{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d491909f",
   "metadata": {},
   "source": [
    "### Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8c6979",
   "metadata": {},
   "source": [
    "Let's first grab some example data from the GitHub repository:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ee313f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T17:54:06.135612Z",
     "iopub.status.busy": "2026-03-10T17:54:06.135028Z",
     "iopub.status.idle": "2026-03-10T17:54:06.891693Z",
     "shell.execute_reply": "2026-03-10T17:54:06.889695Z",
     "shell.execute_reply.started": "2026-03-10T17:54:06.135552Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import requests, io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "demo_data_file = requests.get(\"https://github.com/TutteInstitute/temporal-mapper/raw/docs/docs/data/mapper_demo_data.npy\")\n",
    "X = np.load(io.BytesIO(demo_data_file.content)) \n",
    "X = X[np.argsort(X[:,0])]\n",
    "# shape (n_samples, n_features), the 0th feature is time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8fa04b2",
   "metadata": {},
   "source": [
    "We'll use projection to the $x$-axis as our Morse-type function, and a sklearn clusterer to do single-linkage clustering. The `time_index` keyword argument in `fit` determines which axis is the Morse-type function, by default it is -1.\n",
    "Here's how you fit a TemporalMapper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "562e65d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T17:54:06.893328Z",
     "iopub.status.busy": "2026-03-10T17:54:06.892689Z",
     "iopub.status.idle": "2026-03-10T17:54:54.899778Z",
     "shell.execute_reply": "2026-03-10T17:54:54.897015Z",
     "shell.execute_reply.started": "2026-03-10T17:54:06.893280Z"
    }
   },
   "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",
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       "    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,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);\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=AgglomerativeClustering(distance_threshold=0.1,\n",
       "                                                 linkage=&#x27;single&#x27;,\n",
       "                                                 n_clusters=None),\n",
       "               data=array([[ 0.78120236],\n",
       "       [ 0.94058764],\n",
       "       [ 0.8615557 ],\n",
       "       ...,\n",
       "       [-0.90433927],\n",
       "       [ 0.31418258],\n",
       "       [-1.10363033]], shape=(23000, 1)),\n",
       "               n_slices=10,\n",
       "               time=array([-3.20536694, -3.20242743, -3.1924893 , ...,  3.20039771,\n",
       "        3.20529909,  3.21932717], shape=(23000,)))</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([-3.205...hape=(23000,))</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([[ 0.78...pe=(23000, 1))</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\">Agglomerative...clusters=None)</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\">10</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=\"default\">\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;time&#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...x7be8e02fe840&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: AgglomerativeClustering</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"clusterer__\"><pre>AgglomerativeClustering(distance_threshold=0.1, linkage=&#x27;single&#x27;,\n",
       "                        n_clusters=None)</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>AgglomerativeClustering</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html\">?<span>Documentation for AgglomerativeClustering</span></a></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('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=n_clusters,-int%20or%20None%2C%20default%3D2\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\">n_clusters: int or None, default=2<br><br>The number of clusters to find. It must be ``None`` if<br>``distance_threshold`` is not ``None``.</span>\n",
       "        </a>\n",
       "    </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('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%22euclidean%22\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\">metric: str or callable, default=\"euclidean\"<br><br>Metric used to compute the linkage. Can be \"euclidean\", \"l1\", \"l2\",<br>\"manhattan\", \"cosine\", or \"precomputed\". If linkage is \"ward\", only<br>\"euclidean\" is accepted. If \"precomputed\", a distance matrix is needed<br>as input for the fit method. If connectivity is None, linkage is<br>\"single\" and affinity is not \"precomputed\" any valid pairwise distance<br>metric can be assigned.<br><br>For an example of agglomerative clustering with different metrics, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py`.<br><br>.. versionadded:: 1.2</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;euclidean&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the output of the computation of the tree.<br>By default, no caching is done. If a string is given, it is the<br>path to the caching directory.</span>\n",
       "        </a>\n",
       "    </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('connectivity',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=connectivity,-array-like%2C%20sparse%20matrix%2C%20or%20callable%2C%20default%3DNone\">\n",
       "            connectivity\n",
       "            <span class=\"param-doc-description\">connectivity: array-like, sparse matrix, or callable, default=None<br><br>Connectivity matrix. Defines for each sample the neighboring<br>samples following a given structure of the data.<br>This can be a connectivity matrix itself or a callable that transforms<br>the data into a connectivity matrix, such as derived from<br>`kneighbors_graph`. Default is ``None``, i.e, the<br>hierarchical clustering algorithm is unstructured.<br><br>For an example of connectivity matrix using<br>:class:`~sklearn.neighbors.kneighbors_graph`, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`.</span>\n",
       "        </a>\n",
       "    </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('compute_full_tree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=compute_full_tree,-%27auto%27%20or%20bool%2C%20default%3D%27auto%27\">\n",
       "            compute_full_tree\n",
       "            <span class=\"param-doc-description\">compute_full_tree: 'auto' or bool, default='auto'<br><br>Stop early the construction of the tree at ``n_clusters``. This is<br>useful to decrease computation time if the number of clusters is not<br>small compared to the number of samples. This option is useful only<br>when specifying a connectivity matrix. Note also that when varying the<br>number of clusters and using caching, it may be advantageous to compute<br>the full tree. It must be ``True`` if ``distance_threshold`` is not<br>``None``. By default `compute_full_tree` is \"auto\", which is equivalent<br>to `True` when `distance_threshold` is not `None` or that `n_clusters`<br>is inferior to the maximum between 100 or `0.02 * n_samples`.<br>Otherwise, \"auto\" is equivalent to `False`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('linkage',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=linkage,-%7B%27ward%27%2C%20%27complete%27%2C%20%27average%27%2C%20%27single%27%7D%2C%20default%3D%27ward%27\">\n",
       "            linkage\n",
       "            <span class=\"param-doc-description\">linkage: {'ward', 'complete', 'average', 'single'}, default='ward'<br><br>Which linkage criterion to use. The linkage criterion determines which<br>distance to use between sets of observation. The algorithm will merge<br>the pairs of cluster that minimize this criterion.<br><br>- 'ward' minimizes the variance of the clusters being merged.<br>- 'average' uses the average of the distances of each observation of<br>  the two sets.<br>- 'complete' or 'maximum' linkage uses the maximum distances between<br>  all observations of the two sets.<br>- 'single' uses the minimum of the distances between all observations<br>  of the two sets.<br><br>.. versionadded:: 0.20<br>    Added the 'single' option<br><br>For examples comparing different `linkage` criteria, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;single&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('distance_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=distance_threshold,-float%2C%20default%3DNone\">\n",
       "            distance_threshold\n",
       "            <span class=\"param-doc-description\">distance_threshold: float, default=None<br><br>The linkage distance threshold at or above which clusters will not be<br>merged. If not ``None``, ``n_clusters`` must be ``None`` and<br>``compute_full_tree`` must be ``True``.<br><br>.. versionadded:: 0.21</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('compute_distances',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.cluster.AgglomerativeClustering.html#:~:text=compute_distances,-bool%2C%20default%3DFalse\">\n",
       "            compute_distances\n",
       "            <span class=\"param-doc-description\">compute_distances: bool, default=False<br><br>Computes distances between clusters even if `distance_threshold` is not<br>used. This can be used to make dendrogram visualization, but introduces<br>a computational and memory overhead.<br><br>.. versionadded:: 0.24<br><br>For an example of dendrogram visualization, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</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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       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "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",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        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*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "TemporalMapper(clusterer=AgglomerativeClustering(distance_threshold=0.1,\n",
       "                                                 linkage='single',\n",
       "                                                 n_clusters=None),\n",
       "               data=array([[ 0.78120236],\n",
       "       [ 0.94058764],\n",
       "       [ 0.8615557 ],\n",
       "       ...,\n",
       "       [-0.90433927],\n",
       "       [ 0.31418258],\n",
       "       [-1.10363033]], shape=(23000, 1)),\n",
       "               n_slices=10,\n",
       "               time=array([-3.20536694, -3.20242743, -3.1924893 , ...,  3.20039771,\n",
       "        3.20529909,  3.21932717], shape=(23000,)))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import temporalmapper as tm \n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "## Single-linkage clustering\n",
    "clusterer = AgglomerativeClustering(\n",
    "    linkage = \"single\",\n",
    "    distance_threshold = 0.1,\n",
    "    n_clusters = None,\n",
    ")\n",
    "\n",
    "mapper = tm.TemporalMapper(\n",
    "    clusterer = clusterer,\n",
    "    n_slices = 10\n",
    ")\n",
    "mapper.fit(X, time_index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab53c4e",
   "metadata": {},
   "source": [
    "The output of Mapper is a directed graph; you can access the graph as a NetworkX object via `TemporalMapper.graph`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "921c5e96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T17:54:54.910529Z",
     "iopub.status.busy": "2026-03-10T17:54:54.903665Z",
     "iopub.status.idle": "2026-03-10T17:54:54.939918Z",
     "shell.execute_reply": "2026-03-10T17:54:54.933998Z",
     "shell.execute_reply.started": "2026-03-10T17:54:54.910447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7be8dec5d4d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapper.graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adefa472",
   "metadata": {},
   "source": [
    "You can also plot the graph using `TemporalMapper.temporal_plot`. We set `layout='semantic'` so our graph will align with our input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d047c14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T17:54:54.942032Z",
     "iopub.status.busy": "2026-03-10T17:54:54.941526Z",
     "iopub.status.idle": "2026-03-10T17:54:56.327557Z",
     "shell.execute_reply": "2026-03-10T17:54:56.313903Z",
     "shell.execute_reply.started": "2026-03-10T17:54:54.941981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(12,8))\n",
    "ax1.set_title(\"Input Data\")\n",
    "ax1.scatter(\n",
    "    X[:,0], X[:,1], s=0.2, alpha=0.5\n",
    ")\n",
    "ax2.set_title(\"Output Graph\")\n",
    "ax2 = mapper.temporal_plot(ax=ax2, layout='semantic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a2af9e",
   "metadata": {},
   "source": [
    "If you have Plotly installed, you can also get an interactive HTML/JS plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "644e3f26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-10T17:54:56.330685Z",
     "iopub.status.busy": "2026-03-10T17:54:56.329927Z",
     "iopub.status.idle": "2026-03-10T17:55:00.270510Z",
     "shell.execute_reply": "2026-03-10T17:55:00.268093Z",
     "shell.execute_reply.started": "2026-03-10T17:54:56.330631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hoverinfo": "none",
         "line": {
          "color": "#000000",
          "width": 1
         },
         "marker": {
          "showscale": false
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