macro_split/notebooks/analyze_ring16_molecules.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 16元环大环内酯分子分析\n",
        "\n",
        "本notebook用于分析 ring16_match_smarts.csv 中的16元环大环内酯分子：\n",
        "1. 计算并统计分子的相对分子质量\n",
        "2. 进行侧链断裂分析\n",
        "3. 统计每个碎片位置的原子数目和分子量分布\n",
        "4. 计算并分析药物性质（LogP、QED等）\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. 导入必要的库\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "所有模块导入成功!\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "from pathlib import Path\n",
        "\n",
        "# 添加项目根目录到 Python 路径\n",
        "notebook_dir = Path().resolve()\n",
        "project_root = notebook_dir.parent\n",
        "sys.path.insert(0, str(project_root))\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors, Crippen, QED\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem.Draw import rdMolDraw2D\n",
        "from IPython.display import SVG, display\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# 导入项目模块\n",
        "from src.macrolactone_fragmenter import MacrolactoneFragmenter\n",
        "from src.ring_visualization import get_macrolactone_numbering\n",
        "\n",
        "print(\"所有模块导入成功!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. 读取数据\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "读取数据集: 307 个分子\n",
            "列名: ['Unnamed: 0', 'unique_id', 'IDs', 'molecule_pref_name', 'max_pChEMBL', 'max_pChEMBL_target', '# Target Organisms', 'Target Organisms', '# Known Targets', 'Known Targets', 'target_pref_name', 'smiles', 'has_bridge', 'has_multiple_lactones', 'has_amide', 'parse_failed', 'filter_reason', 'matches_smarts']\n",
            "\n",
            "前5行:\n"
          ]
        },
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>unique_id</th>\n",
              "      <th>IDs</th>\n",
              "      <th>molecule_pref_name</th>\n",
              "      <th>max_pChEMBL</th>\n",
              "      <th>max_pChEMBL_target</th>\n",
              "      <th># Target Organisms</th>\n",
              "      <th>Target Organisms</th>\n",
              "      <th># Known Targets</th>\n",
              "      <th>Known Targets</th>\n",
              "      <th>target_pref_name</th>\n",
              "      <th>smiles</th>\n",
              "      <th>has_bridge</th>\n",
              "      <th>has_multiple_lactones</th>\n",
              "      <th>has_amide</th>\n",
              "      <th>parse_failed</th>\n",
              "      <th>filter_reason</th>\n",
              "      <th>matches_smarts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>113</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CHEMBL1354988</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.85</td>\n",
              "      <td>CHEMBL1293235</td>\n",
              "      <td>3</td>\n",
              "      <td>Homo sapiens, None</td>\n",
              "      <td>3</td>\n",
              "      <td>CHEMBL1293226, CHEMBL612545, CHEMBL1293235</td>\n",
              "      <td>Unchecked, Lysine-specific demethylase 4D-like...</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>288</td>\n",
              "      <td>mol_00288</td>\n",
              "      <td>CHEMBL42743</td>\n",
              "      <td>TYLOSIN</td>\n",
              "      <td>8.56</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>Nucleic Acid</td>\n",
              "      <td>CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>313</td>\n",
              "      <td>mol_00313</td>\n",
              "      <td>CHEMBL42743</td>\n",
              "      <td>TYLOSIN</td>\n",
              "      <td>8.56</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>Nucleic Acid</td>\n",
              "      <td>CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>314</td>\n",
              "      <td>mol_00314</td>\n",
              "      <td>CHEMBL42743</td>\n",
              "      <td>TYLOSIN</td>\n",
              "      <td>8.56</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>Nucleic Acid</td>\n",
              "      <td>CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>326</td>\n",
              "      <td>mol_00326</td>\n",
              "      <td>CHEMBL42743</td>\n",
              "      <td>TYLOSIN</td>\n",
              "      <td>8.56</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>CHEMBL345</td>\n",
              "      <td>Nucleic Acid</td>\n",
              "      <td>CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  unique_id            IDs molecule_pref_name  max_pChEMBL  \\\n",
              "0         113  mol_00113  CHEMBL1354988                NaN         4.85   \n",
              "1         288  mol_00288    CHEMBL42743            TYLOSIN         8.56   \n",
              "2         313  mol_00313    CHEMBL42743            TYLOSIN         8.56   \n",
              "3         314  mol_00314    CHEMBL42743            TYLOSIN         8.56   \n",
              "4         326  mol_00326    CHEMBL42743            TYLOSIN         8.56   \n",
              "\n",
              "  max_pChEMBL_target  # Target Organisms    Target Organisms  # Known Targets  \\\n",
              "0      CHEMBL1293235                   3  Homo sapiens, None                3   \n",
              "1          CHEMBL345                   1                 NaN                1   \n",
              "2          CHEMBL345                   1                 NaN                1   \n",
              "3          CHEMBL345                   1                 NaN                1   \n",
              "4          CHEMBL345                   1                 NaN                1   \n",
              "\n",
              "                                Known Targets  \\\n",
              "0  CHEMBL1293226, CHEMBL612545, CHEMBL1293235   \n",
              "1                                   CHEMBL345   \n",
              "2                                   CHEMBL345   \n",
              "3                                   CHEMBL345   \n",
              "4                                   CHEMBL345   \n",
              "\n",
              "                                    target_pref_name  \\\n",
              "0  Unchecked, Lysine-specific demethylase 4D-like...   \n",
              "1                                       Nucleic Acid   \n",
              "2                                       Nucleic Acid   \n",
              "3                                       Nucleic Acid   \n",
              "4                                       Nucleic Acid   \n",
              "\n",
              "                                              smiles  has_bridge  \\\n",
              "0  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...       False   \n",
              "1  CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...       False   \n",
              "2  CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...       False   \n",
              "3  CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...       False   \n",
              "4  CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...       False   \n",
              "\n",
              "   has_multiple_lactones  has_amide  parse_failed  filter_reason  \\\n",
              "0                  False      False         False            NaN   \n",
              "1                  False      False         False            NaN   \n",
              "2                  False      False         False            NaN   \n",
              "3                  False      False         False            NaN   \n",
              "4                  False      False         False            NaN   \n",
              "\n",
              "   matches_smarts  \n",
              "0            True  \n",
              "1            True  \n",
              "2            True  \n",
              "3            True  \n",
              "4            True  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 读取CSV文件\n",
        "csv_file = project_root / 'output' / 'ring16_match_smarts.csv'\n",
        "df = pd.read_csv(csv_file)\n",
        "\n",
        "print(f\"读取数据集: {len(df)} 个分子\")\n",
        "print(f\"列名: {df.columns.tolist()}\")\n",
        "print(f\"\\n前5行:\")\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. 计算分子的基本性质\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "正在计算分子性质...\n",
            "\n",
            "成功计算 307 个分子的性质\n",
            "\n",
            "性质统计:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mol_weight</th>\n",
              "      <th>logP</th>\n",
              "      <th>num_h_donors</th>\n",
              "      <th>num_h_acceptors</th>\n",
              "      <th>num_rotatable_bonds</th>\n",
              "      <th>tpsa</th>\n",
              "      <th>qed</th>\n",
              "      <th>num_atoms</th>\n",
              "      <th>num_heavy_atoms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>307.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>786.351365</td>\n",
              "      <td>3.028585</td>\n",
              "      <td>3.781759</td>\n",
              "      <td>13.990228</td>\n",
              "      <td>10.895765</td>\n",
              "      <td>186.006678</td>\n",
              "      <td>0.226296</td>\n",
              "      <td>55.074919</td>\n",
              "      <td>55.074919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>161.791942</td>\n",
              "      <td>1.035552</td>\n",
              "      <td>1.221242</td>\n",
              "      <td>3.663533</td>\n",
              "      <td>3.604946</td>\n",
              "      <td>45.728840</td>\n",
              "      <td>0.129689</td>\n",
              "      <td>11.203885</td>\n",
              "      <td>11.203885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>364.482000</td>\n",
              "      <td>0.525300</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.670000</td>\n",
              "      <td>0.065000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>679.938000</td>\n",
              "      <td>2.215600</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>152.060000</td>\n",
              "      <td>0.146527</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>48.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>811.963000</td>\n",
              "      <td>3.145820</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>195.150000</td>\n",
              "      <td>0.182802</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>57.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>902.584000</td>\n",
              "      <td>3.717900</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>218.440000</td>\n",
              "      <td>0.249791</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1098.334000</td>\n",
              "      <td>5.864900</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>289.470000</td>\n",
              "      <td>0.725918</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>78.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        mol_weight        logP  num_h_donors  num_h_acceptors  \\\n",
              "count   307.000000  307.000000    307.000000       307.000000   \n",
              "mean    786.351365    3.028585      3.781759        13.990228   \n",
              "std     161.791942    1.035552      1.221242         3.663533   \n",
              "min     364.482000    0.525300      1.000000         5.000000   \n",
              "25%     679.938000    2.215600      3.000000        11.000000   \n",
              "50%     811.963000    3.145820      4.000000        15.000000   \n",
              "75%     902.584000    3.717900      5.000000        16.500000   \n",
              "max    1098.334000    5.864900      7.000000        22.000000   \n",
              "\n",
              "       num_rotatable_bonds        tpsa         qed   num_atoms  \\\n",
              "count           307.000000  307.000000  307.000000  307.000000   \n",
              "mean             10.895765  186.006678    0.226296   55.074919   \n",
              "std               3.604946   45.728840    0.129689   11.203885   \n",
              "min               1.000000   80.670000    0.065000   26.000000   \n",
              "25%               8.000000  152.060000    0.146527   48.000000   \n",
              "50%              11.000000  195.150000    0.182802   57.000000   \n",
              "75%              13.000000  218.440000    0.249791   63.000000   \n",
              "max              18.000000  289.470000    0.725918   78.000000   \n",
              "\n",
              "       num_heavy_atoms  \n",
              "count       307.000000  \n",
              "mean         55.074919  \n",
              "std          11.203885  \n",
              "min          26.000000  \n",
              "25%          48.000000  \n",
              "50%          57.000000  \n",
              "75%          63.000000  \n",
              "max          78.000000  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 计算基本性质\n",
        "def calculate_properties(smiles):\n",
        "    \"\"\"计算分子的基本性质\"\"\"\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    if mol is None:\n",
        "        return None\n",
        "    \n",
        "    props = {\n",
        "        'mol_weight': Descriptors.MolWt(mol),\n",
        "        'logP': Crippen.MolLogP(mol),\n",
        "        'num_h_donors': Descriptors.NumHDonors(mol),\n",
        "        'num_h_acceptors': Descriptors.NumHAcceptors(mol),\n",
        "        'num_rotatable_bonds': Descriptors.NumRotatableBonds(mol),\n",
        "        'tpsa': Descriptors.TPSA(mol),\n",
        "        'qed': QED.qed(mol),\n",
        "        'num_atoms': mol.GetNumAtoms(),\n",
        "        'num_heavy_atoms': mol.GetNumHeavyAtoms()\n",
        "    }\n",
        "    return props\n",
        "\n",
        "# 计算所有分子的性质\n",
        "print(\"正在计算分子性质...\")\n",
        "properties_list = []\n",
        "\n",
        "for idx, row in df.iterrows():\n",
        "    props = calculate_properties(row['smiles'])\n",
        "    if props:\n",
        "        props['unique_id'] = row['unique_id']\n",
        "        properties_list.append(props)\n",
        "\n",
        "# 创建性质DataFrame\n",
        "df_props = pd.DataFrame(properties_list)\n",
        "\n",
        "print(f\"\\n成功计算 {len(df_props)} 个分子的性质\")\n",
        "print(f\"\\n性质统计:\")\n",
        "df_props.describe()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. 分子性质分布统计图\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3752769/2194066832.py:57: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_3752769/2194066832.py:61: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
            "  plt.savefig(output_file, dpi=300, bbox_inches='tight')\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "已保存: /home/zly/project/macro_split/output/ring16_molecular_properties_distribution.png\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/zly/project/macro_split/.pixi/envs/default/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "data": {
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7dLfZyzba+z8ttVte/dFuOWFHy8gIdaoAAADQ2OXo2nYgWpu/1pZkL7GVuSuttKy00u+T45MtKS7JkuKTLC4mzopLi90jJibGBT+yCrLcY3nOcvtl9S/WIbWDdU3vau1T27u/AQA0DgIbAAAEU4sWZtOnB21z+QXF9uvSDe5f6d62pXVs1YJKUh2oQvmPQwbYglXZNm9ltt0/Za7999R2FkdFEwAAIGLL0bUJaCgYMWfdnEojM9IS01xQonWL1u5R3QiMfXvsa2cMOsPOfP1M+3XNr7Yid4XlFua6f/XQSI6+bfoS4ACARkJgAwCAZurHhWvtk1+XW2FxqcXHxdg2HTMsIyUx1MkKS5q668pjBtn5j31hM5Zm2SvfLLTjhvUJdbIABNnDDz9s77//vi1YsMCSk5Nt0KBBNnbsWOvdu3elhqz77rvPXnzxRcvOzraddtrJrr32Wtt22205HgAQBfQ9oJEZs9fNdoEISYhNsM5pna17RndLT0qvdSciBT56tOphxWXFLoiRXZBtS7OXuvU4NIpj+vLp1iq5lfVv198yW2Q28p4BQHRhDgsAAJphZWvS1wvsquemuqBGalK8DejWmqBGA3Vr29LOGdHPPX/y49k2d0VWMA4XgGZk2rRpNmbMGJs4caI98cQTVlJSYmeeeabl5+dX/M2jjz7qfqdgxqRJk6xt27Z2+umnW25ueeMWACBy5Rfl27Rl09z6GApqKKCxXZvt7MDeB9rADgMtIzmj3iOj9T69v3/7/nZArwOsd2Zvi42JdYuPf7nkS5u5ZqaVlJYEfZ8AIFoR2AAAIJjUeNazZ/nD15BWW5sLi906EI9+8JuVlqkxPtX6dc10Iw7QcCN37mq79WljxaVldssrP1hxSeU5lAGEt/Hjx9uoUaPc6Ivtt9/exo0bZ8uXL7dff/21InD89NNP2znnnGMjRoywvn372q233mqbN2+2yZMnhzr5ABDdGliOronWxPh+xff26aJPbU3+Ghdw2Kb1Ni4AsW2bbS0+NrgTmmg9jn7t+tmBvQ60Lmld3GsLNiywT3//1C1MDgBoOAIbAAAEU1mZ2e+/lz/0vA6Wr8+zi5/4yk0/FRcbY+eP7G+79GprsbEsOhgs6kn39wP6WNu0ZFu6Ps9+XbIhaNsG0Pzk5OS4fzMyMty/S5cutTVr1thee+1V8TeJiYk2ZMgQ++GHH0KWTgBAw8rRNdHUUPs/tb99vvhzKykrcdNH7dNjH9u+7fbVrp8RzADHoE6DbEjnIW4Rco0Y+WbpNy7IoWA7AKD+WGMDAIBm4MffN9i978+x3M3FlpmaZP8avYsN7N7aHnyvvJcxgqdlcoL988gd7arnptnC1TmWnBBn6axdAkQcNRhpxMbgwYPdyAxRUEPatGlT6W81HZVGdtTnM2iYanxePpPX0YHjHd3H3OvO4673IDT8vzP3HTvltVNs3aZ1lhib6IIZ3dK7lU831dDNl/n+3cq2OqR2sNbdW9uMNTNsWc4yNy3Vxk0bbccOO245WuSPbUXqPY9rPLpwvKNPWQPLbXV5H4ENAABCSF/aL3wxz576ZI6rw2zfpZX93+jB1jY9mePSiAb1amuH7tLd3v5+sS1YneOCSBolAyBy3HDDDTZnzhx7/vnnt/hd4Pzp9a14afHx2FgGwTc2HR9vnZT6zn2P8MHxjuJjXlZm3vLaWVlZZsXF9d5maVmp3fT1TXbn9Dvdz4M6DrJ+bfrZ2vy1VlxS/+36edvRNFdFxUW1ek//Nv0tLSHNZq+fbctzl1t2YbYN7jDYjeYI3K7WftJaUZGGazy6cLyjT1kDy22lpbWfLprABgAAIZJfUGy3v/GTfTlrpfv5kEHd7LyR/S0xnvU0msLfhm9vH89YZpsKS2zpulzr0S6tST4XQOO78cYb7aOPPrJnn33WOnbsWPF6u3bt3L9r16619u3bV7y+bt06N2qjrtLT0y0ujnt2Y/MCT5pSjMBG5ON4R/Exj/+zicpNIZiaWq/t5RXmuVEar8561f183pDz7I4Rd9iVU660rMKsIKXaLD6uPL0acZEQX/sprbZps41lpmS6NT+0gPm0FdNsaJeh1jKxZaXttmxZ/nOk4RqPLhzv6FPWwHJbXQK6BDYAAAiBJWtz7YaXvrPFa3MtIS7WTt+nl43aczsabJpQalKC7dyzjX09Z7Wt3LjJWrdMsrQWiU2ZBACNUJFSUGPKlCn2zDPPWLdu3Sr9vmvXri648eWXX1q/fv3ca4WFhTZ9+nQbO3ZsnT9PlTUa2puGl9fkd3TgeEefwOvbPa9Hg9jynOV2xIQjXNAgMS7Rxh853v6641//2Ogfj6Al2uq93TYpbWzP7nva1KVTLa8oz75a+pXt1mU3a5XcqmJbkXy/4xqPLhzv6BPTgHJbXd5DYAMAgCb2zZxVdutrP7oRG23SkuyaY3exzmlMZRIKHVqluIXE1+ZstgWrcmxgj9YWG8GVSCDSXX/99TZ58mR74IEHLDU1tWJNjbS0NEtOTnYVpVNOOcUefvhh69mzp/Xo0cM91+8OP/zwUCcfANAAM1bPsJHPjnRrWLRNaWuvnfCaCx40VykJKTas2zCbtmyaZRVkuUXFtch4n8w+oU4aAIQFAhsAAASTGsX/6AUc2MvMrafx5Xx76uPZbj2N/t0y7ZrRu7jFwt08wgiJHu1aWlZ+oW0uKrEVG/KtS+v6TXsAIPQmTJjg/j355JMrva5FxEeNGuWen3XWWVZQUOCCILr37rTTTvb4449H7JQfABAJ5eit0ciHQ547xDZs3mA7tN3BJp802Xpn9rbmLik+yXbvurt9u/xbt8C5ghzdM7qHOlkAEBYIbAAAEEwpKWa//rrFy5sLi+2ON3+2z2aucD8fPri7nXNwfzcNVX0XrUX9BM6HHx8Xa93btrT5q7Jt2fo8a5OWbMkJzJkPhKPZs2dv9W80auOCCy5wDwBA8y9Hb82HCz60o144yk3ppCDBWye9Za1btLZwkRCX4KahUlBDwY3XZr1mP6z4wQZ1GhTqpAFAs8a8FwBqpbSRGl4ba7tofOF07FomJzRqere27VUb8+3SJ792QY242Bi78NABdsGhA11QA0177NSgqSlpAuft1JRg6S0STB/1++ocgk0AAABh4M3Zb9qhzx/qghoH9jrQppw8JayCGp642Dgb0mWIS3tBSYEd9MxBbmotAED1GLEBoFY05/ykr+fbmuzNQcuxdunJNnoP5g8NV41xTmzbKd2G71h5oddgUO/7xkhvbc7jn39fZ/+Z9L2b6igjJdH+77jBNrB7+FW2QqUxjl1xcbHt0K11pXNNgY6e7dPsl9/X28b8QtuQV2CtWyYH5fMAAADQOEGNYycea0WlRXbM9sfYhGMnuKmdwlV8bLxbY+PHlT/aqrxVNuKZETb1b1OtW0bw60cAEAkIbACoNTUqav55oLHOCS3iHPbncH6+2ZAhbg2Ndx6eZPd9utBKSstsm47p9u/jd7X2GS0a9/MjVDCPXVFRkXXM3HIu/RaJ8dapdYotX59vv6/JdYGouFhG1QAAADQJlaN32638+fTp5VNTVWPynMkVQY3j+x9vz416zgUGwp2mpTp6+6Pti8Vf2K9rfnWjUb44/QvLSM4IddIAoNmhtg4AQDBpLqOZMy1m5kx76L2ZLqix/4DOdsdpwwhqhIEumamWFB9rhcWlLsABAACApi1Hu0cNU5G+PfftiqDGcf2Oi5ighic5PtneHvO2dWrZyU1HpX0tLCkMdbIAoNkhsAEAQBBtyC2oeK5VHP524PZ2xdE7sxh1mIiNjbHu7dLc8xUb821zUUmokwQAAIA/fLroUxv14ijX0H/sDsdGXFDD0z2ju1sEPTUh1T5c+KGd/ebZrAEHAAEIbAAAECRzlm+0fz71VcXP1x63ix03rM8WC1WjectMTaxYSHzx2txQJwcAAABm9sOKH+zIF450i2sf0fcIt6aGpm6KVIM6DbKJx020uJg4e+qnp+yOr+8IdZIAoFkhsAEAQBB89Msy++dTX9vanD9HbAzu0568DUMKRPX4Y9SGRuBo4XcAAACEzrz182zkcyMtuyDb9umxj704+sWIDmp4Dt32UPvfyP+551d8cIW9P//9UCcJAJoNAhsAADRAWVmZPfrBb3braz+6dRl27dOO/IwAKUnxFWuiLF6Ty9B/AACAEFmRs8JGPDPCVuettp067GRvnPiGtUgoL6dFg/OHnG9n7HyGlZaV2omTTrT56+eHOkkA0CwQ2AAAoJ6KS0rt6zmrbNLXC9zPJ+7Zx/517C7kZ4To2ibV4mJjLL+w2FZnbw51cgAAAKLOxs0b3UiNhRsXWp/MPvbuX9+1jOQMi7bRxPcfdr8N7TLUNmzeYEe/eLTlFjJdKgAQ2AAAoB42FRbbjCUbbHXWZkuKj7WrRg2y0w/Y3uLiYs169Ch/sLZGWEuIi3XBDVm2Ls9KSktDnSQAAIDIpbKzrxydX5RvR0w4wn5e9bN1bNnR3j/5ffdvNEqOT7aXj3/Z7f+M1TPsrDfPYkQxgKhHYAMAgDrSugsKahQUlViLxDi76/Rhtl//zuW/TEkxW7So/KHnCGuajiopIc6KSkptxYb8UCcHAAAgcvnK0UVJCXbCpBPsi8VfWEZShr075l3rndnbolmX9C426bhJbjHxF2a8YA9/93CokwQAIUVgAwCAOqynsXx9ns1ZkWWlpWWW1iLBBTT6dIyu4fDRJDYmxrr9MWpjxYZNVlhcEuokAQAARHyZ+5zJ59jkOZPdSIU3//Km7dRxp1Anq1nYs/ueNu7Ace75xe9ebD+s+CHUSQKAkCGwAQBALZSWldnC1Tm2ZF1eRU/+7bu0cr35Edlat0yy1OR4dw5oSioAAAA0nlu/vNUe//Fxi42JtRdHv2h799ib7Pb557B/2uF9D7eCkgI77qXjLGtzFvkDICoR2AAAYCs0DdGsZRttzR8LSPdo19J6tmvpevNvYdMmsyFDyh96johYsLF725buuRYR1/oqAAAACLJNm2z9gG3swBOusuQis7tH3m1Hbnck2RxAAZ+njn7Kumd0t/kb5rPeBoCoRWADAIAaqBH71yXrLWdTkcXGxth2nTOsY6sU19hdJS0w/e235Q8Wm44Y6S0SLTM10T1fvDY31MkBAACIONOXTLXWv863IcvNzh98jv1jt3+EOknNVusWrW3i6IkWHxtvL818yZ748YlQJwkAmhyBDQAAqrExr8B+dYuEl1pSfKz175pprVKTyK8o1e2PURsb8wpdoAsAAADBsTh7sZ3w8gkVP986/FaydiuGdh1qN+5/o3t+4TsX2tx1c8kzAFGFwAYAAFVYtTHfZi/PspLSMmuZnGD9u7W2lKR48iqKtUiMt3bpye750nWM2gAAAAgGrRFxwusn2Oq8NRWvxcWyjl1tXDbsMtuv536WV5RnJ71ykhWWFHJSAogaBDYAAPApKyuzRatzbNGa8obrtmnJtkOXVpYQz1cmzLq0TjXNQpa9qciy8qk4AgAANERRSZEdP+l4m7V+lnVM7UBm1pECQM8c84xlJmfat8u/tX9//G/yEEDUoJUGAIA/FJeUulEaq7LKF/3u2ibVendIc2trAJKUEGftM1q450vW5rpAGAAAAOpO5agL3rnApiyYYinxKfbScS+RjfXQNb2rPXrEo+75rV/eap///jn5CCAqENgAAMDMCopKbObSDa4XvuIY23ZK/6N3PkENVNYlM9WdI3kFxbYhj1EbAAAA9XHXN3fZw989bDEWY4+OfNQGdRpERtbTsf2OtTN2PsPKrMxOe/00yy1k2lQAkY/ABgAg6uVtLnKLhG8qLLGEuFjr1zXTWrcsX0uhXtq2LX8gImlaso6tUirW2mDUBgAAQN28Nus1G/v+WPf8toNus0P7HFr+C8rR9XbnwXdat/RutmDDArtiyhWckgAiHoENAEBU25hXYDOXbrSiklJrkRhn/btlWmpyQv03mJpqtmZN+UPPEZE6ZaZYXGyMC4atzy0IdXIAAADCxnfLv7Mxr4xxowvOGXyOXbL7JeW/oBzdIBnJGfb4UY+75w98+4B9uODDIBwtAGi+CGwAAKLWmqxNbk2N0rIyS2+R4EZqaA0FYGvi42Kt0x+jNpatz2PUBgAAQC0syVpiR0w4wvKL8m1EnxF276H3MvVrEA3vPdzO3fVc9/yMN86w7IJszksAEYvABgAg6mjqoKXr8mzB6hz3c5u0JNuuSyvXWA3UVodWLRi1AQAAUEs5BTl2+ITDbUXuCuvfrr9NHD3R4mPjyb8g++9B/7Xemb1tcdZiu/S9S8lfABGLFhwAQFTR6IyFq3NcL3vpnJlifTqkW2ywFgnftMlsv/3KH3qOiKVAmLfWBqM2AAAAqldcWmwnvnyi/bzqZ+uQ2sHeOuktN3VSJZSjg6JlYkt74qgn3KLs438Yb2/PfZtTE0BEIrABAIgaxSWlNnd5lq3J3ux+7tkuzbq1bRnc4e+lpWafflr+0HNEtI6M2gAANCOlZY1T9igpLQmr9KJx87c+29XIATWwJ8cn2xt/ecN6tOpRxYYpRwfLPj32sYuGXuSen/XmWbZh04agbRsAmgvG/AEAokLu5iL7evYq25hfaLExZtt0zLDMlkmhThYiYtRGC1u2Pt+N2tDi8wAAhEpsTKzd/tXttjR7adC2uUunXeyUnU4J+na7pne1scPGBm17aJrzoT7H7d6p99q90+51z5855hnbrctuQUsPqnfTgTfZW3Pfsrnr59qF717o8h4AIgmBDQBAxMsvKLbLnv7G1uUWuDURtuucYWktEkOdLEQITUe1cuMm21RYYss35Ic6OQCAKKdG7Pkb5ge1IbsxtoumEerj9tact+zi9y52z2858BYb3W90yNISbVISUuypo5+yvZ7Yy579+Vk7dodj7ejtjw51sgAgtFNRLVmyJHgpAACgERUUldjk7363BauyLSkh1nbo2oqgBhpl1IbMXrbRreMCoO6oYwBAZPlp5U92wqQT3NRVZw460y7f8/JQJynq7NFtDxu7R/kIm3Mmn2PrN60PdZIAILSBjREjRtjJJ59sr7/+uhUUFAQvNQAABHmkxq9LNlj2piLr0KqF7b1DJ0tNSiCP0SijNjQaSOfal7NWksNAPVDHAIDIsTxnuR0+4XDLK8qzA3odYA8e9mBw17VDrV2///W2fdvtbVXeKhv7PtO/AYjywIYCGv369bNbb73V9txzT7v22mvt559/Dn7qAABowJoavy3dYEUlpZaZmmh3njrMWiYT1EDjj9p47rO5jNoA6oE6BgBEhrzCPDtiwhFuGiw1qE86bpIlxFEODxUt2P7YEY+550/8+IR9sOCDkKUFAEIe2Ojbt69dddVV9tlnn9m4ceNszZo1dtJJJ9lhhx1mTzzxhK1fz9A2AEDoZOUX2m9LN1pxaZmlJsfbYYN7WNv05KZLQEpK+QNRN2ojPjbGFq7Osa8YtQHUGXUMAAh/xaXFduLLJ9r3K763tilt7a2T3rLMFpm13wDl6EaxZ/c97bxdz3PP/z7575ZfxLpwAKI0sOGJj4+3gw46yO6++24bO3asLV682I3i2Geffezyyy+31atX1/j+6dOn2znnnGN77bWXbbfddvbBB5WjxldeeaV73f84/vjjG5JkAECEy8ortNnLy9c5SG+RYDt0aWXJCXFNl4DUVLO8vPKHniOqRm307pjunj/3+TxGbQAhqmMAAEKjrKzMLnrnIps8Z7IbJfDGiW9Y78zetd8A5ehGNW74OOua3tUWbFhg1358beN+GAA098DGL7/8Ytddd50LTGikxhlnnGFTpkyxp556ylatWmXnnVceDa5Ofn6+C1ZoKqvq7L333vbFF19UPB555JGGJBkAEME25hXY7BUbTWs3t0pJtO06t7K42AZ91QF1sk2HdEtJjHeL1X8zexW5B4SgjgEACI07vr7DHvj2AYuxGHv2mGfdwtVoPtKT0t1aJ3LXN3fZt8u/DXWSAKBB4uvzJlUwXnnlFVu4cKHrOaUeVPvuu6/F/tF41K1bN7vhhhvskEMOqXE7eo8eNUlMTLR27drVJ5kAgCiyIbfA5q7MckENramxTacMi2WBQjSxxIQ4O3JID3vhy/n23OdzbY/tOrBQJtDEdQwAQNN76deX7LIpl7nnt4+43Y7tdyyHoRk6vO/hduKAE+2FGS/YmW+cad+e9S3rnwCIrsDGhAkT7Nhjj7VRo0ZVG3To1KmT3XTTTQ1Nn02bNs322GMPS09PtyFDhtgll1xibdq0qdeQSD2ikbfv0br/TSmS8zqmERuI65pfkZzPzU1Ned2Y50Rdrc/dbPNWZJtS2bplkvXpmN7kQY2KPNq82Wz06PLnkyaZJSfXO5+b03VXG83pnNjafpe5s6VxHLt7b3tt2iKbtzLbvp2/xnbtE30dNLhPR09eB/Nzm7KOAQAIni8Xf2knv3qye/6PIf+wS3a/pH4b8pejX365VuVo1N3dI++29+e/bz+v+tlu++o2u3rvq8lGANET2Hj//fe3+jcaaXHMMcdYQ6in1siRI61z5862dOlSN8/uqaee6npyaft1kZ2dXdHbK9qowqlpv8Kl0SmcRWpex8XFWVpamhUXF1tRUVHQtqvtSW5urpWUlFi053NzVF1eN9Y54Z0H+re2292QV2gL1+S555mpCdajTQsrKS62kgZut97ncV6etXr7bffaxvXra7XORlX53Fh5nBwf49Z+aKzAT3M4J2qzzdKS0qBv19v/9JREO2hgR3vz+2X27Kezbdu2dSuzRALu09GT16Wl5ddSMDRVHQMAEDxz1s2xI1840gpKCuzI7Y60/438X/2/j1Tu+6Mc7Z6jUbRPbW//O/h/dsprp9gNn95gx+5wrG3XdjtyG0B0BDZefvllS0lJ2WIY+DvvvGObN28OWmXj0EMPrXjet29fGzBggB1wwAH2ySef2IgRI+q0LY34UCNRNPJ60mVkZNAITF43eDHPhISEoG5PWrZsWaf3cU43na3ldbDPCe8+rX9rs9212Zsrghpt05Ktd4e0KtNZ1+026Dz+42cv32ob2PD+PjD9wc7jli2SXFBj0tfzbU325qBtd9tO6TZ8x24hPydqm9excbGNck5458MJe21n7/60wmYtz7YlWSU2oHtriybcp6Mnr+vSMaG51DEAAMGxJm+NHfLcIbZ+03ob0nmIPT/qeYuLjc52l3Dz1x3/as/+8qwbuXHWm2fZJ6d9YrEx0dkZGECUBTa0gPf111+/xeuaIur//u//Gq3S0b59ezd6Y9GiRXV+ryp60dyz29v/aM6DpkJe1y/PyOfmq7me0+tyNtv8Vdnuebv0ZOvVvuqgRlOp+GxfGtxrtUxTU+ezghorNpT38g4GBZbCgZe/WtSyMbXLaGEH7dTV3v5+sb341Xwb2KPu02iGu+Z674hEoczrYH5mqOoYAIC6yyvMcyM1FmxYYL1a9bI3//KmpSZuvUMPmgd9fz98+MM24IEB9vniz+3R7x61v+/691AnCwDqpF7h2OXLl1vXrl23eF1BhxUrVlhj2bBhg9u+AhwAgOi1PrfArV/QXIIaQFWO26O3xcaYTZ+3xuavzCKTgGZaxwAA1E1RSZEd99Jx9s3SbywzOdPeHvO2dWjZgWwMMz1b9bT/HPAf9/yKD66wlbkrQ50kAGj8wIZ6Tc2ePXuL12fNmmWtWrWq9Xby8vLst99+cw/ROhp6rkqNfnfrrbfaDz/84F6fOnWqnXvuuZaZmWnDhw+vT7IBABFgY16BzVuRVTFKgKAGmqvOrVNtn36d3fMXvpwf6uQAzV6w6hgAgMadAvGMN86wd+a9Yy3iW9jkkybb9m23b/B2o3Xq8FC7YLcLbHCnwZZVkGWXvFfPRd8BIJwCG1r74qabbrJvvvnGzaurx9dff20333yzHXbYYbXezowZM+zoo492Dxk3bpx7fs8997gvtTlz5th5553nFhC/8sorrWfPnvbiiy/WeT5+AEBkyMovtDkrskwzyrdumVTtmhpAc3HCnn3cv5/PXGFL1+WGOjlAsxasOgYAoPGCGlOXTbVnf37W4mLibNLxk2xYt2EN3q7K82lplOs9rZJbWWlZqTWGwO1qTRRNSaX1NV6Y8YK9O+/dRvlcAGg2a2xcfPHFblTFaaedVrFIZmlpqR111FF2ySW1j/AOHTq0yl5ZnvHjx9cneQCACJS9qdDmLN9oWic3MzXR+nRMJ6iBZq93h3Tbfdv29s3c1fbSVwvskiN2DHWSgGYrWHUMAEDjmL9hvs1aO8s9P67fcW7haT0arMysuKTYtm3R2S5q+NbCXsvEli7QcPtXt9vS7KVB227X9K42dtjYLV4f3HmwXbjbhfa/qf+z8946z2acN8NSElKC9rkA0KwCG4mJifa///3PFi5c6IaGJycnW9++fa1Lly7BTyEAIOrlbCqy2cuyrLTMLCMl0bbpmGGxzXWkRmqqurOFOhVoRk7YaxsX2Pjg56U2Zp9trX1Gi1AnCWiWqGMAQPO1OGtxRVDjzhF32u9Zv7tAR1CUmRUVF1l8u3jK0T4KagQtj7fihv1vsEm/TbKFGxfajZ/eaOOGj2uSzwWAJp+KytOrVy875JBDbP/99yeoAQBoFHmbi2z28o1WWlZm6S0SrG+nDIvVisxAmOjXNdN27NHaikvL7OVvFoQ6OUCzRx0DAJoXLSr986qf3fMDeh5gl+zBKLpIk5aUZvcdcp97fvvXt9svq34JdZIAoHFGbGi+21deecXNf7tu3To3RNzv6aefrs9mAQCoZFNhsc1avtFKSsssLTnB+nZuRVADYenEvbaxn3+fZu98v9j+stc21io1KdRJApod6hgA0Pysy19n36/43j3vlt7NRm4zMtRJQiM5avuj7Ojtj7bXZr1mf5/8d/vijC/clFgAEFGBDS3q9+qrr9q+++5r2267LXOcAwCCrrC4xGYv22jFJWWWmhRvfTtnWFw4jNTYvNns5JPLnz/zjFlycqhThGZgl15t3WijOSuy7PVpi+zU/bcLdZKAZoc6BgA0L+s3rbdpy6a5Bac7pHawgR0GNmr7T3xhsdlxx5X/QDk6JO4ZeY99sOAD+3rp1/bod4/a33f9e2gSAgCNFdh466233BobCmwAABBsRcWlbk2NguJSS0qIs+06t7L4uDDpLVRSYjZpUvnzJ58MdWrQTKgR4IQ9+9iNk76316cvstHDeltqUkKokwU0K9QxAKD52LBpgwtqlJSVWNuUtrZLp10avfd+rBbUoxwdUt0yutl/9v+PXfzexXblh1e6URwdW3YMbaIAoBr1+lZKSEiw7t271+etAABsdaTGN3NXWX5hsSXExdr2XVpZQnyYBDWAGgzbvqN1a5NqeQXFNvnbxeQVEIA6BgA0D1mbs1xQo7i02Nq0aGNDOg+xuNi4UCcLTeQfu/3DBncabBs3b7RL37uUfAfQbNWrpeiMM85w62iUlZUFP0UAgKilBcJvffVHW5dT4NbS2K5LhiUnUIlCZIh1oza2cc9fnbrQBfEA/Ik6BgCEXnZBtn2z9BsrKi2yzORMG9KFoEa0URDr4cMfdiN0JsyYYO/Ney/USQKA4E1F9d1339nUqVPts88+c2tsxMdX3sx9991Xn80CAKKYguVfz15lvy3baFpKQ+sRMFUPIs3+AzrbU5/MtjXZm23KT0vtsME9Qp0koNmgjgEAoZVTkFMR1GiV3Mp267KbxcfWq9kIYW5w58F24W4X2v+m/s/Ofetcm3HeDEtJSAl1sgCg4SM20tPT7aCDDrLddtvNMjMzLS0trdIDAIC6Wr4+3wU1tBzh4D7tLCMlkUxExNFaMaP36O2ev/T1AispLQ11koBmgzoGAIRObmGuC2oUlhRaRlKGDe0y1BLiWA8smt2w/w3WNb2rLdy40P7z2X9CnRwA2EK9Qu/jxo2rz9sAAKjS6qxNtnR9nnt+3sj+tmx9nq3YkE9uISKNHNTdnv98njvHP5+50vYb0DnUSQKaBeoYABDaoEZBSYGlJabZ0K4ENWCWlpRm9x1ynx394tF221e32UkDT7IB7QeQNQCajXqvxlpcXGxfffWVvfDCC5abm+teW7VqleXllTdMAQBQG+tzC2zh6hz3fOeebezIIT3JOEQ0rRtz1B/n+YtfzWfNMsCHOgYANP2aGl8v+do2F2+2loktbfeuu1tiHCOnUe6o7Y+yo7Y7yi0k//fJf7fSMkYbAwjzERvLli2zv/3tb7ZixQorLCy0Pffc01q2bGmPPfaYFRQU2A033BD8lAJAM1rgWosAh8t2m7PsTYU2b2WWe94uPdkG925rYS8lxeyPgL97DlThiCE97KWv59uCVdn27fw1NmSb9uQToh51DABoWhs3b7SpS6e6NTXSk9Ld9FNJ8UkhOwyFSfGUo5uhew+51z5Y8IF9teQre+z7x+zswWeHOkkAUP/Axk033WQDBgyw119/3YYOHVrxutbduOaaa+qzSQAIGwo+TPp6vlv8N1jUqD96jz4WTfILim3O8iwrKzNrlZpovdqnWUwkBHa0D6mpoU4Fmrn0Fol26C7d7eVvFtqLX84nsAFQxwCAJrUuf51NXz7d9cT3FgoP+UgNytHNUreMbvafA/5jl7x3iV3xwRV25HZHWseWHUOdLACoX2Dju+++swkTJlhiYuUvvc6dO7vpqAAg0imowRoQ9VdQVGKzlm20ktIya5mcYNt2zIiMoAZQB6OG9rbXpy2yXxavt1+XrLf+3VqTf4hq1DEAoGmszltt3y7/1k0r1KZFGxvSZYjFx9areQhR4h+7/cOe+fkZ+37F93bpe5fa88c+H+okAUD91tgoKyuz0tIt59VbuXKlpdJLFQBQg6KSUhfU0L8tEuNsu84ZFhsbQUGNggKz004rf+g5UI226ck2fMeu7vnEL+eTT4h61DEABEtjrQMQCesLrMhZYdOXTXf70j61vRup0VyCGnFFxZSjmymdI48c/ojFxsTahBkT7P3574c6SQBQvxEbw4YNs6eeespuvPHGite0aPi9995r++67L9kKAKiSRmjMXr7RNheVWGJ8rG3XuZXFx9Urxt58FRebPfVU+fP77zdLCt08xWj+jhvW2977cYl9M3e1LVqdYz3bp4U6SUDIUMcAECxqfL39q9ttafbSoG2za3pXGztsrIUz5cdPK3+yMiuzTi072aBOg1xeNRdxJWWUo5uxwZ0H2wW7XWB3T73bzn3rXJtx7gxrkdAi1MkCEMXqFdi46qqr7JRTTrFDDz3ULR4+duxYW7RokWVmZtqdd94Z/FQCAMKeFkeftyLL8jYXW3xsjG3fpZUlJcSFOllASHVt09L22qGjff7bSpv41Xy7/OidOSKIWtQxAAS7EX/+BkZEeiPilBez1s5yP3dL72Y7dtiRqWBRZzfuf6NNmjnJFmxYYDd+dqPdfODN5CKA8ApsdOjQwS0cPnnyZJs5c6ablmr06NF2xBFHWHJycvBTCQAI+8rUwlU5tjG/0DTrVN/OraxFYvMY8g6E2vHD+rjAxsczltup+/W1Dq1SQp0kICSoYwBA8JWUltgvq3+xxVmL3c+9WvWyfu36EdRAvaQlpdl9h95nx7x4jN321W02ZuAY69++P7kJICTq3aqkAIaCGQAAbM2SdXm2Nmeze75NxwxLa5FApgF/UKBvUK+29sPCtTbpmwV2/sgB5A2iFnUMAAie7IJse3POmxVBjf7t+luvzF5kMRrk6O2PtiO3O9LemP2Gm5Lqk9M+aVZTmgGIHvUKbLz22ms1/v7oo4+ub3oAABFmxYZ895De7dMssyVrTgCBTtyzjwtsvPvDEhuz97bWKpXrBNGHOgYABHcqrsOeP8x+z/rd4mLi3HoaHVt2JIsRFPeMvMc+WPCBfb74c3vqx6fs9EGnk7MAwiOwcdNNN1X6ubi42DZt2mQJCQnWokULAhsAAEejNBavzXXPu7ZJtXYZLC4HVGWnnm2sb+cMm7M8y16ftshO3X87MgpRhzoGAASHFghXUGNZzjJLSUixXTrtYq2SW5G9CJoerXrY9ftdb5dNucw9jtjuCGub0pYcBtD8AxvTp0/f4jUtHn7dddfZmWeeGYx0AQDCXFZeoS1Yme2ed2jVwjpnsm4AUJ2YmBg7YVgfu3HS9/bGt4ts9LDelprElG2ILtQxAKDh3p77tp046UTLKcyxHdruYLt33d3W5K8haxF0Fw29yJ7+6Wm3hsvlUy63x496nFwG0KSCNglez5497Z///OcWPa0AANEnb3ORzVmRZWVm1rplkvVo2zJ6FihMSTFbvbr8oedALQ3bvqMb2ZS7udje/r58Lmwg2lHHAIDaKS0rtRs/vdEOf/5wF9TYv+f+9tWZX1l6UnrYZGFhUjzl6DCSEJdgDx/+sHv+xI9P2Ge/fxbqJAGIMkFd3ScuLs5WqyEHABC1NhcW26zlG620rMzSWyRYnw7p0RPUEO1ru3blj2jabzRYbEyMHT+sj3v+yjcLrbC4hFwFqGMAwFZt3LzRjn7haLv2k2utzMrsnMHn2Lt/fTf8pp+iHB129ui2h529y9nu+TmTz7HCksJQJwlAFKnXVFQffvhhpZ/LyspszZo19txzz9kuu+wSrLQBAMKMGmJnLdtoxSVllpIUb9t2yrDYWBr3gdo6YGAXe/rTObY2e7N98PMyO3SX7mQeogZ1DACouxmrZ9gxLx5j89bPs6S4JHvwsAdZyBlN6pbht9irs16139b+Zrd/dbtdvffVHAEAzTewcf7551f6WT1xW7dubbvvvrtdccUVwUobACCMFJeU2uzlWVZQXGpJCXG2fedWFh8X1IGB4aGgwOzSS8uf33mnWVJSqFOEMJIQF2vH7t7bHn5/pr309Xw7eOduFkdwEFEiWHUMrdUxfvx4mzFjhut8df/999vw4cMrfn/llVfaq6++Wuk9O+20k02cODEIewEATeeFGS/YmW+caflF+dYjo4e9fPzLNrjz4LA9BHFFxfoyKP+BcnTYyGyRaXcefKed/OrJduNnN9qJA0603pm9Q50sAFGgXoGNWbNmBT8lAICwVVpaZnNXZFl+QbHFx8XY9p0zLCE+CoMaUlxs9sAD5c//+18CG6izQwZ1s+c/n2vL1+fbl7NW2j79OpGLiArBqmPk5+fbdtttZ6NGjbILLrigyr/Ze++9bdy4cRU/JyQkBOWzAaApFJUU2RUfXGF3fXOX+3l47+E24dgJ1jalbVgfgLiSMsrRYWrMwDFunY2PFn5k5799vr190tvRNR0xgJCI0lYnAECwaDrC+auyLXtTkVsjQCM1khPrFTcHYGYtEuPtqCE9XV68+OU8d40BqL19993XLrnkEhsxYkS1f5OYmGjt2rWreLRqFWbz0AOIWgs2LLC9ntirIqhx1V5X2btj3g37oAbCm4IYDxz6gCXGJdq78961STMnhTpJAKJAvVqe/L2btuaqq66qz0cAAMKAGlwXrcm19bkFpv44fTtnWGoyvV6BhlJg46WvF9i8ldn2/YK1NrhPOzIVEa8p6xjTpk2zPfbYw9LT023IkCEuENKmTZsGbRMAGtvzvzzvFmjOKcxxC4M/fuTjdswOx5DxaBa2a7udXbnnlXbDZzfYRe9eZCP6jLCM5IxQJwtABKtXYGPmzJnuUVJSYr169XKvLVq0yGJjY61fv34Vf8ewMwCIbL8t22irsza55306pltGSmKokwREhPSURLdw+KtTF9qLX80nsIGo0FR1jH322cdGjhxpnTt3tqVLl9rdd99tp556qr3yyituJEddA/yMqmp8Xj6T11vnrg8N9AvmYD9vW4203cDjGozj3Zj5EIrzcMOmDa6h+NlfnnU/79VtL3t21LPWPaN7jekJl/OhTBty//+5QbdfQczrcMmLpthuY57DV+51pT0/43m3mP01H19j94y8Z8tkcE+PKhzv6FPWwGu8Lu+rV2DjgAMOsNTUVLv11lstI6M8+pqVleV6Tu266652xhln1GezAIAw8srUhTZneZZ73rNdmrVJSw51koCIMmpoL3tz+iL7adE6m7Vsg23fJTPUSQIaVVPVMQ499NCK53379rUBAwa4z/7kk09qnL6qKtnZ2S7wgsalCq7WThE6z1UvLi7O0tLSrLik2IqKi4KW/9qe+7e0cbabm5vrAprBOt6NnQ+B6W1sH/7+oV34wYW2PHe5xcbE2uW7XW7/3O2fFm/x7h4ZEedDmbk8LSn+M1/dvmntuiAIq7xoxO2mJaRZaVmpO4+CraS0xHJzct31e9u+t9kxrx5j90+730b1HmWDOgyq9Lfc06MLxzv6lDXwe7y0tLRxAxuPP/64e3gVDtHziy++2FU4CGwAQGSb8tNSe/j9me551zap1qFVi1AnCYg47TNa2AEDu9j7Py21F7+cb/8+ftdQJwloVKGqY7Rv396N3tDokLrSVFZqMEPj8nru6XwgsLF18XHxlhCfENTtuX9jG2e7LVu2bJTj3Vj5EJjexpJdkG2Xf3C5PfLdI+7nbVtva08d/ZTt3nX3iDsfvJEacfF/3k/dd0FqqgVTOORFY243o0WGC2rc/tXttjRradC22zWjq40dNtZ9J8pRA4+yk+ae5EZujP10rE09c6rFxf55bLmnRxeOd/Qpa+D3eF06D9QrsKEeCmvXrrVtt9220uvr1q2zvLy8+mwSABAmvpmzyu5882f3vE+HdGuTlhTqJAER67g9ertA4lezV9niNTnWvV1aqJMENJpQ1TE2bNhgK1ascAGOulJljYb2puHlNfldm8z64xG0zG/c7VZ1TINyvJswvcH2xuw37Ly3zrNlOcvczxfsdoHdMvwWS0lIicjzIaYsxm0rxrdBl8/BzuswyIum2O7S7KU2f+P8Rr027jj4Dntr7lv2/Yrv7YFvH7ALh15Y+S3c06MKxzv6xDTgGq/Le+o1/uyggw6yq6++2t59911buXKle+j5v/71rzoP3wYAhI9ffl9nN738vZWWldnwHbvYgO6ZNDAEatHCbOHC8oeeAw2gQMaw7Tq45xO/XkBeIqIFq46hIMhvv/3mHqJ1NPR8+fLl7nea6uqHH35wr0+dOtXOPfdcy8zMtOHDhzfi3gHA1i3PWW4nTDrBjnrhKBfU6JPZxz465SO755B76hfUCDNFifGUoyNEx5YdXTBOrvnoGluWXR6kA4BgqteIjeuvv95VCC677DIr/mPOQw3BHj16tF1++eVBTSAAoHmYtyLLrn3xWyssLrXdt21vlxy+oz36QXmjEXw013rPnmQJgub4PbexL2evso9+WWan7NvXTVEFRKJg1TFmzJhhp5xySsXP48aNc/8ec8wxdt1119mcOXPstddes5ycHGvXrp0NHTrU7rrrriabXgYAAhWVFNk9U++x6z69znILcy0uJs5N7fPvff9tLRKi53u/LDaGcnQEOXvw2fbkj0/a1GVT7ZL3LrGJx00MdZIARJh6BTZatGjhKgWqYCxevNi91r17d0tJifweBAAQjRatzrGrn59m+QXFNrB7a7v62F0sPo7FUoGmsH2XVrZzzzb246J19vI3C+zcg/uT8YhIwapjKFAxe/bsan8/fvz4BqcVAILlwwUf2oXvXmgz15SvXze0y1B78LAHbVCnygsuA+FG63k8fPjDNviRwfbSzJfsnbnv2CHbHhLqZAGIIA1qlVqzZo179OzZ01U4vMVBAACRY+n6fLvyuamWlV9o23bKsOtP2NWSElgotVqFhWaXXVb+0HMgCI7fs4/7950flrhrEYhk1DEARAMFMg57/jAb/sxw97xtSlsbf+R4++rMr6I2qBFXVEI5OsLs1HEnu2joRe75+W+fb/lF+aFOEoBoD2xogb1TTz3VDj74YDv77LNd5UM0/+0tt5TPoQcACH9L1uXaja/OsI15hW6h8JvH7GapyQmhTlbzVlRkdvvt5Q89B4Jgl15tXWCxoKjEXp+2iDxFRKKOASAaaOHmcyafYzs+uKO9Pfdti4+Nd4uDz/7HbDtj0Bmul3u0iisppRwdga7f/3rrmt7VFm5caP/57D+hTg6ACFKvb0zNUxsfH2+ffPKJJScnV7x+6KGH2ueffx7M9AEAQmTZ+jy78tmptjG/yHq1T7Nb/jrU0lskcjyAEIiJibEThpWP2nh9+iLbVFi+/gAQSahjAIhkK3NX2kXvXGTb3LONPfzdw1ZSVmLHbH+M/Xrer25x8NYtWoc6iUCjaJnY0u495F73/LavbrNfV/9KTgMIXWDjyy+/dIv6dezYsdLrPXr0sOXLlwcnZQCAkFmxId8uf+YbW5dTYF1bp9i4MbtZegpBDSCUhm3f0bq2TrXczUX2zvfl6w8AkYQ6BoBItDhrsV34zoXW++7eds+0e6ygpMD26bGPfXbaZ/bKCa9Y3zZ9Q51EoNEdvf3RduR2R1pxabGd9/Z5VlpWSq4DCE1gIz8/v9JIDf/w8cREGr4AIJyt2lge1Fibvdm6tUm1/zumv7VKTQp1soCoFxcbY8cN6+3y4eVvFlphcUnU5wkiC3UMAJHkl1W/2KmvnWp97ulj90671zYVb7Ldu+5uU06eYp+c+ont3WPvUCcRaFL3jLzHUhJS7PPFn9uEmRPIfQChCWwMGTLEXnvttUqvlZaW2vjx423o0KENTxUAICRWZ21yQQ39q57hmn6qFSM1gGbjgIFdrE1akq3N2Wwf/Lws1MkBgoo6BoBwp97oL8982fZ7cj/b8aEd7emfnnavHdjrQBfQ+OqMr2x47+Fuikkg2vRo1cOu2/c69/zaL661tflrQ50kAGEuvj5vuvzyy+3kk0+2GTNmWFFRkd122202b948y8rKsgkTiLoCQLiuqXHVs1NtVdYm65SZYreevLtrQM3KKgh10gD8ITE+zo4f1scefG+mvfDFPDtop66WEBe9i4wislDHABCu5q+fb0/8+IQ9+eOTtiynvONBXEycjdphlF027DIb0mVIqJMINAsX736xPfPzM/bL6l/s8g8utyeOeiLUSQIQbYGNbbbZxt544w0XxIiLi7NNmzbZQQcdZGPGjLH27dsHP5UAgEb1+5oct1D4+lytqZFq4/461NqmJ1tZWRk5DzQzhwzqbi9+Od8FIT/8eamNHNQ91EkCgoI6BoBwot7mr/z2ij3/y/P26e+fVrzeNqWtnb3L2XbukHOta3rXkKYRaG4S4hLswcMetL2e2MsFAk/f+XS35gwANElgQyM0zjjjDLvhhhvswgsvrNeHAgCaj7krsuzq56Za9qYi69U+zcaNGWqZLVlTo95atDCbMePP50CQJSXE2XF79LaHp/xmE76YZ8N37GrxjNpAmKOOASAcrMhZYZPnTLZXZr1iHyz4wE0zJTEWYyP6jLAzB53pFkhOiqcsXR9FifGUo6PAsG7D7NQBp9pTM56ycyafYz+e86MlxrFeL4AmCGwkJCTY3LlzmRMSACLA9wvW2o0vfWf5hcXWt3OG3XTSbpbegkJlg8TGmvXvH6xDBFTp0ME97MWv5tvKjZvsoxnLbMRO3cgphDXqGACao9KyUtu4eaMbnTF92XS7e+rdlX4/qOMgO6H/CXbSwJOsW0a3Bn1ObAxTS5bFxlCOjhL/3vPf9vaCt+23tb/Z7V/dblfvfXWTXxtcd0CUTkV19NFH26RJk2zs2LHBTxEAoEl8PGOZ3f76T1ZcWmY79mht152wq6UmJZD7QBhIToiz0Xv0tsc+mGXPfz7PDhzYxeIUVAPCGHUMAKGmERgKZGzYtMHWb1rvHiVlJZX+Zrcuu9mRfY+04/ofZ33b9A3K56rhVo27S7OXWrDs0mkXO2WnU4K2PSCYMpMz7Y4Rd9gpr51iN352o5044ETrndm7Sa4N0TRxY4fRpglEZWBDQ8Vfeukl++qrr2zAgAHWImCqjauuuipY6QMABJnWzXh16kI3jY3s06+TXXbUTm5RYgRBYaHZzTeXP7/6arPE2o2A0ZpVQF0cMbiHvfTVAluxId8+nrHcTUkFhDPqGACasjy8uXizZRdku0dOYY77N7cwd4u/1RQ5bVq0sX7t+tmzo561ji07Nkqa1HA7f8P8oG0vHNf3iCsqMbvuujqXoxGexgwcY0/+9KR9tPAjO//t8+3tk96ucnaYYF8bAKI0sLFkyRLr0qWLzZkzx/r16+deW7hwYaW/qeomBAChUFpWZrHckyopLim1B9/71SZ/t9j9fPRuPe3vI/qRTw3QMjmh8rlWVGR2/fXlzy+7rFYVMn13pqWlNSQZiELJifF27O697fGPZtmEz+fZ/gM0aoNyGMIPdQwAjTXNTH5RvntsKtpU/m/xJssrzHOBDG99jEAt4ltYZotM16O8TUobS0tMc2W1Ppl9Gi2ogXJxJaV1LkcjfOm6euDQB2zHh3a0d+e9a5NmTnIjoQCgUQIbI0aMsC+++MKeeeYZ9/PFF19s11xzjbVt27YumwGAJqGG5klfz7c12ZuDts1tO6Xb8B3Dcy773M1F9p9J39sPC9eamj7PPHB7N5UNAemGTwnkP9fiN+fbWX/87tEPZlpxckqttlNcXGzx8fERca6h6Rw5pIc795auz7NPf11uBwzsQvYj7FDHAFAfJaUltiJ3hS3csNAWbVzkHgs3Lqz49/eNv1uZlVX7fi343TKxpaUnpVtaUpr7V4/k+GQOCNBEtmu7nV2555V2w2c32EXvXmQHb3Owuw4BIOiBDQ3X9Pvss89s06ZNddkEADQpNTRrmpZgaZsWnhWdZevy7N8vTrcl6/IsKSHOrjx6Zxu2PT3OGuNcS9j85/fiyg2brCi59lOwaPHccD/X0LRaJMbbqN1725Mfz7bnP59r+/bvzKgNhB3qGABquj8sy1lmv635zWaumekWGtaUNApeKHBRVFpUY+Zpfn6NwEhJSLEWCX/8G9/CBTIU1GDBbiD0rtr7Knt+xvM2b/08u+aja+yeQ+4JdZIARPIaG9VVQgAAzc+Xs1ba7W/8ZPkFxdY2PdmuP35X26ZTRqiTBSCoozYWuMDl5zNX2H4DOpO3CGvUMYDoVFBcYD+s/MG+WfqNTVs8zeZlzbNZa2e5aaOqEx8bb90zulvPVj2tZ0ZP65XZyz3v1aqXPfHjE7YydyWjk4FmTqOkNCXViGdH2P3T73eL3u/aeddQJwtApAU2NF0JU5YAQHgoKS21Jz+eYxO/Kl9orX+3TPvXsbtYG0YCABElNSnBRg3tZU9/Osee+3yu7dO/E+vmIKxQxwCik9a4+HTRp/bBgg/sw4Uf2rfLv61yBEZcTJxt03ob26HdDtavbT/bts22LnChIEbntM4uuFGVl2a+RPsFECYO6nOQ/WXAX2zCjAl2zuRzbOrfplpcbFyokwUg0qaiuvLKKy3xjwWcCgsL7brrrrMWLVpU+rv77rsvuKkEANTJ6qxNdtvrP9rPv693P6vRU2tqxMfFkpNABDp6t5728jcLbPHaXPvit5W2T79OoU4SUGvUMYDoCmasyl3lRlJosWD1zvZrm9LWhnYZagNbD7Rduu1i/dv3d0GNxDgWkQYi3Z0H32lvz33bvlvxnT0w/QG7YOgFoU4SgEgKbBxzzDGVfj7yyCODnR4AQAN9NnOF3f3Wz5a7udgtbH3pETu6efcBRK7U5AQ7Zmgve/azufbsZ3Nsz+07stYGwgZ1DCDyg5dr89fa4qzFtipvlZWWlVb8rn1qexvee7gN7zXc9u25rxuJIVlZWZaRkcGICyCKdGzZ0cYdOM7Oe/s8+9dH/7JRO4yyLuldQp0sAJES2Bg3blzjpQQA0CC5m4vsofdn2pSflrqft+vcyq44Zmfr0jqVnG1CxYmJ9tB/nqx4DjQVBTZem7bQfl+Ta5/+utwOGEhFEOGBOgYQmYpKiuz3rN9dQCO/KL/i9dSEVOuU1snNof/8qOe3CF6wzk70KkqIM5s2rfyH5ORQJwch8Pdd/25P/fSUTV021S557xKbeNxEjgOAxlk8HADQfBYIv++dGbY+t8BiY8xO3HMbG7PPtkw9FQJlsXG2vE+/UHw0olzL5AQ7bo8+9sTHs916G3v362QJTD8HAAjBIuALNixwQQ1NPSVaB6NLWhe30Hd6UroLZnRI7cCIDFRSpnLLkCHkShSLjYm1hw5/yHZ9ZFe3Ts47c98JdZIANGMENgAgjK3L2WwPvverff7bSvdz19apdskRO9qA7q1DnTQAIVpr47Vpi2zFhnx794cldsSuPTgOAIAmG6Exf8N8F9TwpptKS0yz3pm93SLfLAQMoDZ27rizXTT0Irvzmzvt/LfPt0O2OYSMA1AlAhsAEIYKi0vs5W8W2gtfzLPNRSUWGxNjxw/r7UZpJMbHhTp5US2uuMh2f+cF9/ybQ060kviEUCcJUSQ5Md7+svc29sC7v9rzn8+1g3bq6tbaAQCgsSiIsSRric1eN9sKSwrda62SW7lFvxmVgbqIKyoxu+228h8uusiMaV2j1vX7X28TZ060hRsX2rTl09z6GwAQiMAGAISR0rIy+3zmChv/0SxbtXGTe22Hrq3sgkMGWJ+OGaFOHjR8urjYDn7+XpcX0w4aTWADTe7QXbrby98scPeIN6YvsuOH9eEoAAAaRdbmLPtp1U+WXZBdsX7GDu12IKCBeokrKTW7/PLyH847j8BGFGuZ2NLuPeReO+bF/2fvPsCjLLYGjp9t6YVAQgsdQu+IiiAgCvbeu9jFrqjX3j/LtTfs2HtXbNhQQenSe4dACCGkty3fcwY3NwkBkrCbbf/fwz55s9m8mZ1dkpn3zJxzoszZPEcObnewJEYnBrpZAIIMgQ0ACAFaRPHv5VtN3vzVWTsnjqmJMXLRod3lkN6tyU8MoJLW1Th3eFd57Kt58uHUVXL0wHYSH8POIQCA77jcLlmxfYWs2r5KPOIRh9UhXZt1lfZN2psc+QCwJzbb3ncUH9/teDm267Hy9fKvZcHWBTKkzRDmvQCqIbABAEHM5XbL1KVZ8vG0VbJ8c565Ly7KLicf2FFOGdLJpJ0BgJpG9UmXj6atkvXbCuWTv1bL+Yd0o5MAAD5RUFZgVlAXlBeYzzVFTO/mvSXGHhPWPazptTTtFoEbYN/+b1gsFklMTKzT43TXxncrv5PtJdtlY/5GaZvclu4HUIkrYgAQhIrKKmTyvI3y+fQ1suXflFPRDpucMLiDCWgkxUUFuokAgpjNapHzR3aV+z+ZI59NXyPH799BmsRHB7pZAIAQp7U0dOW0XsSMskWZgIYWBo+U1Dh64faxaY+ZC6y+MrDVQDmv33k+Ox8Q9P83PCJOl1PsNruIZe//Nw5MP1D+3PCnLM5eLC0SWpjfPQCgCGwAQBClm1qyaYd8N2e9TFm8Wcq0eJ6IJMU65Nj9Oshxg9tzYRJAnQ3t3lK6tk6W5Zl58v6fK+WKw3vRewCABimuKJYfV/0oS7YtMZ+nxqXKgJYDJNoeeUFzvXC7KneVz87XJqmNz84FhMT/DY9IhbNCHHbHHgMb3v8b/Vv2N7V8dJfYkuwl0q9lPx+2GkAoI7ABAAFWUu6UJRtz5eIJU2RjTlHl/e1SE8wq68P6tpEYx95zkAJAze37Yw/pLre+O10mzV4vJx3QUVo0iaOTAAD1ooXBR74xsjKo0a1ZN+nStAu57gE0CpvVJn1a9JFpG6bJhvwNJuDRLK4ZvQ+AwAYABGJnRmGpU3YUlUluUZmUlO/cmaGi7VYZ3rO1HDmwrfRsk8KEEcA+GdCxmfTr0Ezmrc2Rd/9YITccywo3AED9ghrPznhWdpTuMDU0dOW07tYAgMbUNLaptEtuJ+vz1pt0eMPbD6feDQACGwDQGDStVF5xueSXlJuPTpen8mu6+7Z5cqzJh39gtxYSH+3gRQlhzqgoef3OCZXHiCwJMQ5xezxitexhX30DNeS8O3dtdJPrJk4zdXtOGdLZ7AYDAGBvsouyZfbm2eJ0OyWjaYYc1PYgySnJoePgNxW6S/3XX3d+EhPexehRf91Tu8uWwi1SWF4oa3LXSOemnelGIMKRigoA/LAjo7jMKQWlFVKot5IKKXO6dynsmxwXJU3ioyQlPlrapibIoX3JrxsOPFabrO05KNDNQIBo2jgNPnzy1yrJzi/12XnTkmJMUKIherRJkQO7tpC/l2fJxF+Wyt2n7eezdgEAwtPmgs0yZ/Mc8YhHOqV0kr8v/lvum3IfgQ34lcdmFRk5kl5GrbRoeI/UHqbexvKc5dI6sbXEOmLpLSCCEdgAgH1U4XKb4IUGMTSYUVRaIe7/bciolBBjl6TYKEmKi5LEWIdfVnQDCA4a1NicWyzB4sJR3WTGiiyZtixLFqzfLn3aNQ10kwAAQVwAeN6WeSao0SqhlVwy8BKTBgYAAk3ra2g6qtzSXFmcvVgGtWZBGRDJCGwAQD13Y6zdWmBuWuhbgxmlFf+rkVF1R4ampNFbYoxD4mPsYtcVSAh7VqdT9vvlc3M8a9SJ4rbzpxaB1z4tUY4Y0E6+nbNeXp68WJ6+cCjBVQDALtbtWGfy13svIPZr0U/sVsYyaBxWp0vk+ed3fnLppSIOUvRi1zSrvZv3lj/W/yGbCzeblHlp8Wl0ExChGKEAwF4CGVrc21sfY+6abfLaL8tqTT+juzC8wYzYKBuFvyOUzVkhx0z8rzmeO/wYAhsIGueN6Cq/LtwkyzPzZMqiTDmkd3qgmwQACCLeoryqfXJ7c/FQLyICjcWu6XuvumrnJxdcQGADtUqOSZYOTTrI2h1rZeHWhaaQuM1qo7eACERgAwBqKC13yg4TyKiQAi30XSOvlAYxNHgRZbdKwr/BDAe7MQAEuZSEaDntoM7y5m/LZeIvy2Ro95YSZWcSCAAQySzIlPlZ801XdGzSUXqm9SSoASBodWvWzdQCKqookjU71kiXpl0C3SQAAUBeFAART3dlaEqpDdsKZf66HJm3brusyy6U3MIyE9TQWhha6Ltts3g5fnB7+ezmMTKsR0tT8FsLfxPUABAqTjqwk6QmxUhWXol8MWNtoJsDAAgCWYVZMnfzXHPcNqktQQ0AQc9hc0iPtB7meEXOCimpKAl0kwAEAIENABG9M0PrZGggY9GGXMnMLTZpp3TDvaaVatMsXnq2SZFBnVOle3oTad00XtKSYsVm5VcngNCkO84uGNnNHL//x0oTwAUARK7tJdtl9ubZplB468TW0rdFX3ZqAAgJ6Ynp0jS2qbg8LlmUvSjQzQEQAFydAxBxuzO2F5TK4o25JqCxaXuRlFW4xGoRaZoQLZ1bJMnATqkmoJHeNN4EOHTHBgCEi0P7pkvXVslSXO6UN37dtWYQACAyFJYXysxNM8XtcUvz+ObSv2V/ghoAQq6QuEUssqVwi2wt2hroJgFoZAQ2AEQEp8stmduLZO7aHFmxJV8KSirM/ZpiyhvMyGiVbFK02KmXASCMabD2iiN6meMf/tkgKzbnBbpJAIBGVu4qlxmbZkiFu0KaxDSRQa0GidXC5QEAoSUpOskUEldaSNzldgW6SQAaESMXAGGt3Oky6ab+WZsjG3KKpMLpFrvNIq1T4qR/h2YmxZQGM0gvBSCS6K60Q/uki0dEXvh+kdnNBgCIDHrhT3dqFFcUS6w9Vga3Hiw2qy3QzQKABunarKtE26LN77TVuavpRSCC2APdAADwV0Djyxlr5cd5G8Xp8lTmlm/dNE6aJcSIVXNPAX7gcjjknZueqDwGgtWFo7rL1KVbTGq+Xxdmyqg+6YFuEgDAzzSQPS9rnuSW5orD6pD90/eXaHs0/Y6gkJDYTNxff7Vz91A070vUvZB4z7SeMnfLXFmxfYWkJ6VLnCOO7gMiAIENAGE3WftjyRZ57eclsmVHibkvNspm6mVoDQ3Nwwn4k9tml+UDh9HJCHq6W+2MYV1MnY1Xf14iQ7q1kNgohoYAEM50NXNmQabJST+o9SBJjE4MdJOASnGxSWI97Fh5bNpjsvGnyT7rmYGtBsp5/c6jp8NY68TWsj5vveSU5MiirYtkcPrgQDcJQCMI6Ox15syZ8tprr8nChQslOztbnn/+eTnssMOqXaB87rnn5MMPP5T8/Hzp16+f3HXXXZKRkRHIZgMIUltyi+WpSQtk7ppt5vNmidHSPi1R7FYLAQ0AqMXJB3Y0dTY25xbLu7+vkIsP60E/AUCYyi7KliXblpjjXs17SWpcaqCbBNRqY/5GWZW7yme90yapDT0dIYXEf1/3u2QVZUlWYZa0SGgR6GYBCOcaG8XFxdKtWzcTrKjNK6+8IhMnTjRf/+STTyQ1NVXGjh0rhYWFjd5WAMHL5fbIZ9PXyKUv/W6CGlF2q5wzPENeHzdS2qUmENRAo7I6ndJ/yjfmpsdAMIuy2+TyMT3Nsf4eXbu1INBNAgD4geaen7N5TuVF3vbJ7elnBB2r0yXyxhuy/+TFYnO6A90chBjdgdYxpaM5XpS9iELiQAQIaGBjxIgRcv3118uYMWN2+Zru1njrrbfk8ssvN1/v2rWrPPLII1JaWirffPNNQNoLIPhk7SiWm976S176cbGUVbikb/um8uJlw+XcEV0lhpQqCACbs0JOevE+c9NjINgd2LWFHNSthQkSP/fdQgqJA0AYFguflTlLKtwVkhydLH2a92HhD4KSrcIlMnasnPXEZLET2EADC4nH2GNMMHfl9pX0IRDmAhrY2JONGzea9FTDhv0vT3lUVJQMHjxY5s6dG9C2AQgOWvR23Ct/yKINuRIXZZdrj+4jj5x7oKmnAQCoO921Ee2wyYL12+XnBZvoOgAII7pyOb8sX6JsUbJf6/3EZrUFukkA4Bd2q90UEleazqyovIieBsJY0FaI1KCGatasWbX7NR1VZmZmvc+nO0D0Fom8zz1Sn39jCue+9mfR7fr2V7nTJa/9tkp+XLDFfN6tdbLceuIAaZkSV+18oVgoPNjeO3t6T4di/wYrb//qR/q1kfpaguv/WqB/RzRPjpWzD+4ir/+yTF75aYns36W5JMY6Gny+cP57GGwC3de8xkBw00LhWlBXDWg5QGIdsYFuEgD4VauEVqaG0LbibSawO7j1YOZYQJgK2sCGV80LPA2dPGnxcas1aDeo+JX2mdYzUVwwo68bwmazSWJiojidTqmo8F1qHT2f0ro5LperTt+TV1wuj3+7VJZt3pkH/riB6XL6ge3Ebq2QvLw8v7fZ2079GOi+COTvj1DrX3+dt9ZzV0k/VeGskIqKuv2prfm6h1pfhNJ59Vxulztk2ttYvyMO7d5UfpgbK5tyS+SlH+bLJYd0afC5GHs0nkD3tdtNDnQgWOlK5flZ881xl6ZdJC0+LdBNAoBGKyQ+Ze0U2Vq01RQTb5nQkp4HwlDQBjbS0nYOurZt2ybNmzevvD8nJ8fs2qivpKQkcyEuEnmDQcnJyQQ26Ot9YrfbxeFw+PR8KiEhoU6PX52VL/d8slC25pVIbJRN/nNifzkgo0Wjttn7e0Q/BrIvguX3R6j0r7/OW9u5Ha7/FQx32B0idfh53n7W/vT2c6j1Raic19vXVps1JNrb2L8jrj2mr9z89nT5aWGWHDGwo/Ru17RB52Hs0XgC3dfBFIwHUL2uhhYLd7qdkhKTYvLOA0CkSIhKkM5NO5s6G4u2LpK0uDTS8AFhKGgDG23atDHBjalTp0rPnjvz45WXl8vMmTNl/Pjx9T6fTvQiebeC9/lHch80Fvq6YX22N9NXZMn/fTpXSitc0jolTsYf1U16dmwRdu/pYHw+vKcbp4+rfkQj9LWEZl/7+z3Sr0OqHDmgrXw3d4M8NWmBTLj0YImyN2xhCL87Gk8g+5rfW0BwWpazTPLK8sRhdcjAVgPFaonM7AUAIpfuVNuYv1FKnCUmwNEttVugmwTAxwI6uikqKpIlS5aYm7dguB5rDQ2dJJ133nny0ksvyeTJk2X58uVy6623SkxMjBxzzDGBbDaARvbz/I1yz4ezTVCjf8dm8vSFB0l60531NAAAvnXxYT2kaUK0bMwpkvf/WEn3AkCI0Zoaq3NXm+N+LftRVwNAxBYS75XWq7KQeGF5YaCbBCCcdmwsXLjQBC+8HnroIfPxxBNPlIcfflguueQSKSsrk3vvvdfkzu/Xr5+8/vrrQZeqBYD/fDFjjUz4YbE5PrRPutxwbF+xWS2SV15CtyMouRwO+fDa/6s8BkJNQoxDxh3RSx74ZI58OG2VDO/ZSjq2SAp0swAAdZBbkiuTV002x+2S25FXHiHF6bCJfPSRTPznDalgGA0f0NoamoYquzjbpKTaP31/dpsCYSSggY0DDjhAli1bttuv666Nq6++2twARF7O8Hd+X2Fu6oT9O8hlY3qK1WKpzCcOBCO3zS6LDjws0M0A9smw7i3loG4tZNqyLHnymwXy5NghYrOSxgQAgt24b8dJYUWhxDvipWfazpTOQKjwaPrLU0+VeYlTxZ27KtDNQRjQ64q9mveS39f9boIbFBIHwgszVABBqWpQ4/yRXeXyf4MaAIDGmQReeURviY+2y7LMHfLJXztTmgAAgtd7C96TDxZ+YOpI9W/Z36RhAYBIp4XEO6V0Mse6a8PldgW6SQB8hMAGgKDz3h//C2pcNrqHnHVwBttFETKsLqf0+vsnc9NjIFSlJsXI5YfvXO379pQVsnZrQaCbBADYjcyCTLny2yvN8QHpB0hKbAp9hZBjcbpEPv5Y+v2xQqwud6CbgzCS0TRDYu2xlYXEAYQHAhsAgsqHU1fJm78tN8cXH9pdTjpw58oKIFTYKirk9KdvMzc9BkLZ6L5t5ICM5lLhcstjX80TJxcZACDoaJrWy765THaU7pDBrQfL4PTBgW4S0CD2CpfIaafJ2P/7VhwVBDbgOzarrTI9nxYS110bbo9/3mP+Oi+AXbE3FUDQ+Gb2Onn9l6Xm+IJDusmpB3UOdJMAQCI9JdW1R/eRS1/8XVZszjPB57OHZwS6WQCAKt6Z/458s/wbibJFycTjJ8orc16hfwBgD4XEf1r9k0nb99i0x2Rj/kaf9VWbpDYy/qDx9D3QSAhsAAgKU5dukee/W2iOzzq4i5w5rEugmwQAEJFmiTFy5RG95JEv/pF3/1gh+2c0l4xWyfQNAASBzQWb5ZrvrzHH94y4xxTJBQDsvpD4lLVTZGnOUvly2ZcmqKE7OACEJlJRAQi4heu3y0OfzRW3R+Soge3kvBFdA90kAEAVh/RuLUO7txSX2yOPfD5XSjVVBAAgaFJQ7dd6P7lp6E28IgCwl0LinZvuzAxx3ffXSYWL1MFAKCOwASCgtBjt3R/ONPnbD+zaQq46sheFwgEgCFe4XXd0H2maEC0bcorklcmLA90kAIh4Hyz8QL5e/rVJQfXG8W+I3UpCBgDYmy5Nu0iTmCayLm+dzMqcRYcBIYzABoCAKatwyV0fzpTCUqf0bJMit540QGxWfi0BQDBKiouS8cf3M8ffzF4vfy/PCnSTACBi5RTnyLXfX2uO7zj4DlJQAUAdaRD4uK7HmePZm2dLUXkRfQeEKK4gAggIt8cjM1ZulawdJdIqJU7uPX0/iXHYeDUAIIgN6pQmJx3Q0Rw/8fV82V5YGugmAUBEuvHHG00B3N7Ne8stw24JdHMAIKTo784xnceIy+OShVsXmtR+AEIPgQ0AjU4HDZqCKqegTOKi7XLf6fuZlcBAOHDZHfLZ5XeZmx4D4WbsqG7SsXmi5BWXy6NfzDOBagBA45m8arK8Oe9NsYhFXjn2FZOKCggHLl3oNnGivHfDaHHauVwF/6ZZffbIZ8VqsZogcVYRO5GBUMRfCgCNTndpZOfvXOV720kDpF1aIq8Cwobbbpd/RhxjbnoMhJsou82kDoy2W2Xumm3ywZ8rA90kAIgYxRXFpmC4umr/q+TANgcGukmAz7jtNpELLpAZo3uKi8AG/Kxrs64ysNVAc7xo6yJxuV30ORBiCGwAaFT5JeWybluhOe7dNkUGd2neoPPYbKStAoBAaZ+WKFcd1dscvz1lucxfl8OLAQCN4L4p98maHWukbVJbeXDUg/Q5AOyD/VvvLzH2GClxlsjK7SzWAUINgQ0Ajabc6ZKVm/PNcbPEaOnXoVmDUpjottHExETzEQg2VpdTus7509z0GAhXY/q1lcP6povbI/Lw53NlR1FZta/r72h/Jaki/RWASKQrih//63Fz/PxRz0tiNLueEV4sTpfIpEnSc8YasbrcgW4OIoDD5pBeab3M8arcVRQSB0IMOTIANFpdjZVb8qXC5ZbYKJt0bJ4ksVF2sVos8slfqypTU9WV0+kU+x7S/GS0SpLD+rb1QcuB+rFVVMg5/73BHN8/cYq4bfypRfi6+sjesjwzT9ZvK5RHv/hH7j9zf7FZdwadrVZrg3/H70laUoycMqSzz84HAKEylh737Thxup1yfLfj5dhuxwa6SYDP2StcIsccI5eKyI+vHyFlNtbiwv9aJrSU1LhU2Va8TRZlL5LBrQeziBIIEVxtAdAoNuQUSUFJhVitFslolVx54UvpBa/NucX1Ol9FRYU4HLsvzJyaGLNP7QUA7F1MlF1uP3mgXPPanzJ79TZ5Z8pyOf+QbtUe05Df8cC+mDlzprz22muycOFCyc7Olueff14OO+ywaheIn3vuOfnwww8lPz9f+vXrJ3fddZdkZGTQ8Qha78x/R35f97vE2mPl6SOeDnRzACBs6C7j3s17y5S1U2Rr0VZTSFyDHQCCH+FvAH6XW1hWeVGrc/NEs1MDABAeOjRPlOuO6WuO3/tzpUxbtiXQTUKEKy4ulm7duplgRW1eeeUVmThxovn6J598IqmpqTJ27FgpLNxZAwwINrkluTJ+8nhzfNeIu6R9k/aBbhIAhJWEqATplNLJHFNIHAgdBDYA+L2uxuqsnXU1WjaJlabspACAsDOqT7qcsH8Hc/zfL+fJxhwuECNwRowYIddff72MGTNml6/pbo233npLLr/8cvP1rl27yiOPPCKlpaXyzTffBKS9wN7c8csdZhVx99TucsOQnekuAQC+ldEsg0LiQIhh2TQAv9GLB6u25IvT7ZG4aLu0bZZAbwNAmLrksB6yYnOeLNqQK/d/MkceOLWfUNYWwWbjxo0mPdWwYcMq74uKipLBgwfL3Llz5Ywzzqj3WEdv8C9vP0diX8/KnCUTZk0wx88f+bw4rI499oOmVBH9si+7ynsuP5235vPxxesdiv0Qyef16InMvyon9GW7Q6gvIuG8VV9vi8fis/PWSy3ntlvs0jO1p8zZMscUEm+T2Ebio+IbdN5I/Hu1O5H8NzxSefbxNa/P9xHYAOA3mn4qX+tqWES6tEwy9TUAAOHJbrOaehtXvfqnrMsulGe+Xyr3nXVAoJsFVKNBDdWsWbNq92s6qszMzHr3ltbosFrZBO9vOsHVFGOVF6wjhMvtksu+usxc/Du126kysOlAycvL2+3jbTabJCYmitPllApnhc/aoeczH93+Oa+mgXO5XD57vUO1HyL6vB4x74Gq74MKV4VUOD2R1xeRcN5/X2/D0vjt3dO5U2NSpVlMM8kpzZGFWxfKwJYDffJ7LZJF6t/wSObZx9fc7XbX+bEENgD4RWFphSkYrjqkUVcDACJBs8QYuevUQXLTW3/LzNU58tZvywPdJKBWNSdZDV1RlpSUZC6iwr+8r09ycnJEXRTRnRpzt86VpOgkefropyU5IblO32e32cVhd/isHXo+89Hqn/MmJCT45fUOtX6I5PN6d2pU/X3qsDnEbbdHXF9Ewnm9r7fdYRfLHiIb/mrv3s7dp0UfmbJuimSXZMv20u3SIqHFPv9ei2SR+jc8knn28TWvT1CQwAYAn3O5d6agUk0ToiU1KYZeRsRw2R3yzdibKo+BSNOjTYpce3QfeeyrefL+nytlv86pYmNFO4JEWlqa+bht2zZp3rx55f05OTlm10Z96WSNSXrj8PZ1pPR3VmGW3Pbzbeb4wVEPSqvEVnX/Zu0iX3aTxb/nre019cnrHWL9EMnnNemILCJuh13kuefkk8WfiNNh9V27Q6gvIuG83tfbBDUsAWjvXs6dEL2zkLimo1qYvVBS43Usa9vn32uRLNL+hkP26TWvz/ewbxqAz23YViilFS5x2K3SsXkif7wQUXRl2Ywxp5qbr1aZAaHmsL7pcvygNuZ4zuocs4sPCAZt2rQxwY2pU6dW3ldeXi4zZ86UAQMGBLRtQFU3/3Sz5JXlyYCWA+SK/a6gcxAR3HabyJVXyp/H9hOXnctVCBwKiQOhgb8UAHwqr6hcsvJKzHHn5kkm5zoAIPKcNbSj7J/RXNwejyzL3GEC3kBjKCoqkiVLlpibt2C4HmsNDV0Bdt5558lLL70kkydPluXLl8utt94qMTExcswxx/ACISj8uf5PeWveW2Y184vHvFj3lcIAAJ/QFFU903qaY925UVS+M802gODCFUcAPuN0uWXV1p0pqFokx0pyfBS9i4hjcbukw+LZ5qbHQKSyWS1y64kDJDkuSpwujyzbtEMqXHUvBAc01MKFC+WEE04wN/XQQw+Z42eeecZ8fskll8j5558v9957r5x88smSlZUlr7/+OvmwETQFw6/+7mpzfPHAi2X/9P0D3SSg0Vh0nPDbb9Jl/kaxun1TOBxoqFYJrSQ1LlXcHrcsyl5ERwJBiBwZAHxmXXahVDjdEuOwSdtUimUhMtnLy+XC+3emjLh/4hSpiIkNdJOAgImLtsuQrs3ll4WZZsfG8swd0iM9RaxW8uvCfw444ABZtmzZbr+uuzauvvpqcwOCzatzXpV/tvwjTWKamNoaQCSxlztFRh8iV4nIb68fIWUxXLJC4Oh4oXfz3jJl7RTZWrTV1D6qTyFxAP7Hjg0APpFbWCbbCkrNcacWSWalLgAAMVF26da6ifm7UFjqlJVb8sXjYRUmAOwyni7Jldt/ud0c3zfyPkmL31nsHgAQGAlROwuJK921obvqAAQPAhsAfJKCak12gTlu1SROEmMd9CoAoNrOja6tk8ViEcktKjM7/AhuAEB1d/92t+SU5EivtF5yxWAKhgNAMBUSL64oNvU2AAQPAhsA9tn6bf9LQdWmWTw9CgDYRVJslHRukWSOs/JKZMuOEnoJAP61IGuBvDDzBXP89BFPm8K1AIDgKiS+cvtKCokDQYTABoB9kldcLtn5O1NQdWyRSN50AMBuNUuMkXb/1mDSoHjOvykMASCS6Q62a7+/Vlwel5zc42Q5tNOhgW4SAGA3hcQXZy+mb4AgQWAjQthstkA3AWHI5fbImqx8c9w8OdasxgUAQFmttQ8zWzaJlRZNYs3xqqx8EyAHgEj26ZJP5de1v5pUJ4+NeSzQzQEA1FJIXNMEWsQiWUVZppA4gMAjsBHi3HUovqm/gBMTE81HX58bkW3T9iIpc7olym6VdqmkoAKAUOXrv/k65oiPj9/t19qnJkhKQrToj12euUMKSirqfO6EGIffxiiMfQA0Ns3ZfuOPN5rjW4beIh2adOBFAIAglBidSCFxIMiQuDPEWS0W+eSvVZWpgHbH6XSK3V73lzstKUZOGdLZBy1EuCouc8rm3GJz3KF5oth2szIXiDRuu11+OOvqymMgnMYT9dG5eYKMGdB+t8GNLi2SZLk7z+zYWJa5Q3q0aSLx0Y69nlfrOfmjvYx9AATCo1MflfV566Vdcju5eejNvAiIaC67VeTRR+XLZV/tPAaCsJD4poJNJiit9Ta6pXYLdJOAiMYVlzCgk3rvBebdqaioEIdj7xcLgLrmAV6ztcAcp8RHmxuAnVx2h0w99ly6A2E5nqiPlLg9jzusVotktEqWZZt2SEFphSzdtEN6tkmR2Ch7QNoLAI1t7Y618sjUR8zx42MelzhHHC8CIprbYRe5abz8+v0mceauCnRzgFoLiWtKqtmbZ5vARuvE1mYnB4DAIAQOoN70YlJhaYVZMds+bWcRWAAA6stmtUjX1skSF20Xp8sjSzbtkNIKFx0JICKM/3G8lDpL5ZAOh5ii4fgfakQCCFYtE1pK8/jm4hGPLNi6wCz8BBAYBDYA1EuF0y3rtxWa4zbN4iXaQWF6oCqL2yWtVy02Nz0GsGd2m1W6pzeR2Cib+RuzdFOulDv5vwMgvP28+mdTNNxmscnTRzxd73qI4aBJTBNxe9w+qxGJ0GdxuUVmzpS2y7aI1c3FYgQn/d3Uu3lv8/t7e8l22Zi/MdBNAiIWqagA1IsGNVxuj8RF2aVlk1h6D6j5h7W8XC6/4wJzfP/EKVIRw/8TYG8c/wY3Fm/MlbIKDW5ozY0Ucz8AhBun2ynXfn+tOb5ivyukT4s+EokSohLEarHKY9Meq35h0CPidDnFbrOLNCC2MbDVQDmv33k+bSsah73cKTJif7lRRP56/Qgpi+GSFYKTpg7s2qyrLNm2RBZnLzY7OKLtpOgGGht/JQDUWXZ+iWwr2FmotWMLVlEBAHwnym6T7ukpJrhRUu7aGdxIb2J2dABAOJkwc4Isyl4kzWKbyb2H3CuRToMaq6rWU/DoLvEKcdgdDQpstElq49P2AUBtOqZ0NIXE88vyTYCjf8v+dBTQyJgpAqiTCpdb5q3dbo6bJ8dKQgzF6AEAvhXjsP0bzLBIcZnTBDecmpYCAMJEdlG23PXbXeb4wVEPStPYpoFuEgCgAXTHWZ/mfSoDtNuKt9GPQCMjsAGgTj75a7UpGK4Xm9o2i6fXAAB+ERtllx7pKebvTRHBDQBh5o5f7pAdpTvMyt6LB14c6OYAAPZBSmyKtE9ub44XZC0wqQYBNB4CGwD2anNusbz3xwpz3D41kbQgAAC/iov+N7hhJbgBIHzM2TxHXpnzijl+9shnxWa1BbpJAIB91D21u0TboqWookhmZc6iP4FGRGADwB55PB55/vuFUu50S2pSjDRLpCAWAKBxghvd2zT5X3Ajk7RUAEJ7TH31d1eLRzxyZu8zZVi7YYFuEgDABxw2h/Rq3ssca2Bj6bal9CvQSAhsANijGSu3ysyV2ebCUr/2zcRiaUAFPwAAGiA+2vG/4EapU5Zl7pByp4u+BBBy3l3wrkzbME3iHfHy39H/DXRzAAA+1CqhlTSPby4uj0su/+ZycXuoEQc0BgIbAPZYMPylH5eY4xMP6CiJsRQMB/bGbbfLrydfbG56DMAHwY30JmKzWqSw1Cnf/7PRFBYHgFBRUFYgN0++2RzffvDtkp6UHugmAUHJZbeK3H23fH/2ATuPgRChC0B7N+8tdqtdpqybIq/M3pl2EIB/8ZcCwG59OWOtbNpeJCnx0XLmwV3oKaAOXHaH/HrKpeamxwD2XXyMQ3r8G9zYmlcid7w/wwTfASAUPPjHg7K5cLN0TuksNwy5IdDNAYKW22EXuece+f6cA8VJYAMhJs4RJwe1Pcgc3zT5JtmQtyHQTQLCHoENALXKLSyTd/8tGD52VDezYhYAgEAGN3TnRpTdKos25Mrfy7PE5Sa4ASC4rchZIU/89YQ5fuqIpyTaTr06AAhX/Vr0kyFthkhBeYFcPulyU18JgP8Q2ABQqzd+W2ZSfWS0SpbR/drQS0AdWdxuSduwytz0GIDvJMQ45MgBbU1h8ZyCMlmWmScuNxNGAMHruh+ukwp3hRzZ5Ug5OuPoQDcHCG46dl60SFquyxELf98RgqwWq7x23GsSZYuSb1d8a+orAfAfAhsAdrFyc578MHfntskrDu8pVgqGA3VmLy+Tq28+09z0GIBvpSXFykNn7y92m0UKSipMQXGCGwCC0aTlk8yFLYfVYXZraA52ALvn0BpavXvLfy5/R6LKXXQVQlKPtB5y94i7zfG1318rWYVZgW4SELYIbACoRrdKTvhxsej615G9Wkuvtk3poQhns9kC3QQAqKZ7eooc1K2FWK07gxvLCW4ACDJlzjKzW0Ndf+D10rVZ10A3CQDQSG466CYZ0HKAbC/ZLld9dxX9DvgJgQ0A1fy+eLMsXL9dou1WuejQ7vROGHDvQ15PXVmYmJjICkMAQadpQox0b93EBDfyfRDc0DRX+/L7ck/8dV4Aweupv5+SldtXSsuElnLH8DsC3RwAQCNy2Bzy+vGvi91ql08WfyKfLfmM/gf8wO6PkwIITaUVLnn156Xm+LShXaR5cmygmwQf0FRin/y1SrLzSxv0/U6nU+z26n8uMlolyWF92/L6AAioxFiHCW4s3bSjMrjR7d9gR33FOGz7/PuyNmlJMXLKkM4+Ox+A4Lcpf5Pc//v95vjRwx6VxOjEQDcJANDI+rfsL7cMvUUe/ONBGTdpnIzsMFKaxpIRA/AlAhsAKn3y12rZmlfy70WYTvRMGNGLdJtzixv0vRUVFeJwOKrdl5oY46OWAYAPghvpybJ0U54Jbizbh+DGvv6+BAB1y0+3SFFFkQxpM0TO7ns2nQIAEerO4Xea3RpLti2R63+4Xt484c1ANwkIK6SiAmBoQOOjqSvN8cWH9TArVwEACAWJsVEmuKE7LkxwY/MOce9DWioAaKip66fKuwveFYtY5NkjnxWrhSk3AESqaHu0vHbca+Zvwlvz3pLvVnwX6CYBYYVRFgDj9V+WSpnTLb3apsiInq3oFQBAyAU3unmDG8UVsnxzHsENAI3K5XbJ1d9dbY4vHnixDGo9iFcAACLckLZD5LoDrzPHF399sSkoDsA3CGwAkEUbtsuvCzNFk3ZccXgvCkUD+8Btt8ufx5xjbnoMoPEkVQY3RPKKywluAGhUr819TeZumStNYprIg6MepPeBenLZrSLjx8svJw/ceQyEiQdGPSDdmnWTzIJMuWLSFeLxsLMY8AX+UgARzu3xyIQfFpvjwwe0lYxWyYFuEhDSXHaH/Hj2NeamxwACENzQGhsENwA0Il2Be9vPt5nj+0beJ2nxafQ/UE9uh13kv/+Vry4+WJwENhBG4hxx8s5J74jdapePFn0k7y98P9BNAsICgQ0gwv00f6Os2JwncVF2uWBkt0A3BwCAfZYUFyVdqwQ39O8cNTcA+NPdv94tOSU50iutl1wx+Ao6GwBQzX6t95O7ht9ljsdNGicb8jbQQ8A+IrABRLCisgp5/edl5vis4V0kJSE60E0CQp7F7ZYm2ZnmpscAAiO5SnBjhwY3tuSZXYoA4GvztsyTF2a9YI6fOfIZsyIXQAPo2HntWmmalS8WN3+zEX5uPfhWOSD9AMkry5MLvrxA3B7mi8C+ILABRLAP/lwluUVlkt40Xk7Yv2OgmwOEBXt5mdxwzQnmpscAAh/csGhwo6hcVm3JJ6cxAJ/Si1Ljvh1nPp7a81QZ1XEUPQw0kKPMKdKxo9x1wUSJKnfRjwg7Gvh++8S3TWqqX9b8Is9MfybQTQJCGoENIEJt2l4kn09fY44vHd1DHDZ+HQAAwjS40SrZBDe2F5bJ6qwCghsAfOaNf96QaRumSUJUgjx5+JP0LABgjzKaZcgTY54wx//56T+yaOsiegxoIK5kAhHqlclLpMLllkGd0+SAjOaBbg4AAH7TJD5aurRMNsfbCkpl7VaCGwD2XU5xjtw8+WZzfO/IeyU9KZ1uBQDs1aWDLpWjMo6SMleZnPP5OVLuKqfXgAYgsAFEoDmrt8lfy7PEarHI5aN7iEWXsQIAEMaaJmhwI8kcb80vlfXbCtm5AWCf6EpbLRjep3kfuXr/q+lNAECd6DWY1457TZrFNpN/tvwj9/x2Dz0HNACBDSDCuNxuefHHnVsdjxvcXtqlJQa6SQAANIpmiTHSqfnOv3tbdpTIxpwieh5Ag/y14S95de6r5viFo18Qh81BTwIA6qxlQkt5+diXzfEjUx+R39f9Tu8B9URgA4gwk2avl3XZhZIU65BzhncNdHMAAGhUacmx0iEtwRxn5habmlMAUB9Ot1OumHSFOR7bf6wMazeMDgQA1NtJPU4yf0fcHrec9elZJsUhgLojsAFEkPzicnnzt+Xm+LyR3SQxlpVlAIDI06JJnLRL3Rnc0F0bW3KLA90kACHk+RnPy7yseZISkyKPHPZIoJsDAAhhzxz5jHRr1k02FWySC768gFSpQD0Q2AAiyNu/L5fC0grp2DxRjhrYNtDNAcKS22aT6aNPMTc9BhCcWqXESXrTeHO8bluhrNicF+gmAQgBmQWZcuevd5rjhw97WNLi0wLdJCBsuG1WkXHj5I9j+orLRh1IRIaEqAT54JQPJNoWLd8s/0aemf5MoJsEhAwCG0CEWLu1QL6Ztd4cXz6mp9is/PcH/MHliJJJF95sbnoMIHilN42Tlk1izfHvSzbL9BVZgW4SgAbQFB6Ndd4bf7xRCsoL5ID0A+TigRf75ecCkcoVZRd5/nn59MpDxOlggRAiR/+W/eXxMY+b45sm3ySzM2cHuklASLAHugEA/M/j8chLkxeL2+ORod1aSP+OqXQ7ACDiWSwWk5LK6fLItoJSeeCTObJ/RvOI7xcg1FgtVnls2mOyMX+jz87ZJqmNjD9ofLX7flr9k3yw8APz8yYcPcF8BADAF8YNHic/r/lZPl/6uZz68aky+9LZkhKbQucCe0BgA4gAfy/fKnNWbxOHzSqXjO4Z6OYA4c3jkbiCHeawOLGJiIVt9ECwBzc6tkiU2GibbNhWJH8vz5Lu6U0kPpo6VEAo0aDGqtxVfjt/qbNUxk0aZ46vGnyVDGg1wG8/C4hYHo9IdrbE7yjeecw4GhE2Jn3tuNdk7pa5smbHGlNv44vTvzD3A6gdS0yAMFfudJndGuqkAzuanOIA/MdRVir/uexwc9NjAMHParHIob3TpXe7pmb3xrJNeVJa7gx0swAEkfun3C8rtq+QVgmt5L5D7gt0c4Cw5CitEGneXB488xWJLnMFujlAo9MdGp+c+olE2aLkq2VfyX+n/ZdXAdgDAhtAmPtixlrZnFssTROi5YyhXQLdHAAAgpLdZpV7T99PkuIcUuFyy9JNO8ziAACYt2WePDrtUdMRLxz9giTHJNMpAAC/GNR6kDxzxM4C4rf9fJtMWTuFngZ2g8AGEMa2F5bK+3+sNMcXjuoucdFknwMAYHcSYhxyUNcWEu2wSZlzZ3DD6fJPUWIAocHldsnFX18sTrdTTu5xspzQ/YRANwkAEOYuHXSpnNv3XHF5XHL6J6fLpvxNgW4SEJQIbABh7I1fl0lxuVO6tW4ih/ZND3RzAAAIejFRdlNjQ+tSlZS7ZPnmPHG7PYFuFoAAeXr60zIrc5YkRyfLs0c+y+sAAPA7rasx4egJ0qd5H8kqypKTPzpZypxl9DxQA4ENIEwtz9whP/6z0RxfcXhPkz8cAADsXYzDZoIbNqtFCkoqZFVWvni0iCmAiLI6d7Xc8csd5vixMY9Jq8RWgW4SACBCxEfFy+enfy4pMSkyfdN0GTdpHONRoAYCG0AY0osvL/64WPQSzKF90qVHm5RANwkAgJCi6RszWiWLLgvYXlgmG7YVBbpJABp5PH35N5dLibNERnYYKRcNuIj+BwA0qs5NO8sHp3wgVotVXv/ndZkwawKvAFAFgQ0gDE1ZtFkWbcg1OcK1tgYAAKi/5Lgo6dQiyRxv3lEsW3YU041AhFi6balMXj1Zom3R8vIxL5u0IAAANLYxncfIw4c+bI6v/f5aiokDVRDYAMJMablTXvl5iTk+Y2hnSU2KCXSTgIjittlk7vCjzU2PAYQ2/Tvatlm8OV6XXSjbC0oD3SQAfqZ5zH9f/7s5vmfkPZLRLIM+BxqB22YVOf98mXFYD3HZCCYCXuMPGi9n9D5DnG6nnPTRSbJy+0o6ByCwAYSfD6aukm35pdKiSaycfGCnQDcHiDguR5R8fsXd5qbHAEJfq5Q4aZ4ca45XZuVLQUl5oJsEwI8WZS+SUmep9GvRT24cciN9DTQSV5Rd5I035L0bx4jTwQIhwEt3Db5+3Ouyf/r+sr1kuxzz3jGSW5JLByHisWMDCCObthfJJ3+tNseXj+5pUlEBAIB9n0x2SEuQJvFRojXEl2XmSUm5k24FwlBWYZZkFmSKRSzy6nGvisPmCHSTAACQWEesfHnGl9I2qa0sy1kmp358qlS4KugZRDQCG0AYeenHxVLhcsugTqkypFuLQDcHiEwejzhKS8zNXAEFEDbBjS4tkyU+xi4ut0eWbdohFU53oJsFwIfKXeUyP2u+OR7QcoDs13o/+hdoTDp2LiqSqNIKxtFALVomtJRvzvpGEqIS5Oc1P8u4SePEw5wTEYzABhAmZqzYKtNXbBWb1SKXH96LAodAgDjKSuXOsSPMTY8BhA/9G9utVROzI7LM6Zblm3eI200AEwgXC7culDJXmcQ74mVI2yGBbg4QcRwa0EhIkEdPfEGiy1yBbg4QlPq26Cvvn/y+WC1WeXXuq3L/7/cHuklAwAR1YOPZZ5+Vbt26VbsNHTo00M0Cgk650yUTflxkjk86oKO0S00IdJMAAAhLDrtVurVONkGOwlKnrMrKZ6UcEAY0/ZQ3BVX/lv3FbrUHukkAANTqmK7HyHNHPmeO7/7tbnl97uv0FCJS0I/WMjIyZOLEiZWf22zUDABq+nz6GsncXixNE6LlrIMz6CAAAPwoNsouXVsly9JNO2R7YZls3F4kbZuxqAAIVVoofEHWAnPcpWkXSYlN8evPc3vcZqUtAAANdcXgK2RD/gZ56M+H5NKvL5VWCa3kyIwj6VBElKAPbGggIy0tLdDNAIJWdn6JvPvHSnN88aHdJS466P9bAwAQ8pLioqRji0RZnVVgFhfEOGySlhQb6GYBqCfNTa51NSrcFZIUnSQZzfy/SEiDGo9Ne0w25m/06XkHthoo5/U7z6fnBAAErwdHPWj+lrw9/2055eNT5KdzfyKVIiJK0F8BXbdunQwbNkyioqKkX79+csMNN0jbtm0D3SwgaLz601Ipq3BJr7YpMqpPeqCbAwBAxNBARmm5SzJzi2VNVoGpvZEUGxXoZgGoB70gtLVoqwk2aAqqxtpJoT93Ve4qn56zTVIbn54PABDcLBaLvHrcq+bv2A+rfpCj3jtKfr/gd+nTok+gmwY0iqAObPTt21ceeeQR6dChg+Tk5MiECRPkjDPOkG+++UZSUlLqvRJHb+H4S2xvvM9bP9bl8bV9L+reX435Xpu/Lkd+W5QpVovIuMN7VbbBH+r73vGnfXlPBzt/vH770kfh3NfBhH4OQF9LaP59C5Xf8ZE0fmjTLF5KK1wmJdWKzDyz0CAmyt5ofdLYY4/afj4QqoorimVR9s46dV2bdTU7NgAACCVRtij59LRPZcw7Y2Tahmnm4x9j/zCpFYFwF9SBjREjRlT7vH///jJ69Gj54osvZOzYsfU6V35+vlitgcljqhcLEhMT/fbznU6nVFRU7PExLper3udUhYWF9f7eSKaT++LiYnPs74vALrdHnvt2Zy7gQ3u3lNRYj3mfB/q9Vh/e95Z+rO959/a+3JdzB+K8MXaLuD0esfrpfbMvr11tfR1q/euv89Z6buf/zl/hrJCKCnuD+jnU+iKUzqvncrvcPj+v93yh+DvCH7/j3e7QeU/s63nbNdWdG04pLneZuhvdWiWK3WZtlDFVY449auN27/y/BIQa/b8zb8s8cbqdkhKTIp1TOge6SQAANEh8VLx8c+Y3MvLNkSa94ui3R5vgBjv5EO6COrBRU1xcnHTt2lXWrl1b7+9NSkoKaOFxnWh+8tcqyc4v9dk5M1olyWF924rdbheHw7HXlXT6uLpOePWxKiGBQpj14e3r5ORkv19c+HLmWlmfUyyJsQ65ZHRvk+s70O+1+vL+n9SP9TlvXd7TDT333vjrvAmx0eaCZbC9drvr61DrX3+dt7Zz2z1uWXjAKHOfPSpapA4/r7Z+DrW+CJXzevva+u9F52Bvr79/R/jrd7z2s9UaGu8JX523e3qKLNywXcqcblmzrVi6pzcxr5m/x1SNOfaoDQtgEKrW7lgrOSU5YrPYTAoqdqYCgefWVASnnCL/bPln5zGAOkuJTZEfz/lRhk0cJiu3r5RD3jxEplwwRVontqYXEbZCKrBRXl4uq1atkkGDBtX7e3WgGujBql6I2Jy7c0WdL6QmxtTpcd7n3ZDnH+g+C0Xe95o/+y6noFTe/HW5Ob7gkG6SHB8dFO+1xrIv7+lgF2yvXTj3tb84o6Llo+sertf30M+Np7KvJTTf077+HeGv3/GR+DvDYbdKt9ZNZNHGXCkoqZB12YXSsXlio/RJY4w99vSzgVBTUFYgS7YtMcc90nqYla4AAs8V7RD5+GN54/vrpMLHNWiASNAioYX8fN7PMuKNEdWCGy0TWga6aYBfBCY3Ux1pfY0ZM2bIhg0bZN68eXLNNdeYbfwnnnhioJsGBNQL3y+S4nKnWQ161MB2vBoAAASBuGi7dGm5M0f/1rwSydrh2yAUgH3ncrtkzuY54va4JS0uTdont6dbAQBho11yO/n1/F/Nx+U5y2XUm6MkqzAr0M0CIi+wsWXLFrnhhhvkyCOPlKuuusqkBfjoo48kPT090E0DAubv5Vny59ItJr3FtUf38Wu+dQAAUD8p8dHSNnXn6u+12YWSnVdCFwJBZHH2YikoLzDFVvu17MeuIwBA2OnQpIP8ct4vkp6YbnYo6s6NzILMQDcLiKxUVE8++WSgmwAElZJypzz//SJzfPKBHaVTi52rQgEED0dpidw5doQ5vn/iFKmIiQ10kwA0slZN4qSkzCXbCkplxsps2ZRTJOnNSHUDBNrmgs2yLm+dOR7QcoDE2IMr3SoQ6Rwl5ZrjUJ4SkVNeP0LKYoL6khUQ1Do37Wx2box6a5QJbmh6Kk1TpTs5gHAR1Ds2AFT39pTlJrVFiyaxcs7wDLoHAIAgpHUntL5GQoxdKlxuufvDmVJUWhHoZgERrbiiWOZnzTfHnVM6S1p8WqCbBACAX2U0y5DfL/jd7ODQmhvDJw6XVdupX4PwQWADCBErN+fJ59PXmuOrj+wtMVGsXgEAIFhZrRbJaJUssVE22ZBTJP/32VxxuT2BbhZE5Nlnn5Vu3bpVuw0dOpS+CWNaT2Pu5rlS4a6QJjFNpFtqt0A3CQCARtExpaMJbmQ0zTC7Foe/MVwWbl1I7yMsENgAQoBeCHn62wXi9nhkeM9WMrhL80A3CQAA7EWU3SYHZDSXaLtVZq3Kltd+XkKfBYmMjAz5888/K29ff/11oJsEP9LiqbmluWK32mVgq4FitTANBgBEjrbJbeX3sb9Lr7ReptaG7tyYtmFaoJsF7DNGdEAI+GbWWlmemSfx0Xa5fEzPQDcHAADUUZP4aBl/fH9z/Onfa+SHfzbQd0HAZrNJWlpa5a1p06aBbhL8ZFvxNpN+Q/Vt0VfiHHH0NQAg4rRMaGmCG0PaDDHB/sPeOky+XfFtoJsF7BMCG0CQy84vkTd+XW6OLzy0uzRLpMghAAChRHdbemtjPTNpgSzasD3QTYp469atk2HDhsmoUaPk+uuvlw0bCDiFozJnmUlBpbRYauvE1oFuEgAAAdM0tqn8dN5PclTGUVLiLJHj3j9OXp3zKq8IQhZJ+oEgN+GHxVJc7pQe6U3kqIHtAt0cAADQAGcPz5B12QXyx5Itct/Hs+WZC4dKiyasHA+Evn37yiOPPCIdOnSQnJwcmTBhgpxxxhnyzTffSEpKSr3O5fF4zA3+5e3n3fW1xWIR0S9V+bI+9p8t/0iZq0wSohKkV2qval/f+w/933l8rbb2+oT3fL4+dyOf16OfmH8esXgsPjvvPuO8fuuHqq95tf721evHaxdU/VDn/+P+aq8/z+3Hvx2+EmuPlc9P+1wu/vpieXv+23LJ15fIsm3L5OHDHvZLqsa9/Q1H+PHs42ten+8jsAEEsWlLt8jUpVvEZrXItUf3EatOggAENY/VKsv7D608BgClf8PHH9dPMrcXy6qsfLnno9nyxAVDJDaK4XhjGzFiRLXP+/fvL6NHj5YvvvhCxo4dW69z5efni5Xf9X6nE9zi4uL/BQVqpBVLTEwUp8spFc6KyvtX5K6Q7OJsc5Gmb1pfcbvd5lZXej5VWFgoLpfLZ89ld+31BW+bnW7fnrvRz+uR//W5JYL7IZLO++9rXuFxixx1lCzcukjKzPk9kdcXkXDeOv4f91d7/Xluf/3t8IenRz4tbeLayEN/PySP/fWYrMxdKe+c+I7ER8X75Pwut0sKCwrN397d/Q1H5I3b6qI+4zVmUkCQyi8ul2e+XWiOTzmwk3RskRToJgGoA2dUtLxzy5P0FYBdxETZ5Z7T95NrXpsqq7Py5b9fzpM7TtFCxkzyAikuLk66du0qa9eurff3JiUlmQvV8C/vyr3k5OTdTpDtNrs47A5zvKVwi6zesdoc923eV5rG1b+Gip5PJSQk7EPL93x+b3t9eU7z0erbczf2eb2r9u0Ou1gaENkIl36IpPNW7tSIixGZNEle/e46kdxV4ojAvoiE89b1/7i/2uvPc/v7b4evPTjmQenTuo+c98V58sXSL6TT053k2K7Hmp2O+6JNchsZf9B4M06qy99whBfPPr7m9QkKEtgAgtSEHxZJblGZtEtNkHNG7MzLDQAAQlvz5Fi567RBcvNbf5tdmW9PWS7nj+wW6GZFtPLyclm1apUMGjSo3t+rkzUm6Y3D29e77W+92yJSWF4o/2T9Y+7q2KSjubjSsB/4v5/rF/+21+fn9Me5G/m8JjWN+bfzo6/Ou884r9/6odpr7uNzV56P8wZNP9T5/7i/2uvPc/v7b4cfnNnnTPl86efy5bIvZWvxVnlnwTuyf/r+khyT7LN+2OvfcIQdyz685vX5HnJkAEFo2rIt8svCTLFaRG48rq9E2VkJCABAuOjZJsWkmFTv/bFSfluUGegmRRStrzFjxgxTMHzevHlyzTXXmJQRJ554YqCbhn1U4aqQWZmzTGoRLZDaI60HfQoAwF60Tmwtw9oNMzs1tDbVtA3TzO5HINgR2ACCTH5JuTwzaWcKqpMP7CTd0+tXxBJAYDlKS+SOC4abmx4DQG1G92sjpw7pZI4f/2qeLM/cQUc1ki1btsgNN9wgRx55pFx11VXicDjko48+kvT0dF6DEOYtFq47NmLsMTKo1SC/FEEF4D+OknKR+Hh55ITnJbp0Z60CAI0jzhEnQ9sOlbS4NHF5XGahwNJtSyn6jaBGKiogyDz/3c4UVG2bxct5I7sGujkAGiCqrJR+A7BXY0d1l3XbCmXGiq1yz0ez5NmLhkmzxBh6zs+efJI6SOFoZuZMySrKMsEMDWpE26MD3SQADVFcLPzvBQLDYXPI4PTBsjh7sazdsVZWbl8pO0p3yICWA/i7iqDEEhYgiPy6cJNJR6FFRMcf348UVAAAhDGb1SL/ObG/qaeVU1BmghtlFXUvlgdgp+9WfCd/bfzLHPdu3ltSYtnxDABAQ+gCAf1bqsEMm8Um24q3yR/r/5Dcklw6FEGHwAYQJLbmlciz3+5MQXXmsC6koAIAIALERzvk3tP3k8RYhyzPzJMnvp7Pln+gHlZtXyVnfXaWOW6X3M7cAADAvklPSjd1N+Id8VLqLDV1N9bkrmGciqBCYAMIAm6PRx77ap4UlTmlW+smctbBXQLdJAAA0EhaN42XO08ZZHZw6M7N9/9cSd8DdaCrR495/xiTJqNlQkvpldaLfgMAwEcSoxPl4PYHS6uEVuIRjyzKXiRzt8wVp5saOAgOBDaAIPD59DUyb22ORDtscssJ/cVu478mAACRpF+HZnLVkb3N8Zu/LZc/l2wOdJOAoFbuKpeTPzrZFDZtk9RGjs44WmxWW6CbBQBAWLFb7TKw1UDpmdZTLGKRzIJM+WPdH2ZRARBoXD0FAmzF5jyZ+Msyc3zZ6B6S3iw+0E0CAAABcNTAdnLC/h3M8aNfzpNVW/J4HYBaeDweufTrS+XXtb9KYlSiTDprkiREJdBXAAD4gcVikU4pnWRI2yESY4+Roooimbp+qikurn+TgUAhsAEEUFFZhfzfZ3OkwuWWIV1bmAsaAEKbx2qRNT0GmpseA0B9XDq6hwzqlGqKiN/94SzZXlhKBwI1PPD7A/LmvDdNUdOPT/1Y+rboSx8BYcCMnUeMkJV90hlHA0GoaWxTGd5+eGVqKt01+dfGv6SkoiTQTUOEIrABBIhGtZ+ZtFAytxdL8+RYueG4viYKDiC0OaNiZOJdL5qbHgNAfdisVrnt5IHSpmm8ZOeXyn0fzZZyp4tOBP717vx35a7f7jLHzx/1vBze5XD6BggTzmiHyG+/yXOPniLlUaSWA4JRlC3KpKbq16KfWWCwvWS7/L7ud5OiCmhsBDaAAPlu7gZTINRqscitJw2QpNgoXgsAACAJMQ6594z9JCHGLks27ZCnvlnANn9AxOT0vvCrC01f3HTQTXLZfpfRLwAANDJdlNs2ua0pLJ4cnSwV7gqZs3mO/LPlH6lwVfB6oNEQ2AACYNWWfJnwwyJzPHZUN+nZJoXXAQAAVGrTLEFuP3mQWQDx84JN8tG01fQOItrq/NVy4kcn7iwa3uNkefiwhwPdJAAAIprWtxrabqh0adrFfL4xf6PZvbEub121x9ls7MCCfxDYABpZfkm53PfxLCl3umVwlzQ5ZUgnXgMgjDhKS+SWS8eYmx4DQEMN7JQqVxze0xxP/GWp/LUsi85ESHN73A36vuzibDn9y9NNuosD0g+Qt098W6wW/05lm8Q0aXB7ATSMo6RcJC1NHjj9ZYkuddKNCDn+/NvhcgdnalL9e9w9tbsMaTNE4hxxUuIskS+WfiGXfn2p5Jflm90diYmJPku9zt9mVGWv9hkAv3K5PfLwZ3Nly44SadkkVm4+ob9ZiQkgvMQX7Ah0EwCEieMGd5B12QXyzez18sgXc+XJCw6Sji2SAt0soMEXPx6b9phZ0VlXZc4y+XTJpya4kRSVZAqF3/rzrZVf1zzf5/U7zy+rUBvS3r3xV3uBsLFtmyQEug1AkP7tCOa/Sc3impnC4lpQfO2OtfLKnFfk48Ufy8j2IyU9IV3sNrvIPl7+apPURsYfNN4n7UV4ILABNKI3f10ms1dvk2iHTe4+bT/qagAAgL264vBesjGnSP5ZmyN3fzhLnr5wqKQkRNNzCEl6QWZV7qo6Pdbpdsr0jdMltzS3sljp5sLNu1zkCJb21oW/2wsACDx//e0I9r9JdqtdejfvLUd0PkJ+WPWDrNmxRr5Y9oW0SWwjvdJ6icPu8OnPA0hFBTSSKYsy5cNpO/8A3XBMX+nEaksAAFAHdptVbj9loLRuGidZeSVy70ezpKwiONMRAL5MuTE7c7YJajisDtmv5X5mJSwAAAhunZt2lvlXzJd+LfqZzzcWbJQp66dIViFpVeFbBDaARrBkY6489tU8c3zygR1lZO/W9DsAAKizpNgouf+MwZIQ45Alm3bI41/NE7fHQw8iLGn+7Llb5pr0UzaLTfZvvb8kRiUGulkAAKCOdDHCyA4j5cD0AyXWHiulzlKZmTnTLFrQY8AXCGwAfrYlt9ikjdBi4ftnNJeLDu1OnwMAgHpr0yxB7j5tkNitFpmyeLO8/dtyehFhx+PxyIKsBbKlcIvJU75f6/0kJTYl0M0CAAANkBqXKkPTh0rnlM5iEYtJKfnb2t9k3Y515m8+sC8IbAB+VFBSIXe8P0PyisulS8skue2kAWKz8t8OAAA0TN/2zeTaY/qY4/f+XCmrtuTTlQgbeoFjcfZi2ZC/wVz8GNBygKTFpwW6WQAAYB/YrDbpkdpDhrUbJsnRyaaG1oKtC2TahmlSUFZA36LBKB4O+Em50yX3fzJbNuQUSWpijNx7+mCJjeK/HBDuPFaLbOrUo/IYAHxtTL+2ZvHEH4s3S1MtIu5iOz/Cw/Kc5abQqOrXsp+0SmwV6CYBaERm7LzffrI+bz3jaCAMJcckm+DG2h1rZem2paaO1u/rfjc1OTKaZpgACFAfXGUF/MDpcsv/fTpX5q3Nkdgom9x3xmBJTYqhr4EI4IyKkZcefDPQzQAQ5k4+sJO56Qr3vDwCGwht+j7WoMaK7SvM573SekmbpDaBbhaARuaMdojMnClPfH+dlOeuov+BMGSxWKRjSkdpmdBSFm5dKFlFWbJy+0rZlL9Jeqb1NPfrY4C6ICcO4GNayPOJr+fLX8uzxGGzyj2n7yedWybRzwAARCAt9u2vIt8UD0e4BDV01aY3qKGpKvSCBwAACF+xjlhTR2tQq0GmuHiJs0Rmb54tMzbNkMLywkA3DyGCHRuAjydmr09ZLb8s3CI2q0XuOGWg9O+QSh8DABChYhw2sVos8slfqyQ733c7K9KSYuSUIZ0puoiQHzsv2bZEVueurtypQVADAIDIoDszNO1k8/jmZtfGqtxVkl2cbdJTdUrpJF2adhG7lUvX2D3eHYAPV02+8MNimbxgi+imuZuO7ycHdm1B/wIRxlFWKleNP90cP/fYh1IRTRo6AGKCGptzi+kKoEpQY1H2IpNnW/Vu3ls6NOlA/wARzF5aLtKhg9xVsl0ufWSYlEWTbx+IBFpbo1tqN0lPSpdFWxeZ4IYGOjbmbzSLHkhPhd0hsAH4gMvtkacnzZcf/tloghrXHN1HDumdTt8CkcjjkZRtmyuPAQBAzT+VHlmwdYEpEKz6tugr7ZLb0U1AhLPo0HndOmmqnzCOBiJOQlSC7J++v6m7oQEOb3qq1LhUswACqInABuCDQuH//XKe/LYoU6wWkXGHZciRA9rSrwAAAEANbo9b5mfNlw35G8zn/Vr0k7bJjJ0BAMDO9FS6QyMtLq0yPdW24m0yZe0UKSgrkLzSPEmOSaarYBDYCKFVTWVOt5SWO6W03CWlTpdszSsxBarXbi2QcqfbpEJyuz2iixwsFjE7B/QXgt1mEZvFIg67TaLsVomy2yTaYTU5n2Oj7GK3UUO+oYrLnPLQZ3NkxspsU1PjPyf2l76t43z62gMAAADhoMxZJj+s/KEyqDGg5QCTdgIAAKC29FRtktqY1JVbi7aa3RsZz2bI/YfcLxcNvIj6GyCwEawqnG7JLymXolKnFJZVmI8auKgrfah5tMdj0iQZ5a5aH+uwWSUu2i7x0XZJiHFIfAzxrrrQwNLdH86S1Vn5JmB0+8kD5YCM5pKXl1fn1wkAAACIBLrC8sQPT5Tl25eLRSwyoNUAaZ3YOtDNAgAAQSw+Kl4Gtx5sAhu6g0Prb1w+6XJ5buZz8sSYJ2R059GBbiICiCvYQUKDFgXFFbKjuEzyisulpJYghO7C0F0WMQ67xETZpH1qgozqky6/L95svs9qsZibPs4b2NCdHk6XR8rKy8VjsUp5hVvKnC4pq3BJaYXL7PSocLnNz9Sb14rNeSYg0r9DM+nXIVUSYx2N3CPBTfvnrg9myvbCMkmJj5Z7Tt9Puqc3Mf0NAAAA4H8yCzLlyHePNCmoHFaHDGw1UNLi0+giAACwV5qNpkVCCzkg/QDJaJYh9/x2jyzculDGvDNGjs44Wh4b85h0T+1OT0YgAhsBpGmjNCChF8d3FJX/b2fFv+Ki7JIQ65AE3U0R45DYKJv5z+zVu11TGdGrtSzemGuCE3uimzAcDket9SE0iKIplYrKKqSwtMJ8XljqlK9nrTM3rRvRpWWyDOiYKgM7pZqfG6npqzRw8d3cDTLhh0UmKNQ+LUHuP2OwtGhC+ikAAACgpiXZS+SId48whcJbxLeQUR1HSUF5AR0FAADqnZ7qmgOukXP6niP3TblPnp/5vExaMUl+WPWDjNtvnNw98m5pGtuUXo0gBDYCcGG8oLRCtuWXmoBG1WCG1sLQ1f/JcVGSFBslDrv/gwcaoEiM1ZsGPWIrgx16f4smsfLPmhxZv61Qlm/OM7cPp60yKas05dJB3VrKfl3STJ2OSKDBn2e+XSC/Lsw0n+/fJU3+c+IAE3QCgEoWi2xN71h5DABApJq2YZoc+/6xsr1ku2Q0zZAfzvlBnp7+NIENALXy6NC5Z0/ZXLiFcTSA3dLgxVNHPCVX7HeF3DT5Jvl6+dfyzIxn5O35b8vdI+6WcYPHicPGtbpIEBlXpINAfnG5rNycJyu35JsUUF5am6FpQrQ0TYiRhBh7tR0ZgaJBjVYpcXLF4b3M5xqEmbtmm7nNXp1tdpf8sjDT3LQ+x4BOqTK0Wws5sGsLaRIfLeFId8U8/uU82bi9yKT7uuCQbnLqQZ3MMQBUVREdI8899iGdAgCIaF8t+0pO/+R0KXWWmtQR35z1jaTGpQa6WQC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            "text/plain": [
              "<Figure size 1600x1200 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 导入绘图库\n",
        "try:\n",
        "    import seaborn as sns\n",
        "    import matplotlib.pyplot as plt\n",
        "except ImportError:\n",
        "    print(\"需要安装seaborn和matplotlib:\")\n",
        "    print(\"pixi add seaborn matplotlib\")\n",
        "    raise\n",
        "\n",
        "# 设置绘图样式\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "# 绘制分子质量分布图\n",
        "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
        "fig.suptitle('Distribution of 16-membered Macrocycle Esters Molecular Properties', fontsize=16, fontweight='bold')\n",
        "\n",
        "# 1. 分子量分布\n",
        "ax = axes[0, 0]\n",
        "sns.histplot(df_props['mol_weight'], kde=True, ax=ax, bins=30, color='steelblue', alpha=0.7)\n",
        "ax.set_title(f'Molecular Weight Distribution  (n={len(df_props)})', fontsize=12)\n",
        "ax.set_xlabel('Molecular Weight (Da)', fontsize=10)\n",
        "ax.set_ylabel('Frequency', fontsize=10)\n",
        "ax.axvline(df_props['mol_weight'].mean(), color='red', linestyle='--', label=f'Mean: {df_props[\"mol_weight\"].mean():.1f}')\n",
        "ax.legend()\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 2. LogP分布\n",
        "ax = axes[0, 1]\n",
        "sns.histplot(df_props['logP'], kde=True, ax=ax, bins=30, color='green', alpha=0.7)\n",
        "ax.set_title(f'LogP Distribution (n={len(df_props)})', fontsize=12)\n",
        "ax.set_xlabel('LogP', fontsize=10)\n",
        "ax.set_ylabel('Frequency', fontsize=10)\n",
        "ax.axvline(df_props['logP'].mean(), color='red', linestyle='--', label=f'Mean: {df_props[\"logP\"].mean():.2f}')\n",
        "ax.legend()\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 3. QED分布\n",
        "ax = axes[1, 0]\n",
        "sns.histplot(df_props['qed'], kde=True, ax=ax, bins=30, color='orange', alpha=0.7)\n",
        "ax.set_title(f'QED Distribution (n={len(df_props)})', fontsize=12)\n",
        "ax.set_xlabel('QED值', fontsize=10)\n",
        "ax.set_ylabel('Frequency', fontsize=10)\n",
        "ax.axvline(df_props['qed'].mean(), color='red', linestyle='--', label=f'Mean: {df_props[\"qed\"].mean():.3f}')\n",
        "ax.legend()\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 4. TPSA分布\n",
        "ax = axes[1, 1]\n",
        "sns.histplot(df_props['tpsa'], kde=True, ax=ax, bins=30, color='purple', alpha=0.7)\n",
        "ax.set_title(f'TPSA Distribution (n={len(df_props)})', fontsize=12)\n",
        "ax.set_xlabel('TPSA (Ų)', fontsize=10)\n",
        "ax.set_ylabel('Frequency', fontsize=10)\n",
        "ax.axvline(df_props['tpsa'].mean(), color='red', linestyle='--', label=f'Mean: {df_props[\"tpsa\"].mean():.1f}')\n",
        "ax.legend()\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# 保存图片\n",
        "output_file = project_root / 'output' / 'ring16_molecular_properties_distribution.png'\n",
        "plt.savefig(output_file, dpi=300, bbox_inches='tight')\n",
        "print(f\"已保存: {output_file}\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MacrolactoneFragmenter 已初始化\n",
            "  环大小: 16\n",
            "\n",
            "准备开始批量处理...\n"
          ]
        }
      ],
      "source": [
        "# 创建16元环大环内酯断裂器\n",
        "fragmenter = MacrolactoneFragmenter(ring_size=16)\n",
        "\n",
        "print(f\"MacrolactoneFragmenter 已初始化\")\n",
        "print(f\"  环大小: {fragmenter.ring_size}\")\n",
        "print(f\"\\n准备开始批量处理...\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "开始批量处理 307 个分子...\n",
            "\n",
            "\n",
            "Processed 307 molecules successfully\n",
            "\n",
            "批处理统计:\n",
            "  成功处理: 307 个分子\n",
            "  总碎片数: 2684\n",
            "  平均碎片数: 8.74\n",
            "  碎片数范围: 7 - 9\n"
          ]
        }
      ],
      "source": [
        "# 批量处理所有分子\n",
        "print(f\"开始批量处理 {len(df)} 个分子...\\n\")\n",
        "\n",
        "batch_results = fragmenter.process_csv(\n",
        "    csv_file,\n",
        "    smiles_column=\"smiles\",\n",
        "    id_column=\"unique_id\",\n",
        "    max_rows=None  # 处理所有分子\n",
        ")\n",
        "\n",
        "print(f\"\\n批处理统计:\")\n",
        "print(f\"  成功处理: {len(batch_results)} 个分子\")\n",
        "\n",
        "total_fragments = sum(len(r.fragments) for r in batch_results)\n",
        "print(f\"  总碎片数: {total_fragments}\")\n",
        "\n",
        "# 统计每个分子的碎片数\n",
        "fragment_counts = [len(r.fragments) for r in batch_results]\n",
        "print(f\"  平均碎片数: {np.mean(fragment_counts):.2f}\")\n",
        "print(f\"  碎片数范围: {np.min(fragment_counts)} - {np.max(fragment_counts)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "碎片DataFrame:\n",
            "  形状: (2684, 7)\n",
            "  列: ['fragment_id', 'parent_id', 'parent_smiles', 'cleavage_position', 'fragment_smiles', 'atom_count', 'molecular_weight']\n",
            "\n",
            "前10行:\n"
          ]
        },
        {
          "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>fragment_id</th>\n",
              "      <th>parent_id</th>\n",
              "      <th>parent_smiles</th>\n",
              "      <th>cleavage_position</th>\n",
              "      <th>fragment_smiles</th>\n",
              "      <th>atom_count</th>\n",
              "      <th>molecular_weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>mol_00113_frag_0</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>4</td>\n",
              "      <td>*O</td>\n",
              "      <td>2</td>\n",
              "      <td>17.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>mol_00113_frag_1</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>5</td>\n",
              "      <td>*C</td>\n",
              "      <td>2</td>\n",
              "      <td>15.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mol_00113_frag_2</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>6</td>\n",
              "      <td>*OC1OC(C)C(O)C(N(C)C)C1O</td>\n",
              "      <td>14</td>\n",
              "      <td>190.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mol_00113_frag_3</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>7</td>\n",
              "      <td>*CCN1CC(C)CC(C)C1</td>\n",
              "      <td>11</td>\n",
              "      <td>140.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mol_00113_frag_4</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>9</td>\n",
              "      <td>*C</td>\n",
              "      <td>2</td>\n",
              "      <td>15.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>mol_00113_frag_5</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>10</td>\n",
              "      <td>*=O</td>\n",
              "      <td>2</td>\n",
              "      <td>16.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>mol_00113_frag_6</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>13</td>\n",
              "      <td>*C</td>\n",
              "      <td>2</td>\n",
              "      <td>15.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>mol_00113_frag_7</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>15</td>\n",
              "      <td>*COC1OC(C)C(O)C(OC)C1OC</td>\n",
              "      <td>15</td>\n",
              "      <td>205.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>mol_00113_frag_8</td>\n",
              "      <td>mol_00113</td>\n",
              "      <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
              "      <td>16</td>\n",
              "      <td>*CC</td>\n",
              "      <td>3</td>\n",
              "      <td>29.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>mol_00288_frag_0</td>\n",
              "      <td>mol_00288</td>\n",
              "      <td>CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...</td>\n",
              "      <td>4</td>\n",
              "      <td>*O</td>\n",
              "      <td>2</td>\n",
              "      <td>17.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        fragment_id  parent_id  \\\n",
              "0  mol_00113_frag_0  mol_00113   \n",
              "1  mol_00113_frag_1  mol_00113   \n",
              "2  mol_00113_frag_2  mol_00113   \n",
              "3  mol_00113_frag_3  mol_00113   \n",
              "4  mol_00113_frag_4  mol_00113   \n",
              "5  mol_00113_frag_5  mol_00113   \n",
              "6  mol_00113_frag_6  mol_00113   \n",
              "7  mol_00113_frag_7  mol_00113   \n",
              "8  mol_00113_frag_8  mol_00113   \n",
              "9  mol_00288_frag_0  mol_00288   \n",
              "\n",
              "                                       parent_smiles  cleavage_position  \\\n",
              "0  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                  4   \n",
              "1  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                  5   \n",
              "2  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                  6   \n",
              "3  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                  7   \n",
              "4  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                  9   \n",
              "5  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                 10   \n",
              "6  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                 13   \n",
              "7  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                 15   \n",
              "8  CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...                 16   \n",
              "9  CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]...                  4   \n",
              "\n",
              "            fragment_smiles  atom_count  molecular_weight  \n",
              "0                        *O           2             17.01  \n",
              "1                        *C           2             15.04  \n",
              "2  *OC1OC(C)C(O)C(N(C)C)C1O          14            190.22  \n",
              "3         *CCN1CC(C)CC(C)C1          11            140.25  \n",
              "4                        *C           2             15.04  \n",
              "5                       *=O           2             16.00  \n",
              "6                        *C           2             15.04  \n",
              "7   *COC1OC(C)C(O)C(OC)C1OC          15            205.23  \n",
              "8                       *CC           3             29.06  \n",
              "9                        *O           2             17.01  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 转换为DataFrame便于分析\n",
        "df_fragments = fragmenter.batch_to_dataframe(batch_results)\n",
        "\n",
        "print(f\"碎片DataFrame:\")\n",
        "print(f\"  形状: {df_fragments.shape}\")\n",
        "print(f\"  列: {df_fragments.columns.tolist()}\")\n",
        "print(f\"\\n前10行:\")\n",
        "df_fragments.head(10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. 统计每个位置的碎片分布\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "已准备 14 个位置的碎片数据\n",
            "  位置 3: 0 个碎片\n",
            "  位置 4: 305 个碎片\n",
            "  位置 5: 307 个碎片\n",
            "  位置 6: 307 个碎片\n",
            "  位置 7: 304 个碎片\n",
            "  位置 8: 0 个碎片\n",
            "  位置 9: 307 个碎片\n",
            "  位置 10: 307 个碎片\n",
            "  位置 11: 0 个碎片\n",
            "  位置 12: 0 个碎片\n",
            "  位置 13: 263 个碎片\n",
            "  位置 14: 0 个碎片\n",
            "  位置 15: 277 个碎片\n",
            "  位置 16: 307 个碎片\n"
          ]
        }
      ],
      "source": [
        "# 准备数据：按位置收集碎片\n",
        "position_fragments = {}\n",
        "for position in range(3, 17):  # 位置3-16\n",
        "    position_data = df_fragments[df_fragments['cleavage_position'] == position]\n",
        "    position_fragments[position] = position_data.to_dict('records')\n",
        "\n",
        "print(f\"已准备 {len(position_fragments)} 个位置的碎片数据\")\n",
        "for pos, frags in position_fragments.items():\n",
        "    print(f\"  位置 {pos}: {len(frags)} 个碎片\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 7. 绘制每个位置的碎片分布图（参考test_align_two_molecules.ipynb的绘图逻辑）\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "已保存: /home/zly/project/macro_split/output/atom_count_distribution_ring16.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1600 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "已保存: /home/zly/project/macro_split/output/molecular_weight_distribution_ring16.png\n"
          ]
        },
        {
          "data": {
            "image/png": 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YBUuj2X0DsBrhp3SUCmgq9aVSQPuOTFRw36P39r+ArXSaGvGli9sqnwK4/mm6i5IuLCo4o3pWeb1AogJgCmL6BlZ904nnpUmTJu6iri6oKvgS7Khf1aPvxVHVsReE8A0o6btXClWPd5/m5lXgw6NRh17QTCOXfAN2uog7ZswY9xn1fF2s9YIg2h4UfAmWl9bT9wKz5hFWPep7zWs0W34U0FcqVpXt4YcfznWfXtML7usCs0ZB+lL6U63zXUJNF/qVLtWXf0r3vPjul9q2lEpZQXV9Fxp1re1f6WuVslo0AlWfQUGSQPOfe0t+6YQ1+lHBHe1jCoDpuy8MBQA0aln7ul7L/z0VzNRI98MVis/sS9uzb9YCjRjWsUjbqEYs+84br+9HmRNCsX0eij6D/3vp+O/7GbU9+5dJQblhw4blfA/+KdA16l1tR36Uvlmvr+OUOjUUNEtEYSjo6ctLVx1oO9OIdv+gXWEpgOkdWxVgUoDTd7S4siD4d3Lwp2wbClj5vqbaO9W/jve+xwONhNXIzfwo+O/VvY7VvnWi706BMb22AslKm+1R2QMFS33rTmnodVzW9qk2zDcltPZP304Hh9OeB6IU4RpZrufrWOONsFbw1zcjilI/6/itfVCfU+cBvtuJb6BY9/umr1a7pjZL76FjZ8+ePXON2A0Fper2Oq1oBLDqUudVXqeErl275jt6/nDPLdQO+AZG1a5qmhbVhbYbLTquBZr6JJjvQ/uBb9uosuocQWXUa/t2JpFHHnkk1/nFoahzhz6Tzku06HPrtbU9azoK3+OlsiQU9hyxoG1FqM+NdS6g9lTPV8dIbde++7L2IY9G4Gskuu93o2k5tG3p+ZqKw6OMADrmeHzLrGOZ2gM9R4veQ+2YMsNoSgIAAACEFoFxAACAMPEfreSbvvNQo8VFF7Z9XXfdde5ipIKflSpVsgcffDBXIEAXav1HY+XFf9SiRhN5QQHNteo/AmjatGk5t/1HHSmlq1JQe2lQdcGvU6dO7qJ3uKgedBFcFxc1gkoBIwXOOnfu7FIm+474UerS/OZ2V/3qQqqXSlkXTX2DHocqh+8Ib11U1cV0jez0RhjrIrgupvsGxr3R3fmlUddFcN+0vJrHVReUNTJMi4I0qnff1wx2e/ANdojShCsFvDf3sba1YEa3+W9T+qyqT82563vxXxeMFeiPNN95kcU3RXR+fEd46fP5BnR0kV7fgwIgmpc9VO69996czjQKqhZ2DlIFI5RSV9u1tldlF/C9EK9Rdr6BgGigDgsKzHhUbh1fdBxU/avjho6P+R0/I719KlDkG2zX/uXN+azPo3TLvscZfQ8alZwXBfS0X6oOvON2OKYnCNRpQgFmBa50HNH8uh593lBlINAx00sfrsCmRlT7jkgNJgOLOkJ4dFxT4F71Jqo7fQce7dOBRrz6Hju0j3sjPn2Dz6LgmlK26/tVBxn/1OdKsZ0ftcH169d3t5s2bZor4Ca+o7YPpz3PKyCq71H7hhavHfJ/H31+b5tV0Peuu+7KNWrX9328UdoetddqsxQg18hgZVMIZhqLglDday54384OCqp6tP8V5bmFfz3rO1SHKS9Dgf4qlbj/sSvY70OdLX3p86isah/02vrefdPTa6oD/4wP+dE2rcwEOr8777zz3Dasz63bmsvcl85zwnWOGOpzYwXtdS6letOUNv7p3X33VdW5b3uv77RDhw7uvfW5lJHB229FHSq8zpG+5w06n/JtDxRgVzum9lhTFQAAACC0mGMcAAAgTBRs0UhE/0CTLo5pdNuh+M9xrYtvvnQBUCPPfdOn6jn5za3r0egpX4caaeh7EdR/pJvvhedI0Qi8yy67zF2YPhRdjNTF7LxGF2q0eMOGDQtdFl209g3iKUCtgLY3Ss27qK1AnEYR6sKzvg89xj8w7ptVwP870wgyLYf63oLZHjQKylfHjh1z/a/AqUYsHyqY49F7+s8vrICx7/P9U2JHgn8gPNjAjEakeaMh9f0phbMubCu4pkXf8SmnnJITdDtcGvWo0Y2h4H8c0QV9BRV850pW9gAFbqOF/7FQnU+8gLDnhBNOyPc5kd4+/VPv++9j3nfje6z13+f9j9n5zYFdVPzTICtArICoF/TRKHjfEZs6pqljkB6nQLPm/A1Eo0UPlT1AgT9vJOp//vOfnPVt27Z1x+z8OnRoLmvfoKXm5D7UHOj+wT9f6pjiG+jy3x69QJ3HN6PBobYv1bF/lg7//db3mH047Xle5y6B+L+P5kvOj+aEVzp1HRv92xj//VXbh+aSV8aNUFKHCR2r1e77jpTXMaBLly6H7DxW2HMLbfP+7WVh59PO6/vwP8b5H1O87ci3g4ee065du0O+p0aWK+jr25kkP75p84v6HDHU58b+nVby21f931sBfy15UedIHfvVuUWdG9QRRKPYtf788893HRiOOuood96gY4rOG3w7MwAAACA0GDEOAAAQJgqyBbpArXX+c5oGE7Tzv/AeaF2wKZCDHRnr0UVeb9SL/3MPlYo0L/6pin3TJBeURv4Fc+Hak186UY0YOhyB5hn3DXj7Bru9x+qzK5juP5rL97UKk9462FGv+n7zuzCc17q8eCnE8wuoBZuquqjowrT/NhNMJwJv1PUdd9yRKzCmwIDSxL7++us2dOhQF8gOJm1/MIItVzACHUf858j23x6Kar8Nlv+271/eQJ8rv2NcJLZP/88QzPE8v8+gjiqR4N/ZQ51mfPcDbau+c55rdKWXZlmBad0OtPhmw8iLAmCB0gwHk4GlMMdP/zmy8zse+s8T75tiW/wzbuS3fSmQ7BtUD7TNqw3Lq00uSHse6L3zOj8p6Pv4tkH+x5RA+3CgdYdLo3cDTeuioGQwWVAKe27hX1caSa+6LaiCfB+hPEdUhqNgg+L+7UCozhHzEurP7V++/NqCwzkP07FLnWV9twN9Fo1o17mCAuzqVDRu3LgCvwcAAADyx4hxAACAMNIIN83BWdCL+KKLob4X5jVnqf+FVf85aIMd8er72roAH0zQ0xvx7P8eSs+pOU0LSheQfUc86nUKQxf4fVOBK0ChtNNKKerVl+o82BSivkGewtDoRV+aF9Q36OObQl23NT+1vPPOO7nm6dRFVN+Lu/71ruDToUaM+gdr8uJ/4T1QQOhQ8x3nd2FZ/AM9kaaUqL4dJJRGtSBzqWsUoVJIa95dze+rEdcaUamRqd4oM6Wy1ahE/yDZ4aZ8PxyBOkv4r8uv445/p5LC7rcF4b/tB/oM/ttnfp8hEtun/2cItD8V5DMc7nGqsPyzaQQKLvqvK1myZEjeW9+RApqaK9mjY7wyNhS0/nXs9A3gF3S/O9SxNdhjbyA6/ioY57tN+m/z2oa94//htOcF+cz+26Pe51D7jRdU9H/dYI5DoaL2X/Nj+343OnYX5bmF//am9sAbPV8QBfk+gjmmBHuO6J2XeJQCXvNfq1OR9m/Nuz5+/PiAzw3VOWK4zo3999X8tmn/19H7Hmpf9z0eqmOsRoVrLnSNaNd5g0aUeyPR1cHgX//6lztvUPYiAAAAhAaBcQAAgDDSxUCla12yZIn7Xxe6gr1A2KhRI/v2229z/teFNKVc9L3w55/aVM8JhtJMemlndeF68uTJAUfi+Y768y7uKY2xbypWBXR1ofhQfOce9VLZeqOzdQFac9QWhi6m+wbr9NkuuOCCXCN8/NMYFyVdOK5Zs6atX78+J9W2Nw+pglm+35HviHD/eTl9R5Z7n8vXJZdcctC8s3l9Z4eiNJ6+c4Rqu/Odr3z16tUuLW5R0sVoL4iSV9AmVPRZNHLL1xlnnFHgOW51gV5BOd/AnC5qeyO+tF0rW4A3/7D/Bfei/pyBKEA0cODAnP9V577HGfGdq9p/v9X27Hsc8p3rOJBQfGb/45pS9er459txxDfwFeg5kaaAspcGXJQy2n+OYx3j89vno4F/CmwdG7Sde0Fade7xDVppH/E6hmhOaS2HQwHNxx9/POeYr/TUwQTeNXJYKa69dOqaxkIjYvM7RvrOJRxO+mzKPuHbUcd/2/DdRw+nPS8IvY86AXkUHPVPY53X+6i8viOQtb/6dhLUiHLfKUhCScdfBfG3bt3q/teo3EBZI0J5bqG2XvPMa//wPVdSh6pQ0THO9/vQMUUBVY/KPm/evIOeEwydn/nS/Oq+gXh1+MtLYc8Rg20rivLc+FD0Or7H8cGDB7vOOgXZ13S80jz1Wjyvvfaa60jn0ecjMA4AABA6pFIHAAAIs6uuuspdPNZyqDk5ffmPgnv66adt7ty57sK3LvwplbPvnKmanzPYlMsKAvq64YYb3KgnLzCpv5ofU+kdr7nmmlypHXv27JnrubrYrXSj3mhnjXhROe+8885cj9Oc2r70mrpwq4vLuiCogFth6GKt7wXVX3/91aWm9C7u6oJufqmhi4J/OnUvba06RWhkskcXPr2gjn9qW//XUGpu31GiL7zwgruY6jsaXQF4XRx/8MEHg85MEGiOzZdeesmNhNZ2oDrUtlbUQSLfoLS2pYKkrw2WXvfll192F7J9R+drVN71118f9OtMmTLFbbNffPFFrlGO2pb959L1Daz4B94VXAh38E1BhH//+99ue9PxQx0EfOcXVyDcN/Ww/36r7U6jH7WoE4BvYCaQUHxmpQ1v1qxZzv8qt7ZJHQe1jSr4o+Ojr9NOO82iicrje5zScVP7r74HpfV/6qmncgWT9D1oTtpoo2OYb8B+586dOYFqfS8jR47M9XgFIQ9n9LQ/dYbQXORemxrscU6Be9/j3O+//+6CWr7zQOu7UKcL7R86RvgHFcNp+PDhOfulOi099thjue73DaodTnteEP7vozZeHdp8A5gaHaw5rW+55RY333yg8nojklUenS8onbRGYhcmTXUwNLpenYG8bSbY4PThnlucddZZuf7Xd6hOC95z1HarrsaOHVuoz+V/jqj05zNnznTfh+pU25BvgFudAfw73AU7Gl3BbdE+ruN+fvtGYc8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            "text/plain": [
              "<Figure size 2000x1600 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "所有分布图已生成完成!\n"
          ]
        }
      ],
      "source": [
        "# 准备数据\n",
        "properties_to_plot = [\n",
        "    ('atom_count', 'Atom Count', 'Number of Atoms'),\n",
        "    ('molecular_weight', 'Molecular Weight', 'Molecular Weight (Da)')\n",
        "]\n",
        "\n",
        "ring_size = 16\n",
        "positions_to_plot = list(range(3, ring_size + 1))  # 位置3-16\n",
        "\n",
        "# 为每个属性创建图表\n",
        "for prop_key, prop_title, prop_label in properties_to_plot:\n",
        "    fig, axes = plt.subplots(4, 4, figsize=(20, 16))\n",
        "    fig.suptitle(f'{prop_title} Distribution for {ring_size}-Membered Macrolactones', \n",
        "                 fontsize=16, fontweight='bold')\n",
        "    \n",
        "    axes = axes.flatten()\n",
        "    \n",
        "    for idx, position in enumerate(positions_to_plot):\n",
        "        ax = axes[idx]\n",
        "        \n",
        "        if position in position_fragments and len(position_fragments[position]) > 0:\n",
        "            # 提取该位置的所有属性值\n",
        "            values = [frag[prop_key] for frag in position_fragments[position]]\n",
        "            \n",
        "            # 绘制分布图（使用KDE平滑）\n",
        "            sns.histplot(values, kde=True, ax=ax, bins=30, color='steelblue', alpha=0.7)\n",
        "            ax.set_title(f'Position {position}\\n(n={len(values)})', fontsize=10)\n",
        "            ax.set_xlabel(prop_label, fontsize=9)\n",
        "            ax.set_ylabel('Frequency', fontsize=9)\n",
        "            ax.grid(True, alpha=0.3)\n",
        "        else:\n",
        "            ax.text(0.5, 0.5, f'Position {position}\\nNo fragments', \n",
        "                   ha='center', va='center', transform=ax.transAxes, fontsize=10)\n",
        "            ax.set_title(f'Position {position}', fontsize=10)\n",
        "    \n",
        "    # 隐藏多余的子图\n",
        "    for idx in range(len(positions_to_plot), len(axes)):\n",
        "        axes[idx].axis('off')\n",
        "    \n",
        "    plt.tight_layout()\n",
        "    \n",
        "    # 保存图片到output目录\n",
        "    output_file = project_root / 'output' / f'{prop_key}_distribution_ring{ring_size}.png'\n",
        "    plt.savefig(output_file, dpi=300, bbox_inches='tight')\n",
        "    print(f\"已保存: {output_file}\")\n",
        "    plt.show()\n",
        "\n",
        "print(\"\\n所有分布图已生成完成!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "碎片数据已保存: /home/zly/project/macro_split/output/ring16_fragments_analysis.csv\n",
            "分子性质已保存: /home/zly/project/macro_split/output/ring16_molecular_properties.csv\n",
            "\n",
            "所有分析结果已保存!\n"
          ]
        }
      ],
      "source": [
        "# 保存碎片数据\n",
        "output_fragments_csv = project_root / 'output' / 'ring16_fragments_analysis.csv'\n",
        "df_fragments.to_csv(output_fragments_csv, index=False)\n",
        "print(f\"碎片数据已保存: {output_fragments_csv}\")\n",
        "\n",
        "# 保存分子性质数据\n",
        "output_props_csv = project_root / 'output' / 'ring16_molecular_properties.csv'\n",
        "df_props.to_csv(output_props_csv, index=False)\n",
        "print(f\"分子性质已保存: {output_props_csv}\")\n",
        "\n",
        "print(\"\\n所有分析结果已保存!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 9. 延伸分析建议与总结\n",
        "\n",
        "### 已完成的分析\n",
        "\n",
        "1. **基本性质统计**\n",
        "   - ✅ 分子量、LogP、QED、TPSA等药物性质\n",
        "   - ✅ 氢键供受体数、可旋转键数等\n",
        "   - ✅ 生成了分布图并保存\n",
        "\n",
        "2. **侧链断裂分析**\n",
        "   - ✅ 每个位置（3-16）的碎片数量和类型\n",
        "   - ✅ 碎片的原子数和分子量分布\n",
        "   - ✅ 所有数据保存为CSV格式\n",
        "\n",
        "### 延伸分析建议\n",
        "\n",
        "基于当前分析，以下是值得进一步探索的方向：\n",
        "\n",
        "#### 1. **药物性质深度分析** ⭐⭐⭐（强烈推荐）\n",
        "\n",
        "**LogP分析的价值：**\n",
        "- LogP反映分子的亲脂性，对膜通透性和药物分布至关重要\n",
        "- 大环内酯通常LogP较高（从当前分析可以看到平均值和分布）\n",
        "- 建议：分析不同侧链对LogP的贡献，找出对亲脂性影响最大的位置\n",
        "\n",
        "**QED（定量估计药物相似性）分析：**\n",
        "- QED综合考虑分子量、LogP、氢键等因素，评估\"类药性\"\n",
        "- 大环内酯往往违反\"Lipinski五规则\"（分子量>500），但仍可能是好药\n",
        "- 建议：比较高QED和低QED分子的侧链差异\n",
        "\n",
        "**TPSA（极性表面积）分析：**\n",
        "- TPSA与口服生物利用度密切相关（一般<140Ų为佳）\n",
        "- 建议：分析侧链极性原子（O、N）的分布模式\n",
        "\n",
        "**代码示例：**\n",
        "```python\n",
        "# 分析LogP与侧链的关系\n",
        "# 1. 将分子按LogP分为高、中、低三组\n",
        "# 2. 比较三组在各个位置的侧链类型差异\n",
        "# 3. 找出对LogP贡献最大的位置\n",
        "\n",
        "df_props['logP_group'] = pd.cut(df_props['logP'], bins=3, labels=['Low', 'Medium', 'High'])\n",
        "```\n",
        "\n",
        "#### 2. **侧链特征分析** ⭐⭐⭐\n",
        "\n",
        "**极性侧链vs疏水侧链：**\n",
        "```python\n",
        "def classify_fragment_polarity(smiles):\n",
        "    \\\"\\\"\\\"分类碎片的极性\\\"\\\"\\\"\n",
        "    mol = Chem.MolFromSmiles(smiles)\n",
        "    if mol:\n",
        "        # 统计极性原子(O, N)\n",
        "        polar_atoms = sum(1 for atom in mol.GetAtoms() \n",
        "                         if atom.GetSymbol() in ['O', 'N'])\n",
        "        total_atoms = mol.GetNumAtoms()\n",
        "        return polar_atoms / total_atoms if total_atoms > 0 else 0\n",
        "    return 0\n",
        "\n",
        "# 为每个碎片计算极性比例\n",
        "df_fragments['polarity_ratio'] = df_fragments['fragment_smiles'].apply(\n",
        "    classify_fragment_polarity\n",
        ")\n",
        "\n",
        "# 分析每个位置的极性偏好\n",
        "position_polarity = df_fragments.groupby('cleavage_position')['polarity_ratio'].mean()\n",
        "```\n",
        "\n",
        "#### 3. **结构-活性关系（SAR）分析** ⭐⭐⭐\n",
        "\n",
        "如果您的数据中有生物活性数据（如`max_pChEMBL`）：\n",
        "\n",
        "```python\n",
        "# 读取活性数据\n",
        "df_with_activity = pd.merge(\n",
        "    df_props, \n",
        "    df[['unique_id', 'max_pChEMBL']], \n",
        "    on='unique_id'\n",
        ")\n",
        "\n",
        "# 分析高活性分子的侧链特征\n",
        "high_activity = df_with_activity[df_with_activity['max_pChEMBL'] > 7]\n",
        "low_activity = df_with_activity[df_with_activity['max_pChEMBL'] < 5]\n",
        "\n",
        "# 比较两组在各个位置的碎片分布差异\n",
        "# 找出\"特权侧链\"（privilege side chains）\n",
        "```\n",
        "\n",
        "#### 4. **碎片多样性分析** ⭐⭐\n",
        "\n",
        "```python\n",
        "# 统计每个位置的独特碎片类型\n",
        "unique_fragments_per_position = {}\n",
        "for position in range(3, 17):\n",
        "    position_data = df_fragments[df_fragments['cleavage_position'] == position]\n",
        "    unique_smiles = position_data['fragment_smiles'].unique()\n",
        "    unique_fragments_per_position[position] = {\n",
        "        'total': len(position_data),\n",
        "        'unique': len(unique_smiles),\n",
        "        'diversity_ratio': len(unique_smiles) / len(position_data)\n",
        "    }\n",
        "```\n",
        "\n",
        "#### 5. **聚类分析** ⭐⭐\n",
        "\n",
        "```python\n",
        "from rdkit.Chem import AllChem\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# 基于碎片指纹进行分子聚类\n",
        "def get_fragment_fingerprint(mol_result):\n",
        "    \\\"\\\"\\\"为每个分子生成碎片指纹\\\"\\\"\\\"\n",
        "    # 基于侧链组成创建特征向量\n",
        "    fp = [0] * 14  # 位置3-16\n",
        "    for frag in mol_result.fragments:\n",
        "        pos = frag.cleavage_position\n",
        "        if 3 <= pos <= 16:\n",
        "            fp[pos-3] = frag.molecular_weight\n",
        "    return fp\n",
        "\n",
        "# 聚类找出具有相似侧链模式的分子家族\n",
        "```\n",
        "\n",
        "#### 6. **3D性质分析** ⭐（可选）\n",
        "\n",
        "如果需要更深入的3D分析：\n",
        "```python\n",
        "from rdkit.Chem import AllChem, Descriptors3D\n",
        "\n",
        "# 生成3D构象\n",
        "mol = Chem.MolFromSmiles(smiles)\n",
        "AllChem.EmbedMolecule(mol)\n",
        "AllChem.MMFFOptimizeMolecule(mol)\n",
        "\n",
        "# 计算3D描述符\n",
        "pmi1 = Descriptors3D.PMI1(mol)  # 主惯性矩\n",
        "npr1 = Descriptors3D.NPR1(mol)  # 归一化主惯性比\n",
        "```\n",
        "\n",
        "### 推荐的下一步优先级\n",
        "\n",
        "1. **优先级1（必做）：LogP、QED和TPSA与侧链的关系分析** ⭐⭐⭐\n",
        "   - 对药物设计最直接相关\n",
        "   - 实现简单，计算快速\n",
        "   - 能提供实用的设计指导\n",
        "\n",
        "2. **优先级2（重要）：如果有活性数据，进行SAR分析** ⭐⭐⭐\n",
        "   - 找出活性关键侧链\n",
        "   - 指导后续合成策略\n",
        "\n",
        "3. **优先级3（有价值）：碎片多样性和聚类分析** ⭐⭐\n",
        "   - 发现侧链组合规律\n",
        "   - 可能发现新的设计策略\n",
        "\n",
        "### 其他潜在分析指标\n",
        "\n",
        "**Lipinski规则检查：**\n",
        "```python\n",
        "def check_lipinski(props):\n",
        "    violations = 0\n",
        "    if props['mol_weight'] > 500: violations += 1\n",
        "    if props['logP'] > 5: violations += 1\n",
        "    if props['num_h_donors'] > 5: violations += 1\n",
        "    if props['num_h_acceptors'] > 10: violations += 1\n",
        "    return violations\n",
        "\n",
        "df_props['lipinski_violations'] = df_props.apply(check_lipinski, axis=1)\n",
        "```\n",
        "\n",
        "**Veber规则（口服药物性）：**\n",
        "- TPSA ≤ 140 Ų\n",
        "- 可旋转键数 ≤ 10\n",
        "\n",
        "**立体化学分析：**\n",
        "- 统计手性中心数量\n",
        "- 分析每个位置的立体化学偏好\n",
        "\n",
        "### 总结\n",
        "\n",
        "**已验证的重要指标：**\n",
        "- ✅ LogP：反映亲脂性，影响膜通透性\n",
        "- ✅ QED：综合类药性评分\n",
        "- ✅ TPSA：极性表面积，影响生物利用度\n",
        "\n",
        "**下一步建议：**\n",
        "1. 分析LogP/QED与侧链的关系（最重要）\n",
        "2. 如果有活性数据，进行SAR分析（非常有价值）\n",
        "3. 碎片多样性分析（了解侧链变化空间）\n",
        "4. 聚类分析（发现分子家族）\n",
        "\n",
        "**关键洞察：**\n",
        "大环内酯作为天然产物来源的药物，往往不完全遵循Lipinski规则，但仍能表现出良好的药物性质。\n",
        "理解侧链对这些性质的贡献，可以帮助：\n",
        "- 优化现有分子的成药性\n",
        "- 设计新的类似物\n",
        "- 预测哪些位置的修饰最有价值\n",
        "\n",
        "希望这些分析建议对您的研究有帮助！\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "================================================================================\n",
            "分析完成！\n",
            "================================================================================\n",
            "\n",
            "处理了 307 个分子\n",
            "生成了 2684 个碎片\n",
            "\n",
            "所有结果已保存到: /home/zly/project/macro_split/output\n",
            "\n",
            "生成的文件:\n",
            "  - ring16_molecular_properties_distribution.png (分子性质分布图)\n",
            "  - atom_count_distribution_ring16.png (原子数分布图)\n",
            "  - molecular_weight_distribution_ring16.png (分子量分布图)\n",
            "  - ring16_fragments_analysis.csv (碎片数据)\n",
            "  - ring16_molecular_properties.csv (分子性质数据)\n"
          ]
        }
      ],
      "source": [
        "# 完成！\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"分析完成！\")\n",
        "print(\"=\"*80)\n",
        "print(f\"\\n处理了 {len(batch_results)} 个分子\")\n",
        "print(f\"生成了 {total_fragments} 个碎片\")\n",
        "print(f\"\\n所有结果已保存到: {project_root / 'output'}\")\n",
        "print(\"\\n生成的文件:\")\n",
        "print(\"  - ring16_molecular_properties_distribution.png (分子性质分布图)\")\n",
        "print(\"  - atom_count_distribution_ring16.png (原子数分布图)\")\n",
        "print(\"  - molecular_weight_distribution_ring16.png (分子量分布图)\")\n",
        "print(\"  - ring16_fragments_analysis.csv (碎片数据)\")\n",
        "print(\"  - ring16_molecular_properties.csv (分子性质数据)\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
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