StartingKit_HiggsML_Uncertainty_Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting Kit - FAIR UNIVERSE: HIGGSML UNCERTAINTY CHALLENGE\n",
    "\n",
    "For Overview and Descriptions of the competition, please visit the [competition page](https://www.codabench.org/competitions/2977/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`COLAB` determines whether this notebook is running on Google Colab.\n",
    "For local setup check : [Local Setup](./pages/Local_setup.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLAB = 'google.colab' in str(get_ipython())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if COLAB:\n",
    "    # clone github repo\n",
    "    !git clone --depth 1 https://github.com/FAIR-Universe/HEP-Challenge.git\n",
    "\n",
    "    # move to the HEP starting kit folder\n",
    "    %cd HEP-Challenge/\n",
    "\n",
    "    # Install required packages\n",
    "    # %pip install -r conda/requirements.txt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy_Submission\n",
    "By this point you should have a clone of the repo which contains `HiggsML_Dummy_Submission.zip` which you can submit to the Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard library imports\n",
    "import datetime\n",
    "import json\n",
    "import os\n",
    "from contextlib import closing\n",
    "from sys import path\n",
    "from zipfile import ZipFile, ZIP_DEFLATED\n",
    "\n",
    "# Third-party imports\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root directory is /home/chakkappai/Work/Fair-Universe/HEP-Challenge\n"
     ]
    }
   ],
   "source": [
    "\n",
    "root_dir = os.getcwd()\n",
    "print(\"Root directory is\", root_dir)\n",
    "\n",
    "# The directory of the input data\n",
    "input_dir = os.path.join(root_dir, \"input_data\")\n",
    "\n",
    "# The directory where results and other outputs from the participant's code will be written\n",
    "output_dir = os.path.join(root_dir, \"sample_result_submission\") \n",
    "\n",
    "# The directory of the ingestion program \n",
    "program_dir = os.path.join(root_dir, \"ingestion_program\") \n",
    "\n",
    "# The directory of the scoring program\n",
    "score_dir = os.path.join(root_dir, \"scoring_program\")\n",
    "\n",
    "# Add the directory to the path\n",
    "path.append(program_dir) \n",
    "path.append(score_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from systematics import systematics # import the systematics function from the ingestion program"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Data\n",
    "***\n",
    "\n",
    "⚠️ Note:\n",
    "\n",
    "By default the repository has a sample dataset at [input_data](./input_data/). This is a small sample data is for demonstration only to get a view of what the data looks like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Training and inference you may want to use the much Bigger public dataset. For this set `USE_PUBLIC_DATASET` to `True` and the code below uses the  `Neurips2024_public_dataset()` funtion in the `dataset` module to download and read the public dataset. else the code loads the small sample data for quick execution of this notebook\n",
    "\n",
    "\n",
    "⚠️ Note:\n",
    "\n",
    "If you have already downloaded the public_data from the competition website, make sure that it is placed in the right folder `HEP-Challenge/` so that you don't download it again. Setting `USE_PUBLIC_DATASET` to `True` will download the public_data if not found in the `HEP-Challenge` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_PUBLIC_DATASET = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`USE_PUBLIC_DATASET` determines whether to use a public dataset provided for the participants or use a small subset of the data for quick execution of this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import Data # import the Data class from the ingestion program\n",
    "\n",
    "if USE_PUBLIC_DATASET:\n",
    "    from datasets import Neurips2024_public_dataset as public_dataset\n",
    "    data = public_dataset()\n",
    "else:\n",
    "    data = Data(input_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function loads the downloaded data in the public_data folder or downloads the data from codabench using `wget` in the absence of the downloaded data, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:09,849 - datasets             - INFO     - Train data loaded successfully\n"
     ]
    }
   ],
   "source": [
    "# load train set\n",
    "data.load_train_set() # load the training set to memory, this may take a few seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:09,908 - datasets             - INFO     - Test data loaded successfully\n"
     ]
    }
   ],
   "source": [
    "# load test sets\n",
    "data.load_test_set() # load the test set to memory, this may take a few seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Visualize\n",
    "***\n",
    "The purpose of the `Dataset_visualise` class is to visulaise the training dataset,\n",
    "data is the dictionary of different fields like dataframe, label, weights etc. upon initiallisation you will see a table of these fields\n",
    "\n",
    "Initialisation you can give a list a columns of Data you are interested to have plots for. If no inputs are given then all the columns of the DataFrame are taken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "General Structure of the data object is a dictionary\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| Key             | Value                           | Type                                |\n",
      "+=================+=================================+=====================================+\n",
      "| labels          | Array of shape (198748,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| weights         | Array of shape (198748,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| detailed_labels | Array of shape (198748,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| data            | DataFrame of shape (198748, 28) | DataFrame                           |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| settings        | Dictionary with 7 keys          | <class 'dict'>                      |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "import visualization as viz\n",
    "\n",
    "data_vis = data.get_train_set()\n",
    " \n",
    "# when No systematics parameters are passed, the systematics function will return the original dataset with post process cuts\n",
    "data_vis = systematics(data_vis) \n",
    "train_visualize = viz.Dataset_visualise(\n",
    "    data_set=data_vis,\n",
    "    columns=[\n",
    "        \"PRI_met\",\n",
    "        \"PRI_had_pt\",\n",
    "        \"DER_mass_vis\",\n",
    "        \"DER_mass_transverse_met_lep\",\n",
    "        \"DER_deltar_had_lep\",\n",
    "\n",
    "    ],\n",
    "    name=\"Train Set\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `examine_dataset` methods give you more general infomation as tables to give an idea of the general nature of the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "+--------------------+-----------+\n",
      "| Key                | Value     |\n",
      "+====================+===========+\n",
      "| Dataset name       | Train Set |\n",
      "+--------------------+-----------+\n",
      "| Number of events   | 198748    |\n",
      "+--------------------+-----------+\n",
      "| Number of features | 28        |\n",
      "+--------------------+-----------+ \n",
      "\n",
      "[*] --- Detailed Label Summary\n",
      "+------------------+----------------+--------------------+\n",
      "| Detailed Label   |   Total Weight |   Number of events |\n",
      "+==================+================+====================+\n",
      "| diboson          |       3397.27  |                713 |\n",
      "+------------------+----------------+--------------------+\n",
      "| htautau          |        972.486 |                204 |\n",
      "+------------------+----------------+--------------------+\n",
      "| ttbar            |      41132.7   |               8638 |\n",
      "+------------------+----------------+--------------------+\n",
      "| ztautau          |     900920     |             189193 |\n",
      "+------------------+----------------+--------------------+\n",
      "| Total Signal     |        972.486 |                204 |\n",
      "+------------------+----------------+--------------------+\n",
      "| Total Background |     945450     |             198544 |\n",
      "+------------------+----------------+--------------------+\n",
      "\n",
      "[*] --- Examples of all features\n",
      "\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRI_lep_pt</th>\n",
       "      <th>PRI_lep_eta</th>\n",
       "      <th>PRI_lep_phi</th>\n",
       "      <th>PRI_had_pt</th>\n",
       "      <th>PRI_had_eta</th>\n",
       "      <th>PRI_had_phi</th>\n",
       "      <th>PRI_jet_leading_pt</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>...</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_had_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>DER_sum_pt</th>\n",
       "      <th>DER_pt_ratio_lep_had</th>\n",
       "      <th>DER_met_phi_centrality</th>\n",
       "      <th>DER_lep_eta_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.412001</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>1.975</td>\n",
       "      <td>28.997999</td>\n",
       "      <td>0.351</td>\n",
       "      <td>-1.838</td>\n",
       "      <td>54.056000</td>\n",
       "      <td>1.636</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>26.354</td>\n",
       "      <td>...</td>\n",
       "      <td>24.689272</td>\n",
       "      <td>1.513</td>\n",
       "      <td>95.622383</td>\n",
       "      <td>5.151764</td>\n",
       "      <td>2.631094</td>\n",
       "      <td>12.235117</td>\n",
       "      <td>134.820007</td>\n",
       "      <td>0.876336</td>\n",
       "      <td>0.701390</td>\n",
       "      <td>2.553498e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.563000</td>\n",
       "      <td>0.586</td>\n",
       "      <td>3.058</td>\n",
       "      <td>28.274000</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.905827</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>2.977730</td>\n",
       "      <td>4.905827</td>\n",
       "      <td>67.836998</td>\n",
       "      <td>1.399271</td>\n",
       "      <td>-1.390982</td>\n",
       "      <td>-2.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.320000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>2.521</td>\n",
       "      <td>29.041000</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>-0.864</td>\n",
       "      <td>26.881001</td>\n",
       "      <td>-1.195</td>\n",
       "      <td>0.180</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.722971</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>3.280057</td>\n",
       "      <td>12.030704</td>\n",
       "      <td>78.242004</td>\n",
       "      <td>0.768569</td>\n",
       "      <td>1.092476</td>\n",
       "      <td>-2.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.546000</td>\n",
       "      <td>2.164</td>\n",
       "      <td>1.908</td>\n",
       "      <td>38.452999</td>\n",
       "      <td>0.426</td>\n",
       "      <td>-2.151</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>...</td>\n",
       "      <td>40.089237</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>2.822702</td>\n",
       "      <td>40.089237</td>\n",
       "      <td>62.999001</td>\n",
       "      <td>0.638338</td>\n",
       "      <td>1.252621</td>\n",
       "      <td>-2.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.172001</td>\n",
       "      <td>-1.224</td>\n",
       "      <td>1.808</td>\n",
       "      <td>34.316002</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-1.314</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.194582</td>\n",
       "      <td>-25.000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>3.431422</td>\n",
       "      <td>5.194582</td>\n",
       "      <td>63.488003</td>\n",
       "      <td>0.850099</td>\n",
       "      <td>1.412232</td>\n",
       "      <td>-2.500000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "   PRI_lep_pt  PRI_lep_eta  PRI_lep_phi  PRI_had_pt  PRI_had_eta  PRI_had_phi  \\\n",
       "0   25.412001       -0.555        1.975   28.997999        0.351       -1.838   \n",
       "1   39.563000        0.586        3.058   28.274000        0.735        0.084   \n",
       "2   22.320000        0.801        2.521   29.041000       -0.735       -0.864   \n",
       "3   24.546000        2.164        1.908   38.452999        0.426       -2.151   \n",
       "4   29.172001       -1.224        1.808   34.316002        0.200       -1.314   \n",
       "\n",
       "   PRI_jet_leading_pt  PRI_jet_leading_eta  PRI_jet_leading_phi  \\\n",
       "0           54.056000                1.636               -0.244   \n",
       "1          -25.000000              -25.000              -25.000   \n",
       "2           26.881001               -1.195                0.180   \n",
       "3          -25.000000              -25.000              -25.000   \n",
       "4          -25.000000              -25.000              -25.000   \n",
       "\n",
       "   PRI_jet_subleading_pt  ...   DER_pt_h  DER_deltaeta_jet_jet  \\\n",
       "0                 26.354  ...  24.689272                 1.513   \n",
       "1                -25.000  ...   4.905827               -25.000   \n",
       "2                -25.000  ...  17.722971               -25.000   \n",
       "3                -25.000  ...  40.089237               -25.000   \n",
       "4                -25.000  ...   5.194582               -25.000   \n",
       "\n",
       "   DER_mass_jet_jet  DER_prodeta_jet_jet  DER_deltar_had_lep  DER_pt_tot  \\\n",
       "0         95.622383             5.151764            2.631094   12.235117   \n",
       "1        -25.000000           -25.000000            2.977730    4.905827   \n",
       "2        -25.000000           -25.000000            3.280057   12.030704   \n",
       "3        -25.000000           -25.000000            2.822702   40.089237   \n",
       "4        -25.000000           -25.000000            3.431422    5.194582   \n",
       "\n",
       "   DER_sum_pt  DER_pt_ratio_lep_had  DER_met_phi_centrality  \\\n",
       "0  134.820007              0.876336                0.701390   \n",
       "1   67.836998              1.399271               -1.390982   \n",
       "2   78.242004              0.768569                1.092476   \n",
       "3   62.999001              0.638338                1.252621   \n",
       "4   63.488003              0.850099                1.412232   \n",
       "\n",
       "   DER_lep_eta_centrality  \n",
       "0            2.553498e-07  \n",
       "1           -2.500000e+01  \n",
       "2           -2.500000e+01  \n",
       "3           -2.500000e+01  \n",
       "4           -2.500000e+01  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[*] --- Description of all features\n",
      "\n"
     ]
    },
    {
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       "      <th>PRI_lep_pt</th>\n",
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       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_had_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>DER_sum_pt</th>\n",
       "      <th>DER_pt_ratio_lep_had</th>\n",
       "      <th>DER_met_phi_centrality</th>\n",
       "      <th>DER_lep_eta_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "      <td>198748.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.054996</td>\n",
       "      <td>-0.000968</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>40.249287</td>\n",
       "      <td>-0.001081</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>3.693349</td>\n",
       "      <td>-16.965092</td>\n",
       "      <td>-16.972872</td>\n",
       "      <td>-15.893928</td>\n",
       "      <td>...</td>\n",
       "      <td>29.902918</td>\n",
       "      <td>-21.682478</td>\n",
       "      <td>3.686550</td>\n",
       "      <td>-21.877384</td>\n",
       "      <td>2.829471</td>\n",
       "      <td>16.627081</td>\n",
       "      <td>101.731911</td>\n",
       "      <td>0.821258</td>\n",
       "      <td>0.144721</td>\n",
       "      <td>-21.906357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.441554</td>\n",
       "      <td>1.251236</td>\n",
       "      <td>1.814227</td>\n",
       "      <td>15.207740</td>\n",
       "      <td>1.265052</td>\n",
       "      <td>1.812097</td>\n",
       "      <td>49.492981</td>\n",
       "      <td>11.715323</td>\n",
       "      <td>11.702966</td>\n",
       "      <td>26.580822</td>\n",
       "      <td>...</td>\n",
       "      <td>37.783791</td>\n",
       "      <td>8.965435</td>\n",
       "      <td>107.350243</td>\n",
       "      <td>8.889267</td>\n",
       "      <td>0.538228</td>\n",
       "      <td>14.000933</td>\n",
       "      <td>82.928406</td>\n",
       "      <td>0.382247</td>\n",
       "      <td>1.257475</td>\n",
       "      <td>8.309285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>-2.500000</td>\n",
       "      <td>-3.142000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>-2.673000</td>\n",
       "      <td>-3.142000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-111.600006</td>\n",
       "      <td>0.591869</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>46.078999</td>\n",
       "      <td>0.053625</td>\n",
       "      <td>-1.414214</td>\n",
       "      <td>-25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.334000</td>\n",
       "      <td>-0.991000</td>\n",
       "      <td>-1.573000</td>\n",
       "      <td>31.452000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.572000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.200467</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>2.691232</td>\n",
       "      <td>7.957961</td>\n",
       "      <td>61.244999</td>\n",
       "      <td>0.599296</td>\n",
       "      <td>-1.383972</td>\n",
       "      <td>-25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.514000</td>\n",
       "      <td>-0.004000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.157001</td>\n",
       "      <td>-0.006000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.926866</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>3.010935</td>\n",
       "      <td>13.484183</td>\n",
       "      <td>71.712997</td>\n",
       "      <td>0.748874</td>\n",
       "      <td>0.778724</td>\n",
       "      <td>-25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.799000</td>\n",
       "      <td>0.989000</td>\n",
       "      <td>1.578000</td>\n",
       "      <td>44.409000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1.563000</td>\n",
       "      <td>32.238998</td>\n",
       "      <td>-1.401000</td>\n",
       "      <td>-1.775000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.206376</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>-25.000000</td>\n",
       "      <td>3.139685</td>\n",
       "      <td>21.322845</td>\n",
       "      <td>103.128998</td>\n",
       "      <td>0.945720</td>\n",
       "      <td>1.390993</td>\n",
       "      <td>-25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>515.867004</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.142000</td>\n",
       "      <td>464.765015</td>\n",
       "      <td>2.668000</td>\n",
       "      <td>3.142000</td>\n",
       "      <td>780.448975</td>\n",
       "      <td>4.834000</td>\n",
       "      <td>3.142000</td>\n",
       "      <td>490.860992</td>\n",
       "      <td>...</td>\n",
       "      <td>705.035400</td>\n",
       "      <td>29.464001</td>\n",
       "      <td>4245.830078</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>5.649345</td>\n",
       "      <td>404.146729</td>\n",
       "      <td>1582.538086</td>\n",
       "      <td>11.784817</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PRI_lep_pt    PRI_lep_eta    PRI_lep_phi     PRI_had_pt  \\\n",
       "count  198748.000000  198748.000000  198748.000000  198748.000000   \n",
       "mean       31.054996      -0.000968       0.001295      40.249287   \n",
       "std        14.441554       1.251236       1.814227      15.207740   \n",
       "min        20.000000      -2.500000      -3.142000      26.000000   \n",
       "25%        23.334000      -0.991000      -1.573000      31.452000   \n",
       "50%        27.514000      -0.004000       0.000000      37.157001   \n",
       "75%        33.799000       0.989000       1.578000      44.409000   \n",
       "max       515.867004       2.500000       3.142000     464.765015   \n",
       "\n",
       "         PRI_had_eta    PRI_had_phi  PRI_jet_leading_pt  PRI_jet_leading_eta  \\\n",
       "count  198748.000000  198748.000000       198748.000000        198748.000000   \n",
       "mean       -0.001081      -0.002889            3.693349           -16.965092   \n",
       "std         1.265052       1.812097           49.492981            11.715323   \n",
       "min        -2.673000      -3.142000          -25.000000           -25.000000   \n",
       "25%        -1.000000      -1.572000          -25.000000           -25.000000   \n",
       "50%        -0.006000       0.000000          -25.000000           -25.000000   \n",
       "75%         0.999000       1.563000           32.238998            -1.401000   \n",
       "max         2.668000       3.142000          780.448975             4.834000   \n",
       "\n",
       "       PRI_jet_leading_phi  PRI_jet_subleading_pt  ...       DER_pt_h  \\\n",
       "count        198748.000000          198748.000000  ...  198748.000000   \n",
       "mean            -16.972872             -15.893928  ...      29.902918   \n",
       "std              11.702966              26.580822  ...      37.783791   \n",
       "min             -25.000000             -25.000000  ...       0.011628   \n",
       "25%             -25.000000             -25.000000  ...       9.200467   \n",
       "50%             -25.000000             -25.000000  ...      16.926866   \n",
       "75%              -1.775000             -25.000000  ...      33.206376   \n",
       "max               3.142000             490.860992  ...     705.035400   \n",
       "\n",
       "       DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "count         198748.000000     198748.000000        198748.000000   \n",
       "mean             -21.682478          3.686550           -21.877384   \n",
       "std                8.965435        107.350243             8.889267   \n",
       "min              -25.000000        -25.000000          -111.600006   \n",
       "25%              -25.000000        -25.000000           -25.000000   \n",
       "50%              -25.000000        -25.000000           -25.000000   \n",
       "75%              -25.000000        -25.000000           -25.000000   \n",
       "max               29.464001       4245.830078           625.000000   \n",
       "\n",
       "       DER_deltar_had_lep     DER_pt_tot     DER_sum_pt  DER_pt_ratio_lep_had  \\\n",
       "count       198748.000000  198748.000000  198748.000000         198748.000000   \n",
       "mean             2.829471      16.627081     101.731911              0.821258   \n",
       "std              0.538228      14.000933      82.928406              0.382247   \n",
       "min              0.591869       0.011628      46.078999              0.053625   \n",
       "25%              2.691232       7.957961      61.244999              0.599296   \n",
       "50%              3.010935      13.484183      71.712997              0.748874   \n",
       "75%              3.139685      21.322845     103.128998              0.945720   \n",
       "max              5.649345     404.146729    1582.538086             11.784817   \n",
       "\n",
       "       DER_met_phi_centrality  DER_lep_eta_centrality  \n",
       "count           198748.000000           198748.000000  \n",
       "mean                 0.144721              -21.906357  \n",
       "std                  1.257475                8.309285  \n",
       "min                 -1.414214              -25.000000  \n",
       "25%                 -1.383972              -25.000000  \n",
       "50%                  0.778724              -25.000000  \n",
       "75%                  1.390993              -25.000000  \n",
       "max                  1.414214                1.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show data summary\n",
    "train_visualize.examine_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "The `histogram_dataset` method gives a set of histograms (for the columns defined before)\n",
    "It provides insights into the range and frequency of values for a specific feature. By analyzing the histogram, we can identify any patterns or anomalies in the data. This helps in understanding the underlying distribution and making informed decisions during the data analysis process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fvvpvDAAAsEtiYqLuuuuuOo8LAgMDVXKO2VCeOi4gsQcAVBEScvKJm/o4bm0MHDhQAwcOPOPzTz75RNLJaXk9e/bUjBkzdPz4cd18881q1aqVWrZsqc6dO1v3f+qpp6w/v/vuu2c9l1HfVQsAgDO4ekwgSSkpKbr11lvP+Ly6ccGFF16o0NDQBjUuILEHAFTRrJl7L6Nx6aWXasKECZo/f74kaezYsWratKlrgwJcKMjfv26L4J7Ox0eqrLT95CyoC3g0dx8TSGcfF4SGhro0JlcgsQcAGEqrVq309ttvV/msuLjYRdEAruddWmr7M/6JiVUW1K0zFtQF4GJnGxc0RN6uDgAAAAAAANiOxB4AAAAAAAMjsQcAAAAAwMBI7AEAAAAAMDAWzwMAVHXkiFRY6Pjjsno2AADGwpjAMEjsAQBVFRbavsJ2dWq5evbWrVs1evRoXXjhhbJYLDKZTLr11ls1YsSIOp1u69atWrRokf71r3/ZGnG96Nmzp9544w1dcMEFrg4FAIDqucGY4O6771Z0dDRjghqQ2AMA3E5sbKy18y0qKlJycrKuueYatW7d2sWRAQAAZ7ryyiu1bNkySYwJqkNiDwBwa2VlZfLx8VFwcLDWrFmjV199VaWlpSovL9fs2bPVoUMH7dmzR3PmzFFpaamaNm2q9PT0Ksd4/fXX9fHHH2vJkiXau3evJk6cqMrKSiUkJOizzz7T+vXrNXHiRB09elS5ubkaP368mjdvrlmzZqmsrEzNmjXT9OnTFR0dreHDh2vMmDHq3Lmz9u3bp7S0NH3yySeaOHGimjRpol27dikvL0/33XefBg0apKNHj2r8+PH6888/demll6qsrMxFVxKoBxUVUm6u7fWZjgugDmozJti9e7emTp3a4MYEJPYAALezc+dODRgwQGazWb///rv69u2r888/X8uXL9cLL7yg5s2ba+XKlVqyZIleeOEFTZ48WRMmTFCPHj20bNkyvf7667r++uslSatWrdK6deu0ZMkSNWrUSBMnTtQDDzyg7t2767XXXlNlZaX1vKGhoXrhhRdUXl6uG2+8UfPnz1d8fLzWrFmjhx56SO+99161cf/5559atmyZfvjhB6WlpWnQoEFasGCBrrzySi1dulRfffWV1qxZU5+XDnCu4mJpyxbb69dyOi6Ahuu7776r05jgkUce0SOPPNLgxgSsig8AcDuxsbHKzMxUVlaWvvzyS/3222966aWX9Nxzz+mLL77Qs88+q/fff1/FxcUqKCjQoUOH1KNHD0nS0KFD9eijj0qSfvjhBz3++ONKS0tT48aNdfToUe3fv1/du3eXJA0aNKjKeePj4yVJv/32m0JCQqzbffv21e+//67jx49XG/c111wjLy8vXX755Tp69Kgkadu2berXr58k6eqrr1arVq0cc5EAAGgArrzyyjqNCfLz8xvkmIDEHgDg1po0aaK+fftq8+bNGjx4sPbt26err75aw4cPlyT5+fnJy8vLun9ZWZn27t0rSWrcuLEWLlyoefPm6cSJE/Lx8ZHFYjnnuQIDAyVJZrP5jDKLxWL9Jv/UMSoqKqrsExAQIElV4vHy8qpyTh8fn9o3HgAAWDEmODcSewCAW6usrNS2bdsUGBgoLy8vjR49Wp07d9b69etVWVmp4OBghYeH64svvpAkZWZm6tlnn5UkRUVFqWfPnurUqZMWLFig4OBgtWrVSp9++qkkKSsr66znvOSSS3T06FHt2LFDkrR69Wq1bNlSoaGhatasmX766SdJ0scff1xj/F26dFFmZqYkaceOHfr999/tuyAAADRQtRkTRERENMgxAc/YAwCqCgk5+dxrfRy3lk49Y+/l5aWKigrFxMRoxowZmjJlivr27SsvLy9de+21ysnJkSTNnDlTc+fO1T//+U81a9ZM8+bN06+//mo93oQJE9S/f3+lpKRo3rx5mjRpkubPn6+YmBjrN/Kn8/f3V0ZGhmbMmKGSkhI1bdpUGRkZkqSRI0dq4sSJeu+999SrV68a2zJ27FhNnDhRycnJuuSSS5iKDwAwDjcYE5x6xr62Y4J//vOfevLJJxvcmMDLUt38Aw9RVlamnTt3KjY21jolwl45OTnq2LGjQ45VZ7m59r1PMjGxykI3ubm5io6Orn39pCSpLvs7kUt/Lw5GW9yTJ7Zl9+7duuKKK1wdjl2Ki4vVuHHjWu27aNEiDRkyROHh4Vq3bp2ysrK0cOHCeo6wZqd+D6f/jdVH/4V6uK4O7pfr6nhsrIJ37nTJue2u78IxhSf9e+5sXDvbGOG6ueOYoC59fF25+5jgdDX1XdyxBwA0KC1bttSdd94pX19fhYSEaNasWa4OCQAAuIAnjQlI7AEADUpqaqpSU1NdHQYAAHAxTxoTsHgeAKDaVWFR/7j+AAB3QZ/kWrZef+7Yo+4qKk4+T2iLkBCpWTPHxgPALj4+PjKZTPL393d1KA2WyWSSry9dMgDAtRgTuJ6tYwKXjCKysrK0ePFiVVRUaMSIERo2bFiV8t27d2vy5MkqLi5WQkKCpk2bVqVx3333nYYMGaKdti4UA/sUF9u+UE5SEok94GZCQ0OVl5enqKgoeXszkcvZzGaz8vLy1LRpU1eHAgBo4BgTuJY9YwKnJ/Z5eXnKyMjQqlWr5O/vr1tvvVWdO3dW69atrfuMHz9eM2fOVPv27TVp0iStWLFCQ4cOlSSVlJRoxowZMplMzg4dADzS+eefr3379mnPnj2uDsVm5eXlhr670LhxY51//vmuDgMA0MC545jA6H18Xdk6JnB6Yr9p0yYlJiYqNDRUkpSUlKTs7GyNGTNGkrR//36Vlpaqffv2kk4uaLBgwQJrYv/UU09pxIgR+s9//uPs0AHAI3l7e+vCCy90dRh2ycnJUbt27VwdBgAAhuaOYwL6+NpxemJ/8OBBhYWFWbfDw8O1Y8eOc5aHhYUpLy9PkrRhwwaVlpbqxhtvtOncjp66n5OT49Dj1VZLk0nltj7jLql5bKwK/lI/tw7HO1v92vI/cEB/HDpkU93actXvpT7QFvdEW9wTbQEAAA2V0xN7s9ksLy8v67bFYqmyfa7y/Px8LV68WK+99prN546NjVVAQIDN9U+Xk5Ojjh07OuRYdZabK0VH216/SRMFn1Y/NzdX0XU53l/q10mLFmphT+w1cOnvxcFoi3uiLe7JU9tSVlbGejIAAKBGTl8RITIyUvn5+dbt/Px8hYeHn7P80KFDCg8P18aNG3X06FENGzZMAwYMkCQNGDBARUVFzgseAAAAAAA34/Q79l27dtXChQtVUFCgRo0aad26dZoxY4a1PCoqSgEBAdY7FpmZmerWrZtuueUW3XLLLdb9YmJilJmZ6ezwAQAA4Ci8QhcAHMLpiX1ERITGjRuntLQ0mUwmDR48WPHx8Ro1apTGjh2ruLg4paena8qUKSoqKlLbtm2Vlpbm7DABAABQ33iFLgA4hEveY5+SkqKUlJQqny1dutT6c5s2bbRy5cpqj+FOr2AAAAAAAMBVnP6MPQAAAAAAcBwSewAAAAAADIzEHgAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AADgMllZWerXr5/69Omjt95664zy3bt3KzU1VUlJSZo8ebIqKiqqlH/33XeKjY11VrgAALglEnsAAOASeXl5ysjI0LJly/TBBx/onXfe0U8//VRln/Hjx2vq1Klau3atLBaLVqxYYS0rKSnRjBkzZDKZnB06AABuhcQeAAC4xKZNm5SYmKjQ0FAFBQUpKSlJ2dnZ1vL9+/ertLRU7du3lySlpqZWKX/qqac0YsQIZ4cNAIDb8XV1AAAAoGE6ePCgwsLCrNvh4eHasWPHOcvDwsKUl5cnSdqwYYNKS0t144032nz+nTt32lz3dC1NJpXn5tpcv3lsrArsrJ9rY31HnNtV9f0PHNAfhw7ZfG5JysnJsat+Q8a1sw3XzTZct5qR2AMAAJcwm83y8vKyblsslirb5yrPz8/X4sWL9dprr9l1/tjYWAUEBNh1DElSbq4UHW17/SZNFGxH/eOSom2tb+e5XVq/RQu1sOPcOTk56tixo831GzKunW24brbhup1UVlZW7RfSTMUHAAAuERkZqfz8fOt2fn6+wsPDz1l+6NAhhYeHa+PGjTp69KiGDRumAQMGSJIGDBigoqIi5wUPAIAbIbEHAAAu0bVrV23evFkFBQUqKSnRunXr1K1bN2t5VFSUAgICrFMwMzMz1a1bN91yyy36+OOPlZmZqczMTGtZkyZNXNIOAABcjcQeAAC4REREhMaNG6e0tDTdfPPN6t+/v+Lj4zVq1Ch9++23kqT09HTNmTNHN954o06cOKG0tDQXRw0AgPvhGXsAAOAyKSkpSklJqfLZ0qVLrT+3adNGK1eurPYYe/bsqZfYAAAwChJ7AAAAGE9FxcmFC20U5sswGIDn4F80AAAAGE9xsbRli83V/eLjHRgMALgWz9gDAAAAAGBgJPYAAAAAABgYiT0AAAAAAAZGYg8AAAAAgIGR2AMAAAAAYGCsiu8qR45IhYW21S0pcWwsAAAAAADDIrF3lcJCae1a2+omJjo2FgAAAACAYTEVHwAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMQeAAAAAAADI7EHAAAAAMDASOwBAAAAADAwEnsAAAAAAAzM19UBAAAAAHVlqpAKD9te39s7yHHBAICLkdgDAADAcCorpV27bK9/SXcfxwUDAC7GVHwAAAAAAAyMxB4AAAAAAANjKj6cq6JCys21vX5IiNSsmePiAQAAAACDI7GHcxUXS1u22F4/KYnEHgAAAABOQ2IPAACABqexv5hFCMBjkNgDAACgwfEpK5HWfmn7AZhFCMCNsHgeAAAAAAAGRmIPAAAAAICBkdgDAAAAAGBgJPYAAAAAABgYiT0AAAAAAAZGYg8AAAAAgIGR2AMAAAAAYGAk9gAAAAAAGJivqwMAAACAa5gqpMLDttcPqZD8HBcOAMBGJPYAAAANVGWltGuX7fU79SaxBwB3wFR8AAAAAAAMjMQeAAAAAAADI7EHAAAAAMDAXPKMfVZWlhYvXqyKigqNGDFCw4YNq1K+e/duTZ48WcXFxUpISNC0adPk6+ur7du3a/bs2TKZTIqKitLcuXPVtGlTVzTB0P66UE5lZagO12HhHBbKAQAAAAD34fTEPi8vTxkZGVq1apX8/f116623qnPnzmrdurV1n/Hjx2vmzJlq3769Jk2apBUrVmjo0KF67LHHtHjxYrVu3Vrp6el6+eWX9dBDDzm7CYb314VyCgoq1Lx57euzUA4AAAAAuA+nT8XftGmTEhMTFRoaqqCgICUlJSk7O9tavn//fpWWlqp9+/aSpNTUVGv56tWr1bp1a5lMJuXl5SkkJMTZ4QMAAAAA4FacntgfPHhQYWFh1u3w8HDl5eWdszwsLMxa7ufnpz179qh79+7aunWrkpOTnRc4AAAAAABuyOlT8c1ms7y8vKzbFoulynZN5TExMdq0aZOWL1+ucePGafny5bU+986dO+2MvqqcnByb67Y0mVSem2tT3eaxsSqwsa4kNWvbQQUFVR+q/+t2dcrLy5Xnotj9DxzQH4cOVbuPPb8Xd0Nb3BNtcU+0BQAANFROT+wjIyO1fft263Z+fr7Cw8OrlOfn51u3Dx06pPDwcJWVlenzzz9X7969JUk33XST5s6dW6dzx8bGKiAgwM4WnJSTk6OOHTvafoDcXCk62ra6TZoo2Na6kkr9/dW8+XnW7YKCw1W2a+Lv769oF8WuFi3Uopr6dv9e3AhtcU+0xT15alvKysoc/qU0AADwPE6fit+1a1dt3rxZBQUFKikp0bp169StWzdreVRUlAICAqx3KzIzM9WtWzf5+vpq2rRp1gHOmjVr1KFDB2eHDwAAAACAW3H6HfuIiAiNGzdOaWlpMplMGjx4sOLj4zVq1CiNHTtWcXFxSk9P15QpU1RUVKS2bdsqLS1NPj4+ysjI0NSpU1VZWamIiAjNmjXL2eEDAAAAAOBWXPIe+5SUFKWkpFT5bOnSpdaf27Rpo5UrV55RLyEhQatWrar3+AAAAAAAMAqXJPYAAACAoVVUnFwzyVYhIVKzZo6LB0CDRmIPAAAA1FVxsbRli+31k5JI7AE4DIk96sxslg7X/u14VYRUSH6ODadOjhyRCgttq8sX6wDgeFlZWVq8eLEqKio0YsQIDRs2rEr57t27NXnyZBUXFyshIUHTpk2Tr6+vtm/frtmzZ8tkMikqKkpz585V06ZNXdQK+5gqpEIb+1VJCnBpzwpXsGc8IzGmATwRiT3qzGyWdu2yrW6n3q5N7AsLpbVrbavLF+sA4Fh5eXnKyMjQqlWr5O/vr1tvvVWdO3dW69atrfuMHz9eM2fOVPv27TVp0iStWLFCQ4cO1WOPPabFixerdevWSk9P18svv6yHHnrIha2xXWWl7f2qJLXv5uW4YGAI9oxnJMY0gCdy+uvuAAAAJGnTpk1KTExUaGiogoKClJSUpOzsbGv5/v37VVpaqvbt20uSUlNTreWrV69W69atZTKZlJeXp5CQEFc0AQAAt8AdewAA4BIHDx5UWFiYdTs8PFw7duw4Z3lYWJjy8vIkSX5+ftqzZ4/uuOMO+fr62nS3fufOnXZE/z8tTSaV27GIWrO2HVRQYMdcfEm5Np7f3nOXl5crz462N4+NVYGLYpdsv26SfbFLkv+BA/rj0CGb6ppMLZWbW27zuQ8c8NehQ3/YXF+ScnJy7KrfUHHdbMN1qxmJPQAAcAmz2Swvr/9NI7dYLFW2ayqPiYnRpk2btHz5co0bN07Lly+v0/ljY2MVEBBgRwv+v9xcKTra5uql/v5q3vw8u0KItvH89p7b39/f5nNLkpo0UbCLYpdsv26S7IpdktSihVrYWN/OPzm1aCFFR7ewuX5OTo46duxoewANFNfNNly3k8rKyqr9Qpqp+AAAwCUiIyOVn59v3c7Pz1d4ePg5yw8dOqTw8HCVlZXp448/tn5+0003ac+ePc4JGgAAN0RiDwAAXKJr167avHmzCgoKVFJSonXr1qlbt27W8qioKAUEBFinYGZmZqpbt27y9fXVtGnTrHcu1qxZow4dOrikDQAAuAOm4gMAAJeIiIjQuHHjlJaWJpPJpMGDBys+Pl6jRo3S2LFjFRcXp/T0dE2ZMkVFRUVq27at0tLS5OPjo4yMDE2dOlWVlZWKiIjQrFmzXN0cAABchsQeAAC4TEpKilJSUqp8tnTpUuvPbdq00cqVK8+ol5CQoFWrVtV7fAAAGAFT8QEAAAAAMDASewAAAAAADIzEHgAAAAAAA+MZexhLRcXJl7eeQ0uTqdryJhUhkprVQ2AAAAAA4Bok9jCW4mJpy5ZzFpfn5krR0ecs90lMEok9AAAAAE/CVHwAAAAAAAzMrsT+/vvv16ZNmxwVCwAAMDDGBQAAuIZdU/FvuOEGPf/885o2bZqGDBmiQYMGKTQ01EGhAQAAI2mo44KSEunEYdvrB5odFwuMo6REOnjuZYFqrOtKvr5h1S1pVK2QEKkZT0UCDmdXYn/TTTfppptu0s8//6z33ntPgwcP1lVXXaXhw4crPj7eUTECAAADaKjjgrJyadcu2+t3uN5hocBAysqltZ/ZVjcx0bGx1FVJiZ+2brWtblISiT1QH+x+xt5sNis3N1e//fabKisrdd555+nJJ5/UggULHBEfAAAwEMYFAAA4n1137DMyMrRq1Sq1atVKQ4cO1bPPPis/Pz+dOHFCPXr00NixYx0VJwAAcHOMCwAAcA27EvuCggItXbpUbdq0qfJ5UFCQnn76absCAwAAxsK4AAAA17BrKn5lZeUZnfepb+OvvfZaew4NAAAMhnEBAACuYdMd+yeeeEJ5eXnKyclRQUGB9fOKigrt3bvXYcEBAAD3x7gAAADXsimxHzx4sH788Uft2bNHSUlJ1s99fHzUvn17R8UGAAAMgHEBAACuZVNiHxcXp7i4OF1zzTWKiIhwdEwAAMBAGBcAdefvVaGEcNteBt/cK0QS74wD8D82JfYPPPCAnn32WY0cOfKs5VlZWXYFBQAAjINxAVB33qXFKlq5xaa6UQ8kicQewOlsSuxHjRolSXr88ccdGgwAADAexgUAALiWTavix8bGSpI6deqkFi1aqFOnTjpx4oS++uorXXHFFQ4NEAAAuDfGBQAAuJZdr7ubOnWqli5dqp9//llTpkzRvn37NGnSJEfFBgAADIRxAQAArmFXYr9z5049+eSTWr9+vQYOHKg5c+Zo//79jooNAAAYCOMCAABcw67E3mKxyNvbW19++aUSExMlSaWlpQ4JDAAAGAvjAgAAXMOuxP7CCy/UqFGjtG/fPnXq1EkPP/ywYmJiHBUbAAAwEMYFAAC4hk2r4p8yZ84crV+/Xh07dpSfn58SEhJ08803Oyg0AABgJIwLGh6zWTp82Pb6IRWSn+PCcSpThVRoR9sDzY6Lpa4qKqTcXNvre3s3clwwABzCrsQ+KChICQkJOnbsmHbt2qX4+Hj98ssvatu2raPiw1kYuSNpyAMAAPB0jAsaHrNZ2rXL9vqdehu3X6+stK/tHa53WCh1Vlwsbdlie/3YWB/HBQPAIexK7J999lm98sorOu+886yfeXl5acOGDXYHhnMzckfSkAcAAODpGBcAAOAadiX2mZmZWrdunSIiIhwVDwAAMCjGBQAAuIZdi+e1aNGCzhsAAEhiXAAAgKvYdce+S5cumjdvnnr16qXAwEDr5zxLBwBAw8O4AAAA17ArsV+1apUkKTs72/oZz9IBANAwMS4AAMA17ErsP/nkE0fFAQAADI5xAQAArmHXM/bFxcWaPn26RowYoaNHj2rq1KkqLi52VGwAAMBAGBcAAOAadiX2M2fOVHBwsA4fPqyAgAAVFRVp6tSpjooNAAAYCOMCAABcw67Efvfu3Ro3bpx8fX3VqFEjpaena/fu3Y6KDQAAGAjjAgAAXMOuxN7bu2r1ysrKMz4DAAANA+MCAABcw67F866++mr985//VGlpqT7//HO9+eab6ty5s6NiAwAABsK4AAAA17Dra/RHHnlEQUFBCg4O1vz589WmTRtNmDDBUbEBAAADYVwAAIBr2HzHfv369Xr55Ze1Z88eBQYGKiYmRh06dFBAQIAj4wMAAAbAuAAAANexKbFfs2aNMjIyNHbsWLVp00ZeXl769ttvNWvWLJWVlalPnz6OjhMAALgpxgUAALiWTYn9G2+8oddee00tW7a0fnbppZeqXbt2mjRpEh04AAANCOMC1/Ly8tbhw7bVDTQ7NhYjsee6SQ372tmjokLKzbW9vo+PVFlpe/2QEKlZM9vrA+7KpsS+uLi4Sud9ysUXX6yysjK7gwIAAMbBuMC1zGZp1y7b6na43qGhGIo9101q2NfOHsXF0pYtttdPTLSvflISiT08k02JvY+PzznLLBZLjfWzsrK0ePFiVVRUaMSIERo2bFiV8t27d2vy5MkqLi5WQkKCpk2bJl9fX+Xk5GjOnDkymUwKDQ3V7NmzFRUVZUsTAACAg9g7LgBQN438KpQQbvtt7+ZeIZLIbgFPYtfr7myRl5enjIwMrVq1Sv7+/rr11lvVuXNntW7d2rrP+PHjNXPmTLVv316TJk3SihUrNHToUI0fP17PP/+82rRpo5UrV2rmzJlavHixs5sAAAAAuIx3SbGKVtp+2zrqgSSR2AOexabEfs+ePerQocMZn1ssFpWXl1dbd9OmTUpMTFRoaKgkKSkpSdnZ2RozZowkaf/+/SotLVX79u0lSampqVqwYIEGDx6sBx54QG3atJEkxcTE6M0337QlfAAA4ED2jAsAAID9bErs169fb/MJDx48qLCwMOt2eHi4duzYcc7ysLAw5eXlyd/fXwMGDJAkmc1mLVq0SL1797Y5DgAA4Bj2jAsAAID9bErs7Xmu3Ww2y8vLy7ptsViqbNdUXl5erokTJ6qiokJ33313nc69c+dOm+M+m5ycHJvrtjSZVG7jkqDN2nZQQYHty7hWVlacUb8uxztbfWfUlU7+/vNquG651ZT7xBZVW16dAwf8dejQHzbVtZU9f2Puhra4J9rinozWFta7AQDAtZz+jH1kZKS2b99u3c7Pz1d4eHiV8vz8fOv2oUOHrOXFxcW65557FBoaqsWLF8vPz69O546NjVVAQICdLTgpJydHHTt2tP0AublSdLRNVUv9/dW8+Xk2n9rHx7dK/YKCw3U63l/r23PuuvL391d0NdctNze32vKjTZpUW16dFi2k6OgWNtW1hd1/Y26Etrgn2uKeTm9LWVmZw7+UBgAAnsfb2Sfs2rWrNm/erIKCApWUlGjdunXq1q2btTwqKkoBAQHWuxWZmZnW8vHjxys6Olrz58+Xv7+/s0MHAAAAAMDtOP2OfUREhMaNG6e0tDSZTCYNHjxY8fHxGjVqlMaOHau4uDilp6drypQpKioqUtu2bZWWlqbvvvtOGzZsUOvWrTVw4EBJJ5/PX7p0qbObAAPz97L99TBNKng1DAA4Gq/ABQDAfk5P7CUpJSVFKSkpVT47PUE/9Tq701155ZXas2ePU+KD5/Iutf31MD6X8GoYAHAkXoELAIBjOH0qPgAAgFT1FbhBQUHWV+CecrZX4GZnZ6u8vPyMV+AeOHDAFU0AAMAtkNgDAACXONsrcPPy8s5ZzitwAQA4O5dMxQcAAHDlK3Alx70GN6Is0OGvoa0rV72G1hmvsT0Xe1//K9l+3STXXjv7r3uZza//laTY2OY21z9Zt8DOc9te3xWvLz6d0V5n6i64bjUjsQcAAC7hylfgSo57De7RHbkOfQ2tLVz1Gtr6fo1tdex9/a9k+3WTXHvt7L/uATZf95OO21y/SRMpOjrY5jPbW9/Zry8+nSe9mtWZuG4n1fQKXKbiAwAAl+AVuAAAOAZ37AEAgEvwClyYzdJhG2eUB5odGwtQkyNHpMJC2+v7+obVvFM9sTf2kBCpGS+Hcmsk9gAAwGV4BW7DZjZLu3bZVrfD9Q4NBahRYaG0dq3t9ePj6/7IkKPYG3tSEom9uyOxd5GSEukE31DXWU3f7FdWhlZbbuRrZ883rXzLCgAAAHguEnsXKSvnG2pb1PTNfkFBhZo3P3e5ka+dPd+08i0rAAAA4LlYPA8AAAAAAAMjsQcAAAAAwMBI7AEAAAAAMDASewAAAAAADIzEHgAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMQeAAAAAAAD83V1AAA8n69vmHJzbasbEiI1a+bYeAAAAABPQmIPoN6VlPhp61bb6iYlkdgDAAAA1WEqPgAAAAAABsYdewAAAKABaeRXoYRwG5+Rk9TU3187FezAiJynokI2Px5YUuLYWABHIrEHAAAAGhDvkmIVrdxic/2we66X1MJh8ThTcbG0xcamJyY6NhbAkZiKDwAAAACAgZHYAwAAAABgYCT2AAAAAAAYGM/Y2yjM19f2lTck+ZtZfQMAAAAAYD8Sexv5lZTI5hdzS/K+itU3AAAAAAD2I7EHasnfy473o0hSSIjUrJnjAgIAAAAAkdgDtXeiWIc/tP3VMH79k3SksPaJvcnUssr3CLw71TZHjkiFhbbX5/sYAAAAuDsSe6CWzGZp1y7b68f0ltZurv3+ubnlio7+37Yr351qb3Ls7d3IccHUUWGhtHat7fWTkkjsAQAA4N5I7AHUyN7kODbWx3HBAAAAAKiC190BAAAAAGBgJPYAAAAAABgYiT0AAAAAAAZGYg8AAAAAgIGR2AMAAAAAYGCsig8AAAAAbs6e1w+XlDg2FrgfEnsAAAAAcHP2vH44MdGxscD9MBUfAAAAAAADI7EHAAAAAMDAmIoPwK1VVEi5ubbX55kyAAAAeDoSewBurbhY2rLF9vo8UwYAAABPR2IPAAAAoNYaB5iVEG7bdLrmXiGSmjk2IAAk9gAAAABqz6f0hIpWfmZT3agHkkRiDzgei+cBAAAAAGBgJPYAAAAAABgYiT0AAAAAAAZGYg8AAAAAgIGR2AMAAAAAYGAk9gAAAAAAGBiJPQAAAAAABsZ77AEnaeRXoYTw3Frvf1nAMTVt+r/9m3uFiPe+AgAAAPgrlyT2WVlZWrx4sSoqKjRixAgNGzasSvnu3bs1efJkFRcXKyEhQdOmTZOv7/9CnT9/vnx8fHT//fc7O3TAZt4lxSpauaXW+x8rOCyf5udZt6MeSBKJPQAAABoSX98w5db+3tgZQkKkZg1gCO30xD4vL08ZGRlatWqV/P39deutt6pz585q3bq1dZ/x48dr5syZat++vSZNmqQVK1Zo6NChOn78uObMmaOPPvpII0eOdHboAAAAAAAnKinx09atttdPSmoYib3Tn7HftGmTEhMTFRoaqqCgICUlJSk7O9tavn//fpWWlqp9+/aSpNTUVGv5hg0bdNFFF+mOO+5wdtgAAAAAALglp9+xP3jwoMLCwqzb4eHh2rFjxznLw8LClJeXJ0m6+eabJUkLFy606dw7d+60qd7ZtJSUa8eckGZtO6ig4LBNdSsrK2yue676dTmePeevj9j/qrpyd4/9r07fv7y8zOa/uQMH/HXo0B821ZUkk6mlcnPLba4fG9vc5thP1i2w89y21z/btcvJybH5eO6GtrgnT2pLTXg8DwAA+zk9sTebzfLy8rJuWyyWKts1ldsjNjZWAQEBDjnWgS1bFB0dbXP9Un9/NT/t+em68PHxtbnu2eoXFByu0/HsOb+jY/+rmtrizrH/1V/b4u8fYPPfXIsWUnR0C5vqSlJurmTHn7uk4zbH3qSJFB0dbPOZ7a3/12uXk5Ojjh072nw8d0Jb3NPpbSkrK3Pol9LuhsfzAABwDKdPxY+MjFR+fr51Oz8/X+Hh4ecsP3ToUJVyAADgGXg8DwAAx3B6Yt+1a1dt3rxZBQUFKikp0bp169StWzdreVRUlAICAqzTEDMzM6uUAwAAz3C2x/NOPX53tvK/Pp531113ycfHx3kBAwDgppw+FT8iIkLjxo1TWlqaTCaTBg8erPj4eI0aNUpjx45VXFyc0tPTNWXKFBUVFalt27ZKS0tzdpgAAKCeufLxPMlxa+9ElAU6fQ2WvzLS+jGOqu/K6+aI8xtt3Z+/srW+PWsGSa5de8fec8fH+9u1hoo9ax7Vx5pDztPSrr8Z18buPC55j31KSopSUlKqfLZ06VLrz23atNHKlSvPWZ8FcgAAML7IyEht377duu3sx/MctfbO0R25Tl2D5WyMsn6MI+u78ro54vxGWvfnbGytb8+aQZJr196x99zSAbvWg7FnzSNHrznkTFu2HLDrb8aVsTtSTevuOH0qPgAAgMTjeQAAOIpL7tgDAGp25IhUWGh7/ZAQqVkzx8UDOBqP5wENTyO/CiWE2z6turlXiCQ6N+CvSOwBwE0VFkpr19pePymJxB7uj8fzgIbFu6RYRSu32Fw/6oEkkdgDZ2IqPgAAAAAABkZiDwAAAACAgZHYAwAAAABgYDxjDxiEPYvNNKlgoRkAAADAU5HYAwZhz2IzPpew0AwAAIAr+fsHKdf2FwKopMRxscDzkNgDAAAAQD0rLfW26203iYmOiwWeh2fsAQAAAAAwMBJ7AAAAAAAMjKn4NvL2DtLhw7bXDzQ7LhYAAAAAQMNFYm8jU4WP9uyyvX6H6x0WCgAAANAg2POWIElq7sWbguCZSOwBAAAAGII9bwmSpKgHeFMQPBPP2AMAAAAAYGAk9gAAAAAAGBiJPQAAAAAABkZiDwAAAACAgZHYAwAAAABgYCT2AAAAAAAYGK+7AxoAf68KKdf2d742qeCdrwAAAIC7IrEHGgDv0mJps+3vfPVJ5J2vAAAAgLtiKj4AAAAAAAbGHXugATCbpcOHba9vqXBcLIAzHDkiFRbaVtfHR6qstP3cISFSMya4AAA8SIV9T3W6tG80cux1QWIPNABms7Rrl+31Y3o7LhbAGQoLpbVrbaubmChtsf3JFSUlGWMAAAANUSO/CiWE25blNfdquGsOFRcbt280cux1QWIPAAAAoEHwLilW0UrbsryoB1hzCO6LZ+wBAAAAADAwEnsAAAAAAAyMqfgAamTP82iS1NTfXzsV7MCIAAAAAJxCYg+gRvY8jyZJYfdcL6mFw+IBAABwNm50wJ2R2AMAAABADbjRAXfGM/YAAAAAABgYd+wB1LvGAWbeGQsAAADUExJ7APXOp/SEilZ+ZlNd3hnrOkeOSIWFttUNCZGa8WsDAABwChJ7AKhGRYWUe9pkA5OpZZXtmvj4SJWVtp27pMS2eo5SWCitXWtb3V69bP9SQOKLAQAAgLogsQeAahQXS1tOWycnN7dc0dG1r5+YWLV+XSQm2lbPHfz1utVVUhKJPQDAs9jzaKLE44moHok9AAAAANQzex5NlHg8EdVjVXwAAAAAAAyMO/YA3FojvwqmrQEAAADVILEH4Na8S4pVtNL2h7WZtgYAADyBPTc7XH2j46+LEdeFt3cjxwbjoUjsAQAAAMDN2XOzw9U3OuxZVDc21sexwXgoEnsAHo2p/AAAoKFjPOT5SOwBeDR7p/K3fqSXEsL/90L2ywKOqWnT2neMdIQAAMDVeLTR85HYA0A1/toRHis4LJ/m59W6Ph0hAAAA6huJPQDUIyMvdAMAACAxld8ISOwBoB65cqGbmlagNZlaVlteUmLzqe1W19Vz/9oWV8YOAICnYSq/+yOxBwA3Ze+34wEnQrR687k70dzcckVHn7t+YqLNp7ZbXVfP/WtbXBk7AACoyp4xTVN/f+1UsIMj8jwk9gDgphy98N9f1bQQINPmAACAI9gzprnowW5KCC+3+dwNZTxDYg8AHqqmTrSmhQBr+mKgOg2lEwUAAPXLp/SEilZ+ZnN9e8YzktSkwhhjGhJ7AMBZ2fPtur2daCtvH1WEV9Z6/7/OPuCLBQAAINk/A9LnEmOsD0BiDwBwOHs7Ub/7EutU/6+zD5htAAAAHMHfq44r+p4uJERq5pwxBYk9AMDjuHK2gVGm7AEAgJp5lxZLm228WZGU5NmJfVZWlhYvXqyKigqNGDFCw4YNq1K+e/duTZ48WcXFxUpISNC0adPk6+urP/74Q+PHj9fhw4d18cUXKz09XY0bN3ZFEwAAHqqhTNlzF4wJAACwn7ezT5iXl6eMjAwtW7ZMH3zwgd555x399NNPVfYZP368pk6dqrVr18pisWjFihWSpGnTpmno0KHKzs5WbGysnn/+eWeHDwAAHIQxAQAAjuH0xH7Tpk1KTExUaGiogoKClJSUpOzsbGv5/v37VVpaqvbt20uSUlNTlZ2dLZPJpK+++kpJSUlVPgcAAMbEmAAAAMdw+lT8gwcPKiwszLodHh6uHTt2nLM8LCxMeXl5OnLkiJo0aSJfX98qn9eGxWKRJJWX2/7+w7+qlFleIf421zfJYnN9e+qerb6PObBOx3On2P+qpra4c+x/9de2GCn2M+p7GTh2/n+pl3M7ur5b/f9iqVRZWZnN9SVZ65/qt071Y57GFWMCyfHjApOlkn9jXVDfldfNIec30L+xZ9Tnb862+i78mzP0dXfx/6vlssjL38b6lZWSnWMCaxw1jAm8LE4eLSxevFhlZWV68MEHJUkrVqzQzp07NX36dElSTk6Onn76aS1btkyS9Ntvv2n06NF6/fXXNWTIEH366aeSpIqKCl111VX69ttvazzn8ePH9cMPP9RPgwAAqGeXX365goODXR2Gw7liTCAxLgAAGNe5xgROv2MfGRmp7du3W7fz8/MVHh5epTw/P9+6fejQIYWHh6t58+Y6fvy4Kisr5ePjc0a96jRu3FiXX365/Pz85OXl5bjGAABQjywWi0wmk8cuCueKMYHEuAAAYDw1jQmcnth37dpVCxcuVEFBgRo1aqR169ZpxowZ1vKoqCgFBAQoJydHHTt2VGZmprp16yY/Pz8lJCRo9erVSklJ0QcffKBu3brV6pze3t4eeacDAOD5AgMDXR1CvXHFmEBiXAAAMKbqxgROn4ovnXy1zYsvviiTyaTBgwdr1KhRGjVqlMaOHau4uDh9//33mjJlioqKitS2bVvNmTNH/v7+2r9/vyZOnKjDhw+rRYsWeuaZZ9S0aVNnhw8AAByEMQEAAPZzSWIPAAAAAAAcw+mvuwMAAAAAAI5DYg8AAAAAgIGR2AMAAAAAYGAk9gAAAAAAGBiJPQAAAAAABkZiX0dZWVnq16+f+vTpo7feesvV4dikqKhI/fv31759+yRJmzZtUkpKivr06aOMjAwXR1d7ixYtUnJyspKTkzVv3jxJxm3Ls88+q379+ik5OVmvvvqqJOO25ZS5c+dq4sSJkozbluHDhys5OVkDBgzQgAED9M033xi2LZ988olSU1PVt29fzZw5U5Ixfy/vvvuu9fcxYMAAdezYUdOnTzdkWyQpMzPT+u/Y3LlzJRnz9wLn8cT+wpk8oW9yNk/qC53JU/pdZ/O0ft6pLKi1P//809KjRw/LkSNHLMXFxZaUlBTLjz/+6Oqw6uTrr7+29O/f39K2bVvL3r17LSUlJZbu3btbfv/9d4vJZLLceeedlo0bN7o6zBp9+eWXlr/97W+WsrIyS3l5uSUtLc2SlZVlyLZs3brVcuutt1pMJpOlpKTE0qNHD8vu3bsN2ZZTNm3aZOncubPl0UcfNezfmNlstlx77bUWk8lk/cyobfn9998t1157reXAgQOW8vJyy2233WbZuHGjIdtyuh9++MFyww03WP744w9DtuXEiROWq6++2nL48GGLyWSyDB482LJhwwZDtgXO4Yn9hTN5Qt/kbJ7UFzqTp/a7zmb0ft7ZuGNfB5s2bVJiYqJCQ0MVFBSkpKQkZWdnuzqsOlmxYoWeeOIJhYeHS5J27Nih6OhotWrVSr6+vkpJSTFEm8LCwjRx4kT5+/vLz89Pl156qX777TdDtqVTp05644035Ovrq8OHD6uyslKFhYWGbIskHT16VBkZGRo9erQk4/6N/fLLL5KkO++8UzfddJPefPNNw7Zl/fr16tevnyIjI+Xn56eMjAw1atTIkG053ZNPPqlx48Zp7969hmxLZWWlzGazSkpKVFFRoYqKCjVp0sSQbYFzeFp/4Uye0jc5myf1hc7kqf2usxm9n3c2Evs6OHjwoMLCwqzb4eHhysvLc2FEdTdr1iwlJCRYt43apssuu0zt27eXJP32229as2aNvLy8DNkWSfLz89OCBQuUnJysLl26GPb3IklTp07VuHHjFBISIsm4f2OFhYXq0qWLnnvuOb322mtavny5/vjjD0O2JTc3V5WVlRo9erQGDBigZcuWGfb3csqmTZtUWlqqvn37GrYtTZo00QMPPKC+ffuqe/fuioqKMmxb4Dye1F84k6f0Tc7mSX2hM3liv+tsntDPOxuJfR2YzWZ5eXlZty0WS5VtIzJ6m3788UfdeeedmjBhglq1amXotowdO1abN2/WgQMH9NtvvxmyLe+++65atGihLl26WD8z6t/YVVddpXnz5ik4OFjNmzfX4MGDtWDBAkO2pbKyUps3b9bs2bP1zjvvaMeOHdq7d68h23LK8uXLdccdd0gy7t/Y999/r/fee0//93//p88//1ze3t6G/X8fzuUJ/YUzeVLf5Gye1Bc6kyf2u87mCf28s/m6OgAjiYyM1Pbt263b+fn51intRhUZGan8/HzrtpHalJOTo7Fjx2rSpElKTk7Wtm3bDNmWn3/+WeXl5briiivUqFEj9enTR9nZ2fLx8bHuY5S2rF69Wvn5+RowYICOHTumEydOaP/+/YZsy/bt22UymawDQYvFoqioKEP+jZ1//vnq0qWLmjdvLknq3bu3Yf/GJKm8vFxfffWVnnrqKUnG/Xfsiy++UJcuXXTeeedJklJTU/Xyyy8b9veC+udJ/YUzeVLf5Gye1Bc6k6f1u87mKf28s3HHvg66du2qzZs3q6CgQCUlJVq3bp26devm6rDs0q5dO/3666/WKUMffvihIdp04MAB3XfffUpPT1dycrIk47Zl3759mjJlisrLy1VeXq4NGzbo1ltvNWRbXn31VX344YfKzMzU2LFj1bNnT7300kuGbMvx48c1b948lZWVqaioSO+//74eeughQ7alR48e+uKLL1RYWKjKykp9/vnnuvHGGw3ZFknas2ePLrroIgUFBUky7v/7bdq00aZNm3TixAlZLBZ98sknhm0LnMOT+gtn8qS+ydk8qS90Jk/rd53NU/p5Z+OOfR1ERERo3LhxSktLk8lk0uDBgxUfH+/qsOwSEBCgp556Svfff7/KysrUvXt33Xjjja4Oq0Yvv/yyysrKrN/kSdKtt95qyLZ0795dO3bs0M033ywfHx/16dNHycnJat68ueHacjZG/Rvr0aOHvvnmG918880ym80aOnSorrrqKkO2pV27dho5cqSGDh0qk8mka665RrfddpsuueQSw7VFkvbu3avIyEjrtlH/xq699lp99913Sk1NlZ+fn+Li4nT//ffrmmuuMVxb4Bye3l84k1H/3XA2T+oLncnT+l1n85R+3tm8LBaLxdVBAAAAAAAA2zAVHwAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMQeAAAAAAAD43V3QAOyb98+3XDDDbr88sutn1ksFqWlpSkxMfGMshMnTigyMlKzZ89Wq1attHXrVs2YMUMffvhhvce6aNEitWnTRr179673cwEA4Gmc1ecPHz5cw4YNs+v1Y9OnT1ezZs10//3321R/x44dWrlypaZPn25zDIDRkdgDDUxgYKAyMzOt23l5eerfv7/+9a9/nVFmsVg0c+ZMZWRk6JlnnnFqnFu3blXr1q2dek4AADyJUfp8e/3000/Ky8tzdRiAS5HYAw1cRESEoqOj9eWXX55RVlZWpoMHD+r888+v0zGHDx+utm3b6uuvv1ZBQYGGDBmiQ4cOadu2bSopKdH8+fMVExOj48ePa9asWfrhhx9kMpnUpUsXTZgwQe+884527typefPmycfHRzfccIOjmgsAQINVH32+JG3YsEEvv/yyDh06pC5dumjmzJny9vbWCy+8oA0bNqi0tFQlJSV69NFHdcMNN6ioqEiTJ0/W999/r/DwcPn4+Khjx47VnmPr1q1KT09Xy5Yt9csvvygwMFBPPfWUgoKCtGDBAh0/flyPPfaY5syZU+f4AU9AYg80cP/973/1+++/q127diotLdWAAQNkNpt1+PBhNW3aVH369NFdd91V5+Pu379fy5cv1zfffKMhQ4Zo8eLFmjhxombPnq0333xTM2bM0OzZs9W2bVs99dRTqqys1MSJE/Xqq69q1KhRys7O1rBhw0jqAQBwkPrq84uLi7V8+XKVl5frhhtu0H/+8x+1aNFCmzZtss4O+Oijj7RgwQLdcMMNWrBggQIDA5Wdna0jR45o4MCBNSb2krRz5049+uijSkhI0Ntvv63x48dr1apVGjt2rNauXUtSjwaNxB5oYE515JJUWVmpZs2a6Z///KciIyOrTMv7/PPPNX78ePXo0UONGzeu83lOJeStWrWSJF133XWSpAsvvFDbtm2TJG3cuFHffvutVq5caY0NAAA4hrP6/H79+snHx0eNGjXSRRddpMOHDyshIUHz5s1TVlaWcnNz9c0336i4uFiStHnzZk2aNEleXl5q3rx5rb/Eb9OmjRISEiRJgwYN0vTp03XkyJE6xwt4IhJ7oIH56zN1p+zbt6/K9nXXXac77rhDDzzwgD766CM1adKkTufx9/evsu3n53fGPmazWc8++6wuvfRSSVJhYaG8vLzqdB4AAHB2zurzfX3/l1J4eXnJYrFo165duvfee3X77bfrmmuu0dVXX61p06ZZ97NYLNaffXx8anWes+1X27qAp+N1dwDO6c4771Tjxo21YMGCejn+tddeq9dee00Wi0Xl5eW655579Oabb0o62VFXVFTUy3kBAEBVju7zv/rqK8XGxuqOO+5Qp06dtGHDBlVWVko6+UXCypUrZTabdezYMW3YsKFWx/z+++/1/fffS5LeeecdXXXVVQoJCWHMAIjEHkA1/Pz89Pjjj+utt97SDz/84PDjT548WSdOnFBKSopSUlJ0+eWXa+TIkZKknj176plnntH777/v8PMCAICqHN3n9+/fX0eOHFHfvn3Vr18/BQUF6dixYyoqKtL9998vX19f9e3bV6NHj67y2r3qnH/++Zo/f75SUlL08ccfa968eZKk9u3ba+/evRozZozdcQNG5WU5fR4MAAAAALiZrVu3asaMGfrwww9dHQrglnjGHkCdbdmy5Zwrz3bu3FmTJk1yckQAAKA+OLPPf/DBB/Xrr7+etey2225z2HkAT8QdewAAAAAADIxn7AEAAAAAMDASewAAAAAADIzEHgAAAAAAAyOxBwAAAADAwEjsAQAAgAaCdbONgd8T6orX3QFONHz4cG3bts267ePjo+DgYMXFxekf//iHunTpIknat2+fevXqVe2xvvjiC4WFhWnhwoVatGhRlTIvLy81btxYl112mUaPHq3rr7/e4W0xqpiYGE2YMEH/+Mc/XB0KAOA09JH17+OPP9Znn32m6dOnuzoUVMOW39PChQv1yiuv6L///W89RgZ3RmIPOFmHDh306KOPSpIqKip08OBBLV++XHfccYfS09PVv39/674PPfSQOnfufNbjhIaGWn8ODAzU66+/bt2urKzUH3/8oSVLlui+++7Te++9pzZt2tRPgwzmnXfeUcuWLV0dBgDgLOgj69frr7+uoKAgV4eBGvB7gi1I7AEnCwkJUfv27at8duONN2rEiBF68skndd1111k/j46OPmPfs/H29j5jv44dOyouLk5JSUn697//3WAGLTWpzfUEALgGfSQA2IZn7AE34O3trXvvvVfHjx9Xdna2w47bpEmTOtfZunWrYmJitGXLFt1yyy2Kj49X//79tX37dm3fvl0333yz2rVrp6FDhyo3N9dar6ioSDNnzlSPHj0UGxurxMREPfrooyosLLTu880332jYsGG66qqr1KlTJ40dO1b79++vdXlNJk6cqKSkpDM+T01N1YQJEySdnIr/8ssvSzp512bevHm6/vrrFRsbq379+untt9+u8zUDANQf+sjaldfk1KMOGzduVExMjPbt26eFCxcqNTVVs2fPVkJCgm699VZJ0sGDB/XYY4/p2muvVdu2bXXttddq1qxZKi8vl3TycYiYmBh98skn+sc//qF27drpuuuu0+LFi6uc8/3331dycrLi4uLUrVs3zZ49W2VlZZKkXr16aerUqVX2P3bsmGJjY7Vy5UpJ0okTJzRjxgx17dpV8fHxGj58uL777jvr/qtWrVLnzp310ksvqXPnzkpKSlJJSUmtrtUbb7yhPn36KDY2VsnJyVq9enWtr+Xp1+Djjz/W7bffrnbt2qlXr15at26dfvrpJw0dOlTt2rXTzTffrB07dlSp++GHHyolJUVxcXHq3bu3/vWvf1X7e7JVdeeRTo6Jli9frnvuuUft2rVTz5499eabb9p8PrgWiT3gJjp16iQfH58qz0aZzWZVVFSc8Z/ZbD6j/unlZWVl+umnn/TYY4/J19dXycnJdY5n/PjxGjx4sBYtWiSz2awHH3xQkyZN0u23367Zs2fr559/rvLs18MPP6xPPvlEDz/8sF5++WXdeeed+vDDD/X8889LkkpKSnTXXXcpIiJCzz//vGbMmKHvvvtODz30UK3Ka6N///767bff9P3331s/27t3r3bt2nXWa/Dyyy/rvffe04MPPqiXX35Z1113nZ588kl9/vnndb5eAID6Qx9pfx/5xBNP6Morr1SHDh30zjvvKDw8XJK0Z88effvtt1q4cKFGjx4ts9mskSNH6rvvvtMTTzyhl156SQMGDNAbb7yhd955p8oxH3vsMbVr104vvPCCevToofnz5+vTTz+VJP33v//VpEmT1L9/f7388ssaPXq0li9fbl3zIDk5WevWrVNlZaX1eOvXr5ck9enTRxaLRffcc48++ugjPfjgg3r22Wfl7++v4cOH6/fff7fWOX78uFatWqX09HSNGzdOkmq8VosWLdLcuXPVr18/vfDCC+rataseeughrVmzptbX85TJkyfrmmuu0eLFixUZGalHH31U9913n5KTk/X000+rqKhI48ePt+7//vvv6+GHH9bVV1+txYsX6+abb9acOXP00ksvVft7qquaznNKenq6goKCtHDhQt1www2aMWOGVqxYYdM54VpMxQfchI+Pj0JDQ3Xo0CHrZ6c6qL/q16+fMjIyrNsnTpxQ27Ztq+zj7e2tK6+8UkuXLj2jrDaGDx+uv/3tb5KkP/74Q0888YTmzp2rm2++WZL0448/Wr/VLSsrk8lk0pNPPqlu3bpJkjp37qz//ve/1oWQfvzxRx09elTDhw/XVVddJUlq1qyZtmzZIrPZXGO5t3fN30N26dJF559/vrKzs63TKtesWaNmzZrpmmuuOWP/7du3KzY21tqmzp07KzAwUI0aNarz9QIA1B/6SPv7yNatW6tJkyYKCgqq8mhCRUWFJk2apLi4OEnSgQMH1LRpU02ePNnal3bp0kWff/65vvrqKw0fPtxat2/fvho7dqy1TWvXrtVnn32m7t27KycnR40aNdI//vEP+fv7q1OnTvLz85Ofn58kKSUlRS+++KK2bdtmXRhxzZo16tatm0JCQvT5559ry5YtevXVV9W1a1dJ0nXXXafk5GQtXrxYc+bMkXRy9t3YsWOtj2ns2LGj2mtVVFSkJUuWaOTIkXrwwQclSddee62Ki4v19NNPq2/fvjVey9PdeOONGjVqlDWWkSNHKiUlRcOGDZMkHTlyRFOmTFFhYaGaNGmiZ555RikpKdbZCtdee628vLz0/PPPa+jQoef8PdWF2Wyu8TynnuG/5JJL9PTTT0uSunXrpgMHDuiFF17QkCFDbDo3XIfEHnBjjzzyiBITE8/4/PRFgaSTCwOdGkAUFBTomWeekdls1oIFCxQVFWXTuePj460/n3/++ZKk2NjYKjEcP35ckhQQEKBXXnlF0smpab/99pt+/PFH/fzzzwoICJB0suMIDQ3V6NGjlZycrO7du6tLly7q1KlTrcprw8fHR3379lV2dra1s16zZo1uvPFG+fqe+c/dVVddpfnz52v48OHq3bu3evbsec6BIgDAvdBH1q2PrM6ll15q/blFixb617/+JbPZrN9++806E+7w4cNnLD57euLp7e2t8PBwnThxwlp24sQJ3XTTTerbt6969OihwYMHy8vLS5J02WWX6fLLL9eaNWvUpUsXHT16VFu3btW8efMknXzsoVGjRrr66qtVUVFhPc+1116rTz75pEocrVu3tv5c07X6+uuvVVZWpuuvv77Kcbt166b33ntPe/fuVatWrWp97c72t3DqSxLp5JcKklRYWKj8/HwdPHjwrOdesGCBduzYcda/6br69ddfa32efv36Vanbq1cvrV27Vn/++aciIyPtjgXOQ2IPuImysjIdO3ZMERER1s9atWpVpXM4F29v7yr7xcbGqn///ho5cqTee+89m1ZWbdy48RmfBQYGnnP/DRs2aM6cOdq7d6+aNWum2NhYBQYGWqdENmnSRG+++aaee+45vf/++3rrrbcUEhKicePGaejQoTWW11b//v31r3/9S3v27FGjRo303XffafLkyWfd96677lKjRo20cuVKzZ49W7Nnz1anTp2Unp5e5fcAAHAt+kjH9JFnExQUdMY1ePfddzV//nwdOnRIYWFhateunQICAs54t/pf2+zt7W3dJyEhQc8//7xeffVVLVmyRM8//7xatWqluXPnqmPHjpJO3rV/9dVX9cQTT2j9+vXy8/NTjx49JElHjx5VSUlJlS9MTjl11/+U5s2bW3+u6VodPXpUkqzrCfxVfn5+nRL7uvwtnDr3ww8/rIcffvis53aEupznr1P9T13Lo0ePktgbDIk94Ca2b9+uiooKa2dnj/POO0+PPfaYxo8frwULFmjixIkOiPDcfvvtNz3wwAMaOHCg3nzzTWtH8MADD+jnn3+27nfZZZdp/vz5Ki8vV05Ojl5//XVNmzZNbdu2Vbt27Wosr4327durVatWWrdunfz9/dWiRYtzXlMfHx/dfvvtuv322/XHH3/o448/1sKFCzV58uQznkEDALgOfaRj+sja2LZtmx5//HHde++9+vvf/25N9AYPHlznY/Xs2VM9e/bU8ePH9dlnn2nx4sW6++67tWnTJvn7+ys5OVnPPPOMtm/fruzsbPXq1cv6OFxwcLDOO+88vfjii3U+b3XXKjg4WJL03HPPnfVL/IsvvrjO56utU+eeOnVqlTv9p1xwwQVOP8+RI0eqlB0+fFhS1S9LYAwsnge4AYvFoiVLlig0NFR9+vRxyDFvuukmdejQQW+++aZ++eUXhxzzXL777juZTCbddddd1gHLiRMnlJOTY/3m/rPPPlOXLl1UUFAgf39/denSRY8//rikk88n1lReF8nJydq4caPWrVunfv36Waf9/dWdd95pfUavZcuWSktLU+/evXXgwAGbrgMAwPHoIx3XR9bmWfyvv/5aXl5euueee6zJXV5enn744Ycz7thXZ+HChdbntIODg5WcnKx//OMfOn78uIqKiiRJUVFRat++vbKysrRlyxalpKRY63fs2FEFBQUKCgpSXFyc9b+srCz9+9//Pud5a7pW7dq1k5+fnw4fPlzluD/++KOee+65WrfPFqceE8jLy6ty7qNHj+rZZ5+1Xpfa/J4ccR5J2rhxY5W6GzZs0CWXXGLzon1wHe7YA05WWFior7/+WtLJBWvy8vL07rvv6quvvlJ6erqaNGlinUKVm5tr3fevLrzwwhq/TZ04caL+9re/ae7cuTZ9411bV1xxhXx8fPTPf/5Tt912m44cOaJXXnlFhw4dkr+/v6STz6BZLBaNGTNGo0aNkp+fn15//XWFhISoc+fO1ul75yqvi1ML8kjSjBkzzrlfx44dtXjxYoWFhSkuLk4///yzsrOzNWLECNsvBgDAZvSR9dtHhoSEaPfu3dq6des57/LHxcXJbDZr9uzZuvHGG3XgwAEtXrxY5eXlKikpqfW5OnfurOeee05TpkxRcnKyjh07phdeeEEdO3as8rtJSUnRrFmzFBwcbF0kT5J69OihuLg43XXXXRozZoxatGihdevW6a233tK0adPOed6armXz5s01fPhwPfXUUzp27Jji4+P1/fffKyMjQ7169bLpNYi15evrq/vvv19PPfWUpJOLEu7bt09PP/20LrroIuud9L/+nqp7zMOe80jS559/runTp6tnz57auHGj1q9fr/nz5zumwXAqEnvAyf7zn/9YV9L18/OzJpVvv/32GZ3sM888c87jzJs3TwMGDKj2XO3atVO/fv300Ucf6YsvvtC1115rfwPO4uKLL9bcuXO1aNEi3XXXXQoLC1O3bt00aNAgTZ8+XXl5eYqIiNBLL72kp59+WhMmTJDJZFJ8fLxeffVVawdfU3lttW7dWpdffrnKy8t15ZVXnnO/U6/1efvttzV//nydf/75GjFihMaMGWPX9QAA2IY+sn77yNtvv13jxo3TyJEj9frrr591ny5duuixxx7T66+/rvfee0+RkZHq27evfH199frrr1vfZV+TTp066ZlnntGSJUv04YcfKiAgQN26dTvj0Ye+fftq9uzZSkpKqvLsvI+Pj15++WWlp6frn//8p4qKihQdHa05c+YoNTX1nOcNDQ2t8VqNHz9ezZs314oVK7RgwQKFh4c7rf//+9//rsDAQL322mt65ZVXFBoaqhtvvFHjxo2zzjD86++pQ4cO9XIeSRo5cqR2796te++9VxdeeKEyMjJ04403Oqy9cB4vS13m1AAAAAAADC8mJkYTJkzQP/7xD1eHAgfgjj3QQJjNZuvqu+fi5eUlHx8fJ0VUexaLRZWVlTXud7ZX2gEAUBP6SJzibtezsrKyxrUNvL297X4uH8bH/+FAA/Hcc89p0aJF1e4TFRV1xrth3cG2bduUlpZW434bNmxw2IqyAICGgz4Sp7z//vt67LHHatxvz549Tojm5LT8bdu2VbvPwIEDrc/To+FiKj7QQOTl5engwYPV7uPv76+YmBgnRVR7RUVF+vXXX2vcLyYmxroQEQAAtUUfiVOOHDmiffv21bhfXFycE6KRfvnlFxUXF1e7T7NmzfjSBg0jsTebzSouLpafn985X3sFAIC7sVgsMplMaty4MdMsHYhxAQDAaGoaEzSIqfjFxcX64YcfXB0GAAA2ufzyyxUcHOzqMDwG4wIAgFGda0zQIBL7U6/OuPzyy10+BWnnzp2KjY11aQxGxHWzDdet7rhmtuG62aam61ZeXq4ffvihyiugYD9Hjws88e/f09rkae2RPK9NntYeyfPa5GntkYzVpprGBA0isT81zc7f318BAQEujkZuEYMRcd1sw3WrO66ZbbhutqnNdWO6uGPVx7jAE//+Pa1NntYeyfPa5GntkTyvTZ7WHsl4bTrXmIAH9gAAAAAAMDASewAAAAAADIzEHgAAAAAAA2sQz9gDAM7NZDJp3759Ki0ttfkYvr6+2r17twOjahhOXbfAwEBdcMEFLJIHAHCp6sYEntjXu2ObbB0TkNgDQAO3b98+BQcH66KLLrJ5kbbi4mI1btzYwZF5vuLiYgUFBenw4cPat2+fLr74YleHBABowKobE3hiX+9ubbJYLDaPCZiKDwANXGlpqc477zxWXncRLy8vnXfeeXbNmAAAwBEYE7iWPWMCEnsAAB24i3H9AQDugj7JtWy9/kzFBwBUceSIVFhYtzrl5X7y969+n5AQqVkz2+MCAADOdfqYoDZ9fW0xJnC8ek3ss7KytHjxYlVUVGjEiBEaNmxYlfLdu3dr8uTJKi4uVkJCgqZNmyZfX19t375ds2fPlslkUlRUlObOnaumTZtq27Ztuv/++xUZGSlJuvLKKzVnzpz6bAIANDiFhdLatXWrU1ZmUUBA9fskJdWuE8/OztaSJUtUUVEhi8WiAQMGaOTIkRo1apRmzpypiIiIugVXjZiYGO3Zs8dhxwMAwJOcPiaoTV9fW7UdE0iMC2qr3hL7vLw8ZWRkaNWqVfL399ett96qzp07q3Xr1tZ9xo8fr5kzZ6p9+/aaNGmSVqxYoaFDh+qxxx7T4sWL1bp1a6Wnp+vll1/WQw89pJ07d+rOO+/U3XffXV9hAwBcKC8vT3PnztWqVavUrFkzFRcXa/jw4br44ou1dOlSV4cHAACciHFB7dVbYr9p0yYlJiYqNDRUkpSUlKTs7GyNGTNGkrR//36Vlpaqffv2kqTU1FQtWLBAQ4cO1erVq+Xn5yeTyaS8vDzFxMRIkr799lsdOnRIH374oaKiovTEE0+oRYsW9dUEAICTHTlyRCaTybpoTOPGjfXUU08pICBAPXv21BtvvKGIiAg98cQTysnJUUREhLy8vHTvvfdKkl588UUFBgbq559/VkxMjNLT0+Xv76+MjAxt3rxZx44dU3h4uDIyMnT++ee7sqkAAKAG9T0uOHLkiCIjIz1iXFBvif3BgwcVFhZm3Q4PD9eOHTvOWR4WFqa8vDxJkp+fn/bs2aM77rhDvr6+euihhyRJwcHB6tu3r/r06aO3335b48aN0/Lly+urCQBOseWh69PxIBVqqU2bNurVq5d69+6tK664Qp07d1ZKSoqio6Ot+yxfvlwlJSXKzs7WH3/8oZSUFGvZf//7X61Zs0bh4eEaMmSIvvjiC1166aX65ZdftHz5cnl7e2vChAn697//rTvvvNMVTYQHCvP1lXJzbT8A/0YCwFnV97igpKRE06ZN84hxQb0l9mazucqKfhaLpcp2TeUxMTHatGmTli9fbk3gp0+fbi2/7bbb9PTTT+v48eMKDg6uVUw7d+60p0kOk5OT4+oQDInrZhtHXLeWJpPKs7Jsru+fkqI//PzsjsNZGtrfmq+vr4qLi63b5eV+Kiuz1Pk4ZWVl1ZaXl3upuNhU43EmTJig22+/XZs3b9bmzZs1ZMgQzZw5U2azWSUlJfrss880cOBAnThxQqGhoerUqZP1m/xLLrlEwcHBKikpUXR0tA4ePKjOnTtr7NixevPNN5Wbm6v//Oc/ioyMtLb59La7wqnzl5eXN7i/PU/hV1Iibd1q+wHq8rApADQw06ZN07333qsvvvhCX3zxhYYMGaL09HRr+ZdffqkhQ4bIy8tLUVFR6tKli7Xssssus67Pdumll+rYsWOKjo7Wo48+qnfffVc//PCDvv76a1144YVOb5ej1VtiHxkZqe3bt1u38/PzFR4eXqU8Pz/fun3o0CGFh4errKxMn3/+uXr37i1JuummmzR37lyZzWa9+OKLuuuuu+Tj42Otd/rPNYmNjVWAo1Z8sFFOTo46duzo0hiMiOtmG4ddt9xc6bRvRuusRQu1sKe+EzXEv7Xdu3ercePG1m1/f9V5cZyysrIa/33195caN65+Od2NGzfqxIkT6tevny6++GINHTpUK1as0Icffihvb281atRI/v7+8vf3t8bs4+OjwMBASVJQUJD1cz8/P/n7++vXX3/Vww8/rNtvv139+/dXQECA/Pz8rPud3nZnKy4utp7f399f7dq1q1JeVlbmNl9KAwDgbKePCwYNGqRBgwZpxYoVWrlypXUfHx8fmc3ms9Y/fWzi5eUli8WinTt3WscFvXr1UkBAgCyWut/QcDf19h77rl27avPmzSooKFBJSYnWrVunbt26WcujoqIUEBBgvTuRmZmpbt26ydfXV9OmTbMOZNasWaMOHTrI29tb69ev19r/vyzjBx98oHbt2ikoKKi+mgAAcLLAwEA9/fTT2rdvn6STs7l2796tK664wrpP165dtXr1alksFuXl5Wnbtm3VvvP1q6++UqdOnXTbbbfpoosu0saNG1VZWVnvbQEAAPap73FBdHS0x4wL6u2OfUREhMaNG6e0tDSZTCYNHjxY8fHxGjVqlMaOHau4uDilp6drypQpKioqUtu2bZWWliYfHx9lZGRo6tSpqqysVEREhGbNmiVJmjt3rh5//HE999xzat68uebNm1df4QNAgxUScnJmcF2Ul3vV6j32NUlMTNSYMWM0evRomUwnp+1fd911uu+++5T1/x8HGTJkiL7//nulpKQoLCxMLVu2VGBgoEpKSs56zH79+mnMmDHWZ+5iY2OtAwQAAHBup48JatPX1+W4tVHf4wKz2ewx44J6fY99SkpKlcULJFV5LUGbNm2qTKM4JSEhQatWrTrj88suu4zF8gCgnjVrVvfHfYuLTTVOs6+tgQMHauDAgWd8/sknn0g6OS2vZ8+emjFjho4fP66bb75ZF154oUJDQ9W5c2fr/k899ZT153ffffes5zLqu2oBAHCG08cEjuzr66I+xwWnPxInGXtcUK+JPQAAjnbppZdqwoQJmj9/viRp7Nix1lerAgCAhoVxwUkk9gAAQ2nVqpXefvttV4cBAADcAOOCk+pt8TwAAAAAAFD/SOwBAAAAADAwEnsAAAAAAAyMxB4AAAAAAANj8TwAQFVHjkiFhXWq4lderlq9yL4W79HbunWrRo8erQsvvFAWi0Umk0m33nqrRowYUaeYtm7dqkWLFulf//pXnerVt549e+qNN97QBRdc4OpQAACo3mljglr19bXFmECSY8cEJPYAgKoKC6W1a+tUxVJWJgUEVL9TUlKtOnFJio2NtXa+RUVFSk5O1jXXXKPWrVvXKS4AAGCH08YEterra4sxgcOR2AMA3FpZWZl8fHwUHBysNWvW6NVXX1VpaanKy8s1e/ZsdejQQbt379bUqVNVWlqqpk2bKj09vcoxXn/9dX388cdasmSJ9u7dq4kTJ6qyslIJCQn67LPPtH79ek2cOFFHjx5Vbm6uxo8fr+bNm2vWrFkqKytTs2bNNH36dEVHR2v48OEaM2aMOnfurH379iktLU2ffPKJJk6cqCZNmmjXrl3Ky8vTfffdp0GDBuno0aMaP368/vzzT1166aUqKytz0ZUEAMDYHD0m+OmnnzRt2jSPGBOQ2AOofxUVUm6ubXVrOVULnmXnzp0aMGCAzGazfv/9d/Xt21fnn3++li9frhdeeEHNmzfXypUrtWTJEr3wwgt65JFH9Mgjj6hHjx5atmyZXn/9dV1//fWSpFWrVmndunVasmSJGjVqpIkTJ+qBBx5Q9+7d9dprr6mystJ63tDQUL3wwgsqLy/XjTfeqPnz5ys+Pl5r1qzRQw89pPfee6/auP/8808tW7ZMP/zwg9LS0jRo0CAtWLBAV155pZYuXaqvvvpKa9asqc9LBwCAR6nPMcHUqVP10EMPecSYgMQeaAhseGa6ipIS+85fXCxt2WJb3TpM1YLn+Ou0u5EjR+qll17Sc889p08++US//vqrtm3bJm9vbxUUFCg/P189evSQJA0dOlTSyefpfvjhBz3++ON65pln1LhxYx09elT79+9X9+7dJUmDBg3SG2+8YT1vfHy8JOm3335TSEiIdbtv376aOnWqjh8/Xm3c11xzjby8vHT55Zfr6NGjkqRt27bp6aefliRdffXVatWqlYOuEgAAnq8+xwQHDhzwmDEBiT3QENjwzHQViYmOiwWooyZNmqhv3776v//7P73//vu66aabdPXVVysmJkZvvfWW/Pz85OXlZd2/rKxMBw8elCQ1btxYc+bM0axZs3TdddfJx8dHFovlnOcKDAyUJJnN5jPKLBaL9Zv8U8eoqKiosk/A/3/28PR4vLy8qpzTx8enTu2H+/P2DtLhw7bXDyqRGjkuHADwWIwJzo3X3QEA3FplZaW2bdumwMBAeXl5afTo0ercubPWr1+vyspKBQcHKyIiQl988YUkKTMzU88++6wkKSoqSj179lSnTp20YMECBQcHq1WrVvr0008lSVlZWWc95yWXXKKjR49qx44dkqTVq1erZcuWCg0NVbNmzfTTTz9Jkj7++OMa4+/SpYsyMzMlSTt27NDvv/9u3wWB2zFV+GjXLtn8X1m5q1sAAMbg6DHBBRdc4DFjAhJ7AIDbOfU83c0336wBAwYoMDBQzzzzjK644gr17dtXycnJatasmf744w9J0j//+U8999xzGjBggFavXq0JEyZUOd6ECROUlZWlXbt2ad68eXr++ec1cOBA7dixw/qN/On8/f2VkZGhGTNmqH///nrrrbeUkZEhSRo5cqSWLVumgQMHqrS0tMa2jB07Vnv37lVycrKWLl3KVHwAAOqgPscE06dP95gxgZeluvkHHqKsrEw7d+5UbGysdUqEq+Tk5Khjx44ujcGIuG62sV633Fz7p+Lb+oy8vfWTkqToaNvPXUcN8W9t9+7duuKKK/73gQ1rMpSXl8vfQe+xr2+LFi3SkCFDFB4ernXr1ikrK0sLFy50SSzFxcVq3LixpLP8HuRe/ZcncfR13bdpl355/kub68dPSFJovPP+nasNT/u30NPaI3lemzytPZIx21TdmKBWfX1tucmYICMjQ8OGDXOLMcHpbBkT8Iw9AKCqZs3q3Nmaiovl//8TVHfXsmVL3XnnnfL19VVISIhmzZrl6pAAAHBPp40JjNTX11ZkZKTHjAlI7AEADUpqaqpSU1NdHQYAAHCxm266Sbfddpurw3AInrEHAAAAAMDASOwBANW+7gX1j+sPAHAX9EmuZev1J7EHgAYuMDBQhw8fpiN3EYvFosOHD591JV4AAJyJMYFr2TMm4Bl7AGjgLrjgAu3bt0/5+fk2H8OhK+U2IKeuW2BgoC644AJXhwMAaOCqGxN4Yl/vjm2ydUxAYg8ADZyfn58uvvhiu46Rk5Ojdu3aOSiihoPrBkny96o4+VpSW7nJa6MAGF91YwJP7LM8qU0k9gAAAC7kXVosbd5i+wGSkkjsAaCB4xl7AAAAAAAMjMQeAAAAAAADI7EHAAAAAMDASOwBAAAAADAwEnsAAAAAAAyMxB4AAAAAAAMjsQcAAC6TlZWlfv36qU+fPnrrrbfOKN+9e7dSU1OVlJSkyZMnq6Kiokr5d999p9jYWGeFCwCAW6rXxN7Wznr79u1KTU1VSkqKRo8erWPHjkmSCgsLddddd6lv374aNmyY8vPz6zN8AABQj/Ly8pSRkaFly5bpgw8+0DvvvKOffvqpyj7jx4/X1KlTtXbtWlksFq1YscJaVlJSohkzZshkMjk7dAAA3Eq9Jfb2dNaPPfaY5s2bp6ysLLVu3Vovv/yyJGn+/PlKSEjQmjVrdMstt2jWrFn1FT4AAKhnmzZtUmJiokJDQxUUFKSkpCRlZ2dby/fv36/S0lK1b99ekpSamlql/KmnntKIESOcHTYAAG7Ht74OfHpnLcnaWY8ZM0bS2TvrBQsWaOjQoVq9erX8/PxkMpmUl5enmJgYSdLGjRutd/779++v6dOny2Qyyc/Pr76aAQAA6snBgwcVFhZm3Q4PD9eOHTvOWR4WFqa8vDxJ0oYNG1RaWqobb7zR5vPv3LnT5rqni1CgCgoO21y/vLxcebm5Ntf3P3BAfxw6ZHP9c8nJyXH4MV3J09ojeV6bPK09kue1ydPaI3lOm+otsbens/bz89OePXt0xx13yNfXVw899NAZdXx9fdWkSRMVFBQoIiKivpoBAADqidlslpeXl3XbYrFU2T5XeX5+vhYvXqzXXnvNrvPHxsYqICDArmNI0r5Nu9S8+Xk21/f391d0dLTtAbRooRb21D+LnJwcdezY0aHHdCVPa4/keW3ytPZIntcmT2uPZKw2lZWVVfuFdL0l9rZ21qfExMRo06ZNWr58ucaNG6fly5efcQ6LxSJv79o/TeCob+bt5SnfCjkb1802OTk5amkyqdyOu0HNY2NV4KL69XUnqjr8rdmG62abhnzdIiMjtX37dut2fn6+wsPDq5Sfvp7OoUOHFB4ero0bN+ro0aMaNmyYtWzAgAF666231KRJE+cEDwCAG6m3xN7WzrqsrEyff/65evfuLUm66aabNHfuXEkn7/ofOnRIkZGRqqioUHFxsXWqf2046pt5exjpWyF3wnWzjfW65eZK9tzNadJEwa6qXw93oqrD35ptuG62qem61fTtvNF17dpVCxcuVEFBgRo1aqR169ZpxowZ1vKoqCgFBARYr1NmZqa6deumW265Rbfccot1v5iYGGVmZrqiCQAAuIV6Wzyva9eu2rx5swoKClRSUqJ169apW7du1vLTO2tJ1s7a19dX06ZNsw5k1qxZow4dOkiSunfvrg8++ECStHr1aiUkJPB8PQAABhUREaFx48YpLS1NN998s/r376/4+HiNGjVK3377rSQpPT1dc+bM0Y033qgTJ04oLS3NxVEDAOB+6u2O/emdtclk0uDBg62d9dixYxUXF6f09HRNmTJFRUVFatu2rdLS0uTj46OMjAxNnTpVlZWVioiIsK5+/8ADD2jixIlKTk5WcHCw0tPT6yt8AADgBCkpKUpJSany2dKlS60/t2nTRitXrqz2GHv27KmX2AAAMIp6S+wl2zvrhIQErVq16ozPQ0ND9cILLzg+UAAAAAAADKrepuIDAAAAAID6R2IPAAAAAICBkdgDAAAAAGBgJPYAAAAAABgYiT0AAAAAAAZGYg8AAAAAgIGR2AMAAAAAYGAk9gAAAAAAGBiJPQAAAAAABkZiDwAAAACAgZHYAwAAAABgYCT2AAAAAAAYmK+rAwAAAGjIzGbp8GHb6weVSI0cFw4AwIBI7AEAAFzIbJZ27bK9fnwyiT0ANHQk9gDcW0WFlJtre/2QEKlZM8fFAwAAALgZEnsA7q24WNqyxfb6SUkk9gAAAPBoLJ4HAAAAAICBkdgDAAAAAGBgTMUHjOLIEamwsE5VWppMJ59PLympp6Bqx1QhFdq44nNIheTn2HAAAAAAj0JiDxhFYaG0dm2dqpTn5krR0VJiYj0FVTuVlbav+NypN4k9AAAAUB0SewAAAAPz97Lj7SG8OQQAPAKJPQAAgIF5lxZLm218ewhvDgEAj0BiDwAAYGBms3TYxnVM/AqlI2e52W8ytazVJABu+AOAeyCxBwAAMDCz2fZ1TGJ6S2s3n/l5bm65oqNrrs8NfwBwD7zuDgAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMXzAKAeHTkiFRbaVpfVpgEAAFAbJPYAUI8KC6W1a22ry2rTAAAAqI16nYqflZWlfv36qU+fPnrrrbfOKN+9e7dSU1OVlJSkyZMnq6KiQpKUk5OjwYMHa8CAARoxYoT2798vSdq2bZs6d+6sAQMGaMCAAXrsscfqM3wAAAAAANxevSX2eXl5ysjI0LJly/TBBx/onXfe0U8//VRln/Hjx2vq1Klau3atLBaLVqxYYf185syZyszMVEpKimbOnClJ2rlzp+68805lZmYqMzNTc+bMqa/wAQAAAAAwhHpL7Ddt2qTExESFhoYqKChISUlJys7Otpbv379fpaWlat++vSQpNTVV2dnZKi8v1wMPPKA2bdpIkmJiYnTgwAFJ0rfffqsvvvhCKSkpGj16tPVzAAAAAAAaqnpL7A8ePKiwsDDrdnh4uPLy8s5ZHhYWpry8PPn7+2vAgAGSJLPZrEWLFql3796SpODgYA0fPlxZWVnq3r27xo0bV1/hAwAAAABgCPW2eJ7ZbJaXl5d122KxVNmuqby8vFwTJ05URUWF7r77bknS9OnTreW33Xabnn76aR0/flzBwcG1imnnzp02t8eRcnJyXB2CITX069bSZFJ5bm6d6+Xm5qp5bKwKbKh7ir31m7XtoIKCwzbVLS8vV54d5/Y/cEB/HDpUpzqO/FszmVoqN7fcproHDvjr0KE/HBZLfWvo/4/aiusGAADsVW+JfWRkpLZv327dzs/PV3h4eJXy/Px86/ahQ4es5cXFxbrnnnsUGhqqxYsXy8/PT2azWS+++KLuuusu+fj4WOud/nNNYmNjFRAQYE+z7JaTk6OOHTu6NAYj4rpJys2VoqPrWCVX0dHRUpMmCq5j3SrsrF/q76/mzc+zqa6/v//JNtiqRQu1qEN9R/+t2fBrs2rRQoqObuGwWOoT/4/apqbrVlZW5jZfSgMAAPdVb1Pxu3btqs2bN6ugoEAlJSVat26dunXrZi2PiopSQECA9U5FZmamtXz8+PGKjo7W/Pnz5e/vfzJQb2+tX79ea///e6M++OADtWvXTkFBQfXVBAAAAAAA3F693bGPiIjQuHHjlJaWJpPJpMGDBys+Pl6jRo3S2LFjFRcXp/T0dE2ZMkVFRUVq27at0tLS9N1332nDhg1q3bq1Bg4cKOnk8/lLly7V3Llz9fjjj+u5555T8+bNNW/evPoKHwAAAAAAQ6i3xF6SUlJSlJKSUuWzpUuXWn9u06aNVq5cWaX8yiuv1J49e856vMsuu0zLly93fKAAAAAAABhUvU3FBwAAAAAA9Y/EHgAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMQeAAAAAAADI7EHAAAAAMDASOwBAAAAADAwX1cHAAD1qaREOphb+/1NppbKPW3/kBCpWTPHxwUAAAA4Cok9AI9WVi6t/az2++fmlis6+n/bSUkk9gAAAHBvTMUHAAAuk5WVpX79+qlPnz566623zijfvXu3UlNTlZSUpMmTJ6uiokKStH37dqWmpiolJUWjR4/WsWPHnB06AABug8QeAAC4RF5enjIyMrRs2TJ98MEHeuedd/TTTz9V2Wf8+PGaOnWq1q5dK4vFohUrVkiSHnvsMc2bN09ZWVlq3bq1Xn75ZVc0AQAAt0BiDwAAXGLTpk1KTExUaGiogoKClJSUpOzsbGv5/v37VVpaqvbt20uSUlNTreWrV69W69atZTKZlJeXp5CQEFc0AQAAt8Az9gAAwCUOHjyosLAw63Z4eLh27NhxzvKwsDDl5eVJkvz8/LRnzx7dcccd8vX11UMPPVTn8+/cudOO6P8nQoEqKDhsc/3KygqX1S8vL1Nu7tlXGD3X56c7cMBfhw79YdO5nS0nJ8fVITicp7XJ09ojeV6bPK09kue0icQeAAC4hNlslpeXl3XbYrFU2a6pPCYmRps2bdLy5cs1btw4LV++vE7nj42NVUBAgB0tOGnfpl1q3vw8m+v7+Pi6rL6/f4CiT18x9P/Lzc096+d/1aKFFB3dwqZzO1NOTo46duzo6jAcytPa5GntkTyvTZ7WHslYbSorK6v2C2mm4gMAAJeIjIxUfn6+dTs/P1/h4eHnLD906JDCw8NVVlamjz/+2Pr5TTfdpD179jgnaAAA3BB37AEAgEt07dpVCxcuVEFBgRo1aqR169ZpxowZ1vKoqCgFBARY76hkZmaqW7du8vX11bRp0xQZGanY2FitWbNGHTp0cGFLjKuRX4USws+ccn9ZwDE1bVrzVPwmFSGSeCcoALgaiT0AAHCJiIgIjRs3TmlpaTKZTBo8eLDi4+M1atQojR07VnFxcUpPT9eUKVNUVFSktm3bKi0tTT4+PsrIyNDUqVNVWVmpiIgIzZo1y9XNMSTvkmIVrdxyxufHCg7LpxbT+30uSRKJPQC4Hok9AABwmZSUFKWkpFT5bOnSpdaf27Rpo5UrV55RLyEhQatWrar3+AAAMAKesQcAAAAAwMBI7AEAAAAAMDASewAAAAAADIzEHgAAAAAAAyOxBwAAAADAwEjsAQAAAAAwMBJ7AAAAAAAMjMQeAAAAAAAD83V1AABQHbNZOnzY9vqWCsfFAgAAALgjEnsAbs1slnbtsr1+TG/HxQIAAAC4o1ol9vfff79uu+02de3atb7jAQCHauRXoYTw3Frvf1nAMTVt+r/9m1SESGpWD5EBnofxAgAArlGrxP6GG27Q888/r2nTpmnIkCEaNGiQQkNDa6yXlZWlxYsXq6KiQiNGjNCwYcOqlO/evVuTJ09WcXGxEhISNG3aNPn6+ionJ0dz5syRyWRSaGioZs+eraioKBUWFuqRRx7R3r171bx5c82fP19hYWE2NRxAw+BdUqyilVtqvf+xgsPyaX6eddvnkiSR2AO1Y+t4AQAA2KdWi+fddNNNevPNN/X888/r8OHDGjx4sMaPH68dO3acs05eXp4yMjK0bNkyffDBB3rnnXf0008/Vdln/Pjxmjp1qtauXSuLxaIVK1ZYP585c6YyMzOVkpKimTNnSpLmz5+vhIQErVmzRrfccotmzZpla7sBAICD2TJeAAAA9qv1qvhms1m5ubn67bffVFlZqfPOO09PPvmkFixYcNb9N23apMTERIWGhiooKEhJSUnKzs62lu/fv1+lpaVq3769JCk1NVXZ2dkqLy/XAw88oDZt2kiSYmJidODAAUnSxo0blZKSIknq37+/PvvsM5lMJpsaDgAAHK+u4wUAAGC/Wk3Fz8jI0KpVq9SqVSsNHTpUzz77rPz8/HTixAn16NFDY8eOPaPOwYMHq0yTDw8Pr/KN/V/Lw8LClJeXJ39/fw0YMEDSycHBokWL1Lt37zPq+Pr6qkmTJiooKFBERIQNTQcAAI5ky3gBAADYr1aJfUFBgZYuXWq9i35KUFCQnn766bPWMZvN8vLysm5bLJYq2zWVl5eXa+LEiaqoqNDdd9991nNYLBZ5e9d60oF27txZ633rU05OjqtDMKSGft1amkwqz639InCn5ObmqnlsrApsqHuKvfWbte2gggLb3llXWVlhc11b65++f1FRkX6242/PZGqp3Nxym+oeOOCvQ4f+sPncztbQ/x+1lSddN1vGCwAAwH61SuwrKyvP6KTHjh2rBQsW6Nprrz1rncjISG3fvt26nZ+fr/Dw8Crl+fn51u1Dhw5Zy4uLi3XPPfcoNDRUixcvlp+fn6STd/0PHTqkyMhIVVRUqLi4uE6L8sTGxiogIKDW+9eHnJwcdezY0aUxGBHXTVJurhQdXccquYqOjpaaNFFwHetWYWf9Un9/NT9tQbq68PHxtbmuLfULCg5X2b9Jkya6IL6tzee34ddm1aKFFB3dwuZzOxP/j9qmputWVlbmNl9K14Yt4wUAAGC/ahP7J554Qnl5ecrJyVFBQYH184qKCu3du7faA3ft2lULFy5UQUGBGjVqpHXr1mnGjBnW8qioKAUEBFgHNZmZmerWrZukk4vnRUdHa9q0aVXuyHfv3l0ffPCBRo8erdWrVyshIcGa9AMAANewZ7wAAADsV21iP3jwYP3444/as2ePkpKSrJ/7+PhYF707l4iICI0bN05paWkymUwaPHiw4uPjNWrUKI0dO1ZxcXFKT0/XlClTVFRUpLZt2yotLU3fffedNmzYoNatW2vgwIGSTt6pX7p0qR544AFNnDhRycnJCg4OVnp6uv1XAADcVEXFyTv+tgoJkZrxpj44gT3jBQAAYL9qE/u4uDjFxcXpmmuusWmBupSUFOsq9qcsXbrU+nObNm20cuXKKuVXXnml9uzZc9bjhYaG6oUXXqhzHABgRMXF0pYtttdPSiKxh3PYO14AAAD2qTaxf+CBB/Tss89q5MiRZy3Pysqql6AAAIBxMF4AAMC1qk3sR40aJUl6/PHHnRIMAAAwHsYLDZe/F88MAYA7qDaxj42NlSR16tRJe/fuVatWrbRx40bt2rVLaWlpTgkQAAC4N8YLDZd3abG0mWeGAMDVavUS+KlTp2rp0qX6+eefNWXKFO3bt0+TJk2q79gAAICBMF4AAMA1apXY79y5U08++aTWr1+vgQMHas6cOdq/f399xwYAAAyE8QIAAK5Rq8TeYrHI29tbX375pRITEyVJpaWl9RoYAAAwFsYLAAC4Rq0S+wsvvFCjRo3Svn371KlTJz388MOKiYmp79gAAICBMF4AAMA1ql0875Q5c+Zo/fr16tixo/z8/JSQkKCbb765nkMDAABGwngBAADXqNUd+6CgICUkJKiwsFC7du1SfHy8fvnll/qODQAAGAjjBQAAXKNWd+yfffZZvfLKKzrvvPOsn3l5eWnDhg31FhgAADAWxgsAALhGrRL7zMxMrVu3ThEREfUdD+C5jhyRCgttr19S4rhYAKAeMF4AAMA1apXYt2jRgk4asFdhobR2re31//8K0wDgrhgvAADgGrVK7Lt06aJ58+apV69eCgwMtH7etm3begsMAAAYC+MFAABco1aJ/apVqyRJ2dnZ1s94Zg4AAJyO8QIAAK5Rq8T+k08+qe84AACAwTFeAADANWr1urvi4mJNnz5dI0aM0NGjRzV16lQVFxfXd2wAAMBAGC8AAOAatUrsZ86cqeDgYB0+fFgBAQEqKirS1KlT6zs2AABgIIwXAABwjVol9rt379a4cePk6+urRo0aKT09Xbv/X3v3Hhdlmf9//M0ZERXxCx6wsM3SEk2F8pB5KkVFwsjKNDFNy06WbRippWlmGZ6yLLO27WClmYq2hpauu3nIlK0U16ytZEUNEVAOchhm5veHP2cd5TjDMAy8no+HD7nv677u63Nf3MD1mfu+r/vwYUfHBqCOMJRKWVm2/zOanH0EAGoD4wUAAJyjSs/Yu7tb5/9Go/GydQDqL6NROnTI9vrd+9dYKADqMMYLAAA4R5US+xtvvFGvvvqqioqK9M033+ijjz5Sjx49HB0bAABwIYwXAABwjip9jP7000/Lz89PTZo00ZIlS9SxY0dNmzbN0bEBAAAXwngBAADnqPSK/VdffaV3331XR44cka+vrzp06KDu3bvLx8enNuIDAAAugPECAADOU2Fi/+WXX2rx4sWaMmWKOnbsKDc3Nx08eFDz5s1TcXGxBg8eXFtxAgCAOorxAgAAzlVhYv/BBx/or3/9q9q0aWNZd/XVV+uGG27Q9OnT+UMNAAAYL8ApcnKk3NyqbWswtFFamvU6D4/zk8PaqmlTqXlz2+sDQE2qMLEvKCiw+iN9wVVXXaXi4mKHBQUAAFwH4wU4Q26utGVL1bZNSytRaKj1up49pW+/tb39yEgSewB1R4WT53l4eJRbZjabazwYAADgehgvAADgXFV63R0AAABQ40pLddk98lXkX9pUEpfMAUCqJLE/cuSIunfvftl6s9mskpIShwUFAHWFyWTzmFOSVFhYc7EAdRXjBdisoMDm++E9ekaKxB4Azqswsf/qq69qKw4AqJMMhqo/w1mWnj1rLhagrmK8AACAc1WY2IeEhNRWHAAAwEUxXgAAwLkqnDzPXps2bdKwYcM0ePBgrVq16rLyw4cPKzY2VpGRkZoxY4ZKS0utypcsWaJly5ZZlr/77jv16NFDMTExiomJ0bPPPuvI8AEAgIPZOlZISUnRyJEjFRMTo3Hjxun48eO1HToAAHWGwxL7jIwMLV68WB9//LE2bNig1atX6z//+Y/VNvHx8Xr++ee1ZcsWmc1mrVmzRpKUl5en6dOn67333rPaPjU1VRMmTFBSUpKSkpI0f/58R4UPAAAczJ6xQnx8vF588UUlJSUpOjpaL774ojMOAQCAOsFhif3u3bvVs2dPBQQEyM/PT5GRkUpOTraUHz9+XEVFRerataskKTY21lK+bds2tWvXTuPHj7fa58GDB7Vz505FR0dr8uTJOnnypKPCBwAADmbrWKGkpERPPPGEOnbsKEnq0KEDYwIAQIPmsNfdnTp1SkFBQZbl4OBgHThwoNzyoKAgZWRkSJJGjBghSVa34UtSkyZNNHToUA0ePFiffPKJpk6dqk8//dRRhwAAABzI1rGCt7e3YmJiJEkmk0mvv/66brvttmq3n5qaakf0/9NSvsrOzrK5vtFY6rT6FdWtyj4NhlKd+O2sTW1LUnBYsbJsfPWIR1i+0qpR99Jtw8IClZaWbVPbknTypLdOnz5hc/2akJKS4tT2a1p9Ox6p/h1TfTseqf4ck8MSe5PJJDc3N8uy2Wy2Wq6svCxz5syxfH3vvfdq4cKFysvLU5MmTaoUU039AbdXfTl5apur91sbg0Eldrw3LTAsTNk21E9LS7O57gXNO3Wvk4NOR9W/ePuSkuJqDRwvZc/A0dUGna7+M+osDbnf7B0rlJSUKCEhQaWlpXrooYeq3X5YWJh8fHxsjP5/0ncfUmBgC5vre3h4Oq1+eXWzs7OqtE83N0+lpzezqW1JauPlo9DQUJvqnvH3r3LdtLS0y7b195dCQ6s2hixL69ZSaGhrm+vbKyUlReHh4U5rv6bVt+OR6t8x1bfjkVzrmIqLiyvMZx2W2Ldq1Ur79++3LGdmZio4ONiqPDMz07J8+vRpq/JLmUwmrVixQg8++KA8PDws6y/+ujI19QfcHq508tQl9aLf0tIkGwcvkiR/fzWpZn3LQMaGuhcr8vauc4NOR9W/dDDr7W37oFOyb+DoSoPOevEz6gSV9Vtlf8RdnT1jhYKCAj388MMKCAjQm2++KS8vr9oLHACAOsZhz9j37t1be/bsUXZ2tgoLC7V161b17dvXUh4SEiIfHx/LlYqkpCSr8ssCdXfXV199pS3//4XSGzZs0A033CA/Pz9HHQIANGg5Oec/j6rKP4OhjdVyTo6zo4crsGesEB8fr9DQUC1ZskTe3t5OiR8AgLrCYVfsW7ZsqalTpyouLk4Gg0EjR45Uly5dNGnSJE2ZMkWdO3dWYmKiZs6cqfz8fHXq1ElxcXEV7vOVV17Rc889pzfeeEOBgYFasGCBo8IHgAYvN1f6/5+lViotrcTqhpTISKl5c8fEhfrD1rHCv//9b23btk3t27fXHXfcIen88/krV6508hEBAOAcDkvsJSk6OlrR0dFW6y7+o9uxY0etXbu23PqPP/641fI111zDZHkAalUjr1JFBNsxN4JbU0lkuEB5bBkrXH/99Tpy5EitxAcAgCtwaGIPAK7OvbBA+Wu/tbl+yBORIrEHAACAIznsGXsAAAAAAOB4XLEHAACAy/F2q/qjUtf4nFWzZtbb8qgUgPqExB4AAAAux72o6o9Knc3Oksclrz7lUSkA9Qm34gMAAAAA4MJI7AEAAAAAcGEk9gAAAAAAuDASewAAAAAAXBiJPQAAAAAALozEHgAAAAAAF0ZiDwAAAACACyOxBwAAAADAhXk6OwAAAACgtjXyKlVEcJrN9f1Lm0pqXnMBAYAdSOwBAADgFCaTlJVlW11fk31tuxcWKH/ttzbX9/hTpEjsAdQVJPYAAABwCpNJOnTItrrd+9doKADg0njGHgAAAAAAF8YVe6CWFBZK52y83VCSmpZKXjbWNZRKuXa0be/tjgAAAAAch8QeqCXFJbbfbihJN91me2JvNNrXNrc7AgAAAHUXt+IDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MWfEBAACAavJ2K5XS0mzfQdOmUvPmNRcQgAaNxB4AAACoJveiAmnPt7bvIDKSxB5AjeFWfAAAAAAAXBhX7AEXYTJJWVnVq2M0BigrS/I1OSYm1G2ldt4lWlhYc7EAAADAcUjsARdhMkmHDlWvTnZ2qQIDpe79HRIS6riCAulbO+4S7dmz5mIBAACA43ArPgAAAAAALozEHgAAAAAAF+bQxH7Tpk0aNmyYBg8erFWrVl1WfvjwYcXGxioyMlIzZsxQaWmpVfmSJUu0bNkyy3Jubq4efPBBDR06VGPGjFFmZqYjwwcAAAAAoM5zWGKfkZGhxYsX6+OPP9aGDRu0evVq/ec//7HaJj4+Xs8//7y2bNkis9msNWvWSJLy8vI0ffp0vffee1bbL1myRBEREfryyy911113ad68eY4KHwAAAAAAl+CwxH737t3q2bOnAgIC5Ofnp8jISCUnJ1vKjx8/rqKiInXt2lWSFBsbaynftm2b2rVrp/Hjx1vtc8eOHYqOjpYkDR8+XP/85z9lMBgcdQgAAACAY1x4dYkt/3JynB09gDrGYbPinzp1SkFBQZbl4OBgHThwoNzyoKAgZWRkSJJGjBghSVa34V9ax9PTU/7+/srOzlbLli0ddRgAAABAzbPn1SWRkXY1nZMj5ebaXr9pU6l5c7tCsJkrxw44ksMSe5PJJDc3N8uy2Wy2Wq6svCrMZrPc3at+00Fqamq19u8oKSkpzg7BJbl6v7Us9lV2djVfRH8Ro7HUpvrZ2Vk217W37Zqo74y2L97e3vZLSoqVZuPL5MPCApWWlm1z27Vd/+LjPHnSW6dPn7C57YbE1X+3AXA9ubnSli2214+MdF5y7MqxA47ksMS+VatW2r9/v2U5MzNTwcHBVuUXT353+vRpq/KyBAcH6/Tp02rVqpVKS0tVUFCggICAKscUFhYmHx+fqh+EA6SkpCg8PNypMbiiOtFvdn5EfC6nUIGBLWyu7+HhWe362dlZCgxsYVNde9uuqfq13faFPqup9r29fRQaGmpTXX9/KTS0ic1t12b9tLQ0q+Ns3VoKDW1tc9sNRWW/24qLi+vMh9IAAKDuclhi37t3by1btkzZ2dlq1KiRtm7dqrlz51rKQ0JC5OPjYxnUJCUlqW/fvhXus1+/ftqwYYMmT56szZs3KyIiQl5eXo46BMCanR8Ru3frWYPBAAAAAMB5Dps8r2XLlpo6dari4uI0YsQIDR8+XF26dNGkSZN08OBBSVJiYqLmz5+vIUOG6Ny5c4qLi6twn0888YR++OEHRUVF6eOPP9bzzz/vqPABAAAAAHAJDrtiL0nR0dGWWewvWLlypeXrjh07au3ateXWf/zxx62WAwIC9NZbb9VskAAAAAAAuDCHXbEHAAAAAACOR2IPAAAAAIALI7EHAAAAAMCFOfQZewBo6Bp5lSoi2Lb32Ae6NZXEy3YBAABQMRJ7AHAg98IC5a/91qa6IU9EisQeAHCpknOlalnsqzMHbPvg2MeLD46B+obEHgAAAHAhpvwC7X/xbwoMbGFT/Q58cAzUOyT2QBUVFkrnsmyv72uquViA+i4nR8rNtb1+06ZSc8asAACggSCxB6qouEQ6dMj2+t3711goQL2Xmytt2WJ7/chIEnsAANBwMCs+AAAAAAAujMQeAAAAAAAXRmIPAACcZtOmTRo2bJgGDx6sVatWXVZ++PBhxcbGKjIyUjNmzFBpaalV+ZIlS7Rs2bLaChcAgDqJZ+wBAIBTZGRkaPHixVq3bp28vb01atQo9ejRQ+3bt7dsEx8frxdffFFdu3bV9OnTtWbNGo0ePVp5eXmaP3++/va3v2nixIlOPArA9TTyKlVEsG2vypMk/1JelwfUNST2AADAKXbv3q2ePXsqICBAkhQZGank5GQ99thjkqTjx4+rqKhIXbt2lSTFxsbqtdde0+jRo7Vt2za1a9dO48ePd1L0gOtyLyxQ/tpvba7v8SdelwfUNdyKDwAAnOLUqVMKCgqyLAcHBysjI6Pc8qCgIEv5iBEj9OCDD8rDw6P2AgYAoI7iij0AAHAKk8kkNzc3y7LZbLZarqzcXqmpqTWyn5byVXZ2ls31jcZSp9WvqG5V9llXYy/LpdvaG3tJSYky0my/nT0wLEzZNtZv3qm7pKp9j8pi77Hn5+fr15QUm+uXJ6UK+zQY2igtrcTmNk6e9Nbp0ydsquvpGaTCQq9q1Gijb789aVlq1Mig0tJMm9quK6ryPXI19eWYSOwBAIBTtGrVSvv377csZ2ZmKjg42Ko8M/N/g+DTp09bldsrLCxMPj4+du8nffchBQa2sLm+h4en0+qXVzc7O6tK+6yLsZelrOOxN3Zvb2+FhobaXF/+/mpiY/0ib29JqpW+K4u/v7/adulkc/2ypKSkKDw8vNLt0tIke7q9dWspNLS1TXXT0qS9e6uzfZrVORIZKYWGXmlT23VBVb9HrsSVjqm4uLjCD6S5FR8AADhF7969tWfPHmVnZ6uwsFBbt25V3759LeUhISHy8fGxXE1JSkqyKgcAAOeR2AMAAKdo2bKlpk6dqri4OI0YMULDhw9Xly5dNGnSJB08eFCSlJiYqPnz52vIkCE6d+6c4uLinBw1AAB1D7fiAwAAp4mOjlZ0dLTVupUrV1q+7tixo9auXVtu/ccff9xhsQEA4CpI7AEAAABUmbdb6fkHzm3RtKnUnFflATWNxB4AAABAlbkXFUh7vrWtcmQkiT3gADxjDwAAAACACyOxBwAAAADAhZHYAwAAAADgwnjGHgAAAKhlhlIpN8u2ur6mmo0FqExOjmQwtGHOxDqMxB4AAACoZUajdOiQbXW796/RUIBK5eZKmzaVKDTUtvrMmeh43IoPAAAAAIAL44o9AAAAgNpRWqqy7uduYzCUuf5S/qVNJXHpF7gUiT0AAACA2lFQIH377WWrS9LSVJX7vD16RorEHrgct+IDAAAAAODCHJrYb9q0ScOGDdPgwYO1atWqy8oPHz6s2NhYRUZGasaMGSotLZUknThxQmPGjNGQIUP08MMPq6CgQJL03XffqUePHoqJiVFMTIyeffZZR4YPAAAAAECd57DEPiMjQ4sXL9bHH3+sDRs2aPXq1frPf/5jtU18fLyef/55bdmyRWazWWvWrJEkvfDCCxo9erSSk5MVFham5cuXS5JSU1M1YcIEJSUlKSkpSfPnz3dU+AAAAAAAuASHJfa7d+9Wz549FRAQID8/P0VGRio5OdlSfvz4cRUVFalr166SpNjYWCUnJ8tgMGjfvn2KjIy0Wi9JBw8e1M6dOxUdHa3Jkyfr5MmTjgofAAAAAACX4LDJ806dOqWgoCDLcnBwsA4cOFBueVBQkDIyMpSTkyN/f395enparZekJk2aaOjQoRo8eLA++eQTTZ06VZ9++qmjDgEAAAAok8kkZWXZXt/XVHOxwDWU80KAKmvalHfBo3wOS+xNJpPc3Nwsy2az2Wq5vPJLt5NkWZ4zZ45l3b333quFCxcqLy9PTZo0qVJMqampNh1LTUtJSXF2CC7J2f3WsthX2dm2/wU3GkudUj87O8tpbddEfWe0ffH2zjx2L7dCXeNzoPINy9G4JEhpaSU21w8LC1RaWnaVt0+7aLRy8qS3Tp8+YXPbBkMbu2K3t/3a5OzfbQBsYzJJhw7ZXr97/xoLBS6inBcCVFlkJIk9yuewxL5Vq1bav3+/ZTkzM1PBwcFW5ZmZmZbl06dPKzg4WIGBgcrLy5PRaJSHh4elnslk0ooVK/Tggw/Kw8PDUu/irysTFhYmHx8fO4/MPikpKQoPD3dqDK6oLvTbmQNpCgxsYXN9Dw/PWq+fnZ2lwMAWTmm7purXdtsX+sxZ7V/M21Aij69sHwH4Xx+p0Cq8Oqjc+v5SaGjVPjhNS0uzaisoSPL0bG1z24WFVXrrUblat5ZCQ21rPydHys21ve3qXFGp7HdbcXFxnflQGgDqAm+3UkUE237Z27+0qXhdHuojhyX2vXv31rJly5Sdna1GjRpp69atmjt3rqU8JCREPj4+lkFNUlKS+vbtKy8vL0VERGjz5s2Kjo7Whg0b1LdvX7m7u+urr75SaGiohg0bpg0bNuiGG26Qn5+fow4BAGAje69K9OxZc7FUV26utGWL7fW5ogIAjuNeVKD8tbb/gfH4U6RI7FEfOWzyvJYtW2rq1KmKi4vTiBEjNHz4cHXp0kWTJk3SwYMHJUmJiYmaP3++hgwZonPnzikuLk6SNGvWLK1Zs0bDhg3T/v379eSTT0qSXnnlFX3wwQeKiorS559/rhdffNFR4QMAAAAA4BIcdsVekqKjoxUdHW21buXKlZavO3bsqLVr115WLyQkRB9++OFl66+55homywMAAAAA4CIOu2IPAAAAAAAcj8QeAAAAAAAX5tBb8QEAAACgrvB2s/1l8syoj7qMxB4AAABAg+BeVCDtsW1WfY+ezKiPuovEHgAAAECtMJRKuVmXrzcaA5RVxvpL+ZpqPiY4XqntN0qoaVNeI1sVJPaoVTk5598RXV0GQxulpfGDDaBq7BlAFBbWbCwAgP8xGqVDhy5fn51dqsDAyut371/jIaEWFBRI39p2o4QiIxn/VwWJPWpVbq60ZUv166WllSg0tAZ+sG39ZEGSt4nRPuAq7BlA9OxZs7EAAAA4Gok9GhZbP1mQ5N6N0T4AAACAuofEHgAAAAAq4e1Wqojgqj/ndY3PWTVr9r/tA92YVR+OQ2IPAAAAAJVwLypQ/tqqP+d1NjtLHoEtLMshTzCrPhzH3dkBAAAAAAAA25HYAwAAAADgwkjsAQAAAABwYTxjDwD1VCOv6k3ycykm+QEAoObY+3fZv5S/yygfiT0A1FPuhdWb5OdSTPIDACiLySRlZdlW19dUs7G4Env/Lru1i1Ramm1/lz08JKPR5qZVWGh7XdQOEnsAAAAAVWYySYcO2Va3e/8aDaVBMRikLVtsq9uzp/St7Z8pqGdP2+uidvCMPQAAAAAALowr9gAAAABQx9nzjD7z5tR/JPYAAAAAUMfZ84w+8+bUf9yKDwAAAACACyOxBwAAAADAhXErPgAAAADUY/Y8ny9deEYfdRmJPVyKf2mOlJZr+w54CSdQZdUZBFzjc1bNmv1vWybpAQCg7rDn+XzpwjP6qMtI7OFSPApypW9tfIGnxEs4gWqoziDgbHaWPAJbWJaZpAcAgPqjkVepbru2wOpD/OrgA3/HI7FHteXkSLk2XjR39gVzQ6mUm2VbXV9TzcYCAACA2mUySVmMBavNvbBAZ1f/zepD/Oqw5wP/0lIpzfanCOThIRmNZZcZDG0q3XfTplJzF/hMgsQe1ZabK22x8aK5sy+YG43SoUO21e3ev0ZDAQAAQC0zmRgLOoM9z/j7nGuqzXtsz6x79pS+LecGxLS0EoWGVlw/MpLEHgDQQNXMJD0u8FcUAABUyp5n/Hm8r2pI7FGrAt1yFBFc/fv4L0zM5W1i8jvAFdg7SU/7p2+16XfFBXwwAABA/cDFgqohsUet8irKVf7a6t/Hf2FiLvcnmPwOaAic+cFAQxkAAADgChw5Jrj0rT5l8S91jXEBiT0AoN6xZxBg790CrjIAAACgIahoTHDpW33K0ujqW21/3XYtzrzn0MR+06ZNevPNN1VaWqpx48ZpzJgxVuWHDx/WjBkzVFBQoIiICL3wwgvy9PTUiRMnFB8fr6ysLF111VVKTExU48aNlZubq6efflrHjh1TYGCglixZoqCgIEceAsrgX2rb7fSS1EjcSg+gbrP3yoDHn3gWsDpqeqwAAEBNci8qkPbYOC6oxZn33B2144yMDC1evFgff/yxNmzYoNWrV+s///mP1Tbx8fF6/vnntWXLFpnNZq1Zs0aS9MILL2j06NFKTk5WWFiYli9fLklasmSJIiIi9OWXX+quu+7SvHnzHBU+KuBRcP52elv+uZeWODt8AEAd4YixAgAADZHDEvvdu3erZ8+eCggIkJ+fnyIjI5WcnGwpP378uIqKitS1a1dJUmxsrJKTk2UwGLRv3z5FRkZarZekHTt2KDo6WpI0fPhw/fOf/5TBYHDUIdRfOTnnXwZp4z8msAMA1ARHjBUAAGiIHHYr/qlTp6xukw8ODtaBAwfKLQ8KClJGRoZycnLk7+8vT09Pq/WX1vH09JS/v7+ys7PVsmXLCmMxm82SpJKSunG1uLi4WDp7VsrPt20HHh7nX8huq6Ii6bvvbK5e2qmb3Jp621TXILNNdT1MvnJr6q0SmeXmbVvbks7Xr+XYnVn/Qr+5YuzOavtCnzmr/ZqqW9v161K/2Vvf7rbNxvO/56uoom0v/N268HesvnHEWKEqanpcYJSp3p3vl/5MO6Jte+vb8zvK3radXd8gszwCqvY9qum27a3fUM45qW79bayJtp11zjmy36py3tmVfxiNUjXGBBXGUcmYwM3soNHCm2++qeLiYj355JOSpDVr1ig1NVVz5syRJKWkpGjhwoX6+OOPJUlHjx7V5MmT9f777+vuu+/WP/7xD0lSaWmpunXrpoMHDyosLEw//PCD5Q/5LbfconXr1lX6nH1eXp5+/vlnRxwmAAAOd+2116pJkybODqPGOWKsUBWMCwAArqq8MYHDrti3atVK+/fvtyxnZmYqODjYqjwzM9OyfPr0aQUHByswMFB5eXkyGo3y8PCwqhccHKzTp0+rVatWKi0tVUFBgQICAiqNpXHjxrr22mvl5eUlNze3mjtIAAAcyGw2y2Aw1NtJ4RwxVqgKxgUAAFdT2ZjAYYl97969tWzZMmVnZ6tRo0baunWr5s6daykPCQmRj4+PUlJSFB4erqSkJPXt21deXl6KiIjQ5s2bFR0drQ0bNqhv376SpH79+mnDhg2aPHmyNm/erIiICHl5eVUai7u7e7280gEAqP98fX2dHYLDOGKsUBWMCwAArqiiMYHDbsWXzr/CZsWKFTIYDBo5cqQmTZqkSZMmacqUKercubN++uknzZw5U/n5+erUqZPmz58vb29vHT9+XAkJCcrKylLr1q21aNEiNWvWTGfOnFFCQoKOHTumJk2aKDExUW3btnVU+AAAwMFqeqwAAEBD5NDEHgAAAAAAOJbDXncHAAAAAAAcj8QeAAAAAAAXRmIPAAAAAIALI7EHAAAAAMCFkdgDAAAAAODCSOwd7PXXX1dUVJSioqK0YMECSdLu3bsVHR2twYMHa/HixU6OsO565ZVXlJCQIIk+q4rt27crNjZWQ4cO1YsvviiJfquKpKQky8/oK6+8Iol+K09+fr6GDx+u9PR0SeX30+HDhxUbG6vIyEjNmDFDpaWlzgq5Tri031avXq3hw4crOjpazz77rEpKSiTRb65q06ZNGjZsmAYPHqxVq1Y5OxybVfXn21XUt/HX0qVLNWzYMEVFRem9996T5NrHc0F9GuuNHTtWUVFRiomJUUxMjH788UeXP6b6NLb87LPPLN+bmJgYhYeHa86cOS57PGUyw2F27dplvueee8zFxcXmkpISc1xcnHnTpk3mfv36mf/73/+aDQaDecKECeYdO3Y4O9Q6Z/fu3eYePXqYn3nmGXNhYSF9Von//ve/5j59+phPnjxpLikpMd97773mHTt20G+VOHfunPnGG280Z2VlmQ0Gg3nkyJHmbdu20W9l+OGHH8zDhw83d+rUyXzs2LEKfy6joqLM33//vdlsNpufffZZ86pVq5wYuXNd2m+//fabedCgQea8vDyzyWQyT5s2zfzee++ZzWb6zRX98ccf5gEDBphzcnLMBQUF5ujoaPMvv/zi7LCqrTo/366gvo2/9u7dax41apTZYDCYCwsLzQMGDDAfPnzYZY/ngvo01jOZTOY+ffqYDQaDZZ2rH1N9Hlv+/PPP5kGDBplPnDhRL47nAq7YO1BQUJASEhLk7e0tLy8vXX311Tp69KhCQ0N1xRVXyNPTU9HR0UpOTnZ2qHXKmTNntHjxYk2ePFmSdODAAfqsEl999ZWGDRumVq1aycvLS4sXL1ajRo3ot0oYjUaZTCYVFhaqtLRUpaWl8vf3p9/KsGbNGs2aNUvBwcGSyv+5PH78uIqKitS1a1dJUmxsbIPuv0v7zdvbW7NmzZK/v7/c3Nx07bXX6sSJE/Sbi9q9e7d69uypgIAA+fn5KTIy0iW/b1X9+XYV9W38ddNNN+mDDz6Qp6ensrKyZDQalZub67LHI9W/sd5vv/0mSZowYYJuv/12ffTRRy5/TPV5bDl79mxNnTpVx44dqxfHc4GnswOoz6655hrL10ePHtWXX36p++67T0FBQZb1wcHBysjIcEZ4ddbzzz+vqVOn6uTJk5KkU6dO0WeVSEtLk5eXlyZPnqyTJ0+qf//+uuaaa+i3Svj7++uJJ57Q0KFD1ahRI914442cb+WYN2+e1XJ5/XTp+qCgoAbdf5f2W0hIiEJCQiRJ2dnZWrVqlebPn0+/uaiyfg4OHDjgxIhsU9Wfb1dRH8dfXl5eeu211/SXv/xFQ4YMcfnvUX0b6+Xm5qpXr1567rnnZDAYFBcXp4kTJ7r0MdXXseXu3btVVFSkoUOH6osvvnD547kYV+xrwS+//KIJEyZo2rRpuuKKK+Tm5mYpM5vNVssN3WeffabWrVurV69elnUmk4k+q4TRaNSePXv00ksvafXq1Tpw4ICOHTtGv1Xip59+0ueff66///3v+uabb+Tu7q6jR4/Sb1VQ3s8lP69Vk5GRoXHjxunOO+9Ujx496DcXVV+/b/XluOrb+GvKlCnas2ePTp486dJ/q+rjWK9bt25asGCBmjRposDAQI0cOVKvvfaaSx9TfR1bfvrppxo/frwk1z/vLsUVewdLSUnRlClTNH36dEVFRem7775TZmampTwzM9Ny6xukzZs3KzMzUzExMTp79qzOnTun48ePy8PDw7INfXa5//u//1OvXr0UGBgoSbrtttuUnJxMv1Vi586d6tWrl1q0aCHp/O3P7777Lv1WBa1atSrzd9ml60+fPk3/XeLXX3/VxIkTNXbsWE2YMEHS5f1Jv7mGVq1aaf/+/Zbl+vL7oryfb1dSn8Zfv/76q0pKSnTdddepUaNGGjx4sEv/ja+PY739+/fLYDBYPqwwm80KCQlx2XNOqp9jy5KSEu3bt08vv/yypPrxu+5iXLF3oJMnT+rRRx9VYmKioqKiJEk33HCDfv/9d6WlpcloNOqLL75Q3759nRxp3fHee+/piy++UFJSkqZMmaKBAwfqnXfeoc8qMWDAAO3cuVO5ubkyGo365ptvNGTIEPqtEh07dtTu3bt17tw5mc1mbd++nZ/RKiqvn0JCQuTj46OUlBRJ5986QP/9T35+vh544AE98cQTlqReEv3monr37q09e/YoOztbhYWF2rp1a734vrn678H6Nv5KT0/XzJkzVVJSopKSEm3btk2jRo1y2eOpj2O9vLw8LViwQMXFxcrPz9f69ev11FNPufQx1cex5ZEjR9SuXTv5+flJcu3fC2Xhir0DvfvuuyouLrZ8KiRJo0aN0ssvv6zHH39cxcXF6tevn4YMGeLEKOs+Hx8f+qwSN9xwgyZOnKjRo0fLYDDo5ptv1r333qs//elP9FsF+vTpo3//+9+KjY2Vl5eXOnfurMcff1w333wz/VaJin4uExMTNXPmTOXn56tTp06Ki4tzcrR1x9q1a3X69Gm99957lldWDRw4UE888QT95oJatmypqVOnKi4uTgaDQSNHjlSXLl2cHZbdXP3vbn0bf/Xr108HDhzQiBEj5OHhocGDBysqKkqBgYEueTxlcfVzbsCAAfrxxx81YsQImUwmjR49Wt26dXPpY6qPY8tjx46pVatWlmVXP+8u5WY2m83ODgIAAAAAANiGW/EBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MxB4AAAAAABfG6+4AAADQIKWnp2vQoEG69tprJUkmk0mNGzdWXFychg0bpnXr1mnevHlq27atVb0OHTpowYIFl5WbzWbl5+crIiJCc+fOlY+PT60fk61ef/11dezYUbfddpuzQ3E5x44d04IFC7Rs2bIKt1u2bJlycnL0/PPP11JkaEhI7AEXwMCjcjExMfrwww/VtGlTZ4cCAHAhvr6+SkpKsiwfP35c999/vzw8PCRJERERWrFiRbn1Ly0vLi7Wvffeq/Xr12vUqFGOC7yG7d27V+3bt3d2GC7pxIkT+v33350dBho4EnvARTDwqNjFfQMAgK1CQkI0ZcoUvfvuuzb9fTxz5ozy8/PVrFmzCrfbu3evFi1apNatW+v3339Xo0aN9OCDD+rDDz/U77//rsGDB2v69OkymUx66aWX9OOPP6qgoEBms1kvvviiwsPDtX//fr388ssymUySpIceekiRkZHlri/PqlWrlJqaqgULFsjDw0Pbtm3TmTNndOzYMfXv318jR47UnDlzVFBQoMzMTHXs2FFLliyRj4+POnfurAcffFC7du3SqVOnNHHiRI0ePVqZmZl65plnlJOTI0nq16+fnnzySY0aNUrjx4+3xPPqq69KkuLj4/XZZ5/pk08+kclkUkBAgJ577jldffXVSkhIsIpnwIABZR5fSUmJEhMTtW/fPhmNRl1//fWaOXOm/P397f4+SNL27dv15ptvymAwyNfXV88884y6dOmimTNnKiMjQw888IDefffdqpwmysjI0Jw5c3Ty5EkZDAZFRUVp8uTJSk9P19ixY3XLLbfoxx9/lNls1vPPP6+IiIgq7RcNF4k94KIa4sBj9erV+vvf/6633npLkvTrr7/q/vvv144dO3T99ddrz549MhqNZQ4kAACoqo4dO+rnn3+WJO3fv18xMTFW5XFxcbrzzjutyouLi3XmzBm1a9dOEyZM0NChQytt5+DBg5o1a5auv/56TZw4UW+//bY++OAD5efnq2/fvnrggQd04sQJnTp1SqtXr5a7u7vefvttrVy5UuHh4Vq2bJnGjx+vqKgo/fTTT1q9erUiIyPLXV+eMWPGKDk5WWPGjNGgQYO0bds2FRUV6W9/+5sk6ZVXXtGIESMUExMjg8Gg2NhY7dixw5JMN2/eXJ9++qlSU1N177336s4779SaNWvUtm1b/eUvf9G5c+c0Y8YM5eXl6a677tK6desUGRkpo9GojRs36sMPP9R3332nDRs2aNWqVWrUqJF27typxx57TF9++aUkWcUzbty4Mo/v7bffloeHh9atWyc3NzctWrRIiYmJmj17tt3fh8LCQi1evFgffPCBmjdvrl9++UXjx4/X1q1b9eKLL2ru3LlVTuql8x9k3H///Ro4cKCKi4s1adIkXXnllerSpYtOnDihG2+8UXPmzNE//vEPPfnkk/r73/8uLy+vKu8fDQ+JPeDCGtrAIyoqSomJicrMzFRQUJDWrVun2NhYy10LksodSDRp0sSWLgYANEBubm7y9fWVVPU74kwmk5YvX64vvvhCQ4YMqVI7bdu21fXXXy9JuvLKK9WkSRN5e3srMDBQjRs31tmzZ9WtWzc1a9ZMn376qY4dO6a9e/eqcePGkqShQ4dqzpw52r59u3r37q2nnnqqwvXVER4ebvk6Pj5eu3bt0sqVK3X06FGdOnVK586ds5TfeuutkqROnTqppKRE586d0y233KIHH3xQJ0+eVO/evfXnP/9ZTZo00bBhw7RgwQJlZmbq3//+t9q1a6d27dppzZo1SktLs7pYkZubqzNnzlwWT3nHt2PHDuXl5Wn37t2SJIPBoBYtWtTI92Hfvn06deqU7r//fks9Nzc3/fe//61mz0rnzp3Tvn37dPbsWS1dutSy7qefflKXLl3UrFkzRUdHSzp/gcLDw0NHjhxRWFhYtdtCw0FiD7iwhjbw8Pf316BBg7Rx40bdf//92rRpk1atWmW1TXkDCQAAqurgwYOWeW2qyt3dXY899pi+//57JSQkWO4uq4i3t7fVsqfn5UPzHTt2aN68eRo/frxuvfVW/elPf9LGjRslSaNGjdKAAQO0a9cuffPNN3r99deVnJxc7vrqzKnj5+dn+fqpp56S0WjU0KFD1b9/f508eVJms9lSfmG/bm5uks7P5dOlSxdt27ZNe/bs0bfffqu77rpLK1euVFhYmCIjI/XFF1/o+++/11133SXp/PxBMTExio+PtyyfOnXKcmfhxfGUd3wmk0nTp09Xv379JEkFBQUqLi6u9Fir8n0wmUzq1auXlixZYll38uRJBQcHa//+/ZW2cem+zGazPv30UzVq1EiSlJ2dLR8fH+Xk5FhdsLiw/aXrgEvxujvAhdkz8AgJCVFCQkKV6lR14PHQQw9JOv/J/b333mspGzVqlDZu3Kibb75ZO3fu1O23367i4uJy11fk7rvv1oYNG/TNN9/o6quv1hVXXGFVfmEgcc899+j48eO66667lJqaWqXjBADg999/1/LlyzVhwgSb6s+aNUu7du3S119/XSPx7Nq1SwMGDNDo0aMVFhamr7/+WkajUdL5v6+HDx9WbGys5s6dq9zcXGVmZpa7viIeHh4qLS0ts2znzp169NFHNWzYMEnSjz/+aImhPImJiVq+fLluu+02zZgxQ+3bt9cvv/wi6fzf8vXr1+tf//qX5U69Pn366G9/+5tOnTolSfrkk080bty4Mvdd3vH16dNHq1atUklJiUwmk5577jktWrSowjirqlevXtq1a5d+/fVXSdI//vEP3X777SoqKpKHh4cMBkOV9+Xv76+uXbvqvffek3T+zoR7771X27Ztk3Q+yf/nP/8p6fxz/V5eXtUe76Hh4Yo94KIuDDxmzJih7OzsatefNWuWoqKi9PXXX9fIq20uHngUFRVp5cqVVgOPyZMnKzY2VoMHD1a/fv2UmZmpp59+usz1l87uf7GuXbtKkt544w2NHz/+svLExESZzWbFx8fr1ltv1ZEjR/TLL79w+xoAoExFRUWWR9nc3d3l4+Ojp556Sv3799e6devKfNTtwnPcZbnyyis1adIkzZ8/X7fccovdb54ZNWqU/vznPys6OlqlpaW6+eabtXXrVplMJj399NN66aWXtGTJErm5uemxxx5T27Zty11fkYEDB2rRokVlJqhTp07Vo48+Kj8/P/n7++vGG2+s9Bb0cePGKSEhQcOHD5e3t7c6dOigqKgoSVJYWJg8PDw0ZMgQS//06dNHkyZN0oQJE+Tm5iZ/f3+9/vrrlrsALlbe8T3yyCN65ZVXdMcdd8hoNOq6666r8kWMyrRv315z5szRU089JbPZLE9PT7355ptq3Lix2rdvLx8fH40cOVKfffZZmTFfKjExUXPnzlV0dLRKSko0fPhw3X777UpPT5ePj4+SkpKUmJgoX19fvfHGG1yxR6XczBffRwOgTrr0dXcXBh7jxo3T0KFDy33d3YWBx7p167Rly5bLbtV/7bXXlJSUpM2bN5c78Ni7d6/mzp2rL774QpI0Z84cNW/eXI8//rgkqUePHvrwww/l4eGhP//5zzIajVYDjx07duhf//qXXnrpJZlMJrm5uen222/X+PHjtX///jLXV+aDDz7Q8uXL9c9//tNyN0GHDh0sk+clJCQoIyPDMpB44YUXLrvrAAAAoK5JT09XdHS0vv/+e2eHAhdDYg8AAAA4wJNPPlnu+80XL16sP/3pT7UWyzvvvKNNmzaVWfbAAw/o9ttvr7VYalttfR/y8/M1ZsyYMssaN26sjz/+uNJ9kNjDViT2ABh4AAAAAC6MxB4AAAAAABfGrPgAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MxB5AhZiGAwAAAKjbSOyBSowdO1YdOnSw/Lv++uvVo0cPTZw4UXv27LFsl56ebrVdWf8yMzMlScuWLbusrGPHjgoPD9eoUaO0Y8eOGom9Q4cOevfdd6u8/bJly9StWzfL8v79+zVlypQaiaUqqhtvdQwcOFBz5syp8vZ79+5Vhw4ddPDgQYfEAwAAANQUT2cHALiC7t2765lnnpEklZaW6tSpU/r00081fvx4JSYmavjw4ZZtn3rqKfXo0aPM/QQEBFi+9vX11fvvv29ZNhqNOnHihN5++209+uij+vzzz9WxY0fHHFAVrV27ttzX4AEAAACoG0jsgSpo2rSpunbtarVuyJAhGjdunGbPnq1bbrnFsj40NPSybcvi7u5+2Xbh4eHq3LmzIiMjtXHjRqcn9gAAAADqPm7FB2zk7u6uRx55RHl5eUpOTq6x/fr7+9tU7/fff9cDDzygbt26adCgQfrmm28u2yYrK0vTpk3TTTfdpG7dumny5Mk6duxYmftLSEjQ+vXr9csvv6hDhw7au3evJOnAgQOaNGmSIiIiFBYWpsjISH366aeWeuvWrVOPHj30zjvvqEePHoqMjFRhYWGVj+PMmTN66qmn1K1bN/Xo0UMvvfSSDAaDpfzUqVN69tln1adPH3Xq1El9+vTRvHnzVFJSYtkmMzNTU6ZMUXh4uG655RZt2LChyu1XJDU1VePGjdMNN9ygnj17au7cuVbHNnbsWM2ePVsLFixQRESEevbsqTlz5qi4uLhG2gcAAADKwhV7wA433XSTPDw89P333+vmm2+WJJlMJpWWll62rbu7u9zdrT9Lu3g7o9GoY8eO6ZVXXpGnp6eioqKqHEd+fr7Gjh2r//u//9Orr76qnJwcJSQkWG1TVFSkuLg4FRUVaebMmWrUqJFWrFih++67Txs3blSzZs2stn/kkUeUnZ2t3377TYmJiWrfvr1OnDihuLg49evXT0uXLlVpaak++eQTzZo1S127drXcYZCXl6d169YpMTFRBQUFatSoUZWP5Z133tHo0aO1fPlyffvtt3rrrbd0xRVXaOzYsTKZTJo4caLc3Nw0a9Ys+fv7a+fOnXrnnXd05ZVXauzYsTIajXrggQeUn5+vuXPnymw2a+HChcrIyKhyDGX5z3/+o/vuu09du3bVkiVLlJWVpYULFyo9PV0rVqywbPfFF1/oyiuv1Msvv6w//vhDCxcu1NmzZ7Vw4UK72gcAAADKQ2IP2MHDw0MBAQE6ffq0Zd3UqVPL3HbYsGFavHixZfncuXPq1KmT1Tbu7u66/vrrtXLlysvKKrJu3Trl5OTos88+U+vWrSVJzZo10+OPP27ZZsOGDfr999+1adMmXX311ZKkXr16acCAAfrwww/12GOPWe3zyiuvVGBgoE6cOGF5ZCAlJUVdu3ZVYmKivLy8JEk33HCDevToof3791sSe6PRqClTplg9olBVvXv31nPPPWeJb/v27dq7d6/Gjh2rjIwMNWvWTDNmzLC01atXL33zzTfat2+fxo4dqx07dujIkSNavXq1Je527dopNja22rFcbPny5WrRooXefvtteXt7W/Y7ZswY7du3TzfeeKPl2N955x0FBgZKktzc3DRnzhxNnTpVbdu2tSsGAAAAoCwk9kANe/rpp9WzZ8/L1l88cZ50fvK8jz76SJKUnZ2tRYsWyWQy6bXXXlNISEi12vzXv/6la6+91pLUS9Ktt94qDw8Py/LevXsVGhqq0NBQy50Cvr6+Cg8P17fffntZYl+Wfv36qV+/fiouLtZPP/2ko0ePWmaNv/hWeElq3759tY7hgotn5ZekkJAQ5ebmSpJat26tDz/8UCaTSUePHtXRo0f1008/KSsrS23atJF0vi+aNWtmNX9Bp06dqt2nl9q7d69uvfVWubu7W/qva9eu8vf31549eyyJfa9evSxJvXT++zBnzhylpKSQ2AMAAMAhSOwBOxQXF+vs2bNq2bKlZd0VV1yhzp07V1rX3d3daruwsDANHz5cEydO1Oeffy4/P78qx5Gbm6vmzZtbrfPw8LBKMM+cOaPffvutzDsB2rVrV6V2jEajXn75Za1evVoGg0FXXnmlIiIiJF3+vvuL266OS2/bd3d3t9r3Z599piVLluj06dMKCgrSDTfcIB8fH8s2ZfWFJAUFBdkUzwVnzpzR6tWrtXr16svKLrzGsKx2LvTD2bNn7WofAAAAKA+JPWCH/fv3q7S0VOHh4Xbvq0WLFnr22WcVHx+v11577bJn5CsSEBCgX3/91Wqd2Wy2SiabNGmijh076sUXX7ys/oVbyyvz5ptvas2aNXrllVfUr18/+fn5qbCwUGvXrq1yrPb47rvv9Nxzz+mRRx7RfffdZ0maR44cadkmICBAWVlZl9U9c+aMXW37+/vr1ltv1b333ntZ2cUfJFzazoVYWrRoYVf7AAAAQHmYFR+wkdls1ttvv62AgAANHjy4RvZ5++23q3v37vroo4/022+/Vblejx499Msvv+jo0aOWdXv27LG6Pb579+5KT09XSEiIOnfurM6dOyssLEx//etftWPHjjL3e+lkfz/88IPCwsI0dOhQyx0FF2bfv/SKvSP88MMPcnNz08MPP2xJ6jMyMvTzzz9b2u/Ro4fy8vK0Z88eS73ff/9d//3vf+1qOzw8XL/99pvCwsIs/de6dWstXLhQv/zyi2W7vXv3Ws2U//XXX8vd3d1yZwMAAABQ07hiD1RBbm6ufvjhB0nnZ7LPyMjQZ599pn379ikxMVH+/v6WK7VpaWmWbS91YUK6iiQkJOiee+7RK6+8YjXbekVGjBihv/zlL5o8ebKmTp2qoqIiLV682DLBnXT+qvaHH36oCRMm6MEHH1RAQIBWr16trVu36vbbby9zv02bNtUff/yhXbt2WRLalStX6qOPPtK1116rgwcP6o033pCbm5uKioqqFKs9OnfuLJPJpJdeeklDhgzRyZMn9eabb6qkpMSSTN9888268cYbFR8fr6efflp+fn5asmSJVV/Y4pFHHtGoUaP0xBNP6M4771RJSYmWL1+ukydP6vrrr7dsd+bMGT388MMaP3680tLStHjxYo0ePdrqcQ0AAACgJpHYA1Xwr3/9S/fcc48kycvLS0FBQercubM++eQT3XDDDVbbLlq0qNz9LFiwQDExMRW2dcMNN2jYsGH629/+pp07d6pPnz6Vxufj46P3339fc+fOVUJCgpo2baqpU6fq1VdftWzj7++vVatWacGCBZo9e7ZKSkp0zTXXaPny5erXr1+Z+73nnnv097//XQ899JAWLFigBx98UJmZmXr99ddVXFysdu3a6bnnntMXX3yh77//vtI47dWrVy89++yzev/99/X555+rVatWGjp0qDw9PfX++++rpKRE3t7eevPNN/XSSy9p3rx58vT01IQJE/TVV1/Z1XZYWJjef/99LVmyRFOmTJGPj4+6d++uBQsWWCXtffr00VVXXaUnn3xS/v7+euCBB/Twww/be+gAAABAudzMtXH/LAA0AGPHjpWfn1+V77QAAAAAagJX7IE6zGQyyWQyVbiNm5ub1Wvt6hqz2Syj0Vjpdp6etfPrqD70KQAAAHAxEnugDnvjjTf0+uuvV7hNSEiItm/fXksRVd93332nuLi4Srfbtm1brbznvT70KQAAAHAxbsUH6rCMjAydOnWqwm28vb3VoUOHWoqo+vLz8/X7779Xul2HDh2q/No9e9SHPgUAAAAu1iASe5PJpIKCAnl5ecnNzc3Z4QAAUCVms1kGg0GNGze+7PWTAAAAFzSIW/ELCgr0888/OzsMAABscu2116pJkybODgMAANRRDSKxv/D+6muvvbbcW31TU1MVFhZWm2HVWfSFNfrDGv1hjf6wRn/8T030RUlJiX7++WfL3zEAAICyNIjE/sLt997e3vLx8Sl3u4rKGhr6whr9YY3+sEZ/WKM//qem+oLHyAAAQEV4YA8AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIU1iGfsK2IwGJSeni4PDw8dPnzY2eHUCZ6enrXaF76+vmrbti2TQwEAAACADRp8Yp+enq4mTZooKChI/v7+zg6nTigoKFDjxo1rpS2z2aysrCylp6frqquuqpU2AQAAAKA+afC34hcVFalFixbMOOwkbm5uatGihYqKipwdCgAAAAC4pAaf2Eu8RsjZ6H8AAAAAsB2JPQAAAAAALqzBP2N/qZwcKTe35vfbtKnUvHnVtk1OTtbbb7+t0tJSmc1mxcTEaOLEiZo0aZJefPFFtWzZssbi6tChg44cOVJj+wMAAAAA1C6nJPavv/66vvzyS0lSv379NG3aNKvyw4cPa8aMGSooKFBERIReeOEFeXp66sSJE4qPj1dWVpauuuoqJSYm1vgkb7m50pYtNbpLSVJkZNUS+4yMDL3yyitat26dmjdvroKCAo0dO1ZXXXWVVq5cWfOBAQAAAABcWq3fir97927t3LlT69ev14YNG3To0CF99dVXVtvEx8fr+eef15YtW2Q2m7VmzRpJ0gsvvKDRo0crOTlZYWFhWr58eW2H73A5OTkyGAyWyeQaN26sl19+We3bt9fAgQOVnp4ug8Gg6dOnKzIyUnFxcRo3bpz27t2rvXv3asKECXrkkUcUGRmpKVOmqKSkRJK0ePFi3X333YqMjNTYsWN1+vRpZx4mAAAAAKCG1PoV+6CgICUkJMjb21uSdPXVV+vEiROW8uPHj6uoqEhdu3aVJMXGxuq1117TXXfdpX379umNN96wrL/vvvsUHx9f24fgUB07dtStt96q2267Tdddd5169Oih6OhohYaGWrb59NNPVVhYqOTkZJ04cULR0dGWsu+//15ffvmlgoODdffdd2vnzp26+uqr9dtvv+nTTz+Vu7u7pk2bpo0bN2rChAnOOEQADYG9zzVV5/klAACABq7WE/trrrnG8vXRo0f15Zdf6pNPPrGsO3XqlIKCgizLQUFBysjIUE5Ojvz9/eXp6Wm1vj564YUX9Mgjj2jnzp3auXOn7r77biUmJlrKd+3apbvvvltubm4KCQlRr169LGXXXHONWrVqJen8hyZnz55VaGionnnmGX322Wf6/fff9cMPP+jKK6+s9eMC0IDY+1xTVZ9fAgAAgPMmz/vll1/00EMPadq0aWrXrp1lvclksnr9mdlslpubm+X/i1X3NWmpqamXrfP09FRBQYEkqaCgQCUlXiouNldrv1VRUuKmggJDpdt98803OnfunCIjIzVkyBANGTJE3bt31+rVq2UymVRYWChJKiwstMRtNBott+5ffDwX1u/bt0/Tp0/Xfffdp759+8poNKq4uNjquC9V1jpHKikpUUpKSq22WR11OTZnoD+s0R/WUlJS1MZgUElams378D55UifqwSNDnBsAAKA2OCWxT0lJ0ZQpUzR9+nRFRUVZlbVq1UqZmZmW5dOnTys4OFiBgYHKy8uT0WiUh4eHMjMzFRwcXK12w8LC5OPjY7Xu8OHDaty4sQoKCtS4cWN5e0uXbFIjvL2lxo29K90uICBAr776qm666Sa1bdtWZrNZv/32m8LCwvT777+rUaNGuuWWW7Rt2zYNGzZMp06dUkpKiiZMmCCz2SwPDw/LhIKenp7y8fFRamqqevbsqXHjxiknJ0cvvviiBg8ebNnu0gkIL/RFbfL29tYNN9xQq21WVUpKisLDw50dRp1Bf1ijP6xZ+iMtTbroEaJqa91are2pXwfUxLlRXFxc5ofSAAAAF6v1xP7kyZN69NFHtXjxYqtbyC8ICQmRj4+PZUCUlJSkvn37ysvLSxEREdq8ebOio6O1YcMG9e3bt8bja9r0/B2gjthvVfTs2VOPPfaYJk+eLIPh/BX+W265RY8++qg2bdokSbr77rv1008/KTo6WkFBQWrTpo18fX0tV/MvNWzYMD322GOWZ/HDwsKUnp5u/0EBAAAAAJyu1hP7d999V8XFxXr55Zct60aNGqXt27drypQp6ty5sxITEzVz5kzl5+erU6dOiouLkyTNmjVLCQkJevPNN9W6dWstWrSoxuNr3tz5j3XecccduuOOOy5bv337dknSjh07NHDgQM2dO1d5eXkaMWKErrzySgUEBKhHjx6W7S/u488++6zMtniHPQAAAAC4tlpP7GfOnKmZM2detv7ee++1fN2xY0etXbv2sm1CQkL04YcfOjQ+V3D11Vdr2rRpWrJkiSRpypQpCggIcGpMAAAAAADncNrkebDdFVdcYfUmAQAAAABAw+Xu7AAAAAAAAIDtSOwBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYk+ddKidHys2t+f02bVql9+jt3btXkydP1pVXXimz2SyDwaBRo0Zp3Lhx1Wpu7969ev311+vcWwQGDhyoDz74QG3btnV2KAAAAABQL5DYXyo3V9qypeb3GxlZpcReksLCwiwJeX5+vqKionTzzTerffv2NR8XAAAAAMClkdjXccXFxfLw8FCTJk305Zdf6r333lNRUZFKSkr00ksvqXv37jp8+LCef/55FRUVqVmzZkpMTLTax/vvv6+vv/5ab7/9to4dO6aEhAQZjUZFRETon//8p7766islJCTozJkzSktL0+OPP642bdpo3rx5Ki4uVvPmzTVnzhyFhoZq7Nixeuyxx9SjRw+lp6crLi5O27dvV0JCgvz9/XXo0CFlZGTo0Ucf1Z133qkzZ84oPj5ef/zxh66++moVFxc7qScBAAAAoH7iGfs6KDU1VTExMYqOjtbAgQN100036f/+7//06aef6q233tLGjRs1ceJEvf3225Kkp59+Wo888og2bdqkYcOG6f3337fsa926ddq6daveeustNWrUSAkJCXriiSeUlJSkK664Qkaj0bJtQECAvvzyS/Xu3VtPPfWUnnvuOW3cuFGjRo3SU089VWncf/zxhz7++GO9+eabWrBggSTptdde0/XXX69NmzZpzJgxOn36dA33FgAAAAA0bCT2dVBYWJiSkpK0adMm7dq1S0ePHtU777yjN954Qzt37tTSpUu1fv16FRQUKDs7W5mZmRowYIAkafTo0XrmmWckST///LOee+45xcXFqXHjxjpz5oyOHz+ufv36SZLuvPNOq3a7dOkiSUpLS1PTpk0ty0OHDtV///tf5eXlVRj3zTffLDc3N1177bU6c+aMJOm7777TsGHDJEk33nijrrjiiprpJAAAAACAJBL7Os/f319Dhw7Vnj17NHLkSKWnp+vGG2/U2LFjJUleXl5yc3OzbF9cXKxjx45Jkho3bqxly5ZpwYIFOnfunDw8PGQ2m8tty9fXV5JkMpkuKzObzZar+xf2UVpaarWNj4+PJFnF4+bmZtWmh4dH1Q8eAAAAAFApEvs6zmg06rvvvpOvr6/c3Nw0efJk9ejRQ1999ZWMRqOaNGmili1baufOnZKkpKQkLV26VJIUEhJiuZX/tddeU5MmTXTFFVfoH//4hyRp06ZNZbbZrl07nTlzRgcOHJAkbd68WW3atFFAQICaN2+u//znP5Kkr7/+utL4e/XqpaSkJEnSgQMH9N///te+DgEAAAAAWCGxr4MuPGM/YsQIxcTEyNfXV4sWLdJ1112noUOHKioqSs2bN9eJEyckSa+++qreeOMNxcTEaPPmzZo2bZrV/qZNm6ZNmzbp0KFDWrBggZYvX6477rhDBw4csFylv5i3t7cWL16suXPnavjw4Vq1apUWL14sSZo4caI+/vhj3XHHHSoqKqr0WKZMmaJjx44pKipKK1eu5FZ8AAAAAKhhbuaK7s2uJ4qLi5WamqqwsDDL7eIXHD58WNddd50KCgrUuHFjp7/H3tFef/113X333QoODtbWrVu1adMmLVu2zGobS1/Uogvfh7ooJSVF4eHhzg6jzqA/rNEf1iz9kZZm36tDIyOl0NCaC8wJauLcqOjvFwAAwAW87u5SzZvXiQTcUdq0aaMJEybI09NTTZs21bx585wdEgAAAADADiT2DUxsbKxiY2OdHQYAAAAAoIbwjD0AAAAAAC6MxF6q8BVwcDz6HwAAAABs1+ATe19fX2VlZZFcOonZbFZWVlaZs/MDAAAAACrX4J+xb9u2rdLT05Wens6Mw/9fSUmJvL29a609X19ftW3bttbaAwAAAID6pMEn9l5eXrrqqquUkpJSZ1+3VttSUlJ0ww03ODsMAAAAAEAVNPhb8QEAAAAAcGUk9gAAAAAAuDASewAAAAAAXBiJPQAAAAAALsxpk+fl5+dr1KhReuutt6xmRD98+LASEhIsy9nZ2WrWrJm++OILrV+/XgsXLlSLFi0kSf3799fUqVNrPXYAAAAAAOoKpyT2P/74o2bOnKmjR49eVnbdddcpKSlJklRYWKi77rpLs2fPliSlpqYqISFBw4cPr8VoAQAAAACou5xyK/6aNWs0a9YsBQcHV7jdihUrdOONNyoiIkKSdPDgQa1fv17R0dF6+umndfbs2doIFwAAAACAOsspif28efMsyXp58vLytGbNGj322GOWdUFBQXrkkUe0ceNGtW7dWnPmzHF0qAAAAAAA1GluZrPZ7KzGBw4cqA8++MDqGfsLVq1apSNHjpSbvJ89e1aDBg3Sd999V2k7xcXFSk1NtTteAEDVtDEYVLJpk831vaOjdcLLqwYjcm1hYWHy8fFxdhgAAKCOctrkeZX5+uuv9dBDD1mW8/Ly9Pnnn+v++++XJJnNZnl4eFRrnxUNjFJSUhQeHm5zvPUJfWGN/rBGf1ijP6xZ+iMtTQoNtX1HrVurtT3164CaODf4YBoAAFRFnXzdndls1qFDh9StWzfLOj8/P73zzjv68ccfJUkfffSRBg0a5KwQAQAAAACoE+pMYj9p0iQdPHhQ0vlX3Hl5eVldXffw8NCSJUs0e/ZsDR06VIcOHVJ8fLyzwgUAAAAAoE5w6q3427dvt3y9cuVKy9ctWrTQrl27Lts+IiJC69evr5XYAAAAAABwBXXmij0AAAAAAKg+EnsAAAAAAFwYiT0AAAAAAC6MxB4AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIWR2AMAAAAA4MJI7AEAAAAAcGEk9gAAAAAAuDASewAAAAAAXBiJPQAAAAAALozEHgAAAAAAF0ZiDwAAAACACyOxBwAAAADAhZHYAwAAAADgwkjsAQAAAABwYST2AAAAAAC4MBJ7AAAAAABcGIk9AAAAAAAujMQeAAAAAAAXRmIPAAAAAIALI7EHAAAAAMCFkdgDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwpyX2+fn5Gj58uNLT0y8re/311zVgwADFxMQoJiZGq1atkiSdOHFCY8aM0ZAhQ/Twww+roKCgtsMGAAAAAKBOcUpi/+OPP+ree+/V0aNHyyxPTU3VokWLlJSUpKSkJI0ZM0aS9MILL2j06NFKTk5WWFiYli9fXotRAwAAAABQ9zglsV+zZo1mzZql4ODgMstTU1O1YsUKRUdHa86cOSouLpbBYNC+ffsUGRkpSYqNjVVycnJthg0AAAAAQJ3jlMR+3rx5ioiIKLOsoKBA1113neLj47V+/Xrl5uZq+fLlysnJkb+/vzw9PSVJQUFBysjIqM2wAQAAAACoczydHcClGjdurJUrV1qWJ0yYoOnTp2v06NFyc3Oz2vbS5cqkpqZWWJ6SklKt/dVn9IU1+sMa/WGN/rCWkpKiNgaDStLSbN6H98mTOnH6dA1G5RycGwAAoDbUucT+xIkT2r17t0aOHClJMpvN8vT0VGBgoPLy8mQ0GuXh4aHMzMxyb+UvT1hYmHx8fMosS0lJUXh4uN3x1wf0hTX6wxr9YY3+sGbpj7Q0KTTU9h21bq3W9tSvA2ri3CguLq70Q2kAAIA697o7X19fvfrqqzp27JjMZrNWrVqlQYMGycvLSxEREdq8ebMkacOGDerbt6+TowUAAAAAwLnqTGI/adIkHTx4UIGBgZozZ44efvhhDRkyRGazWePHj5ckzZo1S2vWrNGwYcO0f/9+Pfnkk84NGgAAAAAAJ3Pqrfjbt2+3fH3xc/WRkZGW2e8vFhISog8//LBWYgMAAAAAwBXUmSv2AAAAAACg+kjsAQAAAABwYST2AAAAAAC4MBJ7AAAAAABcGIk9AAAAAAAujMQeAAAAAAAXRmIPAAAAAIALI7EHAAAAAMCFkdgDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MxB4AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIWR2AMAAAAA4MJI7AEAAAAAcGGezg4AAFD/FBZK57Jsr+9XKDWquXAAAADqNRJ7AECNKy6RDh2yvX6XKBJ7AACAqiKxBwDUOd5upVJamu07aNpUat685gICAACow0jsAQB1jntRgbTnW9t3EBlJYg8AABoMJs8DAAAAAMCFkdgDAAAAAODCSOwBAAAAAHBhTkvs8/PzNXz4cKWnp19W9vXXXysmJka33367HnnkEZ09e1aStH79evXp00cxMTGKiYnR4sWLaztsAAAAAADqFKdMnvfjjz9q5syZOnr06GVl+fn5mj17tj7//HO1bNlSS5cu1bJlyzRz5kylpqYqISFBw4cPr/2gAQAAAACog5xyxX7NmjWaNWuWgoODLyszGAyaNWuWWrZsKUnq0KGDTp48KUk6ePCg1q9fr+joaD399NOWK/kAAAAAADRUTkns582bp4iIiDLLmjdvrkGDBkmSioqK9Pbbb+u2226TJAUFBemRRx7Rxo0b1bp1a82ZM6fWYgYAAAAAoC5yM5vNZmc1PnDgQH3wwQdq27btZWV5eXl69NFH1bZtW7300kuXlZ89e1aDBg3Sd999V2k7xcXFSk1NrZGYAQCVa1nsq/0vbrS5/sAZg5Szeb3N9b2jo3XCy8vm+nVNWFiYfHx8nB0GAACoo5zyjH1lTp06pQceeEA9e/bU9OnTJZ1P9D///HPdf//9kiSz2SwPD49q7beigVFKSorCw8Ptiru+oC+s0R/W6A9r9Ie1C/1x5kCaAgNb2Lwfb29vhYaG2h5I69ZqbU/9GlAT5wYfTAMAgKqoc6+7MxqNmjx5soYOHaoZM2bIzc1NkuTn56d33nlHP/74oyTpo48+styyDwAAAABAQ1VnrthPmjRJU6ZM0R9//KF///vfMhqN2rJli6TzV9rnzZunJUuWaPbs2SoqKlK7du20YMECJ0cNAAAAAIBzOTWx3759u+XrlStXSpI6d+6sn376qcztIyIitH697c9cAgAAAABQ39S5W/EBAAAAAEDVkdgDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwuxL7xx9/XLt3766pWAAAAAAAQDXZldgPGjRIy5cvV2RkpN59912dOXOmhsICAAAAAABVYdfr7m6//Xbdfvvt+vXXX/X5559r5MiR6tatm8aOHasuXbrUVIwAAGfIyZFyc6tVpY3BIKWlydtU6KCgAAAAcCm732NvMpmUlpamo0ePymg0qkWLFpo9e7b69++vKVOm1ESMAABnyM2VtmypVpWStDQpNFTu3Xo6KCgAAABcyq7EfvHixVq3bp2uuOIKjR49WkuXLpWXl5fOnTunAQMGkNgDAAAAAOBgdiX22dnZWrlypTp27Gi13s/PTwsXLrQrMAAAAAAAUDm7Js8zGo2XJfUXrtL36dPHnl0DAAAAAIAqsOmK/axZs5SRkaGUlBRlZ2db1peWlurYsWM1FhwAAAAAAKiYTYn9yJEj9csvv+jIkSOKjIy0rPfw8FDXrl1rKjYAAAAAAFAJmxL7zp07q3Pnzrr55pvVsmXLmo4JAAAAAABUkU2J/RNPPKGlS5dq4sSJZZZv2rTJrqAAAAAAAEDV2JTYT5o0SZL03HPP1WgwAAAAAACgemyaFT8sLEySdNNNN6l169a66aabdO7cOe3bt0/XXXddjQYIAAAAAADKZ9fr7p5//nmtXLlSv/76q2bOnKn09HRNnz69pmIDAAAAAACVsCuxT01N1ezZs/XVV1/pjjvu0Pz583X8+PGaig0AAAAAAFTCrsTebDbL3d1du3btUs+ePSVJRUVFNRIYAAAAAAConF2J/ZVXXqlJkyYpPT1dN910k/785z+rQ4cONRUbAAAAAACohE2z4l8wf/58ffXVVwoPD5eXl5ciIiI0YsSIGgoNAAAAAABUxq4r9n5+foqIiFBubq4OHTqkLl266Lfffqup2AAAAAAAQCXsumK/dOlS/eUvf1GLFi0s69zc3LRt2za7AwMAAAAAAJWzK7FPSkrS1q1b1bJly5qKBwAAAAAAVINdt+K3bt3a5qQ+Pz9fw4cPV3p6+mVlhw8fVmxsrCIjIzVjxgyVlpZKkk6cOKExY8ZoyJAhevjhh1VQUGBP+AAAAAAAuDy7EvtevXppwYIFSklJ0aFDhyz/KvPjjz/q3nvv1dGjR8ssj4+P1/PPP68tW7bIbDZrzZo1kqQXXnhBo0ePVnJyssLCwrR8+XJ7wgcAAAAAwOXZldivW7dOycnJio+P1+OPP67HH39cU6ZMqbTemjVrNGvWLAUHB19Wdvz4cRUVFalr166SpNjYWCUnJ8tgMGjfvn2KjIy0Wg8AAAAAQENm1zP227dvt6nevHnzyi07deqUgoKCLMtBQUHKyMhQTk6O/P395enpabUeAAAAAICGzK7EvqCgQAsXLtSvv/6qpUuXatGiRXrmmWfUuHFjm/dpMpnk5uZmWTabzXJzc7P8f7FLlyuTmppaYXlKSkq19lef0RfW6A9r9Ie1+tofbQwGlaSlVbteWlqamnfqruzsLJvbLikpUYYNbV/gffKkTpw+bXP9mlJfzw0AAFC32JXYv/jiiwoODlZWVpZ8fHyUn5+v559/XgsXLrR5n61atVJmZqZl+fTp0woODlZgYKDy8vJkNBrl4eGhzMzMMm/lr0hYWJh8fHzKLEtJSVF4eLjNcdcn9IU1+sMa/WGtXvdHWpoUGlrNKmkKDQ1Vkbe3AgNbVF6hHN7e3gqtZttWWrdWa3vq14CaODeKi4sr/VAaAADArmfsDx8+rKlTp8rT01ONGjVSYmKiDh8+bFdAISEh8vHxsVzlSEpKUt++feXl5aWIiAht3rxZkrRhwwb17dvXrrYAAAAAAHB1diX27u7W1Y1G42XrqmrSpEk6ePCgJCkxMVHz58/XkCFDdO7cOcXFxUmSZs2apTVr1mjYsGHav3+/nnzySXvCBwAAAADA5dl1K/6NN96oV199VUVFRfrmm2/00UcfqUePHlWuf/HkeytXrrR83bFjR61du/ay7UNCQvThhx/aEzIAAAAAAPWKXVfsn376afn5+alJkyZasmSJOnbsqGnTptVUbAAAAAAAoBI2X7H/6quv9O677+rIkSPy9fVVhw4d1L1793InpwMAAAAAADXPpsT+yy+/1OLFizVlyhR17NhRbm5uOnjwoObNm6fi4mINHjy4puMEAAAAAABlsCmx/+CDD/TXv/5Vbdq0say7+uqrdcMNN2j69Okk9gBQDxQWSueq+Sp6ozFAWVmSr8kxMQEAAOByNiX2BQUFVkn9BVdddZWKi4vtDgoA4HzFJdKhQ9Wrk51dqsBAqXt/h4QEAACAMtg0eZ6Hh0e5ZWaz2eZgAAAAAABA9dg1Kz4AAAAAAHAum27FP3LkiLp3737ZerPZrJKSEruDAgAAAAAAVWNTYv/VV1/VdBwAAAAAAMAGNiX2ISEhNR0HAAAAAACwAc/YAwAAAADgwkjsAQAAAABwYST2AAAAAAC4MBJ7AAAAAABcmE2T5wEA4Egmk5SVZXt9v0KpUc2FAwAAUKeR2AMA6hyTSTp0yPb6XaJI7AEAQMPBrfgAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MxB4AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIWR2AMAAAAA4MJI7AEAAAAAcGEk9gAAAAAAuDBPZzS6adMmvfnmmyotLdW4ceM0ZswYS9nhw4eVkJBgWc7OzlazZs30xRdfaP369Vq4cKFatGghSerfv7+mTp1a6/EDAAAAAFBX1Hpin5GRocWLF2vdunXy9vbWqFGj1KNHD7Vv316SdN111ykpKUmSVFhYqLvuukuzZ8+WJKWmpiohIUHDhw+v7bABAAAAAKiTav1W/N27d6tnz54KCAiQn5+fIiMjlZycXOa2K1as0I033qiIiAhJ0sGDB7V+/XpFR0fr6aef1tmzZ2szdAAAAAAA6pxaT+xPnTqloKAgy3JwcLAyMjIu2y4vL09r1qzRY489ZlkXFBSkRx55RBs3blTr1q01Z86cWokZAAAAAIC6qtZvxTeZTHJzc7Msm81mq+ULNm7cqNtuu83yPL0kvfHGG5avJ06cqEGDBlWr7dTU1ArLU1JSqrW/+oy+sEZ/WKM/rNXX/mhZ7Kvs7Kxq18vOzpLRWGpT3QvsrZ+fn69f68D3pb6eGwAAoG6p9cS+VatW2r9/v2U5MzNTwcHBl2339ddf66GHHrIs5+Xl6fPPP9f9998v6fwHAh4eHtVqOywsTD4+PmWWpaSkKDw8vFr7q6/oC2v0hzX6w1p97o8zB9IUGNii8g0vkp2dpcDAFvLw8Kx23YvZW9/f319tu3SyuX5NqIlzo7i4uNIPpQEAAGr9VvzevXtrz549ys7OVmFhobZu3aq+fftabWM2m3Xo0CF169bNss7Pz0/vvPOOfvzxR0nSRx99VO0r9gAAAAAA1De1fsW+ZcuWmjp1quLi4mQwGDRy5Eh16dJFkyZN0pQpU9S5c2dlZ2fLy8vL6uq6h4eHlixZotmzZ6uoqEjt2rXTggULajt8AHAdOTlSbq7N1b1NhTUYDAAAABzFKe+xj46OVnR0tNW6lStXWr5u0aKFdu3adVm9iIgIrV+/3uHxAUC9kJsrbdlic3X3bj1rMBgAAAA4Sq3fig8AAAAAAGoOiT0AAAAAAC6MxB4AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIWR2AMAAAAA4MJI7AEAAAAAcGEk9gAAAAAAuDASewAAAAAAXBiJPQAAAAAALszT2QEAAByjsFA6l2V7fV9TzcUCAAAAxyGxB4B6qrhEOnTI9vrd+9dYKAAAAHAgbsUHAAAAAMCFkdgDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYiT0AAAAAAC6MxB4AAAAAABdGYg8AAAAAgAsjsQcAAAAAwIWR2AMAAAAA4MJI7AEAAAAAcGEk9gAAAAAAuDBPZwcAAEBN83YrldLSbKvctKnUvHnNBgQAAOBAJPYAgHrHvahA2vOtbZUjI0nsAQCAS3HKrfibNm3SsGHDNHjwYK1ateqy8tdff10DBgxQTEyMYmJiLNucOHFCY8aM0ZAhQ/Twww+roKCgtkMHAAAAAKBOqfUr9hkZGVq8eLHWrVsnb29vjRo1Sj169FD79u0t26SmpmrRokXq1q2bVd0XXnhBo0ePVlRUlN544w0tX75c8fHxtX0IAFBlOTlSbq5tdbkjHAAAAFVR64n97t271bNnTwUEBEiSIiMjlZycrMcee8yyTWpqqlasWKHjx4/rxhtv1DPPPCN3d3ft27dPb7zxhiQpNjZW9913H4k9gDotN1fassW2utwRDgAAgKqo9cT+1KlTCgoKsiwHBwfrwIEDluWCggJdd911io+PV2hoqBISErR8+XKNGTNG/v7+8vQ8H3JQUJAyMjKq1XZqamqF5SkpKdXaX31GX1ijP6zRH9Yq6g+DoY3S0kps2q8xy6yTJ/+wNSz5ujVSdnaWzfWNxlKb6mdnZ9lc1962LygpKVGGjZPneZ88qROnT9vc9sX4WQEAALWh1hN7k8kkNzc3y7LZbLZabty4sVauXGlZnjBhgqZPn67Ro0dbbSfpsuXKhIWFycfHp8yylJQUhYeHV2t/9RV9YY3+sEZ/WKusP9LSpNBQ2/Yd6JmmgP0HKt+wHEXdeiowsIXN9T08PKtdPzs7S4GBLWyqa2/bF/P29laorR3furVa21r3IjXxs1JcXFzph9IAAAC1Pnleq1atlJmZaVnOzMxUcHCwZfnEiRNau3atZdlsNsvT01OBgYHKy8uT0Wgssx4AAAAAAA1RrSf2vXv31p49e5Sdna3CwkJt3bpVffv2tZT7+vrq1Vdf1bFjx2Q2m7Vq1SoNGjRIXl5eioiI0ObNmyVJGzZssKoHAAAAAEBDVOuJfcuWLTV16lTFxcVpxIgRGj58uLp06aJJkybp4MGDCgwM1Jw5c/Twww9ryJAhMpvNGj9+vCRp1qxZWrNmjYYNG6b9+/frySefrO3wAQAAAACoU2r9GXtJio6OVnR0tNW6i5+rj4yMVGRk5GX1QkJC9OGHHzo8PgAAAAAAXEWtX7EHAAAAAAA1h8QeAAAAAAAXRmIPAAAAAIALI7EHAAAAAMCFOWXyPAANS06OlJtrW92mTaXmzWs2HldhMklZWbbX9zXVXCwAAACou0jsAThcbq60ZYttdSMjG25ibzBIRw7ZXr97/xoLBQAAAHUYt+IDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwEnsAAAAAAFwYk+cBDYA9s9JLrj0zfUM+dgAAADQMJPZAA2DPrPSSa89M35CPHQAAAA0Dt+IDAAAAAODCuGIPAA7kX5qjiGDbngVopMIajgYAAAD1EYk9ADiQR0Gu8tfa9iyA+6M9azgaAAAA1Efcig8AAAAAgAvjij0AVKC0VEpLK7/cYGhTYXnj0pqPCQAAALgYiT0AVKCgQPr22/LL09JKFBpafnlMt5qPCQAAALgYt+IDAAAAAODCSOwBAAAAAHBhJPYAAAAAALgwnrEHaklOjpRr2+vMJUlNm0rNm9dcPK6issnrKlPIq+ABAABQz5HYA7UkN1faYtvrzCVJkZENM7GvbPK6yvTkVfAAAACo57gVHwAAAAAAF8YVewCoQKBbjiKCy3+G4hqfs2rWrPxnBRqJZwEAAADgWE5J7Ddt2qQ333xTpaWlGjdunMaMGWNV/vXXX2vZsmUym81q27at5s+fr2bNmmn9+vVauHChWrRoIUnq37+/pk6d6oxDANBAeBXlKn9t+c9QnM3Okkdgi3LL3R/lWQAAAAA4Vq0n9hkZGVq8eLHWrVsnb29vjRo1Sj169FD79u0lSfn5+Zo9e7Y+//xztWzZUkuXLtWyZcs0c+ZMpaamKiEhQcOHD6/tsAEAAAAAqJNqPbHfvXu3evbsqYCAAElSZGSkkpOT9dhjj0mSDAaDZs2apZYtW0qSOnTooE2bNkmSDh48qKNHj2rFihXq0KGDnnvuOTVr1qy2D8HpmF0dABzI3lcx8EsWAADUslpP7E+dOqWgoCDLcnBwsA4cOGBZbt68uQYNGiRJKioq0ttvv62xY8dKkoKCgjRhwgR1795dixYt0pw5c7Rw4cIqt52amlpheUpKSnUOxWkMhjbatKnE5vrR0d7y8jpR4Tau0he1pSb6w2Boo7Q0279vJ0966/Tpir9vtdV2dfvDnvbDwgKVlpZtU92aqF/SqVjZ2VkVblNRudFYWml9R9R1Zv3s7Cynx15SUqIMG5PzwIwMZf/tbza37R0drRNeXpL4XQoAAGpHrSf2JpNJbm5ulmWz2Wy1fEFeXp4effRRdezYUXfccYck6Y033rCUT5w40fIBQFWFhYXJx8enzLKUlBSFh4dXa3/OkpYmhYbaXr91ayk0tHW55a7UF7WhpvrD0d+32mrblv6wp31/fyk0tIltlWugvrd3mgIreIY+OzurwnIPD88KyytiT11n1b/QH86O3dvbW6F2nHRN7PyBaR0aWiO/O4qLiyv9UBoAAKDWX3fXqlUrZWZmWpYzMzMVHBxstc2pU6c0evRodejQQfPmzZN0PtH/61//atnGbDbLw8OjVmIGAAAAAKCuqvXEvnfv3tqzZ4+ys7NVWFiorVu3qm/fvpZyo9GoyZMna+jQoZoxY4blar6fn5/eeecd/fjjj5Kkjz76qNpX7AEAAAAAqG9q/Vb8li1baurUqYqLi5PBYNDIkSPVpUsXTZo0SVOmTNEff/yhf//73zIajdqy5fwrpsLCwjRv3jwtWbJEs2fPVlFRkdq1a6cFCxbUdvgAXExl76GvDO+hBwAAQF3nlPfYR0dHKzo62mrdypUrJUmdO3fWTz/9VGa9iIgIrV+/3uHxAXWRPRN1Fzbg3LSy99BXhvfQAwAAoK5zSmIPoPoKCqRvv7Wtbk9yUwAAAKDeqvVn7AEAAAAAQM0hsQcAAAAAwIVxKz6AOo3J7wAAAICKkdgDqNOY/A4AAACoGIk9qi0nR8q18QJq06ZS8+bOadve9u1tuyHPTA/UNpNJysqyrW7TUsmrZsMBAABwKBJ7VFturrTFxguokZH2Jfb2tG1v+/a2zcz0QO0xmaRDh2yre9NtJPYAAMC1kNgDcDj/Utufk+cZeQAAAKBiJPYAHM6jwPbn5HlGHgAAAKgYiT2ASvmX5khp56+4tzEYpLS0atX3NnHVHQAAAHAUEnsAlfIoyJW+PX/FvSQtTQoNrVZ9925cdQcAAAAchcQetaq0tNoXe63YO7O8Le0bDG2UltawZ7W/eIZxozGg2rON+5pqPiYAAAAA55HYo1YVFEjffmt7fXtnlrel/bS0EoWGuvas9oFutk9eJ0mehkL98P9nGM/OLlVgYPXqd+9vc9MAAAAAKkFiD7gIe5LzpkWF+m3tP21umwnsAAAAgLqLxB5wEV5FzCwPAAAA4HIk9kAtsfd2eN7nDgAAAKAsJPZAFdmbmHM7PAAAAABHILF3kpwcKdfGHLEhz85uL1uS82t8zqpZszQSc6CBuPgtELbwK5Qa1Vw4AAAAlSKxd5LcXGmLbY9Lu/Ts7M5my3PqZ7Oz5BHYgsQcaCBMJunQIdvrd4kisQcAALWLxB4uhefUAQAAAMAaiT1qFc+pAwAAAEDNIrFHtfmX8j51AAAAAKgrSOxtZM/kd5JzJ8DzK87RmQPlB9+y2FdnDqSVW+5tKFS+jck5iTkAAAAA1CwSexvZM/md5NwJ8Exnc3VgafnBZ2dnKTCwRbnl3UnOAQAAAKDOILF3QUwgBwAAAAC4wCmJ/aZNm/Tmm2+qtLRU48aN05gxY6zKDx8+rBkzZqigoEARERF64YUX5OnpqRMnTig+Pl5ZWVm66qqrlJiYqMaNGzvjEOxmT3LOc+oAAAAAgAtqPbHPyMjQ4sWLtW7dOnl7e2vUqFHq0aOH2rdvb9kmPj5eL774orp27arp06drzZo1Gj16tF544QWNHj1aUVFReuONN7R8+XLFx8fX9iFIsm8COcm+5JzEHAAAAABwQa0n9rt371bPnj0VEBAgSYqMjFRycrIee+wxSdLx48dVVFSkrl27SpJiY2P12muv6a677tK+ffv0xhtvWNbfd999VUrszWazJKmkpKTC7YqLi6t8HKb8HBVstf2quXFsN7k19baprkFmm+tWpb6HybfCcnvad3Tsjqh/oT9cMXZHtF3Z+VHT7df1fufnxRo/L5LBbLT8PanO35WyXPi7deHvGAAAQFnczLU8WlixYoXOnTunqVOnSpI+++wzHThwQHPnzpUkff/991qwYIE++eQTSVJaWpoefPBBffjhhxo5cqT++c/zyXRpaam6du2q1NTUStvMy8vTzz//7KAjAgDAsa699lo1adLE2WEAAIA6qtav2JtMJrm5uVmWzWaz1XJ55ZduJ+my5fI0btxY1157rby8vKpcBwAAZzObzTIYDC47nwwAAKgdtZ7Yt2rVSvv377csZ2ZmKjg42Ko8MzPTsnz69GkFBwcrMDBQeXl5MhqN8vDwuKxeRdzd3bnSAQBwSb6+vs4OAQAA1HHutd1g7969tWfPHmVnZ6uwsFBbt25V3759LeUhISHy8fFRSkqKJCkpKUl9+/aVl5eXIiIitHnzZknShg0brOoBAAAAANAQ1foz9tL5192tWLFCBoNBI0eO1KRJkzRp0iRNmTJFnTt31k8//aSZM2cqPz9fnTp10vz58+Xt7a3jx48rISFBWVlZat26tRYtWqRmzZrVdvgAAAAAANQZTknsAQAAAABAzaj1W/EBAAAAAEDNIbEHAAAAAMCFkdgDAAAAAODCSOwBAAAAAHBhDSqx37Rpk4YNG6bBgwdr1apVl5W//vrrGjBggGJiYhQTE1PmNvVNfn6+hg8frvT09MvKDh8+rNjYWEVGRmrGjBkqLS11QoS1q6L+aGjnx+uvv66oqChFRUVpwYIFl5U3pPOjsr5oaOfG0qVLNWzYMEVFRem99967rLwhnRtS5f3R0M4PAADgBOYG4o8//jAPGDDAnJOTYy4oKDBHR0ebf/nlF6ttHnroIfO//vUvJ0VY+3744Qfz8OHDzZ06dTIfO3bssvKoqCjz999/bzabzeZnn33WvGrVqlqOsHZV1h8N6fzYtWuX+Z577jEXFxebS0pKzHFxceatW7dabdNQzo+q9EVDOjf27t1rHjVqlNlgMJgLCwvNAwYMMP/6669W2zSUc8Nsrlp/NKTzAwAAOEeDuWK/e/du9ezZUwEBAfLz81NkZKSSk5OttklNTdWKFSsUHR2tOXPmqLi42EnR1o41a9Zo1qxZCg4Ovqzs+PHjKioqUteuXSVJsbGxl/VXfVNRf0gN6/wICgpSQkKCvL295eXlpauvvlonTpywlDek86OyvpAa1rlx00036YMPPpCnp6eysrJkNBrl5+dnKW9I54ZUeX9IDev8AAAAztFgEvtTp04pKCjIshwcHKyMjAzLckFBga677jrFx8dr/fr1ys3N1fLly50Raq2ZN2+eIiIiyiy7tL+CgoKs+qs+qqg/Gtr5cc0111gSs6NHj+rLL79Uv379LOUN6fyorC8a2rkhSV5eXnrttdcUFRWlXr16qWXLlpayhnRuXFBRfzTE8wMAANS+BpPYm0wmubm5WZbNZrPVcuPGjbVy5UpdffXV8vT01IQJE/SPf/zDGaHWCZX1V0PTUM+PX375RRMmTNC0adPUrl07y/qGeH6U1xcN9dyYMmWK9uzZo5MnT2rNmjWW9Q3x3JDK74+Gen4AAIDa1WAS+1atWikzM9OynJmZaXXL9YkTJ7R27VrLstlslqenZ63GWJdc2l+nT58u9xb1hqAhnh8pKSm6//779ec//1l33HGHVVlDOz8q6ouGdm78+uuvOnz4sCSpUaNGGjx4sI4cOWIpb2jnRmX90dDODwAA4BwNJrHv3bu39uzZo+zsbBUWFmrr1q3q27evpdzX11evvvqqjh07JrPZrFWrVmnQoEFOjNi5QkJC5OPjo5SUFElSUlKSVX81NA3t/Dh58qQeffRRJSYmKioq6rLyhnR+VNYXDe3cSE9P18yZM1VSUqKSkhJt27ZN4eHhlvKGdG5IlfdHQzs/AACAczSYywYtW7bU1KlTFRcXJ4PBoJEjR6pLly6aNGmSpkyZos6dO2vOnDl6+OGHZTAY1L17d40fP97ZYde6i/sjMTFRM2fOVH5+vjp16qS4uDhnh1frGur58e6776q4uFgvv/yyZd2oUaO0ffv2Bnd+VKUvGtK50a9fPx04cEAjRoyQh4eHBg8erKioqAb7u6Mq/dGQzg8AAOAcbmaz2ezsIAAAAAAAgG0azK34AAAAAADURyT2AAAAAAC4MBJ7AAAAAABcGIk9AAAAAAAujMQeAAAAAAAXRmIP1JL09HRdd911iomJUUxMjKKjozVq1Cht3rxZkrRu3TqFh4dbyi/8mzZtWpnlt99+uwYOHKhp06apuLi4WrEkJydr7NixlW43cOBAHTx4UJI0YcIEZWdnV/Ooy5eQkKB3333Xrn28++67SkhIqHCb9PR0devWza52AAAAgLqswbzHHqgLfH19lZSUZFk+fvy47r//fnl4eEiSIiIitGLFinLrX1peXFyse++9V+vXr9eoUaMcF7ikXbt2OXT/AAAAAGxDYg84UUhIiKZMmaJ3333XpsT8zJkzys/PV7NmzSrddunSpdq0aZMCAgIUGhpqWV9SUqLExETt27dPRqNR119/vWbOnCl/f3/LNs8++6wkady4cXr77bf1008/acWKFSopKVF2drZGjBihJ598Unv37tW8efPk5+engoICff755/L29i43pu+//16jRo3S6dOndc0112jhwoXy8/PT2rVrtXr1ahkMBp09e1aTJk3S6NGjZTAY9OKLL2r37t1q0aKFWrRooSZNmlSrz958801t3bpVJpNJISEhmjVrllq2bKmxY8fq+uuvV0pKinJychQTE6MpU6ZUa98AAACAM5DYA07WsWNH/fzzz5Kk/fv3KyYmxqo8Li5Od955p1V5cXGxzpw5o3bt2mnChAkaOnRohW18/fXX2rp1qzZs2CBfX189+uijlrK3335bHh4eWrdundzc3LRo0SIlJiZq9uzZlm3mz5+vdevW6f3331fz5s01bdo0vfzyy2rXrp0yMjI0YMAAxcXFSZJ++eUXff311woJCan02DMyMvTBBx/I29tbd911l7Zu3apBgwbps88+09tvv63mzZvrhx9+0Pjx4zV69Gh9/PHHOnr0qP72t7+ptLRU9913X7US+w0bNujnn3/WZ599Jk9PT61evVozZ87UypUrJUm///67PvnkExUWFuruu+9W586dNWDAgCrvHwAAAHAGEnvAydzc3OTr6yup6rfim0wmLV++XF988YWGDBlSaRt79uzRoEGDLFfh77zzTn344YeSpB07digvL0+7d++WJBkMBrVo0aLCeN966y3t2LFDX3zxhX799VeZzWYVFhZKklq3bl2lpF6SbrvtNjVq1EiSdM011yg7O1uNGzfWW2+9pX/84x86evSofvrpJ507d85yHMOHD5e3t7e8vb0VHR2tI0eOVKktSfr73/+ugwcPWj4oMZlMlrgl6Z577pGXl5e8vLw0ZMgQ7dy5k8QeAAAAdR6JPeBkBw8e1LXXXlutOu7u7nrsscf0/fffKyEhQW+99Valdcxms+XrC8/0S+eT2+nTp6tfv36SpIKCggon4zt37pzuuOMO3XbbbYqIiNCdd96pr7/+2rJ/Pz+/Kh+Hp+f/fgW5ubnJbDbrjz/+0D333KO7775b4eHhGjJkiP7+97+XWf/i46gKk8mkiRMnavTo0ZLOP4Zw9uzZMuMxm81yd2d+UQAAANR9jFoBJ/r999+1fPlyTZgwwab6s2bN0q5du/T1119XuF3fvn2VnJys3NxcmUwmqwn8+vTpo1WrVqmkpEQmk0nPPfecFi1adNk+PDw8VFpaqrS0NOXn5+vJJ5/UwIEDtXfvXkvdmpCamqrAwEA98sgj6tOnjyWpNxqNuuWWW7RhwwYVFxeruLjY8kaBqurTp4/Wrl2r/Px8SefnHbjw1gFJ2rhxo0wmk86ePasvv/xSAwcOrJFjAgAAAByJK/ZALSoqKrI8Q+/u7i4fHx899dRT6t+/v9atW1fmM/YXnn8vy5VXXqlJkyZp/vz5uuWWW+Tj41Pmdv369dORI0d05513qmnTpurYsaNycnIkSY888oheeeUV3XHHHTIajbruuuvKfIXckCFDNHbsWC1dulT9+/fX0KFD5e3trWuvvVbt27dXWlpahRPlVdXNN9+stWvXasiQIXJzc9NNN92kwMBApaWladSoUfrvf/+r4cOHXzYJYFXcddddysjI0N133y03Nze1bt1aL7/8sqW8qKhII0eOVEFBgUaPHq1evXrZfTwAAACAo7mZL74/FwAaqLFjx2rMmDFVmrMAAAAAqEu4Yg/UE08++aR+//33MssWL16sP/3pT7Uaz2+//aapU6eWWXbVVVdpyZIlNdbWSy+9pL1795ZZ9uyzz6pnz5411hYAAABQ13DFHgAAAAAAF8bkeQAAAAAAuDASewAAAAAAXBiJPQAAAAAALozEHgAAAAAAF0ZiDwAAAACACyOxBwAAAADAhf0/U5fg+JcOdH4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1224x1296 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show data summary\n",
    "train_visualize.histogram_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Histogram\n",
    "\n",
    "The stacked histogram is a visualization technique used to display the distribution of categories within a dataset for a perticular feature. It is particularly useful when comparing the distribution of different groups or classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diboson (713,)\n",
      "diboson (713,)\n",
      "htautau (30,)\n",
      "ttbar (8638,)\n",
      "ttbar (8638,)\n",
      "ztautau (189193,)\n",
      "ztautau (189193,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_visualize.stacked_histogram(\"PRI_jet_leading_eta\",mu_hat=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chakkappai/.local/lib/python3.10/site-packages/seaborn/axisgrid.py:186: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  figlegend = self._figure.legend(handles, labels, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1007.08x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_visualize.pair_plots(sample_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vis = data.get_train_set()\n",
    "\"\"\" \n",
    "when No systematics parameters are passed, and dopostprocess is True .\n",
    "This mean derived quantities are calculated and Post process cuts are applied\n",
    "\"\"\"\n",
    "syst_train_data = systematics(data_vis,tes=0.9, dopostprocess=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_visualize.histogram_syst(syst_train_data['data'],syst_train_data['weights'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For event vise plots we want sytematics plots without without post process cuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vis = data.get_train_set()\n",
    "# when No systematics parameters are passed, and dopostprocess is false .This mean only derived quantities are calculated\n",
    "data_vis = systematics(data_vis, dopostprocess=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "General Structure of the data object is a dictionary\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| Key             | Value                           | Type                                |\n",
      "+=================+=================================+=====================================+\n",
      "| labels          | Array of shape (220790,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| weights         | Array of shape (220790,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| detailed_labels | Array of shape (220790,)        | <class 'pandas.core.series.Series'> |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| data            | DataFrame of shape (220790, 28) | DataFrame                           |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n",
      "| settings        | Dictionary with 7 keys          | <class 'dict'>                      |\n",
      "+-----------------+---------------------------------+-------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "train_visualize = viz.Dataset_visualise(\n",
    "    data_set=data_vis,\n",
    "    columns=[\n",
    "        \"PRI_met\",\n",
    "        \"PRI_had_pt\",\n",
    "        \"DER_mass_vis\",\n",
    "        \"DER_mass_transverse_met_lep\",\n",
    "        \"DER_deltar_had_lep\",\n",
    "\n",
    "    ],\n",
    "    name=\"Train Set\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "syst_train_data = systematics(data_vis,tes = 0.9, dopostprocess=False)  # you can use other systematics like jes, softmet, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x864 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_visualize.event_vise_syst(syst_train_data['data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Program\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from ingestion import Ingestion\n",
    "\n",
    "ingestion = Ingestion(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## MODEL\n",
    "***\n",
    "* For the competition model should be defined as a class and saved a file called model.py.\n",
    "* Two models are available in this notebook one is a ML + counting method.\n",
    "    1. Histogram + Likelihood method - set `MODEL_TYPE` to *histogram* to use this model\n",
    "    2. ML + counting method - set `MODEL_TYPE` to *internal_ml* to use it\n",
    "        * The same model is available in [simple_stat_only_model](./simple_stat_only_model/model.py), - set `MODEL_TYPE` to *simple_stat_only* to use this model\n",
    "* A slightly more advance model which can work with 1 systematics is available in [simple_one_syst_model](./simple_one_syst_model/model.py) \n",
    "    * set `MODEL_TYPE` to *simple_one_syst* to use this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model_list = [\n",
    "    \"histogram\",\n",
    "    \"simple_stat_only\",\n",
    "    \"simple_one_syst\",\n",
    "    \"internal_ml\",\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the model type below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_TYPE=\"simple_stat_only\" # histogram, simple_stat_only, simple_one_syst, internal_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if MODEL_TYPE not in model_list:\n",
    "    raise ValueError(f\"Model type {MODEL_TYPE} not in {model_list}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Notice when you External Models its imported into the notebook*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "if MODEL_TYPE == \"simple_stat_only\":\n",
    "    submission_dir = os.path.join(root_dir, \"simple_stat_only_model\")\n",
    "    path.append(submission_dir)\n",
    "    from model import Model\n",
    "    \n",
    "elif MODEL_TYPE == \"simple_one_syst\":\n",
    "    submission_dir = os.path.join(root_dir, \"simple_one_syst_model\")\n",
    "    path.append(submission_dir)\n",
    "    from model import Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are defining a bunch a helper function for the `Model` class to do statistics and data splting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `calculate_saved_info` Function\n",
    "\n",
    "Calculates `saved_info` for \"mu\" computation from a model and training data. \n",
    "\n",
    "- **Inputs**:\n",
    "  - `model`: Predictive model.\n",
    "  - `train_set`: Dictionary with training data and labels.\n",
    "  - `file_path`: Optional path to save the results (default is \"saved_info.pkl\").\n",
    "\n",
    "- **Returns**: Dictionary with calculated `beta` and `gamma`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "if MODEL_TYPE == \"internal_ml\":\n",
    "    def calculate_saved_info(model, train_set):\n",
    "\n",
    "\n",
    "        score = model.predict(train_set[\"data\"])\n",
    "\n",
    "        print(\"score shape before threshold\", score.shape)\n",
    "\n",
    "        score = score.flatten() > 0.5\n",
    "        score = score.astype(int)\n",
    "\n",
    "        label = train_set[\"labels\"]\n",
    "\n",
    "        print(\"score shape after threshold\", score.shape)\n",
    "\n",
    "        gamma = np.sum(train_set[\"weights\"] * score * label) + 0.1\n",
    "\n",
    "        beta = np.sum(train_set[\"weights\"] * score * (1 - label)) - 0.1\n",
    "\n",
    "        saved_info = {\"beta\": beta, \"gamma\": gamma}\n",
    "\n",
    "        print(\"saved_info\", saved_info)\n",
    "\n",
    "        return saved_info\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `compute_mu` Function\n",
    "\n",
    "Calculates \"mu\" and its uncertainties from scores and weights. It uses a dictionary for additional parameters. \n",
    "\n",
    "- **Inputs**:\n",
    "  - `score`: Array of scores.\n",
    "  - `weight`: Array of weights.\n",
    "  - `saved_info`: Dictionary with \"beta\" and \"gamma\".\n",
    "\n",
    "- **Returns**: A dictionary with \"mu_hat\", \"del_mu_stat\", \"del_mu_sys\", and \"del_mu_tot\".\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if MODEL_TYPE == \"internal_ml\":\n",
    "    def compute_mu(score, weight, saved_info):\n",
    "\n",
    "        score = score.flatten() > 0.5\n",
    "        score = score.astype(int)\n",
    "\n",
    "        mu = (np.sum(score * weight) - saved_info[\"beta\"]) / saved_info[\"gamma\"]\n",
    "        del_mu_stat = (\n",
    "            np.sqrt(saved_info[\"beta\"] + saved_info[\"gamma\"]) / saved_info[\"gamma\"]\n",
    "        )\n",
    "        del_mu_sys = abs(0.1 * mu)\n",
    "        del_mu_tot = (1 / 2) * np.sqrt(del_mu_stat**2 + del_mu_sys**2)\n",
    "\n",
    "        return {\n",
    "            \"mu_hat\": mu,\n",
    "            \"del_mu_stat\": del_mu_stat,\n",
    "            \"del_mu_sys\": del_mu_sys,\n",
    "            \"del_mu_tot\": del_mu_tot,\n",
    "        }\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a custom `train_test_split()` build around the sklearn train_test_slipt. This is so it works well with the dictionary setting we have and as a bonus also does reweighting which is essential for getting the statistics right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if MODEL_TYPE == \"internal_ml\":\n",
    "    from sklearn.model_selection import train_test_split as sk_train_test_split\n",
    "\n",
    "    def train_test_split(data_set, test_size=0.2, random_state=42, reweight=False):\n",
    "\n",
    "        data = data_set[\"data\"].copy()\n",
    "        train_set = {}\n",
    "        test_set = {}\n",
    "        full_size = len(data)\n",
    "\n",
    "        print(f\"Full size of the data is {full_size}\")\n",
    "\n",
    "        for key in data_set.keys():\n",
    "            if (key != \"data\") and (key != \"settings\"):\n",
    "                data[key] = np.array(data_set[key])\n",
    "\n",
    "        train_data, test_data = sk_train_test_split(\n",
    "            data, test_size=test_size, random_state=random_state\n",
    "        )\n",
    "\n",
    "        for key in data_set.keys():\n",
    "            if (key != \"data\") and (key != \"settings\"):\n",
    "                train_set[key] = np.array(train_data.pop(key))\n",
    "                test_set[key] = np.array(test_data.pop(key))\n",
    "\n",
    "        train_set[\"data\"] = train_data\n",
    "        test_set[\"data\"] = test_data\n",
    "\n",
    "        if reweight is True:\n",
    "            signal_weight = np.sum(data_set[\"weights\"][data_set[\"labels\"] == 1])\n",
    "            background_weight = np.sum(data_set[\"weights\"][data_set[\"labels\"] == 0])\n",
    "            signal_weight_train = np.sum(train_set[\"weights\"][train_set[\"labels\"] == 1])\n",
    "            background_weight_train = np.sum(train_set[\"weights\"][train_set[\"labels\"] == 0])\n",
    "            signal_weight_test = np.sum(test_set[\"weights\"][test_set[\"labels\"] == 1])\n",
    "            background_weight_test = np.sum(test_set[\"weights\"][test_set[\"labels\"] == 0])\n",
    "\n",
    "            train_set[\"weights\"][train_set[\"labels\"] == 1] = train_set[\"weights\"][\n",
    "                train_set[\"labels\"] == 1\n",
    "            ] * (signal_weight / signal_weight_train)\n",
    "            test_set[\"weights\"][test_set[\"labels\"] == 1] = test_set[\"weights\"][\n",
    "                test_set[\"labels\"] == 1\n",
    "            ] * (signal_weight / signal_weight_test)\n",
    "\n",
    "            train_set[\"weights\"][train_set[\"labels\"] == 0] = train_set[\"weights\"][\n",
    "                train_set[\"labels\"] == 0\n",
    "            ] * (background_weight / background_weight_train)\n",
    "            test_set[\"weights\"][test_set[\"labels\"] == 0] = test_set[\"weights\"][\n",
    "                test_set[\"labels\"] == 0\n",
    "            ] * (background_weight / background_weight_test)\n",
    "\n",
    "        return train_set, test_set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## The Model Class\n",
    "***\n",
    "We import a class named `Model` from the submission file (`model.py`). This `Model` class has the following methods:\n",
    "- `init`: receives train set and systematics class as input\n",
    "- `fit`: can be used for training\n",
    "- `predict`: receives one test set and outputs a dictionary with the following keys\n",
    "    - `mu_hat` : predicted mu $\\hat{\\mu}$\n",
    "    - `delta_mu_hat`: $\\Delta{\\hat{\\mu}}$ bound for $\\mu$\n",
    "    - `p16`: 16th percentile\n",
    "    - `p84`: 84th percentile\n",
    "\n",
    "In this example code, the `Model` class implements an XGBoost model which is trained to predict both the TES and the class label. You can find the code in `HEP-Challenge/sample_code_submission/model.py`. You can modify it the way you want, keeping the required class structure and functions there. More instructions are given inside the `model.py` file. If running in Collab, click the folder icon in the left sidebar to open the file browser.\n",
    "\n",
    "#### ⚠️ Note:\n",
    "In real setting i.e. the challenge itself, the submitted model is initialized once with train set and systematics class and the `predict` is called multiple times, each time with a different test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if MODEL_TYPE == \"internal_ml\":\n",
    "    from xgboost import XGBClassifier\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "    class Model:\n",
    "\n",
    "        def __init__(self, get_train_set=None, systematics=None):\n",
    "\n",
    "            self.get_train_set = get_train_set\n",
    "            self.systematics = systematics\n",
    "            self.re_train = True\n",
    "                    \n",
    "            self.model = XGBClassifier(eval_metric=[\"error\", \"logloss\", \"rmse\"],)\n",
    "            self.name = \"model_XGB\"\n",
    "            self.scaler = StandardScaler()\n",
    "            \n",
    "\n",
    "        def fit(self):\n",
    "            \"\"\"\n",
    "            Trains the model.\n",
    "\n",
    "            Params:\n",
    "                None\n",
    "\n",
    "            Functionality:\n",
    "                This function can be used to train a model. If `re_train` is True, it balances the dataset,\n",
    "                fits the model using the balanced dataset, and saves the model. If `re_train` is False, it\n",
    "                loads the saved model and calculates the saved information. The saved information is used\n",
    "                to compute the train results.\n",
    "\n",
    "            Returns:\n",
    "                None\n",
    "            \"\"\"\n",
    "\n",
    "\n",
    "            train_set = self.get_train_set() # train_set is a dictionary with data, labels, and weights\n",
    "            \n",
    "            training_set, holdout_set = train_test_split(\n",
    "                train_set, test_size=0.5, random_state=42, reweight=True\n",
    "            )\n",
    "            \n",
    "            del train_set\n",
    "            \n",
    "            training_set = self.systematics(training_set)\n",
    "\n",
    "            weights_train = training_set[\"weights\"].copy()\n",
    "            train_labels = training_set[\"labels\"].copy()\n",
    "            class_weights_train = (\n",
    "                weights_train[train_labels == 0].sum(),\n",
    "                weights_train[train_labels == 1].sum(),\n",
    "            )\n",
    "\n",
    "            for i in range(len(class_weights_train)):  # loop on B then S target\n",
    "                # training dataset: equalize number of background and signal\n",
    "                weights_train[train_labels == i] *= (\n",
    "                    max(class_weights_train) / class_weights_train[i]\n",
    "                )\n",
    "                # test dataset : increase test weight to compensate for sampling\n",
    "\n",
    "            training_set[\"weights\"] = weights_train\n",
    "                    \n",
    "            self.scaler.fit_transform(training_set[\"data\"])\n",
    "\n",
    "            X_train_data = self.scaler.transform(training_set[\"data\"])\n",
    "            self.model.fit(X_train_data,training_set[\"labels\"], training_set[\"weights\"])\n",
    "            \n",
    "            holdout_set = self.systematics(holdout_set)\n",
    "            \n",
    "            self.saved_info = calculate_saved_info(self.model, holdout_set)\n",
    "\n",
    "            holdout_score = self.model.predict(holdout_set[\"data\"])\n",
    "            holdout_results = compute_mu(\n",
    "                holdout_score, holdout_set[\"weights\"], self.saved_info\n",
    "            )\n",
    "                \n",
    "            print(\"Holdout Results: \")\n",
    "            for key in holdout_results.keys():\n",
    "                print(\"\\t\", key, \" : \", holdout_results[key])\n",
    "\n",
    "\n",
    "        def predict(self, test_set):\n",
    "            \"\"\"\n",
    "            Predicts the values for the test set.\n",
    "\n",
    "            Parameters:\n",
    "                test_set (dict): A dictionary containing the test data, and weights.\n",
    "\n",
    "            Returns:\n",
    "                dict: A dictionary with the following keys:\n",
    "                * 'mu_hat': The predicted value of mu.\n",
    "                * 'delta_mu_hat': The uncertainty in the predicted value of mu.\n",
    "                * 'p16': The lower bound of the 16th percentile of mu.\n",
    "                * 'p84': The upper bound of the 84th percentile of mu.\n",
    "            \"\"\"\n",
    "\n",
    "            test_data = test_set[\"data\"]\n",
    "            test_weights = test_set[\"weights\"]\n",
    "\n",
    "            test_data = self.scaler.transform(test_data)\n",
    "            predictions = self.model.predict_proba(test_data)[:, 1]\n",
    "        \n",
    "            result_mu_cal = compute_mu(predictions, test_weights, self.saved_info)\n",
    "\n",
    "            print(\"Test Results: \", result_mu_cal)\n",
    "\n",
    "            result = {\n",
    "                \"mu_hat\": result_mu_cal[\"mu_hat\"],\n",
    "                \"delta_mu_hat\": result_mu_cal[\"del_mu_tot\"],\n",
    "                \"p16\": result_mu_cal[\"mu_hat\"] - result_mu_cal[\"del_mu_tot\"],\n",
    "                \"p84\": result_mu_cal[\"mu_hat\"] + result_mu_cal[\"del_mu_tot\"],\n",
    "            }\n",
    "\n",
    "            return result\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "if MODEL_TYPE == \"histogram\":\n",
    "    import matplotlib.gridspec as gridspec\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    class Model:\n",
    "        def __init__(self, get_train_set=None, systematics=None):\n",
    "            \"\"\"\n",
    "            Parameters:\n",
    "            - get_train_set (function): A function that returns the training set.\n",
    "            - systematics (list): systematics paramter\n",
    "\n",
    "            Attributes:\n",
    "            - get_train_set (function): A function that returns the training set.\n",
    "            - systematics (list): A list of systematics.\n",
    "            - n_bins (int): Number of bins.\n",
    "            - mu_range (numpy array): Range of mu values.\n",
    "            - epsilon (float): A small value used for numerical stability.\n",
    "            - plot (bool): Flag indicating whether to plot the results.\n",
    "            \"\"\"\n",
    "            self.get_train_set = get_train_set\n",
    "            self.systematics = systematics\n",
    "\n",
    "            # Default values for other attributes\n",
    "            self.n_bins = 10\n",
    "            self.mu_range = np.linspace(0.001, 10.0, 400)\n",
    "            self.epsilon = 1e-20\n",
    "            self.plot = True\n",
    "            \n",
    "\n",
    "        def fit(self):\n",
    "            \"\"\"\n",
    "            Trains the model.\n",
    "\n",
    "            Params:\n",
    "                None\n",
    "\n",
    "            Functionality:\n",
    "                This function can be used to train a model. If `re_train` is True, it balances the dataset,\n",
    "                fits the model using the balanced dataset, and saves the model. If `re_train` is False, it\n",
    "                loads the saved model and calculates the saved information. The saved information is used\n",
    "                to compute the train results.\n",
    "\n",
    "            Returns:\n",
    "                None\n",
    "            \"\"\"\n",
    "\n",
    "            # Get the training set\n",
    "            train_set = self.get_train_set() # train_set is a dictionary with data, labels, and weights\n",
    "\n",
    "            # Apply systematics to the training set\n",
    "            train_set = systematics(train_set)\n",
    "            \n",
    "            # Separate the signal and background data and weights\n",
    "            data_sig = train_set[\"data\"].values[train_set[\"labels\"]==1, 0]\n",
    "            weights_sig = train_set[\"weights\"][train_set[\"labels\"]==1]\n",
    "\n",
    "            data_bkg = train_set[\"data\"].values[train_set[\"labels\"]==0, 0]\n",
    "            weights_bkg = train_set[\"weights\"][train_set[\"labels\"]==0]\n",
    "\n",
    "            # Set the bin range for histogram calculation\n",
    "            self.bin_range=(\n",
    "                1.05*min(np.min(data_sig), np.min(data_bkg)),\n",
    "                0.95*max(np.max(data_sig), np.max(data_bkg))\n",
    "            )\n",
    "            \n",
    "            # Calculate the signal template histogram\n",
    "            self.template_sig, _ = np.histogram(\n",
    "                data_sig,\n",
    "                weights=weights_sig,\n",
    "                range=self.bin_range, \n",
    "                bins=self.n_bins, \n",
    "                density=False\n",
    "            )\n",
    "            \n",
    "            # Calculate the background template histogram\n",
    "            self.template_bkg, _ = np.histogram(\n",
    "                data_bkg,\n",
    "                weights=weights_bkg,\n",
    "                range=self.bin_range, \n",
    "                bins=self.n_bins, \n",
    "                density=False\n",
    "            )\n",
    "            \n",
    "            \n",
    "\n",
    "\n",
    "        def predict(self, test_set):\n",
    "            \"\"\"\n",
    "            Predicts the values for the test set.\n",
    "\n",
    "            Parameters:\n",
    "                test_set (dict): A dictionary containing the test data, and weights.\n",
    "\n",
    "            Returns:\n",
    "                dict: A dictionary with the following keys:\n",
    "                * 'mu_hat': The predicted value of mu.\n",
    "                * 'delta_mu_hat': The uncertainty in the predicted value of mu.\n",
    "                * 'p16': The lower bound of the 16th percentile of mu.\n",
    "                * 'p84': The upper bound of the 84th percentile of mu.\n",
    "            \"\"\"\n",
    "            # Convert test data and weights to numpy arrays, and select the first column of the data (Lepton_pt)\n",
    "            test_data = np.array(test_set[\"data\"])[:, 0]\n",
    "            test_weights = np.array(test_set[\"weights\"])[:]\n",
    "\n",
    "            # Calculate the histogram of test data\n",
    "            hist_test, _ = np.histogram(\n",
    "                test_data,\n",
    "                weights=test_weights,\n",
    "                range=self.bin_range, \n",
    "                bins=self.n_bins, \n",
    "                density=False\n",
    "            )        \n",
    "\n",
    "            # Initialize an array to store the negative log-likelihood values\n",
    "            nll2D = np.zeros((len(self.mu_range)))\n",
    "\n",
    "            # Iterate over mu values and calculate the negative log-likelihood\n",
    "            for mu_iter, mu_val in enumerate(self.mu_range):\n",
    "                N = hist_test + self.epsilon\n",
    "                nexp = mu_val * self.template_sig + self.template_bkg + self.epsilon\n",
    "\n",
    "                # Calculate the negative log-likelihood for fixed N, variable nexp\n",
    "                nll = - (N * np.log(nexp) - nexp)\n",
    "                nll2D[mu_iter] = np.sum(nll)\n",
    "\n",
    "            # Calculate the difference between the negative log-likelihood and its minimum value\n",
    "            nll_diff = nll2D-np.min(nll2D)\n",
    "            \n",
    "            # Create a mask to select only NLL values withing 4 of the minimum value for the fit\n",
    "            fitrange_mask = nll_diff < 4.0\n",
    "            \n",
    "            # Fit a parabola to the selected data points\n",
    "            z = np.polyfit(\n",
    "                self.mu_range[fitrange_mask], \n",
    "                nll_diff[fitrange_mask], \n",
    "                2,\n",
    "            )\n",
    "\n",
    "            poly = np.poly1d(z)\n",
    "\n",
    "            deriv1 = poly.deriv()\n",
    "            \n",
    "            # Find the minimum value of nll_diff and its corresponding mu value\n",
    "            min_mu_ind = np.argmin(nll_diff)\n",
    "            central = self.mu_range[min_mu_ind]\n",
    "\n",
    "            # Find the extrema of the polynomial and select the minimum value closest to the mu with the minimum nll_diff\n",
    "            extrema = (deriv1).roots\n",
    "            minimum = extrema[np.argsort(np.abs(extrema-central))][0]\n",
    "\n",
    "            # Find the intersections of the polynomial with the threshold value\n",
    "            # Threadhold 0.5 corresponds to 1 sigma in the chi2 distribution\n",
    "            threshold = .5\n",
    "            intersects = (poly - threshold).roots\n",
    "            \n",
    "            # Select 2 intersections closest to central value (only needed for polynomials with order > 2)\n",
    "            intersects = intersects[np.argsort(np.abs(intersects-central))][:2] \n",
    "\n",
    "            # Ensure first intersect is smaller than second intersect\n",
    "            intersects = np.sort(intersects)\n",
    "\n",
    "            # Plot the results if self.plot is True\n",
    "            if self.plot:\n",
    "                fig = plt.figure(figsize=(7,5))\n",
    "                outer = gridspec.GridSpec(1, 1, wspace=0.2, hspace=0.2)\n",
    "                subplot = fig.add_subplot(outer[0])\n",
    "\n",
    "                subplot.plot(self.mu_range, nll_diff, label='Nll_diff')\n",
    "                subplot.plot(self.mu_range, poly(self.mu_range), label='Fit')\n",
    "                subplot.set_ylim(-0.0, 4.5)\n",
    "\n",
    "                subplot.set_ylabel('NLL Difference')\n",
    "                subplot.set_xlabel('mu')\n",
    "\n",
    "                subplot.legend()\n",
    "                plt.show()\n",
    "\n",
    "            # Create a dictionary to store the results\n",
    "            result = {\n",
    "                \"mu_hat\": minimum,\n",
    "                \"delta_mu_hat\": abs(intersects[1] - intersects[0])/2,\n",
    "                \"p16\": intersects[0],\n",
    "                \"p84\": intersects[1],\n",
    "            }\n",
    "\n",
    "            return result\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you can defined you model you can submit it with the ingestion function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:22,462 - ingestion            - INFO     - Initializing Submmited Model\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# initialize submission\n",
    "ingestion.init_submission(Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fit_submission` then calls fit method of the model and concludes the training phase of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:22,483 - ingestion            - INFO     - Fitting Submmited Model\n",
      "2024-09-27 11:18:22,628 - datasets             - INFO     - Train data loaded successfully\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full size of the data is 220790\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chakkappai/.local/lib/python3.10/site-packages/xgboost/core.py:726: FutureWarning: Pass `sample_weight` as keyword args.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score shape before threshold (99347,)\n",
      "score shape after threshold (99347,)\n",
      "saved_info {'beta': -0.1, 'gamma': 0.1}\n",
      "Holdout Results: \n",
      "\t mu_hat  :  1.0\n",
      "\t del_mu_stat  :  0.0\n",
      "\t del_mu_sys  :  0.1\n",
      "\t del_mu_tot  :  0.05\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# fit submission\n",
    "ingestion.fit_submission()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:26,465 - datasets             - INFO     - Test data loaded successfully\n"
     ]
    }
   ],
   "source": [
    "# load test set\n",
    "data.load_test_set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEST SETTINGS\n",
    "\n",
    "Here you can set the test settings. This will determine which all systematics are applied and how many pseudo experiments are run\n",
    "\n",
    "⚠️ Note: As you can see by default *tes* or *Tau-hadron Energy scale systematics* is applied, when running stat-only method turn this to `False` to get meaningfull results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_SETTINGS = {\n",
    "\"systematics\": {  # Systematics to use\n",
    "    \"tes\": True, # tau energy scale\n",
    "    \"jes\": False, # jet energy scale\n",
    "    \"soft_met\": False, # soft term in MET\n",
    "    \"ttbar_scale\": False, # ttbar scale factor\n",
    "    \"diboson_scale\": False, # diboson scale factor\n",
    "    \"bkg_scale\": False, # Background scale factor\n",
    "    },\n",
    "\"num_pseudo_experiments\" : 4 , # Number of pseudo-experiments to run per set\n",
    "\"num_of_sets\" : 3, # Number of sets of pseudo-experiments to run\n",
    "} \n",
    "\n",
    "USE_RANDOM_MUS = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_settings = TEST_SETTINGS.copy()\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "    \n",
    "if USE_RANDOM_MUS:\n",
    "    test_settings[ \"ground_truth_mus\"] = (np.random.uniform(0.1, 3, test_settings[\"num_of_sets\"])).tolist()\n",
    "    \n",
    "    random_settings_file = os.path.join(output_dir, \"random_mu.json\")\n",
    "    with open(random_settings_file, \"w\") as f:\n",
    "        json.dump(test_settings, f)\n",
    "else:\n",
    "    test_settings_file = os.path.join(input_dir, \"test\", \"settings\", \"data.json\")\n",
    "    with open(test_settings_file) as f:\n",
    "        test_settings = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next is the prediction phase this is where the pseudo experiments are defined and the model is **repeatedly** called to Evalute each pseudo dataset. \n",
    "\n",
    "**⚠️ Note: In the Real Challenge the prediction step is parallelised.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:26,502 - ingestion            - INFO     - Calling predict method of submitted model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 441.0, 'del_mu_stat': 0.0, 'del_mu_sys': 44.1, 'del_mu_tot': 22.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 581.0, 'del_mu_stat': 0.0, 'del_mu_sys': 58.1, 'del_mu_tot': 29.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 431.0, 'del_mu_stat': 0.0, 'del_mu_sys': 43.1, 'del_mu_tot': 21.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 531.0, 'del_mu_stat': 0.0, 'del_mu_sys': 53.1, 'del_mu_tot': 26.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 581.0, 'del_mu_stat': 0.0, 'del_mu_sys': 58.1, 'del_mu_tot': 29.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 431.0, 'del_mu_stat': 0.0, 'del_mu_sys': 43.1, 'del_mu_tot': 21.55}\n",
      "Test Results:  {'mu_hat': 650.9999999999999, 'del_mu_stat': 0.0, 'del_mu_sys': 65.1, 'del_mu_tot': 32.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 341.0, 'del_mu_stat': 0.0, 'del_mu_sys': 34.1, 'del_mu_tot': 17.05}\n",
      "Test Results:  {'mu_hat': 511.0, 'del_mu_stat': 0.0, 'del_mu_sys': 51.1, 'del_mu_tot': 25.55}\n",
      "Test Results:  {'mu_hat': 571.0, 'del_mu_stat': 0.0, 'del_mu_sys': 57.1, 'del_mu_tot': 28.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 461.0, 'del_mu_stat': 0.0, 'del_mu_sys': 46.1, 'del_mu_tot': 23.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Results:  {'mu_hat': 631.0, 'del_mu_stat': 0.0, 'del_mu_sys': 63.1, 'del_mu_tot': 31.55}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# predict submission\n",
    "ingestion.predict_submission(test_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Last step combines the result from all the pseudo experiments and prepare it for scoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:55,599 - ingestion            - INFO     - Computing Ingestion Result\n"
     ]
    }
   ],
   "source": [
    "ingestion.compute_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# save result\n",
    "!rm -rf $output_dir/*\n",
    "ingestion.save_result(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Score\n",
    "***\n",
    "1. Compute Scores\n",
    "2. Visualize Scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from score import Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Score\n",
    "score = Scoring()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:55,837 - score                - INFO     - Read ingestion results from /home/chakkappai/Work/Fair-Universe/HEP-Challenge/sample_result_submission\n"
     ]
    }
   ],
   "source": [
    "score.load_ingestion_results(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next stage computes the score the submitted model by evaluting the results from ingestion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-27 11:18:55,857 - score                - INFO     - Computing scores\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n",
      "Set 0\n",
      "------------------\n",
      "MAE (avg): 883.1193616241264\n",
      "RMSE (avg): 205718.15514338785\n",
      "Interval: 45.35000000000001\n",
      "Coverage: 0.0\n",
      "Quantiles Score: -4.379689438003629\n",
      "------------------\n",
      "Set 1\n",
      "------------------\n",
      "MAE (avg): 1158.5530618256698\n",
      "RMSE (avg): 354427.52364815376\n",
      "Interval: 59.59999999999992\n",
      "Coverage: 0.0\n",
      "Quantiles Score: -4.652929563422983\n",
      "------------------\n",
      "Set 2\n",
      "------------------\n",
      "MAE (avg): 956.2009079346985\n",
      "RMSE (avg): 249741.13460956834\n",
      "Interval: 49.099999999999966\n",
      "Coverage: 0.0\n",
      "Quantiles Score: -4.459136612160804\n",
      "\n",
      "\n",
      "==================\n",
      "Overall Score\n",
      "==================\n",
      "[*] --- RMSE: 269962.271\n",
      "[*] --- MAE: 999.291\n",
      "[*] --- Interval: 51.35\n",
      "[*] --- Coverage: 0.0\n",
      "[*] --- Quantiles score: -8.451\n",
      "[*] --- Ingestion duration: None\n",
      "[✔]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute Score\n",
    "score.compute_scores(test_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize scatter plot of ground truth mu and predicted mu\n",
    "viz.visualize_scatter(ingestion_result_dict=ingestion.results_dict, \n",
    "                  ground_truth_mus=test_settings[\"ground_truth_mus\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Submission\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Prepare the submission**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zipdir(archivename, basedir):\n",
    "    '''Zip directory, from J.F. Sebastian http://stackoverflow.com/'''\n",
    "    assert os.path.isdir(basedir)\n",
    "    with closing(ZipFile(archivename, \"w\", ZIP_DEFLATED)) as z:\n",
    "        for root, dirs, files in os.walk(basedir):\n",
    "            # NOTE: ignore empty directories\n",
    "            for fn in files:\n",
    "                if fn[-4:] != '.zip' and fn != '.DS_Store':\n",
    "                    absfn = os.path.join(root, fn)\n",
    "                    zfn = absfn[len(basedir):]  # XXX: relative path\n",
    "                    z.write(absfn, zfn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submit : Submission_24-09-27-11-18.zip to the competition\n",
      "You can find the zip file in `HEP-Challenge/\n"
     ]
    }
   ],
   "source": [
    "if MODEL_TYPE != \"histogram\" and MODEL_TYPE != \"internal_ml\":\n",
    "    the_date = datetime.datetime.now().strftime(\"%y-%m-%d-%H-%M\")\n",
    "    code_submission = 'Submission_' + the_date + '.zip'\n",
    "    zipdir(code_submission, submission_dir)\n",
    "    print(\"Submit : \" + code_submission + \" to the competition\")\n",
    "    print(\"You can find the zip file in `HEP-Challenge/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "c9e001b0608738f9411416229c98988c04b997dc526fb61c5e4e084e768e3249"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}