{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "871617d6",
   "metadata": {},
   "source": [
    "# 基于函数的可视化操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ae75df",
   "metadata": {},
   "source": [
    "## 函数plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1368418",
   "metadata": {},
   "source": [
    "最常用的绘图函数为`plt.plot()`函数。例如，可以用`plt.plot()`函数绘制一条直线，并使用`plt.ylabel()`函数给图像加上一个y轴的标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a12e4466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b9fad9",
   "metadata": {},
   "source": [
    "通常，Matplotlib模块不会立即显示绘制的图形，以便后续向图形中添加更多的信息。只有在调用函数`plt.show()`后，绘制的图形才会显示。\n",
    "\n",
    "默认情况下，Python绘制的图形会以弹窗的形式显示，并提供了一些按钮对生成的图形进行一些简单的操作。\n",
    "\n",
    "如果使用Jupyter Notebook，plt.show()函数则可以省略，Python也不会弹出绘图窗口，而是在Jupyter Notebook的输出中内嵌图片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e55d446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'some numbers')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61deb346",
   "metadata": {},
   "source": [
    "plt.plot()函数可以绘制基本的点线图，它的使用方式主要有：\n",
    "\n",
    "```python\n",
    "plt.plot(x, y)\n",
    "plt.plot(x, y, format_str)\n",
    "plt.plot(y)\n",
    "plt.plot(y, format_str)\n",
    "```\n",
    "\n",
    "例如，指定x，y的参数作为点的x轴坐标和y轴坐标时，plt.plot()函数会用一条直线连接两个相邻的点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08ae7bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1167f6d90>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4], [1,4,9,16])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde3a7b5",
   "metadata": {},
   "source": [
    "不指定x参数时，x的值默认为range(len(y))。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c246ddcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11692df40>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,4,9,16])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4608e1",
   "metadata": {},
   "source": [
    "默认情况下，plt.plot()函数用蓝色实线连接两个相邻的坐标点。可以使用格式字符串即参数format_str控制plt.plot()函数绘制图形的格式。\n",
    "\n",
    "颜色控制符如下表所示。\n",
    "\n",
    "字符|\t颜色\n",
    "--|--\n",
    "'b'\t|蓝色，blue\n",
    "'g'\t|绿色，green\n",
    "'r'\t|红色，red\n",
    "'c'\t|青色，cyan\n",
    "'m'\t|品红，magenta\n",
    "'y'\t|黄色，yellow\n",
    "'k'\t|黑色，black\n",
    "'w'\t|白色，white\n",
    "\n",
    "点线控制符如下表所示。\n",
    "\n",
    "字符|\t类型\t|字符\t|类型\n",
    "--|--|--|--\n",
    "'-'\t|实线\t|'--'\t|虚线\n",
    "'-.'\t|虚点线\t|':'\t|点线\n",
    "'.'\t|点\t|','\t|像素点\n",
    "'o'\t|圆点\t|'v'\t|下三角点\n",
    "'^'\t|上三角点\t|'<'\t|左三角点\n",
    "'>'\t|右三角点\t|'1'\t|下三叉点\n",
    "'2'\t|上三叉点\t|'3'\t|左三叉点\n",
    "'4'\t|右三叉点\t|'s'\t|正方点\n",
    "'p'\t|五角点\t|'*'\t|星形点\n",
    "'h'\t|六边形点1\t|'H'\t|六边形点2\n",
    "'+'\t|加号点\t|'x'\t|乘号点\n",
    "'D'\t|实心菱形点\t|'d'\t|瘦菱形点\n",
    "'_'\t|横线点\t| |\t\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae46496",
   "metadata": {},
   "source": [
    "可以单独或者组合使用颜色和点线控制符来控制绘图的格式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e65ecd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1169a44c0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4], [1,4,9,16], 'ro')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ac9ee3",
   "metadata": {},
   "source": [
    "图的显示范围可以通过plt.axis()函数控制："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "210867a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 6.0, 0.0, 20.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n",
    "plt.axis([0, 6, 0, 20])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105419d0",
   "metadata": {},
   "source": [
    "还可以同时传入多组数据，将其绘制在同一张图上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a40c9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "675a59f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0., 5., 0.2)        # 数组也可以作为plt.plot()函数的参数\n",
    "plt.plot(t, t, 'r--',             # 红色虚线\n",
    "         t, t ** 2, 'bs',         # 蓝色正方形点\n",
    "         t, t ** 3, 'g^')         # 绿色上三角点\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c458f19",
   "metadata": {},
   "source": [
    "plt.plot()函数还支持使用关键字参数来控制绘图的格式。例如，可以使用参数linewidth改变线条的宽度，参数color改变颜色："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97f288fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-np.pi,np.pi)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, linewidth=2.0, color='r')   # 线宽2，颜色红色\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e366d5",
   "metadata": {},
   "source": [
    "可以通过函数`plt.subplot()`将多张子图画在同一张图片上，其用法为：\n",
    "```python\n",
    "plt.subplot(rows, cols, fignum)\n",
    "```\n",
    "\n",
    "表示在一个`rows`×`cols`大小的画布上，在第`fignum`张图上进行可视化操作，其中`fignum`的计数从1开始。比如`plt.subplot(3, 4, 8)`时，对应的子图排序是：\n",
    "\n",
    "```\n",
    "1,2,3,4\n",
    "5,6,7,8\n",
    "9,10,11,12\n",
    "```\n",
    "\n",
    "当绘制子图的总数小于10时，可以使用一个三位数字替代上面的用法，如`plt.subplot(3, 3, 1)`可以写成`plt.subplot(331)`。\n",
    "\n",
    "一个绘制两个子图的例子如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "567e0ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "plt.subplot(211)                            # 定位到第1张子图\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "plt.subplot(212)                            # 定位到第2张子图\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a6a2745",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}