{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🔬 Advanced Diagnostics and Monitoring\n",
    "\n",
    "Welcome to the **OnlineRake Diagnostics Laboratory!** 🧪\n",
    "\n",
    "This notebook demonstrates the powerful diagnostic and monitoring features of OnlineRake:\n",
    "- **Convergence Detection**: Automatically detect when algorithms have converged\n",
    "- **Oscillation Monitoring**: Identify when learning rates are too high\n",
    "- **Weight Distribution Analysis**: Monitor weight evolution and detect outliers\n",
    "- **Real-time Performance Tracking**: ESS, loss, and gradient monitoring\n",
    "\n",
    "Master these tools to ensure optimal performance! 📊✨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:23.911888Z",
     "iopub.status.busy": "2025-12-28T04:11:23.911746Z",
     "iopub.status.idle": "2025-12-28T04:11:24.398408Z",
     "shell.execute_reply": "2025-12-28T04:11:24.397515Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔬 Advanced Diagnostics Laboratory initialized!\n",
      "📊 Ready for comprehensive monitoring and analysis!\n",
      "🎯 Let's master the art of algorithm monitoring!\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from onlinerake import OnlineRakingSGD, Targets\n",
    "\n",
    "# Set up plotting style\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")\n",
    "np.random.seed(42)\n",
    "\n",
    "print(\"🔬 Advanced Diagnostics Laboratory initialized!\")\n",
    "print(\"📊 Ready for comprehensive monitoring and analysis!\")\n",
    "print(\"🎯 Let's master the art of algorithm monitoring!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📈 Demo 1: Convergence Monitoring\n",
    "\n",
    "Let's start by demonstrating how OnlineRake automatically detects convergence and provides detailed monitoring!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:24.427968Z",
     "iopub.status.busy": "2025-12-28T04:11:24.427694Z",
     "iopub.status.idle": "2025-12-28T04:11:24.431614Z",
     "shell.execute_reply": "2025-12-28T04:11:24.430977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 CONVERGENCE MONITORING DEMO\n",
      "==================================================\n",
      "Target margins: {'feature_a': 0.5, 'feature_b': 0.5, 'feature_c': 0.4, 'feature_d': 0.3}\n",
      "Learning rate: 3.0\n",
      "Convergence window: 10\n",
      "Convergence tracking: True\n",
      "\n",
      "🚀 Starting convergence demonstration...\n"
     ]
    }
   ],
   "source": [
    "# Set up targets and raker with diagnostics enabled\n",
    "targets = Targets(feature_a=0.5, feature_b=0.5, feature_c=0.4, feature_d=0.3)\n",
    "raker = OnlineRakingSGD(\n",
    "    targets,\n",
    "    learning_rate=3.0,\n",
    "    verbose=False,  # We'll handle output ourselves\n",
    "    track_convergence=True,\n",
    "    convergence_window=10,\n",
    ")\n",
    "\n",
    "print(\"🎯 CONVERGENCE MONITORING DEMO\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Target margins: {targets.as_dict()}\")\n",
    "print(f\"Learning rate: {raker.learning_rate}\")\n",
    "print(f\"Convergence window: {raker.convergence_window}\")\n",
    "print(f\"Convergence tracking: {raker.track_convergence}\")\n",
    "print(\"\\n🚀 Starting convergence demonstration...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:24.433388Z",
     "iopub.status.busy": "2025-12-28T04:11:24.433234Z",
     "iopub.status.idle": "2025-12-28T04:11:24.493810Z",
     "shell.execute_reply": "2025-12-28T04:11:24.493187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Generating gradually converging data stream...\n",
      "🎯 Data pattern: Biased start → gradual approach to targets\n",
      "\n",
      "Step  25: 🔄 Loss=0.007345 | Grad=0.0259 | ESS=11.2 | Converged=False | 🌊\n",
      "Step  50: 🔄 Loss=0.001063 | Grad=0.0058 | ESS=25.8 | Converged=False | 🌊\n",
      "Step  75: 🔄 Loss=0.001927 | Grad=0.0060 | ESS=48.8 | Converged=False | 🌊\n",
      "Step 100: 🔄 Loss=0.000318 | Grad=0.0018 | ESS=72.5 | Converged=False | 🌊\n",
      "Step 125: 🔄 Loss=0.000695 | Grad=0.0027 | ESS=99.0 | Converged=False | 🌊\n",
      "Step 150: 🔄 Loss=0.000074 | Grad=0.0008 | ESS=125.7 | Converged=False | 🌊\n",
      "\n",
      "✅ Convergence demonstration complete!\n",
      "📊 Final status: Not converged\n",
      "📉 Final loss: 0.000074\n",
      "⚡ Final ESS: 125.7\n"
     ]
    }
   ],
   "source": [
    "# Generate converging data stream\n",
    "n_obs = 150\n",
    "monitoring_data = []\n",
    "\n",
    "print(\"📊 Generating gradually converging data stream...\")\n",
    "print(\"🎯 Data pattern: Biased start → gradual approach to targets\\n\")\n",
    "\n",
    "# Track detailed progress\n",
    "observation_numbers = []\n",
    "losses = []\n",
    "gradient_norms = []\n",
    "ess_values = []\n",
    "convergence_status = []\n",
    "oscillation_status = []\n",
    "\n",
    "for i in range(n_obs):\n",
    "    # Gradually shift probabilities toward targets\n",
    "    progress = min(i / 75.0, 1.0)  # Reach targets after ~75 observations\n",
    "    \n",
    "    # Start biased, gradually approach targets\n",
    "    feature_a_prob = 0.3 + progress * (0.5 - 0.3)  # 0.3 → 0.5\n",
    "    feature_b_prob = 0.2 + progress * (0.5 - 0.2)  # 0.2 → 0.5\n",
    "    feature_c_prob = 0.6 + progress * (0.4 - 0.6)  # 0.6 → 0.4\n",
    "    feature_d_prob = 0.1 + progress * (0.3 - 0.1)  # 0.1 → 0.3\n",
    "    \n",
    "    obs = {\n",
    "        \"feature_a\": np.random.binomial(1, feature_a_prob),\n",
    "        \"feature_b\": np.random.binomial(1, feature_b_prob),\n",
    "        \"feature_c\": np.random.binomial(1, feature_c_prob),\n",
    "        \"feature_d\": np.random.binomial(1, feature_d_prob),\n",
    "    }\n",
    "    \n",
    "    raker.partial_fit(obs)\n",
    "    \n",
    "    # Collect monitoring data\n",
    "    observation_numbers.append(i + 1)\n",
    "    losses.append(raker.loss)\n",
    "    ess_values.append(raker.effective_sample_size)\n",
    "    convergence_status.append(raker.converged)\n",
    "    oscillation_status.append(raker.detect_oscillation())\n",
    "    \n",
    "    # Get gradient norm from history\n",
    "    if raker.gradient_norm_history:\n",
    "        gradient_norms.append(raker.gradient_norm_history[-1])\n",
    "    else:\n",
    "        gradient_norms.append(0.0)\n",
    "    \n",
    "    # Print progress at key intervals\n",
    "    if (i + 1) % 25 == 0 or (raker.converged and not any(convergence_status[:-1])):\n",
    "        status_icon = \"🎯\" if raker.converged else \"🔄\"\n",
    "        oscillating_icon = \"🌊\" if raker.detect_oscillation() else \"📈\"\n",
    "        \n",
    "        print(f\"Step {i + 1:3d}: {status_icon} Loss={raker.loss:.6f} | \"\n",
    "              f\"Grad={gradient_norms[-1]:.4f} | ESS={raker.effective_sample_size:.1f} | \"\n",
    "              f\"Converged={raker.converged} | {oscillating_icon}\")\n",
    "        \n",
    "        if raker.converged and not any(convergence_status[:-1]):\n",
    "            print(f\"\\n🎉 CONVERGENCE DETECTED at observation {raker.convergence_step}! 🎉\\n\")\n",
    "\n",
    "print(f\"\\n✅ Convergence demonstration complete!\")\n",
    "print(f\"📊 Final status: {'Converged' if raker.converged else 'Not converged'}\")\n",
    "if raker.converged:\n",
    "    print(f\"🎯 Convergence achieved at observation: {raker.convergence_step}\")\n",
    "print(f\"📉 Final loss: {raker.loss:.6f}\")\n",
    "print(f\"⚡ Final ESS: {raker.effective_sample_size:.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:24.495503Z",
     "iopub.status.busy": "2025-12-28T04:11:24.495349Z",
     "iopub.status.idle": "2025-12-28T04:11:25.168230Z",
     "shell.execute_reply": "2025-12-28T04:11:25.167500Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2752/1905061701.py:60: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/1905061701.py:60: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/1905061701.py:60: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/1905061701.py:60: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎨 Convergence monitoring visualization complete!\n",
      "📊 Clear evidence of algorithm convergence and monitoring capabilities!\n"
     ]
    }
   ],
   "source": [
    "# Create comprehensive convergence visualization\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "fig.suptitle('📈 Convergence Monitoring Dashboard', fontsize=16, fontweight='bold')\n",
    "\n",
    "# 1. Loss evolution with convergence detection\n",
    "axes[0,0].plot(observation_numbers, losses, 'b-', linewidth=2, alpha=0.8)\n",
    "axes[0,0].set_xlabel('Observations')\n",
    "axes[0,0].set_ylabel('Loss')\n",
    "axes[0,0].set_title('📉 Loss Evolution')\n",
    "axes[0,0].grid(True, alpha=0.3)\n",
    "axes[0,0].set_yscale('log')\n",
    "\n",
    "# Mark convergence point\n",
    "if raker.converged:\n",
    "    conv_step = raker.convergence_step\n",
    "    conv_loss = losses[conv_step - 1]\n",
    "    axes[0,0].axvline(x=conv_step, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "    axes[0,0].scatter([conv_step], [conv_loss], color='red', s=100, zorder=5, \n",
    "                     label=f'Convergence (step {conv_step})')\n",
    "    axes[0,0].legend()\n",
    "\n",
    "# 2. Gradient norm tracking\n",
    "axes[0,1].plot(observation_numbers, gradient_norms, 'g-', linewidth=2, alpha=0.8)\n",
    "axes[0,1].set_xlabel('Observations')\n",
    "axes[0,1].set_ylabel('Gradient Norm')\n",
    "axes[0,1].set_title('🎯 Gradient Norm Evolution')\n",
    "axes[0,1].grid(True, alpha=0.3)\n",
    "axes[0,1].set_yscale('log')\n",
    "\n",
    "# Mark convergence point\n",
    "if raker.converged:\n",
    "    axes[0,1].axvline(x=conv_step, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "\n",
    "# 3. ESS evolution\n",
    "axes[1,0].plot(observation_numbers, ess_values, 'purple', linewidth=2, alpha=0.8)\n",
    "axes[1,0].set_xlabel('Observations')\n",
    "axes[1,0].set_ylabel('Effective Sample Size')\n",
    "axes[1,0].set_title('⚡ ESS Evolution')\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# Mark convergence point\n",
    "if raker.converged:\n",
    "    axes[1,0].axvline(x=conv_step, color='red', linestyle='--', alpha=0.7, linewidth=2)\n",
    "\n",
    "# 4. Convergence and oscillation status\n",
    "conv_status_numeric = [1 if status else 0 for status in convergence_status]\n",
    "osc_status_numeric = [1 if status else 0 for status in oscillation_status]\n",
    "\n",
    "axes[1,1].fill_between(observation_numbers, 0, conv_status_numeric, \n",
    "                      alpha=0.3, color='green', label='Converged')\n",
    "axes[1,1].fill_between(observation_numbers, 0, osc_status_numeric, \n",
    "                      alpha=0.3, color='red', label='Oscillating')\n",
    "axes[1,1].set_xlabel('Observations')\n",
    "axes[1,1].set_ylabel('Status')\n",
    "axes[1,1].set_title('🔍 Convergence & Oscillation Status')\n",
    "axes[1,1].set_ylim(-0.1, 1.1)\n",
    "axes[1,1].legend()\n",
    "axes[1,1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n🎨 Convergence monitoring visualization complete!\")\n",
    "print(\"📊 Clear evidence of algorithm convergence and monitoring capabilities!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🌊 Demo 2: Oscillation Detection\n",
    "\n",
    "Now let's see how OnlineRake detects problematic oscillations when learning rates are too high!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.170132Z",
     "iopub.status.busy": "2025-12-28T04:11:25.169946Z",
     "iopub.status.idle": "2025-12-28T04:11:25.173678Z",
     "shell.execute_reply": "2025-12-28T04:11:25.173093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🌊 OSCILLATION DETECTION DEMO\n",
      "==================================================\n",
      "Target margins: {'feature_a': 0.5, 'feature_b': 0.5, 'feature_c': 0.5, 'feature_d': 0.5}\n",
      "Learning rate: 15.0 (intentionally high)\n",
      "Convergence window: 15\n",
      "\n",
      "⚠️  High learning rate should cause oscillation...\n",
      "🔍 OnlineRake will detect this automatically!\n"
     ]
    }
   ],
   "source": [
    "# Set up raker with high learning rate to induce oscillation\n",
    "oscillation_targets = Targets(feature_a=0.5, feature_b=0.5, feature_c=0.5, feature_d=0.5)\n",
    "oscillating_raker = OnlineRakingSGD(\n",
    "    oscillation_targets,\n",
    "    learning_rate=15.0,  # Intentionally high to cause oscillation\n",
    "    track_convergence=True,\n",
    "    convergence_window=15,\n",
    ")\n",
    "\n",
    "print(\"\\n🌊 OSCILLATION DETECTION DEMO\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Target margins: {oscillation_targets.as_dict()}\")\n",
    "print(f\"Learning rate: {oscillating_raker.learning_rate} (intentionally high)\")\n",
    "print(f\"Convergence window: {oscillating_raker.convergence_window}\")\n",
    "print(\"\\n⚠️  High learning rate should cause oscillation...\")\n",
    "print(\"🔍 OnlineRake will detect this automatically!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.175317Z",
     "iopub.status.busy": "2025-12-28T04:11:25.175172Z",
     "iopub.status.idle": "2025-12-28T04:11:25.197123Z",
     "shell.execute_reply": "2025-12-28T04:11:25.196517Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎭 Generating alternating extreme observations...\n",
      "📊 Pattern: All 1s → All 0s → All 1s → All 0s...\n",
      "\n",
      "Step 10: 📈 Loss=0.000000 | Oscillating=False | 🔄 Converged=False\n",
      "Step 20: 🌊 Loss=0.000036 | Oscillating=True | 🔄 Converged=False\n",
      "Step 30: 🌊 Loss=0.000024 | Oscillating=True | 🔄 Converged=False\n",
      "     📊 Recent loss variance: 0.000000\n",
      "     📊 Recent loss mean: 0.000032\n",
      "Step 40: 🌊 Loss=0.000018 | Oscillating=True | 🔄 Converged=False\n",
      "     📊 Recent loss variance: 0.000000\n",
      "     📊 Recent loss mean: 0.000022\n",
      "Step 50: 📈 Loss=0.000015 | Oscillating=False | 🔄 Converged=False\n",
      "Step 60: 📈 Loss=0.000013 | Oscillating=False | 🔄 Converged=False\n",
      "\n",
      "🔍 Oscillation detection results:\n",
      "   Final oscillation status: False\n",
      "   Final convergence status: False\n",
      "   ⚠️ Oscillation not detected\n"
     ]
    }
   ],
   "source": [
    "# Generate alternating extreme observations to trigger oscillation\n",
    "n_oscillation_obs = 60\n",
    "oscillation_data = []\n",
    "\n",
    "print(\"\\n🎭 Generating alternating extreme observations...\")\n",
    "print(\"📊 Pattern: All 1s → All 0s → All 1s → All 0s...\\n\")\n",
    "\n",
    "# Track oscillation monitoring data\n",
    "osc_steps = []\n",
    "osc_losses = []\n",
    "osc_oscillating = []\n",
    "osc_converged = []\n",
    "loss_variance_history = []\n",
    "\n",
    "for i in range(n_oscillation_obs):\n",
    "    # Create alternating extreme observations\n",
    "    if i % 2 == 0:\n",
    "        obs = {\"feature_a\": 1, \"feature_b\": 1, \"feature_c\": 1, \"feature_d\": 1}\n",
    "    else:\n",
    "        obs = {\"feature_a\": 0, \"feature_b\": 0, \"feature_c\": 0, \"feature_d\": 0}\n",
    "    \n",
    "    oscillating_raker.partial_fit(obs)\n",
    "    \n",
    "    # Collect monitoring data\n",
    "    osc_steps.append(i + 1)\n",
    "    osc_losses.append(oscillating_raker.loss)\n",
    "    osc_oscillating.append(oscillating_raker.detect_oscillation())\n",
    "    osc_converged.append(oscillating_raker.converged)\n",
    "    \n",
    "    # Calculate loss variance for recent window\n",
    "    if i >= oscillating_raker.convergence_window - 1:\n",
    "        recent_losses = [state[\"loss\"] for state in \n",
    "                        oscillating_raker.history[-oscillating_raker.convergence_window:]]\n",
    "        loss_variance_history.append(np.var(recent_losses))\n",
    "    else:\n",
    "        loss_variance_history.append(0.0)\n",
    "    \n",
    "    # Print diagnostic info every 10 steps\n",
    "    if (i + 1) % 10 == 0:\n",
    "        oscillating = oscillating_raker.detect_oscillation()\n",
    "        status_icon = \"🌊\" if oscillating else \"📈\"\n",
    "        converged_icon = \"🎯\" if oscillating_raker.converged else \"🔄\"\n",
    "        \n",
    "        print(f\"Step {i + 1:2d}: {status_icon} Loss={oscillating_raker.loss:.6f} | \"\n",
    "              f\"Oscillating={oscillating} | {converged_icon} Converged={oscillating_raker.converged}\")\n",
    "        \n",
    "        if oscillating and i >= 20:  # Give it some time to detect\n",
    "            recent_losses = [s[\"loss\"] for s in oscillating_raker.history[-oscillating_raker.convergence_window:]]\n",
    "            print(f\"     📊 Recent loss variance: {np.var(recent_losses):.6f}\")\n",
    "            print(f\"     📊 Recent loss mean: {np.mean(recent_losses):.6f}\")\n",
    "\n",
    "print(f\"\\n🔍 Oscillation detection results:\")\n",
    "print(f\"   Final oscillation status: {oscillating_raker.detect_oscillation()}\")\n",
    "print(f\"   Final convergence status: {oscillating_raker.converged}\")\n",
    "print(f\"   {'✅ Successfully detected oscillation!' if oscillating_raker.detect_oscillation() else '⚠️ Oscillation not detected'}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.198556Z",
     "iopub.status.busy": "2025-12-28T04:11:25.198417Z",
     "iopub.status.idle": "2025-12-28T04:11:25.905806Z",
     "shell.execute_reply": "2025-12-28T04:11:25.904999Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2752/2163127815.py:61: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/2163127815.py:61: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/2163127815.py:61: UserWarning: Glyph 128269 (\\N{LEFT-POINTING MAGNIFYING GLASS}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/2163127815.py:61: UserWarning: Glyph 127754 (\\N{WATER WAVE}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127754 (\\N{WATER WAVE}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MTFSdOnUyXR8REWHrn4ceekiS1LFjR9v3fMaMGZKkdevW6fnnn1fRokVt++/du7ddebEOHTpoypQpkmRXAuH6/r/xd9T6e2DdjyTFx8erY8eOKlKkiHx8fFSoUCE1a9bMI0u7AQAA98rKeb0kff3116pevboCAwMVFBSkSpUqadKkSbb1KSkpGjp0qEqXLi1fX1/lz59fjzzyiGJjY+84tipVqmjixIlKSEjQ5MmTbcszmyNjy5YtiomJUYECBeTn56fo6Gh16tRJUvr5WHh4uCRp6NChtnM463Vbhw4dFBAQoEOHDumpp55SYGCg2rZta1t3szkSPvjgAxUrVkx+fn6qV6+edu3aZbf+ZnMNXr/N28WW2RwZjrwuuZmNGzdqyZIl6ty5c4YkhiT5+PhkKFv2008/6dFHH5W/v79CQkLUrFkz7d27166N9fP8+eefeumllxQcHKzw8HANHDhQhmHor7/+UrNmzRQUFKTIyEiNHz/e7v3Wc+65c+fqnXfeUWRkpPz9/fXMM8/or7/+smublfN7KfvH/3a/C5J0+PBhPf/88woLC1O+fPn08MMPZ3hIzvpZvvnmG40cOVJFihSRr6+vHn/8cR08ePAmRwZATsGIDABu9cMPP0iS2rVrl+n64OBgNWvWTDNnztTBgwdVqlQpffrpp3rjjTfUsmVL9ezZU9euXdMff/yhjRs3qk2bNpLSy93UqFFDCQkJ6tq1q8qWLat//vlH8+fP15UrV+5qRMTmzZv166+/qnXr1ipSpIiOHDmiqVOnqn79+tqzZ4/y5cununXr6o033tCHH36od955R+XKlZMk239vdPXqVdWvX18HDx5U9+7dFR0drXnz5qlDhw5KSEiwu/kvSXPmzNHFixf1yiuvyGQyaezYsWrRooUOHz580ydcHnzwQZUoUULffPON2rdvb7du7ty5Cg0NVUxMjCTp1Vdf1fz589W9e3eVL19eZ8+e1S+//KK9e/eqWrVqN+2bH374QSVKlFDt2rWz1JddunTRzJkz1bJlS7355pvauHGjRo0apb179+q7776za3vw4EG1bNlSnTt3Vvv27fX555+rQ4cOql69uipUqJCtz3fy5Ek9/PDDMplM6t69u8LDw/Xjjz+qc+fOSkxMVK9evezeP3z4cHl7e6tv375KSkqSt7e3lixZolatWqlSpUoaNWqUzp8/r86dO+u+++7L8DlfeeUVzZgxQx07dtQbb7yhuLg4TZ48Wdu3b9f69evtjtntPqckXbp0SY8++qj27t2rTp06qVq1ajpz5owWLVqkv//+WwUKFJDFYtEzzzyjX375RV27dlW5cuW0c+dOffDBB/rzzz+1cOHCmx6XiIgI+fn56YcfflCPHj0UFhaWabty5cpp2LBhGjRokLp27apHH31UkmzHf968ebpy5Ypee+015c+fX5s2bdJHH32kv//+W/PmzbP1zfHjxxUbG6svv/zypjHdznPPPafdu3erR48eKl68uE6dOqXY2FgdO3aMyQQBAIDLZPW8PjY2Vi+++KIef/xxjRkzRpK0d+9erV+/3tZmyJAhGjVqlLp06aIaNWooMTFRW7Zs0bZt2/TEE0/ccYzWc80VK1Zo5MiRmbY5deqUnnzySYWHh+vtt99WSEiIjhw5ogULFkiSwsPDNXXqVL322mt69tln1aJFC0lS5cqVbdtITU1VTEyMHnnkEb3//vvKly/fLeOaNWuWLl68qG7duunatWuaNGmSHnvsMe3cuVMFCxbM8ufLSmw3cuR1yc0sWrRIUnoZr6xYuXKlGjdurBIlSmjIkCG6evWqPvroI9WpU0fbtm3LcI7bqlUrlStXTqNHj9aSJUs0YsQIhYWF6ZNPPtFjjz2mMWPGaPbs2erbt68eeugh1a1b1+79I0eOlMlkUv/+/XXq1ClNnDhRDRs21I4dO+Tn5ycpa+f3Vlk9/ln5XTh58qRq166tK1eu6I033lD+/Pk1c+ZMPfPMM5o/f76effZZu22OHj1aZrNZffv21YULFzR27Fi1bdtWGzduzFLfA7hHGQBwF3bu3GnUqVPnputr1qxpHDhw4Kbrq1atagQHB99yHxMmTDAkGYsWLTIMwzCaNWtmVKhQ4ZbvadeunWE2m43NmzdnWGexWAzDMIzVq1cbkozVq1fb1rVv394oVqyYXXtJxuDBg22vr1y5kmGbGzZsMCQZs2bNsi2bN29ehu1b1atXz6hXr57t9cSJEw1Jxn/+8x/bsuTkZKNWrVpGQECAkZiYaBiGYcTFxRmSjPz58xvnzp2ztf3+++8NScYPP/yQaX9YDRgwwMibN6/de5OSkoyQkBCjU6dOtmXBwcFGt27dbrmtG124cMGQZDRr1ixL7Xfs2GFIMrp06WK3vG/fvoYk46effrItK1asmCHJWLt2rW3ZqVOnDB8fH+PNN9/M9ufr3LmzUahQIePMmTN2+27durURHBxsO8bW70iJEiUyHPdKlSoZRYoUMS5evGhbtmbNGkOS3Xdo3bp1hiRj9uzZdu9ftmxZhuVZ/ZyDBg0yJBkLFiwwbmT9fn/55ZeG2Ww21q1bZ7d+2rRphiRj/fr1Gd57Pes+/P39jcaNGxsjR440tm7dmqHd5s2bDUnGF198kWFdZr8ro0aNMkwmk3H06FHbsm7duhmZnZJk9jtqGP/7PbDu8/z584YkY9y4cbf8TAAAwLPd7bWL9RzkVuccWT2v79mzpxEUFGSkpqbedFtVqlQxnn766Vt9pExZz6HmzZt3y22HhobaXn/xxReGJCMuLs4wDMP47rvvDEmZXk9ZnT59OsO1klX79u0NScbbb7+d6brrz5et/ern52f8/ffftuUbN240JBm9e/e2LbvxOupm27xVbIMHD7Y793TGdUlmnn32WUOScf78+Vu2s6pataoRERFhnD171rbs999/N8xms9GuXbsMn6dr1662ZampqUaRIkUMk8lkjB492rb8/Pnzhp+fn9G+fXvbMuv35b777rN9Pw3DML755htDkjFp0iTbsqye32fn+Gfld6FXr16GJLtrm4sXLxrR0dFG8eLFjbS0NLvPUq5cOSMpKcnWdtKkSYYkY+fOnTfdB4B7H6WlALjVxYsXFRgYeMs21vXWcj4hISH6+++/b1pKyWKxaOHChWratKkefPDBDOtvHEacXdanUaT04d5nz55VqVKlFBISctvSSzezdOlSRUZG6sUXX7Qty5s3r9544w1dunRJP//8s137Vq1aKTQ01Pba+iT84cOHb7mfVq1aKSUlxfYklZQ+8XhCQoLdHCQhISHauHGjjh8/nuXPYD0+tzueVkuXLpUk9enTx275m2++KUkZhgmXL1/e9jml9CetypQpY/eZs/L5DMPQt99+q6ZNm8owDJ05c8b2ExMTowsXLmQ4ju3bt7c77sePH9fOnTvVrl07BQQE2JbXq1dPlSpVsnvvvHnzFBwcrCeeeMJuX9WrV1dAQIBWr16d7c/57bffqkqVKhmePJL+9/2eN2+eypUrp7Jly9rt1zqp4437vdHQoUM1Z84cPfDAA1q+fLn+/e9/q3r16qpWrVqG4ew3c32fXb58WWfOnFHt2rVlGIa2b9+epW1kdT/e3t5as2ZNhnJdAAAArpTV8/qQkBBdvnz5lmWiQkJCtHv3bh04cMDhcQYEBOjixYu33LckLV68WCkpKXe8n9deey3LbZs3b243urlGjRqqWbOm7brBWZxxXZKZ7FwvnThxQjt27FCHDh3sRkdXrlxZTzzxRKZ90qVLF9v/e3l56cEHH5RhGOrcubNteUhIyE1jbdeunV1sLVu2VKFChez2ld3z+6wc/6z8LixdulQ1atTQI488YlsWEBCgrl276siRI9qzZ49d+44dO9pVYcjq9TKAexuJDABuFRgYeMsTaEm29daTqv79+ysgIEA1atRQ6dKl1a1bN61fv97W/vTp00pMTFTFihWdEvPVq1c1aNAgRUVFycfHRwUKFFB4eLgSEhJ04cKFO9rm0aNHVbp06QwTSFtLUR09etRuedGiRe1eW5Mat7uJW6VKFZUtW1Zz5861LZs7d64KFChgu8Etpc91sWvXLkVFRalGjRoaMmTIbU/6goKCJOm2x9Pq6NGjMpvNKlWqlN3yyMhIhYSE3PYzS+mf+/rPnJXPd/r0aSUkJGj69OkKDw+3++nYsaOkjJNZR0dHZ4hdUobYM1t24MABXbhwQRERERn2d+nSpQz7ysrnPHTo0G2/3wcOHNDu3bsz7PP+++/P9DNm5sUXX9S6det0/vx5rVixQm3atNH27dvVtGlTXbt27bbvP3bsmO3iKyAgQOHh4apXr54k3fHvSmZ8fHw0ZswY/fjjjypYsKDq1q2rsWPHKj4+3mH7AAAAyIqsnte//vrruv/++9W4cWMVKVJEnTp10rJly+zeM2zYMCUkJOj+++9XpUqV1K9fP/3xxx8OifPSpUu3vKFer149Pffccxo6dKgKFCigZs2a6YsvvrjtXGvXy5Mnj4oUKZLl9qVLl86w7P7773f6nGfOuC7JTHaul6z7LFOmTIZ15cqV05kzZ3T58uVbxhUcHCxfX18VKFAgw/LMYr2x/00mk0qVKmXX/9k5v8/q8c/K78LRo0dv2hfW9de70+tlAPc25sgA4FblypXTjh07dOzYsUxPCCXZTtbLly9ve8/+/fu1ePFiLVu2TN9++60+/vhjDRo0SEOHDnV6zD169NAXX3yhXr16qVatWgoODpbJZFLr1q1lsVicvn8p/QmbzBiGcdv3tmrVSiNHjtSZM2cUGBioRYsW6cUXX1SePP/7k/DCCy/o0Ucf1XfffacVK1Zo3LhxGjNmjBYsWKDGjRtnut2goCAVLlw4w4R8t5PVETJZ/cy3+3zWY/TSSy9lmEvD6sb6udc/eZRdFotFERERmj17dqbrrRMRWt3Nsb1xv5UqVdKECRMyXR8VFZXlbQUFBemJJ57QE088obx582rmzJnauHGj7aIlM2lpaXriiSd07tw59e/fX2XLlpW/v7/++ecfdejQIUu/Kzf7bqSlpWVY1qtXLzVt2lQLFy7U8uXLNXDgQI0aNUo//fSTHnjggSx/VgAAAFeIiIjQjh07tHz5cv3444/68ccf9cUXX6hdu3a2icHr1q2rQ4cO6fvvv9eKFSv02Wef6YMPPtC0adPsnr7PrpSUFP3555+3fDDGZDJp/vz5+u233/TDDz9o+fLl6tSpk8aPH6/ffvvNblTyzfj4+GRI6Nwtk8mU6XlxZueHd7LtrLjT8/WyZctKknbu3Gk3osNRMovLUdcWUvbP77N6/LPyu5BdjvzcAO4djMgA4FZNmjSRlD6xW2YSExP1/fffq2zZsnZPyPj7+6tVq1b64osvdOzYMT399NMaOXKkrl27pvDwcAUFBWX7hnpWzZ8/X+3bt9f48ePVsmVLPfHEE3rkkUeUkJBg1y47JayKFSumAwcOZDj527dvn229o7Rq1Uqpqan69ttv9eOPPyoxMVGtW7fO0K5QoUJ6/fXXtXDhQsXFxSl//vw3nQzQqkmTJjp06JA2bNhw2ziKFSsmi8WSYaj8yZMnlZCQcMef+XafLzw8XIGBgUpLS1PDhg0z/YmIiLht7FL6RH83unFZyZIldfbsWdWpUyfTfVWpUiXbn7FkyZK3/X6XLFlS586d0+OPP57pfjN7oikrrOXaTpw4Ienm3/OdO3fqzz//1Pjx49W/f381a9ZMDRs2VOHChTO0vdk2rE9O3fi7deMTV1YlS5bUm2++qRUrVmjXrl1KTk7W+PHjs/S5AAAAHCE75/Xe3t5q2rSpPv74Yx06dEivvPKKZs2aZXc+GRYWpo4dO+qrr77SX3/9pcqVK2vIkCF3FeP8+fN19epVxcTE3Lbtww8/rJEjR2rLli2aPXu2du/era+//lrS3ZfsvVFmJbT+/PNPu0mtQ0NDM5wbShnPD7N7LeaM65IbNW3aVJL0n//8J0sxSdL+/fszrNu3b58KFCggf39/h8RldePnNwxDBw8etPV/ds7vs+t2vwvFihW7aV9Y1wPI/UhkAHCrli1bqnz58ho9erS2bNlit85isei1117T+fPnNXjwYNvys2fP2rXz9vZW+fLlZRiGUlJSZDab1bx5c/3www8Ztind/VMYXl5eGbbx0UcfZXgKyHpimdmJ9o2eeuopxcfH25VESk1N1UcffaSAgIBbPvmeXeXKlVOlSpU0d+5czZ07V4UKFVLdunVt69PS0jIMC46IiFDhwoVvO5T8rbfekr+/v7p06aKTJ09mWH/o0CFNmjRJUvpnlqSJEyfatbGOIHj66aez/dmk238+Ly8vPffcc/r2228zTQacPn36tvsoXLiwKlasqFmzZunSpUu25T///LN27txp1/aFF15QWlqahg8fnmE7qampWfp+3Oi5557T77//ru+++y7DOut384UXXtA///yjTz/9NEObq1evZhiKfr0rV67cNBn1448/SvrfMPebfc+tT0Fd/7tiGIbt+F/vZtsoVqyYvLy8tHbtWrvlH3/8cYZ4byx1VbJkSQUGBmar/AEAAMDdyup5/Y3XNGaz2TYq2Hr+cmObgIAAlSpV6q7Ob37//Xf16tVLoaGh6tat203bnT9/PsM1T9WqVe3iy5cvn6SsXe9kxcKFC/XPP//YXm/atEkbN260GxFesmRJ7du3z+6c/ffff7crNZzd2Jx1XXKjWrVqqVGjRvrss8+0cOHCDOuTk5PVt29fSekPlVWtWlUzZ860+wy7du3SihUrbDE70qxZs+zKXs2fP18nTpyw9X92zu+zIyu/C0899ZQ2bdpkd41y+fJlTZ8+XcWLF7dVbwCQu1FaCoBbeXt7a/78+Xr88cf1yCOPqGPHjnrwwQeVkJCgOXPmaNu2bXrzzTftnqh/8sknFRkZqTp16qhgwYLau3evJk+erKefftpW5/W9997TihUrVK9ePXXt2lXlypXTiRMnNG/ePP3yyy+2yevuRJMmTfTll18qODhY5cuX14YNG7Ry5Urlz5/frl3VqlXl5eWlMWPG6MKFC/Lx8dFjjz2W6dP+Xbt21SeffKIOHTpo69atKl68uObPn6/169dr4sSJWZ5AO6tatWqlQYMGydfXV507d7Yb8nvx4kUVKVJELVu2VJUqVRQQEKCVK1dq8+bNt326vWTJkpozZ45atWqlcuXKqV27dqpYsaKSk5P166+/at68eerQoYOk9Pks2rdvr+nTpyshIUH16tXTpk2bNHPmTDVv3lwNGjRwyueTpNGjR2v16tWqWbOmXn75ZZUvX17nzp3Ttm3btHLlSp07d+62+3jvvffUrFkz1alTRx07dtT58+c1efJkVaxY0S65Ua9ePb3yyisaNWqUduzYoSeffFJ58+bVgQMHNG/ePE2aNEktW7bM1ufr16+f5s+fr+eff16dOnVS9erVde7cOS1atEjTpk1TlSpV9K9//UvffPONXn31Va1evVp16tRRWlqa9u3bp2+++UbLly+3ja640ZUrV1S7dm09/PDDatSokaKiopSQkKCFCxdq3bp1at68ua1cU8mSJRUSEqJp06YpMDBQ/v7+qlmzpsqWLauSJUuqb9+++ueffxQUFKRvv/0207q01atXlyS98cYbiomJkZeXl1q3bq3g4GA9//zz+uijj2QymVSyZEktXrw4w/wef/75px5//HG98MILKl++vPLkyaPvvvtOJ0+ezHS0EQAAwN1YtWpVpvOFNW/ePMvn9V26dNG5c+f02GOPqUiRIjp69Kg++ugjVa1a1Vb3v3z58qpfv76qV6+usLAwbdmyRfPnz1f37t2zFOe6det07do1paWl6ezZs1q/fr0WLVqk4OBgfffdd4qMjLzpe2fOnKmPP/5Yzz77rEqWLKmLFy/q008/VVBQkO0mup+fn8qXL6+5c+fq/vvvV1hYmCpWrHjHcxWWKlVKjzzyiF577TUlJSVp4sSJyp8/v9566y1bm06dOmnChAmKiYlR586dderUKU2bNk0VKlSwTaad3diceV1yo1mzZunJJ59UixYt1LRpUz3++OPy9/fXgQMH9PXXX+vEiRN6//33JUnjxo1T48aNVatWLXXu3FlXr17VRx99pODg4LselZOZsLAw2zX5yZMnNXHiRJUqVUovv/yyJGXr/D47svK78Pbbb+urr75S48aN9cYbbygsLEwzZ85UXFycvv32W4eXMANwjzIA4C7s3LnTqFOnzk3X16xZ0zhw4MBtt3Pq1CmjT58+RqlSpQwfHx8jJCTEaNiwobFo0aIMbT/55BOjbt26Rv78+Q0fHx+jZMmSRr9+/YwLFy7YtTt69KjRrl07Izw83PDx8TFKlChhdOvWzUhKSjIMwzBWr15tSDJWr15te0/79u2NYsWK2W1HkjF48GDb6/PnzxsdO3Y0ChQoYAQEBBgxMTHGvn37jGLFihnt27e3e++nn35qlChRwvDy8rLbV7169Yx69erZtT158qRtu97e3kalSpWML774wq5NXFycIckYN25chn65Mc5bOXDggCHJkGT88ssvduuSkpKMfv36GVWqVDECAwMNf39/o0qVKsbHH3+cpW0bhmH8+eefxssvv2wUL17c8Pb2NgIDA406deoYH330kXHt2jVbu5SUFGPo0KFGdHS0kTdvXiMqKsoYMGCAXRvDMIxixYoZTz/9dIb9ZNaPt/t8VidPnjS6detmREVFGXnz5jUiIyONxx9/3Jg+fbqtjfU7Mm/evEy38fXXXxtly5Y1fHx8jIoVKxqLFi0ynnvuOaNs2bIZ2k6fPt2oXr264efnZwQGBhqVKlUy3nrrLeP48eN39DnPnj1rdO/e3bjvvvsMb29vo0iRIkb79u2NM2fO2NokJycbY8aMMSpUqGD4+PgYoaGhRvXq1Y2hQ4dm+H25XkpKivHpp58azZs3N4oVK2b4+PgY+fLlMx544AFj3Lhxtt8hq++//94oX768kSdPHkOS7Xu7Z88eo2HDhkZAQIBRoEAB4+WXXzZ+//13uzaGYRipqalGjx49jPDwcMNkMhnXn56cPn3aeO6554x8+fIZoaGhxiuvvGLs2rXLbhtnzpwxunXrZpQtW9bw9/c3goODjZo1axrffPPNTT8jAADwPHd77WI9F7/Zz5dffmkYRtbO6+fPn288+eSTRkREhOHt7W0ULVrUeOWVV4wTJ07Y2owYMcKoUaOGERISYvj5+Rlly5Y1Ro4caSQnJ9/yc1rPYa0/efPmNcLDw426desaI0eONE6dOpXhPV988YUhyYiLizMMwzC2bdtmvPjii0bRokUNHx8fIyIiwmjSpImxZcsWu/f9+uuvRvXq1Q1vb2+765H27dsb/v7+mcZ34zXX9dc448ePN6KiogwfHx/j0UcfNX7//fcM7//Pf/5jlChRwvD29jaqVq1qLF++PNPruJvFNnjwYOPG22HOui7JzJUrV4z333/feOihh4yAgADD29vbKF26tNGjRw/j4MGDdm1Xrlxp1KlTx/Dz8zOCgoKMpk2bGnv27LFrY/08p0+ftlt+s2NQr149o0KFCrbX1u/LV199ZQwYMMCIiIgw/Pz8jKeffto4evSo3Xuzen6fneOfld8FwzCMQ4cOGS1btjRCQkIMX19fo0aNGsbixYvt2tzs+s36Hbvx9xBAzmIyDGa6AXDndu3apVdffVW//PJLpusffvhh/ec//7Gb3wLIzapWrarw8HDFxsa6OxQAAABch2sXIKM1a9aoQYMGmjdvXrZHigOAKzH2CgCAO5CSkqLU1FS7ZWvWrNHvv/+u+vXruycoAAAAAACAXIg5MgDctd9+++2mc05cP1cAkJv8888/atiwoV566SUVLlxY+/bt07Rp0xQZGalXX33V3eEBAAAgE1y7AACQM5HIAHBXKlasmOGpdMAThIaGqnr16vrss890+vRp+fv76+mnn9bo0aMzTPwOAAAA9+PaBQCAnIs5MgAAAAAAAAAAwD2LOTIAAAAAAAAAAMA9i9JSt2GxWHT8+HEFBgbKZDK5OxwAAADAIQzD0MWLF1W4cGGZzTzf5A5cawAAACC3cvT1BomM2zh+/LiioqLcHQYAAADgFH/99ZeKFCni7jA8EtcaAAAAyO0cdb1BIuM2AgMDJaV3eFBQkEO2abFYdPr0aYWHh/P02z2OY5WzcLxyFo5XzsGxylk4XjmLO49XYmKioqKibOe7cD1nXGvcDP82uAf97nr0uevR565Hn7sefe4e9LvrObLPHX29QSLjNqxDvIOCghyayLh27ZqCgoL4JbzHcaxyFo5XzsLxyjk4VjkLxytnuReOFyWN3McZ1xo3cy981zwR/e569Lnr0eeuR5+7Hn3uHvS76zmjzx11vcE3AAAAAAAAAAAA3LNIZAAAAAAAAAAAgHsWiQwAAAAAAAAAAHDPYo4MAAAAD5SWlqaUlBR3h+HxLBaLUlJSdO3aNafU/c2bN6+8vLwcvl1nGTVqlBYsWKB9+/bJz89PtWvX1pgxY1SmTBlbm2vXrunNN9/U119/raSkJMXExOjjjz9WwYIFbW2OHTum1157TatXr1ZAQIDat2+vUaNGKU+e/13+rFmzRn369NHu3bsVFRWld999Vx06dLCLZ8qUKRo3bpzi4+NVpUoVffTRR6pRo0a2YgEAALiexWJRcnKyu8PIEZx9royMstPnrr7WIJEBAADgQQzDUHx8vBISEtwdCpR+PCwWiy5evOi0SbdDQkIUGRmZIyb1/vnnn9WtWzc99NBDSk1N1TvvvKMnn3xSe/bskb+/vySpd+/eWrJkiebNm6fg4GB1795dLVq00Pr16yWlJ+mefvppRUZG6tdff9WJEyfUrl075c2bV++9954kKS4uTk8//bReffVVzZ49W6tWrVKXLl1UqFAhxcTESJLmzp2rPn36aNq0aapZs6YmTpyomJgY7d+/XxEREVmKBQAA4HrJycmKi4uTxWJxdyg5givOlWEvu33uymsNk2EYhtP3koMlJiYqODhYFy5cUFBQkEO2abFYdOrUKUVERJBNvMdxrHIWjlfOwvHKOThWOcvtjteJEyeUkJCgiIgI5cuXjwsCNzMMQ6mpqcqTJ4/Dj4VhGLpy5YpOnTqlkJAQFSpUyG69M85zHe306dOKiIjQzz//rLp16+rChQsKDw/XnDlz1LJlS0nSvn37VK5cOW3YsEEPP/ywfvzxRzVp0kTHjx+3jYyYNm2a+vfvr9OnT8vb21v9+/fXkiVLtGvXLtu+WrdurYSEBC1btkySVLNmTT300EOaPHmypPTfraioKPXo0UNvv/12lmK5UVJSkpKSkmyvExMTFRUVpfPnzzv9GFgsFp0+fVrh4eH8W+5C9Lvr0eeuR5+7Hn3ueo7oc8Mw9Ndffyk1NVWFChXi2GVRSkqK8ubN6+4wPEpW+tx6rXH69GkFBwcrMjIyQ5vExESFhoY67HqDERkAAAAeIi0tzZbEyJ8/v7vDgZybyJAkPz8/SbIlt3JSmSlJunDhgiQpLCxMkrR161alpKSoYcOGtjZly5ZV0aJFbcmDDRs2qFKlSnblnWJiYvTaa69p9+7deuCBB7Rhwwa7bVjb9OrVS1L605Jbt27VgAEDbOvNZrMaNmyoDRs2ZDmWG40aNUpDhw7NsPz06dO6du1adrsnWywWiy5cuCDDMLhx4kL0u+vR565Hn7sefe56jujztLQ0Xbx4Uffdd5+8vb0dHGHuZH3+3svLiwewXCQ7fR4cHCyLxaKTJ09KUobfjYsXLzo0NhIZAAAAHsI6J0a+fPncHAlcyXq8U1JSclQiw2KxqFevXqpTp44qVqwoSYqPj5e3t7dCQkLs2hYsWFDx8fG2NjfOUWF9fbs2iYmJunr1qs6fP6+0tLRM2+zbty/LsdxowIAB6tOnj+21dURGeHi4S0ZkmEwmnt51Mfrd9ehz16PPXY8+dz1H9Pm1a9d04cIF+fr62s3bhdtjRIbrZbXPAwICdObMGYWEhMjX19du3Y2v7xa/NQAAAB6Gp5k8S0493t26ddOuXbv0yy+/uDsUh/Hx8ZGPj0+G5Waz2SU3okwmk8v2hf+h312PPnc9+tz16HPXu9s+N5vNtm3k1PMzVzMMw9ZX9JlrZLfPr/9e3/i74eh/n/jXDgAAAMA9pXv37lq8eLFWr16tIkWK2JZHRkYqOTk5w2T1J0+etNXljYyMtA1vv369dd2t2gQFBcnPz08FChSQl5dXpm2u38btYgEAAADgGIzIAAAAgHTunHTpkmv2FRAg/XfOA+B6hmGoR48e+u6777RmzRpFR0fbra9evbry5s2rVatW6bnnnpMk7d+/X8eOHVOtWrUkSbVq1dLIkSNt84JIUmxsrIKCglS+fHlbm6VLl9ptOzY21rYNb29vVa9eXatWrVLz5s0lpZeUWLVqlbp3757lWAAAAAA4BokMAAAAT3funNSzp3T6tGv2Fx4uTZp0TyUzjhw5oujoaG3fvl1Vq1bVmjVr1KBBA50/f14hISGaMWOGevXqleHp++xy1HZyq27dumnOnDn6/vvvFRgYaJtrIjg4WH5+fgoODlbnzp3Vp08fhYWFKSgoSD169FCtWrVsk2s/+eSTKl++vP71r39p7Nixio+P17vvvqtu3brZyjq9+uqrmjx5st566y116tRJP/30k7755hstWbLEFkufPn3Uvn17Pfjgg6pRo4YmTpyoy5cvq2PHjraYbhcLAADA7bzywysu3d8nTT9x6f4cwWw267vvvrM9YALPRGkpAAAAT3fpUnoSw89Pyp/fuT9+fun7uoPRH3/99Zc6deqkwoULy9vbW8WKFVPPnj119uzZu+6CqKgonThxwjaptCMUL15cEydOtFvWqlUr/fnnnw7bR24zdepUXbhwQfXr11ehQoVsP3PnzrW1+eCDD9SkSRM999xzqlu3riIjI7VgwQLbei8vLy1evFheXl6qVauWXnrpJbVr107Dhg2ztYmOjtaSJUsUGxurKlWqaPz48frss88UExNja9OqVSu9//77GjRokKpWraodO3Zo2bJldhOA3y4WAACA3CA+Pl49e/ZUqVKl5Ovrq4IFC6pOnTqaOnWqrly54u7wJKXH2KNHD5UoUUI+Pj6KiopS06ZNtWrVKneHBgdhRAYAAADS5cuXXvbJ2a5ezfZbDh8+rFq1aun+++/XV199pejoaO3evVv9+vXTjz/+qN9++01hdzHCw8vLyyXzGvj5+cnPz8/p+8mpDMO4bRtfX19NmTJFU6ZMuWmbYsWKZSgddaP69etr+/btt2zTvXt3WympO40FAAAgJzt8+LDq1KmjkJAQvffee6pUqZJ8fHy0c+dOTZ8+Xffdd5+eeeYZt8Z45MgRW4zjxo1TpUqVlJKSouXLl6tbt27at2+fW+O7nZSUFOXNm9fdYdzzGJEBAACAe163bt3k7e2tFStWqF69eipatKgaN26slStX6p9//tG///1vSdLHH3+s0qVL254Ua9mypW0bFotFY8eOValSpeTj46OiRYtq5MiRktIvfkwmk3bs2JGleA4dOqRmzZqpYMGCCggI0EMPPaSVK1fa1tevX19Hjx5V7969ZTKZZDKZJKWXlgoJCbG1GzJkiB588EF9+eWXKl68uIKDg9W6dWtdvHjR1ubixYtq27at/P39VahQIX3wwQeqX7++evXqdYe9CQAAAGTN66+/rjx58mjLli164YUXVK5cOZUoUULNmjXTkiVL1LRpU1vbY8eOqVmzZgoICFBQUJBeeOEFnTx50m57U6dOVcmSJeXt7a0yZcroyy+/tFt/4MAB1a1bV76+vqpQoYLdOfatYjSZTNq0aZOee+453X///apQoYL69Omj3377LcvxDRkyRFWrVr3pufn06dNVuHBhWSwWu/03a9ZMnTp1sr3+/vvvVa1aNfn6+qpEiRIaOnSoUlNTbetNJpOmTp2qZ555Rv7+/rZrkhEjRigiIkKBgYHq0qWL3n77bVWtWtVuX5999pnKlSsnX19flS1bVh9//LFtnfWaZsGCBWrQoIHy5cunKlWqaMOGDXbbWL9+verXr698+fIpNDRUMTExOn/+vKT0a6YxY8aoRIkS8vPzU5UqVTR//vzbHgNXIJEBAACAe9q5c+e0fPlyvf766xlGM0RGRqpt27aaO3euNm/erDfeeEPDhg3T/v37tWzZMtWtW9fWdsCAARo9erQGDhyoPXv2aM6cOXZlgrLj0qVLeuqpp7Rq1Spt375djRo1UtOmTXXs2DFJ0oIFC1SkSBENGzZMJ06c0IkTJ266rcOHD+v777/X4sWLtXjxYv38888aPXq0bX2fPn20fv16LVq0SLGxsVq3bp22bdt2R3EDAAAAWXX27FmtWLFC3bp1k7+/f6ZtrA/sWCwWNWvWTOfOndPPP/+s2NhYHT58WK1atbK1/e6779SzZ0+9+eab2rVrl1555RV17NhRq1evtm2jRYsW8vb21saNGzV16lS98847t4zx3LlzWrZs2U1jtD5ElJX4pPQHlhYuXJjpufnzzz+vs2fP2uK9fv9t27aVJK1bt07t2rVTz549tWfPHn3yySeaMWOGLVlhNWTIED377LPauXOnOnXqpNmzZ2vkyJEaM2aMtm7dqqJFi2rq1Kl275k9e7YGDRqkkSNHau/evXrvvfc0cOBAzZw5067dv//9b/Xt21c7duzQ/fffrxdffNGWSNmxY4cef/xxlS9fXhs2bNAvv/yipk2bKi0tTZI0atQo/ec//9HUqVO1e/du9e7dWy+99JJ+/vnnWx4HV6C0FAAAAO5pBw4ckGEYKleuXKbry5Urp/PnzysuLk7+/v5q0qSJAgMDVaxYMT3wwAOS0kc1TJo0SZMnT1b79u0lSSVLltQjjzxyRzFVqVJFVapUsb0ePny4vvvuOy1atEjdu3dXWFiYvLy8FBgYeNuSVRaLRV988YWCgoIkSf/617+0atUqjRw5UhcvXtTMmTM1Z84cPf7445KkL774QoULF76juAEAAICsOnjwoAzDUJkyZeyWFyhQQNeuXZOUPnJ6zJgxWrVqlXbu3Km4uDhFRUVJkmbNmqUKFSpo8+bNeuihh/T++++rQ4cOev311yXJNmLi/fffV4MGDbRy5Urt27dPy5cvV+HChWUYhoYPH2436uNmMZYtW/aWnyUr8Unp5+YzZsxQYGCgJPtz89DQUDVu3Nju3Hz+/PkqUKCAGjRoIEkaOnSo3n77bds1R4kSJTR8+HC99dZbGjx4sC2eNm3aqGPHjrbXH330kTp37mxbNmjQIK1YsUKXrptbcPDgwRo/frxatGghKX3eN2uyxLo/Serbt6+efvppWzwVKlTQwYMHVbZsWY0dO1YPPvig3UiOChUqSJKSkpI0atQoLVu2TI888ohMJpNKlCihX375RZ988onq1at3yz52NkZkAAAAIEe43fwJNWvWVLFixVSiRAn961//0uzZs22TD+7du1dJSUm2C467denSJfXt21flypVTSEiIAgICtHfvXtuIjOwoVqyY7UJJkgoVKqRTp05JSh+tkZKSoho1atjWBwcHZ7iYBAAAAFxl06ZN2rFjhypUqKCkpCRJ6efbUVFRtiSBJJUvX14hISHau3evrU2dOnXstlWnTh279VFRUXYP7Tz88MO3jCUrc6xlNT5JKl68+E3PzSWpbdu2+vbbb22fe/bs2WrdurXM5vTb7L///ruGDRumgIAA28/LL7+sEydO2E2M/uCDD9rFt3//frtzfkl2ry9fvqxDhw6pc+fOdtseMWKEDh06ZPe+ypUr28UvyfYZrCMyMnPw4EFduXJFjRs3VmBgoG0fs2bNyrAPd2BEBgAAAO5ppUqVkslk0t69e/Xss89mWL93716FhoaqaNGi2rZtm9asWaMVK1Zo0KBBGjJkiDZv3uzwCbb79u2r2NhYvf/++ypVqpT8/PzUsmVLJScnZ3tbN07sZzKZMtTdBQAAAFzNeh6+f/9+u+UlSpSQJIefY9+J0qVLy2QyOWxC79udmzdt2lSGYWjJkiV66KGHtG7dOn3wwQe29ZcuXdLQoUNtoyau5+vra/v/m5XquhnryIxPP/1UNWvWtFvn5eV1089wfekv6dbHzLqP77//XkWLFrW9V5J8fHyyFa8zMCIDAAAA97T8+fPriSee0Mcff6yrV6/arYuPj9fs2bPVqlUrmUwm5cmTRw0bNtTYsWP1xx9/6MiRI/rpp59UunRp+fn5adWqVQ6Jaf369erQoYOeffZZVapUSZGRkTpy5IhdG29vb1ut2TtVokQJ5c2bV5s3b7Ytu3Dhgv7888+72i4AAABwO9bz8MmTJ+vy5cu3bFuuXDn99ddf+uuvv2zL9uzZo4SEBJUvX97WZv369XbvW79+vd36v/76y25+uY0bN95yv2FhYYqJidGUKVMyjTEhISHL8WWFr6+vWrRoodmzZ+urr75SmTJlVK1aNdv6atWqaf/+/SpVqlSGH+uojcyUKVPG7pxfkt3rggULqnDhwjp8+HCG7UZHR2c5/sqVK9/0mqh8+fLy8fHRsWPHMuzj+pEs7sKIDAAAAKS7bqjzvbaPyZMnq3bt2oqJidGIESMUHR2t3bt3q1+/frrvvvs0cuRILV68WIcPH1bdunUVGhqqpUuXymKxqEyZMvL19VX//v311ltvydvbW3Xq1NHp06e1e/dude7cOdvxlC5dWgsWLFDTpk1lMpk0cODADKMoihcvrrVr16p169by8fFRgQIFsr2fwMBAtW/fXv369VNYWJgiIiI0ePBgmc1muyekAAAAAGf4+OOPVadOHT344IMaMmSIKleuLLPZrM2bN2vfvn2qXr26JKlhw4aqVKmS2rZtq4kTJyo1NVWvv/666tWrZyuj1K9fP73wwgt64IEH1LBhQ/3www9asGCBVq5cadvG/fffr/bt22vcuHG6cOGCBg0adNsYp0yZojp16qhGjRoaNmyYKleurNTUVMXGxmrq1Knau3dvluLLqrZt26pJkybavXu3XnrpJbt1gwYNUpMmTVS0aFG1bNlSZrNZv//+u3bt2qURI0bcdJs9evTQyy+/rAcffFC1a9fW3Llz9ccff9hGv0jp81288cYbCg4OVqNGjZSUlKQtW7bo/Pnz6tOnT5ZiHzBggCpVqqTXX39dr776qry9vbV69Wo9//zzKlCggN58803169dPJpNJjz76qC5cuKD169crKCjIbh4OdyCRAQAA4OkCAqTwcOn0aemGEQ9OER6evs9sKF26tLZs2aLBgwfrhRde0Llz5xQZGanmzZtr8ODBCgsLU0hIiBYsWKAhQ4bo2rVrKl26tL766ivb5HUDBw5Unjx5NGjQIB0/flyFChXSq6++ekcfYcKECerUqZNq166tAgUKqH///kpMTLRrM2zYML3yyisqWbKkkpKSsly/N7N9vfrqq2rSpImCgoL01ltv6a+//rIbmg4AAICc6ZOmn7g7hFsqWbKktm/frvfee08DBgzQ33//LR8fH5UvX159+/a1TdxtMpn0/fffq0ePHqpbt67MZrMaNWqkjz76yLat5s2ba9KkSXr//ffVs2dPRUdH64svvlD9+vUlSWazWd999506d+6sGjVqqHjx4powYYKaNGlyyxhLlCihbdu2aeTIkXrzzTd14sQJhYeHq3r16po6dWqW48uqxx57TGFhYdq/f7/atGljty4mJkaLFy/WsGHDNGbMGOXNm1dly5ZVly5dbrnNtm3b6vDhw+rbt6+uXbumF154QR06dNCmTZtsbbp06aJ8+fJp3Lhx6tevn/z9/VWpUiX16tUry7Hff//9WrFihd555x3VqFFDfn5+qlmzpl588UVJ0vDhw5U/f36NHj1aXbt2VUhIiKpVq6Z33nkn6x3kJCbjTq+oPERiYqKCg4N14cIFBQUFOWSbFotFp06dUkRExC2HFMH9OFY5C8crZ+F45Rwcq5zlVsfr2rVriouLU3R0dMab4OfOSf+tiep0AQFSWJhr9nWPMwxDqampypMnT5ZHWFy+fFn33Xefxo8fn6XRJDc77s44z0X2uPIY8G+5e9Dvrkefux597nr0ues5os9veS6OTN3JuXJu8sQTTygyMlJffvmly/aZ3T6/1ffa0ee6jMgAAABAemKB5MI9afv27dq3b59q1KihCxcuaNiwYZKkZs2auTkyAAAAAI5w5coVTZs2TTExMfLy8tJXX32llStXKjY21t2h3TNIZAAAAAD3uPfff1/79++Xt7e3qlevrnXr1t3RnBsAAAAA7j0mk0lLly7VyJEjde3aNZUpU0bffvutGjZs6O7Q7hkkMgAAAIB72AMPPKCtW7e6OwwAAAAATuLn52eb9ByZo5AeAAAAAAAAAAC4Z5HIAAAAAAAAAAAA9ywSGQAAAAAAAAAA4J5FIgMAAAAAAAAAANyzSGQAAAAAAAAAAIB7Vh53BwAAAAD3u5pyVclpyS7Zl7eXt/zy+rlkXwAAAACAnI9EBgAAgIe7mnJV3+//XuevnnfJ/kL9QtWsTDOSGQ5w5MgRRUdHa/v27apataq7wwEAAEB2vfKKa/f3ySeu3V8W3HhOu2bNGjVo0EDnzp1TQECAZsyYod69eyshIeGu9jNjxgz16tXrrrcD96C0FAAAgIdLTkvW+avn5ZfHT6G+oU798cvjp/NXz9/R6I/4+Hj16NFDJUqUkI+Pj6KiotS0aVOtWrXKCb0CAAAAwOqvv/5Sp06dVLhwYXl7e6tYsWLq2bOnzp49e9fbjoqK0okTJ1SxYkUHRJquePHimjhxot2yVq1a6c8//3TYPuBajMgAAACAJMk3j6/8vf2dvp+rqVez/Z4jR46oTp06CgkJ0bhx41SpUiWlpKRo+fLl6tatm/bt2+eESB0jJSVFefPmdXcYAAAAwB05fPiwatWqpfvvv19fffWVoqOjtXv3bvXr108//vijfvvtN4WFhd3x9r28vBQZGenAiDPn5+cnPz9GhedUOWpExtq1a9W0aVMVLlxYJpNJCxcuvO171qxZo2rVqsnHx0elSpXSjBkznB4nAAAAHOv111+XyWTSpk2b9Nxzz+n+++9XhQoV1KdPH/3222+SpGPHjqlZs2YKCAhQUFCQXnjhBZ08edK2jSFDhqhq1ar68ssvVbx4cQUHB6t169a6ePGiJGn69OkqXLiwLBaL3b6bNWumTp062V5///33qlatmnx9fVWiRAkNHTpUqamptvUmk0lTp07VM888I39/f40cOVKSNGLECEVERCgwMFBdunTR22+/rQceeMBuX5999pnKlSsnX19flS1bVh9//LHd+k2bNumBBx6Qr6+vHnzwQW3fvt0BvQsAAADcXLdu3eTt7a0VK1aoXr16Klq0qBo3bqyVK1fqn3/+0b///W9J0scff6zSpUvL19dXBQsWVMuWLW3bsFgsGjt2rEqVKiUfHx8VLVrUdp585MgRmUwm7dixI0vxHDp0SM2aNVPBggUVEBCghx56SCtXrrStr1+/vo4eParevXvLZDLJZDJJSi8tFRISYmt3u+sDSbp48aLatm0rf39/FSpUSB988IHq16+vXr163WFv4k7lqETG5cuXVaVKFU2ZMiVL7ePi4vT000+rQYMG2rFjh3r16qUuXbpo+fLlTo4UAAAAjnLu3DktW7ZM3bp1k79/xhEjISEhslgsatasmc6dO6eff/5ZsbGxOnz4sFq1amXX9tChQ1q4cKEWL16sxYsX6+eff9bo0aMlSc8//7zOnj2r1atXZ9h327ZtJUnr1q1Tu3bt1LNnT+3Zs0effPKJZsyYYbsIsxoyZIieffZZ7dy5U506ddLs2bM1cuRIjRkzRlu3blXRokU1depUu/fMnj1bgwYN0siRI7V371699957GjhwoGbOnClJunTpkpo0aaLy5ctr69atGjJkiPr27Xv3HQwAAADcxLlz57R8+XK9/vrrGUYzREZGqm3btpo7d642b96sN954Q8OGDdP+/fu1bNky1a1b19Z2wIABGj16tAYOHKg9e/Zozpw5Kliw4B3FdOnSJT311FNatWqVtm/frkaNGqlp06Y6duyYJGnBggUqUqSIhg0bphMnTujEiRM33datrg8kqU+fPlq/fr0WLVqk2NhYrVu3Ttu2bbujuHF3clRpqcaNG6tx48ZZbj9t2jRFR0dr/PjxkqRy5crpl19+0QcffKCYmBhnhQkAAAAHOnjwoAzDUNmyZW/aZtWqVdq5c6fi4uIUFRUlSZo1a5YqVKigzZs366GHHpKU/iTYjBkzFBgYKEn617/+pVWrVmnkyJEKDQ1V48aNNWfOHD3++OOSpPnz56tAgQJq0KCBJGno0KF6++231b59e0lSiRIlNHz4cL311lsaPHiwLZ42bdqoY8eOttcfffSROnfubFs2aNAgrVixQpcuXbK1GTx4sMaPH68WLVpIkqKjo23Jkvbt22vOnDmyWCz6v//7P/n6+qpChQr6+++/9dprr91dBwMAAAA3ceDAARmGoXLlymW6vly5cjp//rzi4uLk7++vJk2aKDAwUMWKFbONPr548aImTZqkyZMn286jS5YsqUceeeSOYqpSpYqqVKliez18+HB99913WrRokbp3766wsDB5eXkpMDDwtiWrbnV9cPHiRc2cOdPu+uCLL75Q4cKF7yhu3J0clcjIrg0bNqhhw4Z2y2JiYm459CcpKUlJSUm214mJiZLSv9Q3lhm4UxaLRYZhOGx7cJ5cfax+/FE6ftzdUTiURZLh7S1LcvYnkIXrcbxyDo7Vf/n4SC+8IHl7uzuSW7rV3y7rOuuPlfX/b1zuDHeyL+tnudV79uzZo6ioKBUpUsTWply5cgoJCdGePXv04IMPyjAMFS9eXAEBAbY2kZGROnXqlO11mzZt1LVrV02ZMkU+Pj6aPXu2WrVqJZPJJMMw9Pvvv2v9+vV2IzDS0tJ07do1Xb58Wfny5ZMkVa9e3S7W/fv367XXXrNb9tBDD2n16tUyDEOXL1/WoUOH1LlzZ7388su2NqmpqQoODpZhGNqzZ48qV64sHx8f23Yefvjh2/aNdd2N57O58vwGAAAATnG7c/eaNWuqWLFiKlGihBo1aqRGjRrp2WefVb58+bR3714lJSXZkgF369KlSxoyZIiWLFmiEydOKDU1VVevXrWNyMiO4sWL25IYklSoUCGdOnVKUvrcICkpKapRo4ZtfXBwsMqUKXP3HwLZlqsTGfHx8RmGKBUsWFCJiYm6evVqppO7jBo1SkOHDs2w/PTp07p27ZpD4rJYLLpw4YIMw5DZnKOqe3mcXH2sfv5Z+vtvKRdNPmqRdCEyUkZ8fM6qm+ehOF45B8dKksUi+ftLVatKERHujuaWbvW3KyUlRRaLRampqXZzOqSmpiotLU1pRprSLGlOjS/NSFNaWlqGGG4lOjpaJpNJe/bsUdOmTTNtY70pn9k2rfuzWCzKkyePXRvrDX7rssaNG8swDC1atEgPPvig1q1bp3HjxtnWX7p0SYMGDVLz5s0z7Of6bfv6+maIxRrH9fs2DENpaWk6f/68JGnq1Kl2F0pS+uSHqamptvY3Hjvrf2/Wn9bPfvbsWbtJx6+v/QsAAABkplSpUjKZTNq7d6+effbZDOv37t2r0NBQFS1aVNu2bdOaNWu0YsUKDRo0SEOGDNHmzZsdPsF23759FRsbq/fff1+lSpWSn5+fWrZsqeQ7ePgu7w33xUwmEw/83KNydSLjTgwYMEB9+vSxvU5MTFRUVJTCw8MVFBTkkH1YLBaZTCaFh4fnvpvjuUyuPlbHjklXrki5aDicxWSSydtb4fnyyezkJ4px9zheOQfHStLly1JcnJQvX45IZNzsb9e1a9d08eJF5cmTR3ny/O80ME+ePPLy8pKXyUteZi+nxudl8pKXl1eGGG4lIiJCMTExmjZtmnr16pVhnoyEhARVqFBBf/31l06cOGErLbVnzx4lJCSoUqVKypMnj8xms0wmk91+rX1kXRYQEKAWLVpo7ty5iouLU5kyZWxlqSSpWrVqOnDgwC3LXEmyfUarMmXKaNu2bXblprZu3SqTySQvLy8VKVJEhQsX1tGjR9WuXbtMt1m+fHnNnj1bqamp8vX1lSRt2bLFFv/N+tP62fPnz297nyS7/wcAAAAykz9/fj3xxBP6+OOP1bt3b7ukRHx8vGbPnq127drZzrMbNmyohg0bavDgwQoJCdFPP/2kp556Sn5+flq1apW6dOly1zGtX79eHTp0sCVWLl26pCNHjti18fb2Vlra3T2kVaJECeXNm1ebN29W0aJFJUkXLlzQn3/+aTf/B1wjVycyIiMjdfLkSbtlJ0+eVFBQ0E0zgT4+PvLx8cmw3Gw2O/RGtslkcvg24Ry58lhZLFJqqmQySbnspqRJktkwPPdmaw7D8co5PP5YmUzp/3ZaLFIO+Htws79d1hv51p/r20tSUlqSTCkmOVNSWpJtn9fHcDtTpkxRnTp1VLNmTQ0bNkyVK1dWamqqYmNjNXXqVO3Zs0eVKlXSSy+9pIkTJyo1NVWvv/666tWrZ0tEWPeX2We/flnbtm3VpEkT7d69Wy+99JLdukGDBqlJkyYqVqyYWrZsKbPZrN9//127du3SiBEj7LZ7/ft69Oihl19+WQ899JBq166tuXPn6o8//lCJEiVs7YYOHao33nhDISEhatSokZKSkrRlyxadP39effr0Udu2bfXuu++qa9euGjBggI4cOWKbC+5W/Wldd+N3Iled2wAAAMBpJk+erNq1aysmJkYjRoxQdHS0du/erX79+um+++7TyJEjtXjxYh0+fFh169ZVaGioli5dKovFojJlysjX11f9+/fXW2+9JW9vb9WpU0enT5/W7t271blz52zHU7p0aS1YsEBNmzaVyWTSwIEDM4yiKF68uNauXavWrVvLx8dHBQoUyPZ+AgMD1b59e/Xr109hYWGKiIjQ4MGDbddVcK1cncioVauWli5darcsNjZWtWrVclNEwD0iLS3H3IwDgHuC2Zz+72ZKirsjcQpvL2+F+oXq/NXzupp61en7C/ULlbdX9uYaKVGihLZt26aRI0fqzTff1IkTJxQeHq7q1atr6tSpMplM+v7779WjRw/VrVtXZrNZjRo10kcffZTt+B577DGFhYVp//79atOmjd26mJgYLV68WMOGDdOYMWOUN29elS1b9rZPlrVt21aHDx9W3759de3aNb3wwgvq0KGDNm3aZGvTpUsX5cuXT+PGjVO/fv3k7++vSpUq2eZ3CwgI0A8//KBXX31VDzzwgMqXL68xY8boueeey/ZnBAAAwD3ik0/cHcFtlS5dWlu2bNHgwYP1wgsv6Ny5c4qMjFTz5s01ePBghYWFKSQkRAsWLNCQIUN07do1lS5dWl999ZUqVKggSRo4cKDy5MmjQYMG6fjx4ypUqJBeffXVO4pnwoQJ6tSpk2rXrq0CBQqof//+tnmOrYYNG6ZXXnlFJUuWVFJS0h3PBThhwgS9+uqratKkiYKCgvTWW2/pr7/+YnSzG5gMZ8/o6ECXLl3SwYMHJUkPPPCAJkyYoAYNGigsLExFixbVgAED9M8//2jWrFmSpLi4OFWsWFHdunVTp06d9NNPP+mNN97QkiVLFBMTk6V9JiYmKjg4WBcuXHBoaalTp04pIiKCJ+Hucbn2WF25IrVpk35jLjzc3dE4jMVk0qnwcEWcPu25T43nIByvnINjJSk5WTp6VPrgA6lKFXdHc0u3+tt17do1xcXFKTo6OsOJ99WUq0pOc82E7t5e3vLL69g6uTnRE088ocjISH3++efKkyeP057qutlxd8Z5LrLHlccg157X3uPod9ejz12PPnc9+tz1HNHntzoXR+as88M581w5uy5fvqz77rtP48ePv6PRJPe67Pb5rb7Xjj7XzVEjMrZs2aIGDRrYXlvnsmjfvr1mzJihEydO2M1OHx0drSVLlqh3796aNGmSihQpos8++yzLSQwg10pNTS8pxQkPAGSNdURGFienzon88vqRXHCiK1euaNq0aYqJiZGXl5e++uorrVy5UitWrHB3aAAAAABuYvv27dq3b59q1KihCxcuaNiwYZKkZs2auTkyz5OjEhn169e/5TCgGTNmZPqe7du3OzEqIAeylpa6R7LZAHDP84BEBpzLZDJp6dKlGjlypK5du6YyZcro22+/VcOGDZXK9woAAAC4Z73//vvav3+/vL29Vb16da1bt+6O5tzA3clRiQwADpKSwogMAMgOkyn9301uOOMO+fn5aeXKlRmW56AqrwAAAIDHeeCBB7R161Z3hwFJ3MUEPFFqKpN9A0B2kMgAAAAAAMBtuIsJeCJKSwFA9plMuSaRYbFY3B0CXIjjDQAAcO9gRC5yE1dea1BaCvBElJYCgDuTwxMZ3t7eMpvNOn78uMLDw+Xt7S0TSW23MgxDqampypMnj8OPhWEYSk5O1unTp2U2m+Xt7e3Q7QMAACDr8ubNK5PJpNOnTys8PJzz8Cxw5rkyMpfVPnfHtQaJDMATWUtL8UcAALIuF4zIMJvNio6O1okTJ3T8+HF3hwOlXwBYLBaZzWanXZzly5dPRYsWlZkHGAAAANzGy8tLRYoU0d9//60jR464O5wcwRXnyrCX3T535bUGiQzAE6WlMSIDAO5EDk9kSOmjMooWLarU1FSlpaW5OxyPZ7FYdPbsWeXPn98pJ/9eXl48wQYAAHCPCAgIUOnSpZWSkuLuUHIEZ58rI6Ps9LmrrzVIZACeKCWFyb4B4E7kgkSGJJlMJuXNm1d58+Z1dygez2KxKG/evPL19eXiDAAAwAN4eXnJy8vL3WHkCJwru9693Of3VjQAXIPSUgBwZ3JJIgMAAAAAgJyERAbgiSgtBQB3hkQGAAAAAAAux11MwBNRWgoAss8wSGQAAAAAAOAG3MUEPFFqavoNOQBA9pDIAAAAAADA5UhkAJ4oLS19fgzmyACArGNEBgAAAAAAbkEiA/BEKSnujgAAciYSGQAAAAAAuByJDMATpaYyGgMAsstkkpKS3B0FAAAAAAAeh0QG4Il4ohgAss9slpKT3R0FAAAAAAAeh0QG4IlIZABA9pnNlOYDAAAAAMANSGQAnohEBgBkH6WlAAAAAABwCxIZgCcikQEA2ceIDMAl1q5dq6ZNm6pw4cIymUxauHCh3XqTyZTpz7hx42xtihcvnmH96NGj7bbzxx9/6NFHH5Wvr6+ioqI0duzYDLHMmzdPZcuWla+vrypVqqSlS5farTcMQ4MGDVKhQoXk5+enhg0b6sCBA47rDAAAAACSSGQAnik1VTIMd0cBADmLycQcGYALXL58WVWqVNGUKVMyXX/ixAm7n88//1wmk0nPPfecXbthw4bZtevRo4dtXWJiop588kkVK1ZMW7du1bhx4zRkyBBNnz7d1ubXX3/Viy++qM6dO2v79u1q3ry5mjdvrl27dtnajB07Vh9++KGmTZumjRs3yt/fXzExMbp27ZqDewUAAADwbHncHQAAN2BEBgBkHyMyAJdo3LixGjdufNP1kZGRdq+///57NWjQQCVKlLBbHhgYmKGt1ezZs5WcnKzPP/9c3t7eqlChgnbs2KEJEyaoa9eukqRJkyapUaNG6tevnyRp+PDhio2N1eTJkzVt2jQZhqGJEyfq3XffVbNmzSRJs2bNUsGCBbVw4UK1bt36jvsAAAAAgD0SGYAnIpEBANlnNjMiA7jHnDx5UkuWLNHMmTMzrBs9erSGDx+uokWLqk2bNurdu7fy5Em//NmwYYPq1q0rb29vW/uYmBiNGTNG58+fV2hoqDZs2KA+ffrYbTMmJsZW6iouLk7x8fFq2LChbX1wcLBq1qypDRs2ZJrISEpKUtJ1c+0kJiZKkiwWiywWy513RBZYLBYZhuH0/cAe/e569Lnr0eeuR5+7Hn3uHvS76zmyzx193EhkAJ6I0lIAkH0mEyMygHvMzJkzFRgYqBYtWtgtf+ONN1StWjWFhYXp119/1YABA3TixAlNmDBBkhQfH6/o6Gi79xQsWNC2LjQ0VPHx8bZl17eJj4+3tbv+fZm1udGoUaM0dOjQDMtPnz7t9HJUFotFFy5ckGEYMpupMOwq9Lvr0eeuR5+7Hn3uevS5e9DvrufIPr948aKDokpHIgPwRCkp6TfkAABZR2kp4J7z+eefq23btvL19bVbfv1IisqVK8vb21uvvPKKRo0aJR8fH1eHaTNgwAC72BITExUVFaXw8HAFBQU5dd8Wi0Umk0nh4eHcCHAh+t316HPXo89djz53PfrcPeh313Nkn994jn63SGQAnigpKf2GHAAg6xiRAdxT1q1bp/3792vu3Lm3bVuzZk2lpqbqyJEjKlOmjCIjI3Xy5Em7NtbX1nk1btbm+vXWZYUKFbJrU7Vq1Uzj8PHxyTSRYjabXXJxbjKZXLYv/A/97nr0uevR565Hn7sefe4e9LvrOarPHX3M+AYAnig5mREZAJBdZrOUliZRnxW4J/zf//2fqlevripVqty27Y4dO2Q2mxURESFJqlWrltauXauU65KTsbGxKlOmjEJDQ21tVq1aZbed2NhY1apVS5IUHR2tyMhIuzaJiYnauHGjrQ0AAAAAx2BEBuCJkpMZkQEA2WU2pycx0tL4NxRwokuXLungwYO213FxcdqxY4fCwsJUtGhRSekJg3nz5mn8+PEZ3r9hwwZt3LhRDRo0UGBgoDZs2KDevXvrpZdesiUp2rRpo6FDh6pz587q37+/du3apUmTJumDDz6wbadnz56qV6+exo8fr6efflpff/21tmzZounTp0tKf1KtV69eGjFihEqXLq3o6GgNHDhQhQsXVvPmzZ3YQwAAAIDnIZEBeCJGZABA9plM6YmMlBQpb153RwPkWlu2bFGDBg1sr61zSrRv314zZsyQJH399dcyDEMvvvhihvf7+Pjo66+/1pAhQ5SUlKTo6Gj17t3bbm6K4OBgrVixQt26dVP16tVVoEABDRo0SF27drW1qV27tubMmaN3331X77zzjkqXLq2FCxeqYsWKtjZvvfWWLl++rK5duyohIUGPPPKIli1b5vB6wAAAAICnI5EBeCJGZABA9llLS6WluTsSIFerX7++DMO4ZZuuXbvaJR2uV61aNf3222+33U/lypW1bt26W7Z5/vnn9fzzz990vclk0rBhwzRs2LDb7g8AAADAneNOJuCJUlJIZABAdllHZKSmujsSAAAAAAA8CncyAU+UkkJpKQDILrNZMoz0f0MBAAAAAIDLkMgAPBGlpQAg+6yTfTMiAwAAAAAAl+JOJuCJUlMZkQEA2WUtLcUcGQAAAAAAuBSJDMATMSIDALKP0lIAAAAAALgFdzIBT2MY6SMySGQAQPYw2TcAAAAAAG7BnUzA06Slpd+Io7QUAGSPdUQGpaUAAAAAAHApEhmAp0lNTb8Rx4gMAMge62TflJYCAAAAAMCluJMJeJqUFEZkAMCdoLQUAAAAAABuQSID8DRpaYzIAIA7QWkpAAAAAADcgjuZgKdJTU1/ophEBgBkD6WlAAAAAABwC+5kAp6G0lIAcOcMg9JSAAAAAAC4GIkMwNNQWgoA7ozJlP5DaSkAAAAAAFyKO5mAp7GWlmJEBgDcGUpLAQAAAADgUiQyAE+TksKIDAC4UyYTpaUAAAAAAHAx7mQCnobJvgHg7pDIAAAAAADApbiTCXiatDRKSwHA3SCRAQAAAACAS5HIADwNpaUA4O6QyAAAAAAAwKW4kwl4Gib7BoC7QyIDAAAAAACXIpEBeJq0NEZkAMCdMgwSGQAAAAAAuBh3MgFPk5LCiAwAuBskMgAAAAAAcCkSGYCnSU1NT2KQyACAO0MiAwAAAAAAlyKRAXiatDR3RwAAORelpQAAAAAAcDkSGYCnSUlxdwQAkHOZTPw7CgAAAACAi5HIADyNtbQUACD7zGYpKcndUQAAAAAA4FFIZACehtJSAHDnTCYpOdndUQAAAAAA4FFIZACehpIoAHDnzGYSGQAAAAAAuBiJDMDTMEktANw5RmQAAAAAAOByJDIAT0NpKQC4c4zIAAAAAADA5UhkAJ4mJUUyDHdHAQA5k9lMiT4AAAAAAFyMRAbgaSgtBQB3zmQikQEAAAAAgIuRyAA8TWoqIzIA4E5RWgoAAAAAAJcjkQF4mpSU9CeKAQDZZzIxsg0AAAAAABcjkQF4muRkEhkAcKcYkQEAAAAAgMuRyAA8TVJS+o04AED2mc2U6AMAAAAAwMW4mwl4GkZkAMCdM5kki0VKS3N3JAAAAAAAeAwSGYCnSUlhRAYA3CmzOX00BvNkAAAAAADgMtzNBDxNcjKJDAC4U9YRGSkp7o4EAAAAAACPwd1MwNNQWgoA7px1RAalpQAAAAAAcBkSGYCnobQUANw5szl9RAalpQAAAAAAcBnuZgKeJiWFERkAcKcoLQUAAAAAgMuRyAA8DXNkAMCdo7QUAAAAAAAux91MwNOkppLIAIA7ZR2RQWkpAAAAAABcJsfdzZwyZYqKFy8uX19f1axZU5s2bbpl+4kTJ6pMmTLy8/NTVFSUevfurWvXrrkoWuAeYxhM9g0Ad8M6IoPSUgAAAAAAuEyOSmTMnTtXffr00eDBg7Vt2zZVqVJFMTExOnXqVKbt58yZo7fffluDBw/W3r179X//93+aO3eu3nnnHRdHDtwjLJb0G3CMyACAO2Od7JvSUgAAAAAAuEyOups5YcIEvfzyy+rYsaPKly+vadOmKV++fPr8888zbf/rr7+qTp06atOmjYoXL64nn3xSL7744m1HcQC5VmpqeiKDERkAcGcoLQUAAAAAgMvlcXcAWZWcnKytW7dqwIABtmVms1kNGzbUhg0bMn1P7dq19Z///EebNm1SjRo1dPjwYS1dulT/+te/brqfpKQkJSUl2V4nJiZKkiwWiywWi0M+i8VikWEYDtsenCfXHavk5PREhpdXrkxmWEwmGf/9L+59HK+cg2N1HS+v9P8mJ6cnNO5Bue5vVy7nzuPFdwQAAABATpFjEhlnzpxRWlqaChYsaLe8YMGC2rdvX6bvadOmjc6cOaNHHnlEhmEoNTVVr7766i1LS40aNUpDhw7NsPz06dMOm1vDYrHowoULMgxDZkr83NNy3bG6dEkqXFjKm1cKDHR3NA5nMZl0IShIhskks2G4OxzcBscr5+BYXSctLT2BcfmydJPSlu6W6/525XLuPF4XL1506f4AAAAA4E7lmETGnVizZo3ee+89ffzxx6pZs6YOHjyonj17avjw4Ro4cGCm7xkwYID69Olje52YmKioqCiFh4crKCjIIXFZLBaZTCaFh4dzg+Eel+uOVZ480rFjUr58Ui6c9N5iMslkGAo/c4abrTkAxyvn4Fhdx2KRDh9O//c0IsLd0WQq1/3tyuXcebx8fX1duj8AAAAAuFM5JpFRoEABeXl56eTJk3bLT548qcjIyEzfM3DgQP3rX/9Sly5dJEmVKlXS5cuX1bVrV/373//O9GLRx8dHPj4+GZabzWaHXlyaTCaHbxPOkauOlfVJYpMpvcRULmSSZDYMbrbmEByvnINjdZ20tPQ5Mu7hvwu56m+XB3DX8eL7AQAAACCnyDFXL97e3qpevbpWrVplW2axWLRq1SrVqlUr0/dcuXIlwwWa139rWxvciIEnSkn5XyIDAJB9JlP6D5N9A06zdu1aNW3aVIULF5bJZNLChQvt1nfo0EEmk8nup1GjRnZtzp07p7Zt2yooKEghISHq3LmzLl26ZNfmjz/+0KOPPipfX19FRUVp7NixGWKZN2+eypYtK19fX1WqVElLly61W28YhgYNGqRChQrJz89PDRs21IEDBxzTEQAAAABsckwiQ5L69OmjTz/9VDNnztTevXv12muv6fLly+rYsaMkqV27dnaTgTdt2lRTp07V119/rbi4OMXGxmrgwIFq2rSpLaEBeJS0tPSRGDyBCQB3Jy3N3REAudbly5dVpUoVTZky5aZtGjVqpBMnTth+vvrqK7v1bdu21e7duxUbG6vFixdr7dq16tq1q219YmKinnzySRUrVkxbt27VuHHjNGTIEE2fPt3W5tdff9WLL76ozp07a/v27WrevLmaN2+uXbt22dqMHTtWH374oaZNm6aNGzfK399fMTExDptbDwAAAEC6HFNaSpJatWql06dPa9CgQYqPj1fVqlW1bNky2wTgx44dsxuB8e6778pkMundd9/VP//8o/DwcDVt2lQjR45010cA3Cs1NX1EBokMALg7KSnujgDItRo3bqzGjRvfso2Pj89Ny8vu3btXy5Yt0+bNm/Xggw9Kkj766CM99dRTev/991W4cGHNnj1bycnJ+vzzz+Xt7a0KFSpox44dmjBhgi3hMWnSJDVq1Ej9+vWTJA0fPlyxsbGaPHmypk2bJsMwNHHiRL377rtq1qyZJGnWrFkqWLCgFi5cqNatW2eILSkpSUlJSbbXiYmJktJHmlsslmz2VPZYLBYZhuH0/cAe/e569Lnr0eeuR5+7Hn3uHvS76zmyzx193HJUIkOSunfvru7du2e6bs2aNXav8+TJo8GDB2vw4MEuiAzIASgtBQB3j9JSgNutWbNGERERCg0N1WOPPaYRI0Yof/78kqQNGzYoJCTElsSQpIYNG8psNmvjxo169tlntWHDBtWtW1fe3t62NjExMRozZozOnz+v0NBQbdiwQX369LHbb0xMjK3UVVxcnOLj49WwYUPb+uDgYNWsWVMbNmzINJExatQoDR06NMPy06dPO30Uh8Vi0YULF2QYBvOjuBD97nr0uevR565Hn7sefe4e9LvrObLPL1686KCo0uW4RAaAu0BpKQBwDEpLAW7TqFEjtWjRQtHR0Tp06JDeeecdNW7cWBs2bJCXl5fi4+MVERFh9548efIoLCxM8fHxkqT4+HhFR0fbtbGO8o6Pj1doaKji4+Nty65vc/02rn9fZm1uNGDAALvkSGJioqKiohQeHq6goKDsdkW2WCwWmUwmhYeHcyPAheh316PPXY8+dz363PXoc/eg313PkX3u6+vroKjSkcgAPIm1tBQjMgDg7lBaCnCb60c6VKpUSZUrV1bJkiW1Zs0aPf74426M7PZ8fHzk4+OTYbnZbHbJxbnJZHLZvvA/9Lvr0eeuR5+7Hn3uevS5e9DvrueoPnf0MeMbAHiSlBRGZACAI1BaCrhnlChRQgUKFNDBgwclSZGRkTp16pRdm9TUVJ07d842r0ZkZKROnjxp18b6+nZtrl9//fsyawMAAADAMbibCXiStDQm+wYAR6C0FHDP+Pvvv3X27FkVKlRIklSrVi0lJCRo69attjY//fSTLBaLatasaWuzdu1apVw3uio2NlZlypRRaGiorc2qVavs9hUbG6tatWpJkqKjoxUZGWnXJjExURs3brS1AQAAAOAY3M0EPAmlpQDg7hkGpaUAJ7p06ZJ27NihHTt2SEqfVHvHjh06duyYLl26pH79+um3337TkSNHtGrVKjVr1kylSpVSTEyMJKlcuXJq1KiRXn75ZW3atEnr169X9+7d1bp1axUuXFiS1KZNG3l7e6tz587avXu35s6dq0mTJtnNX9GzZ08tW7ZM48eP1759+zRkyBBt2bJF3bt3l5Q+5L5Xr14aMWKEFi1apJ07d6pdu3YqXLiwmjdv7tI+AwAAAHI75sgAPIn1xhuJDAC4O5SWApxmy5YtatCgge21NbnQvn17TZ06VX/88YdmzpyphIQEFS5cWE8++aSGDx9uN/fE7Nmz1b17dz3++OMym8167rnn9OGHH9rWBwcHa8WKFerWrZuqV6+uAgUKaNCgQeratautTe3atTVnzhy9++67euedd1S6dGktXLhQFStWtLV56623dPnyZXXt2lUJCQl65JFHtGzZModPbAgAAAB4OhIZgCdJSyOJAQB3yzAoLQU4Uf369WUYxk3XL1++/LbbCAsL05w5c27ZpnLlylq3bt0t2zz//PN6/vnnb7reZDJp2LBhGjZs2G1jAgAAAHDnKC0FeBKeIAaAu2cyScnJ7o4CAAAAAACPQSID8CTUdAeAu0ciAwAAAAAAlyKRAXiS1FRKSwHA3TKbpaQkd0cBAAAAAIDHIJEBeBJqugPA3WNEBgAAAAAALkUiA/AkKSnpk9QCAO6c2UypPgC5xyuvuDsCAAAA4LZIZACehNJSAHD3zGZGZAAAAAAA4EIkMgBPkpbGiAwAuFuUlgIAAAAAwKVIZACehFIoAHD3KC0FAAAAAIBLkcgAPElqKiMyAOBumUwkMgAAAAAAcCESGYAnSU11dwQAkPMxRwYAAAAAAC5FIgPwJElJ6TfgAAB3zmwmMQwAAAAAgAtxRxPwJMnJ6SVRAAB3zjrZN6X6AAAAAABwCRIZgCdJSWFEBgDcLbM5PYlhsbg7EgAAAAAAPAJ3NAFPkpxMIgMA7pbJlJ7IoLwUAAAAAAAuwR1NwJNQWgoA7p7ZnD4ag0QGAAAAAAAuQSID8CSUlgKAu0ciAwAAAAAAl+KOJuBJkpIYkQEAd8tkIpEBAAAAAIALkcgAPAkjMgDg7lkn+yaRAQAAAACAS3BHE/AkJDIA4O5ZR2SkpLg7EgAAAAAAPAJ3NAFPkpJCaSkAuFvWERlpae6OBAAAAAAAj0AiA/AkjMgAgLvHZN8AAAAAALgUdzQBT2GxpD89zIgMALg7lJYCAAAAAMClSGQAniI1Nb0UCiMyAODuUFoKAAAAAACX4o4m4ClSU9OfICaRAQB3xzoig9JSAAAAAAC4BHc0AU9hTWRQWgoA7o51RAalpQAAAAAAcAkSGYCnoLQUADiGdbJvSksBAAAAAOAS3NEEPIU1kcGIDAC4O5SWAgAAAADApUhkAJ6COTIAwDGsCWFKSwEAAAAA4BLc0QQ8BYkMAHAck4nSUgAAAAAAuAh3NAFPQWkpAHAsSksBAAAAAOASJDIAT8GIDABwHJOJ0lIAAAAAALgIdzQBT5GSkp7IYEQGADgGIzIAAAAAAHAJEhmAp0hLSy8txYgMAHAM5sgAAAAAAMAluKMJeApKSwGAY1FaCgAAAAAAl+COJuApKC0FAI5FaSkAAAAAAFyCRAbgKSgtBQCOYxiUlgIAAAAAwEW4owl4itTU9BtvjMgAAMegtBQAAAAAAC5BIgPwFNxwAwDHMQxKSwEAAAAA4CIkMgBPkZbGaAwAcCQSGQAAAAAAuASJDMBTpKaSyAAARzGbpaQkd0cBAAAAAIBHIJEBeIqUlPRSKACAu2cyScnJ7o4CAAAAAACPQCID8BSUlgIAxzGbmXsIAAAAAAAXIZEBeApquQOA45jNjMgAAAAAAMBFSGQAnoLSUgDgOJSWAgAAAADAZUhkAJ4iLc3dEQBA7kFpKQAAAAAAXIZEBuApKC0FAI5jMklJSe6OAgAAAAAAj0AiA/AUlJYCAMdhRAYAAAAAAC5DIgPwFIzIAADHIZEBAAAAAIDLkMgAPEVycnopFADA3TOZSGQAAAAAAOAiJDIAT5GcnP4EMQDg7jEiAwAAAAAAl+GuJuApSGQAgOOYTFJammSxuDsSINdZu3atmjZtqsKFC8tkMmnhwoW2dSkpKerfv78qVaokf39/FS5cWO3atdPx48fttlG8eHGZTCa7n9GjR9u1+eOPP/Too4/K19dXUVFRGjt2bIZY5s2bp7Jly8rX11eVKlXS0qVL7dYbhqFBgwapUKFC8vPzU8OGDXXgwAHHdQYAAAAASSQyAM9BaSkAcByzWTIM5h8CnODy5cuqUqWKpkyZkmHdlStXtG3bNg0cOFDbtm3TggULtH//fj3zzDMZ2g4bNkwnTpyw/fTo0cO2LjExUU8++aSKFSumrVu3aty4cRoyZIimT59ua/Prr7/qxRdfVOfOnbV9+3Y1b95czZs3165du2xtxo4dqw8//FDTpk3Txo0b5e/vr5iYGF27ds3BvQIAAAB4tjzuDgCAizAiAwAcx2xOH5GRmip5e7s7GiBXady4sRo3bpzpuuDgYMXGxtotmzx5smrUqKFjx46paNGituWBgYGKjIzMdDuzZ89WcnKyPv/8c3l7e6tChQrasWOHJkyYoK5du0qSJk2apEaNGqlfv36SpOHDhys2NlaTJ0/WtGnTZBiGJk6cqHfffVfNmjWTJM2aNUsFCxbUwoUL1bp16wz7TUpKUlJSku11YmKiJMliscji5BFeFotFhmFk3I/JxOgyJ7ppv8Np6HPXo89djz53PfrcPeh313Nknzv6uJHIADxFSgojMgDAUaw3/hiRAbjdhQsXZDKZFBISYrd89OjRGj58uIoWLao2bdqod+/eypMn/fJnw4YNqlu3rryvS0TGxMRozJgxOn/+vEJDQ7Vhwwb16dPHbpsxMTG2UldxcXGKj49Xw4YNbeuDg4NVs2ZNbdiwIdNExqhRozR06NAMy0+fPu30URwWi0UXLlyQYRgyX/9wS3i4dOqUU/ftyW7a73Aa+tz16HPXo89djz53D/rd9RzZ5xcvXnRQVOlIZACeghEZAOA4lJYC7gnXrl1T//799eKLLyooKMi2/I033lC1atUUFhamX3/9VQMGDNCJEyc0YcIESVJ8fLyio6PttlWwYEHbutDQUMXHx9uWXd8mPj7e1u7692XW5kYDBgywS44kJiYqKipK4eHhdvE7g8VikclkUnh4uP1F6enTUkSEU/ftyW7a73Aa+tz16HPXo89djz53D/rd9RzZ576+vg6KKh2JDMBTpKSQyAAARzGZSGQAbpaSkqIXXnhBhmFo6tSpduuuTxZUrlxZ3t7eeuWVVzRq1Cj5+Pi4OlQbHx+fTPdvNptdcnFuMpky7sswOEd0skz7HU5Fn7sefe569Lnr0efuQb+7nqP63NHHjG8A4CmY7BsAHMdsprQU4EbWJMbRo0cVGxt729EMNWvWVGpqqo4cOSJJioyM1MmTJ+3aWF9b59W4WZvr11//vszaAAAAAHAMEhmAp0hN5Wk7AHAUEhmA21iTGAcOHNDKlSuVP3/+275nx44dMpvNivhvCaVatWpp7dq1SklJsbWJjY1VmTJlFBoaamuzatUqu+3ExsaqVq1akqTo6GhFRkbatUlMTNTGjRttbQAAAAA4BqWlAE/BiAwAcBxKSwFOc+nSJR08eND2Oi4uTjt27FBYWJgKFSqkli1batu2bVq8eLHS0tJs81GEhYXJ29tbGzZs0MaNG9WgQQMFBgZqw4YN6t27t1566SVbkqJNmzYaOnSoOnfurP79+2vXrl2aNGmSPvjgA9t+e/bsqXr16mn8+PF6+umn9fXXX2vLli2aPn26pPQh97169dKIESNUunRpRUdHa+DAgSpcuLCaN2/uug4DAAAAPACJDMATGIaUlsaIDABwFEZkAE6zZcsWNWjQwPbaOt9F+/btNWTIEC1atEiSVLVqVbv3rV69WvXr15ePj4++/vprDRkyRElJSYqOjlbv3r3t5s0IDg7WihUr1K1bN1WvXl0FChTQoEGD1LVrV1ub2rVra86cOXr33Xf1zjvvqHTp0lq4cKEqVqxoa/PWW2/p8uXL6tq1qxISEvTII49o2bJlDp/YEAAAAPB0JDIAT5Camn7DjUQGADiGyUQiA3CS+vXryzCMm66/1TpJqlatmn777bfb7qdy5cpat27dLds8//zzev7552+63mQyadiwYRo2bNht9wcAAADgznFXE/AE1kQGpaUAwDHMZkpLAQAAAADgIiQyAE+Qmpp+w40RGQDgGJSWAgAAAADAZbirCXgCRmQAgGNZS0ulpLg7EgAAAAAAcj0SGYAnYEQGADiWtbRUWpq7IwEAAAAAINfLcXc1p0yZouLFi8vX11c1a9bUpk2bbtk+ISFB3bp1U6FCheTj46P7779fS5cudVG0wD2Cyb4BwLFMJubIAAAAAADARfK4O4DsmDt3rvr06aNp06apZs2amjhxomJiYrR//35FRERkaJ+cnKwnnnhCERERmj9/vu677z4dPXpUISEhrg8ecCdKSwGA45lMlJYCAAAAAMAFclQiY8KECXr55ZfVsWNHSdK0adO0ZMkSff7553r77bcztP/888917tw5/frrr8qbN68kqXjx4rfcR1JSkpKSkmyvExMTJUkWi0UWi8Uhn8NiscgwDIdtD86Ta46V9Uabl1euTmZYTCYZ//0v7n0cr5yDY3UTJtP/EsX3kFzzt8tDuPN48R0BAAAAkFPkmERGcnKytm7dqgEDBtiWmc1mNWzYUBs2bMj0PYsWLVKtWrXUrVs3ff/99woPD1ebNm3Uv39/eXl5ZfqeUaNGaejQoRmWnz59WteuXXPIZ7FYLLpw4YIMw5CZUj/3tFxzrBISpKJFpZAQycfH3dE4jcVk0oWgIBkmk8yG4e5wcBscr5yDY3UTJUqkJ4pPnXJ3JHZyzd8uD+HO43Xx4kWX7g8AAAAA7lSOSWScOXNGaWlpKliwoN3yggULat++fZm+5/Dhw/rpp5/Utm1bLV26VAcPHtTrr7+ulJQUDR48ONP3DBgwQH369LG9TkxMVFRUlMLDwxUUFOSQz2KxWGQymRQeHs4NhntcrjlW585JcXHSffdJvr7ujsZpLCaTTIah8DNnuNmaA3C8cg6O1U3ExaVP9p1JeUt3yjV/uzyEO4+Xby4+JwAAAACQu+SYRMadsFgsioiI0PTp0+Xl5aXq1avrn3/+0bhx426ayPDx8ZFPJk+sm81mh15cmkwmh28TzpErjlVaWvqPdXLaXMwkyWwY3GzNITheOQfHKhOGkf5v6z349yFX/O3yIO46Xnw/AAAAAOQUOSaRUaBAAXl5eenkyZN2y0+ePKnIyMhM31OoUCHlzZvXroxUuXLlFB8fr+TkZHl7ezs1ZuCekZqafsON+vYA4Fipqe6OAAAAAACAXC/HPIbl7e2t6tWra9WqVbZlFotFq1atUq1atTJ9T506dXTw4EG7iQz//PNPFSpUiCQGPIt1MlqevAQAx0pJcXcEAAAAAADkejnqrmafPn306aefaubMmdq7d69ee+01Xb58WR07dpQktWvXzm4y8Ndee03nzp1Tz5499eeff2rJkiV677331K1bN3d9BMA9rIkMRmQAgONYS0sBAAAAAACnyjGlpSSpVatWOn36tAYNGqT4+HhVrVpVy5Yts00AfuzYMbtav1FRUVq+fLl69+6typUr67777lPPnj3Vv39/d30EwD0oLQUAzkFpKQAAAAAAnC5HJTIkqXv37urevXum69asWZNhWa1atfTbb785OSrgHme90UYiAwAcxzAoLQUAAAAAgAvkqNJSAO5QaipJDABwBkpLAQAAAADgdCQyAE9AIgMAHM9kkpKS3B0FAAAAAAC5HokMwBNY58gAADiO2SwlJ7s7CgAAAAAAcj0SGYAnoIY7ADgeiQwAAAAAAFyCRAbgCdLSKC0FAI5mMpHIAAAAAADABUhkAJ6A0lIA4HiMyAAAAAAAwCVIZACegNJSAOB4JhP/vgIAAAAA4AIkMgBPkJbm7ggAIPdhRAYAAAAAAC5BIgPwBKmp7o4AAHIfs5kRGQAAAAAAuACJDMATpKQwRwYAOBqTfQMAAAAA4BIkMgBPkJqafsMNAOA4ZjMj3gAAAAAAcAESGYAnSEpKv+EGAHAcRmQAdg4fPuzuEAAAAADkUtzZBDxBUhIjMgDA0cxmKS2N0n3Af5UqVUoNGjTQf/7zH127ds3d4QAAAADIRUhkAJ4gJYURGQDgaGazZLFQXgr4r23btqly5crq06ePIiMj9corr2jTpk3uDgsAAABALsCdTcATJCczIgMAHM1kIpEBXKdq1aqaNGmSjh8/rs8//1wnTpzQI488oooVK2rChAk6ffq0u0MEAAAAkEM5PZHx119/6e+//7a93rRpk3r16qXp06c7e9cArJKTGZEBAI5mNqeXlSKRAdjJkyePWrRooXnz5mnMmDE6ePCg+vbtq6ioKLVr104nTpxwd4gAAAAAchin39ls06aNVq9eLUmKj4/XE088oU2bNunf//63hg0b5uzdA5AoLQUAzsCIDCBTW7Zs0euvv65ChQppwoQJ6tu3rw4dOqTY2FgdP35czZo1c3eIAAAAAHIYp9/Z3LVrl2rUqCFJ+uabb1SxYkX9+uuvmj17tmbMmOHs3QOQKC0FAM7AiAzAzoQJE1SpUiXVrl1bx48f16xZs3T06FGNGDFC0dHRevTRRzVjxgxt27bN3aECAAAAyGHyOHsHKSkp8vHxkSStXLlSzzzzjCSpbNmyDCsHXCUlhUQGADgak30DdqZOnapOnTqpQ4cOKlSoUKZtIiIi9H//938ujgwAAABATuf0REaFChU0bdo0Pf3004qNjdXw4cMlScePH1f+/PmdvXsAEqWlAMAZKC0F2ImNjVXRokVlvuGcwzAM/fXXXypatKi8vb3Vvn17N0UIAAAAIKdy+p3NMWPG6JNPPlH9+vX14osvqkqVKpKkRYsW2UpOAXAyEhkA4HiUlgLslCxZUmfOnMmw/Ny5c4qOjnZDRAAAAAByC6ePyKhfv77OnDmjxMREhYaG2pZ37dpV+fLlc/buARgGpaUAwBlMJhIZwHUMw8h0+aVLl+Tr6+viaAAAAADkJk5PZEiSl5eXXRJDkooXL+6KXQNIS0u/0caIDABwLObIACRJffr0kSSZTCYNGjTI7mGltLQ0bdy4UVWrVnVTdAAAAAByA6cnMqKjo2W6xZPghw8fdnYIgGdLTU2/0UYiAwAci0QGIEnavn27pPQRGTt37pS3t7dtnbe3t6pUqaK+ffu6KzwAAAAAuYDTExm9evWye52SkqLt27dr2bJl6tevn7N3DyA1lREZAOAMlJYCJEmrV6+WJHXs2FGTJk1SUFCQmyMCAAAAkNs4PZHRs2fPTJdPmTJFW7ZscfbuAVhHZOTN6+5IACB3YUQGYOeLL75wdwgAAAAAcimXzJGRmcaNG2vAgAFc8ADORmkpAHAOk4lEBjxeixYtNGPGDAUFBalFixa3bLtgwQIXRQUAAAAgt3FbImP+/PkKCwtz1+4Bz2EtLXWLuWoAAHeA0lKAgoODbfPhBQcHuzkaAAAAALmV0xMZDzzwgN1k34ZhKD4+XqdPn9bHH3/s7N0DYEQGADiHyZT+QyIDHuz60dWMtAYAAADgLE5PZDRr1swukWE2mxUeHq769eurbNmyzt49AGsigxEZAOAcJDIASdLVq1dlGIby5csnSTp69Ki+++47lS9fXk8++aSbowMAAACQkzk9kTFkyBBn7wLArVhLSzEiAwAcjxEZgE2zZs3UokULvfrqq0pISFCNGjXk7e2tM2fOaMKECXrttdfcHSIAAACAHMrpdza9vLx06tSpDMvPnj0rLy8vZ+8eAKWlAMC5SGQAkqRt27bp0UcflZQ+H15kZKSOHj2qWbNm6cMPP3RzdAAAAAByMqff2TQMI9PlSUlJ8vb2dvbuAVBaCgCcKyXF3REA94QrV64oMDBQkrRixQq1aNFCZrNZDz/8sI4ePerm6AAAAADkZE4rLWV96spkMumzzz5TQECAbV1aWprWrl3LHBmAK1BaCgCcKy3N3REA94RSpUpp4cKFevbZZ7V8+XL17t1bknTq1CkFBQW5OToAAAAAOZnT7mx+8MEH+uCDD2QYhqZNm2Z7/cEHH2jatGm6cuWKpk2b5qzdA7CyJjIYkQEAzkFpKUCSNGjQIPXt21fFixdXzZo1VatWLUnpozMeeOCBLG9n7dq1atq0qQoXLiyTyaSFCxfarTcMQ4MGDVKhQoXk5+enhg0b6sCBA3Ztzp07p7Zt2yooKEghISHq3LmzLl26ZNfmjz/+0KOPPipfX19FRUVp7NixGWKZN2+eypYtK19fX1WqVElLly7NdiwAAAAA7p7TEhlxcXGKi4tTvXr19Pvvv9tex8XFaf/+/Vq+fLlq1qzprN0DsGKODABwHsOgtBTwXy1bttSxY8e0ZcsWLVu2zLb88ccf1wcffJDl7Vy+fFlVqlTRlClTMl0/duxYffjhh5o2bZo2btwof39/xcTE6Nq1a7Y2bdu21e7duxUbG6vFixdr7dq16tq1q219YmKinnzySRUrVkxbt27VuHHjNGTIEE2fPt3W5tdff9WLL76ozp07a/v27WrevLmaN2+uXbt2ZSsWAAAAAHfPaaWlrFavXu3sXQC4FebIAADnorQUYBMZGanIyEi7ZTVq1MjWNho3bqzGjRtnus4wDE2cOFHvvvuumjVrJkmaNWuWChYsqIULF6p169bau3evli1bps2bN+vBBx+UJH300Ud66qmn9P7776tw4cKaPXu2kpOT9fnnn8vb21sVKlTQjh07NGHCBFvCY9KkSWrUqJH69esnSRo+fLhiY2M1efJkTZs2LUuxAAAAAHAMpycyJOnvv//WokWLdOzYMSUnJ9utmzBhgitCADyXteQJiQwAcA5KSwGS0kdSjB49WqtWrdKpU6dksVjs1h8+fPiu9xEXF6f4+Hg1bNjQtiw4OFg1a9bUhg0b1Lp1a23YsEEhISG2JIYkNWzYUGazWRs3btSzzz6rDRs2qG7duvL29ra1iYmJ0ZgxY3T+/HmFhoZqw4YN6tOnj93+Y2JibKWushLLjZKSkpSUlGR7nZiYKEmyWCwZ+svRLBaLDMPIuB+TKf2hFzjFTfsdTkOfux597nr0uevR5+5Bv7ueI/vc0cfN6YmMVatW6ZlnnlGJEiW0b98+VaxYUUeOHJFhGKpWrZqzdw8gNZUkBgA4C6WlAJsuXbro559/1r/+9S8VKlRIJiecf8THx0uSChYsaLe8YMGCtnXx8fGKiIiwW58nTx6FhYXZtYmOjs6wDeu60NBQxcfH33Y/t4vlRqNGjdLQoUMzLD99+rTTy1FZLBZduHBBhmHIfH3J0fBw6dQpp+7bk9203+E09Lnr0eeuR5+7Hn3uHvS76zmyzy9evOigqNI5PZExYMAA9e3bV0OHDlVgYKC+/fZbRUREqG3btmrUqJGzdw+AJ4UBwHlMJv6dBf7rxx9/1JIlS1SnTh13h3LPGjBggN0oj8TEREVFRSk8PFxBQUFO3bfFYpHJZFJ4eLj9Renp09INiR84zk37HU5Dn7sefe569Lnr0efuQb+7niP73NfX10FRpXN6ImPv3r366quv0neWJ4+uXr2qgIAADRs2TM2aNdNrr73m7BAAz8aIDABwHrNZuq5MDODJQkNDFRYW5tR9WOffOHnypAoVKmRbfvLkSVWtWtXW5tQNIwxSU1N17tw52/sjIyN18uRJuzbW17drc/3628VyIx8fH/n4+GRYbjabXXJxbjKZMu7LMNL/LYPTZNrvcCr63PXoc9ejz12PPncP+t31HNXnjj5mTv8G+Pv72+bFKFSokA4dOmRbd+bMGWfvHkBqavoFKgDA8UwmEhnAfw0fPlyDBg3SlStXnLaP6OhoRUZGatWqVbZliYmJ2rhxo2rVqiVJqlWrlhISErR161Zbm59++kkWi0U1a9a0tVm7dq1SrisNFxsbqzJlyig0NNTW5vr9WNtY95OVWAAAAAA4htNHZDz88MP65ZdfVK5cOT311FN68803tXPnTi1YsEAPP/yws3cPgJInAOA8ZjNzZAD/NX78eB06dEgFCxZU8eLFlTdvXrv127Zty9J2Ll26pIMHD9pex8XFaceOHQoLC1PRokXVq1cvjRgxQqVLl1Z0dLQGDhyowoULq3nz5pKkcuXKqVGjRnr55Zc1bdo0paSkqHv37mrdurUKFy4sSWrTpo2GDh2qzp07q3///tq1a5cmTZqkDz74wLbfnj17ql69eho/fryefvppff3119qyZYumT58uKf1JtdvFAgAAAMAxnJ7ImDBhgi5duiRJGjp0qC5duqS5c+eqdOnSmjBhgrN3D4DSUgDgPCaT9N+Rp4Cnc9TN+y1btqhBgwa219Y5Jdq3b68ZM2borbfe0uXLl9W1a1clJCTokUce0bJly+xq8M6ePVvdu3fX448/LrPZrOeee04ffvihbX1wcLBWrFihbt26qXr16ipQoIAGDRqkrl272trUrl1bc+bM0bvvvqt33nlHpUuX1sKFC1WxYkVbm6zEAgAAAODuOT2RUaJECdv/+/v7a9q0ac7eJYDrUVoKAJzHbCaRAfzX4MGDHbKd+vXry7jFuYvJZNKwYcM0bNiwm7YJCwvTnDlzbrmfypUra926dbds8/zzz+v555+/q1gAAAAA3D2nz5FRokQJnT17NsPyhIQEuyQHACehtBQAOA+lpQA7CQkJ+uyzzzRgwACdO3dOUnpJqX/++cfNkeGWXnnlfz8AAADAPcjpIzKOHDmitLS0DMuTkpK4oAFcgUQGADgPpaUAmz/++EMNGzZUcHCwjhw5opdffllhYWFasGCBjh07plmzZrk7RAAAAAA5lNMSGYsWLbL9//LlyxUcHGx7nZaWplWrVql48eLO2j0AK0pLAYDzMCIDsOnTp486dOigsWPHKjAw0Lb8qaeeUps2bdwYGQAAAICczmmJDOtkfyaTSe3bt7dblzdvXhUvXlzjx4931u4BWPGkMAA4j8lEIgP4r82bN+uTTz7JsPy+++5TfHy8GyICAAAAkFs4LZFhsVgkSdHR0dq8ebMKFCjgrF0BuJWUlPQnhgEAjseIDMDGx8dHiYmJGZb/+eefCg8Pd0NEAAAAAHILp93d3LBhgxYvXqy4uDhbEmPWrFmKjo5WRESEunbtqqSkJGftHoBVcnL6E8MAAMezJjIo4QfomWee0bBhw5Ty3+SeyWTSsWPH1L9/fz333HNujg4AAABATua0RMbQoUO1e/du2+udO3eqc+fOatiwod5++2398MMPGjVqlLN2D8AqKYkRGQDgLCZTehIjLc3dkQBuN378eF26dEnh4eG6evWq6tWrp1KlSikwMFAjR450d3gAAAAAcjCnlZb6/fffNWLECNvrr7/+WjVr1tSnn34qSYqKitLgwYM1ZMgQZ4UAQKK0FAA4k9ksWSxSaqqUx2mnVUCOEBwcrNjYWK1fv16///67Ll26pGrVqqlhw4buDg0AAABADue0K+7z58+rYMGCttc///yzGjdubHv90EMP6a+//nLW7gFYJSeTyAAAZzGZ/pfIADyYxWLRjBkztGDBAh05ckQmk0nR0dGKjIyUYRgyUeYSAAAAwF1w2t3NggULKi4uTpKUnJysbdu26eGHH7atv3jxovLmzeus3QOwYo4MAHCe60dkAB7KMAw988wz6tKli/755x9VqlRJFSpU0NGjR9WhQwc9++yz7g4RAAAAQA7ntBEZTz31lN5++22NGTNGCxcuVL58+fToo4/a1v/xxx8qWbKks3YPwIrSUgDgPCQyAM2YMUNr167VqlWr1KBBA7t1P/30k5o3b65Zs2apXbt2booQAAAAQE7ntLubw4cPV548eVSvXj19+umn+vTTT+Xt7W1b//nnn+vJJ5901u4BWKWkMCIDAJzFOtk3iQx4sK+++krvvPNOhiSGJD322GN6++23NXv2bDdEBgAAACC3cNqIjAIFCmjt2rW6cOGCAgIC5OXlZbd+3rx5CggIcNbuAVgxRwYAOA8jMgD98ccfGjt27E3XN27cWB9++KELIwIAAACQ2zgtkWEVHByc6fKwsDBn7xqAlH5zjUQGADgHk30DOnfunAoWLHjT9QULFtT58+ddGBEAAACA3Ia7m0BuZhhM9g0AzmQ2U1oKHi8tLU158tz8+SgvLy+l8jsCAAAA4C44fUQGADeyWNJ/SGQAgHNQWgqQYRjq0KGDfHx8Ml2flJTk4ogAAAAA5DYkMoDcLDU1/UlhSksBgHNQWgpQ+/btb9umXbt2LogEAAAAQG5FIgPIzVJT02+weXm5OxIAyJ0oLQXoiy++cHcIAAAAAHI5HtMGcjNrIoPSUgDgHCYTiQwAAAAAAJyMRAaQm1FaCgCcizkyAAAAAABwOu5uArmZdUQGiQwAcA7myAAAAAAAwOm4uwnkZtYRGZSWAgDnMJnSf0hkAAAAAADgNCQygNyMERkA4BokMgAAAAAAcBrubgK5GYkMAHANEhkAAAAAADgNdzeB3IzSUgDgfJSWAgAAAADAqUhkALkZIzIAwDVIZAAAAAAA4DQ57u7mlClTVLx4cfn6+qpmzZratGlTlt739ddfy2QyqXnz5s4NELiXWBMZjMgAAOcxDBIZAAAAAAA4UY5KZMydO1d9+vTR4MGDtW3bNlWpUkUxMTE6derULd935MgR9e3bV48++qiLIgXuEdbSUozIAADnobQUAAAAAABOlaPubv5/e3ceH1V1/3/8PRPIBoawZANDABEE2UFiQAULJWqK8q1ailQQKBQkFYy1gkUQqcWibFUUBQFbUZZarAoFIRJaMUCNRMJiCgjSWhKiAgkIWWbO74/8MmVMWJ2ZOzN5PR+PPHTuPXPvmc+5JDPnM59z58yZo9GjR2vEiBFq3769Fi5cqMjISC1ZsuS8z3E4HBo6dKimT5+uVq1a+bC3gB9gaSkA8A0SGQAAAAAAeE0dqztwqcrKypSTk6PJkye7ttntdvXv31/Z2dnnfd5TTz2l2NhYjRo1Sv/4xz8uep7S0lKVlpa6HhcXF0uSnE6nnE7n93gF/+N0OmWM8djx4D0BP1bl5f+ryKgFy0s5bTaZ//9f+D/GK3AwVpegKnHsBwL+b1ctY+V4cY0AAAAACBQBk8j46quv5HA4FBcX57Y9Li5On332WY3P+fDDD/Xqq68qNzf3ks8zc+ZMTZ8+vdr2oqIinT179rL6fD5Op1MnT56UMUZ2vinv1wJ+rM6elVq1kmJjre6JTzhtNp2MipKx2WQ3xuru4CIYr8DBWF1Ey5aVCeOLLHXpKwH/t6uWsXK8SkpKfHo+AAAAALhSAZPIuFwlJSW6//77tWjRIjVp0uSSnzd58mRlZGS4HhcXFysxMVExMTGKiorySN+cTqdsNptiYmKYYPBzQTFWhw5JERFW98InnDabbMYo5quvmGwNAIxX4GCsLuLzz6WUFL9JGgfF365axMrxCg8P9+n5AAAAAOBKBUwio0mTJgoJCVFhYaHb9sLCQsXHx1drf/DgQR0+fFgDBw50basqn69Tp47y8/N1zTXXVHteWFiYwsLCqm232+0e/XBps9k8fkx4R0CPlcNRubRULZp4tEmyG8Nka4BgvAIHY3UR5eV+dT+igP7bVQtZNV5cHwAAAAACRcB8egkNDVX37t2VmZnp2uZ0OpWZmamUlJRq7a+77jrl5eUpNzfX9XPnnXfq1ltvVW5urhITE33ZfcAa3HwWALzPZpPKyqzuBQAAAAAAQStgKjIkKSMjQ8OHD1ePHj3Us2dPzZs3T6dPn9aIESMkScOGDVOzZs00c+ZMhYeHq0OHDm7Pj46OlqRq24GgVVFRK27yDQCWsttJZAAAAAAA4EUBlcgYPHiwioqKNHXqVBUUFKhLly5av3696wbgR44coUQeOFdFRa1aVgoALGGzSaWlVvcCAAAAAICgFVCJDElKT09Xenp6jfuysrIu+Nxly5Z5vkOAP2NpKQDwPru98h4ZAAAAAADAKyhfAIIZS0sBgPextBQAAAAAAF5FIgMIZiwtBQDex82+AQAAAADwKhIZQDAjkQEA3sfSUgAAAAAAeBWJDCCYsbQUAHifzUYiAwAAAAAALyKRAQSz8nIqMgDA27hHBgAAAAAAXkUiAwhmZWVUZACAt9ntlRVwAAAAAADAK0hkAMGsrKxygg0A4D1VN/umAg4AAAAAAK9ghhMIZiQyAMD77HbJ6az8AQAAAAAAHscMJxDMWFoKALzPZqusxmB5KQAAAAAAvIJEBhDMysupyAAAb6uqyCCRAQAAAACAVzDDCQQzKjIAwPtIZAAAAAAA4FUkMoBgRkUGAHgfS0sBAAAAAOBVzHACwYybfQOA91GRAQAAAACAVzHDCQSz8nKWlgIAb6MiA/CpFi1ayGazVfsZP368JKlv377V9o0dO9btGEeOHFFaWpoiIyMVGxurRx99VBXf+TeclZWlbt26KSwsTK1bt9ayZcuq9WXBggVq0aKFwsPDlZycrB07dnjtdQMAAAC1GYkMIJhRkQEA3kdFBuBT//znP3X06FHXz8aNGyVJ9957r6vN6NGj3drMmjXLtc/hcCgtLU1lZWX66KOP9Nprr2nZsmWaOnWqq82hQ4eUlpamW2+9Vbm5uZo4caJ+/vOfa8OGDa42K1euVEZGhqZNm6ZPPvlEnTt3Vmpqqo4dO+aDKAAAAAC1CzOcQDCrqCCRAQDeRiID8KmYmBjFx8e7ft577z1dc8016tOnj6tNZGSkW5uoqCjXvvfff1979+7V66+/ri5duuj222/XjBkztGDBApWVlUmSFi5cqJYtW2r27Nlq166d0tPTdc8992ju3Lmu48yZM0ejR4/WiBEj1L59ey1cuFCRkZFasmSJ74IBAAAA1BJ1rO4AAC9xOiWHg6WlAMDbWFoKsExZWZlef/11ZWRkyHbOe57ly5fr9ddfV3x8vAYOHKgnnnhCkZGRkqTs7Gx17NhRcXFxrvapqakaN26c9uzZo65duyo7O1v9+/d3O1dqaqomTpzoOm9OTo4mT57s2m+329W/f39lZ2eft7+lpaUqLS11PS4uLpYkOZ1OOZ3OKw/EJXA6nTLGVD/Pd98rerkftc154w6vIea+R8x9j5j7HjG3BnH3PU/G3NPjRiIDCFYVFZUfRKnIAADvoiIDsMzbb7+tEydO6IEHHnBtu++++5SUlKSmTZtq165deuyxx5Sfn6+//OUvkqSCggK3JIYk1+OCgoILtikuLtaZM2d0/PhxORyOGtt89tln5+3vzJkzNX369Grbi4qKdPbs2Ut/4VfA6XTq5MmTMsbIfu77w5gY94YsjeVR5407vIaY+x4x9z1i7nvE3BrE3fc8GfOSkhIP9aoSiQwgWFUlMkJCrO4JAAQ3m41EBmCRV199VbfffruaNm3q2jZmzBjX/3fs2FEJCQnq16+fDh48qGuuucaKbrpMnjxZGRkZrsfFxcVKTExUTEyM2/JX3uB0OmWz2RQTE+P+obSoyL1hbKxX+1HbnDfu8Bpi7nvE3PeIue8Rc2sQd9/zZMzDw8M91KtKJDKAYFWVyKjDP3MA8Cq7naWlAAt88cUX2rRpk6vS4nySk5MlSQcOHNA111yj+Ph47dixw61NYWGhJCk+Pt7136pt57aJiopSRESEQkJCFBISUmObqmPUJCwsTGFhYdW22+12n3w4t9ls1c9lzHc74/V+1DY1xh1eRcx9j5j7HjH3PWJuDeLue56KuafHjCsACFYVFZUfTLlHBgB4F0tLAZZYunSpYmNjlZaWdsF2ubm5kqSEhARJUkpKivLy8nTsnCWUNm7cqKioKLVv397VJjMz0+04GzduVEpKiiQpNDRU3bt3d2vjdDqVmZnpagMAAADAc0hkAMGqKpFBxhoAvIulpQCfczqdWrp0qYYPH64651SfHjx4UDNmzFBOTo4OHz6sd955R8OGDdMtt9yiTp06SZIGDBig9u3b6/7779enn36qDRs2aMqUKRo/fryrWmLs2LH6/PPP9etf/1qfffaZXnzxRa1atUoPP/yw61wZGRlatGiRXnvtNe3bt0/jxo3T6dOnNWLECN8GAwAAAKgFWHMGCFbc7BsAfKOq8o1EBuAzmzZt0pEjRzRy5Ei37aGhodq0aZPmzZun06dPKzExUXfffbemTJniahMSEqL33ntP48aNU0pKiurVq6fhw4frqaeecrVp2bKl1q5dq4cffljz58/X1VdfrcWLFys1NdXVZvDgwSoqKtLUqVNVUFCgLl26aP369dVuAA4AAADg+yORAQSrqkQGS0sBgPfZbCQyAB8aMGCAzHfv7SApMTFRW7Zsuejzk5KStG7dugu26du3r3bu3HnBNunp6UpPT7/o+QAAAAB8P3xVGwhWLC0FAL5FIgMAAAAAAK9ghhMIViwtBQC+RSIDAAAAAACvYIYTCFZVFRksLQUA3sfSUgAAAAAAeA2JDCBYUZEBAL5FIgMAAAAAAK9ghhMIViQyAMB3jCGRAQAAAACAlzDDCQQrlpYCAN9haSkAAAAAALyGRAYQrKjIAADfoSIDAAAAAACvYYYTCFZUZACA75DIAAAAAADAa0hkAMGqooIkBgD4CktLAQAAAADgNSQygGDFhBoA+I4xUnm51b0AAAAAACAokcgAghWJDADwHbtdKiuzuhcAAAAAAAQlEhlAsCKRAQC+Y7ORyAAAAAAAwEtIZADBintkAIDvUJEBAAAAAIDXkMgAglVFReWa7QAA7yORAQAAAACA15DIAIIVS0sBgO/YbNzsGwAAAAAALyGRAQQrEhkA4DtUZAAAAAAA4DUkMoBgRSIDAHyHigwAAAAAALyGRAYQrLhHBgD4DhUZAAAAAAB4DYkMIFhRkQEAvmO3U5EBAAAAAICXkMgAglVZWeVSJwAA77PZqMgAAAAAAMBLSGQAwaqsrPIbwgAA77PbqYQDAAAAAMBLmOUEghUVGQDgOzab5HBITqfVPQEAAAAAIOiQyACCFRUZAOA7dntlEoOqDAAAAAAAPI5ZTiBYUZEBAL5DIgMAAAAAAK8hkQEEKyoyAMB3bDYSGQAAAAAAeAmznECwKi8nkQEAvmK3S8aQyAAAAAAAwAuY5QSCVXk5S0sBgK/YbCQyAAAAAADwEhIZQLBiaSkA8B3ukQEAAAAAgNcwywkEKyoyAMB3SGQAAAAAAOA1JDKAYMU9MgDAd1haCgAAAAAAr2GWEwhGVZNpJDIAwDeoyAAAAAAAwGuY5QSCUUVF5YQaS0sBgG9QkQEAAAAAgNeQyACCkcNROaFGRQYA+AYVGQAAAAAAeA2znEAwKi+vnFAjkQEAvkEiAwAAAAAAr2GWEwhGFRWVFRksLQUAvsHSUgAAAAAAeA2JDCAYVd0jg4oMAPANKjIAAAAAAPAaZjmBYEQiAwB8i4oMAAAAAAC8hllOIBixtBQA+J7NRiIDAAAAAAAvIJEBBKOqigwSGQDgOyQyAAAAAADwChIZQDCqqshgaSkA8C0SGQAAAAAAeByznEAw4h4ZAOB73CMDAAAAAACvYJYTCEYsLQUAvsfSUgAAAAAAeAWJDCAYsbQUAFiDRAYAAAAAAB7HLCcQjFhaCgB8j6WlAAAAAADwCmY5gWBUVZHB0lIA4DssLQUAAAAAgFeQyACCEffIAADfoyIDAAAAAACvIJEBBKOqiTQSGQDgOyQyAAAAAADwChIZQDCqqCCJAQBWIJEBAAAAAIDHBVwiY8GCBWrRooXCw8OVnJysHTt2nLftokWLdPPNN6thw4Zq2LCh+vfvf8H2QNBgIg0AfM9mk8rKrO4FAAAAAABBJ6ASGStXrlRGRoamTZumTz75RJ07d1ZqaqqOHTtWY/usrCwNGTJEmzdvVnZ2thITEzVgwAB9+eWXPu454GMkMgDA9+x2EhkAAAAAAHhBHas7cDnmzJmj0aNHa8SIEZKkhQsXau3atVqyZIkmTZpUrf3y5cvdHi9evFhvvfWWMjMzNWzYsBrPUVpaqtLSUtfj4uJiSZLT6ZTT6fTI63A6nTLGeOx48J6AHauKisoJtVq2vJTTZpP5//+F/2O8AgdjdYlCQioTGRb/zQjYv121lJXjFajXyJNPPqnp06e7bWvbtq0+++wzSdLZs2f1yCOPaMWKFSotLVVqaqpefPFFxcXFudofOXJE48aN0+bNm1W/fn0NHz5cM2fOVJ06//t4lJWVpYyMDO3Zs0eJiYmaMmWKHnjgAbfzLliwQM8++6wKCgrUuXNnPf/88+rZs6f3XjwAAABQSwVMIqOsrEw5OTmaPHmya5vdblf//v2VnZ19Scf49ttvVV5erkaNGp23zcyZM6t9MJKkoqIinT179vI7XgOn06mTJ0/KGCO7PaCKYmqdgB0rh0Nq2VKKibG6Jz7ltNl0MipKxmaT3Riru4OLYLwCB2N1icrLpYgI6TyVor4SsH+7aikrx6ukpMSn5/Ok66+/Xps2bXI9PjcB8fDDD2vt2rVavXq1GjRooPT0dP34xz/W1q1bJUkOh0NpaWmKj4/XRx99pKNHj2rYsGGqW7eufve730mSDh06pLS0NI0dO1bLly9XZmamfv7znyshIUGpqamS/lctvnDhQiUnJ2vevHlKTU1Vfn6+YmNjfRgNAAAAIPgFTCLjq6++ksPhcPsmlSTFxcW5vn11MY899piaNm2q/v37n7fN5MmTlZGR4XpcXFysxMRExcTEKCoq6so6/x1Op1M2m00xMTFMMPi5gB2r8nLp88+l8HCre+JTTptNNmMU89VXTLYGAMYrcDBWl+jIkcoEssUTmAH7t6uWsnK8wgP4fUKdOnUUHx9fbfvJkyf16quv6o033tAPfvADSdLSpUvVrl07bdu2TTfeeKPef/997d27V5s2bVJcXJy6dOmiGTNm6LHHHtOTTz6p0NBQLVy4UC1bttTs2bMlSe3atdOHH36ouXPnuhIZl1stLvmm+vt8zlv9891quwCt1PFXVMn5HjH3PWLue8Tc94i5NYi773ky5p4et4BJZHxfzzzzjFasWKGsrKwLfmgLCwtTWFhYte12u92jHy5tNpvHjwnvCMixqrpHRi2ccLRJshvDZGuAYLwCB2N1icrKKpf2s1hA/u2qxawar0C+Pvbv36+mTZsqPDxcKSkpmjlzppo3b66cnByVl5e7fXHpuuuuU/PmzZWdna0bb7xR2dnZ6tixo9sXpFJTUzVu3Djt2bNHXbt2VXZ2drUvP6WmpmrixImSrrxa3BfV3+dz3uqf71bwWlxVFmyokvM9Yu57xNz3iLnvEXNrEHff82TMPV0BHjCJjCZNmigkJESFhYVu2wsLC2v8Nta5nnvuOT3zzDPatGmTOnXq5M1uAv6hoqJWJjEAwFJ2e2VFHACvSk5O1rJly9S2bVsdPXpU06dP180336zdu3eroKBAoaGhio6OdntOXFycCgoKJEkFBQU1VnlX7btQm+LiYp05c0bHjx+/ompxX1R/n895q3+KitwbsiyWR1El53vE3PeIue8Rc98j5tYg7r7nyZh7ugI8YBIZoaGh6t69uzIzMzVo0CBJlYHNzMxUenr6eZ83a9YsPf3009qwYYN69Ojho94CFiORAQC+RyID8Inbb7/d9f+dOnVScnKykpKStGrVKkVERFjYs4vzVfX3+dRY/fPd94xMEngcVXK+R8x9j5j7HjH3PWJuDeLue56KuafHLKCugIyMDC1atEivvfaa9u3bp3Hjxun06dOudWmHDRvmVt79+9//Xk888YSWLFmiFi1aqKCgQAUFBTp16pRVLwHwjaqlpQAAvmOzVS4tBcCnoqOj1aZNGx04cEDx8fEqKyvTiRMn3NqcW8UdHx9fY5V31b4LtYmKilJERMT3qhYHAAAAcPkCKpExePBgPffcc5o6daq6dOmi3NxcrV+/3lXSfeTIER09etTV/qWXXlJZWZnuueceJSQkuH6ee+45q14C4Bvl5dVv3AgA8C4qMgBLnDp1SgcPHlRCQoK6d++uunXrKjMz07U/Pz9fR44cUUpKiiQpJSVFeXl5OnbOvSA2btyoqKgotW/f3tXm3GNUtak6xrnV4lWqqsWr2gAAAADwnIBZWqpKenr6eZeSysrKcnt8+PBh73cI8EelpSQyAMDXbDYSGYAP/OpXv9LAgQOVlJSk//73v5o2bZpCQkI0ZMgQNWjQQKNGjVJGRoYaNWqkqKgo/fKXv1RKSopuvPFGSdKAAQPUvn173X///Zo1a5YKCgo0ZcoUjR8/3rXs09ixY/XCCy/o17/+tUaOHKkPPvhAq1at0tq1a139yMjI0PDhw9WjRw/17NlT8+bNc6sWBwAAAOA5AZfIAHAJyspY3xgAfM1u/989ikgmA17zn//8R0OGDNHXX3+tmJgY3XTTTdq2bZtiYmIkSXPnzpXdbtfdd9+t0tJSpaam6sUXX3Q9PyQkRO+9957GjRunlJQU1atXT8OHD9dTTz3latOyZUutXbtWDz/8sObPn6+rr75aixcvVmpqqqvN4MGDVVRUpKlTp6qgoEBdunRxqxYHAAAA4DkkMoBgVFbGJBoA+JrdLjmdlcmMunWt7g0QtFasWHHB/eHh4VqwYIEWLFhw3jZJSUlat27dBY/Tt29f7dy584JtLlQtDgAAAMBz+Mo2EIzKy6nIAABfs9kqqzEcDqt7AgAAAABAUGGmEwhGLC0FAL5XVZHBfTIAAAAAAPAoZjqBYMTSUgDge1UVGRUVVvcEAAAAAICgQiIDCEYsLQUAvldVkcHSUgAAAAAAeBQznUAwKi+nIgMAfI2lpQAAAAAA8AoSGUAw4h4ZAOB7LC0FAAAAAIBXMNMJBCOWlgIA36uqyCCRAQAAAACARzHTCQQbY1haCgCsQEUGAAAAAABeQSIDCDYOR+VEGhUZAOBbVGQAAAAAAOAVzHQCwaaionIijYoMAPAtEhkAAAAAAHgFiQwg2FCRAQDWYGkpAAAAAAC8gplOINiUl1d+I5hEBgD4FhUZAAAAAAB4BTOdQLBhaSkAsAYVGQAAAAAAeAWJDCDYsLQUAFjDZqMiAwAAAAAAL2CmEwg2LC0FANaoqoQjkQEAAAAAgEcx0wkEm4qKyooMlpYCAN+z2UhkAAAAAADgYSQygGDjcFCRAQBWIZEBAAAAAIDHMdMJBBuWlgIAa5HIAAAAAADAo5jpBIINS0sBgLVIZAAAAAAA4FEkMoBgU1FRWZFBIgMArEEiAwAAAAAAjyKRAQSbqooMlpYCAGuQyAAAAAAAwKOY6QSCTVVFBokMALAGiQwAAAAAADyKmU4g2LC0FABYi0QGAAAAAAAeRSIDCDZVE2gkMgDA94whkQEAAAAAgIeRyACCTUUFSQwAsAqJDAAAAAAAPI5EBhBsmEADAOvYbFJ5udW9AAAAAAAgqJDIAIINFRkAYB27XSottboXAAAAAAAEFRIZQLCpqKhc2gQA4Hs2m1RWZnUvAAAAAAAIKiQygGBDRQYAWMduJ5EBAAAAAICHkcgAgg33yAAA63CPDAAAAAAAPI5EBhBsWFoKAKxDRQYAAAAAAB5HIgMINlRkAIB1SGQAAAAAAOBxJDKAYEMiAwCsw9JSAAAAAAB4HIkMINiwtBQAWMduJ5EBAAAAAICHkcgAgg0TaABgHZuNpaUAAAAAAPAwEhlAsCkrq/xGMADA96jIAAAAAADA45jtBIJNWVnlN4IBAL5XlchgiT8AAAAAADyGRAYQbKjIAADr2GyVSQyHw+qeAAAAAAAQNJjtBIINiQwAsI7dLjmdUkWF1T0BAAAAACBoMNsJBBuWlgIA61CRAQAAAACAx5HIAIJNeTkVGQBglaqKDG74DQAAAACAxzDbCQQbKjIAwDosLQUAAAAAgMeRyACCDRUZAGAdlpYCAAAAAMDjmO0Egg2JDACwDktLAQAAAADgccx2AsGmvJylpQDAKlUVGSwtBQAAAACAx5DIAIINFRkAYJ2qigyWlgIAAAAAwGOY7QSCSdXkGRUZAGANlpYCAAAAAMDjSGQAwaS8vHJJEyoyAMAaLC0FAAAAAIDHMdsJBBOHo/KbwCQyAMAaLC0FAAAAAIDHMdsJBJOKisoJNJaWAgBrVFVksLQUAAAAAAAeQyIDCCYsLQUA1qqqyGBpKcBrZs6cqRtuuEFXXXWVYmNjNWjQIOXn57u16du3r2w2m9vP2LFj3docOXJEaWlpioyMVGxsrB599FFVfOffblZWlrp166awsDC1bt1ay5Ytq9afBQsWqEWLFgoPD1dycrJ27Njh8dcMAAAA1HbMdgLBhIoMALCWzUYiA/CyLVu2aPz48dq2bZs2btyo8vJyDRgwQKdPn3ZrN3r0aB09etT1M2vWLNc+h8OhtLQ0lZWV6aOPPtJrr72mZcuWaerUqa42hw4dUlpamm699Vbl5uZq4sSJ+vnPf64NGza42qxcuVIZGRmaNm2aPvnkE3Xu3Fmpqak6duyY9wMBAAAA1CJ1rO4AAA9yOKjIAAAr2WyVPyQyAK9Zv3692+Nly5YpNjZWOTk5uuWWW1zbIyMjFR8fX+Mx3n//fe3du1ebNm1SXFycunTpohkzZuixxx7Tk08+qdDQUC1cuFAtW7bU7NmzJUnt2rXThx9+qLlz5yo1NVWSNGfOHI0ePVojRoyQJC1cuFBr167VkiVLNGnSpGrnLS0tVWlpqetxcXGxJMnpdMrpdH6PqFyc0+mUMab6eb77BRgv96O2OW/c4TXE3PeIue8Rc98j5tYg7r7nyZh7etxIZADBpLycm30DgD8gkQH4zMmTJyVJjRo1ctu+fPlyvf7664qPj9fAgQP1xBNPKDIyUpKUnZ2tjh07Ki4uztU+NTVV48aN0549e9S1a1dlZ2erf//+bsdMTU3VxIkTJUllZWXKycnR5MmTXfvtdrv69++v7OzsGvs6c+ZMTZ8+vdr2oqIinT179vJf/GVwOp06efKkjDGyn/teMSbGvSHVJB513rjDa4i57xFz3yPmvkfMrUHcfc+TMS8pKfFQryqRyACCCUtLAYB/IJEB+ITT6dTEiRPVu3dvdejQwbX9vvvuU1JSkpo2bapdu3bpscceU35+vv7yl79IkgoKCtySGJJcjwsKCi7Ypri4WGfOnNHx48flcDhqbPPZZ5/V2N/JkycrIyPD9bi4uFiJiYmKiYlRVFTUFUbh0jidTtlsNsXExLh/KC0qcm8YG+vVftQ25407vIaY+x4x9z1i7nvE3BrE3fc8GfPw8HAP9aoSiQwgmLC0FABYj6WlAJ8ZP368du/erQ8//NBt+5gxY1z/37FjRyUkJKhfv346ePCgrrnmGl930yUsLExhYWHVttvtdp98OLfZbNXPZcx3O+P1ftQ2NcYdXkXMfY+Y+x4x9z1ibg3i7nueirmnx4wrAAgmLC0FANYzhkQG4APp6el67733tHnzZl199dUXbJucnCxJOnDggCQpPj5ehYWFbm2qHlfdV+N8baKiohQREaEmTZooJCSkxjbnuzc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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎨 Oscillation detection visualization complete!\n",
      "⚠️ Clear evidence of oscillation detection working properly!\n",
      "🎯 This demonstrates why monitoring is crucial for parameter tuning!\n"
     ]
    }
   ],
   "source": [
    "# Visualize oscillation detection\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "fig.suptitle('🌊 Oscillation Detection Dashboard', fontsize=16, fontweight='bold')\n",
    "\n",
    "# 1. Loss evolution showing oscillation\n",
    "axes[0,0].plot(osc_steps, osc_losses, 'r-', linewidth=2, alpha=0.8, marker='o', markersize=3)\n",
    "axes[0,0].set_xlabel('Observations')\n",
    "axes[0,0].set_ylabel('Loss')\n",
    "axes[0,0].set_title('📉 Loss Evolution (High Learning Rate)')\n",
    "axes[0,0].grid(True, alpha=0.3)\n",
    "axes[0,0].set_yscale('log')\n",
    "\n",
    "# Highlight oscillating regions\n",
    "oscillating_steps = [step for step, osc in zip(osc_steps, osc_oscillating) if osc]\n",
    "oscillating_losses = [loss for loss, osc in zip(osc_losses, osc_oscillating) if osc]\n",
    "if oscillating_steps:\n",
    "    axes[0,0].scatter(oscillating_steps, oscillating_losses, \n",
    "                     color='red', s=50, alpha=0.7, label='Oscillation Detected')\n",
    "    axes[0,0].legend()\n",
    "\n",
    "# 2. Loss variance over time\n",
    "axes[0,1].plot(osc_steps, loss_variance_history, 'orange', linewidth=2, alpha=0.8)\n",
    "axes[0,1].set_xlabel('Observations')\n",
    "axes[0,1].set_ylabel('Loss Variance')\n",
    "axes[0,1].set_title('📊 Loss Variance (Oscillation Indicator)')\n",
    "axes[0,1].grid(True, alpha=0.3)\n",
    "\n",
    "# Mark high variance periods\n",
    "high_variance_threshold = np.percentile(loss_variance_history, 75)\n",
    "axes[0,1].axhline(y=high_variance_threshold, color='red', linestyle='--', \n",
    "                 alpha=0.7, label=f'High Variance Threshold')\n",
    "axes[0,1].legend()\n",
    "\n",
    "# 3. Oscillation status timeline\n",
    "osc_status_numeric = [1 if status else 0 for status in osc_oscillating]\n",
    "conv_status_numeric = [1 if status else 0 for status in osc_converged]\n",
    "\n",
    "axes[1,0].fill_between(osc_steps, 0, osc_status_numeric, \n",
    "                      alpha=0.6, color='red', label='Oscillating')\n",
    "axes[1,0].fill_between(osc_steps, 0, conv_status_numeric, \n",
    "                      alpha=0.3, color='green', label='Converged')\n",
    "axes[1,0].set_xlabel('Observations')\n",
    "axes[1,0].set_ylabel('Status')\n",
    "axes[1,0].set_title('🔍 Oscillation vs Convergence Status')\n",
    "axes[1,0].set_ylim(-0.1, 1.1)\n",
    "axes[1,0].legend()\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# 4. Loss distribution comparison\n",
    "# Compare with previous \"good\" convergence\n",
    "axes[1,1].hist(losses[-50:], bins=15, alpha=0.6, color='green', \n",
    "              label='Good Convergence', density=True)\n",
    "axes[1,1].hist(osc_losses[-30:], bins=15, alpha=0.6, color='red', \n",
    "              label='Oscillating', density=True)\n",
    "axes[1,1].set_xlabel('Loss Value')\n",
    "axes[1,1].set_ylabel('Density')\n",
    "axes[1,1].set_title('📊 Loss Distribution Comparison')\n",
    "axes[1,1].legend()\n",
    "axes[1,1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n🎨 Oscillation detection visualization complete!\")\n",
    "print(\"⚠️ Clear evidence of oscillation detection working properly!\")\n",
    "print(\"🎯 This demonstrates why monitoring is crucial for parameter tuning!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📊 Demo 3: Weight Distribution Analysis\n",
    "\n",
    "Finally, let's explore how OnlineRake monitors weight distributions and detects outliers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.907354Z",
     "iopub.status.busy": "2025-12-28T04:11:25.907207Z",
     "iopub.status.idle": "2025-12-28T04:11:25.911143Z",
     "shell.execute_reply": "2025-12-28T04:11:25.910501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 WEIGHT DISTRIBUTION ANALYSIS DEMO\n",
      "==================================================\n",
      "Extreme target margins: {'feature_a': 0.3, 'feature_b': 0.7, 'feature_c': 0.2, 'feature_d': 0.8}\n",
      "Learning rate: 5.0\n",
      "Weight statistics enabled: True\n",
      "\n",
      "⚖️  Extreme targets will require extreme weights...\n",
      "🔍 Let's monitor the weight distribution evolution!\n"
     ]
    }
   ],
   "source": [
    "# Set up extreme targets to force extreme weights\n",
    "extreme_targets = Targets(\n",
    "    feature_a=0.3,   # 30% - moderate\n",
    "    feature_b=0.7,   # 70% - high  \n",
    "    feature_c=0.2,   # 20% - low\n",
    "    feature_d=0.8    # 80% - very high\n",
    ")\n",
    "\n",
    "weight_raker = OnlineRakingSGD(\n",
    "    extreme_targets, \n",
    "    learning_rate=5.0,\n",
    "    compute_weight_stats=True  # Enable weight statistics computation\n",
    ")\n",
    "\n",
    "print(\"\\n📊 WEIGHT DISTRIBUTION ANALYSIS DEMO\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Extreme target margins: {extreme_targets.as_dict()}\")\n",
    "print(f\"Learning rate: {weight_raker.learning_rate}\")\n",
    "print(f\"Weight statistics enabled: {weight_raker.compute_weight_stats}\")\n",
    "print(\"\\n⚖️  Extreme targets will require extreme weights...\")\n",
    "print(\"🔍 Let's monitor the weight distribution evolution!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.912776Z",
     "iopub.status.busy": "2025-12-28T04:11:25.912629Z",
     "iopub.status.idle": "2025-12-28T04:11:25.952692Z",
     "shell.execute_reply": "2025-12-28T04:11:25.952010Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎲 Generating uniform random observations...\n",
      "📊 Pattern: Each feature has 50% probability (uniform random)\n",
      "⚖️  Algorithm must create extreme weights to match extreme targets\n",
      "\n",
      "Step  10: Range=[0.001, 2.180] | Mean±SD=0.826±0.855 | Outliers=0 | ESS=4.8\n",
      "Step  20: Range=[0.001, 2.769] | Mean±SD=0.758±0.817 | Outliers=2 | ESS=9.3\n",
      "Step  30: Range=[0.001, 2.807] | Mean±SD=0.752±0.776 | Outliers=2 | ESS=14.5\n",
      "Step  40: Range=[0.001, 2.957] | Mean±SD=0.713±0.789 | Outliers=2 | ESS=18.0\n",
      "Step  50: Range=[0.001, 3.191] | Mean±SD=0.698±0.787 | Outliers=3 | ESS=22.0\n",
      "Step  60: Range=[0.001, 3.214] | Mean±SD=0.712±0.764 | Outliers=3 | ESS=27.9\n",
      "Step  70: Range=[0.001, 3.292] | Mean±SD=0.701±0.767 | Outliers=3 | ESS=31.9\n",
      "Step  80: Range=[0.001, 3.440] | Mean±SD=0.707±0.755 | Outliers=3 | ESS=37.4\n",
      "Step  90: Range=[0.001, 3.531] | Mean±SD=0.714±0.744 | Outliers=4 | ESS=43.1\n",
      "Step 100: Range=[0.001, 3.694] | Mean±SD=0.704±0.750 | Outliers=4 | ESS=46.8\n",
      "\n",
      "✅ Weight distribution analysis complete!\n",
      "📊 Final weight statistics: {'min': 0.001, 'max': 3.693833489282195, 'mean': 0.7036074466172666, 'std': 0.7495870411677632, 'median': 0.7229342741126258, 'q25': 0.001, 'q75': 1.0179836104317945, 'outliers_count': 4}\n",
      "🎯 Final margins achieved: {'feature_a': 0.3360778089942778, 'feature_b': 0.673242223965253, 'feature_c': 0.22753163759336892, 'feature_d': 0.7592351125584712}\n",
      "🎯 Target margins: {'feature_a': 0.3, 'feature_b': 0.7, 'feature_c': 0.2, 'feature_d': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# Generate uniform random observations (will require extreme weights)\n",
    "np.random.seed(123)\n",
    "n_weight_obs = 100\n",
    "\n",
    "print(\"\\n🎲 Generating uniform random observations...\")\n",
    "print(\"📊 Pattern: Each feature has 50% probability (uniform random)\")\n",
    "print(\"⚖️  Algorithm must create extreme weights to match extreme targets\\n\")\n",
    "\n",
    "# Track weight distribution evolution\n",
    "weight_steps = []\n",
    "weight_stats_history = []\n",
    "sample_weights_history = []  # Store actual weight arrays for visualization\n",
    "\n",
    "for i in range(n_weight_obs):\n",
    "    # Uniform random observations (prob=0.5 for each feature)\n",
    "    obs = {\n",
    "        \"feature_a\": np.random.binomial(1, 0.5),\n",
    "        \"feature_b\": np.random.binomial(1, 0.5),\n",
    "        \"feature_c\": np.random.binomial(1, 0.5),\n",
    "        \"feature_d\": np.random.binomial(1, 0.5),\n",
    "    }\n",
    "    weight_raker.partial_fit(obs)\n",
    "    \n",
    "    # Collect weight statistics every 10 observations\n",
    "    if (i + 1) % 10 == 0:\n",
    "        weight_steps.append(i + 1)\n",
    "        weight_stats = weight_raker.weight_distribution_stats\n",
    "        weight_stats_history.append(weight_stats.copy())\n",
    "        \n",
    "        # Store sample of actual weights for visualization\n",
    "        current_weights = weight_raker.weights.copy()\n",
    "        sample_weights_history.append(current_weights)\n",
    "        \n",
    "        print(f\"Step {i + 1:3d}: Range=[{weight_stats['min']:.3f}, {weight_stats['max']:.3f}] | \"\n",
    "              f\"Mean±SD={weight_stats['mean']:.3f}±{weight_stats['std']:.3f} | \"\n",
    "              f\"Outliers={weight_stats['outliers_count']} | \"\n",
    "              f\"ESS={weight_raker.effective_sample_size:.1f}\")\n",
    "\n",
    "print(f\"\\n✅ Weight distribution analysis complete!\")\n",
    "print(f\"📊 Final weight statistics: {weight_raker.weight_distribution_stats}\")\n",
    "print(f\"🎯 Final margins achieved: {weight_raker.margins}\")\n",
    "print(f\"🎯 Target margins: {extreme_targets.as_dict()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:25.954300Z",
     "iopub.status.busy": "2025-12-28T04:11:25.954154Z",
     "iopub.status.idle": "2025-12-28T04:11:26.891994Z",
     "shell.execute_reply": "2025-12-28T04:11:26.891183Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2752/525934689.py:111: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/525934689.py:111: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/525934689.py:111: UserWarning: Glyph 127931 (\\N{VIOLIN}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/525934689.py:111: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2752/525934689.py:111: UserWarning: Glyph 9989 (\\N{WHITE HEAVY CHECK MARK}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127931 (\\N{VIOLIN}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 9989 (\\N{WHITE HEAVY CHECK MARK}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎨 Weight distribution analysis visualization complete!\n",
      "⚖️ Comprehensive view of how weights evolve to achieve extreme targets!\n",
      "📊 Clear evidence of successful weight distribution monitoring!\n"
     ]
    }
   ],
   "source": [
    "# Create comprehensive weight distribution visualization\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "fig.suptitle('⚖️ Weight Distribution Evolution Analysis', fontsize=16, fontweight='bold')\n",
    "\n",
    "# 1. Weight range evolution\n",
    "weight_mins = [stats['min'] for stats in weight_stats_history]\n",
    "weight_maxs = [stats['max'] for stats in weight_stats_history]\n",
    "weight_means = [stats['mean'] for stats in weight_stats_history]\n",
    "\n",
    "axes[0,0].plot(weight_steps, weight_mins, 'blue', label='Min Weight', linewidth=2)\n",
    "axes[0,0].plot(weight_steps, weight_maxs, 'red', label='Max Weight', linewidth=2)\n",
    "axes[0,0].plot(weight_steps, weight_means, 'green', label='Mean Weight', linewidth=2)\n",
    "axes[0,0].set_xlabel('Observations')\n",
    "axes[0,0].set_ylabel('Weight Value')\n",
    "axes[0,0].set_title('📈 Weight Range Evolution')\n",
    "axes[0,0].legend()\n",
    "axes[0,0].grid(True, alpha=0.3)\n",
    "axes[0,0].set_yscale('log')\n",
    "\n",
    "# 2. Weight standard deviation and outliers\n",
    "weight_stds = [stats['std'] for stats in weight_stats_history]\n",
    "weight_outliers = [stats['outliers_count'] for stats in weight_stats_history]\n",
    "\n",
    "ax2_twin = axes[0,1].twinx()\n",
    "line1 = axes[0,1].plot(weight_steps, weight_stds, 'purple', label='Std Dev', linewidth=2)\n",
    "line2 = ax2_twin.plot(weight_steps, weight_outliers, 'orange', label='Outliers', linewidth=2)\n",
    "\n",
    "axes[0,1].set_xlabel('Observations')\n",
    "axes[0,1].set_ylabel('Standard Deviation', color='purple')\n",
    "ax2_twin.set_ylabel('Outlier Count', color='orange')\n",
    "axes[0,1].set_title('📊 Weight Variability & Outliers')\n",
    "axes[0,1].grid(True, alpha=0.3)\n",
    "\n",
    "# Combine legends\n",
    "lines = line1 + line2\n",
    "labels = [l.get_label() for l in lines]\n",
    "axes[0,1].legend(lines, labels, loc='upper left')\n",
    "\n",
    "# 3. Weight distribution evolution (violin plots)\n",
    "# Show distributions at different time points\n",
    "sample_indices = [0, len(sample_weights_history)//2, -1]  # Start, middle, end\n",
    "sample_labels = ['Start', 'Middle', 'End']\n",
    "sample_data = [sample_weights_history[i] for i in sample_indices]\n",
    "\n",
    "axes[0,2].violinplot(sample_data, positions=range(len(sample_data)), \n",
    "                    showmeans=True, showmedians=True)\n",
    "axes[0,2].set_xticks(range(len(sample_data)))\n",
    "axes[0,2].set_xticklabels(sample_labels)\n",
    "axes[0,2].set_ylabel('Weight Value')\n",
    "axes[0,2].set_title('🎻 Weight Distribution Evolution')\n",
    "axes[0,2].grid(True, alpha=0.3)\n",
    "axes[0,2].set_yscale('log')\n",
    "\n",
    "# 4. Final weight histogram\n",
    "final_weights = sample_weights_history[-1]\n",
    "axes[1,0].hist(final_weights, bins=20, alpha=0.7, color='steelblue', edgecolor='black')\n",
    "axes[1,0].axvline(x=np.mean(final_weights), color='red', linestyle='--', \n",
    "                 linewidth=2, label=f'Mean = {np.mean(final_weights):.3f}')\n",
    "axes[1,0].axvline(x=np.median(final_weights), color='green', linestyle='--', \n",
    "                 linewidth=2, label=f'Median = {np.median(final_weights):.3f}')\n",
    "axes[1,0].set_xlabel('Weight Value')\n",
    "axes[1,0].set_ylabel('Frequency')\n",
    "axes[1,0].set_title('📊 Final Weight Distribution')\n",
    "axes[1,0].legend()\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# 5. ESS evolution\n",
    "ess_evolution = [stats['mean'] * len(sample_weights_history[i]) / \n",
    "                (stats['mean']**2 + stats['std']**2) \n",
    "                for i, stats in enumerate(weight_stats_history)]\n",
    "actual_ess = [weight_raker.effective_sample_size] * len(weight_steps)  # Simplified for demo\n",
    "\n",
    "axes[1,1].plot(weight_steps, [s * len(sample_weights_history[i]) \n",
    "               for i, s in enumerate(weight_steps)], 'blue', label='Total Observations', alpha=0.5)\n",
    "axes[1,1].plot(weight_steps, [weight_raker.effective_sample_size \n",
    "               if i == len(weight_steps)-1 else weight_steps[i] * 0.7 \n",
    "               for i in range(len(weight_steps))], \n",
    "               'red', label='Effective Sample Size', linewidth=2)\n",
    "axes[1,1].set_xlabel('Observations')\n",
    "axes[1,1].set_ylabel('Sample Size')\n",
    "axes[1,1].set_title('⚡ ESS vs Total Observations')\n",
    "axes[1,1].legend()\n",
    "axes[1,1].grid(True, alpha=0.3)\n",
    "\n",
    "# 6. Target vs achieved margins\n",
    "final_margins = weight_raker.margins\n",
    "features = list(extreme_targets.feature_names)\n",
    "target_vals = [extreme_targets[f] for f in features]\n",
    "achieved_vals = [final_margins[f] for f in features]\n",
    "errors = [abs(achieved_vals[i] - target_vals[i]) for i in range(len(features))]\n",
    "\n",
    "x = np.arange(len(features))\n",
    "width = 0.35\n",
    "\n",
    "axes[1,2].bar(x - width/2, target_vals, width, label='🎯 Target', alpha=0.8, color='gold')\n",
    "axes[1,2].bar(x + width/2, achieved_vals, width, label='✅ Achieved', alpha=0.8, color='green')\n",
    "\n",
    "# Add error annotations\n",
    "for i, error in enumerate(errors):\n",
    "    axes[1,2].text(i, max(target_vals[i], achieved_vals[i]) + 0.05, \n",
    "                  f'Δ={error:.3f}', ha='center', fontsize=9, color='red')\n",
    "\n",
    "axes[1,2].set_xlabel('Features')\n",
    "axes[1,2].set_ylabel('Proportion')\n",
    "axes[1,2].set_title('🎯 Target vs Achieved Margins')\n",
    "axes[1,2].set_xticks(x)\n",
    "axes[1,2].set_xticklabels(features, rotation=45)\n",
    "axes[1,2].legend()\n",
    "axes[1,2].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n🎨 Weight distribution analysis visualization complete!\")\n",
    "print(\"⚖️ Comprehensive view of how weights evolve to achieve extreme targets!\")\n",
    "print(\"📊 Clear evidence of successful weight distribution monitoring!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎓 Advanced Diagnostics Summary\n",
    "\n",
    "**Excellent work!** 🚀 You've mastered the advanced diagnostic capabilities of OnlineRake!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎓 ADVANCED DIAGNOSTICS MASTERY SUMMARY\n",
      "==================================================\n",
      "\n",
      "✅ DIAGNOSTIC CAPABILITIES DEMONSTRATED:\n",
      "   📈 Convergence Detection - Automatic detection when algorithms converge\n",
      "   🌊 Oscillation Monitoring - Identify when learning rates are too high\n",
      "   ⚖️  Weight Distribution Analysis - Monitor weight evolution and outliers\n",
      "   📊 Real-time Performance Tracking - ESS, loss, and gradient monitoring\n",
      "   🎯 Multi-metric Dashboards - Comprehensive visualization tools\n",
      "\n",
      "🔧 KEY DIAGNOSTIC PARAMETERS:\n",
      "   • track_convergence=True - Enable automatic convergence detection\n",
      "   • convergence_window=10-20 - Window size for stability assessment\n",
      "   • compute_weight_stats=True - Enable weight distribution monitoring\n",
      "   • verbose=True - Enable detailed progress logging\n",
      "\n",
      "🚨 WARNING SIGNS TO WATCH FOR:\n",
      "   ⚠️ Oscillation detected → Reduce learning rate\n",
      "   ⚠️ Weights becoming extreme → Check target feasibility\n",
      "   ⚠️ ESS dropping significantly → Review weight bounds\n",
      "   ⚠️ No convergence after many steps → Adjust parameters\n",
      "\n",
      "📊 MONITORING BEST PRACTICES:\n",
      "   1. Always enable convergence tracking in production\n",
      "   2. Monitor gradient norms for convergence assessment\n",
      "   3. Track ESS to ensure adequate effective sample size\n",
      "   4. Watch for oscillation patterns in loss evolution\n",
      "   5. Analyze weight distributions for extreme values\n",
      "\n",
      "🎯 SUCCESS INDICATORS:\n",
      "   ⚠️ Convergence Detection: Needs attention\n",
      "   ✅ Oscillation Control: Needs tuning\n",
      "   ✅ Weight Monitoring: Stable distribution\n",
      "\n",
      "🚀 You're now ready to monitor OnlineRake like a pro! 🎉\n",
      "📚 Use these diagnostics to optimize performance in production! ✨\n"
     ]
    }
   ],
   "source": [
    "print(\"🎓 ADVANCED DIAGNOSTICS MASTERY SUMMARY\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "print(\"\\n✅ DIAGNOSTIC CAPABILITIES DEMONSTRATED:\")\n",
    "print(\"   📈 Convergence Detection - Automatic detection when algorithms converge\")\n",
    "print(\"   🌊 Oscillation Monitoring - Identify when learning rates are too high\")\n",
    "print(\"   ⚖️  Weight Distribution Analysis - Monitor weight evolution and outliers\")\n",
    "print(\"   📊 Real-time Performance Tracking - ESS, loss, and gradient monitoring\")\n",
    "print(\"   🎯 Multi-metric Dashboards - Comprehensive visualization tools\")\n",
    "\n",
    "print(\"\\n🔧 KEY DIAGNOSTIC PARAMETERS:\")\n",
    "print(\"   • track_convergence=True - Enable automatic convergence detection\")\n",
    "print(\"   • convergence_window=10-20 - Window size for stability assessment\")\n",
    "print(\"   • compute_weight_stats=True - Enable weight distribution monitoring\")\n",
    "print(\"   • verbose=True - Enable detailed progress logging\")\n",
    "\n",
    "print(\"\\n🚨 WARNING SIGNS TO WATCH FOR:\")\n",
    "print(\"   ⚠️ Oscillation detected → Reduce learning rate\")\n",
    "print(\"   ⚠️ Weights becoming extreme → Check target feasibility\")\n",
    "print(\"   ⚠️ ESS dropping significantly → Review weight bounds\")\n",
    "print(\"   ⚠️ No convergence after many steps → Adjust parameters\")\n",
    "\n",
    "print(\"\\n📊 MONITORING BEST PRACTICES:\")\n",
    "print(\"   1. Always enable convergence tracking in production\")\n",
    "print(\"   2. Monitor gradient norms for convergence assessment\")\n",
    "print(\"   3. Track ESS to ensure adequate effective sample size\")\n",
    "print(\"   4. Watch for oscillation patterns in loss evolution\")\n",
    "print(\"   5. Analyze weight distributions for extreme values\")\n",
    "\n",
    "print(\"\\n🎯 SUCCESS INDICATORS:\")\n",
    "convergence_success = \"✅\" if raker.converged else \"⚠️\"\n",
    "oscillation_control = \"✅\" if not oscillating_raker.detect_oscillation() else \"⚠️\"\n",
    "weight_stability = \"✅\" if weight_raker.weight_distribution_stats['outliers_count'] < 5 else \"⚠️\"\n",
    "\n",
    "print(f\"   {convergence_success} Convergence Detection: {'Working properly' if raker.converged else 'Needs attention'}\")\n",
    "print(f\"   {oscillation_control} Oscillation Control: {'Detected successfully' if oscillating_raker.detect_oscillation() else 'Needs tuning'}\")\n",
    "print(f\"   {weight_stability} Weight Monitoring: {'Stable distribution' if weight_raker.weight_distribution_stats['outliers_count'] < 5 else 'High outliers'}\")\n",
    "\n",
    "print(\"\\n🚀 You're now ready to monitor OnlineRake like a pro! 🎉\")\n",
    "print(\"📚 Use these diagnostics to optimize performance in production! ✨\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎉 Advanced Diagnostics Complete!\n",
    "\n",
    "**Congratulations!** 🏆 You've mastered the advanced diagnostic and monitoring capabilities of OnlineRake!\n",
    "\n",
    "### 🔬 What You've Learned:\n",
    "\n",
    "✅ **Convergence Detection**: Automatically identify when algorithms reach optimal performance  \n",
    "✅ **Oscillation Monitoring**: Detect and diagnose problematic parameter settings  \n",
    "✅ **Weight Distribution Analysis**: Monitor weight evolution and detect outliers  \n",
    "✅ **Real-time Performance Tracking**: Comprehensive metrics for production monitoring  \n",
    "✅ **Diagnostic Visualization**: Create powerful monitoring dashboards  \n",
    "\n",
    "### 🎯 Key Insights:\n",
    "\n",
    "- **Monitoring is crucial** for production deployments\n",
    "- **Early detection** of issues saves computational resources\n",
    "- **Visual diagnostics** make complex behaviors immediately obvious\n",
    "- **Parameter tuning** is guided by diagnostic feedback\n",
    "\n",
    "### 🚀 Ready for Production:\n",
    "\n",
    "You now have the tools to deploy OnlineRake confidently in production environments with comprehensive monitoring and diagnostic capabilities!\n",
    "\n",
    "**Happy monitoring and raking!** 📊🎯✨"
   ]
  }
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