQ3x8PHx8fMQfxObNm0Mmk+HSpUvieS1btsS1a9eQmJgIAGjTpg3kcjlOnDiRyma/fv3QqVMn1K1bFwDg5+cHIHki06BBA9y6dUs8V+MM0jisFAoFGjRogJs3b6ayu3jxYkm/KBkhCAJ8fHzE/U2bNmHWrFnifvv27dGjRw9xPygoCK9evRL3R44ciZ07d2bLvQLAlClTMGTIkEwHNfoSHR2NiIgIcZAoBY6OjqhWrVqODCyzA5MWGcaiRYswZcoUSW1OnjwZVatWldxhuW7dOjx48EBSm+lBEjdu3BCdJ0eOHMGUKVNELW7atCkGDRoknv/69WsEBgaKb5Pc3NywfPly8XiFChWwcOFCvHz5EkePHkWnTp2wbds2tG/fHkWKFMGgQYPg6emJkJAQg+538eLFGDBgAIKDg/V6xjt37mD8+PEoUaIEfvrpJ9StWxe3bt3C6tWrYWtri8TERMTHx+Pjx48G3VdaODk5oUaNGpLazE2YtMgwNm3aBBcXF0nfyK5YsQLOzs4GTd4yYvfu3bh69aqkNjPC19dXfEPu7e2Nn3/+WRyX1apVC99++6046QkLC8O9e/dgY2MDAPDw8ICbm1u2vUAaMWIESIpR8CkJCAjATz/9hI4dO2LMmDF62VUoFEhMTNQa86VFyZIloVAo8OLFizSPp4w0cHZ2RqlSpSSLDM2NmPRIfw4dOoTBgweLjtkqVarg0aNHBtkqWrQoihYtikOHDqFw4cJ4/PixXtc3b94c5ubmOH36dJrHT58+jaNHj6b7okcul2P27Nl4/Pgx1q1bp/f9f0pAQIDoqAkICMCkSZNEJ2zBggUhCAJ69uyJQoUK4f3797h58ybs7e0BAF5eXhg7dqzonEuLx48fQxAE0RmsL5rgiCJFiuDKlSsYMmQI9uzZo/P1UVFRsLOzy/B3yN7eHu/fvzd4vJYWzs7OcHBwQL58+SSzmSvIbL0bAG9d1sVlx5ZX1tt+/PhRTNqWMtljnTp12KpVK3H/6NGjqco7tmrVilWrVk21fnLbtm3cvXs3379/TwBctmyZeOznn3+mmZmZuFY2Li6OCoWC7u7u4jlTpkyhhYWF1nra58+f08LCItuqjsydO5dyuVysHPf27VvGxcXpdG1iYiLr16/P+fPni23x8fFGuU8NgYGBkldSM5EMDFj7b9Ii/YmNjRXzpUnFsWPHuHbtWkltRkdH08HBIcOqO1KyY8cOAuDVq1dJkh8+fEg3x0ZadO/eXSsZZlpaFBsby8OHD9PFxYWFCxcWK6pVqFCBQ4YM4apVq3jz5k2dNDA8PFynJObh4eE8evQox40bxwoVKhAA8+XLxwEDBvDGjRs6P99/CZMWZQ+JiYmSV6e5cuUKFyxYIKlNtVrNatWqsXPnzpLaTQ9NDk1NMt7o6Gi9EsaOGTOGnTp1EveNOS7S3Gta1WAFQWCbNm1oZ2dn9MT6JUqUoIuLS5rHNGPvgIAAo96DMTBEi5iL9CivaJFKpaKfn5+4r8lrZGjZ96+++oply5alu7u7QQmZ27Vrx8qVK6d5rEuXLqxevXqmyenbtm1LW1tbBgYG6t2/hpcvX9LMzIyzZs0imazZKXM9jhkzhnK5nI8fP07z+oULF7JGjRri3yAtLdq0aRMB8NGjRwbd48aNGwmAL168oCAIPHfunF75hr755huWKVPGoL7/S+iqRbqI0ywAfZCilG1ObXlFoMjkgYBCoeD06dPFtmnTptHMzExMNBgTE0MLCwutSmBr164lgHQH/IIg0MzMTKu84datWwmA//zzj9hWrVo1fvXVV+L+rl27CCDVRCQkJERnR44hvHjxQhS1N2/ecOfOnTonUEwLjZCePHmSJUqUyFMlEz8lK3+H9DBGMnBjYODEzaRFnzHh4eGSVm36lHfv3vHBgwckkwc3GzZsyFICRY0WPXz4kE5OTjx//ny65wqCQF9fXy5ZsoTdu3dnkSJFRKeSXC5nuXLl2KFDB7q6unLhwoXctm0bT548yevXr/PBgwd88uQJnz17xsePH/PevXu8fPkyDxw4wNWrV3PKlCniIFZj09LSkp06deK6dev44cOHTJ/FpEUmLTLxP2JjY42SKF9DTEwMb968STJZGzZs2JCl5PkaLdIktN6xY4ck9/kp7du3p5OTE2NiYlId00wO169fn6mdsLAwLlq0iO3atWPVqlXZokULLliwgFFRUVSpVJlWEq1Tpw67du2a5rFVq1alqnysVCrzRGLbLDiRcoUe5VUtOn/+fKqX/vowb948AjB4/KL5zGrGJylRKpU6OYaeP39OW1tbNm7cWK+qkkqlUnyZRiYHLKSlfd7e3pTL5fzxxx8ztKf57iYkJLBSpUpcuHCh1vFRo0axQIECBlc/01T4NDSReMeOHTMsYJUSQRAkH8eo1WpJKyUbCymdSNEABABKAFH/7kfpYlzqLS8IlJeXF9etW0eSbNq0qdaH9fLly2JJSQ1du3ZlqVKlxB+4yMhI2tjYaFVXu3LlitZEoECBAhw7dqy4f/36dQLg4cOHxbZ+/fqxQoUK4v7Tp08JgBs3biSZPZni4+Li6OzszB49ekhu++bNm+zbt6/RS/CqVCoOHz6c8+bNk9Tu6tWrWahQIUnfGo4bN45VqlSRzJ4xMXDiZtIiPTh79iyXLFkiadWOmzdv8vXr15LZI7NHi5KSkgiA5ubmkvf35MkT9urVK92SsWkhCAIDAwO5b98+/vbbb/z6669Zr149FixYUHQE6bpZWFiwevXq7NevH+fNm8cLFy7opSuHDx/O8hvMT1myZAnt7OyM4pySGpMWGZ+7d+9y/vz5Ojk0deX+/fuSR5sIgpAteqT57kpdBejNmzccMGCAwW/5M0IThfR///d/JJMnvr169SKZXOnKwcGBzZo1y3ByqFQq+fvvv9Pa2poAWKtWLfbt25cNGzYkAJYsWZL29va8cOFChvfSpk0bNmvWLM1j8+fPJwDx5aiXlxetrKwYHBxswFNnL1lwIuUKPcoLWhQcHMzffvtNXBFBJkciAzA4qnH37t0EYHCl0zdv3lAul9PNzU1sM0SL9u7dSwAcNGiQzk6aadOmMV++fBlGD4aFhbFMmTIsU6aMTtUWyeQqaqNHj+bZs2e12itXrswuXbroZCMtBg0axNKlS2u17d27ly1atNDJ4VO7du10HdApefXqFYsXL84///zT0FtNhbe3N21tbXnx4kXJbBoLyZxIuWnLCwLVs2dPsVyjxjut8eomJSWxQIECHDZsmHj+li1bCIDXrl0T2yZOnEiFQkF/f3+qVCra29trlZm1trbmpEmTxP23b98SAFeuXCm2ubu7U6FQiBNItVrN/Pnzi1FPo0eP5uDBg40+YDp58iRfvHhh1D7i4+P5888/Mzo62ij2Bw4cqLU0UAouX77MCRMmSBp54eXlxd9+++2zfuOWW7a8oEWTJk1ioUKFJP081K5dm23atJHMHpk86G/btq1RHcIqlYp9+/bVWjpsDNRqNX/99VeDw+LJ5MHX48ePefXqVR47dox79uzhtm3buGXLFnbt2pVt2rThqVOnePv2bb5+/drgN3oanjx5wtGjR/P58+dZspOS8+fPc9q0aXotEcwpTFpkfFauXEmZTJZuWWVD6Nmzp+TLEnbu3Mn69etn6furCz/99BPXrFlj1D7IZGeuVA6lzp0708HBQYxC8vDwYLdu3RgbG0tXV1cqFAqtJUKf8vbtWzZv3pwA2LNnz1RR5JcuXWLBggVpa2urFRmRFl999RXr1KmT5rGpU6cyX7584v6DBw84YcKEz9qJlFu2vKBFR44coUwm4927d7Xay5Yty759+xpkc/z48QTA77//3uD76tSpE0uWLClGqXh7e7Ny5coZfqfSQuNEdXFx0cmp8v79e3p5eaV7PDIyko0bN6alpWWWlsRv3ryZ+/btIwAuWbLEYDt169Zlhw4dtNoOHTrE1q1bMyQkJNPrCxcuzBEjRmR6niAIHDFiBM+dO2fwvX5KWFgYv//+e96/f18ym8ZCykgkGYBvAMz4d78kgIa6GJd6ywsCJQiCOHC+e/cuAdDT01M8PnjwYBYqVEh8QxsZGUlLS0u6urqK54SGhtLGxoZdu3alSqXitWvXxA9dTEwMAWjlBlKr1VQoFFpe7G3btqVad1qnTh1xnf/MmTO1ltpJyfz587WioozNuXPnaG5unq19msgaBr79N2mRnkg9ib9z5w6vX78uqc21a9dqOdalZOvWrdkyWdPw6NEjWltb08PDI9v6NJE1TFqUPUitRY8fP85wCakh7N+/n926dcuyYzYtzp07Rzc3t2x7yfP+/XsWKVKEEydOzLKtO3fupBp3avD19aVcLteKjv+UZ8+esUyZMsyfPz+3b9+e7nkXL14kgEzHpr2HzhHDAAAgAElEQVR792b16tXTPPbdd9/R2dk5w+tzK1mIRMoVepRXtCg6OjrVkqIBAwawePHiBtkLCgpivXr10s1rpAsaB8uRI0dIJjtV27Vrx6ioKL3sCILAWbNmEQDbtm3L0NDQVOfcv3+frq6umTqZQkJCWK9ePZqZmfHgwYN63UdKEhMTWa1aNdasWTNL+ZCUSiUtLS21gij0ISEhgQA4c+ZMg67/LyGlE2ktgNUAHv27XxCAjy7Gpd7yikBpEASBxYoVY8+ePcW2gwcPEgBPnz4ttg0ZMoR2dnZa68yXLl2qtfxMw7Vr19IMm3R0dNRyRGlCj48fPy629e3bV2uJmzFISEhggwYNtCKnsgMpl2Kkx7t37yS1JwiC5EuDVCqV3j86OYGBEzeTFpnQGUEQ2LNnT7Zr184ok8L0CAwMNPpEUWotIim5FqnVap1D33MSkxaZyA4mTpzIGjVqGC1iOi1evnwpSf6N/v37087OLlUkmSAIbN26NQsVKpTuUsUXL16wePHidHBwEPNAZcSAAQOYP3/+DKO0+/Xrl+7S/e7du7NWrVqp7lPKKDhjkQUnUq7Qo7ysRR4eHgSgtcxNH1asWEEAfPr0qUHXJyUlsUSJEpJFent6etLCwoJOTk7cunWr1hhoxYoVLFasWLpL2ARB4O7du+no6Ehra2seO3Ysy/cTGhrKFi1asGrVqgbbePjwIQFwy5YtaR6PjY1NM1+bBk1al5SBHZkRGRmZoU1D+Jy0SI7MaUTyRwAJAEAyAsBnVqNOGu7evYvx48fj9evXAACZTIY+ffrgxIkTYinJjh07wtbWVqtU/ahRoxAVFYUdO3aIbePGjUO7du3g6uqKpUuXiu0bN26EpaUlWrdurdW3jY2N2AeQXAYVAF6+fCm2ValSBc+fP0dAQIDmh0dyLCwscOHCBaxdu9Yo9tOjdOnSAAAfHx94eHhIbn/JkiUoWbIkoqOjJbM5ffp0VKhQAWq1WjKbxYsXx88//yyZvVyGSYt05NWrV/jhhx/w8OFDyWxevHgR58+fl8weAISEhBitNLVMJsPevXtx6NAhyOW6/NRJQ+nSpSGTyfDs2TPMnDlTcvv79u1DkSJF8PTpU8lsrl+/HsWKFcObN28ks9msWTMMHDhQMnu5DJMW6YhSqcSoUaNw9epVyWzevn0bx44dk/S3882bN1CpVJLZ+5QlS5bg6tWrsLGxMVofn1KiRAkoFAqEhYXhp59+Mujv9eLFC+zbtw/ff/+9WMpbg4eHBy5evIh+/fqhUKFCqa798OEDOnTogNjYWJw/fx4NGjTItL9GjRohLi4Oy5YtS/ccQRDS1fS3b9+iSJEiWm2DBw9GkyZNMu07D2PSIx2ZPHkyjhw5kqq9WbNmAIArV67oZe/p06fYv38/2rVrBwA4fPiwQfdlbm6OcePG4cKFCzh58iQSEhIMsqPBxcUFt27dQtmyZTF06FDUqlUL69evR3h4OMaNG4d//vkHJUqU0LomPj4ee/bsQZMmTTBgwACULl0aPj4+6Nq1a5buRWP7ypUr6NOnD6ZMmYKYmBi9bdy6dQsAULdu3VTHXr16hSJFimDbtm3pXv/8+XMAQLly5XTq78mTJ7C3t8fBgwf1vtf0cHd3h7Ozs9HGvdmNLiNrpUwmUyA5ESBkMpkTkhO4mfiEp0+fYtOmTVofjh49eiAxMRGnTp0CAFhZWaFfv37Yv38/4uLiACSLV926dbFs2TLxWoVCAS8vLygUCkyePBmdO3fG6NGjsXnzZri6uqJgwYJafZubm0OpVIr7mh/Rt2/fim0VK1aEIAioVq2a5JObuLg4zJw5E/Hx8bC2toa5ubmk9nVl3bp1WL58uZZDTQratGmDmTNnSvrF79mzJ5YsWaL1/5ZV3Nzc0K1bN8ns5TJMWqQjwcHB2LVrFz58+CCZzQULFmDSpEmS2SOJFi1awMXFRTKbQPIEY9asWQgPD4eZmRmsra0lta8rXl5e8PDw0HLkS0GjRo3g7u6O/PnzS2azVatWWL58OfLlk27e8cMPP+Dbb7+VzF4uw6RFOvL27VscPnwYAQEBktncuHEjvvnmG8hkMslsDh48ONXLOSlYvHgxgoODIZPJUKBAAcnt68K5c+ewatUq3L59W+9rlyxZAjMzM4wdO1arXRAEeHp6okCBAvjxxx9TXadSqdC/f38EBQXh6NGjqFWrlk79DRo0CAUKFMDdu3fTPScpKSndMearV69QvHhxrbaBAwdi/PjxOvWfRzHpkQ6oVCrs3bs3zc9W7dq1YW9vjwsXLuhl8+DBg+jbty+KFCmCL774Avv27TP4/lxdXVGoUCGMHDkSFSpUyPJ8o0aNGvD29sbOnTshl8sxevRoODg4oH79+pgwYQJ+/vlnuLm5wdXVFa1bt4aDgwMGDBiAsLAwrF+/Hjdu3EDVqlWzdA8aNm7cCJlMhjp16sDDwwNnzpzR24a3tzdsbW3TvKdixYph/PjxqFevXrrX+/v7AwAqVaqkU3/lypXDnDlzUKdOHb3vNT06deqE+fPnSzrvy1EyC1UCMBjAEQAhAOYB8AfQX5cwJ6m3vBAq+WlGfaVSycKFC7N3795im6ac5NatW8W2nTt3EgD37dunZe/Zs2ccOXIkS5YsSXNzcw4fPjzNyjvlypXTquhGkvb29hwzZoy4f+nSJQLguHHjePv27Sw/a0r2799PmUzGy5cvS2pXX6KiovJEMtf/OjBsCYlJi/REymVVHz584OPHjyWzp1KpuGXLFsmTXd+8eZNmZmbphjxnF0qlUqvMtInciUmLsgcptSgqKkry5KQHDx7k3r17JbUZEhJCa2vrHM/BIQhCmmW7MyM8PJzW1tYcOnRoqmOaSlApx7EpmT59ut5LRzQMHjw4w7xG7du3Z+PGjVO1JyUlUS6Xc8aMGXr3mRswRIuYi/Qor2tRr169tKpl60JcXJyYpPv3338ngCwVE1qwYAEBcOrUqQbbSIvY2Fg6OTmxdu3a/PLLL1msWDGam5tToVDQwcGBjRo14vjx43n27FnJS9DHx8ezcOHCYlU0Q7SIJKtVq8ZOnToZfB+jR49mwYIF80TxoZxGVy3SVaCqAPgRwBgAVXW5xhhbXhGoTxkzZgwtLS3FXDVqtZoVK1Zk8+bNxXNUKhUrV67M6tWrp/kFVqlUGZZLLlKkCEeNGqXVVqZMGQ4ZMkTcDwgIIABu2rQpq4+UJs+ePTOKXUNQq9UGr21Oj/j4+CxVJ0iLsLAwSe9TpVLx5cuX2ZoDxhCyMFgyaZGJTAkICMg1AwVBECTP2aZUKunj4yNW35SCiIgISTVcrVbzzZs3Rq26JwUmLTJhTEJCQsRy87kBfbRo4cKFBMB79+5ptavValavXp2VK1dmUlIS79+/r5Xn49y5cwSgUxWktNBUmAoLC0vzeJMmTdi2bdtU7c+ePUtzjCsIAt+9e5et+agMwVAtYi7Ro7yuRWvWrCEAg1+WvXjxggA4Z84cg+8hNjaWJUqUYN26dSV35kREREhaEVpX/vjjDwLg2bNntdr1yR/55s0bAuCCBQvSPUcQBD59+jTd/I7NmjXTmnfrQnx8PB88eKDXNRmhyc+W2/Mi6apFmS5nk8lk20g+Jrma5CqSj2QyWfqLDv/DTJs2DWvWrEnVPnDgQCQkJGDv3r0AALlcjpEjR+Lq1au4f/8+gOTla3PmzMHDhw/h6ekJIDnH0rp16xAXFweFQpFu+K4gCPjw4QMcHR212m1sbLTWnRYtWhRA8hp3KdGE5ZUvX15Su1nBxcUF7du3l3T5mYeHBxo1aoT3799LZrNZs2aYPHmyZPY2bNiAkiVLSprbJLdg0iLdWbx4MWbNmiWZvRcvXmDVqlUICwuTzObdu3e1lttKgUaLypUrJ+lSl6wwc+ZM1K5dW9J8agcPHkSDBg1w7949yWx+/fXX+PrrryWzd+bMGTg7O+PmzZuS2cwtmLRId7Zu3YoxY8ZIZu/jx49YtmyZpOOYf/75B0FBQZLZA/6nRcWLF4eVlZWktg3F09MT5cuXF3ODZIRKpcKaNWvQqlUr1K5dW+vYwYMH8fDhQ/z666/w8/NDzZo1xZQNUVFRcHFxQeXKlbFixQqD7vPy5csAgGfPnqV5PDIyMs2lgZr/Q02OTA1Pnz6Fk5MTDhw4YND95HZMeqQbJ0+exPDhwxEVFZXm8c6dOwMAjh8/rpM9kliyZAn8/PwAAGXKlEHr1q3h6elp8Nzj7du3mDhxIu7cuSNZblmNFtnb28PBwUESm7qSmJiI+fPno1GjRvjyyy/F9nPnzqF8+fLidz0zTp8+DQBi7qm0iIiIQMWKFbFly5ZUxwRBwP3793VeVqvBw8MDNWrUQGRkpF7XpYdSqUTBggUN1sbchi45kaqn3Pl33W36iw7/w/j4+ODx48ep2ps0aYLKlStj69atYtt3330HKysrrSTQffv2RbNmzTB9+nSEh4fjr7/+gqura6b9hoSEQKVSoVSpUlrtFhYWSExMFPctLS0BAEePHtX72dIjMTER5cqVw7p16ySzKQVDhw6Fu7u7pE6kPn364ODBg5LmIlm8eLGka/Vbt26NP/74I8fywBgZkxbpyIMHDzLMKaEv3t7eGDt2rKQO1CFDhmDEiBGS2SOJxo0bY8aMGZLZlIKePXti3rx5UCgUktls3bo19uzZgwoVKkhmc9q0aZg7d65k9mrXro3Vq1ejbNmyktnMRZi0SEeePHkCb29vyew9fvwYkyZNkrRowLRp08QJpFT0798fQ4cOldRmVunQoQPmzJmTKkF2Whw7dgxBQUGpxickMW/ePFSsWBH9+/dHzZo1sXXrVrRo0QJA8t/y1atX2LJli8FjpW+++QZAcn6jtAgPD08zkXdgYCCA5Ml8SkqWLAkPDw80bNjQoPvJA5j0SAeCgoJw+vRpcS70KWXKlEGNGjV0niOFhYVhypQpuHTpktg2YsQIPH/+3OAiJCtWrMCMGTPQvn17TJ8+XfxMZ4WpU6eiTZs2OZLMeeXKlQgODsbcuXO1Xuw1bdoUv/zyi87BB8eOHUPRokUzzE9UqFAh7Ny5E/369Ut1LCAgAFFRUXrnN+revTt27dolWZ7ffPnyYc2aNejSpYsk9nKc9EKUAEwHEA1ABSDq339HA/gA4Hddwpyk3vJyqOTcuXMJgP7+/mLbqFGjaGFhoRV6d+/ePSoUCg4ZMoQqlUqntaPHjh0jgFT5iBo2bMiOHTuK+yqVig4ODlr5mbLKu3fvOGrUKJ4/f14ymyY+f6BH2LZJi3IezdIkpVIpmc2rV6/y+vXrktlLSEjgpEmTuGPHDslsmvj8MWlR3kKzNEnKJWJ3796VfAwzZ84cLlmyRFKb2UmHDh1YrFixVJp/6tQpAuDGjRtTXXP9+nXKZDJOmDAhS30HBwcTANevX5/qmFqtpkKh4PTp01Mdmz59Os3MzCT9ncpO9NEi5kI9+hy06JdffqFCodBp2ZdmaZImVQmZvPzJ0dGRPXv2NKj/x48f89ixY3zx4gVtbW3ZsmXLLC9rW7duHadNm5YlG4bw+vVr2tnZsXPnzlmyEx8fTxsbG44cOdJgG1u3biUA+vr6Zule/ivoqkW6iFSODIzS2vKyQL1584ZmZmb86aefxLanT59SLpen+nLPmDGDALhz506dbE+dOpXm5uaMiYnRaq9Xrx7/n73zDovqWrv4GjqiIKLYe29YYjeWWKLRWLHEeqMxFmKMRqNGjS0m+sUWNRZs0Rv1ir2igh0RI6jEgqJiQUEEQXoZhrO+P7gz13EGGGb24KDze57zJOxzZu0z4KzZe593v2+PHj3U2urUqcMBAwbo+S4KF7GxsdyzZ4/QfcU3b94UmpQ8Li6OV69eFXaPCoWC4eHh781+2zcPsxeZKawkJyfzyJEjQj+X9+/fp7+/vzC9pKQkXrt2TWjekOfPn+eY18RUMHuRmQ+J9PR0+vr65pqL8cGDBwTAhQsXapzr0qULy5Urp5brLDw8nL6+vmzWrBnLli2rNqnWB2X+k0WLFmmce/nyJQFw9erVGufc3d1Zq1YtrZpRUVE55koxFfTxIpqQH70PXnT9+nUC4KZNm/TWmDVrFi0sLAzOMbh9+3YCKJSJ4iVJYv/+/Wlra8v79+9rvUahUNDPzy/PnEMHDx4kAJ48eTLPfqOjo3nq1CmNOdWECRNYtGjRfM+1FAoFb926pXcycG28evWK4eHhwvSMgchFJAsAwwH89N+fKwJooYu46MOUDSo6Opru7u48d+5cjtf069ePLi4uTElJUbV98cUXdHBwUBtoZ2Zmsm3btrSxseGKFSty7VeSJNasWVNrksEGDRqoRR09ePCAdevW5aeffpqPd5Y7L168EKYlGuXK89tJIQ2hefPm7Nq1qzC9tWvXEoCwwU1MTEyOAyxTQs+Jm9mLdECSJA4YMIB79uwRprl3716h1c4iIiJ49uxZNS80lBcvXphMMu23uXTpEgHwwIEDwjQHDRrEGjVqCNM7ceIEAfDSpUvCNIsWLWpwVIKxMXuRcfHw8OAff/whTM/X15erVq0Spvf69Wv6+PgwLi5OmGZUVJTJFreIiIgggFyjpGbMmEELCws+f/5crT04OJgAuHjxYq3XI5dqbfkhKCiIAPjFF19onFNO8rV5aZ06ddinTx+tmm5ubjmeMxUMWEQyCT8ydS+aP38+586dm+s1kiSxevXq7NKlS556gYGBXLp0qcYD/IiICNrY2HDChAn5uj+5XE5vb2/VvEqSJH755ZcEwMOHD+dLS0l0dPQ7icz766+/8kyELZfL6eDgoFZFXBsDBgxgqVKldHofnp6eBKCxSN6wYUOd/qZvk5GRkeOCur706dOHbm5uwvSMgchFpPUA1gK4+9+fnQEE6iIu+jBlg3ry5Anr16+f6wf93LlzGpUjQkJCaGFhwalTp6pdGxkZSSsrK9ra2vLu3bs5ah4/fpwAuG3bNo1zlSpVUivNOnfuXAJg27Zt8/PWciQuLo4AhA7oRBIbG8u///5baHWgwMBAoaWFHz9+zMOHDwubTMvlcm7evJl37twRomcs9Jy4mb1IB+RyORs0aMC1a9cK0+zevTubNWsmTO/PP/8kAGHVwCRJorOzM6dMmSJETzTp6en08/MTGuVz69YtBgYGCtOLjo7mwYMHhVZv2bFjh/CKlqIxe5Fx6dy5s9atR/ri4eFBFxcXYXrnz58nAPr6+grTbNq0qUkvWFy4cCHHRTO5XE5XV1et23FGjx7NIkWKaLz2zp07dHV15UcffSRk8UwZiTRnzhyNcwcOHCAABgUFqbWnp6fT0tKSs2fP1qp56NAhjepQpoYBi0gm4Uem7kVffvmlWsXqnJgzZw4tLCzyjD5ZunQpATAhIUHj3JgxY2hra5uvCBblNk5PT09VW2pqKps1a0YHBwe9dkG4u7sX+IJFSEgIixYtynbt2uUZ+RMQEMCoqKgcz7969Yo2NjacNGmSTn1HRETwwoULatudlQ/XtUU26sL+/fv1rtinjTNnzvDQoUPC9IyByEWk6//974032v7RRVwH7e4AQgE8BDAzr+tN3aDyQpIk1q9fnw0bNlR7Yv7ll1/SxsaGjx8/Vrv+5s2bdHV1pYuLi9Yvv9evX7NmzZqsUaOGRqlnSZJYpEgRfv/996q28PBwNm/enC1bthTyfl6/fs2VK1ea95iayTd6TtzMXvSOUCgUQqOGXr58yfPnzwsrUZ+Zmck//viDFy5cEKJn5sPB1LyI+fSjD9GLRC7GxsfH8+LFi0K3mm7btk3vyIF3zaFDhwiAR44cUWuPiYmhra0tx40bp/Ga33//XWsJ7zeRJInBwcH09fVlfHx8rvcQHR1NAFyzZo3GOeXE/e2FrFu3bhFAoc6JZ8AikkmMjd4XLwoNDSUALl26NNfrsrKymJycrDUCOiwsjJaWljovfpDZuX8uXbqksTMhMjKSlStXpqura74XMw4dOiQ0ijwvYmNjWatWLbq6uvLZs2cG661YscLgHSW7d+8mAAYEBBh8Px8KIheR/gZg+YZJlXrTqPQ9/qsZBqAaABsA/wCol9tr3geDUj6BP3XqlKrt2bNntLe358CBAzWuV25BA8CRI0fy/PnzjIuL4/nz59m4cWNaW1trnTi9fv1aqwl2796dzZs3F//GTBRvb+9ctxjml9DQUJ44cUKYXkJCAgMCAvIcVOWH+/fv8+XLl8L0jIGeEzezF5kptFy8eJHHjh0TphceHs5jx44xLS1NiF56enqeTwXzy+PHjzW2xJgapuRF1MOPzF5kJr9cv36du3fv1nru888/Z9myZTW2jvz2228EoJG/JDU1la6urmzYsGGO0U2BgYFs3LgxARAAXVxcco38CgsLIwAuW7ZM49z48eNZokQJjXblRDGnyebz58/56NGjHPs0BQxYRDKJsdH75EWtWrVi3bp1Ddoi/9VXX9HGxoZPnjwx+H7u3btHV1dXli1bVmhUjEhSUlL48ccf08bGhn5+fjq9JjQ0lNu3b6dcLtc4l5WVxRo1arB169Y630NycjK9vb3VFrBGjx5NJycnvbf1BQcHqxXFMpSXL1/mmCfKVBC5iDQMwBEAzwH88t8V6YG6iOeh2xrAqTd+/hHAj7m9xpQNKiQkhL169eKNGzdyvS49PZ3lypVj+/bt1doXLlyosbi0ePFinjp1isnJyZw2bRodHR1VX8IAWKpUKR4/flxrP8o95fv371dra9asGVu1amXAO/0fKSkpfPnypdDE1aJp2LCh3lUStDFjxgza2NgI07tw4UKeT/DyS5EiRTS2R5oaek7czF6kA7GxsezVq5fQxc7169fTy8tLmF5ISAi9vb2F6aWnpzMqKkrrQMRU6N27Nxs1aiRMb8OGDQQgbJFGGUpvSELRt6lfv77QaqDGwJS8iHr4kSl7EZm9nULbdnt92b17Nzds2CBM78mTJ0IXYxUKBV+8eCFMzxhMnjyZjo6OGu3R0dG0srLitGnT1NqzsrJYrVo1tmvXTuM1a9asUY1JtU0c9+7dSxsbG1aoUIGenp709vZmvXr1WLJkScbGxmq9P+X41d3dXeNcly5dtD4InTlzJq2trXNMX9CrVy82btxY6zlTwYBFJJMYG5m6F3l4ePC3337T6dotW7bkmSPw1KlTueb8CQ8Pp52dHYcNG6ZTn9HR0Tx69GiOn4tbt27R1dWVpUqV4tWrV3XSfPHihUbOJmOQkpLCrl27UiaT5WusuGrVKgLQuo3+8OHDBMD//Oc/OuspiwIoo6+ysrJYpkwZDho0SGeNt6lZs6bW/Gz6MmnSJDo5OQnTMwbCFpGytVAHwDcAJgKoq8trdNAcAGDzGz+PAPCHluvGAggCEFSpUiWj/cIM5caNG2zcuLFO4XIrV67U+MJNT09nrVq1WLVqVdUHvlixYmpJSePj4+nl5cUVK1Zw7969uYZ0KyOe3synNGjQINrb22ssYOnLtm3bhOY1MQZPnz4VElKpJDw8XGhIZFxcHE+cOMGYmBhhmnv27BFaQc4YGDBYMntRHkRHR7Nx48bct2+fME3RSUmVA35RXLx4UXheE9FERkYKfRL+8uVLBgQECMv5lpaWxhMnTgitGnL8+HGT32JoSl5EHf2osHgRSbZo0UJo3sS+ffuyYcOGwvSUiVhFjROePXumkdfE1IiJidH6ZF05Nn072uj06dOElorBmZmZrFKlClu2bMmAgACN3DAnTpyglZUV27ZtqzYxVibozmlC7+3trbU/kixfvrzWvDadO3dm06ZNc3zPFy5cEPrgwhjo60V8h2OjwuRF/fr14/Tp03W6NikpiY6Ojhw6dGiO10yZMoVFixbNVWfWrFkEwCtXruTZp4+PDwHw4sWLOV4TGhrKKlWq0N7ennv37s1T09ramjNnzszzOkOIjY1lu3btKJPJ+Oeff+brtfHx8bx3755GlJAkSWzdujUrV66crwiitLQ0BgQEqHZjXLlyhQD4119/5eu+3uTixYt5Bojkh2vXrgktfGMMRC8iOQNwA9BUeejyujw0B2oxpzW5vcbUV7l1JTk5maVLl2anTp3U2pVRKcqM/pmZmXqHUo4fP57FihVTixK6f/8+69Wrxx49euh/829w9+5drl+/3uTLyZsxPQyYuJm96B1giBdp4+nTpxqJUQ0hMjKSa9euFbpgbObDwJS8iHr40YfmRQqFQqgXRUVFMTAwUFh+tpSUFK5Zs4YhISFC9AqSFi1asEmTJhrtgwcPprOzs1qyWvJ/W8i0JYkNCwtj8eLF2ahRI62Jh3OrlrR582YC0MgTqizm8nb0hyRJdHFx4ZgxY/J6iyaNgYtI73xs9L550XfffUdra+scq1ArFIo8E8knJiayXLlybNq0aZ67NuLj43n16tU8c75FRUWxdevWBMApU6bk+iBp/fr1Oi1g6UtISAhr1apFGxsbodHqvr6+BGBwgZgZM2bQysoqx+guM9oRtogE4GcAzwCcB3Duv8dZXcTz0H2vtpDkF+UTn7e3MU2dOpUQUAq6Tp067Natm0Z7tWrVcl1Zf5+QJIlr1qwRWqnsypUrPHPmjDC96OhoXrhwQVi4qTJ5ZXR0tBA9Y6HPYMnsRWYKM3/++afOIei6cOvWLR49elTYhDopKYnnzp0TWp3tzp07+apO8y4wJS+iHn5k9iIz+WXv3r0a45h79+5pzaP5+vVr2traai3D3bp1a1avXp1hYWFqVWYVCgXbtm1LJyenHKMvR48eTVdXV63nZs+eTQsLC41cMsoKx29v1X7y5EmeE84HDx5oLEqZGgYsaJvE2Oh986L79+9TJpNprRKYH5SLrb///rugO8vevTJx4kQCoJubm9CHcrogSRK3bNlCBwcHurq66pwD6W1OnTrFgwcPami3bNmSFSpUyPe24OjoaB44cIAxMTGUJIlVqlRh9+7d9bo3JX5+fgwLC4HTKuAAACAASURBVDNI402ePn0qNMeSMRC5iBQKwEYXsfwcAKwAPAJQFf9L2FY/t9eYskE9e/aM3bp103mBIS0tjZUqVdIoiZqRkcFmzZrR0dGRP/30E//973/n+16Ug4G3Devhw4csUqQIv/nmm3xrakOSJL548YJPnz4VoicaZVj5unXrhGm6u7uzTp06wvS8vLwIgLdu3RKil5GRQRhQyrKg0HPiZvYiHZDL5ezevbvQKjU7d+4UuiUlMjKSXl5eQhPKx8bGmuzW2szMTNra2vKHH34QpvnDDz/Q1tZW2CJSYGAgoaUqkyGUK1eOo0ePFqZnDEzJi6iHH5myF5Hk8OHDuXz5cmF63t7enD9/vjC9169fc9++fUITwCcmJqqlEjA1atWqpZFvaOHChZTJZBq/B2XutcDAQLX2a9euqcaZ69atIwDVgvHatWsJINfx64IFCwhAax47d3d3rWNYZf6Ut6tX7dmzhwByXaT/+OOP+cknn+R43hQwYBHJJMZGpu5FP/zwQ76/g/v06cMSJUpofdAbEBDA6dOn57nQIUkSP/vsMzo4OOS6GJGens6DBw/mK3H20aNHWaZMGVpYWNDDw0MjNUZqaipv3ryps54u3L9/n926dSMAduzY0SDv7Natm8Y2VOXnefPmzfnWO3nypCqXVUBAAAHke4vd2zg6OvK7774zSONNRowYwcqVKwvTMwYiF5H2A3DVRSy/B4AeAO4jO/v/7LyuN2WDioyMZPPmzXNMdK2Nv/76Sy0BmJInT56wZMmStLW1Zc+ePfN9LzNnzqSFhYXGE+Dly5cTAGfMmJFvTW1IksQyZcronDTuXRAVFZVjxRB9iIiIEGrI0dHRPH36tLBIpMzMTB46dMjkQ+n1nLiZvUgHJEli8+bN9foCzolBgwZp3eagL0ePHiUEl1xt3bo127RpI0xPNLGxsUKrJsbExAh9+piYmMizZ88KjWI8ceKExuTT1DA1L2I+/ciUvYjMTii/YMECYXozZsxg8eLFhS2e3r59O8f8O/oybNgwli1bVui2O5EkJiaqTfwkSWK9evXYtm1bjWs//vhj1qtXT+O9jB07lvb29nz9+rVqG45CoWBsbCydnZ3ZqVOnXN+/snS3tnQITZs2ZbNmzTQeUH755Zd0dXXV0J00aRLt7e1z3ZJ44cIFnj9/PsfzpoABi0gmMTYydS+aMGECx40bl6/X+Pv7EwBXrlypcc7T05O2trYai5raCA8Pp6OjI9u3b5/jtrbU1FQC4C+//JKve3z9+jW/+eYbWlhYsGjRopw1a5aqyuqcOXNoaWmZ5xY5XQgPD+c333xDa2trFitWjKtXr85zO19epKamqkUcpqamskqVKnRzc9OraFNycjKvX7/OpKQkjhs3jvb29lq30+YHPz8/oRXxgoKC8rVW8C4QuYjUDEAEgFPIzv5/BMARXcRFH6ZuUPklKyuLzZs3Z5kyZTT+kfv7+9POzo5NmjTJ1yJIfHw8nZ2d2a9fP41zygRjIieX+/fvF5pwzMyHgZ4TN7MXvSNEV2CMj4/nzZs3hSWFJrPDovUNqTbz4WL2osKFaC9KT0/n9evXhVYwunz5Mk+cOGHwBKugUC6kvb0dTLlN7O3I5uTkZBYtWpRffvmlhtb06dMpk8nyfNi2dOlSAtAY+2ZlZdHBwYGTJk3SeE39+vW15vRs1aqV1spxhQ0DFpFMwo/eVy/q0KEDy5UrpxFxlN/8bMqCR7/++muO1wQFBekdoR0SEsKBAwdSJpPRxsaGQ4cO5caNG7l37169x1qZmZn08fHh4MGDaWVlRSsrK44bNy7HPFGGMmfOHALg2bNnDdJJTU2lk5MThw8fLujOPixELiLdATAJwCcAOigPXcRFH++jQV29epUA1KqwKfH29qaNjQ0bNmyoc8LY7777jgC0VudSVrz4ECZaWVlZnDBhAv39/YVpJicn09PTU+j2vatXr/Ly5cvC9F6/fs2///67QEp6GoKeEzezF5kplMycOZPHjh0Tqunp6Sk04vDOnTs8c+aMsOiJ9PR0Xr58WWgkqDEwe5GZD4nffvtNY5vZzz//rHWb2JIlSwhAI6/R9u3biTeqSO3cuZOBgYGMiYlhkSJFdIpOnzdvHgFoLAo+fPiQAPjdd9+pJfKOj4+nTCbjwoUL1a5PS0ujjY1NntuU/P39VdEZpooBi0gm4UfvqxedPXuWALh69WqDdCRJ4uDBg2lpaWnUeVhoaCi//fZbFi9enABYvHhxDhw4kGvWrOHly5dzjcxJSUnhtWvX6OnpyWHDhtHFxUWlMWXKFI08ZYawdetWrlixQjXmuHXrFq2trQ1a+Dl8+DDPnTunqh5+7tw5g+4xOjqax48fF1pAKigoyGTTwCgRuYh0QRehgjhM3aCGDBmi8QWnC+PHj6eFhYVG2L+fnx979uzJYsWKsXTp0vTx8clVR5lfJ6ecR//3f/9HAEIz6JPZ5VpF5l8RwYMHD1iyZEnu3r1bmKYykktbJRJ96d27Nxs0aCBM7/jx48K3CRkDPSduZi/SkalTp6qqPIrg9u3b/Oabb4TmDTlz5ozQzyeZPQExtdLaiYmJrFy5cq5PH/PLy5cvCYArVqwQpvndd9+xaNGiwhaRQkJCCMDkvhvexuxFxmXp0qXs27evML2IiAhOnjxZaAT01atXuWnTJmF6ZHaKg99//92kopGU0e9ff/21WnvDhg358ccfa1zfvHlzNmvWTKO9c+fOrF69OiVJYlZWFosWLcpvv/2W8+fPJwCdipmMHz+eJUqU0Gjfu3cvARCA2vZfZfnzt8fBymTbueVyS0tLe29zRdKE/MjUvWjHjh1s06ZNvqMZJUlihw4dWLp0abUHtCkpKZw2bVqec7M3iY+PZ40aNVi2bFmt2+BCQkL4+++/C/keTktL46FDhzh06FA6OTmpPlcA6OzszFq1arFp06Zs2rQp69aty1KlSqld4+rqymHDhnHfvn35TnCtCwMHDlRVaJTL5WzatClLlSpl0Jb6Bg0asGfPnmzRogXr1Klj8O/xyJEjBCC0wl3ZsmXfm1yRupjTCgCLkZ2lX2gp2/wepm5Qw4YN488//5zv18XHx7N8+fJs0KCBWsjhtm3b6OLiQl9fX9atW5cAOGTIEN6+fVvt9enp6Vy8eDEtLCzYpk2bHD/sAwYMoI2NDT/99NN832NujBo1imXKlBFWIlcU6enpWpM26oskSXz8+LGQvcVKnjx5IjSvSUxMDI8ePSo0YbEx0HPiZvYiHfnhhx/o4eEhTO/ChQt0cnISGjU3cOBA1qhRQ5gemf1E3dbW1uSeOCsUCo3y2IYgSRIjIiKEVlJ78eKF0L9vUlISvb2939fqbGYv0pEVK1YIXUQKDw9nkSJFhD4Mmz59Ou3s7ISOF3bs2EGZTKY1KvxdIkmS2kT40aNHWhekle2//fabWntkZCQtLCw4d+5cVVtMTAyfPn3KMmXKaN1upo0ePXqwcePGGu0//PADbWxs6OPjozYBXLBgAWUymUZEwPz58ymTyXId88jlcvr4+PD+/fs63du7woBFJJPwI1P3ot27d7NLly565ci5fPkyAajN8RQKBR0dHTU+I3lx8+ZNFilShK1bt9aYr61fv54AhEaqKPM6eXp68uDBg1yyZAk9PDw4aNAg9uzZkz179qS7uzvHjh3Ln3/+mV5eXrx//36B5HRTzqdmzpxJANy/f79BevHx8dy/fz8BcM2aNQbfX2JiIv39/YXu7jh//rzJp4ERuYh0TsshpJRtfg9TNyhDUEaQfP/996q2N59gpaamcs6cObS3tycAVqpUie7u7vz8889ZokQJAqC7u3uOCxySJLFChQr8/PPPhS/2PH/+XKMiwLskOjraZBNamslGz4mb2YveEZIkCf9MPX/+XFUSWhRxcXEmFSYcGxvLzMzMd30bZnLB7EWFC2N40cuXL4U/eJHL5TpF5BQUSUlJWv125cqVBMAHDx6otSsTX79dTWr16tVao42UEUS6btutXr06Bw4cqNHevn17tmjRQqO9U6dObNSokUZ7586d6ebmplOfpo4Bi0gm4Ufvuxf17duXRYsWVXtIpW+kofLzMmTIEDWN169fCy1uoSQ4OFi4pr5kZmYyNjZW9fOJEycIgGPGjBGi7+7uzuLFiwt94P+hIWwRyZSO992gPDw8CIDe3t45XhMTE8Pff/+d7u7urFu3Lt3c3DhixAj6+vrmqh0aGqo1caJIJEkyOAu+oWRmZrJJkyYcPHiwUF1Jkjh9+vRcS8jml6ioKG7fvl3oAtyVK1f4zz//CNMzFvoOlkzleN+96H3gXXsRmb1dtUWLFsK3tCxatChPz88PaWlp3LRpE8PDw4Vp3rx5U2gIuLEwe5EZY2MKXjR58mRWrlxZY2LVsWNHrVvqO3TowPr16+fZvnbtWu7Zs4eff/45y5cvr9NWoaSkJMpkMo2qfRkZGbSzs2PXrl159+5dVXt6ejrt7Ow0ymynpaXRzs5OaxLuN3nw4AEvXrxo8gv6Zi8ybUJDQ2ltbS1sK5Iy59jUqVML7MG3KXjRqlWrWKJECT5+/JhhYWEsUaIE3dzcDH6ouGvXLlVk4o8//ijkXvfs2SN0t0hkZCTPnDkj/AGqaIQuIgHoCWA6gLnKQ5fXiT5M3aCOHDnCunXr6r2KnJqaSjc3N7q4uPDx48ckyYULFwrZx63Mh/TkyROuWLGCW7duNVjzbUaPHs1mzZoJDQnPL5IkcfPmzULzFpHZod0ODg7ctm2bME3lkwiRJbDbtm3L9u3bC9MzFgY8cTN7kQ4EBgbSzc1N6Jffhg0b8hys55ft27fnOxRcFxYuXMhKlSoJTYaoD0eOHOHGjRuFaqamprJUqVJq20kM5dq1awTAPXv2CNMcPnw4K1asKEzPWJi9yLg8e/aMH330EQ8cOCBMc9++fRwyZIjQidexY8c4c+ZMYXpKtm3bRmdnZ43k1AWNv7+/Rl62xMREWllZccaMGWrtsbGxtLCw4Jw5c9TaY2JiaGFhwdmzZ5PMHm+5ublx0KBBtLKy4rRp03S6l/Pnz2uNWrp06ZIqH8uSJUtU7X5+fgSg8W/I19eXAPIsl/3TTz/RwsJCaDVQY2DIIpIp+JGpe5FcLmerVq0MSpA9bdo0ymQy1QOS8+fPs1+/fnpFvUiSxIkTJ2psk/P39+f48eOFP3xSfl527dolVDe/hISEcPr06YyLi2O9evXo7OzMhw8fGqzbp08furq60t7eXi2fmr5IksTixYtz3LhxBmspUVboe1+21lohD2Qy2QYARZCd9X8zgAEArub1ug+R4sWLo3bt2khLS9Pr9fb29ti3bx+aN2+OPn36wM/PD3fv3oWFhYXB97Z79240a9YMlStXxuHDh1GuXDmMGjXKYN03+fzzz1G3bl1YWeX5z8ooyOVy2NjY4KuvvhKuXbVqVcTGxiq/rIXQr18/3LlzBzVq1BCmuXnzZr3//Zk6Zi/SHWdnZ1SsWFGo5uPHjxEcHAySkMlkQjTPnj2L0NBQTJs2TZgmAHTv3h3x8fFwcHAQppkflF7Uq1cv4dr29vaIjIxEenq6MM3GjRvj4cOHKFWqlDDNhQsXIioqSpieKWH2It1xcXGBi4sL7O3thWm+fPkSt2/fRlJSEhwdHYVoXrt2Dbt378bcuXOF3mvHjh3Rv39/oZ+t/KD0ojZt2qBNmzZq506dOgWFQoEePXqotfv4+ECSJPTs2VOt3dvbG5IkoW/fvgAAmUyGGzduwNPTE3v27MHgwYN1uqdLly4BAFq3bq3WfvbsWchkMgQHB6NMmTKqdl9fX1hYWKBjx45q1587dw6Wlpb4+OOPc+1vwoQJ+OSTT2Bra6vT/RU2zH6kG9bW1ihfvjycnJz01pg7dy527dqFCRMm4OrVq0hJScGdO3cQGRmJWrVq5UtLJpNh1apVSEhIwE8//QRLS0v8+OOPCAsLw/79+zFjxgxUqVJF73t9m+bNmwMAIiIihGnmB6UX1a1bFwsWLED37t3x4MED+Pj4oHr16gbr/9///R/q1auHyZMnw9XV1WA9mUyGe/fuITMz02AtJb169cLp06eF/l3fKXmtMgG4+dZ/iwLw0WWFSvRh6qvcojh16hQtLS3ZtWtXIfmLlFXF/vjjD5IUmuA1Jwo6AiAoKIiVKlUqFNsnzOj3xM3sRe8fqampRg/jjo+PL9AcaU+ePGG1atWER0OaMQ5mLzJDZm+NMrZPpKSkFGgBkri4ODZq1CjHNAajRo2is7OzxjavESNG0MXFRWNr2hdffMEyZcpoREgMGDCA5cqV0/n316lTJ615jNq3b88mTZpotLds2ZKtWrXSaP/oo4/Ytm1bnfosDOjjRTQhP/pQvGjPnj0EwOXLlwvRUygUHDZsGAFwzpw5TE9PN9rWS+VnVKFQMDEx0Sh9aCMjI4NdunThDz/8wIyMDPbq1Ut49daBAwfSwcFBSBTSh46uXqRLiIvycWeqTCYrByATQFX9lqw+DLJ///rz6aefYtOmTfD19cWQIUMMXgVdsmQJihcvjpEjRwKA6imbofeZE8+ePUP9+vWxZs0ao+hrw9XVFbVq1UKFChWEa1+5cgUdOnTAgwcPhGlmZGRg9uzZuHPnjjDNR48eYe/evUhOThamaWKYvSifGOszLgp7e3vIZDKj3WdSUhLatGmDqVOnGkVfG8qI1GrVqgnXjoiIQIsWLRAQECBUd+HChUI1X716hZ07dyImJkaYpolh9qJ8YupeZGdnZ1QvysrKQq9evTB48OAC+104ODigdu3aWiMkSOLMmTP45JNP1KLHSeLUqVPo1q0bLC0t1e7f19cXXbt2hYWFBdLS0tCqVSscOHAAp0+fRvfu3XWKJk1KSoKfnx8+/fRTtfaEhARcvnwZLi4uOHnypKo9Li4OgYGB6Nq1q9r1UVFRuHbtmkYU1dtkZmZi27ZtCA8Pz/PeCjFmP8oHygmwvgwYMAC9evXCnDlz8PDhQ4Pvx9LSEtu3b8eYMWOwaNEiTJo0Sch9akP5GR0zZgw6deqEjIwMofo5YW1tjTp16qBWrVoYPHgwjh49irVr12Lo0KEGa5NEy5YtsXfvXkydOlVIFBIAbN++Hbt37xaipWTnzp0IDQ0Vqvku0WUR6ahMJisOYCmA6wCeAPiPMW+qMDN69GiNL0d9GDVqFFatWoUDBw6gSpUqmDlzpl4658+fx6FDh/D999+jWLFiqvY///wTderUEbolQknZsmXRt29fdOrUSbj2m5DEZ599huvXr6NixYrw9fVF+fLlhfeTkJCAhIQEtfBqQ7l79y6WLl0q5AtIyeHDhzFo0CCkpKQI0zQxzF6UD+bNm4c6deoI1ZwyZQpGjx4tVPPkyZOoWrUqXrx4IVQXAIoWLaoa8BmbixcvQi6Xw8nJCd7e3mjYsKHwPqKiopCZmSlskARkL/gsWbIEV65cEabp7++P4cOHIywsTJimiWH2onywefNmlCpVCqmpqcI0ly5diu7duwvTA4AbN26gVq1aCAwMFKoLZE8UBw8ejH79+gnduquNoKAgxMfHw8bGBl5eXujSpYvGNXfv3kV4eDi6deum1n7nzh1ER0drvOaff/5BbGysanwbHR0Na2trREdHIz4+Hp07d9bp3k6cOIHMzEwNT/bx8YFCocCNGzfg7e2taj958iQkScJnn32mdr2vry8AaNy/tvc5atQo1Ra69xSzH+nIyZMn4erqivv37+utIZPJsH79etjY2GDUqFH497//jY8++ggKhUJvTUtLS2zcuBE//vgjNm7ciC5dusDNzQ179+7VWzM33N3d4e7ubvQtnv7+/ti1axdkMhl+/fVX7NmzB4cOHcKaNWvg4eEhpI/ExETcv38fjo6OmDZtmhBNAPD09MTOnTuF6SUkJGD48OHYt2+fMM13Tm5hSsheZGrzxs+2AJx0CXEyxlEYQiX/+OMPjYoThuoBYKVKlRgZGZmv175+/ZrVq1dntWrVNLawnT17loMGDVIrVWksli1bxosXLwrXPXnypCoJY2EkKSlJaKLH9PR0Xrt2TZieMUE+w7bNXpR/9uzZw8mTJwtNdD9nzhx+++23wvTI7Mo53bt3Z2hoqFBdbWzbtk1ogl8ld+/eJQCNxLWFhbS0NKHlcDMzM3nz5k2TT2RLmr2oILhw4QInTJggtBLp2rVrOXjwYKH+lpCQwC5dujAgIECYZk54e3vT09NTePLcuLg4Ojg45Fkue9WqVQSgKuKiZMWKFQTAp0+fam1//vy5WrtyjPrkyROd7m/AgAF0dXXV2Co3fPhwlihRgunp6YyPj1e1DxkyhKVKldL4PQ0aNIilS5fO8/eXlZXF0NDQd15kQRfy60U0MT8qDF507949jh49Wkhi4+3btxMAR4wYwV69egnbRrVp0yZaWVnRwcFBlYbEmNy4cYO//PKL8O/rzMxM1Rzt+fPnbNKkCS0tLYUXdlL+HUTrZmVlMTY2VpieJEl8/PgxX7x4IUzTWOjqRboYVIAuQgVxFAaDMgb79u1jkSJFWLp0aY1qFjmRkpLCTp060crKipcuXTLyHeZMYmIiq1atqlGaVV9iY2N59epVktkfyL/++kunkrL6cvPmTeGDPDN6D5bMXmRGbxQKBVu2bMnevXsL0UtJSaGfn5/qZy8vL6alpQnR1kZISEiB5lP5UDB7kZl3weDBg9moUSMh4xe5XM4zZ86ofj58+DDj4uJyfU3//v1ZpUoVjfZevXqxZs2aGu19+/ZltWrVSJIPHz5U5VMZNWoUS5UqpVM+pLi4ONrY2Gg8iMjIyKCTkxO//PJLjffl7OzMkSNHarQXK1aMX3/9dZ59Fib08SKakB99aF4kSRIHDhxIKysr1bxEFH5+fixTpgzt7e25adMmo+ZrmzlzJsuUKSMkR1JWVhZ9fHxU93v69GkeOHCAZcqUYdGiRent7W1wH29y69Yturi4sFWrVua5mkBELiItAOAOQKaLoDGPwmJQWVlZak9SRHDz5k3WrVuXANivXz/evHkzx2vv3bvHZs2aUSaT8d///neuuhEREUZPRp2cnKwyp6tXr7Jjx4588OCBzq9/80ljz549WaFCBaFPH3MiOjqaDg4OOpet1ZXHjx+zV69euf4N80tMTAwXLVqk89PAd42eEzezF+UTSZLynEzoQ0JCgnDNuLg4nj17Vrjum8jlcr569YokGRkZyVatWuXL/zIzM1WDo4kTJ9Le3l6lZ0wyMjJYqVIluru7C9VNSUnhZ599xvPnzwvTVCgUnDdvHm/duiVM05iYvajgKCxelJqaKnyy8zaSJKkiF9LS0ti2bVsePXpU59dnZWWpFqCUUUK3b9/W6bUKhYLOzs4cNWqUhqazszNHjx6tca+lSpXiyJEjKUkSW7duzSZNmlCSJDZr1oydO3fWqd81a9YQgEbE9PHjxwmAbdu25b59+1TtPj4+BMCDBw+qXX/mzBkC0Cmq9LfffqO/v79O9/euMWARyST86EP0ori4OFaqVIlVq1bViN4zlBcvXrBTp04EwD59+hg1gkW5K0WSJPbo0YNbtmzR+bWSJKkSgR88eJAAeOzYMSoUCi5evJhWVlasXr26UcYE1apVIwAGBgYK1Z0wYQLXrVsnVNPT05MnTpwQqmksRC4iJQGQAMgBJP7350RdxEUfhcWgGjRowCFDhgjVHDRoEFu0aMFffvmFDg4OBMCPP/6Yv/76K/ft20dfX19u3bqVgwcPppWVFZ2cnHSqDtSmTRvWqVOnwKoXHTt2jA0aNFAN/P7zn/9w2LBhKgPy9/fnhg0bVOe3bt3KYsWKqX4ODAwUuviSG1lZWdy1a5fwbTbnzp1jxYoVGRYWJkzzxIkTBFAgYfgi0HPiZvaifNK7d2+tVW0MYfr06SxXrpzwCMBhw4axePHiTE5OFqqbE3///Tfd3NxUn+9z585x4MCBqsHUzZs36enpyWfPnpEkfX19WbRoUYaEhJAkQ0NDCyzKU5Iknjp1SvhkKCQkhLVr11aLYhChaWlpKbTqijExe1HBMGnSJJYuXVroWGPt2rW0s7MTvjg1Z84cWlhYFNhDmbCwMLZt25anTp0ime0tffr0US0KPX78mJs2beKdO3dIZn/GSpQooYpMf/nyJY8fP67z7/b69esEwB07dqi137p1S+u2kEePHhEA169fTzLbO48fP06SdHJyooeHR559ZmVlsU6dOtT273X48OF0cnJikyZNuHnzZlX7hAkTWKRIEY10DB4eHrS3t8/zuyIxMZF2dnZctGhRnvdnChiwiGQSflRYvGjlypW0tLQUtgB9+fJlWlpaUiaTCZ+fbN68mQBoY2NDZ2dnbtq0yagRN/Hx8ezevTs3btxIMjvtRo8ePVQPmuLi4rhx40bVXCMxMZFly5blmjVrSGY/8Nq9ezdDQkLYrl07AuDAgQOFB1aQ/1t87tOnj1BdhULBTp06cc6cOcI0JUlixYoV+dVXXwnTNCbCFpFM6SgsBrVhwwaNJyeGsmPHDq5du5aSJPHVq1dcvHgxGzZsqNpvqjxKlizJ7777TudcR9euXeOjR4+E3mt+WLp0KZs2bar6eeLEiWpP1Pz9/TllypT3rmSjMRbtXr16ZbSyoKLRd7BkKkdh8SIvLy/h+8RPnTrFJUuWCN+69eDBAwYHBwvVzA87duxg9erVVROTZcuWEYAqOuDhw4ecMGGCkFwKpoQkScL9KDExkSkpKUI1jYXZiwoGX19frlq1SuiWzODgYM6ZM0do3goy+7v03LlzQjXzw9mzZ1mnTh3VOOjAgQMEwFWrVpHMntiNGjVK7y00OeUx2rBhAwHw4cOHau379u0jAI3+kpKSCIBLlizJs0/lhO+vv/5Sa09OTqaDg4Nqa5rSizIzM1m6dGkOGDBA7XpJklihQgX27dtXp/ealpZmlGg1Y2D2ooLh+vXrXLJknwDW4AAAIABJREFUidA8WYsWLSIAfv/998I0yexFmSNHjvDu3bts3749AbB58+ZGyTOrjbt379LNzU0VQaNcgJ48ebLqmvHjx/PkyZMks6M4FyxYQFtbWzo5OXHbtm1Gme/ExMSwTJkybNCggdFyL4q+b7lcLvy7ylgIXUQC4AygBYD2ykOX14k+CotBFSRxcXG8fv06L168yPv37xsUHWAK+TZiY2P5/Pnzd7oYkpWVxf79+6uFVYsiNTW1wKK+TBkDnriZvegDwBS8KCEhgc+ePXvnyaHHjRtnlOSa6enpRs0nV1gwe5GZ3DAFL0pJSWF4eLiwhdnBgwezXLlyGmORL7/8Umt+o9mzZ9PS0pI//fQTZ8+erTr/+PFjnRLaSpLEVq1asVKlShq/T2VS3LejIU+fPk0AGuOwK1euEAC3bduWr/dcGDBkEckU/OhD9iJJkjhixAgC4P79+43SR3p6Ov/66y+WL1+eANijRw/huZh0uYfw8HCN/EmZmZncunUrK1asSAAcNGgQIyIijHIPkiSxcePGtLCwMEo6lrcjHz9EdPUiC+SBTCYbA+AigFPI3nd7CsD8vF73oRMTE4OQkBChmnK5HIcPH0ZWVpaqzdnZGU2aNEG7du1Qs2ZNWFpa6qX9/fff47PPPoMkSaJuVy9KlCiB8uXLw8rK6p3dQ1xcHJ4/f47k5GTh2jNmzMBHH32k9jc0lEePHmHo0KEIDQ0VpmmKmL1IPxISEnDjxg2hmpIkwcfHB/Hx8UJ1AeD3339H06ZNhZYD1wdHR0dUqFDB6CVwcyMjIwPh4eGIjo4Wrr1+/XpUq1YNcXFxwjRTUlIwYMAABAQECNM0RcxepB9paWm4evWqUE2SCAgIQHh4uFBdANi7dy9q1qyJly9fCtfOD0WKFEHFihVRpEgRg7VIws/PDx06dIBMJlM7FxQUhGbNmmm037p1C7Vr10ZUVBTCw8NV55VjpKJFi+ba59GjR3HlyhXMmjULNjY2aue2bt2KsmXLYtCgQWpj5t27d8PBwQGfffaZ2vV79uyBjY0N+vbtm+f7HD58OI4fP57rde8DZj/KP5mZmbh8+bIwPZlMho0bN8LNzQ3Dhg1DYGCgMG0A8PPzQ7Vq1dCkSRPcv38fixcvxpUrV9CiRQt06tQJR44cETqvyAlbW1tUrFgRxYoVA5D9nb9+/XrUrl0bo0ePRunSpXHu3Dl4eXmhXLlyRrmH33//HcHBwahWrRqaN28uVPuff/5B6dKlcebMGaG6kydPxvbt24VqmgJ5LiIB+A5AcwBPSX4CoAmAGKPe1XtA165d4eHhIVTz2LFj6Nu3L3x8fITqAkCDBg3QrFmzAjEhU6dkyZK4fPkyRo4cKVy7bdu26NOnj96LfdoICwvD6dOn3+nCWwFh9iI9GDVqFPr27at8WimEmzdvolu3bti5c6cwTSVmL/oftra2OH78OObOnStcu379+ujbty9KlCghTPPx48e4evUqUlJShGmaKGYv0oM5c+agffv2Qv99vHr1Ch06dMDq1auFaSpp0KABmjZtKlz3XfL8+XNERkaiTZs2au3JyckICQnROim7e/cu6tWrh40bN2Lr1q2qduV3ytuLTm+SkZGBH374QTXJfJN79+7hwoUL6N+/P3r37o1atWoBANLT07Fnzx70799fbeFMkiTs378fXbp0gZOTU67vMzY2FkFBQYiMjMz1uvcEsx/lk40bN6Jt27Z4+PChME1LS0tERkbCwsICPXv2FKpdu3ZtNG7cGDY2NihSpAhmzpyJJ0+eYOnSpXjw4AH69OmDKlWqYM6cObhz546wfrVBElevXsXEiRNRvnx5eHh4wMXFBYcOHcLVq1fRsWNHo/Xt5+eH6dOno2/fvggJCYGFhS7LGLpTpEgRuLu7o3HjxsI0s7KyEBAQ8H4+6M8rVAlA4H//GwzAVvn/uoQ5iT4KU6jk6dOnef36daGacrmcx48fN29BMBKJiYn86aefCmUoY2H7NwH9ktmavUgP/v77b168eFH4Nsrjx4+bxFaP95HMzEzOnTvXKNWsjI0kSYWq1K7ZiwqOkJAQnjx5Unh1VV9fXyYlJQnVfF9R5ld6uwiHn58fAfDIkSNq7XK5nBYWFvzmm280tMLCwvLczjZv3jwCUOVMeZNJkybR2tpaI+elMgeTj4+P1nt8O69SbhSmsZE+XkQT8qPC5EURERE8cOCA8PH+xYsXGRAQQBcXF1arVs1oW7reRC6Xc//+/ezevTstLCwIgLVr1+aUKVN4/PhxIUmtExISeOzYMU6aNIlVqlQhANra2nLIkCG8dOlSgaTpePLkCYsVK8bKlSsbJVG3sXkfvUiX0IXnMpmsOIBDAHxlMtlrAB/E0r4hdO7cWbimtbU1evToIVz3TUJDQzF+/Hj89ddfqFChglH7MjVOnjyJxYsXo1u3bmjbtq1QbYVCgT179mDAgAEa4dyGQBIymUxoZJMJY/YiPWjRooVRdI3tRVFRURg5ciSWLl2KRo0aGbUvUyMoKAiLFy9G3bp18cUXXwjX9/LyQs+ePfPchpIfsscd2VEJuUUmvCeYvUgP6tati7p16wrX7dKli3DNN0lKSsKoUaMwZswYdO/e3ah9GZsrV67A2tpa40n7zZs3AUCj/e+//4YkSVojesqWLQsgO7pJGwEBAVi0aBGGDRuGbt26qZ1LTEzEn3/+idatW2tEE2zZsgXly5dHp06d1Nq9vLxgZ2eHPn365Pk+zWMjsx/lRrly5dCvXz/huu3atQMAeHt7o3PnzujatSvOnz+PUqVKCdHPzMzEd999h8aNG2Ps2LEAsueG/fv3R//+/fHy5Uvs378fhw8fxrp167By5UrIZDLUqVMHbm5uqFevHqpXr47y5cujVKlSKFasGGxtbSGTyZCRkYHk5GS8evUKERERCAsLw927dxEcHIyQkBCQhJ2dHTp37oyffvoJ/fv3R/HixYW8r7xISEhAz549kZycjG7duuUZiagPvr6+qF27NipVqiRU9732Il1WmpQHgA4AegOwyc/rRB2FaZWbJP/55x9u2LBBuO6WLVs4adIk4bpkdonXatWqMTAw0Cj6pk5YWJhRdI8ePUoAPHTokFDddevW8aOPPip0EQswsAqJ2YvyR1hYGJctWyb8adHhw4c5dOhQozyFioyMZM2aNVVlrD80jOVFt2/fJgCuXLlSqO6JEydYp04dhoaGCtU1NmYvKlhevHjBZcuWCa/e5+/vz969ewuvGklmVyFr2rQpPT09hWsXNJ988gmbN2+u0e7h4UFHR0cNLz9//nyOkUQkWaNGDfbs2VOjPTw8nGXLlmXVqlW1Rg2sWLGCAGhtbc1vv/1W1R4REUELCwvOmjVL7fqMjAyWLFmSAwcOzPM93rt3j5UqVSqwClaiMNSL+I79qLB5UXx8PNetW8fw8HChunfu3GGvXr24b98+2tvbs2HDhoyOjhairVAo2L17d86ePTvPa1NTU3n69GkuWLCAvXr1YtWqVTUqeud1VKxYkT179uT8+fN5+vTpd7JTIz09nZ07d6aVlRV3795tlHvIzMxk+fLl+fnnnwvVTUhIYKVKlejl5SVU19jo6kU5RiLJZDI7AOMB1ABwC8AWkhdELFx9KOzduxfLly/HF198IXTV9PHjxwgODoZcLhca1QJk5wC4d+8erK2tAWTvQRe959TU+OOPP9CuXTs0atQI1apVM0ofPXv2xOnTp/HJJ58I1S1VqhRq1KhRYE8D3gVmLzKcCxcuYPr06fjss89Qr149YbqvXr3CrVu3EBMTA1dXV2G6QPZT7jt37nxQXrRnzx6UKFECXbp0MZoX1a9fH/7+/nBzcxOqa2tri2rVqqFy5cpCdU0JsxcZzr179zBt2jTUr19faFSPXC7HP//8g0ePHgn1OCA7cbQyggcovF5EEjdu3MDgwYM1zoWEhKBevXqqKMKLFy8iPDxc9fS8YsWKWjV79+6NNWvWIDIyUpVINywsDN27d0dycjJ8fHw0xr9yuRwrV65Eu3btsG7dOjg7O6vObdmyBZIkYdSoUWqv8fHxwatXr3TKVZmRkYGGDRuievXqeV5bmDH7kWG8fv0aHh4eICk0h62NjQ2CgoIwZcoUHDlyBL1790bHjh1x+vRpVfSevlhaWuLIkSM6eZG9vT06d+6stjMmLS0NT58+RWRkJGJiYpCUlAS5XA6SsLGxQbFixeDi4oJy5cqhSpUqcHBwMOh+DUWhUKB37944c+YMtm3bptW7RGBlZQV/f3+kp6cL1U1ISECrVq1QpUoVobomQ06rSwC8AOwAMA7ZIZKrdFmVMuZR2Fa5X716xdjYWOG6crm8QPafbtq0iZ9++qnwJ4amREJCAitUqMBx48YZrY+C+FsVNpCPJ25mLzKc5ORko+zNVygUBZL/xtvbmy1atBD2NM8UUSgU/Oijj/jpp58azTPMXqSJ2YsKlqysLD58+NAo2qJzLWkjKCiIjRo14oMHD4zel2jCw8MJgGvXrtU4V6pUKX711Veqn/v378969epx+fLlBMCYmBitmo8ePaKNjQ1btGjBXbt28ZdffqGTkxNLlCihkXdJyZYtWwiAx48fV2tXKBSsXLkyO3furPEad3d3uri4vNd5+PLjRTRBPypsXkSS9+/fN4rum1507tw5Ojg4sFq1akK97/Hjx2zcuLFRytybAgqFgsOGDSMAFi1a1GjjP/O4SBNdvSi3Ryn1SA4n6QlgAIB2gtatPhhcXFyEVr5RYm1tDZlMhsTERDx48EC4/pv9WFlZvZf7OJUfAEdHRwQEBGDNmjVG6Sc1NRVNmzbF/v37hWvfu3cPmZmZwnVNELMXGYiDg4NRyq1aWlrCwsIC6enpuH37tnB9JUofUj59e98gCUtLS5w8eRL79u0zSk4hSZLQuXNnrF27Vrj2w4cPkZaWJlzXBDF7kYFYWFgYLULE2toaWVlZCA4ONoo+ULi9SPl7eTvHXHx8PGJiYlC7dm3l4gR27NiBM2fOICkpCQDUooXepGrVqvDy8sLt27cxdOhQzJ49G61atUJQUBBatWqlcX1mZiZ++eUXlChRAhcvXlQ75+3tjadPn2LcuHFq7bGxsThy5AhGjBiRZ/T9s2fPEB8fn+s17xFmPzKQmjVrGkXX2toaJHHt2jV07NgRZ86cQXx8PNq2bYugoCAhfVhaWhZaL8oLhUKBkSNHYufOnfj5559x584dYXml3mbWrFkYOXKk8IrAMTExiIqKEqppauS2iKSanZJUFMC9vJc8e/YMffr0waVLl4TqkkSnTp0wZMgQ1Ze+aP71r3/h2LFjsLW1RVJSEp48eWKUfgoahUKBr776Cr/++isAoEKFCkYz4bi4ODg7Owvf6pOZmYlPPvlEo2Tue4rZiwSQmJiI4cOHY9euXcK1R4wYgU8//VR4KLCSrl27wt/fH8WLF4dCocDdu3eN0s+7YN68efj6669BEiVLlkSxYsWM0k9ycjJcXFzg6OgoXHvIkCHo2rWrcF0TxOxFApAkCd988w2WLl0qXHvWrFlo06YNXrx4IVwbyF6ACQoKQuXKlUESt27dMko/xuCff/4BAI2trGFhYQCyUyX06tULcrkc9vb2KFOmDJKSkmBvb5/rw8S+ffvixYsXCA4OxuPHj3Hy5ElUrVpV67Xbtm3Do0eP0LJlS42tMmvXrkW5cuXQt29ftfZdu3YhMzMT//rXv/J8j1OnTkWDBg2ETwhNFLMfCeDnn3/G999/L1zX09MTzZo1w/Xr19GyZUv4+/vD3t4eHTp0wMGDBw3Wr1ixIgIDA9G0aVMAKFRelBtpaWlo164ddu3ahXnz5mHOnDnCk12/ib29fZ4epw/Lly9H1apV3+tF7dyqszWSyWSJ//1/GQD7//4sA0CS4kei7yEuLi4IDQ0Vvhopk8mwZMkSODs7G7USjlJ7woQJOHPmDB4+fPjO98gaiqWlJRQKBeRyudH7qlChAs6cOSP8b2RhYYGNGzcKX5wyUcxeJIBixYrhwYMHePXqlXDtmTNnIi4uDnZ2dsK1lSg/QwsWLMDy5csRGhqaY56OwoQyKjIrKwtWVroUTNUPR0dH7N27V/hDB5JYvnz5hxKJZPYiAVhYWODFixdGWTD18PBAkyZNUKZMGeHaSpRetG3bNowZMwb+/v5ao25Mjdu3b6Nq1aoav/enT58CAEqXLo0nT56o5duUy+WwtbXNU9vR0THPKpopKSmYP38+WrdujePHj6udCwkJwalTp/Dzzz+rPdQjiU2bNqFZs2YaleO08eOPP+LBgwfvZQS9Fsx+JICYmBjExsaCpNCx+rBhwyCTyVSLtnXq1MGVK1fQp08f9O/fH/PmzcPcuXMNyq+mvN+zZ8+ic+fO8PLywqBBg4Tc/7sgNjYWffv2xd9//43KlStj/PjxRu9z7ty5RgnGGDNmDGrVqvVe56x9p3v583sUxv22ZMHstzR2H/fv3+eOHTsKrD9jcOzYMT579owkjZ7HRZIkrl69mgkJCUbtp7ACAVVI3uVh9qJ318fLly+5bt26AuvPGAQEBDA4OJhk9v0b+z1s2rSJL168MGofhRWzF70b3gcvSkpK4rJly1TjCVP3okaNGvGzzz5Ta7t37x4nTpyoynv09tho4sSJdHZ2FtL//PnzCYC7du3SODd27Fja2tpq5F66cuUKAXD9+vVC7sGUMXvRu6GgvSgtLY3/+te/CIA9evTgq1evDNaXy+VctmyZqjqlqXuRNi5cuMCyZcvSxsaGXl5eRp+nnTx5klevXjVqH4UVXb2o8JWXKITIZDKQVD3tEc2iRYvg7u6O7L+7cahZsyaGDRsGALh06RKaN2+Ox48fG60/0cTFxeGLL77AokWLAMDolVWuXbuGyZMnw8vLS7h2UFAQVq5c+aE8+TcjEOVTK2N50datW9GuXTuj5upydXXFhAkTAGTn4nFzc8P169eN1p9oMjMz8cUXX2D69OkAsv8mxowmjYyMxKRJk7Bq1Srh2s+ePcP8+fPx+vVr4dpm3m/e9CJjjF28vb3RpEkTo/7bLFq0KKZOnQoLCwvExcWhSZMmOHnypNH6MwSSePjwIWrVqqXW/vXXX2Pnzp2wtbWFi4uLxtjIysoKCoXhO6WePHmCJUuWwM7ODocPH1Y7Fx0dje3bt2PkyJEoWbKk2rkNGzagWLFiqvFnTqSmpmLGjBmIiIgw+F7NfFgovSgqKgoZGRnC9QMDA+Hm5qbaNmpnZ4c///wTa9euxenTp9G4cWON/GD5xdraGlOnToWdnR3kcjnatWuH7du3i7j9AuHgwYPo0qULoqKicOzYMQwaNMio8zSSmDVrFiZOnCj8+0epHRoaKlTXFDEvIhUQc+bMQcOGDY2yN9LBwQGOjo4Fsj0LyP6ytra2Vm2lMubilSFkZWXBx8cHAFCiRAn4+voaZSKljWbNmuHGjRv46quvhGsfPnwYP//884ey59+MYDZv3oxq1arh/v37wrVLlCgBR0dHVTJWY5OYmAg7OzuULl0agOl6EUmcOnUKkiTB2toahw4dwt69ewuk73LlyuHGjRv46aefhGv7+Pjg119/RWJiYt4XmzHzFr6+vqhatSrOnDkjXLt06dJwcHAosH+bCQkJKFKkiMmOiyIiIpCSkoLatWvDz88PycnJAIAtW7agU6dOKFu2rNbFbAcHB6SkpBj0fkji22+/haWlJS5evKgxDlu9ejXkcjmmTp2q1h4XFwcvLy8MGTIkz62P/v7+WLFiRaF6uGnGdLh37x4qV66Mf//738K1y5YtC3t7e7VxkUwmg4eHBy5fvgw7Ozt07NgR06dPF5JXMikpCY6OjqptVKbmRW8SFBSEr7/+Gv3790f9+vVx9uzZAsmvKJPJcPbsWezevVv4Q7y7d+9i1apVuHr1qlBdk0SXcCVTOQprqCRJ3rx5k+vXr2d6erpw7XcRtqjsMysri126dOGmTZsK/B7yYsOGDQSQY5lZY5CZmclbt24ZvZ/IyEij92FMYA7bfmdERUVx8eLFTExMNIp+QfvRm/2NGDGCCxYsKND+dcHHxyfHbRzGQpIk3rhxw+j9mL3I7EX6kp6eznnz5vHly5dG0X+XXvTjjz9y7NixJrOt5PTp0wTAbdu2EQAXLVqkOte1a1e2bNlS6+tWrFhBAIyNjdW77//85z8EwGXLlmmci4+Pp5OTE93d3TXOLV26lAB48+ZNnfoxe5HZi/RFkiQuWrSIjx49Mpp+TiQlJXHs2LEEwDp16tDPz09ofxs2bGC/fv2YnJxssK5ILl++TAsLCwLgt99+a5T5sTaCg4ONvlUuJiaGcrncqH0YE129yByJVEA0bNgQ48eP1ylBYX5RrqI+f/4cY8eOLZBtTso+ExISYGdnp0rCmJGRgefPnxu9f21kZmZi69atOHv2LABg5MiR2LdvH1q2bFlg97Bs2TI0bdrUKFEeAFR/27JlyxpF38z7T+nSpTFz5kyjVQGTyWSIj4/H2LFjERMTY5Q+3u4PyK66+ObWMJJ49OiR0fvXBkns379fFW3UpUsX7N69GwMHDiywe9izZw+aNGmi8kPRmL3IjKHY2tpi/vz5RisQIZPJkJ6ejsmTJ+PevXtG6ePt/pRIkgTyf4l635UXAdlJd7dt2wYA6NixI/bt24cpU6aozsfGxsLFxUXra8uXLw8Aeo/rIiMjMW7cOADQWhlyzZo1SEhIwKxZs9TaFQoF1q1bh48//hgNGzbMtQ+zF5kxFJlMhtmzZ+dYVVCEflZWFhYuXIgLFy6onStatCg8PT1x8uRJpKamol27dhgzZoxB46c3vSgjIwNyuRxFihQBkF2FMXudoOC5fv06Fi9ejAULFqBDhw5wdHTEzp07sXr1aqPMj9/m6dOnaN26NebNm2cUfaUXlSxZ0mhVv00KXVaaTOUozKvcSvbv309PT0+jaB8/fpyOjo78+++/jaKfG8pV7x07dtDS0pL//PNPgfSblZXFiIgIkqRCoWCVKlU4atSoAulbG7GxsWpJf0Vy/fp1Fi9enOfOnTOKfkEC8xO3d8758+f566+/GkU7ODiYxYoV46FDh4yinxtKL/L39ycAHjlypMD6ff78uern9u3bs0OHDgXStzZSU1O5cuVKKhQK4dqRkZEsUaIEd+7cKVy7oDF70bsnODiY06dPN0rUzvPnz1mqVCmuWbNGuHZeKN9PeHg4LS0tuXr16gLr+00v+vLLL1m8eHFaWloyMzNT49rq1atz6NChWnWuXbtGANy7d2++70GhULBz5860t7fnrFmzmJKSonY+Pj6ezs7O/PzzzzVee/DgQQLg/v37c+0jNTWVlStX5pIlS/J9f6aG2YvePWFhYZw0aZJRomKSkpJYu3Ztfv/997leM23aNFpZWdHJyUktWbYhKL0oJSWFzs7OnDJlisGauhIZGamK/Bk+fLgq+mjo0KEaifSNjSRJ3Lx5s1EKjUiSxBYtWtDDw0O4dkGjqxe9c9PJz/E+GNSAAQPYokULo4XSicjybwhPnjzhokWLVO/vt99+Y79+/YS+37i4ONX//397dx4f09n+D/xzTxYRJHaxr31qqzUUVXtRj1ZptYrSSuvLo4u2sdRaRam9LaW11FJ7F9oKLSF2KrEmEUTsiSULSWQzM9fvj5Hzo0QSMnNmJp/36zWvus049zWncTnnOvfSu3dvqVGjhpYgL1++rMvw8cuXL1vlZu1ep0+flt69e8vNmzet2o8t8GJJf8OGDZPKlStLUlKSVY6vdy66fv26fPnll9oOiatWrZIXXnjhiaZl/Ft8fLyWb8aMGSOFCxfWbpSio6OtnhMe5vr163ly0fkoV69elXfeeUciIyOt2o8tMBfpb/78+VKsWDE5d+6cVY6fl3/nH0diYqJ88803cvbsWRGxFLife+45OXXqVJ71kZSUJOnp6SIismjRIgEg58+fFxHL1Io+ffpIxYoVH/pnS5cuLQMHDnzoeykpKeLq6iojR47MdUzDhg0TALJ48eKHvj9+/HgBICEhIQ+817JlS6lUqdJDi173SkxMlE8++UR27tyZ6/jsDXOR/rZs2SIFCxaUffv2WeX4cXFxObpHCQ8PlxdffFEASKVKlWThwoV5Mj0qPT1dli5dKgcOHBARkUuXLkmjRo1kz549T3zsTGlpadp10LZt20QpJQsXLpROnToJAKlevbps2bIlz/rLCaPRqA04sJaMjAyZNGmSTZctsBYWkexUfHx8tv8o5oX169fLn3/+afV+sjNt2jR5/fXXtfbbb78tfn5+Wnvv3r0SGhqqta9fvy5XrlzRkuXZs2dl0aJF2sXRlClTxM3NTVJSUkREZPPmzbJ8+XJdbtYyJSYmSpUqVbK8CKMH8WJJf0lJSdrfI2vatWuXLFq0yOr9ZGf58uXSunVr7QJu7Nix0rFjR+39w4cPS3BwsNaOj4+XK1euaAWZq1evytKlS+X69esiIrJmzRoBIGfOnBERkX/++Ufmz5+v67oDRqNRmjZtKh06dLCbtVjsHXOR/jIyMiQhIcHq/YSFhclXX31l9X6ys2XLFmnatKlW4P7hhx/E19dXK+hHRETIrl27tM/funVLrly5or2fmJgoK1as0Iq4+/fvF4PBIH///beIWK6bZsyYcd9T/g4dOmS57pGXl5d89NFHWcbbrFkzadq0aa6+408//SQApGjRog8d1XHt2jUpXLjwQ9dCOnTokACQ2bNn56pPR8dcpD+z2WyT0TFXrlyR0aNHZ/uAPTAwUJ599lmtmPTNN9/k6YO/kJAQadmypYSFhYmI5Z6qXr162nXNhQsXZMeOHdo9WHJyskRHR2v52mg0yurVq+Xw4cMiYnl45u7uLgsWLBARkT179kjt2rUFgBQrVkxmzpypHcuWRowYIWXKlNGu3+jRWESyc2lpaVZbBNBoNEqTJk2kU6dOdncjMXr0aBk2g6geAAAgAElEQVQzZozWrlOnjnTv3l1rP/XUUwJAIiIiRES0RSBPnz4tIiIHDhyQGTNmaBdf9uKbb76RQ4cOWeXYGRkZMm7cOJsP+7QmXizZD5PJJBcuXLDa8V999VWpU6eOLhcOj/LNN9/Ie++9p7VfeuklqV+/vtbOfGoWEBAgIiI7d+4UANoTtDNnzsikSZOs/nQrt37++WerTeEzm80yefJkbYSDM2Aush9ms9mqP1vDhg0THx8fu/u3dM2aNfLKK69o12sffviheHl5ae8PGjRIAMi8efNExDJ6AIDMnz9fREQSEhJk7NixjxzZVKdOHXnllVce+p6Hh4cMGzYsyz87adKk+0Y2ZScgIEDc3NykQYMGWU7vf//998XFxUVOnjz5wHuvvfaaeHt7Z3ud9+233+Z40W1HwFxkX6yZi7777jspVKiQhIeHZ/tZs9ksAQEB8txzzwkA8fb2lo8//li7T8pL27Ztk86dO2szHubMmSMAtJHl06ZNEwDi7++vxebm5ibDhw/X2qNGjZKZM2dKhw4dBIB4eXnJ2LFj75tFYmvh4eEyZcoUqx1/xYoVTjEaMhOLSHbMbDZL8+bNpVWrVlYr8ly/fv2B+ef26OjRo/ddBKxatUoWLFigDT9PSEiQc+fO6TrSKCspKSlWG35/r507d4qLi4su68tYCy+W7EefPn2kevXqVtsZIzk52e5u2h4mIiJC/vnnH639559/yoIFC7QLydu3b8uZM2fsrhgmYnlwYI0Lyn+LjIwUT09PmTVrltX7shXmIvsxZswY8fb2ttrTYqPReN9aQfbq/Pnz9+3QtHPnTpk/f76286vRaJSTJ0/matpqsWLFslyro0CBAtpN4MNcuHBBDAbDI9dyyfTbb7+Ju7u7NGrUKMvRZREREeLq6iqDBg164L3Tp0+LwWDIdvrcrVu3pHTp0vLBBx9kG5OjYC6yH4sXLxZXV1dthE5eM5vNj/Xwbt++ffL666+Lq6urAJDnnntO5s+fb7VrrJiYGNm+fbt2D3b48GFZsGDBfdP9IiIiJCkpSSIiImTMmDFSsWJFASDlypWTKVOm2GSUaVZyUqR7UiaTSerWrfvQtd0cFYtIdm7dunU2mROalpYmfn5+VkuE+Vnfvn2lXLlyVltT5l5RUVF2N6rsSfBiyX7s2LFDfvrpJ6v/fJnNZvH395egoCCr9pMfjRs3TgoVKmSTEUIXL1506K1r/425yH6Eh4fL7NmzbTLlf8aMGU6xdkVOpKenCwD54osvHvp+wYIFtZEFWenfv7+4u7tnOfLHZDLJl19+KUqp+0ZsPkyXLl2kSJEicu3atQfeGzBggHh4eORo4dvY2FinWCMyE3OR/bhx44Z8/vnnNpnyv3bt2lxPs7169ap89dVX2lQxFxcXad26tcyYMUNOnDhhk/sFo9EoBw8elHHjxkm9evUEgBgMBunYsaOsX79e9+uE7du3i1JK1q1bZ/W+bt++LVevXrV6P7bCIhKJiMi5c+ekbNmydrEmibMJCwuTn376yap9ZM5Ldja8WMp/EhISpHbt2vdNZ6W8ER0dbfXdp5iL7PPFXJR7d+7ckdatW2e5I5mziY6OFgBZTi0rWrSovP/++488xrVr18THx0cqVKhw34hNs9ksQUFB0qxZMwEgr732mkycODHLtV7++OMPASAzZsx44L3z58+Lm5ubDBky5JGxnDlzxqkeqmViLsqf3nrrLXn++ecfq+hiNpvl6NGjMnr0aKlbt64AEABSqlQp6dGjh0ybNk22bdsmV69efaK/M2azWWJiYuSvv/6SL7/8Urp27Sre3t5a4ahly5Yye/Zsu5ref+fOHZkyZYpVC4Fnz561y5kyTyqnuUhZPusYfH19JTg4WO8w8tT8+fNx5MgR/PDDD1brIzExEV5eXgCAtLQ0eHh4WK0vZxcXF4eAgAC89dZbVu/rt99+w2uvvYbAwEC0adPG6v3ZklIqRER89Y7jcTljLvr111+xYsUK/Pzzz3BxcbFKH4mJiShSpAiUUsxFTyg1NRXLly/HwIEDoZSyal8hISFo2rQpfvzxR/Tr18+qfdkac5H92b17NyZMmIANGzagcOHCVukjJSUFbm5ucHNzc/pcFBYWhrp162LNmjV44403Hni/YsWKeOGFF7BkyZJHHufYsWPo0qULoqOj0aRJE5QqVQqhoaG4ePEivLy88PXXX6N///5Z5qOUlBTUrVsXHh4eOHr0KNzd3e97f/DgwVi8eDEiIyNRqVKlhx7j8uXLqFWrFvz9/TF+/PgcngHHwFxkf8LCwvDBBx9gxYoVKF++vFX6MBqNSE9PR6FChZCeng43NzcYDIbHOtbFixexdetW7Ny5E3v37kVUVJT2XtGiRfHUU0+hcuXKKFu2LEqWLAlvb294enrCzc0NAHDnzh2kpKTg1q1biI2NRUxMDM6fP4/IyEjcvHlTO9bTTz+NVq1aoW3btnjhhRdQsmTJJzsJeUREsHTpUvTo0QPe3t5W7ev27duoVasW2rVrh6VLl1q1L1vLaS5ytUUwlLXY2FhcvnwZ6enpKFCggFX6yCwgRUREoEOHDli4cCFefPFFq/Tl7GbOnIlZs2ahdevWWV7k5JV27dph/PjxaNmypVX7IQIsF/jR0dGIj49HqVKlrNJHZi66du0ann/+eQwfPhzvvvuuVfpydj/99BMGDRqE+vXro1mzZlbtq06dOvjiiy/QrVs3q/ZDlOny5cuIjo7Gf/7zH6sc39PTE4Al77Vr1w4dO3bEF198YZW+9Hbr1i0AyPKmqmjRokhISMj2OPXr10d4eDiWLl2KDRs2ICYmBk2aNEHPnj0xc+ZMeHh4PLKg/fnnn+PcuXMICgp6oIB04cIFLF68GAMGDHjktVXZsmUxadIkvPLKK9nGS/SkChQogAsXLuDChQtWKyK5urrC1dUVJpMJvXr1QqFChbBixYrHejhUqVIl+Pn5wc/PD4DlHvPo0aMICwvDqVOnEBkZiRMnTmDr1q1aXsiKt7c3fHx8UKlSJfTq1Qu1atVC3bp10bBhQxQrVuyxvqu1nT59GgMHDkRMTAxGjRpl1b48PT0xdepU1KhRw6r92DOORNKZyWSCwWCw+pNkwJJM/Pz8MHv2bFSrVs3q/TkTs9kMg8GAjIwMhIaGolGjRlbrS0RgNputNhrEHvCJm/0REZhMJri6Wv/ZQkpKCvz8/DB06FA8++yzVu/PmWTmIhHBwYMHrV5AMplMzEV2zBlzEWB5Om+LXGQymTB06FC0b9/eaQsTW7duRceOHbF79+6HPpRq164d0tPTsXfv3lwdNzMXAcCBAwcemYuCg4PRrFkzvPPOO1i4cOED77/33ntYvnw5IiMjUbFixYceg7nIvjEXPRkRwbRp01CgQAEMHTrU6v3duXMHSUlJSElJwZ07dwBYClqenp7w8vLSRic5gntz0T///ANfX9/HHs2VE8xFFtY7w5QjLi4uUEohLi4OEydOhNlstlpfJUuWxMaNG7UC0pIlS5CYmGi1/pzFokWL0LJlS6SmpsLd3d2qBSQA+O6779ChQwf+vyGbUkrB1dUVKSkp+OKLL5CSkmK1vjw9PbF69WqtgLR27VrExMRYrT9nERAQgAYNGuDGjRtQSlm9gLRp0yY0atQIly5dsmo/RP/m6uoKo9GIadOm4dq1a1brx8XFBd9++61WQNq8eTNOnTpltf70kJnLCxYs+ND3K1SokOu/44cOHUKdOnW0c/WoXJSWlob+/fvDx8cH06dPf+D906dP48cff8SgQYOyLCAdPXoUtWrVwrFjx3IVJ9GTcnV1hYjghx9+QFhYmNX6UUphxIgRWgHp4MGD2L9/v9X6c3NzQ/HixVGhQgVUrVoVVatWRcWKFVGiRAmHKiBFRUWhQYMG2LVrFwCgadOmVi0gXblyBbVr18a2bdus1oejYBHJTmzatAkTJ05ESEiITfoLDw/HwIED8f3339ukP0dWunRplCxZEhkZGTbpz9vbG6VKlUKRIkVs0h/RvQ4dOoQJEyZgy5YtNukvNjYW7733HiZOnGiT/hxZmTJlUKxYMZvlInd3d5QuXdpq0xuJHiUqKgrjx4/HypUrbdJfRkYGunTpgpo1a1q1iG5r6enpAJDluk9Vq1bFlStXtM/lRJkyZVC0aFGYTKZsP/vZZ58hPDwcixcvRtGiRR94f8yYMfDw8Hjk9BMRgY+Pj9WmFBE9SkJCAsaOHYsFCxbYpD8RwbBhw+Dn55ejv2P5WebaTraY0QNY/p0oW7ZslgXvfCUnq2/by8uZV/43m8023/3m0KFD2la6ly9fdsoV5h9XUFCQrF+/Xms7404gegJ3IbFrts5FYWFhcuvWLRGx7AKUmppq0/7t2bFjx+T777/X2sxFeYu5yL7pkYsmTpwoRqNREhMTJT4+3qb9W8PKlSsFgERERDz0/VWrVgkAOX78+COPc/78eZk+fbrWzkkuytyN7YMPPnjo+wcOHBAAMm7cuGyP5eyYi+xbZGRklrsOWkNCQoKcPHlSRCw7jdnTzmd6i42NlQkTJmj3rbwuyls5zUUciWQnlFLa4lxBQUG4cOGC1fv09fWFq6sr0tPT0aFDB/Tt29fqfToCEcHEiRPx5ZdfatMLrV3hNplM6N27NzZv3mzVfohyIjMXnThxAocPH7Z6f7Vr14aXlxdEBL1790abNm1g+XeMvv32W0yYMAFJSUkArJ+LAGDo0KHZ7tREZAuZuejSpUvYvn271furXbs2xowZAxcXF3zyySdo0KCBw49KysylWU3xqFevHgDgyJEjjzzOqlWrMGHCBJw/fx5A9rno3Llz6NevHxo2bIhp06Y9NK5PP/0UPj4+8Pf3f+gxZsyYYfWlHohyonr16jAYDEhISMDvv/9u9f6KFi2KmjVrAgCmT5+O2rVr4/Lly1bv1xFs3boVkyZNwsGDBwHY5rpo/fr1GDx4sM1GgjsC7s5mZ5KTk9GzZ0+88MILWLVqlU36dHd3x+jRo1GmTBkAloJGenq6tntJfiAi2LBhA9q0aYNixYphxYoV8PLysuq82nslJCQgPDwcFy9etEl/RNkxm8148803UaRIEezbt88m/0grpTB8+HDExsZq/SUlJeW7qZ1BQUGoWLEiqlevjhkzZuDLL7+02TlIT09HWFiY1XYLJXocgwYNwvHjxxEZGWmzn81BgwahXr162rWQo+aizFyaVSGmZs2a8PLywp49e9CvX7/73jt+/DgyMjLg6+sLf39/9O7dG5UrV862z9u3b6N79+4wm834+eefHzqVbt26ddi7dy++//77h55XEUFoaChSUlJsNlWFKDvjxo3DkiVLcO7cOZQuXdomffbs2RMmkwkVKlQA4Li56EmcP38eUVFRaNeuHd544w00adIE1atXt1n/YWFhOH78OB9w3isnw5Xs5eXsQyUzHThwQJvaoYe5c+dKxYoVJTo6WrcYbC0yMlIMBoN88cUXusWQlpamW9+2Bg7bdgihoaG6DqH+888/pXjx4nL06FHdYrC1W7duiZeXl7z99tu6xXDnzp18M72ZucgxXLp0ScLCwnTr/9ixY1KkSBHZvHmzbjE8rnXr1gkAOXHiRJafefnll6VKlSr3TQsxGo1So0YNad26da76MxqN0q1bNzEYDFmer9u3b0ulSpWkQYMGj8w1ZrNZ0tPTc9W/o2IucgyJiYmyf/9+3fq/evWqlChRQhYtWqRbDHro0KGDVKlSRVuGRQ/55T4tp7mI09ns0LPPPgsvLy8YjUasXbsWlv+ftlO/fn1069YNPj4+ACyLW9o6BlvI3BEEsAxTDQwMxGeffWbTGLZs2YL//e9/MBqNfPJPdqdOnTooV64cRARr166F0Wi0af9VqlRBt27dUKtWLQDAhQsXnHKRyejoaMydOxcA4OXlhc2bN2PevHk2jeHYsWPo3bs3kpOT4erq6tTb15LjqVChAmrXrg0A2LhxI5KTk23af9GiRfHqq6+iSZMmACw79KSlpdk0hseVeW3xqIWzu3btivPnz2PXrl2YOXMmjEYjXFxcsHbtWvz666857ktE8MEHH2Djxo2YM2cOOnfu/NDPTZkyBRcvXsTXX3/9QK65fPkyevTogWvXrkEpBXd39xz3T2RtRYoU0XYj3L59u813lnVzc8Prr7+Oli1bArBsTnLr1i2bxmALqamp+Oabb7TvNnfuXOzatQuurrabRHXr1i306NEDp0+fBgDep/1bTipN9vLKL1XuTEuWLBEAsnPnTt1iSExMlOLFi8snn3yiWwzWMmTIEPH29tZ11NekSZOkfv36kpiYqFsMegCfuDmUoKAgASA//vijbjEYjUapWbOmdO/eXbcYrGXmzJni6upq80WE77V8+XKpXLmyXL58WbcY9MBc5FjOnj0rLi4uui/E3KlTJ2nQoIFDLOgaGBgoACQoKCjLz8TGxoq7u7u8+OKLAkACAwNz3Y/ZbJZPP/1UAMiIESOy/Nzp06fF3d1d+vTpk2W8Pj4+cuzYsVzH4MiYixzLzZs3pWjRotK3b19d4xg8eLCULl1aUlJSdI0jrx0+fFgAyJIlS3SL4cSJE1K2bFn566+/dItBDznNRbonndy88luCMplMsmXLFl1jyMjIkEWLFklISIiIiERHR8uMGTPk5s2busb1OE6fPi3t2rXTdiC5fv26XL16VZdY7r3wvH37ti4x6IkXS45ny5YtNt2Z5N9MJpP8/PPP2j/mqampMmnSJImJidEtpsd148YN6d69u/z+++8iYskBZ8+e1SUW5iLmIkcTGBio+xSnwMBAWb16tYhY/g5NmTJFIiMjdY0pK4cOHRIAsnHjxgfeS09Pl/79+8t3330n/fr1E09PT9m1a1eu+zCZTDJkyBABIO+//36WxTWz2Sxt27YVLy+vB3I3cxFzkaPZu3ev7vdDISEhMn/+fK09d+5cOXLkiI4RPb5hw4bJqFGjtPajpuBaE3MRp7M5PIPBgE6dOgGwTL366aefbB6Dm5sb/Pz80KhRIwDApk2b4O/vj7i4OABAfHw87ty5Y/O4ckJEcOLECRw7dgwAULJkSVy5ckXb3aBUqVLaYuK2dPLkSTRv3hxRUVEAkK8WMCfH1alTJxgMBly/fh3z5s2D5d8Z2zEYDHj11VfRsWNHAMDu3bsxduxYhIeHAwASExPtehelqKgo7N+/H4Blasy5c+cQGxsLwJIDqlWrZvOYrl69iueeew6HDh3S4iCyd+3atYO7uztu376NGTNm6DLFtV27dujVqxcAy/XZ2LFjsWPHDgBAWlqaXU0vKVGiBABo+ebq1asIDAwEYNlYJSYmBvHx8fjss8+Qnp6OFStW5Or4ycnJeO211zBv3jz4+/vjm2++yXIh7KVLl2LHjh2YNm2atmRC5jE6duyIP//8EwBzETmGFi1awNvbG0ajETNmzNDlGqRRo0YYNGgQAMv0q88++wzr168HYLkPun79us1jyqmEhAQEBARo7bi4OMTHx2vtunXr2jwmo9GI3r17Y8GCBQCYix4pJ5Ume3nlxyp3pv79+0uZMmV0nXqV6d6nbX5+flKlShVthILeQ7uNRqM2HcNkMkn58uWlW7du2vt6xycicuTIEaldu7acPHlS71B0Az5xc1iff/65FCxYUKKiovQORaKiorTcM3nyZClcuLAkJCSIiP5/181msxw4cEAbMdGiRQt55pln7ntfbxcuXJC6des+1sgDZ8Fc5LiWL18uBoNB9uzZo3coEh0dLcnJySIismrVKnF3d5fQ0FAR0f/v+u3btwWAjB8/XkRE+vTpI8WKFdNy073x+fv7CwD5+eefc3TskJAQqVmzphgMBpkzZ84jP3vlyhUpWrSoPP/88w+Mak1ISJBmzZrJ2rVrc/HNnAtzkePauXOnKKVk1apVeociCQkJEhsbKyKWjZoMBoMEBASIiP65SEQkIiJCbty4ISIiY8eOFYPBoI1KtIf40tLSpGvXrjJ9+nS9Q9FNTnOR7kknN6/8nKBSU1Pl9OnTeofxgL/++ku+++47rd2hQwf53//+p7VTU1Ot2n9GRoacOnVKa/fo0UPq1Kmjtffs2SPXrl2zagw5lZnURSTf7HyUFV4sOS6TyaTdHNmTAwcOyJdffqm1BwwYIF27dtXa1s5FJpPpvsJw5s3Y5MmTRcRys3X+/HmrxpBTsbGx2sUacxFzkSPTa7rDo4SHh8uoUaO0QsmECROkYcOG2q5C1s5FZrNZIiMjtZ2E5s2bJwCkatWqIiJy8uRJCQ8Pf+ifTU1NlebNm0uBAgVkzZo1WfZx48YN+fjjj8XFxUXKli2b7RpKZrNZunTpIgULFrzvmu3mzZvaeWEuYi5yZPaYiy5evChjxozRHq4tW7ZMnnrqKW337bS0NKsWbsxms1y6dEmb8rdp0yYBIN7e3iJi2XXz0KFDdlE8Sk1NlaSkJBFhLmIRyYl9/fXXMm7cOLv4S3cvs9ksI0eOlLlz52rtkiVL3je/dfXq1bleN8BoNGrf9cSJEzJ9+nTt4uzTTz8VDw8P7YlaQECArFy50u7OzfHjx8Xb21tWrlypdyh2gRdLzuG3334TPz8/XbdczcqsWbPuW3y3SZMm0rt3b629ceNGbX20nDKZTFruOXfunMyaNUvi4uJE5P/fqF28eFFERPbv3y9ffPGF7usl/Ft0dLSUL1/+voJbfsZc5BwOHDggPXr00G4C7MmKFStk8ODBWrtnz57SvHlzrb1t27ZcbxluNpu1G50bN27It99+q62rFhAQcN9C2qdOnZKyZctK69atc3Ts2NhYadGihQCQDh06yLp16yQ8PFxCQ0Nl3bp10r9/f/H09BSllLz77rtaDnyUH374QQDI119/rf1eSkqKPPPMM/Luu+/m6rs7K+Yi5xAZGSkvvviibmuuPsrmzZvljTfe0K5jRowYIWXLltXa+/fvl23btuXqmGazWbsGTElJkQULFkhwcLCIWEYd4Z5NWeLi4mTcuHFy+PDhPPpGecNsNkvHjh2lQ4cOuq79aS9YRHJSZrNZ/Pz8pHv37nZfKU1LS5OJEyfK5s2bRUQkPj5eAGhDBOPi4qRQoUKycOFCERGJiYmR+vXrS7169bSpMsuWLROllFy6dElERObPny8AtHZwcLCsXr1ae+Jmr9LS0mTw4MHaDWZ+x4sl5zBp0iR59tlnHWLhwdmzZ8tPP/0kIpY8WrRo0ftGTZYrV07Gjh2rtRs1aiT169eX3bt3i4hluLrBYNB2y9yyZct9u2dGRkbKjz/+qD3xs1dms1n8/f3z3c5HWWEucg7Lly+X//znP3Yz8vhRli1bdl8xpVGjRtK5c2et3axZM3nrrbe09ksvvST169fXRgadP39eXF1dZenSpSJi2TQEgNaOjY2V77//XhttIGIpXNWoUSPHMaanp8uUKVOkYsWKAuC+l7e3t7z77rtZjmb6t5MnT4qnp6e0b9/+gRu0r776Ktc3rc6Kucg5bNu2TSpWrOgQS1b88ccf8vnnn2vtV199VZ5++mmt3atXL2nVqpXWHjx4sNSvX19mzpwpIpbriYIFC2pTZdPS0kQppT3AMxqN8u2339rtpgP3WrZsmaxYsULvMOxCTnORq7XXXKK8pZTCwoULcefOHbi4uCA+Ph4GgwFFixbVO7QHFChQAGPGjNHaXl5eOHnypBarUgrvvfceatWqBcCycG6VKlUgInBxcQEAPPPMMxgzZgwKFCgAAOjTpw969eqlHaNx48Zo3LixLb9WjokIlixZgl69eqFQoUL47rvv9A6JKE+NHj0a/v7+KFCgANLT0xEXF4dy5crpHdZDDR069L52cHAwXF0t/wSKCHr37n1fLqlcuTJMJhM8PDy09siRI1G2bFkAQKtWrXDjxg2ULFkSAFC9enVUr17dFl/lsaxduxatW7eGj48Ppk+frnc4RHnqrbfewuuvv44CBQrAbDbj4sWLqFKlit5hPVS/fv3ua2/YsAGpqalau1u3bihdurTWLl++PJRSKFy4MADLQtn+/v6oU6cOAKBatWqIjo7WFqouUaIEBg4ceF8ftWrVwi+//ILU1FQULFgw2xjd3d0xcuRI+Pv7Izg4GOfOnYPBYEC1atVQv359uLu75+i7pqWloVevXvD09MTy5cthMBiwbds2lCtXDrVr18bw4cNzdBwiR9G+fXucOXNGu285d+4cqlatqnNUD9e1a1d07dpVa8+bNw83btzQ2u3atcPNmze1dqlSpVC5cuX77uM+/fRTPPfccwAs932XLl3SrpNcXFzw/vvv2+KrPJajR48iMTERrVq1eiAvU/aUpeDkGHx9fSU4OFjvMOyGiKBz5864fv06goODtcIL2YcjR46gcePGmDNnDj788EO9w7ErSqkQEfHVO47HxVz0oEGDBuGPP/7AyZMn4eXlpXc4dI+rV6+ievXqGDhwIGbPnq13OHaFucj5TJs2DRMnTsSxY8d02fXQHv3666949dVXceDAATz77LM26/f//u//8MMPP2DTpk3o0qUL0tLS8NRTT6FRo0bYuHGjzeJwBMxFzmfdunXo27cvduzYoRVayD6ICJo1a4a0tDQcOXIEBgM3rM+U01zEkUgOTCmF0aNHIzo6mgUkO5L5pK9hw4bYt2+fTS/YiPTywQcf4JlnnmEByY5k5iIfHx/s3LkT9evX1zskIqvr06cP7ty5Y7dP//WQeR2yf/9+m12TLFmyBD/88ANGjBiBdu3aQUTg4eGBzZs3o3LlyjaJgUhPnTp1wogRI9C0aVO9Q6G7jEYjRARubm5Ys2YNChYsyALSY+JZc3CtWrVCr169AABbtmyBv78/7ty5o3NU+dfevXtRtWpVhISEAACaNWsGpZTOURFZX506dTBkyBAAwPHjxzFgwAAkJSXpHFX+deDAAXh6emLlypUAAF9fX7i5uekcFZH1lS9fHqNHj2iooiQAABuTSURBVIZSCtHR0ejVqxeuXbumd1i6Kl++PKpWrYqdO3fapL89e/Zg0KBB6NChA4YOHYomTZpg7ty5AIC6deuiSJEiNomDSE/e3t6YOHEi3NzckJycjF69euHMmTN6h5VvZWRk4IUXXtCm0VatWlWbBky5xyKSE9m9ezf+/vtvGI1GvUPJt2rWrIlmzZqhWLFieodCpJsDBw5g69at963zQbZVsWJFAEClSpV0joRIP0eOHEFgYCDi4+P1DkV37du3x/bt25GRkWHVfs6ePYvu3bujSpUqWLt2LcqUKYMmTZrgP//5j1X7JbJnp06dwvbt23Hp0iW9Q8m33N3d8eyzz6Jhw4Z6h+IUWERyIpMnT8a+fftQsGBBpKenY9WqVXCkNa8c1aFDh/DBBx9ARFCiRAls2LCB6zBQvjZw4EBERESgdOnSEBH89NNPHCFpA5GRkfDz80N6ejrKly8PEcHzzz+vd1hEuvnvf/+L8+fPaxt4rFu3Drdv39Y5Kn289NJLSExMxPbt263Wx40bN/Diiy/CZDLhmWeegYhAKYUlS5agU6dOVuuXyN41btwY586dQ7t27QAAf/zxx32LWJN1JCUlYciQITh9+jQAYOrUqVxEO4+wiORkMnfvWL58Ofr06YMDBw7oHJHz++eff/Dbb7/h8uXLeodCZDcKFSoEANi+fTveeustrF+/XueInN/p06fx66+/IiwsTO9QiOxGZi46c+YM3nzzzXy7uHynTp3g7e2tTXHNazdv3kTnzp1x6dIlzJo1C1u3bsXBgwet0heRI8rMRTdv3kTfvn0xcuRInSNyfsnJyVi3bh127NihdyhOh7uzOSkRwfbt29G+fXsAlu2s69Wrl+NtWenRtm7dCoPBgPbt28NsNiM5OZkLCucCdyHJXwIDA9G2bVsYDAacOHECVatW1Qre9GRCQkIQFRWFnj17ArBcnGZuv0vZYy7KX/bu3YuGDRvC09MTZ86cQdGiRVGqVCm9w7KZIUOGYPHixbh48SJKly6dZ8e9efMmWrdujbCwMPz+++/o0qULc1EuMRflL6GhoShZsiR8fHwQExODtLQ0bgaQR65du4bVq1dj6NChAHhdlFs5zUUcieSklFJaAenGjRto06YNPvnkE52jcg4mkwkff/wxpk6dCgAwGAwsIBE9Qvv27WEwGJCRkYGuXbvijTfe0DskpzF+/HiMGTMGJpMJAHihRPQIzz33HDw9PSEiePvtt9GmTZt8Ne3/ww8/REZGBubMmZNnx7x+/TratWuH0NBQeHh4oEWLFgCYi4gepW7dutqizsOHD4evr2++nWqb11auXIkRI0ZoU9iYi6yDRaR8oFSpUlizZo1WRLp69SqnO+TSzZs3MWXKFKSnp8PFxQUbN27EH3/8oXdYRA7F3d0dq1evxueffw7AsgX9/v379Q3KwaSlpWHWrFnaWgrff/89Dh48CBcXF50jI3IcSiksWrQIc+bMgVIKZrMZO3bscPqC0tNPP41evXphzpw5uHjx4hMdy2QyYfLkyfD19UVERARWr16N06dP84aNKJemTJmChQsXatPdgoKCtAdDlD0RwYYNG7TdJ99//32EhoZyMX8rYxEpn+jatau22POkSZPg6+vL3UpyITg4GKNGjUJgYCAAoHr16vDw8NA5KiLH06JFCzRp0gQA8N1336FFixYIDw/XOSrHceHCBQwfPhzr1q0DYNm6mzdtRLlXq1YtvPDCCwCADRs2oF27dggICNA5KuubOnUqlFIYOHAgzGbzYx9n9erVGDNmDOLi4rB9+3a8/vrrKFeuXB5GSpQ/VKhQAT169ABg2VGybdu2mD9/vs5ROQ6j0Yhhw4bh66+/BmB5YPnUU0/pHJXzYxEpH5owYQJWrVqF4sWLA7A8yc4c8kcWIoLp06dj3rx5AIAOHTrg1KlT6NKli86RETmPQYMGYdWqVahduzYAYNWqVQgJCdE5KvuzbNkyjB8/HoBlJEF4eDiGDBmic1REzuOll17C8uXL0blzZwBAQEAAAgMDnXJkUqVKlTBjxgz89ddfGDt2bK7+bEBAAN577z188MEHeOutt1C3bl2Eh4ejWbNmVoqWKH+pX78+1q5di/79+wMADhw4gF9++eWJCr7OaP/+/ejbty+MRiPc3Nzw119/aQ/XyDZYRMqHSpQoge7duwMA4uPj8emnn2Lp0qUALMUTZ7xoyqnExEQAlqHuO3fuxN69e7X3OCySKG8VKlQIb775JgDLk6TPPvsM06dP195nLrIIDg5GYGAgjEYjAOYiorzm5uaGt956S5sWOnXqVIwaNUp739ly0aBBg/Duu+/iyy+/xKhRox45dSY5ORkmkwlmsxlr167F0qVLMXfuXAwdOhTBwcGoXLmyDSMncm4GgwGvv/46ihQpAgCYP38+PvzwQ9y5cweA8+Wi3MjIyEBaWhoAy+LZQUFBOHv2LACgWrVqcHV11TO8fIdFpHyuePHiOHv2LPz9/QEABw8eROPGjREREaFzZLb3/fffo3z58oiLiwMA/Pzzz1i1apXOURHlD66urjhx4gRmzpwJALh8+TLq1KmDPXv26ByZ7QUEBMDHx0dbu2769OnYvXs3L5CIbOTvv//GmjVroJRCamoq6tevj19++UXvsPKMUgoLFizAwIEDMWXKFLRq1Qq7du164Ab12LFjKFeuHIYOHYr69etj+fLlqF27Nnbv3o3Zs2ejQIECOn0DovxhyZIl2LFjBwoUKAARQevWrbVZEvnJtWvXUKlSJSxcuBAA8PLLL+Ps2bN4+umndY4s/2IRiVCmTBltatvt27dRoEABlC9fHgCwb98+BAUFOWXlOy4uDhMmTNAKZs899xwGDx6sfVeueURkW15eXlruiYuLQ/HixbU1NsLCwvDnn39qo3GcSUpKCmbOnIndu3cDAJo2bYq3335bW2TTw8MDSik9QyTKVzw8PLTttmNjY1GuXDntOik6Ohpr1qxBamqqniE+MRcXFyxYsADLli1DZGQkWrdujfLly6Nx48bo2LEj+vbtCz8/PyQnJ2Pu3LlQSmHFihU4fPgwWrZsqXf4RPmCi4uLNvo4OTkZPj4+KFy4MADL5iSLFy/GrVu39AzRatavX49FixYBsNyr9unTBw0aNABgGbHFIra+lCMVB3x9fSU4OFjvMPKVl156CWFhYTh79iyUUrh48SLKly/vsDsBRUVFISMjAzVr1sSNGzdQoUIFzJkzB4MHD9Y7tHxFKRUiIr56x/G4mIts76OPPsKiRYtw7do1FC5cGFeuXEGpUqXg7u6ud2iPJSYmBlevXkXDhg1x584dlC9fHgMGDMDUqVP1Di1fYS6i3JozZw4+/vhjnD17FtWqVcP169dRuHBheHp66h3aY4mPj8fRo0cRExODX3/9FZs2bQIAlC1bFtWqVUOLFi3QrVs3NG7cmMVsK2IuotzauHEjXnnlFWzbtg3t27fHzZs3ISIoVqyY3qE9luTkZISEhKB169YAgNdeew0XLlzAP//8w9xjQznNRSwi0SPdvn0bUVFReOaZZyAiqFGjBpo2bYrVq1cDAG7evGnXOwOlpqYiOjoa1atXh4igXLlyeP7557XF1+Li4lCiRAmdo8x/eLFEuZWRkYHQ0FA0atQIgGWx+6SkJBw8eBCA/eeiO3fuICoqSht63bx5cxiNRhw6dAgAc5FemIsot8xmM0JCQrRdJgcOHIiNGzciJiYGBoMBN2/ehLe3t93e9JhMJpw+fRq1atUCAPTu3Rvbtm1DTEwMXFxctFGg9hq/s2IuotwSEQQHB6NRo0ZwcXHBlClTMH78eFy9ehXFixfHrVu3UKRIERgM9jnxSEQQERGBp59+GgaDAWPGjMHUqVNx7do1lChRAgkJCfD29rbb+J1VTnORLv9XlFI9lVJhSimzUsphE2Z+UKhQITzzzDMALBdOkydPhp+fHwBLgalMmTLaQrgmkwnh4eGPXKDR2q5du4Z9+/Zp7Zdffhk9e/YEYFkDYNmyZfjqq6+093nTlr8xFzkOd3d3rYAEAJ988gmGDRsGwHIhUq9ePfzvf//T3g8PD9cWotRDQkICgoKCtPaQIUPQvHlzLT9Onz5d29AAYC4i5iNHYTAYtAISAPTr1w9TpkzRbnS6deuGF198UXv/9OnT2mKwekhOTkZQUBAyMjIAWHJPnTp1EBsbCwAYPnw4/vzzTy3+EiVKsICUzzEXOQalFJo0aaLNDvnvf/+L6dOna1NvP/roI61YDFhmYyQlJekSKwCkp6dj//79iI+PBwCsW7cOtWvXxtGjRwEA77zzDrZt26Y9ECxWrBgLSHZMr/8zoQB6ANilU//0GFxcXNCrVy906NABgGU3pYkTJ6JNmzYAgIiICNSpU0dbjDomJgazZs3CpUuXAOTdjgJGo1E71t69ezFq1CitPXHiRHTu3Fm7URsxYgQmT56s/dmOHTtq6xwQgbnIYXXp0gWvvfYaAEsB29/fHy+//DIAy01T3bp1MWXKFABAWloavvrqK4SHhwPIu1yUuWMRAISGhmLkyJHaBdqSJUvQtm1bXL9+HQDw7rvvYtGiRdrnW7ZsiTp16uRJHOQ0mI8cUMuWLTFgwACtPWDAAPTr1w+AJde0atUKgwYN0t6fM2eONoIy8zNPSkS09eIuXryIcePG4dy5cwAsi4S3bdtWu1Hr0aMHVqxYoa372KBBAzRt2pSFI7oXc5EDqlevHj766COt3b179/vaffr0QdeuXbX2kiVLsHXrVq2d17no1q1bmDhxojbi+uTJk2jRogX+/vtvAEDbtm2xaNEiVKpUCQBQvXp1tGnTxmGXTMl3Mrd01+MFIAiAb04/37hxYyH7FRcXJ0uXLpVLly6JiMimTZsEgOzfv19rFytWTI4ePSoiIvv375d+/frJlStXREQkODhYRo4cKSNHjpTr16+LiMjOnTulZ8+eEhsbKyIiCxYsEIPBIPHx8SIiMnv2bPHw8JC4uDgREQkLC5Pdu3eL0Wi03RenXAMQLDrmnn+/mIucy+3bt2XVqlUSGhoqIiLh4eECQFauXCkiIidPnpQiRYrI77//LiIiZ8+elb59+8qxY8dERCQqKkrLRREREdox3njjDQkLCxMRkS1btoirq6uEhISIiMiGDRvEzc1Njhw5IiIi58+fl23btklqaqrtvjjlmr3lIsllPmIusm8mk0l++eUX2bt3r4hYcpNSSiZNmiQiIikpKVKoUCGZO3euiIgkJSVJnz59JDAwUEREEhIStFy0b98+ERG5ceOGvPnmm7J9+3YRseQzd3d3Wb9+vYiIHDt2TAwGg2zcuFFERGJjYyUgIEASExNt98Up15iLyNr++usv2bJli9YuW7as+Pn5ae1q1arJ8OHDtfa7774ra9as0dqjR4+WkSNHyqZNm0RExGw2S58+fbTP3L59WwoWLCjTpk0TEZHExERRSsnXX38tIiIZGRny22+/yY0bN6z3JemJ5TQX2f1+wUqpgQAGAtAqlWSfihcvjv79+2vtLl26IDY2FkWKFAEAlCtXDr1794aPjw8Ay0ilnTt3alNOjh8/jhkzZkAphX79+qFUqVKIj4/HiRMnkJSUhBIlSqBhw4YYPXq01sfgwYPx4YcfasMda9eubauvS/kMc5Hj8PT0xJtvvqm1a9WqhZs3b8LNzQ0AULBgQbzzzjuoVq0aAMt6RHv37sXAgQMBABcuXMDMmTMBAK1atcLTTz+NlJQUHDlyBAkJCQCAGjVqYNiwYdoCll26dEFKSgpcXS3/rFauXBmVK1e2zRemfIW5yHEYDAb06NFDa3t6eiIpKUkbLZ2RkYGBAwdqoxKTkpKwf/9+bTrcrVu3MGvWLIgIKlasiObNm8NsNuPQoUPo1q0bAMDHxwdDhw5FjRo1AAB16tRBSkqKtnNRiRIl7pteR5RXmIscS8eOHe9rR0VFabtMigh69uwJX19frb1nzx5Ur15d+/z06dMhIjCZTOjSpQuUUjh69Ki21ICnpyc+/PBD7RhFihTB7du3UbBgQQCAm5sbXnnlFat/T7INqy2srZTaBsDnIW+NFpGNdz8TBMBfRHK0EhsXbSNyDrZcQJK5iIiyYuvFbPM6HzEXETkH5iIisgc5zUVWG4kkIh2sdWwiopxiLiIie8F8RET2gLmIiJ4ElzwnIiIiIiIiIqJs6VJEUkp1V0pdBtAcwCal1F96xEFE+RtzERHZC+YjIrIHzEVElB1dFtYWkd8A/KZH30REmZiLiMheMB8RkT1gLiKi7HA6GxERERERERERZYtFJCIiIiIiIiIiyhaLSERERERERERElC0WkYiIiIiIiIiIKFssIhERERERERERUbZYRCIiIiIiIiIiomyxiERERERERERERNliEYmIiIiIiIiIiLLFIhIREREREREREWWLRSQiIiIiIiIiIsoWi0hERERERERERJQtFpGIiIiIiIiIiChbLCIREREREREREVG2WEQiIiIiIiIiIqJssYhERERERERERETZYhGJiIiIiIiIiIiyxSISERERERERERFli0UkIiIiIiIiIiLKFotIRERERERERESULRaRiIiIiIiIiIgoWywiERERERERERFRtlhEIiIiIiIiIiKibLGIRERERERERERE2WIRiYiIiIiIiIiIssUiEhERERERERERZYtFJCIiIiIiIiIiyhaLSERERERERERElC0WkYiIiIiIiIiIKFssIhERERERERERUbZYRCIiIiIiIiIiomyxiERERERERERERNliEYmIiIiIiIiIiLLFIhIREREREREREWWLRSQiIiIiIiIiIsoWi0hERERERERERJQtJSJ6x5BjSqkbAC7oHUc2SgKI1TsIB8dz+OTs/RxWFpFSegfxuJiL8g2ewydn7+eQucj67P1nwBHwHD45ez+HzEXWZ+8/A46A5/DJ2fs5zFEucqgikiNQSgWLiK/ecTgynsMnx3NI/Bl4cjyHT47nkPgz8OR4Dp8czyHxZ+DJ8Rw+OWc5h5zORkRERERERERE2WIRiYiIiIiIiIiIssUiUt77Qe8AnADP4ZPjOST+DDw5nsMnx3NI/Bl4cjyHT47nkPgz8OR4Dp+cU5xDrolERERERERERETZ4kgkIiIiIiIiIiLKFotIeUwp1VMpFaaUMiulHH7ldVtSSnVWSp1SSkUqpUbqHY8jUkotUUpdV0qF6h0L6Yu56PExFz055iK6F/PR42EuenLMRXQv5qLHx3z0ZJwtF7GIlPdCAfQAsEvvQByJUsoFwDwALwKoDeBNpVRtfaNySEsBdNY7CLILzEWPgbkozywFcxH9f8xHucRclGeWgrmI/j/mosfAfJQnlsKJchGLSHlMRE6KyCm943BATQFEikiUiGQAWAOgm84xORwR2QUgXu84SH/MRY+NuSgPMBfRvZiPHgtzUR5gLqJ7MRc9NuajJ+RsuYhFJLIX5QFcuqd9+e7vERHZEnMREdkD5iIishfMR3QfV70DcERKqW0AfB7y1mgR2WjreJyEesjvcetAokdgLrIK5iKix8B8lOeYi4geA3ORVTAf0X1YRHoMItJB7xic0GUAFe9pVwAQrVMsRA6BucgqmIuIHgPzUZ5jLiJ6DMxFVsF8RPfhdDayF4cAPKWUqqqUcgfQC8DvOsdERPkPcxER2QPmIiKyF8xHdB8WkfKYUqq7UuoygOYANiml/tI7JkcgIkYA7wP4C8BJAOtEJEzfqByPUmo1gP0AnlZKXVZK+ekdE+mDuejxMBflDeYiuhfzUe4xF+UN5iK6F3PR42E+enLOlouUCKczEhERERERERHRo3EkEhERERERERERZYtFJCIiIiIiIiIiyhaLSERERERERERElC0WkYiIiIiIiIiIKFssIhERERERERERUbZYRKKHUkqZlFJHlVKhSqn1SilPvWMCAKXUqDw4Rk+lVJhSyqyU8s2LuIjIepw8H01XSkUopY4rpX5TShXNi9iIKO85eS6aeDcPHVVK/a2UKpcXsRFR3nPyXPS5UurK3e93VCnVJS9io7zFIhJlJVVEGohIXQAZAAbl9A8qpVysFxZynZweEk8ogB4AduVJRERkbc6cj7YCqCsi9QCcBvBZXgRGRFbhzLlouojUE5EGAP4EMC5PIiMia3DmXAQAs+9+vwYiEpAHcVEeYxGJcmI3gBoAoJTaoJQKuTuSZ2DmB5RSyUqpL5RSBwE0V0qNU0odulsh/0Eppe5+LkgpNVsptUspdVIp1UQp9atS6oxSatI9x+urlPrnbgX6e6WUi1JqKoCCd39vZVafe1g8934ZETkpIqesfdKIyCqcLR/9LSLGu80DACpY79QRUR5ytlyUeE+zEACxzmkjojzmVLmIHISI8MXXAy8AyXf/6wpgI4DBd9vF7/63ICwjekrcbQuA1+/588Xv+fUKAC/d/XUQgK/u/vojANEAygIoAOAygBIAagH4A4Db3c99B6DfvXHd/fWjPndfPFl8xyAAvnqfa7744uvRr/yQj+5+7g8AffU+33zxxdfDX86eiwBMBnDp7ncopff55osvvh7+cuZcBOBzAOcBHAewBEAxvc83Xw++XEH0cAWVUkfv/no3gMV3f/2hUqr73V9XBPAUgDgAJgC/3PPn2yqlhgPwBFAcQBgsiQQAfr/73xMAwkQkBgCUUlF3j9kSQGMAh+4WxgsCuP6QGNs/4nP/joeIHJfT5yOl1GgARgArH/U5ItKVU+ciERkNYLRS6jMA7wMYn9VniUhXzpyL5gOYCEuhaSKAmQAGZPFZ0gmLSJSVVLHMi9copdoA6ACguYikKKWCAHjcfTtNREx3P+cBS7XZV0QuKaU+v+dzAJB+97/me36d2XYFoAAsE5Hs1gZ51Oe0eIjI4Tl1PlJK9QfQFUB7ufsYjojsklPnonusArAJLCIR2SunzUUicu2e77QQljXayM5wTSTKDW8ACXcTU00AzbL4XGYiilVKFQbwWi77CQTwmlKqNAAopYorpSrffe+OUsotB58jIufmFPlIKdUZwAgAL4tISi5jIyL9OUsueuqe5ssAInIZHxHpy1lyUdl7mt1hmZZHdoYjkSg3tgAYpJQ6DuAULIvAPkBEbt6tHJ+AZU7rodx0IiLhSqkxAP5WShkA3AEwBMAFAD8AOK6UOiwifR7xuSzdHeb5LYBSADYppY6KSKfcxEhEunOKfARgLixrDWy9O9z7gIjkeJcVItKds+SiqUqpp2EZbXABudjtiYjsgrPkomlKqQawTGc7D+D/chMf2YbiyHkiIiIiIiIiIsoOp7MREREREREREVG2WEQiIiIiIiIiIqJssYhERERERERERETZYhGJiIiIiIiIiIiyxSISERERERERERFli0UkIiIiIiIiIiLKFotIRERERERERESULRaRiIiIiIiIiIgoW/8Pd2iRh0qXSPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa959df2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors_list = [[0,1],\n",
    "                [1,2],\n",
    "                [2,3],\n",
    "                [3,4],\n",
    "               ]\n",
    "fig = my_project.plot_pdfs(factors_list)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Experimental design\n",
    "+++++++++++++++++++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:03.656668Z",
     "start_time": "2018-04-27T14:51:03.647063Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 2 Entropy flux   0.002021\n",
      "Experiment 3 Entropy flux   0.026705\n",
      "Experiment 4 Entropy flux   0.000073\n",
      "Experiment 5 Entropy flux  -0.000179\n",
      "Application 1 Entropy flux  -0.028620\n",
      "Experiment 1 Entropy flux   0.000000\n",
      "Experiment 5 Entropy flux  -0.00\n",
      "Experiment 2 Entropy flux   0.001914\n",
      "Experiment 3 Entropy flux   0.026593\n",
      "Experiment 4 Entropy flux   0.000074\n",
      "Application 1 Entropy flux  -0.028582\n",
      "Experiment 1 Entropy flux   0.000000\n",
      "Experiment 5 Entropy flux   0.000000\n",
      "No low-information measurements\n"
     ]
    }
   ],
   "source": [
    "my_project.remove_low_information_measurements()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:03.791565Z",
     "start_time": "2018-04-27T14:51:03.657956Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.12547508  0.          0.          0.          0.        ]\n",
      " [-0.13014049  0.24328481  0.          0.          0.        ]\n",
      " [-0.09975008 -0.13704602  0.07786079  0.          0.        ]\n",
      " [ 0.0688092  -0.38530355  0.01937665  0.13471687  0.        ]\n",
      " [ 0.39525824  0.06522259 -0.1889297  -0.04926863  0.11986361]]\n"
     ]
    }
   ],
   "source": [
    "print (my_project.solution.alpha)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Parameter uncertainty analysis\n",
    "++++++++++++++++++++++++++++++\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:06.128636Z",
     "start_time": "2018-04-27T14:51:03.794628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa958cc3b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors_list = [[0,1],\n",
    "                [1,2],\n",
    "                [2,3],\n",
    "                [3,4],\n",
    "               ]\n",
    "fig = my_project.plot_pdfs(factors_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.139030Z",
     "start_time": "2018-04-27T14:51:06.130004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa958b78240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factors_list = [[0,4],\n",
    "                [1,3],\n",
    "                [2,4],\n",
    "                [0,2],\n",
    "               ]\n",
    "fig = my_project.plot_pdfs(factors_list)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true,
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "Here we show the covariance matrix of the model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.333887Z",
     "start_time": "2018-04-27T14:51:08.140464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa959ed05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_project.plot_covariance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.338669Z",
     "start_time": "2018-04-27T14:51:08.335158Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.74    0.25    0.48    1.29\n",
      "Parameter 2                    :       1    2.72    0.26    0.55     1.3    1.74\n",
      "Parameter 3                    :       1    2.72    0.57    0.37     1.8    1.45\n",
      "Parameter 5                    :       1    2.72   -0.17    0.83    0.84    2.29\n",
      "Parameter 7                    :       1    2.72    0.36    0.92     1.4    2.52\n"
     ]
    }
   ],
   "source": [
    "#my_project.interpret_model()\n",
    "print(my_project.interpret_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.396976Z",
     "start_time": "2018-04-27T14:51:08.339883Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter name                   Value   Uncert \n",
      "Parameter 1                    :       1    2.72\n",
      "Parameter 2                    :       1    2.72\n",
      "Parameter 3                    :       1    2.72\n",
      "Parameter 5                    :       1    2.72\n",
      "Parameter 7                    :       1    2.72\n"
     ]
    }
   ],
   "source": [
    "print(my_project.print_model_values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.478039Z",
     "start_time": "2018-04-27T14:51:08.398785Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Name                  Value  Unc    OptVal OptUnc MdlVal MdlUnc\n",
      "Experiment 2        :   0.25   0.05   0.24   0.05  -0.60   0.62 \n",
      "Experiment 3        :   0.20   0.05   0.21   0.05   0.50   0.62 \n",
      "Experiment 4        :   0.70   0.08   0.70   0.08   0.80   0.62 \n",
      "Application 1       :   0.00   0.00  -0.68   0.11   0.00   0.51 \n",
      "Experiment 1        :   0.40   0.05  -0.15   0.09   0.20   0.62 \n",
      "Experiment 5        :   0.40   0.80   1.11   0.34   0.50   0.62 \n"
     ]
    }
   ],
   "source": [
    "print(my_project.validate_solution())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T14:51:08.532472Z",
     "start_time": "2018-04-27T14:51:08.480481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "project\n",
      "6 measurements\n",
      "Experiment 2 (Active): 1\n",
      "Experiment 3 (Active): 2\n",
      "Experiment 4 (Active): 3\n",
      "Application 1 (Application): 0\n",
      "Experiment 1 (Inconsistent): 0\n",
      "Experiment 5 (Low Information): 4\n",
      "\n",
      "Name                  Value  Unc    OptVal OptUnc MdlVal MdlUnc\n",
      "Experiment 2        :   0.25   0.05   0.24   0.05  -0.60   0.62 \n",
      "Experiment 3        :   0.20   0.05   0.21   0.05   0.50   0.62 \n",
      "Experiment 4        :   0.70   0.08   0.70   0.08   0.80   0.62 \n",
      "Application 1       :   0.00   0.00  -0.68   0.11   0.00   0.51 \n",
      "Experiment 1        :   0.40   0.05  -0.15   0.09   0.20   0.62 \n",
      "Experiment 5        :   0.40   0.80   1.11   0.34   0.50   0.62 \n",
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.74    0.25    0.48    1.29\n",
      "Parameter 2                    :       1    2.72    0.26    0.55     1.3    1.74\n",
      "Parameter 3                    :       1    2.72    0.57    0.37     1.8    1.45\n",
      "Parameter 5                    :       1    2.72   -0.17    0.83    0.84    2.29\n",
      "Parameter 7                    :       1    2.72    0.36    0.92     1.4    2.52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(my_project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter name                   Value   Uncert  FactVal FactUnc NewVal  Uncert \n",
      "Parameter 1                    :       1    2.72   -0.74    0.25    0.48    1.29\n",
      "Parameter 2                    :       1    2.72    0.26    0.55     1.3    1.74\n",
      "Parameter 3                    :       1    2.72    0.57    0.37     1.8    1.45\n",
      "Parameter 5                    :       1    2.72   -0.17    0.83    0.84    2.29\n",
      "Parameter 7                    :       1    2.72    0.36    0.92     1.4    2.52\n"
     ]
    }
   ],
   "source": [
    "print(my_project.interpret_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpret_model_as_arrays(project,measurement=0):\n",
    "    '''Takes a project and returns the model parameter values and uncertainties as arrays\n",
    "    \n",
    "    :param project: The MUM-PCE project whose parameters are being identified\n",
    "    :type project: MUM-PCE Project\n",
    "    :key measurement: A measurement identifier, either int or string\n",
    "    :returns: The new model parameter values and new uncertainties as NumPy arrays\n",
    "    '''\n",
    "    meas = project[measurement]\n",
    "    \n",
    "    print(meas)\n",
    "    \n",
    "    x = project.solution.x\n",
    "    cov = project.solution.cov\n",
    "    \n",
    "    new_values = []\n",
    "    new_uncertainties = []\n",
    "    \n",
    "    for active_num,param in enumerate(meas.active_parameters):\n",
    "        value = meas.model.get_parameter(param)\n",
    "        this_x = x[active_num]\n",
    "        this_std = 2*np.sqrt(cov[active_num,active_num])\n",
    "        this_unc = meas.parameter_uncertainties[active_num]\n",
    "\n",
    "        multiplier = this_unc ** this_x\n",
    "        new_value = value*multiplier\n",
    "        new_uncertainty = this_unc ** (this_std)\n",
    "        \n",
    "        new_values += [new_value]\n",
    "        new_uncertainties += [new_uncertainty]\n",
    "    new_values = np.array(new_values)\n",
    "    new_uncertainties = np.array(new_uncertainties)\n",
    "    \n",
    "    return new_values,new_uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 2 (Active): 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([ 0.47803445,  1.29799983,  1.76326957,  0.84261713,  1.43934579]),\n",
       " array([ 1.28524602,  1.73639604,  1.45217576,  2.29051459,  2.51677464]))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpret_model_as_arrays(my_project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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