{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei'] #或者把\"SimHei\"换为\"KaiTi\"\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "program_data = [[\"towers\", 1880, 10152, 18.52, 10144, 100, (10144/10152)*100, 0.0788],\n",
    "                [\"dhrystone\", 4384, 286216, 1.53, 286080, 100, (286080/286216)*100, 0.0475],\n",
    "                [\"vim\", 126712, 3528864, 3.59, 3528518, 100, (3528518/3528864)*100, 0.0098],\n",
    "                [\"bash\", 35548, 2818904, 1.26, 2818690, 100, (2818690/2818904)*100, 0.0076],]\n",
    "# {\n",
    "# \"测试程序\" : \"towers\",\n",
    "# \"原程序体积\" : 10152,\n",
    "# \"优化后体积\" : 10144,\n",
    "# },\n",
    "\n",
    "data = pd.DataFrame(program_data, columns=[\"测试程序\", \"数据段体积\", \"原程序体积\", \"数据段占比\",\n",
    "                                           \"优化后体积\", \"原体积比\", \"缩减后体积比\", \"代码缩减效率\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>测试程序</th>\n",
       "      <th>数据段体积</th>\n",
       "      <th>原程序体积</th>\n",
       "      <th>数据段占比</th>\n",
       "      <th>优化后体积</th>\n",
       "      <th>原体积比</th>\n",
       "      <th>缩减后体积比</th>\n",
       "      <th>代码缩减效率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>towers</td>\n",
       "      <td>1880</td>\n",
       "      <td>10152</td>\n",
       "      <td>18.52</td>\n",
       "      <td>10144</td>\n",
       "      <td>100</td>\n",
       "      <td>99.921198</td>\n",
       "      <td>0.0788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dhrystone</td>\n",
       "      <td>4384</td>\n",
       "      <td>286216</td>\n",
       "      <td>1.53</td>\n",
       "      <td>286080</td>\n",
       "      <td>100</td>\n",
       "      <td>99.952483</td>\n",
       "      <td>0.0475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vim</td>\n",
       "      <td>126712</td>\n",
       "      <td>3528864</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3528518</td>\n",
       "      <td>100</td>\n",
       "      <td>99.990195</td>\n",
       "      <td>0.0098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bash</td>\n",
       "      <td>35548</td>\n",
       "      <td>2818904</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2818690</td>\n",
       "      <td>100</td>\n",
       "      <td>99.992408</td>\n",
       "      <td>0.0076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        测试程序   数据段体积    原程序体积  数据段占比    优化后体积  原体积比     缩减后体积比  代码缩减效率\n",
       "0     towers    1880    10152  18.52    10144   100  99.921198  0.0788\n",
       "1  dhrystone    4384   286216   1.53   286080   100  99.952483  0.0475\n",
       "2        vim  126712  3528864   3.59  3528518   100  99.990195  0.0098\n",
       "3       bash   35548  2818904   1.26  2818690   100  99.992408  0.0076"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'bar_label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-51069a34305b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"缩减后体积比\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtick_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"测试程序\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'优化后体积'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'edge'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'edge'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Original code size(%)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Reduced code size(%)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lower right'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'bar_label'"
     ]
    },
    {
     "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": [
    "\n",
    "total_width, n = 0.6, 2\n",
    "width = total_width / n\n",
    "x = list(range(len(data[\"测试程序\"])))\n",
    "p1 = plt.bar(x, data[\"原体积比\"], width=width, label='原程序体积')\n",
    "for i in range(len(x)):\n",
    "    x[i] = x[i] + width\n",
    "p2 = plt.bar(x, data[\"缩减后体积比\"], width=width, tick_label=data[\"测试程序\"], label='优化后体积')\n",
    "plt.bar_label(p1, label_type='edge')\n",
    "plt.bar_label(p2, label_type='edge')\n",
    "plt.legend([p1,p2],['Original code size(%)','Reduced code size(%)'], loc='lower right')\n",
    "plt.ylim(99.9,100.01)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "p1 = plt.bar(data[\"测试程序\"], data[\"代码缩减效率\"], width=width, label='代码缩减效率')\n",
    "plt.bar_label(p1, label_type='edge')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# 第1个坐标轴\n",
    "ax1 = fig.add_subplot()\n",
    "p1 = ax1.bar(data[\"测试程序\"], data[\"代码缩减效率\"], width=width)\n",
    "plt.bar_label(p1,label_type='edge')\n",
    "plt.ylabel('代码缩减效率（%）')\n",
    "ax1.legend(loc=\"best\")\n",
    "\n",
    "# 第2个坐标轴\n",
    "ax2 = fig.add_subplot(sharex=ax1, frameon=False)\n",
    "p2, = ax2.plot(data[\"测试程序\"], data[\"数据段占比\"], \"r\", marker='*', ms=10)\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position(\"right\") # 画在右边\n",
    "plt.ylabel('程序数据段占比（%）')\n",
    "\n",
    "plt.legend([p1,p2],['代码缩减效率','程序数据段占比'])\n",
    "\n",
    "\n",
    "# x轴\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "\n",
    "pic1 = fig.add_subplot(1,2,2)\n",
    "\n",
    "total_width, n = 0.6, 2\n",
    "width = total_width / n\n",
    "x = list(range(len(data[\"测试程序\"])))\n",
    "p1 = pic1.bar(x, data[\"原体积比\"], width=width, label='原程序体积')\n",
    "for i in range(len(x)):\n",
    "    x[i] = x[i] + width\n",
    "p2 = pic1.bar(x, data[\"缩减后体积比\"], width=width, tick_label=data[\"测试程序\"], label='优化后体积')\n",
    "plt.bar_label(p1, label_type='edge')\n",
    "plt.bar_label(p2, label_type='edge')\n",
    "plt.legend([p1,p2],['原程序体积占比（%）','缩减后体积占比（%）'])\n",
    "plt.ylim(99.9,100.03)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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