{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97a5b3cf",
   "metadata": {},
   "source": [
    "### Interactive Growth Map\n",
    "\n",
    "A growth map of a Mapper graph is a visualization that displays each topics relative size and growth. It is inspired by a [similar visualization often used to display stock market data](https://stockcharts.com/marketcarpet/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc167dc1",
   "metadata": {},
   "source": [
    "Let's demonstrate a growth map by fitting a `TemporalMapper` to a small dataset of 10,000 arXiv machine learning papers. The paper's titles and abstracts were concatenated and embedded using the sentence transformer [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), and then reduced to 2D with UMAP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4946e6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T13:17:33.830517Z",
     "iopub.status.busy": "2026-03-10T13:17:33.830392Z",
     "iopub.status.idle": "2026-03-10T13:18:14.615622Z",
     "shell.execute_reply": "2026-03-10T13:18:14.614921Z",
     "shell.execute_reply.started": "2026-03-10T13:17:33.830503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>id</th>\n",
       "      <th>created</th>\n",
       "      <th>authors</th>\n",
       "      <th>arxiv</th>\n",
       "      <th>doi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>automated rating of recorded classroom present...</td>\n",
       "      <td>effective presentation skills can help to succ...</td>\n",
       "      <td>1801.00453</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>[akzharkyn izbassarova, aidana irmanova, a. p....</td>\n",
       "      <td>cs.ai</td>\n",
       "      <td>10.1109/icacci.2017.8125872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accelerating deep learning with memcomputing</td>\n",
       "      <td>restricted boltzmann machines (rbms) and their...</td>\n",
       "      <td>1801.00512</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>[haik manukian, fabio l. traversa, massimilian...</td>\n",
       "      <td>cs.ai</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>accelerating deep learning with memcomputing</td>\n",
       "      <td>restricted boltzmann machines (rbms) and their...</td>\n",
       "      <td>1801.00512</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>[haik manukian, fabio l. traversa, massimilian...</td>\n",
       "      <td>cs.lg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>accurate reconstruction of image stimuli from ...</td>\n",
       "      <td>in neuroscience, all kinds of computation mode...</td>\n",
       "      <td>1801.00602</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>[kai qiao, chi zhang, linyuan wang, bin yan, j...</td>\n",
       "      <td>cs.ai</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>deep learning: a critical appraisal</td>\n",
       "      <td>although deep learning has historical roots go...</td>\n",
       "      <td>1801.00631</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>[gary marcus]</td>\n",
       "      <td>cs.lg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  automated rating of recorded classroom present...   \n",
       "1       accelerating deep learning with memcomputing   \n",
       "2       accelerating deep learning with memcomputing   \n",
       "3  accurate reconstruction of image stimuli from ...   \n",
       "4                deep learning: a critical appraisal   \n",
       "\n",
       "                                            abstract          id     created  \\\n",
       "0  effective presentation skills can help to succ...  1801.00453  2018-01-01   \n",
       "1  restricted boltzmann machines (rbms) and their...  1801.00512  2018-01-01   \n",
       "2  restricted boltzmann machines (rbms) and their...  1801.00512  2018-01-01   \n",
       "3  in neuroscience, all kinds of computation mode...  1801.00602  2018-01-02   \n",
       "4  although deep learning has historical roots go...  1801.00631  2018-01-02   \n",
       "\n",
       "                                             authors  arxiv  \\\n",
       "0  [akzharkyn izbassarova, aidana irmanova, a. p....  cs.ai   \n",
       "1  [haik manukian, fabio l. traversa, massimilian...  cs.ai   \n",
       "2  [haik manukian, fabio l. traversa, massimilian...  cs.lg   \n",
       "3  [kai qiao, chi zhang, linyuan wang, bin yan, j...  cs.ai   \n",
       "4                                      [gary marcus]  cs.lg   \n",
       "\n",
       "                           doi  \n",
       "0  10.1109/icacci.2017.8125872  \n",
       "1                               \n",
       "2                               \n",
       "3                               \n",
       "4                               "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import temporalmapper as tm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests, io\n",
    "from sklearn.cluster import DBSCAN\n",
    "from fast_hdbscan import HDBSCAN\n",
    "import datamapplot as dmp\n",
    "response = requests.get(\n",
    "    'https://github.com/TutteInstitute/temporal-mapper/raw/refs/heads/docs/docs/data/ai_arxiv_coordinates.npy'\n",
    ")\n",
    "map_data = np.load(io.BytesIO(response.content))\n",
    "\n",
    "response = requests.get(\n",
    "    'https://github.com/TutteInstitute/temporal-mapper/raw/refs/heads/docs/docs/data/ai_arxiv_data.feather'\n",
    ")\n",
    "df = pd.read_feather(io.BytesIO(response.content))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6e439aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T13:19:06.637843Z",
     "iopub.status.busy": "2026-03-10T13:19:06.637596Z",
     "iopub.status.idle": "2026-03-10T13:19:07.480476Z",
     "shell.execute_reply": "2026-03-10T13:19:07.479901Z",
     "shell.execute_reply.started": "2026-03-10T13:19:06.637807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TemporalMapper(clusterer=HDBSCAN(min_cluster_size=20),\n",
       "               data=array([[ 4.82239962, -1.57145286],\n",
       "       [ 1.20892036, -3.84591722],\n",
       "       [ 1.20193553, -3.85584664],\n",
       "       ...,\n",
       "       [11.05540466, 10.08293819],\n",
       "       [ 8.87438393, -1.76646364],\n",
       "       [11.04866219, 10.09112167]], shape=(10000, 2)),\n",
       "               n_slices=8, slice_method=&#x27;data&#x27;,\n",
       "               time=array([  0.,   0.,   0., ..., 480., 480., 480.], shape=(10000,)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TemporalMapper</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('time',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">time</td>\n",
       "            <td class=\"value\">array([  0., ...hape=(10000,))</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('data',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">data</td>\n",
       "            <td class=\"value\">array([[ 4.82...pe=(10000, 2))</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('clusterer',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">clusterer</td>\n",
       "            <td class=\"value\">HDBSCAN(min_cluster_size=20)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_slices',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_slices</td>\n",
       "            <td class=\"value\">8</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_neighbors</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('overlap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">overlap</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inclusion_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">inclusion_threshold</td>\n",
       "            <td class=\"value\">0.01</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('slice_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">slice_method</td>\n",
       "            <td class=\"value\">&#x27;data&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('density_based',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">density_based</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kernel',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kernel</td>\n",
       "            <td class=\"value\">&lt;function squ...x70ee33ec6a20&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kernel_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">kernel_params</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>clusterer: HDBSCAN</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"clusterer__\"><pre>HDBSCAN(min_cluster_size=20)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>HDBSCAN</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"clusterer__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_cluster_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_cluster_size</td>\n",
       "            <td class=\"value\">20</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cluster_selection_method',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cluster_selection_method</td>\n",
       "            <td class=\"value\">&#x27;eom&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('allow_single_cluster',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">allow_single_cluster</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_cluster_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_cluster_size</td>\n",
       "            <td class=\"value\">inf</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cluster_selection_epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cluster_selection_epsilon</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('cluster_selection_persistence',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">cluster_selection_persistence</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('semi_supervised',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">semi_supervised</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ss_algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">ss_algorithm</td>\n",
       "            <td class=\"value\">&#x27;bc&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
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       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
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       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
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       "    // Check Jupyter theme\n",
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       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
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       "            parseFloat(match[3])\n",
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       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
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       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
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       "\n",
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       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
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       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "TemporalMapper(clusterer=HDBSCAN(min_cluster_size=20),\n",
       "               data=array([[ 4.82239962, -1.57145286],\n",
       "       [ 1.20892036, -3.84591722],\n",
       "       [ 1.20193553, -3.85584664],\n",
       "       ...,\n",
       "       [11.05540466, 10.08293819],\n",
       "       [ 8.87438393, -1.76646364],\n",
       "       [11.04866219, 10.09112167]], shape=(10000, 2)),\n",
       "               n_slices=8, slice_method='data',\n",
       "               time=array([  0.,   0.,   0., ..., 480., 480., 480.], shape=(10000,)))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute a time column T which is the number of days since Jan 01, 2018.\n",
    "def date_to_T(date):\n",
    "    d0 = pd.Timestamp('2018-01-01')\n",
    "    delta = date-d0\n",
    "    return delta.days\n",
    "\n",
    "df[\"date\"] = pd.to_datetime(df[\"created\"])\n",
    "df[\"T\"] = df[\"date\"].apply(\n",
    "    lambda x: date_to_T(x)\n",
    ")\n",
    "time = df[\"T\"].to_numpy().reshape(-1,1)\n",
    "\n",
    "clusterer = HDBSCAN(\n",
    "    cluster_selection_method='eom',\n",
    "    min_cluster_size=20,\n",
    ")\n",
    "mapper = tm.TemporalMapper(\n",
    "    clusterer = clusterer,\n",
    "    slice_method = 'data',\n",
    "    n_slices = 8,\n",
    "    kernel=tm.kernels.square\n",
    ")\n",
    "X = np.concatenate([map_data, time],axis=1)\n",
    "mapper.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8963513",
   "metadata": {},
   "source": [
    "Now that we've fit a Mapper graph, we can use `tm.plotting.growth_map` to generate a growth map. The size of each square indicates the number of data points in the corresponding topic, and it's colour represents the growth of that topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4accf3e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T13:19:07.590050Z",
     "iopub.status.busy": "2026-03-10T13:19:07.589806Z",
     "iopub.status.idle": "2026-03-10T13:19:09.998332Z",
     "shell.execute_reply": "2026-03-10T13:19:09.997777Z",
     "shell.execute_reply.started": "2026-03-10T13:19:07.590032Z"
    }
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tm.plotting.growth_map(mapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd364db1",
   "metadata": {},
   "source": [
    "\n",
    "By default, growth_map displays topics across the entire time range, but we can pass an index parameter to show only the topics at a certain time slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ae0c9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T13:19:46.160423Z",
     "iopub.status.busy": "2026-03-10T13:19:46.160193Z",
     "iopub.status.idle": "2026-03-10T13:19:46.210796Z",
     "shell.execute_reply": "2026-03-10T13:19:46.210226Z",
     "shell.execute_reply.started": "2026-03-10T13:19:46.160406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         },
         "domain": {
          "x": [
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          ],
          "y": [
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          ]
         },
         "hovertemplate": "<b>%{label}</b><br>Count: %{value}<br>Growth: %{color:.2f}",
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         "colorbar": {
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        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
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             },
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             }
            },
            "type": "bar"
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          "barpolar": [
           {
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             },
             "pattern": {
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              "solidity": 0.2
             }
            },
            "type": "barpolar"
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          "carpet": [
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             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
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            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
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          ],
          "choropleth": [
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             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
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             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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             ],
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            "type": "contour"
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            },
            "type": "contourcarpet"
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          ],
          "heatmap": [
           {
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            },
            "colorscale": [
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          "histogram": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tm.plotting.growth_map(mapper, index=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8fba78c-aca9-4ffc-a9e7-cc14b89b4210",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Temp Topic Modelling (dev)",
   "language": "python",
   "name": "ttm-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}