fleet-assignment/modelos.ipynb

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   "source": [
    "# Alocação de Frotas (Fleet Scheduling) - Aeronaves C-97\n",
    "\n",
    "Este projeto visa realizar a alocação de frota (Fleet Assignment) baseada nos princípios do livro referenciado *Airline Operations and Scheduling*.\n",
    "Diferentemente do modelo anterior que utilizava aeronaves de carga, este foca exclusivamente na aeronave de passageiros **C-97**.\n",
    "\n",
    "## Objetivos:\n",
    "1. Remover cálculos de aeronaves de carga.\n",
    "2. Analisar as demandas diárias para a aeronave C-97.\n",
    "3. Identificar os esquadrões detentores dessas aeronaves e a quantidade de aeronaves (matrículas únicas).\n",
    "4. Calcular os 5 trechos mais relevantes do ano de 2025 utilizando estatística e gráficos.\n",
    "5. Realizar o Fleet Assignment para os 5 trechos, visando minimizar o custo total da operação (Modelo 1 proposto).\n"
   ]
  },
  {
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   "execution_count": 1,
   "id": "fb50d855",
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   "source": [
    "# Importação de bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pulp\n",
    "import pymap3d as pm\n",
    "import math\n",
    "import networkx as nx\n",
    "\n",
    "# Configuração de estilo para os gráficos\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Constantes globais\n",
    "CSV_FILEPATH = \"SISCO_AEREA_01-01-2025_A_31-12-2025_1911251714Z.csv\"\n",
    "JSON_AEROPORTOS = \"airports.json\"\n",
    "CAPACIDADE_C97 = 30 # Capacidade média de passageiros do C-97 (Brasília)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4cd9a7",
   "metadata": {},
   "source": [
    "## 1. Leitura e Limpeza dos Dados\n",
    "Vamos carregar os dados, filtrar apenas para o modelo `C-97` e tratar os campos de localidades e datas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3404c328",
   "metadata": {
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   "source": [
    "# Carregamento do banco de dados\n",
    "df = pd.read_csv(CSV_FILEPATH, low_memory=False)\n",
    "df_aeroportos = pd.read_json(JSON_AEROPORTOS, orient='index')\n",
    "\n",
    "# Tratamento básico de valores nulos expressos como 'NIL'\n",
    "df.replace('NIL', 0, inplace=True)\n",
    "\n",
    "# Filtro para a aeronave C-97 (Passageiros) e remoção de registros de carga\n",
    "df_c97 = df[df['Modelo'] == 'C-97'].copy()\n",
    "\n",
    "# Filtrando decolagens e pousos conhecidos\n",
    "df_c97 = df_c97[df_c97['Localidade Decolagem'] != 0]\n",
    "df_c97 = df_c97[df_c97['Localidade Pouso'] != 0]\n",
    "\n",
    "# Criação da coluna Trecho\n",
    "df_c97['Trecho'] = df_c97['Localidade Decolagem'] + '-' + df_c97['Localidade Pouso']\n",
    "\n",
    "# Conversão da coluna PAX para numérico\n",
    "df_c97['PAX'] = pd.to_numeric(df_c97['PAX']).fillna(0)\n",
    "\n",
    "# Tratamento de datas\n",
    "df_c97['Dep TimeStamp'] = pd.to_datetime(df_c97['Data de Decolagem'] + ' ' + df_c97['Hora de Decolagem'], format='mixed', dayfirst=True)\n",
    "df_c97['Data Apenas'] = df_c97['Dep TimeStamp'].dt.date\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fb80a2",
   "metadata": {},
   "source": [
    "## 2. Análise dos Esquadrões e Matrículas (Aeronaves)\n",
    "Identificaremos quais esquadrões operam o C-97 e qual a disponibilidade de aeronaves (número de matrículas únicas), pois isso definirá as restrições de frota.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "142209ce",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esquadrões que operam o C-97: ETA1, ETA3, ETA5, ETA6, ETA7, ETA2, PAMA SP\n",
      "Total de matrículas únicas (aeronaves disponíveis): 14\n",
      "\n",
      "Aeronaves por esquadrão:\n",
      " - ETA1: 1 aeronaves\n",
      " - ETA2: 1 aeronaves\n",
      " - ETA3: 3 aeronaves\n",
      " - ETA5: 1 aeronaves\n",
      " - ETA6: 4 aeronaves\n",
      " - ETA7: 3 aeronaves\n",
      " - PAMA SP: 1 aeronaves\n",
      "\n",
      "Bases inferidas por esquadrão (pela moda de decolagens):\n",
      " - ETA1: SBBE\n",
      " - ETA2: SBNT\n",
      " - ETA3: SBGL\n",
      " - ETA5: SBCO\n",
      " - ETA6: SBBR\n",
      " - ETA7: SBMN\n",
      " - PAMA SP: SBGR\n"
     ]
    }
   ],
   "source": [
    "# Obtendo esquadrões e matrículas únicas\n",
    "esquadroes = df_c97['Emissor'].unique()\n",
    "matriculas_unicas = df_c97['Matrícula'].unique()\n",
    "total_aeronaves = len(matriculas_unicas)\n",
    "\n",
    "print(f\"Esquadrões que operam o C-97: {', '.join(esquadroes)}\")\n",
    "print(f\"Total de matrículas únicas (aeronaves disponíveis): {total_aeronaves}\")\n",
    "\n",
    "# Aeronaves por esquadrão\n",
    "aeronaves_por_esquadrao = df_c97.groupby('Emissor')['Matrícula'].nunique().to_dict()\n",
    "print(\"\\nAeronaves por esquadrão:\")\n",
    "for esq, qtd in aeronaves_por_esquadrao.items():\n",
    "    print(f\" - {esq}: {qtd} aeronaves\")\n",
    "    \n",
    "# Identificar a base principal de cada esquadrão (aeroporto com mais decolagens)\n",
    "bases_esquadroes = {}\n",
    "for esq in aeronaves_por_esquadrao.keys():\n",
    "    base = df_c97[df_c97['Emissor'] == esq]['Localidade Decolagem'].mode()[0]\n",
    "    bases_esquadroes[esq] = base\n",
    "\n",
    "print(\"\\nBases inferidas por esquadrão (pela moda de decolagens):\")\n",
    "for esq, base in bases_esquadroes.items():\n",
    "    print(f\" - {esq}: {base}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f845c354",
   "metadata": {},
   "source": [
    "## 3. Análise Estatística das Demandas Diárias\n",
    "Para definir os trechos mais relevantes do ano, calcularemos a demanda de passageiros (PAX) agregada diariamente. Em seguida, extraímos a **média de passageiros diários** e o **desvio padrão** para cada trecho. A relevância será determinada pela maior média de demanda diária de PAX.\n",
    "Começaremos focando em 5 trechos (alvo variável).\n"
   ]
  },
  {
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       "      <th></th>\n",
       "      <th>Trecho</th>\n",
       "      <th>Media_PAX_Diario</th>\n",
       "      <th>Desvio_Padrao_PAX</th>\n",
       "      <th>Total_PAX</th>\n",
       "      <th>Dias_Operados</th>\n",
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       "      <th>223</th>\n",
       "      <td>SBGL-SBBR</td>\n",
       "      <td>0.980822</td>\n",
       "      <td>4.097998</td>\n",
       "      <td>358.0</td>\n",
       "      <td>38</td>\n",
       "      <td>9.421053</td>\n",
       "      <td>13604.0</td>\n",
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       "      <th>69</th>\n",
       "      <td>SBBR-SBGL</td>\n",
       "      <td>0.794521</td>\n",
       "      <td>3.483710</td>\n",
       "      <td>290.0</td>\n",
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       "      <td>0.487671</td>\n",
       "      <td>2.420831</td>\n",
       "      <td>178.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.476190</td>\n",
       "      <td>3738.0</td>\n",
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       "      <th>272</th>\n",
       "      <td>SBGR-SBBR</td>\n",
       "      <td>0.476712</td>\n",
       "      <td>2.481277</td>\n",
       "      <td>174.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.285714</td>\n",
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       "      <td>SBGL-SBGR</td>\n",
       "      <td>0.460274</td>\n",
       "      <td>2.575601</td>\n",
       "      <td>168.0</td>\n",
       "      <td>19</td>\n",
       "      <td>8.842105</td>\n",
       "      <td>3192.0</td>\n",
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       "      <td>SBSJ-SBBR</td>\n",
       "      <td>0.339726</td>\n",
       "      <td>2.016330</td>\n",
       "      <td>124.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6.888889</td>\n",
       "      <td>2232.0</td>\n",
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       "      <td>SBGR-SBGL</td>\n",
       "      <td>0.353425</td>\n",
       "      <td>1.885025</td>\n",
       "      <td>129.0</td>\n",
       "      <td>17</td>\n",
       "      <td>7.588235</td>\n",
       "      <td>2193.0</td>\n",
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       "      <td>SBSJ-SBGL</td>\n",
       "      <td>0.284932</td>\n",
       "      <td>1.705169</td>\n",
       "      <td>104.0</td>\n",
       "      <td>19</td>\n",
       "      <td>5.473684</td>\n",
       "      <td>1976.0</td>\n",
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       "      <th>253</th>\n",
       "      <td>SBGL-SBSJ</td>\n",
       "      <td>0.263014</td>\n",
       "      <td>1.619550</td>\n",
       "      <td>96.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>1920.0</td>\n",
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       "      <th>53</th>\n",
       "      <td>SBBR-SBAN</td>\n",
       "      <td>0.336986</td>\n",
       "      <td>2.304767</td>\n",
       "      <td>123.0</td>\n",
       "      <td>13</td>\n",
       "      <td>9.461538</td>\n",
       "      <td>1599.0</td>\n",
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       "        Trecho  Media_PAX_Diario  Desvio_Padrao_PAX  Total_PAX  Dias_Operados  \\\n",
       "223  SBGL-SBBR          0.980822           4.097998      358.0             38   \n",
       "69   SBBR-SBGL          0.794521           3.483710      290.0             34   \n",
       "72   SBBR-SBGR          0.487671           2.420831      178.0             21   \n",
       "272  SBGR-SBBR          0.476712           2.481277      174.0             21   \n",
       "237  SBGL-SBGR          0.460274           2.575601      168.0             19   \n",
       "437  SBSJ-SBBR          0.339726           2.016330      124.0             18   \n",
       "277  SBGR-SBGL          0.353425           1.885025      129.0             17   \n",
       "441  SBSJ-SBGL          0.284932           1.705169      104.0             19   \n",
       "253  SBGL-SBSJ          0.263014           1.619550       96.0             20   \n",
       "53   SBBR-SBAN          0.336986           2.304767      123.0             13   \n",
       "\n",
       "     Media_PAX_Dias_Operados  Score_Relevancia  \n",
       "223                 9.421053           13604.0  \n",
       "69                  8.529412            9860.0  \n",
       "72                  8.476190            3738.0  \n",
       "272                 8.285714            3654.0  \n",
       "237                 8.842105            3192.0  \n",
       "437                 6.888889            2232.0  \n",
       "277                 7.588235            2193.0  \n",
       "441                 5.473684            1976.0  \n",
       "253                 4.800000            1920.0  \n",
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Demanda diária por trecho\n",
    "demanda_diaria = df_c97.groupby(['Trecho', 'Data Apenas'])['PAX'].sum().reset_index()\n",
    "\n",
    "# Criar um range de datas para todo o ano de 2025 para contabilizar os dias sem voo\n",
    "import itertools\n",
    "datas_2025 = pd.date_range(start='2025-01-01', end='2025-12-31').date\n",
    "todos_trechos = demanda_diaria['Trecho'].unique()\n",
    "idx = pd.MultiIndex.from_product([todos_trechos, datas_2025], names=['Trecho', 'Data Apenas'])\n",
    "demanda_diaria_completa = pd.DataFrame(index=idx).reset_index()\n",
    "\n",
    "# Mesclar com os dados reais e preencher NaN com 0\n",
    "demanda_diaria_completa = pd.merge(demanda_diaria_completa, demanda_diaria, on=['Trecho', 'Data Apenas'], how='left').fillna({'PAX': 0})\n",
    "\n",
    "# Estatísticas (Média e Desvio Padrão) por trecho\n",
    "estatisticas_trechos = demanda_diaria_completa.groupby('Trecho').agg(\n",
    "    Media_PAX_Diario=('PAX', 'mean'),\n",
    "    Desvio_Padrao_PAX=('PAX', 'std'),\n",
    "    Total_PAX=('PAX', 'sum'),\n",
    "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
    ").reset_index()\n",
    "\n",
    "# Média PAX por dias operados (usado para o Fleet Assignment Model)\n",
    "estatisticas_trechos['Media_PAX_Dias_Operados'] = estatisticas_trechos.apply(\n",
    "    lambda row: row['Total_PAX'] / row['Dias_Operados'] if row['Dias_Operados'] > 0 else 0, axis=1\n",
    ")\n",
    "\n",
    "# Nova métrica solicitada: PAX * Dias_Operados\n",
    "estatisticas_trechos['Score_Relevancia'] = estatisticas_trechos['Total_PAX'] * estatisticas_trechos['Dias_Operados']\n",
    "\n",
    "# Ordenar pelas rotas com maior métrica (PAX * Dias_Operados)\n",
    "estatisticas_trechos = estatisticas_trechos.sort_values(by='Score_Relevancia', ascending=False)\n",
    "\n",
    "# Definir número de trechos alvo\n",
    "NUM_TRECHOS = 10\n",
    "top_trechos = estatisticas_trechos.head(NUM_TRECHOS)\n",
    "\n",
    "display(top_trechos)\n",
    "\n",
    "# Gráfico\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=top_trechos, x='Trecho', y='Score_Relevancia', hue='Trecho', palette='viridis', legend=False)\n",
    "plt.title(f'Top {NUM_TRECHOS} Trechos Mais Relevantes (PAX * Dias Operados)')\n",
    "plt.ylabel('Score de Relevância (PAX * Dias Operados)')\n",
    "plt.xlabel('Trecho (Origem - Destino)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fcb682",
   "metadata": {},
   "source": [
    "## Diagrama de Rede dos Trechos\n",
    "Para melhor visualização dos trechos mais relevantes, abaixo temos um diagrama de rede conectando as origens aos destinos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab894f9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:53.282929Z",
     "iopub.status.busy": "2026-06-08T00:02:53.282778Z",
     "iopub.status.idle": "2026-06-08T00:02:53.427660Z",
     "shell.execute_reply": "2026-06-08T00:02:53.427163Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criação do grafo direcionado\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# Adiciona arestas com peso baseado no Score de Relevância\n",
    "for _, row in top_trechos.iterrows():\n",
    "    origem, destino = row['Trecho'].split('-')\n",
    "    peso = row['Score_Relevancia']\n",
    "    G.add_edge(origem, destino, weight=peso)\n",
    "\n",
    "# Configuração do layout do grafo - k maior afasta mais os nós\n",
    "pos = nx.spring_layout(G, k=2.5, iterations=100, seed=42)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "# Define o tamanho do nó\n",
    "tamanho_no = 1200\n",
    "\n",
    "# Desenha os nós\n",
    "nx.draw_networkx_nodes(G, pos, node_size=tamanho_no, node_color='skyblue', edgecolors='black')\n",
    "# Desenha as arestas, informando o node_size para que a seta não fique escondida sob o nó\n",
    "nx.draw_networkx_edges(\n",
    "    G, pos, \n",
    "    edgelist=G.edges(), \n",
    "    arrows=True, \n",
    "    arrowstyle='-|>', \n",
    "    arrowsize=25, \n",
    "    edge_color='gray', \n",
    "    width=2, \n",
    "    node_size=tamanho_no, \n",
    "    connectionstyle='arc3,rad=0.15'\n",
    ")\n",
    "# Desenha os rótulos (nomes dos aeroportos)\n",
    "nx.draw_networkx_labels(G, pos, font_size=12, font_family='sans-serif', font_weight='bold')\n",
    "\n",
    "plt.title('Diagrama de Rede - Top Trechos Relevantes', fontsize=14)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6417a6c5",
   "metadata": {},
   "source": [
    "## 4. Fleet Assignment Model (FAM) - Nível Brasil\n",
    "Conforme o MODELO 1 solicitado (aeronaves podem pernoitar em qualquer hub), não há restrição de base ou esquadrão específico. Toda a frota de C-97 opera a nível nacional.\n",
    "\n",
    "**Modelo Matemático:**\n",
    "- **Variáveis de decisão:** $X_r$ (Quantidade de voos alocados no trecho $r$)\n",
    "- **Função Objetivo:** Minimizar a distância total da operação (Distância do trecho $\\times$ voos no trecho). Como as aeronaves não precisam retornar a uma base fixa, desconsideramos voos de posicionamento.\n",
    "- **Restrições:**\n",
    "  1. O número de voos alocados para o trecho deve suprir a demanda diária operada daquela rota.\n",
    "  2. O total de voos atribuídos no dia não pode exceder o total da frota de C-97 disponível (14 aeronaves).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37aa1929",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:53.430410Z",
     "iopub.status.busy": "2026-06-08T00:02:53.430191Z",
     "iopub.status.idle": "2026-06-08T00:02:53.461295Z",
     "shell.execute_reply": "2026-06-08T00:02:53.460683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== RESULTADO DO FLEET ASSIGNMENT (NÍVEL BRASIL) ==\n",
      "Status: Optimal\n",
      "Distância Total da Operação: 5703.02 km\n",
      "\n",
      "Alocações (Voos Diários Globais):\n",
      "Trecho: SBGL-SBBR  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBGL  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBGR  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBGR-SBBR  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBGL-SBGR  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBSJ-SBBR  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBGR-SBGL  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBSJ-SBGL  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBGL-SBSJ  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBAN  | Demanda Média PAX:  30 | Alocados: 1 voo(s)\n",
      "\n",
      "Total de voos alocados na malha diária: 10 de 14 aeronaves disponíveis.\n"
     ]
    }
   ],
   "source": [
    "# Preparação dos dados para o modelo\n",
    "rotas_lista = top_trechos['Trecho'].tolist()\n",
    "\n",
    "# Dicionário de distâncias apenas dos trechos diretos\n",
    "def calc_distancia(icao1, icao2):\n",
    "    try:\n",
    "        lat1, lon1, alt1 = df_aeroportos.loc[icao1, ['lat', 'lon', 'elevation']]\n",
    "        lat2, lon2, alt2 = df_aeroportos.loc[icao2, ['lat', 'lon', 'elevation']]\n",
    "        # Retorna a distância oblíqua em km\n",
    "        return pm.geodetic2aer(lat1, lon1, alt1, lat2, lon2, alt2)[2] / 1000.0\n",
    "    except KeyError:\n",
    "        return 9999.0 # Penalidade caso aeroporto não exista no json\n",
    "\n",
    "distancias_voo = {}\n",
    "for r in rotas_lista:\n",
    "    origem, destino = r.split('-')\n",
    "    distancias_voo[r] = calc_distancia(origem, destino)\n",
    "\n",
    "# Demanda de voos por rota\n",
    "voos_requeridos = {}\n",
    "for _, row in top_trechos.iterrows():\n",
    "    voos = math.ceil(row['Media_PAX_Dias_Operados'] / CAPACIDADE_C97)\n",
    "    voos_requeridos[row['Trecho']] = max(1, voos)\n",
    "\n",
    "# MODELAGEM COM PULP\n",
    "prob = pulp.LpProblem(\"Fleet_Assignment_C97_Brasil\", pulp.LpMinimize)\n",
    "\n",
    "# Variáveis (número de voos em cada rota)\n",
    "X = pulp.LpVariable.dicts(\"X\", rotas_lista, lowBound=0, cat='Integer')\n",
    "\n",
    "# Função Objetivo: Minimizar distância voada nos trechos\n",
    "prob += pulp.lpSum([distancias_voo[r] * X[r] for r in rotas_lista])\n",
    "\n",
    "# Restrição 1: Suprir a demanda\n",
    "for r in rotas_lista:\n",
    "    prob += X[r] >= voos_requeridos[r]\n",
    "\n",
    "# Restrição 2: Limite global de frota (nível Brasil)\n",
    "prob += pulp.lpSum([X[r] for r in rotas_lista]) <= total_aeronaves\n",
    "\n",
    "# Solução\n",
    "prob.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "# Exibição dos resultados\n",
    "print(\"== RESULTADO DO FLEET ASSIGNMENT (NÍVEL BRASIL) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob.status]}\")\n",
    "print(f\"Distância Total da Operação: {pulp.value(prob.objective):.2f} km\\n\")\n",
    "\n",
    "print(\"Alocações (Voos Diários Globais):\")\n",
    "voos_totais = 0\n",
    "for r in rotas_lista:\n",
    "    qtd = int(X[r].varValue)\n",
    "    voos_totais += qtd\n",
    "    print(f\"Trecho: {r:10} | Demanda Média PAX: {voos_requeridos[r]*CAPACIDADE_C97:3.0f} | Alocados: {qtd} voo(s)\")\n",
    "\n",
    "print(f\"\\nTotal de voos alocados na malha diária: {voos_totais} de {total_aeronaves} aeronaves disponíveis.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca983df",
   "metadata": {},
   "source": [
    "## 5. Modelo 2: Minimização de Aeronaves\n",
    "Neste modelo, o objetivo é encontrar o **número mínimo absoluto de aeronaves** necessário para operar a malha diária estabelecida (os top trechos).\n",
    "Considerações solicitadas:\n",
    "- O tempo de voo é estimado usando a velocidade média real do C-97 em 2025.\n",
    "- As aeronaves podem voar até 24 horas por dia (não consideraremos o tempo de solo no chão nesta versão).\n",
    "- Se houver desbalanceamento de malha (mais voos saindo de A do que chegando em A), o modelo criará voos de reposicionamento vazios.\n"
   ]
  },
  {
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   "id": "34085659",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Velocidade média calculada para o C-97: 391.76 km/h\n",
      "\n",
      "== RESULTADO DO MODELO 2 ==\n",
      "Status: Optimal\n",
      "Número mínimo absoluto de aeronaves C-97 necessárias: 1\n",
      "Tempo total de voo diário (incluindo reposicionamento vazio): 1004 min (16.73 h)\n",
      "Taxa de ocupação da frota (Tempo Voo / Tempo Disponível): 69.7%\n",
      "\n",
      "Voos de Reposicionamento Vazio Gerados (Y) para fechar o ciclo diário:\n",
      " -> De SBAN para SBSJ: 1 voo(s) vazio(s) | Tempo de voo: 129 min\n",
      "\n",
      "== QUADRO DE HORÁRIOS DIÁRIOS ==\n",
      "| Aeronave   | Origem   | Destino   | Tipo                     | Partida   | Chegada   | Duração   |\n",
      "|------------|----------|-----------|--------------------------|-----------|-----------|-----------|\n",
      "| Aeronave 1 | SBSJ     | SBGL      | Passageiro               | 00:00     | 00:42     | 42 min    |\n",
      "| Aeronave 1 | SBGL     | SBSJ      | Passageiro               | 00:42     | 01:24     | 42 min    |\n",
      "| Aeronave 1 | SBSJ     | SBBR      | Passageiro               | 01:24     | 03:33     | 129 min   |\n",
      "| Aeronave 1 | SBBR     | SBGR      | Passageiro               | 03:33     | 05:43     | 130 min   |\n",
      "| Aeronave 1 | SBGR     | SBGL      | Passageiro               | 05:43     | 06:35     | 52 min    |\n",
      "| Aeronave 1 | SBGL     | SBGR      | Passageiro               | 06:35     | 07:27     | 52 min    |\n",
      "| Aeronave 1 | SBGR     | SBBR      | Passageiro               | 07:27     | 09:37     | 130 min   |\n",
      "| Aeronave 1 | SBBR     | SBGL      | Passageiro               | 09:37     | 11:57     | 140 min   |\n",
      "| Aeronave 1 | SBGL     | SBBR      | Passageiro               | 11:57     | 14:17     | 140 min   |\n",
      "| Aeronave 1 | SBBR     | SBAN      | Passageiro               | 14:17     | 14:35     | 18 min    |\n",
      "| Aeronave 1 | SBAN     | SBSJ      | Vazio (Reposicionamento) | 14:35     | 16:44     | 129 min   |\n"
     ]
    }
   ],
   "source": [
    "# 1. Calcular a velocidade média do C-97 em 2025\n",
    "# Converte a string 'HH:MM:SS' para timedelta e extrai horas\n",
    "df_c97_spd = df_c97.copy()\n",
    "df_c97_spd['Tempo_Horas'] = pd.to_timedelta(df_c97_spd['Tempo de Voo']).dt.total_seconds() / 3600\n",
    "\n",
    "def get_dist_row(row):\n",
    "    try:\n",
    "        lat1, lon1, alt1 = df_aeroportos.loc[row['Localidade Decolagem'], ['lat', 'lon', 'elevation']]\n",
    "        lat2, lon2, alt2 = df_aeroportos.loc[row['Localidade Pouso'], ['lat', 'lon', 'elevation']]\n",
    "        return pm.geodetic2aer(lat1, lon1, alt1, lat2, lon2, alt2)[2] / 1000.0\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "df_c97_spd['Dist_km'] = df_c97_spd.apply(get_dist_row, axis=1)\n",
    "\n",
    "# Filtra voos válidos para calcular a velocidade média\n",
    "df_valid = df_c97_spd[(df_c97_spd['Tempo_Horas'] > 0) & (df_c97_spd['Dist_km'] > 0)].copy()\n",
    "df_valid['Velocidade'] = df_valid['Dist_km'] / df_valid['Tempo_Horas']\n",
    "vel_media_c97 = df_valid['Velocidade'].mean()\n",
    "print(f\"Velocidade média calculada para o C-97: {vel_media_c97:.2f} km/h\")\n",
    "\n",
    "# 2. Configurar os nós (aeroportos) e demandas da malha das rotas top\n",
    "aeroportos_malha = set()\n",
    "for r in rotas_lista:\n",
    "    o, d = r.split('-')\n",
    "    aeroportos_malha.add(o)\n",
    "    aeroportos_malha.add(d)\n",
    "aeroportos_malha = list(aeroportos_malha)\n",
    "\n",
    "# Matriz de tempo de voo entre todos os nós da malha em MINUTOS (inteiro)\n",
    "tempos_voo_min = {}\n",
    "for o in aeroportos_malha:\n",
    "    for d in aeroportos_malha:\n",
    "        if o == d:\n",
    "            tempos_voo_min[(o, d)] = 0\n",
    "        else:\n",
    "            dist = calc_distancia(o, d)\n",
    "            tempos_voo_min[(o, d)] = int(round((dist / vel_media_c97) * 60))\n",
    "\n",
    "# Demanda fixa D_{i,j}\n",
    "D = {(o, d): 0 for o in aeroportos_malha for d in aeroportos_malha}\n",
    "for r in rotas_lista:\n",
    "    o, d = r.split('-')\n",
    "    D[(o, d)] = voos_requeridos[r]\n",
    "\n",
    "# 3. Modelagem Matemática (Pulp)\n",
    "prob2 = pulp.LpProblem(\"Minimizar_Aeronaves\", pulp.LpMinimize)\n",
    "\n",
    "# Variáveis de reposicionamento vazio Y_{i,j}\n",
    "Y = pulp.LpVariable.dicts(\"Y\", [(o, d) for o in aeroportos_malha for d in aeroportos_malha], lowBound=0, cat='Integer')\n",
    "\n",
    "# Variável de número de aeronaves N\n",
    "N = pulp.LpVariable(\"N\", lowBound=0, cat='Integer')\n",
    "\n",
    "# Função Objetivo: Minimizar N\n",
    "# Para desempatar soluções com mesmo N, damos um pequeno peso pro tempo de reposicionamento vazio (para pegar o roteamento mais curto também)\n",
    "prob2 += N + 0.0001 * pulp.lpSum([Y[(i, j)] * tempos_voo_min[(i, j)] for i in aeroportos_malha for j in aeroportos_malha])\n",
    "\n",
    "# Restrição de conservação de fluxo para cada aeroporto (Chegadas == Partidas)\n",
    "for no in aeroportos_malha:\n",
    "    chegadas = pulp.lpSum([D[(i, no)] + Y[(i, no)] for i in aeroportos_malha])\n",
    "    partidas = pulp.lpSum([D[(no, j)] + Y[(no, j)] for j in aeroportos_malha])\n",
    "    prob2 += chegadas == partidas\n",
    "\n",
    "# Restrição de tempo (O tempo total de voo da malha não pode exceder N * 1440 min)\n",
    "tempo_total_voo = pulp.lpSum([(D[(i, j)] + Y[(i, j)]) * tempos_voo_min[(i, j)] for i in aeroportos_malha for j in aeroportos_malha])\n",
    "prob2 += tempo_total_voo <= N * 1440\n",
    "\n",
    "# Solução\n",
    "prob2.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 2 ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob2.status]}\")\n",
    "print(f\"Número mínimo absoluto de aeronaves C-97 necessárias: {int(N.varValue)}\")\n",
    "\n",
    "tempo_gasto = sum([(D[(i, j)] + int(Y[(i, j)].varValue)) * tempos_voo_min[(i, j)] for i in aeroportos_malha for j in aeroportos_malha])\n",
    "print(f\"Tempo total de voo diário (incluindo reposicionamento vazio): {int(tempo_gasto)} min ({tempo_gasto/60:.2f} h)\")\n",
    "if N.varValue > 0:\n",
    "    print(f\"Taxa de ocupação da frota (Tempo Voo / Tempo Disponível): {(tempo_gasto / (N.varValue * 1440)) * 100:.1f}%\\n\")\n",
    "\n",
    "print(\"Voos de Reposicionamento Vazio Gerados (Y) para fechar o ciclo diário:\")\n",
    "gerou_vazio = False\n",
    "for i in aeroportos_malha:\n",
    "    for j in aeroportos_malha:\n",
    "        if Y[(i, j)].varValue > 0:\n",
    "            print(f\" -> De {i} para {j}: {int(Y[(i, j)].varValue)} voo(s) vazio(s) | Tempo de voo: {tempos_voo_min[(i,j)]*int(Y[(i,j)].varValue)} min\")\n",
    "            gerou_vazio = True\n",
    "if not gerou_vazio:\n",
    "    print(\" Nenhum voo de reposicionamento vazio foi necessário! A malha já é circular/balanceada.\")\n",
    "\n",
    "# GERAR TABELA DE HORÁRIOS COM TABULATE E DATETIME\n",
    "from tabulate import tabulate\n",
    "import datetime\n",
    "\n",
    "# 1. Construir a lista de todos os voos a serem realizados\n",
    "voos_para_fazer = []\n",
    "\n",
    "# Cópia para saber quantos voos de passageiros faltam alocar\n",
    "D_copy = {k: v for k, v in D.items()}\n",
    "\n",
    "for i in aeroportos_malha:\n",
    "    for j in aeroportos_malha:\n",
    "        total_voos = D[(i, j)] + int(Y[(i, j)].varValue)\n",
    "        for _ in range(total_voos):\n",
    "            if D_copy[(i, j)] > 0:\n",
    "                tipo = 'Passageiro'\n",
    "                D_copy[(i, j)] -= 1\n",
    "            else:\n",
    "                tipo = 'Vazio (Reposicionamento)'\n",
    "            voos_para_fazer.append({'origem': i, 'destino': j, 'tipo': tipo, 'tempo': tempos_voo_min[(i, j)]})\n",
    "\n",
    "# 2. Simular a alocação sequencial de horários\n",
    "tabela_horarios = []\n",
    "aeronave_id = 1\n",
    "\n",
    "while voos_para_fazer:\n",
    "    voo_atual = voos_para_fazer.pop(0)\n",
    "    hora_atual = datetime.datetime(2025, 1, 1, 0, 0, 0)\n",
    "    \n",
    "    tempo_delta = datetime.timedelta(minutes=int(voo_atual['tempo']))\n",
    "    hora_chegada = hora_atual + tempo_delta\n",
    "    \n",
    "    tabela_horarios.append([\n",
    "        f\"Aeronave {aeronave_id}\",\n",
    "        voo_atual['origem'],\n",
    "        voo_atual['destino'],\n",
    "        voo_atual['tipo'],\n",
    "        hora_atual.strftime('%H:%M'),\n",
    "        hora_chegada.strftime('%H:%M'),\n",
    "        f\"{voo_atual['tempo']} min\"\n",
    "    ])\n",
    "    \n",
    "    hora_atual = hora_chegada\n",
    "    local_atual = voo_atual['destino']\n",
    "    \n",
    "    # Continuar traçando o caminho para esta aeronave até que não caiba mais nas 24h\n",
    "    while True:\n",
    "        prox_voo = None\n",
    "        for idx, v in enumerate(voos_para_fazer):\n",
    "            if v['origem'] == local_atual:\n",
    "                if (hora_atual - datetime.datetime(2025, 1, 1, 0, 0, 0) + datetime.timedelta(minutes=int(v['tempo']))).total_seconds() <= 1440 * 60:\n",
    "                    prox_voo = voos_para_fazer.pop(idx)\n",
    "                    break\n",
    "        \n",
    "        if prox_voo:\n",
    "            tempo_delta = datetime.timedelta(minutes=int(prox_voo['tempo']))\n",
    "            hora_chegada = hora_atual + tempo_delta\n",
    "            tabela_horarios.append([\n",
    "                f\"Aeronave {aeronave_id}\",\n",
    "                prox_voo['origem'],\n",
    "                prox_voo['destino'],\n",
    "                prox_voo['tipo'],\n",
    "                hora_atual.strftime('%H:%M'),\n",
    "                hora_chegada.strftime('%H:%M'),\n",
    "                f\"{prox_voo['tempo']} min\"\n",
    "            ])\n",
    "            hora_atual = hora_chegada\n",
    "            local_atual = prox_voo['destino']\n",
    "        else:\n",
    "            break\n",
    "            \n",
    "    aeronave_id += 1\n",
    "\n",
    "print(\"\\n== QUADRO DE HORÁRIOS DIÁRIOS ==\")\n",
    "print(tabulate(tabela_horarios, headers=[\"Aeronave\", \"Origem\", \"Destino\", \"Tipo\", \"Partida\", \"Chegada\", \"Duração\"], tablefmt=\"github\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b655f18a",
   "metadata": {},
   "source": [
    "## 6. Modelo 3: Minimização de Aeronaves com TAT (Turnaround Time)\n",
    "Nesta variação do Modelo 2, adicionamos uma restrição crítica da vida real: o Tempo de Solo ou **Turnaround Time (TAT)**.\n",
    "Considerações:\n",
    "- TAT estipulado: 40 minutos (0.66 horas) por cada voo (tempo necessário para desembarque, reabastecimento, verificações e embarque).\n",
    "- O tempo total de dedicação de uma aeronave por ciclo passa a ser a soma do seu Tempo de Voo + 40 minutos de TAT no aeroporto de destino.\n"
   ]
  },
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   "id": "1ff4f6ff",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "== RESULTADO DO MODELO 3 (COM 40 MIN DE SOLO) ==\n",
      "Status: Optimal\n",
      "Número mínimo absoluto de aeronaves C-97 necessárias: 2\n",
      "Tempo total de voo puro: 1004 min (16.73 h)\n",
      "Tempo total gasto em solo (TAT): 440 min (7.33 h)\n",
      "Tempo total operacional comprometido: 1444 min (24.07 h)\n",
      "Taxa de ocupação da frota (Tempo Operacional / Tempo Disponível): 50.1%\n",
      "\n",
      "== QUADRO DE HORÁRIOS DIÁRIOS (COM 40 MIN TAT) ==\n",
      "| Aeronave   | Origem   | Destino   | Tipo                     | Partida   | Chegada   | Duração Voo   |\n",
      "|------------|----------|-----------|--------------------------|-----------|-----------|---------------|\n",
      "| Aeronave 1 | SBSJ     | SBGL      | Passageiro               | 00:00     | 00:42     | 42 min        |\n",
      "| Aeronave 1 | SBGL     | SBSJ      | Passageiro               | 01:22     | 02:04     | 42 min        |\n",
      "| Aeronave 1 | SBSJ     | SBBR      | Passageiro               | 02:44     | 04:53     | 129 min       |\n",
      "| Aeronave 1 | SBBR     | SBGR      | Passageiro               | 05:33     | 07:43     | 130 min       |\n",
      "| Aeronave 1 | SBGR     | SBGL      | Passageiro               | 08:23     | 09:15     | 52 min        |\n",
      "| Aeronave 1 | SBGL     | SBGR      | Passageiro               | 09:55     | 10:47     | 52 min        |\n",
      "| Aeronave 1 | SBGR     | SBBR      | Passageiro               | 11:27     | 13:37     | 130 min       |\n",
      "| Aeronave 1 | SBBR     | SBGL      | Passageiro               | 14:17     | 16:37     | 140 min       |\n",
      "| Aeronave 1 | SBGL     | SBBR      | Passageiro               | 17:17     | 19:37     | 140 min       |\n",
      "| Aeronave 1 | SBBR     | SBAN      | Passageiro               | 20:17     | 20:35     | 18 min        |\n",
      "| Aeronave 1 | SBAN     | SBSJ      | Vazio (Reposicionamento) | 21:15     | 23:24     | 129 min       |\n"
     ]
    }
   ],
   "source": [
    "# 3. Modelagem Matemática Modelo 3 (Pulp) com TAT\n",
    "prob3 = pulp.LpProblem(\"Minimizar_Aeronaves_TAT\", pulp.LpMinimize)\n",
    "\n",
    "# Variáveis\n",
    "Y3 = pulp.LpVariable.dicts(\"Y3\", [(o, d) for o in aeroportos_malha for d in aeroportos_malha], lowBound=0, cat='Integer')\n",
    "N3 = pulp.LpVariable(\"N3\", lowBound=0, cat='Integer')\n",
    "\n",
    "TAT_min = 40\n",
    "\n",
    "# Função Objetivo\n",
    "prob3 += N3 + 0.0001 * pulp.lpSum([Y3[(i, j)] * tempos_voo_min[(i, j)] for i in aeroportos_malha for j in aeroportos_malha])\n",
    "\n",
    "# Restrição de fluxo\n",
    "for no in aeroportos_malha:\n",
    "    chegadas = pulp.lpSum([D[(i, no)] + Y3[(i, no)] for i in aeroportos_malha])\n",
    "    partidas = pulp.lpSum([D[(no, j)] + Y3[(no, j)] for j in aeroportos_malha])\n",
    "    prob3 += chegadas == partidas\n",
    "\n",
    "# Restrição de tempo com TAT\n",
    "# Cada voo (D ou Y) consome tempo de voo + TAT\n",
    "tempo_total_operacional = pulp.lpSum([(D[(i, j)] + Y3[(i, j)]) * (tempos_voo_min[(i, j)] + TAT_min) for i in aeroportos_malha for j in aeroportos_malha])\n",
    "prob3 += tempo_total_operacional <= N3 * 1440\n",
    "\n",
    "# Solução\n",
    "prob3.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 3 (COM 40 MIN DE SOLO) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob3.status]}\")\n",
    "print(f\"Número mínimo absoluto de aeronaves C-97 necessárias: {int(N3.varValue)}\")\n",
    "\n",
    "tempo_voo_puro = sum([(D[(i, j)] + int(Y3[(i, j)].varValue)) * tempos_voo_min[(i, j)] for i in aeroportos_malha for j in aeroportos_malha])\n",
    "total_voos_realizados = sum([(D[(i, j)] + int(Y3[(i, j)].varValue)) for i in aeroportos_malha for j in aeroportos_malha])\n",
    "tempo_solo_total = total_voos_realizados * TAT_min\n",
    "\n",
    "print(f\"Tempo total de voo puro: {tempo_voo_puro} min ({tempo_voo_puro/60:.2f} h)\")\n",
    "print(f\"Tempo total gasto em solo (TAT): {tempo_solo_total} min ({tempo_solo_total/60:.2f} h)\")\n",
    "print(f\"Tempo total operacional comprometido: {tempo_voo_puro + tempo_solo_total} min ({(tempo_voo_puro + tempo_solo_total)/60:.2f} h)\")\n",
    "if N3.varValue > 0:\n",
    "    print(f\"Taxa de ocupação da frota (Tempo Operacional / Tempo Disponível): {((tempo_voo_puro + tempo_solo_total) / (N3.varValue * 1440)) * 100:.1f}%\\n\")\n",
    "\n",
    "# GERAR TABELA DE HORÁRIOS COM TABULATE E DATETIME\n",
    "voos_para_fazer3 = []\n",
    "D_copy3 = {k: v for k, v in D.items()}\n",
    "\n",
    "for i in aeroportos_malha:\n",
    "    for j in aeroportos_malha:\n",
    "        total_voos3 = D[(i, j)] + int(Y3[(i, j)].varValue)\n",
    "        for _ in range(total_voos3):\n",
    "            if D_copy3[(i, j)] > 0:\n",
    "                tipo = 'Passageiro'\n",
    "                D_copy3[(i, j)] -= 1\n",
    "            else:\n",
    "                tipo = 'Vazio (Reposicionamento)'\n",
    "            voos_para_fazer3.append({'origem': i, 'destino': j, 'tipo': tipo, 'tempo': tempos_voo_min[(i, j)]})\n",
    "\n",
    "tabela_horarios3 = []\n",
    "aeronave_id3 = 1\n",
    "\n",
    "while voos_para_fazer3:\n",
    "    voo_atual = voos_para_fazer3.pop(0)\n",
    "    hora_atual = datetime.datetime(2025, 1, 1, 0, 0, 0)\n",
    "    \n",
    "    tempo_delta = datetime.timedelta(minutes=int(voo_atual['tempo']))\n",
    "    hora_chegada = hora_atual + tempo_delta\n",
    "    \n",
    "    tabela_horarios3.append([\n",
    "        f\"Aeronave {aeronave_id3}\",\n",
    "        voo_atual['origem'],\n",
    "        voo_atual['destino'],\n",
    "        voo_atual['tipo'],\n",
    "        hora_atual.strftime('%H:%M'),\n",
    "        hora_chegada.strftime('%H:%M'),\n",
    "        f\"{voo_atual['tempo']} min\"\n",
    "    ])\n",
    "    \n",
    "    # Próxima partida só depois do TAT de 40 min\n",
    "    hora_atual = hora_chegada + datetime.timedelta(minutes=40)\n",
    "    local_atual = voo_atual['destino']\n",
    "    \n",
    "    while True:\n",
    "        prox_voo = None\n",
    "        for idx, v in enumerate(voos_para_fazer3):\n",
    "            if v['origem'] == local_atual:\n",
    "                if (hora_atual - datetime.datetime(2025, 1, 1, 0, 0, 0) + datetime.timedelta(minutes=int(v['tempo']))).total_seconds() <= 1440 * 60:\n",
    "                    prox_voo = voos_para_fazer3.pop(idx)\n",
    "                    break\n",
    "        \n",
    "        if prox_voo:\n",
    "            tempo_delta = datetime.timedelta(minutes=int(prox_voo['tempo']))\n",
    "            hora_chegada = hora_atual + tempo_delta\n",
    "            tabela_horarios3.append([\n",
    "                f\"Aeronave {aeronave_id3}\",\n",
    "                prox_voo['origem'],\n",
    "                prox_voo['destino'],\n",
    "                prox_voo['tipo'],\n",
    "                hora_atual.strftime('%H:%M'),\n",
    "                hora_chegada.strftime('%H:%M'),\n",
    "                f\"{prox_voo['tempo']} min\"\n",
    "            ])\n",
    "            hora_atual = hora_chegada + datetime.timedelta(minutes=40)\n",
    "            local_atual = prox_voo['destino']\n",
    "        else:\n",
    "            break\n",
    "            \n",
    "    aeronave_id3 += 1\n",
    "\n",
    "print(\"== QUADRO DE HORÁRIOS DIÁRIOS (COM 40 MIN TAT) ==\")\n",
    "print(tabulate(tabela_horarios3, headers=[\"Aeronave\", \"Origem\", \"Destino\", \"Tipo\", \"Partida\", \"Chegada\", \"Duração Voo\"], tablefmt=\"github\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1763af22",
   "metadata": {},
   "source": [
    "# Alocação de Frotas - Aeronaves C-99A\n",
    "Agora replicaremos a mesma lógica e os mesmos modelos (1, 2 e 3) para a aeronave C-99A (que será nomeada como Modelos 4, 5 e 6).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c4f2389",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:54.856067Z",
     "iopub.status.busy": "2026-06-08T00:02:54.855898Z",
     "iopub.status.idle": "2026-06-08T00:02:55.197782Z",
     "shell.execute_reply": "2026-06-08T00:02:55.197166Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de matrículas únicas C-99A (aeronaves disponíveis): 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trecho</th>\n",
       "      <th>Media_PAX_Diario</th>\n",
       "      <th>Desvio_Padrao_PAX</th>\n",
       "      <th>Total_PAX</th>\n",
       "      <th>Dias_Operados</th>\n",
       "      <th>Media_PAX_Dias_Operados</th>\n",
       "      <th>Score_Relevancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>SBBR-SBGL</td>\n",
       "      <td>3.038356</td>\n",
       "      <td>9.585856</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>56</td>\n",
       "      <td>19.803571</td>\n",
       "      <td>62104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>SBGL-SBBR</td>\n",
       "      <td>3.079452</td>\n",
       "      <td>10.280769</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>50</td>\n",
       "      <td>22.480000</td>\n",
       "      <td>56200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>SBGR-SBBR</td>\n",
       "      <td>0.846575</td>\n",
       "      <td>4.933461</td>\n",
       "      <td>309.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15.450000</td>\n",
       "      <td>6180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>SBBR-SBGR</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>4.424820</td>\n",
       "      <td>250.0</td>\n",
       "      <td>17</td>\n",
       "      <td>14.705882</td>\n",
       "      <td>4250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>SBGL-SBGR</td>\n",
       "      <td>0.564384</td>\n",
       "      <td>3.161295</td>\n",
       "      <td>206.0</td>\n",
       "      <td>17</td>\n",
       "      <td>12.117647</td>\n",
       "      <td>3502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SBBR-SBAF</td>\n",
       "      <td>0.594521</td>\n",
       "      <td>4.223166</td>\n",
       "      <td>217.0</td>\n",
       "      <td>11</td>\n",
       "      <td>19.727273</td>\n",
       "      <td>2387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SBAF-SBBR</td>\n",
       "      <td>0.495890</td>\n",
       "      <td>3.539124</td>\n",
       "      <td>181.0</td>\n",
       "      <td>8</td>\n",
       "      <td>22.625000</td>\n",
       "      <td>1448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>SBBR-SBSJ</td>\n",
       "      <td>0.386301</td>\n",
       "      <td>3.265929</td>\n",
       "      <td>141.0</td>\n",
       "      <td>10</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>1410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>SBGL-SBSJ</td>\n",
       "      <td>0.235616</td>\n",
       "      <td>2.077970</td>\n",
       "      <td>86.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9.555556</td>\n",
       "      <td>774.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>SBGR-SBGL</td>\n",
       "      <td>0.147945</td>\n",
       "      <td>1.121727</td>\n",
       "      <td>54.0</td>\n",
       "      <td>14</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>756.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trecho  Media_PAX_Diario  Desvio_Padrao_PAX  Total_PAX  Dias_Operados  \\\n",
       "48   SBBR-SBGL          3.038356           9.585856     1109.0             56   \n",
       "127  SBGL-SBBR          3.079452          10.280769     1124.0             50   \n",
       "158  SBGR-SBBR          0.846575           4.933461      309.0             20   \n",
       "50   SBBR-SBGR          0.684932           4.424820      250.0             17   \n",
       "136  SBGL-SBGR          0.564384           3.161295      206.0             17   \n",
       "31   SBBR-SBAF          0.594521           4.223166      217.0             11   \n",
       "0    SBAF-SBBR          0.495890           3.539124      181.0              8   \n",
       "61   SBBR-SBSJ          0.386301           3.265929      141.0             10   \n",
       "144  SBGL-SBSJ          0.235616           2.077970       86.0              9   \n",
       "163  SBGR-SBGL          0.147945           1.121727       54.0             14   \n",
       "\n",
       "     Media_PAX_Dias_Operados  Score_Relevancia  \n",
       "48                 19.803571           62104.0  \n",
       "127                22.480000           56200.0  \n",
       "158                15.450000            6180.0  \n",
       "50                 14.705882            4250.0  \n",
       "136                12.117647            3502.0  \n",
       "31                 19.727273            2387.0  \n",
       "0                  22.625000            1448.0  \n",
       "61                 14.100000            1410.0  \n",
       "144                 9.555556             774.0  \n",
       "163                 3.857143             756.0  "
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Filtro e Limpeza C-99A\n",
    "df_c99 = df[df['Modelo'] == 'C-99A'].copy()\n",
    "df_c99 = df_c99[df_c99['Localidade Decolagem'] != 0]\n",
    "df_c99 = df_c99[df_c99['Localidade Pouso'] != 0]\n",
    "df_c99['Trecho'] = df_c99['Localidade Decolagem'] + '-' + df_c99['Localidade Pouso']\n",
    "df_c99['PAX'] = pd.to_numeric(df_c99['PAX']).fillna(0)\n",
    "df_c99['Dep TimeStamp'] = pd.to_datetime(df_c99['Data de Decolagem'] + ' ' + df_c99['Hora de Decolagem'], format='mixed', dayfirst=True)\n",
    "df_c99['Data Apenas'] = df_c99['Dep TimeStamp'].dt.date\n",
    "\n",
    "CAPACIDADE_C99 = 50\n",
    "matriculas_unicas_c99 = df_c99['Matrícula'].unique()\n",
    "total_aeronaves_c99 = len(matriculas_unicas_c99)\n",
    "\n",
    "print(f\"Total de matrículas únicas C-99A (aeronaves disponíveis): {total_aeronaves_c99}\")\n",
    "\n",
    "# Demanda diária\n",
    "demanda_diaria_c99 = df_c99.groupby(['Trecho', 'Data Apenas'])['PAX'].sum().reset_index()\n",
    "\n",
    "import itertools\n",
    "todos_trechos_c99 = demanda_diaria_c99['Trecho'].unique()\n",
    "idx_c99 = pd.MultiIndex.from_product([todos_trechos_c99, datas_2025], names=['Trecho', 'Data Apenas'])\n",
    "demanda_diaria_completa_c99 = pd.DataFrame(index=idx_c99).reset_index()\n",
    "demanda_diaria_completa_c99 = pd.merge(demanda_diaria_completa_c99, demanda_diaria_c99, on=['Trecho', 'Data Apenas'], how='left').fillna({'PAX': 0})\n",
    "\n",
    "estatisticas_trechos_c99 = demanda_diaria_completa_c99.groupby('Trecho').agg(\n",
    "    Media_PAX_Diario=('PAX', 'mean'),\n",
    "    Desvio_Padrao_PAX=('PAX', 'std'),\n",
    "    Total_PAX=('PAX', 'sum'),\n",
    "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
    ").reset_index()\n",
    "\n",
    "estatisticas_trechos_c99['Media_PAX_Dias_Operados'] = estatisticas_trechos_c99.apply(\n",
    "    lambda row: row['Total_PAX'] / row['Dias_Operados'] if row['Dias_Operados'] > 0 else 0, axis=1\n",
    ")\n",
    "estatisticas_trechos_c99['Score_Relevancia'] = estatisticas_trechos_c99['Total_PAX'] * estatisticas_trechos_c99['Dias_Operados']\n",
    "estatisticas_trechos_c99 = estatisticas_trechos_c99.sort_values(by='Score_Relevancia', ascending=False)\n",
    "\n",
    "NUM_TRECHOS_C99 = 10\n",
    "top_trechos_c99 = estatisticas_trechos_c99.head(NUM_TRECHOS_C99)\n",
    "\n",
    "display(top_trechos_c99)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=top_trechos_c99, x='Trecho', y='Score_Relevancia', hue='Trecho', palette='magma', legend=False)\n",
    "plt.title(f'Top {NUM_TRECHOS_C99} Trechos C-99A (PAX * Dias Operados)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Rede C-99\n",
    "G_c99 = nx.DiGraph()\n",
    "for _, row in top_trechos_c99.iterrows():\n",
    "    origem, destino = row['Trecho'].split('-')\n",
    "    G_c99.add_edge(origem, destino, weight=row['Score_Relevancia'])\n",
    "pos_c99 = nx.spring_layout(G_c99, k=2.5, iterations=100, seed=42)\n",
    "plt.figure(figsize=(12, 8))\n",
    "nx.draw_networkx_nodes(G_c99, pos_c99, node_size=1200, node_color='lightcoral', edgecolors='black')\n",
    "nx.draw_networkx_edges(G_c99, pos_c99, edgelist=G_c99.edges(), arrows=True, arrowstyle='-|>', arrowsize=25, edge_color='gray', width=2, node_size=1200, connectionstyle='arc3,rad=0.15')\n",
    "nx.draw_networkx_labels(G_c99, pos_c99, font_size=12, font_weight='bold')\n",
    "plt.title('Diagrama de Rede - C-99A', fontsize=14)\n",
    "plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742232e8",
   "metadata": {},
   "source": [
    "## MODELO 4: Minimização de Distância (Custo)\n",
    "Equivalente ao Modelo 1, mas para C-99A.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffd85ca9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:55.199228Z",
     "iopub.status.busy": "2026-06-08T00:02:55.199102Z",
     "iopub.status.idle": "2026-06-08T00:02:55.213916Z",
     "shell.execute_reply": "2026-06-08T00:02:55.213276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== MODELO 4 (C-99A) ==\n",
      "Status: Infeasible\n",
      "Distância Total: 6280.78 km\n",
      "\n",
      "Trecho: SBBR-SBGL  | Alocados: 1 voo(s)\n",
      "Trecho: SBGL-SBBR  | Alocados: 1 voo(s)\n",
      "Trecho: SBGR-SBBR  | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBGR  | Alocados: 0 voo(s)\n",
      "Trecho: SBGL-SBGR  | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBAF  | Alocados: 1 voo(s)\n",
      "Trecho: SBAF-SBBR  | Alocados: 1 voo(s)\n",
      "Trecho: SBBR-SBSJ  | Alocados: 1 voo(s)\n",
      "Trecho: SBGL-SBSJ  | Alocados: 1 voo(s)\n",
      "Trecho: SBGR-SBGL  | Alocados: 1 voo(s)\n"
     ]
    }
   ],
   "source": [
    "# Preparação Modelo 4\n",
    "rotas_lista_c99 = top_trechos_c99['Trecho'].tolist()\n",
    "\n",
    "distancias_voo_c99 = {}\n",
    "for r in rotas_lista_c99:\n",
    "    origem, destino = r.split('-')\n",
    "    distancias_voo_c99[r] = calc_distancia(origem, destino)\n",
    "\n",
    "voos_requeridos_c99 = {}\n",
    "for _, row in top_trechos_c99.iterrows():\n",
    "    voos = math.ceil(row['Media_PAX_Dias_Operados'] / CAPACIDADE_C99)\n",
    "    voos_requeridos_c99[row['Trecho']] = max(1, voos)\n",
    "\n",
    "prob4 = pulp.LpProblem(\"Fleet_Assignment_C99_Brasil\", pulp.LpMinimize)\n",
    "X_c99 = pulp.LpVariable.dicts(\"X_c99\", rotas_lista_c99, lowBound=0, cat='Integer')\n",
    "\n",
    "prob4 += pulp.lpSum([distancias_voo_c99[r] * X_c99[r] for r in rotas_lista_c99])\n",
    "\n",
    "for r in rotas_lista_c99:\n",
    "    prob4 += X_c99[r] >= voos_requeridos_c99[r]\n",
    "\n",
    "prob4 += pulp.lpSum([X_c99[r] for r in rotas_lista_c99]) <= total_aeronaves_c99\n",
    "\n",
    "prob4.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"== MODELO 4 (C-99A) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob4.status]}\")\n",
    "print(f\"Distância Total: {pulp.value(prob4.objective):.2f} km\\n\")\n",
    "for r in rotas_lista_c99:\n",
    "    print(f\"Trecho: {r:10} | Alocados: {int(X_c99[r].varValue)} voo(s)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f293c10",
   "metadata": {},
   "source": [
    "## MODELO 5: Minimização de Frota C-99A (S/ TAT)\n",
    "Equivalente ao Modelo 2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "948d033b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:55.215350Z",
     "iopub.status.busy": "2026-06-08T00:02:55.215231Z",
     "iopub.status.idle": "2026-06-08T00:02:55.785030Z",
     "shell.execute_reply": "2026-06-08T00:02:55.784476Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Velocidade média C-99A: 536.65 km/h\n",
      "\n",
      "== RESULTADO DO MODELO 5 (C-99A) ==\n",
      "Status: Optimal\n",
      "Aeronaves mínimas: 1\n",
      "Tempo total de voo: 922 min (15.37 h)\n",
      "\n",
      "| Aeronave   | Origem   | Destino   | Tipo       | Partida   | Chegada   | Duração   |\n",
      "|------------|----------|-----------|------------|-----------|-----------|-----------|\n",
      "| Aeronave 1 | SBSJ     | SBGL      | Vazio      | 00:00     | 00:30     | 30 min    |\n",
      "| Aeronave 1 | SBGL     | SBSJ      | Passageiro | 00:30     | 01:00     | 30 min    |\n",
      "| Aeronave 1 | SBSJ     | SBBR      | Vazio      | 01:00     | 02:34     | 94 min    |\n",
      "| Aeronave 1 | SBBR     | SBSJ      | Passageiro | 02:34     | 04:08     | 94 min    |\n",
      "| Aeronave 2 | SBGR     | SBGL      | Passageiro | 00:00     | 00:38     | 38 min    |\n",
      "| Aeronave 2 | SBGL     | SBGR      | Passageiro | 00:38     | 01:16     | 38 min    |\n",
      "| Aeronave 2 | SBGR     | SBBR      | Passageiro | 01:16     | 02:51     | 95 min    |\n",
      "| Aeronave 2 | SBBR     | SBGR      | Passageiro | 02:51     | 04:26     | 95 min    |\n",
      "| Aeronave 3 | SBAF     | SBBR      | Passageiro | 00:00     | 01:42     | 102 min   |\n",
      "| Aeronave 3 | SBBR     | SBAF      | Passageiro | 01:42     | 03:24     | 102 min   |\n",
      "| Aeronave 4 | SBGL     | SBBR      | Passageiro | 00:00     | 01:42     | 102 min   |\n",
      "| Aeronave 4 | SBBR     | SBGL      | Passageiro | 01:42     | 03:24     | 102 min   |\n"
     ]
    }
   ],
   "source": [
    "# Velocidade Média C-99A\n",
    "df_c99_spd = df_c99.copy()\n",
    "df_c99_spd['Tempo_Horas'] = pd.to_timedelta(df_c99_spd['Tempo de Voo']).dt.total_seconds() / 3600\n",
    "df_c99_spd['Dist_km'] = df_c99_spd.apply(get_dist_row, axis=1)\n",
    "df_valid_c99 = df_c99_spd[(df_c99_spd['Tempo_Horas'] > 0) & (df_c99_spd['Dist_km'] > 0)].copy()\n",
    "df_valid_c99['Velocidade'] = df_valid_c99['Dist_km'] / df_valid_c99['Tempo_Horas']\n",
    "vel_media_c99 = df_valid_c99['Velocidade'].mean()\n",
    "print(f\"Velocidade média C-99A: {vel_media_c99:.2f} km/h\")\n",
    "\n",
    "aeroportos_malha_c99 = set()\n",
    "for r in rotas_lista_c99:\n",
    "    o, d = r.split('-')\n",
    "    aeroportos_malha_c99.add(o)\n",
    "    aeroportos_malha_c99.add(d)\n",
    "aeroportos_malha_c99 = list(aeroportos_malha_c99)\n",
    "\n",
    "tempos_voo_min_c99 = {}\n",
    "for o in aeroportos_malha_c99:\n",
    "    for d in aeroportos_malha_c99:\n",
    "        if o == d:\n",
    "            tempos_voo_min_c99[(o, d)] = 0\n",
    "        else:\n",
    "            dist = calc_distancia(o, d)\n",
    "            tempos_voo_min_c99[(o, d)] = int(round((dist / vel_media_c99) * 60))\n",
    "\n",
    "D_c99 = {(o, d): 0 for o in aeroportos_malha_c99 for d in aeroportos_malha_c99}\n",
    "for r in rotas_lista_c99:\n",
    "    o, d = r.split('-')\n",
    "    D_c99[(o, d)] = voos_requeridos_c99[r]\n",
    "\n",
    "prob5 = pulp.LpProblem(\"Minimizar_Aeronaves_C99\", pulp.LpMinimize)\n",
    "Y5 = pulp.LpVariable.dicts(\"Y5\", [(o, d) for o in aeroportos_malha_c99 for d in aeroportos_malha_c99], lowBound=0, cat='Integer')\n",
    "N5 = pulp.LpVariable(\"N5\", lowBound=0, cat='Integer')\n",
    "\n",
    "prob5 += N5 + 0.0001 * pulp.lpSum([Y5[(i, j)] * tempos_voo_min_c99[(i, j)] for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "\n",
    "for no in aeroportos_malha_c99:\n",
    "    chegadas = pulp.lpSum([D_c99[(i, no)] + Y5[(i, no)] for i in aeroportos_malha_c99])\n",
    "    partidas = pulp.lpSum([D_c99[(no, j)] + Y5[(no, j)] for j in aeroportos_malha_c99])\n",
    "    prob5 += chegadas == partidas\n",
    "\n",
    "tempo_total_voo_c99 = pulp.lpSum([(D_c99[(i, j)] + Y5[(i, j)]) * tempos_voo_min_c99[(i, j)] for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "prob5 += tempo_total_voo_c99 <= N5 * 1440\n",
    "\n",
    "prob5.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 5 (C-99A) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob5.status]}\")\n",
    "print(f\"Aeronaves mínimas: {int(N5.varValue)}\")\n",
    "\n",
    "tempo_gasto_c99 = sum([(D_c99[(i, j)] + int(Y5[(i, j)].varValue)) * tempos_voo_min_c99[(i, j)] for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "print(f\"Tempo total de voo: {int(tempo_gasto_c99)} min ({tempo_gasto_c99/60:.2f} h)\\n\")\n",
    "\n",
    "voos_para_fazer_c99 = []\n",
    "D_copy_c99 = {k: v for k, v in D_c99.items()}\n",
    "for i in aeroportos_malha_c99:\n",
    "    for j in aeroportos_malha_c99:\n",
    "        total_voos = D_c99[(i, j)] + int(Y5[(i, j)].varValue)\n",
    "        for _ in range(total_voos):\n",
    "            if D_copy_c99[(i, j)] > 0:\n",
    "                tipo = 'Passageiro'\n",
    "                D_copy_c99[(i, j)] -= 1\n",
    "            else:\n",
    "                tipo = 'Vazio'\n",
    "            voos_para_fazer_c99.append({'origem': i, 'destino': j, 'tipo': tipo, 'tempo': tempos_voo_min_c99[(i, j)]})\n",
    "\n",
    "tabela_horarios_c99 = []\n",
    "aeronave_id = 1\n",
    "\n",
    "while voos_para_fazer_c99:\n",
    "    voo_atual = voos_para_fazer_c99.pop(0)\n",
    "    hora_atual = datetime.datetime(2025, 1, 1, 0, 0, 0)\n",
    "    tempo_delta = datetime.timedelta(minutes=int(voo_atual['tempo']))\n",
    "    hora_chegada = hora_atual + tempo_delta\n",
    "    \n",
    "    tabela_horarios_c99.append([f\"Aeronave {aeronave_id}\", voo_atual['origem'], voo_atual['destino'], voo_atual['tipo'], hora_atual.strftime('%H:%M'), hora_chegada.strftime('%H:%M'), f\"{voo_atual['tempo']} min\"])\n",
    "    hora_atual = hora_chegada\n",
    "    local_atual = voo_atual['destino']\n",
    "    \n",
    "    while True:\n",
    "        prox_voo = None\n",
    "        for idx, v in enumerate(voos_para_fazer_c99):\n",
    "            if v['origem'] == local_atual:\n",
    "                if (hora_atual - datetime.datetime(2025, 1, 1, 0, 0, 0) + datetime.timedelta(minutes=int(v['tempo']))).total_seconds() <= 1440 * 60:\n",
    "                    prox_voo = voos_para_fazer_c99.pop(idx)\n",
    "                    break\n",
    "        if prox_voo:\n",
    "            tempo_delta = datetime.timedelta(minutes=int(prox_voo['tempo']))\n",
    "            hora_chegada = hora_atual + tempo_delta\n",
    "            tabela_horarios_c99.append([f\"Aeronave {aeronave_id}\", prox_voo['origem'], prox_voo['destino'], prox_voo['tipo'], hora_atual.strftime('%H:%M'), hora_chegada.strftime('%H:%M'), f\"{prox_voo['tempo']} min\"])\n",
    "            hora_atual = hora_chegada\n",
    "            local_atual = prox_voo['destino']\n",
    "        else:\n",
    "            break\n",
    "    aeronave_id += 1\n",
    "\n",
    "print(tabulate(tabela_horarios_c99, headers=[\"Aeronave\", \"Origem\", \"Destino\", \"Tipo\", \"Partida\", \"Chegada\", \"Duração\"], tablefmt=\"github\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e218deab",
   "metadata": {},
   "source": [
    "## MODELO 6: Minimização de Frota C-99A (C/ 40 min TAT)\n",
    "Equivalente ao Modelo 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4c89733",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:55.786359Z",
     "iopub.status.busy": "2026-06-08T00:02:55.786235Z",
     "iopub.status.idle": "2026-06-08T00:02:55.800410Z",
     "shell.execute_reply": "2026-06-08T00:02:55.799867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "== RESULTADO DO MODELO 6 (C-99A C/ TAT 40 MIN) ==\n",
      "Status: Optimal\n",
      "Aeronaves mínimas: 1\n",
      "Tempo total operacional comprometido: 1402 min (23.37 h)\n",
      "\n",
      "| Aeronave   | Origem   | Destino   | Tipo       | Partida   | Chegada   | Duração   |\n",
      "|------------|----------|-----------|------------|-----------|-----------|-----------|\n",
      "| Aeronave 1 | SBSJ     | SBGL      | Vazio      | 00:00     | 00:30     | 30 min    |\n",
      "| Aeronave 1 | SBGL     | SBSJ      | Passageiro | 01:10     | 01:40     | 30 min    |\n",
      "| Aeronave 1 | SBSJ     | SBBR      | Vazio      | 02:20     | 03:54     | 94 min    |\n",
      "| Aeronave 1 | SBBR     | SBSJ      | Passageiro | 04:34     | 06:08     | 94 min    |\n",
      "| Aeronave 2 | SBGR     | SBGL      | Passageiro | 00:00     | 00:38     | 38 min    |\n",
      "| Aeronave 2 | SBGL     | SBGR      | Passageiro | 01:18     | 01:56     | 38 min    |\n",
      "| Aeronave 2 | SBGR     | SBBR      | Passageiro | 02:36     | 04:11     | 95 min    |\n",
      "| Aeronave 2 | SBBR     | SBGR      | Passageiro | 04:51     | 06:26     | 95 min    |\n",
      "| Aeronave 3 | SBAF     | SBBR      | Passageiro | 00:00     | 01:42     | 102 min   |\n",
      "| Aeronave 3 | SBBR     | SBAF      | Passageiro | 02:22     | 04:04     | 102 min   |\n",
      "| Aeronave 4 | SBGL     | SBBR      | Passageiro | 00:00     | 01:42     | 102 min   |\n",
      "| Aeronave 4 | SBBR     | SBGL      | Passageiro | 02:22     | 04:04     | 102 min   |\n"
     ]
    }
   ],
   "source": [
    "# Modelo 6\n",
    "prob6 = pulp.LpProblem(\"Minimizar_Aeronaves_C99_TAT\", pulp.LpMinimize)\n",
    "Y6 = pulp.LpVariable.dicts(\"Y6\", [(o, d) for o in aeroportos_malha_c99 for d in aeroportos_malha_c99], lowBound=0, cat='Integer')\n",
    "N6 = pulp.LpVariable(\"N6\", lowBound=0, cat='Integer')\n",
    "\n",
    "TAT_min = 40\n",
    "\n",
    "prob6 += N6 + 0.0001 * pulp.lpSum([Y6[(i, j)] * tempos_voo_min_c99[(i, j)] for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "\n",
    "for no in aeroportos_malha_c99:\n",
    "    chegadas = pulp.lpSum([D_c99[(i, no)] + Y6[(i, no)] for i in aeroportos_malha_c99])\n",
    "    partidas = pulp.lpSum([D_c99[(no, j)] + Y6[(no, j)] for j in aeroportos_malha_c99])\n",
    "    prob6 += chegadas == partidas\n",
    "\n",
    "tempo_total_op_c99 = pulp.lpSum([(D_c99[(i, j)] + Y6[(i, j)]) * (tempos_voo_min_c99[(i, j)] + TAT_min) for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "prob6 += tempo_total_op_c99 <= N6 * 1440\n",
    "\n",
    "prob6.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 6 (C-99A C/ TAT 40 MIN) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob6.status]}\")\n",
    "print(f\"Aeronaves mínimas: {int(N6.varValue)}\")\n",
    "\n",
    "tempo_voo_c99_6 = sum([(D_c99[(i, j)] + int(Y6[(i, j)].varValue)) * tempos_voo_min_c99[(i, j)] for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "tot_voos_6 = sum([(D_c99[(i, j)] + int(Y6[(i, j)].varValue)) for i in aeroportos_malha_c99 for j in aeroportos_malha_c99])\n",
    "solo_tot_6 = tot_voos_6 * TAT_min\n",
    "\n",
    "print(f\"Tempo total operacional comprometido: {tempo_voo_c99_6 + solo_tot_6} min ({(tempo_voo_c99_6 + solo_tot_6)/60:.2f} h)\\n\")\n",
    "\n",
    "voos_para_fazer_c99_6 = []\n",
    "D_copy_c99_6 = {k: v for k, v in D_c99.items()}\n",
    "for i in aeroportos_malha_c99:\n",
    "    for j in aeroportos_malha_c99:\n",
    "        tot = D_c99[(i, j)] + int(Y6[(i, j)].varValue)\n",
    "        for _ in range(tot):\n",
    "            if D_copy_c99_6[(i, j)] > 0:\n",
    "                tipo = 'Passageiro'\n",
    "                D_copy_c99_6[(i, j)] -= 1\n",
    "            else:\n",
    "                tipo = 'Vazio'\n",
    "            voos_para_fazer_c99_6.append({'origem': i, 'destino': j, 'tipo': tipo, 'tempo': tempos_voo_min_c99[(i, j)]})\n",
    "\n",
    "tabela_horarios_c99_6 = []\n",
    "aeronave_id = 1\n",
    "\n",
    "while voos_para_fazer_c99_6:\n",
    "    voo_atual = voos_para_fazer_c99_6.pop(0)\n",
    "    hora_atual = datetime.datetime(2025, 1, 1, 0, 0, 0)\n",
    "    tempo_delta = datetime.timedelta(minutes=int(voo_atual['tempo']))\n",
    "    hora_chegada = hora_atual + tempo_delta\n",
    "    \n",
    "    tabela_horarios_c99_6.append([f\"Aeronave {aeronave_id}\", voo_atual['origem'], voo_atual['destino'], voo_atual['tipo'], hora_atual.strftime('%H:%M'), hora_chegada.strftime('%H:%M'), f\"{voo_atual['tempo']} min\"])\n",
    "    hora_atual = hora_chegada + datetime.timedelta(minutes=40)\n",
    "    local_atual = voo_atual['destino']\n",
    "    \n",
    "    while True:\n",
    "        prox_voo = None\n",
    "        for idx, v in enumerate(voos_para_fazer_c99_6):\n",
    "            if v['origem'] == local_atual:\n",
    "                if (hora_atual - datetime.datetime(2025, 1, 1, 0, 0, 0) + datetime.timedelta(minutes=int(v['tempo']))).total_seconds() <= 1440 * 60:\n",
    "                    prox_voo = voos_para_fazer_c99_6.pop(idx)\n",
    "                    break\n",
    "        if prox_voo:\n",
    "            tempo_delta = datetime.timedelta(minutes=int(prox_voo['tempo']))\n",
    "            hora_chegada = hora_atual + tempo_delta\n",
    "            tabela_horarios_c99_6.append([f\"Aeronave {aeronave_id}\", prox_voo['origem'], prox_voo['destino'], prox_voo['tipo'], hora_atual.strftime('%H:%M'), hora_chegada.strftime('%H:%M'), f\"{prox_voo['tempo']} min\"])\n",
    "            hora_atual = hora_chegada + datetime.timedelta(minutes=40)\n",
    "            local_atual = prox_voo['destino']\n",
    "        else:\n",
    "            break\n",
    "    aeronave_id += 1\n",
    "\n",
    "print(tabulate(tabela_horarios_c99_6, headers=[\"Aeronave\", \"Origem\", \"Destino\", \"Tipo\", \"Partida\", \"Chegada\", \"Duração\"], tablefmt=\"github\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4845bd74",
   "metadata": {},
   "source": [
    "# Alocação de Frotas - Mix (C-97 + C-99A)\n",
    "Agora vamos analisar a demanda de forma unificada (soma dos passageiros transportados por C-97 e C-99A) e colocar as duas frotas para trabalhar em conjunto nos Modelos 7, 8 e 9.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5818c70c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:55.801763Z",
     "iopub.status.busy": "2026-06-08T00:02:55.801641Z",
     "iopub.status.idle": "2026-06-08T00:02:56.285567Z",
     "shell.execute_reply": "2026-06-08T00:02:56.285011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trecho</th>\n",
       "      <th>Total_PAX</th>\n",
       "      <th>Dias_Operados</th>\n",
       "      <th>Media_PAX_Dias_Operados</th>\n",
       "      <th>Score_Relevancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>SBGL-SBBR</td>\n",
       "      <td>1482.0</td>\n",
       "      <td>83</td>\n",
       "      <td>17.855422</td>\n",
       "      <td>123006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>SBBR-SBGL</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>83</td>\n",
       "      <td>16.855422</td>\n",
       "      <td>116117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>SBGR-SBBR</td>\n",
       "      <td>483.0</td>\n",
       "      <td>40</td>\n",
       "      <td>12.075000</td>\n",
       "      <td>19320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>SBBR-SBGR</td>\n",
       "      <td>428.0</td>\n",
       "      <td>36</td>\n",
       "      <td>11.888889</td>\n",
       "      <td>15408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>SBGL-SBGR</td>\n",
       "      <td>374.0</td>\n",
       "      <td>34</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>SBBR-SBAF</td>\n",
       "      <td>341.0</td>\n",
       "      <td>18</td>\n",
       "      <td>18.944444</td>\n",
       "      <td>6138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>SBBR-SBSJ</td>\n",
       "      <td>251.0</td>\n",
       "      <td>22</td>\n",
       "      <td>11.409091</td>\n",
       "      <td>5522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>SBGR-SBGL</td>\n",
       "      <td>183.0</td>\n",
       "      <td>30</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>5490.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>SBGL-SBSJ</td>\n",
       "      <td>182.0</td>\n",
       "      <td>27</td>\n",
       "      <td>6.740741</td>\n",
       "      <td>4914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>SBSJ-SBBR</td>\n",
       "      <td>212.0</td>\n",
       "      <td>23</td>\n",
       "      <td>9.217391</td>\n",
       "      <td>4876.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Trecho  Total_PAX  Dias_Operados  Media_PAX_Dias_Operados  \\\n",
       "264  SBGL-SBBR     1482.0             83                17.855422   \n",
       "85   SBBR-SBGL     1399.0             83                16.855422   \n",
       "321  SBGR-SBBR      483.0             40                12.075000   \n",
       "88   SBBR-SBGR      428.0             36                11.888889   \n",
       "280  SBGL-SBGR      374.0             34                11.000000   \n",
       "65   SBBR-SBAF      341.0             18                18.944444   \n",
       "110  SBBR-SBSJ      251.0             22                11.409091   \n",
       "328  SBGR-SBGL      183.0             30                 6.100000   \n",
       "297  SBGL-SBSJ      182.0             27                 6.740741   \n",
       "504  SBSJ-SBBR      212.0             23                 9.217391   \n",
       "\n",
       "     Score_Relevancia  \n",
       "264          123006.0  \n",
       "85           116117.0  \n",
       "321           19320.0  \n",
       "88            15408.0  \n",
       "280           12716.0  \n",
       "65             6138.0  \n",
       "110            5522.0  \n",
       "328            5490.0  \n",
       "297            4914.0  \n",
       "504            4876.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pywkNzJXarfdZry90azGPCmtpvbDeCxXi0gxpdGoLvV/ari36fs0KMr0CbkpV1uyrZrz1vAoqoy9BiE7HnypmK/BUqrV+Rx2vahfNsodSGn7ontLhllK/mwqwqdK/PrQGW8egBrWUceGlh+v31nsfE70mr8q8BtL0WAXomtVOzPEFAJEgUyC+0qoAAKQAdeLVOV+wYEGytydC5FFldc12ay9vb3s1pF0aSNJSCGVJxLVXNwBEGtZgAwCANE+Fuq6++mr74IMPUvulIB6aadf7pMwFgmsAGQ0BNgAASPMUqCmVXunzqgaPtEvrvpXKr5R/AMhoSBEHAAAAAMAHzGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA/YBzuVLFu2zO2Jmi1bttR6CQAAAACAeJw6dcoV2yxfvnx8DyXATi0KrtmCHAAAAADStsTEbcxgpxJv5rpMmTKp9RIAAAAAAPFYsWKFJRRrsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYadyZM2dS+yWkSbQLAAAAgLQma2q/AMQtS5Ys9niHp+2vP9fTVP9z9TVX2Yi3XqM9AAAAAKQpBNjpgILrP1asSu2XAQAAAACIAyniAAAAAAD4gAAbAAAAAIBIDLBHjRplLVu2jHLbV199ZU2bNrXy5ctb3bp17dVXX7Xjx48H7z9x4oS98MILVr16dfeYbt262d69e6M8x88//2xNmjSx66+/3urXr2+ffvpplPv9eA4AAAAAQMaVpgLsyZMn27Bhw6LctnTpUuvcubPdeuutNmPGDOvbt6/NnTvXBcOe559/3n744QcbMWKEvfvuu7Z+/Xp74okngvf//fff1q5dO6tVq5ZNnz7dmjVrZj169HABs5/PAQAAAADIuNJEkbMdO3a4wHnRokV25ZVXRrlvypQpVrVqVWvfvr37Xvd37drV+vTp44Lsffv22cyZM+3tt9+2SpUquccMGTLEzTAvW7bMzUYrYC5RooT7OSlWrJitWrXKxo4d62as9f8n9zkAAAAAABlbmpjBXrlypWXLls1mz57t0q9DPfroo9azZ88ot2XOnNlOnTplhw8ftl9++cXdVq1ateD9RYsWtYIFC9qSJUuCs+DRg2A9Xj8bCAR8eQ4AAAAAQMaWJmawta5aHzEpVapUlO8VWE+YMMFKly5t+fPnd7PPF154oeXIkSPK4y655BLbvn27+1qfCxUqdM79x44dczPgfjyHXktiKTA/evRorPdnypTJcuXKlejnzSjU9gxuAAAAAAgnxRyKzdJNgJ1Qp0+fduue//rrL7de2wuysmfPfs5jFSyrcJmoIFr0x3jfnzx50pfnSAoNFqxevTrW+xVcRx9gwP/ZsGGDe+8AAAAAIJxiihfTdYCtdPAnn3zSFi9ebG+88YaVLVvW3Z4zZ84YA1wFxt7srwLl6I/xvtdj/HiOpFBafPHixWO9P6GjJBmV0viZwQYAAAAQTuvWrUvwY9NFgL1z505r27atbdmyxcaNG2eVK1cO3qe07f3797tgN3RUQT+jNdRSuHBh933058ydO7flzZvXl+dICgXQ+nkkDenzAAAAAMItMROfaaLIWVwOHDhgDz/8sNuTWmnhocG1VKxY0c6ePRssVOalDmtdtfdYVQbXzHeohQsXWoUKFVzBND+eAwAAAACQsaX5yPDll1+2zZs322uvveYKie3atSv4cebMGTfD3KBBA7dtl7b5Wr58uT311FNWpUoVK1eunHuOli1butsHDRrk9rMeP368zZs3z9q0aePu9+M5AAAAAAAZW6ZAGlvE2qtXL5cKPmnSJBdAaw9qr9BYdAsWLLDLLrvMVeIeMGCAff755+72G2+80QXLqgzu+e6771yQvnHjRvczjz/+uN1xxx3B+/14jsRYsWKF+1ymTJl4H1v/5qb2x4pVSfp/IlHpMqVs3oJpqf0yAAAAAGQAKxIRu6W5ADujIMBOOgJsAAAAAGkxdkvzKeIAAAAAAKQHBNgAAAAAAPiAABsAAAAAAB8QYAMAAAAA4AMCbAAAAAAAfECADQAAAACADwiwkaFpr3XQLgAAAIAfsvryLEA6lSVLFuvS5xVbt+Gf1H4paUbxokXs9Zd6pfbLAAAAANIdAmxkeAqu/1i7LsO3AwAAAIDkIUUcAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAkRZgjxo1ylq2bBnlttWrV1uLFi2sXLlyVrduXZs4cWKU+8+ePWvDhw+3WrVquce0bdvWNm/enOLPAQAAAADI2NJMgD158mQbNmxYlNv27dtnrVq1siJFiti0adOsU6dONmjQIPe1Z+TIkfb+++9bv379bMqUKS5YbtOmjZ08eTJFnwMAAAAAkLFlTe0XsGPHDuvbt68tWrTIrrzyyij3TZ061bJly2YvvviiZc2a1YoVK2abNm2y0aNHW9OmTV0APH78eOvevbvVqVPH/czQoUPdTPT8+fPtzjvvTJHnAAAAAAAg1WewV65c6YLX2bNn2/XXXx/lvqVLl1qVKlVcUOupVq2abdy40Xbv3m1r1qyxI0eOWPXq1YP358uXz0qVKmVLlixJsecAAAAAACDVZ7C1nlkfMdm+fbtdc801UW675JJL3Odt27a5+6Vw4cLnPMa7LyWe4+KLL0707w0AAAAAiCypHmDH5fjx45Y9e/Yot+XIkcN9PnHihB07dsx9HdNjDhw4kGLPkVSBQMCOHj0a6/2ZMmWyXLlyJfn5I53eO7VhUtG+4W1fAAAAIBKoT6zYId0H2Dlz5gwWGvN4AW3u3Lnd/aLHeF97j/EC05R4jqQ6deqUq04eG/3/SlVHzDZs2BAcIEkK2je87QsAAABEiugTrukywC5UqJDt3Lkzym3e9wULFrTTp08Hb1OF79DHlChRIsWeI6m09rx48eKx3p/QUZKMqmjRosmewUb42hcAAACIBOvWrUvwY9N0gF25cmW3bdaZM2csS5Ys7raFCxe6jv9FF11kefPmtTx58rgK5F5wfPDgQVu1apXbszqlniM5AV5yZsAzOtLnaV8AAAAg3BIzMZfqVcTjoi2wDh8+bL1793ajBtOnT7cJEyZYu3btgtP0CoK1J/WCBQtcRfCuXbu6Ged69eql2HMAAAAAAJCmZ7A1Ozx27Fjr37+/NW7c2AoUKGA9evRwX3ueeOIJl+bdp08fV4xMs83jxo1z6dcp+RwAAAAAgIwtU4BFlqlixYoV7nOZMmXifWz9m5vaHytWpcCrSh9Klyll8xZM8+35GjzY0f5Ym/B1FZGudIni9unkkan9MgAAAIB0F7ul6RRxAAAAAADSCwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAACAtBpgHz58OBxPCwAAAABAmpU1KT908uRJe/fdd23x4sXu60Ag4G7X56NHj9q6devs999/9/u1AgAAAAAQWQH2wIED7b333rNrrrnG9u7dazly5LD8+fPbn3/+aadOnbLOnTv7/0oBAAAAAIi0FPH58+dbq1atbPbs2daiRQsrXbq0ffTRR+72Sy+91M6ePev/KwUAAAAAINICbM1a33jjje5rzWKvWLHCfV2wYEF77LHHbO7cuf6+SgAAAAAAIjHAzps3r1t7LVdccYVt27YtWNjsyiuvdN8DAAAAAJCRJCnArlSpkk2aNMmOHTvmAuxcuXLZl19+6e5btmyZ5cmTx+/XCQAAAABA5AXYKmL222+/uXTwrFmz2gMPPGDPPvusNWnSxF5//XW77bbb/H+lAAAAAABEWoBdokQJ++yzz6x9+/bu+27dulmnTp3s4osvtg4dOliPHj18fZGnT592gftNN91k5cuXtwcffNAF+J7Vq1e7YmvlypWzunXr2sSJE6P8vIquDR8+3GrVquUe07ZtW9u8eXOUx/jxHAAAAACAjCtJAbYUKFDAatas6b7OlCmTC7ZHjx7tZrezZ8/u52u0t956y1Up79evn82cOdOKFi1qbdq0sZ07d9q+fftcRfMiRYrYtGnTXKA/aNAg97Vn5MiR9v7777ufnzJliguW9fPeOnI/ngMAAAAAkLEleB/sN954w5o1a+YqhevruCjgVpDqF63vvvPOO+2GG25w3/fq1csF3JrF3rBhg2XLls1efPFFl65erFgx27Rpkwv2mzZt6gLg8ePHW/fu3a1OnTru54cOHepmorWtmJ536tSpyX4OAAAAAEDGlqgAW1tzpUaAfdFFF9nXX3/tUrgLFy5sH374oZslL1mypAu0q1Sp4gJjT7Vq1WzUqFG2e/du27p1qx05csSqV68evD9fvnxWqlQpW7JkiQuOly5dmuznAAAAAABkbAkOsNesWRPj1ymhd+/e1qVLF7v55pstS5YsljlzZhsxYoRL6d6+fbvbizvUJZdc4j5ruzDdLwrMoz/Gu8+P50iKQCBgR48ejXOgQhXaETNVsVcbJhXtG972BQAAACKB+sSKHXwNsKP7559/bPHixXbPPfe47//++2+3ZlmzzP/5z3/MT+vWrXN7b7/55ptuBl2z1krXfu+99+z48ePnrPnOkSOH+3zixAkXJEhMjzlw4ID72o/nSIpTp0654mqxUXCtWXLETMsDvPcmKWjf8LYvAAAAECkSWmcsSQG21j4/+uijLtj1AuyDBw/a7NmzXZCtPbKjzwgnlWaQVaV8woQJbv9tKVOmjAu6NYudM2fOcwqNKSiW3Llzu/tFj/G+9h7jzQ778RxJoXXfxYsXj/X+hI6SZFQqdpfcGWyEr30BAACASKDYM6GSFGAPHjzYKlSoEGUttrbPWrBggasiPnDgQBs7dqz54ffff3czvQqqQ11//fX23XffudlyVRMP5X2vAQBt8eXdppTy0MdouzEpVKhQsp8jqQGeAngkDenz4UX7AgAAAJaoibkkbdO1cuVKa926dZTZXC9l+uGHH3ZBsV8U/MratWuj3P7nn3/alVdeaZUrV7ZffvnFzpw5E7xv4cKFbvZNxdFUCC1Pnjy2aNGi4P2abV+1apX7WfHjOQAAAAAAGVuSAmwF1jt27IjxPu0prSJkfilbtqxVrFjRevbs6YLejRs32rBhw+znn3+2xx57zG2jdfjwYVcITVP306dPd+nk7dq1C+bKa1249rXWDLsKtHXt2tUF7vXq1XOP8eM5AAAAAAAZW5JSxLX/8/Dhw+3aa6+NkiKtQmdaF63tvPyiYP2tt95yQfUzzzzjioppfbcCYKWJi9LR+/fvb40bN7YCBQpYjx493NeeJ554wqV59+nTxxU006zzuHHj3Bpo0Sx1cp8DAAAAAJCxZQokoYrRrl27rHnz5q4A2WWXXWb58+d3M9ebN29230+ePNkFqYjdihUr3Ofoa8tjUv/mpvbHilU05/+ULlPK5i2Y5lt7NHiwo/2xNuGFCyJd6RLF7dPJI1P7ZQAAAADpLnZL0gy2guc5c+a4VOpff/3V9u/f74qBKY26SZMmdt555yXlaQEAAAAASLeSvA+2ql8roNYHAAAAAAAZXZID7A0bNti3335rR48etbNnz55TxrxTp05+vD4AAAAAACI3wJ41a5b16tXLYlu+TYANAAAAAMhokhRgjxw50mrUqGEvvfSS26oqMRtvAwAAAAAQiZK0YfXWrVutTZs2VrhwYYJrAAAAAACSGmAXLVrUbdEFAAAAAACSEWB369bNpYkvWrTITpw4kZSnAAAAAAAgoiRpDXb//v1tz5499sgjj8R4v9Zkr1q1KrmvDQAAAACAyA6wGzVq5P8rAQAAAAAgowXYnTt39v+VAAAAAACQ0QJs0drrtWvX2smTJ4P7YZ89e9aOHTtmS5cute7du/v5OgEAAAAAiLwAW8XNunTpYgcOHIjx/vPOO48AGwAAAACQoSQpwB46dKhdeOGF1q9fP5s9e7ZlzpzZmjRpYt9995198MEHNmbMGP9fKQAAAAAAkRZgKzX8pZdesltvvdUOHTpkU6ZMsdq1a7uPU6dO2VtvvWWjR4/2/9UCAAAAABBJ+2BrrXXBggXd11dccYX99ddfwftuu+02tugCAAAAAGQ4SQqwixQp4maxpWjRoq6w2fr16933p0+ftiNHjvj7KgEAAAAAiMQAu2HDhjZo0CB77733LH/+/Fa6dGm3Hvurr76yN99804oXL+7/KwUAAAAAINIC7DZt2ljz5s3t999/d9/37dvXVq9ebR07dnQz2T169PD7dQIAAAAAEHlFzlQ1vGfPnsHvy5QpY19++aULrq+66irLkyePn68RAAAAAIDInMHu1KmTffHFF65iuEdBddmyZQmuAQAAAAAZUpJmsP/99197/PHH7fzzz7f69evbXXfdZRUqVPD/1QEAAAAAEMkB9qxZs+zvv/+2Tz75xObOnWsffvihXXbZZdaoUSMXbGvrLgAAAAAAMpIkpYhLsWLFrEuXLvb555/bRx99ZLfeeqvNnDnTzWjfd999/r5KAAAAAAAiNcCOvi+2Au4SJUq4Amj//POPH08LAAAAAEBkp4jL0aNHXeVwpYj/+OOPLrCuXbu2DR8+3H0GAAAAACAjSVKArdTw7777zo4fP+6Kmz377LN2++23W968ef1/hQAAAAAARGqAvXbtWmvbtq0raqbiZgAAAAAAZHRJCrDnzZsX5fsTJ05Y9uzZLVOmTH69LgAAAAAAMsYa7PXr17v11j/99JMdPnzYVRL/+OOP7aqrrrKWLVv6+yoBAAAAAEjjklRFfPXq1XbPPffYypUrrWHDhhYIBNztWbJksQEDBtiMGTP8fp0AAAAAAETeDParr75qpUuXtvHjx7vvJ0+e7D736dPHpYtPnDjRGjdu7O8rBQAAAAAg0mawf/vtN3vkkUcsa9as56y7vuOOO2zjxo1+vT4AAAAAACI3wM6RI4fboism+/fvdwXPAAAAAADISJIUYNesWdMVONu+fXvwNs1kHzlyxKWN16hRw8/XCAAAAABAZK7Bfvrpp+2+++6z+vXrW8mSJV1w/corr9iGDRtcwbMhQ4b4/0oBAAAAAIi0GezChQvbrFmz7OGHH3YBdZEiRezo0aN255132vTp0+3yyy/3/5UCAAAAABCJ+2BfeOGF1rVrV39fDQAAAAAAkR5gz5w5M1FPfPfddyfl9QAAAAAAENkBdq9evRL8pFqTTYANAAAAAMhIEhxgL1iwILyvBAAAAACAjBBgX3rppTHefujQIdu5c6crbJYlSxb3AQAAAABARpOkKuKyaNEia9asmVWpUsUaNmxof/31l3Xr1s1t1wUAAAAAQEaTpAD7559/ttatW1vOnDmte/fubqsu0Z7YEydOtHfeecfv1wkAAAAAQOQF2MOGDbObb77ZJk2aFNwLW9q3b29t2rSxjz76yO/XCQAAAABA5AXYq1evtqZNmwYrhoeqWbOmbdmyxZ9XBwAAAABAJAfYefPmtV27dsV437Zt29z9AAAAAABkJEkKsJUePnToUFuxYkXwNs1kb9++3d5++22rU6eOn68RAAAAAIDI2aYrlKqF//7773bvvffaxRdf7G576qmnXIBduHBh9zUAAAAAABlJkgLs888/3xUymzlzpi1cuND279/v0sJbtmxpTZo0sVy5cvn/SgEAAAAAiLQAW7Jnz+5msPUR3dq1a61EiRLJfW0AAAAAAERmgL1nzx6bP3++W29dt25du+SSS6Lcf/DgQbeF19SpU+2PP/7w+7UCAAAAAJD+A+zly5db69at7dChQ+77IUOG2MSJE61kyZLue6WM67Z9+/ZZ2bJlw/eKAQAAAABIz1XEX3/9dbe2esyYMTZlyhS79NJL7bXXXrNjx45Zu3bt7LnnnrMsWbJY//793Qw2AAAAAAAZSYJnsFeuXGldunSxWrVque+fffZZe+SRR1xF8e+++84eeOAB69q1q+XJkyecrxcAAAAAgPQ9g63U8Kuvvjr4vVLDT548ab/88ou98847LuAOZ3CtiuV33HGHlSlTxho0aGCfffZZ8L5///3XzaJXqFDBbrjhBrcO/MyZM1F+fvLkyW7/bqWvazBg1apVUe734zkAAAAAABlXggNsBZuqHO7JkSOH+9y9e3erVq2ahdOsWbOsd+/e9uCDD9qnn35qd955p9tre9myZXbq1Cm3NlyUuv7888/bBx98YG+++Wbw52fMmGEDBw50M/DTp0+3yy67zFq1amV79+519/vxHAAAAACAjC3BAXZsrr32WgunQCDg1n8/9NBDLsAuUqSIdejQwWrUqGGLFy+2zz//3LZu3eqC32uuucZuueUWF3y/++67boZd3n77bWvRooU1atTIihcvbgMGDHDryVWYTfx4DgAAAABAxpbsAFtbdoXThg0bbMuWLdawYcMot48bN86ldC9dutSuu+46O//884P3aUb98OHDtnr1are12MaNG6169erB+7NmzWqVKlWyJUuWuO/9eA4AAAAAQMaWqH2wP/74Y1fQzJtZVnD94YcfnrMftm7v1KmTbwG2HD161KVxa92z0rM1i629uLdv326FChWK8jPe69m2bZsLhKVw4cLnPGbNmjXuaz+eAwAAAACQsSUqwI5p+62YbvMzwNYssvTs2dM6d+7s1nwrpbtjx46uuNrx48ctX758UX7GWx9+4sQJt42YhK4f9x6j+8WP50gKDVJo4CA2akeloSNmel/UhklF+4a3fQEAAIBI4E0u+xpgp9ZMbbZs2dxnzV43btw4uO5bM9kKsHPmzBlcJ+3xgt7cuXO7+yWmx3jBqx/PkRQqrqYU9NjouUuVKpXk5490ym7wBj+SgvYNb/sCAAAAkSL6ZKsvM9iJdfbsWbv11ltdgbDQLb4So2DBgu6zio+FUqGxb775xqpUqWJ//vlnlPt27twZ/FkvrVu3FStWLMpjvOdWenhynyOpgwf6PVJrfXt6V7Ro0WTPYCN87QsAAABEgnXr1iX4sWENsNU5V4Gy6DO/iaHiY+edd579/vvvrqiYRwGxKopXrlzZ7ZGtVHJvH+6FCxe6n9Fe3RppUKCwaNGiYJGy06dPu8Jm2sta/HiOpAZ4miFH0pA+H160LwAAAGCJmphLdhXxcFN6dps2bdye1J988on9888/9tZbb9mPP/7o9qHWlloFChSwJ5980qWxf/nllzZkyBB79NFHg9P4+lrp5NrLWqMP//3vf92663vuucfd78dzAAAAAAAytrDOYPtFBc00mzZ06FDbsWOHS9MeMWKEVa1a1d0/duxYe+GFF+zee+91W21pVlk/49Hthw4dsmHDhtn+/futdOnSLljOnz9/sFhZcp8DAAAAAJCxpYsAWzRbrY+YXHHFFTZ+/Pg4f15F0vQRGz+eAwAAAACQcaX5FHEAAAAAANIDAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAASOsBtvYL0x7T2k8aAAAAAIBIluQq4idPnrSPP/7YfvrpJ9u1a5cNGDDAFi9ebNddd52VLVvWPSZz5sw2adIkP18vAAAAAACRM4O9d+9ea9q0qfXv3982bdpky5cvt+PHj9s333xjLVu2tGXLlvn/SgEAAAAAiLQAe+DAgXbkyBGbO3euzZgxwwKBgLt9+PDhVqZMGfcZAAAAAICMJEkB9tdff21dunSxK664wq2z9uTIkcMeffRRW7lypZ+vEQAAAACAyAywT5w4YRdccEGM92XJksVOnTqV3NcFAAAAAEDkB9hKA3///fdjvG/OnDlWunTp5L4uAAAAAAAiv4q40sMfeeQRu+uuu6x27douTfyTTz6xESNG2A8//GBjx471/5UCAAAAABBpM9iVKlWyd955x3LlyuWCaRU5mzBhgtuua9SoUVatWjX/XykAAAAAAJE2g/3zzz9b+fLlbcqUKW57rgMHDliePHnsvPPO8/8VAgAAAAAQqTPYjz/+uM2fP999nTNnTitYsCDBNQAAAAAgQ0tSgJ0vXz4XWAMAAAAAgGSkiLdr185eeukl27Bhg5UsWdJy5859zmMqV66clKcGAAAAACDjBNh9+/Z1n4cOHeo+q4q4RwXP9P3q1av9eo0AAAAAAERmgD1x4kT/XwkAAAAAABktwK5SpYr/rwQAAAAAgIwWYIvWXw8fPtwWL15sBw8etAsvvNDtj92pUycrVqyYv68SAAAAAIBIDLDXrVtnzZs3tyxZsljdunXt4osvtl27dtnXX39t33zzjX300UcE2QAAAACADCVJAfagQYPssssus0mTJlnevHmDtx86dMgefvhhV/zsjTfe8PN1AgAAAAAQeftgL1myxNq3bx8luBZ9/9hjj7n7AQAAAADISJIUYGfNmtVy5MgR433Zs2e3kydPJvd1AQAAAAAQ+QF2mTJl7P3333d7XofS95MnT7bSpUv79foAAAAAAIjcNdhdunSx+++/3xo1amT169e3AgUKuCJn8+bNc9XF33nnHf9fKQAAAAAAkRZgawZ77NixNnjwYFfMTDPXmTJlcjPXY8aMscqVK/v/SgEAAAAAiMR9sKtVq2ZTpkxx6621D3a+fPns9OnT5xQ+AwAAAAAgI0jSGuxTp05Z37597d5777VcuXJZwYIFbdmyZVa9enV79dVX7ezZs/6/UgAAAAAAIi3AHjFihM2ePdsaNGgQvK1UqVLWvXt3mzp1qksfBwAAAAAgI0lSivicOXOsZ8+e1rx58+BtF1xwgT3yyCNuC6+JEye6/bABAAAAAMgokjSDvW/fPrv88stjvO+qq66y7du3J/d1AQAAAAAQ+QG2gujPP/88xvu++uoru+KKK5L7ugAAAAAAiPwU8Yceesh69epl+/fvt1tuucUuuugi27t3r3399df22Wef2csvv+z/KwUAAAAAINIC7LvvvtuOHDliI0eOtPnz5wdvv/DCC+3ZZ5919wMAAAAAkJEkeR/sBx980B544AHbsGGDm8nW1lxXX321nX/++f6+QgAAAAAAIm0N9vLly619+/Y2c+ZM932mTJnsp59+slatWlnLli2tdu3aNm7cuHC9VgAAAAAA0n+AvWbNGhdEr1692nLnzu1uW7FihfXv399VFNfe2B07drShQ4fal19+Gc7XDAAAAABA+k0RHzVqlJUsWdImTJhguXLlcrdpv2sZNGiQu092795tkyZNcsXPAAAAAADIKBI8g71kyRI3g+0F1/LDDz+42WsvuJYbbrjBVq1a5f8rBQAAAAAgEgJsFTIrVKhQ8Pu///7b9u3bZ1WrVo3yOAXgJ0+e9PdVAgAAAAAQKQH2BRdcYHv27Al+v3DhQlfkrHr16lEep8A7f/78/r5KAAAAAAAiJcCuUqWKTZ061QKBgJ0+fdqmTZtmOXLksFq1agUfo5nryZMnW4UKFcL1egEAAAAASN9Fzjp06GD33XefK16mIHvr1q3WqVMny5s3r7tfAbeCa+2LPXDgwHC+ZgAAAAAA0m+AffXVV7sZ7PHjx7tU8bZt29r9998fvH/YsGGWNWtWe/PNN+3aa68N1+sFAAAAACB9B9hSvHhxGzBgQIz3ffzxx1agQAHLnDnBWecAAAAAAGTMADsuBQsW9OupAAAAAABId5huBgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAGTHA3rBhg5UvX96mT58evG316tXWokULK1eunNWtW9cmTpwY5WfOnj1rw4cPt1q1arnHtG3b1jZv3hzlMX48BwAAAAAg40pXAfapU6ese/fudvTo0eBt+/bts1atWlmRIkVs2rRp1qlTJxs0aJD72jNy5Eh7//33rV+/fjZlyhQXLLdp08ZOnjzp23MAAAAAADK2dBVgjxgxwvLkyRPltqlTp1q2bNnsxRdftGLFilnTpk3tkUcesdGjR7v7FQCPHz/ennjiCatTp46VLFnShg4datu3b7f58+f79hwAAAAAgIwt3QTYS5YssQ8//NBeeeWVKLcvXbrUqlSpYlmzZg3eVq1aNdu4caPt3r3b1qxZY0eOHLHq1asH78+XL5+VKlXKPadfzwEAAAAAyNj+L6JMww4ePGg9evSwPn36WOHChaPcp1nka665Jsptl1xyifu8bds2d79E/zk9xrvPj+dIikAgECXdPbpMmTJZrly5kvz8ke7YsWOuDZOK9g1v+wIAAACRQH1ixQ4RE2A///zzrrBZw4YNz7nv+PHjlj179ii35ciRw30+ceKECxIkpsccOHDAt+dI6ppyFVeLjYJrzZIj9oJ33nuTFLRveNsXAAAAiBTRY8F0G2DPnDnTpXDPmTMnxvtz5sx5TqExBcWSO3dud7/oMd7X3mO82WE/niMptO67ePHisd6f0FGSjKpo0aLJnsFG+NoXAAAAiATr1q1L8GPTfICtSt579uxxxcVC9e3b1+bOnWuFChWynTt3RrnP+75gwYJ2+vTp4G2qEh76mBIlSriv/XiOpAZ4CuCRNKTPhxftCwAAAFiiJubSfICt7bKUwh2qXr16rqJ3o0aNbNasWW7brDNnzliWLFnc/QsXLnSzbxdddJHlzZvXVR5ftGhRMDjWmu5Vq1a5fa+lcuXKyX4OAAAAAEDGluariGsG+YorrojyIQp8dZ+21Dp8+LD17t3bTd1Pnz7dJkyYYO3atQvmyisIVqC+YMECVxG8a9eubtZagbr48RwAAAAAgIwtzc9gx0eB9tixY61///7WuHFjK1CggKs4rq89mu1WmreqkGs2XDPW48aNc2ug/XoOAAAAAEDGlilAFaNUsWLFCve5TJky8T62/s1N7Y8Vq1LgVaUPpcuUsnkLpvn2fA0e7Gh/rE144YJIV7pEcft08sjUfhkAAABAuovd0nyKOAAAAAAA6QEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAADJKgL1//3577rnn7MYbb7QKFSrY/fffb0uXLg3e//PPP1uTJk3s+uuvt/r169unn34a5edPnDhhL7zwglWvXt3Kly9v3bp1s71790Z5jB/PAQAAAADIuNJFgP3UU0/ZsmXLbMiQITZt2jS79tprrXXr1rZ+/Xr7+++/rV27dlarVi2bPn26NWvWzHr06OECZs/zzz9vP/zwg40YMcLeffdd93NPPPFE8H4/ngMAAAAAkLFltTRu06ZN9uOPP9r7779vFStWdLc9++yz9v3339ucOXNsz549VqJECevatau7r1ixYrZq1SobO3asm23esWOHzZw5095++22rVKmSe4wCdc1SK2jXbLQC5uQ+BwAAAAAgY0vzM9gXXnihjR492sqUKRO8LVOmTO7j4MGDLlVcQXCoatWq2S+//GKBQMB99m7zFC1a1AoWLGhLlixx3/vxHAAAAACAjC3NB9j58uWz2rVrW/bs2YO3ff75525mWynd27dvt0KFCkX5mUsuucSOHTtm+/btc7PPCtJz5MhxzmP0s+LHcwAAAAAAMrY0nyIe3a+//mrPPPOM1atXz+rUqWPHjx+PEnyL9/3JkyddkBz9flGwrMJl4sdzJIVmx48ePRrr/Zqlz5UrV5KfP9LpfVEbJhXtG972BQAAACKB+sSKHSIuwP7yyy+te/furpL4oEGDgkGuguBQ3vcKTnPmzHnO/aLA2Ate/XiOpDh16pStXr061vv13KVKlUry80e6DRs2uCAwqWjf8LYvAAAAEClimnBN1wH2e++9Z/3793eFxV599dXgL1i4cGHbuXNnlMfq+9y5c1vevHld6re2+VKAHNooeozWUPv1HEmRLVs2K168eKz3J3SUJKPSOvjkzmAjfO1LG8eN7AAAAID0Yd26dQl+bLoIsFVBvF+/ftayZUvr3bt3lMBIVb0XL14c5fELFy50s9yZM2d2lcfPnj3rCpV5hcw0M6d11ZUrV/btOZJCv4eCeCQN6fNpv33PnD1jWTJn8eX1RBLaBQAAIP1IzMRcmg+wFcgOGDDAbr31VrdX9e7du4P3KXVbQXfjxo1dyrg+f/vttzZv3jy3xZZohrlBgwbWp08f9zwKGvr27WtVqlSxcuXKucf48RwAzqXg+snXX7F1//5D8/xP8cuK2LAuvWgPAACACJTmA2xVDNda5S+++MJ9hFIw/Morr9jIkSPttddec/tZX3bZZe7r0G23NPutwLhz587u+xtvvNEFy56rr7462c8BIGYKrlduSHhaDQAAAJBeZQqwEDBVrFixwn0O3d87NvVvbmp/rFiVAq8qfShdppTNWzDNt+dr8GBH+2MtAWCwfUsUt08nj/Stfe98uiMBdojriha3T17zr30BAACQdmK3NL8PNgAAAAAA6QEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgCkQ2fOnkntl5Bm0TYAACC1ZE21/xkAkGRZMmexARNfsH+2b6IVQxQpdIX996G+tAkAAEgVBNgAkE4puP7r3z9T+2UAAADgf0gRBwAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAA0Zw9e4Y2iQVtAwBA7LLGcR8AABlS5sxZ7IPJL9nOHZtS+6WkKZcUvMLuf7BPar8MAADSLAJsAABioOB6y5a/aBsAAJBgpIgDAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAQIoKnD1Li9M2ABCRsqb2CwAAABlLpsyZ7Zdp79nh3TtS+6WkKXkuLmgVm7ZI7ZcBAEgGAmwAAJDiFFwf2LaFlg+DwNmAZcqcibYNU7sEAgHLlIn2pV2AmBFgAwAARBAFkevnLbXjew+l9ktJM3Lmz2tX1a/ky3MpuP77l112/PApX54vEuTMk82KVSyQ2i8DSBMIsAEAACKMguujuw6k9suIWAqujx44mdovIyKRIRDedqF9w98uBNgAAAAA0gQFOev+PWzHTpxJ7ZeSZuTKkcWKX5bHt/bddviQnTxD+3qyZ8lihfPkNb8QYAMAAABIMxRcHz1OABguCq5PEGCHDdt0AQAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgJ8LZs2dt+PDhVqtWLStXrpy1bdvWNm/e7Mf7AAAAAABI5wiwE2HkyJH2/vvvW79+/WzKlCku4G7Tpo2dPHkyfO8QAAAAACBdIMBOIAXR48ePtyeeeMLq1KljJUuWtKFDh9r27dtt/vz54X2XAAAAAABpHgF2Aq1Zs8aOHDli1atXD96WL18+K1WqlC1ZsiRc7w8AAAAAIJ0gwE4gzVRL4cKFo9x+ySWXBO8DAAAAAGRcmQKBQCC1X0R6MGvWLOvRo4etXr3aMmf+v3EJ3bZz506bMGFCop7v119/NTV9tmzZ4nxcpkyZbM/uvXbq1Kkkv/ZIoza76OL8rv2Sy7Xvvv126tRpX15bJMiWLatddOEF/rXvwf126jTtG2zfrFntonzJb1+17f7D++z0Gdo2VNYsWe2CPBf60r6HD++3s7RvFJmzZLU8efw5fk8eOWxnz55J1vNEmsyZs1j28/L40r6nj52wwBm6eME2yZLJsubK4du17fTJMxY4m+ynihiZMptlzZ7Ft/Y9dSbgy3NFCrVJtiyZfGvfMwHaN3qbZMkUd/sqFtPjKlSoYPHJGu8j4OTMmTO4Ftv7Wk6cOGG5cuVKdCvpDQr9HBcFk4i9DZNLwSTC2L75aN9wta8CSYSvfRVIInztq0AS4WtfBZMIT9u69s2eheYNY/sqmDTz57kiiV/tq2DSfHqujNK+mTJlSnD7E2AnkJcartnqIkWKBG/X9yVKlLDEKl++fKJ/BgAAAACQdrEGO4FUNTxPnjy2aNGi4G0HDx60VatWWeXKlcP1/gAAAAAA0glmsBMoe/bs1qJFCxs0aJDlz5/fLr30UnvttdesUKFCVq9evfC+SwAAAACANI8AOxG0B/bp06etT58+dvz4cTdzPW7cuHgLlQEAAAAAIh9VxAEAAAAA8AFrsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AABBmZ8+epY0BIAMgwAYAn9GRDi/aN7wCgUCY/4eMZe7cue5z5sx0ucJ5PuC8gPRmy5Ytqf0SECZZw/XEQGJ8+umn9vfff9vBgwft/vvvtyJFili2bNloRKQL8+bNs40bN9qxY8fc8VuoUCHX2aNDTfump6A6U6ZM7mt9PnPmjGXJkiW1X1a6N2DAAJs8ebJVrFjRChYsmNovJ+J89NFHtnDhQnv55Zcte/bsnHeRbkybNs0dv0888YTVqFEjtV9ORDp58qQ7L8R0nQs3AmykuoEDB9qMGTPsoosusq1bt9qCBQvstddes0qVKqXoH0OkItALr0GDBtnMmTMtR44ctn//fpsyZYq7cF522WUcv7RvuqBO3g8//OCC6ipVqthDDz3kgmvOv8kPrnVu0PmA4Np/p0+fdgPzq1evdm393//+lyAb6cb5559vJ06csHfffdf1c6tXr57aLymifPzxx27wTde1qlWrWtOmTd3EXUpd1zIFyAVDKtJFUcH1mDFj7PLLL3cHfqtWrVxnZOzYsbw3yfDzzz9biRIlLH/+/HSUw9yBfvPNN+2KK66wzZs3W+/eve2aa66xYcOGuZM4A0S0b1r26quv2qxZs6xs2bJugPPPP/+0zp07uw8k79wwffp0e++996xkyZIuGMyalTkNvx09etTGjx9vn3/+uZUvX9769OlDkI1047vvvrPhw4fbBRdcYK1btybI9vG6pvPv9ddfb+vWrXPnCQ0cd+jQIcX6ZCwIQqrp37+/+wOYNGmSlStXzs1gX3zxxW4GhbVUyQ+un376aXvrrbds37597oTCWFp4jt+JEyda5cqV7ZJLLnEdPAXXp06dcunhtDvtm9aDQI3y6zzx9ttv2zvvvGONGjWyL774wvbu3cs5I4mUrqxlTzo3RA+uV65c6edbmKGpn5A7d2579NFH7ZZbbrFly5bZSy+95NJCdf6lH5F0n332mRtwQ3h4/bEbb7zRHn/8cZf9Nm7cONd3gz/XNU3SjR492r766iu77rrr3Gct40spBNhIFUOGDHEz17Nnzw52QEJPPFrDiqRTqlGDBg1s8eLFNmrUqAQF2QTgCff666+7tNrox686dQUKFLD//Oc/wcd6o6VKUwLtm5YGiJR9ofREjfLrGNYgpzoiOlYVuJB9kXjqIKtN27Zta6VKlXLBnhdcjxw50lq0aGHbt2/3/f3MiLwgWseq2jumIJvrWuLt2LHDunbtan379nVfwx+hx2Jof6x27doE2T4Obuq6psHNMmXKuBR8ufXWW93ER+igW7jPDQTYSHGbNm1y6VxKXfYCEa8jp2Iw77//vu3cudOtbVXqrf5Ydu/eTackkZ555hkXaGttZVxB9q5du1wlSzrTCaMZKI2Oli5d2vLly+du84pB6djVMazjVZ1pLX346aef7PDhw66AH2jftEDHpjKHVC8gehB46NAhd17OmTNnjDOABCxxUybL3Xff7a5fX375ZbDAjmZSlC6uwTkGkMMbZP/2228uRVQdaq5riaO/by3R07lB1zoF2QwI+cM7Fr1zaGxBtgboVqxY4dP/mnG8+uqr9uGHH9qECRPs2muvddc11cYRZWMULlw4yjKdcJ8bWIONFKeDXqP8L774oivyoDRbUZqiAhLNCOoEr5O7Am1dJDWjots/+OADy5UrF+9aDNavX+8uhJqJUiEHr2CGishpnc8NN9xg7dq1swsvvDBKcP3ss8/a77//bt98843rDNIhiZ8ugEoB1ZIGdUB0vGoQQx+qJXDllVfakiVLXDqSl5KkIhs6xtURRNw0+vzJJ5/QvmGg1O82bdq48+qTTz5pN998c5R2V3rdVVddZcWLF3fn6mrVqrnj+dJLL3V1BkIrsiJm//77r73xxhtucFiBytq1a13WloLuWrVq0WxJoEJmBw4ccB833XTTOcehV8zzyJEjLjX022+/dde72267jfZOIg1UqA219On5558PDgyFFonas2eP/fLLLy7VWYNyOJcmOTSxpP6Wzqf6CBXant9//73LLrrzzjupg5EI+vu//fbb3QCx+lmKF7zg2ruuFS1a1A0oq611fVMQrqBbBWnD0S8jwEaqBtkvvPCCC050wdT6PwWDCkR08VRQrYBRI0+LFi1yF8qrr76adyyWGamvv/7azZyqg6F17OrQeZ0Qjex5QXbHjh3dwIZSv9Te6oio7ZVOg4TTSVsdaJ20NeOnWW21s9peHQ11PJSepE6KBj84fhOH9g1vsKKdGlT4pWXLlm45idb/aYBIM4CahV2zZo07/yo4VAdQAfbUqVNdGjliFrq1mQoejhgxwubMmeMCP1USV6ePyuyJp3bU7iI6pypIeeCBB1wxs+jbIHrtr4yhZs2aWZ06daxnz54crgmgIFmZbPp7V00cBYF58+Z1g++PPfbYOUG2aAJEgYvOFcqI0fIoRKV+mGpaaGJIx6/aT/2tuAI69Yt1jlabsl1t/Ly/e8UV99xzjx0/fty1oSaZNNim61r9+vVdv0zHuAp5btu2zf2cgmv13VRkzm8E2EgROsFoBE8jRzVr1nQdDQUfCpzV0fvrr7/c7LVG971969iHNWE0K6IsAGUEeGtOlLqsE8by5ctddWBR8KfRUQXZ2q5AlVdVyERZARrJQ+w0OPHPP/+4ZQ0KqL32UhCojrNO2GpfFYjyjls60snPvlBKrda6077+U4aQjllR0KylDKpmqwHOUN7MiwZClZ2BqHSOVSdN54bo2yKq7dRJ1nHsXd9C00MRv8GDB7tzgD4rY0jtpuBEbR5K1z1lZeTJk8d9rzXERYoUcVkatHfctOOFMthEM4DKVNGAm3eMal27ZrIrVKgQnBTRYL7Wu2oZhJZGqXYDotJyEGWwaIBIO7qob6C//9AsQlGfV5WuNbsqqiGgJX3e3u5IOLVl48aN3TlYg0JajqpsIhWi9SirUEv2dO5WLBKu6xoBNsJOnTgd5Bq500GtESSN4GmUVH8M6tjpRKKgUBdSYUuThNEMqmavFWR7gbRHF0gNXmhrAu0P6r0XSlfS+6BUO10YvZM6YqY21ECERud17Gomr0uXLlajRg13v9ZcawRUJ2lt0aXOB4ND/mVfqH11XlBnmfZNGu0FqrRl7Rms41Oj/ApENPOkc69mqVR8q3v37lHOvwwSxU3HZr9+/dxMqWb9tYbyvPPOizI7tXHjRlehXedqpS4qWys0CEfsdK1S++q6pf5CKA266Xzh1XLRDKyuc0qtVXCic7bWYxYrVowmjsPQoUNdZoqCbKXNarBN7acgUFWX1W9THyE0yNZxrgF6ZRUwQB8zTRope0LnVK+v4FH2pmZQNQmiWi7KJNLgvDI01c/QsayBOQV/iJ0mNzQ4r8kPXb/UXsrOVFzRvHlzW7VqlWt/LYkS77ybUv0zzvBIkXL5Onlrtk8zJEo90slDnTh1onXyUbqXZkk0syrq3IVWFkdU3iyI0o+17kQn6dCCROrQ6QL44IMPuounRkRFJ3wFMJolVOeD4DpuWgul4E4DGGovHcdqu6VLlwYfozZu0qSJC2DUGVTqvU7ebBETP7WrZvfUcdNIv84VOlZ1XvCKvKh9dV6gfZPegdZxqQBPHeZff/01OHihDonOCRqcUwda696F4DphvBk+BSU6F99xxx3Wq1cvl5mlNGXR+nUty1EHWoGJBpsJrhNGQYgyhlTl3qNZJ2VrqZDc/fff7wqbKQ1UA5xeIKhsGJ1XCK7jzyycP3++KyarzBUvTVbBtSZBdLzq3PHHH3+42UCl2uprnY+1tIzgOnZKn1fBMm3b6fXZFFgr4GvVqpUbDNJ5QX1kBYQaVFaArfoXaleC67i98sorrj+mgXnFDtpDXJmGouub+hNqewXhancF1d6uAikRXDsBIEwGDhwYqFy5cmDdunVRbn/jjTcCjzzySOCff/4JrFixInDixAl3+/fffx+45ZZbArfeeivvSQLs378/cMMNNwTGjx8f5fbPP/88ULFixcAPP/zgvv/4448DJUuWDIwbNy74mD179tDG8Rg0aFCgUqVKgb/++st9f/bsWff5lVdeCTz44IOBTZs2BX777bfg4ydNmhRo1qxZ4OGHHw7s2LGD9o3HjBkz3N/677//fs59Y8eODZQoUSLQv3//4G0TJ06kfRNpyJAhgapVqwaWLFkSOHz4cPC8IXPmzAls377dfb18+fJAy5YtA82bNw98+umnHLsJdPLkyUDr1q0D3bp1c22p4/Wmm25y51udByZMmBA4fvy4e+zBgwcDHTt2DFSpUsW9F975BLHTeaB27dqBvXv3uvYaPny46yNcf/31gaeeespd2/T5zjvvDOzevdv9jNrb61MgZt6x99prrwW6d+8eOHr0aJT7R40a5c4bugbqOH7ggQcCf/zxh7vvl19+Cdx1112BVatW0bxxtK2ua/Xq1XPHqM65/fr1C9SsWTNQvnx5d15etGhR4Ntvv3Xff/bZZ8Gf5bwQP51nK1SoEOUY7N27t2tv8dpQ54Lbb7/d3f7zzz8HTp8+HUhJBNgIC3Xe1EFWMO3xDu4BAwYESpcu7S6cekznzp1dsK37v/zyy0DDhg3d94ibguSbb7458P7777vvT5065T6rMx164tHJXQMdocEK4jZ16lR3bE6fPj1425kzZ9znvn37ugtlrVq1AmXKlAn06tUrSsekRYsWgW3bttHEsfAufmpHdTTUrl7byltvvRWoUaNG4MUXX3QdaXVMPKNHj6Z9E+jrr78ONGjQwJ0PvGDQ884777jjW53orVu3BoPsVq1auZ+ZN28ex28C6JqlIKVu3bqBv//+292mzx999JFrX30o+Hv55ZcDq1evDmzYsIHBt0ScIxTMNWrUyJ0PqlWrFrj22mvd90uXLg0OGOncoYHQ2bNnc8wmkNpM/QX1tUaMGBGlzdUH0wC9AhKZP39+4LbbbnPXuUOHDrnbGMCInwbc2rRp445NnQeuu+46NzC/Zs2aKNc79RcGDx7MsZtAmuDQ+UDtGHos/vTTT4GmTZsGfvzxR3fMavLOC7J1nOtat3jx4kBKIsBGWKxdu9adOPQxbdq04O1vv/12oFy5ci5wUYdDsyU6+bz77rvufp30jxw5wruSQI8//nigSZMmgX379p1zn3fB/Oabb9wo3q+//kq7JpDaUwNAmpHWSHNoAF22bFk3Kq3R58mTJ7tOn3f8hs4Qwr/si9DH0b5x8/7u1XF+8sknA8eOHYty/8iRI90sqjIC1OGrXr16MMjWrEv79u0Dmzdv5vCNhzdgrNlVBX/PPPNM8L4+ffq4oFvH7f333++ucZrZ1iw2YrdlyxYXmOzcuTN4mwZ7NDismVZlvXh//zrO9R4oQ079DK/DjYR76KGHXB8idGZPfTD130LVqVMn0LNnT5o2DitXrnTXLWW8eQMRynLT8TtmzBg3WOTd7k2GaJJEg5pkDSXMsmXL3Ln0ueeec9/ruPWud6+++qq7r379+i4rQMesBpm9IPu+++5z70dK+r8dtwEfae2D1lVrnYS3tYsK7GhdsNZN1K5d2z1O60x0m9a0aq2l1v6FbgSP/6PtyrR2RB+qpipaN6UiUWpjbV2iwkVeFXZvfaCqg6oAj9YCImG0Fm3GjBlurZnWSGkdq9arqXCcKoJqz0/RGisVOtLWJh4V2UDctB5Ke1R6+6Z6RbVUsEj1Gbwq7doySgWOtBaT9k0Y/d1r/b/WAWtNn9rYK+qitWizZs1yx7NqX1SsWNFVW37qqafceVhrsVX5lsq18Z9/vW1htF5VW0JprZ8qAescod0atC5Y1zett9S6dxWL0rZHiJkq/ardVHldxba0Zv3ee+912xvGtJe1t5ZSO2hovTvbxyWczrdaj6o+gVcAUZXDdZ7QedhbN6zziNZcq3Cfar147U4F/Kh0PlU1dW3DpWK9Oo9qjbUKc+oj9JgVr4/77rvvugJd0Qv4IWY6h3bu3NnVDFCxTq1h92oOqQ+sgrTapUhbnKl+zty5c61SpUquX6w12SmNSAa+UcU+VZ9UMKfN27UtgQroqIOsoiQqRjB69GgXFHodam2rof0BdUJPscIDEdD5uOuuu1ynToMSqrSqE4w6e/re2wJC7a1gRfuwqlp49K0hEJUGgLRFiYrl6JhUe6noloJsVV3W8aqiXNpqxzt+1fHQ46JvGYO4KZBWQSK1rzpuXnEdXQxDO3EqHKVgxuvcIWHUeVZgrfOFeOdWnZN1rvAG2/QeaKBD74eOeSG4jv/8q21gdF7w2koDbhMmTHCVbBVEa9BTwbU3sKHKy4idKtlr4EeDmepDaEBCwYe2fgrd/mnx4sVuBwwV3NKODl6lcA1yeoPOiP/6poBD1y/tb60gRMVnY9oSSucR3a9zg1cQleA6Kh2zOnY1caRgWsfkp59+6iqB33rrrcHHqZ+mwp3eFrUa7FTb6jhXFXwkLMC+77773Nc6ZtVv0DGqgnwqEudNfCjIVoFDDRx52/alBgJshHUETycTba2jk5C2LtH9oSN46uypzL4qVSLhnQ915tRZVjtr6xdV+1TlSY3mq5Kt2lQdO31WkO2NSCNmmrXThVEzIRr00dZmOpGr06bqyxq4UOdEs9PeKL+oI61Ot94bxI7si5SnTob+9tWR8/YVVyDt7desQQxt06Wq+A0aNAjeRgc6/vOvZvsVcHiZFhoY0myrZkk0oOxtmcigccLaV9lCuqZ5QZyubZpZ9Sqxe5TppmNaW0yq2rIGkTR4TMXlxF/fVIFdW0726NHD9b90XevQoYNre9m8ebPbQUMfGsDQ9lGIauDAge7YVcV6r90effRRt1OABoeVqanBePV9FejpcdqSS22pjAG1K32zuGlgU4Nq2pJPg5pqOy/IVoasJpb0Huhc7GVm6EODRRoM9TI6U0WKJqQjIml9lNb0qcjAv//+69ab3H333W49pUfrrVU5/N5773XrqLw1gioS5VWnxLlUEE4FyrS+x6PiDVrft3DhwigVJ1XYQYWj2rVrF+jUqVNg5syZbk0bEnb8aq261p56lZdDq1trnaXW9HgFdjh+E05/5/q7V5tqza8KyHm03lfrpfQYtbFn165d7n3Qeuzo6wER+/o/rUf1ij/pnKv269Chwzk7OYQWjNG52qsmjsSdf0Pp/Kv6IirSJyldsTY9ev311wOlSpVyBfYktF5A48aN3bVMaydfeOGFYOFTHecqoqqfYTeM5F/fDhw44PpkesyNN97o2lvnae0qoPouOo/gXLpmac2vVy9Ef+/e33yXLl1cYS3tkqEK1l7RSPXH/vzzT1fzwjtPI3ZaV61zrQpFqlaICnB6uzJopxbVxNH5QzFH9PdGx3Ns172Ukkn/pE5oj0gZwdMIZ+gIntbtaIRJ+y0/8cQTbsRUo3dan6Z1Ed7+10uWLHGjz9rDGedSCoz2ndSsSJkyZez48ePBNavad1mp+FoDrHbXOj8v7dNLX0b8NAKqtZNK0wrdE1zte9NNN7k9VjXirFm/vXv3uhFUbz27ZgQ4fhM/+6eRfd3uzfIp+0K3K1U5evaFUvK9WUIkLHtIewY/88wz7pjV7ToHazZbNRp0TIvaVqP+mkFhBjB551+dNx5++GE3I6XzsPZk9fYTR+x0ndJMqvas1iyqZvu8ZQrKbFNafsOGDd25YN68eS4tfMyYMTSpT9e3OnXquOubQgDNsO7YscNla+n4lRtuuMH9jI5znEvnT51n1Ybq73rLxLQMUucOnXe9fu5PP/3k+smaUUXCqM+gNlZNC6XQa8ljly5d3HH57LPPusfoNmVuak22aom0a9fOtb3qX6SFvhm9cCSZLoBKldMfgII8pRiJLohKPVJqhwpDiYro1K9f33r16uVSxhVsa+0UnefYOx9KkVMhB60104k5tPPx559/ujZXCrPW++jiqNu99hfSPeOmdTtKS1RnWO0bml6kASFdGNW2WmOtjnO9evVcZ0XrgX///fdgWhISn/p57Nix4PGponE6T/z444/uvKDBIRU3q1y5MmvTkrj+T6m0Ol7Vjup8PPfcc67opM4nOs51TGsdIMF18s+/s2fPdum0Cv5UmEtrAdXxU9Et0u1jp79ztZmKFqkvoQGMu+++2/Up9KHbFASKCvKpD6Hj2qvTgORd39SWWgOsc0Hr1q3duUIBChJGdRjU59V5Vceu+rZqc/WHdc71CvlqeY4CbPWHCbATPjCk2kETJ04M9rE0YKz6N7puabJD/Qcdu94xq2UQSs1XrYG0EFw7qTp/jnRNW20pRWbQoEFRtnVR2ob2/NNn7bfasWNHlzqn7QtEe4GSuhw/pRAprV5pMV5a/bhx41zKorf9gHhbnXn73SJ+2hN40qRJrt28lE6P0o209Zb2cNcet0qVU6qtl8aorXbYp92f1E9tt6HzgcfbvgQJS5/THquh2xNpf9V77rknMHDgQJdK520Lo+N17ty57nhWiqj2A1UaPvw7/yrlVttLkW4fNx2X2orT27LMa2ctVXjiiSdc+4ZujSjff/+92+bM60PA/+vbqlWrojwudPkZ/j8tSdA+90pP9q5V2kpS7aw9mLVdn5ZKine/+rraOuqzzz6jGRNA51q150cffRTc59o7Fnv06OFiiZo1a7prn5aOaEtVnUt0Xbv55pujLOdJbcxgI8VG8L777js3gsd2UbHT6JzaVB+qRqtRfI3wK8Xr66+/dgWLdJvS7z1KC1UKjVeJGfFTYSele2uGScsWdPw++eSTLr1Lx69mqbyKlBp5VhqzZqmUKqr3he12Ykf2RdrMHlL1YKqxh+/8q/RxCkHFTUsSvGUiSj3u3r272ypO/QWlf2oGStlCXvtquZlmXFU9XAUmvQJ98P/6tmHDhigZWWRfRKW2U1upErgyANq0aeOWhqid1d5a5qCMC2+ppLdMT6nhOp9o6Q7ip0whnROUGaRrlnYdEi3XmT9/vrueaWcBZV9o+Y5mtbX0SYVode5IS/1gAmwkilIztO5MJxhd7LR9kU7ESvtetmyZS6dTuqKCam8tsDoqKq/PVgRxo/MRfqpGqWrgOh615lcnZaUaqROifYNVEXzw4MFunY93/GpASKmibF8U/tRPr1NH5y5uCqJFacxa+xu6/s9bd+2t/9NuDtoPW9uWeFg+4v/5l22i4qbq6kpX9raSVPvpOPaqLGvtZKdOndx2Rjpe77zzTncMqz+hVFF1pgmw48b1LXzHrgI+DVIosF67dq0btPRoaYhqDakfrJ1F9Bj1MXRMa0BDx662rkX8tOe9Bor1N6+/fS2D0vIx9R30vTdxp60PtRxK9ynATou1AgiwkWCM4IUPnY/w0wn6m2++cVsTeUG11qlqex2NQKtgnwJrfYSOQGuvZgV8mr1G7Mi+SNvr/0IDbAYwouL8G17aWktrKjVz6q2N9LbP0XGsLXjOO++8YMaAztWaydK5Wsc1tQLix/UtPBRYa+ZU26F6hTk1+69zqGotiGZNNZOtQSMV4FI2i45vFeDSR2iBOZxLtVd0DtD2e2pb1QNQ+ypzSPUBVMBT5xC1v9pY/TcVn9UMd9GiRS3NSu0cdaQP2s6lRo0abosdbfeg9dXbtm2Lsk7HW4vSr18/d5+3FlNbcaWldRFpzcSJE92aEm3/4tG6E9G2D952Dlq31qpVK7cmUOspteZEbRv6c4j9+FUbv/fee+74jV43QG2s+0qWLBkYPHhw8PZhw4YFypYty/EbD7XdU0895bbUaN68eXArMx2zbdq0cecFvQeha4VFba21l2y3Ez/W/4UH59/wUf/g6NGjbm31lClTzjmeVaNFa4C1Dc/o0aOD54xHH33UnYu1hR/Xt/hxfQufl19+2dUKCb12bdq0yfUNtK2ZtoPSdmZeXSGtHdb1Tscv/d74aUstrV9XO6rdtK2cZ8GCBW7LOPV5Q7dN9X5O7Z+W6+Ewg414MYIXtsEtN/uk9FitgQqtenj48GHX7l999ZXb3kgpSNoOQiP8eqzSajXCn2aqJaZhqvyt9M7Q2RNvhk9bkmi5g0ZLlbqo2RRVv1bbaqRU2z0wAh03Zv/Cj+wh/3H+DT+dV5XuqfoVuqZ5pk+f7nYY0BIGzUJpCY6W5ihdvEOHDi61VpXvVSH4mmuuSYFXmn5xfQsPbxnNtm3boqzr1UyqtjNTdoWWi2gGVUtL1D/TMa1lk3q8lu5oqQNip204dfyqz6U206y/t/xJ6tat694HVcNXqriW7WgpmSqGK1tLfTOdP9IqAmzEa9WqVS5t1kuPUeDx77//upOJLpQKErUGUNvtNGvWzJ2UlLqoz/rjIT0mZnQ+Usbq1atdKrhXwEXHry6a2t5IH6KLpIqUaF9WdQj79evnbtfxq4IaiBmpn+HH+r/w4PybMnS+1ZprDXJqez4NKOtDHWp1mrUdnzrJ6kQrcNGSHW1xpoAb8eP6Fh7eMhotDVMgqMH49evX28aNG109IaWMq3CZjm3VH9Ke2P/8848r5KsUZ8RNRSN1TlA7litXLsp9qoWjATkNUtx8880u5V4TJBp4UyFJ1RnR5FJa75sRYCNWjOCFH52P8B6/mhHRntXqxHn7g2vUU4GzBo50gdTsiU7o2ntRJ/AmTZq4wnya7b7qqqvC+ArTL2b/UgbZQ+HF+Tf85wl1jrUuVZlX6hQrY0gD8e3bt3fn3lA67yq4RsLalutb+Pu/KlimgaEFCxa4wmUq2KnjV8epHiO6X/UEtKMAEmbTpk2ujxUaXGuwSBN3Kgqn2WwF2DpPqOaIaryof6YCc+khuBYCbMSKEbzwovMR/uNXxctUlVIj0CqgoRO4TtAaBVUxKAXYqv6rmRONpKpwhirVNmzYkEJQ8bQtqZ/hR/ZQ+HD+TZnzhJbdaCcGdYp37Njhqv0q6FZAogEOUTV2pZGrQ62Otc7bFOKLv225voX32PWCbA0OPfDAAy6o9nYP8D7Lt99+645rVcNHwiver1+/3vXLtAuD+l/KKFR2oXYZ0fZcmuXWzLUqtitdXOcMZRuGppGnZQTYiBUjeOFF5yNlaLsXncQ1a61URI1It2zZ0gXWXgdPAbeq2HoXTDp38WP2L3zIHgo/zr8pQwNxmmnV7LQ+REvMtKxM51tVD1aNAVUZVxDOdoiJw/UtvOcIbb+lgQwvs0LrrZXZpoF4BYM6ZrXTiLb5U3YGEqZ06dJuL2tlA2hiQ5XCtdOFzgVaVqpzxf3332/VqlVzO2EoVdzb4SW9IMBGrBjBCz86H+GntK5u3bq5PWx1UYxpBFrpXxqB1t7YiB+zf+FF9lDK4PwbHqH7rCu41paHmrHSukqdd7Ve1Uun1ffKLNIabK1fReJwfQsfZV8ouNb6a31oSYP2XtaH1mHrPh3T7733npUsWTKMryTy1K1b1wXVv/32mx06dMgFz6qVo9oMOn/ovKBCcpqxDt1iMj0hwEacGMHzH52PlL9IqiOt4Fr+/PNP17nTBVIpixqB1lpXfVb1cMSP2b/wInso/G0rBH/+UEdYu10UKVLEzfQpBdzrJCu41oy1Ckj+97//dfvctmjRws346XyrmSwF3PpZJB7Xt/C265YtW1yWgPZnf/TRR92xq4w3ZcUpqNYMa+HChcP0KiLT2f9Nbmj2WjVvvPo4oXS/Zrglva5tz6S9ulL7RSDtn2RCR/C0VVT0EbwhQ4YEqzQj4Z0PtW30zofWnYR2Pu666y46H8k8fhVI61hV+qHWY3sFS9TR3r9/v9v2geM38bwAxeOlfoqX+qmichq8YHYq6YGgRvpjW/+nLU7+/vtvGzZsGCmKMeD8G17aPUS7iegcq9kndZq1ZZGXLqsARVsg1qhRw/r37x88pr1jOPQ4R+JwfUu+0HNpdDp2FVBrW08V6ospEETS2vl0tL7D4sWL3f1Kv9+9e7eri6NiZ+o7pNfsAAJsJHoET4UJVF6fEbz40fkIP63P0QCGOml33HHHOSfwrVu3utQj7amqYiXff/+9qyyu9VMqclazZs10UzQjPc7+jRw5kq36kshb/+fR9kbR1//pQwNy6bUTEk6cf8NL22wp+6dnz56uvoWOxR9//NHGjBnjtvXU2kr1GUqUKGHPP/988NzhzeuELkNDzLi+hccff/zhJjBiC7J1XGrfZU10aNvO0GM3pq9xrokTJ7p21SBFaHuFDgxpyy0NymmSQ4Pxeowmo/Q47eySnq9rBNhgBC9M6HyE39ChQ13xMm3hoHU8CqQHDhzosgRElWlV0EwFMhT8hY6YIn7M/qUesoeSh/Nv+NtXM9eh60/VOS5fvrwbjFcxSVm+fLnbS5hAJPG4voWHMtheeOEFl2mh/kFsQbb6FdRlSZp9+/a5gQkNEGmg4p577nG3a5cAZRJq4u6+++5ztz/55JPuPmXGKvNNuwloIFmp+OmaUsSRMa1YsSL49ZkzZ865/+zZs4Gnnnoq0Lt3b/d16O0xfY3/079//0DFihUDq1evjtJW119/fWDMmDHB237//XfaMBltXLly5cD333/v2vGTTz4JlC5dOjB69Ohge/fo0cMdwxyniTd8+PBAnTp1Atdee22gevXqgSFDhgQOHToUvP/ff/8N1K5dO/DMM89EaV/vXEKbJ93p06eDbVyuXLnA2LFjXXuuW7cu8OabbwYGDBgQmD59emDr1q3J+F8iF+ff8Hr11VcDVapUCaxdu9Z9f+rUKfd3f+LEiUCLFi0CX3zxRZTH6z7OB4nD9S181qxZ4/oFd911V+C9996LsR/snYORdH/99VfgueeeC9SsWTMwderU4O1btmxx5w/dpzaPKf6IBATYGdSXX34ZqFWrVmDixInB22I6yI8fP57Cryz9o/OROh1oBX/NmjULPP/888Hbjhw5Qscuie1btWpVF8QtWbIk0LVrV3dB1ECG16733Xefu0BGH3DzvqdDHb+4OhYKrjXA0bdvXzp7icD5N7w+//zzQIkSJdy5Ifrf+QcffOAG5D777LPA8uXLA+vXrw/zq4lMXN/CT4ND3bt3D9x5552xBtly4MCBwNGjR1PgFUWmdevWBfr06RMlyFbcofN0pPcRyJfMoFSIqHLlyi69VmkxKkISvXiOUhRJj0mc+fPn2/jx413hoWuuucalzHlpyUqn++WXX1xbr1ixwhWB0RYEsRXYQMyUAq51fx9++KHbvsFbp6r21FZbKr7nyZ07t/v8v8FE2jqJqZ8VK1Z0qZ8qRKK1lWpXFeWLnvoZ29eIef1f9HOuR8eqCkeqPkDfvn1Z/5dAnH/DT/0GnQ/efvttt+66UqVK7natu1ahPa1Z1dp3Fd4TXePOP/98V8gztEYGYsb1LTxUOEsfopor6p+1b9/eHccqpiVeP9izc+dOV9xMfYvXXnuN/kMSFCtWzB555BH3tdZZa/mel5Yf6TW2CbAzKBUdUdEnXRSjn1y8Dp9XMVFFjBTAsIVR/Oh8hNeSJUvs3XfftUaNGgX3RvSOUxXYUcGMq6++2gXgCrZV1VZBogaLWH+dsM7drFmzXFuqA6JCZjoX6LOC6SuvvDL4WAXaOlcIwXTS1//FFGSrPTXQEX2AkwGMuHH+Db8LL7zQ3nrrLevQoYP16NHD7V89b948GzdunDt/6LygdZYKZlTDQYX5/vnnH3deRty4voWH+rk///yzLVu2zPUXVBVcu4moDxEaZCvg8wpybd++3QYPHuwGldXnYCIkYWIaMC72vyBb7fvqq6+6SRFtzxXp/QaKnGXgEby8efO6UWadXHQhbN68uQuyQzGCl3gakFDnQ1V+Qzsfzz33XKydD53s6YDETxXBVWlyzpw51rBhQ+vWrZu7fdSoUW77OM2UaE9KZQiIBoXUIdQWMaGVQBHz7N8TTzzhsi8aN24cpUKqOh8vvviim1XVucPLvkDirV271m1fpnOvqqd659zo2UNsCZM0nH/9p2NVx6f6DNre0GtnDdLrXKtZKRXkqlWrVow/r2riXjYRYsf1zX8K6DTgrsEgFc/SdU3XLg2+e9tKbtiwwe124fWD7777bjcIqmui+hts4Rn/cau/cZ0H1P+K7dr1119/2aRJk9xAh/pj5cqVs0hGgJ3BR/DUgQ4NslXVL/oInmYFNYKn4BDnovOR8idzpYcrjVlphzqpK7jW7IlmWbWFkaqHK5DRe6NBDAXi6Xm7h5Sq+qnqvxr80X610VM/1c7qXJP6mXx//vmna9foA5vRR//JHoof59/wUlqnAg11oLUd39NPP2333nuvu09bdWp3Bs28vvPOO8Ftj7zBubj2GEbMuL75R8GxzrPDhw8PHpuesWPHuuV8mvioX79+lCBbM6zaQkrbH5YqVYpDNQ6a3FBW1qZNm9z5QNvxKY7IFMtkhnYVUNXwTp06WdOmTSO6bQmwMwBG8MKHzkfqZF7o4qc0ZtUQ0H2aYdVoaPR9g5E4zP6FB9lD4cH5N7yUzTJz5kw3GK9sIG+Pa517vQF3nTMee+wxl+2mAXmdh8kUSjiub+GhTEEN/lx33XVuL/bQjCxlECm7UDPamlXVbOrtt9/uBut0rK9evdpNOjEoHzdNasyYMcMNUmhJnibl1N6hS8m890JZWd4yUw0oN2jQwB544AGLaKldZQ3h9f777wduuummKFtyebRdlLbfUcVPUcVPr6ribbfd5raHWblyJW9RLLRVjiorf/fdd67S8rBhwwLXXXddsNKyV4FS1Zb1Hvz6668RXzXRb9pyq1WrVu5YVNXw0C23tm/f7raOUnVKbSkV2/YatHncFT7//PPPwLZt26Ics82bN3fHstpcx3dsVE0cST+G1f7eOXfSpEnBn9H7odv1M6HnE/wfzr/h9dJLL7nrm6qBe7QNl65n6lccO3bMbc/l7eCg22+99dbA4sWLOUwTiOtb+GzevNmdP6NvGadK1qp0/9NPP7nve/XqFahQoYLrw8mGDRtc3wJx+/TTTwN169aNMbb4559/3DXMa8cZM2YEHn/8cbfLwODBg915RY+JdATYEUwXQ3Xmxo0bd06gMWrUKBdcqyOtTt/cuXODHb727du7/W1Dt0BCVHQ+wu+VV14J1KhRIzBz5szAsmXL3ADFvn37glsYyc6dO4NBtj57InVfRT9pQOiOO+5wW0FVqlQp8OGHHwbv279/f6BDhw7u9tALqHcOoX2TfwyrAxh9YFPbxRw+fDjw9NNPB66//vrAqlWrfH3PIwXn3/B699133VZcXidYwbSncePGgYcffjhQv359t32fOtpekK1juGHDhmzvmQBc38Jr48aNrl+waNEi9/3JkyfdgJC2qNV52KM+hI71d955J8yvKLKMHDky0Lt37+CEhvoG2vd60KBB7ppXuXLlQMuWLd0Ah7ZE03uhc0eTJk0yzHWNADuCMYIXHnQ+0lbmhRdkK1DU/qGIH7N/4Uf2UHhw/g0vZaUo+NM5NjQzyJtxLV26tLtfH/Xq1QvcddddbmBeDh48GBz8ROy4vvkveqaaBoVq1arl9mCO63Haz/3mm28ObNq0KQyvKnJpRloBtDfgrok8DbDp/PDII4+4PoYm8DQY5w3a60PniIyCbboimNY8nHfeeZYvXz73vdanag2KikCpeIP2tZWnnnrKraPQ3qwqbBR9/QT+jwq9qDq42lBr0x5//HFX/Em0Nk3reVQkTlUnv/rqK1u/fr3bEk3bFGjdmtarsbd43LReR4XJ1I4qTBLb2qk+ffq421SgRGt5jh07Zj/88IPt3bvXvT+ImQqYqaqqiryoKJxoPaWKIK5cudJtz6UtzXTe0GPatGljPXv2dD+nbZDg7zGs+7T+T9vFeOv/VJyH9X/n4vwbPt4xqmrfDz/8sOs7qJjk8ePHXWEzFYTSNe7NN9901cL12GrVqrlK4qrGrGuc6mPoA7Hj+hYehw4dcn0rHZfZs2d31zDthqFtJ/Vx1113BfvBut+j87T6a/QZ4hZ6DRNVWtd5QeupVdxMReJUkV1Fz3Ttyp8/v2tb1WdQ4bgMeT1L7Qgf/mEEL2XaVmtLRowYEbjhhhsCAwcOdLdp9E4pMd9++23wsd98841LPdJnhD/zYseOHYE9e/bQ1HFg9i9lkD3kL86/4afaC5r1O378uPt+69atbgZbs4BaX121atVguq2XFqqUUNVr+eGHH1LgFUYGrm/+U1abZkqVgqwljrt27XK3a5mjlizcfffdgY8//jjKz2gpztChQ12/Tccx4j8/6Nygpaei9dWTJ08ONGvWzNUY0bJTr//lLSFbvny5m8VWlmFGxAx2BGEEL7xtq1FPjeBpqyJtL6ARvY8++sh++eUX27hxo9sqqkqVKsE9bFXxWtkAGklF+DMvVMUSsWP2L+WQPeQvzr/hpUwVZf8ow0rnUVVV1n623nZcU6dOddkrur6FUiaMtuFS1gsShuub/5XudRx27drVtmzZYj/99JO1bt3a3nvvPTdr2rdvX/eY1157zW0ndeONN7rZVlVv/+2331w2UfHixTl8E3l+KFiwoMsc1DaTsW3FN3v2bNf/9bI8Mxp6/hH4B6ADX38AF198sUs9/Prrr91J5PTp0y4w9NJjjhw54lK+vvjiC5e+nCdPntT+NdIkOh8pm3qk41cn7Dlz5rgOnbftVt26daM8Tse7UpJ0O+JvX1I/OYbTI86/KR+gaFmIBjN1Lg4NsrXlp5aLaAB56NChrl+hNPICBQqE+VWmX1zfwkfLlrRUL3QP9goVKrhjWgG0litUrFjRbR/37bffugF5Hce6FtauXdvtx3z55ZeH8RVG9vlBMYMXXGuiQ8tJNNmhPbFnzJjhtlFVbJFRl40QYEcARvBStm3pfPiLzIvwYvYv/DiGw4Pzb+oGKOpAK8jWOktRMK3ZKO1nq59R51nrVxE7zg3hoYxBHX/z5s1zQbKCOx2bCvBUj2X69Om2efNmq1Onjpu1fuihh9xxfOLECRdgI3nnhz179gQn5TRZp2Bak3Vy0UUXuQk9BeEZcu31/xBgp3OM4KVe29L5SD4yL8KL2b/w4xgOD86/qRugqMOs4mUKUDTbp8Jnmq1SESN1njUT6F0XETPODeHJCNBxqQC6VKlSLtDT8eulIU+aNMndpmKRyujs1auXK8Sn2WplXnjBdfTMAiTu/PDxxx+7SSedH5RF+Oijj1q5cuXc7LWKp1577bVuOWVGlkkLsVP7RSDpfwBvvfXWOX8AGk264447rFGjRlFG8LSGVet/GMHzp21DOx/qeOjETucjebNTqrCqtVNKKdLadj1G7ayBjehrp0aMGOEusEhc+3qpXTt27HCdZH3ceeedLvVTlPqpQSXNVjE7xTGcGjj/hj9AUcCsmabevXu7jrHn7bffdu2vCuEKUJYvX+4qAavmhXbQ0NKdevXqsdtIPLi+hdeiRYvcrgvqK7Rt29aqV6/uljyqD6bbtbxMwbQG6hQMav01lcLDc35Q+3fr1i2s73e6lNpV1pC0iqr//POP2yf4nnvuCSxbtizK/W+99VbguuuuC7Ru3dpV+FMla+0THL3KePTvkbS2HTx4cLDqqiopbtiwgaaMx0svvRSoVKlSlH2uVW1dFWnXr18fvG3jxo2u8rWqgKpC6IMPPuj2YtV7hKS1b+jxqaqfqhRcs2bNwLBhw9zxW6ZMmRj3HwfHcLhx/k05CxcudNc4VQD2dmbQubVixYqBr7/+2u2H7Z1LtKvD7t27o1QQR+y4voWHqthPmTIlMHHiRLf3+h9//BFo2rRpoEOHDoFnnnnGVbr/8ccfo/Rvp02bFrjllltc1WuE7/zADi7nIsBOx/gDSDttS+cj4bTFWalSpYJBsraG8bbNuPHGGwODBg0KdOnSJTBjxozgSVudOq/Nkfz2ffLJJwMzZ84M7Nu3z22/8cYbb7jAWtugEVxzDKc2zr/hQYASflzfwkOTRHfccUegQYMGbmstDbZ7A8cafC9durQbIPZ4W0X169cv0KJFi8ChQ4fC9MoiB+cHf7EGO51ZvHixS5NVqmeNGjXs+eefd9sQKD1ZqVtfffWVDR8+3N3nZf8rzVMVxbXdEcLTtlqTJlrjg5ixdiptta9Swr3UzyZNmrhq7aR++tvGrP9LOM6/4aWlH19++aVbd7pz5077/PPP3XKcxx9/3F5//XX7/vvv3de6voWuUV21apVbS6mtExE7zg3hPXa1ZEnHqc67+/fvd4X2REv0lAqulPylS5e6+2+44Qa3bG/YsGGulsCUKVPYJScBbcz5wWc+B+wII0bwaNtIQOZF2mpfsi/C38akz8WNa1v421fpszpuDx486LJbdu3aFbx/8eLFbhawbdu2ge+//z54+9ChQ93xu2bNmjC/wsjBucFfyqjSsal2jamtP/vss8CqVasCc+fODdx7771u+d6vv/4aePvtt1nulECcH8KDGex0ghE82jY9I/Mi7bYv2Rfhb2Oyh2LHtS28tD+tZqc1+1e1alV3m7cvrQpF7du3z6644gpXqEj7WutDs9U63sePH+8qCZcoUSLMrzJ949wQPirGefjwYStevLj7/uzZs26mWvss68Oj7VNVmOvdd9+1jh07uoK0OnapdB83zg/hQ4CdDvAHQNumZ6Qe0b7pHcdweHBtCz8ClPDi3BBe2bNntxw5cridQzRApArWc+fOdfuLt2rVylWz1i4vXbp0sQ8++MCefvpplxquwc+rr746zK8u/eP8ED4E2OkAfwC0bXrF7BTtm95xDIcP17bwI0AJH84N4XfNNde42jb//e9/7cCBA+5r7bPsBdBZs/7/MEZbSS1ZssTVFRk9enRwv2vEjfND+BBgpwP8AdC26RGzU7RvescxHF5c28KPACU8ODekjIIFC7pZay1z0HKG8uXLu7RvLXPwljft2rXLFem88sor3fdeATTEj/ND+BBgpwP8AdC26RGzU7RvescxHF5c28KPACU8ODeknMsuu8zuv//+c273Zq8nTpzoKuOXLVvWfa/q90gYzg/hQ4CdDvAHQNumR8xO0b7pHcdweHFtSxkEKP7j3JA6NJP9999/u2BaKeP6fvbs2S7I1nZySDzOD+GRSaXEw/TcSEGDBw+2+fPnuwqKnGRo27Qywt+uXTvbtm1bnGunmjdvbnXr1nVrp44ePcraKdo3zeAYTn1c21IuQNEewkgYzg2p4+eff7bOnTu7nRouuugiu/zyy61Xr14uGwbJx/nBP8xgp0OM4NG26QGzU7RvescxnLK4toWfBjZHjBgRJUDRdkYEKInDuSF1VK9e3WbNmmW7d++2Cy64wB3D3rZzSD7OD/4hwE6H+AOgbdMLUo9o3/SOYzjlcG0LPwIU/3BuSL121wf8x/nBP6SIp1P//vsvI3i0bbpC6hHtm95xDIcf1zakR5wbAIRiBjudYgSPtk1vmJ2ifdM7juHw49qG9IhzA4BQzGADSDHMTtG+6R3HMADODQDiQoANAAAAAIAPMvvxJAAAAAAAZHQE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD7I6seTAACAtK9Xr142Y8aMOB9TpUoVmzRpUlj+/0WLFtlDDz1kEydOtKpVq4bl/wAAIDURYAMAkEF07NjRmjdvHvx+5MiRtmrVKnvjjTeCt+XJkyeVXh0AAOkfATYAABlEkSJF3Icnf/78lj17ditXrlyqvi4AACIFa7ABAEDQ9OnTrVSpUvbRRx9ZzZo1Xcr4unXr3H1ffvmlNWnSxMqUKePue+mll+zo0aNRWu+3336zRx991CpUqGDVqlWzp556ynbs2BHlMevXr7fWrVvb9ddf755n0KBBdvr06eD9J06csDfffNPq16/v/q969erZ6NGj7ezZs7xTAIA0jRlsAAAQxZkzZ2z8+PHWv39/27dvnxUrVszmzJlj3bt3t4YNG9qTTz5pW7ZssaFDh7rg+5133rFMmTK5dPMWLVq4wHngwIHueQYPHuyC6ZkzZwaf/+WXX7b27dtbmzZtbMGCBTZmzBgrVKiQ+9lAIODuU6DeuXNnK1mypFu7PWzYMNu8ebP169ePdwsAkGYRYAMAgHMoyK1Tp477WkGvZplr1arlPnuuvPJKe+SRR+zbb791j3377bftggsucMF5jhw53GMuueQS69atm/3111/Bn1OhM60HF81ya2Z84cKFLsD+7rvv7KeffrIhQ4ZYgwYN3GM0y50zZ057/fXX3c9effXVvGMAgDSJFHEAAHCOa6+9NkpK9/bt261u3bouldv7qFy5siuK9uOPP7rH/fLLL3bjjTcGg2spX768ffXVV1Ger1KlSsGvNfN96aWX2sGDB933ixcvtqxZs7r08FCNGjUK3g8AQFrFDDYAADhH7ty5g1/v37/ffX7hhRfcR3Q7d+4MPu6iiy6KtzVz5coV5fvMmTO7WXI5cOCAXXjhhZYlS5YojylQoID7fOjQId4tAECaRYANAADilC9fPve5R48eruhZdOeff777nDdvXtu7d+859yuFPHQGOy56Lq371vrt0CDbC+IVfAMAkFaRIg4AAOJ01VVXuZnpf//911X19j4KFizoipipuJmX+q108ZMnTwZ/Vvc99thjtnLlygS1sgJ4pZ/Pmzcvyu2zZ892nytWrMi7BQBIs5jBBgAAcdJMcteuXe25555zX990001uzfTIkSPdFlzXXXede5wKl913333Wrl07V4zs+PHjrvp32bJlXaGyZcuWxdvSWsNdtWpV69Onj3tuVRHXumtVGm/cuLEVL16cdwsAkGYRYAMAgHg1a9bMzjvvPBs7dqx9+OGHbo229rpWVfHLL7/cPUb7Z0+aNMnNamsrLxVAq127ttveK3v27AlqZRU9GzVqlA0fPtwmTJjgUs4vu+wyt592q1ateKcAAGlapoBXVQQAAAAAACQZa7ABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAAWPL9P4+sWUelMpNsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+27K9t/rhgVZS8/P7psFWtyfSddE6U0E/RNEKrp5vXXub9d+Mbr+l2yzp75F+cKL/vnfu3Gnek4wfugAACh7BFgCKgFblbB2JNXzq9FANWdpFVfeXzWmtoo2GHv2jW/9w1rCkAUenFGvY1XCkFUit/uqaTw2bWgXW6ct6f/0DW6epTpkyxUy9zMv00MGDB5vwqlXQjz/+2Kzf1PCl+3Hq1FmdxqkhRoPkw9IQpGPVypa+Hh2fhjsNIlo91HCtz5V1qm1GGkD0HOrPa3VNw7cGLg2POuas51IfV4OZTrXW16PTgDXo6HrljJ2IH5Q+pwYaDUC61lKDjlZDZ86cKV988YXMmTPHhCfdU1jDlQbejB8SaFdlrT7qe6bddzVE6e+JrvfU9zojDcx6bvT3Sz8g0fHr75Ye19BbGLRBlv6u6evT3zWtVupUbD3fGi71eXP7oKaw6LR5/Z3V3wFt5qVVUm249be//c2qIOfn903/rer6Zb2vvmd6XM/tP/7xD/PeZKyG678JfZ80zOpsCQ3VOuNBf8/0vdGKsa5df9Cp7gCA3Lnonj/3uQ8AwInoNEwNeVkDmv4Rrk1s7tccCAAAwNmwxhYAihndT1O7GGfdN9PWpCq3yicAAIAzYioyABQzOo1Yp0zqtGOtzuoaVJ2urFMptcFOQUwfBgAAcCRMRQaAYkibR3399demO3JCQoJZr6ndbHUtZ27bvwAAADgjgi0AAAAAwKmxxhYAAAAA4NQItgAAAAAAp0awBQAAAAA4NYItAAAAAMCpEWwBAAAAAE6NYAsAAAAAcGoEWwAAAACAUyPYAgAAAACcGsEWAAAAAODUCLYAAAAAAKdGsAUAAAAAODWCLQAAAADAqRFsAQAAAABOjWALAAAAAHBqBFsAAAAAgFMj2AIAAAAAnBrBFgAAAADg1Ai2AAAAAACnRrAFAAAAADg1gi0AAAAAwKkRbAEAAAAATo1gCwAAAABwagRbAAAAAIBTI9gCAAAAAJwawRYAAAAA4NQItgAAAAAAp0awBQAAAAA4NYItAAAAAMCpEWwBAAAAAE6NYAsAAAAAcGoEWwAAAACAU3O19wCKu2vXrklMTIwkJydLamqquLu7i4eHh/j7+0vFihXtPTwAAAAAcHoE2wJ069YtWbdunWzfvl127Nghkdsj5WT0yRzvHxgQKC1btZQWLVpIq1atpHPnzuLqylsCAAAAAPnhcufOnTv5+gnc4/z58zJ58mT5ctKXEnsmVrw9vSXYP1hC/EMkNCBU/Kv5i4e7h7i7ukvqrVRJTk2WmAsxEhUdJfti9sn+mP2SkJQgdWrXkVGjR8krr7wi1apV40wDAAAAQB4QbB9CVFSUTPjzBImYEyGlS5WW/m36y7CwYSbQliqV9+XLt2/fNgF3+urpMn/rfEm/nS6DwwfL+LfHS2ho6MMMEQAAAACKPYLtA0hLS5OJEyfKBx98ILV8a8mIbiMkvGO4+Hj5PPQbEpcQJ3M2zJHvV30vZ6+elXfeeUfGjh0rbm5uD/3YAAAAAFAcEWzzac+ePfLC8y+Yau2oJ0bJr/r/Ssq4lSnwNyYlLUU+n/+5fLnoS2ncuLF88+035isAAAAAIDO2+8mHSZMmScuWLSXpapLMfme2vDX4rUIJtUofVx9fnyfxSqJ5Xn1+AAAAAEBmBNs80qnHY8aMkWFdhsncP86VxoFFUz3V59HnG9p5qHl+HQcAAAAA4L/YWyYPNEyOHz9efj3o12bqsYuLixQlrd6+O/xdqVSukhmHPr+uuwUAAAAAEGzvS6f/2kLtawNes9vvjIbZ1we+bq6PGzdOKlSoIKNHj7bbeAAAAADAUdA86j6NonRtq04/1oppUVdqs6PbDr839T2ZsW6GREZG0lAKAAAAQIlHsM1lS582rduYxk26xrWwmkQ9aMfkQe8PkrK+ZWXrtq1sBQQAAACgRKN5VC7ravfu3St/efkvDhVqlY7no5c+MuP76KOP7D0cAAAAALArKrbZ0MDYokULGdlnpNlyx1F9PPtjmbxssuzYsUNCQ0PtPRwAAAAAsAsqttmYOGGi1PKtZTogOzJtZlWzUk0zXgAAAAAoqQi2WZw/f14i5kTIiG4jHG4KclY6vuHdhsvsiNly4cIFew8HAAAAAOyCYJvFlClTpHSp0hLeMVycweCOg814ddwAAAAAUBIRbDO4deuWfDnpS+nfpr/4ePmIM9Bx6ngnfTFJ0tPT7T0cAAAAAChyrkX/lI5r3bp1cjr2tHz68qfm9sZDG2XWxlly5OwRSUxJNCEy1C9Ungt7TurXrG/u89q/X5PdJ3dnehytoHq6e0rd6nVlZM+R0jigcbbPN3vTbPlk4Sfm+kuPvSQvPvbiPff58+w/y9KdS3Mc85Ptn5RhXYfJzHUzzfjDwsIe6hwAAAAAgLOhYpvB9u3bxdvTW0L8Q2TprqUy9ruxsuP4DklKTRJvD2+5dvOarN2/VsZMGiOHYg9lOpEaZKuUryKVy1cWn7I+Jgjvid4jb371ppy6fCrbk79ox6JM12/fvp3jG+Xu6m4eP+tFxxUSECJenl5m/AAAAABQ0lCxzUC3zQn2D5ZSpUrJNyu/Mcf6NO8jbw14yzRqOnv1rLw++XW5cP2CTF07VT589kPrZ3s27Sm/Hfhb6/b5a+fl5c9flvikeFm8Y7GM7jU604k/fOawHDt3zFR3lT7m1qNbpd2j7bJ9o5oGNpW/vfi3HN/IYL9gM34AAAAAKGmo2GYQuT3SVGvVpfhL5qtWRG3dkXVrnTf7vSlDOw617peT6hWrS2C1QHM9JTXlnu8vjFxovrap30ba1m9rri/YvuCB30gdj44fAAAAAEoaKrb/ce3aNTkZfVJe7/W6ud08qLlsObJFZm2aJZsObZL2DdpLy3otpXnd5tKhYYdcT+qt9Fuy68QuORh70NzuFNwp0/dT0lJkxZ4VVkW4lEsps55Xn+dy/GUznTm/QgNC5evlX5vXUbFixXz/PAAAAAA4K4Ltf8TExJiv/tX8zVedfqzrY2OvxMqZq2dMwNWLrqXt16qfvNrjVfFw97BO5Lxt88wlqzG9x5gwnNGafWvkZvJNsxa3Q4MO4uLiIhW9Ksq1hGtm2rI2p8pq29Ft0ml8p3umJ3/26mfmekD1APP11KlTBFsAAAAAJQpTkf8jOTnZfLWFVZ1K/N0b38nbT74tHRt2NFOSlTaSmrlxpkyYPSHb5lF60ZBqWzs7bd002Xpka6b7Loq82zTqscaPiZurm7iWdpWezXpaU5Tv3LmTp+ZRFbwqWN/3cPPI9DoAAAAAoKSgYvsfqampVoBUabfS5HrCdendrLe5aMfig2cOytcrvzZBdc3+NXLlxpUcm0clJCfIh7M+lA0HN8h7P74ns383W8qWKStnrpyR3dF3tweas2WOuWR07to52X5su7R+pHW+mke5u90dd0rKvet5AQAAAKA4o2L7H+7ud4Nh6q1Usz622x+7Sfhfwq2terRTcqM6jazuxlpV1e1/cuLl4SUDWg8w128k3ZCYSzHWtj7ZVWQzmr9tfr7fyNS0u8G8TJm7ja4AAAAAoKSgYvsfHh7/mcqbmiyN/BqJVxkvSUhJkL/O/au8+/S7pqGTVmGnrZ9m7qfV11q+tXI8sVrxXb1vtbmua2h9y/lK+u10WbJziTk2ousIGd5leKaf0WnIny36zDSS0tBc0TvvTaCS05IzvQ4AAAAAKCkItv/h73+3aVTMhRhpEtTETCvWKcS7T+42ldvynuVNwycNp0qbR+m6Wpvlu5ebrsbq9p3bpkqr1V8VFhImVX2qWl2PVY8mPUw4zkiP/XPJP01XZW0i9WyXZ/P8RkafjzZf/fz88vULoNXjuLg4OXfunJw/f17c3NykXbt2Urr03TXCAAAAAODoCLb/oVvkBAYESlR0lPRv11+6N+luGkbN3DRTDsceNqFWb9erUU/C24VL5+DOmU6kNpXSi61C61rKVapXqC5dQ7rKKz1eydQ0yr+Kv7XHbUZaodUOytuPbjfV22c6P5PnN1LHHRQYlGtH5PT0dLl06ZIJsBkvWdfl6rTs1q0zr/EFAAAAAEflcud+Cz5LkKeeekpiomJk+tjp4myGThwqgU0C5ccffzS3NaxmDbAaajXc3k+/fv2kefPMWxQBAAAAgKOiYptBixYtZMnCJaYDsjaLchY63gOnDki/Z/vJpEmT5Pr16w/VHblevXoFOj4AAAAAKEzOk96KQKtWreRm0k3ZF7NPnMm+6H2SkJRg1steuHDhoUJt1apVpXz58gU6PgAAAAAoTATbDDp37ix1ateR6audayry9DXTxa+Onzz55JMP/VhUawEAAAA4G4JtBq6urjJq9CiZv3W+xCXEiTPQcep4ddwNGzaUESNGmM7GD4pgCwAAAMDZEGyzePnll82WPnM2zBFnELEhwoxXx62CgoLMdW9v73w/lm7xo+GefmIAAAAAnAldkbMxbOgw2bxmsyz5cImUcSsjjiolLUX6/KGPtA9rL9OmT8v0vWvXrsnUqVPl6tWr+X5cDcVaua1fv74JymXKOO45AAAAAACCbTaioqLMdjcj+4yUtwa/5bC/JR/P/lgmL5ssO3bskNDQ0Hu+n5CQID/88IOcO3fugZ9Du0NruG3QoIG5eHl5PeSoAQAAAKBgEWxz8P7778v7770vs9+ZLY0DG4uj2Xtyrwz5YIi8+6d35Z133snxftohecaMGRIdHX3fx+zWrZvExsbKiRMn5NatW/d838XFRfz8/MxaXg25Pj4+D/06AAAAAOBhEWxzkJaWJm1at5HEK4ky949zHWpKsk5BHvjeQPGu4i1btm65b7MoDalz5syRgwcP5nifKlWqyC9+8QvrtWsQPnr0qBw+fFji4+Oz/ZmaNWuakKsXX1/fh3xVAAAAAPBgCLa52Lt3r7Ro0UKGdRkm7w5/11Qs7U0bO7039T2ZsW6GREZGSuPGeasm3759WxYvXmymLWenXbt20rNnz2yfT6cyayjWy5UrV3IMxraQW61aNYc4VwAAAABKBoLtfUyaNEnGjBkjvx70a3ltwGtib5/+9Kl88tMnZlyjRo3K189qSF29erWsX7/+nu/pNkG6lvZ+P3/58mUr5J4/fz7b+1WoUMEKubVr1ybkAgAAAChUBNs8mDhxoowfP17eGPiGCbf2qEZqqNRQ++m8T814xo4d+8CPtXXrVlm6dKl1W6cy/+53vzNb/eSHdl7WgHvo0CE5ffp0jh2WdT2uhlx/f3+zpRAAAAAAFCSCbR599NFHMm7cOBnx2AgZN3Rcka651TW1E2dMlO9Xfv/QoTZj5+effvrJTFHWjsrh4eEP9Xg3btwwAVcvJ0+ezHYvXA8PD3n00UdNyK1bt26+gzQAAAAAZIdgmw86/ff111+XoOpB8tFLHxVJt2Ttfjz2q7Fy4vwJ+fTTT2X06NEF9ti6dlabRDVp0kTKli1bYI+blJRkmk5pyD127Jikp6ffcx+tEus+uVrNfeSRR9grFwAAAMADI9g+QEOpF55/wXwd+fhIMzW5MKq3WqX9bN5n8q/F/zINor759ps8N4pyJKmpqaa7sobcI0eOmNtZ6fRkreBqyNWKbkGGbAAAAADFH8H2Aeh2ODo1+YMPPpCalWrK8G7DZXDHweLj9fD7usYlxEnEhgiZumqqnL161uxRq1OP77eljzPQbYd0j1xdl6sVXa3sZqXrlwMCAkzI1Uv58uXtMlYAAAAAzoNg+5DrVCdOmCizI2ZL6VKlpX+b/jKs6zAJCQiRUqVK5flxdJ3rvuh9Mm31NFmwbYGk306XIYOHyLjx48z61+JIX3NMTIzVfErX6GZHuyrbmk9VqlSpyMcJAAAAwPERbAuAbnszZcoU+XLSl3I69rR4eXpJsF+whPiHSGhAqARUDxAPNw9xd3OX1LRUSU5Llujz0RIVHSX7YvbJgVMHJCEpQerUriOjx4yWl19+2ewFW1Joo6kzZ85Y2whpt+Xs6DmxhdyqVauyjRAAAAAAg2BbwFNtdY/Y7du3y44dOyRye6ScOHkix/sHBQZJy1YtpUWLFtKqVSvp1KlTie8UrCH34sWLVsjV69nR6m1wcLCpaGvIBQAAAFByEWwLmVYfT506JcnJyZKSkmK6/+q2N35+flKxYsXCfnqnd+XKFTNVWUOuVnWzU716dRNw9VKuXLkiHyMAAAAA+yLYwmnEx8dbIVfX52a3V25gYKDpHq3TlfVDBAAAAADFH8EWTunmzZuyf/9+08Aru0quq6ur2TpIq7j16tUzWwoBAAAAKJ4ItigW05V1X2ENudk1nvL09JRGjRqZkFunTh2aTgEAAADFDMEWxa67soZcreYmJibec58KFSqYgKvTlStXrmyXcQIAAAAoWARbFEvp6ely/PhxU8XVdbnasTqrGjVqmJAbEhJC0ykAAADAiRFsUexpN2oNtxpyT5w4cU/TKRcXFwkKCjIhV/fJpekUAAAA4FwItihRbty4YaYp63Tlc+fOZdt0SsOthty6devSdAoAAABwAgRblFiXL1+2mk5dv379nu+XLVvWNJ3S9bi1atWi6RQAAADgoAi2KPF0anJsbKzVdCopKemec1KxYkWr6ZSvr2+JP2cAAACAIyHYAlmaTh07dsxUcQ8fPpxt06maNWuagKvVXG9vb84fAAAAYGcEWyCXplMHDx60mk7d84/HxcWsw7U1nXJ3d+dcAgAAAHZAsAXyID4+Xvbt22dC7vnz5+/5vpubmwm3WsnVDsulSpXivAIAAABFhGAL5NPFixdNwNVLXFzcPd8vV66cNGvWzFwqVKjA+QUAAAAKGcEWeIimU6dOnTIBV5tOJScn33OfevXqSfPmzaV+/fpsHQQAAAAUEoItUAC0yZQ2ndq9e7ccOXLEhN6MtMlU06ZNTcjVDssAAAAACg7BFihgN27ckF27dplLdvvj6hpcDbi6Jrd06dKcfwAAAOAhEWyBQqJVW+2mvGPHDrN10O3btzN9v2zZstKkSRNp0aIFe+MCAAAAD4FgCxSBmzdvmmnKWsW9evXqPd/39/c3Abdhw4bi6urKewIAAADkA8EWKOIqbnR0tOzcudPskZuenp7p+56enmbLIA25VapU4b0BAAAA8oBgC9hJYmKi7Nmzx0xVvnLlyj3fr1Onjgm4wcHBZp9cAAAAANkj2AIOsm2QVnF126CsVdwyZcpYVdxq1arZbZwAAACAoyLYAg4kKSlJ9u7da0LuxYsX7/l+rVq1TEflkJAQcXd3t8sYAQAAAEdDsAUctIobGxtrAu6+ffvMPrkZaagNDQ01VdwaNWrYbZwAAACAIyDYAg4uOTlZoqKiTMg9f/78Pd/XYKtVXA26Om0ZAAAAKGkItoATVXHPnTtnmk1p0E1LS8v0fW0wpVOUtYpbs2ZNcXFxsdtYAQAAgKJEsAWcUEpKipmirFXcs2fP3vN9bTKlVVxtOuXh4WGXMQIAAABFhWALODmt4mrA1SquBt6MXF1dpVGjRtKqVSvTeAoAAAAojgi2QDGRmppqtgvSkKuNp7KqXbu2tGnTRho2bCilS5e2yxgBAACAwkCwBYoh3SpI1+Lq1kHafCqj8uXLmwqursX19PS02xgBAACAgkKwBYoxbTCla3G3bt0qFy5cuGeacpMmTUwVt0qVKnYbIwAAAPCwCLZACemoHB0dbQLu4cOH7/l+3bp1pW3btuYr3ZQBAADgbAi2QAlz9epVE3B3795t1uVmVLlyZVPB1W7K7u7udhsjAAAAkB8EW6CE0rW3Gm415F6/fj3T93SLIF2Dq2txfXx87DZGAAAAIC8ItkAJd/v2bTly5Ihs2bJFYmJiMn1PpyUHBwebKq52VWaaMgAAABwRwRZApj1xtYKrDafS09MznRndB1cDrgZdtgsCAACAIyHYArjHzZs3JTIy0lwSEhIyfa9cuXLWdkFly5bl7AEAAMDuCLYAcnTr1i1ru6Dz58/fs12QNpnSKm7VqlU5iwAAALAbgi2APG0XpOtvNeAeOnTonu8HBQWZ7YLq1avHOlwAAAAUOYItgHy5du2abNu2TXbu3HnPdkG+vr7SunVradq0KdsFAQAAoMgQbAE8kJSUFGu7IA27GZUpU0aaN29uQm6FChU4wwAAAChUBFsAD71d0NGjR812QdHR0Zn/A+PiIg0bNjTrcOvUqcM0ZQAAABQKgi2AAqMNprSCGxUVdc92QTVq1DDrcBs1asR2QQAAAChQBFsABU63CNKtgrZv337PdkHe3t5muyCdpuzh4cHZBwAAwEMj2AIo1O2C9u/fb6YpZ90uyN3d3QTcdu3aiZeXF+8CAAAAHhjBFkCRbBd06tQpa7sgvZ1xP1xtNNW+fXvx8fHh3QAAAEC+EWwBFKmrV6/Kxo0bZc+ePZnW4ZYqVUqaNGkiHTp0MNsGAQAAAHlFsAVgF/Hx8bJ582bZsWOHpKWl/fc/Si4upsFUx44dpVq1arw7AAAAuC+CLQC70uZSOkV527ZtZm/cjOrXry+dOnWS2rVr2218AAAAcHwEWwAOITk52XRR1kZTiYmJmb4XGBhoKrj6VSu6AAAAQEYEWwAORacl79y5UzZt2mSmK2dUq1YtU8HVSi4BFwAAADYEWwAOSRtLaYMpbTSlDacyqlq1qgm4wcHBpukUAAAASjaCLQCHdvv2bTlw4ICsX79eLl68mOl7lSpVMl2UtZty6dKl7TZGAAAA2BfBFoBT0L1vjxw5YgLumTNnMn2vfPnyZh9c3Q/Xzc3NbmMEAACAfRBsAThdwI2OjjYB9+TJk5m+V7ZsWWnbtq20atVKPDw87DZGAAAAFC2CLQCnFRsbawKuVnIzKlOmjLRu3dqEXA27AAAAKN4ItgCc3oULF2TDhg2yf/9+U9G10WnJOj1ZpynrdGUAAAAUTwRbAMXGlStXTBdl7aasTadstLGUNpjSRlPacAoAAADFC8EWQLETFxdn9sHV/XBv3bplHde9b0NCQqRjx45myyAAAAAUDwRbAMVWQkKCbNmyRbZv3y4pKSmZvtegQQMTcGvVqmW38QEAAKBgEGwBFHvJycmybds2E3KTkpIyfS8oKEi6dOkifn5+dhsfAAAAHg7BFkCJkZqaKjt27JDNmzfLjRs3Mn3vkUcekbCwMKlRo4bdxgcAAIAHQ7AFUOLoulttMKWNpq5du5bpe8HBwSbgVq5c2W7jAwAAQP4QbAGUWNo5WQPu2rVrTcOpjE2mtIuyTlGuUKGCXccIAACA+yPYAijxtIKrU5TXr19vGk7ZlCpVSlq0aCGdO3cWb2/vEn+eAAAAHBXBFgAyrMHVJlM6RVkbTtm4urpKmzZtzD64np6enC8AAAAHQ7AFgCw01Oo+uNpFOS0tzTpepkwZad++vQm5eh0AAACOgWALADnQack6PTkyMlLS09Ot42XLljV74LZq1cpUcwEAAGBfBFsAuA9tLLVu3TrZtWuX3Llzxzperlw502CqadOmUrp0ac4jAACAnRBsASCPrly5ImvWrJF9+/ZlOl6xYkWzRVBISIjpqAwAAICiRbAFgHy6cOGCrF69Wg4fPpzpeNWqVU3AffTRRwm4AAAARYhgCwAPKDY2VlatWiUnT57MdLxWrVrSrVs3CQwMJOACAAAUAYItADykEydOmIB75syZTMcDAgJMwK1Tpw7nGAAAoBARbAGgAGhTqSNHjpiAe/HixUzfq1+/vpmiXL16dc41AABAISDYAkABB9z9+/ebNbhXr17N9D1tLtW1a1fx9fXlnAMAABQggi0AFALd93bPnj2ydu1aiY+P/+9/dF1czPZAuk2Qj48P5x4AAKAAEGwBoBDdunVLIiMjZf369ZKYmGgd131vW7RoIZ06dRJvb2/eAwAAgIdAsAWAIpCamipbtmyRTZs2SUpKinXczc1N2rRpI+3btxdPT0/eCwAAgAdAsAWAIpSUlGTC7datWyUtLc067uHhYcKthlx3d3feEwAAgHwg2AKAHdy8edNMT96xY4dZj2tTrlw5s0VQkyZN2AMXAAAgjwi2AGBH169fNw2mtNGUdlS2qVmzpvTq1Uv8/Px4fwAAAO6DYAsADuDy5cuyYsUKOXz4cKbjwcHB0r17d6lYsaLdxgYAAODoCLYA4EBOnDghy5cvlwsXLmTqoNy2bVvTQblMmTJ2HR8AAIAjItgCgIO5ffu27N69W1atWiUJCQnWcS8vLwkLC5NmzZpJqVKl7DpGAAAAR0KwBQAHpdsCaYMp3SYoY4OpatWqSc+ePSUoKMiu4wMAAHAUBFsAcHDXrl0z628PHDiQ6Xj9+vVNwPX19bXb2AAAABwBwRYAnMSpU6dk2bJlcvbsWeuYTklu1aqVdOnSRTw9Pe06PgAAAHsh2AKAE9Etgfbu3SsrV66UGzduWMc11Gq4bdmypWk2BQAAUJIQbAHACaWmpsqmTZtk48aNcuvWLet45cqVzfTkevXqiYuLi13HCAAAUFQItgDgxOLj4031Vqu4GdWtW9cE3KpVq9ptbAAAAEWFYAsAxcCZM2fM+tvTp09bx7Ri26JFC+natavZKggAAKC4ItgCQDFaf7t//37TQTkuLs46XqZMGencubO0bt1aXF1d7TpGAACAwkCwBYBiJi0tzex9u2HDBrMW16ZixYrSo0cPadCgAetvAQBAsUKwBYBi6ubNm7Jq1SrZtWtXpuMBAQFm/W2NGjXsNjYAAICCRLAFgGLu/PnzZv1tdHR0puNNmzaVbt26Sbly5ew2NgAAgIJAsAWAErL+9vDhw/Lzzz/L1atXreNubm7SsWNHadeunbkOAADgjAi2AFCCpKeny7Zt22Tt2rWSkpJiHffx8ZHu3btLo0aNWH8LAACcDsEWAEqgxMREWb16tezYscNUc21q164tvXr1Ml8BAACcBcEWAEqwS5cuyfLly+XYsWOZjjdu3Nh0UPb29rbb2AAAAPKKYAsAkKNHj5qAe/nyZetseHh4yGOPPSYtWrRgejIAAHBoBFsAgLX+Vqcm6xTl5ORk66zUqlVLnnjiCbYHAgAADotgCwDIJCEhwXRP3rNnz3//Z+HiIq1bt5awsDApU6YMZwwAADgUgi0AIFu67+2iRYsyTU/WPW+1uVRwcDDTkwEAgMMg2AIAcp2evHnzZrM90K1bt6zjdevWlccff1wqVarE2QMAAHZHsAUA3Ne1a9dk6dKlcuTIEetY6dKlpVOnTtKhQwdxdXXlLAIAALsh2AIA8kT3uz18+LAsWbJE4uPjreO+vr6mehsUFMSZBAAAdkGwBQDkS2pqqqxZs0a2bNliwq5NaGio9OzZk71vAQBAkSPYAgAeyIULF0xzqdOnT1vHtGNyt27dpGXLllKqVCnOLAAAKBIEWwDAA9OK7a5du2TFihWSlJRkHa9Zs6bZ+1a/AgAAFDaCLQDgoSUmJpq9b3fv3v3f/8G4uJjKrVZwPTw8OMsAAKDQEGwBAAUmJibGTE++dOmSdczb29vsfduoUSP2vgUAAIWCYAsAKPC9b7WxlO59m5aWZh3XrsnaPVm7KAMAABQkgi0AoFBcv37d7H2rWwRl3Pu2Y8eO5sLetwAAoKAQbAEAhcq2921cXJx1rFKlSqZ6W7duXc4+AAB4aARbAECR7H2rU5N1ivLt27et4yEhIWbv23LlyvEuAACAB0awBQAUmYsXL5rmUqdOncq0921YWJi0atWKvW8BAMADIdgCAIp871vdFki3B8q4922NGjWkb9++7H0LAADyjWALALDb3rcrVqyQXbt2ZTque98+9thj7H0LAADyjGALALArnZas05N1mrKNl5eX2ftW1+C6uLjYdXwAAMDxEWwBAA6x9+3WrVtlzZo1mfa+rVevnvTr10/Kly9v1/EBAADHRrAFADgM3RJI9749dOhQpuZSWr1t2rQp1VsAAJAtgi0AwCH3vl24cKHcvHnTOqZ73mr11sfHx65jAwAAjodgCwBwSNoxedmyZbJnzx7rmLu7u6neNmvWjOotAACwEGwBAA7tyJEjpnp748YN61hQUJD079+f6i0AADAItgAAh5ecnGyqt7r/bcbqbc+ePaV58+ZUbwEAKOEItgAAp3H06FFZsGDBPdVbXXtboUIFu44NAADYD8EWAOB01dvly5fLrl27MlVvu3fvLi1btqR6CwBACUSwBQA4pWPHjpnqbXx8vHUsICDArL2tWLGiXccGAACKFsEWAOC0UlJSTPV2586d1jE3NzdTvW3VqhXVWwAASgiCLQDA6R0/ftxUb+Pi4qxj/v7+pnpbqVIlu44NAAAUPoItAKDYVG9//vln2bFjR6bq7WOPPSatW7emegsAQDFGsAUAFCsnTpyQ+fPnU70FAKAEIdgCAIpl9XbFihUSGRlpHXN1dTXV2zZt2lC9BQCgmCHYAgCKrZMnT5rq7fXr161jfn5+Zu2tr6+vXccGAAAKDsEWAFCspaammurt9u3bM1Vvu3XrZqq3pUqVsuv4AADAwyPYAgBKhOjoaFO9vXbtmnWsdu3aMmDAAKlcubJdxwYAAB4OwRYAUKKqtytXrpRt27Zlqt6GhYVJ27Ztqd4CAOCkCLYAgBInJiZG5s2bR/UWAIBigmALACiR0tLSTPV269at1rHSpUub6m27du2o3gIA4EQItgCAEu3UqVOmenv16lXrWK1atcza2ypVqth1bAAAIG8ItgCAEk+rt6tWrZItW7Zkqt527dpV2rdvT/UWAAAHR7AFAOA/Tp8+baq3V65cybTv7aBBg6RChQqcJwAAHBTBFgCALNXb1atXm+rtnTt3zLEyZcpI3759JSQkhHMFAIADItgCAJDD2ts5c+ZIXFycdaxJkybSp08fE3QBAIDjINgCAJCD5ORkWbRokezbt886VrFiRQkPD5fatWtz3gAAcBAEWwAAcqHTkffu3SuLFy+W1NTUu//zdHExjaU6duxIYykAABwAwRYAgDzQ7YB0avKZM2esY/7+/qaxlI+PD+cQAAA7ItgCAJBH6enpsnbtWtmwYYPVWMrDw8M0lmrUqBHnEQAAOyHYAgCQTzExMTJ37txMjaWaNm1qGku5u7tzPgEAKGIEWwAAHrCx1MKFC2X//v3WsUqVKpnGUrVq1eKcAgBQhAi2AAA8IJ2OvGfPHlmyZInVWKpUqVKmsVSHDh1oLAUAQBEh2AIA8JBoLAUAgH0RbAEAKMDGUuvXr7eOaWOpfv36SXBwMOcYAIBCRLAFAKCAG0vptkDx8fHWMRpLAQBQuAi2AAAUsKSkJFm0aNE9jaUGDx4sNWvW5HwDAFDACLYAABRSY6ndu3ebxlJpaWlWY6mwsDBp3749jaUAAChABFsAAArRlStXzNTks2fPWscCAgJk0KBBUr58ec49AAAFgGALAEARNJZas2aNbNiwIVNjqf79+0vDhg05/wAAPCSCLQAARSQ6Olrmzp2bqbFUs2bNpHfv3uLu7s77AADAAyLYAgBQxI2lFi5cKAcOHLCO+fr6Snh4OI2lAAB4QARbAAAcpLFUt27dTGMpFxcX3hMAAPKBYAsAgAM1lgoMDJSBAwfSWAoAgHwg2AIAYOfGUqtXr5aNGzdaxzw9PU1jqQYNGvDeAACQBwRbAAAcwMmTJ01jqRs3bljHmjdvbhpLubm52XVsAAA4OoItAAAO1FhqwYIFcvDgQetYtWrV5MknnzQNpgAAQPYItgAAOFhjqV27dsnSpUutxlK6FZCuu2XPWwAAskewBQDAAV26dElmzpwply9fto61bdtWunfvLqVLl7br2AAAcDQEWwAAHFRqaqqZmrxv3z7rmJ+fnwwZMkTKlStn17EBAOBICLYAADj41OTt27fLsmXL5Pbt2+aYl5eXDB482GwNBAAACLYAADiF2NhYmTVrlsTHx5vbLi4uEhYWJh07djTXAQAoyajYAgDgJBITE82WQMeOHbOO1a9f3zSW0r1vAQAoqQi2AAA42dTkdevWyZo1a6xjFSpUMFsC1axZ065jAwDAXgi2AAA4oePHj0tERITZ+1Zpp+Q+ffpI8+bNmZoMAChxCLYAADipuLg4mT17tll/a9O4cWPp27evuLm52XVsAAAUJYItAABOLD09XZYvXy7btm2zjlWtWlWeeuop8fX1tevYAAAoKgRbAACKAd3rVve81b1vlbu7uwwYMECCg4PtPTQAAAodwRYAgGLi8uXLMnPmTLl06ZJ1rG3bttK9e3ezBhcAgOKKYAsAQDGiFduFCxdKVFSUdaxOnToyZMgQKV++vF3HBgBAYSHYAgBQDLcEioyMlGXLlpk1uKps2bIyePBgCQoKsvfwAAAocARbAACKqTNnzsisWbNM92Tl4uIiXbt2lU6dOrElEACgWCHYAgBQjCUmJsrcuXPl2LFj1rFHHnlEBg0aJJ6ennYdGwAABYVgCwBACZiavH79elm9erV1zMfHx2wJVLNmTbuODQCAgkCwBQCghDhx4oRERESYKq7STsm9evWSli1bMjUZAODUCLYAAJQg8fHxZt1tbGysdSw0NFT69u1r9r4FAMAZEWwBAChhtFPyzz//LFu3brWOValSxUxNrly5sl3HBgDAgyDYAgBQQu3fv1/mz59v9r5VWrHt37+/NGrUyN5DAwAgXwi2AACUYJcvX5aZM2fKpUuXrGOtW7eWnj17mjW4AAA4A4ItAAAlnFZsFy1aJHv37rWO1a5dW5588kkpX768XccGAEBeEGwBAIDZEmjHjh2ydOlSswZXeXl5ydNPPy116tThDAEAHBrBFgAAWM6ePWumJsfFxZnbOh1ZOyY3bdqUswQAcFgEWwAAkElSUpLZEujkyZPWsbZt20qPHj2kVKlSnC0AgMMh2AIAgHvodORly5bJ9u3brWP16tWTwYMHi4eHB2cMAOBQCLYAACBHkZGRsmTJErl9+7a57evrK8OGDTNfAQBwFARbAACQq+joaLPuVqcoK63YDhkyROrWrcuZAwA4BIItAAC4r2vXrsn06dOt/W5dXFykV69eZs9bvQ4AgD0RbAEAQJ6kpKTInDlz5MiRI9ax5s2by+OPP266JwMAYC8EWwAAkK/9bletWiUbNmywjvn7+8uTTz5p9r0FAMAeCLYAACDf9u7dK/Pnzzfdk1WFChVk6NChUq1aNc4mAKDIEWwBAMADOXPmjMyYMUNu3rxpbru5uUl4eLg0aNCAMwoAKFIEWwAA8MDi4+Plxx9/lLNnz1rHunXrJh07dqSpFACgyBBsAQDAQ0lLSzPTkvft22cdCwkJkf79+5sqLgAAhY1gCwAACqSplDaU0sZSNjVr1pSnn35aypcvzxkGABQqgi0AACgwhw4dMlsCaRVXeXt7m6ZStWrV4iwDAAoNwRYAABSoCxcumKZS169fN7d1j1udlty4cWPONACgUBBsAQBAgUtISJBZs2ZJTEyMdaxDhw7y2GOP0VQKAFDgCLYAAKBQ6B63ixcvlp07d1rH6tevb7YEKlOmDGcdAFBgCLYAAKBQm0pt27ZNli1bZq6rKlWqyLBhw6RixYqceQBAgSDYAgCAQnfixAkzNTk5Odnc9vT0lKeeekoCAgI4+wCAh0awBQAAReLKlSumqdTly5fN7VKlSkmfPn2kZcuWvAMAgIdCsAUAAEVGK7YRERFy7Ngx65gG2969e5vuyQAAPAiCLQAAKFK3b9+WFStWyObNm61jgYGB8uSTT5opygAA5BfBFgAA2MXu3btl4cKFpnuy0mZS2lRKm0sBAJAfBFsAAGA3p0+flh9//NHse6vc3d1lyJAh8sgjj/CuAADyjGALAADsKi4uzjSVOn/+vHWsV69e0rZtW7uOCwDgPAi2AADA7lJTU2XevHly4MAB61ibNm1MwHVxcbHr2AAAjo9gCwAAHMKdO3dkzZo1sm7dOutYcHCwDBo0SFxdXe06NgCAYyPYAgAAh7Jz507TVEqDrvLz85OhQ4fSMRkAkCOCLQAAcDhHjx6VWbNmSVpamrlduXJlefbZZ6VChQr2HhoAwAERbAEAgEM6e/asTJs2zeqY7O3tLc8884zUqFHD3kMDADgYgi0AAHBY165dkx9++EGuXLlibQf05JNPSr169ew9NACAAyHYAgAAh5aYmGi2A9I9b1WpUqWkX79+0rRpU3sPDQDgIAi2AADA4ela27lz58rBgwetY127dpXOnTuzHRAAgGALAACcw+3bt2XZsmWybds261izZs2kb9++pooLACi5qNgCAACnoVsAbd68WX7++Wfr2COPPCJDhgwx628BACUTwRYAADidffv2yU8//STp6enmds2aNWXYsGGmczIAoOQh2AIAAKcUHR0tP/74oyQnJ5vbusft8OHDxdfX195DAwAUMYItAABwWhcvXjTbAcXHx5vbnp6epnJbp04dew8NAFCECLYAAMCpaaidNm2aXLhwwdx2dXWV8PBwadiwob2HBqCE7r8dExNjZpOkpqaa9f8eHh7i7+8vFStWtPfwii2CLQAAcHopKSkyc+ZMOXHihHWsT58+0rp1a7uOC0DxduvWLVm3bp1s375dduzYIZHbI+Vk9Mkc7x8YECgtW7WUFi1aSKtWrcyWZfphHB4ewRYAABQL2khq/vz5snfvXutY+/btpXv37ux1C6BAnT9/XiZPnixfTvpSYs/EirentwT7B0uIf4iEBoSKfzV/8XD3EHdXd0m9lSrJqckScyFGoqKjZF/MPtkfs18SkhKkTu06Mmr0KHnllVekWrVqvEsPgWALAACK1XZAq1atkg0bNljHQkJCZMCAAVRFADy0qKgomfDnCRIxJ0JKlyot/dv0l2Fhw0ygzc9+2rovtwbc6auny/yt8yX9droMDh8s498eL6GhobxTD4BgCwAAip3IyEhZvHixCboqICBAnn76abPODQDyKy0tTSZOnCgffPCB1PKtJSO6jZDwjuHi4+Xz0CczLiFO5myYI9+v+l7OXj0r77zzjowdO1bc3Nx4o/KBYAsAAIqlw4cPy+zZs80aOFW1alV55plnxMfn4f8QBVBy7NmzR154/gVTrR31xCj5Vf9fSRm3MgX+PClpKfL5/M/ly0VfSuPGjeWbb78xX5E3ea+XAwAAOJFHH31UXnjhBSlbtqy1NdCUKVOs7skAcD+TJk2Sli1bStLVJJn9zmx5a/BbhRJqlT6uPr4+T+KVRPO8+vzIGyq2AACgWLt69arZ61a/qjJlyshTTz0lQUFB9h4aAAemU4/Hjx8vIx4bIeOGjiu0QJtT9XbijIny/crvZcKECTJu3Lgie25nRbAFAADFXkJCgkyfPl3OnDljbmuTF20oxTQ/ALmF2l8P+rWZeuzi4lLkJ0p7BHw27zP55KdPzHh03S1yRrAFAAAlpvmLrrk9cuSIdeyxxx6TDh06sB0QAItO/x0zZowJta8NeM3uZ+bTnz414faLL76Q0aNH23s4DotgCwAASgzdYmPJkiWma7KNrmPr06dPvrbqAFB8G0XpfxOGdRkm7w5/1yE+9NLK7XtT35MZ62aY/3Yx0yR7BFsAAFCi6B+JGzdulJUrV2ZqNDV48GC21wBK+KyONq3bmMZNc/84t0jX1OZlze2g9wdJWd+ysnXbVv5blQ0+mgQAACWKVmA6duwogwYNsqq0ujXQt99+a9biAiiZdB3r3r175S8v/8WhQq3S8Xz00kdmfB999JG9h+OQqNgCAIAS68SJEzJz5kxJSUkxt319feW5556T8uXL23toAIqQBsYWLVrIyD4jzZY7jurj2R/L5GWTZceOHRIaGmrv4TgUgi0AACjRdF9b3Q7oxo0b5raPj48Jt5UqVbL30AAUkWFDh8nmNZtlyYdLHK5am3VKcp8/9JH2Ye1l2vRp9h6OQ2EqMgAAKNGqVasmL730klSsWNHcjouLk6+//louXrxo76EBKALnz5+XiDkRMqLbCIcOtUrHN7zbcJkdMdt8KIf/ItgCAIASr0KFCvLiiy9K1apVzbm4efOmfPPNN3L27NkSf26A4m7KlClSulRpCe8YLs5gcMfBZrw6bvwXwRYAAEBEypUrJ88//7zUrFnTnI+kpCTTUComJobzAxRTt27dki8nfSn92/QXHy8fcQY6Th3vpC8mSXp6ur2H4zBYYwsAAJCBNpKaPn26FWhdXV3l6aeflnr16nGegGJm1apV8thjj8ncd+dK48DG5tjGQxtl1sZZcuTsEUlMSTRBMtQvVJ4Le07q16xv7vPav1+T3Sd3Z3osraJ6untK3ep1ZWTPkdI44O7j2UTFRMkPa3+Q/af3y42kG+Lt4S0NazeUoZ2GSou6LXIc4+Idi2VixERxd3WXle/f3aZs74m9ZvsfHX9YWFghnBnnQ8UWAAAggzJlysizzz5rBVmt6GjQPXDgAOcJKGa2b98u3p7eEuIfYm4v3bVUxn43VnYc3yFJqUkmfF67eU3W7l8rYyaNkUOxhzL9vAbZKuWrSOXylcWnrI8Jwnui98ibX70ppy6fsu638/hOE4Y1NNtC7c3km7LlyBb5zVe/kY0HN+Zr3CEBIeLl6WXGj7sItgAAAFm4ubnJ0KFDJTg42Ny+ffu2zJ49W3bvzlyhAeDcdNucYP9ga0/rb1Z+Y772ad5Hlv5xqSz8w0KZ8dYMqVahmqTeSpWpa6dm+vmeTXvKnLFzZO7YuTJv/Dz58bc/SnnP8ua+Wmm1+W7Nd5J+O11aP9JaFry9wDyu/oxWgG/fuS1frfwqX+PW8Qb7BZvx4y7X/3wFAABABqVLl5bBgweLu7u7CbR37tyRefPmSWpqqrRu3ZpzBRQDkdsjJezR/07lvRR/yXzViqqtQ3LNSjXlzX5vmqnHvuV9c3286hWrS2C1QFO1TUm9uz+2uhx/2arwli1T1lyv6F1R3uj7hqw/sN5MM84vrTKv3b423z9XXBFsAQAAcqmK9O/f34Tbbdu2mWNLliwx63A7derEeQOc2LVr1+Rk9El5vdfr1rHmQc3N9OBZm2bJpkObpH2D9tKyXktpXre5dGjYIdfHu5V+S3ad2CUHYw+a252C//vfiGZBzSTmUoyZ0jzkf4dIhwYdzLpafeysa3HzKjQgVL5e/rV5HRX/s11ZSUawBQAAyIWLi4v07t3brL1dv369OaYNWzTcatMZ/T4A52NrEOdfzd869taAt8z62NgrsXLm6hkTcPWildZ+rfrJqz1eFQ93D+v+87bNM5esxvQeY8KwjTaT0mZUB04fMNVb28+5lXaT7k26yy8f/6VZo5sfAdUDzNdTp04RbFljCwAAcH8aXrt16ybdu3e3jm3cuFEWL15spigDcD7Jycnma8agqlOJv3vjO3n7ybelY8OOZkqy0kZSMzfOlAmzJ2TbPEovFb0qms7Iatq6abL1yFbrfuU8y8kXo76QPz/7Z3ms8WPmviotPU2W7Fwi//PN/+T7vyUebh6ZXkdJR8UWAAAgjzp06GAqt4sWLTK3IyMjzZrbAQMGWM1nADgH/berMq5vTbuVJtcTrkvvZr3NRRvHHTxzUL5e+bUJqmv2r5ErN65kah7124G/tW4nJCfIh7M+lA0HN8h7P74ns38326yp1WnK+rg6nblzo87mvsfPHzcBePnu5Wb68oHYA9KoTqMcx5t1doi7291x6+wR0BUZAAAgX1q2bCmDBg2y/sjcu3evzJo1y2wLBMA5aHXUVunUDsZK18d2+2M3Cf9LuLVVj35gpWFzdK/R1s/p9j858fLwkgGtB5jruq2Prqs9e/WsedxBHw0y2wjZ6H63v+n/G+u2Pq5WhYf9bZj89pv/hmXdQkiVdb/bdMomNe3uuPXDNhBsAQAA8q1x48by1FNPmc7J6tChQ2avW1sFCIDj0ACr62m1AdyCBQtkypQp8tFHH8mcOXPufj/1bsBt5NdIvMp4met/nftXq5OxVmGnrZ9mrmv1tZZvrRyfSyu+q/etNtf1wy/fcr6mq3Jt39rm2GeLPpPTl09bgfrb1d+a66VcSpmgq8FZ1/dGxUSZQKwV461H705pDqoelPl1pf1nKrXHf6dSl2RMRQYAAHgADRo0kGeeeUZmzJghaWlpcuLECZk6dao5xh+agH3cuHFDYmNj5ezZs3LhwgVziY+Pz/a+FSpUMF9jLsRIk6AmZkqyTivWKcS6tY9WbnVP2pvJN80etEqbR+m6WhudRqzdk5XuR6tVWlsFOCwkTKr6VDXXx4aPldcnvy7RF6Plmb8/Yx43MTXRTFFWQ9oPkRoVa5ipzd+s+sY8p95Px6Tre3Xt7rOdn800/ujz0earn59fIZxJ5+Nyh44HAAAAD+z06dPyww8/WOvcqlevLsOHDxcvr7uVHwCFIz09Xc6dO2eCrO0SFxeXp5/VUFutWjV56zdvSe/GveXtYW9b39tyeIvM3DRTDsceloSUBNNAql6NehLeLlw6B99dH/vav18z4TcjrdC6lnI1VdquIV3llR6vWHvhqv2n9ssP636QfTH7JD4p3gRk3fP2iRZPyOMtHreWN+ja2y+WfiGHYg+Z5lJayX0+7HlpU79Npuf7cNqHsvboWjl+4vhDncfigmALAADwkPSPa63WJibeXQtXuXJlGTFihJQvX55zCxQQrbzqB0m2EKv/7jTc5kbXn2qArVq1qvlqu25bl6pLCmKiYmT62OlO9z4NnThUApsEyo8//mjvoTgEpiIDAAA8pBo1asiLL74o3333nZkKefnyZfn666/lueeeY39J4AFoMzYNrhpkz5w5Y77qv63cuLm5Sc2aNaV27drmorMnfHx8ct1rukWLFrJk4RKzltWZOpvreA+cOiBDXhpi76E4DIItAABAAdAq7UsvvWTC7bVr1+T69evy1VdfmXBbpUoVzjGQC51CrOHVFmQ11Gp4y02lSpWsEKsXrcbmN5y2atVKbibdNNODGwc2dpr3aF/0PklISjDjx11MRQYAAChAWlX6/vvv5dKlS+a2p6enWXOrlSQAd2nDNe1UfOzYMTl+/LiZ5ZAbd3d3qVWrVqYgW7Zs5u1vHrQyHBQYJO2C2snElyY6zdsz7qtxsuXkFjlx8oTVnb2kI9gCAAAUMF1rq2tuteqkdD3fsGHDxN/fn3ONEkn71Wp4tQVZDbW57f2sMyAyhlid9VBYU4X//Oc/y4cffCib/r5JfLx8xNHFJcRJ+9+0l3f++I6MHz/e3sNxGARbAACAQto7U/e2PXXqlLnt6uoqTz/9tNSrV4/zjRLzb0C3wdIgq4E2p213dA2shtfAwECpU6eOqczqTIeicv78ebNlzu+H/F5e7PWiOLqvln0l/zv7f820bZ1+jbsItgAAAIU43VI7luof9korTkOGDJGGDRtyzlEsq7I6S8FWldXgldPOotoxvG7duuaDnqCgILvv/Txs6DDZvGazLPlwSaYtehxNSlqK9PlDH2kf1l6mTZ9m7+E4FIItAABAIdLplnPmzJGDBw/e/ePLxUUGDBggTZo04bzD6d28edOEWNvFtuVVVroONCAgwAqzOtU4t27FRS0qKkqaN28uI/uMlLcGvyWO6uPZH8vkZZNlx44dEhoaau/hOBSCLQAAQCHT7q7z58+XPXv2WMf69OkjrVu35tzD6WjX7/3798uBAwesdeTZ0fBqC7K6vly343Fk77//vrz/3vsy+53ZDtkhee/JvTLkgyHy7p/elXfeecfew3E4BFsAAIAioFMylyxZItu3b7eOdevWTTp16sT5h8PT9bEaZvWi2/Hk1LlYpxXbwmyFChXEmT58Sk9Plzat20jilUSZ+8e5DjUlWacgD3xvoHhX8ZYtW7c4/IcE9kCwBQAAKMJwu2rVKtmwYYN1rGPHjibgOtK0TMA2zVirshpmbU3QsqpevboJsXrRBlCOvPWM/vvT16TdmW0X3ZZLg3pqaqppxNS+fXtp0aKFDOsyTN4d/q5D/LvUcb839T2ZsW6GREZGSuPGjldNdgSu9h4AAABASaF/JD/22GNm+5+VK1eaYxpy9Q9XPe4If0SjZNM1sroeXMNsdHR0ts2fNMw2atTIXCpWrCiORiuvOl06Y4C1XVJSUnL8uQsXLph1q5999pmMGTNGfMv7ymsDXhN7+2zeZ/L9yu9l0qRJhNpcEGwBAACKmFZpNdwuXrzY3N64caMJtVRuYa9teQ4fPiz79u0z2/PotNzs1suGhISYMKvXHY12YtaGShper169mu1ruB9tbqX/DkePHm2Cse4Rq8Few609PnTS5/70p0/l03mfysSJE2XUqFFFPgZnQrAFAACwg1atWpmvtnCrlVv94zksLIzKLQqdTr09cuSIqcwePXrUVDmz0mqsBlkNtFWrVnXY30vtPD5z5kyzvdaD0inUgwYNsm6PGzfOvF79evXGVRk3dFyRrrnVNbUTZ0w0lVoNtWPHji2y53ZWBFsAAAA7hltbUym1fv16s9dt165deU9Q4LSKqZXNvXv3mlCbXRDU/WVtYbZGjRoOG2YzcnV1lUqVKpmpxA+qTZs25rVnpGFSG2C9/vrrsu3INvnopY+KpFuydj8e+9VYOXH+hHzxxRemgoz7o3kUAACAnW3dulWWLl1q3dZg26VLF7uOCcWHNkzauXOnucTFxd3zfS8vL2vNbJ06dZwizGal2w5Nnjz5gaYg67KAN954Qzw9PbP9vn4Q8MLzL5ivIx8faaYmF0b1Vqu0up72X4v/ZdbSfvPtN6ypzQeCLQAAgAPYsmWLLFu2zLqtU5I7d+5s1zHBeelMAG3+pF10Dx06dE/g0xDXsGFDU5nVPWZ1poCz07XqK1asyPfP3W/brbNnz0pCQoLMmDFDPvjgA6lZqaYM7zZcBnccLD5ePg85apG4hDiJ2BAhU1dNlbNXz5o9arVazJY++UOwBQAAcBCbN2+W5cuXW7fZ5xb5lZSUJLt37zaNlK5cuXLP9x955BGznY1uz+PIW/M8aJj//vvv5eTJk3n+Ga1W61Rj3YM3Kw2zCxYsMI211BNPPGGquxMnTJTZEbOldKnS0r9NfxnWdZiEBITk68MB/aBhX/Q+mbZ6mizYtkDSb6fLkMFDZNz4caYzM/KPYAsAAOBANm3aJD///LN1W7cB0i7KQG6BLjY21oRZ7WyctRGUhrdmzZqZQKtrRoszDfO6LY42lMqLxx9/3GrkZqPnT5cHrFu3LtP2QHr++vbta66fP39epkyZIl9O+lJOx54WL08vCfYLlhD/EAkNCJWA6gHi4eYh7m7ukpqWKslpyRJ9PlqioqNkX8w+OXDqgCQkJUid2nVk9JjR8vLLL5t9dPHgCLYAAAAOPqWye/fu0qFDB7uOCY5HQ1dUVJSZbpxd4yTdvqZly5bSoEGDYledzc7x48dl0aJFZquevNCuz7/85S+tc6MfEGhTLZ01oVsGZfXUU0+Z6dsZaYDWpm/bt283HyxEbo+UEydP5PicQYFB0rJVSxOSNVDrFGhtfoWHx1kEAABwMBpi9Y/slStXmtsacrWhT/v27e09NDgArRZqmNVQq9v2ZOTh4SFNmjQxgdYR95stDDplWMOoNney0X8v+m8oN7qO3RZq9YMBfQzdxze3IJyVhlJ9HL3YaLA+deqU2R9YP3zQ6cv6vvj5+WX7GCgYBFsAAAAHpNOPdR3e6tWrzW2dnqx/rLdr187eQ4MdaGVQ95zVQKvTjrOqVauWCbPa2bikNB3S4KrrifXfhq4tttEA2atXL4mIiMi28qp02q82ztJQvGbNGlNtvV8Qzus0bg2vBNiiR7AFAABwUNoVWf/Y1j+8lVaUtEGN7rmJkkErfjrNVbtmawjLSAOsNhrSQKt7zpYkly9fNtOOtfOzjVZFe/ToYdYT64dAgwcPNutgs9sCSLfU0nO6du3aTOtoc2KrusJxEWwBAAAcmO5nq+FW/wBXut+t/tHeunVrew8NhSgxMdEEr23btt0TvKpWrWrCrO51qoGrpFWudQ26rmvN2CRLq69apfX29raO1axZ00wRtk3pt6lSpYqp8uZUzc1OcW+6VRwQbAEAABycVpc03GqXVrVkyRITbrN2c4Xzi4+PN52xd+7cKWlpaZm+p9OMtVpfu3Zt8/6XNDExMbJw4UJTrc0YOHUbHt2+KDu6Ll2bSmWs7F66dCnfz02wdXwEWwAAACcKt1qpUosXLzbhRit3cH5aPdywYYPs2bMn09RZnXquzaC0oZivr6+URLp+Vhuoadi3sa031xkN2e1Bm/H8DRo0yGwBlHEdbn75+Pg88M+iaBBsAQAAnID+Ia/TKjXcagBSusZQj+vWIXBO2o1X309tDJWxeZF229X3VcNbSQ1VOtVYmzrpNHydmp1xinG/fv2kevXqeXqc8uXLy8iRI800/sOHDz/QWKjYOj6CLQAAgJPQENutWzdT0dPpqkqnZurx5s2b23t4yIfTp0+bQKv7pmaka2Z1innbtm3Fy8urRJ5TDfiHDh0ya2OvXLliHdfKrP7+6/nRSmx+g+nTTz9tukrr+tqs07zz8vNwbARbAAAAJ6Ihtnv37uaP/82bN5tjCxYsMMe1Gywcl75nJ0+eNNPJM675VGXLljVhVkNbSe6+q1sZafDUfWCzri/WjscPU722rUvX9bjz58+/5z3IDcHW8RFsAQAAnIz+ga5/5GtQ0s65Sv9Q1+NNmza19/CQhb5POgVWA+3Zs2fvmSarDY604l5S9p/NaY3xqlWrzJTsjHRPWv1d14ZZBUX3mH3uuefMNko6Pfl++9cqgq3jI9gCAAA4IQ2xPXv2NH+Ub9261RybN2+eOa7NhuA4gVYDW9ZOvNoIShtC6ZY9pUuXlpJK185qt28NmRmbZun50ZkJjz76aKF0gNbHvH79ep5CrU6BLslVdGdBsAUAAHBS+se57t2pf5zrfqfqp59+Msc1MMHxptRWq1ZNOnXqJA0bNsz3OtHith+tfiCjVeyM+/TqlGztAK4V7MIM/Lq22TaVX/+96IcM+m8oNTU122ptSdxeydkQbAEAAJyY/sHdu3dvE2616pUx3IaGhtp7eCXOtWvXTNOjrFNqdSpt586dzfrOkhyS9Pc0KirKVLHj4uIydYHWDtAaMLWBVmHS59V/IzY680HXN2sXal2vfuLEiUz3ZxqycyDYAgAAODkNSn369DGhQbu+6te5c+ea4yEhIfYeXolgrym1zkQbZ2kV+9y5c5mO67pw3cpK1xsXxRZCERER1p62DRo0kDZt2lgBdvjw4Wa/3OXLl1vVW12TC8dHsAUAACgGNDQ9/vjjJtTq3p/6dc6cOea4dpRF8ZxS6wx0fbEG2qNHj2Y6XrduXdMYSqdnFxWtFOtWS0o7LPfv3z/TBw62faG1sr5ixQpT3dVOynB8LnfysmIaAAAATkH/tNO9bbXqZPtDfciQIRIcHGzvoRUrjjCl1hk6Hetevbt3787UpEmDrAZaDbZFae/evWYmg9L1zS+++GKBdluGfRFsAQAAihkNEbpWcNeuXeY24bb4Tal1ZBcuXJCNGzfKvn37MgVaPS96frSxWVE3ztIq7bfffmumIitdl26bgoziganIAAAAxYwG2X79+plQYauW6bpCPa7dePHgU2p1eqp21LX3lFpH7QStFVrd4igjrVxrBVsbNNljr17d1ufHH3+0Qq1ONW7dunWRjwOFi4otAABAMaVNjObPny979uwxt7VK9uSTT5qGOci7mzdvyurVq00F3BGm1DoSPR9awdZAq18z0nXGGmZ1jaq99oHVBlBfffWVqSKrwMBAefbZZ0v8uufiiGALAABQzMPtvHnzzPpCW7h9+umnpX79+vYemsNLS0szU2o3bdpkrtuUK1dOunXrZpcptY4UaLVyrU2zzpw5k+l7en7at29vGme5u7vbdYxaqbVVkCtVqiSvvPKKeHp62m1MKDwEWwAAgBIQbnXfTm12pLRLr1attHqF7B0/flwWLVpk9qW10ZDWsWNHu02pdZTfJd2jVyu0Fy9ezPQ9DY465bhJkyYOURHVaeP6wYRtOrSG2sqVK9t7WCgkBFsAAIASEkh0+x8NJUqD2XPPPVdsu8JqtU7XxOo+svkJWQkJCbJs2TLrQwClVVldl9mlSxfx8vKSkrqtkU5p16CYMezbpmRr4NfO245Swda15TpTQenacv0gpyRPGS8JCLYAAAAlhDbPmTlzptX8SNc9vvDCC8Wy6ZF2hdYtj/z8/EyAv1+4tTXa0m7HSUlJ1nH9+b59+0qVKlWkJNI1qrov8ubNm+XGjRuZvqcfinTq1EkeeeSRTHvB2tupU6fku+++s5pF9enTh2ZRJQDBFgAAoATRtaLTpk2T6Ohoc1srkLqfp1Y2i4uzZ8/Kv//9b+u2To/t3r17jve/fPmymXZsOye20K+NoZo1a+ZQoa2oaOU6MjJStm7dminoq6CgIBNo/f39He7caAdkfe8TExPN7ZYtW8rjjz/ucONEwSPYAgAAlDApKSny/fffW01/dH/Rl156SXx8fKQ40Nd24sSJTMe0apt1TbFOr9WptdoAyVbdUyEhIdKrVy/x9vaWkkSr1lrt1ArtgQMHMp0Tpd20dcpxrVq1xFF/r7UDsm3tLx2QSxaCLQAAQAmkVbhvvvnGCgHa+Ecrt84e5jTQarDNSjv1jhkzxuqIGxMTIwsXLjTVWpsKFSrIE088IfXq1ZOSJDk52XTN1gqtrkvOSCudoaGhpupdtWpVcVQawrUD8tGjR81tOiCXPARbAACAErw/69dffy1Xr141t3Wt7fPPP++026FoxXHKlCn3bD9j07BhQ7NeduXKlWb9bcbw1q5dO+natWuJ6nasU7Y1zO7bty/TdkZKfweaNm1q9qCtWLGiOPr7rl2/bVta6TTyl19+mQ7IJQzBFgAAoASLi4sz0zfj4+PNbZ1mOmLECLM9irM5ePCgaY6VG92yRxsi2ejr1bBbvXp1KQn0tWuQ1enGGmyz0mZZ2gFaOxy7urqKo9NQu2TJEtm+fbu5zVZWJRfBFgAAoIS7cuWKqdxqwyDb2sRnnnmmQIKNbg2j0351uquGKg2WWlHTxkMFWQnU7Yy++OKLTFOLc6PjeOyxx0xzIUfZoqYw6ZRzDbO6ZY+uRc16LnTvWQ20ztYhe/Xq1bJu3Tqr8v7UU0+ZtcAoeQi2AAAAkAsXLpg1txpAVf369U1IyM8esNqMSUOGVs80REVuj5ST0SdzvH9gQKC0bNXSBCqd8tq5c+cHDtO7du2S+fPn5+m+GqxHjhzp8FNsH5a+H1rF1unG2hQqK61Sa7DXNbQabp2NbkG0fPly6/bAgQNNQEfJRLAFAACAERsba/b/tK231O7AgwYNum9F8/z58zJ58mT5ctKXEnsmVrw9vSXYP1hC/EMkNCBU/Kv5i4e7h7i7ukvqrVRJTk2WmAsxEhUdJfti9sn+mP2SkJQgdWrXkVGjR8krr7ySr8qhBrjPPvvMmk6dF9rdVyu2xZFWyTXM6r68tm1vbPSDA31fNdDWrFnTabfByfpBhnaxbtu2rV3HBPsi2AIAAMBy8uRJ+eGHH6ytXpo3b27WoGYXgKKiomTCnydIxJwIKV2qtPRv01+GhQ0zgTY/03t1GrEG3Omrp8v8rfMl/Xa6DA4fLOPfHm+qifmt3OWVNsoKCAiQ4tIITLfo2b9/f7bV2cqVK5sw27hxY6dtDmajVehZs2aZ9bWqS5cupvEXSjaCLQAAADI5fPiwacKkgVNpx+AePXpY4VYruhMnTpQPPvhAavnWkhHdRkh4x3Dx8Xr4fXDjEuJkzoY58v2q7+Xs1bPyzjvvyNixY3PsVqzrRT/55BOzfVF+6f69o0ePdtqgp9VYDXkaZqOjo62gZ6MfLmgTKJ3qrWuanbU6m3U7p2nTplkfvLRu3Vp69+5dLF4bHg7BFgAAAPfQzrkRERHW7bCwMLMGVpsPvfD8C6ZaO+qJUfKr/r+SMm4F30E5JS1FPp//uXy56EtTZfzm22/M16zWrFkja9eufeDn0YpweHi4OAtdA60fPOj7oyHP9uFD1uqsrjVt1qyZeHl5SXGdKq+vccCAAYRaGARbAAAAZEsbQC1cuNC6rYHiL3/5i9StUVc+eukjaRx4b9AsaHtP7pWxX42VE+dPyKeffmoqrDbaxVmrtVn3YM0PbST1u9/9zqHDkXaTPnLkiKnMHj161KpWZqSNsBo1amTWz1atWtWhX8+DdnXWzt225maPPvqoaW5WEjpaI28cf3MqAAAA2IVOYdVQpetX169fLytXrpQRj42QcUPHFUqVNjsanuf+ca5MnDFRxowZYxojjRs3znxv0aJFDxVqK1SoIB06dHDIEKiv69ixYybMaqjN7nXqVGpbmK1Ro4ZDvo6CoO/5999/b4VaXRc9ZMgQQi0yIdgCAAAgR7q+durUqSbU/nrQr83U46IOUBqi3x3+rlQqV0nGjx9vnv+3v/2tWV96P9oF2NfX10zPzXjRYzmt27UXrcQeP37chNlDhw6ZDxWy8vb2NutmNczWrl272IbZjHss6/RjbY6ltJPz0KFDC2SPZRQv/EYAAAAgR5MmTZJ//vOfJtS+NuA1u50pDXCvD3zdXNeKrVYrNdzoVj+qbNmyUqVKlUwhVm/7+Pg4dPjTBlC6Vlars7p21laVzEibW2mY1eqsNoEqKdNvdfqxhlqdcq70/Xz22WelTJmimS0A58IaWwAAAGRLG0XpFjHDugwzFVNHCIja+fe9qe/JjHUzZOPGjWY6sVbxnKVJkjZ7OnPmjAmyWp3V69nR8NawYUMTZgMDA6V06dJSkpw7d85MP7Z1u9Z9jUeMGOE07zOKHsEWAAAA99A1nW1at5HEK4lmjWtRranNa8fkQe8PkrK+ZWXrtq0ON6U4q/j4eBNiNcxqdTa7qqzS16FNkXSacd26dUvsdFvtfqzT33UrJ6UfXAwfPtxpt2VC0SiZ/1oAAACQK92ndu/evRLxxwiHCrVKx6NdmYd8MEQ++ugjs9etI9Hp0adOnbKqsjqlNic6vbZevXrm4ufnV2LDrE1MTIzZp9a2vrhOnTryzDPPmO7VQG6o2AIAACATDbTaEXlkn5Hy1uC3HPbsfDz7Y5m8bLLZlkj3o7Xn9OirV69aQTY6OjrHbs0a0IKCgkxFVsOsrhXGXVrNnj59urVuWqdga6Mod3d3ThHui2ALAACATIYNHSab12yWJR8ucbhqbdYpyX3+0Efah7WXadOnFelz37hxw0yZtTV+un79eo73rVWrlhVk9XpJaf6UH7ql0cyZM609evVc6T61jj7NHI6DYAsAAADL+fPnzZTY3w/5vbzY60WHPzNfLftK/nf2/8rp06dNg6HCoGFLmxlpkLVd4uLicry/bsmjwUzDrFZntWMzcnbgwAGJiIgwjbVUgwYNZPDgwSV+Wjbyp2RP4gcAAEAmU6ZMkdKlSkt4x3CnODODOw6Wv835mxm37nFbUM2eNCjbQqyGWlslMTtagdVteGxV2apVqzpEB2lnmfb+008/mencShtnDRw4sMR1gcbDo2ILAAAAQ9c2BgUGSbugdjLxpYlOc1bGfTVONp/YLCejT+Y7EOlr1uCqQVa33tGvOs04Nzo9Vjv11q5d21S3AwICWAf6AHbu3CkLFiywbjdt2lT69evHVG08EIItAAAAjFWrVsljjz0mc9+dKzdSbsisjbPkyNkjkpiSKD5ePhLqFyrPhT0n9WvWN/d/7d+vye6TuzOdPa32erp7St3qdWVkz5HSOKCx9b0/z/6zLN25NNP9S7mUEg93D/Gr7CcjwkZI5+DO1ve+WvGVfL3q63veHXdXd/Et5yvtHm0nr/Z8VU6cPWG2/9Hxh4WF5fhualVQpxBnnFKsodY2BTYnlSpVMiHWdtEpz6yTfXD6PqxZs0bWrVtnHdP9kh9//HEq3XhgTEUGAACAsX37dvH29JYzV8/IhIgJd/9YLO0q3h7ecu3mNVm7f61sPrxZ/m/k/0mD2g2ss6ZBVu9zR+6YkHg94brsid4jb371pnz9+tcmtGYNpj5lfcz19NvpEpcYJ4fOHJI//PAH+eTlT6RZULN7wnIl70rmuj5HQnKCnLt2TuZsmSMXrl+QCcMniJenlxm/LdjqdjG6zc6FCxfMxXY9pz1krbG5u5sGTxmDLGtkC45WyLVKq1OQbdq2bSs9e/Yk1OKhEGwBAABg6LY5wf7B8u3qb83tPs37yFsD3jKdkc9ePSuvT37dBMmpa6fKh89+aJ21nk17ym8H/ta6ff7aeXn585clPileFu9YLKN7jc50hpsGNpW/vfg367YG2zGTxsjpy6dl3rZ59wTbWpVqyQ+/+cG6rWH4/83/f+a+Gw9tlItxFyXYL1iWLFli1rpqgL127Vqe3tXKlStnCrG6ryzV2MKRlJQkP/74o9mr1qZXr17Spk0bQi0eGsEWAAAARuT2SAl7NExWHVxlbmsV1rbdT81KNeXNfm+aqce+5X1zPWPVK1aXwGqBpmqbkppy37Or1dtGdRqZYKtb+NyPVnA7BXcywdYWjEP8Q2R+5Hw5dOhQjj9Xrlw5M43YVpHVr56enrz7RUA/aPjhhx/kypUr5rarq6uEh4dLw4YNOf8oEARbAAAAmOChzZde7/W6XE+5LluObJFZm2bJpkObpH2D9tKyXktpXre5dGjYIdezdSv9luw6sUsOxh40tzWA5karr0fPHpWtR7bm+f461Tlic4Q1DbpO5ToSGhAqXy//2lQFy5cvbzoT60WDrF70OlOK7UPXMk+fPl0SExPNbS8vLxk2bJj5YAEoKARbAAAAWNND/av5S+sGrc362NgrsWa9rQZcvWiI7Neqn7za41XT8MlGK6e26mlGY3qPMWE4q21Ht0mn8fcG2PC24fJ4i8fvOX7q8qls76+V2zf6viFly5SVgOoB5pg2IOrUqRNTWx1oj9q5c+eatbW2qd/PPPOMVKxY0d5DQzFDsAUAAIDVVEkDq04l/u6N72Rl1EpZu2+tmX58M/mmJKUmycyNM+VS3CV5/5n372keZavY6tparaxOWzfNdEduU79Njs2j9P43km+Yr0t2LpFHaz16T7jVAKtdmeMS4szjqt7NessznZ8xU57NuN3uBu0yZcoQah2k8/GWLVtk+fLl1jHdFumpp55i+jcKBcEWAAAApouwLXSm3Uoz0301POpFOx0fPHNQvl75tZkyvGb/Grly4+5ayeyaR2nX4g9nfSgbDm6Q9358T2b/brapqubUPEqf75OFn5iq719/+qtpHlWjYo17mkdpZ+Z3pr1j1u5qh+aMAdjdzd18TUm5/xpdFC79fdFGXpGRkdaxxo0bS//+/fO9zzCQV6XyfE8AAAAUW7rNjdobs1e6/bGbhP8l3EwBVtolWJs72bobazVOQ2ZOvDy8ZEDrAeb6jaQbEnPpv11ws+Pm6iZD2g8x17Vyeyg2+wZQFb0rygfPfCAVvCqY6rGGXFvATk1LtSq2sO8HJDNmzMgUart06SIDBw4k1KJQEWwBAAAgHh4eVnXUq4yXuf7XuX+Vy/GXrSrstPXTzHWtvtbyzbnxj1ZgV+9bba67uLiIbznf+1b4Vu5dad2uXL5yjvfVcPub/r+xuiHrtj8qOS050+tA0bt69ap89dVXcvToUesDkQEDBkjXrl2ZHo5Cx1RkAAAAmP1f1dnLZ820Yp1CrGtrtXJb3rO8WWNrW9+qzaN0Xa3N8t3LTfdkdfvObVOlTb11t4IaFhImVX2qZjrD5nE/Crfur6HZFky1MhziF5LrOxIWGiZd9nYx05H1os996eol8z0/Pz/eTTs4fPiwaRJlmwqulfOnn35aAgPvroEGChvBFgAAAKZLbWBAoERFR8nbw942zaBmbpoph2MPm1Crt+vVqCfh7cKlc3DnTGdMpwXrxVahdS3lKtUrVJeuIV3llR6v3HN2NfReir9k3V+bQ2mVVptM/aL3L/JU3XtrwFsmIGvV9h8L/iGNajSSoMCgPHfb1WZZcXFx91x0f9Xu3bubLWlwf1ptX7Nmjaxfv9465uvra0JtlSpVOIUoMi53dJEEAAAASjztWBsTFSPTx053unMxdOJQCWwSKD/++KMJWzdv3pTr169nG171kluTKd0uqFu3bkU6fmek+9JGRETIiRMnrGMNGzY0049Z64yiRsUWAAAARosWLWTJwiUmGOr6SGeh4z1w6oB07d9VJk6caHV4flA+Pne3IkLOzpw5IzNnzpT4+HhzW6vsWulu164d62lhFwRbAAAAGK1atZKbSTdlX8w+aRzY2GnOyr7ofZKQlGDC+MOGWt2OJjg4uMDGVtzoZM8dO3bI0qVLJT397pprnbY9ZMgQs08tYC/O81EcAAAAClXnzp2lTu06Mn21c01Fnr5muvjV8ZOXXnrpoR+rfv364un538ZY+K+0tDSZN2+eLFq0yAq1derUkVGjRhFqYXcEWwAAABjaOGnU6FEyf+t8iUuIc4qzouPU8eq4NZQ+99xzD7VfauPGzlOpLuqtfKZMmSJ79uyxjrVp00aef/55KVeunF3HBiiCLQAAACwvv/yy2dZnzoY5TnFWIjZEmPHquJVuL6MdeR9kjbDugVuvXr1CGKVzO3LkiPzrX/+SCxcumNtubm4yePBg6d2790N9iAAUJIItAAAALNWrV5fB4YPl+1XfS0pazp2DHYGOb+qqqTJk8BCpVq2adfyRRx4xaz7zsm1QRo0aNTJVa9x169YtWbZsmUyfPt3qIq1b+bz66qsSEpL7XsNAUWO7HwAAAGQSFRUlzZs3l5F9Rspbg99y2LPz8eyPZfKyyaaZUWho6D3f37t3r8ydOzfPj6fb1DRt2lSK0rVr1yQmJsbsq6uNr9zd3U3l2N/fP8978haG8+fPm3N38eJF6xhb+cCR8ZEUAAAAMtGQ+M4778j7770vPZr3cMgOyXtP7pV/Lf6XvPund7MNtbb1shoWtdlRXmhjpOPHj0vXrl1NZbIwKqDr1q2T7du3mzAeuT1STkafzPH+gQGB0rJVS7MNk3as1uZehV1R1q2TNm/eLKtXr7YaROl0Y93KR9fU5rcKDhQVKrYAAADItgNum9ZtJPFKosz941wp41bGoaYgD/jTAPGq4iXbtm0zaz5zo0Ft+fLleX58DW/NmjUzQbIg9rTV6ufkyZPly0lfSuyZWPH29JZg/2AJ8Q+R0IBQ8a/mLx7uHuLu6i6pt1IlOTVZYi7ESFR0lNl6aX/MfrOdkXas1iZZr7zySqap1wXl+vXr8tNPP5kKso0+T3h4uFStWrXAnw8oSARbAAAAZEun8mq1cFiXYfLu8Hcdolqn+6i+N/U9mbZmmvzmN78x6z11Te39rF27VtasWZPj9zt06CC7du2SxMRE65hWKrVS2rFjR7NX64NM6Z7w5wkSMSdCSpcqLf3b9JdhYcNMoM1PcyutomrA1W2YtAO0NsvSddDj3x6fY7U6v+dUx7p48WJrLa1q3769hIWFse4YToFgCwAAgBxNmjRJxowZI78e9Gt5bcBrdj9Tn/70qXzy0yfSt29fadmypTmm4a5Xr165hk8NbytWrJBNmzbd872aNWuagKyhbsuWLabCmzHglSlTRnr06GHWHecl3Gu1e+LEifLBBx9ILd9aMqLbCAnvGC4+Xj4Fsr2RdqzW5l5nr541U8bHjh1736p1TpKSksxU7f3791vHtEo9cOBA9qaFUyHYAgAAIFca0saPHy9vDHzDhFt7VG41mGqo/XTep/KLX/zCdG/WSqaNp6en9OzZU5o0aZLj+PQxvvrqK4mNjc10XENx27Ztrdtatd24caOZ5qzrYm20oZMG6sqVK+c4Tt3n9YXnXzAV0FFPjJJf9f9VoUzj1unYn8//XL5c9KVZS/zNt9/kew/eEydOmKnHN27csI7pY/Tp08c0sAKcCcEWAAAA9/XRRx/JuHHjZMRjI2Tc0HFFuuZWQ9zEGRPl+5Xfm5BdtmxZ0004OwEBAfL4449LlSpV7vmeNkP629/+ZqqUGb311lvi7e19z/018K1cudKE1YzTk3XtrU5dzrqHq1a3X3vtNalbo6589NJHRdJ0S5tojf1qrJw4f0I+/fRTGT16dJ4qyvq6tm7dah3TIKuhXbc8ApwRwRYAAAB5osHt9ddfl6DqQXYNbnPmzDEV0dzUqVNHevfubaYZ2xw+fFhmzJiR6X467VbDaNaQmrWyuXDhwkxhWpsp9evXT2rXrp2pqm3v4D9hwgTzAUROzp49a6q0ly5dso4FBQWZrY7Kly9fRCMGCh7BFgAAAPlqKKVTbfXryMdHmqnJhTXV9rN5n5ktfbJOtb169ap89tlneXqccuXKmQZQOkV52bJlcuDAgXvuo/uzDhkyJNeGTlrl1OZTuv5WpzTb6NZAum5XQ62uQ9apx/aaqq3nS9cfa8jWdbf3G79uHaTb+LRu3dohGoMBD4NgCwAAgHzRkKRTk7U5Us1KNWV4t+EyuOPgAmuOFLEhQqaumpprc6R///vfpvpYUDQ0a8Ok+wW8c+fOyfz5880WPkr3pNXmS47WXOuLL76wpiWfPHlSFixYkKnirGuUdRuf7KZsA86IYAsAAIAHotOBJ06YKLMjZv93O5uuwyQk4AG2s4neJz+s/kEWbltotrMZMniIjBs/LsftbPK7N21e6NZGTzzxxH3DrY5XuyfrlOh//OMf8kzXZxxuO6QZ62aYBlgaxHUbIxudct2lSxezlU9u068BZ0OwBQAAwEPR6uWUKVPky0lfyunY0+Ll6SXBfsFmv9bQgFAJqB4gHm4e4u7mLqlpqZKclizR56MlKjrK7M+6P3q/JKYkSsUKFeWt374lr7zyilSrVi3X59TGTn//+98L/J1r166d2drnfiFVq9ZtWreRxCuJMvePc4t0TW1epnEPfG+gJN5JlBdfetEKsH5+fmZdcG5dnQFnRbAFAABAgdCtcdavX2+m5+7YsUMit0fKiZMncrx/UGCQtGzV0lR3da9YDV7PP/+81K1bN0/P9+2330p0dHSBv3ta0dS1s7l5//335f333peIP0aY8O5otOnW4A8Gm9eiQV3X0uq+v45QVQYKA8EWAAAAhUbXdZ46dUqSk5MlJSXFBFjdWkZDbMWKFc19tKHTrFmzzHXdbkYbOeWFhmftVlwYNAjqlj7Z0cZZOm15ZJ+R8tbgt8RRfTz7Y/n3kn/L2nVrzdRjoDhztfcAAAAAUHxpeLUF2Jw8+uijZm/axMREOXTokPmqt+9HuxkvXrzYrHktaCtWrBB3d3fTUTkrXVdcy7eW6YDsyLSZ1eLti+Xzzz4n2KLYy/uqfgAAAKAQ6BpQ21Y+6enppiKaFxp+8zpt+UFoaN65c+c964kj5kTIiG4jHGpdbXZ0fNqxWpt7Xbhwwd7DAQoVwRYAAAB217x5c+u6dvHNuFdsbkJCQgpxVGK2ydEOyDbaJEs7QId3DBdnoNsw6Xh13EBxRrAFAACA3el+qrVr1zbXL168mOc9ahs0aCCuroW7um7btm1Wcyzt/KzbGhXEnr1FQcep4530xSRTDQeKK9bYAgAAwCE0a9ZMYmNjzXWdAlyrVq37/oyug9U1uvv37y+UMbm5uUnHjh3N9XXr1pntjD59+VPZeGijzNo4S46cPWK2KtIAGeoXKs+FPSf1a9Y393/t36/J7pO7Mz2eVk893T2lbvW6MrLnSGkccHcKdkanLp2Saeunyc7jO+XKjSvm/lUrVJWwkDAZ2GaglPMsZ93X9hwDWg+Q3w78bbavQfcWnrluphl/WFhYAZ8hwDEQbAEAAOAQtCPy0qVLzR6xUVFRZpsa7aCcl+nI2QVbXbeb23pdfWwfH59sLxUqVBAvLy+zFZGNbmPk7ektZ66ekQkRE8wx19Ku4u3hLdduXpO1+9fK5sOb5f9G/p80qN3A+jkNpnqfO3LHNLq6nnBd9kTvkTe/elO+fv1r8avsZ9135d6VMjFiotmLVvmU9ZGk1CQ5evaoufy8+2f55JVPpKJ37g25Mp2fgBCzt7COn2CL4opgCwAAAIegWwE1adJEIiMjTbjVqm1etqmpV6+e+VndTsimV69eZt9WbTB1/fp1OXnypPX96tWry7PPPive3t75Gp9uLxTsHyzfrv7W3O7TvI+8NeAt06Tp7NWz8vrk1+XC9Qsyde1U+fDZD62f69m0Z6Zq6vlr5+Xlz1+W+KR4WbxjsYzuNdocP3H+hEyYPUFSb6VKy3otzWPX9q0t6bfTZcWeFfLXn/4qJy+eNI//2hOv5XncGs6D/YLN+IHiimALAAAAh9GmTRsTbJVWGNu2bZupapodXWOr1V5bB2Pdg1Z/zhZw1ZUrV+Srr74yWwlpZ2NtCvX000/f97EzitweKWGPhsmqg6vMba3C2joj16xUU97s96aZFuxb3jfXx6lesboEVgs0VduU1P+G8R/W/WBCbQWvCvLBMx+Yx7dNX+7VrJd5roTkBHm01qOSXyH+IbJ2+9p8/xzgLAi2AAAAcBiVK1c2Fdhjx46ZSuvhw4fNfrX3o9OWPT09pUaNGibkZuXr62uqtN9++62kpqbKkSNHZP78+TJgwABxcXG57+Nfu3ZNTkaflNd7vS7XU67LliNbZNamWbLp0CZp36C9qbA2r9tcOjTskOvj3Eq/JbtO7JKDsQfN7U7BnazvbTi44e5radLDCrUZdQ3pKg8qNCBUvl7+tXkd99tXGHBGBFsAAAA4XNVWg63aunVrnoKtrpfVSm1uatasKUOHDpUffvjBdAjes2ePmarcs2fP+z5+TEyM+epfzV9aN2ht1sfGXok162014OpF19L2a9VPXu3xqni4/3dt8Lxt88wlqzG9x5gwrOIT400TKjPOSjWt+5y7dk5++eUv7/nZOWPnSH4EVA8wX0+dOkWwRbHEdj8AAABwKHXr1jUVVlug1KnDBSUwMFDCw8OtKu3mzZvNlOf7SU5ONl81sOpU4u/e+E7efvJt6diwo1Vd1SZPMzfONOtkM9LAW6V8FXOp6FXRTC1W09ZNk61Htt7zXLbvK11feyn+0j2X/PJw88j0OoDihmALAAAAh6KhU6u2Nlq1LUjBwcHyxBNPWLe1E3N0dHSuP6PTl5W7q7uk3UoznY17N+stE0dMlEV/WCSTxkySNvXvjnnN/jVmm56MzaO0wqqX+W/PN/fXQByXGCfv/fieqdSWL1veCsi63Y+NNo9aP2G9uYwbPO6BX7O7m7v5mrHBFlCcEGwBAADgcLQ7snY6Vrr1T0JCQoE+fosWLaRdu3bmum7BM3PmTLP+NLf9ctXemL3S7Y/dJPwv4XLq8t0Aqg2oGtVpZHU3vnPnjtn+JydeHl5m31l1I+mGxFy6O83Ztj531b5V1rTkjG7fuf3Arzc17W4wt51ToLgh2AIAAMDhaJBs3vzu+lNdD1sYW9Xomlyd9qySkpJkxowZVmU2K9t+urUq1RKvMl7m+l/n/lUux18217Vb8bT108z1smXKSi3fWjk+r1Z8V+9bbVWnfcvdnXb9bOdnTUX46o2rMva7sXL68mnr/gu2L5D/W/x/OT7mrdu3TBjOetEuyyo57T9TqfOwLzDgjGgeBQAAAIfUunVr2bJli6mA6jrYDh06SOnS/11/+rC00jpkyBCZPHmy2Q7o4sWL8tNPP8mTTz55T6dkf39/8/Xs5bNmT1qdQqxb+2jltrxnebmZfNOsh1XaPErX1dos373cdE+2VV21SmsLnGEhYVLVp6q5rlsAvT3kbZkYMVF2ndwlz/z9GfEp62PW7trub+uanNWiyEXmklXv5r3NY0afvzvV2s/Pr0DOHeBoCLYAAABwSBUqVJBHH31UDh06JDdv3pQDBw5IaGhogT6HVjC1U7KGW11/evDgQVm7dq107Zp5ax3dIicwIFCioqPk7WFvm/WwMzfNlMOxh02o1dv1atST8Hbh0jm4c6af1WCqF6WB2bWUq1SvUN1s3/NKj1cy3bdb425Sr2Y9mbt5rmw7uk0uxl00zaQCqwZKs6Bm8niLxx9oH1sdd1BgEB2RUWy53NGPwAAAAAAHpE2ddO9ZVatWLXnllcxBsKAcPXpUpk27O5VYadVWm0xl9NRTT0lMVIxMHztdnM3QiUMlsEmg/Pjjj/YeClAoWGMLAAAAh6VTgKtVq2aunzlzRmJjYwvleR555JFM++DqlOQLFy7c03DqQMwB02zKmeh4D5w6YMYPFFcEWwAAAJTYrX8yat++vTXVOS0tTaZPn56pG3OrVq3kZtJN2RezT5zJvuh9kpCUYMYPFFcEWwAAADg0DZtly5Y113WdbXx8fKGF6H79+knNmjXN7bi4OJk1a5bpyqw6d+4sdWrXkemrnWsq8vQ108Wvjp8ZP1BcEWwBAADg0FxdXa1ptDqtVjskFxY3Nzd5+umnxdvb29yOiYmRpUuXWuMYNXqUzN86X+IS4sQZ6Dh1vDruguwoDTgagi0AAAAcnk6j1e15lAZb3Xe2sJQvX96EW1sQjIyMNBf18ssvm2195myYI84gYkOEGa+OGyjOCLYAAABweOXKlZOmTZua67otj+5vW5hq164tffv2tW4vWbLEVG+rV68ug8MHy/ervpeUtBRxZDq+qaumypDBQ6wGXEBxRbAFAACAU+jUqZNVtdUmUoVZtVUapNu2bWtNgdb1trqf7vi3x8uZK2fk8/mfiyP7bN5ncvbqWRk3fpy9hwIUOoItAAAAnEKFChWKtGqrevToIUFBQea6dkieN2+ehISEyDvvvCNfLvpS9p7cK45Ix/Wvxf8y47R1egaKM5c7d+7csfcgAAAAgLy4fv26fPbZZ6aCWqZMGfn1r38tHh4ehXryNNB+8cUX1tY/PXv2lJYtW0qb1m0k8UqizP3jXCnjVsahpiAPfG+geFfxli1bt5iGWEBxR8UWAAAATlW1bdKkSZFWbb28vGTQoEHW7RUrVsilS5fkm2+/kePnjsvEGRPFUWpFOg4dz8kLJ+Xrb74m1KLEINgCAADAadfaarBNTk4u9OesW7eutG/f3lzXanFERIQ8+uijpnr8/crvHWa9ra6r1fHouBo3bmzv4QBFhmALAAAAp1KxYsUir9qqbt26Sc2aNc31q1evmk7Jo0ePlgkTJsg/5v5DPv3pU7tVbvV5P5n7iXzy0ycyceJEGTVqlF3GAdgLwRYAAABOWbV1cXGxOiQXRdVW97UdPHiwuLu7m9t79uyRvXv3yrhx40yY1FD53tT3inwbIH0+fd5P531qxjF27NgifX7AERBsAQAA4NRVWw21Gm6LQqVKleSJJ56wbi9atMhUbzVMaoOpGetmyKD3BxVZt2R9Hn0+fV59fkItSiqCLQAAAJxS586draptUa21Vbp21RaqU1NTzXrb9PR0My05MjJSyvqWlSEfDJGPZ39caNVbfVx9fH0efT59Xn1+oKQi2AIAAMAp2atqq/r06WOqt+rs2bOyatUqK/Ru3bZV3v3TuzJ52WTp84c+8tWyryQuIa5AnlcfRx9PH1cfX59Hn49GUSjp2McWAAAATkunAX/++eemeZLuZ/vGG28U+r62Nhpop0yZYrokq+HDh5vuyTZRUVEyccJEmR0xW0qXKi392/SXYV2HSUhAiNXVOS/08fdF75Npq6fJgm0LJP12unTs2NG81gEDBhTKawOcDcEWAAAATm3evHmye/ducz0sLMxMUS4qmzdvluXLl1v73Y4ZM8Z8zej8+fMmAH856Us5HXtavDy9JNgvWEL8QyQ0IFQCqgeIh5uHuLu5S2paqiSnJUv0+WiJio6SfTH75MCpA5KQlCB1ateR0WNGS7Vq1SQ2NtY8tgZc7dZsm5INlFQEWwAAADg1e1Zt9TmnTZsmx44dM7fr1asnzzzzTLZB89atW7J+/XrZvn277NixQyK3R8qJkydyfOygwCBp2aqltGjRQlq1amU6Qbu6usrkyZPlzJkz1v10GnLfvn3Fzc2tkF4l4PgItgAAAHB6P/30k9l+R3Xo0EG6d+9eZM998+ZNmTRpkiQkJJjbPXv2lHbt2uXpZ69duyb//Oc/JTo62gTfl156ScqXLy9+fn5mDXF2ZsyYIYcPH850TPfXffrpp83PAiURzaMAAADg9Lp06WL2mbV1SNbAWFS8vb1l0KBB1m1tJKVV5LzQ8NqgQQOpXbu2BAQESEhIiGmIlVOotVWJs1vv++9//9uaogyUNARbAAAAOD0Ngm3atDHXdeudFStWFOnza9Mo2/Nr5XXBggXZBtDseHp6WtcTExPve3/dYiinyvE333xjVa6BkoRgCwAAgGJBm0bZGjcdOHBATp06VaTPr02cfHx8zHWdWrxr1648/VzZsmXzFWzT0tJy/J6Gep2WrQ2tbN2agZKAYAsAAIBioUyZMqYrss3SpUvzXDUtCO7u7tKvXz/rtobLGzduFGmwzditefr06WZ/X6AkINgCAACg2GjWrJnZDkedO3dO9u7dW+RTknWNrEpJSZHFixcXeLDNaSpyVtqpWTsoX758OU/3B5wZwRYAAADFRqlSpUxXYpuVK1fmOQgWFH1+W1g9dOiQmRZd1BVbmytXrphwa9uOCCiuCLYAAAAoVoKCguTRRx8113Uq8MaNG4v0+TWo9unTx7q9ZMkSSUpKyvX+hVGxtdHKse61u3Xr1nz9HOBMCLYAAAAodnr06GGqt2rTpk0SFxdXpM/fqFEjqV+/vtWtWNfb5sTW8Erdb02urhnOT8U2488tW7YsT2t+AWdEsAUAAECx4+vrK61bt7a239EpyUXJxcVFnnjiCdNQSu3evVtOnDiR4z64bm5u5vr99r/V1/IwWyJ5eHg88M8DjoxgCwAAgGK7/Y9tj9ioqCiJjY0t0ucvX768dO/e3bq9cOHCbKutGoIrVapkrl+7di3XbXoeZL2wjkMr2K+++qoVoIHihmALAACAYklDbdeuXa3bOhW3KLf/US1bthQ/Pz8rtK5evTrb+9mCrYba3KZN52caco0aNSQ8PFxef/11ad++PdVaFGsEWwAAABRbGiwrV65srmvFdt++fUX6/FqN1b1tS5cubW5v2bJFzpw5k2Owvd905LxUbHUa9vPPP28qtKGhodZzA8UZwRYAAADFljaQ6tWrl3V7xYoVD9R86WFosO7SpYu5rhXjBQsWSHp6eo7BVrfoyUlOY3d1dc30mgMCAkyoBkoKgi0AAACKtXr16pmLio+PN12Si5pOBa5WrZq5fuHChXu2IMprxdbW6TljR+WwsDB58803pU6dOubYpUuX5OLFiwX8CgDHRrAFAABAsdezZ0+rgrl+/Xq5fPlykT6/Tgfu379/pjFkXEubMdjqWtycVK9eXZo3by6BgYHm8X7961+bJlm6F25wcLB1vwMHDhTaawEcEcEWAAAAxV6VKlWkXbt25rpOA9bpwEXdSKpmzZo5bkFUrlw5azpxblORbWt2n3vuOWnWrFmmKcgZg+3+/fuL/PUB9kSwBQAAQImgHZJtldFTp05JZGRkkY9B19pmtwVRfrb8yW1bH1sHZq1I65RkoKQg2AIAAKBE0D1ctdqZsZFUblvrFPUWRHnd8ic3jRo1ylS1BUoKgi0AAABKDO0WrGtUbVvnLFq0qMin7LZo0SLTFkS2AJrXBlK5adiwoXWd6cgoSQi2AAAAKFF69Ogh3t7e5vrRo0eLfG9bbSSlzaxsfv75Z7ONT0EEW12r6+/vb63VpTsySgqCLQAAAEoUDw8PeeKJJ6zbS5culcTExCIdwyOPPJJpC6LNmzcXSLDNOh25qEM7YC8EWwAAAJQ4DRo0sLoIa6jVta723IJow4YN4u7uXiDBVqcj2x5Xt/2hOzJKAoItAAAASqQ+ffqY6q3au3evHDt2rMi3IGrZsqW5rlORtUuzbfuehwm2Os1a1xLbHqeo9+wF7IFgCwAAgBJJA2DGta4LFy6UlJSUIh2DdkguU6aMub57925r7e+DbvljU7duXeu6bm0EFHcEWwAAAJRYTZs2laCgIHNdt9hZtWpVkT5/2bJlzd62NrZgnZ6ebtbePihbAylFsEVJQLAFAABAiaVrUfv27Wv2uFXbtm2T06dPF+kYWrdubTWOSkpKso6fP3/+gR+zRo0a1rRmgi1KAoItAAAASrSKFStKWFiYdXv+/Ply69Ytu23/Y/MwgVQfs1atWub69evXH6r6CzgDgi0AAABKvDZt2kjNmjXNedBmS+vWrSvSc1K/fn0JDAzMdOxhK8d+fn4F9liAoyPYAgAAoMQrVaqU9O/f33y1bb9TlFN4dUp0r169rG161JkzZ0y35IIItjExMQ89RsCREWwBAAAAEalWrZrVyEn3fo2IiMi05rUonr9Zs2bWbR2DhtsHVadOHSsoU7FFcUewBQAAAP6jY8eOVkdhXZe6YMECEzCLigZrW9VYPczeurqNkIZldeHCBUlOTi6QMQKOiGALAAAA2P44LlVKwsPDxdPT09w+ePCg7Nixo8jOT/ny5SUkJMS6vX///od6PK3aKg3nsbGxDz0+wFERbAEAAIAs4VLX29osW7ZMLl68WGTnqFu3btZ17Wh88+bNAllny7Y/KM4ItgAAAEAWDRo0kFatWpnruvWPrrd9mEZO+eHj42O2ILJZuXLlAz8WnZFRUhBsAQAAgGz06NFDqlataq5rxXb58uVFdp4aN25sXY+KipLExMQHrj5XqFDBXNepyOnp6QU2RsCREGwBAACAbLi5ucmQIUPE1dXV3I6MjDRrbotqX1sbDaObN29+6KqtVp7PnTtXIOMDHA3BFgAAAMhBlSpVpHfv3tbt+fPnS1xcXKGfr+rVq5tgbbNt27YHrtqyzhYlAcEWAAAAyEXz5s0lODjYXNctc+bMmSO3b98u9O7Mto7GKjU19YGrtgRblAQEWwAAACAXLi4u0rdvX9PUydZdeP369YV+zjIG0oep2lauXNnavkjHXpT78gJFhWALAAAA3IcGQ93fVkOuWrt2rcTExBRpsNWq7ZYtW/L9ODpm22MlJSXJlStXCmyMgKMg2AIAAAB5oOGwS5cu5rpWPXVKsgbFwlKrVi0rSNts3br1gZ6zZs2a1vVLly4VyPgAR0KwBQAAAPKoU6dO4u/vb67Hx8fL3LlzC229rbu7u9SoUSPTsQdda1upUiXrOhVbFEcEWwAAACCvfzyXKmWmJNvWrB49elRWrVpVJNORbdXbB6naZgy2V69eLcARAo6BYAsAAADkQ/ny5eXJJ5+0gubGjRslKiqqUM5hvXr1rOsVKlR44LW2BFsUdwRbAAAAIJ8CAwOlV69emfa3PXv2bIGfx4CAAKs6fOPGDVMxtnVI1oCbVx4eHlK2bFlznYotiiOCLQAAAPAAWrduLc2aNTPXb926JTNmzDDhsyCVLl1aGjZsaD2HbW9b3U93z549+XosX19f81XHmJaWVqDjBOyNYAsAAAA8AJ2K/Pjjj1thUwPjzJkzTQAtSI0aNbKu2yq2tqptfvakZToyijOCLQAAAPCAXF1d5amnnjLrblVsbKwsWrQoX4EzL9ORbdOIT58+bQXpy5cvy/Hjx/P8OBUrVrSuMx0ZxQ3BFgAAAHgI3t7eMnToUBNy1e7du03n4oKiVdqM05F1f1ub/DyPbSqyItiiuCHYAgAAAA9J95sdMGCAdXv58uX5qqbmZzrytWvXxMfHx1w/duxYnvelZSoyijOCLQAAAFAAQkJCpGPHjua6TkWePXt2gVVG/f39xcvLywqzzZs3z1S1jY6OlrVr15rpyTkh2KI4I9gCAAAABaRbt25Sv359q3Px9OnTJSUlpUCmIwcHB5vr6enpZs2tm5ubuR0ZGSnffvutrFmzRiIiInJ8DLb8QXFGsAUAAAAKsFNyeHi4VKlSxdzWCuqcOXPk9u3bBTodWTsi22RsVHXz5s1cH8NWtY2Pj2fLHxQrBFsAAACgAJUpU8Y0k9IKqTpy5IisXr36oR+3XLlyVpX20qVL2QZTd3f3XB8j43RkXasLFBcEWwAAAKCAaYB88sknTQVXbdiwQXbs2PFAj6VbCM2aNUs+//zz+1ZZbcE3t3HZ0BkZxcndnuQAAAAAClRQUJD06tVLli5dam7r/ra6Nta2dU9udOry4cOHZfPmzWbv2rzKT8WWYIvihGALAAAAFJLWrVtLXFycCai6FlabOz377LMSGBiY489cv35dfvjhh1w7HOfkfmt5MwbbvG4TBDgDpiIDAAAAhUSnIvfo0UOaNGlidTSeMWOGnDt3Ltv7JyYmyo8//vhAoTY/zaMUFVsUJwRbAAAAoJDDbf/+/a1tgFJTU2Xq1Kn3VEx1eyA9fv78+Qd+Lu12rOE4J56enqa5lbpx48YDPw/gaAi2AAAAQGH/0V2qlAwZMkT8/PzMbQ2f33//vRUuda9bDbUZK7murvlfNajTne/XpErX+drGABQXBFsAAACgCGjH4mHDhknVqlXNbV17q2FWv+qa2jNnzmS6/61bt+SRRx7J9/NosM1tra0t2CYlJRXI/rqAIyDYAgAAAEVE97YdPny4VKhQwdy+ePGifPHFFzl2Pq5cubL07NnT2jYoLzQonzx58r7B1jb9GSgOCLYAAABAESpXrpyMGDHCCpg6DTkn+/fvl7Zt28ozzzxjrY3Ni127duUp2DIdGcUFwRYAAAAoYuXLlxdfX988NYPSam69evXklVdeue/P2PaxPXjwYI6hVRtI2RBsUVwQbAEAAIAipFv+zJo1K8fpx1lFRUVZ05I13GrIzUmdOnXMV107u3fv3mzvQ8UWxRHBFgAAACgiGjgjIiLkyJEjef6ZAwcOWE2edI2uNqBq165dtvcNCgrKNB1ZuyRnRbBFcUSwBQAAAIqAhtO5c+eaacL5odOFT5w4kWnrIG0oNXDgQCldunSm+3p7e1tVW21MlbXTsiLYojgi2AIAAABFYOnSpbJv374H+tnsfq5JkybywgsvmG2EbPR6s2bNcmwide3aNTMFWi/R0dGmOdXu3bvNccCZudzJbn4CAAAAgAKjf3JPnDhR0tLSHujntSPyb3/7W3F1db3ne1evXjVrdrXbsk5T1uf429/+Zvap1e9pBVf3to3cHikno3PeBigwIFBatmopLVq0kFatWknnzp2zfT7AERFsAQAAgCKwceNGWbt27QOH26eeekoaNmx43/udP39e3n33Xflxxo8SFx8n3p7eEuwfLCH+IRIaECr+1fzFw91D3F3dJfVWqiSnJkvMhRiJio6SfTH7ZH/MfklISpA6tevIqNGjTMOqatWqPdCYgaJCsAUAAACKiIba48ePy6FDh+Tw4cOSnJyc558NDg6WJ598MtfuyRP+PEEi5kRI6VKlpX+b/jIsbJgJtLouNz9rgTXgTl89XeZvnS/pt9NlcPhgGf/2eAkNDc3z4wBFiWALAAAA2GnbH13nqs2kNOTevHkz1/trOP3d735npiVnDcs6zfmDDz6QWr61ZES3ERLeMVx8vHweeoxxCXEyZ8Mc+X7V93L26ll55513ZOzYsZnW9QKOgGALAAAAOMAa3NjYWBNy9XL9+vVs79elSxfp2rWrdXvPnj3ywvMvmGrtqCdGya/6/0rKuGUOvgUhJS1FPp//uXy56Etp3LixfPPtN+Yr4CgItgAAAICDhdwLFy6YgKtTlnXbHhsNk4MGDTLXJ02aJK+99prUrVFXPnrpI2kcWPhBc+/JvTL2q7Fy4vwJ+fTTT2X06NGF/pxAXhBsAQAAAAd2+fJlWb9+vVmPGx4ebqYi69Tj8ePHy4jHRsi4oeMKpUqbW/V24oyJ8v3K72XChAkybty4IntuICcEWwAAAMCJ2ELtrwf92kw9dnFxsUtV+bN5n8knP31ixqPrbgF7YmMqAAAAwEno9GNbqH1twGt2G4eG6dcHvm6ua8W2QoUKTEuGXVGxBQAAAJyANopq2bKlDOsyTN4d/q5dKrXZVW7fm/qezFg3QyIjI2koBbsh2AIAAAAOTrf0adO6jSReSZS5f5xbpGtq87LmdtD7g6Ssb1nZum0rWwHBLvK+UzMAAAAAu9B1rHv37pW/vPwXhwq1SsejXZl1fB999JG9h4MSiootAAAA4MA0MLZo0UJG9hkpbw1+SxzVx7M/lsnLJsuOHTskNDTU3sNBCUPFFgAAAHBgEydMlFq+tUwHZEemzaxqVqppxgsUNYItAAAA4KDOnz8vEXMiZES3EQ43BTkrHd/wbsNldsRsuXDhgr2HgxKGYAsAAAA4qClTpkjpUqUlvGO4OIPBHQeb8eq4gaLEGlsAAADAAd26dUuCAoOkXVA76du+r8zaOEuOnD0iiSmJ4uPlI6F+ofJc2HNSv2Z9c//X/v2a7D65O9NjaMj0dPeUutXrysieI6VxQON7nufUpVMybf002Xl8p1y5ccXcv2qFqhIWEiYD2wyUcp7lrPtm9xy67ZCHm4dUr1BdHm/5uOw5skc2n9gsJ6NPSunSpQvt/AAZUbEFAAAAHNC6devkdOxpCaodJGO/Gys7ju+QpNQk8fbwlms3r8na/WtlzKQxcij2UKaf02BapXwVqVy+sviU9TFBeE/0Hnnzqzfl1OVTme67cu9Keenzl2RR5CI5d+2c+Vl9jqNnj8q/lv9LfvnlL81zZWV7Dr1U8q4kt9JvycmLJ+X/Fv+fVPWtasat4weKimuRPRMAAACAPNu+fbt4e3qb8Kn6NO8jbw14y6xlPXv1rLw++XW5cP2CTF07VT589kPr53o27Sm/Hfhb6/b5a+fl5c9flvikeFm8Y7GM7jXaHD9x/oRMmD1BUm+lSst6Lc1j1/atLem302XFnhXy15/+asKqPv5rT7yWaWxZnyMhOUHemPKGHD5zWDYd3iRenl5m/GFhYbzjKBIEWwAAAMAB6bY5wf7BcjH+ormtlVpbAyntPvxmvzfNtGDf8r65Pk71itUlsFqgqdqmpKZYx39Y94MJtRW8KsgHz3xgHt82fblXs17muTSwPlrr0fuO1cvDS1rWbWmCbVxinAT7BZvxA0WFYAsAAAA4oMjtkRL2aJhcT7kuW45skVmbZsmmQ5ukfYP2psLavG5z6dCwQ66PoVOEd53YJQdjD5rbnYI7Wd/bcHCD+dqjSQ8r1GbUNaRrnsapzxFzKUZW71ttbmsQruZVTdZuX5uv1ws8DIItAAAA4GCuXbtmmi+93ut1ad2gtVkfG3slVs5cPWMCrl50nWu/Vv3k1R6vioe7h/Wz87bNM5esxvQeY8Kwik+MN2tvbdVfG11nq+tqs5ozdk6m2zk9h67p/dXjv5JDMYfk6+Vfm9dRsWLFhzwbwP0RbAEAAAAHExMTY776V/M3U4m/e+M7WRm1UtbuW2umH99MvmmaPM3cOFMuxV2S95953/pZDby2CqxWU3Vtra6bnbZumumO3KZ+m0zPpVOPbfR+l+Iv3Xd8+hw6Vfl6wnXrMV7u/rL0bdlXKnpXlOSUZHP81KlTBFsUCYItAAAA4GCSk+8GQ63Ept1KMwGyd7Pe5nL79m05eOagfL3ya9l6ZKus2b/GbNOTW2OnD2d9aKYev/fjezL7d7OlfNnyJvxqQNbtfmy0edT6CevNdW00NTFiYrbjsz2Hrqn9/Xe/N8+/ZOcS0+DKjNvNI9PrAAob2/0AAAAADiY1NdV8PX7+uHT7YzcJ/0u4tVVPqVKlpFGdRlZ34zt37mS7JU/Gxk4DWg8w128k3TDrYZVtfe6qfausackZ3b5z+77j1PW0f3jyD2Yv29OXT8ufZvzJBG93N3fz/ZSU/zarAgoTwRYAAABwMO7ud4OhXxU/8SrjZa7/de5f5XL8ZasKO239NHO9bJmyUsu3Vo6PpRVfW2MnDaC+5e52UX6287Pi7uouV29cNfvkajC13X/B9gVmT9q80EZWA1sPNNe18/KcLXMkNe1uMC9T5m4XZ6CwMRUZAAAAcDAeHnen8qanp5spvzqFWNfWauW2vGd5M4VY18MqbR6la15tlu9ebron26quWqXVbX1UWEiYVPWpaq7rFkBvD3nbTDfedXKXPPP3Z0zzJ127a7u/rWvy/YzuPdp0btbmU5N/nizjB4/P9DqAwkawBQAAAByMv7+/+RpzIUb6t+tv1sPO3DRTDsceNqFWb9erUU/C24VL5+DOmX5Wg6lebBVa11KuUr1CdbN9zys9Xsl0326Nu0m9mvVk7ua5su3oNrkYd9E0ggqsGijNgprJ4y0ez9M+tlo1/n3470335oSUBPlu9XfmuJ+fXwGeFSBnLnd0Uj4AAAAAhxIUGGT2sX172NvibD6c9qGsPbpWjp84bu+hoIRgjS0AAADggFq2ain7YvaJM9Jx6/iBokKwBQAAABxQixYt5EDMAdNl2JnoeA+cOmDGDxQVgi0AAADggFq1aiU3k246XdV2X/Q+SUhKMOMHigrBFgAAAHBAnTt3ljq168j01dPFmUxf8//bu2OfKq8wgMMvAkLVxNimgZhKkdFwWZS61KTpH2BNYaiJUxmgg85cGps0JIXBCRzAxGoDsR1Eq6bdagcdtOAixE2gWCkdxJgGC6LQfHfqYmNtvPceeZ6FhXznvXf75Xznnm+jYVdDYX4oFmELAABlqKqqKjq7OuPyzcvxaOlRpCCbM5s3m7uysrLU47CBCFsAAChTHR0dhftqL1y/ECkYuz5WmDebG4pJ2AIAQJmqr6+Pto/bYuTqSKysrkQ5y+YbvToa7W3tUVdXV+px2GCELQAAlLGez3vi/oP7cfLyyShng5cGY35xPvI9+VKPwgYkbAEAoIzlcrk4fvx4DP8wHLdnbkc5yuY69eOpwpzZvFBsFevr6+tFXxUAAHhhq6ursf+9/fH4weO4+MXFqKmuKatXkA99eSi2vb0tbty8EdXV1aUeiQ3Iji0AAJS5LBbPfnM27v5+N/q+64ty2ZvK5sjmmfljJs6cPSNqKRlhCwAACWhpaYnBwcEY+WmkbM7bZudqs3myubL5oFSqSrYyAADwn3R1dcXDhw+jp6ensFt69KOjUVFRUfRvMVt74PuBGLg0EH19fdHZ2Vn0GeCfhC0AACQkn88XYjb7u/jnYuQ/yRf1zG12pjZ7/Tjbqc2itru7u2hrw/P48SgAAEjQ0NBQHDt2LJrqm6L/0/5o2d1SlF8/7v66O6YXpmNgYKCwgwzlwBlbAABIUBaVExMTseWtLdHe2x4nzp8o7Ka+Ctlzs+dn62TrZeuKWsqJHVsAAEj8KqD+/v7o7e2NnW/ujCMfHom299ti+9bt//vZj5Yexdj1sRi9Ohrzi/OFe2qzV49d6UO5EbYAAPAamJycjL6v+uL82Pmo3FQZB/cfjMMfHI7mxubYtOnFX9RcW1uLqdmpOPfzubjyy5V4tvYs2tvaI9+Tj1wu90o/A7wsYQsAAK+RhYWFOH36dAwPDce93+7F1je2xp6GPdH8bnPkGnPRWN8YtdW1sbl6czxZfRLLq8sxuzAbk7OTMfXrVNyZuxNLfy3Frnd2RddnXdHR0RF1dXWl/ljwr4QtAAC8hp4+fRrXrl2L8fHxuHXrVkyMT8T0zPRz/79pd1Psa90Xe/fujdbW1jhw4EBUVblEhTQIWwAA2CCyO3Dn5uZieXk5VlZWoqamJmpra6OhoSF27NhR6vHgpQlbAAAAkua6HwAAAJImbAEAAEiasAUAACBpwhYAAICkCVsAAACSJmwBAABImrAFAAAgacIWAACApAlbAAAAkiZsAQAASJqwBQAAIGnCFgAAgKQJWwAAAJImbAEAAEiasAUAACBpwhYAAICkCVsAAACSJmwBAABImrAFAAAgacIWAACApAlbAAAAkiZsAQAASJqwBQAAIGnCFgAAgKQJWwAAAJImbAEAAEiasAUAACBpwhYAAICkCVsAAACSJmwBAABImrAFAAAgacIWAACApAlbAAAAkiZsAQAASJqwBQAAIGnCFgAAgEjZ39G34zCzUB38AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Filtro e Limpeza Unificados (Mix)\n",
    "df_mix = df[df['Modelo'].isin(['C-97', 'C-99A'])].copy()\n",
    "df_mix = df_mix[df_mix['Localidade Decolagem'] != 0]\n",
    "df_mix = df_mix[df_mix['Localidade Pouso'] != 0]\n",
    "df_mix['Trecho'] = df_mix['Localidade Decolagem'] + '-' + df_mix['Localidade Pouso']\n",
    "df_mix['PAX'] = pd.to_numeric(df_mix['PAX']).fillna(0)\n",
    "df_mix['Dep TimeStamp'] = pd.to_datetime(df_mix['Data de Decolagem'] + ' ' + df_mix['Hora de Decolagem'], format='mixed', dayfirst=True)\n",
    "df_mix['Data Apenas'] = df_mix['Dep TimeStamp'].dt.date\n",
    "\n",
    "# Demanda diária\n",
    "demanda_diaria_mix = df_mix.groupby(['Trecho', 'Data Apenas'])['PAX'].sum().reset_index()\n",
    "\n",
    "todos_trechos_mix = demanda_diaria_mix['Trecho'].unique()\n",
    "idx_mix = pd.MultiIndex.from_product([todos_trechos_mix, datas_2025], names=['Trecho', 'Data Apenas'])\n",
    "demanda_diaria_completa_mix = pd.DataFrame(index=idx_mix).reset_index()\n",
    "demanda_diaria_completa_mix = pd.merge(demanda_diaria_completa_mix, demanda_diaria_mix, on=['Trecho', 'Data Apenas'], how='left').fillna({'PAX': 0})\n",
    "\n",
    "estatisticas_trechos_mix = demanda_diaria_completa_mix.groupby('Trecho').agg(\n",
    "    Total_PAX=('PAX', 'sum'),\n",
    "    Dias_Operados=('PAX', lambda x: (x > 0).sum())\n",
    ").reset_index()\n",
    "\n",
    "estatisticas_trechos_mix['Media_PAX_Dias_Operados'] = estatisticas_trechos_mix.apply(\n",
    "    lambda row: row['Total_PAX'] / row['Dias_Operados'] if row['Dias_Operados'] > 0 else 0, axis=1\n",
    ")\n",
    "estatisticas_trechos_mix['Score_Relevancia'] = estatisticas_trechos_mix['Total_PAX'] * estatisticas_trechos_mix['Dias_Operados']\n",
    "estatisticas_trechos_mix = estatisticas_trechos_mix.sort_values(by='Score_Relevancia', ascending=False)\n",
    "\n",
    "NUM_TRECHOS_MIX = 10\n",
    "top_trechos_mix = estatisticas_trechos_mix.head(NUM_TRECHOS_MIX)\n",
    "\n",
    "display(top_trechos_mix)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=top_trechos_mix, x='Trecho', y='Score_Relevancia', hue='Trecho', palette='cubehelix', legend=False)\n",
    "plt.title(f'Top {NUM_TRECHOS_MIX} Trechos MIX (C-97 + C-99A)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Rede Mix\n",
    "G_mix = nx.DiGraph()\n",
    "for _, row in top_trechos_mix.iterrows():\n",
    "    origem, destino = row['Trecho'].split('-')\n",
    "    G_mix.add_edge(origem, destino, weight=row['Score_Relevancia'])\n",
    "pos_mix = nx.spring_layout(G_mix, k=2.5, iterations=100, seed=42)\n",
    "plt.figure(figsize=(12, 8))\n",
    "nx.draw_networkx_nodes(G_mix, pos_mix, node_size=1200, node_color='plum', edgecolors='black')\n",
    "nx.draw_networkx_edges(G_mix, pos_mix, edgelist=G_mix.edges(), arrows=True, arrowstyle='-|>', arrowsize=25, edge_color='gray', width=2, node_size=1200, connectionstyle='arc3,rad=0.15')\n",
    "nx.draw_networkx_labels(G_mix, pos_mix, font_size=12, font_weight='bold')\n",
    "plt.title('Diagrama de Rede - Unificada', fontsize=14)\n",
    "plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dc1e44",
   "metadata": {},
   "source": [
    "## MODELO 7: Minimização de Distância (Custo) com Mix de Frota\n",
    "A malha será atendida pelas duas aeronaves. O modelo decide quem alocar para cobrir a **demanda em passageiros** de cada rota, visando minimizar o número total de voos * distância (simplificação de custo de combustível), e não exceder o tamanho da frota física de cada modelo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e94d5e84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:56.286990Z",
     "iopub.status.busy": "2026-06-08T00:02:56.286869Z",
     "iopub.status.idle": "2026-06-08T00:02:56.353873Z",
     "shell.execute_reply": "2026-06-08T00:02:56.353236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== MODELO 7 (MIX C-97 e C-99A) ==\n",
      "Status: Optimal\n",
      "Distância Total da Operação Combinada: 7064.88 km\n",
      "\n",
      "Trecho: SBGL-SBBR  | Demanda PAX: 18 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBBR-SBGL  | Demanda PAX: 17 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBGR-SBBR  | Demanda PAX: 12 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBBR-SBGR  | Demanda PAX: 12 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBGL-SBGR  | Demanda PAX: 11 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBBR-SBAF  | Demanda PAX: 19 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBBR-SBSJ  | Demanda PAX: 11 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBGR-SBGL  | Demanda PAX: 6 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBGL-SBSJ  | Demanda PAX: 7 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n",
      "Trecho: SBSJ-SBBR  | Demanda PAX: 9 | Oferecido: 30 | Alocados -> C-97: 1, C-99A: 0\n"
     ]
    }
   ],
   "source": [
    "# Preparação Modelo 7\n",
    "rotas_lista_mix = top_trechos_mix['Trecho'].tolist()\n",
    "\n",
    "distancias_voo_mix = {}\n",
    "for r in rotas_lista_mix:\n",
    "    origem, destino = r.split('-')\n",
    "    distancias_voo_mix[r] = calc_distancia(origem, destino)\n",
    "\n",
    "demanda_pax_mix = {}\n",
    "for _, row in top_trechos_mix.iterrows():\n",
    "    demanda_pax_mix[row['Trecho']] = row['Media_PAX_Dias_Operados']\n",
    "\n",
    "prob7 = pulp.LpProblem(\"Fleet_Assignment_Mix_Brasil\", pulp.LpMinimize)\n",
    "\n",
    "X_97 = pulp.LpVariable.dicts(\"X_97\", rotas_lista_mix, lowBound=0, cat='Integer')\n",
    "X_99 = pulp.LpVariable.dicts(\"X_99\", rotas_lista_mix, lowBound=0, cat='Integer')\n",
    "\n",
    "# Minimizar distância combinada\n",
    "prob7 += pulp.lpSum([distancias_voo_mix[r] * (X_97[r] + X_99[r]) for r in rotas_lista_mix])\n",
    "\n",
    "# Restrição de demanda (Em PAX)\n",
    "for r in rotas_lista_mix:\n",
    "    prob7 += (X_97[r] * CAPACIDADE_C97) + (X_99[r] * CAPACIDADE_C99) >= demanda_pax_mix[r]\n",
    "\n",
    "# Restrição de uso da frota global para o dia (simplificado a 1 perna por aeronave aqui)\n",
    "prob7 += pulp.lpSum([X_97[r] for r in rotas_lista_mix]) <= total_aeronaves\n",
    "prob7 += pulp.lpSum([X_99[r] for r in rotas_lista_mix]) <= total_aeronaves_c99\n",
    "\n",
    "prob7.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"== MODELO 7 (MIX C-97 e C-99A) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob7.status]}\")\n",
    "print(f\"Distância Total da Operação Combinada: {pulp.value(prob7.objective):.2f} km\\n\")\n",
    "\n",
    "for r in rotas_lista_mix:\n",
    "    c97_alloc = int(X_97[r].varValue)\n",
    "    c99_alloc = int(X_99[r].varValue)\n",
    "    capacidade_gerada = (c97_alloc * CAPACIDADE_C97) + (c99_alloc * CAPACIDADE_C99)\n",
    "    print(f\"Trecho: {r:10} | Demanda PAX: {demanda_pax_mix[r]:.0f} | Oferecido: {capacidade_gerada} | Alocados -> C-97: {c97_alloc}, C-99A: {c99_alloc}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c7d162",
   "metadata": {},
   "source": [
    "## MODELO 8: Minimização Absoluta de Frota Mix (S/ TAT)\n",
    "Diferente do Modelo 7, permitimos ciclos completos (vários voos por aeronave no dia) para minimizar a quantidade real de fuselagens exigidas para a operação. O modelo fará os reposicionamentos vazios necessários e alocará o voo ao tipo ideal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f74805e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:56.355316Z",
     "iopub.status.busy": "2026-06-08T00:02:56.355184Z",
     "iopub.status.idle": "2026-06-08T00:02:56.391012Z",
     "shell.execute_reply": "2026-06-08T00:02:56.390340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "== RESULTADO DO MODELO 8 (MIX SEM TAT) ==\n",
      "Status: Optimal\n",
      "Aeronaves C-97 necessárias: 0\n",
      "Aeronaves C-99A necessárias: 1\n",
      "Frota total mobilizada: 1 aeronaves\n",
      "\n",
      "Alocações para satisfazer a Demanda:\n",
      " -> Trecho SBGL-SBBR : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBBR-SBGL : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBGR-SBBR : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBBR-SBGR : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBGL-SBGR : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBBR-SBAF : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBBR-SBSJ : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBGR-SBGL : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBGL-SBSJ : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n",
      " -> Trecho SBSJ-SBBR : C-97 faz 0 voo(s) | C-99A faz 1 voo(s)\n"
     ]
    }
   ],
   "source": [
    "# Malha Aeroportuária\n",
    "aeroportos_malha_mix = set()\n",
    "for r in rotas_lista_mix:\n",
    "    o, d = r.split('-')\n",
    "    aeroportos_malha_mix.add(o)\n",
    "    aeroportos_malha_mix.add(d)\n",
    "aeroportos_malha_mix = list(aeroportos_malha_mix)\n",
    "\n",
    "# Matrizes de tempo exclusivas de cada aeronave na malha mix\n",
    "t_voo_97 = {}\n",
    "t_voo_99 = {}\n",
    "for o in aeroportos_malha_mix:\n",
    "    for d in aeroportos_malha_mix:\n",
    "        if o == d:\n",
    "            t_voo_97[(o, d)] = 0\n",
    "            t_voo_99[(o, d)] = 0\n",
    "        else:\n",
    "            dist = calc_distancia(o, d)\n",
    "            t_voo_97[(o, d)] = int(round((dist / vel_media_c97) * 60))\n",
    "            t_voo_99[(o, d)] = int(round((dist / vel_media_c99) * 60))\n",
    "\n",
    "prob8 = pulp.LpProblem(\"Minimizar_Frota_Mix\", pulp.LpMinimize)\n",
    "\n",
    "# D = Voos de Demanda (agora variável)\n",
    "D_97 = pulp.LpVariable.dicts(\"D_97\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "D_99 = pulp.LpVariable.dicts(\"D_99\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "\n",
    "# Y = Voos de Reposicionamento Vazios\n",
    "Y_97 = pulp.LpVariable.dicts(\"Y_97\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "Y_99 = pulp.LpVariable.dicts(\"Y_99\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "\n",
    "N_97 = pulp.LpVariable(\"N_97\", lowBound=0, cat='Integer')\n",
    "N_99 = pulp.LpVariable(\"N_99\", lowBound=0, cat='Integer')\n",
    "\n",
    "# Objetivo: Minimizar a soma de aeronaves. \n",
    "# Adicionamos pequenas penalidades para desempate:\n",
    "# 1. Preferir tempo de voo menor nos reposicionamentos\n",
    "# 2. C-99 é mais cara/maior, penalidade irrisória para preferir C-97 se ambas puderem (custo indireto)\n",
    "penalidade_voo_97 = pulp.lpSum([(D_97[(i, j)] + Y_97[(i, j)]) * t_voo_97[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "penalidade_voo_99 = pulp.lpSum([(D_99[(i, j)] + Y_99[(i, j)]) * t_voo_99[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "prob8 += N_97 + N_99 + 0.001 * N_99 + 0.00001 * (penalidade_voo_97 + penalidade_voo_99)\n",
    "\n",
    "# Restrição de Demanda em PAX\n",
    "# Todas as rotas que não são Top10 tem demanda = 0\n",
    "for i in aeroportos_malha_mix:\n",
    "    for j in aeroportos_malha_mix:\n",
    "        r_name = f\"{i}-{j}\"\n",
    "        demanda = demanda_pax_mix.get(r_name, 0)\n",
    "        prob8 += (D_97[(i, j)] * CAPACIDADE_C97) + (D_99[(i, j)] * CAPACIDADE_C99) >= demanda\n",
    "\n",
    "# Restrições de Fluxo Circulares (separados por tipo)\n",
    "for no in aeroportos_malha_mix:\n",
    "    # C-97\n",
    "    chegadas_97 = pulp.lpSum([D_97[(i, no)] + Y_97[(i, no)] for i in aeroportos_malha_mix])\n",
    "    partidas_97 = pulp.lpSum([D_97[(no, j)] + Y_97[(no, j)] for j in aeroportos_malha_mix])\n",
    "    prob8 += chegadas_97 == partidas_97\n",
    "    \n",
    "    # C-99\n",
    "    chegadas_99 = pulp.lpSum([D_99[(i, no)] + Y_99[(i, no)] for i in aeroportos_malha_mix])\n",
    "    partidas_99 = pulp.lpSum([D_99[(no, j)] + Y_99[(no, j)] for j in aeroportos_malha_mix])\n",
    "    prob8 += chegadas_99 == partidas_99\n",
    "\n",
    "# Restrições de tempo operacional diário\n",
    "prob8 += pulp.lpSum([(D_97[(i, j)] + Y_97[(i, j)]) * t_voo_97[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix]) <= N_97 * 1440\n",
    "prob8 += pulp.lpSum([(D_99[(i, j)] + Y_99[(i, j)]) * t_voo_99[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix]) <= N_99 * 1440\n",
    "\n",
    "# Restrições Físicas\n",
    "prob8 += N_97 <= total_aeronaves\n",
    "prob8 += N_99 <= total_aeronaves_c99\n",
    "\n",
    "prob8.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 8 (MIX SEM TAT) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob8.status]}\")\n",
    "print(f\"Aeronaves C-97 necessárias: {int(N_97.varValue)}\")\n",
    "print(f\"Aeronaves C-99A necessárias: {int(N_99.varValue)}\")\n",
    "print(f\"Frota total mobilizada: {int(N_97.varValue) + int(N_99.varValue)} aeronaves\\n\")\n",
    "\n",
    "print(\"Alocações para satisfazer a Demanda:\")\n",
    "for r in rotas_lista_mix:\n",
    "    origem, destino = r.split('-')\n",
    "    d97_val = int(D_97[(origem, destino)].varValue)\n",
    "    d99_val = int(D_99[(origem, destino)].varValue)\n",
    "    print(f\" -> Trecho {r:10}: C-97 faz {d97_val} voo(s) | C-99A faz {d99_val} voo(s)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe80d00",
   "metadata": {},
   "source": [
    "## MODELO 9: Minimização Absoluta de Frota Mix (C/ TAT 40 MIN)\n",
    "Por fim, a versão que soma a demanda, usa as duas frotas simultaneamente, permite múltiplos voos por dia, gera reposicionamentos e pune **40 minutos** no chão (TAT) para cada perna voada por qualquer um dos aviões.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97b2d5b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-08T00:02:56.393978Z",
     "iopub.status.busy": "2026-06-08T00:02:56.393575Z",
     "iopub.status.idle": "2026-06-08T00:02:56.413806Z",
     "shell.execute_reply": "2026-06-08T00:02:56.413342Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "== RESULTADO DO MODELO 9 (MIX COM 40 MIN TAT) ==\n",
      "Status: Optimal\n",
      "Aeronaves C-97 necessárias: 0\n",
      "Aeronaves C-99A necessárias: 1\n",
      "Frota total mobilizada: 1 aeronaves\n",
      "\n",
      "Tempo total de voo da frota combinada: 886 min (14.77 h)\n",
      "Tempo de solo desperdiçado (TAT 40 min em 12 voos): 480 min (8.00 h)\n"
     ]
    }
   ],
   "source": [
    "# Modelo 9\n",
    "prob9 = pulp.LpProblem(\"Minimizar_Frota_Mix_TAT\", pulp.LpMinimize)\n",
    "\n",
    "D_97_t = pulp.LpVariable.dicts(\"D_97t\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "D_99_t = pulp.LpVariable.dicts(\"D_99t\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "Y_97_t = pulp.LpVariable.dicts(\"Y_97t\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "Y_99_t = pulp.LpVariable.dicts(\"Y_99t\", [(o, d) for o in aeroportos_malha_mix for d in aeroportos_malha_mix], lowBound=0, cat='Integer')\n",
    "\n",
    "N_97_t = pulp.LpVariable(\"N_97t\", lowBound=0, cat='Integer')\n",
    "N_99_t = pulp.LpVariable(\"N_99t\", lowBound=0, cat='Integer')\n",
    "\n",
    "TAT_min = 40\n",
    "\n",
    "penalidade_97 = pulp.lpSum([(D_97_t[(i, j)] + Y_97_t[(i, j)]) * t_voo_97[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "penalidade_99 = pulp.lpSum([(D_99_t[(i, j)] + Y_99_t[(i, j)]) * t_voo_99[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "prob9 += N_97_t + N_99_t + 0.001 * N_99_t + 0.00001 * (penalidade_97 + penalidade_99)\n",
    "\n",
    "for i in aeroportos_malha_mix:\n",
    "    for j in aeroportos_malha_mix:\n",
    "        r_name = f\"{i}-{j}\"\n",
    "        demanda = demanda_pax_mix.get(r_name, 0)\n",
    "        prob9 += (D_97_t[(i, j)] * CAPACIDADE_C97) + (D_99_t[(i, j)] * CAPACIDADE_C99) >= demanda\n",
    "\n",
    "for no in aeroportos_malha_mix:\n",
    "    prob9 += pulp.lpSum([D_97_t[(i, no)] + Y_97_t[(i, no)] for i in aeroportos_malha_mix]) == pulp.lpSum([D_97_t[(no, j)] + Y_97_t[(no, j)] for j in aeroportos_malha_mix])\n",
    "    prob9 += pulp.lpSum([D_99_t[(i, no)] + Y_99_t[(i, no)] for i in aeroportos_malha_mix]) == pulp.lpSum([D_99_t[(no, j)] + Y_99_t[(no, j)] for j in aeroportos_malha_mix])\n",
    "\n",
    "# TAT INCLUSO AQUI\n",
    "prob9 += pulp.lpSum([(D_97_t[(i, j)] + Y_97_t[(i, j)]) * (t_voo_97[(i, j)] + TAT_min) for i in aeroportos_malha_mix for j in aeroportos_malha_mix]) <= N_97_t * 1440\n",
    "prob9 += pulp.lpSum([(D_99_t[(i, j)] + Y_99_t[(i, j)]) * (t_voo_99[(i, j)] + TAT_min) for i in aeroportos_malha_mix for j in aeroportos_malha_mix]) <= N_99_t * 1440\n",
    "\n",
    "prob9 += N_97_t <= total_aeronaves\n",
    "prob9 += N_99_t <= total_aeronaves_c99\n",
    "\n",
    "prob9.solve(pulp.PULP_CBC_CMD(msg=False))\n",
    "\n",
    "print(\"\\n== RESULTADO DO MODELO 9 (MIX COM 40 MIN TAT) ==\")\n",
    "print(f\"Status: {pulp.LpStatus[prob9.status]}\")\n",
    "print(f\"Aeronaves C-97 necessárias: {int(N_97_t.varValue)}\")\n",
    "print(f\"Aeronaves C-99A necessárias: {int(N_99_t.varValue)}\")\n",
    "print(f\"Frota total mobilizada: {int(N_97_t.varValue) + int(N_99_t.varValue)} aeronaves\\n\")\n",
    "\n",
    "tempo_97_voo = sum([(int(D_97_t[(i, j)].varValue) + int(Y_97_t[(i, j)].varValue)) * t_voo_97[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "tempo_99_voo = sum([(int(D_99_t[(i, j)].varValue) + int(Y_99_t[(i, j)].varValue)) * t_voo_99[(i, j)] for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "\n",
    "total_voos = sum([(int(D_97_t[(i, j)].varValue) + int(Y_97_t[(i, j)].varValue) + int(D_99_t[(i, j)].varValue) + int(Y_99_t[(i, j)].varValue)) for i in aeroportos_malha_mix for j in aeroportos_malha_mix])\n",
    "\n",
    "print(f\"Tempo total de voo da frota combinada: {tempo_97_voo + tempo_99_voo} min ({(tempo_97_voo + tempo_99_voo)/60:.2f} h)\")\n",
    "print(f\"Tempo de solo desperdiçado (TAT 40 min em {total_voos} voos): {total_voos * TAT_min} min ({(total_voos * TAT_min)/60:.2f} h)\")\n"
   ]
  }
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