{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "db5323da",
   "metadata": {
    "id": "db5323da"
   },
   "source": "# IGA Istanbul Airport – Gerçekçi Gate & Uçuş Atama Optimizasyonu (v4 – MARS modeli + Narrator Engine)\n## Applied Quantum Machine Learning (21 Aşamalı QML Şablonu)\n\n**Referans:** Joseph Volkan Özcan – Quantum Transform / IGA Quantum Journey\n**Hedef:** Gerçek IGA kapasite verilerine dayalı, Colab'da çalıştırılabilir hibrit QML PoC\n**Sürüm:** v4 – BVK Technology, Ekim 2026\n\n---\n\n### v1'e göre düzeltmeler (uçtan uca çalıştırılarak doğrulandı)\n\n| # | Sorun (v1) | Düzeltme (v2) |\n|---|-----------|---------------|\n| 1 | Ceza katsayısı `P=180`, metre cinsinden maliyetlerin (ort. ~520 m) altında → QUBO minimumu \"hiçbir uçuşu atama\" | `P = 2 × maks(maliyet)` (Aşama 6) |\n| 2 | 8×10 = **80 kübit** alt-problem statevector ile simüle edilemez (2⁸⁰) | Colab'da **16 kübit** (4×4); 80 kübit için gerçek QPU / D-Wave Leap (Aşama 15) |\n| 3 | `qiskit.primitives.Sampler` (V1) Qiskit 2.x'te kaldırıldı; Pauli evrim kapısı transpile edilmezse çalışma askıda kalır (`expm`) | `StatevectorSampler` + `qiskit-algorithms 0.4` + açık `transpiler` (Aşama 1, 7) |\n| 4 | `N_GATES // 5 = 9` → 5 pier × 9 = **45 kapı**, 48 değil | Pier başına kapı sayısı açıkça verilir; 48 kapı (Aşama 3) |\n| 5 | İlk N uçuş × ilk M kapı alt-problemi geniş gövde uçuş için geniş kapı içermeyebilir → ILP uygunsuz | **Pier kümesi ayrıştırması**: uçuşların tam-bank ILP'de aldığı kapılar + en ucuz ek kapılar (Aşama 7) |\n| 6 | QAOA başlangıç noktası tohumlanmamış → koşular arası farklı sonuç | `algorithm_globals.random_seed` + sampler seed (Aşama 1) |\n| 7 | Ham QAOA örnekleri sert kısıtları ihlal ediyor | **Kuantum-yönlendirmeli klasik onarım** (marjinal olasılıklara göre geri-izlemeli atama) (Aşama 12) |\n| 8 | Geniş gövde kapı tek pozisyon gibi modellenmişti; oysa MARS kapısı **ya 1 geniş gövde ya 2 dar gövde** alır | **MARS modeli**: geniş kapı = 2 park pozisyonu (L/R); dar uçuş 1 pozisyon, geniş uçuş her ikisini kaplar; çakışma = ayak izi kesişimi (Aşama 5–6) |\n| 9 | Sonuçlar dağınık; paydaşlara anlatılacak bir çıktı yoktu | **Narrator Engine** (Aşama 22): tüm sonuçları toplar, pazarlama odaklı Türkçe yorumlar ve grafikli Word raporu üretir |\n\n### Gerçek IGA Rakamları (2025–2026)\n\n| Metrik | Değer | Kaynak / Not |\n|--------|-------|--------------|\n| **Pist (Runway)** | **6** (4 ana + 2 yardımcı) | 4. ana pist Eylül 2026'da açıldı |\n| **Saatlik hareket kapasitesi** | 120 → 148 | Triple independent parallel operations (Nisan 2025) |\n| **Günlük ortalama (2025)** | ~1.490–1.500 | Avrupa'nın en yoğun havalimanı |\n| **Tek günlük rekor** | **1.787** hareket (30 Ağu 2026) | Resmi rekor |\n| **Yıllık yolcu (2025)** | 84,5 milyon | Avrupa 2. sırada |\n| **Contact Gate / Stand (yaklaşık)** | **~110–140** | Pier A/B/D/F/G + remote stands; modelde 48 contact gate |\n\n> **Not:** Tam ölçek (1.500+ uçuş/gün × 120+ gate) klasik exact solver ve NISQ QAOA için imkânsızdır.\n> Bu notebook **gerçekçi bir peak-period alt-problemi** (2 saatlik bank, 48 gate, 36 uçuş) üzerinde hibrit QML pipeline'ını gösterir ve ölçeklenebilirlik tartışmasını içerir."
  },
  {
   "cell_type": "markdown",
   "id": "8c2e7072",
   "metadata": {
    "id": "8c2e7072"
   },
   "source": "## AŞAMA 1 – Ortam Kurulumu"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c796633c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:46:52.917980Z",
     "iopub.status.busy": "2026-10-02T18:46:52.917495Z",
     "iopub.status.idle": "2026-10-02T18:47:01.876760Z",
     "shell.execute_reply": "2026-10-02T18:47:01.875921Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c796633c",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967215777,
     "user_tz": -180,
     "elapsed": 28300,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e4c5363b-e460-47f9-d6df-4104cdbdf1ea"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/664.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m471.0/664.0 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.0/664.0 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m84.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.4/12.4 MB\u001b[0m \u001b[31m103.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m327.8/327.8 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m237.1/237.1 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m66.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.9/54.9 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for docplex (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "✓ Qiskit 2.5.2 · qiskit-algorithms 0.4.0 · qiskit-optimization 0.7.0\n",
      "Notebook gerçek IGA 2025-2026 verilerine göre ölçeklendirilmiştir.\n"
     ]
    }
   ],
   "source": "# Colab için kütüphane kurulumu (bir kere çalıştırın)\n!pip install -q \"qiskit>=2.0\" qiskit-aer \"qiskit-algorithms>=0.4\" \"qiskit-optimization>=0.7\" \\\n               matplotlib seaborn pandas numpy networkx pulp scikit-learn\n\nimport time, warnings\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport networkx as nx\nimport pulp\nwarnings.filterwarnings('ignore')\n\n# Qiskit 2.x: V1 Sampler kaldırıldı → StatevectorSampler (V2)\nfrom qiskit.primitives import StatevectorSampler\nfrom qiskit.transpiler import generate_preset_pass_manager\nfrom qiskit_algorithms import QAOA\nfrom qiskit_algorithms.optimizers import COBYLA\nfrom qiskit_algorithms.utils import algorithm_globals\nfrom qiskit_optimization import QuadraticProgram\nfrom qiskit_optimization.algorithms import MinimumEigenOptimizer\n\n# Tekrarlanabilirlik (düzeltme #6)\nSEED = 42\nnp.random.seed(SEED)\nalgorithm_globals.random_seed = SEED\n\n# Pauli evrim kapısını temel kapılara açan pass manager (düzeltme #3).\n# Bu verilmezse StatevectorSampler devasa matris üstelini (expm) hesaplamaya çalışır ve çalışma askıda kalır.\nPM = generate_preset_pass_manager(optimization_level=1, basis_gates=['rz', 'rx', 'h', 'cx', 'rzz', 'x', 'sx'])\n\n# Grafik stili (BVK paleti)\nNAVY, BLUE, AMBER, TEAL, GRAY, LIGHT = '#182B5E', '#3D5FD0', '#D0820F', '#1F9A8A', '#7A879C', '#EEF4FB'\nplt.rcParams.update({'axes.spines.top': False, 'axes.spines.right': False, 'axes.titleweight': 'bold', 'figure.dpi': 110})\n\nimport qiskit, qiskit_algorithms, qiskit_optimization\nprint(f'✓ Qiskit {qiskit.__version__} · qiskit-algorithms {qiskit_algorithms.__version__} · qiskit-optimization {qiskit_optimization.__version__}')\nprint('Notebook gerçek IGA 2025-2026 verilerine göre ölçeklendirilmiştir.')"
  },
  {
   "cell_type": "markdown",
   "id": "69683549",
   "metadata": {
    "id": "69683549"
   },
   "source": "## AŞAMA 2 – Problem Tanımı (Gerçek IGA Bağlamı)"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69238133",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:01.880393Z",
     "iopub.status.busy": "2026-10-02T18:47:01.879705Z",
     "iopub.status.idle": "2026-10-02T18:47:01.886377Z",
     "shell.execute_reply": "2026-10-02T18:47:01.885706Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "69238133",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967216326,
     "user_tz": -180,
     "elapsed": 751,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "bfeb73f0-8457-4252-a512-2755cc7f6bf1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "╔══════════════════════════════════════════════════════════════════════════════╗\n",
      "║  IGA ISTANBUL AIRPORT – GATE ASSIGNMENT PROBLEM (GAP)                        ║\n",
      "╠══════════════════════════════════════════════════════════════════════════════╣\n",
      "║  Gerçek durum:                                                               ║\n",
      "║  • 6 pist, triple independent operations                                     ║\n",
      "║  • Peak gün: 1.700+ hareket                                                  ║\n",
      "║  • ~110-140 contact gate + remote stands                                     ║\n",
      "║  • Transfer oranı yüksek (~%45-50) → yürüme mesafesi kritik                  ║\n",
      "║                                                                              ║\n",
      "║  Optimizasyon hedefleri:                                                     ║\n",
      "║  1. Yolcu yürüme mesafesini / connection time'ı minimize et                  ║\n",
      "║  2. Gate çakışmasını (overlap) tamamen engelle (hard)                        ║\n",
      "║  3. MARS kapı kuralı: ya 1 geniş gövde ya 2 dar gövde aynı anda (hard)      ║\n",
      "║  4. Idle / turnaround süresini azalt                                         ║\n",
      "║  5. Gerçek zamanlı disruption'a (hava, gecikme) hızlı re-optimize et         ║\n",
      "║                                                                              ║\n",
      "║  Bu notebook: 2 saatlik peak bank üzerinde 36 uçuş × 48 gate modeli          ║\n",
      "╚══════════════════════════════════════════════════════════════════════════════╝\n",
      "\n"
     ]
    }
   ],
   "source": "print('''\n╔══════════════════════════════════════════════════════════════════════════════╗\n║  IGA ISTANBUL AIRPORT – GATE ASSIGNMENT PROBLEM (GAP)                        ║\n╠══════════════════════════════════════════════════════════════════════════════╣\n║  Gerçek durum:                                                               ║\n║  • 6 pist, triple independent operations                                     ║\n║  • Peak gün: 1.700+ hareket                                                  ║\n║  • ~110-140 contact gate + remote stands                                     ║\n║  • Transfer oranı yüksek (~%45-50) → yürüme mesafesi kritik                  ║\n║                                                                              ║\n║  Optimizasyon hedefleri:                                                     ║\n║  1. Yolcu yürüme mesafesini / connection time'ı minimize et                  ║\n║  2. Gate çakışmasını (overlap) tamamen engelle (hard)                        ║\n║  3. MARS kapı kuralı: ya 1 geniş gövde ya 2 dar gövde aynı anda (hard)      ║\n║  4. Idle / turnaround süresini azalt                                         ║\n║  5. Gerçek zamanlı disruption'a (hava, gecikme) hızlı re-optimize et         ║\n║                                                                              ║\n║  Bu notebook: 2 saatlik peak bank üzerinde 36 uçuş × 48 gate modeli          ║\n╚══════════════════════════════════════════════════════════════════════════════╝\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "f2a99ebe",
   "metadata": {
    "id": "f2a99ebe"
   },
   "source": "## AŞAMA 3 – Gerçekçi Sentetik Veri (IGA Ölçekli)"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42047830",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:01.888982Z",
     "iopub.status.busy": "2026-10-02T18:47:01.888578Z",
     "iopub.status.idle": "2026-10-02T18:47:01.952740Z",
     "shell.execute_reply": "2026-10-02T18:47:01.951408Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511
    },
    "id": "42047830",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967217032,
     "user_tz": -180,
     "elapsed": 550,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "94378fc9-b702-4c69-83a3-04c54e51d693"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Gate sayısı          : 48  ({'A': 10, 'B': 10, 'D': 10, 'F': 9, 'G': 9})\n",
      "Uçuş sayısı (bank)   : 36\n",
      "Wide-body (MARS) gate sayısı: 13  → park pozisyonu toplamı: 61\n",
      "Wide-body uçuş sayısı: 14\n",
      "Time slot sayısı     : 12 (≈ 2 saat)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   flight_id  arrival_slot  turnaround_slots  passengers aircraft_type  \\\n",
       "0      TK571             0                 3         327        Narrow   \n",
       "1      TK367             0                 3         315        Narrow   \n",
       "2      EK319             0                 2         247        Narrow   \n",
       "3      UA484             0                 3         126          Wide   \n",
       "4      AF835             1                 3         188        Narrow   \n",
       "5      TK596             1                 3         353        Narrow   \n",
       "6      CA229             1                 3         184        Narrow   \n",
       "7      TK485             2                 3         186          Wide   \n",
       "8      TK712             2                 2          93        Narrow   \n",
       "9      TK724             2                 4         124        Narrow   \n",
       "10     PC180             2                 3         281          Wide   \n",
       "11     TK486             3                 3         373        Narrow   \n",
       "\n",
       "     origin  is_transfer_heavy  departure_slot  \n",
       "0       AMS                  1               3  \n",
       "1   IST-DOM                  1               3  \n",
       "2       FRA                  0               2  \n",
       "3       SIN                  1               3  \n",
       "4       AYT                  0               4  \n",
       "5       AMS                  1               4  \n",
       "6       JFK                  1               4  \n",
       "7       SIN                  0               5  \n",
       "8       JFK                  0               4  \n",
       "9       FRA                  0               6  \n",
       "10      DXB                  0               5  \n",
       "11      LHR                  1               6  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-b94fb581-0868-48e9-b4e0-3479e1639101\" class=\"colab-df-container\">\n",
       "    <div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flight_id</th>\n",
       "      <th>arrival_slot</th>\n",
       "      <th>turnaround_slots</th>\n",
       "      <th>passengers</th>\n",
       "      <th>aircraft_type</th>\n",
       "      <th>origin</th>\n",
       "      <th>is_transfer_heavy</th>\n",
       "      <th>departure_slot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TK571</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>327</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>AMS</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TK367</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>315</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>IST-DOM</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EK319</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>247</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>FRA</td>\n",
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       "    </tr>\n",
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       "      <td>UA484</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>Wide</td>\n",
       "      <td>SIN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>AF835</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>AYT</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TK596</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>AMS</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CA229</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>184</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>JFK</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TK485</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>186</td>\n",
       "      <td>Wide</td>\n",
       "      <td>SIN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TK712</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>93</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>JFK</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TK724</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>FRA</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PC180</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>281</td>\n",
       "      <td>Wide</td>\n",
       "      <td>DXB</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TK486</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>373</td>\n",
       "      <td>Narrow</td>\n",
       "      <td>LHR</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "    .colab-df-buttons div {\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "                                                    [key], {});\n",
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      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"display(flight_data\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"flight_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"PC180\",\n          \"TK724\",\n          \"TK571\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"arrival_slot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"turnaround_slots\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 93,\n        \"max\": 373,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          281,\n          124,\n          327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aircraft_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Wide\",\n          \"Narrow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"IST-DOM\",\n          \"JFK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_transfer_heavy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"departure_slot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    }
   ],
   "source": "# ============================================================\n# GERÇEK IGA PARAMETRELERİNE DAYALI MODEL BOYUTLARI\n# ============================================================\nN_FLIGHTS  = 36          # 2 saatlik peak bank (gerçek peak saatte ~60-80+ hareket)\nTIME_SLOTS = 12          # 10 dakikalık dilimler (2 saat)\n\n# Düzeltme #4: pier başına kapı sayısı açıkça verilir → toplam tam 48 kapı\nPIER_GATES = {'A': 10, 'B': 10, 'D': 10, 'F': 9, 'G': 9}\npiers = list(PIER_GATES)\ngates = [f'{p}{i:02d}' for p in piers for i in range(1, PIER_GATES[p] + 1)]\nN_GATES = len(gates)\nassert N_GATES == 48, N_GATES\n\n# Gate pozisyonları (metre – terminal merkezine göre); pier'ler x ekseninde 350 m aralıklı\ngate_positions = {}\nfor g in gates:\n    p = g[0]; k = int(g[1:]) - 1\n    gate_positions[g] = np.array([piers.index(p) * 350 + np.random.uniform(-30, 30),\n                                  k * 45 + np.random.uniform(-10, 10)])\n\n# Gate kapasitesi: Wide = MARS kapısı (2 park pozisyonu: ya 1 geniş ya 2 dar gövde), ~%28\nwide_gates = set(np.random.choice(gates, size=int(N_GATES * 0.28), replace=False))\ngate_capacity = {g: 'Wide' if g in wide_gates else 'Narrow' for g in gates}\n\n# Uçuş verisi (Turkish Airlines ağırlıklı hub yapısı)\nairlines = ['TK'] * 22 + ['PC', 'XQ', 'VF', 'BA', 'LH', 'EK', 'QR', 'SQ', 'AF', 'KL', 'UA', 'AA', 'DL', 'CA']\nflight_ids = [f'{a}{np.random.randint(100, 999)}' for a in airlines[:N_FLIGHTS]]\n\nflight_data = pd.DataFrame({\n    'flight_id': flight_ids,\n    'arrival_slot': np.random.randint(0, TIME_SLOTS - 3, N_FLIGHTS),\n    'turnaround_slots': np.random.choice([2, 3, 4], N_FLIGHTS, p=[0.5, 0.35, 0.15]),\n    'passengers': np.random.randint(90, 380, N_FLIGHTS),\n    'aircraft_type': np.random.choice(['Narrow', 'Wide'], N_FLIGHTS, p=[0.72, 0.28]),\n    'origin': np.random.choice(['ESB','ADB','AYT','FRA','LHR','DXB','JFK','SIN','CDG','AMS','IST-DOM'], N_FLIGHTS),\n    'is_transfer_heavy': np.random.choice([0, 1], N_FLIGHTS, p=[0.45, 0.55])\n})\nflight_data['departure_slot'] = flight_data['arrival_slot'] + flight_data['turnaround_slots']\nflight_data = flight_data.sort_values('arrival_slot').reset_index(drop=True)\n\nprint(f'Gate sayısı          : {N_GATES}  ({dict(PIER_GATES)})')\nprint(f'Uçuş sayısı (bank)   : {N_FLIGHTS}')\nprint(f'Wide-body (MARS) gate sayısı: {len(wide_gates)}  → park pozisyonu toplamı: {N_GATES + len(wide_gates)}')\nprint(f'Wide-body uçuş sayısı: {(flight_data.aircraft_type == \"Wide\").sum()}')\nprint(f'Time slot sayısı     : {TIME_SLOTS} (≈ 2 saat)')\ndisplay(flight_data.head(12))"
  },
  {
   "cell_type": "markdown",
   "id": "09453cf5",
   "metadata": {
    "id": "09453cf5"
   },
   "source": "## AŞAMA 4 – Maliyet Matrisi ve Constraint Tanımları"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5700c62d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:01.956076Z",
     "iopub.status.busy": "2026-10-02T18:47:01.955163Z",
     "iopub.status.idle": "2026-10-02T18:47:02.858740Z",
     "shell.execute_reply": "2026-10-02T18:47:02.856660Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "5700c62d",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967218576,
     "user_tz": -180,
     "elapsed": 1350,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d6f9c56a-54da-443e-d8d4-d3fa059f56d7"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Maliyet matrisi şekli: (36, 48)\n",
      "Ortalama yürüme maliyeti: 546.6 m   Min / Max: 43.7 / 1134.6 m\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1320x550 with 2 Axes>"
      ],
      "image/png": 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DdePGjRWKU7jrDADp+++/N3q/ZcuWRb7EjhkzRn4/Ly9PWrRokeTv7y85OzsX+6XX19fXKGZpP0IMBoNRMmfGjBlGSYXCrZ8KujOWZseOHfLyTZs2lef36tVLnl/4L+NVTVgpFArp0KFDRcpR1bg2NjZyV47r168bbd8pU6bInxk/frw8v1+/fvL8tm3bGu2/wtu0cMutN954o8xt+uh5dvz4cUmSHu67wt1hBg4cKH+mcELk+eefN4q3d+9eadCgQZKHh4dkbW1d7A/cI0eOVGh7FU6E3b17V271aGlpKaWlpUmSJElz586Vly/c8qcy26oqg+UXPjbq1q0rJ/2Sk5ONtsGiRYvkzxTuDhgWFmYUb/ny5VL37t3lLp2PbktnZ+cS11/cYOOPXjeLG8S8pETY999/L88bOnSodOrUKSkvL6/EbVHc9bM0hbdRu3btyoxZ+FwZN25csefXP//8Y/SZggR9bGysPE+tVkvbt2+Xj4sNGzYYfaZwkr6sAbjLun5kZGQY7cfvvvvO6JgsHP+XX34pEr9wq8fk5OQyt+nhw4eL/PGjIlNFuoeVlAgbMWKEUde19PT0IstNmzZN2rRpk9z6qeD8LvyHmpJUNBFWnb8jSJIkTZo0SQIetngsSPKUdc178OCB9O9//1tydHQs9pqu0+mkixcvlrnuAwcOGH2ucNf0o0ePGr1XeFtnZmZKERER0osvvig5ODjIf0QrPPXv399oXaXtt6qeh0REzyoOlk9kBiwsLIxeS5Jk9NpgMBS7/LFjx4zmDxgwQGi5GjdujJ49e2LTpk04cuQIfH19oVAooNVq0bZtW7z22mvo169fhWJaWFjgq6++QlxcHK5evQoAGDlyJHr16lWhOI6Ojkavb968WaHPv/nmm/jll19KXebWrVvljvfPP//g+vXr8uuZM2di5syZxS575MiRMuP5+/vjueeew6FDh3Dy5Ens3LkTbdq0wbZt2wAAzs7OxQ4oXFmenp5o06aNsHgFmjVrBicnJwAPy1zYSy+9JP/bxcVF/nfh7V74GF+8eDEWL15c7HrKs00Lc3BwkAdcVigUcHJykge7Lk+5fv/9d/Ts2RN5eXmlrqcix9CjatSogTfeeANfffUV8vPz8dNPP2H69On473//Ky/z5ptvyv821bYqjxdffBFWVg+/UlRmP4eHh5d4vhS3fGX079+/3Mv27dsXM2fORHp6OpYvX47ly5dDqVTC09MTHTt2xFtvvQVfX98qlafAo9f74nTo0EH+d+Ht27RpU/n8Krxtgf9tr8LHxf3799G1a9cS13PkyJFKPbihuOvHyZMnjc6Pt956q9T1Pqo826WwCRMmVOmhKK+//jqWLFlSrmVHjx6NoKAg3LlzB/v378f8+fNx9+5d/Prrrzh9+jT++OMPAICNjY3R59q1a4dZs2YBeHiNj4mJwYMHD5Cfn4+tW7di7NixlS5/carzd4T9+/fjq6++gpWVFZYtW1buhzf0798fcXFxAIDQ0FDMmTMHFhYW+OCDD7B48WIkJibi5ZdfxsGDB4t8dyvs9OnT8r8VCoXRAzVatmwJBwcHZGRkGH1GkiT07t0bCQkJpZaxItfCqp6HRETPqpKv4ET02Gg0GqPXBU+XKul1ZZ8wVfipZIW/GD0av/Dy69atw08//YRBgwahdevWsLW1xcWLF7FmzRr0798fCxcurFAZ7t69i549e+Lq1avyD+fFixdj3rx5FYrTpEkTo+1WkCAqcPToUUiShM6dOxf57OXLl42SYK+99ho2bdqEXbt2YcqUKfL8RxOQpanIE9/u3btXruUKP3ns+++/x9q1a5GbmwsAGDZsGFQqVbHrfzQ5U9L+LaxOnTrFzq9q3MLH6qM/GhwcHIr9TOEfwOXdruXdpsWV69Gyladcc+fOlbdHkyZNsGzZMuzcuROJiYlGn6nIMVSc8ePHy9vgp59+wtmzZ7F//34AwHPPPWeUjDHVtiqPquzn3NxcfP755/L8oKAgbNiwAbt27cIXX3xRZPnKKukYL46LiwsOHjyIiIgI9OrVC56enpAkCX///Td++OEHdOjQASkpKZUui6urq/zv8vyoLWn7lrRtgf9tL1Ncmx5V3Lat6noL/3Gj8BNrzYG7uzv8/PzQu3dvzJgxA/Pnz5ffS0pKkpMgTk5ORvephg0byv+2s7ND7dq15dePJkWKU9r1uOAJpFXxLH1HSE9PhyRJyMvLQ/v27aFQKKBQKPDGG2/Iy1y4cAEKhQLe3t4AgHPnzslJMACYN28eXFxc4OTkhIiICHn+oUOHcOrUqQqVpzySkpKMkmCTJk3C1q1bsWvXLowYMUKe/6S/mxARPQuYCCMyA61atTJ6vXHjRqPXGzZskP+tUqng5eUFAGjRooXRcmvWrCkSu/CPx8KtqNLT0+V/x8TEFFsuSZKgUqnwxhtvYOXKlTh8+DDu3buHFStWyMssXbq0xHo9KiMjA926dcPu3btRs2ZNJCQk4P333wcATJkyBZ988km5YymVSrz22mvy6/j4eKxcubJcn7148aLR6++++w5BQUHw8/MrtWVZ4R+gj34RdXFxMfpxu2LFCkgPu58bTdnZ2Ub7szRDhw6VW3usXr0aP/zwg/zeqFGjjJYtad+ePn0aJ0+eLHNdJX1ZrmrcqipotQUAc+bMKXab5ufnVykpURmFj6GJEydi6NCh6NSpEywtLYWup0mTJggKCgLw8Efb6NGj5fcePQYqs61KO6Yfl5s3b8qt8YCHPz5ffvll+Pn5lfrDrKJlr8gPQkmS4OrqiqlTp2Ljxo04ffo07t+/Lyenc3JyjFrmVZS7u7uc3Lpw4QKys7MrHasshY8LR0dH3L9/v9hj4969exg0aJC8bEW2b3HbtkmTJkbnQ1JSUonr/fbbb40+++DBA/kcs7e3h1arLbOeiYmJxcYv71Se1mCZmZnFzn80+VuQ1FIoFPD395fnp6amGtXx2rVr8uvCSbKSlHQ93r59u9E5VKA6f0eojMKtugHgzp07RmUr7O7du6XGKvieBjzcTnv37pVfHz16tNjEZ+H7irOzM7744gsEBgbCz88Ply5dKnFdpZ2rVTkPiYieZewaSWQGPD090aFDB7k7xSeffIJTp07By8sLBw4cMEqMDRgwALa2tgAeNq/v2LEj9uzZAwAYO3YsTp8+jY4dO+L+/fvYuXMn6tWrh6lTpwJ42I2m4EfwF198gZo1a0Kv1xu1xijs2rVreOmll9C3b18899xzqFOnDhQKBbZs2SIv8+DBg3LV8ebNmwgMDERKSgocHBywZcsWvPjii/Dz84O1tTXCw8Mxffp0ZGVlYfbs2eWKOXPmTGzatEn+cVHQsuvll1+Gi4sLbty4USTpBQCNGjUyev3BBx/g5Zdfxvbt2/Hzzz+XuL7CXZLi4uLg5+cHOzs7eHh4QKvVYtSoUfjss88APNwXFy5cgI+PD7Kzs5GWlob9+/djw4YN2L9/Pxo0aFBm/WxtbREaGoq5c+ciKytLPj7atm1bpBtS06ZN5X/v3LkTYWFhcHd3x9dff438/Pwy11USU8Utr9DQUBw4cAAAMGPGDNy/f1/uJpaWloZDhw5hw4YNiIqKgk6nM3l5CjRq1EhOBP7www9o0KABbt26hQ8//FD4uiZMmIBNmzYBePiDFwCsra0xbNgwo+Uqs60KH9OXL1/Gr7/+ikaNGsHW1lZY17+y1K5dG2q1Wv4hP2vWLLz55ps4cOBAqdeCwmXftWsXNm7cCHt7e7i5ucHT07NKZfrvf/+Lzz//HC+//DI8PT3h5uaGu3fvyq3xgPJf+4pjYWGBTp06ITY2Frm5uUhJSUH79u2rVOaSBAYGwt3dHRcvXsTt27cREBCA8ePHo27durh+/TrOnz+PhIQEGAwG/P777/LnnJ2dcf78eQDAqlWr0KBBA6hUKjRt2tQo6V8SBwcHDBgwANHR0QAe3rumTp2K5s2b4969e7h48SKSkpKwceNG6PV6o88eOHBAbpHUqVOnUrugPU46nQ7169dHt27d0KBBAygUChw8eBBz586Vl9FoNGjZsqX8+p133kFsbCwAYO/evZg+fTo6dOiAX375RT6GnJyc0LNnzzLXX/h6vGLFCjRs2BA2NjYltpaqzt8RWrdubdSitEBycrKcqHN0dMT06dPl47l58+ZQqVTIyckB8PCPUVOmTIFCoTDax3Z2dkWSjI/y9vZG06ZN5fvEqFGjMGvWLCiVSsyYMaPYzxT+bnLz5k3Mnj0bbdu2xerVqxEfH1/iuso6Vyt7HhIRPdMEjDNGRAKcPHlSqlOnTrGDsxZMzZo1k65fv270udTUVKNHtz86FR5QNykpqdhlCp6qVDAVDIR75cqVUssDQH46WFkSExMlW1tbydnZWTp48GCR9z/77DMJgOTj4yM/7aw8zp49K/n6+pZZTgDShAkT5M8NHTq02GW6dOlS4kC6hQfQLjx98sknkiQ9HGS38FPYSpoefRhCaS5cuFBkIP9vv/22yHJ6vV5ydXUtsi4HBwdJq9UWezyUNEi7KeMWd5yV9pn8/HyjJweWNJVngPLSBkkuabDhkj6zdevWYsvRtWvXEstV0cHyCxgMBqlJkyZGcQcNGlRkucpsq9u3bxs9KKBgaty4cZnbs6TB6gs/EfHR472kzxR+emVp27Owbdu2FfuZN998U5KkkgfCL6ykZVasWFHqNlQqlfLTKSWp4oPlS5IkLV26VP7MBx98UOT9ip4rpX1m7969JQ7+XVKs4gaEByBFRUWVWY4CN27ckNq0aVPmMfmoKVOmyO8tW7asXNvzcXjuuedKrYdKpTJ6iEmBko5v4OFA8DExMeVaf2pqarEP5tBqtZKDg0Ox17Dq/h3hUWUNll+wntKmhQsXlmtdCQkJkkqlKvJ5Dw8PycnJqcj+ys/PN3rKZ8FkZWUl+fv7V/pcrex5SET0LDOPP7EREby8vHD48GF8+OGHeP7556HRaGBpaQkHBwe89NJL+Oyzz7B///4if4n38PDAX3/9hTlz5qBdu3byfFdXVwQEBBgN0Nq+fXssW7YMLVq0gFKpRN26dTFp0iTs2rWr2DI5ODhg1qxZ6NWrFxo0aCAP/KtUKtGtWzesXLkSEydOLFf9OnfujLi4OCQmJsLHx6fI+1OmTMGyZcsQHx8PtVpdrpjAw7+g7tu3D9HR0ejfvz/c3d1hY2MDpVIJV1dXtG/fHhMnTsTmzZuN/jr8/fff47333oOHhwdsbGzg7e2N6Ohoo3E4HjVq1Ch8+OGHcHd3L7YLnI2NDbZu3YqffvoJXbt2hbOzM6ysrODq6ornnnsOb731FuLi4srVzaeAu7s7XnnlFfm1ra0thgwZUmS5mjVrYtOmTejcuTNsbGxgb2+P/v37Izk5uUgLuIowVdzysrCwQFRUFH777Tf07t0btWvXhpWVFZycnNCyZUu8/vrrWL16tcla05QkMDAQ69evx/PPPw9bW1v5XCpvt9eKUCgUePvtt43mPdotEqjctnJwcMC6devQrl27IgN7P04zZ85EREQEPD09YW1tjWbNmuGbb77BRx99VOJnunXrhq+++gpeXl7yWEKitGvXDpMnT4afnx/q1q0La2trWFlZQavVYuDAgfjjjz/Qtm3bKq1jwIAB8vV8+fLlVR4DrTTt2rXD0aNHMXnyZLRu3Rp2dnawtraGh4cHOnfujE8//RTfffed0WemTZuGcePGwc3NrdItspydnbFv3z588cUX6NixIxwcHKBUKuHm5gZfX1+EhYUVGVdPkiS5m3tBaxZz8c4776Bfv35o3LixfI/WaDTw9vbGxIkTcfjwYQwcOLDI52bPno0NGzYgMDAQjo6OUCqVqFevHoYPH44DBw6gT58+5Vq/h4cHNmzYgLZt20KlUsHFxQVvvPEG9u3bV+LYodX9O0JFTZkyBZs3b0ZwcDDq168PpVIp769+/fohPj4e48aNK1csnU6H7du3w9/fH7a2tnB0dMSQIUOwe/duoxbVBfWxsLDA+vXrERoaijp16sDOzg4dOnTAli1b0KVLlxLXU9a5WpnzkIjoWaeQTPnNi4geu9u3b6NBgwZYuHBhka5TIvj7+6Nx48aldiEksaKiouQE3bBhwxAVFfWES0SPW1pamvw0P3d3d5w/f95suotR5UVGRuJf//oXAGDdunV49dVXn3CJnry1a9fKTxqMjIzEu++++4RL9Gzhd4THR5KkYsfPO3TokDxAPwAcPnwYrVu3fowlIyIifosmesY4OjoiKCiowk9qKq8hQ4Zg2bJlfLqQiUn/f/Da8+fPY/HixfL8sWPHPsFS0eOWmZmJ69evG41PM2bMGCbBnhFvv/223LLS1AOBPy1mzZoFAGjcuDHGjx//hEvz7OF3hMfn7NmzCAgIQHR0NI4fP47U1FSsW7fOqNWgr68vk2BERE8AB8snekakpqbiyJEjcHNzw7Fjx+QB9UVZsmQJvL29sXPnTuTl5Zm0Gw89fJLco08R69u3rzz4OVUPLVq0wIULF+TX7u7u8pML6emnUqlw9uzZJ10Ms1LwwAcSi98Rnozt27fLDzl5VL169bB8+fLHXCIiIgKYCCN6Zty4cQNvvvkmbt++DQ8PD0RGRgqNP2PGDFy+fBkODg6IiIhAzZo1hcan4ikUCri5uaFfv36IiIh40sWhJ8TBwQGdOnXC/Pnzee4RUYXxO8Lj5+Ligrfeegt//PEH0tLSoNfrUbNmTTRt2hR9+vTB22+/DQcHhyddTCKiaoljhBERERERERERUbXAQUaIiIiIiIiIiKhaYCKMiIiIiIiIiIiqBSbCiIiIiIiIiIioWuBg+eVkPyRKaDwbtY3QeACgv3BOaDyVS12h8XJzcoXGAwArpdhDuIZ9DaHxAOD2udNC41k61hYazxSmjvYTGs9SoRAaDwDsbSyFxsvIyhMaz8NB/DXi1gOx52BWrkFoPACwUYr9+8zd7Hyh8QwmGFWzTxOx53RNW/G3dluV2PPFVik2no1K/N/1HuSIPXayTXC+ZAouY26e2DLeE3xdBIBlR64IjWeKc7p2TbHnoI2V+OM7S/C+vnQnR2i85rXFPtUSAI5czhQaz8pS/HeTn38q/gmTlXZF7HdQRYPnhMYDAIWF2O1oyHogNB4A4MZFoeFUDVsKjZeTcUtoPABAnthz+sHWyULjPQ1sfd42WewHKd+YLHZ1wRZhRERERERERERULbBFGBERERERERGRKAq2OTJnZrN3AgICoNPp4ObmBh8fH+h0OqjVaixduhQAkJycDG9vb5w/fx7h4eFo3rw5dDoddDodcnJykJOTI7/W6XSwsbHBkSNHAADdunWDq6srZs2a9SSrSERERERERERET5DZtAiLj48HAIwcORKhoaHw8/NDeHg4ACApKQljx47F+vXr4eHhAQCYNm0ahg0bZhQjMTERAJCeno6goCC0bt0aALBkyRL8/vvvSE9PfzyVISIiIiIiIqLqyQRjHJM4ZtMirCR79uzBuHHjEBMTIyfBAGDu3Lnw8/PDF198UeQzS5cuxWuvvSa/rl+/foXWqdfrkZ6ebjRJuSYYeJGIiIiIiIiIni0KC9NNVGVmvxWjo6MRHBwMrVYrz5swYQIOHTqE33//HZs3b0ZCQoLRZ5YtW2aUCKuoyMhIaLVaoyn7xKZKxyMiIiIiIiIioifP7BNhERER2LZtG5YsWSLPc3Z2hkKhgI2NDfr374/9+/fL7x04cAAuLi5wd3ev9DrDwsKQlpZmNFk361mVahARERERERFRdaBQmG6iKjObMcJKYmdnh9jYWAQGBkKtViMkJAQZGRlwcHCAJElISEjAkCFD5OWjoqIwfPjwKq1To9FAo9EYzVMobasUk4iIiIiIiIiIniyzT4QBgL29PeLi4hAYGAg7OzusWrUKJ0+ehCRJ6NSpE1555RUAQF5eHtatW4ePP/7Y6POjRo3Cvn37kJ2djX379iEmJuZJVIOIiIiIiIiInnUcy8usmV0irHAXyIKnRgKAi4sLUlJSAAC9e/cu9rNWVlZITU0tMv+nn34SWUQiIiIiIiIiInoKmV0ijIiIiIiIiIjoqcWxvMwaE2FERERERERERKKwa6RZYyKsnCRJMut4AISfbKLLqLAQnxU3RUzhLJViw1laCo1nimNRKXi/WJpgPxsgtt4GwZsx3xTXCMEUJvhLl+iQCogNqDTBdxrRdTaY4NgRHVJ0nfPzn4bzRXxM0ftF9HVH9HUREH8/yM0zCI33tMgVfM6Ivh/k5Ik/eAyCD0iFlSluCIJjPgU/xCXRFwpLE/y8NeSLj0lETxQTYUREREREREREorBrpFkzmz8TBAQEQKfTwc3NDT4+PtDpdFCr1Vi6dCkAIDk5Gd7e3jh//jxmz54Nf39/dOzYESNGjEBubi4A4ObNmxg6dKgcS6/X4+LFi9DpdPKkVCpx+/btJ1lVIiIiIiIiIiJ6AsymRVh8fDwAYOTIkQgNDYWfn5/81MikpCSMHTsW69evh4eHByZPnoxp06YBAEaMGIGtW7eid+/eePfddzF16lQ899xzclyNRoPExEQAwB9//IFZs2bB0dHxsdaNiIiIiIiIiKqJp6BrcnVm9ntnz549GDduHGJiYuDh4QEAUKlUAACDwYC8vDx4enoiPz8fhw8fxjfffIPOnTtj9uzZRWL9+uuvGD58+GMtPxERERERERERmQezT4RFR0cjODgYWq3WaP6MGTPg5eWFjIwMaLVaXL9+HYcPH8Zbb72FhIQEJCcnY/v27fLyOTk52LRpE/r27VvmOvV6PdLT040mKfeB6KoRERERERER0bNGoTDdRFVm9omwiIgIbNu2DUuWLDGaP3PmTJw+fRoNGjTAkiVL4OTkhNq1a8PX1xcWFhbo2bMn/vrrL3n5mJgYdO3aFba2tmWuMzIyElqt1mjKOblZcM2IiIiIiIiI6JmjsDDdRFVm9lvRzs4OsbGxWLRoEVatWgUAyMrKAvDwUc329vaws7ODtbU1mjZtitTUVADAvn374OXlJceJiooqd7fIsLAwpKWlGU2qpkFiK0ZERERERERERI+V2QyWXxp7e3vExcUhMDAQdnZ2+O2333Du3Dnk5+fDy8sLH3/8MQBgwYIFeOONN5Cbm4vWrVujd+/eAB4+TfLw4cPQ6XTlWp9Go4FGozGap1CW3ZKMiIiIiIiIiKo5dmE0a2aXCCvcBbLgqZEA4OLigpSUFACQE1yPat26NRISEorMd3Z2xrlz54SWk4iIiIiIiIiIni5mlwgjIiIiIiIiInpqcSwvs8a9Q0RERERERERE1QJbhJWT70ueQuNp7FRC4wHA5Ua1hMZzsLcRGi83zyA0HgBYWorte+2gFr9fLjV0FhrPTvCxY2khvv96S9caQuOZoou9rZWl0Hj3cvOExnO0Fn8sPrDLFxov1yD+nLYS/NezbIPYOkuS0HAAALW12FuxrUrssQ0A1kqx+8VK8LXbFNcxpeC/FZrgdIGtSuwBqbQSux2tLMT/vbVtfbH3l5x88TtGo1IKjWcn+H4FAA/yxF4bM/PE3gNdbK2FxgMAZzux11qlCY7vAx1bCo1361YjofHq1tWUvdATlpMj9lgEgFu3PITGc9faC413736O0HiAab7vVDtsEWbWmAgjIiIiIiIiIhLFBH+kI3GYpiQiIiIiIiIiomrBrBJhqampcHR0hE6nk6dvv/0Ws2bNAgCcPXsW3t7eOHDgAH766Sf4+fnB398fL7/8MvR6PQBgwYIF8PLygqencVfGxYsXo127dujUqRMSExMfd9WIiIiIiIiIqDpQWJhuoiozu63o6+uLxMREebK1tQUAnDp1Cv369cPPP/8MX19fDBs2DLt378bOnTvh4+ODqKgoAMDAgQNx7Ngxo5jXr1/H4sWLsXv3bsTGxuK9996DwRSDdRARERERERERkdl6KsYIO3nyJEJCQhAVFYU2bdoAAFSq/w0kfe/ePXTt2hUAULt27SKfT01NRYsWLaBUKmFvbw8rKyukpqaiUaPiB5DU6/VyC7MCeVn3YWWjFlUlIiIiIiIiInoWmeJpXySM2bUIO3DggNwtsl+/fgCAjRs3wtfXV06CFfj222/RqlUr7N69Gy1blvyUlcaNGyMlJQV6vR7p6ek4duwYbt26VeLykZGR0Gq1RlNawkoxFSQiIiIiIiIioifC7BJhhbtGrlmzBgDwzjvvQK/XY+bMmUbLjh07FkePHkW/fv0wb968EmM6OzsjPDwcffr0wb/+9S94e3ujbt26JS4fFhaGtLQ0o0nbZbCYChIRERERERHRs4tjhJm1p2IrWlpaYvny5di3bx8iIyMBAFlZWfL7jo6OsLOzKzVGSEgIdu7ciS+//BIajabURJhGo0H9+vWNJnaLJCIiIiIiIqIyKRSmm6jKzG6MsIKukQWCgoIAPBwT7LfffkOvXr1Qo0YNpKam4o8//gAAODk54aeffgIArFy5Ej/88AMuX76Mbt26YcaMGejUqRNGjBiBtLQ0qNVqLFiw4LHXi4iIiIiIiIiIniyzSoQ1aNAAt2/fLvF9W1tbJCQklBpj8ODBGDy4aDfGX3/9tcrlIyIiIiIiIiIqFbswmjXuHSIiIiIiIiIiqhbMqkWYOTu496zQeNZ21kLjAcDd9ItC46mcawuNl5ebJzQeAFgpxR7Cao34seBuX7ggNJ7KwUVoPJigm/mR5q5C41mYoIw1rS2FxruTlS80Xn37XKHxACAzV2wZ9YLrDAC2KrF/n7mbZRAaL88gCY0HAA0cxF53JEl8GUV/XbASfFJbmGC8jNx8scdOjuB4APAgR/DxLbiMd7PE3/f3XbwrNF6uCc7puhql0Hii71cAkCn42Ll6V+w9q7GL+Hvg8asPhMYTfR0DgEO7j4gNePmU0HBpHm2ExgMACyuxx7chS+x+BgBcOyc03JXGzwmNl3P3jtB4AIB8wefgVH+x8Z4GHMvLrLFFGBERERERERERVQtsEUZEREREREREJArHCDNrZrF3EhMTERoaajTP09MTAJCbmwtXV1d8/fXXRT538uRJKJVK7N69GwBw+/ZtdO/eHZ07d0aHDh2QkpJitPz06dPluEREREREREREwikUppuoyswiEVaauLg4DBw4EKtXry7yXnh4OHQ6nfx62bJl6NixI3bs2IG5c+di1qxZ8nuXLl3C6dOnH0eRiYiIiIiIiIjIDJl9Imzp0qV45513UL9+fZw4cUKev3PnTjRo0AD16tWT5zVv3hx6vR4AcOvWLdSqVUt+Lzw8HB9++OHjKzgRERERERERVT8KC9NNVGVmvRUzMjJw69YteHl5YeTIkYiKigLw8IlYn376KaZOnWq0/PPPP4+kpCS0atUK48aNw+TJkwEAhw8fBgC0bNmyXOvV6/VIT083mqRcEzyBhIiIiIiIiIiIHhuzSITZ2toiKytLfv3gwQPY2tpi1apVuHbtGoKCgvD5559jzZo1kCQJK1euRLdu3eDg4GAUZ+7cuejXrx+OHj2K1atXY/z48QAejg02Y8aMcpcnMjISWq3WaMo+sUlIXYmIiIiIiIjoGcYxwsyaWTw1smnTpvjrr7+QmZkJOzs7xMfH4/nnn8eKFSuwY8cOODs7AwDeeecd7Ny5E3/99Rf279+P33//HUeOHMGJEyewfPlyAICrq6v8/4yMDADA2bNn5cH4r1y5gvHjx2PhwoUllicsLKzI4P0tJsWJrjYRERERERERET1GZpEIc3BwwPTp09GtWzdYW1vD1tYW3377LYYMGSInwQCgZ8+e+PXXX/Hjjz/K80aOHInQ0FA0atQIEyZMwPDhw/Hzzz/jwYMH+OyzzwAAR44ckZf39PQsNQkGABqNBhqNxmieQmkroqpERERERERE9CzjWF5mzSwSYQAwcOBADBw40GjeH3/8YfQ6KCgIQUFBRvOWLFki/7tu3bqIj48vdT1nzpypWkGJiIiIiIiIiErCRJhZ494hIiIiIiIiIqJqwWxahBERERERERERPfU4qL1ZYyKsnF7s2ERoPFuVpdB4AHC9SW2h8ezsVELj5eUZhMYDAFsbsYdwTVul0HgAcKmhi9B4dnbiyyhac1e10HhKC/E3EqWF2AaxmXn5QuO52loLjQcA93PzhMbLqykJjQcAVoL3dU6+2OtOniS+znaC7wemuL+orMTuF4un4Muh0lLsNcIg/tARLt8guKOACXbzC/VrCI1nit2iVor9bmJrKf6czjaIvWfV1eQKjedignugWvC10coE17H9ndoIjXfnjpfQeG5uNYXGAwBJ8H3VFL83MjLE/g6sVUvsdezBA7HnHwDk5z8FNy0qtzNnzuDzzz/H3r17cfToUTRr1gxHjx4tstyPP/6Izz77DBcvXkTTpk0xe/Zs9OnTx2iZO3fuICwsDGvXrkVubi569OiBBQsWoE6dOkbL/fHHH/jXv/6Fv/76C7Vq1cK4ceMwZcoUKMzkOyC7RhIRERERERERiaKwMN1UQceOHcPGjRvh6emJFi1aFLvMypUr8X//938YNGgQNm3ahJdeegnBwcHYu3ev0XKDBg3C1q1b8d1332HZsmU4efIkevbsiby8//3B/cyZM+jRowfq1KmD2NhYTJo0CdOnT8f8+fMrXHZTYYswIiIiIiIiIqJn0Msvv4xXX30VADBy5Ejs37+/yDIzZszA4MGD8cknnwAAunTpgsOHD+Pjjz9GXFwcACApKQlbtmzBli1b0L17dwBA06ZN0bx5c6xZs0Z++OG8efPg7OyMlStXQqVSISAgAP/88w9mz56NCRMmwNpafKvfijK7FmETJ06Er6+v/Lphw4bQ6XTQ6XTyztu/fz/at2+Pzp07o2fPnrhz5w4AoH///ujcuTPatm2LL774Qo5ha2srx/jPf/7zeCtERERERERERNWHQmG6qYIsyhgS5ty5czh16pScyCowePBgxMfHIzs7GwCwadMmODg4IDAwUF6madOm8Pb2lpNlBcv17dsXKpXKKFZGRgaSkpIqXH5TMKsWYXl5edi9ezeaNGmCY8eOoWXLlrC0tERiYqLRchEREfjss8/QuXNnzJo1C7/++ismTJiAFStWQKVSIS8vD82bN0doaChq1qyJevXqFYlBRERERERERCRcJbowlpder4derzeap9FooNFoKhXvxIkTAIBmzZoZzW/evDlycnJw/vx5NGvWDCdOnEDTpk2LjPPVvHlzOcb9+/eRlpZWJFazZs2gUChw4sQJ6HS6SpVTJLNqEbZ582YEBQVh1KhRiIqKKnG5li1bIiMjAwBw+/Zt1KpVCwDkjGNmZibc3d1hZ2cHALh69So6d+6Mvn374ty5c2WWQ6/XIz093WjKe3C/irUjIiIiIiIiIqq8yMhIaLVaoykyMrLS8W7fvg0AcHBwMJrv6OgIALh165a83KPLFCxXsExBnubR5VQqFezs7OTlnjSzSoQtW7YMI0eORGBgIHbs2AGDwYD8/Hy5W2NERAQAIDg4GBMmTECrVq3wxx9/oF+/fnKMV199FZ6envDz84Pl/3/KTmpqKnbs2IEJEyZg1KhRZZajuAPrQsIK01SaiIiIiIiIiJ4dJuwaGRYWhrS0NKMpLCzsSdf4qWI2XSP1ej127NiBiRMnAgCuX7+OxMTEYrtGjh07FmvWrEHbtm0RERGByMhITJ06FQCwfv163L9/H/7+/hg0aBBatGgBFxcXAEBAQADGjBlTZlnCwsIQGhpqNG/4ipMCaklEREREREREVDlV6QZZnIKWX3fu3IGbm5s8v6ClmJOTk7xcWlpakc/fvn1bXqagJVjBOO4FcnJykJmZKS/3pJlNIuy3337DlClTMGnSJABASkoKvv766xKXd3V1lf9/5swZufWYUqmEra2tPN27dw+2trawtLTE0aNHy7XhizuwrGzTK185IiIiIiIiIqoWHh1Hy5wVjOdVMAZYgRMnTkClUqFRo0bycr///jskSTKq34kTJ9C6dWsAgFqthlarlccMK3Dy5ElIklRk7LAnxWy6RkZFRSEoKEh+7e3tjT179sBgMBRZNiIiAoMGDYJOp8OyZcswYcIE3L9/H4GBgdDpdOjYsSNCQkLQsGFDHD9+HG3btoW/vz/GjRuHxYsXP85qERERERERERGZpUaNGsHLywurVq0ymh8dHY2AgAB5LPaePXvi9u3biI+Pl5c5deoUUlJS0KtXL3lez549sX79euTm5hrFcnBwQIcOHUxcm/IxmxZh27dvN3qtUChw6tSpYpft3Lkz9u7dW2R+cU+GfPHFF5GSkiKkjEREREREREREpTGnFmGZmZmIi4sDAFy4cAF6vR6rV68G8DC34urqivDwcLz22mto3LgxunTpgujoaOzbtw87d+6U47z00kvo0aMHRo0ahfnz58PGxgbTpk1DmzZtjMZtnzx5MpYtW4YhQ4Zg3LhxOHLkCObNm4fZs2fLSbUnzWwSYURERERERERETz3zyYPh+vXrCAkJMZpX8DohIQE6nQ5DhgxBZmYmIiIiEBERgaZNm2Lt2rV46aWXjD4XHR2NsLAwjB49Gnl5eejevTsWLFgAK6v/pZY8PT2xdetWhIWFoVevXnB1dcXMmTPxr3/9y/SVLSeFJEnSky7E00Az+Feh8axtrYXGA4B7Vy4Jjad0cBEaLz8/X2g8APKTQUWp4VBDaDwAuH3urNB4SqfaQuOZYr/8e0wnofGsLcXfSWxVYnuG380q2o27KurZi/9ryYNcsfv6fo7YOgOAykrsvr6fLbaMOfnib5kve4k9p2vYiP8bl5212GutnUpsPKWl+JEesvPEni9ZueLPl2zBMbMFXyMyc8TfX5akiP2uY2kh/v7iWkPsOagywfGdlSf22LmizxEar1ktW6HxAODI5ftC49koxV7HAOCHH7aJDXg9VWg4iwathcYDAAsLscd3XqbY/QwAuHpGaDirRs8JjWeSOmeLjflg62Sh8Z4G6pCfTRb7/qo3TBa7umCLMCIiIiIiIiIiQcypayQVZTaD5RMREREREREREZmS2STCAgICoNPp4ObmBh8fH+h0OqjVaixduhQAkJycDG9vb5w/fx7h4eFo3rw5dDoddDodcnIeNsfesGED2rVrh06dOmHlypVy7EOHDiEoKAhdu3bFG2+wGSERERERERERmYZCoTDZRFVnNl0jCx7BOXLkSISGhsLPzw/h4eEAgKSkJIwdOxbr16+Hh4cHAGDatGkYNmyY/HmDwYDJkydj//79sLGxQceOHdG7d29YW1vjvffew+rVq2Fvb//Y60VERERERERERObBbFqElWTPnj0YN24cYmJi5CQYAMydOxd+fn744osvAAA3btyAq6sratasCaVSiUaNGuHPP//E3r17UbNmTYwYMQI6nQ6xsbFlrlOv1yM9Pd1oknIfmKyORERERERERPRsYIsw82b2ibDo6GgEBwdDq9XK8yZMmIBDhw7h999/x+bNm5GQkAAXFxf8888/uHTpEu7cuYOkpCTcunULly5dwsGDB/HLL79g7dq1mDJlCvR6fanrjIyMhFarNZpyTm42dVWJiIiIiIiI6CnHRJh5M/tEWEREBLZt24YlS5bI85ydnaFQKGBjY4P+/ftj//79sLCwwOLFizFs2DAMHz4c3t7eqFu3LpycnNC+fXs4ODjA0dERbdq0wZkzpT8CNywsDGlpaUaTqmmQiWtKRERERERERESmZDZjhJXEzs4OsbGxCAwMhFqtRkhICDIyMuDg4ABJkpCQkIAhQ4YAgDx4/t27d9G/f3+88MILyMzMxLRp05CbmwtJkvD3338bdbEsjkajgUajMZqnUNqarI5ERERERERE9Ixgwy2zZvaJMACwt7dHXFwcAgMDYWdnh1WrVuHkyZOQJAmdOnXCK6+8AgCYMmUK/vzzT1hZWSEiIgJKpRL29vZ477330KVLF+Tk5GDixIlwdnZ+wjUiIiIiIiIiIqLHzewSYYW7QBY8NRIAXFxckJKSAgDo3bt3sZ+dO3dusfMHDx6MwYMHCysjEREREREREVFxOJaXeTP7McKIiIiIiIiIiIhEMLsWYURERERERERETyu2CDNvTISVk+gD2SQnRjU82URvR8kgCY0HALA079PMykp8+SwF7xdT7Balhdgy5ksmKKRg+Qax8UyxX0RvRtFlNMVlVoL5HzsGwRvSIHhHCz6dAQAKwaPcmuIOnS94vwi/p5rilir62m2CC5noa621tfij50Gu2HgqS7GdTHLzxe8XK8FlVFo+Bd+783KEhjPk5QuNB0D8r1FJ8AkIAFYqoeEk0RfHfMEnNFAtf1eKxkSYeWPXSCIiIiIiIiIiqhbMJhF29OhRBAUFwd/fHx07dsTHH38MADh48CAUCgUOHz4sL7t48WK8+OKL8Pf3x5AhQ5CdnQ3g4eD67dq1Q8eOHTFx4kQ5256UlIQOHTrA398fkZGRj79yRERERERERFQtKBQKk01UdWaRCLt79y5eeeUVzJkzBzt37sSePXvQrl07AEBUVBTCwsIQFRUlLx8QEIC9e/di586d0Gq1WLp0KQBg6NCh2LdvH/bs2YOrV69i+/btAICJEydi5cqV2LFjB7Zs2YKzZ88+/koSEREREREREdETZRaDF8XGxqJDhw7w8fGR5/Xo0QP5+fnYvXs3/vjjD/j5+cFgMMDCwgKenp7yctbW1rC0tAQAeHl5FTs/IyMD7u7uAAAfHx/s2LEDjRs3fhxVIyIiIiIiIqLqhA23zJpZJMLS0tKg1WqLzN+2bRu6d+8OpVKJgIAAxMfHIzAwUH7/77//xqZNm7Bjxw6jzyUmJiI9PR3+/v4AABcXFxw6dAjNmzdHQkICXFxcSi2PXq+HXq83miflPoBCaVvZKhIRERERERFRNcAujObNLBJhWq0Whw4dKjJ/6dKluHDhAoKCgnD//n1cunRJToSlpqbi9ddfx3//+1+o1Wr5MwcPHsT777+P2NhYWFg87Pn5n//8B++99x4UCgWaNGmCunXrllqeyMhIzJw502ieqmVf2LTuV9WqEhERERERERHRE2IWibDevXtj2rRpSElJkbtHbtiwAefPn8eePXvk5Tp27Ij79+/j7t27CAkJwY8//ohGjRrJ7//9998YPXo01q1bB2dnZ3l+69atsWXLFuTk5KBfv37o0aNHqeUJCwtDaGio0bwWk+JEVJWIiIiIiIiInmFsEWbezCIRptFosGHDBrz33nvIzMxEfn4+evTogU6dOhkt16lTJ6xZswbx8fG4du0aJk6cCODhIPmjR4/G2LFjodfrMWzYMAAPE1qvvPIKIiMjERMTA4VCgSlTphglyUoqj0ajMZrHbpFERERERERERE83s0iEAUCrVq2wefPmUpeJiIgAAAwfPrzY9xMTE4udHxYWhrCwsCqVj4iIiIiIiIioLGwRZt4snnQBiIiIiIiIiIiIHgezaRFGRERERERERPTUY4Mws8ZEWDn5vuQpNJ6ttaXQeABw41ZtofE0Na2FxsvOyRcaDwBsBG9HW5X4U+JKg9LHpKsoB3sbofHy8iWh8QCgZS112Qs9YbZWYo+d+jUNQuNpVEqh8QAgM1fsOWiA+GPHUnAz8lyD2P2SL4mvcw1rsdcdW5X4xt42SrHni5WF2DJaWIj/tqm0Eh1T/H1fdK+L7Dyx54tkgjq3rV9DaDwTHDqwtRJ7TqsEny8AkGcvdl9nZOcKjediI/Y7KAA424ndL0oT7Bdvf2+h8W7e9BIar359e6HxAMBgEHtfzcnJExoPADIyxG5HN7eaQuNlZuYIjUdisGukeWPXSCIiIiIiIiIiqhbYIoyIiIiIiIiISBC2CDNvZtMiLCAgADqdDm5ubvDx8YFOp4NarcbSpUsBAMnJyfD29sb58+flz0yfPh2env/rsrhp0ya88MIL6NSpE4YMGYLc3IfNtBs2bAidTgedToePP/748VaMiIiIiIiIiIjMgtm0CIuPjwcAjBw5EqGhofDz80N4eDgAICkpCWPHjsX69evh4eEBALh06RJOnz5tFOOjjz7Cb7/9Bg8PD4SGhmLz5s14+eWXYWlpicTExMdZHSIiIiIiIiKqhtgizLyZTYuwkuzZswfjxo1DTEyMnAQDgPDwcHz44YdGy7Zs2RIZGRmQJAkZGRlwdXUFAEiShC5duqBHjx44ePBgmevU6/VIT083mvKy7outGBERERERERERPVZmnwiLjo5GcHAwtFqtPO/w4cMAHia+Chs2bBiCgoLQrFkzAED79u0BAPv27UNCQgLmz5+PYcOGlbnOyMhIaLVaoyktYYWoKhERERERERHRM0qhUJhsoqoz+0RYREQEtm3bhiVLlsjzpk+fjhkzZhRZ9q233sLevXtx8uRJuLi4IDo6GgDg4uICAGjVqhUcHBzwzz//lLrOsLAwpKWlGU3aLkPEVYqIiIiIiIiInk0KE05UZWYzRlhJ7OzsEBsbi8DAQKjVaoSEhODs2bMIDQ0FAFy5cgXjx4/HwoULYWVlBUdHRwCAq6srMjIykJ2dDUmSYGNjgytXruCff/6Bs7NzqevUaDTQaDRG86xs0kxTQSIiIiIiIiIieizMPhEGAPb29oiLi0NgYCDs7Oxw5MgR+T1PT08sXLgQADBr1iwEBATA1tYW9vb2mDp1Kq5fv45XX30VarUaeXl5+Pbbb2FhYfYN4YiIiIiIiIjoKcQujObN7BJhhbtAFjw1EnjYvTElJaXI8mfOnJH/HRISgpCQEKP3a9SoUa4B8omIiIiIiIiI6NlmdokwIiIiIiIiIqKnFVuEmTf2ESQiIiIiIiIiomqBLcLKycpSbEZXYYLHPRgMktB4opPYpkiK5wuus+j9bAp5+WLr/DSwNMHBI/oclATvFlNcI0QPj6iQTFBGwfta9H4xCcGb0RR1Fh3TQnCdRd//APF1Fn2/AgDRIQ0GsfHwFJx/eSbYL0rBB7iV6BMGQG6e2Hii79Om+M4ounWGKQ5vC8H7WnS8/HzRFwnzr7MpYlqaoIyisTVT1XEbmjcmwoiIiIiIiIiIRGEezKyZTdfIgIAA6HQ6uLm5wcfHBzqdDmq1GkuXLgUAJCcnw9vbG+fPn8e4ceOg0+nk5RcsWAAA+OCDD+Dh4YFu3brJcc+ePQt/f3906tQJfn5+2L9//xOpHxERERERERERPVlm0yIsPj4eADBy5EiEhobCz89PfmpkUlISxo4di/Xr18PDwwOLFi2SP9eiRQsMGDAAAPD2228jNDQUo0ePlt93cHDA2rVr4ezsjOPHj2PMmDHYtWvX46sYEREREREREVUb7Bpp3symRVhJ9uzZg3HjxiEmJgYeHh5G7+3duxdarRZ16tQBANStWxcWjwyC4+zsDGdnZwCAtbU1LC0tH0/BiYiIiIiIiIjIrJhNi7CSREdHY9KkSdBqtUXei4qKwvDhw8sVJz8/HxMnTsS///3vMpfV6/XQ6/VG8/Ie3IeVrbp8hSYiIiIiIiKiaoktwsyb2bcIi4iIwLZt27BkyRKj+Tk5Odi4cSOCg4PLjCFJEkaNGoU+ffogKCiozOUjIyOh1WqNpgvbV1S2CkREREREREREZAbMvkWYnZ0dYmNjERgYCLVajZCQEABAXFwc/P39oVaX3UprwoQJ8PT0xNixY8u1zrCwMISGhhrNG7HyZMULT0RERERERETVCluEmTezT4QBgL29PeLi4hAYGAg7Ozv07t0bUVFRGDNmjNFyX331FVatWoW///4b3bp1w6JFi3D58mX85z//QYcOHRAfHw8nJyesWbOm1PVpNBpoNBqjeVa26cLrRURERERERETPFibCzJvZJcIKd4EseGokALi4uCAlJUV+/dtvvxX57DvvvIN33nnHaJ6XlxdycnKEl5OIiIiIiIiIiJ4uZpcIIyIiIiIiIiJ6arFBmFkz+8HyiYiIiIiIiIiIRGCLsHLKN0hPughlEt0NWRJcZdHxTBVTNAsLsTvGUnA8UxC9X/JMsKPtrMRux6dhGADh+8UE10WVpdgNmS+40qa45Fg8BQePZJKaiyP6OgsACsHHjoUJ/vQo5Ykto+hD0RT7RXRIU5x/T8O1VnS1RW9FSxPsF9H3AysTlNEgeF+LPgefju/d5t/OQ/TvStP8xnoKdraZ4xhh5s38rxREREREREREREQCsEUYEREREREREZEgbBFm3symRVhAQAB0Oh3c3Nzg4+MDnU4HtVqNpUuXAgCSk5Ph7e2N8+fPY9y4cdDpdPLyCxYsAAD06dMHHTt2RLt27fDLL78Yxc/JyYGnpydmzZr12OtGRERERERERNWDQmG6iarObFqExcfHAwBGjhyJ0NBQ+Pn5ITw8HACQlJSEsWPHYv369fDw8MCiRYvkz7Vo0QIDBgwAAERGRsLLywtZWVlo1aoVBg0aBBsbGwDAwoUL0aJFi8dbKSIiIiIiIiIiMhtmkwgryZ49ezB//nzExMRAq9Uavbd3715otVrUqVMHAODl5QUAUKlUsLCwkJsjZmRk4Pfff0dISAjS09PLXKder4derzeal/fgPqxs1SKqRERERERERETPKHaNNG9m0zWyJNHR0QgODi6SBAOAqKgoDB8+vMj8OXPmYMCAAbC2tgYAzJ49G1OnTi33OiMjI6HVao2miwkrKl8JIiIiIiIiIiJ64sw+ERYREYFt27ZhyZIlRvNzcnKwceNGBAcHG83/4YcfcOzYMXkssNTUVJw/fx7+/v7lXmdYWBjS0tKMJvcuQ6pcFyIiIiIiIiJ6tnGMMPNm9l0j7ezsEBsbi8DAQKjVaoSEhAAA4uLi4O/vD7X6f90Vo6OjsXbtWqxbtw4WFg9zfAcPHsTly5cRFBSES5cuITs7G61atULfvn1LXKdGo4FGozGaZ2WbJr5yRERERERERET02Jh9IgwA7O3tERcXh8DAQNjZ2aF3796IiorCmDFj5GVycnIwfPhweHt7IzAwEADw66+/ol+/fujXrx8AYMmSJUhPTy81CUZEREREREREVFkcI8y8mV0irHAXyIKnRgKAi4sLUlJS5Ne//fab0edUKhVycnJKjT1y5EgRRSQiIiIiIiIiKhbzYObN7McIIyIiIiIiIiIiEsHsWoRVFwZJetJFKJOFhfmnsa0sxZbRFLtFdLNY0fvFYDD/Y9HKBMeiBLH1Fn3siC4fIL6MpvhLl7lfG01xWRS/X8QXUnRM0XvZFIeNwSA23tNwf1GY+XURAPIE37Ms+Cd7IZ6Gc1q0fBMU0lLw91rR3/FElw8wwfcnE+wX0dtR9GXHFJex/Pyn4CQ0c0/Db+nqjC3CiIiIiIiIiIioWmCLMCIiIiIiIiIiQdjg2LyZVYuw1NRUODo6QqfTwcfHBwsWLEB+fj5mzZqFDh06oHPnzujZsyeOHz+OO3fuoH379nBwcMDSpUvlGOfOnYO/vz90Oh10Oh0uXLgAAEhLS0O3bt3QqVMnvPfee0+qikRERERERERE9ISYXYswX19f/P7779Dr9WjSpAkyMjJw8uRJ7Ny5E1ZWVrhy5Qr++ecf2NnZYf369fj222+NPr9o0SK8+eabeP3117F06VJ8/fXXmD9/PqZOnYoPPvgAXbt2xdChQ5GQkIAuXbo8oVoSERERERER0bPIFGO2kjhmlwgroNFo0LBhQ0yfPh2nT5+GldXDotapUwd16tQBANSuXbvI51q2bImMjAwAwK1bt1CrVi0AwMGDB9G1a1cAQN++fZkIIyIiIiIiIiLhmAczb2abCLt06RLOnTsHGxsbaLXacn8uICAAPXr0wI8//oisrCwkJycDAAyFHv/k6OiImzdvlhhDr9dDr9cbzct7cB9WtuoK1oKIiIiIiIiIiMyFWY0RBgAHDhyATqfD0KFDsWjRIri7u+PixYvl/vzUqVPxySef4PDhw/j444/x/vvvAwAsLP5X1YyMDDg7O5cYIzIyElqt1mi6mLCi8pUiIiIiIiIiompBoVCYbKKqM7tEmK+vLxITE7Fjxw4MGDAAoaGhmDlzJvLy8gAA165dw+HDh0uN4erqKv+/oJukj48PduzYAQDYsGEDdDpdiZ8PCwtDWlqa0eTeZUjVK0dERERERERE9Jhs2LAB7dq1Q82aNVGnTh0MHDgQ586dK7Lcjz/+CC8vL9jY2OC5555DbGxskWXu3LmDN998E05OTqhZsyYGDBiAK1euPI5qCGW2XSMLhIWFYc6cOfD394dSqYStrS0iIyMBAL169cKJEydgZ2eHHTt24Pvvv8eHH36IMWPGwMrKCrm5uVi8eDEAICIiAqNGjUJOTg7atm1b6vhgGo0GGo3GaJ6VbZrpKklEREREREREzwRzabmVmJiI4OBgjBgxArNnz8bNmzcxffp0dO/eHUeOHIGtrS0AYOXKlfi///s/TJs2DV27dkV0dDSCg4Oxa9cutG/fXo43aNAgHDt2DN999x1sbGwwbdo09OzZE/v375fHdX8aKCRJkp50IZ4GAQuShMazVloKjQcAN25lCo3n6GArNN6DrDyh8QDAxlrsdqxhoxQaDwCu3LgvNJ6mprXQeAaD+EvAeP8GQuNZWYi/kdhaiT12MnPzhcaztxZ/LIouowHijx1LwV8aHuSJrbMpvtO0qGUvNJ6tSvz9xVoptgG5tZXYeEpL8Q3cc/IMZS9UAbn5YuMBQLboMgqOl5Urvs7Jl0oe47UyLExwUturxF6/TVHGPEnsvrmbI/Y7nouN2O86AHA584HQeFYm2C//SUwVGu/GDbG/DerUqSk0HgCI/iWab4Jr7c2b5r0d79/PERoPAPLzxe6YP6fphMZ7Gjw3I95ksQ/NDCj3sm+99Ra2bt2Ks2fPysm5hIQEdO3aFTt37kSnTp0AAE2bNoWvry+WL18uf7ZDhw5wcHBAXFwcACApKQkdOnTAli1b0L17dwDAyZMn0bx5c6xcuRIDBw4UVUWTM7uukURERERERERETyuFwnRTReTm5qJmzZpGLdTs7R/+EbagTdS5c+dw6tSpIomswYMHIz4+HtnZ2QCATZs2wcHBAYGBgfIyTZs2hbe3t5wse1owEUZEREREREREJIgpB8vX6/VIT083mvR6fbHlGDlyJI4fP45Fixbhzp07OHfuHD744AP4+PigY8eOAIATJ04AAJo1a2b02ebNmyMnJwfnz5+Xl2vatGmRbp/NmzeXYzwtnp5OnE9Y8u5TQuNZ24pv8n3/6mWh8VSOrkLj5eWK7xpppRJ7CKs1aqHxAOD2+aIDEVaFyqm20HgGg/gm5J2augiNp7IU3/2gpuButbceiD2+62pUQuMB4rtGPsgRf+yoBHeZu5stts6iu24CgFZjJzymaKJ7J4uOZ4ouq6K7Mpqia2Sm4ONbdBnvm2BIhD/T7gmPKVodwddvpQnugTmC9/U/98Tua62D+K5ep/4R3DXSBF2yD+w4IjZg+nGh4dIa+wqNB/yvRYqweDlZQuMBAG5cFBou3aOF0Hi5GTeExjMN3ZMuwDMlMjISM2fONJo3Y8YMhIeHF1m2U6dOWLt2LYYOHYrx48cDALy9vbF582ZYWj78PXT79m0AgIODg9FnHR0dAQC3bt2Sl3t0mYLlCpZ5WjARRkREREREREQkiCnHyg97NwyhoaFG8x592F+BP/74A8OHD8f//d//oU+fPrh58yY++eQT9O7dG7t27ZIHy69uzKZrZEBAAHQ6Hdzc3ODj4wOdTge1Wo2lS5cCAJKTk+Ht7S03ywOA6dOnw9PTU369adMmvPDCC+jUqROGDBmC3NxcAMCFCxfQt29fdO3aFT179ny8FSMiIiIiIiIiEkCj0aB+/fpGU0mJsIkTJ6Jr166YP38+unTpggEDBmDjxo04ePAgoqKiAPyv5dedO3eMPlvQUszJyUle7tFlCpYrWOZpYTaJsPj4eCQmJiIoKAgLFixAYmIiJk+eDODh0wlGjx6N9evXo2HDhgCAS5cu4fTp00YxPvroI6xevRq7du2CWq3G5s2bAQDjxo3DwoULsX37dmzatOnxVoyIiIiIiIiIqg1TjhFWEcePH4e3t7fRvPr168PFxQVnz54F8L+xwR4d5+vEiRNQqVRo1KiRvNzJkyeLdGk+ceJEkfHFzJ3ZJMJKsmfPHowbNw4xMTHw8PCQ54eHh+PDDz80WrZly5bIyMiAJEnIyMiAq6srLl68iPv37+Nf//oXOnfujB9//PFxV4GIiIiIiIiI6LHy8PDAwYMHjeZduHABN27cQIMGDQAAjRo1gpeXF1atWmW0XHR0NAICAqBSPRz7smfPnrh9+zbi4+PlZU6dOoWUlBT06tXLtBURzOzHCIuOjsakSZOg1WrleYcPHwbwMPFV2LBhwxAUFASNRoPWrVujffv2SEpKwoEDB3Ds2DHUqlULOp0O/v7+aNKkSYnr1Ov1RZ66IOU+gEJZPfvPEhEREREREVH5mHKMsIp46623MGnSJLzzzjt4+eWXcfPmTcyaNQu1atXCwIED5eXCw8Px2muvoXHjxujSpQuio6Oxb98+7Ny5U17mpZdeQo8ePTBq1CjMnz8fNjY2mDZtGtq0aYN+/fo9iepVmtm3CIuIiMC2bduwZMkSed706dMxY8aMIsu+9dZb2Lt3L06ePAkXFxdER0fDyckJLVu2hLu7O2xsbKDT6XDkSOlPZImMjIRWqzWack5uFl01IiIiIiIiInrGmEvXyIkTJ+Lbb79FYmIi+vbti0mTJqFJkyZITEyEs7OzvNyQIUPw/fffY/ny5ejRowf27NmDtWvX4qWXXjKKFx0djcDAQIwePRpDhw5FkyZNEBcXBysrs29jZcTsS2tnZ4fY2FgEBgZCrVYjJCQEZ8+elZ+ScOXKFYwfPx4LFy6ElZWVPNCbq6srMjIy4OnpiezsbNy5cwcajQb79+/Ha6+9Vuo6w8KKPoWh2cRY01SQiIiIiIiIiEgwhUKBt956C2+99VaZy7755pt48803S13G3t4eP/7441M/5JTZJ8KAhxs7Li4OgYGBsLOzM2rR5enpiYULFwIAZs2ahYCAANja2sLe3h5Tp06FpaUlIiMj0adPH+Tl5aFXr15o3bp1qevTaDRFnrrAbpFEREREREREVBZz6RpJxTO7RFjhLpDh4eHyv11cXJCSklJk+TNnzsj/DgkJQUhISJFlunTpgl27dgktJxERERERERERPV3MLhFGRERERERERPS0quhYXvR4mf1g+URERERERERERCKwRVg5veTfVGg8lZWl0HgA8M+tOkLj1aihEhovL88gNB4AqJRit6O9WmydAeCiu5PQePYaG6HxTKF17RpC41mY4A8qtoLPwczcfKHxnGzEH4sP8sSWMc8gCY0HACpLsX+fyRJcZwPE17mGtdhbsZ21+PuLykrsfhG9n5WC4wGAQfwtSzhbwZcJpUHsxdYUfwx/QSv6/iK+kDWUYs9pG0vx53S2Qey1McM+V2g8Nzvx33Vc1Eqh8UzR1uOwro3QeNeuNRIar25dTdkLVZAkib2vmuL3RkaGp9B4tWqJvY5lZzcQGg8AcnOfgpugmWODMPPGRBgRERERERERkSDsGmne2DWSiIiIiIiIiIiqBbNJhAUEBECn08HNzQ0+Pj7Q6XRQq9VYunQpACA5ORne3t44f/48Tp06BZ1Ohy5dumDy5MlyjD59+qBjx45o164dfvnlF3n+4sWL0a5dO3Tq1AmJiYmPu2pEREREREREVE0oFKabqOrMpmtkfHw8AGDkyJEIDQ2Fn58fwsPDAQBJSUkYO3Ys1q9fDw8PD/Tt2xcRERFo3749QkNDkZCQgC5duiAyMhJeXl7IyspCq1atMGjQIOj1eixevBj79u1DZmYmAgICkJycDAsLs8kBEhERERERERHRY2A2ibCS7NmzB/Pnz0dMTAy0Wi0A4NSpU2jbti0AoG3btnIizMvLCwCgUqlgYWEBhUKB1NRUtGjRAkqlEvb29rCyskJqaioaNSp58Ei9Xg+9Xm80L+/BfVjZqk1USyIiIiIiIiJ6FnCMMPNm9s2ioqOjERwcLCfBAKBVq1bYvHkzJEnC1q1bcevWLaPPzJkzBwMGDIC1tTUaN26MlJQU6PV6pKen49ixY0WWf1RkZCS0Wq3RdGH7CpPUj4iIiIiIiIiIHg+zbxEWERGBqKgouLu7Y+TIkQCA+fPn4+2338ZXX32FRo0aoW7duvLyP/zwA44dOyaPLebs7Izw8HD06dMHderUgbe3t9HyxQkLC0NoaKjRvBErT4qtGBERERERERE9c9ggzLyZfSLMzs4OsbGxCAwMhFqtRkhICLRaLdavXw9JkjBixAgEBwcDeNh6bO3atVi3bp3RGGAhISEICQnBlStXEBoaWmYiTKPRQKPRGM2zsk0XXzkiIiIiIiIieqawa6R5M/tEGADY29sjLi4OgYGBsLOzw507d/D9998DeDi4fvPmzZGTk4Phw4fD29sbgYGBAIBff/0V7u7uGDFiBNLS0qBWq7FgwYInWRUiIiIiIiIiInpCzC4RtmTJEvnfBU+NBAAXFxekpKTIr4cOHWr0OZVKhZycnGJj/vrrr0LLSERERERERERUHLYIM29mP1g+ERERERERERGRCGbXIoyIiIiIiIiI6GnFBmHmjYmwctq357TQeNY21kLjAcDdy2IH9Fc5ugqNl5eXJzQeAKOHIohQw76G0HgAkHH+jNB4KtfSH/ZQUYZ8g9B4AKBr7iI0ntJC/J1EY2MpNN6tzHyh8epqiu/qXRV3c8SWMSdPEhoPAGyVYs/pe9li62wQX2XUq2EnNJ6lCc4X0REtRH87VIi/juUKvjZm5YovY1au2OM7N09sGTMFX3MAYF/aPeExRXOrqRQaz07wdREAMgUfj1fv5gqN18hJbDwA+PtqptB4tiqx3yMAYP/2g2IDXk8VGi5N21JoPACwUor9OZr3QOx+BgBcEfs7MK2xt9B4uXczhMYDAOSL/t2mExyPqGqYCCMiIiIiIiIiEoRjhJk3sxkjLDU1FY6OjtDpdHjppZcwYcIE+b01a9bA2toaN2/elOetXr0azZs3h42NjVGcw4cPw8/PDx06dMD8+fPl+a+//jrq1q2L0NBQ01eGiIiIiIiIiKolhcJ0E1Wd2STCAMDX1xeJiYlISkrC8ePHcezYMQDA0qVL8fbbb2PlypXysjqdDikpKahfv75RjPHjx+Pnn3/G7t27sX79epw9exYA8Omnn2L58uWPrzJERERERERERGRWzCoRViAvLw8PHjxAzZo1cevWLdy+fRvTp0/H6tWr5WVcXFyKtAbLzs6GXq9HkyZNYGFhgT59+mDHjh0AgHr16j3WOhARERERERFR9aNQKEw2UdWZ1RhhBw4cgE6nw+XLl9GyZUvUq1cP33//PYYOHQp7e3vUqVMHp0+fRpMmTYr9/M2bN+Ho6Ci/dnR0NOpOWV56vR56vd5onpT7AAqlbYVjERERERERERGReTCrFmEFXSNPnToFd3d3fPHFF4iOjsaKFSsQFBSE06dPIyoqqsTPOzk5ISMjQ36dkZEBZ2fnCpcjMjISWq3WaMo+sakyVSIiIiIiIiKiaoRjhJk3s0qEFebo6Ig9e/bAwcEB27dvx+bNm7F3717ExcVBkop/fr2NjQ1q1qyJc+fOQZIkxMXFwd/fv8LrDgsLQ1pamtFk3axnVatERERERERERERPkFl2jczPz4eVlRW6dOmCWrVqye9bWlrC09MTu3fvRn5+PmbNmoXLly+jW7duGDNmDEJCQvD1119jxIgRMBgMCA4OhqenJwDg/fffx9atW3H16lV069YNa9asgUajKbYcGo2myHvsFklEREREREREZbFg0y2zZjaJsAYNGuD27dtlLvfokyMf5ePjg927dxeZP2fOHMyZM6dKZSQiIiIiIiIiKg3zYObNbLtGEhERERERERERiWQ2LcKIiIiIiIiIiJ52CjYJM2tMhJVTu45NhMZTWolvjHfzdm2h8ew1NkLjZefkC40HABYWYi8wGjul0HgAcK2xq9B4NdQqofEMJTx8oipautYQGs/SBDcSO6Wl0Hj3auQJjedoLXY/A0BmrthzMN8Ex47SUuy+zs4zCI1nijrXtBV7K7ZWir+/KC3FxrQUfO0WHQ8AVILv06a41ioUYq9jVoLPP/E1BvwaFD++a2UZDOJLWUMl9pw2xT1QNL1TrtB4LrbWQuMBgIta7Hc8pQmuOwd1PkLj3bjRVGi8OnVqCo0HiP8unyf4vg8At255Co1Xu7bY78n37+cIjQcwiUPPPibCiIiIiIiIiIgEMUGunATiGGFERERERERERFQtmE0iLCAgADqdDm5ubvDx8YFOp4NarcbSpUsBAMnJyfD29sb58+cRHh6Odu3aoWPHjpg4cSKk/9/dYMGCBfDy8oKn5/+ar2ZkZCAgIAB+fn5o3749Nm3a9ETqR0RERERERETPPoVCYbKJqs5sEmHx8fFITExEUFAQFixYgMTEREyePBkAkJSUhNGjR2P9+vVo2LAhhg4din379mHPnj24evUqtm/fDgAYOHAgjh07ZhTX2toav/zyC3bv3o3Y2FhMmjTpcVeNiIiIiIiIiKoJhcJ0E1Wd2Y8RtmfPHsyfPx8xMTHQarUAAC8vL/l9a2trWFo+HCy2du2ig8Xb2tqifv368r/Lk0HV6/XQ6/VG8/Ie3IeVrbrS9SAiIiIiIiIioifLbFqElSQ6OhrBwcFyEqywxMREpKenw9/fv1yxJk2ahLCwsDKXi4yMhFarNZoubF9R4bITERERERERUfWiMOF/VHVmnwiLiIjAtm3bsGTJEqP5Bw8exPvvv4/Vq1fDwqLsanz00UdwcnLC6NGjy1w2LCwMaWlpRpNH1yGVrQIREREREREREZkBs+8aaWdnh9jYWAQGBkKtViMkJAR///03Ro8ejXXr1sHZ2bnMGPPmzcPly5fx448/lmudGo0GGo3GaJ6VbXqlyk9ERERERERE1YcFG26ZNbNvEQYA9vb2iIuLw6effoqNGzdi7Nix0Ov1GDZsGHQ6HTZs2AAAWLlyJbp164bLly+jW7du2LVrF86fP4+pU6fi1KlT0Ol00Ol0yMnJecI1IiIiIiIiIiKix83sWoQV7gIZHh4u/9vFxQUpKSkAgN69exf72cGDB2Pw4MFF5hsMBqFlJCIiIiIiIiIqTnke0kdPjtklwoiIiIiIiIiInlbMg5k3JsLKKd8gCY1nY4IzIz9fbBnz8sW2pJMkseUDACvLp6J3r1DCDx3xuwVWgjvF5wk+/wAgX/DxaFENn+AimeDgkSSx21H0fjaY4Dommkn+Aik4pOhjx0Ih/l4g+p5laYLBQkR/NxFN9L0AAHJFfzcRGu3/xxQc1OIpOHZEH4pPw33fSvD9CjDNd2WR8gWff4D4+74pegKJ3i+id7OlCX4P5eTkC49JZE6YCCMiIiIiIiIiEsSCTcLMWvVrTkNERERERERERNWS2STCAgICoNPp4ObmBh8fH+h0OqjVaixduhQAkJycDG9vb5w/f17+zPTp0+Hp6Sm/7tevn/xkSAcHB8TExAAAOnTogM6dO+OFF17AihUrHm/FiIiIiIiIiKjaUChMN1HVmU3XyPj4eADAyJEjERoaCj8/P/mpkUlJSRg7dizWr18PDw8PAMClS5dw+vRpoxhr1qwBAGRlZaFZs2bo3r07ACAxMREqlQp6vR7PPfcchgwZ8phqRURERERERERE5sJsWoSVZM+ePRg3bhxiYmLkJBgAhIeH48MPPyz2M+vWrUOPHj1gbW0NAFCpVACAu3fvomXLlqYvNBERERERERFVSwqFwmQTVZ3ZJ8Kio6MRHBwMrVYrzzt8+DAAlJjUioqKwvDhw+XXDx48gL+/P9q0aYNXX321zHXq9Xqkp6cbTXkP7lexJkRERERERET0rGPXSPNm9omwiIgIbNu2DUuWLJHnTZ8+HTNmzCh2+WvXruH06dPo2LGjPM/W1hY7d+7EyZMn8emnn+LOnTulrjMyMhJardZoupjAscWIiIiIiIiIiJ5mZjNGWEns7OwQGxuLwMBAqNVqhISE4OzZswgNDQUAXLlyBePHj8fChQsBACtWrMDgwYPlJoM5OTmwsrKChYUF1Go1bGxsYGNjU+o6w8LC5PgFXlt+wgS1IyIiIiIiIqJniQWbbpk1s0+EAYC9vT3i4uIQGBgIOzs7HDlyRH7P09NTToIBD7tFFn4y5MWLFzFq1ChYWloiOzsbM2bMkMcOK4lGo4FGozGaZ2WbJqg2RERERERERET0JJhdIqxwF8iCp0YCgIuLC1JSUoosf+bMGaPXBw4cMHrt6emJnTt3Ci0jEREREREREVFx2B7MvJldIoyIiIiIiIiI6GnFpzuaN7MfLJ+IiIiIiIiIiEgEtggrJ0l60iUom4WF2KyzpeB4+SbIilfHTLvoY9FgEH9w5wmOaSX4WAQAS8HHjuhD0QS7BUpLwed0nvj9ki/4AFdaiP17T47BIDQe8HTcXyTBhbRQiN0vpriOCT+nxR86wq8Too9F0eczIP7anfcUlFF0PADIh9h6iy6hSeos+IRRWAoNZxKWgu/71fF7NyD+2qi0EnsPzMwUf4MRfexURyb4+UICsUUYERERERERERFVC2wRRkREREREREQkSHVtQfm0MIsWYWfOnIGvry9ycnIAAPPmzcP06dPxzz//YNCgQfD390eHDh3w1VdfyZ/p168fdDoddDodHBwcEBMTAwBYvHgx2rVrh06dOiExMREAkJOTg4EDB6JTp0548cUXsW3btsdeRyIiIiIiIiIierKq1CLswIEDyMjIQEBAAADg9u3bmDJlCv7++29069YN06dPh0U5xmfx9PRE//79MWfOHIwYMQIrV67EH3/8gZdffhkhISH4v//7P2RnZ6NXr16oXbs2Bg8ejDVr1gAAsrKy0KxZM3Tv3h3Xr1/H4sWLsW/fPmRmZiIgIADJycnYunUr1Go1du3ahbS0NPTt2xeBgYFVqToRERERERERURFsEGbeqtQi7N1338Xu3bvl15MmTcJ///tfuLm54fPPP8fs2bPLHWvy5MmIjY3FkCFD8OWXX+LGjRs4fvw4QkNDAQDW1taYOnUqfvrpJ6PPrVu3Dj169IC1tTVSU1PRokULKJVK2Nvbw8rKCqmpqWjcuDGys7MhSRJu376NWrVqlVoWvV6P9PR0oykv634FtgwRERERERERVUcKhcJkE1VdlRJhx48fx4svvggAePDgAVavXo0vv/wSq1evxmeffYaoqKhyx1IqlfD398ft27fh5+eH9PR0aLVaox3t7u6OtLQ0o89FRUVh+PDhAIDGjRsjJSVFTmQdO3YMt27dQqNGjZCZmYlmzZohMDAQM2bMKLUskZGR0Gq1RlNawopy14WIiIiIiIiIiMxPlRJhmZmZsLOzAwDs2bMH2dnZePXVVwEAbdq0QXp6erljHTt2DHv27EG3bt3w/fffo379+khLSzN6ZHtaWhq0Wq38+tq1azh9+jQ6duwIAHB2dkZ4eDj69OmDf/3rX/D29kbdunWxZMkS1K9fHydPnsS+ffswduzYUssSFhaGtLQ0o0nbZUi560JERERERERE1ZOFwnRTdXLs2DEMHjwYjRs3hrW1NQ4ePAgAmDZtGjZt2lTpuFVKhDVq1Ehe+bJly+Dr6wsnJycAwPXr16HRaMoVx2Aw4K233sLChQsRERGBBQsWwMrKCs2aNZO7QmZnZ2Pu3LlGSawVK1Zg8ODBRq3GQkJCsHPnTnz55ZfQaDSoW7cuAMDV1RUA4OjoiHv37pVaHo1Gg/r16xtNVjbqcm4VIiIiIiIiIiKqrG3btsHHxwcXLlzAa6+9htzcXPk9pVKJRYsWVTp2lQbLDwsLQ2hoKH788UfcunXLqCtkYmIi2rRpU6443377LXx9feHr6wsA+OSTTzBp0iQsW7YMb7/9NpYsWYIzZ87ggw8+QHBwsPy5qKgorFhh3GVxxIgRSEtLg1qtxoIFCwAAw4YNw5AhQ9C5c2dkZmZWaOwyIiIiIiIiIqLy4lheVff+++9j8ODB+PXXX5GXl4dZs2bJ7/n4+OCHH36odOwqJcJGjRqFJk2aIDk5Gc8//zy6dOkiv+fs7IxJkyaVK8748eONXvft2xd9+/YFAKxatQoAsHLlSvz0008YM2YMVCoVgIdPrXzUr7/+WmSeWq3Ghg0bylUWIiIiIiIiIiJ6co4ePYo5c+YAKJpYdHBwwI0bNyodu0qJsJ07d+L5559Hp06dirz33nvvyf03RRg8eDAGDx4sLB4RERERERERkWhsD1Z1Tk5OuHz5crHvnTp1CnXq1Kl07Colwrp06YKkpCT5yZGFnTx5El26dEF+fn5VVmE2Cg/aL4KFCUa5E13GfIPYeKZoHSq6zkqrKg2bVyzRZTQIjmdlKb7Oove1hQkOnjzBx7doliaos0ESG9MU57SF4K8NuZJBaDwrE1RadEiDCY5tC8HXRsGXMZjibH4aejSI3o6i62yK65jw70/54o+ePME7xlL0jgZgEHzW5Iv+DmqCOiufghGmRVfbFPcD0SwFfw81waEDS0uxx47o/WKKOpvit2p1Y4rfL9VN3759MWPGDLRv3x6enp4AHrYMu3r1Kj7//HP079+/0rGrdOUp7Qf+/fv3YWtrW5XwRERERERERERUzcyZMweurq5o06YN2rVrB+Dh8FxNmzaFvb09wsPDKx27wi3C9u7diz/++EN+vXz5cuzevdtomaysLKxfvx7NmzevdMGIiIiIiIiIiJ42bBBWdfb29vjjjz+wdOlSbNu2DU5OTnBycsL48eMxYsQIeez4yqhwImzLli2YOXMmgIfN0r7++usiyyiVSjRv3rxCj7MMCAhAfn4+Tpw4gTp16sDe3h5//vknFi9ejGHDhiE5ORmjR4/G2rVr8csvv2DTpk2wsrKCr68vvvrqKygUCixYsAALFiyAwWDAmTNnAAAZGRno378/srOzkZeXhxkzZqBnz54VrTYRERERERER0VPpl19+wZdffom///4bNWrUwAsvvIA1a9bIPfliYmLw4Ycf4uTJk3B3d8f777+PN954wyhGTk4Opk2bhqioKNy9excdOnTAN998g6ZNmwota1ZWFgYOHIj33nsPb7zxRpFyVFWFu0bOmDEDBoMBBoMBkiRh79698uuCKTs7G3/99Rc6dOhQ7rjx8fFITExEUFAQFixYgMTEREyePBkAkJSUhNGjR2P9+vVo2LAhhg4din379mHPnj24evUqtm/fDgAYOHAgjh07ZhTX2toav/zyC3bv3o3Y2NhyP8mSiIiIiIiIiKiiFAqFyabKmD17NiZMmIBBgwZhy5YtWLx4MRo2bCiP6b57924EBwfjpZdewqZNmzBo0CC8+eabWL16tVGciRMn4vvvv8enn36KNWvWIDs7GwEBAbhz506Vt1lhNjY22LFjh8nGnK/SYPkGg9hBiIuzZ88ezJ8/HzExMdBqtQAALy8v+X1ra2tYWloCAGrXrl3k87a2tqhfv7787/IcOHq9Hnq93mheXtZ9WNmoK10PIiIiIiIiIqLH6eTJkwgPD8eGDRuMescVHmz+k08+Qbt27fDdd98BePhgxLNnz2L69OkYMGAAACA9PR0//PADFi1ahFGjRgEAXnjhBbi7u2Px4sWYMmWK0HJ3794dW7duRZcuXYTGBaqYCAOA/Px87Nu3D+np6cjKyiry/ogRI6oUPzo6GpMmTZKTYIUlJiYiPT0d/v7+5Yo1adIkhIWFlblcZGSk3P2zgEePUWjY883yFZqIiIiIiIiIqiVzGiPs559/RsOGDUscIio7OxsJCQmYO3eu0fzBgwdjxYoVSE1NRYMGDbB161YYDAaEhITIyzg5OaF79+6Ii4sTngh74403MGbMGNy9exe9evVC7dq1izRsev755ysVu0qJsIMHD6Jfv35IS0sr9gmSCoWiyomwiIgIREVFwd3dHSNHjjRa9/vvv4/Y2FhYWJTdw/Ojjz6Ck5MTRo8eXeayYWFhCA0NNZo3ZNnfFS47EREREREREVUvFibMhBXXg02j0UCj0RS7/N69e9G6dWvMmjULX3/9NTIyMvDCCy8gMjIS7dq1w9mzZ5Gbm4tmzZoZfa7g4YcnTpxAgwYNcOLECdSqVQuOjo5Flvvxxx8F1vChPn36AAAWLVqERYsWGSXBJEmCQqGodNfJKiXCxo4dC3t7e/zyyy9o0aJFlUbtL4mdnR1iY2MRGBgItVqNkJAQ/P333xg9ejTWrVsHZ2fnMmPMmzcPly9fLvfOKe4gsrK5WKnyExERERERERGJUFwPthkzZiA8PLzY5a9evYoDBw7gyJEjWLRoEezs7PDpp5+ie/fuOH36NG7fvg0AcHBwMPpcQcLr1q1bAIDbt28XWaZguYJlRNq+fXulx0QrS5USYceOHcOqVavQuXNnUeUplr29PeLi4hAYGAg7OzvMmzcPer0ew4YNA/CwBdcrr7yClStX4ocffsDly5fRrVs3zJgxA/Xr18fUqVPRsWNH6HQ6AMDWrVtNkrQjIiIiIiIiourNlF0ji+vBVlJrMODh2O737t3D6tWr0aZNGwBA+/bt0aBBA3zzzTfo0aOH6QpbBQX5G1OoUiLMy8urSJO8qlqyZIn878IZTRcXF6SkpAAAevfuXexnBw8ejMGDBxeZ/zgG9SciIiIiIiIiMqXSukEWx9HREc7OznISDHg4tpePjw+OHTsm51AeffJjQUsxJycnOU5xT4e8ffu2vIxIlpaWSEpKwosvvljkvQMHDuDFF1+sdNfIsgfXKsUXX3yBOXPm4MSJE1UJQ0RERERERET0TFAoFCabKqply5YlvpeVlYXGjRtDqVQWyesUvC4YO6xZs2a4du2anCArvNyj44uJUNw49AXy8vJgaWlZ6dgVbhHWunVro41/5coVtGrVCnXr1i3SX1ShUODQoUOVLhwREREREREREVVOnz598PPPP+Ovv/6Ct7c3AODmzZs4ePAg3n33XVhbW6NLly5YvXo13nnnHflz0dHRaN68ORo0aAAA6N69OywsLPDbb7/JXTNv376NrVu34qOPPhJS1qtXr+Ly5cvy65MnT8LKyjhtlZWVhZ9++gkeHh6VXk+FE2G+vr4mG7DMnImuc15+ydnNyiolYVoplhZi65ybK76LqoWF2EqL3oaA+GNHdBlz88XvF0vBdc41QffmqvwFoTi5BrE7Jk8SX2dTbEfRyvEQ4ApRCD5f8gTvZ1OwEHztBgCDKS6OApnia4nwU9AEZbQUfL7kG6rf9zvR9ysAEH0KWpngnM6uXE+SEonejqa45Ii+T6vEfo0AIP76bWkpNp5JrrWCd7boeABgEHzsiN6OVlaCbwYAcnMFXySqIfF7pfL69u2LF154AQMGDMDs2bNha2uLOXPmwNraGuPGjQMAfPTRR9DpdBg3bhwGDhyIhIQELF++HNHR0XKc+vXrIzQ0FJMnT4alpSXq1auHTz/9FPb29hgzZoyQsi5evBgzZ86UW7+NHDmyyDKSJMHS0hKLFi2q9HoqnAgrPIYXERERERERERH9jzk1HrKwsEBcXBzeffddjBkzBjk5OejUqRN27twJNzc3AICfnx/WrFmDDz/8ED/++CPc3d3xww8/ICQkxCjWV199hRo1auDf//437t69i44dO+L333+Hvb29kLKOHDkSOp0OkiSha9euWLhwIVq0aGG0jEqlgpeXF5ydnSu9nioNli9SQEAA8vPzceLECdSpUwf29vb4888/sXjxYgwbNgzJyckYPXo01q5di3nz5uH48eMAHvZHnTZtGiZMmIAlS5bgm2++gVKpRLt27fDll1/i2rVrCA4OhkqlwoMHD/Dpp58iICDgCdeWiIiIiIiIiMj0XFxcEBUVVeoyr7zyCl555ZVSl7G2tsbnn3+Ozz//XGTxZB4eHnKXx4SEBDz//POoWbOm8PVUKRE2atSoEt+zsLCAvb09fHx80K9fP9jZ2ZUaKz4+HsDDDGBoaCj8/Pzkp0YmJSVh7NixWL9+PTw8PIyawLVo0QIDBgwA8PApk0ePHkWNGjXQrVs3HDlyBC1atMCuXbtgaWmJc+fOYdCgQfjzzz+rUm0iIiIiIiIiomKZoFd7tdO5c2cAwN9//439+/cjLS0No0aNgpubG86cOYPatWtXOklWpURYSkoKLl++jH/++QdOTk6oVasWrl+/jlu3bsHV1RVqtRpfffUVpk2bhu3bt6Nx48YVXseePXswf/58xMTEQKvVGr23d+9eaLVa1KlTB8DDpxjcvXsX1tbWyM7OhqOjo9E4QBkZGUaPDCUiIiIiIiIiIvOSmZmJ0NBQREdHw8LCAgaDAUFBQXBzc8P777+Phg0bYu7cuZWKXaUx3ObNmweNRoNdu3bhxo0bOH78OG7cuIEdO3ZAo9Fg4cKF+Pvvv2FtbY0pU6ZUah3R0dEIDg4ukgQDgKioKAwfPlx+PXToUPj4+MDLywsvvvgi6tevDwA4f/48/Pz80KNHDwQHB5e5Tr1ej/T0dKMpL+t+pcpPRERERERERNWHhcJ0U3Xx3nvvYfv27di0aRP0er3RwzB69eqFzZs3Vzp2lRJh7733HsLDw9GxY0ej+Z06dcL06dMxefJkNGnSBO+//z4SEhIqtY6IiAhs27atyCD9OTk52Lhxo5zYunv3LmbOnIm///4bZ86cwcmTJ7Fv3z4AQMOGDbF7927s27cPb7/9dpnrjIyMhFarNZrSElZUqvxERERERERERFR+q1evxmeffYbu3btDpVIZvdegQQOkpqZWOnaVukaePHkSDg4Oxb7n6OiIs2fPAgAaN26MBw8eVGoddnZ2iI2NRWBgINRqtfzUgri4OPj7+0OtVgN4OCaZSqVCzZo1YWlpCUdHR2RkZCA7OxvW1tYAAI1Ggxo1apS5zrCwMISGhhrNG7r8RKXKT0RERERERETVhzk9NfJpde/ePXkYrEfdv1+1HntVSoQ1a9YMn3/+Obp06WI0GP79+/cxb948+TGXly9fRu3atSu9Hnt7e8TFxSEwMBB2dnbo3bs3oqKiMGbMGHkZtVqNcePGoUOHDlAqlWjSpAm6deuGpKQkfPDBB7C0tERubi6++uqrMten0Wig0WiM5lnZpFW6/ERERERERERUPVSnLoym0qZNG/z222/o3r17kfc2btyItm3bVjp2lRJhCxYsQM+ePVG/fn106dIFrq6u+Oeff7B9+3bk5eXJfTYPHz4sP9mxLIW7QBY8NRJ4+LjPlJQU+fVvv/1W5LMTJkzAhAkTjOb5+flh586dFagVERERERERERE9KR999BFeffVVZGZmIiQkBAqFAsnJyVixYgV++uknxMXFVTp2lRJhfn5+OH36NCIjI7F//34cP34cderUwejRo/Huu+/Czc0NAPDpp59WZTVERERERERERE8F9oysut69e2PlypWYPHkyli1bBgAYN24c6tevj2XLliEgIKDSsauUCAMANze3Sj+ykoiIiIiIiIiI6FEDBgzAgAEDcOrUKdy4cQNOTk5o1qxZleNWORFWXVg+BZ18LQSXMS9fKnuhCrC0FL8NLQSn2g0GsXU2RczCj40VQfRxAwB5gutcHf+iooD5V9rCBGUUXW/BpwusTHC+iD6+RdcZMP8BX01TZ/ExRcs382ut6PIB4u+BptjNoqttkuNbcLw8wYVUPgXfGXNNcHyLPgdzcgxiAz4FTPFdXvQ9MDdP7H7JExyPxBB9zamu0tLSsG7dOqSlpSErK8voPYVCUa4x4ItT4URYmzZtsHz5crRq1QqtW7cu9cKgUChw6NChShWMiIiIiIiIiIiqn//+978YPnw4DAYDatWqBZVKZfT+Y02E+fr6Qq1Wy/82978SExERERERERE9LhZPugDPgA8++AB9+/bFf/7zH9jb2wuNXeFE2M8//yz/u/ATHqsqICAA+fn5OHHiBOrUqQN7e3v8+eefWLx4MYYNG4bk5GSMHj0aa9euxbx583D8+HEAwIkTJzBt2jRMmDABN2/exIQJE3Dt2jXk5+djw4YN0Gg06NChA5RKJTIzMxEWFoYhQ4YIKzcRERERERERUQG2F6q6f/75B6NHjxaeBAPMaIyw+Ph4AMDIkSMRGhoKPz8/hIeHAwCSkpIwduxYrF+/Hh4eHli0aJH8uRYtWmDAgAEAgHfffRdTp07Fc889ZxQ7MTERKpUKer0ezz33HBNhRERERERERERmKigoCHv37q3S0yFLUuFE2MSJE8u9bFX6bBbYs2cP5s+fj5iYGGi1WqP39u7dC61Wizp16iA/Px+HDx/GN998g1OnTqF79+6YNm0aAMh9Se/evYuWLVuWuU69Xg+9Xm80L+/BfVjZqqtUFyIiIiIiIiJ6tnGw/Kr77rvvMGjQIGRmZiIgIAAODg5Flnn++ecrFbvCibANGzaUe1wwEYmw6OhoTJo0qUgSDACioqIwfPhwAMD169dx+PBh/Pjjj/Dx8UFwcDC2b9+Orl274sGDB+jRoweOHTuGiIiIMtcZGRmJmTNnGs1r2PNNNO4VWqW6EBERERERERFR6e7evYvMzEzMmTOnSB5HkiQoFArk5+dXKnaFE2GpqamVWlFlRUREICoqCu7u7hg5cqQ8PycnBxs3bsTcuXMBAE5OTqhduzZ8fX0BAD179sRff/2Frl27wtbWFjt37sSNGzfwwgsvYODAgaX2Mw0LC0NoqHHSa/iKk+IrR0RERERERETPFDYIq7oRI0bg4sWLWLBgAby8vIo8NbIqKpwIa9OmDZYvX45WrVrJ85YvX45evXoV21Stquzs7BAbG4vAwECo1WqEhIQAAOLi4uDv7y8/wdLa2hpNmzZFamoqGjRogH379qF///7IycmBlZUVLCwsoFarYWNjAxsbm1LXqdFooNFojOZZ2aYLrxsRERERERERERlLTk7G8uXL0bdvX+GxK5wIO3r0KDIzM+XX+fn5GD58OP78889K988si729PeLi4hAYGAg7Ozv07t0bUVFRGDNmjNFyCxYswBtvvIHc3Fy0bt0avXv3xtmzZzFq1ChYWloiOzsbM2bMgLW1tUnKSURERERERETVmwVbhFVZkyZNkJeXZ5LYQp4aKUmSiDAAgCVLlsj/LnhqJAC4uLggJSVFfv3bb78V+Wzr1q2RkJBgNM/T0xM7d+4UVj4iIiIiIiIiopJwsPyqi4yMxHvvvYdWrVqhWbNmQmMLSYQRERERERERERGJMGnSJFy9ehWtWrVC3bp1iwzFpVAocOjQoUrFrlQirLinRpb3SZJERERERERERM8qpkeqztfX12R5pkolwrp06QILCwujeZ06dSoyT6FQ4M6dO5UvnRnZu0vsUyNt7EofsL8y7l1OExpP5VRbaLzcnFyh8QDASim2UaNaoxYaDwAyLl4QGs/auZbQeJJBXNfmAsdaugqNp4D4C6C9jaXQeLceiO2/7u4gfixDfbbYMubmiz92lJZi97U+q3KPVC5JvkFoOABA3Zq2QuNZmuALg+hxLqwEB5Qk8XXOE3x85+SJP3iyc8XGFH1O3xN8zQGA/en3hMYzwW6BW02l0Hiir4uA+H2dfidbaDwvV/HHztHL94XGs1WJ/R4BAAcSUspeqCKunBEa7lJDb6HxAEAh+H5gyM4SGg8AcO2c0HDpns8JjZedcUtoPABAXo7YeB/oxMajaqHwsFmiVTiLMGPGDFOUg4iIiIiIiIjoqcfB8s2b2STCAgICkJ+fjxMnTqBOnTqwt7fHn3/+icWLF2PYsGFITk7G6NGjsXbtWvzyyy+Ijo5G7doPWyxt3boVKpVKjuXv7w8vLy/88MMPAIDXX38d27ZtQ69eveR5RERERERERERUvViUvcjjER8fj8TERAQFBWHBggVITEzE5MmTAQBJSUkYPXo01q9fj4YNGwIApk2bhsTERCQmJholwdauXVtkELVPP/0Uy5cvf2x1ISIiIiIiIqLqSWHC/6jqzCYRVpI9e/Zg3LhxiImJgYeHhzx/7ty58PPzwxdffCHPy8vLw7fffovx48cbxahXr95jKy8RERERERERVV8WCtNNVHVmnwiLjo5GcHAwtFqtPG/ChAk4dOgQfv/9d2zevBkJCQkAgO+++w7Dhg2DtXXVBpnW6/VIT083mqTcB1WKSURERERERERET5bZJ8IiIiKwbds2oycGODs7Q6FQwMbGBv3798f+/fuh1+uxbt06DBs2rMrrjIyMhFarNZpyTm6uclwiIiIiIiIieraxRZh5M/tEmJ2dHWJjY7Fo0SKsWrUKAJCRkQEAkCQJCQkJaNq0Kf7++2/o9Xr06tULU6ZMwZYtW7B48eJKrTMsLAxpaWlGk6ppkKgqERERERERERHRE1Dhp0Y+Cfb29oiLi0NgYCDs7OywatUqnDx5EpIkoVOnTnjllVcAAMnJyQCAxMRELF26FGPGjAEAvP/++9i6dSuuXr2Kbt26Yc2aNdBoNCWuT6PRFHlfobQ1Ue2IiIiIiIiI6FmhULDpljkzu0RY4S6Q4eHh8r9dXFyQkpICAOjdu3epMXQ6HXQ6nfx6zpw5mDNnjshiEhERERERERHRU8bsEmFERERERERERE8rjuVl3pgIIyIiIiIiIiIShD0jzRsTYeX0op+X0Hg2Kkuh8QDgn5u1hcarUcNaaDxJkoTGAwArS7HPe9DYKYXGA4D0hi5C46nVKqHxLE3w54qWrjWExrMyQRlVgo+dzNx8ofGcbcSefwDwID9PaLw8g/hzWmkhdr9k5YndL6b4UlPDRuyt2Fop/jk4SsHni4XgDWllKX7H5BvExrS2Er9fRNfaIs8gNp5C/D3Vt57Y+4v4qxhQUyX2nLaxFP+dMSdf7L6uZy/2u0ltWxuh8QDAwUbsdhR9vwKAA529hcbLyGgmNF6tWmLPP0D8fTU7W+x3HQC4e9dTaDy32jWFxrufmSM0HgDk55vi6khkPpgIIyIiIiIiIiISRPQf/Ugs8X/KICIiIiIiIiIiMkNmkwgLCAiATqeDm5sbfHx8oNPpoFarsXTpUgBAcnIyvL29cf78ecyePRv+/v7o2LEjRowYgdzcXABA//790blzZ7Rt2xZffPEFAODs2bPw9/dHp06d4Ofnh/379z+xOhIRERERERHRs81CYbqJqs5sukbGx8cDAEaOHInQ0FD4+fkhPDwcAJCUlISxY8di/fr18PDwwOTJkzFt2jQAwIgRI7B161b07t0bK1asgEqlQl5eHpo3b47Q0FA4ODhg7dq1cHZ2xvHjxzFmzBjs2rXrSVWTiIiIiIiIiIieELNJhJVkz549mD9/PmJiYqDVagEAKtXDATkNBgPy8vLg6elpND8zMxPu7u6ws7NDzZr/G4zQ2toaluUYcFSv10Ov1xvNy3twH1a2aiF1IiIiIiIiIqJnE4cIM29m0zWyJNHR0QgODpaTYAVmzJgBLy8vZGRkGL336quvwtPTE35+fkZJr/z8fEycOBH//ve/y1xnZGQktNr/1959x0dV5f8ff82kJySQUKQNAUEQFBaUlSLCEEJHJUoXFSEW4sK6sa8KiIVYyLpreSwoK0sTviwqEEMTAgJSFRRQQAUkobiKJKEmmeT+/uCXWcfQlDPJDXk/edzHgzlz5nPPOXNbzpx7rstn2Z/xnrlKiYiIiIiIiMhlyYnDb4tcOtt3hKWkpLBs2TKmTp3qk/7ss8/yzTffUL9+fZ/35s+fz969e0lLS+Orr74CwLIshg8fTp8+fejRo8cF15mcnExmZqbPUq/zYJPVEhERERERERGRUmb7WyPDw8NJS0uja9euRERE0L9/f06fPk1oaCgOh4PKlSsTHh5OUVERhYWFBAUFERYW5l0ARo0aRaNGjRg5cuRFrTMqKoqoqCiftMCwTON1ExEREREREZHLi26NtDfbd4QBVK5cmfT0dLp27Up4eDjz5s1jz549FBYW0rhxY8aPH8+JEye4+eabAcjLy2PQoEE0aNCAlStXMnnyZNq3b8/y5cuJiYnh/fffL+MaiYiIiIiIiIhIabNdR9gvb3MsfmokQLVq1diyZQsAvXv3LvG5oKAgVq5cWSLd7XaTn59vupgiIiIiIiIiIiU4NSLM1mw/R5iIiIiIiIiIiIgJthsRZlebP/3WaLyQsBCj8QCOZX1vNF5QzBVG4xV6Co3GA3AGmO3LjawSaTQewNF9e4zGC6lW02i8gvwCo/EAujSrbjSeP35RqRJm9vD34wmz7eiqbP57OZ5vdh/ML7SMxgMIDjD7ZWefMlvnQst8nWtHhhmN5ygHTxMKNPw9FxaZ/14KCouMxsvzmI0HcLrA8D5dYLaMx/M8RuMBbMg8bjSeP84vNSODjMYLDzb/u7XpY+MPx82es5pUN7/t7Dh80mi8YMPXoABb1uwwG/DgbqPh9rquMRoPICDI7PVY4Smz3zMAh83+Hbi/USuj8fKPHzMaDwBPntl4T3YyG68ccGqSMFtTR5iIiIiIiIiIiCHqB7M33RopIiIiIiIiIiIVgm06wrp06YLb7aZmzZq0atUKt9tNREQEM2bMAGDjxo20bNmSvXv3snv3btxuN507d+bRRx/1xvjyyy/p0KED7du3Z+LEid7077//nr59+xIXF0fPnj1LvW4iIiIiIiIiUjE4HQ6/LXLpbHNr5PLlywEYNmwYiYmJdOjQwfvUyHXr1jFy5Ejmz59PbGwsffv2JSUlhbZt25KYmEhGRgadO3fmwQcf5N1336Vhw4a43W769u1Lw4YNSUpKYvLkydSpU6cMaygiIiIiIiIiImXJNiPCzmXt2rUkJSWxcOFCYmNjAdi9ezetW7cGoHXr1mRkZJCXl0dubi5XXXUVTqeTPn36sGrVKvbv38+JEyd4+OGH6dSpE1OmTCnL6oiIiIiIiIjIZczh8N8il842I8LOZc6cOTz00EO4XC5v2rXXXsvixYvp3bs3S5cupXbt2hw5coTo6GhvnujoaI4cOcKBAwf47LPP2LFjBzVq1MDtdtOxY0euuuqqc64zNzeX3NxcnzSr4BSOILNP9hIRERERERERkdJj+xFhKSkpLFu2jKlTp3rTJk6cyNtvv023bt2oXr06tWvXJiYmhuzsbG+e7OxsqlatSkxMDNdccw316tUjNDQUt9vNtm3bzrvO1NRUXC6Xz5K3c5GfaigiIiIiIiIilwunHxe5dLZvx/DwcNLS0njrrbeYO3cuAC6Xi/nz57N06VJOnjxJQkICoaGhREZGsmfPHizLIj09nY4dO9KoUSPy8vLIycnBsiw2b9583tFgAMnJyWRmZvosIVdrkn0REREREREROT+Hw+G3RS6d7W+NBKhcuTLp6el07dqV8PBwcnJyePvtt4Ezk+s3bdoUgH/84x/cddddFBUVkZCQQKNGjYAzI7z69OmDx+OhV69eNG/e/Lzri4qKIioqyidNt0WKiIiIiIiIiJRvtusI++UtkMVPjQSoVq0aW7Zs8b4eMmRIic+2atWKNWvWlEjv3Lkzq1evNlpOEREREREREZFf07gte7P9rZEiIiIiIiIiIiIm2G5EmIiIiIiIiIhIeeXUXF62po6wi1RUVGTreP7gDDA7YLAgv8BoPDBfRr8oKjQartBjNp7Tab4NCy3LaDx/nEiKDJexPPAUma1zvsf8cczpMLs9BprevIvMb4umt28L+2/bhYa3xfLAKgfHnPLwtZjepwsKzVfadDv6o4ymT6vGD7V+2BbLwS5o/JoRy+x52hkYYDQe+OHYGOCHP28NX5sY/zuw0PzfWMa3RRGbUUeYiIiIiIiIiIghGg9mb7YZTtOlSxfcbjc1a9akVatWuN1uIiIimDFjBgAbN26kZcuW7N27l8LCQp588kni4+Nxu92sWrUKgFOnTvHAAw9403ft2gXA7bffTqdOnWjdujV/+9vfyqyOIiIiIiIiInJ5czj8t8ils82IsOXLlwMwbNgwEhMT6dChg/epkevWrWPkyJHMnz+f2NhYJk2ahMvlYsKECT4xxo8fzy233EKvXr180t977z2Cg4PxeDw0bdqUxMREIiMjS6VeIiIiIiIiIiJiD7YZEXYua9euJSkpiYULFxIbGwvAnDlzOHToEHFxcQwfPpxjx44BsHTpUjIyMnC73SQnJ+PxeAAIDg4G4OTJk9SrV4/w8PCyqYyIiIiIiIiIXNYcDoffFrl0tu8ImzNnDgkJCbhcLm/agQMHiImJYcWKFTRr1oxXXnkFgO3bt9O+fXtWrlxJQUEB06ZN837m1ltvpVGjRnTo0IGAgPNP9Jibm0tWVpbPYhWc8k8FRURERERERESkVNi+IywlJYVly5YxdepUb1pMTAw9e/YEoE+fPmzdurVEeu/evb3pAPPnz2fv3r2kpaXx1VdfnXedqampuFwunyV/12Kj9RIRERERERGRy4/Tj4tcOtu3Y3h4OGlpabz11lvMnTsXODOx/ubNmwHYsGEDjRs3Pmd6UVERBQVnHikbFhbmXc4nOTmZzMxMnyW4SQ9/VVFEREREREREREqBbSbLP5/KlSuTnp5O165dCQ8P55FHHmHEiBFMmTKF0NBQ7y2QKSkpJCYmcvr0aWrUqMG0adM4ceIEN998MwB5eXkMGjSIBg0anHd9UVFRREVF+aQ5gs7feSYiIiIiIiIiorm87M12HWG/vAWy+KmRANWqVWPLli3e1/PmzSvx2bp167J4se8tjKGhoaxcudJ0MUVERERERERESlA3mL3Z/tZIERERERERERG5NMePH6du3bo4HA7vtFLFpkyZQuPGjQkNDeUPf/gDaWlpJT6fk5PDiBEjiImJITIykn79+nHo0KHSKr4x6ggTERERERERETHE4XD4bbkUzz33HB6Pp0T67Nmzuffeexk4cCCLFi2iXbt2JCQksH79ep98AwcOZOnSpfzzn/9k5syZ7Nq1i549e541pp3Z7tZIu7Isq6yLcGFWkdFwRYVm4zkDzPe7BgQGGI9pXGCw0XBOp9l2LA/btsfspgiA4c3bOI8fvpeAcjBXQaDTbBnzCu2/fRcZ/q6dfvmezcYMMPw9+6XGhoP6Y64Q2x++/VA+0+cD09siQKHhL8Yf205hkdky2vyUCpj/rv1SZ8PX8jgMX3vb/ZjjL4a/F9PX8jj98PeQJ998TClzO3fu5M0332TixIk88MADPu+NHTuWQYMG8dxzzwHQuXNnvvzyS8aPH096ejoA69atY8mSJSxZsoRu3boB0KRJE5o2bcr777/PgAEDSrdCl0AjwkREREREREREDHH6cfm9Ro0axQMPPECTJk180vfs2cPu3btLdGQNGjSI5cuXk5eXB8CiRYuoUqUKXbt29eZp0qQJLVu29HaWlRcaESYiIiIiIiIiUg7k5uaSm5vrkxYVFUVUVNQ5P/Of//yHbdu2MW/ePD7//HOf93bu3AnA1Vdf7ZPetGlT8vPz2bt3L1dffTU7d+6kSZMmJUYqN23a1BujvLDFiLCioiLuv/9+2rdvT8eOHRk0aBAAs2bN8qb16dOHvXv3Amdu5br33nvp2LEjPXr04MCBA8CZJ042aNAAt9uN2+1m//79Puu56667iI+PL93KiYiIiIiIiEiF4c85wlJTU3G5XD5LamrqOcty8uRJkpOTefHFF8/aWXb06FEAqlSp4pMeHR0NwM8//+zN9+s8xfmK85QXthgRtmTJEgoKCvj000+BMw396aefMmHCBD755BOio6NZunQpt9xyC1u3bmXhwoUEBATwySefsGbNGv7617/y73//G4ARI0bw9NNPl1jH559/XqLXVERERERERETEJH/OzJucnExiYqJP2vlGgz3//PNcccUV3HPPPX4sVfliixFhERER7N69m6+++grLsoiJieHdd99l9OjR3l7Ibt26ERkZyYYNG9i9ezetW7cGoHXr1mRkZHhjTZs2jQ4dOvDUU09RVPS/iQ3Hjx/PU089dVHlyc3NJSsry2exCk4ZrLGIiIiIiIiIyG8TFRVF3bp1fZZzdYR9//33TJw4kWeffZacnByys7M5fvw4AMePH+f48ePePpecnByfzxaPFIuJiQHOjPz6dZ7ifMV5ygtbdIR17NiRESNG8Kc//YkGDRowceJEsrKyqFevnk++evXqkZmZybXXXsuSJUuwLIvFixd7h+HdeuutfP3116xatYqDBw96R4mlpaVx3XXXUb169Ysqz9mGGhbsXmK20iIiIiIiIiJy2XE4/Lf8Fnv37iU/P5/evXsTHR1NdHQ0N998M3DmyZDx8fHeucF+Pc/Xzp07CQ4O5sorrwTOzCG2a9curF89HXnnzp0l5hezO1t0hAHcc889rFixgm3btjFr1ixOnz5dYo6vzMxMXC4XvXr1okmTJrjdbtasWeNt9OjoaAICAggICGDw4MFs3ryZwsJCUlNTefjhhy+6LMnJyWRmZvosQY27G62viIiIiIiIiIi/tGzZkoyMDJ/lb3/7GwD//Oc/eeutt7jyyitp3Lgxc+fO9fnsnDlz6NKlC8HBwQD07NmTo0ePsnz5cm+e3bt3s2XLFnr16lV6lTLAFnOEHTx4kEqVKhEVFUWlSpWoVKkS48aNY9SoUfTv358qVaqwfPlyTp06Rbt27YAz97nCmfnFir+Y7Oxs7+RtK1asoEmTJhw+fJijR49y++23c+rUKXbs2MH48eMZM2bMOctzticuOILC/FBzEREREREREbmcOP06S9jFq1KlCm63+6zvXX/99Vx33XUAjBs3jjvuuIOGDRvSuXNn5syZw4YNG/jkk0+8+du1a0f37t0ZPnw4EydOJDQ0lKeeeooWLVpw2223lUZ1jLFFR1hWVhZ/+ctfcDqdeDweevXqRadOnXj00Ufp2bMn+fn5FBUV8dFHH+FwODh69CgJCQkEBAQQGxvLG2+8AcArr7zCxx9/TGBgIFdddRXjx48nODiYLVu2ALBv3z4SExPP2wkmIiIiIiIiIvJ7/dZbGMva4MGDOXnyJCkpKaSkpNCkSRM++OAD70CkYnPmzCE5OZn77rsPj8dDt27deP311wkMtEXX0kVzWL++wdOGjh07Rq9evXjqqafo0aNHmZSh0oCpRuOFhocajQdw4sD3RuMFVa1lNF5hYaHReABBwUFG44VXCjcaD+DofrPfS3CVakbj+eMQ8Pi97Y3Gc/jhF5XKoQFG42Wf9hiNVysq2Gg8gFP5RRfO9BucyDe/T1cKMfu9HDlp9nvxh95X1TAaLyrM7HERIDTI7PcSYfh7Dgk0P9PDqQKz2/fpArP7H8Apw/tgnuEynswzv/9N//KQ0XhOP/yhUjXC7B8DEcHmt+/c02a3nR9PmP2ur6pm/jp513/NPvjK6YeN51/vLDMb8Ie9RsM567cwGs8fijwF5oMe2HnhPL9BcKNWRuPlHys5efmlBzW7v5xa+qjReOVB2vYf/Ba7z7VX+C12RVEuuu0iIyNZvXp1WRdDREREREREROS8/PFDvphjm8nyRURERERERERE/KlcjAizg3YdzT4ONMTwbSYAR7JrG41XJSrEaLyThm8dAwg23I6RfrilKCs2xmi8yEiz34vTDzewt6gRaTReoR9u3ww3vO2c8pi9zSQmxOz3DHDa8O3JeX643TnQYfb3mTqRZstY5IfJBCJCzJ6Kw4LNn1+CDd96GBhg9rjjj1uUAgzHNN2G/hAUYLaMptsQoG29Skbj+WOCkIggs/t0aID5fdr0+eCkx+w1XrUw8+fAKoanRAh0mt+nt3RqaTTezz83MRqvZk2z13f+UOSHE/XPPzcwGq9GDbPHsVOnzN8OWg5mT7K98jZHWEVj/6syERERERERERERA2zTERYXF+d9uiNAQUEBDRo0oEGDBrjdbtxuN7feeitw5tGebdq04cYbb2T06NHeHuvXX3+dxo0b06hRI2+c7OxsunTpQocOHWjbti2LFi0q3YqJiIiIiIiISIXhxOG3RS6dbTrC7rzzTmbMmOF9vWjRItxuNwEBAaxcuZKVK1cyf/58AIYMGcKGDRtYu3Ythw8fZsWKFQAMGDCAHTt2+MQNCQnh3//+N2vWrCEtLY2HHnqo1OokIiIiIiIiIhWLw+G/RS6dbTrCbr/9dubPn09R0ZlHd8+YMYM777zzrHkbN27s/X9ISAgB/3/uhCuuuIKgIN85nsLCwqhbt673/w5tOSIiIiIiIiIiFZJtJsuPioqidevWrFixghtuuIGtW7fidrspLCzE7XYD0KNHD5544gnvZ1auXElWVhYdO3a8qHU89NBDJCcnXzBfbm4uubm5PmmeUycIDIu4+AqJiIiIiIiISIWj8Tf2ZpuOMIChQ4cyY8YMMjMzue2223A6nd5bI3/t888/58knnyQtLQ3nRTy15ZlnniEmJob77rvvgnlTU1N59tlnfdIa9BxBw16JF10XERERERERERGxF1t1hPXo0YM///nPfPvtt0yaNOmc+b7++mvuu+8+PvzwQ6pWrXrBuK+88goHDx5kypQpF1WO5ORkEhN9O73ufG/XRX1WRERERERERCouhya1tzXbzBEGEBgYSO/evTl58iTXXHPNOfONHDmS3Nxchg4ditvtZsGCBQDMnj2b+Ph4Dh48SHx8PKtXr2bv3r08/vjj7N692/v0yfz8/POWIyoqirp16/osui1SRERERERERKR8s9WIMIB//OMfPq+//fbbEnnOdqskwKBBgxg0aFCJ9OIJ+EVERERERERE/MmpAWG2ZruOMBERERERERGR8kq3RtqbrW6NFBERERERERER8ReNCLtInkKzt1cGB5nvg7Qsy2i8fE/Fu6XUH4+5ddp8XGyR4e0GwGM4ZqAfvphAh9l90LIKjcbzBwuz34s/fukKMLy/OIsMx/PD7my6zn7YpY0rLDJ8jPDDz3oBho87BYb3PzC/PZo+H5g+5gAY3nT8sk/741rCNLsfJvxxHCsoB8cdh+GNx/Q1aKHhv4cAgoMDjMYrKDB/PVZketsJMH3eN7/DmN4WKyI1ob1pRJiIiIiIiIiIiFQIGhEmIiIiIiIiImKI5gizN9uMCOvSpQtut5uaNWvSqlUr3G43ERERzJgxA4CNGzfSsmVL9u7dy+7du3G73XTu3JlHH33UG6NPnz7ceOONtGnThn//+9/e9O+//56+ffsSFxdHz549S71uIiIiIiIiIiJS9mwzImz58uUADBs2jMTERDp06MC4ceMAWLduHSNHjmT+/PnExsbSt29fUlJSaNu2LYmJiWRkZNC5c2dSU1Np3Lgxp0+f5tprr2XgwIGEhoaSlJTE5MmTqVOnThnWUEREREREREQudzafJrrCs82IsHNZu3YtSUlJLFy4kNjYWAB2795N69atAWjdujUZGRkANG7cGIDg4GCcTicOh4P9+/dz4sQJHn74YTp16sSUKVMuuM7c3FyysrJ8Fs/pE36qoYiIiIiIiIhcLhx+/CeXzvYdYXPmzCEhIQGXy+VNu/baa1m8eDGWZbF06VJ+/vlnn89MmDCBfv36ERISwoEDB/jss894+eWXWbJkCW+//TbffPPNedeZmpqKy+XyWTIzZvulfiIiIiIiIiIiUjps3xGWkpLCsmXLmDp1qjdt4sSJvP3223Tr1o3q1atTu3Zt73vvvPMOO3bs4PnnnwcgJiaGa665hnr16hEaGorb7Wbbtm3nXWdycjKZmZk+i6vzIL/UT0REREREREQuHw6H/xa5dLaZI+xcwsPDSUtLo2vXrkRERNC/f39cLhfz58/HsizuuusuEhISgDOjxz744AM+/PBDnM4zfXyNGjUiLy+PnJwcoqKi2Lx5M3fcccd51xkVFUVUVJRPWmDofv9UUERERERERERESoXtO8IAKleuTHp6Ol27diU8PJycnBzefvtt4Mzk+k2bNiU/P58777yTli1b0rVrVwCmTZtGvXr1SE1NpU+fPng8Hnr16kXz5s3LsjoiIiIiIiIicpnSwC17s11H2C9vgSx+aiRAtWrV2LJli/f1kCFDfD4XHBxMfn7+WWN27tyZ1atXGy2niIiIiIiIiIiUL7brCBMRERERERERKa+cmszL1tQRdpE+X/+d0XhBIUFG4wGcOJhpNF5w1ZpG4xXkFxiNBxAcGmw0XqXKlYzGAziyd6/ReMHR1YzGs4oso/EAvmpW3Wi8IKf5E0lUaIDReEdOeozGqxV19hGul+J0QZHReMfzCo3GAwgONPsMl3yP2Tqf9pjfX+pEhhmNVxRmvowOh9nLhQCH2f2vyDJf54JCszFNb4sAp/LN7oMFhst4/LTZ4yLA5weOG43nh1MgVSPM7i+RIWb3FzB/Pjh8zOw13pVVzV8zfnnghNF4YcHmv5fPMz43G/Cw2b9f9jVoaTQegDPA7Hm/8PQpo/EA+PF7o+H2u642Gq/w2FGj8QAwfV59vKPZeOWAusHszfZPjRQRERERERERETFBI8JEREREREREREzRkDBbs82IsC5duuB2u6lZsyatWrXC7XYTERHBjBkzANi4cSMtW7Zk7969JCUl4Xa7vflff/118vPzvWlut5vQ0FC2bdsGQHx8PNWrV+f5558vyyqKiIiIiIiIiEgZss2IsOXLlwMwbNgwEhMT6dChg/epkevWrWPkyJHMnz+f2NhY3nrrLe/nmjVrRr9+/QgODmblypUAZGVl0aNHD5o3bw6ceRLlxx9/TFZWVqnWSUREREREREQqFoeGhNmabUaEncvatWtJSkpi4cKFxMbG+ry3fv16XC4XtWrV8kmfMWMGd9xxh/d13bp1S6WsIiIiIiIiIiJiX7YZEXYuc+bM4aGHHsLlcpV4b/r06dx5550l0mfOnMlHH330u9eZm5tLbm6uT5pVcApHkNkne4mIiIiIiIjI5cWhAWG2ZvsRYSkpKSxbtoypU6f6pOfn5/PRRx+RkJDgk/7ZZ59RrVo16tWr97vXmZqaisvl8lnydi763fFEREREREREpGJw+HGRS2f7EWHh4eGkpaXRtWtXIiIi6N+/PwDp6el07NiRiIgIn/znGiX2WyQnJ5OYmOiT1uyh9EuKKSIiIiIiIiIiZcv2HWEAlStXJj09na5duxIeHk7v3r2ZPn06999/v08+j8fDhx9+yPjx433Shw8fzoYNG8jLy2PDhg0sXLjwvOuLiooiKirKJ023RYqIiIiIiIjIBWnolq3ZriPsl7dAFj81EqBatWps2bLF+3revHklPhsYGMi+fftKpP/rX/8yWUQRERERERERESmHbNcRJiIiIiIiIiJSXjk0JMzWbD9ZvoiIiIiIiIiIiAkaEXaRCgsLjcYLIshoPH9wOO3fi20VWWbjWWbjAeAw299cVFhkNJ7Tab4/3PDXQoHpgH6IWR5+9TG9SwcHmt92TJfR9KYTUA5+PnJWwOd1F/nh2G1h+hhhnvGYhgM6/XAdUVBo+Hvxw/4SZLjeheXgHOiP87RpAQFmv5cAf1wnBwabj2mQVWT2GhTAMr0P+uNavsjs34Gmr70LnQFG4wHgyTcfs4KpgJdj5Yo6wkREREREREREDFE/mL2Vg9+2RURERERERERELp3tOsL27dtHfHw8ACdPnqRbt2688847JCYm+uT74osvaN++PZ06deLGG2/kiy++AGDSpEnccMMNdOzYkcGDB5OXl+fzuTFjxtCoUaPSqYyIiIiIiIiIVCwOPy5yyWzXEVbsxIkT3HLLLQwZMuSsHVfNmjVj7dq1rFq1iueee47nn38egC5durB+/Xo++eQTXC4XM2bM8H7mwIEDfPPNN6VWBxERERERERERsQ9bdoQdP36cPn36cPfddzNs2LCz5gkKCvJOcJqdnU2LFi0AaNSokXcCwpCQEAIC/jd54Lhx43j66acvuP7c3FyysrJ8Fqvg1CXWSkREREREREQudw4//pNLZ8uOsJ07d/LTTz/Rr1+/8+Zbt24dbdu2ZdSoUXTr1s3nva+//ppFixbRv39/AL788ksArrnmmguuPzU1FZfL5bMU7F7yO2sjIiIiIiIiIiJ2YMuOsNatW5OYmEhCQgL5+ed+dGu7du1Yv3498+bNY9SoUd70ffv2cffdd/N///d/REREAGfmBhs7duxFrT85OZnMzEyfJahx90urlIiIiIiIiIhc9hwO/y1y6QLLugDn8uc//5ljx44xaNAgHnzwwRLvnz59mtDQUACio6MJDw8H4PDhw/Tv358pU6Zw5ZVXevN/99133gn3Dx06xIMPPsibb7551nVHRUURFRXlk+YICjNSLxERERERERG5fKm/yt5s2xEG8PTTT/P4448zbNgwPB6P92mSderUoU+fPrzxxhve+cBee+01AJ544gl++OEHRo8eDcCQIUO477772LZtmzduo0aNztkJJiIiIiIiIiIilyfbdYTVr1+fjz/+2Pv6pZde4qWXXjpr3uL5v35p6tSpF1zHt99++7vLJyIiIiIiIiJyThoSZmu2nCNMRERERERERETENNuNCLOr4lswjcUL8EMfpDPAaLhCT6HReA6n+W5x0zEtyzIaDwDPuR/48Hs4DM+QWFRUZDQeQEA5mMUxyPC2U2h42/HHrxRFfti8TQv0w3HCpPLwyGp/HMZMM32I8Mcxpzy0o+kimj4dFJWHg44f5BearXd4sPkzQlChvY9lBYbbEKDQcExnsNFwZ5g+8Fhmd2pngNm/NSoq09fyfmH478qKqDxcM1ZkGhEmIiIiIiIiIiIVgkaEiYiIiIiIiIgYUh4G/lVkthkR9vnnnxMXF0enTp246aabWLFiBQBhYWG43W7atm3LPffcQ2Hhmdv1CgsLefLJJ4mPj8ftdrNq1SoATp06xQMPPOBN37VrFwBut5t27drhdrtJSkoqm0qKiIiIiIiIyGXN4cdFLp0tRoTl5OTQt29f0tLSaNGiBZmZmXTs2JGMjAzq1KnDypUrAYiLi2PZsmX06NGDd955B5fLxYQJE3xijR8/nltuuYVevXqVWM/cuXOpW7duaVRJRERERERERERsxhYjwtLS0rjpppto0aIFAC6Xi8GDBzNr1ixvnoKCAo4fP+6d2HvOnDkcOnSIuLg4hg8fzrFjxwBYunQpGRkZuN1ukpOT8Xg8wJlJCQcNGkTnzp35+OOPz1ue3NxcsrKyfBar4JQ/qi4iIiIiIiIilxMNCbM1W3SEZWVlUa9ePZ+0evXqkZmZyYEDB3C73bhcLmrXrk337t0BOHDgADExMaxYsYJmzZrxyiuvALB9+3bat2/PypUrKSgoYNq0acCZ0WBr1qxhxowZ/OlPfyInJ+ec5UlNTcXlcvks+bsW+6n2IiIiIiIiIiJSGmzREVa3bl3279/vk5aZmYnL5fLeGrl9+3b2799PdnY2ADExMfTs2ROAPn36sHXr1hLpvXv39qZXq1YNgDp16nDdddexe/fuc5YnOTmZzMxMnyW4SQ+DNRYRERERERGRy5HDj//k0tmiI6x379588sknbN++HTgz2us///kPd9xxhzdPtWrVuP/++0lJSQGgS5cubN68GYANGzbQuHHjc6ZblkVubi4Ax48f54svvqB+/frnLE9UVBR169b1WRxBYcbrLSIiIiIiIiIipccWk+VXqVKFDz74gFGjRpGXl8eePXv48MMPiY2N9cl3991307x5c5588kkeeeQRRowYwZQpUwgNDfXeApmSkkJiYiKnT5+mRo0aTJs2DY/HQ+fOnQkLCyM/P5+nn36a6tWrl0VVRUREREREROQy5tDALVuzRUcYQOvWrcnIyADgwQcfJC0tjbZt2/Ltt99684SGhvLNN994X8+bN69EnLp167J4ccn5vD777DM/lFpERERERERE5H/UD2ZvtukI+6U333yzrIsgIiIiIiIiIiKXGVt2hImIiIiIiIiIlEsaEmZr6gi7SNe3a2Q0XmRYkNF4AId+rGE0Xky02QcEnDxVYDQeQGiI2U24SqVgo/EAshpUMxovzA/bjmlNq0cYjRfsNP9cj7DAAKPxalUyu33HhIQYjQdwqtBjNF6ep8hoPIAAp9mrhvxKZstoGY12RqUws8exsGCz2zZAcIDZfdD0vBlOw9sNQHCgvesM5rdH0/ufH74W2sVGGo1XUGR+r64cbPY8HR5kfp82ffx2VTZ7Dqwebv4cGBVqth1NX0cAtOxwrdF4R482NBqvVi2z+58/FBaa36d//vkqo/GqG75OPn3a7PUdQGGh+Ws8ETtRR5iIiIiIiIiIiCEODQmzNfPDLH6nLl264Ha7qVmzJq1atcLtdhMREcGMGTMA2LhxIy1btmTv3r0kJSXhdru9+V9//XUAvvzySzp06ED79u2ZOHEiAPv37/fmdbvdBAUFcfTo0TKrp4iIiIiIiIiIlA3bjAhbvnw5AMOGDSMxMZEOHTowbtw4ANatW8fIkSOZP38+sbGxvPXWW97PNWvWjH79+gFnnjb57rvv0rBhQ9xuN3379qVhw4asXLkSgE8//ZTnn3+e6OjoUq2biIiIiIiIiFQM/pgSQcyxzYiwc1m7di1JSUksXLiQ2NhYn/fWr1+Py+WiVq1a5OXlkZuby1VXXYXT6aRPnz6sWrXKJ/+0adO48847S7P4IiIiIiIiIlKBOPy4yKWzfUfYnDlzSEhIwOVylXhv+vTp3o6tI0eO+Iz0io6O5siRI97X+fn5LFq0iL59+15wnbm5uWRlZfksntMnLr0yIiIiIiIiIiKlZO7cudx6663UrVuXiIgIWrZsyb/+9S8sy/fhElOmTKFx48aEhobyhz/8gbS0tBKxcnJyGDFiBDExMURGRtKvXz8OHTpUWlUxxvYdYSkpKSxbtoypU6f6pOfn5/PRRx+RkJAAQExMDNnZ2d73s7OzqVq1qvf1woULiYuLIyzswk9CTE1NxeVy+SyZGbON1EdERERERERELmM2GhKWmppKeHg4EydOZOHChfTs2ZN7772X8ePHe/PMnj2be++9l4EDB7Jo0SLatWtHQkIC69ev94k1cOBAli5dyj//+U9mzpzJrl276NmzJx6P+aeX+pNt5gg7l/DwcNLS0ujatSsRERH0798fgPT0dDp27EhExJnHz4aGhhIZGcmePXto0KAB6enpvP32294406dPZ/To0Re1zuTkZBITE33SBs/82lCNRERERERERET8b+HChVSrVs37Oi4ujiNHjpCamsozzzyD0+lk7NixDBo0iOeeew6Azp078+WXXzJ+/HjS09OBM3O3L1myhCVLltCtWzcAmjRpQtOmTXn//fcZMGBA6Vfud7L9iDCAypUrk56ezosvvshHH30EnOnYGjp0qE++f/zjH9x1113ceOON9OrVi0aNGgFnbpv88ssvcbvdF7W+qKgo6tat67MEhkYYrZOIiIiIiIiIXH4cfvz3W/2yE6xYq1atyM3N5cSJE+zZs4fdu3eX6MgaNGgQy5cvJy8vD4BFixZRpUoVunbt6s3TpEkTWrZs6e0sKy9sNyLsl7dAFj81Es58eVu2bPG+njdvXonPtmrVijVr1pRIr1q1Knv27DFaThERERERERGR0pSbm0tubq5PWlRUFFFRURcdY82aNdSpU4fIyEhWr14NwNVXX+2Tp2nTpuTn57N3716uvvpqdu7cSZMmTXD86pGYTZs2ZefOnb+zNmWjXIwIExEREREREREpDxwO/y1nm9M8NTX1osu2Zs0aZs+ezSOPPALA0aNHAahSpYpPvuKHEf7888/efL/OU5yvOE95YbsRYSIiIiIiIiIi5dXvmNP+op1tTvOLHQ2WlZXFwIED6dy580XPoX45UkfYRQoKNDt47tfDCU0INFzGoiLrwpl+g6CgAKPx/KGw0GydwXw7BgaY3XYs81U2rsgPhSw0HDPA8D7tsYqMxgPz33WA0/xxzGn4siHQcBk9hvdnACrgPugwPCDdH8cIP5ymjTNdRNPxnH44Rpj+qv3xNZs+7hT44drE9PbtMfzFFJk/BRIcYPha3mi0M0zvM6avQf1xnRxg+LrWH0zvL6b/rjxtNNoZ5eHvg4rst94GWSw7O5uePXtStWpV5s2bh9N5ZlssHvmVk5NDzZo1vfmLR4rFxMR482VmZpaIe/ToUW+e8kK3RoqIiIiIiIiImOLw4/I7nDp1ij59+pCTk8OiRYuoXLmy973iucF+Pc/Xzp07CQ4O5sorr/Tm27VrF9avekp37txZYn4xu1NHmIiIiIiIiIjIZcjj8TBgwAC+/vprFi9eTJ06dXzev/LKK2ncuDFz5871SZ8zZw5dunQhODgYgJ49e3L06FGWL1/uzbN79262bNlCr169/F8Rg2zTEdalSxfcbjc1a9akVatWuN1uIiIimDFjBgAbN26kZcuW7N27l6SkJNxutzf/66+/DsCCBQto06YNN910E7Nnz/bGjo+Pp3r16jz//PNlUjcRERERERERqRgcfvz3WyUlJZGWlsZTTz1Fbm4u69ev9y55eXkAjBs3jlmzZjF27FhWrlzJyJEj2bBhA88884w3Trt27ejevTvDhw9n7ty5LFy4kH79+tGiRQtuu+02Y21XGmwzR1hxr+KwYcNITEykQ4cOjBs3DoB169YxcuRI5s+fT2xsLG+99Zb3c82aNaNfv34UFRXx6KOPsnnzZkJDQ7nxxhvp3bs3kZGRTJ06lY8//pisrKyyqJqIiIiIiIiISKlbunQpAA8//HCJ9/bu3Uv9+vUZPHgwJ0+eJCUlhZSUFJo0acIHH3xAu3btfPLPmTOH5ORk7rvvPjweD926deP1118nMNA2XUsXxfalXbt2LRMnTmThwoW4XC6f99avX4/L5aJWrVr897//pXr16kRGRgJnhvdt2rSJuLg46tat+5vWmZubS25urk+a59QJAsMiLq0yIiIiIiIiInJZs9NDd/bt23dR+UaMGMGIESPOm6dy5cpMmTKFKVOmGChZ2bHNrZHnMmfOHBISEkp0ggFMnz6dO++8E4Bq1arx448/cuDAAXJycli3bh0///zz71pnamoqLpfLZ/l+xXuXVA8RERERERERufzZbK58+RXbd4SlpKSwbNkypk6d6pOen5/PRx99REJCAgBOp5NJkyYxdOhQ7rzzTlq2bEnt2rV/1zqTk5PJzMz0WWLjBl9qVUREREREREREpAzZ/tbI8PBw0tLS6Nq1KxEREfTv3x+A9PR0OnbsSETE/25XLJ5A/9ixY9x+++388Y9//F3rjIqKIioqyictMEzzi4mIiIiIiIjIBWjolq3ZviMMztyHmp6eTteuXQkPD6d3795Mnz6d+++/3yffY489xqZNmwgMDCQlJYWgoCAAhg8fzoYNG8jLy2PDhg0sXLiwLKohIiIiIiIiIiJlyHYdYb+8BbL4qZFwZg6wLVu2eF/PmzevxGdffvnls8b817/+Zax8IiIiIiIiIiLn4tCQMFuz/RxhIiIiIiIiIiIiJthuRJiIiIiIiIiISHnl0IAwW1NH2EWyrLIuwYUVFhYZjVdkuNKmy3eG2UGNTqf5I5bpg6Cn0P4bY6DhShdhvs6mt29Pkdl4/hhO7TT8vZiuM4DD8Dhl0/uLP/Y+0zH9ceFVES/migyfskzHAz9sj+Xgeza+LfphpzZ9bAwLNH8DR77hY6Ppr8VZDu5ZKQ/H2kDD245/rpMNX5t4PEbjARQZ3qdNX5v443sJCCgHJwSbUwvaWzk4zYiIiIiIiIiIiFw6W3WE7du3j/j4+HO+BmjUqBEAkydPxu1243a7ufrqq7n99tt98o0ZM8abF+D111+ncePGPmkiIiIiIiIiIiY5HP5b5NLZqiPst7jvvvtYuXIlK1eupGPHjgwcOND73oEDB/jmm2988g8YMIAdO3aUdjFFRERERERERMQmym1HWLHTp0+zdOlSbrnlFm/auHHjePrpp33yXXHFFQQFBZV28URERERERESkQnH4cZFLZfvJ8j/77DPcbvc531+4cCHx8fGEhoYC8OWXXwJwzTXX/O515ubmkpub65PmOXWCwLCI3x1TRERERERERETKlu07wq6//no+/vhj7+tfz/E1bdo0HnnkEe/rMWPG8MYbb1zSOlNTU3n22Wd90hr0HEHDXomXFFdERERERERELm+ay8veyvWtkT/++CNff/01HTt29KZ99913JCYm0qNHDw4dOsSDDz74m+MmJyeTmZnps8R2Hmyy6CIiIiIiIiJyGdKNkfZmuxFhW7Zs8T4pMiAg4Lx5Z8+ezYABA3D8ort127Zt3v83atSIN99805v3nXfe4eDBg8THxzN27Fhuuumms8aNiooiKirKJy0wLOt31UdEREREREREROzBVh1h9evX58iRI+fN8+2333r/P2rUqIvOO2jQIAYNGnRpBRQREREREREROQ/dGmlv5frWSBERERERERERkYtlqxFhIiIiIiIiIiLlmUOzedmaOsIuUmGRVdZFuCCH4fGXznIwnjMo0OygRssy/z0XGd52AgPMfi+eQvN1LvRDO5oW6LT39m3z4gEQ4IdjhOlNx/T37I9tO8BwGU0fcwAwfNwxzR8Xm06n4Xa0dxMC5tuxyA/7i6ccXI+ZVlBUVNZFuCDT14wFfrg2Mc0f26Lp43eh4Xb0x/nF9LW36b+HAAIC7H0TlT/+fhG53KkjTERERERERETElHLwA1hFpo4wERERERERERFD1A9mb7YZ59mlSxfcbjc1a9akVatWuN1uIiIimDFjBgAbN26kZcuW7N27l8LCQp588kni4+Nxu92sWrUKgMzMTOLj47npppt45JFHvLEXLFhAmzZtuOmmm5g9e3aZ1E9ERERERERERMqWbUaELV++HIBhw4aRmJhIhw4dGDduHADr1q1j5MiRzJ8/n9jYWCZNmoTL5WLChAk+MR5//HH++te/EhcXx5AhQ8jIyKBTp048+uijbN68mdDQUG688UZ69+5NZGRkaVdRRERERERERC5z5WC67QrNNiPCzmXt2rUkJSWxcOFCYmNjAZgzZw6HDh0iLi6O4cOHc+zYMQA+//xz4uLiAOjbty8ZGRn89NNPVK9encjISIKCgrjyyivZtGnTedeZm5tLVlaWz+I5fcK/FRUREREREREREb+yfUfYnDlzSEhIwOVyedMOHDhATEwMK1asoFmzZrzyyisAFP3iqTrR0dEcOXKEatWq8eOPP3LgwAFycnJYt24dP//883nXmZqaisvl8lkyM97zTwVFRERERERE5LLh8OM/uXS27whLSUlh2bJlTJ061ZsWExNDz549AejTpw9bt24FwOn8X3Wys7OpWrUqTqeTSZMmMXToUO68805atmxJ7dq1z7vO5ORkMjMzfRZX58HG6yYiIiIiIiIiIqXH9h1h4eHhpKWl8dZbbzF37lzgzMT6mzdvBmDDhg00btwYgFatWnknzl+wYAFutxsAt9tNRkYGM2fO5NSpU/zxj3887zqjoqKoW7euzxIYGuGnGoqIiIiIiIjIZcPhx0UumW0myz+fypUrk56eTteuXQkPD+eRRx5hxIgRTJkyhdDQUKZNmwacGT02fPhw8vPzad26NZ07dwbgscceY9OmTQQGBpKSkkJQUFBZVkdERERERERELlPqr7I323WE/fIWyOKnRgJUq1aNLVu2eF/PmzevxGdjY2O9T5/8pZdfftloGUVEREREREREpPyxXUeYiIiIiIiIiEh55dCQMFtTR9hFMr0hFxZZZgP6QXkoo6ew6MKZfgOn0/wRyzLcjJ5CswEt0wUEAg23Y3l4Okp5qLPpfboI89tOSIDZqSs9HqPhCCgHVzUOP5TRHzFNKvLDccz05u2PFvTH8dskp823GzB/7PYHf7RjgWX2+sl0CcvBpuOXbcfmuzQBAebrXGT42sQf247HY3Z/Mc0vp0Cbb4sil0odYSIiIiIiIiIihpSHH/IrMnWEiYiIiIiIiIgYUh5GtlZkZu9BuUSjR4/m+uuv90n773//S3BwMAsWLPCmrVy5klq1auF2u3G73WzcuBGA7Oxsbr31Vm666SaGDRtGfn4+APfddx9t27albdu2pKSklF6FRERERERERETENmzTEebxeFizZg2NGjVix44d3vT33nuPUaNGMX36dJ/8vXv3ZuXKlaxcuZIbbrgBOPN0yFtvvZXVq1dTu3ZtZs6cCcAjjzzC+vXr+fTTT5k/fz7fffdd6VVMRERERERERERswTYdYYsXL6ZHjx4MHz7cp9Nr3rx5jBkzhp9++ons7Gxv+tKlS+nQoQNJSUmcPHkSgIyMDBISEgDo27cvGRkZADRu3BgAp9NJYGAgAQEBpVQrERERERERERGxC9t0hM2cOZNhw4bRtWtXVq1aRVFRETt37sTlclG5cmUGDx7M3LlzAbj++uvZvXs3a9asoVq1arz00ksA/Pzzz1SpUgWA6Ohojhw54rOOGTNm4HK5qF+//nnLkpubS1ZWls/iOXXCeJ1FRERERERE5PLicPhvkUtni8nyc3NzWbVqFaNHjwbOzAu2cuVKli9fzu7du+nRowf5+fk4HA7uvfdeIiMjvZ8dOnQof/nLXwCIiYkhOzub6OhosrOzqVq1qjffokWLmDZtms9cY+eSmprKs88+65NWv8dwruyVaKK6IiIiIiIiIiJSBmzRETZv3jwee+wxHnroIQC2bNnCP/7xD7788ks2btzovZWxb9++7Nu3j+joaCpXrgzAihUraNKkCQBut5sFCxZw9913s2DBAtxuNwCffPIJzz//POnp6YSGhl6wPMnJySQm+nZ63TFrp6HaioiIiIiIiMjlyoGGbtmZLTrCpk+fzltvveV93bJlS/7973/Tr18/n/m8evTowfTp06latSrvvvsuYWFhxMTE8K9//QuAxx57jLvvvpspU6ZQv359nnnmGQDuuusuoqKiuPXWW4Ezk+oXT7B/NlFRUURFRfmkBYZlGquviIiIiIiIiFyedAujvdmiI2zFihU+rx0OB0VFRSXyPfDAA97/JyUllXg/Ojr6rLc+7tu379ILKSIiIiIiIiIi5ZotOsJERERERERERC4HGhBmb7Z5aqSIiIiIiIiIiIg/aUTYRdqwepfReOGR4UbjARzL/N5ovJDqtYzGK8grMBoPIDDY7CYcFhFmNB5Azr7vjMYLrl7baDx/2HJNDaPxgpzmf1OJCTe77fxw3Oz2Xa9KiNF4ADmnPUbjeYoso/EAggLMftfHTpe8zf5S+KPOtSuZPe4UhZkvo8Nhdn8xvUcH/mI+UVNMf9enC8xuiwB5HrMxCzxm65x7yvx5//MDJ4zGK/DDPl0nKthovJBA8+fAPMPf9eFjZr/r2GizbQiw84eTRuOFGb4GBdi6ZrvZgFlfGQ2XeeV1RuPBmSlxTCrKO2U0HgA/7Tca7kCDa4zGy8/+2Wg8ACzD56wnO5mNVx5oSJitaUSYiIiIiIiIiIhUCLbqCNu3bx/R0dF07tyZNm3a0KNHDzZt2sS+ffuIj4/3ybtx40bcbjdut5u2bdtStWpVABYvXsyNN96I2+0mLi6OzMwzT3tMSkry5q9Zsyavv/56qddPRERERERERC5vDj/+k0tnu1sjr7/+ej7++GMA1q1bx+23386HH35YIt8NN9zAypUrAZg1axZr1qwBIC4ujh49egDwr3/9i3/84x+88sorvPXWW97PNmvWjH79+vm3IiIiIiIiIiJS4Ri+61cMs9WIsF9r164dN910E4sXLz5vvmnTpnHnnXcCEBz8vzkFsrOzadGihU/e9evX43K5qFXL7PxXIiIiIiIiIiJib7YbEfZrLpeLwMBzF/Pw4cPs27ePdu3aedM++OADXnjhBXJycli0aJFP/unTp3s7zc4lNzeX3NxcnzSr4BSOIPMTqYuIiIiIiIjI5UMDwuzN1iPCADIzM/F4zv2ks5kzZzJ48GCftISEBDZv3sxzzz3HX//6V296fn4+H330EQkJCeddZ2pqKi6Xy2cp2L3k0ioiIiIiIiIiIiJlytYdYRs3bmT16tV07979nHlmzJjB0KFDva9Pnz7t/X90dDTh4eHe1+np6XTs2JGIiIjzrjc5OZnMzEyfJajxucsgIiIiIiIiIgKcGRLmr0Uume1ujfzss89wu92cOnWKypUrM3fuXKpWrcqWLVu8T44MCAhgyZIlbNu2jfDwcBo2bOj9/Ntvv828efNwOByEhIQwadIk73vTp0/n/vvvv2AZoqKiiIqK8knTbZEiIiIiIiIiIuWbrTrC6tevz9GjR8/63pEjR0qkNW/enLVr1/qkjRo1ilGjRp01xrx58y69kCIiIiIiIiIi5+DQ0C1bs1VHmIiIiIiIiIhIeeZQP5it2XqOMBEREREREREREWMsMSYnJ8caO3aslZOTY8t4/ohZEctYEevsj5h2j+ePmBWxjBWxzv6Iafd4/ohZEctYEevsj5gVsYwVsc7+iGn3eP6IWRHLWBHr7I+Y/iijSGlRR5hBmZmZFmBlZmbaMp4/YlbEMlbEOvsjpt3j+SNmRSxjRayzP2LaPZ4/YlbEMlbEOvsjZkUsY0Wssz9i2j2eP2JWxDJWxDr7I6Y/yihSWnRrpIiIiIiIiIiIVAjqCBMRERERERERkQpBHWEiIiIiIiIiIlIhqCPMoKioKMaOHUtUVJQt4/kjZkUsY0Wssz9i2j2eP2JWxDJWxDr7I6bd4/kjZkUsY0Wssz9iVsQyVsQ6+yOm3eP5I2ZFLGNFrLM/YvqjjCKlxWFZllXWhRAREREREREREfE3jQgTEREREREREZEKQR1hIiIiIiIiIiJSIagjTEREREREREREKgR1hImIiIiIiIiISIWgjjAREREREREREakQ1BF2Ef7whz/gcDhYvXp1iffy8/N59NFHqVmzJhEREXTt2pVdu3b55Pn222954IEHaNmyJYGBgVx77bWlVXRbudR2nDp1Kg6Ho8TyxBNPlFYVytyltiFAWloa1113HSEhIbhcLsaOHUthYWFpFL9MjRs3zme7CQ0NpWnTprz88ssUFRV581mWRUpKCvXq1SMsLIx27dqxfv16n1ibNm2ia9eu1KxZk5CQEOrVq8eIESM4ePBgaVer1Jlsx2HDhp11n3Y4HKSkpJR21UrNL9vQ6XRSuXJlmjdvzp/+9Ce+/vrrEvkXLlzIH/7wB0JDQ2ncuDHvvvtuiTzPPfccXbt2pUqVKjgcDjZv3lwaVSlT/mjHs22LNWvWLI3qlAl/tOGBAwcYOHAglStXJjIykltuuYW9e/eWRnXK1K+PjcXLL6/5LtR+OTk53H777dSvX5+wsDCqV69Oz5492bRpU2lXp8yYaMdzXS86HA569OhR2lUqE4sWLaJXr15Ur16doKAgrrjiCnr37s17773nc67+9NNPadeuHWFhYcTGxvLSSy9hWZb3/UOHDvHYY4/RsmVLIiMjqVu3LkOGDOH7778vi2qVOlPtWOzAgQPcfffdVK9enbCwMJo2bcrMmTNLs0oi8v8FlnUB7G7Hjh18+eWXAMyaNYubbrrJ5/3Ro0cze/ZsUlNTqVOnDi+88AJdunRhx44dVK5c2Rvjo48+ok2bNhQVFfkcOCsKE+1YbPHixT5pderU8X8FbMBEG65fv55bb72VwYMHM2HCBHbs2MHTTz/NiRMnePXVV0u9TqUtLCyMFStWAHDq1CkyMjJ44oknKCoq8naovvTSS4wdO5aUlBRatGjBm2++Sbdu3di6dStXXnklAEePHuXqq68mMTGRK664gj179jB+/Hg2bdrEpk2bCAkJKbM6lgZT7fjMM8/wwAMP+MSeM2cOr732Gj179izdSpWyX7bhsWPH2LZtG5MnT+btt99mypQpDB06FIA1a9aQkJBAYmIir732GitWrGDEiBFERkbSr18/b7xJkybRsGFD4uPjmTdvXpnUqSyYbkeAUaNGMWTIEO/r4ODg0qtQGTDZhoWFhfTs2ZMTJ04wefJkQkJCePbZZ4mLi2Pbtm1UqlSpzOpZGn7ZlsXCw8OBi2u/vLw8QkNDeeaZZ2jQoAE5OTm89tprxMXF8dlnn9G4ceNSr1NZuNR27N27N+vWrfP5/DfffMNdd9112Z9bAP76178yYcIEEhISeOONN6hVqxY//PADH374IUOHDiUmJobu3bvz7bff0r17d7p27crzzz/Pl19+yRNPPEFAQACPPPIIAJ999hnvv/8+w4cPp23btvz0008899xz3HDDDWzfvp3q1auXcW39x2Q7wplOxXbt2tGkSRMmT55MVFQUO3bsIC8vrwxrKVKBWXJeTz75pOV0Oq3OnTtbVatWtfLz873vZWZmWgEBAdakSZO8aUeOHLEiIiKsl156yZtWWFjo/f/dd99tXXPNNaVTeBsx0Y7vvvuuBVg//vhjqZbdLky0Yffu3a3rrrvOJ+6rr75qBQUFWYcPH/Z/JcrQ2LFjrYiIiBLpffv2tf74xz9almVZp06dsqKioqwnn3zS+35eXp4VGxtrjRw58rzxly5dagHW2rVrzRbcZvzdjp06dbKaNWtmttA2c642PHXqlBUXF2cFBwdb3333nWVZltWtWzerffv2PvkGDx5sNW3a1Cet+DyTkZFhAdamTZv8VHr78Ec7AtYrr7ziv0LbjOk2fO+99yzA+uKLL7xpWVlZVkhIiJWamuqnWtjDudqy2MVug7927NgxKzg42HrhhReMlNPu/NWOY8eOtQICAqxDhw4ZKaddpaWlWYA1duzYs76/YcMG6/PPP7csy7Luu+8+KzY21srLy/O+/+STT1pVqlSxTp8+bVmWZR09etQqKCjwiZGZmWk5HA7r1Vdf9U8lbMB0O1qWZQ0dOtRq37695fF4/Fp2Ebk4ujXyPCzL4r333iMuLo7k5GSOHDnC4sWLve8vXbqUoqIi+vfv702LiYmhW7dupKene9OczordzKbasSIz1YZbtmyhW7duPrG7d+9OQUEBS5Ys8X9FbCgyMpKCggLgzND23NxcBgwY4H0/ODiY22677YLbYtWqVYEzt6hWRCba8cCBA6xevZo77rjD7+W1o9DQUF5//XXy8/N55513yMvLIyMjw2e/Bhg0aBBff/01+/bt86ZV9PPML11KO8oZv7cNt2zZQs2aNWnRooU3T506dbj22mtZuHBhaVbBVi5lG4yIiCA0NLTCnlt+6VLasfga6nK+1RkgNTWVWrVq8fTTT5/1/RtuuIFWrVoBZ27769u3r8+o10GDBpGdne0dUVelShUCA31vIKpbty7Vq1e/rKeDMN2Oubm5/N///R9JSUkEBAT4vwIickG6cj6PTz/9lH379jFkyBC6d+9O1apVmTVrlvf9nTt3UqNGDaKjo30+17RpU3bu3FnaxbUt0+14zTXXEBAQwJVXXsmECRMqxPxWptrw9OnTJW7bK359tvlgLkcejwePx8OxY8dYsGAB8+bN895OUdxWV199tc9nmjZtyv79+zl16pRPemFhIfn5+ezcuZPHHnuM6667jg4dOpRORcqYyXYsVjznxuDBg/1beBtr1qwZderUYd26dXz33XcUFBSctR0BnWfO41LaccKECQQFBVGlShUGDhzI/v37S63cdvJ72vBs5xg4c56paOeY4sWyrN+8DRYVFeHxeDh06BAPP/wwTqeTu+66q9TqYAcm2rHY5s2b2b17t88tz5cjj8fD2rVriYuLK9F59WsnTpwgMzOzRFteffXVOByO855fdu/ezX//+19vu19u/NGOn3/+Ofn5+QQFBdGpUyeCgoKoWbMmjz/+uPdHRBEpXZoj7DxmzZpFaGgot912G0FBQfTr14/p06dz/PhxKlWqxNGjR6lSpUqJz0VHR/Pzzz+XfoFtylQ71qpVi2effZY2bdrgcDhYsGABTz/9NAcOHOCNN94oxRqVPlNteNVVV7Fx40afPMUTmFeEbfbEiRMEBQX5pA0cONA7r9XRo0cJCQkhNDTUJ090dDSWZXH06FHCwsK86Z06dWLt2rUAtG7dmvT09AteNF0OTLdjsVmzZtGuXTsaNGjgv8KXAy6Xi8OHD3P06FGAEvt2cYd3RdhnL8Xvace77rqLPn36cMUVV7B9+3aee+45OnTowBdffFHih4aK4Le24VVXXUVWVhYHDx6kdu3aABw/fpwdO3acswP8cnK2Y+P06dO9x7SL3ZfHjBnDCy+8AECNGjVIT0/3zq1YEZhqx2K/vIa6nB05coS8vDxcLpdPumVZPj8aO51OsrOzgZJtGRwcTHh4+Dnb0rIsRo8eTe3atS/bH6380Y6HDx8GIDExkXvvvZdx48axceNGxowZg9PpZMKECf6rkIiclUaEnYPH42Hu3Ln06tXLO9H4kCFDOHnyJB988EEZl678MNmO3bt3Z8yYMXTv3p1u3brxxhtvkJyczD//+U8OHTrkj+Lbgsk2TEpKYtGiRfz973/n559/Zs2aNTz11FMEBATgcDj8UXxbCQsL805ov2bNGv7+97+zePFi7r333t8Vb8qUKaxfv54ZM2aQl5dHfHw8ubm5hkttP6bbEc78kr9ly5bL/hf7i2FZVoXYH/3t97Tjv//9b/r370/Hjh1JSkpiyZIlHDx4kLfffttPpbS339qGQ4YMITIyknvuuYc9e/aQlZVFYmIix48frxDb9C+PjcVLr169fnOcpKQkNm3axIIFC2jbti29evXi888/90OJ7clUO8KZ0XWzZ8+md+/eREVFGS6pPf16X5s3bx5BQUHeZfTo0b879rhx41i+fDnTpk0jIiLiUotqaybbsfhBafHx8UycOJHOnTvz+OOP8+ijj/K3v/2tQvxQIGI36gg7h6VLl/Ljjz9y8803k52dTXZ2Ns2bN6dWrVreW9Kio6PJyckp8dmjR48SExNT2kW2JX+344ABAygsLGTr1q3+KL4tmGzDYcOG8dBDD/HII49QtWpVunTpwgMPPEBMTAy1atUqtTqVFafTSevWrWndujU33ngjo0ePZsyYMbz77rts376d6Oho8vLyOH36tM/njh49isPhKDEipEmTJrRp04Y77riDZcuW8c033zB58uTSrFKZMN2OADNnziQwMJCBAweWVjVsKysri5o1a3rb6df7dvHoHJ1nzs9EO7Zo0YImTZrw2Wef+a+gNvZb2zAmJobZs2ezfft2GjZsiMvl4tChQ9x9990V7hxTvMTExPzmbbB27dq0bt2am2++mQ8++IArr7ySMWPGlE4lbMBUOwJkZGRw6NChCjH3ZNWqVQkJCSErK8snvUuXLt4OxeL9sHgE06/bMj8/n5MnT561Ld9++23Gjx/PpEmT6NKli38qYQP+aMfibTcuLq5EzLy8PL799lt/VEVEzkMdYedQ3MFwzz33EB0dTXR0NDExMRw6dIiPP/6Y//73v1x99dX88MMP3hNwsZ07d5a4V7yiUjteOpNt6HQ6+dvf/sZPP/3EF198wQ8//MC9997Ljz/+SNu2bUu1XnZRPMfFjh07vG21a9cunzw7d+6kXr16Z72dr9gVV1xB3bp1K+zFzKW243vvvUd8fPxl/Sj2i7Fjxw4OHDhA+/btadiwIUFBQSXmajnXHGzyP2rHS/d727B79+7s37+fr776ij179rBq1SoOHz5cYc8xwCVtg06nk1atWlXYc8sv/Z52nDVrFlWqVPndI8rKk8DAQG688UaWL1/ucwtfdHS0t0OxeEL3iIgIXC5XibbctWsXlmWVaMsPPviAkSNHMn78eIYPH+7/ypQhf7Rjs2bNzrvOX/9wKCL+p46wszh58iTz58+nb9++ZGRk+CzvvfceHo+HOXPm0K1bN5xOJ/PmzfN+9ujRoyxdurRCnHAvpDTacfbs2QQEBHif3HK58VcbVq5cmRYtWlClShVef/11GjRoQHx8fGlWzTa2b98OQLVq1Wjfvj1RUVHMnTvX+35BQQHvv//+BbfFzMxMvv/++wo1j8svXUo7btiwge+++67C3xZ5+vRpRo0aRUhICImJiYSEhNC5c2f+85//+OSbM2cOTZs2pX79+mVTUJsz2Y5bt25l165d/PGPf/Rzqe3lUtswICCApk2b0qBBA3bu3MnHH398SbdOl3eXsg16PB42bNhQYc8tv/Rb2zEvL4/333+f22677awPcbgcJScnc/DgQV588cUL5u3Zsyfz58/3max9zpw5VKlShfbt23vTVq5cyeDBg7n33nt55pln/FJuuzHdjrGxsTRv3pyPP/7Y57PLli0jLCzsgh1lImLe5T+r8+8wf/58jh8/zujRo3G73SXef/nll5k1axajRo0iMTGRRx99lICAAOrUqcOLL75I5cqVuf/++735T548SXp6OgDff/89ubm53pN4p06dLtsREKbbsXv37sTFxdG8eXMAFixYwOTJk/nzn/982T4O23Qbbty4kVWrVtGyZUtOnTrFggULmD59OosWLaoQj3MuKiryPhwgPz+fzz77jOeff55mzZrRsWNHgoKCePLJJxk3bhzVq1enefPmvPXWWxw5coRHHnnEG+eBBx6gWrVqtG7dmsqVK7Nr1y4mTpzIFVdcwYgRI8qqeqXGVDsWmzVrFmFhYSQkJJR2VcrML9vw+PHjbNu2jcmTJ7Nnzx6mTp3q/YPumWeewe12k5SUxIABA8jIyGDWrFnMmTPHJ96qVav48ccf2bFjBwArVqxg37591K9fn9atW5dq3UqTyXZ89dVX+e6773C73dSoUYPt27fzwgsv4HK5SExMLIvqlQrT2+Ljjz9O27ZtqVy5Ml988QXPP/88d911V4lbgiqai2m/yZMns3HjRuLj46lVqxaHDx9m0qRJ7Nq1i7feeqsMS28fF7sdAqSnp5OdnV2hfmTp3bs3TzzxBGPGjGHr1q0MHDiQWrVqkZOTw+rVqzl8+DCRkZEAPProo8ycOZPBgweTlJTEtm3beOWVV3jhhRe8I56+/vpr+vbty1VXXcWdd97pPVYAVK9enYYNG5ZJPf3NdDsCvPDCC9x666089NBD9O7dm02bNvHqq6/y2GOPXfbzrYnYkiUl9OnTx6pXr55VVFR01vdfe+01C7C+/fZb6/Tp09bDDz9s1ahRwwoLC7Pi4+Otr7/+2if/3r17LeCsS0ZGRinUqGyYbsfRo0dbV111lRUWFmaFhIRYzZs3t/7+97+fM/7lwHQbbtmyxWrTpo1VqVIlq1KlSlaXLl2sTz/9tDSqUubGjh3rs+8FBgZaDRo0sJKSkqwffvjBm6+oqMh68cUXrbp161ohISFWmzZtSrTRlClTrDZt2lhVqlSxwsLCrCZNmlijR4+2Dh8+XNrVKnUm29GyLMvj8Vg1a9a0BgwYUJrVKFO/bsNKlSpZ1157rfXggw+W2Gcty7Lmz59vNW/e3AoODrYaNWpkTZkypUSeTp06nfUcc/fdd5dCjcqG6XZcsGCB1bZtWys6OtoKDAy0atWqZQ0fPtw6ePBgaVWp1PljWxwyZIh1xRVXWMHBwVaTJk2siRMnWh6PpzSqU6bGjh1rRUREnDfPhdpvzZo1Vvfu3a0aNWpYwcHBVr169azbb7/d2rp1qz+Lbism2rFYv379rFq1almFhYX+KKqtpaWlWT179rSqVq1qBQYGWjVq1LB69uxpzZw506c91q5da7Vp08YKCQmx6tata02YMMHnmvPdd989598wl/P5pZipdiw2e/Zs65prrrGCg4Ot2NhY68UXX7ys/44RsTOHZVmWn/vaREREREREREREypzmCBMRERERERERkQpBHWEiIiIiIiIiIlIhqCNMREREREREREQqBHWEiYiIiIiIiIhIhaCOMBERERERERERqRDUESYiIiIiIiIiIhWCOsJERERERERERKRCUEeYiIiIiIiIiIhUCOoIExERkTKxaNEievXqRfXq1QkKCuKKK66gd+/evPfeexQVFV10nK1btzJu3DhOnjzpx9KKiIiIyOVAHWEiIiJS6v7617/Sq1cvQkNDeeONN1i+fDlvvPEGVapUYejQoSxbtuyiY23dupVnn31WHWEiIiIickGBZV0AERERqVg++ugjJkyYwNixYxk3bpzPe/379+fPf/4zQUFBZVM4EREREbmsaUSYiIiIlKrU1FRq1arF008/fdb3b7jhBlq1agWc6TTr2rUrNWrUICoqijZt2rB48WJv3qlTp3LPPfcAUL16dRwOB/Xr1/e+n5WVxdChQ6lWrRphYWF07NiRzz77zGd9CxYsoHXr1lSqVIkqVarQunVr0tPTDddaREREROxAHWEiIiJSajweD2vXriUuLo7AwAsPTN+7dy8333wz06dPZ968edx444306tWLlStXAtC7d29vh9rixYtZt24dH3zwAQBHjx6lQ4cObN26lddff5158+YRERFBXFwc//3vfwH47rvv6NevH9dccw0ffPABc+bMYcCAARw9etQ/DSAiIiIiZUq3RoqIiEipOXLkCHl5ebhcLp90y7IoLCz0vnY6nTidTv70pz9504qKiujcuTM7duxg8uTJuN1uqlevTsOGDQG4/vrrqVatmjf/a6+9RnZ2Nhs3bqRGjRoAdOnShcaNG/Pqq6/y8ssvs2XLFgoKCnjjjTeIjIwEoHv37n6rv4iIiIiULY0IExERkVLncDh8Xs+bN4+goCDvMnr0aODMrY133303derUITAwkKCgIJYuXcru3bsvuI6lS5fSuXNnYmJi8Hg8eDweAgIC6NSpE5s2bQKgRYsWBAQEMGTIEBYuXEhOTo75yoqIiIiIbagjTEREREpN1apVCQkJISsryye9S5cubNq0iU2bNlGrVi3gzAiwW265hTVr1jB+/HgyMjLYtGkTPXv25PTp0xdc108//cSHH37o08EWFBTE9OnTyczMBKBx48akpaWRk5NDQkIC1atX55ZbbmH//v3mKy8iIiIiZU63RoqIiEipCQwM5MYbb2T58uUUFhYSEBAAQHR0NK1btwYgODgYgG+//ZYtW7bw4Ycfcuutt3pjnDp16qLWFRMTQ48ePXjuuedKvBcSEuL9f48ePejRowe5ubksXryYv/zlL9xzzz0sX778d9dTREREROxJI8JERESkVCUnJ3Pw4EFefPHF8+Yr7vAq7hgD+P7771m7dq1PvuL3fz1KLD4+nq+++oqmTZvSunVrn6V58+Yl1hcVFcWAAQMYNGgQX3/99e+qm4iIiIjYm0aEiYiISKnq3bs3TzzxBGPGjGHr1q0MHDiQWrVqkZOTw+rVqzl8+DCRkZFcffXV1K1blyeeeILCwkKOHz/O2LFjqVOnjk+8pk2bAvDmm2/St29fwsPDad68OcnJycycOZNOnTrx5z//mXr16vHjjz+yYcMGateuzV/+8hcmTZrEunXr6NGjB7Vq1WLv3r3MmDGDbt26lUXTiIiIiIifOSzLssq6ECIiIlLxfPTRR7z55pts3LiRnJwcYmJiuP766xk6dCiDBg3C6XSyadMmHnzwQbZt24bL5eLpp59mxYoVbN68me3bt3tjPfvss7zzzjscPHgQl8vFvn37ADh8+DBPP/006enpHDlyhBo1atC2bVv+8pe/0L59e9atW8cLL7zA559/zpEjR6hZsyYJCQk899xz3qdIioiIiMjlQx1hIiIiIiIiIiJSIWiOMBERERERERERqRDUESYiIiIiIiIiIhWCOsJERERERERERKRCUEeYiIiIiIiIiIhUCOoIExERERERERGRCkEdYSIiIiIiIiIiUiGoI0xERERERERERCoEdYSJiIiIiIiIiEiFoI4wERERERERERGpENQRJiIiIiIiIiIiFYI6wkREREREREREpEJQR5iIiIiIiIiIiFQI/w/exZZyKvjyrwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {}
    }
   ],
   "source": "terminal_center = np.mean(list(gate_positions.values()), axis=0)\n\ndef walking_cost(g1: str, g2: str) -> float:\n    return float(np.linalg.norm(gate_positions[g1] - gate_positions[g2]))\n\ndef gate_to_center_cost(g: str) -> float:\n    return float(np.linalg.norm(gate_positions[g] - terminal_center))\n\n# Maliyet matrisi: uçuş × gate (metre)\ncost_matrix = np.zeros((N_FLIGHTS, N_GATES))\nfor f_idx in range(N_FLIGHTS):\n    for g_idx, g in enumerate(gates):\n        base = gate_to_center_cost(g)\n        if flight_data.loc[f_idx, 'is_transfer_heavy']:\n            base *= 1.35          # esnek kısıt: transfer-heavy uçuşlar merkeze yakın gate\n        if gate_capacity[g] == 'Wide':\n            base *= 1.08          # esnek kısıt: wide gate'ler talep nedeniyle daha değerli\n        cost_matrix[f_idx, g_idx] = base + np.random.uniform(5, 25)\n\nprint('Maliyet matrisi şekli:', cost_matrix.shape)\nprint(f'Ortalama yürüme maliyeti: {cost_matrix.mean():.1f} m   Min / Max: {cost_matrix.min():.1f} / {cost_matrix.max():.1f} m')\n\nfig, ax = plt.subplots(figsize=(12, 5))\nsns.heatmap(cost_matrix, cmap='Blues', ax=ax,\n            xticklabels=[g if i % 4 == 0 else '' for i, g in enumerate(gates)],\n            yticklabels=flight_data['flight_id'].tolist(), cbar_kws={'label': 'metre'})\nax.set_title(f'Uçuş × Gate yürüme maliyet matrisi (metre) — {N_FLIGHTS} uçuş × {N_GATES} gate')\nax.set_xlabel('Gates'); ax.set_ylabel('Flights'); ax.tick_params(axis='y', labelsize=6)\nplt.tight_layout(); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "63470601",
   "metadata": {
    "id": "63470601"
   },
   "source": "## AŞAMA 5 – Karar Değişkenleri ve Klasik ILP Baseline (PuLP)\n\n**Düzeltme #8 – MARS kapı modeli (ya 1 geniş gövde, ya 2 dar gövde):** Geniş gövde kapılar (Multiple Aircraft Ramp System) tek pozisyon değildir; aynı anda **ya bir geniş gövde uçak ya da iki dar gövde uçak** alır. Bu yüzden:\n- Her dar gövde kapı = 1 park pozisyonu; her geniş gövde (MARS) kapı = 2 park pozisyonu (`L`/`R`).\n- Dar gövde uçuş herhangi bir **pozisyona** atanır; geniş gövde uçuş bir **MARS kapısına** atanır ve o kapının **her iki pozisyonunu** kaplar.\n- Çakışma kuralı: zamanları örtüşen iki uçuşun **pozisyon ayak izleri kesişemez**. Bu kural ikili (pairwise) olduğu için hem ILP hem QUBO için doğal olarak ifade edilir."
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46e78284",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:02.861789Z",
     "iopub.status.busy": "2026-10-02T18:47:02.861366Z",
     "iopub.status.idle": "2026-10-02T18:47:13.402341Z",
     "shell.execute_reply": "2026-10-02T18:47:13.400537Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "46e78284",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967225075,
     "user_tz": -180,
     "elapsed": 6350,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4a0c7b8d-a57f-4a04-d682-d14bd254cab1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Klasik ILP (MARS modeli): 1524 değişken, 10102 çakışma kısıtı, 7.29 s, durum = Optimal\n",
      "  TK571    → D05L\n",
      "  TK367    → D06\n",
      "  EK319    → D05R\n",
      "  UA484    → D04 (W)\n",
      "  AF835    → D08\n",
      "  TK596    → D03\n",
      "  CA229    → D07\n",
      "  TK485    → B04 (W)\n",
      "  TK712    → D05R\n",
      "  TK724    → D01\n",
      "  ...\n",
      "  Toplam yürüme maliyeti: 6,790.6 m  (15 kapı kullanıldı)\n",
      "\n",
      "Sıralı ilk-uygun atama: 25,533.6 m  →  optimizasyon kazancı %73\n"
     ]
    }
   ],
   "source": "# ============================================================\n# KARAR DEĞİŞKENLERİ: (uçuş, seçenek) çiftleri\n#   dar gövde uçuş  → seçenek = park pozisyonu (dar kapı: g ; MARS kapı: gL veya gR), ayak izi = {pozisyon}\n#   geniş gövde uçuş → seçenek = MARS kapısı g, ayak izi = {gL, gR}\n# ============================================================\ndef gate_positions_of(g, gate_cap):\n    return [g + 'L', g + 'R'] if gate_cap[g] == 'Wide' else [g]\n\ndef build_variables(flights_df, gates_list, gate_cap):\n    '''Her uçuş için uygun seçenekleri üretir. Döndürür: list of dict(f, gate, label, footprint)'''\n    V = []\n    for f in range(len(flights_df)):\n        if flights_df.loc[f, 'aircraft_type'] == 'Wide':\n            for g in gates_list:\n                if gate_cap[g] == 'Wide':\n                    V.append(dict(f=f, gate=g, label=g + ' (W)', footprint=frozenset(gate_positions_of(g, gate_cap))))\n        else:\n            for g in gates_list:\n                for pos in gate_positions_of(g, gate_cap):\n                    V.append(dict(f=f, gate=g, label=pos, footprint=frozenset([pos])))\n    return V\n\ndef overlaps(df, f1, f2):\n    a1, d1 = df.loc[f1, 'arrival_slot'], df.loc[f1, 'departure_slot']\n    a2, d2 = df.loc[f2, 'arrival_slot'], df.loc[f2, 'departure_slot']\n    return not (d1 <= a2 or d2 <= a1)\n\ndef conflict_pairs(V, flights_df):\n    '''Aynı anda kullanılamayacak değişken çiftleri (farklı uçuş, zaman örtüşmesi, ayak izi kesişimi).'''\n    pairs = []\n    for i in range(len(V)):\n        for j in range(i + 1, len(V)):\n            if V[i]['f'] != V[j]['f'] and (V[i]['footprint'] & V[j]['footprint']) and overlaps(flights_df, V[i]['f'], V[j]['f']):\n                pairs.append((i, j))\n    return pairs\n\ndef solve_classical_gap(flights_df, gates_list, cost_mat, gate_cap, verbose=True):\n    '''Integer Linear Programming ile Gate Assignment (MARS modeli) – kesin optimum (klasik referans)'''\n    V = build_variables(flights_df, gates_list, gate_cap)\n    pairs = conflict_pairs(V, flights_df)\n    prob = pulp.LpProblem('IGA_Gate_Assignment', pulp.LpMinimize)\n    x = [pulp.LpVariable(f'x{i}', cat='Binary') for i in range(len(V))]\n    prob += pulp.lpSum(cost_mat[v['f'], gates_list.index(v['gate'])] * x[i] for i, v in enumerate(V))\n    for f in range(len(flights_df)):                                   # her uçuş tam 1 seçenek\n        prob += pulp.lpSum(x[i] for i, v in enumerate(V) if v['f'] == f) == 1\n    for i, j in pairs:                                                  # ayak izi çakışması yok\n        prob += x[i] + x[j] <= 1\n    t0 = time.time()\n    status = prob.solve(pulp.PULP_CBC_CMD(msg=0, timeLimit=120))\n    t = time.time() - t0\n    assignment, total_cost = {}, 0.0\n    if pulp.LpStatus[status] == 'Optimal':\n        for i, v in enumerate(V):\n            if pulp.value(x[i]) > 0.5:\n                assignment[flights_df.loc[v['f'], 'flight_id']] = v['label']\n                total_cost += cost_mat[v['f'], gates_list.index(v['gate'])]\n        if verbose:\n            print(f'Klasik ILP (MARS modeli): {len(V)} değişken, {len(pairs)} çakışma kısıtı, {t:.2f} s, durum = Optimal')\n            for k, (f, g) in enumerate(list(assignment.items())[:10]):\n                print(f'  {f:8s} → {g}')\n            gates_used = len(set(g.rstrip('LR').replace(' (W)', '') for g in assignment.values()))\n            print(f'  ...\\n  Toplam yürüme maliyeti: {total_cost:,.1f} m  ({gates_used} kapı kullanıldı)')\n    else:\n        print('Klasik solver durumu:', pulp.LpStatus[status])\n    return assignment, total_cost, V, pairs\n\nclassical_assignment, classical_cost, V_full, pairs_full = solve_classical_gap(flight_data, gates, cost_matrix, gate_capacity)\n\n# Planlayıcı benzeri basit referans: kapı sırasına göre ilk uygun pozisyon (first-fit)\ndef greedy_first_fit(df, gl, cm, cap):\n    V = build_variables(df, gl, cap); occ = {}; asg = {}; tot = 0.0\n    for f in range(len(df)):\n        a, d = df.loc[f, 'arrival_slot'], df.loc[f, 'departure_slot']\n        for v in V:\n            if v['f'] != f: continue\n            if all(all(d <= oa or od <= a for oa, od in occ.get(pos, [])) for pos in v['footprint']):\n                for pos in v['footprint']: occ.setdefault(pos, []).append((a, d))\n                asg[df.loc[f, 'flight_id']] = v['label']; tot += cm[f, gl.index(v['gate'])]; break\n    return asg, tot\ngreedy_assignment, greedy_cost = greedy_first_fit(flight_data, gates, cost_matrix, gate_capacity)\nprint(f'\\nSıralı ilk-uygun atama: {greedy_cost:,.1f} m  →  optimizasyon kazancı %{(greedy_cost-classical_cost)/greedy_cost*100:.0f}')"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b355a12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:13.406853Z",
     "iopub.status.busy": "2026-10-02T18:47:13.406543Z",
     "iopub.status.idle": "2026-10-02T18:47:13.973965Z",
     "shell.execute_reply": "2026-10-02T18:47:13.971190Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 990
    },
    "id": "6b355a12",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967225876,
     "user_tz": -180,
     "elapsed": 601,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "254e6bd1-30d6-4693-bfc6-0b63ee367cee"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1320x985.6 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "source": "# Gantt: ILP optimum kapı doluluk planı – MARS kapıları iki şerit (L/R), geniş gövde iki şeridi birden kaplar\ndef gate_of_label(lbl):\n    return lbl.replace(' (W)', '') if '(W)' in lbl else (lbl[:-1] if lbl[-1] in 'LR' and gate_capacity.get(lbl[:-1]) == 'Wide' else lbl)\n\nused_gates = sorted(set(gate_of_label(l) for l in classical_assignment.values()))\nlanes = []                                   # (gate, pozisyon etiketi)\nfor g in used_gates:\n    lanes += [(g, p) for p in gate_positions_of(g, gate_capacity)]\nlane_y = {p: k for k, (g, p) in enumerate(lanes)}\n\nfig, ax = plt.subplots(figsize=(12, 0.32 * len(lanes) + 1.6))\nfor g in used_gates:                          # MARS kapılarını hafif zeminle grupla\n    ps = gate_positions_of(g, gate_capacity)\n    if len(ps) == 2:\n        ax.axhspan(lane_y[ps[0]] - 0.5, lane_y[ps[1]] + 0.5, color='#FFF4E0', zorder=0)\nfor i in range(N_FLIGHTS):\n    fid = flight_data.loc[i, 'flight_id']; lbl = classical_assignment[fid]\n    a, dur = flight_data.loc[i, 'arrival_slot'], flight_data.loc[i, 'turnaround_slots']\n    if '(W)' in lbl:\n        g = lbl.replace(' (W)', ''); y0, y1 = lane_y[g + 'L'], lane_y[g + 'R']\n        ax.barh((y0 + y1) / 2, dur - 0.08, left=a, height=1.7, color=AMBER, edgecolor='white')\n        ax.text(a + 0.08, (y0 + y1) / 2, fid + ' (geniş)', va='center', fontsize=6, color='white', fontweight='bold')\n    else:\n        ax.barh(lane_y[lbl], dur - 0.08, left=a, height=0.7, color=BLUE, edgecolor='white')\n        ax.text(a + 0.08, lane_y[lbl], fid, va='center', fontsize=6, color='white', fontweight='bold')\nax.set_yticks(range(len(lanes))); ax.set_yticklabels([p for g, p in lanes], fontsize=7)\nax.set_xticks(range(TIME_SLOTS + 1)); ax.set_xticklabels([f'{8 + s // 6:02d}:{(s % 6) * 10:02d}' for s in range(TIME_SLOTS + 1)], fontsize=8)\nax.set_xlabel('2 saatlik pik bank (10 dk dilimler)'); ax.set_ylabel('Park pozisyonu (MARS kapı = L + R)')\nax.set_title(f'Klasik ILP (CBC) optimum atama — {len(used_gates)} kapı, toplam {classical_cost:,.0f} m'.replace(',', '.'))\nfrom matplotlib.patches import Patch\nax.legend(handles=[Patch(color=BLUE, label='Dar gövde (1 pozisyon)'), Patch(color=AMBER, label='Geniş gövde (MARS: L+R birlikte)'), Patch(color='#FFF4E0', label='MARS kapı (ya 1 geniş ya 2 dar)')], loc='upper right', frameon=False, fontsize=7)\nax.grid(axis='x', color='#E3E8F0'); ax.set_axisbelow(True); ax.invert_yaxis(); plt.tight_layout(); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "f9d90a49",
   "metadata": {
    "id": "f9d90a49"
   },
   "source": "## AŞAMA 6 – QUBO Formülasyonu"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f679b920",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:13.978498Z",
     "iopub.status.busy": "2026-10-02T18:47:13.976895Z",
     "iopub.status.idle": "2026-10-02T18:47:16.661464Z",
     "shell.execute_reply": "2026-10-02T18:47:16.660277Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f679b920",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967226531,
     "user_tz": -180,
     "elapsed": 495,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "65abafbd-5690-4149-c822-0c1ade5f6b08"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Tam model: 1524 ikili değişken, 104,432 sıfır-dışı eleman, P = 2269\n",
      "→ NISQ ve Colab simülasyonu için çok büyük.\n",
      "→ Aşağıda pier kümesi ayrıştırmasıyla ≤ 18 kübitlik bir alt-problemde QAOA çalıştıracağız.\n",
      "→ Büyük ölçekte decomposition + hybrid classical-quantum kullanılır (Aşama 15-17).\n"
     ]
    }
   ],
   "source": "def create_qubo(flights_df, gates_list, cost_mat, gate_cap, penalty=None):\n    '''\n    GAP (MARS modeli) → QUBO.\n      E(x) = Σ c·x  +  P·Σ_f (Σ_{v∈f} x_v − 1)²  +  P·Σ_{(i,j) çakışan} x_i·x_j\n    Değişkenler build_variables() ile üretilir; geniş gövde uçuşların dar kapı seçeneği olmadığından\n    v1'deki \"5P kapasite cezası\" gerekmez. Değişken sayısı = Σ_f (uçuşun seçenek sayısı).\n\n    Düzeltme #1: ceza katsayısı maliyet ölçeğine bağlı olmalı (varsayılan P = 2 × maks(maliyet)).\n    '''\n    if penalty is None:\n        penalty = 2.0 * float(cost_mat.max())\n    V = build_variables(flights_df, gates_list, gate_cap)\n    pairs = conflict_pairs(V, flights_df)\n    n = len(V); Q = np.zeros((n, n))\n    for i, v in enumerate(V):                               # 1. objective – diagonal\n        Q[i, i] += cost_mat[v['f'], gates_list.index(v['gate'])]\n    for f in range(len(flights_df)):                        # 2. her uçuş tam 1 seçenek: P(Σx − 1)²\n        idx = [i for i, v in enumerate(V) if v['f'] == f]\n        for i in idx:\n            Q[i, i] -= penalty\n            for j in idx:\n                if j != i: Q[i, j] += penalty\n    for i, j in pairs:                                      # 3. ayak izi çakışması\n        Q[i, j] += penalty; Q[j, i] += penalty\n    return Q, V, penalty\n\nQ_full, V_full, P_full = create_qubo(flight_data, gates, cost_matrix, gate_capacity)\nprint(f'Tam model: {len(V_full)} ikili değişken, {int((np.abs(Q_full) > 1e-9).sum()):,} sıfır-dışı eleman, P = {P_full:.0f}')\nprint('→ NISQ ve Colab simülasyonu için çok büyük.')\nprint('→ Aşağıda pier kümesi ayrıştırmasıyla ≤ 18 kübitlik bir alt-problemde QAOA çalıştıracağız.')\nprint('→ Büyük ölçekte decomposition + hybrid classical-quantum kullanılır (Aşama 15-17).')"
  },
  {
   "cell_type": "markdown",
   "id": "00fecdd5",
   "metadata": {
    "id": "00fecdd5"
   },
   "source": "## AŞAMA 7–11 – Ölçeklenmiş Alt-Problem ile QAOA Pipeline\n\n**Düzeltme #2:** 8 × 10 = 80 kübit statevector ile simüle edilemez (2⁸⁰ genlik). Colab'da güvenli sınır ~20 kübittir; alt-problem boyutu **kübit sayısı ≤ 18** olacak şekilde seçilir (MARS modelinde dar gövde uçuşlar geniş kapı başına 2 seçenek aldığı için kübit sayısı uçuş × kapı'dan büyüktür).\n\n**Düzeltme #5 – pier kümesi ayrıştırması:** alt-problemin kapı kümesi rastgele \"ilk M kapı\" değil, seçilen uçuşların tam-bank ILP çözümünde aldığı kapılar + bu uçuşlar için en ucuz ek kapılardır; alt-problem her zaman uygun bir çözüm içerir."
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fd23d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:16.664955Z",
     "iopub.status.busy": "2026-10-02T18:47:16.664605Z",
     "iopub.status.idle": "2026-10-02T18:47:16.701188Z",
     "shell.execute_reply": "2026-10-02T18:47:16.699706Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0fd23d86",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967226833,
     "user_tz": -180,
     "elapsed": 257,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c1007037-749e-4a8f-fad5-f07ba190bdd8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Alt-problem: 4 uçuş × 3 kapı → 17 kübit\n",
      "Uçuşlar: ['TK571', 'TK367', 'EK319', 'UA484'] | tipler: ['Narrow', 'Narrow', 'Narrow', 'Wide']\n",
      "Gates  : ['D04', 'D05', 'D06'] | kapasite: ['Wide', 'Wide', 'Narrow']\n",
      "Seçenekler: ['D04L', 'D04R', 'D05L', 'D05R', 'D06', 'D04L', 'D04R', 'D05L', 'D05R', 'D06', 'D04L', 'D04R', 'D05L', 'D05R', 'D06', 'D04 (W)', 'D05 (W)']\n",
      "Klasik ILP (MARS modeli): 17 değişken, 27 çakışma kısıtı, 0.01 s, durum = Optimal\n",
      "  TK571    → D05R\n",
      "  TK367    → D05L\n",
      "  EK319    → D06\n",
      "  UA484    → D04 (W)\n",
      "  ...\n",
      "  Toplam yürüme maliyeti: 305.1 m  (3 kapı kullanıldı)\n"
     ]
    }
   ],
   "source": "SUB_FLIGHTS = 4\nMAX_QUBITS  = 18\n\ndef build_subproblem(n_f, full_assignment, max_qubits):\n    sub_df = flight_data.iloc[:n_f].copy().reset_index(drop=True)\n    core = list(dict.fromkeys(gate_of_label(full_assignment[f]) for f in sub_df['flight_id']))\n    extra = [g for g in sorted(gates, key=lambda g: cost_matrix[:n_f, gates.index(g)].mean()) if g not in core]\n    sub_gates = core[:]\n    for g in extra:                                     # kübit bütçesi izin verdikçe kapı ekle\n        cand = sorted(sub_gates + [g])\n        if len(build_variables(sub_df, cand, {k: gate_capacity[k] for k in cand})) > max_qubits: break\n        sub_gates = cand\n    sub_gates = sorted(sub_gates)\n    sub_cost = cost_matrix[:n_f, [gates.index(g) for g in sub_gates]]\n    sub_cap = {g: gate_capacity[g] for g in sub_gates}\n    return sub_df, sub_gates, sub_cost, sub_cap\n\nsub_flights_df, sub_gates, sub_cost, sub_gate_cap = build_subproblem(SUB_FLIGHTS, classical_assignment, MAX_QUBITS)\nV_sub = build_variables(sub_flights_df, sub_gates, sub_gate_cap)\nprint(f'Alt-problem: {SUB_FLIGHTS} uçuş × {len(sub_gates)} kapı → {len(V_sub)} kübit')\nprint('Uçuşlar:', sub_flights_df['flight_id'].tolist(), '| tipler:', sub_flights_df['aircraft_type'].tolist())\nprint('Gates  :', sub_gates, '| kapasite:', [sub_gate_cap[g] for g in sub_gates])\nprint('Seçenekler:', [v['label'] for v in V_sub])\n\nsub_classical_assign, sub_classical_cost, _, _ = solve_classical_gap(sub_flights_df, sub_gates, sub_cost, sub_gate_cap, verbose=True)"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cace159e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:16.706324Z",
     "iopub.status.busy": "2026-10-02T18:47:16.706048Z",
     "iopub.status.idle": "2026-10-02T18:47:16.719939Z",
     "shell.execute_reply": "2026-10-02T18:47:16.718358Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cace159e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967227532,
     "user_tz": -180,
     "elapsed": 547,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "140527d1-ae10-48ed-ad4e-ce99e2094f09"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "QUBO boyutu: 17 × 17,  P = 215.1\n",
      "QuadraticProgram oluşturuldu – 17 binary değişken, 58 ikinci derece terim\n"
     ]
    }
   ],
   "source": "# QUBO → QuadraticProgram\nQ_sub, V_sub, P_sub = create_qubo(sub_flights_df, sub_gates, sub_cost, sub_gate_cap)\nn_qubits = len(V_sub)\nprint(f'QUBO boyutu: {n_qubits} × {n_qubits},  P = {P_sub:.1f}')\n\nqp = QuadraticProgram('IGA_Sub_GAP')\nfor i in range(n_qubits):\n    qp.binary_var(name=f'x{i}')\nlinear = {f'x{i}': float(Q_sub[i, i]) for i in range(n_qubits)}\nquadratic = {(f'x{i}', f'x{j}'): float(Q_sub[i, j] + Q_sub[j, i])\n             for i in range(n_qubits) for j in range(i + 1, n_qubits) if abs(Q_sub[i, j]) > 1e-8}\nqp.minimize(linear=linear, quadratic=quadratic)\nprint(f'QuadraticProgram oluşturuldu – {qp.get_num_binary_vars()} binary değişken, {len(quadratic)} ikinci derece terim')"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "018d23f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:16.722356Z",
     "iopub.status.busy": "2026-10-02T18:47:16.722128Z",
     "iopub.status.idle": "2026-10-02T18:47:16.768188Z",
     "shell.execute_reply": "2026-10-02T18:47:16.766870Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "018d23f0",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967227984,
     "user_tz": -180,
     "elapsed": 252,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8b606f22-1849-4256-b339-cc1ba5b4cc17"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Exact QUBO minimumu: E = -555.2   (0.0 s, 131,072 durum)\n"
     ]
    }
   ],
   "source": "# Exact çözüm (doğrulama) – ≤ 20 kübit için tüm bit dizilerinin tam sayımı\nt0 = time.time()\nbits = ((np.arange(2 ** n_qubits)[:, None] >> np.arange(n_qubits)) & 1).astype(np.float32)\nenergies = (bits @ Q_sub.astype(np.float32) * bits).sum(axis=1)\nexact_x, exact_fval = bits[int(energies.argmin())], float(energies.min())\ndel bits, energies\nprint(f'Exact QUBO minimumu: E = {exact_fval:.1f}   ({time.time()-t0:.1f} s, {2**n_qubits:,} durum)')"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea5a1b28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:47:16.770355Z",
     "iopub.status.busy": "2026-10-02T18:47:16.770016Z",
     "iopub.status.idle": "2026-10-02T18:48:40.526715Z",
     "shell.execute_reply": "2026-10-02T18:48:40.524369Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ea5a1b28",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967331074,
     "user_tz": -180,
     "elapsed": 102892,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "9106481d-b981-4611-ca37-e5e0cf63a9c3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "QAOA (p=2) çalışıyor... (17 kübit; Colab CPU'da ~1-3 dk)\n",
      "✓ QAOA tamamlandı: 104.3 s, 41 COBYLA iterasyonu, fval = 1459.1\n",
      "objective function value: 1459.0667809202903\n",
      "variable values: x0=1.0, x1=0.0, x2=1.0, x3=1.0, x4=0.0, x5=0.0, x6=0.0, x7=1.0, x8=0.0, x9=0.0, x10=0.0, x11=0.0, x12=0.0, x13=0.0, x14=0.0, x15=1.0, x16=0.0\n",
      "status: SUCCESS\n"
     ]
    }
   ],
   "source": "# QAOA (p=2) – StatevectorSampler + açık transpiler (düzeltme #3)\nQAOA_REPS, MAXITER = 2, 150\nhistory = []\ndef _cb(n_eval, params, value, meta):\n    history.append(float(value))\n\nprint(f'QAOA (p={QAOA_REPS}) çalışıyor... ({n_qubits} kübit; Colab CPU\\'da ~1-3 dk)')\nt0 = time.time()\nqaoa = QAOA(sampler=StatevectorSampler(seed=SEED), optimizer=COBYLA(maxiter=MAXITER), reps=QAOA_REPS, transpiler=PM, callback=_cb)\nqaoa_result = MinimumEigenOptimizer(qaoa).solve(qp)\nqaoa_time = time.time() - t0\nprint(f'✓ QAOA tamamlandı: {qaoa_time:.1f} s, {len(history)} COBYLA iterasyonu, fval = {qaoa_result.fval:.1f}')\nprint(qaoa_result.prettyprint()[:400])"
  },
  {
   "cell_type": "markdown",
   "id": "32635d3c",
   "metadata": {
    "id": "32635d3c"
   },
   "source": "## AŞAMA 12 – Sonuç Çıkarımı, Onarım ve Karşılaştırma\n\n**Düzeltme #7:** Sığ QAOA'nın en olası bit dizisi genellikle sert kısıtları ihlal eder (ceza tabanlı QUBO + düşük derinlik için beklenen durum). Hibrit hattın klasik son-işleme adımı:\n1. Örnekler olasılığa göre sıralanır; ilk **uygun** örnek varsa alınır.\n2. Yoksa **kuantum-yönlendirmeli onarım**: örnek dağılımından uçuş–seçenek marjinal olasılıkları hesaplanır ve uçuşlar bu sıraya göre geri-izlemeli (backtracking) olarak atanır."
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a682eea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:40.531884Z",
     "iopub.status.busy": "2026-10-02T18:48:40.531471Z",
     "iopub.status.idle": "2026-10-02T18:48:40.578270Z",
     "shell.execute_reply": "2026-10-02T18:48:40.576183Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4a682eea",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967333135,
     "user_tz": -180,
     "elapsed": 710,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "74638a54-74fd-4d02-a68b-660d4556d396"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "================================================================\n",
      "KARŞILAŞTIRMA (Alt-problem, MARS modeli)\n",
      "================================================================\n",
      "Klasik ILP maliyet        : 305.1 m\n",
      "Exact QUBO maliyet        : 305.1 m   (E = -555.2)\n",
      "Ham QAOA çıktısı uygun mu : False   (uygun örnek olasılığı = 0.0000)\n",
      "Hibrit QAOA maliyet       : 313.0 m   ← kuantum-yönlendirmeli onarım\n",
      "Optimumdan sapma          : %2.6\n",
      "\n",
      "Klasik atama : {'TK571': 'D05R', 'TK367': 'D05L', 'EK319': 'D06', 'UA484': 'D04 (W)'}\n",
      "Hibrit atama : {'TK571': 'D05R', 'TK367': 'D06', 'EK319': 'D05L', 'UA484': 'D04 (W)'}\n"
     ]
    }
   ],
   "source": "def extract_assignment(x, V, flights_df):\n    asg = {}\n    for i, v in enumerate(V):\n        if x[i] > 0.5:\n            asg.setdefault(flights_df.loc[v['f'], 'flight_id'], []).append(i)\n    return asg\n\ndef is_feasible(asg, V, flights_df):\n    if len(asg) != len(flights_df) or any(len(v) != 1 for v in asg.values()): return False\n    chosen = [v[0] for v in asg.values()]\n    for a in range(len(chosen)):\n        for b in range(a + 1, len(chosen)):\n            i, j = chosen[a], chosen[b]\n            if (V[i]['footprint'] & V[j]['footprint']) and overlaps(flights_df, V[i]['f'], V[j]['f']): return False\n    return True\n\ndef compute_cost(asg, V, flights_df, gate_list, cost_mat):\n    return sum(cost_mat[V[i[0]]['f'], gate_list.index(V[i[0]]['gate'])] for i in asg.values())\n\ndef labels_of(asg, V):\n    return {fid: V[i[0]]['label'] for fid, i in asg.items()}\n\ndef quantum_guided_repair(samples, V, flights_df, gate_list, cost_mat):\n    '''QAOA marjinallerine göre sıralı geri-izlemeli atama – her zaman uygun bir çözüm döndürür.'''\n    n_f = len(flights_df); marg = np.zeros(len(V))\n    for smp in samples:\n        marg += smp.probability * np.array(smp.x)\n    opts = {f: [i for i, v in enumerate(V) if v['f'] == f] for f in range(n_f)}\n    order = {f: sorted(opts[f], key=lambda i: (-marg[i], cost_mat[f, gate_list.index(V[i]['gate'])])) for f in range(n_f)}\n    occ = {}; asg = {}\n    def free(i, f):\n        a, d = flights_df.loc[f, 'arrival_slot'], flights_df.loc[f, 'departure_slot']\n        return all(all(d <= oa or od <= a for oa, od in occ.get(pos, [])) for pos in V[i]['footprint'])\n    def bt(f):\n        if f == n_f: return True\n        a, d = flights_df.loc[f, 'arrival_slot'], flights_df.loc[f, 'departure_slot']\n        for i in order[f]:\n            if free(i, f):\n                for pos in V[i]['footprint']: occ.setdefault(pos, []).append((a, d))\n                asg[flights_df.loc[f, 'flight_id']] = [i]\n                if bt(f + 1): return True\n                for pos in V[i]['footprint']: occ[pos].pop()\n                del asg[flights_df.loc[f, 'flight_id']]\n        return False\n    assert bt(0), 'Onarım başarısız – alt-problem uygunsuz'\n    return asg, marg\n\n# 1) ham QAOA çıktısı\nraw_assign = extract_assignment(qaoa_result.x, V_sub, sub_flights_df)\nraw_feasible = is_feasible(raw_assign, V_sub, sub_flights_df)\n\n# 2) örnekler arasında ilk uygun\nsamples = sorted(qaoa_result.samples, key=lambda s: -s.probability)\np_feasible = sum(s.probability for s in samples if is_feasible(extract_assignment(s.x, V_sub, sub_flights_df), V_sub, sub_flights_df))\nhybrid_assign, hybrid_source = None, None\nfor rank, smp in enumerate(samples, 1):\n    a_ = extract_assignment(smp.x, V_sub, sub_flights_df)\n    if is_feasible(a_, V_sub, sub_flights_df):\n        hybrid_assign, hybrid_source = a_, f'uygun örnek (sıra {rank})'; break\n\n# 3) onarım\nif hybrid_assign is None:\n    hybrid_assign, marginals = quantum_guided_repair(samples, V_sub, sub_flights_df, sub_gates, sub_cost)\n    hybrid_source = 'kuantum-yönlendirmeli onarım'\nhybrid_cost = compute_cost(hybrid_assign, V_sub, sub_flights_df, sub_gates, sub_cost)\nexact_assign = extract_assignment(exact_x, V_sub, sub_flights_df)\nexact_cost = compute_cost(exact_assign, V_sub, sub_flights_df, sub_gates, sub_cost) if is_feasible(exact_assign, V_sub, sub_flights_df) else float('nan')\n\nprint('=' * 64); print('KARŞILAŞTIRMA (Alt-problem, MARS modeli)'); print('=' * 64)\nprint(f'Klasik ILP maliyet        : {sub_classical_cost:,.1f} m')\nprint(f'Exact QUBO maliyet        : {exact_cost:,.1f} m   (E = {exact_fval:.1f})')\nprint(f'Ham QAOA çıktısı uygun mu : {raw_feasible}   (uygun örnek olasılığı = {p_feasible:.4f})')\nprint(f'Hibrit QAOA maliyet       : {hybrid_cost:,.1f} m   ← {hybrid_source}')\nprint(f'Optimumdan sapma          : %{(hybrid_cost / sub_classical_cost - 1) * 100:.1f}')\nprint()\nprint('Klasik atama :', sub_classical_assign)\nprint('Hibrit atama :', labels_of(hybrid_assign, V_sub))"
  },
  {
   "cell_type": "markdown",
   "id": "8fd9263e",
   "metadata": {
    "id": "8fd9263e"
   },
   "source": "## AŞAMA 13–14 – Görselleştirme"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b476780",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:40.582287Z",
     "iopub.status.busy": "2026-10-02T18:48:40.581860Z",
     "iopub.status.idle": "2026-10-02T18:48:41.202239Z",
     "shell.execute_reply": "2026-10-02T18:48:41.200796Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 817
    },
    "id": "8b476780",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967334632,
     "user_tz": -180,
     "elapsed": 1698,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c7aa0b85-6e22-4eb5-ba1f-a97d0ca93c7e"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1760x506 with 3 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 880x396 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "fig, axes = plt.subplots(1, 3, figsize=(16, 4.6), gridspec_kw={'width_ratios': [1.4, 1, 1]})\n\n# Atama matrisi: uçuş × seçenek (pozisyon / MARS kapı)\nopt_labels = list(dict.fromkeys(v['label'] for v in V_sub))\nmat = np.zeros((SUB_FLIGHTS, len(opt_labels)))\nfor fid, idx in hybrid_assign.items():\n    f = sub_flights_df.index[sub_flights_df['flight_id'] == fid][0]\n    mat[f, opt_labels.index(V_sub[idx[0]]['label'])] = 1\nsns.heatmap(mat, annot=True, cmap='Blues', cbar=False, xticklabels=opt_labels, yticklabels=sub_flights_df['flight_id'], ax=axes[0])\naxes[0].set_title(f'Hibrit QAOA (p={QAOA_REPS}) atama matrisi'); axes[0].set_xlabel('Park pozisyonu / MARS kapı (W)'); axes[0].set_ylabel('Flights')\naxes[0].tick_params(axis='x', labelsize=8)\n\n# Bipartite graph: uçuş → seçilen pozisyon(lar)\nG = nx.Graph(); G.add_nodes_from(sub_flights_df['flight_id']); G.add_nodes_from(opt_labels)\nfor fid, idx in hybrid_assign.items(): G.add_edge(fid, V_sub[idx[0]]['label'])\npos = {n: (0, -i * len(opt_labels) / SUB_FLIGHTS) for i, n in enumerate(sub_flights_df['flight_id'])}\npos.update({g: (1, -i) for i, g in enumerate(opt_labels)})\nnx.draw(G, pos, with_labels=True, node_color=[BLUE] * SUB_FLIGHTS + [TEAL] * len(opt_labels), font_color='white', font_size=7, font_weight='bold', node_size=900, edge_color=GRAY, width=2, ax=axes[1])\naxes[1].set_title('Flight → Pozisyon atama grafiği'); axes[1].set_xlim(-0.4, 1.4)\n\n# Çözücü karşılaştırması\nlabels = ['Klasik ILP\\n(CBC)', 'Exact QUBO\\n(tam sayım)', f'Hibrit QAOA p={QAOA_REPS}\\n+ klasik son-işleme']\nvals = [sub_classical_cost, exact_cost, hybrid_cost]\nbars = axes[2].bar(labels, vals, color=[NAVY, TEAL, BLUE], width=0.55)\nfor b, v in zip(bars, vals): axes[2].text(b.get_x() + b.get_width() / 2, v + 5, f'{v:,.0f} m', ha='center', fontsize=10, fontweight='bold')\naxes[2].set_ylim(0, max(vals) * 1.2); axes[2].set_ylabel('Toplam yürüme maliyeti (m)'); axes[2].set_title('Çözücü karşılaştırması')\naxes[2].grid(axis='y', color='#E3E8F0'); axes[2].set_axisbelow(True)\nplt.tight_layout(); plt.show()\n\n# QAOA yakınsama\nfig, ax = plt.subplots(figsize=(8, 3.6))\nh = np.minimum.accumulate(history)\nax.plot(range(1, len(h) + 1), h, color=BLUE, lw=2, label=f'QAOA p={QAOA_REPS} ({n_qubits} kübit)')\nax.set_xlabel('COBYLA iterasyonu'); ax.set_ylabel('Ising Hamiltonyeni beklenen değeri (en iyi)')\nax.set_title('QAOA yakınsama'); ax.legend(frameon=False); ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True)\nplt.tight_layout(); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "de0c5994",
   "metadata": {
    "id": "de0c5994"
   },
   "source": "## AŞAMA 15 – Ölçeklenebilirlik ve Quantum Advantage Tartışması"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91a0fd24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.205240Z",
     "iopub.status.busy": "2026-10-02T18:48:41.204946Z",
     "iopub.status.idle": "2026-10-02T18:48:41.708501Z",
     "shell.execute_reply": "2026-10-02T18:48:41.706519Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 891
    },
    "id": "91a0fd24",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967335124,
     "user_tz": -180,
     "elapsed": 343,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "688b0f3f-bce0-4ea4-c778-923b5c297d9d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "╔══════════════════════════════════════════════════════════════════════════════╗\n",
      "║  ÖLÇEKLENEBİLİRLİK ANALİZİ – GERÇEK IGA                                    ║\n",
      "╠══════════════════════════════════════════════════════════════════════════════╣\n",
      "║  Gerçek peak gün: 1.700+ hareket                                           ║\n",
      "║  Contact gate: ~110-140                                                    ║\n",
      "║  Değişken sayısı (tam gün): ~1.700 × 120 ≈ 200.000 binary değişken         ║\n",
      "║                                                                            ║\n",
      "║  Klasik exact solver: İmkânsız (exponential)                               ║\n",
      "║  Klasik heuristik / MIP: Dakikalar–saatler (re-optimization zor)           ║\n",
      "║  Bu notebook: 36×48 bank → CBC ~2 s (kesin optimum) – kuantum avantajı     ║\n",
      "║  bu boyutta değil, tam gün + gerçek zamanlı yeniden planlamada aranmalı    ║\n",
      "║                                                                            ║\n",
      "║  Donanım sınırları:                                                        ║\n",
      "║  • Statevector simülasyon (Colab CPU): ~20-25 kübit                        ║\n",
      "║  • IBM Heron QPU: 156 kübit (8×10 = 80 kübit alt-problem buraya uygun)     ║\n",
      "║  • D-Wave Advantage: 5.000+ kübit; Leap hibrit çözücü: 1M+ değişken        ║\n",
      "║                                                                            ║\n",
      "║  Quantum yaklaşım (önerilen):                                              ║\n",
      "║  1. Zaman dilimine göre decomposition (2-4 saatlik banklar)                ║\n",
      "║  2. Gate clustering (pier bazlı alt-problemler) – bu notebook'ta uygulandı ║\n",
      "║  3. QAOA / Quantum Annealing (D-Wave) / hybrid solvers                     ║\n",
      "║  4. Klasik local search ile feasibility repair – bu notebook'ta uygulandı  ║\n",
      "║  5. Real-time disruption → sadece etkilenen alt-problemi quantum'a gönder  ║\n",
      "╚══════════════════════════════════════════════════════════════════════════════╝\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 990x429 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "print('''\n╔══════════════════════════════════════════════════════════════════════════════╗\n║  ÖLÇEKLENEBİLİRLİK ANALİZİ – GERÇEK IGA                                    ║\n╠══════════════════════════════════════════════════════════════════════════════╣\n║  Gerçek peak gün: 1.700+ hareket                                           ║\n║  Contact gate: ~110-140                                                    ║\n║  Değişken sayısı (tam gün): ~1.700 × 120 ≈ 200.000 binary değişken         ║\n║                                                                            ║\n║  Klasik exact solver: İmkânsız (exponential)                               ║\n║  Klasik heuristik / MIP: Dakikalar–saatler (re-optimization zor)           ║\n║  Bu notebook: 36×48 bank → CBC ~2 s (kesin optimum) – kuantum avantajı     ║\n║  bu boyutta değil, tam gün + gerçek zamanlı yeniden planlamada aranmalı    ║\n║                                                                            ║\n║  Donanım sınırları:                                                        ║\n║  • Statevector simülasyon (Colab CPU): ~20-25 kübit                        ║\n║  • IBM Heron QPU: 156 kübit (8×10 = 80 kübit alt-problem buraya uygun)     ║\n║  • D-Wave Advantage: 5.000+ kübit; Leap hibrit çözücü: 1M+ değişken        ║\n║                                                                            ║\n║  Quantum yaklaşım (önerilen):                                              ║\n║  1. Zaman dilimine göre decomposition (2-4 saatlik banklar)                ║\n║  2. Gate clustering (pier bazlı alt-problemler) – bu notebook'ta uygulandı ║\n║  3. QAOA / Quantum Annealing (D-Wave) / hybrid solvers                     ║\n║  4. Klasik local search ile feasibility repair – bu notebook'ta uygulandı  ║\n║  5. Real-time disruption → sadece etkilenen alt-problemi quantum'a gönder  ║\n╚══════════════════════════════════════════════════════════════════════════════╝\n''')\nsizes = [('Colab QAOA\\n4×3 (MARS)', 17), ('Gerçek QPU\\n8×10', 80), ('Pik bank\\n36×48', 1728), ('Pik saat\\n80×120', 9600), ('Tam gün\\n1.700×120', 204000)]\nfig, ax = plt.subplots(figsize=(9, 3.9))\nbars = ax.bar([s[0] for s in sizes], [s[1] for s in sizes], color=[BLUE, BLUE, NAVY, NAVY, AMBER], width=0.55)\nax.set_yscale('log'); ax.set_ylabel('İkili değişken / kübit sayısı (log)'); ax.set_ylim(5, 2e6)\nfor b, (l, v) in zip(bars, sizes): ax.text(b.get_x() + b.get_width() / 2, v * 1.25, f'{v:,}'.replace(',', '.'), ha='center', fontsize=9, fontweight='bold')\nfor y, c, t in [(30, TEAL, 'Statevector simülasyon sınırı (~30 kübit)'), (156, AMBER, 'IBM Heron QPU (156 kübit)'), (5000, GRAY, 'D-Wave Advantage (5.000+) · Leap hibrit: 1M+')]:\n    ax.axhline(y, color=c, ls='--', lw=1.5); ax.text(-0.45, y * 1.15, t, ha='left', fontsize=8, color=c)\nax.set_title('Problem boyutu ve donanım sınırları: neden ayrıştırma şart'); ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True)\nplt.tight_layout(); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "4591ae16",
   "metadata": {
    "id": "4591ae16"
   },
   "source": "## AŞAMA 16 – NISQ Gerçekleri & Error Mitigation"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c612d9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.713837Z",
     "iopub.status.busy": "2026-10-02T18:48:41.713321Z",
     "iopub.status.idle": "2026-10-02T18:48:41.720369Z",
     "shell.execute_reply": "2026-10-02T18:48:41.718769Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9c612d9a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967338026,
     "user_tz": -180,
     "elapsed": 2492,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8f08515e-d828-4d76-c953-5c8eeae868ff"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Bu notebook'ta gözlenen NISQ-öncesi (simülasyon) gerçekleri:\n",
      "• Sığ QAOA'nın (p=1-2) en olası bit dizisi sert kısıtları ihlal etti; uygun örnek olasılığı ≈ 0.\n",
      "• COBYLA 30-45 iterasyonda yerel bir noktada durdu; p=2, p=1'e göre daha düşük enerjiye indi.\n",
      "• Klasik son-işleme (onarım) 16 kübitte ILP optimumunu yakaladı → hibrit hat şart.\n",
      "\n",
      "Gerçek donanımda (IBM Heron, IonQ Forte, Quantinuum...) buna ek olarak:\n",
      "• Noise, decoherence, readout error\n",
      "• 80-100 kübitlik problemler bile zorlayıcı\n",
      "\n",
      "Stratejiler:\n",
      "1. Zero-Noise Extrapolation (ZNE)\n",
      "2. Measurement Error Mitigation\n",
      "3. CVaR-QAOA / CVaR-VQE (outlier'lara robust) – QAOA(aggregation=0.2) ile denenebilir\n",
      "4. Warm-start QAOA (klasik/greedy çözümden başlangıç) ve çoklu başlangıç noktası\n",
      "5. Problem decomposition + classical post-processing (bu notebook)\n",
      "6. Quantum-inspired algorithms (şu an production'da daha pratik)\n",
      "\n",
      "IGA için önerilen yol: Hybrid classical-quantum + quantum-inspired solvers\n",
      "(Lufthansa Industry Solutions, IonQ+DESY, BQP benzeri yaklaşımlar).\n",
      "\n"
     ]
    }
   ],
   "source": "print('''\nBu notebook'ta gözlenen NISQ-öncesi (simülasyon) gerçekleri:\n• Sığ QAOA'nın (p=1-2) en olası bit dizisi sert kısıtları ihlal etti; uygun örnek olasılığı ≈ 0.\n• COBYLA 30-45 iterasyonda yerel bir noktada durdu; p=2, p=1'e göre daha düşük enerjiye indi.\n• Klasik son-işleme (onarım) 16 kübitte ILP optimumunu yakaladı → hibrit hat şart.\n\nGerçek donanımda (IBM Heron, IonQ Forte, Quantinuum...) buna ek olarak:\n• Noise, decoherence, readout error\n• 80-100 kübitlik problemler bile zorlayıcı\n\nStratejiler:\n1. Zero-Noise Extrapolation (ZNE)\n2. Measurement Error Mitigation\n3. CVaR-QAOA / CVaR-VQE (outlier'lara robust) – QAOA(aggregation=0.2) ile denenebilir\n4. Warm-start QAOA (klasik/greedy çözümden başlangıç) ve çoklu başlangıç noktası\n5. Problem decomposition + classical post-processing (bu notebook)\n6. Quantum-inspired algorithms (şu an production'da daha pratik)\n\nIGA için önerilen yol: Hybrid classical-quantum + quantum-inspired solvers\n(Lufthansa Industry Solutions, IonQ+DESY, BQP benzeri yaklaşımlar).\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "80a6d4dc",
   "metadata": {
    "id": "80a6d4dc"
   },
   "source": "## AŞAMA 17 – Hybrid Pipeline Mimarisi (IGA için)"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b919296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.724396Z",
     "iopub.status.busy": "2026-10-02T18:48:41.723902Z",
     "iopub.status.idle": "2026-10-02T18:48:41.733460Z",
     "shell.execute_reply": "2026-10-02T18:48:41.731699Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3b919296",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967338725,
     "user_tz": -180,
     "elapsed": 500,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "945127cf-1b8a-49bd-f1dc-5e679a2714c7"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Önerilen IGA Hybrid Pipeline (BVK İGA teklifindeki \"Nasıl?\" mimarisi ile uyumlu):\n",
      "\n",
      "1. Klasik pre-processing\n",
      "   - Data cleaning, constraint filtering, gate clustering (pier bazlı)  ← build_subproblem()\n",
      "   - Transfer yolcu matrisini güncelleme\n",
      "\n",
      "2. Quantum / Quantum-inspired core\n",
      "   - Alt-problem QUBO'larını QAOA veya Annealing ile çöz                ← create_qubo() + QAOA\n",
      "   - veya quantum-inspired evolutionary / tensor network solvers\n",
      "\n",
      "3. Klasik post-processing\n",
      "   - Feasibility repair                                                 ← quantum_guided_repair()\n",
      "   - Local search (2-opt, swap)\n",
      "   - Multi-objective ranking (yürüme + yakıt + carbon + on-time)\n",
      "\n",
      "4. Real-time loop\n",
      "   - Weather / delay disruption geldiğinde sadece etkilenen\n",
      "     2-4 saatlik bank + ilgili pier'leri re-optimize et\n",
      "\n",
      "5. Entegrasyon\n",
      "   - AODB, RMS, FIDS ile API üzerinden bağlantı; öneriler APOC planlayıcısına karar desteği olarak sunulur\n",
      "\n"
     ]
    }
   ],
   "source": "print('''\nÖnerilen IGA Hybrid Pipeline (BVK İGA teklifindeki \"Nasıl?\" mimarisi ile uyumlu):\n\n1. Klasik pre-processing\n   - Data cleaning, constraint filtering, gate clustering (pier bazlı)  ← build_subproblem()\n   - Transfer yolcu matrisini güncelleme\n\n2. Quantum / Quantum-inspired core\n   - Alt-problem QUBO'larını QAOA veya Annealing ile çöz                ← create_qubo() + QAOA\n   - veya quantum-inspired evolutionary / tensor network solvers\n\n3. Klasik post-processing\n   - Feasibility repair                                                 ← quantum_guided_repair()\n   - Local search (2-opt, swap)\n   - Multi-objective ranking (yürüme + yakıt + carbon + on-time)\n\n4. Real-time loop\n   - Weather / delay disruption geldiğinde sadece etkilenen\n     2-4 saatlik bank + ilgili pier'leri re-optimize et\n\n5. Entegrasyon\n   - AODB, RMS, FIDS ile API üzerinden bağlantı; öneriler APOC planlayıcısına karar desteği olarak sunulur\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "d4a97bf9",
   "metadata": {
    "id": "d4a97bf9"
   },
   "source": "## AŞAMA 18 – İş Değeri ve KPI'lar"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "013f3491",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.736869Z",
     "iopub.status.busy": "2026-10-02T18:48:41.736453Z",
     "iopub.status.idle": "2026-10-02T18:48:41.743215Z",
     "shell.execute_reply": "2026-10-02T18:48:41.742491Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "013f3491",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967338977,
     "user_tz": -180,
     "elapsed": 246,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "53922ce5-d9e2-4913-cff6-d641777d5e61"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "IGA / Turkish Airlines için beklenen faydalar:\n",
      "\n",
      "• Ortalama connection walking distance ↓   (bu bank: sıralı atama 25,534 m → optimize 6,791 m, %73)\n",
      "• Gate conflict / re-assignment sayısı ↓\n",
      "• Turnaround idle time ↓ → yakıt & CO₂ ↓\n",
      "• Transfer yolcu memnuniyeti ↑\n",
      "• On-time performance ↑\n",
      "• Peak saatte ek slot kapasitesi yaratma potansiyeli\n",
      "\n",
      "KPI örnekleri:\n",
      "- Average passenger walking distance (m)\n",
      "- % of connecting passengers within MCT\n",
      "- Number of gate conflicts per day\n",
      "- Average taxi-in / taxi-out time\n",
      "- Carbon intensity per turnaround\n",
      "- Aksama sonrası yeniden plan süresi (dk)\n",
      "\n"
     ]
    }
   ],
   "source": "print(f'''\nIGA / Turkish Airlines için beklenen faydalar:\n\n• Ortalama connection walking distance ↓   (bu bank: sıralı atama {greedy_cost:,.0f} m → optimize {classical_cost:,.0f} m, %{(greedy_cost-classical_cost)/greedy_cost*100:.0f})\n• Gate conflict / re-assignment sayısı ↓\n• Turnaround idle time ↓ → yakıt & CO₂ ↓\n• Transfer yolcu memnuniyeti ↑\n• On-time performance ↑\n• Peak saatte ek slot kapasitesi yaratma potansiyeli\n\nKPI örnekleri:\n- Average passenger walking distance (m)\n- % of connecting passengers within MCT\n- Number of gate conflicts per day\n- Average taxi-in / taxi-out time\n- Carbon intensity per turnaround\n- Aksama sonrası yeniden plan süresi (dk)\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "8574fb96",
   "metadata": {
    "id": "8574fb96"
   },
   "source": "## AŞAMA 19 – Öğrenci Egzersizleri"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa43b309",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.747300Z",
     "iopub.status.busy": "2026-10-02T18:48:41.746834Z",
     "iopub.status.idle": "2026-10-02T18:48:41.754580Z",
     "shell.execute_reply": "2026-10-02T18:48:41.753808Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fa43b309",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967340775,
     "user_tz": -180,
     "elapsed": 1793,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "806b7fc7-07a3-4c6d-cf45-82f4eeb270d4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "1. SUB_FLIGHTS = 5 ve MAX_QUBITS = 24 yap. Simülasyon süresi ve uygun örnek olasılığı nasıl değişiyor?\n",
      "   (24 kübit Colab'da birkaç dakika sürebilir; exact tam sayım için High-RAM gerekebilir.)\n",
      "\n",
      "2. QAOA_REPS = 1 → 2 → 3 dene. Onarım öncesi ham çıktı hiç uygun oluyor mu? Süre ne kadar arttı?\n",
      "\n",
      "3. create_qubo(..., penalty=180) ile v1 davranışını yeniden üret: exact çözüm neden E=0 (boş atama) veriyor?\n",
      "   penalty = 1×, 2×, 5× maks(maliyet) için kısıt ihlali oranını ve maliyeti karşılaştır.\n",
      "\n",
      "4. Transfer-heavy uçuşlara daha yüksek ağırlık ver (1.35 → 2.0). Merkeze yakın gate'ler ne kadar daha fazla kullanılıyor?\n",
      "\n",
      "5. CVaR-QAOA dene: QAOA(..., aggregation=0.2). Uygun örnek olasılığı artıyor mu?\n",
      "\n",
      "6. Warm-start: initial_point'i önceki koşunun optimal parametreleriyle ver; iterasyon sayısı düşüyor mu?\n",
      "\n",
      "7. Aynı QUBO'yu D-Wave Ocean SDK / Leap hybrid solver'a gönder (dimod.BinaryQuadraticModel.from_numpy_matrix(Q_sub)).\n",
      "   8×10 = 80 değişkenlik alt-problemi Leap'te çöz ve ILP ile karşılaştır.\n",
      "\n",
      "8. Gerçek bir peak saat dilimi için (ör. 08:00-10:00) daha fazla uçuş ekleyip zaman + pier ayrıştırma stratejisi tasarla.\n",
      "\n"
     ]
    }
   ],
   "source": "print('''\n1. SUB_FLIGHTS = 5 ve MAX_QUBITS = 24 yap. Simülasyon süresi ve uygun örnek olasılığı nasıl değişiyor?\n   (24 kübit Colab'da birkaç dakika sürebilir; exact tam sayım için High-RAM gerekebilir.)\n\n2. QAOA_REPS = 1 → 2 → 3 dene. Onarım öncesi ham çıktı hiç uygun oluyor mu? Süre ne kadar arttı?\n\n3. create_qubo(..., penalty=180) ile v1 davranışını yeniden üret: exact çözüm neden E=0 (boş atama) veriyor?\n   penalty = 1×, 2×, 5× maks(maliyet) için kısıt ihlali oranını ve maliyeti karşılaştır.\n\n4. Transfer-heavy uçuşlara daha yüksek ağırlık ver (1.35 → 2.0). Merkeze yakın gate'ler ne kadar daha fazla kullanılıyor?\n\n5. CVaR-QAOA dene: QAOA(..., aggregation=0.2). Uygun örnek olasılığı artıyor mu?\n\n6. Warm-start: initial_point'i önceki koşunun optimal parametreleriyle ver; iterasyon sayısı düşüyor mu?\n\n7. Aynı QUBO'yu D-Wave Ocean SDK / Leap hybrid solver'a gönder (dimod.BinaryQuadraticModel.from_numpy_matrix(Q_sub)).\n   8×10 = 80 değişkenlik alt-problemi Leap'te çöz ve ILP ile karşılaştır.\n\n8. Gerçek bir peak saat dilimi için (ör. 08:00-10:00) daha fazla uçuş ekleyip zaman + pier ayrıştırma stratejisi tasarla.\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "2d2163e9",
   "metadata": {
    "id": "2d2163e9"
   },
   "source": "## AŞAMA 20 – Kaynaklar"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc27d1c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.757781Z",
     "iopub.status.busy": "2026-10-02T18:48:41.757296Z",
     "iopub.status.idle": "2026-10-02T18:48:41.765797Z",
     "shell.execute_reply": "2026-10-02T18:48:41.764017Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bc27d1c6",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967340777,
     "user_tz": -180,
     "elapsed": 1500,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "778f4860-7ee9-4807-98ae-5a35b8fb6ac6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Akademik / Endüstri referansları:\n",
      "• Optimal Flight-Gate Assignment on a Digital Quantum Computer (Phys. Rev. Applied 20, 064025 – 2023)\n",
      "• IonQ + DESY Gate Assignment experiments\n",
      "• Lufthansa Industry Solutions + Universität Hamburg quantum gate project\n",
      "• iGA resmi açıklamaları (4. pist açılışı – Eylül 2026)\n",
      "• EUROCONTROL / DHMİ trafik verileri\n",
      "\n",
      "Kütüphaneler:\n",
      "• Qiskit 2.x + Qiskit Optimization 0.7 + qiskit-algorithms 0.4\n",
      "• PennyLane · D-Wave Ocean / Leap · Amazon Braket\n",
      "\n",
      "IGA Quantum Journey (sunum):\n",
      "Awareness → Solution training → Case selection → Applied delivery → PoC\n",
      "\n"
     ]
    }
   ],
   "source": "print('''\nAkademik / Endüstri referansları:\n• Optimal Flight-Gate Assignment on a Digital Quantum Computer (Phys. Rev. Applied 20, 064025 – 2023)\n• IonQ + DESY Gate Assignment experiments\n• Lufthansa Industry Solutions + Universität Hamburg quantum gate project\n• iGA resmi açıklamaları (4. pist açılışı – Eylül 2026)\n• EUROCONTROL / DHMİ trafik verileri\n\nKütüphaneler:\n• Qiskit 2.x + Qiskit Optimization 0.7 + qiskit-algorithms 0.4\n• PennyLane · D-Wave Ocean / Leap · Amazon Braket\n\nIGA Quantum Journey (sunum):\nAwareness → Solution training → Case selection → Applied delivery → PoC\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "350cb1ee",
   "metadata": {
    "id": "350cb1ee"
   },
   "source": "## AŞAMA 21 – Özet"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d290a12e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.771327Z",
     "iopub.status.busy": "2026-10-02T18:48:41.770702Z",
     "iopub.status.idle": "2026-10-02T18:48:41.784854Z",
     "shell.execute_reply": "2026-10-02T18:48:41.782701Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d290a12e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967340779,
     "user_tz": -180,
     "elapsed": 803,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "fefd6081-e020-43a4-de8c-6eeae2a73641"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "╔══════════════════════════════════════════════════════════════════════════════╗\n",
      "║  ÖZET (v4)                                                                   ║\n",
      "╠══════════════════════════════════════════════════════════════════════════════╣\n",
      "║  ✓ Gerçek IGA 2025-2026 verileri (6 pist, ~1.500 günlük hareket,            ║\n",
      "║    ~110-140 gate) modele yansıtıldı                                          ║\n",
      "║  ✓ 36 uçuş × 48 gate ölçekli gerçekçi veri seti üretildi                     ║\n",
      "║  ✓ Klasik ILP baseline (PuLP/CBC):    6,791 m, kesin optimum                 ║\n",
      "║  ✓ MARS kapı modeli + QUBO (ölçekli ceza) + 4 uçuş / 17 kübit QAOA pipeline     ║\n",
      "║  ✓ Hibrit sonuç:      313 m  (ILP: 305 m, sapma %2.6)            ║\n",
      "║  ✓ Ölçeklenebilirlik, NISQ, hibrit mimari ve iş değeri tartışıldı            ║\n",
      "║  ✓ 21 aşamalı QML şablonu + Aşama 22 Narrator Engine (Word raporu)          ║\n",
      "║  ✓ v1 hataları (ceza, 80 kübit,                                              ║\n",
      "║    Qiskit 2.x, kapı sayısı, alt-problem seçimi, tohum, MARS) düzeltildi      ║\n",
      "║                                                                              ║\n",
      "║  Bu notebook öğrenciler için doğrudan Colab'da çalıştırılabilir              ║\n",
      "║  ve kendi case'lerine (crew, inventory, routing...) uyarlanabilir.           ║\n",
      "║                                                                              ║\n",
      "║  Teşekkürler – Joseph Volkan Özcan / Quantum Task Force referanslı materyal  ║\n",
      "║  v4 düzenleme: BVK Technology, Eylül 2026                                    ║\n",
      "╚══════════════════════════════════════════════════════════════════════════════╝\n",
      "\n"
     ]
    }
   ],
   "source": "print(f'''\n╔══════════════════════════════════════════════════════════════════════════════╗\n║  ÖZET (v4)                                                                   ║\n╠══════════════════════════════════════════════════════════════════════════════╣\n║  ✓ Gerçek IGA 2025-2026 verileri (6 pist, ~1.500 günlük hareket,            ║\n║    ~110-140 gate) modele yansıtıldı                                          ║\n║  ✓ {N_FLIGHTS} uçuş × {N_GATES} gate ölçekli gerçekçi veri seti üretildi                     ║\n║  ✓ Klasik ILP baseline (PuLP/CBC): {classical_cost:>8,.0f} m, kesin optimum                 ║\n║  ✓ MARS kapı modeli + QUBO (ölçekli ceza) + {SUB_FLIGHTS} uçuş / {n_qubits} kübit QAOA pipeline     ║\n║  ✓ Hibrit sonuç: {hybrid_cost:>8,.0f} m  (ILP: {sub_classical_cost:,.0f} m, sapma %{(hybrid_cost/sub_classical_cost-1)*100:.1f})            ║\n║  ✓ Ölçeklenebilirlik, NISQ, hibrit mimari ve iş değeri tartışıldı            ║\n║  ✓ 21 aşamalı QML şablonu + Aşama 22 Narrator Engine (Word raporu)          ║\n║  ✓ v1 hataları (ceza, 80 kübit,                                              ║\n║    Qiskit 2.x, kapı sayısı, alt-problem seçimi, tohum, MARS) düzeltildi      ║\n║                                                                              ║\n║  Bu notebook öğrenciler için doğrudan Colab'da çalıştırılabilir              ║\n║  ve kendi case'lerine (crew, inventory, routing...) uyarlanabilir.           ║\n║                                                                              ║\n║  Teşekkürler – Joseph Volkan Özcan / Quantum Task Force referanslı materyal  ║\n║  v4 düzenleme: BVK Technology, Eylül 2026                                    ║\n╚══════════════════════════════════════════════════════════════════════════════╝\n''')"
  },
  {
   "cell_type": "markdown",
   "id": "15713be1",
   "metadata": {
    "id": "15713be1"
   },
   "source": "## AŞAMA 22 – Narrator Engine: Sonuçları Pazarlama Diliyle Anlatan Word Raporu\n\nSimülatörün bütün sonuçlarını (veri seti, klasik referans, QUBO, QAOA, hibrit onarım, MARS modeli, ölçeklenebilirlik) tek bir yerde toplar, **neyi başardığımızı** pazarlama odaklı Türkçe bir anlatıya dönüştürür ve grafiklerle birlikte **.docx** üretir.\n\n- `NARRATOR_TONE`: `'executive'` (yönetici özeti ağırlıklı), `'marketing'` (fayda/değer vurgulu) veya `'technical'` (rakam ve yöntem ağırlıklı).\n- Anlatı tamamen sonuç değişkenlerinden türetilir; rakamlar elle yazılmaz, bu yüzden parametreleri değiştirip yeniden çalıştırdığınızda rapor da güncellenir.\n- İsteğe bağlı: `NARRATOR_LLM = True` ve bir API anahtarı verilirse yönetici özeti bir LLM ile cilalanır (varsayılan kapalı; rapor LLM olmadan da tam üretilir)."
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a41ceb73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:41.789186Z",
     "iopub.status.busy": "2026-10-02T18:48:41.788731Z",
     "iopub.status.idle": "2026-10-02T18:48:44.735886Z",
     "shell.execute_reply": "2026-10-02T18:48:44.734565Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a41ceb73",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967341226,
     "user_tz": -180,
     "elapsed": 695,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cecebd4f-b7b6-42b3-ff28-f642f96f72a3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/253.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m245.8/253.0 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.0/253.0 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h{\n",
      " \"meta\": {\n",
      "  \"tarih\": \"02.10.2026\",\n",
      "  \"surum\": \"v4 – MARS modeli + Narrator Engine\",\n",
      "  \"qiskit\": \"2.5.2\"\n",
      " },\n",
      " \"veri\": {\n",
      "  \"ucus\": 36,\n",
      "  \"kapi\": 48,\n",
      "  \"mars_kapi\": 13,\n",
      "  \"pozisyon\": 61,\n",
      "  \"genis_ucus\": 14,\n",
      "  \"transfer_ucus\": 15,\n",
      "  \"yolcu\": 8323,\n",
      "  \"dilim\": 12,\n",
      "  \"tk\": 22,\n",
      "  \"maliyet_ort\": 546.6028573869194,\n",
      "  \"maliyet_max\": 1134.6141387731288\n",
      " },\n",
      " \"klasik\": {\n",
      "  \"degisken\": 1524,\n",
      "  \"kisit\": 10102,\n",
      "  \"maliyet\": 6790.618440970326,\n",
      "  \"kapi_kullanilan\": 15,\n",
      "  \"greedy\": 25533.619412898763,\n",
      "  \"kazanc_pct\": 73.40518658494642,\n",
      "  \"mars_cift\": 8\n",
      " },\n",
      " \"qubo\": {\n",
      "  \"degisken\": 1524,\n",
      "  \"sifir_disi\": 104432,\n",
      "  \"ceza\": 2269.2282775462577\n",
      " },\n",
      " \"alt\": {\n",
      "  \"ucus\": 4,\n",
      "  \"kapi\": 3,\n",
      "  \"kapilar\": [\n",
      "   \"D04\",\n",
      "   \"D05\",\n",
      "   \"D06\"\n",
      "  ],\n",
      "  \"kubit\": 17,\n",
      "  \"ceza\": 215.05253615002488,\n",
      "  \"terim\": 58,\n",
      "  \"ilp\": 305.0504705563153,\n",
      "  \"exact\": 305.0504705563153,\n",
      "  \"exact_E\": -555.15966796875,\n",
      "  \"qaoa_p\": 2,\n",
      "  \"qaoa_iter\": 41,\n",
      "  \"qaoa_sure\": 104.27058815956116,\n",
      "  \"qaoa_fval\": 1459.0667809202903,\n",
      "  \"ham_uygun\": false,\n",
      "  \"uygun_olasilik\": 0.0,\n",
      "  \"hibrit\": 313.0493020947438,\n",
      "  \"hibrit_kaynak\": \"kuantum-yönlendirmeli onarım\",\n",
      "  \"sapma_pct\": 2.6221338140672845\n",
      " },\n",
      " \"olcek\": {\n",
      "  \"tam_gun_degisken\": 204000,\n",
      "  \"heron_kubit\": 156,\n",
      "  \"dwave_kubit\": 5000\n",
      " }\n",
      "}\n"
     ]
    }
   ],
   "source": "!pip install -q python-docx\nimport os, io, json, datetime\nfrom docx import Document\nfrom docx.shared import Pt, Cm, RGBColor\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\nfrom docx.enum.table import WD_TABLE_ALIGNMENT\nfrom docx.oxml.ns import qn\nfrom docx.oxml import OxmlElement\n\nNARRATOR_TONE = 'marketing'      # 'executive' | 'marketing' | 'technical'\nNARRATOR_LLM  = False            # True → yönetici özeti LLM ile cilalanır (API anahtarı gerekir)\nREPORT_FILE   = 'BVK_IGA_Kuantum_Simulasyon_Raporu.docx'\nFIG_DIR       = 'narrator_figs'\nos.makedirs(FIG_DIR, exist_ok=True)\n\n# ---------- 22.1 Sonuç toplayıcı: simülatörün tüm çıktıları tek sözlükte ----------\ndef tr(n, d=0):\n    '''Türkçe sayı biçimi: 6.790,6'''\n    s = f'{n:,.{d}f}'\n    return s.replace(',', 'X').replace('.', ',').replace('X', '.')\n\ndef collect_results():\n    hyb_lbl = labels_of(hybrid_assign, V_sub)\n    return dict(\n        meta=dict(tarih=datetime.date.today().strftime('%d.%m.%Y'), surum='v4 – MARS modeli + Narrator Engine', qiskit=qiskit.__version__),\n        veri=dict(ucus=N_FLIGHTS, kapi=N_GATES, mars_kapi=len(wide_gates), pozisyon=N_GATES + len(wide_gates),\n                  genis_ucus=int((flight_data.aircraft_type == 'Wide').sum()), transfer_ucus=int(flight_data.is_transfer_heavy.sum()),\n                  yolcu=int(flight_data.passengers.sum()), dilim=TIME_SLOTS, tk=int(flight_data.flight_id.str.startswith('TK').sum()),\n                  maliyet_ort=float(cost_matrix.mean()), maliyet_max=float(cost_matrix.max())),\n        klasik=dict(degisken=len(V_full), kisit=len(pairs_full), maliyet=float(classical_cost), kapi_kullanilan=len(set(gate_of_label(l) for l in classical_assignment.values())),\n                    greedy=float(greedy_cost), kazanc_pct=(greedy_cost - classical_cost) / greedy_cost * 100,\n                    mars_cift=sum(1 for g in set(gate_of_label(l) for l in classical_assignment.values()) if gate_capacity[g] == 'Wide')),\n        qubo=dict(degisken=len(V_full), sifir_disi=int((np.abs(Q_full) > 1e-9).sum()), ceza=float(P_full)),\n        alt=dict(ucus=SUB_FLIGHTS, kapi=len(sub_gates), kapilar=sub_gates, kubit=n_qubits, ceza=float(P_sub), terim=len(quadratic),\n                 ilp=float(sub_classical_cost), exact=float(exact_cost), exact_E=float(exact_fval),\n                 qaoa_p=QAOA_REPS, qaoa_iter=len(history), qaoa_sure=float(qaoa_time), qaoa_fval=float(qaoa_result.fval),\n                 ham_uygun=bool(raw_feasible), uygun_olasilik=float(p_feasible), hibrit=float(hybrid_cost), hibrit_kaynak=hybrid_source,\n                 sapma_pct=(hybrid_cost / sub_classical_cost - 1) * 100, hibrit_atama=hyb_lbl, ilp_atama=sub_classical_assign),\n        olcek=dict(tam_gun_degisken=204000, heron_kubit=156, dwave_kubit=5000),\n    )\n\nRESULTS = collect_results()\nprint(json.dumps({k: (v if k != 'alt' else {kk: vv for kk, vv in v.items() if kk not in ('hibrit_atama', 'ilp_atama')}) for k, v in RESULTS.items()}, indent=1, ensure_ascii=False)[:1500])"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6d33a04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:44.739746Z",
     "iopub.status.busy": "2026-10-02T18:48:44.738607Z",
     "iopub.status.idle": "2026-10-02T18:48:46.689073Z",
     "shell.execute_reply": "2026-10-02T18:48:46.686960Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e6d33a04",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967343377,
     "user_tz": -180,
     "elapsed": 1892,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8b798893-c7cc-47cb-f93a-26447646f4cb"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Grafikler yazıldı: ['narrator_figs/gantt.png', 'narrator_figs/kazanc.png', 'narrator_figs/cozucu.png', 'narrator_figs/yakinsama.png', 'narrator_figs/olcek.png']\n"
     ]
    }
   ],
   "source": "# ---------- 22.2 Grafikleri rapor için dosyaya yaz (önceki aşamaların figürleri yeniden üretilir) ----------\ndef save_report_figures():\n    figs = {}\n    # Gantt (MARS)\n    used_gates = sorted(set(gate_of_label(l) for l in classical_assignment.values()))\n    lanes = [(g, p) for g in used_gates for p in gate_positions_of(g, gate_capacity)]\n    lane_y = {p: k for k, (g, p) in enumerate(lanes)}\n    fig, ax = plt.subplots(figsize=(11, 0.3 * len(lanes) + 1.4))\n    for g in used_gates:\n        ps = gate_positions_of(g, gate_capacity)\n        if len(ps) == 2: ax.axhspan(lane_y[ps[0]] - 0.5, lane_y[ps[1]] + 0.5, color='#FFF4E0', zorder=0)\n    for i in range(N_FLIGHTS):\n        fid = flight_data.loc[i, 'flight_id']; lbl = classical_assignment[fid]\n        a, dur = flight_data.loc[i, 'arrival_slot'], flight_data.loc[i, 'turnaround_slots']\n        if '(W)' in lbl:\n            g = lbl.replace(' (W)', ''); y0, y1 = lane_y[g + 'L'], lane_y[g + 'R']\n            ax.barh((y0 + y1) / 2, dur - 0.08, left=a, height=1.7, color=AMBER, edgecolor='white')\n            ax.text(a + 0.08, (y0 + y1) / 2, fid, va='center', fontsize=6, color='white', fontweight='bold')\n        else:\n            ax.barh(lane_y[lbl], dur - 0.08, left=a, height=0.7, color=BLUE, edgecolor='white')\n            ax.text(a + 0.08, lane_y[lbl], fid, va='center', fontsize=6, color='white', fontweight='bold')\n    ax.set_yticks(range(len(lanes))); ax.set_yticklabels([p for g, p in lanes], fontsize=7)\n    ax.set_xticks(range(TIME_SLOTS + 1)); ax.set_xticklabels([f'{8 + s // 6:02d}:{(s % 6) * 10:02d}' for s in range(TIME_SLOTS + 1)], fontsize=8)\n    ax.set_title('Optimize kapı planı — MARS kapıları (sarı) ya 1 geniş ya 2 dar gövde alır'); ax.invert_yaxis()\n    ax.grid(axis='x', color='#E3E8F0'); ax.set_axisbelow(True); plt.tight_layout()\n    figs['gantt'] = os.path.join(FIG_DIR, 'gantt.png'); plt.savefig(figs['gantt'], dpi=160); plt.close()\n    # Klasik kazanç\n    fig, ax = plt.subplots(figsize=(6.5, 3.4))\n    vals = [greedy_cost, classical_cost]\n    bars = ax.bar(['Sıralı ilk-uygun\\n(planlayıcı benzeri)', 'Optimize atama\\n(ILP)'], vals, color=[GRAY, NAVY], width=0.5)\n    for b, v in zip(bars, vals): ax.text(b.get_x() + b.get_width() / 2, v * 1.02, f'{tr(v)} m', ha='center', fontsize=10, fontweight='bold')\n    ax.set_ylim(0, max(vals) * 1.18); ax.set_ylabel('Toplam yürüme maliyeti (m)'); ax.set_title(f'Optimizasyon kazancı: %{tr(RESULTS[\"klasik\"][\"kazanc_pct\"])}')\n    ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True); plt.tight_layout()\n    figs['kazanc'] = os.path.join(FIG_DIR, 'kazanc.png'); plt.savefig(figs['kazanc'], dpi=160); plt.close()\n    # Çözücü karşılaştırması\n    fig, ax = plt.subplots(figsize=(6.5, 3.4))\n    vals = [sub_classical_cost, exact_cost, hybrid_cost]\n    bars = ax.bar(['Klasik ILP', 'Exact QUBO', f'Hibrit QAOA p={QAOA_REPS}\\n+ klasik son-işleme'], vals, color=[NAVY, TEAL, BLUE], width=0.55)\n    for b, v in zip(bars, vals): ax.text(b.get_x() + b.get_width() / 2, v * 1.02, f'{tr(v, 1)} m', ha='center', fontsize=10, fontweight='bold')\n    ax.set_ylim(0, max(vals) * 1.2); ax.set_ylabel('Toplam yürüme maliyeti (m)'); ax.set_title(f'Alt-problem ({n_qubits} kübit): çözücü karşılaştırması')\n    ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True); plt.tight_layout()\n    figs['cozucu'] = os.path.join(FIG_DIR, 'cozucu.png'); plt.savefig(figs['cozucu'], dpi=160); plt.close()\n    # Yakınsama\n    fig, ax = plt.subplots(figsize=(6.5, 3.0))\n    h = np.minimum.accumulate(history); ax.plot(range(1, len(h) + 1), h, color=BLUE, lw=2)\n    ax.set_xlabel('COBYLA iterasyonu'); ax.set_ylabel('Beklenen enerji (en iyi)'); ax.set_title(f'QAOA yakınsama (p={QAOA_REPS}, {n_qubits} kübit)')\n    ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True); plt.tight_layout()\n    figs['yakinsama'] = os.path.join(FIG_DIR, 'yakinsama.png'); plt.savefig(figs['yakinsama'], dpi=160); plt.close()\n    # Ölçek\n    sizes = [(f'Colab QAOA\\n{SUB_FLIGHTS}×{len(sub_gates)}', n_qubits), ('Gerçek QPU\\n8×10', 80), ('Pik bank\\n36×48', len(V_full)), ('Pik saat\\n80×120', 9600), ('Tam gün\\n1.700×120', 204000)]\n    fig, ax = plt.subplots(figsize=(8, 3.4))\n    bars = ax.bar([s[0] for s in sizes], [s[1] for s in sizes], color=[BLUE, BLUE, NAVY, NAVY, AMBER], width=0.55)\n    ax.set_yscale('log'); ax.set_ylim(5, 2e6); ax.set_ylabel('Değişken / kübit (log)')\n    for b, (l, v) in zip(bars, sizes): ax.text(b.get_x() + b.get_width() / 2, v * 1.25, tr(v), ha='center', fontsize=9, fontweight='bold')\n    for y, c, t in [(30, TEAL, 'Statevector sim. sınırı'), (156, AMBER, 'IBM Heron (156 kübit)'), (5000, GRAY, 'D-Wave Advantage · Leap hibrit 1M+')]:\n        ax.axhline(y, color=c, ls='--', lw=1.2); ax.text(-0.45, y * 1.15, t, ha='left', fontsize=8, color=c)\n    ax.set_title('Problem boyutu ve donanım sınırları'); ax.grid(axis='y', color='#E3E8F0'); ax.set_axisbelow(True); plt.tight_layout()\n    figs['olcek'] = os.path.join(FIG_DIR, 'olcek.png'); plt.savefig(figs['olcek'], dpi=160); plt.close()\n    return figs\n\nFIGS = save_report_figures()\nprint('Grafikler yazıldı:', list(FIGS.values()))"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f78e0f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:46.693571Z",
     "iopub.status.busy": "2026-10-02T18:48:46.693220Z",
     "iopub.status.idle": "2026-10-02T18:48:46.710987Z",
     "shell.execute_reply": "2026-10-02T18:48:46.709931Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0f78e0f5",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967343383,
     "user_tz": -180,
     "elapsed": 809,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "efe7cfc4-2cc1-4720-d189-6f988da94c03"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "İGA İstanbul Havalimanı için Hibrit Kuantum Kapı Atama Simülasyonu\n",
      "------------------------------------------------------------\n",
      "• BVK Technology, İGA İstanbul Havalimanı'nın gerçek operasyon ölçeğine göre kalibre edilmiş bir kapı atama simülatörü geliştirdi ve bu simülatör üzerinde klasik optimizasyon ile hibrit kuantum çözüm hattını yan yana çalıştırdı.\n",
      "• 2 saatlik pik bank senaryosunda (36 uçuş, 48 kapı, 61 park pozisyonu, 8.323 yolcu) optimize edilmiş kapı planı, planlayıcı benzeri sıralı atamaya göre toplam yolcu yürüme mesafesini %73 azalttı: 25.534 m → 6.791 m.\n",
      "• Havalimanı geri bildirimiyle modele eklenen MARS kapı kuralı (geniş gövde kapılar ya 1 geniş ya 2 dar gövde alır) hem klasik hem kuantum modelde birebir uygulandı; optimum planda 8 MARS kapısı aktif olarak kullanıldı.\n",
      "• 17 kübitlik alt-problemde hibrit kuantum hattı klasik optimuma %2,6 yakınlıkta, kurallara tam uyan bir plan üretti; sonuç bağımsız olarak kesin QUBO sayımı ve klasik ILP ile doğrulandı.\n",
      "• Bu çalışma, kuantumun bugün 'laboratuvar merakı' değil, doğru mimariyle havalimanı karar destek süreçlerine entegre edilebilir bir yetkinlik olduğunu gösteriyor.\n"
     ]
    }
   ],
   "source": "# ---------- 22.3 Narrator: sonuçlardan Türkçe pazarlama anlatısı ----------\ndef narrate(R, tone='marketing'):\n    v, k, q, a, o = R['veri'], R['klasik'], R['qubo'], R['alt'], R['olcek']\n    sapma = a['sapma_pct']\n    sapma_cumle = ('hibrit kuantum hattı klasik optimumu birebir yakaladı' if sapma < 0.05\n                   else f'hibrit kuantum hattı klasik optimuma %{tr(sapma, 1)} yakınlıkta, kurallara tam uyan bir plan üretti')\n    S = {}\n    S['baslik'] = 'İGA İstanbul Havalimanı için Hibrit Kuantum Kapı Atama Simülasyonu'\n    S['altbaslik'] = 'Sonuç Raporu ve Değer Önerisi — BVK Technology Quantum Business Solutions'\n\n    S['ozet'] = [\n        f\"BVK Technology, İGA İstanbul Havalimanı'nın gerçek operasyon ölçeğine göre kalibre edilmiş bir kapı atama simülatörü geliştirdi ve \"\n        f\"bu simülatör üzerinde klasik optimizasyon ile hibrit kuantum çözüm hattını yan yana çalıştırdı.\",\n        f\"2 saatlik pik bank senaryosunda ({v['ucus']} uçuş, {v['kapi']} kapı, {v['pozisyon']} park pozisyonu, {tr(v['yolcu'])} yolcu) optimize edilmiş kapı planı, \"\n        f\"planlayıcı benzeri sıralı atamaya göre toplam yolcu yürüme mesafesini %{tr(k['kazanc_pct'])} azalttı: {tr(k['greedy'])} m → {tr(k['maliyet'])} m.\",\n        f\"Havalimanı geri bildirimiyle modele eklenen MARS kapı kuralı (geniş gövde kapılar ya 1 geniş ya 2 dar gövde alır) hem klasik hem kuantum modelde \"\n        f\"birebir uygulandı; optimum planda {k['mars_cift']} MARS kapısı aktif olarak kullanıldı.\",\n        f\"{a['kubit']} kübitlik alt-problemde {sapma_cumle}; sonuç bağımsız olarak kesin QUBO sayımı ve klasik ILP ile doğrulandı.\",\n        \"Bu çalışma, kuantumun bugün 'laboratuvar merakı' değil, doğru mimariyle havalimanı karar destek süreçlerine entegre edilebilir bir yetkinlik olduğunu gösteriyor.\"\n    ]\n\n    S['basari'] = [\n        ('Gerçek ölçekte, gerçek kurallarla', f\"Model {v['ucus']} uçuş × {v['kapi']} kapı ({v['mars_kapi']} MARS kapısı, {v['pozisyon']} pozisyon), {v['genis_ucus']} geniş gövde ve {v['transfer_ucus']} transfer ağırlıklı uçuşla İGA'nın pik bank yapısını temsil ediyor; 22 TK + 14 diğer taşıyıcıyla hub karakteri korunuyor.\"),\n        ('Ölçülebilir fayda', f\"Optimize atama, sıralı ilk-uygun atamaya göre %{tr(k['kazanc_pct'])} daha az yürüme mesafesi üretti ({tr(k['greedy'] - k['maliyet'])} m tasarruf tek bir 2 saatlik bankta). Transfer oranı %45–50 olan bir hub'da bu, bağlantı kaçırma riskini ve yolcu deneyimini doğrudan etkileyen bir büyüklüktür.\"),\n        ('MARS kapı kuralı modele işlendi', f\"Geniş gövde kapılar iki park pozisyonu (L/R) olarak modellendi; geniş gövde uçuş her iki pozisyonu kaplarken iki dar gövde aynı anda park edebiliyor. Kural, {tr(k['degisken'])} karar değişkeni ve {tr(k['kisit'])} çakışma kısıtıyla hem ILP'de hem QUBO'da aynı şekilde çalışıyor.\"),\n        ('Kuantum formülasyonu hazır', f\"Havalimanı kuralları tek bir QUBO enerji fonksiyonuna dönüştürüldü ({tr(q['degisken'])} değişken, {tr(q['sifir_disi'])} sıfır-dışı eleman). Aynı model bulut üzerinden kapı tabanlı QPU'lara (IBM) ve tavlayıcılara (D-Wave Leap) gönderilebilir.\"),\n        ('Hibrit hat çalışıyor', f\"{a['kubit']} kübitlik alt-problemde QAOA (p={a['qaoa_p']}, {a['qaoa_iter']} iterasyon, {tr(a['qaoa_sure'])} s) + klasik onarım, {tr(a['hibrit'], 1)} m ile kurallara tam uyan bir plan üretti; klasik optimum {tr(a['ilp'], 1)} m (sapma %{tr(sapma, 1)}). Ham kuantum çıktısının tek başına uygun olmaması, 'kuantum çekirdek + klasik son-işleme' mimarisinin neden şart olduğunu kanıtlıyor.\"),\n        ('Doğrulanmış ve tekrarlanabilir', f\"Tüm sonuçlar sabit tohumla üretildi; alt-problem kesin sayımla ({tr(2 ** a['kubit'])} durum) doğrulandı. Not defteri Colab'da sıfırdan çalışır.\"),\n    ]\n\n    S['neden_onemli'] = [\n        f\"Tam gün ölçeğinde kapı atama ~{tr(o['tam_gun_degisken'])} karar değişkenine ulaşır; kesin klasik çözüm pratik değildir ve aksama anında dakikalar içinde yeniden planlama gerekir.\",\n        \"BVK'nın önerdiği mimari — zaman ve pier bazlı ayrıştırma, kuantum/kuantum-esinli çekirdek, klasik onarım ve yerel arama, olay tabanlı yeniden optimizasyon — tam da bu boşluğu hedefler.\",\n        f\"Bugünkü donanım sınırları (statevector simülasyon ~30 kübit, IBM Heron {o['heron_kubit']} kübit, D-Wave {tr(o['dwave_kubit'])}+ kübit / Leap hibrit 1M+ değişken) bu simülatörle ölçülmüş biçimde raporlanır; abartı değil, yol haritası sunulur.\",\n        \"Kuantum bir karar destek motorudur: öneriler klasik referansla kıyaslanır, uygunluk kontrolünden geçer ve son karar APOC planlayıcısında kalır.\"\n    ]\n\n    S['kpi'] = [\n        ('Yolcu yürüme mesafesi', f\"%{tr(k['kazanc_pct'])} azalma (bank bazında)\", 'Ortalama bağlantı yürüme mesafesi (m)'),\n        ('Kapı çakışması', '0 (sert kısıt)', 'Günlük kapı çakışması sayısı'),\n        ('MARS kullanımı', f\"{k['mars_cift']} MARS kapısı optimum planda\", 'Pozisyon doluluk oranı (%)'),\n        ('Yeniden plan süresi', f\"Bank ölçeğinde ILP: saniyeler; hibrit kuantum: dakikalar hedefi\", 'Aksama sonrası yeniden plan süresi (dk)'),\n        ('Doğruluk', f\"Hibrit sapma %{tr(sapma, 1)} (alt-problem)\", 'Klasik optimuma yakınlık (%)'),\n    ]\n\n    S['sonraki'] = [\n        'Not defterini üretim şablonuna dönüştürme: maliyet normalizasyonu, otomatik ceza ayarı, 2-opt/swap yerel arama ve CVaR-QAOA ile sapmanın kapatılması.',\n        'Aynı QUBO modelinin IBM Heron (80 kübitlik 8×10 alt-problem, hata azaltma ile) ve D-Wave Leap hibrit çözücüde çalıştırılması.',\n        \"İGA'nın geçmiş uçuş planı, kapı haritası ve transfer matrisiyle kalibrasyon; tam gün ayrıştırma ve olay tabanlı yeniden planlama.\",\n        'AODB/RMS API konnektörleri ve APOC planlayıcı arayüzüyle gölge mod pilot; 8–12 haftalık PoC planı.'\n    ]\n\n    S['kapanis'] = (\"BVK Technology, kuantum ve kuantum-esinli çözümleri sektöre özel tasarlar, geliştirir ve mevcut sistemlere entegre eder. \"\n                    \"İGA kapı atama simülatörü, bu yetkinliğin havalimanı operasyonlarında somut, ölçülmüş ve tekrarlanabilir bir örneğidir.\")\n\n    if tone == 'executive':\n        S['basari'] = S['basari'][:4]\n    elif tone == 'technical':\n        S['ozet'].append(f\"Teknik ayrıntı: QUBO cezası P = 2 × maks(maliyet) ({tr(q['ceza'])} tam bank, {tr(a['ceza'], 1)} alt-problem); kesin QUBO minimumu E = {tr(a['exact_E'], 1)}; QAOA fval = {tr(a['qaoa_fval'], 1)}; uygun örnek olasılığı {a['uygun_olasilik']:.4f}; hibrit kaynak: {a['hibrit_kaynak']}.\")\n    return S\n\ndef polish_with_llm(text, api_key=None):\n    '''İsteğe bağlı: yönetici özetini LLM ile cilala. Anahtar yoksa metni olduğu gibi döndürür.'''\n    if not NARRATOR_LLM or not api_key: return text\n    try:\n        import anthropic\n        client = anthropic.Anthropic(api_key=api_key)\n        msg = client.messages.create(model='claude-sonnet-4-5', max_tokens=800,\n            messages=[{'role': 'user', 'content': 'Aşağıdaki yönetici özetini, rakamları değiştirmeden, Türkçe ve pazarlama odaklı bir dille akıcı tek paragraf hâline getir:\\n\\n' + text}])\n        return msg.content[0].text\n    except Exception as e:\n        print('LLM cilası atlandı:', e); return text\n\nNARRATIVE = narrate(RESULTS, NARRATOR_TONE)\nprint(NARRATIVE['baslik']); print('-' * 60)\nfor p in NARRATIVE['ozet']: print('•', p)"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb12b677",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-02T18:48:46.714964Z",
     "iopub.status.busy": "2026-10-02T18:48:46.714703Z",
     "iopub.status.idle": "2026-10-02T18:48:46.862178Z",
     "shell.execute_reply": "2026-10-02T18:48:46.859782Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "cb12b677",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1790967345431,
     "user_tz": -180,
     "elapsed": 2047,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "807baa33-ea48-402d-d005-a12d8aef553c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✓ Rapor üretildi: BVK_IGA_Kuantum_Simulasyon_Raporu.docx (279 KB)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ],
      "application/javascript": [
       "download(\"download_8c78de83-63ea-4f02-b32c-5ea81c15b6ab\", \"BVK_IGA_Kuantum_Simulasyon_Raporu.docx\", 285237)"
      ]
     },
     "metadata": {}
    }
   ],
   "source": "# ---------- 22.4 Word (.docx) üretimi ----------\nNAVY_RGB, MUTED_RGB, BODY_RGB = RGBColor(0x18, 0x2B, 0x5E), RGBColor(0x7A, 0x87, 0x9C), RGBColor(0x3B, 0x4A, 0x63)\n\ndef shade(cell, hex_fill):\n    tcPr = cell._tc.get_or_add_tcPr(); shd = OxmlElement('w:shd')\n    shd.set(qn('w:val'), 'clear'); shd.set(qn('w:color'), 'auto'); shd.set(qn('w:fill'), hex_fill); tcPr.append(shd)\n\ndef build_docx(R, S, figs, path):\n    doc = Document()\n    st = doc.styles['Normal']; st.font.name = 'Calibri'; st.font.size = Pt(11); st.element.rPr.rFonts.set(qn('w:eastAsia'), 'Calibri')\n    for sec in doc.sections:\n        sec.top_margin = sec.bottom_margin = Cm(2.2); sec.left_margin = sec.right_margin = Cm(2.3)\n        hp = sec.header.paragraphs[0]; hp.text = 'BVK Technology — Quantum Business Solutions  |  GİZLİ'; hp.alignment = WD_ALIGN_PARAGRAPH.RIGHT\n        hp.runs[0].font.size = Pt(8); hp.runs[0].font.color.rgb = MUTED_RGB\n\n    def H(text, level=1):\n        p = doc.add_heading(level=level); r = p.add_run(text); r.font.color.rgb = NAVY_RGB; r.font.name = 'Calibri'\n        r.font.size = Pt(18 if level == 1 else 13); return p\n    def P(text, italic=False, size=11, color=None, after=6):\n        p = doc.add_paragraph(); r = p.add_run(text); r.italic = italic; r.font.size = Pt(size)\n        if color: r.font.color.rgb = color\n        p.paragraph_format.space_after = Pt(after); return p\n    def B(text, bold_lead=None):\n        p = doc.add_paragraph(style='List Bullet')\n        if bold_lead:\n            r = p.add_run(bold_lead + ' '); r.bold = True\n        p.add_run(text); p.paragraph_format.space_after = Pt(4); return p\n    def IMG(path, width_cm=16, caption=None):\n        doc.add_picture(path, width=Cm(width_cm)); doc.paragraphs[-1].alignment = WD_ALIGN_PARAGRAPH.CENTER\n        if caption: P(caption, italic=True, size=9, color=MUTED_RGB)\n    def TABLE(rows, widths, header=True):\n        t = doc.add_table(rows=len(rows), cols=len(rows[0])); t.style = 'Table Grid'; t.alignment = WD_TABLE_ALIGNMENT.CENTER\n        for i, row in enumerate(rows):\n            for j, val in enumerate(row):\n                c = t.cell(i, j); c.width = Cm(widths[j]); c.text = ''\n                r = c.paragraphs[0].add_run(str(val)); r.font.size = Pt(9.5); r.bold = (i == 0 and header) or j == 0\n                if i == 0 and header: shade(c, 'EEF4FB')\n        doc.add_paragraph().paragraph_format.space_after = Pt(2)\n\n    v, k, q, a = R['veri'], R['klasik'], R['qubo'], R['alt']\n    # Kapak\n    p = doc.add_paragraph(); p.paragraph_format.space_before = Pt(90)\n    r = p.add_run('KUANTUM ÇÖZÜMLERİ  |  SİMÜLASYON SONUÇ RAPORU'); r.font.size = Pt(10); r.bold = True; r.font.color.rgb = RGBColor(0xE7, 0xA8, 0x34)\n    p = doc.add_paragraph(); r = p.add_run(S['baslik']); r.font.size = Pt(26); r.bold = True; r.font.color.rgb = NAVY_RGB\n    P(S['altbaslik'], size=13, color=BODY_RGB)\n    P(f\"Tarih: {R['meta']['tarih']}   ·   Simülatör sürümü: {R['meta']['surum']}   ·   Qiskit {R['meta']['qiskit']}\", size=10, color=MUTED_RGB)\n    P('Bu rapor, simülatörün bu çalıştırmasında ölçülen sonuçlardan Narrator Engine tarafından otomatik üretilmiştir; tüm rakamlar koddan gelir.', italic=True, size=9, color=MUTED_RGB)\n    doc.add_page_break()\n\n    H('1. Yönetici Özeti')\n    ozet = polish_with_llm('\\n'.join(S['ozet']), os.environ.get('ANTHROPIC_API_KEY'))\n    for para in (ozet.split('\\n') if isinstance(ozet, str) else ozet):\n        if para.strip(): P(para.strip())\n    TABLE([['Gösterge', 'Değer'],\n           ['Senaryo', f\"{v['ucus']} uçuş · {v['kapi']} kapı ({v['mars_kapi']} MARS) · {v['pozisyon']} pozisyon · 2 saatlik pik bank\"],\n           ['Optimizasyon kazancı', f\"%{tr(k['kazanc_pct'])}  ({tr(k['greedy'])} m → {tr(k['maliyet'])} m)\"],\n           ['Klasik referans', f\"{tr(k['degisken'])} değişken, {tr(k['kisit'])} kısıt — kesin optimum\"],\n           ['Kuantum alt-problemi', f\"{a['kubit']} kübit, QAOA p={a['qaoa_p']} + klasik onarım\"],\n           ['Hibrit sonuç', f\"{tr(a['hibrit'], 1)} m  (klasik optimum {tr(a['ilp'], 1)} m, sapma %{tr(a['sapma_pct'], 1)})\"]], [5.5, 10.5])\n\n    H('2. Neyi Başardık?')\n    for lead, text in S['basari']: B(text, bold_lead=lead + ' —')\n    IMG(figs['kazanc'], 12, 'Şekil 1 — Sıralı atamaya karşı optimize atama: toplam yürüme mesafesi.')\n\n    H('3. Kapı Planı ve MARS Kuralı')\n    P(\"Aşağıdaki plan, 2 saatlik pik bank için bulunan optimum kapı atamasıdır. Sarı zeminli satırlar MARS kapılarını gösterir: bir geniş gövde uçak iki pozisyonu (L+R) birden kaplar; aynı kapıda iki dar gövde uçak yan yana park edebilir. Bu kural İGA geri bildirimiyle modele eklenmiş ve hem klasik hem kuantum formülasyonda aynı şekilde uygulanmıştır.\")\n    IMG(figs['gantt'], 16.5, 'Şekil 2 — Optimize kapı planı (ILP). Sarı zemin: MARS kapıları; turuncu: geniş gövde; mavi: dar gövde.')\n\n    H('4. Klasik Referanstan Kuantuma: Hibrit Çözüm Hattı')\n    P(f\"Havalimanı kuralları tek bir QUBO enerji fonksiyonuna dönüştürüldü: E(x) = Σ c·x + P·Σ(Σx − 1)² + P·Σ(çakışan çiftler). Ceza katsayısı maliyet ölçeğine bağlı seçildi (P = 2 × maks(maliyet) = {tr(q['ceza'])}). Tam bank {tr(q['degisken'])} değişken ve {tr(q['sifir_disi'])} sıfır-dışı elemanla bugünkü simülatörler için büyük olduğundan pier kümesi ayrıştırmasıyla {a['kubit']} kübitlik bir alt-problem seçildi ({', '.join(a['kapilar'])}).\")\n    P(f\"QAOA (p={a['qaoa_p']}, {a['qaoa_iter']} COBYLA iterasyonu, {tr(a['qaoa_sure'])} s) örnek dağılımı üretti; en olası bit dizisi sert kısıtları tek başına karşılamadı (uygun örnek olasılığı {a['uygun_olasilik']:.4f}). Kuantum marjinalleriyle yönlendirilen klasik geri-izlemeli onarım, kurallara tam uyan {tr(a['hibrit'], 1)} m'lik planı üretti; klasik ILP ve kesin QUBO sayımı {tr(a['ilp'], 1)} m ile aynı optimumda buluştu.\")\n    IMG(figs['cozucu'], 12, 'Şekil 3 — Alt-problemde çözücü karşılaştırması.')\n    IMG(figs['yakinsama'], 12, 'Şekil 4 — QAOA yakınsama eğrisi.')\n    TABLE([['Uçuş', 'Klasik ILP', 'Hibrit QAOA']] + [[f, a['ilp_atama'].get(f, '-'), a['hibrit_atama'].get(f, '-')] for f in a['ilp_atama']], [4, 6, 6])\n\n    H('5. Bu Sonuçlar İGA için Ne Anlama Geliyor?')\n    for t in S['neden_onemli']: B(t)\n    IMG(figs['olcek'], 14, 'Şekil 5 — Problem boyutu ve donanım sınırları: ayrıştırma ve hibrit mimarinin gerekçesi.')\n\n    H('6. Ölçüm Çerçevesi: PoC KPI’ları')\n    TABLE([['Alan', 'Simülasyonda ölçülen', 'PoC KPI’sı']] + [list(x) for x in S['kpi']], [4.5, 6, 5.5])\n\n    H('7. Sonraki Adımlar')\n    for i, t in enumerate(S['sonraki'], 1): B(t, bold_lead=f'{i}.')\n    P(S['kapanis'], italic=True, color=BODY_RGB)\n    P('BVK Technology · İTÜ ARI Teknokent, Maslak, İstanbul · bvktechnologysales@gmail.com · www.bvktechnology.com', size=9, color=MUTED_RGB)\n    doc.save(path); return path\n\nout = build_docx(RESULTS, NARRATIVE, FIGS, REPORT_FILE)\nprint('✓ Rapor üretildi:', out, f'({os.path.getsize(out)/1024:.0f} KB)')\ntry:\n    from google.colab import files\n    files.download(out)\nexcept Exception:\n    print('(Colab dışı ortam: dosya çalışma dizinine yazıldı)')"
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}