Maltplotlib绘图.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNDk61iIvHPN0j39111YTdq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/leiyi-ihb/ColabPractics/blob/main/Maltplotlib%E7%BB%98%E5%9B%BE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaJMfpZowrha"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Maltpltlib是常用的python绘图工具包，这里对该包进行一般绘图进行讲解，尽量做到输入数据就能出图\n",
        "### plot\n",
        "参考:https://zhuanlan.zhihu.com/p/33270402"
      ],
      "metadata": {
        "id": "gRRCZNzMxZDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.patches import Shadow\n",
        "import matplotlib.pyplot as plt\n",
        "'''\n",
        "matplotlib的初级之 ---> plot图\n",
        "Processing: 创建数据 --> 创建画板 --> 画图并装饰 --> 注释和标题 --> 图例 --> 坐标轴 --> show.plit()\n",
        "'''\n",
        "\n",
        "# step01：定义x和y坐标轴上的点【如果x,y都有，则元素个数必须相等】\n",
        "x=[1, 2, 3, 5] #x坐标轴上点的数值\n",
        "y=[1, 4, 9, 16] #y坐标轴上点的数值\n",
        "\n",
        "# step02：创建画板。这个是第一个figure对象\n",
        "plt.figure() #无参数默认画板1\n",
        "\n",
        "# step03：使用plot绘制线条及其装饰\n",
        "# 基本参数：第1个参数是x的坐标值，第2个参数是y的坐标值\n",
        "'''\n",
        "color：线条颜色，值r表示红色（red）\n",
        "marker：点的形状，值o表示点为圆圈标记（circle marker）\n",
        "linestyle：线条的形状，值dashed表示用虚线连接各点\n",
        "linewidth: 线宽\n",
        "label：图例显示，需通过plt.legend()显示\n",
        "'''\n",
        "plt.plot(y, label=\"Line plot\") #如果只有一个坐标，那么一定就是y轴, x轴就是默认\n",
        "plt.plot(x, y, color=\"red\", marker=\"o\", linestyle=\"dashed\", linewidth=3, label=\"DashLine\")\n",
        "\n",
        "# step04: 添加注释和标题等\n",
        "plt.xlabel('x label') #x轴文本\n",
        "plt.ylabel('y label') #y轴文本\n",
        "plt.title('Title') #标题\n",
        "\n",
        "#一种特殊的注释\n",
        "plt.annotate('here', xy=(2,5), xytext=(2, 10),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.01),\n",
        "            ) #添加注释 参数名xy：箭头注释中箭头所在位置，参数名xytext：注释文本所在位置，arrowprops在xy和xytext之间绘制箭头, shrink表示注释点与注释文本之间的图标距离\n",
        "'''\n",
        "更为详细的注释\n",
        "plt.annotate(r'$2x+1 = %s$'% y0,xy = (x0,y0),xycoords = 'data',\n",
        "             xytext=(+30,-30),textcoords = 'offset points',fontsize = 16\n",
        "             ,arrowprops = dict(arrowstyle='->',\n",
        "                                connectionstyle=\"arc3,rad=.2\"))\n",
        "'''\n",
        "\n",
        "# step05：图例，一定是与plit.plot()里边的label参数配合的\n",
        "plt.legend(loc = 'upper right', labels = ['up','down'])\n",
        "# 如果不配合使用，就要下面这样写\n",
        "# plt.legend(handles = [l1,l2],labels = ['up','down'],loc = 'best') #l1和l2分别表示画的两条线\n",
        "legend = plt.legend(labels = ['hu','tang'],loc = 'upper right',shadow = True)\n",
        "frame = legend.get_frame()\n",
        "frame.set_facecolor('r') # 图例加颜色 \n",
        "\"\"\"\n",
        "legend( handles=(line1, line2, line3),\n",
        "           labels=('label1', 'label2', 'label3'),\n",
        "           'upper right')\n",
        "    shadow = True 设置图例是否有阴影[一般不需要]\n",
        "    The *loc* location codes are::\n",
        "          'best' : 0,         \n",
        "          'upper right'  : 1,\n",
        "          'upper left'   : 2,\n",
        "          'lower left'   : 3,\n",
        "          'lower right'  : 4,\n",
        "          'right'        : 5,\n",
        "          'center left'  : 6,\n",
        "          'center right' : 7,\n",
        "          'lower center' : 8,\n",
        "          'upper center' : 9,\n",
        "          'center'       : 10,\n",
        "\"\"\"\n",
        "\n",
        "# step05：坐标轴\n",
        "#设置图形的4个边框\n",
        "ax = plt.gca()\n",
        "ax.spines['top'].set_color('none')\n",
        "# ax.spines['bottom'].set_color('none')\n",
        "# ax.spines['left'].set_color('none')\n",
        "ax.spines['right'].set_color('none')\n",
        "\n",
        "#示图范围\n",
        "'''\n",
        "axis：坐标轴范围，如果不定义就是出全图\n",
        "语法为axis[xmin, xmax, ymin, ymax]，\n",
        "也就是axis[x轴最小值, x轴最大值, y轴最小值, y轴最大值]\n",
        "'''\n",
        "plt.axis([0, 6, 0, 20])\n",
        "\n",
        "# FinalStep：显示图形\n",
        "plt.show() # 如果不使用plt.show()图表是显示不出来的，因为可能你要对图表进行多种的描述，所以通过显式的调用show()可以避免不必要的错误"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OKAjZSJIyR2B",
        "outputId": "7b502b30-56ac-40f5-fb56-2ce1a678a9ab"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}