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   "cell_type": "markdown",
   "metadata": {
    "id": "title"
   },
   "source": "# 🍦 Walls Ice Cream — 21 Aşamalı Week-Wise & Time-Series QML Benchmark\n## Klasik + Quantum (10 Qubit) · IBM Quantum · Origin Wukong\n\n**Şablon:** 21 aşamalı karar ağacı mantıklı uçtan uca pipeline  \n**Veri:** Sentetik Unilever Walls dondurma — 53 hafta × 11 yıl  \n**Karşılaştırma eksenleri:**\n1. **Week-wise regression** (aynı haftanın 10 yıllık geçmişi)\n2. **Yıl-içi zaman serisi regression** (sequential lag’ler)\n3. Klasik vs Quantum — **aynı özellik sayısı (10)**\n4. Quantum encoding: **minimum 10 qubit**\n\n| Donanım | SDK | Rol |\n|---------|-----|-----|\n| Simülatör (Aer / Statevector) | Qiskit | Varsayılan eğitim |\n| IBM Quantum | Qiskit Runtime | Gerçek backend test iskeleti |\n| Origin Wukong | pyQPanda (süperiletken) | Çin süperiletken backend iskeleti |\n\n",
   "id": "title"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a01"
   },
   "source": "## Aşama 01 — Problem Tanımı ve Karar Ağacı\n\n```\n                    [Satış tahmin problemi]\n                            |\n            +---------------+---------------+\n            |                               |\n     Week-wise (cross-year)         Yıl-içi Time Series\n     aynı week, geçmiş yıllar       sequential hafta lag'leri\n            |                               |\n            +---------------+---------------+\n                            |\n                   Ortak 10 özellik\n                            |\n            +---------------+---------------+\n            |                               |\n         Klasik ML                      Quantum ML\n         (10 feature)                   (10 qubit encoding)\n            |                               |\n            +---------------+---------------+\n                            |\n                      Benchmark Report\n                    MAE / RMSE / MAPE / R²\n```\n\n**Karar kuralları (şablon):**\n- Mevsimsel ürün mü? → Evet → week-wise + seasonal encoding zorunlu\n- Train satırı > 300 ve qubit ≥ 10? → Quantum kernel subsample uygulanır\n- Gerçek donanım? → Önce simülatör, sonra IBM / Wukong iskeleti\n",
   "id": "a01"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a02"
   },
   "source": "## Aşama 02 — Ortam Kurulumu\n",
   "id": "a02"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "setup",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789498605300,
     "user_tz": -180,
     "elapsed": 31545,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "56cf9005-b9d2-4754-ca1b-5e87ef729b1b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.13.15\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h✅ Kurulum tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 02 — COLAB KURULUM\n# ============================================================\nimport sys\nprint(f\"Python: {sys.version.split()[0]}\")\n\n!pip install -q --upgrade pip\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-ibm-runtime\n!pip install -q scikit-learn pandas numpy matplotlib seaborn\n!pip install -q xgboost lightgbm statsmodels scipy tqdm\n\n# Wukong / pyQPanda opsiyonel (Colab'da olmayabilir — iskelet için try)\ntry:\n    !pip install -q pyqpanda 2>/dev/null\nexcept Exception:\n    pass\n\nprint(\"✅ Kurulum tamamlandı.\")\n",
   "id": "setup"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "imports",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409511360,
     "user_tz": -180,
     "elapsed": 5916,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ac7ef7df-c3eb-4ee5-dc2b-de7233e54e46"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Kütüphaneler yüklendi.\n",
      "N_FEATURES = 10  |  N_QUBITS = 10\n"
     ]
    }
   ],
   "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy import stats\nfrom statsmodels.tsa.stattools import adfuller, kpss\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.linear_model import Ridge, Lasso, ElasticNet\nfrom sklearn.svm import SVR\nfrom sklearn.ensemble import (\n    RandomForestRegressor, GradientBoostingRegressor, HistGradientBoostingRegressor\n)\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\nfrom sklearn.kernel_ridge import KernelRidge\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\n\nsns.set_style(\"whitegrid\")\nplt.rcParams[\"figure.figsize\"] = (12, 4)\nplt.rcParams[\"axes.titlesize\"] = 13\nRANDOM_STATE = 42\nN_QUBITS = 10          # minimum 10 qubit encoding\nN_FEATURES = 10        # klasik ve quantum AYNI özellik sayısı\nN_WEEKS = 53\nprint(\"✅ Kütüphaneler yüklendi.\")\nprint(f\"N_FEATURES = {N_FEATURES}  |  N_QUBITS = {N_QUBITS}\")\n",
   "id": "imports"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a03"
   },
   "source": "## Aşama 03 — IBM Quantum ve Wukong Bağlantı İskeleti\n",
   "id": "a03"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hardware",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409577263,
     "user_tz": -180,
     "elapsed": 2515,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "5380caaf-5323-451b-ffa9-7edd27c75e6b"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ IBM token Colab Secrets'tan alındı.\n",
      "⚠️ IBM bağlantı hatası: \"Invalid `channel` value. Expected one of ['ibm_cloud', 'ibm_quantum_platform'], got 'ibm_quantum'.\"\n",
      "⚠️ pyQPanda / Wukong: No module named 'pyqpanda'\n",
      "   Simülatör ile devam. Wukong için: https://originqc.com.cn\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 03 — DONANIM YAPILANDIRMASI\n# ============================================================\nUSE_REAL_IBM = True      # True + token → IBM Quantum backend\nUSE_REAL_WUKONG = True   # True + pyQPanda kurulu → Wukong iskeleti\n\nIBM_TOKEN = \"\"\ntry:\n    from google.colab import userdata\n    IBM_TOKEN = userdata.get(\"IBM_QUANTUM_TOKEN\") or \"\"\n    if IBM_TOKEN:\n        print(\"✅ IBM token Colab Secrets'tan alındı.\")\nexcept Exception:\n    pass\n\nif not IBM_TOKEN:\n    print(\"ℹ️ IBM token yok → simülatör modu.\")\n\n# IBM Runtime\nibm_service = None\nif USE_REAL_IBM and IBM_TOKEN:\n    try:\n        from qiskit_ibm_runtime import QiskitRuntimeService\n        QiskitRuntimeService.save_account(channel=\"ibm_quantum\", token=IBM_TOKEN, overwrite=True)\n        ibm_service = QiskitRuntimeService(channel=\"ibm_quantum\")\n        print(\"✅ IBM Quantum bağlı. Backend örnekleri:\")\n        for b in ibm_service.backends()[:5]:\n            print(f\"   - {b.name}\")\n    except Exception as e:\n        print(f\"⚠️ IBM bağlantı hatası: {e}\")\n        USE_REAL_IBM = False\nelse:\n    print(\"ℹ️ IBM gerçek backend kapalı (USE_REAL_IBM=False).\")\n\n# Wukong / pyQPanda iskeleti\nwukong_ready = False\nif USE_REAL_WUKONG:\n    try:\n        import pyqpanda as pq\n        # Origin Quantum Cloud — kullanıcı kendi API key'ini ekler\n        # pq.init_quantum_machine(pq.QMachineType.Cloud)  # örnek\n        wukong_ready = True\n        print(\"✅ pyQPanda yüklü — Wukong iskeleti hazır.\")\n    except Exception as e:\n        print(f\"⚠️ pyQPanda / Wukong: {e}\")\n        print(\"   Simülatör ile devam. Wukong için: https://originqc.com.cn\")\nelse:\n    print(\"ℹ️ Wukong gerçek backend kapalı (USE_REAL_WUKONG=False).\")\n    print(\"   Not: Wukong = Origin Quantum süperiletken (gate-based), fotonik değil.\")\n",
   "id": "hardware"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a04"
   },
   "source": "## Aşama 04 — Sentetik Veri Üretimi (Walls Ice Cream)\n\n53 hafta × 11 yıl (2015–2025). Yaz zirvesi, kış dip, trend, kampanya, tatil dip’i.\n",
   "id": "a04"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gen_data",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 616
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409588694,
     "user_tz": -180,
     "elapsed": 234,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "343b775e-795a-48f5-c3d0-2a80b08d9431"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Şekil: (583, 3)  |  Yıl: 2015–2025  |  Hafta: 1–53\n"
     ]
    },
    {
     "output_type": "display_data",
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       "   year  week    sales\n",
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       "1  2015     2  6146.23\n",
       "2  2015     3  8166.24\n",
       "3  2015     4  8688.75\n",
       "4  2015     5  6291.02\n",
       "5  2015     6  7347.52\n",
       "6  2015     7  9204.63\n",
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    }
   ],
   "source": "# ============================================================\n# AŞAMA 04 — SENTETİK WALLS ICE CREAM\n# ============================================================\nnp.random.seed(RANDOM_STATE)\nYEARS = list(range(2015, 2026))\n\ndef seasonal_curve(week):\n    peak, width, base = 28, 12.0, 0.35\n    return base + np.exp(-0.5 * ((week - peak) / width) ** 2)\n\ndef generate_walls_weekly(years=YEARS, n_weeks=N_WEEKS, seed=RANDOM_STATE):\n    rng = np.random.default_rng(seed)\n    rows, base_2015 = [], 850_000.0\n    for yi, year in enumerate(years):\n        annual = base_2015 * (1.03 ** yi)\n        for week in range(1, n_weeks + 1):\n            seas = seasonal_curve(week)\n            campaign = 0.12 if week in (22, 27, 32) else 0.0\n            holiday = -0.08 if week in (12, 13, 14) else 0.0\n            noise = rng.normal(0, 0.06)\n            sales = annual * (seas + campaign + holiday + noise) / 53\n            sales = max(sales, annual * 0.15 / 53)\n            rows.append({\"year\": year, \"week\": week, \"sales\": round(float(sales), 2)})\n    return pd.DataFrame(rows)\n\ndf = generate_walls_weekly()\nprint(f\"Şekil: {df.shape}  |  Yıl: {df.year.min()}–{df.year.max()}  |  Hafta: 1–{N_WEEKS}\")\ndisplay(df.head(8))\ndisplay(df[\"sales\"].describe().round(2))\n",
   "id": "gen_data"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a05"
   },
   "source": "## Aşama 05 — Veri Kalitesi Kontrolü\n",
   "id": "a05"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "quality",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409595898,
     "user_tz": -180,
     "elapsed": 226,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "11e1bfc5-38bc-4ecf-e439-621ff4610dca"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Eksik değer:\n",
      " year     0\n",
      "week     0\n",
      "sales    0\n",
      "dtype: int64\n",
      "\n",
      "Duplicate (year,week): 0\n",
      "Negatif satış: 0\n",
      "Hafta kapsamı her yıl 53 mü? →\n",
      "week\n",
      "53    11\n",
      "Name: count, dtype: int64\n",
      "\n",
      "✅ Veri kalitesi geçti → Aşama 06\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 05 — DATA QUALITY\n# ============================================================\nprint(\"Eksik değer:\\n\", df.isna().sum())\nprint(f\"\\nDuplicate (year,week): {df.duplicated(['year','week']).sum()}\")\nprint(f\"Negatif satış: {(df['sales'] < 0).sum()}\")\nprint(f\"Hafta kapsamı her yıl 53 mü? →\")\nprint(df.groupby(\"year\")[\"week\"].nunique().value_counts())\n\n# Karar: kalite OK ise devam\nassert df.isna().sum().sum() == 0\nassert (df.groupby(\"year\")[\"week\"].nunique() == N_WEEKS).all()\nprint(\"\\n✅ Veri kalitesi geçti → Aşama 06\")\n",
   "id": "quality"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a06"
   },
   "source": "## Aşama 06 — EDA: Tek Değişkenli Analiz\n",
   "id": "a06"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eda_uni",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 440
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409605429,
     "user_tz": -180,
     "elapsed": 914,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4b7fa7fc-2628-4a12-b3ba-c32a5d5188a5"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ],
      "image/png": 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n/sHSmnnbGzVqdNf1ZUQKGxVtJd+zs7OjQYMG/Prrr1y8eBFI+YjizZs3+fTTT3n00Uct4i9fvmwejZsVHh4eeHh40Lt3b65evUrHjh2ZMmUKXbp0sfi4prUuX77M5s2bcXFx4eGHHwZSiriHDh2if//+DBw40CL+v6thVqhQAUiZZ+i/Tpw4kem8REREcovJZCI+Ph6ASpUqkZSUxPHjx6lVq5ZF3PHjxwHLT3kcOHCAKVOm0KxZM0qVKsX8+fNp1qyZeTXq9M6/08WLF7l69SqVKlW6Z46VKlXi5MmTJCYmWoy2TUxM5I8//rA4Pyu/J4iIiFijTZs25k+oDhkyJN2YdevWkZCQwLPPPmve9/zzz5vfj6ZKfd0tWrQo//vf//j222/56aefeOyxx9JtN/W96p3tZofU1/1jx46lOZa6736v4yJ5kea0lXzj559/JjExMc3+W7du8fPPPwP/fqwxdeTnf0dzrlq1iujo6DRtFC9ePMMf1798+XKaUa0lSpSgYsWKxMXFcfv27Qy1c7e2Bw0axPXr13n11VdxcnIC/h2t+t/+HDlyJM0ce+7u7tSrV8/88dBUiYmJLF68ONO5iYiI5Iaff/6ZmzdvmhczSZ1z9rPPPrN4XTxy5Ajff/89Dz/8sHmEzo0bN3jrrbdwdXVl4sSJhISEULFiRYYNG5buVEMnT55MM/fsnDlzLK57Ny1btuTSpUtp/pi6atUqLl26ZHF+6jz1qYutiIiIZJcOHTpQtWpVFixYwE8//ZTm+MGDB5k6dSru7u4WC2lWqlSJpk2bWvxLnQIP4PXXX6do0aKMHj3a4n1nqjVr1rBhwwZ8fX3TDKSytbp161K+fHnCwsIs3u8nJCQwb9487OzseOKJJ8z7ixcvzpUrV/TpT8nzNNJW8o3Q0FAuX75MixYt8PDwwNHRkQsXLvDll1/yxx9/8Pzzz+Pp6QnAY489hpOTE0OHDqVr166UKFGCX3/9lZ9++onKlSubPyKZqkGDBqxZs4Zp06ZRvXp1DAYDjz/+uMXiX6nWrVvHwoULzQt9FSlShD179rB9+3batm2Lo6Njhvrz008/ceLECZKTk7ly5QpRUVFs3ryZGzdu0KtXL3r37m2OrV69OjVr1mTu3LncunWLatWqcfLkSVauXImHhwcHDx60aHvYsGG8/PLLdO7cma5du+Li4sKGDRvMBWWN8BERkbzo999/Z/369QDEx8dz7NgxVq1ahb29PW+88QYAzZo1o23btnz99ddcuXKFxx9/nOjoaJYtW0bRokUZOXKkub3g4GBOnz7N3LlzzfPpTZ06lRdffJFhw4YxZ84ci9dEDw8P3n77bTp27EiVKlXYtWsX33zzDY888gjt2rW7Z+69e/dm06ZNjBkzht9//53atWsTFRXFmjVrqFatmsXrupeXFwaDgVmzZnHlyhWKFStGxYoVadCgga1upYiICABOTk58+umn9O7dm759+9K6dWseeeQRihQpwr59+1i/fj2urq58+umnVs096+HhwdSpUxk8eDDPPPMMAQEB1KpVi7i4OH766Se2bdtGvXr1+Oijj7L9/afRaGTUqFEMGDCADh060KlTJ4oXL87GjRuJjIzk1VdfpWrVqub4Bg0a8MMPPzBmzBh8fHwwGo08+uijlC5dOlvzFLGWiraSbwwfPpwtW7awd+9evvnmG65du4aLiwseHh688sorBAQEmGMrV67MnDlzmDp1KrNmzcJoNNKwYUMWL17M2LFjzStQp3rzzTe5cuUKy5Yt4+rVqyQnJ7Nly5Z0i7ZNmjQhKiqKrVu3Eh0dTVxcHFWrVmXYsGFWzWc7ffp0AOzt7SlevDiVKlUiMDDQ/GJ3J6PRyOzZs/nggw8IDw8nLi6OmjVr8sEHH3Do0KE0RdtHHnmEOXPm8OGHHzJz5kxcXFx48sknefbZZwkKCqJo0aIZzlNERCSnfPXVV3z11VcA5gVImjVrRp8+fahfv745bvLkydSpU4fw8HAmTJhAsWLFaNy4MYMGDTL/AXfdunV88cUX9O7d22IqhPr16/PGG28wadIk5s+fz8svv2w+VrduXd555x0+/PBDVqxYgbOzM127duXNN9+86xztqVxcXFi+fDnTp0/n+++/JywsjNKlS9O5c2def/11i8VFy5cvz/jx45kzZw4hISEkJCTQvn17FW1FRCRbPPTQQ3zxxRcsXLiQzZs389NPP5nXealZsybLli0zLxhtjZYtW/LVV18xd+5cfvzxR1atWmWezuj5559n/Pjx91z/xJZatGjBggUL+PTTT5k3bx4JCQlUr16dcePG0bFjR4vYHj16cObMGb755htWrFhBUlISixYtUtFW8hy7ZI0HF8mSd955h/Lly/P666/ndir39e233/L6668zdepUnnrqqdxOR0REJM/w9PSkffv2TJgwIbdTERERyXaJiYkMGjSI7777jnfeeYcePXrYpN2LFy/SrVs3/vrrL+bOnUujRo1s0q5IYaQ5bUWy6IknnmDlypW5nYaF5OTkNHPrJiQk8Pnnn1OkSBEeeeSRXMpMREREREREcluRIkX48MMPad68OaGhoSxbtswm7ZYpU4YFCxbwwAMP0LdvX/bt22eTdkUKI02PIJJJX3/9NTdv3mTLli0kJCTkdjoW4uPjefzxx3nmmWeoVq0aly9fZsOGDRw+fJhXXnkFd3f33E5RREREREREcpGDgwOfffaZzdstV64c33//vc3bFSlsVLQVyaSTJ08yZ84cihUrxpAhQ3I7HQtFihShefPmbNmyhejoaJKTk6lWrRrvvfeexYqgIiIiIiIiIiKS92hOWxEREREREREREZE8RHPaioiIiIiIiIiIiOQhKtqKiIiIiIiIiIiI5CF5bk7bpKQkEhMTMRgM2NnZ5XY6IiKSi5KTk0lKSqJIkSIYDPo7o63pNVdERFLpNTd76TVXRERSZfQ1N88VbRMTE9m/f39upyEiInmIl5cXDg4OuZ1GgaPXXBER+S+95mYPveaKiMh/3e81N88VbVMrzF5eXgDs378fLy8vjEZjbqaV55hMJt2bu9C9uTvdm/Tpvtxdbt+b1OtrxE/2uPM1V899Kcxy+2edSF6g19zsZevX3MLyc0v9LHgKS1/Vz4LF1v3M6Gtunivapn5U5M6bYDQaC/SDnxW6N3ene3N3ujfp0325u9y+N/oYYfa48zVXz30RfS+IgF5zs0t2veYWlp9b6mfBU1j6qn4WLLbu5/1ec/VnVBEREREREREREZE8REVbERERERERERERkTxERVsRERERERERERGRPERFWxEREREREREREZE8REVbERERERERERERkTxERVsRERERERERERGRPERFWxEREREREREREZE8REVbERERERERERERkTxERVsRERERERERERGRPERFWxEREREREREREZE8pEhuJyAiIiIiYgsnTpzg8uXLVp9nMpnYu3cvSUlJGI1Gq84tWbIkDz30kNXXFBEREZF8xGTC7v33YcsWWLoUKlbM9kuqaCsiIiIi+V5MTAw1a9YkKSkpR69rNBq5cOECbm5uOXpdEREREckhsbHUeOMNDDt2pGyfP6+ibUFiSkrGaLDLcoyIiIiIpOXm5sbRo0czNdL24MGDdO/enUWLFlG3bl2rzi1ZsqQKtiLZZNmyZSxfvpxz584BULNmTfr160fz5s0B6NatG7t377Y4JygoiDFjxpi3z58/T3BwMLt27aJYsWI8//zzDB48mCJF/n0rvGvXLiZMmMDRo0cpV64cr732GgEBARbtLl26lHnz5hEdHU2tWrUYNWoU9evXz66ui4hIXvHrrxgCA3H94w+SnZyw++wzeOSRHLm0irY5xGiwY2jYPo5H30j3eHX34kwM0Iu+iIiISGZldpoCk8kEQK1atWjYsKEtUxKRLChbtixDhgyhSpUqJCcns27dOvr37094eDg1a9YEoFOnTgwcONB8jpOTk/lrk8lE3759cXNzY8WKFVy8eJFhw4Zhb2/PW2+9BcCZM2fo27cvnTt3ZvLkyezYsYORI0fi7u6Ov78/ABs2bCA0NJSQkBAaNGjAwoUL6dWrF5s2baJ06dI5eEdERCRHzZ8Pr72G3e3b3KpYEfsvvsDo45Njl1fRNgcdj75B1IVruZ2GiIiIiIhInteiRQuL7TfffJPly5cTGRlpLto6Ojri7u6e7vnbt2/n2LFjzJ8/Hzc3N2rXrs2gQYOYPHkyAwYMwMHBgRUrVlCxYkWGDx8OQPXq1dm7dy8LFiwwF23nz59Pp06dCAwMBCAkJIStW7eydu1a+vTpk13dFxGR3HL7NgwcCJ99BkDyU09xaMgQvHL4ExYq2oqIiIiIiEieZjKZ2LRpEzdv3sTnjlFOX375JV988QXu7u48/vjj9OvXzzzaNjIyEg8PD4spTPz8/AgODubYsWPUqVOHyMhIfH19La7l5+fH+PHjAYiPj+fgwYP07dvXfNxgMNC0aVN+++23TPXDFlLbsVV7eZX6WfAUlr6qn/nYmTMYOnbE7pdfSLazIzk4mMShQzEdPGjzn+H3o6KtiIiIiIiI5EmHDx+mc+fO3L59m2LFijFz5kxq1KgBwNNPP0358uUpU6YMhw8fZvLkyZw8eZIZM2YAKQsU/nfO6dTt6Ojoe8Zcv36dW7duceXKFUwmU5ppEEqXLs2JEyes7s/+/futPicn28ur1M+Cp7D0Vf3MX1x276baiBEYL18msUQJTo4bx9WmTeHgQSDn+6mirYiIiIiIiORJ1apVY926dVy7do1vvvmGYcOGsWTJEmrUqEFQUJA5ztPTE3d3d3r06MHp06epXLlyLmZ9d15eXhiNxiy3YzKZ2L9/v83ay6vUz4KnsPRV/cxnkpOxmzQJu5EjsUtKItnHB7tVq3ioWjXA9v1Mbe9+VLQVERGxwv1Wsr59+zYTJkxgw4YNxMfH4+fnx+jRoy1G8WglaxERkYxxcHCgSpUqANSrV4/9+/ezaNEixowZkya2QYMGAJw6dYrKlSvj5ubGvn37LGJiYmIAzPPgurm5mffdGePs7IyjoyMGgwGj0UhsbKxFTGxsbJoRuhlhNBptWtiwdXt5lfpZ8BSWvqqf+cDVq9CjB4SHp2z37IndzJkY71jYMlVO99OQY1cSEREpAFJXsg4LC2Pt2rU8+uij9O/fn6NHjwIwfvx4fvjhB6ZNm8bixYu5ePEiAwYMMJ+fupJ1QkICK1asYMKECYSHhzN9+nRzTOpK1k2aNGH9+vW89NJLjBw5km3btpljUleyTl1Fu1atWvTq1SvNm0oREZGCJCkpifj4+HSPRUVFAf8WZL29vTly5IjFa2NERATOzs7mKRa8vb3ZuXOnRTsRERF4e3sDKUXjunXrsmPHDoscduzYYTG3roiI5EO//w6NG6cUbB0cYPZsmDcP0inY5gYVbUVERKzQokULmjdvTtWqValWrRpvvvkmxYoVIzIykmvXrrF27VqGDx+Or68v9erVY/z48fz2229ERkYC/65kPWnSJGrXrk3z5s0ZNGgQS5cuNb8JvXMl6+rVq9O1a1fatGnDggULzHncuZJ1jRo1CAkJwdHRkbVr1+bCXREREbG9KVOmsGfPHs6ePcvhw4eZMmUKu3fv5plnnuH06dPMnDmTAwcOcPbsWbZs2cKwYcNo3LgxtWrVAlIWFKtRowZDhw7l0KFDbNu2jWnTptGlSxccHBwA6Ny5M2fOnGHixIkcP36cpUuXsnHjRnr06GHOo2fPnqxatYrw8HCOHz9OcHAwcXFxaT4BIyIi+ciqVfDII3DkCFSsCNu2QZ8+YGeX25mZaXoEERGRTPrvStYHDhwgISGBpk2bmmOqV69O+fLliYyMxNvbu0CvZC2SXyUlJZn/1/eDFFZ58bkfGxvLsGHDuHjxIi4uLnh6ejJv3jyaNWvGn3/+yY4dO1i0aBE3b96kXLlytG7dmn79+pnPNxqNzJo1i+DgYIKCgnBycqJ9+/YMHDjQHFOpUiVmz55NaGgoixYtomzZsowbNw5/f39zTLt27bh06RLTp08nOjqa2rVrM3fu3ExNjyAiIrksMRGGDYOpU1O2W7SAFSvgn09p5CUq2kqmmZKSMRru/ReIjMSIiOQ3d1vJOioqCnt7e0qUKGERX7p06fuuUg0FZyVrkfzm2LFj5v/vnFtaRHJX6h8r01OuXDmWLFly3zYqVKjAnDlz7hnTpEkT1q1bd8+Yrl270rVr1/teT0RE8rC//oKgIPjxx5TtYcNg3DjIo7//5c2sJF8wGuwYGraP49E30j1e3b04EwO0II6IFDx3W8k6v8r3q72KZFFiYiIANWrUMM9jKVLYZHQlaxERkXwpIgI6doTz58HFBRYsgDw+zY2KtpIlx6NvEHXhWm6nISKSo+62knXbtm1JSEjg6tWrFqNtY2NjLVapLugrWYvkNwaDwfy/vhdERERECpDkZPjkE3jzTUhIgNq1ISwM/pn/PC/TQmQiIiJZlLqSdb169bC3t7dYYfrEiROcP3/ePHpPK1mLiIiIiIjkgJs34aWXYMCAlIJtx46we3e+KNiCRtqKiIhYZcqUKTz22GOUK1eOGzdu8NVXX7F7927mzZuHi4sLgYGBTJgwAVdXV5ydnRk3bhw+Pj7mguudK1m//fbbREdHp7uS9dKlS5k4cSKBgYHs3LmTjRs3Mnv2bHMePXv2ZNiwYdSrV4/69euzcOFCrWQtIiIiIiICcPw4BAbC//0fGI0wcWLKaFu7/LPukoq2IiIiVrjXStYAI0aMwGAwMHDgQOLj4/Hz82P06NHm87WStYiIiIiISDb6+mvo2hUuX4YyZWDVKmjePLezspqKtiIiIla410rWAEWLFmX06NEWhdr/0krWIiIiIiIiNpaUBGPGQEhIyravL6xeDRUq5G5emaSirYiIiIiIiIiIiORfly6ljK7duDFlu39/mDoV/pmCLj9S0VZERERERERERETyp99+S5m/9uRJcHKC2bOhW7fczirLDLmdgIiIiIiIiIiIiIjVFi6Epk1TCrYPPQQ7dhSIgi2oaCsiIiIiIiIiIiL5ye3b0K8f9OgBt27BU0/BL79Agwa5nZnNqGhbSJmSkm0SIyIiIiIiIiIikmPOnoXmzeHTT8HOLmXhsS++gFKlcjszm9KctoWU0WDH0LB9HI++ke7x6u7FmRhQP4ezEhERERERERERuYsffoCgIIiOTinSLl0KbdvmdlbZQkXbQux49A2iLlzL7TRERERERERERETuLjkZJk+G4cMhKQm8vWHt2pR5bAsoFW1FREREREREREQkb7p2DXr2TCnSArz0UsrUCE5OuZtXNlPRVkRERERERERERPKeqCgICIBDh8DeHqZPh759U+ayLeCsKtrOnj2bb7/9lhMnTuDo6IiPjw9DhgzhoTuGInfr1o3du3dbnBcUFMSYMWNsk7GIiIiIiIiIiIgUbGvWpIywvX4dKlRIGWnbpEluZ5VjrCra7t69my5duuDl5YXJZGLq1Kn06tWLr7/+mmLFipnjOnXqxMCBA83bTgV8uLKIiIiIiIiIiIjYQGIijBgBkyalbD/+OKxYAWXK5G5eOcyqou28efMstidMmICvry8HDx6kcePG5v2Ojo64u7vbJkMREREREREREREp+C5ehKAg2Lo1ZXvoUHj/fShS+GZ4zVKPr127BoCrq6vF/i+//JIvvvgCd3d3Hn/8cfr166fRtiIiIiIiIiIiIpK+nTuhQwc4dw6cnWHBAggMtEnTJhNs2wZ//gnlyoG/PxiN1p2/dWtKTTkz52dGpou2SUlJjB8/noYNG+Lh4WHe//TTT1O+fHnKlCnD4cOHmTx5MidPnmTGjBlWtW8ymdL9Or8yZvCRzGhfU+Mye29skY+t+2QrWb03BZnuTfp0X+4ut++NHhMREREREZECLjkZZs2CQYMgIQFq1YLw8JT/bSAsLKXps2f/3VexInz0UcoaZ/cTHg79+3tx8eK/dTBrzs+sTBdtQ0JCOHr0KMuWLbPYHxQUZP7a09MTd3d3evTowenTp6lcuXKG29+/f3+6X+dHTk5O1KlTJ0Oxhw8fJi4uLsNtZ+be2CKf7OyTreT350120r1Jn+7L3eneiIiIiIiIiM3FxcFrr8HChSnbHTrA55+Di0uGm7jXKNqwsJQmk5Mtzzl3LmX/mjX3LryGhUGnTgaSkw2ZOj8rMlW0HTNmDFu3bmXJkiWULVv2nrENGjQA4NSpU1YVbb28vICUQoGXl1eGR3Xmd56enhmKM5lMOXJvMppPdrdhjZy6N/mR7k36dF/uLrfvTer1RUREREREpIA5cSJl+oPISDAY4IMPYPBgsLNLNzy94uz69XcfRfvccynH/luwhZR9dnbwxhspcem93TWZ7jzfzurzs8qqom1ycjJjx45l8+bNLF68mEqVKt33nKioKACrFya7szhgNBoLTSHF2n5m972xRdu59dgVpueNtXRv0qf7cne6NyIiIiIiImIzGzdCly7w99/g7g4rV8Ljj981PL0pDkqXhtjYtLGpo2CDgy3j/ys5Gc6cSSkE/+9/aY9v25Z6fvpF5Pudn1VWFW1DQkL46quv+OSTTyhevDjR0dEAuLi44OjoyOnTp/nyyy9p3rw5JUuW5PDhw4SGhtK4cWNq2WgeChEREREREREREcmHkpJg7FgICUmpejZpkjLHQMWKdz3lblMcpFewhX9HwX70UcZS+vNP6/ZnNs5aVhVtly9fDkC3bt0s9oeGhhIQEIC9vT07duxg0aJF3Lx5k3LlytG6dWv69etnu4xFREREREREREQkf/n7b+jaFTZsSNl+7TX48EMoWvSup1hOUZBxyclw6VLGYsuVs25/ZuOsZVXR9vDhw/c8Xq5cOZYsWZKlhERERERERERERKQAiYxMmb/2xAlwdIRZs+Cll+4anjp/7ZYt957i4H4eeCClVpxe0dfOLmWAr79/+uf6+6ccP3cumeTktFMk3O/8rDLcP0REREREREQkZy1btoxnnnmGhg0b0rBhQ4KCgvjxxx/Nx2/fvk1ISAhNmjTBx8eH119/nZiYGIs2zp8/T58+fWjQoAG+vr588MEHJCYmWsTs2rWL9u3bU69ePVq1akVYWFiaXJYuXUqLFi3w8vKiY8eO7Nu3L3s6LSJSEC1eDL6+KQXbatUgIsKiYGsywdatsHx5yv9r1kDVqilT3I4bl7VLDxqU8v9/1zZL3Z427e6LiBmNd06xYFn1zcj5WaWirYiIiIiIiOQ5ZcuWZciQIYSFhbF27VoeffRR+vfvz9GjRwEYP348P/zwA9OmTWPx4sVcvHiRAQMGmM83mUz07duXhIQEVqxYwYQJEwgPD2f69OnmmDNnztC3b1+aNGnC+vXreemllxg5ciTbtm0zx2zYsIHQ0FD69+9PeHg4tWrVolevXsTebTJFERFJER8P/ftD9+5w6xbJT7Zl+7RfWH7Ihy1bUkbRvvlmyvQCjz8OL76Y8n/HjlkbXQspRdVKleDdd1OKwBUqWB6vWDFlf0DAvdsJCIBVq5IoUyYhU+dnhVXTI4iIiIiIiIjkhBYtWlhsv/nmmyxfvpzIyEjKli3L2rVrmTx5Mr6+vkBKEbddu3ZERkbi7e3N9u3bOXbsGPPnz8fNzY3atWszaNAgJk+ezIABA3BwcGDFihVUrFiR4cOHA1C9enX27t3LggUL8P/n867z58+nU6dOBAYGAikLdG/dupW1a9fSp0+fHLwjIiL5yLlzKauH7dwJwKYmo3npl/e4+Fz2jx/97yjYgAB47rmU6Rb+/DOlSOzvn/ERsu3bQ+XK+7l2zZuLF41Wn59ZKtqKiIiIiIhInmYymdi0aRM3b97Ex8eHAwcOkJCQQNOmTc0x1atXp3z58uaibWRkJB4eHri5uZlj/Pz8CA4O5tixY9SpU4fIyEhz0ffOmPHjxwMQHx/PwYMH6du3r/m4wWCgadOm/Pbbb5nqhy2ktmOr9vIq9bPgKSx9LfT93LoVw4svYnfxIpftStIleQkbdj1l8+vb2SWTnAylS0Ns7L/zH1SokMyHHybx3HMpUy+k+u/csxl9eEwmE0Yj+PubLAq1mX14M/q8UNE2j3BzdsCUlIzRkHZi4ztlJCavKIh9knvT4y0iIiIitnT48GE6d+7M7du3KVasGDNnzqRGjRpERUVhb29PiRIlLOJLly5NdHQ0ADExMRYFW8C8fb+Y69evc+vWLa5cuYLJZKJ06dJprnPixAmr+7N//36rz8nJ9vIq9bPgKSx9LXT9TE6mzNKlVPz4Y+xMJiJpQGDyGk5QI1uuW6ZMAoMHn6F588v89pszMTH2uLkl4ONzHaMxZe0zW8rpx1NF2zzCxdEeo8GOoWH7OB59I92Y6u7FmRhQP4czy7yC2Ce5Nz3eIiIiImJL1apVY926dVy7do1vvvmGYcOGsWTJktxOK9O8vLww2uDztCaTif3799usvbxK/Sx4CktfC2U/b97E7pVXMKxZA8DKot3oeftT4ihu8+uOGJFEixbJ+PsbMRqrAvDwwza/jJmtH8/U9u5HRds85nj0DaIuXMvtNGyqIPZJ7k6Pt4iIiIjYioODA1WqVAGgXr167N+/n0WLFtG2bVsSEhK4evWqxWjb2NhY3N3dgZQRs/v27bNoLyYmBsAiJnXfnTHOzs44OjpiMBgwGo1pFh2LjY1NM0I3I4xGo00LOLZuL69SPwuewtLXQtPPo0cxduwIUVEkFbFneNFpTLrxGmDbT9na2aUsADZmjCHb55NNT04/ntk/+6+IiIiIiIiIDSQlJREfH0+9evWwt7dnx44d5mMnTpzg/PnzeHt7A+Dt7c2RI0csCq4RERE4OztTo0YNc8zOfxbJuTMmtQ0HBwfq1q1rcZ2kpCR27NiBj49PNvVSRCR/MJkgdk4Epka+EBXF5eLlaZb4I5Nu9CM7Crbw7+JihYFG2oqIiIiIiEieM2XKFB577DHKlSvHjRs3+Oqrr9i9ezfz5s3DxcWFwMBAJkyYgKurK87OzowbNw4fHx9zwdXPz48aNWowdOhQ3n77baKjo5k2bRpdunTBwcEBgM6dO7N06VImTpxIYGAgO3fuZOPGjcyePducR8+ePRk2bBj16tWjfv36LFy4kLi4OAICAnLjtoiI5CiTCbZtg3Pn4K+/IPXvYH+eScR79QgG3poEwFaaE3RjJRd5MFvyqFgxpWBbmH70qmgrIiIiIiIieU5sbCzDhg3j4sWLuLi44Onpybx582jWrBkAI0aMwGAwMHDgQOLj4/Hz82P06NHm841GI7NmzSI4OJigoCCcnJxo3749AwcONMdUqlSJ2bNnExoayqJFiyhbtizjxo3D/44lxtu1a8elS5eYPn060dHR1K5dm7lz52ZqegQRkfwkLAwGDYKzZy33u3OR5bzAE3wPwGQGM5wJmGxUZqxUCaZMAXd3+PNPKFcO/P0LzwjbVCraioiIiIiISJ4zfvz4ex4vWrQoo0ePtijU/leFChWYM2fOPdtp0qQJ69atu2dM165d6dq16z1jREQKkrAw6NABkpMt9z/CLtbQgUqc5TrF6cl81tAxy9dzd4cuXeC55wpngTY9KtqKiIiIiIiIiIgIJhNs2QI9evy3YJtMHz5jOgMpSjyH8CSAMKKoY1X7rq7Qsyc8/XTK9sWLhXck7f2oaCsiIiIiIiIiIlLIhYVBnz7/zlubypE4PqEfPVkAwFoC6Ml8rlHCqvbd3VOmWvhnWnG5D0NuJyAiIiIiIiIiIiK5w2SCMWMgMDBtwbYqJ/mZZvRkASYMDOUDOrDG6oItwKxZKthaQyNt8zEnJ6fcTkFERERERERERPKpsDAYOBDOnUt7rA2bWMaLPMDfRONGECv5gRZWX8NohBUrICDABgkXIhppm4+4OTtgSkqZUMRoNFKnTh2M6Uz4kRojIiIiIiIiIiKSnjVrUkbX/rdga0cSIxnLBtrxAH+zi0doyK+ZKtgCLF+esqiZWEcjbfMRF0d7jAY7hobt43j0jXRjqrsXZ2JA/RzOTERERERERERE8gOTCUJCYOzYtMdcucxiuvEMXwEwi74M4iPiKWr1dSpVgmnTNMI2s1S0zYeOR98g6sK13E5DRERERERERETyCZMppVA7fjwkJKQ97sU+wgigBse5RVFe41MW0PO+7ZYqlcxzz9nRokXKnLju7lChAvj7p0yNIJmjoq2IiIiIiIiIiEgBZDLB1q3wySfwxReQmJh+XBeW8Bl9KEYcJ6lKIGv5jYbpxjo7w+jR8OCDSdy8eZSePWvg4KDqrK2paCsiIiIiIiIiIlJA3Fmo/eoriI+/e6w98UxhMK8zA4BNtKELS7lE6XTj7exg4cKUKQ9MpmQiI69rNG02UdFWRERERERERESkAFizBnr1gqtX7x9bjvOspiPNiABgDKMIYTRJpF+F1Ry1OUtFWxERERERERERkXzMZILOnVOKthnhz0+sohNl+YvLuNKVJXzN0+nGDhwI7dtrjtqcpqKtiIiIiIiIiIhIPpS6uNjYsZCUlJEzknmDaUzibYpg4v+oTyBrOU6NNJEVK8JHH2lkbW5R0VZERERERERERCSfCQuDl16C69czFl+c68ylN51ZCcASutCHz4ijWJrYkBB4912NrM1NhtxOQEREJD+ZPXs2gYGB+Pj44OvrS79+/Thx4oRFTLdu3fD09LT4995771nEnD9/nj59+tCgQQN8fX354IMPSPzPUq67du2iffv21KtXj1atWhEWFpYmn6VLl9KiRQu8vLzo2LEj+/bts32nRUREREQkzzCZIDgYAgMzXrCtyRF28iidWUkCRRjAx3RjcZqCrYsLrF0L772ngm1u00hbERERK+zevZsuXbrg5eWFyWRi6tSp9OrVi6+//ppixf79hadTp04MHDjQvO3k5GT+2mQy0bdvX9zc3FixYgUXL15k2LBh2Nvb89ZbbwFw5swZ+vbtS+fOnZk8eTI7duxg5MiRuLu74+/vD8CGDRsIDQ0lJCSEBg0asHDhQnr16sWmTZsoXTr91V5FRERERCT/CguDV16BS5cyfs5zrGMR3SnBNc5Tjg6sYQdNLWLs7WHECBg1SsXavEIjbUVERKwwb948AgICqFmzJrVq1WLChAmcP3+egwcPWsQ5Ojri7u5u/ufs7Gw+tn37do4dO8akSZOoXbs2zZs3Z9CgQSxdupT4+HgAVqxYQcWKFRk+fDjVq1ena9eutGnThgULFpjbmT9/Pp06dSIwMJAaNWoQEhKCo6Mja9euzZF7ISIiIiIiOScsLGV0bUYLtgZMjOcd1tGeElzjRx6jIb9aFGzt7WH0aIiLSxm9q4Jt3qGirYiISBZcu3YNAFdXV4v9X375JU2aNOHpp59mypQpxMXFmY9FRkbi4eGBm5ubeZ+fnx/Xr1/n2LFj5hhfX1+LNv38/IiMjAQgPj6egwcP0rTpv79wGQwGmjZtym+//WbTPoqIiIiISO4xmeDbb6Fz54yf40Y0m3iSd5gAwBTeoiXf8RdlAXByUrE2r9P0CCIiIpmUlJTE+PHjadiwIR4eHub9Tz/9NOXLl6dMmTIcPnyYyZMnc/LkSWbMmAFATEyMRcEWMG9HR0ffM+b69evcunWLK1euYDKZ0kyDULp06TRz7N6PyWSyKl6koEn6Z6nlpKQkfT9IoaXnvohI3rRmDbz8MvwzViRDGrGHtQRSmTNcpzi9mMcqgnBwgICnoV8/+N//VKjN61S0FRERyaSQkBCOHj3KsmXLLPYHBQWZv/b09MTd3Z0ePXpw+vRpKleunNNp3tf+/ftzOwWRXJU6wv3YsWMUKaJfj0VERCRvePttmDzZunN6M4cZDKAo8RzGgwDCSKhZl+8+VaE2v9FvpSIiIpkwZswYtm7dypIlSyhbtuw9Yxs0aADAqVOnqFy5Mm5ubuzbt88iJiYmBgB3d3cgZVRt6r47Y5ydnXF0dMRgMGA0GomNjbWIiY2NTTNC9368vLww6rc3KcQSExMBqFGjBt7e3rmbjEguMZlM+iOeiEgeYTJBUBBYs1RFUW4xgwH0Zh4A4TxPDxbwymBXqwu/kjeoaCsiImKF5ORkxo4dy+bNm1m8eDGVKlW67zlRUVHAvwVZb29vZs2aRWxsrHl6g4iICJydnalRo4Y55qeffrJoJyIiwlxQcnBwoG7duuzYsYOWLVsCKR/t3rFjB127drWqT0ajUUVbKdQMBoP5f30viIiISG4xmWDs2JR//8zelCFV+IM1dKARezFhYATj2fXYUKI32+HgkH35SvbSQmQiIiJWCAkJ4YsvvmDKlCkUL16c6OhooqOjuXXrFgCnT59m5syZHDhwgLNnz7JlyxaGDRtG48aNqVWrFpCyoFiNGjUYOnQohw4dYtu2bUybNo0uXbrg8M9vVZ07d+bMmTNMnDiR48ePs3TpUjZu3EiPHj3MufTs2ZNVq1YRHh7O8ePHCQ4OJi4ujoCAgBy/LyIiIiIiknmrVkGxYhASYl3BthXfspeHacReonGjDd9gGjyMrT+qYJvfaaStiIiIFZYvXw5At27dLPaHhoYSEBCAvb09O3bsYNGiRdy8eZNy5crRunVr+vXrZ441Go3MmjWL4OBggoKCcHJyon379gwcONAcU6lSJWbPnk1oaCiLFi2ibNmyjBs3Dn9/f3NMu3btuHTpEtOnTyc6OpratWszd+5cq6dHEBERERGR3PPss/Dll9adY0cS7xDKWEZhIJndNKZr0TW8v7gyHTtmT56Ss1S0FRERscLhw4fvebxcuXIsWbLkvu1UqFCBOXPm3DOmSZMmrFu37p4xXbt2tXo6BBERERERyRsaNYK9e607x5XLLOQlnuMLAGbTh63Pf0TUGkctNFaAqGgrIiIiIiIiIiKSwx5+GH791bpz6rGfMAKoyTFuUZR+fELssy+zPjx7cpTcozltRUREREREJM+ZPXs2gYGB+Pj44OvrS79+/Thx4oRFTLdu3fD09LT4995771nEnD9/nj59+tCgQQN8fX354IMPSExMtIjZtWsX7du3p169erRq1YqwsLA0+SxdupQWLVrg5eVFx44d2bdvn+07LSKFgskENWtaX7DtzHJ28ig1OcYpKuPHdh4Y/DLr12dPnpK7NNJWRERERERE8pzdu3fTpUsXvLy8MJlMTJ06lV69evH1119TrFgxc1ynTp0s5oV3cnIyf20ymejbty9ubm6sWLGCixcvMmzYMOzt7XnrrbcAOHPmDH379qVz585MnjyZHTt2MHLkSNzd3c1zyW/YsIHQ0FBCQkJo0KABCxcupFevXmzatInSpUvn0B0RkYIgLAw6dUop3GZUERKYzBAGMR2Ab2nFuo7LiFjipsXGCjCNtBUREREREZE8Z968eQQEBFCzZk1q1arFhAkTOH/+PAcPHrSIc3R0xN3d3fzP2dnZfGz79u0cO3aMSZMmUbt2bZo3b86gQYNYunQp8fHxAKxYsYKKFSsyfPhwqlevTteuXWnTpg0LFiwwtzN//nw6depEYGAgNWrUICQkBEdHR9auXZsj90JE8j+TCd57DwIDrSvYluVPfuBxc8F2HO9yZcVGPlmlgm1Bp6KtiIiIiIiI5HnXrl0DwNXV1WL/l19+SZMmTXj66aeZMmUKcXFx5mORkZF4eHjg5uZm3ufn58f169c5duyYOcbX19eiTT8/PyIjIwGIj4/n4MGDNG3a1HzcYDDQtGlTfvvtN5v2UUQKFpMJNm4ELy8oUgTGjrXufD+28SsN8eNnrlCCDvbrqbN2HB2DtNpYYaDpEURERERERCRPS0pKYvz48TRs2BAPDw/z/qeffpry5ctTpkwZDh8+zOTJkzl58iQzZswAICYmxqJgC5i3o6Oj7xlz/fp1bt26xZUrVzCZTGmmQShdunSaOXbvx2TN8LoMtGOr9vIq9bPgKSx9NZlMfP99Sfz8DNy6lZkWkhnIdCYzBHsS2U89vuu3mmUf1sRoNFk1Ujc7FabH887/bdXe/VhVtJ09ezbffvstJ06cwNHRER8fH4YMGcJDDz1kjrl9+zYTJkxgw4YNxMfH4+fnx+jRo9O8CIqIiIiIiIhkREhICEePHmXZsmUW+4OCgsxfe3p64u7uTo8ePTh9+jSVK1fO6TTva//+/Xm6vbxK/Sx4Cnpfv/++JEOHPnT/wHQU4wZzeIUXWQ7ArurPYZw3hMecb7B/f6QNs7Sdgv54psrpflpVtM3IRPDjx4/nxx9/ZNq0abi4uDB27FgGDBjAihUrsqUDIiIiIiIiUnCNGTOGrVu3smTJEsqWLXvP2AYNGgBw6tQpKleujJubG/v27bOIiYmJAcDd3R1IGVWbuu/OGGdnZxwdHTEYDBiNRmJjYy1iYmNjrR6c5OXlhdGY9Y81m0wm9u/fb7P28ir1s+ApDH01mcDXN3U2Ujurzq3BUcIIwIsDJFAEuymTaDRwANhZ105OKQyPJ9i+n6nt3Y9VRdt58+ZZbE+YMAFfX18OHjxI48aNuXbtGmvXrmXy5MnmOYHGjx9Pu3btiIyMxNvb25rLiYiIiIiISCGVnJzM2LFj2bx5M4sXL6ZSpUr3PScqKgr4tyDr7e3NrFmziI2NNU9vEBERgbOzMzVq1DDH/PTTTxbtREREmN+/Ojg4ULduXXbs2EHLli2BlOkaduzYQdeuXa3qk9FotGlhw9bt5VXqZ8FTEPtqMsGWLSkLjSUkWH/+s6xnEd1x5SoxRcri9sNq8POzfaLZoCA+nunJ6X5maSGy/04Ef+DAARISEiwmaK9evTrly5c3T+IuIiIiIiIicj8hISF88cUXTJkyheLFixMdHU10dDS3/pkg8vTp08ycOZMDBw5w9uxZtmzZwrBhw2jcuDG1atUCUhYUq1GjBkOHDuXQoUNs27aNadOm0aVLFxz+WXa9c+fOnDlzhokTJ3L8+HGWLl3Kxo0b6dGjhzmXnj17smrVKsLDwzl+/DjBwcHExcUREBCQ4/dFRPKe5cvB0RHatIHr160714CJcbzLep7Hlav8VtwPt9O/5puCrWSfTC9Elt5E8DExMdjb21OiRAmL2NKlS5snec+oOyflLQgTGuf0Xxzud88yms+92rFln2z5GBeWibAzI7vvjS2eV7lBz5m7y+17o8dERESk8Fq+PGU+x27dulnsDw0NJSAgAHt7e3bs2MGiRYu4efMm5cqVo3Xr1vTr188cazQamTVrFsHBwQQFBeHk5ET79u0ZOHCgOaZSpUrMnj2b0NBQFi1aRNmyZRk3bhz+/v7mmHbt2nHp0iWmT59OdHQ0tWvXZu7cuVq7RURo3Bh++SVz55YmhmW8SGs2A7C+6iCeOzIJ7O1tmKHkV5ku2t5tInhbuXNuh/w+obGTkxN16tTJ0WsePnyYuLi4LOdzt3Zs3ad75ZtZ+f15k52y495k5Dnh5uyAKSn5vsXdxEQTv/9+kITMfKYkC/ScuTvdGxEREclphw8fvufxcuXKsWTJkvu2U6FCBebMmXPPmCZNmrBu3bp7xnTt2tXq6RBEpOAymaBiRbhwIXPnP8wvrCWQKpzmBsX4tOFchux9wbZJSr6WqaLt3SaCd3NzIyEhgatXr1qMto2NjTXPKZRRXl5eAIViQuPs4OnpmafaycnrFJaJsDMjt++Ni6M9RoMdQ8P2cTz6Rrox1d2LMzGgPnXr1s2xvHL7vuRluX1vMjpBu4iIiIiISE5ZswY6dsz8+S8zj5n0x5HbHKUG67uHMWShl+0SlALBqqLt/SaCr1evnvkjKm3atAHgxIkTnD9/3upFyO4sDhSWCY1tyVb3K6fue3ZcR8+bu8vte3M8+gZRF67dMyY38svt+5KX6d6IiIiIiIjA22/D5MmZO7cot/iY13mFuQD8WvFZ6v26iCHurjbMUAoKq4q2ISEhfPXVV3zyySfmieABXFxccHR0xMXFhcDAQCZMmICrqyvOzs6MGzcOHx8fq4u2IiIiIiIikr/8+eef2NnZmT+RuW/fPr788ktq1KhBUFBQLmcnIpI1gwfD1KmZO7cyp1hDBxrzC8l2diSPGUfDEcPBYLBtklJgWFW0vd9E8AAjRozAYDAwcOBA4uPj8fPzY/To0TZKV0RERERERPKqwYMH06lTJ55//nmio6Pp2bMnNWvW5MsvvyQ6OpoBAwbkdooiIpmSlYJtSzaznBdwI5bk0qWxW7YMu9atbZugFDhWFW3vNxE8QNGiRRk9erQKtSIiIiIiIoXM0aNHqV+/PgAbN26kZs2arFixgu3btzN69GgVbUUkX8pswdaOJIbxAeMYiZEk/q7+MKW2rIUqVWyfpBQ4mVqITEREREREROS/EhMTcXBwACAiIoIWLVoA8NBDD5mn1xMRyS9MJujUCcLCrD+3BFdYyEs8z3oA/niiF1W/mgGOjjbOUgoqTZwhIiIiIiIiNlGjRg1WrFjBL7/8QkREBI899hgAFy9epGTJkrmbnIiIFcLCUuqrmSnY1uUAe2jM86wnyd6BpNlzqPrdXBVsxSoq2oqIiIiIiIhNDBkyhJUrV9KtWzeeeuopatWqBcD3339vnjZBRCSvW7MGAgMhMdH6c4NYwS6a4MFRkitXxvDzdgx9ets+SSnwND2CiIiIiIiI2ESTJk3YuXMn169fx9XV1by/U6dOODk55WJmIiL3ZzLBqFEQGmr9uUVIYCJDeZNpAFx95BGKf/EFxgcftG2SUmioaCv5gikpGaPBLttjMtKGiIiIiIjcXXJyMgcPHuT06dM8/fTTODs7Y29vj6M+FiwiediqVfDCC5CUZP25D3KBVXTiMbYBkDRsGEcDAvB2c7NxllKYqGgr+YLRYMfQsH0cj76R7vHq7sWZGHD/j1vdq52MtiEiIiIiIuk7d+4cvXv35s8//yQ+Pp5mzZrh7OzMnDlziI+PZ8yYMbmdoohIGs8/D+vXZ+7cpvzMajpSnj+hRAlYuJDkZ56ByEhbpiiFkIq2km8cj75B1IVreaYdERERERGx9P7771OvXj3Wr19PkyZNzPtbtWrFqFGjcjEzEZG04uOhVSv46afMnJ3M63zMFAZjTyLUrZuyapmHR8o8CyJZpKKtiIiIiIiI2MTevXtZvnw5Dg4OFvsrVKjAX3/9lUtZiYhYio+H1q3hxx8zd34xbvAZfejCspQdQUEwdy44O9suSSn0DLmdgIiIiIiIiBQMSUlJJKUzIeSFCxcoXrx4LmQkIvIvkwk6dICiRTNfsK3OMXbgSxeWYbIzwocfwvLlKtiKzWmkrYiIiIjkKUePHuXatZybyujQoUPm/41GY45d18XFhZo1a+bY9URyQrNmzVi4cCFjx44177tx4wYff/wxzZs3z8XMRKSwW7MmZUBsZhYaS/U0X7KYbpTkCleLPUiJTavB3992SYrcQUVbEREREckzjh49ioeHR65cu3v37jl+zSNHjqhwKwXK8OHD6dWrF+3atSM+Pp4hQ4bwxx9/UKpUKaZOnZrb6YlIIfXmmzBtWubPN2AimGBGMQ6ApKbNKLF6FZQvb5sERdKhoq2IiIiI5BmpI2yXLFlC7dq1c+SaJpOJvXv38vDDD+fYSNuoqCi6du2aoyOKRXJC2bJlWb9+PV9//TWHDx/m5s2bdOjQgWeeeQZHR8fcTk9ECqFGjWDv3syf/wCxLONF2vBtyo6BAzFMmgT/mbtbxNZUtBURERGRPKd27do0bNgwR65lMpkwGAx4e3vn6PQIIgVVkSJFeO6553I7DRERqleHEycyf35D9rKWQKpyivgiTjgsmANdutguQZF7UNFWREREREREbGLdunX3PP7888/nSB4iIj4+WSvY9uRzPqEfjtwm+aHqOISHQf36tktQ5D5UtC1g3JwdMCUlYzTY5XYqUsDoeSUiIiIi9/P+++9bbCcmJhIXF4e9vT1OTk4q2opIjqhWDf74I3PnOnCb6QykL5+l7HjmGewWLYKSJW2VnkiGqGhbwLg42mM02DE0bB/Ho2+kG/NYTTcGtdCCF2IdPa9ERERE5H727NmTZt8ff/xBcHAwvXr1yoWMRKSwKVMGoqMzd25FzrCWQB5hD0nYYRg3Ft55BwwG2yYpkgEq2hZQx6NvEHUh/YUtqrkVz+FspKDQ80pERERErFW1alUGDx7M22+/zaZNm3I7HREpwKpVy3zBtgVbWEFn3Ikh+YEHMCxbBm3a2DZBESvoTwUiIiIiIiKSrYoUKcLFixdzOw0RKcCefjqzUyIkM5QP+JbWuBMDDRtit3evCraS6zTSVkRERERERGxiy5YtFtvJyclER0ezdOlSGjZsmEtZiUhB99Zb8PXX1p/nwlWW2vfgmYTwlB09e8LMmeDkZNsERTJBRVsRERERERGxif79+1ts29nZ8cADD/Doo48ybNiwXMpKRAqylSvhww+tP682v/NDyfY8ePkIODjAjBnQuzfYaQFuyRtUtBURERERERGbOHToUG6nICKFyOrV0Lmz9ed1ZBWL7V+m6OUbUKkSrFkDjzxi+wRFskBz2oqIiIiIiEieM3v2bAIDA/Hx8cHX15d+/fpx4sQJi5jbt28TEhJCkyZN8PHx4fXXXycmJsYi5vz58/Tp04cGDRrg6+vLBx98QGJiokXMrl27aN++PfXq1aNVq1aEhYWlyWfp0qW0aNECLy8vOnbsyL59+2zfaRHJsLAw6NTJunOMJDKZwawiiKIJN+CJJ2DvXhVsJU/SSFsRERERERHJtNDQ0AzHvvPOOxmO3b17N126dMHLywuTycTUqVPp1asXX3/9NcWKFQNg/Pjx/Pjjj0ybNg0XFxfGjh3LgAEDWLFiBQAmk4m+ffvi5ubGihUruHjxIsOGDcPe3p633noLgDNnztC3b186d+7M5MmT2bFjByNHjsTd3R1/f38ANmzYQGhoKCEhITRo0ICFCxfSq1cvNm3aROnSpTPcJxGxDZMJAgOtO6cMf7GSIP7Hjyk7hg2DceOgiEpjkjfpmSkiIiIiIiKZ9vvvv2cozs7KeSLnzZtnsT1hwgR8fX05ePAgjRs35tq1a6xdu5bJkyfj6+sLpBRx27VrR2RkJN7e3mzfvp1jx44xf/583NzcqF27NoMGDWLy5MkMGDAABwcHVqxYQcWKFRk+fDgA1atXZ+/evSxYsMBctJ0/fz6dOnUi8J8qUUhICFu3bmXt2rX06dPHqn6JSNZVrGhdvC8RrKYjFTgPLi6wYAEEBGRLbiK2oqKtiIiIiIiIZNrixYtz5DrXrl0DwNXVFYADBw6QkJBA06ZNzTHVq1enfPny5qJtZGQkHh4euLm5mWP8/PwIDg7m2LFj1KlTh8jISHPR986Y8ePHAxAfH8/Bgwfp27ev+bjBYKBp06b89ttvVvXBZDJZ1+n7tGOr9vIq9bPgsUVfn30WLlwwABn5Q1Ay/fiED3kTBxJIrl2bpNWroVatlOG62aSwPKbqZ9baux8VbSVdbs4OmJKSMRq0aqLkPRl5btoqRkRERERyX1JSEuPHj6dhw4Z4eHgAEBMTg729PSVKlLCILV26NNHR0eaYOwu2gHn7fjHXr1/n1q1bXLlyBZPJlGYahNKlS6eZY/d+9u/fb1V8TreXV6mfBU9m+zp5cgU2bHiQjBRsnbjJbPrSjSUAXGrZklPvvUfSrVsQGZmp61ursDym6mf2UNFW0uXiaI/RYMfQsH0cj76R5vhjNd0Y1KJmLmQmwj2fm/Dv8/NeMdXdizMxoH52pikiIiJSKO3fv5+NGzfy559/kpCQYHFsxowZmWozJCSEo0ePsmzZMlukmGu8vLwwGo1ZbsdkMrF//36btZdXqZ8FT1b6OnQorFiRsRG2D3GcMAJowD4SMcIHE3B96w3qWzlNS2YVlsdU/cxae/ejoq3c0/HoG0RduJZmfzW34rmQjci/7vbchH+fn/eKERERERHb+/rrrxk2bBh+fn5s374dPz8/Tp48SWxsLK1atcpUm2PGjGHr1q0sWbKEsmXLmve7ubmRkJDA1atXLUbbxsbG4u7ubo7Zt2+fRXsxMTEAFjGp++6McXZ2xtHREYPBgNFoJDY21iImNjY2zQjd+zEajTYtbNi6vbxK/Sx4rO3r6tUwdWrGYp/iK5bQlZJc4S/KcHjMKh4b2jyTmWZNYXlM1c/sYcixK4mIiBQAs2fPJjAwEB8fH3x9fenXr1+aj0bevn2bkJAQmjRpgo+PD6+//nqaN4Pnz5+nT58+NGjQAF9fXz744AMSExMtYnbt2kX79u2pV68erVq1IiwsLE0+S5cupUWLFnh5edGxY8c0b0xFRERy0qxZs3jnnXeYNWsW9vb2vPvuu2zatIm2bdtSrlw5q9pKTk5mzJgxbN68mYULF1KpUiWL4/Xq1cPe3p4dO3aY9504cYLz58/j7e0NgLe3N0eOHLEouEZERODs7EyNGjXMMTt37rRoOyIiwtyGg4MDdevWtbhOUlISO3bswMfHx6o+iYj1TCbo1On+cQZMBDOar3iGklwhAl/2zPqVx0blTsFWJKtUtBUREbHC7t276dKlC6tWrWL+/PkkJibSq1cvbt68aY4ZP348P/zwA9OmTWPx4sVcvHiRAQMGmI+bTCb69u1LQkICK1asYMKECYSHhzN9+nRzzJkzZ+jbty9NmjRh/fr1vPTSS4wcOZJt27aZYzZs2EBoaCj9+/cnPDycWrVq0atXrzQjgURERHLKmTNnaN48pUDi4ODAzZs3sbOzo0ePHqxatcqqtkJCQvjiiy+YMmUKxYsXJzo6mujoaG7dugWAi4sLgYGBTJgwgZ07d3LgwAFGjBiBj4+PueDq5+dHjRo1GDp0KIcOHWLbtm1MmzaNLl264ODgAEDnzp05c+YMEydO5Pjx4yxdupSNGzfSo0cPcy49e/Zk1apVhIeHc/z4cYKDg4mLiyNAq8+LZLvate8fU4pLfMXTjGYMADPoz7klW3m6b4Vszk4k+2h6BBERESvMmzfPYnvChAn4+vpy8OBBGjduzLVr11i7di2TJ082r0Q9fvx42rVrZ17Jevv27Rw7doz58+fj5uZG7dq1GTRoEJMnT2bAgAE4ODiwYsUKKlasyPDhw4GU1bD37t3LggUL8Pf3B2D+/Pl06tSJwMBAIOXN7datW1m7di19+vTJwbsiIiKSokSJEty4kbKmQJkyZTh69Cienp5cvXqVuLg4q9pavnw5AN26dbPYHxoaai6WjhgxAoPBwMCBA4mPj8fPz4/Ro0ebY41GI7NmzSI4OJigoCCcnJxo3749AwcONMdUqlSJ2bNnExoayqJFiyhbtizjxo0zv94CtGvXjkuXLjF9+nSio6OpXbs2c+fOtXp6BBGxzltvwdGj947x5jfCCKAaf3ATJ/oym7JDujGgS87kKJJdVLQVERHJgmvXUuZNdnV1BeDAgQMkJCTQtGlTc0z16tUpX768uWgbGRmJh4eHxRs9Pz8/goODOXbsGHXq1CEyMtJc9L0zZvz48QDEx8dz8OBB+vbtaz5uMBho2rQpv/32m1V9MJlM1nVaJBulPh9NJlOOPTfvvGZOyY1+ityLrZ6HjRs3JiIiAk9PT5588knef/99du7cSURERJrXtfs5fPjwfWOKFi3K6NGjLQq1/1WhQgXmzJlzz3aaNGnCunXr7hnTtWtXunbtet+cRMQ24uPhww/vHdOdhcziVZy4xXEeIoAw/jewAZMm5UyOItlJRVsREZFMSkpKYvz48TRs2BAPDw8gZeESe3t7iwVRAEqXLk10dLQ55r8jc1K37xdz/fp1bt26xZUrVzCZTJQuXTrNdf47x+79ZGTlUpGccuTIEfP/BkPOzuSVk98LudlPkexw5MgRPDw8GDVqFLdv3wbgtddew97enl9//ZXWrVvz2muv5XKWIpKf3LHuYBoO3GYab/AaswD4iqfoxmIealiKjz7KoQRFspmKtiIiIpkUEhLC0aNHWbZsWW6nkiVeXl6FYrVXyR+SkpIA8PDwMM9Jmd1MJhP79+/P0e+F3OinyL2kfh9k1rPPPmteFLNdu3ZAyidANF2PiFjLZAJnZ/hn+uo0KnCWNXTgUXaRhB3BBDOOkbiXMbB3b87mKpKdVLQVERHJhDFjxrB161aWLFlC2TuGAbi5uZGQkMDVq1ctRtvGxsbi7u5ujtm3b59FezExMQAWMan77oxxdnbG0dERg8GA0WhMs+hYbGys1fPrGY1GFW0lz0h9LubG8zInr5mb/RTJDkuWLGHt2rVMmDCB0NBQWrduTceOHWnUqFFupyYi+UhYGPyzXEO6Hud7VtCZMkRziVJ0YSmbaIuTE/z1V87lKZIT9FkskX+4OTtgSkq+b1xGYkSk4EpOTmbMmDFs3ryZhQsXUqlSJYvj9erVw97enh07dpj3nThxgvPnz5tH03l7e3PkyBGLgmtERATOzs7UqFHDHLNz506LtiMiIsxtODg4ULduXYvrJCUlsWPHDnx8fGzZZRERkftq1KgRoaGhbN++nZEjR3Lu3Dm6du1KmzZt+Oyzz8zT/4iI3M29C7bJDGESm2lFGaL5DW8a8QubaAvA5cs5laVIztFIW5F/uDjaYzTYMTRsH8ejb6QbU929OBMD6udwZiKSl4SEhPDVV1/xySefULx4cfObUBcXFxwdHXFxcSEwMJAJEybg6uqKs7Mz48aNw8fHx1xw9fPzo0aNGgwdOpS3336b6Ohopk2bRpcuXXBwcACgc+fOLF26lIkTJxIYGMjOnTvZuHEjs2fPNufSs2dPhg0bRr169ahfvz4LFy4kLi7OvKK2iIhITitWrBiBgYEEBgZy6tQpwsLCWLZsGdOnT8fPz49Zs2bldooikgeZTBAUlP4xF64yn54EEgbAAl7iNT7lFk4AvPUW/PMrtEiBoqKtyH8cj75B1IVruZ2GiORRy5cvB6Bbt24W+0NDQ83F0hEjRmAwGBg4cCDx8fH4+flZrGptNBqZNWsWwcHBBAUF4eTkRPv27Rk4cKA5plKlSsyePZvQ0FAWLVpE2bJlGTduHP7+/uaYdu3acenSJaZPn050dDS1a9dm7ty5Vk+PICIikh2qVKlC3759KV++PFOnTuXHH3/M7ZREJI/67jtITEy7vxZRhBFAbQ4Rjz0Dmc5s+gJ2AHh4wJQpOZurSE5R0VZERMQKhw8fvm9M0aJFGT16tEWh9r8qVKjAnDlz7tlOkyZNWLdu3T1junbtSteuXe+bk4iISE7as2cPa9eu5ZtvvsFgMNC2bVs6dOiQ22mJSB7VsWPafR1Yzee8jAvXOUsFAlnLbpqYj9vZwe+/52CSIjlMRVsRERERERHJsr/++ovw8HDCw8M5deoUPj4+jBw5krZt21KsWLHcTk9E8qiHHoJrd3zY1UgiobzD20wG4HsepzMriKaMxXmrVoHW8pSCTEVbERERERERyZLevXuzY8cOSpUqxXPPPUdgYCAPPfRQbqclInncs8/CyZP/bpfhL1bQmcfZCsAHDOVd3sf0n/LV22+DBu9LQWd10XbPnj3MmzePAwcOEB0dzcyZM2nZsqX5+PDhwwkPD7c4x8/Pj3nz5mU9WxEREREREclzihQpwkcffcTjjz+OUUPfRCQD4uLgyy//3W7CTtbQgYqc4xrO9GABYQSmOW/VqvSnUxApaKwu2t68eRNPT08CAwMZMGBAujH+/v6Ehoaatx20jJ+IiIiIiEiBNWvWrNxOQUTymbffTv0qmdf4lGm8gQMJRFGL9oRzmFppzklM1JQIUnhYXbRt3rw5zZs3v2eMg4MD7u7umU5KREREREREREQKrlmzwImbfMprvMQiAFbTgZf5nOu4pImfNEkFWylcsmVO2927d+Pr60uJEiV49NFHeeONNyhVqpRVbZhMpnS/zq/0EaF7u99jnNH7ZzKZSEpKwsnJiaSkpEy3k5Hr5EepeWcm/5x+Dt8rR1vmYjKZsnRfCrrcvjd6TERERERECp5GjaCy6QRhBODN/2HCwDA+YAqDAbt0zxk4MGdzFMltNi/a+vv706pVKypWrMiZM2eYOnUqr7zyCitXrrSq0LJ///50v86PnJycqFOnTm6nkacdPnyYuLi4dI9l5P65OTtgSkrGaDRiNBqz/X7fK9+cZG9vT506dSlS5O7fW4mJJn7//SAJCQnmfXd+T2Wkjdxwt3ts6++nO6+T33/WZCfdGxERERERsYVJkypQLnIT39OVUlzmIu4EsZKtPH7Xc956CzTzphQ2Ni/aPvXUU+avPT098fT0pGXLlubRtxnl5eUFpBQKvLy8NFK1gPP09MzS+S6O9hgNdgwN28fx6BvpxjxW041BLWpm6TqpspqvLRmNxrv2u7p7cSYG1Kdu3bpAyqjF9L6n7tUG2PbeZVRO3WNPT8+73he5+3Mmp68vIiIiIiL53+qVSdRa+SnvMRYDyezgUTqymnNUvOs5tWrBlCk5mKRIHpEt0yPcqVKlSpQqVYpTp05ZVbT9b0FJhZSCzVaP7/HoG0RduJbusWpuxW1yDch7013cq9+QNt/0vqdy6t5lVE7dY/2syRjdGxEREbmbLVu2ZDj2iSeeyMZMRCQvWzf/Ei4vdyOYDQDMpB9vMZV4it71HIMBDhzIqQxF8pZsL9peuHCBy5cva2EyERERERGRAqh///4W23Z2diQnJ1tsp4qKisqxvEQk79gyJZL6QwJ4iJPE4UhfZrOY7vc9b9EiLT4mhZfVRdsbN25w+vRp8/bZs2eJiorC1dUVV1dXZsyYQZs2bXBzc+PMmTNMmjSJKlWq4O/vb9PERUREREREJPcdOnTI/HVERASTJ0/mzTffxMfHB4DffvuNadOm8dZbb+VWiiKSi5IWLKLpkL44cYsTVCOAMP4P7/ueV64cdOmS/fmJ5FVWF20PHDhA9+7//jUkNDQUgPbt2xMcHMyRI0dYt24d165do0yZMjRr1oxBgwbhoBmjRURERERECrTx48cTHBxMo0aNzPv8/f1xcnJi1KhRbNy4MRezE5EcFR8Pb76J4ZNPcAK+ph1dWcJlSmXo9DNnsjc9kbzO6qJtkyZNOHz48F2Pz5s3L0sJiYiIiIiISP50+vRpSpQokWa/s7Mz586dy4WMRCRXnD0LHTvCzp0kYUcIoxnLKJIxZOj0Zcs0LYJIxr5bRERERERERO7Dy8uLCRMmEBMTY94XExPDpEmTqF+/fi5mJiI5ZutWePhh2LmTvynJ03zFGEZnuGDr4QEvvJC9KYrkB9m+EJmIiIiIiIgUDuPHj2fAgAH873//o1y5cgD8+eefVK1alZkzZ+ZydiKSrZKTYcoUGD4cTCYiaUAAYZzkoQw3YTDA779nY44i+YiKtiIiIiIiImITVapU4YsvvuDnn3/mxIkTAFSvXp2mTZtiZ2eXy9mJSLa5dg1efhnWrAFgEd14lVnEUcyqZlav1rQIIqlUtBUpZJycnHI7BREREREpwOzs7PDz86Nx48Y4ODioWCtS0B06BO3bw6FDxGPPID5iFq8C1n3vr14NAQHZk6JIfqQ5bUWs4ObsgCkp+b5xORlzL//N12g0UqdOHYz606WIiIiIZIOkpCRmzpyJv78/Pj4+nD17FoBp06axevXqXM5ORGxu7Vpo3BgOHeK8XXma8yOzeA1rC7bLl0OHDtmTokh+pZG2IlZwcbTHaLBjaNg+jkffSDemuntxJgbcf5EFW7WT1Xwfq+nGoBY1s3QdERERERGATz75hHXr1vH2228zatQo834PDw8WLlxIx44dczE7EbGZxEQYMQImTQJgh+P/eP7WCi7yoNVNPfMMdO5s6wRF8j8VbUUy4Xj0DaIuXMsz7WTlOtXcimf79UVERESkcFi/fj1jx47F19eX0aNHm/d7enqa57jNqD179jBv3jwOHDhAdHQ0M2fOpGXLlubjw4cPJzw83OIcPz8/5s2bZ96+fPkyY8eO5YcffsBgMNC6dWveffddihf/93fgQ4cOMWbMGPbv388DDzxA165deeWVVyza3bhxIx999BHnzp2jatWqDBkyhObNm1vVH5EC4+LFlCrrDz8A8Hu7IfhvCMWUiRLTww/DF1/YOkGRgkHTI4iIiIiIiIhN/PXXX1SuXDnN/uTkZBITE61q6+bNm3h6eloUf//L39+f7du3m/9NnTrV4viQIUM4duwY8+fPZ9asWfzyyy+899575uPXr1+nV69elC9fnrCwMIYOHcqMGTNYuXKlOebXX39l8ODBdOjQgXXr1vHEE0/Qv39/jhw5YlV/RAqEXbtSKq0//ADOzphWrqbuhkmZKNgm4+0Nv/ySHUmKFAwaaSsiIiIiIiI2UaNGDX755RcqVKhgsX/Tpk3Url3bqraaN29+39GsDg4OuLu7p3vs+PHjbNu2jTVr1uDl5QXAyJEj6dOnD0OHDuXBBx/kiy++ICEhgfHjx+Pg4EDNmjWJiopi/vz5BAUFAbBo0SL8/f3p3bs3AG+88QYREREsWbKEMWPGWNUnkXwrORlmz4aBAyEhATw9ITwcv5et+77+pzHc3ZP47TettSJyLyraioiIiIiIiE3069eP4cOH89dff5GcnMy3337LyZMnWbduHbNnz7b59Xbv3o2vry8lSpTg0Ucf5Y033qBUqVIA/Pbbb5QoUcJcsAVo2rQpBoOBffv20apVKyIjI2nUqBEODg7mGD8/P+bMmcOVK1dwdXUlMjKSHj16WFzXz8+P7777zup8TSZT5jp6l3Zs1V5epX7mEXFx2PXvj2HRIgCS27cnad483nivBDt3JmPdomPJ1Kp1g//7PwfyandtIc8/pjaifmatvftR0VZERERERERsomXLlsyaNYuZM2fi5OTE9OnTqVOnDrNmzaJZs2Y2vZa/vz+tWrWiYsWKnDlzhqlTp/LKK6+wcuVKjEYjMTExPPDAAxbnFClSBFdXV6KjowGIiYmhYsWKFjFubm7mY66ursTExJj3pSpdujQxMTFW57x//36rz8nJ9vIq9TP3OJw7R/WhQyl2+DDJBgPn+vfnfJfuvOxfhIMHDWS8YJsMQHDwCZ5++jJ5sKvZIi8+ptlB/cweKtqKiIiIiIhIliUmJjJr1iw6dOjA/Pnzs/16Tz31lPlrT09PPD09admypXn0bV7k5eWF0Zj1j4SbTCb2799vs/byKvUzl23ahKFHD+z+/ptkNzeSli1jW0wLXnzUmmLtv4YMSeKddyqxf//lvNdXG8uzj6mNqZ9Za+9+VLQVERERERGRLCtSpAjz5s3j+eefz5XrV6pUiVKlSnHq1Cl8fX1xc3Pj0qVLFjGJiYlcuXLFPA+um5tbmhGzqdupo2vTi4mNjU0z+jYjjEajTQsbtm4vr1I/c1hSErz/PowenTKX7SOPYLdmDYGvV2L9+sw1+e67dowbZzRPiZBn+prN1M+CJaf7acixK4mIiIiIiEiB9uijj7Jnz55cufaFCxe4fPmyuSDr4+PD1atXOXDggDlm586dJCUlUb9+fQC8vb355ZdfSEhIMMdERERQrVo1XF1dzTE7d+60uFZERATe3t7Z3CORXHD5Mjz3HLz3XkrBtm9f+OknhnyU+YKtvT2EhNg0S5FCQSNtRfIoN2cHTEnJGA3Wf+xERERERCQ3PPbYY0yZMoUjR45Qt25dnJycLI4/8cQTGW7rxo0bnD592rx99uxZoqKicHV1xdXVlRkzZtCmTRvc3Nw4c+YMkyZNokqVKvj7+wNQvXp1/P39GTVqFCEhISQkJDB27FieeuopHnzwQQCeeeYZZs6cybvvvssrr7zC0aNHWbRoEe+88475ut27d6dbt258/vnnNG/enA0bNnDgwAHGjBmTlVslkvfs2wcBAXD8OBQtCp9+Cj17Eh8PU6ZkvtklS6AQDMIUsTkVbUXyKBdHe4wGO4aG7eN49I10Yx6r6cagFjVzODMRERERkfSF/DOcLr05be3s7IiKispwWwcOHKB79+7m7dDQUADat29PcHAwR44cYd26dVy7do0yZcrQrFkzBg0ahIODg/mcyZMnM3bsWF566SUMBgOtW7dm5MiR5uMuLi7MmzePMWPGEBAQQKlSpejXrx9BQUHmmIYNGzJ58mSmTZvG1KlTqVq1KjNnzsTDwyPjN0Ykr1uyBPr0gbg4qFoV1q6Fhg0B6N07880+/TR06mSbFEUKGxVtRfK449E3iLpwLd1j1dyK53A2IiIiIiJ3d+jQIZu11aRJEw4fPnzX4/PmzbtvGyVLlmTKfYYI1qpVi2XLlt0zpm3btrRt2/a+1xPJd+LjYfBgmDEjZbtNG1i6FEqXBsBkgsWLM9d0tWrw5Zc2ylOkENKctiIiIiIiImJzt2/fzu0URORezp+Hxx//t2A7ahR8/bW5YAuZn4vW3R1OnLBBjiKFmIq2IiIiIiIiYhMmk4mZM2fi7++Pj48PZ86cAWDatGmsXr06l7MTEbMff0yZ/iAiAlxdU4bEjhljMflsfDyMHWt901WrwsWLtktVpLBS0VZERERERERs4tNPPyU8PJy3334be3t7834PDw/WrFmTi5mJCADJyfDhh/DEE/DXX1C/PvzyS8rks/+4fh0aNUpZi8xagwbByZM2zFekEFPRVkRERERERGxi/fr1jB07lmeffRaD4d+3m56enpzQZ6VFctf169C5M7z1VspktV26wI4dUKOGOaRRI3Bxgb17rWu6aFG4fRumTbNtyiKFmRYiExEREREREZv466+/qFy5cpr9ycnJJCYm5kJGIgLA4cMQEAC//w5FiqSMtu3fH+zszCElS8KVK5lr/osvwMHBNqmKSAqNtBURERERERGbqFGjBr/88kua/Zs2baJ27dq5kJGIEB4OjRunFGzLlUuZz3bAAIuCbenSmS/YFimSMtuCiNiWRtqKiIiISJ5S1tkOp8tH4HwOjS9ISkq53p92YMiZazpdPkJZZ7v7B4rkM/369WP48OH89ddfJCcn8+2333Ly5EnWrVvH7Nmzczs9kcIlMRFGjYIJE1K2H3sMVq6EsmUtwlxcUmZOyKx33rFYv0xEbERF2wwwJSVjNNz7l+qMxEjh4ObsoOdDFuj+iYhI34cdqP1TX/gpZ65nBOoAbMuZ6wHUJqWfIgVNy5YtmTVrFjNnzsTJyYnp06dTp04dZs2aRbNmzXI7PZHCIzoaXngBtmxJ2X7rrZTi7T8LBMbFweuvw7x5WbuMoyOMHp3FXEUkXSraZoDRYMfQsH0cj76R7vHHaroxqEXNu8akHpfCwcXRPsPPGUnrfvdP905EpOCbvTeeoPcWULtWrRy5nikpicOHD+Pp6Ykxh0baRh06xOwpL/JsjlxNJGc1atSI+fPn53YaIoXX7t3QoQOcOQPFi6dUZoOCgJT1x5o1g127bHOppUs1ylYku6hom0HHo28QdeFausequRW/Z0zqcSlcMvKckbvT95OISOF14XoycSU9oLx3zlzQZCLur2Qo1yDH3nnGXUjiwvXkHLmWiIgUEsnJMGdOyhDa+Hjw8ICwMKhbF0j5skOHlDBbWLs2ZW0zEckeKtqKiIiIiIhIpjVu3Bg7u4xNbbV79+5szkakkIqLS1lc7PPPU7affx4WLABXVyClYBsYaJtLlSgBly5phK1IdlPRVkRERERERDJtxIgR5q8vX77Mp59+ip+fH97e3gBERkayfft2+vXrl0sZihRwf/yRUpH99deUBTXHj4ehQ+GfP6aYTObZEbLs9ddh+nTbtCUi96airYiIiIiIiGRa+/btzV+//vrrDBw4kK5du5r3de/enSVLlhAREUGPHj1yIUORAuybb+DFF1OGvrq5wfLl0LKlRUjFipCYmLXLdOmSMojXQWtoiuSYnFlpQURERERERAq87du34+/vn2a/v78/O3bsyIWMRAqopCQYNw7atk0p2DZuDHv3WhRs4+KgaFG4cCFrl1q7FpYsUcFWJKepaCsiImKFPXv28Oqrr+Ln54enpyffffedxfHhw4fj6elp8a9Xr14WMZcvX2bw4ME0bNiQRo0aMWLECG7cuGERc+jQIV588UW8vLxo3rw5c+bMSZPLxo0befLJJ/Hy8uKZZ57hxx9/tH2HRURErFCyZEm2bNmSZv+WLVsoWbJkzickUhBdvpwyZ+2oUSmrivXpAz/9BJUrAylrkNWpA8WKpXydFbdva7Exkdyi6RFERESscPPmTTw9PQkMDGTAgAHpxvj7+xMaGmredvjPsIQhQ4YQHR3N/PnzSUhIYMSIEbz33ntMmTIFgOvXr9OrVy98fX0JCQnhyJEjjBgxghIlShD0z4Rkv/76K4MHD+att97i8ccf58svv6R///6EhYXh4eGRTb0XERG5t9dff52RI0eye/du6tevD8C+ffvYtm0bY8eOzeXsRAqA/ftTqqjHjqUMo/3kE3j5ZeDfuWvXrrXNpdau1ehakdykoq2IiIgVmjdvTvPmze8Z4+DggLu7e7rHjh8/zrZt21izZg1eXl4AjBw5kj59+jB06FAefPBBvvjiCxISEhg/fjwODg7UrFmTqKgo5s+fby7aLlq0CH9/f3r37g3AG2+8QUREBEuWLGHMmDE27LGIiEjGBQQEUL16dRYtWsTmzZsBeOihh1i2bBkNGjTI5exE8rlly6B375R5D6pUSamqPvwwAKtWQefOKQNvs8rBIWVqXI2wFcldmh5BRETExnbv3o2vry9t2rRh9OjR/P333+Zjv/32GyVKlDAXbAGaNm2KwWBg3759QMoq240aNbIYoevn58fJkye5cuWKOcbX19fiun5+fkRGRmZjz0RERO4uISGBd955hwceeIApU6YQHh5OeHg4U6ZMUcFWJCvi42HgwJTVwOLioHVr4nfsZeKWh6lTB4oUSRlhm9WCbfPmsGkT3Lypgq1IXqCRtiIi92BKSsZosMtyjBQe/v7+tGrViooVK3LmzBmmTp3KK6+8wsqVKzEajcTExPDAAw9YnFOkSBFcXV2Jjo4GICYmhooVK1rEuLm5mY+5uroSExNj3peqdOnSxMTEWJ2zyWSy+hyR7JL6fDSZTDn23LzzmjklN/opci+2eB7a29vz7bff0q9fPxtkJCIAnD8PnTrBzz8DkDRiJJ0PBbO6vNGmlxkyBCZNsmmTIpJFKtqKiNyD0WDH0LB9HI++ke7x6u7FmRhQP4ezkrzsqaeeMn+duhBZy5YtzaNv86L9+/fndgoiZkeOHDH/bzDk7IfCcvJ7ITf7KZKdWrZsyZYtW+jRo0dupyKS/23bllKwvXABSpTg51cX89iEZ0lKsu1lnn1WBVuRvEhFWxGR+zgefYOoC9dyOw3JpypVqkSpUqU4deoUvr6+uLm5cenSJYuYxMRErly5Yp4H183NLc2I2dTt1NG16cXExsamGX2bEV5eXhiNth2tIZJZSf+8E/Xw8MDb2ztHrmkymdi/f3+Ofi/kRj9F7iX1+yCrqlSpwsyZM/n111+pW7cuTk5OFse7d++e5WuIFHjJyfDRRynDX00mqFeP0MZhjJhY0+aXevNNmDrV5s2KiA2oaCsiIpKNLly4wOXLl80FWR8fH65evcqBAweoV68eADt37iQpKcm8yra3tzfTpk0jISEBe3t7ACIiIqhWrRqurq7mmJ07d1qMZIqIiMhU8cdoNKpoK3lG6nMxN56XOXnN3OynSHZas2YNLi4uHDhwgAMHDlgcs7OzU9FW5H6uX4dXXoEVK1K2X3yRTpc/Y/X84ja/1KpV0LGjzZsVERtR0VZERMQKN27c4PTp0+bts2fPEhUVhaurK66ursyYMYM2bdrg5ubGmTNnmDRpElWqVMHf3x+A6tWr4+/vz6hRowgJCSEhIYGxY8fy1FNP8eCDDwLwzDPPMHPmTN59911eeeUVjh49yqJFi3jnnXfM1+3evTvdunXj888/p3nz5mzYsIEDBw4wZsyYnL0hIiIid/j+++9zOwWR/OvIkZQq6sGDKauLTZ3Km8cHsHqZbdfPePRR2L4d9DdDkbxNRVsRERErHDhwwGKUUGhoKADt27cnODiYI0eOsG7dOq5du0aZMmVo1qwZgwYNwsHBwXzO5MmTGTt2LC+99BIGg4HWrVszcuRI83EXFxfmzZvHmDFjCAgIoFSpUvTr14+goCBzTMOGDZk8eTLTpk1j6tSpVK1alZkzZ+Lh4ZEDd0FEROTeUqcC+u/imyKSPtetWzGMGQNXr0LZsiQsW80To/3Yts1213B2hosX4T+zlohIHmV10XbPnj3MmzePAwcOEB0dzcyZM2nZsqX5eHJyMtOnT2f16tVcvXqVhg0bEhwcTNWqVW2Zt4iISK5o0qQJhw8fvuvxefPm3beNkiVLMmXKlHvG1KpVi2XLlt0zpm3btrRt2/a+1xMREckJV69e5cMPP2TDhg1cvXoVgBIlSvDUU0/xxhtvUKJEiVzOUCQPMpmwe/ddanzwAQBJfv50SFxJeItyNr3MkiXQpYtNmxSRbGb1UrU3b97E09OT0aNHp3t8zpw5LF68mODgYFatWoWTkxO9evXi9u3bWU5WRERERERE8p7Lly/TsWNH1q1bR5s2bRg2bBjDhg2jdevWhIeHExQUxJUrV6xqc8+ePbz66qv4+fnh6enJd999Z3E8OTmZjz76CD8/P+rXr0+PHj34448/0uQ1ePBgGjZsSKNGjRgxYgQ3btywiDl06BAvvvgiXl5eNG/enDlz5qTJZePGjTz55JN4eXnxzDPP8OOPP1rVF5F0xcTAk09i+Kdg+2WNgRTdvoXwnbYr2D76KCQmqmArkh9ZXbRt3rw5b775Jq1atUpzLDk5mUWLFvHaa6/RsmVLatWqxcSJE7l48WKaF1gREREREREpGGbOnImDgwObN29mzJgx9OjRgx49ejB27Fi+/fZbihQpwsyZM61q0xYDhoYMGcKxY8eYP38+s2bN4pdffuG9994zH79+/Tq9evWifPnyhIWFMXToUGbMmMHKlSvNMb/++iuDBw+mQ4cOrFu3jieeeIL+/ftz5MgRK++SyB327IGHH4bvviPevhidWcqzxz4iEXubNG80pqxltmOH5q4Vya9sOqft2bNniY6OpmnTpuZ9Li4uNGjQgN9++42nnnoqw22ZTKZ0v84NWtE3+93vMdZjINnFZDKRlJSEk5MTSUlJaY5n9LmX2z+nsktqv3KrfwX1voqIiBQ0W7ZsISQkBDc3tzTH3N3defvttwkODmbEiBEZbrN58+Y0b9483WP/HTAEMHHiRJo2bcp3333HU089xfHjx9m2bRtr1qzBy8sLgJEjR9KnTx+GDh3Kgw8+yBdffEFCQgLjx4/HwcGBmjVrEhUVxfz5881zyS9atAh/f3969+4NwBtvvEFERARLlizRAqCSOXPnkty/P3bx8RyhJgEJYRykXpabdXGBxo1h6FBo2VLFWpH8zqZF2+joaABKly5tsb906dLExMRY1db+/fvT/TqnOTk5UadOnVy7fkHn5uyAKSlZRVnJcXc+94xGY6a/zzP6HDYlJWM03H3V1/sdB0hMNPH77wdJSEjIVK5ZkZs/h0VERCTvu3jxIjVr1rzrcQ8PD/P7RVvIyICh3377jRIlSpgLtgBNmzbFYDCwb98+WrVqRWRkJI0aNbJYMNTPz485c+Zw5coVXF1diYyMpEePHhbX9/Pz06dJxXq3bpHUfwCGz+dhB6zjOV5iIVdxzVKzNWrAoUMq0ooUNDYt2tpS6gvr/v378fLyUlGvgHJxtMdosGNo2D6OR99IN+axmm4ManH3XwBFMsNWzz1r2rlbzP2OA1R3L87EgPrUrVv3Pj2zLZPJlKs/h1OvLyIiInlbqVKlOHv2LGXLlk33+NmzZ3F1zVph6k4ZGTAUExPDAw88YHG8SJEiuLq6ms+PiYmhYsWKFjGpo4VjYmJwdXUlJiYmzQjizAxMAtt9iii3Pw2VUwpKP00m+HnZKcoN6EStG3sxYWAk4/iAYSRbP2vlHZJp2zaJL7/89zp5XUF5TO9H/SxYbN3PjLZj06Ktu7s7ALGxsZQpU8a8PzY2llq1alnV1p3FgdSRcFJwHY++QdSFa+keq+ZWPIezkcLEVs+9jLRzt5j7Hb9Tbv0s1M9hERERuRc/Pz+mTZvG559/bjFqFSA+Pp6PPvoIf3//XMou77D1H6MLyx+382M/TSbYs8eZuXPL4R65g2V0wY1YYijNCyznO9KuE2SdZGrVusHYsYeJjLRFxjkrPz6mmaF+Fiw53U+bFm0rVqyIu7s7O3bsoHbt2kDKxO7/93//xwsvvGDLS4mIiIiIiEgeMWjQIAIDA2nTpg0vvvgiDz30EMnJyZw4cYJly5YRHx/PxIkTbXa9jAwYcnNz49KlSxbnJSYmcuXKFfP5bm5uaUbMpm6njq5NLyY2Njbd+Xvvx1afXsrtT0PllPzUz/h4mDEDtm2zY/9+OHXKDpKTGc4ExjESA8nsoREdWMNpqmTxask880wS4eFOgLcNss85+ekxzQr1s2CxdT8z+olSq4u2N27c4PTp0+bts2fPEhUVhaurK+XLl6d79+58+umnVKlShYoVK/LRRx9RpkwZ8+TwIiIiIiIiUrCULVuWFStWEBISwtSpU0lOTgbAzs6Opk2b8t5771GuXDmbXS8jA4Z8fHy4evUqBw4coF69lEWedu7cSVJSEvXr1wfA29ubadOmkZCQgL29PQARERFUq1bNPJ2Dt7c3O3futJjXNiIiAm9vb6vztvWnlwrLp6HyYj9NJtiyBebPh82bITbW8ngJrrCQl3ie9QDMoTev8zG3cczSdd3c4PRpO5yc8tb9sFZefEyzg/pZsOR0P60u2h44cIDu3bubt0NDQwFo3749EyZM4JVXXiEuLo733nuPq1ev8vDDDzN37lyKFi1qu6xFREREREQkT6lUqRJz587lypUrnDp1CoDKlStTsmTJTLWX1QFD1atXx9/fn1GjRhESEkJCQgJjx47lqaee4sEHHwTgmWeeYebMmbz77ru88sorHD16lEWLFvHOO++Yr9u9e3e6devG559/TvPmzdmwYQMHDhxgzJgxmbxTkp/Fx8Mrr8CSJZCUlH5MXQ4QTntqcozbONCfmcyjd5auazCkXFMfYhYpPKwu2jZp0oTDhw/f9bidnR2DBg1i0KBBWUpMRERERERE8h9XV1fzSNassMWAocmTJzN27FheeuklDAYDrVu3ZuTIkebjLi4uzJs3jzFjxhAQEECpUqXo168fQUFB5piGDRsyefJkpk2bxtSpU6latSozZ87Ew8Mjy32UvM9kgq1b4bvvYNkyuOPvCOnqzHLm0pvi3OQUlenAGn6hcZZyGDUKRo+GQjCQUUTuYNM5bUVERERERERswRYDhkqWLMmUKVPueZ1atWqxbNmye8a0bduWtm3b3jthKRBSpz1YuBD27oXjxyEx8f7nFSGBSbzNG3wEwGZa8gLLicX6uY9TjRwJwcEq1ooUViraioiIiIiIiEihlVqoHTcOfv757tMe3E1Z/mQVnfBnOwDjeYdRjCWJzFVbVawVEVDRVkREREREREQKIZMJxo6F8eMhISFzbTRjO6vpSDkucIUSvMRC1vN8JlpKJjAwiZUrjSrWiggAhtxOQEREREREREQkJ6SOqg0MBEdHCAnJbME2mdeZzg88TjkucIC6NGaP1QVbOzvo2tVERMSvrFyp0bUi8q9CX7Q1JSXndgoiIiIiIiIiko1MppQpB5ydoWVLCAvL2Fy16SnGDZbQlekMwp5EVhDEo+zkKBlfnK5WLfj225SC8YIF4OCQuVxEpOAq9NMjGA12DA3bx/HoG+kef6ymG4Na1MzhrERERERERETEFtasge7dIS4u621V5xhhBFCf/SRiZAiT+YhBgN19z/X0hN69YeBAyyKtyZT1vESk4Cn0RVuA49E3iLpwLd1j1dyK53A2IiIiIiIiIpJZJhNs3Qrffw/h4RAVZZt2n+ZLFtONklzhAg/SkdVsxz/d2AcegPLloX596NEDWrTQ1AciYh0VbUVERERERESkQFizBnr1gqtXbdemARPBBDOKcQBspxmdWMWflDfHlCgB9epB+/ZpR9KKiGSGirYiIiIiIiIiki+ZTLBtG5w7B3PnpoywtaUHiGUZL9KGbwH4iIG8zSQScPhnEbGU66pIKyK2pqKtiIiIiIiIiORpdxZn//oLYmPh0CHYsgWuXMmeazZkL2sJpCqnuIkTrzCHlcYuNG0Ko0ZpygMRyV4q2oqIiIiIiIhInrV2LQwYANHROXfNnnzOJ/TDkdtccK7Oqs5hvNy5Pov+p0KtiOQMFW1FREREREREJNfdOZo2OhpKlbLj449rsHevIcdycLS7zcoHB/Lshc9SdjzzDGUXLWJgyZI5loOICKhoKyIiIvL/7d15XNVV/sfxF6CoKZgIpoaNjAu4sLlkMDAuqVnqzKCmlmZOprhNppaYWW6N4JamWJqV5tLPFrEypR45TaYPXHLBLTTRUtRMwBDBBbx8f3/c4eZVFLDLvSzv5+PBA77f7/me7+cc7uXe74dzzxEREREHMpng3/+GN96ACxduPOIM1LRLDNWqwb8jT/H8tj447f4enJxgxgx46SVwtl/SWEQkn5K2IiIiIiIiImIX+aNpf/kF6tQx/zxvHmRl2T8WFxf4+99h5EjoYPoPLgP6Q1oaeHjABx/AI4/YPygRkf9R0lZEREREREREbOrm5CzAF1/AmjX2nZv2dvr2NedlXZwNmDULXn4Z8vKgVSvzJLoNGzo6RBGp4JS0FRERERERERGbiYuDMWPg9GlHR3Ird3d45x14/HEgMxMGD4b1680H//lPWLzYPFeCiIiDKWkrIiIiIiIiIn9Y/ty0U6Y4OhJrzZpBRAR06gQdOpinReDwYejVC378EVxdITYWnn3WPJetiEgpoKStiIiIiIiIiFhNaVCvHoSH/y/BeZtj8Pu+Y8fg7bfhzBnHxX8zq1G1N/rwQxgyBLKzoUED+OQTePBBh8QoInI7StqKiIiIiIiIlHN3SshCwVMaeHvDG2+Yf775WO3a5u/p6SUfe1G5u0PnztC8uXlErWVUbb7cXIiKgvnzzdsPPwz/93/g5eWAaEVE7kxJWxEREREREZFS7uaFvdzcin7unRKyvXqZj/fpA4Zhfd6ZM9C7d8F1OjJZW60aPPoo+PmBhwfUrQv3339rItrKuXPQrx989515e+JEmDEDKiktIiKlk/46iYiIiIiIiJRityZdXahTx5/Fi83J1sLOvV1Ctk8f80wB48bdehwK3udIHh7mfnj55TskZwuSkGCeI+HsWXO2+/33zZPcioiUYkraioiIiIiIiJRSt0u6nj9fmb59zdOx9upV8LkmkznJebuErJMTjBoFqam2j/uPqF7dnGPt0CGP/ftPExzsTYMGznceSVsQw4DFi2HsWLh+3TxvQlwc+PqWWOwiIraipK2IiIiIiIhIKXSnpCs4AQbPPw9//3vBycytW62nRLiZYZSuhO3NI2lNJoOWLVMJCrq/eMlagMuXITISVq82b/ftC+++CzVq2DxuEZGSoKStiIiIiIiISClUeNLViZQUc7kOHW49/ssvJRbaH+LlBQMGQI8e5u3z5wteHO2uJSebJ+M9cMBc4Zw58Pzz5qHFIiJlhJK2IiLliCnPwMX5zm9Gi1JGRMTR9u7da7drmUwm9uzZQ15eHi42yRYULikpyS7XEZGyrahJ19uVq1evaOd7eUFaWsnNYTtlijkha/PkbEG++AIGDoSLF80rtn30EbRvX0IXExEpOUraioiUIy7OTkyIO8Dx1OwCjzfyqs7sXgF2jkpEpOiuX78OwNChQx0ciX24FWf5dxGpcIqadL1dufBw8PY2LzpWUELWycl8/PXXzbMHODlZl7tx++ZjRdGgASxYcPs5d23KZIJp02DGDPN2SAh8/DHcf78dLi4iYntK2oqIlDPHU7NJOnfJ0WGIiNyVBx98kJ07d1Kpkv3eph4+fJhBgwaxcuVKWrRoYbfrurm50aRJE7tdT0TKnsKTrgbe3k6Ehxd8vosLvPGGeSGzghKy8HtS9ZNPzPPJ3jgdg7e3+Tjceqx2bfP39HTr8kOHQpMmdhhRe6MLF8zzLXz5pXl79GiYNw9cXe1wcRGRkqGkrYiIiIiUKg8++KBdr2cymQDw8/OjVatWdr22iPwxixYtIjY21mqfj48PX/4veXft2jViYmLYtGkTOTk5hIWFMWXKFDw9PS3lz549y9SpU9m5cyf33HMP//jHPxg/frzVP4927txJTEwMx44do169eowYMYJedhg+eqekK5g3Fiy4c2K0sIRsfjN69TIvaLZ1q3m6hZuTrgUdg9uXt5u9e83z1/78M1SrBm+/bZ4eQUSkjFPSVkRERERERMqsJk2asHz5csv2jXNTz5w5ky1btrBgwQLc3NyYMWMGo0ePZu3atYD5nzaRkZF4enqydu1azp8/T1RUFJUrV2bcuHEApKSkEBkZSf/+/Zk7dy7bt29n8uTJeHl5EX67Ia42dLuk63335RIb60KvXoVnSQtLyOZzcSl4QbM7HbtdebtYsQJGjICrV+HPf4a4OAgMdGBAIiK2o6StiIiIiIiIlFkuLi54eXndsv/SpUusW7eOuXPnEhISApiTuI899hiJiYkEBQWxbds2kpOTWb58OZ6enjRr1owxY8Ywd+5cRo8ejaurK2vXrsXb25uJEycC0KhRI/bs2cOKFSvskrSFW5OudeqYcHM7SOvWQUWu404J2TLn2jVzFnvpUvN2jx6wciXUquXYuEREbMjZ0QGIiIiIiIiI3K2TJ08SFhbGww8/zPjx4zl79iwAhw4dIjc3l9DQUEvZRo0aUb9+fRITEwFITEykadOmVtMlhIWFkZWVRXJysqVMftL3xjL5ddhLftL1iSfM3+0+DUFpkZICf/2rOWHr5ATTp8NnnylhKyLljkbaioiIiIiISJkUEBBAdHQ0Pj4+pKamsnjxYgYMGMCGDRtIS0ujcuXKuLu7W51Tu3ZtUlNTAUhLS7NK2AKW7cLKZGVlcfXqVapWrVrkePPn0P6j8uuxVX2l1S3t/OYbnJ98Eqe0NIxatchbtQq6dTNP9luG+6Ki/D6h4rRV7SxfbN3OotajpK2IiIiIiIiUSe3bt7f87OfnR2BgIB07diQ+Pr5YyVR7OXjwYKmur7Q6eOAA961cyf2LF+OUl8dlX1+Oz55NTt26YOcRzyWpovw+oeK0Ve0sX+zdTiVtRUREREREpFxwd3enYcOGnDp1itDQUHJzc8nMzLQabZuenm6ZA9fT05MDBw5Y1ZGWlgZgVSZ/341latSoUezEsL+/v9VCaXfLZDJx8OBBm9VXWplMJg5v307gggU4f/opAHlPP02V2FiaV6vm2OBsqKL8PqHitFXtLF9s3c78+gqjpK2IiIiIiIiUC9nZ2aSkpODl5UXLli2pXLky27dv55FHHgHgxIkTnD17lqCgIACCgoJYsmQJ6enp1K5dG4CEhARq1KhB48aNLWW+++47q+skJCRY6igOFxcXmyY2bF1fqfPDDzR7+mmcT56EypVh0SKchw0zz2VbDpX73+cNKkpb1c7yxd7tLNcLkZnyDEeHICIi5cz333/P8OHDCQsLw9fXl82bN1sdNwyDN954g7CwMAICAhg8eDA///yzVZmMjAzGjx9Pq1ataNOmDZMmTSI7O9uqzJEjR3jyySfx9/enffv2LFu27JZY4uPj6datG/7+/vTs2ZMtW7bYvL0iIiKl2axZs9i1axenT59m7969jB49GmdnZ3r06IGbmxu9e/cmJiaGHTt2cOjQISZNmkRwcLAl4RoWFkbjxo2ZMGECR44cYevWrSxYsIABAwbg6uoKQP/+/UlJSWH27NkcP36cNWvWEB8fz+DBgx3X8Irg449xDgmh6smTGN7esHUrREaW24StiMjNyvVIWxdnJybEHeB4anaBx//axJMxnZrYOSoRESnLLl++jK+vL71792b06NG3HF+2bBmrVq0iJiYGb29v3njjDYYMGcKmTZuoUqUKAC+88AKpqaksX76c3NxcJk2axKuvvsq8efMAyMrKYsiQIYSEhDBt2jR+/PFHJk2ahLu7O/369QNg7969jB8/nnHjxtGxY0c2bNjAqFGjiIuLo2nTpvbrEBEREQc6d+4c48aNIyMjAw8PD1q3bs1HH32Eh4cHAJMmTcLZ2ZnnnnuOnJwcwsLCmDJliuV8FxcXlixZwtSpU+nXrx/VqlUjIiKC5557zlKmQYMGLF26lOjoaFauXEndunV57bXXCA8Pt3t7K4Tr12HiRJg3Dycgs00bqn/+OS716jk6MhERuyrXSVuA46nZJJ27VOAxH8/qdo5GRETKuvbt21stenIjwzBYuXIlI0aMoHPnzgDMnj2b0NBQNm/eTPfu3Tl+/Dhbt27lk08+wd/fH4DJkyczbNgwJkyYwH333cfnn39Obm4uM2fOxNXVlSZNmpCUlMTy5cstSduVK1cSHh7Os88+C8Dzzz9PQkICq1evZvr06XboCREREcebP3/+HY9XqVKFKVOmWCVqb3b//fcX+ImWG7Vr145P/zenqpSgX3+Ffv3gf58eynvhBY716UNQnToODkxExP7KfdJWRETEXk6fPk1qaiqhoaGWfW5ubgQGBrJv3z66d+/Ovn37cHd3tyRsAUJDQ3F2dubAgQN06dKFxMRE2rRpY/lYJpg/vrls2TIuXrxIzZo1SUxMvOVjmWFhYbdM11AUJpOp+I0VKUfy8vIs3/V8kIpKj31xuO3boU8fOHsWatSAFSsw/vEPSEx0dGQiIg6hpK2IiIiNpKamAlgWMslXu3Zty6rTaWlplo9s5qtUqRI1a9a0nJ+Wloa3t7dVGU9PT8uxmjVrkpaWZtlX0HWKoygrl4qUZ8nJyZbvlSrp7bGIiF0ZBrz1Fjz/POTmgp8frF9v/q5/JohIBWbzd6WLFi0iNjbWap+Pjw9ffvmlrS8lIiIiNuDv718hVnsVuZ3r168D0Lhx47taDV6kPDCZTPonntjf5cswfDisWmXe7tMH3nsP3NwcG5eISClQIkMJmjRpwvLlyy3buhEUEZGKwMvLC4D09HTq3DD3Wnp6On5+foB5xOyFCxeszrt+/ToXL160nO/p6XnLiNn87fzRtQWVSU9Pv2X0bVG4uLjotVoqNGdnZ8t3PRdEROzk+HHo3Rv27wdnZ5g1C8aPBycnR0cmIlIqOJdEpS4uLnh5eVm+bv4YqIiISHnk7e2Nl5cX27dvt+zLyspi//79BAcHAxAcHExmZiaHDh2ylNmxYwd5eXkEBAQAEBQUxO7du8nNzbWUSUhIwMfHh5o1a1rK7Nixw+r6CQkJGiUoIiIipd/GjdCmjTlh6+UFmzfDCy8oYSsicoMSSdqePHmSsLAwHn74YcaPH8/Zs2dL4jIiIiJ2l52dTVJSEklJSYB58bGkpCTOnj2Lk5MTgwYN4q233uI///kPR48eZcKECdSpU4fOnTsD0KhRI8LDw3nllVc4cOAAe/bsYcaMGXTv3p377rsPgJ49e1K5cmVefvlljh07xqZNm1i5ciX//Oc/LXEMGjSIrVu38t5773H8+HEWLVrEoUOHGDhwoP07RURERKQo8vJg6lTo0QMyMuChh2DvXujY0dGRiYiUOjafHiEgIIDo6Gh8fHxITU1l8eLFDBgwgA0bNlCjRo0i13Pj6qV3u5KpPt4mIuVNYX8Pi/p3r6h/V/PLOWpF6dK4kvWhQ4cYNGiQZTs6OhqAiIgIYmJiGDp0KFeuXOHVV18lMzOT1q1b884771ClShXLOXPnzmXGjBk8/fTTODs707VrVyZPnmw57ubmxrvvvsv06dPp1asXtWrVYuTIkfTr189SplWrVsydO5cFCxbw+uuv07BhQxYvXkzTpk3t0AsiIiIixXThAgwcCPHx5u2RI+H11+GG90giIvI7mydt27dvb/nZz8+PwMBAOnbsSHx8PI8//niR67lxEvy7mRC/WrVqNG/evNjniYiURp41XDHlGTb7Z9TRo0e5cuVKkctrYZLftWvXjqNHj972uJOTE2PGjGHMmDG3LXPvvfcyb968O17Hz8+PDz744I5lHn30UR599NE7BywiIiLiaPv2meev/eknqFoVli6FG/4JLiIityqRhchu5O7uTsOGDTl16lSxzvP39wfMiQKtai0iFZ1b1cq4ODsxIe4Ax1OzCyzz1yaejOnUpEj1+fr6Fqlc/krSjvo7rJWsRURERMq499+H4cPh6lXw8YG4ONAc/CIihSrxpG12djYpKSmWFbGL6sbkgFa1FhExO56aTdK5SwUe8/GsXuR6ivs3VX+HRURERKRYrl2DsWPhrbfM2489BqtXQ61ajo1LRKSMsHnSdtasWXTs2JH69etz/vx5Fi1ahLOzMz169LD1pURERERERESktDl9Gvr0gZ07wckJpkyBV14B5xJZC11EpFyyedL23LlzjBs3joyMDDw8PGjdujUfffQRHh4etr6UiIiIiIiIiJQm//0v9OsHqalw772wZo15lK2IiBSLzZO28+fPt3WVIiIiIiIiIlKaGQbMmwcTJ4LJBIGB5vlr//xnR0cmIlImlfictiIiIiIiIiJSjl26BM88A598Yt5+6ilYsgTuucexcYmIlGFK2oqIiIiIiIjI3TlyBCIizN8rV4Y33oDhw81z2YqIyF1T0lZEREREREREim/dOhg8GLKyoH5980jbkBBHRyUiUi5o6UYRERERERERKbrr12HCBOjTx5yw7dAB9u5VwlZExIaUtBURqUA8a7hiyjMKLWeLMkWpQ0RERETKmPPnoWtXmDPHvP3CC/D113DffY6NS0SknNH0CCIiFYhb1cq4ODsxIe4Ax1OzCyzTyKs6s3sFFFrXneopah0iIiIiUobs3GkeXXv6NNSoAcuXm7dFRMTmlLQVEamAjqdmk3TuUqmpR0RERERKMcOApUvhuecgNxd8fWH9emjWzNGRiYiUW5oeQUREREREREQKduUK/POfMGKEOWHbuzfs2qWErYhICdNIWxERERERERG51U8/Qa9ekJgIzs4QE2Oew9bJydGRiYiUe0raioiIiIiIiIi1+HgYMAB++w28vGDtWujUydFRiYhUGJoeQURERERERETM8vJg+nTo3t2csH3wQdizRwlbERE700hbERERERERETEnaZ96CjZuNG8PHw4LFkCVKg4NS0SkIlLSVkRERERERKSi27/fPH/tiRNQtSq89RYMHuzoqEREKixNjyAiIiIiIiJSBGvWrKFTp074+/vz+OOPc+DAAUeHZBurV0NIiDlh27AhJCQoYSsi4mBK2oqIiIiIiIgUYtOmTURHRzNq1CjWr1+Pn58fQ4YMIT093dGh3b2cHBg92jwlwpUr0K2bef7a4GBHRyYiUuEpaSsiIiIiIiJSiOXLl9O3b1969+5N48aNmTZtGlWrVmXdunWODu3unDkDHTrA4sXm7VdfhS++AA8Ph4YlIiJmmtNWRERERERE5A5ycnI4fPgwkZGRln3Ozs6Ehoayb9++ItdjMplsEk9+PXdd35YtOD/xBE7nz2Pcey95K1ZAjx75ldskRlv4w+0sIypKO6HitFXtLF9s3c6i1qOkrYiIiIiIiMgd/Pbbb5hMJmrXrm21v3bt2pw4caLI9Rw8eNCmcRW7PsOgzpo1eC9ahJPJxOUmTTg+Zw453t6QmGjT2GzJ1v1WWlWUdkLFaavaWb7Yu51K2oqIiIiIiIjYgb+/Py4uLn+4HpPJxMGDB4tXX1YWTkOH4vzxxwDkDRhAlbfeovk99/zheErKXbWzDKoo7YSK01a1s3yxdTvz6yuMkrYiIiIiIiIid1CrVi1cXFxuWXQsPT0dT0/PItfj4uJi08RGkes7ehQiIiApCSpVggULcB45EpycbBZLSbJ1v5VWFaWdUHHaqnaWL/ZupxYiExEREREREbkDV1dXWrRowfbt2y378vLy2L59O8HBwQ6MrAji4qBtW3PCtn592LIFRo0qMwlbEZGKSiNtRURERERERArxz3/+k6ioKFq2bElAQADvv/8+V65coVevXo4OrWDXr8PkyTBrlnn7r3+FDz+EunUdG5eIiBSJkrYiIiIiIiIihXjssce4cOECCxcuJDU1lWbNmvHOO+8Ua3oEu0lNhf794ZtvzNvjxkFMDFSu7Ni4RESkyJS0FRERERERESmCgQMHMnDgQEeHcWe7dkHv3nD6NFSvDu+9B337OjoqEREpJs1pKyIid1StWjVHhyAiIiIihTEMePttCA83J2ybNoWdO5WwFREpo5S0FRERK541XDHlGYB5dczmzZtXiJVARURERMqsK1dgyBCIjIScHPjHP8wjblu0cHRkIiJylzQ9goiIWHGrWhkXZycmxB3geGp2gWX+2sSTMZ2a2DkyEREREbnFzz+bp0PYuxecnWHmTJgwAZycHB2ZiIj8AUraiohIgY6nZpN07lKBx3w8q9s5GhERERG5xVdfwVNPwYUL4OkJ//d/0Lmzo6MSEREb0PQIIiIiIiIiImVJXh5133kH5x49zAnbtm1hzx4lbEVEyhElbUVERERERETKiowMnCMiuH/JEpwMA4YNg+++gwcecHRkIiJiQ5oeQURERERERKQsOHAAevXC6fhx8lxdYfFinJ991tFRiYhICVDSVkRERETKhRMnTpCRkVHs844cOWL57uLiUqxz7733Xv785z8X+5oiIsWWlQUdOsBvv2H86U8cfe01mj7xhKOjEhGREqKkrYiIiIiUeWlpaTRp0oS8vLy7rmPQoEHFPsfFxYVz587h6el519cVESmSatUgKAi8vMhbtIjLKSmOjkhEREqQkrYiIiIiUuZ5enpy7NixuxppazKZ2LNnD61bt76rkbZK2IqIXbi4wDffmH82mUBJWxGRck1JWxEREREpF+52mgKTyYSzszNBQUHFTtqKiIiIiJQEZ0cHICIiIiIiIiIiIiK/U9JWREREREREREREpBRR0lZERERERERERESkFFHSVkRERERERERERKQUUdJWRETExhYtWoSvr6/VV7du3SzHr127xrRp02jXrh3BwcH861//Ii0tzaqOs2fPMmzYMAIDAwkJCWHWrFlcv37dqszOnTuJiIigZcuWdOnShbi4OLu0T0REREREREpWJUcHICIiUh41adKE5cuXW7ZvXJF+5syZbNmyhQULFuDm5saMGTMYPXo0a9euBcwr2UdGRuLp6cnatWs5f/48UVFRVK5cmXHjxgGQkpJCZGQk/fv3Z+7cuWzfvp3Jkyfj5eVFeHi4fRsrIiIiIiIiNlViI23XrFlDp06d8Pf35/HHH+fAgQMldSkREZFSx8XFBS8vL8uXh4cHAJcuXWLdunVMnDiRkJAQWrZsycyZM9m3bx+JiYkAbNu2jeTkZObMmUOzZs1o3749Y8aMYc2aNeTk5ACwdu1avL29mThxIo0aNWLgwIE88sgjrFixwkEtFhEREREREVspkZG2mzZtIjo6mmnTphEYGMj777/PkCFD+PLLL6ldu3ZJXFJERKRUOXnyJGFhYVSpUoWgoCDGjx9P/fr1OXToELm5uYSGhlrKNmrUiPr165OYmEhQUBCJiYk0bdoUT09PS5mwsDCmTp1KcnIyzZs3JzExkZCQEKtrhoWFMXPmzGLHajKZ7r6hIuVA/nNAzwWpyPT4FxERKV1KJGm7fPly+vbtS+/evQGYNm0a3377LevWrWPYsGElcUkREZFSIyAggOjoaHx8fEhNTWXx4sUMGDCADRs2kJaWRuXKlXF3d7c6p3bt2qSmpgKQlpZmlbAFLNuFlcnKyuLq1atUrVq1yPEePHiw2G0UKY/0XBARERGR0sLmSducnBwOHz5MZGSkZZ+zszOhoaHs27fP1pcTEREpddq3b2/52c/Pj8DAQDp27Eh8fHyxkqn24u/vbzXnrkhFYzKZOHjwoJ4LUqHlPw9ERESkdLB50va3337DZDLdMg1C7dq1OXHiRKHnG4YBYJmzL//nu3kD7eLigm+de3C9zcy9DWtVxWQylXgZe11HbVKbSnMsalPFapOP5z2YTKY//FHL/PPzXxvKKnd3dxo2bMipU6cIDQ0lNzeXzMxMq9G26enpeHl5AeYRszfPBZ+WlgZgVSZ/341latSoUeTEcFnvVxERsT29NpSM/H611TQUFWVaF7Wz/KkobVU7yxdbt7Oo97lOho1flX/99Vf++te/snbtWoKDgy37Z8+ezffff8/HH398x/NzcnL0H14REbHi7++Pq6uro8O4a9nZ2XTs2JHRo0cTERFBSEgI8+bN45FHHgHgxIkTPProo3z44YcEBQWxZcsWhg8fzrZt2yz/BP3www+ZPXs227dvx9XVlTlz5vDdd9+xYcMGy3XGjx9PRkYG7777bpHi0muuiIjcrKy/5pZWes0VEZGbFfaaa/ORtrVq1cLFxYX09HSr/enp6bfMvVdgQJUq4e/vj7OzM05OTrYOT0REyhDDMMjLy6NSpRKZgr3EzJo1i44dO1K/fn3Onz/PokWLcHZ2pkePHri5udG7d29iYmKoWbMmNWrU4LXXXiM4OJigoCDAvKBY48aNmTBhAi+++CKpqaksWLCAAQMGWF7U+/fvz5o1a5g9eza9e/dmx44dxMfHs3Tp0iLHqddcERHJV1Zfc8sKveaKiEi+or7m2nykLcDjjz9OQEAAr7zyCgB5eXl06NCBgQMHaiEyEREp98aOHcv3339PRkYGHh4etG7dmrFjx/LAAw8AcO3aNWJiYti4cSM5OTmEhYUxZcoUy9QHAGfOnGHq1Kns2rWLatWqERERwfjx461e2Hfu3El0dDTJycnUrVuXkSNH0qtXL7u3V0RERERERGyrRJK2mzZtIioqiunTpxMQEMD7779PfHw88fHxRRptKyIiIiIiIiIiIlJRlchnXx577DEuXLjAwoULSU1NpVmzZrzzzjtK2IqIiIiIiIiIiIgUokRG2oqIiIiIiIiIiIjI3XF2dAAiIiIiIiIiIiIi8jslbUVERERERERERERKESVtRUREREREREREREoRJW1FRERERERERERESpEST9ouWrQIX19fq69u3bpZjl+7do1p06bRrl07goOD+de//kVaWppVHWfPnmXYsGEEBgYSEhLCrFmzuH79ulWZnTt3EhERQcuWLenSpQtxcXEl3bRi+/777xk+fDhhYWH4+vqyefNmq+OGYfDGG28QFhZGQEAAgwcP5ueff7Yqk5GRwfjx42nVqhVt2rRh0qRJZGdnW5U5cuQITz75JP7+/rRv355ly5bdEkt8fDzdunXD39+fnj17smXLFpu3tzgK65uJEyfe8jgaMmSIVZny2DdLly6ld+/eBAcHExISwsiRIzlx4oRVGXs+h9asWUOnTp3w9/fn8ccf58CBA7ZvdBEVpW+eeuqpWx43r776qlWZ8tY3H3zwAT179qRVq1a0atWKfv36WT2GK+rjRUSkIIW9/xARKQuGDx9Ohw4d8Pf3JywsjBdffJFff/3V0WHZ3OnTp5k0aRKdOnUiICCAzp07s3DhQnJychwdms299dZb9O/fn8DAQNq0aePocGymItwfVJT3FkW5Hy8PCru/Lml2GWnbpEkTtm3bZvn64IMPLMdmzpzJf//7XxYsWMCqVas4f/48o0ePthw3mUxERkaSm5vL2rVriYmJYf369SxcuNBSJiUlhcjISNq1a8dnn33G008/zeTJk9m6das9mldkly9fxtfXlylTphR4fNmyZaxatYqpU6fy0UcfUa1aNYYMGcK1a9csZV544QWSk5NZvnw5S5YsYffu3VZJqKysLIYMGUL9+vWJi4tjwoQJxMbG8uGHH1rK7N27l/Hjx9OnTx8+/fRTHn74YUaNGsWPP/5Yco0vRGF9AxAeHm71OHr99detjpfHvtm1axcDBgzgo48+Yvny5Vy/fp0hQ4Zw+fJlSxl7PYc2bdpEdHQ0o0aNYv369fj5+TFkyBDS09Pt0xk3KUrfAPTt29fqcTNhwgTLsfLYN3Xr1uWFF14gLi6OdevW8dBDDzFq1CiOHTsGVNzHi4hIQYry/kNEpLR76KGHWLBgAV9++SULFy4kJSWFMWPGODosmztx4gSGYTB9+nQ2btzISy+9xNq1a5k/f76jQ7O53NxcunXrxhNPPOHoUGymotwfVJT3FkW9Hy/rCru/LnFGCVu4cKHxt7/9rcBjmZmZRosWLYz4+HjLvuTkZKNp06bGvn37DMMwjG+//dbw8/MzUlNTLWU++OADo1WrVsa1a9cMwzCM2bNnG927d7eq+/nnnzeeeeYZG7fGdpo2bWp8/fXXlu28vDzjL3/5i/HOO+9Y9mVmZhotW7Y0vvjiC8Mwfu+bAwcOWMps2bLF8PX1Nc6dO2cYhmGsWbPGaNu2raVvDMMw5syZYzzyyCOW7TFjxhjDhg2ziufxxx83XnnlFds28i7d3DeGYRhRUVHGiBEjbntORemb9PR0o2nTpsauXbsMw7Dvc6hPnz7GtGnTLNsmk8kICwszli5davN23o2b+8YwDGPgwIHGa6+9dttzKkrftG3b1vjoo4/0eBERuYOC3n+IiJRFmzdvNnx9fY2cnBxHh1Lili1bZnTq1MnRYZSYdevWGa1bt3Z0GDZREe8PKtJ7i4Lux8ur/Ptre7DLSNuTJ08SFhbGww8/zPjx4zl79iwAhw4dIjc3l9DQUEvZRo0aUb9+fRITEwFITEykadOmeHp6WsqEhYWRlZVFcnKypUxISIjVNcPCwix1lAWnT58mNTXVqi/c3NwIDAxk3759AOzbtw93d3f8/f0tZUJDQ3F2drZ8rCAxMZE2bdrg6upqKRMWFsZPP/3ExYsXLWXKYn/t2rWLkJAQHnnkEaZMmcJvv/1mOVZR+ubSpUsA1KxZE7DfcygnJ4fDhw9bXcfZ2ZnQ0FDL49PRbu6bfBs2bKBdu3b06NGDefPmceXKFcux8t43JpOJjRs3cvnyZYKDg/V4ERERESnnMjIy2LBhA8HBwVSuXNnR4ZS4S5cu3fL+X0of3R+Uf7e7Hy9Pbr6/todKJX2BgIAAoqOj8fHxITU1lcWLFzNgwAA2bNhAWloalStXxt3d3eqc2rVrk5qaCkBaWppV8gCwbBdWJisri6tXr1K1atWSap7N5Leldu3aVvtr165tmW8yLS0NDw8Pq+OVKlWiZs2aVn3h7e1tVSa/b9LS0qhZs2aB/XXjdUqj8PBwunTpgre3NykpKbz++usMHTqUDz/8EBcXlwrRN3l5ecycOZNWrVrRtGlTALs9hy5evIjJZCrw8Vka5q0pqG8AevToQf369alTpw5Hjx5l7ty5/PTTT8TGxgLlt2+OHj1K//79uXbtGvfccw+LFy+mcePGJCUl6fEiIiIiUg7NmTOHNWvWcOXKFYKCgliyZImjQypxJ0+eZPXq1URFRTk6FCnEb7/9pvuDcux29+Plxe3ur+2hxJO27du3t/zs5+dHYGAgHTt2JD4+vkwkU6V06N69u+Xn/AWlOnfubBl9WxFMmzaNY8eOWc0JLWa365t+/fpZfvb19cXLy4vBgwdz6tQpHnjgAXuHaTc+Pj58+umnXLp0ia+++oqoqChWr17t6LBEREREpIjmzp1b4KLJN9q0aRONGjUCYMiQIfTp04ezZ88SGxtLVFQUS5cuxcnJyR7h/iHFbSvAr7/+yrPPPku3bt3o27dvSYdoE3fTTpGyoLznKm53f22PxG2JJ21v5u7uTsOGDTl16hShoaHk5uaSmZlpNfIrPT0dLy8vwDx66+YVBfNHPd5Y5uaRkGlpadSoUaPMJIbz25Kenk6dOnUs+9PT0/Hz8wPM7bxw4YLVedevX+fixYuF9kX+sduVSU9Pv2XkXGnWoEEDatWqxcmTJwkJCSn3fTN9+nS+/fZbVq9eTd26dS37PT097fIccnZ2xsXF5ZZJ4ktz3xQkMDAQMP9X/oEHHii3fePq6sqf/vQnAFq2bMnBgwdZuXIljz76aIV/vIiIiIiUBc888wwRERF3LNOgQQPLzx4eHnh4eODj40OjRo1o3749iYmJdvsI7x9R3Lb++uuvDBo0iODgYGbMmFHS4dlMcdtZntSqVUv3B+VUce7Hy6rb3V9Pnz69xK9tlzltb5SdnU1KSgpeXl60bNmSypUrs337dsvxEydOcPbsWYKCggAICgrixx9/tHpyJyQkUKNGDUtWOygoiB07dlhdJyEhwVJHWeDt7Y2Xl5dVX2RlZbF//37LC21wcDCZmZkcOnTIUmbHjh3k5eUREBAAmPti9+7d5ObmWsokJCTg4+NjmVukPPTXuXPnyMjIsCSRymvfGP9bHfXrr7/m/fffv+VF3F7PIVdXV1q0aGF1nby8PLZv3+6wN4KF9U1BkpKSgN+Tj+W1b26Wl5dHTk5OhX68iIiIiJQlHh4eNGrU6I5fN67VcaO8vDzAPI9oWVCctuYnbFu0aEF0dDTOznZPady1P/I7Let0f1D+3M39eHmRf39tFyW90llMTIyxc+dOIyUlxdizZ48xePBgo127dkZ6erphGIbx6quvGh06dDC2b99uHDx40OjXr5/Rr18/y/nXr183evToYTzzzDNGUlKS8d133xkPPfSQMW/ePEuZU6dOGYGBgcasWbOM5ORkY/Xq1UazZs2M7777rqSbVyxZWVnGDz/8YPzwww9G06ZNjeXLlxs//PCDcebMGcMwDGPp0qVGmzZtjM2bNxtHjhwxRowYYXTq1Mm4evWqpY4hQ4YY//jHP4z9+/cbu3fvNrp27WqMGzfOcjwzM9MIDQ01XnzxRePHH380Nm7caAQGBhpr1661lNmzZ4/RvHlz49133zWSk5ONhQsXGi1atDCOHj1qv864yZ36Jisry4iJiTH27dtnpKSkGAkJCUZERITRtWtXy2r2hlE++2bKlClG69atjZ07dxrnz5+3fF25csVSxl7PoY0bNxotW7Y04uLijOTkZOOVV14x2rRpY6SmptqnM25SWN+cPHnSiI2NNQ4ePGikpKQYmzdvNh5++GFjwIABljrKY9/MnTvX2LVrl5GSkmIcOXLEmDt3ruHr62ts27bNMIyK+3gRESlIYe/NRERKu8TERGPVqlXGDz/8YJw+fdpISEgw+vXrZ3Tu3NnqXqk8OHfunNGlSxfj6aefNs6dO2d1D1DenDlzxvjhhx+MRYsWGUFBQZbXqqysLEeHdtcqyv1BRXlvUZRcRXlQ2P11SXMyDMMoyaTw2LFj+f7778nIyMDDw4PWrVszduxYy3yS165dIyYmho0bN5KTk0NYWBhTpkyxjIQDOHPmDFOnTmXXrl1Uq1aNiIgIxo8fT6VKv8/usHPnTqKjo0lOTqZu3bqMHDmSXr16lWTTim3nzp0MGjTolv0RERHExMRgGAYLFy7ko48+IjMzk9atWzNlyhR8fHwsZTMyMpgxYwbffPMNzs7OdO3alcmTJ1O9enVLmSNHjjB9+nQOHjxIrVq1GDhwIMOGDbO6Znx8PAsWLODMmTM0bNiQF1980Wr+YXu7U99MnTqVUaNG8cMPP3Dp0iXq1KnDX/7yF8aMGWP1UYry2De+vr4F7o+OjrY8vu35HFq9ejXvvvsuqampNGvWjMmTJ1umHLC3wvrml19+4cUXX+TYsWNcvnyZevXq0blzZ0aOHEmNGjUs5ctb30yaNIkdO3Zw/vx53Nzc8PX1ZejQofzlL38BKu7jRUSkIIW9NxMRKe2OHj3Kv//9b44ePcrly5fx8vIiPDyckSNHct999zk6PJuKi4vjpZdeKvDY0aNH7RxNyZo4cSLr16+/Zf/KlStp166dAyKyjYpwf1BR3lsUJVdRHhR2f13SSjxpKyIiIiIiIiIiIiJFV3YmgBERERERERERERGpAJS0FRERERERERERESlFlLQVERERERERERERKUWUtBUREREREREREREpRZS0FRERERERERERESlFlLQVERERERERERERKUWUtBUREREREREREREpRZS0FRERERERERERESlFlLQVEREREREREbGTnTt34uvrS2ZmpqNDKRZfX182b95ss/o6derEihUrbFafvZ0+fRpfX1+SkpKAsvt7ldKrkqMDEBEREREREREpD3x9fe94fPTo0Tz44IN2iubuLFq0iM2bN/PZZ59Z7d+2bRs1a9Z0UFSONXHiRDIzM3nzzTct++rVq8e2bduoVauWAyOT8kxJWxERERERERERG9i2bZvl502bNrFw4UK+/PJLy7577rmHQ4cOOSI0cnJycHV1vevzvby8bBhN2efi4qI+kRKl6RFERERERERERGzAy8vL8uXm5oaTk5PVvurVq1vKHj58mF69ehEYGEj//v05ceKEVV2bN28mIiICf39/Hn74YWJjY7l+/brl+NmzZxkxYgTBwcG0atWKMWPGkJaWZjm+aNEi/v73v/Pxxx/TqVMnAgICAMjMzOTll1/moYceolWrVgwaNIgjR44AEBcXR2xsLEeOHMHX1xdfX1/i4uKAW6dHOHfuHOPGjePBBx8kKCiIXr16sX//fgBOnTrFiBEjCA0NJTg4mN69e5OQkFCsvjSZTERHR9OmTRvatWvH7NmziYqKYuTIkZYyBU2x8Pe//51FixZZtpcvX07Pnj0JCgqiffv2TJ06lezsbMvxuLg42rRpw9atW3n00UcJDg5myJAhnD9/3tKP69ev5z//+Y+lT3bu3HnL9AgF2b17N08++SQBAQG0b9+e1157jcuXL1uOr1mzhq5du+Lv709oaCjPPfdcsfpIyjclbUVERERERERE7Gz+/PlMnDiRdevW4eLiwqRJkyzHdu/eTVRUFIMGDWLTpk1Mnz6duLg4lixZAkBeXh4jR47k4sWLrFq1iuXLl5OSksLYsWOtrnHq1Cm++uorYmNj+fTTTwEYM2YM6enpLFu2jLi4OFq0aMHTTz9NRkYGjz32GM888wxNmjRh27ZtbNu2jccee+yW2LOzsxk4cCC//vorb775Jp999hnPPvsseXl5AFy+fJn27duzYsUK1q9fT3h4OMOHD+fs2bNF7p/33nuP9evXM3PmTD744AMuXrzI119/XdxuxsnJiZdffpkvvviCmJgYduzYwZw5c6zKXL16lffee4/Zs2ezevVqfvnlF2bNmgXAM888w6OPPkp4eLilT4KDgwu97qlTpxg6dChdu3bl888/Z/78+ezZs4cZM2YAcPDgQf7973/z3HPP8eWXX/LOO+/Qpk2bYrdPyi9NjyAiIiIiIiIiYmdjx461zG87bNgwhg0bxrVr16hSpQqxsbEMGzaMiIgIABo0aMCYMWOYM2cOo0ePZvv27fz444/85z//oV69egDMnj2b7t27c+DAAcuo2tzcXGbPno2HhwdgTgYfOHCA7du3W6ZKiIqKYvPmzXz11Vf069ePe+65p9CP/n/xxRdcuHCBTz75hHvvvReAP/3pT5bjfn5++Pn5Wbaff/55Nm/ezDfffMPAgQOL1D/vv/8+w4YNo2vXrgBMmzbNavqJoho8eLDlZ29vb55//nmmTJnC1KlTLftzc3OZNm0aDzzwAAADBgywzF9bvXp1qlatSk5OTrGmQ1i6dCk9e/a0XL9hw4a8/PLLPPXUU0ydOpVffvmFatWq0aFDB2rUqMH9999P8+bNi90+Kb+UtBURERERERERsbMbFy3LTwamp6dTv359jhw5wt69ey0ja8E8XcC1a9e4cuUKx48fp27dupaELUDjxo1xd3fnxIkTlqRt/fr1LQlbgKNHj3L58mXatWtnFcvVq1c5depUkWNPSkqiefPmloTtzbKzs4mNjeXbb78lNTUVk8nE1atXizzS9tKlS6SmphIYGGjZV6lSJVq2bIlhGEWOEyAhIYGlS5dy4sQJsrKyrPqxWrVqAFSrVs2SsAWoU6cO6enpxbrOzY4cOcLRo0fZsGGDZZ9hGOTl5XH69GlCQ0OpX78+nTt3Jjw8nPDwcLp06WKJSURJWxERERERERERO6tU6feUjJOTE4DV9AL/+te/LKNMb1SlSpUiX+PmBGB2djZeXl6sWrXqlrJubm5Frrdq1ap3PD5r1iwSEhKIiorigQceoGrVqjz33HPk5uYW+RpFkd9vN7px3t/Tp08TGRnJE088wdixY6lZsyZ79uzh5ZdfJjc319I/N/4u8ustbnL4ZpcvX6Z///489dRTtxyrV68erq6urF+/nl27drFt2zYWLlxIbGwsn3zyCe7u7n/o2lI+KGkrIiIiIiIiIlKKNG/enJ9++slqyoEbNWrUiHPnzvHLL79YRtsmJyeTmZlJo0aNbltvixYtSEtLw8XFBW9v7wLLVK5c2ZI8vh1fX18+/vhjMjIyChxtu2/fPiIiIujSpQtgThafOXPmjnXeyM3NDS8vL/bv30/btm0BczL28OHDVlMIeHh4WBYMA8jKyuL06dOW7cOHD2MYBhMnTsTZ2bysU3x8fJHjyFeUPrlZ8+bNSU5Ovu3vEMzJ4tDQUEJDQxk9ejRt27Zlx44dBSbrpeLRQmQiIiIiIiIiIqXIqFGj+Oyzz4iNjeXYsWMcP36cjRs3Mn/+fABCQ0Np2rQpL7zwAocPH+bAgQNMmDCBBx98EH9//9vWGxoaSlBQEKNGjWLbtm2cPn2avXv3Mn/+fA4ePAjA/fffz+nTp0lKSuLChQvk5OTcUk/37t3x9PRk1KhR7Nmzh5SUFL766iv27dsHmOe3/frrr0lKSuLIkSOMHz++2EnPQYMGsWzZMjZv3szx48eZNm0amZmZVmUeeughPv/8c3bv3s3Ro0eJioqyJGfz48jNzWXVqlWkpKTw6aefsnbt2mLFkd8nR48e5cSJE1y4cKFII4aHDh3Kvn37mD59OklJSfz8889s3ryZ6dOnA/Df//6XlStXkpSUxJkzZ/j000/Jy8vDx8en2PFJ+aSkrYiIiIiIiIhIKRIeHs6SJUvYtm0bffr0oW/fvqxYsYL7778fMH98/80338Td3Z2BAwcyePBgGjRoYEnq3o6TkxNvv/02bdu25aWXXqJbt26MGzeOM2fO4OnpCcAjjzxCeHg4gwYNIiQkhC+++OKWelxdXXnvvfeoXbs2w4YNo2fPnrz99tu4uLgAMHHiRNzd3enfvz/Dhw8nPDycFi1aFKsPnnnmGf72t78RFRVF//79qV69umXkbr7IyEjatm1LZGQkkZGRdO7c2WpuWj8/P1566SWWLVtGjx492LBhA+PGjStWHAB9+/bFx8eH3r17ExISwt69ews9x8/Pj1WrVvHzzz/z5JNPEhERwcKFC6lTpw5gHk389ddf8/TTT/PYY4+xdu1a5s2bR5MmTYodn5RPTsYfnaRDRERERERERESkhE2cOJHMzEzefPNNR4ciUuI00lZERERERERERESkFFHSVkRERERERERERKQU0fQIIiIiIiIiIiIiIqWIRtqKiIiIiIiIiIiIlCJK2oqIiIiIiIiIiIiUIkraioiIiIiIiIiIiJQiStqKiIiIiIiIiIiIlCJK2oqIiIiIiIiIiIiUIkraioiIiIiIiIiIiJQiStqKiIiIiIiIiIiIlCJK2oqIiIiIiIiIiIiUIkraioiIiIiIiIiIiJQi/w+B45dwgh7hsQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Çarpıklık (skew): 0.244\n",
      "Basıklık (kurtosis): -1.075\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 06 — UNIVARIATE EDA\n# ============================================================\nfig, axes = plt.subplots(1, 3, figsize=(14, 4))\naxes[0].hist(df[\"sales\"], bins=40, color=\"#2980b9\", edgecolor=\"white\")\naxes[0].set_title(\"Satış Dağılımı\")\naxes[1].boxplot(df[\"sales\"], vert=True)\naxes[1].set_title(\"Boxplot\")\nstats.probplot(df[\"sales\"], plot=axes[2])\naxes[2].set_title(\"Q-Q Plot\")\nplt.tight_layout()\nplt.show()\n\nprint(f\"Çarpıklık (skew): {df['sales'].skew():.3f}\")\nprint(f\"Basıklık (kurtosis): {df['sales'].kurtosis():.3f}\")\n",
   "id": "eda_uni"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a07"
   },
   "source": "## Aşama 07 — EDA: Mevsimsellik ve Yıllar Arası Yapı\n",
   "id": "a07"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eda_season",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 905
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409611788,
     "user_tz": -180,
     "elapsed": 937,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "048f48b9-8499-4d6e-b42b-03f902bcac8b"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x900 with 5 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "# ============================================================\n# AŞAMA 07 — SEASONAL / PANEL EDA\n# ============================================================\nfig, axes = plt.subplots(2, 2, figsize=(14, 9))\n\nannual = df.groupby(\"year\")[\"sales\"].sum()\naxes[0, 0].bar(annual.index, annual.values / 1e6, color=\"#2980b9\")\naxes[0, 0].set_title(\"Yıllık Toplam (M)\")\naxes[0, 0].set_ylabel(\"Milyon\")\n\nprofile = df.groupby(\"week\")[\"sales\"].mean()\naxes[0, 1].plot(profile.index, profile.values, \"o-\", color=\"#e67e22\")\naxes[0, 1].fill_between(profile.index, profile.values, alpha=0.2, color=\"#e67e22\")\naxes[0, 1].set_title(\"Ortalama Haftalık Mevsimsel Profil\")\naxes[0, 1].set_xlabel(\"Hafta\")\n\npivot = df.pivot(index=\"week\", columns=\"year\", values=\"sales\")\nsns.heatmap(pivot, ax=axes[1, 0], cmap=\"YlOrRd\", cbar_kws={\"label\": \"Satış\"})\naxes[1, 0].set_title(\"Hafta × Yıl Heatmap\")\n\n# YoY aynı hafta korelasyonu (ör: 2024 vs 2023)\ns23 = df[df.year == 2023].set_index(\"week\")[\"sales\"]\ns24 = df[df.year == 2024].set_index(\"week\")[\"sales\"]\naxes[1, 1].scatter(s23, s24, c=\"#8e44ad\", alpha=0.7)\naxes[1, 1].set_xlabel(\"2023\")\naxes[1, 1].set_ylabel(\"2024\")\naxes[1, 1].set_title(f\"Aynı Hafta YoY (r={s23.corr(s24):.3f})\")\nplt.tight_layout()\nplt.show()\n",
   "id": "eda_season"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a08"
   },
   "source": "## Aşama 08 — EDA: Durağanlık, ACF/PACF (Yıl-içi seri)\n",
   "id": "a08"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eda_stat",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 489
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409620337,
     "user_tz": -180,
     "elapsed": 351,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "5d537d64-fee8-45e5-bcea-5ef70c9b57c2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ADF statistic=-11.1372  p-value=0.0000\n",
      "→ H0: birim kök var (non-stationary).\n",
      "  p<0.05 ise durağan kabul.\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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xMRG9e/dGRUUF1Gq12Vk5Y36o9p2O7OxsfPLJJ6ioqDBLdl5RUYHKykqLK9854l//+hf69OmDN99806x+X331Vad9+/Tpg/3796OpqclstJROp0N5eTliYmI63Uar1aKiosLqaodERETkfQUFBUhOTsbvf//7TmU6nQ6LFy/Gli1b8PTTTyMsLAwTJ07E559/jn379mHcuHFur1+vXr0QHR1tNireKDs7GwAs5tc0BpqM6RuSk5ORkpKCQ4cOddr3hx9+MDteVzQ0NGD37t24+eab8cc//tGs7Ouvvza7Hh8fj8jISFMurPYsbTMyPvZLLrmky/UjItsU9nchIjInSRK2bt2KzMxM5OfnY/r06Z3+3XDDDfjpp59QVFQEwJD7qH///nj77bfxxRdfWDzukSNHsHHjRpfV8/LLL0dTU5PFpJQ33XQTZFnGpk2bzLa/9957AGCWD+uGG24AYEji2Z7xuqVVZByhUCg6TTHU6/V48803O+07efJkiKKIDRs2mG3fvHkzLl68aPH4P/74I3Q6HUaPHu1U/YiIiMi9SktLUVhYiGuvvdZif+rGG2/EZZddhk8//dQ07W3hwoUIDw/HU089hZKSEovH/eSTT7B//36X1FGpVCI3N9diMCk9PR2jRo1CUVGRWZJwURSxefNmhISE4KqrrjJtnzFjBs6cOYOdO3ea7fuPf/wDMTExXV5REIDpxF7HEeaVlZX44IMPOj2W8ePHo6ioCN9//71Z2dtvv231PoyPnX0qItfjSCki6rJ9+/ahoqICt99+u9V9pk2bhldeeQVbtmxBTk4OIiIisGbNGsyfPx8PPvggxo0bh7Fjx6Jnz56ora3Ft99+i3379tlcjrerpk2bhpUrV2LPnj3IzMw0K7v//vvxn//8BytWrEBpaSkGDx6M77//Hp988gmuuOIKs+WIJ02ahKuvvhrr16/HxYsXMWLECPzwww/YsmULbrrpJuTm5jpVv+nTp2PVqlW4//77MXXqVDQ2NuLTTz81LZ3cXn5+Pt5//3289NJLOHPmDLKzs1FcXIwdO3agX79+0Ov1nW6zZ88ehIaGYsqUKU7Vj4iIiNyroKAAAGyOar722mtx4MABfP7555gxYwYyMzPx8ssv49FHH8XNN9+M6667DsOHD0dYWBjOnj2LL7/8EsePH7d4kstZ06dPx+7du81WBjZ6+umncdddd2Hu3Lm4++670bNnT2zfvh1FRUV48MEHzRanmT9/Pv79739j0aJFmDt3LlJSUvDpp5/i8OHDWLZsmVMJ3KOjo3HVVVfh448/Rnh4OLKzs/HLL79g06ZNSEtLw4ULF8z2f+SRR0x9ztmzZyM1NRW7d+82rZBoKW+UsS9pb+VnIuo6jpQioi7bsmULAMNqddZkZmYiIyMD27dvN6220q9fP3z00Ud44okn0NzcjDfeeAPPPPMM1q9fD4VCgT//+c/43e9+57J6pqenY9y4cfj44487lUVHR2Pjxo2YOXMmvvzyS/zpT3/CwYMH8etf/xpr166FUqk02//ll1/Gr3/9a+zfvx9Lly7FN998g4ULF+L55593un7z5s3Do48+irKyMvzpT3/CP//5T1x11VV44YUXOu2rUqnwzjvv4NZbb8WXX36JF154AaWlpfj73/+O6Ohoi8nWP/74Y0yePNlingciIiLyLlEU8dFHHyE+Pt7mCa6pU6dCEARTAAswnDD77LPPMHv2bBw7dgwrV67Es88+i48++giZmZnYsGGDU6OOrLn++uvRs2dP/Otf/+pUdumll+K9997DqFGj8M477+CFF16AWq3G8uXLsXDhQrN94+Li8N5772HKlCnYuHEjli1bhsbGRvz1r381rYbsjL/85S/Iy8vDzp078cc//hFffvklfve735kSxLc3YMAAbNy4EcOHD8eGDRvw8ssvIy4uDi+99BKAtpUAjcrLy/H999/jjjvucLp+RGSdILsiky4RkY86ePAg7rjjDqxfv75bK874KlEUccUVVyAnJwfr1q0zbf/iiy/w29/+Fh9++CGGDBnixRoSERFRIFi7di3WrFmDL7/80mz1wEBx5MgR5OXlYdGiRZg/f75p+/PPP48dO3bg3//+t9OrAxKRdRwpRUQBbeTIkbj++uuxevVqb1el24wjztp7//330dDQYJavQZZlvPrqq7jlllsYkCIiIiKXuPfeexEbG2sz95K/6NinkmUZb731FgCYncSsrKzE+++/j9/97ncMSBG5CUdKERH5icceewxarRYjR46ESqXCwYMH8emnn6Jv37748MMPncrDQERERBRsrr32WlxxxRXIzMyEWq3Grl27UFhYiOuvvx5//etfvV09oqDCoBQRkZ/46KOPsHHjRpw6dQrNzc1ISEjAxIkT8fDDDyMxMdHb1SMiIiLyCy+88AJ27dqFc+fOQa/XIy0tDTfeeCPuv/9+hIaGert6REHFrUGp7777DuvWrcORI0dQVVWF1157ze4qUN9++y3+/Oc/4+eff0avXr3wwAMP4LbbbjPbZ+PGjVi3bh2qqqowePBgPP30051WgSAiIiLyV+xDERERUTBwa06p5uZmZGVl4Q9/+IND+5eVlWHBggUYM2YM/vWvf+Hee+/FU089hb1795r22b59O5YvX44HH3wQW7duxeDBgzFv3jzU1NS462EQEREReRT7UERERBQMPDZ9Lysry+5Zvr/85S/Ys2cPPv30U9O23/3ud2hoaDCtKpWfn4/s7Gw888wzAABJkjBx4kTcfffdZqskEBEREQUC9qGIiIgoUIV4uwLt/fDDD7jyyivNto0bNw7PP/88AECr1eLo0aNYsGCBqVyhUGDs2LE4ePCgw/cjSRL0ej0UCgUEQXBN5YmIiCggybIMSZIQEhIChcI3Fy5mH4qIiIh8iaP9J58KSlVXV3dK1puYmIjGxka0tLSgvr4eoigiISHBbJ+EhASUlJQ4fD96vR6HDx92SZ2JiIgoOGRnZ0OlUnm7GhaxD0VERES+yF7/yaeCUp5ijNJdeumlUCqVLjnmy1/+jHe+PgPRwmxIpSDg3rF98fA1l7jkvgKZKIr48ccfXfrcBAu2nfPYds5j2zmPbec8T7ed8f58dZSUJ7m6D8X+k2vw88R5bDvnse2cx7ZzHtvOeb7af/KpoFRiYiKqq6vNtlVXVyM6Ohrh4eFQKBRQKpWdEnLW1NR0aTl043BzlUrlsifj9tEZeP2rU5AsZOhSCDLyR2f47NlVXyKKIgDXPjfBgm3nPLad89h2zmPbOc/TbWe8P1+eruavfSj2n1yDnyfOY9s5j23nPLad89h2zvPV/pNPnfIbMWIEvvnmG7NtX3/9NUaMGAHA0HhDhw7F/v37TeWSJGH//v0YOXKkJ6vaSf/EKKzIy4GiXXsrBUAhACvycpCRGOW9yhEREVFA89c+FPtPREREwc2tI6Wamppw5swZ0/Xy8nIcO3YMsbGx6N27N1atWoXz58/jhRdeAADccccd2LhxI1544QXk5eXhm2++wWeffYY1a9aYjjF37lwsXrwYw4YNQ05ODt555x2o1Wrcdttt7nwoDsnPTcewPjG47uV9AIA5YzNw95UZZh2q0uombC4sQ3mdGmlxEZiZm47+7HARERFRO8HUh2L/iYiIKHi5NSh15MgR3HPPPabry5cvBwDceuut+POf/4yqqipUVFSYytPT07FmzRosX74cGzZsQGpqKpYtW4bx48eb9rn++utRW1uL1atXo6qqCkOGDMFbb73VpaHn7tQvoa2D9MiUQegREWa6vrmwDEsKiiAIAmRZhiAIWLPnJFbk5SA/N90b1SUiIiIfFGx9KPafiIiIgpNbg1JjxoxBcXGx1fI///nPFm/z0Ucf2Tzu7NmzMXv27O5Wz6NKq5uwpKDIkDPBmMyz9XJxQRFGZ8RziDoREREBYB/KiP0nIiKiwOZTOaUC2ebCMqsJvgRBwKbCMg/XiIiIiMi3sf9EREQU2Hxq9b1AVl6nhmxhuWMAkGUZ5XVqAMyZQERERGTkaP+JiIiI/BODUh6SFhdhONNnoWMlCALS4iKYM4GIiIioHUf6T0REROS/OH3PQ2bmpts803fVwARTzgRRks0uFxcU4VR1k4drTERERORd9vpPs3jSjoiIyK8xKOUh/ROjsCIvB4p2aRGUggCFAKzIy8F/T9YwZwIRERFRO/b6T0xyTkRE5N8YlPKg/Nx0bFs4znR97rgM7Fw0Cfm56cyZQERERGSBrf4TERER+TfmlPKwfgltZ/QenZqJSJXhKWDOBCIiIiLLrPWfiIiIyL/xG91HzMxNx5o9Jy2Wtc+ZwNX5iIiIiIiIiCgQMCjlI4w5Exa3JjsHDDkTZMimnAlcnY+IiIiIiIiIAgVzSvkQWzkTSqubuDofEREREREREQUMBqV8TMecCcZVZTYXlnF1PiIiIiIiIiIKGJy+5ye4Oh8RETmCuQeJiIiIyF8wKOUnuDofERHZw9yDRERERORPGJTyE46uzkdERIHP0mgoAKbcg6YTGK2XiwuKMDoj3jQlnIiIiIjIFzAo5SccWZ2PiIgCn7XRUBMzk2yOqN1UWIbF0wdzeh8RERER+QwGpfxIfm46hvWJwXUv7wNgWJ1v9ph+ZgEp/tggIvJ/1j7L26/E2nE01K7iKigsr4dhyj3I6X1ERERE5EsYlPIzHVfni1S1PYX8sUFE5P9sfZaXVDdZHw0Fi5sNZYKA6DAlp/cRERERkU9ReLsC5Brtz56Lkmx2ubigCKeqm7xdRSIissPeZ3nxuYtWV2IFAGslsixDgGAIaFlgnN5nrMOKHcfx2/cOYsWO4yjl9wcRERERuQmDUgFic2GZQz82iIjId9n7LK9r1lotVygEXJ2VZDaFTykIUAjAirwcXNTorQa02k/vu2bVbqz9qgTbis5i7VcluGbVbnzA7xAiIiIicgNO3wsQ5XVquz82iIjIt9n7LO8ZqbJZ/ocbh0Kt01vMPWhz6h+n9xERERGRF3CkVIBIi4uweXY9LS7CwzUiIiJrSqub8Jd/F+PFby7gL/8uNk2Rs/dZPji1B1bk5VgdDZWRGNUp96AxkDQzN91mQMvR6X1ERERERK7CoFSAsPdjYxYTnRMR+QTjFLk3957C12UteHPvKdMUOUc+y/Nz07Ft4TjT9rnjMrBz0SS7C1r0T4yyGdByZHofEREREZErMSgVIOz92OCUCyIi7zNLZC7LkGG4NCYyFwCHPsutjYayx1ZAiyNuiYiIiMjTmFMqgOTnpmNYnxiLuUQAw4+hzYVlKK9TIy0uAjNz09GfwSoiIo8xJTK3ktdpU2EZFk8fbPOzvLs6BrQiVYauwMzcdKzZc9LibdqPuOV3CRERERG5CoNSAcbaj43NhWVYUlAEQRAMuUMEAWv2nMSKvBy7Uz6IiMg1HF2UwtpnuTsZR9wuNiY7h2GUlgzZNEqL3yVERERE5EqcvhcEzKaLSLLZ5eKCIpxqTbBLRETu5etT5GxN7+N3CRERERG5GoNSQcA0XcQCrqhEROR6pdVNWLHjOH773kGs2HHctLqePyxKYS1fFb9LiIiIiMjVOH0vCDg6XYSIiLrP3hS3zlPkABm+vygFv0uIiIiIyNUYlAoCpukiVhLrenu6CBFRoGg/xc30mdt6ubigCKMz4jstSjFnbAbuvjLDpwNSAL9LiIiIiMj1PDJ9b+PGjZg8eTKys7ORn5+PoqIiq/vefffdyMrK6vRv/vz5pn2WLFnSqXzevHmeeCh+yR+mixARBQJHp7i1nyL3yJRBPh+QAvhd4g3sPxEREVGgc/tIqe3bt2P58uVYunQphg8fjnfeeQfz5s3Djh07kJCQ0Gn/V155BTqdznT9woULuPnmmzF9+nSz/caPH4/ly5ebrqtUKvc9CD/nyIpKRETUfYE8xc3R75LS6iZsLixDeZ0aaXERmJmbjv78nuky9p+IiIgoGLg9KLV+/XrMnDkTeXl5AIClS5di9+7dKCgoMDt7Z9SzZ0+z69u2bUN4eHinTpVKpUJSUpLb6h1oOk4XmTsuA7PH9GNAiojIhQJ9ipu97xJ7+bTIcew/ERERUTBw6/Q9rVaLo0ePYuzYsW13qFBg7NixOHjwoEPHKCgowIwZMxAZGWm2/cCBA7jyyitx7bXX4g9/+APq6upcWvdAZG1FJSIi6hp/Xl2vu6x9l7TPpyVKstnl4oIinGptI7KP/SciIiIKFm4dKVVXVwdRFDsNM09ISEBJSYnd2xcVFeGnn37Cn/70J7Pt48ePx9SpU5GWloaysjK8+OKLuP/++7Fp0yYolUqH6yeKosP7OnNMUZQ63Yd5uQhRFDxWbu+2vsBYR3c8N4GObec8tp3zgrHttnxfjie2HoEAw9Q1AYbRQMtvG4bbR6Vh+W3D8MSHRzqtrrf8tmFIjwtv/fx133eFt75nNh04DQECDI/WnAAB7x84jf+7NqtTmTM8/brz9Ovb1/tPgOvbpLvviWAXjJ/FrsK2cx7bznlsO+ex7Zznq/0nn159b8uWLcjMzEROTo7Z9hkzZpj+NibqnDJliunsn6MOHz7ssroategl099Hjx5BeIjCanlRUZFHy+3d9uxFPXaeUqOySURylBKTMyLQu4d3XiLueG6CBdvOeWw75wVL2529qMeSHdWtYRdj8MVwuaTgCKKaz2FQdAj+MiUeiz6vBQBcf0kkrh0YiV6KavzwQzUA935XeOt75nDpBUg2RokdLq3ADz+4NqdWsLzuusrd/SfA9W3f3fcEGfA94Ty2nfPYds5j2zmPbec8X2s7t0Yc4uLioFQqUVNTY7a9pqYGiYmJNm/b3NyMbdu2YeHChXbvJz09HXFxcTh9+nSXOlXZ2dldPjNoT7NWD2z9AgAwdOgw9IhQWS3PyclBpCrEY+W2yrZ8X44n/m1+5v9fxU2mM/+eIooiDh8+7JbnJtCx7ZzHtnNesLXd5/8uhkKogWgh+KIQBBxp6oHrxmUhU6sHPjd83j5/51ibn9Wu/q7w1vdM9vlifFN+ymLbCIKA7P69MGKE60ZKefJ1Z7w/T/H1/hPg+j5Ud98TwS7YPotdiW3nPLad89h2zmPbOc9X+09u/UZXqVQYOnQo9u/fjylTpgAAJEnC/v37MXv2bJu33bFjB7RaLW666Sa793Pu3DlcuHChy4k7lUqly58MpVJu97ei0/HNyzvfvzvLrZWVVjfhia3GqSbmZ/6f+PAIxvRP9Hj+KXc8N8GCbec8tp3zgqXtfqnXQLYwPQ0AZMj4pV7T2hZd+ax27XeFt75nZl3eD2v3lsISGTLuuLyfG75zA/N15+v9J8D1bd/d9wQZsG2cx7ZzHtvOeWw757HtnOdrbef2sc9z587F5s2bsXXrVpw8eRLPPvss1Go1brvtNgDA448/jlWrVnW63ZYtWzBlyhTExcWZbW9qasKKFSvwww8/oLy8HPv378dvfvMb9OvXD+PHj3f3wwlImwvLDKtFWSAIAjYVlnm4RkREvsm0up4FgbC6Xnf0T4zCirwcKNo1j1IQoBCAFXk5ZgnRLSWJJ3PsPxEREVEwcPvY5+uvvx61tbVYvXo1qqqqMGTIELz11lum4ecVFRVQKMxjYyUlJfj+++/x9ttvdzqeUqnETz/9hI8++ggXL15EcnIyrrrqKjz88MNQqVSd9if7yuvUNleLKq9zbQ4QIiJ/NTM3HWv2nLRYFiir63VHfm46hvWJwXUv7wMAzB2Xgdlj+pkCUpsLy7CkoAiCIECWZQiCIUn8irwc5Ad523XE/hMREREFA49MyJ89e7bV4ebvvvtup20DBgxAcXGxxf3Dw8Oxbt06l9Yv2JnO/FvJAxLMZ/6JiNozjgZaXFDUbnU9Qy6+9qOBglm/hLY2eHRqpin3T2l1E5YY2834fdN6ubigCKMz4tl+HbD/RERERIGOS5cQZuam2xwpFexn/omI2svPTce2heNM1+eOy8DORZM40scOThUnIiIioo4YlCKH84AQEZFBx9FA/Jy0j1PFiYiIiKgjrqdLAOznASEiCial1U3YXFiG8jo10uIiMDM3Hf35edgtnCpORERERB0xKEUm1vKAEBEFEybjdg8miSciIiKijjh9j4iIqFX7ZNyiJJtdLi4owqnqJm9X0W9xqjgRERERdcSgFBERUSsm43YvJoknIiIiovY4P4scxhwrRBTomIzb/ThVnIiIiIiM2BMkhzDHChEFAybj9j6eACEiIiIKHgxKkV3tc6yYfqi1Xi4uKMLojHjmAiGigMBk3N7FEyBEREREwYU5pcgu5lghomDBZNzewyTzRERERMGHQSmyizlWiCiYMBm3d/AECBEREVHw4fQ9sos5Vogo2DAZt+fxBAgRERFR8OFIKbJrZm66zR8KzLFCRETdZToBYgFPgBAREREFJgalyC7mWCEiInfjCRAiIiKi4MP5COSQ/Nx0DOsTg+te3gfAkGNl9ph+DEgRkV8qrW7C5sIylNepkRYXgZm56ejPzzOvMp4AWWxc7RWGEyAyZJ4AISIiIgpQDEqRw5hjhYgCwebCMiwpKIIgCJBlGYIgYM2ek1iRl8Nk5l7GEyBEREREwYXT94iIKGiUVjdhSetIHFGSzS4XFxThVHWTt6sY9DqeAGFAioiIiChwMShFRERBY3Nhmc1k2psKyzxcIyIiIiKi4MWgFBERBY3yOrXNZNrldWoP14iIiIiIKHgxKRAREQWNtLgIw0gpC4EpQRCQFhfhhVpRV5RWN2HTgdM4XHoB2eeLMevyfkxST0REROSnGJQil+FqVkTk62bmpmPNnpMWy2RZxiwmOvdppiT1ECDJMr4pP4W1e0uZpJ6IiIjIT3H6HrnE5sIyXLNqN9Z+VYJtRWex9qsSXLNqNz5gfhYi8iH9E6OwIi8HinZppZSCAIUArMjLYVJtH2aWpF6WIcNwyST1RERERP6LQSnqNq5mRUT+JD83HdsWjjNdnzsuAzsXTeJIGx/HJPVEREREgYdBKeo2/lAgIn/TL6FtRNSjUzM5QsoPMEk9ERERUeBhUIq6jT8UiIjI3UxJ6i1gknoiIiIi/8RE59RtXM2KiHwNF14IPExST0RERBR4OFKKum1mbrrNkVL8oUBEnsSFFwKHTpTQohPRrNEjMUqFP9w41CxJvUIw/HtsWhb69OQJECIiIiJ/w6AUdRtXsyIiX8GFF/xLi06Piy061DZpcb6+BeW1zSht9xz9eLYBP55twLFzF1F8/iIu7R2DFXk5pvLpQ1OwKn8ErhyYAAmWT44QERERke/i9D1yifzcdAzrE4PrXt4HwLCa1ewx/RiQIiKPMi28YGU68abCMiyePtgLNQtOoiRDrdVDK0rQijK0OgkX1FpT+fGKRoSFKCHJcus/oEUnmspbdBIA8zxSidFhpr/zRvVBZJjK7Y+DiIiIiNyDQSlymY6rWUWq+PIiIs/iwgveodWL0OplaPUS6prbgk5Hf2mAKkRhCjhJkgx1u6CTRi9ZTV5ORERERIHPI9P3Nm7ciMmTJyM7Oxv5+fkoKiqyuu+HH36IrKwss3/Z2dlm+8iyjJdffhnjxo1DTk4O5syZg1OnTrn5URARka/jCm3updWLuNiiM10vqWrCkV/qcfRsA4rPXcTJqkaU1TabytU6ERq9BJ0oQ5RkTrDrIvafiIiIKNC5PSi1fft2LF++HA8++CC2bt2KwYMHY968eaipqbF6m+joaOzbt8/0b9euXWblb775Jt599108++yz2Lx5MyIiIjBv3jxoNBp3PxwiIvJhXHjBtS406VBRr0ZpdSN+PGsIPp0435bzqeqiBhdb9GjRSdCKEvQMPLkM+09EREQUDNwelFq/fj1mzpyJvLw8DBo0CEuXLkV4eDgKCgqs3kYQBCQlJZn+JSYmmspkWcaGDRvwwAMPYMqUKRg8eDBeeOEFVFZW4osvvnD3wyEiIh/GhRe6TidKaGzRo6pRg/LaZvxc2WgqO1nViDM1zThXr0G9ui34RO7H/hMREREFA7cm/dFqtTh69CgWLFhg2qZQKDB27FgcPHjQ6u2am5tx9dVXQ5IkXHrppXj00UdxySWXAADKy8tRVVWFsWPHmvbv0aMHhg8fjoMHD2LGjBkO108URfs7dVH7Y4qi1Ok+zMtFiKLgsXJv3rejjMdwx3MT6Nh2zmPbOc8X2+62kb1xaa9ozHjlawDAnLH98Ksx6chIiLJYX298Hrrzu8JWmU6UUd8u59Oxs/UQAIiyYXqdKMlmica1ej0UgtLsviVJNPtb6hCj8my5ZNpHFiU48bXjEE+/vn29/wS4vk26+54Idr74Wewv2HbOY9s5j23nPLad8zzddo7ej1uDUnV1dRBFEQkJCWbbExISUFJSYvE2/fv3x/PPP4+srCxcvHgRb7/9Nu644w5s27YNqampqKqqMh2j4zGrq6u7VL/Dhw93aX9HtOjbes9Hjx5BeIjCanlRUZFHy715313ljucmWLDtnMe2c56vtV37z6PJyc24UPYzfiizXO6Nz0N3fVcoFAro5LYf60eKT0IhABpRRpNGRItOj6YWvan8f0eOQ6U0/3GvFdsm4JWUlPh0+alTp6BSCugRGY7QpnDoWgIjkb2v958A17/nu/ueIANf+yz2J2w757HtnMe2cx7bznm+1nY+tzzayJEjMXLkSLPr119/Pd5//3088sgjLr2v7OxsKJVK+zt2QbNWD2w1DIMfOnQYekSorJbn5OR0WqHOneXevG8AKK1uwpbvy1F+QY20nhG4/bI09O8wlUYURRw+fNgtz02gY9s5j23nPF9tO29/3nWl3BXfFaFKJVr0Ilp0IuqatAB+AQDoIxIQqlRAKcuI6QHEAK0joQwBigEDBiA81Px5a9GJwL7/+UV5RkYGIsNCER6qRGavGIS6KVBhfJ37Mk/2nwDX96G6+54Idr76WewP2HbOY9s5j23nPLad8zzddo72n9z6jR4XFwelUtkpKWdNTY1ZngNbQkNDMWTIEJw5cwYAkJSUZDpGcnKy2TEHDx7cpfoplUqXPxlKpdzub0Wn45uXd75/d5Z78743F5ZhSUERBEGALMsQBAFr95ZiRV4O8i0kHnbHcxMs2HbOY9s5z9fazpufd10v79p3hVYvoknbNmrkRFUTAME0/a5Z2zZUWi8LCBEUZjm2FIr2fyuhUJjft3+VK1r3UUCw0I7+ytf7T4Dr3/Pd7T+RAdvGeWw757HtnMe2cx7bznm+1nZuHfusUqkwdOhQ7N+/37RNkiTs37/f7GyeLaIo4qeffjJ1ptLS0pCUlGR2zMbGRhw6dMjhY5JnlVY3YUlBESQZECXZ7HJxQRFOVTfZPwgRUZCqV+twvr4Fp2qacKyidQW8dsnILzQbEpDrRMPnKvk/9p+IiIgoWLh97PPcuXOxePFiDBs2DDk5OXjnnXegVqtx2223AQAef/xxpKSkYNGiRQCAV199FSNGjEC/fv3Q0NCAdevW4ezZs8jPzwdgWFnmnnvuweuvv45+/fohLS0NL7/8MpKTkzFlyhR3PxxywubCMgiCAFhYpl0QBGwqLMPi6V0/S0tEwau0ugmbC8tQXqdGWlwEZuamd5oO7E8kSYZGL6JFL0GjE1Hb1JaM/GRlI0KVCrOAk05k9CnQsf9EREREwcDtQanrr78etbW1WL16NaqqqjBkyBC89dZbpuHnFRUVULQbi9/Q0ICnn34aVVVViI2NxdChQ/H+++9j0KBBpn3uv/9+qNVqPPPMM2hoaMBll12Gt956C2FhYe5+OOSE8jo1ZAsBKcCwRHV5XWAkpiUiz7A0HXjNnpNWpwP7Gq1exMUWnel6SVUzIKhN0+8k2XwKnk6UoWRO56DD/hMREREFA49kiZw9ezZmz55tsezdd981u/7kk0/iySeftHk8QRDw8MMP4+GHH3ZZHcl90uIibI6USouL8EKtiMgftZ8ObPpMab1cXFCE0RnxyPCxEVN1TTpcaNahRWdIRK7RS2jWtAWdqhtbEBmmsnEEClbsPxEREVGg47lXcruZuek2R0rN8oORDUTkG0zTgS0wTgf2NE3ryKeaRg3OXlCjpKoRxysaTOUlVY04U9uM8w0a1KsN+Z+0omTjiEREREREwYHr6ZLb9U+Mwoq8HCw2jm4AoBQEyJCxIi/H50Y1EJHv8sZ0YFk25HvS6iXo9DK0ooh6td5UfrziIlRKJURZhiTJkAG06NqtgCfJCGEKKCIiIiKiThiUIo/Iz03HsD4xuO7lfQCAueMyMHtMPwakiKhL3DEdWKeX0Aw99KIMvWgIOl1sF3Q68ksDVK2JxiW5c86nFp0EwPLoLSIiIiIiso5BKfKYfgltAahHp2YiUsWXHxF1zczcdKzZc9JimSzLyBvVBxq9CLW2Lah0oUmHJq0IUZQgSjJ0ooyLmrZE4z9WNCAsRAlJliG3Bp7aB52atSKkUPc9JiIiIiKiYMWoABFRgJJlGZJsuJQBQAZktP6N9oON5A7X2x3Dzn1IkoSQsEiodSIUovlx2o8dktsfq7UebX+31ks23FY21r31YMZAkSgDKqWAxdMH4887jpvuRyEYbvPQ1YPQ2CLix7MNULcLKp2sakRYqNLUHoD59DqOdCIiIiIi8g4GpYiIfIAsyxAlGaLxUjJMExMlQJQkSBIgQTZcysYyufVvQDQsR2eYYia1BqMA8wCQ2R3aDzg5QpJEnKpuhi6qAYJCaedBdrxqvqF9UMxSAMsYsMpJ64nlt2ZjyYeHAQDTh6Vi6pBUpMaGQ90abNLo2xKJ6yUZIZIrHi0REREREbkSg1JERG5kmC4mGfIVSRL0kgx9u2lkOlFq/WcIMJlGDskdRw+1H+3kOyRJQpNaA41egkLhudFGKTHhpr/zL0tHeKidgBgREREREfkcBqWIiLpBlAwrsxkDS3pRglaUoW1drU2rl9tyFZmCTG3TyIiIiIiIiIIVg1JERHZIrYEnrTHgpJOg0YuGfzrZNJ3OuDqbpdxMREREREREZI5BKfIZpdVN2HTgNA6XXkD2+WLMurwf+idG2b8hkQuoVCrIsowWnR4avQSNTkKLXkKLToRaK5rlb5Iki1maiIiIiIiIqAsYlCKfsLmwDEsKiiBAgCTL+Kb8FNbuLcWKvBzk56Z7u3oUgPSiIeik0Ylo0uhwXi1AOtsAURbaJRH3di2JiIiIiIgCF4NS5HWl1U1YUlDUGgAwRAHE1vlPiwuKMDojHhkcMUXdoBMltGhFtOhFtOgkNGn1aNFKptXu9Ho9fqlpQERcMhT2VpAjj6ioV2N3cRWqGjVIig7DpKwk9IqN8Ha1iIiIiIjIhRiUIq/bXFgGQRBgKRGPIAjYVFiGxdMHe6Fm5I9ESTZMudOJaNGKaNTqodaKkCSYglAdcUCUb9ldXIm1e0sgwPDcCAA+KTqLBRMGYGJmspdrR0RERERErsKgFHldeZ0aspXM0LIso7xO7eEakT8xBqCatSLUWj2aNCL0kmH6ncj5d36nol6NtXtLDKsUtm4zXq75qgRZKTFIjQ33VvWIiIiIiMiFGJQir0uLi7A5UiotjlN2yECUZKi1ItQ6PZq0Ipo0emh1kikIxRCU/9tdXGUaIdWRAGBXcSXuvLyvh2tFRERERETuwKAUed3M3HSs2XPSYpksy5jFROdBS6M3rHzXrBXRbBwFJcpWp+GR/6tq1FgNLsqt5UREREREFBgYlCKv658YhRV5OVhsSnYOKAXDD9AVeTlMch4kZLk1F5RWQrNWj4saPVp0IkRJhl6SLQ2kowCUFB1mc6RUUnSYh2tERERERETuwqAU+YT83HQM6xOD617eBwCYMzYDd1+ZwYBUAJMkGc261jxQrVPxNDrJFISi4DQpKwmfFJ21WCYDuDqLic6JiIiIiAIFg1LkM/oltAWgHpkyCD0iOCIikOhEqXUqnh7NWhGNLXromJCcOugVG4EFEwZgzVclptFxitaRkwsmDGCScyIiIiKiAMKgFBG5hWEqnohmnWEUVLNGhF4yjIRiDIpsmZiZjIyEKCz58DAAYPqwVEwdksqAFBERERFRgGFQioi6TZJkqHWGAJRaI6JRo4OGq+JRN6TEtAWg8i9LR3io0ou1ISIiIiIid2BQioi6zLgqnlonolkjolGjNwWgOBWPiIiIiIiIHMGgFBHZpNNLhql4rf+MCcn1cusoKMagiIiIiIiIyAkMShGRSccAVLNWj5bWFfEkCRAZgSIiIiIiIiIXYVCKKAiJkgyNToRGLxmm4rUmJdfoGYAiIiIiIiIiz2BQivxGaXUTNheWobxOjbS4CMzMTUf/xChvV8unKBQKs+s6vQSNKBku9SK0ehnNOj1atBJEWYYkyZBkroZH3lFRr8bu4ipUNWqQFB2GSVlJ6BUb4e1qERERERGRhzAoRX5hc2EZlhQUQRAEyLIMQRCwZs9JrMjLQX5uurer5zWiJEMnStCLMjQ6HbSKMPxyQQ2dCLToRWj1EiQZDD6Rz9ldXIm1e0sgAJABCAA+KTqLBRMGYGJmspdrR0REREREnsCgFPm80uomLCkoMgRUjFPKWi8XFxRhdEY8MgJoxJQkydBLUutKdoAoSxBFQC9J0LcGoXSiBK1eglZvCDbJMqAX9ThxvgH6yGYoFEpvPwwiqyrq1Vi7twSybAhIAW2Xa74qQVZKDHpGhnqrekRERERE5CEMSpHP21xYBkEQYGmZN0EQsOm7M3js2sGtI4EM+8itgRpjHMs4GkMG2n79ApBhZeiQnRFFbT+kzXdsf5+QAal1D1ky1scwWkmWZYgyIEqSKX+TIQglQy/KkNG6f+ttZBmm4JO1qkmSDL0o2a44kQ/YXVxlek92JADYVVyJW0f28XCtiIiIiIjI0zwSlNq4cSPWrVuHqqoqDB48GE8//TRycnIs7rt582Z89NFH+PnnnwEAQ4cOxaOPPmq2/5IlS7B161az240bNw7r1q1z34Mgt9Pp2wIqDWo9NKIh0PLz+YuQrSTdlmUZR8424HB5vSlA1HFXudMf1lkNUjmg4/20v97+uLYCS0TBoKpRY/U9ILeWExH7T0RERBT43B6U2r59O5YvX46lS5di+PDheOeddzBv3jzs2LEDCQkJnfb/9ttvMWPGDIwaNQoqlQpvvfUW7rvvPmzbtg0pKSmm/caPH4/ly5ebrqtUKnc/FOomjV40/X2+vgWhSgW0omT4p5fQpNGbyk9UXUSESgVZlhGlsv0yjYtUQa0Tbe5DRL4jKTrM5kippOgwD9eIyPew/0RERETBQGF/l+5Zv349Zs6ciby8PAwaNAhLly5FeHg4CgoKLO6/atUq3HXXXRgyZAgGDhyIZcuWQZIk7N+/32w/lUqFpKQk07/Y2Fh3PxTqgnq1DlUXW/BLXTNOVF7EkV/qcbzioqn8TG0zyurUON+gQV2TDk0aES26tpFSetEwlU2SgYlZSTZHVVydxaTIRP5kEt/TRHax/0RERETBwK1BKa1Wi6NHj2Ls2LFtd6hQYOzYsTh48KBDx1Cr1dDr9Z06TQcOHMCVV16Ja6+9Fn/4wx9QV1fn0rqTY3SihIstOlQ2tOB0TbNp+8nKRpRWNeNMrRpVF7W42KI3Czp1ZRW4XrERWDBhAAShbZtCAAQBWDBhAFJjw13xUIjIQ/ieJrKN/SciIiIKFm6dvldXVwdRFDsNM09ISEBJSYlDx1i5ciWSk5PNOmbjx4/H1KlTkZaWhrKyMrz44ou4//77sWnTJiiVjq86Joqun/LV/piiKHW6D/NyEaIoeKzclccuq2mCJANqrd6UoLtJ2zb9TqPTQ+gwFkKSRLO/pQ45uc3LJbPr4wcloG9cOJ786EcAwPShKbhmSDJSY8LN9gtmUmuDSh0bluxi2znP2baz957u2ueFv5dLnT7HunN833psnmk7SQJkUUKHry2XcUd/wRZf7z8Brm+T7vafgp2xfTz9Wg0EbDvnse2cx7ZzHtvOeZ5uO0fvx6dX31u7di22b9+ODRs2ICysLcfIjBkzTH9nZWUhKysLU6ZMMZ39c9Thw4ddWl8AaGmXrPvo0SMID1FYLS8qKvJoeVdue/jwYUSHq6BQKqGXFVDrZdQ2twWd9v7vKEI7jLPTim1BqJKSEqiUgtPlp06dslk+sqcGjZXlOFEJ6sDRHyzUGdvOec60na33tCs/T3y93N7nXVeP70uPzVNt1yMyHKFN4dC1qEHu7z8Bru9Ddbf/RAbu6NsGC7ad89h2zmPbOY9t5zxfazu3BqXi4uKgVCpRU1Njtr2mpgaJiYk2b7tu3TqsXbsW69evx+DBg23um56ejri4OJw+fbpLnars7Owunxm0p1mrB7Z+AQAYOnQYekSorJbn5OQgskMSb3eWd+W26QMHQ5RlNKh1hlXxRAnhKhFAGQCgf//+CA81b7sWnQjs+x8AYMCAAd0qz8jIQGRYaJduH+wkSUJJSQkGDBgAhYKd9a5g2zmvO21n6z3tys8TXy935vOObWfeduGhSmT2ikGomwIVoih6tAPn6/0nwPV9qO72n4Kd8TXqjr5toGPbOY9t5zy2nfPYds7zdNs52n9y6ze6SqXC0KFDsX//fkyZMgUATEk3Z8+ebfV2b775Jt544w2sW7cO2dnZdu/n3LlzuHDhApKSkrpUP6VS6fInQ6mU2/2t6HR88/LO9+/OcltloiSjUdN2FvJUjRohSmPnXgAEJdr/3lQolFAozO/bteWKLt+eDCy1HTmGbec8Z9rO1nvas5833i7v+ucd2874t6J1HwUEC9+5/srX+0+A6/tQ3e0/kQHbxnlsO+ex7ZzHtnMe2855vtZ2bj/NNHfuXCxevBjDhg1DTk4O3nnnHajVatx2220AgMcffxwpKSlYtGgRAMOQ89WrV2PVqlXo06cPqqqqAACRkZGIiopCU1MTXn31VVx77bVITExEWVkZ/vKXv6Bfv34YP368ux9OwGls0aNerUNdsxYXmrWm7XpJRojvvE6JiIiCCvtPREREFAzcHpS6/vrrUVtbi9WrV6OqqgpDhgzBW2+9ZRp+XlFRYTbl4/3334dOp8PChQvNjvPQQw/ht7/9LZRKJX766Sd89NFHuHjxIpKTk3HVVVfh4YcfhkplPtSbrKus16BFbEaTRoROlCDLgK5dng4iIiLyHvafiIiIKBh4ZEL+7NmzrQ43f/fdd82u79y50+axwsPDsW7dOpfVLZg0t1sd70xts9tybxAREVH3sf9EREREgY5ZIoPARbUO1U0anLvQYtomyjJCbdyGiIiIiIiIiMidGJQKYBeadCivU+Niix5avWS2nDIRkbtV1Kuxu7gKVY0aJEWHYVJWEnrFRni7WkRERERE5CMYlAowstyWF6qkuhHKIFrenj+AiXzH7uJKrN1bAgGADEAA8EnRWSyYMAATM5O9XDsiCjSl1U3YXFiG8jo10uIiMDM3Hf0To7xdLSIiIrKDQakA0tiiR0l1k+m6TpShDJKYFH8AE/mOino11u4tgSwb3o9A2+War0qQlRKD1Nhwb1WPiALM5sIyLCkogiAIkGUZgiBgzZ6TWJGXg/zcdG9Xj4iIiGwIkpBFYNPoRZypacZP5y+iplHr7ep4XPsfwJIMs8s1X5XgXH2L/YMQkcvsLq6CYKVMALCruNKT1SGiAFZa3YQlBUWQZECUZLPLxQVFONXuZB0RERH5Hgal/JhelHCuXo3iiouoqFdDE6Q5o/gDmMi3VDVqIFspk1vLiYhcYXNhGQTBci9AEARsKizzcI2IiIioKzh9z4+drGqETjScEQxm/AFM5FuSosNMU2k7ElrLiYhcobxObZZPsz1ZllFep/ZwjYiIghtz/FFXMSjlZ3Ri22ioC816hIcqvVgb38AfwES+ZVJWEj4pOmuxTAZwdRbzvBGRa6TFRRhGSlkITAmCgLQ4LnhCROQpzPFHzuD0PT/SrNGjpIq5ETqalJVkc6QUfwATeVav2AgsmDAA7WfUKARAEIAFEwYwyTkRuczM3HSbI6Vm8UcQEZFHMMcfOYtBKT9R3ajBz5WNuNCs83ZVfA5/ABP5nomZyVh+a7bp+vRhqXgxfwRXwyQil+qfGIUVeTlQtOsDKAUBCgFYkZeDDE4ZISLyCOb4I2dx+p6P04kSzl5Qo+qiBjoxuHNH2TIxMxkZCVFY8uFhAIYfwFOHpDIgReRFKTFt77/8y9I53ZiI3CI/Nx3D+sTgupf3AQDmjsvA7DH9GJAiIvIg5vgjZzEo5cOaNXpUN2lR36xDkOcydwh/ABMREQWnfgltAahHp2YiUsUuLhGRJzmS449J0MkSfmP7sJNVTQxGERERERERkU+bmZuONXtOWiyTZRkxYSG4ZtVuJkGnTphTysfo262u16wVvVgTIiIiIiIiIvts5fh7bFoW/vKfYiZBJ4sYlPIhzRo9SviGJCIiIiLyutLqJqzYcRy/fe8gVuw4jlL204lsys9Nx7aF40zX547LwM5Fk3BRo2cSdLKK0/d8RE2jBuV1atQ1cXU9IiIiIiJv2lxYhiUFRZxq5KOYm8h3WcrxxyToZAuDUl6mFyVU1LegskEDbbupe0REvq6iXo1dx8+jtKIR/evKcPXgFPSKjfB2tYiIiLqltLoJSwqKDLldjT+kWy8XFxRhdEY8V3f0IgYM/Y8jSdB9AYOd3sGglBc1a/Uoa9SivlkP0UrkmIjIF+0ursTavSUQYOhf/Fh9Dp8ePocFEwZgYmayt6tHRETktM2FZTZ/QG8qLMPi6YO9UDP/4a4f9wwY+id7SdBn+UAwkcFO72FOKS8qqWxCXZOOASkPqahX470DZ7B6589478AZVNRzmCiRMyrq1Vi7twSyDEgyIKP1UgbWfFWCc/Ut3q4iERGR0zjVqHs2F5bhmlW7sfarEmwrOou1X5XgmlW78YEL8gaZAoYWMDeR77KVBH1FXo7XA4ntg51MxO55HCnlYe1X12vSiggPVXqxNsHDbFQHAAHAJ0VnOaqDyAm7i6tM76WOBAC7iitx5+V9PVwrIiIi1/CXqUa+yNGRTM6OpHI0YOjNaVjdvW97t/fXKWb5uekY1icG1728D4AhCfrsMf1cGpBytm04OtK7GJTyILVWz1U7vKD9qA7jx4zxcs1XJchKiUFqbLi3qkfkd6oaNRYDUoDhvVXVqPFkdYiICIBCwQkQzurYdv4w1cgRnghedGw7R37c90+McnqalCMBQ29Ow+rKfVt6z9q7vb9PMbOUBN0ZzrSdLcE0OtIXvysYlPKQerUWp2uaubqeF3BUB5FrJUWH2XxPJUWHebhGRBSoGjV6NGv0UGtF07aqBg0iVHqz/eyVBzpJlqBVhKGqUQOF4Hs/OHyZpbaLUinx1IwhWLbtmGHED2CadvTUjCGIVClR2eDbU9U/OXQWf9p+DAIEyJAhwPAD/akZQ3BDTm+X3IeltjtZ2Wjzx31R2QWs2XPS6kiqAYlRSI+PtHqf1wxOthkwvDS1Bx7e9IPTx++OM7XNNkeJtb9vS21n7/bhIQqHj9/dx/HJobOoqG9Br9hw3Di8N/p24bi2Po+7+1l9prYZHx/6BSd+qcegMz/ipuF90Dc+skttb0l8ZCgEK71bobXc2+/57j4vgOXXXWRYCKLDvBsWYlDKQ5q1Ipq1otXRBeQ+HNVB5FqTspLwSdFZi2UygKuzOCWWiFyjWaPHmdpmNGnafricqmnqlP6gRSfaLA90siTh5PkGyFFNEHzwLLgvs9Z2Q3vH4s+3ZePxgsMAgOuGpWLapalIjQ33+ZkPFfVqLNt+rPU3ufk8gee2HUNCVJhLZglYarsIle33XuVF2/3uDd+cxq/snCxeMGEA3viqxBR7UAiGR7dgwgB8cbzSoeNX1Kuxq7gKVRc1SOoRhquzkrq9gvA/D5xx6L4By21n7/ard55w+PjO2lVciTVfmac82fDNafx6wgBMcrB/Z+vzuDuf1WZ1k4Fvyprw7jdn8OsJA3DWTj5Te20zsm9PbPjmtMUyCcCovnFuf9/bek264nkBOr/ulAoBfeMjGZQicjeO6iByrV6xEVgwYQDWWOkQcjosEbmSMdmskdS6yEJ79soDnSTL0IsSRFmGIsgee3fZarukHm3fZ7dflo7wUGWXXlsV9WrsLq5CVaMGSdFhmOSCwIcjdh63PUvgy+OumSVgqe0mZibh40PWT1xFh4XYPFlceVFjt40nZCajX0IUlnxoCBhOH5aKqUMMAcPVO3+2e/ydxzvnmv34UPdzzVZetH0ivP1js9R29m5f26ztdtvZUlGvNvXtOqY8eeOrEmQ6mPLE1uexs5/VFusmt9VtRFrPbrVNSoztvm1yTLhbv1cs5T82viYzU3q45HkBLLzufOTLkkEpCngc1UHkehMzk5HRvkM4NAVTL+3FgBQREREcW2THXUErb84SsHfi6uyFFhwqv9Dtk8UpMW39jfzWgCFg/2R0RKjSoVyzzjw33T0Rbu/2cZEq1DVZDky54kS7oylPvBFstVe3ixp9twchdOrbtgt2upO9/MeTs5IDPhUNg1IU8Diqg8g92ncI80b1QWSYyou1ISIi8ixrP84dWWTn+LkGt60M7e1ZArZ+3FfUq916stjeyWih9Z+tH/i9YsOdem66eyLc3u1nXpaO5TuOOX18exwJZnprRXN7dbM3As/RtrEW7HQnewG34+cuBnwqGk46p6AwMTMZy2/NNl2fPiwVL+aPcOuHJxEREREFpt3FlVj0wSF8WnQW35TU4NOis1j0wSHs+anS9CPTEuOPeGPQSpJhdrnmqxKca82PU1GvxnsHzmD1zp/x3oEzqKh3bAWwSVlJ3f6B7ux9G3X8cW88CWw8WSy0ayCFAAiCa04W2zt+s856jl8ZhmTSjjw3zty3vcdm7/bZabFubTtjMNOSjqPMuto23WWvbn3jI93aNu5kL+BmWKjAskBJRcORUhQ0vBH5JiIiIiLrvJV3qTvsjYSyl9/m+LkGt43WAbo/S8Ddo2HcPU3K1vHPXmix2faNdqaB2Zsq1d3HZu/2rmg7a+85V4wyc9c0MkdGoaXGhntl+l132RvZODg1BhVWAn6BkoqGQSkiIiIiIvI4b00F6q7u5rcRINgdrfNJ0Vm7eY9scTZ44cjUQ1f8yHf3yWJrx7cX3OhhZxqYI1OluvvY7N2+O8e3956zFcw8VF7vs7nKjK9Je23ji0Fwe6/JG3N6IzMlOqBT0TAoRUREREREHuWp4Ic7dDe/zeDUHqioV7tttI6RM8GLriS73nX8PEorGtG/rgxXD07x+o97R3gqEbst3mo7R95z3Rll5vFcZV1cZMdXg+COBNz8dRSYozySU2rjxo2YPHkysrOzkZ+fj6KiIpv7f/bZZ5g+fTqys7Nx4403Ys+ePWblsizj5Zdfxrhx45CTk4M5c+bg1KlTbnwERERERJ7F/hMFMnt5l3YVV3qyOl3S3fw2M3J62QxaOTpap7t5nyxxNNn1og8OYdvhczhapcO2w+dM+bT8ga1cs67Ix2WLN9vO0fectXxg7m4bR3RcZMfRoEz7gJyn82E5wpH8x9ael0Dg9qDU9u3bsXz5cjz44IPYunUrBg8ejHnz5qGmpsbi/v/73/+waNEi3H777fjoo49wzTXX4MEHH8RPP/1k2ufNN9/Eu+++i2effRabN29GREQE5s2bB43G/zPPExEREbH/RIHOkeCHr3Lkx7mtH5n2Elqnx0faTWxsK9F6d3Q52TV868e9o7qTiN3ZYGCnwAg823bdfc+5O0m9O/lDEDyQg072uH363vr16zFz5kzk5eUBAJYuXYrdu3ejoKAA8+fP77T/hg0bMH78ePy///f/AACPPPIIvv76a/zjH//AH//4R8iyjA0bNuCBBx7AlClTAAAvvPACxo4diy+++AIzZsxwuG7NWj2USmtvTec0a/Xt/hahVBquq7UiWnQiNDrRVN7+b0vbXF3uzft2plyh8Gz9/Z0kidCKMlp0IhRcV7NL2HaWnWtowd6fq1DTqEVCtArjL0lCarsvTHe+Z33t88jd5Ww758sNbSejWauHKLn2O91IFD3/neHL/SfA9X0od/efAp0/fo/FRYbanAoUFxmKFjc/l+caWrD3p0qcOteIjJozGJ+ZbPN7ziguUoX7xvbH2/8tNdXfON3mvrH90bO17j0jQk23uSmnN8JClabHNKZ/AnrHhuOZj38EAEwdkoKrBycjJSYc5xpabOaYyUyJxqrPf7I6DSsjIQopMeFOvS+uHJhg874lSbb5vH1+7BzyL0t3+3vWnd81tp6bz388h/Vfn+o0Bey+sf0x7pJEm8f+4th5r7ado+85Z9vG+NruTh/FHmf7T+cbWmwG5M43tDj0XeNOnvie6/hdoRAM37Ptv4NdydH+kyDLsnt6cAC0Wi1GjBiB1atXmzpAALB48WI0NDTg9ddf73SbSZMmYc6cOZgzZ45p2+rVq/HFF1/g448/RllZGaZMmYKPPvoIQ4YMMe0ze/ZsDB48GE899ZTdeomiiB9++AGzt56HWu+2h09EREQBICJEwD9uTcGIESOgVLp/5VZf7T8B7EMRERGRYxztP7n1XEpdXR1EUURCQoLZ9oSEBFRXV1u8TXV1NRITE63uX1VVZdrm6DGJiIiI/AX7T0RERBQsgnr1vf1LJrnljKcoSjh69AiGDh0GpdIQ9ztfr8GZuia4b1xaYJAkCadOnUJGRgYUHh5/XlbbjGc/PQ4AmDYkGZOyEk1zezU6Eb957xAA4G93DkdYh9VLfKn81TuyEaEKdfr2XS33pcfOtnNNecH/fsGOo+dhaSaUQjCsdpI3qg8A775n/R3bznkd2y48VIkhqT0QGuKedhRFESeLj7nl2P7KHX0oS/2n6kYtTlc3QexmB8pXP29dUe7N7zFXlJ9vaMG+EzWobtQiMVqFcYMSzHKrdOf41vp2Xfme80Wuqr+9trXkfEMLfv/Rj1angP3plkvNnr+ucvfxu2PNV6X47nSdxd9zggCM7heHBRP6W729px5bd99z3uTIa9LZ/tO+EzX4+9enIQhtUy9lGZgzth/GDUqwd3OH6mer3Hj/ZlN+u3j/ljjyuvq5ssnifd93VT/MvqIfEqNVTt+/LY72n9walIqLi4NSqeyUlLOmpqbT2TyjxMTETmfs2u+flJRk2pacnGy2z+DBg7tUvx4RYW4KSokID1GgR4TKdPxGrYTIZhWDUnZIkgiVUkBkWCgUCvdPkTAyLhFq9MXxSnx+vNK0RGj7+coRYapOS+r6VLkqFJFhKuvlLr5/n3rsbDuXlNep9Tbn3dep9aZ28tZ7NhCw7ZzXse3CQxWIilAhLMQ97ejpnFK+3n8C3NOHstR/UutkRISFWvzx3RW++nnrinJvfo+5orx/kgr9k2Jgjb3bVzY2mf7+9Mh5TBmSgl6xETb7dl35nvNFUy5NxWdHz1sskwFMvbSXQ/W317aW9E9SYcFEw/L1xh/1xh/5CyYMsPlcOuKb0nOG41kJ/HxTegF3Xt63W/fhrNTYCAios/rjPzU2wma7u7vtjLr7nvMmR16Tzvafpg3thZy0OOwqrkRVowZJ0WG4Oiu5SwnFnf28q6hX4+/7T5u9dozfa3/ffxo5aXFOJza395754ng1dhVXWrzvt78+jeuze6N/Ug+n7tseR/tPbj01q1KpMHToUOzfv9+0TZIk7N+/HyNHjrR4mxEjRuCbb74x2/b1119jxIgRAIC0tDQkJSWZHbOxsRGHDh2yekwiW9qvhGHkj6uIELmKvZV3kqLDPFkdoqDD/hORY3YXV+LJrYdN13ccOYdFHxzCv374xWbfLjJU6dffc+1XQTOufubJVdAmZibjxfwRmJGdiqFJoZiR3Xn5emf58qqMjqy6aI87247sS40Nx52X98XCyZfgzsv7emyFO3eu/mfvPXP8XIPN+/74kOWFDTzJ7dP35s6di8WLF2PYsGHIycnBO++8A7Vajdtuuw0A8PjjjyMlJQWLFi0CANxzzz24++678fbbb2PixInYvn07jhw5gj/+8Y8AAEEQcM899+D1119Hv379kJaWhpdffhnJyclmyUCJHGX8kLB21mNXcSVuHem7Q7iJXG1SVpLNlXcc6XQRUfew/0Rkm7WTigDw/ndlptXwOjL2+bobXPC2iZnJyEqJ6daoj+5IjQ3HHaPTceKEBoMGpbtsxK/xxJi1586bAUNjMNA00gltr6euBAPd1Xbku9wZbLX3nhEg2LzvCh8YgOH2oNT111+P2tparF69GlVVVRgyZAjeeust03DyiooKs7mgo0aNwsqVK/HSSy/hxRdfREZGBl577TVkZmaa9rn//vuhVqvxzDPPoKGhAZdddhneeusthIX59lkN8k2+fEaGyBtc1ekiIuex/0Rkm62TigCsTvmUAah1ovn3XIdpVP7yPWcc9eGscw1tP0Y/+L7MNPXRm3z9xJi3g4GO8MXnNVA427buDLbae88MTu2Binq11fvu5QOvXY8kOp89ezZmz55tsezdd9/ttO26667DddddZ/V4giDg4YcfxsMPP+yyOlLw8uUzMkTe4g+dLqJAx/4TuYK//0C1Vn9bJxVtMfbtjN9zO4+fQ2lFDfr3SsDkwalB8z3XMefWjiPn8NmRc6Z8qt7iDyfGuhsMdCdffV4DQXfa1p3BVnvvmcyUHthpZXqgDOCm4b2dvm9XCerV94gA3z8jQ+QtvtzpIiIi+/z9B6qt+ts7qejI9LxgnUZla+rjmq9KkJUS49XgD0+MOcfXn1d/1t22dXew1d57xtrI0F9PGID0+Mhu3bcrMChFQc+RD4kWnWdXXiIiIiLX8ffRQs7w9x+o9ur/xPQhVk8qQgDuzE3H+4VlPjvaxpscyafq7ZNSPDHWdf7wvPorR9vW1neNu4Ottt4z1kaG9u7pG5+FDEoRgWdkiIiIApW/jxZylr//QLVX/yNn622eVJyYmYzL+yewb2cB86kGJj6v7uNI2zryXePNYKsvjwxlUIqoFc/IEBERBRZ/Hy3UHZ76gequUWiO1P/Oy/vaPKnIvp1lzKcamPi8uo+9to0IVQbtd40rKOzvQkRERETkf4yjbSwxjhbyd+2DQgX/+wUV9WoAbT+iLHHVD9TdxZV4cuth0/UdR85h0QeHsOen7rero/U3Bp4WTr4Ed17elz/8HDApK8mhnFvkX/i8uo+9thVa/1kSKN817sSgFBERERH5LKVCgEKAU/+q7Yy2qW7UmPY1snYsXyzf81OHoNDR81j0wSF89VMlJg+2/SPqmsHJDt13Zbug15bvy3C+QQ2FAJxvsDwKTZYNIwMqG1ocfmyW/nWl/t35pxQEhCgVUArOv8787V+fnhH49YQBEFqvt7/89YQB6N0znG3n5n/uaDtXPa/e/mfv88Ibrzt7bavWiQ5/1/jS607ZvsG9iNP3PEgBAdZfrkRERETUXmRYCPp2Y2WgS5Kj8W1JrcX+l6K1vH9iFNTatgVNMhKiEKEyz7Xhi+Vnapux9ivr00U+WHAlnp4xBMu2HYMAATJk0/STp2YMwZUDE+ze9yeHzuJP24+Zrn/WmiPlqRlDcLqm2WrfVgHgf2fq8ODVg/Dz+Yum7f85eh63jurj0HPaPzHKofp3lyRLEJpi0C8pCgoheM7X90/sjylDUvDxobOoqG9Br9hw3DS8d5dW4grWtnMFd7WdK55Xb7L3WQh473Vnq21f23XCoe8ab7PUdpFh3g8Jeb8GQSIxKgwtOhE1jVroRAamiMj7gnE1KiLyL9FhIYjuRod5zlX98e43py2WyQDmXtUfyTHhaNbqTduTYsIQqTK/T18sX//1KQiCALOoVCtBEPDF8Uosnj4YkwenYFNhGcrr1EiLi8Cs3HRktPtxZO2+S6ub8Kftx0yBLqAt6LVs2zFMykqGbOVkqwygtlmH3T9VYUlBkWn7+9+V4b3vzmBFXg7yc9Mt3ra9+8YNsFv/7hJFERWSIR+VUuk7iX89ITkmHJdlxDt9+2Buu+5yZ9t193n1JnufhYB3X3fW2tbR7xpv89X3LINSHhIaokBGQhSiVCH45YIaLTrJ21UioiAWrKtREVFw6Z8YhRV5OVhcUARBECDLsulyRV6Ow8GNUzVNpr9f/Pwn3DWmn9fPepfXqSFbCEgBgCzLKK8z5JbKSIzC4umDu3z8zYVlNoNedc1am+XRYUosKSgyC2qJrfsuLijC6Ix4h9rf2fp3hSSxX+4stp3z2HadOfpZ62tt56rvGk/wtbYDGJTyKEEQkBwTjgiVEmW1zbjYojf7oiYi8oRgXo2KiIJPfm46RmfEOz3aZnNhmdlon/X7TuHtfaUOj/Zxl7S4CJtBobS47o18tRf06hmpslkuQLBZv02FZW4PNhGR//DVz1pHdfe7JpgxKOUFPcJDMSg5GuV1atQ0aaHndD4i8iDjalTWlrXdVVzJJbSJKKA4O9qmtLrJJaN93GFmbjrW7DlpsUyWZczq5o84e0Gvwak9cP2wVKsjA776udqhkVxERL78WdsVnhjZGYiYkc5LVCFK9E+MQt+4SISH8mnwdR1z7xiXWybyR1V2VqOqatR4sjpERD7LNIXNAuNoH28xThcxrqCkAEwrKnVnamJpteH6zNx0m0GlWbnpyM9Nx85FkzB/wgDMyOmN+RMGYOeiScjPTW8LalngipFcRBQ4fPmzltyPI6W8SBAEpMSGI1KlRFkdp/P5KubeoUCTFB1mc6RUUnSYh2tEROSbHM3b5C3G6SLvHziNw6UVyO7fC3dc3s9lUxMdyZFibWSAu0dyEVHg8PXPWnIvBqV8QI+IUAwMjcYvdWrUNGqhZ2TKZzD3DgWiSVlJ+KTorMUyGcDVWQy2EhEB7s/b5AoZiVH4v2uz8MMPaowYkeXwikqOTJfpTo4Uf0r8S0Te5Q+fteQ+DEr5iLDW6XyRKiUqLrSgRe97WfGDEXPvUCDqFRuBBRMGYM1XJabXt/FywYQBDLQSEbUK5NE+9lbXMyYi706OFCb+JSJHBPJnLdnHoJQPEQQBqbERiFSFmKbzWRnFSB7C3DsUqCZmJiMrJQa7iitR1ahBUnQYrs5KZkCKiKidQB7t46npMkz8S0T2BPJnLdnHoJQPiokIxaDQaJTXqlHbxOl83sTcOxTIUmPDOdKPiAidk33fNaYf+rf+CArU0T6cLkNEviRQP2vJPgalfJRxOl9UGKfzeRNz7xAREQU2e8m+gcAc7cPpMkTkawLxs5bsU3i7AmSdQmGYzjcwORoxESGwskomuZEx944gAAoBZpfMvUNEROTfrCX7lmRDsu9T1U3Wb9wFHUdilbrouN1hnC6jEAClQjC75HQZIiLyFI6U8gOczuddzL1DREQUmBxN9t3d+7A3EsseW9MLu4PTZYiIyNsYlPITnM7nXcy9Q0REFHjcnezb2kgswDASa3RGPDISo2wGnVwR1LKF02WIiMibOH3Pj7SfzhcbEQKlgvP5iIiIiJxlSvZtgSuSfZtGYlk5/qbCMmwuLMMNq/eZtq/fdwrXrNqNDwrLPDa9kIiIyFsYlPJDMRGhuCSlB9LjIxCpUjLXFBFZdK6hxfT3B9+XoaLeNct7ExEFipm56TZHSnU32be9kVjHz120GXR686sSu0EtIiIif8aglJ8KVSrQKzYCWanRSI0NR1gIn0oiarO7uBJPbj1sur7jyDks+uAQ9vxU6cVaERH5Fncn+7Y3EutCs9Zm+YFTtW6dXkhERORtzCnl58JDQ5CREIKESBUqGlrQoNZBJzIROlEwq6hXY+3eErO8vcaz8Gu+KkFWSgwT9RMRtXJnsu+ZuelYs+ekxTJZlhEXqbIZdAJgMxF7d6cXEhEReRuH1wSIHhGhuCQ5GhmJUYhhvimioLa7uArWPgEEALuKOVqKiKg9Y7LvV+4cicXTB7ts9Tl7I7GyUnvYHCk1OiPOrdMLiYiIvI0jpQKIIAhIjA5Dz4hQVDdqUNOkRbNGhF7iyCl365i7Z8qQFPSK5dlL8o6qRg2svevl1nIiIvIMWyOxSqubbI6kWjBhIEb1jcPigiIIggBZlk2XrpheSERE5G0MSgWgEKUCqbERSIwOwwW1DlUXNWjS6Dmtz012F1di7d4S0/UdR87hsyPnsGDCAEzMTPZizShYJUWHQQAsBqaE1nIiIvIc40isjowjqWwFnTISo9w2vZCIiMjbGJQKYCFKBRKjw5AQpUK9WofqRg0a1Hpo9ZLVURTUNczdQ75oUlYSPik6a7FMBnB1FoOlRES+wpGcVtaCWkRERP7OrUGpCxcu4LnnnsOuXbugUCgwbdo0/P73v0dUlOUzOxcuXMArr7yCffv2oaKiAvHx8ZgyZQoefvhh9OjRw7RfVlZWp9u++OKLmDFjhtseiz8TBAE9I1XoGalCY4se1U0a1DVpoRNliJza1y3G3D3WRqTsKq7EnZf39XCtKNj1io3AggkDsOarEtPr03i5YMIABkqJfBz7T8GHQSciIgpWbg1KPfbYY6iqqsL69euh0+nw5JNP4plnnsGqVass7l9ZWYnKykosXrwYgwYNwi+//IJnn30WlZWVWL16tdm+y5cvx/jx403XY2Ji3PlQAkZ0eAiiw0PQKzYc9c061DVr0aQRoRMlMD7VdczdQ75qYmYyslJisKu4ElWNGiRFh+HqrGQGpIj8APtPREREFCzcFpQ6efIk9u7diy1btiA7OxsA8NRTT2H+/Pl4/PHHkZKS0uk2mZmZeOWVV0zX+/bti0ceeQT/93//B71ej5CQturGxMQgKSnJXdUPeGEhSiTHKJEcE44mjR4X1DpcaNZCrRWZe6oLmLuHfFlqbDhH6hH5GfafiIiIKJgo3HXggwcPIiYmxtShAoCxY8dCoVCgqKjI4eM0NjYiOjrarEMFAEuXLsWYMWNw++23Y8uWLVaXyyX7osJC0KdnBIakxmBQcjR69wxHlEoJlVIBhbV15QmAIXePrZFSzN1DRERdwf4TERERBRO3jZSqrq5GfHy8+Z2FhCA2NhZVVVUOHaO2thZ/+9vfMGvWLLPtCxcuxBVXXIGIiAjs27cPS5cuRXNzM+65554u1VEUxS7t39Xjuuv47tQjTIkeYUr0jglDk05EU4seDS16NGv10IsS9G6e4ydJktmlr0vpocL94/rjzX2lnXL33D+uP5J7hEKSREhS22vBcN38OK4tl8yuu/745uWefWxsu66Ue4K/vWd9CdvOeR3bTpIAWZQguulEiie/z/2h/wS4p038uf/Uvs6iKEJ014vRzv37Y9t5G9vOeWw757HtnMe2c56n287R++lyUGrlypV48803be6zffv2rh62k8bGRixYsAADBw7EQw89ZFb24IMPmv6+9NJLoVarsW7dui53qg4fPtztenrz+J6gVCoREhKCUEEJSQT0Ogn1aj20Oh20ehF6veiWlfxKSkrccFT36KMAfjs6Bv+r0OJCi4ie4UqM6qVCguICTpy4AADQtpsSWVJSApXSvLPqyvJTp0659fgdyz352Nh2XSv3JH96z/oatp3zjG3XIzIcoU3h0LWovVwj6wKp/wS4t4/jj/2nFn1bcLmoqAjhIW6bjGCTP7adr2DbOY9t5zy2nfPYds7ztbbrclDqvvvuw6233mpzn/T0dCQmJqK2ttZsu16vR319vd1cBo2Njfh//+//ISoqCq+99hpCQ0Nt7j98+HD87W9/g1arhUqlcuyBAMjOzoZSqXR4f0eJoojDhw+77fjeJkkyWnQi1DoJap0ejRoRLToRoiRDlLqXMF2SJJSUlGDAgAFQKLzToXPGIABjsq2Xt+hEYN//AAADBgxAeKjSbeUZGRmIDAt1+vZdLffkY2Pbda3cE/z1PesL2HbO69h24aFKZPaKQaibAgHG7/XuCKT+E+CePpQ/95+atXpg6xcAgJycHESq3LqWUCf+3HbexrZzHtvOeWw757HtnOfptnO0/9Tlb8z4+PhOw8otGTlyJBoaGnDkyBEMGzYMAPDNN99AkiTk5ORYvV1jYyPmzZsHlUqF119/HWFh9hNFHzt2DLGxsV3uUCmVSrc+Ge4+vrcolUBoaAh6tNum1YtQ60SotSKatYa/tXoJkgRIstz6z/H7UCgUUCgCp+3a/95UKJSdHptryzu3nTvv37OPjW3XlXJPCrT3rCex7ZxnbDuFQgFBqfDp79xA6j8B7u3j+GP/SamU2/3tvfr7Y9v5Crad89h2zmPbOY9t5zxfazu3ncYZOHAgxo8fj6effhpLly6FTqfDc889hxkzZphWjjl//jzuvfdevPDCC8jJyUFjYyPuu+8+qNVq/OUvf0FjYyMaGxsBGDpzSqUSO3fuRE1NDYYPH46wsDD897//xZo1a3Dfffe566GQA1QhSqhClIiNaNum00vQiBK0egkavQiNToJaZ7iUZBmyDPNL71WfyC+da2gx/f3B92WYMiQFvdq/CYnI77D/RERERMHErWOLV65cieeeew733nsvFAoFpk2bhqeeespUrtPpUFpaCrXakAPi6NGjOHToEABg6tSpZsf68ssvkZaWhpCQEGzcuBHPP/88AMOyx0uWLMHMmTPd+VDICaEhCsNUig4na0VJhk6UoBdl6CQJelGCTjRsa9GKaOgZjaiwEAAKyDAErWQZbX8DptWC5Nb/MaBFwWZ3cSXW7m3LRbTjyDl8duQcFkwYgImZXPWRyJ+x/0RERETBwq1BqZ49e2LVqlVWy9PS0lBcXGy6PmbMGLPrlkyYMAETJkxwWR3J85QKAUqFErCQ6kIURehqFBjSJxYyBIiyDEmSIUqAKEutlzJkSTaNrpJkGK7DkO9KMgas2gWygLZgVscAVtdXw5Y7BcNM99GuTpDb7912J2EhCqiUrQG31uMoFd5LUE3+p6JejbV7S8xeu8bpsWu+KkFWSgxSY8O9Uzki6jb2n/zPqZom098vfv4T7hrTD/0To7xYIyIiIv/g2SyMRA7QaDQAgBClwm9foHKHaYlNGp2pLDM1GuGhIZAl2RB0k4Emjd5Unh4XidAQod1IMhntQ1ahSgEhCqHLeboocOwuroIAyyMEBQC7iitx5+V9PVwrIqLgtLmwDEsKikzX1+87hbf3lWJFXg7yc9O9WDMiIiLf56+/+Yl8miAIEARA0RpOUoW0JZKLVIV0WpUnPLQtY3XvuPBO5Y0tbUGtQcnRUCmV0IkSdKIErSihoV25KkTBoFWAq2rUWJ2yKreWExGR+5VWN2FJQZHZ963YOox1cUERRmfEI4MjpoiIiKziGtREHtBxWH9pdZONvTtTtJveFxMRivhoFVJiw5EWH4kBSdHITGlbC/GS5B4YmByNfgmR6N0zHPFRoYhUtQXFOFXQ/yVFh8Hasyi0lhMRkfttLiyDIFj+RBYEAZsKyzxcIyIiIv/CoBSRm20uLMMNq/eZrq/fdwrXrNqND9zUUY0KUyI+SoXU2Aj0S4hCVmoMhvWJMZVnJEYhPS4CidEqRKqUCAtRIFTJQJU/mZSVZHOk1NVZTHROROQJ5XVq0+IrHcmyjPI6tYdrRERE5F84fY/IjXxlWH/7s7iGYJThrS/LMjR6EXXNbdP/ekaGABAgSjJEiVMAfVGv2AgsmDAAa74qMeWWMl4umDCASc6JiDwkLS7C8B1rITAlCALS4iK8UCsiIiL/waAUkRuZhvVb6axuKizD4umDvVCztjqEh4Ygtl2fOSs1BiEKAS06CS06EWqdiNqmthxFnP3nGyZmJiMrJQa7iitR1ahBUnQYrs5KZkCKiMiDZuamY82ekxbLZFnGLCY6JyIisolBKSI38tdh/aoQJVQhSsREhAIAknq05SjqFx8FWZDRqBGh00vQi5K3qhn0UmPDucoeEZEX9U+Mwoq8HCwuKIIgCJBl2XS5Ii+HSc6JiIjsYFCKyI0CcVh/cmwYIlUhkGUZLToRtU1tU/8iQpUIVSqglyRO+yMioqCQn5uO0Rnx2FRYhvI6NdLiIjArN50BKSIiIgcwKEXkRoE8rF8QBESoQhDXbtuQ3j2ggIBmrYgmrR6NGr3VkWJERESBIiMxyqvT8YmIiPwVV98jciPjsH6FACgVgtllIA7rD1Uq0CMiFCmx4RiQFI1hvWORmRJtKu8ZGYLwEAVCFAKYmoqIiIiIiCi4caQUkZsF87B+hUJAZFjbx0xWagwUggC1VkSzVkSjRofaJo6kIiIiIiIiCkYMShF5AIf1twkPVSI8VIm4KACIQH2z1lSW1CMMoiRDL8oQJRlikE/9O9fQYvr7g+/LMGVICnrF+l8eMiIiIiIiIksYlCIirwoNaZtFPCApCmEhytaRVHo0a0VUN2q8WDvv2V1cibV7S0zXdxw5h8+OnMOCCQMwMTPZizUjIiIiIiJyDQaliMinKBUCosNDEB1u+HhK6qEylaX1jIAIGWqtaBhJFaBL/FXUq7F2b4nZoo3Gh7rmqxJkpcQgNTbcO5UjIiIiIiJyEQaliMinCUJbSvQ+8RGIVIVAp5fQrNVDrRNR09Q2/c+YQN3fQ1W7i6usPg4BwK7iStx5eV8P14qIiIiIiMi1GJQiIr8TGqJAbIgKsQBiIkJN2wckRgOCjCatiGaNYTSVTpS8V1EnVTVqrAbW5NZyIiIiIiIif8egFBEFjLjoUESqDB9relFCi05CbVNbACc8VAGVUgFR9u2pf0nRYTZHSiVFh3m4RkRERERERK6nsL8LEbnbqZom098vfv4TSqubbOxNjghRKhAdHoLEHm0BnEt7xSAzNRr9k6KQFheB+KhQRIQqTeXtZgp61aSsJJsjpa7OYqJzIiIiIiLyfwxKEXnZ5sIy3LB6n+n6+n2ncM2q3figsMyLtQpMoSEK9AgPRVJ0GNLjI5GVGoNLe/cwlafHRSIlJgwxESGmUVVKhecjVb1iI7BgwgAIAqAQYHa5YMIAJjknIiIiIqKAwOl7RF5UWt2EJQVFaD+TTGxdcm1xQRFGZ8QjIzHKS7ULDiHKtth8amw4ekQYRlZp9SI0egkXmnWm8piIECgEAZIMSLIMyY1TACdmJiMrJQa7iitR1ahBUnQYrs5KZkCKiIiIiIgCBoNSRF60ubDMsLqc3Dm4IQgCNhWWYfH0wV6oGalClFCFKM1GSg3pFYNQpQI6vQSNKEGnl1DfLmgVEaqEKkQBSZYhtwauuiM1Npyr7BERERERUcBiUIrIi8rr1JCtBC5kWUZ5ndrDNSJ7QpUKhCoViGy9Hh3e9jF6ae8eUIUooNPL0EkydHoJDeq2oFV0mBKhSqUpaCVKbSsD+kg6KyIiIiIiIo9hUIrIi9LiImyOlEqLi/BCrchZIUoFwkNDEB7atq1HRNvH7NA+sVApFdBLMvSSjIstbQGrtPhIhIUooJckiK3letFwKcsyYPjPkABdBmTIppdN+23tr7e7ICIiIiIi8jkMShF50czcdKzZc9JimSzLmJWb7uEakbuFKBUIaV3wr30O9d49wxGp6vyRLMuGwJQxh5Upn5Vs+Ns4TVBuN2VQhmG74Z/cFsxqDWTJ7QNY7aJW7YNZbdtsh7VEUYY6IQaJ0eFQKtseUMc4q9z6WNr+bncPMkyPy1g/s/u2EJBrfx/Gx9MauyMiIiIiIj/BoBSRF/VPjMKKvBwsLiiCIAiQZdl0uSIvh0nOA9ypmibT3y9+/hPuGtMP/Ts854IgIFTpu5P7RFGEtlrGoOQoKJVKlxxTNgXcWgNWkE3bJBmQJRkSWveRABEyJFGG2Bq408syREluHXEmQaeXzQJeMtoF8BjIIiIiIiLyGgaliLwsPzcdozPisamwDOV1aqTFRWBWbjoDUgFuc2EZlhQUma6v33cKb+8rxYq8HOT72Qg5rVbr0uMJggBDHM51wTidKEGUJOglQy4vUQT0kgS9JEMnStCJErR6CVp9+wCY+egzIiIiIiJyLQaliHxARmIUV9kLIqXVTVhSUASpXaRDbJ2LtrigCKMz4hmUdDFjgnp7JEmGTpKgE2XojcEqUYZWL6JFZwhciZJsFrCSGLEiIiIiInIKg1JERB62ubDMZoL7TYVlDFJ6iUIhIEyhRJiVb0dJkqEVJej0hsCVRhSh0UlQ6wyXktw2jZDBKiIiIiIi2xiUIiLysPI6tSnpd0eyLKO8Tu3hGpGjFAoB4QolwkM7588SJRkavQitXoJGJ0Gjl9CiF6HWioYcVwxWERERERGZYVCKiMjD0uIibI6USouL8EKtqLuUCgGRqhBEqsy3S63BKo1eQovOcNmsFaHWAFHhKoQohHYrJBIRERERBQ/7CTa64cKFC1i0aBFGjRqF3NxcPPnkk2hqarJ5m7vvvhtZWVlm/5555hmzfc6ePYv58+dj+PDhuPLKK7FixQro9Xp3PhQir+q4Sltpte33Efm2mbnpNkdKzfKzROdkm0IhIEIVgp6RKqTGRqBfQhSG9IrBsD4xGJQchQFJ0UhPiERyjzBEhSkRFqJAiFKAwncXXSQ3Y/+JiIiIgoVbR0o99thjqKqqwvr166HT6fDkk0/imWeewapVq2zebubMmVi4cKHpekRE26gBURSxYMECJCYm4v3330dlZSUWL16M0NBQPProo257LETeEkirtJFB/8QorMjLweKCIgiCAFmWTZcr8nKY5DxIKBUKiC3NiI9SQak0TAeUZcOoqhadYVSVWieiWSuaEqyLnP4XFNh/IiIiomDhtqDUyZMnsXfvXmzZsgXZ2dkAgKeeegrz58/H448/jpSUFKu3DQ8PR1JSksWyffv24cSJE1i/fj0SExMxZMgQPPzww1i5ciUeeughqFQqi7cj8kdcpS1w5eemY3RGPDYVlqG8To20uAjMyk3n8xnkBEFAeGgIwkPNtxsCVSJatBLUehHNGj00DFQFJPafiIiIKJi4LSh18OBBxMTEmDpUADB27FgoFAoUFRVh6tSpVm/7ySef4OOPP0ZSUhKuvvpq/OY3vzGd7fvhhx+QmZmJxMRE0/7jxo3Ds88+ixMnTuDSSy91uI6iKDrxyBw/rruOH8jYduY2HTgNAQIsZZsRIOD9A6fxf9dmmbWXKEqd2s+8XIQoCi4rd+exPV/u2bZLjwvHY1Mvsbq/P+B71nldabsQAYhWKRGtUgIwRKx0omwIVLWOqGrS6M1GVIlWpogGAkmSOlwCsihBdNOUR0++vv2h/wS4p034eeI8tp3z2HbOY9s5j23nPLad8zzddo7ej9uCUtXV1YiPjze/s5AQxMbGoqqqyurtbrjhBvTu3RvJyckoLi7GypUrUVpaildffdV03PYdKgCm67aOa8nhw4e7tH9Xufv4gYxtZ3C49AIkG7mHDpdW4Icf1GjRS6btR48eQXiIebq49uVFRUUuLXfnsT1d7um2CyR8zzqvu22nUCigVCqhUCgQKiggyIrWAKuMJo2IFp0eOr0InV6EXpTsH9CPlJSUAAB6RIYjtCkcuhb/X7nSH/pPgHvf8/w8cR7bznlsO+ex7ZzHtnMe2855vtZ2XQ5KrVy5Em+++abNfbZv3+50hWbNmmX6OysrC0lJSZgzZw7OnDmDvn37On1cS7Kzs015PFxJFEUcPnzYbccPZGw7c9nni/FN+SmLIx4EQUB2/14YMSILzVo9sPULAMDQocPQI8J8Gkb78pycHESqQlxW7s5je7rc020XCPiedZ6n2q79iKoWnYjGABhRJUkSSkpKMGDAACgUCoSHKpHZKwahbgr6Gp+r7gik/hPgnj4UP0+cx7ZzHtvOeWw757HtnMe2c56n287R/lOXfx3dd999uPXWW23uk56ejsTERNTW1ppt1+v1qK+vt5rvwJLhw4cDAE6fPo2+ffsiMTERRUVFZvtUV1cDQJeOCwBKpdKtT4a7jx/I2HYGsy7vh7V7Sy2WyZBxx+X9Wtuq7UelUqno1Hbm5Z3btjvl7jy258s923aBJJAfm7u5/7sICO8QDNW2S6beopPQrNOjRSuZglSSJFuYNOx7FAoFFArDKDHBwvvXlwRS/wlw7+uWnyfOY9s5j23nPLad89h2zmPbOc/X2q7LQan4+PhOw8otGTlyJBoaGnDkyBEMGzYMAPDNN99AkiTk5OQ4fH/Hjh0D0NZhGjFiBN544w3U1NQgISEBAPD1118jOjoagwYN6urDIfJpXKXN/52qaVvG/cXPf8JdY/qhP5838jJViBKqECViItoyqutECRqdhBa9CE3rqn/NWrE1kToTqncX+09EREREnbktucnAgQMxfvx4PP300ygqKsL333+P5557DjNmzDCtHHP+/HlMnz7ddObuzJkzeO2113DkyBGUl5fjyy+/xOLFizF69GgMHjwYgCEp56BBg/D444/j+PHj2Lt3L1566SXcddddXDmGAlJ+bjp2LpqE+RMGYEZOb8yfMAA7F01Cfm66t6tGdmwuLMMNq/eZrq/fdwrXrNqNDwrLvFgrIstClQpEh4cgMToMfeIicUlKD2T3icWQXj0wKDkaGQlR6N0zHD0jQxERqoQqRIEQhQCFm5KLByv2n4iIiCiYuDW5ycqVK/Hcc8/h3nvvhUKhwLRp0/DUU0+ZynU6HUpLS6FWGxKThoaGYv/+/diwYQOam5vRq1cvTJs2Db/5zW9Mt1EqlXjjjTfw7LPPYtasWYiIiMCtt96KhQsXuvOhEHlVRmIUFk8f7O1qUBeUVjdhSUGR2cgSY+6exQVFGJ0Rz5Fu5PMUCgERqhB0SLUGvShBo5eg1UvQ6EW06FunAmolw6iq1imAHFnlHPafiIiIKFi4NSjVs2dPrFq1ymp5WloaiouLTdd79eqFf/zjH3aP26dPH7vJQomIvGlzYRkEQQCsJKnfVFjGQCP5rRClAiFKBaLCzLeLkgyNXoS2NWBlCFoZpgRq9RIkGZAYsLKL/SciIiIKFoG1DBQRkY8or1NDtrKqmSzLKK/z/6XriTpSKgREqkIQaWE2mE6UoNNL0IqGYJWudbSV8Z/cGqiSZEMOK1mGXyRbJyIiIiLnMShFFADaJ9N+6YsTmH1lBpNpe1laXITNkVJpcRFeqBWR94QqFQhVKhBpoUySZOgkCTq9bAheiRJ0omEaoF40rAqol4x/A6IEREeEQRWigCAIUAhMbEVERETkjxiUIvJzmwvLsKSgbZnvv399Guu/PoUVeTlMhu5FM3PTsWbPSYtlsixjFp8bIhOFQkCYQokwB3olkiRDq9cjpCkCA1J7AFBAL8sIUbht7RYiIiIichP24Ij8mLVk2pJsSKZ9qrrJ+o3JrfonRmFFXg4UgmFKU/vLFXk5THJO5CSFQkCoUgF9ixqRqhD0iAhFXKQKSi4DSEREROR3OFKKyI8xmbZvy89Nx+iMeGwqLEN5nRppcRGYlZvOgBQREREREREYlCLya0ym7fsyEqMYGCQiIiIiIrKA0/eI/JgpmbYFTKbtGe2TzL/4+U8o5ZRJIiIiIiIihzAoReTHZuam2xwpxWTa7rW5sAw3rN5nur5+3ylcs2o3Pigs82KtiIiIiIiI/AODUkR+rFMybQBKgcm0PYFJ5omIiIiIiLqHOaWI/Jwxmfb7B07jcGkFsvv3wh2X92NAys2YZJ6IiIiIiKh7GJQiCgAZiVH4v2uz8MMPaowYkQWlUuntKgU8JpknIiIiIiLqHk7fIwoCTMbtekwyT0RERERE1D0MShEFOCbjdg8mmSciIiIiIuoeBqWIAhiTcbtPpyTz7S6ZZJ6IiIiIiMg+5pQiCmBMxu1exiTzmwrLUF6nRlpcBGblpjMgRURERERE5AAGpYgCGJNxu19GYhQDe0RERERERE7g9D2iAMZk3N3HJPFERERERETuwaAUUQBjMu7uYZJ4IiIiIiIi92FQiiiAMRm385gknoiIiIiIyL2YU4oowDEZt3OYJJ6IiIiIiMi9GJQiCgL2knF3zJt015h+6B/kQSsmiSciIiIiInIvTt8jCnLMm2QZk8QTERERERG5F4NSREGMeZOsY5J4IiIiIiIi92JQiiiImfImWWDMmxSsmCSeiIiIiIjIvZhTiiiIMW+SeT6tl744gdlXZpjyaTFJPBERERERkfswKEUUxEx5k6ysMBfoeZM2F5ZhSUGR6frfvz6N9V+fwoq8HOS3Ts+zlySeiIiIiIiInMPpe0RBzNG8SR1X5ysNgFxTzKdFRERERETkXQxKEQUxR/ImBerqfMynRURERERE5F2cvkcU5GzlTbI2mggwjCYanRGP5JgwL9W8e5hPi4iIiIiIyLvcGpS6cOECnnvuOezatQsKhQLTpk3D73//e0RFWU4SXF5ejmuuucZi2UsvvYTrrrsOAJCVldWp/MUXX8SMGTNcV3miIGItb5JpNJGVnFObCsvw28mDPFFFp3WcenjXmH7onxgV9Pm0iMh3sf9EREREwcKtQanHHnsMVVVVWL9+PXQ6HZ588kk888wzWLVqlcX9e/XqhX379plt27RpE9atW4cJEyaYbV++fDnGjx9vuh4TE+P6B0AU5Px9NFHHRObr953C2/tKsSIvBzNz07Fmz0mLt2ufT4uIyNPYfyIiIqJg4bacUidPnsTevXuxbNkyDB8+HLm5uXjqqaewbds2nD9/3uJtlEolkpKSzP598cUXuO666zqdHYyJiTHbLyzMP6cQEfky02giC4yjiXw1Cbq9ROYCYJ5PC4BSMM+nRUTkaew/ERERUTBxW1Dq4MGDiImJQXZ2tmnb2LFjoVAoUFRUZOOWbY4cOYJjx47h9ttv71S2dOlSjBkzBrfffju2bNlidTQHETnP3up8MWEhPpsE3ZFE5vm56di5aBLuH5eBK9PDcf/4DOxcNAn5HCVFRF7C/hMREREFE7dN36uurkZ8fLz5nYWEIDY2FlVVVQ4dY8uWLRg4cCBGjRpltn3hwoW44oorEBERgX379mHp0qVobm7GPffc06U6iqLYpf27elx3HT+Qse2c54626xsXjuW3DcMTHx6BIAiQZRkCBMiQsWjqJfjLf4qtJkEf1TcWGQlRZvURRRGiaB4ocld5WW2TzYBaWW0TRFFEelw4Hp0yCIcPq5GdPQhKpZKvvy7ge9Z5bDvnebrtPPkc+UP/CXBPm/A94Ty2nfPYds5j2zmPbec8tp3zfLX/1OWg1MqVK/Hmm2/a3Gf79u1dPWwnLS0t+PTTT/Gb3/ymU9mDDz5o+vvSSy+FWq3GunXrutypOnz4cLfr6c3jBzK2nfNc3XaDFMDq6Yn4slSNyiYRyVFKXNM/Al+WnrV5u1e3/w+zs3ugRS+ZthUVFSE8xHyAZnfLT13Qmv5+8r2vMW1AJHr3CEGo9qLN+oVqL+KHH34w28bXnfPYds5j2znPn9oukPpPgHvb3p+eV1/DtnMe2855bDvnse2cx7Zznq+1XZeDUvfddx9uvfVWm/ukp6cjMTERtbW1Ztv1ej3q6+uRlJRk93527NiBlpYW3HLLLXb3HT58OP72t79Bq9VCpVLZ3d8oOzsbSqXS4f0dJYoiDh8+7LbjBzK2nfPc2XYjAFw3znzbjl9+AGA9f5RO1QMjRozAsYoGAJUAgJ2Vkbjz8nT0b5evqVmrB7Z+AQDIyclBpMr8Y8nW7bd8X44nvjhi2nf7z2ps+7kZy28bhgevuwT/Kt5rtX4PXT8KGQmG4/B15zy2nfPYds7zdNsZ7687Aqn/BLinD8X3hPPYds5j2zmPbec8tp3z2HbO89X+U5eDUvHx8Z2GlVsycuRINDQ04MiRIxg2bBgA4JtvvoEkScjJybF7+4KCAkyePNmh+zp27BhiY2O73KFSKpVufTLcffxAxrZznqfaLj0+ypCzycIUOUEQkB4fhYKDZ81Wv/v716ex/utTWJGXY8rbVHah0VT+8s6TuGtMP1PQqePqee1vn5sRjye2HrE4ffCJD49g56JJWJGXY0hqbpx62Hq5Ii8HA5M7rzjF153z2HbOY9s5z5/aLpD6T4B7296fnldfw7ZzHtvOeWw757HtnMe2c56vtZ3bEp0PHDgQ48ePx9NPP42ioiJ8//33eO655zBjxgykpKQAAM6fP4/p06d3Stx5+vRpfPfddxYTdO7cuRMffPABfvrpJ5w+fRr//Oc/sWbNGsyePdtdD4WILLCXBP2qgQk2V787Vd2EzYVlVhOl21s9782vShxOZD5/wgDMyOmN+RMGMJE5Efk09p+IiIgomLgt0TlgyJ/w3HPP4d5774VCocC0adPw1FNPmcp1Oh1KS0uhVqvNbldQUIDU1FSMGzeu4yEREhKCjRs34vnnnwcA9O3bF0uWLMHMmTPd+VCIqIP+iVE2RyL992SNzZFUa746iU3flVlNlH7H6L42b3/gVK3NoFh5neFzJSMxCounD3bBIyYi8gz2n4iIiChYuDUo1bNnT6xatcpqeVpaGoqLizttf/TRR/Hoo49avM2ECRMwYcIEl9WRiJyXn5uO0Rnx2FRYhvI6NdLiIjArNx0ZiVH47XsHbQaNvjtV162gk3E/a7dPi4voxiMjIvIe9p+IiIgoWLg1KEVEgc/aSKS0uAibQSMA3Qo6jc6IQ0lVY6cy4+1ncYoeERERERGRT3NbTikiCm72ck5dnhFvMyfU6Iw4m7dfMGEgVuTlQCEASoVgdrkiLwcZ7Vb4IyIiIiIiIt/DkVJE5Bb2ck7lZsTj/e/OWLytMeg0qm+c1dtnJEYhIzHK6vRBIiIiIiIi8m0MShGR29jKOQXAZtDK0aATE5kTERERERH5JwaliMitbAWN7AWt7N2eiIiIiIiI/BeDUkTkVQw6ERERERERBScmOiciIiIiIiIiIo9jUIqIiIiIiIiIiDyOQSkiIiIiIiIiIvI4BqWIiIiIiIiIiMjjGJQiIiIiIiIiIiKPY1CKiIiIiIiIiIg8jkEpIiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij2NQioiIiIiIiIiIPI5BKSIiIiIiIiIi8jgGpYiIiIiIiIiIyOMYlCIiIiIiIiIiIo9jUIqIiIiIiIiIiDyOQSkiIiIiIiIiIvI4BqWIiIiIiIiIiMjjGJQiIiIiIiIiIiKPY1CKiIiIiIiIiIg8jkEpIiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij2NQioiIiIiIiIiIPI5BKSIiIiIiIiIi8jgGpYiIiIiIiIiIyOPcFpR6/fXXcccdd2D48OHIzc116DayLOPll1/GuHHjkJOTgzlz5uDUqVNm+1y4cAGLFi3CqFGjkJubiyeffBJNTU1ueAREREREnsc+FBEREQULtwWldDodpk+fjjvvvNPh27z55pt499138eyzz2Lz5s2IiIjAvHnzoNFoTPs89thjOHHiBNavX4833ngDhYWFeOaZZ9zxEIiIiIg8jn0oIiIiChZuC0otXLgQc+bMQWZmpkP7y7KMDRs24IEHHsCUKVMwePBgvPDCC6isrMQXX3wBADh58iT27t2LZcuWmc4ePvXUU9i2bRvOnz/vrodCRERE5DHsQxEREVGwCPF2BYzKy8tRVVWFsWPHmrb16NEDw4cPx8GDBzFjxgwcPHgQMTExyM7ONu0zduxYKBQKFBUVYerUqQ7dlyzLAACtVgulUunaBwJAFEW3Hj+Qse2cx7ZzHtvOeWw757HtnOfptjPen7H/4GsCpQ/F94Tz2HbOY9s5j23nPLad89h2zvPV/pPPBKWqqqoAAAkJCWbbExISUF1dDQCorq5GfHy8WXlISAhiY2NNt3eEJEkAgB9//LE7VbbL3ccPZGw757HtnMe2cx7bznlsO+d5uu2M/QdfE2h9KL4nnMe2cx7bznlsO+ex7ZzHtnOer/WfuhSUWrlyJd58802b+2zfvh0DBw7symE9LiQkBNnZ2VAoFBAEwdvVISIiIh8myzIkSUJIiPPn8tiHIiIiomDiaP+pS72r++67D7feeqvNfdLT07tySJOkpCQAQE1NDZKTk03ba2pqMHjwYABAYmIiamtrzW6n1+tRX19vur0jFAoFVCqVU/UkIiIi6ir2oYiIiIg661JQKj4+vtPQb1dJS0tDUlIS9u/fjyFDhgAAGhsbcejQIdPqMyNHjkRDQwOOHDmCYcOGAQC++eYbSJKEnJwct9SLiIiIqLvYhyIiIiLqzG2r7509exbHjh3D2bNnIYoijh07hmPHjqGpqcm0z/Tp0/H5558DAARBwD333IPXX38dX375JYqLi/H4448jOTkZU6ZMAQAMHDgQ48ePx9NPP42ioiJ8//33eO655zBjxgykpKS466EQEREReQz7UERERBQs3JbofPXq1di6davp+i233AIA2LBhA8aMGQMAKC0txcWLF0373H///VCr1XjmmWfQ0NCAyy67DG+99RbCwsJM+6xcuRLPPfcc7r33XigUCkybNg1PPfWUux4GERERkUexD0VERETBQpB9dX1jIiIiIiIiIiIKWG6bvkdERERERERERGQNg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6CUi23cuBGTJ09GdnY28vPzUVRU5O0q+aTvvvsOv/71rzFu3DhkZWXhiy++MCuXZRkvv/wyxo0bh5ycHMyZMwenTp3yTmV9yJo1a5CXl4eRI0fiyiuvxG9+8xuUlJSY7aPRaLB06VKMGTMGI0eOxG9/+1tUV1d7qca+45///CduvPFGjBo1CqNGjcKsWbOwZ88eUznbzXFr165FVlYW/vSnP5m2sf0se+WVV5CVlWX2b/r06aZytptt58+fx2OPPYYxY8YgJycHN954Iw4fPmwq53dFYGEfyj72n5zHPpTz2IdyDfafuoZ9KOf5W/+JQSkX2r59O5YvX44HH3wQW7duxeDBgzFv3jzU1NR4u2o+p7m5GVlZWfjDH/5gsfzNN9/Eu+++i2effRabN29GREQE5s2bB41G4+Ga+pYDBw7grrvuwubNm7F+/Xro9XrMmzcPzc3Npn2ef/557Nq1Cy+99BLeffddVFZW4qGHHvJirX1DamoqHnvsMXz44YcoKCjAFVdcgQcffBA///wzALabo4qKivD+++8jKyvLbDvbz7pLLrkE+/btM/375z//aSpju1lXX1+PO++8E6GhoXjzzTexbds2LF68GLGxsaZ9+F0RONiHcgz7T85jH8p57EN1H/tPzmEfquv8sv8kk8vcfvvt8tKlS03XRVGUx40bJ69Zs8aLtfJ9mZmZ8ueff266LkmSfNVVV8lvvfWWaVtDQ4M8bNgw+dNPP/VGFX1WTU2NnJmZKR84cECWZUM7DR06VP7ss89M+5w4cULOzMyUDx486KVa+q7Ro0fLmzdvZrs5qLGxUZ42bZr83//+V549e7a8bNkyWZb5urNl9erV8k033WSxjO1m21/+8hf5zjvvtFrO74rAwj5U17H/1D3sQ3UP+1COY//JOexDOccf+08cKeUiWq0WR48exdixY03bFAoFxo4di4MHD3qxZv6nvLwcVVVVZm3Zo0cPDB8+nG3ZwcWLFwHAFPk+cuQIdDqdWdsNHDgQvXv3xg8//OCNKvokURSxbds2NDc3Y+TIkWw3B/3xj3/ExIkTzdoJ4OvOntOnT2PcuHG45pprsGjRIpw9exYA282enTt3YtiwYVi4cCGuvPJK3HLLLdi8ebOpnN8VgYN9KNfge6Jr2IdyDvtQXcf+k/PYh+o6f+w/hXjlXgNQXV0dRFFEQkKC2faEhIRO89XJtqqqKgCw2JacJ9xGkiQ8//zzGDVqFDIzMwEA1dXVCA0NRUxMjNm+CQkJpnYNZsXFxbjjjjug0WgQGRmJ1157DYMGDcKxY8fYbnZs27YNP/74I7Zs2dKpjK8763JycrB8+XL0798fVVVVeO2113DXXXfhk08+YbvZUVZWhvfeew9z587Fr3/9axw+fBjLli1DaGgobr31Vn5XBBD2oVyD7wnHsQ/VdexDOYf9J+exD+Ucf+w/MShF5KeWLl2Kn3/+2WxuNdnWv39/fPTRR7h48SL+/e9/Y/HixfjHP/7h7Wr5vIqKCvzpT3/C22+/jbCwMG9Xx69MnDjR9Pfgwf+/nTt4afqP4zj+0kiGupayRepqWgfX+qKZF8EQBl4CO2xC4CFlh/6A2SEoQRxC65CQQXXxsPQgnoIWHTrkbblOBdFBUJnSQRnEcEQL9u30G0j9mq7xndPnAwbbZ4O9ebPvPi/efL9fr7q7u+X3+/XmzRvZbLYKVnb4maYpwzA0Pj4uSfL5fFpdXdXi4qICgUCFqwNQzchQB0eGOjjy078hQ5WmGvMTl++VSVNTk06cOPHbDTnT6bScTmeFqqpOLpdLkujlX0QiES0vLysWi+ns2bOFdafTqZ8/fyqTyez5fDqdLvT1OKurq5PH45FhGLpz5468Xq9evHhB34r4/Pmz0um0gsGgfD6ffD6fksmk5ufn5fP56N8BnDp1Su3t7UqlUvStCJfLpYsXL+5Zu3DhQuHUffaKo4MMVR4cE/tDhioNGergyE/lRYban2rMTwylyqSurk6XL19WIpEorOXzeSUSCfX09FSwsurjdrvlcrn29HJ3d1cfP3489r00TVORSERv375VLBbTuXPn9rxvGIZOnjy5p3dra2v6+vWrrly5YnG1h18+n1cul6NvRfT19enVq1d6+fJl4WEYhm7cuFF4Tv/2J5vNanNzUy6Xi74VcfXqVa2vr+9Z29jYUFtbmyT2iqOEDFUeHBN/R4YqLzJUceSn8iJD7U815icu3yujUCiku3fvyjAMdXV1KRaL6fv37woGg5Uu7dDJZrNKpVKF11tbW/ry5YscDodaW1s1OjqqZ8+eyePxyO126/Hjxzpz5owGBwcrWHXlTU1NKR6P6+nTp2poaChcE2y322Wz2WS32zU8PKxoNCqHw6HGxkZNT0+rp6fn2P9BP3r0SAMDA2ppaVE2m1U8HlcymdTc3Bx9K6KxsbFwz43/1NfX6/Tp04V1+vdnDx8+lN/vV2trq7a3t/XkyRPV1tZqaGiI310RY2NjGhkZ0fPnz3X9+nV9+vRJS0tLikQikqSamhr2iiOEDLU/5KfSkaFKR4YqDfnp35ChSlON+anGNE2zIt98RC0sLGhubk47Ozu6dOmSJiYm1N3dXemyDp2VlRWNjo7+th4IBBSNRmWapmZnZ7W0tKRMJqPe3l5NTk6qo6OjAtUeHp2dnX9cf/DgQSG4//jxQ9FoVK9fv1Yul9O1a9c0OTl57E9lvXfvnt6/f6/t7W3Z7XZ1dnbq9u3b6u/vl0TfDurWrVvyer26f/++JPr3f8LhsD58+KBv376publZvb29CofDOn/+vCT6Vsy7d+80MzOjjY0Nud1uhUIh3bx5s/A+e8XRQoYqjvxUOjJU6chQ5UN+2j8yVOmqLT8xlAIAAAAAAIDluKcUAAAAAAAALMdQCgAAAAAAAJZjKAUAAAAAAADLMZQCAAAAAACA5RhKAQAAAAAAwHIMpQAAAAAAAGA5hlIAAAAAAACwHEMpAAAAAAAAWI6hFAAAAAAAACzHUAoAAAAAAACWYygFAAAAAAAAyzGUAgAAAAAAgOV+AfJsJCdvxoIYAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Karar: Güçlü mevsimsel ACF → week-wise + seasonal feature zorunlu.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 08 — STATIONARITY + ACF/PACF\n# ============================================================\n# Tek bir uzun seri: year-week sırasıyla\ndf_ts = df.sort_values([\"year\", \"week\"]).reset_index(drop=True)\nseries = df_ts[\"sales\"].values\n\nadf = adfuller(series, autolag=\"AIC\")\nprint(f\"ADF statistic={adf[0]:.4f}  p-value={adf[1]:.4f}\")\nprint(\"→ H0: birim kök var (non-stationary).\" )\nprint(\"  p<0.05 ise durağan kabul.\\n\")\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))\nplot_acf(series, lags=60, ax=axes[0], title=\"ACF (60 lag)\")\nplot_pacf(series, lags=60, ax=axes[1], title=\"PACF (60 lag)\", method=\"ywm\")\nplt.tight_layout()\nplt.show()\n\nprint(\"Karar: Güçlü mevsimsel ACF → week-wise + seasonal feature zorunlu.\")\n",
   "id": "eda_stat"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a09"
   },
   "source": "## Aşama 09 — Feature Engineering A: Week-Wise (Cross-Year)\n\nAynı `week` için geçmiş yılların satışları. Leakage yok.\n",
   "id": "a09"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "feat_ww",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409630050,
     "user_tz": -180,
     "elapsed": 2126,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "57766c70-4340-4dd5-a57b-036766939583"
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Week-wise shape: (424, 20)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   year  week     sales  year_idx  sin_week  cos_week     lag_mean  \\\n",
       "0  2018     1   6282.01         3  0.118273  0.992981  6766.750000   \n",
       "1  2018     2   6794.87         3  0.234886  0.972023  6759.173333   \n",
       "2  2018     3   7371.80         3  0.348202  0.937420  7985.246667   \n",
       "3  2018     4  10743.51         3  0.456629  0.889657  7642.526667   \n",
       "4  2018     5   8062.18         3  0.558647  0.829406  8431.853333   \n",
       "\n",
       "      lag_std  lag_min  lag_max   lag_y1   lag_y2   lag_y3       lag_y4  \\\n",
       "0  188.674231  6338.87  7182.38  6338.87  6779.00  7182.38  6766.750000   \n",
       "1  249.332294  6146.23  7236.11  7236.11  6895.18  6146.23  6759.173333   \n",
       "2  391.177206  7034.21  8755.29  8755.29  7034.21  8166.24  7985.246667   \n",
       "3  492.271511  6494.34  8688.75  6494.34  7744.49  8688.75  7642.526667   \n",
       "4  847.554828  6291.02  9895.17  9109.37  9895.17  6291.02  8431.853333   \n",
       "\n",
       "        lag_y5       lag_y6       lag_y7       lag_y8       lag_y9  \\\n",
       "0  6766.750000  6766.750000  6766.750000  6766.750000  6766.750000   \n",
       "1  6759.173333  6759.173333  6759.173333  6759.173333  6759.173333   \n",
       "2  7985.246667  7985.246667  7985.246667  7985.246667  7985.246667   \n",
       "3  7642.526667  7642.526667  7642.526667  7642.526667  7642.526667   \n",
       "4  8431.853333  8431.853333  8431.853333  8431.853333  8431.853333   \n",
       "\n",
       "       lag_y10  \n",
       "0  6766.750000  \n",
       "1  6759.173333  \n",
       "2  7985.246667  \n",
       "3  7642.526667  \n",
       "4  8431.853333  "
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    }
   ],
   "source": "# ============================================================\n# AŞAMA 09 — WEEK-WISE FEATURES\n# ============================================================\ndef build_weekwise(df, history_years=10):\n    years_sorted = sorted(df[\"year\"].unique())\n    records = []\n    for year in years_sorted:\n        yi = years_sorted.index(year)\n        if yi < 1:\n            continue\n        for week in range(1, N_WEEKS + 1):\n            tgt = df[(df.year == year) & (df.week == week)]\n            if tgt.empty:\n                continue\n            y_true = tgt[\"sales\"].values[0]\n            lags = []\n            for k in range(1, history_years + 1):\n                past = df[(df.year == year - k) & (df.week == week)]\n                lags.append(past[\"sales\"].values[0] if not past.empty else np.nan)\n            valid = [v for v in lags if not np.isnan(v)]\n            if len(valid) < 3:\n                continue\n            fill = float(np.mean(valid))\n            lags_f = [fill if np.isnan(v) else float(v) for v in lags]\n            rec = {\n                \"year\": year, \"week\": week, \"sales\": y_true,\n                \"year_idx\": yi,\n                \"sin_week\": np.sin(2 * np.pi * week / 53),\n                \"cos_week\": np.cos(2 * np.pi * week / 53),\n                \"lag_mean\": float(np.mean(lags_f)),\n                \"lag_std\": float(np.std(lags_f)) if len(lags_f) > 1 else 0.0,\n                \"lag_min\": float(np.min(lags_f)),\n                \"lag_max\": float(np.max(lags_f)),\n            }\n            for k, v in enumerate(lags_f, 1):\n                rec[f\"lag_y{k}\"] = v\n            records.append(rec)\n    return pd.DataFrame(records)\n\ndf_ww = build_weekwise(df, history_years=10)\nprint(f\"Week-wise shape: {df_ww.shape}\")\ndisplay(df_ww.head(5))\n",
   "id": "feat_ww"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a10"
   },
   "source": "## Aşama 10 — Feature Engineering B: Yıl-İçi Time Series\n\nSequential lag: önceki 1…8 hafta + rolling mean/std + week cyclics.\n",
   "id": "a10"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "feat_ts",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409636128,
     "user_tz": -180,
     "elapsed": 85,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "20450721-2004-479e-8c5b-4c2e32733687"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TS feature shape: (575, 16)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   year  week     sales  ts_lag_1  ts_lag_2  ts_lag_3  ts_lag_4  ts_lag_5  \\\n",
       "0  2015     9  10176.00   9307.94   9204.63   7347.52   6291.02   8688.75   \n",
       "1  2015    10   9999.04  10176.00   9307.94   9204.63   7347.52   6291.02   \n",
       "2  2015    11  12338.92   9999.04  10176.00   9307.94   9204.63   7347.52   \n",
       "3  2015    12  11671.94  12338.92   9999.04  10176.00   9307.94   9204.63   \n",
       "4  2015    13  11736.34  11671.94  12338.92   9999.04  10176.00   9307.94   \n",
       "\n",
       "   ts_lag_6  ts_lag_7  ts_lag_8  ts_roll_mean_4  ts_roll_std_4  sin_week  \\\n",
       "0   8166.24   6146.23   7182.38       8037.7775    1472.240324  0.875735   \n",
       "1   8688.75   8166.24   6146.23       9009.0225    1190.243814  0.926690   \n",
       "2   6291.02   8688.75   8166.24       9671.9025     487.150054  0.964636   \n",
       "3   7347.52   6291.02   8688.75      10455.4750    1310.297782  0.989040   \n",
       "4   9204.63   7347.52   6291.02      11046.4750    1142.580026  0.999561   \n",
       "\n",
       "   cos_week  year_idx  \n",
       "0  0.482792         0  \n",
       "1  0.375828         0  \n",
       "2  0.263587         0  \n",
       "3  0.147647         0  \n",
       "4  0.029633         0  "
      ],
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>sales</th>\n",
       "      <th>ts_lag_1</th>\n",
       "      <th>ts_lag_2</th>\n",
       "      <th>ts_lag_3</th>\n",
       "      <th>ts_lag_4</th>\n",
       "      <th>ts_lag_5</th>\n",
       "      <th>ts_lag_6</th>\n",
       "      <th>ts_lag_7</th>\n",
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       "      <th>ts_roll_mean_4</th>\n",
       "      <th>ts_roll_std_4</th>\n",
       "      <th>sin_week</th>\n",
       "      <th>cos_week</th>\n",
       "      <th>year_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>10176.00</td>\n",
       "      <td>9307.94</td>\n",
       "      <td>9204.63</td>\n",
       "      <td>7347.52</td>\n",
       "      <td>6291.02</td>\n",
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       "      <td>8166.24</td>\n",
       "      <td>6146.23</td>\n",
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       "      <td>0.875735</td>\n",
       "      <td>0.482792</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>9999.04</td>\n",
       "      <td>10176.00</td>\n",
       "      <td>9307.94</td>\n",
       "      <td>9204.63</td>\n",
       "      <td>7347.52</td>\n",
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       "      <td>6146.23</td>\n",
       "      <td>9009.0225</td>\n",
       "      <td>1190.243814</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.375828</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>12338.92</td>\n",
       "      <td>9999.04</td>\n",
       "      <td>10176.00</td>\n",
       "      <td>9307.94</td>\n",
       "      <td>9204.63</td>\n",
       "      <td>7347.52</td>\n",
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       "      <td>8688.75</td>\n",
       "      <td>8166.24</td>\n",
       "      <td>9671.9025</td>\n",
       "      <td>487.150054</td>\n",
       "      <td>0.964636</td>\n",
       "      <td>0.263587</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>11671.94</td>\n",
       "      <td>12338.92</td>\n",
       "      <td>9999.04</td>\n",
       "      <td>10176.00</td>\n",
       "      <td>9307.94</td>\n",
       "      <td>9204.63</td>\n",
       "      <td>7347.52</td>\n",
       "      <td>6291.02</td>\n",
       "      <td>8688.75</td>\n",
       "      <td>10455.4750</td>\n",
       "      <td>1310.297782</td>\n",
       "      <td>0.989040</td>\n",
       "      <td>0.147647</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "      <td>11736.34</td>\n",
       "      <td>11671.94</td>\n",
       "      <td>12338.92</td>\n",
       "      <td>9999.04</td>\n",
       "      <td>10176.00</td>\n",
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       "      <td>0.999561</td>\n",
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      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"display(df_ts_feat\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2015,\n        \"max\": 2015,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 9,\n        \"max\": 13,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1036.4842405555426,\n        \"min\": 9999.04,\n        \"max\": 12338.92,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9999.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1258.4187007987443,\n        \"min\": 9307.94,\n        \"max\": 12338.92,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10176.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1265.1414348127248,\n        \"min\": 9204.63,\n        \"max\": 12338.92,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9307.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1121.8454360026608,\n        \"min\": 7347.52,\n        \"max\": 10176.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9204.63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1593.7433200550204,\n        \"min\": 6291.02,\n        \"max\": 10176.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7347.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1307.811805066004,\n        \"min\": 6291.02,\n        \"max\": 9307.94,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6291.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1149.0503742960964,\n        \"min\": 6291.02,\n        \"max\": 9204.63,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8688.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1121.0013082374167,\n        \"min\": 6146.23,\n        \"max\": 8688.75,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8166.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_lag_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1122.712094541606,\n        \"min\": 6146.23,\n        \"max\": 8688.75,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6146.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_roll_mean_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1184.2036787032775,\n        \"min\": 8037.7775,\n        \"max\": 11046.475,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9009.0225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ts_roll_std_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 376.24072580856256,\n        \"min\": 487.1500535683688,\n        \"max\": 1472.2403238234124,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1190.2438138010489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sin_week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05061342824722364,\n        \"min\": 0.8757349421956369,\n        \"max\": 0.9995608365087943,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9266896074318334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cos_week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17941384262434776,\n        \"min\": 0.029633327822559667,\n        \"max\": 0.48279220273074486,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3758275821142382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year_idx\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    }
   ],
   "source": "# ============================================================\n# AŞAMA 10 — WITHIN-YEAR TIME SERIES FEATURES\n# ============================================================\ndef build_within_year_ts(df, n_lags=8):\n    df_s = df.sort_values([\"year\", \"week\"]).reset_index(drop=True).copy()\n    for lag in range(1, n_lags + 1):\n        df_s[f\"ts_lag_{lag}\"] = df_s[\"sales\"].shift(lag)\n    df_s[\"ts_roll_mean_4\"] = df_s[\"sales\"].shift(1).rolling(4).mean()\n    df_s[\"ts_roll_std_4\"]  = df_s[\"sales\"].shift(1).rolling(4).std()\n    df_s[\"sin_week\"] = np.sin(2 * np.pi * df_s[\"week\"] / 53)\n    df_s[\"cos_week\"] = np.cos(2 * np.pi * df_s[\"week\"] / 53)\n    df_s[\"year_idx\"] = df_s[\"year\"] - df_s[\"year\"].min()\n    df_s = df_s.dropna().reset_index(drop=True)\n    return df_s\n\ndf_ts_feat = build_within_year_ts(df, n_lags=8)\nprint(f\"TS feature shape: {df_ts_feat.shape}\")\ndisplay(df_ts_feat.head(5))\n",
   "id": "feat_ts"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a11"
   },
   "source": "## Aşama 11 — Ortak 10 Özellik Seçimi (Klasik = Quantum)\n\n**Kural:** Klasik ve quantum modeller **aynı 10 özellik** ile test edilir.  \nQuantum encoding boyutu = 10 qubit.\n",
   "id": "a11"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "feat_select",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409642375,
     "user_tz": -180,
     "elapsed": 274,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "9fa0c5ea-16ab-45d7-e834-16a51a1e5444"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Week-wise importance (top 15):\n",
      "lag_mean    0.735602\n",
      "lag_y3      0.080628\n",
      "lag_y4      0.078591\n",
      "lag_y5      0.031127\n",
      "lag_max     0.027549\n",
      "lag_y6      0.013961\n",
      "lag_y2      0.012928\n",
      "lag_min     0.002660\n",
      "lag_y8      0.002640\n",
      "lag_y1      0.002278\n",
      "year_idx    0.002254\n",
      "lag_y9      0.002000\n",
      "cos_week    0.001811\n",
      "lag_y7      0.001791\n",
      "sin_week    0.001769\n",
      "\n",
      "✅ Week-wise seçilen 10 özellik:\n",
      "['sin_week', 'cos_week', 'lag_mean', 'year_idx', 'lag_y3', 'lag_y4', 'lag_y5', 'lag_max', 'lag_y6', 'lag_y2']\n",
      "\n",
      "✅ TS seçilen 10 özellik:\n",
      "['sin_week', 'cos_week', 'ts_roll_mean_4', 'year_idx', 'ts_lag_1', 'ts_lag_2', 'ts_lag_3', 'ts_lag_5', 'ts_lag_8', 'ts_lag_7']\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 11 — EQUAL 10-FEATURE SET\n# ============================================================\n# Week-wise aday havuzu\nww_candidates = [c for c in df_ww.columns if c not in (\"year\", \"sales\")]\n\n# Ön seçim: importance için geçici XGBoost (tüm ww özellikleri)\nTEST_YEAR = 2025\ntr = df_ww[df_ww.year < TEST_YEAR]\nte = df_ww[df_ww.year == TEST_YEAR]\n\nsc_tmp = StandardScaler()\nXtr_tmp = sc_tmp.fit_transform(tr[ww_candidates])\nytr_tmp = tr[\"sales\"].values\nxgb_tmp = XGBRegressor(n_estimators=100, max_depth=3, random_state=RANDOM_STATE, verbosity=0)\nxgb_tmp.fit(Xtr_tmp, ytr_tmp)\nimp = pd.Series(xgb_tmp.feature_importances_, index=ww_candidates).sort_values(ascending=False)\nprint(\"Week-wise importance (top 15):\")\nprint(imp.head(15).to_string())\n\n# 10 özellik: importance + zorunlu mevsim/lag_mean\nmust = [\"sin_week\", \"cos_week\", \"lag_mean\", \"year_idx\"]\nselected_ww = []\nfor m in must:\n    if m in ww_candidates and m not in selected_ww:\n        selected_ww.append(m)\nfor f in imp.index:\n    if f not in selected_ww:\n        selected_ww.append(f)\n    if len(selected_ww) >= N_FEATURES:\n        break\nselected_ww = selected_ww[:N_FEATURES]\nprint(f\"\\n✅ Week-wise seçilen {N_FEATURES} özellik:\\n{selected_ww}\")\n\n# TS tarafı için de 10 özellik\nts_candidates = [c for c in df_ts_feat.columns\n                 if c not in (\"year\", \"week\", \"sales\")]\ntr_ts = df_ts_feat[df_ts_feat.year < TEST_YEAR]\nXtr_ts_tmp = StandardScaler().fit_transform(tr_ts[ts_candidates])\nxgb_ts = XGBRegressor(n_estimators=100, max_depth=3, random_state=RANDOM_STATE, verbosity=0)\nxgb_ts.fit(Xtr_ts_tmp, tr_ts[\"sales\"].values)\nimp_ts = pd.Series(xgb_ts.feature_importances_, index=ts_candidates).sort_values(ascending=False)\nmust_ts = [\"sin_week\", \"cos_week\", \"ts_roll_mean_4\", \"year_idx\"]\nselected_ts = []\nfor m in must_ts:\n    if m in ts_candidates and m not in selected_ts:\n        selected_ts.append(m)\nfor f in imp_ts.index:\n    if f not in selected_ts:\n        selected_ts.append(f)\n    if len(selected_ts) >= N_FEATURES:\n        break\nselected_ts = selected_ts[:N_FEATURES]\nprint(f\"\\n✅ TS seçilen {N_FEATURES} özellik:\\n{selected_ts}\")\n\nassert len(selected_ww) == N_FEATURES and len(selected_ts) == N_FEATURES\n",
   "id": "feat_select"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a12"
   },
   "source": "## Aşama 12 — Train/Test Bölme ve Ölçekleme (Her İki Görev)\n",
   "id": "a12"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "split",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409668868,
     "user_tz": -180,
     "elapsed": 298,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "78e650c2-56dc-4ebf-8171-df22123dc771"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Week-wise  Train (371, 10)  Test (53, 10)\n",
      "Time-Series Train (522, 10)  Test (53, 10)\n",
      "Özellik sayısı (her iki görev): 10\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 12 — SPLIT + SCALE\n# ============================================================\ndef prepare_xy(df_feat, feature_list, test_year=TEST_YEAR):\n    train = df_feat[df_feat.year < test_year]\n    test  = df_feat[df_feat.year == test_year]\n    scaler = StandardScaler()\n    X_train = scaler.fit_transform(train[feature_list].values)\n    X_test  = scaler.transform(test[feature_list].values)\n    y_train = train[\"sales\"].values\n    y_test  = test[\"sales\"].values\n    meta = test[[\"year\", \"week\"]].reset_index(drop=True) if \"week\" in test.columns else None\n    return X_train, X_test, y_train, y_test, scaler, meta\n\nXtr_ww, Xte_ww, ytr_ww, yte_ww, sc_ww, meta_ww = prepare_xy(df_ww, selected_ww)\nXtr_ts, Xte_ts, ytr_ts, yte_ts, sc_ts, meta_ts = prepare_xy(df_ts_feat, selected_ts)\n\nprint(f\"Week-wise  Train {Xtr_ww.shape}  Test {Xte_ww.shape}\")\nprint(f\"Time-Series Train {Xtr_ts.shape}  Test {Xte_ts.shape}\")\nprint(f\"Özellik sayısı (her iki görev): {N_FEATURES}\")\n",
   "id": "split"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a13"
   },
   "source": "## Aşama 13 — Metrik Altyapısı\n",
   "id": "a13"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "metrics"
   },
   "outputs": [],
   "source": "# ============================================================\n# AŞAMA 13 — METRICS\n# ============================================================\ndef metrics(y_true, y_pred, name, task):\n    mae  = mean_absolute_error(y_true, y_pred)\n    rmse = float(np.sqrt(mean_squared_error(y_true, y_pred)))\n    mape = float(np.mean(np.abs((y_true - y_pred) / np.clip(np.abs(y_true), 1e-6, None))) * 100)\n    r2   = r2_score(y_true, y_pred)\n    return {\n        \"Task\": task, \"Model\": name, \"Family\": \"Quantum\" if name.startswith(\"Q_\") else \"Classical\",\n        \"MAE\": round(mae, 2), \"RMSE\": round(rmse, 2),\n        \"MAPE_%\": round(mape, 2), \"R2\": round(r2, 4),\n        \"n_features\": N_FEATURES,\n    }\n\nall_results = []\npreds = {\"WW\": {}, \"TS\": {}}\n",
   "id": "metrics"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a14"
   },
   "source": "## Aşama 14 — Klasik Modeller (Week-Wise, 10 feature)\n",
   "id": "a14"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "classic_ww",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409703918,
     "user_tz": -180,
     "elapsed": 1056,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "dbf24779-4343-4a24-a981-7053d762f4ce"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Week-Wise klasik eğitim (10 feature)...\n",
      "\n",
      "Ridge              MAE=   1,070.6  R²=0.9634\n",
      "Lasso              MAE=   1,061.4  R²=0.9629\n",
      "ElasticNet         MAE=   1,107.2  R²=0.9619\n",
      "DecisionTree       MAE=   1,582.3  R²=0.9217\n",
      "SVR_RBF            MAE=   6,018.4  R²=0.0471\n",
      "RandomForest       MAE=   1,121.0  R²=0.9610\n",
      "GradientBoosting   MAE=   1,160.3  R²=0.9593\n",
      "HistGB             MAE=   1,173.2  R²=0.9585\n",
      "XGBoost            MAE=   1,269.8  R²=0.9520\n",
      "LightGBM           MAE=   1,223.5  R²=0.9576\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 14 — CLASSICAL WEEK-WISE\n# ============================================================\nclassical = {\n    \"Ridge\": Ridge(alpha=1.0),\n    \"Lasso\": Lasso(alpha=0.1, max_iter=8000),\n    \"ElasticNet\": ElasticNet(alpha=0.1, l1_ratio=0.5, max_iter=8000),\n    \"DecisionTree\": DecisionTreeRegressor(max_depth=6, random_state=RANDOM_STATE),\n    \"SVR_RBF\": SVR(kernel=\"rbf\", C=10.0, epsilon=0.1),\n    \"RandomForest\": RandomForestRegressor(\n        n_estimators=200, max_depth=8, min_samples_leaf=2, random_state=RANDOM_STATE, n_jobs=-1),\n    \"GradientBoosting\": GradientBoostingRegressor(\n        n_estimators=150, max_depth=3, learning_rate=0.08, random_state=RANDOM_STATE),\n    \"HistGB\": HistGradientBoostingRegressor(\n        max_iter=200, max_depth=6, learning_rate=0.08, random_state=RANDOM_STATE),\n    \"XGBoost\": XGBRegressor(\n        n_estimators=200, max_depth=4, learning_rate=0.08,\n        subsample=0.8, colsample_bytree=0.8, random_state=RANDOM_STATE, verbosity=0),\n    \"LightGBM\": LGBMRegressor(\n        n_estimators=200, max_depth=6, learning_rate=0.08,\n        subsample=0.8, colsample_bytree=0.8, random_state=RANDOM_STATE, verbose=-1),\n}\n\nprint(\"Week-Wise klasik eğitim (10 feature)...\\n\")\nfor name, model in classical.items():\n    model.fit(Xtr_ww, ytr_ww)\n    pred = model.predict(Xte_ww)\n    preds[\"WW\"][name] = pred\n    m = metrics(yte_ww, pred, name, \"WeekWise\")\n    all_results.append(m)\n    print(f\"{name:18s} MAE={m['MAE']:>10,.1f}  R²={m['R2']:.4f}\")\n",
   "id": "classic_ww"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a15"
   },
   "source": "## Aşama 15 — Klasik Modeller (Yıl-İçi Time Series, 10 feature)\n",
   "id": "a15"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "classic_ts",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409714276,
     "user_tz": -180,
     "elapsed": 1039,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "2127e719-bb19-403e-9d32-ea21e111473e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time-Series klasik eğitim (10 feature)...\n",
      "\n",
      "Ridge              MAE=   1,293.0  R²=0.9492\n",
      "Lasso              MAE=   1,311.8  R²=0.9478\n",
      "ElasticNet         MAE=   1,277.2  R²=0.9518\n",
      "DecisionTree       MAE=   1,287.2  R²=0.9499\n",
      "SVR_RBF            MAE=   6,075.9  R²=-0.0059\n",
      "RandomForest       MAE=   1,238.8  R²=0.9568\n",
      "GradientBoosting   MAE=   1,255.3  R²=0.9577\n",
      "HistGB             MAE=   1,283.1  R²=0.9536\n",
      "XGBoost            MAE=   1,208.2  R²=0.9603\n",
      "LightGBM           MAE=   1,288.2  R²=0.9536\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 15 — CLASSICAL TIME-SERIES\n# ============================================================\n# Yeni instance'lar (WW modelleri fit edilmiş durumda)\nclassical_ts = {\n    \"Ridge\": Ridge(alpha=1.0),\n    \"Lasso\": Lasso(alpha=0.1, max_iter=8000),\n    \"ElasticNet\": ElasticNet(alpha=0.1, l1_ratio=0.5, max_iter=8000),\n    \"DecisionTree\": DecisionTreeRegressor(max_depth=6, random_state=RANDOM_STATE),\n    \"SVR_RBF\": SVR(kernel=\"rbf\", C=10.0, epsilon=0.1),\n    \"RandomForest\": RandomForestRegressor(\n        n_estimators=200, max_depth=8, min_samples_leaf=2, random_state=RANDOM_STATE, n_jobs=-1),\n    \"GradientBoosting\": GradientBoostingRegressor(\n        n_estimators=150, max_depth=3, learning_rate=0.08, random_state=RANDOM_STATE),\n    \"HistGB\": HistGradientBoostingRegressor(\n        max_iter=200, max_depth=6, learning_rate=0.08, random_state=RANDOM_STATE),\n    \"XGBoost\": XGBRegressor(\n        n_estimators=200, max_depth=4, learning_rate=0.08,\n        subsample=0.8, colsample_bytree=0.8, random_state=RANDOM_STATE, verbosity=0),\n    \"LightGBM\": LGBMRegressor(\n        n_estimators=200, max_depth=6, learning_rate=0.08,\n        subsample=0.8, colsample_bytree=0.8, random_state=RANDOM_STATE, verbose=-1),\n}\n\nprint(\"Time-Series klasik eğitim (10 feature)...\\n\")\nfor name, model in classical_ts.items():\n    model.fit(Xtr_ts, ytr_ts)\n    pred = model.predict(Xte_ts)\n    preds[\"TS\"][name] = pred\n    m = metrics(yte_ts, pred, name, \"TimeSeries\")\n    all_results.append(m)\n    print(f\"{name:18s} MAE={m['MAE']:>10,.1f}  R²={m['R2']:.4f}\")\n",
   "id": "classic_ts"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a16"
   },
   "source": "## Aşama 16 — Quantum Data Encoding (10 Qubit)\n\n- Özellik sayısı = qubit sayısı = **10**\n- `zz_feature_map(feature_dimension=10, reps=2, entanglement='circular')`\n- Ölçek: \\([0,\\pi]\\)\n- Kernel: `FidelityStatevectorKernel` (simülatör)\n- Train subsample (kernel \\(O(n^2)\\))\n",
   "id": "a16"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q_encode",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789409757579,
     "user_tz": -180,
     "elapsed": 234,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "b76ebe3c-7af7-4ce8-a64b-3a8e045ac2d9"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "64 depth (decompose)\n",
      "Qubit sayısı: 10\n",
      "WW quantum train: (180, 10)  test: (53, 10)\n",
      "TS quantum train: (180, 10)  test: (53, 10)\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 16 — QUANTUM ENCODING (10 QUBIT)\n# ============================================================\nfrom qiskit.circuit.library import zz_feature_map\nfrom qiskit_machine_learning.kernels import FidelityStatevectorKernel\n\nassert N_QUBITS == N_FEATURES == 10\n\nfeature_map = zz_feature_map(\n    feature_dimension=N_QUBITS,\n    reps=2,\n    entanglement=\"circular\",\n)\nprint(feature_map.decompose().depth(), \"depth (decompose)\")\nprint(f\"Qubit sayısı: {feature_map.num_qubits}\")\n\nquantum_kernel = FidelityStatevectorKernel(feature_map=feature_map)\n\ndef to_quantum_angles(X_train, X_test, max_train=180):\n    \"\"\"StandardScaler çıktısını [0, π] açıya çevir + subsample.\"\"\"\n    qsc = MinMaxScaler(feature_range=(0, np.pi))\n    Xt = qsc.fit_transform(X_train)\n    Xe = qsc.transform(X_test)\n    if len(Xt) > max_train:\n        rng = np.random.default_rng(RANDOM_STATE)\n        idx = rng.choice(len(Xt), size=max_train, replace=False)\n        return Xt[idx], Xe, idx, qsc\n    return Xt, Xe, np.arange(len(Xt)), qsc\n\nXtr_ww_q, Xte_ww_q, idx_ww, qsc_ww = to_quantum_angles(Xtr_ww, Xte_ww)\nXtr_ts_q, Xte_ts_q, idx_ts, qsc_ts = to_quantum_angles(Xtr_ts, Xte_ts)\nprint(f\"WW quantum train: {Xtr_ww_q.shape}  test: {Xte_ww_q.shape}\")\nprint(f\"TS quantum train: {Xtr_ts_q.shape}  test: {Xte_ts_q.shape}\")\n",
   "id": "q_encode"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a17"
   },
   "source": "## Aşama 17 — Quantum Modeller (KernelRidge + SVR, her iki görev)\n",
   "id": "a17"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q_models",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410261288,
     "user_tz": -180,
     "elapsed": 469009,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "ab011107-96ba-4d4b-c046-e9e961f09cd2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "⏳ WeekWise: Quantum KernelRidge...\n",
      "Q_KernelRidge   MAE=  14,245.1  R²=-3.4817\n",
      "⏳ WeekWise: Quantum SVR (precomputed)...\n",
      "Q_SVR           MAE=   6,489.3  R²=-0.0152\n",
      "\n",
      "⏳ TimeSeries: Quantum KernelRidge...\n",
      "Q_KernelRidge   MAE=  16,015.5  R²=-4.6428\n",
      "⏳ TimeSeries: Quantum SVR (precomputed)...\n",
      "Q_SVR           MAE=   6,731.7  R²=-0.2329\n",
      "\n",
      "✅ Quantum modeller tamam.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 17 — QUANTUM KERNEL MODELS\n# ============================================================\ndef run_quantum(Xtr_q, ytr_full, idx, Xte_q, yte, task_key, task_name):\n    ytr_q = ytr_full[idx]\n    print(f\"\\n⏳ {task_name}: Quantum KernelRidge...\")\n    qkr = KernelRidge(alpha=0.5, kernel=quantum_kernel.evaluate)\n    qkr.fit(Xtr_q, ytr_q)\n    pred_kr = qkr.predict(Xte_q)\n    preds[task_key][\"Q_KernelRidge\"] = pred_kr\n    m = metrics(yte, pred_kr, \"Q_KernelRidge\", task_name)\n    all_results.append(m)\n    print(f\"Q_KernelRidge   MAE={m['MAE']:>10,.1f}  R²={m['R2']:.4f}\")\n\n    print(f\"⏳ {task_name}: Quantum SVR (precomputed)...\")\n    K_tr = quantum_kernel.evaluate(x_vec=Xtr_q)\n    K_te = quantum_kernel.evaluate(x_vec=Xte_q, y_vec=Xtr_q)\n    qsvr = SVR(kernel=\"precomputed\", C=10.0, epsilon=0.1)\n    qsvr.fit(K_tr, ytr_q)\n    pred_svr = qsvr.predict(K_te)\n    preds[task_key][\"Q_SVR\"] = pred_svr\n    m = metrics(yte, pred_svr, \"Q_SVR\", task_name)\n    all_results.append(m)\n    print(f\"Q_SVR           MAE={m['MAE']:>10,.1f}  R²={m['R2']:.4f}\")\n    return qkr, qsvr\n\nqkr_ww, qsvr_ww = run_quantum(Xtr_ww_q, ytr_ww, idx_ww, Xte_ww_q, yte_ww, \"WW\", \"WeekWise\")\nqkr_ts, qsvr_ts = run_quantum(Xtr_ts_q, ytr_ts, idx_ts, Xte_ts_q, yte_ts, \"TS\", \"TimeSeries\")\nprint(\"\\n✅ Quantum modeller tamam.\")\n",
   "id": "q_models"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a18"
   },
   "source": "## Aşama 18 — IBM Quantum Gerçek Backend (İskelet)\n\nProduction için: Session + SamplerV2 + transpilation + error mitigation.  \nEğitimde varsayılan: Statevector simülatör sonuçları geçerlidir.\n",
   "id": "a18"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ibm_real",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410266937,
     "user_tz": -180,
     "elapsed": 121,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "0bac30ee-14e6-4306-9cc5-bbaaf7cb1ca0"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ℹ️ IBM gerçek backend atlandı — FidelityStatevectorKernel sonuçları geçerli.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 18 — IBM REAL BACKEND SKELETON\n# ============================================================\nif USE_REAL_IBM and ibm_service is not None:\n    try:\n        from qiskit_ibm_runtime import SamplerV2 as Sampler, Session\n        from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n        from qiskit_machine_learning.kernels import FidelityQuantumKernel\n        from qiskit_machine_learning.state_fidelities import ComputeUncompute\n\n        backend = ibm_service.least_busy(\n            operational=True, simulator=False, min_num_qubits=N_QUBITS\n        )\n        print(f\"IBM backend: {backend.name}  (qubits≥{N_QUBITS})\")\n        print(\"\"\"\n        # Örnek production iskeleti:\n        # pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n        # with Session(backend=backend) as session:\n        #     sampler = Sampler(mode=session)\n        #     fidelity = ComputeUncompute(sampler=sampler)\n        #     qk = FidelityQuantumKernel(feature_map=feature_map, fidelity=fidelity)\n        #     ...\n        \"\"\")\n    except Exception as e:\n        print(f\"IBM gerçek backend hatası: {e}\")\nelse:\n    print(\"ℹ️ IBM gerçek backend atlandı — FidelityStatevectorKernel sonuçları geçerli.\")\n",
   "id": "ibm_real"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a19"
   },
   "source": "## Aşama 19 — Origin Wukong (pyQPanda) İskeleti\n\nWukong = **süperiletken** gate-based (Origin Quantum). Fotonik (Jiuzhang) değildir.  \nQiskit feature map → pyQPanda devre çevirisi veya native angle encoding ile kernel tahmini yapılabilir.\n",
   "id": "a19"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wukong",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410272140,
     "user_tz": -180,
     "elapsed": 98,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "9ff65d7d-e00b-4b16-a668-b0f2918502fa"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ℹ️ Wukong atlandı. Kurulum: pip install pyqpanda + Origin Cloud hesap.\n",
      "   Dokümantasyon: Origin Quantum / pyQPanda\n",
      "   Bu notebook'ta quantum sonuçlar Qiskit Statevector kernel'den gelir.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 19 — WUKONG / pyQPanda SKELETON\n# ============================================================\nif wukong_ready:\n    try:\n        import pyqpanda as pq\n        print(\"pyQPanda sürümü yüklü.\")\n        print(\"\"\"\n        # Wukong cloud örnek iskelet (kullanıcı API key ile doldurur):\n        # machine = pq.init_quantum_machine(pq.QMachineType.Cloud)\n        # qubits = machine.qAlloc_many(10)\n        # prog = pq.QProg()\n        # # Angle encoding / ZZ-benzeri entanglement katmanları\n        # for i, x in enumerate(features_10):\n        #     prog << pq.RY(qubits[i], x)\n        # for i in range(9):\n        #     prog << pq.CNOT(qubits[i], qubits[i+1])\n        # # fidelity / overlap tahmini → klasik KernelRidge\n        # result = machine.run(prog, shots=1024)\n        \"\"\")\n    except Exception as e:\n        print(f\"Wukong runtime: {e}\")\nelse:\n    print(\"ℹ️ Wukong atlandı. Kurulum: pip install pyqpanda + Origin Cloud hesap.\")\n    print(\"   Dokümantasyon: Origin Quantum / pyQPanda\")\n    print(\"   Bu notebook'ta quantum sonuçlar Qiskit Statevector kernel'den gelir.\")\n",
   "id": "wukong"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a20"
   },
   "source": "## Aşama 20 — Benchmark Raporu (Eşit Feature · İki Görev)\n",
   "id": "a20"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "report",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410279577,
     "user_tz": -180,
     "elapsed": 421,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "91a831f0-ef7d-49de-9632-143da7e242fd"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "==============================================================================\n",
      "BENCHMARK — Aynı n_features=10  |  Quantum encoding=10 qubit\n",
      "==============================================================================\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "          Task             Model     Family       MAE      RMSE  MAPE_%  \\\n",
       "0   TimeSeries           XGBoost  Classical   1208.23   1444.80    7.88   \n",
       "1   TimeSeries      RandomForest  Classical   1238.81   1506.15    7.84   \n",
       "2   TimeSeries  GradientBoosting  Classical   1255.28   1490.55    8.29   \n",
       "3   TimeSeries        ElasticNet  Classical   1277.24   1591.53    8.45   \n",
       "4   TimeSeries            HistGB  Classical   1283.09   1561.98    8.34   \n",
       "5   TimeSeries      DecisionTree  Classical   1287.25   1622.32    8.55   \n",
       "6   TimeSeries          LightGBM  Classical   1288.25   1562.36    8.48   \n",
       "7   TimeSeries             Ridge  Classical   1292.98   1633.52    8.72   \n",
       "8   TimeSeries             Lasso  Classical   1311.81   1656.76    8.86   \n",
       "9   TimeSeries           SVR_RBF  Classical   6075.94   7271.60   32.64   \n",
       "10  TimeSeries             Q_SVR    Quantum   6731.69   8050.49   35.71   \n",
       "11  TimeSeries     Q_KernelRidge    Quantum  16015.47  17222.62   83.10   \n",
       "12    WeekWise             Lasso  Classical   1061.36   1396.62    7.64   \n",
       "13    WeekWise             Ridge  Classical   1070.64   1386.13    7.73   \n",
       "14    WeekWise        ElasticNet  Classical   1107.17   1415.32    7.98   \n",
       "15    WeekWise      RandomForest  Classical   1121.02   1430.96    7.21   \n",
       "16    WeekWise  GradientBoosting  Classical   1160.32   1461.87    7.44   \n",
       "17    WeekWise            HistGB  Classical   1173.20   1477.23    7.45   \n",
       "18    WeekWise          LightGBM  Classical   1223.49   1493.19    7.83   \n",
       "19    WeekWise           XGBoost  Classical   1269.77   1589.02    8.15   \n",
       "20    WeekWise      DecisionTree  Classical   1582.29   2028.54    9.86   \n",
       "21    WeekWise           SVR_RBF  Classical   6018.36   7077.23   33.74   \n",
       "22    WeekWise             Q_SVR    Quantum   6489.34   7305.25   40.00   \n",
       "23    WeekWise     Q_KernelRidge    Quantum  14245.10  15348.64   73.98   \n",
       "\n",
       "        R2  n_features  Rank_in_Task  \n",
       "0   0.9603          10             1  \n",
       "1   0.9568          10             2  \n",
       "2   0.9577          10             3  \n",
       "3   0.9518          10             4  \n",
       "4   0.9536          10             5  \n",
       "5   0.9499          10             6  \n",
       "6   0.9536          10             7  \n",
       "7   0.9492          10             8  \n",
       "8   0.9478          10             9  \n",
       "9  -0.0059          10            10  \n",
       "10 -0.2329          10            11  \n",
       "11 -4.6428          10            12  \n",
       "12  0.9629          10             1  \n",
       "13  0.9634          10             2  \n",
       "14  0.9619          10             3  \n",
       "15  0.9610          10             4  \n",
       "16  0.9593          10             5  \n",
       "17  0.9585          10             6  \n",
       "18  0.9576          10             7  \n",
       "19  0.9520          10             8  \n",
       "20  0.9217          10             9  \n",
       "21  0.0471          10            10  \n",
       "22 -0.0152          10            11  \n",
       "23 -3.4817          10            12  "
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Task</th>\n",
       "      <th>Model</th>\n",
       "      <th>Family</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAPE_%</th>\n",
       "      <th>R2</th>\n",
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       "      <th>Rank_in_Task</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1208.23</td>\n",
       "      <td>1444.80</td>\n",
       "      <td>7.88</td>\n",
       "      <td>0.9603</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>RandomForest</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1238.81</td>\n",
       "      <td>1506.15</td>\n",
       "      <td>7.84</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1255.28</td>\n",
       "      <td>1490.55</td>\n",
       "      <td>8.29</td>\n",
       "      <td>0.9577</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1277.24</td>\n",
       "      <td>1591.53</td>\n",
       "      <td>8.45</td>\n",
       "      <td>0.9518</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>HistGB</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1283.09</td>\n",
       "      <td>1561.98</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.9536</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1287.25</td>\n",
       "      <td>1622.32</td>\n",
       "      <td>8.55</td>\n",
       "      <td>0.9499</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>LightGBM</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1288.25</td>\n",
       "      <td>1562.36</td>\n",
       "      <td>8.48</td>\n",
       "      <td>0.9536</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>Ridge</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1292.98</td>\n",
       "      <td>1633.52</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.9492</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1311.81</td>\n",
       "      <td>1656.76</td>\n",
       "      <td>8.86</td>\n",
       "      <td>0.9478</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>SVR_RBF</td>\n",
       "      <td>Classical</td>\n",
       "      <td>6075.94</td>\n",
       "      <td>7271.60</td>\n",
       "      <td>32.64</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>Q_SVR</td>\n",
       "      <td>Quantum</td>\n",
       "      <td>6731.69</td>\n",
       "      <td>8050.49</td>\n",
       "      <td>35.71</td>\n",
       "      <td>-0.2329</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TimeSeries</td>\n",
       "      <td>Q_KernelRidge</td>\n",
       "      <td>Quantum</td>\n",
       "      <td>16015.47</td>\n",
       "      <td>17222.62</td>\n",
       "      <td>83.10</td>\n",
       "      <td>-4.6428</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1061.36</td>\n",
       "      <td>1396.62</td>\n",
       "      <td>7.64</td>\n",
       "      <td>0.9629</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>Ridge</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1070.64</td>\n",
       "      <td>1386.13</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.9634</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1107.17</td>\n",
       "      <td>1415.32</td>\n",
       "      <td>7.98</td>\n",
       "      <td>0.9619</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>RandomForest</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1121.02</td>\n",
       "      <td>1430.96</td>\n",
       "      <td>7.21</td>\n",
       "      <td>0.9610</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1160.32</td>\n",
       "      <td>1461.87</td>\n",
       "      <td>7.44</td>\n",
       "      <td>0.9593</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>HistGB</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1173.20</td>\n",
       "      <td>1477.23</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.9585</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>LightGBM</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1223.49</td>\n",
       "      <td>1493.19</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.9576</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1269.77</td>\n",
       "      <td>1589.02</td>\n",
       "      <td>8.15</td>\n",
       "      <td>0.9520</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>Classical</td>\n",
       "      <td>1582.29</td>\n",
       "      <td>2028.54</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.9217</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>SVR_RBF</td>\n",
       "      <td>Classical</td>\n",
       "      <td>6018.36</td>\n",
       "      <td>7077.23</td>\n",
       "      <td>33.74</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>Q_SVR</td>\n",
       "      <td>Quantum</td>\n",
       "      <td>6489.34</td>\n",
       "      <td>7305.25</td>\n",
       "      <td>40.00</td>\n",
       "      <td>-0.0152</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WeekWise</td>\n",
       "      <td>Q_KernelRidge</td>\n",
       "      <td>Quantum</td>\n",
       "      <td>14245.10</td>\n",
       "      <td>15348.64</td>\n",
       "      <td>73.98</td>\n",
       "      <td>-3.4817</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
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     "text": [
      "\n",
      "--- Görev × Aile ortalaması ---\n"
     ]
    },
    {
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     "data": {
      "text/plain": [
       "                            MAE       RMSE  MAPE_%     R2\n",
       "Task       Family                                        \n",
       "TimeSeries Classical   1751.888   2134.157  10.805  0.857\n",
       "           Quantum    11373.580  12636.555  59.405 -2.438\n",
       "WeekWise   Classical   1678.762   2075.611  10.503  0.865\n",
       "           Quantum    10367.220  11326.945  56.990 -1.748"
      ],
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       "      <th>MAE</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">TimeSeries</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">WeekWise</th>\n",
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       "type": "dataframe",
       "summary": "{\n  \"name\": \"    print(f\\\"{task:12s} \\u2192 {best['Model']:16s}  MAE={best['MAE']:,\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.712056382133,\n        \"min\": 1678.762,\n        \"max\": 11373.58,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11373.58,\n          10367.22,\n          1751.888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5727.46991771713,\n        \"min\": 2075.611,\n        \"max\": 12636.555,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12636.555,\n          11326.945,\n          2134.157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAPE_%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.46722960638732,\n        \"min\": 10.503,\n        \"max\": 59.405,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          59.405,\n          56.99,\n          10.805\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.728602325579831,\n        \"min\": -2.438,\n        \"max\": 0.865,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -2.438,\n          -1.748,\n          0.857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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     "metadata": {}
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "--- Her görevde en iyi model ---\n",
      "WeekWise     → Lasso             MAE=1,061.4  R²=0.9629\n",
      "TimeSeries   → XGBoost           MAE=1,208.2  R²=0.9603\n"
     ]
    }
   ],
   "source": "# ============================================================\n# AŞAMA 20 — FULL BENCHMARK\n# ============================================================\nbench = pd.DataFrame(all_results)\nbench = bench.sort_values([\"Task\", \"MAE\"]).reset_index(drop=True)\nbench[\"Rank_in_Task\"] = bench.groupby(\"Task\").cumcount() + 1\n\nprint(\"=\" * 78)\nprint(\"BENCHMARK — Aynı n_features=10  |  Quantum encoding=10 qubit\")\nprint(\"=\" * 78)\ndisplay(bench)\n\nprint(\"\\n--- Görev × Aile ortalaması ---\")\ndisplay(bench.groupby([\"Task\", \"Family\"])[[\"MAE\", \"RMSE\", \"MAPE_%\", \"R2\"]].mean().round(3))\n\nprint(\"\\n--- Her görevde en iyi model ---\")\nfor task in [\"WeekWise\", \"TimeSeries\"]:\n    best = bench[bench.Task == task].iloc[0]\n    print(f\"{task:12s} → {best['Model']:16s}  MAE={best['MAE']:,.1f}  R²={best['R2']}\")\n",
   "id": "report"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "viz_bench",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410289626,
     "user_tz": -180,
     "elapsed": 1091,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cef84f8e-a133-4aea-8952-3cc09aa83f37"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1300x800 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "# Görseller\nfig, axes = plt.subplots(1, 2, figsize=(14, 6))\nfor ax, task, title in zip(axes, [\"WeekWise\", \"TimeSeries\"], [\"Week-Wise\", \"Time Series\"]):\n    sub = bench[bench.Task == task].sort_values(\"MAE\")\n    colors = [\"#9b59b6\" if f == \"Quantum\" else \"#3498db\" for f in sub[\"Family\"]]\n    ax.barh(sub[\"Model\"], sub[\"MAE\"], color=colors, alpha=0.85)\n    ax.set_xlabel(\"MAE\")\n    ax.set_title(f\"{title} — MAE (10 feature)\")\n    ax.grid(True, axis=\"x\", alpha=0.3)\nplt.tight_layout()\nplt.show()\n\n# Prediksiyon overlay — WeekWise\nweeks = meta_ww[\"week\"].values if meta_ww is not None else np.arange(1, len(yte_ww) + 1)\nbest_ww = bench[bench.Task == \"WeekWise\"].iloc[0][\"Model\"]\nbest_ts = bench[bench.Task == \"TimeSeries\"].iloc[0][\"Model\"]\n\nfig, axes = plt.subplots(2, 1, figsize=(13, 8), sharex=False)\naxes[0].plot(weeks, yte_ww, \"o-\", label=\"Gerçek\", color=\"#2c3e50\", linewidth=2)\naxes[0].plot(weeks, preds[\"WW\"][best_ww], \"s--\", label=f\"Best {best_ww}\", alpha=0.85)\nif \"Q_KernelRidge\" in preds[\"WW\"]:\n    axes[0].plot(weeks, preds[\"WW\"][\"Q_KernelRidge\"], \"D--\", label=\"Q_KernelRidge\", color=\"#9b59b6\", alpha=0.8)\naxes[0].set_title(\"Week-Wise Test (2025)\")\naxes[0].legend()\naxes[0].grid(True, alpha=0.3)\n\n# TS test: 2025 noktaları sıralı\naxes[1].plot(yte_ts, \"o-\", label=\"Gerçek\", color=\"#2c3e50\", linewidth=1.5, markersize=3)\naxes[1].plot(preds[\"TS\"][best_ts], \"s--\", label=f\"Best {best_ts}\", alpha=0.85, markersize=3)\nif \"Q_KernelRidge\" in preds[\"TS\"]:\n    axes[1].plot(preds[\"TS\"][\"Q_KernelRidge\"], \"D--\", label=\"Q_KernelRidge\", color=\"#9b59b6\", alpha=0.8, markersize=3)\naxes[1].set_title(\"Time-Series Test (2025 sıralı)\")\naxes[1].legend()\naxes[1].grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()\n",
   "id": "viz_bench"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a21"
   },
   "source": "## Aşama 21 — 2026 Forecast, Karar Özeti ve Kapanış\n",
   "id": "a21"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "forecast",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 876
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1789410305224,
     "user_tz": -180,
     "elapsed": 434,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f253e92a-91a6-4dcd-fa7d-53ed83a11c9f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "2026 forecast modeli (Week-Wise): Lasso\n",
      "2026 toplam: 1,068,036\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   week  forecast_2026\n",
       "0     1        10363.4\n",
       "1     2        10434.9\n",
       "2     3        11700.3\n",
       "3     4        12139.4\n",
       "4     5        12348.3\n",
       "5     6        12874.4\n",
       "6     7        12758.3\n",
       "7     8        14173.6\n",
       "8     9        13990.2\n",
       "9    10        14805.1"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-f3af113c-df63-46c1-9737-470778a148ad\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>forecast_2026</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10363.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10434.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11700.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12139.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12348.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>12874.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12758.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>14173.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13990.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>14805.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3af113c-df63-46c1-9737-470778a148ad')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-f3af113c-df63-46c1-9737-470778a148ad button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-f3af113c-df63-46c1-9737-470778a148ad');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
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       "\n",
       "\n",
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       "  </div>\n"
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      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"forecast_2026\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1495.538309959179,\n        \"min\": 10363.4,\n        \"max\": 14805.1,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          13990.2,\n          10434.9,\n          12874.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "source": "# ============================================================\n# AŞAMA 21 — 2026 WEEK-WISE FORECAST (en iyi WW model)\n# ============================================================\nbest_ww_name = bench[bench.Task == \"WeekWise\"].iloc[0][\"Model\"]\nprint(f\"2026 forecast modeli (Week-Wise): {best_ww_name}\")\n\nfuture_year = 2026\nyears_sorted = sorted(df[\"year\"].unique())\nrows = []\nfor week in range(1, N_WEEKS + 1):\n    lags = []\n    for k in range(1, 11):\n        past = df[(df.year == future_year - k) & (df.week == week)]\n        lags.append(past[\"sales\"].values[0] if not past.empty else np.nan)\n    valid = [v for v in lags if not np.isnan(v)]\n    fill = float(np.mean(valid)) if valid else 0.0\n    lags_f = [fill if np.isnan(v) else float(v) for v in lags]\n    rec = {\n        \"week\": week,\n        \"year_idx\": years_sorted.index(years_sorted[-1]) + 1,\n        \"sin_week\": np.sin(2 * np.pi * week / 53),\n        \"cos_week\": np.cos(2 * np.pi * week / 53),\n        \"lag_mean\": float(np.mean(lags_f)),\n        \"lag_std\": float(np.std(lags_f)) if len(lags_f) > 1 else 0.0,\n        \"lag_min\": float(np.min(lags_f)),\n        \"lag_max\": float(np.max(lags_f)),\n    }\n    for k, v in enumerate(lags_f, 1):\n        rec[f\"lag_y{k}\"] = v\n    rows.append(rec)\n\nfut = pd.DataFrame(rows)\n# eksik kolon varsa 0\nfor c in selected_ww:\n    if c not in fut.columns:\n        fut[c] = 0.0\nX_fut = sc_ww.transform(fut[selected_ww].values)\n\nif best_ww_name in classical:\n    y_fut = classical[best_ww_name].predict(X_fut)\nelif best_ww_name == \"Q_KernelRidge\":\n    Xf = qsc_ww.transform(X_fut)\n    y_fut = qkr_ww.predict(Xf)\nelif best_ww_name == \"Q_SVR\":\n    Xf = qsc_ww.transform(X_fut)\n    K_f = quantum_kernel.evaluate(x_vec=Xf, y_vec=Xtr_ww_q)\n    y_fut = qsvr_ww.predict(K_f)\nelse:\n    model = classical.get(\"XGBoost\", list(classical.values())[0])\n    y_fut = model.predict(X_fut)\n\nforecast = pd.DataFrame({\"week\": range(1, N_WEEKS + 1), \"forecast_2026\": np.round(y_fut, 1)})\nprint(f\"2026 toplam: {forecast['forecast_2026'].sum():,.0f}\")\ndisplay(forecast.head(10))\n\nplt.figure(figsize=(13, 5))\nplt.plot(meta_ww[\"week\"], yte_ww, \"o-\", label=\"2025 Gerçek\", color=\"#2c3e50\")\nplt.plot(forecast[\"week\"], forecast[\"forecast_2026\"], \"D-\", label=f\"2026 ({best_ww_name})\", color=\"#e67e22\")\nplt.title(\"2025 Gerçek vs 2026 Week-Wise Forecast\")\nplt.xlabel(\"Hafta\")\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()\n",
   "id": "forecast"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "close"
   },
   "source": "---\n\n### Karar Özeti (Decision Tree Çıktısı)\n\n| Soru | Cevap |\n|------|--------|\n| Week-wise mi TS mi daha iyi? | Benchmark tablosundaki MAE’ye bakın (genelde week-wise mevsimsel üründe önde) |\n| Klasik mi Quantum mu? | Aynı 10 feature ile kıyaslandı; gradient boosting çoğu zaman önde |\n| 10 qubit yeterli mi? | Encoding boyutu = feature boyutu; bilgi kaybı minimize edildi |\n| IBM / Wukong? | İskelet hazır; üretimde Session + error mitigation / pyQPanda Cloud |\n\n### 21 Aşama Tamamlandı\n\n1 Problem & karar ağacı → 2 Kurulum → 3 Donanım → 4 Veri → 5 Kalite →  \n6–8 EDA → 9–10 Feature (WW + TS) → 11 Ortak 10 özellik → 12 Split → 13 Metrik →  \n14–15 Klasik → 16 Encoding (10 qubit) → 17 Quantum → 18 IBM → 19 Wukong →  \n20 Benchmark → 21 Forecast & kapanış\n\n**Ana mesaj:** Eşit özellik / eşit qubit kuralı adil karşılaştırma sağlar.  \nDondurma gibi mevsimsel serilerde week-wise regression güçlü bir bazdır; quantum kernel pipeline’a entegre edilebilir, NISQ’da klasik boosting sıklıkla daha düşük hata verir.\n",
   "id": "close"
  }
 ]
}