{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Compose custom legends\n\nComposing custom legends piece-by-piece.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>For more information on creating and customizing legends, see the following\n   pages:\n\n   * `legend_guide`\n   * :doc:`/gallery/text_labels_and_annotations/legend_demo`</p></div>\n\nSometimes you don't want a legend that is explicitly tied to data that\nyou have plotted. For example, say you have plotted 10 lines, but don't\nwant a legend item to show up for each one. If you simply plot the lines\nand call ``ax.legend()``, you will get the following:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport matplotlib as mpl\nfrom matplotlib import cycler\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "N = 10\ndata = (np.geomspace(1, 10, 100) + np.random.randn(N, 100)).T\ncmap = plt.cm.coolwarm\nmpl.rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n\nfig, ax = plt.subplots()\nlines = ax.plot(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Since the data does not have any labels, creating a legend requires\nus to define the icons and labels.\nIn this case, we can compose a legend using Matplotlib objects that aren't\nexplicitly tied to the data that was plotted. For example:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from matplotlib.lines import Line2D\n\ncustom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n                Line2D([0], [0], color=cmap(.5), lw=4),\n                Line2D([0], [0], color=cmap(1.), lw=4)]\n\nfig, ax = plt.subplots()\nlines = ax.plot(data)\nax.legend(custom_lines, ['Cold', 'Medium', 'Hot'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "There are many other Matplotlib objects that can be used in this way. In the\ncode below we've listed a few common ones.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from matplotlib.lines import Line2D\nfrom matplotlib.patches import Patch\n\nlegend_elements = [Line2D([0], [0], color='b', lw=4, label='Line'),\n                   Line2D([0], [0], marker='o', color='w', label='Scatter',\n                          markerfacecolor='g', markersize=15),\n                   Patch(facecolor='orange', edgecolor='r',\n                         label='Color Patch')]\n\n# Create the figure\nfig, ax = plt.subplots()\nax.legend(handles=legend_elements, loc='center')\n\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}