survey_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92b0e85d0c608f46",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Dissertation Survey Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc67172abe5ffb4",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Power Analysis\n",
    "\n",
    "1. Define the Null Hypothesis ($H_0$) and Alternative Hypothesis ($H_a$):\n",
    "    * $H_0$: The AI-generated music is indistinguishable from human-composed music (e.g., 50% of people believe it's human-composed).\n",
    "    * $H_a$: The AI-generated music is distinguishable from human-composed music (e.g., significantly more or less than 50% believe it's human-composed).\n",
    "2. Effect Size:\n",
    "   * The effect size here would be the difference in proportions (by the 6-point Likert scale).\n",
    "   * For example, we consider the AI successful if, instead of 50% (chance level), 60% or more respondents believe the music is human-composed. \n",
    "   * The effect size in this context is 0.1 (60% - 50%).\n",
    "3. Significance Level ($\\alpha$):\n",
    "    * Commonly set at 0.05. This is the probability of rejecting the null hypothesis when it is actually true (Type I error).\n",
    "4. Power (1 - $\\beta$):\n",
    "    * Typically set at 0.8 or 80%. This is the probability of correctly rejecting the null hypothesis when it's false (thus detecting an effect if there is one).\n",
    "5. Sample Size:\n",
    "    * 500 respondents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c26e6c09174434ff",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:32.526602400Z",
     "start_time": "2024-01-04T03:02:27.052415200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Required sample size: 387.1677468578107\n",
      "Is current sample size of 500 adequate? Yes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: omegaconf 2.0.6 has a non-standard dependency specifier PyYAML>=5.1.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of omegaconf or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n"
     ]
    }
   ],
   "source": [
    "!pip install -q -U statsmodels\n",
    "from statsmodels.stats.power import NormalIndPower\n",
    "from statsmodels.stats.proportion import proportion_effectsize\n",
    "\n",
    "alpha = 0.05  # Significance level\n",
    "power = 0.8   # Desired power\n",
    "p1 = 0.5      # Proportion under null hypothesis (50%)\n",
    "p2 = 0.6      # Proportion under alternative hypothesis (60%)\n",
    "effect_size = proportion_effectsize(p1, p2)  # Effect size\n",
    "current_sample_size = 500\n",
    "\n",
    "# Calculate required sample size\n",
    "analysis = NormalIndPower()\n",
    "sample_size = analysis.solve_power(effect_size=effect_size, power=power, alpha=alpha, ratio=1.0)\n",
    "print(f\"Required sample size: {sample_size}\")\n",
    "\n",
    "# Assess if current sample size is adequate\n",
    "is_adequate = current_sample_size >= sample_size\n",
    "print(f\"Is current sample size of 500 adequate? {'Yes' if is_adequate else 'No'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIhCAYAAACizkCYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADObUlEQVR4nOzdd1wT5x8H8M8lQNjIUhnuAU4cCG4F96oWt9ZtXVD3wr1Q6x64V917W6vW2lr3ry5ctc4qKCpDBGST+/1BcxIZggYS4fN+vXiR3D25+16e5JJvnnGCKIoiiIiIiIiI6IvItB0AERERERFRXsDkioiIiIiISAOYXBEREREREWkAkysiIiIiIiINYHJFRERERESkAUyuiIiIiIiINIDJFRERERERkQYwuSIiIiIiItIAJldEpFWavo45r4ueP7HeiYhIFzC5IsqjHjx4gBEjRqBOnTqoWLEi6tati+HDh+P+/ftai8nJyQnLly8HACQkJGD27Nk4evSotH78+PHw9PTM8vaWL18OJycn6f61a9cwYMAAjcXbpUsXODk54eTJkxrbZno+Po4vFRQUBCcnJxw4cEBj2wRS6sfJySnDvzp16khlo6OjMWjQILi4uKBGjRr4999/8dNPP6FOnTqoXLkyVq5cqZGY0nsdZeTatWsYNGgQ3N3dUbFiRTRs2BATJkxAYGCgWjlPT0+MHz9eI/HpAk9PzzR1Va5cOdSoUQPdunXDn3/+qbXYrly5AicnJ1y5ciVHtp/V98Lbt28xZ84cNG7cGBUrVoSbmxt69eqFX3/9NUfi+lyafG/HxMRg+fLlaNmyJSpXrozq1aujS5cu2Lt3r9qPFQcOHICTkxOCgoK+eJ9E+YGetgMgIs17+PAhOnfujCpVqmDSpEmwtrbGq1evsG3bNnTq1AlbtmxBlSpVcj2u3bt3o3DhwgCAN2/eYPPmzZgzZ460fsiQIejZs2eWt9exY0fUq1dPur937148fvxYI7E+efIEN27cQNmyZbFr1y40a9ZMI9vNDQULFsTu3btRtGhRjW/b1tYW/v7+6a7T19eXbh86dAi///47pkyZgjJlysDGxgY//vgjGjZsiL59+8LR0VEj8aT3OkrPpUuX0L9/fzRp0gR+fn4wMzPD8+fPsXHjRnTo0AF79+6Vni9/f3+YmppqJD5d0aBBAwwZMkS6n5SUhOfPn2Pt2rUYMmQI9u3bB2dn51yPq0KFCti9ezdKly6d6/tWiYuLQ/fu3ZGcnIwBAwagWLFiiIqKwi+//AIfHx9MmDABvXr10lp8OUEURQwaNAhPnjzBgAEDUKZMGcTHx+P8+fOYPHkyHj58iAkTJgAAGjZsiN27d6NgwYJajpro68DkiigP2rRpEywtLbFu3Tro6X14mzdu3BjNmzfHypUrsXbt2lyP61MJXXaTgcKFC0vJmqYdOHAADg4OGDhwIEaPHo1nz56hWLFiObIvTTMwMMix5Dmr246IiAAAdOvWDYIg4MWLF1AqlWjcuDFq1KiRI7FlZvXq1ahcuTKWLFkiLXN3d0eDBg3QpEkTbNq0CVOnTgUAlC9fPtfjy2lWVlZp6s3V1RUuLi5o2bIljhw5opXkytTUVCs/9KR24sQJPH78GCdPnkTx4sWl5Y0bN0ZcXByWLVuG7777DnK5XHtBati1a9dw5coVbNy4Ua3FuWHDhpDJZNi2bRu+//572NrawsrKClZWVlqMlujrwm6BRHlQaGgoRFGEUqlUW25sbIwJEyagRYsWastPnz4NLy8vVKpUCXXq1MGsWbMQExMjrV++fDmaNGmCP/74A23atEHFihXRrFkzHDp0SG07mzdvRvPmzVGpUiXUq1cP06ZNQ3R0tLRe1S0wKCgIjRo1AgD4+vpKXQFTdwucPHky6tSpg+TkZLV9+Pn5wd3dHYmJiWrd6caPH4+DBw/ixYsXUreZ9u3bo0uXLmmen969e6NPnz4ZPn/Jyck4dOgQPDw80LhxYxgbG2P37t1pynl6emLZsmX48ccfUbt2bVSuXBn9+vXDv//+q1Zu79698PLyQpUqVVC5cmW0bdsWv/zyS7r73r59O5ycnPD06VO15YcPH0a5cuUQHBwMpVKJxYsXw9PTExUrVoSnpycWLlyIxMREAGm7Dn2qPAD06NEjW10yM9OjRw+p+6ezszM8PT2lbU+YMEGtC+SnXnsAcPPmTfTt2xfVqlVDzZo1MXLkSLx+/TrD11F6VO+JjxUsWBCTJk1S+4KZulug6jWW3p/qGLN6HKll5fUdFxeHadOmoX79+qhYsSKaN2+ODRs2ZLjNz2Fubg4AEARBWhYfH4958+ahQYMGqFixItq0aYPjx4+rPU6pVGLFihVo2LAhXFxcMGjQIPzyyy9q3cfS6+b78Wvz426BqnONv78/3NzcULduXbx79w5AyvuoVatWUpfO5cuXp3n+Tp06hW+++QaVK1fGt99+m6Vu0KGhodIxfWzgwIEYMmQIEhISpGWnT59Gt27dULVqValetm/fLq1XHdOlS5fQo0cPVK5cGQ0bNsTevXvx5s0b+Pj4oGrVqmjQoAF++umnNI87f/48unfvjsqVK6Np06bYsWNHpvG/fPkSI0eOhJubG1xcXNCrVy/cu3cv08eEhIRkeMzdunXDiBEjpNfEx90CM+sarBIREYEpU6agdu3aqFSpEjp16oRLly5lGhNRXsHkiigPatiwIV6+fIkuXbpg+/btePz4sfTFsnnz5vj222+lskePHoW3tzdKliyJFStWwMfHB0eOHMGQIUPUvoyGhIRgxowZ6NmzJ9auXQtHR0eMGzdO6oZ37NgxzJ8/H927d8eGDRvg7e2Nw4cPY+bMmWniK1iwoNS1bPDgwel2M2vbti1CQ0PVxmIolUr88ssvaNWqlVoXNCClS2GDBg1ga2uL3bt3o2HDhujQoQNu3LiBZ8+eSeWCg4Nx5coVeHl5Zfj8/fnnnwgJCUG7du1gaGiIFi1a4ODBg2pfsFS2bNmCJ0+eYM6cOZg1axbu3LmDcePGSeu3b9+OKVOmoHHjxlizZg0WLFgAAwMDjB49Gq9evUqzvTZt2kChUODw4cNqyw8dOoRatWrBzs4O69atw86dO+Ht7Y2NGzeia9eu2LBhA1atWpXu8WSl/NSpUzPs7vexpKSkdP9Ur5epU6eiQ4cOAFK6gi5evFitvlWJalZee/fu3cN3330nfeGfPn067ty5g379+mXpdaTSsGFD3LhxAz169MC+ffvUxll17NgRjRs3TvdxHTt2xO7du9X+qlevDhMTE7Rs2TLLx/GxrLy+Z8+ejT///BPjxo3Dhg0b0KhRI8ybNw/79+//dCV9RBRFtbqKjY3F/fv3MW7cOOjr66N169ZSOW9vb+zatQt9+vTBqlWrULVqVYwYMULtx5R58+Zh5cqVaN++PZYvXw5zc3Op5e9LvXz5EmfPnsXixYvh6+sLCwsLrFmzBpMnT0atWrWwevVqdO/eHevWrcPkyZOlx505cwZDhw6Fk5MTVqxYgRYtWmDMmDGf3F+9evWgp6eHXr16wd/fHzdv3pR+eFD9YGJkZAQA+OOPP+Dt7Y0KFSpg5cqVWL58OYoUKYIZM2YgICBAbbsjR46Ep6cn1qxZgxIlSmDq1Kno2bMnypQpg5UrV6Jy5cqYM2cObt26pfa4ESNGoHz58lixYgVq166N6dOnZ5hghYeHo0uXLrh79y4mT56MhQsXQqlUonv37pl2kXZzc4OxsTFGjhyJ+fPn48qVK4iLiwMAFC9eHN9//z1sbGzSfezH7wc/Pz/IZDLpPR8fH49evXrht99+w4gRI+Dv74/ChQujf//+TLAofxCJKE9asmSJWKlSJbFs2bJi2bJlRXd3d3HUqFFiQECAVEapVIr169cX+/Xrp/bYixcvimXLlhV///13URRFcdmyZWLZsmXFixcvSmVevHghli1bVtywYYMoiqI4efJksVmzZmJycrJU5vDhw+KWLVuk+2XLlhWXLVsmiqIoBgYGimXLlhX3798vrR83bpzo4eEhxebh4SH6+vqmievmzZtqcaX3eFEUxcjISLFy5cri0qVLpWWrVq0Sq1evLsbGxmb43Pn4+IitW7eW7l+9elUsW7aseOTIEbVyHh4eooeHh5iUlCQtW758uVi2bFkxPDxcFEVRnDNnjjh//ny1x925c0csW7aseOzYsXSPY+TIkaKHh4eoVCpFURTF4OBg0dnZWTx69KgoiqLYt29fsU+fPmrb3Lp1q3jo0CFRFNM+t58qn1Xjxo2TXk/p/a1fv14q+/ExfRxTVl97P/zwg1inTh0xLi5OKnP9+nXRw8NDvHfvXrqvo/TEx8eLkydPFsuVKyfFW79+fXHy5Mni48eP1cp6eHiI48aNS3c7mzZtEp2cnMRff/01W8fxsay8vps1ayZOmjRJ7XH+/v4ZbjMjHh4e6dZX+fLlxU6dOomXL1+Wyp4/f14sW7as+PPPP6ttY/To0WKdOnXExMREMSIiQqxQoYI4d+5ctTK9e/cWy5YtKwYGBoqimPb9KIppXweXL18Wy5YtK8Wget389ddf0mNU7+MpU6aobWvPnj1i2bJlxQcPHoiiKIpeXl5ix44d1cqsWbMmS6+PkydPirVr15aem8qVK4t9+/YVjx8/rlZu3bp1aV4bb9++FcuWLSuuWbNG7ZhSv+9v3rwpli1bVhwzZoy0LDw8XCxbtqy4adMmtcelfk2IoigOHjxYrFOnjqhUKtM8f4sWLRIrVaokBgUFSeXj4+PFRo0aiT/88EOmx/zXX3+JjRo1ko65QoUKYvfu3cXdu3erndP279+vVq8fH3vjxo3FTp06ifHx8aIoiuLu3bvVXseimPJ67969u+jl5ZVpTER5AVuuiPKoYcOG4dy5c1i4cCE6dOgAU1NTHD16VJrQAkiZtOHVq1fw9PRU+1W7Ro0aMDU1xYULF9S2mXpshGqsk6rrU82aNfH06VN4eXnB398ft2/fRps2bdCjR4/Pil8QBHzzzTc4ffq01GL0888/o3jx4nBxccnSNszMzNC0aVMcOXJEWnbw4EG0bNkShoaG6T4mPDwcv//+O5o1a4bIyEhERkaiTJkycHBwSLdrYKVKldTGYqiel9jYWAApXaNGjx6NyMhI3Lx5E4cPH5a6EKXXEgYAHTp0wIsXL3D16lUAKa1WJiYmaNKkCYCUsUIXLlxAt27dsH79ejx69Ajfffcd2rZtm+72sls+M7a2tti3b1+6f9nZXlZfe9euXUP9+vWhUCikx1atWhVnzpxBuXLlsrw/AwMDzJgxA2fPnoWfnx/atGkDpVKJ3bt345tvvsGpU6c+uY1z585h3rx5GDJkiNTSld33kEpWXt/u7u7Ys2cPvv/+e2zbtg2BgYHw9vZGw4YNs3zcKh4eHlI9LVq0CHZ2dqhYsSL8/f3h7u4ulbt06RIEQUCDBg3UjsfT0xMhISF4+PCh1LKj6pKp8s0332Q7roykrtsbN24gLi4uzXOs6nJ44cIFxMXF4e7du/Dw8FDbzsddoDPStGlT/PHHH1i/fj369u2LUqVK4eLFixg+fDiGDh0qtUD2798fc+fOxfv373Hnzh0cP34ca9asAZD2/Vy1alXptrW1NQConbssLS0BAFFRUWqPS92zQBVbSEhImq7CQEp9lStXDoUKFZKeF5lMhvr16+PixYuZHrOrqytOnTqFbdu2YdCgQahUqRJu3ryJyZMno0ePHlJLVkaSkpIwdOhQxMbGYtmyZTAwMJBisrW1RYUKFaSYkpOT4eHhgTt37kjdPInyKk5oQZSHWVhYoHXr1lKXn3v37mHMmDGYP38+2rRpI006MH36dEyfPj3N49+8eaN2X9U1BgBkspTfZlRfOlq2bAmlUokdO3ZI3WUcHBwwevRoqftUdrVt2xarVq3CuXPnUK9ePZw6dSrbs3Z16NABR44cwdWrVyGXy/Hvv//ixx9/zLD8kSNHpPFcqcfUAMCLFy/w+PFjlCpVSlqW+jkBPjwvqrEMz58/x5QpU3Dp0iXo6+ujZMmS0sQBYgZdxmrWrAlHR0ccOnQINWrUwKFDh9CyZUspwejfvz9MTEywf/9+LFiwAPPnz0eZMmUwadIk1KxZM832sls+MwYGBqhUqVK2HpOerL72IiIipC+mmmBra4sOHTpIXZguX76MMWPGYNq0aWjcuLFUfx978uQJRo4cifr16+OHH37I9nGk51Ov74kTJ6Jw4cI4cuQIZs6ciZkzZ6Jq1aqYNm1atiefKFCggFRvlSpVgpOTE9q3b4/vv/8ee/bskb4YR0REQBRFVKtWLd3tvHnzRvpy/PEkB4UKFcpWTJkxMTGRbque44wus6CKSRRFKWFRyc4Md/r6+qhXr540A+nr168xa9YsnDx5En/88Qc8PDwQHh6OqVOn4vTp0xAEAcWKFYOrqyuAtO/n9Gac/Ph8kZ6Pn0fV6//du3ewtbVVWxcREYFnz56hQoUK6W4rNjY2033KZDLUqFFDmmTm3bt3WLx4MXbu3Il9+/bhu+++y/CxM2fOxPXr17Flyxa1mCMiIhASEpJhTCEhIbCwsMhwu0RfOyZXRHnM69ev0b59ewwbNgwdO3ZUW1e+fHmMGDEC3t7eCAwMlAazjx07Fm5ubmm2ld0PQFUiFxUVhfPnz2PdunUYM2YMqlev/llfvEqUKIHKlSvjl19+gUwmQ2RkZLZ/HXdzc0PRokVx4sQJyGQylCxZMtPZyfbv3y+NMUktJiYGQ4YMwc6dOzFp0qQs7VupVGLAgAHQ19fHvn37UK5cOejp6eHRo0dpxlSlJggCvv32W2zduhVdu3bF06dP1RJCmUyG7t27o3v37ggLC8PZs2exevVq/PDDD+m2lHyqvOqLdW7K6mvPzMwM4eHhadafPXs2yy1XAQEBGDx4MObPn682cQWQksj269cPc+bMwdu3b9NN5N69e4fBgwfDxsYGCxYsUJv84UveQ596fRsYGGDw4MEYPHgwXr58id9//x0rV67EqFGj8PPPP2fp2DNSunRpDB06FPPmzYO/vz9GjhwJIOX5NjY2llq3P1asWDFpbFFYWBhKliwprVMlQSqCIKSZcCKzST4yonqOFyxYoDabn4qNjQ0KFCgAmUwmTU6RUUzp6dKlC0qUKJFmOv9ChQrBz88Pp06dwqNHj+Dh4YHRo0fjyZMn+Omnn1C1alUYGBggNjYWe/bsyfZxZeTt27dqM6eGhYUBQLqvTTMzM7i5uWHs2LHpbiuj9/bw4cMRERGhNqEGkPJ6nTJlCo4fP45Hjx5lGOO2bduwa9cuzJgxI00ibmZmhuLFi2PBggXpPlZTl2Eg0lXsFkiUx9jY2EBPTw87duxAfHx8mvVPnjyBQqFAsWLFULJkSVhbWyMoKAiVKlWS/goVKoSFCxd+csap1IYPHw5vb28AKR+uLVq0wJAhQ5CUlJTur/dZnda4bdu2OHfuHH7++WdUq1YNRYoUybBseq0OgiDAy8sLp0+fxpkzZ9J0uUnt9u3bePDgAby8vODu7q725+HhgZo1a+Lw4cOf7C6j8vbtWzx9+hQdOnRApUqVpGnxVRdtTW+mLhUvLy9ERkbixx9/RKlSpdS6E3Xp0gWzZs0CkPKFy8vLC927d0dkZKTa7IyfWz43ZPW15+rqigsXLqh1ubp37x4GDBiAu3fvZul1VLx4ccTGxmLLli3pPudPnz6Vppz+WFJSEoYPH47Q0FCsWLEiTWvEl76HMnp9x8XFoVmzZti4cSMAwN7eHt27d0erVq3w8uXLTx5zVvTq1Qtly5bFxo0bpRku3dzcEBMTA1EU1Y7nwYMHWLFiBZKSklC1alUYGRnhxIkTats7c+aM2n0TExO8fftW7Tx07dq1bMfp4uICfX19vH79Wi0mPT09LFq0CEFBQVAoFKhatSpOnTql1oL0cUzpcXBwwIkTJ9JcTBqA1BWvbNmyUvxNmzaFu7u7lLhk5f2cHadPn1a7f+LECTg4OKR7qQo3Nzc8ffoUJUqUUHtuDh8+jH379mX4/ihWrBguX76Mmzdvpln35s0bxMTESMf8sYsXL2LOnDno3LkzOnfunG5MwcHBsLa2VovpwoULWL9+fZ6a0p4oPWy5Ispj5HI5pk2bBm9vb7Rv3x7du3dHqVKlEBsbiwsXLmD79u0YNmyY9Iv6iBEjMGXKFMjlcnh4eCAyMhIrV67E69evM+zWkZ6aNWti6tSp+PHHH1G/fn1ERkbC398fxYsXT7cLk5mZGYCU/vkfJw+ptWzZEnPnzsXx48c/ORuZubk5QkNDpVYNVZcgLy8vqYtfZuOC9u/fD319fTRt2jTd9W3btsXFixdx/PjxTGcbVLG2toaDgwO2b9+OwoULw9zcHOfOnZNaBVTjstJjb2+P2rVr4/z58xg9erTauho1amDjxo2wsbFB1apV8fr1a2zatAlubm6wsrJK0zrwqfIA8OjRIyQkJHzyGk8JCQnpfiFTcXJyylLXJ7lcnqXX3pAhQ9C5c2cMHDgQPXv2RFxcHJYsWYLKlSujTp06UtKV2evIwsIC48aNw9SpU9GtWzd06tQJRYoUQVRUFH799VccPHgwTYuUyty5c3Hx4kWMHz8e79+/Vzt2U1NTlC5d+oveQxm9vg0NDVGhQgX4+/tDX19fmp7/4MGDahe0vnfvHgwMDD7rIrx6enqYMGECevfujdmzZ2Pt2rVo0KABatSogSFDhmDIkCEoVaoUbt26hWXLlqFevXrS68Xb2xuLFi2CoaEhatWqhXPnzqWZrt3DwwNbt27FxIkT0aFDBzx48ACbNm3K9pdrS0tL9O/fH0uXLkV0dDTc3d3x+vVrLF26FIIgSOeXkSNHolevXvDx8UHnzp3x9OlTrF69+pPbHzFiBK5cuYIOHTqgZ8+eqFq1KmQyGW7fvo2NGzeifv36qF+/PoCU2QOPHj2KChUqoHDhwrh+/TrWrl0LQRAyfT9nx6ZNm6BQKFClShWcOnUKv//+OxYuXJhu2d69e+Pw4cPo3bs3+vbtC0tLSxw/fhx79uyBr69vhvvo27cvTp8+jT59+qBbt25wd3eHkZERHjx4gI0bN6JMmTLpnuOeP3+O4cOHo1ixYujUqRMCAgLUktnSpUvDy8sL27ZtQ58+fTBo0CDY2dnh4sWLWLduHb777rs0M70S5TVMrojyoIYNG2LPnj3YsGEDVq9ejfDwcBgYGKB8+fJYvHixWvLQsWNHmJiYYP369di9ezeMjY1RrVo1LFiwINNWoo916dIFiYmJ2LVrF3bs2CF96RozZky6H6ampqbo06cPdu/ejbNnz2Y48N/Kygp169bFhQsX0Lx580xj8PLywtmzZ+Ht7Y2hQ4dKYzQKFSoEZ2dn2NjYZNg9MT4+Hj///DPq1KmDAgUKpFumadOmmD59Onbt2pWl5AoAVq5cCT8/P4wfP176Erxq1SrMnj0bV69ezXTCj4YNG+LSpUtpEsJhw4bBwMAA+/fvx4oVK2BmZgZPT0+MGjUq3e1kpfz06dPx4sWLT/7SHxISku6v1SqHDh3Kcne9rLz2ypcvj61bt2LhwoUYPnw4TE1N0aBBA4wePRoGBgYwMDBI8zpK7/XWpUsXFCtWDFu2bMGiRYsQEREBExMTVK5cGZs3b1ab1CE11fMxd+7cNOvc3NywdevWL3oPZfb6njFjBpYsWYKNGzciJCQE1tbW6NChA4YNGyaV8fHxgYODA7Zu3Zr5k52BWrVqoVmzZjh58iR+//13eHh4YO3atVi6dCnWrFmDsLAwFCpUCH369JFapgHg+++/h4mJCTZu3IgtW7agevXqGDRoEFasWCGVqVOnDsaNG4etW7fi5MmTUrKY3rXnPmX48OGwtbXFjh07sH79elhYWKBWrVoYOXKk9EONq6sr1q1bh0WLFsHHxweOjo6YPXs2Bg0alOm2HR0dcfDgQaxZswZHjx7FunXrIIoiihUrhn79+qFnz55S4j137lxp/BuQ0io6ffp0aVynJkyYMEGKp2TJkli2bJlaQp1aoUKFsGvXLixcuBDTpk1DfHw8ihcvDj8/P2lcYXosLCywe/durFu3DmfOnMHOnTuRmJgIBwcHtG7dGgMGDEh30p+rV6/i3bt3ePfuHdq3b59m/ZYtW+Du7o7t27dj4cKFmD9/PqKiouDg4IBRo0ahb9++n//EEH0lBDGjEdVERHnE69ev4eHhgWXLlmV4PSNd1L9/fygUCrUvrES66sCBA/D19cVvv/3GcTWf4cqVK+jZs6eUoBDR14ktV0SUZ/3999/47bffcPLkSRQvXlyaulnXrVixAk+fPsX58+czvHgoERER6R4mV0SUZ8XHx2PTpk0oVKgQFi1alOE027rmzJkzeP78OcaOHZvhlNhERESke3SiW2BCQgK8vLwwefJkqSk8MDAQkydPxs2bN2Fvb48JEyagbt260mMuXryI2bNnIzAwEC4uLvDz81Pr2/7TTz9hw4YNiI6ORosWLTB58uQsDbImIiIiIiL6HFr/GTc+Ph4jR47Ew4cPpWWiKMLb2xs2NjbYv38/2rZtCx8fH2n62ZcvX8Lb2xteXl7Yt28frKysMGTIEGnGmpMnT8Lf3x8zZszA5s2bERAQgPnz52vl+IiIiIiIKH/QanL16NEjdOrUCc+fP1dbfvnyZQQGBmLGjBkoVaoUBg4ciCpVqmD//v0AgL1796JixYro27cvypQpgzlz5uDFixf43//+ByBltppevXrBw8MDlStXxvTp07F//36NTZNKRERERET0Ma0mV//73//g7u6O3bt3qy0PCAhA+fLlYWxsLC2rXr26dH2RgIAAuLq6SuuMjIxQoUIF3Lx5E8nJybh9+7ba+ipVqiAxMRH379/P2QMiIiIiIqJ8S6sTWnTr1i3d5SEhIdLFP1Wsra3x6tWrT66PjIxEfHy82no9PT0UKFBAejwREREREZGmaX3MVXpiY2NhYGCgtszAwAAJCQmfXB8XFyfdz+jxREREREREmqaTU7ErFApERESoLUtISJCuFq5QKNIkSgkJCTA3N4dCoZDuf7w+u7MFhodHQetzKSYmQrFzOyAApt6DEB4Vp/2YCIIAWFmZ6cZrhADk7zpxdTVBWJgMv/zyHs7OSm2HAyB/14euYp3oFtaH7mGd6BZdqg9VLFmhk8lVoUKF8OjRI7VloaGhUle/QoUKITQ0NM36cuXKoUCBAlAoFAgNDUWpUqUAAElJSYiIiICtrW224lAqofXKRFwCTEcOTbn9fV/diIkgCCn/WR+6Iz/Xib6+iKgoICREQNmy2o4mRX6uD13FOtEtrA/dwzrRLbpUH6pYskInuwW6uLjg7t27Uhc/ALh27RpcXFyk9deuXZPWxcbG4t69e3BxcYFMJkOlSpXU1t+8eRN6enpwdnbOvYMgIsollpYpnzrh4dk4+xMREZHG6WRy5ebmBjs7O/j6+uLhw4dYu3Ytbt26hQ4dOgAA2rdvj+vXr2Pt2rV4+PAhfH194ejoKF2AuFu3btiwYQNOnz6NW7duYdq0aejUqRMvIkxEeZKVVUpy9fYtkysiIiJt0snkSi6XY+XKlQgJCYGXlxeOHDmCFStWwN7eHgDg6OiI5cuXY//+/ejQoQMiIiKwYsUKCP+12bVq1QoDBw7ElClT0LdvX1SuXBljxozR5iEREeUYVXLFlisiIiLt0pkxV//884/a/WLFimHbtm0Zlm/QoAEaNGiQ4foBAwZgwIABGouPiEhXWVqm/GdyRZQ+pVKJ5OQkbYehRhCAuLg4JCYmaH08CaVgneiW3K4PuVwPMtmXtzvpTHJFRESfRzXmit0CidKKj4/F27chAHTv23J4uAxKpW7M8EkpWCe6JXfrQ4ClpS0Uii8bRsTkiojoK2dtzeSKKD1KpRJv34bAwMAQpqYW0vABXSGXC0hO1r2kLz9jneiW3KoPURQRHf0Ob9+GoGBBxy9qwWJypesUCrzbvgcAYKFQALGxWg6IiHSNquUqLEy3vjgSaVtKV0ARpqYWMDBQaDucNPT0ZEhKYiuJLmGd6JbcrA9TUwuEh8ciOTkJMpnBZ2+HyZWu09NDQpPmKfPr67G6iCgtzhZIlDlda7EiIt2jqfOETs4WSEREWccxV0RERLqByZWuS0yEYtd2KHZuBxITtR0NEekgVctVRASQnKzdWIiIiPIz9jPTdQkJMB86OOV23x7ajYWIdJKq5UoUBURECNIEF0T0derQoQ1evQqW7guCAFNTM7i4VMGIEWNRqFDhHI/Bx2cAqlatjn79Bmpke35+0wAAEydOS3f9/ft/Y+3aFbh9+xYAEWXLOqN3736oUaOmRvafHRs2rMGNG9fg77/2sx7/11+XsXHjWjx48A/09PRQsaILvv9+MJydy2lk+6Tb2HJFRPSV09MDzM1VXQO1HAwRacTQoaNw+PAJHD58AgcO/IwZM2bjyZPHUpKS02bPno+uXXPnR903b15j2LBBqFTJBevXb8GGDVtRrZorRo8ehrt37+RKDJpy//7fGD9+FJo0aYHNm3dh1aoNKFy4MIYOHYTg4JcAgK5de2D27PlajpRyCpMrIqI8QNV6xQsJE+UNpqamsLa2gbW1DWxtC6JGjZro338Qrl+/iujo6Bzfv7m5BYyNjXN8PwBw9uzvsLNzQJ8+36NYseIoWrQ4+vUbiCpVquP48SO5EoOm/PrrL3Bzqwkvr45wdCyCkiVLY/RoX1hZWeP06VMAAGNjY5ibW2g5Usop7BZIRJQHWFuLePaMk1oQfYooAjExubtPY2NAExOR6evrA4B0DZ6oqCgsWTIP5879CSMjIzRs6IkhQ4ZCoTAEAAQE3MCSJfPx/PkzuLnVhL29AyIjIzFx4rR0u6Z16NAGffsOQMuWbdS6Bapayx48+AdhYaFYtWoDChSwzNK+nz17hjp16gEADA0N0z0umUzAq1cvERQUCEfHItLySZOmQS6XS/ePHj2EnTu34uXLFzAxMYGnZ1MMHz4acrkcfn7TYGlphVevgnHhwp+ws7PHlCkz8ccfZ3DgwB4YGRnjhx9GwtOzMYKDX6Jjx28wZcosrFy5FHFxsWjevDV8fIZDTy/tFNwBATewbNkiPH36BI6OjujbdwAaNmyU7rEIggyPHj3C27fhsLS0+m+ZgCVLVkjJaurn3sdnAG7evJ5mO/v2HUXhwnY4e/Z3rFu3EsHBL1GyZCkMGTIMVatWT3ffpBuYXBER5QFsuSL6NFEEWrc2xl9/yT9dWIPc3JJw9GjsFyVYL14EYevWn+DuXlv6kj537gwkJSVh1aoNiI+Pw5IlC7Bo0Tz4+k5BeHgYxowZjnbtvDBt2mycOPEzdu3ahsaNm33W/k+ePI7ZsxfA2toaRYoUxcSJYzLc99u3bzF27HC0beuF6dNn49dfT2LTpnVo0aJ1utv29GyCLVs2onv3DqhWzRU1arijZs3aKFmytFTmxo1rWLJkPqZMmYmyZZ1x//49zJw5Ba6uNdCggScAYO/enRg6dBQGDBiC2bOnY+jQwfDwaIQ1a37C3r27MH/+bDRs6Cltc9OmtZg+fQ6Sk5Mwc+YUGBkZwdv7B7XYwsJCMXbscAwYMATu7rVx9+5t+PlNh6WlFVxcqqY5ltat2+Lw4f1o374N3Nzc4erqhlq16sLBwTHdY589ez4S/5uwTKlUwtd3NAoXtkPhwnZ4+PAB/PymYfRoX5QvXwGXLl3A6NFDsXnzLrUklHQLuwUSEeUBTK6IskYQvo4JXxYsmIMmTeqhSZN68PSsjT59uqNEiRKYMmUGgJRk69y5s5g8eSZKlSqN8uUrYty4Sfjll2OIjo7Gb7/9CktLSwwePBTFihXHwIHeKFWqzGfH4+xcHnXr1ke5chU+ue8zZ35FgQIp+1Z18StXrnyG27a0tMK6dVvQunVbPHz4ACtXLkPPnl0wbNhgvH0bDgAwMjLG+PGT0aCBJ+zs7OHh0Rhlyjjh6dMn0nacnMrh2287wNGxCJo0aY74+DgMHz4axYoVR4cOnREVFYnw8HCp/JAhQ+HiUgXVqrmif/9BOHr0EERR/fVx4MBeuLq6oX37znB0LIJmzVrim2++xZ49O9I9luLFS2Dt2s1o2NATN29ex5IlC9C5cztMnjwecXFxacqbm1tI3T/37duN6Ogo+PpOBgDs2rUVbdq0Q9OmzeHoWAQdO3ZBzZq1cfDgvqxXHOU6tlwREeUBvJAw0acJAnD0aOxX0S2wX7+BaNDAEzEx77Fx41oEBwdj4EAfWFgUAAD8++9TKJVKfPttC7XHKZVKBAUF4tmzf1G6dBm1C6NWrFgJ79+//6xjsLOzk25/at///vs0zb6dnSsgLi42w+0XLFgIY8ZMwKhR4/HgwX38/vtv2LdvF378cRbmzl0EZ+dyUCgU2LBhDZ4+fYzHjx8hKCgQbm4fZhO0t3eQbisUClhaWkndFBUKBQAgMTFBKlOpUpVU8ZVHRMRbREREqMX17NlTXLhwDk2a1JOWJSUloUiRohkeS4kSJTFlykwkJSXhzp1bOH36FI4ePQhraxsMHz463cecO/cH9u3bhdWrN8LExBQA8O+//+LJk9M4cuSAVC4xMRFubrUy3DdpH5MrXadQ4N36zRAAmCsUQGzGJyYiyr+YXBFljSAAJibajuLTLC2tpK5fM2f+iP79e2L8+FFYu/Yn6OnpITk5Gaampli/fmuax9ra2sLQ0BAfNcJAX98AQEpyJaST7SVncqE8AwOFWrnM9g0gnX3rIZ2GGwDA1q0/oVy58nB1dYNMJoOzc3k4O5eHnZ0d/P2XAACuXLkEX9/RaN68JWrWrI0+fQZg4cK5attJPT4L+DA2LSN6eh++BiuVyf89Rv15SU5ORtOmLdCzZ98MH5uav/8SNGvWEmXKlIWenh6qVKmGKlWqwcTEBBcunEv3MUFBgfDzm4Zhw0ajTBkntX13794LzZu3UiuvShRJN7FboK7T00PCN98ioe23KfMtExGlQ9UtMCyMyRVRXqOvr4/x4yfh0aMH2L17OwCgaNFiiI6OhiAIcHQsAkfHIoiPj8eKFUuRkJCI4sVL4MGD+2oJ04MH/6htMyZVE15MTIzUBe9TPrXvkiVLZbrvj925E4D9+3enWW5qaoYCBSwBAEePHkSrVt9g7NiJaN26HYoVK44XL4KyFG9GHj78ENP9+3/DxsZWahlUKVKkmDTRhurv3LmzOHXql3S3+ddfl3H8+NEMjqVAmuVxcXGYOHEM6tSph2+++VZtXdGixRAc/EJt30eOHMDlyxezf7CUa5hcERHlAWy5IsrbypWrgFat2uKnnzYgNDQExYuXgLt7bUyfPgl//30X//xzH35+0xAbGwMzMzM0adIMycnJWLJkAZ4//xe7dm3DtWv/k7bn7Fwejx8/xJkzp/H8+TPMm+cHmSxrE318at+NGjVFXFwcli5N2feOHVtw+3ZAhtv77rveuHTpAubOnYn79/9GUFAgfvvtV6xcuQxdunQHkDI26c6dADx+/AhPnjzG7NnTERYWioSEhAy3+ylLly7E/fv38NdfV7B+/Wp4eXVMU8bLq+N/FzheicDA5zh16gTWrl2BwoXt0tki0KtXf+zfvxurVi3H48eP8Pz5vzh27DB27NgiHUtq8+f7ITk5GQMH+iA8PAxhYaEICwtFfHwcOnXqhtOnT2Hv3l148SIIe/bswO7dOzLtkkjax6YQXZeUBIPjRyEAQK9u2o6GiHSUquWKyRVR3jVwoDf++OM3rFy5DFOmzMTkyTOwePE8DBs2BHK5HO7utTBixBgAgEJhiPnzl2LePD/07t0Nrq5uqFevobQtV1c3dO7cDfPm+UEul6Fz5+4IDQ3JciyZ7dvc3BwLFy7HggVz0Lt3N7i4VEWzZi3TTBahUqmSC5YuXY3Nm9djxAhvxMfHoUiRoujT53u0adMOANC370DMnj0NAwf2homJKWrVqoN27TqotT5lV6NGTTBmzHCIohLt2nXAd9/1TlOmcGE7/PjjIqxatRw7d26FjU1B+PgMR9OmLdJuEICnZ2MYGOhj585tOHRoHxITk1CqVGn4+k5B3boN0pQ/eTKlBczLS73r34QJU9GyZRtMnjwDGzeuxcqVS+Hg4IipU/1QpUq1zz5mynmCmNErnRAaGpWmz3Cue/8etiX++3UkOhqhsUrtx0QQBMDGxkw3XiMEgHVy544Mnp4mKFhQiTt3Pm/Auibl9/rQRfmxThITExAWFgxra7v/xhvpFj09GZKSlLm2P9X1qiZOnJZr+9RFqutc7d17BHZ29mrrcrtOKHO5WR+ZnS9U58+sYLdAIqI8QNUtMDxcyDdfnImIiHQNkysiojxA1S0wKUlAdLSWgyEiIsqnOOaKiCgPMDICjIxExMYKCA8XYGbG5isiUpffuwOq2NnZ4/z5q9oOg/IotlwREeURnNSCiIhIu5hcERHlEanHXREREVHuY3JFRJRHqFqumFwRERFpB8dc6ToDA0QuWwUBgJmBARAbp+2IiEhH8ULCRERE2sXkStfp6yO+S3cIAmCmrw+AyRURpY8tV0RERNrFboFERHkEx1wRERFpF5MrXZeUBINfT0D/1AkgKUnb0RCRDmO3QCIiIu1it0BdFx8Pi+6dUm635ZVBiShj7BZIRESkXWy5IiLKI9hyRZS3REZGYvnyxejY8Rs0alQH3bt3wJ49O6BUKrUdmuThw39w+3ZAhuuTkpKwYcMadOzYFh4eteDl1QrLly9CTMz7XIwyRXDwS9St64rg4Jef9fhPHcuXbj8zb9+Go2/f75CUB3sxJSYmom/f7nj7NlzboWgEW66IiPIIjrkiyjvevYvAwIF9YGNji/HjJ8POzh5//30XixfPx4sXQRgxYqy2QwQATJgwBn36fI9KlVzSXb9q1TL89dcVjBs3EQ4OjnjxIghLly5AYGAg5s1bnMvRfplPHUvBgoVw+PAJFChgqfF9r1y5DO3bd4KeXu5+dd+wYQ02bVqntqxo0WLYsWO/2rL9+/dg586tCA8PQ6lSZTBixBiUL18xS+v19fXRvn1nrFy5DBMnTsvxY8ppbLkiIsojVN0C2XJF9PVbvdof+vr6WLhwOapXrwF7ewc0atQU48dPxoEDe/H8+TNthwgAEEUx0/XHjx9D//6D4erqBjs7e7i6umH06Am4ePEcQkNDcylKzfjUscjlclhb20Aul2t0v8HBL3Hu3Fk0adJco9vNqhIlSuLw4RPS38qVG9TW//bbKfj7L0afPt9jw4ZtKF26LEaO/EFqifrUegBo2rQFzp//E69eBefqseUEJldERHmEquUqJkZAHK/aQJSh94nvM/yLS4rLctnYpNgslc2uhIQEnD59Cu3bd4JCoVBbV6dOPSxdugqFC9ul2w1tw4Y18PEZIK376af1aN7cA4sW/ZjustevX2HcuBFo1KgOOnRog40b1yI5ORnAh25uZ8+eQadObeHpWRtjxw5HZOQ7AICPzwC8ehWM2bOnw89vWrrHIpMJuH79L7WujBUrVsLWrXtQoEABAMCtWzcxeHA/NGpUB40b18Xo0UOlxEsVw8WL59GhQxs0aVIPS5YswJMnj9CvXw80blwXY8cOR0zMe6nsqVMn0K5dCzRv3hBLlizIsCtdZsf+OceSuj42bFiDunVd0/z98suxbO/78OEDcHevCQMDA2nZixdBqFvXFRcunMOwYYPRqFEddO3qhbt372QY/+eSy/VgbW0j/anqTWXXru1o06YdWrX6BiVKlMSYMb4wNDTEsWNHsrQeSGm9qlHDHYcPH9B4/LmN3QKJiPIIMzNAT09EUpKAt28F2Nll/osyUX5VYp1dhusaF22KHa33SfcrbCqFmKSYdMvWtq+LQ+2OS/ddt1ZEWFxYmnJvhkRmK74XL4IQGxsDZ+cKadYJgoBq1VyzvK1btwKwYcNWtYQg9bKJE8eidOky2LRpO0JDQzF//mzIZDL07t1fKr9lyyZMm+YHUQTGjx+JnTu3YeBAb8yePR+9e3dDly7foWXLNunuv2PHrli/fjX+/PMP1K5dF66ubnBzq4USJUoCAKKjozF27HB07twdkyfPQGhoCGbPnoFt2zZh+PAx0na2bfsJc+cuwtOnjzF9+iRcvnwBo0aNg0JhiPHjR+Ho0UOoX98DALBp01pMnz4HyclJmDlzCoyMjDBwoLdaXKIopnvsenpy9OzZ77OOJbWuXXugXbv20v2fftqA//3vEurVa5jhvj9+3lWuXLmI9u07qS179OgBBEHA7t3b0bt3f9jaFsTChXOxevVyLF++Rq3sli0bsXXrpnSPSWXr1r0oXLhwuuuCgp6jbdvmMDBQoGLFShg40Ecqm5iYiAcP7qNHjz5SeZlMBldXN9y9e+uT61OrUcMdBw/uTVNXXxudTq7CwsIwffp0XLx4EZaWlhg8eDC8vLwAAIGBgZg8eTJu3rwJe3t7TJgwAXXr1pUee/HiRcyePRuBgYFwcXGBn58fihQpoq1DISLKcYKQ0jUwJERAWBiTK6KvVXR0FADA1NT0i7fVqVNXODg4AoDUwqVadvXq//DqVTDWrv0JMpkMRYsWh7f3cMyePV3tS36/fgOl8TFNmzbH/fv3AADm5haQyWQwNTXNMNbevfvD3t4BBw/uxZEjB3Ho0H4YG5tg2LBRaNXqG8THx6FXr/7o0qU7BEGAvb0DGjb0xN9/302zndKly6B06TJYtmwRGjduhho1agIAXF3d8OzZv1LZIUOGwsWlCgCgf/9BWLVqOQYMGKK2vWvX/srw2DNKrj51LKkZGxvD2NgYAHDp0gUcP34Eq1ZtgKmpaZafdyBlEo3Hjx+hWLESassfPXoIU1MzTJ8+B5aWKWO86tatn27LT7t27eHp2STdY1KxsbFJd3n58hUxYcI0FC1aDGFhodi0aR28vftj69bdMDY2wbt3EUhOToaVlZXa46ysrPDs2b+fXJ9a8eIl8OjRQyQnJ2u8a2Vu0tnkShRFeHt7Q6lUYsuWLXj9+jXGjRsHU1NTNGnSBN7e3ihbtiz279+P06dPw8fHB8ePH4e9vT1evnwJb29v/PDDD6hXrx5WrFiBIUOG4MiRIxCEr2wsgoEBouYsgCAApgYGQCz7+hBRxqysRISEcNwVUWaefp/xuA65oP6l7m6fxxmWlQnqoyuu9tBMlywLCwsAQFRU9lq80mNnZ5/hsmfPniIy8h2aNWsgrVMqlYiPj8e7dxHSMkfHDz9OGxubZHvGuqZNW6Bp0xZ49y4CV65cxv79uzF37kyUKlUGzs7l0KJFa+zevR0PHz7Av/8+xaNHD9JMkGFv7yDdVigUKFzYTu1+YmKidL9SpSrSbWfn8oiIeIuIiA/Hk5Vjt7AokO1jUdVbasHBLzFz5hT88MNIlC3rnO19R0VFQqlUponn0aMHqFevgZRYqfaVuq5UzM0tYG6eNrasqFWrjnS7dOkyKF++Ijp0aI0zZ35F69btPmubGbGwsIBSqURk5DtYWlp9+gE6SmeTqzt37uDGjRs4ffo0ihQpgvLly6N///7YsGEDzMzMEBgYiF27dsHY2BilSpXCpUuXsH//fvzwww/Yu3cvKlasiL59+wIA5syZgzp16uB///sf3N3dtXxk2aSvj7h+A1KSK319AEyuiChjnNSC6NNM9E20XjYz9vaOMDU1xT///I1y5dJ2DRw/fiTat++MIkWKpln38bid1ON0Pl6WnJyMokWLY+7chWnKmJiYIiYmpTukvr6+2rpPTWKh8ujRQ/zyyzH88MMIAICFRQE0bdocHh6N0LlzO1y//hesra3Rv38PODmVg6urO7755ltcvHged+/eVtvWxy0ZMlnG0waknlFPqUz+r7z6OTGjY5fLBZiYpG2Fy8qxeHg0VntMfHw8Jk4ci5o1a6t1EfzU865O+O841Kfff/ToIb77rrfasocPH8DFpWqabX5pt8DUzMzMUKRIMQQFBQFIeR7kcjnCw9WnUQ8PD4e1tfUn16emVKa8rgTh654SQmejDwwMhJWVlVpXPicnJ9y5cwfXrl1D+fLlpeZWAKhevTpu3rwJAAgICICr64f+yEZGRqhQoYK0nogor+KFhIm+fnp6emjUqCn279+j1iIDAOfP/4nz5/+EjY0t9PRSkh5VEgQAL1++yPJ+ihQphtevX6FAAUs4OhaBo2MRBAe/wIYNa7Lc0yezcsnJydi9ezsePLivtlxfXx+GhoYoUMASf/75O8zMLDBv3hJ06tQVLi5Vs3UM6Xn48B/p9v37f8PGxjZNy09Gx75uXfrHnpVj+djixfMQHx+HMWMmZGnf6T3vFhYWkMvlePfunbQsOjoawcEvUbas00fH/QBlyqgvA1K6BW7atCPTv4y6BX4sJiYGL14EwdraRjr+smWdce3a/6QySqUS1679hQoVKn9yfWrv3kVALpen2wL4NdHZlisbGxtERUUhNjYWRkZGAIBXr14hKSkJISEhKFiwoFp5a2trvHr1CgA+uT6rdKIHYXIy9C9fTLndupluxERSPbA+dAfrJIW19YfkSpvPBetD9+THOvmaj7Vv3wH4/vteGDnSB337DkDBgoVw48ZVrFixDB07dkWJEiWhVCpRsGAh7NixBX37DkBAwA1cunQ+3S/Y6XFzq4nChQtjxozJGDjQG9HRUZg3bzZcXd2yPObF0NAQz579i8jId2m6njk5OaN27boYP34UBg36AZUqVUZYWBhOnDiGhIQENGzoiQsXzuH161e4evV/sLOzx++/n8bZs2fg7Fw+28+ZytKlCzF+/CRERUVh/frVaSaDyOzYa9RwT/fYs3IsqROgY8cO4bffTmHx4hWIjY2RLjSsUBhm63mXyWQoVaoMHj9+KI0je/z4IeRyOUqWLC2Ve/UqGFFRkShTpmya2L+kW6C//xLUqVMPhQvbITQ0BBs2rIFcLkPjxs2kMl26dIef3zQ4O5dHuXIVsGfPDsTGxqJVqzZZWq/y6NFDlC3rpPUhPIKQ9tyRnZB0NrlycXFBwYIFMXPmTEyaNAkhISHYtCmlSTMhISFNM7eBgQESEhIAALGxsZmuzypra7MvOAINef8eaNcq5XZ0tG7ERBLWh+7J73Xi8N+whLg4BWxsFJkXzgX5vT50UX6qk7i4OISHyyCXC9DT083OOhnFVahQQaxbtwnr16/BjBmTERn5Dg4OjhgwYBC8vDpCLpcBkGHixKlYtGgeevToBFdXN/Tu3Q8XL174bz0gl8ukfXy8TE9PhgULlmDhwnkYMKA3jI2N4OnZGD/8MAJ6erJ0tyGTCRCED89n+/adsGLFUgQFPcePP6bt5jZ79o/YtGkDNm1ai9evX8HQ0Ag1a9bC6tXrYW5uhqZNm+HWrZuYPHkcBEFAuXLlMXToCKxbtwZKZVK6MajiUN1XfRlXlW3SpCnGjBkOUVTCy6sjevfuC5lM/XgUCv1Mjz09nzoW1UQkcrkMJ0/+gtjYWAwa1FdtGy1btsGUKdOzte9atWrjzp0AdOrUGQDw5MlDFCtWHCYmRlKZx48f/NdlzzHd2D9XaOgbTJ8+Ee/evUOBApZwcamC9es3w9b2Q5e+Zs2aIzIyAhs2rEZYWBjKlHHCkiX+KFjQNkvrVe7cCUDt2nXSPAe59d5VKgXIZDJYWprA0NDws7cjiFntOKsFt27dwvDhwxEcHPxfn9z+mDNnDtq3b4/Y2FgsXvzhyt47duzAzp07cfToUbRq1QrfffcdunbtKq0fPnw4bGxsMGnSpCzvPywsClp/dt6/h03x/wZtRkcjLE6p/ZgIgpDyBUUnXiMEgHWi4u+vj+nTDdGpUyJWrNDeGE3Wh+7Jj3WSmJiA0NBgWFvbQV8/7dgjbdPTkyEpSfnpgpQlwcEv0bHjN9i790i6E3lkhS7WyYsXQejX7zscOnTii77067LY2Fh8+20LbNq0Q63ucrM+EhMTEBYWDBubtOcL1fkzK3S25QoAKleujDNnziAkJASWlpa4cOECLC0tUbRoUVy4cEGtbGhoqNQVsFChQmmu+h0aGopy5cpla/+iCO1/AH20f52IiSSsD92T3+sk9ZgrXXge8nt96KL8VCf55Tgpb3NwcEStWnVx6tQv+Oabb7UdTo44deoX1K5d77OTYk360nOkbraRA4iIiEDXrl3x9u1b2NraQk9PD3/88Qfc3Nzg4uKCu3fvIi7uw6+y165dg4tLyrSdLi4uuHbtmrQuNjYW9+7dk9YTEeVVVlac0IKIKK/x9h6OAwf2ppngJC9ITEzEgQN74e09TNuhaITOJlcFChRATEwM5s+fj8DAQOzduxf79+9H//794ebmBjs7O/j6+uLhw4dYu3Ytbt26hQ4dOgAA2rdvj+vXr2Pt2rV4+PAhfH194ejo+PVNw05ElE2qS54wuSKi/MbOzh7nz1/VidYPTbOxscFPP+1IMy1+XqCvr4/Nm3dKMxB+7XQ2uQKAxYsXIzAwEG3atMHmzZuxdOlSVK5cGXK5HCtXrkRISAi8vLxw5MgRrFixAvb2KW8mR0dHLF++HPv370eHDh0QERGBFStWaH32ESKinKZqueJ1roiIiHKfTo+5KlmyJLZu3ZruumLFimHbtm0ZPrZBgwZo0KBBhuuJiPIi1Zird+8EJCUBejp9liciIspb+LGr6/T1ET1lJgQBMNHXB2LjtR0REemwAgU+jMJ9+1aArS1H9BMREeUWJle6zsAAsT7DUpIrAwMATK6IKGN6eikJVkSEwOSKiIgol+n0mCsiIsq+1NOxExERUe5hcqXrkpOhd+Ma9G5cA5KTtR0NEX0FOKkFERGRdrBboK6Li4NlM4+U29HR2o2FiL4KbLkiIiLSDrZcERHlMbyQMBERkXYwuSIiymNULVdv32o5ECL6LB06tEHduq7p/l2/fhUAEBQUiN69u8HTszbWrVuF8+fP4ttvW6JRozq4cuXSZ+9bFEUcOLA30zJ//XUZgwf3RaNGddCsWQOMGjUU9+//La3fsGENfHwGfHYMGfn4mDXl4sXz+OGHgWjWrAGaN/eEr+9oPH36RGPb14QXL4Jw6dKFDNfHxLzH0qUL8e23LeHhUQtdunyLTZvWITExMRejTBEc/BJ167oiOPjlZz0+PDwMs2dPR+vWTVC/fk306NEJ+/bt0tj2cxq7BRIR5TEcc0X09Rs6dBQaNWqSZrm5uQUAYP/+PQCArVv3wMzMHEOHDoKbW0306fM9LC0tP3u/N29ex6JFP8LLq2O66+/f/xvjx4+Ct/dwTJw4HQkJ8di/fw+GDh2EzZt3ws7OHl279kDHjl0+O4aMfHzMmrBnz06sXbsC/foNwujRvlAqk7Bly0/w9v4eq1dvRNGixTSyny81d+5MVKlSDbVq1Ul3/axZ0xAZ+Q4zZsyBjY0tHj16gEWL5iEi4i1GjBiby9F+PlEUMXr0MNjb22PhwuWwtLTAzZs3sXDhXCQmJqFr1+9QsGAhHD58AgUKfP7rPCcxuSIiymNULVdhYUyuiL5WpqamsLa2yXD9+/fRKF26DBwcHKX7lStXQeHCdl+0X1HM/PINv/76C9zcaqolX6NH++Latas4ffoUevToDWNj4y+KISMfH/OXevEiCKtWLcP48ZPRrFlLAICengyTJ8/AwIG9sWnTOkydOksj+/pSmdVLdHQ0zp37Axs3bkOZMk4AADs7e8TExGDePD8MGzYaMtnX0Vnt8eNHePDgPpYuXQUzMzPo6clQsKAdXr58gaNHD6Jr1+8gl8szfW9oG5MrIqI8xtqaLVdEmXr/PuN1cjlgaJi1sjIZYGT06bImJtmL7xP8/Kbhl1+OAQBOnPgZhQvb4dWrYMyZMwObNq3Dvn1H8fr1Kyxa9COuXv0fLC2t0LJlG/Tq1Q9yuRwAcPnyRaxduwLPnv0LR8ei+OGHEXBwcMTQoYMAAHXrumLZstWoVs1Vbd+CIMOjR4/w9m04LC2t/lsmYMmSFVJStWHDGty4cQ3+/mvh4zMAN29eT3MM+/YdReHCdjh79nesW7cSwcEvUbJkKQwZMgxVq1b/5DHv3XsEVlbW2LBhDU6fPonIyHeoXr0GRo4ch0KFCiM4+CU6dvwG/fsPwq5d29G0aXOMHDlObZunT5+EubkFmjRprrZcJpNh4sTpMDAwSHM8Kh06tEHfvgPQsmUb+PgMQKlSpXHx4gUkJydh/vyl6NWrS5p9Z3asPj4DUKOGOwICbuDmzRsoWLAQRowYA3f3WvDzm4abN6/j5s3raeJIef5T6uCvv/4nJVcA0KCBJ8qVKw9BSPksePr0CZYvX4Tbt28hOTkJzs7lMXbsRBQvXgLXr1/F7NnT0bt3f6xduxIJCQno0aM3KlSohHnz/BASEoL69Rti4sRpkMlk8PEZgGrVXPHXX1fw4MF9ODmVw7hxk1CsWPE0dRcVFYUlS+bh3Lk/YWRkhIYNPTFkyFAoFIZpyspkKbH+9dcVeHo2lpa3b99ZqidV3e7dewQ3blzD7NnT02ynb98B6Nt3wCffBzmByRURUR7zYcwVkyui9NiWyLh1J75xU0Tu2Cfdt6lQCkJMTLplE2rXxbtDx6X71q4VIQsLS1Mu5E3kF0Sb1rBhoxEfH//f7VEQBAH9+/dEly7foUmT5hBFERMnjkXp0mWwadN2hIaGYv782ZDJZOjduz+ePHmMceNGoE+f79GoUVP88ccZ+PqOwo4d++HnNw8TJ47F4cMnpC6IqbVu3RaHD+9H+/Zt4ObmDldXN9SqVTfD1qTZs+dL436USiV8fUejcGE7FC5sh4cPH8DPbxpGj/ZF+fIVcOnSBYwePRSbN++Co2ORTI+5QAFLzJ07E7dvB2DSpOkwN7fAqlXL4es7CuvXb5Ued+tWADZs2AqlUpkmtkePHsLJqVy6rTrFi5fIYm2kOH78KBYt8oe+voGUZKbed1aOdcuWjRg1ajxGjRqP1av98eOPs7Bv31EMGzYagYHPUbFiZfTs2SfNvk1MTNGiRWusXLkUx44dQq1adeDq6obq1d1QtGhx6bkfN24EatRwx6hR4xEdHY1Fi37EqlXL8OOPiwEAoaEh+PPPP+Dvvwbnz5/D6tXLUbp0GUyYMA3v3kVg0qSxqF/fAw0apMxivW3bTxg0yAfjxk3Cpk1rMXr0MGzfnna83ty5M5CUlIRVqzYgPj4OS5YswKJF8+DrOyVN2ZIlS6N69RqYMmU8tm1zQq1atVG9ujtcXKrCzMwsTflGjZrA3b1Wqno4hp07t6Jly28++T7IKV9HG2F+pq+P96PHI2bMeEBfX9vRENFXgFOxE339FiyYgyZN6qn9ffddJwApXQYVCgUUCgWsrW1gZWUNmUwGU1NTWFpa4tq1v/DqVTDGjp2IokWLo1o1V3h7D8eePTsBAD//fBiVKrmgd+/+KFKkKHr06I1OnbohJiZGGstkbW0D/XS+dxQvXgJr125Gw4aeuHnzOpYsWYDOndth8uTxiIuLS1Pe3NwC1tY2sLa2wb59uxEdHQVf38kAgF27tqJNm3Zo2rQ5HB2LoGPHLqhZszYOHtyXZjsfH/P79+9x8uRxjBw5DtWquaJ06TKYOnUmnj9/hr/+uiI9rlOnrnBwcESRIkXTbDM6OgqmpqafUTtp1a5dF5UqucDZuVy6+87KsdaqVRctW7aBg4MjevXqhzdvXiM8PAympqbQ09ODkZFRugkvAIwbNwnDh4+GQqHA7t07MGbMcHTo0AYXL54HAMTHx6Ndu/bw8UlpoXRyckaLFq3VJu5ISkqCj89wFC1aHO3bd4RSqYSXVydUrFgJderUQ+nSZfH8+b9SeXf32ujUqRuKFy+BceMmISLirdpzD6R0vTx37iwmT56JUqVKo3z5ihg3bhJ++eUYojO4xNC8eUvQv/8gxMbGYvPmTRg6dBC6dvXC3bt30pRVKAyl11d4eBg2b16PSZOmo3Dhwp98H+QUtlzpOgMDxIydAEEAjA0MAMRrOyIi0nGpJ7QQxZQuI0T0QcjT4IxXftRdKPTu44zLftTiEXY17Ze/z9Wv30A0aOCptkxPL2tf2549e4rIyHdo1qyBtEypVCI+Ph7v3kXg+fNncHIqp/aY778fDAAICwv95PZLlCiJKVNmIikpCXfu3MLp06dw9OhBWFvbYPjw0ek+5ty5P7Bv3y6sXr0RJiYpCc2///6LJ09O48iRA1K5xMREuLnVSncbqQUGPodSqUT58hWlZebmFihatBiePXsqTURhZ2ef4TbMzS0QFaWZVsXChdPuJ/W+s3KsqRNAk/+6kiYlJWVp/zKZDB06dEGHDl0QGhqCS5cuYPfu7Zg0aRz27DkEGxtbtGvXASdO/Iz79+/h+fN/8c8//8DKykptO/b2DgAgddlLfQwKhQIJCQnS/UqVXKTbxsYmKFKkKJ49e4qSJUulOu6nUCqV+PbbFmr7USqVCAoKVEtGU++nd+/+6N27P169eoE///wTu3Ztw/jxI7Fv39F0jz8qKgoTJ45Fhw5dULt2XQCffh9YWBTI9Dn9XEyuiIjyGFXLVXKygMhIwCL9HzqJ8q/sjIHKqbKfYGlplaZrXFYlJyejaNHimDt3YZp1JiamWU7S0uPvvwTNmrVEmTJloaenhypVqqFKlWowMTHBhQvn0n1MUFAg/PymYdiw0WpjgpKTk9G9ey80b95KrbxCofhkHKrxUB9LTlYiOVn5yXIA4ORUDrt3b4MoitK4JJXffvsVV65cxIQJU9OsU8X+qXhSL8vKsaZXL5+aYAQArl+/itu3A9CrVz8AgI2NLdq0aYcGDTzw7bctcetWAGrWrI3vv+8JC4sCqFu3Pho3bobnz//Fzp3b1Lb1cQzpHXtGZZVKJQRB/QeH5ORkmJqaqnXVVLG1tU2z7I8/fsPbt2/x7bcdAACOjkXQqVNXuLvXQvfuHfDkyaM0swSKooiZM6egYMFC0o8Eqn1n9j7IKewWqOuUSsjv/w35/b+BdPoLExF9zNAQMDZm10Ci/KpIkWJ4/foVChSwhKNjETg6FkFw8Ats2LAGgiDA0bEoHj16qPaYQYP64vTpk5l+mQZSrnF1/Hja1gNTUzMUKFAgzfK4uDhMnDgGderUwzfffKu2rmjRYggOfiHF6OhYBEeOHMDlyxc/eYwODo6Qy+W4e/e2tOzduwgEBT3P8vTpnp6NERkZiV9/Pam2PDk5Gbt2bUNsbCwAQF9fHzGpxt3FxMTg7dvwLO1D5UuOFcg8yYmKisTmzRvStDoaGRlDLtdDgQIFcOPGNYSGhmDZstXo1q0natRwx+vXr7KUvGXk0aMH0u3o6Gi8eBGI0qVLq5UpWrQYoqOj/3vdpRx3fHw8VqxYioSEtNfgevUqGD/9tB7x8epdTFXdN9Obfn3z5g34+++7mDZtttpEFZ96H+QUJle6LjYWVvXdYVnPHfjvTU5E9Cm81hXR1y06OhphYaFp/mKz8F3Aza0mChcujBkzJuPx40cICLiBefNmw9DQEHK5HO3atcetWzewa9c2BAUFYuvWTXj69DGqVKkGo/9mP7x//29pAonUevXqj/37d2PVquV4/PgRnj//F8eOHcaOHVvQpUv3NOXnz/dDcnIyBg70QXh4mHQc8fFx6NSpG06fPoW9e3fhxYsg7NmzA7t370h3fNTHjI2N0abNt1i8eB6uX7+KR48eYsaMlNaLGjXcs/AMA4UL26FPn+8xd+5M7N69HYGBz3H37h1MmjQWL14EYdAgHwCAs3N5PH78EGfOnMbz588wb54fZLLszTb3JccKAEZGRggKCkw3qatTpz6KFSuO4cOH4Pz5PxEc/BIBATcwbdoEFC1aDFWqVIOFhQViY2Nx7twfCA5+iaNHD2H//j1fdJHhX389gV9+OYZ//32KuXNnoFAhO1Stqj67ZPHiJeDuXhvTp0/C33/fxT//3Ief3zTExsakO0FFy5ZtIJfLMWKED65e/R9evnyJv/66gqlTJ6BBA4803Tz/+usyfvppPcaNmwi5XCa9viIj333yfZBT2C2QiCgPsrQUERTEliuir9WyZQuxbFna7kz9+w/65Exncrkcc+cuwpIl8zFgQC8YGRnDw6MxfHyGAUhp9Zk1ax5Wr16OtWtXonjxkvjxx8WwsbGFubkFatRwx+DBfTFtml+acV+eno1hYKCPnTu34dChfUhMTEKpUqXh6zsFdes2SBPLyZO/AAC8vNS7w02YMBUtW7bB5MkzsHHjWqxcuRQODo6YOtUPVapUy9Jz5OMzHP7+SzBp0jgkJibC1dUNS5aszLQr4Md69uyLggULYd++3diwYS0UCgUqV3bB6tUbpBkQXV3d0LlzN8yb5we5XIbOnbsjNDQky/sAgIoVK33RsbZu3Q5z5szAs2dPsXHjdrV1enp6WLp0FdavX43Fi+chLCwU5uYWqF+/IcaNmwSZTIaKFSujd+/+WLjwRyQkJKBUqdIYOXIc5s6diZCQN9k6FpUmTZrj8OEDWLBgDlxcqmHBgmXpdm2cPHkGFi+eh2HDhkAul8PdvRZGjBiT7jbNzS2wcuUGrFu3EjNnTsG7dxGwsrJG06Yt0KdP2tf9qVMnkJSUhPHjR6ktr1KlGvz912b6Psgpgvgl7YF5XGhoFLT+7Lx//2HK2OhohMYqtR8TQRAAGxsz3XiNEADWycc6djTC2bN68PePRadOWRsQrUmsD92TH+skMTEBYWHBsLa2g75+1r9w5xY9PRmSktjlX5ewTrLGx2cAqlatjn79BubofnKzPjI7X6jOn1nBboFERHkQuwUSERHlPiZXRER5EC8kTERElPs45oqIKA9SJVdhYUyuiIhIs/z912o7BJ3FlisiojzI2potV0RERLmNLVe6Tl8fMUOGAgJgrK8PxKadFpWI6GPsFkj0AefuIqJP0dR5gsmVrjMwwPtpsyAIgLGBAQAmV0T0aarkilOxU34mk6V00ElOTgKg0G4wRKTTUs4TH84bn4vJFRFRHqSaLZDJFeVnMpkc+vqGiI6OgFwuhyDo1mgIpVJAcjJb1XQJ60S35FZ9iKISUVERMDAwzPYFoj/G5ErXKZWQBQVCEABYldd2NET0lWC3QCJAEARYWFghLOwVwsNfazucNGQyGZRKXlNJl7BOdEtu1ocgyGBubgVB+LLPTSZXui42FtaulVJuR0drNxYi+mqoJrSIixMQEwMYG2s5ICIt0dPTR8GCjkhKStR2KGoEAbC0NMHbt+/zzUWddR3rRLfkdn3o6el/cWIFMLkiIsqTTEwAfX0RiYkC3r4VYGzMbwqUfwmCAH19A22HoUYQAENDQ+jrJ/KLvI5gneiWr7U+dKvzMRERaUTKL34cd0VERJSbmFwREeVRnNSCiIgodzG5IiLKo1TJFSe1ICIiyh1MroiI8ih2CyQiIspdTK6IiPIodgskIiLKXZwtUNfp6SG2T38AgJGeHoAE7cZDRF8NXuuKiIgod+l0y1VwcDAGDhyIatWqwdPTEz/99JO07t69e+jYsSNcXFzQvn173LlzR+2xx44dQ+PGjeHi4gJvb2+Eh4fncvQaolAg+sdFeD9vEaBQaDsaIvqKsOWKiIgod+l0cjV8+HAYGxvjwIEDmDBhApYsWYJff/0VMTExGDBgAFxdXXHgwAFUrVoVAwcORExMDADg1q1bmDhxInx8fLB7925ERkbC19dXy0dDRJS7OKEFERFR7tLZ5Ordu3e4efMmBg8ejOLFi6Nx48aoV68eLl26hOPHj0OhUGDs2LEoVaoUJk6cCBMTE5w4cQIAsG3bNrRo0QLt2rWDs7Mz5s2bh7NnzyIwMFDLR/UZRBFCaCiE0FB8VVdQIyKt44QWREREuUtnkytDQ0MYGRnhwIEDSExMxJMnT3D9+nWUK1cOAQEBqF69OgQh5QuDIAioVq0abt68CQAICAiAq6urtC07OzvY29sjICBAG4fyZWJiYFO+JKzLlQT+a5kjIsoKJldERES5S2cntFAoFJgyZQpmzpyJLVu2IDk5GV5eXujYsSN+++03lC5dWq28tbU1Hj58CAB48+YNChYsmGb9q1evshWDoAvfRz6KQSdiIqkeWB+6g3WSlo3Nh26Buf28sD50D+tEt7A+dA/rRLfoUn1kJwadTa4A4PHjx/Dw8ECfPn3w8OFDzJw5E7Vq1UJsbCwMDAzUyhoYGCAhIWUmvbi4uEzXZ5W1tdmXHYAmGKk3LupETCRhfege1skHqg+DqCgBFhZm0NfP/RhYH7qHdaJbWB+6h3WiW762+tDZ5OrSpUvYt28fzp49C0NDQ1SqVAmvX7/GqlWrUKRIkTSJUkJCAgwNDQGktHqlt97IyChbMYSFRWl/mNP797BJdVcnYiIIQsqbnfWhO1gnaSUnA4JgClEU8OBBNAoVyr0nhvWhe1gnuoX1oXtYJ7pFl+pDFUtW6GxydefOHRQrVkxKmACgfPnyWL16NVxdXREaGqpWPjQ0VOoKWKhQoXTX29raZisGUdSBOSQ+2r9OxEQS1ofuYZ18IJMB1tYiQkMFvH4toGDB3H9iWB+6h3WiW1gfuod1olu+tvrQ2QktChYsiGfPnqm1QD158gSOjo5wcXHBjRs3IP73TIuiiOvXr8PFxQUA4OLigmvXrkmPCw4ORnBwsLSeiCi/cHBIOU++fKkDndaJiIjyOJ1Nrjw9PaGvr49Jkybh6dOnOHPmDFavXo0ePXqgefPmiIyMhJ+fHx49egQ/Pz/ExsaiRYsWAICuXbvi8OHD2Lt3L+7fv4+xY8eiYcOGKFKkiJaPiogodzk4KAEAQUE6e7onIiLKM3S2W6CZmRl++ukn+Pn5oUOHDrCyssLgwYPRuXNnCIKANWvWYOrUqdizZw+cnJywdu1aGBsbAwCqVq2KGTNmYNmyZXj37h3q1KmDmTNnavmIPpOeHuI6dwMAGOrpAcjepBxElL+pWq5evGDLFRERUU7T2eQKAEqXLo1Nmzalu65y5co4ePBgho/18vKCl5dXToWWexQKRC1fDUEADBUKIIrJFRFlnarl6uVLtlwRERHlNH7aEhHlYY6OKS1XQUFsuSIiIsppTK50nSgC79+n/H1NU6UQkU6wt2fLFRERUW7R6W6BBCAmBrYl7FJuR0drNxYi+uqoWq6CgwUkJQF6POsTERHlGP6USUSUhxUsKEJfX0Rycsq1roiIiCjnMLkiIsrDZDLAzo4zBhIREeUGJldERHmcasbAFy94yiciIspJ/KQlIsrj7O1VMwbylE9ERJST+ElLRJTHOTqqZgxkt0AiIqKcxOSKiCiPc3DgmCsiIqLcwEl5dZ1cjvg27QAACrkcgFKr4RDR10c15ordAomIiHIWkytdZ2iIyA1bIAiAwtAQiE7UdkRE9JVRtVyxWyAREVHO4s+YRER5nGrMVXi4DO/fazkYIiKiPIzJFRFRHmduDpiaqlqveNonIiLKKfyU1XXv38O2oDlsbM3Bn5yJ6HOpWq84qQUREVHOYXJFRJQPfJgxkKd9IiKinMJPWSKifMDeXjVjIFuuiIiIcgqTKyKifMDRkWOuiIiIcho/ZYmI8oEP17piyxUREVFOYXJFRJQPcMwVERFRzuOnLBFRPqBquXr5UoAoajkYIiKiPEpP2wHQJ8jliG/cFAIAA7kcgFLbERHRV8jOToQgiIiLExAWJsDGhhkWERGRpjG50nWGhojcsQ+CANgYGgLRidqOiIi+QgoFYGsr4s0bAS9fMrkiIiLKCewWSESUT6hmDAwK4qmfiIgoJ/ATlogon1CNu3rxgjMGEhER5QR2C9R179/DpkKplNtv3mg3FiL6qtnbc8ZAIiKinMTk6isgxMRoOwQiygMcHdlyRURElJP48yURUT6hutYVx1wRERHlDH7CEhHlE6mvdUVERESax+SKiCifULVcvXolIJFXdSAiItI4JldERPmEjY0IAwMRoiggOJitV0RERJrG5IqIKJ+QyT7MGPjyJU//REREmsbZAnWdTIaE2nUhANCXyQAotR0REX3FHB2V+PdfGYKC2HJFRESkaUyudJ2REd4dOg5BAGyMjID3UdqOiIi+YrzWFRERUc7hpysRUT7Ca10RERHlHCZXRET5iGrGQLZcERERaZ7OfroeOHAATk5Oaf6cnZ0BAPfu3UPHjh3h4uKC9u3b486dO2qPP3bsGBo3bgwXFxd4e3sjPDxcG4fx5d6/h3W5ErByLgG8f6/taIjoK6e61hXHXBEREWmeziZXLVu2xPnz56W/P/74A8WKFUPPnj0RExODAQMGwNXVFQcOHEDVqlUxcOBAxMTEAABu3bqFiRMnwsfHB7t370ZkZCR8fX21fESfTxYWBllYmLbDIKI8QNVyxdkCiYiINE9nP10NDQ1ha2sr/R05cgSiKGL06NE4fvw4FAoFxo4di1KlSmHixIkwMTHBiRMnAADbtm1DixYt0K5dOzg7O2PevHk4e/YsAgMDtXxURETapWq5evdOQHS0loMhIiLKY76K2QIjIiKwbt06zJo1CwYGBggICED16tUhCCndWgRBQLVq1XDz5k14eXkhICAA33//vfR4Ozs72NvbIyAgAEWKFMnyfgVd6DXzUQw6ERNJ9cD60B2sk6wxMwMsLES8eyfg5UsZnJxy5vIOrA/dwzrRLawP3cM60S26VB/ZieGrSK527tyJggULonnz5gCAkJAQlC5dWq2MtbU1Hj58CAB48+YNChYsmGb9q1evsrVfa2uzL4haQ4zUGxd1IiaSsD50D+vk04oVA27dAqKiTGBjk7P7Yn3oHtaJbmF96B7WiW752upD55MrURSxd+9e9O/fX1oWGxsLAwMDtXIGBgZISEgAAMTFxWW6PqvCwqIgip8ZuKa8f4/U3310IiaCIKS82VkfuoN1knWFCxvh1i093LsXB1fXxBzZB+tD97BOdAvrQ/ewTnSLLtWHKpas0Pnk6vbt23j9+jVatWolLVMoFGkSpYSEBBgaGma63sjIKFv7FkVovTLx0f51IiaSsD50D+vk0+ztP1zrKqefK9aH7mGd6BbWh+5hneiWr60+dD65OnfuHFxdXWFhYSEtK1SoEEJDQ9XKhYaGSl0BM1pva2ub8wFrmkyGxCpVAQD6MhmAnBkfQUT5h6NjyqdUUJDOzmlERET0VdL5T9Zbt26hWrVqastcXFxw48YNiP+lsaIo4vr163BxcZHWX7t2TSofHByM4OBgaf1XxcgIEafO4t2vZ4FstrwREaVHNWPgixc6MEqYiIgoD9H55Orhw4dpJq9o3rw5IiMj4efnh0ePHsHPzw+xsbFo0aIFAKBr1644fPgw9u7di/v372Ps2LFo2LBhtmYKJCLKq1TXunrxQuc/AoiIiL4qOv/JGhoaCnNzc7VlpqamWLNmDa5duyZNvb527VoYGxsDAKpWrYoZM2ZgxYoV6Nq1KywsLDBnzhxthE9EpHNULVcvXwpQsqcxERGRxgii+DUNEctdoaHan50EMTGwqucGAJDf/xuhMcnaj4kgCICNjZluvEYIAOskOxITgSJFTKFUCrh9OxqFCmn+CWN96B7WiW5hfege1olu0aX6UMWSFTrfcpXviSLkgc8hD3z+dU2VQkQ6S18fUkL18iXHXREREWkKkysionxINe6KMwYSERFpDj9ViYjyIUdHzhhIRESkaUyuiIjyIXt7zhhIRESkafxUJSLKh9hyRUREpHlMroiI8iG2XBEREWmenrYDoE8QBCQ5OQMA9AT+wkxEmsGWKyIiIs1jcqXrjI3x9tz/UubXNzYGYqK0HRER5QGq2QLfvJEhPh5QKLQcEBERUR7A/iBERPmQlZUIQ0Ne64qIiEiTmFwREeVDgvCh9erlS34UEBERaQI/UXVdTAws67mhQF03ICZG29EQUR7i4JAy7iooiC1XREREmsAxV7pOFKH3z33pNhGRprDlioiISLP4iUpElE+x5YqIiEizmFwREeVTH6Zj50cBERGRJvATlYgon1JdSJizBRIREWkGkysionxK1XIVGCjjkE4iIiINYHJFRJRPqVqu3r8XEBmp5WCIiIjyACZXuk4QkFykKJKLFE25MA0RkYYYGwNWVhx3RUREpCmf9WkaGBiIH3/8EUOGDMGbN2+wb98+XL16VdOxEQAYGyP82h28vX4n5ZsQEZEGqaZjf/GCP94QERF9qWwnV3/99Re++eYbvHjxAufOnUN8fDyePHmC3r1749SpUzkRIxER5ZAP07Gz5YqIiOhLZfvTdP78+Rg1ahSWLVsGPb2UaxCPHTsWo0ePxrJlyzQeIBER5ZwPFxJmyxUREdGXynZy9eDBAzRo0CDN8kaNGuH58+caCYpSiY1FgaYNYNGkARAbq+1oiCiPYcsVERGR5uhl9wEODg64ffs2ihQporb8jz/+gIODg8YCo/8oldC/eUO6TUSkSY6OHHNFRESkKdlOroYPH47x48fj9u3bSE5OxqFDhxAUFISff/4Z8+bNy4kYiYgoh9jbp/xo8/IlW66IiIi+VLY/TZs0aYLt27cjLCwMZcqUwW+//YaEhARs374dLVu2zIkYiYgoh6harl6+FJCcrOVgiIiIvnLZbrkKDg6Gs7Nzmlaq+Ph4HDp0CO3atdNUbERElMMKFRIhl4tIShLw5o0AOztR2yERERF9tbLdcuXp6YkRI0YgJiZGbXlUVBR8fX01FhgREeU8uRxSQsVxV0RERF8m28mVKIoIDAzEt99+i3/++ScnYiIiolykmjHwxQuOuyIiIvoS2f4kFQQBK1euRJMmTdClSxfs27dPWk45Q2ltDaW1tbbDIKI8SnWtq6AgnseJiIi+RLbHXImiCLlcjtGjR6NKlSrw9fXF1atXMWLEiJyIj0xMEPb3UwgCYGNiAsRGaTsiIspjVC1XnDGQiIjoy3zRJ2njxo2xb98+3Lt3D/369dNUTERElIvYckVERKQZ2U6uatSoAX19fel+sWLFsGfPHpQrVw6iyFmmiIi+Nmy5IiIi0oxsf5Ju3boV5ubmassMDQ0xf/583L9/X2OB0X9iY2HRriUs2rYEYmO1HQ0R5UGqlivOFkhERPRlsjTmqmfPnvD394e5uTl69uyZYTlBELB582aNBUcAlEoYXDwv3SYi0jRHx5RzS2ioDLGxgJGRlgMiIiL6SmUpuXJzc5O6Arq5ueVoQKklJCRgzpw5OHbsGPT19dGhQweMGDECgiDg3r17mDp1Kh48eIDSpUtj+vTpqFixovTYY8eOYcmSJQgJCUHdunUxc+ZMWFlZ5VrsRERfCwsLwNhYREyMgJcvBZQqxS7eREREnyNLyZWPj0+6t3ParFmzcOXKFWzYsAHv37/HiBEjYG9vj2+++QYDBgxAmzZtMHfuXOzcuRMDBw7Er7/+CmNjY9y6dQsTJ07E9OnT4ezsDD8/P/j6+mLNmjW5FjsR0ddCEICSJZW4c0eOhw9lKFUqWdshERERfZWyPObq5cuXWLduHSIjIwEA8fHxmD17Ntq0aYOePXvijz/+0GhgERER2L9/P2bOnInKlSujVq1a6Nu3LwICAnD8+HEoFAqMHTsWpUqVwsSJE2FiYoITJ04AALZt24YWLVqgXbt2cHZ2xrx583D27FkEBgZqNEYiorzCySmla+A//8i1HAkREdHXK0vJ1d27d9GmTRvs378f79+/BwCMGzcOO3bsQMOGDVG3bl2MGzcOZ86c0Vhg165dg6mpqVo3xAEDBmDOnDkICAhA9erVpQsXC4KAatWq4ebNmwCAgIAAuLq6So+zs7ODvb09AgICNBYfEVFe4uycklz9/TdnDCQiIvpcWeoWuGTJErRu3RrTp08HAAQGBuLEiRPo1q0bRo0aBQCwsrLCmjVr4OnpqZHAAgMD4eDggEOHDmH16tVITEyEl5cXBg8ejJCQEJQuXVqtvLW1NR4+fAgAePPmDQoWLJhm/atXr7IVg6ALE2d9FINOxERSPbA+dAfr5Ms4O6d0BfznH5lGnkPWh+5hnegW1ofuYZ3oFl2qj+zEkKXk6saNGxg3bpx0/+zZsxAEAS1btpSWVa9eHbNmzcr6nj8hJiYGz549w65duzBnzhyEhIRgypQpMDIyQmxsLAwMDNTKGxgYICEhAQAQFxeX6fqssrY2+7KD0AQjGWBsLN3ViZhIwvrQPayTz1O7dsr/R4/ksLQ0g1xDvQNZH7qHdaJbWB+6h3WiW762+shScpWUlASFQiHdv3jxIszMzFCtWjVpWWJiotrFhb84MD09REdHY+HChXBwcACQMu5r586dKFasWJpEKSEhAYaGhgAAhUKR7nqjbM4vHBYWBZ24LvKzVxAEwNrERHdiyucEIeXNzvrQHayTL2NqChgbmyImRsDVq9FfPGMg60P3sE50C+tD97BOdIsu1YcqlqzIUnJVrlw5XLhwAV26dEF4eDguXLiAZs2aQSb70Df/6NGjcHZ2/ryI02FrawuFQiElVgBQokQJBAcHw83NDaGhoWrlQ0NDpa6AhQoVSne9ra1ttmIQRWi9Mj+mizHlZ6wP3cM6+TyCAJQpo0RAgBz37slRsmSSRrbL+tA9rBPdwvrQPawT3fK11UeWRi77+Phg7ty58PHxQefOnSGTyTBw4EAAwD///IM5c+Zg48aN6Nu3r8YCc3FxQXx8PJ4+fSote/LkCRwcHODi4oIbN25A/O+ZFkUR169fh4uLi/TYa9euSY8LDg5GcHCwtJ6IiNL6MGMgJ7UgIiL6HFn6BK1Tpw62bdsGR0dHNG7cGPv27UOpUqUAAIcOHcKlS5ewcOFCeHh4aCywkiVLomHDhvD19cX9+/dx7tw5rF27Fl27dkXz5s0RGRkJPz8/PHr0CH5+foiNjUWLFi0AAF27dsXhw4exd+9e3L9/H2PHjkXDhg1RpEgRjcWXa+LiYN6tA8y7dgDi4rQdDRHlYakntSAiIqLsE0RRdxvaoqKiMHPmTPz6668wMjJCt27d4O3tDUEQcOvWLUydOhWPHz+Gk5MTpk+fjvLly0uPPXDgAJYtW4Z3796hTp06mDlzJiwtLbO1/9BQ7ffxxPv3sC1hl3I7OhqhsUrtx0QQBMDGxkw3XiMEgHWiCadPy9GtmzHKlUvG2bMxX7Qt1ofuYZ3oFtaH7mGd6BZdqg9VLFkqq8vJlbbpQmUyudJNuvSGpxSsky8XGCigenVT6OuL+PffaHzJHEWsD93DOtEtrA/dwzrRLbpUH9lJrtj3g4iIAACOjiJMTEQkJgp48oQfD0RERNnFT08iIgKQ8sucszMntSAiIvpc/PQkIiKJk1PKpBb37/PjgYiIKLs+69Pz2rVrGDp0KNq2bYvg4GCsXbsWP//8s6ZjIyKiXKaajp3JFRERUfZl+9Pz1KlTGDBgABwcHPD06VMkJSVBT08P48ePx44dO3IiRiIiyiW81hUREdHny/anp7+/P6ZNm4Zx48ZBLpcDAPr27YvZs2dj06ZNGg8w3zMxQcibSISGRAImJtqOhojyuHLlUpKrJ09kiI/XcjBERERfmWwnV8+ePUOVKlXSLK9cuTJev36tiZiIiEhLChcWYW4uIjlZwOPHbL0iIiLKjmx/cpYuXRrnzp1Ls/zgwYMoXbq0RoIiIiLtEASOuyIiIvpcetl9gK+vLwYNGoTLly8jMTERq1evxrNnz3Dnzh2sWrUqJ2LM3+LiYO49IOX2np3ajYWI8gVn52T89Zec466IiIiyKdvJlaurK3755Rdp8oqIiAhUqVIF8+bNg729vcYDzPeSk6E4eki6TUSU01TXumLLFRERUfZkO7kCAFtbWwwbNkzTsRARkQ74MGOgXMuREBERfV2ylFz16NEDgiBkaYNbtmz5ooCIiEi7VMnV06cCYmMBIyMtB0RERPSVyFJy5e7untNxEBGRjihYUISlpYi3bwU8eiRDpUpKbYdERET0VchScuXj45Pu8rdv30Imk8HCwkKjQRERkfakzBiYjMuX9XD/PpMrIiKirMr2aGWlUoklS5agTp06qF27NmrWrIkGDRpg7dq1OREfERFpgWpSC84YSERElHXZntBizpw5OHXqFEaNGoWKFStCqVTi9u3bWLZsGRISEjJs5SIioq/Hh2tdcVILIiKirMp2cnX48GH4+/vDzc1NWubs7AwHBweMHj2ayZWmGRsj5GkwBAGwMTYGYqO1HRER5QOcjp2IiCj7sv2paWhoCH19/TTLzc3NszyjIGWDIAAmJil/fH6JKJeoWq6eP5fh/XstB0NERPSVyHZyNXbsWEyYMAG///47IiIiEB0djatXr2Ly5Mno1asXXr58Kf0REdHXycZGhI1NSoL18CFbr4iIiLIi290CR48eDQAYPHiw1FIliiIA4O+//8bixYshiiIEQcDff/+twVDzqfh4mI3+74LNmzdqNxYiylecnZU4f16G+/dlqFKFMwYSERF9SraTq99++y0n4qCMJCXBcPeOlNsbOCMjEeUeJyclzp9XTWqRpO1wiIiIdF62kysHBwcAQGhoKBISEtKst7e3//KoiIhI61TjrjgdOxERUdZkO7k6ceIEpk6disjISLXl7ApIRJS38FpXRERE2fNZ17lq2bIlvvvuOxgaGuZETEREpAOcnZMBAEFBMkRFAWZmWg6IiIhIx2U7uYqJiUHPnj1RokSJnIiHiIh0RIECQKFCSrx+LcM//8jg6spJLYiIiDKT7b4e3bp1w6ZNm9Idb0VERHnLh3FXci1HQkREpPuy3XLVvHlz9OrVC4cOHYKNjU2aCwdzNkEioryjXDkl/vwTuH+f466IiIg+JdvJ1ZgxY1CmTBm0bt2aY65yg7ExQu89gSAA1sbGQGy0tiMionxE1XLF5IqIiOjTsp1cBQUFYdWqVShSpEhOxEMfEwSINjaAkHKbiCg3OTmlTGrBGQOJiIg+Lduflh4eHrh48WJOxEJERDpG1XL16pUM795pORgiIiId91kXEfbz88OhQ4dQpEgRyOXqg5znzJmjseAIQHw8TKf4ptxe5a/dWIgo3zE3BxwclHjxQob79+Vwd0/WdkhEREQ6K9stV2FhYWjVqhWKFy+eJrGiHJCUBKNN62G0aT2QlKTtaIgoH+K4KyIioqz5rIsIExFR/uHkpMSZMxx3RURE9CnZTq5EUcRvv/2Ghw8fIjn5Q/eQhIQE3Lt3D+vXr9dogEREpF3OzpzUgoiIKCuy/Uk5c+ZMjBw5EmfPnsXKlStx6dIl7N69G+vWrUPRokU1Gtyvv/4KJycntb+hQ4cCAO7du4eOHTvCxcUF7du3x507d9Qee+zYMTRu3BguLi7w9vZGeHi4RmPLLcpUt/8EwNEORJTbnJ3ZLZCIiCgrsv1Jefz4cSxYsAC7du1C0aJFMW3aNPz+++9o1aoVEhMTNRrco0eP4OHhgfPnz0t/s2bNQkxMDAYMGABXV1ccOHAAVatWxcCBAxETEwMAuHXrFiZOnAgfHx/s3r0bkZGR8PX11WhsueGYgR7qWJlI91sAqGZpgmMG2W5wJCL6bGXKpCRXISEyhIXxkhBEREQZyXZyFR0djYoVKwIAypYti1u3bkFPTw8DBw7E2bNnNRrc48ePUbZsWdja2kp/5ubmOH78OBQKBcaOHYtSpUph4sSJMDExwYkTJwAA27ZtQ4sWLdCuXTs4Oztj3rx5OHv2LAIDAzUaX046ZqCHfuaGCJapf5EJlgnoZ27IBIuIco2pKVC0aEqCxa6BREREGcv2N/QiRYrg3r17sLe3R5kyZXDr1i20b98eoigiKipKo8E9fvwYtWvXTrM8ICAA1atXh/DfRXUFQUC1atVw8+ZNeHl5ISAgAN9//71U3s7ODvb29ggICMjWxY/fJ8ZAFMU0y+WCHIZ6hqnKvc9wGzJBBiM9o2yVTQYwyVQBMTEGSIz5UCDhPUR9AKKICQqgRQKgmq8xJjEGItLGCgACBBjrG0v3Y5NioRSV6ZYFABN9k88qG5cUh2Qx446L2SlrrGcs1W98cjySlBnPlJidskZ6RpAJKV8OE5ITkKjMuLU1s7KCABglyPA+8T1EETCUG0Iuk2dpu6nLJiYnIkGZkGFZhVwBPZletssmKZMQnxyfYVkDmQH05frZLpusTEZcclyGZfVl+jCQG2S7rFJUIjYp9ovKquokPjkeBjIFgJQxojFJMWnKqujJ9KCQZ61sdt73OX2OUMnO+/5LzxGlyynxPFgPt/6OQ+3aBpmWBT7UR2r56RzxMV04RxjqKaTb+fEcoZKd931OniOM9PPWOSK1r/p7RFK89Nmenrx8jtDF7xHvE95nWB+5eY4QBAE2MMvwMWqPz1KpVPr27YsxY8bAz88PLVu2hJeXF/T09HDjxg1Ur149u5vLkCiKePr0Kc6fP481a9YgOTkZzZs3x9ChQxESEoLSpUurlbe2tsbDhw8BAG/evEHBggXTrH/16lW2Yqj0UxlEJaRNGBsXa4qdrfdJ9ytsKpXhCbe2fV0c/va4dN91a0WExYWlW7ZKwar4teNZXNGX46VcBiyvgNi3z1B8WMr62CWFpLbGV7blcaX7X6iTmHJiabavIf55ez/d7RYxK4rrPT+MSWt7qDluvrmRbllrQ2vc7/dUut/1WHtcfHk+3bLGesZ4NvDDc9r35Hc4/exUumUBIMQ7Urrt/dsAHH18KMOy/w4Ilk6io88Ow+77OzIs+3ffJ7AxsgEATLngi013Mp5U5VqP2yhqXgwAMOfKDKy4uSzDsue6XIGzdTkAwNLrCzD/r7kZlj3V4XdULZTy+l93axWmX5qcYdlD7X5GHYd6AICtf2/C+D9HZ1h2e6s9aFq8OQBg/8M9GHpmcIZl1zfbjLalvwUAHH96FP1P9sqw7DLPVeharjsA4PfA0+j+c6cMy86tvwD9Kg0AAFx5dRHtDrXKsOzUWjPhUy3lBXs79Caa7vPIsOyYGuMx1m0CAOBh+D+ot8s9w7LeVYZiWp1ZAIAXUYGovrVShmX7VuqPH+svAgCExYWh3MaSGZbt7NwN/o1WAwBikmJQYp1dhmXblGqHjc23SPczK5vT5wiVervcEBj1PN2yTpbOON/tf9L9Lz5H1Er584uxwWDhibQ403OEvjGe8xwBQDfOERuab0bfgj0hCPn7HNGnYn/Ma6D9c8SuNinnCEHII+eI/3zN3yMEARh5ciRWXl2ZYdm8fI7Qte8Rt0Juoukq3ThHmBmYIdI3MsPHpJbt5Kpjx44oXrw4jI2NUapUKfj7+2Pv3r2oWLEifvjhh+xuLkMvX75EbGwsDAwMsGTJEgQFBWHWrFmIi4uTlqdmYGCAhISUbDwuLi7T9V/KwEAPNjapstdMhiDo68vVygqyjAvr6aWUTX16FWXAM8v0y8dYGMPmv9tyvYy76shkgloMenoZX59M+Kisvn4m1zIToFbW4BNdFVOXVSg+UdbaDCYGKSdFQ4V+pmWtrUxhY5KybSNDg0zLWlmZwqbAf2WNMi9raWkixWxsrMi0bIECH8qamGRe1sLCWCpramKYeVnzD2XNzDIva25mJJU1f22UaVkzM0OprEW4caZlTU1SlY3OvKyJiUIqWyDeJNOyxsYfyloqMy9rZGQglY3WM820rKHiQ1nxfca/jqWU1ZfKftzK8jGF4qP3fSZy+hyhIsukrFxPplZWU+cIZTKyfo4AYG3NcwSgG+cIM9OU84K1tRnMX+Xfc4SRoW6cI1TvDWtrszx1jvjav0d8Sl4+R/B7xH9ls3GOSI8gptfvTUdERETAwsJCaqo9efIkxowZAzc3Nzg7O2P06A+Z+vz58/H48WOsXr0aVapUwbJly1C/fn1pfceOHdGyZUv06dMny/t//uq1VroFXtCXo52FcUqXwIyqRxBwKAZSyxWb83O/W6C1lRnCwqPYLTAVbXcLtLYyw7uIWHYLhOa7/Ny+LUOb1iawtFTinzspz3dGZYEP9REXpZROY/npHPExXThHGOopUMjWEmFhUUhMzn/nCBVd6hZobW2GsLAoRCd8/eeI1L7W7xGCAJhaGOB16Ft2C9SB7xFKMRkmFvrSd63My+Z8t8CihQtl+Bi1x2epFFJm4Dtw4ACGDh0KKysrhIeHY/Lkybh48SKsrKzQt29fdO/ePauby5ICBQqo3S9VqhTi4+Nha2uL0NBQtXWhoaFSV8BChQqlu97W1jZb+zfWM87wzZV6ubFe5tlydsu6JyTDPlmJYD0j6CUmwm/iRADARD8/JBoYQBBF2ClFuCe8l06DRnqZ/xKQOgZDeea/RnxuWYU8819EslM2dXkDmUL6sqzJsvoyA+jLMv/VKaOyggCYGJggVu/DF8fP2a6eTB96ssx/UfucsnJBD8Z6mb+9P6esTJBn+fWenbICZF9cVqoTmTLVa03IxvszO2U1/77/nLLZed9/6TmikhMgJBnj7RsBb95Eo2BBMcOyQKr6ED98KOanc4SmymryHKFKiEUxf54j0iurC+cIUcwb5whNlNWFc4RCTwFjPZMMv/+lLpvXzhGpy+rKOeLj71oZlc3pc4SQSevyx7I07dPFixfRrVs3PH/+HElJKZn/yJEjcfHiRYwbNw4jR47E+vXrsX///qzv+RPOnTsHd3d3xMZ+yCz//vtvFChQANWrV8eNGzekViVRFHH9+nW4uLgAAFxcXHDt2jXpccHBwQgODpbW6zo5gFnRKb8AGCQkYMyCBRizYAH0ExMh/HfMs6LjkXlnHCIizTE2BooXTzn/cMZAIiKi9GXpE3LVqlUYNGgQNm7ciIIFC+Lhw4e4fPkyevTogS5duqBVq1YYM2YMtmzZ8umNZVHVqlWhUCgwadIkPHnyBGfPnsW8efPQv39/NG/eHJGRkfDz88OjR4/g5+eH2NhYtGjRAgDQtWtXHD58GHv37sX9+/cxduxYNGzYMFszBWpb64QkbIiMQ2GleqpurxSxITIOrRMybt4mIsoJTk4pXXB4MWEiIqL0ZekT8s6dO2jdurV0/88//4QgCGjWrJm0rGLFivj33381FpipqSk2bNiA8PBwtG/fHhMnTkTnzp3Rv39/mJqaYs2aNbh27Zo09fratWthbJzSpF21alXMmDEDK1asQNeuXWFhYYE5c+ZoLLbc0johCRfCP/St/gXAtbfvmVgRkVY4O6eMm2ByRURElL4sjbkSBEFtYgfVOKsKFSpIy6KiomBo+On+r9lRpkwZbNq0Kd11lStXxsGDBzN8rJeXF7y8vDQajzak/gpTH0AokMFwUyKinOXkxAsJExERZSZLn5BVq1bFiRMnAADPnz/HlStX0KRJE7UyO3fuRKVKGc8ZT0REXzdVy9U//8gzHexNRESUX2Wp5WrEiBHo3bs3Tp06hRcvXqBAgQIYPDjlImSXLl3Ctm3b8Oeff2Lz5s05GiwREWlP6dJKyOUi3r0T8OqVADs7ZlhERESpZSm5qlixIn7++WecOnUKMpkMLVq0gJWVFQDg9u3bUCqV2LJlC6pWrZqjwRIRkfYoFECpUko8eCDHrVsy2NllfI0ZIiKi/CjL17kqVKgQevTokWb5gAEDNBoQfcTICOF/XoEgAJZGRkBsxhcPJCLKaTVqJOPBAzmuXJGjWTMmV0RERKlxVLKuk8mQ7FwOyc7lABmri4i0y909JaG6ciXLv80RERHlG/y2TkREWaZKrm7elCHVNd6JiIgITK50X0ICjOfNhvG82UBCgrajIaJ8rnhxEYUKKZGYKODmTbm2wyEiItIpTK50XWIiTBbMhfH8uUBiorajIaJ8ThA+tF5dvszkioiIKLUsdZr39fXN8gbnzJnz2cEQEZHuq1kzGUeO6OPKFSZXREREqWW75So2NhYHDx7Eo0ePYGRkBHNzcwQFBeHIkSOQccIFIqI8T9Vy9ddfciRzwkAiIiJJllquUrdGDR8+HD4+PvDx8VErs379ely6dEmz0RERkc4pX14JU1MRUVEC7t2ToVIlpbZDIiIi0gnZbmr6448/0Lp16zTLGzVqhKtXr2okKCIi0l1yecr1rgCwayAREVEq2U6uSpQogf3796stE0UR27dvh5OTk8YCIyIi3fXheldMroiIiFSyfRXIiRMnYtCgQTh16pSUTN29exdxcXFYv369xgMkIiLdU7PmhxkDRTFlFkEiIqL8LtvJlaurK06dOoVffvkFjx8/BgD0798frVq1grm5ucYDzPcMDfH25O8QBKCAoSEQG6PtiIiIULVqMvT1Rbx+LcOzZwKKFxe1HRIREZHWZTu5AgArKyu0bdsWz58/R6lSpZCYmAhTU1NNx0YAIJcjqWr1lF+F5ex+Q0S6wcgIcHFR4upVOS5flqN48SRth0RERKR12R5zFR8fj4kTJ8LNzQ0dOnTAmzdvMH78ePTr1w/v3r3LiRiJiEgHqcZd/e9//OGHiIgI+Izkav78+Xj8+DEOHjwIhUIBAPjhhx/w9u1bzJo1S+MB5nsJCTDyXwoj/6VAQoK2oyEiktSsmdJadfkykysiIiLgM5KrU6dOYeLEiWozAzo5OWHmzJn4888/NRocAUhMhOmMyTCZPhlITNR2NEREEtV07I8eyREayhktiIiIsp1cvX//HkZGRmmWK5VKJCcnayQoIiLSfVZWgLMzp2QnIiJSyXZy5enpicWLFyM6OlpaFhgYiFmzZqFBgwYaDY6IiHSbmxuTKyIiIpVsJ1dTpkyBTCaDm5sbYmNj0b59ezRt2hTm5uaYPHlyTsRIREQ6SnW9K05qQURE9BlTsb99+xbLly9HYGAgHj9+jKSkJJQoUQKlSpXKifiIiEiHqWYMvHVLhvfvARMTLQdERESkRdlOrrp27Yo1a9agYsWKKFKkSE7EREREX4kiRUQ4OCjx4oUM16/LUa8ex94SEVH+le1ugTY2NggLC8uJWIiI6Cukar3ilOxERJTfZbvlqnz58hgyZAgqVaoEBwcHGBgYqK2fM2eOxoIjAIaGiDj4MwQBsDA0BGJjtB0REZEaN7dkHDigz0ktiIgo38t2cgUA33zzjabjoIzI5UisUw+CkHKbiEjXqCa1uHpVjqQkQO+zPlmIiIi+ftn+CGTLFBERpebsrISFhYh37wTcuSNDlSpKbYdERESkFVlKrvz9/dGvXz8YGRnB398/07I+Pj4aCYz+k5gIwy2bUlquRg7VdjRERGnIZCldA3/9VQ+XL8uZXBERUb6VpeTqypUr6NmzJ4yMjHDlypUMywmCoLHA6D8JCTDzHZ1y22eQdmMhIsqAu3tKcnXlihyDBiVqOxwiIiKtyFJytXXr1nRvExERAR9mDLxyRQ5RBPhbGxER5UfZHnN16NChTNe3a9fuM0MhIqKvVZUqyVAoRISGyvDkiYDSpUVth0RERJTrsp1cLVu2TO1+cnIywsLCoKenh8qVKzO5IiLKhxQKoGrVZFy+rIfLl/VQujS7BhIRUf6T7eTqzJkzaZa9f/8eU6ZMgZOTk0aCIiKir4+7e0pydeWKHN99x+SKiIjyH5kmNmJiYoIffvgBmzZt0sTm0jVgwACMHz9eun/v3j107NgRLi4uaN++Pe7cuaNW/tixY2jcuDFcXFzg7e2N8PDwHIuNiIg+XO/q8mVek4+IiPInjSRXAHD//n0olTkz/e7PP/+Ms2fPSvdjYmIwYMAAuLq64sCBA6hatSoGDhyImJgYAMCtW7cwceJE+Pj4YPfu3YiMjISvr2+OxEZERClcXZMhCCL+/VeGV684owUREeU/2e4W2KNHjzRTrr9//x7//PMPevfuram4JBEREZg3bx4qVaokLTt+/DgUCgXGjh0LQRAwceJE/Pnnnzhx4gS8vLywbds2tGjRQhr/NW/ePHh4eCAwMBBFihTReIw5SqHAu+17AAAWCgUQG6vlgIiI0mdhAZQvr8Tdu3JcuSJHxYrajoiIiCh3ZTu5cnd3T7PMwMAAo0ePRq1atTQSVGo//vgj2rZtizdv3kjLAgICUL16dSnJEwQB1apVw82bN+Hl5YWAgAB8//33Unk7OzvY29sjICDg60uu9PSQ0KR5yrTGetmuLiKiXOXuniwlV/36aTsaIiKi3JXtb+s+Pj45EUe6Ll26hKtXr+Lo0aOYNm2atDwkJASlS5dWK2ttbY2HDx8CAN68eYOCBQumWf/q1ats7V+XrtOiikWXYsrPWB+6h3WiG2rWTMbGjR/GXbE+dAffI7qF9aF7WCe6RZfqIzsxZDu5ys7YpTlz5mR385L4+HhMnToVU6ZMgaGhodq62NhYGBgYqC0zMDBAQkICACAuLi7T9VllbW32GZFrWGIisH17yu3u3XUjJpKwPnQP60S7WrRI+X/3rhyRkawPXcQ60S2sD93DOtEtX1t9ZDu5srCwwPbt2+Hk5IQqVarAwMAAf//9Ny5duoQmTZrA1NRUI4H5+/ujYsWKqFevXpp1CoUiTaKUkJAgJWEZrTcyMspWDGFhURC1fR3M9+9h06dPyu2OHREWGaf9mAiCkPJm14nXCAFgnegKQ0OgaFETPH8uw6VLgKsr60NX8D2iW1gfuod1olt0qT5UsWRFtpOrwMBA9O3bFyNGjFBbvmnTJly5cuWLWqtS+/nnnxEaGoqqVasCgJQsnTx5Eq1bt0ZoaKha+dDQUKkrYKFChdJdb2trm60YRBFar0x8tH+diIkkrA/dwzrRPnf3ZDx/LsP580D16qwPXcP3iG5hfege1olu+drqI9tTsV+8eFGahS+1hg0b4tKlS5qICQCwdetWHD16FIcOHcKhQ4fg6ekJT09PHDp0CC4uLrhx4wbE/55pURRx/fp1uLi4AABcXFxw7do1aVvBwcEIDg6W1hPR/9u77/Cm6v6N4+8k3S0F2rKRIbKEUqAMURTEATJkKz4KKqgoy/E8ynCgIENBHJUhIoLiBGQo4g8URFEBLQIyZVNAsGW3TUeS8/sjNhDKaLFtTtv7dV25mpyT8Uk/SdM753u+RyT/NG/uPt7Vjz/6uBAREZEClutwVa1aNb744guvZYZh8OGHH1KnTp08K6xSpUpUrVrVcwoNDSU0NJSqVavSrl07Tp8+zZgxY9i1axdjxozBbrdzxz+D/e+55x4WLVrE3Llz2b59O8888wytW7cufDMFiogUQlkHE167FnK5q6uIiEihluthgc8++yz9+/dn2bJl1K5dG4AtW7bgdDqZPn16nhd4IWFhYbzzzjuMHDmSzz//nNq1azN9+nRCQkIAaNSoEaNGjeKtt97i1KlT3HDDDYwePbpAahMRKe5q1nQREeHi+HErv/9uo1kzp69LEhERKRAWw8j9KMZjx46xZMkS9u7dS1BQENdccw0dO3YkMDAwP2r0maQk3+9AR0oKZapXcJ9PTibJ7vJ9TYLFAlFRJczxGhFAPTGbRx4JYuFCfwYPTuf557X5ygz0HjEX9cN81BNzMVM/smrJiSs6Km1kZCTdunXjwIED1KhRg8zMzCIXrERE5Mq1b+9g4UJ/vvrKn+eeyzDFcUpERETyW67DVXp6OqNGjWLBggWAe/a+V155BbvdzqRJkyhZsmSeF1msBQZyasZsLEB4YCDY7b6uSETksm67zUFAAOzda2X7dit167p8XZKIiEi+y/WEFhMmTGD37t0sWLDAs7Vq8ODBnDhxgpdffjnPCyz2/PzIuLMrGZ27gt8VbWgUESlwYWFw++3u80uW6G+XiIgUD7kOV8uWLePZZ5/1TGYBULt2bUaPHs0PP/yQp8WJiEjh1a2b++fXXytciYhI8ZDrcJWSkkJwcHC25S6XC6dTM0LlOYeDgMULCFi0ABwOX1cjIpJjnTqB1WqwebONffu005WIiBR9uQ5Xbdq04fXXXyc5OdmzLCEhgZdffplWrVrlaXECpKdT8qH7CX/ofkhP93U1IiI5FhUF11/v/tJNW69ERKQ4yHW4euGFF7BarTRr1gy73U737t25/fbbCQ8P57nnnsuPGkVEpJDq0MG9xX3JEn8fVyIiIpL/cv1VYokSJYiLiyMhIYHdu3fjcDioXr06NWrUyI/6RESkEGvf3sHw4fDbb1aOHrVQrpwOHiMiIkVXrrZcJScnY/9nKvCrrrqK1q1bc+utt1KjRg0SExN55pln8qVIEREpnCpWNGjc2IlhWFi6VEMDRUSkaMtRuDpy5AgPPPAATZs2pXHjxvTv359Tp04B4HQ6ee+992jbti2rVq3K12JFRKTwad8+a2igwpWIiBRtOQpXo0aN4tChQ7z66qu8/vrrJCYmMm7cOI4ePUrPnj157bXX6NixI998801+1ysiIoVMhw6ZAPz0k42TJ31bi4iISH7K0deI8fHxvPHGG7Ro0QKAa6+9lq5du7J9+3YMw+Czzz4jOjo6XwsVEZHCqUYNgzp1nGzfbmPZMj/uukuHlRARkaIpR1uuTp8+7TVhRZUqVcjMzKRSpUrMmzdPwSo/BQRw+q2pnHlrKgQE+LoaEZEroqGBIiJSHOQoXBmGgc1m81pms9kYPHgw/v6aXjdf+fuT3ute0u+5F/S7FpFCKmtK9pUr/UhJ8XExIiIi+STXx7k6V2hoaF7VISIiRVj9+i6qVHGRlmZh5UptvRIRkaIpx59wS5cuJSwszHPZ5XKxbNkyIiMjva7XpUuXPCtOAIeDgJXfus/f1dW3tYiIXCGLxT00cNq0AJYs8aNjR+13JSIiRU+OwlXFihWZOXOm17LIyEg++ugjr2UWi0XhKq+lp1Py3rvc5zsn+7YWEZF/oUMHd7havtyPjAztRioiIkVPjsLVihUr8rsOEREp4po2dVK2rIu//7ayerWNNm2cvi5JREQkT/2rfa5ERERyymqFO+7QrIEiIlJ0KVyJiEiByZqSfelSP5zacCUiIkWMwpWIiBSYG25wUrKkQVKSlV9/tV3+BiIiIoWIwpWIiBSYgAC4/XYNDRQRkaJJ4UpERApU1gGFv/7aD8PwcTEiIiJ5SF8bml1AAGfGTcRigbCAALCn+boiEZF/pXVrB8HBBgkJVv74w0qDBi5flyQiIpIntOXK7Pz9Sev3CGn9HgF/f19XIyLyr4WEQJs2Z7deiYiIFBUKVyIiUuCyhgZqvysRESlKFK7MzunE/6cf8f/pRzRvsYgUFbfd5sDPz2DHDhs7d+qjSEREigZ9opldWhqlunagZJcOkKb9rUSkaChZEm680f2FkYYGiohIUaFwJSIiPnHurIEiIiJFgcKViIj4RLt2DiwWg99/t3HwoMXX5YiIiPxrClciIuITZcsaXHede2jg559rNlQRESn8FK5ERMRn7rsvE4DZs/1xOHxcjIiIyL+kcCUiIj5z550OoqJc/PWXlaVLte+ViIgUbgpXIiLiM4GBZ7devf++hgaKiEjhZupwtX//fvr160ejRo1o3bo1M2bM8KxLSEjggQceoGHDhrRv357Vq1d73fbnn3+mY8eOxMTE0KdPHxISEgq6/Lzh70/yC6NJGTka/PWPh4gUPX36ZGK1Gqxe7ceOHab+WBIREbkk036KuVwuHnnkEUqXLs2CBQt46aWXmDp1Kl9++SWGYTBw4ECioqKYP38+nTt3ZtCgQRw+fBiAw4cPM3DgQLp168a8efOIiIhgwIABGIbh42d1BQICsA96HPugxyEgwNfViIjkucqVDdq1c+9wNXOmvkQSEZHCy7ThKikpibp16/Liiy9SrVo1WrVqRYsWLYiPj2fNmjUkJCQwatQoatSoQf/+/WnYsCHz588HYO7cudSvX5++fftSs2ZNxo0bx6FDh1i3bp2Pn5WIiFxI377uoYGff+7PmTM+LkZEROQKmTZclS1bljfeeIOwsDAMwyA+Pp5ff/2VZs2asXHjRq699lpCQkI814+NjWXDhg0AbNy4kSZNmnjWBQcHU69ePc/6QsXpxO/3ePx+jwen09fViIjkixtvdFKzppOUFIumZRcRkUKrUEzN1KZNGw4fPszNN99M27ZtGTt2LGXLlvW6TmRkJEeOHAEgMTHxkutzymKGY1qmp1G67c3u88nJ5qhJPH1QP8xDPTGX3PbDYnFvvRo+3MbMmf7065epXuYxvUfMRf0wH/XEXMzUj9zUUCjC1VtvvUVSUhIvvvgi48aNw263E3De/kcBAQFkZGQAXHZ9TkVGlvh3heeFYO+Ni6aoSTzUD/NRT8wlN/0YMADGjIGdO21s2lSCW27Jx8KKMb1HzEX9MB/1xFwKWz8KRbiKjo4GID09nf/97390794du93udZ2MjAyCgoIACAwMzBakMjIyCA8Pz9XjHjt2Bp/PgZGSQtQ5F01Rk2CxuN/s6od5qCfmcqX96NkzkPffD+D11zOJiUnLvwKLIb1HzEX9MB/1xFzM1I+sWnLCtOEqKSmJDRs2cOutt3qWXXPNNWRmZlKmTBn27NmT7fpZQwHLlStHUlJStvV169bNVQ2Ggc+byXmPb4qaxEP9MB/1xFxy248HH8zk/fcDWLrUj4QEC5Urq5l5Te8Rc1E/zEc9MZfC1g/TTmhx8OBBBg0axNGjRz3LNm/eTEREBLGxsWzZsoW0tLPfasbHxxMTEwNATEwM8fHxnnV2u52tW7d61ouIiDnVqeOiZUsHLpeFDz7QxBYiIlK4mDZcRUdHU69ePUaMGMGuXbtYtWoVEyZM4NFHH6VZs2ZUqFCB4cOHs3PnTqZPn86mTZvo0aMHAN27d2f9+vVMnz6dnTt3Mnz4cCpXrkzz5s19/KxERORyHnzQPS37nDn+pKf7uBgREZFcMG24stlsTJkyheDgYO6++26effZZevfuTZ8+fTzrEhMT6datG4sXL2by5MlUrFgRgMqVKxMXF8f8+fPp0aMHJ0+eZPLkyVjMMN2IiIhc0h13OKhQwUVSkpXFi007el1ERCQbi2EUplGMBSspyfc70JGRQcgbE7FYIOTll0g6ne77mgSLBaKiSpjjNSKAemI2/7YfkyYFMH58ILGxTpYuTc37AoshvUfMRf0wH/XEXMzUj6xacsK0W67kHwEBpD4zgtRnRsB508uLiBRV992Xib+/QXy8jY0b9VElIiKFgz6xRETEdMqWNejUyQHAzJn6YklERAoHhSuzc7mwbd+Gbfs2cLl8XY2ISIHp29d9vMIFC/w4ftzHxYiIiOSAwpXZ2e1E3NSc0jc2h/MOnCwiUpQ1beqifn0naWkWPv5Y07KLiIj5KVyJiIgpWSzQr597WvZZswJwOn1ckIiIyGUoXImIiGl17ZpJqVIGBw5Y+e47m6/LERERuSSFKxERMa2QELjnHvfWK01sISIiZqdwJSIipvbAAxlYLAYrVvixe7cOBi8iIualcCUiIqZWvbrBrbe6d7h67bVAH1cjIiJycQpXIiJies88kw7A/Pl+/PGHPrpERMSc9Alldv7+pA4YQurAIeCvqYhFpHiKiXHRtWsmhmFhzBhtvRIREXNSuDK7gABSXnyZ1BdfhgDtzC0ixdewYen4+bn3vfrxR80cKCIi5qNwJSIihUL16gb33++eOXD06EAMw8cFiYiInEfhyuxcLqwH9mM9sB9cLl9XIyLiU089lUFoqMGGDTa+/NLP1+WIiIh4UbgyO7udyCbRRMRGg93u62pERHyqTBmDAQMyABgzJpDMTB8XJCIicg6FKxERKVQeeyyDqCgXe/damTNHE/2IiIh5KFyJiEihEhYG//2ve+vVxIkBJCf7uCAREZF/KFyJiEih07t3JtWquUhMtPLOO5pJVUREzEHhSkRECp2AABgxwn1g4bffDiApyeLjikRERBSuRESkkLrzTgcxMU5SUiy8/rq2XomIiO8pXImISKFktcLzz7u3Xs2a5c++fdp6JSIivqVwZXZ+ftgffAj7gw+Bn47pIiJyrptuctK6tYPMTAvjxwf6uhwRESnmFK7MLjCQ5FcmkfLqJAjUPw4iIufL2nr1xRf+/PGHPtZERMR39CkkIiKFWnS0i27d3EcTHj1aX0KJiIjvKFyZnWFgSUrCkpQEhuHrakRETGn48HT8/Q2+/96PVatsvi5HRESKKYUrs0tNJeraq4msezWkpvq6GhERU6pa1eDBB89uvXK5fFyQiIgUSwpXIiJSJDzxRAZhYQabNtn4/HNNACQiIgVP4UpERIqEqCiDJ57IAOC554I4fFhTs4uISMFSuBIRkSJjwIAMGjd2cvq0hSeeCNKuqiIiUqAUrkREpMjw84O4uDSCgtyTW3zwgb+vSxIRkWJE4UpERIqUmjVdjBjhPvbVyJGB7Nun4YEiIlIwFK5ERKTIeeSRTFq0cJCaamHIkCDNHigiIgVC4crs/PxIu/s/pN39H/d4FxERuSyrFd58M42QEIM1a/yYPl3DA0VEJP8pXJldYCBn4qaR/PY0CAz0dTUiIoVGtWoGL73kHh44ZkwgO3fqI09ERPKXPmlERKTI6tMnk9atHaSnWxg8OAiHw9cViYhIUWbqcHX06FGGDBlCs2bNuPHGGxk3bhzp6e5vIRMSEnjggQdo2LAh7du3Z/Xq1V63/fnnn+nYsSMxMTH06dOHhIQEXzyFf88wICXFfdKcwiIiuWKxwBtvpBEebrB+vY233w7wdUkiIlKEmTZcGYbBkCFDsNvtfPTRR7z++uusXLmSN954A8MwGDhwIFFRUcyfP5/OnTszaNAgDh8+DMDhw4cZOHAg3bp1Y968eURERDBgwACMwhhOUlMpU70CUdUqQGqqr6sRESl0KlY0GDs2DYAJEwLYvNm0H30iIlLImfYTZs+ePWzYsIFx48ZRs2ZNmjRpwpAhQ/jqq69Ys2YNCQkJjBo1iho1atC/f38aNmzI/PnzAZg7dy7169enb9++1KxZk3HjxnHo0CHWrVvn42clIiK+0LOngzvuyCQz08KgQUFkZPi6IhERKYpMO/1cmTJlmDFjBlFRUV7Lk5OT2bhxI9deey0hISGe5bGxsWzYsAGAjRs30qRJE8+64OBg6tWrx4YNG2jevHmOa7CY4dAo59VgiprE0wf1wzzUE3MxWz8sFpg4MZ1162xs3WrjtdcCGDGieCUss/WkuFM/zEc9MRcz9SM3NZg2XIWHh3PjjTd6LrtcLubMmcN1111HYmIiZcuW9bp+ZGQkR44cAbjs+pyKjCxxhdXnoWDvjYumqEk81A/zUU/MxUz9iIqCadOgZ094881A7r47kFx831ZkmKknon6YkXpiLoWtH6YNV+ebMGECW7duZd68ecyaNYuAAO+dkgMCAsj4Z5yH3W6/5PqcOnbsjO/nkEhJ4dxtd6aoSbBY3G929cM81BNzMWs/WreG7t2DmD/fn/vuc7JiRSrBwb6uqmCYtSfFlfphPuqJuZipH1m15EShCFcTJkxg9uzZvP7669SqVYvAwEBOnjzpdZ2MjAyCgoIACAwMzBakMjIyCA8Pz9XjGoYJJug77/FNUZN4qB/mo56Yixn7MXZsGqtX29i1y8Z//xvE22+nmWLYSUExY0+KM/XDfNQTcyls/TDthBZZRo8ezfvvv8+ECRNo27YtAOXKlSMpKcnreklJSZ6hgBdbX6ZMmYIpWkRETKt0aZg8OQ2bzWDuXH/eekvTs4uISN4wdbh6++23+fTTT5k0aRIdOnTwLI+JiWHLli2kpaV5lsXHxxMTE+NZHx8f71lnt9vZunWrZ32hYrOR3qkL6Z26gM3m62pERIqEm25yMnas+7iJY8YE8tVXhWIgh4iImJxpw9Xu3buZMmUKDz/8MLGxsSQmJnpOzZo1o0KFCgwfPpydO3cyffp0Nm3aRI8ePQDo3r0769evZ/r06ezcuZPhw4dTuXLlXM0UaBpBQZx+7wPOzPwA/hn2KCIi/96DD2by0EPuIeQDBwaxcaNpPxJFRKSQMO0nyXfffYfT6WTq1Km0bNnS62Sz2ZgyZQqJiYl069aNxYsXM3nyZCpWrAhA5cqViYuLY/78+fTo0YOTJ08yefJkLMVpUL2IiFzWqFHptGnjwG630Lt3MH/9pc8JERG5chbDKEy7iBWspCTfz06SxWKBqKgSpqqpOFM/zEc9MZfC1I/Tp6FDhxB27LDRoIGTRYtSCQ31dVV5rzD1pDhQP8xHPTEXM/Ujq5acMO2WK/lHSgplyoYTVSYcUlJ8XY2ISJETHg5z5tiJjHSxaZONQYOCcLl8XZWIiBRGClciIlLsVa1qMGtWGgEBBkuW+DNunGYQFBGR3FO4EhERAZo3d/L66+5ZaN98M5DPPtMMgiIikjsKVyIiIv/o2dPBk0+6p2h/6qkg1qzRITBERCTnFK5ERETOMXRoBp06ZZKZaeHBB4PYt08zCIqISM4oXImIiJzDaoW4uDRiYpwcO2blvvuCOXXK11WJiEhhoHAlIiJynpAQ+PBDOxUquPjzTxt33x3CyZO+rkpERMxO4crsbDbSb72djFtvB5vG/ouIFJTy5Q0++shO6dIG69fb6N49hOPHfV2ViIiYmcKV2QUFcfrjeZz+ZB4EBfm6GhGRYqV+fRcLFqQSFeXijz9sdO0aQmKi9sESEZELU7gSERG5hGuvdbFwoZ1y5Vxs22aja9dgjh5VwBIRkewUrkRERC6jVi0XixalUrGiex+szp1DOHxYAUtERLwpXJldSgpR1coTWbU8pKT4uhoRkWLr6qsNFi1KpUoVF3v2WLnzzhAOHFDAEhGRsxSuCgFLaiqW1FRflyEiUuxVrWqwcGEq1aq5OHDASufOIezZo4AlIiJuClciIiK5ULmyweLFqdSs6eTQIStduoSwc6c+TkVEROFKREQk18qXN1iwwE7duk6OHLHSuXMw27bpI1VEpLjTJ4GIiMgVKFvW4Isv7NSv7yQpyUrXrsH8/rs+VkVEijN9CoiIiFyhyEiD+fNTadTIyfHj7kkuPvvMz9dliYiIjyhciYiI/AulS8O8eam0besgPd3C4MHBPP98IA6HrysTEZGCpnBldlYrGde3JPP6lmBVu0REzKhECZg9285TT6UD8M47Adx9dzDHjmkmQRGR4kT/rZtdcDCnFn7NqUVfQ3Cwr6sREZGLsFph2LAMZs60ExJi8OOPfrRtG8LmzfqoFREpLvQXX0REJA917Ohg6dJUqlZ1HwurQ4cQFi7UflgiIsWBwpWIiEgeq1vXxbJlKbRu7cBut/DII8GMHh2A0+nrykREJD8pXJldSgqRdasTUac6pKT4uhoREcmh0qXhk0/sDBrk3g8rLi6Qe+8N5uRJ39YlIiL5R+GqELAeO4b12DFflyEiIrlks8ELL2QwbZqd4GCDFSv8aNs2VPthiYgUUfrrLiIiks+6dXPw1VepVK7sYu9eK23bhvD66wGarl1EpIhRuBIRESkA0dEuli1LpV27TDIzLYwbF0iHDiH8+ac+ikVEigr9RRcRESkgUVEGs2enMXmynZIlDX7/3cYtt4QwebK/JrsQESkCFK5EREQKkMUCPXs6+OGHFG65xUF6uoWXXgrizjtD2LNHBx0WESnMFK5ERER8oEIFg48/tvP662mEhRn8+quNm28O5d13/XG5fF2diIhcCYUrs7NayWzYiMyGjcCqdomIFCUWC9x7byarVqVw443uY2I9+2wQ3bsHs3+/tmKJiBQ2+m/d7IKDOblsFaeWr4LgYF9XIyIi+eCqqwzmzrUzfnwaISEGP/3kR+vWoUyb5k9Ghq+rExGRnFK4EhERMQGrFfr2zWTlyhSuu85BSoqFF14IolWrUJYvt2EYvq5QREQuR+FKRETERKpXN1i40M5rr6URFeVi924r994bwt13B7N9uz62RUTMTH+lzS41lYjY+pRuXB9SU31djYiIFACrFXr3zmTt2hQGDUonIMDg++/9uPnmEIYNC+TYMe2PJSJiRoUiXGVkZNCxY0fWrl3rWZaQkMADDzxAw4YNad++PatXr/a6zc8//0zHjh2JiYmhT58+JCQkFHTZecMwsCUcwJZwAI0JEREpXkqUgBdeyODHH1No3z4Tp9PCzJkBXHddKNOn+5OZ6esKRUTkXKYPV+np6Tz11FPs3LnTs8wwDAYOHEhUVBTz58+nc+fODBo0iMOHDwNw+PBhBg4cSLdu3Zg3bx4REREMGDAAQ+FEREQKoerVDWbNSuOLL1KpV8/JqVMWnnsuiFatQvj2W+2PJSJiFqYOV7t27eKuu+7iwIEDXsvXrFlDQkICo0aNokaNGvTv35+GDRsyf/58AObOnUv9+vXp27cvNWvWZNy4cRw6dIh169b54mmIiIjkiZYtnXz7bapnf6xdu2z85z8hdOkSzA8/KGSJiPiaqcPVunXraN68OZ999pnX8o0bN3LttdcSEhLiWRYbG8uGDRs865s0aeJZFxwcTL169TzrRURECiubzb0/1po1Z/fH+uUXP3r0CKFDhxC++04hS0TEV/x8XcCl/Oc//7ng8sTERMqWLeu1LDIykiNHjuRofU5ZzLC/8Hk1mKIm8fRB/TAP9cRc1I/8V7IkjByZwcMPZxIXF8CcOf789puNe+4JoWFDJ089lUG7do5svVBPzEH9MB/1xFzM1I/c1GDqcHUxdrudgIAAr2UBAQFk/HOkxcutz6nIyBL/rtC8EOy9cdEUNYmH+mE+6om5qB/5LyoK3n0XRo+GiRNh6lTYsMFGnz7BNGgAzz0H3bu7ZyAE9cRs1A/zUU/MpbD1o1CGq8DAQE6ePOm1LCMjg6CgIM/684NURkYG4eHhuXqcY8fO+H5oRWoqpWrXAcDPYjFHTYLF4n6zqx/moZ6Yi/pR8Pz8YNgweOghC9Om+fPeewFs2mThrrugVi0nTz6ZwUMPBXPqlHpiBnqPmI96Yi5m6kdWLTlRKMNVuXLl2LVrl9eypKQkz1DAcuXKkZSUlG193bp1c/U4hmGC2c+DQzjx4zosFogKCcFI9f0LTM4yxWtEvKgn5qJ+FLzISINnn81g4MAM3n03gOnTA/jzTxuPPRbMuHHQu3cA996bSVSUGmMGeo+Yj3piLoWtH6ae0OJiYmJi2LJlC2lpaZ5l8fHxxMTEeNbHx8d71tntdrZu3epZLyIiUtSVKgVPP53B+vXJPPtsOpGRLg4cgDFjAmnYMJSBA4NYv75Q/hsgImJahfKvarNmzahQoQLDhw9n586dTJ8+nU2bNtGjRw8Aunfvzvr165k+fTo7d+5k+PDhVK5cmebNm/u4chERkYJVogQ8/ngGGzakMHs2NG7sJCPDwty5/rRrF8rtt4fw6ad+nPN9pYiIXKFCGa5sNhtTpkwhMTGRbt26sXjxYiZPnkzFihUBqFy5MnFxccyfP58ePXpw8uRJJk+ejMUM043kVmoqpW9sRqmWzSA11dfViIhIIRUUBH36wP/9XyrffJPCXXdlEhhosGGDjSFDgmnYMJTRowM4cKAQflaKiJiExTAK0yjGgpWUZIL9m1JSKFO9gvt8cjJJdpfvaxL3PnBRJczxGhFAPTEb9cN8LtSTpCQLH3/sz6xZ/hw86P6+1Wo1aN3ayV13ZdKunYNzDikpeUjvEfNRT8zFTP3IqiUnCuWWKxEREfn3oqIMhgzJYN26FGbNsnPTTQ5cLgsrVvjx6KPB1K8fxuOPB7F6tQ2Xy9fVioiYn8KViIhIMefnB+3bO5g3z86aNck89VQ6Vaq4SE628Mkn/nTrFkJsbChjxgTw55/610FE5GL0F1JEREQ8rr7aYNgw99asxYtT6d07g/Bwg0OHrLz5ZiAtW7onwXj3XX+OHtX+WSIi51K4EhERkWysVrjuOievvZbO5s3JzJhh5/bbHfj5uSfBePbZIBo0CKVjx2CmTvVn/34FLRGRQnkQYRERESk4QUFw550O7rzTQVKShYUL/Zg3z5/1622sW+fHunV+jBwJ9es7ad/eQYcODurUcVEYJ+kVEfk3FK7MzmLBeVUVAGz6lBIRER+LijJ46KFMHnook0OHLCxd6sfXX/vx8882Nm92n159NZDq1V3/BK1MGjd2YdVYGREpBjQV+yWYYerHLGaajlLUDzNST8xF/TCf/O7JsWMWli2zsWSJP6tW2UhPP/uFYFSUi9atnbRp46B1aydRUXpR6D1iPuqJuZipH7mZil1brkRERORfi4w0uOceB/fc4yA5Gb77zr1Fa/lyP5KSrMybZ2XePH8sFoMGDVy0aePg5pudxMY68ff3dfUiInlD4UpERETyVFgYdO7soHNnBxkZsG6djZUrbaxY4ceWLTY2bnSfXn8dSpQwuPFGB23aOGnVykGVKob21RKRQkvhyuzsdkp1buc+//NPvq1FREQklwICoGVLJy1bOnn++QyOHrWwcqWN77/34/vvbRw/buXrr/35+mv35qtKlVy0aOHk+uudXH+9g+rVFbZEpPBQuDI7lwv/Db97zouIiBRm5coZ9OrloFcvB04nbNpkZcUKP1autLF+vY1Dh84OIXRf3/VP0HKfrrlGsxCKiHkpXImIiIhP2GzQqJGLRo0y+O9/ISUFfvvNxi+/2Pj5Z3fYOnrUyoIFVhYscIetMmVcNG/u3lcrNtZFTIyT4GAfPxERkX8oXImIiIgphIZCq1ZOWrVyAmC3Q3y8O2j98ouN336zkZho5auvrHz1lTts+fkZ1KvnokmTrMDlpFo1DSUUEd9QuBIRERFTCg4+u78WQFoa/P67jV9/tREfb/WErawJMt57z327yEgXsbEuGjVy0qCBkwYNXJQrp7m1RST/KVyJiIhIoRAUBC1aOGnRwh22DAMOHrQQH28jPt69ZeuPP6wcO2Zl2TIry5ad/TenbFkXDRq4aNDASXS0ezhhpUrawiUieUvhSkRERAoliwWuusrgqqscdOniACA9HTZvdm/V2rjRHbZ27rTy999Wvv3Wyrffnv3XJyLCRXS0i/r1XdSt66RuXRc1a7oICvLVMxKRwk7hqhBwRUYCYPVxHSIiImYXGAixse5hgZAJuCfK2LrVyqZN7rC1aZON7dutHD9uZdUqK6tWnb29zWZw9dUu6tY99+SkalUDqz6IReQyFK7MLjSUY9v2YrFAVGgo2M/4uiIREZFCJTQUmjZ10bTp2UOapKfD9u3uoLVli5Vt26xs22bj5EkLO3fa2LnTxuLFZ+8jJMSgVi33lq2aNV1cc42LWrVcVKvmIiDAB09KRExJ4UpERESKncBAiIlxERNzNnAZBhw9amHr1rNha9s2K3/+aSU11cKGDTY2bLB53Y/NZlCtmkHNmk5P8KpRw8XVVxtERGifLpHiRuFKREREBPc+XOXLG5Qv76RNGydZwwodDti718qOHVZ27XLvw5V1SkmxsHu3hd27rXzzjff9hYe7hxhWr3725L5sEBWl2QtFiiKFK7Oz2yl5T3csAN8u83U1IiIixY6fH56tUucyDPjrLws7d7pD159/ugPXnj1WDh+2cvr0hbd2gTt4XXMNVKwYRJUqBlWruqha1UWVKi4qVzY0qYZIIaVwZXYuFwE/r/acFxEREXOwWKBiRYOKFc8e+DhLairs3+8OWnv3Wti718revd7Ba/16WL/e/4L3Xb68O2hVqWJ4AlelSmd/hoQUxDMUkdxSuBIRERHJYyEheGYbPJ/d7g5ex4+H8scfaRw4YP3nZGH/fvf+XUeOWDlyxMq6dRe+/8hIF5UqeQeuSpUMKlRwUaGCQblyhibaEPEBhSsRERGRAhQc7A5eUVFwww2ZGOfsfmUYcOyYhQMHLF6h6/BhK4cOWUhIsJKcbOHYMSvHjsGmTdmHHAJYLO79uipUMKhY0UX58gYVKxqUL382fJUr56JUKTTphkgeUrgSERERMQmLBaKi3MGoceML7w5w+jQkJLjD1sGD7p+HDlk5eDBri5eFjAwLiYkWEhMvHsAAAgPdQatsWXfYKlfOoHz5s+fLlHGfIiMN/C88glFEzqFwJSIiIlKIhIdDvXou6tUDcGZb73K5t34dOWLh8GELf/1l/ee8lb/+ci8/etTKyZMW0tOztpIBXDyEAUREuChTxh3EskKX++QiKsodwCIj3cEwNDQ/nrmI+SlciYiIiBQhViue4BMdDRcKYODe9+vvvy0cPeoOW+eeP3LEwt9/u7d+JSVZcLksHD9u5fhx2LHj8jUEBxvZAldkpPvYX1k/S5c+e75UKQPbpbOdSKGgcFUIGP9MCaQh0SIiIpJXgoOhalWDqlUN4OIzEjudcPx41jBD98kdvKyey8eOuU9JSe6tYXa7hYQECwkJOavFYjEoVQpP6MoKXKVLu0+lSmVfVrq0QViY9hkTc1G4MrvQUJL2HXGPwQ4NBfsZX1ckIiIixYjNdnZL2OUYBqSkQFLS2bDl/mklKcnCiRMWjh/3Pp06ZcEwLJw4ASdO5C4p2WwGJUsalCwJpUq5z2f/yT/XcZ/Cw7N+uo9hJpKX9JISERERkTxhsUBYGISFGVSrdvkwBpCZ6Q5VWcHr2DELJ09a/lnGOefdp6zLaWkWnM6skHZl9YaGeoeuqCgICgoiLMx9OTwcSpTIOu99uUQJ95YzBTQ5l14OIiIiIuIz/v5Qtqx7oozcsNvh1Cl32Dp50sKpU/zz05Lt56lTcPp01nkLKSnuLWQpKe7zhw97VZSrOkJCDMLCDEqUgPDwrPPuy1nnw8LcQS7r/LnLw8LctwkNde8vJ4WbwpXZpaUR3vc+9/5WXy7ydTUiIiIiphAc7J44o3z53IUyAIfDPaX9qVMWr9AFwRw+nMapUxbOnHGvO33aHczOvZyc7N5yBpCaaiE11cLff//755QV1LLCWNb5cwNYaKjxz4lsy7Jul7U+IED7pBU0hSuzczoJ/HaZ57yIiIiI/Dt+fhAR4Z5AA9zhzH2MMUhK8j6w88VkZEByMpw54w5eyckWzpzBE8LOnLGQkoLXOvdP9/Ks88nJ4HTmfVBzP8/sgSzrZ0iI97KQEO/ruddnv21ICJrZ8RIUrkREREREcikgIHtAuxKGAWlp7rCVnOz+6R6umLXs7PmzP7OfP3dZero7rDkc7iGR7q1yeSckxDgvfJ0NYVnDIS+1Ba50afcw0NDQordlrciGq/T0dF566SWWLVtGUFAQffv2pW/fvr4uS0RERETEw2I5O8SxTBn4N0Eti8MBqamcF7y8zycnZ20p816fmpr9Nqmp7uu7XN5b2JKS/l2d7uds/HNw6rMHqc463XabO8QWJkU2XL366qts3ryZ2bNnc/jwYYYOHUrFihVp166dr0sTEREREck3fn4QHu6eYMPt3wc2w4D09PMDG/8ENXf4yjp/7la3rOGQ5y4/ftwd3Ox2CwcOWDhwACD7WMOSJWHbtsI1I2MhKjXnUlNTmTt3Lu+++y716tWjXr167Ny5k48++kjhSkREREQklywWCAqCoCCDyEj4t4EtJQWvA1K7f1o8PxMTrTRtaitUwQqKaLjavn07DoeDRo0aeZbFxsYybdo0XC4XVs1zKSIiIiLiM1n7abmPh+bKtt49wUgJkpLI0QQjZlEkw1ViYiKlS5cm4JxBmlFRUaSnp3Py5EkiIiJydD9WqwmaabVAiRJnL5qhJvHsfKl+mId6Yi7qh/moJ+aifpiPemIuZupHbibdKJLhym63ewUrwHM5IyMjx/cTEVHi8lfKb5El3Adi+EdEqA9rkWxM8RoRL+qJuagf5qOemIv6YT7qibkUtn4UyfFxgYGB2UJU1uWgoCBflCQiIiIiIkVckQxX5cqV48SJEzgcDs+yxMREgoKCCA8P92FlIiIiIiJSVBXJcFW3bl38/PzYsGGDZ1l8fDzR0dGazEJERERERPJFkUwawcHBdOnShRdffJFNmzbx7bffMnPmTPr06ePr0kREREREpIiyGIav59/IH3a7nRdffJFly5YRFhZGv379eOCBB3xdloiIiIiIFFFFNlyJiIiIiIgUpCI5LFBERERERKSgKVyJiIiIiIjkAYUrERERERGRPKBwZVKGYTBx4kSuu+46mjVrxquvvorL5bro9Tds2ECvXr1o1KgRbdu2Ze7cuQVYbdGX235k2b9/Pw0aNCiACouH9PR0RowYQZMmTWjZsiUzZ8686HW3bt1Kz549iYmJoXv37mzevLkAKy0ectOPLL/99hu33HJLAVRXPOWmJ99//z2dO3emUaNGdOrUie+++64AKy0ectOPxYsX07ZtWxo0aECvXr3YtGlTAVZafFzJ362DBw/SqFEj1q5dWwAVFi+56cdjjz1G7dq1vU4rV64swGpzyBBTeu+994xWrVoZv/76q/HLL78YLVu2NGbMmHHB6/79999GkyZNjNdee83Yu3ev8dVXXxnR0dHGypUrC7boIiw3/chy+PBho23btkatWrUKqMqib9SoUUanTp2MzZs3G8uWLTMaNWpkLF26NNv1UlJSjBtuuMEYP368sWvXLmP06NHG9ddfb6SkpPig6qIrp/3Isn37duP66683br755gKssnjJaU+2bdtm1KtXz5g9e7axb98+Y86cOUa9evWMbdu2+aDqoiun/fj111+N+vXrGwsXLjQOHDhgjB8/3mjWrJmRnJzsg6qLttz+3TIMw+jXr59Rq1YtY82aNQVUZfGRm37cdtttxqJFi4y///7bc0pPTy/gii9P4cqkWrVqZcyfP99zeeHChRf9h+Tjjz822rVr57Xs+eefN5566ql8rbE4yU0/DMMwli9fblx33XVGp06dFK7ySEpKihEdHe314TZ58mTjvvvuy3bduXPnGm3atDFcLpdhGIbhcrmM2267zauH8u/kph+GYRiffPKJ0bBhQ6NTp04KV/kkNz2ZMGGC0a9fP69lffv2NSZNmpTvdRYXuenH119/bUyZMsVz+cyZM0atWrWMjRs3FkitxUVu/24ZhmEsWrTI6NWrl8JVPshNP9LT0426desae/bsKcgSr4iGBZrQ0aNH+euvv2jatKlnWWxsLIcOHeLvv//Odv0bb7yRcePGZVuenJycr3UWF7ntB7iH2zz++OM8++yzBVVmkbd9+3YcDgeNGjXyLIuNjWXjxo3Zhmhu3LiR2NhYLBYLABaLhcaNG7Nhw4aCLLlIy00/AH744QdeeeUVHW8wH+WmJ127duV///tftvs4c+ZMvtdZXOSmH3fccQePPfYYAGlpacyaNYvIyEhq1KhRoDUXdbn9u3XixAkmTJjAqFGjCrLMYiM3/dizZw8Wi4WrrrqqoMvMNYUrE0pMTASgbNmynmVRUVEAHDlyJNv1K1euTMOGDT2Xjx07xpIlS2jRokX+FlpM5LYfAC+//DK9evXK/+KKkcTEREqXLk1AQIBnWVRUFOnp6Zw8eTLbdc/tF0BkZORF+yW5l5t+AEyZMoXbb7+9ACssfnLTkxo1alCnTh3P5Z07d/LLL7/ocyMP5fY9AvDLL7/QqFEj3n77bUaMGEFoaGgBVVs85LYn48ePp2vXrtSsWbMAqyw+ctOPPXv2EBYWxjPPPEPLli3p0aMHq1atKuCKc8bP1wUUV2lpaRw9evSC61JTUwG8XmxZ5zMyMi57v4MHDyYqKoq77747j6ot+vKrH5J37Ha7Vw/g4n242HXVr7yTm35IwbjSnhw/fpzBgwfTuHFjTTaSh66kHzVr1uSLL75g5cqVDBs2LNuXp/Lv5KYnP//8M/Hx8Xz11VcFVl9xk5t+7Nmzh7S0NFq2bMkjjzzC8uXLeeyxx/jss8+Ijo4usJpzQuHKRzZu3EifPn0uuO7pp58G3C+swMBAz3mA4ODgi95nSkoKAwYMYN++fXz88ceXvK54y49+SN4KDAzM9sc263JQUFCOrnv+9eTK5aYfUjCupCdJSUk8+OCDGIbBW2+9hdWqAS155Ur6ERUVRVRUFHXr1mXjxo18+umnCld5KKc9SUtL44UXXmDkyJH6e5aPcvMeGTBgAL1796ZkyZIA1KlThy1btvD5558rXIlb8+bN2bFjxwXXHT16lAkTJpCYmEjlypWBs0PTypQpc8HbJCcn89BDD3HgwAFmz55NtWrV8qXuoiqv+yF5r1y5cpw4cQKHw4Gfn/tPV2JiIkFBQYSHh2e7blJSkteypKSkbEMF5crlph9SMHLbk6NHj3q+VPrggw+IiIgo0HqLutz0Y9OmTdhsNurVq+dZVqNGDXbv3l2gNRd1Oe3Jpk2bSEhIYMiQIV63f/jhh+nSpYv2wcojuXmPWK1WT7DKcvXVV7Nr164Cqzen9BWVCZUrV46KFSsSHx/vWRYfH0/FihUv+M+hy+Vi0KBBHDx4kA8//FBjg/NYbvsh+aNu3br4+fl5TUoRHx9PdHR0tm/bY2Ji+P333zEMA3Afp2z9+vXExMQUZMlFWm76IQUjNz1JTU3loYcewmq1MmfOHMqVK1fA1RZ9uenHvHnzmDRpkteyLVu2cPXVVxdEqcVGTnvSoEEDli1bxsKFCz0ncO9P/fjjjxdw1UVXbt4jw4YNY/jw4V7Ltm/fbsr3iD4BTeqee+5h4sSJrF27lrVr1/Laa695DVs7fvw4KSkpgPuP8tq1a3n55ZcJDw8nMTGRxMTEi+4wK7mXm35I/ggODqZLly68+OKLbNq0iW+//ZaZM2d6+pCYmEhaWhoA7dq14/Tp04wZM4Zdu3YxZswY7HY7d9xxhy+fQpGSm35IwchNT9555x0OHDjAK6+84lmXmJio2QLzUG76cffdd7NmzRpmz57Nvn37eOutt9i0aZNm18xjOe1JUFAQVatW9TqB+8vWyMhIXz6FIiU375E2bdrw5ZdfsnDhQvbv38/bb79NfHw89913ny+fwoX5eCp4uQiHw2GMHTvWaNKkidG8eXNjwoQJnmP2GIZh3HzzzcZbb71lGIb72CS1atXKdrrUcRskd3LTj3OtWbNGx7nKQ6mpqcYzzzxjNGzY0GjZsqXx/vvve9bVqlXL6zhWGzduNLp06WJER0cbPXr0MLZs2eKDiou23PQjy/z583Wcq3yU055kHeD8/NPQoUN9VHnRlJv3yIoVK4yOHTsa0dHRRrdu3Yz4+HgfVFz0Xcnfrax1Os5V3stNPz7//HPj9ttvN+rXr2907drVWLdunQ8qvjyLYfwzbkZERERERESumIYFioiIiIiI5AGFKxERERERkTygcCUiIiIiIpIHFK5ERERERETygMKViIiIiIhIHlC4EhERERERyQMKVyIiIiIiInlA4UpERERERCQPKFyJiOSTNm3aULt27Que1q5dC8D+/fvp3Lkz0dHRvPHGG3z33XfcdNNNxMTE8OOPP17xYxuGwUcffXTJ6/z000/06tWLmJgYYmNjeeihh9i8ebNnfVxcHL17977iGi5XX+/evdm9e/cF1w8bNoxhw4ZdsI64uDhiY2Np0qQJycnJDBgwgAYNGuRbrQVh7dq11K5d29dl5IvzX4vn9ja/ZGRk0LVrV44dO5avjyMicj6LYRiGr4sQESmK2rRpw/3330/79u2zrStZsiQBAQGMGTOGdevWERcXR8mSJenTpw/16tVj4MCBREZGEhQUdEWPvW7dOnr37s2OHTsuuH7z5s3ce++9PPPMM7Rs2ZL09HTmzJnDV199xeLFi6lcuTIpKSlkZmZSqlSpK6rhUr744gvWrl3LK6+8csH1Wf98jx8/3quOU6dO0axZM0aPHs0NN9zAtm3bePLJJ/nkk08oW7YsZcuWzfNaC0JGRganTp2iTJkyvi4lz53/Wjxz5gwAJUqUyNfHnT9/Pr/++ivjx4/P18cRETmXtlyJiOSjEiVKUKZMmWyngIAAAJKTk6lTpw5VqlShZMmSnDlzhtjYWCpVqnTFwQrcWwsu5csvv+SGG27g3nvvpWrVqtSqVYuXXnqJMmXK8PXXXwMQGhqaL8HKMAymTp3KPffck6Prn1tHcnIyAC1atKBSpUqcOXOGqKgo6tevX2iDFUBAQECRDFaQ/bVYokSJfA9WAJ06dWLFihUcOnQo3x9LRCSLwpWIiI8MGzaML774goULF1K7dm3atGnDoUOHGDFiBG3atAHgr7/+4tFHHyUmJoY2bdrw9ttv43Q6Pffxww8/0LVrV2JiYrjzzjv55ZdfOHjwIH369AHwGoJ4LqvVyo4dO7yGTVksFmbOnMldd90FeA/H69279wWHN2b947p8+XLat29PTEwMPXr0YN26dRd93qtXr8ZutxMTE+NZ9ttvv9GlSxcaNGjA448/jt1u96zLquPgwYOe38utt95KmzZtGDZsGIcPH6Z27dp88cUXAHz66ae0adOGRo0aZdt616ZNGyZMmEDLli3p0qULhmHw559/0rt3bxo0aEDbtm29hrDFxcXx3//+l5EjR9K4cWNatGjBu+++61nvcDiYNGkSLVu2JDY2liFDhnDixAnAvTXq5Zdfpnnz5jRv3pz//e9/nDx58oK/k3OHBR48eJDatWuzbNkybr31VqKjo+nfv/9FbxsXF8eTTz7J8OHDiYmJoW3btnz33XeXfM67d++mX79+NG7cmBtvvJG3334bl8uVo/tLT09nwoQJtGrVioYNG/Loo4/y119/edU+efJkmjZtSv/+/bO9Fs8fFrhy5Uq6du1KgwYNaN++PcuWLfOs6927N1OnTqVfv36e/pw7XPbrr7+mbdu2REdH0759e7799lvPuoCAAK6//no+++yzC/7eRETyg8KViIiPPPvss9xxxx3ccccdrF69ms8//5zy5cszYsQI5s2bh2EYDBo0iMjISBYsWMC4ceP48ssvmTZtGgA7d+7kscce47bbbmPRokV07NiRAQMG4O/vT1xcHOAOMo0aNcr22D169OD48ePcfPPNPPbYY3z44YccOHCASpUqXXBrVVxcHKtXr2b16tX88MMP1K9fn7Zt21KpUiW2b9/O0KFDeeyxx1i8eDF33nknDz/8MPv377/g8/7xxx9p0aIFFosFgOPHj9O/f3+uv/56Fi5cyDXXXMM333yT7XYVKlRg7ty5AMydO5dFixYxYsQIypcvz+rVq2nfvj0rVqzg7bff5vnnn2fBggXExsbSp08fTp065bmfL7/8kvfee4/x48eTnp7Oww8/TGxsLIsXL2bo0KFMmTKFhQsXeq7/f//3fwQGBrJgwQL69evHxIkT2bt3LwBvvvkmCxYsYOzYsXz22WccO3aMkSNHAjBp0iQ2b97Mu+++ywcffEBycjKPP/745V4WHtOmTWPSpEnMmTOHP/74g/fff/+i112+fDmGYfDFF1/QvXt3hgwZwq5duy74nE+cOMF//vMfypYty9y5cxk5ciRz5szhgw8+yNH9jRw5kuXLl/PKK6/w6aef4nA4GDBggCecAaxfv5758+czdOjQS74Wf/nlFwYPHkznzp1ZtGgRPXv25Mknn/Ta92/atGl06NCBr776ijp16vD888/jcrk4duwYzzzzDP379+ebb76he/fuPPXUU14h9IYbbvhX+y6KiOSWn68LEBEpykaOHMno0aO9llWsWJElS5ZQokQJz9C/rCFhNpuNEiVKEBERwS+//MLhw4eZO3cuVquVq6++mqFDhzJ8+HAGDhzIvHnzaNy4MQMGDADgkUceITU1leTkZEqWLOl1v+erUaMGc+fOZdq0aXz//fesWLGCl19+mXbt2jF+/HiCg4O9rn9u4Jo4cSJnzpxh7NixALz33nvcdddddOrUCYA+ffrw66+/8sknn1xw4oKtW7fSsmVLz+WlS5cSERHB008/jcViYfDgwaxatSrb7Ww2GxEREQBERER4hpfZbDbP85wxYwb9+/fn5ptvBuCJJ57ghx9+YPHixZ6tcHfeeadnK9HcuXOJjIzkiSeeAKBatWocOnSIDz74gC5dunie+9ChQ7HZbDz00EO8++67bN68mWrVqvH5558zdOhQbrrpJgBeeuklli5dit1uZ86cOcyfP9/zWK+++irNmzdnx44dOZq8YsiQITRo0ABwD3H7448/LnrdkiVLMmrUKAICAqhRowY//PCDJ9yc/5w/+OADgoODGT16NH5+ftSoUYPExEQmT57MAw88cMn7e/TRR1m0aBHvvvsu1113HeB+PbRu3ZqffvqJ6tWrA3D//fdTpUoVABITE4ELvxY/+ugj2rZt63nc6tWrs2nTJmbOnMmkSZMAaNWqFd26dQPgscceo3PnziQmJnLs2DEyMzMpX748lSpVom/fvtSuXZvAwEDP/deoUYPt27fjdDqx2WyX/Z2LiPxbClciIvloyJAh3H777V7L/Pxy9qd39+7dnDx5ktjYWM8yl8tFWloaJ06cYO/evdSrV8/rNlkhISkp6bL3f8011zBx4kQcDge///47S5Ys4fPPP6dMmTI899xzF7zNt99+y4cffsinn35KWFiYp86lS5d6Db/KzMz0ClDnOn78OKVLl/Zc3rVrF3Xq1PFsyQKIjo72GhqYU7t372bChAmef8zBPYxt3759nsuVKlXynN+zZw/bt2/32qJy/j/ilStX9rocGhqKw+HgxIkTnDx50qsH11xzDYMHD+bPP/8kMzOTXr16edXncrnYt29fjsJV1apVPefDwsLIzMy86HXr16/v2Y8v6/K5MzGe+5x3795NvXr1vF6HjRo1IjExkdOnT1/y/vbt24fL5fIa0lmqVCmqV6/O7t27PeHq3Me7lN27d2f7HTVq1Ij58+d7LlerVs1zPus153A4qFu3Lq1bt+bBBx+kevXq3HLLLfTs2dPri4FSpUrhcrk4efIkkZGROapJROTfULgSEclHkZGRXv8k54bD4eDqq69mypQp2daVKFEixyHtQl555RU6d+5MnTp18PPzo2nTpjRt2pSwsDBWrlx5wdvs37+fYcOG8eyzz1K3bl3PcqfTycMPP+zZ0pPlYhNyWCwWr/3GIPukB/7+/lcUrpxOJyNGjKBFixZey7P+KQe8tmw4HA5atGjBCy+8cNH79Pf3z7bMMIxL/v6znt/HH39MSEiI17qc/pN/oce9mPNrcTqdWK1nR/6f+5zPPZ8la0hfVt0Xu78L3TZr/bnDAi92vfNdrJZz7+tiv3+LxcI777zDpk2b+O6771i+fDkff/wxH3/8sef1mfW6Oje4i4jkJ+1zJSJiUtWrV+fw4cNERERQtWpVqlatysGDB3nrrbewWCxUrVqV7du3e92mV69eLFmy5LL/TK5evdpr60CW8PBwz9C7c9ntdgYNGsTNN9/smfDi3DoPHjzoqbFq1ap89tln/PDDDxd87MjISK/9YmrWrMnWrVu9Ate2bdsuWf/FVK9enSNHjnjVMm3aNDZs2HDR6+/du5fKlSt7rr9hwwY+/PDDyz5WeHg4pUuX9urBtm3buOmmm7jqqquw2WycPHnSc79hYWGMGzcuX469tGPHDq9Asnnz5otuHatevTpbtmzx2hL2+++/ExER4Rn+ebH7u+qqq/Dz8/P6fZ44cYL9+/d7tlqd71KvxerVq7Nx40avZb///vtF7+tcu3fv5pVXXqFBgwY8+eSTLFmyhAoVKnjtY3XixAn8/Py8tpSKiOQnhSsRkXx05swZEhMTs51SU1Mve9uWLVtSqVIlnn76aXbs2MFvv/3G888/T3BwMDabjXvuuYfffvuN999/n/379/POO++wc+dOmjRp4hkatXnzZtLT07Pd94ABA5gzZw4TJ05kx44d7Nmzh3nz5jFjxgzP/i/neuGFF3A6nfz3v/8lKSnJ8zzS0tJ44IEH+Prrr/nggw84cOAAs2bNYtasWV7Duc517bXXes3g16FDB+x2O2PGjGHPnj3MmDGD+Pj4nP2Cz/Pggw8ye/ZsFi5cyIEDB5gwYQJLly6lRo0aF7z+nXfeSVpaGi+88AK7d+9m1apVjBkzJsdbl3r37s2bb77JmjVr2LlzJ2PGjKFhw4aEhYXRs2dPXnzxRdauXcuuXbt45pln2L9/P5UrV76i53YpCQkJTJgwgT179jB16lS2bNlCjx49LnjdTp06kZGR4XnO3377LXFxcdxzzz2eIHSx+wsNDaVnz56MHj2atWvXsn37dp5++mnKly/PDTfccMHHu9Rr8YEHHuD//u//mD17Nvv27WPWrFksX748R9P0h4eH88knnzBlyhQSEhL4/vvvOXToENdee63nOjt27KBu3braciUiBUbDAkVE8tHYsWM9Ez+c6/HHH/dMRHExNpuNqVOnMnr0aO666y5CQkJo166dZ5KCKlWqEBcXx2uvvcakSZOoWbMm06ZNo1y5cpQuXZobbriBXr16MWnSpGz7fd1xxx0EBAQwc+ZMPvnkEzIzM6lduzZjx47llltuyVbL4sWLAffkAucaN24c3bp149VXXyUuLo5XX32VKlWq8Nprr9G0adMLPq8bb7yRYcOGeYZ2lSxZkhkzZvDiiy/SuXNnmjZtSufOnS97rK4Lad++PUlJSbz11lskJSVxzTXXMHXq1IsGvbCwMN59913Gjh1Lly5dKFWqFPfeey/9+/fP0eM98sgjnDlzhieeeAKHw0Hr1q15/vnnAfdU+6+88gpDhgwhMzOTpk2bMn369HyZWCEmJobjx4/TpUsXqlWrxvTp07nqqqsueN2wsDBmzJjBmDFj6NKlCxEREdx///1ez/lS9zd06FDP88rIyOD6669n1qxZXvtonat27dper8Xz68567UyYMIHq1avzxhtvZBvWeSFlypQhLi6OiRMnMm3aNCIjI3nqqae89vWLj4/3TDYiIlIQLMaVfHqJiIhcIafTSdu2bRk3btxFA5jkXFxcHOvWrcvRUEZf3J+vpKamctNNN7Fw4cJ82VooInIhGhYoIiIFymaz8cgjj/Dpp5/6uhQpwr788ktat26tYCUiBUrhSkREClyPHj04fPiw13ThInklIyODjz76yDOEVkSkoGhYoIiIiIiISB7QlisREREREZE8oHAlIiIiIiKSBxSuRERERERE8oDClYiIiIiISB5QuBIREREREckDClciIiIiIiJ5QOFKREREREQkDyhciYiIiIiI5IH/B74oDtn3A6KmAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Range of effect sizes to consider\n",
    "effect_sizes = np.linspace(0.01, 0.5, 50)  # Varying from a small to moderate effect size\n",
    "\n",
    "# Calculate required sample sizes for each effect size\n",
    "sample_sizes = [analysis.solve_power(effect_size=proportion_effectsize(p1, p1 + es), \n",
    "                                     power=power, alpha=alpha, ratio=1.0) \n",
    "                for es in effect_sizes]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(effect_sizes, sample_sizes, color='blue', label='Required Sample Size')\n",
    "plt.axhline(y=current_sample_size, color='green', linestyle='--', label='Current Sample Size ($n=500$)')\n",
    "plt.axvline(x=effect_size, color='red', linestyle='--', label='Effect Size for Current Sample Size')\n",
    "plt.scatter(effect_size, current_sample_size, color='cyan')\n",
    "plt.title('Sensitivity Analysis: Effect Size vs. Required Sample Size')\n",
    "plt.xlabel('Effect Size (difference in proportions)')\n",
    "plt.ylabel('Required Sample Size')\n",
    "plt.ylim(0, 1000)\n",
    "plt.yticks(np.arange(0, 1100, 100))\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(\"Images/Analysis/SensitivityAnalysis.svg\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:33.301322200Z",
     "start_time": "2024-01-04T03:02:32.529131300Z"
    }
   },
   "id": "7173c316007ab293"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Implications of Power Analysis\n",
    "\n",
    "The effect size of -0.2, calculated from the proportions under the null hypothesis ($p_1 = 0.5$) and the alternative hypothesis ($p_2 = 0.6$), is a measure of the magnitude of the difference we are testing. \n",
    "\n",
    "1. **Magnitude of Difference**: An effect size of -0.2 suggests a small to moderate difference between the proportions we are comparing. I.e., the difference between 50% of respondents identifying a piece as AI-generated and 60% doing so is considered to be modest.\n",
    "\n",
    "2. **Implications for Sample Size**: The sensitivity plot shows that for an effect size of -0.2, our current sample size of 500 is more than sufficient. This is evident given the line representing the current sample size is above the curve of required sample sizes for this effect size. Hence, we have enough participants to reliably detect even this relatively small difference, reducing the likelihood of a Type II error (failing to detect a real effect).\n",
    "\n",
    "3. **Statistical Power**: A larger sample size increases the statistical power of the test, which is the probability that the test correctly rejects the null hypothesis when it is false. With 500 respondents, our study is well-powered to detect even small differences between the proportions, assuming other parameters like the significance level ($\\alpha$) remain constant.\n",
    "\n",
    "4. **Confidence in Results**: The adequacy of our sample size lends confidence to our results, suggesting that if the study did not find significant differences in certain comparisons (e.g., some of the Chi-Square test results), it is less likely due to insufficient data and more likely reflective of the actual similarities in perceptions across different groups.\n",
    "\n",
    "This effect size, in conjunction with the power analysis, implies that our study design is robust for detecting even small differences in perceptions of AI and human compositions, and assures that the sample size is adequate to give the statistical tests enough sensitivity to detect the desired effects."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cb999b0ef53583a0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr>"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e6127086d8256660"
  },
  {
   "cell_type": "markdown",
   "id": "5e2f31f5a477cfa2",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Survey Results ($n = 500$)\n",
    "\n",
    "**Think human:**\n",
    "   * Q1. AI Sample #1 (model 2)  : 50.60%\n",
    "   * Q2. AI Sample #2 (model 3)  : 54.60%\n",
    "   * Q4. AI Sample #3 (model 13) : 48.20%\n",
    "    \n",
    "**Think AI:**\n",
    "   * Q3. Human Sample #1.        : 51.20%\n",
    "   * Q5. Human Sample #2.        : 29.60%\n",
    "    \n",
    "**Demographics:**\n",
    "   * Music teacher/professional  : 08.20% \n",
    "   * Music student               : 12.40%\n",
    "   * Amateur musician            : 28.20%\n",
    "   * Music listener/enjoyer      : 51.20%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5842f1033f93017",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "###  Results per Question\n",
    "\n",
    "| Question                 | #1 (AI) | #2 (AI) | #3 (Human) | #4 (AI) | #5 (Human) |\n",
    "|--------------------------|---------|---------|------------|---------|------------|\n",
    "| AI - Strongly believe    | 8.20    | 8.40    | 10.20      | 7.00    | 5.60       |\n",
    "| AI - Mostly believe      | 21.80   | 17.80   | 21.40      | 22.20   | 11.00      |\n",
    "| AI - Somewhat believe    | 19.40   | 19.20   | 19.60      | 22.60   | 13.00      |\n",
    "| Human - Somewhat believe | 15.40   | 16.20   | 18.00      | 18.00   | 20.80      |\n",
    "| Human - Mostly believe   | 27.00   | 26.00   | 20.60      | 23.00   | 29.40      |\n",
    "| Human - Strongly believe | 8.20    | 12.40   | 10.20      | 7.20    | 20.20      |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50aa829c7a52a721",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:33.693636100Z",
     "start_time": "2024-01-04T03:02:33.306322100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                       Q1     Q2     Q3  \\\n Who do you believe composed the following audi...                        \nAI - Strongly believe                               0.082  0.084  0.102   \nAI - Mostly believe                                 0.218  0.178  0.214   \nAI - Somewhat believe                               0.194  0.192  0.196   \nHuman - Somewhat believe                            0.154  0.162   0.18   \nHuman - Mostly believe                               0.27   0.26  0.206   \nHuman - Strongly believe                            0.082  0.124  0.102   \n\n                                                       Q4     Q5  \n Who do you believe composed the following audi...                \nAI - Strongly believe                                0.07  0.056  \nAI - Mostly believe                                 0.222   0.11  \nAI - Somewhat believe                               0.226   0.13  \nHuman - Somewhat believe                             0.18  0.208  \nHuman - Mostly believe                               0.23  0.294  \nHuman - Strongly believe                            0.072  0.202  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5</th>\n    </tr>\n    <tr>\n      <th>Who do you believe composed the following audio sample?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AI - Strongly believe</th>\n      <td>0.082</td>\n      <td>0.084</td>\n      <td>0.102</td>\n      <td>0.07</td>\n      <td>0.056</td>\n    </tr>\n    <tr>\n      <th>AI - Mostly believe</th>\n      <td>0.218</td>\n      <td>0.178</td>\n      <td>0.214</td>\n      <td>0.222</td>\n      <td>0.11</td>\n    </tr>\n    <tr>\n      <th>AI - Somewhat believe</th>\n      <td>0.194</td>\n      <td>0.192</td>\n      <td>0.196</td>\n      <td>0.226</td>\n      <td>0.13</td>\n    </tr>\n    <tr>\n      <th>Human - Somewhat believe</th>\n      <td>0.154</td>\n      <td>0.162</td>\n      <td>0.18</td>\n      <td>0.18</td>\n      <td>0.208</td>\n    </tr>\n    <tr>\n      <th>Human - Mostly believe</th>\n      <td>0.27</td>\n      <td>0.26</td>\n      <td>0.206</td>\n      <td>0.23</td>\n      <td>0.294</td>\n    </tr>\n    <tr>\n      <th>Human - Strongly believe</th>\n      <td>0.082</td>\n      <td>0.124</td>\n      <td>0.102</td>\n      <td>0.072</td>\n      <td>0.202</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "population = 500\n",
    "dfs = pd.read_excel(\"Data/Survey/SurveyResults.xlsx\", sheet_name=[f\"Q{i}\" for i in range(2, 8)])\n",
    "\n",
    "results = pd.concat([dfs[f\"Q{i}\"].iloc[:, 1] for i in range(2, 7)], axis=1)\n",
    "results.columns = [f\"Q{i}\" for i in range(1, 6)]\n",
    "results = pd.concat([results, dfs[\"Q2\"].iloc[:, 0]], axis=1)\n",
    "results.rename(columns={results.columns[-1]: results.columns[-1][3:]}, inplace=True)\n",
    "results.index = results.iloc[:, -1]\n",
    "results.drop(columns=results.columns[-1], inplace=True)\n",
    "results = results.iloc[1:, :] \n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7ba12586986a48c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:33.703695200Z",
     "start_time": "2024-01-04T03:02:33.696636200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                                   Percentage  Count\nMusical Ability Level                                               \nMusic Teacher/professional (classically trained...      0.082     41\nMusic student (college-level or equivalent expe...      0.124     62\nAmateur musician (hobbyist, some background in ...      0.282    141\nMusic listener/enjoyer (little or no experience)        0.512    256",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Percentage</th>\n      <th>Count</th>\n    </tr>\n    <tr>\n      <th>Musical Ability Level</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Music Teacher/professional (classically trained, undergraduate degree or higher)</th>\n      <td>0.082</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>Music student (college-level or equivalent experience, including AP)</th>\n      <td>0.124</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>Amateur musician (hobbyist, some background in composition/theory)</th>\n      <td>0.282</td>\n      <td>141</td>\n    </tr>\n    <tr>\n      <th>Music listener/enjoyer (little or no experience)</th>\n      <td>0.512</td>\n      <td>256</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demographics = dfs[\"Q7\"].iloc[:, 1]\n",
    "demographics.index = dfs[\"Q7\"].iloc[:, 0]\n",
    "demographics = demographics.iloc[1:]\n",
    "demographics = pd.concat([demographics, demographics.apply(lambda x: int(x * population))], axis=1)\n",
    "demographics.columns = [\"Percentage\", \"Count\"]\n",
    "demographics.index.name = \"Musical Ability Level\"\n",
    "demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d9b20ec90d4698e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:33.998822800Z",
     "start_time": "2024-01-04T03:02:33.705713200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the demographic data\n",
    "x_labels = ['Music teacher/professional', 'Music student', 'Amateur musician', 'Music listener/enjoyer']\n",
    "plt.figure(figsize=(10, 5)) \n",
    "plt.bar(x_labels, demographics['Count'], color='blue', width=0.5)\n",
    "for i, v in enumerate(demographics['Count']):\n",
    "    plt.text(i, v + 5, str(int(v)), color='black', ha='center')\n",
    "plt.title(\"Demographics\")\n",
    "plt.ylabel(\"Sample Size ($n=500$)\")\n",
    "plt.ylim(0, 275)\n",
    "plt.savefig(\"Images/Analysis/Demographics.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fdaed759aa7df",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Chi-Square Analysis\n",
    "\n",
    "Hypothesis:\n",
    "* $H_0$: There is no statistically significant difference between the frequency of respondents identifying a music piece as AI-generated and the frequency of respondents identifying it as human-composed.\n",
    "* $H_a$: There is a statistically significant difference between the frequency of respondents identifying a music piece as AI-generated and the frequency of respondents identifying it as human-composed.\n",
    "\n",
    "In other words,\n",
    "* $H_0$: Any observed difference in the identification of AI and human compositions is due to random chance or sampling variability.\n",
    "* $H_a$: The pattern of responses is not what would be expected by random chance alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e286271dc40a1752",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:33.999821Z",
     "start_time": "2024-01-04T03:02:33.938541200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         Chi-Square statistic   p-value Reject null hypothesis  \\\nQuestion                                                         \nQ1                   0.036001  0.849513                  False   \nQ2                   2.120487   0.14534                  False   \nQ3                   0.144021  0.704316                  False   \nQ4                   0.324105  0.569151                  False   \nQ5                  43.423096       0.0                   True   \n\n         Degrees of freedom                   Expected values  \nQuestion                                                       \nQ1                        1  [[251.5, 248.5], [251.5, 248.5]]  \nQ2                        1  [[261.5, 238.5], [261.5, 238.5]]  \nQ3                        1  [[247.0, 253.0], [247.0, 253.0]]  \nQ4                        1  [[245.5, 254.5], [245.5, 254.5]]  \nQ5                        1  [[301.0, 199.0], [301.0, 199.0]]  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Chi-Square statistic</th>\n      <th>p-value</th>\n      <th>Reject null hypothesis</th>\n      <th>Degrees of freedom</th>\n      <th>Expected values</th>\n    </tr>\n    <tr>\n      <th>Question</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Q1</th>\n      <td>0.036001</td>\n      <td>0.849513</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[251.5, 248.5], [251.5, 248.5]]</td>\n    </tr>\n    <tr>\n      <th>Q2</th>\n      <td>2.120487</td>\n      <td>0.14534</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[261.5, 238.5], [261.5, 238.5]]</td>\n    </tr>\n    <tr>\n      <th>Q3</th>\n      <td>0.144021</td>\n      <td>0.704316</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[247.0, 253.0], [247.0, 253.0]]</td>\n    </tr>\n    <tr>\n      <th>Q4</th>\n      <td>0.324105</td>\n      <td>0.569151</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[245.5, 254.5], [245.5, 254.5]]</td>\n    </tr>\n    <tr>\n      <th>Q5</th>\n      <td>43.423096</td>\n      <td>0.0</td>\n      <td>True</td>\n      <td>1</td>\n      <td>[[301.0, 199.0], [301.0, 199.0]]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Convert percentages to counts for the binary outcome\n",
    "results_counts = results.apply(lambda x: x * population)\n",
    "\n",
    "# For \"Think AI\",    sum the counts of \"AI - Strongly believe\", \"Mostly believe\", and \"Somewhat believe\"\n",
    "# For \"Think human\", sum the counts of \"Human - Somewhat believe\", \"Mostly believe\", and \"Strongly believe\"\n",
    "binary_outcomes = pd.DataFrame({\n",
    "    'Think AI': results_counts.iloc[0:3].sum(),     # Sum the first three rows for AI beliefs\n",
    "    'Think Human': results_counts.iloc[3:6].sum()   # Sum the next three rows for Human beliefs\n",
    "})\n",
    "\n",
    "# Calculate the 'Think human' counts for each question\n",
    "think_human_counts = binary_outcomes['Think Human']\n",
    "\n",
    "# Expected count for \"Think human\" and \"Think AI\" if there was a 50-50 chance (null hypothesis)\n",
    "expected_count = population / 2\n",
    "chi2_results = {}\n",
    "for q in [f\"Q{i}\" for i in range(1, 6)]:\n",
    "    think_human_count = think_human_counts[q]\n",
    "    think_ai_count = population - think_human_count\n",
    "    \n",
    "    observed_values = np.array([\n",
    "        [think_human_count, think_ai_count],  # Observed counts\n",
    "        [expected_count, expected_count]      # Expected counts if guesses were random\n",
    "    ])\n",
    "    \n",
    "    chi2_stat, p_val, dof, ex = chi2_contingency(observed_values, correction=False)\n",
    "    reject_H0 = p_val < 0.05\n",
    "\n",
    "    chi2_results[q] = {\n",
    "        'Chi-Square statistic': chi2_stat,\n",
    "        'p-value': p_val,\n",
    "        'Reject null hypothesis': reject_H0,\n",
    "        'Degrees of freedom': dof,\n",
    "        'Expected values': ex\n",
    "    }\n",
    "\n",
    "# Here, failing to reject the null hypothesis implies that respondents could not reliably distinguish between AI and human-composed music (our intended result).\n",
    "chi2_results_df = pd.DataFrame(chi2_results).T\n",
    "chi2_results_df.index.name = \"Question\"\n",
    "chi2_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8186c0563d88e39b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:34.291274800Z",
     "start_time": "2024-01-04T03:02:33.961258400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reformat the data for plotting as sns.countplot() requires\n",
    "binary_outcomes_df = binary_outcomes.reset_index()\n",
    "binary_outcomes_df = pd.melt(binary_outcomes_df, id_vars='index', value_vars=['Think AI', 'Think Human'])\n",
    "binary_outcomes_df.columns = ['Question', 'Response', 'Count']\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x='Question', y='Count', hue='Response', data=binary_outcomes_df)\n",
    "plt.title(\"Binary Outcomes (Think AI vs. Think Human) per Question\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.ylim(0, 400)\n",
    "plt.savefig(\"Images/Analysis/BinaryOutcomes.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "525f8a0c03741770",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:34.522732800Z",
     "start_time": "2024-01-04T03:02:34.265815200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(chi2_results_df.index, chi2_results_df['p-value'], color='green', width=0.5)\n",
    "for i, v in enumerate(chi2_results_df['p-value']):\n",
    "    plt.text(i, v + 0.01, str(round(v, 3)), color='black', ha='center')\n",
    "plt.title(\"Initial Chi-Square Test Result per Question\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.ylim(0, 1)\n",
    "plt.savefig(\"Images/Analysis/ChiSquareInitial.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f5f61ed4632c44",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Chi-Square Initial Results\n",
    "\n",
    "**Q1, Q2, Q3, and Q4: Fail to Reject Null Hypothesis**\n",
    " * This indicates that there is no statistically significant difference in the respondents' ability to identify these music pieces as AI-generated or human-composed.\n",
    " * These findings suggest that the level of musical ability does not significantly impact the respondents' perceptions of these particular music samples, pointing to a high level of realism in the AI-generated music for these samples.\n",
    "\n",
    "**Q5: Reject Null Hypothesis**\n",
    " * This significant result suggests that respondents were able to distinguish this human-composed piece from the AI-generated compositions, indicating that certain qualities of this composition were more identifiable to the listeners.\n",
    "\n",
    "#### Implications\n",
    "* **Perceptual Uniformity in AI Compositions**: The lack of significant differences for Q1, Q2, Q3, and Q4 underscores the effectiveness of the AI model in creating music that challenges listeners' ability to discern its origin, reflecting the AI's capability in mimicking human compositions.\n",
    "* **Distinguishing Human Compositions**: The significant result for Q5 highlights that certain human-composed music can possess distinct characteristics that make it more recognizable to listeners, contrasting with the AI-generated pieces.\n",
    "* **Overall AI Model Performance**: The general inability of respondents across various backgrounds to reliably identify the origin of most music samples (except for Q5) demonstrates the AI model's success in producing compositions that closely emulate human creations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a7d15f4e93e07c",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Kruskal-Wallis Test\n",
    "\n",
    "Hypothesis:\n",
    "* $H_0$: The distribution of responses (AI or Human) is the same across different levels of musical ability. In other words, the median response is equal across all groups.\n",
    "* $H_a$: There is a difference in the distribution of responses among at least one of the levels of musical ability.\n",
    "\n",
    "In other words,\n",
    "* $H_0$: The level of musical ability does not affect the ability to correctly identify AI-generated versus human-composed music.\n",
    "* $H_a$: At least one group’s ability to identify AI-generated versus human-composed music is statistically different from the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddcc1f6ca8c161ae",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:34.664610Z",
     "start_time": "2024-01-04T03:02:34.518982100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "             Q1  Q2  Q3  Q4  Q5  Demographic\nID                                          \n-499838105    2   1   1   5   5            4\n-420969952    2   2   5   5   5            4\n 1053933326   4   5   6   3   5            4\n-1235923469   4   5   2   5   5            4\n-1928203393   5   2   5   1   6            3\n...          ..  ..  ..  ..  ..          ...\n-1960238835   1   4   3   2   5            3\n-750259040    4   3   4   3   3            4\n 598529083    6   4   3   5   6            4\n-2004161357   5   3   5   6   4            4\n 358739295    6   6   6   6   6            4\n\n[500 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5</th>\n      <th>Demographic</th>\n    </tr>\n    <tr>\n      <th>ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>-499838105</th>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-420969952</th>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1053933326</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n      <td>3</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-1235923469</th>\n      <td>4</td>\n      <td>5</td>\n      <td>2</td>\n      <td>5</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-1928203393</th>\n      <td>5</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1</td>\n      <td>6</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>-1960238835</th>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>5</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>-750259040</th>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>598529083</th>\n      <td>6</td>\n      <td>4</td>\n      <td>3</td>\n      <td>5</td>\n      <td>6</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-2004161357</th>\n      <td>5</td>\n      <td>3</td>\n      <td>5</td>\n      <td>6</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>358739295</th>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pull the individual questions from the \"Individuals Coded\" sheet\n",
    "individuals_coded = pd.read_excel(\"Data/Survey/SurveyResults.xlsx\", sheet_name=\"Individuals Coded\", index_col=0)\n",
    "individuals_coded.drop(columns=individuals_coded.columns[0:2], inplace=True)\n",
    "individuals_coded.drop(columns=individuals_coded.columns[-7:], inplace=True)\n",
    "individuals_coded.columns = [f\"Q{i}\" for i in range(1, len(individuals_coded.columns) + 1)]\n",
    "individuals_coded.rename(columns={individuals_coded.columns[-1]: \"Demographic\"}, inplace=True)\n",
    "\n",
    "sample_codes = {\n",
    "    \"1\": \"AI - Strongly believe\",\n",
    "    \"2\": \"AI - Mostly believe\",\n",
    "    \"3\": \"AI - Somewhat believe\",\n",
    "    \"4\": \"Human - Somewhat believe\",\n",
    "    \"5\": \"Human - Mostly believe\",\n",
    "    \"6\": \"Human - Strongly believe\"\n",
    "}\t\t\n",
    "\n",
    "demographic_codes = {\n",
    "    \"1\": \"Music teacher/professional\",\n",
    "    \"2\": \"Music student\",\n",
    "    \"3\": \"Amateur musician\",\n",
    "    \"4\": \"Music listener/enjoyer\"    \n",
    "}\n",
    "\n",
    "individuals_coded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97ebfb10586545c1",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:34.683368700Z",
     "start_time": "2024-01-04T03:02:34.666854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "             Q1  Q2  Q3  Q4  Q5  Demographic\nID                                          \n-499838105    1   1   1   0   0            4\n-420969952    1   1   0   0   0            4\n 1053933326   0   0   0   1   0            4\n-1235923469   0   0   1   0   0            4\n-1928203393   0   1   0   1   0            3\n...          ..  ..  ..  ..  ..          ...\n-1960238835   1   0   1   1   0            3\n-750259040    0   1   0   1   1            4\n 598529083    0   0   1   0   0            4\n-2004161357   0   1   0   0   0            4\n 358739295    0   0   0   0   0            4\n\n[500 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Q1</th>\n      <th>Q2</th>\n      <th>Q3</th>\n      <th>Q4</th>\n      <th>Q5</th>\n      <th>Demographic</th>\n    </tr>\n    <tr>\n      <th>ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>-499838105</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-420969952</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1053933326</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-1235923469</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-1928203393</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>-1960238835</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>-750259040</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>598529083</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>-2004161357</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>358739295</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the dataframe to be binary coded for the sample questions\n",
    "# 1 = Think AI, 0 = Think Human\n",
    "binary_coded = individuals_coded.copy()\n",
    "for i in range(0, len(binary_coded.columns) - 1):\n",
    "    binary_coded.iloc[:, i] = binary_coded.iloc[:, i].apply(lambda x: 1 if str(x) in [\"1\", \"2\", \"3\"] else 0)\n",
    "binary_coded = binary_coded.apply(pd.to_numeric)\n",
    "binary_coded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "304b37b77dd8ba6e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:35.247356Z",
     "start_time": "2024-01-04T03:02:34.684873700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAHUCAYAAACwD+y9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC7qUlEQVR4nOzddVwU6R8H8M/SKS0GigKCSCiCjaKYeHad3Z7d3d0tiJ1nIYpdJ2ecesaJ2GIgdiEpuSzs7w9+jCwLCLgCyud9r3mdO/PMzDMzy+x85/nOMyKpVCoFERERERERkQIoFXQFiIiIiIiI6NfBIJOIiIiIiIgUhkEmERERERERKQyDTCIiIiIiIlIYBplERERERESkMAwyiYiIiIiISGEYZBIREREREZHCMMgkIiIiIiIihWGQSUT0i5FKpQVdBSIiIirCGGRSoTF27FjY2Nhg69atBV2VfHX9+nXY2NjIDfb29qhXrx4mTJiA0NDQgq4mZRAbG4uNGzeibdu2qFq1KqpXr47OnTvDx8cHEomkwOr19OlTdOnSRWacjY0NPD09f/i637x5AxsbG/j5+Sl82T169JD5+6hYsSKcnJzQrl077Ny5s0D3eWGWk2Pfo0cP9OjRQyHr+/fffzFy5EjUr18f9vb2qFmzJgYOHIhLly4pZPn5JSkpCe3atcO///773cv6/Pkzxo4dixo1asDZ2RljxozBp0+fvjlfly5dMv1tuHfv3nfXKb1JkyZlup70Q9r3IyffFU9PT9jY2OSqDnmZJ+238/r16wpbZlEgkUiwatUquLm5oXLlyujatSvu3LnzzflWrlyZ6Xdjy5YtCq2fn5/fN7+Pacc1J8f4W98TRc2TJiUlBfXr14eNjQ3u37+faZlJkybB3d1d+HzgwAH88ccfuV5XYadS0BUgAoAvX77A398f1tbW8PHxQZ8+fSASiQq6WvlqxowZsLOzEz7HxsYiICAAGzduREhICHx9fQuwdpTe27dv0b9/f4SHh6Nnz56oWrUqEhMT8e+//2L+/Pk4fvw4vL29oaurm+91O336NAIDA2XG+fj4oESJEvleF0WrVKkSZs6cCQBITk5GVFQU/vnnHyxcuBA3b97EqlWroKTEe6e5lbZPv9fChQuxfft2NG7cGOPHj4epqSlCQ0Nx5MgR9O/fH5MmTUKfPn0Usq4fbf369ShRogRq1679zbIpKSlZfu8kEgkGDBiAmJgYzJo1CxKJBMuXL0e/fv3g5+cHVVXVTOeTSqV4/Pgx+vTpg2bNmslMs7S0zP0GZWPIkCHo3Lmz8Nnb2xsPHz6El5eXME5HRyfHy+vYsSPq1q2r0DqSrOy+c9+yaNEiHDhwAGPHjkXp0qWxbds29O7dG4cPH4a5uXmW8wUFBaF69eoYO3aszPhSpUrlqR5ZqV+/Pnx8fITPFy5cwLp16+Dl5QUTE5NcL8/Ozg4+Pj6wsrJSZDWzdOXKFXz+/BkWFhbYt28f5s2b98152rdvj927d+PAgQPo0KFDPtQyfzDIpELh+PHjAICpU6eiV69euHbtGmrVqlXAtcpfVlZWqFKlisy4OnXqQCwWY9OmTXj27Fm+nSQpaykpKRg7dixiY2Nx6NAhmR/Y+vXrw8PDAz179sScOXOwdOnSAqzpVxm/Vz8rHR0duW1xd3eHhYWFENy3atWqYCr3E1PEeeXQoUPYvn17poGkh4cH5s6di+XLl6NZs2YoWbLkd6/vR/r06RM2btyIvXv3ZlsuNDQU+/btw6VLl7B///5My5w+fRoPHz7EiRMnhP1sa2uLFi1a4NSpU1l+X1+9eoXY2Fi4ubn98L/fsmXLomzZssJnQ0NDqKmp5Xm9JUqU+CVuahVmw4YNQ9myZdGtWzeUKVMmx/O9f/8ee/fuxdSpU9G1a1cAgKurK5o2bYpNmzZlGxA9evQI7dq1++HfR0NDQxgaGgqfnz9/DiD178bMzCzXy8vsd+NH8vPzg5OTE+rWrYt169Zh0qRJ37xJIxKJMHDgQMyZMwctWrSAhoZGPtX2x+ItXyoUDh48iFq1aqFmzZowNzfHvn37hGl9+/ZFu3bt5OYZMmSIzA/0zZs30b17d1SuXBnVq1fHxIkTER4eLkz38/NDpUqV4Ovrizp16qB69ep49uwZkpOTsXHjRrRo0QKOjo6oUqUKOnfujGvXrsms78KFC2jXrh0cHR3RtGlTHD9+HI0bN5ZJRYuMjMSMGTNQu3ZtODg4oFOnTrh69ep37ZtixYoBgEzL7pMnTzBw4EBUrVoVVatWxdChQ/H69WuZ+Xbs2IFmzZrBwcEBdevWxaxZsxATEyNMt7Gxwa5duzBx4kQ4OTmhdu3amD9/PhITE2WWc/LkSbRr1w5OTk6oU6cOZsyYgaioKGG6p6cnGjdujAsXLqBly5awt7dH06ZNcfjw4VzVJyUlBRs3bkTjxo2FZfz555/Z7pu09MwTJ05g0KBBqFy5MurXr4+1a9ciJSVFpqyvry9+++032Nvbo379+vD09ERycrIwfdKkSejVqxdmzpyJqlWronnz5jLT01y9ehWBgYGYMGFCpndwnZyc0KtXLxw9ehSvXr0S9lFmKT0ZUxkTExOxZMkSuLm5wd7eHi1btsTJkydl5rl//z569eoFZ2dnODk5oXfv3rh9+7awnrTWh/TLzrieT58+YfLkyXBzc4OjoyM6dOiAv//+W65uu3fvxtSpU1G9enU4OTlh5MiR+Pz5s/yByODjx48YOHAgHB0d4ebmhjVr1gj7cvHixXB0dMSXL19k5vH29oazszPi4+O/ufyMunfvDlNTU5nzBpCzY96vXz/4+PigUaNGcHR0ROfOnRESEoLz58+jZcuWqFy5Mjp27IhHjx7JLPvKlSvo2rUrnJ2dUaNGDYwdOxbv37+XKRMYGIhu3bqhSpUqqF+/Pnbs2IHevXtj0qRJAL5+f7dt24ZmzZqhcuXKOHjwIADA398fXbt2hZOTE+zt7dGsWTPs3r1bWHZaOtfly5fRrVs3ODo6okmTJtizZ4/c/omJiZE5jiNGjJA5jhlTIMViMVatWoWGDRvC0dERLVq0wKFDh7I9BmvXroWjoyN69+6d6fShQ4fC1dUVERERMvXft28fGjRogKpVq+LKlSs52rc5+XvKzbkho23btqFUqVKwt7fPdPrdu3cxbtw4NGjQALt27YKbm1uWy7p8+TLKly8vE8hbWVnB0tISFy9ezHK+tO9bxYoVs61remnb/K2U1+8llUqxadMm1K9fH46Ojvj9999x9+5dYXrG49OjRw9MnToVGzduRP369eHg4IDOnTvLzJPRu3fvUL9+fbRr1w7R0dEKqXfGFEVAPsU/7Xt59epV9OjRA46Ojqhfvz58fX3x6dMnDBs2DE5OTnBzc8P27dtllhUUFIRhw4ahZs2asLOzQ926dTFv3jwkJCQIZb7nvJqem5sbTp8+jSZNmmDw4ME5vs64evUqJBIJGjduLIxTU1ND/fr1s/0+hoeH4+PHj7C1tc1VPd3d3b+Z8qoIFy5cQKtWreDg4CB3/ZEx9TWn1yzpicVi9O3bFzVq1JD7LUgvKioK/v7+aNCgAVq0aIH4+HgcOXIkR9vQoEEDJCYmCr8BvwK2ZFKBe/r0Ke7du4fVq1cDANq0aQNvb298/vwZxsbGaNWqFSZOnIiXL18KqRzR0dH4559/MHr0aADAf//9hz59+qBmzZpYtWoVoqKisHr1avTs2RMHDhwQ7golJydj69atmD9/PiIiImBpaYklS5Zg7969wjOhHz9+xNq1azFy5EhcuHABmpqauHbtGoYMGYIGDRpg5MiRePnyJWbOnCkTkCUmJqJXr174/PkzRo8ejeLFi+PgwYPo378/Nm/e/M2W2ZSUFJnnymJiYnDjxg1s2bIFjo6OKF++PAAgJCQEnTt3hoWFBRYvXgyJRIJ169ahS5cuOHLkCIyMjHD8+HEsXboUEydOhI2NDZ4/f47FixcjPj4eixcvFtaxevVqVK5cGatWrUJwcDBWrVqF0NBQrFq1CkDqhf+aNWvQtWtXjB49Gq9fv8bq1atx+/Zt7N+/X9ivoaGhmDNnDgYPHozSpUtjy5YtmDhxIhwcHGBpaZmj+syaNQt+fn4YOHAgnJyc8N9//2HBggWIjo7G0KFDs913s2bNgpubGzw9PREQEAAvLy/ExcVh/PjxAIANGzZg5cqV6N69OyZPnoxHjx7B09MT79+/x4IFC4Tl3Lx5E+rq6li7di3i4uKgrKwst65z585BWVlZ7mIlvd9++w2bNm3C33//neP0QKlUiqFDh+LWrVsYMWIELC0tcfbsWYwePRpisRht2rRBTEwM+vfvj5o1a8LT0xNisRjr1q1Dv379cOHCBXTs2BEfPnzAgQMHskyR/fz5Mzp06AB1dXWMHj0aBgYG8PPzw9ChQ7FkyRKZGzcrV65E48aNsWLFCrx+/RoLFy6EsrIyVqxYke22eHp6ok2bNli7di0CAwOxfv16xMTEYMqUKejQoQO2bt2K06dPo2PHjsI8R44cQfPmzaGpqZmj/ZWekpISatWqhRMnTkAikUBFRSXHxzwwMBCfPn3CpEmTkJiYiFmzZuGPP/6ASCTCiBEjoKmpiZkzZ2LcuHE4ceIEAODw4cOYOHEiWrRogYEDByIiIgJr1qzB77//jkOHDsHIyAjBwcHo3bs37O3tsWLFCkRERGDFihWIjo7Gb7/9Jre/pk6dCh0dHVSuXBkXLlzA0KFD0bNnTwwfPhwJCQnYs2cP5syZA3t7e1SuXFmYd/To0WjTpg0GDRqEv//+G7NnzwYAoZUCAHbu3ImWLVti9erVCA4OxqJFiwAAa9asyXR/jhs3DhcvXsTgwYNRuXJlXLx4EZMmTYKqqipatGghVz4oKAivX79G9+7ds3zMwdDQEOvXr5cb7+XlhWnTpiEhIQFOTk452re58a1zQ2aOHTsmd2MzKSkJZ86cwZ9//onbt2/DwcEBc+fORfPmzaGurp7lsoKDg1GuXDm58WXLlkVISEiW8z169AhaWlpYsmQJzp07h7i4ONSsWROTJ0+GhYVFpvMUL15cJs0wo9ykvGYnICAAYrEY06dPh0QiwaJFizB48GBcvHgRKiqZX1aeOXMGlpaWmDZtGqRSKRYvXozhw4cL59P0QkND0bt3b+jr62Pbtm3CjdasZPztTD8+r8aMGYM//vgDQ4YMwcaNGzFz5kyULVsWHh4e6NatG/bs2YOFCxeiatWqcHR0xKdPn4QbSosWLYKamhr++ecfbNu2DcWLF5d53i6v59X0fv/9d3To0AH+/v7YtWsXevfuDWtra3Tv3h2tWrXK8jwaHBwMbW1tubRTc3NzfPr0CbGxsdDW1pabLygoCEBqMLdo0SJ8+vQJFSpUwOjRo7O9yeLl5QWxWJzj7cqrGTNmYNSoUShevDg2bdqESZMmoWLFilnepPnWNUt6EokEo0ePxv3797Ft27ZsA+1jx44hOTkZLVu2hImJCWrWrAkfHx9069btm9ugrq6OBg0a4NixYzkq/zNgkEkF7uDBg9DX1xcu2tu2bQtPT08cOHAAgwYNQpMmTTB79mwcP35cCDb++usvJCcnCxc8y5cvR/ny5bFhwwbhB6ty5cr47bffcPDgQZk/2EGDBqF+/frC50+fPmH06NEyd3nV1dUxfPhwPH78GFWqVIGnpycqVKgALy8v4SLKyMgIY8aMEeY5cuQIgoKCsH//fuEisF69eujRoweWLVv2zbtTmbUA6OnpoWHDhhg/frzw/IWXlxc0NTWxfft24aKhVq1aaNSoETZv3oyJEyfixo0bMDMzQ7du3aCkpITq1atDS0tLpgUS+Hrhp6KiAjc3NygpKWHhwoUYPnw4jI2NsW7dOnTq1AkzZswQ5rG2tka3bt1k9mt8fDzmz58vBNLlypVDgwYNcPHiRVhaWn6zPiEhIdi/f7/www6kpvCIRCJs2LABXbt2hYGBQZb7zs7ODsuWLRP2eVxcHHbs2IHBgwdDKpXC29sbv//+O6ZNmyYsW19fH9OmTUOfPn1QoUIFAKk/JnPmzMk21evt27cwMDCAlpZWlmXSUs/evn2bZZmM/v33X1y6dAkrV65E8+bNAQB169ZFfHw8li1bhhYtWuDZs2eIiIgQngMFAAsLC/j4+CA2NlYmTS2r9KBt27YhPDwcZ86cQenSpQGk3hXv3bs3lixZghYtWgjfNWtrayxcuFCY9+7duzh9+vQ3t6Vu3bpCIFe3bl3ExMRgz549GDJkCCwtLeHk5IQjR44IQeatW7fw4sULIfjJC2NjYyQlJSEyMhLq6uo5PuaxsbFYtWqVcGFx48YN7Nu3D9u3bxe+zy9fvsTixYsRHR0NHR0dLFu2DK6urli+fLmw/rTW7y1btmDChAnYsGEDdHV1sXnzZuGCz8LCQubZtzQeHh5o37698Pn48eNo27Ytpk6dKoxzcnJCjRo1cP36dZkgs3HjxkK5unXr4tOnT/D29kaXLl2Ec5WDgwOWLFkCIPVccefOnSxbLZ48eYIzZ85gypQp6NWrlzDP27dvcf369UyDzLQsiozBlFQqlcsGUFJSknmWrGvXrsIzhykpKTnat7mR3bkhs6ArODgYoaGhcHR0lBnv7u6OyMhIeHh4YMqUKTLHIDtfvnzJ9Dk3bW1txMbGZjlfUFAQ4uLiUKxYMaxduxZv377F2rVr0a1bNxw+fBimpqZy83xPimtuqKmpYePGjdDX1weQetN32rRpePbsWZYX9RKJBFu2bBH2eWxsLCZOnIhHjx7JtBhHRESgT58+0NDQwLZt26Cnp/fN+mTVev492rdvL9wg1NLSQqdOneDo6IiRI0cCSG1h/uuvv3Dr1i04OjriyZMnsLW1xerVq4VtrF27Nq5cuYLr16/LBJl5Pa9mpKysjKZNm6Jp06Z49uwZdu3ahUWLFmH58uU4efIkjI2N5eb58uVLpt/7tMAyJiYm0yAzrfUuNDQU8+bNg1gsxq5duzBo0CBs3Lgxy2dwK1WqlOvtyot58+ahXr16AFJ/fxs3bowbN25k+X381jVLmpSUFEyaNAnXr1/Htm3bZPrNyIyfnx/q1asnBPHt2rXD+PHjcevWLeE3OzsODg44efIkYmJiFHZTqCAxXZYKVFJSEo4ePYpGjRohISEB0dHR0NbWhrOzM/bv34+UlBRoaWmhUaNGMmmDJ06cQK1atWBqaor4+HjcuXMHbm5ukEqlkEgkkEgkKFOmDCwtLYUUrDQZ70ItX74cvXr1Qnh4OG7evImDBw/i6NGjAFJTJMRiMQIDA9GkSROZu/TNmjWTuWt79epVmJiYwM7OTqhDcnIyGjRogPv378sFeBnNnj0bBw4cwP79+zFw4EAoKyujR48eWLhwoczzCdeuXUP16tWhoaEhrEdHRwcuLi5CT4g1a9ZESEgI2rVrBy8vL9y7dw8tW7aUS5dq2bKlzDY0bdoUQGrL8O3btyEWi+UuKl1cXFC6dGncuHFDZnz6i5u0QCcuLi5H9bl27RqkUinc3d2FbZJIJHB3d0diYiICAgKy3Xdt2rSR+dy0aVMkJSUhMDAQgYGBSEhIyHTZAGS+H/r6+gp5lijtIjqzdNusXL16FSKRCG5ubnL1DA0NxdOnT1GhQgUYGhpi0KBBmDFjBs6ePQtjY2OMHz8+x/W+ceMGnJychAAzTatWrRAaGio8/wLIB6olSpTIUTqrh4eHzOcmTZogKSlJ6MGwffv2uHnzphCEHzp0COXLl4eTk1OOtiEzaa9tEYlEuTrmenp6MhcVaRdm6YOI9BfTISEhCA0Nlfu7KFu2LJycnIS/i2vXrqFevXoyLQqZ7XdA/pzUv39/LFq0CLGxsbh//z5OnjyJDRs2AIBcq0Dbtm1lPjdp0gShoaEyrWTOzs4yZczMzLJMQUz7W2vSpInMeE9PT8ydOzfTebJqMTpw4ADs7OxkhilTpsiUSb/tOd23uZHduSEzaQFzxme/0v6mRSJRrjpcye51Qtl1bjd69Gjs2rULkydPhouLC1q3bo0tW7bgy5cv2LlzZ5bzpf++Zxxycz7KjpWVlfA3AXzdVxlT4DPOk/6iOS1Izng+6d+/P54+fYopU6Zke2MxvbTfzoxDp06dcrpJctKfi9Jaz9OfE9LqlrbNrq6u2LVrF9TV1fHs2TP8/fffWLduHcLDw+X+ZvN6Xs2OSCQSvk/p/53Rt15vldV328PDA+vXr8eGDRtQp04dNGjQAOvXr0f58uWzzIgAUn8Ds/tOKoqLi4vw77Tv47fSrLO7ZkmzbNkyHDt2DD179oSDg0O2ywsKCsKDBw/QpEkTREdHIzo6GjVr1oSWlla2GQbplS5dGsnJyfjw4UOOyhd2bMmkAnXhwgWEhYUJPwoZXbp0CW5ubmjdujWOHj2KoKAgGBsb4/r160JLSXR0NFJSUrBp0yZs2rRJbhkZU5kytkDdu3cPs2fPxr1796CpqQkrKyvhWTupVIrIyEgkJyfLpWkpKyvL/NBGRkYiNDQ0yztdoaGh2d6VLV++vHASq1y5MlRVVeHl5QV1dXWZu6CRkZE4efKk3LN6AIRgtHnz5khJScGePXvg7e0NT09PlC5dGuPGjRNayQDI3Q1P28aoqChhP2V2N9TY2FjugiL9xXTaD1XaD9q36hMZGQkAcmmEaT5+/Jjp+Ky2I20/REVFCRfAWXUPnv5VApndwc2odOnS+Oeff7JMKwK+Xqjmpte9yMhISKXSLO92fvr0Cba2tti9ezfWrVuHU6dOwcfHBxoaGmjdujWmTZsGNTW1b64nKioq044i0o5z+h/mjClXSkpKOXoHZ8ZUrPTHA0j9PixYsABHjhxBv379cOrUqe/uvv3jx4/Q0NCAvr6+8H3KyTHP6m5xVi3VacvO6u/i4cOHAFKfYcostTOz+TKuKzw8HDNnzoS/vz9EIhHMzc2Fi6iM+z+7v+Gslp/dcUzbvtykpaZ9zzO23Dds2FCmJWHw4MFy86avW073bW5kd27ITNp5LeN3/++//8aZM2ewY8cOdOjQAZUrV0a3bt3g4eGR7d+djo5Opi2WMTEx2fY+nVkLTNqN07TUxYzevHmDhg0bZrnM6tWrf/M595zI7PsEZJ+emtm5JLN54uPjYWZmhuXLl8PHxydHAX363870Lly48M15s5LZeSG7VP6UlBSsWLECu3fvRlxcHEqWLAlHR8dMU6nzel7NbJ3nzp3Drl27cPXqVVSoUAGTJk1C69ats+w4JrvvI4Asv5OlSpWS+z1TVVVFnTp15J6FT69x48bZZvQ8fvw4y2m5kf47mfH6IyvZXbOkCQkJQbVq1bBjxw78/vvvmWYQpEm7hp08eTImT54sM+3UqVOYMmXKN1vm07Yjuxs2PxMGmVSgDh48iDJlymD+/Pky46VSKYYNG4Z9+/bBzc0NtWrVgomJCU6dOgUTExOoq6sLd9q1tbUhEonQu3fvTIOU7H4Y0p5xS+sgwsLCAkpKSrh48SLOnDkDIPViS1VVVe7B/JSUFOGiCEg9OZcrV05Izcoot72iDR48GP7+/lizZg3q168Pa2trYT21a9fO9Fm/9K2SLVq0QIsWLfDlyxdcvnwZmzZtwvjx4+Hs7CycKNM64UiTto2GhobCyTCtK+70QkNDc9Wj3bfqk/bMzY4dOzIN3L4VrGXcjrCwMACpxy7tudlly5Zl+mxUZhe02XF3d8euXbvg7++P1q1bC+ODgoJgaWkJVVVV/PXXXwAgpO+k3VVOTk4W0rkz/tDr6upCS0sry1aKtJQ7CwsLLF26FMnJybh79y6OHDmCvXv3omzZsujfv/8366+np5fpe1fTxuW09SA7GS/g075XaYGLtrY2mjVrhlOnTsHa2hpxcXEy+zK3JBIJrl+/jqpVq0JZWVn4PinqmKeXdmMps446QkNDhf1XokSJTMuEhYVl+UxdmnHjxuH58+fYvn07nJycoKamhvj4+Ex7MI2IiJDpGTT9dz8v0vZdeHi4TOt4cHAwIiMj5VpFgdSUVFNTU5w+fVrm0YSMvUR+6yZITvdtTv6e0mR3bshM2joytoKoqKjgt99+w2+//YY7d+5gx44dmDJlChYvXoxevXph4MCBmS6vfPnymXYU8urVK7mU3DQSiQTHjh1DuXLl5Fr3ExISZPZpesWLF8/0Zm2anNxEK2g7duzAo0ePMGDAAOzcuVOhqbAikUiuNTdjy1Vebdy4Edu3b8fs2bPRpEkTIVj7Ua+j8PX1xbp16/Du3TvUq1cPW7duRZ06db45n4WFBWJiYhAeHi7zPXr58iVKly6dZXB68eJFJCQkCNlOaRITE7P8PgLAunXr8uWZzB9l7ty5qF69Ojw8PDB79mx4e3tnWk4sFuPYsWNo0qQJunfvLjPtzZs3mDJlCg4dOvTN73Pab6cifocLA6bLUoEJDQ3FpUuX8Ntvv6FGjRoyQ82aNdGsWTNcvHgRHz9+hLKyMlq2bInz58/j9OnTaNSokXDHR0dHB5UqVcLz58/h4OAgDBUqVICnp2e2L9N9/vw5IiMj0bNnT1hZWQl3s/755x8AqYGksrIyqlatKtf75rlz52TSPapXr47379/DyMhIph5XrlzB5s2bM+1EJjsqKirCe9XSdyue1iuura2tsA57e3ts374dZ8+eBQCMGjVKeH5VV1cXHh4eGDJkCCQSiUwrzrlz52TWeebMGYhEItSsWROVK1eGmpqa8HqZNDdv3sS7d+9y9HxBmm/VJ62VJiIiQmbfhYeHY/Xq1TLBfGb8/f3ltkNTUxOVK1cWWoU/fvwos2wVFRWsWLECb968yfF2AKnPp7m4uGDZsmUyd2hHjx6Nhg0bYufOndiyZQuaNGki9J6Xdlc8fQpMxhTg6tWrIy4uDlKpVKaeT548wdq1ayGRSHD69GnUrFkToaGhUFZWhpOTE2bNmoVixYrh3bt3ALJOd0pTrVo1BAYGyt1dPnr0KExMTLJ9T1pOZWxBOHHihHA80nTo0AFPnjzBjh07ULt27WzvEH+Lj48PQkND0aVLFwBQ+DFPr3z58jAxMZH7u3j9+jVu374t/F1Uq1YNly5dkukc7OHDhzlad0BAAJo0aYIaNWoIgVn6c1J6Gb/7p0+fRunSpWUCz9xICyIznhuWLVsmdzMwjZKSEoYNG4YbN25gx44dmZZ5//69TG/Smcnpvs3J31Oa7M4NmUm7oZVdulrlypWxYsUKnDt3Du3btxduSGbG1dUVwcHBePbsmTDu2bNnCA4OzjIoUFFRgZeXl/AcbZoHDx7g1atXqFGjRqbzqampyXzfMw7furlRGJiYmKBevXrw8PDA6tWrv+tvNSNtbW1ERETI/E1+61GMnAoICICVlRXat28vBJgfP37EkydPvqsDoqz4+/vDzc0Np06dwsaNG3MUYAIQ3vua/hlQsViMCxcuZLuM06dPY/LkyTK/xXFxcbhw4UKW30cgtTfd7L6ThZ2xsTFMTEwwZswY/P333zh16lSm5c6dO4fIyEh07txZ7nq2ffv2KFeuXI5SZtOud7/n97AwYUsmFZjDhw9DIpFkmSLZpk0b+Pr6Yv/+/Rg+fDhat26NrVu3QklJSS4tNq3DmLFjx6JVq1ZCL7J37tzBkCFDsqxD+fLloaOjI3R+o6KigjNnzgh3g9OekxgxYgR69OiBESNGoEOHDnj37p3QG27aXfV27dph165d6NOnDwYNGoSSJUvi33//xaZNm9C9e/csX7qdHScnJ7Rq1QpHjhzBqVOnhOCsc+fOGDhwILp06QJ1dXX4+PgIrZ5A6jOQM2fOxOLFi1GvXj1ER0fDy8sL5cqVk0nDun37NsaNG4fWrVsjKCgInp6e6NSpk9BK+ccff2Dt2rVQVVVFgwYN8ObNG6xevRpWVlZyz4Jl51v1UVVVRatWrTB9+nS8ffsW9vb2CAkJwcqVK2FmZpZpa1R6p06dgpGREdzc3HDjxg3s3r0bo0ePhpaWFrS0tNC/f3+sXr0aMTExqFGjBj5+/IjVq1dDJBLl6hUBQOoF9fLlyzFgwAC0a9cOvXr1gpOTE8aPH4958+Zh/vz5UFVVxbhx44R53NzcsHDhQsyYMQP9+vXD+/fvsXbtWpmWBTc3N1SrVg1DhgwROsi5e/cu1qxZg7p168LQ0BBVq1ZFSkoKhg4dij/++APa2to4deoUvnz5IrTsp7VEHT9+HJUrV5Zrce7Tpw+OHj2K3r17Y9iwYdDX18fhw4dx7do1LFiwIM8v+E7vr7/+gqmpKWrXro3Lly/Dx8cHI0eOlElBc3Z2Rvny5XHjxg2sXLkyR8uNiYkRXteSkpKCiIgIYfmtWrUS9oGBgYFCj3l6SkpKGDNmDCZPniycbyIiIuDl5QU9PT0hw2DQoEE4efIk+vfvj759+yI6OhqrV6+GkpJSts/iAYCjoyOOHTsGOzs7lChRArdu3cLGjRshEonknt3atm0b1NXVUaVKFfz11184f/68TKc5uVWxYkU0a9YMS5cuRUJCAmxtbfHPP//g/PnzwutxMtOpUye8efMGCxcuxD///IMWLVqgdOnSiIqKwuXLl3HkyBHhPJKVnO7bnPw9pcnu3JAZCwsLlCpVCgEBATKveciMqakpxo4dK3QGk5nmzZtj/fr1GDBggPAS++XLl8Pa2lrm2eWHDx9CTU1NeNXJ8OHDMXHiREyYMAGtW7cWfnNsbW1zde79WU2ZMgWXLl3CzJkzsWXLFoUss0GDBvjzzz8xdepU4SbXtm3bcn0DODOOjo7w9vbGxo0bUaVKFbx8+RIbNmyAWCzO9fOW4eHhePXqldxzrOmtXbs2y558s1O6dGm0bdsWCxcuRGJiIsqVK4dt27YhOjpaJhPm1atXCA8PF55b7N+/P06fPo0BAwZg4MCBwiNK8fHxGD58eK7r8bPp3LkzDh8+jPnz56N27dpyaa8HDx6EkZERatasmen8rVq1wpo1a3D9+vVsg/KAgAC4uLjkqZf1wohBJhUYPz8/VKhQQUgDzcjZ2RlmZmbw9fXFkCFDULFiRVhbWyMiIkLudSCurq7YsmULvLy8MGLECKiqqsLOzg7btm3Ltrc9XV1deHt7Y8mSJRg5ciS0tbVha2uLXbt2YcCAAbh58ybc3d3h4uICT09PrF69GkOGDEHp0qUxffp0jB49Wriw0dLSwu7du7F8+XIsXboUX758QenSpTF27Fj07ds3z/tp3Lhx8Pf3x5IlS1C/fn1UrFgRu3fvxsqVKzFhwgRIpVJYW1tj7dq1wvM4nTt3RlJSEvbt24c9e/ZAQ0MDtWrVwvjx42WC3V69euHjx48YNmwYDAwMMGjQIJm0r7ReZnft2gUfHx/o6+ujWbNmGDVqVLa9q2aUk/osXLgQGzZswL59+/DhwwcYGRmhefPmGDVq1DcvAkaOHIkbN27Ax8cHJUuWxIwZM4RWLSC1JdXExAR79uzB5s2boaenh1q1amHMmDHZPheVlRIlSsDHxwe7d+/GiRMnsGnTJigrK6N8+fLo0qWL8DqRoUOHolevXihfvjwWL16MdevW4Y8//oClpSXmzp0r05GKkpISNm7ciNWrV2PDhg0ICwuDqakp+vTpI7QCFy9eHJs3b8bq1asxdepUxMfHCy32aT9uTZo0wZEjRzBp0iR06NABs2bNkqm7iYkJ9u7di+XLl2PevHlISkpCxYoV4e3tne3zXLkxdepUnDhxAtu3b4eJiQmmTJmCnj17ypWrX78+wsPD0ahRoxwt9+HDh/j9998BpN7c0dbWhrW1NWbNmiXzOhRA8cc8vXbt2kFbWxsbNmzA0KFDoaOjg7p162LMmDHC86jm5ubYsmULlixZghEjRsDIyAgDBw7EunXrvpm2uGjRIpnvR7ly5TB79mwcPXoUN2/elCmbloa1YcMGWFhYYM2aNXIpbbm1dOlSeHl5YceOHcKrntasWfPN4zRmzBi4u7tj37598PLywqdPn6ChoQErKysMGzYMHTp0kHmOPTM52bc5+XtK861zQ2aaNm2Kf/75R3if6bdkd7GvpqaGbdu2Yf78+Zg+fbrwHNvkyZNl5hs2bBhKly4tPDPZpk0bqKmpYfPmzRg6dCg0NTXRuHFjjBkzRiFBUWFXvHhxjBkzBnPmzMHhw4flOnDKizp16mDixIn4888/cebMGdjZ2cHLyyvTHp9zK+11Ozt37sTatWtRsmRJtG7dWughPTo6+puvYklz4cIFTJ48GTt37swyIMlLgJlmzpw5KFasGDZt2oS4uDjhWil9Fou3tzcOHTokPDNpaWmJXbt2YcWKFZg6dSrEYjGqVauG+fPn5/rRmZ+RkpIS5syZg/bt22Px4sUyr8H6+PEjrly5gs6dO2f5t9m6dWt4enpi3759WR7TxMREXL9+HaNGjfoRm1AwpET0Tf7+/tL79+/LjHvy5InU2tpa6u/vX0C1+j7W1tbSNWvWFHQ1vsvr16+l1tbW0oMHDxZ0VWSkpKRIT5w4IT106FBBV6XQSklJkXp4eEjnz59f0FX5If7991/pf//9JzMuKipKamdnJ92xY8d3L//atWtSa2tr6bVr1757Wb+i7zk3fPjwQWpvby+9cePGD6gZ0beNHz9eGhgYWNDVoHx06NAhaZ06daTx8fEFXRWFYUsmUQ5cvnwZJ0+exLhx41C+fHl8/PgR69atg4WFBVxdXQu6elTIiEQimV586auYmBhs374d9+7dw+vXr+Veq/OrePDgAdasWYMxY8bAzs4OkZGR2LZtG3R1dTN91yQVHqampujduzc2bdqEatWqFXR1qIh59uwZ7ty5I5eFQr+ulJQUbN26FcOGDcuy86WfEYNMohyYOHEiNDQ0sG7dOnz69An6+vqoW7cuxo4dm2kX5USUOQ0NDezbtw8pKSlYsGDBL5tq1bdvX4jFYuzduxfv37+HlpYWqlevLvfeWyqchg8fjo4dO+Ly5cu8kUj5ytDQENu3b8/VIyn0czt48CBMTEwUkrpdmIik0jy8nIeIiIiIiIgoE3yFCRERERERESkMg0wiIiIiIiJSGAaZREREREREpDAMMomIiIiIiEhh2LssUS4Z995X0FWgfDSjf+YvTqZf038vowu6CpSPDizbVNBVoHwU8Z9XQVeB8pFGIY1yNJ2GKXR58YE5/14nJiZi9uzZ+Ouvv6ChoYG+ffuib9++cuV69OiBGzduyI1v164dFi5cmKN1FdLdT0RERERE9IsRFVwi6ZIlS3D//n3s2LED7969w8SJE1GqVCk0a9ZMppynpyeSkpKEz3fu3MGoUaPQtWvXHK+LQSYREREREdEvLC4uDr6+vti0aRPs7OxgZ2eHp0+fYvfu3XJBpr6+vvDv5ORkrFy5Ev3794eDg0OO18dnMomIiIiIiPKDSKTYIYeCgoIgkUjg5OQkjHN2dsadO3eQkpKS5Xx+fn6IiorCgAEDcrWZbMkkIiIiIiLKDwpOlxWLxRCLxTLj1NTUoKamJjMuNDQUBgYGMuONjY2RmJiIyMhIGBoayi1bKpVi8+bN6NmzJ7S1tXNVL7ZkEhERERER/YQ2bNgAZ2dnmWHDhg1y5eLj4+UCz7TPGYPUNNevX8eHDx/QqVOnXNeLLZlERERERET5IRcprjkxcOBA9OnTR2ZcxmASANTV1eWCybTPGhoamS77zJkzqFevnswzmjnFIJOIiIiIiCg/KDhdNrPU2MyYmpoiIiICEokEKiqpIWBoaCg0NDRQrFixTOe5dOkShg3L2ytXmC5LRERERET0C7O1tYWKigpu374tjAsICICDgwOUlORDwvDwcLx+/RrOzs55Wh+DTCIiIiIiovxQQL3Lampqok2bNpg1axbu3r0Lf39/bN26FT179gSQ2qqZkJAglH/69CnU1dVhZmaWp81kkElERERERJQfREqKHXJh8uTJsLOzQ69evTB79mwMHz4cTZo0AQC4urri5MmTQtmwsDAUK1YMojw+Q8pnMomIiIiIiH5xmpqaWLx4MRYvXiw37fHjxzKfmzdvjubNm+d5XQwyiYiIiIiI8oOCe5ctrBhkEhERERER5QcF9y5bWBWNrSQiIiIiIqJ8wZZMIiIiIiKi/MB0WSIiIiIiIlIYpssSERERERER5Q5bMomIiIiIiPID02WJiIiIiIhIYZguS0RERERERJQ7bMkkIiIiIiLKD0WkJZNBJhERERERUX5QKhrPZBaNUJqIiIiIiIjyBVsyiYiIiIiI8gPTZYmIiIiIiEhhisgrTIpGKE1ERERERET5gi2ZRERERERE+YHpskRERERERKQwTJclIiIiIiIiyh22ZBIREREREeUHpssSERERERGRwjBdloiIiIiIiCh32JJJRERERESUH5guS0RERERERArDdFkiIiIiIiKi3GFLJhERERERUX5guiwREREREREpDNNliYiIiIiIiHKHLZlERERERET5gemyREREREREpDBFJMgsGltJRERERERE+aJAg0wbGxvY2Njg3bt3ctP27t0LGxsbeHp6KmQ9169fz/V8jx49wq1bt757/d/i7u4OPz8/hS+3QYMG+Pjxo8KX+/LlS7Ru3RoODg5YtWqVwpcPAH5+fnB3d/8hy84or9+Pn526qhJW9a2OYO92eLCqNYY0s8myrK2ZHo5PaYjXGzvgn7nN4FqxeKblVvaphglt7H9Ulek7SJLEOLdtBTYNa49tY7og8MzBLMs+vnYOu6b0w/pBrXBwwWh8fP5YmCaVShF4+gB2TuyFTcPa4++tyyFOiM+PTaBcUFUSoX8NM6zvaA/PdpXgUdHkm/MYa6tiUyd7VCyuLTO+kbURVraxxcZO9hjuag5tNeUfVW3KI3U1Fayb2RXv/1mC53/Nx8gemf9+ntk0EvGBXnLD+pnd5Mqund4FUwc2/9FVpzxITEzEzOlT4FrTBQ3dXLFj+9ZvznMr4CaaN20oM66ynU2mw7Ejh39QzQlAasc/ihwKqQJvyVRVVcW5c+fkxvv7+0OkoB13+fJlODk55Xq+oUOH4sWLFwqpQ357+fIlNDQ0YGpqqvBl79q1CwBw4sQJ9OnTR+HLB4DmzZvjwIEDP2TZlGrW71VQpZwB2i4+j/E7b2J8a3u0dDGTK6erqYoD4+vjybto1Jt2GicC3mDHCFcY66rLlBvuURE93Czzq/qUS//u34xPL56izfhFcOs+DP8d3Y1nNy/JlXv35D7Ob1uJai27osvcDShhWQnHVk0TAskHF0/ixtFdqNmuN9pNXo7YiDCc3bgovzeHvqFz1VIob6SFhf7B2H7jDdo6mqJaGb1s5+ld3QwaqrIBZA1zfXR2KoXdAe8w58wzGGmrole10j+y6pQHC0e3RdVKZeHxxxqMWuiDKX94oG2jKnLlOo/dhHKNJgtDx9EbkChOwkZf2XPBmF6N0LddnXyqPeXWimVL8PD+fWzaugNTps/EBm8vnD1zOsvyT588xrjRIyFNkcqM//vCZZmhd9/+KFWqNOq7N8xiSaQQIiXFDoVUgdfMxcVFLsiMiYlBYGAgKlWqpJB1mJiYQE1NTSHL+llcvXoVtWrV+iHLjomJQcWKFVG2bFno6WV/0ZJXGhoaMDQ0/CHLJkBLTRnd61lg6p5A3H0ZgZO33sLz5CP0b2QtV7ZznXKITZBg3I6bCPkUg8WH7+P5hy+oUj71+OhoqGDr0DoY8Zst3oTF5vemUA4kJSbg4aXTqNtlEEzMK8Ciah1UbdYB984dlSsbFxUOl5ZdYVOrIfRMSqJaq65IjP2CiHevAAD3/j6KKk3aw7pGAxiVLoeG/cbixd0biPjwOr83i7KgrqyE+paG+PPmW7yMiEfAm2icePgJjW2Ms5yndjl9aKjIt1C2qGSCEw8/4ebrKLyJSsDewPcoo69RmG+eFzlaGmro3aYWxi05iNtBb3D0/F2s2OGPQb+7yZWNiI7Dx7Av+Bj2BaERMZg9rBVW7PDHrYepf9+62hrYs7QfxvZpjNfvw/N7UygH4uLicOigLyZMngrbSnZo2Kgxevftj317d2da3nf/PvTs1hlGRkZy04xNTIQhITEBe3f/iZlz5kFXV/dHbwYVAQUeZDZs2BA3btxATEyMMO7ChQtwcXGBtvbXlJ1JkyZh0qRJMvOmT3O8evWqkMLZsGFD7Nu3L9NycXFxmDFjBmrUqIEaNWpg+vTpSExMlKtXjx498PbtW0yePFlY75MnT9CjRw84OjqiadOm2L376x+0VCrF+vXr4e7uDnt7e7i6usLLy0uYLpFIsGLFCri6usLZ2RkjRoxARESEMP3p06fo3LkzHBwc0KZNGzx69EiY9v79ewwaNAiVK1eGu7s7vLy8kJycDCA1rbRz584YOnQonJ2dcfToUWF/pAWZ7u7u2L59O1q2bIkqVargjz/+QGhoKADg+vXrcHd3x8yZM+Hs7IyNGzcKy/Xw8ICjoyPatWuH//77TzgOfn5+OHz4MGxsbPDmzRuIxWLMmzdP2Kfjxo1DZGSkUP+dO3eiQYMGcHBwQLt27XDz5k1hWto+cXR0RI8ePfD06VNh/enTZYODg9GvXz9UrVoVdevWhZeXF1JSUgAAnp6eGDt2LGbOnImqVauiVq1a2LRpkzBvTEwMJk+ejFq1asHe3h7NmjWDv7+/3DEvSuzK6kNVWQk3nn4Wxl1/+hlVLQzlLh7rVCyO04FvkSL9ege08Zyz8L/7HgBgbqIDDVUluM/6Cy9DGWQWRp9fP0dKsgQlrL7euCtZwQ4fnz+G9P9/R2msqtWDS4suAACJOBG3/zoEzWL6MChVFgAQ/fkDTC2+plZr6xtBU1cPH549AhUOZQ00oKwkwtPPccK4x59iYWmkhcxiQx01ZfzuVArbbsjeKNBQUUI5Qy389zpKZjmTTzyBVJpxKVRQHG1KQ1VFGdfuPBfG/Rv4HNXszbPNCOvRqiYMimlh+bazwrhypY2grqaKWl0WI+Rt2A+tN+XNk8dBkEgkqFLla4aeU1Vn3Lt7R7guSu/KpX8wd8FidO/ZO9vlenutQfWatVCzVm1FV5kyYrps/rC2toapqSn++ecfYdzZs2fRqFGjHC8jOTkZo0aNQrNmzXDq1CmMHDkSs2fPxrNnz+TKTps2DQEBAfD29sbWrVsREBCQ6XOFnp6eKFGiBKZMmYKpU6ciISEBAwYMEAK5iRMnwtvbG4cPHwYAHD58GDt27MD8+fNx+vRpDB06FJ6ennjw4AEAYPXq1Th06BAWLFgAHx8fhIWFYebMmcL6Dhw4gP79++Po0aPQ09MTpkmlUgwbNgxGRkY4dOgQFi5ciGPHjmH9+vXCvIGBgbCyssL+/fvh6uoKqVSK//77D9WrV5fZnv79+8PHxwfx8fEYPny4MO3t27cQi8Xw8/NDixYt4Ofnh7lz52LgwIE4fPgwateujT/++AMfP37E1KlT4eHhAQ8PD1y+fBklS5bEihUrcP/+fWzatAk7d+5ETEwMRo4cCQB4+PAhlixZgpkzZ+LUqVNwcXHBqFGjkJKSgrNnz8LHxwerVq3C8ePHYWxsjMmTJ8sdi/DwcHTt2hXFixeHr68vZs6ciV27dmHnzp1CmTNnzkBdXR2HDh1Cv379sGzZMoSEhAAA5s+fj5CQEGzduhXHjx+Hi4sLpk6dCrFY/M3v1q/KVF8TYTGJSEr++oP0KSoBmmoqMNSRTYM1L66Dz18SsaJ3NTxY3RqnpzdCdauvLSIPXkei66pLeP2ZAWZhFRcZDg0dPSirqArjNIsZIDlJjITY6Eznef0wEBuHtMV/x3bDtfNAqGlo/n8+fcRGfL34TEpMQGLsFyTEZL4cyn96mqr4kihBcrrUuOgECdRUlKCjLt9a2dW5FC4/D8fbKNkbrsV1UzOAiqmrYHoTK6xpWwl/1CoDLdUCv3SgdEoY6+FzZCySJMnCuE/h0dDUUIORvnaW843t3Rhr95xHbPzX38J7T96i/cj1eMVWzELrc2go9PUNoJouQ8/IyBiJiYkyN/jTrPL0RqPGTbJd5vt373DqxHH8MWiIoqtLmWG6bP5p2LChkDIrFotx5coVNGyY83zwL1++IDIyEsbGxjAzM0OrVq2wbds2mJjIdnQQFRWF06dPY8aMGXB2doadnR3mzJmDUqVKyS1TX18fysrK0NXVha6uLo4dOwYjIyOMGjUK5cqVg7u7OwYNGiQEOiVLlsTChQtRq1YtmJmZoUuXLjAxMcHTp08hlUqxf/9+jB49GvXq1YOVlRVmz56NChUqCOvr0qULGjVqhPLly6NHjx4ICgoCAFy7dg3v3r3D3LlzYWFhgRo1amDixIkyAZZIJMLgwYNhaWkJQ0NDBAUFoWTJkjKprO3bt0fr1q1hY2ODBQsWIDAwEE+ePBGm9+/fH+bm5ihVqhT+/PNP9OjRA23atIGFhQXGjRsHa2tr7Nq1C7q6utDQ0ICGhgZMTEwgFouxa9cuzJ49G46OjrCxscGSJUtw48YNPH78GG/fvoVIJEKpUqVgZmaGUaNGYenSpUhJScHbt2+hqqqKUqVKoWzZspg+fbpcazUAHD9+HJqampg7dy4sLS3RqFEjjBw5Eps3b5Y5XhMnToS5uTn69+8PfX193L9/HwBQrVo1zJkzB7a2tihXrhz69u2LyMhIhIUV3bu0mmoqECfJ3vEU//8CRU1F9rSgra6Ckb/Z4mNkPDovv4h/g0LhO74+Shlq5Vt96ftIxAkyASYAKKumfk5OSsp0HqPS5dBxhidqtO6Bv7euwIfg1JbKCtXccOukD8LfvYIkSYzLPhtSl5Ms+YFbQLmhrqIESYZnr5L+/1lFSfbv266EDqxNtHH4vnwncWnpsz2rlcbxB5/gefkFSutpYFDtsj+o5pQXmhqqECfJ/v0lilM/q6tm/qa6ei4VULq4Prb6/fvD60eKFZ8QL/cIWNrnpDzePD/kdwCV7Ozh6Fj5u+tHlKZQvCezYcOGGDFiBCQSCa5evQpra+tMc8ezoq+vjy5dumDatGnw9vZGgwYN0L59e7nnBV++fInk5GTY2dkJ41xcXODi4vLNdTx//hxBQUEyHQglJydDWTn1R7hmzZq4c+cOli9fjuDgYDx69AihoaFISUlBREQEIiMjZdZrZWUl05pYpkwZ4d+6urpCCm9wcDAiIyPh7OwsTE9JSUFCQoKQbmtkZAQNDQ1hembPY1atWlVmXfr6+ggODhaeezQz+9rhS3BwMIYOHSozf5UqVRAcHCy3X16/fo2kpCR07txZZnxKSgpevHiBevXqwdraGi1btkSlSpXQsGFDdOzYESoqKvjtt9+wa9cuNGzYEFWqVEGjRo3QoUMHuXUEBwfDzs4OKipfv65OTk4IDQ1FdHS0UP+0YwEA2trakEhSf2TbtGkDf39/7N+/H8+fPxdal9NSjouixKRkqGVojVD7/wVlvFh2vySnSHHvZQQWH04N2u+9ikR9+xLoVLscVh1/mD8Vpu+irKqGZIlsMJkWXKqoaWQ2C7T0DKClZwCTspb48DwIDy6cQAlLW7i07Iqo0PfYO2MglJRVYOfmAeMyFlDT4E2HwiIpOQUqSrIpVKr//yxOl72gqixCn+pm2PHfWyQly+e/prWEHn/wCYFvU8+1W66/xvzmNtDXVEFkPG8sFAaJiRKoZQgm1dVSP8clZB50tG3khL/+fYiI6LhMp1Phpa6uLpeJlfY5/bVgbpz96ww6/t752wVJMQpxiqsiFYogMy2ACggIgL+/Pxo3bixXRiQSQZruIZC0ACLNrFmz0K1bN/j7+8Pf3x8+Pj7w9vaGm9vXB99VVWXv5OeGRCJBrVq1MGPGjEyn+/r6YsGCBejYsSOaNGmCiRMnomfPngAgExxlJX2AlHG9FhYW8Pb2lpuW9mC2urpseuPVq1fRu3dvmXEZ65CcnAyldHe00y8j4/LSymeW658WqO3ZswdaWrIXmUZGRtDU1ISvry9u3LiB8+fPw8/PD3v37oWfnx9MTU1x6tQpXLlyBefPn8eWLVuwf/9+IQU5u/qk1SVt/Zkd27Tvy4QJExAYGIjWrVsLLcy///67XPmi5H1EHIx01KGsJBIuJE31NBCXKEFUnOyP18fIeDx9L5sK+fzDF5Q21My3+tL30TYwRkJMFFKSk6H0/3NNXHQEVNTUoa4lm073MeQxlJSUYGL+NdPCsFRZhP+/4x9VdQ00GzwViXGxEIkANU1tbB31O4oZK74na8qbiLgk6KqrQEkEpDVo6mmqIFGSgrh0N5EsjbRgqquOEXXNZeYf38ACl0LCcezBJwDA++ivabRp/zbSUmOQWUi8C42Esb42lJWVkPz/mwimRsUQFy9G5JfMXy/UpLYt5m04mZ/VJAUpXtwUkZERkEgkwrXd58+h0NDQgG6xYrle3of37/E8+BkaNGCPsvlFUW/PKOwKRbqsiooK3NzccO7cOZw/fz7T5zFVVVURG/v1ma/Xr792UBAaGorZs2fD3NwcgwcPxsGDB1GzZk25XmvLlCkDZWVlIRUVSH1VStu2bb9Zx/LlyyMkJARmZmYwNzeHubk5bt++jT///BNA6ns9hw4diilTpqBNmzYwMDBAWFgYpFIpihUrBgMDA5n1Pnr0CPXq1UNCQsI31/vu3TsYGhoK633z5g3WrFmT6Zc0KSkJd+/elWn5BCCz7pcvX+LLly+wscn8vYjly5fHnTt3ZMbduXMH5cuXlyubtk8jIyOF+uno6GDhwoUICwtDYGAgNmzYgJo1a2Ly5Mk4ffo0EhMTERAQgAsXLsDX1xf169fH7NmzceTIEbx48UImjTetPg8ePEBSurS+wMBAGBoaQl9fP9v9FxMTg+PHj2PlypUYMWIEGjdujKio1E4spEW454r7ryKRlJwCF8uvGQM1rE1wOyRcrkOPm8FhsCtjIDPOqqQuXvEZzJ+GcRkLKCmr4MPzdB2KPb2P4uWsIcqQPvno0hlcPbhNZtynF89gUDI12+Jf380IunIW6lraUNPUxseQx0iMj0MJS8X0Bk7f72VEPJJTpLAy/noDwdpEGyFhcUj/5x0cFoexRx5h6sknwgCktlb63fmIsNgkhMcloazB19aRUsU0kCKV4nNs0X2mvbC58/gNkiTJqOFQThhX28kSAQ9fZvo7Z6SvDYsyJrh6+7ncNCr8bCraQkVFBXfv3BbGBd4KgJ29g0zjQU7du3sHJUqURMlMHh0j+h6FIsgEUlNmfX19YWRkJJM6msbBwQFXrlzB1atX8eTJE8yZM0dovdLT08PZs2exYMECvHr1Cv/99x+CgoLkXoGio6ODNm3aYP78+bh79y7u3buHlStXombNmpnWSUtLC8+fP0dkZCRatWqFhIQEzJgxA8HBwbh48SLmz58vpPUaGBjg6tWrCAkJwf379zF69GgkJSUJKQw9evTA6tWrce3aNTx9+hTz589HlSpVvpna4OrqitKlS2P8+PF4/Pgxbt68ienTp0NTUzPT1s87d+7A2tpabrk7d+7E33//jaCgIEyZMgV16tRBuXLlMl1n7969sWvXLhw+fBghISFYtmwZgoKCMk1l1dHRQceOHTFr1ixcv34dz549w4QJE/Dy5UuYmZlBQ0MDa9euha+vL968eYMTJ04gLi4ONjY2SElJwZIlS3D27Fm8efMGfn5+0NTUlKtXy5YtIRaLhX3v7+8PT09PdOnS5Zt3g9TU1KCpqYm//voLb968waVLlzBnzhwAKNId/8SLk+Fz5QWW9XKBU3lDeFQtjaHNbLDhbOpFZnE9DeF9edvPP0OlMnqY0MYe5YvrYFJbe5Qz0YHvvy8LchMoF1TVNVCxdiNc3OmJjyGP8fzWv7h95iAcG7UGAMRGhUMiTm2hsnPzwNugO7hz9jAiP77F9cN/4lPIY1RunHozTlvfCP8d3Y2PIY/x6cVT+G9aAvv6v0FDh13eFxbiZCkuhYSjT/XSKG+oCWezYmhuWxxnHqf2Jq2noQJVZRGSkqX4FCOWGQAgPC4J0YmprZRngkLRzrEE7EvooKy+BvpUL42A11GISmArZmERn5CEXcdvYM3UznCuVBYt6ztiVI+GWLvnAgDA1EgXGupfs30qWZZCfIIYL9h77E9JU1MTLVu3wbw5s3D/3l2c+9sfO7dvRdfuqdlzn0NDv9mAkd6zZ09hYcl3XOcnkUik0KGwKhTpskBqMCWRSLLsVbZ169a4desWhgwZAl1dXYwcORIvX6Ze5KqpqcHb2xsLFixAq1atoK2tjQ4dOqBjx45yy5kyZQrmz5+PPn36QFVVFc2bN8fo0aMzXWeXLl2wbNkyvHjxAl5eXti0aRMWLFiANm3aQF9fH926dcPAgQOF5U6ZMgWtW7eGkZERPDw8oKmpKbyK5I8//sCXL18watQoSCQS1K9fH9OnT//mflFWVsa6deswd+5cdOrUCVpaWmjWrBkmTpyYaflr165lGjS3bdsWK1aswLt37+Dm5obZs2dnuc7mzZvj8+fPWLNmDUJDQ2Fra4utW7fCMouT0KRJk7B48WKMGDECSUlJqFatGjZu3AhlZWXY2tpi/vz58Pb2FjpZWrp0KSwtLWFpaYkRI0Zg4cKFCA0NFdKCMz5Lq6Ojg82bN2P+/Plo06YNDA0N0atXL2HfZ0dNTQ1Lly7F4sWL8eeff8LMzAyDBw/GqlWr8OjRoyy3qSiYvjcQS3u64NDEBvgSn4TFh+/jRMAbAMDD1W0wbPN17Lscgjdhcei07CIWdq+KEb/Z4sm7aHRZ+Q8+RGaehkWFU53f/8DFXV44vHQi1DW1Ub11D1g6uwIAto/pCvc+Y2Dr2gQm5hXgMXQGrvltw9WDW2FU2hwtx8yHjkFqj8IODVsh+vNHHF81HSKRCNa1GqJ2h34FuWmUiT0B79C7uhmmNLJEXFIK/O5+wM3/v4rEq70dNl59hUvPI76xFODko1CoKoswsHZZaKgo4dbbaGy/8eZHV59yaeLyg1gzpTNObxqJ6Jh4zFt/AkfOpWYkvfBfiAEz/sSuY6mvcjM10s0yjZZ+DuMmTMb8ObPQv08v6OjqYPDQ4UIPsg3ru2LOvIVo3bZdjpYVFvYZxYr9mHeeUxYKb1yoUCJpUc4ZLCLc3d0xbNgwtGuXsxMOZc+4975vF6Jfxoz+NQq6CpSP/nvJV7EUJQeWbfp2IfplRPzn9e1C9MvQKDRNabK0O277dqFciPXto9DlKUoh3f1ERERERES/lsKc4qpIDDKJiIiIiIjyAYNM+mVk7GWXiIiIiIjoR2GQSURERERElA/YkklEREREREQKU1SCzELznkwiIiIiIiL6+bElk4iIiIiIKD8UjYZMBplERERERET5gemyRERERERERLnElkwiIiIiIqJ8UFRaMhlkEhERERER5YOiEmQyXZaIiIiIiIgUhi2ZRERERERE+aCotGQyyCQiIiIiIsoPRSPGZLosERERERERKQ5bMomIiIiIiPIB02WJiIiIiIhIYYpKkMl0WSIiIiIiIlIYBplERERERET5QCQSKXTIjcTEREyZMgUuLi5wdXXF1q1bsyz7+PFjdOnSBY6OjmjZsiWuXbuWq3UxyCQiIiIiIsoPIgUPubBkyRLcv38fO3bswMyZM+Hl5YXTp0/Llfvy5Qv69u0LKysrHDt2DI0bN8awYcMQFhaW43UxyCQiIiIiIvqFxcXFwdfXF1OnToWdnR0aN26M/v37Y/fu3XJlDx06BC0tLcyaNQvm5uYYMWIEzM3Ncf/+/Ryvjx3/EBERERER5YOC6vgnKCgIEokETk5OwjhnZ2esX78eKSkpUFL62vZ448YNNGzYEMrKysK4gwcP5mp9bMkkIiIiIiLKB4p+JlMsFiMmJkZmEIvFcusNDQ2FgYEB1NTUhHHGxsZITExEZGSkTNnXr1/D0NAQ06dPR506ddCpUycEBATkajsZZBIREREREf2ENmzYAGdnZ5lhw4YNcuXi4+NlAkwAwueMQWlcXBw2btwIExMTbNq0CdWqVUO/fv3w/v37HNeL6bJERERERET5QNHpsgMHDkSfPn1kxmUMJgFAXV1dLphM+6yhoSEzXllZGba2thgxYgQAoFKlSrhy5QqOHDmCQYMG5aheDDKJiIiIiIjygaKDTDU1tUyDyoxMTU0REREBiUQCFZXUEDA0NBQaGhooVqyYTFkTExNYWFjIjCtXrlyuWjKZLktERERERPQLs7W1hYqKCm7fvi2MCwgIgIODg0ynPwBQpUoVPH78WGbc8+fPUbp06Ryvj0EmERERERFRfiig92RqamqiTZs2mDVrFu7evQt/f39s3boVPXv2BJDaqpmQkAAA6Ny5Mx4/fgxPT0+8fPkSq1evxuvXr9G6descr49BJhERERERUT5QdO+yuTF58mTY2dmhV69emD17NoYPH44mTZoAAFxdXXHy5EkAQOnSpbF582acP38eLVq0wPnz57Fx40aYmprmeF18JpOIiIiIiOgXp6mpicWLF2Px4sVy0zKmxzo7O8PPzy/P62KQSURERERElA8U3fFPYcUgk4iIiIiIKB8UlSCTz2QSERERERGRwrAlk4iIiIiIKD8UjYZMBplERERERET5gemyRERERERERLnElkwiIiIiIqJ8UFRaMhlkEhERERER5YOiEmQyXZaIiIiIiIgUhi2ZRERERERE+aCotGQyyCQiIiIiIsoPRSPGZLosERERERERKQ5bMolySVVNtaCrQPnoekhUQVeB8pGRrkZBV4Hy0YCZwwq6CpSP3kUkFHQVKB9ZmBTO8znTZYmIiIiIiEhhikqQyXRZIiIiIiIiUhi2ZBIREREREeWDItKQySCTiIiIiIgoPzBdloiIiIiIiCiX2JJJRERERESUD4pIQyaDTCIiIiIiovzAdFkiIiIiIiKiXGJLJhERERERUT4oIg2ZDDKJiIiIiIjyg5JS0YgymS5LRERERERECsOWTCIiIiIionxQVNJl2ZJJRERERERECsOWTCIiIiIionxQVF5hwiCTiIiIiIgoHxSRGJPpskRERERERKQ4bMkkIiIiIiLKB0yXJSIiIiIiIoUpKkEm02WJiIiIiIhIYdiSSURERERElA+KSEMmg0wiIiIiIqL8wHRZIiIiIiIiolxiSyYREREREVE+KCINmQwyiYiIiIiI8gPTZYmIiIiIiIhyiS2ZRERERERE+aCINGQyyCQiIiIiIsoPTJclIiIiIiIiyiW2ZBIREREREeWDItKQySCTiIiIiIgoPzBdloiIiIiIiCiX2JJJRERERESUD4pIQyaDTCIiIiIiovzAdFkiIiIiIiKiXGJLJhERERERUT4oIg2ZDDKJiIiIiIjyA9NliYiIiIiIiHKJLZlERERERET5oIg0ZDLIJCIiIiIiyg9MlyUiIiIiIiLKJbZkEhERERER5QO2ZFKhZmNjAxsbG7x7905u2t69e2FjYwNPT0+FrOf69evfvZz0rl69iuDg4DzPr8g6nTp1CmFhYQpZ1s9GXUUJy3tWRdCqlri9tDkGNq6QZdmKpYvhyAQ3PPdqg3MzG6G2jYnM9AENrXBrcXM8XdMKy3tWhaaa8o+uPuWSqpIIA2qVwcbfHeDV3g7NbU2+OY+xthq2dHaAramO8Hl3jyqZDhWLa//oTaBcUFESoYtTCSz6rQLmNrNCAyvDb85jqKWKpS2sYWWslel0dytDzGxiqeiqkgLweBct4sRErFw4Ex2auaJr64Y4uHdHlmVv/PsPhvbuhLaNa2Jwrw64dvlCpuUunfsLHq6Vf1CNKT2RSLFDYcUg8yemqqqKc+fOyY339/dX2F2Sy5cvw8nJSSHLStO7d298/vxZocvMi7dv32LUqFGIj48v6KoUiOkdHFDZ3AAdV1zC5N23MbaFLX6rWlqunK6mCnxG1cWTd1/gPvssTt56i62Da8JIVx0A8FvVUhjb0hYTdt1Ch+WX4GxhhGntHfJ7c+gbujiXQnlDLSw4+wzbb7xBO8cSqF5WL9t5+tYwg4bq1xsGYXFiDPG9LzP8GxKB4M+xeBoa+6M3gXKhtX1xlNXXgNflV/C98wEeNkaoUko323k6VTaFukrmlwVGWqrwqGj8I6pKCsDjXbRs9l6Bp0EPsWj1JgwdMwW7t23ApfNn5cqFPHuCuVPHoMlvrbF22340b9UB86eNxfOnj2XKxXyJxrrVi/Or+lREMMj8ibm4uMgFmTExMQgMDESlSpUUsg4TExOoqakpZFmFjVQqLegqFBhNNWV0dS2P6T53cO9VJE7dfgfvM0/Qt4H8XetOtcwRmyjBxN238CI0FsuOPULIpxhUNjcAAPR3t8Kmv5/B/94H3HkZgQm7bqFLHXO2ZhYi6ipKaGBlhD9vvsWL8HjcfB2F4w8/obFN1q2ZtcsbQENV9idCKgWiEiTCUFxXDdXK6mHdlVdILrp/ToWOmrIItcz1cPDeJ7yJSsTd9zHwfxqOuhYGWc7jYlYsy4ADAH6vUgJvohJ+RHXpO/F4Fy0J8XE4c+wQBo2cACsbW9Rxa4iOXXvj2MF9cmXPnz2JKlWro3XHbihlVhYt23eGY9Vq+OfcXzLltnivRMlSZvm1CUWeSCRS6FBYMcj8iTVs2BA3btxATEyMMO7ChQtwcXGBtvbX1LVJkyZh0qRJMvOmTzm9evUqWrduDQcHBzRs2BD79u3LtFxcXBxmzJiBGjVqoEaNGpg+fToSExMzrdvOnTvRoEEDODg4oF27drh58yYAwN3dHQDQs2dPeHp6ws/PTxiXpkePHjKpvl5eXqhVqxZq1KgBX19fmbJisRjz5s0T6jRu3DhERkYCAN68eQMbGxv89ddfaNSoERwcHDBw4EBhesOGDYX/+/n5ZbOnfz12ZnpQVRbhZvDXVOHrzz7DqbyhXOpFbWsTnLn9DinpggiPBedx7v4HKImAyuUMce3J15bpgOfhUFVWQiWz7FvJKP+UNdCAspIIT9K1Nj7+FAsrYy1k9vOko6aMLlVLYsu1N9kut7NTKZx/Gob30ZmfB6hglNbTgLJIhJCwOGHc8/B4mBtoZHq8tVSV0MrOBD63P2S6vGplikFVWYRrL6N+UI3pe/B4Fy3Pnz2BJFkCW4cqwjg7Ryc8fngPKSkpMmUbebRCn0Ej5ZYRF/tF+PfdwJu4G3gTnXsO+GF1JllMl6VCz9raGqampvjnn3+EcWfPnkWjRo1yvIzk5GSMGjUKzZo1w6lTpzBy5EjMnj0bz549kys7bdo0BAQEwNvbG1u3bkVAQABWrVolV+7hw4dYsmQJZs6ciVOnTsHFxQWjRo1CSkoKDhw4AADw9PRE3759v1k/Hx8f7Ny5EwsWLMD27dtx8OBBmekrVqzA/fv3sWnTJuzcuRMxMTEYOVL2hLp+/XqsWLECu3btwr1797Bt2zYAEAJWX19fNG/ePEf761dhqq+B8BgxktI1P32OToSmmjIMtWVbrsuaaCMsJhFLu1fFnaW/4fik+qhmaQQA0NNSg6aaMj6mu+OdnCJFRKwYJQ0082dj6Jv0NVXxJVGC5HR3CqLik6CmogQddfkW524upXEpOAJvs2nJsDbRhpWJNo7e//RD6kx5V0xDBbHiZJnW5S8JEqgpK0E7kwyDtg6muPEqCh++iOWm6agpo5Vdcfjc/gA2VhdOPN5FS3jYZ+jp6UNVVVUYp29oBLE4EdFRkTJly5azgEUFG+Hzy+fPcDvgBio71wCQeqN+zZI5GDJmMtTV1fOl/lR0MMj8yTVs2FBImRWLxbhy5YrQQpcTX758QWRkJIyNjWFmZoZWrVph27ZtMDGRTaOLiorC6dOnMWPGDDg7O8POzg5z5sxBqVKl5Jb59u1biEQilCpVCmZmZhg1ahSWLl2KlJQUGBqmdkagp6cn09qalf3796NXr15o0KABbG1tMW/ePGFafHw8du3ahdmzZ8PR0RE2NjZYsmQJbty4gcePvz5vMGLECDg6OqJy5cpo2bIl7t27BwBCXQwNDaGhoZHjffYr0FRTgViSLDMuUZJ6B1RNRfaiRFtdBcOa2eBjVDy6rbmMq08+Y98oV5Qy0BRSYhOTZJcllqRkm4pF+UtdWQmSDPmsSf8POFWVZY+TXQkd2BTXxqF7mbdypGlQwQg3X0UiIj5JsZWl76amLIIkRfZ4p31WUZK97W1togVLI02ceZx5B2htHYpnGZBQ4cDjXbQkJsRDVVX2ZnDa56SkrM/HUZERmDdtLCo5VEGtug0AAHu3b4SVtS2cq9f+cRUmOUyXpZ9Cw4YNcenSJUgkEly9ehXW1tYwMjLK8fz6+vro0qULpk2bhgYNGmDOnDnQ1dWFnp5squPLly+RnJwMOzs7YZyLiwt69Oght0xXV1dYW1ujZcuWaNu2LbZu3QoLCwuoqOT+jTnBwcGwtbUVPltZWUFLK7UnvNevXyMpKQmdO3eGk5MTnJyc4ObmhpSUFLx48UKYx9zcXPi3jo5OtifhoiIhKVkumEwLCuPFEpnxySlS3H8diWXHHuH+6yjM97uP4I8x6FCzrBBcqqvKLktNRQnxYtnAkwqOODkFKsqyP0Sq/7/4TLu5AACqyiL0q1kG266/kWnlzkhJBDiX0cPlkIgfU2H6LknJUrngIu2zODnd8VYSoXOVEth/56Nw0yG9isW1Ud5QE6eDCr6jNsoaj3fRoqamjqQk2ZsAaZ+zumEeER6GSSMHQJqSgqlzl0FJSQkvnj/FqaMHMHDkhB9eZ5JVkOmyiYmJmDJlClxcXODq6oqtW7dmWXbw4MHC2yzShvPnz+d4XXxP5k/O2dkZABAQEAB/f380btxYroxIJJLp5EYikQ0iZs2ahW7dusHf3x/+/v7w8fGBt7c33NzchDLp0zK+RVNTE76+vrhx4wbOnz8PPz8/7N27F35+fjA1NZWrW0YZ65exg560YDU5OTWI2bNnjxB4pjEyMhKevcxN3YuKDxHxMNRRg7KSSEihNNHTQLxYgqgMLVMfo+Lx7H2MzLjnH7+glIEWwmPFiBcnw6SYOp59SH3GQ1lJBANtNZkUWipYEfFJ0FVXgZIIwrO1+pqqSJSkIC7dzQBLIy2Y6qpjlFs5mfknuFvg0vNwbL2e+oxmBRNtKCsB9999ARU+UQkSaKspyxzvYhoqEEtSEJ/0NegwN9CAsbYa+lWX7VV6UC0z3HgVBRUlEfQ1VbGgeerrjZREqX/fS1tYY93V13geVjR75i5seLyLFiOT4oiKikSyRALl/18PRYR/hrq6BrR15HsU/hz6EZNGpD5vudhrC/QNUrO4rlz4G1++RKPv778BAFL+f0OibeOaGD5+Otyb/JYfm0P5bMmSJbh//z527NiBd+/eYeLEiShVqhSaNWsmVzY4OBhLly5FrVq1hHEZG6GywyDzJ6eiogI3NzecO3cO58+fxx9//CFXRlVVFRERX1scXr9+Lfw7NDQU3t7emDx5MgYPHozBgwejX79+OHfunEyQWaZMGSgrKyMoKAguLi4AUl+VsnbtWhw6dEhmfYGBgbh27RoGDx6MmjVrYuzYsahduzYCAgLknn1UVVVFbOzXzkikUinevPna2UiFChVw7949IQX4zZs3iI6OlqlTZGSk0NoZFhaGqVOnYvLkyVBWzr5308KcYvCjPXgThaRkKZwtDHHjWWraVA0rI9x+EYGMne7eeh6Omtay6dNWJXRx6MZrSKXAnRfhqGFljKv/7/zHxcIQSclSPHzDTiMKi5fh8UhOkcLKWFvo/Me6uDaeh8XJPHcVHBaHMYcfysy7ok0lbLr2Gvfffw0oLY218CIsPtPWECp4b6ISkCyVopyBJp6HpwYGFkaaeBWZIHO8X0YkYM5Z2XcWz2hsiX2BHxD0KfV78teTr2mVlUvqws3SAGsuv0JUvOzNQCo4PN5Fi0UFG6goq+DRg7uwr1wVAPDgbiAq2NpBSUk2QTEhPg7Txw6BkpISFq3ZDEOjr6+ladWhCxo0+XpNFvTwHpbOmYK12/ZD3zDnGXGUe0oFdP0ZFxcHX19fbNq0CXZ2drCzs8PTp0+xe/duuSBTLBbjzZs3cHBwkHuELqeYLvsLaNiwIXx9fWFkZIQyZcrITXdwcMCVK1dw9epVPHnyBHPmzBFa9/T09HD27FksWLAAr169wn///YegoCC5V6Do6OigTZs2mD9/Pu7evYt79+5h5cqVqFmzptz6NDQ0sHbtWvj6+uLNmzc4ceIE4uLiYGOT+vC5lpYWnj59ii9fvsDe3h6RkZH4888/8fr1ayxcuBBRUV+Dk+7du2Pnzp04c+YMnjx5gqlTpwonUR0dHXTs2BGzZs3C9evX8ezZM0yYMAEvX76Emdm3u+LW1EztmCYoKEgm0C0K4sXJ8L36Eou7OaGyuQGaVSmFQU2ssfnv1A6fTIqpC6+v2HkxBJVK62FsS1uUM9HG+FaVYG6ijYPXXwEAtl98jsFNrdGsSilUNjfAom5O2H0phOmyhYg4WYpLz8PRt6YZLIw04VxGD79VKo7Tj0IBAHoaKlBVFiEpWYqPX8QyAwBExCUhOuHrRWYZfc1sOwWigpWULMWNV1HoVKUEyuprwKGkDtytDHEhOBwAoKuuDFUlEZJSpPgcmyQzAEBkggQx4mTEiJNlpn35f+cyn2OTeIOhEOHxLlo0NDTRyKMlvJbNw+NH9/HvP+dwcO9OtOnYFUBqx0CJiann5307t+D92zcYO3WuMC087DNiY75At5geSpmVFQZj4+IAgFJmZaGl9e0+MyjvFJ0uKxaLERMTIzOIxfLPVQcFBUEikcDJyUkY5+zsjDt37sj1TPz8+XOIRKJM44qcYpD5C3B1dYVEIsmyV9nWrVujadOmGDJkCPr3748WLVqgePHUk4mamhq8vb0RFBSEVq1aYdSoUejQoQM6duwot5wpU6agYsWK6NOnDwYMGIAaNWpg9OjRcuVsbW0xf/58bN68GR4eHli/fj2WLl0KS8vUdzD26NEDS5YsgaenJ8qVK4eJEydi3bp1aNOmDaRSKZo2bSpT9xEjRmDu3Lno2rUr6tSpg2LFignTJ02ahFq1amHEiBHo1KkTVFRUsHHjxm+2YgKpHf6kbXPGV6MUBbN87+Luy0gcHFsPC7pUwbKjD3Ey8B0A4O6yFmjlknpieRMehy6rL6OJY0mcn9UYjR1Loofnv/gQmfojduS/N/A8FYQl3Z3gM9oVt0LCMe/gvQLbLsrcrptvERIWj6mNrdC7emkcvPMBN1+n3tDx7miPWuZZv1MvI73/92ZJhdeh+5/wOjIBw13LoqOjKU4Ffcbd/6e9z/eoACezYt9YAv1MeLyLlgHDx8HKphImjegP7xUL0L3fYNRxS70G7Na6If75+wwA4MpFfyQmJmDUH93RrXVDYVi/eklBVp8UbMOGDXB2dpYZNmzYIFcuNDQUBgYGUFP72nGUsbExEhMThUfM0jx//hw6OjqYMGECXF1d0aFDB1y8eDFX9RJJi/Ib6YnyoOQfB79diH4Z7nUtC7oKlI+MdItWT9NERcmoOuUKugqUjyxMCuf5vKn3dYUu71h/J7mWSzU1NZlgEgAOHz6M1atXy3Te8/r1azRq1AgXL15EiRIlhPFeXl7YtGkTZs6ciUqVKuHs2bNYt24dfHx84ODgkKN68ZlMIiIiIiKifKCk4EcyMwsoM6Ouri4XjKZ9ztgz8ZAhQ9CjRw+ho5+KFSviwYMH2L9/f46DTKbLEhERERER/cJMTU0REREh8xaH0NBQaGhoyDyKBgBKSkpyPclaWFjg48ePOV4fg0wiIiIiIqJ8IBKJFDrklK2tLVRUVHD79m1hXEBAABwcHOR6Jp40aRImT54sMy4oKAgWFhY5Xh+DTCIiIiIionyg6N5lc0pTUxNt2rTBrFmzcPfuXfj7+2Pr1q3o2bMngNRWzYSE1E4d3d3dcezYMRw+fBgvX76El5cXAgIC0L179xyvL89BZlhYGEJCQoTPJ0+eRGhoaF4XR0RERERERD/I5MmTYWdnh169emH27NkYPnw4mjRpAiD1bRUnT54EADRp0gQzZ87EunXr0KJFC5w7dw6bN2/O0SsC0+Spd9mrV69i6NCh6N27N0aMGAEA6Ny5M4KDg7F+/Xo4OzvndpFEPw32Llu0sHfZooW9yxL9uti7bNFSWHuXbbHhP4Uu7/jAagpdnqLkqSVz8eLFGDRokBBgAsC+ffvQv39/LFiwQGGVIyIiIiIi+lUoiRQ7FFZ5CjJfvHiBZs2ayY338PDAs2fPvrtSRERERERE9HPKU5BpYWGBU6dOyY0/d+4cypYt+92VIiIiIiIi+tUUVO+y+U0lLzONGjUKQ4YMwZUrV2BnZwcAePz4MW7evAlPT0+FVpCIiIiIiOhXUIjjQoXKU0tmvXr1cOjQIVSqVAnPnz/Hq1evULFiRZw4cQJubm6KriMRERERERH9JPLUkgkAFSpUwKRJkxRZFyIiIiIiol+WUhFpysxxkNmzZ094eXmhWLFi6NGjR7Y5wDt37lRI5YiIiIiIiH4VRSTGzHmQWb16daiqqgIAatSo8cMqRERERERERD+vHAeZw4YNy/TfRERERERE9G2FuUdYRcrTM5lJSUnw8/NDUFAQEhMTIZVKZaYvXLhQIZUjIiIiIiL6VRSRGDNvvcvOmDEDixYtQmhoqFyASUREREREREVXnloyT58+DW9vb9SqVUvR9SEiIiIiIvolsXfZbOjq6qJ48eKKrgsREREREdEvq2iEmHlMlx0yZAjmz5+PV69eMV2WiIiIiIiIBDluyaxYsaJMb0hSqRRNmzbNtOyjR4++v2ZERERERES/EPYum8HOnTt/ZD2IiIiIiIh+aUpFI8bMeZBZvXp1uXHPnj1DcHAwlJSUYGNjg7Jlyyq0ckRERERERPRzyVPHP58/f8aIESNw69Yt6OnpISUlBTExMahTpw5WrlwJXV1dRdeTiIiIiIjop1ZU0mXz1PHP1KlToaKiAn9/f1y/fh3//fcfTp8+jYSEBMyaNUvBVSQiIiIiIvr5iUSKHQqrPLVk3rhxA/v374eZmZkwztzcHNOmTUPXrl0VVjkiIiIiIiL6ueSpJbNMmTJ4/Pix3Ph3796hVKlS310pIiIiIiKiX41IJFLoUFjlqSWzffv2mD17Nh48eAAnJyeoqKjg0aNH2LlzJ9q1a4fDhw8LZdu0aaOgqhIREREREf282LtsNnbs2AFdXV2cOXMGZ86cEcZra2vLjBOJRAwyiYiIiIiIipA8BZnnzp1TdD2IiIiIiIh+aYU5xVWR8hRkAkB4eDhCQkKQkpICAJBKpRCLxXj48CH++OMPhVWQiIiIiIjoV1A0Qsw8Bpn79+/HnDlzIJFIIBKJIJVKAaRG5o6OjgwyiYiIiIiIiqg89S67fv16DBo0CHfv3oWRkRHOnz+P48ePw9bWFo0bN1Z0HYmIiIiIiH56SiKRQofCKk9B5qdPn9CmTRuoqanBzs4Ot2/fhpWVFaZMmQJfX19F15GIiIiIiOinJxIpdiis8hRkGhoaIjw8HABgYWGBR48eAQBMTU3x8eNHxdWOiIiIiIiIfip5CjI9PDwwceJE3Lp1C3Xr1oWfnx/OnDmDtWvXwtzcXNF1JCIiIiIi+umJRCKFDoVVnjr+GTduHHR1dREREYGGDRuiffv2mDlzJvT19bFw4UJF15GIiIiIiOinV4jjQoXKU5C5Y8cOdOjQASVKlAAAjB49GqNHj1ZoxYiIiIiIiOjnk+feZZOSkhRdFyIiIiIiol8We5fNRosWLbBu3Tq8ePECYrFY0XUiIiIiIiL65RSV3mXzlC77zz//4N27dzh06FCm09N6myUiIiIiIqKiJU9B5qJFixRdDyIiIiIiol9aYe4RVpHyFGRWr15d0fUg+mlY2ZgWdBUoH9mV0i3oKlA+2nP+eUFXgYh+kO6OJQu6CpSPLEw0CroKmcrTs4o/oTwFmRUrVswyCldVVYWJiQk8PDwwcuRIqKqqflcFiYiIiIiI6OeRpyBz1qxZ8PLywvDhw1GlShVIpVLcv38fnp6eaN++PaytrbF27VpIpVKMHz9e0XUmIiIiIiL66TBdNhtbtmzBggULUK9ePWFcxYoVUbJkScyZMwcjRoyAqakphg8fziCTiIiIiIgIgFLRiDHzlhb8+fNnlChRQm68sbExPn78CAAwMTFBbGzs99WOiIiIiIiIfip5CjLr1KmDOXPm4O3bt8K4t2/fYv78+ahZsyaSk5Nx8OBBWFtbK6yiREREREREPzMlkWKHwipP6bLz5s3D6NGj0bBhQ+jr60MqlSI6Ohqurq6YO3cuLl68iL1798Lb21vR9SUiIiIiIvop8ZnMbOjr62Pbtm148eIFHj9+DGVlZVhZWaFcuXIAgNq1a+Pq1atFZicSERERERFRqjwFmQCQkJCAGzduIDg4GMnJyfj06RP09PRgYGAADY3C+V4aIiIiIiKiglKYU1wVKU/PZD558gRNmjTBunXr8O7dO7x79w4bNmxA8+bN8ezZM0XXkYiIiIiI6KcnEil2KKzy1JI5f/581KlTB3PnzoWKSuoikpKSMH36dCxYsABbt25VaCWJiIiIiIjo55Cnlszbt29jwIABQoAJAKqqqhgwYAACAwMVVjkiIiIiIqJfhZJIpNChsMpTkGliYoJXr17JjX/16hW0tbW/u1JERERERES/GiUFD4VVntJlO3fujGnTpmHkyJFwdHQEANy5cwdr1qxBx44dFVpBIiIiIiIi+nnkKcjs168f4uPjsWzZMkRFRQEAjI2N0bt3b/Tt21ehFSQiIiIiIvoVFOIMV4XKU5ApEokwfPhwDB8+HGFhYVBXV4eOjo6i60ZERERERPTLKMzPUSpSnt+T6e/vj+fPn0MsFstNGzZs2HdVioiIiIiIiH5OeQoyJ06ciJMnT8LW1hbq6uoy00RFJDonIiIiIiLKjaISKuUpyDx79iy8vLzg5uam6PoQERERERH9kpSKSJCZp55vTU1NYWBgoOi6EBERERER0U8uTy2Zc+fOxaxZs9CjRw+UKlUKSkqysWq1atUUUjkiIiIiIqJfBTv+ycbt27cRFBSEyZMny00TiUR49OjRd1eMiIiIiIjoV1JEYsy8BZkbN27E+PHj0bVrV7mOf4iIiIiIiKjoytMzmWpqamjQoAEDTCIiIiIiohxSEil2yI3ExERMmTIFLi4ucHV1xdatW785z5s3b+Dk5ITr16/nbjtzV7VUo0ePxuLFi/Hq1SukpKTkZRFERERERERFikjB/+XGkiVLcP/+fezYsQMzZ86El5cXTp8+ne08s2bNQlxcXK63M0/psmvXrsWnT59w4cKFTKfzmUwiIiIiIqLCIS4uDr6+vti0aRPs7OxgZ2eHp0+fYvfu3WjWrFmm8xw9ehSxsbF5Wl+egsxFixblaWVERERERERFVUG9JzMoKAgSiQROTk7COGdnZ6xfvx4pKSlybwuJiIjA0qVLsXXrVrRo0SLX68tTkFm9enUAQExMDF69egUrKyuIxWLo6OjkZXFERERERES/PEUHmWKxGGKxWGacmpoa1NTUZMaFhobCwMBAZryxsTESExMRGRkJQ0NDmfKLFi1C27ZtUaFChTzVK0/PZIrFYkybNg3Vq1dHhw4d8PHjR0yaNAn9+vVDVFRUnipCREREREREObdhwwY4OzvLDBs2bJArFx8fLxd4pn3OGKT++++/CAgIwJAhQ/JcrzwFmUuWLMGzZ89w6NAhoYfZ4cOHIyIiAvPmzctzZYiIiIiIiH5VIpFIocPAgQMREBAgMwwcOFBuverq6nLBZNpnDQ0NYVxCQgJmzJiBmTNnyozPrTyly/71119Yu3YtbGxshHE2NjaYO3cu+vbtm+fKEBERERER/aoUnS6bWWpsZkxNTREREQGJRAIVldQQMDQ0FBoaGihWrJhQ7u7du3j9+jVGjBghM/+AAQPQpk0bzJkzJ0f1ylOQGRsbC01NTbnxKSkpSE5OzssiiYiIiIiI6AewtbWFiooKbt++DRcXFwBAQEAAHBwcZDr9cXR0xF9//SUzb5MmTTBv3jzUqVMnx+vLU7qsu7s7VqxYgZiYGGHc69evMW/ePLi5ueVlkURERERERL80kUixQ05pamqiTZs2mDVrFu7evQt/f39s3boVPXv2BJDaqpmQkAANDQ2Ym5vLDEBqS6iRkVGO15enIHPGjBlQUVFBjRo1EB8fj/bt26Nx48YoVqwYpk+fnpdFEhERERER/dKURCKFDrkxefJk2NnZoVevXpg9ezaGDx+OJk2aAABcXV1x8uRJhW1nntJlIyMj0bZtW9jZ2cHGxgYvX75E3bp1YWFhobCKERERERERkWJoampi8eLFWLx4sdy0x48fZzlfdtOykqsg8+rVq1i4cCGePn0KqVQqjBeJRDh27BgmTZok5PgSERERERHRV4ru+KewynG67OXLl9G/f39UrFgRf/75J65du4YHDx7g+vXr2L59OywsLNCnTx8EBgb+yPoSERERERH9lArqmcz8luOWzLVr16J3794YP368zHg9PT3UqFEDNWrUgJ6eHtatW4eNGzcqvKJERERERERU+OW4JTMoKAht27bNtkzHjh3x8OHD764UERERERHRr0YJIoUOhVWOWzITEhKgp6eXbRkDAwOEh4d/d6WIiIiIiIh+NYU5xVWRctySKZVKZV7UmRmRSCTTIRAREREREREVLbnqXfbUqVPQ0dHJcvqXL1++u0JERERERES/oqLSu2yOg8xSpUph69at3yxXsmTJ76rQr8bPzw+TJ0/GvHnz0LFjxx+yDrFYjMOHD6NTp04/ZPkFZdKkSQCARYsWKaQcyVJTFmF0Q0u4VTCGWJKCvTffwifgbaZlF7S2RV0rI5lxEw89wL/PI6CqLMKAOuZoWNEEmqrKCHwdhVXnghEaI86PzaAcSk4S49o+b7y8fQUqqmqwa9Qedo3aZTtPTNhHHJk3GA0Hz0IJa0dh/MNzh3H/7EEkJcShXNW6qPH7IKioafzoTaBcUFNRwrSWFdGoUnEkSlKw/fJL7LjyMtOya7pVhrttcZlxQ/8MxMXHn1FMQwX/TmsgMy0iVoy6Cy/+sLpT7vF4Fy1icSJ2rF2Cm1fOQ1VNHc3bd0fz9t0yLXvl3Gkc3rMJYaGfYG5pje4Dx8DSxk6YfvXCGRzYsR6REZ/hULUm+o2cCl09/XzakqJJqYjky+Y4yDx37tyPrMcv68SJEyhbtiyOHDnyw4LMEydOYP369b9ckDl16lSFliNZQ9zKo6KpLkb53oNpMQ1MbVYBH6MTcOFpmFzZckZamHPiMQJeRQrjviRKAAB9a5dFXSsjzD35GJFxSRhcrzzmtbLFwD138mtTKAdu+m1B2KunaDpyIWLCP+HKzuXQNiyOclVds5zn2l4vSBITZMa9DLyM2yd2o27vcdDUNcDlP1fg5qGtqPn7kB+9CZQLY5tWgF2pYui3NQCl9DUwv7093kXG4+yDT3JlLU10MNH3Hq4Ff+1TITo+KXVacW1ExIrRxvOqMI2PxRQ+PN5Fy77NaxDy9BEmL/LG54/vsWH5HBgXL4HqdRvKlHt8PxBbVs1Dv1FTUcHWEf7HD2DZ9JFYueMoNDS1EPz4ATavmoc+wyahrKU1/ly3HBtXzMbY2SsLaMvoV5LjZzIp98LCwnD16lUMHToUN2/exOvXr3/Ien7VHwBdXV3o6uoqrBx9paGihBb2plh9/jmefIrFpWdh2PPfW7RzKiVXVlVZhJJ6Ggj6+AXhcUnCkJSc+r3zsDPFpisvcftNNF6Ex2PJ2aeoVFIXZvps2SoskhIT8PTfM6jecSCMylrBvEpt2DXugKCLx7Kc5/mN80hKjJcb//D8EVRq0BplHGrAuJw1anUZjmf/noVEnJDJUqggaKoqob1LaSw6+RiP3n/B349CsfXyC3StWUaurKqyCKUNNHD/TTTCYsTCkPb3bWGig5dhcTLTwmOT8nuTKBs83kVLQkI8Lpw5ih6DxqKcVUW41GmA3zp2x9ljvnJlIyPC0LpLX9Rx90DxkqXRtms/xHyJxttXIQCAs8f2o3rdRnBt9BvKlq+AQeNn485//+LTh8yzmkgxisp7Mhlk/kCnT5+Grq4uWrVqheLFi+PIkSPCNHd3dxw4cADt27eHo6Mj+vbti7dv32L48OGoXLkyWrdujadPnwrlfX190axZM9jb26NGjRqYPXs2kpOTcf36dUyePBlv376FjY0N3rx5A6lUirVr18LV1RUuLi4YNGgQ3r17JyzLxsYG169fFz77+fnB3d0dAHD9+nW4u7tj5syZcHZ2zvSdpz169MCWLVvQp08fODo6okOHDnj58iWmT58OJycnNGnSBDdu3BCWZ2NjIzP/pEmThBTX6OhoDB8+HC4uLqhWrRrGjRuHmJgYuXIAcOTIETRr1gyVK1dG586dhdflpC8nlUqxfv16uLu7w97eHq6urvDy8pKp+7p169CvXz84OjqiadOmuHTpUm4O6y/Bqrg2lJWVcP9dtDDu7ttoVCqhI9cZdlkDTUAqxbtI+SBCBGDuycf472Wk3DRt9Vw98k0/UMSb50hJlsDEwlYYZ2pph88vHkOakiJXPiEmOrV1sstwmfEpKckIe/kUphXshXEm5SsiJTkJ4W9CftwGUK7YlNCFipIIgekyDwJfRsLBTE/ugqS8sTakAN5EyN9QAACL4tp48Tnux1WWvhuPd9Hy6vlTJEskqGD79REGG7sqCH78ACkZzuc16jZC6y59AQDixAScOrwXxfQNUbpseQBAcNB9VLR3EsobmZjCyKQEgoPu58OWFF1KIpFCh8KKQeYPdOLECdSvXx9KSkpwd3fH4cOHZVodV61ahbFjx2LPnj14+PAh2rZti9q1a+PAgQPQ1NTEihUrAAA3btzAvHnzMGbMGJw+fRqzZ8/GgQMH8Pfff8PJyQlTpkxBiRIlcPnyZZQsWRK7du3CsWPHsHz5cvj4+MDIyAh9+/ZFUlLO7ka+ffsWYrEYfn5+aNGiRaZl1q5di06dOsHPzw9fvnxBhw4dYGxsjAMHDqBChQqYN29ejta1Zs0ahIaGYu/evdi5cyeCgoLg7e0tV+7SpUuYOnUqevXqhaNHj8Le3h4DBw6EWCz73N/hw4exY8cOzJ8/H6dPn8bQoUPh6emJBw8eCGXWr1+P3377DcePH0fFihUxffp0uRPzr85IWw1R8UmQpHz9PkbEiaGuqgw9Tdng0NxICzHiZExrboPDA6tjQ9fKqFHOAAAgBRDwKgpfEiRC+Q5VSyEyLgnBobH5si30bfHR4VDX0YOyiqowTkNXH8lJYiTGynfYdvPgJljVbAiDUuYy48VxsUhOEkNL7+vzuUrKylDXLoa4yM8/bgMoV4x11REZlwRJ8te/77AYMTRUlaGvqSpT1sJEGzEJEizsYI/zE+th76DqcK1gJDPdVE8dewdVx98T6mJpJwcY66jl27bQt/F4Fy1R4Z+hq6cHFdWvx7aYviGSxImIiY7KdJ4HgTfQv119HN69Gd0HjoaGphYAIDI8DAZGJjJlixkYIvyzfJo1UW4xyPxB3r9/j1u3bqFRo0YAgCZNmuD169cICAgQyrRr1w61a9eGvb09atasiQoVKqBLly6oUKECWrVqhefPnwMAtLS0MH/+fDRp0gRmZmZo1qwZKlWqhKdPn0JNTQ26urpQVlaGiYkJlJWVsXnzZkyYMAE1atSApaUl5syZg6ioqFy12PXv3x/m5uYoVUo+fRIAGjRoAA8PD1hZWaFRo0bQ0dHBiBEjYGlpiU6dOgl1/5a3b99CW1sbZmZmsLW1xerVq9G+fXu5cj4+PmjRogW6dOkCc3NzTJgwAS1atEBUlOwJtWTJkli4cCFq1aoFMzMzdOnSBSYmJjKtwm5ubmjXrh3Kli2LwYMH4/379wgNDc3xvvkVaKgoISlZNrAW//+zqrLsaaGsoSY0VJRw40UExvo9wLWQCCxqWwk2pvI9TbtaGqKzixk2XH4hE8BSwZKIE6GsInvzQPn/FyjJEtmbT++CAvEx+AEcPbrILSdZnAgAUFKRvXBVVlFFcg5vYtGPp6mmLPw9pxFLUj+rqcj+fZc30YaGqjKuPP2MQTtu4dKTz/DqXgV2pYoJ03XUVbDk5GOM87kHk2LqWNvDqcj0jvgz4PEuWhITE6CiKhv4q/7/syQp8w73zMpZYu6anWjX4w9sXD4Hzx7dS7cs2fO5qqoqkrJYDilGUUmXZT7bD3LixAmoq6vD1TW1U43q1atDT08Phw4dgouLCwCgTJmvz0toaGigdOnSMp/TWh7t7e2hoaGBNWvW4NmzZ3j8+DFevnwpLDu92NhYfPjwAaNHj5Z5r2lCQgJevHiR4/qbmZnleLqGhgZKlSoF0f+/6enr/i09e/bEkCFDUKtWLdSqVQtNmzZFy5Yt5cqFhISgc+fOwmc1NTVMnDhRrlzNmjVx584dLF++HMHBwXj06BFCQ0NlWirLlSsn/DvtlTwSiSTjon5pickpcsGk2v8/J0hkL1Z2XH2NA7feISYxGQAQHBoLG1MdtHIsgaVnnwnl6loZYtZvFeEX+A7H7338wVtAuaGsqobkDN/xtKBQRU1dGCcRJ+LqHk/U7DxUZnz65QBASobANFmSlGl5KhiJScnC33OatGAjPilZZvz6C8+x++orRP8/G+HxhxhUKlUMHaqVxoMj0Wiz5l9IpUDi/88LY/bewfmJbnA008Pt15m3mlD+4vEuWlTV1OSCybSgUE0j874Q9AyMoGdgBHNLawQH3cffJ/1gZesAVVU1SDJcryUlJUFdnX0q/EhFpYWvqGxnvjtx4gQSEhLg7OyMSpUqwdHREVFRUTh9+jQSElKfbVNWVpaZJ31QmN6lS5fQrl07fP78GXXr1sWaNWtQtWrVTMsmJ6f+oKxevRqHDx8WhtOnT6Ndu8xfV5A2T3rq6tlfMKpkaBXJqu6iTG6xpA/oatWqhYsXL2LmzJlQU1PDjBkzMg0eM64vK76+vujduzcSExPRpEkTbN++HSVKlJApo5rhrh3w63aelJXPMWLoaapCOd3hMdRWQ0JSMmISZIMRKSAEmGlehsfBJF0KVUMbY8xpURHH7n2A5wU+m1fYaOkZITEmCinp/tbjoyOgrKoONU1tYdznF08Q8/kDLmyaj92j22H36NRzhv/aGbi6xxPq2rpQVlVDfHSEME9KcjISY6OhqWeYfxtE2foUnQh9LVUop2t+MtJRQ7w4WSa1HQCkUggBR5rnobEwLZb6G5CQlCIEHAAQHpuEyLgkFC/GmwqFBY930WJgVBxfoqKQnPz1OEZFhEFNXR1a2rKdID5//BAvngXJjCtVtjxioiMBAIbGJoiMkO1RPioiDPqGxj+m8lSkMMj8AUJCQvDw4UNMmzZNJtBbuXIlYmJicPbs2Vwtz9fXF+3bt8ecOXPQsWNHWFpa4tWrV0JglD6QK1asGIyMjBAaGgpzc3OYm5ujZMmSWLp0KUJCUi/+VVVVERv79Xm5H9Xrbdq6AAid+QDAmzdvhH9v374dDx48QNu2bbF69WosXLgQf/31l9xyzM3NERT09USZnJwMd3d3mfRjANi7dy+GDh2KKVOmoE2bNjAwMEBYWFiRCyK/5emnWCQnp6DS/1OkAMCxdDEEfYhBxj01pWkFTGpaQWaclYk2XoandhzhXFYP0zys4Xf7PVady1maNOUvwzIWUFJWQWjI17+hT8EPYGxeAaJ0N4iMy1mj7azNaDnZSxgAoHa3kajSsgdESkowMq+Aj8Ffn3EODXkEJWUVGJqVz78NomwFffgCSYoUjmX0hHFVzfVx/200Mp4K57Wzw9y2lWTGVSypi+ehcdBWV8aVqfVRrbyBMK24rjoMtFQRws5hCg0e76LF3MIayioqePboa+c8Tx7cQfkKleRu+F/86wj2b1srM+7F0yCUKpN6vrasaI8nD24L08JCPyI89CMsK9qDfhyRSKTQobBikPkDnDhxAvr6+vj9999hbW0tDM2bN4eVlRUOHz6cq+Xp6+sjMDAQjx8/xtOnTzFp0iSEhoYKnd5oamoiKioKL168gEQiQe/evbFq1SqcO3cOL168wLRp03Dr1i1YWFgAABwcHLBr1y68ePECf//9N/z8/BS9CwQVKlSAhoYG1q9fj9evX2Pz5s1Cr7AA8OHDB8yZMwe3b9/GixcvcObMGVSqVEluOT169MDRo0dx6NAhvHz5EgsXLoRUKoWdnZ1MOQMDA1y9ehUhISG4f/8+Ro8ejaSkJLkOgoq6REkKTj38hHGNLFHRVAd1rQzR2aU0fG+l9kJsqKUqpFtdDg5HE1sTNK1UHKX1NdC7Zhk4li6Gg4HvoCwCJjWtgNtvorH7xhsYaqkKgwof4ik0VNQ0YFmjIa7t9cLnF0/w6va/eODvB9sGrQEA8VHhkIgToaKmjmLFS8kMAKClbwRNXX0AQMV6LfDg7EG8uv0vPr94gmt716JCnWZQUWN6VWGRkJSCo4HvMaOVLexLF4O7rQl6u5pj99VXAFJbudT///d9ISgULSqXRKsqJVHGUBODGljAqaw+9lx7hdjEZNx6EYmJzW1gX7oYbEvqYunvDrj8NAxPP8ZkVwXKRzzeRYu6hgbqNmqObV6L8PzxQ9z89wJOHtyFpm1SHymKDP8M8f/fb9zAoy0e3rmJM4f34cPbVzj450Y8f/JQKNvwt/a48vcpXDhzBK9CnmLDslmoUt0VxUuUznL99P1ECh4KKz6T+QOcOHECLVu2hJqafI9sXbp0wfz587NML83MsGHDMHnyZPz+++/Q0dGBm5sbunTpgkePHgFIfQ7R3NwcLVu2xJ49e9CvXz/ExsZixowZiImJgb29PbZs2QI9vdS7nNOnT8e0adPQokULODg4YMSIEVi/fr1iNj4DHR0dzJ07FytXrsSff/6Jxo0bo1u3boiISE23GzlyJL58+YLBgwcjLi4O1apVw9KlS+WWU61aNcycORNr165FaGgo7O3tsX79emhkeP5gypQpmDJlClq3bg0jIyN4eHhAU1NT2Ff0ldeFEIxtZInVnRwQmyjB1n9f4Z9nqWkzRwbXwILTT3DqwSf88ywMK/4ORq8aZVC8mDpehMVh3MEH+BCdiEoldVGimAZKFNPAkcE1ZJY/3Ocebr/hMzyFRbUOA3Bt71qcWT0JqhraqNKiO8yd6gAA9k/ujjo9RsOqVuNvLqe8ixtiwj7i6l4vJEuSYF6lDlza9v3R1adcWnLqMaa3ssXWvs74kijB2r+fw/9hao+RFye5YerB+zgS+B7+Dz9h3rEg/FG/PErqaeDZp1gM2nlLeGXR1IP3Md7DGt49naCmrITzQZ+w8Pjjgtw0ygSPd9HSdcBobPdahAWTBkNLWwftuv+BanUaAACGd2uOAWNmoF7jFihnVREjpy+B7/Z18Nm2FmbmFhg/bw0MjYsDACrYOqLPiMnw+3MDYr5Ew6FqDfQdOaUgN41+ISIp8wiJcqXu8ssFXQXKRx5VShZ0FSgf7TnPlG+iX9XWvtULugqUj6pb6H27UAHYFfDm24Vyobtz9p11FhS2ZBIREREREeWDwpziqkh8JpOIiIiIiIgUhi2ZRERERERE+aAQdwirUAwyiYiIiIiI8kFhfu2IIjFdloiIiIiIiBSGLZlERERERET5oKi08DHIJCIiIiIiygdMlyUiIiIiIiLKJbZkEhERERER5YOi0Y7JIJOIiIiIiChfMF2WiIiIiIiIKJfYkklERERERJQPikoLH4NMIiIiIiKifMB0WSIiIiIiIqJcYksmERERERFRPiga7ZgMMomIiIiIiPJFEcmWZbosERERERERKQ5bMomIiIiIiPKBUhFJmGWQSURERERElA+YLktERERERESUS2zJJCIiIiIiygcipssSERERERGRojBdloiIiIiIiCiX2JJJ/2vv3uNiyv8/gL+m+1XpIlQqITZURNZmrbJyrbDuv5DVol3Wd5fVuq9clvzs94clkjvJIouwtGGXzSW33FpEJdd0U9JlZvr90RrGTGuGMYN5PXucx6M553M+533m00zzns/nfA4REREREakBZ5clIiIiIiIileFwWSIiIiIiIiIlsSeTiIiIiIhIDbSlJ5NJJhERERERkRpoyy1MOFyWiIiIiIiIVIY9mURERERERGqgox0dmUwyiYiIiIiI1IHDZYmIiIiIiIiUxJ5MIiIiIiIiNeDsskRERERERKQyHC5LREREREREpCT2ZBIREREREakBZ5clIiIiIiIileFwWSIiIiIiIiIlsSeTiIiIiIhIDTi7LBEREREREamMluSYHC5LREREREREqsMkk4iIiIiISA10BAKVLsooLy/H5MmT4e3tDV9fX6xevbrGsrt27UJAQABatmyJgQMHIi0tTaljcbgskZI8G1prOgRSo+MZ+ZoOgdTIyspE0yGQGlmaGWg6BFKjgrIKTYdApNHhsgsWLMDFixexbt063LlzB5MmTUL9+vXRtWtXqXKpqamYMmUKZs+ejVatWmHz5s0ICwtDcnIyTE1NFToWezKJiIiIiIjeY6Wlpfjll18wZcoUuLu749NPP8XIkSOxadMmmbK5ubkIDw9HUFAQHB0d8eWXX6KwsBAZGRkKH489mUREREREROqgoa7M9PR0CIVCeHl5Sda1bt0a0dHREIvF0NF51vfYrVs3ye9lZWVYu3YtrK2t4erqqvDxmGQSERERERGpgUDFWWZFRQUqKqSHghsYGMDAQPpygNzcXNSuXVtqvY2NDcrLy1FYWAgrKyuZulNSUjBixAhUVVVh4cKFCg+VBThcloiIiIiI6J20YsUKtG7dWmpZsWKFTLknT57IJJ5PH7+YpD7VuHFj7NixA+PGjUNERATOnTuncFzsySQiIiIiIlIDJSeEfalRo0YhNDRUat2LySQAGBoayiSTTx8bGRnJrdvGxgY2NjZo1qwZzp8/jy1btsDT01OhuJhkEhERERERqYGqL8mUNzRWHjs7OxQUFEAoFEJPrzoFzM3NhZGREWrVqiVVNi0tDbq6unB3d5esc3V1VWriHw6XJSIiIiIieo81a9YMenp6UkNeT58+jRYtWkhN+gMA27Ztw6JFi6TWXbp0CQ0bNlT4eEwyiYiIiIiI1EGg4kVBxsbGCA4OxsyZM5GWloakpCSsXr0aQ4cOBVDdq1lWVgYAGDBgAI4fP45169YhMzMTixcvRlpaGoYPH67w8ZhkEhERERERqYFAxT/K+P777+Hu7o5hw4bhhx9+wNixY9GlSxcAgK+vL/bu3QsAcHd3x9KlS7Ft2zYEBgbiyJEjiI2NhZ2dneLnWVVVVaVUdERabmzCFU2HQGqUlVui6RBIjQpL5M+wR+8nS7OXX8dE748vfZ01HQKpUcAHtpoOQa7Um49UWp+3S62XF9IATvxDRERERESkBqqeXfZtxeGyREREREREpDLsySQiIiIiIlIDLenIZJJJRERERESkFlqSZXK4LBEREREREakMezKJiIiIiIjUQNnbjryrmGQSERERERGpAWeXJSIiIiIiIlISezKJiIiIiIjUQEs6MplkEhERERERqYWWZJkcLktEREREREQqw55MIiIiIiIiNeDsskRERERERKQynF2WiIiIiIiISEnsySQiIiIiIlIDLenIZJJJRERERESkFlqSZXK4LBEREREREakMezKJiIiIiIjUgLPLEhERERERkcpwdlkiIiIiIiIiJbEnk4iIiIiISA20pCOTSSYREREREZFaaEmWyeGyREREREREpDLsySQiIiIiIlIDzi5LREREREREKsPZZYmIiIiIiIiUxJ5MIiIiIiIiNdCSjkwmmURERERERGqhJVkmh8sSERERERGRyrAnk4iIiIiISA04uywRERERERGpDGeXfY+5ubnBzc0Nd+7ckdkWFxcHNzc3LFmyRCXHOXHihFL75OTkwM3NDTk5OUrVcevWLRw5cuSV4lSV7du3Y9q0aa9dj5+fH3bs2KGCiOjf6OkIMNirHub3aILZ3RrDr5HVS/exMtHHwl5uaGRjIne7f2MrzOziqupQSQX0dQUY+7EzNg/zwtr/8UBwC7uX7lPHzADxoa3QvJ65ZJ2uQIBhbR2wZogHNoR4ItTHETpa8g/zXWKgK8CkLo2QGO6DhC/aYEDr+jWWnRvYFH9885HU8qFLbQDVfzdjPnbGtjBvJIb7YHZgU9iaGajrNEhBfH1rl8qKcmxeOg+ThnTF1BFBSP41rsayp44cQGT4QHw7wA+LIkYj6+plyTaxSIRd65djSmggJg76FKujpuFRYb46ToG0gFYmmQCgr6+P5ORkmfVJSUkQqOgrhqNHj8LLy0stdUyePBlpaWmvdazXlZKSgg8//PC169m2bRu6d++ugojo3wQ3r4MGtY2w5Gg2tp67h65NbeBZ3/xf9xngUReGevLfNqxN9NGtqe2bCJVUINTHEY1sTTF1z9+IPpqFga3t0f6fRKImY3ydYKyvK7VuiLc9/JrYYMkfmZix9yo87M3x+YcN3mTo9ArGfOwMNzsz/GfbRSxKzsDwdo7o2NhabllnaxNE7r2K4OiTkiU1uxAAMOLDBujQyAqR+64ifEsa9HQEmB3YVI1nQorg61u7/LpuGbIz0vHVrP9Dvy++wb74NTj71yGZchmXzyPu5x/RtX8ovv+/DWjYtDmWR05A+ZNSAMDBHRtx5ujvCJ0wC98sWInSkkfY8N9IdZ+O1hGoeHlbaW2S6e3tLZNklpSU4OzZs/jggw9UcgxbW1sYGLzeN76qqENdTpw4gXbt2r12PVZWVjAyMlJBRFQTA10BPnS2xLa0+8gpKkPa3WL8fi0PHzes+UOJt0MtGOrX/JYxwLMucorK3kS49JoM9XTwaVNbxPyVjRt5pTieWYgd5++ih3udGvfp2MgKxga6Muu7u9fB+pM5OHOrCDfySrHszyx0bWYLoxq+fCD1M9LTQc8Wdlh86AauPniMP6/nIy71Nvp41pMpq68rQF0LI1y5V4z80krJUimqAgB0da+DVUezcT7nEbLynyDq4HU0q2sOB0u+R78t+PrWLuVlT5CStBt9P/8ajq5u8GjXEZ17D8afe7fLlH1UkIeAfsPQ5pMA2NS1R0D/UJSWPMK9W5kAALFYhN4jxqKRuyfqObqgY49+uHFFsx0WWkFLskytfdfw9/fHyZMnUVJSIll3+PBheHt7w9TUVLIuIiICERERUvs+P4Q1JSUFQUFBaNGiBfz9/bFlyxa55UpLSzF9+nT4+PjAx8cH06ZNQ3l5+UvjVORYEREROHnyJJYuXYqQkBAAwN27dzF69Gh4eHjAz88PS5cuhUgkAgDs2LEDISEhWLx4MXx8fODt7Y158+ahqqpKctwtW7bAz88PXl5eCAkJwd9//y3Z5ufnh6ioKPj6+iI4OBhVVVW4fv06ateuDSur6iGXqamp6NOnD1q2bIlevXrht99+k3pO582bh/Hjx8PDwwMdO3bEzp07pep/OlxWLBZj1apV8Pf3R8uWLaViWb58OXr16iX1fK1evRqDBw8GADx69AgTJ05Eq1at4Ovri8jISJSVVSdBJ06cgJ+fH2bMmIHWrVtj5cqVL22L94m9hRF0BQLczCuVrMvIewInK2O571cmBroIal4HW87elVtfW0cLGOjpICWz8M0ETK/FxdoEejoCpN9/9n53+V4JmtQxk9ve5oa6GO7jiJ//zJRab2GkBxMDXVx98KyezPxS6OvqoJGtKejt0MjWFLo6Orh4p1iyLu32I3xQT7a9HWsbA1VVuCvnCyIBgNn7ruLUP72azzM15JQObwu+vrXL7czrEAlFcHFrIVnXsFlLZF27DLFYLFXW6yM/BPQbBgCoKC/H4d3xMLeojbqOzgCAbgNGwKNdRwBAcWEBUpJ2o3Hz1xuBR/SU1iaZTZo0gZ2dHf744w/JuoMHD6Jz584K1yESiTB+/Hh07doV+/btw9dff40ffvgB169flyk7depUnD59GsuWLcPq1atx+vRp/Pe//1XJsaZMmQIvLy+MGDECS5YsQVVVFb766itYW1sjISEB8+bNw+7duxEdHS2p7+zZs7h58ybi4uIwbdo0rF+/Hn/99RcAIDk5GUuXLsW0adOQkJCA1q1bY+jQoSgqKpLsv3v3bsTGxuLHH3+EQCCQGiqbm5uLUaNGoU+fPti9ezdGjhyJiIgIpKamSvbftGkT3N3dsWfPHnTp0gUzZsxAcfGzD0RP/fzzz1i9ejUmT56MhIQE2NvbY+TIkSgtLUWPHj1w9epV3Lx5U1J+37596NGjBwBgypQpKC4uRlxcHJYtW4YLFy5g1qxZkrK3b99GRUUFduzYgZ49eyrcFu+DWkZ6eFwhgujZ9wooLhfCQFcHpnK+3e7Tog5OZhfhXnGFzDYzA10E/ksCSppX20Qfj8qEEIqfNXjhk0oY6unA3Eg2Wfj8wwZIvvoQtwqkE4/iciEqRWJYmz4bXWHzz/V5teTUQ5phbWaAoieVUu1dUFoJQz1d1DKWbidnKxOUVIgwpVsTJHzRBisGt4SPsyUAoArA6ewiFJcJJeU/86qPwtJKZOQ+VsepkAL4+tYujwryYFrLAnr6+pJ15hZWqKyowOPiIrn7/J2WiomDP8X++DXo8/k4GBpLz6uwNy4WU0J74caVNAQP/+qNxk/Vs8uq8udtpbVJJlDdm/l0yGxFRQWOHTsGf39/hfcvLi5GYWEhbGxs4ODggMDAQKxZswa2ttLXpRUVFWH//v2YPn06WrduDXd3d8yaNQv169c8EYMyxzI3N4e+vj5MTExgaWmJ48eP486dO4iMjETDhg3h4+ODSZMmYf369ZL6RCKRZHtQUBCaNm2KCxcuAABWrVqFUaNGoVOnTnB2dsb48eNhb2+PXbt2SfYPDAyEm5sbmjatvjbn+SRz06ZNaN++Pf7nf/4HTk5OCAoKwoABA7Bu3TrJ/m5ubggLC4OjoyO+/vprlJWV4dq1a1LnXFVVhY0bN+Lrr7+Gv78/XF1dERkZCV1dXezatQsNGjRAy5YtsX//fgDVSePly5fRtWtXZGdnIykpCVFRUXBzc0PLli0RGRmJhIQEqWR25MiRcHJyUqot3gcGugKpDyQAIPwn49R7YZYHN1sTNLQywf70h3Lr6tPCDieyCuUmoPR2MNTTQaVI+hvup8Mh9XWl29vDvhaa1TVD/BnZidHEVUBKZgFC2jjA2lQfJvq6CG3nCKFYLPN3Q5ojr70r/nlsoCv9b7+BlTGM9HRwMrMQE3ZcwvGbBZgX/AHc7Mxk6vV1tcIAb3usOJol8/5BmsPXt3apKC+TSjABSB4LKyvl7lOvQUNMXBiL7oM+x8bFc3Hz74tS29t8EoAJUavQpKU3lv3wDZ6U8kukN0kgUO3yttLqr6b8/f0xbtw4CIVCpKSkoEmTJrC2lj8xgjyWlpYYNGgQpk6dimXLlqFTp07o27cvLCwspMplZWVBJBLB3d1dss7b2xve3t4qPxYAZGRkoLCwEK1bt5asE4vFKCsrQ0FBAQDA2toaZmbPPkSYmZlBKBRK9o+KisKiRYsk28vLy5GZmSl5bG9vL/ldJBLhzJkzWLBgAQDgxo0bOHTokNSERZWVlXBxcZE8dnZ2ljo2AMnxn8rLy0NhYSE8PDwk6/T19dG8eXNkZGQAAHr06IGEhASMGTMG+/btQ9u2bWFtbY20tDSIxWJ8/PHHUnWKxWJkZWVJHjs4OMg8f9qgUlQl86FB758PIxXPfVjR1xFggGc9bD1/D5VyPlQ2rWMKZytjxP3OXsy3WaVQDP0XkounHz7Lhc/a20BXgPAOTog+moUKkfwkIuZYNib6u2LNEE88qRRh65k7cLM1Q2ml6M2dACmlQk57P00uy4TSyci647ew7ewdlJRXt1/Gw1I0qWOGXi3s8Pdzwy99Xa0ws4cbdpy9i8SL99/wGZAy+PrWLvr6BjLJ5NPHBobyr5WuZWmFWpZWcHBpjMy/L+PYb7/Cxa25ZLttverPQiFfT8X0kX2QdvwIfPw4ASO9Hq1OMp8mYadPn0ZSUhI+/fRTmTICgUDqWsUXE6GZM2diyJAhSEpKQlJSEuLj47Fs2TJ07NhRUkb/hW+cXpUix3oaY8OGDbFs2TKZOszNq2cPlTeZ0NPzFIlEmDx5ssxMsc8npYaGhpLfL126BGdnZ6lksVevXhg9erTU/np6z/7c5D0nzz/PLx7jeSKRSHLdQffu3TF//nxkZWXht99+Q//+/SVlzM3NsX277IXwdnZ2OH/+/L8e431XVCaEqYEudATV314DQC1DPVQIxXhS+exDiVNtY9iaGWCkj3QyPqa9I05mF0FPR4DaJvqY16MJAEBHAOjqCLCwlxuW/5WNjLwnajsnqlleaSVqGelJtXdtY32UC0V4XP7sw2PjOmaoV8sIEZ82ktp/RrcmSL76EMuPZqGoTIipiX/DzFAXFcIqCATAMB9HPGBP9lvjYUkFLIz1oSuAZEi8lakByipFKCmT/h9WBUgSzKey8kvhYv1sOJ2fmw2mdm2MX9PuYemRm6C3C1/f2sXC2haPHxVBJBJCV7f6c9WjwnzoGxjC2FR6BELWtSvQ0dGBo6ubZF1dR2fJxD8XTx2DQ8MmsLSuHoGnb2AIa7v6KHlUqJZz0VZvceejSmn1cFk9PT107NgRycnJOHTokNzrMfX19fH48bNhA7du3ZL8npubix9++AFOTk4YM2YMtm/fjnbt2snMWuvo6AhdXV2kp6dL1iUlJaF3794Kx6rosQDAxcUFd+7cgZWVFZycnODk5IScnBwsXrxYoduzuLi44N69e5J9nZycEB0djXPnzskt/+KtS1xcXJCVlSW1/++//47du3crfL5AdUJsY2MjddzKykpcunRJ0itap04dtG3bFtu3b0d6ejq6dOkiiaG4uBgCgUASQ1lZGRYsWICKCv6zzCkqg6iqCs5WxpJ1Da1NkFX4BM+n+lkFT/DDgev4MfmGZAGAuDN3kXg5F79efIA5SRmSbYlXclFUJsSPyTeQXcCZZt8WNx6WQiiugludZx9APqhrhmsPHku197UHJRi1JQ3jt1+SLACw9I+b2Jx6GwDwn04u8LSvhZJyESpEYng7WqCwtBK3CviFwtviWu5jiMRifPDc/Q9b1DdH+v0SvNh/9X1AI0zqIp10NLY1RXZ+dXu2crTA1K6NsePcPfzfISaYbyO+vrWLg0tj6OrpIvPvS5J1N66koUGjZtDRkf5YfzxpD3ZvXCG17lbG37BzcAIA7Fz3M04e3i/ZVvakFLl3bqGug/ObOwHi7LLawt/fH7/88gusra3h6Ogos71FixY4duwYUlJScPXqVcyaNUvSC2dhYYGDBw9i7ty5yM7OxqlTp5Ceni5zCxQzMzMEBwdjzpw5SEtLw4ULF/DTTz8pdbuPlx3LxMQEmZmZyMvLg6+vL+zt7TFx4kT8/fffSE1NxbRp02BsbAxdXdlJXV4UGhqKdevWYefOncjOzkZUVBT27dsHV1dXueVfTDIHDx6Mixcv4qeffkJmZiZ2796NRYsWvdJ1j8OHD8fixYuRnJyMjIwMyay8z99Hs2fPnli7di0++ugjyfBhV1dXdOjQARMmTEBaWhouXbqE77//HqWlpahVq5bScbxvKkVVOJldhAGeddHA0ggt65nBv7EVjmRUD6c2N9SFvo4AleIqPHxcKbUAQGGZECUVIpRUiKS2lZSLIP5nH3nDa0kzKkRiJF99iPAOTmhkawofJ0sEe9TF7osPAACWxnow0BWgQlSFu4/KpRYAyHtciaJ/esCKy4QIaeOABrWN0byeOUZ95IRt5+7KJC+kOeVCMfZfysW3nV3R1M4Mvq5WGOhtj23/XIdnZaIPg39uSXEsIx9dmtkioJkt7C2NMKydI1rY18L2c3ehKwAiAhrhXM4jbD6VAysTfcnCa/TeHnx9axcDQyO0/aQbtkYvRNa1K0g78QeSf43DJz37AaieGKjin7sXtO8SiKsXTuPw7q14cOcW9sbFIuvaZXzSq3rUV4dufZC8czMunU7B3ewbWP/TLNjUs0ezVq9/OzoirR4uCwC+vr4QCoU1ziobFBSEM2fOIDw8HObm5vj6668l1/QZGBhg2bJlmDt3LgIDA2FqaorPPvsM/fr1k6ln8uTJmDNnDkJDQ6Gvr4/u3bvjP//5j8JxvuxY/fr1w+TJkzFy5EgkJCRg+fLliIyMRP/+/WFiYoKuXbti0qRJCh2re/fuePjwIRYvXoyHDx+iUaNGWL58udR1lE9VVFTgypUr8PT0lKyzt7dHdHQ0Fi5ciNjYWNjZ2SEiIgKBgYEKn+9TI0aMQElJCaZNm4aSkhJ4eXlhw4YNklulAECXLl0wc+ZMqcQTABYsWIDZs2dj+PDh0NPTQ4cOHTB16lSlY3hf7bhwHwM862JcByc8qRRh75VcnP/nlgdzuzfBxtN3cCJb/kx19O6JTbmF8A5OmNPTDaUVIsSl3kFKZvWXCutDvPDfwzeQfDXvpfVsPHUbo32d8GNgU5RVivHrhXvYxWv03jpLj9zEt/6u+G+/5nhcIcSav7Lxx/V8AMDO0W0xd/817L/8AH9cz8ei329gaDtH1DE3RGZeKSbuuIx7j8rxQT0z1K1lhLq1jLBzdFup+sdtvYBzOY80cWokB1/f2qX3iLHYGr0QS6aPg7GJKboN/BweH1ZfOjV1RBCGjJ0MH7/ucHR1w8hJc7Fn00rs3hiNeg0aYsyMRZLhsR269UFF2RNsXbEQJUWFaOrZFl9Mni/TI0qq9TbPCKtKgqoXL4Qj0rCPP/4YEydOlLkHZk0yMzMRHByMY8eOSd3j9E0Zm3DljR+D3h5ZuSUvL0TvjcISDqfXJpZmsvMT0PvrS19nTYdAahTwge3LC2lAdn65SutrYPV2zi+i9T2Z9PYoKSlBeno68vLyUKdOHYXKHz16FPHx8ejRo4daEkwiIiIiIvp37A+nt8apU6cwcuRIdOzYEa1atVJon6lTp6KoqEipocdERERERJqgJfP+sCeT3h6dOnWqcQZbeczMzJCamvrmAiIiIiIiUiEFbvTwXmBPJhEREREREakMezKJiIiIiIjUQju6MplkEhERERERqQGHyxIREREREREpiT2ZREREREREaqAlHZlMMomIiIiIiNSBw2WJiIiIiIiIlMSeTCIiIiIiIjUQaMmAWSaZRERERERE6qAdOSaHyxIREREREZHqsCeTiIiIiIhIDbSkI5NJJhERERERkTpwdlkiIiIiIiIiJbEnk4iIiIiISA20ZXZZ9mQSERERERGpg0DFixLKy8sxefJkeHt7w9fXF6tXr66x7OHDhxEUFAQvLy/06tULv//+u1LHYpJJRERERET0nluwYAEuXryIdevWYcaMGVi6dCn2798vUy49PR1fffUV+vbti507d2LgwIH4+uuvkZ6ervCxOFyWiIiIiIhIDTQ1WLa0tBS//PILYmJi4O7uDnd3d1y7dg2bNm1C165dpcru2bMH7dq1w9ChQwEATk5OSE5Oxr59+9C0aVOFjsckk4iIiIiISA00Nbtseno6hEIhvLy8JOtat26N6OhoiMVi6Og8G+Dau3dvVFZWytRRXFys8PGYZBIREREREb2DKioqUFFRIbXOwMAABgYGUutyc3NRu3ZtqfU2NjYoLy9HYWEhrKysJOtdXV2l9r127RpSUlIwcOBAhePiNZlERERERERqIFDxz4oVK9C6dWupZcWKFTLHffLkiUzi+fTxi0nq8/Lz8zF27Fi0atUK/v7+Cp8nezKJiIiIiIjUQNXDZUeNGoXQ0FCpdS8mkwBgaGgok0w+fWxkZCS37ocPHyI0NBRVVVVYvHix1JDal2GSSURERERE9A6SNzRWHjs7OxQUFEAoFEJPrzoFzM3NhZGREWrVqiVT/v79+5KJf9avXy81nFYRHC5LRERERET0HmvWrBn09PRw7tw5ybrTp0+jRYsWMj2UpaWlGDlyJHR0dLBx40bY2dkpfTwmmURERERERGogEKh2UZSxsTGCg4Mxc+ZMpKWlISkpCatXr5b0Vubm5qKsrAwAsGLFCmRnZ2P+/PmSbbm5uZxdloiIiIiIiJ75/vvvMXPmTAwbNgxmZmYYO3YsunTpAgDw9fXFvHnz0KdPH/z2228oKytDv379pPbv3bs3fvzxR4WOJaiqqqpS+RkQvcfGJlzRdAikRlm5JZoOgdSosKTmGfbo/WNp9vLrmOj98aWvs6ZDIDUK+MBW0yHIVfRErNL6LIzfzoGp7MkkIiIiIiJSA1XPLvu2ejtTXyIiIiIiInonsSeTiIiIiIhIDbSkI5NJJhERERERkVpoSZbJ4bJERERERESkMuzJJCIiIiIiUgOBlnRlMskkIiIiIiJSA84uS0RERERERKQk9mQSERERERGpgZZ0ZDLJJCIiIiIiUgstyTI5XJaIiIiIiIhUhj2ZREREREREasDZZYmIiIiIiEhlOLssERERERERkZIEVVVVVZoOgoiIiIiIiN4P7MkkIiIiIiIilWGSSURERERERCrDJJOIiIiIiIhUhkkmERERERERqQyTTCIiIiIiIlIZJplERERERESkMkwyiYiIiIiISGWYZBIREREREZHKMMkkIiIiIiIilWGSSURERERERCrDJJOIJPLy8jB79mx06tQJHh4e6NGjB2JjYyEUCmXKFhYWon379sjJydFApKQKirR3RkYGRowYgVatWsHPzw/R0dEQi8UajJpelSLtfeHCBQwcOBAeHh4ICAjAzp07NRcwvRZl3s+FQiGCgoKwZMkSDURKqqBIe69duxZubm5Sy/z58zUYNb3P9DQdABG9He7fv49BgwbBxcUFUVFRsLOzw4ULF7Bw4UIcP34cK1asgI5O9fdSRUVFGD16NPLy8jQcNb0qRdq7vLwcX3zxBdq2bYtt27bh1q1biIiIgLm5OYYMGaLpUyAlKNLejx8/RlhYGHr37o2oqCicPXsWkydPhqOjI1q3bq3pUyAlKPN+DgCrV69Geno6OnfurMGo6VUp2t7Xr1/H4MGDER4eLtnX2NhYg5HT+4xJJhEBAObOnQt7e3usXLkSurq6AABHR0d4enqiR48eiIuLw5AhQ5CamopJkybB1NRUwxHT61CkvR0dHVFUVIQffvgBBgYGaNiwIYYPH47du3czyXzHKNLebdq0wccff4zvvvsOAoEAjo6OWLNmDc6cOcMk8x2j6Ps5AGRlZWH9+vVo1KiRJkOm16Boe2dkZCA4OBi2trYajpi0AYfLEhEKCgqQlJSEsLAwyT+op+rXr4++ffti69atAICjR4+ib9++HFb1DlO0vZs1a4aff/4ZBgYGUmVKSkrUGS69JkXbu0mTJliwYAEEAgHEYjGSk5Nx8+ZNtGnTRkOR06tQ5v0cAKZPn46xY8fCyspK3aGSCijT3jdu3ICzs7MGoiRtxCSTiHDp0iUIhUK0bNlS7vZWrVohPT0dFRUVGD9+PMLDw2X+mdG7Q9H2trCwgI+Pj2R9WVkZtm7dinbt2qkrVFIBZV7fAFBRUYGWLVtizJgxCAoKgqenpxqjpdelTHtv374d5eXl6N+/v5qjJFVRtL0fPnyIwsJCJCQkwM/PD926dUNsbCyqqqrUHDFpCyaZRISCggIAqHEIrIWFhVQ5ere9SnuLxWJERETg8ePHGDVq1JsPklTmVdo7Pj4eCxcuxN69e7FmzZo3HySpjKLt/fDhQyxatAizZs2CQCBQW3ykWoq2940bNwAA1tbWWL58OUaNGoXly5dj3bp16gmUtA6vySQiWFpaAqiePMDBwUFm+6NHjwAA5ubm6gyL3hBl21soFGLSpEk4fPgwVq9ezet53jHKtreBgQHc3d3h7u6OBw8eYMOGDQgNDVVbvPR6FG3vOXPmoE+fPmjSpIk6wyMVU7S9mzdvjuPHj6N27doAADc3N+Tn5yMuLg7Dhw9XV7ikRdiTSURwd3eHnp4eLl68KHf72bNn4eLiAhMTEzVHRm+CMu1dWVmJ8ePHIzk5GStXrkSrVq3UHC29LkXbOy8vD3/++afUtkaNGnEEwztG0fZOSkrC+vXr4eXlBS8vL6SmpmLFihXo0aOHmiOm16HM+/nTBPMpV1dX3L9/Xx1hkhZikklEsLKyQufOnREdHS25p9aGDRswcuRInDx5EgkJCejXr5+GoyRVUaa9p0+fjmPHjiEmJgZt27bVZNj0ihRt77S0NPznP/9BWVmZZN+LFy+iYcOGmgqdXoGi7X3gwAHs2rULO3fuxM6dO9G8eXMMHDgQK1eu1PAZkDIUbe9ffvkFAQEBUtdgXrlyha9vemOYZBIRAGDKlCkoLi5GWFgYUlNT4ePjg9LSUoSEhMDS0hJDhw7VdIikQoq097Fjx7Bjxw5ERETAyckJubm5yM3NRX5+vqbDJyUp0t6ffPIJzM3NMX36dNy8eRO7d+/GqlWrMGbMGE2HT0pSpL2dnJykFiMjI1hYWMDe3l7T4ZOSFGnv9u3bIzc3F/Pnz0dWVhYSExMRExODkSNHajp8ek8JqjitFBH9Iy8vDz///DN+//13FBQUoH79+vDz88PBgwfRoEEDzJs3D3Xq1AEA5OTkwN/fH7///rvc60Do7fey9ra3t0d8fLzMfvb29khOTtZAxPQ6FHl9FxcXIzIyEufPn0ft2rUxZswYjmJ4Rynzfg4AISEhaNu2LcaOHavBqOlVKdLe2dnZiIqKQnp6OqytrREWFoZBgwZpOnR6TzHJJKKXKi0tRXx8PAYMGMDrMrUA21u7sL21C9tbu7C9SVOYZBIREREREZHK8JpMIiIiIiIiUhkmmURERERERKQyTDKJiIiIiIhIZZhkEhERERERkcowySQiIiIiIiKVYZJJREREREREKsMkk4iIZOTl5WH27Nno1KkTPDw80KNHD8TGxkIoFKrt+Pv27ZM8dnNzw4kTJ97Y8eLj4/HTTz/JrJ86dSqWLFmiVF3nzp3DwIED4eXlhYCAAPzyyy+vFNOqVavg5+f3Svv+myVLlsDNzQ0pKSky2yIiIhAREaFwXbt27UJAQABatmyJgQMHIi0tTZWh/quQkJB/bRtvb2+4ublJLY8fP37t4+bk5MDNzQ05OTky25R9/tRl69atcv++iYjeFCaZREQk5f79++jXrx9u3ryJqKgo7NmzB19++SU2bdqEMWPGQCwWv/EYFi5ciCNHjkgeHz16FF5eXm/kWAUFBVixYgU+//xzqfUxMTFKJ4i5ubkICwtD27ZtkZCQgHHjxiEyMhKHDx9Wqp5bt25h6dKlSu2jqD179qBBgwbYuXPna9WTmpqKKVOmIDw8HImJifDy8kJYWJhKErnXdf/+fRQXFyMpKQlHjx6VLNp6M/o+ffrgwIEDuHnzpqZDISItwSSTiIikzJ07F/b29li5ciW8vb3h6OiI7t27Y+PGjUhNTUVcXNwbj6Gqqkrqsa2tLQwMDN7IsTZt2gRfX1/UqlULAFBSUoJx48YhJiYG9erVU6qupKQk2NjY4JtvvoGzszN69OiB4OBg7N69W6l6ZsyYgWbNmim1jyIuXbqE7OxsjBkzBgcOHHithDA3Nxfh4eEICgqCo6MjvvzySxQWFiIjI0OFEb+ajIwM2NrawtHREba2tpJFIBBoOjSN0NPTQ+/evRETE6PpUIhISzDJJCIiiYKCAiQlJSEsLAy6urpS2+rXr4++ffti69atAIATJ07Azc1NqsyLwwUPHjyI7t27w8PDA5999hlOnjwp2Zaeno6BAwfCw8MDHTp0kPTcLVmyBAkJCUhISJAMF31+uGx5eTmioqLQsWNHeHp6YvTo0bh79y6AZ0MZDxw4gM6dO6NFixYYNWoUCgsL5Z6vWCxGfHw8OnfuLFmXk5OD8vJy7NixA46Ojko9fx06dMC8efNk1peUlChcx86dO/HkyRN89tln/1ru6bBXeUtN9uzZg6ZNmyIgIACVlZU4cOCAwnG9qFu3bhgzZgwAoKysDGvXroW1tTVcXV3llr9//z7GjRuHNm3aoHnz5ujduzdOnz4NQLF2O3jwIAICAuDp6YlZs2ZBJBLVGNv169fh4uKi0Hns2LGjxudR3pBYRe3YsUNmuPPzQ3wjIiIQFRWF8ePHw8PDA927d8fly5fx008/wdvbGx9//LHUkPHTp09j0KBB8PDwgKenJ8LCwvDgwQPJsUJCQrB48WL4+PjA29sb8+bNk/qyxt/fH4mJiXj06NErnxMRkaKYZBIRkcSlS5cgFArRsmVLudtbtWqF9PR0VFRUvLSu9PR0TJo0CWPGjMGuXbsQGBiIsLAwZGVlAQC+++47NGvWDHv27MGcOXOwatUqHDlyBCNGjEC3bt3QrVs3bNu2TabeGTNm4ODBg5g/fz62bNkCoVCI8PBwqWG80dHRWLRoETZu3IgLFy5gzZo1cmO8evUq8vPz0a5dO8m6pk2bYsWKFXBwcHjpOb7IwcEBnp6eksd5eXlITEzEhx9+qND++fn5WLhwIWbNmvXSXrcRI0ZIDQV9fpGnqqoK+/btQ6dOnWBqaooPP/wQCQkJCp9bTVJSUuDl5YWlS5di8uTJMDU1lVtuwoQJEIlE2LJlC3bu3Ak7OzvMnDlTqkxN7Xb9+nWMHz8egwYNwvbt2yEUCiUJqjwZGRl48uQJQkJC4Ovri7CwsBqHinbv3r3G51HZnmxlrVu3Dm3btsWuXbtgaWmJYcOGIS8vD/Hx8fDz88OMGTMgFotRXFyMUaNG4aOPPsKePXsQGxuL7OxsrFy5UlLX2bNncfPmTcTFxWHatGlYv349/vrrL8l2V1dXWFhY4NSpU2/0nIiIAEBP0wEQEdHbo6CgAABqTBQsLCykyv2b2NhY9O/fH7169QIADB06FKdOnUJcXBwiIiJw+/Zt+Pv7w97eHo6OjlizZg0cHBxgamoKIyMjAICVlZVUnUVFRfj1118RExMjSQwXLlyITz75BMeOHZP0Xo0bN06SKPfq1QsXLlyQG+OlS5fg4ODwRobilpWVYezYsbCxscGAAQMU2mfu3Lno3bs3GjduXGPMT5mamtbYTvKcPn0ad+/elfTadunSBdOmTcPt27dhb2+vcD0vaty4MXbs2IFDhw4hIiJCJtEGqhPczp07IyAgAHXr1gUADBkyBF988YVUuZrabfv27fD29sbw4cMBANOmTcOhQ4dqjOnGjRsoKirCN998AzMzM8TExGD48OFITEyEmZmZVFkjIyPJ35syevbsKfNFQEVFheTvXRHNmzfH4MGDJfXNnTsXU6dOhZGREUJCQhAXF4eHDx9CIBAgPDwcoaGhEAgEcHR0RJcuXaQmWhKJRIiMjISZmRkaNmyItWvX4sKFC/joo48kZRo1aoTLly/D399f6fMlIlIGk0wiIpKwtLQEUD20UV5P3tOhdubm5i+tKyMjA/v27UN8fLxkXWVlJXx9fQEAo0aNwqJFixAfH49PPvkEQUFBsLW1/dc6MzMzIRaL4eHhIRWzi4sLMjIyJEmmk5OTZLuZmRkqKyvl1pefn4/atWu/9FyU9fjxY4SHhyMzMxObN2+GsbHxS/f5888/ce7cOcyePVuhY0RHR2PFihVyt509e1ZmXWJiIuzt7fHBBx8AqB4+OX36dPz6668IDw9X6Jjy2NjYwMbGBs2aNcP58+exZcsWmSRTIBBg0KBB2Lt3L86cOYObN2/i4sWLMpNI1dRuGRkZUteo6uvr/+s1q7GxsaisrJQk4QsXLkTHjh1x6NAhmSRw165dmDFjhtx6EhMTUb9+fbnbVq5cCTs7O6l1CxcurDEmeZ5/jRkZGcHGxkaS8BoaGgKoTlwdHBwQHByMtWvX4sqVK7h+/Tr+/vtvtGrVSrK/tbW1VAJtZmYmMxu0paUl8vLylIqRiOhVMMkkIiIJd3d36Onp4eLFi3KTzLNnz8LFxQUmJiZyh3MKhULo6VX/axGJRAgLC0NwcLBUmacfor/44gt069YNSUlJSE5OxrBhwxAZGYl+/frVGN/TD94vEolEUgmLvr7+S88VqE5+/u3avldRUlKCkSNHIjs7G+vWrYOzs7NC++3duxf37t2TDK0VCoWorKyEl5cXYmJi4O3tLVV+4MCB6Natm0J1i0Qi7N+/HwUFBZIkE6i+JvVVk8y0tDTo6urC3d1dss7V1VXuxD9isRgjRozAo0eP0L17d/j5+aGyshJfffWVVLl/a7cXJ4P6t7IGBgZSvdOGhoZwcHDA/fv3Zcr6+flJfWnxvDp16tR4jPr168u8Rp7vWa7p9fG8p6+Vp3R05F/FdP/+ffTt2xfu7u5o3749+vfvj8OHD+P8+fOSMvJ64198zsRicY3HICJSJSaZREQkYWVlhc6dOyM6OhqdO3eGnp4eNmzYgCNHjuCLL75AQkICRo8eDeDZh/ySkhJJD0pOTo4kqXJxcUFOTo5U79SCBQvg4uKCwMBAREVFISwsDKGhoQgNDcX06dPx22+/oV+/fhAIBDIfkAHA0dERenp6OHfuHDp06ACgeuhuVlaWwhO9PM/GxqbGSYFehVgsxldffYWcnBxs2LChxklw5JkwYYLkuQWAAwcOYMOGDdiwYYNMjxlQ3Sv1tOf5ZVJSUpCfn48lS5ZIJb3Hjh3Djz/+iDNnzkj1iili27ZtuH37NmJjYyXrLl26JJXEPnX9+nWcOnUKKSkpkiHQmzZtAiCbCMnTuHFjqd5ZsViM9PR0NG3aVKZsVVUVPv30U4SHh6NPnz4AgNLSUmRlZaFhw4Yy5c3MzGSG0KqCvr6+1Oy9VVVVrzyR0MGDB2FhYSHVc71hwwaFnrvnFRQUoEmTJq8UAxGRMvh1FhERSZkyZQqKi4sRFhaG1NRU+Pj4oLS0FCEhIbC0tMTQoUMBVH/wNzIyQnR0NG7duoVVq1bh8uXLknqGDx+OvXv3Yv369cjOzsbatWuxdu1aODs7w9DQEGfOnEFkZCRu3LiBCxcuIDU1VZKgGBsb4/bt2zI9T6ampujXrx8iIyNx4sQJpKenY+LEiahbt67UtWeKatasGXJycpS6lUdhYSGKi4vlbtu2bRtOnDiB2bNno1atWsjNzUVubq4kka2oqEBubq7c3lNra2s4OTlJFmtra+jp6cHJyemVrhl8XmJiIho3bowuXbqgSZMmkmXw4MGwtLSs8Z6Z+fn5NT43AwYMwPHjx7Fu3TpkZmZi8eLFSEtLk1w3+bxatWpBR0cHiYmJuH37Nvbv3y+ZZVWRSaT69++PixcvYvny5bhx4wbmz5+PO3fuyC0rEAjwySefYMmSJThx4gSuXbuG7777DnXr1kXHjh1feixVad68OQoLC7FhwwbcunUL8+bNQ1FR0SvVZWlpiTt37iAlJQW3bt3CypUrceDAAYWeu+ddvXpVqueZiOhNYZJJRERS6tSpg61bt8LFxQXffvstPvvsM+Tn5+Pzzz8HAIwePRoPHjyAmZkZIiMjkZiYiJ49eyI9PR1DhgyR1OPp6YkFCxZg8+bN6N69O7Zu3Yr//d//RZs2bQAAP/30k+RWHZ9//jm8vb0lwzaDgoJw8+ZNBAYGyvTWTJo0Ce3bt8e4ceMwaNAgGBoaYu3ata80eY+bmxtsbW3lXsNYk7Fjx2LOnDlyt/32228Qi8UYNWoUfH19JcvYsWMBVA839vX1ldxyRR0qKipw8OBBubdEMTQ0RJ8+fbBv3z6Ul5fLbP/ss8+wevVqufW6u7tj6dKl2LZtGwIDA3HkyBHExsbK7XWtW7cuZs6ciZiYGPTs2RMrV67E1KlToaenJ/XFRE2cnJywfPlyJCYmIjg4GLm5uf+aME6cOBEBAQH49ttv0a9fPwiFQqxcuVLmtjxvkrOzMyZNmoTly5cjODgYVVVVCAgIeKW6unXrhsDAQIwbNw59+/bFiRMnMGnSJGRkZCicaN64cQOPHz9G27ZtXykGIiJlCKqUHWtBRERaq7S0FPHx8RgwYABMTEw0HY5KLFmyBHfu3JF7f0t5bt26hZiYGMyaNeuVjjdp0iRMmDDhpZMcvQ2OHz+OtLQ0mVlg6d2zdOlS3L17t8YvSIiIVIk9mUREpDATExOEhoa+NwkmUH0rjWPHjil8bWZsbCy6du36SsfKzs5Gfn7+O5FgAtXX/XXq1EnTYdBrqqysxK+//ooRI0ZoOhQi0hLsySQiIq23efNm3Lt3D998881Ly1ZWVio8e+2LqqqqIBKJZGYVfVu9zrnS22PLli3IycnBhAkTNB0KEWkJJplERERERESkMhwuS0RERERERCrDJJOIiIiIiIhUhkkmERERERERqQyTTCIiIiIiIlIZJplERERERESkMkwyiYiIiIiISGWYZBIREREREZHKMMkkIiIiIiIilfl/xQzAKm9H0yMAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.heatmap(binary_coded.groupby('Demographic').mean(), annot=True, cmap='Blues', fmt='.2f')\n",
    "plt.title(\"Average Response per Question by Demographic Group (< 0.5 = Think Human, > 0.5 = Think AI)\")\n",
    "plt.yticks(np.arange(0.5, len(demographic_codes.keys()), 1), demographic_codes.values(), rotation=0)\n",
    "plt.xlabel(\"Question (1, 2, 4 = AI, 3 and 5 = Human)\")\n",
    "plt.savefig(\"Images/Analysis/ResponseHeatmap.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae11d3c8545e755b",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:37.086539200Z",
     "start_time": "2024-01-04T03:02:35.251361100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 5 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(20, 5))\n",
    "fig.suptitle(\"Response Count by Musical Ability Level\", fontsize=16)\n",
    "for i, q in enumerate([f\"Q{i}\" for i in range(1, 6)]):\n",
    "    sns.countplot(ax=axes[i], x='Demographic', hue=q, data=binary_coded)\n",
    "    axes[i].set_title(f\"{q}\")\n",
    "    axes[i].set_xticks(np.arange(0, len(demographic_codes.keys()), 1))\n",
    "    axes[i].set_xticklabels(demographic_codes.keys(), rotation=0)\n",
    "    axes[i].set_xlabel(\"Musical Ability Level\")\n",
    "    axes[i].set_ylabel(\"Count\")\n",
    "    if i != 0:\n",
    "        axes[i].get_legend().remove()\n",
    "    else:\n",
    "        axes[i].legend(title=\"Response\")\n",
    "        handles, labels = axes[i].get_legend_handles_labels()\n",
    "        axes[i].legend(handles=handles, labels=['Think Human', 'Think AI'], title=\"Response\")\n",
    "fig.tight_layout(pad=1.0)\n",
    "fig.text(0.5, -0.02, \"(1 = Music teacher/professional, 2 = Music student, 3 = Amateur musician, 4 = Music listener/enjoyer)\", ha='center')\n",
    "plt.savefig(\"Images/Analysis/ResponseCountAbility.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc02b533c7e37d4e",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:37.108868400Z",
     "start_time": "2024-01-04T03:02:37.089049900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         Kruskal-Wallis Statistic   p-value Reject null hypothesis\nQuestion                                                          \nQ1                       8.387896   0.03864                   True\nQ2                       3.334107  0.342924                  False\nQ3                       2.805512  0.422593                  False\nQ4                       9.162945  0.027201                   True\nQ5                       5.216878  0.156588                  False",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Kruskal-Wallis Statistic</th>\n      <th>p-value</th>\n      <th>Reject null hypothesis</th>\n    </tr>\n    <tr>\n      <th>Question</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Q1</th>\n      <td>8.387896</td>\n      <td>0.03864</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>Q2</th>\n      <td>3.334107</td>\n      <td>0.342924</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Q3</th>\n      <td>2.805512</td>\n      <td>0.422593</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Q4</th>\n      <td>9.162945</td>\n      <td>0.027201</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>Q5</th>\n      <td>5.216878</td>\n      <td>0.156588</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import kruskal\n",
    "\n",
    "results_kw = {}\n",
    "\n",
    "for q in [f\"Q{i}\" for i in range(1, 6)]:\n",
    "    # Split the data into groups by demographic\n",
    "    groups = [binary_coded[binary_coded['Demographic'] == int(code)][q] for code in demographic_codes.keys()]\n",
    "    stat, p = kruskal(*groups)\n",
    "    reject_H0 = p < 0.05\n",
    "    results_kw[q] = {\n",
    "        'Kruskal-Wallis Statistic': stat, \n",
    "        'p-value': p,\n",
    "        'Reject null hypothesis': reject_H0,\n",
    "    }\n",
    "\n",
    "results_kw_df = pd.DataFrame(results_kw).T\n",
    "results_kw_df.index.name = \"Question\"\n",
    "results_kw_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4ce18c7ad36e00",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Kruskal-Wallis Initial Results\n",
    "\n",
    "**Q1 and Q4: Reject Null Hypothesis**\n",
    " * This implies there is a statistically significant difference in how respondents from different demographic groups perceived the music.\n",
    " * In other words, the level of musical ability appears to influence respondents' ability to distinguish between AI and human compositions for these particular music samples.\n",
    "\n",
    "**Q2, Q3, and Q5: Fail to Reject Null Hypothesis**\n",
    " * This suggests that there is no significant difference across the demographic groups in terms of their perceptions of these music samples.\n",
    " * The ability to identify these pieces as AI or human-composed does not seem to be significantly influenced by the respondents' level of musical ability.\n",
    "\n",
    "#### Implications\n",
    " * **Variation in Perception by Demographics**: The results for Q1 and Q4 indicate that musical background may play a role in how certain pieces of music are perceived.\n",
    " * **Consistency Across Groups**: The results for Q2, Q3, and Q5 suggest a consistency in perception across different levels of musical experience, indicating that these pieces might have qualities that make them uniformly identifiable (or unidentifiable) as AI or human compositions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7529a643d698db",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Post-Hoc Analysis\n",
    "\n",
    "Since the data has insufficient variability and/or size to perform Dunn's test, we will instead use a Chi-Square Test on the larger samples and a Fisher's Exact Test on the smaller samples.\n",
    "\n",
    "\n",
    "### Chi-Square Test of Independence (Larger Groups)\n",
    "Hypothesis:\n",
    "* $H_0$: The distribution of responses (AI or Human) is the same across the demographic groups being compared.\n",
    "* $H_a$: The distribution of responses differs significantly across the demographic groups.\n",
    "\n",
    "In other words,\n",
    "* $H_0$: There is no significant association between the demographic groups and the belief about the music's origin (AI or Human).\n",
    "* $H_a$: There is a suggested association between the demographic groups and the belief about the music's origin.\n",
    "\n",
    "\n",
    "### Fisher's Exact Test (Smaller Groups)\n",
    "Hypothesis:\n",
    "* $H_0$: There is no significant association between the demographic groups and the belief about whether the music is AI or human-composed.\n",
    "* $H_a$: There is a significant association between the demographic groups and the belief about the music's origin.\n",
    "\n",
    "In other words,\n",
    "* $H_0$: The proportions of responses are similar across the groups being compared.\n",
    "* $H_a$: The proportions of responses differ between the groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e6ca2488d6e409c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:42.162109900Z",
     "start_time": "2024-01-04T03:02:37.110873800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insufficient data or variability for Dunn's test on Q1.\n",
      "Insufficient data or variability for Dunn's test on Q4.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: omegaconf 2.0.6 has a non-standard dependency specifier PyYAML>=5.1.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of omegaconf or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n"
     ]
    }
   ],
   "source": [
    "!pip install -q scikit-posthocs\n",
    "import scikit_posthocs as sp\n",
    "\n",
    "for question in ['Q1', 'Q4']:  # Questions where Kruskal-Wallis test was significant\n",
    "    data_for_test = binary_coded[[question, 'Demographic']].copy()\n",
    "    data_for_test['Demographic'] = data_for_test['Demographic'].map(demographic_codes)\n",
    "\n",
    "    # Check group sizes and variability\n",
    "    valid_groups = []\n",
    "    for group_name, group_data in data_for_test.groupby('Demographic'):\n",
    "        if len(group_data) > 1 and group_data[question].nunique() > 1:\n",
    "            valid_groups.append(group_name)\n",
    "\n",
    "    # Perform Dunn's test if valid groups are found\n",
    "    if len(valid_groups) > 1:\n",
    "        filtered_data = data_for_test[data_for_test['Demographic'].isin(valid_groups)]\n",
    "        dunn_test_result = sp.posthoc_dunn(filtered_data, val_col=question, group_col='Demographic', p_adjust='bonferroni')\n",
    "        print(f\"Post-hoc comparisons for {question}:\\n{dunn_test_result}\\n\")\n",
    "    else:\n",
    "        print(f\"Insufficient data or variability for Dunn's test on {question}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ea9bdab0fb5eda5",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:42.226607200Z",
     "start_time": "2024-01-04T03:02:42.163110400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         Chi-Square statistic   p-value Reject null hypothesis  \\\nQuestion                                                         \nQ1                   2.040548  0.153154                  False   \nQ4                   1.284357   0.25709                  False   \n\n         Degrees of freedom                                    Expected values  \nQuestion                                                                        \nQ1                        1  [[75.29471032745592, 65.70528967254408], [136....  \nQ4                        1  [[68.90176322418137, 72.09823677581863], [125....  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Chi-Square statistic</th>\n      <th>p-value</th>\n      <th>Reject null hypothesis</th>\n      <th>Degrees of freedom</th>\n      <th>Expected values</th>\n    </tr>\n    <tr>\n      <th>Question</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Q1</th>\n      <td>2.040548</td>\n      <td>0.153154</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[75.29471032745592, 65.70528967254408], [136....</td>\n    </tr>\n    <tr>\n      <th>Q4</th>\n      <td>1.284357</td>\n      <td>0.25709</td>\n      <td>False</td>\n      <td>1</td>\n      <td>[[68.90176322418137, 72.09823677581863], [125....</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Assuming the larger groups are 'Amateur musician' (code 3) and 'Music listener/enjoyer' (code 4)\n",
    "chi2_posthoc_results = {}\n",
    "for question in ['Q1', 'Q4']:\n",
    "    contingency_table = pd.crosstab(\n",
    "        binary_coded[binary_coded['Demographic'].isin([3, 4])]['Demographic'],\n",
    "        binary_coded[question]\n",
    "    )\n",
    "    chi2, p, dof, ex = chi2_contingency(contingency_table)\n",
    "    reject_H0 = p < 0.05\n",
    "    chi2_posthoc_results[question] = {\n",
    "        'Chi-Square statistic': chi2,\n",
    "        'p-value': p,\n",
    "        'Reject null hypothesis': reject_H0,\n",
    "        'Degrees of freedom': dof,\n",
    "        'Expected values': ex\n",
    "    }\n",
    "\n",
    "chi2_posthoc_results_df = pd.DataFrame(chi2_posthoc_results).T\n",
    "chi2_posthoc_results_df.index.name = \"Question\"\n",
    "chi2_posthoc_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4773f9016b00ec67",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:42.230121400Z",
     "start_time": "2024-01-04T03:02:42.186222600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "             Odds ratio   p-value Reject null hypothesis\nComparison                                              \nQ1 - 1 vs. 2     0.9472       1.0                  False\nQ1 - 2 vs. 1     0.9472       1.0                  False\nQ4 - 1 vs. 2   0.319212  0.008658                   True\nQ4 - 2 vs. 1   0.319212  0.008658                   True",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Odds ratio</th>\n      <th>p-value</th>\n      <th>Reject null hypothesis</th>\n    </tr>\n    <tr>\n      <th>Comparison</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Q1 - 1 vs. 2</th>\n      <td>0.9472</td>\n      <td>1.0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Q1 - 2 vs. 1</th>\n      <td>0.9472</td>\n      <td>1.0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Q4 - 1 vs. 2</th>\n      <td>0.319212</td>\n      <td>0.008658</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>Q4 - 2 vs. 1</th>\n      <td>0.319212</td>\n      <td>0.008658</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import fisher_exact\n",
    "\n",
    "# Assuming the smaller groups are 'Music teacher/professional' (code 1) and 'Music student' (code 2)\n",
    "fisher_exact_results = {}\n",
    "for question in ['Q1', 'Q4']:\n",
    "    for group1 in [1, 2]:\n",
    "        for group2 in [1, 2]:\n",
    "            if group1 != group2:\n",
    "                table = pd.crosstab(\n",
    "                    binary_coded[binary_coded['Demographic'].isin([group1, group2])]['Demographic'],\n",
    "                    binary_coded[question]\n",
    "                )\n",
    "                oddsratio, p_value = fisher_exact(table)\n",
    "                reject_H0 = p_value < 0.05\n",
    "                fisher_exact_results[f\"{question} - {group1} vs. {group2}\"] = {\n",
    "                    'Odds ratio': oddsratio,\n",
    "                    'p-value': p_value,\n",
    "                    'Reject null hypothesis': reject_H0,\n",
    "                }\n",
    "\n",
    "fisher_exact_results_df = pd.DataFrame(fisher_exact_results).T\n",
    "fisher_exact_results_df.index.name = \"Comparison\"\n",
    "fisher_exact_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c5836ccce45649c",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:42.818843600Z",
     "start_time": "2024-01-04T03:02:42.221607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 4 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi2_posthoc_results_df['p-value'] = pd.to_numeric(chi2_posthoc_results_df['p-value'], errors='coerce')\n",
    "fisher_exact_results_df['p-value'] = pd.to_numeric(fisher_exact_results_df['p-value'], errors='coerce')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 5))\n",
    "fig.suptitle(\"Post-Hoc Test Results\", fontsize=16)\n",
    "sns.heatmap(chi2_posthoc_results_df[['p-value']].T, annot=True, cmap='Blues', fmt='.2f', ax=axes[0])\n",
    "axes[0].set_title(\"Chi-Square Test\")\n",
    "sns.heatmap(fisher_exact_results_df[['p-value']].T, annot=True, cmap='Blues', fmt='.2f', ax=axes[1])\n",
    "axes[1].set_title(\"Fisher's Exact Test\")\n",
    "fig.tight_layout(pad=1.0)\n",
    "plt.savefig(\"Images/Analysis/PostHocTestResults.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ae261e698fc36eb",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:43.648464900Z",
     "start_time": "2024-01-04T03:02:42.819850700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.lineplot(data=binary_outcomes_df, x='Question', y='Count', hue='Response')\n",
    "plt.title(\"Cumulative Response Distribution\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.ylim(0, 400)\n",
    "plt.savefig(\"Images/Analysis/CumulativeResponse.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e7eafad6cba8c2d",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:43.822828600Z",
     "start_time": "2024-01-04T03:02:43.656463800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "               Lower     Upper\nComparison                    \nQ1 - 1      0.460441  0.759071\nQ1 - 2      0.474670  0.718879\nQ1 - 3      0.435253  0.600208\nQ1 - 4      0.376732  0.498268\nQ2 - 1      0.240929  0.539559\nQ2 - 2      0.343543  0.591940\nQ2 - 3      0.323253  0.485257\nQ2 - 4      0.427049  0.549513\nQ3 - 1      0.310777  0.616052\nQ3 - 2      0.312066  0.558901\nQ3 - 3      0.471130  0.635253\nQ3 - 4      0.454406  0.576844\nQ4 - 1      0.568046  0.846588\nQ4 - 2      0.312066  0.558901\nQ4 - 3      0.471130  0.635253\nQ4 - 4      0.427049  0.549513\nQ5 - 1      0.055556  0.285907\nQ5 - 2      0.135336  0.348535\nQ5 - 3      0.222387  0.373358\nQ5 - 4      0.270609  0.385641",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Lower</th>\n      <th>Upper</th>\n    </tr>\n    <tr>\n      <th>Comparison</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Q1 - 1</th>\n      <td>0.460441</td>\n      <td>0.759071</td>\n    </tr>\n    <tr>\n      <th>Q1 - 2</th>\n      <td>0.474670</td>\n      <td>0.718879</td>\n    </tr>\n    <tr>\n      <th>Q1 - 3</th>\n      <td>0.435253</td>\n      <td>0.600208</td>\n    </tr>\n    <tr>\n      <th>Q1 - 4</th>\n      <td>0.376732</td>\n      <td>0.498268</td>\n    </tr>\n    <tr>\n      <th>Q2 - 1</th>\n      <td>0.240929</td>\n      <td>0.539559</td>\n    </tr>\n    <tr>\n      <th>Q2 - 2</th>\n      <td>0.343543</td>\n      <td>0.591940</td>\n    </tr>\n    <tr>\n      <th>Q2 - 3</th>\n      <td>0.323253</td>\n      <td>0.485257</td>\n    </tr>\n    <tr>\n      <th>Q2 - 4</th>\n      <td>0.427049</td>\n      <td>0.549513</td>\n    </tr>\n    <tr>\n      <th>Q3 - 1</th>\n      <td>0.310777</td>\n      <td>0.616052</td>\n    </tr>\n    <tr>\n      <th>Q3 - 2</th>\n      <td>0.312066</td>\n      <td>0.558901</td>\n    </tr>\n    <tr>\n      <th>Q3 - 3</th>\n      <td>0.471130</td>\n      <td>0.635253</td>\n    </tr>\n    <tr>\n      <th>Q3 - 4</th>\n      <td>0.454406</td>\n      <td>0.576844</td>\n    </tr>\n    <tr>\n      <th>Q4 - 1</th>\n      <td>0.568046</td>\n      <td>0.846588</td>\n    </tr>\n    <tr>\n      <th>Q4 - 2</th>\n      <td>0.312066</td>\n      <td>0.558901</td>\n    </tr>\n    <tr>\n      <th>Q4 - 3</th>\n      <td>0.471130</td>\n      <td>0.635253</td>\n    </tr>\n    <tr>\n      <th>Q4 - 4</th>\n      <td>0.427049</td>\n      <td>0.549513</td>\n    </tr>\n    <tr>\n      <th>Q5 - 1</th>\n      <td>0.055556</td>\n      <td>0.285907</td>\n    </tr>\n    <tr>\n      <th>Q5 - 2</th>\n      <td>0.135336</td>\n      <td>0.348535</td>\n    </tr>\n    <tr>\n      <th>Q5 - 3</th>\n      <td>0.222387</td>\n      <td>0.373358</td>\n    </tr>\n    <tr>\n      <th>Q5 - 4</th>\n      <td>0.270609</td>\n      <td>0.385641</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.proportion import proportion_confint\n",
    "\n",
    "# Confidence intervals for the proportions of responses in each demographic group\n",
    "confidence_intervals = {}\n",
    "for q in [f\"Q{i}\" for i in range(1, 6)]:\n",
    "    for group in demographic_codes.keys():\n",
    "        group_count = binary_coded[binary_coded['Demographic'] == int(group)][q].sum()\n",
    "        group_size = len(binary_coded[binary_coded['Demographic'] == int(group)][q])\n",
    "        ci = proportion_confint(group_count, group_size, alpha=0.05, method='normal')\n",
    "        confidence_intervals[f\"{q} - {group}\"] = {\n",
    "            'Lower': ci[0],\n",
    "            'Upper': ci[1]\n",
    "        }\n",
    "        \n",
    "confidence_intervals_df = pd.DataFrame(confidence_intervals).T\n",
    "confidence_intervals_df.index.name = \"Comparison\"\n",
    "confidence_intervals_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b9a4fdc9458b74ee",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-04T03:02:45.595359800Z",
     "start_time": "2024-01-04T03:02:43.680753200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 5 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(20, 5))\n",
    "fig.suptitle(\"Confidence Intervals for Proportions of Responses\", fontsize=16)\n",
    "for i, q in enumerate([f\"Q{i}\" for i in range(1, 6)]):\n",
    "    sns.lineplot(ax=axes[i], data=confidence_intervals_df[confidence_intervals_df.index.str.contains(q)], x='Comparison', y='Lower')\n",
    "    sns.lineplot(ax=axes[i], data=confidence_intervals_df[confidence_intervals_df.index.str.contains(q)], x='Comparison', y='Upper')\n",
    "    axes[i].set_title(f\"{q}\")\n",
    "    axes[i].set_xticks(np.arange(0, len(demographic_codes.keys()), 1))\n",
    "    axes[i].set_xticklabels(demographic_codes.keys(), rotation=0)\n",
    "    axes[i].set_xlabel(\"Musical Ability Level\")\n",
    "    axes[i].set_ylabel(\"Proportion\")\n",
    "    axes[i].set_ylim(0, 1)\n",
    "    for j, v in enumerate(confidence_intervals_df[confidence_intervals_df.index.str.contains(q)]['Lower']):\n",
    "        axes[i].text(j, v - 0.075, str(round(v, 2)), color='black', ha='center')\n",
    "    for j, v in enumerate(confidence_intervals_df[confidence_intervals_df.index.str.contains(q)]['Upper']):\n",
    "        axes[i].text(j, v + 0.025, str(round(v, 2)), color='black', ha='center')\n",
    "fig.tight_layout(pad=1.5)\n",
    "fig.text(0.5, -0.02, \"(1 = Music teacher/professional, 2 = Music student, 3 = Amateur musician, 4 = Music listener/enjoyer)\", ha='center')\n",
    "plt.savefig(\"Images/Analysis/ConfidenceIntervals.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7906f9d886d868f",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Post-Hoc Results\n",
    "\n",
    "#### Chi-Square Test\n",
    "\n",
    "**Interpretation**:\n",
    " * **No Significant Difference in Perception**: The results suggest that there is no statistically significant difference in the way the larger demographic groups (e.g., \"Music listener/enjoyer\" and \"Amateur musician\") perceived the music as AI or human-composed for Q1 and Q4. \n",
    "    * This implies that their ability to identify whether the music was AI-generated or human-composed was similar across these groups.\n",
    " * **Consistency Across Groups**: The lack of significant differences indicates a consistency in perception across the largest demographic groups for these particular pieces of music.\n",
    "\n",
    "**Implications**:\n",
    " * **AI Model Performance**: Since there was no significant difference in perception across the demographic groups for Q1 and Q4, this aligns with the overall finding that listeners, regardless of their musical background, found it challenging to distinguish between AI-generated and human-composed music. \n",
    "    * This supports the success of the AI models in creating music that is not easily distinguishable from human compositions.\n",
    " \n",
    " * **General Audience Perception**: The results particularly highlight that the general audience (music listeners/enjoyers) did not perceive the music differently from other more musically trained groups, suggesting that the AI's output was uniformly convincing or ambiguous.\n",
    "\n",
    "\n",
    "#### Fisher's Exact Test\n",
    "\n",
    "**Q1 Results**:\n",
    " * Groups 1 and 2 (Music teacher/professional and Music student):\n",
    "    * The p-values are 1.0 for both comparisons. \n",
    "    * This indicates that there is no statistically significant association between these demographic groups and their beliefs about whether Q1 was AI or human-composed. \n",
    "    * Essentially, both groups responded similarly to Q1.\n",
    "\n",
    "**Q4 Results**:\n",
    " * Groups 1 and 2 (Music teacher/professional and Music student):\n",
    "    * The p-values are approximately 0.00866 for both comparisons.\n",
    "    * This is below the typical alpha level of 0.05, suggesting that there is a statistically significant difference in how these two groups perceived the music sample in Q4.\n",
    "    * In other words, the ability to identify Q4 as AI-generated or human-composed seems to differ significantly between these two groups.\n",
    "\n",
    "**Interpretation and Implications**:\n",
    " * **Q1 (General Agreement)**: The lack of a significant difference in Q1 implies that both music teachers/professionals and music students had similar difficulty (or ease) in identifying the music's origin.\n",
    "   * This might suggest that Q1's composition was such that it equally challenged or fooled both groups.\n",
    "\n",
    " * **Q4 (Significant Difference)**: The significant difference observed in Q4 suggests that one group was better able to distinguish the origin of the music than the other. \n",
    "   * This could be due to the specific characteristics of the music sample in Q4, which may have been more easily identifiable as AI or human-composed by one group over the other."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d9a737d65bd345",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Summary of Statistical Analyses and Findings\n",
    "\n",
    "1. **Chi-Square Tests**: The initial Chi-Square tests were performed to assess whether there was a statistically significant difference in the frequency of respondents identifying music pieces as AI-generated versus human-composed. These tests indicated a general uniformity in perception across the larger demographic groups for most samples, suggesting that the AI model was effective in creating compositions indistinguishable from those composed by humans.\n",
    "\n",
    "2. **Kruskal-Wallis Test**: Subsequent Kruskal-Wallis tests were conducted to examine if there were differences in perceptions of AI-generated versus human-composed music across various demographic groups based on their musical ability. The tests revealed significant differences in perception for certain music samples (notably Q1 and Q4, produced by the two weaker models), suggesting that listeners' abilities to distinguish between AI and human compositions varied based on their musical background and supporting the hypothesis that musical training influences music perception. Interestingly, the test did not reveal a significant difference across groups for Q2 (generated by the best AI model weights). This suggests a uniformly high level of realism in the AI's composition, as the model successfully blurs the lines between AI-generated and human-composed music for listeners across all levels of musical expertise.\n",
    "\n",
    "3. **Post-Hoc Chi-Square and Fisher's Exact Tests**: To further explore the nuances in perception among different groups, post-hoc analyses were conducted. Subsequent Chi-Square tests, performed for the larger demographic groups (Amateur musician and Music listener/enjoyer), found no significant differences in the ability of these groups to differentiate between AI and human compositions for most of the samples, indicating a general uniformity in perception across these groups. These tests confirmed the initial findings of no significant differences in how these groups perceived most of the music samples, reinforcing the AI model's effectiveness in creating compositions that were broadly indistinguishable from human compositions. The Fisher's Exact tests, applied to the smaller demographic groups (Music teacher/professional and Music student), revealed significant differences in perception for Q4 (produced by the worst of the three models), indicating distinct perceptual capabilities between groups like music teachers/professionals and music students. This highlighted that certain nuances in AI-generated music might be more discernible to those with specific types of musical training. However, this also contrasted with the findings for Q3, where such differences were not observed, underscoring the exceptional performance of the best AI model in mimicking human composition. \n",
    "\n",
    "4. **Overall Interpretation**: The collective findings from these statistical analyses provide a comprehensive understanding of the perception of AI-generated music. While the AI model demonstrated a high level of proficiency in creating compositions that were broadly indistinguishable from human compositions, particularly in Q2, there were instances (e.g., Q4) where specific listener groups with certain musical expertise could discern differences. These insights emphasize the sophistication of the AI model and its potential in the field of music composition while also highlighting the subtle yet impactful role of musical training in the perception of music.\n",
    "\n",
    "### Analysis Conclusion\n",
    "\n",
    "This analysis revealed that the AI model was generally successful in creating music that listeners, irrespective of their musical background, found challenging to distinguish from human compositions. The model successfully created pieces indistinguishable from human compositions, with the performance of the best model weights (in Q2) being particularly noteworthy. However, the experiment also brought to light the intricacies of musical perception, particularly among those with specialized training, offering a deeper understanding of the interplay between AI-generated art and human expertise. As such, this research contributes significantly to the fields of AI in creative arts and musicology, showcasing both the capabilities and limitations of current AI technology in music composition and the nuanced dynamics of music perception."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f27ff683ef35610",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Conclusions\n",
    "\n",
    "The primary objective of this research was to assess the capability of a novel AI model to generate realistic four-part (SATB) choral music compositions that are indistinguishable from those composed by humans. The survey data, collected from a diverse range of respondents with varying levels of musical expertise, provided a rich dataset for evaluating this objective. The statistical analyses conducted — the Chi-Square, Kruskal-Wallis, and post-hoc tests — have yielded insightful results.\n",
    "\n",
    "**Performance of the AI Model**: The findings from the Kruskal-Wallis tests, particularly for questions Q1 and Q4, indicated a significant difference in the perception of music samples across the larger demographic groups. However, the subsequent Chi-Square tests on these larger groups revealed no significant difference in the ability to distinguish between AI and human compositions. This suggests that, overall, the AI model successfully created music that was, to a large extent, indistinguishable from human compositions, meeting the primary goal of the AI's performance.\n",
    "\n",
    "**Perception Across Musical Abilities**: The lack of significant differences in the Chi-Square tests for the larger demographic groups suggests a general consistency in how the music was perceived, regardless of the listeners' musical background. This consistency highlights the AI model's effectiveness in creating compositions that uniformly challenge or fool listeners across different levels of musical experience.\n",
    "\n",
    "**Differences in Smaller Groups**: The Fisher's Exact tests conducted on the smaller demographic groups revealed a notable exception. Particularly for Q4, a significant difference was observed between music teachers/professionals and music students. This suggests that certain nuances in the AI-generated music might be more discernible to those with specific types of musical training or experience.\n",
    "\n",
    "**Implications for AI in Music Composition**: The overall difficulty experienced by respondents across all levels of musical ability in distinguishing AI compositions from human ones is a testament to the sophistication and realism of the AI-generated music. This finding is particularly significant in the context of the evolving role of AI in creative fields like music composition. It underscores the potential of AI to not only assist in but also autonomously create compositions that resonate with a wide audience.\n",
    "\n",
    "## Final Remarks\n",
    "\n",
    "This research has demonstrated that the novel AI model used in this study is capable of generating music compositions that are broadly indistinguishable from those created by humans, as evidenced by the general inability of respondents from diverse musical backgrounds to reliably identify the origin of the compositions. The subtle differences observed in smaller, more specialized groups open avenues for further exploration into how AI can be fine-tuned to enhance its mimicry of human compositions or create distinctively AI-flavored music. These findings contribute valuable insights into the field of AI-generated classical music and pave the way for future research in this intersection of technology and art."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}