redisxann_usearch_implement_approximate_query_for_molecular_geometries.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOKAVdt/YsD+t44knJmG150",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/weedge/doraemon-nb/blob/main/redisxann_usearch_implement_approximate_query_for_molecular_geometries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run RedisxANN-usearch redis Server"
      ],
      "metadata": {
        "id": "9VJeZqCgx6w1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IDNXhB1ExUgk"
      },
      "outputs": [],
      "source": [
        "!rm -rf redis && git clone https://github.com/redis/redis && \\\n",
        "  cd redis && \\\n",
        "  git checkout 7.0 && \\\n",
        "  make REDIS_CFLAGS='-Werror' BUILD_TLS=yes && make install && \\\n",
        "  which redis-server && redis-server --version"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/weedge/RedisXANN.git --recursive"
      ],
      "metadata": {
        "id": "Mmywa_jfxk2c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!curl https://sh.rustup.rs -sSf > rustup.sh"
      ],
      "metadata": {
        "id": "MgjOHK3FxmmD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!sh rustup.sh -y"
      ],
      "metadata": {
        "id": "N-5m13wxxo8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cd RedisXANN && source \"$HOME/.cargo/env\" && \\\n",
        "  cargo clean && \\\n",
        "  cargo build --lib --manifest-path rust/usearch/Cargo.toml --release"
      ],
      "metadata": {
        "id": "Pvzlb-kJxsGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!redis-server --daemonize yes \\\n",
        "  --loadmodule RedisXANN/target/release/libredisxann_usearch.so is_remove_serialized_file 1 \\\n",
        "  --port 6666 \\\n",
        "  --dbfilename dump.6666.rdb"
      ],
      "metadata": {
        "id": "jAqfLnWPxwzh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ps -ef | grep redis-server | grep -v grep"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSWGURiXAH83",
        "outputId": "a197d120-37a8-4b33-df21-815b269419b2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root       16209       1  0 03:19 ?        00:00:00 redis-server *:6666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data\n",
        "1. https://pubchem.ncbi.nlm.nih.gov/\n",
        "2. https://pubchem.ncbi.nlm.nih.gov/sources/\n",
        "3. 生物分子数据：https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/\n",
        "4. 说明：https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/README-Compound-SDF\n",
        "5. 数据格式：https://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_sdtags.txt ， https://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_sdtags.pdf\n",
        "\n",
        "本示例使用的测试数据为PubChem开源数据集，共计11012行数据，上述下载地址提供的数据为SMI格式，数据示例如下，两列分别为化学分子式和唯一ID。\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "B5BW2WMmx3jn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 已经格式化为sim格式数据\n",
        "!wget https://static-aliyun-doc.oss-cn-hangzhou.aliyuncs.com/file-manage-files/en-US/20230518/ypqh/Compound_168000001_168500000.smi -O oss_Compound_168000001_168500000.smi"
      ],
      "metadata": {
        "id": "AsTV4mEf_mAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 格式： 分子式, CID(唯一的化合物 ID)\n",
        "!head -n 10 oss_Compound_168000001_168500000.smi\n",
        "!cat oss_Compound_168000001_168500000.smi | grep \"smiles\" | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ALw91BdLegG",
        "outputId": "5a000953-70f6-469a-86d7-c737aa574fd1"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC,168000001\n",
            "CC(C)CN1C(=O)C2SCCC2N2C(=S)NNC12,168000002\n",
            "CC1=C[NH+]=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1cccc(C(F)(F)F)c1,168000003\n",
            "CC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1cccc(C(F)(F)F)c1,168000004\n",
            "CCCCC1NC(SCC(=O)NCc2ccco2)C2C(=O)N(C)C(=O)N(C)C2N1,168000005\n",
            "COC(=O)c1ccc(CN2NC3N(CCN3c3ccccc3)C(=O)C2=O)o1,168000006\n",
            "Cc1ccc(C2NC(SCCN3CCCC3)C3C(=O)N(C)C(=O)N(C)C3N2)cc1,168000007\n",
            "O=C(Nc1ccccc1Cl)C1NNNC1c1ccccc1,168000008\n",
            "CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1,168000009\n",
            "O=C(Nc1ccccc1-c1cn2c(n1)CCCC2)C1CC(=O)N(c2ccccc2)C(=O)N1,168000010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numpy matplotlib rdkit redisx"
      ],
      "metadata": {
        "id": "sZ2BqYaZ1RnJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "如果下载sdf文件，需要转换为sim格式："
      ],
      "metadata": {
        "id": "kGx43jWSGeYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/Compound_168000001_168500000.sdf.gz -O Compound_168000001_168500000.sdf.gz\\\n",
        "  && rm -rf Compound_168000001_168500000.sdf && gunzip Compound_168000001_168500000.sdf.gz"
      ],
      "metadata": {
        "id": "NimWdXKNzeMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sdf文件格式：https://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_sdtags.pdf\n",
        "!head -n 100 Compound_168000001_168500000.sdf\n",
        "!tail -n 50 Compound_168000001_168500000.sdf\n",
        "!cat Compound_168000001_168500000.sdf | grep \"smiles\" | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35PP6xdXLPn0",
        "outputId": "fff2b9ed-9c6b-4ffb-f0ae-79d9efe20211"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "168000001\n",
            "  -OEChem-10062315042D\n",
            "\n",
            " 49 51  0     1  0  0  0  0  0999 V2000\n",
            "    7.1892    0.4628    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    8.9212   -2.5372    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4639    2.4972    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.0000    2.5373    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    7.1892   -2.5372    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    8.0552   -1.0372    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4292   -2.5719    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4407    0.4974    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    6.3231   -1.0372    0.0000 C   0  0  3  0  0  0  0  0  0  0  0  0\n",
            "    6.3231   -2.0372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4292   -0.5026    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    7.1892   -0.5372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.5231   -1.0164    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    8.0552   -2.0372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.6590   -0.5131    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.5231   -2.0580    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    7.1892   -3.5372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    8.9212   -0.5372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.7911   -1.0097    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.5806    1.0074    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.5921    2.0073    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.7088    0.5174    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.7319    2.5173    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.8486    1.0274    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.8602    2.0273    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4754    3.4972    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.0116    3.5372    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    6.3279   -0.4172    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.0594   -0.0397    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.2623   -0.0366    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.9874   -2.3701    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    6.5692   -3.5372    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    7.1892   -4.1572    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    7.8092   -3.5372    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    9.2312   -1.0742    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    9.4581   -0.2272    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    8.6112   -0.0003    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.4832   -0.4716    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.2529   -1.3177    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.0990   -1.5479    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.7016   -0.1026    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    3.7391    3.1372    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.3081    0.7236    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    6.0954    3.4900    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    5.4826    4.1171    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    4.8555    3.5043    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.6315    3.5301    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    2.0187    4.1572    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "    1.3916    3.5444    0.0000 H   0  0  0  0  0  0  0  0  0  0  0  0\n",
            "  1 12  2  0  0  0  0\n",
            "  2 14  2  0  0  0  0\n",
            "  3 21  1  0  0  0  0\n",
            "  3 26  1  0  0  0  0\n",
            "  4 25  1  0  0  0  0\n",
            "  4 27  1  0  0  0  0\n",
            "  5 10  1  0  0  0  0\n",
            "  5 14  1  0  0  0  0\n",
            "  5 17  1  0  0  0  0\n",
            "  6 12  1  0  0  0  0\n",
            "  6 14  1  0  0  0  0\n",
            "  6 18  1  0  0  0  0\n",
            "  7 10  2  0  0  0  0\n",
            "  7 16  1  0  0  0  0\n",
            "  8 11  2  0  0  0  0\n",
            "  8 20  1  0  0  0  0\n",
            "  9 10  1  0  0  0  0\n",
            "  9 11  1  0  0  0  0\n",
            "  9 12  1  0  0  0  0\n",
            "  9 28  1  0  0  0  0\n",
            " 11 13  1  0  0  0  0\n",
            " 13 15  1  0  0  0  0\n",
            " 13 16  2  0  0  0  0\n",
            " 15 19  1  0  0  0  0\n",
            " 15 29  1  0  0  0  0\n",
            " 15 30  1  0  0  0  0\n",
            " 16 31  1  0  0  0  0\n",
            " 17 32  1  0  0  0  0\n",
            " 17 33  1  0  0  0  0\n",
            " 17 34  1  0  0  0  0\n",
            " 18 35  1  0  0  0  0\n",
            " 18 36  1  0  0  0  0\n",
            " 18 37  1  0  0  0  0\n",
            " 19 38  1  0  0  0  0\n",
            " 19 39  1  0  0  0  0\n",
            " 19 40  1  0  0  0  0\n",
            " 20 21  2  0  0  0  0\n",
            " 20 22  1  0  0  0  0\n",
            " 21 23  1  0  0  0  0\n",
            " 22 24  2  0  0  0  0\n",
            " 22 41  1  0  0  0  0\n",
            " 23 25  2  0  0  0  0\n",
            " 23 42  1  0  0  0  0\n",
            " 24 25  1  0  0  0  0\n",
            " 24 43  1  0  0  0  0\n",
            " 26 44  1  0  0  0  0\n",
            " 26 45  1  0  0  0  0\n",
            "\n",
            "> <PUBCHEM_OPENEYE_CAN_SMILES>\n",
            "CCC(CO)N1CC(CC1=O)C#C\n",
            "\n",
            "> <PUBCHEM_OPENEYE_ISO_SMILES>\n",
            "CC[C@@H](CO)N1CC(CC1=O)C#C\n",
            "\n",
            "> <PUBCHEM_CACTVS_TPSA>\n",
            "40.5\n",
            "\n",
            "> <PUBCHEM_MONOISOTOPIC_WEIGHT>\n",
            "181.110278721\n",
            "\n",
            "> <PUBCHEM_TOTAL_CHARGE>\n",
            "0\n",
            "\n",
            "> <PUBCHEM_HEAVY_ATOM_COUNT>\n",
            "13\n",
            "\n",
            "> <PUBCHEM_ATOM_DEF_STEREO_COUNT>\n",
            "1\n",
            "\n",
            "> <PUBCHEM_ATOM_UDEF_STEREO_COUNT>\n",
            "1\n",
            "\n",
            "> <PUBCHEM_BOND_DEF_STEREO_COUNT>\n",
            "0\n",
            "\n",
            "> <PUBCHEM_BOND_UDEF_STEREO_COUNT>\n",
            "0\n",
            "\n",
            "> <PUBCHEM_ISOTOPIC_ATOM_COUNT>\n",
            "0\n",
            "\n",
            "> <PUBCHEM_COMPONENT_COUNT>\n",
            "1\n",
            "\n",
            "> <PUBCHEM_CACTVS_TAUTO_COUNT>\n",
            "-1\n",
            "\n",
            "> <PUBCHEM_COORDINATE_TYPE>\n",
            "1\n",
            "5\n",
            "255\n",
            "\n",
            "> <PUBCHEM_BONDANNOTATIONS>\n",
            "4  10  5\n",
            "6  12  3\n",
            "\n",
            "$$$$\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "from rdkit import Chem\n",
        "\n",
        "def converter(file_name):\n",
        "    mols = [mol for mol in Chem.SDMolSupplier(file_name)]\n",
        "    outname = file_name.split(\".sdf\")[0] + \".smi\"\n",
        "    out_file = open(outname, \"w\")\n",
        "    for mol in mols:\n",
        "        smi = Chem.MolToSmiles(mol)\n",
        "        name = mol.GetProp(\"_Name\")\n",
        "        out_file.write(\"{},{}\\n\".format(smi, name))\n",
        "    out_file.close()\n"
      ],
      "metadata": {
        "id": "lH9amhu91b5p"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "converter(\"/content/Compound_168000001_168500000.sdf\")"
      ],
      "metadata": {
        "id": "DOb7RR5Y1jLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Impl demo"
      ],
      "metadata": {
        "id": "eC7tQ6w4G_S1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "from redis import ResponseError\n",
        "from rdkit.Chem import Draw, AllChem\n",
        "from rdkit import DataStructs, Chem\n",
        "from rdkit import RDLogger\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "RDLogger.DisableLog('rdApp.*')\n"
      ],
      "metadata": {
        "id": "TFT-GuU2HE6L"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from random import random\n",
        "from redisx.client import Client\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# change the following configuration for your redis.\n",
        "REDIS_HOST = \"localhost\"\n",
        "REDIS_PORT = 6666\n",
        "REDIS_DB = 0\n",
        "REDIS_USERNAME = \"\"\n",
        "REDIS_PASSWORD = \"\"\n",
        "\n",
        "\n",
        "def get_client() -> Client:\n",
        "    return Client(\n",
        "        host=REDIS_HOST,\n",
        "        port=REDIS_PORT,\n",
        "        db=REDIS_DB,\n",
        "        username=REDIS_USERNAME,\n",
        "        password=REDIS_PASSWORD,\n",
        "    )\n",
        "\n",
        "\n",
        "def get_random_vectors(dim: int, n: int):\n",
        "    return [[(random()/100)-(random()/100) for _ in range(dim)] for _ in range(n)]\n",
        "\n",
        "\n",
        "def get_numpy_random_vectors_f16(dim: int, n: int):\n",
        "    #return np.random.default_rng().standard_normal(10, dtype=np.float32)\n",
        "    return np.random.uniform(-0.1, 0.1, (n, dim)).astype(np.float16)\n",
        "\n"
      ],
      "metadata": {
        "id": "dlLouOYyHSRA"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Union\n",
        "\n",
        "from redis import ResponseError\n",
        "from redisx.define import UsearchQuantizationType,DistanceMetric\n",
        "from redisx.ann_usearch import VectorType\n",
        "\n",
        "cli = get_client()\n",
        "\n",
        "def create_index(index_name: str, dim:int):\n",
        "    try:\n",
        "        return cli.create_index(\n",
        "            index_name, dim,\n",
        "            metric=DistanceMetric.L2,\n",
        "            quantization=UsearchQuantizationType.F32)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return None\n",
        "\n",
        "def get_index(index_name: str):\n",
        "    try:\n",
        "        return cli.get_index(index_name)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return None\n",
        "\n",
        "def delete_index(index_name: str):\n",
        "    try:\n",
        "        return cli.del_index(index_name)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return False\n",
        "\n",
        "def add_vector(index_name: str, name: str, vector: Union[VectorType, str]):\n",
        "    try:\n",
        "        return cli.add_vector(index_name, name, vector)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return None\n",
        "\n",
        "def get_vector(index_name: str, name: str):\n",
        "    try:\n",
        "        return cli.get_vector(index_name, name)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return None\n",
        "\n",
        "def del_vector(index_name: str, name: str):\n",
        "    try:\n",
        "        return cli.del_vector(index_name, name)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return False\n",
        "\n",
        "def kann_search(index_name: str, k: int, query_vector: Union[VectorType, str]):\n",
        "    try:\n",
        "        return cli.kann_search(index_name, k, query_vector)\n",
        "    except ResponseError as e:\n",
        "        print(e)\n",
        "        return False\n",
        "\n",
        "def test_search():\n",
        "    dim = 1024\n",
        "    print(\"delete_index res {}\".format(delete_index(\"test_idx0\")))\n",
        "    print(\"create_index res {}\".format(create_index(\"test_idx0\", dim)))\n",
        "    print(\"get_index res {}\".format(get_index(\"test_idx0\")))\n",
        "    print(\"\\n======= kann search =====\\n\")\n",
        "    for i, vector in enumerate(get_random_vectors(dim, 10)):\n",
        "        name = \"n%i\" % i\n",
        "        print(\"add_vector {} {} res {}\".format(\n",
        "            name, vector[0:10], add_vector(\"test_idx0\", name, vector)))\n",
        "        # print(\"get_vector res {}\".format(get_vector(\"test_idx0\", name)))\n",
        "\n",
        "    print(\"after add, get_index res {}\".format(get_index(\"test_idx0\")))\n",
        "    k = 10\n",
        "    for i, query_vector in enumerate(get_random_vectors(dim, 3)):\n",
        "        print(\"{}. kann_search k {} query_vector {} res {}\".format(\n",
        "            i, k, query_vector[0:10],\n",
        "            kann_search(\"test_idx0\", k, query_vector)))\n",
        "\n",
        "    print(\"delete_index res {}\".format(delete_index(\"test_idx0\")))\n",
        "\n",
        "def test_search_f16():\n",
        "    dim = 1024\n",
        "    print(\"delete_index res {}\".format(delete_index(\"test_idx0\")))\n",
        "    print(\"create_index res {}\".format(create_index(\"test_idx0\", dim)))\n",
        "    print(\"get_index res {}\".format(get_index(\"test_idx0\")))\n",
        "    print(\"\\n======= kann search =====\\n\")\n",
        "    for i, vector in enumerate(get_numpy_random_vectors_f16(dim, 10)):\n",
        "        name = \"n%i\" % i\n",
        "        print(\"add_vector {} {} res {}\".format(\n",
        "            name, vector[0:10], add_vector(\"test_idx0\", name, vector)))\n",
        "        print(\"get_vector res {}\".format(get_vector(\"test_idx0\", name)))\n",
        "\n",
        "    print(\"after add, get_index res {}\".format(get_index(\"test_idx0\")))\n",
        "    k = 10\n",
        "    for i, query_vector in enumerate(get_numpy_random_vectors_f16(dim, 3)):\n",
        "        print(\"{}. kann_search k {} query_vector {} res {}\".format(\n",
        "            i, k, query_vector[0:10],\n",
        "            kann_search(\"test_idx0\", k, query_vector)))\n",
        "\n",
        "    print(\"delete_index res {}\".format(delete_index(\"test_idx0\")))"
      ],
      "metadata": {
        "id": "o5sLnCtEHWgD"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "INDEX_NAME=\"MOLSEARCH_TEST\""
      ],
      "metadata": {
        "id": "C-JZc57zIMJ3"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_index():\n",
        "    \"\"\"\n",
        "    创建用于存储分子结构的向量索引：\n",
        "    * 本示例的索引名称为\"MOLSEARCH_TEST\"。\n",
        "    * 向量维度为512。\n",
        "    * 计算向量距离函数为L2(欧几里得距离,similarity值越小，越相似)。\n",
        "    \"\"\"\n",
        "    ret = get_index(INDEX_NAME)\n",
        "    if ret is None:\n",
        "        create_index(INDEX_NAME, 512)\n",
        "    print(\"create index done\")\n"
      ],
      "metadata": {
        "id": "3XjFnbXDIAfH"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "build_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBf871uYVvN4",
        "outputId": "8ae0f73c-1c9f-4d4a-b1a2-bb837b13ef90"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index: usearch.MOLSEARCH_TEST does not exist\n",
            "create index done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_index(INDEX_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4wR3fZiaNVX",
        "outputId": "2b839de1-b454-4237-debe-4a9326c39c9a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'usearch.MOLSEARCH_TEST',\n",
              " 'dimensions': 512,\n",
              " 'metric': 'L2sq',\n",
              " 'quantization': 'F32',\n",
              " 'connectivity': 10,\n",
              " 'expansion_add': 128,\n",
              " 'expansion_search': 3,\n",
              " 'serialization_file_path': '/content/0.usearch.MOLSEARCH_TEST.idx',\n",
              " 'serialized_length': 112,\n",
              " 'index_size': 0,\n",
              " 'index_capacity': 10,\n",
              " 'index_mem_usage': 336}"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def do_load(file_path):\n",
        "    \"\"\"\n",
        "\n",
        "    您需要输入分子结构数据集的路径，该方法会自动提取分子结构的向量特征（smiles_to_vector），并将数据写入Vector中。\n",
        "    存入格式为：\n",
        "    * 向量索引名称为“MOLSEARCH_TEST”。\n",
        "    * Key为分子结构，唯一的化合物 ID (CID) ，例如“168000001”。\n",
        "    * 特征信息为512维向量信息。\n",
        "    * “smiles”为化学分子式，例如“CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC”。\n",
        "    \"\"\"\n",
        "    num = 0\n",
        "    lines = []\n",
        "    with open(file_path, 'r') as f:\n",
        "        for line in f:\n",
        "            if line.find(\"smiles\") >= 0:\n",
        "                continue\n",
        "            lines.append(line)\n",
        "            if len(lines) >= 10:\n",
        "                parallel_submit_lines(lines)\n",
        "                num += len(lines)\n",
        "                lines.clear()\n",
        "                if num % 10000 == 0:\n",
        "                    print(\"load num\", num)\n",
        "    if len(lines) > 0:\n",
        "        parallel_submit_lines(lines)\n",
        "    print(\"load done\")\n",
        "\n",
        "\n",
        "def parallel_submit_lines(lines):\n",
        "    \"\"\"\n",
        "    并发写入的调度方法。\n",
        "    \"\"\"\n",
        "    with ThreadPoolExecutor(len(lines)) as t:\n",
        "        for line in lines:\n",
        "            t.submit(handle_line, line=line)\n",
        "\n",
        "\n",
        "def handle_line(line):\n",
        "    \"\"\"\n",
        "    单个分子结构的写入处理。\n",
        "    \"\"\"\n",
        "    if line.find(\"smiles\") >= 0:\n",
        "        return\n",
        "    parts = line.strip().split(',')\n",
        "    try:\n",
        "        ids = parts[1]\n",
        "        smiles = parts[0]\n",
        "        vec = smiles_to_vector(smiles)\n",
        "        insert_data(ids, smiles, vec)\n",
        "    except Exception as result:\n",
        "        print(result)\n",
        "\n",
        "\n",
        "def smiles_to_vector(smiles):\n",
        "    \"\"\"\n",
        "    提取分子结构的向量特征，从SMI格式转换为Vector。\n",
        "    \"\"\"\n",
        "    mols = Chem.MolFromSmiles(smiles)\n",
        "    fp = AllChem.GetMorganFingerprintAsBitVect(mols, 2, 512 * 8)\n",
        "    hex_fp = DataStructs.BitVectToFPSText(fp)\n",
        "    vec = list(bytearray.fromhex(hex_fp))\n",
        "    return vec\n",
        "\n",
        "\n",
        "def insert_data(id, smiles, vector):\n",
        "    \"\"\"\n",
        "    将分子结构的Vector写入Tair Vector中。\n",
        "    \"\"\"\n",
        "    key = \"{}${}\".format(id, smiles)\n",
        "    return add_vector(INDEX_NAME, key, vector)\n",
        "\n",
        "\n",
        "def do_search(search_smiles,k):\n",
        "    \"\"\"\n",
        "    需要输入待查询分子结构，在reidsxann-usearch索引中，查询并返回与该目标最为相似的k个分子结构。\n",
        "    先提取待查询分子结构的向量特征，再通过USEARCH.SEARCH.KANN命令查询到k个（本示例为10）最近的分子结构数据，\n",
        "    分子结构数据在vector node name中\n",
        "    \"\"\"\n",
        "    vector = smiles_to_vector(search_smiles)\n",
        "    result = kann_search(INDEX_NAME, k, vector)\n",
        "    print(\"topK {} q {} result {}\".format(k,vector,result))\n",
        "    if result == None:\n",
        "      return None\n",
        "    print(\"与查询目标分子结构最为相似的10个分子结构如下：\")\n",
        "    matchs = []\n",
        "    for i,item in enumerate(result[\"vals\"]):\n",
        "        name = str(item[\"name\"]).split(\".\"+INDEX_NAME+\".\")[1]\n",
        "        id_smiles = name.split(\"$\")\n",
        "        print(\"{} => {} {}\".format(i,name.split(\"$\"),item))\n",
        "        matchs.append(id_smiles[1])\n",
        "    return matchs\n"
      ],
      "metadata": {
        "id": "A3wThNceItZ-"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vec = smiles_to_vector(\"CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC\")\n",
        "print(len(vec),vec)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZE5SBI4UUFd",
        "outputId": "c481c49d-32dc-45e1-c740-566470ba49f1"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "512 [32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 128, 0, 0, 1, 32, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 32, 32, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insert_data(168000001,\"CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC\",vec)"
      ],
      "metadata": {
        "id": "bsy4-URfVGWv"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name  = \"{}${}\".format(168000001, \"CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC\")\n",
        "name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zTqVTCgAagOZ",
        "outputId": "9f28b710-dbe2-4ddc-9d05-037b89b551c5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'168000001$CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_index(INDEX_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8l8dIZma5_I",
        "outputId": "127dacec-a9ae-481d-ca10-4c37a136bd81"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'usearch.MOLSEARCH_TEST',\n",
              " 'dimensions': 512,\n",
              " 'metric': 'L2sq',\n",
              " 'quantization': 'F32',\n",
              " 'connectivity': 10,\n",
              " 'expansion_add': 128,\n",
              " 'expansion_search': 3,\n",
              " 'serialization_file_path': '/content/0.usearch.MOLSEARCH_TEST.idx',\n",
              " 'serialized_length': 2256,\n",
              " 'index_size': 1,\n",
              " 'index_capacity': 10,\n",
              " 'index_mem_usage': 20971792}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_vector(INDEX_NAME,name)"
      ],
      "metadata": {
        "id": "wBQaR7zfar8d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "do_search(\"CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC\",1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGWupeYjbRH0",
        "outputId": "d437c543-bae1-42d3-c8ab-fe1b72554c21"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topK 1 q [32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 8, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 128, 0, 0, 1, 32, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 32, 32, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 1, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 8, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] result {'size': 1, 'vals': [{'similarity': '0', 'name': 'usearch.MOLSEARCH_TEST.168000001$CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC', 'id': 483599729863688204}]}\n",
            "与查询目标分子结构最为相似的10个分子结构如下：\n",
            "0 => ['168000001', 'CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC'] {'similarity': '0', 'name': 'usearch.MOLSEARCH_TEST.168000001$CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC', 'id': 483599729863688204}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del_vector(INDEX_NAME,name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N57H-soa-OA",
        "outputId": "1f641f8b-af77-48c9-f356-3593465a115d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_index(INDEX_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ex1ZcnXibDsq",
        "outputId": "55704b3e-8425-4dfb-c08e-3328d0dfbdfb"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'usearch.MOLSEARCH_TEST',\n",
              " 'dimensions': 512,\n",
              " 'metric': 'L2sq',\n",
              " 'quantization': 'F32',\n",
              " 'connectivity': 10,\n",
              " 'expansion_add': 128,\n",
              " 'expansion_search': 3,\n",
              " 'serialization_file_path': '/content/0.usearch.MOLSEARCH_TEST.idx',\n",
              " 'serialized_length': 2256,\n",
              " 'index_size': 0,\n",
              " 'index_capacity': 10,\n",
              " 'index_mem_usage': 20971792}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 写入示例数据\n",
        "do_load(\"./oss_Compound_168000001_168500000.smi\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qX1zbfcrb8vZ",
        "outputId": "7412a2f0-3d26-4df7-b389-e0c87dbc5aa6"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node: usearch.MOLSEARCH_TEST.168000001$CCC1=CN=C2C(C(=O)N(C)C(=O)N2C)/C1=N/c1ccc(OC)cc1OC already exists\n",
            "load num 10000\n",
            "load done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_index(INDEX_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjA4eVgRcTOV",
        "outputId": "be8b1b0e-6091-46c9-d70d-181635c8d39f"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'usearch.MOLSEARCH_TEST',\n",
              " 'dimensions': 512,\n",
              " 'metric': 'L2sq',\n",
              " 'quantization': 'F32',\n",
              " 'connectivity': 10,\n",
              " 'expansion_add': 128,\n",
              " 'expansion_search': 3,\n",
              " 'serialization_file_path': '/content/0.usearch.MOLSEARCH_TEST.idx',\n",
              " 'serialized_length': 23610016,\n",
              " 'index_size': 11012,\n",
              " 'index_capacity': 22020,\n",
              " 'index_mem_usage': 37925088}"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 在MOLSEARCH_TEST索引中，查询与\"CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1\"最为相似的10个分子结构。\n",
        "matchs = do_search(\"CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1\",10)\n",
        "matchs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hM-593Mfcim5",
        "outputId": "aac98827-a150-4106-c98a-8c4090462197"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topK 10 q [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 136, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 32, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 192, 0, 0, 4, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 16, 128, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 64, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 8] result {'size': 10, 'vals': [{'similarity': '0', 'name': 'usearch.MOLSEARCH_TEST.168000009$CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1', 'id': 483600173604536332}, {'similarity': '29534', 'name': 'usearch.MOLSEARCH_TEST.168003114$Cc1cc(C)cc(N2CCN(CC(=O)NC3CCCC3)C(=O)C2=O)c1', 'id': 483600180919336972}, {'similarity': '60222', 'name': 'usearch.MOLSEARCH_TEST.168000210$COc1ccc(N2CCN(CC(=O)Nc3cc(C)cc(C)c3)C(=O)C2=O)cc1OC', 'id': 483600173973766156}, {'similarity': '61123', 'name': 'usearch.MOLSEARCH_TEST.168001000$COc1ccc(N2CCN(CC(=O)Nc3ccc(C)cc3)C(=O)C2=O)cc1OC', 'id': 483600175701622796}, {'similarity': '64524', 'name': 'usearch.MOLSEARCH_TEST.168003038$CCN1CCN(c2cc(C)cc(C)c2)C(=O)C1=O', 'id': 483600180734853132}, {'similarity': '67591', 'name': 'usearch.MOLSEARCH_TEST.168003095$O=C(CN1CCN(c2cccc(Cl)c2)C(=O)C1=O)NC1CCCC1', 'id': 483600180869136396}, {'similarity': '70376', 'name': 'usearch.MOLSEARCH_TEST.168000396$COc1ccc(N2CCN(Cc3ccc(C)cc3)C(=O)C2=O)cc1OC', 'id': 483600174359511052}, {'similarity': '71121', 'name': 'usearch.MOLSEARCH_TEST.168002227$CCOC(=O)CN1CCN(C2CC2)C(=O)C1=O', 'id': 483600178738429964}, {'similarity': '73197', 'name': 'usearch.MOLSEARCH_TEST.168000441$Cc1cc(C)cc(NC(=O)CN2CCN(c3ccc(F)c(F)c3)C(=O)C2=O)c1', 'id': 483600174443528204}, {'similarity': '73817', 'name': 'usearch.MOLSEARCH_TEST.168000820$Cc1cc(C)cc(N2CCN(CC#N)C(=O)C2=O)c1', 'id': 483600175282389004}]}\n",
            "与查询目标分子结构最为相似的10个分子结构如下：\n",
            "0 => ['168000009', 'CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1'] {'similarity': '0', 'name': 'usearch.MOLSEARCH_TEST.168000009$CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1', 'id': 483600173604536332}\n",
            "1 => ['168003114', 'Cc1cc(C)cc(N2CCN(CC(=O)NC3CCCC3)C(=O)C2=O)c1'] {'similarity': '29534', 'name': 'usearch.MOLSEARCH_TEST.168003114$Cc1cc(C)cc(N2CCN(CC(=O)NC3CCCC3)C(=O)C2=O)c1', 'id': 483600180919336972}\n",
            "2 => ['168000210', 'COc1ccc(N2CCN(CC(=O)Nc3cc(C)cc(C)c3)C(=O)C2=O)cc1OC'] {'similarity': '60222', 'name': 'usearch.MOLSEARCH_TEST.168000210$COc1ccc(N2CCN(CC(=O)Nc3cc(C)cc(C)c3)C(=O)C2=O)cc1OC', 'id': 483600173973766156}\n",
            "3 => ['168001000', 'COc1ccc(N2CCN(CC(=O)Nc3ccc(C)cc3)C(=O)C2=O)cc1OC'] {'similarity': '61123', 'name': 'usearch.MOLSEARCH_TEST.168001000$COc1ccc(N2CCN(CC(=O)Nc3ccc(C)cc3)C(=O)C2=O)cc1OC', 'id': 483600175701622796}\n",
            "4 => ['168003038', 'CCN1CCN(c2cc(C)cc(C)c2)C(=O)C1=O'] {'similarity': '64524', 'name': 'usearch.MOLSEARCH_TEST.168003038$CCN1CCN(c2cc(C)cc(C)c2)C(=O)C1=O', 'id': 483600180734853132}\n",
            "5 => ['168003095', 'O=C(CN1CCN(c2cccc(Cl)c2)C(=O)C1=O)NC1CCCC1'] {'similarity': '67591', 'name': 'usearch.MOLSEARCH_TEST.168003095$O=C(CN1CCN(c2cccc(Cl)c2)C(=O)C1=O)NC1CCCC1', 'id': 483600180869136396}\n",
            "6 => ['168000396', 'COc1ccc(N2CCN(Cc3ccc(C)cc3)C(=O)C2=O)cc1OC'] {'similarity': '70376', 'name': 'usearch.MOLSEARCH_TEST.168000396$COc1ccc(N2CCN(Cc3ccc(C)cc3)C(=O)C2=O)cc1OC', 'id': 483600174359511052}\n",
            "7 => ['168002227', 'CCOC(=O)CN1CCN(C2CC2)C(=O)C1=O'] {'similarity': '71121', 'name': 'usearch.MOLSEARCH_TEST.168002227$CCOC(=O)CN1CCN(C2CC2)C(=O)C1=O', 'id': 483600178738429964}\n",
            "8 => ['168000441', 'Cc1cc(C)cc(NC(=O)CN2CCN(c3ccc(F)c(F)c3)C(=O)C2=O)c1'] {'similarity': '73197', 'name': 'usearch.MOLSEARCH_TEST.168000441$Cc1cc(C)cc(NC(=O)CN2CCN(c3ccc(F)c(F)c3)C(=O)C2=O)c1', 'id': 483600174443528204}\n",
            "9 => ['168000820', 'Cc1cc(C)cc(N2CCN(CC#N)C(=O)C2=O)c1'] {'similarity': '73817', 'name': 'usearch.MOLSEARCH_TEST.168000820$Cc1cc(C)cc(N2CCN(CC#N)C(=O)C2=O)c1', 'id': 483600175282389004}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1',\n",
              " 'Cc1cc(C)cc(N2CCN(CC(=O)NC3CCCC3)C(=O)C2=O)c1',\n",
              " 'COc1ccc(N2CCN(CC(=O)Nc3cc(C)cc(C)c3)C(=O)C2=O)cc1OC',\n",
              " 'COc1ccc(N2CCN(CC(=O)Nc3ccc(C)cc3)C(=O)C2=O)cc1OC',\n",
              " 'CCN1CCN(c2cc(C)cc(C)c2)C(=O)C1=O',\n",
              " 'O=C(CN1CCN(c2cccc(Cl)c2)C(=O)C1=O)NC1CCCC1',\n",
              " 'COc1ccc(N2CCN(Cc3ccc(C)cc3)C(=O)C2=O)cc1OC',\n",
              " 'CCOC(=O)CN1CCN(C2CC2)C(=O)C1=O',\n",
              " 'Cc1cc(C)cc(NC(=O)CN2CCN(c3ccc(F)c(F)c3)C(=O)C2=O)c1',\n",
              " 'Cc1cc(C)cc(N2CCN(CC#N)C(=O)C2=O)c1']"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit import Chem\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def to_images(data):\n",
        "    imgs = []\n",
        "    for smiles in data:\n",
        "        print(smiles)\n",
        "        mol = Chem.MolFromSmiles(smiles)\n",
        "        img=Chem.Draw.MolToImage(mol,size=(500,500))\n",
        "        imgs.append(img)\n",
        "        plt.imshow(img)\n",
        "        plt.show()\n",
        "    return imgs\n"
      ],
      "metadata": {
        "id": "QiehH5-PcvY6"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images = to_images(matchs)\n",
        "images"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R48vsAE6czkk",
        "outputId": "1de155d1-469d-44aa-d948-121aaa83fa76"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CCOC(=O)N1CCC(NC(=O)CN2CCN(c3cc(C)cc(C)c3)C(=O)C2=O)CC1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cc1cc(C)cc(N2CCN(CC(=O)NC3CCCC3)C(=O)C2=O)c1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COc1ccc(N2CCN(CC(=O)Nc3cc(C)cc(C)c3)C(=O)C2=O)cc1OC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COc1ccc(N2CCN(CC(=O)Nc3ccc(C)cc3)C(=O)C2=O)cc1OC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CCN1CCN(c2cc(C)cc(C)c2)C(=O)C1=O\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O=C(CN1CCN(c2cccc(Cl)c2)C(=O)C1=O)NC1CCCC1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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F3t7eSEpKQl5eHhwcHIzdzMfTaoG9e+XAKSgA5s4FXn9dnrknhFxdAgB8fIABA4DsbHk/Ozu5uoaSw5ee2zP9dvv374/Nmzdj9+7d8PPzM2z38fFBQUEBMv622Fpqaip8fHwM+/x9tl/R10X7/J2NjQ3UanWxBxE9YG9vj8qVK+PcuXPQ/VUKSaVSoXr16rh+/Tqys7ON3MLHKAqgRYvk+538/IDly4E33ngQUJcuAdHRwHvvAffvy/dHubrK9QrVagbUC+CpfsNCCPTv3x/r1q3Drl27ULFixWLP161bF1ZWVti5c6dhW3x8PBITExEZGQkAiIyMRFxcHNLS0gz7bN++HWq1GqGhoc/zWoheWLa2tggMDERiYqLhXimVSoWQkBDcuHEDOTk5Rm7hY2RnA5MnAxMmAM2aydeY6teXg0cI4NQpuXe1Z49cTYL1+V5IT/Vbj46Oxs8//4wNGzbAycnJcA3J2dkZdnZ2cHZ2Rp8+fTB48GC4ublBrVbjo48+QmRkJBr8VbKkZcuWCA0NRc+ePTFt2jSkpKRg9OjRiI6Oho2NTcm/QqIXgEqlQvny5aFSqXD58mUUFBTgzp078PX1RVJSErKysiCEgCRJxm6qfP0pMVGe9LBrF9CrFzBkiNw7kiR5KO+PP4CxY+VrT/PnAy1b8rrTi+pppgwCeOxj8eLFhn3y8vLEhx9+KFxdXYW9vb148803xa1bt4r9O9euXRNt2rQRdnZ2wsPDQwwZMkQUFhY+cTs4BZ3oUXv27BGVKlUSUVFRokKFCkKSJOHg4CBUKpX45ptvREZGhrGbKE8f37NHiCZNhAgIEGLhQnma+V/T5kVWlhDz5gnh7S3EK68IcfLkg+fIrDzpeVwSwvRu087KyoKzszMyMzN5fYroL3FxcejevTsSEhJQs2ZNNG/eHAcPHsTx48fh5OSEV155BU2aNEGbNm3g5+dX5r0qnU6H0zt2oObw4bDQauWe0ptvPhjGu31bHvJbuBBo3hwYPRoICeF1JzP1pOdxDvISmTghBO7cuYOffvoJN27cwGuvvYbp06fD398fOTk5uHnzJjZu3IgVK1Zg7969mDJlCpo1a4Zu3bqhRo0acHFxgZWVVamFlhAC2dnZ+OGHHzD/66/xTdOmeO2jj4CwMDmg9Hp5LawhQ+TrT+++C4wYATg7y8N/9EJjT4rIhAkhcPXqVYwbNw6///47evTogQkTJsDJyckQOkV/4tnZ2di/fz+2bNmCI0eO4MqVK6hWrRo6dOiABg0aoHbt2o/c41gSUlJSMGPGDCxduhQtW7bEjBkzUM7HRw4grRY4ehT49FM5qIYOBXr3liudM6DM2hOfx0t52LFU8JpU6UlMFOLbb4X4++WLq1fl7dnZxmkXPUqn04njx4+Ltm3bCm9vbzF79mxx7949Q1mkf5Kfny/i4uLEkiVLRJcuXYSbm5sICAgQb7zxhpg9e7ZISEgQOp1O6PX6//y3/o1erxdXrlwRvXr1Eu7u7mLEiBHi5s2bhn9Tr9UK/bp1QoSFCREcLMTmzQ/KIJHZe9LzOEOKitmzR4iQECESEopv37ZN3n7zplGaRX+j0WjE1q1bRZUqVURwcLDYunXrE9e+LKLVakV2dra4ePGi+PLLL0XdunWFn5+fKF++vIiKihK//fabuHHjhsjPz3/q9mm1WnHo0CEREREhypcvLxYtWiTy8vIMwZeVlSV+mjdP3K5SReibN5dr8mm1T/1zyHQ96Xmc16SoGL1engH890Hgou1kfJmZmVixYgUmTZqE0NBQTJw4EREREU9d9sjCwgKOjo6oWrUqqlSpgg8++AAHDhzA9u3bsW/fPrz11luoXLky2rZti4YNGyI8PBxubm7/ee3q/v372LZtG0aMGAG1Wo1FixahVatWsLCwgBACN2/exIwZM/DTsmWY2acPevTtK68JxeE9egyGFD1W0RI+D39NxiUEkJOTg88++wyLFy9GixYtMHHiRFSuXPm56/IV1f9r1aoVmjZtioSEBMTGxmLbtm2YN28eFi9ejGrVqqFdu3Zo3749AgICoFKpigWWEAL5+flYtGgRZs6cieDgYEybNg01a9Y0BNSNGzcwcOBA7N+/H//vvffQ6uOPIXl5Pe+hITPGiRNUzO7d8uSqDh2Ah6+hX74M7NwJxMYC5coZq3UlR/w1I87CwgIuLi7KLr4KedmkhARg2rQD+O23PujVqwuGDBkCFxeXUpuVp9frUVBQgNTUVGzevBmrV69GQkICCgoK0LBhQ/Ts2RM1a9aEj48PbG1tkZaWhilTpmDZsmV4/fXXMWPGDHh5eUGSJBQWFuLw4cMYNmwY7ty5gxEjRuDtt9+GnZ2dMm4wpjLHKej0XAoLiw/vabXm05vS6XT4/fff8d1338HGxgavvfYamjRpgsDAQFj80+qvRqTXAwcOyAXCExPr48svf0abNlXh6OhYqj9XpVLB1tYWFSpUQHR0NKKionDw4EHs2bMHBw8eRFRUFCpVqoTmzZujWrVq2LJlCw4dOoTBgwfjgw8+gIeHBwBAq9Vi1apV+Oyzz2BlZYXZs2ejZcuWijzWpDwMKXqESiXPBH64NOPWrcCJE8ZrU0kQQkCr1WLr1q0YOnQobty4AQcHB+zYsQMVKlRAo0aN8NZbb6FevXqwtLSEJElG/ZRfNOS6fTvw8cfyJZuvvrJG69Z1jVIhyNHR0TAcePPmTZw5cwZbtmzBTz/9hPz8fOTm5iIoKAgNGzaEm5sbhBC4f/++YfgvJCTkma+f0YuL7xR6YeTk5GDRokXo27cv3N3dsXHjRly+fBnLli1DSEgIduzYgTfeeANNmjTBV199hZMnTyI9PR36hy/OlZGiAuGzZsm3DVWoIK/1164dYGVV5s0pxsbGBoGBgejQoQPmzp2L2NhYTJo0CY0bN8atW7fw/vvv49ChQ0hKSsKwYcMwZcoUtGzZEkuXLkWDBg0YUPR0SnmWYangFPTSs2uXEJUqyfdFPWzLFnm7qU5BT0lJEWPGjBEeHh6iR48eIj4+vtg9QBqNRpw9e1bMmzdPvPnmm8Lb21v4+/uLHj16iPnz54vz588/1z1DTystTYiPPhLCy0uIXr0e/X0oUWpqqpg5c6awsrISo0ePFp06dRI+Pj5i9OjRyqgbSIrC+6TomZw6JUT37o+G0eHD8va7d43SrGem1+tFYmKieOutt4Srq6sYOnSoSE1N/cfA0el0Ii0tTRw7dkx89tlnokaNGsLDw0O89NJL4r333hN79uwX2dlaUVhYOnVPdTohLl8WomtXucbqqFFCpKaaTo3V5ORkUb58eeHm5ib8/f3F0qVLRXZ2dpkGPJkGFpilZ5KeDhw5Is/sK1raB5BXVjh7Fmjd2nTqfRYWFiI2NhYDBgxASkoKhg8fjv/7v/+DtbX1f15rEvIHONy/fx/Hjh3DTz/9hKNHj8LauissLIbhzTdt0KQJULWqvIp5SVy60unkBWpHjQJu3ZInSnTvLg/vmcoEuHv37qFLly44c+YMVq1ahSZNmnB4jx6Ls/vomVy7BnzyiXxi/OUXoEoVefvBg/JJ89w50wipwsJCrFu3DpMnTwYAfP3112jZsuUTr1lWNGnC0dERr776Kho3bowLFy7g/HlnbNpkjW++kZc5atgQeOUV4NVX5YLdzxomGg2wbh0wfjzg4AB89ZV8G4ApHOuH2djYoHLlysjIyEDt2rUZUPTcGFL0WHo98P338moKDg7Gbs2TE0JAo9Hghx9+wOTJkxEcHIyvv/4aNWrUeK4pz5aWlqhRowaqV5fX30tMlAt2L1sm/3fWLHnx2B49gPBwwNGxeA/o9Glg3z55BYpq1R5s37YNyM+XV6mYPFkOuunTgZdeMr2AAh6E1Jo1a1DAEiVUAkzwz4BKm0oF9OsnT33eu9d07o8Sf1U0GDFiBMaMGYN27drhxx9/NFQ8KAmSBLi6yqtMDBgA7N8P/Pwz0KYNcPEi8PbbQKNGcuDs2QOkpMjH78QJOfDHjZMDqci6dcD69fJ0/27dgBUrHqxgYYosLCwQGBgIjUaDGzduGLs5ZAZM9E+BSlv16kCzZvIadI0bG7s1T+b69esYMmQI9u/fjw8//BADBgyAp6dnqfysop6QnZ18nJo0katyHD0K7NgBLFoEfPcdULcu8M478o3RdnZyaG3ZAvTsWTyImjaVH5aWpnP96XEkSYKnpyfc3d1x/vx51KlTx9hNIhPHnhQ9loODXB4pPR1YvPhBHb+iQrN6vTJ6WOKvG3RjYmLQrVs3nDp1ChMnTsSnn35qqHhQFiws5KG6Hj2Ab76Re1EjRwJ378pDfZaW8gSL7t2BBQuAmzeLf7+VlWlNkPg33t7e8PDwQFxcnLGbQs9JCAGt0KJQFBrWJSsUhbitvY0CfdkM57InRf+oWjXgww+BmTPlWX0AkJYGrF0LeHoC9erJxauNUf2gSEFBAX755RdMnjwZDg4OWLRoEZo1a2a0ahEqFaBWA05O8sy/vn2B3FxgzRo5yN54Qx5G/fJL+dqTOfL29oanpyfOnDkDIQRr85kgIQTStGk4ev8o4vPjoRM6VLKphCaOTXBbexu9rvfCAv8FCHcIL/W2MKQIwON7RZIEvPmmfN3k11/lGWhZWfLXp04BoaHyZIE33pBnuRVVQiiLc5IQAnl5eVi4cCFmzJiBGjVqYOzYsYiMjFTESbGoCZaW8iroRXx85Ot9H30EtG9vnLaVNhcXF3h7e+P48eO4f/8+HExp5g3JxZe1dzA8eThO559GA/sGsJKssDlzM/bl7EN3t+4o1BdCj7KpxMKQIgghD+sdP178hArIs9Q++UQepsrLAypVAlavlkNqyRJg40b56/LlgU6d5FDz9pa/r7TqhwohkJycjC+++AKrVq1C27ZtMX78ePj5+SkioP5L06Zyz3T+fLlwbyms2G5UlpaWCA4OxoEDB3D9+nWEhoYau0n0lL69+y1O5Z3CHP85qGtfFxIkpGvTcbXgKuxUdmXaFl6TesHp9fJNuh9+KH+6v3lT7h05OcnPSxJQpw4weLB8crWykof6WrYEfvpJniTw6adyD2HuXHmSRb9+wNKlwPnzcjX1kiSEwLlz59C/f3+sXr0aH3zwAebOnWtY38gUWFjIMwNTUoCYGGO3pnRUrVoVWq0WV69eNXZT6CnpoMPq9NV4zek1RDpEwl5lDzuVHXytffGy48tQlXFssCf1AhMCOHkSGDQIuHoVmDRJnqXWsKE8zbqInZ08iaJLl+K9I5VKnjrdv7889friRXnNqbVr5arpQUFy4HXoINC0qYCVldzLeZbejhACer0eBw8exNixY3HlyhVMnDgR3bp1g62t7XMeibIlSYCfn9w7HTGi+OKS5iIoqAp8fKogPV2CEOYxIeRFka3LRrouHRWsK8BSMn5EGL8FVOaEkG8g3bpVXpLD1VW+cbdFi3+usO3oKD8eR5Lk3pWHBxARIfcSTp+We1N//AFs25YPN7eO6NDhZbRr1w4VK1aEWq1+qp6PRqPB2rVrMXr0aDg6OmLlypWoX78+rIxdEvwJ+foCkZEPvra0lEP/yhXAHBemDQ6ujlu31iA21hbdupXe0C+VPK3QQoIEK0kZf1sMqRdQbq48NDdnDlCrFjBxIlC79vN/2pUkeaafm5tcJuiVV+ST8MmTudiwwQvff/895s6di+bNm6Nx48Zo0KABqlevDsv/uHP1zp07WLJkCaZPn47IyEhMnDgRNWrUMInrT0Vat34wQ1II4MIFIDVVLoNUNLRqThwcLODnp8aNG0B2tvlddzNnags1bCQb3NLegl7ooZKMO4zOkHqBFE2QmDgRWL5cPmmOHw9Urlw6P8/CQq79FxzsjpYtv8aff/6JmJgYLFu2DFu2bIG/vz/Cw8PRoUMHtGjRAvb29sWmjgshcPfuXUyYMAHLly9Hp06dMGbMGPj7+5tUQD3Otm3Apk3AvHnyLElzFBoq3+Ccns6QMgVCCOihh7VkjYaODXEs9xhuFt5Eeavy8vMwzo2RDKkXhF4PxMUBY8bIJXoGDQKiox+dzVcaJEmCm5sbwsPDUbduXbz77ruIi4vDypUrsWvXLmzZsgUeHh5466230KJFC1SuXBnOzs6Ij4/HoEGDcPr0aQwePBgff/yxIchMnZ8fEB9fvESSuQkLk8tq3b1bfJVnUp5CUYgDOQewNXMrBnkNwhCvIehxrQcG3RiE9zzeg5PKCRfyL6BQFKKRY6MybRtD6gWg18vFTUeOlIeYJkwA/u//yr4+nCRJsLCwgJOTExo2bIgGDRogISEBBw4cwI4dO7BgwQJ8/fXXePnllxEcHIwtW7bg6tWrqFWrFrp06WJW99uEhMgr76alwWwnFlSvLs9gvHfPfF+jOcjV5WJdxjqMuzUOFWwqIFOXiRDbECwKWIRV6avwTdo38gdNCze0c24HJwsnvKZ+DW4WbmXSPobUc9DpdCgsLISFhYXhuoqSPuULIVBQoMPatXpMnmwFGxsJc+fKteaUcCFbpVKhcuXKqFSpEtq3b4/vvvsOI0eOxJUrV/DHH3/AyckJXbt2xbFjx3D//n1jN7dEubrK1+6uXpXXkTLVgrL/pnx5+X2WlMSQUiIhBO7r72NW2izMuz0PYXZhmOU/C1VsqsBCskC4Qziq21VHhi4DAgIOKgeoVWrooMNon9FwtPiHmVQlzAz/NMpGTk4OFi9ejB9++AG1a9dGx44dUbFiRXh7e8Pd3R0qlcrogZWeno6FCxdhxYrrqFJlMr780h2BgcpbAkKSJLi6uiIsLAwuLi4YP348AgMD4e/vj9jYWGzcuBF37twxmxI7kiRP6w8OlidQFBaaX0hJkjwbtGJF+X45rda45bOoOL3QI7kwGZ+lfIbV6avRxbULPvP9DG4WbsX+xuxUdo/cvGsJS7hauv79nyw1ZvanUTaEENi1axc+//xz5OXlITExEStXrkRAQABCQ0NRo0YN1K5dG9WrV0dgYOB/zl4rDUlJSZg+fTqWL1+Ot9/uhhEjJJQvr9xPs5IkwcPDA56enrh27Ro6d+4MAAgMDDRUmDCXkAIAW1v5BH7xonwCN0d2dvJrvHSJIaUkeqHH6bzTGH9rPA7nHsZIn5Ho7d4bbpZlM3z3tBhST6loxtmyZcsghMD69evh4eGBtLQ0HDhwALt27cL8+fNha2sLR0dHlC9fHk2aNMHLL7+MsLAw2NnZwcrKChYWFqVywtXr9YiPj8ewYcMQExODoUOH4v3334erq6tiA6qIh4cHvL29ce7cOQBycNnb2yMgIACXLl2CVquFtZmc6Wxt5VmVGzfKVeXNUVFIbdokD2maGyEECgsLcfToUezatQtvvvkmgoKCYGNjY7QCx/9FK7Q4lHMI/ZP6I0efg9n+s9HBuUOZlzp6GgypZ7B27Vrs2rUL06ZNQ+PGjWFhYQEhBJo2bYpRo0YhLS0NR44cwaFDhxAbG4vly5djxowZsLOzQ0REBBo3boxatWrB19cXvr6+UKvVJfKG1mg0OHToEAYMGICCggJ888036NKliyKGHp+Eh4cHfHx8cObMGej1elhYWMDe3h5BQUE4d+4cdGZ0prO0BCpUkG+qvnEDcHc3dotKnq2tXOvxxg15koi53Q+Wl5eHRYsWYeTIkdBqtZg+fTqaNWuGLl26IDw8HAEBAbCzU8bJXwiBHH0OVqevxrhb4xBkE4T5vvMR6RAJCco+NzCkntKlS5cwc+ZMNGvWDO3atTOs+PrwJ6dy5cqhY8eO6NixIzIzMxEfH4/Lly/j4sWLOH78OKZPn47CwkIEBgaiSpUqCAsLQ926dVG7dm14eHg8U6Dk5+fjl19+wWeffQY3NzfMmjULr7zySomtSFsWHBwc4Ofnh5iYGNy7dw+enp6ws7NDYGAgfv/9d2jNaFxMkuRCvK6u8jWbmjWN3aKSJ0lyTUcHB3m6vZ+fsVtUctLT07Fo0SJ8+eWXiIiIQIcOHZCeno7du3cjOjoaISEhiIiIQLNmzfDqq6/CuSzu9fgXufpcTE2dih/u/IBIx0hMKjcJoXahig8ogCH1VHJycvDll1+ioKAAAwYMgNcT1LNxdnZG/fr1Ub9+fWg0GmRlZSEzMxOxsbE4cOAA9u7diz179sDa2hp2dnZ46aWX0LhxYzRv3hx+fn6wtbWFtbX1P/aGhBC4f/8+Zs6ciblz56JmzZqYM2cOgoKCTKbgahFJklD5rzuLr1y5Ak9PT9ja2iIwMBCJiYnIycmBkxl9HPf2lntQZ88auyWlp1w5eRbjuXNA8+bGbs3zE0IgNTUVkyZNwi+//ILOnTtjzJgxKFeuHHQ6HT788ENcuHABP//8MzZv3oy1a9eiXLlyaNOmDbp16wY/Pz/Y2dmV2eiGXq/HjRs3MDNtJn6x+gVvub2Fod5D4WvlaxIBBQAQJigzM1MAEJmZmWX2MwsLC8WPP/4ovLy8xGeffSb0ev0z/1t6vd7w0Gq14vz58+Lbb78V7733nnj11VdF5cqVhb29vahRo4bo06ePWLhwodizZ4+4cuWK0Gg0hp+t0+nE5cuXRZ8+fYS7u7sYPHiwSElJea62Gdv27dtFQECAWLp0qRBCPlarV68WHh4eYvfu3cZtXAm7fVuI5s2FaNdOCBP+lf2r69eF6NpViOnTjd2S56fVakVsbKxo0aKFKF++vJg8ebLIy8t75O+t6G/75s2b4vvvvxcdO3YUwcHBwsnJSbRr104sWLBAxMbGiry8vFJtr16vFwcPHhTNmjUT1ZtWFwuSFoj7uvuKOT886XmcPaknIP769DRv3jxUqVIFffr0ea5PQQ9/r4WFBapVq4Zq1arh3XffRUpKCq5evYpLly7h2LFjOH36NH755Re4uLigUqVKqFq1KmrWrIk6derAxsYGo0aNwokTJzBy5Ej07t0brq5lNzW0NFSqVAkADEs8SJIEb29vODs748KFC2jatKkRW1eyXFzk4rLnzslrddnbG7tFJUcIeUKIi4tcequoLFJRcWNLy38uZqxEOp0OO3fuxLhx45CSkoIZM2agffv2hgr84q9VQ/8+7N+7d2907twZ586dQ0xMDDZs2IBhw4YhMDAQDRo0wKuvvorWrVvDpYTrRun1evz+++8YNWoUsrOzMXbsWLzt/TasVCZ00IuUSWSWsLLuSRUWFoqhQ4cKX19fsWnTJqHT6Ur9Z+p0OpGTkyNu374tzp07J+bOnSu6du0qgoKChL+/vyhfvrwIDAwUwcHBYtWqVSI/P7/U21QWCgoKREhIiOjVq5fhNf35558iPDxc9OvXz8itK3njxuWKqlWTxIUL2cZuSonS64UYOVKI2rWF2LPnQU+xoEDuOf74o3Hb9zTy8vLE8uXLhZ+fn6hTp444fPiwKCwsLLZPQUGB6NWrl/jkk0/EsWPHRHp6+iP7FBYWijt37oijR4+Kjz76SFSqVEn4+vqK0NBQMWzYMHH8+HFx7969R77vaej1epGdnS0WLFggKlasKF5++WURExMjCgoKFNODKvKk53GG1H/Q6/Vix44dwsfHRwwZMkRkZ5f9yeTh4cGcnBxx8OBBMWTIEOHl5SVmzJhRJqFZVvR6vejYsaNo2bKlSE5OFkIIkZOTI1q3bi2aNGkitFqtkVtYslav3iiCgoLE1q1bjd2UEqXXC9GvnxD29kJ06iRERoa8XaMRon59IebNM277noRerxepqali0qRJws3NTXTt2lWcO3fusSf73NxcERUVJUJCQoSzs7Po0KGDmDt3rjhy5IjIzc195N/V6/Xi1q1b4qeffhJvv/22qFq1qnBxcRHt2rUzfN+zDAfevn1bjBgxQnh4eIj//e9/4tq1a4oLpyIc7isB4q+bSOfMmQMPDw/069cPjv+0qFIpenh40MHBAQ0bNoStrS1WrVoFe3t7k5sg8W8kSUJoaCi2bduGu3fvwtfXF/b29vD390dMTAzu3r37RBNWTEVgYDnk5uYiMTHRrG5WLlK/vlwWae1auV6kqRBCIC0tDcOGDcPmzZvRvXt3DB06FP7+/o/d387ODnPmzEF8fDyOHDmC9evXY/To0fD29ka9evXQsmVLtG3btthwvI+PD7p164b27dvj4sWLOHr0KNasWYMRI0bA19cX4eHhaNmyJdq0aQM3N/lG2396fwghcO3aNXz++edYu3YtoqKiMGTIEPj6+pr8e4oh9S8KCwuxYsUK7N27FwsXLjRcL1ECtVoNIQSysrKM3ZQSV7NmTaz6+Wdk3L0LQP7DbFe3LqokJUGVlGRWqwRWrVoVer0eV69eNcuQqlIFqFMH+OYbIDxc/lrptFotzp07h8GDB+Py5csYPXo0+vTp868zSyVJgpOTE+rVq4datWqhR48euHLlClatWoWNGzdi165dGDduHDp27IguXbogKCgIrq6usLS0hFqtRv369VGnTh306NEDly9fxpo1a7Bhwwbs3LkT48ePR+fOndGxY8di31dEp9MhNjYWH3/8MS5fvoxx48bhvffeg62trVm8nxhS/+Ls2bP45ptv8MYbb+D1119X1C+8KKSys7ON3ZQS16h6dXxqYYGKmZmGyqQdAgLktUauXAHq1jV2E0uMg4MDAgICcOPGDWRnZxv9fpqSJklAt27Ali3AggXyJAol0+l02LJlC8aNG4fCwkLMmjUL7du3f6pKJ5aWlnB2dkadOnVQu3ZtfPzxx9i3bx+2b9+OP/74Az/88APCw8PRpk0bhIeHo06dOnBwcIClpSVcXFwQHh6OevXqYfDgwdizZw+2b9+OjRs34ttvv0WdOnXQtm1bhIeHo3bt2rCwsMAff/yBTz/9FLa2tpg3bx7at29vlFJspcV8XkkJy8/Px7Rp0+Dg4ID3339fcctEqNVqAEB2drbZfQIv7+yM/3f/vnyX6+uvy9PAqlQBNBqzLKkdGhqKpKQkZGRkmF1IAXJ5pI8/BgYOBI4dk399gPx5Q6UCAgIeFD021q9VCAGtVovly5dj4sSJ8PT0xKxZs9CkSZPnGk4vmp3apUsXvP7667h8+TKOHz+ONWvWYPz48fDy8kLNmjXRtm1btG3bFp6enoYZgl5eXujatStef/11xMfH4+jRo1i7di3Gjh0LHx8f1KxZE56envjtt9/g5+eHL7/8ErVr1zargAIA87mYUYK0Wi2WLl2KPXv2oF+/foiIiFBcCNjY2MDGxga5ublmVS4IgFxPp0oVufpqYaG8LSBAnqN97ZpZFbuTJAlhYWG4ceMG0tPTjd2cUhMRAbRrB8yaBRStujJvHtCwIdChA7BqFXD9uvycKOMFYMVft5iMHTsWw4cPR/369fHTTz89d0D9nYODA2rWrInevXvj119/xaFDhxAVFYVr165h7NixqF+/PgYMGIDdu3fjxo0bhgorjo6OqFOnDt5//32sX78eBw8eNAwn/vjjj6hRowZWrlyJ8PBwWJnSvP4nZF6RWwKEEDh//jwWLVqEWrVqoVevXootLeTi4oK8vDxoNBrz+vRkYyNXXy1axwKQP2qHhADJyXIhODO6LvXSSy/h1q1buHfvnsn3ioUAMjIeXXrE3h6IigJ27gRu3pT369RJ/jxy+DDwwQfyr7xJE6BxYyAyUq7IURaHIiEhAWPGjMGOHTvQvXt3jB492jBRoaQVLfzp4OCA6tWr46WXXkLfvn2xf/9+7NmzB3v27MFPP/2EGjVq4PXXX0dkZCTCw8Nhb29v+L6wsDCEhYXh7bffxssvv4zIyEh4eXmZ9Pvm35jRma1kaDQafPfdd0hOTsZXX31lGFZToodDSmnDkc+lKKQ2bXrQa5IkIDQUiIkBsrLMKqT8/PygUqlMfjkSIeTO78iRQOvW8orQD3++Cw4G+vYFhg6VtzdsKM/+u3VL/r61a+Ue1cqVQFAQ8MorwDvvyJ1qCwv5LVBSh0bIt9/g9OnTGDVqFE6dOoVPP/0Uffr0KdO/eUmS4OnpiU6dOqF169ZISEjAiRMnsGHDBkydOhUeHh4ICQlB586d0aZNG3h7excbDgwODsaVK1dQUFBglr0ogMN9xYi/1olatmwZoqOjER4ebuwm/Ss3NzdDSJkVS0t5jYfcXPljNyCfncLCgMREwIyGxYpmhVWoUMGkK73n5wPbt8u9o8uX5c8Q0dHAhx8+uN6kUgE9egC//gq0aSP/Sq2s5JHc114D5swBTp0Cpk6Vi9KuWCEHVceOwI8/yutS5eYKQ3WH51FYWIiNGzeiW7duSEhIwPfff4+BAweW2IoEz8Le3h6hoaHo0aMHfv75Z8TExKBXr17IzMw0DAdu377dsL+1tTWqVq2K+Ph4FJjREPjfsSf1kKSkJHz++eeoVasWunfvbih5olQuLi64ffu2+YWUJAGennJl0odLhFetKo8l3b1rVpMn7O3tUaFCBVy8eBE6nc7kPhHn5wOLFwMzZsgFZadMkXtJjxslt7ICHlfZqiiwPD2Bnj3lsIuNBXbvBvbskSdeVKyoRYsW+1GrVgqaNHkF5cuXf6b2ZmdnY8WKFZg8eTKCgoIwadIkREZGKuJ+w6Jeko2NDUJDQzFu3Dj0798f+/fvx+HDhw0FmAE5pIKCgvDbb78xpF4E+fn5WLx4MS5duoTvvvsOgYGBxm7Sf3JxcUF+fr75hRQgX5Dw8JAL2wHyWczNTX5cvix/9FbotcKnZW9vj8DAQPzxxx8m1ZMSQu7sfvEFsGgR8PLLwKRJQLVqz/f5oWjp+UaNgAYNgN69gT//BDZsyMauXT9i2bLf4O/vj1atWuGtt95C5cqV/3WlgAftFbh37x6mTZuGxYsX49VXX8Vnn32GihUrKiKg/om7uzveeOMNtGnTBpaWlobXaGlpicDAQGRmZuL27dvw9vY2cktLB0MK8pv3wIED+OGHH9C5c2e0bdtW0W/aIm5ubrh//z7y8/ON3ZSS5+0tjxnFxj7oNTk6ysOAZ88+esHDhNna2qJixYpISkpCenq6SVxfFEIefps4EdixQw6SIUPkzxUl1cGVJHnk188PKF8eaNzYBZmZX2LXrrb4+eefsXr1asyePRv169dH586dUb9+fQQFBcHJyemRsCq6YXrkyJHYs2cPevTogYkTJ8LBwcEkrgEW9a7+vs3Hxwdubm44e/YsqlevbqTWlS6GFOR1ombOnAlXV1cMGDDAJAIKkNeqys/PN8+uvpubHFQnTz4oEe7gAPj7y1fZ9Xpjt7DEqFQqw4l14sSJiIqKQv369RU97HfiBDB8uPyrGD4ceP99+ddTWiQJsLCQ4Obmhs6dO6Ndu3Y4c+YMDh48iD/++AOjRo2Cu7s7GjdujFdffRXNmjVDuXLlIEkSdDodYmJiMGHCBFy8eBGTJk3CO++8Y5QSZyXN09MTnp6eOFc04mCGXviQ0uv1WLp0KY4fP47JkycjODjYJD5ZAYCrqyvy8vLMsydlaSmvPX7woHwDb9Wq8lmwQgV5HrNWK88CNBNNmzZFeHg4Vq5cid9//x0NGjRAVFQUIiIi4OjoCGtra6O/L4WQ7wjYvVsOprw8YOZM+T6nsvxVSJIEW1tb1K9fH3Xr1kW3bt1w9epVbN68Gb/88gu2bdsGNzc3w3BgWloaPvnkEwgh8P3336NJkyaP9EpM1cMhZcozQ//NCx1SQgicOHECs2fPRsuWLdG1a1fF3hP1OGY7u69ISIgcRleuyCFlZSWHVH6+HFwhIcZuYYmpXLkyfv/9d6xduxZHjhzBsWPHEBUVBbVajU6dOqFNmzYICQmBj4+P0Xr6OTl6fPedhBkzJFSpIk+WKJrTYqxzo4WFBby9veHl5YUGDRpgyJAh2LFjBzZs2IDffvsN3333HXQ6HRo2bIhp06YhLCzMZEZKnoSHhwfKlSuHY8eOoaCgwGzC92EvdEilp6dj0aJFyM/Px9ChQ0t84bHS9vB9UmYpKEgOqWvX5K8lCfD1BZyc5HEmMwopSZJgbW2Nt99+G//73/+QlJSEI0eOYN++fVi9ejWWLVuG2rVr4+WXX0a7du0QGhpapsOBt2/fxrx5a/HTT1F45RVbjBsnf25Qygf3oh6Em5sbunbtig4dOuDs2bNYsGABfvvtN0RHR6NmzZpm19OwsrJChQoVsG/fPiQnJyuqCHZJMZ+PFE+paOXKNWvWYODAgSZ50dHFxQUajQb5+fklcu+I4gQHy0N8aWkPauXUqwfMnSvfBWqmLC0tUbFiRXTt2hXTp0/H7t27MWHCBOTn52PevHno2LEjOnXqhFWrViEtLQ15eXml9vsXQuDKlSv46KOPMG/eBPTuvQNz5igroB7H1tYW9erVQ8eOHaFSqZCfn28IKI1Gg4SEBNy7d8/IrSwZVapUgV6vx5UrV4zdlFLxwoZUXFwcxo8fj0aNGqF79+4mNwRQ9Mnb1tbWUGTW7KjVwA8/AO++C9y4AezfL5dK8vcHzLAQ69+pVCo4ODggMDAQ77//PrZv344NGzagW7duyM7ORr9+/dCoUSMMHz4cmzdvRlJSUolOYddqtThy5AiioqJw+PBhjBo1HMOGtYa7u7ID6mFVq1aFJEm4fPmyYduxY8fQsmVLrFmzxogtKzlFIfXnn38auyml4oUc7issLMT06dNx584djBw50mTrXllYWMDR0RFZWVnmGVKSJPeYNm0Cvv1WvmpvYyPf0FuvnnyHZ2Cg6Zwxn9HD98UUrVeUnJyMEydOYMeOHdi4cSN+/vlnhIWFITIyEv/73/9Qo0YNwwevZ3lvFxYWYtOmTZg4cSIKCwvx1VdfoVWrViZXI9Lf3x/W1ta4du0atFotLC0t4eHhAbVajQsXLhi7eSWicuXKEEIgISHBLCdPmFb3oYTo9XqkpaXBzs4Onp6exm7OM7OwsIBarUZmZib0ZjQl20AIeW2H0aOBFi2An36S6+MsWACcPi3fOWpGJZKelKWlJSpUqIA333wTM2bMwMGDBzFr1ixYW1tj2bJlaN++PV5//XUsWbIEycnJyM3NfaoPMbm5uVi4cCH69+8PtVqN1atXo3379rCzsyvFV1U6bGxsEBQUZCjgCwDlypWDm5sbzp07ZxZ/N66urvD29kZSUhJycnKM3ZwS90KGlI2NDXr37g2dToc5c+YYlu42NRYWFnBycjLfkALkUga1agF9+sg1dzw8gBo1gHHjgL175TtKTfB3VxIkSYK9vT18fX3Ro0cPbNy4ERs3bsS7774LnU6HoUOH4tVXX8XQoUOxZs0aJCYm/uv7RAiBGzduYNKkSRgzZgxat26NpUuXolq1aiY16/VhkiShRo0aSE1Nxe3btwHIa7H5+/vj5s2bZrE8StFrvHXrFu7+tZq1OXkhQwoAunbtivHjx+PUqVP44osvUFi0JIQJMfvhPiGA48flq/QPz7yUJPm6lIsLEB//wobU31lZWaFmzZoYO3YsFi9ejOXLl6Ndu3b4448/8OGHH6Jnz54YNWoUjhw5Ar1eb6gEDsgBlZSUhMGDB2PRokUYMGAAPv/8cwQGBpr88FFoaChu375tOIFLkoTg4GBoNBokJSUZuXUlIzQ0FGlpaWYzGeRhpjXAXIJUKhV69eqFjIwMfPnllwgMDMSAAQNMakjD7If7tFp5FTxX10evO9naylUpzPCP8nlZWFjA398ffn5+aNq0KUaMGIE9e/bgxx9/xC+//ILFixcjJCQE3bp1Q+PGjeHi4oLr16+jf//+uHfvHr744gv06NHDpP4W/k2NGjVw+/ZtpKamGq7ZVKtWDfn5+UhISECtWrWM3cTnFhYWhpSUFNy5c8fsrku9sCElSRLs7OzQt29fJCcn4+uvv4avry/eeustWFtbG7t5T6RouO/evXvmGVKWlnI5pIyMR6ueFxYCOTlyUNFjFdV7K1qG/I033sD58+exfft27NixAyNHjoRKpUK1atWQmJgIT09PzJs3D82bN1d0Saan5ePjA0dHR1y/fh06nQ6WlpaGkLp+/bqxm1ciKlWqhIKCAty8edPsQuqFHe4r4uzsjOHDhyM0NBSTJk3CyZMnTWbozOyH+yQJqF1bvu6UlfVgWE8IeYXetDTl37CjIDY2NqhduzY++eQTfP/991i1ahUaN26M06dPQ61W45tvvkHLli3NKqAA+XVXqlQJly9fNgzrBwQEwNLSEjdu3DDJof6HSZIEBwcH+Pn54c8//zQsO28uniqk5s+fj7CwMKjVaqjVakRGRmLbtm2G5/Pz8xEdHQ13d3c4Ojqic+fOSE1NLfZvJCYmom3btrC3t4eXlxeGDh1q9INarlw5zJw5E25ubvjggw/w559/msRJ3+yH+wCgXz95JbzFi+XKE8nJD2b2NWsmL9vKkHoqKpUK/v7+ePXVV7F8+XLcuHEDMTExCA8PN7n7BZ+EjY0NqlSpgosXLxqKMRctGJiYmIisrCwjt/D52dnZITg4GOfPnzf50P27p3pH+vn5YerUqThx4gSOHz+OZs2a4Y033jBU4P3444+xadMmrF69Gnv37sXNmzfRqVMnw/frdDq0bdsWBQUFOHToEJYuXYolS5Zg7NixJfuqnpIkSahevTomTJiAnJwcjBw5Ejdu3DBqm56E2c/ukySgbl15Cvq2bXJg9e0LDBwoh9PQoRzuew5FhVodHR1hb29vVkNEDyuahv5wT0qSJISGhiI5ORmZmZlGbuHzs7W1RaVKlXDp0iWjf+gvceI5ubq6iu+++05kZGQIKysrsXr1asNzFy5cEABETEyMEEKIrVu3CpVKJVJSUgz7zJ8/X6jVaqHRaJ74Z2ZmZgoAIjMz83mbX0xBQYH48ccfhaurq/j4449FZmam0Ov1JfozSpJWqxVffvmlcHJyEnfv3jV2c0pPQYEQ164JceSIEAcPCnH6tBDp6cZuFZkIvV4vlixZIuzs7MSFCxcM2+bOnSsCAwPFyZMnFf13/iS0Wq2YNWuWcHBwELdu3TJ2c57Ik57Hn7lvr9PpsHLlSuTm5iIyMhInTpxAYWEhWrRoYdgnJCQEAQEBiImJAQDExMSgRo0axVaQbNWqFbKysv51PRSNRoOsrKxij9JgZWWFrl27YvDgwVi8eDEWLVqk6K6zSqWCo6Mj8vLyzHNNqSJF1c/r15fXJQ8LKz4lnehfSJKEcuXKwdXVFWfPnjVsf+mllwwz4kydSqWCn58frK2tcenSJWM3p0Q9dUjFxcXB0dERNjY26Nu3L9atW4fQ0FCkpKTA2tr6kUri3t7eSElJAQCkpKQ8ssRx0ddF+zzOlClT4OzsbHj4+/s/bbOfmI2NDd577z3873//w8yZM7FhwwbFLuldNEPR2toaGRkZxm4OkWJ5eXnB3d0d58+fByD/7Xh6esLZ2RlXr141iWvQ/0aSJHh7e8PNzc3wGs3FU4dU1apVERsbiyNHjqBfv36Iiooq9YMyYsQIZGZmGh6lfQOel5cXxo8fj6pVq2LSpEk4dOiQYt/EFhYWUKlUSExMNHZTiBTLy8sLHh4eOHv2rOFv2dHREQEBAbhw4YJZXNMtWkre3FbpfeqQsra2RlBQEOrWrYspU6agZs2a+Prrr+Hj44OCgoJHPtGnpqbCx8cHgHwQ/z7br+jron0ex8bGxjCjsOhRmiRJgq+vLxYuXAhJkjBs2DDFdaGFELh37x7i4uKQn59frMozERXn6ekJHx8fXLx40TCE7+joiIoVK+Ls2bOKHS15Gj4+PvD09ERcXJxZhG6R555vqtfrodFoULduXVhZWWHnzp2G5+Lj45GYmIjIyEgAQGRkJOLi4pCWlmbYZ/v27VCr1QgNDX3eppQoSZJQuXJlTJ48GWlpaRg3bpwi6mIJIXDr1i389NNP+PDDDzF37lzY29ubxV3zRKXFysoKgYGBuH//PpKTkwEA9vb2CAgIQHx8vFmc1B0dHVG+fHmkpaWZRU3CIk8VUiNGjMC+fftw7do1xMXFGcqtdO/eHc7OzujTpw8GDx6M3bt348SJE+jduzciIyPRoEEDAEDLli0RGhqKnj174vTp0/j9998xevRoREdHK3LZYwsLC7Rq1QrDhg3D7t278cUXXxjlxlm9Xo+cnBzEx8fj888/R8uWLTFs2DCcOnUK7777LrZt24aIiIgybRORqalatSq0Wi2uXr0KQB6h8ff3R1ZW1iMjPKZIkiRUqVIFBQUFuFa0mrUZeKqySGlpaejVqxdu3boFZ2dnhIWF4ffff8drr70GAJg1axZUKhU6d+4MjUaDVq1aYd68eYbvt7CwwObNm9GvXz9ERkbCwcEBUVFRmDhxYsm+qhJkbW2N//f//h+SkpKwcOFCeHp6YuDAgWVSOqmwsBDXrl3DsWPH8Ouvv2Lfvn1wcXFBREQE2rRpgw4dOsDJyQnAs60ZRPQiKQqpK1euoHnz5pAkCf7+/nB0dMTZs2cRGBho7CY+laLLK15eXoZt1apVg0ajwdWrV1G3bl0jtq7kPFVIff/99//6vK2tLebOnYu5c+f+4z4VKlTA1q1bn+bHGp1KpcLAgQORkJCAWbNmITQ0FG3bti21n1dYWIhTp05h9erVOHjwIOLi4lC1alUMHDgQTZo0QZ06deDo6FhqP5/IHAUFBUGn0+HatWuG+na+vr5wcnLCxYsX0a5dO2M38Ynl5eVh3rx5uHr1KkaNGgVfX18A8mssCilz8cIWmH0akiTBw8MD48ePx+3btzF06FC4u7ujfv36JVJGRgiBgoICpKenY+/evViyZAnOnj0LSZJQu3Zt/Pzzz2jQoAGcnZ1hZWXFXhPRM3BzczMsDpibmwtHR0f4+flBrVbj3LlzJlGYVQiBmzdv4osvvsCqVavQqVOnYqM6vr6+0Ol0uHr1KvLy8syikj1D6glJkoSgoCB88cUX6NmzJ4YPH4558+ahWrVqz/XGzszMxMmTJ7F7926sX78eycnJqFmzJnr37o0333wTYWFhUKlUiv/jIVK6ovJnRav0Ojo6wtvbGx4eHrhy5Qry8/MVfVIXQuDs2bMYM2YMDh8+jL59+2LYsGGGNufm5uLnn39Gfn4+tFqt2ZRHYkg9BUmSUKtWLUyePBn9+/fHhAkT8N1338HR0fGJQ6Ro0sW9e/ewceNGbNu2DceOHUNeXh5at26NyZMno1atWihfvrzJroZKpFTVqlVDbGws0tPTDZXQg4KCsG/fPqSkpKBixYrGbuIjxF+LUx48eBCjR4/G9evXMWHChGJrft2/fx/z58/HjBkzEBAQgPfff998LgmUWmGmUlRatfuehF6vFxqNRsydO1d4e3uLwYMHi9zc3P/8Pp1OJ+7duydOnDghxo0bJypVqiR8fX1F7dq1xfTp08WVK1dETk6OydcQI1IqvV4vNm7cKNzc3MTOnTsNf2sLFiwQlSpVEnv27DFyCx8vLy9PLFu2TAQFBYmwsDCxf/9+UVBQIISQzyupqaniww8/FO7u7qJ79+4iKSlJ6HQ6I7f6vz3peZw9qackSRKsra0RFRWFlJQULFy4EJUqVcK777772KECrVaLixcv4siRI/jjjz+wc+dOuLq6olmzZmjdujWaNWsGFxcXDucRlYHKlStDo9EUWxwwNDQUOTk5iqzacvfuXSxevBjTpk1DREQEpk6ditDQUEiSBL1ejzNnzmDChAk4fPgwhgwZgg8++ABuZrYyAEPqGTk4OGDgwIE4d+4cpk+fjqCgILz22muGsNFoNDh16hR+/vln7N+/HwkJCahSpQpGjx6NRo0aoVq1aubTHScyAZIkGW54/fPPP6HT6aBSqVClShXk5+cjOTkZer1eEWtqCSGQlpaGKVOmYNmyZejSpQs+/fRTVKhQAZIkQQiBI0eOYNCgQUhOTsbUqVPRpUsX2NvbG7vpJY4h9Rzc3Nwwbdo0fPDBB+jXrx/mzZsHf39/HD9+HN9//z0uXLgAFxcXREZGYvr06YiIiIC9vT0sLCzYcyIyAnt7e1SuXBnnz5+HVquFlZUVXF1dERQUhPT0dGg0GqNPntDpdLh06RKio6Nx8eJFDBo0CJ988glsbW0ByBMkNm/ejE8//RTlypXDjz/+iFdeeQWWluZ5OjfPV1VGJElCpUqVMHz4cPTp0wddu3aFnZ0dtFot6tevj8GDB6Nly5aoXbu2Ij6dEb3o7OzsULFiRezfv99Qr8/S0hKTJ0+Gp6en0U/0BQUF2LVrF8aNG4fMzExMnz4dnTt3NgRUVlYWZs6ciQULFqB+/fqYNGkSwsLCzPpDL0PqOUmShMaNG2PEiBGYO3cufHx8MHDgQNSuXRve3t5Gf9MT0QO2trYIDAzEjz/+iPv378PR0REqlQpt2rQxaruEENDpdFi9ejXGjRsHBwcHfPvtt4iIiIC1tTWEELh9+zbGjRuHX3/9FR07dsTIkSMRGBho1gEFMKRKRNEaVP/3f/9nmFgBsFQRkdKoVCq4urpCq9Xi999/R8+ePY3dJAghcPfuXXz77beYMWMGmjRpgs8//xxVq1Y1TJCIjY3FmDFjEBsbi+HDh+ODDz6Ag4PDC3GOYUiVEAsLC97XRKRwkiTB1dUVDg4O+Prrr1FQUIAWLVogICDAKCd8IQQSExMxceJEbNiwAb169cLgwYMNC7vq9Xrs3LkTo0aNQnp6OiZPnoyePXu+UCM0L84rJSIC0KxZM7z99ttYvnw5hg8fDl9fX7Rp0wZdunRBSEgIbG1ty6TKi16vx59//ono6GicPXsWQ4YMQb9+/eDi4gIhBDQaDX799VdMmjQJarUaCxYsQOPGjV+ogAIASQiFLjn7L7KysuDs7IzMzMxSXwCRiMyLEAJ6vR5ZWVnYs2cP1qxZg5MnT+LWrVsICQnBO++8g0aNGiEoKAhqtbpUwqqgoAC//fYbRo8eDb1ej/Hjx6NDhw6G2px37tzBnDlzsGDBAoSHh2POnDlmd/3pSc/jDCkieqEVFBTgzJkzOHz4MPbs2YPdu3fD1dUVkZGRaN68OZo2bYqAgIASm6FbWFiIJUuWYOrUqfDx8cHkyZPRtGlTw/1P169fx7Rp07Bq1Sp069YNw4YNQ/ny5c0qoACGFBHRU9HpdMjIyEBCQgK2bNmCFStWIDMzE66urmjcuDF69uyJGjVqwMHB4ZmG3IQQuHfvHubMmYNFixahQYMGmDhxIkJDQ6FSqaDX63HhwgV88sknOH78OEaMGIE+ffqUWm/O2BhSRETPoOiUmJeXh+3bt2Pz5s04fvw4rl+/juDgYPzvf//Dyy+/jKpVq8LV1fWJA+Tu3bvo378/du7ciZ49e2Ls2LGGANJoNNi7dy8GDhwIlUqF8ePHo1OnTmY9GYshRURUAjQaDS5duoSDBw9i165d2LlzJ9RqNerWrYvmzZujdevWqFChwn8OB966dQt9+/bFK6+8YqhSLkkS8vLysHz5ckydOhVeXl6YMmUKGjZsCCsrqzJ6hcbBkCIiKkE6nQ7Z2dlISkrChg0bsGbNGqSlpcHBwQGNGjVCr169ULNmTTg6Oj52cVK9Xo+MjAzY29vDxsYGAJCTk4Np06Zh4cKFiIyMxKxZsxAYGPhCVKhhSBERlYKiU2Z+fj727t2LTZs24ejRo7h06RKqVKmCdu3aoUmTJqhevTrc3d0fOxyo1+tx+fJlfPbZZ9i2bRv69OmDwYMHw9PT0yyvPz0OQ4qIqAxoNBpcuXLFsBzPjh07YG9vj1q1aqF58+bo0KEDKlSoAACGGXxnzpzBkCFDEBcXh1GjRiEqKgrOzs5GfiVliyFFRFRGxF+r5+bm5iI1NRXr16/H2rVrkZiYCEtLS0RGRiIqKgohISE4cOAAJk6cCBsbG0yYMAFt27Y1DP+9SBhSRERGlJ2djYMHD2Lz5s04efIkYmNjoVKpUFhYiOrVq2PBggWoV6/eCzO893cMKSIiIxNCoKCgANevX8eBAwewbNkyeHt7Y8CAAWjYsKGxm2dUDCkiIgXR6XS4f/8+LCwsYGdn98L2oIo86Xn8xapUSERkJBYWFnBycjJ2M0yO+U/GJyIik8WQIiIixWJIERGRYjGkiIhIsRhSRESkWAwpIiJSLIYUEREpFkOKiIgUiyFFRESKxZAiIiLFYkgREZFiMaSIiEixGFJERKRYDCkiIlIshhQRESkWQ4qIiBSLIUVERIrFkCIiIsViSBERkWIxpIiISLEYUkREpFgMKSIiUiyGFBERKRZDioiIFIshRUREisWQIiIixWJIERGRYjGkiIhIsRhSRESkWAwpIiJSLIYUEREpFkOKiIgUiyFFRESKxZAiIiLFYkgREZFiMaSIiEixGFJERKRYDCkiIlKs5wqpqVOnQpIkDBo0yLAtPz8f0dHRcHd3h6OjIzp37ozU1NRi35eYmIi2bdvC3t4eXl5eGDp0KLRa7fM0hYiIzNAzh9SxY8ewcOFChIWFFdv+8ccfY9OmTVi9ejX27t2LmzdvolOnTobndTod2rZti4KCAhw6dAhLly7FkiVLMHbs2Gd/FUREZJ7EM8jOzhbBwcFi+/btokmTJmLgwIFCCCEyMjKElZWVWL16tWHfCxcuCAAiJiZGCCHE1q1bhUqlEikpKYZ95s+fL9RqtdBoNE/08zMzMwUAkZmZ+SzNJyIiI3vS8/gz9aSio6PRtm1btGjRotj2EydOoLCwsNj2kJAQBAQEICYmBgAQExODGjVqwNvb27BPq1atkJWVhXPnzj3252k0GmRlZRV7EBGR+bN82m9YuXIlTp48iWPHjj3yXEpKCqytreHi4lJsu7e3N1JSUgz7PBxQRc8XPfc4U6ZMwYQJE562qUREZOKeqieVlJSEgQMHYvny5bC1tS2tNj1ixIgRyMzMNDySkpLK7GcTEZHxPFVInThxAmlpaahTpw4sLS1haWmJvXv3Yvbs2bC0tIS3tzcKCgqQkZFR7PtSU1Ph4+MDAPDx8Xlktl/R10X7/J2NjQ3UanWxBxERmb+nCqnmzZsjLi4OsbGxhke9evXQvXt3w/9bWVlh586dhu+Jj49HYmIiIiMjAQCRkZGIi4tDWlqaYZ/t27dDrVYjNDS0hF4WERGZg6e6JuXk5ITq1asX2+bg4AB3d3fD9j59+mDw4MFwc3ODWq3GRx99hMjISDRo0AAA0LJlS4SGhqJnz56YNm0aUlJSMHr0aERHR8PGxqaEXhYREZmDp5448V9mzZoFlUqFzp07Q6PRoFWrVpg3b57heQsLC2zevBn9+vVDZGQkHBwcEBUVhYkTJ5Z0U4iIyMRJQghh7EY8raysLDg7OyMzM5PXp4iITNCTnsdZu4+IiBSLIUVERIrFkCIiIsViSBERkWIxpIiISLEYUkREpFgMKSIiUiyGFBERKRZDioiIFIshRUREisWQIiIixWJIERGRYjGkiIhIsRhSRESkWAwpIiJSLIYUEREpFkOKiIgUiyFFRESKxZAiIiLFYkgREZFiMaSIiEixGFJERKRYDCkiIlIshhQRESkWQ4qIiBSLIUVERIrFkCIiIsViSBERkWIxpIiISLEYUkREpFgMKSIiUiyGFBERKRZDioiIFIshRUREisWQIiIixWJIERGRYjGkiIhIsRhSRESkWAwpIiJSLIYUEREpFkOKiIgUiyFFRESKxZAiIiLFYkgREZFiMaSIiEixGFJERKRYDCkiIlIshhQRESkWQ4qIiBSLIUVERIrFkCIiIsViSBERkWIxpIiISLEYUkREpFgMKSIiUiyGFBERKRZDioiIFIshRUREisWQIiIixWJIERGRYjGkiIhIsRhSRESkWAwpIiJSLIYUEREpFkOKiIgUy9LYDXgWQggAQFZWlpFbQkREz6Lo/F10Pv8nJhlSd+/eBQD4+/sbuSVERPQ8srOz4ezs/I/Pm2RIubm5AQASExP/9cW96LKysuDv74+kpCSo1WpjN0exeJyeDI/Tk+FxejJCCGRnZ8PX1/df9zPJkFKp5Etpzs7OfBM8AbVazeP0BHicngyP05PhcfpvT9LJ4MQJIiJSLIYUEREplkmGlI2NDcaNGwcbGxtjN0XReJyeDI/Tk+FxejI8TiVLEv81/4+IiMhITLInRURELwaGFBERKRZDioiIFIshRUREimWSITV37lwEBgbC1tYWEREROHr0qLGbVKb27duH9u3bw9fXF5IkYf369cWeF0Jg7NixKFeuHOzs7NCiRQtcvny52D737t1D9+7doVar4eLigj59+iAnJ6cMX0XpmjJlCsLDw+Hk5AQvLy907NgR8fHxxfbJz89HdHQ03N3d4ejoiM6dOyM1NbXYPomJiWjbti3s7e3h5eWFoUOHQqvVluVLKVXz589HWFiY4cbTyMhIbNu2zfA8j9HjTZ06FZIkYdCgQYZtPFalRJiYlStXCmtra/HDDz+Ic+fOiffee0+4uLiI1NRUYzetzGzdulWMGjVKrF27VgAQ69atK/b81KlThbOzs1i/fr04ffq06NChg6hYsaLIy8sz7NO6dWtRs2ZNcfjwYbF//34RFBQk3nnnnTJ+JaWnVatWYvHixeLs2bMiNjZWvP766yIgIEDk5OQY9unbt6/w9/cXO3fuFMePHxcNGjQQDRs2NDyv1WpF9erVRYsWLcSpU6fE1q1bhYeHhxgxYoQxXlKp2Lhxo9iyZYu4dOmSiI+PFyNHjhRWVlbi7NmzQggeo8c5evSoCAwMFGFhYWLgwIGG7TxWpcPkQqp+/foiOjra8LVOpxO+vr5iypQpRmyV8fw9pPR6vfDx8RHTp083bMvIyBA2NjZixYoVQgghzp8/LwCIY8eOGfbZtm2bkCRJJCcnl1nby1JaWpoAIPbu3SuEkI+JlZWVWL16tWGfCxcuCAAiJiZGCCF/GFCpVCIlJcWwz/z584VarRYajaZsX0AZcnV1Fd999x2P0WNkZ2eL4OBgsX37dtGkSRNDSPFYlR6TGu4rKCjAiRMn0KJFC8M2lUqFFi1aICYmxogtU46EhASkpKQUO0bOzs6IiIgwHKOYmBi4uLigXr16hn1atGgBlUqFI0eOlHmby0JmZiaAB8WJT5w4gcLCwmLHKSQkBAEBAcWOU40aNeDt7W3Yp1WrVsjKysK5c+fKsPVlQ6fTYeXKlcjNzUVkZCSP0WNER0ejbdu2xY4JwPdTaTKpArN37tyBTqcr9ksGAG9vb1y8eNFIrVKWlJQUAHjsMSp6LiUlBV5eXsWet7S0hJubm2Efc6LX6zFo0CA0atQI1atXByAfA2tra7i4uBTb9+/H6XHHseg5cxEXF4fIyEjk5+fD0dER69atQ2hoKGJjY3mMHrJy5UqcPHkSx44de+Q5vp9Kj0mFFNGziI6OxtmzZ3HgwAFjN0WRqlatitjYWGRmZuLXX39FVFQU9u7da+xmKUpSUhIGDhyI7du3w9bW1tjNeaGY1HCfh4cHLCwsHpkxk5qaCh8fHyO1SlmKjsO/HSMfHx+kpaUVe16r1eLevXtmdxz79++PzZs3Y/fu3fDz8zNs9/HxQUFBATIyMort//fj9LjjWPScubC2tkZQUBDq1q2LKVOmoGbNmvj66695jB5y4sQJpKWloU6dOrC0tISlpSX27t2L2bNnw9LSEt7e3jxWpcSkQsra2hp169bFzp07Ddv0ej127tyJyMhII7ZMOSpWrAgfH59ixygrKwtHjhwxHKPIyEhkZGTgxIkThn127doFvV6PiIiIMm9zaRBCoH///li3bh127dqFihUrFnu+bt26sLKyKnac4uPjkZiYWOw4xcXFFQv07du3Q61WIzQ0tGxeiBHo9XpoNBoeo4c0b94ccXFxiI2NNTzq1auH7t27G/6fx6qUGHvmxtNauXKlsLGxEUuWLBHnz58X77//vnBxcSk2Y8bcZWdni1OnTolTp04JAGLmzJni1KlT4vr160IIeQq6i4uL2LBhgzhz5ox44403HjsFvXbt2uLIkSPiwIEDIjg42KymoPfr1084OzuLPXv2iFu3bhke9+/fN+zTt29fERAQIHbt2iWOHz8uIiMjRWRkpOH5oinDLVu2FLGxseK3334Tnp6eZjVlePjw4WLv3r0iISFBnDlzRgwfPlxIkiT++OMPIQSP0b95eHafEDxWpcXkQkoIIebMmSMCAgKEtbW1qF+/vjh8+LCxm1Smdu/eLQA88oiKihJCyNPQx4wZI7y9vYWNjY1o3ry5iI+PL/Zv3L17V7zzzjvC0dFRqNVq0bt3b5GdnW2EV1M6Hnd8AIjFixcb9snLyxMffvihcHV1Ffb29uLNN98Ut27dKvbvXLt2TbRp00bY2dkJDw8PMWTIEFFYWFjGr6b0vPvuu6JChQrC2tpaeHp6iubNmxsCSggeo3/z95DisSodXKqDiIgUy6SuSRER0YuFIUVERIrFkCIiIsViSBERkWIxpIiISLEYUkREpFgMKSIiUiyGFBERKRZDioiIFIshRUREisWQIiIixWJIERGRYv1/nmSsnR4JXRUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COc1ccc(N2CCN(Cc3ccc(C)cc3)C(=O)C2=O)cc1OC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CCOC(=O)CN1CCN(C2CC2)C(=O)C1=O\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cc1cc(C)cc(NC(=O)CN2CCN(c3ccc(F)c(F)c3)C(=O)C2=O)c1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cc1cc(C)cc(N2CCN(CC#N)C(=O)C2=O)c1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>,\n",
              " <PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x500>]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    }
  ]
}