Mastering Subplot Legends in Matplotlib

mastering-subplot-legends-in-matplotlib.ipynb

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",
            "_dom_classes": [],
            "_figure_label": "Figure 3",
            "_image_mode": "full",
            "_message": "",
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            "grid_template_rows": null,
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            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
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            "object_fit": null,
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          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/JAEarly/922dfa76aa3f0195fefb1a1530663b31/mastering-subplot-legends-in-matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mastering Subplot Legends in Matplotlib\n",
        "\n",
        "In this notebook, I go through how to improve your subplot legends in matplotlib.\n",
        "\n",
        "When using subplots, you might want to have a shared legend between plots.  \n",
        "\n",
        "This notebook is shows you how to create a legend that sits outside the subplots."
      ],
      "metadata": {
        "id": "0o6XLQZk6iUm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1 - Basic Setup and Plotting\n",
        "\n",
        "We'll experiment with plotting some bar charts, using two subplots.  \n",
        "The data is grouped into four categories (Group 1 to Group 4), and for each group we have five different values (A to E).  \n",
        "Each plot has its own set of random values.  \n",
        "In this section, we first setup the notebook, generate our random samples, then create a basic plot (without a legend!)."
      ],
      "metadata": {
        "id": "tA0vvZst7BER"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZn8FZVcXvI5",
        "outputId": "14bd8f37-f663-4846-be80-924d56b31049"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing packages...\n",
            "Done\n"
          ]
        }
      ],
      "source": [
        "print('Installing packages...')\n",
        "\n",
        "# Used for setting the plotting style (https://github.com/garrettj403/SciencePlots)\n",
        "!pip install -q SciencePlots\n",
        "# Used for interactive subplots adjust\n",
        "!pip install -q ipympl\n",
        "\n",
        "print('Done')\n",
        "\n",
        "# Basic imports\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.patches import Patch\n",
        "from matplotlib.lines import Line2D\n",
        "import scienceplots\n",
        "import numpy as np\n",
        "\n",
        "# Enable interactive plots for subplots adjust\n",
        "%matplotlib widget\n",
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()\n",
        "\n",
        "# Fixing random state for reproducibility\n",
        "np.random.seed(12345)\n",
        "\n",
        "# Setup plotting\n",
        "plt.style.reload_library()\n",
        "plt.style.use(['science', 'bright', 'no-latex'])\n",
        "plt.rcParams.update({'xtick.labelsize': 12,\n",
        "                     'ytick.labelsize': 12,\n",
        "                     'axes.titlesize': 20,\n",
        "                     })"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate some synthetic data\n",
        "x_labels = ['A', 'B', 'C', 'D', 'E']\n",
        "n_groups = 4\n",
        "n_repeats = 4\n",
        "group_labels = ['Group {:d}'.format(group_idx + 1) for group_idx in range(n_groups)]\n",
        "y_repeats = []\n",
        "xs = np.arange(len(x_labels)) * (n_groups + 1)\n",
        "for _ in range(n_repeats):\n",
        "    ys = [np.random.uniform(0.5, 0.95, len(x_labels)) for _ in range(n_groups)]\n",
        "    y_repeats.append(ys)\n",
        "\n",
        "# Plot the synthetic data (handles axis formatting)\n",
        "def plot(axis, repeat_idx):\n",
        "    # Plot a set of bars for each group\n",
        "    for group_idx in range(n_groups):\n",
        "        axis.bar(xs + group_idx, y_repeats[repeat_idx][group_idx], label=group_labels[group_idx])\n",
        "\n",
        "    # Configure axis\n",
        "    axis.set_ylim(0, 1)\n",
        "    axis.set_xticks(xs + (n_groups - 1)/2)\n",
        "    axis.set_xticklabels(x_labels)\n",
        "    axis.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=True)\n",
        "\n",
        "# Initial plot\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "fig.patch.set_facecolor('white')\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "619bdfd709584a91ac8ab800b04db811",
            "d2a2335b70284b2080cb258923d123ec",
            "0ed1b9ac737e4d39890f70dd0f39bbde",
            "445df53a6af14ae59536b17f4a7b3dca"
          ]
        },
        "id": "fOwhNa8kX9cL",
        "outputId": "3ec4c492-662e-44bd-da5e-e82ee60894b0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1EAAAERCAYAAACaQi3vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcpElEQVR4nO3df6zV9X0/8CcXIbd1DqSIZO3Ws6KU4JaAgN5ktT2WSjeMXTs1nWXtcEhHtEts2KJNs+4k7TLratfZzVhHKWtQoibrugU7cNqPXVxJQcu6yg+Z+PEPtqFSr60Cyo/z/cN5C/ec65ePcM+5l/t4JCb3fPic83kdQnzmeT/vz+czrtlsNgMAAMAJ6en2AAAAAKOJEgUAAFDBkCVq8+bNOe+887JmzZo3/IC777478+bNy/z587Ny5cpYHQhAp8gqALqhbYn61re+lb/6q7/KpEmT3vDNP/7xj7Ny5cps2LAhP/jBD/L444/njjvuGJZBAeBYsgqAbmlbohYsWJB77rknZ5111hu++etf/3oWL16cqVOnpqenJ3/wB3+QO++8c1gGBYBjySoAuqVtiXrHO95xQm/evHlzZs2aNfB69uzZeeKJJ3LgwIFTMx0ADEFWAdAtJ3Vjib179x63jGLy5MlpNpt5/vnnT3owADgVZBUAp9oZJ/sB48aNa9nW7oLdyy+/PC+//HKSpFarpVarneyhARjFyrJMWZZJkjPPPDPr168ftmPJKgDejKGy6qRK1LRp09Lf3z/wur+/P+PGjcs555zTsu+CBQvSaDRO5nAAnKaGMx9kFQCnwrH5cFLL+RYsWJCdO3cOvN62bVsuuOCCvOUtbzmZjwWAU0ZWAXCqVSpRzz//fC655JL85Cc/SZJcd911eeCBB7Jv374cPXo0a9asyYoVK4ZlUAA4EbIKgOHWtkQ99thjqdfr2bp1a2655Zb8zu/8TpLkwIED2bFjR/bv358k+bVf+7V86UtfyqJFi3LxxRdnzpw5uf766zs3PQBjlqwCoFvaXhM1b968FEXRsv2Xf/mX89xzzx23bcmSJVmyZMmwDAcAQ5FVAHTLSV0TBQAAMNYoUQAAABUoUQAAABUoUQAAABUoUQAAABUoUQAAABV0rESVZZlGo9H2drQAjF1FUaQsy26PkURWAdDe4Kxq+5yo4VCr1dJoNDp1OABGiXq9PmJKi6waXV74w5sr7X/2124ZpkmA093grLKcDwAAoAIlCgAAoIKOLedjdLFEgtPJY/96WaX9533gwWGaBAA4HShRnHIzvji/0v5P3bRlmCYBAIBTz3I+AACACpQoAACACkbdcr7Lbr6/0v4P3nL1ME0CAACMRc5EAQAAVKBEAQAAVDDqlvMBANA57roLrTpWosqyTKPRSL1eT71e79RhARjhiqJIWZbdHiOJrAK6xzMNR7bBWdWxElWr1dJoNDp1OABGiXq9nqIouj1GElkFQHuDs8pyPhil/MYKAKA73FgCAACgAiUKAACgAiUKAACgAiUKAACgAiUKAACgAiUKAACgArc4Z0Rx224AAEY6JQoAGHNmfHF+pf2fumnLME0CjEZKFAAA/J8X/vDmSvuf/bVbhmkSRjLXRAEAAFTQsRJVlmUajUaKoujUIQEYBYqiSFmW3R4jiawCoL3BWdWx5Xy1Wi2NRqNThwNglKjX6yOmtMgqANoZnFWW8wEAAFTgxhInyF184OS5WBcAOB04EwUAAFCBM1EAMEJUPVubOGMLjD2P/etllfaf94EHT/kMShQAA6oGUzI84cTYcdnN91fa/8Fbrh6mSQBOXNvlfAcPHszSpUvT19eX+fPnZ+PGjW3f/LOf/Swf//jHs2DBglx00UVZunRpXnrppWEdGAASWQVA97QtUY1GI81mM5s2bco999yT3/3d383evXtb9vv85z+fZ555Jps2bcqmTZvyzDPP5Atf+MKwDw0AsgqAbmkpUUePHs2qVauybNmyJMnMmTMzd+7crF27tuXNTzzxRC666KKMHz8+PT09ueiii/LDH/5w+KcGYEyTVQB0U0uJ2r17d/bt25dZs2YNbJs9e3a2bGm9ZffixYvz8MMP5+WXX87+/fvz8MMP5+KLLx7eiQEY82QVAN3UcmOJ15dCTJo0aWDb5MmTs23btpY333DDDXnqqafyrne9K81mMx/5yEfyuc99bhjHBQBZBVV5Th+cWkPenW/cuHHHvW42my37/Pmf/3n+4z/+I88880yS5IorrsiaNWty3XXXtexblmUajUaSpF6vp16vn8TYAIx2RVGkKIokr2XEmyGrABhOQ2VVS4maNm1akqS/v7/tz8e6/fbb89WvfjW9vb1JkhUrVuRTn/pU22Cq1WoDwTTWjIR72TN8/HaP4TAW/l0dW1Kq5oOsAqAThsqqlmuiZsyYkSlTpmTnzp0D27Zt25YFCxa0fOirr76aCRMmDLyeMGFCfvazn53CsQGglawCoJtazkT19PRk+fLlWb16dS655JLs2rUrW7duzd13353t27fnU5/6VDZu3Jjx48fnAx/4QO699958+MMfTpLcc889ufTSSzv9HQAYY0ZyVnl4LMDp7w2fE9XX15drrrkm69aty/Tp0/Piiy9mx44dOXToUJLkb//2bwf2u/jii3P48OHcddddHf0CAIxNsgqAbml7Y4ne3t6sWbOmZXtfX1/27Nkz8HratGm59957h204ABiKrAKgW9qeiQIAAKA9JQoAAKCCIZ8TdToaC7cMBgCGl0eXAM5EAQAAVKBEAQAAVDCmlvMBdJLnBQHA6aljZ6LKskyj0UhRFJ06JACjQFEUKcuy22MkkVUAtDc4qzp2JqpWq6XRaHTqcACMEvV6fcSUFlkFQDuDs8o1UQAAABW4JgoAcNtugAqciQIAAKhAiQIAAKhAiQIAAKjANVEAAEBHzfji/Er7P3XTlmGa5M1xJgoAAKACJQoAAKACy/kAAEaBy26+v9L+D95y9TBNAjgTBQAAUEHHSlRZlmk0GimKolOHBGAUKIoiZVl2e4wksgqA9gZnVceW89VqtTQajU4dDoBRol6vj5jSIquAscYy0RMzOKss5wMAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKhAiQIAAKigYyWqLMs0Go0URdGpQwIwChRFkbIsuz1GElkFQHuDs+qMTh24Vqul0Wh06nAAjBL1en3ElBZZBaeHy26+v9L+D95y9TBNwulicFZ1rETRef4HAgAAp55rogAAACpwJgrgNDPji/Mr7f/UTVuGaRIAOD05EwUAAFCBEgUAAFBB2xJ18ODBLF26NH19fZk/f342btw45Af8+7//exYuXJj3vve9mT17dr761a8O27AA8DpZBUC3tL0mqtFopNlsZtOmTXnyySfT19eX7du359xzzz1uv6effjo33nhjHnjggUydOjVPPPFEVq9e3ZHBARjbZBUA3dJyJuro0aNZtWpVli1bliSZOXNm5s6dm7Vr17a8+ctf/nKuvfbaTJ06NUlywQUX5LbbbhvmkQEY62QVAN3UUqJ2796dffv2ZdasWQPbZs+enS1bWu/e9NBDD+XVV1/N4sWL8xu/8Rv54z/+4xw8eHB4JwZgzJNVAHRTS4nau3dvkmTSpEkD2yZPnpxnn3225c1lWebOO+/MN7/5zRRFkW3btuXGG28cvmkBILIKgO4a8jlR48aNO+51s9ls2eeVV17Jxz72sYElEjfeeGM+9KEP5Y477khPz/H9rCzLNBqNJEm9Xk+9Xj/J0QEYzYqiSFEUSV7LiDdDVgEwnIbKqpYSNW3atCRJf39/25+PdfbZZx93Ae873vGOvPLKK3n++edb9q/VagPBBADHlpSq+SCrAOiEobKqpUTNmDEjU6ZMyc6dOwfCZdu2bVm8eHHLh86ZM+e4pRPPPfdcJk6cmLe97W2neHxgpLjs5vsr7f/gLVcP0ySMZbIKgG5quSaqp6cny5cvH7j9665du7J169YsWbIk27dvz8KFC3PkyJEkyfLly7Nu3brs378/SbJ69er83u/9XsaPH9/BrwDAWCOrAOimIZ8TtWLFivT19eXw4cNZt25dpk+fnrIss2PHjhw6dCjjx4/PRz/60ezevTt9fX0566yz8u53vztf+cpXOvwV4OQ5uwKjj6wCoFvalqje3t6sWbOmZXtfX1/27Nlz3LbPfOYz+cxnPjMswwHAUGQVAN0y5N35AAAAhvLCH95caf+zv3bLME3SeS3XRAEAADA0JQoAAKACJQoAAKACJQoAAKACJQoAAKCCjpWosizTaDRSFEWnDgnAKFAURcqy7PYYSWQVAO0NzqqO3eK8Vqul0Wh06nDAaWbGF+dX2v+pm7YM0yScavV6fcSUFlkFQDuDs8pyPgAAgAqUKAAAgAqUKAAAgAqUKAAAgAqUKAAAgAo6dnc+AADGlsf+9bJK+8/7wIPDNAmcWs5EAQAAVOBMFACcJjxPDaAzlCgAADgF/CJj7LCcDwAAoIKOnYkqyzKNRiP1ej31er1Th4URzW+sICmKImVZdnuMJLIKgPYGZ1XHSlStVkuj0ejU4QAYJer1eoqi6PYYSWQVAO0NzirL+QAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACpQogAAACroWIkqyzKNRiNFUXTqkACMAkVRpCzLbo+RRFYB0N7grDqjUweu1WppNBqdOhwAo0S9Xh8xpUVWAdDO4KyynA8AAKACJQoAAKACJQoAAKACJQoAAKACJQoAAKCCtiXq4MGDWbp0afr6+jJ//vxs3LjxDT/k0KFDOf/8893RCICOkVUAdEvbW5w3Go00m81s2rQpTz75ZPr6+rJ9+/ace+65bT/krrvuyrPPPjusgwLAsWQVAN3Scibq6NGjWbVqVZYtW5YkmTlzZubOnZu1a9e2/YCXXnop9913Xz70oQ8N76QA8H9kFQDd1FKidu/enX379mXWrFkD22bPnp0tW7a0/YDbbrstf/RHf5Tx48cP35QAcAxZBUA3tZSovXv3JkkmTZo0sG3y5Mltl0A899xz+d73vperrrpqGEcEgOPJKgC6qe01UUkybty44143m82WfT7/+c/ns5/97AkdqCzLgYt56/V66vX6iU8JwGmnKIoURZHktYx4M2QVAMNpqKxqKVHTpk1LkvT397f9+XW7d+/O008/nfe///0nNECtVnNHJAAGHFtSquaDrAKgE4bKqpYSNWPGjEyZMiU7d+4cCKNt27Zl8eLFx+33yCOP5L//+78HPnTHjh3p7e1NURT5xje+kV/91V8dnm8CwJgnqwDoppZronp6erJ8+fKsXr06SbJr165s3bo1S5Ysyfbt27Nw4cIcOXIk1157bR577LGBU1y/+Zu/maVLl6YoCqEEwLCSVQB005DPiVqxYkX6+vpy+PDhrFu3LtOnT09ZltmxY0cOHTo0cIejV199NYsWLRr47d6PfvSj/MM//ENHvwQAY4+sAqBb2pao3t7erFmzpmV7X19f9uzZc9y2iRMnDlxsBQCdIqsA6JaW5XwAAAAMTYkCAACoQIkCAACoQIkCAACoQIkCAACoQIkCAACooGMlqizLNBoNt5gF4DhFUaQsy26PkURWAdDe4Kxq+5yo4VCr1dJoNDp1OABGiXq9PmJKi6wCoJ3BWWU5HwAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAVKFAAAQAUdK1FlWabRaKQoik4dEoBRoCiKlGXZ7TGSyCoA2hucVWd06sC1Wi2NRqNThwNglKjX6yOmtMgqANoZnFWW8wEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFSgRAEAAFTQsRJVlmUajUaKoujUIQEYBYqiSFmW3R4jiawCoL3BWXVGpw5cq9XSaDQ6dTgARol6vT5iSousAqCdwVllOR8AAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFbUvUwYMHs3Tp0vT19WX+/PnZuHFj2zf/8z//cxYtWpT3v//9ufDCC3P77bcP67AA8DpZBUC3tL3FeaPRSLPZzKZNm/Lkk0+mr68v27dvz7nnnnvcfp/+9Kdz//33Z+7cufnf//3fXHDBBXnnO9+Z3/7t3+7I8ACMXbIKgG5pORN19OjRrFq1KsuWLUuSzJw5M3Pnzs3atWtb3nzDDTdk7ty5SZLp06fn0ksvHfI3gQBwqsgqALqppUTt3r07+/bty6xZswa2zZ49O1u2bGl586c//enjXh88eDDnnHPOMIwJAD8nqwDoppYStXfv3iTJpEmTBrZNnjw5zz777Bt+0E9/+tNs3rw511577SkeEQCOJ6sA6Ka210Qlybhx44573Ww23/CDbrrppnzuc5/LO9/5zrZ/XpZlGo1GkqRer6der1ebFIDTSlEUKYoiyWsZ8WbIKgCG01BZ1VKipk2bliTp7+9v+3M7d911VyZMmJAbbrhhyH1qtdpAMAHAsSWlaj7IKgA6YaisalnON2PGjEyZMiU7d+4c2LZt27YsWLCg7Qd/61vfyne/+9389V//dZJk165dp3BsAGglqwDoppYS1dPTk+XLl2f16tVJXguarVu3ZsmSJdm+fXsWLlyYI0eOJEm+973v5fbbb8/f/M3f5OWXX85LL72UL3zhC539BgCMObIKgG5q+7Dd15+90dfXl2uuuSbr1q3L9OnT8+KLL2bHjh05dOhQkuSaa65JURSZOnVqzjrrrJx11ll55plnOvoFABibZBUA3dL2xhK9vb1Zs2ZNy/a+vr7s2bNn4PWxPwNAJ8kqALql7ZkoAAAA2lOiAAAAKlCiAAAAKlCiAAAAKlCiAAAAKlCiAAAAKuhYiSrLMo1GI0VRdOqQAIwCRVGkLMtuj5FEVgHQ3uCsavucqOFQq9XSaDQ6dTgARol6vT5iSousAqCdwVllOR8AAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFShQAAEAFHStRZVmm0WikKIpOHRKAUaAoipRl2e0xksgqANobnFVndOrAtVotjUajU4cDYJSo1+sjprTIKgDaGZxVlvMBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABUoEQBAABU0LESVZZlGo1GiqLo1CEBGAWKokhZlt0eI4msAqC9wVl1RqcOXKvV0mg0OnU4AEaJer0+YkqLrAKgncFZZTkfAABABUoUAABABUoUAABABUoUAABABUoUAABABW1L1MGDB7N06dL09fVl/vz52bhx45AfcNttt2XevHmZN29e/vIv/3LYBgWAY8kqALql7S3OG41Gms1mNm3alCeffDJ9fX3Zvn17zj333OP2+5d/+Zf83d/9XbZu3ZokmTNnTmbPnp3LL7982AcHYGyTVQB0S8uZqKNHj2bVqlVZtmxZkmTmzJmZO3du1q5d2/Lmr33ta/nYxz6W3t7e9Pb2ZsmSJbnzzjuHf2oAxjRZBUA3tZSo3bt3Z9++fZk1a9bAttmzZ2fLli0tb968efMJ7QcAp5KsAqCbWkrU3r17kySTJk0a2DZ58uQ8++yzLW/eu3fvCe0HAKeSrAKgm8Y1m83msRseffTRvOc978krr7ySiRMnJkn+9E//NI8++mgefvjh4948YcKErF+/PosWLUqSPPTQQ1m0aFGOHDnScqDLL788L7/8cpKkVqulVqsNx/cBYJQoyzJlWSZJzjzzzKxfv/6E3yurAOiEobKq5cYS06ZNS5L09/e3/Xnwvv39/QOv+/v7c84557QdoEo4AsAbkVUAdFPLcr4ZM2ZkypQp2blz58C2bdu2ZcGCBS1vXrBgwQntBwCnkqwCoJtaSlRPT0+WL1+e1atXJ0l27dqVrVu3ZsmSJdm+fXsWLlw4sARixYoVWbduXQ4ePJiDBw/mnnvuyYoVKzr7DYbZ3XffncmTJ+fVV1/t9igj0oMPPpg5c+Zk3Lhxed/73pf3vOc9Oe+88/KJT3wiBw8e7PZ4I9IjjzySyy67LPV6PZdcckkuu+yy/P3f/71/Y8cY/O/qve99bxYsWJBbb701hw4d6vZ4I8rgv6t6vX7cf6crWXU8WTU0OVWdnPr/k1PVnJZZ1WzjwIEDzd///d9vXnzxxc158+Y1N2zY0Gw2m83vf//7zV/6pV9qHjhwYGDfL33pS80LL7yweeGFFzZvvfXWdh83ql199dXNt771rc1/+qd/6vYoI9Z3v/vdZpLmoUOHms1ms/ncc881zz777OaXv/zlLk828nznO99p/sqv/Epz165dA9seeOCB5hlnnNH84Q9/2L3BRqDB/66ef/755gc/+MHm4sWLm0eOHOnydCPL4L+r173vfe/rzkAdIqt+Tla9MTl14uTUiZNT1ZxuWdX2Ybu9vb1Zs2ZNy/a+vr7s2bPnuG0rV67MypUrT3G1GxlefPHFjB8/PldccUXuu+++XHHFFd0eaVSYOnVqzj///PzXf/1Xt0cZUY4ePZrrr78+n/3sZ3PeeecNbP+t3/qtXHXVVV2cbHR429veljVr1uRd73pX1q5dm0984hPdHmnEu/XWW7s9wrCSVa+RVdXJqfbk1MmRU2/OaM2qluV8/Ny3v/3tXHnllbnmmmvy7W9/O6+88kq3RxoVdu7cmW3btmXhwoXdHmVEefzxx/P0008P3CHsWKtXr86v//qvd2Gq0WX69On54Ac/mPvvv7/bo4xoRVGk0Wjkoosu6vYodICsqk5OtSenTp6cOnGjPavanoniNRs2bMjXv/719PT0ZPz48fnOd76TD3/4w90ea8RauHBhDh8+nB/96Ef55Cc/mY985CPdHmlEeeqpp5Ikb3/721v+7C1veUunxxm1arVaNmzY0O0xRqSFCxdm3Lhx6e/v9/+qMURWnTg59cbk1Kkhp97Y6ZJVzkQNob+/P7/wC7+Q3t7eTJw4MVdeeWXuvffebo81oj300EN59NFHs3fv3vz4xz/Odddd1+2ROA0dPXq02yOMWA899FCKoshXvvKVbo9Ch8iqauQUnSCn3tjpklXORA3hH//xH/P9739/4I4hL7zwQnbv3p0DBw74bcz/x1vf+tasWLEiV155ZW6//faceeaZ3R5pRHh9ffmePXs8wPMklGV53Fp9Wo3qux1Riax6c+RUe3Lq1JBTJ2a0Z5UzUUPYsGFDHnvssRRFkaIosnnz5vT09HgQ4wkaP358ms3mwC2GSebOnZvzzz8/GzdubPmzZcuW5fHHH+/CVKPL//zP/2Tjxo258soruz3KqPDII490ewSGmax68+RUKzl18uRUdaM1q5SoNl544YWcccYZmTBhwsC2iRMnZvHixbnvvvu6ONnocPTo0dx3332ZN29efvEXf7Hb44wYPT09ueuuu/IXf/EXx90R6hvf+Eb+8z//M3PmzOnecKPAT37yk1x77bWp1+v5+Mc/3u1xRoU/+7M/6/YIDCNZ9ebJqfbk1MmRU2/OaM0qy/kGefHFF3PppZfmpz/9adavX5/LL788SbJ+/fr84Ac/yJ49e3L99dfnjjvu6PKkI8ODDz6YP/mTP0ny8wsF9+/fn7e//e3W5bdRr9fzzW9+M5/85Cdz+PDhHD16NO9+97vzwAMPpKfH7zReN/jfVbPZzP79+3PVVVdl5cqV/q6O8W//9m8DAfTRj34048aN6/JEdIKsOnFyqho5dWLkVDWnY1aNazabzW4PAQAAMFqoyQAAABUoUQAAABUoUQAAABUoUQAAABUoUQAAABUoUQAAABX8P0E889PnncJEAAAAAElFTkSuQmCC' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "619bdfd709584a91ac8ab800b04db811"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, the plot needs a legend to understand what the different colours mean. Let's try adding a simple legend."
      ],
      "metadata": {
        "id": "Ocil7XOZ76ii"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 - Subplot Legend"
      ],
      "metadata": {
        "id": "-GtdamSP-0FS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Standard legend\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "fig.patch.set_facecolor('white')\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "axes[0].legend(loc='best')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "54896026c17e478b984043ccbdad9b8b",
            "861f6b0d7363424f9740c534390cd157",
            "ebfd7023ae39420ba78d20c58151096b",
            "a0f2e880b08c45f6a0e34fc8b4640634"
          ]
        },
        "id": "ZdX0tui5bKhl",
        "outputId": "970384f6-244f-4e2a-a44a-fad154a65826"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "54896026c17e478b984043ccbdad9b8b"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we try and add a basic legend to the left plot, evening when using the 'best' location, the legend overlaps with plotted data.  \n",
        "Even if we change the legend layout (below), it still overlaps."
      ],
      "metadata": {
        "id": "PTQa1GQn85l5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Multi-column legend\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "fig.patch.set_facecolor('white')\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "axes[0].legend(loc='best', ncol=n_groups)\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "c9f223df9894443ebe71d5b6599426be",
            "6a8ad399204e4411bbee8712c500a1d1",
            "a1acbe088c1c4bb1a6721a2d02ce31f0",
            "a93cb6da890b4bc580e5bc3174f8c694"
          ]
        },
        "id": "T70fBhiydQz5",
        "outputId": "071ccab7-97ea-41e8-d47b-6047499f5c83"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img src='data:image/png;base64,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' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9f223df9894443ebe71d5b6599426be"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As there isn't enough space in the plots, the legend needs to go outside.  \n",
        "Rather than adding the legend to a particular axis, it can be added to the figure itself.  \n",
        "The location is then relative to the entire plot.  \n",
        "\n",
        "However, to do this, we need to manually create the entries in the legend.  \n",
        "In this case we create a set of `Patch` handles and pass that to `fig.legend`"
      ],
      "metadata": {
        "id": "gvQd1OVU9Ndv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Legend below both axes\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "\n",
        "# Create legend in fig rather than an axis\n",
        "def add_fig_legend(fig, loc='lower center', ncol=4):\n",
        "    # Get colours for current style\n",
        "    colours = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "    # Set up handles (the bits that are drawn in the legend)\n",
        "    handles = []\n",
        "    for group_idx in range(n_groups):\n",
        "        # Create a simple patch that is the correct colour\n",
        "        colour = colours[group_idx]\n",
        "        handles.append(Patch(edgecolor=colour, facecolor=colour, fill=True))\n",
        "    # Acutally create our figure legend, using the handles and labels\n",
        "    fig.legend(handles=handles, labels=group_labels, loc=loc, ncol=ncol)\n",
        "\n",
        "add_fig_legend(fig)\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "1e2c20e7e02c42dfa02b135eb23b87a8",
            "23e79c790b894a93b6340e199f400f75",
            "6a2f9539a64e49749acd40c72f0748be",
            "4b98a62faf7245d7a89a1437585a0de7"
          ]
        },
        "id": "9vE5LVLjdw6d",
        "outputId": "ef6e3c9c-66b2-485b-ce63-ebcab786b691"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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XV1dbr00ku5oxEAwDFkFQDALSFvcW4Yhvx+v0pLS7V161Z5vV6Vl5erd+/eIVeycOFC7dmzJ6KDAgBwOrIKAOCWoCNRDQ0NWrRokXJzcyVJffv2VUZGhpYuXRpyBYcPH9ZLL72k0aNHR3ZSAAD+H1kFAHBTUImqqqpSdXW1+vfvH3guPT1dGzZsCLmC2bNn61e/+pUSEhIiNyUAAKchqwAAbgoqUbt375Ykde/ePfBcUlJSyFMg9u7dq3feeUfjxo2L4IgAADRGVgEA3BTymihJiouLa/TY7/cHLfPoo4/qwQcfbNWGLMsKXMzr8/nk8/laPyUAoN0xTVOmaUr6OiPOBFkFAIik5rIqqESlpKRIkmpqakL+fEpVVZW++OILXXvtta0awOPxcEckAEDA6SXFbj6QVQAAJzSXVUElKi0tTT179lRlZWUgjMrKypSdnd1oubfffltffvllYKUVFRVKTEyUaZpasmSJLr744si8EwBAh0dWAQDcFHRNVHx8vPLy8lRYWChJ2rZtmzZt2qScnByVl5crKytL9fX1mjRpkjZu3Bg4xDVy5EhNnDhRpmkSSgCAiCKrAABuavZ7ovLz8+X1enXy5EmtWLFCqampsixLFRUVqqurC9zh6MSJExoxYkTgt3ubN2/WK6+84uibAAB0PGQVAMAtIUtUYmKiioqKgp73er3auXNno+e6dOkSuNgKAACnkFUAALcEnc4HAAAAAGgeJQoAAAAAbKBEAQAAAIANlCgAAAAAsIESBQAAAAA2UKIAAAAAwAbHSpRlWTIMg1vMAgAaMU1TlmW5PYYksgoAEFrTrAr5PVGR4PF4ZBiGU5sDAMQIn88XNaWFrAIAhNI0qzidDwAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGADJQoAAAAAbKBEAQAAAIANlCgAAAAAsMGxEmVZlgzDkGmaTm0SABADTNOUZVlujyGJrAIAhNY0qzo5tWGPxyPDMJzaHAAgRvh8vqgpLWQVACCUplnF6XwAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGADJQoAAAAAbHCsRFmWJcMwZJqmU5sEAMQA0zRlWZbbY0giqwAAoTXNqk5Obdjj8cgwDKc2BwCIET6fL2pKC1kFAAilaVZxOh8AAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA0hS1Rtba0mTpwor9erzMxMlZSUhHzx3/72N40YMULXXnutfvSjH2nu3LkRHRYAgFPIKgCAW0Le4twwDPn9fpWWlmrr1q3yer0qLy9X7969Gy1333336eWXX1ZGRob+97//acCAAbrooot00003OTI8AKDjIqsAAG4JOhLV0NCgRYsWKTc3V5LUt29fZWRkaOnSpUEvvuuuu5SRkSFJSk1N1TXXXNPsbwIBAAgXsgoA4KagElVVVaXq6mr1798/8Fx6ero2bNgQ9OL77ruv0ePa2lr16tUrAmMCAPANsgoA4KagErV7925JUvfu3QPPJSUlac+ePS2u6ODBg1q/fr0mTZoU5hEBAGiMrAIAuCnkNVGSFBcX1+ix3+9vcUXTp0/XI488oosuuijkn1uWJcMwJEk+n08+n8/epACAdsU0TZmmKenrjDgTZBUAIJKay6qgEpWSkiJJqqmpCflzKAsXLlTnzp111113NbuMx+MJBBMAAKeXFLv5QFYBAJzQXFYFnc6Xlpamnj17qrKyMvBcWVmZBg8eHHLFr776qt566y0988wzkqRt27aFcWwAAIKRVQAANwWVqPj4eOXl5amwsFDS10GzadMm5eTkqLy8XFlZWaqvr5ckvfPOO5o7d67mzZunI0eO6PDhw3rsscecfQcAgA6HrAIAuCnkl+2e+u4Nr9er8ePHa8WKFUpNTdWBAwdUUVGhuro6SdL48eNlmqaSk5PVrVs3devWTTt27HD0DQAAOiayCgDglpA3lkhMTFRRUVHQ816vVzt37gw8Pv1nAACcRFYBANwS8kgUAAAAACA0ShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2OlSjLsmQYhkzTdGqTAIAYYJqmLMtyewxJZBUAILSmWRXye6IiwePxyDAMpzYHAIgRPp8vakoLWQUACKVpVnE6HwAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYINjJcqyLBmGIdM0ndokACAGmKYpy7LcHkMSWQUACK1pVnVyasMej0eGYTi1OQBAjPD5fFFTWsgqAEAoTbOK0/kAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2OBYibIsS4ZhyDRNpzYJAIgBpmnKsiy3x5BEVgEAQmuaVZ2c2rDH45FhGE5tDgAQI3w+X9SUFrIKABBK06zidD4AAAAAsIESBQAAAAA2UKIAAAAAwAZKFAAAAADYQIkCAAAAABtClqja2lpNnDhRXq9XmZmZKikpaXYFs2fP1qBBgzRo0CD9/ve/j9igAACcjqwCALgl5C3ODcOQ3+9XaWmptm7dKq/Xq/LycvXu3bvRcn//+9/1pz/9SZs2bZIkDRw4UOnp6Ro1alTEBwcAdGxkFQDALUFHohoaGrRo0SLl5uZKkvr27auMjAwtXbo06MXPP/+8br/9diUmJioxMVE5OTlasGBB5KcGAHRoZBUAwE1BJaqqqkrV1dXq379/4Ln09HRt2LAh6MXr169v1XIAAIQTWQUAcFNQidq9e7ckqXv37oHnkpKStGfPnqAX7969u1XLAQAQTmQVAMBNcX6/33/6E++++66uuOIKHT9+XF26dJEkPfzww3r33Xe1bt26Ri/u3LmziouLNWLECEnSm2++qREjRqi+vj5oQ6NGjdKRI0ckSR6PRx6PJxLvBwAQIyzLkmVZkqRzzjlHxcXFrX4tWQUAcEJzWRV0Y4mUlBRJUk1NTcifmy5bU1MTeFxTU6NevXqFHMBOOAIA0BKyCgDgpqDT+dLS0tSzZ09VVlYGnisrK9PgwYODXjx48OBWLQcAQDiRVQAANwWVqPj4eOXl5amwsFCStG3bNm3atEk5OTkqLy9XVlZW4BSI/Px8rVixQrW1taqtrdXy5cuVn5/v7DsAAHQ4ZBUAwE0hv2z31HdveL1ejR8/XitWrFBqaqoOHDigiooK1dXVSZJGjhyp3NxcDRs2TMOGDdMdd9zR7r53Y9myZUpKStKJEyfcHiUqrV27VgMHDlRcXJyuvvpqXXHFFerTp48mTJig2tpat8eLSm+//baGDx8un8+nK6+8UsOHD9ef//xnPmOnafq5uuqqqzR48GA9+eSTgf/+4GtN95XP52v0v/aMrPoGWdU8cso+curbkVP2tMus8qNFt9xyi//ss8/2v/76626PErXeeustvyR/XV2d3+/3+/fu3evv0aOH/+mnn3Z5sujzxhtv+L/3ve/5t23bFnhu9erV/k6dOvk//vhj9waLQk0/V/v27fNff/31/uzsbH99fb3L00WXpvvqlKuvvtqdgeA4sqpl5FTrkVOtR07Z096yKuSRKHztwIEDSkhI0I033qiXXnrJ7XFiRnJysi699FJ9/vnnbo8SVRoaGjRlyhQ9+OCD6tOnT+D5G264QePGjXNxsthw3nnnqaioSG+99VbIL1RFsCeffNLtEeAAsso+cio0cqptyKkzE6tZRYlqwapVqzR27FiNHz9eq1at0vHjx90eKSZUVlaqrKxMWVlZbo8SVT766CN98cUXgdssn66wsFA/+MEPXJgqtqSmpur666/Xyy+/7PYoUc00TRmGoSFDhrg9ChxAVtlHToVGTrUdOdV6sZ5VQbc4xzfWrFmjxYsXKz4+XgkJCXrjjTd08803uz1W1MrKytLJkye1efNmTZ48WWPGjHF7pKiyfft2SdL5558f9GdnnXWW0+NIkn722Ovafzh8/+DqcW5XvfTQ6BaX2bVrl5566iklJSWpvr5elZWV8ng8euKJJ1q1DY/HozVr1oRjXH3++eeaNm2aOnXqpL/85S9hWWcoNfc/Jv/Bw2FbX9x3zlXS7x8Kej4rK0txcXGqqalRVlaWpk6desb7OVzWr1+vOXPmKCMjQ5WVlRoyZIjy8vLCvp2h80Zo35Gvwra+5HN66oO7S1pcpq2f5XBZtWqVjh49qmHDhunEiRO6+eabVVxcrPj48P6e9JN3fqaTJ/aHbX2duvTQ5Ve1fOQs3Pu4rTm1Z88eZWRkaObMmbr77rvPaIZoFo05FYvCmVPt0elZFcv/rqZENaOmpkbnnnuuEhMTJUljx47VypUrY/r/7Eh788031alTJx09elRjxozRnXfeqcWLF7s9FloQzgLVmvXV1tZq9OjReuWVV3ThhRdKkk6cOKHbb7+91dtoaGho04yn++CDD5Sdna2Skpb/sdxW4SxQLa3v1N/BkpISTZo0SaWlpWe8n8Nl165d+vWvf60hQ4aorq5OKSkpGjNmjJKTk8O6nXAWqNasLxyf5XCoqanRWWedpZEjRyovL0+WZemvf/2r3n//fQ0bNiys2wpngWrN+iKxj9uSUw0NDXrooYeUmZl5xttHxxDOnGqPTv09NE1Tpmm6Pc4Zo0Q147XXXtP7778fuGPI/v37VVVVpWPHjvHbmG9x9tlnKz8/X2PHjtXcuXN1zjnnuD1SVDh1fvnOnTvl8XjcHcYlxcXF8ng8gX8QSVKXLl0CR4GWLFmimTNnKj8/X++9954kqb6+Xvfff7/OO+887d+/X6Zpqk+fPlqwYIEKCgpkWZZKSko0efJkmaapgwcP6p577tF3v/tdXXbZZfr44481evRoTZgwIWienJwcFRUVOfLenXTo0CH95Cc/adV+3r59uyorK/X+++9r2rRpgf3cr18/TZ48uc37efToxkcmO3XqpM6dO0d+J0SYnc9yJPfxa6+9ps2bN+u///2vli1bpn379unAgQMhv3Q41oRzH7/++uuBdZzax6deu3XrVl1wwQXf+t+LWbNmKTc3V88991zk37xLyKnwsCyr0TVlCC2m78wnrolq1po1a7Rx48ZAS16/fr3i4+P5NvtWSkhIkN/vD3xPC6SMjAxdeumlIY965Obm6qOPPnJhKmdVVVUpNTU18HjHjh0qKCjQjTfeKMuyNGnSJPXv319DhgzRAw88IElavHix6urq9NBDD2natGnasmWLfvzjHzf6np8RI0YEAv+HP/yhfD6f0tLS9OCDD2rZsmWaPn26qqurHX2vbjq1n99++21JLe/nF198UfPnz9eiRYsC+3n27NmaO3euysvLw7qf582bpwceeEDdu3eP6Pt3gp3PciT38elZlZ+fr549e6pr16765JNPHNsXkRLOfXx6mT+1j0+d7njFFVd86+d43bp1OvvsszV06FBn3rxLyKm227Vrl0pKSjR27Fi3R4kZp7Iq1lCiQti/f3/Qb0u7dOmi7Oxs7nzUCg0NDXrppZc0aNAgfec733F7nKgRHx+vhQsX6oknnmh0R6glS5Zoy5YtGjhwoHvDOSQtLU27du0KPL7ooos0Y8YMbdmyRYcPf3OK2ve///3Az1u2bNEll1yir776SpMmTVJycrIuueSSb93WqWW6du2q5OTkwLn+HcGp/fzb3/5W0rfv58zMTG3evLnRfr344ov16aeffuu2Wrufly9friNHjui+++474/cVTex+liOxj5tm1W233SbTNNWtWzc988wzbX+TLov0Pi4uLtagQYPUtWvXb/0cv/766zp27JgKCgq0ZcsWrV27VkuWLAnbe40W5FTbnMopn8+nX/ziF26PEzNOZVWs4XS+Jg4cOKBrrrlGBw8eVHFxceALGYuLi/Xhhx9q586dmjJliubPn+/ypNFh7dq1uv/++yV9c6Hg0aNHdf7552vlypUuTxd9fD6fXnjhBU2ePFknT55UQ0OD+vXrp9WrV4f9IvBoNGrUKD3++OOyLCvw2/ZQRyz/9a9/afbs2ZK+Pnd61apVWr58ucaNG6d///vfuvzyy4PW/Z///KfR46qqKklfX1exZ88epaWlReAdue+f//xnIIBuvfVWxcXFqb6+XqZpql+/foHlQu3nuLi4wM+XX355o6MXVVVVIe/EdSb7edGiRTp8+LAeeughbdmyRV27dlXfvn3P4N1Gj9Z+liO1j0/PqoKCAl177bUaMmSI3njjDR07dkylpaUxn1Xh2scHDx7U448/LumbnCotLdVVV12llStX6sUXX/zWz/GcOXMCP1dUVCgzM1OTJk0K59uNGh09p1qr6b9//H6/jh49qnHjxmnq1KnsqyZCZVWsi/P7/X63hwDgjuEzwn8L1rUFt7T457t27dKsWbPUo0cP1dfX6/PPP1daWppmzJih9957T5MnT9bYsWM1ffp09erVS/X19YG7zO3fv18DBgzQ5MmTJUkPP/ywampqNGDAAC1YsEAjR45UQUGBDMPQl19+KY/How8//FA33XRTyH/wrFq1Si+88IIqKys1YcIE/eY3vwn7/pCk/b+cEfZ19ni+oMU/j5b9vGrVKk2YMEEZGRmSpOrqaj377LNhPw8+bVb4L/bfPn1Di38eLfv4gw8+0FNPPaWMjAwdOnRIO3fu1HPPPRf261E3/mN4WNcnSYOuW9vin0fLPj6lsLBQ8+bN0/nnn68pU6bohhtuCPs+ARAbKFFAB+bGLc6dYBiGPB6PJk6c6PYokpy7xbnTomk/u3GLcydE0z524xbnToimfQwgdlCiALQrn376qe655x716NFDc+bMaXRnL4QP+zny2MeRxz4GcKYoUQAAAABgA1e9AQAAAIANlCgAAAAAsIESBQAAAAA2UKIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGz4Pzi1O3Tx7taYAAAAAElFTkSuQmCC' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e2c20e7e02c42dfa02b135eb23b87a8"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This works - sort of!  \n",
        "\n",
        "The legend looks good: it's outside the axes and positioned as we want, but now it's overlapping with the x axis labels.  \n",
        "To get around this, we can adjust the subplot layout.  \n",
        "\n",
        "The easiest way to do this is to use matplotlib's subplot tool which allows you to interactively change the layout.  \n",
        "**Note:** Using the subplot tool requires extra steps in Jupyter notebooks (included in the first cell above):\n",
        "```\n",
        "# Used for interactive subplots adjust\n",
        "!pip install -q ipympl\n",
        "\n",
        "# Enable interactive plots for subplots adjust\n",
        "%matplotlib widget\n",
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()\n",
        "```\n",
        "We want to adjust the 'bottom' parameter. This will move the subplots up, leaving space for the legend."
      ],
      "metadata": {
        "id": "UYS3J_Zn-Da4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Legend below both axes with subplot adjust tool\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "add_fig_legend(fig)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show subplot tool\n",
        "plt.subplot_tool()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683,
          "referenced_widgets": [
            "ce587086c4a84b5c91024cb0ff04150c",
            "32b9e16652fd47cdbff0e0de817526f4",
            "35ed791b9c6d43e0a723a146dcc7ad2f",
            "05ce97fc1eaa44b8833884430e4f82ca",
            "558092579ffc4747bf23bc862ba71675",
            "a8a8b05b923f437483de2fe098404772",
            "42afafa223334ef1b06cc15cb0e28e1e",
            "e2c88ab3531c43d0bc58ca83fd850463"
          ]
        },
        "id": "10yKR50SgG7f",
        "outputId": "f825419a-2f36-4f8b-b6fe-b9e6a1165fcd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Subplot configuration tool\n",
              "                </div>\n",
              "                <img 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' 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              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce587086c4a84b5c91024cb0ff04150c"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.widgets.SubplotTool at 0x7fef84f2d460>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img src='data:image/png;base64,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' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "558092579ffc4747bf23bc862ba71675"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems a value of around 0.14 for the 'bottom' parameter is enough to leave space for the legend.  \n",
        "We can now permantely set that using `plt.subplots_adjust` (which needs to be called after `plt.tight_layout()` otherwise it won't work).  \n",
        "**Note:** Using the subplot tool is not strictly necessary, you can just set the values via trial and error instead."
      ],
      "metadata": {
        "id": "D_TdZOwF_RhG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot below both axes with adjusted subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
        "plot(axes[0], 0)\n",
        "plot(axes[1], 1)\n",
        "add_fig_legend(fig)\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.tight_layout()\n",
        "\n",
        "# Adjust the subplots with the new bottom values, leaving space for the legend\n",
        "plt.subplots_adjust(bottom=0.14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "f93e51a3958d4a059f747902d3a6c221",
            "ce540922cfd94e84804d3b1aa4a6887b",
            "9a2b12afc25741258b182582cf34fd1d",
            "09c65ee8208c4f1ab94b3bfbb3684e80"
          ]
        },
        "id": "2IBMJesDpTLw",
        "outputId": "40b59b7b-e18f-48b3-c0fb-00013b11b491"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
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              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img src='data:image/png;base64,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' 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              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f93e51a3958d4a059f747902d3a6c221"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Much better!  \n",
        "The legend is now clearly visible and well positioned between the two axes.  "
      ],
      "metadata": {
        "id": "caapetZPAINl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3 - Additional Examples\n",
        "\n",
        "Below, we provide additional examples of using legends outside the subplots.  \n",
        "\n",
        "First, let's move the legend and add some more axes."
      ],
      "metadata": {
        "id": "Tn3U-7zyAZXC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Legend to right with four axes\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 5))\n",
        "plot(axes[0][0], 0)\n",
        "plot(axes[0][1], 1)\n",
        "plot(axes[1][0], 2)\n",
        "plot(axes[1][1], 3)\n",
        "# Add the figure on the right of the plot this time\n",
        "add_fig_legend(fig, loc='center right', ncol=1)\n",
        "fig.patch.set_facecolor('white')\n",
        "plt.tight_layout()\n",
        "\n",
        "# Remove space from the right handside to make space for the legend\n",
        "plt.subplots_adjust(right=0.9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449,
          "referenced_widgets": [
            "5b4fe29429e244afabc47ad916348d2b",
            "18453cadd437405f8e02f6a0f5e39998",
            "9674f2f4c19e4a0d9a9d8922e9075863",
            "f425e01879614a468593ef6b79d6d1e1"
          ]
        },
        "id": "V8jX7GiXt_iX",
        "outputId": "3f766283-f2c6-465e-c47a-ff0e5052b2fb"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' width=864.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b4fe29429e244afabc47ad916348d2b"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This approach can also be used if we only have one axis, and with other plot types (e.g., lines)."
      ],
      "metadata": {
        "id": "I2DszdrFBOXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Legend below with single axis and lines\n",
        "line_styles = ['-', ':', '--', '-.']\n",
        "line_labels = ['Line {:d}'.format(i + 1) for i in range(4)]\n",
        "\n",
        "fig, axis = plt.subplots(nrows=1, ncols=1, figsize=(6, 4))\n",
        "lines_xs = np.linspace(0, 1, 100)\n",
        "axis.plot(lines_xs, lines_xs, ls=line_styles[0])\n",
        "axis.plot(lines_xs, np.ones_like(lines_xs) * 0.45, ls=line_styles[1])\n",
        "axis.plot(lines_xs, np.sin(lines_xs * np.pi * 5) / 2 + 0.5, ls=line_styles[2])\n",
        "axis.plot(lines_xs, np.cos(lines_xs * np.pi * 3) / 6 + 0.2, ls=line_styles[3])\n",
        "\n",
        "# Get colours for current style\n",
        "colours = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "handles = []\n",
        "for idx in range(4):\n",
        "    handles.append(Line2D([0], [0], color=colours[idx], ls=line_styles[idx]))\n",
        "fig.legend(handles=handles, labels=line_labels, loc='lower center', ncol=4)\n",
        "fig.patch.set_facecolor('white')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.subplots_adjust(bottom=0.14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "1a2467a80e9147798b2e3cb4494dc34f",
            "3d4fb59b8e174dbfb500cb97b66f5818",
            "4033e28ff53346208e9811cbd78dd1ee",
            "3a6bccc32f074dd58f533a09ab33b14e"
          ]
        },
        "id": "-JNhmEtntxY-",
        "outputId": "d4f838fc-b121-4eda-f32f-25d1e130f76a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' 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              "            </div>\n",
              "        "
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a2467a80e9147798b2e3cb4494dc34f"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And that's my guide on Mastering Subplot Legends in Matplotlib. If you have any questions, please find my details here: www.jearly.co.uk\n",
        "\n",
        "Thanks for reading, and happy plotting!"
      ],
      "metadata": {
        "id": "iak9jFNiBhKP"
      }
    }
  ]
}