{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "import matplotlib.colorbar as colorbar # Import colorbar module\n",
        "import numpy as np\n",
        "\n",
        "# 创建一个图形和一个轴来放置颜色条\n",
        "# fig is the entire window or page\n",
        "# ax is the area where the plot will be drawn\n",
        "# We specify the position and size of the axes: [left, bottom, width, height]\n",
        "fig, ax = plt.subplots(figsize=(1.5, 8))\n",
        "fig.subplots_adjust(left=0.5)\n",
        "\n",
        "# 创建一个自定义的颜色映射来精确匹配 \"DiscoClassic\"\n",
        "# 颜色从蓝色 -> 青色 -> 品红色 -> 红色\n",
        "colors_list = ['blue', 'cyan', 'magenta', 'red']\n",
        "custom_cmap = colors.LinearSegmentedColormap.from_list(\n",
        "    'disco_classic_custom', colors_list)\n",
        "\n",
        "# 定义颜色条的范围\n",
        "# 根据新图片，刻度从 0 到 0.16\n",
        "vmin = 0\n",
        "vmax = 0.16\n",
        "norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
        "\n",
        "# 创建颜色条\n",
        "# 我们使用 ColorbarBase，它允许我们在没有关联图的情况下绘制颜色条\n",
        "cb = colorbar.ColorbarBase(ax, cmap=custom_cmap, # Use colorbar.ColorbarBase\n",
        "                                norm=norm,\n",
        "                                orientation='vertical')\n",
        "\n",
        "# 设置刻度\n",
        "# 我们希望刻度从 0 到 0.16，每隔 0.02 个单位一个刻度\n",
        "ticks = np.arange(vmin, vmax + 0.01, 0.02)\n",
        "cb.set_ticks(ticks)\n",
        "\n",
        "# 将图形保存为 SVG 文件\n",
        "# 我们将文件名设置为 'colorbar_discoclassic.svg'\n",
        "# 您可以更改为您喜欢的任何名称\n",
        "# bbox_inches='tight' 会裁剪掉图像周围多余的空白\n",
        "plt.savefig('colorbar_discoclassic.svg', format='svg', bbox_inches='tight')\n",
        "\n",
        "# plt.show() # 我们注释掉了这行，因为现在是保存文件而不是显示"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 150x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "8CSO85D8b53Y",
        "outputId": "c8268356-7699-478c-b764-d9f22cf3a512"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}