Data_normalized.ipynb

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        "<a href=\"https://colab.research.google.com/github/AnaChavezHdz/HANNA/blob/main/Data_normalized.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "source": [
        "!pip install -q rdkit-pypi\n",
        "!pip install -q torch-geometric"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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      "cell_type": "code",
      "source": [
        "import rdkit\n",
        "print(f\"rdkit_version: {rdkit.__version__}\")\n",
        "import torch_geometric\n",
        "print(f\"pythorch_version: {torch_geometric.__version__}\")"
      ],
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        "id": "cR_54OQfsPqE",
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      "outputs": [
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          "name": "stdout",
          "text": [
            "rdkit_version: 2022.09.5\n",
            "pythorch_version: 2.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git clone https://github.com/DIFACQUIM/HANNA.git"
      ],
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          "text": [
            "Cloning into 'HANNA'...\n",
            "remote: Enumerating objects: 49, done.\u001b[K\n",
            "remote: Counting objects: 100% (49/49), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 49 (delta 20), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (49/49), 767.65 KiB | 4.19 MiB/s, done.\n",
            "Resolving deltas: 100% (20/20), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "g41apkJjrJJJ"
      },
      "outputs": [],
      "source": [
        "import HANNA\n",
        "\n",
        "from HANNA.Graph_from_smiles import from_smiles\n",
        "from HANNA.Graph_to_smiles import to_smiles\n",
        "\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# Read data set\n",
        "url = \"https://raw.githubusercontent.com/DIFACQUIM/HANNA/main/DATASETS/UNPD_subset_C_4998_curada_NPL_score.csv\"\n",
        "DATA = pd.read_csv(url)\n",
        "print(f\"Data_shape: {DATA.shape}\")\n",
        "DATA.head(2)"
      ],
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          "height": 130
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        "id": "kovJwRHxrPo1",
        "outputId": "06eb4a38-f3bb-4c44-dbbe-4599cd2816a0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data_shape: (4998, 4)\n"
          ]
        },
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              "                ID                                      SMILES_chiral  \\\n",
              "0  UNPD_subset_C_1  CN[C@H]1CC[C@]23C[C@]24CC[C@]2(C)[C@@H]([C@H](...   \n",
              "1  UNPD_subset_C_2                                       NC(=O)/C=C\\I   \n",
              "\n",
              "                                    SMILES_no_chiral  NPL_score  \n",
              "0  CNC1CCC23CC24CCC2(C)C(C(C)N(C)C)CCC2(C)C4C=CC3...      2.700  \n",
              "1                                         NC(=O)C=CI      0.441  "
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "## Normalize NPL-score values"
      ],
      "metadata": {
        "id": "IYld0AXvrSLs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "DATA[[\"NPL_normalized\"]] = scaler.fit_transform(DATA[[\"NPL_score\"]])\n",
        "DATA.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nCVD35p5rQKa",
        "outputId": "7375897f-0948-417e-85d4-b48cbb432cfb"
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      "execution_count": 6,
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              "                ID                                      SMILES_chiral  \\\n",
              "0  UNPD_subset_C_1  CN[C@H]1CC[C@]23C[C@]24CC[C@]2(C)[C@@H]([C@H](...   \n",
              "1  UNPD_subset_C_2                                       NC(=O)/C=C\\I   \n",
              "\n",
              "                                    SMILES_no_chiral  NPL_score  \\\n",
              "0  CNC1CCC23CC24CCC2(C)C(C(C)N(C)C)CCC2(C)C4C=CC3...      2.700   \n",
              "1                                         NC(=O)C=CI      0.441   \n",
              "\n",
              "   NPL_normalized  \n",
              "0        0.794118  \n",
              "1        0.442577  "
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              "      <th>NPL_normalized</th>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30b9f8fb-8bc4-46e6-a9e3-bb5ec2757f60 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-30b9f8fb-8bc4-46e6-a9e3-bb5ec2757f60');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f16e4198-f5d5-4f36-99ef-8ed419b88836 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA.to_csv(\"UNPD_subset_C_4998_curada_NPL_normalized.csv\", sep=\",\", index=False)"
      ],
      "metadata": {
        "id": "xofPXPousYbg"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Built molecular graphs"
      ],
      "metadata": {
        "id": "1dGTA4RN765v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSAu4qv36yaq",
        "outputId": "07273180-9a28-4bbf-a6a3-59f8702406d7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_smiles = list(DATA['SMILES_chiral']) #get smiles strings from file\n",
        "Y = np.asarray(DATA['NPL_score']) #get NPL values from file\n",
        "\n",
        "# Shown the first value\n",
        "print(f\"X_smiles: {X_smiles[0]}\")\n",
        "print(f\"NPL_normalized: {Y[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsOk0biS79rr",
        "outputId": "50b29f7f-3633-4a69-f100-702aadbe34c3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_smiles: CN[C@H]1CC[C@]23C[C@]24CC[C@]2(C)[C@@H]([C@H](C)N(C)C)CC[C@@]2(C)[C@@H]4C=C[C@H]3C1(C)C\n",
            "NPL_normalized: 2.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "graphs_list = list()\n",
        "for y, x in zip(Y, X_smiles):\n",
        "    graphs_list.append(from_smiles(y,x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MsLDa1m8qeD",
        "outputId": "004e2ccc-fed0-42e6-8c72-ecd181f561d6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "tensor([2.8590])\n",
            "tensor([3.3840])\n",
            "tensor([2.6440])\n",
            "tensor([2.8800])\n",
            "tensor([2.0090])\n",
            "tensor([2.6110])\n",
            "tensor([0.9560])\n",
            "tensor([2.2890])\n",
            "tensor([1.3590])\n",
            "tensor([2.3880])\n",
            "tensor([2.1250])\n",
            "tensor([1.3210])\n",
            "tensor([0.8070])\n",
            "tensor([1.4390])\n",
            "tensor([2.8530])\n",
            "tensor([2.0280])\n",
            "tensor([2.9520])\n",
            "tensor([2.4840])\n",
            "tensor([1.1740])\n",
            "tensor([1.4490])\n",
            "tensor([0.5010])\n",
            "tensor([1.4220])\n",
            "tensor([2.0700])\n",
            "tensor([1.0250])\n",
            "tensor([2.5910])\n",
            "tensor([2.1210])\n",
            "tensor([3.2980])\n",
            "tensor([0.7750])\n",
            "tensor([1.8870])\n",
            "tensor([1.6110])\n",
            "tensor([1.6220])\n",
            "tensor([-0.6640])\n",
            "tensor([1.7040])\n",
            "tensor([2.1430])\n",
            "tensor([0.8870])\n",
            "tensor([2.5140])\n",
            "tensor([1.9860])\n",
            "tensor([1.4370])\n",
            "tensor([2.2180])\n",
            "tensor([1.4770])\n",
            "tensor([2.7250])\n",
            "tensor([0.4150])\n",
            "tensor([2.0120])\n",
            "tensor([1.8220])\n",
            "tensor([2.8500])\n",
            "tensor([3.0030])\n",
            "tensor([1.8340])\n",
            "tensor([3.6860])\n",
            "tensor([3.5380])\n",
            "tensor([0.3690])\n",
            "tensor([3.1990])\n",
            "tensor([2.5600])\n",
            "tensor([2.2210])\n",
            "tensor([1.7980])\n",
            "tensor([-0.3060])\n",
            "tensor([1.0500])\n",
            "tensor([3.1390])\n",
            "tensor([2.5400])\n",
            "tensor([0.5640])\n",
            "tensor([1.1930])\n",
            "tensor([1.7460])\n",
            "tensor([2.8830])\n",
            "tensor([2.7700])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save\n",
        "import torch\n",
        "torch.save(graphs_list,\n",
        "           \"/content/drive/MyDrive/HANNA_molecular_fingerprint/Final_notebooks/UNPD_C_graphs2.pt\")"
      ],
      "metadata": {
        "id": "zo2agGkP-Gq5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rebuilt molecular graphs"
      ],
      "metadata": {
        "id": "e-qeeYvtAl7w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smi = X_smiles[333]\n",
        "print(f\"smi: {smi}\")\n",
        "mol = Chem.MolFromSmiles(smi)\n",
        "mol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "-gp15Nm_C5XQ",
        "outputId": "e24e3a46-a7a4-4b85-87a7-80bd7bb3a067"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "smi: COc1cc2c3c(c1OC)-c1c(cccc1OC)C[C@@H]3NCC2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7b373661c120>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "molecular_graph = from_smiles(Y[333], X_smiles[333])\n",
        "molecular_graph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1ZbYxjRAp4W",
        "outputId": "916c46cf-e2d9-4aa7-8359-c97152d43e23"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1.3920])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[23, 9], edge_index=[2, 52], edge_attr=[52, 3], y=[1], smiles='COc1cc2c3c(c1OC)-c1c(cccc1OC)C[C@@H]3NCC2')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smi = to_smiles(molecular_graph)\n",
        "print(f\"smi: {smi}\")\n",
        "mol = Chem.MolFromSmiles(smi)\n",
        "mol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "KeRCnlL8BJWa",
        "outputId": "e95e1ad5-d3a2-4d09-b3ab-0a35c8eb2788"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "smi: COc1cc2c3c(c1OC)-c1c(cccc1OC)C[C@@H]3NCC2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7b373661c7b0>"
            ],
            "image/png": 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kqrQRagsso7rgwYMHbDabxWKVlZVpOhdCtcT/+uuvCSE7duw4c+aMnHos/a00NTWNHj1auST20aNH9vb2ADBr1iyVZYxQ22AZ1RETJ04EgC1btmg6EZKUlAQANjY2Kvn0XLkktri4WCqVUg/vDxkypD3cCEaIgmVUR9C6p9vrcLlcANi8eXN5eXlbxvn999+px5Oo9af//ve/qQJ97949FWWKkAq07H+DtNS4ceMsLS2pPd0++OADzSZjaWlZVVW1YsWKFStWmJmZOTs78/l8BwcH5TetHEehUHA4nF9++cXd3X379u2//vqroaHhiRMnbGxs1Jo/Qm8E96nXHV9++eWOHTu++Wbb998v1mAaUVFRM2fO1NPT4/F4d+/era6ubnGAhYUFn893bqZFh9DmCgsL7ezszp075+PjI5fLDxw4MHXqVDWfAUJvBsuo7sjMvPfhh/KGBtvSUtDX10wO6enpw4cPr6+v/+WXX6gN9UpLS4VCYX5+vkAgEAqFGRkZDx8+bPGuTp069ezZk5qrUl/t7e0Zf+8ZV1BQ4ObmVl5evnr16tDQULpPCaHXwTKqU1xd4eZNOHwYpkzRQPSysrKBAwfeu3dv0aJFP//888sOKykpEYlEVFWlVFZWtjimU6dOPB6Pqqfh4eG5ubkTJ06MjY1lMnHbG9TuYBnVKTt2wJIl8NFH8L//0R26oaFhxIgRaWlpw4cPP3fu3BvtAUU9PCoUCpVfy8rKlD+1tLQ0NDTMyMigWpoi1N5gGdUpFRXQpQvI5XD3LtD8MUxQUFB0dLSdnV1aWhrVdaktHj16lJWVJRaLY2Njz50798EHH1y/fl0leSKkclhGdU1AAMTGwqZN8NVX9AXduHHjN998Y2xsfO3atT59+qhw5NraWmtr6+rqapFIRK2jQqi9wTtNumb2bACAffvoi3jmzJlVq1Yxmczo6GjV1lAA6NChw5QpUwAgKipKtSMjpCo4G9U1Mhl06wb370NqKri5qT2cWCx2d3evqqpav359SEiIOkIkJSV5eHh06dLl7t27enp66giBUFvgbFTXsFgQGAgAEB6u9liVlZUTJkyoqqoKCAj45ptv1BRl+PDhvXr1unfv3rlz59QU4g1IpbBzJwQFQXAwREYCzkIQllGdNGcOAMCBA1Bfr8Yocrk8MDAwJyenf//+kZGRymWe6hAUFAQAERER6gvRWgsWQFoahITAokWwfz+sX6/phJDm4UW9bvLzAz4fVqyATp3UFWLJkiU7duywtLS8fv16t27d1BUGAABKSkrs7OzYbHZpaamZmZlaY706D3B3h/x8oJZzPXwIXC48eABstsZSQu0Azka13q5dkJ399PsnT+DbbwEAPDzAxeVpDc3MhL17VRw0IiJix44dbDb78OHD6q6hANC1a1dPT8+GhoZDhw6pO9ariMXA54NySez778N778Hdu3D8OKSmQlWVJnNDmoNlVOudOgUlJU+/r62FAwcAAE6ehM8/f/p6URFcuKDKiNeuXaMe9Ny1a5eHh4cqh3652bNnA0A4DXd8X4HDAan0H69IpcBmQ2AguLuDqSmYm8OwYTB/PmzfDufOwYMHGkoU0Qo7POmsuXNh8WKIjVXxsEVFRf7+/o2NjUuXLp03b56KR385f39/U1PT1NRUkUjE4/Foi/sPfD6IxSCRgIkJAEBeHshk0KkT+PmBSAQiEVRWQnIyJCc/e4u1dfj48ekcDp/P5/F4vXv3trCw0EzySG2wjOqC//4XTpwAAKitffbixx/D4sWQkADUY+hZWZCfD87OYG8Pb71qqL6+PiAg4MGDB2PHjt20aVObE38DhoaGU6ZM+e2338LDw2kO/YyFBXz6KUycCEuXQkMDrFsHGzaAqSlERz89oLQUhEIQCJ5+vX0bysrib98+npqqHKMtnQNR+4QfMWm9cePAywsGDAAAKC+HZcsgNxfGjIGtW0FPD/z9YcMGOHYMuneHjRsBANhscHQEPh8cHMDZGfh86N27VR2hCCHTp0+PiYlxcnJKSUmh/6Oea9euDR061NLSsqSkhMWifQagnIQmJkJyMujrw6hR4O7+qrcQAoWFV+7cSc3IoLqxiESiFp0D3d3d//zzT3XmjdQOZ6O6gM+HoUMBAO7d+8frvXvDhAnw00/QrRs4OoKXF4hEUFwMQiEIhdkAJQCjAYDDASuro6NHZ9vZyakGoI6OjuznPn1et25dTExMx44dY2NjNfJx+ZAhQ7hcrlgsTkxM9PX1pTX27dswahRs3gzBweDlBV5erXoXgwH29h729h7e3srX7t69KxaLs7KyqMLq/upCjLQBllEdFxoKzs7QrRsEB0NwMACARAI5ORARcePq1Ys9e44WCEAshqKiHUeO5NbUPO2rxGKxbG1tmzcAzc3N/e6775hM5oEDB/h8vqZOZ+bMmSEhIeHh4bSW0YoK8PeHx4/h8uWn/xHboHv37t27d/duVliR1tPU7iVIVaKiSF7e0+8lErJtGyGE/PYbKS19+uLFi+TAgZbv2r9//9y5c6nvq6tJ//7D16xZw+fzqf3fn/89MTIyAoD//Oc/dJzSy5WUlOjp6XE4nEePHtEUUiolI0YQADJ4MMF99NCL4GxU6wUFPfu+Y0f44gsAgOYfoY8c+eI3ikSi3bt3U99XV5d99NFHtbW1AoFALpcrj2GxWAYGBjKZTC6XGxkZff7556pO/8106dJl7Nixp0+fPnTo0MKFC+kIuWgRXL4MNjZw9KjGNhVA7RuuG313MZlM9t+oRzm7d+9O/ahz5879+vX7+OOP4+LiKioq6uvre/fuXVdXFxcXp9GUAQBmzZoFtC0g3bEDfv0VDA3hxAm6G7gi7YGz0XdXr169lAs/IyMjAcDV1XXo0KGjRo0KDg62s7NrfvDs2bPT09PDw8M//vhj+lNtbtKkSWZmZjdu3MjIyHBxcVFjpPPnYdkyYDAgLAwGDlRjIKTlcDaKnhkyZMjVq1dDQ0Nb1FAAmD59ur6+fmJiYnFxsSZSe8bAwIDaHJQq/epSUABTp4JMBv/3fzBtmhoDIe2HZfQdZWtrO4BaawoAACNGjDA3N3/F8ebm5uPHj1coFNHKpeaaQz0YGhUV1dTUpI7xJRLJvhUroK4OJk6E775TRwikS3D5PWqtkydPjhs3zsnJSSwWq7UtXmu4uLhkZmbGxcVNmDBBtSMrFIpJkyYlJCR87eu7ISYGcB899Do4G0Wt5e3tbWNjc+fOnZSUFE3nAjNmzAD1dCBdtWpVQkKCubn53O3bsYai1tBbu3atpnNA2oHJZD58+DA5OVlPT2/8+PGaTcbBweGnn366c+dOfn5+QUFBdXU1h8Pp1ObuqkePHl2yZAmLxYqPj29+0wOhV8CLevQGsrOzuVyuiYlJWVkZtSBfU06dOjV+/HgzM7OKigrliyYmJo6OjsqWH87Ozjwej8ls7SXXzZs3hw0bVldXt3PnzgULFqgncaSDsIyiN+Pu7p6amhodHT19+nRN5ZCdne3u7v7kyZN58+b17dtXIBBQT6mXl5e3ONLY2JjL5VIllWJnZ/fCwnr//n03N7fi4uI5c+bso3NjVaT9sIyiN7N79+4FCxaMHTs2MTFRIwlUVlYOGjQoJycnICDgyJEjzT/sqqyszM/PFwgEQqGQ+lpQUNDiN5zD4fTs2VPZK8DZ2ZnL5crl8jFjxiQlJQ0bNuz8+fMcZX97hFoByyh6M1VVVdbW1o2NjQUFBba2tjRHl8vl48eP/+OPP/r163f16tW6ujqpVNqlS5eXHf/48WOqPR1VWEUi0b0WXbAA9PX1zczM7t+/b2dnl5aW1rlzZzWfBNI1WEbRG5s2bVpMTMy6detWrVpFc+gvvvhi+/bt1D56VlZWY8aMycnJOXnyZP/+/Vs5QlVVVW5ubvMZa2FhIZPJ1NfXj4qK8vPzU2v+SDdprCkK0lqnT58GAHt7e4VCQWdc6jl6Npt9+fJlQgi1H5S1tXVJSUlbhq2qqpoyZQoALF++XEWZoncLllH0xuRyObUbaFJSEm1Bk5OT9fX1AeDXX38lhPz8888AYGBgkJqa2vbBqf7zlpaWUqm07aOhdw0uv0dvjMlkBgUFAY37dJaWlk6ZMqWxsfHLL7/85JNPkpKSli1bxmAwwsLC3Nzc3nrYkpKS9evXnz592t3dncfjPXjw4MyZMypMG70rNF3HkVbKzs5mMBjGxsbV1dXqjlVXVzdw4EAAGDt2bFNTU0FBAfUpUEhISBtH/u9//wsAH374ISFk48aNABAQEKCKlNG7BcsoektDhgwBgIiICLVGUSgUVD8ne3v7R48eVVdX9+nTh6p9MpmsjYNXVFTo6+szmcyioqKysjIWi0VrX32kK/CiHr0lqs2Suq/rv//+e2ofPeo598DAwMzMTC6XGxMT88LNTt6Iubn5hAkTFArF/v37raysvLy8pFLpwYMHVZI5eodouo4jbVVVVWVkZMRgMPKUW0Gp2okTJ5hMJpPJTEhIIISEhIQAgLm5eU5OjqpCnDx5EgCcnJwUCsXhw4cBwNXVVVWDo3cEllH09gIDAwFg7dq16hhcKBSamJjA3/voHT16lMFgsFisc+fOqTCKTCbr2rUrACQnJzc2Nr733nsAcOvWLRWGQDoPL+rR21Ne1ysUCtWO/Pjx4/Hjx0skkqCgoBUrVty8eXPmzJmEkG3bto0ePVqFgfT09KjmAOHh4RwOh9oiRR3995Au03QdR1pMLpdT0zcfHx+BQKCqRZdSqdTT0xMAXF1da2tr79+/Ty1TnT17tkrGbyE7OxsATExMamtrr1+/DgAWFhaNjY3qiIV0EpZR1CZeXl7Kf5JZLJaDg8O4ceNWrlwZERFx48aN2tratxjzs88+AwBra+vi4mKpVOrh4QEAQ4cObVDbNvGDBg0CgP379xNCqG3yjh8/rqZYSPdgGUVtIpVKAwMDuVyug4PD8w3oWCyWk5OTn59fSEhIdHT0X3/9VVdX9+oBw8LCAMDAwCAlJYUQQu1d2r179wcPHqjvLHbv3g0AY8aMIYRs2bIFACZOnKi+cEjHYGsSpDJSqTQnJ0fZ8kMgEGRnZ8vl8haHWVtbN+9T169fP2NjY+pHV69eHT16tFQqDQsLCw4O/vHHH5ctW2ZoaJiUlKTWXvRVVVU2NjYNDQ35+fmGhoZdu3YlhBQXF1tZWakvKNIdmq7jSJc1NDTcunXr4MGDq1atCggI4HK5bDa7xW8gg8Gwt7f39fWdP3++mZkZAKxcuZIQkpiYyGKxGAxGTEwMDalOmzYNAEJDQwkh1BYpP/74Iw1xkQ7A2SiiVVNTU3FxcfM+dUKhsL6+nvqptbW1tbV1WlpaXV2dg4NDeXn5mjVr6NkuLDEx0dvb297ePi8v7/jx4wEBAb17987MzKQhNNJ2WEaRhslkMqoBaFRUVFxc3MiRIy9evAgAiYmJhw4d2rt3Lz2bOSsUCjs7u+Li4suXL7u7u3ft2vXRo0fp6emurq40REdaDdeNIg1jsVhcLjcgICAyMtLIyOjy5cv5+fkA4OXlFRYWRk8NBQAmk0lt2kwtIKWu8WlrYYW0GpZR1F6YmJhMmjSJELJ//36NJDB79mwGg3H48OGamhrqyYLo6OjGxkaNJIO0CJZR1I4oH4vSyL0mR0fHIUOG1NbWHjt2rH///v369Xv8+PH//vc/+jNB2gXLKGpHRo8ebWtrW1BQcOXKFY0k0Lxt1axZswCv61ErYBlF7UjzG5QaSWDq1KnGxsaXL1/Oy8ubMWOGvr7+6dOny8rKNJIM0hZYRlH7Qt2gPHLkSHV1Nf3RjY2Nqc1BL1y4YGFhMWbMGJlMtnz5cvozQVoEyyhqX3r27Dl06FDqBqVGEvj2229zcnI++eQTAOBwOABA9StB6GWwjKJ2h56++i/Ts2fPHj16AEBxcfGlS5cAwMnJSSOZIG2By+9Ru1NTU2NtbV1bW5uTk0NVNPrV19ePGDHi+vXrw4YNO3v2rIGBgUbSQFoBZ6Oo3WA0VIcAAAOTSURBVKFuUBJCIiMjNZIAIWTu3LnXr1+3t7c/fvw41lD0alhGUXtEXddHRESovK9+a6xfv/7gwYPUPnpUX2qEXgEv6lF7RAjp2bNnfn7++fPnR40aRWfouLg4f39/6ptx48bRGRppKZyNovaIwWBoZAGpSCSaNWuWQqHYsGED1lDUSjgbRe1UYWGhg4ODgYFBWVlZp06daIj4+PHjQYMG5ebmBgUFRUVF0RAR6QacjaJ2ys7OzsPDo76+/ujRozSEk8lkU6ZMyc3NdXV13bNnDw0Rkc7AMoraLzoXkC5ZsuTChQtWVlZxcXFGRkY0REQ6Ay/qUftVW1trbW1dXV0tEom4XK76Av3+++/BwcEGBgaXLl2idglFqPVwNorarw4dOkyePBkAmncgLS4uVm2Uq1evfvrppwCwc+dOrKHoLeBsFLVrV65cGTFiRJcuXe7evaunpyeRSExNTTkcTo8ePZpvL8rj8Z7f3rk17t696+bm9vDhw6+++mrTpk0qzx+9C7CMonaNEOLo6JiXl5eYmDh27FihUOjp6fnw4cMWhxkZGfF4POdm7O3t9fT0Xj14TU3N0KFDMzIyvL29T548+drjEXohLKOovQsNDV2zZs3YsWMTExOpV548eZKXl9d8e9GCgoIWv8lsNtvR0VE5Y3VwcOjdu7e+vr7yAELI1KlTDx8+3KtXr5SUFFNTU1rPCukQLKOovcvKyurTpw8AuLi4+Pj4UGWRy+V26NBBeUxlZSW1V7NIJBIIBCKR6PlbqBwOJzQ0dOXKldRf16xZExoaamZmlpqa6ujoSNvpIN2DZRRpAR8fn9OnT7d40drauvnt0b59+3bs2FH5U4lEkpOTk5+fr5y0isXiPXv2zJs3DwConeiZTGZ8fLyvry+tJ4N0DpZRpAUIIfHx8RUVFcXFxdSs886dO1KptMVhtra2PB6Pz+crvza/VK+pqWEwGB06dLh9+zbVGXrbtm1ffPEFvaeCdBCWUaSVZDJZUVFR89ujIpGorq6uxWFmZmbK6Sqfz+/du7dEIvHy8rp79+7MmTMjIiI0kjzSMVhGkY6Qy+XUJbzy9qhIJKqvr3/hwf369UtJSWn+iRNCbw3LKNJlpaWlyumqQCBIT09vaGgwNTW9fPmyi4uLprNDOgLLKHqHEEIyMjL69Onzdmv1EXohLKMIIdQm+G8yQgi1CZZRhBBqEyyjCCHUJlhGEUKoTf4frnYacpWcbOMAAAHaelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuNQAAeJx7v2/tPQYg4GWAACYglgBiaSBuYGRjSADSjMwcDBpAmpmJzQFMs7A5ZIBoZkaEAHYarhFqEKY6uEGYDBxGgGkmJjYFFRDNwg7hM8LFoTQjWB8DIzcDIwMjkwITcwYTM0sCC2sGEytbAht7BhM7hwIHJwMblwIXdwYTN08CD28GEy9fAh9/BhO/gIKAIAO3EIOQMIOwCIOIKIOoGAM7UwI/V4IYC4MwK4OwOAujCAvQeDYmdjZWFmY2Hl4+fi5uNiFhVjYgJSIqxsIqLK7FCLQfGrAMEtvSjR2y7JIdQJxNsSvtf9wqArPTs/bZyS7UALMVrvfuX21rAWbLnGU+MJ/9jj2IPf314f3W2QvtQOzF1v57baUn2YLYZZPS7DOnzgWrqYqVcJi7owbMZrcPdWDK4ASbI3j5i51s96r9IHaA5ad9mfXyB0DstZwptpdCK8Fs9klH7A0qW8DsCVryDi077MDsumW/7C2OnATrPW1i5cDhdHUfiH2lrMfBSlgTLM7H8Gv/nUf8YPUR1+UOnDOMAIubTKg9MGO/JJh9eHP/Ac5ZvmC3/WxNOrDL4TeYrbEx8MDa7RlgNWIATztxKrd78qMAAAJVelRYdE1PTCByZGtpdCAyMDIyLjA5LjUAAHicfVXLbhsxDLz7K/QDEfjSg4cekjhNiyI20Dr9h977/+hQC0cKIHQ3Iqz1LCnODJ1Tiuvn+cefv+njkvPplBL958/d028lotNbig/p6eX1+yU93x6f7k+er++X268klqThHdyfsY+369v9CafnJLmTCpWkuRSqvSTKNK75pqRr4qyi3AS45lZ5h1Pko6yNnSoSV7WutsEZcA+cmTpLjxO03uoOWAIomYqrG47QuHvXDbAeGYtSjbazcpHGG2ALIB4X5BwVG6vtgH304kQVPXNG7yyywTm4kaydGgUl3tH9DseQBtypsiOhZLbqbdcK86hsrTO+R1MK4hvtkHI0Y00JzYAoc0HjO+ghDZVSI6nm7qXXbfkQJ5gsDSI+WGb15ltkGfaxPhpBde9EvK1eR06UlMiJ7K0q7VjnNuh09AOWojeuhX2HDIEMxDukDGStZGVLkx/m8KIMc+CgYl1kV17oMJx1GE0jazcrvMuK9y9BpFfo3cYBiErfQodOBnWoDiYxcaw7oiRkQtICuyMpzlzFeUc+5vtbIAWDoz3KgzMqO+jL5fxp6o/fgafr5Tx/B+KWOe7YJJ1TbbHm8MZd5ohik+ocRMNqc9wMq8+hYiyfs8Ox1hlhwJmXWbAILIvnLQLrYm2LwLZY2CJwWawaWxhxsSSP0Bbr8Qh9sRiPWr5YiSPI6hiOILwYgyOILPrHFuouOhuI0EVNG6eZWUq8o5NnqUehjw7CH6MyBvbr6xde5V7Fjf39HwQ+n/4Bx3Qy9B88V2cAAAE+elRYdFNNSUxFUyByZGtpdCAyMDIyLjA5LjUAAHicJZA7buRADESv4nAG6Gnw//EkCyjZyI42MhzpEJv48C5qMvGxuoqqr7/fX8efb5bj1PN23j6P+3meen/MOMOL3E7l+3F8yNvP7aFbMszWg3ZTk+t6PmRbcfWwMnMG4t2urAs7sRIBom2p1DbMWtjXc1BxQsZbVTTXcx6W8BDPgLtMjuWYG4dzr6dtrUYMUASZj8gqOse6i4jHx9UTGtusnT1h5B6pSNON9xXXUeTkOAofyWo8uiaKlMVblFnGXQtbaLqSG0RxLiNPNtvk4pFiQwESaqVzAH6Ic+nOtuBxKVLB7E5RV0dMhY6wyMq4isQt2khO9PnSuBJqgD+75FQLA21TiDqkhxj6pGiIkMAql6iDW6+KiLzWff3/986bfn4B1dpnMBGzgPQAAAAASUVORK5CYII=\n"
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    }
  ]
}