{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bearing Autoencoder — One AE, Per-Bearing Dynamic Threshold\n",
    "\n",
    "**Key idea:**\n",
    "- A **single** Conv1D autoencoder is trained once on healthy signals (MC nuisance).\n",
    "- At deployment each physical bearing gets its **own** `AdaptiveTau` instance.\n",
    "- The sliding window tracks μ and σ *locally* for that bearing → no cross-contamination.\n",
    "- The z-score anchor `z_MC` is shared (derived from the simulator calibration set).\n",
    "\n",
    "Steps:\n",
    "1. Imports & seeds\n",
    "2. Simulator\n",
    "3. Generate dataset (healthy train/val + per-regime test)\n",
    "4. Build & train autoencoder (once)\n",
    "5. Reconstruction fidelity check\n",
    "6. Per-regime MSE distributions\n",
    "7. MC threshold calibration → derive `z_MC`\n",
    "8. `AdaptiveTau` class — per-bearing sliding window\n",
    "9. **Per-bearing experiment** — each bearing gets its own tracker\n",
    "10. Window size sensitivity\n",
    "11. Latent space PCA\n",
    "12. Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Imports & seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version: 2.16.2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import deque\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, Model\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "print('TF version:', tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal length: 1200 samples\n"
     ]
    }
   ],
   "source": [
    "from bearing_simulator import BearingSignalSimulator\n",
    "\n",
    "sim = BearingSignalSimulator()\n",
    "SIG_LEN = len(sim.t)\n",
    "print(f'Signal length: {SIG_LEN} samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Generate dataset\n",
    "\n",
    "- **Train / Val:** healthy only (MC) — the AE sees only M_healthy\n",
    "- **Test:** all four regimes — never used for training\n",
    "\n",
    "> Each test split is kept separate so we can simulate *independent bearings* later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (800, 1200, 1)  Val: (200, 1200, 1)\n",
      "  Test [healthy]: (200, 1200, 1)\n",
      "  Test [outer_race]: (200, 1200, 1)\n",
      "  Test [inner_race]: (200, 1200, 1)\n",
      "  Test [ball_fault]: (200, 1200, 1)\n"
     ]
    }
   ],
   "source": [
    "REGIMES = ['healthy', 'outer_race', 'inner_race', 'ball_fault']\n",
    "COLORS  = ['#01696f', '#a12c7b', '#da7101', '#006494']\n",
    "LABELS  = ['Healthy', 'Outer Race', 'Inner Race', 'Ball Fault']\n",
    "\n",
    "N_TRAIN = 800   # healthy train\n",
    "N_VAL   = 200   # healthy val  (also used for MC calibration)\n",
    "N_TEST  = 200   # per regime for test\n",
    "\n",
    "# ── Healthy train/val ──────────────────────────────────────────────────────\n",
    "X_healthy = np.array([sim.sample('healthy', mc=True)\n",
    "                       for _ in range(N_TRAIN + N_VAL)])\n",
    "scaler = StandardScaler()\n",
    "X_healthy_sc = scaler.fit_transform(X_healthy)\n",
    "\n",
    "X_train = X_healthy_sc[:N_TRAIN, :, np.newaxis].astype('float32')\n",
    "X_val   = X_healthy_sc[N_TRAIN:, :, np.newaxis].astype('float32')\n",
    "\n",
    "# ── Test sets — one array PER regime ──────────────────────────────────────\n",
    "# X_test_by_regime[i] shape: (N_TEST, SIG_LEN, 1)\n",
    "X_test_by_regime = {}\n",
    "for regime in REGIMES:\n",
    "    sigs = np.array([sim.sample(regime, mc=True) for _ in range(N_TEST)])\n",
    "    X_test_by_regime[regime] = scaler.transform(sigs)[:, :, np.newaxis].astype('float32')\n",
    "\n",
    "print(f'Train: {X_train.shape}  Val: {X_val.shape}')\n",
    "for k, v in X_test_by_regime.items():\n",
    "    print(f'  Test [{k}]: {v.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Autoencoder architecture\n",
    "\n",
    "```\n",
    "Encoder: Conv1D(32) → Conv1D(64) → Conv1D(128) → Flatten → Dense(16)\n",
    "Decoder: Dense → Reshape → Conv1DTranspose(128) → Conv1DTranspose(64) → Conv1DTranspose(1)\n",
    "```\n",
    "Trained **once**. All per-bearing logic lives in `AdaptiveTau`, not in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-06-30 21:13:36.227768: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2 Pro\n",
      "2026-06-30 21:13:36.227915: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2026-06-30 21:13:36.227923: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.92 GB\n",
      "2026-06-30 21:13:36.228074: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2026-06-30 21:13:36.228084: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"bearing_autoencoder\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"bearing_autoencoder\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ signal_in (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">600</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">544</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,448</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">19200</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ latent (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">307,216</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">19200</span>)          │       <span style=\"color: #00af00; text-decoration-color: #00af00\">326,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">600</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose_2              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,800</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ signal_out (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)        │            <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ signal_in (\u001b[38;5;33mInputLayer\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m1\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m600\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │           \u001b[38;5;34m544\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │        \u001b[38;5;34m16,448\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m19200\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ latent (\u001b[38;5;33mDense\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │       \u001b[38;5;34m307,216\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m19200\u001b[0m)          │       \u001b[38;5;34m326,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m65,664\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv1DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m600\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │        \u001b[38;5;34m65,600\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv1DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d_transpose_2              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │        \u001b[38;5;34m32,800\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv1DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ signal_out (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m1\u001b[0m)        │            \u001b[38;5;34m33\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">847,601</span> (3.23 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m847,601\u001b[0m (3.23 MB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">847,601</span> (3.23 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m847,601\u001b[0m (3.23 MB)\n"
      ]
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LATENT_DIM = 16\n",
    "\n",
    "def build_autoencoder(sig_len, latent_dim):\n",
    "    inp = keras.Input(shape=(sig_len, 1), name='signal_in')\n",
    "    x   = layers.Conv1D(32,  kernel_size=16, strides=2, padding='same', activation='relu')(inp)\n",
    "    x   = layers.Conv1D(64,  kernel_size=8,  strides=2, padding='same', activation='relu')(x)\n",
    "    x   = layers.Conv1D(128, kernel_size=4,  strides=2, padding='same', activation='relu')(x)\n",
    "    conv_shape = x.shape[1:]\n",
    "    x   = layers.Flatten()(x)\n",
    "    latent = layers.Dense(\n",
    "        latent_dim,\n",
    "        activity_regularizer=keras.regularizers.l1(1.5e-4),\n",
    "        name='latent'\n",
    "    )(x)\n",
    "    y = layers.Dense(conv_shape[0] * conv_shape[1], activation='relu')(latent)\n",
    "    y = layers.Reshape(conv_shape)(y)\n",
    "    y = layers.Conv1DTranspose(128, kernel_size=4,  strides=2, padding='same', activation='relu')(y)\n",
    "    y = layers.Conv1DTranspose(64,  kernel_size=8,  strides=2, padding='same', activation='relu')(y)\n",
    "    y = layers.Conv1DTranspose(32,  kernel_size=16, strides=2, padding='same', activation='relu')(y)\n",
    "    y = layers.Conv1D(1, kernel_size=1, padding='same', activation='linear', name='signal_out')(y)\n",
    "    if y.shape[1] > sig_len:\n",
    "        y = layers.Cropping1D((0, y.shape[1] - sig_len))(y)\n",
    "    autoencoder = Model(inp, y,      name='bearing_autoencoder')\n",
    "    encoder     = Model(inp, latent, name='encoder')\n",
    "    return autoencoder, encoder\n",
    "\n",
    "ae, encoder = build_autoencoder(SIG_LEN, LATENT_DIM)\n",
    "ae.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse')\n",
    "ae.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training (healthy only — done once)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-06-30 21:13:37.196618: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 43ms/step - loss: 0.6965 - val_loss: 0.3075 - learning_rate: 0.0010\n",
      "Epoch 2/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.2033 - val_loss: 0.1419 - learning_rate: 0.0010\n",
      "Epoch 3/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.1312 - val_loss: 0.1150 - learning_rate: 0.0010\n",
      "Epoch 4/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.1027 - val_loss: 0.0945 - learning_rate: 0.0010\n",
      "Epoch 5/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0879 - val_loss: 0.0837 - learning_rate: 0.0010\n",
      "Epoch 6/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0795 - val_loss: 0.0753 - learning_rate: 0.0010\n",
      "Epoch 7/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0742 - val_loss: 0.0733 - learning_rate: 0.0010\n",
      "Epoch 8/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0715 - val_loss: 0.0718 - learning_rate: 0.0010\n",
      "Epoch 9/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0700 - val_loss: 0.0705 - learning_rate: 0.0010\n",
      "Epoch 10/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0692 - val_loss: 0.0695 - learning_rate: 0.0010\n",
      "Epoch 11/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0683 - val_loss: 0.0692 - learning_rate: 0.0010\n",
      "Epoch 12/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0677 - val_loss: 0.0689 - learning_rate: 0.0010\n",
      "Epoch 13/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0675 - val_loss: 0.0687 - learning_rate: 0.0010\n",
      "Epoch 14/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0673 - val_loss: 0.0687 - learning_rate: 0.0010\n",
      "Epoch 15/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0690 - val_loss: 0.0714 - learning_rate: 0.0010\n",
      "Epoch 16/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0693 - val_loss: 0.0743 - learning_rate: 0.0010\n",
      "Epoch 17/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0727 - val_loss: 0.0742 - learning_rate: 0.0010\n",
      "Epoch 18/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0772\n",
      "Epoch 18: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0772 - val_loss: 0.0784 - learning_rate: 0.0010\n",
      "Epoch 19/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0697 - val_loss: 0.0693 - learning_rate: 5.0000e-04\n",
      "Epoch 20/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0672 - val_loss: 0.0677 - learning_rate: 5.0000e-04\n",
      "Epoch 21/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0661 - val_loss: 0.0673 - learning_rate: 5.0000e-04\n",
      "Epoch 22/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0654 - val_loss: 0.0672 - learning_rate: 5.0000e-04\n",
      "Epoch 23/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0653 - val_loss: 0.0672 - learning_rate: 5.0000e-04\n",
      "Epoch 24/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0651 - val_loss: 0.0671 - learning_rate: 5.0000e-04\n",
      "Epoch 25/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0650 - val_loss: 0.0670 - learning_rate: 5.0000e-04\n",
      "Epoch 26/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0648 - val_loss: 0.0670 - learning_rate: 5.0000e-04\n",
      "Epoch 27/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0647 - val_loss: 0.0669 - learning_rate: 5.0000e-04\n",
      "Epoch 28/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0645 - val_loss: 0.0668 - learning_rate: 5.0000e-04\n",
      "Epoch 29/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0644 - val_loss: 0.0667 - learning_rate: 5.0000e-04\n",
      "Epoch 30/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0643 - val_loss: 0.0666 - learning_rate: 5.0000e-04\n",
      "Epoch 31/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0641 - val_loss: 0.0666 - learning_rate: 5.0000e-04\n",
      "Epoch 32/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0640 - val_loss: 0.0665 - learning_rate: 5.0000e-04\n",
      "Epoch 33/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0639 - val_loss: 0.0664 - learning_rate: 5.0000e-04\n",
      "Epoch 34/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0637 - val_loss: 0.0663 - learning_rate: 5.0000e-04\n",
      "Epoch 35/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0636 - val_loss: 0.0662 - learning_rate: 5.0000e-04\n",
      "Epoch 36/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0634 - val_loss: 0.0661 - learning_rate: 5.0000e-04\n",
      "Epoch 37/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0633 - val_loss: 0.0660 - learning_rate: 5.0000e-04\n",
      "Epoch 38/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0631 - val_loss: 0.0659 - learning_rate: 5.0000e-04\n",
      "Epoch 39/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0629 - val_loss: 0.0658 - learning_rate: 5.0000e-04\n",
      "Epoch 40/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0628 - val_loss: 0.0658 - learning_rate: 5.0000e-04\n",
      "Epoch 41/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0627 - val_loss: 0.0665 - learning_rate: 5.0000e-04\n",
      "Epoch 42/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0642 - val_loss: 0.0693 - learning_rate: 5.0000e-04\n",
      "Epoch 43/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0671 - val_loss: 0.0701 - learning_rate: 5.0000e-04\n",
      "Epoch 44/100\n",
      "\u001b[1m24/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0683\n",
      "Epoch 44: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0686 - val_loss: 0.0713 - learning_rate: 5.0000e-04\n",
      "Epoch 45/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0670 - val_loss: 0.0675 - learning_rate: 2.5000e-04\n",
      "Epoch 46/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0635 - val_loss: 0.0660 - learning_rate: 2.5000e-04\n",
      "Epoch 47/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0631 - val_loss: 0.0659 - learning_rate: 2.5000e-04\n",
      "Epoch 48/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0627 - val_loss: 0.0658 - learning_rate: 2.5000e-04\n",
      "Epoch 49/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0624\n",
      "Epoch 49: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0624 - val_loss: 0.0659 - learning_rate: 2.5000e-04\n",
      "Epoch 50/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0619 - val_loss: 0.0652 - learning_rate: 1.2500e-04\n",
      "Epoch 51/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0618 - val_loss: 0.0652 - learning_rate: 1.2500e-04\n",
      "Epoch 52/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0617 - val_loss: 0.0652 - learning_rate: 1.2500e-04\n",
      "Epoch 53/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0616 - val_loss: 0.0651 - learning_rate: 1.2500e-04\n",
      "Epoch 54/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0615 - val_loss: 0.0651 - learning_rate: 1.2500e-04\n",
      "Epoch 55/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0615 - val_loss: 0.0651 - learning_rate: 1.2500e-04\n",
      "Epoch 56/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0614 - val_loss: 0.0651 - learning_rate: 1.2500e-04\n",
      "Epoch 57/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0614 - val_loss: 0.0651 - learning_rate: 1.2500e-04\n",
      "Epoch 58/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0613 - val_loss: 0.0650 - learning_rate: 1.2500e-04\n",
      "Epoch 59/100\n",
      "\u001b[1m24/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0610\n",
      "Epoch 59: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0613 - val_loss: 0.0650 - learning_rate: 1.2500e-04\n",
      "Epoch 60/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0611 - val_loss: 0.0651 - learning_rate: 6.2500e-05\n",
      "Epoch 61/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0611 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 62/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0611 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 63/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0611 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 64/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0610 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 65/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0610 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 66/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0610 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 67/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0610 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 68/100\n",
      "\u001b[1m23/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0607\n",
      "Epoch 68: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0609 - val_loss: 0.0650 - learning_rate: 6.2500e-05\n",
      "Epoch 69/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0609 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 70/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0609 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 71/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0609 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 72/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0609 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 73/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 74/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 75/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 76/100\n",
      "\u001b[1m23/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0606\n",
      "Epoch 76: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 3.1250e-05\n",
      "Epoch 77/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 1.5625e-05\n",
      "Epoch 78/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 1.5625e-05\n",
      "Epoch 79/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0608 - val_loss: 0.0649 - learning_rate: 1.5625e-05\n",
      "Epoch 80/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0649 - learning_rate: 1.5625e-05\n",
      "Epoch 81/100\n",
      "\u001b[1m23/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0605\n",
      "Epoch 81: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.5625e-05\n",
      "Epoch 82/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 83/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 84/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 85/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 86/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 87/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 88/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 89/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 90/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 91/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 92/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 93/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 94/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 95/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 96/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 97/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 98/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 99/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Epoch 100/100\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.0607 - val_loss: 0.0648 - learning_rate: 1.0000e-05\n",
      "Restoring model weights from the end of the best epoch: 100.\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    keras.callbacks.EarlyStopping(\n",
    "        monitor='val_loss', patience=20, restore_best_weights=True, verbose=1\n",
    "    ),\n",
    "    keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor='val_loss', factor=0.5, patience=5, min_lr=1e-5, verbose=1\n",
    "    ),\n",
    "]\n",
    "\n",
    "history = ae.fit(\n",
    "    X_train, X_train,\n",
    "    validation_data=(X_val, X_val),\n",
    "    epochs=100, batch_size=32,\n",
    "    callbacks=callbacks, verbose=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "ax.plot(history.history['loss'],     color='#01696f', label='train')\n",
    "ax.plot(history.history['val_loss'], color='#a12c7b', ls='--', label='val')\n",
    "ax.set_xlabel('Epoch'); ax.set_ylabel('MSE'); ax.set_title('Training curve')\n",
    "ax.legend(); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Reconstruction fidelity (healthy val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x750 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def per_sample_mse(model, X):\n",
    "    Xr = model.predict(X, verbose=0)\n",
    "    return np.mean((X[:, :, 0] - Xr[:, :, 0]) ** 2, axis=1)\n",
    "\n",
    "n_show = 3\n",
    "idx    = np.random.choice(len(X_val), n_show, replace=False)\n",
    "preds  = ae.predict(X_val[idx], verbose=0)\n",
    "\n",
    "fig, axes = plt.subplots(n_show, 1, figsize=(10, 2.5 * n_show), sharex=True)\n",
    "for k, ax in enumerate(axes):\n",
    "    ax.plot(X_val[idx[k], :, 0], color='#01696f', lw=0.8, label='original')\n",
    "    ax.plot(preds[k, :, 0],      color='#da7101', lw=0.8, ls='--', label='reconstructed')\n",
    "    ax.set_ylabel('Amplitude'); ax.legend(loc='upper right', fontsize=8)\n",
    "axes[-1].set_xlabel('Sample')\n",
    "fig.suptitle('Healthy — original vs reconstructed', y=1.01)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Per-regime MSE distributions\n",
    "\n",
    "Healthy signals should cluster at low MSE; fault regimes should drift higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy       mean=0.0627  std=0.0244\n",
      "Outer Race    mean=0.2916  std=0.0972\n",
      "Inner Race    mean=0.4969  std=0.1824\n",
      "Ball Fault    mean=0.1941  std=0.0597\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_per_regime = {r: per_sample_mse(ae, X_test_by_regime[r]) for r in REGIMES}\n",
    "\n",
    "for r, label in zip(REGIMES, LABELS):\n",
    "    m = mse_per_regime[r]\n",
    "    print(f'{label:12s}  mean={m.mean():.4f}  std={m.std():.4f}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "for i, (regime, label, color) in enumerate(zip(REGIMES, LABELS, COLORS)):\n",
    "    mse = mse_per_regime[regime]\n",
    "    ax.scatter(\n",
    "        np.full_like(mse, i) + np.random.uniform(-0.2, 0.2, len(mse)),\n",
    "        mse, alpha=0.4, s=10, color=color, label=label)\n",
    "    ax.boxplot(mse, positions=[i], widths=0.3,\n",
    "               boxprops=dict(color=color), whiskerprops=dict(color=color),\n",
    "               capprops=dict(color=color), medianprops=dict(color='white', lw=2),\n",
    "               flierprops=dict(marker=''))\n",
    "ax.set_xticks(range(len(REGIMES))); ax.set_xticklabels(LABELS)\n",
    "ax.set_ylabel('Reconstruction MSE'); ax.set_title('Anomaly score per regime')\n",
    "plt.grid(axis='y', ls='--', lw=0.5, alpha=0.7)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. MC threshold calibration → derive z_MC\n",
    "\n",
    "From the **healthy val set** we compute the static reference threshold and the\n",
    "z-score that encodes its statistical meaning:\n",
    "\n",
    "$$z_{\\text{MC}} = \\frac{\\tau_{\\text{MC}} - \\mu_{\\text{MC}}}{\\sigma_{\\text{MC}}}$$\n",
    "\n",
    "This z-score is **shared across all bearings** — it represents the desired\n",
    "false-alarm rate on healthy operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MC calibration  μ=0.0633  σ=0.02362\n",
      "τ_MC (98th pct) = 0.11925   →  z_MC = 2.36914\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_mc = per_sample_mse(ae, X_val)   # healthy MC calibration set\n",
    "\n",
    "mu_mc  = mse_mc.mean()\n",
    "std_mc = mse_mc.std()\n",
    "\n",
    "PERCENTILE = 98\n",
    "tau_mc = np.percentile(mse_mc, PERCENTILE)\n",
    "z_mc   = (tau_mc - mu_mc) / std_mc\n",
    "\n",
    "print(f'MC calibration  μ={mu_mc:.4f}  σ={std_mc:.5f}')\n",
    "print(f'τ_MC ({PERCENTILE}th pct) = {tau_mc:.5f}   →  z_MC = {z_mc:.5f}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "ax.hist(mse_mc, bins=30, color='#01696f', alpha=0.7, label='Healthy MC val')\n",
    "ax.axvline(tau_mc, color='#a12c7b', lw=2, label=f'τ_MC = {tau_mc:.5f}')\n",
    "ax.set_xlabel('MSE'); ax.set_ylabel('Count')\n",
    "ax.set_title('MC calibration — healthy MSE distribution')\n",
    "ax.legend(); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. AdaptiveTau — per-bearing sliding window\n",
    "\n",
    "One instance per physical bearing. Each instance:\n",
    "- Maintains its own rolling buffer of MSE values.\n",
    "- Computes μ_t and σ_t locally.\n",
    "- Applies **τ_t = μ_t + z_MC · σ_t** (shared z, local scale).\n",
    "- Updates the buffer **only** with non-anomalous samples → no contamination.\n",
    "- Can be pre-seeded with the MC val distribution for a warm, meaningful start.\n",
    "\n",
    "```\n",
    "bearing_A  →  AdaptiveTau(z_mc, window_size=WINDOW_SIZE)\n",
    "bearing_B  →  AdaptiveTau(z_mc, window_size=WINDOW_SIZE)   # independent!\n",
    "bearing_C  →  AdaptiveTau(z_mc, window_size=WINDOW_SIZE)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaptiveTau defined  |  z_MC=2.39  default window=200\n"
     ]
    }
   ],
   "source": [
    "# ── Hyperparameter ──────────────────────────────────────────────────────────\n",
    "WINDOW_SIZE = 200   # <-- main tuning knob; try 100 / 200 / 500\n",
    "\n",
    "\n",
    "class AdaptiveTau:\n",
    "    \"\"\"\n",
    "    Per-bearing sliding-window threshold estimator.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    z_mc        : float  – shared z-score from MC calibration\n",
    "    window_size : int    – rolling buffer length (healthy samples only)\n",
    "    warmup      : int    – min samples before using dynamic τ; static τ_mc used before\n",
    "    tau_init    : float  – static fallback during warm-up (defaults to tau_mc)\n",
    "    \"\"\"\n",
    "    def __init__(self, z_mc: float, window_size: int = 200,\n",
    "                 warmup: int = 50, tau_init: float = None):\n",
    "        self.z_mc        = z_mc\n",
    "        self.window_size = window_size\n",
    "        self.warmup      = warmup\n",
    "        self.tau_init    = tau_init\n",
    "        self._buf        = deque(maxlen=window_size)\n",
    "\n",
    "    def seed(self, mse_array: np.ndarray):\n",
    "        \"\"\"Pre-fill buffer with known-healthy values (e.g. MC val set).\"\"\"\n",
    "        for v in mse_array[-self.window_size:]:\n",
    "            self._buf.append(float(v))\n",
    "\n",
    "    def update(self, mse_value: float):\n",
    "        \"\"\"\n",
    "        Process one observation.\n",
    "        Returns (mu_t, sigma_t, tau_t, is_anomaly).\n",
    "        Buffer is updated only when the sample is NOT anomalous.\n",
    "        \"\"\"\n",
    "        if len(self._buf) < max(2, self.warmup):\n",
    "            tau   = self.tau_init if self.tau_init is not None else np.inf\n",
    "            mu    = float(np.mean(list(self._buf))) if self._buf else 0.0\n",
    "            sigma = float(np.std(list(self._buf)))  if len(self._buf) > 1 else 0.0\n",
    "        else:\n",
    "            arr   = np.array(self._buf)\n",
    "            mu    = float(arr.mean())\n",
    "            sigma = float(arr.std())\n",
    "            tau   = mu + self.z_mc * sigma\n",
    "\n",
    "        is_anomaly = bool(mse_value > tau)\n",
    "        if not is_anomaly:\n",
    "            self._buf.append(float(mse_value))\n",
    "        return mu, sigma, tau, is_anomaly\n",
    "\n",
    "\n",
    "print(f'AdaptiveTau defined  |  z_MC={z_mc:.2f}  default window={WINDOW_SIZE}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Per-bearing experiment\n",
    "\n",
    "We simulate **4 independent bearings** running in parallel:\n",
    "\n",
    "| Bearing | Scenario | Stream |\n",
    "|---------|----------|--------|\n",
    "| A | Always healthy | 200 healthy samples |\n",
    "| B | Outer-race fault develops | 100 healthy → 100 outer_race |\n",
    "| C | Inner-race fault develops | 100 healthy → 100 inner_race |\n",
    "| D | Ball fault develops | 100 healthy → 100 ball_fault |\n",
    "\n",
    "Each bearing gets its **own** `AdaptiveTau` instance, seeded identically from `mse_mc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bearing                           FAR (healthy)   DR (fault)\n",
      "------------------------------------------------------------\n",
      "Bearing A (always healthy)        0.030 (6/200)          N/A\n",
      "Bearing B (outer-race fault)      0.040 (4/100) 1.000 (100/100)\n",
      "Bearing C (inner-race fault)      0.040 (4/100) 1.000 (100/100)\n",
      "Bearing D (ball fault)            0.040 (4/100) 0.910 (91/100)\n"
     ]
    }
   ],
   "source": [
    "# ── Define bearing scenarios ─────────────────────────────────────────────────\n",
    "# Each scenario: list of (regime, n_samples)\n",
    "BEARINGS = {\n",
    "    'Bearing A\\n(always healthy)':      [('healthy',    200)],\n",
    "    'Bearing B\\n(outer-race fault)':     [('healthy',    100), ('outer_race', 100)],\n",
    "    'Bearing C\\n(inner-race fault)':     [('healthy',    100), ('inner_race', 100)],\n",
    "    'Bearing D\\n(ball fault)':           [('healthy',    100), ('ball_fault', 100)],\n",
    "}\n",
    "\n",
    "# ── Build streams and run per-bearing AdaptiveTau ────────────────────────────\n",
    "bearing_results = {}\n",
    "\n",
    "for bearing_name, scenario in BEARINGS.items():\n",
    "    # build stream\n",
    "    stream_X, stream_regime = [], []\n",
    "    for regime, n in scenario:\n",
    "        arr = X_test_by_regime[regime][:n]\n",
    "        stream_X.extend(arr)\n",
    "        stream_regime.extend([regime] * n)\n",
    "    stream_X = np.array(stream_X)  # (N, SIG_LEN, 1)\n",
    "\n",
    "    # compute MSE (batch — model is shared)\n",
    "    stream_mse = per_sample_mse(ae, stream_X)\n",
    "\n",
    "    # create a fresh AdaptiveTau for THIS bearing only\n",
    "    tracker = AdaptiveTau(z_mc=z_mc, window_size=WINDOW_SIZE,\n",
    "                          warmup=50, tau_init=tau_mc)\n",
    "    tracker.seed(mse_mc)   # warm start from MC val\n",
    "\n",
    "    mus, sigmas, taus, flags = [], [], [], []\n",
    "    for val in stream_mse:\n",
    "        mu_t, sig_t, tau_t, flag = tracker.update(val)\n",
    "        mus.append(mu_t); sigmas.append(sig_t)\n",
    "        taus.append(tau_t); flags.append(flag)\n",
    "\n",
    "    bearing_results[bearing_name] = dict(\n",
    "        mse=stream_mse,\n",
    "        taus=np.array(taus),\n",
    "        mus=np.array(mus),\n",
    "        sigmas=np.array(sigmas),\n",
    "        flags=np.array(flags),\n",
    "        regime=stream_regime,\n",
    "        scenario=scenario,\n",
    "    )\n",
    "\n",
    "# ── Print per-bearing metrics ─────────────────────────────────────────────────\n",
    "print(f'{\"Bearing\":<30} {\"FAR (healthy)\":>16} {\"DR (fault)\":>12}')\n",
    "print('-' * 60)\n",
    "for bname, res in bearing_results.items():\n",
    "    scenario = res['scenario']\n",
    "    n_healthy = sum(n for r, n in scenario if r == 'healthy')\n",
    "    n_fault   = sum(n for r, n in scenario if r != 'healthy')\n",
    "    far = res['flags'][:n_healthy].sum() / n_healthy if n_healthy else float('nan')\n",
    "    dr  = res['flags'][n_healthy:].sum() / n_fault   if n_fault   else float('nan')\n",
    "    label = bname.replace('\\n', ' ')\n",
    "    far_str = f'{far:.3f} ({int(res[\"flags\"][:n_healthy].sum())}/{n_healthy})'\n",
    "    dr_str  = f'{dr:.3f} ({int(res[\"flags\"][n_healthy:].sum())}/{n_fault})' if n_fault else 'N/A'\n",
    "    print(f'{label:<30} {far_str:>16} {dr_str:>12}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8c/dj1mc34n41j8l5wb58xh_k100000gn/T/ipykernel_22071/1818303132.py:31: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
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+O9TgJz/5yaNury6r36sdmPo0tEac1LKo6PWLX/yibbTRRkOuX/dZw8c/9KEPdTtzxx9/fPeYTjvttIHrjGcbmROeTx3mVc/nyiuv7LaZ2sn6yEc+0v287qeeQ/1ebRul1vO01A5WXW/DDTfstt2KMGeccUZ7xzveMWR0yI477tgdXlZzCtQhId/+9rdH3EZqR/Wvf/1rF2cqfNRO70knndT9t9bD8DkyprWexvO8dt999+5QgMsuu6x7Pn033nhjd9vHHnvsdK370Yz3tTunqXVSkaLCxzvf+c7usLnPfe5zXeyr97jh804ccMAB3c55ba8VbirC1ntbvRf21fqq7aKWQW2bFejqkMSKGLVca12PpoJYbQf99V3L9/LLL++29zrMYzzva+WTn/xke9nLXtb22muvLlR97Wtfa7vuumv73ve+1wWG4a/HM888s3s+NbqnHudY38PH87qf1mux3l9qJF9tU7Xchm/LffX/AXfcccejLq916RBEYK7SA2BUp5xySq/eKs8///zev/71r95NN93U+9rXvtZbcsklewsuuGDv5ptv7t1www29CRMm9I4++ughv/u73/2uN3HixCGXb7HFFt3tnXjiiWNa6nXd+rr88ssHLrvxxht7T3jCE3o777zzwGX77bdfb7nlluvdcccdQ35/jz326C2++OK9Bx98sPt+0qRJvYceemjIde66667eMsss03vta187cNnf/va37n4XW2yx3j//+c8h1+//rJZN3z777NNdduSRRw657vrrr9/bYIMNBr7/5je/2V3vE5/4xMBlkydP7m211VaPus2RXHjhhd316r/Dl+lpp502cFk9x2WXXbb3ile8YuCyus+63plnnjlw2QMPPNBbZZVVhtzmlClTequuumpvu+226/7dV8tw5ZVX7m277bYDl73//e/vfvdlL3vZkMe5//77d5dfffXV3ffTs408ls9n8LZQalurbX6whRdeuFvvY/Xwww93t3HYYYcNXLbnnnv21l133SHXO/vss7vHcMwxxwxcVtvtZptt9qhtpL9dD3bGGWd01/v5z38+7vU0tefVfy+o7b/cc889vQUWWKB3yCGHDLlePe555pmne52Od90PttZaa3XbwuBlMJbX7kivkdFet7PTTjvt1Jt//vl7119//cBlt956a2/RRRftbb755o9azttss82Q7fXggw/uluPdd9/dfX/ffff1llhiid7rX//6Iffzj3/8o3vPG375cLXdbb/99lO9zljf10baFmt7X3vttbv3tsHq9uadd97e73//+yGXj/U9fKyv+6mp/z+p65533nnd97WcV1hhhd6BBx446v8HjfRVrzWAuYnDXgDGYJtttuk+iVtxxRW7ERs1/Lk+ja7DEeqY6RrRUJ/q1qdj/a/6JLqGVA8fll4jKOqT6rGqIfT1iX3fU5/61O6T8R/96Efdp5L19+k3v/nNbkRF/XvwY6hPLO+5557u08xScxb0P6mrx/zvf/+7m8ujhuj3rzPYK17xiu55j9Xw2f/rk976ZL6vhnfXJ771yXVfjUJ4y1ve0mZErY/Bx6TXc6xPcwffd01Qu9xyy3VDvPtq5EH/E/6+q666qv3lL3/p5qWoUTr9ZVmHQ9QnrnWoxvDRL8Mff31q3b/PMt5t5LF+PiOtx/rd+gR6etWn2HUbg+fIqX/XIU+DD1Oo5zVx4sQhn6rXdttfpoPV4QXDP52uT8/LSNvztNbTeNSn3jWapj7B/799xP9TIxPqMdTrdHrW/WjG+9qdk9T71I9//OPuUI3BhwrV9lvbZY3yGb5t1XY8eORObYN1OzWypj/qpw6Fqm1o8HKt5VSjNKa1XGu0TW139dqY0fe14dtijSaq99263kjrpg45W3PNNQe+H897+Fhf99Ma9VEjDuuwulLLuUYy1WiV4YfzlPr/m1rew7/6vw8wt3DYC8AYfPrTn+4mfaudsvqjsebfqJ32Un881x+stSMzkuFnRalgMniocP1hW0OZ++pndXhJ30i3W4+lhnjX8Pl6HLUTUMP962sk//znPwf+XfMZ1LDtmv+gjnvvq8MchhvpstHUcfPDQ0kNW68dgb7acak/3IdP8DraWQbGaoUVVnjUIQ513zUnxOD7rvsZfr1al4P1d4amdihOrbO6/dHWUQ2Jr/XSnx9ivNvIY/18+jvug++71Lqsnf7pUXNe1PZU8a/mFOkvp9oWameszrIyeBupADS151UqANS8DbXTNngb7z+n4aa1nsardhjrtLi/+tWvuvke6nC4ms+nDtHoG++6n5rxvHanR+34jjSvxlhUdBgtlNZt1vvVSOuwDmuqmFPzn9QcF2PZBgdv1/35l4ab1nZaZ/Cqnfp6L61DsOows5qL6FnPeta439dKHd5y1FFHdbFx8PwbI52eePj6quUz1vfwsb7up7aO6/VS4WLwnFUVjGrbuuCCC9oLX/jCR70f1QcAAHM78QNgDOpT9+ETGPbVH+71h2h9sj3S2SCG78QN/oSw1KSLtVMz+FPB4ZMVTk3/U/saKTDaDm7/D/raAa0JHOsT2JpnoeZxqMdcx7735zGZ2mOdmll1JowZue/Bn8iPd3nWnA2jzaMwfJ0ON3zHZLzbyGP9fGbm/Zf6VP+73/1uNzpjpAhQZ4CpeTFG2lGcmhpNUWesqG25nls9j3q+tSM72tw0g433/oarT+or3tToj4of9d+KKTXXw/Su+9GM9bU72nMa6RP94SpATG9IqVNgT29EGsm0tsH++q35LmoUzXAVqqem5lup5VZzDtWolJpP5eMf/3g78cQTh5yBaCzvazVvTs33UbdZczJVvKuoVfPq1LY9rffV8byHz6iaG+S2227rAkh9DVchcnj8AEghfgDMoPr0uP4gr52G+hRxvGryv8GHOAz+BL6MNCz7z3/+c7fT1f9Ess5yUDs30/p0ribOrGHnNRR/8E7SaKc4nNlqB6mGow8/vW9/JMCsvu9rrrmmW1eDn/uf/vSnIdfrT3BZnxyP9dPOWkeDdxrr+dQOTX/CxRndRmb38xmL8YSD2t4qfNSkijXB42D1eA8//PDu7B41UWU9r/r0+f777x8SBoY/r/rkva5XIz/qLCh9UzuMYVrrabzPq862UWd9Oeuss7pTX9chL3WoQ01C2Tez1v1YX7v994/hZ8fpHy4yNRURRjrbyVhMLZTW+1S93oevw1KjWCoY1SGF49HfrisCTe92XSPs6hDE+qrtreJFTYQ63tMv1yErNUKkDkWskU19FT/GopbPWN/Dx/q6H03FjVpmNZpxuNq26nDOCkDjCd8AcwtzfgDMoJoxvz4drJ2w4Z+M1/fDT7s5XB37XX/w9r8Gz+9Rakj94OPG69PZ+rSyPp2r+62vmpuj/gCvP4qHGzyMvf8p5uDHWadArfuYHer49Rqu//nPf37gstr5HOkP8ZntJS95Sbv11luHnH6yIszwYea1/GvHqs7sUDtEw410WMDwx19n8ij9M7PM6DYyu5/PWHf8Rzr97GijFmrHveZOqLkKBn/VaUMrcvRPtVnPq+ayqFDSVzuF/WU6tW25DD7kZLzrabzPq3/oS62HGjlQ85fU94PNrHU/1tdu7RzXdYefDrtGJExL7cAPfi8az9fznve8qT72er+q963Bo0PqDCc1MqKi13gPp6r3kvqdOlxq8CFAY92uhy/32gbrcJLxnjK2//wqRAweXVPPsw6JGuvvj/U9fKyv+5HU4ZUVOCrYDX8d1ledZabOIvSd73xnTI8bYG5j5AfADKodyzrW+z3veU/3B28NS69P8ep46voUrSaiqx286VXHo9cf+oNPdVtqZ6rvwx/+cDeioo7brslEK6jUfAgVTerUjPXvUn/01h+/O++8c3f6xXqM9SlfXX+kHeOZrZZNHUJ0yCGHdJ+616lu6w/t/uOb0cMQpqaWy6c+9anu9KQ1L0MNTa8h88PnH6lPoWtHtnaIaw6C+lS45mm55ZZbumVcO1x1CMdgtRxr2HsdblE7o7WzXxM5rrvuurNsG5mVz2csKqrUtlUjHmqkQ41sGH660lI7anU/tf2OpLbp2r5r9ESderYOJakd6Xe/+93dsqpts7bZ4XN41OOuT+qPOeaYbue3nlMdvjB4HoPhprWexvO8Bu+M1rqs9dffiR1sZq37sb52F1988e6wmwo79Xqq+6/5KIbPiTK71TKoUSUVOvbff//usJQ61W3FhlqH41XrvwJZzdPx7Gc/u5uIukZQ1Clgv//973fbUL0+RlPLrU6zW+u7RoDUaW4rKFQAGK9aH7W91HZV21Mt6wptFVMGz9MzNWN9Dx/r634k9V5bcaNeAyOpiXprGVaIHBzxaqRhvVaGq/mvBp8WGGCO91ifbgZgTtY/7eJll102zevWaVyf//znd6fKrK/VV1+995a3vKX3pz/9aeA6derKOoXlWNV91218+ctf7k5XWqfWrNMsjnQ6w9tvv7277oorrtibb775ulOjbr311r2TTjpp4Dp1SsMPfvCDvZVWWmngtr73ve91p3Ssy4afFvPYY4991P2Mdqrbes7D9U8xOlidMrhOcVqnuKxTOO677769X/7yl9316jTC03Oq25GW6fDnVOr0o3W604UWWqi31FJLdad2PPfcc0c8ReRvfvOb3stf/vLu9Ky1rOq2dtttt94FF1zwqOd37bXX9nbZZZfuOT3xiU/svfWtb+395z//manbyOx8PrWOpnaa1/LHP/6xO0VpnfK5fjbaaW+PO+647ueD72e4U089tbvOOeec031/55139l796ld3p1qubaT+XY9/+HZXp5qu0/DWKU/rervuumt3+tS6Xj2X6VlPoz2vkZZB31577TVwetbRjGXdT+1Ut2N97ZZaf3Va5Nou6nm+8Y1v7F1zzTWP6aluy5VXXtmdcnmRRRbpHtsLXvCC3sUXXzym99zRTuFb39dt1vqvU4A/4xnP6N5TBp8efCRHHXVUb6ONNuq2nVrXtT7qtMN1itrpeV87+eSTB96j67bqeYx0vf57+kjG8h4+3tf9YDvssEO3jOrUuKOpZVf33T/l7tROdTt4+wSYG8xT//NYBxgAHt9qeHh9ol2nvJza8Pk5Tc0PUCNwalj68LksmHNYTwCAOT8AmK0Gn9Z38HwONYy9hq8DAMDMZs4PAGarAw44oAsgm266aXe8f81jUKcrrYkLnWEAAIBZQfwAYLbaaqut2nHHHddNwlinP61JAWvkx/RMNAgAAGNhzg8AAAAgmjk/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAAABBN/AAAAACiiR8AAABANPEDAAAAiCZ+AAAAANHEDwAAACCa+AEAAABEEz8AAACAaOIHAAAAEE38AAAAAKKJHwAAAEA08QMAAACIJn4AAAAA0cQPAAAAIJr4AQAAAEQTPwAAAIBo4gcAAAAQTfwAAAAAookfAAAAQDTxAwAAAIgmfgAAAADRxA8AAAAgmvgBAAAARBM/AAAAgGjiBwAE+ulPf9rmmWeedvfdd7fHg1e/+tXtgx/8YOzy2XfffdtOO+00y+9nyy23bAcddNBUr3Pqqae2JZZYYobu59xzz23rrbdemzJlygzdDgCMlfgBALNoZ7V2rvtfSy65ZHvRi17Ufvvb386W5f3c5z633XbbbW3xxRdvs8vqq6/eFlhggfaPf/yjzU5XX311+8EPftDe9ra3zdb7fTx42tOe1j7xiU/M9Nut18J8883XvvKVr8z02waAkYgfADCL1A5eBYj6uuCCC9rEiRPbS1/60lm+vB955JE2//zzt2WXXbYLL7PDRRdd1P7zn/+0XXbZpX3pS19qs9MJJ5zQdt1117bIIovM1vtlxgPh8ccfbzECMFuIHwAwi9QoiAoQ9VVD/N/97ne3m266qf3rX/8auE59v9tuu3WHETzpSU9qO+64Y7vhhhsGfn7ZZZe1bbfdti211FLdKI4tttiiXXnllUPupwLHZz/72fayl72sLbzwwu3oo49+1GEd/UMVfvSjH7U11lijCwX9ONM3adKkbvREXa9GqrzrXe9q++yzz5gOtzj55JPbnnvu2R1+8sUvfrHNLpMnT27f+MY32g477DDk8tNPP7095znPaYsuumi3/Oux/fOf/xzxNnq9Xlt66aW72+mr9bXccssNiTu1Ph988MHu+4997GNtnXXW6Zb3iiuu2Pbff/92//33dz974IEH2mKLLTbk9srZZ5/dXf++++5rDz/8cHvrW9/a3ccTnvCEttJKK7UPfehD03y+H/3oR7vfqfXzlre8pQtdfQ899FA79NBD21Oe8pTufjbeeONuO+i788472ytf+cru5wsttFD3+M8444ypHgJz4403toMPPnhgBNNgo21LP//5z7tRHcNHANXhNJttttnA97XOLr/88nb99ddP83kDwIwSPwBgNqgd4y9/+cttlVVW6XZcS+24brfddt0O+i9+8Yv2y1/+cmBHsnaOS+0oV4Cone9f//rXbdVVV20veclLussHO+KII9rOO+/cfve737XXvva1Iz6G2nGvnecKA7WD+ve//73bWe77yEc+0h2GcMopp3SP5d577+122KelHstZZ53VXvWqV3Wh5p577umez+xQhxHV/VXoGKyW7Qc+8IHukJh6DhWUaqTBSGqnfvPNNx8IBXfddVf7wx/+0I1k+eMf/9hd9rOf/axtuOGGXTQo8847bzdq4fe//3030uUnP/lJe+c739n9rMLDHnvs0S3Hwer7GhlT67t+9zvf+U4788wz25/+9KduudchJlNz4YUXdqGg/lv3WUGrvvoqpvzqV79qX/va17rlUqNhalv6y1/+0v38v//9b9tggw3a97///XbNNde0N7zhDV2suvTSS0e8v29961tthRVWaEceeeTACKaxbEu1LJ/+9Kd3Pxu8Puo5Dt42n/rUp7Zllllmtm0rADzO9QCAmW6fffbpTZgwobfwwgt3X/V/ucstt1zviiuuGLjO6aef3ltttdV6U6ZMGbjsoYce6i244IK9H/3oRyPe7uTJk3uLLrpo77vf/e7AZXXbBx100JDrXXjhhd3ld911V/f9Kaec0n1/3XXXDVzn05/+dG+ZZZYZ+L7+feyxxw58P2nSpN5Tn/rU3o477jjV53rSSSf11ltvvYHvDzzwwO75zw7f/va3u+U8eBmO5LLLLuue/3333Tfi8jn++ON7a621Vvfvs88+u7fxxht3z/uzn/1sd9k222zTe+973zvq7Z911lm9JZdccuD7Sy65pHtct956a/f97bff3ps4cWLvpz/9aff9AQcc0Ntqq62m+bj7anmutNJK3Trp23XXXXu777579+8bb7yxu79bbrllyO9tvfXWvfe85z2j3u7222/fO+SQQwa+32KLLbr111f3+fGPf3zI74xlW/rIRz7SW2ONNQa+/+Y3v9lbZJFFevfff/+Q21p//fV7RxxxxJiWAQDMCCM/AGAWecELXtCuuuqq7qs+Xa9RHi9+8Yu7QwlKjUq47rrrupEANeKjvurQl/qEvn8owO23395e//rXdyM+6rCXOpyiRpHUJ+2DDR/5MJIatfCMZzxj4Ps6fKJ/KEiNnqj72mijjQZ+PmHChG6kwLTUYS416qOv/l0jQYaPTumrx95/vmP9Gm1izBqdUYejDD8k44orrugOq6jRBbV863Ch/n2PpH5+7bXXdock1SiPOuSjvmo0SI1auPjii7vv+84///y29dZbd4eQ1O3XCIo6rKR/WEwtx7XWWmtg/pMa9VOHttSoiFKjUGq7WG211bpDjX784x9PcznX7dU6GWn91YifOgTomc985pDlVs+lvy3Vz2s0TB3uUttZ/bwOXRltmUzvttR/frVt12ilUiNU6vCuGhUz2IILLjiwzABgVpo4S28dAB7HakevDnPp+8IXvtAFjM9//vPtqKOO6iJGxYWRduxrDopSh7zUTvUnP/nJbue5dvQ33XTTgcNiBt/XtNQ8DINVMPi/gSPTr4JB7eBW3Kk5QvpqR7sOv6hwM9zyyy/f7fiPRx0eMZKaC6V2nmt51CSv/Tk3KjTVVy3bWpa1g1/fD19uff0gULGgvmrelJorpA4FqnlXKoDUGXRKHUJTE9e++c1v7q5Xv1eHJe23337d7fcPjXnd617XPv3pT3dzvdQhL695zWsGIs2zn/3s9re//a398Ic/7EJKhYFtttnmUfOETGv99U8VW9tShZGKPoMDSelPBHvsscd221GdvaU/X0nNwzHaMpmRbenJT35yF5/qea+88srd8xw8/0jfv//974FtHQBmJfEDAGaT2kGsuSJqtEJ/B/jrX/96t6NYIzpGUnNvfOYzn+nm+ehPkHrHHXfM9MdWUaYCQ+3o90cnVMCoyVVr8s+pTXRa16+d/MFqp7d+NlL8qLPeDI5CM6L/2CrC9P9d83RUMPrwhz/cTUZaamLNaa2bmozznHPO6ebxeP7zn99FjJpE9HOf+1w3sqYfmCowVHQ47rjjuvVZau6O4WoETM0DUvN71OOrkDVYrfPdd9+9+6q5QGp+jooBFVPGa/311+/WV42+GDyp6PBtqSbU7Y/Sqefw5z//ua255pqj3m4Fpbrd6VHxpyZYrXlDapTI8573vCE/749wqscOALOaw14AYBapHec640V91QSaBxxwQPcJff/MJHvttVc3cqF2SGvSxxoJUJ+O12EQN998c3edOtylJo6s37/kkku636lDBWaFenx1xpEKADUJ54EHHthN/jna6XJrNEQ9ttrBXXvttYd81Y5vPd4KCbNSjRqoiFQjL/rqUJfaaa9T4P71r3/tJhatwz2mpQ5rqbOfVESp0RIVNirs1OiR/mEzpcJNPff+7dcyOPHEEx91e0984hPby1/+8vaOd7yjvfCFL+wiQF+dLabuq0JNBYg6TKhGmtSZdqZHHe5S28bee+/dTVRa21KNxqn1WROc9rel8847rzuEp7anN77xjd2hTlNTk7DWhKa33HLLuKNbjbSpwFOjnGrUy3A1Yqg/kgkAZjXxAwBmkXPPPbebC6G+6rSjNaqidnL7c0fUyILasayd9dpJrtOG1qET9Yl4fyRIjZ6oAFE7+DWvRIWRGikyK9RhKxUyage6dkgrANQObJ2KdSQVFWqERZ1lZrh6LvVVj39Wq9Ay+NChCiI1x0Qt6xrVUCNA6swk01KBo0Y5DJ7bo/49/LJ11123ixd1SEyFnrrv0U5T2z8UZvgZeGqekGOOOaYbUVJnkalDaX7wgx8MjCSZHjXaptbdIYcc0s0lUqcorm2utq9y+OGHd9tRrdN6PhVbpnUa4zrTSz22Grkx3sNT6rnU3B+1/OpxDVfxp4JN/zAhAJiV5qlZT2fpPQAAc6U6LKICRs1HMZaRE4+VOoyodvbrEKI5bRRBjQo5+OCD26233jowJ8njScWfmkS2QtlgNYqk1lkdjlRzggDArGbODwCgU2ehqbOO1AiIOmTnU5/6VHf4xJ577jlHL6E6DOi0006bJXOhTK+ahPW2227rRp3U4SWPt/BRZw+qM9B89atffVT4KDWapOayET4AmF2M/AAABiZT3WOPPdo111zTnbmjDumonff+BKiM3RFHHNGdCaaWXc2h0j/jyuNFHVZTc45U+Pn4xz/+WD8cABA/AAAAgGwmPAUAAACiiR8AAABANPEDAAAAiOZsL6Oc2q9OSbfooou2eeaZZ/avFQAAAGCaapL2++67ry2//PJt3nlHH98hfoygwseKK6447aUMAAAAzBFnrVthhRVG/bn4MYIa8dFfeIsttlibWzz0yKT2x9tvbxPnnbdNnOCIJgAAAMZm0uQpbdKUKW31ZZZpC8w396SCe++9txu80N+PH83c84xmo/6hLhU+5rb4sciDD7YnzDexLTDRqgUAAGCM+5OTJrX/PjKp2weem+JH37SmrDA8AAAAAIg29+Wcx9jkyZPbI4880uZEDz8yqfUmTWpTWq9N7vUe64cDMNPNO2FCm2cqE1kBAMBIxI9xuP/++9vNN9/czSY7J6rHNWHy5DZ5nnnafx/rBwMwC0xprS34xCe2iQssYPkCADBm4sc4RnxU+FhooYXa0ksvPUeeAnfKlF57aNIj3WPzuSiQpgLvv//973bvXXe1RZ78ZCNAAAAYM/FjjOpQl/rDu8LHggsu2OZEFT/apAniBxDrSU96UjcKb8rkyW2Cw18AABgjAwTGaU4c8QHweOE9ePZZ9PTT24RbbhlyWX1flwMAzG3Ej7nsj/7DDz984PtDDz20nXrqqbPt/j9w5JHts5/5TEtw+mmntUUXXrjdddddcc8NYEZV4Fj67W9vy++000AAqf/W93W5AAIAzG3Ej7nIIoss0r7yla+0++67b7rnLZnbzPvFL7Z2001DL7zppv+7fAZ84xvfaBs85zntnHPOedwsy1np6quuauedd96oP7/sssva2w8+eKbc1/nnn9822WijtsH667ctN9+8XfO73w387Aff/35bZ6212lprrtm+OGgbqftff91125prrNGOPuqogct32nHHgQD2ePGzn/2sPWH++ds3zjpr4LKzzjyzu6x+Vh566KH2zne8o62x+upt0403bttus0279NJLx3T7Jxx/fHv44Yeneb3jPvrRgX/feuutbZ+9956u58Os8eBWW7VHnva0Nt8NN3TBY4FLL+3+W9/X5fVzAIC5ifgxi02aPLnd9eCD3X9n1AILLND22muv9pkRRihceeWVbZNNNm4bPvvZ7XWveU3773//73wvz1x11Xb4e9/bNt5ww3bhhRe2pyy3XDvk7W9v666zTtvl5S9vF110UXvBllt2O4W//vWvu9+55JJL2habbdbtYG6z9dbtxhtvbI+FChzzvelNbf5tt/3/A8hNN3Xf1+XTG0BqwsS//OUv7YMf+tCQHcC+z590Unveppu2DTfYoO396lcPnNq4dgAPPeSQ9txNNukiVC3b/3nf+7rrbbnFFu2KK65o273whW311VZr55x9dpuZar6ZSf95eJZ8zYyzF13929+2C84/f9SfH/uRj7Q3v/nNbWZYeqml2tnf+U674je/aYe9733twAMP7C6fNGlSt8N+7o9/3C659NL28eOOa3feeWf3s4Pe9rZ22umnt99dc0370bnnDgSTPfbYo1vfjzdrrbVW+9Y3vznwff177bXXHvj+8MMOa3fffXf77e9+1351ySXtCyef3P79/5bltJxwwgljih8fO+64gX8vv/zy7UunnTbu58GsM/kpT2m3nn32QAB5yvbbD4SPurx+DgAwNzHh6Sz01zvuaF+8+JJ2+733tmUWW6y99rkbt6cvtdQM3Wbt6G2yySYDO3x9++yzTzvppM+3dTd4djvwgAPa5048sR140EHdz1ZYccV2yWWXdf+uncEdXvaydtzHPtZe8uIXtxM/85l2wU9+0u24fvSYY9o3vvWttsYaa7Sf/PSnbcKECe173/1u+/CHPtQ+e+KJbXabsu22rff0p7d5/vrXLng8csopbb7XvKb7vi6vn0+Ps88+u+2www5t0003bdddd1274447hvz8Fbvs0l7/hjd0/66d6Qokr9xzz+77+eabr138/yLRUR/4QHvGKqu0y6644v9j7z7gmyrXP4A/adK9GKWUvTcICoKACCqKigNFxAniVhyIehWvgnj1jwvEwRXHVe91IldBrwNFECeKggiyVJRN2XQ3bdL8P8+TvOlJmrZpm3HOye+rIc3JOjnzfZ/zvO9LN95wA9115530yZIltH3bNrrs0kvpvDFjKFScpeX07tBZFA5jv51GtuSEKtM5KMC3ffv2kc1mo6ZNm9Ktt91Gl19xhe+8OZ304MyZZC8tpeXLltGD//gHnXHmmd7n8/Ly6M8//6QuXbvKY87A+HblSvk7OytLghUnnniiBJX+99FH8j016duvn/fvAQMGSNaAyu7o2bMntfJUykadcQZ9vnQpDTvpJAmM9DnmGJk+7qKL6OOPP6beffrQWaNH08hTTqG/3X03hRoHIDmDQou3Hx4xipcZd9rpLzMzU+6LCgt9AqYc+ExKSqr2u4JdV0rXrl1p9+7dVFRUJMGvvXv3UpcuXdzfXVREb7z+Om35/XeZX9auXTu5afFvuPqqq2jtzz/LseLWKVOopLiY9u7ZIxk5/Pp3Fy2im268kX5es0aWx4SJE+n2qVMlaMjBlYEDBtDAQYPozrvuoksvvlj2LV5Xd//tb7Tiiy+kqd8906bRhePG1WnZQ2hwgGP/vHkS+FD4MQIfAAAAYEQIfoQJV1w48LE5dz+1zMyQe378wOgzyGa11vtzebSZs88+2yelnysRXMkaNGgQlTrK6dLLLqO5s2d7gx8XjB3r03RmxIgR8jdf6eUKaVxcHPXq3dub4cHNAK668kqpsFZUVFCjxo0pKtq0obKlSyXwIQGQ4cNlMgc+eDo/Xx8czHhg5kypWJ133nm0aNEin+fXrVtHMx94gPLz8igvP9+n0qldlozXhVqWXOHkSmrXbt2kMml0HADiG/eHkpWVRTfedFPA13HFd/qMGbRxwwZ65NFHA2Yl8falHD9woDSh4Ep3nz596Ltvv6W+ffvKNszLcOlnn9Hf7703YNDjxZdeqtJ3y8iRI+VvrnS31FyN5mwCDozwuvCf/vXXX8vfGRkZUinPz8+Xv0Np586dtPX3332mtWjVSn4rf+fKb7+t8p4zzjpL7tevX09HNc1xOnXp4g1ONGRdaZ151ln00YcfynrgvzmIwf7cupXatGlD6enpNb7/l19+oW3bttHadeu8QS4O3syZM4dWfPWVHGvYQw8/LCO0cFDj1JNPlkAGB8j+9dJLtOqnn+Q1/DkKT+fsLA4q8rEp1pol6Qn38ZE9ebLPNH6MzA8AAAAwIgQ/wqTAbpeMDw58NElNkWn8mKc3TnE/ri/u6JQrfGdqrq7XhK80K1w5V7hioR7z36ofC76Kz5Wwa665hjb8+itde801FDVt2kjGhwp8MH5c38DH/v37pbJ9+WWXuT+rrEyCFZx5oNxw/fWSHdK9Rw/pBFXb7CfFb5hj7fLTLttQNCXRsibFS4ZGOPBnh9O+3FwJ2ilDhg6VdcDLaOodd9Bbb75Jxx53nGQAsNNOP11uteEAyksvvkhfePqpqK8mTZvS/n37Qh784ABCdna2zzSVScEBtcFDh1b7Xg4K+Wd+hNqFF14ozVt4PXATMBX8CFaHDh0ksHTbrbfS2eecQ6dVk4n1zoIF9MrLL8vxhbNNtmzZIsumOpw9xFkkvE+xxtEKvsY41bmpaurCGR8c+FB9gCAAAgAAAEaDPj/CJD0xUZq67MnLp8NFxbQnL08e8/SG4orD0KFD6V1Pm/1GjRpJ5YjT/tnbb71FJw4bVu/PL8jPp1YtW8rfr0V7SMOdO6Wpi5Y89u8ENUiLFy2SK+S//f673P7avl2CG9xcQCkuKqLs5s2l34IFCxaQHnCWCjdNCcettqFDGzq0KFf0VR80bOiQIbRy5UoJXnDTlPyCAvr2m29o8JAh8jxnfkhzCL+bNgj3119/SXbS2wsWeJvJtOBMD82wnJz10aJFC7kFmq5wc50kv6BWKPDv5kwI7U0FIjlbxv851eSFpaal+UyvqclLfddV5y5dJHjB2z4331I6duokWSuBmuVocVBi9Zo1NGzYMOnk9J4ATYc4e2z+c8/RZ59/Tj+tWSNZZ2V+TYFAn1KWL/fp48M+cKBPHyD8PAAAAICRIPgRJty0hfv46J6TTSXlZdQ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o8BQAAAAAAKInkk0FjNApZawLdbZGpDIdzN6fhhGazDmGn0LJt1wn01WfQWbIagkGMj8CQOYHAACA+SDzA0ItUlfLozH0ppmH+jSLSG5/oWTU+TaiumbaOMsdps78QPCjAQtPb+zlDtqYm0tJ8TZKtBlnYwUAAIgEBD/AqE1DotVUgDM+eDQORTpHHDAw5N8DAOFTl0Cm0+TBD3R4CgAAAACg46Yh0WgqYIROKQHA+KPrRJIhgh/z5s2j9u3bU1JSEg0aNIhWrVoV1PvefvttslgsNGbMmLDPIwAAAADEDjX0pQpAcIZEuDIxIj30ZqwPhwlgJghkGij4sWDBApo6dSrNmDGD1qxZQ3379qVRo0bR/v37a3zftm3b6M4776Rhw4ZFbF4BAAAAIHZE8opqJIfeRKeUAOaAQKbBgh9z5syha6+9liZNmkQ9e/ak+fPnU0pKCr388svVvsfpdNJll11GM2fOpI4dO0Z0fgEAAAAgNpj1imqkM00AIDwQyDRQ8KOsrIxWr15NI0eO9E6Li4uTxytXrqz2fQ8++CBlZ2fT1VdfHdT32O126SRFewMAAAAAiNUrqpHMNAEIZUfE/vseP1ZDusYaBDINFPw4ePCgZHE0b97cZzo/zs3NDfieb775hv71r3/Riy++GPT3zJo1S3qHVbc2bdo0eN4BAAAAwLxwRRVAX4ENNQKTNviogpQ8PZYDIAhkGiD4UVcFBQV0xRVXSOAjKysr6PdNmzZNhsVRt507d4Z1PgEAAAAgNq6o4ko0QMMFE9iI5AhMYEy6HryXAxhWq5X27dvnM50f5+TkVHn91q1bpaPTc845xzutoqJC7m02G23ZsoU6depU5X2JiYlyAwAAAAAIVqAmINqmIarCph0BRttcprrPAABf/oEN7liY+9fRBjZU8FHtXzwCk9QHwzACExiTrjM/EhISqH///rRs2TKfYAY/Hjx4cJXXd+/endavX09r16713s4991w6+eST5W80ZwEAAACASMGVaIDIDi0dyRGYwHh0nfnBeJjbiRMn0oABA2jgwIE0d+5cKioqktFf2IQJE6hVq1bSb0dSUhL17t3b5/2NGjWSe//pAAAAAADhhCvRAKHdnziQoTI6AgU2qhuBCZkfoPvMDzZ+/Hh64oknaPr06dSvXz/J4FiyZIm3E9QdO3bQ3r17oz2bAAAAAABV4Eo0QGSGljb7CEwQA8EPdvPNN9P27dtlSNoffviBBg0a5H1uxYoV9Oqrr1b7Xn5u8eLFEZpTAAAAgMhBZ5r6X461VdgAILj9qLbABkZgAlMEPwAAAADAV6wN6xiuAEU4lyOuRAOERjCBjWBHYILYheAHAAAAgAHFUmea4QxQhHM54ko0QGgEG9jge//OTbUjMEFss7hcLle0Z0Jv8vPzKTMzk/Ly8igjI4OMwl7uoI25uZQUb6NEm+77sgUAAIiokmI75W09QJ1bZVNycpIplr7/sKlmHdbRP4PCf5jLhv7ecC5HDsyoYTi136euVAMA6OXY4ix3kLPUQY06Z5M1wWa6+jsyPwAAAAAMKlY60wx2mEs9LkdciQaAUIu1Zo+hguAHAAAAgEHFUmea4QxQxNJyBADji6Vmj6GE4AcAAACAAcVaZ5rhClDE2nIEAOMLdzacWSH4AQAAAGBAsdSZZjgDFLG0HAFijZmHA4+VZo+hZJxeTAAAAADAS3Vop+3wTl0NNFtnmv4BCvU7VUCkIb83lpZjQ6DjVjBqvxja44Z/58ZG3r+ry4ZD5kf1MNpLABjtBQAAwHzMONpLLEHlW7+VSO1QowCxMkqUGX+bE6O9AAAAAABEF0ZNiR50rghGZOZ+MdBcr37Q7AUAAAAAAKrl38yIK5HMDJVIMDfVL4baZs3SLwaa69UPOjwFAAAAAIAaoXPF8DFzp5zRZuZhrJENV3cIfgAAAAAAgOEqkWYIGqj+VLSjFqn+HHi6kX6L3mAYa/CH4AcAAAAAABiqEmmWoAH6Uwkf9IsB/hD8AAAAAAAAQ1Uiwx00iFRWiZk75dRDsxAeiUi7HNXyxghFsQlD3QaAoW4BAADMB0PdAphrqGFtRooSiqBBNIb25eCNtlNOya4ZMDCk3wFQGwx1CwAAAAAAuhPJPi/02LliuDphjXRTFD32pwJgRmj2AgAQwIBu3Xwe/9+MGfTft96q17K69LzzaMumTfL38888452+a8cOOm/kSCx/AADQfZ8XjTtk09Azhstt+Nmn0roN62t8/RsL36S/PzRd/r5h6mT65PNPq7yGpx8z9Djv5777waI6zRP/3tGXjKFfPY8fC1HQIJJNUfTYnwqAWSH4AQAQQS8++yyWNwAANFiksxMyMzLp2yVfym3qTbfRo089HpLPfXTmLO/njj33/DoHDchup4qclhI0eCwuLmRBg0gN7avH/lQAzArBDwCAOtr+11905bhxkrVx2fnnSwYHe/PVV2nMaafR6BEjaMr111N5ebnP+554+GHKz8ujs0eMoPvuvFOmORwOunPyZDp9yBC65ZpryOVy0bdffUW3XXed933vvP66ZJ4AgP6ZYfcvrkcAAQAASURBVOhNMIZodpRZUFggwRD257a/aNTY0TTsrJNpxDkja80Iqc2td99OJ511Cg08dQg9/XzlBYP2fbt4/37+1Rfpkfvucvf1kZhIpbOfoQeWf0ZHLRbqFx9PN9UxaBBov437eTUlX3N52JuioFNOgMhB8AMAIAAVpFC3Re+8431uxt/+Rg/Nnk3vf/45TZ46lR554AGZftZ559HipUvpoxUrqFl2Nn38/vs+n3nn3/9OGZmZ9OGKFfTQE0/ItK2//UbX33orffrtt3TowAH66fvvaciwYbTp11+poKBAXrN44UIaO3481hOAzpll6E0wjkhlJ7C8/DxpmnLs8IH094dm0O033irTc5o3pw/efI++/vgLevaxp+j+h2fUKdhw94xp7mYvpw6hA88/Sw9Mm05ffbycvvv0K3r/k//RrmqCDc7efaXjUWfPPuTKbk7T/3afBGS++Xo1za1Dh6SB9lsOfKSMPYviDh+iiiZNw94URY/9qQCYkS3aMwAAoEcqSKGozIuiwkL68Ycf6IYJE+Sxi68EpaTI35s2bKAnZ82SoEVBfj4lJiXV+j0dOnemLp7+RXr26UO7du6k4wcPlkDKx4sX09Dhw6mwsJC69ewZpl8KAOFqhsCVUL5SHK5mCADVdZQZjswP1eyFLf7oA7rjvr/RB28tIru9jO6cfjdt2LSBrFYrHTx0MOiRVKj3MdLs5ayevd1Ndh6eQXN/XUev/PEbOZ1O2r13D/32x2/UuprfIsGBRQsbFDQIuN9eczlZHA5y2WxU8spbVHFsf1mmqllRNEe4AYD6Q/ADAKAOKlwuymrWzCcwokybMoVeevNN6ty1K/3npZckkFGbhIQE799xVqsU9tjYiy+mu26+mfbv30/nX3QR1hGAgZohqAqSGrYyEs0QIPb4d5SpDbbx9HBuc2ecejpdf/tN8vc//zWf2rVuQy89NZ+Kiouo95Bjgw422A4doLh+x1HKA/fK4z9atqL5v/xMn3+4jDIzMuiK668ke1mZ+/daLN7PKPNMC+t+26SpN/ChfR0CHwDGhWYvAAB1kJ6eLsGPZZ+6e63nYIUayaW4uFie40LZ/957L/BBVxPgqEmbdu3IarPRgtdeo3PHjsU6AjBIfx6RbIYAsS2aHWX+sHoVtW/bztv/R07zHAlOvLHwrTr1VWIpKqKk/3vA+zsOPDqXUjMzKSM9nXbv3U0rvnVnmrCM9AzasWun9Ke1ZNlnAT/bGuQ5NtA8VdlvX3rdG/jQvg4ZHwDGheAHAEAdPTl/vmR2cMemZ510En331Vcy/ba77pIOTy8591zq3qtXwPdeMH68vEd1eFqT0WPGUM/evalpVhbWEYBB+vOorhkChquEUIt0R5mqz48ho06i+x9+gJ5+5EmZfs2Eq+iVN/4tzx0+ciSoz6ouSNh7+CnUrXNX6n/yIJp81210woATvM/ff9e9dPbFY+isi871Bl78XTr2YjrhtBNpyrQ76vTbQrnfotNjAP2yuHhoAfCRn59PmZmZlJeXRxkZGYZZOvZyB23MzaWkeBsl2tCiCcDouNnL6aNH02lnulNwAaBhSortlLf1AHVulU3JybX3yRNsE4OKlq2p+L2P5XXe13DK/Euv+/T5gaYvAFX3JyVa+0iN+3Yd50nbn4l6n/bzwxGUAgglZ7mDnKUOatQ5m6wJNtPV35H5AQCgQ2cMG0YHDxygU0eNivasAEANw4oKi28zBO4kkUeJsP6+JWLNEACMwj/YEO6RVCLZfMi/89S4n1b5/FZ0egwQXcYJ5wAAxJAlX38d7VkAgGpS9VWHiKyieQ7F7d7lHSWCMz5keExPRQedJALUHGzw73A00h2Kqu9S+2tDOjdFp8cA+oZmLwGg2QsAAID51LXZC6ewaytEcsV67FkS7FC4yQtnfvhMQxMXgDrtW2r/MstIKpzxoQ2SSnbLgIFRnSeAYKDZCwAAAECMd3DqH/iw33ibO719zy4ih8PnvRjdBaBmHODw70fDLCOpoNNjAP1Cnx8AAAAAtbTdj//3v7yBD872KJ94tbtfgJatKW5frs97MboLQGzSW38mAOALwQ8AAACAWjo4TXzuKZmuRnbxXrX2dHaKig7UBYZDNadQdp4KAKGHDk8BAAAAguzgtOTZF72BD6no7N6lm44bwRhqGw6VYbsxplB2ngoAoYfgBwAAAEAd2u6rCisqOhCKJlUcYOPtCsOhmkOgAIdZ+jMBMDo0ewEAAACoZ9t9M3fcCOFpquLfpIozi/yHfgUAgNBD8AMAAADAD9ruQ02j/2gDZDy9PgEQzvjQwihBAADhhWYvAAAAAH7QpAXC2VSltiZVAAAQesj8AAAAAAgATVogHE1VMBxqcDAiDgCEGoIfAAAAAAARaqpi9iZVoQhahLqZEQAAQ/ADAAAAAKCeTVX8K/rBZBSVPjrXJ2NEZZbwdCN3lhsoaBH/3NOUcsFZPkGL2oIh/s2M4n5a5dMBcV2bGQEAMAQ/AAAAAAAi2FTFrE2q/IMWCbMepKRZMyluzy6qaNlang8mgwMj4gBAzAY/5s2bR+3bt6ekpCQaNGgQrVq1qtrXvvfeezRgwABq1KgRpaamUr9+/ei115AaBwAAAAD1Y/amKqHiH7RIfO6pyictVYNINWVwmGVEnEj3XYK+UgAMPNrLggULaOrUqTR//nwJfMydO5dGjRpFW7Zsoezs7Cqvb9KkCf3973+n7t27U0JCAn344Yc0adIkeS2/DwAAAACgLjD6T/BU0II7hVUqmudQ3O5d3mnBdBRrhhFxVDMg7e/VBoBYKLN9Iv19AEaj+8yPOXPm0LXXXisBjJ49e0oQJCUlhV5++eWArx8xYgSdf/751KNHD+rUqRPddtttdMwxx9A333wT8XkHAAAACAau1uqfWZuqhFqgoIW/2jI4zDIiTqT7Lgnl9+GYBGak6+BHWVkZrV69mkaOHOmdFhcXJ49XrlxZ6/tdLhctW7ZMskROOumkal9nt9spPz/f5wYAAAAQCRjZAswiYNCiZWuK25dbp45izdLMKNJ9l4Tq+3BMArPSdfDj4MGD5HQ6qXnz5j7T+XFuru9BVCsvL4/S0tKk2cvo0aPpmWeeodNOO63a18+aNYsyMzO9tzZt2oT0dwAAAABUByNbgFn4By2ksm2pfN5+421BZXCYaUSc+vZdUt/Mi1D0lYJjEpiVroMf9ZWenk5r166lH3/8kR5++GHpM2TFihXVvn7atGkSMFG3nTt3RnR+AQAAIHZhZAswC/+ghQRDdu+iilatqXTaDCqbNj3oDA6zNDOqzxDJDcm8CMWQzDgm6ROaIpk8+JGVlUVWq5X27dvnM50f5+TkVPs+bhrTuXNnGenljjvuoAsvvFCyO6qTmJhIGRkZPjcAAACASDHLyBYA2qCFNxjy7sdUfuOths7gqI/69l1S38yLUPaVgmOSvqApUgwEP7jZSv/+/aXfDqWiokIeDx48OOjP4fdwvx4AAAAAehSKq7UAemSWDI76qG/fJfXNvAhlXyk4JukLmiLFQPCDcZOVF198kf7973/Tpk2b6MYbb6SioiIZ/YVNmDBBmq0onOGxdOlS+vPPP+X1s2fPptdee40uv/zyKP4KAAAAAHOPbAH6hFT56GlI3yX1ybwIVV8pOCbpD5oixUjwY/z48fTEE0/Q9OnTpRkL9+WxZMkSbyeoO3bsoL1793pfz4GRm266iXr16kVDhw6ld999l15//XW65pprovgrAAAAAMw9soUeoKIfur4jILqZL/XNvAhFpg2OSfqEpkgNZ3HxeLDgg4e65VFfuPNTI/X/YS930MbcXEqKt1GizRbt2QEAANCV/2fvTuDkqKq2gT+99+yTySQz2RMSAoRACNlYZA8gQZBXBAQEBBQFRCUii8ruCwiIfCiKIsiiAuLLIqIgsiORaDAkkBAgELLOZJ+99/p+53ZXp6unZ6anp5eq6uePY6b3qlvL9D117rk93UG0rd6CKWNGoqLCDzORTqikNad2WqSTI52QchgekM+OfurQgNQr2OVQY2KgK/iSOSAd6EJOt0r22G48J5l7v9Dle3+IhiOIBiKonzISLq/bdv1302d+EBEREdldOddFyBeOie+NqfLWZIbMC56TzIVDkfKDwQ8iIiIisjx29PtuF84kZC35qt1B9mGGgJgdMPhBREREVECsQ1G8NmFHvzfO2mFNzLyg9P2BATETBj9uvfVW9PT0JG//85//NEwz29HRoQqSEhEREdkdC04Wt03Y0e/dHpxJiMgeGBAzYfBDpp2VAIfuuOOOw4YNuyL73d3d+NWvfpXvjyUiIiIyHdahKF6bsKPfG1PliYgKGPxInzyGk8kQERFRuWIdiuK1CTv6vTFV3pw4FI6oNKwzfw0RERGRBel1KKSTr5Pb5TzFaCHaRC8CmTplsB5oKecpgzOtN2cSMu+UzKJc91WiQmPBUyIiIqICYh2K4rUJx8ST2XEoHJHNMj9+85vfoLq6Wv0eiUTwwAMPoLGxUd1OrQdCREREZGfpdSgku0E6+Xq9i6EM87Aqtkn2GQKpWSx625VzFosd6NlI+nlBz34a6rAvIipB8GP8+PG49957k7ebm5vx8MMP93oOERERUSn4H/8DHPOPLUqnMr0ORXrHpxw7smyTgXFohL1xKByRTYIfa9bEx6oRERERmc3IZ59A7U9uROzXdxdlvL2V61CkZx7I7eie0+B6f0VyuXMJGlm5TUo1NCI1Y2goM+KQuYd9MfODqLAcGqdj6aW9vR11dXVoa2tDbW0trCIYjmBFSwv8Hjd8btayJSIiStXTHUTPouWY+b1vwLXu017DUJh23nfmgQQl5LbmdsMRiSDw4ztVB1wPGsltBi3yKz0oJ7iP2nvYF7cvlVo0HEE0EEH9lJFwed2267/nveDpokWL8Je//MVw30MPPYRJkyZh5MiRuOCCCxAMBvP9sUREREQDCo1sxo6HH8/7NKt2zzyIDWtIBj7kX7md2oFjJkLhhkakKvdZguwwjSynZCYqnbwHP2644Qa89957ydvLly/H+eefj/nz5+PKK6/EM888g5tvvjnfH0tERESUldgodioHog9DSQZALjg7GfiQf+U2g0aFxVmCcs9YksCcHgDRMy3kfjMEQCRDSjKlUoOt+vHGDCprMXugjYoQ/Fi6dCmOOuqo5O1HH30U8+bNU0VQFy5ciLvuugt//OMf8/2xRERERFlxbirMNKvlkHkQuOHHhtvMRCjO0IiuJ/5myMThvmrtaWQ5JbP1AwlWCLRREYIfO3bsQFNTU/L2q6++iuOOi0/hJObMmYN169bl+2OJiIiIBuTd3IJhZ53CTmWOmQf+a64w3GbQqDA4NCI/GUsc1mYNVgwkWCXQRgUOfkjg45NPPlG/h0IhvP322zjggAOSj3d0dMDj8eT7Y4mIiIgGVP/vN5PFTlUnafZcQ2dJOp1Wlq+rp+mZB92/fshQ80NuMxOhcDg0Ind2rpVitewIOwcSGGizprwHPxYsWKBqe7z++uu46qqrUFlZiUMOOST5+LJlyzB58uR8fywRERHRgDYf/wW0/+i2XuPtQ2eeg8BV1xpmLLFapyKfV0/TMw+cO7Yban7IbTsFjcyIQyNyY9daKVbMjrB7IMHOgTa7yvv8NTfeeCO+8IUv4LDDDkN1dTUeeOABeL3e5OP3338/jjnmmHx/LBEREVFWAqecgYoKv7FTcfP18SuMnz9ZfXFNn2bUCtO4pl89TZ9CczBXT/X1lddIe+i3o3tOg+v9Fcnb+jS4VmgfKu9pZOV+M3eki3l8mzmQIIEPqwQS+gq0WXk/szuHpmlaId5Y5tiV4IfL5TLcv337dtTU1Jh66Eu28wSbTTAcwYqWFvg9bvjc1pmXmYiIqBh6uoNoW70FU8aMNAQ/+uswmf3KY7r0oI2w2joQDTU7InWfTz0mrD6bip2Pb6utm53+bqSKhiOIBiKonzISLq91+pPZ9t/zvkbnnXdeVs+TDBAiIiIis6Rc619k9SuPVvwCa8Wrp0T5kp6xlHp82yFDya7HtxUzdtKHBqb/HbHD/mZHec/8cDqdmDBhAmbOnIn+3vrJJ5+EWTHzg4iIqHwyP3RSZC+1U6GmF509F1ZitaunRJQ9ux7fVs3YkeVODbQJWW4rBz6iNs/8yHvw4+KLL8YjjzyiAiDnnnsuvvzlL6OhoQFWwuAHERFReQU/7NCpsGsaNhHZ//i2YyDBiqI2D37kfbaXu+++G5s2bcLll1+OZ555BuPGjcOpp56K559/vt9MECIiIiIzdCpUxkdKYUGrzBKRnoZtt2l8icpZPo9vM06Zy9mNqBgKEs7x+Xw4/fTT1c+nn36qZny56KKLEIlE8N5776lCqERERERmYJex23avd0BUzvJ1fA80xCT1s4jspuC5LFIDxOFwqKyPaDRa6I8jIiIiKtugQaZlTZ2qloisKx/Ht52mzOVQGRqsvA97EcFgUNX9OProozF16lQsX74cP//5z7F27VpmfRAREZHpMOWaiMqBHtjVAyBS5NmKdUP0DJbUoYl6BovcX8ohPFRGwQ8Z3jJq1Cjccsst+NznPod169bh8ccfx4IFC1QWCBEREREREZV2ytxUVpsyNz2DRWbrSq3dZKUMFrL4VLfjx49XU93KcJe+PPHEEzArzvZCRERUflPd0tAxDZ3I/Owwu5Wd1sNMopztZXDOPvtsHHHEEaivr1fTzfT1Q0RERET2ke809ELOSGHG2S6KqdzXv5zZZXYru2SwkMULnsrMLkRERERUXvJZSLGQM1KU+2wX5b7+5c4us1sJ2W/lHJNKbjPzg/rCIhxEREREZKpCioUcz1/utQLKff3LnQQ2Aj++03BM6seu3G+lwIddMlgyYXZWYTD4QURERESmSkMv5IwUdpntIlflvv5kj9mt0jNYYrPnGvZredyqgQzOZFM4DH4QERER2VgxryBmTEP/6pfh/O+SQX9+Icfzl3utALOtP69yk10zWHIJZDA7q3AY/CAiIiKyqWJeQcyYht4wHM7t21B58oJkACTbz+9rPH8+0tkL+d5WYKb151VueytkYMsKGSy5BDKYnVU4DH4QERER2VS2X7zz0UHJlIbe89tHoLndcEQiqDj39KzrSxRyPL/dawVYbf15ldu+GNjKPZCRa3YWs6j6x+AHERERURl/8c5XByVTGnps5ix0/99fkxkg2X7xL+R4fivVCigEs60/r3LbFwNbfQcyAj+8wXD+Sw8255KdxWDTwByapmlZPK+stLe3o66uDm1tbaitrYVVBMMRrGhpgd/jhs+d91mMiYiILK2nO4i21VswZcxIVFT4UU4k40ICDzp1tX/23IyZAOlT1OajCGZ/n9/fF3npPKV3EPIxFWch39sKzLj+uewjZP59S51fZNjbhvXJ55Rbcd30qaSFZMSpwPDMWYbHJYAs7ZfLOTkf5/JoOIJoIIL6KSPh8rpt139n5gcRERGRjQ10BXEoV96zSbHOtb5EIcfzm61WQLFT1c22/maqQUK5y5R5oEQiZVtcOD0g0f3rh5JDASUo5Hru2V5DAXPNzmIW1cAY/CAiIiIyoXx0iLOt75DL+PJsUqzNVl/CjMo9VZ37iM2HuXxhAZytLWUb2EoPZEQ/e7zK+EgGQC44u1eweSgz2ZhtJiezYfCDiIiIyKYd4myvIOZy5T2b8fxmqy9hRuVeF4H7iH1kzDzYGB/uEhsztiyDn33VQur5xf39Bihyzc5iFpUNgh933303Jk6cCL/fj3nz5mHx4sV9Pvfee+/FIYccgmHDhqmf+fPn9/t8IiIiIrt2iLO5gpjrlfdsUqyHcgWzXJR7qjr3EXvJlHkQa2qO17co0+BneiBDzqn+H12T92wYZlHZIPjx2GOPYeHChbj22mvx9ttvY8aMGTj22GOxefPmjM9/5ZVXcPrpp+Pll1/GokWLMG7cOBxzzDHYsMH+kUUiIiKyh3x2iAe6gjiUK+/ZpFgXsr6EXaZ1LPdUde4j9pEp8wCuXYUzyz34WcgABbOobDDbi2R6zJkzBz//+c/V7VgspgIal1xyCa688soBXx+NRlUGiLz+7LPPzuozOdsLERGR/VhxtpdizYCR6+wfmWYxKFbGgj40KPXz0mdNGErnqpgzopSyHe2s0PsIGRVj5iirM/t5K8rZXkonFAphyZIlauiKzul0qtuS1ZGN7u5uhMNhNDQ09PmcYDCoAh6pP0RERESlVMyx27lceS91inUha2UUswhpqdvRzsq9nkqxMfOg9MO8zDaTk9mYetjL1q1bVeZGU1OT4X653dJirBrclyuuuAKjR482BFDS3XzzzWpeYP1HMkuIiIiISsUKHeJSd3QKWSujmJ3mUrejnZV7PZViY/2W7NuJAYrS2DUAy4ZuueUWPProo6oOiBRL7ctVV12l6oroJPODARAiIiIqlfQOsd6J0zvghRh6MVj656emWOvLWazl02tlpA4NyketjPT21t+/EJ1mM7SjnRVqH6HMMu2vzDwgszB15kdjYyNcLhdaW1sN98vt5ubmfl97++23q+DH3//+d+y77779Ptfn86G2ttbwQ0RERFQqVrmCWuormIUcGlTMIqSlbkc7F5wt96k/rbCNiIrF1MEPr9eLWbNm4cUXX0zeJwVP5faBBx7Y5+tuvfVW3HjjjXjuuecwe/bsIi0tERERUf5YtUNsl6FB5d5pNlNdFDsPHyv3bURUTKYOfggZjnLvvffiwQcfxMqVK3HhhReiq6sL5557rnpcZnCRYSu6H//4x7j66qtx//33Y+LEiao2iPx0dnaWcC2IiIiIKJ8KWSuj3DvNdikmWuh6KmbPqsj3NjL7+hJZvubHaaedhi1btuCaa65RQYz99ttPZXToRVDXrl2rZoDR/fKXv1SzxHzxi180vM+1116L6667rujLT0RERET5V8haGVaouVJqxayLYsZ9ZKApS1M/3w7byArrOxTFnNqaSsehaZpWws83JSl4KrO+tLW1War+RzAcwYqWFvg9bvjcpo9rERERFVVPdxBtq7dgypiRqKjouxA6kWBnKDuSTZBaTFRlycyea/udKD07SOrByLAoM84mk49tZKX1HayBAjtmqrNUaNFwBNFABPVTRsLldduu/87gxxAaz2wY/CAiIuobgx9E+ZV+5V9YvSNst/XP5zJaYX1zYefAzmBFbR78MH3NDyIiIiIiMhfWRSnujEBm2EZmX9+hDg/S20ayZMox8FEOGPwgIiIiKiMsWkhWKCZqBWafESjf28js6zsUdg3skBGDH0RERERlglNfUr5IDQSphZB6ZVy/gl4ONRKskPmSz21khfUdCjsHdmgXBj+IiIiIyoQVpicl65DOc/qVcblt98CHlTJf8rWNrLK+ubB7YId2YfCDiIiIqEyGq3Bsu/3ZdViT2dar3DJf7Ly+dg7skJF1SrgSERERlamBpmIU2XY+9LHtqVNfcmy7PeRzPzETs65Xps+0c+aLXddXX37JfEsP7Ejgoxjrx6m1i4OZH0RERERlNFyFY9vty67Dmuy6XmQepRzCxVpMxcPgBxEREZHJ5Wu4Cse225tdhzXZdb2IBIN7xcPgBxEREVGZTMXIse32Z9cpO+26XmQdhao7w+Be8TD4QURERGQB+RiuYueihWTvYU12XS+yRhCj0ENTGNwrDgY/iIiIiEwun8NVynl6UrvLdT8x20wq6Thci/Il1yBGoYemMLhXHAx+EBEREZkch6tQofYTKxRb5P5P+ZJrEKOQQ1MY3CseTnVLREREZHJmmIqR7LmfpHcGpY6GDCcx00wq3P8pX/TjQQ946FN+ZxPEKNQ04enBvfRl5Dk+fxyapml5fD9baG9vR11dHdra2lBbWwurCIYjWNHSAr/HDZ+bcS0iIspdzcMPo/vIIxEds+tLnWvDBlS+9BI6zrJmR7unO4i21VswZcxIVFT4S704RKaReuVZx5lUyM4k4yM1iKGGiM2eW7LjRDKsUoOW+ucVO/ARDUcQDURQP2UkXF637frvHPZCREREvQIfIxYuxOiTTlIBDyH/ym25Xx4nsgqz17MwAxZbpHKSS32NQg9NYS2m4mDwg4iIiAwk4yM8cSI8a9aogIdv8WL1r9yW++VxIiuwQj0LM2CxRSoXuQYxWHfGHhj8ICIiIgMZ6rLxqaeSAZAxxx+fDHzI/alDYYjMrNAzNNgBiy1mj1lE1pdrEIPThNuDdQbyEBERUdFIgGPz3XerwIdObjPwQeVS3LBcsNji4LKIUved9BoQLDxsfkMpnpvpMU4Tbi0seJoBC54SEVG502t8SMaHzuqZHyx4Wr5yKW5YTsxSbNFKGTLps+IwmEZ2EGXBUyIiIirXwIcEPDY8+6yhBoheBJXICljPYmDlVmwxl+ErenaAPjxCgmkMfBBZC2t+EBERkYFMZ5ta4yM4d66hBog8TmQFrGdB+SyCy1lxiKyNwQ8iIiIy6DjrLGy54w7DEBe9CKrcL48TWQFnaKB8FsFlFhGRtbHmRwas+UFERGQ/rPlRnljPgtKlFyoVA9XtYM0PKgdR1vwgIiIiIrKmcqtnQQPLZfgKs4iIrI9T3RIRERERUdnoa/hKf5kfQ5kilYjMgTU/iIiIiIioLAylCC6ziIisjcEPIiIiIiIqCxy+QlS+OOyFiIiIiIjKAoevEJUvZn5Q2ap5+GG4NhhTG+W23E9ERERE9sThK0TliZkfVJYkwDFi4UKEJ07ExqeeQnTMGBX4GH3SSfCsiU971nEWC1cRERERERHZATM/qCx1H3mkCnxIoEMCHr7Fi5OBD7lfHiciIiIiIiJ7YPCDypJkekjGhx4AGXP88cnAh54JQkRERERERPbAYS82EdjZjY3/+Rg7N22BKxyDIxRFtCeMWDDS67kOh6P3G2S4K3mnI+XX9Nfqt/t83Pg8+cfp88BTXwFPfSU8wxL/1vrhcBU3FicBjs13360CHzq5zcAHEQ1mCJ1kiqWeN2QIXeVLLxmGzmX7PCIiIiIqDAY/+tHW1gZN05K3PR4PKisrEY1G0dnZ2ev5dXV16l95TJ6TqqKiAl6vF8FgEIFAwLgR3G5UVVWpz2pvb+/1vjU1NXA6nejq6kIkYgxm+P1++Hw+bP1gI175/qPGF2qAO/FRkYre6+cKAA4NiHoBzWV8zBEBXGEg5gRiPvT9vv7egRNnEHDGgKgH0NL2MEcUcIUAzQFE/akPAO4aPyorqlRAJFbrlo9JPqbaSXPBBSeijpj6gdMBp8cJp8cNt9cNn8+v2jAQCUKLxOI/0RhikSg8YScQiSEYC0ODBofHhapAGz7zpx8blq/+y+di0Vk/RKB+eHxdHE545TBxOBBEyLhADsALj9o2YS2CKOQz40GnaHcYjq4I0B2B5nVCq3SqoI/T74bT7VLtLu8ru1cQYTi9Ljh9bvXj8nlQUVOFytHD4Gz0wVnrMwSG3B4PfH6/2scC3d29tmtVTY36t7urC1osZnhMXievD4VCCAeDxv3B7Ya/ogKxWAw9XV293reyuloFt3q6uxFL2b+lzb0+PzxuN8LhMII9PXJn4jGo9qnwy/tq6E4cNw6nQ7WpwwlUVFfD5XEjGAggmrZ/e3w+ddxE5H3TjhuH04nKqir1e1dHR6/l9VdWwuVyqdfJ61P124YOB6qqq/tsQ2kjaatQMIhwKGRoB5fTpd43Fomgu6sbUC/VoMU01RiVldVALIbuzi5EQ2Fo4Vi8LWMavF7ZNm5EohGEI7JPuOP7i9cNT4UXFYlzhN6G6twU09S/FRWVcDical3UuSfevOr/vNKGPl+8DWWbq8ccWbVhRWUlnH20ocfrVe8r2ywg29zQhA61v6g27Ow0nEf7a8PUbSPtIvtaqfdvIesp6yv7dyhlPxz22GMYcdVVyYyx9tpaeDZuxKQzzoBv7dr4+f7cc1H3hz+oGkPB8ePxyR/+gPDo0ep5u515JryffqqWafMXvmBsw9RtI9s8vQ1z3b9L3YahkNruqWQfk30tdf9OJfu+nEdkPxvqOSLQE0JPoBsdXZ3wej2qDXuCAbVtDcvr8cDv8yMSjaK7x9iGso41VfH9u7O7U53bDO3gr1B/14OhIIJp+7ecJ+V8GI1F0ZVh29RWx7dNV0/iWE5tBznPyrYJhxBIa0O3y4XKingbyrqlq66Mt2F3T486x6TyeX3wybaJhNGT1obyGnmt6Ojs2PV3OaFK9kOnCz2BHoTTto3X44Vfzj1yPgz09NmGsrzp5wi9DWU9ZX2zbUM5s9Uk2rCzu0vtq4Y29MvfKo/aLrJ9jG3oRmVi/5bXppPlleWW/UH2i1SynrK+sh/J/pRK9rGqikr1e3tn7/Nsf20o20W2T6Y2dDodqK7spw0rKtV+EQgGEEo/f3s8an+SfUz2tXy1ob5tBmrDTPu33oaZ9m+9DQfavzO34RD274r4eZbnCJ4jyukcEY1E4IrGvycO1Oft6OjodY6QvrKcYzL1efW+tLxGXpuutrZWtWF/fWn5ftKT9r1T2jBbDH70480331QbSDd27FjMnDlTbcjXXnut1/NPOOEE9e/SpUuxY8cOw2PyOnn9pk2bsHz5csNjI0aMwAEHHKA2cqb3PeaYY1SA47333kNra6vhsb333hu77bYbuh1B9BxUA9lXnW6n6mD7XD6Mcjapv2QfOzb2OqmPi41QnffNjp3ogPGPX71WheGoRY8WxEbHNnWf/no3nBgbHanu+NS1GdLtTzUq0oAKzYvtznbsdKa+r4bqaAWGd1SoL6Ibq9tVZooWThzIsRAalwaANcCOPZ2I7mp6pebjGHw7gZ6RDnSNNUZcvG1A7eoYYm5g+769M0galsZUQKZtihPhWqB6axuOfP5hVHXuQJu/Hm+O/jwO3vg0andsxuz7r8Ufjz0LnVV1cHcB9aviB/XW/Xu/77D3YnAFgY6JDgQbjMtUuUlTP6FaoH2KE5Djv21XgKjhvfj7btvX2StIVLcqBk8X0DXGgZ5mJ1wVXhXoEVVdHtS3+RDyRrFlRNqXfc2BMa1VqtPb0tiFiNu41RvbKlARdKO9KoS26sSXi8RiVwY9aGyvRMQVw8ZhHSpwlAwiRWIYvjQKiTntnAyEaxyGzlj1pxr82zQEhjvQOcHYDp4OoO7DmAp4bZuZoQ2Xx+COOdG5hweh4bLvOuFwu9Ry1XX5UdvjQ48njC01XUBUAgkS1NLgCsQw/OP4yW7z7lFo8tZ6x97pwIjNFfDDg531QXRVyj4mYa/E8ra7UbvDg5A3hq1NiTaU+IRsmyjQvNan/khsHhtCNNmG8ScM2+iCr8uJjoYIOofFA2wqCBGLwbddQ80aDVEfsGPv3uva+HZ8m+/cw4lI/PvWrjZco8G/XUPPCAe6xqW1YTtQ/6lDwmvYtq+x7UXDshicEaB9shOh+N+jpKr1Gio2awjWAx27OdW+4a71q4yrmuH1mHHgPPhH1WL54v/0+gM2fcZMVFZWYe3qj7ClpSW+TyR+RtSOwKgRo9Ed7MJH61erQZQSpHN6XPBVVWLWwQep93h/2bJend1pM2eitr4eLRs2YOOnnxoeGzFqFCbvuac6zy7/z396dWbnHXaY+v2jlSvRnfKHU1pkyh57YfjIEdi6qQWffrzaEJcd1tiIPfbZR/1RT39fMeeQQ1Tnfs2HH6Jt+3bDYxOnTkXzmDHYuW0bVq9cmby/sq4Ow0aNQlWidtCq88/HgT//OXytrehoasJ/6uqwezCoMj66R49G5dq1GHPyyVj0zW+q53lbW1XgZPOsWb2WyVdRgZkHHKB+X7l0aa8Ax97774+aujq0rF+PTevWGR5rGjMGk6ZOVYGP9PeVdZR1FR++916vIMbUffZBQ2MjtmzahHUff2x4rGHECEydPl0FMDK14dzDDlPH3yerVqF9507DY7vtsQdGjh6NHVu34uNVqwyPyb4g+4Qcc5ned+aBB6qgzNrVq7F9yxbDY+N22w1jJkxAe1sbPkj72ypBkxlz56rfVyxdqrZ9JBJDqL0H27etw0GzDlDBhk/Xr8W6lvWG104YPQ5TJ+2Ozq5O/Hv5EsNjXrcHh82Lt+E7K5f3+sI5c9oMNA4bjvUtG/Hxuk8Mj40a0YzpU6epL4VvvfPvXut69MHxelPvfbgSbR2JPxgJ03efhlEjm9G6dTPe//gDw2PD6xuw/977qe8Rmd73sLmfgdfpxQeffIgtO7YaHps6cQomjBmP7Tt3YNmqdw2P1VTV4ID95qjfFy9bgphmPEccOHOu+nL9ybo12LB5k+GxiWMmYPeJk9HR1YH/vPtfw2PyZf3QOQer3/+74p1enejZ02diWN0wrNu0Hms2GM8RY0aOwrTd91IdgfR1lQsWRx10uPr93Q9WqM9Ote8e09HUOBItW1rwwZqPDI+NGNaI/abtq4JDmdrwiHmHqoCMtP22ncZzxJ67TcW4UWOxdcc2vPvhCsNjdTV1mLvvLPV7pvc9eP8DVCdk9dpPsGlLi+Gx3cZNwuTxk7Czo021U3qA6OBZB6rf3373vwhFjOeIOfvMQn1tHdZuXIdPNxrPEeOax2LPyVNVECJ9maSDd8QBh6rfl73/Lrp6jOeI/fbcByOGj8DG1o34aK3xHNE0fCT23XO6CmBkWtejDjxcnSNWfvQ+drQbzxHTJu+JMc2jsWXbVqxY/b7hsWG19Zi9z/7qHJHpfQ+ZfZAKVn64ZjVat202PDZl/G6YNG4idrbtxNL3jeeIqooqHLT/PPW77KPpgcF5M+bwHMFzRNmdI7SYhtENTWieNl4FIdL7prKOxx13nPp9yZIlvYIYc+bMQXNzM9auXYv33zcey6NGjcLs2bPV38BMfd7jjz9enSOWLVuGbdvi/U/djBkzMH78eLS0tOCdd4zrOnz4cEyfPh3ZcGjpYSBS2RcS0ZKNJhEoK2R+SCRxR1s7PtyyBT4JfLjdlrmqKx2pcEcAkfYgPAEgvLMbXe3yvrFdnTwN8Dg9KvIZjkYQiUXUY7FwFLFQFAhrcIfj3duQKxrPrnBJJ9qhOtIVHr+6gh6WUI0Ww+iX/4q9fv8zdA9vwn++9iNEh42Ee3srZt17NSq3t2LZ/3wd62cfBScc8DrikYlALL4uyfVV2RseOB2OZOaHfKa70gNXhQfeqir4q/0IB8OqExILRBALhhGTYUkOB3wur3qbYCwUX49gGFF5TiACR3sYoU0d6NrWjpgWzZiV0yt7Rm/jnn6yckLxzr0EiWKeLLNyElw98beTjr0KNGR6XxcQ86a9bwwqQCStFh1kBpIzDNWpz/S+WWc2ZcpA0tvQGV+fQbVhIrMplzbU3zda6QS8sn864ZSsHodDLY+0ocpscmtqv9Zk386mDfVtU6g2HGx2mMMBv9sHT12Fel89YBJLZGO5ow44Nadqv2QbyuEekyF7mtqfNC2KsEeD2v3VuSB+7nEHHXC4HGrbyB9o/b0lAJVx/3Y61OskMCjbRtrcNakOVRMbUDlxOComNMA3skYFeeR5gUD8iqQMxZOssv4yP9Rjra3Y7fTTk7NEidQMD/08G1m9GhNOPVVlhOhCEyZg09NPI9TU1Ps8WwaZH5KVF+kMAsEofF6/2o7dPV0quCnZYeoc7nTEMz/k6mtHF8KBIGIhOZdGVDBU3lN+orK/SMfFldjekhnolqwzuVoM9b6qzQMhdG5qw+jh9airqoHL4UQwHFKvVeHRRDN7XG743F6VXdATNraDHGtVvnh0vjvUg1j6tvH44Ha61BfNUDRt2zhd6nFpw+6wcV8S1Yn37QkF1N8qw7Zxe9VyhaNhBNO+xMp6VEgbahq6Qj29t423Qv2tCoSDiMTSto3LowI68oU+EDH+zZbXyGtFV7C710WUSvnbKlk5Gd5Xb0O5P9CrDR2o8unv25MSmja2YTAS3zbZtuGA28btVZ37freNZCBlaMMqb0V8/864bTzwuDxqWWWZM20b0RnsfTz214bJbZOhDQfaNhUen/rOlKkNPU43fB7Zv2PoKUgbxtAdCgyyDfX9u+827Hv/9qvAV773b/nu6HImzhGxtDbkOSKrNuQ5whrnCP+oGpV5npr5IcEP+ZtqlcwPeU9ZLhm5kdp/T8fgRz/Bj4Eaz2y6AkH8Z+06NFRXosqb3suhdFYYgy+du9C2TgQ2tiEaUNGdxAPJZ8T/P3m/1s9jKc9JPiXD64V0xis8cFV64VLBHK8amhNPrUhkV+ijfxJDWPTXxYdbqBuJjmfi98QwjF3LmRgOEtPUukU6Q4h2BlVnKNqjer/GxnA64UoMA9GHg6jPTmsvTQJJiR/5PbFgydo18WVPWY/Ecmd8POV1at3UciSG7MhdHpfKdHAkhl9JxzrZHolOd7IN9NuJjllW2z8aQzQoAbGwyr5Jvlb+lwiayNChXe/pSBlmk9jo8aZOtrkMyer8cDM63m9B5/ut6PxgM6Ldxj/EfZH1VdvA71FDtNQnJLODJIAXUcO+7MJdVwHfiGoVHJFsGdlXpcOur7O0h7vKi4Ztn2DOfT9Ivm7pZT9Fx+S9k7u7/kvN6vcw49bvJJ/3zhX/L/482ZTuxL6UyHySTC/5V+1brvj9u/ah+HtKEEA/ZtRxI9sxsS+o/SHxrwRS1O8SGHA64wFcdfzF9wt5jqvaB7f8VPngqor//ZDtmfwJRRKvi79m1z4lz0zc1od/JB6PRiVI0gPHziAi27vVuSy0rQvhth5E2gOIdmW33xEREVHhHHHXmaif0qR+j4Yj6mJs/ZSRcHndtuu/M/gxhMYzk4+3bsW9ry/C+62b0VRbjS/N3h/jG4aVerGIyOQkUCId2109dcOvu+6Tq/BZFCWWoJN0bMPtPYi0BVQAR71W78y7ZNhOvKOcrFuSCKKld9KTHXhXSmAnuitwpi9TMmiQeG8V/EmsWzLIlvhXOvLda7aj66Mt6Fq9RQWCpEMeX+/4iuvLlY3KUDuO/eRh1IR3pXB3eOrx/KSz0O2tHfTzyo4DKriqtl0yuJfYDinDrGS7x2sixWsjyY9se7Vt5XE19Cw+/EztU4khcsmgbyKwKds2JtlHElBKBmtT9/1dgV0iIqJycOC1J6F2YqP6ncGPMmS14IcU1bnu2eewYlML/G43tnV1YfeRI/CtIw6Du8gzqJD1RaIxdIWCqPL6uP9QWZIOcqQjiNCWDgS3dCK4uQORjkAieCPBlnggx92yEfvc9l1UbGtBd/1IvHP01zHj+V+hsn0zumpH4K2Tr0Kgejj8Hdsw74mbUdW+Rd3/ztEXYMYLv07e/tfnr0BPxbB4tpJklkghXD3DRLKYEpk18SyeRCAnEQyQbA3J1FD/ViYy/qTjn8ysigcOkr/Lv6nZR/JvNIZIVyLzqiueRSJBAJXhoxdjlqs/KZlHIjULRd1OBC4knLVhZxt6whGVKdTm1eAfUYPPzJoGf2N1fJav2gqVTSPLXcyZvnq6g2hbvQVTxoxERUWGsWlERERlLGrzzA/rrBH1qSMYRGt7O0bX1SISi8HldGBLZ5fqwNZVZBjIT9SHtdt34E9vL1X7z4jqKnxx//2YQURlRzr1UvNDfqomj+jzeTUP/1cFPqRo6ZannlKFRrdsOFkVP5UiqPvsA3SctUANsat6YIt63tannlLFU7duOAXexPP2nelEx1knwi7aenrw6PMvosLjVn+D5LYEQj577FT+TSIiIqKSYfDDBmp8PjTV1iYzP7Z2dWHqyBHqyj3RYDI+JPCxess2NXRK/pXbzCAiykyvDZRaO0j+lWlvU2sHZfs8u5C/PRI8lXOIaG3vwOQRjep+ZpYRERFRqVhiTMTdd9+NiRMnqplN5s2bh8WLF/f5XJkO9uSTT1bPl6t3d955J+xOxi6fd9A87DFypLq6tlvjcHXFnkNeiku+1MsVTvnXiiRTSDI+JPAhV2vlXz2DiOzB6vuoGUngIrVospDb6QGNbJ9nh20pf3vkb9DkEcPV3yQJfMjtjW1tuOvlV3HL8y+qfyXTjIiIiKhYTJ/58dhjj2HhwoW45557VOBDghnHHnssVq1ahZEjR/Z6fnd3N3bbbTeccsopuPTSS1EudmtsxA+PO4azvZSIHYaL9He1lqzPDvsoWWdbyvJI1pheP0hIwIOZZURERFQqps/8uOOOO/C1r30N5557LqZNm6aCIDI/8P3335/x+XPmzMFtt92GL33pS/D5suu0yTzEUiQl9ceqGSC1FX64ZcYEKslwERnjrg8XMeMV2Vyu1jKDyPrsso+StbalnDski0z+ZWYZERERlZqpMz9CoRCWLFmCq666Knmf0+nE/PnzsWjRorx9zs0334zrr78+b+9H5SX9S72wasHZ9Ku1DHzYg5320XJn5m3ZXz0PZpYRERFRqZk6RWDr1q2IRqNoamoy3C+3W1pa8vY5ElyRaXH0n3Xr1uXtvcn+9C/1re2dagy+DBeR21YdLpJ6tZbswW77aDkz67aUoTj91fNgZhkRERGVGns3gBoeI/MBp/4QZYtf6snsuI/aRzG25WCLqWY7FEfPLLvy2KPUv2arU0JERET2ZuphL42NjXC5XGhtbTXcL7ebm5tLtlxEVhkuYpdpJe2yHlbdR9n+5XO+yaWY6mCG4uiZZURERETFZurgh9frxaxZs/Diiy/ipJNOUvfFYjF1+5vf/GapF4/I1F/qrTAjhJXXw4oBgVz2UbO2f7krxPkmNYNDAhl6BocEWvrbxwtdz8OKxxoRERGZj+m/Rcg0t/feey8efPBBrFy5EhdeeCG6urrU7C/i7LPPNhRElSKpS5cuVT/y+4YNG9TvH330UQnXgqi4rDQjhBXXY6D6BnZh1vanwsh1RpZCDsUpl2ONiIiIyjzzQ5x22mnYsmULrrnmGlXkdL/99sNzzz2XLIK6du1aNQOMbuPGjZg5c2by9u23365+DjvsMLzyyiuwM03TEIxE5DfEYhrUf9rAr3M4BngcAzwhB/KZDocDToe8e/xfpzP/n1OuzDwjhNXXI9er41ZkxvanwhlKBkchhuKU07FGREREhWf64IeQIS59DXNJD2hMnDhRBQHKUU84jA07d8IJB9wul7pPAiDyv14KGPDoL5gim0YFPuQ/R0oQRA+AOJwqCOJKCWj1Jx48if8bjWkIRMLwuz1wOR2IaZr68twZCqLS61XPkfsMr1XLGv/sPtcnQ3ukPl3/XX+OfEb8R4WhEo85IbEdWS5ZR2ObaKoNkNouiX9VezikPeR2dttEXutxudFQWYGPt25XQ8VaOzqxW+NweF1uhKPRjK9JpwfPUvch9XtiO+q3+3pt6vOyWu7EeqttkXjL1PWIxmLY3N6B3UY0wu10IRAO97n+qW3Z3zoORF+P1HVpD/So9hxRXYlqnw/R6hha2juwo7sLtX5jQCAS25WuL9swNX1f9vH+2slwnKSsUz5ku0+ZeXpSDoXIPz2DQx/mNNgMjnwPxWHwjYiIiMou+EHZkQ53KBLF8KpK+DzuZDepvw7OQIGiwYSRIlENPeEQKjxeuF0OY8c+w7JCDxBo8SyVqBZTncVYRD41u6yV+DJq2NjWjhdXrsL27gAaKv04aq89VEfxxfdXYXtXAA1Vfhy9154YVVerOpO73rv/zzF0fgdekLhE3z21860HEbR+3igeZHBAc8T/TbaaCoL0H6DJZM7E8djR3YPNnV0YVlmpbq/dsT3lGdm83672SQ14pN5Oldq2qW2XuKNvGQI/yfWYkFiPjk4Mq6rC7Anjkqnv+mekBgV6B6b6Dlz1WtuMy2i8MxrV4HI4sHrrdjRWVWJrVxfGDRumph7d1tWdfN6mtja8sPID7OjuxrDKCswYNwbvrNuA7d09KqBz1J57oLm2tnc7pdAzozIF24ZCjkkXnHC5nCog5nH1DsrpjtpzKrpD76p1a66tweFTd8fWzs5EcE8zBCD1gJ0j8btkoO06Jzh3BdPSghjdoZAKUKYGhPrbX9bt2IFnlsWXSc53x+8zXR3bmd5frW9iH9Dbzq0CrC4VAJLPdGVY99TstGhiPaozBK3i65x9cLKci6nqGYly/o//CdiVoZgp0Chbrr7Cr4KfErTd3N6JSY0N6vGOQCDZ/moLpwShM79X/LmBYAjd4bAKikeYbUhERIRqr9c232MG4tDKNU2iH+3t7airq0NbW5ulpr3d1tmFv698X12pleBHMW3Y2Ya/vbsC27p6MLyqAsdNn4Yx9XVF+WwJujyw6F/4dPtONFZXYmtnN8YNi3/2uh1tyfsmNNTjKwcekAzMWIUcovHOQnaHauqzwhHJhgnB794VkOr9/MzvuysYod+xK8Qw0Aky2+dlXM/4DcNS6YG1Sj2wlrIsxnVJvpn6va/OcLb6yrTY2NaG599bmdzfPyv7e11tsnctAZIH//UW1m7fieHVldjS0YWdEgSpqlTHp74/nnPgASrwkKmt4h3DxL9p7ZG+bT3uwbRzvE0kk0ZlKMXi7d4faf/kfuSOt0pq26QG9uK/OdDS1o5/vL8KO3oCGFbhV8EePUChr6p6jgSIenpUgEgCLaPlOWlrm/pZsiyP/uc/WLezXbX99q4ejKuvw+lzZ6ugRjZielBPLapkL0nApvfzJKjR0t6OF1aswk61jJU4dtoeaK6rMwTe9PbIZuhexn0qcZce5FTBo5QOvWTGqaelvVTWQdZFPz/Ej594NpYeLE3fr2S7S7BJMuQksKFve335VVZQYjlUoCK2a/v2tZv03juNQVMJbMf0/U0PdiSeoNYhsdyZtLR14B/vv68CoPF9ZA+Mqk0EsRPrqt4qETRONmni8XSBQBhd63Zg3MgG+PzezCtERERUJjxOFyYNa0Clx6NuR8MRRAMR1E8ZCZfXbbv+u3XWiExLOiMS+NCDD/Kv3C5WoEE6xdIJlc+u8fvVfZIlIFLvk+fIc2tcpU/ZH/RV+hyjsR4XUOkb+mGugg8hY1ZPMddT1qPCRCfgicMbcP7BB/XKdNL1hILY0R3AiJoqtf9JdsOn27djt8ZG1FZUqHWVLKVgJAyvO/P+GO8AZ071KE2wsXJQ+8v/rV6KzR1d6hiUf99c/bHhnJB8Tmdn4jmdvZ6TSUcgiK5QBKPralTb+txudIbC8LicqPHndmxnCv5I5zwcieGl9z/A+p1tKoi1bsdO/H3FKpx1wFy4E0Pz9G57THXw+/6MgWKXxhpNKcEDPcukj9ftyjIzZprpr04lGXL/WLlK7ZvDKuPZcKPra1NekxqA3BWUyTT0ry/G3TV+Q88KkmV0SyhHDbnatY8n9/Xke+y6JQGP3UeO6PNYG6yAIwS3rwfDKirgz3F/ISIisgNN09CmMinLJxfCPL0JsqxMwYdiBhrkC7F0ACXoIrZ2yjCE+mTmh37fhIZh6rk0ONl2tDMNe7IzWce+9u/0fVKyBuorKrAz0AOP2zmk/bHUwcZ8nRNyPW9kOt6HemxnHFLmcKA7FsbOnqAqtinLKAGPtkBQBTp8Hmt1nGW/ef3D1Whpjweb5N/XPvzIVPtNLsdaOZ+DiIiIaHBYLp2GTO+MSCq/jMOWzojcLlagQb7kSodchhEEwlHVETp+n73VT+p98hx+Ic69o+33uJIdbbk/PUAiQ49+8eo/1b9yu5yl75OThjfgq585ABMbhg15f0wPGsi/etDASueEXM8bmY73Qh3bpT635ZNZ9xs5l0g2T/o5ZbB4DiIiIqKBMPODhkzvjOjZAaUINEgmglzBTL/ql+k+yl42V+etkIkwGPm6epxpn9xn9Jghv3chMh9KcU4Yynmjr+O9FOthFWbcb/I1fMtu5yAiIiIqDAY/KC+K1RkZbGp0tunSlHuHqdTDnvIp37U00ve/fOyPVumQZ3NOGMp5o1jHthnObflgtv0mnwELO52DiIiIqHAY/KD87UwMNNhuTHo2HSYzXlHOhZWuHlulQ57NOaHU541sjslSL6Md95t8Bizscg4iIiKiwmLwg6gMpgMuZIfJbFeUc2W1q8d26ZCb8ZjMJiBitUCm2fabfAYs7HIOIiIiosJi8IMswaodjWyyCoTZ122gDpOZrijnilePy0tfx+Sx0/bC8ytW9huktGog00zyHbCwwzmIiIiICovBDzJ9wMIOHY2+sgo+3roFr3242tLrNtQrymYJbA21M2aW9bCDYrRlpmNyS1c3/rzsXbS0d/Q59MlKw6PMLt8BC7NktRAREZE5MfhBZVXFv1Qd1ExZBeOG1eOVDz7Cuh1tZduJMltgK9fOmNnWw8qK1ZaZjsnm2lp0BIP9Dn0qxvAoKwTS8rWMDFgQERFRsTiL9klUNlIDFn6PK9mpl/sHK72jIf/K7Y5gAB2B4KDeUzpVDyz6F37x6j/Vv3K72FkFExrqEQhHVVbB4VOnYEd3oNe6yTqXg3zuJ/mkOmN+36AyPsy4HsUm6zvQMTnQc/pqy2A4OujjPZdj8sR9p6OxqhJbO7vREQiogIgESFLrUOhBk/6ek+36mu08ZZZlzKXdiIiIiAbCzA/Ku/6ujFbAO6irhZmuzg6rrMTjS/6rAgfZXhk2Q6p6elaBKOcZCsxSYHSoV7DNsh5mz9bI5jmZ2lIyo37zz0XoDIbyngmSKdNnoKFP2Q6PyiWDxQznqYEUehmZRUVERESFwswPyru+rozu7O4Z9NXC9KuzMlRE7xAN5ip7Xxkkxc6ySM0qyHTl2W4zFPR3BXcwV9DNfAXbDOtRStlkvmSbHZPelps7OrG1sxOb2tsLllWTnumjB0QuOuxg9W+mgMVAz8k1G8gs56lSLSOzqIiIiKiQmPlB+d+pMlwZ1WdQyOVqYerV2Ug0hl+/sWjQV9nNOpOHnWcoGOgKbqmnp8zXFexSr0epZZP5km12THpbjq6rg8fpwrCqirxl1WST6ZNNHYr+npNrNtBQz1PFqBVSyHMps6iIiIiokBj8sLFSFs1L79QP9Uut3tGQdcrli7eZZ/LIV8E/MxVJzDawUMrgTz47WkNZDzNtt0J1hgfTYU5tS6/LjYffWpy3YEApi6kW+jxVrHUrZLDPrEFqIiIisgcGP2xqMF+EC9X5Su3US62PfHypHcoX72w7qKXqMPX1+dkYyjIWYvsPJrCQHvwpVjAg3x2tXIJYZqhvMNT2zmedjExtma9gwFCyzwarGOepUtYKKVTQstyzqIiIiKiwHJqmsZx6mvb2dtTV1aGtrQ21tbWwim2dXfj7yvcxrLICj/x7SfKLsIyfl7oSmb4IZ+p8NdXUFqTzmc+OXqE6yAN1mPpry0J8frZFEqVeRS7LWKjOd+9ligcWzpo3F6FopM/tVuxgQCmDD0PZblY9JnM9bgf7ukxt21xbo6axrfC4VUBOaopIrR2p2yE1PwqhWIE8qasjdWukvkix1q2QCtlugZ4QOj7djgnNw+G3YNsQERHli6ZpaAsEMLlhOKq88Yt/0XAE0UAE9VNGwuV1267/bp01oqx1h8JZXXXPdLXwsf/8F9U+76BmUhnq1cJcvujma6hIqkzt8edl76oOU3pbylS7bqdzwGUezLr1d/VW9PU+uQ7fKOTV4kxXcGdNGKeGMPTV0R7s8uSjs22XYTe5yHV/68tQ62Tk83WZ2rY9GEStz4eW9o6iDako1pA2uw0XKcT5nYiIiIjBDxuq9Hqy+iKc3kGIaRre3bgJI2pqMKq2piCp0+lfas2Q9j/YDlO2U+0Odt366gx/vHULXvtwdZ/vk2vHp9Cd775qN/QV2BjM8uQ6tWqmrKZSdbSK0WHtr9Oc6/5mBX21rZ7JZaUhFdns6xwuQkRERDQwTnVrQ5KR0NcUqqlTj6ZPK9kqHXwNaKqpLso0i2ab1jDTlKUjqipx4r7TBz3Vbi7rlunzh1X68coHH/V6n2A4mtyOuU6ZW4wpWvVpRGWoy0DTY2a7PH21bWqbZHqOZDXd/+aiIU1rm0/5nuo4fVrhgabxHcz+VqpjMtf176ttxyWGFfU3jW0plzvTY9meR7KZonewn09ERERkJ8z8sKlM6fyZriCmDk3YrXE4RtbUYHtXD5wOR79XovMxJruvK8/ZDinJt76unuYy1W4uWRWZPv/Q3SfjyaXvGt5HAi+/+ecidAZDhivBgx2+UcyrxdlkOWS7PJnaNr1NpN2KndWUi3wNu8mluGe2+1sxh+LkOzsiU9uaaUjFQFkdgz2PDHbdzJR5R0RERFRoDH7YkNSoCEYi6neHQ2qABBGJaXhq6TLVSWyoqsBHW7ap22fMmY2TZ+6HQCSsOgibOzrwj5XvY3t3AKNqa3HY1CkIRsLoCmoIROKdiM2dHXhhxfsqSCLvdfS0PTG6rv/CsHpZXU3+S/wejWmo8XmxbsdOhKNRbOvqRn1FBf6w+D/Y2R3I+r11DlnZRPGe1M+Vz9y1DOlXN42dzVq/H1/cf6ahw9QVjGcnSEDI5XSi1u/F2h1tyWUer7JBHOgJh5Pv5oADdX4f1u7YiUgsporRjm8YBpfDiVAk2uc6jKiuxhlz5iTbWtRXyPu0IRaLYXNnF3Z29yAci6qslE+27cBflr2Ls+bNU8vqc7sR02IIReLbPvNaJhtMFYE8+4B5CIRD8CfWVwIFehvqbbqrPRNtuatB1fs4+tgGKVsCR++1B557Lz7kYGx9HY7acw9EtSii4V3v31hVhdPnzFbLI+vvcjl6tZfL4Uq2STQWw5bUNqmuwpptOxCJfYi6Ch/W72hTy9LS1g4tpiWzmvrrRBr2n0zNlqFdhkLvsMrnptefzuZzBlOrJn199QCBnCP0/a2h0q/eQ5ZF9u9MAVB5TA+AVnp9OQdt1Dqn3TY8nvlFyfvl6RKQfGbZu1i7fSeGV1fi463b8ed3luPLc+eo/cfldCAUDSMU7fs80f8ypt02nE8GT9+k0n5PL12uzn+y3Ku3bMPTieVOtqcm5yQ5R7ap9dzW1ZXIPnOgO5Q5I09fv9QtsusI3bUO4aim2kmOIwl8rE6025mpnz8IhSidvuvvRfzf1L1FSz0e5T85D+nNlnhNOLGP+t1ybos/LxgMozMcUkMag5n3MCIiIrIpBj9sxOtyoaGqEm6XU9VYSCVp7V2hMEbV1aK2wg+f24POUFg9t9onnaEK9YVROv8ThjfEO8NuL5xOBzbJ1cH3VmJ7twQn/Op1O7q6VcdKvpT/Y8UqnHXAHJWt0e+X8cSX09Qv4ifMmJ648hjvZMmV+40721VnIJv3Nnzp1vvi6otw4vPksxzxwMWu+42vy/SlXdpBffHWv3wnvnh7XMCC6dNUJ357dw8mDBuGY6btCY/TaejISYdL7n9+xfvY0d2NccOGqc5/DBpC0b6DH/pKeN0uFRgQR+45FX9fsUq9jwRHZF2GVchwEB+GVWjY2tWNtkB3spNrCDn0891e70zoz9E7UwP1t/V2lM5Yjx40kfVPvJf+en0767cbqqrwpdmz0BMJodLjU/uWBIYyfAD8Xk+yg5beNkICJ3+Xtu3pwcjUNnF7UVcRU5klC6bvhWhsjdpOY+rrUV9Zida2DhVM2d7VhbHD6hGUitaJZUi2Vdryp65DsgOWWM/Uzlh6BzO+xFqvx3ptk7SXGZ4LTd1Sr3HI58oOrG4kH+8MBLGxrUMFEt1Ol/p3S2enGm4UP5YiKvgm6xsKR7E92gOHQ97Xkdxe8o6doaB6z89MmYLg+7K/9aCppgYHT56szh+py9vS1oEXE8+pr/Sr7SHB0viyZV4PWXbDsZfSzpn2uUztaQjoqd8d6AwG1PljeFUlauRcpgE7egIqoFvr9vd7nkh9z/4CTcllSZw/EmcTw/IM1Pk3hnmAzlgA7YEAmmrjATkJjLb1BNQQMZ9bgmGA04VEFs+u88ixifNNPx+UOHMZg4/p5Bwv7SQBw9rE5++Uz4+E4XXtareB6Ofa+O/9nDxSAuAZ3yd1eyd+leNanbtTz99p206CtdFE4DCqxY9lJ5xoaW/HX99bof5Wyb4hWS2j6+rRIyEPjxc1Xi8q1N8+IiKi8uV0OOFxuVAuGPywEa/brYat+D1ulQGQKhKtUcEFuboos7l0BYOYPKIRU0aMUAGQvkgH94n/vqM6F/IlXa6kb2prUzN3SOHPuooK9IQjaKqtUb8PlmRDzB4/Hl2SnRKN4fZ/vIzdRgxX7zXY9+4rWyHfJjUOx9yJE9UyV6mr3vHAR3wZdj1vt8ZGzJsUf16lyqqQ5/X95T9V6vtMGt6oPk+uzkvQ6p7X3lBXt2srgEAkooYr7T1qVK/tmJptk/lDdgU/MnXSM9Gbdv2Onepqu2RdNFZX4X/22xdj6+t7Pc/QaUzpsGQKLKSv967F7H2ntO3ciRNU2/Zqk3AYu41oxBFTp+LwqVPVvl7p9ar99ql3lqtlnj56FE6SZU7UcEldVmPGTOaARrbbsT/p7536uXIsSDCqwutVgbT0q9+pny/PldoWn2zdgSqfT832NKmxAcfvszeeXf6eOnanjWrGiTP2SW6jZAc+JSCpf77sT3Mnxo9JldWR1tGWgJW8rwSVRtZUYXNHF5Z8uhbfOPQzalnVkqY1za512/U5qZ+Zqe37bKu0fUkyyGSZP96yDXUVfrX95dw2ffToDMfE4DNrCkW2m5xL5Jxc45chLvHlTj2WZXnlfHjAJOP5JtOy98qakayYWEwFh1Jfp14Lhwr6TU58fl1Fyucn2i31/UrZTrm27eNv/xeb2zvV362W9k5VxPdbRxyGsNeHqqogxtfVo6Ii+yAPERERWR+DH2VCvsx+cf/98Ke3l6rOn3zJldv9BT6EfOGW58sXSAlAyFW2Te1t2NjWrq7ISZFUeS/5cj2UZZP3li+schVSvoyLwb53Mb+g68uc/tnpi+B1uuB1Vw7587xwqVl8xGmz909ux91HjlDbsSLx2ECkjZOdKHdu9Y7lPWRYhQwvkf3i0207VCBEOhYD7U/5JFHqigxtMrVppGqTan98v5EMCCEZT98dMaJXJ9Js1m7fkVwXOR5kXUbX1fW73F+eNyf5mr1GNanXSGBROtK5rq+0VyZtPT0qQ2BMfa06BmQ7tAWCat/flX1UPJKNdeqsmcn1n5I4JjKtr5k68dmck/Xl9bhdqB/gPJK+but29N6PZJ/QOZ2ufj/fTG01WOl/t4Tclvu9rPNORERUthxatgOfy0h7ezvq6urQ1taGWknltghJ4V/R0pIx8yNj5zeLzpA8/66XX1UBCfkiKQGJxmpJ0/apK8qZvlTnu+OXr/e2k8Fux3y2rXR+b3n+RVR43KpjIbclQ+fKY4/KKfunlG1ihvfu/3jrVJk12RxvpVvGeJCy2MGvTMtl9sBWsZY70340ecTwjNvIju3W3z4aDobRtnoLpowZycwPIiKiNFEZEh6IoH7KSLi8btv1362zRlSQjIVsnp/p6uBAV6JzJZ06+YJqxS/jZt6O0hmQbah3BuRfuZ1Lh1W2y1AydMzSJmYMyGXKtFqydh1G1dWpIrH9bbdCrX++ssiKsVylDL6Zabn7y3xI/ywrtttAx2R/+2hKfWUiIiIqMwx+UM4BiUJ9Ybbil3GzG0xnyKqd30LIZ9Aol8DSxp1tqjaLzHg01O2WTwxSmptZA5TFPCa5jxIREVE6Bj8oKwxIWFu+O0Pl0rHIZ9Aol8CS1C+RrI8tHV15q7GTz2VlkNKc7BygtHtWCxERERUOgx9EZaAQnaFy6FiU4gp6emBpY1ubLTuxVFh2DVDaOauFiIiICosFT8us4CmVN6sWNywlMxTh5XYjys8x2dMdZMFTIiKiPrDgKRHZRjlka9jxCjq3G5G5jkkiIiKyHqYHEBENdKJk0IjIVHhMEhER0WDxcgkRERERERER2RqDH0RERERERERkawx+EBEREREREZGtMfhBRERERERERLbG4AcRERERERER2RqDH0RERERERERka5zqNgNN09S/7e3tsJJgOILOjg4EnE41DSARERHtEuoJo7urE+07fQj09LBpiIiIUmjRGLRIDM52P1xe64QK9H673o/vi3XWqIg6OjrUv+PGjSv1ohARERERERFRFv34urq6Ph93aAOFR8pQLBbDxo0bUVNTA4fDAStFvCRgs27dOtTW1pZ6cShH3I72wW1pD9yO9sFtaQ/cjvbBbWkP3I720W7R/qSENCTwMXr0aDidfY+AYOZHBtJgY8eOhVXJjmqlnZUy43a0D25Le+B2tA9uS3vgdrQPbkt74Ha0j1oL9if7y/jQsTAEEREREREREdkagx9EREREREREZGsMftiIz+fDtddeq/4l6+J2tA9uS3vgdrQPbkt74Ha0D25Le+B2tA+7b0sWPCUiIiIiIiIiW2PmBxERERERERHZGoMfRERERERERGRrDH4QERERERERka0x+EFEREREREREtsbgBxERERERERHZGoMfNnH33Xdj4sSJ8Pv9mDdvHhYvXlzqRaIB3HzzzZgzZw5qamowcuRInHTSSVi1apXhOYcffjgcDofh5xvf+Abb1kSuu+66Xttozz33TD4eCARw8cUXY/jw4aiursbJJ5+M1tbWki4zZSbn0PRtKT+y/QSPR3N67bXXcMIJJ2D06NFqez311FOGxzVNwzXXXINRo0ahoqIC8+fPx4cffmh4zvbt23HmmWeitrYW9fX1OP/889HZ2VnkNaH+tmU4HMYVV1yBffbZB1VVVeo5Z599NjZu3DjgcXzLLbewcU10TH7lK1/ptY0++9nPGp7DY9Ia2zLT30z5ue2225LP4TFpjT5HIIvvq2vXrsXxxx+PyspK9T7f+973EIlEYCUMftjAY489hoULF6o5md9++23MmDEDxx57LDZv3lzqRaN+vPrqq+ok869//QsvvPCC+mJ3zDHHoKury/C8r33ta9i0aVPy59Zbb2W7mszee+9t2EZvvPFG8rFLL70UzzzzDB5//HG1zeWL+he+8IWSLi9l9u9//9uwHeW4FKecckryOTwezUfOmfJ3Ty4CZCLnzLvuugv33HMP3nrrLdVxlr+R8kVPJ4GP9957T23zv/zlL+oL/wUXXFDEtaCBtmV3d7f6jnP11Verf5944gn15f3EE0/s9dwbbrjBcCxfcsklbGATHZNCgh2p2+iRRx4xPM5j0hrbMnUbys/999+vgh/ScU7FY9L8fY5LB/i+Go1GVeAjFArhzTffxIMPPogHHnhAXVywFI0sb+7cudrFF1+cvB2NRrXRo0drN998c0mXiwZn8+bNmhySr776avK+ww47TPv2t7/NpjSxa6+9VpsxY0bGx3bu3Kl5PB7t8ccfT963cuVKtZ0XLVpUxKWkXMixN3nyZC0Wi6nbPB7NT46tJ598Mnlbtl1zc7N22223GY5Ln8+nPfLII+r2ihUr1Ov+/e9/J5/zt7/9TXM4HNqGDRuKvAbU17bMZPHixep5n376afK+CRMmaD/96U/ZkCbejuecc472+c9/vs/X8Ji07jEp2/XII4803Mdj0vx9jp1ZfF/961//qjmdTq2lpSX5nF/+8pdabW2tFgwGNatg5ofFSfRtyZIlKo1X53Q61e1FixaVdNlocNra2tS/DQ0Nhvt///vfo7GxEdOnT8dVV12lrn6RuUgKvaSE7rbbbupqlaQFCjk2JbqeenzKkJjx48fz+LTAufV3v/sdzjvvPHUVS8fj0Vo++eQTtLS0GI7Buro6NTxU/xsp/8pQl9mzZyefI8+Xv6WSKULm/rspx6dsv1QyzEVSt2fOnKnS762Wll0OXnnlFZU2v8cee+DCCy/Etm3bko/xmLQmGSLx7LPPqmGD6XhMmrvPsSSL76vyrww7bGpqSj5Hsijb29tV5qRVuEu9ADQ0W7duVWlIqTuikNvvv/8+m9ciYrEYvvOd7+Dggw9WQQ7dGWecgQkTJqiO9bJly9R4Z0nzlXRfMgfpREnan3yBk5TP66+/Hocccgjeffdd1enyer29vpjL8SmPkXnJuOadO3eqsek6Ho/Wox9nmf5G6o/Jv9IJS+V2u9WXQh6n5iXDluRv4umnn65qtei+9a1vYf/991fbT1Kz5aKBnJvvuOOOki4vGYe8SDr9pEmTsHr1anz/+9/HcccdpzpXLpeLx6RFyTAIqSmRPrSXx6T5+xwtWXxflX8z/S3VH7MKBj+ITEDG4UlnObVWhEgdcy7RVinYd9RRR6kvC5MnTy7BklI6+cKm23fffVUwRAJWf/zjH1VxRbKm++67T21bCTzqeDwSmYNcoTz11FNVMdtf/vKXhsekBlrqOVm+0H/9619XBf98Pl8JlpbSfelLXzJ8t5HtJN9pJBtEvuOQNUm9D8l+lckXUvGYtEafo1xw2IvFyXAIiZKnV+OV283NzSVbLsreN7/5TVVg7+WXX8bYsWP7fa50rMVHH33EJjYpiZpPnTpVbSM5BmX4hGQQpOLxaW6ffvop/vGPf+CrX/1qv8/j8Wh++t/B/v5Gyr/pBcJlmITMNsG/o+YNfMhxKoX7UrM++jpOZXuuWbOmaMtIgyNDRuX7rP7dhsek9bz++usqM3mgv5uCx6T5+hzNWXxflX8z/S3VH7MKBj8sTq5ozJo1Cy+++KIhnUluH3jggSVdNuqfXLGSk9CTTz6Jl156SaV/DmTp0qXqX8kAIXOS6TElM0e2kRybHo/HcHzKlwOpCcLj07x++9vfqmEQUtW8PzwezU/Oq/KlLPUYlPHJUstDPwblX/nCJ2OedXJOlr+leoCLzBX4kDpLEqCUuh4DkeNU6rekD20i81i/fr2q+aF/t+Exac1sSfnOIzPDDITHpPn6HLOy+L4q/y5fvtxwsUAPQE+bNg1WwWEvNiDpZOecc44q1jZ37lzceeedauqic889t9SLRgOknf3hD3/A008/rcZI6uPlpBifDJeQDrQ8vmDBAvUFT2p+yDRUhx56qEoRJXO47LLLcMIJJ6ihLjItmEw5LdlYMg5dtqUU/pJjVMafyx8ImXJR/oAccMABpV50ykA6vBL8kHOq1H3Q8Xg0d8AxNRtOipzKl2s55qRYm4xt/tGPfoTdd99dfeGTqVJlONNJJ52knr/XXnupGgQyjbFMhysdbPmSKKn5qcOeqLTbUjrGX/ziF9U0t3LlUuqd6X835XG5GCQ1IySwdcQRR6i/q3Jb/m5++ctfxrBhw7gJTbAd5UdqY8lUqBKYlHPr5ZdfjilTpqjiiYLHpHXOr3pAWaZH/clPftLr9TwmrdHnqMvi+6pMjStBjrPOOktNIS/v8cMf/lC9t6WGFJZ6uhnKj5/97Gfa+PHjNa/Xq6a+/de//sWmNTk5/DL9/Pa3v1WPr127Vjv00EO1hoYGNS3jlClTtO9973taW1tbqRedUpx22mnaqFGj1LE3ZswYdfujjz5KPt7T06NddNFF2rBhw7TKykrtf/7nf7RNmzaxDU3q+eefV8fhqlWrDPfzeDSvl19+OeO5VKbT1Ke7vfrqq7WmpiZ1Lj3qqKN6bd9t27Zpp59+ulZdXa2m7Tv33HO1jo6OEq1R+epvW37yySd9/t2U14klS5Zo8+bN0+rq6jS/36/ttdde2k033aQFAoFSr1pZ6W87dnd3a8ccc4w2YsQINbWmTIP6ta99zTB9puAxaY3zq/jVr36lVVRUqOlS0/GYtEafI9vvq2vWrNGOO+44tb0bGxu17373u1o4HNasxCH/V+oADBERERERERFRobDmBxERERERERHZGoMfRERERERERGRrDH4QERERERERka0x+EFEREREREREtsbgBxERERERERHZGoMfRERERERERGRrDH4QERERERERka0x+EFElEFjY6Ph9mWXXYYHHnggp7Y6/PDD8e6776rfb7311uT9a9aswezZs9n+RERkib+FhXbddddh7Nix2G+//bDXXnvhkUceKernE5G9MfhBRFREqcEPIiKichaNRnvdd+WVV2Lp0qV46qmncMEFFyAcDpdk2YjIfhj8ICIapNWrV+PYY49VWRtHHnmkyuAQ99xzD+bMmYMZM2bgjDPO6PWF7Qc/+AF27typrmh94xvfUPfJc8455xx1heu0006Dpml48cUXcfrppydfd99996nMEyIiolJ45ZVXcPTRR+Okk07C1KlTsXDhQkN2iPyN2meffXDUUUehq6ur37+Vkg35ne98R93/8MMP9/mZe+yxB6qqqrB9+3Z1WwIhs2bNwt57743bb789+bxnn30WM2fOTP7tFVu2bMEXvvAF9RkHHngg/vvf/xasbYjIQjQiIurF5XJpM2bMSP6MGDFC++1vf6seO+aYY7RPPvlE/f7iiy9qX/ziF9Xv27ZtS77+0ksv1X73u9+p3w877DBt+fLl6vfhw4cnnyPv4fF4tPfee0+LxWLqea+99pr6fc8999Ta2tqSr1+2bBm3EhERFZX+N+vll1/WGhoatJaWFi0YDGpTpkzRPv30U/WYdCdeeOEF9ftZZ52lPfTQQ/3+rZS/aZdddlnGz7v22mu1n/3sZ+r3xYsXawcccEDyMf1vbDgcVvevXbtWa21t1SZNmqStX7/e8JwzzjhDvV588MEH2ty5cwvSPkRkLe5SB1+IiMyovr5epd3q9MyLzs5OvP766+rql5DvfXJlSrzzzju4+uqr0dbWpn4qKioG/By5sjVt2jT1u1y5kitjhxxyCE499VT88Y9/xPz589HR0aGuqBEREZXKQQcdhKamJvX79OnT8emnn2L8+PGorq5Wf6uEZGbI37H+/laKU045pc/PueWWW/CLX/wCH3zwAZ588snk/VL/4ze/+Y0aKrN+/Xq8//776OnpUVklY8aMUc9paGhQ//7jH//Ae++9l3ztjh078t4eRGQ9HPZCRDQIsVhMffmTwIj8SMDjzTffVI+df/75uPfee7F8+XJcfvnlCAaDA76fz+dL/u5yuZLjn7/yla/goYceUinBZ599NrcRDTpF3eFwqGFW5UCGhh1zzDHJ23L86J0u6p90IA844AD4/X41JC9f9KEN/XnuuefUZ8p5lcyvr79Xme7v72+lqKys7PNzpObHihUr1AUAGSIaCATw8ccf4+6771bntmXLlqmAx0B/Y//zn/8kP18CNUREDH4QEQ1CbW2t+kL3zDPPqNvyJU+fyUXGOctjoVCozwr1qV8Y+zNp0iS43W4VTNHHMJN1SOdbgg/6z/Dhw/HZz35WfWkv1hXaTZs2oa6uruCfJVd0f/3rX2PevHnqCrBkTck4+zvvvBPd3d0F/3zpGEnG1bXXXpu87//9v/+X8+xM5UbaTa7Ir1q1StUbKpSJEyeqfSKVHBMejwe///3vC/a5ZL6/ldmSmh2SESkXAiQDUs4v8r6S9SGZHUICdy+99BI2bNigbuv1QY444gj88pe/TL6XBF+IiBj8ICIapD/84Q/42c9+poqryXAUvcMgU/RJp+/QQw/Fvvvum/G1UtxUXqMXPO2PFECVq6IjRozgNrIg6dhJAEJ+ZB+RYNbnPve5gn+uFNH1er1obm5WgZdCO+uss9QV/s9//vN4+eWX1VVWCUY8/fTT+Pvf/17wz//Tn/6kOkQHH3xw8j4J+kgQptQkEJqNUs5mIUUpP/OZz2DChAkqSFeKQOFdd91V9M+l0v2tHAz5u/rTn/5U/U2VwuB77rmnyrKUfVaMHDlS7T/HH3+8+pxvfetb6n75XMkSkfvkdbIsREQseEpEZFLnnHOO9tRTT5V6MSjHbff5z3/ecN/rr7+uCgNu3rw5eZ8U7DvllFO0uro6bdiwYdqJJ56YLBAopGDf/PnzVdHB2tpa7dBDD9WWLFlieF95z1/84hfaCSecoFVWVqqCgVKcUO7fsWOHeo4U65XPeO6551Qx3aqqKu3YY4/VNm7cmHwfKSJ4ySWXqOdJYcPLL79cO/vss3utR6rHHntMfU6m/VQK9+7cubPg+8/xxx/fq3hievtLgUVZt+9973uqnZuamlQ7pZL1uPfee7WTTjpJq6ioUAUdn376acNzpHDxZz/7WdV+I0eO1L785S9rW7ZsMXzOxRdfrH37299W2+zwww/PuMyZtlkkEtHOO+88beLEiZrf79emTp2q3Xnnnb1ee99992nTpk3TvF6v1tzcrD5PJ9v7/PPP1xobG7WamhrtiCOO0JYuXdpn28lypP7obSLbfvfdd1ftIMUkf/jDH2qhUKjP9hWyzrL+qW0h9+m/p3+WTopmyu2PPvqoz+UkIiLKB2Z+EBGZkBSTa21txQknnFDqRaE8kOJ/v/vd7zBlypTk1XW52i/TQNbU1KjCgP/85z9VWrdkjOgZA5LqLdlCb7zxBv71r39h9913x4IFC9T96VdH/+d//kfVmznvvPMyLoMMQZHpIaWOzGuvvYa1a9caplD+8Y9/rIYf/Pa3v1XL0t7ejqeeeqrf9ZLnS9FeyfpIJ1knxRh2I20jGVcDefDBB9Xwjrfeegu33norbrjhBrzwwguG51x//fWq2LAMT5J2PvPMM5Np9FI/ReoMSBq+1BKQehVyjMrz0z9HMm+kDWX6676kbzOpkTB27Fg8/vjjqt7BNddcg+9///uq7oFO0vgvvvhiNeWnvO7Pf/6z2qdSi0hu3rwZf/vb37BkyRLsv//+aupRfR3SSVaSTBv63e9+V/2u7w+yT8qwIVkOGUIkw+/k6nuunnjiCbVu0uZ6NpROCmbK8Ag5BoiIiAoqLyEUIiIiMlwZl+mSJUNAfuTP7ahRowxZGw8//LC2xx57qAwJnUwhKVfbn3/++YytGY1G1RX9Z555JnmfvPd3vvMdw/MyZX6kX12/++67VQaETn6/7bbbkrclE2H8+PH9Zn7stddeKlulVGT9ZL1kiuiBMj8+85nPGJ4zZ84c7YorrkjelveRDAddZ2enuu9vf/ubun3jjTeqqTtTrVu3Tj1n1apVyc+ZOXPmgMudaZtlIlkdJ598cvL26NGjtR/84AcZnyuZRZIdFAgEDPdPnjxZ+9WvftXnZ8hU3ulZMOlkv5g1a1bOmR9iwoQJ2k9/+tOM7y9tdt111/W7DEREREPFqW6JiIgKILXgnkyzKFM3HnfccVi8eLGqryAF+D766CN1lT29gKfUYRCSWfDDH/5QjV2XK/pSNFAyOCRrI1U2mQ8yu8LkyZOTt0eNGqXeU8jUzPJZc+fONRTnlWkr+5uJI96PHzzJNhjM7Atf/vKXM2ZRyDSXQmYqGUh6HZ7U9c/0HMkSkVoi+nNke0lNE8nOSSfba+rUqep3aTPdTTfdpH50kkkhmQ59bTOZzeL+++9X21fWTTKA9BlYZDk2btyoMjkykeWTDKP0uh3yPvr+lK3HHntM1VGQ18l7RiIR1RaFItOCF6M4LhERlTcGP4iIiApAOs+pQxJ+85vfqGEgMoTgRz/6kepUSkc500wXepFbGfKybds2NfRAAiYypeSBBx7Yq5CmfNZAZFaN9GEpuQYvdNLhl6lSB+uvf/3roIp89tXxlo6+rIcEl3JZ//TATn/Pke0lw9BkeFA6CaRk2hZS2Dh1WMzo0aMzPk88+uijatjJT37yE7WNJSh22223qWE6eoCgP7J8shwSKEs3mOKvixYtUsN9ZAiQDMuSfVaWTZZL53Q6e+07QynaKsNyWNiZiIgKjcEPIiKiIpCOtHQa9WwFqccgV9hltoK+OvdSN0IyRqT+hFi3bh22bt2a92WTDq7UXfj3v/+tZisSkmXy9ttvJzMPMpFpmL/0pS+pmV3S635I51jqhmSq+yGBnHyQ2hrTpk1TGRXHHHMMCkm21//93/+pKVtl5p5sNDQ0qJ9syLaWKYovuuii5H2pGRsSDJHPlhkzJKso0/K1tLSoZZPn5erNN99U2+cHP/hB8r70LB0JVKRPWyqz/KQHj9K3VaZpvvVMJ6mlQkREVEgseEpERFQAwWBQdUblZ+XKlbjkkkuS2QNCrq43NjaqoIEUe/zkk0/UVXuZqnH9+vXqOVLgVAqUyuslA0BeM1AGQK5k+W6++WYVyFi1ahW+/e1vq4yK/qbLlawGmZL59NNPV8M7pBCodJT/8pe/YP78+WqYSKFJdoIUPS00KTQqGQqyrhIkkg77888/j3PPPTdjp36wZFtL+8l7fvDBB2q6YPmc9CKpkoEhQ1I+/PBDFZySKT2FtLdkjJx00klqiuE1a9aoQIYEMeR9B7McMuxGsj1kHeWznnzyScNzpPCrvOdDDz2kluPaa6/tFQxJJwEZKbS7YcMGQwBPCvnqGU1ERESFxOAHERFRAchsIDIMQX7mzZunOrIyk8fhhx+erMEhnUGpAfGFL3wBe+21F84//3x1JVzPBLnvvvtUAEKu6p911lkqMCKZIoVwxRVXqI792WefrTqiUttCAgv91dOQwMgf/vAH3HHHHWpmmMMOO0zVzZBOugR15PWFJm0mw2ikbkkhyZAVyc6QQIdkmeyzzz74zne+o4aUSEbPUH39619X+4EEk2R/keFOqVkg+jCoO++8U2UDSd2Uz33ucyr4oG8LaQfJ3JGAjAxJkqwcCUZJVk+2TjzxRFx66aX45je/qbJ+JIAigZhUsl3lvssvvxxz5sxRsw/JftMfmelFAjJSdyZ1iMsjjzyignpyPBARERWSQ6qeFvQTiIiIyHKk1oUEZCS748Ybb4SZyRSvEiC66qqrSr0oNAiSASJTJUsWyaRJk9h2RERUUMz8ICIiIpUhIMVYZcjF8uXLceGFF6qhOFLXw+ykMGimWVjI3CQTRLJYGPggIqJiYOYHERERqWKqMkxCajdIUuj06dNxyy23JAugEhEREVkZgx9EREREREREZGsc9kJEREREREREtsbgBxERERERERHZGoMfRERERERERGRr7lIvgFmn99u4cSNqamrgcDhKvThERERERERElIEUau/o6MDo0aPhdPad38HgRwYS+Bg3blyfjUZERERERERE5pq5buzYsX0+zuBHBpLxoTdebW0trCIYjuD91la4nU64XRzRRERElCoS7EF024eY1DgSPp+fjUNERJRCi0WgRSPwNu0Jp9sHq2hvb1fJC3o/vi8MfmSgD3WRwIfVgh/V3d3we9zwublpiYiIDH8nA14Eg1WoqxsGX0UlG4eIiCiFFgkiFg7AW1trqeCHbqCSFewhD1I0GkU4HIYZhcIRaJEIYtAQ1bRSLw4RUd45XS44+hnLSURERESUCYMfg9DZ2Yn169ergipmJMvlikYRdTgQKPXCEBEVQAxAxbBhcPusdzWCiIiIiEqHwY9BZHxI4KOyshIjRoww5SwwsZiGYCSslo3XRYnIbiTAu337drTv2IHqkSOZAUJEREREWWPwI0sy1EW+eEvgo6KiAmYkwQ9EXAx+EJFtNTQ0qCy8WDQKF4e/EBEREVGWmCAwSGbM+CAiKhc8BxMRERFRLhj8ICIiIiIiIioAx8OPARs2Gu/csDF+PxUVgx8Wu+L5wx/+MHn7sssuwwMPPFC0z7/xhhvwy1/8Anbw8EMPoaaqCjt27LDduhERERERUelJgMN96ffh/vyZuwIgGzaq23I/AyDFxeCHhVRXV+P3v/89Ojo6ci7aajXO++8H1q0z3rluXfz+IfjTn/6EWbNn4+mnny6btiykd5YuxQsvvNDn4//+97+x8NJL8/JZ//jHP3DA3LmYNXMmDj/0ULy7fHnysb8++yz22Xtv7D1tGu5P2Ufk82fOmIFpe+2F//3Rj5L3n/T5zycDYOXi1Vdfhd/rxZ8efzx53+N//KO6Tx4TwWAQl3/ve9hrzz1x4Lx5OHr+fCxevDir9//ZXXchFAoN+Lyf3H578veNGzfinLPPzml9iIiIiMxKO/IQaBPHw7FmrQp4OBYvif+7Zq26Xx6n4mHwo8Ai0Sh2dHerf4fK5/PhzDPPxC8yZCi8/fbbOOCAeZiz//746rnnIhCIT3Y7dffd8cPvfx/z5szByy+/jDGjRuG7Cxdixj774Itf+ALeeOMNHHH44apT+K9//Uu95q233sJhhxyiOpjzjzoKn376KUpBAhyeb3wD3qOP3hUAWbdO3Zb7cw2AyGwRH374IW66+WZDB1B3769/jYMPPBBzZs3C2WedpYrdCukAXvbd7+KgAw5QQShp22uuvlo97/DDDsOSJUtw7DHHYM899sDTTz2FfJJiu5GeUEF+8jF18zvLluHFf/yjz8dv+/GPceGFFyIfRjQ24qk//xlL/vtf/ODqq/Htb39b3R+JRFSH/bm//x1vLV6Mn/7kJ9i2bZt67Dvf+hYeevhhLH/3XTz/3HPJgMmXvvQltb3Lzd57740n/u//krfl9+nTpydv//AHP8DOnTuxbPlyLHrrLfzmvvuwPdGWA/nZz36WVfDjjp/8JPn76NGj8eBDDw16PYiIiIhMbcxoRJ7+/a4AyIJTk4EPuV8ep+LhbC8F9PHWrbj/zbfQ2t6OptpanHfQPOzW2Dik95SO3gEHHJDs8OnOOecc/PrX92LGrP3x7Usuwa/uuQff/s531GNjx43DW//+t/pdOoMnnHgifnLHHVhw3HG45xe/wIsvvaQ6rrffeiv+9MQT2GuvvfDSK6/A5XLhL888g1tuvhm/vOceFFvs6KOh7bYbHB9/rAIe4d/+Fp5zz1W35X55PBdPPfUUTjjhBBx44IH46KOPsHXrVsPjJ3/xi/jaBReo36UzLQGS0884Q932eDx4MxEk+tGNN2LylCn495IluPAb38D3LrsMf3vuOXy6Zg3OPOMMfP6kk5Av0UAY/3fwzSiEk/95FdwV3l73S1BAflpbW+F2uzF8+HB869vfxpfPOsu4bNEobrj+egQDAbz04ou44cYb8dnjjks+3tbWho8//hi7T52qbksGxj8XLVK/j2xsVMGKz3zmMyqo9Myzz6rP6c+M/fZL/j579myVNaBnd0ybNg1jxoxRt4/97GfxjxdewCGHHqoCI/vsu6+6/5RTT8Vf//pXTN9nHyw4/njMP/JIXH7FFcg3CUBKBkUq2X9kumxpM5mxJF1dXZ36t6uz0xAwlcCn3+/v87Oy3Va6qVOnYsOGDejq6lLBr02bNmH33XePf3ZXF37/u99h1YcfquUVEyZMUD+pZB3OP+88LP3vf9W54lvf+Q56uruxaeNGlZEjz/+/J5/ERRdeiP++/bZqj7PPOQeXLlyogoYSXJk7ezbmzpuHy773PZzxpS+pY0u21RWXX45XXn5ZDfW78qqr8MVTThlU2xMRERGZxpjRiP7idhX40MltBj6Kj8GPApGOiwQ+3m/ZjNF1tepfuX3d8Z+F2+XK+X1lqt3Pfe5zhpR+6URIJ2vevHkIRMI448wzcedPfpIMfnzh5JMNQ2cOP/xw9btc6ZUOqdPpxN7TpyczPGQYwHlf+YrqsMZiMdQPG4aSGDcOoRdeUIEPFQA57DB1twQ+5H55PBcSzLju+utVx+rzn/88nnzyScPjy5Ytw/XXXYf2tja0tbcbOp2pbSlkW+htKR1O6aRO3WMP1Zm0OgkAyY/UQ2lsbMSFF12U8XnS8b3m2mux4r33cMuPf5wxK0n2L92cuXPVEArpdO+zzz5485//xIwZM9Q+LG34wt//jh98//sZgx73/uY3vWq3zJ8/X/0une7RicCHnk0ggRHZFun3v/766+r32tpa1Slvb29Xv+fTunXrsPrDDw33jRozRq2rfOaif/6z12s+u2CB+nf58uXYmTIcZ/LuuyeDE0PZVqmOW7AAz/7lL2o7yO8SxBAfr16NcePGoaampt/Xv/POO1izZg2WLluWDHJJ8OaOO+7AK6+9ps414kf/+79qeloJahx1xBEqkCEBsvt+8xss/s9/1HPkfXRyv2RnSVBRzk3lNiyJiIiIbGbDRrguusxwl9xm5kfxMfhRIB3BoMr4kMBHQ1Wluk9uy/3DKuO3cyWFTqXDd1zK1fX+yJVmnXTOddKx0G/L73odC7mKL52wr371q3jv3Xfxta9+FSUzbpzK+NADH0Ju5xr42Lx5s+psf/nMM+PvFQqpYIVkHui+8fWvq+yQPffaSxVBTR32U1lRYXi/1PZLbdt8DCVJ5fJ7VIZGIch7F1JrS4sK2ukOOvhgtQ2kjRZ+97t45A9/wMz991cZAOLoY45RPwORAMpv7r0XLyfqVOSqYfhwbG5tzXvwQwIII0eONNynZ1JIQO3Agw/u87USFErP/Mi3L37xi2p4i2wHGQKmBz+yNWnSJBVY+va3voXPnXACju4jE+uPjz2G395/vzq/SLbJqlWrVNv0RbKHJItEjikxrFTBVyIiIqKhShQ31Ye6SMaHBD70GiAMgBQXa34USI3Pp4a6bGxrx/aubmxsa1O35f6hko7DwQcfjP9LjNmvr69XnSNJ+xePPvIIPnNI7sVzOtrbMWZ0fPzZww8/jJJat04NdUmlbqcXQc3SU08+qa6Qf/Dhh+rnk08/VcENGS6g6+7qwsimJlW34LHHzDEFlWSpyNCUQvzIew/02UMhHX29Bo04+KCDsGjRIhW8kKEp7R0d+Ocbb+DAgw5Sj0vmhxoOkfaTGoT75JNPVHbSo489lhwmM0oyPTZsSD5Hsj5GjRqlfjLdr5PhOv60oFY+yHpLJkTqjx6IlGyZ9Mf0IS+iqrracH9/Q15y3VZTdt9dBS9k35fhW7rdJk9WWSuZhuWkkqDEkrffxiGHHKKKnF6ZYeiQZI/d88tf4u//+Af+8/bbKusslDYUiIiIiMiuHC+9bqjxoc2dZagBIo9T8TD4USAytEVqfOzZPBI94RD2bG5St4cy5CXVFVdckax1IGTK20su+SbmzpqFzo4OXPD1r+f83gsvu0zVr5CCp/qV6pJIFDfVa3yEXn3VUAMklwDI448/rmqepDr++OPx5BNPJG//8OqrcdCBB6paEHIFvtzJMAoZhtCfmurqPmch2mOPPbB69erkbRlqJZ1i6VxLtoXUmJEgmwRFhGR9yHCI9B99yIsMgzjl5JNx5113Ydreeyffd86cOXhvxQqVXSDvLYVN5b1kmIsEG5YvW6ayD2RmE9nmum3bt6vnlMu2SnXt9dfjmuuuM9xXVVWlhs7JOUCGqoi1a9fiub/9zfA8qZUjw+JkGIscMzIMJn1fkHORDH+R7bx+/Xq89NJLydfLNsk0a9KRRx2lhr7IewsOeyEiIiKr0s46DZGf3mTM8EgUQZX75XEqHoeW7/x8G5Dx/3K1Vcaw66nwcuVarjZLqne2V2GFpK7LUBfJ+MhX4KMvsZiman7I1V87RLX02V4MNT5SAiLhe+5B7LzzSr2YtidBtpP/539wwIEH4qd33pnxOdLhPuH441Vn+fobbjAUPJVTjExLK8Us9WPntFNOQfOoUfh/d92lMpWksOza9euzWp6bb7pJTZO62267qduS9fR6on6GFOiVDIRYYkiNDN3SZzD6xgUXIBAM4owzzsDV11yTnKL31h//GL9/5BGUy7aS6Wyl0PEjaVlNp592Gr5x0UU47LDDVP0Vqbvy7LPPqqFewxoacPMtt6gAk27ZO++obBwJUkiR1dt+8hM1fOzun/9cFVyWGiVS8FSKoi5+6y1MmDhRBVO/9rWvqUKz37/ySlV4VrLU0gueyv7w8ksvqfeVgqdShDg1U0eytfzDh8NVyuAsWVIw0IVg60pMaRoLX8XQhqASDYXj4cfiU1ymzvSwYaO6CszOEBGVihYJIhYOwNs8DU53/oddF7P/ngmDHwUOfhST3YIfegBEzeqSWiNg3To4X3iBgQ8Lue3WW9XsH6eeZq7otgRKZOjNEUccUepFoSwx+EFDweAHmSXw4b70+8apLlPqAvBqMNHQji8GFnOn2Tz4YZc+MtmUyuxIL444bhwDHxZz0cUXw4ykqC0DH0REVEzSMdPH+6uAx+IlhoKIquNGRDkHFuV4koCikggsyv3yOJU3zvZCRFl7d/lynJdWgHbixIn445/+1O/rpI6E2bI+xFe+8hXYVa7bioiICiwx3l8PeLgXnKruNmSCENGQA4upM6swsEiCwY/BHlQskUJlbPo++6jio2R+dt1WPAcTkS2MGa06ZnrgQ8htBj6IhnZcMbBI/WHwI0tSqE9qaWzZsgUjRowY8vSfhar5EbRZzQ8iotTAhxTXlXlgnAUuIE1EVFAbNqor0qnkNjM/iIaIgUXqB4MfWZJpGceOHauma1yzZg3M2jEIR6Mq+GG+0AwR0dBJ4KNi2DA4nAzxEpFFpRQ3lVT81NR8uZ8BEKKhHV8MLFJfGPwYhOrqajV9YzgchhmFwhF8tHUrfG4XvG5uWiKyH8n4YOCD7IgzFJQPmc5WD3zogY7UVH1Od0uUIwYWaQDsIeeQASI/ZuRwReBwu+H0uOFi8IOIiMgeU59KdudZ5isaTbmRbam2qczqohc3TQRAGPggyj1IzMAiDYTBDyIiIqIS4gwF5SdjMGvMaAa5iIYYJGZgkfrDQdNEREREpZS46p+conHBqb2GRRARlbP0ILFj8RJD7RyVEaIHFtPPmQwsUgKDH0REREQmmaEgFac+JSLadY5kkJiGisEPIiIiIpPOUCD3ExERg8Q0dAx+EBEREZlohoLIX/9oSO9mAISIqLBBYqkp0ut9pJiq3E+2YYngx913342JEyfC7/dj3rx5WLx4cb/Pv/POO7HHHnugoqIC48aNw6WXXopAIFC05SUiIiLKVvoMBdrcWYb0bnmciKisFTBIrBdTNbxP4vPkfgZA7MP0wY/HHnsMCxcuxLXXXou3334bM2bMwLHHHovNmzdnfP4f/vAHXHnller5K1euxH333afe4/vf/37Rl52IiIhoIGqGgp/eZCxumhjfLvdzmlsiKneFDBJnW0yVrM+haZoGE5NMjzlz5uDnP/+5uh2LxVQ2xyWXXKKCHOm++c1vqqDHiy++mLzvu9/9Lt566y288cYbWX1me3s76urq0NbWhtraWlhFMBzBipYW+D1u+NycxZiIiMjwdzLQhWDrSkxpGgtfRSUbh4jIQiQDQwUiUmdzkaEpL70+9CBxSmaJrhxn3NIiQcTCAXibp8Hp9sEqsu2/mzrzIxQKYcmSJZg/f37yPqfTqW4vWrQo42sOOugg9Rp9aMzHH3+Mv/71r1iwYEGfnxMMBlWDpf4QERERERGRORR0GlvOuFUWTB382Lp1K6LRKJqamgz3y+2WlpaMrznjjDNwww034DOf+Qw8Hg8mT56Mww8/vN9hLzfffLOKFOk/kllCREREREREZYAzbpUFUwc/cvHKK6/gpptuwi9+8QtVI+SJJ57As88+ixtvvLHP11x11VUqRUb/WbduXVGXmYiIiIisjzNG2Bu3bxkVUx3e0LuYKmd/sTxTF4ZobGyEy+VCa2ur4X653dzcnPE1V199Nc466yx89atfVbf32WcfdHV14YILLsAPfvADNWwmnc/nUz9EREREREOZMcJQJyClUxXR0/bJkrh9y6eYqrq9bTs0tztZTFVqjfBYtj5TZ354vV7MmjXLULxUCp7K7QMPPDDja7q7u3sFOCSAIkxe25WIiIiILIozRtgbt2/5zLiV3NaRiMoAwR5TOPuLTZg680PINLfnnHMOZs+ejblz5+LOO+9UmRznnnuuevzss8/GmDFjVN0OccIJJ+COO+7AzJkz1UwxH330kcoGkfv1IAgRERERUV4lpifWrw67F5xatjNG2BK3r60ZsrK4rW3L9MGP0047DVu2bME111yjipzut99+eO6555JFUNeuXWvI9PjhD38Ih8Oh/t2wYQNGjBihAh//+7//W8K1ICIiIiLbS8wYoQc+hNxm4MMmuH3LB7e1LTk0jgXJeZ5gswmGI1jR0gK/xw2f2/RxLSIioqIKBroQbF2JKU1j4auoZOtbsOaCpKMbAglSgFDG45ullkZKjQ8dMz9shNu3fJTpttYiQcTCAXibp8Hp9tmu/27qmh9ERERERHqxyfSZF+S23K9m4TDjjBFSNyB9xgiyJm7f8pkhh9vathj8ICIiIiJTs0KxyfQZI7S5s+L/JpZbHifr4va1adAyA25r++Kwlww47IWIiMh+OOzFhmnowxsQ+cOvgVkzTTEUxhJDcyhn3L5Dy6CQ+jeuiy4zBAnNOoSkXLe1ZvNhLwx+DKHxzIY1P4iIiPr5O8maH5anMj5SiokKQycqpbMlU1fauZNCZAllWjvDqjSbBz847IWIiIiIzG/DRnXVOJXmdpt6KAxR2UvMmpKKMyBRqTD4QURERETm1lcBwkhkVwBkwamWSKcnKvegpbrNAsBUAgx+EBEREZGp9VuAMBIxPJdXle3JkrOGlDvOmkImw+AHEREREZma1O6QGh6GjI4xoxH51R2q6Gkq1wXfMXaS2UG2PKvOGlLuOGsKmY271AtARERERDSQXsVLpfP79YVwbNuuMkBiJx4H112/gmP9RriPPw2RZ+Md4mQB1EzvQaaVOtuGYarjIz+P6MP3GGYNYX0XEwctE1NVG4KWT//e9rOmkDkx84OIiIiILH9VOXb+l6GNjXewJADiuO93LIBql0wPPctH6rts2876LhaiAhzp9XckoMXAB5UAgx9EREREZP2hMNJBfvaxZADELVkgLIBqSYZMD30mH8nyYX0XKmOsezN0DH4QERERkT2uKsu0mr++0/AcFkC1oMTQiGQARJ/Jx20csc9ZQ6hcsO5NfjD4QURERET2wGk17UMCWb+43XCXmto4darjRGYIp02lssyGSpn+m3VvssPgBxERERFZH6fVtH0gSzI/VO2P1KmO16xV9V+IyjIbKlHzqFddFcqIwQ8iIiIisjxOq2nzQNbwBpX5IbU/kkVQn/69qvvC4plUrtlQHNY3OAx+EBEREZH9CqAKdpDtE8h66enemR6cNYTKSY7D+lgodRdj1SAiIiIiIovKmAHADrI1A1mJOgfpgSwJfDDTg8o9G0oyPiTwodcA6Wvoi14o1TA8ZsOu91LHWRlNO8zMDyIiIiIiMvdMPoKBLCpTuQ7rY6FUI2Z+EBEREREREdktGyrxHD3Tw73g1Pj7lWmhVGZ+EBEREREREdkxG4qFUpMY/CAiIiKigmGxPSIi6xVKtSMGP4iIiIioIPRie5JynfyinSi2J/erwAgRUY7nl14d+A0beV4ZaNroifE6IYbzcplg8IOIiIiICoLF9ohKy64BAgZWC1so1a5Y8JSIiIiICoPF9ohKxs7TnKYHVlOnfpX7VWFQ4rTRaZj5QURERESFw2J7RCVh68yrRGA1uX4LTjVkOJTbLCb94bTRuzD4QURERESFw2J7RKVh9wABA6s0SAx+EBEREVFhsNgeUWnZOUDAwCoNEoMfRERERGWkmAUQWWyPyIQBgrO+ASz5r7WLoDKwSjlgwVMiIiKiMlHsAojyXuo9pbaAfqU5kYovgRGrFlsksmKAQBUFPesbcGzbDvfxX0Lk2UeBWTMtWQQ1PbCaXlyZ5xfKhMEPIiIiojJRihkSMnakxoy2RAeLyMoyBgj+8GsV+HBEInCfcQGiD99jyVlSGFilXDg0TdNyeqWNtbe3o66uDm1tbaitrYVVBMMRrGhpgd/jhs/NuBYREZHh72SgC8HWlZjSNBa+isrybZyUq7w62xRAJKJe2V6GzCux5L8q8CEZIDqeA0hokSBi4QC8zdPgdPtgt/47a34QERERlRM7F0AkooGnOZ01U2V8pOI5gMoBgx9ERERE5YQzJJS0CCxRyfEcQGWKwQ8iIiKicsEZEvosAitDgZIBkEQ7yf0MgJCt8BxAZYzBDyIiIqIywalnBy4C61i8xDBDhlUKQBJlg+cAKmesiklERERUJjhDQgZpU2S6F5wabysWgSUb4jmAyhkzP4iIiIjKvQBinqaetWztDBaBLSuW3U8tcA4gMjMGP4iIiIhoyB1ES9fOYAHIsmHp/ZSIhoTBDyIiIiIacgfRsrUzWACyrFh2PyWiIWPwg4iIiIiG3kFM1M5Ivm7Bqcnny/290uxNwu4FIMt9iIcZ91NuE6LSYMFTIiIiIspPEdBE7Qz9+UJumzXwYfcCkHoGj2G7pWS6qPW28PrlrIT7KbcJUekw84OIiIiI8lME1KK1M+xaANIqQzyKnglRwv3UKtuEyI4Y/CAiIiKioXcQWTvDfEwwxMN0BUhLvZ9aYJsQ2RWDH0RERERlrteVd+kgHn/aoDqIdq+dYVkmn8a32JkQpthPTbhNWIeEygFrfhARERGVsUw1CBxPPAPH+niAI3r2l5IdRL1TmqkWhp1rZ9gxg8c0WQa51pfJkSn2U5NtE9YhoXLBzA8iIiKiMpbpyrvrwUfjj40dDe3kEwwdxMhPb+qzg2jX2hmWVeohHibNhCjpfmrCbcI6JFQuLBH8uPvuuzFx4kT4/X7MmzcPixcv7vf5O3fuxMUXX4xRo0bB5/Nh6tSp+Otf/1q05SUiIiKyjP5qEDz7mLGTyECGpZhiiEc2LFoo1zbbhHVIqEyYftjLY489hoULF+Kee+5RgY8777wTxx57LFatWoWRI0f2en4oFMLRRx+tHvvTn/6EMWPG4NNPP0V9fX1Jlp+IiIjI9Cw4RS3lPsQjes6XAC1tmluZXaUUw5PSMiFkv5PAh54JYZrhOXYadpMJzwFUBkyf+XHHHXfga1/7Gs4991xMmzZNBUEqKytx//33Z3y+3L99+3Y89dRTOPjgg1XGyGGHHYYZM2YUfdmJiIiILKGMrryXm/QhHqq+w/W3wvXQo8WZXcWKmRAFZsrhYTwHUBkwdfBDsjiWLFmC+fPnJ+9zOp3q9qJFizK+5s9//jMOPPBANeylqakJ06dPx0033YRoNNrn5wSDQbS3txt+iIiIiMqCCWsQCM4+UR71HVQmxE9vMmZ4ZFFfxu6Kuv+b9BxAVFbBj61bt6qghQQxUsntlpaWjK/5+OOP1XAXeZ3U+bj66qvxk5/8BD/60Y/6/Jybb74ZdXV1yZ9x48blfV2IiIiIzMiMV9712ScMHa8SZifYignrO5gyE6KEir3/m/EcQFSWNT8GKxaLqXofv/71r+FyuTBr1ixs2LABt912G6699tqMr7nqqqtUXRGdZH4wAEJERETlwIw1CNKzE1LrQJQiO8F2WN/B1Iq9/5vxHEBUdsGPxsZGFcBobW013C+3m5ubM75GZnjxeDzqdbq99tpLZYrIMBqv19vrNTIjjPwQERERlaOMnZtSXnlPdLz0VHy9EGspsxNspY/6DmxbkyjB/m+6cwBRuQ17kUCFZG68+OKLhswOuS11PTKRIqcfffSRep7ugw8+UEGRTIEPIiIiolyxLkXhsxNScQaaPGB9B2vg/k9UXsEPIcNR7r33Xjz44INYuXIlLrzwQnR1danZX8TZZ5+thq3o5HGZ7eXb3/62Cno8++yzquCpFEAlIiIiyhfWpSgwzj5REKzvYBHc/4nKL/hx2mmn4fbbb8c111yD/fbbD0uXLsVzzz2XLIK6du1abNq0Kfl8qdXx/PPP49///jf23XdffOtb31KBkCuvvLKEa0FERER2Y7ZZM2yF2Qkln12FWU0lxP2fqCAcmqZphXlr65KCpzLrS1tbG2pra2EVwXAEK1pa4Pe44XObupwLERFR0QUDXQi2rsSUprHwVVTmvZOiY12K/GXVGNoypa3LeQrUYmD7s/2pPGmRIGLhALzN0+B0+2zXf2cPmYiIiChXnDWjIDj7RGlxtp0Stz9nXyEqCGZ+ZMDMDyIiIvth5gfRIDCriajsaDbP/DB9zQ8iIiIiU+K4fLIzzjZCRDbD4AcRERFRDjhrBtkaZxshIpth8IOIiIiogLNmEFkOs5qIyIYY/CAiIiLKkQpw6IEP3ZjRDHyQpTGriYjsiLO9EBERERFREmcbISr+9NIyy5IhmL5howpEMoswfxj8ICIiIiIig4wdLmY1ERUk8OG+9PvQJo7fNYwyZehZpK/jkQaNw16IiIiIKKsv6PKF3ECuTMr9RIPAfSk/2I72IBkfEviQQIcKeCxekgx8yP0qI4TygpkfRERERNQvXpmkfOG+xHakNIlC2XrAw73gVHW3IROE8oKZH0RERESU+5XJsaONVyaZDUK57ku8yp01tqPNjBmN6C9uN9ylbjPwkVcMfhARERFRVlcmk53WBaeqf3tJjFOX8escDkOD2Zd4lXuQ2I72smEjXBddZrhL3U4fakhDwuAHERERURFZdpx+hiuTWnMTHOsThfl4BZ+GsC/xKrf12tGy5zKzSSluqoKAf/2jITuKAZD8YfCDiIiIqMj1DgxfaK2SLZHhyiTcLjXshVfwaaj7Eq9yW6sdLX0uMxmZzjY1+0mbO8uQHSWPU34w+EFERERUJJYdp9/Xlcn1G4FI1PBUXsGnnPYlXuW2VDta9lxmQjKNbeSnNxmLmyaGNcn9nOY2fxj8ICIiIioWi47T7/PKpGR9tLQanssr+JTTvsSr3NZqR4uey8xKBTjS22zMaAY+8oxT3RIRERGVYJy+Pp2hFbIl1JXJxNXeTMspHR5ZBwl86FeC2QGirPelREdaOuy8ym2hdrTguYzKGzM/iIiIiIrJovUO0q9MqivP6zfyCj4NeV9SeJXbeu1o0XMZlS8GP4iIiIgsME7fbDMrcJw6ZcNs+y2Zs+YI9xMqBgY/iIiIiEw+Tt+sMyuU/MozmZpZ91syV80R7idULKz5QURERGTycfrpMyuk1tfgzArlSzqNveqwSFaFSWpncL+1r3zWHOF+QsXi0DRNK9qnWUR7ezvq6urQ1taG2tpaWEUwHMGKlhb4PW743IxrERERGf5OBroQbF2JKU1j4auotHSauY4zK9gzaDCYq+WGfSBlHzHNFJncb4n7iWVokSBi4QC8zdPgdPtgt/47h70QERERWUFiZoVUnFlh8OySYp9+tdyxeImhBoMK7pgB91vifkImweAHERERkRVwZgVLBg0KVsgxMcQguS4LTjXUYDDNdKPcb4n7Cdk1+HHrrbeip6cnefuf//wngsFg8nZHRwcuuuiifH8sERERkX3leWaFspZj0CCXIEbBs0zMnlXB/Za4n5Cdgx9XXXWVCnDojjvuOGzYsCF5u7u7G7/61a/y/bFEREREtpXPmRVo8EGDXIMYBc8yMXlWBfdb4n5CZpL3qpjp9VNZT5WIiIjIPDMrUN9Bg74yP3KejSKxjfSAh2SZqPfLx9CUtKyK1GWS+80w9IX7LXE/ITNhzQ8iIiIiC1ABjvTO7JjRDHwUYyjGUOprFGhoilWyKgq53xasngoVHc9vVAwMfhARERFR2cg5aJBrEKNAQ1NUVsVPbzIGXxJBGtNMc1tAdpm1h4gsPOxF/OY3v0F1dbX6PRKJ4IEHHkBjY6O6nVoPhIiIiIjIEkMxBjlUphhDUzIua5lkA+U8FImIypZDy3NRjokTJ8LhcAz4vE8++QRm1d7ejrq6OrS1taG2thZWEQxHsKKlBX6PGz53QeJaRERElhUMdCHYuhJTmsbCV1FZ6sUhK+kniNHf0Bc9O8HwnJT3KocMjYJKaUtdMaf6le1rCKIllqmc6/CwTaxNiwQRCwfgbZ4Gp9sHq8i2/573HvKaNWvy/ZZEREREBcUv7DSYoTLphUz76uyy4GeBJYYi6YVkiznV74CBrb4yc2yMbUJmx/QAIiIiKmv8wk4DGUoQo5yHphRcLkOR8oTDbtgmZD15L3i6aNEi/OUvfzHc99BDD2HSpEkYOXIkLrjgAgSDwXx/LBEREVFeOjGOxUsMQxxYO4DUfsLZdswll1l78mkoMwDZFduEyi34ccMNN+C9995L3l6+fDnOP/98zJ8/H1deeSWeeeYZ3Hzzzfn+WCIiIqLc8As7keWYYqrfAk1jbGlsEyqn4MfSpUtx1FFHJW8/+uijmDdvHu69914sXLgQd911F/74xz/m+2OJiIiIcscv7ESWYoqpfgs0jbGlsU2onIIfO3bsQFNTU/L2q6++iuOOOy55e86cOVi3bl2+P5aIiIgod/zCTpS3Gjq9Ov8yA4rcb6ehSKUedmNGbBMqt+CHBD70aWxDoRDefvttHHDAAcnHOzo64PF48v2xRERERLnhF3Zbd5Cp+MWDDZ3/xPEl99tp+5pi2I3JsE2o7GZ7WbBggart8eMf/xhPPfUUKisrccghhyQfX7ZsGSZPnpzvjyUiIiIq6jSmlGN7c4pQ2yqnGVA4jTHbhKzHoWmals833Lp1K77whS/gjTfeQHV1NR544AF1Wyf1QCQT5H//939hVu3t7airq0NbWxtqa2thFcFwBCtaWuD3uOFzcxZjIiIiw9/JQBeCrSsxpWksfBWVvTrkhmlM9UyEQQY+8vU+5ZRpk95BLtuZMmy4fXXcrjRYPJeWhhYJIhYOwNs8DU63D1aRbf8978NeGhsb8dprr6naH/KTGvgQjz/+OK677rp8fywRERFRQWsHDDRUo5xS/oeEs+vYG4sH20Iph6bxXFr49kWZDjvMe3rAeeedl9Xz7r///nx/NBEREVHphmqUUcp/vjrI7gWnJu8q+ylCbV48mBk91lHqoWk8l5Zw28YiiJ36OdhV3jM/ZJjLyy+/jJ07dyazPzL9EBEREVlF+pdxx+IlhqEb+lCX1IKH0rHnUI4+cHYde2LxYPuf78aONgZyC5ExwHNpyf6WxQ4/GHaW9+DHhRdeqMbayIwvRxxxBO677z48+eSTvX6IiIiILCPbL+NM+R8YO8i2xdk+7H2+66WQw/p4Li3R37JRsLO8Bz/uvvtubNq0CZdffjmeeeYZjBs3Dqeeeiqef/555Lm2KhEREVHxZPNlnBkNA2IH2b7UDCg/valXQFBuy/0s+mvt853W3ATH+o19Z7/lE8+lhTMmi79lNpX34Ifw+Xw4/fTT8cILL2DFihXYe++9cdFFF2HixIno7OwsxEcSERERFdZAX8aZ0ZAVdpDtLZviwWTN8x3cLjXspeDD+mx+Li15wdENA/wtszFnwT/A6YTD4VBZH9FoNOdsEgmc+P1+zJs3D4sXL87qdY8++qj67JNOOimnzyUiIiLK9ss4Mxqyxw4ykQXPd+s3ApFowTMG7HwuLflMNhsG+lu2CXZWkOBHMBjEI488gqOPPhpTp07F8uXL8fOf/xxr165FdXX1oN7rsccew8KFC3Httdfi7bffxowZM3Dsscdi8+bN/b5uzZo1uOyyy3DIIaysTkRERIX/Mm7WjIaSX2UkMjkeI1me7yTro6W14BkDZj2XFq14dgn/ljlf+SfszKHluRCHDG+RjAup9SHT3p555plobGzM+f0k02POnDkqeCJisZh670suuQRXXnllxtdIhsmhhx6qPv/1119XM8889dRTWX9me3s76urqVOHW2tpaWEUwHMGKlhb4PW743HmfxZiIiMjSgoEuBFtXYkrTWPgqKnPqIOmzuiQlMj7M+mV8wGkNLd6RoNKy4jGRjsdIlttWzhvHn6ayP+R8kj6VN6cyHoSUc7CumG3o6Oe4jZ1+EmLhALzN0+B0+2AV2fbf895DvueeezB+/HjstttuePXVV9VPJk888cSA7xUKhbBkyRJcddVVhmE08+fPx6JFi/p83Q033ICRI0fi/PPPV8GPbDJV5Ce18YiIiIhSZezMmbyWQfpVxvQOS6GvMpJ9DRg06OuYMRkeI320S9q2UxkDicCHvr3lX317WyngZZaCo1I3pRQFR7X+/pZFdvWJ7SjvwY+zzz5b1dnIh61bt6osjqamJsP9cvv999/P+Jo33nhDTa+7dOnSrD/n5ptvxvXXXz/k5SUiIiIylbQOiv5lm1dqaahXi1XQQC9+aeXAGo+R7IeiJLZ7+lAUBj4GmQ3VR8FRZs9YMPjxwAMPoFQ6Ojpw1lln4d577x3UUBvJLJG6IqmZHzK0hoiIiMqXHVL6zXCVkWya5ZH6uNUDazxGbJv9ZrpsqCMPMdT4SA0ayv2WO3YspuCzvQyFBDBcLhdaW42FdeR2c3Nzr+evXr1aFTo94YQT4Ha71c9DDz2EP//5z+p3ebyvqXllbFDqDxEREZWvklfkz6cyntaQ8lPws88ijTIMotmYoW3JwBqPESpSMVM7z2RjBaYOfni9XsyaNQsvvvhi8j4peCq3DzzwwF7P33PPPdXMMjLkRf858cQTccQRR6jfmc1BREREVqjIX8wpeqm8ZRXoSwxvSO47C06N71NjRwNul7UDazxGKF/6Ok5SMkHsPJONFZg6+CFkOIoMY3nwwQexcuVKXHjhhejq6sK5556brDGiF0T1+/2YPn264ae+vh41NTXqdwmmEBEREeXjS6wV8Coj5S3QlxgaYhCJ7iqCadHAGo8RyqsMx0l6NpQKcKT/DeHwoaIw/Xyop512GrZs2YJrrrkGLS0t2G+//fDcc88li6CuXbtWzQBDRERElFc2qAPAIoWUt4KfGYaGOFpaVfaHlWf/MMMxYpv6QsRipibn0DRNK/VCWHWeYLMJhiNY0dICv8cNn9v0cS0iIqKiCga6EGxdiSlNY+GrqBxUOrzOapkfRNlSGR8pgT6VyTF3VsahIYaZXST48Wx8aIz+XHba81gkk0MhrKO/48Qifzu0SBCxcADe5mlwun2wW/+dKRNERERE6VgHgMrJAAU/+x0asj4e7Ehi+n551hciDqGyAAY/iIiIyNZqHn4Yrg0b+pzJIhPWAaCykUWgj0UarVNfaKCZe6hweJyYH8dGEBERka0DHyMWLkR44kSseewPCLoAx8YWuE87P55SrhefM2EdAKJiSA/09VW7I+M+zywPU9UXGnAITR/nOzsrdj0VHifmxswPIiIisq3uI49UgQ/PmjWYcMqXUPnOClSccm5WKeWsyE/lgFerzT/sKFscQpPDNM5UVhj8ICIiItuKjhmDjU89pQIg3rVrMfX878K5dr0lis8xfZ2KhYE+m9QXsskU3fnCYBClY/CDiIiIbB8A2Xz33cb7TD5lLa9YEtnDQEHMvNcXSgyhsdL5rmAYDKI0DH4QERGRrUmx05EXXzzklHIzXrFkdggRLB3EzPuwozwNobENBoMoBYMfREREZOvAx+iTTlI1P0Ljx+OD+36C2PixuaWUm+yKJbNDiMwt2yBm3oYdcYrujG3CYBDpGPwgIiIi26p86SUV+JCaH58+/ii6Z0xD+MunQBs72phSbsapIAe4Ysnx7EQmV+RhF5yiOw2DQZSGU90SERGRbXWcdVZy1pfI8Ho0/Oox+G76fyr4Ebnm8viVVbNOBdnHFctkpyltSlJ9msxyLW5IZOdpbLPBKbqznMb5yM8bpnEu9PS3ZB7M/CAiIiLbB0Ck6Kn6/YBZ8WEv6zfC9dCjfaahW+aKJcezE5lbkYddcOYeY1uk11NRAZFt26ENb9h1vuf0t2WDwQ8iIiIqG+HmEeh5/Lemnwoy6/R1jmcnKsksLVnhsIuSSw8GJYcLbtvebx0WsicGP4iIiKisaKObTT8VZFYzQLBjRVQQ+SomzBocJsTpb8sagx9ERERUVhwbWyxR/X+g9HV2rIhKO0vLgO+T72lsKT8sMFyQ05gXBoMfREREVDY8LVtQccq5A9fSsAB2rOyNnR97ZAewBocJmXy4IKcxLxwGP4iIiKhs1PxrCZxr1w9cS8Mi2LGyZ6Aj2fk58vNDGnZB9s4OoBxYYLggpzEvHAY/iIiIqGxsP+mzCNx6HdPQyTQyXeXVpk2F5nbHizLKtJwsylh8Js8OoNxYYrgg65IUjEPTNK1wb29N7e3tqKurQ1tbG2pra2EVwXAEK1pa4Pe44XO7S704REREphIMdCHYuhJTmsbCV1FZ6sUhynglWrILpJOtbksAJBJJtpTZZiUqy23CbWCLgKOq25J6HMlMPi+9bqo6LCroueDU5G2VpTJ3VkE/U4sEEQsH4G2eBqfbB7v135n5QURERERkwqu80fvuMjyVwy6KwxLZAWTv4YLMPCoIBj+IiIiIiMxWX+L6K+G69hbDfRx2URwsJkwlZYG6JFbF4AcRERGRTXCGEIvKdJX3/G+x81NClsgOIFti5lHhMPhBREREZAOcHtFGV3mHN6haH1LzI/KrOzjsgqiMMPOocBj8ICIiIrIBTo9oo6u8Lz2dDIA4VnxgqA0S+elNzD4gsjlmHhUGpwQhIiIisoNE51jPItBnCbDC7BRWmX2hYFd5E8Gr5PrLtnzp6d7rz2EXREQ5Y+YHERERkZ0LZ8ptkwc+3Jd+31jILzEURO5XdUxsjld5C4d1cIhIx+AHERERkV1YcHpEDtehQmFgjfK9P/U6l0qGWhkEaO2CwQ8iIiIiO7Dq9IiJ4TrJZV1wqqEGhpmzVoqJHa/BY2CN8nn8lXuGmh0w+EFERERkA5aeHtGCw3WKiR2vwbVVsnOqB9bGjmZgjYaEgTR7YPCDiIiIyAYsPT2iBYfrFBM7XkMIEolI1PA8BtYobxlqY0cbz7kcBmNqDH4QERER2YQlC2dadbhOMYeicGhQ7kGi40+Do6XV8DwG1ihfGWqGwBqHwZgegx9EREREVDJWHa5T9KEoWQ4NKuvaIJmCROvjbaGu0FsosEbWyFCTwJoKsEmgLSWIq6auJtNh8IOIiIiISsaqw3WKPhQli6FBrA2SOUikNTch8uxjlgmskUUy1MbGz1cSYGOhZmtg8IOIiIiISsqSw3WKORQly6FBrA2SOUgEt6vXdjNzYM3urJidlDFDTQJqzU2mqidjxbYtJgY/iIiIiMi2CtoZKNIsNVkPDSr32iB9BYnkynzqMBezB9ZszKrZSRkz1NIDayWuJ2PVti0mBj+IiIiIyJYK3hko0iw1gxoaVMbTBlu1fkw5sXJ2kiFDTQ+0rd9omkLNVm7bYmHwg4iIiIhsqaCdgSLPUpP10KAynjbYqvVjyopNspNMGWizSdsWEoMfRERERGRPBewMmLLzY+Fpg8u6fky5sUF2kmkDbTZo20Ji8IOIiIiI7KtAnQEzdn5MGZAhSmeT7CRTBtps0raFwuAHEREREdlXATsDZuv8mDEgU24428YAmJ1UOGzbATH4QURERET2VIadAbMFZMoJZ9vIoo2YnVS4/Y9tOyD3wE8hIiIiIrJ+Z0DPgtADIvI4gwJUqAK7MrxKsow420ZadlKirdKzk3g8DnH/Y9sOyKFpmjbw08pLe3s76urq0NbWhtraWlhFMBzBipYW+D1u+NyMaxERERn+Tga6EGxdiSlNY+GrqGTjlNHVeENHS2zYyI4WFTzbSMfZNsgqtEgQsXAA3uZpcLp9sFv/ncNeiIiIiMi2OAyEioqzbRCZFoMfRERERERpWLiScsLZNohMi8EPIiIiIqIULFxJOSnDArtEVsLgBxERERFRP4UrHYuXGDq1qoYIURrOtkFkbpYIftx9992YOHEi/H4/5s2bh8WLF/f53HvvvReHHHIIhg0bpn7mz5/f7/OJiIiIiAwSs08kAyALTu01awxRxtk2fnqTcR9J7EtyP2cWIiot0wc/HnvsMSxcuBDXXnst3n77bcyYMQPHHnssNm/enPH5r7zyCk4//XS8/PLLWLRoEcaNG4djjjkGGzZsKPqyExEREZFFsXAlmazALuvQENk8+HHHHXfga1/7Gs4991xMmzYN99xzDyorK3H//fdnfP7vf/97XHTRRdhvv/2w55574je/+Q1isRhefPHFoi87ERERETssFsXClWQirENDZPPgRygUwpIlS9TQFZ3T6VS3JasjG93d3QiHw2hoaOjzOcFgUM0NnPpDRERENFTssFgUC1eSybAODZHNgx9bt25FNBpFU1OT4X653dLSktV7XHHFFRg9erQhgJLu5ptvRl1dXfJHhsoQERERDRU7LNbEwpVkOqxDQ2Tv4MdQ3XLLLXj00Ufx5JNPqmKpfbnqqqvQ1taW/Fm3bl1Rl5OIiIhsih0WS2LhSjIl1qGhFBxSabPgR2NjI1wuF1pbWw33y+3m5uZ+X3v77ber4Mff//537Lvvvv0+1+fzoba21vBDRERElBfssFhSIQtXEuWEdWgogUMqbRj88Hq9mDVrlqFYqV689MADD+zzdbfeeituvPFGPPfcc5g9e3aRlpaIiIgoA3ZYiGioWIeGUnBIpQ2DH0Kmub333nvx4IMPYuXKlbjwwgvR1dWlZn8RZ599thq2ovvxj3+Mq6++Ws0GM3HiRFUbRH46OztLuBZERERUlthhIaI8YB0aMuCQypy4YXKnnXYatmzZgmuuuUYFMWQKW8no0Iugrl27Vs0Ao/vlL3+pZon54he/aHifa6+9Ftddd13Rl5+IiIjKV3qHRf/C6v78mep+eZzDKIgoqzo0iSv+yeFYifMJzyPlPaTSveDU5F1yu9dwPUpyaJqm7bpJQqa6lVlfpPiplep/BMMRrGhpgd/jhs9t+rgWERFRUQUDXQi2rsSUprHwVVQWdWy2ocMiNmxkh4WIiPKSWahLDbTnQosEEQsH4G2eBqfbZ7v+u+mHvRARERFZGQtnEhFRXnFIZU4Y/CAiIiIiIiKyCNaAyQ3HRhAREVEvNQ8/jO4jj0R0zJjkfa4NG1D50kvoOOssthgREVGJsAZMbhj8ICIiol6BjxELFyI8cSI2PvWUCoBI4GP0SSfBs2aNeg4DIERERKWTsVj2mNEsot0PDnshIiIiA8n4kMCHBDok4OFbvDgZ+JD75XEiIiIiK2Hwg4iIiAwk00MyPvQAyJjjj08GPvRMECIiIiIrYfCDiIiIepEAx+a77zbcJ7cZ+CAiIiIrYvCDiIiIepEaHyMvvthwn9yW+4mIiIishsEPIiIiMkgtbipDXTY8+6yhBggDIERERGQ1DH4QERGRgUxnm1rjIzh3rqEGiDxOREREZCWc6paIiIgM9GlsZVYXvcaHXgRVAh+c5paIiIishpkfVLZqHn64V+q23Jb7iYjKnQQ40oubym0GPoiIiMiKmPlBZUkCHCMWLjRM25g6xl3wCz4REREREZE9MPODypKkcqcW7/MtXmwo7iePExHlK4OMmWZEREREpcXMD5uIBMJoX7sVPZu2IgIHAjFAC8egRaLFWwiHA3DovzqM96t/4z9OtwuuCg9cFV441b8eOH1u42sKTB+7rgc8xhx/vLo/NROEiCgfGWTMNCMiIiIqPQY/+tHW1gZN05K3PR4PKisrEY1G0dnZ2ev5dXV16l95TJ6TqqKiAl6vF8FgEIFAwLgR3G5UVVWpz2pvb+/1vjU1NXA6nejq6kIkEjE85vf74fP50Lp8LZ6/+LfGF2qAO/FRkYre6+cKAA4NiHoBzWV8zBEBXGEg5gRiPvT9vv5EUCOFMwg4Y0DUA2hpe5gjCrhCgOYAov7UBwCn3wO/Ix4QiVY61XPUQ4l/3VEnnJoDUaeGqFvfLvEHXbJM0fhrwp5Y6kOKJ+yE0+FExBVDzLlrmw6fehLmr7kzefu1PU7CtttfS77eoTngicUbJ+RO2aaJ9/ZGXXDAkXxfLaohFo4gForBGYzBEYhBczsQq3DA4XbB4XbA4XSql3siu97X4XKqH6cn/q/X7YO3pgKodcNR5YGrygenN/58l8MJj9ODmBZDMByKbxB9lTQNfrc0rIZgJIiYpsld6n61vE6Pen0kGkE4FkneL/86HS71eDSmIRQNpmzv+Hv4nF4VoApFQ4gh0cYJbvnP6UIkFkVEk/dN2R8cDrW8sn8HY7K88UXWeZ1euNwu1Yaax6HWUwJk0khupwcelxvRWAShSCi5uPJ6h8MJv9urbvaEAol2iD9B/t/n9kFaOhQNIxqLGtZFllW1YSyGoL6uyWVKtKEGBCJBaJpxXT0OD1xOF8LRMCJROUjinytPk8/zONyIReV9Q+ox9Xp5SkxT+7fsI6FYCNFYLPG6+Ad74I5vG0QRdcQS+1g8oCj3y7aRZQ8ivGv/TBwcats4HQhrkfi2UYFG2TNlXd1wO9zyrghr8X3NO6wCnsZq+BtrUDemUd3X1dGBdBWVlXC6XAgGAoiEw8Z28Hrh9fkQjUQQ6OkxPCbLVVldrX7v7uw0nEeFv6ICLrcboWAQ4VDIuC95PPD5/YhFo+jp7u61TFU1NfH37eqCJm2YQl4nrw+FQggHU/ZhOW7cbvW5ss17urp6va8sryy3fKZ8dipZT1nfcDiMUMr5OzR3LurGj4c3kUG29rbb0Pzd78Kzdi2C48dj29y5cEejKpMslHhe84knYv1PfpJ8ngROOg89tFf7y3misqpK/d4lf2/S27CyEq4+to3ehvJ3KGC2NgyF1HZPJfuY7Guyn8j+kq6iqkr9DZT9TPa3VB6fT/1tlTaQtuizDRPtGwx2I9QTRHtnNxq8PtWGPYEgQmHj+3o9blT4fYhEo+jqTntfB1BbHX/fjs5udZ41tG+FH263C4FgCMFQ2nHjdqGyQrZNDJ3dxuNG1NXE31cek+cY2sHvhdfjUe8p753K7XKiqrIi/j2is/c2r6mqiH+P6AkgknZRxO/zwKf27wi6A2nb3OlAdVWl+r29oyv11K1UV1bA5XKiuyeIcNq28Xk98Pu86vPkc1PJ34Wa6soc29CNygpfxjaUc16t3oZd3ervWapKvw8ejxvBUAiBYNpx43apz5X9u6Or97apra5U+3dXdw8iadtG1lPWNxQOoydg3DbSPtJOoq2j93Gzqw0DCKdtm2zbsL2zK/0UgapKP9wD7N/q+2z6/j2ENtT374HaMOP+7fPCK20YCqMnmLkNB9q/M7Xh0PZvP88RPEeU3TlCiwThdkQh37AH6vN2dHSo9kglfWXpM2fq8+p9adWGGb531tbWxs8R/fSl5ftJT9r3Tvlbni0GP/rx5ptvqg2kGzt2LGbOnKk25GuvJTrIKU444QT179KlS7Fjxw7DY/I6ef2mTZuwfPlyw2MjRozAAQccoDZypvc95phjVIDjvffeQ2trq+GxvffeG7vtthvaQ51on+FVJ26HK97B9kZcqNvsl70fG8d0J4MJupEtfngiTuwYFkJ3lfEPY027B1XtXgS9UWwdadxxXVEHmjfF26VlVDeiEnlI6ZQO3+yFL+hCe10InbWJ9008pbLThaoWp+oYbh8fgZa6Y8eiaFwaP+h37OlEdFfTx5fp4xh8O4GekQ50jTWujLcNqF0dQ8wNbN+392iuhvdiKiDTNsWJcG38vuquNhz7pjE1fdY/H8Qfjz0LnVXxg9rdDdSvih/UW/dPed/E+g5bGYMrCHRMdCDYkFgmT/ynsl1DZYuGUC3QPtLZK0AkyyS27euMv500RaI56lbF4OkCusY40NNkXFf/Fg3V6zQV0Nq5l7NXcGn4O/H33THNaQwwSRuujsHXBnQ3O9A9Oq0NdwC1n8RU0GrHPr3bcPh/YypY1ra7E+F43ymp+lMN/m0aAsMd6JxgfF9PB1D3YUztf9tm9n7fYctjKtDWPsmJ0DDjY5Ub420YrAM6Jjt7Be+GrUi04QxnrwBe/coY3D1A5zgHAiOMy1TRqqFqg4ZwFdC2R1obRoDhy+Lvu31vZ6/gX+1HMXjbge5RDvWTyrddQ80aDVEfsGPv3uva+Hb8fXfu4USkKq0N12jwb9fQM8KBrnFpbdgO1H0kATZg+34Z9u9lMTgjQPtkJ0LxXTepar2Gis0agvVAx25pbdgNNHwIuKq82LxnNH6OSPnjKI9JoLN9NBBoSHlAg3rPqk1AqFJD25RdyyuBFDecaNpQAZffjZYxgV3nCPUWGoa3eOENONFeHzacI0RFuwN1rS6EvRq2jTeel+Q5zavlAAO2josg4jP+Ja9vccHf6URXfRQdjWmd+i4Hhm1yI+oGtu2hqWCry+dWwVanx4Wp9bupINPajg3oivTEA5IepwrEjRs/Cc1jxmDb1s1Ys/bjeDBHnWyBj6+8GvNvvlEFNiafcor6rI6RI/GPy69A9/oNmDFqDNDUjEW33479L7kENWvXJp/XM2YMNj/1FHb4/Xj/P/8xLm9FBWYecID6feXSpb0CHHvvvz9q6urQsn49Nq5bFw+0JTopTaNGYeKU3dHV1Yn3lv43sWESgWK3C3M+c4gKpnyw/N14gGlX0h6m7rMPGhobsWXTJqz7+GPjfjZiBKZOn64CGMvTllfMPeww9aXlk1Wr0L5zp4oLhqMRuGMOTBg3EQ3VDdi8aRM+XfcJtEhMnf/l3wqHD+Mrx6gO8Acdqw37g5hcNQEepxsbelrRETF+CWv0DsNwTz06I13YENhseMzrcGNS5Vj1+wddn6qAsQQjY+EerPKswZ6Vzah0erE+tANbw8YvnCM81RjjrUdXNIgPA1sMj8l+sk/laPX7ip4WhCSQnGI3fyNqXX5sCrWjNWy8oDHMXYkJvgYEYxGs7Gnp1Yb7VSWWN7AZ3RJATTHe14AGdyW2hDuxIbTT8FiNy4/J/kZEtRiWd2/s9b57V46Cx+HCx4GtaI8a/6aP9tZhpKcGOyPdWBPcbniswunBHhVN6vd3ujf0CmTKY/KctcEd2B4xtqG8p7x3ZzSIj9LaUJZFlkm8171JBWZTTfGPQLXLh42hNmwOG78gN7irMN43DD2xMFb1GL8Tyf43ozKeuSmPyXNSTfQ1oN5dqd5T3juVbDPZdrIsskzpZJvLtl8d2IqOtDaUfUX2me2RbqxNa8NKlxdT/SPV70u71vd6370qmuFzuvFpcDt2RIwd+yZPLUZ5a9U2k22Xyut0Y1pFs/pdtrls+1S7+0egyuVT+4rsM6kaPVUY6x2G7lgIH/Rs7tVh2jfRhrKPyr6aapJvOOrcFWrfln08ldwvj8sxIcdGOnlfeX85puTYSjXONwzD3VXYFunCuqDxO7Ssh6yPnCOWdRuHGQppB2mPT4Lb0BYxdoqk/aQd5X55PJW0u7S/kPdND8JNrRjJcwTPEWVxjgg0u9R3NiUWxbimeuw3IX5BP71vKhftjzvuOPX7kiVLegUx5syZg+bmZqxduxbvv/++4bFRo0Zh9uzZKjCSqc97/PHHq/P4smXLsG2b8XidMWMGxo8fj5aWFrzzzjuGx4YPH47p06cjGw4t/S8ZqewLiWjJRpMIlBUyPz7YtAm/fe1NrN66FY3VVThx3+kY2zCs11Uvs13VlS/rsVAE0UAYsUAEPocH0Z4wujsS75uS0eB1e1VkT11xj0QMnytZHT6PT71fIKy3byILQAP8Hr/6ch8MSzZEDL4tm7HvDZeionUjAk2j8cl3rsWkO6+Hv3UjekaOwjvX/BTBhhGq3SWDQPSEegxXXw3ZBZGQOqlI/0IN4XG74K3wwydXDiMR9PR0A6GYygjQojE4nQ61TNLlkPeV++QnJkOVolG4w07EOkMItHch2NmDSEdAPa7WVXPAHXMh5tBUtoS6+p/SV/bGZAgREJZMColWxDdaPJMi5oQLLpVZEJVoUMpjTs0Jj+ZSmQM9jgjckqGiv7fDAa8Wz3IJO6Lqs+N5BYntqsn7OhFFDBH9fZPbxgGP5oYGDSGJziT3o8S+Jssb1dS2iUbCiIai0MLx57liDri0Xeuq73/qX3ltVDJEHBmzcuQx2TbGbB9HShvGM4XU+6YFBqUNRVi9r0oz2bWu8r4Op2rDmGQgqTaSBnRAWsjr8KggRdgpWRaO+PI64//6HPEMDdUOEhBQDe/Ylfkh7RuTHI2IIVPFoTnhhUvt8yF5TM/oSUQVZdvIyoSRyPxQySjxdXZpTrV9ZP8MQzqbUYS3dyO0vQuhrV1wd2vWyA7T27+nn/cNAc4oVBA05unjfSXGmGlde+JvJ4ErLS2+5AxDBZckCSzmTXvfGNC8Yz2O+/jB5H1/2fMcbK4Z26sNR3Svx+fe3/W8Z3c/B9v8YxFzOVQbyr6h72tyfLlD8f09UintIftQIqPH4YAr7IAjoqlsn2jUmG2lb5vBtKHDLcEel8pckr9JErCS9ZXMEBVUiWnq82TbSQAh7NmVaaV/tqtbU+sa8WqqDeV1jvRt00cbShC5z23Tz37Y37bJOvsx036ot6Ezvk9k04bp+3e/++EA2ybjftjf/j1QG+r7d6HakOeIfs8Rsm1yacPkthnoPJvpfYPxz+53/y7UebaP9+33PMtzRFZtyHOEPb9HlPocccjtR6N+twZD5kfdhP2gOdyWyfyQ95TlkpEbqf33dAx+9BP8GKjxzELScq979jms2NQCv9uNbV1d2H3kCHzriMNUKiz1NtAY/C133DHo2V4kxa0rFESV12fZdl+7fQf+9PZSbOnswojqKnxx//0wviEtHYNsQwJqoe3diHZLLzt+X2rAS7frPnUrcWfK/fp9moZYcFcwU/6VznP8eZKRlnie6uTrr08EiOQhCRLFP7DXF5J8rnN4Rw9C2zpV8Ce4tRPRLvnWkVKbSL5kBMKIdgYR6Qoi0hlU66OWT34kK8TpUO/l79iO+St/i5rQriuVHZ56PD/pLHR7d/39qAy149hPHkZNeGe/z7Mr+RLqrvKpGk8OvxvuSh/clYnaTzKkT983EoGfxC+7GOpIpb5zIlhkeG4fr1MXtGS/3IlafxWcbia/EhER7XPOTFQ1VyeDH7FwAN7maXAmLgDbqf/Ov/w20BEMorW9HaPrahGJxdQYRum8Ske8riJDqI+SgQ0Zi68XN9WLoFa+9NKgAx92CBpI8EbWYfWWbWiqrVb/ym0G0ewjPUAnnXjfiPgfOxq8ZMA0tAPhCRPQcuddaPr2t1Cz9lOcGPgz1v/2cURGj4J7w0aMPeMUeMM7ERo3Hptu/ylGXXYpatatxYk9T+PTex5BpHl0MpMnPnxFsi0SGTyJLDj1e0zDxp1teOG997E9GEBdbRWOn7kPxjUNV5kb0tdXr9efn/r6RH2aXUWoU4JWIalVFFXBK/lREsEeFaBKDKdUQSvZd1RGk94SqcEsoCMQxM9ffQN+jxvVVRXYEQ2iOxbFl+fNxvMr3i/peTIY6EKwdSWmNI2FryJtbCURERHZGoMfNlDj86GptjaZ+bG1qwtTR45QHRzqW6YAhwRAcsn4sEPQQDrF0imRddCDZgyi2YcdAnRmI4FSfXpsPYNs05+fVgERqQFS89Yb8dleFv8T3kRx00368575c/J59Uvfyvq8I+ebP3+0DKud3WhqqsbK9g6EPlmFb008TBVEM0Nwrbq+ArXNdfFzosON1u4uTGocjr+9txKfbN1u6fMkERERWReDHzYglXrPO2ge7n19Ed5v3YzdGoerjg2/UBaHXYIGEiyTTrF0SkRrewcmj2hkEM0G7BKgs2oGWT4zzYpxvsllCF96cO2gyZOSyybnkWOn7YnfLV5i+fMkERERWReDHzaxW2MjfnjcMfjP2nVoqK5ElTe9ahmVY9BgMJ0YeVyCZnoHRtbBLEE0O9RTKaWhdpjZ/kPPIMtXplmhzze5ZAhlCq6Jiw47BMFIOLlsuS439z8iIiLKBwY/bJYBUlvhV7N0UDHb3ZxBg1w6MfK4ZAOYKdBg1uEaVuqQDaXDbNb2L1eFPN/kmiHUV3BNAh+pwbVclpv7HxEREeULgx9kG5k6o8XqoJotaDCUYQ7yuFnS0M06XCPbDln6/leqgEmuHeahtn8265trm1gp+GSV802uGULZBtcGu9xmPf6JiIjImhj8sJFgJIINO3fC43LC43RDUzNRD11y2sEBRLUYukMhVHq9cDniswIYpCyOPOyEzBgQnyHAKT9wQJJWnJleO8CyyXo/s+xdbO3qRmNVJU7Yd7q6/8/L3sW2zm4Mr6rE5/bdG2Pr69PfYNDraXh5hpd0BAOJyRWM7S/vn+n56Z+fnIhBPT/+Gv212S5jW6AHm9o7MLyqQm2P4dWVaGnvwLauzkEFNmQ91L+JddFvD/i6HPe91DaS33utR1UFNrW1Y2tnJ2r8vSdvN86GmWEKzBylrrfMqPTIv5eowo0ja6rwQesWdfvCQz9j6JCt27ETTy1dhq2dXWisrsK8SRPx1idrkrdPmrEPxgyrH7A99HVI3/ayHMlOZBbZXiOqq/G1zxwUL0jp9cHjdiEcjar3leMvE9mXN3d0qvWs9fvV7CFyuz3Qg1p/fD9K3V6pt2X9BwoQpQeRTp45o9dzMu1za3fswP8lXteY8rqB9k/jsZWYncSigZZcg5T9rUuuGUKDCa4NZrntUk+JiIiIzIHBDxuRjoxMMVjl9UBLbNlcO37ZdnJ1G9va8MLKVdje1YOGqgocvdceGF1X1/f7R7XEZ8T/lc5y8iO1wS1DNKapzuf6nTtVkGPFpla09QRUYGPDjja1PCtbWlVH7kuzZ6ngkKFzrg3QSI7sOvR650+ep36Xpzv0lYlPAZl8LL09tNTtpSWeH/9XTV05yOBMNBaDx+nAJ1t3oKEqiO1dXRjXUI/W9k5s+//s3QecE2X6B/AnbZPtFZbdpfdepNtQQRArZ0MsIJY7ux5nw7+Knt5h5TyVgzt7L3jqnb2goqcoCiIdAenLLrC9ps7/87zZCZNsspvdTZmZ/L5eLiSZTGbeKTvvM8/7vnX1Yc9HXjaeX4PTQTazxTuiRJCi8E7jpGSLhUxGb2BLLo/wfshbRqRYV56nWaxHOeWkplJ5XS11zckWlTIe1SgYZTkF7v/hlmFLy1zbaKedZeVkNZvJLRFZzSbxenNJCaVZbeK7breHXl+9hvaVV1JOagqt21dM32zbQVkpyZSbmkob9h+givp6mjVmtK+smv2mvF8GBD54Wg4ALd+ylcrrGygnJZkmDxxABZmhxzQPVFHX0FT5bwo+higXDrBwYGXH4XIRVOQy757N5V/rV/5+yycCoRK9/P2PIgDCwQle/8r6erp4/FgxP96nXJKHXpKnSU2hX/YVU3kdTzOmxWAOL9PLP/xEe5q+t27/Aaqsb6CLxo0hkym87SsCPiLYahDlz8GfI4EQeTt4g0Jc1h+s3yDKOrcpsFqUmenbXiJY27Ti7Qmg+pYpYN9XvteaYMHWwHkxPkf+95f14hzgXZdh1DU702967pj0P79soLK6OuqanSVec7ChtfMxB8dmTxhH9Q47pTQF5Hgf91s+MVKvR7z2SN4l5r9btXY7pViS/IIlYh/xeMhqMorjPy8tRQQOOchVVltHFfUN8olR4G3F0zc6nWSzWHz7UGC5OO0N5KiuJ3NSNSU1OMMrYAAAAJ0yGQzUJT2VkswmSgQIfugQ3yG3WmK3aV1uiVb8up0OVNWIC1R+5teXTpxA5jArIx3BAZ96p5MKszJENgCve3Vjo/hMfo8vhusdLrJZzJRui39HpO3BlYW2OHPEMPp4wyYqq2ug3p1yafqQQZSV0jxboiVcIdxfWeWbD2/f6UMHU1GWf2CLp/moaRrOzgg2Tbg4u0C5pmcp1oPvKPO8OZNFXj75O37zCJhfW/dnDvQki8qYsiLq/Te/3yU9jXaXV1KS2Sj2tR452ZSfnuGbnvfJ2kYHdclM92WocMWzb6dOlJueKvZR3h+tYeyP8vJLiuX7Zpv/8cav57TheBNlLEnkkeTyDl5GHCicNmQgfbpxi6j898jOFq854OMtFEWwUs4O4lFN7A6xvbJTkyk5KYmyUz10uLZeVHLl9eUssfKm/SXVZhXf59/gymtaQJko16rR6RIVX153HuabP+TXDreL0s3e1/4rG2T9idfdQx4Pie+F2kecboneWv0z7amsEkGmLSUHqc6+mi4YM8avrOWgZou7mmK5QpW3ch4GOXDq+1KIbSuma/pe03f85+N99rglevWnn2hfRZU4fjhIzPvurLGjRRDoSKBEolOHDqZGl1MEO3k+eysqWlix5itY1WBvtoyGpoCu+J2mOCdno32+eStV1DdSToqNpgwa2CyIN6l/P/p8M+9/jVSYmUEnDuhHTo+bDLzxFOXJQarPNnmn43mdPLj5vJjT6SK70011Tic5TbgEAgCAxCVJkriJzNdiSYTgB0BYuKIoV4zlih6/5vfTTdEPNHBllA9arowyvjvYrak5wd6KKt97XEHlaeMpVMU6HKGaJoTCZTD36Int/j15eT/ZuJn2VFSJ7ctlzEEOZWCLp+H3+LNQ07SFaOrTxvUIbL7g96oN5RZOECfJbKBThw3xTdczN0dMZzEfuWvNd77lsuBlq2pspOyUFKqyN1KSxdSm/VFeN3kt7C6HqOB1Sk8Vxxt/zq+5c8kkrvyHow1lwkNnX3Hs0W3aj7jZXX5Gqlh/i8kkMrF4fXNT03zfTzJZfNNwZ82VTdNkp6S2+Btmo1k0wxHfMxpF1gd/Lys5pd37eKh1E0Esh5MKm4JYHDytE0Erk+aCqCJI7HAdCRKbmwJw5vgEhL1BvLVUUl3rDeJV19LX25oHzdNtncRoZi3tf74AvGJeoQLwDvKQOclM6dYkSrJiVDQAAEjs4Edl4A0LncNtD4hK8CHSgYaWKij8miufcmWUf5tfs8D3YpGJEkoksyPCxevbkQBUOIGtWAS/Oroe4WhLEIe3G78f7j7ZKzeHRh/VjVbv3tvh/TEWx1tHyz/UMalc33Cmae+8O3pMyuebJJM5ZFl3JJAZD/HYb1rSlvNGa/tfvAPwAAAAoA0IfkDHd6IWgg98t7GjlYNwggahKqMtVVBjqaWKNVPDMra3wtTRSpVaKpFtrUAFVsgC1yPYPjmssKjD6xrJyn80tRYgamma1vaJcObd3mNy2uBB9Mmmzb7zzege3cT0yrIuramOeSCzo9S230QyGKO2wA4AAACoE4IfEBGBlRGuHDy/8vuQd1TDrbCEqqBcMn6caKuvnE+wu4OtVVDjXbH+7fAh+nrbDtVWoqJ5Bz9e2TCBwrnL3971CNz/IpXBEonKf6QFO7bCOf4CpwlWltyfSmvfi8QxeaiuXowQxX1RyOcbpjzfMD63hZMh1N7zTbTOU5Hcbzq6jC2dN9o6b7UFdgAAAECdEPyAyO1MTZWRcO+ohlPRDVZB4X48nv52JdXaHW2qMMezoh2qX5Kvft0u1icSfWVo7Q5+pPsKaY/AfSLYXf7WliVe6xGLpkDhCufYCmeaYGX5xk8/U5o1SXSKGcnjNtgx2SUjg2rs9mZBStGZalO/GJzNFk6GUHvPN9E+T0Viv4nUMgY7b7R33moMCAIAAIC6hB5PEKCdAgMW/CzfUeWKhs1i8lUQubITTgWFR4qoaWykgzW1dLi2lg5UV7dpPspKVVu+p/w+V3rCnT7UnckeOVnU6HSLivUJ/fuKCp2ynORKVHt0dBlbW36u/LXYEWXANFyJ4Tvk/1jxrXjm163tJx1Z/7YKtk9wnxx8l/+aSceIilQ4la54r0e8hXNshXv8BZYlD1O98cAB+q2sPGLHrfweCzwmzxw+1Dukb9P5hgMifP4J1syrpWnae76J93kq3N/oyDK2dN7o6LzDOU8BAABA4kLmh46FmzocOF1H05nbcke1tQ7pAtOZCzMzyWI0iSE02zKftvTnELj+0brLyYI1s+DmF23tK6UjyxiNFPtwsiFCtdNvz/q3R6h9QnmXPxxa7/Oko78fyU5xA8uytLpGDFWbn57W5o4sgx0TLPC9wGyBSDTzam8HnB3puDNWmW3R7FwUHZcCAABANCH4oVPhXggHS/uXR6Ro7wV0sMqB3OSlPaMmKIMGXDF+6YdVba5ohltBDSwP5XK31udIMIHrFphyHlhOXP68fm3pK6UjzS6iVWEKVYmpsTeK4UlDVTRDrX84ZRvONMrXkeokUct9nrQ22kk4AZFIdoobWJY8zG7n9HQqr2sQQz2Hu42CHRMfrN8oPgvWzEwZ7OpIM6+2rm+4ZdlaQDCWTa+i2bkoOi4FAACAaDJIPMAv+KmurqbMzEyqqqqijIwMzZROWW0dfbp5C2WnJNNrP67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      "text/plain": [
       "<Figure size 1300x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    len(BEARINGS), 1,\n",
    "    figsize=(13, 4.2 * len(BEARINGS) + 1.2),\n",
    "    sharex=False,\n",
    ")\n",
    "\n",
    "for ax, (bname, res) in zip(axes, bearing_results.items()):\n",
    "    t      = np.arange(len(res['mse']))\n",
    "    flags  = res['flags']\n",
    "    scenario = res['scenario']\n",
    "\n",
    "    # shade background per segment\n",
    "    cursor = 0\n",
    "    for regime, n in scenario:\n",
    "        color = COLORS[REGIMES.index(regime)] + '22'\n",
    "        ax.axvspan(cursor, cursor + n, color=color, zorder=0)\n",
    "        ax.text(\n",
    "            cursor + n / 2,\n",
    "            ax.get_ylim()[1] if ax.get_ylim()[1] != 1 else 0.98,\n",
    "            LABELS[REGIMES.index(regime)],\n",
    "            ha='center', fontsize=7,\n",
    "        )\n",
    "        cursor += n\n",
    "\n",
    "    ax.scatter(t[~flags], res['mse'][~flags], s=8,  color='#01696f', alpha=0.5, label='Normal')\n",
    "    ax.scatter(t[ flags], res['mse'][ flags], s=25, color='red', marker='x', zorder=5, label='Alarm')\n",
    "    ax.plot(t, res['taus'], color='#a12c7b', lw=1.5, label=f'τ_t (w={WINDOW_SIZE})')\n",
    "    ax.axhline(tau_mc, color='gray', lw=1, ls='--', alpha=0.6, label='τ_MC static')\n",
    "    ax.fill_between(\n",
    "        t,\n",
    "        res['mus'] - res['sigmas'],\n",
    "        res['mus'] + res['sigmas'],\n",
    "        color='#01696f', alpha=0.12,\n",
    "    )\n",
    "    ax.set_title(bname.replace('\\n', ' — '), fontsize=10)\n",
    "    ax.set_ylabel('MSE')\n",
    "    ax.legend(fontsize=7, loc='upper left', ncol=4)\n",
    "\n",
    "axes[-1].set_xlabel('Stream index (samples per bearing)')\n",
    "fig.suptitle('Per-bearing independent AdaptiveTau — one shared AE', fontsize=12, y=0.995)\n",
    "fig.subplots_adjust(left=0.09, right=0.985, bottom=0.08, top=0.94, hspace=0.35)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Window size sensitivity\n",
    "\n",
    "Sweep `WINDOW_SIZE` over several values and compare FAR vs Detection Rate\n",
    "for the fault bearings (B, C, D). Results are averaged across the three fault scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "window=   50  FAR=0.050  DR=0.950\n",
      "window=  100  FAR=0.050  DR=0.977\n",
      "window=  200  FAR=0.040  DR=0.970\n",
      "window=  500  FAR=0.020  DR=0.960\n",
      "window= 1000  FAR=0.020  DR=0.960\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window_sizes = [50, 100, 200, 500, 1000]\n",
    "sweep = []\n",
    "\n",
    "fault_bearings = [\n",
    "    ('outer_race', 100),\n",
    "    ('inner_race', 100),\n",
    "    ('ball_fault', 100),\n",
    "]\n",
    "\n",
    "for ws in window_sizes:\n",
    "    far_list, dr_list = [], []\n",
    "    for fault_regime, n_fault in fault_bearings:\n",
    "        # stream: 100 healthy then 100 fault\n",
    "        X_h = X_test_by_regime['healthy'][:100]\n",
    "        X_f = X_test_by_regime[fault_regime][:n_fault]\n",
    "        stream = np.concatenate([X_h, X_f], axis=0)\n",
    "        mse    = per_sample_mse(ae, stream)\n",
    "\n",
    "        tr = AdaptiveTau(z_mc=z_mc, window_size=ws,\n",
    "                         warmup=min(ws // 4, 50), tau_init=tau_mc)\n",
    "        tr.seed(mse_mc)\n",
    "        flgs = np.array([tr.update(v)[3] for v in mse])\n",
    "        far_list.append(flgs[:100].sum() / 100)\n",
    "        dr_list.append(flgs[100:].sum()  / n_fault)\n",
    "\n",
    "    sweep.append((ws, np.mean(far_list), np.mean(dr_list)))\n",
    "    print(f'window={ws:5d}  FAR={np.mean(far_list):.3f}  DR={np.mean(dr_list):.3f}')\n",
    "\n",
    "res_sw = np.array(sweep)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.plot(res_sw[:, 0], res_sw[:, 1] * 100, 'o--', color='#a12c7b', label='FAR — healthy (%)')\n",
    "ax.plot(res_sw[:, 0], res_sw[:, 2] * 100, 's-',  color='#01696f', label='Detection Rate (%)')\n",
    "ax.set_xscale('log'); ax.set_xlabel('Window size'); ax.set_ylabel('%')\n",
    "ax.set_title('Per-bearing FAR vs DR across window sizes')\n",
    "ax.legend(); ax.grid(True, alpha=0.3)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Latent space geometry (PCA)\n",
    "\n",
    "Even though the AE was trained only on healthy signals,\n",
    "the 16-dim latent space should show some separation between regimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Concatenate all regimes for PCA visualisation\n",
    "X_all = np.concatenate([X_test_by_regime[r] for r in REGIMES], axis=0)\n",
    "y_all = np.concatenate([[i] * N_TEST for i in range(len(REGIMES))])\n",
    "\n",
    "Z_all = encoder.predict(X_all, verbose=0)\n",
    "pca   = PCA(n_components=2)\n",
    "Z2    = pca.fit_transform(Z_all)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "for i, (label, color) in enumerate(zip(LABELS, COLORS)):\n",
    "    mask = y_all == i\n",
    "    ax.scatter(Z2[mask, 0], Z2[mask, 1], s=12, alpha=0.5, color=color, label=label)\n",
    "ax.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f} %)')\n",
    "ax.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f} %)')\n",
    "ax.set_title('Latent space — PCA (AE trained on healthy only)')\n",
    "ax.legend(markerscale=2); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Summary\n",
    "\n",
    "| Component | Details |\n",
    "|---|---|\n",
    "| **AE** | Trained once on healthy MC signals — shared across all bearings |\n",
    "| **z_MC** | Derived from healthy val set — shared, encodes desired FAR level |\n",
    "| **AdaptiveTau** | One instance **per bearing** — independent buffer, μ_t, σ_t |\n",
    "| **Buffer guard** | Anomalous samples never enter the buffer → no drift contamination |\n",
    "| **Warm-up + seed** | Buffer pre-filled from MC val; τ_MC used as fallback until warm |\n",
    "| **WINDOW_SIZE** | Main deployment knob — tune via Section 10 FAR/DR curve |\n",
    "\n",
    "**Deployment pattern:**\n",
    "```python\n",
    "# At startup — one tracker per physical bearing\n",
    "trackers = {\n",
    "    'bearing_001': AdaptiveTau(z_mc, window_size=200, tau_init=tau_mc),\n",
    "    'bearing_002': AdaptiveTau(z_mc, window_size=200, tau_init=tau_mc),\n",
    "    ...\n",
    "}\n",
    "for name, tr in trackers.items():\n",
    "    tr.seed(mse_mc)   # same warm start for all\n",
    "\n",
    "# At runtime\n",
    "mse = per_sample_mse(ae, new_signal[np.newaxis])[0]\n",
    "mu, sigma, tau, alarm = trackers['bearing_001'].update(mse)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58493f1a",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data_analytics-jTVYsQHW",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}