{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.050383, 0.029803, 0.527975, 1.      ],\n",
       "       [0.063536, 0.028426, 0.533124, 1.      ],\n",
       "       [0.075353, 0.027206, 0.538007, 1.      ],\n",
       "       ...,\n",
       "       [0.944152, 0.961916, 0.146861, 1.      ],\n",
       "       [0.941896, 0.96859 , 0.140956, 1.      ],\n",
       "       [0.940015, 0.975158, 0.131326, 1.      ]], shape=(256, 4))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "\n",
    "mpl.colormaps[\"plasma\"](np.linspace(0, 1, 256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4058693/3103424214.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels([format_spec % val for val in ax.get_xticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x80 with 1 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAABFCAYAAACFUKW1AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAACU9JREFUeJzt3U9IVO0XwPEzajMT+TcCJ8HUkJcgCiNRXPSHX0YLoRZFiyBqUy3alVCQ5SJIMSEhWgVRtEgqpBaFGFIbsYKwP6SIhYuCRmhRJqWWc97Fr5n3zswzo1dn9FG/H4ibzz1z7nOf53hP2mQeVVUBAABWyljoCQAAgMRo1AAAWIxGDQCAxWjUAABYjEYNAIDFaNQAAFiMRg0AgMWy5uMi4+PjMjk5OR+XAgBg3ni9XvH7/Wm9Rtob9fj4uJSVlUkwGEz3pQAAmFeBQECGh4fT2qzT3qgnJyclGAzKp0+fJCcnR0REVFXCPxAt9mgaSxavqiIzzBHJ9V9Q1NH0epnm2mrIEffD3pLch3ENpouJiTNe25Ajdixq7ZzXMb02wbVN8aa1k+nmEROT9H6deUw5EuVNsjemuZrWzjTHZDmcuTxJat50LnbMFOecS9y5RPsVzjPDecTFO2KN+5vgXKKxuPuZQbw4Po+N9x1zzrk3Ho1fkwxDjvDfC5rqIi6HSCSHaS1MNWmah4TzzGA9jfHhe3TOwzkW/ngmOQx1ZMohzhzhnEnio8b+HsPzyDTlMKxr3Fo7980wFndNZx5TfIL8sXszpir/CwZlcnJycTfqsNzcXHsadYKCX5BGnWAs6f0kiZ91o55mPWPPyXQ5Yu5NpptHeDzJfGI/yRKuXYL1iZpPzFxMc032h6Jp8xtyzblRm8Ycc5m3Ru0YW9BGnSiH4X5M8c77mXOjVkODcM4hyV7GnktJozaNhZMZ5pO2Rm3IkeHM8feY9kadYG/m2qglFJL5wJvJAACwGI0aAACL0agBALAYjRoAAIvRqAEAsBiNGgAAi9GoAQCwGI0aAACL0agBALAYjRoAAIvRqAEAsBiNGgAAi9GoAQCwGI0aAACL0agBALAYjRoAAIvRqAEAsBiNGgAAi9GoAQCwGI0aAACL0agBALAYjRoAAItlzdeFRkdHRVVFRERVo37vPJrGksWrqsgMc0Ry/RcUdTS9Xqa5thpyOPMkOpdsrtPeT0yc8dqGHLFjUWvnvI7ptQmubYo3rZ1MN4+YmKT368xjypEob5K9Mc3VtHamOSbL4czlSVLzpnOxY6Y451ziziXar3CeGc4jLt4Ra9zfBOcSjcXdzwzixfF5bLzvmHPOvfFo/JpkGHKEv4ox1UVcDpFIDtNamGrSNA8J55nBehrjw/fonIdzLPzxTHIY6siUQ5w5wjmTxEeN/T2G55FpymFY17i1du6bYSzums48pvgE+WP3ZiwmX7qkvVGrqmRnZ0txcXG6LwUAwLzKzs6O/wIgxdLeqD0ej4yNjcmnT58kNzc33ZfDEjY6OirFxcXUEuaEOkKqhGvJ4/FMHzwH8/at79zcXD4pkBLUElKBOsJiwZvJAACwGI0aAACLpb1R+3w+aWxsFJ/Pl+5LYYmjlpAK1BFSZb5qyaPpfrsaAACYNb71DQCAxWjUAABYjEYNAIDFaNQAAFiMRg0AgMVm1aivXbsmpaWl4vf7pbq6Wl6+fJk0/t69e7Jhwwbx+/2yadMmefz4cdR5VZULFy7I2rVrZeXKlVJbWytDQ0OzmRoWETd19P79e9m/f7+UlpaKx+ORtra2OefE0uFm369fvy7btm2TgoICKSgokNra2rh4nknLk5s66ujokMrKSsnPz5dVq1ZJRUWF3L59OyomZXWkLrW3t6vX69UbN27o+/fv9dixY5qfn68jIyPG+J6eHs3MzNSWlhbt7+/XhoYGXbFihb579y4S09zcrHl5efrgwQN98+aN7t27V8vKyvTXr19up4dFwm0dvXz5Uuvr6/XOnTsaCAT0ypUrc86JpcHtvh86dEivXbumfX19OjAwoEePHtW8vDz9/PlzJIZn0vLjto6ePn2qHR0d2t/frx8+fNC2tjbNzMzUzs7OSEyq6sh1o66qqtKTJ09GPp6amtKioiJtamoyxh88eFDr6uqixqqrq/XEiROqqhoKhTQQCOjly5cj5799+6Y+n0/v3LnjdnpYJNzWkVNJSYmxUc8lJxavue77nz9/NCcnR2/duqWqPJOWq1Q8P7Zs2aINDQ2qmto6cvWt78nJSXn16pXU1tZGxjIyMqS2tlZ6e3uNr+nt7Y2KFxHZs2dPJH54eFiCwWBUTF5enlRXVyfMicVtNnW0EDlhv1Ts+8+fP+X379+yevVqEeGZtBzNtY5UVbq7u2VwcFC2b98uIqmtI1eN+uvXrzI1NSWFhYVR44WFhRIMBo2vCQaDSePDRzc5sbjNpo4WIifsl4p9P3PmjBQVFUUeqDyTlp/Z1tH3798lOztbvF6v1NXVydWrV2X37t0ikto6mrf/5hIAbNPc3Czt7e3y7Nkz8fv9Cz0dLDI5OTny+vVrGRsbk+7ubjl16pSsX79edu7cmdLruPqKes2aNZKZmSkjIyNR4yMjIxIIBIyvCQQCSePDRzc5sbjNpo4WIifsN5d9b21tlebmZunq6pLNmzdHxnkmLT+zraOMjAwpLy+XiooKOX36tBw4cECamppEJLV15KpRe71e2bp1q3R3d0fGQqGQdHd3S01NjfE1NTU1UfEiIk+ePInEl5WVSSAQiIoZHR2VFy9eJMyJxW02dbQQOWG/2e57S0uLXLx4UTo7O6WysjLqHM+k5SdVz49QKCQTExMikuI6cvXWM/3/W9h9Pp/evHlT+/v79fjx45qfn6/BYFBVVQ8fPqxnz56NxPf09GhWVpa2trbqwMCANjY2Gv95Vn5+vj58+FDfvn2r+/bt459CLHFu62hiYkL7+vq0r69P165dq/X19drX16dDQ0MzzomlyW0tNTc3q9fr1fv37+uXL18iv378+BEVwzNpeXFbR5cuXdKuri79+PGj9vf3a2trq2ZlZen169cjMamqI9eNWlX16tWrum7dOvV6vVpVVaXPnz+PnNuxY4ceOXIkKv7u3bv6zz//qNfr1Y0bN+qjR4+izodCIT1//rwWFhaqz+fTXbt26eDg4GymhkXETR0NDw+riMT92rFjx4xzYulyU0slJSXGWmpsbIzE8ExantzU0blz57S8vFz9fr8WFBRoTU2Ntre3R+VLVR3x/1EDAGAxftY3AAAWo1EDAGAxGjUAABajUQMAYDEaNQAAFqNRAwBgMRo1AAAWo1EDAGAxGjUAABajUQMAYDEaNQAAFvsXykTRcs9n4VcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x80 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colorbar import ColorbarBase\n",
    "\n",
    "\n",
    "def create_colorbar(get_color, vmin=0, vmax=1, width=8, height=0.8, dpi=100, num_samples=256, \n",
    "                   title=None, format_spec=\"%.2f\"):\n",
    "    \"\"\"\n",
    "    创建自定义colorbar\n",
    "    \n",
    "    参数:\n",
    "    get_color: 颜色映射函数，接受vmin-vmax之间的值，返回(r,g,b,a)颜色元组\n",
    "    vmin: colorbar最小值\n",
    "    vmax: colorbar最大值\n",
    "    width: 图像宽度\n",
    "    height: 图像高度\n",
    "    dpi: 图像分辨率\n",
    "    num_samples: 颜色采样数量\n",
    "    title: colorbar标题\n",
    "    format_spec: 刻度格式化字符串\n",
    "    \"\"\"\n",
    "    # 创建图形\n",
    "    fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)\n",
    "    \n",
    "    # 生成颜色样本\n",
    "    colors = []\n",
    "    for i in range(num_samples):\n",
    "        # 计算实际值\n",
    "        value = vmin + (vmax - vmin) * (i / (num_samples - 1))\n",
    "        r, g, b, a = get_color(value)\n",
    "        colors.append((r, g, b, a))\n",
    "    \n",
    "    # 创建自定义colormap\n",
    "    custom_cmap = ListedColormap(colors)\n",
    "    \n",
    "    # 直接创建colorbar\n",
    "    ColorbarBase(ax, \n",
    "                cmap=custom_cmap,\n",
    "                orientation='horizontal',\n",
    "                norm=plt.Normalize(vmin, vmax))\n",
    "    \n",
    "    # 设置刻度数量和格式\n",
    "    ax.locator_params(nbins=5)  # 设置大约5个刻度\n",
    "    ax.set_xticklabels([format_spec % val for val in ax.get_xticks()])\n",
    "    \n",
    "    # 设置标题（如果提供）\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "    \n",
    "    # 调整布局\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return fig\n",
    "\n",
    "def get_color(value):\n",
    "    if value == 0:\n",
    "        return (0, 0, 0, 0)\n",
    "    result = mpl.colormaps[\"hot\"](value)\n",
    "    _res = tuple([int(255 * x) for x in result])\n",
    "    r, g, b, a = _res\n",
    "    # return (r, g, b, a)\n",
    "    return (result[0], result[1], result[2], value*3)\n",
    "\n",
    "create_colorbar(get_color, vmax=0.3, width=5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vertex",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}