{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 选择配色方案"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 目录\n",
    "+ 使用color_palette()和set_palette()建立配色方案\n",
    "    + color_palette()可以接受的颜色参数形式\n",
    "    + sns.choose_colorbrewer_palette()能够以互动的方式测试、修改不同的参数\n",
    "    + Qualitative color palettes\n",
    "        + 使用循环颜色系统\n",
    "        + 使用Color Brewer调色板\n",
    "        + 手动指定循环颜色系统\n",
    "        + 使用来自xkcd color survey的颜色名字\n",
    "    + Sequential color palettes\n",
    "        + Sequential palettes中的cubehelix_palette()函数\n",
    "        + sns.choose_cubehelix__palette()能够以互动的方式测试、修改不同的参数\n",
    "        + Sequential palettes中的light_palette()函数和dark_palette()函数\n",
    "        + sns.choose_light_palette()能够以互动的方式测试、修改不同的参数  \n",
    "        + sns.choose_dark_palette()能够以互动的方式测试、修改不同的参数  \n",
    "    + Diverging color palettes\n",
    "        + Diverging palettes中的diverging_palette()函数\n",
    "        + sns.choose_diverging_palette()能够以互动的方式测试、修改不同的参数\n",
    "        \n",
    "        \n",
    "+ 使用set_palette()函数改变配色方案的默认设置\n",
    "\n",
    "\n",
    "+ 临时设置图表配色方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(rc={\"figure.figsize\": (6, 6)})\n",
    "np.random.seed(sum(map(ord, \"palettes\")))\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用color_palette()和set_palette()建立配色方案"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### color_palette()可以接受的颜色参数形式\n",
    "+ HTML十六进制字符串（hex color codes）\n",
    "    + color = '#eeefff'       \n",
    "\n",
    "+ 合法的HTML颜色名字（HTML color names）\n",
    "    + color = 'red'/'chartreuse'      \n",
    "\n",
    "+ 归一化到[0, 1]的RGB元组（RGB tuples）\n",
    "    + color = (0.3, 0.3, 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sns.choose_colorbrewer_palette()能够以互动的方式测试、修改不同的参数\n",
    "只能用于Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.choose_colorbrewer_palette(data_type, as_cmap=False)  \n",
    "+ data_type : {‘sequential’, ‘diverging’, ‘qualitative’}  \n",
    "This describes the kind of data you want to visualize. Note that you can pass substrings（e.g. ‘q’ for ‘qualitative）.\n",
    "+ as_cmap : bool  \n",
    "If True, the return value is a matplotlib colormap rather than a list of discrete colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.95755478915046244, 0.95755478915046244, 0.95755478915046244),\n",
       " (0.90120723387774304, 0.90120723387774304, 0.90120723387774304),\n",
       " (0.83289505032932054, 0.83289505032932054, 0.83289505032932054),\n",
       " (0.75021916137022127, 0.75021916137022127, 0.75021916137022127),\n",
       " (0.64341409276513495, 0.64341409276513495, 0.64341409276513495),\n",
       " (0.53871589525073182, 0.53871589525073182, 0.53871589525073182),\n",
       " (0.44032295626752516, 0.44032295626752516, 0.44032295626752516),\n",
       " (0.34288351570858677, 0.34288351570858677, 0.34288351570858677),\n",
       " (0.22329873945198808, 0.22329873945198808, 0.22329873945198808),\n",
       " (0.1046981975144031, 0.1046981975144031, 0.1046981975144031)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ab9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.choose_colorbrewer_palette('sequential', as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Qualitative color palettes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当需要区分离散的数据集，且数据集之间没有内在的顺序，最好使用qualitative (or categorical) palettes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.2980392156862745, 0.4470588235294118, 0.6901960784313725),\n",
       " (0.3333333333333333, 0.6588235294117647, 0.40784313725490196),\n",
       " (0.7686274509803922, 0.3058823529411765, 0.3215686274509804),\n",
       " (0.5058823529411764, 0.4470588235294118, 0.6980392156862745),\n",
       " (0.8, 0.7254901960784313, 0.4549019607843137),\n",
       " (0.39215686274509803, 0.7098039215686275, 0.803921568627451)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入Seaborn的同时，会引入默认的颜色循环，由6种颜色构成\n",
    "sns.color_palette() # 返回当前默认颜色循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYZJREFUeJzt2DEuRVEYRtH7PAyIUq+QiELCCBTqF61CK2qFEZAoRKLQ\nKxkQkWsCorv7Cmu1f/NVOydnMY7jAEBjbe4BAP+J6AKERBcgJLoAIdEFCIkuQGj9p+P7x+e4ubGs\ntgD8CSdPb8PN3tbiu9uP0d3cWA77q4dpVv0Cj1cHw9Ht6dwzJnN3fD28HBzOPWMyOw/3w8Xqce4Z\nkzi/2h9en8/mnjGZ7d3L4eTpbe4Zs/C9ABASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ\n6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE\nRBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcg\nJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCC3GcZx7A8C/4aULEBJdgJDoAoREFyAk\nugAh0QUIfQHQuR6AY7gr5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c3b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示当前默认颜色循环\n",
    "current_palette = sns.color_palette()\n",
    "sns.palplot(current_palette) # sns.palplot：plot the values in a color palette as a horizontal array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在默认颜色主题的基础上，有6个变种：deep、muted、pastel、bright、dark、colorblind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用循环颜色系统\n",
    "当需要区分的数据集超过颜色循环中的6种颜色时，最简单的方法是使用循环颜色系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9db0240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 最为常用的方法是使用'hls'循环颜色系统\n",
    "sns.palplot(sns.color_palette(\"hls\", 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa34668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可以通过sns.hls_palette函数控制'hls'循环颜色系统的亮度和饱和度\n",
    "sns.palplot(sns.hls_palette(8, l=.3, s=.8)) # l是亮度（lightness），s是饱和度（saturation）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJgAEggkAgWACQPAbQxckyJWg960AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab9e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'husl'循环颜色系统，在亮度和饱和度上分别更加平均\n",
    "sns.palplot(sns.color_palette(\"husl\", 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn2afJVM7zdw9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad39cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可以通过sns.husl_palette函数控制'husl'循环颜色系统的亮度和饱和度\n",
    "sns.palplot(sns.husl_palette(8, l=.9, s=.9)) # l是亮度（lightness），s是饱和度（saturation）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用Color Brewer调色板"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "来自[Color Brewer](http://colorbrewer2.org/)的颜色循环系统\n",
    "+ 缺点  \n",
    "在某些情况下颜色会循环重复\n",
    "+ 优点  \n",
    "某些颜色循环系统对色盲比较友好（尤其是红绿色盲）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYxJREFUeJzt2KFxVVEYRtFz34MqkidIAwyGJjDYGCw0gEgFETQAFoPF\n0AQmQwOJyKMKJjk0kIm7+zJkLXvMp/b8c5Y55wCgsdt6AMBTIroAIdEFCIkuQEh0AUKiCxB69tjj\n3f2c+91SbQH4LxxPDuP09+2D8Xw0uvvdMr5dHddZ9Q84f3U6Xlz82HrGam4u34zv15+2nrGat2cf\nx+uvL7eesYqf736NP18+bz1jNc/ffxjHk8PWMzbhewEgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE\nRBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcg\nJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoA\nIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxBa5pxbbwB4Mly6ACHRBQiJ\nLkBIdAFCogsQEl2A0F/EAR2rRYArWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaed6c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"Paired\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAPJ+A6nOKn97dSF1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad92908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"Set2\", 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 手动指定循环颜色系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYpJREFUeJzt2LEtRmEYhuHz89vBAHqFKA1AYgAajUSiNIhSItFoGEDC\nAEpR6A1gB+RYQHTnPsJ1tW/zVHe+fItxHAcAGitzDwD4T0QXICS6ACHRBQiJLkBIdAFCy5+OH++f\n43JttdoC8CdsPp8NL1vni+9uP0Z3ubY6XB0+TLPqFzi+2R22L1/nnjGZp5ON4eLmdu4Zkzk9PBje\n9nfmnjGJ9bvHYXvvaO4Zk3m6vx42n8/mnjEL3wsAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoA\nIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEF\nCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIku\nQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYDQYhzHuTcA/BteugAh0QUIiS5A\nSHQBQqILEBJdgNAXbbQeA7+ba4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf992b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n",
    "sns.palplot(sns.color_palette(flatui))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用来自xkcd color survey的颜色名字\n",
    "[954 named colors](http://xkcd.com/color/rgb/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JqeTpN3Mr0tZS2jl7j2MvsjpvT9vrop2ytBettPQJMw+shvkhs+k45DAmzJhJacLOp+WW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\nppAXEckxhbyISI79PyAqeMTqZ7/2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb197780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用sns.xkcd_rgb设置颜色名字\n",
    "plt.plot([0, 1], [0, 1], sns.xkcd_rgb[\"pale red\"], lw=3)\n",
    "plt.plot([0, 1], [0, 2], sns.xkcd_rgb[\"medium green\"], lw=3)\n",
    "plt.plot([0, 1], [0, 3], sns.xkcd_rgb[\"denim blue\"], lw=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAABGCAYAAACKcII3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAW5JREFUeJzt1yFKBWEYhtH/igruyOgCNBssZrtBsF1Bwe4ODGZdgNEd\nCQr+NrFo8jKPcA58aSa86WFmNeccABVbSw8A+E6UgBRRAlJECUgRJSBFlICUX6P09v4xxxjOOfen\ntz69meMH2z89GGOM3Z2tsX/x/Nsr/9rL9cGYT3tLz9iY1eHreLi/W3rGxhyfnI3Lx/OlZ2zE1dHt\nWJ/eLD1jEX7fgBRRAlJECUgRJSBFlIAUUQJSRAlIESUgRZSAFFECUkQJSBElIEWUgBRRAlJECUgR\nJSBFlIAUUQJSRAlIESUgRZSAFFECUkQJSBElIEWUgBRRAlJECUgRJSBFlIAUUQJSRAlIESUgRZSA\nFFECUkQJSBElIEWUgBRRAlJECUgRJSBFlIAUUQJSRAlIESUgRZSAFFECUkQJSBElIEWUgBRRAlJE\nCUgRJSBFlIAUUQJSVnPOpTcAfPGlBKSIEpAiSkCKKAEpogSkiBKQ8gnHyi9UhvTn+wAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1c99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用sns.xkcd_palette传入颜色名字列表\n",
    "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
    "sns.palplot(sns.xkcd_palette(colors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential color palettes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当数据集的范围从相对低值（不感兴趣）到相对高值（很感兴趣），最好使用sequential color palettes  \n",
    "\n",
    "常用于kdeplot或corrplot等函数  \n",
    "\n",
    "The Color Brewer library中有大量的sequential color palettes，以占主导地位的颜色命名（如Blues）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAY9JREFUeJzt2CFOXVEYRtF7X1Dk2YYBEASp7AgQZQQETToJBBrBJMjT\nTUfQio4ASSqaDqDBEmQPEyC4u28Da9ljPrXz58xjjAmAxmbtAQDviegChEQXICS6ACHRBQiJLkBo\n77XHf2OMzTxXWwDehO35bnr8evFiPF+N7maep99/n5ZZ9R84Otiffvx6WHvGYj4ff5hufv5Ze8Zi\nLk8Op7Pbu7VnLOLbl0/Tx6vva89YzP316bQ93609YxW+FwBCogsQEl2AkOgChEQXICS6ACHRBQiJ\nLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBI\ndAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC\nogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6AKF5jLH2BoB3w6ULEBJd\ngJDoAoREFyAkugAh0QUIPQOAIB2POdZCeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b3a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"Blues\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "添加后缀_r，倒置sequential color palettes的顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAZNJREFUeJzt2KFOlmEch+Hnc34DN06AQjAT9BQIbkY2j4JGMRFNFBpH\n4WZ0I3AKEMwGiifAho7wcgKO9t6Pk+uq//JL9549m2VZBgCNV7MHALwkogsQEl2AkOgChEQXICS6\nAKHXzx1/P/5Zdrc71RaA/8Ld/cM42Huz+dvt2ejubnfG9vTdOqv+AY8Xt+Pw8tPsGav5cfJ1HH8/\nnz1jNd8+fh5fbq5mz1jF2fsP4/rXz9kzVnO0/3bc3T/MnjGF7wWAkOgChEQXICS6ACHRBQiJLkBI\ndAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC\nogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQ\nEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBosyzL7A0AL4aX\nLkBIdAFCogsQEl2AkOgChEQXIPQEIWUfaqZ0tA8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c04c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"BuGn_r\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "添加后缀_d，使sequential color palettes颜色变深"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYRJREFUeJzt2DEuRVEYRtH7hDcdnUgoaHVCrxedGIXoRK8nOq1XkIjO\ndCiuCYju7ius1f7NV+2cnMU4jgMAjbW5BwD8J6ILEBJdgJDoAoREFyAkugCh9Z+OHx+f43K5UW0B\n+BOu31fD2ebe4rvbj9FdLjeG7eOTaVb9Aq93t8PuxeXcMybzfHkx7N88zj1jMk+nB8PRw9vcMyZx\nf7g1nK9e5p4xmau9neH6fTX3jFn4XgAIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQB\nQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqIL\nEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJd\ngJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoQW4zjOvQHg3/DSBQiJLkBIdAFCogsQEl2A\nkOgChL4Aro4ev4ooW0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9bf4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"GnBu_d\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "#### Sequential palettes中的cubehelix_palette()函数\n",
    "cubehelix调色板系统的亮度是线性变化的，优点在于打印后也能区分不同颜色，且对色盲友好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCAQTAALBBIDgD3EVJUg9tixWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e247f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"cubehelix\", 8)) # 通过cubehelix参数调用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEwACAQTAIJfldsjGDxlD4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9dee390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.cubehelix_palette(8)) # 通过sns.cubehelix_palette()函数调用，与通过cubehelix参数调用的结果有所区别（色域的宽度变小、明暗变化倒置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIJgAEAgmAAS/AI0RIxnluOCrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ddb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start参数（取值0到3）和rot参数（number of rotations，任意值，取值-1到1）\n",
    "sns.palplot(sns.cubehelix_palette(8, start=.5, rot=-.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQDABIPAFiKsldVt8QuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b76160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dark、light参数（控制endpoints的暗度和亮度）、reverse参数（倒置cubehelix_palette）\n",
    "sns.palplot(sns.cubehelix_palette(8, start=2, rot=0, dark=0, light=.95, reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9b81630>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD9CAYAAABz5fboAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE/5JREFUeJzt3V3IZdV9x/HfI06EODNx2mai6LxQpP8mJM1NICISkwsL\nlgYUxIvGEsa8NCGQYCRECfZO8aIRBRNoAyYSY6gGbQiBNCSVXAzVEKEghP6DGF9RGtOZ6Iw6HTNP\nL845zvHMOWfvvfZaa6919vdzM/PMPM9zls94vmedtdfee2t7e1sAgLqcNfQAAADdEW8AqBDxBoAK\nEW8AqBDxBoAKEW8AqNDZuR7o5KtH2JMIAB3t2LVna9mfM/MGgAoRbwCoEPEGgAoRbwCoEPEGgAoR\nbwCoEPEGgAoRbwCoEPEGgAoRbwCoEPEGgAoRbwCoEPEGgAoRbwCoEPEGgAoRbwCoEPEGgAoRbwCo\nEPEGgAoF38PSzM6S9C1JJumUpM+5+69jDQwAsFqfmffHJW27+2WSbpF0W5whAQCaBMfb3X8o6bPT\nDw9KOhJjQACAZsHLJpLk7qfM7DuSrpJ0TZQRAQAabW1vb/f+Jma2V9IvJb3X3V9f9jknXz3S/4EA\nYGR27NqztezPg5dNzOw6M7tp+uEbkv6oyYFLAEBifZZNHpL0bTP7xfT7fMndT8QZFgBgnSjLJm2w\nbAIA3UVfNgEADId4A0CFiDcAVIh4A0CFiDcAVIh4A0CFiDcAVIh4A0CFiDcAVIh4A0CFiDcAVIh4\nA0CFiDcAVIh4A0CFiDcAVIh4A0CFiDcAVIh4A0CFiDcAVKjPDYiBIhx/7pm3fn/uvgMDjgTIh3ij\nePNxjvG5BB6bgHijSF2C3ed7E3LUKijeZna2pHskHZT0Dkm3uvuPIo4LI5Qy2E2PScRRm9ADltdJ\netndPyLpSkl3xxsSxub4c88MEu7SxgB0Ebps8oCkB6e/P0vSyTjDwZiUGEtm4qhFULzd/TVJMrNd\nmkT8azEHhc1WYrQXHX/uGQKOogXv8zazfZL+Q9K97v6v8YaETVZDuGdYSkHJtra3tzt/kZm9R9Ij\nkr7g7o+0+ZqTrx7p/kDYGJsQQWbiGMKOXXu2lv15aLzvlHStpP+WtCVpW9KV7n5i1dcQ7/HahHDP\nI+LIKWq8QxDv8YkR7WNPv9T5a3YePL/343ZBzJES8UZWfcIdEuxVcod8GeKOPog3sgkNd8xoL1NC\nyJch7liHeCOLkHCnjvaiUiM+Q8wxj3gjqVJn201KD7lEzMeOeCOZGmbbTYg4SkW8kcQmhHtR6SEn\n4uNCvBFd7nAfffbIyr87b/+e4O/bRolBJ+LjQLwRVddwh0Z7XbDXSR3ztnJEn4hvNuKNaHKEOzTa\ny5QS8kUxw07ANxfxRhSpwx0z2suUGnKpf8wJ+GYi3ugt9Rp36nAvU2LM+0ScgG8e4o1eNjHcq5QS\n9NCIE/DNQrwRbEzh7iNV9EMiTsA3B/FGEMIdV2jgCfh4EW90RrjT6hpyAj5Oq+IdfBs0YFHpZ06W\n5uizRzq9gB17+qXOP+NNuxEGTgu9ezw2XOonfapZ9++ef6XV5737ot1JHj/E7GeRas2cmylvJpZN\ncIYcp73HinfbWMeSI/ptIs4Syniw5o1Wagl37mh31TfyBBwzxBut1HAGZenhXiYk5qkCLhHxmnDA\nEo1qOLhVY7ilybi7jr3NC13oQeIa/q2xHjNvSKpjuaTWcC/TZSaecgYuMQsv3aqZN7tNUIXY4X7h\nxWMr/+7CC3ZGfaxlfvf8K60DfvTZI40BP/b0S8EBZzdKnXrNvM3sw5Jud/ePNX0uM+9y5bqpQp+Z\nd594rwt1V7HDHnsGLjEL3zTRD1ia2Vck/b2kY+5+adPnE+8y5bpxcO5wxwz2OjFiXlrAJSJekhQH\nLJ+UdHWPr0elcp5J2TXcL7x4LFu4Yz1eirX8vv9Gx597hoOahQuOt7s/LOnNiGNBZrmenKGz7pBw\nD2UW8dAxtP1vTX06/SIiXi62CqJ6uWfbTXIEnIgjxm6TpesxKFuute5QbUMWEsqn/+cPjZ9zcO+7\nOn/febNxdV0Tj70LZV6fHSkz7EwpR4x4cyASK4UsmaQId5tgr/v80Ji/8OKx4gIu9TugOXvhJ+LD\n4iSdEerz9jfHLpM28W4b7q7RbhIa8ZBdKV1PqQ+5KiG7UsrHtU3wlpLjHSvcsaO9KCTipQZcIuIl\n49omkFT2NS1ibZlLHe7QxwhZnw+5HkrIu52+BzVL/v9qUxFvVKUpgDnCPf9YXR8vZGdM6EWtQiMe\nioDnRbxRjZLC3fdxc8zCpbCI95mFE/B8WPMemb5PrlRr3n3XursG9Knfv7zy7/78T/+s0/eaV+pa\n+EyOmx5LrIHHxJo3ouh7YCtUrJNwnvr9y2vDPf85TZ+3TM5ZeOhMvAtm4OUi3qhe22CGxDgk4rnW\nwqU86+EEvEzEG0mluiN6VyHh7vv1fSKe66BmW6Hr4JxWnw7xRmdDLZ0Mrc9SSuhySuqIMwuvF/Ee\nGQ4k9bepEW+LgJeBeCPIWGff8/pGPMeSSpeIE/C6EO8Ryj37LmXdO5XQiEv9Q94WAd88xBvBYs6+\nQ/ctl6TPFkMp/S6VtrPwXAEn4v1wks6IxXjydHnyNkVhXVhinF3Zd8dJqJwn/bQ94afNi2WXd0zc\n9DgdTtLBGWI8aXKtfce+a3tOfU/66TIb7zILb5JjBi6xjBKKmTeyzsDHOvteJnRG3nY23uYFL/YM\nXGIWHhszb6xU0hNmXUxizL77LGHEFjojbzsTbzMLjz0Dl5iF58LMG2/JddGqPrNvKc4FqkqagS/q\n8gLTZhYeawYusQ4+BO6kg1YIeDnaRrzkgEtEvC/ijdZqCHisa3uXHnCpXcRLD7jE5WVDEW90kuM+\nl23WUmsK+JP/+8Lav7/4Ty4M/t4lBlzi+uA5EG90RsDbawr3opCQxwh4zH3gM8zC0yLeCELAm3UN\n96IuIW8KeKzZt5T+zvUEvJ2o8TazLUnflPRBSW9I+rS7P7Xua4h3vWoIuNT/IOZQ8ZbKDbhUZsTH\nFPDY+7yvknSOu18q6WZJd4QODOXr80Rp+8Rs84Rvisi6ILUJ2pB7wGO8AHSR4qJWMzn2hbMfPDze\nl0n6iSS5+2OSPhRtRChSroA3RbxvwJsiXkPAm94hxDyJZybHTR66RnzsAQ+N925J8/+HvGlmnK25\n4XIEXGqehfcJuNQ8C+8a8D67SIZW+516xnx1wtDgviJp1/z3cfdTEcaDwhHwzZPjxsepL3I1xoCH\nxvuwpL+RJDO7RNIT0UaE4hHwM9U8+5by3L2+yyycgDcLjffDkk6Y2WFJX5d0Q7whoQZ9Ax7rQCYB\nXy7kXplSvoi3QcDXY583eqnhVHqp/17wrtsIQ3aPxNwuKHW/kcMqIVdzjHmp2a5bCTdtGyGXhEUS\nfZ8omzgDl7rPwmOHO6bQu9c3abuMwi6U5Zh5I5oSTuYpbQY+s2omnuo0eSnezHuZmCf5MANfj9Pj\nkcUYAi7VcT/MlPGeiXWqfZuAE++3Y9kEUZVwNmbqJRRpmK2EpYVbinfPTJZPuiPeiO7cfQeCI07A\nh3+srtquiccIOE4j3kiGgMdRcrjnxbpn5jrMvk8j3khqLAFPFdiQ75tryWSZkD3i85h9t0e8kdwY\nAi7FnSGHviAMGe62cs++NxXxRhapA96k6zWpF3UJeJ+I9/n6UsLdd/aNds4eegAYj3P3HUi2Bnne\n/j293nJfeMHOxugc3Puu1qedzwd43bbCWLP1UsKNfNjnjexCAl7THvDcSgx301JU333fXd6R1b7f\nm33eQASlhbK08cQQckPjMSLeyC5kJpRr7bvNGYMlBLPNXYGw2Yg3NkquWduQ4Sw92iFXIUR3xBtY\nUHJ8Sg93G313/sR6F1Y74o3R6RuPmZwhrWWZpLQXvtoPVq5DvIEl2kYoR1RriLbU7mcW6/KwIN7Y\nQEMEIEVga5ltS/lm3CyZnMZJOhild1+0u3HPd5sTd+bNhzZ0P3gtsZ4Z4qYMbW3ykolEvIEkaotw\nV11n2kPcjGHTsWyCKqS4GFGbA5elHYAb2oUX7Kwi3Js+65aYeQONZrEa8wWXUt1BHuGYeSO7HBfI\nb7t+2iUwY5yFh8y0pfY/V2bd4XrNvM3saknXuPsnIo0HyK7NwcuZsczCQ1+oYkZbItzrBMfbzO6U\n9NeS/ivecIB4ulwmdhadrhGXNi/ktYZ7bPosmxyW9PlYAwFS6Lr9LGSddra0ELrEUJLQJZISwj2m\nWbfUYuZtZtdLukHStqSt6a+H3P1BM7s88fiA3rreqKHrLHzRqgCWPEPPcUCScMfVGG93v0fSPRnG\nAqy08+D5vbYLhtxpZz5Ofe+7KJ0ZyFJiHnvr36Iu734Id3tsFUR2KW+Hts4sIiG3S4sdcqmMmBPu\nehFvVKPv7HtmPih9Qy7Fj3muiMc8tX1R6mhL4w63xD0sMZA+M+8UZ1tKYSFfJVbQU4U8Vbi7HiAm\n3M1W3cOSeGMwJQZ8JmbIpf4xjxXxlMskhDsN4o3ixFr3Th1yqayYh4S8pGhLhLsL4o0ixT5wmSPk\nM7GCHmOJZTHoffablzjblsYZbol4o2Cpd57kCnrfmMdaJw9V6mxbGm+4JeKNgg2xbXCm1LXznCFP\nuZNkhnCHI94o3pARn1fSbpaUES892hLhlog3KlFKwBfFDvqQIc8RbYlwx0K8UZVSIz4TK+Z91sm7\nxDz0xgiEe3jEG1UqPeIzMWIeeztiH31uBhzjUq7E+zTijY0whpgPGfG+d3An3PERb4xCaXHvOyPP\nFfK+0Zbi3TyBeL8d8QYW5A59aSGPEWwp7h1vCPeZiDfQQo6gD7k+HivYM7FvVUa8z0S8gQCbcvZn\nCinuMUm8z7Qq3lzPG1hjPiYpQj4LYG0R5+bAw2PmDXSUcjZeQ8RThZtZ93KrZt597h4PjNK5+w4k\nC83Og+cXPasteWxjw7IJECjlksp8JEuYjRPt8hBvIIJZyFOui8/kijnBLhvxBiJKGfGZVVENjTqR\nrhMHLIGESjvjs1QcrFyNA5bAAFIe3MS4sWwCZJB6v3jNeHELExRvM9st6T5JuyXtkHSjuz8ac2DA\npsqxLo7NF7ps8mVJP3P3j0o6JOkb0UYEjMRsSWXMM88x/7f3FbpscoekE9Pf75D0epzhAOO0GLEx\nzMoJdz+N8Taz6yXdIGlb0tb010Pu/riZnS/pu5K+mHSUwMhseswJd3/BWwXN7AOS7tdkvfunTZ/P\nVkEgnppjTri7iXpVQTN7n6QHJF3r7k/0GRiA7pYFsIagE+54gmbeZvZvkv5K0tOaLKUcdfer130N\nM28gv5KCTrjDcDMGYOSGCjnR7oebMQAjl/sgKNFOi3gDI5XirE+CnQ/xBrA2usvCTqSHR7wBrEWo\ny8RVBQGgQsQbACpEvAGgQsQbACpEvAGgQsQbACpEvAGgQsQbACpEvAGgQsQbACpEvAGgQsQbACpE\nvAGgQsQbACpEvAGgQsQbACpEvAGgQsQbACoUdBs0M3unpPsl7ZF0QtIn3f3FmAMDAKwWOvP+jKRf\nufvlkr4n6avxhgQAaBI083b3u8xsa/rhfklH4g0JANCkMd5mdr2kGyRtS9qa/nrI3R83s59Ler+k\nK5KOEgDwNlvb29u9voGZmaQfu/vF6z7v5KtH+j0QAIzQjl17tpb9edCat5ndZGbXTT88LunN0IEB\nALoLWvOWdI+ke83sU5q8AByKNyQAQJPeyyZtsWwCAN1FXTYBAAyLeANAhYg3AFSIeANAhYg3AFSI\neANAhYg3AFSIeANAhYg3AFSIeANAhYg3AFQo27VNAADxMPMGgAoRbwCoEPEGgAoRbwCoEPEGgAoR\nbwCoUOg9LItiZn8p6VFJe939/4YezyIze6ek+yXtkXRC0ifd/cVhR7Wcme2WdJ+k3ZJ2SLrR3R8d\ndlSrmdnVkq5x908MPZZ5ZrYl6ZuSPijpDUmfdvenhh3Vcmb2YUm3u/vHhh7LKmZ2tib3zj0o6R2S\nbnX3Hw06qCXM7CxJ35Jkkk5J+py7/zrFY1U/8zazXZL+SZMnSKk+I+lX7n65pO9J+urA41nny5J+\n5u4f1eTG0t8Ydjirmdmdkm6VtPQefwO7StI57n6ppJsl3THweJYys69oEptzhh5Lg+skvezuH5F0\npaS7Bx7PKh+XtO3ul0m6RdJtqR6o+nhL+hdNnhyvDT2QVdz9Lk0iI0n7JR0ZcDhN7pD0z9Pf75D0\n+oBjaXJY0ueHHsQKl0n6iSS5+2OSPjTscFZ6UtLVQw+ihQc0iaE06dbJAceykrv/UNJnpx8eVMLn\nejXLJmZ2vaQbJM2fEvqspO+7+xPTt6mDWxjn1vTXQ+7+uJn9XNL7JV0x4BDf0jDW8yV9V9IXBxyi\npLXjfNDMLh90cKvtlvSHuY/fNLOz3P3UUANaxt0fNrMDQ4+jibu/Jr31TvtBSV8bdkSrufspM/uO\nJu++rkn1OFWfHm9mv5H0vCZP6EskPTZ9u18sMzNJP3b3i4ceyypm9gFN1uhvdPefDj2edabx/gd3\n/7uhxzLPzL4u6T/d/QfTj5919/0DD2upaby/P13iKZaZ7ZP0kKS73f3eocfTxMz2SvqlpPe6e/R3\nsNXMvJdx97+Y/d7MfqtCZrSLzOwmSc+7+32Sjkt6c+AhrWRm79PkLeq17v7E0OOp2GFJfyvpB2Z2\niaTSf5ZFvHNdxczeI+nfJX3B3R8ZejyrmNl1ki5y99s1OQ73R00OXEZXdbwXzN5Sl+geSfea2ac0\nWa87NPB41rlNk4NXd02Xoo66ew1roqV5WNIVZnZ4+nHJ/+bS25cjS3SzpPMk3WJm/6jJeK909xPD\nDusMD0n6tpn9QpO+finVGKteNgGAsdqE3SYAMDrEGwAqRLwBoELEGwAqRLwBoELEGwAqRLwBoELE\nGwAq9P+2qr0xCbMpFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c85f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 默认返回颜色列表，设置as_cmap=True返回colormap对象，可以用于某些Seaborn或matplotlib函数\n",
    "x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T\n",
    "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sns.choose_cubehelix__palette()能够以互动的方式测试、修改不同的参数  \n",
    "只能用于Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.choose_cubehelix_palette(as_cmap=False)\n",
    "+ as_cmap : bool  \n",
    "If True, the return value is a matplotlib colormap rather than a list of discrete colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],\n",
       " [0.8888663743660877, 0.7106793139856472, 0.7158661451411206],\n",
       " [0.8314793143949643, 0.5987041921652179, 0.6530062709235388],\n",
       " [0.7588951019517731, 0.49817117746394224, 0.6058723814510268],\n",
       " [0.6672565752652589, 0.40671838146419587, 0.5620016466433286],\n",
       " [0.5529215689527474, 0.3217924564263954, 0.5093718054521851],\n",
       " [0.43082755198027817, 0.24984535814964698, 0.4439396089963985],\n",
       " [0.29794615023641036, 0.18145907625614888, 0.35317781405034754],\n",
       " [0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAABGCAYAAACzDYzYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAgAgQQAAVH8BRFomGHHFB10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb64f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.choose_cubehelix_palette(as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sequential palettes中的light_palette()函数和dark_palette()函数\n",
    "以某个颜色为种子，从明向暗或从暗向明渐变，产生sequential palettes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAXdJREFUeJzt2KFNREEYRtEZQhVbxBqaWEMHaErAkrWUgKYDDE1gKGLb\nGBoguHcfYc+xYz5182fmWmsA0LjZewDANRFdgJDoAoREFyAkugAh0QUI3f72uMZac8xqC8C/MM9z\nrOf1Yzx/je4cc1zGZZtVf8BhHMbH5WPvGZs5HU7j5fNl7xmbebp7Gg/vD3vP2MTb/ds4vh73nrGZ\nr8evMc/XedD5XgAIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoRE\nFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAk\nugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh\n0QUIiS5ASHQBQqILEBJdgJDoAoTmWmvvDQBXw6ULEBJdgJDoAoREFyAkugAh0QUIfQM61hsOZxh8\nEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d770f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.light_palette(\"green\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAX9JREFUeJzt2LFNw1AYRlEbIbmjo6PLDOyQHbJDVoiyAjtkB3ZghnR0\ndHSuzAIona8ROaf9m6+6enrjsiwDAI2HrQcA3BPRBQiJLkBIdAFCogsQEl2A0OOt4zzPyzRN1RaA\nf+E8nofTchp/u92M7jRNw263W2fVH3C9XofXl9etZ6zm4/Nj2D/vt56xmvev9+HwdNh6xiou35fh\nOB23nrGat/ltOI/nrWdswvcCQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ\n6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE\nRBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcg\nJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgNC7LsvUGgLvhpQsQEl2AkOgChEQXICS6ACHRBQj9\nAA39HaGPv8GIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d8e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.dark_palette(\"purple\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAX5JREFUeJzt2KFNBEEAhtFZcuJquCIwNHGGDtCUgCVYSkDTAYYmMBRx\nNSBIBk/Iuf2WwHt2Rvxrvkx2mXMOABoXWw8A+E9EFyAkugAh0QUIiS5ASHQBQrtzhx8fn3O/P3sF\ngG9OpzEOh7H8dHa2qPv9bizLwzqrfoE578fl5dPWM1bz/n47bm5etp6xmufn6/H4+Lb1jFXc3V2N\n19fT1jNWczwexunvft5Zfi8AhEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJ\nLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBI\ndAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC\nogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCy5xz6w0A/4aXLkBIdAFCogsQEl2AkOgChEQXIPQF\nvhYdaTooEcgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0658d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 倒置调色板的顺序\n",
    "sns.palplot(sns.light_palette(\"navy\", reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P8dizD1JeVca1V+7gnjvun9UoPzz5Hn/98DVc5C7s+tz/Y/OqK2dtfzHXs6PnLLll2eSW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\nsdb55OSSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c32518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 产生colormap对象，而非颜色列表\n",
    "pal = sns.dark_palette(\"palegreen\", as_cmap=True)\n",
    "sns.kdeplot(x, y, cmap=pal);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAY1JREFUeJzt2CFOXVEYRtF7SUM6ADDVlZgXXEVFR0ANlqTjqOw4SLCY\n1xEgKupIDRKNoQMgmMMEyHN3X8Jbyx7zqZ0/Zx5jTAA0DtYeALBPRBcgJLoAIdEFCIkuQEh0AUIf\ndj2OMcY8z9UWgHdhc7md/v34/mo8d0Z3nufp/9PzMqvegKOPh9Pfh8e1Zyzmy6fj6erufu0Zi7k4\n+Tz9/HO79oxF/Pp6Op3/vll7xmKuz75Nm8vt2jNW4XsBICS6ACHRBQiJLkBIdAFCogsQEl2AkOgC\nhEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQX\nICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6\nACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQmscYa28A2BsuXYCQ6AKE\nRBcgJLoAIdEFCIkuQOgF3ygeCssO4agAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f2be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# provide tuples in ``hls`` or ``husl`` space along with the default ``rgb``\n",
    "sns.palplot(sns.light_palette((210, 90, 60), input=\"husl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYlJREFUeJzt2CFKBHEchuG/IuxBtttEPIAgWOwK3sDiCWwWizcw2C2C\n4AFEbPY9yKbxBLtg2HcQnwd+aSZ86WWYvWmaBgCN/bkHAPwnogsQEl2AkOgChEQXICS6AKGt0V2v\n19MYwznn3C/u7up+GhscbHowxhiLxWIsl8ttr/xpq9VqHB0ezz1jZ76+P8fZyfncM3bm7eN1XJ5e\nzz1jJ57fn8bNxe3cM3bm8eVh3F3dzz1jFn4vAIREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoRE\nFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAk\nugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh\n0QUIiS5ASHQBQqILEBJdgJDoAoREFyAkugAh0QUIiS5ASHQBQnvTNM29AeDf8KULEBJdgJDoAoRE\nFyAkugAh0QUI/QAK42oQxhjT6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6fc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seed the palette with any valid ``xkcd`` color\n",
    "sns.palplot(sns.dark_palette(\"muted purple\", input=\"xkcd\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sns.choose_light_palette()能够以互动的方式测试、修改不同的参数\n",
    "只能用于Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.choose_light_palette(input='husl', as_cmap=False)\n",
    "+ input : {‘husl’, ‘hls’, ‘rgb’}  \n",
    "Color space for defining the seed value. Note that the default is different than the default input for light_palette().\n",
    "+ as_cmap : bool  \n",
    "If True, the return value is a matplotlib colormap rather than a list of discrete colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.94054458,  0.95945542,  0.95679586,  1.        ]),\n",
       " array([ 0.87363254,  0.90742758,  0.90267475,  1.        ]),\n",
       " array([ 0.80672051,  0.85539974,  0.84855364,  1.        ]),\n",
       " array([ 0.73741876,  0.80151376,  0.79249963,  1.        ]),\n",
       " array([ 0.67050672,  0.74948591,  0.73837852,  1.        ]),\n",
       " array([ 0.60120497,  0.69559993,  0.68232452,  1.        ]),\n",
       " array([ 0.53429294,  0.64357209,  0.62820341,  1.        ]),\n",
       " array([ 0.46499119,  0.58968611,  0.5721494 ,  1.        ]),\n",
       " array([ 0.39807915,  0.53765827,  0.51802829,  1.        ]),\n",
       " array([ 0.33116712,  0.48563043,  0.46390718,  1.        ])]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4gkeACCe4AEA4gkeACDeF6E+KdTtjNNXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f29438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.choose_light_palette(input='husl', as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sns.choose_dark_palette()能够以互动的方式测试、修改不同的参数  \n",
    "只能用于Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.choose_dark_palette(input='husl', as_cmap=False)\n",
    "+ input : {‘husl’, ‘hls’, ‘rgb’}  \n",
    "Color space for defining the seed value. Note that the default is different than the default input for dark_palette().\n",
    "+ as_cmap : bool  \n",
    "If True, the return value is a matplotlib colormap rather than a list of discrete colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.13333333,  0.13333333,  0.13333333,  1.        ]),\n",
       " array([ 0.15505626,  0.17201694,  0.16963164,  1.        ]),\n",
       " array([ 0.17677918,  0.21070054,  0.20592994,  1.        ]),\n",
       " array([ 0.19927793,  0.2507657 ,  0.24352462,  1.        ]),\n",
       " array([ 0.22100085,  0.2894493 ,  0.27982292,  1.        ]),\n",
       " array([ 0.2434996 ,  0.32951446,  0.31741759,  1.        ]),\n",
       " array([ 0.26522252,  0.36819806,  0.3537159 ,  1.        ]),\n",
       " array([ 0.28772127,  0.40826322,  0.39131057,  1.        ]),\n",
       " array([ 0.30944419,  0.44694683,  0.42760888,  1.        ]),\n",
       " array([ 0.33116712,  0.48563043,  0.46390718,  1.        ])]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBA8AEE/wAADxBA8AEE/wAADxvgAeYSnmLFDTJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaba8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.choose_dark_palette(input='husl', as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diverging color palettes\n",
    "当数据集的低值和高值都很重要，且数据集中有明确定义的中点时，最好使用diverging color palettes  \n",
    "\n",
    "例如，绘制温度相对于基准时间点的变化图，最好使用diverging colormap来同时显示温度相对于基准值的上升和下降  \n",
    "\n",
    "两端的颜色应该具有相似的亮度和饱和度，中间点的颜色不应该喧宾夺主"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xafa22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"BrBG\", 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e0b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"RdBu_r\", 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb17cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coolwarm调色板也是很好的选择，缺点在于中间的颜色和两端的颜色对比较少\n",
    "sns.palplot(sns.color_palette(\"coolwarm\", 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diverging palettes中的diverging_palette()函数\n",
    "diverging_palette()函数使用'husl'颜色系统，需要在函数中设置两个hue参数，也可以选择设置两端颜色的亮度和饱和度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFmoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1f5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.diverging_palette(220, 20, n=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ9cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaba8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.diverging_palette(145, 280, s=85, l=25, n=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAadJREFUeJzt2DFOlEEAhuHZjWxhsi2RC3gJLei4i6HxBnsDmw13obPQ\nS3gBDe0mFJjw01vQ+P5MIM9TzjRf92ZmsyzLAID/tZ09AIC3QVAASAgKAAlBASAhKAAkBAWAxLvn\nLh8fHpbtbvdSWwB4BS4Px/H9cL359/zZoGx3u/Hr09V6qyb7+PN23H27mT1jNedfv4y/v//MnrGa\ns4sP43Q6zZ6xiv1+P+5O97NnrOZ8/34cb3/MnrGa66vP4/JwnD3jxfnyAiAhKAAkBAWAhKAAkBAU\nABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQ\nFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQ\nEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAA\nkBAUABKCAkBisyzL7A0AvAFeKAAkBAWAhKAAkBAUABKCAkBCUABIPAFeNSDrRrVbFwAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb15a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sep参数controls the width of the separation between the two ramps in the middle region of the palette\n",
    "sns.palplot(sns.diverging_palette(10, 220, sep=80, n=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b3d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可以将中间点的颜色设置成暗色而非亮色\n",
    "sns.palplot(sns.diverging_palette(255, 133, l=60, n=7, center=\"dark\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sns.choose_diverging_palette()能够以互动的方式测试、修改不同的参数\n",
    "只能用于Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.choose_diverging_palette(as_cmap=False)\n",
    "+ as_cmap : bool\n",
    "If True, the return value is a matplotlib colormap rather than a list of discrete colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.25199714,  0.49873371,  0.57516028,  1.        ]),\n",
       " array([ 0.43026136,  0.62000665,  0.67878019,  1.        ]),\n",
       " array([ 0.60852558,  0.74127959,  0.7824001 ,  1.        ]),\n",
       " array([ 0.7867898 ,  0.86255253,  0.88602001,  1.        ]),\n",
       " array([ 0.95,  0.95,  0.95,  1.  ]),\n",
       " array([ 0.95457726,  0.76653099,  0.78032569,  1.        ]),\n",
       " array([ 0.91971827,  0.58735877,  0.61174   ,  1.        ]),\n",
       " array([ 0.88485928,  0.40818655,  0.44315432,  1.        ]),\n",
       " array([ 0.85104086,  0.23436275,  0.27960104,  1.        ])]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAABGCAYAAACzDYzYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAcNJREFUeJzt2TFqVFEAhtFvZEjllLapQrYQhWm0Su0OhDQJaOEKsgIL\nBW0C2UFqK20C6hZCqrQpx0oCLxuQ6Z43wXPae4u/ex/3LaZpCgD4vz0ZPQAAGE8QAACCAAAQBABA\nggAASBAAANVy2+Gfu7tpZ7n1CgDwSFytD9u//Lr429nWr/3OctnL00/zrHoAvp++7d35xegZs/n4\n5nVn336OnjGbo1fP+3V9M3rGLA72dttsNqNnzGa1WvX78sfoGbN5un7R7eez0TNm8+zkqJvj96Nn\nzGb3y4eu1oejZ/xzfhkAAIIAABAEAECCAABIEAAACQIAIEEAACQIAIAEAQCQIAAAEgQAQIIAAEgQ\nAAAJAgAgQQAAJAgAgAQBAJAgAAASBABAggAASBAAAAkCACBBAAAkCACABAEAkCAAABIEAECCAABI\nEAAACQIAIEEAACQIAIAEAQCQIAAAEgQAQIIAAEgQAAAJAgAgQQAAJAgAgAQBAJAgAAASBABAggAA\nSBAAAAkCACBBAAAkCACABAEAkCAAABIEAECCAABIEAAACQIAoFpM0zR6AwAwmBcCAEAQAACCAABI\nEAAACQIAIEEAAFT35vQm9XNsU6UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e03c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.choose_diverging_palette(as_cmap=False)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. _palette_contexts:\n",
    "\n",
    "Changing default palettes with :func:`set_palette`\n",
    "--------------------------------------------------\n",
    "\n",
    "The :func:`color_palette` function has a companion called :func:`set_palette`. The relationship between them is similar to the pairs covered in the :ref:`aesthetics tutorial <aesthetics_tutorial>`. :func:`set_palette` accepts the same arguments as :func:`color_palette`, but it changes the default matplotlib parameters so that the palette is used for all plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用set_palette()函数改变配色方案的默认设置\n",
    "color_palette()与set_palette()的关系，类似于axes_style()和set_style()的关系  \n",
    "\n",
    "set_palette()的参数与color_palette()相同  \n",
    "\n",
    "区别在于，set_palette()会改变配色方案的默认设置，从而应用于之后所有的图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sinplot(flip=1):\n",
    "    x = np.linspace(0, 14, 100)\n",
    "    for i in range(1, 7):\n",
    "        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o+pz1iLzJLJizS1+gl6cbf41JtPChsn8Zfd3TGNYHmSVju16EYS+SuzdsLIzj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6zfc/jQkfXwjeWugtkUxbt5uaWW9zrD370gCFyedROqERpxZ5yKM7Ew3A2pdRe6Nn6tdgAtOxND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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c85be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(\"husl\")\n",
    "sinplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 临时设置图表配色方案\n",
    "在with语句里使用color_palette()函数来临时设置图表配色方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tgVM025kRow0NJsLfl8MVVYyM236Kt/UfM8+O5ZCDudM/PsGv9+WjlMn48fpc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zPAQ2/tZYmzI0/csBYPRw0ejpqLlsWgHi1qwvx8OFlcTkNjB9opek/HR0ay72QW2fllhAVNX6Lf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\n1nHvWeIgWXkTVzVbw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d6cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette(\"PuBuGn_d\"):\n",
    "    sinplot()"
   ]
  }
 ],
 "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": "3.5.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "405px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}