Human/Human_sample_lvl_sim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Human sample level analysis\n",
    "\n",
    "Main notebook to run sample-level simulation experiment using human gene expression data from recount2.\n",
    "\n",
    "Make sure to run ```download_data.R``` download raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%load_ext rpy2.ipython\n",
    "\n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects import pandas2ri\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import ast\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import subprocess\n",
    "from plotnine import (ggplot,\n",
    "                      labs,  \n",
    "                      geom_line, \n",
    "                      geom_point,\n",
    "                      geom_errorbar,\n",
    "                      aes, \n",
    "                      ggsave, \n",
    "                      theme_bw,\n",
    "                      theme,\n",
    "                      facet_wrap,\n",
    "                      scale_color_manual,\n",
    "                      guides, \n",
    "                      guide_legend,\n",
    "                      element_blank,\n",
    "                      element_text,\n",
    "                      element_rect,\n",
    "                      element_line,\n",
    "                      coords)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "from simulate_expression_compendia_modules import pipeline\n",
    "from ponyo import utils, train_vae_modules\n",
    "\n",
    "from numpy.random import seed\n",
    "randomState = 123\n",
    "seed(randomState)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in config variables\n",
    "base_dir = os.path.abspath(os.path.join(os.getcwd(),\"../\"))\n",
    "config_file = os.path.abspath(os.path.join(base_dir,\n",
    "                                           \"configs\", \n",
    "                                           \"config_Human_sample_limma.tsv\"))\n",
    "params = utils.read_config(config_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load parameters\n",
    "local_dir = params[\"local_dir\"]\n",
    "dataset_name = params['dataset_name']\n",
    "analysis_name = params[\"simulation_type\"]\n",
    "correction_method = params[\"correction_method\"]\n",
    "lst_num_experiments = params[\"lst_num_experiments\"]\n",
    "train_architecture = params['NN_architecture']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input files\n",
    "rpkm_data_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"data\",\n",
    "    \"input\",\n",
    "    \"recount2_gene_RPKM_data.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.setup_dir(config_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output file\n",
    "normalized_data_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"data\",\n",
    "    \"input\",\n",
    "    \"recount2_gene_normalized_data.tsv.xz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize data\n",
    "if os.path.exists(normalized_data_file) == False:\n",
    "    train_vae_modules.normalize_expression_data(base_dir,\n",
    "                                                config_file,\n",
    "                                                rpkm_data_file,\n",
    "                                                normalized_data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory containing log information from VAE training\n",
    "vae_log_dir = os.path.join(\n",
    "    base_dir, \n",
    "    dataset_name,\n",
    "    \"logs\",\n",
    "    train_architecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input dataset contains 896 samples and 58037 genes\n",
      "Train on 806 samples, validate on 90 samples\n",
      "Epoch 1/40\n",
      "806/806 [==============================] - 297s 368ms/step - loss: 8349.9469 - val_loss: 5660.8708\n",
      "Epoch 2/40\n",
      "806/806 [==============================] - 286s 355ms/step - loss: 5863.6263 - val_loss: 5594.4683\n",
      "Epoch 3/40\n",
      "806/806 [==============================] - 287s 356ms/step - loss: 5779.9224 - val_loss: 5424.6846\n",
      "Epoch 4/40\n",
      "806/806 [==============================] - 288s 357ms/step - loss: 5650.7489 - val_loss: 5391.5148\n",
      "Epoch 5/40\n",
      "806/806 [==============================] - 288s 357ms/step - loss: 5701.9353 - val_loss: 5363.3989\n",
      "Epoch 6/40\n",
      "806/806 [==============================] - 286s 355ms/step - loss: 5679.6096 - val_loss: 5275.3207\n",
      "Epoch 7/40\n",
      "806/806 [==============================] - 286s 355ms/step - loss: 5561.9723 - val_loss: 5273.8338\n",
      "Epoch 8/40\n",
      "806/806 [==============================] - 288s 357ms/step - loss: 5552.7388 - val_loss: 5189.0354\n",
      "Epoch 9/40\n",
      "806/806 [==============================] - 287s 356ms/step - loss: 5455.2930 - val_loss: 5248.0741\n",
      "Epoch 10/40\n",
      "806/806 [==============================] - 288s 357ms/step - loss: 5430.4370 - val_loss: 5190.0948\n",
      "Epoch 11/40\n",
      "806/806 [==============================] - 287s 357ms/step - loss: 5452.8990 - val_loss: 5241.4960\n",
      "Epoch 12/40\n",
      "806/806 [==============================] - 287s 357ms/step - loss: 5380.2683 - val_loss: 5179.4632\n",
      "Epoch 13/40\n",
      "806/806 [==============================] - 288s 357ms/step - loss: 5380.0118 - val_loss: 5171.0953\n",
      "Epoch 14/40\n",
      "806/806 [==============================] - 288s 358ms/step - loss: 5344.0403 - val_loss: 5112.9083\n",
      "Epoch 15/40\n",
      "806/806 [==============================] - 287s 356ms/step - loss: 5217.6982 - val_loss: 5050.1347\n",
      "Epoch 16/40\n",
      "806/806 [==============================] - 295s 366ms/step - loss: 5212.4015 - val_loss: 5038.5889\n",
      "Epoch 17/40\n",
      "806/806 [==============================] - 295s 366ms/step - loss: 5211.7153 - val_loss: 5072.6968\n",
      "Epoch 18/40\n",
      "806/806 [==============================] - 300s 373ms/step - loss: 5196.4393 - val_loss: 5157.2120\n",
      "Epoch 19/40\n",
      "806/806 [==============================] - 292s 363ms/step - loss: 5189.1548 - val_loss: 5082.1386\n",
      "Epoch 20/40\n",
      "806/806 [==============================] - 292s 363ms/step - loss: 5109.0580 - val_loss: 5029.1408\n",
      "Epoch 21/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 5098.3733 - val_loss: 4999.4092\n",
      "Epoch 22/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 5066.8687 - val_loss: 5024.0885\n",
      "Epoch 23/40\n",
      "806/806 [==============================] - 290s 360ms/step - loss: 5022.4480 - val_loss: 4974.2989\n",
      "Epoch 24/40\n",
      "806/806 [==============================] - 295s 366ms/step - loss: 5005.1630 - val_loss: 4952.3451\n",
      "Epoch 25/40\n",
      "806/806 [==============================] - 294s 364ms/step - loss: 5020.8264 - val_loss: 4954.9488\n",
      "Epoch 26/40\n",
      "806/806 [==============================] - 289s 358ms/step - loss: 4972.6981 - val_loss: 4939.8551\n",
      "Epoch 27/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 4969.3321 - val_loss: 4991.4759\n",
      "Epoch 28/40\n",
      "806/806 [==============================] - 292s 362ms/step - loss: 4945.9706 - val_loss: 4933.4278\n",
      "Epoch 29/40\n",
      "806/806 [==============================] - 290s 360ms/step - loss: 4913.6599 - val_loss: 4931.0360\n",
      "Epoch 30/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 4909.5925 - val_loss: 4948.7837\n",
      "Epoch 31/40\n",
      "806/806 [==============================] - 289s 359ms/step - loss: 4982.3353 - val_loss: 4936.0687\n",
      "Epoch 32/40\n",
      "806/806 [==============================] - 290s 360ms/step - loss: 5005.1811 - val_loss: 4904.2703\n",
      "Epoch 33/40\n",
      "806/806 [==============================] - 291s 360ms/step - loss: 4896.4975 - val_loss: 4909.0649\n",
      "Epoch 34/40\n",
      "806/806 [==============================] - 290s 360ms/step - loss: 4854.0739 - val_loss: 4916.7796\n",
      "Epoch 35/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 4879.9702 - val_loss: 4906.8312\n",
      "Epoch 36/40\n",
      "806/806 [==============================] - 296s 367ms/step - loss: 4859.9820 - val_loss: 4882.1506\n",
      "Epoch 37/40\n",
      "806/806 [==============================] - 291s 362ms/step - loss: 4854.2613 - val_loss: 4855.0715\n",
      "Epoch 38/40\n",
      "806/806 [==============================] - 289s 359ms/step - loss: 4846.6032 - val_loss: 4878.8825\n",
      "Epoch 39/40\n",
      "806/806 [==============================] - 288s 358ms/step - loss: 4821.7418 - val_loss: 4840.4799\n",
      "Epoch 40/40\n",
      "806/806 [==============================] - 291s 361ms/step - loss: 4768.4141 - val_loss: 4889.0549\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train VAE\n",
    "if len(os.listdir(vae_log_dir)) == 0:\n",
    "    train_vae_modules.train_vae(config_file,\n",
    "                                normalized_data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run simulation experiment without noise correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed: 46.4min\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed: 46.4min remaining: 69.6min\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed: 46.5min remaining: 31.0min\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed: 46.5min remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed: 46.5min finished\n",
      "                          score\n",
      "number of experiments          \n",
      "1                      0.999991\n",
      "2                      0.901448\n",
      "5                      0.632603\n",
      "10                     0.203977\n",
      "20                     0.118938\n",
      "50                     0.135414\n",
      "100                    0.184654\n",
      "250                    0.378679\n",
      "500                    0.653348\n",
      "                              score\n",
      "number of experiments              \n",
      "1                      2.815872e-07\n",
      "2                      1.577455e-04\n",
      "5                      2.284287e-03\n",
      "10                     1.077649e-03\n",
      "20                     2.040411e-03\n",
      "50                     4.475839e-03\n",
      "100                    2.890957e-03\n",
      "250                    1.965542e-03\n",
      "500                    5.823870e-03\n",
      "                           ymin      ymax\n",
      "number of experiments                    \n",
      "1                      0.999991  0.999992\n",
      "2                      0.901139  0.901757\n",
      "5                      0.628126  0.637080\n",
      "10                     0.201865  0.206089\n",
      "20                     0.114939  0.122937\n",
      "50                     0.126642  0.144187\n",
      "100                    0.178988  0.190320\n",
      "250                    0.374827  0.382532\n",
      "500                    0.641934  0.664763\n"
     ]
    }
   ],
   "source": [
    "# Run simulation without correction \n",
    "corrected = False\n",
    "pipeline.run_simulation(config_file,\n",
    "                        normalized_data_file,\n",
    "                        corrected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run simulation with correction applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed: 38.5min\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed: 38.6min remaining: 58.0min\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed: 38.7min remaining: 25.8min\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed: 38.7min remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed: 38.7min finished\n",
      "                          score\n",
      "number of experiments          \n",
      "1                      0.999991\n",
      "2                      0.998663\n",
      "5                      0.995652\n",
      "10                     0.989930\n",
      "20                     0.981173\n",
      "50                     0.946037\n",
      "100                    0.889085\n",
      "250                    0.699587\n",
      "500                    0.117121\n",
      "                              score\n",
      "number of experiments              \n",
      "1                      2.649890e-07\n",
      "2                      3.519406e-04\n",
      "5                      2.567284e-04\n",
      "10                     3.255820e-04\n",
      "20                     5.076565e-04\n",
      "50                     1.033679e-03\n",
      "100                    3.522318e-03\n",
      "250                    2.876636e-03\n",
      "500                    2.594490e-03\n",
      "                           ymin      ymax\n",
      "number of experiments                    \n",
      "1                      0.999991  0.999992\n",
      "2                      0.997973  0.999353\n",
      "5                      0.995149  0.996156\n",
      "10                     0.989292  0.990568\n",
      "20                     0.980178  0.982168\n",
      "50                     0.944011  0.948063\n",
      "100                    0.882181  0.895988\n",
      "250                    0.693949  0.705226\n",
      "500                    0.112036  0.122206\n"
     ]
    }
   ],
   "source": [
    "# Run simulation without correction \n",
    "corrected = True\n",
    "pipeline.run_simulation(config_file,\n",
    "                        normalized_data_file,\n",
    "                        corrected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_ind = [0,1,2,-3,-2,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File directories\n",
    "similarity_uncorrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    \"saved_variables\",\n",
    "    dataset_name + \"_\" + analysis_name + \"_svcca_uncorrected_\" + correction_method + \".pickle\")\n",
    "\n",
    "ci_uncorrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    \"saved_variables\",\n",
    "    dataset_name + \"_\" + analysis_name + \"_ci_uncorrected_\" + correction_method + \".pickle\")\n",
    "\n",
    "similarity_corrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    \"saved_variables\",\n",
    "    dataset_name + \"_\" + analysis_name + \"_svcca_corrected_\" + correction_method + \".pickle\")\n",
    "\n",
    "ci_corrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    \"saved_variables\",\n",
    "    dataset_name + \"_\" + analysis_name + \"_ci_corrected_\" + correction_method + \".pickle\")\n",
    "\n",
    "permuted_score_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    \"saved_variables\",\n",
    "    dataset_name + \"_\" + analysis_name + \"_permuted.npy\")\n",
    "\n",
    "compendia_dir = os.path.join(\n",
    "    local_dir,\n",
    "    \"experiment_simulated\",\n",
    "    dataset_name + \"_\" + analysis_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output files\n",
    "svcca_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".svg\")\n",
    "\n",
    "svcca_png_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    dataset_name +\"_\"+analysis_name+\"_svcca_\"+correction_method+\".png\")\n",
    "\n",
    "pca_uncorrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    dataset_name +\"_\"+analysis_name+\"_pca_uncorrected_\"+correction_method+\".svg\")\n",
    "\n",
    "pca_corrected_file = os.path.join(\n",
    "    base_dir,\n",
    "    dataset_name,\n",
    "    \"results\",\n",
    "    dataset_name +\"_\"+analysis_name+\"_pca_corrected_\"+correction_method+\".svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pickled files\n",
    "uncorrected_svcca = pd.read_pickle(similarity_uncorrected_file)\n",
    "err_uncorrected_svcca = pd.read_pickle(ci_uncorrected_file)\n",
    "corrected_svcca = pd.read_pickle(similarity_corrected_file)\n",
    "err_corrected_svcca = pd.read_pickle(ci_corrected_file)\n",
    "\n",
    "permuted_score = np.load(permuted_score_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate error bars\n",
    "uncorrected_svcca_err = pd.concat([uncorrected_svcca, err_uncorrected_svcca], axis=1)\n",
    "corrected_svcca_err = pd.concat([corrected_svcca, err_corrected_svcca], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add group label\n",
    "uncorrected_svcca_err['Group'] = 'uncorrected'\n",
    "corrected_svcca_err['Group'] = 'corrected'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>ymin</th>\n",
       "      <th>ymax</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of experiments</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.901448</td>\n",
       "      <td>0.901139</td>\n",
       "      <td>0.901757</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.632603</td>\n",
       "      <td>0.628126</td>\n",
       "      <td>0.637080</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.203977</td>\n",
       "      <td>0.201865</td>\n",
       "      <td>0.206089</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.118938</td>\n",
       "      <td>0.114939</td>\n",
       "      <td>0.122937</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.135414</td>\n",
       "      <td>0.126642</td>\n",
       "      <td>0.144187</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.184654</td>\n",
       "      <td>0.178988</td>\n",
       "      <td>0.190320</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.378679</td>\n",
       "      <td>0.374827</td>\n",
       "      <td>0.382532</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.653348</td>\n",
       "      <td>0.641934</td>\n",
       "      <td>0.664763</td>\n",
       "      <td>uncorrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998663</td>\n",
       "      <td>0.997973</td>\n",
       "      <td>0.999353</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.995652</td>\n",
       "      <td>0.995149</td>\n",
       "      <td>0.996156</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.989930</td>\n",
       "      <td>0.989292</td>\n",
       "      <td>0.990568</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.981173</td>\n",
       "      <td>0.980178</td>\n",
       "      <td>0.982168</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.946037</td>\n",
       "      <td>0.944011</td>\n",
       "      <td>0.948063</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.889085</td>\n",
       "      <td>0.882181</td>\n",
       "      <td>0.895988</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.699587</td>\n",
       "      <td>0.693949</td>\n",
       "      <td>0.705226</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.117121</td>\n",
       "      <td>0.112036</td>\n",
       "      <td>0.122206</td>\n",
       "      <td>corrected</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          score      ymin      ymax        Group\n",
       "number of experiments                                           \n",
       "1                      0.999991  0.999991  0.999992  uncorrected\n",
       "2                      0.901448  0.901139  0.901757  uncorrected\n",
       "5                      0.632603  0.628126  0.637080  uncorrected\n",
       "10                     0.203977  0.201865  0.206089  uncorrected\n",
       "20                     0.118938  0.114939  0.122937  uncorrected\n",
       "50                     0.135414  0.126642  0.144187  uncorrected\n",
       "100                    0.184654  0.178988  0.190320  uncorrected\n",
       "250                    0.378679  0.374827  0.382532  uncorrected\n",
       "500                    0.653348  0.641934  0.664763  uncorrected\n",
       "1                      0.999991  0.999991  0.999992    corrected\n",
       "2                      0.998663  0.997973  0.999353    corrected\n",
       "5                      0.995652  0.995149  0.996156    corrected\n",
       "10                     0.989930  0.989292  0.990568    corrected\n",
       "20                     0.981173  0.980178  0.982168    corrected\n",
       "50                     0.946037  0.944011  0.948063    corrected\n",
       "100                    0.889085  0.882181  0.895988    corrected\n",
       "250                    0.699587  0.693949  0.705226    corrected\n",
       "500                    0.117121  0.112036  0.122206    corrected"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate dataframes\n",
    "all_svcca = pd.concat([uncorrected_svcca_err, corrected_svcca_err])\n",
    "all_svcca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVCCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (8775087274013)>\n"
     ]
    }
   ],
   "source": [
    "# Plot\n",
    "lst_num_partitions = list(all_svcca.index)\n",
    "\n",
    "threshold = pd.DataFrame(\n",
    "    pd.np.tile(\n",
    "        permuted_score,\n",
    "        (len(lst_num_partitions), 1)),\n",
    "    index=lst_num_partitions,\n",
    "    columns=['score'])\n",
    "\n",
    "panel_A = ggplot(all_svcca) \\\n",
    "    + geom_line(all_svcca,\n",
    "                aes(x=lst_num_partitions, y='score', color='Group'),\n",
    "                size=1.5) \\\n",
    "    + geom_point(aes(x=lst_num_partitions, y='score'), \n",
    "                 color ='darkgrey',\n",
    "                size=0.5) \\\n",
    "    + geom_errorbar(all_svcca,\n",
    "                  aes(x=lst_num_partitions, ymin='ymin', ymax='ymax'),\n",
    "                   color='darkgrey') \\\n",
    "    + geom_line(threshold, \n",
    "                aes(x=lst_num_partitions, y='score'), \n",
    "                linetype='dashed',\n",
    "                size=1,\n",
    "                color=\"darkgrey\",\n",
    "                show_legend=False) \\\n",
    "    + labs(x = \"Number of Partitions\", \n",
    "           y = \"Similarity score (SVCCA)\", \n",
    "           title = \"Similarity across varying numbers of partitions\") \\\n",
    "    + theme(\n",
    "            plot_background=element_rect(fill=\"white\"),\n",
    "            panel_background=element_rect(fill=\"white\"),\n",
    "            panel_grid_major_x=element_line(color=\"lightgrey\"),\n",
    "            panel_grid_major_y=element_line(color=\"lightgrey\"),\n",
    "            axis_line=element_line(color=\"grey\"),\n",
    "            legend_key=element_rect(fill='white', colour='white'),\n",
    "            legend_title=element_text(family='sans-serif', size=15),\n",
    "            legend_text=element_text(family='sans-serif', size=12),\n",
    "            plot_title=element_text(family='sans-serif', size=15),\n",
    "            axis_text=element_text(family='sans-serif', size=12),\n",
    "            axis_title=element_text(family='sans-serif', size=15)\n",
    "           ) \\\n",
    "    + scale_color_manual(['#1976d2', '#b3e5fc']) \\\n",
    "\n",
    "\n",
    "print(panel_A)\n",
    "ggsave(plot=panel_A, filename=svcca_file, device=\"svg\", dpi=300)\n",
    "ggsave(plot=panel_A, filename=svcca_png_file, device=\"svg\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncorrected PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting PCA of 1 experiment vs 1 experiments...\n",
      "[0.29599061 0.15003598]\n",
      "Plotting PCA of 1 experiment vs 2 experiments...\n",
      "[0.6385757  0.31959061]\n",
      "Plotting PCA of 1 experiment vs 5 experiments...\n",
      "[0.21613532 0.21514729]\n",
      "Plotting PCA of 1 experiment vs 100 experiments...\n",
      "[0.01238571 0.01028361]\n",
      "Plotting PCA of 1 experiment vs 250 experiments...\n",
      "[0.0107596  0.00648701]\n",
      "Plotting PCA of 1 experiment vs 500 experiments...\n",
      "[0.01045338 0.00591998]\n"
     ]
    }
   ],
   "source": [
    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
    "\n",
    "all_data_df = pd.DataFrame()\n",
    "\n",
    "# Get batch 1 data\n",
    "experiment_1_file = os.path.join(\n",
    "    compendia_dir,\n",
    "    \"Experiment_1_0.txt.xz\")\n",
    "\n",
    "experiment_1 = pd.read_table(\n",
    "    experiment_1_file,\n",
    "    header=0,\n",
    "    index_col=0,\n",
    "    sep='\\t')\n",
    "\n",
    "\n",
    "for i in lst_num_experiments:\n",
    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
    "    \n",
    "    # Simulated data with all samples in a single batch\n",
    "    original_data_df =  experiment_1.copy()\n",
    "    \n",
    "    # Add grouping column for plotting\n",
    "    original_data_df['num_experiments'] = '1'\n",
    "    \n",
    "    # downsample\n",
    "    original_data_df = original_data_df.sample(n=500)\n",
    "    \n",
    "    # Get data with additional batch effects added\n",
    "    experiment_other_file = os.path.join(\n",
    "        compendia_dir,\n",
    "        \"Experiment_\"+str(i)+\"_0.txt.xz\")\n",
    "\n",
    "    experiment_other = pd.read_table(\n",
    "        experiment_other_file,\n",
    "        header=0,\n",
    "        index_col=0,\n",
    "        sep='\\t')\n",
    "    \n",
    "    # Simulated data with i batch effects\n",
    "    experiment_data_df =  experiment_other\n",
    "    \n",
    "    # Add grouping column for plotting\n",
    "    experiment_data_df['num_experiments'] = 'multiple'\n",
    "    \n",
    "    # downsample\n",
    "    experiment_data_df = experiment_data_df.sample(n=500)\n",
    "    \n",
    "    # Concatenate datasets together\n",
    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
    "\n",
    "    # PCA projection\n",
    "    pca = PCA(n_components=2)\n",
    "\n",
    "    # Encode expression data into 2D PCA space\n",
    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)\n",
    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
    "\n",
    "\n",
    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
    "                                               index=combined_data_df.index,\n",
    "                                               columns=['PC1', 'PC2']\n",
    "                                              )\n",
    "                                              \n",
    "    # Variance explained\n",
    "    print(pca.explained_variance_ratio_)  \n",
    "    \n",
    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
    "    \n",
    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
    "    # is represented by label = 5)\n",
    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
    "    \n",
    "    # Concatenate ALL comparisons\n",
    "    all_data_df = pd.concat([all_data_df, combined_data_PCAencoded_df])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'num_experiments' into categories to preserve the ordering\n",
    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
    "num_experiments_cat = pd.Categorical(all_data_df['num_experiments'], categories=['1', 'multiple'])\n",
    "\n",
    "# Convert 'comparison' into categories to preserve the ordering\n",
    "comparison_cat = pd.Categorical(all_data_df['comparison'], categories=lst_num_experiments_str)\n",
    "\n",
    "# Assign to a new column in the df\n",
    "all_data_df = all_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
    "all_data_df = all_data_df.assign(comparison_cat = comparison_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rKI07kd20aZO87NChQzh58iQGDx7s8b66du2KNWvWoKysDNOmTYPFYsGNN96IBx98EP/617+a/dr1DffZ3LpTEovFgri4OJ/r3OdSY9cAd1t86KGHfLZD97XP13lUF68X3iLpehEfH4+XX34ZkiTh7rvvRkpKCrp164bf/OY3OHToUJPLIaLA4HClQdKUmX9vvPFGAMCpU6dw6dIlr18eG7Nx40YAwJ49e+RfptzcF+Y9e/aguLg4oEPftXT0GfdoSenp6T5/darN/ctrbf/617/wzTffAKj5wD1z5gyys7ObHYevHht3bJMnT27w17ybbrrJa1lMTEyzY3Br6Jg29Xi7Y7/66qu95sKoy2Aw+P06SjN16lQ88MAD2Lp1K9asWQO1Wo3NmzcD8N37tWDBAkyePBlvv/02du3ahQMHDuCPf/wjnnnmGfzpT3/CPffc0+TXbuyYBeqYNjTreWtqai+nuy0OHDhQ/iXbF1+/VNdXFq8X/xVp14tp06Zh2LBheOedd7Bz50589NFHWLt2LdauXYtFixbh6aefDthrEVHDmBiEUHJyMvr06YPPP/8cW7duxbx585q87+eff44TJ04AAH744QevLnQ3SZKwefNmLFy4sNEy27Vrh9OnT+PMmTM+u+zdv9y1NMlw/9KUlpbWpASqturqauTm5qK6uhozZszAxo0bkZubi8OHD/v88Kodd13//ve/AdS879qxnThxAo888gi6d+/erNhCzX1cr7/++mYf13CWkpKC4cOH48MPP8T+/fsxePBg/P3vf4fBYMDEiRN97tOhQwfMnz8f8+fPh9PpxNatW5GXl4cFCxYgNze32Ul6S7l7/OoOrelWe4jjYLt48SIqKip89hq4z5nGrgHutjhp0iTcd999LYqH14vgUNr1IiUlBb/+9a/x61//GkII7NixA1OnTsWaNWswa9YsXHfddaEOkSgq8FaiEHvwwQcB1IxTXlpa2uC2te/td98itGjRIoiah8i9/rlnSnb3LDRm4MCBADxvyXArKyvDzp07oVarG/11qTEZGRm45ppr8PXXX+P06dPN2nfJkiU4duwYcnNz8be//Q3Tp0/HsWPHsGTJknr32blzp9ccDEDNXA+A5/wQw4YNAwC/x1kPpd69eyMhIQH79u3D5cuXg/pa7i+yTqczqK/TVO6egc2bN2PPnj34+eefcccddzTpC75Wq8WMGTPQu3dv2O12fP/998EO10tycjJ0Oh1Onz7tdUztdrs8DnxT6XS6FtXN66+/7rVsx44duHjxIq6++mqkpqY2uH8gzyNeL4JDydcLlUqF22+/HXfccQcAyD0+RBR8TAxCbPLkycjJycHPP/+MQYMGIT8/32ubkpIS3HvvvfJzA06nU/7gdj8Q58uQIUOQmpqKr776qkkX1nvuuQdqtRrPPfccvvjiC3m53W7H/PnzUVVVhQkTJtR7X3VzPPLII3C5XJg4caLP2E6ePIm//OUvHst27dqF5557DldddRVeeOEFADWT/Fx11VV47rnn5AcB67JarViwYIHHh9L27dvxxhtvwGg0YubMmfLyuXPnIiUlBU888QTWr1/vdftAZWUl/vrXv+Ls2bN+v/dgMRgMePDBB3Hp0iVMnDjR5y+fX3/9tc8vfc3l/tXU3WsVauPGjYPJZMJbb72F9evXA/B9G9G+ffuwe/dur1tzzpw5I9/D7uve6WDT6/Xo27cvLly4ILdtoGbipwceeKDZX4jbtWuHn3/+2ecX3KZYtWqVx+Rn586dw+LFiwEAv/nNbxrdv2/fvhg6dCj27duHBx54wKsnRJIk7Ny50+NB3obwehF4SrleFBQU4B//+IfXZGoXL17EZ599BoAzdRO1qtYfCCmyoZ55DBpit9vF7Nmz5X2vvfZaMWHCBDF16lRx0003CY1GI481LYQQ7777br1DNNb1m9/8RgAQDz30UJNicU/CptVqxbBhw0ROTo7o0KGDACCuvvpqjwnOhPB/uFIhhFi8eLEAIDQajejVq5eYPHmyuO222+RJq2644QZ523Pnzol27doJtVot9u/f71HOvn37hFqtFu3atRPnzp2Tl7uHBczNzfWYsGjw4MHycJIvv/yyV1yffPKJSEpKkod8vOOOO8SECRNEr1695MmZao/f7X6dukP01dbY8IO+1DdEpRD1H3eXyyWmTZsmD43br18/MXXqVDF06FDRqVMneQz/psbgHpaz7sRfb731lgBqJkWaNGmSmDNnjpgzZ06977+2L7/8Utx0003yP71eLwCInj17yst81Utjpk+fLp9DCQkJwmazeW3jHl4yJSVF3H777SI3N1eMGDFCxMTECKBm4qimqO+4uPlTr7t27RJqtVoAEP369RPjx48XV111lUhOTpbbT1OHK50/f74AIDp16iRyc3PFnDlzxNNPP93ofkDNBGejRo0SRqNRjB49WkyYMEEkJiYKoGZSsNrj+wvR8ARn3bt3FwBEUlKSGDJkiDxxVUpKSrOHjeX1IjKvF9u2bZPP2aFDh4rc3Fxxxx13yJPGjR8/vt7jRESBx8QgwPxJDNw+/fRTMXv2bJGVlSWMRqMwGAyiY8eOYsqUKeKdd96RxwN3TzbW0AeL24EDBwRQM2FR3TG+6/P++++LoUOHioSEBKHX60VWVpZYvHixz7G9W5IYCCHEnj17xPjx4+VJglJTU0XPnj3FokWLxJdffilv5x6je/HixT7LWbRokQAgJkyYIC+r/QH87bffirFjxwqLxSJiY2NFv379xHvvvVdvXD/99JNYuHChuPbaa0VsbKwwm82ia9euYurUqeL111/3OWGREj7o3d58801x++23i+TkZKHT6UR6erro27evWLFihddY8P580AtR8yX7uuuuEwaDoVntvvbkYPX9a0rbruuDDz6Q9589e7bPbX744QfxyCOPiJtvvlmkp6fLE1ANHz7cYy6Qpr6HQCYGQtSce7179xYGg0EkJSWJKVOmiNOnTzd7HgOr1Sruvfde0aFDB6HVar3aSmPzH9hsNrF06VKRmZkp9Hq96Nixo3j44Ye9Zs4Vov62LUTNBFX/8z//I2666SYRFxcnDAaDyMzMFCNGjBAvvPCCz/HzG8LrReRdL4qLi8Vjjz0mhgwZIjIyMoRerxdt27YVAwYMEK+99ppXIkpEwaUSooWD6RMp1IYNG5CXl4fly5djxYoVoQ6HSPHcwxXXvo0oWvB6QUTEZwyIiIiIiAhMDIiIiIiICEwMiIiIiIgIAJ8xICIiIiIi9hgQERERERETAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICoA11AOHq0qVLqKqqCnUYFMWMRiMSExN9rmP7pFBj+ySlaqhtEkU7JgZ+uHTpEp5//nk4nc5Qh0JRTKvV4t577/X6gGP7JCVg+ySlqq9tEhETA79UVVXB6XSiR48eMJvNoQ6HopDVakVBQQGqqqq8PtzYPinU2D5JqRpqm0TExKBFzGYzLyykWGyfpGRsn0REysOHj4mIiIiIiIkBERERERHxViJqZe+//z727t2LwsJC9OvXD4sWLQp1SEQAAIfDgRdffBFHjx5FRUUFkpOTMXnyZNxyyy2hDo0Izz77LD7++GNotf/92H7hhReQkpISwqiIKNIwMaBWlZSUhClTpuCrr75CRUVFqMMhkrlcLiQlJeGxxx5Damoqjh8/jlWrViEtLQ3XXnttqMMjwtixYzFz5sxQh0FEEYyJAbWq/v37AwBOnTrFxIAUJSYmBrm5ufLf1113Hbp164bvvvuOiQEREUUFPmNAROSDzWbDjz/+iI4dO4Y6FCIAwI4dOzB9+nTcd9992LVrV6jDIaIIxB4DIqI6hBB47rnncPXVV6NHjx6hDocIo0ePxuzZs2EymfDtt9/iqaeegslkknthiYgCgT0GRES1CCHwv//7vzh//jwWL14MlUoV6pCI0KVLF8THx0Oj0SA7Oxt33HEHDh48GOqwiCjCsMeAiOj/CCHw4osv4tSpU1i9ejViYmJCHRKRTyqVCkKIUIdBRBGGPQbUqlwuF+x2OyRJgiRJsNvtcDqdoQ6LCACwbt06nDhxAitXroTRaAx1OESyTz75BFVVVZAkCd9++y0++OAD9O3bN9RhEVGEYY8BtarXX38dW7dulf8+ePAghgwZgt/+9rchjIoIKC0txfbt26HT6TB79mx5+aRJkzBlypQQRkZUMwfMCy+8AEmSkJycjNzcXAwaNCjUYRFRhGFiQK1q+vTpmD59eqjDIPKSmpqKd999N9RhEPn01FNPhToEIooCvJWIiIiIiIiYGBARERERERMDIiIiIiICnzFoEavVGuoQKEo1pe2xfVKosH2SUrHdETWMiYEf3MNrFhQUhDgSina+hnpl+ySlYPskpeIw2US+MTHwg1Zbc9huvfVWWCyWZu+v0WgQFxeHiooKuFyuQIcXUOESa7jECQQm1osXL2Lfvn1yW6wtWtpnuMQJRF+swWqf0XYcW0u4xBrstklEEZYYnDp1Chs3bkRhYSEuX74MvV6P9u3bY+TIkbj11ls9tj158iQ2bNiAEydOyFPMz549G2lpaU1+vauvvhrt2rVrdpxCCDidTnTo0AEqlarZ+/siSRJKSkqQlpYGtTpwj46ES6zBiBNQbqxFRUXYt29fg9tEevuMtjqvS8nnfLDaJ+tcuXVel1KPaVPaJlE0i6jEoLKyEsnJyRg4cCDatGmD6upq7N+/H8888wxKS0sxdepUAMDZs2exdOlSdOrUCYsXL4bdbsfmzZuxZMkSPPfcc0hISAjxOyEiIiIial0RlRhkZ2cjOzvbY1nv3r3x888/Y8eOHXJisGnTJuh0OixbtgxGoxEAkJWVhXnz5mHbtm2YNWtWa4dORERERBRSUTFcaXx8PDQaDQDA5XLh8OHD6N+/v5wUADWznmZnZ+PQoUOhCpOIiCig3i9yef0jIqpPRCYGkiTB5XKhvLwc27dvR0FBASZOnAgAKC4uht1uR2Zmptd+mZmZ8noiIqJwVl8SwOSAiOoTUbcSub344ov45z//CaBmBIy77roLt99+OwCgoqICAGA2m732M5vNEELAarUiKSmp9QImIiIiIgqxiEwMJk+ejOHDh6O8vByff/45XnrpJVRXV2P8+PHyNg2NaOBrXXFxMYqLiwEAZWVlqKysBFDTO9FcQghIkgRJkgI6AoS/8TQkXGINRpxAeMVam06nAxDZ7TPa6zyczvm6/G2frPPAxVm7DKXH6tYabZMo2kVkYpCSkoKUlBQAQK9evQAAf/3rXzFkyBDExcUB+G/PQW1WqxUqlQomk8lr3bp167By5Ur575ycHABASUlJwONvidLS0lCH0GSMNXjy8vIAsH22RLjEGi5x1sb22TJNjzMRvu8YtqGkpCqAEdUvXI4pEdWIyMSgrq5du+Kf//wnSkpKkJWVBb1ej8LCQq/tCgsLkZ6eDr1e77Vu7ty5GDNmDICaHoPdu3cDQLPmPXBzj8Ws1WoD+utMaWkpUlNTgzK+tdJjDUacgHJjbewL1fr165GXlxfR7TPa6rwuJZ/zwWqfrPNmxlkifC7+ZVsdVKp4ZcXaBK3RNomiXVQkBseOHYNarUZaWho0Gg369OmD/Px8zJo1Sx6ZqKysDMeOHcPYsWN9lpGeno709HQANROk5OfnA4BfFzwhBNRqNdRqdcC7Q93lBkq4xBrMOIHwihUAHA4HgMhun6zzGuF0zrv52z5Z5zWaGueodsCBIhfKay27PRXyKH1KirUpWqNtEkW7iEoMnn/+eRiNRnTt2hWJiYm4fPkyDh48iAMHDmD8+PHyxGXTp0/HwoULsXr1akyaNEme4Cw+Ph7jxo0L8bsgIiIKjIHtNI1vRET0fyIqMbj22muxe/du7N27F5WVlYiJiUGnTp3wwAMP4NZbb5W3y8jIwOOPP47XXnsNTz31FDQaDbp3746lS5dy1mMiIiIiikoRlRgMGzYMw4YNa9K2WVlZWL16dZAjIiIiIiIKDxE5wRkRERERETUPEwMiIiIiImJiQERERERETAyIiIiIiAhMDIiIiIiICEwMiIiIiIgIETZcaWsym83QarUQwveU8w1x7+OWAuQRAAAgAElEQVTPvg2V6Y4n0OXW/m+gygx0rMGI012eEmPVahs+ddPS0iK+fUZbnfsqU6nnfLDaJ+tcuXXuq0wlHtPG2iZRtOMZ4qcePXrAYrHA6XT6XYbL5QpgRIDFYoEkSZAkKaDlAuETa6DjBJQZq8ViaXD9nDlzACAq2me01LkvSj3ng90+WefKq3NflHhMG2ubRNGOiYGfCgoKkJ2djZSUlGbvK4SAy+WCRqOBSqUKSDySJOH8+fNo06YN1OrA3SEWLrEGI05AubGWlZU1uP7VV1/FhAkTIrp9Rlud16Xkcz5Y7ZN1rtw6r0upx7SxtkkU7ZgY+MlqtcLpdLboIqpSqQJ2EVapVHI8gfzArF1+OMQa6DKVGmtjv7SWlJRETfuMljr3VZZSz/lgt0/WufLq3FdZSjymLelFJYoGfPiYiIiIiIiYGBARERERERMDIiIiIiICEwMiIiIiIkKEPXx89OhR7N+/H8ePH8e5c+dgMpmQlZWFnJwcZGVleWx78uRJbNiwASdOnIBGo0F2djZmz56NtLS0EEVPRERERBQ6EdVj8OGHH6K0tBSjR4/G8uXLcdddd6G8vByLFi3C0aNH5e3Onj2LpUuXwuFwYPHixZg/fz6KioqwZMkSlJeXh/AdEBERERGFRkT1GMybNw+JiYkey3r27Im5c+fizTffxA033AAA2LRpE3Q6HZYtWwaj0QgAyMrKwrx587Bt2zbMmjWrtUMnIiIiIgqpiOoxqJsUAEBsbCw6dOiAc+fOAaiZMfHw4cPo37+/nBQAQGpqKrKzs3Ho0KFWi5eIiIiISCkiqsfAl8rKSpw6dQrZ2dkAgOLiYtjtdmRmZnptm5mZia+++gp2ux16vb6VIyUiIiUTQuCDYslj2ah2mhBFQ0QUeBHVY+DLiy++CJvNhilTpgAAKioqAABms9lrW7PZDCEErFZrq8ZIRETKVzcpAID3i1whiISIKDgiusdg48aN+Oijj3D33Xd7jUrU0HTqvtYVFxejuLgYAFBWVobKykoAgCR5f1A0RggBSZIgSVLApop3x+FPPA0Jl1iDEScQXrHWptPpAER2+4z2Og+nc74uf9tnKOvcWiXqXfdxkQsD0jzjifY6D5fziIg8RWxisGXLFvz973/HjBkzMGrUKHl5XFwcgP/2HNRmtVqhUqlgMpm81q1btw4rV66U/87JyQEAlJSUBDr0FiktLQ11CE3GWIMnLy8PANtnS4RLrOESZ23h2D4LoAJg8bnuMi6jpMQZpKi8hVOdh1OsRBShicGWLVuwZcsWTJs2Tb6FyC09PR16vR6FhYVe+xUWFsrr65o7dy7GjBkDoKbHYPfu3QDg17wHQgg4nU5otdqA/jpTWlqK1NRUqNWBu0MsXGINRpyAcmNt7AvV+vXrkZeXF9HtM9rqvC4ln/PBap+hrPNfXBD4wl5fCW2Q5qPHIJrqvC6lnkdKS0aJlCbiEoOtW7diy5YtmDJlCqZNm+a1XqPRoE+fPsjPz8esWbPkkYnKyspw7NgxjB071me56enpSE9PBwAUFRUhPz8fAPy64AkhoFaroVarA94d6i43UMIl1mDGCYRXrADgcDgARHb7ZJ3XCKdz3s3f9hnKOk9LBlDP8wS+HkBmndcIl/OIiGpE1MPH27Ztw+bNm9GzZ0/06tULx48f9/jnNn36dNjtdqxevRpffvkl8vPzsWrVKsTHx2PcuHEhfAdERKRUI5K9l92W0vpxEBEFS0T1GBw+fBgAcOTIERw5csRr/bvvvgsAyMjIwOOPP47XXnsNTz31FDQaDbp3746lS5ciISGhVWMmai2+Rk/hUIvUXLXb0R3p0fXLrV6vwah2oY6CiCh4IioxeOKJJ5q8bVZWFlavXh3EaIiUo74hFd8vcjE5oCar244+KJYw1ALExrINERFFgoi6lYiIiIKjvuRyz8VWDoSIiIKGiQERERERETExICIiIiIiJgZEUW1kWvQ8OEpEREQNY2JAFAV8PWB8R3pgx0KnyFbfQ+p8eJ2IKHJE1KhERFQ/foGjlhrVToPTRS78C0AHADewTRERRRQmBn4ym83QarUQQjR7X/c+/uzbUJnueAJdbu3/BqrMQMcajDjd5SkxVq224VM3LS0t4ttntNW5rzJDcc5npquRWWfbuoLVPlnnvM7X/q8/GmubRNGOZ4ifevToAYvFAqfT6XcZLpfv4f/8ZbFYIEkSJEkKaLlA+MQa6DgBZcZqsVgaXD9nzhwAiIr2GS117otSz/lgt0/WufLq3BclHtPG2iZRtGNi4KeCggJkZ2cjJSWl2fsKIeByuaDRaAI2a6gkSTh//jzatGkT0PvGwyXWYMQJKDfWsrKyBte/+uqrmDBhQkS3z2ir87qUfM4Hq32yzpVb53Up9Zg21jaJoh0TAz9ZrVY4nc4WXURVKlXALsIqlUqOJ5AfmLXLD4dYA12mUmNt7JfWkpKSqGmf0VLnvspS6jkf7PbJOldenfsqS4nHtCW9qETRgEOSEBEREREREwMiIiIiImJiQEREREREYGJARERERESIwIePq6qq8Prrr+P06dM4deoULl++jJycHEyfPt1r25MnT2LDhg04ceIENBoNsrOzMXv2bKSlpYUgciIiIiKi0Im4HoOKigrs3LkTDocDffv2rXe7s2fPYunSpXA4HFi8eDHmz5+PoqIiLFmyBOXl5a0YMRERERFR6EVcj0Fqaio2b94MlUqFy5cvY+fOnT6327RpE3Q6HZYtWwaj0QgAyMrKwrx587Bt2zbMmjWrFaMmIiIiIgqtiOsxaMr4xi6XC4cPH0b//v3lpACoSSqys7Nx6NChYIdJRERERKQoEZcYNEVxcTHsdjsyMzO91mVmZsrriYiIiIiiRcTdStQUFRUVAACz2ey1zmw2QwgBq9WKpKQkeXlxcTGKi4sB1EypXllZCaBm2vfmEkJAkiRIkhTQ6ef9jach4RJrMOIEwivW2nQ6HYDIbp/RXufhdM7X5W/7ZJ2HT52HyzElIk9RmRi4NXRhqbtu3bp1WLlypfx3Tk4OAKCkpCQ4wfmptLQ01CE0iSRJ8rFTq5XfcRUux9UtLy8PANtnS4RLrOESZ21sny0TLnEC4RUrEUVpYhAXFwfgvz0HtVmtVqhUKphMJo/lc+fOxZgxYwDU9Bjs3r0bAPwa2lQIAafTCa1WG9BfZ0pLS5GamhrQL9rBiNVms+Hs2bNITEyEWq2GyWSCwWBQXJxAcI5rIGJt7AvV+vXrkZeXF9HtM9rqvC4ln/PBap+sc+XWeV1KPaZKS0aJlCYqE4P09HTo9XoUFhZ6rSssLJTX190nPT0dAFBUVIT8/HwA/v3aLYSAWq2GWq0OeHeou9xACXSsTqcTVqsVQgjodDr5ti2tVivfXqCEOOsK5HENdqwA4HA4AER2+2Sd11D6Oe+Lv+2TdV4jnOo8XI4pEdVQ/j0cQaDRaNCnTx/k5+ejqqpKXl5WVoZjx46hX79+IYwuckiSBJvNhsrKSthsNkiSBJfLBQDQarVQq9XQarUQQsjLiYiIiCg0IrLH4Msvv4TNZsOVK1cAAP/5z39w8OBBAECvXr1gMBgwffp0LFy4EKtXr8akSZNgt9uxefNmxMfHY9y4caEMPyJIkoTKykpcuXIFarUaTqcTKpUKWq0WDocDQohQh0hEREREtURkYrB27VqPB54OHjwoJwYvv/wy2rZti4yMDDz++ON47bXX8NRTT0Gj0aB79+5YunQpEhISQhV6WHOPFuHuGaisrIRarYbdbkdVVRWqq6uhUqkghEB1dTXMZjO0Wi00Gg202ohsikRERERhIyK/jb3yyitN2i4rKwurV68OcjTRwd1DUF1dDSGEfAuRTqeD3W6Hw+GAWq2G0WiUEwObzYbExEQ5QSAiIiKi0OG3MQoI961bBoMBLpcL5eXlqK6uRmxsLBwOB5xOJ5xOJ/R6PbRaLWJiYmA0GmEymZgUEBERESkAv5FRQDidTmg0GgA1Q75qNBrodDp5FKLq6mrodDpUVFTIDxwDDc8lQURERESth4kBBYRarfZ4xgCoSRYcDgeqq6sB1AxRWF5eDoPBIA9Pyt4CIiIiImWIyuFKKfBiYmIAAOXl5aioqEBlZSXsdjsqKyu9tnU4HFCpVPLtRU6ns7XDJSIiIqI6+HMtBYS7B0CtVsvPFdjtdp/bSpKEK1euoLS0FFVVVRBCwGAwoE2bNl4TyxERERFR62BiQAEjSRLi4uIAQB6itD4ulwvnzp1DZWWl/MzBhQsX0LZtWxiNRsTExAR0Zk8iIiIiahgTAz+5h9j0Z6Iu9z6BnORLCCHHE+hya/+3Pk6nEy6XC5Ikyc8QNPZgscvlkoc3BYCqqipcvnxZLsdkMgU8zuYKxnENRKyNPZuRlpYW8e0z2urcV5mhPOcbEqz2yTpXbp37KlOJx5TPtRE1jGeIn3r06AGLxdKi++NdLlcAIwIsFovHw7+BVDdWSZJgs9lgt9tx5coVSJIEIYT8TIHNZpMfOm6I+3kDrVYLSZJgt9sRGxsrz4HQ3F6DQB9TIHjHtSWxWiyWBtfPmTMHAKKifUZLnfvSmud8cwS7fbLOlVfnvijxmDbWNomiHRMDPxUUFCA7OxspKSnN3lcIAZfLBY1GE7DhOiVJwvnz59GmTZuA3oJTN1abzYaqqipUVlbC5XLJ8xZIkgSHw4HKykrYbLZmXbjtdrucHFRXV6OiogIajQZxcXFQq9Xyv+bEGSjBOK6BiLWsrKzB9a+++iomTJgQ0e0z2uq8rtY65/0RrPbJOldundel1GPaWNskinZMDPxktVrhdDpbdBFVqVQBuwi7R/kJZJl1y6+urpYnLquoqIAQAk6nEzqdDpIk4eLFi353G7uTA5VKJfc6OJ1OJCYmQqvVwmQyySMfNRZnIN9/MI9rS8ps7JfWkpKSqGmf0VLnvsoK9jmv1PbJOldenfsqS4nHlKPgETWMiQE1WVVVlfwFXq/Xy7cTuVwu2O12SJIk9x40lxACkiTh0qVLUKlUcLlcuHLlCkwmE/R6PaxWK9RqNUctIiIiIgoSDvtCTebuxtVqtdDr9XLPgMPhkGc3bsmvMXa7HU6nU+52ttlsKC0tlZcH475iIiIiIqrBHgNqMr1eD5fLBafTKY861JQHjJurdpm1H252D4VKRERERIEX1YnBlStXsHHjRhw8eBAVFRXIyMjAxIkTMWjQoFCHpkgmkwnx8fHyw8HuW4piY2Nx5coV2Gy2gL+mw+FAVVWV30NvEhEREVHTRHVi8OSTT+KHH37AzJkz0a5dO3z88cf4wx/+ACEEBg8eHOrwFEetViM5ORlmsxnV1dW4fPmyPExpVVUVNBpNwG/3UalUkCQJTqcTNpsNZrM5oOUTERERUY2oTQy++OILfPXVV1i4cKGcBHTv3h2lpaVYv349BgwYAI1GE+IolUetVsNoNMJoNMLlcqG8vBwOh0N+8DgYr1ddXQ2VSoXLly97zIrsnkdBrVb7NecBEREREf1X1H6TOnToEGJjYzFgwACP5UOHDsWFCxfw/fffhyiy8BETEwOdTge73Q4hBGJiYgI+LJ0kSdDpdBBCwGq14vTp0zhz5gxKSkpQUVEBu90Om82Gy5cvw263B/S1iYiUhrdUElEwRW2PwZkzZ5CRkeHVK9CpUyd5fbdu3UIRWtgQQsBkMsk9BS6XC3q9PqAPJNvtdlRWVspDowLAhQsX4HQ6YbFYYLFYEBMTA4fDgerqajgcDhgMBhgMBvYgEFFEEEJg37598t8nTpwAAAwZMiRUIRFRhIraxKCiogJt27b1Wu6+h72ioqK1Q1K82lPbO51OeV4DjUYj/6ofjC/jV65c8bn8woULuHjxIjQajTyRjvsB6ZSUFCQkJECrjdomTkQRonZSUNvevXuZHBBRQEX1t6aGbnupu664uBjFxcUAaqZUd8/O25LJvCRJCuj08/7G0xB3rO5EoLq6GkIIOSEwm824dOkSKisrYbPZWv25DPfsy+7/t9vt8qhJOp2uxQ8rB+O4BqP+69LpdAAiu30G6ziGS6zBPueV2D6jvc59CUTM4dI+W+uYEkWzqE0M4uLifPYKWK1WAPD6Qrlu3TqsXLlS/jsnJwcAUFJSEsQom6+0tDQo5dpsNjgcDrlHoKKiQp7wzH0bD4CQT0LmngzNPVpSoEYxCtZxDZa8vDwA0dM+gyFcYg2XOGtj+wwcpR3DusLxmBJFs6hNDDp27IgDBw7A5XJ5/MpdWFgor69t7ty5GDNmDICaHoPdu3cDANLS0pr92u5fud2ThAWCJEkoLS1FampqQG/nccfqvs9fp9PJv9hUVlbC5XIp6qFf9zE1mUzy7UQtEYzjGoj6b+zLwPr165GXlxfR7TMYcQLhE2uwz3klts9orfPjx4/Xu86fc7yucGmfrdE2iaJd1CYG/fr1w86dO/Hpp59i4MCB8vK9e/ciKSkJXbt29dg+PT0d6enpAICioiLk5+cDgF8XPPe9+Gq1OuDdoe5yA6V2rEIIqFQquTu3urq63vv/Q8XpdMJgMCAuLg5mszlgxyKQxzWY9e/mcDgARHb7DPZxDJdYg3nOK619ss49Ber5gnBpn61xTImiXdQmBr/4xS9w4403Yu3ataiqqkJ6ejo+/vhjHDlyBAsWLOAcBnXExsbKw4O6ewvcH+5KYjKZkJaWhvbt2/PBYyKKCEOGDMHevXu9lhERBVpUf3P63e9+h40bN2Lz5s2oqKhARkYGHnzwQQwaNCjUoSmOXq9HfHw8bDYbrly54jFCUWtTq9WIj4+XJ1XTarUwGAxISEhAYmIiTCYThyoloogyZMgQSJKEkpKSgNw+RETkS1QnBrGxsbjrrrtw1113hTqUsKDX66HX6+F0OhETE4OqqqqQxJGRkYHExER5hKTExETExsZCkqSA33tMREREFC2iOjGg5qusrITVag1Zj4HRaIRer4ckSTAYDDAajTAajfJzD0RERETkHyYG1GTV1dWoqqqSZxXW6XRBfc6gbvlmsxlXX321nAi45yogIiIiopZjYkBN5nA4oFKpoNfr5V/rL1++DCFEwF+rTZs2UKvV8pwEJpMJmZmZiImJCfhrERERERETA2oG91ClGo0GsbGx8pd2XxPFtYTBYIBOp4PJZJLnToiLi2NSQERERBRETAyoyQwGA2w2G+x2O/R6PbRaLXQ6HdRqtd/396vVavmWIK1WKycD7rGq3b0TJpMpwO+GiIiIiGpjYkBNptVqkZCQgCtXrkCn0yE2NhZCCBQXF6O0tLTeB4DdPQwul0t+aNjlciEpKQlqtRoVFRXycvfQo0lJSQBqeincy4mIiIgoePhty09msxlardav++vd+wTy3nwhhBxPoMut/V+NRgOz2eyxjcPhkIcOdc9MWVVVBZ1Oh6SkJMTGxkKlUnk8o1BdXY2UlBTY7XYYjUbYbDao1WrExsb6vG2osfcUjGPqLi/QxzUQsTaWKKWlpUV8+4y2OvdVZmuc8/4IVvtknSu3zn2VqcRjyh+ZiBrGM8RPPXr0gMVigdPp9LsMl8sVwIgAi8UStGFEG4rVaDQiLi4ODocDkiTB4XDAbDajXbt2SEhI8Iip7kVZq9XCaDTK690Tk/l7XAN9TIHgHdeWxGqxWBpcP2fOHAD+H0cgfNpntNS5L6E65xsT7PbJOldenfuixGPaWNskinZMDPxUUFCA7OxspKSkNHtfIQRcLhc0Gk3AJuOSJAnnz5+XR/MJlKbEqtVqkZaWhnPnzsHhcCA2NhbJyclePQvBjDUYxxRQbqxlZWUNrn/11VcxYcKEiG6f0VbndYXynG9MsNon61y5dV6XUo9pY22TKNoxMfCT1WqF0+ls0UVUpVIF7CKsUqnkeIIx829j5ZpMJnn2YfeDw6GINdBlKjXWxn5pLSkpiZr2GS117qusUJ7zDQl2+2SdK6/OfZWlxGPakl5UomjAxIACprGEgIiIKFj2F7lgrbNsVDtNSGIhClf8FkdERERhzW73TgoA4P2iwD+PQhTJmBgQERFRWNt5LtQREEUGJgZERERERBRZzxhUVVXh9ddfx+nTp3Hq1ClcvnwZOTk5mD59us/tT548iQ0bNuDEiRPQaDTIzs7G7NmzkZaW1sqRExERERGFVkT1GFRUVGDnzp1wOBzo27dvg9uePXsWS5cuhcPhwOLFizF//nwUFRVhyZIlKC8vb6WIiYiIqKXuSI+orzNEIRNRPQapqanYvHkzVCoVLl++jJ07d9a77aZNm6DT6bBs2TIYjUYAQFZWFubNm4dt27Zh1qxZrRQ1ERERtYRKpcLINBW2l3jOisxRiYiaJ6ISg6aOa+xyuXD48GEMGTJETgqAmsQiOzsbhw4dYmJAREQURtRqNUa1C3UUROEtKvveiouLYbfbkZmZ6bUuMzNTXk9EREREFC2iMjGoqKgAAJjNZq91ZrMZQghYrb5GRCYiIiIiikyKvZXo2LFjePjhh5u07bPPPovOnTs3+zUauvWo7rri4mIUFxcDAMrKylBZWQkAkCSp2a8rhIAkSZAkKWBTxbvj8CeehoRLrMGIEwivWGvT6XQAIrt9Rnudh9M5X5e/7ZN1Hj51Hi7HlIg8KTYxaN++Pe69994mbZuSktKssuPi4gD8t+egNqvVCpVKBZPJ5LF83bp1WLlypfx3Tk4OAKCkpKRZrx1spaWloQ6hyRhr8OTl5QFg+2yJcIk1XOKsje2zZcIlTiC8YiUiBScGSUlJGDFiRFDKTk9Ph16vR2Fhode6wsJCeX1tc+fOxZgxYwDU9Bjs3r0bAPya80AIAafTCa1WG9BfZ0pLS5Gamgq1OnB3iIVLrMGIE1BurI19oVq/fj3y8vIiun1GW53XpeRzPljtk3Wu3DqvS6nHVGnJKJHSKDYxCCaNRoM+ffogPz8fs2bNkkcmKisrw7FjxzB27FivfdLT05Geng4AKCoqQn5+PgD4dcETQkCtVkOtVge8O9RdbqCES6zBjBMIr1gBwOFwAIjs9sk6rxFO57ybv+2TdV4jnOo8XI4pEdWIuMTgyy+/hM1mw5UrVwAA//nPf3Dw4EEAQK9evWAwGAAA06dPx8KFC7F69WpMmjQJdrsdmzdvRnx8PMaNGxey+ImIiIiIQiHiEoO1a9d63NN48OBBOTF4+eWX0bZtWwBARkYGHn/8cbz22mt46qmnoNFo0L17dyxduhQJCQkhiZ2IiIiIKFQiLjF45ZVXmrxtVlYWVq9eHcRoiIiIiIjCQ1TOY0BERERERJ4irseAiIgiz/tFrlp/SRjVThOyWIiIIhV7DIiISNE8k4L6lxERUcswMSAiIsVqaObcfzI5ICIKKCYGRESkWD+UiHrXOVsxDiKiaMDEgIiIFCuDo0cTEbUaPnzsJ7PZDK1WCyHq/zWrPu59/Nm3oTLd8QS63Nr/DVSZgY41GHG6y1NirFptw6duWlpaxLfPaKtzX2Uq9ZwPZPs0GtVAue/bie5IVwfkvbPOwyPW1mibRNGOZ4ifevToAYvFAqfT/85slyuw98daLBZIktTgPbn+CpdYAx0noMxYLRZLg+vnzJkDAFHRPqOlzn1R6jkf6PZ5Wwqwo6z2EgnDkgCnM3Dvm3UePrEGs20SRTsmBn4qKChAdnY2UlJSmr2vEAIulwsajQYqlSog8UiShPPnz6NNmzZQqwN3h1i4xBqMOAHlxlpWVtbg+ldffRUTJkyI6PYZrDp3uVy4cOGC4mNV8jkfjPZ5R3r0ned1KbnO61LqMW2sbRJFOyYGfrJarXA6nS26iKpUqoBdhFUqlRxPID8wa5cfDrEGukylxtrYL60lJSVR0z4DVebevXu9lg0ZMqTF5dYWLsfUXb5S22e0nOe+ylJqnfsqS4nHtCW9qETRgA8fE1HU85UUNLSciIgoEjExICIiIiIiJgZERERERMTEgIiIiIiIEGEPHx89ehT79+/H8ePHce7cOZhMJmRlZSEnJwdZWVle2588eRIbNmzAiRMnoNFokJ2djdmzZyMtLS0E0RMRERERhU5E9Rh8+OGHKC0txejRo7F8+XLcddddKC8vx6JFi3D06FGPbc+ePYulS5fC4XBg8eLFmD9/PoqKirBkyRKUl5eH6B0QUSjUN/pQoEclIiIiUrKI6jGYN28eEhMTPZb17NkTc+fOxZtvvokbbrhBXr5p0ybodDosW7YMRqMRAJCVlYV58+Zh27ZtmDVrVmuGTkQh5k4CJElCSUkJew6JiCjqRFSPQd2kAABiY2PRoUMHnDt3Tl7mcrlw+PBh9O/fX04KACA1NRXZ2dk4dOhQq8RLRERERKQUEZUY+FJZWYlTp06hQ4cO8rLi4mLY7XZkZmZ6bZ+ZmSmvJyIiIiKKFhF1K5EvL774Imw2G6ZMmSIvq6ioAACYzWav7c1mM4QQsFqtSEpKkpcXFxejuLgYQM2U6pWVlQBqbjtoLiEEJEmCJEkBnX7e33gaEi6xBiNOILxirU2n0wGI7PYZ7XUeTud8Xf62T9Z5+NR5uBxTIvKk2MTg2LFjePjhh5u07bPPPovOnTt7Ld+4cSM++ugj3H333T5HJWrowlJ33bp167By5Ur575ycHABASUlJk2JsLaWlpaEOockYa/Dk5eUBYPtsiXCJNVzirI3ts2XCJU4gvGIlIgUnBu3bt8e9997bpG1TUlK8lm3ZsgV///vfMWPGDIwaNcpjXVxcHID/9hzUZrVaoVKpYDKZPJbPnTsXY8aMAVDTY7B7924A8OsBRSEEnE4ntFptQH+dKS0tRWpqKtTqwN0hFi6xBiNOQLmxNvaFav369cjLy4vo9hltdV6Xks/5YLVP1rly67wupR5TpSWjREqj2MQgKSkJI0aM8GvfLVu2YMuWLZg2bZrHLURu6enp0Ov1KCws9FpXWFgor6+7T3p6OgCgqKgI+fn5AODXBU8IAbVaDbVaHfDuUHe5gS7MVaAAACAASURBVBIusQYzTiC8YgUAh8MBILLbJ+u8Rjid827+tk/WeY1wqvNwOaZEVCPiHj7eunUrtmzZgilTpmDatGk+t9FoNOjTpw/y8/NRVVUlLy8rK8OxY8fQr1+/1gqXiIiIiEgRFNtj4I9t27Zh8+bN6NmzJ3r16oXjx497rL/22mvl/58+fToWLlyI1atXY9KkSbDb7di8eTPi4+Mxbty41g6diIiIiCikIioxOHz4MADgyJEjOHLkiNf6d999V/7/jIwMPP7443jttdfw1FNPQaPRoHv37li6dCkSEhJaLWYiIiIiIiWIqMTgiSeeaNb2WVlZWL16dZCiISIiIiIKHxH3jAERERERETUfEwMiIiIiImJiQERERERETAyIiIiIiAhMDIiIiIiICEwMiIiIiIJmxYoVUKlUGDRokM91ZrM5BFF5euaZZ3DVVVdBo9FEzFxOhYWFUKlUePPNN0Mdipdnn30W27dvD3UYPkXUcKWtyWw2Q6vVQgjR7H3d+/izb0NluuMJdLm1/xuoMgMdazDidJenxFi12oZP3bS0tIhvn9FW577KVOo5H6z2yTpXbp37KlOJx7SxthlMBw4cwN69ezFkyJCQxeDL8ePHsWDBAixZsgSjR49GcnJyqEMKiPT0dOTn56Nr166hDsXLs88+i1GjRmHkyJGhDsULEwM/9ejRAxaLBU6n0+8yXC5XACMCLBYLJEmCJEkBLRcIn1gDHSegzFgtFkuD6+fMmQMAUdE+o6XOfVHqOR/s9sk6V16d+6LEY9pY2wwWk8mE66+/HitXrlRkYgAAd911Fzp37hziaFpOCAG73Q6DwYC+ffuGOpyww1uJ/FRQUICLFy9Cq9U2+59GowEAaDQav/b39U+tVuPixYtQq9UBKzOcYg1GnEqO9eLFiw22z1dffTXi22e01XlrxBmoWIPVPlnnyq3zcDmmjbXNYFq2bBk+/vhj7N+/v8HtLly4gF//+tdISUlBbGws+vTpg507d/r1mjabDQsXLkT79u1hMBiQnZ2NzZs3y+tnzZqF8ePHAwC6dOkClUqFDRs21Fve2bNnMWPGDCQnJyM2NhaDBg3Cl19+Ka//4osvoNPp8Pzzz8vLHA4HevTogX79+slJXWZmJu69916sWbMG7du3h9FoxNixY1FcXOzxetXV1Vi6dCk6duwIg8GAbt26ecTvfg/XX389tm/fjhtuuAEGgwHvvvuuz1uJ3K/7xz/+ERkZGTCbzbjzzjths9nw1Vdf4eabb4bJZELv3r1x7Ngxj9cRQuAPf/gDunbtCoPBgM6dO+OZZ57x2GbF/90a9vXXX2PAgAEwGo24/vrrsWPHDo8Yzpw5gxdeeAEqlcrjmL/77rvo1asXzGYzEhMT0atXr1a/5Yg9Bn6yWq1wOp1QqVR+l+FuEIGgUqnkeAJVZt3ywyHWQJep1Fgb+6W1pKQkatpntNS5r7KUes4Hu32yzpVX577KUuIxbUkvakuNHDkSvXv3xooVK+pNDlwuF375y1/ixx9/xJNPPomMjAysXbsWI0eOxK5du3Drrbc26zVzc3Oxfft2PPbYY7j++uuxdetW5ObmwuVy4Ve/+hUeffRRXHPNNVi6dCn+8Y9/ID09HV26dPFZ1sWLFzFgwACYzWb8+c9/RkJCAv785z9jyJAh+OGHH5CamopevXrhkUceweLFizF8+HBcc801WL58OX744Qd89dVXcnIHANu2bUPHjh2xdu1aXLx4EUuWLMGECROQn58vbzNlyhR88sknWL58Obp164bt27djxowZsFgs+OUvfylvV1RUhPvvvx+PPPIIOnTogA4dOtR7TN555x10794dL730Ek6dOoUFCxbAYDDg0KFDWLBgAdq2bYuHHnoIkydPxrfffgu1uuY39Pvvvx+vvPIKHn74Ydx000349NNP8dBDDyE2Nhbz5s2Ty3c4HJgxYwbuu+8+PProo3jyyScxceJEnDlzBm3atMG2bdswcuRIDBgwAAsXLgRQk5SdPHkSkyZNwrRp0/Dkk09CkiQcPXq09ZNZQc32008/ieXLl4uffvrJr/0lSRJ2u11IkhSwmFwul/jpp5+Ey+UKWJlChE+swYhTCOXG2lAbjJb2GW11XpeSz/lgtU/WuXLrvC6lHtOWXh/9sXz5cmEymYQQQrz33nsCgNi/f7/XOiGEeOeddwQA8cEHH8jLXC6X6Natmxg8eHCzXvfo0aMCgHjhhRc8lo8YMUJ07NhR/vuNN94QAMTp06cbLG/ZsmUiISFB/Pzzz/Iym80mMjIyxKJFi+RlDodD9O7dW/Tu3Vt89NFHQqPRiLVr13qU1bFjRxEXFycuXrwoL9u9e7cAIHbs2CGEEGLv3r0ef7tNnjxZ9O7dW/575syZAoD47LPPPLY7ffq0ACDeeOMNj9ft8P/ZO/c4q6qy8X/X3vucuTPMcHFGMcAILwXeSc0MvIWXRIW8kQmmgb+u4iWjIok0X/N9K7UCy1cwgSxLyxuaiqaIZaZGmZYXMF4YBxDmfs7Ze6/1+2Ptc5g7cznDnJl5vp/PfODsyzrP3uvZ+6xnreey//4mmUxmts2cOdMA5tFHH81sS/fTK6+8Yowx5s033zRKKbNs2bIW33HNNdeYioqKjJ5/+9vfbtN///73vw1gfvGLX7SQ4wtf+EKLttL9UFtba/oTcSUSBEEQhAHOQ1vCzN/WrdmPwRCyw5lnnskRRxzB4sWL293/7LPPUlJS0iIo1XEczjvvPJ5//vluxVc8++yzAJx//vkttl944YVs2rSJ//znP92S/fHHH2fatGmUl5cTBAFBEOC6Lh//+Md58cUXM8d5nscvfvEL/v73v3Pqqady6qmntphRTzNt2jSGDx+e+XzSSScxbNgwXnjhhcz3lZeXc+KJJ2a+LwgCTjrpJF5++eUW92LkyJFMmTKlS9dxwgknEI/HM58nTpyI4zgtYj/SAcvpe/TEE08AMHPmzDayVFVVtbiXjuNw8sknZz5PmDCBeDzO5s2bO5Vr8uTJuK7LRRddxIMPPkhNTU2XrifbiGEgCIIgCAMUrTUPbWk5WHzJwKtbxDjIVRYtWsTatWszA/fm7Ny5k3322afN9oqKCnzfp76+vsvfk47jGTFiRJu2wMYydIft27fzwAMPEIvFWvytXr26jZFx4IEHctRRR5FMJvniF7/YbnujR49ud1s6zmD79u28//77bb5v/vz5BEHQIh6hvbY6orkxAhCPxykoKGhhLKT/n0gkMrIYYxg5cmQLWaZPnw7Q4vpbtwUQi8UybXXExIkTeeihh6ipqeGcc85h1KhRnHXWWbz77rtdvrZsIDEGgiAIgjBAeaSq/dSd/wEO3buiCF1kxowZHHbYYSxevJjjjz++xb7y8nLee++9NudUVVURi8W6VfMgPbP//vvvU15e3qKt9P7uUF5ezvTp01myZEmbfXl5eS0+/+xnP+P5559n8uTJLFiwgGnTplFQUNDimOrq6jbtVFdXU1lZmfm+UaNGdRh829wY6IuYm+aUl5ejlOK5555rM+gHawhlg+nTpzN9+nRqa2tZs2YNV155JXPnzuXJJ5/MSvtdoVuGwRNPPMHq1avZsmULFRUVnH322cyYMaPD4//rv/6Lxx57jKeeeqrXgnaFt99+m3vuuYeNGzdSW1tLPB5nv/324/TTT283YOett95i+fLlvPHGG7iuy6RJk7j00ksz1rQgCIIgCEK2WbRoEeeee26b7ccffzzf//73WbNmTWY2WmvNr3/9a4477rgWwbt7Im10/OpXv2rhynPvvfcyduzYTgN02+Pkk0/mnnvu4eCDD6aoqKjD49IBvddeey1XXHEFkyZN4mtf+xq33npri+PWrl1LTU0NpaWlADz55JPU1tby0Y9+NPN9N998M/F4nMmTJ3dL1mxz0kknAbBjxw4+9alP9bq9eDze6QrCsGHDOO+88/jTn/7E6tWre/193aHLhsHVV1+dSctkouIid999N8ceeywrV65k7Nixbc55/fXXeeaZZ7Ik6p5paGhg5MiRfPzjH2fEiBEkk0mefvppfvCDH1BdXd3Cz27z5s0sXLiQ8ePHc+2115JKpVi1ahXXXXcdP/rRjzKKKgiCIAiCkE3OPvtsJk+ezJNPPtlikH3GGWcwZcoULr74Ym688UbGjBnD0qVLeeONN/jxj3+cOe473/kO3/nOd3jrrbfaHX+B9VmfOXMmCxYsoLGxkQ9/+MP86le/Ys2aNdx9993dlnnBggWsXLmST3ziE3zlK1/hAx/4ANu2beNPf/oT++67L1deeSVaay655BI++MEPcv311xOPx7ntttu45JJLOOuss1r43peUlHDaaadx3XXXsWvXLr72ta8xZcoUPvnJTwJwyimn8KlPfYrp06dz7bXXMnnyZBoaGvjHP/7Bm2++yc9//vNuX0NPmThxIl/4whe4+OKLueaaa/joRz+K7/v861//Yu3atTzwwAPdau/ggw/mqaee4g9/+ANlZWWMHz+e++67j+eff57TTjuNyspK3nnnHe655x5OPfXUPrqq9umSYfDggw/yP//zP3iex6WXXsqhhx7K22+/zd13383zzz/P0UcfzcMPP8zRRx/d1/J2yqRJk5g0aVKLbUcffTTvvfcejz32WAvDYOXKlcRiMRYtWkRhYSFgA0Tmz5/P/fffz5w5c/am6IIgCIIgDBGUUixatIhZs2a12O66Lo8++ijXXHMNX//616mvr2fy5Mk8/PDDTJ06NXOc1powDPdYBfqee+7hG9/4Brfccgvbt29n4sSJ3HPPPcyePbvbMo8YMYIXXniBb37zm3zta19jx44djB49mmOOOSZTC+Hmm2/mz3/+My+++GLG5ebiiy/md7/7HXPnzmXDhg0ZH/9zzjmHMWPGMH/+fHbu3MnJJ5/MsmXLWnznfffdx0033cRPfvITNm3aRGlpKR/5yEeYO3dut+XvLbfeeisHHnggy5Yt4zvf+Q5FRUUceOCBnHfeed1u68Ybb+SKK65g5syZ1NXVcddddzF58mQefPBBFixYwI4dO6ioqODCCy9s13WrT+lK6qLp06cbx3HMI4880mL7rl27zAUXXGCUUqakpMQ88cQTLfbPmTPHOI6TpQRKPWfx4sXmsssuy3wOgsDMnDmzTQovY2w6rnnz5nXa3lBJB2nMwJF1qKUxlHSlQ6/PW5PLz7ykK927ff7g/wVt/nJV1t4wUNOVCm1pL12nkBt0KSvRX/7yF44++ugWxSQASktLWb16NTfeeCMNDQ2ceeaZ3H///X1iwHSHtCVdU1PDI488wssvv8zMmTMz+7du3UoqlWLcuHFtzh03blxmvyAIgiDkOmfu63Lmvi5nVDqZ/wuCIPSELrkS1dXVdVgJD+C6666jsrKSyy67jPPPP58777yTiy++OGtCdpelS5eyZs0awObTvfzyyzNBPGCvB2g3ur+4uBhjDPX19S0i9rdu3ZpJjbVt2zYaGhoAa4R0F2MMWmu01lmLpE/L0RN5OmOgyNoXcsLAkrU5sVgMGNz6OdT7fCA9863pqX5Kn+9ZTrMH15KOzhkI+rk3dFMQhjpdMgz222+/THqrjrjkkksYPnw4559/PnPnzqW2trZXgm3YsIFvfOMbXTr2hz/8IQcccEDm86c//WlOOeUUampq+POf/8wdd9xBMpnM+MCl6ezF0nrfsmXLWhQkueCCCwD2eF/2Nu2l/8pVRNa+I+1/KfrZcwaKrANFzuaIfvaOgSInDCxZhb3Hxo0b+1sEoQO6ZBgcdNBBPPPMMzQ1NbXJQ9ucGTNmsGbNGmbMmMGXv/xlRo0a1WPB9ttvvw6LYrSm9feMGjUqs+2oo44CbAalE088kdLSUkpKSoDdKwfNqa+vRynVJhXXvHnzOOusswC7YpCugteT1KbGGIIgwPO8rM7OVFdXM3r0aBwne3XrBoqsfSEn5K6sexpQ3XXXXcydO3dQ6+dQ6/PW5PIz31f6KX2eu33emly9p7lmjApCrtElw2D69Ok8+uijrFy5kssuu6zTY6dOncpTTz3FaaedRnV1dY8f3vLy8qylaJo4cSJr1qyhqqqK0tJSKisricfj7VqsGzduzOxvTmVlZaboxpYtW1i/fj3bt2/vkTye51FWVsaOHTsIgqBHbXREtmdnBoqsfSkn5J6se9I93/cHvX4OtT7viFx85vtKP6XPLbnY5x2Ra/e0p+9FQRgqdMkwmDFjBvfeey9PP/30Hg0DgCOPPJJnn32WCy64gF27dvVayN6yYcMGHMfJzE65rsuUKVNYv349c+bMyaQr3bZtGxs2bOi0aBtAYWEhsViM3/72t30uuyB0RCwWy+huc0Q/hVxA9FPIVTrSTUEQQJmeRCrlKLfffjuFhYVMnDiR4cOHU1tby7p163j22Wc555xzWuS93bx5M1dddRUHHHAAs2bNyhQ4q6ur61KBs127dtHY2NjXlyQIHVJYWJjJB90a0U+hvxH9FHKVznRTEIY6g8oweOKJJ3jiiSfYvHkzDQ0N5OfnM378eE455RSmTZvW5vg333yTFStW8Prrr+O6LpMnT2bu3LkZlyFBEARBEAShJXV1dSxZsoRXXnmFl19+me3bt/Ptb3+b66+/vr9FE3rJoDIMBEEQBEEQhL5l48aNHHbYYRx66KFMnDiRn//852IYDBK6FGMgCIIgCIIgCABjx45l586dKKXYvn07P//5z/tbJCFLiGEgCIIgCIIgdBkpMDd4EcOgh0jwnNDfSHCnkMuIfgq5ylANPtZas2XLlkxNqn333Ter9TCEwYEYBj1g165d3H777X2SR1sQuorneXzxi19s8wMn+inkAqKfQq7SkW4OZrTWvPTSS9TX12e2VVVVccQRR4hxILRADIMe0NjYSBAEHH744RQXF/e3OMIQpL6+npdffpnGxsY2P26in0J/I/op5Cqd6eZgZsuWLdTX19M830xdXR1btmxhzJgx/SiZkGuIYdALiouLh9SLRRhYiH4KuYzopyDsPZqamrq1XRi6dGn9yBjDKaecwoQJE1i/fv0ej1+/fj0TJkzgtNNO67WAgiAIgiAIQs8pKCjo1nZh6NIlw+B3v/sdTz75JKeeeirHHnvsHo8/9thjmT59Oo8//jgPP/xwr4UUBEEQBEEQesa+++5LcXExSqnMX0lJCfvuu29/iybkGF1yJVq9ejWu67Jo0aIuN/ytb32LZcuWsXLlSs4444weCygMLB566CGeeuopNm7cyLHHHss111yT2bdp0yZuu+02Nm7cSEVFBVdccQUf/vCHW5x733330dTUxJFHHskXv/hFCgsL++MyhEGI7/ssXbqUV199lbq6OkaOHMmnP/1ppk6dCsBll13Grl27MoF4o0aN4sc//nHm/L///e8sXbqUqqoqxo0bx5e//GU+8IEP9MelCIOQH/7wh/zxj3/E83b/LP/4xz9m1KhRAGzbto3bbruNf/7zn5SVlTFnzhyOO+64zLHr1q1j+fLl7Nq1i4MPPpivfOUrjBgxYq9fh5CbOI7DkUcemdWsRI8++igNDQ3U1dUB8Nprr3HfffcBcPrpp8vv9wClS4bBn//8Z4488kgqKiq63PA+++zDUUcdxQsvvNBj4YSBR3l5Oeeddx6vvPJK5mUBEAQB3/3ud5k+fTrf+973eO6557jhhhu44447KC4u5uWXX+aXv/wl3/nOd6ioqOAHP/gBy5Yt48orr+zHqxEGE2EYUl5ezne/+11Gjx7N66+/ntG3gw46CICvf/3rHHnkkW3Ora2t5YYbbmDevHl87GMf44EHHuC73/0uP/3pT3Fdd29fijBImTFjBpdcckm7+2655RYOOOAAvvGNb/Daa69x0003MXbsWPbbbz82b97Mrbfeyte//nUOPvhg7rrrLm655Ra+973v7eUrEHIZx3GyGmh8xRVXsGnTpsznX//61/z6178G4J133mHcuHFZ+y5h79ElU7Gqqorx48d3u/Fx48ZRVVXV7fOEgctxxx3HMcccw7Bhw1ps37BhA8lkknPOOYdYLMa0adPYZ599eP755wF46qmnOOmkkzjggAMoLCxk9uzZPPfccySTyf64DGEQkp+fz+zZs6moqMBxHA455BAOPvhg/vnPf+7x3PXr17PvvvsydepUYrEY5557Lk1NTfz973/fC5ILQ50tW7bw73//m9mzZ5OXl8fhhx/OYYcdxtq1awFYu3YtRxxxBIcddhh5eXnMnj2b119/na1bt/az5MJgZuPGjRhj2v0To2Dg0iXDIBaLkUqlut247/symyYA8O677zJ27NgWy5bjx4/n3XffBaybUXPjc+zYsZliLILQFyQSCd58803Gjh2b2fbDH/6Qz3zmMyxcuJDXXnsts/3dd99toZ+u6zJ27NgWs2WC0Fsee+wxLrroIr785S/zhz/8IbN906ZNjBo1qkV6187enyUlJYwaNUr0UxCEbtMlV6LKysoWP5Jd5bXXXpPAFgGwKdGKiopabCsqKspUP00kEi32K6UoLCyUVGpCn2CM4Uc/+hEf+tCHOPzwwwFYsGABH/zgBwF48sknWbx4MbfddhujR4+mqampTc79oqIi0U8ha3zqU5/i0ksvpaioKOMqVFRUxHHHHdfm/Qg23Wta/xKJRBt/btFPQRB6QpdWDE444QT+9a9/8ac//anLDb/wwgu8/vrrnHDCCT0WThg8FBQUZIyANI2NjZlUafn5+Z3uF4RsYYzhJz/5CTt27ODaa69FKQXAIYccQl5eHnl5eZx++ukccMABvPTSS8Ce9VcQessHP/hBhg0bhuu6TJo0iTPOOIN169YB7b8fGxoaOn1/Nt8vCILQVbpkGFx++eUYY5g7dy7bt2/f4/Hbt29n7ty5KKW47LLLei2kMPD5wAc+wKZNm9BaZ7a98847mawuY8eO5Z133sns27RpE47jyIqTkFWMMSxdupS3336b66+/nvz8/A6PdRwnUyX0Ax/4ABs3bszs01qzcePGFm5IgpBNlFIZ/Rs7dizV1dU0NDRk9rd+fzbXz/r6erZv3y76KQhCt+mSYTBlyhQuu+wyXn/9dQ499FB+9rOfUVtb2+a42tpa7rjjDiZPnsy//vUvLr30Uj760Y9mXWghdwnDkFQqhdYarTWpVIogCJg0aRKxWIwHHngA3/d55plnqKqqytTFOPHEE3nyySd55513aGxsZOXKlRx//PHk5eX18xUJg4lly5bxxhtvsHjx4hauF9u2beMf//gHvu/j+z6PPfYY//73vzNuRsceeyz/93//xzPPPIPv+9x///0UFBTwkY98pL8uRRhkPPfcczQ2NqK15rXXXuPhhx/mmGOOAWwO+gkTJrBq1SqSySSvvvoqL7/8MtOmTQNg6tSpvPTSS7z66qskk0lWrlzJgQceSGVlZX9ekiAIA5AuxRgA/OQnP6GxsZFVq1Yxf/58rrjiCg444IAWOZbffvvtTET6BRdcwNKlS/tMcCE3uffee/nlL3+Z+bxu3TpOPPFEvvrVr/LNb36T22+/nVWrVrHPPvuwcOFCSkpKADj88MM5//zzWbx4MY2NjRx55JHMmzevvy5DGIRUV1fzyCOPEIvFuPTSSzPbZ82axTHHHMMdd9zB1q1b8TyP/fffn29961uZgdWwYcNYuHAhS5cu5bbbbmPcuHF885vflOQKQtZ46KGH+PGPf4zWmpEjRzJ79uwWrrjXXHMNt956K7Nnz6asrIyvfOUr7LfffgDsv//+fOlLX+L2229n586dHHLIIVx99dX9dSmCIAxglEmvVXaR3/zmN3z/+9/nxRdfpL1Tp0yZwtVXX82sWbOyJmSusWXLFu644w4+/vGPM3z48P4WRxiC7Nq1i2effZbPf/7zbdytRD+F/kb0U8hVOtNNQRC6sWKQZubMmcycOZMdO3bwyiuvsGPHDowxjBw5ksMOO0wqLQqCIAiCIAjCAKTbhkGaESNGcNJJJ2VTFkEQBEEQBEEQ+oluGQaPPPIIDzzwAP/5z3/Iy8tj8uTJzJ07t0dVkQVBEARBEARByB26bBjMnj07E1Saji148MEHueWWW/jlL3/JWWed1TcSCoIgCIIgCILQ53TJMLjzzjtZvXo1nudx8cUXc/jhh1NXV8dDDz3E+vXr+exnP8umTZsoLS3ta3lzivr6+v4WQRiidEX3RD+F/kL0U8hVRO8EoXO6ZBisWLECx3F49NFHW8QVfP3rX2fu3Lncfffd/Pa3v2Xu3Ll9JmguEQQBAC+//HI/SyIMddK62N420U+hvxH9FHKV9nRTyD3mzJnD008/3aKA34033sghhxzC2Wef3eLYp59+mmnTprF27VqmTp3a7e8aN24cU6dOZfny5b0TeoDTJcNgw4YNHHPMMe0GGy9cuJAVK1awYcOGrAuXq3ievW3Tpk2jrKys2+e7rktJSQl1dXWEYZht8bLKQJF1oMgJ2ZF1586drF27NqOLzRkq+jlQ5IShJ2tf6edQu497i4Eia1/rpjAwuPHGG5k1a1Ybw+CII45g/fr1HHLIIf0k2eCgS09GbW0tH/zgB9vdl97eXiXkvc3bb7/NPffcw8aNG6mtrSUej7Pffvtx+umnZypEpnnrrbdYvnw5b7zxBq7rMmnSJC699FIqKiq6/H0f+tCHepQH2RhDEATsv//+KKW6fX57aK2pqqqioqICx+lSQesuMVBk7Qs5IXdl3bJlC2vXru30mMGun0Otz1uTy898X+mn9Hnu9nlrcvWedkU3hYHJsGHDMtXChZ7TpafVGNNhhc/0A6+1zp5UPaShoYGRI0dy8cUXs2jRIq688kpGjx7ND37wA+69997McZs3b2bhwoX4vs+1117Ll770JbZs2cJ1111HTU1NP16BIAiCIAhCbnP99dejlOJvf/sbn/70pyktLaW8vJwFCxYQBAFvvPEG06dPp6SkhHHjfWp1RQAAIABJREFUxnHzzTdnzl2+fDlKqRbuQWBdgZRSPP300x1+r1KKhoYGVqxYgVIKpVTGbai98+fMmUNxcTH/+Mc/OOmkkygqKmLUqFF88YtfpLGxcY/XWVtby9VXX8348eMzk81f/epXaWho6M7tGlAMqrW0SZMmMWnSpBbbjj76aN577z0ee+wxzj//fABWrlxJLBZj0aJFFBYWAjBhwgTmz5/P/fffz5w5c/a26IIgCIIgCH1GaAz/aTQ0BlDowf6FCreXK0TnnXcen/nMZ5g3bx5/+MMfuPnmm/F9nyeeeIL/9//+H1dffTWrVq3ia1/7GhMmTODcc8/t1fetX7+eE088kWnTpvGtb30LsCsFneH7Pqeffjrz5s3juuuu4/nnn+e73/0umzZt4sEHH+zwvMbGRj7xiU9kJpMnT57MP/7xDxYtWsSGDRt44oknsrpymSt02TBYsWIFK1asaHefUqrD/Uqpfg/yGTZsWGYlIAxDXnzxRU488cSMUQAwevRoJk2axAsvvCCGgSAIgiAIg4bQGJ7frqn1d2/b3Gg4dqTTK+Pg85//PAsWLADg5JNP5vHHH+f222/nt7/9Leeccw4AU6dO5aGHHmLlypW9NgyOOeYYHMdh1KhRXXYbSqVSXHXVVXz5y18G4JRTTiEWi/GNb3yDdevW8bGPfazd82699Vb+9re/8ac//YmjjjoKgJNOOon99tuPWbNmsWbNGk477bReXU8u0mXHP2NMj/76w8VIa00YhtTU1PDII4/w8ssvM3PmTAC2bt1KKpVi3Lhxbc4bN25cZr8gCIIgCMJg4D+NhlofDLv/any7vTeceeaZLT4ffPDBKKVaDJg9z2PChAls2rSpV9/VG2bPnt3i80UXXQTQabzJQw89xEc+8hEOO+wwgiDI/H3yk5/co8vTQKZLKwa5ED/QHZYuXcqaNWsAq5CXX34506dPB6Curg6A4uLiNucVFxdjjKG+vp7y8vIW+7Zu3crWrVsB2LZtG4lEAujZvUkbTFrrrAZ69VSezhgosvaFnDCwZG1OLBYDBrd+DvU+H0jPfGt6qp/S5wOnzwfKPR0qNHbguNHR9q7SeqwUj8cpLCwkPz+/zfb+SlLjeR4jRoxosS2daGbHjh0dnvfee+/x5ptvZt5Xrdm+fXv2hMwhBlWMQZpPf/rTnHLKKdTU1PDnP/+ZO+64g2QymVnWAjp9qbS3b9myZSxevDjzOb10VlVVlUXJe091dXV/i9BlRNa+I11TRPSz5wwUWQeKnM0R/ewdA0VOGFiyDmYKOxjtdbS9L0kbDclkssX2vhpoB0HAjh07WhgH6XdPa4OhOSNHjqSgoID//d//7XD/YGRQGgajRo1i1KhRABm/sLvvvpsTTzyRkpISYPfKQXPq6+tRSlFUVNRm37x58zjrrLMAu2KQXn7qTnrTNOmUa57nZXV2prq6mtGjR/dJGrtcl7Uv5ITclXVPA6q77rqLuXPnDmr9HGp93ppcfub7Sj+lz3O3z1uTq/c014zRvcX+hSrjTpSmNGa3723Srtx/+9vfOPDAAzPbf//733fp/Ly8PJqamrr1nStXrszEGACsWrUKoNNCaGeeeSY33ngjI0aMYPz48d36voHMoDQMWjNx4kTWrFlDVVUVEyZMIB6Pt0mTBbBx40YqKyuJx+Nt9lVWVlJZWQnYPMjr168H6NELzxiD4zg4jpP15dB0u9lioMjal3LCwJIVbBYGGNz6KX1uGUjPfJqe6qf0uWUg9flAuaeDHVcpjhvpZD0rUU84+uijOfDAA7n66qsJgoCysjLuv/9+nnvuuS6dP2nSJJ5++mkefPBBKisrKSkpaWFgtCYej/Pf//3f1NfXc/TRR2eyEp122mkcf/zxHZ731a9+ld/85jeccMIJXHnllUyePBmtNe+++y6PP/44V111FR/96Ee7ff25TvbeLDnMhg0bcByHiooKXNdlypQprF+/vkUO223btrFhwwaOPfbYfpRUEARBEAQh+7hKMa7I4ZBSh3FFvctG1Cs5XJcHH3yQgw46iPnz5/PZz36WvLw8br/99i6d/6Mf/YgPfehDXHDBBRx99NHMmzev0+NjsRgPPfQQf/jDH5gxYwa33norl19+Ob/+9a87Pa+oqIhnn32WOXPmcMcdd3DGGWdw3nnnceuttzJmzJh2k9gMBgbVisHtt99OYWEhEydOZPjw4dTW1rJu3TqeffZZzjnnHEpLSwEbjX7VVVexZMkSZs2aRSqVYtWqVQwbNqxNiW1BEARBEARhN9dffz3XX399m+3Lly9n+fLlbba3zuDzoQ99iMcee6zNcca0zJLUXluHHnpou6sLU6dObXN+mkmTJu2x4nV7niRFRUUsWbKEJUuWdHruYGJQGQYHHXQQTzzxBE899RQNDQ3k5+czfvx4rrzySqZNm5Y5bsyYMdxwww2sWLGCm266Cdd1mTx5MgsXLswYD4IgCIIgCIIwlBhUhsHJJ5/MySef3KVjJ0yYMKQsQEEQBEEQBEHojCERYyAIgiAIgiAMLZYvX059fX1/izGgEMNAEARBEARBEAQxDARBEARBEARBEMNAEARBEARBEATEMBAEQRAEQRAEgUGWlWhvUlFRged5HebM7Yz0OT05t7M20/Jku93m/2arzWzL2hdyptvLRVk9r/NHdyjo51Dr8/bazNVnvq/0U/o8d/u8vTZz8Z7uSTcFYagjT0gP+dznPgdAEAQ9biMMw2yJA0BZWRlaa7TWWW0XBo6s2ZYTclPWsrKyTvcPJf0cKn3eHrn6zPe1fkqf516ft0cu3tM96aYgDHXEMOghd955J+eeey6jRo3q9rnGGMIwxHVdVJZKkmut2bFjByNGjMBxsuchNlBk7Qs5IXdl3bZtW6f7h4J+DrU+b00uP/N9pZ/S57nb563J1Xu6J90UhKGOGAY9pKqqiiAIevUSVUpl7SWslMrIk80fzObtDwRZs91mrsq6p5nWoaSfQ6XP22srV5/5vtZP6fPc6/P22srFe9qbVVRBGApI8LEgCIIgCIIgCGIYCIIgCIIgCIIghoEgCIIgCIIgCIhhIAiCIAiCIAgCgyz4+NVXX+Xpp5/m9ddfZ/v27RQVFTFhwgQuuOACJkyY0OLYt956i+XLl/PGG2/gui6TJk3i0ksvpaKiop+kFwRBEARBEIT+Y1CtGDz66KNUV1fzqU99im9/+9tcfvnl1NTUcM011/Dqq69mjtu8eTMLFy7E932uvfZavvSlL7Flyxauu+46ampq+vEKBEEQBEEQBKF/GFQrBvPnz2f48OEtth1xxBHMmzeP++67j0MPPRSAlStXEovFWLRoEYWFhQBMmDCB+fPnc//99zNnzpy9LbogCIIgCIIg9CuDasWgtVEAUFBQwP7778/27dsBWzHxxRdf5LjjjssYBQCjR49m0qRJvPDCC3tNXkEQBEEQBEHIFQaVYdAeDQ0NvP322+y///4AbN26lVQqxbhx49ocO27cuMx+QRAEQRAEQRhKDHrDYOnSpSQSCc477zwA6urqACguLm5zbHFxMcYY6uvr96qMgiAIgiAIgtDfDKoYg9bcc889PPPMM3z+859vk5Wos3Lq7e3bunUrW7duBWDbtm0kEgkAtNbdlssYg9YarXXWSsWn5eiJPJ0xUGTtCzlhYMnanFgsBgxu/RzqfT6QnvnW9FQ/pc8HTp8PlHsqCEJLBq1hsHr1an71q1/xmc98hjPPPDOzvaSkBNi9ctCc+vp6lFIUFRW12bds2TIWL16c+bxgwQIAqqqqsi16r6iuru5vEbqMyNp3zJ07FxD97A0DRdaBImdzRD97x0CREwaWrIIgDFLDYPXq1axevZoLL7ww40KUprKykng8zsaNG9uct3Hjxsz+1sybN4+zzjoLsCsGa9euBehR3QNjDEEQ4HleVmdnqqurGT16NI6TPQ+xgSJrX8gJuSvrngZUd911F3Pnzh3U+jnU+rw1ufzM95V+Sp/nbp+3Jlfvaa4Zo4KQaww6w+CXv/wlq1ev5rzzzuPCCy9ss991XaZMmcL69euZM2dOJjPRtm3b2LBhAzNmzGi33crKSiorKwHYsmUL69evB+jRC88Yg+M4OI6T9eXQdLvZYqDI2pdywsCSFcD3fWBw66f0uWUgPfNpeqqf0ueWgdTnA+WeCoJgGVSGwf3338+qVas44ogjOOqoo3j99ddb7D/ooIMAuOiii7jqqqtYsmQJs2bNIpVKsWrVKoYNG8bZZ5/dH6ILgiAIgiAIQr8yqAyDF198EYC//vWv/PWvf22z//e//z0AY8aM4YYbbmDFihXcdNNNuK7L5MmTWbhwIaWlpXtVZkEQBEEQBEHIBQaVYXDjjTd2+dgJEyawZMmSPpRGEARBEARBEAYOg76OgSAIgiAIgiAIe0YMA0EQBEEQBEEQxDAQBEEQBEEQBEEMA0EQBEEQBEEQEMNAEARBEARBEATEMBAEQRAEQRAEgUGWrnRvUlFRged5GGO6fW76nJ6c21mbaXmy3W7zf7PVZrZl7Qs50+3loqye1/mjOxT0c6j1eXtt5uoz31f6KX2eu33eXpu5eE/3pJuCMNSRJ6SHfO5znwMgCIIetxGGYbbEAaCsrAytNVrrrLYLA0fWbMsJuSlrWVlZp/uHkn4OlT5vj1x95vtaP6XPc6/P2yMX7+medFMQhjpiGPSQO++8k3PPPZdRo0Z1+1xjDGEY4rouSqmsyKO1ZseOHYwYMQLHyZ6HWF/KWl4+AhO16Sp61X5fyAl9c1+zIeu2bds63T8U9LMncobG4EdjFE+B57Q9L1dk3RO5/Mz3lX4Otee8Nbnc563J1Xu6J90UhKGOGAY9pKqqiiAIevUSVUpl7SWslMrIk80fzObt96TdVKjZlTI0hXbwXxqDQhdSQUBNAIEBA+S7MCwGTqvvCI0hFYI2hpijiLudy5Dt6+/L+9qbNvc00zqU9LOrbQbasMuHIDIMXAWlcYi3Mg5yQdautpWLzzz0vX4Olee8vbZytc/baysX72lvVlEFYSgghoHQZwTasC1pqPftICxhIKlhpAdNeLjaGgQAiRBcZSiJqRbn7/JNZiAHhmExQ6EnMfNC96kPDKGG/Mi4TGlDnW8oj/dutUoQBEEQBgtiGAh9RjI0NIaQ51qXDWMMiRDqQvBxiDt2QGaMIdSG7UkItCbPhQJX0Ri2HMiFxlAfQJ5rcGUgJ3STwFgDNY2nrHGQCMFR4Dlk9Mpg9c0Yg4MYDoIgCMLQQAwDoc9oHW5ml39tNgmFQQMu1oCoDyGu7OAtGUS+pNoO2NI4gG/AGEDGaUI38YCk2f3SS2lDY6RrSllDoTQOGEMjLjuSoBxDgQvFXls3N0EQBEEYbIhhIPQZMUcRw5DSAIbQgDZQEAOfkECDwVAX2EF/cUwRcxRKRysNjiGpFZ4x1l/V2FndduJFBaFdjDE0BlYHtbHGaCIEFDSFEHegIHJNS4bWtQgDPi4xB5SCxsAass3d3AShv9BYo9bF4PUyaYMgCEJrBp1h0NjYyL333ss777zD22+/TW1tLRdccAEXXXRRm2Pfeustli9fzhtvvIHrukyaNIlLL72UioqKfpB88JHnwMg82J6ysQUuUB63QcYJNKUx+wMXdyHfsYYE2MGYMVDoKbSxsQlog6NgmKdk5lboMnWBXRVwFWijMBiKPKtrCoPbbOkp5oCvDcaAh8aJBl2eMiQ1FEcGqiD0F0ltqCeGmwJHGQo8KPHEOBAEIXsMuijOuro6Hn/8cXzf55hjjunwuM2bN7Nw4UJ83+faa6/lS1/6Elu2bOG6666jpqZmL0o8eFFKURx32b/IYXyRYmyxYkSeg6sUChs7MDzPpTyuMCg7oxulk4w74DkOw+KK8piiLK4YEVfk7SErkSCkCY3JrArEHKs7LgpjbAyLBzQGhjpfUx9oktpkYhCal09Ke67J4EvoT0JjqE0RvTutIdsYQFOY3WJvgiAMbQbdisHo0aNZtWoVSilqa2t5/PHH2z1u5cqVxGIxFi1aRGFhIQATJkxg/vz53H///cyZM2cvSj24cZXCbTag11qj2V29ssizM7lNUc2afNe6FYH16467e1tiYTBgoniU5uN5R1l3NmMMBmjSu491FexXqAi14X0cfA1KGbSBokH3phQGGqEhisuy701HKRxl8MUuEAQhiwy6FYOu5DcOw5AXX3yR4447LmMUgDUqJk2axAsvvNDXYg5ZUtrwfgrqiLM9CY1RLtISTzEyz/6VxlSnWYdsJhlDKjQZ40IQWuMqu1qQ0tYQ0MbGucQdG+QeGMWIOJTGFMPjikIvcmFzoYAANxOQrCiQlSphL2P1dfc7zsGuFpgWx1gXTUEQhGwxJOfBtm7dSiqVYty4cW32jRs3jldeeYVUKkU8Ht/7wg1iQm3YldQ0BdCEw3sJ2OEbRsQNZXlOm0JTaZKhIaFtUGi64Fl6Grgw8rEVhPYocG3cQFMIrqMo8mzsih/5B3mOk3kJJkM7I6uUIg9NWVxltbqsIHQFYwwNgU3AANaQLfbsypWnIIlDUgPK4DmI0SoIQlYZkkOquro6AIqLi9vsKy4uxhhDfX095eXle1u0QU1KG2p9qA+gljh5ITghFCgAjefYwZlS1p3IUYpUaEhoG8gcaKj1bfByoauijDP2hzNPxm9CM7Qx1Pq2RgHY2dYSj0xxPBeDh9XJmLIGgSEdAN/+KlQqNCS1QQFxV3VoyApCb2gMDDW+TbagNexKwnvYdM5xB0DhYle28l2FK3ooCEIWGZKGQZrOXI5a79u6dStbt24FYNu2bSQSCcD6y3cXY4z1s9c6awGNaTl6Ik9nZFNWPzTUBhCG1r/baEOTUdT64GsIsT7ggbEpJctjhgC7XO7FrGuIwhoP+Y7OZC8KQkMMsn5PoW/ua1/0f2tisRgwuPWzMzkbA0Ojbw1Mpax+1fmGmApxlQ109xTUpiChIKagOAYxDFqbNrImQkONv7t8hvINpXG6bBwMlHsKua2ffSVbfz3nxhhSZneMS8xR7EoZGkK7rSkEZWz9FltLw+ChCbWxBSIho699LWt3GajvTkEY6gxJw6CkpATYvXLQnPr6epRSFBUVtdi+bNkyFi9enPm8YMECAKqqqvpQ0u5TXV3d3yJ0SB0uNRQRoFAoUmGCEKhJamqBAjT5hCRwSeJQndTkoUnhsBNFHI2PIo4hwAdsBeUkAfE25dSySy7f1/aYO3cuMHT1sxGXAIfGZrP/KRxS+CgMjcTwo2F+iCKfgJCQ5m+EtKwGqCOGYnfgZ4CiBiiO9LA/GWi6CaKfYPWqERc/ihJQQJyAWuJ24gRFgEMKB4UhnvJpwqEQw/s1u0jV+HgdrG7lEgNRPwVhKDMkDYPKykri8TgbN25ss2/jxo2Z/c2ZN28eZ511FmBXDNauXQvQo5oHxhiCIMDzvKzOzlRXVzN69Ois+kV3R1abbtTO0LYOHva1QSehuNHOviZ0CsfJw1OKuGN/JEvjkO+B44OrIdRRTQNt3Y0KPWiI0k8W59lz8l0ojbQ42/cU+ua+ZqP/9zSguuuuu5g7d+6g1s/O5KzzrY92vrt75tUJbXE9u1oFZcpmJUppOyMbj9s6GxjTQlZtDF6yZXG9IJrlHZnX9RWDgXBPIbf1sy/uI/TPc94QGFRga7goZV0lkyGUaLvC5WvwrIUAGvLyomxajTspLR3OyDyVqf3S17L2hIH67hSEoc6QNAxc12XKlCmsX7+eOXPmZDITbdu2jQ0bNjBjxow251RWVlJZWQnAli1bWL9+PUCPXnjGGBzHwXGcrC+HptvtqhzpeXaH9l2ruiprSls3oUDbwVOhY4i7ClA4CupDGzRcHAPfKOLajvrTg63AwI4UDMem5WsIoMgFo6LfRQccF4a5kOdCadzBUTa2QCnVp/cUundf90Rfywrg+3YmeyDr557oTM6imMHHBq0nA2sA5Dnwvm8HX66CJmV1L88FFdoifI1aURwFc6bbdoC4q0lpyHOsroXYwObuPGsD4Z5CbuvnYHrOtbJxVU40uI+5UBdYPXOUda1MhHYlwVNQ40epn41iH8duGwjvpIH27hSEoc6gNAxeeuklEokETU1NAPznP/9h3bp1ABx11FHk5eVx0UUXcdVVV7FkyRJmzZpFKpVi1apVDBs2jLPPPrs/xe9zUtqQDDS7/N2ZWApcGBGHAq/7L9zQ2GxDjSGZalC1IeRF012pEBLaDsbyoirHATqqdmxnYoscG1j8XgIcDa5nVwZCbNBo3I0qfALKURR68qMgdIznKMpidlY2GUJpzOp6TEFSQYNvjdE8x+qWUlDgQSoE40TuQtqmiwS7WqUDSGiDis5L19oQhJ7gAU1RTQ2lFCmtM7EEvgHHQL0BHYKvwHXAdTSJlCZZu5OamEt+fj4FBQUySBYEIWsMSsPgpz/9aQu/xnXr1mUMg5/97Gfss88+jBkzhhtuuIEVK1Zw00034boukydPZuHChZSWlvaX6H1OU6Cp9aEmZWegwA7Ya1O2iua+hYZir3tVXn1tqI2C5FxlZ7nqAmzWIcd+NtFgKt+FYXEIUpqSaEXAibIQaR0NzFzbTmiswQLpyrM293yJZCASuoDrKAo8h/zQpnVsCg3G2GB2R0FTYFem8j27OrU9CXmOrXXQgAsJcFwN2JWtYR6ZNLmeslmzBKGn5HuKpDYkNShsxfd8BcPiilrf8H7CusAZ7A91zIDjpwgN+G6cPKVpaGjAcRzy8/P7+3IEQRgkDErD4Oc//3mXjpswYQJLlizpY2lyB20M9QE42EwYcce6T6Rn8pOh9c3OcxXxbox5QgN+aAdXRBlgalPRD5oDGDtTG1P2+5TZPdDflgCtbCq+ECuLi40vqA2toVAYiwwHAwWuIeYoQmNwoIUrVJr0DJwguMrqoK8NKjIGwA7svSirS6jh/cD+vzgGqcCwIyygqDGgOO5R4Bm8yLgtiVnXONEuobe4SjE8bt+JgTYE2sZQvZ+wRSBr/AAfmx5XGQiMSxMORQ4EysFxFI6jSaVSYhgIgpA1BqVhILRPFMNGcw8IpXb78duUjuAoTXk7BceMMTSF1jUDoMBT5LsKT1kjwzcQhNZNI4wG/0ZD0kACOwDLi1yHwNYgSBp7bAJ7fNq4cIAA64JkArtyEBq7qpEMwTTPQhQZHq6GIIpYznMMRZ6SWd0hjq+tQdzoW71sCK1RoLEvP23sMekYz/oAApMgoUEHAU2BwfViGZejMIqNKfLo9sqaILTGUYqYMjRoCLQiERiqk5AKQgJMRi9TvsHmYINUaI9rchSJ0CFPKYqNkXedIAhZQQyDIYSDHRQFRlHoGt4PbbCwa6AeO2uvokHTzpShPB4NogzU+5r6wLoFFcZAoUimDMPjNvd2ScyQiAb6xuwuEeVHBoIDpAzsjHLLB6jMaoU2ZJI+JkLrz+26oAKroEbbJfWGaLZ3eNwuuzcENkYhMDazjAeMLrArDg0BGAzDxA980JLSUW2BUBN3FcWeapENK6UNu3yDQpHnGhIGipR1WWuKgo19A6BROJH7mk99lJTUwbEGaJCiwY3j2Vh64g67C+u5/XTxwqChPjCEGlztg++jfQeNxvjpNydE0yxoFE14VCdCGkOHgBgxFae2VjMyD5vQQUGRp/Ck8JkgCD1ADIMhhFKKkhjUpgz5rq2c2Wii2VNj3SiKPVtVOBEaGgJDsQs1gXXzaYj+JYBhMTvgqklpij2FMXag1eBbF6D0TFcUi4zBDqJKPPuhDpdUFPgcNJMxAQQBeEHkPhT9tjWEdsDvOrArZbc1BjZTUb4CP4Awqpw8LG4D9xIhFHsykzYY8bVhV8oOqOKOHehrbYuOpfs7EVoLNebalbA8Bcazq1o7UuDjk46XB6u0bhhZqyjCqMJsGK1OxSN3u/woNqZvK2cIQ4XAgA59mhobCY2tWZBMhoR4u/PjRqYqGIwbow6HJFDqubiuy7aUnRypyDckUPjGUBZvmzZaEARhT4hhMMSIO4qyPLtsPSLPTu83hTYjUKGnIn9sGzzcFEKdgoQPxXG7uhBi/bRDA/mOIaHBN4Z638YZNIS7XZaaGwYedmCfCO1ALQCSHciYNhS0BhPVOAi1HdgpY9uxLh+2hkHctS5HOsr93XzAlvvlf4SekIyqZ8cdm4HIjQzTwJCJj7H6Z6hNWaNWa2gKQpKhJgGYyC2IYPfgKcSxB2LQGDwMAQ6egUQAKHB9g+uA0yzSQIsrh9BNjDEEBsBQ35TCxa54pXxNaBRKmchl0mF3VIsGZdO5BYDBI8Cu+vrGGqx5jp3Y8bVdeRUEQegOYhgMQVyloh+MdP5sQ4idfU27Z2hjYwFqfWj07dipMbDbXGVnXbf5NpXezsg9Q2N/nDx2uwaljYN0tqJECJ62VWg7Ir0nNBDDnuNERoL2bSrTdK7vRPRvFONMaOwgLdDK5pnvkzso9DdpgzONUpHV2Iw8R7E9sEX38hzYlfJpCG0NAjuK0qBavwIjs1IrTBBiPAdwcZTVP4zNXjQ8bgd2KW0D9rUBVxlKYtkrOiUMXkJjqE1Z/WkMYFfogFH4BlJocJ102DFRWoboTNfqbQDaU5kYLBXFiTUG0Kg0frSyle+KLgqC0D3EMBBwlKI0BrVRtVhtoMS18QL5rs0MFITWjQfsz5QH7EzYYZTC/pt2y/CwA/Iw2peP3dEUfXZCCOl4Kkuzu10vMjhCk5nIRUfVjwtdayAksbPEJvL5NkZR6EGxpyQ4dJCS5ygajM3k4hhDKrSz+F6z/o4rQ16UectojQp9FB5og2OCDnQwGv07gAcOMYxyiCZ2SWob6G4MvJ+yqwpxR1lj2UBNKnLhEONAaEbzTGmhMbyf1KSMTUGqDcQdl/rAJ+WHmDCaBfHSmYZaTW+kfTMxNOkQo10KY3ZXKrApogEaA0OBa7PMCYIgdBUxDHpIRUUFnudhTPedVdIhyixVAAAgAElEQVTn9OTcztpMy9OTdl0Fw2N2ZtUx1j2nyY8CLKNUomE081oahx0JaCTKrx0NlDIuQOw2FqCtL3ZXf6Z8IBEFMucp8FwbwEyUarU8z7ouBdrGRgyLaiQoZ7efeW/vcW/va0dtNv+3J3he54/uYNPP1sQcGBazaXHrfetCVuiRiXnxHGsU5ruGmAOGEE1AAw46NNaNyGltGKTXIeyfgajCqt2TCO0QzYnZ50Mbq3/F0fjNVZAIDMnQUKBaDuYGwj1t3m7zf3tCX+lnX9zHdHt98ZynNNQktM0GFxV0rPfTVYzBd+x71XgxkkGIxqAdu3rQMZHx6lutTpKiDIPn5OE4Ll40QaIN7ErpTAKGmNNxzMFA0c+9oZuCMNSRJ6SHfO5znwMgCII9HNkxYRhmSxwAysrK0Fqjdc/DIt3Ihz/h2yBgH1sAKo62GYWUdS16P7U725BvWgYQp92H0kSxwihs21a63f9rj/TwzMO2rSLZ0PZMR0FhZMS4UdVmR4ExGhNmNzA0G/e1PXrT/2VlZZ3uH6z62ZwYNr2t72uKIl1oTNlA9BLPulfEoxoFqcDQgAE/wGiFdmKgWhsGzQM9wfeh1knguA4eChdF3HEIQ/sdroqq0vr2u0KsoZIKId/VFLt2MNacXL+nzcll/cz2fYTs30tfR0UkjSYwsCuqAF8Ws9neTLS/wYeUDm0y0piD9hWodJRW+k3YxnnO/qMDwjBJXc0O8kvLSMby0W4exnFsPJYD9TE7kRKPjOnOisYPFP3sS90UhKGOGAY95M477+Tcc89l1KhR3T7XGEMYhriumzVXF601O3bsYMSIETiOs+cTOsADyl1DQ2hQUdViT2kcx2FUzLrzbE/aHxc3Gv03v4J87EC+taGQZncWGB87tGuLarYnP2bdmEJlXYa0sYZGgbFtxT0ojSliDlm/p5C9+9qcbPT/tm3bOt0/WPWzOcYY/CAk5jrkR6OdWBQIH0YFyXambBB9oOMkMcR0HUob6r2SdCu0XcNKRy8HaO2itSbQIU5eDK01w1TcVuyOZmabsIH39YE1Wks922pdGD0P0QOQh6Yg5mTt+vvinkJu62df6Cb0zb1M+NGA2HVIaWuHpjQ0RSuedRoa/YBGbfAB7RtMzIkqPGIVWKXXXyPjwBi7JAp2m3JBQypWQIo4ATEIDW5oV7pKFWjHvkdDY9+h+V7b6xtIz3xf66YgDHXEMOghVVVVBEHQq5eoUtnzgVdKZeTpbZsxVzHctQGWqVCT9MHzFHmuoik07EwZ+5uEHd6nB/4uEMf+pjWvStycriwAp3NwFLh2kFUHNIZ2RSAW7UxGmZP2yYe4q6zrUrRU4SqyliEmm/e1vbZ72uaeZloHs362bRvSg/lAG2oD61rWFMD7vh1LuZFBqWJFeI4BPwribHZu24bTr0cHHDtwS8UUO5M+yokxrMC6L72XsKsEKQ1FXlSh23PYldQkDRS4dlBXE4DrKQoGwD1Nt5+r+pnta+6Le5l2p/Sjwo4acHxoSAU0GkNTEFCPIoxqbRiwFqaB3ekUVLPWmhsFuzebWD4J19bz8FSI9jy73diJnXhgv7/QtTVsOru+gfPM951uCsJQRwwDoVNijkK54Ln2ReyH2qYEjUb9mTg4olSm7A4e7gkONvBZKWsYFEa+si42vsB1bPKZJhMNyDSMzNMUedZ3l8AGoQ6LIUF3Q4C8KL2urw0Kw/u+1YlEANVNAU3G4KAxgSYwrk0rqsOogl5X9SNymtMh+A4+SfLyQQcuDcYhiALkTZQysj6APMcaBSUOmSxFAVFAqLx1hwRx186ehCYyEALAT1AbhGjfJzAOoRuLyuhp7FsubU60YwS0a8Bao9WYPIzRBGGIEwaYeIhRLk0a4mHMpus1UBrLbmyGIAiDD/mJErqFxrpGpLNDtjYA/PZOinCxRkQ+kCREE8uU7XGAPKxveEWRXRlIhdAY5eJWBlRo3TcSoQ149qIf3e1JaxQUOZDn2W27fEO5QlJHDnLiDpS6iobQFh8zGlw/Qa2foDGME+LioFAotDGgYpHOpt0zuko0yAsDUqFP9ftNJAvzqSOOcWPEPRejrNsSBordKEZGtWhB6moMIWKOoiwG1Sm7EODg44WNmDAPVxkcHZDQitBxCdOKoZQ1CLqlKFGAjXJs2mijCQIbJO9iCFIp3FgcX6dXrwRBEDpGDANhjwQa/NCglJ2JTUVViFvPZ8XZHWjcHulwsRS2kFQ+tp1iD0riUWaZmK2V4CjrPrQtYVORprSNMwh8Ww25MAoujUVBn76x5yml8JQiDG3xoPajGITBRJ6ryPcUDb6mqamJhlQTjdpF6RDjeFHlYidaIUj7bLcXW7AnbMSoTiWpR5OKFxB6DmGoyTOuDSjFpqAEGObtrqsBhgAobsfVWhtDUkOoDa6jyI/0WBj45LswpkhR6xuaEgYdaPIcjRcGhBh8rfBxrdua3l11u/uqGQXSO2BrHYRoPyA/Bi6afOUR8xwcmSgRBGEPiGEgdEoyNOz0IQxszvamqMhZQzuZfzozCppjPTxVpmxPylh/8JS2sQOJ0BoJ6ToJobu7onG6RgIx+xvqKWu4uMruT4Z2EBYaUN3+dRUGMgpDkx8SaIPrusS0j0ZHBmmzCrJub157CuMofOPiB3YQ53jg6xAfFwcocKyh6gIYSEWlqgpdKGiVEsYYYweNYToux9jYmpgYB4OFuKMYkadoNA5J3YR2FH4sH1+n8CNjEx2AE2e34drDvjfR9IsOrR3rK5ow1AaGUtcaocaIbgmC0DFD2jBoamrinnvuYd26ddTV1TFmzBhmzpzJCSec0N+i5QTGGOoCY100otXtpI58/qMsQb3BJ/r503ZmFWXdhRLaGgohtn6BiQb+gYmGdg4kfYjF7bZCzw7G3k/trgAad9KZ6OUHcKgQRJmqNCryT3PRJjI/TbjbTaM3mMhHA8fO0poA47skY9ZH3MEazdUJm0435kJJzP4p0zIoPtCGRGiiQlQ2hscYQyK08TV5HdcAFAYYjlIU5ccpLSqioT5B6HoEOIR4KNfFaGUH87i7K5T1BKXsykOYAsezhoDrUucbQgVgMvrYUU0DQRCGNkPaMPje977Hv//9by655BL23Xdf/vjHP3LLLbdgjOETn/hEf4vX76QzavgG8qOiYcYYGqNUROmYATtHFRUZyMx2BZ0HHADEfFKBIcDgegoPhQ84uGicjItQ+mfSxRYwK/CsO1GRa7PAlMYUNSmDp2zhKdfG49EQyOBqqBAaQ0IrRhXE2Z6opzFlaFSFGGWsJau6E2zcAQo76PLi1jhwrGuSYbdRYFcJNLWpkJ2Ap1zyXYfR+bYGSJnSFHgODYGJ4iLsakHMsW5xStn8NNmvSiD0N0opSsvLqXcaiaWSbDcO+aEtNZ8MAowbFWPpFU4U3JJvaxxoH+U4mFCjtY2BcZRBB6E1Dlw3q6luBUEY+AxZw+Avf/kLr7zyCldddVXGCJg8eTLV1dXcddddHH/88bju0B5VpoOCQ7P798pRdrZpV6p5dePIAjC0LGCwJ3wAhUahUxpfB3a2LEwCCrxY5J5k83e7JiTppygoLqK0IM6IuEOBp0gEIe+nbO76uGtXD9JuRYlQE3NUZnZMG5O5HpkxG0QYq34F+XHKy8qor0vihppYGOKrdLaX3va3stWSnZhNQaRNNLurwNdoEjgxFVXrTutbiBca6kOXPM+m2NUY6kOIK0MQxc/sSBhGFBicaJ1LYkQHKcqhsKiIoqJCUrsaqalvIDAOrtYETsxWPO61mqpMvYPQycPxUxiTxKeAutCjUWscHVDq+OTHXIaXlJBE0eSD9//bu/8Yuar7/v/Pc+69M7O/f/gHXjDBuP5YSSrTNCG0oQnKTxRFARJCKCREwUQE0pI/QtI0NW0ailIhVVWpFCnQJDUkgEn6gyqqkpS2gfwAu6HQIFcqpIU4Cl/b+EfA3l+zM/ee8/3j3Bnvrtfr9XrGOz9eDwlsz525c/be98ze9z3nvE/m6IkNsYGK83l5U+ixJlRZEpGO17WJwa5du+jp6eHNb37znMff8Y538Jd/+Zf87Gc/4zWvec0Kta41GGMYLIQkYKoKkfVEJpQTnTDzVjg+We/ASd8sr0NqDET5WFuf90D4UNs7c47DU2XKE0ewq1exatUg5czz/02F3gHvYaYSJkg7wpCk8czTYz1rimGoxtGqDzeQDfRFnt58zLfG3LY3a45NUrdxgWJPATJHZeIIqTd4O78u/LLeJdyJNT6suofJk4N8RTPvSWdqQ40gxLDnaFalXLGkccpofz+xiTH+2JC5GJjIgHIoJzmYGAqaJNqRrDk29b1UiJmyUHX5GgbZzKzvv9ORf3c6h49iMgeT5QpUHN47bFwk7i1xxCXEMxk9M5P0lwoUIsNUZjlY8cSEXtmBOCSrZeMZRiWgRbpB1yYGv/jFL1i/fv1xvQLnn39+fXu3JwYAPZFlbcExmWcA1sC0Cx0DRQOZr5KeblJQY2x+pwvCRZcLd2SdgbSSD9KOmcZy6JWjDPb2UjERFQejBRivhqFH+8thaMbqYhieMZXBS2VPMfJEJizUlnrHgRkoVj0Fa+jJkwQlCO3JGMNgAker+UJi5EPdIovPakVxl1uNaPYbzVq6z82at+Brg3/y3gnvIAvdZ955KlXHkekpvInoifqoGsvR/HMTmVDetJSv29GXaGhHJ/LeU80c3oX1LCozVUwUE2Up1qdk3lKplhuQGES1CS3Ue16rLiSz1oCJmZmude2G2zvJxFF6Iyj19dNfLFGxIdGecWEifDVvsxIDkc7XtYnB+Pg4Z5111nGP9/f317fPtm/fPvbt2weEJdXL5TIQln0/Vd57nHM45xq6/Pxy27MY7z19kaPHWKYyOJrCeCVcGiURxPkcgeOLl8IpX4QZn19g1S7igDS/i+Yq2MoM3lehNEA1qzCVZoy7COdCAuC9wzlL6sOaCFlerrRoQ9LgPfTHnizzTOeLYMUxeOM5Ug0/a++8qjHNOK7NOP/zJUm4a93J8Tm/nQZIqmWYnsGllmlnKFMIVYhq1VpMI4YH2rDPyjTExZAAGHNsWFFtonOU9ySYCF+dwmcplfI0k0nCTFJiqgqlJNS4r80xgKUfo2Z+5ls1PpvVtmYcy9R5jlYcruqwBtJKhaMzM+Ac05kjyzxFMqrWM12u4OIEiqXGvLmNwuQv70MPVjEKw5Rwee8sIUYzB5GlamKOGM/4DLziqvTGCUMxYKDHeKzJS++ewvFp1+9OkW7XtYkBLD58ZP62e+65h9tvv73+71tvvRWA/fv3N6dxy3TgwIGm7LdMxCQxM0RUgBkKVPBk1dpY69rd2FqC4KAyE1Yni2pDK072RW5mXbjlXRQmzicEJOAmMFGMyzJmbIF94zOkccoMEePVLCxiRRWPIU1TXkmhiMPjmSJhgowCnoSMlCh/h4wZHBmGCTz9J5gk0azj2ixbt24Fuic+AappypFySiUqMWkKTBKFIUS1IWpZFmamN6JSlbH5nV0feg6SQh67tfKTswba+RRcRlpNKUdVDk1O4wtVMiyu6ijgKGM4iKdChclTnHrcbrEJnR+fHpgkVB6K8FQyz3gKvW6G2MBkJWPKlvA+o0qCK/YREs7T7S2oMeG7tzYTzJk89mslI/Khddbk75vPiSFUfKsyQ5ZmWDzZdIUEQ4mUSn01mqVrx/gU6WZdmxgMDAwc1ysAMDExARzrOai56aabuPzyy4HQY/Doo48CsG7dulN+b+89aZoSx3FD78geOHCAtWvXNrTKhPeeSjXliIvpIYzlNwb2ToZuZptUiaspWVrGl6dDxZY4CRdHPg23mfLxrlibV3NZ5K6tc/l2E35p1YYWxUVc3wihHFJCUooZ6SlRyeCVfH5BZFN6bEyU3xDz+aJnMx5W27B4WjnL50dnoaLRYCH8TqzmJVFHCsf3GDT6uDbi/J/sgmr79u1s3bq1o+Oz1s7Mxsw4qExMERUcfSaimnmS6TIVUyDcOq0tcNZAUQSVSoj52clG7a8+/5/3YCOyYj/9xYiRwX68Tcjysr+RDfE3nMDa0tLnuzTzM9+q8dmM2ITGH8uK85gZiH1o69RMhcpkSn/SCz7jV5MpvlolNQk+n59CnC901jDm2J82Dj1b0eylKf2s8r35cDhjwxoxFEmB4SIUbR9n9YSiE/YUjnm7fneKdLuuTQzOO+88fvSjH5Fl2Zx5Bnv27Klvn21sbIyxsTEA9u7dy86dOwGW9YXnvcdai7W24d2htf02ivceZywpYTGx2kJifQlQBUvCDBUyHGmhQJZl4TrM5BVcsgxchpn4FUQGn/RCoRTujNkov8tam8Kcv8Y76r+0jIc0r1aUFImNJ04SLAnlfNGz/rg2+dTSm3d/W0IFmIINw4pWF0ObEuuZyueNFuNwEZb5Wfs5wbFr5HFt5vmvqVbDAPZOjk/vPVUsE1Ww1uCMYdpZnLHMABWTUJ9pbjiWcDZkfYt8bkEye23tvJfApXMmJIdg68UBhVJEmYQ0DZuHC2FegYthtGiIolM/Ns34zLdqfDa7bY06ltZ7jHVYH1YbjvMe1QlvybKIivGkvkLmHTar4qOkQcPcFpLfYKnNOagv9jebrz9usSSEuOyNazdQDNEyj0u7fXeKdLuuneX2pje9ienpaZ544ok5j3//+99ndHSUzZs3r1DLWkvVeY5WYbIKvyqHO+5Tlbz6S95bnRQKuLiIiWOSOApDK7IMKtOYyjTR0f3EEy8RlyeJZsaJjhyEyVegWs4X9Zl3oWZsftFlKCQlhnr76Sv1MtjTw6reXvqihJTQe+H8sVVmh/NrsaoPZUvP7YXz+2CoYMAYrDH0xJb+2LC6BAOJqV+3DSbQo4l1bWc6CzljwRoKSYEyYYXZSurDmGrvwzjqevksv9juTlF+oWVt/l4uvJetjecmvH+SQCHGFmIqFMJAjjzEyw4yA6uKluIykgJpTZElL/kZSiQbm2CtJc0cqfdUM08WJWERsmjWjZJmMbWkAOrJsXMcq/4WbsSYxGCIiCz05aOaeiKWnRSISPvp2h6DN7zhDbzuda/jy1/+MlNTU4yNjfHDH/6Qp59+mltvvbXr1zCAcHfmaDUswGQMHK2GikR21vUWDiKTECXgqxC7lGLsqVarVMvjROUpMAY3uDa/CI/whRibzuCqESSlWZWI/LEZmITfShXCBGIIbejJbwLPzISqGcWIevuMye9yRdCb2PquerxnKg0L+4QhR9AXW2Jr8LHP962koB25PCnMvKdqInqThPFqSuayfIgPzO0daPBq2AbIqvmQjPxq0ISG2UK492qI8iXQHGUHhXyqw1ASet6KFkpKSjtKZAxDieGVLAwTw8JIbwlfKfNSxePxYApQiAkX5flY/2aaveq3c+CqQISJY6LIEmNIiUmo3SgJk+IHEsWmSDfp2sQA4I/+6I+4//77efDBBxkfH2f9+vV85jOf4ZJLLlnpprWEzIeL7tSBz+9wlgh3ORMf5rPZ2h17kxCXEqzvoWgdLi2T9vZAeZLxLCKLCvkY1trOUwrWUsnLPcaxIc1TAENIC/K8g6oPgWo8xB7KHgbjsAJyMYJVhfCagQh6CobCvDuvAzHExtd7FkpRrWtfCUG7K1qYchCZsHAd2HzdvDiPt9kJfm0ycO1CrAGMDe/hMjAZ/YWEBPBRxFQez5GB1M8KfcIlYJyvJq4lCzpTYg3DCdg49EweAg5Ve5iiHCoQkYR5WLOrsDXMQsOFyO+yGJIMVkVFfFykQp6c4pjxoadgKA7D8/pitKaGSJfp6sSgp6eHG2+8kRtvvHGlm9KSMu+ZqIZEwOZzKDMfuscxUPH1wRSkPpRdBJh0FuiFJKGQ9JHgKPkMlxp8rVxedZqengITWKpEeGy480tIPkpx+GU1nZdyHC2Ei/pCBDYvAhMmChsqzhPh6bHhl/F8xpjjypBKZ+iJQvxNZmGYWznN8Fh8YkJGOUc+hKKRyaALQW/jiMFCzGAxzkcuGWYqx3qyChxbQTw2Yc2CsoPEeYa0bkHHMiZfYd3ATNVzaKZCxUP4NpuduDb4+8kBJj3WkzWvp6w/ihgZSHAeplIo2fB9u66QD7EkJKyJ0c0TkW7T1YmBnFiazy1IbBjLn/pw4VXNh2unPvxZu/tp87/nOUN+DyxhJt+ftWHUkCWvCmQ9hbhIwUVYFy6WnA/bLOEiquzCPoejUD2o4sJd1j4besKnU4/JL7QGEoNpbDl3aQPWwEAU7mxWMs+4BZM5PPkF13G9A428yHFhbbMkJiKh7MOyG0mUx2QhXGw5Hyp4VTIYKYTqLuS9CIOJ0aJRXcAYw0yWYXBEJsrXfmnGec/Cl2M6DcRhnQ0bhmhaYhwQYxjtCUPZHGGC8UAcemNLiWnoRHYRaT9KDGRBM1kYmjFSDBfp1RQmqa/5Ssqx3gKo12Op/72WIOT3xaj4cGFUq0EUWcOUD7d7e6I8YXD588jXiCJUFKoY2DcdEoK+HgDDjPGULPTEhsQee710H2MMxnush0IUM5l5DB6Pac61FwCeKIbYWDIiEsJE/MyFJKAUQ09eItcYmE6hbGBNCUqxJfPh86UJ793B+/DtaEOfachom/J9FYWbLoUiznlSW1s5L8ETRhKtK8DqEmQYLKFMbk9eQE69AyKixEAWFRnDcAyV2tAHGy7uaxf6VY71HMxWSxRqv/tq26vUeg0ibD4J09pQWjSd9fzaUjxlgBRKJiQG01koK5rkv8BqkzZrv3ilO01mkJowHjo2MRWfHZu1XiUvG1rrPTjNi58EIgwWQ0oUyuRGefXdvMrQ2h7qQzEwhqL1VKPwmZgJkyEYSEKCLJ0tdR6HoRRFVLHhu7B2x6Thwk6L1hAn4SZLSkJMmI+1rgfO7QtzrOq9u8bo+1NE6pQYyIKSyGBST+p8GBZkQgWVAWAir0Zaqwh/qr9Swq8uwwRQzKvlVRZ8TvhzCrB5b4JNw/hs5z3lDF5yWXjch96FPuMpRbrz1U1qsdAfw9EKJJGlJ7WUCQlpnDjS6gJlcWfL1+UIi+nlf09qZR05NmYugiIJw4XwmXD50LqBOCQmcZQPeSPE4HAhv/jPl+Pw1uCNITYLz4eRzjLjYDL14eaJAWsSEp9SxeBmJ64NY4kTS4mUYmxZkyRkefW2oQKsKh6bjK+6eyKyECUGsqCCNQwVYDwvVzoDYMI8A+/CNdMMy0sMZqtdiy2kdkerNiRpMl8Uqs95ZtIw36HsjlVOGoxgBM9AIVyoKTnoLlmeTQ4XYNqCr4Tr+QyLSTJ8dd7Y6Zh8pdk8AZhdljGpUv96zCfRJIQ47InDuOyRvHTWgUoexy5USCrl62mEco8GY0xYu8BAHBnFZZfIvGc8zeec2HBXviexWFegksGRfJ4WSfXEyUGt67S2PSZ0rS6UVMShhHPJJiSFhIEk9PCmDvoTyLzBeX9KqxeLSPdRYiAnVIoMxkMlhaEo3CHdPx2G/pSi/C5+lk8mXqaT9aZ7wu/GgglrRtWSBGPDuO3U19dCYyqDYR8e74nmXudJ57LGULKeX6UhnlIXksbaWnsheU3yi/3ZZq9afOJgqV2bFSz05vMGBuNj5XRLJgyJq1XJHUigNzL0xEoCupnz4b9aaeTEhjVW0nw9lonp2ndnre91nqT+v7mhmlSBhBLgE6jkt1BiDDEhYa3kpaYN4T0tpmnTnUWksygxWKZ169YRx/GyxmbWXtPIcZ3e+3p7Grlfl+8qsfnd+jhcdBlgKgIzf3JBE6TAkTRccJ1TgsGi4ZdTniyvZlRTIfxCjPC4vLzq6WrGcW3E+Y/jxT+63RCfs9vZF4ceg4kKjGdhzQuXZ521a6qMpH4JdoJLsTkMMGjCHV9HiKeRQugtGE9DIuB9qDg5koRFoWI7NxmYfyxb/ZjW9jv7z+VoVnw24zjW9tfwY+l9KOXsPLENsTEUhx7YNDv2y7dAzFQtaU3JN9TGtB0TUVuZwOXrsYQkuNfYMLE4r4bVF0NfXuWtFIU4rXrPQBT2u9DP1y7xeSZiU6Tb6ROyTB/72McASNPl3y/PssZeVY+MjOCcw7mT3YdfOufIx0eHfVofEgPrIa3O7eU+Nac2+84R5jVUs3AXOPaht4B8XYUsX2NhsuJC70LWuCpFzTiucHrnf2RkZNHt3RKfcKyd/RGc1wt7JuHlvCfLz/qPBf5cSAkoEuGMC+tn5Bdg5TRfbduESlmWvAyvDT1nLiJfZO3kbW2UZh1TaO34bPRxhMYfy9rd+omqw4RpJpQsrCnATApHqmGoW2SALGIK5vxGLuIYsOF7rpJPdemLIDEJ3jhiEypdlWzYj8mTgWoWhtOVory8tAuJQoFj3+Mn0i7x2czYFOl2SgyW6Wtf+xpXXnkla9asOeXXeu/Jsowoiho21MA5x+HDh1m1alVD61Bb5yhmjgqWxIYLox4DR2Zgyh9fjWi2AnMrDc0VusNrcwgW+7UREy7G4gheqoSVOVf3wNEUXqmGCX6Zg6J1GGvDxVpsGjKWthnHtRHn/+DBg4tu74b4XKid/TGsdY7J2UWJ8gpatQnui8VagXChFbtJegp9uHwdD593Mzgb9jNQPDZ52HvPTAbRrBW1l9LW09Wsz3wrx2czjiM0Lz57fUYxtmFxO2so2FDpLYk9QzM+9G4RvlPjNKzTUsjL3iY2TBguRvBylXoP6Ux5klcN99Ebw0szMFkN62V4wu2WNSUYLJiwsNoptLUd4vNMxKZIt1NisEz79+8nTdPT+hI1pnFjkI0x9fY08hemMWHcaiW/M1/MF9E85I6N95/fY1B7vJCP1zhxcrC0ictVIPIQuXCH9nAFzu41DBU9VQ9xvhKydTBaDGPOU28oNqDqS7OOa23fy93nye60dkt81vY9e5+RCf+lhDuo1oS4WZUPtZhOQyzXSue6WX8awlChQmWG4UIfL82EuS1xXtnF5eK68ZcAACAASURBVD1V1oX3MEDFGZIoVB862c/WLse0tv9Wjc9mfM815/szrFsxf5+xhXW9nnTqWC9TKYJz+mB1wVD1MJ56Uhfati72JHmPwNHyFKuKfURRRCn2HK26kJhaGExsvYTz8trbHvHZzNgU6XZKDGRRk6lnwkEhBu8NMy5U2vD52OvMh2ostQv8Wo0XSxhuVDKhZ2GhxMDmz7eEu7CL3cnNCBdzPYQegnIW7rytKXoms3DRNlPN27OEaXaZ9/WhRolFlTo6ROY8Uy5MSO8jxE1tAvtADKOlkGROpvCrSr4WR568FvLx2SNFmK6EOS2vpGHCcSH/bzoLCUFfDNMu9CQkNqxgrBiSpTLGMFq0GBxH89JsQzEMFy3WGIpAwfr8+yx8PxljcM5RxtUvimNrGC1GuHw+gya7i8jpUmIgJ5R5z1QWLpgKNgz6SVPPjAt3rmp3ZrN8DGxCKOWYpnnZ9zgM7ShlcLh6bNhR7e4shBKjxobu8Clqk+vmJgm1SXcx4ULOx8cSkSQKF3rTKRytgqmGqjF9saNgj79TB1B1niPVuYnBUMIJh4FI+yhnntiEicBTab6YnodVxVrJxnwxPR/+7ZO8zKkJset9GLY2hcHaEL+pPxazWT6hc7AQ0ed8vQypkgI5VdYYVpUiVpUW3m7y9S6Wui8RkUZQYiAn5PPKPrNHh8b2WNB4woWS83l99wgGIpg0obegrwA9+RW9IUy2s4QEoUp+wW/C8J8YyPIxSbUEwhB6CIwNF2W1qhuGMKQpi2AmM1g8h2fyoU5RuAO8vwzWOPqTucv4eO8Zr4aKRrUu95nMM5F6hgv65doJrDGsLnombIiFzIehZxY4UPYhEYwN3nl+VQ29YTaPK5+HgMFjTYi76XwCp7Wh12Ewr4OrVYtFRKTTdFRiMDU1xTe/+U1+/vOf88ILL3D06FGuueYaPvShDy34/Oeff557772X5557jiiK2LJlCzfccAPr1q07wy1vTZEJicBMCrEPd0edN5zd6/FT4Q59lq8LVRuDbUyo8V6bCJfUSurlQy/64lBK8mglxZqEog13dws2vFc1n8tQycJrPSE5iU14XWTD+xQiS2Q94DhYDs/rteF9jAl3ecdT6Iv93PKRhG2Jnftzpo2tfigrJPT6eDyGwYKhmtfb7bGGjLD4WKl2QW8Nfc5RzSttQagig4ESGTMuTAS1hCQ086FnqRDZBd5ZRESk/XXUb7jx8XEeeeQRqtUqv/3bv73oc1988UW2bdtGtVrls5/9LJ/85CfZu3cvn/vc5zhy5MgZanFrM8YwlJiwynE+FrsvhuGCZdMAvKoPzu2Ds3tD9YyeOAyzGC3C2T2hB6GSj8keSMIYWpsnGz1kjJZgTW+48zpUMPy/Qdg4AJsH4XWj8JoBWFMMJfoiwkV9JQvjw8uZIzJhXLcnL/lnwpCmNL/Diz9+cnMYrzu3rGSW93hI+ytGhoE4nNOZzGMMDCUmTAwmJJeza6AXYsOaIryq13BOr2G0YBgpQIyjkoWFyoYKhpGCYTgBpyWiRESkg3VUj8HatWt58MEHMcZw9OhRHnnkkRM+94EHHiBJEj7/+c/T29sLwKZNm7j55pt5+OGHuf76689Qq1tbbA1DCdgoTH6LTD721VoGE0/VhSEXhXxYjs1fk1jDcMEzlYWx/ImF0YIPEz4deDLWlWBVae48gN5Z7111nsw4xqth6FExX+G4nMFL055X9Xmm0rB4UMGExMW5MDm5Vsd7/thbY8KF4ytVz0zm8fk8iX4tk9wx+hJLKfZhGNys8f9xPo9gKgVrPI7aglB2zrCg2TXXwxyCkFRgtHKsiIh0to7qMVhqCbMsy3jyySe5+OKL60kBhMRiy5Yt7Nq1q5nNbDvhLr+pr+pacZ6XK2FicuoNFW+IjaEvtvTEtl7fPbKGgcQyUrT0J5b+QsTZvZZzemGEGUYK4cJrKnWMVx3Tmcd5T+Y905nj8IyrD/GI85KTpTj8eTSFzDksoWeiJwl3/VMftg8XQum+hRSjcGe4P4bBONwNTjRevKNEJsTr7MQwJIWGwSQMR+uPYCTvTZivFldVFxLUiguJRs9SZ4OKiIi0oY7qMViqffv2UalU2LBhw3HbNmzYwE9/+lMqlQqFQuHMN64NTFbDfINjk3cdhyue4SRcjBUWuciOrcFiiAn7GK94yvmaCB5PbMLjZRcqFfXlERrlcwzcrGVsjbF5Sb9QiSbxkOVzFtaVFq5IVJNYJQPdyBhD7xIv7vtjiPMeKAP0JOa0asSLiIi0uq5MDMbHxwHo7+8/blt/fz/eeyYmJhgdHT3TTWsLGbPKinpPOQv13Z3zYDwDMQzEoSeh4kJ97VJ0/IV4xYUEoGip1+g+VAkXZEVrqBhPxYVegom8xKTPwnCioQKQDwHKCHd1S3GYfDxUXDwpEFkKYwx9iaVvpRsiIiJyhrRsYrB7925uu+22JT33rrvuYuPGjaf8HotdPM7ftm/fPvbt2weEJdXL5TIwdzzyUnnvcc7hnGvYBWytHctpz2IWamtEuMsfGU85hakslCctZ+GO/kQVJiKPMR6br5Q8VfUMFcJ6CLU2ZvnwjPr7kC9Q5sHm5SKrLtSVJ68K0xdBHIXKRyZf1GcoDvMYUhzF2GJ8GJLUCM04rs04//MlSQJ0dnw26zi2S1vP5Ge+0ZYbnzrn7XPO2+WYishcLZsYnHPOOdxyyy1Leu6aNWtOad8DAwPAsZ6D2SYmJsKdwr659wnvuecebr/99vq/b731VgD2799/Su/dbAcOHGjo/mqX1waohrouODwVYl7GUMFSxWLxJLh8FWPLUQz9VCnke0gxHMXRV1+lAF4+fJAJEuL8dSkwSQGfvy7DMEWMwVHAExGSB0fGJI6phv6ki2v0cW22rVu3Ap0fn83ULm1tl3bOpvg8Pe3STmivtopICycGo6OjXHrppU3Z99jYGIVCgT179hy3bc+ePfXts910001cfvnlQOgxePTRRwGWteaB9540TYnjuKF3Zw4cOMDatWux9vTnlDvv87KgMJOmlLOYqgdMmIxcsqEEKR7Gs1AitJSXiUxdGCY0koTHIDxmgNGiqbf17LVrmHaGyTQkIAZYRUgQwpwDGDahzGnhJGO7m3FMofHHFRrT1pNdUG3fvp2tW7d2bHxC953z+ZrRTmjt+NQ5b91zPl+rHtNWS0ZFWk3LJgbNFEURF110ETt37uT666+vVyY6ePAgu3fv5oorrjjuNWNjY4yNjQGwd+9edu7cCbCsLzzvPdZarG38WPjafk+H956pNCxIZozn5dQymYVgcYRFoIwJEzOHEui3cKgS1hgwJmz3BjITJgh778kIE4lnt81ay0Bs6Yk9Li8tGZkw2bPqPJZQQShewiThZh7TWlsb+cutmW0FqFbDMtKdGJ81OudBI9sJrR2fOudBO53zdjmmIhJ0XGLw1FNPUS6XmZ6eBuCXv/wljz/+OAAXXnghxWIRgA996EN8+tOf5o477uCqq66iUqnw4IMPMjg4yPve974Va38rcISkoGDDfIFqvkhZre572UOB2kW8YSQJY/2nsjAp2VjDSBSGEM1ktQpG0HeCajDzL/xLEar+IiIiInKGdVxi8OUvf3nOmMbHH3+8nhh85Stf4ayzzgJg/fr1fPGLX+S+++7jzjvvJIoiLrjgArZt28bQ0NCKtL0VOUIvQG214MhAluWThPNr98haVhWh34UJxdYaivnFfm2F4dgsPtlbRERERFZWxyUGX/3qV5f83E2bNnHHHXc0sTXtyRJKiJazkBC4PAmIPExXw9+LebWhYnzsgr8UETKH2ftSLiAiIiLSFjouMZDTZ0xYHdYaD/kiYw6IIqh4SGxYa6A3MfRrJVgRERGRjqDEQBZkjWEwMfRHjsEIZgg9B0lk6I0gMkZDg0REREQ6iBIDWZQxhsRCT6wqECIiIiKdrHH1zkREREREpG0pMRARERERESUGIiIiIiKiOQbLtm7dOuI4xnt/yq+tvWY5r11sn7X2NHq/s/9s1D4b3dZmtLO2v1Zsaxwv/tHthvjstnO+0D5b9TPfrPjUOW/dc77QPlvxmJ4sNkW6nT4hy/Sxj30MgDRNl72PLMsa1RwARkZGcM7hnGvofqF92trodkJrtnVkZGTR7d0Un91yzhfSqp/5ZsenznnrnfOFtOIxPVlsinQ7JQbL9LWvfY0rr7ySNWvWnPJrvfdkWUYURQ2r9OOc4/Dhw6xatQprGzdCrF3a2ox2Quu29eDBg4tu74b47LZzPl8rf+abFZ865617zudr1WN6stgU6XZKDJZp//79pGl6Wl+ipoFrARhj6u1pRlnRdmlro/fZqm092Z3WborPbjnnC+2rVT/zzY5PnfPWO+cL7asVj+np9KKKdANNPhYRERERESUGIiIiIiKixEBERERERFBiICIiIiIidNjk42eeeYbHHnuMZ599lkOHDtHX18emTZu45ppr2LRp03HPf/7557n33nt57rnniKKILVu2cMMNN7Bu3boVaL2IiIiIyMrpqB6D7373uxw4cIDLLruMP/3TP+XGG2/kyJEj/MEf/AHPPPPMnOe++OKLbNu2jWq1ymc/+1k++clPsnfvXj73uc9x5MiRFfoJRERERERWRkf1GNx8880MDw/Peez1r389N910E3//93/Pb/zGb9Qff+CBB0iShM9//vP09vYCsGnTJm6++WYefvhhrr/++jPZdBERERGRFdVRPQbzkwKAnp4ezj33XA4dOlR/LMsynnzySS6++OJ6UgCwdu1atmzZwq5du85Ie0VEREREWkVHJQYLmZyc5IUXXuDcc8+tP7Zv3z4qlQobNmw47vkbNmyobxcRERER6RYdnxjcfffdlMtlrr766vpj4+PjAPT39x/3/P7+frz3TExMnLE2ioiIiIistJadY7B7925uu+22JT33rrvuYuPGjcc9fv/99/ODH/yAj3/84wtWJVpsSfX52/bt28e+ffsAOHjwIOVyGQDn3JLaOJv3HucczrmGLRVfa8dy2rOYdmlrM9oJ7dXW2ZIkATo7Prv9nLfTZ36+5canznn7nPN2OaYiMlfLJgbnnHMOt9xyy5Keu2bNmuMe27FjB9/61re47rrreO973ztn28DAAHCs52C2iYkJjDH09fXNefyee+7h9ttvr//71ltvBWD//v1LauOZcuDAgZVuwpKprc2zdetWQPF5Otqlre3SztkUn6enXdoJ7dVWEWnhxGB0dJRLL710Wa/dsWMHO3bs4Nprr50zhKhmbGyMQqHAnj17jtu2Z8+e+vbZbrrpJi6//HIg9Bg8+uijAMta88B7T5qmxHHc0LszBw4cYO3atVjbuBFi7dLWZrQTWretJ7ug2r59O1u3bu3o+Oy2cz5fK3/mmxWfOuete87na9Vj2mrJqEiradnEYLkeeughduzYwdVXX82111674HOiKOKiiy5i586dXH/99fXKRAcPHmT37t1cccUVx71mbGyMsbExAPbu3cvOnTsBlvWF573HWou1tuHdobX9Nkq7tLWZ7YT2aitAtVoFOjs+dc6DdvrM1yw3PnXOg3Y65+1yTEUk6KjE4OGHH+bBBx/k9a9/PRdeeCHPPvvsnO2vfvWr63//0Ic+xKc//WnuuOMOrrrqKiqVCg8++CCDg4O8733vO9NNFxERERFZUR2VGDz55JMAPP300zz99NPHbf/2t79d//v69ev54he/yH333cedd95JFEVccMEFbNu2jaGhoTPWZhERERGRVtBRicGf//mfn9LzN23axB133NGk1oiIiIiItI+OX8dAREREREROTomBiIiIiIgoMRARERERESUGIiIiIiKCEgMREREREUGJgYiIiIiI0GHlSs+kdevWEccx3vtTfm3tNct57WL7rLWn0fud/Wej9tnotjajnbX9tWJb43jxj243xGe3nfOF9tmqn/lmxafOeeue84X22YrH9GSxKdLt9AlZpo997GMApGm67H1kWdao5gAwMjKCcw7nXEP3C+3T1ka3E1qzrSMjI4tu76b47JZzvpBW/cw3Oz51zlvvnC+kFY/pyWJTpNspMVimr33ta1x55ZWsWbPmlF/rvSfLMqIowhjTkPY45zh8+DCrVq3C2saNEGuXtjajndC6bT148OCi27shPrvtnM/Xyp/5ZsWnznnrnvP5WvWYniw2RbqdEoNl2r9/P2mantaXqDGmYV/Cxph6exr5C3P2/tuhrY3eZ6u29WR3WrspPrvlnC+0r1b9zDc7PnXOW++cL7SvVjymp9OLKtINNPlYRERERESUGIiIiIiIiBIDERERERFBiYGIiIiIiNBhk49feOEF7r//fvbs2cPRo0cpFAqcc845vOc97+Ftb3vbcc9//vnnuffee3nuueeIoogtW7Zwww03sG7duhVovYiIiIjIyumoxGBycpLVq1fzlre8hVWrVjEzM8Njjz3GX/3VX3HgwAF+93d/t/7cF198kW3btnH++efz2c9+lkqlwoMPPsjnPvc5/vqv/5qhoaEV/ElERERERM6sjkoMtmzZwpYtW+Y89sY3vpGXXnqJf/mXf5mTGDzwwAMkScLnP/95ent7Adi0aRM333wzDz/8MNdff/2ZbLqIiIiIyIrqijkGg4ODRFFU/3eWZTz55JNcfPHF9aQAYO3atWzZsoVdu3atRDNFRERERFZMRyYGzjmyLOPIkSN85zvf4b/+67/4wAc+UN++b98+KpUKGzZsOO61GzZsqG8XEREREekWHTWUqObuu+/me9/7HgBxHHPjjTfy7ne/u759fHwcgP7+/uNe29/fj/eeiYkJRkdH64/v27ePffv2AWFJ9XK5DIQk5FR573HO4Zxr6PLzy23PYtqlrc1oJ7RXW2dLkgTo7Pjs9nPeTp/5+ZYbnzrn7XPO2+WYishcLZsY7N69m9tuu21Jz73rrrvYuHFj/d8f/OAHede73sWRI0f4yU9+wt/8zd8wMzPD+9///jmvW+yLZf62e+65h9tvv73+71tvvRWA/fv3L6mNZ8qBAwdWuglLprY2z9atWwHF5+lol7a2SztnU3yennZpJ7RXW0WkhRODc845h1tuuWVJz12zZs1x/649duGFFwLw9a9/nbe//e0MDQ0xMDAAHOs5mG1iYgJjDH19fXMev+mmm7j88suB0GPw6KOPAiyrtKn3njRNieO4oXdnDhw4wNq1a7G2cSPE2qWtzWgntG5bT3ZBtX37drZu3drR8dlt53y+Vv7MNys+dc5b95zP16rHtNWSUZFW07KJwejoKJdeemlD9rV582a+973vsX//foaGhhgbG6NQKLBnz57jnrtnz5769tnGxsYYGxsDYO/evezcuZNDhw4tqz1xHDMyMsLhw4dJ03RZ+ziRRt+daZe2NrOd0HptPVnsVavVjo/PbjvnJ9KKn/lmxafOedCK5/xEWu2YLvd7UaRbtGxi0Ei7d+/GWlu/OxVFERdddBE7d+7k+uuvr1cmOnjwILt37+aKK65YdH+9vb0kScI//uM/Nr3tSzU+Ps5TTz3FG97whnqPSKtSWxsjSZI5VbVqFJ+np13a2urtVHw2Xru0E1q7rSeKTREB4733K92IRvnSl75Eb28vmzdvZnh4mKNHj/L444/zox/9iPe///31ca0QFjj79Kc/zcaNG7nqqqvqC5yNj48vaYGzV155hampqWb/SEu2e/du3v3ud/O9733vuLUcWo3a2hi9vb0MDw8vuE3xuXzt0tZWb6fis/HapZ3Q2m1dLDZFul1H9Ri8+tWv5t/+7d/4/ve/z+TkJKVSifPPP59PfepTvO1tb5vz3PXr1/PFL36R++67jzvvvJMoirjgggvYtm3bklY9Hh4ebqkvltq4yTVr1nD22WevcGsWp7Y2n+Jz+dqlre3SzoUoPpenXdoJ7dVWETmmoxKDd77znbzzne9c8vM3bdrEHXfc0cQWiYiIiIi0h+gLX/jCF1a6EdIY/f39vPWtb2258ZwLUVu7Tzsdx3Zpa7u0sx20y7Fsl3ZCe7VVRIKOmmMgIiIiIiLL07hCyCIiIiIi0raUGIiIiIiISGdNPu5GzzzzDI899hjPPvsshw4doq+vj02bNnHNNdewadOm457//PPPc++99/Lcc88RRRFbtmzhhhtuWNYKuadqenqa+++/n8cff5zx8XHWr1/PBz7wAS655JKmv/dCTuXYreRxa2eKz+VTfDZfu8Rnq8UmKD5FOpUmH7e5e++9l5dffpl3vvOdXHHFFfz6r/86zzzzDN/85jd5zWteM+eL98UXX+QP//APGRwc5MYbb+TCCy/kiSee4Dvf+Q5ve9vbKJVKTW3rHXfcwVNPPcV1113HZZddxsTEBN/4xjc4++yz2bBhQ1PfeyFLPXYrfdzameJz+RSfzdcu8dlqsQmKT5GO5aWtvfzyy8c9NjU15T/ykY/4P/7jP57z+J133uk//OEP+8nJyfpjL730kn//+9/vt2/f3tR2Pvnkk/6yyy7zjz322JzH/+RP/sR/9KMf9WmaNvX9F7LUY7eSx63dKT6XT/HZfO0Qn60Ym94rPkU6leYYtLmFFgnq6enh3HPP5dChQ/XHsizjySef5OKLL56zFPzatWvZsmULu3btamo7d+3aRU9PD29+85vnPP6Od7yDX/3qV/zsZz9r6vsvZCnHbqWPW7tTfC6f4rP52iE+WzE2QfEp0qmUGHSgyclJXnjhBc4999z6Y/v27aNSqSzY7bxhw4b69mb5xS9+wfr164miaM7j559/fn17K5h/7Fb6uHUixefyKT6br9Xis11iExSfIp1AiUEHuvvuuymXy1x99dX1x8bHx4Gw4Mx8/f39eO+ZmJhoWpvGx8dP+N6z27fS5h+7lT5unUjxuXyKz+Zrtfhsl9gExadIJ1BVohaye/dubrvttiU996677mLjxo3HPX7//ffzgx/8gI9//OMLVtUwxpxwn4tta4SVfO+lWOzYtXrbzwTF58pSfC6uk+OzHc6v4lOkMygxaCHnnHMOt9xyy5Keu2bNmuMe27FjB9/61re47rrreO973ztnW21J+oXuLk1MTGCMoa+vbxmtXpqBgYETvjcsfEfpTDrRsVvp49ZKFJ8rR/F5cp0an60em6D4FOkkSgxayOjoKJdeeumyXrtjxw527NjBtddeO6cLvGZsbIxCocCePXuO27Znz5769mY577zz+NGPfkSWZXPGytbac9555zXtvU9msWO30setlSg+V4bic2k6NT5bOTZB8SnSaTTHoAM89NBD7Nixg6uvvpprr712wedEUcRFF13Ezp07mZqaqj9+8OBBdu/ezZve9KamtvFNb3oT09PTPPHEE3Me//73v8/o6CibN29u6vufyMmO3Uoft06g+Fw+xWfztXp8tmpsguJTpBNpgbM29/DDD/ONb3yD17/+9Vx66aUcOnRozn+rV6+uP/e8887ju9/9Lrt372ZkZIRf/OIXfOlLX8IYw6c+9ammLjRz9tln8z//8z888sgj9Pf3MzU1xd/93d/x4x//mE984hMLjvdttqUeu5U8bu1O8bl8is/ma4f4bMXYBMWnSKcy3nu/0o2Q5du2bRv//d//fcLt3/72t+f8+//+7/+47777ePbZZ4miiAsuuICtW7cyNjbW7KYyPT3N/fffz49//GPGx8dZv349V111FZdccknT33shp3LsVvK4tTPF5/IpPpuvXeKz1WITFJ8inUqJgYiIiIiIaI6BiIiIiIgoMRAREREREZQYiIiIiIgISgxERERERAQlBiIiIiIighIDERERERFBiYGIiIiIiKDEQEREREREUGIgIi3OGDPnP2stw8PDvOUtb+GrX/0qi63R+B//8R/ceOONbN68mYGBAUqlEhs2bODqq6/m4Ycfxjm3pDZkWca3vvUtPvOZz/CWt7yFvr4+jDHcfPPNjfoxRUREVpxWPhaRlmaMAeCjH/0oEC7Sn3/+eXbt2oX3nmuuuYYdO3bMeU21WuX3f//3+cpXvgLA5s2bee1rX0uhUODnP/85Tz31FM453v72t/Pv//7vJ23DK6+8wsjIyHGP33TTTdx9992n+yOKiIi0hHilGyAishT33nvvnH//67/+K+95z3t46KGH+PCHP8x73/ve+ratW7fywAMPsHnzZrZv387FF18857V79+7lz/7sz3jkkUeW9N5JkvCRj3yEN77xjbzxjW/kpz/9KZ/4xCdO+2cSERFpJUoMRKQtvetd7+IjH/kI27dv55/+6Z/qicE//MM/8MADD3DWWWfxwx/+kLPOOuu415599tncfffdPP7440t6r76+Pr7+9a/X//3ss8825ocQERFpIZpjICJt6zd/8zcB+OUvf1l/7C/+4i8A+MIXvrBgUjDb7/zO7zSvcSIiIm1GiYGItK3x8XEAisUiAIcOHeInP/kJxhiuueaalWyaiIhI21FiICJtyXvPP//zPwNwwQUXAPDTn/4U7z0bN25keHh4JZsnIiLSdpQYiEhbybKM//3f/+WGG25g586dFItFtm7dCsDhw4cBWLNmzUo2UUREpC1p8rGItIVa2dLZBgYGuO+++/i1X/s1gEXXNBAREZHFKTEQkbZQW8fAWsvg4CBbtmzhyiuvnLO+wOrVqwE4ePDgirRRRESknSkxEJG2MH8dg4W87nWvA+CFF17glVde0TwDERGRU6A5BiLSMVavXs1FF12E956HHnpopZsjIiLSVpQYiEhH+cxnPgOEdQwOHDiw6HOfeOKJM9EkERGRtqDEQEQ6ygc/+EGuueYaXnrpJS655BJ27tx53HP279/PLbfcwnXXXbcCLRQREWlNxquMh4i0sFo1olP5qqpWq9x888387d/+LQCvfvWree1rX0uSJOzZs4f//M//JMsy3vWud/HII48saZ+/93u/x9NPPw2Eyc0vvPACa9eu5fzzz68/Z9eufv5khgAAALhJREFUXUtuo4iISKtRYiAiLW05iUHNzp07+epXv8oPf/hD9u7dS5ZlrFu3jt/6rd/iwx/+MJdddtmCZVAX8ta3vpUf/OAHiz5HX6ciItLOlBiIiIiIiIjmGIiIiIiIiBIDERERERFBiYGIiIiIiKDEQEREREREUGIgIiIiIiIoMRAREREREZQYiIiIiIgISgxERERERAQlBiIiIiIighIDERERERFBiYGIiIiIiKDEQEREREREgP8fWkWbK8bS1r8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (8775087183389)>\n"
     ]
    }
   ],
   "source": [
    "# Plot all comparisons in one figure\n",
    "panel_B = ggplot(all_data_df[all_data_df['Comparison'] != '1'],\n",
    "                 aes(x='PC1', y='PC2')) \\\n",
    "    + geom_point(aes(color='No. of experiments'), \n",
    "                 alpha=0.2) \\\n",
    "    + facet_wrap('~Comparison') \\\n",
    "    + labs(x = \"PC 1\", \n",
    "           y = \"PC 2\", \n",
    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
    "    + theme_bw() \\\n",
    "    + theme(\n",
    "        legend_title_align = \"center\",\n",
    "        plot_background=element_rect(fill='white'),\n",
    "        legend_key=element_rect(fill='white', colour='white'), \n",
    "        legend_text=element_text(family='sans-serif', size=12),\n",
    "        plot_title=element_text(family='sans-serif', size=15),\n",
    "        axis_text=element_text(family='sans-serif', size=12),\n",
    "        axis_title=element_text(family='sans-serif', size=15)\n",
    "    ) \\\n",
    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
    "    + scale_color_manual(['#bdbdbd', '#b3e5fc']) \\\n",
    "    + geom_point(data=all_data_df[all_data_df['Comparison'] == '1'],\n",
    "                 alpha=0.1, \n",
    "                 color='#bdbdbd')\n",
    "\n",
    "print(panel_B)\n",
    "ggsave(plot=panel_B, filename=pca_uncorrected_file, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Corrected PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting PCA of 1 experiment vs 1 experiments...\n",
      "Plotting PCA of 1 experiment vs 2 experiments...\n",
      "Plotting PCA of 1 experiment vs 5 experiments...\n",
      "Plotting PCA of 1 experiment vs 100 experiments...\n",
      "Plotting PCA of 1 experiment vs 250 experiments...\n",
      "Plotting PCA of 1 experiment vs 500 experiments...\n"
     ]
    }
   ],
   "source": [
    "lst_num_experiments = [lst_num_experiments[i] for i in pca_ind]\n",
    "\n",
    "all_corrected_data_df = pd.DataFrame()\n",
    "\n",
    "# Get batch 1 data\n",
    "experiment_1_file = os.path.join(\n",
    "    compendia_dir,\n",
    "    \"Experiment_corrected_1_0.txt.xz\")\n",
    "\n",
    "experiment_1 = pd.read_table(\n",
    "    experiment_1_file,\n",
    "    header=0,\n",
    "    index_col=0,\n",
    "    sep='\\t')\n",
    "\n",
    "# Transpose data to df: sample x gene\n",
    "experiment_1 = experiment_1.T\n",
    "\n",
    "for i in lst_num_experiments:\n",
    "    print('Plotting PCA of 1 experiment vs {} experiments...'.format(i))\n",
    "    \n",
    "     # Simulated data with all samples in a single batch\n",
    "    original_data_df =  experiment_1.copy()\n",
    "    \n",
    "    # Match format of column names in before and after df\n",
    "    original_data_df.columns = original_data_df.columns.astype(str)\n",
    "    \n",
    "    # Add grouping column for plotting\n",
    "    original_data_df['num_experiments'] = '1'\n",
    "    \n",
    "    # downsample\n",
    "    original_data_df = original_data_df.sample(n=500)\n",
    "    \n",
    "    # Get data with additional batch effects added and corrected\n",
    "    experiment_other_file = os.path.join(\n",
    "        compendia_dir,\n",
    "        \"Experiment_corrected_\"+str(i)+\"_0.txt.xz\")\n",
    "\n",
    "    experiment_other = pd.read_table(\n",
    "        experiment_other_file,\n",
    "        header=0,\n",
    "        index_col=0,\n",
    "        sep='\\t')\n",
    "    \n",
    "    # Transpose data to df: sample x gene\n",
    "    experiment_other = experiment_other.T\n",
    "    \n",
    "    # Simulated data with i batch effects that are corrected\n",
    "    experiment_data_df =  experiment_other\n",
    "    \n",
    "    # Match format of column names in before and after df\n",
    "    experiment_data_df.columns = experiment_data_df.columns.astype(str)\n",
    "    \n",
    "    # Add grouping column for plotting\n",
    "    experiment_data_df['num_experiments'] = 'multiple'\n",
    "    \n",
    "    # downsample\n",
    "    experiment_data_df = experiment_data_df.sample(n=500)\n",
    "        \n",
    "    # Concatenate datasets together\n",
    "    combined_data_df = pd.concat([original_data_df, experiment_data_df])\n",
    "    \n",
    "    # PCA projection\n",
    "    pca = PCA(n_components=2)\n",
    "\n",
    "    # Encode expression data into 2D PCA space    \n",
    "    combined_data_numeric_df = combined_data_df.drop(['num_experiments'], axis=1)    \n",
    "    combined_data_PCAencoded = pca.fit_transform(combined_data_numeric_df)\n",
    "\n",
    "    \n",
    "    combined_data_PCAencoded_df = pd.DataFrame(combined_data_PCAencoded,\n",
    "                                               index=combined_data_df.index,\n",
    "                                               columns=['PC1', 'PC2']\n",
    "                                              )\n",
    "    \n",
    "    # Add back in batch labels (i.e. labels = \"batch_\"<how many batch effects were added>)\n",
    "    combined_data_PCAencoded_df['num_experiments'] = combined_data_df['num_experiments']\n",
    "    \n",
    "    # Add column that designates which batch effect comparision (i.e. comparison of 1 batch vs 5 batches\n",
    "    # is represented by label = 5)\n",
    "    combined_data_PCAencoded_df['comparison'] = str(i)\n",
    "    \n",
    "    # Concatenate ALL comparisons\n",
    "    all_corrected_data_df = pd.concat([all_corrected_data_df, combined_data_PCAencoded_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'num_experiments' into categories to preserve the ordering\n",
    "lst_num_experiments_str = [str(i) for i in lst_num_experiments]\n",
    "num_experiments_cat = pd.Categorical(all_corrected_data_df['num_experiments'], categories=['1', 'multiple'])\n",
    "\n",
    "# Convert 'comparison' into categories to preserve the ordering\n",
    "comparison_cat = pd.Categorical(all_corrected_data_df['comparison'], categories=lst_num_experiments_str)\n",
    "\n",
    "# Assign to a new column in the df\n",
    "all_corrected_data_df = all_corrected_data_df.assign(num_experiments_cat = num_experiments_cat)\n",
    "all_corrected_data_df = all_corrected_data_df.assign(comparison_cat = comparison_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_corrected_data_df.columns = ['PC1', 'PC2', 'num_experiments', 'comparison', 'No. of experiments', 'Comparison']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363261821295868)>\n"
     ]
    }
   ],
   "source": [
    "# Plot all comparisons in one figure\n",
    "panel_C = ggplot(all_corrected_data_df[all_corrected_data_df['Comparison'] != '1'],\n",
    "                 aes(x='PC1', y='PC2')) \\\n",
    "    + geom_point(aes(color='No. of experiments'), \n",
    "                 alpha=0.2) \\\n",
    "    + facet_wrap('~Comparison') \\\n",
    "    + labs(x = \"PC 1\",\n",
    "           y = \"PC 2\", \n",
    "           title = \"PCA of experiment 1 vs multiple experiments\") \\\n",
    "    + theme_bw() \\\n",
    "    + theme(\n",
    "        legend_title_align = \"center\",\n",
    "        plot_background=element_rect(fill='white'),\n",
    "        legend_key=element_rect(fill='white', colour='white'), \n",
    "        legend_text=element_text(family='sans-serif', size=12),\n",
    "        plot_title=element_text(family='sans-serif', size=15),\n",
    "        axis_text=element_text(family='sans-serif', size=12),\n",
    "        axis_title=element_text(family='sans-serif', size=15)\n",
    "    )\\\n",
    "    + guides(colour=guide_legend(override_aes={'alpha': 1})) \\\n",
    "    + scale_color_manual(['#bdbdbd', '#1976d2']) \\\n",
    "    + geom_point(data=all_corrected_data_df[all_corrected_data_df['Comparison'] == '1'],\n",
    "                 alpha=0.1, \n",
    "                 color='#bdbdbd')\n",
    "\n",
    "print(panel_C)\n",
    "ggsave(plot=panel_C, filename=pca_corrected_file, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}