{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# From linkage matrix to dendrogram\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The :func:`~pdendro.attach_dendrogram` function generates components of a dendrogram from\na linkage matrix and attaches them to a figure. Let us assume that we have the following data\npoints:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "X = [[0.0], [1.0], [3.0], [6.0]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The linkage matrix can be computed using the :func:`scipy.cluster.hierarchy.linkage` function:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import scipy\n\nZ = scipy.cluster.hierarchy.linkage(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then, a dendrogram can be drawn as follows:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly.graph_objects as go\nimport plotly.io as pio\n\nfrom pdendro import attach_dendrogram\n\nfig = go.Figure()\nattach_dendrogram(fig, Z)\npio.show(fig)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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