clustering5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM9G3aXtRqBKG/uhE6eTHPy",
      "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/francescopatane96/Bioactivity-prediction-with-ML/blob/main/clustering5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2D4euSqkBVDd",
        "outputId": "909be0f0-e3f6-46f4-aab8-1cf0c1b84629"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2022.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 36.8 MB 423 kB/s \n",
            "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from rdkit) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rdkit) (1.21.6)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2022.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/volkamerlab/teachopencadd.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myQilAQCBeKl",
        "outputId": "d6e27c7f-de1f-4261-946d-eccf1715b1a2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/volkamerlab/teachopencadd.git\n",
            "  Cloning https://github.com/volkamerlab/teachopencadd.git to /tmp/pip-req-build-v6ekzr6p\n",
            "  Running command git clone -q https://github.com/volkamerlab/teachopencadd.git /tmp/pip-req-build-v6ekzr6p\n",
            "Building wheels for collected packages: teachopencadd\n",
            "  Building wheel for teachopencadd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for teachopencadd: filename=teachopencadd-2022.9.0+2.g8f2c87a-py3-none-any.whl size=108231919 sha256=7a6176ee4ada6615b93831f57cd9186302abdeb9d94f72048fe34ca367b77b90\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ggg1d_3d/wheels/ee/da/82/b2c07c8ddbf7e09a56a283a394ebff26d956480b85a9ef011f\n",
            "Successfully built teachopencadd\n",
            "Installing collected packages: teachopencadd\n",
            "Successfully installed teachopencadd-2022.9.0+2.g8f2c87a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "import random\n",
        "from pathlib import Path\n",
        "\n",
        "import pandas as pd\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "from rdkit import Chem\n",
        "from rdkit import DataStructs\n",
        "from rdkit.ML.Cluster import Butina\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "\n",
        "from teachopencadd.utils import seed_everything\n",
        "\n",
        "seed_everything()  # fix seed to get deterministic outputs"
      ],
      "metadata": {
        "id": "GyKKqPxiBTs2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and have a look into data\n",
        "# Filtered data taken from **Talktorial T002**\n",
        "compound_df = pd.read_csv(\n",
        "    \"TNFB_compounds_lipinski.csv\",\n",
        "    index_col=0,\n",
        ")\n",
        "print(\"Dataframe shape:\", compound_df.shape)\n",
        "compound_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "7hQCDG1LC42R",
        "outputId": "03934860-e189-44d4-f43f-9a9bcecfef95"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe shape: (49, 11)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  molecule_chembl_id   IC50 units  \\\n",
              "0       CHEMBL336546   50.0    nM   \n",
              "1       CHEMBL131636  170.0    nM   \n",
              "2       CHEMBL134490  280.0    nM   \n",
              "3       CHEMBL135133  380.0    nM   \n",
              "4       CHEMBL334485  500.0    nM   \n",
              "\n",
              "                                              smiles     pIC50  \\\n",
              "0  O=C(Nc1cc(C(F)(F)F)cc(C(F)(F)F)c1)c1cnc(Cl)nc1...  7.301030   \n",
              "1  CCCCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C...  6.769551   \n",
              "2  CCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C(F...  6.552842   \n",
              "3       O=C(Nc1ccc(C(F)(F)F)cc1)c1cnc(Cl)nc1C(F)(F)F  6.420216   \n",
              "4         O=C(Nc1cc(Cl)cc(Cl)c1)c1cnc(Cl)nc1C(F)(F)F  6.301030   \n",
              "\n",
              "                                               ROMol  molecular_weight  n_hba  \\\n",
              "0  <img data-content=\"rdkit/molecule\" src=\"data:i...        436.997743      3   \n",
              "1  <img data-content=\"rdkit/molecule\" src=\"data:i...        469.062788      5   \n",
              "2  <img data-content=\"rdkit/molecule\" src=\"data:i...        441.031488      5   \n",
              "3  <img data-content=\"rdkit/molecule\" src=\"data:i...        369.010359      3   \n",
              "4  <img data-content=\"rdkit/molecule\" src=\"data:i...        368.945029      3   \n",
              "\n",
              "   n_hbd    logp  ro5_fulfilled  \n",
              "0      1  5.4387           True  \n",
              "1      1  5.3768           True  \n",
              "2      1  4.5966           True  \n",
              "3      1  4.4199           True  \n",
              "4      1  4.7079           True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14828fb0-6bad-455f-be89-bf8b8c0bee93\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>IC50</th>\n",
              "      <th>units</th>\n",
              "      <th>smiles</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>ROMol</th>\n",
              "      <th>molecular_weight</th>\n",
              "      <th>n_hba</th>\n",
              "      <th>n_hbd</th>\n",
              "      <th>logp</th>\n",
              "      <th>ro5_fulfilled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL336546</td>\n",
              "      <td>50.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>O=C(Nc1cc(C(F)(F)F)cc(C(F)(F)F)c1)c1cnc(Cl)nc1...</td>\n",
              "      <td>7.301030</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>436.997743</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5.4387</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL131636</td>\n",
              "      <td>170.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>CCCCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C...</td>\n",
              "      <td>6.769551</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>469.062788</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5.3768</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL134490</td>\n",
              "      <td>280.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>CCOC(=O)c1cc(NC(=O)c2cnc(Cl)nc2C(F)(F)F)cc(C(F...</td>\n",
              "      <td>6.552842</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>441.031488</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5966</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL135133</td>\n",
              "      <td>380.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>O=C(Nc1ccc(C(F)(F)F)cc1)c1cnc(Cl)nc1C(F)(F)F</td>\n",
              "      <td>6.420216</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>369.010359</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.4199</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL334485</td>\n",
              "      <td>500.0</td>\n",
              "      <td>nM</td>\n",
              "      <td>O=C(Nc1cc(Cl)cc(Cl)c1)c1cnc(Cl)nc1C(F)(F)F</td>\n",
              "      <td>6.301030</td>\n",
              "      <td>&lt;img data-content=\"rdkit/molecule\" src=\"data:i...</td>\n",
              "      <td>368.945029</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.7079</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14828fb0-6bad-455f-be89-bf8b8c0bee93')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-14828fb0-6bad-455f-be89-bf8b8c0bee93 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-14828fb0-6bad-455f-be89-bf8b8c0bee93');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create molecules from SMILES and store in array\n",
        "compounds = []\n",
        "# .itertuples() returns a (index, column1, column2, ...) tuple per row\n",
        "# we don't need index so we use _ instead\n",
        "# note how we are slicing the dataframe to only the two columns we need now\n",
        "for _, chembl_id, smiles in compound_df[[\"molecule_chembl_id\", \"smiles\"]].itertuples():\n",
        "    compounds.append((Chem.MolFromSmiles(smiles), chembl_id))\n",
        "compounds[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHvZx_QmDBUu",
        "outputId": "aa8f7ab6-c17c-43c6-f107-97b58ea8a3ca"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(<rdkit.Chem.rdchem.Mol at 0x7fa5804076c0>, 'CHEMBL336546'),\n",
              " (<rdkit.Chem.rdchem.Mol at 0x7fa57d215620>, 'CHEMBL131636'),\n",
              " (<rdkit.Chem.rdchem.Mol at 0x7fa585a30530>, 'CHEMBL134490'),\n",
              " (<rdkit.Chem.rdchem.Mol at 0x7fa585a30620>, 'CHEMBL135133'),\n",
              " (<rdkit.Chem.rdchem.Mol at 0x7fa57d1ef530>, 'CHEMBL334485')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create fingerprints for all molecules\n",
        "rdkit_gen = rdFingerprintGenerator.GetRDKitFPGenerator(maxPath=5)\n",
        "fingerprints = [rdkit_gen.GetFingerprint(mol) for mol, idx in compounds]\n",
        "\n",
        "# How many compounds/fingerprints do we have?\n",
        "print(\"Number of compounds converted:\", len(fingerprints))\n",
        "print(\"Fingerprint length per compound:\", len(fingerprints[0]))\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5y-nTbl3DDc-",
        "outputId": "d6374d6b-734c-4287-951d-7a72469a8be8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of compounds converted: 49\n",
            "Fingerprint length per compound: 2048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tanimoto_distance_matrix(fp_list):\n",
        "    \"\"\"Calculate distance matrix for fingerprint list\"\"\"\n",
        "    dissimilarity_matrix = []\n",
        "    # Notice how we are deliberately skipping the first and last items in the list\n",
        "    # because we don't need to compare them against themselves\n",
        "    for i in range(1, len(fp_list)):\n",
        "        # Compare the current fingerprint against all the previous ones in the list\n",
        "        similarities = DataStructs.BulkTanimotoSimilarity(fp_list[i], fp_list[:i])\n",
        "        # Since we need a distance matrix, calculate 1-x for every element in similarity matrix\n",
        "        dissimilarity_matrix.extend([1 - x for x in similarities])\n",
        "    return dissimilarity_matrix"
      ],
      "metadata": {
        "id": "d9IDVZRADIRu"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Calculate single similarity of two fingerprints\n",
        "# NBVAL_CHECK_OUTPUT\n",
        "sim = DataStructs.TanimotoSimilarity(fingerprints[0], fingerprints[1])\n",
        "print(f\"Tanimoto similarity: {sim:.2f}, distance: {1-sim:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QauDa9HyDKVO",
        "outputId": "9c170b4a-9ae0-48da-9469-5b8cd6320c07"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tanimoto similarity: 0.80, distance: 0.20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Calculate distance matrix (distance = 1-similarity)\n",
        "tanimoto_distance_matrix(fingerprints)[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yY0K_IF7DMKu",
        "outputId": "50fe54ee-2738-44e1-e43b-48b590fdd046"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2021276595744681,\n",
              " 0.16897506925207761,\n",
              " 0.039893617021276584,\n",
              " 0.05315614617940201,\n",
              " 0.24403183023872677]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Side note: That looked like a list and not a matrix.\n",
        "# But it is a triangular similarity matrix in the form of a list\n",
        "n = len(fingerprints)\n",
        "\n",
        "# Calculate number of elements in triangular matrix via n*(n-1)/2\n",
        "elem_triangular_matr = (n * (n - 1)) / 2\n",
        "print(\n",
        "    f\"Elements in the triangular matrix ({elem_triangular_matr:.0f}) ==\",\n",
        "    f\"tanimoto_distance_matrix(fingerprints) ({len(tanimoto_distance_matrix(fingerprints))})\",\n",
        ")\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iccQP_zbDOX-",
        "outputId": "a84a49f2-d011-4aef-fe89-a035e46de9b2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elements in the triangular matrix (1176) == tanimoto_distance_matrix(fingerprints) (1176)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cluster_fingerprints(fingerprints, cutoff=0.2):\n",
        "    \"\"\"Cluster fingerprints\n",
        "    Parameters:\n",
        "        fingerprints\n",
        "        cutoff: threshold for the clustering\n",
        "    \"\"\"\n",
        "    # Calculate Tanimoto distance matrix\n",
        "    distance_matrix = tanimoto_distance_matrix(fingerprints)\n",
        "    # Now cluster the data with the implemented Butina algorithm:\n",
        "    clusters = Butina.ClusterData(distance_matrix, len(fingerprints), cutoff, isDistData=True)\n",
        "    clusters = sorted(clusters, key=len, reverse=True)\n",
        "    return clusters"
      ],
      "metadata": {
        "id": "5ZcQJY7MDRxW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the clustering procedure for the dataset\n",
        "clusters = cluster_fingerprints(fingerprints, cutoff=0.3)\n",
        "\n",
        "# Give a short report about the numbers of clusters and their sizes\n",
        "num_clust_g1 = sum(1 for c in clusters if len(c) == 1)\n",
        "num_clust_g5 = sum(1 for c in clusters if len(c) > 5)\n",
        "num_clust_g25 = sum(1 for c in clusters if len(c) > 25)\n",
        "num_clust_g100 = sum(1 for c in clusters if len(c) > 100)\n",
        "\n",
        "print(\"total # clusters: \", len(clusters))\n",
        "print(\"# clusters with only 1 compound: \", num_clust_g1)\n",
        "print(\"# clusters with >5 compounds: \", num_clust_g5)\n",
        "print(\"# clusters with >25 compounds: \", num_clust_g25)\n",
        "print(\"# clusters with >100 compounds: \", num_clust_g100)\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1XWfxqKDTxN",
        "outputId": "b18f9fcd-0c68-4f34-969e-4c3a13103d8d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total # clusters:  25\n",
            "# clusters with only 1 compound:  17\n",
            "# clusters with >5 compounds:  2\n",
            "# clusters with >25 compounds:  0\n",
            "# clusters with >100 compounds:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the size of the clusters\n",
        "fig, ax = plt.subplots(figsize=(15, 4))\n",
        "ax.set_xlabel(\"Cluster index\")\n",
        "ax.set_ylabel(\"Number of molecules\")\n",
        "ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters], lw=5);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "lGxKiS9sDWxB",
        "outputId": "5dce5188-633e-4240-d289-fb76389c152c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for cutoff in numpy.arange(0.0, 1.0, 0.2):\n",
        "    clusters = cluster_fingerprints(fingerprints, cutoff=cutoff)\n",
        "    fig, ax = plt.subplots(figsize=(15, 4))\n",
        "    ax.set_title(f\"Threshold: {cutoff:3.1f}\")\n",
        "    ax.set_xlabel(\"Cluster index\")\n",
        "    ax.set_ylabel(\"Number of molecules\")\n",
        "    ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters], lw=5)\n",
        "    display(fig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "edbn5RRCDcQ-",
        "outputId": "18243579-09eb-49cf-f3ee-9688c2e21ad6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAEWCAYAAADB8b79AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcE0lEQVR4nO3deZRlZXnv8e8PGi6jglIi0LSNLsWLKOqtqyiOaBTDZFwY4QoX0Nia5YCzrdFwb0wiJuBVNEttBSERIV5EgoIRHInDRQZBmikx0EAzCIgyKZP93D/ObinaGnZV9TmndtX3s9ZZffZw9vNUrbNW96/f/b47VYUkSZIkqZs2GHYDkiRJkqSZM9RJkiRJUocZ6iRJkiSpwwx1kiRJktRhhjpJkiRJ6jBDnSRJkiR1mKFOkjQnJflfSb44gDqHJfnBDD87aY9JViV56cy7kyRpaoY6SdJQJLl7zGtNkt+O2X7tsPsbtPR8NMkvm9dHk2SS80eSfCnJHUl+leSkQfYrSZo7DHWSpKGoqi3WvoDrgH3H7JtWQEmyqD9dDtQy4JXAbsDTgH2BN05y/mnAzcAS4DHA0f1uUJI0NxnqJElz2cZJ/jHJXUkuSzK69kBza+P7kvwMuCfJoiS7J/lRkl8nuSTJi8acf1iSq5trXbPuaGCSo5sRr2uSvGLM/u2TnJHk9iQ/T/KGiZpNckiSa5uRtr+Y5s96KHBMVa2uqhuAY4DDJqjzMmBH4D1VdUdVPVBVP51mPUnSPGGokyTNZfsBpwBbAWcAn1rn+EHA3s3xbYEzgb8GHgW8G/hKc5vi5sCxwCuqakvgucDFY67zbOAqYBvg74Djxtz6eAqwGtgeOAD42yR7rttokl2ATwOHNOc+Glg85vjzkvx6kp/1KcAlY7YvafaNZ/em3xObAHl+khdOcm1J0jxmqJMkzWU/qKqzqup3wD/RuzVxrGOr6vqq+i1wMHBWc/6aqjoHuAD44+bcNcCuSTatqpuq6rIx17m2qj7X1DkR2A7YNsmOwB7A+6rq3qq6GPg88D/H6fUA4OtVdW5V3Qd8qKkJQFX9oKq2muRn3QK4Y8z2HcAWE8yrWwy8DPgu8Fh6o3r/kmSbSa4vSZqnDHWSpLns5jHvfwNsss78uevHvH8c8Orm1stfN6NizwO2q6p7gNcAbwJuSnJmkiePV6eqftO83YLeiNvtVXXXmHOvBXYYp9ftx/bT1Pxly58T4G7gEWO2HwHcXVU1zrm/BVZV1XHNrZenNLX3mEY9SdI8YaiTJHXZ2MBzPfBPVbXVmNfmVXUUQFV9s6r+iN4o3JXA51pc/0bgUUm2HLNvCXDDOOfeRG+eGwBJNqN3C2Zbl/Hwkcjdmn3j+RkP/9kZZ1uStEAY6iRJ88UXgX2TvDzJhkk2SfKiJIuTbJtk/2Zu3X30RsXWTH45qKrrgR8BH2mu9zTg9U2tdZ0K7NPMndsY+Cum9/fsPwLvTLJDku2BdwEnTHDuV4Gtkxza/KwH0Lsl84fTqCdJmicMdZKkeaEJYPsDHwBupTdy9x56f9dtALyT3sjb7cALgT9veemDgKXNZ78KHFlV3xqn/mXAm4Ev0Ru1+xW9BVYASPL8JHdPUuezwNeAS4GV9BZ9+eyYz9+d5PlNrdvpLSLzbnpz75YD+1fVbS1/JknSPJLxb9WXJEmSJHWBI3WSJEmS1GGGOkmSJEnqMEOdJEmSJHWYoU6SJEmSOmzR1KcM3zbbbFNLly4ddhuSJEmSNBQXXnjhbVU1Mt6xToS6pUuXcsEFFwy7DUmSJEkaiiTXTnTM2y8lSZIkqcMMdZIkSZLUYYY6SZIkSeowQ50kSZIkdZihTpIkSZI6zFAnSZIkSR3Wt1CX5PgktyRZuc7+tya5MsllSf6uX/UlSZIkaSHo50jdCcBeY3ckeTGwP7BbVT0FOLqP9SVJkiRp3utbqKuqc4Hb19n958BRVXVfc84t/aovSZIkSQvBogHXexLw/CR/A9wLvLuqzh/vxCTLgGUAS5YsGVyH07B0+ZnDbmHOWXXU3sNuQZIkSVpQBr1QyiLgUcDuwHuALyfJeCdW1YqqGq2q0ZGRkUH2KEmSJEmdMehQtxo4rXp+AqwBthlwD5IkSZI0bww61J0OvBggyZOAjYHbBtyDJEmSJM0bfZtTl+Rk4EXANklWA0cCxwPHN485uB84tKqqXz1IkiRJ0nzXt1BXVQdNcOjgftWUJEmSpIVm0LdfSpIkSZLWI0OdJEmSJHWYoU6SJEmSOsxQJ0mSJEkdZqiTJEmSpA4z1EmSJElShxnqJEmSJKnDDHWSJEmS1GGGOkmSJEnqMEOdJEmSJHWYoU6SJEmSOsxQJ0mSJEkdZqiTJEmSpA4z1EmSJElShxnqJEmSJKnDDHWSJEmS1GF9C3VJjk9yS5KV4xx7V5JKsk2/6kuSJEnSQtDPkboTgL3W3ZlkR+BlwHV9rC1JkiRJC0LfQl1VnQvcPs6h/wO8F6h+1ZYkSZKkhWKgc+qS7A/cUFWXDLKuJEmSJM1XiwZVKMlmwAfo3XrZ5vxlwDKAJUuW9LEzSZIkSequQY7UPQHYCbgkySpgMXBRkseOd3JVraiq0aoaHRkZGWCbkiRJktQdAxupq6pLgces3W6C3WhV3TaoHiRJkiRpvunnIw1OBn4M7JxkdZLX96uWJEmSJC1UfRupq6qDpji+tF+1JUmSJGmhGOjql5IkSZKk9ctQJ0mSJEkdZqiTJEmSpA4z1EmSJElShxnqJEmSJKnDDHWSJEmS1GGGOkmSJEnqMEOdJEmSJHWYoU6SJEmSOsxQJ0mSJEkdZqiTJEmSpA4z1EmSJElShxnqJEmSJKnDDHWSJEmS1GGGOkmSJEnqsGmFuiQbJHlEv5qRJEmSJE3PlKEuyZeSPCLJ5sBK4PIk7+l/a5IkSZKkqbQZqdulqu4EXgl8A9gJOGSqDyU5PsktSVaO2ff3Sa5M8rMkX02y1Yw7lyRJkiS1CnUbJdmIXqg7o6oeAKrF504A9lpn3znArlX1NODfgfdPo1dJkiRJ0jrahLrPAquAzYFzkzwOuHOqD1XVucDt6+w7u6oebDb/H7B4Wt1KkiRJkh5m0VQnVNWxwLFjdl2b5MXrofbrgH+e6GCSZcAygCVLlqyHcuqKpcvPHHYLc9Kqo/YedguSJEmag9oslLJtkuOSfKPZ3gU4dDZFk/wF8CBw0kTnVNWKqhqtqtGRkZHZlJMkSZKkeavN7ZcnAN8Etm+2/x14+0wLJjkM2Ad4bVW1mZsnSZIkSZpAm1C3TVV9GVgD0MyJ+91MiiXZC3gvsF9V/WYm15AkSZIkPaRNqLsnyaNpVrxMsjtwx1QfSnIy8GNg5ySrk7we+BSwJXBOkouTfGbmrUuSJEmSplwoBXgncAbwhCQ/BEaAA6b6UFUdNM7u46bXniRJkiRpMm1Wv7woyQuBnYEAVzXPqpMkSZIkDdmEoS7JqyY49KQkVNVpfepJkiRJktTSZCN1+05yrABDnSRJkiQN2YShrqoOH2QjkiRJkqTpm3JOXZK/HG9/Vf3V+m9HkiRJkjQdbVa/vGfM+03oPTj8iv60I0mSJEmajjarXx4zdjvJ0cA3+9aRJEmSJKm1Ng8fX9dmwOL13YgkSZIkafrazKm7lN5qlwAb0nv4uPPpJEmSJGkOaDOnbp8x7x8EflFVD/apH0mSJEnSNLS5/XI74PaquraqbgA2TfLsPvclSZIkSWqhTaj7NHD3mO17mn2SJEmSpCFrE+pSVWvn1FFVa2h326YkSZIkqc/ahLqrk7wtyUbN6wjg6n43JkmSJEmaWptQ9ybgucANwGrg2cCyfjYlSZIkSWqnzcPHbwEOHEAvkiRJkqRpmnKkLsmTknw7ycpm+2lJPtj/1iRJkiRJU2lz++XngPcDDwBU1c9oMXKX5Pgkt6wNg82+RyU5J8l/NH9uPdPGJUmSJEntQt1mVfWTdfa1efj4CcBe6+xbDny7qp4IfLvZliRJkiTNUJtQd1uSJwAFkOQA4KapPlRV5wK3r7N7f+DE5v2JwCvbtypJkiRJWleb5829GVgBPDnJDcA1wMEzrLdtVa0NhDcD2050YpJlNKtsLlmyZIblJEmSJGl+a7P65dXAS5NsDmxQVXetj8JVVUlqkuMr6IVJRkdHJzxPkiRJkhayCUNdkndOsB+AqvrYDOr9Isl2VXVTku2AW2ZwDUmSJElSY7I5dVtO8ZqJM4BDm/eHAv8yw+tIkiRJkphkpK6q/vdsLpzkZOBFwDZJVgNHAkcBX07yeuBa4E9nU0OSJEmSFrop59QlWQx8Etij2fVvwBFVtXqyz1XVQRMcesm0OpQkSZIkTajNIw2+QO+2ye2b19eafZIkSZKkIWsT6kaq6gtV9WDzOgEY6XNfkiRJkqQW2oS6XyY5OMmGzetg4Jf9bkySJEmSNLU2oe519BY0uRm4CTgAOLyfTUmSJEmS2mnz8PFrgf0G0IskSZIkaZrarH65E/BWYOnY86vKoCdJkiRJQzZlqANOB46jt+rlmv62I0mSJEmajjah7t6qOrbvnUiSJEmSpq1NqPtEkiOBs4H71u6sqov61pUkSZIkqZU2oe6pwCHAnjx0+2U125IkSZKkIWoT6l4NPL6q7u93M5IkSZKk6WnznLqVwFb9bkSSJEmSNH1tRuq2Aq5Mcj4Pn1PnIw0kSZIkacjahLoj+96FJEmSJGlGpgx1VfX9QTQiSZIkSZq+NnPqJEmSJElzlKFOkiRJkjpswlCX5NvNnx9d30WTvCPJZUlWJjk5ySbru4YkSZIkLQSTjdRtl+S5wH5JnpHkmWNfMy2YZAfgbcBoVe0KbAgcONPrSZIkSdJCNtlCKX8JfAhYDHxsnWMF7DnLupsmeQDYDLhxFteSJEmSpAVrwlBXVacCpyb5UFV9eH0VrKobkhwNXAf8Fji7qs5e97wky4BlAEuWLFlf5SVJkiRpXplyoZSq+nCS/ZIc3bz2mU3BJFsD+wM7AdsDmyc5eJy6K6pqtKpGR0ZGZlNSkiRJkuatKUNdko8ARwCXN68jkvztLGq+FLimqm6tqgeA04DnzuJ6kiRJkrRgTfnwcWBv4OlVtQYgyYnAT4EPzLDmdcDuSTajd/vlS4ALZngtSZIkSVrQ2j6nbqsx7x85m4JVdR5wKnARcGnTw4rZXFOSJEmSFqo2I3UfAX6a5LtAgBcAy2dTtKqOBI6czTUkSZIkSS1CXVWdnOR7wH9vdr2vqm7ua1eSJEmSpFbajNRRVTcBZ/S5F0mSJEnSNLWdUydJkiRJmoMMdZIkSZLUYZOGuiQbJrlyUM1IkiRJkqZn0lBXVb8DrkqyZED9SJIkSZKmoc1CKVsDlyX5CXDP2p1VtV/fupIkSZIktdIm1H2o711IkiRJkmakzXPqvp/kccATq+pbSTYDNux/a5IkSZKkqUy5+mWSNwCnAp9tdu0AnN7PpiRJkiRJ7bR5pMGbgT2AOwGq6j+Ax/SzKUmSJElSO21C3X1Vdf/ajSSLgOpfS5IkSZKkttqEuu8n+QCwaZI/Av4v8LX+tiVJkiRJaqNNqFsO3ApcCrwROAv4YD+bkiRJkiS102b1yzVJTgTOo3fb5VVV5e2XkiRJkjQHTBnqkuwNfAb4TyDATkneWFXf6HdzkiRJkqTJtXn4+DHAi6vq5wBJngCcCRjqJEmSJGnI2sypu2ttoGtcDdw1m6JJtkpyapIrk1yR5DmzuZ4kSZIkLVQTjtQleVXz9oIkZwFfpjen7tXA+bOs+wngX6vqgCQbA5vN8nqSJEmStCBNdvvlvmPe/wJ4YfP+VmDTmRZM8kjgBcBhAM0z8O6f7DOSJEmSpPFNGOqq6vA+1dyJXjD8QpLdgAuBI6rqnrEnJVkGLANYsmRJn1qRFpaly88cdgtzzqqj9p71Nfy9jm99/G4lSdLUppxTl2SnJB9LclqSM9a+ZlFzEfBM4NNV9QzgHnrPwnuYqlpRVaNVNToyMjKLcpIkSZI0f7VZ/fJ04Djga8Ca9VBzNbC6qs5rtk9lnFAnSZIkSZpam1B3b1Udu74KVtXNSa5PsnNVXQW8BLh8fV1fkiRJkhaSNqHuE0mOBM4G7lu7s6oumkXdtwInNStfXg30a/6eJEmSJM1rbULdU4FDgD156PbLarZnpKouBkZn+nlJkiRJUk+bUPdq4PHNowckSZIkSXPIlKtfAiuBrfrdiCRJkiRp+tqM1G0FXJnkfB4+p26/vnUlSZIkSWqlTag7su9dSJIkSZJmZMpQV1XfH0QjkiRJkqTpmzLUJbmL3mqXABsDGwH3VNUj+tmYJEmSJGlqbUbqtlz7PkmA/YHd+9mUJEmSJKmdNqtf/l71nA68vE/9SJIkSZKmoc3tl68as7kBvYeG39u3jiRJkiRJrbVZ/XLfMe8fBFbRuwVTkiRJkjRkbebUHT6IRiRJkiRJ0zdhqEvyl5N8rqrqw33oR5IkSZI0DZON1N0zzr7NgdcDjwYMdZIkSZI0ZBOGuqo6Zu37JFsCRwCHA6cAx0z0OUmSJEnS4Ew6py7Jo4B3Aq8FTgSeWVW/GkRjkiRJkqSpTTan7u+BVwErgKdW1d0D60qSJEmS1MpkDx9/F7A98EHgxiR3Nq+7ktw5mPYkSZIkSZOZbE7dZIFv1pJsCFwA3FBV+/SzliRJkiTNV30NblM4ArhiiPUlSZIkqfOGEuqSLAb2Bj4/jPqSJEmSNF8Ma6Tu48B7gTUTnZBkWZILklxw6623Dq4zSZIkSeqQgYe6JPsAt1TVhZOdV1Urqmq0qkZHRkYG1J0kSZIkdcswRur2APZLsoreg8z3TPLFIfQhSZIkSZ038FBXVe+vqsVVtRQ4EPhOVR086D4kSZIkaT4Y5uqXkiRJkqRZmvA5dYNQVd8DvjfMHiRJkiSpyxypkyRJkqQOM9RJkiRJUocZ6iRJkiSpwwx1kiRJktRhhjpJkiRJ6jBDnSRJkiR1mKFOkiRJkjpsqM+pkyRpMkuXnznsFuacVUftPewWJElzjCN1kiRJktRhhjpJkiRJ6jBDnSRJkiR1mKFOkiRJkjrMUCdJkiRJHWaokyRJkqQOM9RJkiRJUocZ6iRJkiSpwwx1kiRJktRhAw91SXZM8t0klye5LMkRg+5BkiRJkuaLRUOo+SDwrqq6KMmWwIVJzqmqy4fQiyRJkiR12sBH6qrqpqq6qHl/F3AFsMOg+5AkSZKk+WAYI3W/l2Qp8AzgvHGOLQOWASxZsmSgfUmSNJ8tXX7msFuYk1Ydtfesr+Hv9g+tj9+rpMkNbaGUJFsAXwHeXlV3rnu8qlZU1WhVjY6MjAy+QUmSJEnqgKGEuiQb0Qt0J1XVacPoQZIkSZLmg2GsfhngOOCKqvrYoOtLkiRJ0nwyjJG6PYBDgD2TXNy8/ngIfUiSJElS5w18oZSq+gGQQdeVJEmSpPloaAulSJIkSZJmz1AnSZIkSR1mqJMkSZKkDjPUSZIkSVKHGeokSZIkqcMMdZIkSZLUYYY6SZIkSeqwgT+nTpIkSZqOpcvPHHYLc86qo/ZeL9fxd/uH1tfvdpAcqZMkSZKkDjPUSZIkSVKHGeokSZIkqcMMdZIkSZLUYYY6SZIkSeowQ50kSZIkdZihTpIkSZI6zFAnSZIkSR1mqJMkSZKkDhtKqEuyV5Krkvw8yfJh9CBJkiRJ88HAQ12SDYF/AF4B7AIclGSXQfchSZIkSfPBMEbqngX8vKqurqr7gVOA/YfQhyRJkiR1XqpqsAWTA4C9qurPmu1DgGdX1VvWOW8ZsKzZ3Bm4aqCNSj3bALcNuwlpGvzOqmv8zqpr/M5qWB5XVSPjHVg06E7aqqoVwIph96GFLckFVTU67D6ktvzOqmv8zqpr/M5qLhrG7Zc3ADuO2V7c7JMkSZIkTdMwQt35wBOT7JRkY+BA4Iwh9CFJkiRJnTfw2y+r6sEkbwG+CWwIHF9Vlw26D6klbwFW1/idVdf4nVXX+J3VnDPwhVIkSZIkSevPUB4+LkmSJElaPwx1kiRJktRhhjppHEl2TPLdJJcnuSzJEcPuSZpKkg2T/DTJ14fdizSVJFslOTXJlUmuSPKcYfckTSbJO5p/E6xMcnKSTYbdk7SWoU4a34PAu6pqF2B34M1JdhlyT9JUjgCuGHYTUkufAP61qp4M7IbfXc1hSXYA3gaMVtWu9Bb7O3C4XUkPMdRJ46iqm6rqoub9XfT+sbHDcLuSJpZkMbA38Plh9yJNJckjgRcAxwFU1f1V9evhdiVNaRGwaZJFwGbAjUPuR/o9Q500hSRLgWcA5w23E2lSHwfeC6wZdiNSCzsBtwJfaG4Z/nySzYfdlDSRqroBOBq4DrgJuKOqzh5uV9JDDHXSJJJsAXwFeHtV3TnsfqTxJNkHuKWqLhx2L1JLi4BnAp+uqmcA9wDLh9uSNLEkWwP70/sPie2BzZMcPNyupIcY6qQJJNmIXqA7qapOG3Y/0iT2APZLsgo4BdgzyReH25I0qdXA6qpaewfEqfRCnjRXvRS4pqpuraoHgNOA5w65J+n3DHXSOJKE3lyPK6rqY8PuR5pMVb2/qhZX1VJ6E/e/U1X+D7LmrKq6Gbg+yc7NrpcAlw+xJWkq1wG7J9ms+TfCS3BxH80hi4bdgDRH7QEcAlya5OJm3weq6qwh9iRJ88lbgZOSbAxcDRw+5H6kCVXVeUlOBS6it0L2T4EVw+1Kekiqatg9SJIkSZJmyNsvJUmSJKnDDHWSJEmS1GGGOkmSJEnqMEOdJEmSJHWYoU6SJEmSOsxQJ0ma85I8NskpSf4zyYVJzkrypCRLk6yc4TUPS7L9LPvaL8nyaX7mhCQHzKauJElj+Zw6SdKc1jzo96vAiVV1YLNvN2Bb4PpZXPowYCVw4zR6WVRVD67drqozgDNm0YMkSbPmSJ0kaa57MfBAVX1m7Y6quqSq/m3sSc3I26fGbH89yYuSbNiMjq1McmmSdzQjZaP0Hn59cZJNk/y3JN9vRgK/mWS75jrfS/LxJBcAR0xUs6lxbJIfJbl67Whcej6V5Kok3wIeM+bzf1AzySObc3duzjk5yRvW8+9UkjSPOFInSZrrdgUunMXnnw7sUFW7AiTZqqp+neQtwLur6oIkGwGfBPavqluTvAb4G+B1zTU2rqrRFrW2A54HPJneCN6pwJ8AOwO70BtdvBw4fqKaVfW6prcTknwC2LqqPjeLn1+SNM8Z6iRJ893VwOOTfBI4Ezh7nHN2phcez+nd7cmGwE1jjv9zy1qnV9Ua4PIk2zb7XgCcXFW/A25M8p2palbVOUleDfwDsFvL2pKkBcpQJ0ma6y4D2iws8iAPn1awCUBV/aqZg/dy4E3An/LQCNxaAS6rqudMcO17WvZ63zrXnMyENZNsAPxX4DfA1sDqlvUlSQuQc+okSXPdd4D/kmTZ2h1Jnpbk+euctwp4epINkuwIPKs5dxtgg6r6CvBB4JnN+XcBWzbvrwJGkjyn+cxGSZ6ynvo/F3hNM7dvO3pzBKeq+Q7gCuB/AF9obtWUJGlcjtRJkua0qqokfwJ8PMn7gHvpBbi3r3PqD4Fr6M1ZuwK4qNm/A71gtPY/Mt/f/HkC8JkkvwWeQ2808Ngkj6T39+PH6Y0SztZXgT2bvq4Dftz8XPc3i6k8rGaSB4E/A55VVXclOZdeGD1yPfQiSZqHUlXD7kGSJEmSNEPefilJkiRJHWaokyRJkqQOM9RJkiRJUocZ6iRJkiSpwwx1kiRJktRhhjpJkiRJ6jBDnSRJkiR12P8HvQ+mPmyyxfsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cutoff = 0.2\n",
        "clusters = cluster_fingerprints(fingerprints, cutoff=cutoff)\n",
        "\n",
        "# Plot the size of the clusters - save plot\n",
        "fig, ax = plt.subplots(figsize=(15, 4))\n",
        "ax.set_xlabel(\"Cluster index\")\n",
        "ax.set_ylabel(\"# molecules\")\n",
        "ax.bar(range(1, len(clusters) + 1), [len(c) for c in clusters])\n",
        "ax.set_title(f\"Threshold: {cutoff:3.1f}\")\n",
        "fig.savefig(\n",
        "    f\"cluster_dist_cutoff_{cutoff:4.2f}.png\",\n",
        "    dpi=300,\n",
        "    bbox_inches=\"tight\",\n",
        "    transparent=True,\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Number of clusters: {len(clusters)} from {len(compounds)} molecules at distance cut-off {cutoff:.2f}\"\n",
        ")\n",
        "print(\"Number of molecules in largest cluster:\", len(clusters[0]))\n",
        "print(\n",
        "    f\"Similarity between two random points in same cluster: {DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]], fingerprints[clusters[0][1]]):.2f}\"\n",
        ")\n",
        "print(\n",
        "    f\"Similarity between two random points in different cluster: {DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]], fingerprints[clusters[1][0]]):.2f}\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "k3lEYETpDswT",
        "outputId": "2c0f607c-6d85-4033-a3b4-5720b0bd69ea"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters: 33 from 49 molecules at distance cut-off 0.20\n",
            "Number of molecules in largest cluster: 7\n",
            "Similarity between two random points in same cluster: 0.84\n",
            "Similarity between two random points in different cluster: 0.70\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Ten molecules from largest cluster:\")\n",
        "# Draw molecules\n",
        "Draw.MolsToGridImage(\n",
        "    [compounds[i][0] for i in clusters[0][:10]],\n",
        "    legends=[compounds[i][1] for i in clusters[0][:10]],\n",
        "    molsPerRow=5,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "rz__tGBhD1F-",
        "outputId": "de5fbde9-1f25-430d-c2ef-81b5d0691140"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ten molecules from largest cluster:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x400 at 0x7FA57CC27E50>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save molecules from largest cluster so other talktorials can use it\n",
        "sdf_path = str(\"molecule_set_largest_cluster.sdf\")\n",
        "sdf = Chem.SDWriter(sdf_path)\n",
        "for index in clusters[0]:\n",
        "    mol, label = compounds[index]\n",
        "    # Add label to metadata\n",
        "    mol.SetProp(\"_Name\", label)\n",
        "    sdf.write(mol)\n",
        "sdf.close()"
      ],
      "metadata": {
        "id": "sRJlL7K2D3zO"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Ten molecules from second largest cluster:\")\n",
        "# Draw molecules\n",
        "Draw.MolsToGridImage(\n",
        "    [compounds[i][0] for i in clusters[1][:10]],\n",
        "    legends=[compounds[i][1] for i in clusters[1][:10]],\n",
        "    molsPerRow=5,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "2aD1Wrg8D-lv",
        "outputId": "0b5354b4-515b-4c87-e112-957aac220263"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ten molecules from second largest cluster:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x200 at 0x7FA57C68F050>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Ten molecules from first 10 clusters:\")\n",
        "# Draw molecules\n",
        "Draw.MolsToGridImage(\n",
        "    [compounds[clusters[i][0]][0] for i in range(10)],\n",
        "    legends=[compounds[clusters[i][0]][1] for i in range(10)],\n",
        "    molsPerRow=5,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "oTDrWCFGEGR_",
        "outputId": "84cf4d38-23c8-40d7-d33f-9441d6598150"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ten molecules from first 10 clusters:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x400 at 0x7FA57C680D10>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate image\n",
        "img = Draw.MolsToGridImage(\n",
        "    [compounds[clusters[i][0]][0] for i in range(0, 3)],\n",
        "    legends=[f\"Cluster {i}\" for i in range(1, 4)],\n",
        "    subImgSize=(200, 200),\n",
        "    useSVG=True,\n",
        ")\n",
        "\n",
        "# Patch RAW svg data: convert non-transparent to transparent background and set font size\n",
        "molsvg = img.replace(\"opacity:1.0\", \"opacity:0.0\").replace(\"12px\", \"20px\")\n",
        "\n",
        "# Save altered SVG data to file\n",
        "with open(\"cluster_representatives.svg\", \"w\") as f:\n",
        "    f.write(molsvg)"
      ],
      "metadata": {
        "id": "TYu5ERd7ENPP"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def intra_tanimoto(fps_clusters):\n",
        "    \"\"\"Function to compute Tanimoto similarity for all pairs of fingerprints in each cluster\"\"\"\n",
        "    intra_similarity = []\n",
        "    # Calculate intra similarity per cluster\n",
        "    for cluster in fps_clusters:\n",
        "        # Tanimoto distance matrix function converted to similarity matrix (1-distance)\n",
        "        intra_similarity.append([1 - x for x in tanimoto_distance_matrix(cluster)])\n",
        "    return intra_similarity"
      ],
      "metadata": {
        "id": "SbkjGFAtElV2"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Recompute fingerprints for 10 first clusters\n",
        "mol_fps_per_cluster = []\n",
        "for cluster in clusters[:10]:\n",
        "    mol_fps_per_cluster.append([rdkit_gen.GetFingerprint(compounds[i][0]) for i in cluster])\n",
        "\n",
        "# Compute intra-cluster similarity\n",
        "intra_sim = intra_tanimoto(mol_fps_per_cluster)"
      ],
      "metadata": {
        "id": "EB6o0NUvEnK2"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violin plot with intra-cluster similarity\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "indices = list(range(10))\n",
        "ax.set_xlabel(\"Cluster index\")\n",
        "ax.set_ylabel(\"Similarity\")\n",
        "ax.set_xticks(indices)\n",
        "ax.set_xticklabels(indices)\n",
        "ax.set_yticks(numpy.arange(0.6, 1.0, 0.1))\n",
        "ax.set_title(\"Intra-cluster Tanimoto similarity\", fontsize=13)\n",
        "r = ax.violinplot(intra_sim, indices, showmeans=True, showmedians=True, showextrema=False)\n",
        "r[\"cmeans\"].set_color(\"red\")\n",
        "# mean=red, median=blue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "by8B4MSYEogY",
        "outputId": "9c75e668-02e7-4e66-8282-d6ab75aa7971"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:1970: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  result = asarray(a).shape\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-6aa74891ea9e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Intra-cluster Tanimoto similarity\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviolinplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintra_sim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowmeans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowmedians\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowextrema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"cmeans\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# mean=red, median=blue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mviolinplot\u001b[0;34m(self, dataset, positions, vert, widths, showmeans, showextrema, showmedians, quantiles, points, bw_method)\u001b[0m\n\u001b[1;32m   7895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7896\u001b[0m         vpstats = cbook.violin_stats(dataset, _kde_method, points=points,\n\u001b[0;32m-> 7897\u001b[0;31m                                      quantiles=quantiles)\n\u001b[0m\u001b[1;32m   7898\u001b[0m         return self.violin(vpstats, positions=positions, vert=vert,\n\u001b[1;32m   7899\u001b[0m                            \u001b[0mwidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowmeans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshowmeans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mviolin_stats\u001b[0;34m(X, method, points, quantiles)\u001b[0m\n\u001b[1;32m   1462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1463\u001b[0m         \u001b[0;31m# Calculate basic stats for the distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1464\u001b[0;31m         \u001b[0mmin_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1465\u001b[0m         \u001b[0mmax_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1466\u001b[0m         \u001b[0mquantile_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mamin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2878\u001b[0m     \"\"\"\n\u001b[1;32m   2879\u001b[0m     return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n\u001b[0;32m-> 2880\u001b[0;31m                           keepdims=keepdims, initial=initial, where=where)\n\u001b[0m\u001b[1;32m   2881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFNCAYAAACuWnPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcxklEQVR4nO3deZRdZZ3u8e9DAiqjtERFAhKviKDtWJe+iCJi60XbBVdbG7DVVq+iLnHuVpyV1nY50wq2MhmcQC6ol7ZpUJvB2SbgREAwBoUAalRG4RqCv/vH2dUeiqpUhWTXW6nz/axVq87Z+z17//Y+Sc6T933P3qkqJEmS1M5mrQuQJEkadQYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJulOkrwjydda19FKkuVJDm5dx12V5HFJrt/Abfx7ktcPPa8kj93Abd6cZO8N2YY0XxnIpFmU5Lwkb+mr/VzUxzEk2aX7cB//WZtkzdDz5Ruy/ap6SFV9fmPVO1GS5ydZ0df2q+obVXXPDdzGU6rqfRurpm6bW1fVdwCS7Jdk7cbcvrQpW9i6AEkbJsnmVXVb6zr6NPEYq+pKYOuh9V8DvllV72hQnqYxCn9GpQ1lD5nUSJJdu2Gg5ya5JMlNSb6SZMdu/dHA44C3dr0+l3XLlyb5bPf7d8BHkmyZ5AtJfpnkxiQXJXnSNPvfPMmbklzW7ftnSZ45RdufJ3nOJLUv7p7/ZZLvd/v+zfhw51TH0K17cZKLk9zQvfbJQ+vekeScJB9I8ivgjPU8t59MclV3XJckefbQuv26HrWDu2O+IcmpSbaZ7HiH2j+7a//7JJ9Ksm2S45Jcl+QXSZ4xoYaXdef2hiTfTfK4bvnewMeBBwz16O3XrXt8ku91r/lJkpes4xjvluTYJL/uzvtPkzxruOahtkuTfDrJiUmuT3J1kkOTPCLJBd15OjfJ/YZeM2XPZpLFSc5Ksrqr9RtJHj20ftL3r/sz89huP/8OLBg6B3+X5PNJ/nnCvl6YZEWSTHUupPnAQCa1dzCwL7ATsBVwJEBVHQ58A/jHbqhn96HXPIvBB9oi4HUM/i5/AdgNuBdwMnB6kkXr2O+7gOd029oWeDxw+V08hk8BHwG2647jXes6hiQvBt4A/C2wPfBm4AtJHji0zX2Ba4Gdgb9ez3q+CTwCuCeD87k0yZ5D6xcATwYeDjwIeCTwynVsbwGwH/DnwB7AAcB3gS8xON/vAU5MsmV3fIcC/wg8r1t/HHBWkvt3Q3YvBVZ252TrqjovyRLgLOBfutc8H3jPeMiaxN8B/x3Yo6q2BfYH1jVU+0zgdODPutqO687N04H7AAW8cx2vH7YZ8DHg/sB9gYsYvH+bD7WZ8v2rqmuApwC3D52Dk4BPAM9Jcreh5i8Cji/v86d5zkAmtffOqvpNVd0IfA4Ym8FrvllVn6+q26vqlqq6uao+U1U3VdVtVfV+YA2DD+w76XobXg78Q1X9qAZWVdWP7uIxrAH+G3CfqvpDVZ03TftXAUdW1Q+r6o9VdSZwLnDIUJtfVNUHq2pNVd2yPsVU1QlV9dvu/JwC/IhBoBp2RHfefsUgWE133t/cnesrgfOAK6rq36rqjwwC6XYMAjHAC4BPVNX3qmptVZ3Q1fDsyTbcORS4qKqWdq/5LoOA8qIp2q9hMGy7Z5KFVXVVVV2yju2fM6HerYBPd+/7LcBpMzgHwGDIuKrO6M7HrcBbgF2Gjh/u2vt3LvBbBiGRJHt0NS2d4eulTZaBTGrv2qHHvwe2marhkJ8PP0lyjyRHJ1nZDV9dz6DnaVG3fvnQ0NCbuuVbcdd7xCY6iMGH8Y+7IcJXT9N+CXBMN3x2fVfvExj0ro37xV0pJMlmSY4cGi68nkFP2HBv4e1VtXro+XTnfWL7Wxh634YCx/g2dgaumLCNn3XLp7K+r/kMcDzwYeC3GQxZP3CKtkxR7/CfvVuY2Z89kuzQDdtemeRG4Kpu1fA5Xu/3r+sFO44/hdAXAV+uql+u77akTY2BTJrb/jjD5a9lMET0RGC77ht21wGB//rW4PjQ0D8Bqxl8AO/GzNzEIMCNu9/wyq6n62Dg3sBLGAy17b+OY/gF8MKquufQz9ZV9bJ1HONMHcrgg/yvge27c/FDunMxS64Cdp2w7AH8KbhMdmzTveYOul6091bVGIOhw1uAE+9ivevrPcCOwF90w6XjoXH4HE/3/k21fimwT5IHAc9lENCkec9AJs1tvwTW1esxblvgDwyGe7ZI8jYG86cm1fVEfAx4X5KHZmBxkodN8ZILgUOTbN3NS3vr+IokW3QTsnfotnsdgw/b29dxDB8G3tFNKk/Xw/fYJA+ewbFOZ1tgLYPQuVmSFzLoIZtNS4GXJNkrycIkL2Awp+1z3fpfAvdOsu3Qa04GHp3ked1r9mIQbk+YbAdJ9k/y6G7e1q0Mevlun6xtD7ZlEACvS7I18N67sI1fMpjUv2R4YdcT+X+BUxgc19kbWKu0STCQSXPbh4GxblhvXRO2PwRcD1zDYJjrFiYMa07izcCpDOZP3cRgXtRU4e8tDD7sr+3anTJh/cHAT5LczOAbdW+vqvOnOoaqOg54H/BJBgHuSgYhb3M23EnA94AVwNXAngy+WDBrqupzDCbIf4ZBSH4Z8NSqGh/GOxf4KnBFd14eX1VXAE8FDu9e82ngrVV16hS7uU/X5joG78v9gcN6OqSJ3sagN/S3DObGfZv1DINVdTmDLzD8Z3cOnju0+hMMvmhxYjfnTZr34hdXJElzSddr9lNgSVVNOmQrzTcGMknSnJFkIXA0cK+qmuqSH9K845X6JUlzQpIx4HxgJfC0xuVIs8oeMkmSpMac1C9JktSYgUySJKmxTW4O2Q477FC77rpr6zIkSZKmdeGFF/6mqtZ1X2FgEwxku+66K8uWLWtdhiRJ0rSSzOg2Yg5ZSpIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSY70GsiQHJLksyYokR0yy/v5J/iPJj5Kcl2Rxn/VIkiTNRb0FsiQLgGOApwB7Aocm2XNCsw8An6qqhwFHAu/pqx5JkqS5qs8esr2AFVW1sqrWAKcAB01osydwTvf43EnWS5IkzXt9BrKdgKuGnq/qlg37IfCM7vHTgW2S3KvHmiRJkuac1pP6/x54fJLvA48HrgZun9goyWFJliVZtnr16tmuUZIkqVd9BrKrgZ2Hni/ulv2Xqrqmqp5RVY8E3twtu37ihqrq2Koaq6qxRYsW9ViyJEnS7OszkF0A7JZkSZItgEOAM4YbJNkhyXgNbwRO7LEeSZKkOam3QFZVa4HDgbOBS4FTq2p5kiOTHNg12w+4LMnlwH2Ad/dVjyRJ0lyVqmpdw3oZGxurZcuWtS5DkiRpWkkurKqx6dq1ntQvSZI08gxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIa6zWQJTkgyWVJViQ5YpL1uyQ5N8n3k/woyVP7rEeSJGku6i2QJVkAHAM8BdgTODTJnhOavQU4taoeCRwCfKyveiRJkuaqPnvI9gJWVNXKqloDnAIcNKFNAdt2j7cDrumxHkmSpDmpz0C2E3DV0PNV3bJh7wCek2QVcCbwisk2lOSwJMuSLFu9enUftUqSJDXTelL/ocDSqloMPBX4dJI71VRVx1bVWFWNLVq0aNaLlCRJ6lOfgexqYOeh54u7ZcP+N3AqQFV9B7g7sEOPNUmSJM05fQayC4DdkixJsgWDSftnTGhzJfBEgCR7MAhkjklKkqSR0lsgq6q1wOHA2cClDL5NuTzJkUkO7Jq9Dnhxkh8CJwPPr6rqqyZJkqS5aGGfG6+qMxlM1h9e9rahx5cA+/RZgyRJ0lzXelK/JEnSyDOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ11msgS3JAksuSrEhyxCTrP5zkB93P5Umu77MeSZKkuWhhXxtOsgA4BngSsAq4IMkZVXXJeJuqes1Q+1cAj+yrHkmSpLmqzx6yvYAVVbWyqtYApwAHraP9ocDJPdYjSZI0J/UZyHYCrhp6vqpbdidJ7g8sAc7psR5JkqQ5aa5M6j8EOK2qbp9sZZLDkixLsmz16tWzXJokSVK/+gxkVwM7Dz1f3C2bzCGsY7iyqo6tqrGqGlu0aNFGLFGSJKm9PgPZBcBuSZYk2YJB6DpjYqMkDwa2B77TYy2SJElzVm+BrKrWAocDZwOXAqdW1fIkRyY5cKjpIcApVVV91SJJkjSX9XbZC4CqOhM4c8Kyt014/o4+a5AkSZrr5sqkfkmSpJFlIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJaqzXQJbkgCSXJVmR5Igp2vxNkkuSLE/yuT7rkSRJmosW9rXhJAuAY4AnAauAC5KcUVWXDLXZDXgjsE9VXZfk3n3VI0mSNFf12UO2F7CiqlZW1RrgFOCgCW1eDBxTVdcBVNWve6xHkiRpTuozkO0EXDX0fFW3bNiDgAcl+VaS7yY5oMd6JEmS5qTehizXY/+7AfsBi4GvJ/nzqrp+uFGSw4DDAHbZZZfZrlGSJKlXffaQXQ3sPPR8cbds2CrgjKq6raquAC5nENDuoKqOraqxqhpbtGhRbwVLkiS10GcguwDYLcmSJFsAhwBnTGjzJQa9YyTZgcEQ5soea5IkSZpzegtkVbUWOBw4G7gUOLWqlic5MsmBXbOzgd8muQQ4F/iHqvptXzVJkiTNRamq1jWsl7GxsVq2bFnrMiRJkqaV5MKqGpuu3Yx6yLprikmSJKkHMx2y/GmS9yfZs9dqJEmSRtBMA9nDGXwD8vjuemGHJdm2x7okSZJGxowCWVXdVFXHVdVjgDcAbweuTXJSkgf2WqEkSdI8N+M5ZEkOTPJF4Cjgg8ADgH8FzuyxPkmSpHlvplfq/ymDy1K8v6q+PbT8tCT7bvyyJEmSRsdMA9nzquqbwwuS7FNV36qqV/ZQlyRJ0siY6aT+j0yy7KMbsxBJkqRRtc4esiR7A48BFiV57dCqbQGvTSZJkrQRTDdkuQWwdddum6HlNwLP7KsoSZKkUbLOQFZV5wPnJ1laVb+YpZokSZJGynRDlkdV1auBo5Pc6aaXVXXgJC+TJEnSephuyPLT3e8P9F2IJEnSqJpuyPLC7sbih1XV385STZIkSSNl2steVNXtwP2TbDEL9UiSJI2cmV4YdiXwrSRnAL8fX1hVH+qlKkmSpBEy00D2s+5nM+54+QtJkiRtoBkFsqp6Z9+FSJIkjaoZBbIki4DXAw8B7j6+vKr276kuSZKkkTHTe1l+FvgJsAR4J/Bz4IKeapIkSRopMw1k96qqE4Dbqur8qnohYO+YJEnSRjDTSf23db+vTfJXwDXAn/VTkiRJ0miZaSB7V5LtgNcBHwW2BV7TW1WSJEkjZKbfsvxy9/AG4An9lSNJkjR6pru5+EeBO91UfFxVvXKjVyRJkjRipushWzYrVUiSJI2w6W4uftJsFSJJkjSqphuyPKqqXp3kX5lk6LKqDuytMkmSpBEx3ZDlp7vfH+i7EEmSpFE13ZDlhd3v82enHEmSpNEzoyv1J3laku8n+V2SG5PclOTGvouTJEkaBTO9MOxRwDOAH1fVlJfBkCRJ0vqb6b0srwIuNoxJkiRtfDPtIXs9cGaS84E/jC+sqg/1UpUkSdIImWkgezdwM3B3YIv+ypEkSRo9Mw1k96uqh/ZaiSRJ0oia6RyyM5M8uddKJEmSRtRMA9nLgLOS3OplLyRJkjauGQ1ZVtU2fRciSZI0qqa7l+WDq+onSR412fqquqifsiRJkkbHdD1krwUOAz44tGz4WmT7b/SKJEmSRsx0c8iOT3LfqnpCVT0BWMrg8hcXA8/suzhJkqRRMF0g+ziwBiDJvsB7gJOAG4Bj+y1NkiRpNEw3ZLmgqn7XPT4YOLaqTgdOT/KDfkuTJEkaDdP1kC1IMh7angicM7RupheVlSRJ0jpMF6pOBs5P8hvgVuAbAEkeyGDYUpIkSRtonYGsqt6d5D+AHYGvVNX4Nyw3A17Rd3GSJEmjYNphx6r67iTLLu+nHEmSpNEz01snSZIkqScGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqLFXVuob1kuQm4LLWdTSwA/Cb1kU04HGPFo97tHjco2VUj3v3qtpmukYLZ6OSjeyyqhprXcRsS7LM4x4dHvdo8bhHi8c9WpIsm0k7hywlSZIaM5BJkiQ1tikGsmNbF9CIxz1aPO7R4nGPFo97tMzouDe5Sf2SJEnzzabYQyZJkjSvbFKBLMkBSS5LsiLJEa3rmQ1JTkzy6yQXt65lNiXZOcm5SS5JsjzJq1rXNBuS3D3Jfyb5YXfc72xd02xJsiDJ95N8uXUtsynJz5P8OMkPZvptrPkgyT2TnJbkJ0kuTbJ365r6lmT37n0e/7kxyatb1zUbkrym+zft4iQnJ7l765pmQ5JXdce8fLr3epMZskyyALgceBKwCrgAOLSqLmlaWM+S7AvcDHyqqh7aup7ZkmRHYMequijJNsCFwP8agfc7wFZVdXOSzYFvAq+qqu82Lq13SV4LjAHbVtXTWtczW5L8HBirqpG6PlOSk4BvVNXxSbYAtqyq61vXNVu6z7Srgb+oql+0rqdPSXZi8G/ZnlV1a5JTgTOramnbyvqV5KHAKcBewBrgLOClVbVisvabUg/ZXsCKqlpZVWsYHORBjWvqXVV9Hfhd6zpmW1VdW1UXdY9vAi4FdmpbVf9q4Obu6ebdz6bxv6YNkGQx8FfA8a1rUf+SbAfsC5wAUFVrRimMdZ4I/Gy+h7EhC4F7JFkIbAlc07ie2bAH8L2quqWq1gLnA8+YqvGmFMh2Aq4aer6KEfiAFiTZFXgk8L22lcyObujuB8Cvga9W1Sgc91HA64E/ti6kgQK+kuTCJIe1LmaWLAFWA5/shqmPT7JV66Jm2SHAya2LmA1VdTXwAeBK4Frghqr6StuqZsXFwOOS3CvJlsBTgZ2narwpBTKNoCRbA6cDr66qG1vXMxuq6vaqegSwGNir6/aet5I8Dfh1VV3YupZGHltVjwKeAry8m6Yw3y0EHgX8S1U9Evg9MBLzggG6IdoDgf/TupbZkGR7BiNaS4D7AVsleU7bqvpXVZcC7wW+wmC48gfA7VO135QC2dXcMVku7pZpnurmUJ0OfLaqvtC6ntnWDeGcCxzQupae7QMc2M2lOgXYP8ln2pY0e7reA6rq18AXGUzPmO9WAauGen9PYxDQRsVTgIuq6letC5klfwlcUVWrq+o24AvAYxrXNCuq6oSqenRV7Qtcx2Au/KQ2pUB2AbBbkiXd/y4OAc5oXJN60k1uPwG4tKo+1Lqe2ZJkUZJ7do/vweBLLD9pW1W/quqNVbW4qnZl8Pf6nKqa9/97BkiyVfelFbohuyczGOaY16rql8BVSXbvFj0RmNdf2JngUEZkuLJzJfA/kmzZ/dv+RAbzgue9JPfufu/CYP7Y56Zqu8ncXLyq1iY5HDgbWACcWFXLG5fVuyQnA/sBOyRZBby9qk5oW9Ws2Ad4LvDjbj4VwJuq6syGNc2GHYGTum9gbQacWlUjdRmIEXMf4IuDzygWAp+rqrPaljRrXgF8tvsP9krgBY3rmRVd8H4S8JLWtcyWqvpektOAi4C1wPcZnav2n57kXsBtwMvX9eWVTeayF5IkSfPVpjRkKUmSNC8ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUzSnJDkvklOSfKz7jZCZyZ5UJJdk9yla3MleX6S+21gXQcmWa+ryCdZmuSZG7JfSaNlk7kOmaT5q7tY5BeBk6rqkG7Zwxlcp+uqdb12Gs9ncKHVGd/IOMnC7kbAAFTVGXgRakk9s4dM0lzwBOC2qvr4+IKq+mFVfWO4UdfjdfTQ8y8n2a+7IfvSJBcn+XGS13Q9VGMMLj76gyT3SPLoJOd3PXBnJ9mx2855SY5Ksgx41VT77PbxkSTfTrJyvBcsA0cnuSzJ14B7D73+TvtMsl3XdveuzclJXryRz6mkTYg9ZJLmgocCG3Jz8UcAO1XVQwGS3LOqru/u7vH3VbWsuzfqR4GDqmp1koOBdwMv7LaxRVWNzWBfOwKPBR7MoOfsNODpwO7Angx69S4BTpxqn1X1wq62pUn+Gdi+qo7bgOOXtIkzkEmaD1YCD0jyUeDfgK9M0mZ3BsHvq92tihYA1w6t//wM9/WlqvojcEmS+3TL9gVOrqrbgWuSnDPdPqvqq0meBRwDPHyG+5Y0TxnIJM0Fy4GZTIJfyx2nWtwdoKqu6+ac/U/gpcDf8Keer3EBllfV3lNs+/czrPUPE7a5LlPuM8lmwB7ALcD2wKoZ7l/SPOQcMklzwTnA3ZIcNr4gycOSPG5Cu58Dj0iyWZKdgb26tjsAm1XV6cBbgEd17W8CtukeXwYsSrJ395rNkzxkI9X/deDgbi7bjgzmxE23z9cAlwLPBj7ZDW9KGlH2kElqrqoqydOBo5K8Afh/DMLXqyc0/RZwBYM5WpcCF3XLd2IQasb/k/nG7vdS4ONJbgX2ZtAL95Ek2zH49+8oBr1zG+qLwP5dXVcC3+mOa0038f8O+0yyFngRsFdV3ZTk6wyC5Ns3Qi2SNkGpqtY1SJIkjTSHLCVJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmN/X/pPekir+7D4wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the cluster center of each cluster (first molecule in each cluster)\n",
        "cluster_centers = [compounds[c[0]] for c in clusters]\n",
        "# How many cluster centers/clusters do we have?\n",
        "print(\"Number of cluster centers:\", len(cluster_centers))\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CogA_W54JK2X",
        "outputId": "3e6d2e34-b98e-4107-d721-ef04b4e83548"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of cluster centers: 33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort the molecules within a cluster based on their similarity\n",
        "# to the cluster center and sort the clusters based on their size\n",
        "sorted_clusters = []\n",
        "for cluster in clusters:\n",
        "    if len(cluster) <= 1:\n",
        "        continue  # Singletons\n",
        "    # else:\n",
        "    # Compute fingerprints for each cluster element\n",
        "    sorted_fingerprints = [rdkit_gen.GetFingerprint(compounds[i][0]) for i in cluster]\n",
        "    # Similarity of all cluster members to the cluster center\n",
        "    similarities = DataStructs.BulkTanimotoSimilarity(\n",
        "        sorted_fingerprints[0], sorted_fingerprints[1:]\n",
        "    )\n",
        "    # Add index of the molecule to its similarity (centroid excluded!)\n",
        "    similarities = list(zip(similarities, cluster[1:]))\n",
        "    # Sort in descending order by similarity\n",
        "    similarities.sort(reverse=True)\n",
        "    # Save cluster size and index of molecules in clusters_sort\n",
        "    sorted_clusters.append((len(similarities), [i for _, i in similarities]))\n",
        "    # Sort in descending order by cluster size\n",
        "    sorted_clusters.sort(reverse=True)"
      ],
      "metadata": {
        "id": "pskLQ2S9JOnl"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count selected molecules, pick cluster centers first\n",
        "selected_molecules = cluster_centers.copy()\n",
        "# Take 10 molecules (or a maximum of 50%) of each cluster starting with the largest one\n",
        "index = 0\n",
        "pending = 1000 - len(selected_molecules)\n",
        "while pending > 0 and index < len(sorted_clusters):\n",
        "    # Take indices of sorted clusters\n",
        "    tmp_cluster = sorted_clusters[index][1]\n",
        "    # If the first cluster is > 10 big then take exactly 10 compounds\n",
        "    if sorted_clusters[index][0] > 10:\n",
        "        num_compounds = 10\n",
        "    # If smaller, take half of the molecules\n",
        "    else:\n",
        "        num_compounds = int(0.5 * len(tmp_cluster)) + 1\n",
        "    if num_compounds > pending:\n",
        "        num_compounds = pending\n",
        "    # Write picked molecules and their structures into list of lists called picked_fps\n",
        "    selected_molecules += [compounds[i] for i in tmp_cluster[:num_compounds]]\n",
        "    index += 1\n",
        "    pending = 1000 - len(selected_molecules)\n",
        "print(\"# Selected molecules:\", len(selected_molecules))\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3o9Pwd95JQlt",
        "outputId": "7ac9b51d-f17c-4f81-c655-1f5c133e4641"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# Selected molecules: 46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reuse old dataset\n",
        "sampled_mols = compounds.copy()"
      ],
      "metadata": {
        "id": "sZJMGoIuJYEN"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper function for time computation\n",
        "def measure_runtime(sampled_mols):\n",
        "    start_time = time.time()\n",
        "    sampled_fingerprints = [rdkit_gen.GetFingerprint(m) for m, idx in sampled_mols]\n",
        "    # Run the clustering with the dataset\n",
        "    sampled_clusters = cluster_fingerprints(sampled_fingerprints, cutoff=0.3)\n",
        "    return time.time() - start_time"
      ],
      "metadata": {
        "id": "lVBhlkOiJeTd"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(sampled_mols)\n",
        "# NBVAL_CHECK_OUTPUT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yofFXJ1vJgrO",
        "outputId": "a8811fdd-bc61-4c58-9927-81c4968da021"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_sizes = [100, 500, 1000, 2000, 4000]\n",
        "runtimes = []\n",
        "# Take random samples with replacement\n",
        "for size in sample_sizes:\n",
        "    time_taken = measure_runtime(random.sample(sampled_mols, size))\n",
        "    print(f\"Dataset size {size}, time {time_taken:4.2f} seconds\")\n",
        "    runtimes.append(time_taken)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "3yWndEPJJice",
        "outputId": "1210ddf8-ad52-49cf-d2ec-561412d337df"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-eaf4a6008f3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Take random samples with replacement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msize\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msample_sizes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeasure_runtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled_mols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Dataset size {size}, time {time_taken:4.2f} seconds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mruntimes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime_taken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/random.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, population, k)\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sample larger than population or is negative\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0msetsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m21\u001b[0m        \u001b[0;31m# size of a small set minus size of an empty list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Sample larger than population or is negative"
          ]
        }
      ]
    }
  ]
}