{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline #1 - Clustering\n",
    "\n",
    "<img src=\"img/pipeline1.png\" width=\"550\"/>\n",
    "\n",
    "This is the very first phase of the project. The idea here is to run an unsupervised [ML algorithm](https://github.com/ucl-pond/pySuStaIn/tree/master) on cross-sectional clinical data of PD patients, to uncover data-driven disease phenotypes (subtypes) with distinct temporal progression patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline overview\n",
    "\n",
    "This pipeline runs the SuStaIn clustering algorithm on non-motor symtoms (NMS) data. \n",
    "Specifically, it does the following:\n",
    "\n",
    "- Data visualization\n",
    "- Prepares/adjusts the hyperparameters of the algorithm\n",
    "- Runs the algorithm\n",
    "- Model stability tracking\n",
    "- Model performance evaluation\n",
    "\n",
    "**A disclaimer:** the scientific results found in this notebook (and the other two) are all somewhat (if not very) preliminary and serve the bigger purpose of providing a visual representation of what the final products will look like."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dive in!\n",
    "\n",
    "First, let's load the libraries that will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pySuStaIn\n",
    "import wandb\n",
    "from sklearn.metrics.cluster import adjusted_rand_score\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "output_folder = \"/Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/results/\"\n",
    "input_folder = \"/Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let’s load the data. \n",
    "\n",
    "The project will utilize three independent datasets containing participants with PD: a training set, a validation set, and a testing set.\n",
    "\n",
    "1. Canadian Open Parkinson Network (COPN)\n",
    "2. Parkinson's Progression Marker Initiative (PPMI)\n",
    "3. UK Biobank\n",
    "\n",
    "Currently, the two first datasets are available. \n",
    "\n",
    "***To load a dataset, input its abbreviation ('copn' or 'ppmi').***\\\n",
    "\n",
    "The pipeline will need to be rerun from here for every dataset to be loaded. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected dataset: copn\n",
      "prob_nl:\n",
      "shape: (956, 26)\n",
      "[[0.752 0.851 0.741 ... 0.885 0.93  0.874]\n",
      " [0.752 0.851 0.065 ... 0.029 0.017 0.874]\n",
      " [0.062 0.037 0.065 ... 0.029 0.017 0.031]\n",
      " ...\n",
      " [0.062 0.851 0.065 ... 0.029 0.017 0.031]\n",
      " [0.752 0.851 0.065 ... 0.029 0.93  0.874]\n",
      " [0.752 0.851 0.741 ... 0.029 0.017 0.031]]\n",
      "(24856, 4)\n",
      "prob_score:\n",
      "shape: (956, 26, 4)\n",
      "[[[0.06198765 0.06198765 0.06198765 0.06198765]\n",
      "  [0.0371787  0.0371787  0.0371787  0.0371787 ]\n",
      "  [0.0648523  0.0648523  0.0648523  0.0648523 ]\n",
      "  ...\n",
      "  [0.02875    0.02875    0.02875    0.02875   ]\n",
      "  [0.0174964  0.0174964  0.0174964  0.0174964 ]\n",
      "  [0.0314284  0.0314284  0.0314284  0.0314284 ]]\n",
      "\n",
      " [[0.06198765 0.06198765 0.06198765 0.06198765]\n",
      "  [0.0371787  0.0371787  0.0371787  0.0371787 ]\n",
      "  [0.7405908  0.0648523  0.0648523  0.0648523 ]\n",
      "  ...\n",
      "  [0.885      0.02875    0.02875    0.02875   ]\n",
      "  [0.0174964  0.9300144  0.0174964  0.0174964 ]\n",
      "  [0.0314284  0.0314284  0.0314284  0.0314284 ]]\n",
      "\n",
      " [[0.06198765 0.06198765 0.7520494  0.06198765]\n",
      "  [0.0371787  0.8512852  0.0371787  0.0371787 ]\n",
      "  [0.7405908  0.0648523  0.0648523  0.0648523 ]\n",
      "  ...\n",
      "  [0.02875    0.885      0.02875    0.02875   ]\n",
      "  [0.9300144  0.0174964  0.0174964  0.0174964 ]\n",
      "  [0.0314284  0.8742864  0.0314284  0.0314284 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.06198765 0.06198765 0.06198765 0.7520494 ]\n",
      "  [0.0371787  0.0371787  0.0371787  0.0371787 ]\n",
      "  [0.0648523  0.0648523  0.7405908  0.0648523 ]\n",
      "  ...\n",
      "  [0.02875    0.885      0.02875    0.02875   ]\n",
      "  [0.0174964  0.0174964  0.9300144  0.0174964 ]\n",
      "  [0.0314284  0.8742864  0.0314284  0.0314284 ]]\n",
      "\n",
      " [[0.06198765 0.06198765 0.06198765 0.06198765]\n",
      "  [0.0371787  0.0371787  0.0371787  0.0371787 ]\n",
      "  [0.0648523  0.0648523  0.7405908  0.0648523 ]\n",
      "  ...\n",
      "  [0.885      0.02875    0.02875    0.02875   ]\n",
      "  [0.0174964  0.0174964  0.0174964  0.0174964 ]\n",
      "  [0.0314284  0.0314284  0.0314284  0.0314284 ]]\n",
      "\n",
      " [[0.06198765 0.06198765 0.06198765 0.06198765]\n",
      "  [0.0371787  0.0371787  0.0371787  0.0371787 ]\n",
      "  [0.0648523  0.0648523  0.0648523  0.0648523 ]\n",
      "  ...\n",
      "  [0.885      0.02875    0.02875    0.02875   ]\n",
      "  [0.0174964  0.9300144  0.0174964  0.0174964 ]\n",
      "  [0.8742864  0.0314284  0.0314284  0.0314284 ]]]\n",
      "score_vals\n",
      "shape: (26, 4)\n",
      "[[1 2 3 4]\n",
      " [1 2 3 0]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 0]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]\n",
      " [1 2 3 4]]\n"
     ]
    }
   ],
   "source": [
    "# User input for dataset\n",
    "dataset = input(\"Enter dataset (copn/ppmi): \")\n",
    "print(\"Selected dataset:\", dataset)\n",
    "\n",
    "# Dictionary to map dataset names to file paths\n",
    "data_paths = {\n",
    "    \"copn\": input_folder + \"dataset_1_copn/data.xlsx\",\n",
    "    \"ppmi\": input_folder + \"dataset_2_ppmi/data.xlsx\"\n",
    "}\n",
    "\n",
    "# Get the data path based on the dataset\n",
    "data_path = data_paths.get(dataset, \"\")\n",
    "if not data_path:\n",
    "    raise ValueError(\"Invalid dataset name provided\")\n",
    "\n",
    "# Dataset demographics\n",
    "demographics = pd.read_excel(data_path, sheet_name=\"demographics\")\n",
    "\n",
    "# Algorithm inputs \n",
    "\n",
    "# prob_nl input\n",
    "prob_nl = pd.read_excel(data_path, sheet_name=\"1_prob_nl\").values\n",
    "print(\"prob_nl:\")\n",
    "print(\"shape:\", prob_nl.shape)\n",
    "print(prob_nl)\n",
    "\n",
    "# prob_score input\n",
    "tmp = pd.read_excel(data_path, sheet_name=\"2_prob_score\").values\n",
    "original_shape = tmp.shape\n",
    "print(original_shape)\n",
    "\n",
    "desired_shape = (prob_nl.shape[0], 26, 4)\n",
    "prob_score = tmp.reshape(desired_shape)\n",
    "print(\"prob_score:\")\n",
    "print(\"shape:\", prob_score.shape)\n",
    "print(prob_score)\n",
    "\n",
    "# score_vals input\n",
    "score_vals = pd.read_excel(data_path, sheet_name=\"3_score_vals\").values\n",
    "print(\"score_vals\")\n",
    "print(\"shape:\", score_vals.shape)\n",
    "print(score_vals)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization\n",
    "\n",
    "Let's look at the summary statistics and distribution of some basic demographic variables. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    387.000000\n",
      "mean      67.369509\n",
      "std        9.108181\n",
      "min       36.000000\n",
      "25%       62.000000\n",
      "50%       68.000000\n",
      "75%       74.000000\n",
      "max       90.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age in COPN')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "print(demographics['Age'].describe())\n",
    "sns.histplot(demographics['Age'],\n",
    "            bins=30,           # increase \"resolution\"\n",
    "            color='purple',    # change color\n",
    "            kde=True       \n",
    ").set_title(\"Age in \" + dataset.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    384.000000\n",
      "mean      61.403646\n",
      "std        9.677358\n",
      "min       34.000000\n",
      "25%       55.000000\n",
      "50%       62.000000\n",
      "75%       68.000000\n",
      "max       88.000000\n",
      "Name: Onset, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age at onset')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age at onset\n",
    "print(demographics['Onset'].describe())\n",
    "fig = sns.histplot(demographics['Onset'],\n",
    "            bins=30,         \n",
    "            color='red',   \n",
    "            kde=True        \n",
    ").set_title(\"Age at onset in \" + dataset.upper())\n",
    "\n",
    "plt.xlabel('Age at onset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    385.000000\n",
      "mean       7.910130\n",
      "std        5.698714\n",
      "min        0.500000\n",
      "25%        3.500000\n",
      "50%        6.300000\n",
      "75%       10.900000\n",
      "max       30.200000\n",
      "Name: Duration, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Disease duration (years)')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Disease duration\n",
    "print(demographics['Duration'].describe())\n",
    "sns.histplot(demographics['Duration'],\n",
    "            bins=30,          \n",
    "            color='yellow',   \n",
    "            kde=True        \n",
    ").set_title(\"Disease duration in \" + dataset.upper())\n",
    "\n",
    "plt.xlabel('Disease duration (years)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    386.000000\n",
      "mean      10.849741\n",
      "std        3.308896\n",
      "min        1.000000\n",
      "25%        9.000000\n",
      "50%       11.000000\n",
      "75%       13.000000\n",
      "max       17.000000\n",
      "Name: Education, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Education (years)')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years of education\n",
    "print(demographics['Education'].describe())\n",
    "sns.histplot(demographics['Education'],\n",
    "            color='pink',   \n",
    "            kde=True        \n",
    ").set_title(\"Years of education in \" + dataset.upper())\n",
    "\n",
    "plt.xlabel('Education (years)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    401.000000\n",
      "mean       1.324190\n",
      "std        0.468656\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        2.000000\n",
      "max        2.000000\n",
      "Name: Gender, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender in COPN')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender\n",
    "print(demographics['Gender'].describe())\n",
    "sns.countplot(x ='Gender', \n",
    "              data = demographics, \n",
    "              palette=\"Set3\"\n",
    "              ).set_title(\"Gender in \" + dataset.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    349.000000\n",
      "mean      25.031519\n",
      "std        3.566548\n",
      "min        6.000000\n",
      "25%       23.000000\n",
      "50%       25.000000\n",
      "75%       28.000000\n",
      "max       30.000000\n",
      "Name: MoCA, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'MoCA')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the MoCA score (cognition)\n",
    "\n",
    "print(demographics['MoCA'].describe())\n",
    "sns.histplot(demographics['MoCA'],\n",
    "            color='orange',   \n",
    "            kde=True        \n",
    ").set_title(\"MoCA score in \" + dataset.upper())\n",
    "\n",
    "plt.xlabel('MoCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the MoCA in the COPN dataset is very negatively skewed! This will have to be considered when performing statistical analyses on the variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the age vary among stages of the disease?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'H&Y stage')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NxS233IIFCxagsbFRWLXGt4l35MgRzJ49GxMnThTCUdv2AsCECRNw/PhxVFVVCY+rrKzE8ePHhZ978/q40rRp0zBixAi8+uqrqKur63D/yy+/DKvVihtuuAEAcOONNyI+Ph7/7//9v06P//HHH7Flyxbcddddwjy2gdJqtfi///s/FBUVOcyZI6S/qOeHkD7YsmULrFZrl0MUN998Mz777DNs3rwZ9913H15//XX4+fkhOzsbBw8exCeffAKg9YN0yZIluOOOO7B48WLceeedUCqV2LBhA3bs2IHXX3+927YolUo88MADeOSRR2Cz2fC///0PQUFBuOeeexyO++1vf4tHH30UycnJwiTgrtxzzz3YuHEj7r33Xjz44IOw2Wx49dVXOxSra2vSpEl4+umn8Z///AfTp08XKgUnJCQgNTUVcrkcN954I/75z3+itLQUo0ePRmFhIV599VXExcUhISEBer0eWq0Wb731FmQyGWQyGb799lthmMRgMEClUiEtLQ0rVqyAXC5HSkoKCgsL8fnnnwvDIbfccgvWrl2LRYsW4Y9//COio6Pxyy+/4N1338XChQu7fB4TJkzA+++/j7CwMGRlZaGiogIffPABsrOzhZ60gIAAHD16FIcOHcL48eORkZGBL7/8EmlpaYiKisLRo0fxzjvvgGEYYXXa3XffjXXr1uHee+8VAuIbb7wBi8UiFMRMSUnp8fVxJZlMhpdeegm///3vMW/ePKHIYW1tLTZv3ow9e/bgscceE+aXqdVqLF++HIsXL8bNN9+MRYsWYdSoUTAYDPjhhx+wceNGzJw5Ew899JBT2zl16lTceuut+Oyzz5x6XuKbKPwQ0gebN2/G8OHDMWLEiE7vHzduHOLi4vDZZ5/hxx9/BMuy2LBhA9577z1kZmbi8ccfx4svvgi1Wg0ASE1Nxbp16/Dqq6/iiSeeAMuyGDFiBFauXImZM2d225ZRo0bhmmuuwTPPPIOmpiZMnjwZf//73zvMFbryyivBMEyPvT4AN+z1ySef4IUXXsBf//pXaDQa3HfffZ3OSeLdcccdsFgsWL9+PT7++GOoVCpMnjwZf/nLX4Sw8eKLL+Ltt9/G+vXrUV5ejtDQUFx33XV4+OGHIZVK4e/vjzfeeAMvvfQSHnroIWg0GowcORJr167F/fffj8OHDyMnJwfPPfccXnvtNbz//vuoqqpCaGgofvvb3woftGq1GuvWrcPLL7+M//73v2hqakJsbCwee+wx/P73v+/yOTz00ENQKBTYtGkTVq5cCX9/f+Tk5OCxxx4TjvnjH/+IN954A/fffz+2b9+Of//738K8IwBISEjAs88+iy+++AKHDx8GwAWmNWvW4IUXXsATTzwBjUaD+fPnw8/PT/g30JvXx9VGjhyJjRs34oMPPsAnn3yCiooKqNVqpKSkYNWqVR1WivH1hdauXYuNGzeipKQEKpUKqampeOmll7r8cjBQf/3rX7F3715hXhsh/cWwtFMfIU5ntVrx1VdfYeLEiQ7LytetW4f/+7//w4EDBzotEOcK27dvxxNPPIHdu3f3OLmUONeJEydQX1/vUEPHarVixowZuP766/G3v/1NxNYR4ruo54cQF5DJZHj33Xfx4Ycf4oEHHkBwcDDOnz+P1157DTfffLNbgs+OHTtw8uRJrF+/HrfccgsFHxFcvnwZjzzyCJYuXYrs7GyhzlNTU1O3tZMIIa5FPT+EuEhxcTFeeeUVHDhwAI2NjYiJicGNN96IxYsXdzuHxllWr16N1157DePGjcNrr70mFOwj7vXJJ5/g448/RnFxMeRyOTIzM/HQQw/1e+UTIWTgKPwQQgghxKd43FL3t99+26G8Pe/ixYsYM2aMUDyLZzKZ8Oyzz2Ly5MnIysrCY4891qGWCiGEEEIIz6PCz7p16/Daa691uD0/Px+///3vheWjbT3zzDPYu3cvli9fjg8//BAFBQX485//7IbWEkIIIcQbeUT4qaiowB//+EcsW7asQ02Lt99+G7/97W87LUlfUVGBLVu24B//+IdQd+OVV17BoUOHcOzYMTe1nhBCCCHexCNWe50+fRpyuRxffPEFVq5c6bAT8I4dO/Diiy8iODgYd999t8Pj+A3/2u69k5iYiMjISBw6dMhhg7/eGj9+PMxmc7d7GRFCCCHEs1RVVUGhUAh1trrjEeEnJycHOTk5nd7HV/M8cOBAh/sqKioQHBzcYa+iiIgIlJeXd3m97orHNTU1uaWoGCGEEEKcx2q1druJb1seEX76y2AwdLrhoFKphMlk6tc5pVIpoqOjsXPnzoE2jxBCCCFu0lNV/La8OvyoVCqYzeYOt5tMJvj5+XX5uO6CTV9ePEIIIYR4H4+Y8NxfUVFRqK+v7xCAKisrERkZKVKrCCGEEOLJvDr8jBs3Dna7XZj4DACFhYWoqKjAhAkTRGwZIYQQQjyVV4efyMhIXH/99fjHP/6BAwcO4Ndff8Wjjz6K7OxsjBkzRuzmEUIIIcQDeXX4AYDnn38ekydPxp/+9Cfce++9SEpKwuuvvy52swghhBDioWhvr3b4Cc+02osQQgjxHn35/Pb6nh9CCCGEkL6g8EMIIYQQn0LhhxBCCCE+hcIPIYQQQnwKhR9CCCGE+BQKP4QQQgjxKRR+CCGEEOJTvHpjU0IIId6poaEBTU1NAICgoCBotVqRW0R8CYUfQgghbnXp0iX87W9/A19jVy6X47XXXkNwcLDILSO+goa9CCGEuFVBQQFYloWUkUACBhaLBcXFxWI3i/gQCj+EEELcSqfTAQBGhycjPjAaAIQhMELcgYa9CCGEuBUffvxkSljtNgAUfoh7UfghhBDiVo2NjQAAtdwPNtYOgMIPcS8KP4QQQtyKDz9auQp21uZwGyHuQOGHEEKIW3XW80Phh7gThR9CCCFu1dDQAADQKvyE5e78bYS4A4UfQgghbsOyLOrr6wEA/go1WrKPcBsh7kDhhxBCiNs0NzfDYrEAALRyx/DDsiwYhhGxdcRXUJ0fQgghbsP38KhkCsilMvgr1AAAs9kMvV4vYsuIL6HwQwghxG1qamoAAIEKbi8vuVQGtUwFAKitrRWtXcS3UPghhBDiNnzACVBqhNv4/+eDESGuRuGHEEKI2wg9P8rWXdwp/BB3o/BDCCHEbaqrqwE4hp8gpT8AoKqqSpQ2Ed9D4YcQQojb8AEnWOUv3Bak4oIQH4wIcTUKP4QQQtymsrISABDUJvwEKwMc7iPE1Sj8EEIIcQuLxSJMeA5RBQi3B/txQYjCD3EXCj+EEELcoqqqCizLQiGRQSP3E27ng1BjYyMMBoNYzSM+hMIPIYQQtygvLwcAhPgFOlRyVsmU0MhVDscQ4koUfgghhLhFWVkZACDML7DDfaF+QQ7HEOJKFH4IIYS4BR9s+KDTFh+ILl++7M4mER/lUeHn7bffxl133eVw29mzZ7Fw4UKMGTMGOTk5WLNmjcP9drsdr7/+Oq644gqMGTMG999/P4qLi93ZbEIIIb1QWloKAAhTB3W4j7+Nwg9xB48JP+vWrcNrr73mcFtdXR0WLVqEoUOHYtOmTVi6dCmWLVuGTZs2Cce88cYb+Pjjj/H8889j/fr1sNvtuO+++2A2m938DAghhHSFZVmUlJQAACLUwR3uj1CHAGgNSIS4kkzsBlRUVODpp5/GgQMHkJCQ4HDfp59+Crlcjueeew4ymQzJycm4ePEi3nnnHcybNw9msxnvv/8+Hn/8ccyYMQMA8Oqrr+KKK67Ad999hzlz5rj/CRFCCOmgvr4ezc3NYMAgrJNhLz4QXb58GVarFTKZ6B9PZBATvefn9OnTkMvl+OKLL5CZmelw3+HDh5Gdne3wSzBp0iQUFRWhuroaubm5aG5uxuTJk4X7AwICMGrUKBw6dMhtz4EQQkj3+OkIIX4BkEs7BpsApRZKqRw2m41WfBGXEz1a5+TkICcnp9P7ysvLMWLECIfbIiIiAHAT5/hfkOjo6A7HdPfLM3PmzC7vKysr63A+QgghA3Px4kUAQJQmtNP7JQyDCE0IihsrcPHiRcTFxbmzecTHiN7z0x2j0QiFQuFwm1KpBACYTCahGFZnx5hMJvc0khBCSI8uXboEoOvw0/Y+/lhCXEX0np/uqFSqDhOX+VCjVquhUnFFscxms/D//DF+fn7oys6dO7u8r7teIUIIIf1TVFQEAIjSdh1+olvCT2FhoTuaRHyYR/f8REVFddjrhf85MjJSGJ7q7JjIyEj3NJIQQki3jEajsIQ9Rhve5XH8fYWFhWBZ1i1tI77Jo8PPhAkTcOTIEdhsNuG2/fv3IzExEaGhoUhNTYVWq8WBAweE+xsbG3HmzBlMmDBBjCYTQghp5+LFi2BZFv4KNfwV6i6Pi9CEQMpI0NzcjKqqKje2kPgajw4/8+bNg06nw1NPPYW8vDxs3rwZq1evxuLFiwFwc30WLlyIZcuWYefOncjNzcUjjzyCqKgozJ49W+TWE0IIAYD8/HwAQGw3vT4AIJNIEdky9MU/hhBX8Og5P6GhoVi1ahVeeOEFzJ07F+Hh4XjiiScwd+5c4Zg///nPsFqt+Mc//gGj0YgJEybgvffeg1wuF7HlhBBCeHl5eQCAuICepyPE+Ufgsq4KeXl5DmVMCHEmjwo///73vzvclpGRgQ0bNnT5GKlUir/85S/4y1/+4sqmEUII6Sch/PhH9HhsnH8EDpadxoULF1zdLOLDPHrYixBCiHerqalBdXU1GDA9DnsBwJCW3qHCwkLapoi4DIUfQgghLnP+/HkA3BJ3pUzRw9FAiCoAWrkfbDYbCgoKXN084qMo/BBCCHGZc+fOAQDiA6J6dTzDMBjacmxubq7L2kV8G4UfQgghLnP27FkAEAJNb8QHcjXcKPwQV6HwQwghxCUaGxuFDU0TAmN6/bjElmPPnTsHq9XqkrYR30bhhxBCiEvwPTcR6mBoFV1vOdRehCYEfjJuj0aa90NcgcIPIYQQlzh16hSA1p6c3pIwjPCYM2fOOL1dhFD4IYQQ4hInT54EACQHx/X5sfxj+HMQ4kwUfgghhDhdVVUVKioqIAHTp/k+vKSgWADcUnmj0ejs5hEfR+GHEEKI0504cQIAt6WFqhf1fdoLUQUgWOUPm82G06dPO7t5xMdR+CGEEOJ0fPgZHjykX49nGAbDWh7766+/Oq1dhAAUfgghhDiZ1WoVemuGh/Qv/ACtwen48eNgWdYpbSMEoPBDCCHEyc6ePQuj0Qit3A9RmrB+nycpKBYyRoqqqiqUlpY6sYXE11H4IYT4jL179+KZZ57Bq6++Cr1eL3ZzBq2jR48CAEaExEPCMP0+j0IqR0JQjMM5CXEGCj+EEJ+xZcsWnD9/HocOHaJ5JC7CsqwQVFJC4wd8vtQQ7hwUfogzUfghhPgMnU4n/H9TU5OILRm8iouLUVVVBZlEiuSW5eoDwQeoCxcuoKGhYcDnIwSg8EMI8REsy6K5uVn4ue3/E+c5dOgQAGBYUBwUUvmAzxeo1CJGG+7Qo0TIQFH4IYT4BIPBAJvNJvzctheIOM/hw4cBACPDEp12zpGhCQCAgwcPOu2cxLdR+CGE+IT2w1yNjY0itWTwqqiowMWLFyEBg5SQgc/34Y1qCVKnTp2iierEKSj8EEJ8Qvv5IhR+nI/vmUkMioVarnLaecPVwQhXB8Nms9HQF3EKCj+EEJ/QPvzQ5FnnO3DgAAAgzYlDXjz+nPw1CBkICj+EEJ9QX18PAFCpAwAAdXV1IrZm8KmoqEBBQQEYMEgNdUX4SQLAbZtBQ19koCj8EEJ8Qk1NDQDAPygSADfsZbFYxGzSoLJ//34AQGJQDLQKP6efP0IdgnC/IFitVmFSNSH9ReGHEOITamtrAQAa/1BIJFKH28jA8eEnPTzZJednGAajW869b98+l1yD+A4KP4S4WHNzM0pLS2mCrciqqqoAAEo/fyj9/AEA1dXVYjZp0CgpKeFWeTESjHTBkBcvPXwYAG7VF/0+kYGg8EOIC9XW1mLp0qX4y1/+giVLluDixYtiN8ln8eFHpQ4Qwk9lZaWYTRo0fvnlFwDAsOA4p67yai9MHYRoTRhsNhvV/CEDQuGHEBcqLCyE2WwGANjtduTl5YncIt9kNpuFIS6VX4Aw6bmiokLMZg0KLMsKw1AZLT0zrpQewV2DD1yE9AeFH0JciF9hxKPl1eLgQ45UpoBMoYKfJggAUF5eLmKrBof8/HxUVFRALpEhpaUSsyulhyeDAZCbm0vDlqTfKPwQ4kLtww8trxZHWVkZAMBPEwiGYeCnCQRA4ccZ9u7dC4DbgkLphL28ehKo1CIhMAYA9f6Q/qPwQ4gL8curg5Xc6iL6piqOy5cvAwDU2pCW/wYD4EKR3W4XrV3ezmq1CkNemRHD3XbdjJZr7dmzByzLuu26ZPDwivCj0+nw9NNPY9q0acjOzsbjjz8ufKgA3LLHW265BZmZmbj22muxbds2EVtLSCt+km1yoBIAhR+xlJSUAAD8WkKP0s8fEokUFouF5v0MwK+//oqmpiZo5H5ICo5z23VHhSVCxkhRWlpKiwhIv3hF+HnooYewe/duvPDCC1i3bh0MBgPuvvtumM1m5OfnY/HixbjiiiuwefNm3HrrrXjiiSeoDgTxCHz4GdYSfqqqqqinQQTFxcUAAI0/1/PDMBKhF4i/j/Tdnj17AHATnaWM+z5O/GRKpITGO7SBkL7w+PBz9uxZ7N27F8899xyuvPJKDB8+HC+99BIqKyuxbds2fPjhh0hJScEjjzyC5ORk3Hvvvbj22muxatUqsZtOfJzFYhHCT2qwEhKGW3VE837cy2q1CsNeGv9Q4XZ1APf/ly5dEqVd3k6v1wubjGZGum/Ii8cPs/3888+w2Wxuvz7xbh4ffoqKigAA48ePF27TaDSIj4/HwYMHcfjwYUyePNnhMZMmTcKRI0doLJiIqqKiAizLQiVlEKiQIkwlA9A6/4S4R3FxMWw2G2RyJRQqrXA7H4Ro2KR/Dhw4AIvFgnB1MKI1YW6//vDgIdDIVWhsbMTJkyfdfn3i3WRiN6AnERERALiJicnJXGlzm82G8vJyhIaGory8HFFRUR0eYzAYUFdXh5CQkA7nnDlzZpfXKysrQ3R0tBOfAfFV/DyTCLUMDMMgUi1DpcGKkpISpKeni9w638F/gdIEhINhGOF2bWA4AK4WE+k7frhpTMQIh9fVXaQSKdLDh2H/5VPYs2cPxowZ4/Y2EO/l8T0/6enpSEpKwtNPP42KigoYjUa8/PLLqKurg8VigdFohEKhcHgM/zNfXI4QMfAfurEa7t9jjIZbBkw9De5VUFAAANAGOvZOaAK4n2trazuUJCDdq6ysRG5uLhgAGRGuL2zYlTERIwAAhw8fpp3eSZ94fM+PQqHAihUr8MQTT2D69OmQy+W44YYbcNVVV0EikUCpVHYIOfzPfn6d7yy8c+fOLq/XXa8QIX3Bh5zYltDDhyAKP+6Vn58PAPAPjHS4XSZTQK0Nhl5Xh/z8fIwbN06M5nklvrZPYlAsApXaHo52nWhtGMLVwajS1+HAgQO46qqrRGsL8S4e3/MDAMnJydi0aRMOHDiA/fv348UXX0R5eTmGDh2K6OjoDvvzVFZWQq1Ww9/fX6QWE1/HsqzwoTvEnws9Q7RcCCouLobRaBStbb7EZDIJE5q1QREd7te2BCL+74r0jGVZhyEvMTEMI7SBVn2RvvD48KPT6bBw4ULk5uYiKCgIWq0WJSUlOHPmDKZOnYrx48d32OBu//79GDt2LCQSj396ZJAqLy+HTqeDXNLa8xOklCJQIYXdbqd5Jm5SUFAAu90OhVIDpapjD0VAMBd+zp8/7+6mea0LFy4I21mMDHPdDu69lRExTNjugjaqJb3l8elAq9WCZVm88MILuHDhAk6ePIkHHngAkyZNwuTJk3HXXXfh119/xbJly5Cfn4/3338f33zzDe677z6xm058GP9hOkSrgEzCTQZlGAYJAVwv0Llz50Rrmy/hX+eA4KhOJ+X6B3OLG/Lz82G1Wt3aNm/FD3mNCkt0y3YWPQlUapEYFAuAW/ZOSG94fPgBgFdeeQWBgYG48847sXjxYowbNw7Lly8HAAwfPhxvvPEGdu/ejZtvvhmfffYZ/vvf/3ZY/k6IO50+fRoAkNRS3JCXHMD9fObMGbe3yRfx4cc/OKrT+9XaYMjkSphMJmGCOuma1WrF/v37AQCZIg95tcXX/Nm7dy+VOCG94vETngEgMjISK1as6PL+6dOnY/r06W5sESFdY1kWp06dAgCkBDmGn5Rg7udz587BbDZ3WKlInMdmswnhJzA0ttNjGIZBQHA0aiuLkJubi2HDxFu55A2OHTsGnU4Hf4UaSUExYjdHMCo0EV9J9qKsrAz5+fn090h65BU9P4R4k+LiYtTX10MuARICHMNPhJ8MgQpuT6nc3FyRWugbioqKYDQaIZUphG0tOhMYwn2Inz171l1N81r8sFJ6+DBI3LidRU+UMgVSQxMA0NAX6R3P+ddLyCBx5MgRAMCIIBXkEsd5JgzDYFSIyuE44hr80GJgaCyYbj6o+V6hs2fP0jYJ3dDr9Th27BgA9+7g3lt8vaF9+/bR3yPpEYUfQpyMDzWjQzuvMzU6tDX80PwE1+GHHoO6GPLiaQLCIJMrYTQahYKIpKODBw8K21lEaUJ7foCbDQuKg1rGbXfB/90T0hUKP4Q4UVVVFQoKCsAAGN3Sw9PeiCAVFBIGtbW1yMvLc28DfYTZbBaGFQND47o9lmEYofeH9ojq2i+//AKA28FdjO0seiKVSDE6PAkA1/tDSHco/BDiRPyb7rBAJfwV0k6PkUsYpLf0/vAfKMS5zp07B4vFAoVSA7U2uMfjg8OGAAB+/fVXVzfNKzU0NAgrGNPDPXcyMd+2Q4cO0fZGpFsUfghxIn6y5bgIdbfHjW25n+YnuMaJEycAAEFhQ3rVSxHUEn7y8vLQ3Nzs0rZ5owMHDoBlWcT6RyDEL0Ds5nRpSEAUAhQaGAwG4d8AIZ2h8EOIkxQUFKC4uBhSBsjoYr4PLzVIBY1MgsbGRnqTdgH+NQ2JGNqr41XqAPhpg2G322noqxN8bZ/RYckit6R7EobB6HCujXybCekMhR9CnOTHH38EAGSG+UEt7/5XSyphMCFS7fA44hwVFRUoLS0FGEbo0emN4HAuKPErmginrq5OqJfEz6nxZGlhXBuPHj1KQ1+kSxR+CHECo9EoDHlNjtL06jGTWo47duwYamtrXdY2X3P06FEAQGBwNGRyZQ9HtwqJSAAAHD9+HHa73RVN80qHDh0Cy7KI848QdQf33uLbaTKZqFeVdInCDyFOsGfPHhiNRoT7yTAssHcfuFFqOZIDFLDb7dixY4eLW+g7+PATEtm3TTcDgqMgkyvR1NREG522cejQIQCtPSqejmEYjGrZcJVvOyHtUfghZIDsdju++eYbAMAV0do+LQO+Iob7Jv3DDz9QF70TNDU1CZWa+Z6c3pJIpAgOjwcAHD582NlN80o6nU54PUe2VFD2BiNDufBz7Ngx2rCWdIrCDyEDdOLECZSVlUElZZAd2f0qr/bSw/wQrJSisbGRyvI7wZEjR2C326HxD4WfJrDPjw+N4no3Dh48SAUowYUHu92OSHUIQvz6/nqKZWhAJNQyFZqbm4X5SoS0ReGHkAH68ssvAXBzeFSyvv1KSRlG6P356quvaK7JAB04cABAa4jpq+DwIZBIZaiurqZqz2id/J0SGi9yS/pGwkiENvPDoIS0ReGHkAE4f/48cnNzIWWAGbH+/TrHlCgN/GQMysrKaL+vAWhqahK2NQiL7l8hPqlULgyX+XqVYKvVKhR9HBHSu5IBnoRv8/Hjx8VtCPFIFH4GIbvdji+++AIffPABfZi62NatWwEAEyLUCFJ2XtG5JyqZBNOiud6fLVu20HBLPx08eBA2mw2agLBeVXXuSnhLcNq/f79P98RduHABer0eapkKcf4RYjenz5KD4iBhJCgrK0N5ebnYzSEehsLPIHT27FmsX78e33//PVasWEEfpi5SUFCAY8eOgQEwc0j/en14V8ZooZAwKCwspG+q/bRnzx4AQHjMwHYcDw6Ph0yuRG1trbAzvC/ie32GBXMhwtuoZAoMDYgCQHu2kY6871806VFdXZ3w/yaTCUajUcTWDF6bN28GwG1lEe4nH9C5tAoppsVwdX82bdpEgbWPysvLW5anMwiPGTGgc0mkUmHY7KeffnJC67wTv5dXcnD3G8N6suQgbsNa2uWdtEfhZxDS6/UOP9NeRc6Xl5eHo0ePggEwe4C9PrycWH8oJAwKCgpokmYf7d69GwA3YVmp6l2Rye5ExKYA4IbS2v8++YLm5mbk5+cDAJJaAoQ34oPb6dOnfXoIk3RE4WcQ0ul0Dj9T+HG+zz77DAAwIVKNCPXAen14WoVUWPn16aef0pt1L9lsNiH8RMaNdMo5/YMiodYGw2w2+2QJgnPnzoFlWYT6BXpFVeeuxGjDoJQqoNfrcenSJbGbQzwIhZ9BqKmpqdufycCcOXMGJ0+ehJQBrhnq3B2uc+L8oZIyKC4upo0Ze+no0aOor6+HTKFCSGSCU87JMAwih4wCwBWg9LVhSL42TnxAtMgtGRgJI8GQgEgAQG5ursitIZ6Ews8g1D7sNDY2itSSwYdlWaxfvx4At4dXqErm1PNr5BLkxHHDaJ999hlVp+2F7777DgAQFTcSEkn/Vtx1JiI2BRKJFBcvXvS5QnlC+AmMErklAxffMumZwg9pi8LPINTQ0NDtz6T/Dh06hLy8PCgkDGY7udeHd2WsFv5yCSoqKvDDDz+45BqDRUlJScvEXAZR8aOdem65QiWsHPv222+dem5PZrVaUVhYCADCailvxj8Hfg4TIQCFn0GJDzuyQG6svr6+XsTWDB42mw0bNmwAAMyI1SJA4bxehraUUokwnLZ582YYDAaXXGcw2L59OwAgNDIRKj/nTDxvKyYhAwA38bmqqsrp5/dEJSUlsFgsUMkUCFG5JuC7U4w2DAyAmpoa+iJIBBR+BqHa2loAgDyUe+Nqu/Sd9N+uXbtQVlYGjax1aMpVJkdpEKaSobGxEdu2bXPptbxVfX099u7dCwCITcp0yTU0AWEICo0Dy7L4+uuvXXINT8P3kMRow/u0Sa+nUsoUCPMLAgDasoQIKPwMMkajUViaKw8PAtAahkj/GY1GbNy4EQA3ybmve3j1lVTCYE4CF163bdtGAbYT27dvh9VqhX9QJAKCXTcxNzZpDADgxx9/9In5c/yqqGhtmMgtcR7+uVy8eFHklhBPQeFnkKmurgYAMHIZ5C3DXjU1NWI2aVD46quv0NDQgDCVFFOiB15Hpjcyw/wQ76+AyWTCpk2b3HJNb6HT6bBjxw4AwJDkcS69VlDYEGgDwmEymfDNN9+49FqeoLi4GAAQqQ4RuSXOE6HhnktJSYnILSGegsLPIMPPS5Bq/SDV+gHgAhHVjOm/uro6YehpTkIgZBL3DAUwDIObEgMBcL0OpaWlbrmuN9i+fTuMRiPU/qEIjnDtjuMMwyBu2FgA3MTn9nW0BhOWZYWAEKHp//5oniZCzT0XCj+ER+FnkKmsrATAhR+JnwqQMLDZbNT7MwCbNm2CyWRCvL8CmWF+br12UqAS6aEqsCyLTz75xK3X9lSNjY1CD8zQ4RPcMi8lNDIJav9QGAwGfPXVVy6/nlh0Op0Q7vh5MoNBeEv4KSsr87maTaRzFH4GmbKyMgCALEADRsJAqlUDAO1q3E+lpaX48ccfAQA3JQaKMgF0TkIgJAxXzO/s2bNuv76n+fLLL2E0GqEJCEdoZKJbrskwDOJHZAMAvvn220G7gpLvOfZXqKGQOqdyuScIUmrBgIHFYhm0f3ekbyj8DDJtw0/b/16+fFm0Nnmz9evXg2VZjA5VISlQKUobItVyTIrk/h4/+eQTn/7mWlVVJdTciR+R7dYwGhKRAG1gBMwmEz7//HO3Xded+J7jIJVrVzO6m1QiRaCS+x3inyPxbRR+Bhl+XogQfgI1DreT3jt37hyOHDkCBlzvi5iujQ+AQsIgLy8PBw8eFLUtYtq4cSOsVisCQ2MRHD7UrddmGAaJqZMBcFte8F80BhN+eDxIObjCDwAEtjwnmgJAAC8JP1arFf/73/9w1VVXISsrCwsWLMDx48eF+8+ePYuFCxdizJgxyMnJwZo1a8RrrIh0Op2wrF0WxP2iy1v+y6/gIL3Tdo7NpCgNopy0eWl/BSikuCqOW723YcMGn9z2oqCgAHv27AEAJKRMFmUIMjA0FsER8bDZbPj444/dfn1X44sAahXundvmDv4tz4kKHRLAS8LPm2++ic8++wzPP/88tmzZgsTERNx3332orKxEXV0dFi1ahKFDh2LTpk1YunQpli1b5pNLg/n6HFKNChIFt+eULNhfuI9WfPXe0aNHcf78ecglDK510TYWfXVVrD80MgnKy8uFXcx9Bcuy+OijjwAA4bEj4B8UIVpbElOnAAyDI0eO4NSpU6K1wxWE8CNXi9wS59MquOdE4YcAXhJ+duzYgTlz5mDatGmIj4/HX//6VzQ1NeH48eP49NNPIZfL8dxzzyE5ORnz5s3D7373O7zzzjtiN9vt+P14ZCGtQzSyQA0glcBgMKCiokKspnkVu90ubGMxPUaLQKVrtrHoK5VMgtlDuTDLr0DzFfv27cO5c+cgkciQMGKSqG1Ra4MRPZTbR+yjjz6CzWYTtT3OxG+KrJarRG6J86nlXM9P+42fiW/yivATGhqKH3/8ESUlJcL+SgqFAqmpqTh8+DCys7Mhk7Xurj1p0iQUFRUJBf/amzlzZpd/vHkcny/drght7algJBLIW3p/aGO/3vn5559RUlICPxmDmUM8a+7D1GgtQpRS1NfXC7uZD3ZGoxHr1q0DAMQlj4XSTytyi7gl9jK5CsXFxfj+++/Fbo7TGI1GAIByEK304vHPiX+OxLd5Rfh56qmnIJfLMXPmTKSnp+PVV1/F66+/jqFDh6K8vBxRUY47D0dEcF3i3hxk+uPChQsAAHlYkMPt/M95eXlubpH3sVqtwpBpTpw/1C7exqKvZBIG18Rz4fbLL78UtjIZzLZs2YK6ujoo/QIQ17LVhNjkChXiUyYC4CZhD5ahFH4TXQo/ZLCT9XyI+PLy8uDv74+VK1ciMjISn332GR5//HGsXbsWRqMRCoXC4XilkluS3NWwwM6dO7u81syZM53XcDeqrq7meroYBvIwx5VJiohg6HMv4ty5cyK1znvs3r0blZWV0MolmB4jfg9DZ8ZHqLGzuAmVOh22b9+O3/72t2I3yWVKS0uF6tpJo6ZCIvWct6yoISNRcekMdI1VWLduHZYsWSJ2kwbMbDYDAOQe9Do7i0zCPSdfGi4mXfOsr7WdKCsrw2OPPYbHHnsMs2bNQnp6Op577jmkpKRg+fLlUKlUwi8sj//HrVYPvkl7XcnNzQUAyEP8IZE7vnEpWjY4vXTp0qAuzT9QFosFW7ZsAQBcPcQfSqln/npIGQa/aen9+frrrwft3ynLsli9ejVsNhuCI+LdVtCwtxhGguTR0wEAe/fuHRQFKPkaUoznfzT0maRldaAv18kirTz+X/iJEydgsViQnp7ucHtmZiYuXryIqKioDkWr+J8jIyPd1k6xnTx5EgCgiArtcJ9UrYIsUAOWZXHmzBl3N81r7Nq1CzU1NQhUSDAl2jN7fXiZYX6IVsthMBiwfft2sZvjEj///DNOnz4NiUSK5FHTxG5Op/yDIhE1ZBQA4P333/f6EgR8MLDarTDbLDDbLLDYOz4nq90m3M//aR8q7Ky9wzE2e8fJ4e2Psdh6dz0767h61c6yHY6xtrkeAwo/pJXH923y83nOnTuHjIwM4fbz588jISEBmZmZWL9+PWw2G6RSblXO/v37kZiYiNDQjkFgMGJZVlhyq4zu/DkrosNgbWjGr7/+iuzsbHc2zytYLBZs3boVADBrSADkbtq8tL8kDINr4/3xwdlafPPNN7juuuug1Xp2YOuL5uZmYZLzkGHjoFKLW2SyO/Epk1BdUYDS0lJs374dN954o9hNGrAPT20T/j81NAHzR13jcP8XF37C8crzDrf9bfLv4CdrrYJ+saEcH5z80uGYa5MmY0pshsNtK458inpTa+9llCYUS8Y6DuX+VHwMuy4dcbjtgax5iNaGCT83mHR49ZBj7aVJMaNxXfJUAAALCj2klcf3/GRkZGDcuHF48sknsX//fhQVFeG1117Dvn378Ic//AHz5s2DTqfDU089hby8PGzevBmrV6/G4sWLxW662xQVFaGurg6MVApFROc7MStjuDeJ48eP0zefTvz000+ora1FoEKCSVEasZvTK+mhfojRyGE0GoWNPgeLTz/9FA0NDfDTBCE2MUvs5nRLrlBxtX8AbN78ubA/ljdqP39yMOF7geTywTeZm/Sdx/f8SCQSvPnmm3jttdfwt7/9DQ0NDRgxYgRWr16NzMxMAMCqVavwwgsvYO7cuQgPD8cTTzyBuXPnitxy9zlyhPtGpIgJAyPtvCaNMioEjEyK2tpaFBYWIikpyZ1N9GhWqxVffPEFAG6Fl6f3+vAkDIOrh/jjw9zW3p/BMM+toKAAO3bsAAAkp02HpIt/054kIjYFFSW5aKy9jDVr1uCxxx4Tu0n9wgeD21NnYXgIt31IZ5W0bxw+HXOGOQ5FyiWOHyfxgVH4x5TfO9wmZTp+3/7TuNscfuaHp9qaPiQL0+IyHW6TSRz/XQQqtR2uJ2lzPWvL8N1gDnik9zw+/ABAYGAgnn76aTz99NOd3p+RkSEUpfNFhw4dAgCohoR3eQwjlUIZEwbjpQocPHiQwk8bP//8M6qqqqCVSzDZS3p9eJlhfojwk6FSr8f333+Pm266SewmDYjdbsf7778PlmURHjMcQWFxYjepVxiGQXLadBzf+ymOHDmCI0eOYNy4cWI3q89UKq64oanNvBu2ZS5NW1JG0q9d322sHTab41wdmUQmTEbuC64nx3EOUXdt4p8TvxqY+DavCD+kayUlJdy+XRIGqrjuJ3irhkbBeKkC+/fvx+233y7K3kiexm6348svuXkJV8X6Q+GhK7y6wvf+rDtfh6+//hq/+c1vvPqb7Y8//oiCggJIZQokjpwqdnP6ROMfgpjETJQWHMOaNWuQnp7udX8X/LyxLRd2YcuFXV0eN3fEDGRFpnR7rs7m/HTmkQnzEdzDLvKdzflpL0ipxaPZC7q8X2/lahj5+3tW4VIiDu96pycd/PLLLwC4OT0SZfffxJRx4WCkUlRWVlK15xaHDx/G5cuX4SdjMDXau3p9eGPD1QhWStHY2Ihdu3aJ3Zx+a2xsxPr16wEA8SOyoVB63xDe0GHjoVBpUFVVJQylepPBNGm+PYOFK4FC4YcA1PPj1ex2O3766ScAgF9iTI/HS+QyKIdGwFhYht27d2PYsGGubqJHY1lW6PWZFq2FysOqOfeWVMIgJ84fm/LrsW3bNsycOVNY+ehNPvvsMzQ3N0PjHyrsneVtpDI5kkZOQ+6xb/Hll1/iyiuvRHh418PRniYggKsfNTYyFdclT+nyuM7m7rTX2ZyfzsgkPX8MdTbnp690Zq4aOoUfAlDPj1f79ddfUVtbC0Yhh2pI72oaqYdxcyh++eUXny/znpubi/z8fMgYeGw1596aGKmGRiZBVVUVDh48KHZz+qyoqAg//PADACAp7QowEu99awqNSkJgaCwsFgvWrl0rdnP6hC8PorPooZDKu/wjlfQcriUt84J6+tOb+T4yibRX5+pOg6kZABAWFtbtccQ3eO87DBFWxPglxYDp5VwVRWQIpP5qGAwGYcjMV3311VcAgOxIDfwV3tdT0pZCKsEVLQHuq6++8qpyBizLYu3atWBZFmHRwxAY0nMvpidjGAZJo6YBDINDhw55VeVnPhg0mAZf1XD+OflK/TfSPQo/Xqq8vBzHjh0DAGhShvb6cQzDQD1iCADgm2++8aoPSWcqLS3FsWPHwAC4Ks67e31402I0kEsYFBYWCtudeIOjR4/izJkzYCRSJKROFrs5TqHxDxUqP69btw52u72HR3gGPvzUGZsG1XuD0WqG3sr1dFPPDwEo/Hitb7/9FizLQhkTBllA3ybqqofFgZFJUVJSIlSG9jVff/01AGB0qArhfoOj6JlWLsWECG6SsLdseWG324VJzrEJGVD5DZ75GEOHT4BUJkdBQQH2798vdnN6JTIyEhKJBGabBU0tc2QGgxpDPQAgKChoUNTCIgNH4ccLNTQ04McffwQAaEYm9PnxEoUcfi1zf/gtHXxJY2Mj9uzZAwCYETt4PmwB4MpYrhfr6NGjKCsrE7k1Pdu7dy9KS0shkysRlzxW7OY4lUKpRmwSV51648aNsNk67mvlaWQyGSIiIgAAZc3Vg2Z/ryp9PQAgOjq6vy8NGWRotZcX+vrrr2E2myEPDYSii728eqIdlQD9uUs4c+YMzp8/jxEjRji5lZ7rhx9+gMViwRCtHEkB3lWHpSeRajnSQlQ4XcttebFo0SKxm9Qlq9WKTZs2AQDiksZCJh98xediEjJwuehXlJeXY8+ePZgxY4bYTepRbGwsysvLse5065Yp3r6/F7+iLCbGu+eTEeehnh8v09DQgG+//RYAoE1P6nehQqnGD35J3BvBxo0bndY+T2e1WvHdd98BAK6M9R+UhR753p+ffvoJOp3nTlz95ZdfUFVVBbnCD9Hx3rm0vScymQJxSVyP1tatW71i7s/Qob2fQ+gtynTVAICEhARxG0I8BvX8eJmtW7fCZDJBHhoAZVzEgM6lzUiGofAyTp06hVOnTmH06MH5AdTW/v37UV9fjwCFBGPC/MRujksMD1QiWi1Dmd6EXbt2Yc6cOWI3qYO2lbVjEzMhlQ2OeVediR6ahpL8I6ioqMCBAwcwebJnT+rmA0KEOgR/GHMzAO/e34sBg1cOrePaEx/f4bzEN1HPjxepqKgQlrf7jxkx4F4LmVYN9XBu5dcnn3ziFd9KB4rv9ZkarYXMSzYw7SuGYTC9ZS7T999/75F/rydOnEBpaSkkUjnCY1Ngs1pg72Seh91mg81qcfjTfl4Jy9o7HGPvZF6JzeZ4jK2z69k7u57j68eybMfrdTKfx2azwmbl9sSKGpoGANi2bVvvXySRJCYmAgCqDfVgwEAhlXcINUDntXfavyd1VuunsxpB7Y+RS3t3PUm7ICVhmA7HNFsMaLYYIZFIBmWvFukf6vnxIh9//DGsVisUUaH9nuvTnjY9GYaCUhQWFmLPnj248sornXJeT5SXl4e8vDxIAIwP94PJZoeEYTrs4m61s7C1+4BVSBiHN3Y7y8JidzxGxjCQtjuX2WZH26O4D5OeryeXMA7F3zq7npRhOgQ4s40FCxbpIUp8IQWqqqpw9OhRjB8/vuMLIiI+hNptFhz64UMAQEhkIkaN+43DcXmndqGy9JzDbZOuvtdhflBDbRlOHXCcuJ84cipiEx17CY7+tB4mQ5Pws8Y/FFlX3O5wTHHeERTnHXa4bcy026ANaJ1XYjI04fAux+KFMQkZXG2fNk4f+gqNtZcdbisoKEBeXp5HV1cPDQ1FUFAQ6uvrUaqrQkKgd08SLm6sAMD1+njbXmvEdSj8eInTp09zu7czQMD4VKfNVZH6KaFNT0bT0fPYsGEDsrOz4ec3OIeDhA9cAM8f5t4QR4eqcN8ox7ofGy7U4VCl4zLff02OgVrW+prnN5iw8mS1wzE3JwV2WD324pEK1JlaewViNHI8MdaxGvf3xY349lKTw22PZ0UgTtv6Rl1nsuH5Q+UOx0yP0eKW5CCH294+XYX8BrPDbd9++61HhZ+KigqcOHFC7Ga4HcNIwLJ27Nixw6PDD8MwGD58OA4dOoTixgrvDz9N3O/68OHDRW4J8SQUfryAxWLBBx98AABQDx8KebBzl2drUhOgv1CC+vp6bNy4EXfddZdTz+8JGhsbvabWijMx4IJzaWkpYmNjxW4OAAhlBgJDYzFq3HXC7Z0F+mGjZyA5bbrDbZJ2QyKBIdGYPPt+h9s62x5j7PQ74NgN1/F6Q4aNQ1zL8vTW6zkO0yj9/Dter5N5LGkT5gBtevSa6stx6uCXOHDgAH73u99BpVJ1eIynGDFiBA4dOoRLjeU9H+zhLrY8B19a0Up6RuHHC3z55Ze4fPkyJCoF/Mc4/9sLI5UgMHskancewTfffINp06YJ4/6Dxa5du2C1WhGnkeHBzNaJ4p3tK3T78GD8dliQw22KdsNLyYFK/GeK47JZWSfn+tu4yA7DXu1dPSQAOXGOgbb9UFywUtrhetJOrrc4LRxsmyuuya3F6VojduzYgXvuuafD8e7Gsiz27t0LAIiMG9njRGcueHS/9QjDSCDtxaa00h72fgIAiUQK9LBvFcMwvZqgLW0f0kLjoFIHwqhvwMGDBzF9+vQuHim+kSNHAgAuNpTBzto7zK3xFgaLCeUtK73450QIQBOePV5paalQiDBgfCokStesilHGhEOVEAWWZfHuu+/Cau04GdRb2e127Ny5EwBwRaw/lFKJ8Kd9yAAAmYRxOEYplXQykbPjMe3n+wDcnlttj2k/36er67UPZZ1dr7MJ2wqp43HTYlqXvXvCRrZFRUWorKyERCpDaOTgCtg9YRgG4THcl5dDhw6J3JruJSQkQK1Ww2gzo1xXI3Zz+u1iYxlYcMUNg4ODxW4O8SAUfjyY3W7H22+/DYvFAmVMGFQJrh17Dxg/EoxCjqKiInzxxRcuvZY7/frrr6iqqoJaxiArzLdK26cEKRGmksJgMHjEsN+RI1yRuuCwIYN6eXtXQqOSAAAnT56EyWQSuTVdk0gkQk9Jfn2pyK3pP77to0aNErklxNNQ+PFgX331FfLy8sDIZQicNNrlBfmkfkoEZnNveJ9//jkuXrzo0uu5C9/rMyFC02nPy2AmYRhMjuJ6f/gyCWI6fvw4AG5lly/S+IdC6ecPs9mMM2fOiN2cbqWnpwMA8uqKRW5J//Ftz8jI6OFI4mso/HiooqIifPbZZwC44S6pxj2TI1UJ0VAOiYTNZsPKlSthNpt7fpAHq6urw9GjRwEAk6P7tgHsYDExUg0pwy2zFjPQ6vV6FBYWAgCCwuJEa4eYGIZBUCj33M+ePStya7rHB4ZLjeUw2Swit6bv6oxNqDE0QCKRIC0tTezmEA9D4ccDmUwmrFixAjabDcohkfBLdt8qHYZhEDgpDRI/JUpKSvDJJ5+47dqu8NNPP4FlWSQFKBCl9r1hFgDQKqRID+XKF+zatUu0dpw/fx4sy0KlDoBSpRWtHWILDOUmrnt6+ImMjERERARsrB0FXjj0daH2EgBuiTvt5E7ao/DjgdasWcOt7vJTImhymtv3n5KqFAiczG118e233wrzNLwNy7LCh/3EKN/s9eHxz3/v3r2i9eYVFBQAAPyDokS5vqfgn/+lS5c8eqd3hmGQlcUt+z9f631D4Oda2sw/B0LaovDjYX7++Wf8+OOPAICgqRmQKMWpSKqKDYdmJLcPzltvvYWqqipR2jEQ58+fR0VFBZRSZtDu49VbKUFKBCmlaG5uxrFjx0Rpw6VL3DfxttWSfZFKHQCpTA6LxYKysjKxm9OtsWO5TVnP1VyCvV0Vck9mtllQWM9V1+afAyFtUfjxIJcvX8Z7770HgNt0VOmkLSz6yz8rBfLQQDQ3N2P58uVet/ydryeTEeoHpdS3/6lLGAbjwrmuf/51cbeSkhIAgNpf3H/XYmMYBmptCIDW18RTpaamQqVSQWfRo0znPV+A8utKYGVtCA8P95jinsSz+PYnggcxGo149dVXYTQaoYgMgTZd/PL3jFSCoCsywShkyMvLw9q1a3t+kIewWq3C0u4JETTeD7S+DsePH0dTU1MPRzsXy7JC76FKHeDWa3si/jXw9B5VuVyOzExuj7SzNUXiNqYP+LaOHz/e7dMGiHeg8OMBWJbFqlWruF2u/ZRc4PCQHcdl/moETeVWfXz33Xei9Rr01alTp9Dc3IwAuQTDgpQ9P8AHRGnkiNHIYbPZ3D6Pq7GxERYLt2JI6ee7k515Kj/vCD8AhH3hzlYXityS3rGxdmG+jyftaUc8C4UfD/D111/jl19+ARgGwdPHQOrnWR/WqrgIaEdzxdlWrVrlFfV/Dh48CADICPPrdAsLX8XPfTpw4IBbr8v3NMnkKm4LCR8nV3J/D+7ugeuPMWPGQCqVospQj2p9vdjN6dHFhjIYrCZotVraz4t0icKPyE6fPo2PP/4YABAwLgWKCM8swa7NHA5FdCjMZjNeeeUV6HQ6sZvUJbvdjsOHDwMAMn18onN7/Otx6tQp6PX6Ho52Hv7fi0zuWcFeLDI5V7fLk3+PeBqNBqNHc6s/T1cXiNyanp1p6aEaP348pFIK2qRzFH5EVFVVhddffx12ux1+STFQp8aL3aQuMRIGwVdkQqr1Q1VVFZYvX+6xy3Tz8vKg0+ngJ2OQFEgftm1FquUI95PBZrPh1KlTbrsuv5WDL25p0Rl+01NvKSI6YcIEAMCZGs8e+rKzLM62tJFvMyGdofAjEqPRiFdeeQVNTU2QhQQgcKL76/n0lUSpQPCMsWBkUpw8eRLr168Xu0mdOnHiBAAgJUjV6c7nvm5kMNfrwL9O7sAHZcZLdwd3NkbCvQ6e+gWiPX7icJmuGnXGRrGb06WSpgo0mfXw8/MTeqsI6YzHvxMdOHAAKSkpnf6ZOXMmAG656OLFizF27FhMmzYNr732mke/qbAsi3feeQcXL16ERKlAyIwsMDLv6J6VB/sLBRC3bdvmkROg+R6N1GD3bAnibfjw486eH7alRoynB3z34V4Hu90ucjt6JyAgQNgc9LQHT3w+XcUNy40bNw5yOfUykq7JxG5AT7Kysjp8wB4/fhwPPvgglixZAovFgnvvvRcJCQlYv349Ll26hKeeegoSiQR//vOfRWp197788ktuGTbDIPjKMZBqvGteil9CNCx1TWg+VYB3330XMTExSEpKErtZALhhBL6S8DAa8upUYoACDLhh17q6OgQHu36eGf9BZLd77pcSd2JbXgeFQpwipv2RnZ2N06dP43R1AabFZYrdnA7sLCvMScrOzha5NcTTeXzPj0KhQHh4uPBHo9HgxRdfxNy5czFv3jx8++23uHz5Ml566SWMGDECs2bNwqOPPooPP/zQI8fTjx07hg0bNgAAAiaMhCIyROQW9Y9/5nAoY8NhsVjwyiuvoKGhQewmAeC2ULDZbPCXSxCq8o7eNHdTySSI0XBh5Ny5c265phB+bN5VKNNV+BAok3n890/BhAkTwDAMSpsqUW/0vFVqpU2VaDQ3Q6VS0S7upEfe85vX4q233oLBYMCTTz4JADh8+DDS0tIQGBgoHDNp0iTodDqcPXtWKNDlCcrKyrBy5UqwLAv18DhoUoaK3aR+YyQMgqZloPrr/aitrcVrr72Gp556SvQ3c37X8PgABQ2xdCPeX4HSZgsKCwsxadIkl19Po+H2FrNaTC6/ljfgXwf+dfEGQUFBSElJQW5uLs7UFGJKrGcFDL7XZ+zYsR7Zo8ayrDDxn/SdUql06nu6V4Wf2tparF69Go899hiCgoIAAOXl5YiKctwoMSIiAgAXNjoLP/xcoc6UlZUhOjraeY1uYTAY8Morr0Cv10MeHoSACaOcfg13kyjkCJmRheqv9+PcuXNYu3Ytfve734naptJSbvfpaB/dwb23olt6fvjXy9W0Wq6wocViBMuyPh9MLWYjgNbXxVtMnDgRubm5OF1V4FHhh2VZnPHgIS+WZfHss8/i/PnzYjfFa40YMQJPP/200947PH7Yq62PP/4Y/v7+uP3224XbjEZjh5SvVHJzPTwlZbMsi7fffluo4Bx8ZRaYQbLXlCxQ61ABevfu3aK2h/8wj6Lw060oNfe9x117SwUEBHBvWiwLi9nglmt6MrOpGQAceqy9AR8sipsq0GDynBpFl3VVqDfpoFQqPaq3n3gur+r52bJlC26++WaoVK2reFQqVYe5PXzoUas739Np586dXV6ju16h/tq2bRtXcVjCIPjKLI+r4DxQqiER0GYOg+5EHt5//33Ex8cjISFBlLZUV1cDAML8vOqfttuFqbjXp6amxi09MTKZDIGBgaivr4fZqINC6dv7rZkMXHAIC/OuHe6Dg4MxYsQInD9/HmerizAp1jOWk/Mr0MaMGSN8+fUkDMPg6aef9pgv5N7II4a9amtr8d577+GXX35BVVUVVq1ahR07diA1NRWzZs1yWuPays3NRXFxMW644QaH26Oiojp0JVZWVgIAIiMjXdKWvjh79qxQDydg/EgowoPEbZCLaNOTYalugKm0Cq+99hpeeOEFt89nYFlWmHgdIB8cPWuu4q/gJoPbbDY0Nze7ZfglLCwM9fX10DfVwU/T9QozRiLpcQsMlrXD3otyFhKprMc3TLvdBranJecMIJX23Jtos1mBlmX9XZ6KkcBk4CYMh4Z63w732dnZOH/+PE5XF3hE+PH0IS8ewzAOX9yJuPocfoqLi3HnnXfCZDJh3LhxyM3Nhc1mQ2FhId544w288cYbmDFjhtMbevjwYYSGhiI1NdXh9gkTJmDLli3Q6XTCG/j+/fuh0Wg6HOtuDQ0NWL58OVfBOTEG6hFDRG2PKzEMg6Cp6ajetg+VlZV455138PDDD7t1boder4fVyq0m0ipopVd3ZBIGfjIGBisXGN0RfqKiopCXl4fzv+4Efu2693V4Rg4i47r/3W2oLcOpA1t7vOb4GQt73EW+OO8IivMOd3uM0s8fE666q8frnT70FRprL3d7TETsCBj0XEhvP1/RG2RnZ2Pt2rW41FgGnVkPrULcXrxKfS1qjY2Qy+UYM2aMqG0h3qPPX4//85//IDQ0FDt37sSKFSuE4mUvv/wycnJy8NZbbzm9kQBw5swZpKSkdLh91qxZCA8Px8MPP4zc3Fzs2LEDr7zyCn7/+9+LOuPfbrfjrbfeQn19PWSBWgRMHDXoJ3lKlAoEXTkGkDA4dOgQduzY4dbr87uGA1zND5PN3uUfm737b+e9OQf/x97DN30AsNp7PpfZ1ruCd2Zbz+ey9uL5KSTcv8e2r5sruWIhgTeyWa1g7TbIZDKEh4eL3Zw+CwsLQ2JiIlgAuTXib3LM7+WVnp4OPz/vqplGxNPnnp99+/bhX//6FwICAjpUUb799tvx8MMPO6ttDqqqqoQVXm0plUqsWrUKzz77LG677TYEBgZi/vz5WLJkiUva0Vtff/01t32AVIKgKzIhkfvGHBRFaCD8s1LQdCQXa9euRUpKCoYOdc+SfrZNCHnyl+6/fd85IhgTI7sflstvMGHlyeoer/vPCVEIVXX/9/t9cSO+vdR9bZRgpRRPZ/ccEN4+XYX8hu5rWE2IUGNBSvc1pJiWKsNsL8KbM8TFxQEA1P6hyJx8S9ftkvT8nSwwJBqTZ9/f43ESac+/d0OGjUNcUlb3B/Xye0vahDk9DnvVVl5ETUUBYmNjIenFc/VEEyZMQGFhIc7WFGJ89EhR23K2pggAtwUHIb3Vr0/krmq5mM1ml/VuvPvuu13eFx8fj/fff98l1+2PkpKS1kKG41MhD/YXuUXupRkZD3N5DUylVXjzzTfx/PPPu6X+j7s+xAcbd71ufAg2NNf1al5PdxhGAqnMOcFBIpECA2hLW9JehC1Dcx0AuO1LgStMmDABn376KQrqS2GymqGUidPLXmdsRHlzDRiGwdixY0VpA/FOff5EGj9+PN5++21MnjxZmFXPMAzsdjs++eQTn/8HaLVa8cYbb8BqtUIZGw718ME7z6crDMMgcPJoVH25FxcvXsTmzZtx2223ufy6bVf3PZcdBWU3H46yXoT05EAl/jMlpsfj5JKez3X1kADkxHUfgnv7tWFxWjhYdB9YerOhq97KDbO5a2J6REQE1Go19Ho99Lo6aAO8a6WTs+gaqgBAtBWRzhATE4OoqCiUl5cjr74EaWHibG9zrmXYLTU1FQEB3c/tIqStPn91euyxx5Cfn4/Zs2fjiSeeAMMweO+993DLLbfgyJEjeOSRR1zRTq+xbds2FBUVgVHIETjJ83dqdxWpnxKBE9MAAF988QWKiopcfk2lUilso2BhAaVU0uUfaS8Ci4Rhuj0H/0fSi79jmaTncyl6WftJIe35XLIenp/ZxsLcMi/I3989PZMMwyA5ORkA0FRf4ZZrehqWZYXnzr8W3ohhGIwbNw5AawARQ24td21f/9JN+q7P4WfEiBHYuHEjJk6ciAMHDkAqleKXX37B0KFDsX79eowcKe74r5gqKyvx+eefA+CGu6Rq317W6BcfBVV8FOx2Oz744AOX72DNMIxQNK7BRBtodqfR3Lq3lDsniQrhp67cbdf0JCZDEyxmA6RSqVf3/ACtgeN87SXYWffvTm+0mlDUUAYAQhAjpLf6NREjMTERL7/8srPb4vU++ugjmM1mKCJD4JfU83CJLwgYnwpTaRUuXLiAXbt2IScnx6XXi4mJQXV1NSr0FiTRru5dqtBzK7xiYmLc2js5YsQIAEBjXZnbrulJ+OedmJjokftP9cWIESOEYczSpioMCXBvXbX8+lLYWTuio6O9smQAEVefw8+hQ4e6vI9hGGg0GgwZMsTr9qwZqDNnzuDIkSMAw/jEsvbekqpV0GYOR9ORXGzcuBFTpkxxaaGvuLg4/PrrryjT0+7h3SlrCT/8Cix3GTFiBBiGgVHfCJOxGUqV92zs6QwNtVz46axsh7eRSqXIyMjA/v37cb72ktvDz/naSwBAtX1Iv/Q5/Nx1113CB3vbVSJtP+wlEgluvvlmPPfcc5BKB3+xOZZl8cknnwAA1MOHQB7oW8GvJ5qUodCfu4j6+np88803uPnmm112LX4FzaWm7peC+7pLTVz4cfeKI7Vajfj4eBQVFaGhphQRsSPcen2xNdRwe6mJXYDVWcaMGYP9+/fjQt0lzEyY4LbrsiyLvLpioQ2E9FWf5/y8+eabUCgUuO222/DRRx9h+/btWLt2LRYuXAiZTIa//e1v+Pvf/47vv/8e77zzjiva7HGOHz+O/Px8MDIptBneO4nRVRipBP5jhgMAvvrqK+j1epddi59zdqnJDKPV/fMQvIGdZZHXwO0xJMYcvbQ0biJ8fY17NlX1FEZDE4z6RkgkkkEzNzI9PR0AUKarRrPFfRvWVurr0GTWQy6XD4peNOJ+fe75effddzF//nw8+eSTwm1JSUkYP3481Go1vv/+e3z00UcAgDVr1uCBBx5wXms91LZt2wAAfsNiwciksFusYCQMmHa9XqzN3mEPIUYmdeg1Y+0s2HbFIxmppEPhN9Zq69Dzxsh6cT2pFEyblUAsy4K1trueRNJh1/n21wPQoXAja7eDtbW/Htd2VUI0pL/mQ9/YjF27duG6666DK4SHhyM8PBxVVVXIbzQhLYQqvrZ3udkCvdUOlUqFpCT3L1EePXo0tm3bhvrqYrdsquop6qu5nork5OQuN132NsHBwRgyZAiKi4tRUF+K9PBhbrlufj0XnEeOHOn1c6eIOPocfk6fPo0//elPnd43ceJEfPDBBwC4Me2yssE/qbGoqAhnzpwBAOhzL0Gfy41DK+MiEHKV4/LLhv2nYChwrDwceftMMIrWDRPNlbWo/d5xXlXA+FRoRiY43Fb1xR7Ymo3Cz7Jgf4TPmepwjO5UPnS/5jvcFnb9FMhDWuth2JoNqPr8J4dj1KnxCJzg+M209ofDMFfUCT8zchmi7nDcxNZQeBkNv5xyuC34yiyohkaCYRhoRyag4cBpfPPNN7j22mtdVt02IyMDO3fuxMlqA4WfTvxazX1DT0tLE2VYeuTIkZDL5TAbm6HX1UHj330l6sGirop7b8jMzBS5Jc6Vnp7u/vBTVwqAC9KE9EefP33Cw8Nx4MCBTu87cOAAwsK4wmV1dXU+UXRq9+7dYjfBa/glxYBRyFFdXY3Tp0+77DqTJk0CAPxaY+jVHle+hGVZHK3iws/kyZNFaYNCocCoUaMAAHWV4u8N5Q52uw311VxvRUZGhsitcS7+77KwvvstZZzFxtpxqZH7Ys0PoRLSV33u+bnzzjvx8ssvw2Aw4JprrkFoaCiqq6uxY8cOfPTRR/jzn/+M8vJyvPnmm5g4caIr2uwxbDYb9u3bBwAIunIMlNGtFWuZTorMBU4ajYDsUQ63tR+qUkSEILJdj0r7ISgACL/xii4nnPO0o5OhGZXY7lyO15Nq/Dper5MemZCc8T1ug+CXGAPVUMclp23bzsik8IuPgv5CMX7++WdhvoCzjRw5EkFBQaivr0dunRGjQ6n3h1ess6DaaIVCoRC1MFxWVhZOnDiB2soixCX3sK/WINBYexk2qxmBgYGiDDW6UmpqKhiGQa2xEQ0mHQKVrl3wUaarhslmESbPE9IffQ4/9957LwwGA1atWiXM7WFZFv7+/njooYdw//33Y+vWrTCbzXj00Ued3mBPkpubi8bGRjBKOVRxET1uyMhIO86l6XCMhAEj6fmvhZFJe9wOoVfXYxgwvdh0tVfXk3Scm9SeX2I09BeKcfjwYdhsNpcMu0gkEkydOhXbtm3D3jIdhZ829pbpAHB7M7my5EBPsrKysHr1ajTWlcNiNkCuGNx/RzWVRQC45+2tm5l2Ra1WIyEhAYWFhbjYUIaMiOEuvV5RA9fDlJqaOuheS+I+/fqX86c//Qn79u3De++9h2XLlmHt2rVC4MnJycGNN96Ir776CrGxsc5ur0fhh26UMWG92omaAPLwYDAKGfR6vUu3vJg1axYYhkFunQlVBqr5AwDNFhuOtQx5XX311aK2JTw8vOVbO4uaiiJR2+JqLMuitrwQwOCtRMyvuLrU6PptS4pbrkGrvMhA9PsT28/PD1OmTEFAQAA++OADzJ49GytWrIBUKvWZNM5PdFZGhYrcEu/BSBgoIrgJrq6c9xMZGSnMrfiptMll1/Emv5Q1w2JnkZCQgOHDXfvtvDeys7MBADXl+T0c6d10DZUwGXVQKpUuG+oVG1+5+1Kja7ctYVlWuAaFHzIQ/UoptbW1eOeddzBr1iz84Q9/wMGDBzF37lysXbsWO3bscHYbPRLLsrh4kZusKQ8PErcxXkYREQQAwuvnKtdffz0AYH+FXtjLyleZbHbsKuWGvK677jqPWF7Oh5/66hJYLcYejvZe1WVcuMvKyhq0y7L5IFLRXAuTzeKy69QZG9FsMUImkyExMbHnBxDShT7N+dm/fz82bNiAHTt2wGazYdy4cSgtLcXKlSuFNzJfUVtbC5PJBDAMZP6Do2aHu8gCuAmRri6FkJaWhuHDh+PChQv4saQJNyUFufR6nuznsmY0W+2IjIwUbZVXe7GxsUKNmJryQkQOGRyF/9piWRbVZXkAWlchDkbBwcEICQlBbW0tynTVSAiMdsl1SpuqAADx8fGQy+U9HE1I13rV87N69Wr85je/we9+9zucOXMGS5YswQ8//ICVK1f6VJGytiorKwEAUq0fzffpI2kAFxYrKlw7P4BhGNxyyy0AgL1lzag3+ebcH4PVjp3F3NDfTTfd5FFbzvCBoKrsgsgtcY2m+nKYjDqoVKpBvw0Dv4qNDyiuUKqrcrgWIf3Vq0/tf//731AoFFizZg2+/fZbPPDAA4iKivLJ0MPjt2iQKOjbR1/xr5nBYIDd7totKDIyMpCamgqLncX2i40uvZan2lnShGarHTExMbjiiivEbo6DKVOmAADqq0thNjaL3Brnqyw9D4BbXTdYh7x4fCC5rHNd+OHPTUNeZKB6FX6uv/56XLx4EYsXL8aSJUvw/fffw2r1zW/RPKORm6PAyD3nW7S3aLsthslkcum1GIbB/PnzAQCHKvS43OxbG57WmazY3TLX58477/SoXh+Am5jOTb5mUXV5cPX+2O02Ychr2rRpIrfG9fiaO+XNNS45P8uyKNdx505ISHDJNYjv6NWcn5dffhk6nQ5ffvklNm/ejAcffBDBwcHCcmJf7gEifddTcUZnGzZsGCZNmoT9+/djU1497k8LdbiuhGEgb1eU0mpnYWtX1FEhcfy3bmdZWNpVkJYxDKTtzmW22dH2KAYMFNKeryeXMJD0cD0pw0DW4XosWLBQSiX4oqABFjuL1NRUUYsadueKK67AhQsXUFGai5jEzEHzflJbUQSrxYTg4GCfqETMh58afT0sdivkvahX1hcNJh2MNjOkUumgL6NCXK/X/zq1Wi3uvPNO3Hnnnbhw4QI2bdqEL7/8EizL4u9//zuuv/56XH/99Rg2zD17u4iNLxDHWnx7FVF/8BupMgzjtqGA+fPn4+jRo8hvNOOv+xwnWo8OVeG+UWEOt224UIdDlY67z/9rcgzUstYP5vwGE1aerHY45uakQMyI9Xe47cUjFagztf47idHI8cTYSIdjvi9uxLeXHJfkP54VgTht6+tTZ7Lh+UOOS4mnx2hxS3KQw21vn65CfoMZS9PDcKzaAIZhcPfdd3tsqJg0aRI++ugj6Jtq0dxYBW1ghNhNcoqKklwAXLjzhfIfwcHB0Gq10Ol0qNLXIUYb7tTzVzTXAgBiYmJosjMZsH79Rg4fPhx//etfsXv3bixfvhxJSUl49913ccMNN+DGG290dhs9Er8rs93sumWdgxX/mqlUKrd9KISFheHmm292y7U8xab8egBcwUdPHibQarUYP348AKC8+KzIrXEOk1EnbGQ6ffp0kVvjHgzDIC4uDgBQpa93+vkr9dzGyvw1CBmIAfVLymQyXH311bj66qtRXV2Nzz//HJ9//rmz2ubRIiK4b6e2Jj1sZjMYpusPcUba87YPrJ0Fa+u5F4mRSXv8Bs/a7GB7mEjMMEyHfcU6PZfV1uOeXoyk5200AMBu4eaJWeu5Hg7+NXSX66+/Hj/99BPKy8sxOVKNm1t6TCSdvJ63Dw/Gb4cFOdymaDe8lByoxH+mxDjcJuvkXH8bF9lh2Ku9q4cEICfOsceo/VBcsFLa4XrSTq63OC0cO4ob8V1xEwICAnDrrbd2OMbTXHXVVdi3bx+qLl9AYuoUSGXe/c2e6/XhhhtjYmJ6PH6wiImJQW5uLqpagoozVRu4c9KQF3EGpw3KhoWF4f7778f999/vrFN6tODgYMjlclgsFlRu+KHbYwOnjIY6uftvK+bKWtR+f6jH64bPnQ6Ztvu6QrpT+dD92n3VXKlGhYhbZvR4vdofDsNc0f0bmV9SDIKm9rxTdcV6xwKYkZGRXRzpGnK5HPfeey9eeOEF7K/QIztKg8QAZafHyiQMZD3sZiZhGCilPQ8lKXoRDJ15vTqTFTtLuIB59913Q6t17UaTzjBq1ChERkaioqIC1WV5Xl3zh2XtqGjpwbrqqqtEbo178cGk2gU9P3xvki+FSeI6g38g2kUkEgmGDBkidjO82tChQ91+zbS0NFx55ZVgwc3rsdq779XyNnaWxacX6mBjgczMTI8paNgTiUQiBIXLF0/BZrUIf+y2jitL7TabwzE2q6VDDyXL2jscY7d37F212RyPsXV2PXtn13PsXWVZFjarBTXlhTAZmqDRaDBx4sSBvCxeJyoqCgBQa3R+WYlaQ6PDNQgZCOdOx/cxKSkpKCgogN+wOASMT+3yuN4MCSkiQhB5x6wej+vNUJV2dDI0o7qvg9Hbya8hOeN7NezVG/zzq9y6B6zBJNrePPPnz8fx48dR3tCAby814vqEQFHa4Qo/lzUjv9EMpVKJ3//+9x47ybkzM2bMwGeffYbmxirs++5d4faQyESMGvcbh2PzTu1CZek5h9smXX0vZPLWnryG2jKcOrDV4ZjEkVMRm5jpcNvRn9bDZGidbK7xD0XWFbc7HFOcdwTFeYcdbhsz7TZoA1onypsMTTi8a63w85VXXjnoa/u0x/fm1hoanFoA12A1QW81OlyDkIGgnp8B4D+8zZW1kMhlXf7pTThgJEy35xDO1Ys3E0Yq6fk8vQhRABe2ejxXL8IdwNX3sRtMYA0mSKVSJCcn9+pxzubv749FixYB4AoAFjcNjto/1QYrvixsAMAFvPBw5662cbWAgIBB1VMyc+ZMsZvgdhEREWAYBma7Fc0Wg9POW9fSkxQQEAA/Pz+nnZf4Lur5GYDRo0dDKpXC1qiHtUEHWaDnz60Qm7GY2xZk1KhRQrkAMWRnZwu1fz4+X4vHsiI71MvxJnaWxfoLdTDbWYwaNcprP3hnz56NX375BQwjwbgZCyCXqzoN/MNGz0BymuMqKonU8e0sMCQak2c7zkHs7IvI2Ol3wHFGesfrDRk2DnFJWe2u5/gFQunnj+iEdJQVnUR6ejqio12zv5Unk8lkCA4ORm1tLepNOmgVztn3sN7IFer0tkBPPBf1/AyAWq0WipcZL7l2n6rBwljMvU780mYx/e53v0NAQADK9FZ8c8m7t77Yc1mHvAYTlAoF7r//fq+tKzN8+HAkJCSAZe2ovpwHqUzeIdQAXPCQyuQOf9qHJIaRdDhGIunY4ymVOh4j7ex6ks6u5/ga2+1WVLVsZzF79uyBvAxeLSyMGwqsNzb1cGTvNZi48BMaGuq0cxLf5p3vkB6E35hRX3C5x7kxvs7aoIOlqh4SicQjwk9AQADuvfdeAMDO4iYUNbp2qw1XqdRb8FURF97mL1jg1XMiGIYRgkPZxZM9lmzwJFWXL8BqMSE8PBxZWVk9P2CQ4sMPH1icgT8Xf25CBsprws+WLVtw3XXXIT09Hddffz2+/vpr4b6SkhIsXrwYY8eOxbRp0/Daa6/B1ouaOc4wceJEKJVK2BqbYamud8s1vZU+vxQAtwopODhY5NZwJkyYgKlTp4IFsO58Hcw27/mwBbjhrnXn62Cxs0hLS/Pa4a62pkyZAq1WC5NRh5rKIrGb0yssy+Jy0a8AuF4fb+15cwb+d7vJrO/hyN5rMnOb3oaEhDjtnMS3ecVv6NatW/HUU09hwYIF2LZtG+bMmYNHH30Ux44dg8ViEb69r1+/Hs888ww++eQTrFy50i1t8/PzQ3Z2NgBAf77YLdf0RqzNBkNL+PG0irf33HMPgoODUWWwYluRdw1//VDShItNZqhUKixevHhQfOgqFArk5OQAAMpaAoWna6i9DH1TLZRKJa688kqxmyOq1vDT7LRzNpq4IEXhhziLx79TsiyL//3vf7j77ruxYMECDB06FA888ACmTJmCgwcP4ttvv8Xly5fx0ksvYcSIEZg1axYeffRRfPjhhzCb3bOKh++mNxSVwaY3uuWa3sZQVAa70YyQkBCMGzdO7OY40Gq1QnHO3Zd1yKv3juGvsmYLvr7IhbW77757UA0JXH311ZBIJGiovYzmxuqeHyAyPqRdccUVXlFU0pWCgoIAtAYWZ+CDFH9uQgbK48NPYWEhSktLccMNNzjc/t5772Hx4sU4fPgw0tLSEBjYWqtl0qRJ0Ol0OHvWPfsEJScnY8SIEYCdRfO5S265pjdhWRbNZy8C4IKiTOZ5iwzHjBkjFNn75EItTB4+/GWzs1h3vhY2FsjKyhp0vQ2hoaGYMGECAODyxZMit6Z7Rn0jaiqKAPj2RGce/17szKXuzRajw7kJGSjP+xRqp7CwEACg1+tx77334syZM4iLi8MDDzyAnJwclJeXd6j4ye8ZVVZWhszMzA7n7G5eRFlZWb+WqF533XU4f/489OcuQZuWCInCu/cmciZTSRWsdU1QqVTCcIYnWrhwIU6ePInq6mp8UdiAW4d5xrykznxf3IQSnQUajQb33XefVxUz7K1rrrkGBw4cQFXpeSSkTIZcIV5phO6UXToFgJtzRZtuOj/8WOxWmGxcL35AQIBTzkmIx/f86HTcLP8nn3wSc+bMwfvvv4+pU6diyZIl2LdvH4xGY4cqqkolV+XVZHLf8MX48eMRGxsL1mJFcy71/vBYloXuJLfP2NVXX+3RQwJ+fn5YvHgxAK5S8vk6zxzCLNWZ8V0xN9y1aNEij5k87mwpKSmIj4+H3W5DRYln7vZus1mFfbyuueYakVvjGfiAYrCaYOtkO5G+0rf0+kilUqjVzqkbRIjH9/zI5VwPyr333ou5c+cCAEaOHIkzZ87ggw8+gEql6jC3hw89Xf2i7Ny5s8vr9Xe1jEQiwc0334yVK1ei+WwhNKlDqfcHgKm0CpaaBigUClx33XViN6dHaWlpmD17Nr777jusv1CHJ8dFQtnLCtbuYLOz+Ph8Hewst1LNW/bu6g9+2fu7776L8kunEZs4xuN6uKrL8mC1mBAWFoaxY8eK3RyPoNFohP83Ws3QKAZWkZkPP/7+/h7390+8l+e8q3eBr1kyYsQIh9uHDRuGkpISREVFobKy0uE+/md31zuZPHky1/tjtqL5TJFbr+2JWJZF0/ELALhvxd4yXn/HHXcgPDwctSabsF2Ep9hZ0oTSZgu0Wq3X7d3VH1OmTIFarYZR34i6Ks/rUS27eAoAMGvWrEGx0s4Z2vbQ8PtxDQQffjy515h4H4//bU1LS4NGo8GJEyccbj9//jyGDh2KCRMm4MyZM8LwGADs378fGo0GqaldbzbqChKJBLfeeisAoPlsEWwG71g15CrGi+XCXJ85c+aI3ZxeU6lUwuqvvWXNyG/wjL/H8mYLvr3UurrLW8LkQCiVSqE0QnnxGZFb40jXUAVdQyWkUumgm3A+UHxQ0VsG/rtjsJoczkmIM3h8+FGpVLjvvvuwcuVKfPXVV7h06RLefPNN/Pzzz1i0aBFmzZqF8PBwPPzww8jNzcWOHTvwyiuv4Pe//70oOypPmDABSUlJYK026H7Nd/v1PQVrs6PpGFfqf86cOfD39xe5RX0zevRoYfXXhgt1sNrFrd5tZ1lsyKsTVndNnTpV1Pa4Ez9JvrayCCaj82rHDBQfxiZMmOATQbQv+KEvfqLyQBitZodzEuIMHh9+AGDJkiV48MEH8eqrr+K6667DN998g+XLlwvVlVetWgW73Y7bbrsNzz77LObPn48lS5aI0laGYTB//nwAgP5CMawNzivx7k305y/BpjMgKCjIK+b6dObOO+9EYGAgKg1WfF8sbvHDfeXNKGzkihkuWrRo0A93tRUXF8cNe7MsqkrPid0cANxE56rL3JAuH5JJK37Yi++1GQijrfs5nIT0h8dPeOYtWrQIixYt6vS++Ph4vP/++25uUddGjRqFrKwsHDt2DI1HzyPkKt+aCGk3mdHU0us1b948UXdvHwitVot77rkHr7/+OnYUN2FcuBoRavdPYm8yt849uu222wZVMcPeuvLKK3H+/HlUlOQiNilL9PBXW1EIm9WMsLAwYXNj0ooPKkZnhJ+Wnh8KP8SZvKLnxxvNnz8fEokEppJKmMprxG6OW+lO5oM1WzBkyBDMmDFD7OYMyMSJE5GZmQkbC2zKrxdl89ovChtgtLFITEz02SJ6EydOhEKhgKG5HrqGKrGbg8qWHqhp06bRROdO8EHFZLMM+FymlvDj5zewVWOEtEW/tS4SGxsrLJtvPJwLVuQ5I+5ibWwWqlwvWLAAUqlU5BYNDMMwuOeeeyCXy3Gu3oQT1c6rWtsbBQ0mHKrUg2EYLFq0yGc/aNVqtbCUvKrsgqhtsZiNqK8uAQCfmnvVF3xQccacHz5AUfghzuSb76RuMm/ePKjValjrmmDILxG7OW7ReCQXsLPIzMxERkaG2M1xiqioKGF7la2FDTDb3BNk7SyLzfn1AIAZM2Zg2LBhbrmup+JrGlWX5YnSA8erKS8Ay9oRHx+P2NhY0drhyfihbr7XZiD4AEXhhzgThR8XCggIwC233AIAaDp+AXazVeQWuZaprBqmkipIpVIsXLhQ7OY41Q033ICQkBDUmWzYfbnJLdc8VKlHSbMFarUat99+u1uu6ckyMzOhVCphNjaLOvRVU1EAgBuKI53jg4rZNvD3PL7nx1vnDhLPROHHxWbPno3o6GjYjWboTg3epe+s3Y7GQ7kAuG0sBts3YqVSiTvuuAMAsKO4CU3mgZft747JZse2Im6S880330x7GgFQKBTCXn21FYWitMFqNaO+huvFHT9+vCht8AZCzw8NexEPReHHxWQymdAL0ny2CNZGz6lT4kz8sn6tViv0dg02U6ZMQVJSEkw2Ft8Xu7b3Z89lHRrNdoSHh9OeUW2MGzcOAFBbdVGU6zfUlIK12xEZGTnoAr4ztc75GfiEZzP1/BAXoPDjBmPGjOG+sdpZNB7xjDolzmQ3mdF0PA8AcOuttw7aSqwSiUTo/fm5TIc6o2uGMfVWO3aWcOHq1ltvFfa3IxDmkTU3VsNs0rv9+vVVxUI7xF5u78n4zaUtThj2ovBDXIHCjxswDIOFCxe2Ln0vG1xL35t+bV3azlfjHaxGjx6NUaNGwcYC37qo92dXSRMMVhZxcXGYMmWKS67hrQIDAxEfHw+A64VxN37IKz093e3X9iZ8UDFTzw/xUBR+3CQ2NhZXX301AKDx8NlBs/Td2qCDvmVp+8KFC71+aXtv3HbbbQCAQxXNqDM5t/fHYLXjp8tcVfDf/va3Pru0vTv8nn2NdWVuva7ZpIehud6hDaRzrhj24nuTCHEGemd1o3nz5kGj0cBazwUGu8Uq/GFtHSfQsja7wzF2i7XDEl/WznY8xm7veC6rzfEYay+v1y6ksazj9RqPnANYFmPHjvWZb8MjRowQen9+LHHu9iU/l+lgtLGIjY2lCbVdEMJPrXvDT1NdOQBgyJAhg3Zo11n4XhqLfWDhx2q3wcbaHc5JiDN4zfYWg4FWq8W8efOwZs0aNB4+i8bDZ4X7lHERHbbBaNh/CoaCyw63Rd4+E4yidQ6IubIWtd8fcjgmYHwqNCMTHG6r+mIPbM1G4WdZsD/C5zgWaNOdyu+wGWvY9VMgD2ldaWRrNqDq858cjpFKpViwYEFXT3tQuummm3DmzBnsK2/GNUP9oZEPvMfLamexu5QLUzfeeCP1+nSBr3fUrKuFzWaFVOqet7GmhkoAQHJysluu5834XpqBLnVvO2eIwg9xJnp3dbNZs2YhOjpa7GY4Fb+c35eMHj0a8fHxsNhZ7C93zsTbY1V6NFnsCAkJEQr6kY5CQkK4pf8sC32T++bP8bWFEhMT3XZNb9Xa82OFne3YE91b5paeI6lUCpmMvqsT56F/TW4mk8kwf/58vPzyywDDIGzOVEg1KjCSjitHAieNRkD2KIfbGJljD4MiIgSRd8xyPEbaMdOG33iFw5BZZytVtKOToRnl+MbOtJvDI9X4IfKOWTDkX0bjoTPQaDSYO3duF8928GIYBrNnz8a7776LvWU6XBWnhWSAq3/2tMz1mTVrFr3Rd4NhGMTHx+PkyZNobqyBf1CkW67LB62EhAS3XM+bte2lMdssUMn6N1/HTDV+iItQz48Ixo4di1GjRgEsC93JfEjksg4hA+BCjEQuc/jTPrQwEqbjMZ0MlzAyqeMxsl5er10o46/PF2ycO3euz85/mDp1KrRaLepMNpytM/b8gG4UN5lxSWeBTCbDVVdd5aQWDl5xcXEAAL2u1i3Xs1qMwtJ6qu/TM5lMJix+GMjQF1V3Jq5C4UcEDMMIc2SMRWWw1DSI3KK+aT5bBLvBhIiICJ/dZRzgKg5fccUVAICDAxz6OljBFb+cMGECAgMDB9y2wa41/NS55Xr8dUJCQoQdy0nXGIYR5v0MpMozLXMnrkLhRySJiYnCjtCNR8+JulFjX9gMJjSf5rYWuP32231+eGb69OkAgFO1BjRb+rflhdXO4kiVweF8pHuRkdxQl1Hf6Jbr8deJiopyy/UGAz4kDqTWD78xKoUf4mwUfkR02223QSaTwVxeC7OXFD7UncwHa7UhKSmJNnYEEB8fj/j4eNhY4ES1oV/nyK0zQm+1Izg42GfKBQwUH35MhqZOSzs4Gx9+IiIiXH6twYIPLMYB7OxO+3oRV6HwI6Lw8PDWwofHznt874+1SQ/9ea68/5133klLsVtMmjQJQP/DD/+47Oxsek17KTg4GFKpFCxrd8s2FyYDV807PDzc5dcaLJxR6JAfMqPwQ5yN3mlFdtNNN0GlUsFa2wjjxXKxm9OtphMXAJZFeno60tLSxG6Ox8jOzgYAXGgwodnSt14Im53FqdrW8EN6RyKRIDg4GABgMrp2k1kAMBm4lXihoaEuv9ZgwQ97DWTOD99rRPOsiLNR+BFZQEAA5syZA4ALF+7owu8PS10TjIVcRd3bb79d5NZ4lujoaMTFxcHOAufq+7bqq7DRDIOVRUBAAFJSUlzUwsGJDyJ8MHElk5G7RkhIiMuvNVgIPT8DGvainh/iGhR+PMC1114LrVYLW6O+Q0VnT9F04gIArnciKSlJ5NZ4nszMTADc/J2+yG0JS+np6TTk1Ud8z487hr34a/DXJD3je2sMAwg/fM8PhR/ibPRu6wHUajVuvPFGAIDu13ywNs/q/bHUNMBUXAmGYXDrrbeK3RyPlJGRAQA4V2fs09ytc3Wt4Yf0TWv4aXbpdWxWC2wtH8IUfnpPo9EAAIw2U7/PwYcf/lyEOAuFHw9x9dVXIzAwELZmAwz5pWI3x0HTiTwAXFE/KvDWuZSUFEilUjSY7agx9m7Ju9FqR4mOmww6evRoVzZvUAoKCgLg+p4f/vxKpZJ6IPpACD8D6PkxWE0O5yLEWSj8eAilUtna+3PKc3p/zDUNMJVWQSKR+OQ2Fr2lUCiEPZ8KGnv3TfdikxksuBVENJek7/jwYzG6J/wEBQV1ui0M6VzrsNcAen5s1PNDXIPCjweZOXMmgoKCYGs2wlDgGb0//C7vU6dO9bnNS/uKn7Bc1Ni7b7qFLccNHz7cZW0azNw17MWfn4a8+obf9sZg6X/4MVi4YWEKP8TZKPx4EIVCIaz80p0qEH3ll6W2EaYSbq7PTTfdJGpbvAE/Eby0uXd1TUqbufCTnJzssjYNZq1L3V3c82Pkwg/f00R6Rwg/1v7ve8f3Gvn7+zulTYTwKPx4mJkzZyIgIAA2nQGGInHr/uhOFQDgivjFxMSI2hZvEB8fDwC43GyBvReTnktb5vvwjyN9w4cfm9UEm7X/hfR6YqZl7v3Chx99P3t+7Kwdxpbw46ubJxPXofDjYZRKJa699loAQPOpAtGqPlsbm4Wii9Tr0ztRUVFQKpWw2FlUG7rfydpotaPWxE2MHjp0qDuaN+io1erWWjIuLHRIBQ77h++tMVj7tgKSp7eYwD+Kwg9xNgo/Hujqq6+Gn58frA06mEqqRGmDrmXz0rFjx9KHcy9JJBJh48uqHsJPZcv9gYGB9MY+AGFhYQBcW+iQL3BI4adv+PBjY+39qvKsbzPfRyqVOrVthFD48UAajQazZs0CADSfKXT79W0GkzDhmp+DRHqHDz+VPYQfPhzRLuEDw2806srd3Q36Bodrkd5RKBTC5qbNlr7P+2m2cNu+BAQEOLVdhABeEn4qKiqQkpLS4c/mzZsBAGfPnsXChQsxZswY5OTkYM2aNSK3eOCuueYaSKVSmCvrYK6qd+u19bkXATuLYcOG0ZYLfcSHmRpj9+GHv5/fnZz0D//6GZrrXXJ+q8UEq9nocC3Se3xw0Zn7vukvhR/iSjKxG9Abubm5UCqV2LFjh0OdDX9/f9TV1WHRokXIycnBs88+i+PHj+PZZ5+FRqPBvHnzRGz1wISEhGDKlCnYs2cPmnOLoAgf45brslYbmi9wO7fPmTOH6pr0ET80UmfqvtAhfz8/bEP6hy+/4Krww583KCiIChz2Q2BgICorK4Ug0xf8YwIDA53dLEK8I/ycP38eCQkJnXY7f/jhh5DL5Xjuuecgk8mQnJyMixcv4p133vHq8AMAv/nNb7Bnzx4YL1bANs4IqVrl8msaCi+DNVkQFhaG8ePHu/x6gw0ffhp6CD/1LffTPJKB4SuO63V1Ljm/vqkWABAXF+eS8w92rT0/fS9HwPcWUfghruAVw17nzp3rshbK4cOHkZ2dDZmsNcdNmjQJRUVFqK6udlcTXSIhIQGpqakAy0J/vtjl12NZFs3nLgEAZs+eTRtt9gO//LrB3H34aWy5nwrnDQwfSkyGJlgHUEyvK81NNQ7XIX3D10bSWfoefppaAhOFH+IKXtPzExwcjAULFqCwsBDx8fF44IEHMH36dJSXl2PEiBEOx/M9RGVlZZ0OK8ycObPLa5WVlXlUJePZs2cjNzcX+rwSaDOSwbgwkFiq6mGta4JcLseMGTNcdp3BjP+m22yxw86ykHQxbKiz2ByOJ/2j1WoRHh6Oqqoq6BqrERTq3L3ndA3casuEhASnntdXCOGnH3N++MBEXxCIK3j8V3ur1YqCggI0NDTgwQcfxDvvvIMxY8bgD3/4A/bt2wej0QiFQuHwGKVSCQAwmZz/TdDdxo8fj8DAQNgNJhiLK116LX3LXJ/JkyfT8ut+4pf32gEYrJ3XNmFZFjoLV72bws/A8Xuq6eqd+/vB2u1obqx2uAbpGz78NJr7vgUJ9fwQV/L4nh+ZTIYDBw5AKpUKyyZHjx6NCxcu4L333oNKpYLZ7FhDgg89/MZ67e3cubPL63XXKyQGmUyGGTNmYOvWrTDklcAv3jVLo+1mCwwtRQ35Zfak7+RyOZRKJUwmEwxWOzTyjt8vzHYWtpZcRCFz4JKTk3Hw4EE01ju3InqzrhY2mwV+fn7C3CLSN3yvTVM/5vw0tWwoS5W1iSt4fM8PwNW94YMPb/jw4aioqEBUVBQqKx2/8fE/D5alqfwQlOlyNWzNfe8+7g1DURlgsyMuLo72mhogYTdrW+d7sxlbko9EIhF6KUn/8eUYmurKnVoRvbG2DAAwbNgwmv/WT0L4MfUt/NjsNjTTsBdxIY//jb5w4QLGjh2LAwcOONx+6tQpDBs2DBMmTMCRI0dgs7VOMN2/fz8SExMHzUqayMhIjBo1CgBgKLjskmsY8rmihldeeSUtbx8gPvwYrV2En5bbVSoVvdZOkJiYCIVCAYvZ4NRVXw213O9Eamqq087pa/hem2aLHjZ794sA2tJZDGABSKVS2tSUuITHh5/k5GQkJSXhueeew+HDh5Gfn48XX3wRx48fxwMPPIB58+ZBp9PhqaeeQl5eHjZv3ozVq1dj8eLFYjfdqaZNmwagpYfGyaxNeliqG8AwDKZMmeL08/savjfHbOu8F4K/vX1vJukfuVwu9P401JQ45Zwsy6Khhgs/aWlpTjmnL/L394dUKgWLvg19NZq4OULBwcHU60ZcwuP/VUkkErz11lvIyMjAww8/jLlz5+LEiRP44IMPMGLECISGhmLVqlUoLCzE3LlzsWLFCjzxxBOYO3eu2E13qgkTJkAmk8Far4OlzrmbOPKBKi0tjbqYnYAPPxZ75+GHv52GvJyHDyh1Vc4pCaFrqILVYoJKpaJh4AGQSCRC709fJj03mLj91Gi+D3EVj5/wDHBVcF988cUu78/IyMCGDRvc2CL302g0yMzMxJEjR2C8VA55sPO6go2XKgBwq7zIwPGrD81dhB/+9varFEn/jRkzBuvXr0dDTSnsNisk0oG9tdVVXQQApKen06aaAxQSEoKqqioh0PQGH5Qo/BBX8fieH9JqwoQJAFrDijNYm/Sw1jaCYRiMGzfOaef1ZXzBTWsX4Ye/vW1hTjIwQ4YMQUhICOx2K+pbhqsGoraSCz9jxowZ8Ll8XWvV8z70/Bh1Do8lxNko/HiRsWPHQiKRwFqvg7Wp70tHO2Mq4VbGpaamUs0ZJ+FDTRdTfmBrWZEkl8vd1aRBj2EYZGVlAQBqKwoHdC6TsRm6hkqHc5L+4wNMYx96fvheItr7jrgKhR8votVqMXz4cADcsndn4M9D33Cdp+eeH+6/NJziXPxedLWVRQNa8s6Hp2HDhglF+kj/8QGmvh/hh4a9iKtQ+PEymZmZAJwTflibDaYKbuNGCj/Ow4earj5+7WxrnR/iPGlpaVCr1TCb9Giq63/Bw+ryAgCgjX2dRBj2MvY+/NRTzw9xMXr39TKjR48GAJgra8F20bPQW+bqBsBmR2BgIG3c6ER8qLF10fvA/7VRz49zyWQyjB07FgBQXZ7fr3NYTAahvk92drbT2ubLWnt+erdK1WKzotlicHgsIc5G4cfLJCYmQqVSgTVbYa0f2JJ3c0uvz8iRI6nYnhPx4aerbEo9P67DB5bq8vx+DX3VVBQCLIuEhIRBUyFebHyAMVhNMNksPR7PD3kplUra/oW4DL37ehmpVCrM+zFX1Q/oXJaWx/MF4ohz8KGmq89ee7vjiPNkZGRw+/0Zm/s19FVVlgcAmDRpkrOb5rPUajU0Gg0AoMHY8xc2vocoPDycvpQRl6F3Xy/EF12z1DT0+xwsy8Lc8ngq4uZc/Bu2vYv0w99Mb+zOp1AohLk6fJDpLbNJL1R1njhxotPb5sv43p+6Xgx91bcEJBryIq5E4ccLJSUlARhY+LE1G8GaLJBKpRg6dKizmkbQpueni/vZdscR5+J7bWrK88Gyne+v1pma8gIALJKSkmjIy8nCw8MBtAab7tBkZ+IO9O7rhfiwYm1sBmvv/Zt7W9YG7g0mJiaGKg07WW/n/FDPj2tkZGQIq774ndl7g4a8XKdP4cfYOuxFiKtQ+PFCYWFh3L5Qdha2fhY7tNZz4Sc2NtaZTSNoDTVsF30/1PPjWjKZTBj66u2qLy4oXQZAQ16u0JdaP3UmCj/E9ejd1wtJJBJER0cDAKyN/Qw/jVyp+ZiYGKe1i3CE8NNFzw9/O4Uf1+EDTHVZ74a+qsu4kJScnEwfui7Av6Z1xsYej6WeH+IO9O7rpfg3BluzoV+P5x9HbzDO1/Owl+NxxPnS09OhVqthMRvQ2ItVXzUVXGFD6vVxjdZhr+57fqx2G5rMeofHEOIK9O7rpQYcfnRURMxVhPDTxf12UJ0fV2tb8LCmpWJzVyxmAxpquCEvKmzoGvz7jN5q7LbWD9/ro1Qq4e/v75a2Ed9E775eit9zyGYw9evxdiP3uODgYGc1ibQQtrfoYak7hR/X4oNMTUVhtwUPayuKAHCFDSMiItzTOB+j0WigVqsBAPXdDH3xNX7CwsJoQQBxKXr39VL8Dux2g7nPj2WtNrAWGwAgMDDQqe0ibbe36Px+G1V4dov09HQoFAqYDE3QN9V0eVxNZREAYNy4cW5qmW/qzdAX3/NDIZS4Gr37eim+S9hu7rlcfHv8YyQSifBtjDgP3/PTVZFD2tvLPZRKJdLS0gC0Bpz27DYr6quLAUAYJiOuwQea7vb4qjNRgUPiHhR+vJSfnx8AgLVY+/xY/jF+fn7UtewCfKjpqeeHwo/r8YGmvupSp/c31JbBbrMiODgYCQkJbmyZ7xGqPHdT64fvFaLwQ1yNwo+XGkj4sbcJP8T5ZDIZAMDWxXIvq93xOOI6GRkZAIDG+gpYLR3nx9VXXxKOoy8CrsUPezV0U+uHhr2Iu1D48VJyuRwAwNr6UeG55TH8OYhzCeGni2Ev6vlxn/DwcK4mFssK+3a1VVddAqA1JBHX6VXPDw17ETeh8OOlhPDTj+0t+MdQz4Nr8K+rtathr5YeIQqf7jF69GgAQENLBWeexWwUJkKPGjXK7e3yNT1tcdG2xg+FH+JqFH68lLBSqJslvF2i1UYuJYSfroa9Wm6m8OMeI0eOBADUt+v54cNQbGwsrXp0g7a1fsyd1Prhh8MUCoWwmpUQV6FPPx/E5yWa4+Aa/EaxXYUfC/X8uFVKSgoAQN9UA6ultTREUx236Wlqaqoo7fI1arVamGfY2byfhja7udN7E3E1Cj8+rLvCb6T/+FBj6XLCM4UfdwoODhaGXHQNFcLtjfXc/w8fPlyUdvkahmG63eCUHw4LDQ11a7uIb6Lw46Xs/FyffnxDYiT8xpsUflyB7/npKvxQz4/7DRs2DADQVF8JgJv31txQ5XAfcT0+2DR0Uuiwbc8PIa5G4cdL8eGnX93DLY+x2WzObBJp0dvwo1Qq3dYmX8fX8NE1VgMA9M11sNtt8PPzQ1RUlIgt8y1C+Olh2IsQV6Pw46Ws1pb6Pv2YtMy0PEY4B3EqPtSYu6hyyN/OhyTieomJiQCA5saqlv9yIWjo0KE08d+Nug8/zQ7HEOJK9FvvpSwWbrUEI+3HX6GUwo8r8aHG3EXPD387hR/3GTJkCADAqG+EzWaBvqnW4XbiHnywaTQ3d7iPD0QUfog7UPjxUnxw4efv9AXf88MHKOJcbXt+OptXxff80LCX+wQEBECr1QIADLp6NOu48BMXFydms3xOVz0/LMuiseW2kJAQt7eL+B4KP15KCC79qBLMUM+PS/GhhkXnhQ7NLfO1KPy4D8MwiImJAQAYmuthbG4AAK76M3EbPtg0mpodvhiYbGaY7VaHYwhxJQo/XmpAw17U8+NSbUONuZPtR/ieH5VK5bY2EQgTmw3N9TDqGx1uI+7BBxuL3QqjrbXmUmPLfB+NRkNfCohbeFX4KSwsRFZWFjZv3izcdvbsWSxcuBBjxoxBTk4O1qxZI2IL3UcIP/2Z8CxtDT+03N35pFKpsIzd1G7Ss9XOCru9U/hxL36zzMa6MrCsHVKplOaXuJlCoRCGH5tMrfN++DlA1OtD3MVrwo/FYsHjjz8OvV4v3FZXV4dFixZh6NCh2LRpE5YuXYply5Zh06ZNIrbUPYQhq370/LQNTLTc3TX4YNM+/LT9mb7huhe/hLqxrhwA90FLK73cLzg4GIDjpGe+5ycoKEiMJhEf5DU7Wy5fvlz4xsD79NNPIZfL8dxzz0EmkyE5ORkXL17EO++8g3nz5onUUvcY0ITnNoHJarXSBqcuoFQq0dTUBFO7YS9+GEwmk9Hr7mZ8L4/dZnX4mbhXUFAQiouLhU1MAQj/zwcjQlzNK959Dx06hA0bNmDLli2YMWOGcPvhw4eRnZ3t8CEyadIkvP3226iuru6yWNbMmTO7vFZZWZlXTILke2z6M+yFNoGJen5cg9/DqH3Pj5Hm+4imfa8CfdCKg3/dKfwQMXl8n29jYyOeeOIJ/OMf/+gQSsrLyztMWOTH9cvKytzWRjEMZHuLto+h8OMa/JBWx2Ev7u+Nwo/7tQ8/NMQiDv51bzvnR9cSfujvhLiLx/f8PPPMM8jKysINN9zQ4T6j0dihUJzwoWMydXnOnTt3dnlfd71CnkSYqNyv7NP6IJrw7BqtPT+Ow158GOLvJ+6jVqsxbtw4HD9+HCqVCmPHjhW7ST6JDzg6i0G4rclC4Ye4l0eHny1btuDw4cP48ssvO71fpVLBbDY73MaHHrVa7fL2eYZ+pB/icj1NeKaeH/djGAaPPfaY2M3weUL4aTPs1WzmglBgYKAYTSI+yKPDz6ZNm1BTU+MwzwcAnn76aWzfvh1RUVGorKx0uI//OTIy0l3NFFc/em6ot8f1+J4dY7ueHyMNexEfxwectj0/fBCi8EPcxaPDz7Jly2A0Gh1umz17Nv785z/jxhtvxNatW7F+/XrYbDZIWyod79+/H4mJiYN+JQf/fPsTfto+RtqPCtGkZz31/NCwF/FVAQEBAIDmlvBjtlmE6s4Ufoi7ePSE58jISMTHxzv8AbglqpGRkZg3bx50Oh2eeuop5OXlYfPmzVi9ejUWL14scstdjw8tbBebZ3bLTuHH1boOP1zPD4Uf4qv4gGO0mmG129Bs4b7gyuVy+r0gbuPR4acnoaGhWLVqFQoLCzF37lysWLECTzzxBObOnSt201yOn+jN9mO1VtvH8JWIiXN1NexFc36Ir1Or1cKXrmaLAfqWHiB/f3+HxRiEuJJHD3t15ty5cw4/Z2RkYMOGDSK1RjxC+LH2I/y0PEYqlVKhPRcRVnu129nUaKWeH+LbJBIJtFotGhoaYLCYhJ4ffjiMEHfw6p4fX8b3HPQn/NgtNodzEOfresIzzfkhxN/fHwDf82N0uI0Qd6Dw46U0Gg0AwG7q+87srNnicA7ifHywNNKcH0I64Ht59Baj0PND4Ye4E4UfL8UHF7YfO7PbKfy4HF9nqn2RQ+r5IQTCPo0GqwkGK4Uf4n4UfryU8EbBAmwfe3/sRq4wJI2xu44w7EVzfgjpgP/ipbeaYLCYHG4jxB0o/HgpmUwmfHuyGbveyqMzdgN3PNXUcB0+3Bio54eQDoSeH4tR6PnhbyPEHSj8eDG+TLxd37fwY2sJP7SPjuvww15mGwt7m2FJ6vkhpLWXx2gzwWA1O9xGiDtQ+PFifBVrW7OxhyMd8ccP9irYYuLDDQsuAPEM1PNDiBB0DFYzDFYa9iLuR+HHi7WGH0MPRzqy6Sn8uJpcLhcKufFDXzY7C0tLdW3f2XiXkI6E8GMxwWj1tc2oiSeg8OPFIiIiAABWnb6HI1uxLAtbk97h8cT5GIYR3sz5Sc9ta/5Qzw/xZUIRUJsZxpZhLwo/xJ0o/HgxPrzwYaY37CYLWIvV4fHENfg3c0PLPB9DSwhSKpVUWZv4NP53Q2c2wGSj8EPcj96BvVh0dDQAwNrY+/Bja2wGwA158VtkENdoX+WZ/y+9yRNfJ4Qfi77DbYS4A4UfLxYVFQWGYcCaLbAbzZCoeg4z1pbwExMT4+rm+bzWSZ1sy38p/BACcF/crrjiCly8eBEAMHr0aPq9IG5F4ceLKZVKhIWFoaqqCpYGHZSqkB4fY23QAaDw4w78m7m+3bAXvckTXyeRSPDAAw+I3Qziw2jOj5eLi4sDAFjrdb06nj+OfxxxHaGWiRB+7A63E0IIEQeFHy/XGn6aenW8peW4IUOGuKxNhCNMeG6Z62OgOT+EEOIRKPx4OT7EWOp6Dj92k1moBk09P67Xun8RzfkhhBBPQuHHy8XHxwPgen562t2dD0jh4eH0AewG7Ze68yGIhr0IIURcFH68XHR0NORyOViLDTZd95WeLbVc+OEDE3Gt1tVeNOeHEEI8CYUfLyeTyYQhLEttY7fHWuu4+xMSElzdLIK2w152h/9S+CGEEHFR+BkE+DDTU/jh76eeH/egnh9CCPFMFH4GAT78WLsJP6zVBmtDs8PxxLU69PxYKPwQQognoPAzCCQmJgLovufHUt8EsCwCAgIQEtJzMUQycK11fljYWZaGvQghxENQ+BkEhgwZAoZhYDeaYdMbOz2m7ZAXwzDubJ7P4kMOCy4AUfghhBDPQOFnEFAqlcJ2FV31/lhbVnrxvUTE9eRyOZRKJQCgwWyDraUSAYUfQggRF4WfQaKnoS/+dprv41580KkxWgFwexrxu70TQggRB4WfQUIodthJpWfWbhe2taCVXu7FFzrkw49araZhR0IIERmFn0GCDzWd9fxYG/WAzQ6lUonIyEh3N82ntfb82Bx+JoQQIh4KP4PE0KFDAQA2nQF2i9XhPr644dChQyGR0F+5O/Fhp7al54fCDyGEiI8+CQeJgIAABAcHA+i4wzu/pxcfkIj7tA57Uc8PIYR4Cgo/gwgfbtrv8G6t1wFo3QGeuI/Q82Pien5osjMhhIjPK8JPTU0N/vKXv2DSpEnIysrCH/7wB+Tn5wv3nz17FgsXLsSYMWOQk5ODNWvWiNha8fB7fPGVnHkUfsTD9/yYbLSjOyGEeAqvCD9Lly7FxYsX8c4772Djxo1QqVT43e9+B4PBgLq6OixatAhDhw7Fpk2bsHTpUixbtgybNm0Su9luFxsbC6A17ACA3WKFrZnb7Z0PR8R9+PDT1c+EEELcTyZ2A3rS0NCA2NhYLF68GCNGjAAALFmyBDfddBMuXLiAffv2QS6X47nnnoNMJkNycrIQlObNmydy692rteenNfzYGrleoICAAPj7+4vSLl/WPuzQsBchhIjP48NPYGAgXn75ZeHn2tparF69GlFRURg2bBiWL1+O7OxsyGStT2XSpEl4++23UV1djbCwsA7nnDlzZpfXKysrQ3R0tHOfhJvwVZ7tBhPsZiskCpkwBMbfR9yrfdihnh9CCBGfVwx78f75z39i8uTJ2LZtG1544QWo1WqUl5cjKirK4biIiAgAXJDxJWq1GkFBQQAAa0uPD/9fbw103q59+FGpVCK1hBBCCM/je37auueee3D77bdj3bp1WLp0KT7++GMYjUYoFAqH4/j9lEwm0/9v786Dmjj/MIA/QQ6VQQSkpNqOF4Z2CGJQQKtFG8+p2tFaiwU8qgK1FA+0iqLihegoCiMVRa1aRcUpltHRaTsw02M6ohyV1vvCowpymUYEBOL+/rCsjUHlpw2J2eczw9S8+Sb7Jt9Zffouu9vk+2RnZz91G89aFXoVyOVyaDQa6O5VAx0c0XCvWhynlseVHyIi8/NKrfy4u7tDqVQiLi4OnTp1wt69e9G6dWvU1dXp1TWGHin+Q9N4BeeGe49WfHQMPyb15EoPV36IiEzP7MNPZWUljh49ioaGx1cttrKygru7O0pLSyGXy1FaWqr3msbHUryVQ+MhP11VzT//rdYbp5bFw15ERObH7MNPeXk5oqKicPz4cXGsvr4eZ8+eRffu3eHr64v8/HzodDrx+ZycHHTt2hUuLi6mmLJJubq6Anh8m4uHD+r1xqllNR6CbcTwQ0RkemYffhQKBQICArBq1Srk5ubi4sWLiI6OhlarxZQpUzBu3DhUVVUhJiYGly9fxqFDh7Br1y6Eh4ebeuom0Xh2m+5+jXh9n7Zt20ryEKA54GEvIiLzY/bhBwA2bNiAfv36Yc6cORg/fjw0Gg3S0tLQsWNHuLi4YPv27SgqKsLYsWORnJyM+fPnY+zYsaaetkk0rnbpqmuhu18LAE2e7k8tw8bGBjKZTHz85EoQERG1vFfibC8HBwcsW7YMy5Yta/L5nj17Ij09vWUnZaacnJwgk8kgPBTQUPnobu7Ozs4mnpV0/Tv4AAw/RETm4JVY+aHms7a2Rrt27QAAdRWPwk/j3d7J9J68LAMREbU8hh8LJF7o8K5W7zGZnpUVdzkiIlPj38QWyNHREcDj090ZfoiIiB5j+LFAjYe9GvGGpkRERI8x/FigJ8POk2GIiIhIyl6Js73o/9OvXz8UFhaitrYWcrkc7u7upp4SERGR2WD4sUDu7u5Yv369qadB/3BwcIBWqzX1NIiI6B887EVkZEFBQXBxcUFwcLCpp0JERODKD5HRBQQEICAgwNTTICKif3Dlh4iIiCSF4YeIiIgkheGHiIiIJIXhh4iIiCSF4YeIiIgkheGHiIiIJIXhh4iIiCSF4YeIiIgkheGHiIiIJIXhh4iIiCSF4YeIiIgkheGHiIiIJIXhh4iIiCSFd3V/QmlpKXQ6HQYPHmzqqRAREVEzFRcXo1WrVs2q5crPE+zs7GBt/epnwuLiYhQXF5t6GgT2wpywF+aDvTAvltAPa2tr2NnZNatWJgiCYOT5kAk0rlxlZ2ebeCbEXpgP9sJ8sBfmRWr94MoPERERSQrDDxEREUkKww8RERFJCsMPERERSQrDDxEREUkKww8RERFJCk91JyIiIknhyg8RERFJCsMPERERSQrDDxEREUkKww8RERFJCsMPERERSQrDjwXYunUrJk6c+Myau3fvYu7cufD19YWfnx+WL1+OmpqaFpqhZdNoNFi6dCkCAgLg4+ODTz75BHl5eU+t/+uvvxAeHg4fHx8MGDAAiYmJ0Ol0LThjy1VRUYEvv/wSffv2hUqlQlhYGK5cufLUeu4XLaOoqAgqlQqHDh16ag17YVx37tyBh4eHwc/TemLp/bA29QTo5aSlpSExMRF9+vR5Zt3MmTNRU1ODXbt2QavVIiYmBtXV1Vi7dm0LzdRyRUVFoaysDBs2bICLiwv27NmDadOm4bvvvkO3bt30auvr6zFt2jR06dIFBw4cwI0bNxATEwMrKyvMnDnTRJ/AckRERODhw4dITU2Fvb09kpKSMGXKFPz4449o06aNQT33C+Orr6/HvHnzUF1d/cw69sK4zp8/Dzs7O2RlZUEmk4njDg4OTdZbfD8EeiWVlJQI4eHhQq9evYQRI0YIISEhT60tKCgQFAqFcPnyZXHs119/FTw8PISSkpKWmK7FunbtmqBQKIS8vDxx7OHDh8KQIUOExMREg/ojR44ISqVS0Gg04tiBAwcEHx8f4cGDBy0yZ0ul0WiEqKgo4cKFC+LYuXPnBIVCIRQWFhrUc79oGQkJCcKkSZMEhUIhZGRkNFnDXhhfamqqMHr06GbVSqEfPOz1ijpz5gxsbGxw+PBheHt7P7M2Ly8Prq6u6N69uzjm5+cHmUyG/Px8Y0/Vojk5OSE1NRVeXl7imEwmg0wmg1arNajPy8uDp6cnHB0dxbG+ffuiqqoK586da5E5WypHR0ckJCRAoVAAACorK7Fr1y7I5XK4u7sb1HO/ML7c3Fykp6djzZo1z6xjL4zvwoULet/vs0ihHzzs9YpSq9VQq9XNqr1z5w5ef/11vTFbW1u0b98excXFxpieZLRr1w4DBw7UG/vhhx9w/fp1LFq0yKC+pKQEcrlcb+y1114DABQXFz83yFLzLFmyBAcPHoStrS1SUlLQtm1bgxruF8al1Woxf/58LF682OB7fhJ7YXwXL16Ek5MTgoODUVRUhM6dO2PGjBkICAgwqJVCP7jyIwE1NTWwtbU1GLezs8ODBw9MMCPLVVBQgIULF2LYsGEYNGiQwfO1tbUGvbCzswMA9uI/NHnyZGRkZGDUqFGIiIjAmTNnDGq4XxjXsmXLoFKpMHr06OfWshfG1dDQgKtXr+Lvv/9GZGQkUlNT0atXL4SFheH48eMG9VLoB1d+JKB169aoq6szGH/w4EGT/0dMLyYrKwvz5s2Dj48P1q9f32RNU71o/MuEvfjvNB7miouLQ2FhIfbu3Yv4+Hi9Gu4XxpOZmYm8vDwcOXKkWfXshXFZW1vjxIkTaNWqFVq3bg0AUCqVuHTpEnbs2IF+/frp1UuhH1z5kQC5XI7S0lK9sbq6Omg0GvGQC72cvXv3IjIyEu+99x62bNkiruY8qaleND52c3Mz+jwtWWVlJY4ePYqGhgZxzMrKCu7u7gbfOcD9wpgyMjJQUVGBQYMGQaVSQaVSAQBiY2Mxffp0g3r2wvjs7e3F4NOoR48euHPnjkGtFPrB8CMBvr6+KCkpwfXr18WxkydPAgB69+5tqmlZjH379mHlypUIDg7Ghg0bmlwubuTr64uzZ8+iqqpKHMvJyYG9vT3eeuutlpiuxSovL0dUVJTeMn59fT3Onj3b5C96cr8wnvXr1+PYsWPIzMwUf4BHp0/HxcUZ1LMXxnXp0iX4+PjgxIkTeuOnT59u8mQAKfSD4ccC6XQ6lJWVoba2FgDg7e0NHx8fzJkzB3/88QdycnKwdOlSjBkzhqsNL6moqAirV6/G0KFDER4ejvLycpSVlaGsrAz37t1DXV0dysrKxCXkIUOGwNXVFbNnz8b58+eRlZWFDRs2YOrUqc8MTfR8CoUCAQEBWLVqFXJzc3Hx4kVER0dDq9ViypQp3C9akJubGzp37qz3AwAuLi5wc3NjL1pY9+7d0a1bN6xYsQJ5eXm4cuUK4uPjcerUKcyYMUOa/TD1ufb08hYsWKB3nZ+bN28aXFOjvLxciIyMFHr16iX4+/sLsbGxQm1trSmma1FSUlIEhULR5M+CBQuEnJwcQaFQCDk5OeJrrl27Jnz66aeCl5eXMGDAACExMVHQ6XQm/BSWQ6vVCrGxsUL//v2Fnj17ClOnThUuXrwoCAL3C1P793fPXrS8srIyITo6Wujfv7/g5eUlBAYGCrm5uYIgSLMfMkEQBFMHMCIiIqKWwsNeREREJCkMP0RERCQpDD9EREQkKQw/REREJCkMP0RERCQpDD9EREQkKQw/REREJCkMP0RETeAl0IgsF8MPERlFdHQ01Gr1U59Xq9WIjo42GN+2bRvUajV69+6NiIgIgxssJicnw8PDA+np6U2+74ULF6BUKjFnzpwXnvvmzZuxY8eOF349EZk3hh8iMhsHDhwQ73W2du1aFBYWYuHChXo14eHh8PDwwLp16wzuSK3T6bBo0SI4OTkhNjb2heeRlJSEmpqaF349EZk3hh8iMhs//fQT3n77bYSEhGDIkCEYPXo0CgoK9GpsbGwQHx+PmpoaLF++XO+5nTt34vTp04iLi0P79u1bcOZE9Cph+CEis9G1a1dcunQJV65cQUNDA37//Xf4+PgY1Hl6emL69OnIzs7G999/DwC4ceMGNm3ahMDAQAQEBDx1Gw8fPsTGjRuhVquhVCqhVquRkJCA+vp6AICHhweAx4fXGmVlZSEoKAgqlQpKpRIjRoxAWlqa3ntfuXIFoaGh8PHxwTvvvIONGzdi4cKFmDhxot72U1NTMXToUCiVSgwfPhx79ux58S+NiP5v1qaeABFZtoaGhmbXhoWF4dixY4iIiICzszPu37+PpKSkJmsjIiKQnZ2NNWvWYODAgVi5ciVcXV2xYMGCZ25j27Zt2L9/PxYsWIA333wThYWF2LhxI2xsbDBz5kykp6cjMDAQH330EcaPHw/g0YpUREQEJk2ahMjISNTW1mLfvn1YsWIFlEolvL29UVlZiZCQELi4uCA+Ph46nQ5JSUm4ffs2evXqJW5/2bJlOHToEMLDw6FSqZCbm4vVq1dDq9UiIiKi2d8VEb04hh8iMppbt27B09Oz2fW3b9+Gk5MTzp07h4qKCmRlZcHR0bHJWltbW6xevRoTJkxAaGgo8vPzsXfvXtjb2z9zGydPnoRSqcS4ceMAAH5+fmjTpg0cHBwAQAwqcrlc/PPly5cxduxYxMTEiO+jUqng7++PEydOwNvbG3v27MH9+/eRmZkJNzc3AIC3tzeGDx8uvqaoqAgHDx5EVFQUwsLCAAADBgyATCbD1q1bERQUBCcnp2Z/X0T0Yhh+iMhoXF1dkZKS0uRzM2bM0HuclZWF2bNnY/DgwQgMDMTy5cuxdOlSJCUlITMzEwAwZswYvdf07NkTU6dOxbZt2zBt2jT07t37uXPy9/dHQkICgoKCoFarMWjQIISEhDzzNdOnTwcA3L9/H0VFRbhx4wb+/PNPAEBdXR0AICcnByqVSgw+ANCpUyeoVCrxcU5ODgRBgFqt1lsRU6vVSElJQX5+PoYMGfLcz0BEL4fhh4iMxtbWFl5eXk99rlF1dTViYmIwfPhwJCQkAADKy8uRnJyMTZs2IS0tDd7e3gbhBwDeffddbNu2DQMHDmzWnKZPnw57e3tkZGRg/fr1WLduHXr06IHFixejb9++Tb6msrISsbGxyMrKgkwmQ+fOndGnTx8Aj68HVFlZ2eQqV4cOHVBeXg4A0Gg0AICRI0c2uZ0nz14jIuNg+CEik7t69So0Gg1GjRoljn3xxRe4dOkSkpOTAQBBQUH/ybasrKwQHByM4OBgVFRU4Oeff8aWLVsQGRmJ3377TS+UNZo3bx6uXr2KXbt2QaVSwdbWFjU1NTh48KBYI5fLxZDzbxUVFeKf27VrBwDYvXt3k4fnOnbs+F98RCJ6Dp7tRUQm17FjR1hZWeHkyZPimEwmEw83WVlZib+T87ImTJiAVatWAQBcXFzw4YcfIjg4GFqtFlVVVeL2/i0/Px/Dhg2Dv7+/GI5++eUXAI/O3gIAX19fnDp1CmVlZeLrSktLcerUKfFx42rR3bt34eXlJf5UVlYiKSlJXBkiIuPiyg8RmZyzszNCQkKwe/du2NnZwd/fH2fPnsWWLVvQu3dvaLVahIeHIzExEf3793+pbfn6+uLrr79Ghw4doFKpcOfOHezcuRN+fn5wdnYG8GiFpqCgALm5uejTpw969uyJI0eOwNPTE3K5HAUFBUhNTYVMJhMvhjhp0iSkpaVh2rRp4llbmzdvRn19PWQyGYBHp9F/8MEHWLJkCW7dugWlUomioiJs3LgRb7zxBrp06fJSn42Imofhh4jMQnR0NJydnfHtt99i+/btcHNzw+TJkxEaGgqNRoPPP/8c1dXVL72dWbNmwdbWFhkZGfjqq6/g4OAAtVqNuXPnijWfffYZNm/ejNDQUBw7dgxr1qzBypUrsXLlSgBAly5dsHz5chw+fBh5eXkAHgWmb775BnFxcZg/fz7s7e0RFBSENm3aoG3btuJ7x8fHY+vWrThw4ABKSkrg4uKC999/H7Nnz0arVq1e+vMR0fPJBN69j4heAYIgiCso5qiwsBAajUbvF68bGhowaNAgjBw50uA2HURkOlz5IaJXgjkHH+DRNYrmzJmDiIgI+Pn5oaamBunp6bh37x4+/vhjU0+PiP6FKz9ERP+R/fv3Y9++fbh58yZsbGzg7e2NWbNmPfV0fyIyDYYfIiIikhSe6k5ERESSwvBDREREksLwQ0RERJLC8ENERESSwvBDREREksLwQ0RERJLC8ENERESSwvBDREREkvI/3+3AdROVez8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age by H&Y stages\n",
    "sns.violinplot(data=demographics, \n",
    "               x=\"Hoehn\", \n",
    "               y=\"Age\", \n",
    "               split=True, \n",
    "               inner=\"quart\", \n",
    "               palette=\"Set2\"\n",
    "               ).set_title(\"Age by disease stage in \" + dataset.upper())\n",
    "plt.xlabel('H&Y stage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does disease stage differ among genders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/song/Library/Python/3.9/lib/python/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'H&Y stage')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stages by gender\n",
    "demographics['Gender'] = demographics['Gender'].astype(str)\n",
    "\n",
    "sns.countplot(data=demographics, \n",
    "              x='Hoehn', \n",
    "              hue='Gender',\n",
    "              palette=\"Set3\"\n",
    "              ).set_title(\"Disease stage by gender in \" + dataset.upper())\n",
    "plt.xlabel('H&Y stage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputting/adjusting hyperparameters\n",
    "\n",
    "Since the input data is ordinal, the SuStaIn version being used is **Ordinal SuStaIn**. \n",
    "\n",
    "Values to be input by user (hyperparameters of the model):\n",
    "\n",
    "- *n_s_max*: models with 1 to *n_s_max* clusters will be fit by the algorithm\n",
    "- *n_iterations*: number of MCMC iterations \n",
    "\n",
    "Those values need to be adjusted in the process of hyperparameter tuning. For now, this needs to be done manually. Tools (*i.e.* Optuna, WandB sweeps) are currently explored to automate this process. \n",
    "\n",
    "***To set the hyperparameters, input the values directly.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max number of clusters is set to: 4\n"
     ]
    }
   ],
   "source": [
    "# n_s_max\n",
    "n_s_max = input(\"Enter a value for n_s_max: \")\n",
    "print(\"Max number of clusters is set to:\", n_s_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of MCMC iterations is set to: 100000\n"
     ]
    }
   ],
   "source": [
    "# n_iterations\n",
    "n_iterations = input(\"Enter a value for n_iterations: \")\n",
    "print(\"Number of MCMC iterations is set to:\", n_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the algorithm\n",
    "\n",
    "Here, the SuStaIn algorithm is called with the inputs prepared previously. The runs are tracked on wandb. The following code needs to be executed on every run. \n",
    "\n",
    "- For the **training** phase: the output folder (var '*output_path*') has to be empty, which will trigger the algorithm to run on the data provided as input (training set). \n",
    "- Fort the **validation** phase: the output folder has to point to the previously trained model folder. The trained model will be fit to the validation set. \n",
    "- For the **testing** phase: the output folder has to point to the previously trained model folder. The trained model will be fit to the test set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "wandb version 0.17.2 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.17.1"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/notebooks/wandb/run-20240621_002814-rwytirqc</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn/runs/rwytirqc' target=\"_blank\">polar-gorge-11</a></strong> to <a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn' target=\"_blank\">https://wandb.ai/song-y/Brainhack%20pySuStaIn</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn/runs/rwytirqc' target=\"_blank\">https://wandb.ai/song-y/Brainhack%20pySuStaIn/runs/rwytirqc</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found pickle file: /Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/results/532/pickle_files/copn_subtype0.pickle. Using pickled variables for 0 subtype.\n",
      "Found pickle file: /Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/results/532/pickle_files/copn_subtype1.pickle. Using pickled variables for 1 subtype.\n",
      "Found pickle file: /Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/results/532/pickle_files/copn_subtype2.pickle. Using pickled variables for 2 subtype.\n",
      "Found pickle file: /Users/song/Documents/dev/repos/10. brainhack 2024/bhSubtypingPD/brainhack_source/results/532/pickle_files/copn_subtype3.pickle. Using pickled variables for 3 subtype.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox(children=(Label(value='0.001 MB of 0.006 MB uploaded\\r'), FloatProgress(value=0.1646273637374861, max=1.0…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">polar-gorge-11</strong> at: <a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn/runs/rwytirqc' target=\"_blank\">https://wandb.ai/song-y/Brainhack%20pySuStaIn/runs/rwytirqc</a><br/> View project at: <a href='https://wandb.ai/song-y/Brainhack%20pySuStaIn' target=\"_blank\">https://wandb.ai/song-y/Brainhack%20pySuStaIn</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Find logs at: <code>./wandb/run-20240621_002814-rwytirqc/logs</code>"
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      "text/plain": [
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     "metadata": {},
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   ],
   "source": [
    "output_no = random.randint(100, 999)\n",
    "\n",
    "output_path = output_folder + str(output_no)\n",
    "seed = 5\n",
    "\n",
    "biomarkers = ['Cognition', 'Psychosis', 'Depression', 'Anxiety', 'Apathy',\n",
    "            'Impulsivity', 'Insomnia', 'Sleepiness', 'Pain', 'Incontinence',\n",
    "            'Constipation', 'Dizziness', 'Fatigue', 'Speech', 'Drooling',\n",
    "            'Swallowing', 'Eating', 'Dressing', 'Hygiene', 'Handwriting', 'Hobbies',\n",
    "            'Rolling', 'Tremor', 'Arising', 'Walking', 'Freezing']\n",
    "\n",
    "# start a new wandb run to track this script\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=\"Brainhack pySuStaIn\",\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config={\n",
    "    \"dataset\": dataset,\n",
    "    \"output_no\": output_no,\n",
    "    \"n_s_max\": n_s_max,\n",
    "    \"n_iterations\": n_iterations,\n",
    "    \"seed\": seed\n",
    "    }\n",
    ")\n",
    "\n",
    "# create folder for results if it doesn't exist already\n",
    "if not os.path.isdir(output_path):\n",
    "    os.makedirs(output_path)\n",
    "\n",
    "# The initializer for the scored events model implementation of AbstractSustain\n",
    "#         Parameters:\n",
    "#           prob_nl                     - probability of negative/normal class for all subjects across all biomarkers \n",
    "#                                         dim: number of subjects x number of biomarkers\n",
    "        \n",
    "#           prob_score                  - probability of each score for all subjects across all biomarkers\n",
    "#                                         dim: number of subjects x number of biomarkers x number of scores\n",
    "#           score_vals                  - a matrix specifying the scores for each biomarker\n",
    "#                                         dim: number of biomarkers x number of scores\n",
    "#           biomarker_labels            - the names of the biomarkers as a list of strings\n",
    "#           N_startpoints               - number of startpoints to use in maximum likelihood step of SuStaIn, typically 25\n",
    "#           N_S_max                     - maximum number of subtypes, should be 1 or more\n",
    "#           N_iterations_MCMC           - number of MCMC iterations, typically 1e5 or 1e6 but can be lower for debugging\n",
    "#           output_folder               - where to save pickle files, etc.\n",
    "#           dataset_name                - for naming pickle files\n",
    "#           use_parallel_startpoints    - boolean for whether or not to parallelize the maximum likelihood loop\n",
    "#           seed                        - random number seed\n",
    "\n",
    "sustain_input_zscore = pySuStaIn.OrdinalSustain(\n",
    "                            prob_nl,     \n",
    "                            prob_score,      \n",
    "                            score_vals,\n",
    "                            biomarkers,\n",
    "                            25,\n",
    "                            int(n_s_max),\n",
    "                            int(n_iterations), \n",
    "                            output_path, \n",
    "                            dataset, \n",
    "                            True,\n",
    "                            seed)                      \n",
    "samples_sequence,   \\\n",
    "samples_f,          \\\n",
    "ml_subtype,         \\\n",
    "prob_ml_subtype,    \\\n",
    "ml_stage,           \\\n",
    "prob_ml_stage,      \\\n",
    "prob_subtype_stage  = sustain_input_zscore.run_sustain_algorithm()\n",
    "\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.DS_Store', '4subtypes.png', '3subtypes.png']\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "imageList = os.listdir(output_path + '/plots')\n",
    "print(imageList)\n",
    "\n",
    "for image in imageList:\n",
    "    if image.endswith('.png'):\n",
    "        display(Image(filename=output_path + '/plots/' + image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write the results to a file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_subtypes = 3\n",
    "pickle_filename_s = output_folder + '/pickle_files/' + dataset + '_subtype' + str(n_subtypes) + '.pickle'\n",
    "pk = pd.read_pickle(pickle_filename_s)\n",
    "\n",
    "for variable in ['ml_subtype', # the assigned subtype\n",
    "                 'prob_ml_subtype', # the probability of the assigned subtype\n",
    "                 'ml_stage', # the assigned stage \n",
    "                 'prob_ml_stage',]: # the probability of the assigned stage\n",
    "    \n",
    "    print(variable)\n",
    "    \n",
    "    # Add SuStaIn output to dataframe\n",
    "    results = pd.DataFrame()\n",
    "    results.loc[:,variable] = pk[variable] \n",
    "\n",
    "# Write results to excel file\n",
    "writer = pd.ExcelWriter(output_folder + '/' + output_no + '.xlsx', engine='openpyxl', mode='a', if_sheet_exists = 'replace')\n",
    "results.to_excel(writer, sheet_name = n_subtypes)\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model stability tracking\n",
    "\n",
    "In order to find the optimal hyperparameter values for our trained model, we need to evaluate the stability of the obtained models throughout the hyperparameter tuning process. This can be done by using the **Adjusted Rand Index (ARI)**. \n",
    "\n",
    "### Adjusted Rand Index (ARI)\n",
    "\n",
    "The ARI is a measure of the similarity between two data clusterings. The Rand Index computes a similarity measure between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusterings. The raw RI score is then “adjusted for chance” into the ARI score.\n",
    "\n",
    "The ARI ranges from − 1 to + 1:\n",
    " \n",
    "- ARI = 1: Perfect agreement between clusterings\n",
    "- ARI = 0: The expected similarity between two random clusterings (given the same number of clusters and points)\n",
    "- ARI = -1: Perfect disagreement between clusterings\n",
    "\n",
    "***To compare two models, input the 'output_no' of the two models.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First model: # 841\n",
      "Second model: # 532\n",
      "ARI:  0.1443848207063997\n"
     ]
    }
   ],
   "source": [
    "def get_excel_path(no_model):\n",
    "    return output_folder + str(no_model) + \"/\" + str(no_model) + \".xlsx\"\n",
    "\n",
    "def get_input_model(no_model):\n",
    "    return pd.read_excel(get_excel_path(no_model), sheet_name=\"stability\")[\"Stab\"].tolist()\n",
    "\n",
    "no_model_1 = input()\n",
    "no_model_2 = input()\n",
    "\n",
    "print(\"First model: #\", no_model_1)\n",
    "print(\"Second model: #\", no_model_2)\n",
    "\n",
    "print('ARI: ', adjusted_rand_score(get_input_model(no_model_1), get_input_model(no_model_2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, an ARI of 0.14 indicates that the clustering results from the two models show some level of similarity beyond what would be expected by chance, but it is not very high. Hyperparameters therefore need to be adjusted in consequence. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model performance evaluation\n",
    "\n",
    "Clustering requires evaluating the quality and meaningfulness of the clusters created by the model. With no known ground truth labels, evaluation must be performed using the model itself, through internal evaluation metrics, such as the [silhouette coefficient](https://www.sciencedirect.com/science/article/abs/pii/B9780128219294000020). \n",
    "\n",
    "### Silhouette coefficient\n",
    "The silhouette score is a metric that measures how well each data point fits into its assigned cluster, by measuring how similar a point is to its own cluster (cohesion) compared to other clusters (separation). Values range from -1 to 1, where a higher value indicates better-defined clusters.\n",
    "\n",
    "***Input the output_no of a model to compute its silhouette coefficient.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected model: # 841\n",
      "Silhouette coefficient:  0.003243252594606985\n"
     ]
    }
   ],
   "source": [
    "# User input for output_no\n",
    "output_no = input(\"Enter output_no: \")\n",
    "print(\"Selected model: #\", output_no)\n",
    "\n",
    "# TODO This needs to be dynamic\n",
    "tmp = pd.read_excel('/Users/song/Documents/dev/repos/10. brainhack 2024/SubtypingPD/brainhack_source/results/841/841.xlsx', sheet_name=\"3st\")\n",
    "X = tmp.iloc[:, 7: 33].values\n",
    "labels = tmp['ml_subtype'].values\n",
    "\n",
    "# Computing the silhouette coefficient\n",
    "print('Silhouette coefficient: ', metrics.silhouette_score(X, labels, metric='euclidean'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A silhouette coefficient close to 0 indicates poorly defined clusters. In a case like this one, depending where we are in our ML pipeline, we should consider retuning the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, the authors of the algorithm provide the following code for plotting the models' likelihood. These plots help in choosing the appropriate model (most appropriate number of subtypes to fit the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mWLFiNGrUiLCwsBw/hegIAwcO5MsvvyQiIoJPPvmExx9/nCVLlhAZGcmgQYNwcXGhZs2aLF261KELswcOHEipUqV4//33WbVqFcWLF6d169YMHjzYun7KkWMrcrtMhr5xVES4dluF3bt307JlS9zc3KzbBw0axMmTJ21uqCgi8qDTDJSIANfWTY0cOZKWLVvy/PPP4+TkxI8//sjXX3/N5MmT87o8EZF8RTNQImIVGxvL3LlzOXDgAFeuXMHX15fu3btnuy+PiMiDTgFKRERExE66jYGIiIiInRSgREREROykACUiIiJiJ30K7y6oV68emZmZN/1CWBEREclfTp8+jaurKzt27LjlvgpQd0FGRobNVwyIiIhI/nflyhVy+9k6Bai7oHTp0gBs2rQpjysRERGR3Lr+mxluRWugREREROykACUiIiJiJwUoERERETspQImIiIjYSYvIRUREcuHq1atcvnw5r8uQO+Di4oKTk5ND+lKAEhERuQnDMEhISODcuXN5XYo4QPHixfHx8cFkMt1RPwpQIiIiN5EVnkqXLo27u/sdv/FK3jAMg4sXL5KUlARA2bJl76g/BSgREZEbuHr1qjU8lSxZMq/LkTtUuHBhAJKSkihduvQdXc7TInIREZEbyFrz5O7unseViKNk/S7vdD2bApSIiMgt6LLd/cNRv0sFKBERERE7KUCJiIiI2EkBSkRE5DZYLMYDc97OnTszcuTIm+4TFxfHjh077lFF1xw4cIBOnToREBBAcHAwK1asuGfn1qfwREREboPZbGLayjhOJabds3OWL+PJsI5179n57PHyyy8zefJk6tWrd0/Ol5ycTPfu3QkODmbcuHHs3r2bcePGUaRIEUJDQ+/6+RWgREREbtOpxDSO/JmS12U8kFavXo2Liwvjx4/H2dkZX19fTpw4QVRU1D0JULqEJyIPLMNiwbBY8roMkXvi+++/p3379tSuXZtGjRoxcuRIUlJS2Lp1K9WqVePUqVPWfXPaduHCBYYOHUrt2rV5/PHHmT17Npb///9PtWrVABg1ahQjR46kf//+dOnSxeb8R48epVq1avz+++/MmTOHDh06MHfuXBo0aEC9evUYNWoU58+ft+6flpbGmDFjaNiwIXXr1qVLly7s3bvX2r5jxw7q16+Ps/P/5oIaNmzI8ePHOXPmjGMHLwcKUCLywDKZzZjMehmU+9/Zs2cZMGAAoaGhbNy4kcjISLZv384777yT6z6+/vprvLy8WLt2LeHh4SxdutS65mjz5s0AvP7664wePZr27duzbds24uPjrcd/8skn1KpVi0cffRSAvXv3snnzZqKjo5k7dy7bt29n8ODBwLW7hvfq1YuTJ0+ycOFCVq9eTUBAAB06dGD//v3AtTvE+/j42NRYunRpAJvz3i26hCciInKfS0xMJDMzk4ceeohy5cpRrlw5FixYwNWrV0lJyd0lyBo1ahAREQGAr68vR44cITo6mm7duuHt7Q2Ap6cnnp6eNGvWjFKlSrF+/Xr69OmDxWLh008/pXfv3tb+TCYTM2fOpEyZMgCMHTuWXr16cfToURITE9m9ezexsbEUL14cgLCwMHbu3MmKFSuYMmUKly5dwtXV1abGQoUKAZCRkXFH45UbClAiIiL3uerVq9OmTRv69u2Lt7c3jRs3pnnz5jz55JPExcXlqo+6dW0Xr/v7+7NgwQJSU1MpWrSoTZuzszPt2rXj008/pU+fPsTGxnL27FnatGlj3adixYrW8ARQp04dAA4dOsSpU6cwDIMWLVrY9JuZmWkNR25ubmRmZtq0Z7XdizvHK0CJiIg8AKZPn07//v354Ycf+Pnnnxk+fDh169alX79+2fa9evVqtm3mf1zutlgsmEwmXFxccjxfaGgoS5YsYd++faxfv56WLVtSrFgxa/s/j8s6p5OTExaLBQ8PD9auXZut36xZJx8fH+sXA2fJenx9MLtbdPFfRETkPrdnzx4mTZpE5cqV6datG1FRUUyaNInY2FhrMLp+Affx48ez9fHrr7/aPI6Li6N8+fLWL+j9J19fXwIDA/niiy/YtGkT7du3t2k/duwYaWn/uwXErl27gGuXCqtWrcr58+e5fPkyjzzyiPVn0aJFbNq0CYCgoCDi4uJswl5sbCyVKlW6J1/8rBkoERGR21S+jGeBOJ+HhwcxMTG4uLjw4osvkpGRwcaNG6lYsSJ+fn64u7sTFRXF4MGDOXHiBEuXLs3Wx86dO5k6dSrt27dn586dxMTE8Oabb1rb3d3dOXLkCMnJyXh5eQHXZqHeeustihYtSuPGjW36u3jxIuHh4QwZMoQzZ84wfvx4nnnmGcqVK4ePjw/Vq1dnyJAhjB49mrJlyxITE8PatWtZsmSJte/FixczevRoevbsyS+//MKyZcsYN27cbY2RvRSgREREboPFYuTJTS0tFgOz2b4vxPX19WXOnDlERkYSExOD2WymYcOGLFq0CE9PT6ZOncq0adN45pln8PPzY8SIEfTv39+mjxdeeIHjx48TEhJCiRIlGDp0qM2sUo8ePVi8eDFHjhxhwYIFADz99NNMmDCB5557DicnJ5v+ypYtS/Xq1enYsSNOTk60bduWYcOGAdcu40VHRzN16lQGDx5Meno6vr6+REZG0qhRIwBKlizJ4sWLmThxIiEhIXh7exMeHk5ISIjdY3o7TIZh5M296O9jLVu2BLBOM4qISMF06dIljh07RqVKlXBzc8vrcgqckydP0qpVK7744gsqVqxo3T5nzhzWrVvHd999d89rutnv1J73b81AiYiIiEPFx8fzyy+/EBMTQ5MmTWzC0/1CAUpEREQcKjk5mZEjR1KxYkUiIyPzupy7QgFKREREHKpGjRrWT9XlZODAgQwcOPAeVuR4uo2BiIiIiJ0UoERERETspAAlIiIiYicFKBERERE7KUCJiIiI2CnPA9Tff//N8OHDadiwIYGBgfTu3ZsjR45Y2yMiIqhWrZrNT3BwsLXdYrEwe/ZsmjRpQkBAAL169eLkyZM25zhw4ACdOnUiICCA4OBgVqxYYdOemz5EREREsuR5gOrfvz8nTpwgKiqKjz/+GDc3N7p160Z6ejoAv/32G3379mXz5s3Wn48//th6/Lx584iJiWHChAl8+OGHWCwWevbsSWZmJnDtXhTdu3fn4YcfZs2aNfTv359p06axZs2aXPchIiLyT4bF8kCdV2zl6X2gUlJSKFeuHH369KFq1aoA9OvXj3//+9/8/vvv1KpVi8OHD9O7d2+8vb2zHZ+ZmUl0dDTDhg2jefPmAMyYMYMmTZrw9ddf06ZNG1avXo2Liwvjx4/H2dkZX19fa2ALDQ3NVR8iIiL/ZDKbSfpkJpl/n7pn53QtWZ7Szw2+Z+fL0rlzZ8qVK8eUKVNuuE9cXByGYVCvXr17WNk1ycnJtG3blunTp9OgQYN7cs48DVDFihVj+vTp1sdnz55l2bJl+Pj4UKVKFf744w8uXrxI5cqVczz+4MGDXLhwwfrFggBFixalRo0abN++nTZt2rBjxw7q16+Ps/P/nmrDhg1ZuHAhZ86c4a+//rplHznJ+r6cnMTHx1O2bNlcj4OIiBRMmX+fIjPhWF6XkS+8/PLLTJ48+Z4HqMTERF599VVOnz59T8+b55fwsowZM4ZGjRqxYcMGJk6ciLu7O4cOHQLgvffeIzg4mCeeeILx48eTlpYGQEJCAkC2sFK6dGlrW0JCAj4+Ptna4VrQyU0fIiIikv98/PHHtGvXLk/OnW++yqVr16689NJLrFy5kv79+xMTE8OhQ4cwm82ULl2aBQsW8Mcff/DOO+/w+++/s3z5cus6KVdXV5u+ChUqREpKCnDtW5dzagfIyMjIVR85udk3Nd9sdkpERCQvfP/998yaNYsjR47g7u5Os2bNGDVqFAcPHqRLly5s2rSJ8uXLA7B169Zs2y5cuMDQoUP59ttv8fT05MUXX2TAgAGYzWaqVasGwKhRo9i2bRtpaWmkpaXZfGjr6NGjPP3003z++ed8+eWX/Pzzzzz++OOsWLGCq1ev8uSTTzJ69Gg8PDwASEtL45133uGbb77h8uXL1KxZk+HDh1OrVi1rn9988w1DhgyhcePGPPHEE/dqKIF8NANVpUoVHnvsMSZOnEi5cuV4//33efXVV/n555/p168fVatW5YknnmDq1Kls27aNvXv34ubmBpBtsXdGRgaFCxcGwM3NLcd2AHd391z1ISIiUpCdPXuWAQMGEBoaysaNG4mMjGT79u288847ue7j66+/xsvLi7Vr1xIeHs7SpUutAWnz5s0AvP7664wePZr27duzbds24uPjrcd/8skn1KpVi0cffRSAvXv3snnzZqKjo5k7dy7bt29n8ODBABiGYf1E/MKFC1m9ejUBAQF06NCB/fv3W/tcuHAh//nPfzCZTHc6RHbL0wB19uxZNmzYwJUrV6zbzGYzVapUISkpCbPZjJeXl80xWQOfkJBgveyWlJRks09SUhJlypQBwMfHJ8d2gDJlyuSqDxERkYIsMTGRzMxMHnroIcqVK0fdunVZsGABnTt3znUfNWrUICIiAl9fX9q1a0eXLl2Ijo4GsH7Qy9PTE09PT5o1a0apUqVYv349cO12QZ9++ikhISHW/kwmEzNnzqRmzZo0aNCAsWPH8uOPP3L06FFiY2PZvXs3M2fOpHbt2vj6+hIWFkZAQEC2WxHllTwNUGfOnCEsLIwtW7ZYt12+fJn9+/fj6+tLeHg43bp1szlm7969wLUZKz8/Pzw8PNi6dau1PTU1lf379xMUFARAUFAQcXFxXL161bpPbGwslSpVomTJkrnqQ0REpCCrXr06bdq0oW/fvjz++OOMGDGCw4cPU6VKlVz3UbduXZvH/v7+JCYmkpqamm1fZ2dn2rVrx6effgpce989e/aszQezKlasaDNRUadOHQAOHTrEr7/+imEYtGjRgsDAQOvPrl27bO4VmZfydA1U1apVadq0KW+99RZvvfUWxYoVY+HChaSmptKtWzcOHDhAv379iIyMpF27dhw7dozx48fTpk0bfH19AejUqRPTpk2jRIkSlCtXjqlTp+Lj40OrVq0ACA0NZfHixYwePZqePXvyyy+/sGzZMsaNGwdcW/t0qz5EREQKuunTp9O/f39++OEHfv75Z4YPH07dunXp169ftn2vn3TIYjbbzrlYLBZMJhMuLi45ni80NJQlS5awb98+1q9fT8uWLSlWrJi1/Z/HZZ3TyckJi8WCh4cHa9euzdbvP9cs55U8X0T+7rvvMn36dIYMGUJaWhr16tVj5cqVPPTQQzz00EPMnDmTqKgoFi1ahKenJ23btrVeIwUYNGgQV65cISIigkuXLhEUFMSSJUusv5iSJUuyePFiJk6cSEhICN7e3oSHh9tMI96qDxERkYJsz549bNiwgddff53KlSvTrVs31q9fz/DhwxkwYAAA58+ft+5//PjxbH38+uuvNo/j4uIoX778DdcL+/r6EhgYyBdffMGmTZtsblsEcOzYMdLS0vD09ARg165dwLVLhS4uLpw/f57Lly/bzJJFRETg5+dHp06d7B8EB8vzAOXp6cmbb77Jm2++mWP7008/zdNPP33D452cnBg+fDjDhw+/4T7+/v6sWrXqjvoQERH5J9eS5QvE+Tw8PIiJicHFxYUXX3yRjIwMNm7cSMWKFfHz88Pd3Z2oqCgGDx7MiRMnWLp0abY+du7cydSpU2nfvj07d+4kJibG5r3b3d2dI0eOkJycbF2/HBoayltvvUXRokVp3LixTX8XL14kPDycIUOGcObMGcaPH88zzzxDuXLl8PHxoXr16gwZMoTRo0dTtmxZYmJiWLt2LUuWLLmtMXC0PA9QIiIiBZFhseTJXcENiwWT2b4lzL6+vsyZM4fIyEhiYmIwm800bNjQenVn6tSpTJs2jWeeeQY/Pz9GjBhB//79bfp44YUXOH78OCEhIZQoUYKhQ4fSvn17a3uPHj1YvHgxR44cYcGCBcC1SZAJEybw3HPP4eTkZNNf2bJlqV69Oh07dsTJyYm2bdsybNgw4NrERnR0NFOnTmXw4MGkp6fj6+tLZGSkzY2v85LJMAwjr4u432TdB+pm94oSEZH879KlSxw7doxKlSpZb3sjuXfy5ElatWrFF198QcWKFa3b58yZw7p16/juu+/ueU03+53a8/6tGSgRERFxqPj4eH755RdiYmJo0qSJTXi6XyhAiYiIiEMlJyczcuRIKlasSGRkZF6Xc1coQImIiIhD1ahRw/qpupwMHDiQgQMH3sOKHC/ffJWLiIiISEGhACUiIiJiJwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2UoASERG5DRbD8sCct3PnzowcOfKm+8TFxbFjx457VNE13333HaGhoQQGBhIcHMzbb7/NpUuX7sm5dR8oERGR22A2mZkdG82fqQn37JzlivowqGGPe3Y+e7z88stMnjyZevXq3ZPz7dixgwEDBjBo0CBat27NiRMnGDt2LOfOnWPy5Ml3/fwKUCIiIrfpz9QEjiWfzOsyHkgffvghDRo0oG/fvgBUrFiRIUOGEBERwbhx43B1db2r59clPBERkQfA999/T/v27alduzaNGjVi5MiRpKSksHXrVqpVq8apU6es++a07cKFCwwdOpTatWvz+OOPM3v2bCyWa5cTq1WrBsCoUaMYOXIk/fv3p0uXLjbnP3r0KNWqVeP3339nzpw5dOjQgblz59KgQQPq1avHqFGjOH/+vHX/tLQ0xowZQ8OGDalbty5dunRh79691vYePXowYsQIm3OYzWYuX75s08/dogAlIiJynzt79iwDBgwgNDSUjRs3EhkZyfbt23nnnXdy3cfXX3+Nl5cXa9euJTw8nKVLl7JixQoANm/eDMDrr7/O6NGjad++Pdu2bSM+Pt56/CeffEKtWrV49NFHAdi7dy+bN28mOjqauXPnsn37dgYPHgyAYRj06tWLkydPsnDhQlavXk1AQAAdOnRg//79wLWvi/Hz87P2f/nyZZYtW8Zjjz1GiRIl7mi8ckOX8ERERO5ziYmJZGZm8tBDD1GuXDnKlSvHggULuHr1KikpKbnqo0aNGkRERADg6+vLkSNHiI6Oplu3bnh7ewPg6emJp6cnzZo1o1SpUqxfv54+ffpgsVj49NNP6d27t7U/k8nEzJkzKVOmDABjx46lV69eHD16lMTERHbv3k1sbCzFixcHICwsjJ07d7JixQqmTJliU9uVK1cIDw/n999/Z+XKlXc6XLmiACUiInKfq169Om3atKFv3754e3vTuHFjmjdvzpNPPklcXFyu+qhbt67NY39/fxYsWEBqaipFixa1aXN2dqZdu3Z8+umn9OnTh9jYWM6ePUubNm2s+1SsWNEangDq1KkDwKFDhzh16hSGYdCiRQubfjMzM8nIyLDZdv78eQYPHsy2bduIjIzE398/V8/nTilAiYiIPACmT59O//79+eGHH/j5558ZPnw4devWpV+/ftn2vXr1arZtZrPtqh+LxYLJZMLFxSXH84WGhrJkyRL27dvH+vXradmyJcWKFbO2//O4rHM6OTlhsVjw8PBg7dq12fq9fnF4UlISvXr14s8//2TJkiUEBQXdZAQcS2ugRERE7nN79uxh0qRJVK5cmW7duhEVFcWkSZOIjY21BqPrF14fP348Wx+//vqrzeO4uDjKly9P4cKFczynr68vgYGBfPHFF2zatIn27dvbtB87doy0tDTr4127dgHXLhVWrVqV8+fPc/nyZR555BHrz6JFi9i0aRMAKSkpdO3albNnz7Jy5cp7Gp5AAUpEROS+5+HhQUxMDFOnTuXEiRMcOnSIjRs3UrFiRfz8/HB3dycqKoo//viDH3/8kaVLl2brY+fOnUydOpUjR47w0UcfERMTYzN75e7uzpEjR0hOTrZuCw0N5f3338fNzY3GjRvb9Hfx4kXCw8M5dOgQP//8M+PHj+eZZ56hXLlyNGnShOrVqzNkyBBiY2M5ceIEkydPZu3atfj6+gIwefJkTp48ydSpUylRogSnT5+2/uQ0g+ZouoQnIiJym8oV9SkQ5/P19WXOnDlERkYSExOD2WymYcOGLFq0CE9PT6ZOncq0adN45pln8PPzY8SIEfTv39+mjxdeeIHjx48TEhJCiRIlGDp0qM2sUo8ePVi8eDFHjhxhwYIFADz99NNMmDCB5557DicnJ5v+ypYtS/Xq1enYsSNOTk60bduWYcOGAdcu40VHRzN16lQGDx5Meno6vr6+REZG0qhRI65evcrGjRu5fPkyXbt2zfZ8N23aRPny5W9rrHLLZBiGcVfP8ABq2bIlgHWaUURECqZLly5x7NgxKlWqhJubm02bxbBgNt37Czl5dd7bcfLkSVq1asUXX3xBxYoVrdvnzJnDunXr+O677+55TTf7ndrz/q0ZKBERkduQVyGmIISn+Ph4fvnlF2JiYmjSpIlNeLpfKECJiIiIQyUnJzNy5EgqVqxIZGRkXpdzVyhAiYiIiEPVqFHD+qm6nAwcOJCBAwfew4ocL//PA4qIiIjkMwpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiJiJ8Mw0H2oH2y6jYGIiIidTCZTXpcgeUwzUCIiIrfBsFgemPN27tyZkSNH3nSfuLg4duzYcY8qumbjxo20bdsWf39/nnjiCRYtWnTPZgY1AyUiInIbTGYzh96dycWTp+7ZOd0rlKdq2OB7dj57vPzyy0yePJl69erdk/P9+OOPDBs2jFGjRtG8eXMOHDjAiBEjcHV1zfELhh0tzwPU33//zZQpU/jxxx/JyMggKCiIESNG4OvrC8CBAweYOHEi+/bto0SJEnTr1o0uXbpYj7dYLERGRvLRRx+RlpZGUFAQY8eOpUKFCtZ9HNGHiIjIP108eYoLR4/ldRkPpNOnT9O7d286d+4MQIUKFfj000/56aef7kmAyvNLeP379+fEiRNERUXx8ccf4+bmRrdu3UhPTyc5OZnu3bvz8MMPs2bNGvr378+0adNYs2aN9fh58+YRExPDhAkT+PDDD7FYLPTs2ZPMzEwAh/QhIiJS0H3//fe0b9+e2rVr06hRI0aOHElKSgpbt26lWrVqnDr1v5m0nLZduHCBoUOHUrt2bR5//HFmz56N5f9fTqxWrRoAo0aNYuTIkfTv399mogLg6NGjVKtWjd9//505c+bQoUMH5s6dS4MGDahXrx6jRo3i/Pnz1v3T0tIYM2YMDRs2pG7dunTp0oW9e/da29u3b8/gwYOBaxMhP//8M9u3b6dx48YOH7uc5GmASklJoVy5crz11lv4+/vj6+tLv379SEpK4vfff2f16tW4uLgwfvx4fH19CQ0NpVu3bkRFRQGQmZlJdHQ0gwYNonnz5vj5+TFjxgwSEhL4+uuvARzSh4iISEF29uxZBgwYQGhoKBs3biQyMpLt27fzzjvv5LqPr7/+Gi8vL9auXUt4eDhLly5lxYoVAGzevBmA119/ndGjR9O+fXu2bdtGfHy89fhPPvmEWrVq8eijjwKwd+9eNm/eTHR0NHPnzmX79u3WQGQYBr169eLkyZMsXLiQ1atXExAQQIcOHdi/f79NXX/99Re1atWie/fu1KpViw4dOtzJUOVangaoYsWKMX36dKpWrQpc+wUvW7YMHx8fqlSpwo4dO6hfvz7Ozv+70tiwYUOOHz/OmTNnOHjwIBcuXKBRo0bW9qJFi1KjRg22b98O4JA+RERECrLExEQyMzN56KGHKFeuHHXr1mXBggXWy1+5UaNGDSIiIvD19aVdu3Z06dKF6OhoALy9vQHw9PTE09OTZs2aUapUKdavXw9cmyH69NNPCQkJsfZnMpmYOXMmNWvWpEGDBowdO5Yff/yRo0ePEhsby+7du5k5cya1a9fG19eXsLAwAgICrKEtS9GiRfnoo4+YOXMmBw8eJDw8/E6HK1fyfA1UljFjxrB69WpcXV2ZP38+7u7uJCQkWMNVltKlSwMQHx9PQkICAGXLls22T1abI/rIScuWLW/YFh8fn60/ERGRvFK9enXatGlD37598fb2pnHjxjRv3pwnn3ySuLi4XPVRt25dm8f+/v4sWLCA1NRUihYtatPm7OxMu3bt+PTTT+nTpw+xsbGcPXuWNm3aWPepWLEiZcqUsT6uU6cOAIcOHeLUqVMYhkGLFi1s+s3MzCQjI8Nmm4eHBzVq1KBGjRpcvXqVoUOHMnz4cMqVK5er53W78k2A6tq1Ky+99BIrV66kf//+xMTEcOnSJVxdXW32K1SoEAAZGRmkp6cD5LhPSkoKgEP6EBERKeimT59O//79+eGHH/j5558ZPnw4devWpV+/ftn2vXr1arZtZrPtRSuLxYLJZMLFxSXH84WGhrJkyRL27dvH+vXradmyJcWKFbO2//O4rHM6OTlhsVjw8PBg7dq12frNer/esWMHrq6u+Pv7W9uy1mIlJSU9OAGqSpUqAEycOJE9e/bw/vvv4+bmlm0hd1bydHd3x83NDbiWSLP+nLVP4cKFARzSR042bdp0w7abzU6JiIjca3v27GHDhg28/vrrVK5cmW7durF+/XqGDx/OgAEDAGwWcB8/fjxbH7/++qvN47i4OMqXL3/D90pfX18CAwP54osv2LRpE9OnT7dpP3bsGGlpaXh6egKwa9cu4NqlQhcXF86fP8/ly5et+QAgIiICPz8/OnXqxIoVK0hKSuLDDz+0eZ7Ozs5UrFgx94Nzm/I0QJ09e5YtW7bw1FNPWdcomc1mqlSpQlJSEj4+PiQlJdkck/W4TJkyXLlyxbrt4YcfttknK4U6og8REZGcuFcoXyDO5+HhQUxMDC4uLrz44otkZGSwceNGKlasiJ+fH+7u7kRFRTF48GBOnDjB0qVLs/Wxc+dOpk6dSvv27dm5cycxMTG8+eab/6vN3Z0jR46QnJyMl5cXcG0W6q233qJo0aLZPh138eJFwsPDGTJkCGfOnGH8+PE888wzlCtXDh8fH6pXr86QIUMYPXo0ZcuWJSYmhrVr17JkyRIAunXrRqdOnZgxYwbt27dn//79TJ06lS5duljPfzflaYA6c+YMYWFhLF68mCZNmgBw+fJl9u/fT3BwMKVKleLDDz/k6tWrODk5ARAbG0ulSpUoWbIknp6eeHh4sHXrVmv4SU1NZf/+/XTq1AmAoKCgO+5DRETknwyLJU9uamlYLJjM9n0GzNfXlzlz5hAZGUlMTAxms5mGDRuyaNEiPD09mTp1KtOmTeOZZ57Bz8+PESNG0L9/f5s+XnjhBY4fP05ISAglSpRg6NChtG/f3treo0cPFi9ezJEjR1iwYAEATz/9NBMmTOC5556zvgdnKVu2LNWrV6djx444OTnRtm1bhg0bBly7jBcdHc3UqVMZPHgw6enp+Pr6EhkZaf3QV506dVi4cCEzZ85k2bJllChRgh49etCrVy+7x/S2GHmsZ8+eRqtWrYxt27YZv/32mxEWFmYEBQUZf/75p3HmzBkjKCjIGDFihPH7778ba9asMWrVqmWsXbvWevy7775r1K9f3/j222+NAwcOGD169DBatWplZGZmGoZhOKQPewUHBxvBwcF3NjAiIpLn0tPTjf379xvp6el5XUqB9Mcffxh+fn7GsWPHbLbPnj3baNGiRZ7UdLPfqT3v33m+Burdd99l+vTpDBkyhLS0NOrVq8fKlSt56KGHAFi8eDETJ04kJCQEb29vwsPDbT4GOWjQIK5cuUJERASXLl0iKCiIJUuWWBenlSxZ8o77EBERkdyLj4/nl19+ISYmhiZNmtyTNUn3mskw7tG37j1AshaR32yhuYiI5H+XLl3i2LFjVKpUyeaDRnJz+/fvp2PHjlSsWJHIyMhsn4ibM2cO69at47vvvrvntd3sd2rP+3eez0CJiIjI/aVGjRrWT9XlZODAgQwcOPAeVuR4ef5deCIiIiIFjQKUiIjILWi1y/3DUb9LBSgREZEbyPow0cWLF/O4EnGUrN/lnX5QTGugREREbsDJyYnixYtbb8Ds7u6OyWTK46rkdhiGwcWLF0lKSqJ48eLZ7ktlLwUoERGRm/Dx8QHI9q0WUjAVL17c+ju9EwpQIiIiN2EymShbtiylS5fm8uXLeV2O3AEXF5c7nnnKogAlIiKSC05OTg5785WCT4vIRUREROykACUiIiJiJwUoERERETspQImIiEi+YVgsNv/NrxSgREREJN8wmc2cXPURJnP+jij5uzoRERF54GScPp3XJdySApSIiIiInRSgREREROykACUiIiJiJwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2cr6dg06ePElmZia+vr6kpaUxc+ZM/vzzT1q3bs1zzz3n4BJFRERE8he7Z6C+//57nn76aT7++GMAxo4dy4cffkhiYiKjRo3io48+cniRIiIiIvmJ3QFq/vz5PP744/Tv35/U1FS++eYbevfuzbp16+jduzcrVqy4G3WKiIiI5Bt2B6iDBw/StWtXPDw8+OGHH7h69SpPPfUUAI0bN+bEiRMOL1JEREQkP7E7QBUqVIgrV64AsHnzZkqWLImfnx8AZ86coWjRoo6tUERERCSfsXsReZ06dYiOjiY1NZWvvvqKkJAQAPbt20dkZCR16tRxeJFyYxaLAYDZbMrjSkRERB4cds9Avf766yQkJDB06FDKlSvHq6++CkCfPn3IzMxk2LBhDi9SbsxsNik8iYiI3GN2z0BVqFCBjRs38vfff1OqVCnr9rlz51KjRg1cXV0dWqCIiIhIfnNb94EymUy4uLiwadMmkpKSeOqppyhatCguLi6Ork9EREQk37mtADV//nwWLlzIpUuXMJlM+Pv7M3PmTJKTk4mOjtZCchEREbmv2b0G6v3332fOnDl0796d1atXYxjXFjF36tSJkydPMmvWLIcXKSIiIpKf2B2g3nvvPXr37s1rr71GzZo1rdubNWvG4MGD+e677xxaoIiIiEh+Y3eA+uuvv6hfv36ObZUrV+bMmTN3XJSIiIhIfmZ3gCpbtiy7du3KsW3fvn2ULVv2josSERERyc/sDlDPP/88CxYsYMmSJRw/fhyAixcv8tVXX7Fw4ULrjTVz69y5c4wdO5amTZtSp04dOnTowI4dO6zt3bt3p1q1ajY/nTt3trZnZGQwbtw4GjVqRGBgIEOHDuXs2bM259iyZQvt27endu3atG7dmg0bNti056YPEbl/GBZLXpcgIgWc3QGqV69ehISEMG3aNNq0aQNAly5dGDx4MM2bN6dPnz529RcWFsauXbt49913WbNmDdWrV+eVV17h6NGjAPz222+8+eabbN682fozZ84c6/FZbXPmzGH58uUcPXqUQYMGWduPHDlCnz59aNKkCWvXruWFF14gPDycLVu25LoPEbm/mMxmUnd9k9dliEgBZvdtDEwmE+PHj6d79+7ExsaSkpKCp6cnQUFBVK1a1a6+Tpw4wU8//URMTAx169YFYMyYMfz444989tlndOrUib///pvatWvj7e2d7fjExEQ++eQTFixYQL169QB49913ad26Nbt27SIwMJDly5dTrVo1hgwZAoCvry/79+9n8eLFNGrUKFd9iMj95+r55LwuQUQKsNu6DxRApUqVqFSp0h2d3MvLi6ioKGrVqmXdZjKZMJlMpKam8ttvv2EymW54nri4OAAaNmxoU1eZMmXYvn07gYGB7NixgyeeeMLmuIYNGzJx4kQMw8hVHyIiIiLXy1WA6tKlS647NJlMLF++PFf7Fi1alGbNmtls++qrrzhx4gSvv/46hw4dwtPTk/Hjx/PTTz/h7u5O69at6devH66uriQmJuLl5UWhQoVs+ihdujQJCQkAJCQk4OPjk609PT2d5OTkXPWRk5YtW96wLT4+XovpRURE7mO5WgNlGEaufyx3sDhz586djBo1ilatWtG8eXMOHTpERkYG/v7+LF68mFdffZWPPvqIiIgIANLT03P87r1ChQqRkZEBwKVLl7Ltk/U4MzMzV32IiIiIXC9XM1Dvvffe3a6Db7/9lmHDhlGnTh2mTZsGwPjx4xkxYgTFihUDoGrVqri4uDBkyBDCw8Nxc3MjMzMzW18ZGRkULlwYuBaE/rlP1uPChQvnqo+cbNq06YZtN5udEhERkYLvttdAbd68me3bt3Pu3DlKlSpFo0aNrIuw7fX+++8zceJEWrduzdtvv22dEXJ2draGpyyPPvoo8L9Lc+fOnSMzM9NmFikpKYkyZcoA1+5blZSUZNNHUlIS7u7ueHp65qoPERERkevZHaBSUlLo06cPu3fvxtnZmeLFi3Pu3DnmzZtH06ZNmTNnTo6XxG4kJiaGCRMm0LlzZ0aPHo3JZLK2de7cmfLlyzN58mTrtr179+Li4kLFihXx9vbGYrEQFxdHo0aNADh27BiJiYkEBQUBUK9ePbZt22ZzztjYWOrUqYPZbKZu3bq37ENERETkenbfB2rSpEkcO3aMyMhI9u7dy+bNm/nll1+YNWsWu3fvZsaMGbnu69ixY0yaNIknn3ySPn36cObMGU6fPs3p06dJS0vjqaee4tNPP+WDDz7g5MmTbNy4kXfeeYdXXnkFDw8PypQpw7PPPktERARbt27ll19+ISwsjPr16xMQEABcC2G//PIL06ZN48iRI0RHR/Pll1/Ss2dPgFz1ISIiInI9u2eg/vvf/zJs2DCbWwOYzWZatWrF2bNniYyMZMSIEbnq66uvvuLy5ct88803fPON7U3tQkJCmDJlCiaTiffee49Jkybh7e1Nt27d6N27t3W/CRMmMGnSJAYMGABA06ZNrYvM4dolv3nz5jF16lSWL19O+fLlmTp1qnW2KTd9iIiIiFzPZBiGYc8B9evX5+2336ZFixbZ2r7//nuGDBnCzp07HVZgQZS1iPxmC81FJG8l/7garyYv5nUZIpKDw5HzqDKg3z0/rz3v33ZfwgsJCWH+/PlcuHDBZvuVK1d4//337f4uPBEREZGCxu5LeIULF+b48eO0bNmSli1bUqZMGZKTk/n+++9JSEigWLFijBo1Crh2U81JkyY5vGgREZG8YDEsmE12zz3IfcjuALV+/Xo8PDwAbL6QF8DHx8fm8t31n6gTEREp6MwmM5uObKal7+N5XYrkMbsD1HfffXc36hARESkQki+l5nUJkg9oHlJERETETrd1I83Zs2ezc+dOUlOzp3CTycS3337rkOJERERE8iO7A9SYMWPYtGkTTZo0wc/P727UJCIiIpKv2R2gfv75ZyIiIujQocPdqEdEREQk37N7DVSRIkUoX7783ahFREREpECwO0B17NiRJUuWZLuRpoiIiMiDwu5LeJ06dWLdunU0a9aMSpUqUbhwYZt2k8nE8uXLHVagiIiISH5j9wzU2LFjOXbsGKVLl8bNzQ3DMGx+LBbL3ahTREREJN+4rRtpDh06lF69et2NekRERETyPbtnoFxdXXnsscfuRi0iIiIiBYLdAerf//43H3zwgS7ViYiIyAPL7kt4np6efPzxxwQHB+Pv70+RIkVs2k0mE5MmTXJYgSIiIiL5jd0Bau3atRQrVgyAffv2ZWs3mUx3XpWIiIhIPnZbi8hFREREHmR2r4G6laNHjzq6SxEREZF8xe4ZqHPnzjFz5ky2bdtGZmYmhmEAYBgGFy9eJCUlhQMHDji8UBEREZH8wu4ZqMmTJ/Pxxx/zyCOP4OTkhKenJ7Vq1eLy5cukpqYyfvz4u1GniIiISL5hd4D68ccfGThwIPPnz+ell17Cx8eHmTNn8uWXX1KtWjUOHz58N+oUEbkrjP9/SxZDt2YRETvYHaBSU1MJDAwEwNfX1/pJvCJFitCjRw/++9//OrRAEZG7yWQ2k7z5I0xmhy8JFZH7mN2vGF5eXqSlpQFQsWJF/v77b86dOwdAmTJlSExMdGiBIiJ325WU03ldgogUMHYHqEaNGrFgwQL+/PNPHn74YYoVK8a6desA+L//+z+8vLwcXqSIiIhIfmJ3gHrttdf4+++/GTFiBCaTiT59+vD222/ToEEDli1bRmho6N2oU0RERCTfsPs2BuXKlWPjxo0cP34cgO7du1OqVCl27tyJv78/ISEhjq5RREREJF+xO0ABuLm54efnZ3389NNP06RJE4oXL+6oukRERETyLbsv4V25coXIyEg+++wzALZu3Urjxo1p1KgRXbt2JSUlxeFFioiIiOQndgeo2bNnM3/+fFJTUwF46623KF68OKNGjeKPP/5g+vTpDi9SREREJD+xO0Bt2LCBsLAwOnbsyJEjR/j999959dVX6dKlC0OGDNGXDYuIiMh9z+4AlZSURO3atQH473//i9lspmnTpgD4+PhY7xElIiIicr+yO0CVLl2aU6dOAfDdd99RvXp1SpQoAcCuXbvw8fFxbIVySxaLgcVi5HUZIiIiDwy7A1SbNm2YPHkyr7zyCnFxcdb7Pk2cOJE5c+bQtm1bhxcpN2c2mzCbTXldhoiIyAPD7tsYDB48GHd3d7Zv387QoUN5+eWXAdi7dy89evSgX79+Di9SREREJD+xewYq6+7jixcvplevXtbtH374IWFhYZjt/ELOc+fOMXbsWJo2bUqdOnXo0KEDO3bssLZv2bKF9u3bU7t2bVq3bs2GDRtsjs/IyGDcuHE0atSIwMBAhg4dytmzZ232cUQfIiIicncZFktel5Bref7142FhYezatYt3332XNWvWUL16dV555RWOHj3KkSNH6NOnD02aNGHt2rW88MILhIeHs2XLFuvxb775Jps3b2bOnDksX76co0ePMmjQIGu7I/oQERGRu89k5yRMXrqtO5E7yokTJ/jpp5+IiYmhbt26AIwZM4Yff/yRzz77jL///ptq1aoxZMgQAHx9fdm/fz+LFy+mUaNGJCYm8sknn7BgwQLq1asHwLvvvkvr1q3ZtWsXgYGBLF++/I77EBEREblenkY9Ly8voqKiqFWrlnWbyWTCZDKRmprKjh07aNSokc0xDRs2JC4uDsMwiIuLs27LUqlSJcqUKcP27dsBHNKHiIiIyPVyNQO1bds2atWqReHChR168qJFi9KsWTObbV999RUnTpzg9ddfZ926ddlui1C6dGnS09NJTk4mMTERLy8vChUqlG2fhIQEABISEu64j5y0bNnyhm3x8fGULVv2xk9cRERECrRczUD169eP/fv3A9ClSxeOHDlyV4rZuXMno0aNolWrVjRv3pxLly7h6upqs0/W48zMTNLT07O1AxQqVIiMjAwAh/QhIiIicr1czUBZLBa2bNmCj48P27Zt4/jx4zedjXrooYfsLuTbb79l2LBh1KlTh2nTpgHXQkxmZqbNflmPCxcujJubW7Z2uPapuqz6HNFHTjZt2nTDtpvNTomIiEjBl6sA1apVKyIjI5k7dy4mk4kBAwbcdP8DBw7YVcT777/PxIkTad26NW+//bZ1Rqhs2bIkJSXZ7JuUlIS7uzuenp74+Phw7tw5MjMzbWaRkpKSKFOmjMP6EBEREblergJUVrhJTk5m1KhRvPrqqzz88MMOKSAmJoYJEybQuXNnRo8ejcn0vztq16tXj23bttnsHxsbS506dTCbzdStWxeLxUJcXJx1ofixY8dITEwkKCjIYX2IiIiIXC9XAcrJyYnmzZsD1xaUt2/fngoVKtzxyY8dO8akSZN48skn6dOnD2fOnLG2ubm50blzZ0JCQpg2bRohISF8//33fPnllyxevBiAMmXK8OyzzxIREcGkSZMoXLgwb7zxBvXr1ycgIADAIX2IiIiIXM/u+0BNnjwZgB9++IFt27aRmpqKl5cX9erVo0mTJnb19dVXX3H58mW++eYbvvnmG5u2kJAQpkyZwrx585g6dSrLly+nfPnyTJ061ea2BBMmTGDSpEnWy4pNmzYlIiLC2v7oo4/ecR8iIiIi1zMZhmHYc0BmZib9+vVj8+bNODk54eXlRXJyMhaLhYYNG7Jw4cIcP9X2IMlaRH6zheYikreSf1yNV5MXATi9YR7ez+p7PCV3Pv51I8/XfCavy7ivHY6cR5UB9/7/SXvev+2+keacOXOIi4vjnXfe4ZdffmHz5s3s2bOHyZMns3v3bubPn29/xSIiIiIFiN0B6vPPP2fAgAG0a9cOJycnAJydnXnuuecYMGAAn332mcOLFBEREclP7A5QZ8+epUaNGjm21ahRg8TExDsuSkRERCQ/sztAPfzww9bvj/un7du36ytMRERE5L5n96fw/vOf/zBlyhTc3Nx49tlnKVWqFGfOnOHzzz9n0aJFt7zJpoiIiEhBZ3eA6tChA/v372fatGlMnz7dut0wDEJCQujdu7dDCxQRERHJb+wOUGazmYkTJ9KjRw+2bdtGSkoKxYoVo379+vj6+t6NGkVERETyFbsDVBZfX18FJhEREXkg2b2IXERERORBpwAlIiIiYicFKBERERE72R2g1q1bp5tlioiIyAPN7gA1fvx4fvnll7tRi4iIiEiBYHeA8vHx4fz583ejFhEREZECwe7bGLz00ktMnDiRXbt2Ua1aNYoUKZJtn+eee84RtYmIiIjkS3YHqClTpgCwevXqHNtNJpMClIiIiNzX7A5QmzZtuht1iIiIiBQYdgeocuXK2TzOyMjA1dUVk8nksKJERERE8rPbug/U0aNHGTx4MPXr1ycwMJD9+/czbtw43nvvPUfXJyIiIvc5w2LJ6xLsZneAOnDgAM8//zy//vorbdu2xTAMAJycnJg0aRLr1q1zeJEiIiJy/zKZzSR8/U1el2EXuy/hvf322zz22GNER0cDsHLlSgAiIiLIyMhgxYoVhISEOLZKERERua9dTk7O6xLsYvcM1O7du+nWrRvOzs7Z1j0988wzHD9+3FG1iYiIiORLdgeoQoUKcenSpRzbzp07h6ur6x0XJSIiIpKf2R2gGjduzOzZs0lISLBuM5lMXLhwgejoaP71r385tEARERGR/MbuNVDDhw/npZdeonXr1vj5+WEymZgyZQrHjh3DMAzefffdu1GniIiISL5h9wxU2bJl+fTTT+natSuGYfDwww9z8eJF2rRpw9q1a6lQocLdqFNEREQk37B7BgrAy8uLIUOGOLoWERERkQLhtgJUQkICK1asYMeOHaSkpFCyZEkaNmxI586d8fLycnSNIiIiIvnKbd1Is23btsTExODu7s5jjz2Gs7MzixYt4rnnnuPkyZN3o04RERGRfOO2bqRZvnx5Fi1aRKlSpazb4+Pj6dmzJ5MnT2bevHkOLVJERCS/sRgWzCaz9b/yYLH7N75r1y4GDBhgE57g2uLyQYMGsWXLFocVJyIikl+ZTWbW/LpR4ekBZfdvvUSJEly4cCHHNicnJ4oUKXLHRYmIiBQEpy+ezesSJI/YHaBeffVVpk+fzq+//mqz/eTJk8yaNYvevXs7rDgRERGR/ChXa6CCg4NtvvfuzJkzPP/881SoUIFSpUqRkpLCsWPHcHV15auvvqJLly53rWARERGRvJarAFW/fv1sXxz8T/7+/g4pSERERCS/y1WAmjJlyt2uQ0RERKTAuO2PDpw/f56//vorx5/btXDhQjp37myzLSIigmrVqtn8BAcHW9stFguzZ8+mSZMmBAQE0KtXr2z3ojpw4ACdOnUiICCA4OBgVqxYYdOemz5ERETk3nD28MCwWACs/81v7L4P1MGDBxk+fDiHDx++4T4HDhywu5CVK1cyc+ZM6tWrZ7P9t99+o2/fvnTq1Mm6zcnJyfrnefPmERMTw5QpU/Dx8WHq1Kn07NmTzz77DFdXV5KTk+nevTvBwcGMGzeO3bt3M27cOIoUKUJoaGiu+hAREZF7x6mwGyazmZOrPqLCSy/kdTk5sjtAjR07luTkZMLDwylevPgdF5CYmMgbb7zB1q1bqVixok2bYRgcPnyY3r174+3tne3YzMxMoqOjGTZsGM2bNwdgxowZNGnShK+//po2bdqwevVqXFxcGD9+PM7Ozvj6+nLixAmioqIIDQ3NVR8iIiJy72WcPp3XJdyQ3QHq0KFDzJgxgxYtWjikgF9//RUXFxfWr1/P3Llz+fPPP61tf/zxBxcvXqRy5co5Hnvw4EEuXLhAo0aNrNuKFi1KjRo12L59O23atGHHjh3Ur18fZ+f/PdWGDRuycOFCzpw5w19//XXLPnLSsmXLGz6n+Ph4ypYtm+sxEBERkYLF7gBVoUIF0tPTHVZAcHCwzZqm6x06dAiA9957jx9++AGz2UzTpk0ZMmQInp6eJCQkAGQLK6VLl7a2JSQkULVq1WztcC3o5KYPERERkevZHaDCwsKYMmUKpUqVwt/fHzc3t7tRF3AtQJnNZkqXLs2CBQv4448/eOedd/j9999Zvny5Ncj9c51SoUKFSElJAeDSpUs5tgNkZGTkqo+cbNq06YZtN5udEhERkYLP7gBVqVIlDMOga9euObabTCb2799/x4XBtbuev/zyy3h5eQFQtWpVvL29efHFF9m7d681vGVmZtoEuYyMDAoXLgyAm5sbmZmZNv1mZGQA4O7unqs+RERERK5nd4AaNWoU586d46WXXsr2hcKOZjabreEpy6OPPgpcuzSXddktKSmJhx9+2LpPUlIS1apVA8DHx4ekpCSbPrIelylThitXrtyyDxEREZHr2R2g9u/fz+TJk3nmmWfuRj02wsPDSUpKYtmyZdZte/fuBaBKlSpUqFABDw8Ptm7dag0/qamp7N+/33rbg6CgID788EOuXr1qvf1BbGwslSpVomTJknh6et6yDxEREYthwWy67dsnyn3G7r8JpUuXvmeXtp566im2bNlCZGQkf/zxB99//z2vv/46bdq0wdfXF1dXVzp16sS0adPYtGkTBw8eZMiQIfj4+NCqVSsAQkNDOX/+PKNHj+bw4cOsXbuWZcuW0adPH4Bc9SEiIqLwJNezewaqV69ezJw5k0qVKmW7b5OjtWzZkpkzZxIVFcWiRYvw9PSkbdu2DB482LrPoEGDuHLlChEREVy6dImgoCCWLFmCi4sLACVLlmTx4sVMnDiRkJAQvL29CQ8PJyQkJNd9iIiIiFzP7gD19ddfc+rUKZ5++mmKFi2Kh4eHTbvJZOLbb7+9rWJy+s69p59+mqeffvqGxzg5OTF8+HCGDx9+w338/f1ZtWrVHfUhIiIiksXuAOXt7a1LWyIiIvJAsztATZ48+W7UISIiIlJgaEWciIiIiJ3snoHy8/PDZDLddJ8DBw7cdkEiIiIi+Z3dAap///7ZAtSFCxfYuXMnf/zxB8OGDXNYcSIiIiL5kd0BauDAgTdsCw8PZ9++fYSGht5RUSIiIiL5mUPXQIWEhLBx40ZHdikiIiKS7zg0QP3xxx/W75YTERERuV/ZfQkvMjIy2zaLxUJCQgIbN26kRYsWDilMREREJL9ySIAC8PDw4IknnmDUqFF3XJSIiIhIfmZ3gDp48ODdqENERESkwNCNNEVERETslKsZKHsuy5lMJiZNmnTbBYmIiIjkd7kKUFu3br3lPsnJyaSnpytAiYiIyH0vVwHqu+++u2HblStXmDdvHlFRUZQqVYo333zTUbWJiIiI5Et2LyK/3oEDBxg1ahS//fYbzz77LGPGjKFYsWKOqk1EREQkX7qtAHXlyhXmzp3LokWLKF68OJGRkbRs2dLRtYmIiIjkS3YHqP3791tnndq1a0dERARFixa9G7WJiIiI5Eu5DlBXrlwhMjKSxYsX4+Xlxfz583XXcREREXkg5SpA/frrr4wcOZLDhw/z3HPP8frrr+Pp6Xm3axMRERHJl3IVoF588UUsFguenp78+eef9O/f/4b7mkwmli9f7rACRURERPKbXAWoOnXqWP9sGMZN971Vu4iIiEhBl6sA9d57793tOkREREQKDH0XnoiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQBZjFontuiYiI5AUFqALMbDbldQkiIiIPJAUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2ylcBauHChXTu3Nlm24EDB+jUqRMBAQEEBwezYsUKm3aLxcLs2bNp0qQJAQEB9OrVi5MnTzq8DxEREZEs+SZArVy5kpkzZ9psS05Opnv37jz88MOsWbOG/v37M23aNNasWWPdZ968ecTExDBhwgQ+/PBDLBYLPXv2JDMz02F9iIiIiFzPOa8LSExM5I033mDr1q1UrFjRpm316tW4uLgwfvx4nJ2d8fX15cSJE0RFRREaGkpmZibR0dEMGzaM5s2bAzBjxgyaNGnC119/TZs2bRzSh4iIiDieYbFgMuebuRy75HnVv/76Ky4uLqxfv57atWvbtO3YsYP69evj7Py/nNewYUOOHz/OmTNnOHjwIBcuXKBRo0bW9qJFi1KjRg22b9/usD5ERETE8UxmMwlff5PXZdyWPJ+BCg4OJjg4OMe2hIQEqlatarOtdOnSAMTHx5OQkABA2bJls+2T1eaIPnLSsmXLG7bFx8dn609ERESyu5ycnNcl3JY8n4G6mUuXLuHq6mqzrVChQgBkZGSQnp4OkOM+GRkZDutDRERE5Hp5PgN1M25ubtkWcmeFGnd3d9zc3ADIzMy0/jlrn8KFCzusj5xs2rTphm03m50SERGRgi9fz0D5+PiQlJRksy3rcZkyZayXyXLap0yZMg7rQ0REROR6+TpABQUFERcXx9WrV63bYmNjqVSpEiVLlsTPzw8PDw+2bt1qbU9NTWX//v0EBQU5rA8RERGR6+XrABUaGsr58+cZPXo0hw8fZu3atSxbtow+ffoA19YtderUiWnTprFp0yYOHjzIkCFD8PHxoVWrVg7rQ0REROR6+XoNVMmSJVm8eDETJ04kJCQEb29vwsPDCQkJse4zaNAgrly5QkREBJcuXSIoKIglS5bg4uLisD5ERERErpevAtSUKVOybfP392fVqlU3PMbJyYnhw4czfPjwG+7jiD5EREREsuTrS3giIiIi+ZEClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiIiYicFKBERERE7KUCJiIiI2EkBSkRERMROClAi8sAwLJa8LkFE7hMKUCLywDCZzaTu+iavyxCR+4AClIg8UK6eT87rEkTkPqAAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiJyExZDXwEk2SlAiYiI3ITZZGbTkc15XYbkMwpQIiIit5B8KTWvS5B8RgFKRERExE4KUCIiIiJ2UoASERERsZMClIiIiIidFKBERERE7KQAJSIiImKnAhGgEhMTqVatWraftWvXAnDgwAE6depEQEAAwcHBrFixwuZ4i8XC7NmzadKkCQEBAfTq1YuTJ0/a7HOrPkRERESyOOd1Ablx8OBBChUqxLfffovJZLJu9/T0JDk5me7duxMcHMy4cePYvXs348aNo0iRIoSGhgIwb948YmJimDJlCj4+PkydOpWePXvy2Wef4erqmqs+RERERLIUiAB16NAhKlasSOnSpbO1LV++HBcXF8aPH4+zszO+vr6cOHGCqKgoQkNDyczMJDo6mmHDhtG8eXMAZsyYQZMmTfj6669p06YNq1evvmkfInJ/M7t5YFgsmMxm639FRG6mQLxK/Pbbb/j6+ubYtmPHDurXr4+z8/+yYMOGDTl+/Dhnzpzh4MGDXLhwgUaNGlnbixYtSo0aNdi+fXuu+hCR+5vZ1Q2T2Uzy5o8UnkQkVwrMDJSXlxcdO3bk2LFjPPLII7z66qs0bdqUhIQEqlatarN/1kxVfHw8CQkJAJQtWzbbPlltt+qjVKlS2Wpq2bLlDeuNj4/Pdj4Ryf+upJzO6xJEpIDI9//UunLlCkePHiUlJYWBAwcSFRVFQEAAvXv3ZsuWLVy6dAlXV1ebYwoVKgRARkYG6enpADnuk5GRAXDLPkRERESul+9noJydndm6dStOTk64ubkB8Nhjj/H777+zZMkS3NzcyMzMtDkmK/S4u7tbj8nMzLT+OWufwoULA9yyj5xs2rTphjXfbHZKRETuHx6u7lgMCwBmU76fkxAHKhC/7SJFitiEH4BHH32UxMREfHx8SEpKsmnLelymTBnrpbSc9ilTpgzALfsQERHJiZuzG2aTWeHpAZTvf+O///47derUYevWrTbb9+3bR5UqVQgKCiIuLo6rV69a22JjY6lUqRIlS5bEz88PDw8Pm+NTU1PZv38/QUFBALfsQ0REROR6+T5A+fr6UrlyZcaPH8+OHTs4cuQIkydPZvfu3bz66quEhoZy/vx5Ro8ezeHDh1m7di3Lli2jT58+wLW1T506dWLatGls2rSJgwcPMmTIEHx8fGjVqhXALfsQERERuV6+XwNlNptZsGAB06dPZ/DgwaSmplKjRg2WLl1q/eTc4sWLmThxIiEhIXh7exMeHk5ISIi1j0GDBnHlyhUiIiK4dOkSQUFBLFmyBBcXFwBKlix5yz5EREREsuT7AAVQqlQpJk+efMN2f39/Vq1adcN2Jycnhg8fzvDhw2+7DxEREZEs+f4SnoiIiEh+owAlIiIi95RhseR1CXdMAUpERETuKZPZTMLX3+R1GXdEAUpERETuucvJyXldwh1RgBIRERGxkwJUAWWxGHldgoiIyANLAaqAMptNeV2CiIjIA0sBSkRERMROClAiIiIidlKAEhERkXzJ2cPDes+o/HbvKAWo+4TFYmhhuYiI3FecCrthMps5ueojTOb8FVkKxHfhya1pUbmIiNyvMk6fzusSsslfcU5ERESkAFCAEhEREbGTApSIiIiInRSgREREROykACUiIiJiJwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2UoASERERsZMClIiIiIidFKBE5IGQ377JXUQKNgUoEXkg5LdvcheRgk2vKCIiIiJ2UoASERERsZMClIiIyB2yGBYshtbZPUic87oAERGRgs5s0nzEg0a/cRERERE7KUCJiIiI2EkBqgCyWIy8LkFEROSBpgBVAJnNprwuQURE5IGmACUiInID+mSd3IgClIiIyA3o03VyI/qbcR+xWAytjxIRkXzrfvpOSgWo/89isTB79myaNGlCQEAAvXr14uTJk3ldll3MZpPWR4mISL5lMptJ+PqbvC7DIRSg/r958+YRExPDhAkT+PDDD7FYLPTs2ZPMzMy8Lk1ERAqArPVSWjd1c5eTk/O6BIdQgAIyMzOJjo5m0KBBNG/eHD8/P2bMmEFCQgJff/11XpcnIveI2c0Dw2K5ry4zyL1jNplZ8+tGrZt6QJgMw3jgF8388ssvvPDCC3z55ZdUqlTJur1Dhw5UrVqVcePGZTumZcuWN+zv1KlTODk5UbZs2btSr4jcPktmOmbXwtb/3mibyPXSr1yisLPbTf8McOHyRYq4uOdJjQXF1fR0nAoXvuF/c9zn4kWc3O/+uMbHx+Pk5MTevXtvua9iMpCQkACQLfCULl3a2mYPk8mEs3PB+ZrB+Ph44uPj87qM+4rG1PEcNaZZAen6oJTTtgeB/p7m3vUh6UZ/Bkg9k6IxvYWskHSj//5zW3x8PEkpKfekNmdnZwoVKpS7fe9yLQVCeno6AK6urjbbCxUqRMoNfmmbNm2663XdK1mzaffTc8prGlPH05g6nsbU8TSmjpdfx1QzUICb27V/QfxzwXhGRgaFCz9Y/yIVERGRW1OA4n+X7pKSkmy2JyUlUaZMmbwoSURERPIxBSjAz88PDw8Ptm7dat2WmprK/v37CQoKysPKREREJD/SGiiurX3q1KkT06ZNo0SJEpQrV46pU6fi4+NDq1at8ro8ERERyWcUoP6/QYMGceXKFSIiIrh06RJBQUEsWbIEFxeXvC5NRERE8hkFqP/PycmJ4cOHM3z48LwuRURERPI53UhTRERExE5aRC4iIiJiJwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2UoC6T/zxxx+8+uqr1KtXj3r16hEWFkZiYqLNPu+99x6tWrWiVq1aPPvss6xZs8amPS4ujmrVqmX7uf4O7Vu2bKF9+/bUrl2b1q1bs2HDBps+MjIyGDduHI0aNSIwMJChQ4dy9uzZu/fE7yJHjGluxkNjajuma9asoW3btgQEBNCqVSuioqK4evWqtX39+vU5/j09deqUdZ8vvviCZ555Bn9/f5577jm2bNlic47k5GSGDh1KUFAQ9evXZ9y4cdYvFS9oHDGmuRkPjWlijvueOHGCgIAAm79/oNfTf3LEmOa711NDCryMjAyjRYsWRu/evY3ffvvN2L9/v9GxY0fjueeeMywWi2EYhvHhhx8a/v7+xvr1640//vjDWLVqlVG9enXjm2++sfazcuVK44knnjCSkpJsfjIyMgzDMIzDhw8btWrVMt59913j8OHDxuLFi40aNWoYP//8s7WPkSNHGk888YSxfft2Y8+ePcZzzz1ndOzY8d4OiAM4akxvNR4aU9sx/fTTT42aNWsaH374oXHixAljw4YNRp06dYw5c+ZY+3nnnXeMTp06Zft7euXKFcMwDGPLli1GzZo1jeXLlxuHDx82pkyZYjz22GPG4cOHrX106tTJCA0NNfbt22f8/PPPRosWLYzw8PB7OyAO4KgxvdV4aExtxzTL4cOHjeDgYKNq1arGyZMnbdr0evo/jhrT/PZ6qgB1Hzh+/LgxaNAg4++//7Zu++abb4yqVataty1atMhYvny5zXH//ve/jTfffNP6+I033jD69u17w/OMGTPGeP755222hYWFGT169DAMwzASEhIMPz8/47///a+1/ejRo0bVqlWNnTt33v4TzAOOGNPcjIfG1HZM//Of/xijR4+2OS4yMtJo1qyZ9XHPnj2NCRMm3PA8PXr0MF577TWbbS+99JIxZswYwzAMY+fOnUbVqlVt3vx//PFHo1q1akZCQsLtPr084Ygxzc14aExtx9QwDGPBggVGQECAERISkuObvV5P/8cRY5ofX091Ce8+8MgjjzBr1ixKlCgBwF9//cUHH3xAzZo18fLyAqBnz5506dIFgMuXL7Nx40aOHDlC48aNrf389ttv+Pr63vA8O3bsoFGjRjbbGjZsSFxcHIZhEBcXZ92WpVKlSpQpU4bt27c75sneI44Y09yMh8bUdkyHDRvGK6+8YnOc2WwmJSXF+vhmf08tFgs7d+7MNqYNGjSwGXNvb2+bPurXr4/JZLKOd0HhiDG91XhoTLOPKcC3337L5MmTGTFiRI796PX0fxwxpvnx9VRf5XKf6dGjBz/99BPFihVj+fLlmEwmm/YdO3bQuXNnLBYLoaGhtGzZ0tr2+++/4+XlRfv27UlMTKRq1aoMGTIEf39/ABISEvDx8bHpr3Tp0qSnp5OcnExiYiJeXl4UKlQo2z4JCQl36Rnffbc7prkZD42p7ZjWrVvXZr+0tDQ++OADmjRpAkBKSgqJiYns2LGDmJgYkpOT8ff3Z/jw4VSqVInU1FQuXryY45hmjVdiYiJly5a1aXd1daV48eLEx8ffrad8193umN5qPDSmOf+//9FHHwHYrGm6nl5Pc3a7Y5ofX081A1UAnDp1KsfFiFk/1y+AGz58OKtXryYgIIBu3bple/GqVKkS69at46233uKLL75g2rRpAMTHx5OWlsbFixeJiIhg3rx5lCpVik6dOnH48GEALl26hKurq01/WY8zMzNJT0/P1g5QqFAhMjIyHDomd+pejGluxkNjmvOYAly4cIF+/fqRkZFBeHg4cO1NCcAwDCZPnszMmTPJyMjg5Zdf5syZM1y6dAkg25hdP14aU9sxvdV4aExvPqY50eup48c0P76eagaqAChTpgwbN268YXuxYsWsf65evToAM2fOpEWLFqxZs4YBAwZY20uWLEnJkiXx8/Pj7NmzREZG8tprr1G2bFm2b99O4cKFcXFxAaBWrVrs37+f9957j3HjxlGoUCEyMzNtzp31uHDhwri5uWVrh2ufeihcuPDtD8BdcC/GNDfjoTHNeUxPnz5Nnz59OHXqFEuWLKF8+fIA1KtXjy1btuDl5WX9l2tkZCTNmzdn7dq1vPDCCwDZxuz68brZmLq7u9/OU79r7sWY3mo8sv61rjHNPqY3otdTW44Y0/z4eqoAVQC4uLjc9Fp6fHw8e/bsoXXr1tZt7u7ulC9fnqSkJAB++OEHHnroIapUqWLdp1q1amRmZnLu3DlKly5N0aJFbfo1m834+vpaP2patmxZa39ZkpKScHd3x9PTEx8fH86dO0dmZqZNyk9KSqJMmTK3PwB3wb0Y09yMh8bUdkwBjhw5Qs+ePbFYLKxcuZJHH33Upp+sdRRZChcuTPny5UlMTKR48eK4u7vnOKZZ4+Xj48O3335r0379/wf5yb0Y01uNh8Y05zG9Fb2e/o8jxjQ/vp7qEt594ODBg7z22mscPXrUui01NZVjx45Z/1LPnDmTefPm2Ry3Z88eihcvTqlSpfjhhx8IDAzk5MmT1vYrV65w8OBBa0CoV68e27Zts+kjNjaWOnXqYDabqVu3LhaLxWbR6LFjx0hMTCQoKMjhz/tucsSY5mY8NKa2Y3ry5Em6du1K4cKF+fDDD7OFp1WrVtGgQQMuXrxo3Xb+/HmOHz9OlSpVMJlM1KlTJ9uYbt26lXr16gEQFBREQkICJ06csLZn7f/P9UL5nSPG9FbjoTHNPqa3otdTW44Y03z5emr35/Yk38nIyDDatWtnPP/888bevXuNffv2GV27djWCg4ONtLQ0wzAMY8OGDUa1atWM9957zzh+/LixatUqo1atWsZ7771nGIZhpKWlGS1atDA6dOhg7N271zh48KARFhZmBAUFGadPnzYMwzAOHTpk1KxZ05g6dapx+PBhY8mSJdnusREWFmYEBwcbsbGx1ntsdOrU6d4Pyh1yxJgaxq3HQ2NqO6adOnUygoKCjAMHDmS7f45hGMZff/1l1KtXz+jfv79x6NAh45dffjG6detmPPHEE8alS5cMw7j28fnq1asb0dHRxuHDh423337b8Pf3t37E3mKxGP/5z3+MkJAQY8+ePcaWLVuMFi1aGCNHjsybgbkDjhjT3IyHxtR2TK8XGxub7SP3ej215YgxNYz893qqAHWfSExMNMLCwowGDRoYgYGBxsCBA42//vrLZp9PPvnEePbZZ43HHnvMaNWqlbF69Wqb9hMnThgDBw406tevb9SuXdvo0aOH8dtvv9ns8/333xtt2rQxHnvsMaN169bGhg0bbNovXLhgjB492qhXr55Rr149IywszDh79uzdedJ3mSPGNDfjoTG9NqYJCQlG1apVb/iTZd++fUb37t2NunXrGnXq1Mnx97Ju3TrjySefNGrVqmWEhITYvIAahmGcOXPGGDhwoBEQEGA0aNDAeOONN6wBrKBxxJjmZjw0pn/luO+N3uz1emrLEWOa315PTYZhGPbPW4mIiIg8uLQGSkRERMROClAiIiIidlKAEhEREbGTApSIiIiInRSgREREROykACUiIiJiJwUoERERETspQImIiIjYSQFKRO6Zzp0707lz5zw7/8iRIwkODrY+Dg4OZuTIkQCcOnWKatWqsXbt2nx/jjt1fU0icnuc87oAEZG8EhkZiYeHR4E/h4jcewpQIvLAqlGjxn1xDhG593QJT0TynZ9++omXX36ZunXr0qBBA4YOHUp8fLzNPrt27aJjx44EBATQvHlzli9fTrdu3ey6NHWzS1mGYTBq1Cj8/f3ZvHmzdfu3335L+/btqVWrFo0bN+att97i4sWLdp3j9OnTDBo0iMDAQOrXr8+YMWO4cOGCtf3q1ausXLmStm3b4u/vT/PmzZk2bRoZGRl2j9PBgwfp3r07gYGBtGjRgvXr1+d6fETkxhSgRCRf+eSTT+jRowdly5bl3XffZdSoUezatYuXXnqJv//+G4AjR47QrVs3AN59910GDhxIVFQUcXFxDqvjrbfe4vPPPycyMpLHH38cgM8++4z+/ftTuXJl5s6dy4ABA1i/fj39+vXDnu9lnzVrFmXLlmXevHl07dqV1atXExkZaW0fO3YskydP5oknnmD+/Pl07NiR999/3+Y8uRmnxMREOnXqRFpaGlOnTuW1115j2rRpJCYmOmycRB5UuoQnIvmGxWJh2rRpPP7440yfPt26vU6dOjzzzDMsWbKE8PBwFi5ciKenJ4sXL6Zw4cIAVK5cmf/85z8OqWP69OmsWrWKyMhImjZtClybkZo2bRpNmjRh2rRp1n0rVqxIt27d+P7772nevHmu+n/qqacYNWoUAI0aNeKnn34iNjYWgMOHD/Pxxx8zdOhQevfuDUDjxo0pXbo04eHh/PDDD9YabjVOy5Yt4+rVq0RFRVGiRAkAKlWqxIsvvnjHYyTyoNMMlIjkG8eOHeP06dO0adPGZvvDDz9MYGAg27ZtAyA2NpamTZtawxNAYGAg5cqVu+MaVq5cSVRUFM8++6xNIDp69CgJCQkEBwdz5coV609QUBAeHh789NNPuT5HvXr1bB6XL1+e1NRUAOtzfPbZZ232efbZZ3FycmLr1q25Hqe4uDgCAgKs4Qmgdu3aPPTQQ7muVURypgAlIvnGuXPnAChVqlS2tlKlSpGWlgbA2bNnKVmyZI773KmDBw/y+OOP8/nnn7N///5stY0bN46aNWva/Jw/f56kpKRcn+P64AdgNputl+ZSUlIA8Pb2ttnH2dkZLy8v0tLScj1OKSkpeHl5Zdvnn32LiP10CU9E8o3ixYsDcObMmWxtp0+ftoYBHx+fHPf5+++/qVy58h3V8Nprr9GlSxeeffZZIiIi+Oijj3BycqJo0aIAhIeHU79+/WzHFStW7I7O+89+Tp8+bTOjdvnyZZKTk/Hy8sr1OHl5eeW4T1YAE5HbpxkoEck3KlWqhLe3N59//rnN9pMnT7J7927q1KkDQFBQED/++KPNp9L279/PqVOn7riGUqVK4ebmxtixY/n1119ZunQpcG2NVcmSJTl16hS1atWy/pQpU4bp06fbzFbdiaxwtmHDBpvtGzZs4OrVq9StWzfX49SwYUN27dpls2j88OHDnDx50iG1ijzINAMlIvdUQkICy5Yty7a9atWq/Otf/yIsLIxRo0YxdOhQ2rVrR3JyMpGRkRQrVozu3bsD0LdvXzZu3EjPnj3p0aMHqampzJo1C7PZjMlkckidzZo1o3Xr1syZM4ennnqKChUqMGTIEMaOHYuTkxMtWrQgNTWVefPmkZiYSM2aNR1y3ipVqhASEsLs2bNJT08nKCiIAwcOEBkZSYMGDWjSpAlmszlX49S1a1c+/vhjXnnlFQYOHMjVq1eZMWMGLi4uDqlV5EGmACUi99Qff/zB5MmTs21//vnn+de//kX79u0pUqQICxcupH///nh4eNCkSRPCwsKsa3ceeeQRlixZwjvvvMOgQYMoWbIkffr0Yf78+RQpUsRhtb7++uts3ryZMWPGsGzZMl544QWKFCnC4sWLWbVqFe7u7tSpU4dp06ZRoUIFh5134sSJPPLII6xZs4ZFixZRunRpunTpQr9+/TCbr104yM04eXl58cEHHzBx4kRGjhxJkSJF6NmzJxs3bnRYrSIPKpNhz81LRETygS1btuDi4mLzabbU1FT+9a9/ER4eTpcuXfKwOhF5EGgGSkQKnF9//ZXZs2cTFhZGzZo1OXfuHEuXLsXT0zPbR/tFRO4GBSgRKXB69OhBZmYmH3zwAfHx8bi7u1O/fn0mT55sc88jEZG7RZfwREREROyk2xiIiIiI2EkBSkRERMROClAiIiIidlKAEhEREbGTApSIiIiInRSgREREROykACUiIiJiJwUoERERETv9P62/wvZwCXCWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import pickle\n",
    "\n",
    "# go through each subtypes model and plot MCMC samples of the likelihood\n",
    "for s in range(4):\n",
    "    pickle_filename_s           = output_folder + str(output_no) + '/pickle_files/' + dataset + '_subtype' + str(s) + '.pickle'\n",
    "    pickle_filepath             = Path(pickle_filename_s)\n",
    "    pickle_file                 = open(pickle_filename_s, 'rb')\n",
    "    loaded_variables            = pickle.load(pickle_file)\n",
    "    samples_likelihood          = loaded_variables[\"samples_likelihood\"]\n",
    "    pickle_file.close()\n",
    "\n",
    "    _ = plt.figure(0)\n",
    "    _ = plt.plot(range(100000), samples_likelihood, label=\"subtype\" + str(s))\n",
    "    _ = plt.figure(1)\n",
    "    _ = plt.hist(samples_likelihood, label=\"subtype\" + str(s))\n",
    "    \n",
    "_ = plt.figure(0)\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.xlabel('MCMC samples')\n",
    "_ = plt.ylabel('Log likelihood')\n",
    "_ = plt.title('MCMC trace')\n",
    "    \n",
    "_ = plt.figure(1)\n",
    "_ = plt.legend(loc='upper right')\n",
    "_ = plt.xlabel('Log likelihood')  \n",
    "_ = plt.ylabel('Number of samples')  \n",
    "_ = plt.title('Histograms of model likelihood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will analyse these plots along with the stability and performance metrics, in order to choose the most fitting model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is next?\n",
    "\n",
    "In conclusion, Pipeline #1 implements SuStaIn on non-motor symptoms (NMS) datasets. The next steps for this pipeline will be to automate hyperparameter tuning, as well as implementing cross-validation, which is provided with the algorithm."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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