{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [],
   "authorship_tag": "ABX9TyPKFx5thNnTi/HxhW43BYXt"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "id": "o9VH_z9w5yWT",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1791133973070,
     "user_tz": -180,
     "elapsed": 648,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "d78ac49d-9ed1-4805-b276-2d4b00ad5798"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--- 64 ÇEVRİM SONU ELDE EDİLEN KARARLI ROTA MATRİSİ ---\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "import numpy as np\nimport matplotlib.pyplot as plt\n\nclass QAMRE_GoldenModel:\n    def __init__(self, num_nodes=16, iterations=64):\n        self.num_nodes = num_nodes\n        self.iterations = iterations\n\n        # Başlangıç Durum Matrisi (x_ij)\n        self.state = np.zeros((num_nodes, num_nodes), dtype=int)\n\n        # Donanımsal Ağırlık Katsayıları (FPGA bit kaydırma - shift operatörleri için)\n        self.w_cost = 1      # Hat kalitesi çarpanı\n        self.w_penalty = 40  # Topoloji kısıt cezası\n        self.threshold = 85  # Kararlılık eşiği (Theta)\n\n        # Evrensel LFSR Başlangıç Çekirdeği (Seed)\n        self.lfsr = 0x1A4C\n\n    def get_lfsr_noise(self):\n        \"\"\"FPGA içindeki 16-bit LFSR kuantum tünelleme gürültüsünün emülasyonu\"\"\"\n        # Taps: 16, 14, 13, 11 (XOR mantığı)\n        bit = ((self.lfsr >> 0) ^ (self.lfsr >> 2) ^ (self.lfsr >> 3) ^ (self.lfsr >> 5)) & 1\n        self.lfsr = (self.lfsr >> 1) | (bit << 15)\n        # Gürültüyü yerel potansiyele uygun ölçekle (Bit kaydırma)\n        return (self.lfsr & 0xFF) >> 3\n\n    def run_simulation(self, src, dst, link_cost, freq_penalty):\n        energy_history = []\n\n        # Başlangıç durumu: İlk saat çevriminde tüm hücreler kapalı, rastgele arama başlıyor\n        for cycle in range(self.iterations):\n            next_state = np.copy(self.state)\n            total_system_energy = 0\n\n            # Tam Paralel Donanım Emülasyonu (Tüm hücreler aynı anda değerlendirilir)\n            for i in range(self.num_nodes):\n                for j in range(self.num_nodes):\n                    if i == j: continue\n\n                    # 1. Telsiz RF ve Spektrum Katmanı Girdileri\n                    c_ij = link_cost[i, j]\n                    p_freq = freq_penalty[i, j]\n\n                    # 2. Topolojik Ceza (Giriş/Çıkış akış dengesi)\n                    # Düğüme giren ve çıkan aktif hat sayısının 1 olmasını zorlar\n                    out_flow = np.sum(self.state[i, :])\n                    in_flow = np.sum(self.state[:, j])\n                    topology_penalty = abs(out_flow + in_flow - 2)\n\n                    # 3. Yerel Potansiyel Hesaplama (Delta E_ij)\n                    delta_E = (self.w_cost * c_ij) + (self.w_penalty * topology_penalty) + p_freq\n\n                    # 4. Stokastik Gürültü Enjeksiyonu (LFSR)\n                    n_t = self.get_lfsr_noise()\n\n                    # 5. Durum Geçiş Kuralı (LUT Karşılaştırıcı Emülasyonu)\n                    if (delta_E + n_t) < self.threshold:\n                        next_state[i, j] = 1\n                    else:\n                        next_state[i, j] = 0\n\n                    # Sistemin toplam enerjisini kaydet\n                    total_system_energy += delta_E * next_state[i, j]\n\n            # Matrisi güncelle (Clock Cycle bitti)\n            self.state = np.copy(next_state)\n\n            # Kaynak düğüme giriş, hedef düğümden çıkış olamaz kısıtı (Zorunlu maske)\n            self.state[:, src] = 0\n            self.state[dst, :] = 0\n\n            energy_history.append(total_system_energy)\n\n        return self.state, energy_history\n\n# ==========================================\n# TESTBENCH: SENARYO OLUŞTURMA VE ÇALIŞTIRMA\n# ==========================================\nif __name__ == \"__main__\":\n    NUM_NODES = 16\n    SRC_NODE = 0\n    DST_NODE = 15\n\n    # Varsayılan hat maliyetleri (Düşük = İyi SNR)\n    link_cost_matrix = np.random.randint(10, 50, size=(NUM_NODES, NUM_NODES))\n    freq_penalty_matrix = np.zeros((NUM_NODES, NUM_NODES), dtype=int)\n\n    # Kasıtlı bir temiz rota (Optimum Path) oluşturalım: 0 -> 4 -> 8 -> 15\n    link_cost_matrix[0, 4] = 5; link_cost_matrix[4, 8] = 5; link_cost_matrix[8, 15] = 5\n\n    # Jamming (Karıştırma) Senaryosu Enjeksiyonu\n    # 0 -> 4 hattı temizken, aniden bu frekansa 255 (Maksimum) ceza basılıyor\n    freq_penalty_matrix[0, 4] = 255\n\n    # Alternatif rotayı (0 -> 7 -> 15) temiz bırakalım\n    link_cost_matrix[0, 7] = 8; link_cost_matrix[7, 15] = 8\n\n    # Modeli Başlat\n    qamre_core = QAMRE_GoldenModel(num_nodes=NUM_NODES, iterations=64)\n    final_route_matrix, energy_trend = qamre_core.run_simulation(SRC_NODE, DST_NODE, link_cost_matrix, freq_penalty_matrix)\n\n    # 1. Rota Matrisi Çıktısı (optimized_mesh_vector)\n    print(\"--- 64 ÇEVRİM SONU ELDE EDİLEN KARARLI ROTA MATRİSİ ---\")\n    aktif_hatlar = np.argwhere(final_route_matrix == 1)\n    for hat in aktif_hatlar:\n        print(f\"Düğüm {hat[0]} -> Düğüm {hat[1]}\")\n\n    # 2. Enerji Yakınsama Grafiği (Matplotlib)\n    plt.figure(figsize=(10, 5))\n    plt.plot(range(64), energy_trend, marker='o', linestyle='-', color='b')\n    plt.title(\"Q-AMRE Kuantum Esintili Donanım Motoru - Enerji Yakınsama Grafiği\")\n    plt.xlabel(\"Saat Çevrimi (Clock Cycles)\")\n    plt.ylabel(\"Toplam Sistem Maliyeti (Energy)\")\n    plt.axvline(x=63, color='r', linestyle='--', label=\"T64: Rota Validasyon Kesmesi (Interrupt)\")\n    plt.grid(True)\n    plt.legend()\n    plt.show()"
  }
 ]
}