{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 数据绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 二列数据绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据文件中的数据绘制数据曲线，一般的做法是打开文件，读取文件中的数据到列表中，再绘制数据曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "1. 打开文件创建文件对象：\n",
    "with open('文件路径/文件名', 'r', encoding='utf-8') as 文件对象名:\n",
    "2. 遍历文件对象\n",
    "for line in 文件对象名:\n",
    "3.当前行切分为列表\n",
    "line.strip().split('分隔符')\n",
    "4.列表元素转数值类型： float(x)\n",
    "5.绘制曲线：plt.plot(x, y)       \n",
    "6. 显示绘制结果： plt.show()                              # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  # 使jupyter中可以使用matplotlib绘制图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例 9.4 读文件绘制数据曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有一包含两列数据的9.4 DOS.csv文件，两列数据分别代表x,y坐标，利用文件中的数据绘制数据曲线（数据文件可通过扫描二维码下载）。下面仅给出文件前几行数据："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"images/ch9/9.4 dos.csv\" target=\"_blank\">9.4 dos.csv</a>\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "-58.55742805,0\n",
    "-58.54690537,0\n",
    "-57.47359261,0.00011105\n",
    "-57.46306993,0.000141095\n",
    "-57.45254726,0.000178771\n",
    "-57.44202458,0.000225883\n",
    "......\n",
    "20.78353541,0.000164749\n",
    "20.79405809,0.000146059\n",
    "20.80458076,0.000129131\n",
    "20.81510343,0.000113849\n",
    "20.82562611,0.000100099"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "欲利用文件中的数据绘制曲线，需打开并逐行读取文件中的数据。  \n",
    "要注意读取每行数据时，行末有一个换行符“\\n”需要用replace()替换掉或用strip()函数过滤掉。  \n",
    "读取的数据是字符型，需要转换成float()函数或eval()函数转换为数字型。  \n",
    "再将其附加到列表中，构建分别包含x和y坐标的两个列表，再利用matplotlib绘图。  \n",
    "绘图结果如图所示。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with open('images/ch9/9.4 dos.csv', 'r', encoding='utf-8') as file:  # 打开文件创建文件对象file\n",
    "    ls = [line.strip().split(',') for line in file]                   # 逐行读取文件中的数据创建二维列表\n",
    "\n",
    "x_ls = [float(x[0]) for x in ls]        # 列表x_ls为ls子列表中序号为0的元素的浮点类型\n",
    "y_ls = [float(x[1]) for x in ls]        # 列表y_ls为ls子列表中序号为1的元素的浮点类型\n",
    "plt.plot(x_ls, y_ls, linewidth=1)       # 绘制数据曲线\n",
    "plt.show()                              # 显示绘制结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这种方法容易理解，但稍显繁琐，可利用列表推导式对其进行简化。  \n",
    "打开文件后，先将每行数据去掉行末的换行符后切分为列表，存入列表ls中，再用列表推导式将ls中的元素分别放入lsx、lsy两个列表中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def read_csv(file):\n",
    "    with open(file) as data:\n",
    "        ls = [line.strip().split(',') for line in data]  # 读文件到二维列表\n",
    "    return ls\n",
    "\n",
    "\n",
    "def plot_dos(ls):\n",
    "    x = [float(x[0]) for x in ls]  # 列表推导式\n",
    "    y = [float(x[1]) for x in ls]\n",
    "    plt.plot(x, y)  # 绘制数据曲线\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    lst = read_csv('images/ch9/9.4 dos.csv')\n",
    "    plot_dos(lst)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E5TDDHzpHqc0ROC4jq2sKPQJCRovfIxAhWBuIEAjKqeUItBR7BC4ja2hKZg15OQINmhbfdNOq7QmA9BGsDUQIBOVIjqDuEagYuhd4BIamRmjaUbFc6DEmpwW1iBAIynFDIybSKgQ1j0BJH4F3UdY0irV8dCxvwJQhd2sCEQJBOfWGsvQur3d9IVAhhLbLyGgaDC2+Md+BEFgSGloTiBAIymFmENLuEbh+sljBsR2vs1ij+DyCqu1iLJeR0NAaQYRAUA7XdhanN1nsKEwW2y4jo1MyHoEIwZogESEgokki+jwR3UtE9xDRc5KwQ4gHlxmaRiCk2SPwQ0MqcgSOlyPQY80RuBjLZyRHsEZYUQiI6KtE9HoiGo34vB8B8HVmPgvAeQDuifj4whARzhGkdC+N0j4C2+8s1rX4hLZiOxgXj2DNsJpH8M8AXgrgYSL6HBH9NBFlBzkhEU0AeD6AjwMAM5vMPDfIMYXhRqqG1PYR2H5nsa5RbA1lVcuVZPEaYkUhYOb/ZObXAtgF4AsAfhHAY0T0SSJ6cZ/nfBKAGQCfJKLbiOhjRFRsfhIRXUFEe4lo78zMTJ+nEoYBaShT20dgu27NI4ivj8DBWF6SxWuFrnIEzFxi5s8y808D+EkAzwDQ7546A8AFAP6Rmc8HsAzgyjbnvIqZ9zDznunp6T5PJQwDLgMEaShT7hEQxdZZXLEcjHboI7j94By+dNuhWOwQoqErISCiTUT0NiK6EcCXAXwD3sW8Hw4BOMTMN/tff36AYwmnAEHVUJr3ETgqk8X+PgJdj08IqrbbsWroXZ+/A7/92TtisUOIBmOlB4noVwG8FsBZ8C7Y72TmGwc5ITMfJaKDRHQmM98H4BIAdw9yTGG48aaPBjmCpK1JhnofgarQUHwege24cJkxktVbhMB1GYdmywC8eUQ5Q1dujzA4q3kEzwHwQQDbAdwC4EUAQEQ7iehZA5z3bQA+Q0R3wgszfWCAYwlDDkuyOBQaUnNsQ/NzBDEIQdV2kTN0ZPVWIThZMpEzNGwYzdX2GgvDz4oeATO/GQCI6B8BuABeCOD/AViElzy+sJ+TMvPtAPb0873CqYfLQCblDWUq+wisUNVQHEJQsRzkMxoyOsG0G893fKmKDaM5uMyYL1vYOJ5Xbo8wOCsKQYhnM/MFRHQbADDz7KBlpEJ6CBrK0rqPwHUZzEBGV5MsdvzQkBFTQ1ngEWQMrcUjmFmsYnosh7LlYKEiHsGpQrdCYBGRDr8diIim4XkIgrAqwbVJU7Sha9gJkrmaohi+5TB0Lb6GssAjyOqtQhB4BAsVC/NlEYJThW5HTHwUwJcAbCSi9wP4HiSuL3QJI5wjSNqa+HGZ/REQULaPIKPH11BWtV3kMzoMjVoW05xYMrGumEUxZ2CxYiu3RYiGrjwCZv4MEd0Kr8KHALySmWUshNAV9aFz6UwW18o7SU3DlxWaNRRHQ1nFcpAzNC801CRsy1VvGN1ixUZVuo5PGboNDYGZ7wVwr0JbhDWK6wYeQTr3ETjBmGhF00E9j0CDTgTLUb9DuGq7yGV0ZHUNdlNoqGTZmChkkM9oqMo+41MGGUMtKKc+dC6+McnDRG0EhCKPwHb90JNOSvYdNFPzCNrkCEpVB8WsgXxGR8USj+BUQYRAUE596Fw6y0cdt+4RqFpMk6mNmFB/8Q1yBBmdYDXlJEqmg0JWRz6joSIewSmDCIGgHGYGpTxHoBMpq+rxdhYHDWWRH74Fr2pIR0bXWpLFZcv2PAJDR8UWIThVECEQlMPwwkKU0llDgUegagSEt48gaChTrwSLFRujOQPZNn0Ey1UHI1l9oNAQM+PQbCkKU4UuESEQlOMGY6iJUrmXxvEv1Jqizl/bcWFoXkNZHFVDixUb43kDhkYtQlA2PSHIDRAauu3gHC7+82+n8kNDUogQCMrxksWU6oYyzyNQ8/MHG8pUCU0zS1ULoznD7yxuKh81bYwEoaE+PYKK6fjHktBSXIgQCMoJhs5RShvKHFftLKBgH4ERkxAslG2M5Y22ncVlP1mcy2io9pkjKPuexEIEncmLFQtfvevIwMdZ64gQCMpxXdRCQ+n0CLxkrqYsWeyHniieWUNHFyrYPJFvXz5qOijmBssRBN8XxayiG+6fwW985kepLFvuBRECQTmuXzWU2oYyV+0GsVqOQFffp/G1u47gurufwGnri9A1AoCGn2nZtDGSMZAzBvcIohhREexGkLlHKyNCICiHgfpimhR+MrMV9xEE+wji8AhuOziHl567BWdtHgOABq+AmeuhIUNvKS3tlkAIouhDCATgxHJ14GOtZUQIBOW4tRxBuhvKdEWhMcsPDRmapjz0dmS+ghfv3gQizxvI6BpMXwhMxwURkDU0ZA2t71lDQbI4is7kRT+8tCAD8FZEhEBQTn1ncTpzBDUhUJTMdVzX31AG5dNHS1UbI9n6+smMTrD8C37ZdFDIeI/ljNZms26pROgRBOGlUlUqkFai66FzgtAv4RETaawNd/zOYk1Rnb/l1IVGtdB6yeD6ZSOja7VwVPixKHIEUQjBQtkCEVAyxSNYCRECQTm1oXNaOstHLae+XF7lPgIiKM8RlCwvBxAQHjNRtsIegd53aKgmBBGMsV6s2Ng8nkdJehJWREJDgnK8qiFK7ayh2phoDYo2lAX7CDTlfQTNoaHwmIkgURzc339oyI1sjPVixcamkBCksVihG0QIBPXUcgTpTBbXqoYUCWF4H4FyITC9MdMB4QmkjR7BAMliy8HUSDaa0FDF8j0CLzT03D//P3xu78GBj7vWECEQlFPPEaRzeX3Q+auyszjIEagODZXbhIbaeQS5TP8eQdl0MFHIRFQ1ZGPzRN0jODJfwfcfPD7wcdcaIgSCcupD59I6a8itzwJS8OPbrlvbWaw69LHcFBoyQuWjYY8gq/efLK7YnkdQHtAjcFxGybQxPZZryBGk0StdDRECQTnB0LnUzxpSlCz2Qk+aco/AcRmm4yJvhHIEzeWjvkgYugYialll2Q1l08FUMTNwaGipYqOYM1DM6iiHqoYkT9CKCIGgHA6FhlLpEThq+wis0NA5la9v8Ilf80dLAI3lo2GPAAi8gt6FoGI5mChkBw4NLVQsjOczGMkaKJlOzUNpno8kJCgERKQT0W1E9JWkbBDiwWWA4P1LoQ7A9ss7NUU7ix3X9auSSGlDWclsDAsBjZ3FJbMxf9BvnqBiuZgayfQdWgpYqFgYyxvIZ3WULKfWXBZFWepaI0mP4B0A7knw/EJMMDM0DdC0tDaUufWGL8XJYpUeQanaeKEH/GSxf2GtROQRlGtVQ4NdsBcr3rjskYyOsunUxlrLLuVWEhECItoO4KUAPpbE+YV4qS+mSWeOwAvd+H0ESmYN1ZPFKnMEzaWjAJA1QuWjZqMQ9LuToGw5mIjAI/CEIIORrI6Sadc9AhGCFpLyCD4M4F0AOko+EV1BRHuJaO/MzIwSI76x/ygeeGJRybGFOm7DYpr0KUGQLFY1fdUJewQKhaBs2S0egaHVy0ebQ0PZNsvtu6FiOpgsZFAd2COwMJ43UMjqKFsuFioWNo7lUJYu4xZiFwIiehmAY8x860rPY+armHkPM++Znp5WYsuv/eut+NP/keiUaupD55LLESQ5j95yXejBcnlF00eDhjKVHkGwmD5Mprl8NJwj6HPMRMV2MFXMohKBRzCaNzCSNVA2bSyUbWwczw183LVIEh7BcwG8gogeBXAtgBcS0b8lYAcARNLGLqyMm3DV0PGlKs77k2/2VcoYBY5T9wjU7SNQV5UUUDIdjLQNDdVzBCPZ5tBQbz+w5bhwXMZozhg4hLNYsUKhIQeLFQsbx/Iom5IsbiZ2IWDmdzPzdmbeBeA1AP6PmV8ftx0B/bbBC93jMoOQXENZMIL45LIZ+7kBf5WkX+evInRjOW7t+CqFoGy1rxoKKpVKpt0mWdzbxTxIOOcz/Q+t+/QPHsWjx5exWPWSxYWsnyz2Q0OSI2gl9X0EUlOsHk64oSwIXSxVkxlFbLtu/RO7ip3FTrCYRs3xAzqFhmojJiwX+YZkce9byiqWi0JWRz6j9X3Bfs9/7sc1tzxWSxYXMoFHYGP9aLb2+yDUSVQImPl6Zn5ZkjYI6gkvpkmifDS4GCU1ith2GbquMFkcLK9X7RG0CQ2FcwSVpsf7KR+tWA5yho68ofdVPhq81xoRFiu2lyzO6KjYDuZKFtYXc7AcN5VlzCuReo9AUI/LDE2jxKaPhpOZSeA4jEwQulFUPmpomucRKM8RNHoE3ogJPzRk2chn6peUfnIEQcK539LTYMroXMnEYsXCaM6AphFyhoaZxSomCplYprSeaqReCOSDgXrqQ+eSSRaboVk4SVAfQx39PoLgeLqm3iMomW3KR0OhoVLVwWhoe1muj/LRWo7Arzjq9ZP7slnPBy35oSEAGMkaOLpQwXgh0+DFCB4iBEkbkALqQ+cS8gjsZD0Cb/poMIY7+mNnNO/PWLVHsGzaDRd6oDFHsOCXawb086m+bDrIZ7xJrRmtd4+i5OeBZpetWmcxABQyOo4tVDCWN/w9y/KXH0ZWVQrK4VrVUDI5guBClVRhgGUzsrqaqp5gvAQAvzw1+tf34MkSbnn0JJarrZ3F4cU0S1ULY7lM7bF+GsrKllNLOAfLbcIJ6NVY9rubT5ZMlKp1IRjJ6nj4+DLG8xlve5p4BA2IRyCxIeUwkGgfQfCpMikhqNoOchk1OYKgYgiAsqqhD//vA/idz92B5aqNYq4pR+CvqvT+cVOOoPcS0PC8olxG77nPp1S1sWNdASeXzVrVEOAJgeMyxvKGJ1AiBA2kVghEAOKj3lAGuAn8/QV/9EmFA6q2i5yhK6kaCkpTAdQ8jqh/t4Op08cWqyh2CA0tV72wEVF9RHWuj73F4YS0V0La2/cvmw62TXpCsGzaGPPtDbyKqWIWGaM+KE/wSK0QBK340kegHtcFiJDYrKHgYpTUp0BPCBR5BC7D0L0/YyI13cUn/Ea8u48sdCwfXay05g/6aShbNh0U/HN4oaEePQKz7gUQUW13QsZ/jYpZvSGvIXikVgiSvjikCZcZlOCsIXMYQkOGrqRs0XYZmdCiGBUTSI8vVbFxLAfTdlsu9jlDQ9VyGxKztcf62EdQNm0Uax5B770Ey1WnFr6i0P2BHUTU93jstUxqhSC4KPS7YFvoHpe9hGZyHkGym6lM20Uuo0HTol+TaDveQLuAjAIhOLls4vydkwCAqWKm4bFizhvxvFTt5BEMEhrSex4Q5y3PWbkGJmOIR9BMaquGTMcFEWoVD4I6HJeh+zmCJF7t4D1O6r2u2m69aihiIbT8ZrUAXSM4Ef+cJdPBmZvH8Y39T2DdSLbhsZGsgWXTwVLVaigdBYJkb+9CMOWfI/A2esGrbNLxM+dva/hdu+zczShZXmlpNlTpJHikVggsh1HMGpI0igGXAS1JjyBB72+hYnljEzKanyyO9vjBeIkAQ9dgRXyS5aqN83dM1o4fppg1aktf2nkEvYReK5aD+ZKF7VMFAEFoqHePYKqYxV+/+ryGxPWbLtqFN120C4Cf15C/+wbSGxqyXYxkdckRxEDYI0iioaxqu369e/zv9dPf903c/8QSRnOGIo/AhR7yCKJuKnNchum4+PGnTuPRD7205fGRnI5S1cF82cJ4oTFs1GtD2a9+ei8+u/dgzSPIZ7SeQ0PLfo4hLAJAfeghUC95FeqkVghMx0UxZyhd5CF4OLURC8kNnSvmjNj/+MOfOlXNuLFdRibsEWjRCl7Zr+vXNGr7eDFrYNm0cWLJxIZiY9goZ/Q2ffS7DxwHAKwfDUJDfYSWqq2D8ZqREROtpFcI/I5Fl1kGUCmmPnQuufLRYtaIPS48W6rvPwiGnwHRJoydUB8B4IVuovx9LlVXTr4GHsHJZRPrmoQga/SWLN61fgQAcNbmcQD9ewTNg/Ga6XeF5lomtUJgOS6yhiY1xTEQDJ2jhBrKLMcre4y7ZLBsOtg+VcCXf/O5tbBE1OEhy/GW3gQYEVcNtZs4GmYko3sewXIV60ZzDY/lehSCqu3ixitfWBOUXB+jqEumg5Hcyh6BhIZaSbEQMLK6V1MsvxRqcVz4OYIEPYKcHvv7XLa82TwnUDQjAAAdQklEQVTP8BOtACIPD4VHTAB+H0GEns9qn7AN3fswdXiu0hIa6tUjKIfGSwBejqHXZPFytd6H0Imk8kXDTIqFwEVG1xqGZglqqIWGtIQaypxkcgRly0G+6aKkadGu67Rct6GSx9A12BG6XeVVPAIA2DCaw52H5rBpIt9wf68jJsqm0zDmeixnYLnHrXLt9io3I1VDraRWCEw7EALxCFRTrxpKziMYzRmx//FXLAd5o/FPLGqPwPR7FAKMyD0Cp2W+UDPrR7NgBnZMjTTc38uIiKA6KRd6vcYLGSxUrJ7sLZlOy2C8ZrxksXz4C5NeIQjlCOTTgVpcN7yhLJnpo55HEO+5K5bTsshF06LtJTDtxounoUebIyg3LaRvRyA82SbR66XqJ5g6Gi77HM9nsFDu1SNoXZ7TTE5yBC2kuKHM+yQliSP1OKERE0mEhoJk8cxiNdbzlk235SIafbLYbbgAR91HUDJbxayZ9758N47MV1ru72Xuf3N+AADGC0Z/HkEXoSFpJG0k1UKQ0UlyBDEwDKGhJJLFlTYXNxWhoUxLsji6n7Nqu8gbKwvBs09f3/Z+b0REd6EhbzNZkxDkM1gody8ErsttX/Nmek1ip4HUhoYsmyVHEBNeshiJLq9PKlmcy7QJDUUohmaTR5DRtUhDQxXLaVg20ws5o/vO/XKbMJqXI+g+NFS2vCmvnZrfAqLaUHZssYLvPjAz8HGGgdQKQdVxkQlyBCIEShkKjyBrxJ4gjMsjyOr1c3hjqKP7fa5Yva2KDBN88u6mm7xddVKvHkE3iWKgN09lJT594wG84eM/XBMNqakVAsuvtshKvFApzAyX4ecIkttHUMzFP2DQSxY3VQ1FHMM3HRcZIzxiQou0aqhiOQ3J6F7QNYJO3SWvw7uKA8YLBuZ7EILlVbqgA3rxVFbipodPAAAeO1ka+FhJE7sQENEOIvo2Ed1NRPuJ6B1x2wCEOosNyRGoxOX6djJVy9VXw3RcjCbUUNbsEUTdR2DaLnK6umRx1XZbwlu90G08vt1rFXxdNrv79D5XtjA5kln1ef2Mt27mb667H3sPzOL8nZM4PFse6FjDQBIegQ3gd5l5N4AfA/CbRLQ7biPqyWLJEagkCAsB6parr0ZSQ+fKZmtYRUloKPSJXdcJVuQ5gv6FoNumsrLZKgREhKmRbMPMppWYLZmYbNqX0NamzGDJ4icWKvjotx7A7/3UmTht3QiOLtQrpg6cWO77uEkSuxAw8xFm/pF/exHAPQC2xW1HuKFMcgTqCLqKgejDIt1STxbHnCOwWy+iUSeLgw75gIxGcCLMEVTt/pPFQOARrP6JvlMH81Qxi5PLqwvBVd95CLc9NofJwuoeQT+7lMMcOFHCBTsn8Zs/cQY2TeTxhC8E+w7P48f/8no8PnfqeQiJ5giIaBeA8wHc3OaxK4hoLxHtnZmJPjNvOl7VkMwaUkswcA4I4tfxv9a1zuK4y0fbfMr1PILoztHiEWhapIJXsVYvH12JbpvK2o3jAICpkQzmSu3zBPcdXcSjx5dRtR184Kv34qPfeqBlAmpbmzL6QB7BwZMl7FjndVFvGqsLQZAzuO/oYtvv++T3H8F/3n647/OqJDEhIKJRAF8A8FvMvND8ODNfxcx7mHnP9PR05Oe3/HZ2GUCllnBoSNcT8giSCg21KYlUkSxW2VBWtb3tav3Sbalmu9AQ4HsEHUJDr/vYTXjDJ27GbY/N1UZxB6OsV6LXqajNzJWtmuBsmcjj8TlPCG5+5CQKGb0mDM38yX/fjb/+5v19n1cliTSUEVEGngh8hpm/mIQN9aFzGixbksWqcF3UQkNRj0juFtNxUczqsVcNldvU4EddQlttnjUU8YiJwT2C7hKz7ZLFQOARtBeCiuXi+JKJ6++bwa8873RUbQcv2r2pO5sG+F1YrFgY8+cvbZ0s4Mh8Ga7LuOXRk7jsnM0dhQvwCieGkdiFgLxhIh8HcA8z/03c5w8IhMCQHIFSgvESQDI5AsdluMzIZ/REZg21JIsjfg0sh1s8gijDb5Eki50ucgSW07LzGADWjXTOETAznjxdxD/d8BD+9Zefhec9pbvIQXbAPoKlio1N496k1S2TeRyZr2Df4/NYV8zirC1jOLbQOsokeE+iLO2NkiRCQ88F8AYALySi2/1/L4nbiGCPbVZCQ0ppCA35NeVxrquszZTyBT/Oc5et1llDWsSVU6btNHkE0W4oG6SzGAguuv2HhiZHsm1zBKbtomq7uOycLQCAC3et69qmXldoNrNYsTGa90RrQzGHpYqNL912GJeevRlTHYTrxLIJQyMs9ThWOy5i9wiY+XsAEneQgk9SUj6qlnDVUH0CKaDH9BtQ9ZOpmka10FQmppNX2gxs0ynaVZXNyWJvZ3G0oaHcoMnibnMEbZLF64pZ3H5wruX+uZKJyZEMfv0FT8bLztvSk9cyaGhoqWrXvBdNI2yayOGT338UX3nbxTgyX8Fcmya4YwtVPHl6FA8cW6xN4x0mUt9ZnDGirbIQGgl7BIBXORSn8IbHNMct+hXbaYmvqwgNhctH9WEsHx0gRzA5ksFsycTDM0u4/O+/X/PoZksWJkeyKOaM2o7jbhm0j2CxamMsX/8MvX3SS1CfvXUcE4VM227omaUKtkzmMZI1sDiEXkHKp4/KPgLVOG49RwDEnycwnXoyNaOTVxiweoVhJLT7lKtR1KGhJo8g8qFz/c8aArpfTtOuwgrwPILZkol9jy/gjoNzOLZYxabxPGZLJqa66CJub5M+UB/BYsVqEIK/evV5WKxYIKLOQrBYxYbRHCYK3vykiS76HeIktR6B6Q+dkxyBWoLJowFRV7Wshml77zMQ3dTJbmk3P0ePeDFN8yfpqDeURVI+2sUHrU7jo6dGsphdtmpNWo8e9zp357rsIm7HwKGhio2xfP1Cvm2yUPNKVhKC6bEcxvK9zU+Ki/QKgV0fMZFESWNaaA0NxesRWA0eQbyhoarltoRVol5M0/xJWkX56MA5gi4uup0W4Ez5HkEgBMECnLmS1VUXcTuyfhSg38KBcI6gmU5CcHzJxIbRXF/rN+MgvUJQayiT0JBKvORsY+drlGOSVyMcOolTCGzHhe021vjXbIjw9635k7QxZDmCQWYNAfD6PxwXjxxfxmnrR2pCMFuyMNVFF3E7NI2QNTRU+hw8F64aaiaf0QD23pcwM0tVbBjNYjyfwWIPOxbiIr1C4M9xz8iqSqWYtgtD4XTM1ag2CAHFJvoV223ZwQsg8pEmzXX+UY6YcFyG7XKLmPXCamGYiuWgYjkomXbbWUNEhHXFLO46PI9n7pzC0XnPMwiqhvplopDBbQdnu5pjFIaZUTLtjuswicj71N/kFRxfrGJ6NIfxvNHTjoW4SLcQSI5AOc3lmnrEMezVMO3G0FBcOYJ2i+sBIBNxnsJb8RjeUBad0FYsr+qpWcx6YTWP4N1fvAu/8C83Ydl0UOwQbtk8nsdcycIFp02FPAITU33mCACvUe0X/uVmvPXff9TT91Us77qhr1D+OdFmj8LMUhUbxrzQkHgEQ0Qwo8ULF0iOQBW249bmwABeDDv2qqFQsjiu97psOm1j69kIQ5HM3JKQjnJD2aDNZMDq00e/vu8ofvTYHErV9h4BgFoYZvfW8drI59mS1XfVEIDagLtePYLlFbyBgOY8gesyDs+WsX2qgLG8IUIwTAQegYyhVovlcENoSI953lDVql+Q48wRdLqIZg2K7PfNcrzS3HAOptvkbDdU7cESxYE9KwlfcPEvW07H7WLBz7d1ohBKFvdfNQQAT/jHObbYOg5iJUrVzp5LQLMQHF2oYKKQwUjWwFjekGTxMBGEDKJO3gmNhKt2gGRyBMEF2esjiOe97nRhi3I1arvy1EFLI8NE5xF0ticIOwXrTNvxuy8+E3/78+dheiyHuZIJ03Z9j6B/IXjnT52Jt7/wDCyUrRV/H6/8wp34+r4jta+XO+QywjQLwYETJZzmT0X1ksXDJwSpbSir5QgMyRGoxHZdGA05gnirhsKfauP0/jqVQ0ZpQ7uBcFEtZveOP1gzWc2eDqEhZsZC2Vq11+Dc7RM4d/sEAGDDaA7HFiuYXTa72j3QiVc9czsA4N9ufgyzJa+0sx3X3nIQh+fKuNSfaVQy7Z49gvuOLuApm8YAAGP5DBbKEhoaGqqh8lHJEajDchiG1ugRxJksrlj1hqhsjO91p3LIKPMU7c4x6NKVhuNbdlsx64V8Rke5Q5lm2XKgaeipy3azP/9/vjxYjiBgfTGLE0vt8wTBJ/fwfoHlavtNamGahWD/4wvYvcVrOBsvGFisDp9HkEohYOaG0JDkCNRhO22qhmIODSUxa8gLDbX3CKK7ULcKQT6CxewBy1Vn1cToaozmDCx3mK0ThHcyPQxg2zKRxz1HFjBeyDTknvpl/WgWx5ca8wSLFQvMjCPzFWybLODRE6Va+Gil0tGAcV8I/va6+3F4roy7jyzg7K2eEIwNaR9BKkNDpr+4XtNkeb1qmnfqxp8jqCeLszH2jJRW8AiiGkVcMm2M5Fo9gsoAc3Rajj+gR1DMGR1/3rmSiYlCBn/8st1dV+9snxrBDx85ifUDhIXCrB/NtQjBue/7Jj74M+di62QBuzaMwHJcHFusYMtEwfMIcqt7BN994DhuuH8GZcvBQzNLtREU0kcwRIRry2VnsVospzFHkOSYhzi7yMtm+7BKlOWjC00zb4DuN4J1w3IXFTKrMZY3sNThE/Cc7xE894wNePl5W7s63kVPXo//uesIdm0oDmRXwPRoriE0dMIXhTsPzeHofBlbJgrYNlXA4Vmvka0bj2CikMEN98+gkNHxie89gh1TI7XfhWH1CNIrBEG4wCBZVakQ22VkQjmCfEZrab9XSThZvFoFS5R0Cg1F6ZUsVuzaysSAfGawyZphovAIRnMGls3OQtBrd/BFT96AnKHhGTsmB7IrYH0xixPLdY9g/+Pe+vSHji3j8bkKtkzksW2ygMP+rKNlszuPAADe+sIzYLuMPbumao8F5aNxLkjqhtSGhpKYP5NG7CaPIJ/R+57x0g8Vy8HGMa8ipJjVUepwUYqaTqGhKL2S5nHIgOcRRPX6rtTt2y3F3AoeQbn3MRFZQ8Md7/3JWt5nUNaP5nDnofrim/2PL+Cyczbj1gOzeNKGIs7bMQnTdmtCUKqu7hEEFUI/ceZGPPeMDdg2Wag9lvfHjnhlzYOJbJSk0iMIV1tkJVmslPCsHyAQgpg9Aj805MWr4zm3t4ugTR9BhCMmFit2WyGIzCNYodu3W8bynRexzPnLZXol32aGU794yWIvNHT/E4v49n3H8OLdmzBftvDQzJLnEYRCQ0tdVA2tK2Zxx3t+Eru3juMZOyYxPdZYmjo+hE1lqRSCkllv9pHpo2opm41/OPmMFlkysxuqVn1LWDGnoxTTdijPI2j984py8F3zXHwgCA1F6BEMWDWUMzS4Lrf9mWeX+18uExUb/KqhiuXgso98Fz985CSe8+T12LluBHsPzGLzRB5bJ+qhoYWKhfEuyl0nVvi5xoewlyCVQrAcmideyOoox/gJNW00d9jmDB1lM77Xe6labwAqrhCvjpr5stX2YpAzoguNdQ4NOZHEoJerrVVJvUJE3uveRoBnSxYmCzGti+vA+mIOJ5aruOfIAs7aPIb7/uxSbJko1DqBt08VsH1dAYdmg6mng28X8+YNiUeQOMuhsrugzjnKheJCnZLZ2P1ayEb3ibUbFis2xv2LpfdexyNCC5X2F4yxvIGliBqKTrYZs2DoGgw9mjzBbMnEugHGOASMdighne8jRxA1G8a8qqH9j3u1/kFhQfDhZSyfwY6pERyaLYGZPZsHFAJvOc1weQSpTBaHy+J0jVD0F0oP2x7RtUDz4pS8EW+OYLFq1aZXjmQ7NzdFzXyHvbRRLiaZWay0xJ8BYNJvaBq0K3i2zxh+M50mbg6yXCYqilkdjsu49cBsQyXSJU/bWH9OzkAxa2BmsYr58uCvyXi+/RazJEmnR1C1UQz9kbRbJCFEg7dKMbny0XAcvZjT4w0NdfAIohKC40tmeyEYyWCu3Nt45XYMOs8nYP1oY4lm7filwT9dDwoRYcNoDt978DjO2jxWu//yZ2zDR197fu3rHetGcHC2FEloaPNEHkf8nMOwkEohONm01GJYR8OuBRbKFsZy9T+c0bwRa6JsoVLPB43l4kvSzS63H5McZXx4ZrHadljaZCGLudLg5/CWvwx+od40lsfR+UrL/TMLVWwcyw98/EHZNlXAzGIVZ4aEoJkd60bwyPGSFy4bUBy3T9WTz8NCKoXg+GLjtMHxQgbzEfzhCK2cXDaxfrT+hzPdpqVfFabtYrFi1f5wN03kGgaIqaJsOiiZTtv4etBZOmgyd75kwXbcthfqyZEM5kqDeQQVy8FCxcb6DlM5e2HTRL7ldZ8vW2B4Q9iS5uyt49A1WjHks3NdAbceOIl1xWxDOXQ/bJusJ5+HhUSEgIguJaL7iOhBIroy7vMfa4qtbh/CN2atcHzJxPpi/bXeOJ7veRlIvzyxUMGG0Vxtzv2GYg6LVVt5aOrwXAlbJvLQ2gxTyxoaJkcyeGJhsNfgwZklPHnjaNt6+o3judoCl345NFvC1on8iisZu2XLRB6H5xrtOXiyhG2Thcj6AQbhysvOws1/cMmKzzlj4yi+vu8odkyNDHy+06dHcd/RRQDAPUcWhiJfELsQEJEO4O8BXAZgN4DXEtHuOG24/4lFnLFxtPb16dNFPDizFKcJqaBiOTi+VMXmibr7v2Uij0OzpViqtO472vg+axphx1QBDx5T+17feWgeu/1pk+04fXoUDw34+7b30ZM4d9tE28fO3DyOe48sDnT82w/O4+wOx++Vc7ZN4PaDcw333X5wDmdv6/waxUnO0DvuIwh4zukbMFuycP7OwUdbPHm6iKrt4ANfvQc/8w834vK/+17b0FmcJOGXPQvAg8z8MAAQ0bUALgdwd9QnuuvQPK655TFYtgvbZZiOi1LVxnzZaogHPv+p03jTJ2/BUtVG8AEo7LmHL1mNHj23vb/xezs8p8Mxw8/vcLMhrND5OKs/H93YOcDPMlsyccHOqQZXetN4HtNjObz92tswUciAa9/DYPZus3/b9W/7/4GZa88Pfw3/ea5b/16GJ/hvumhX+CfGi3Zvwh9+eR/O2ToOt8153dDt4Lwuc8s5mdvbCzD2HV7Au19yFjrxvDM24P3/cw/O3znpn4/hsndu1z9R+Gtu+r/LXpXLx954Ycfj/9U37oOmeclQZu+1cX3b2x2z+f93HJrD+15+dsefoRfO3TaBhbKFd1x7G7K6hpLl4IePnMSHfubcSI4fB5sn8vjUL12Ip28fXAiICL/z4jPx7z88gK+8/WJ8c/8T+Ll/vhEXn7EBAIEICPtJ73vF2Q0TfFVAcQ8/IqJXAbiUmX/F//oNAJ7NzG9tet4VAK4AgJ07dz7zwIEDPZ/r0ePL+N6Dx5HVNRi6N3I6o2s4e+s4dqxrdPF++MhJ3Hd0odnY+s32d4NCjzTev/rzO9xscJc7HqcLG8J0dcwefxZ08fxnnjbVEmc+PFfGDffNwHFdgLyzer/83h+B5t/2/wOFnxN6HkKPaVT/A/IeIxSyOp53xoaGEE3VdvD1fUexULa8713lvFqbc2pN31ez0b89Xsjg2U9a1zHsYTkuvr7vKOZKJjSNoNXOU/9ZNC04D9XsCp6jEbB1soBzVvjEfvvBOew7PF+zt/ZzEDV83ekc64pZPOtJ6zoev1cOz5Xx3ftnQAQUsga2TORx4a7ojn+qc+NDx/HI8eXaB44wr71wR9+7F4joVmbes+rzhlUIwuzZs4f37t0bl4mCIAhrgm6FIIlk8WEAO0Jfb/fvEwRBEBIgCSG4BcBTiOhJRJQF8BoA/5WAHYIgCAISSBYzs01EbwXwDQA6gE8w8/647RAEQRA8EunmYOavAvhqEucWBEEQGkllZ7EgCIJQR4RAEAQh5YgQCIIgpBwRAkEQhJQTe0NZPxDRDIBuWos3ADiu2Jx+ELt6Z1htE7t6Z1htS4NdpzHz9GpPOiWEoFuIaG83XXRxI3b1zrDaJnb1zrDaJnbVkdCQIAhCyhEhEARBSDlrTQiuStqADohdvTOstoldvTOstoldPmsqRyAIgiD0zlrzCARBEIQeESEQBEFIOae0EBDRC4jodiLaT0Q3hO6/lIjuI6IHiejKBGya9+26nYjeMwx2Ndl4IRHZ/pKg4L43EtED/r83xmzP5UR0p/967SWii4fErtf5dt1FRDcS0XmhxxJ9L4noLCL6ARFVieidTY8Ny+/ZUNjh2/IJIjpGRPtC960jouv8363riGgqAbt2ENG3iehu/zr2jkRs83avnnr/AEzC23O80/96o/9/HcBDAE4HkAVwB4DdMdr1AgBfaXN/onY12fF/8Ka/vsq/bx2Ah/3/T/m3p2K0aRT1fNXTAdw7JHZdFJwPwGUAbh6W9xLARgAXAng/gHcO6e9Z4naE7Hk+gAsA7Avd9xcArvRvXwngzxOwawuAC/zbYwDuB7A7bttOZY/gFwB8kZkfAwBmPubf/ywADzLzw8xsArgWwOUJ2RhmWOx6G4AvADgWuu+nAFzHzCeZeRbAdQAujcsgZl5i/zceQBH1ta1J23Wjf14AuAneNj1gCN5LZj7GzLcAsJoeSty2IbMDAMDM3wFwsunuywFc7d++GsArYzUKADMfYeYf+bcXAdwDYFvctp3KQvBUAFNEdD0R3UpEv+jfvw3AwdDzDvn3xclziOgOIvoaEZ09LHYR0TYAPw3gH5seGgbbfpqI7gXwPwDePCx2hfhlAF/zbw+TXc0Mi23DYsdKbGLmI/7towA2JWkMEe0CcD6AmxGzbYkspokIA8AzAVwCoADgB0R0U7ImAQB+BG++xxIRvQTAlwE8JWGbAj4M4PeZ2SWipG1pgJm/BOBLRPR8AH8K4EUJm1SDiH4CnhBcvNpzhVMTZmYiSqyWnohG4Xnqv8XMC+G/zzhsO6U8AiL6zSAJC+BxAN9g5mVmPg7gOwDOA3AYwI7Qt23374vLrlFmXgJqm9gyRLQhCbva2LYHwLVE9CiAVwH4ByJ6ZRK2he0ioq3B/b4Lf3pSr1mzXUT0dAAfA3A5M5/wn5b4exl+zZpIxLYhtmMlniCiLQDg///YKs9XAhFl4InAZ5j5i4nYFndyJMIky9MAfAueZzACYB+Ac/yvHwbwJNSTVGfHaNdm1BOfzwLwGABK2q42dn4KjcniR+AlZKf82+titOWM0Gt2AbwLBg2BXTsBPAjgoqb7h+a9BPA+NCaLh8K2YbGjyaZdaEwW/yUaE7J/kYBNBODTAD7cdH+stiX2pkT0Iv4evMqhffBcquD+l8DLvj8E4A9jtumtAPb7v/g3hS8iSdrVxs6aEPhfv9m/6D0I4JdituX3/dfsdgA/AHDxkNj1MQCzvl23A9g7LO8lvA8chwAsAJjzb48Pg23D8ho12XINgCPwkuuH4IX61sP7MPkAgP9FjB8yQnZdDK844s7Q79lL4rZNRkwIgiCknFMqRyAIgiBEjwiBIAhCyhEhEARBSDkiBIIgCClHhEAQBCHliBAIQp8Q0SeJ6Nea7nslEX2t0/cIwjAiQiAI/XMNgNc03fca/35BOGWQPgJB6BIiej2At8PrlL0ZwG/Aa066gJmPEFERwAEApzPzQnKWCkJviEcgCF1ARE8D8PMAnsvMzwDgAHgdvBkxr/af9nIA14sICKcaIgSC0B2XwJt2e4s/wO8SeEtXwuEhCQsJpySn8hhqQYgTAnA1M7+74U5vXvAWf43lRWjNGQjC0CMegSB0x7cAvIqINgK1nbKnsZdk+yy8LVJfY+ZKkkYKQj+IEAhCFzDz3QD+CMA3iehOeGszt/gPXwNvF4aEhYRTEqkaEgRBSDniEQiCIKQcEQJBEISUI0IgCIKQckQIBEEQUo4IgSAIQsoRIRAEQUg5IgSCIAgp5/8DjXa0/YUH+PAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 支持中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']    # 宋体\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 正常显示负号 -\n",
    "\n",
    "\n",
    "def read_csv(file):\n",
    "    \"\"\"接受一个文件名变量为参数，读文件，每行切分为一个子列表。\n",
    "    返回二维列表。\n",
    "    \"\"\"\n",
    "    with open(file) as data:\n",
    "        ls = [line.strip().split(',') for line in data]\n",
    "    return ls\n",
    "\n",
    "\n",
    "def plot_dos(ls):\n",
    "    \"\"\"接受二维列表为参数，绘制曲线，无返回值。\"\"\"\n",
    "    x = [float(x[0]) for x in ls]\n",
    "    y = [float(x[1]) for x in ls]\n",
    "    plt.plot(x, y, linestyle='-', linewidth=1)  # 绘制数据曲线\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    \"\"\"增加图名和轴标签\"\"\"\n",
    "    plt.title('（Density of States）')\n",
    "    plt.xlabel('eV')\n",
    "    plt.ylabel('eV')\n",
    "\n",
    "\n",
    "# 函数调用与输入输出\n",
    "if __name__ == '__main__':\n",
    "    lst = read_csv('images/ch9/9.4 dos.csv')\n",
    "    plot_dos(lst)\n",
    "    draw_label()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在NumPy中，使用loadtxt函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入NumPy数组。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.85574280e+01  0.00000000e+00]\n",
      " [-5.85469054e+01  0.00000000e+00]\n",
      " [-5.74735926e+01  1.11050000e-04]\n",
      " ...\n",
      " [ 2.08045808e+01  1.29131000e-04]\n",
      " [ 2.08151034e+01  1.13849000e-04]\n",
      " [ 2.08256261e+01  1.00099000e-04]]\n"
     ]
    },
    {
     "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": [
    "# 利用numpy读文件绘制数据曲线\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = np.loadtxt('images/ch9/9.4 dos.csv', delimiter=',')\n",
    "print(data)\n",
    "plt.plot(data[0:, 0], data[0:, 1])  # 切片方法获取两列数据\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于有标题行的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(data[1:, 0], data[1:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读数据到dataframe,用数据的列标题绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot('xlabel', 'ylabel', data=obj)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2d,Intensity,E\n",
    "4,0,2.5\n",
    "4.03,0,2.3\n",
    "4.06,0,3.8\n",
    "4.09,,4.2\n",
    "4.12,,5.6\n",
    "4.15,0,1.0\n",
    "4.18,0\n",
    "4.21,0\n",
    "4.24,0\n",
    "......\n",
    "44.95,5.66224\n",
    "44.98,6.46755\n",
    "45.01,7.59091"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"images/ch9/XRD_AFOtxtd.csv\" target=\"_blank\">XRD_AFOtxtd.csv</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在Pandas库中，使用read_csv()函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入为dataframe类型。  \n",
    "可通过列标签获取其中第 i 列数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1377: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py:237: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py:239: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('images/ch9/XRD_AFOtxtd.csv')\n",
    "plt.plot('2d', 'Intensity', data=df)   # 根据列标题获取数据\n",
    "plt.show()   # 显示绘制结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在Pandas库中，使用read_csv()函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入为dataframe类型。  \n",
    "可通过dataframe.iloc[:,i]获取其中第 i 列数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# 利用pandas读文件绘制数据曲线\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('images/ch9/XRD_AFOtxtd.csv')\n",
    "plt.plot(df.iloc[1:, 0], df.iloc[1:, 1])  # 索引两列数据为数组\n",
    "plt.show()  # 显示绘制结果"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<img src=\"https://www.educoder.net/api/attachments/3394575?type=image/png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 多列数据绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当数据文件中包括多列数据时，可用循环的方法，每次读两列数据绘图，重复执行，完成所有数据的读取和绘制。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例 9.5 多列数据绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有一包含多列数据的PDOS.csv文件，8列数据分别代表4组x,y坐标，利用文件中的数据绘制数据曲线（数据文件可通过扫描二维码下载）。  \n",
    "下面仅给出文件部分数据："
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "-58.55742805,0,-58.55742805,0,-58.55742805,0,-58.55742805,0\n",
    "-58.54690537,0,-58.54690537,0,-58.54690537,0,-58.54690537,0\n",
    "-57.47359261,0.000110663,-57.16843505,0.000112165,-57.04216296,0.000102657,-57.47359261,0.00011105\n",
    "-57.46306993,0.000140603,-57.15791238,0.000131517,-57.03164029,0.000116436,-57.46306993,0.000141095\n",
    "-57.45254726,0.000178149,-57.14738971,0.000153782,-57.02111761,0.0001317,-57.45254726,0.000178771\n",
    "......\n",
    "18.15286686,0.034078085,17.67934652,1.146470801,20.6256953,0.000119975,16.14303609,0.670546987\n",
    "18.16338954,0.031011768,17.6898692,1.125145182,20.63621797,0.000110874,16.15355876,0.673103152\n",
    "18.17391221,0.028196187,17.70039187,1.102515945,20.64674065,0.000102179,16.16408144,0.67741828\n",
    "18.18443488,0.025618465,17.71091454,1.078811693,20.65726332,9.39E-05,16.17460411,0.683354996\n",
    "18.19495756,0.023266078,17.72143722,1.054125932,22.33036852,0,16.18512679,0.690765294"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"images/ch9/PDOS.csv\" target=\"_blank\">PDOS.csv</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用循环的方法读取数据比较容易理解，每次读取相邻的2列数据作为一组坐标，取消10和11行的注释，可获得区域放大的效果，绘图结果如图9.11所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "with open('images/ch9/PDOS.csv', 'r', encoding='utf-8') as file:\n",
    "    ls = [line.strip().split(',') for line in file]  # 逐行读取文件中的数据创建二维列表\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "for i in range(0, 8, 2):                # i取偶数0，2，4，6，i+1的值分别为1，3，5，7\n",
    "    x_ls = [float(x[i]) for x in ls]    # 列表x_ls为ls子列表中序号为i的元素的浮点类型\n",
    "    y_ls = [float(x[i+1]) for x in ls]  # 列表y_ls为ls子列表中序号为i+1的元素的浮点类型\n",
    "    plt.plot(x_ls, y_ls, linewidth=2)  # 绘制当前数据的曲线，颜色通过索引获得\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10和11行的的作用是获得区域放大的效果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MYick4tisNhztkybC7nfUwfGXdX7B3AkIkwVrshMA1z4dFunNuPbtw7V7N4bUVFIuvJCEKZOxT5qEv7aW+o9XRNu8iBGKYFcCwc75h4BWzYyllM9IKadJKadlZWWdeK8mgPT7qXhqMQB9rrqqdTqfuwHKdqoMimCM+nRkjVKv5Wrzrbl4JjZCItLnw7FFZbHY+1mgoQxS+qv+KJ3FZIWc8VhTA2ERHcfu1dS8o5yAlPPPRwR68aR89atAz55OFIpg3wVcKYQwAOMAXRvcDp6yMpw7dzYVeEgpKf/DH3Hu2IEpK4s+V1/V+obiLSqDInu06hnSHlmBzcoTPOxYmTzjKtiPv74ec79+mKsCueUjzoeuRtPypmEJbjwGhhdreif1H30EQMpXLmo6lnz+AgAaVq1Cut1RsSvSmNq/hMXAS8APgNeklDoJ9hRIt5uShx6ieuky1ZHOZMI6Yjh4fWocmBDkPvIIBru99Y3HAg2N+k0J7YOCHnaFEuxYmzzTKp1vb8DbGX5B1xfuNxlb6j8B7WH3ZjxlZbj2FSDsdhKmNm/Sm7OzsQ4fhmtfAY6tW0+uc+gBtOthSymLpZTzpZTTpZS/6A6j4pWShx6ieslSMJmw5Oerqrydu3Dt3YsxNZW8J58kad7ck28sCuRf54Uo2BnDQBig8gB4XRjT0hB2O/76enx1deH7Qp2kSbDHDFPtYI1WyJ/X9YVzxjd52O6CAp0p0ktpXKu6VSZMn9YUDgmSMFPVNjSsXdftdnUHoXjYmhCo/3wV1UuWIiwWBr34Avbx4/HV1+Pauw/p9WAfP/5kzzpIUQc9bJMV+uRD5X44XoDoOxZzbi7uAwfwFBVjHJkcni/VSRo3BwQ72w9HUKX2bfVG6SiZIzDZTRgtPnyNjXjLy9ueD6np0TSsUYKdOPuMk84lzJxB1Qsv0LhuHfzg9u42LeLoSscwIKWk7PdqQG7mD36Affx4QI3wSpgymcQZM04t1o4q5SmbbM3ZH6Fw4sZjMI5dEt2wiPf4cTyHCxF2OzZDoToY7DLYVUwWyBqFJVlNnXEfOhSedTVxRbC1Q3B0XEsSp6ve6o7Nm3tkHFsLdhho3LAB165dGDMzSb/+uo7dXKTS38gZD0Zz6PedsPEYK8UzTel8EyYgitargwNP/sXqNDnjsSSrTBEt2L0Pb1UVniNHEDYb1uHDTzpvTEvDkp+P9Hhw7u15G9NasMNA9ZKlAPS5/DIMVmvHbu5oOCRIZmAOZMVeIHYmzzTFryeMU9kvwhBabnmo5IxrFuzDh8O3riYucG4PDK4eM0Z1gGwD2/hxgWu3dZtd3YUW7C7id7moX6ES9VMvuaSdq9sguOHYb/LprzuRjGHq9fh+IHYmzzQGO/QNSFTVmNljwZYSvg/o20KwD2nB7m0ERdg2buwpr7GPUyFJxzYt2JoTaFi1Ws0sHDMay6BBHV/gWAczRIIEB/RWHgApY2LyjHS7cQZ+SewplepgOMMhoEMivRxHwMMO7hO1RZOHvVULtuYE6j74AICU8zuRZ1xfBrVHwZLU7DGHSkI62NLAXQ/1ZTFRPOPctQvpdmMZMgTjcVXpyIBTDGPoLAnpWPqmA+ApLET6fOFdXxPTBB2Ck2ahtsA2ejSYTGo6UWNjd5nWLWjB7gJSShpWrQIg6ZyzO75AMBySO6lzTZGavOz9mAKjwjylpVETseb+IZPgSCAPdsCM09zROQy5IzDZfUiPJ6YaXmkii6e0DG9ZGYbk5NM+zRpsNlWw5vf3uBmgWrC7gLugAG95OaasrDZ3rNslWOGY18H4dZD0gGAfL8BgtWLMzASfD2+UmnA1xa+H5YKzWrVTTRsY/g/KGoElSYdFehuu3ao7pW306Na9eNrAPjYQFtm5K+J2dSdasLtAw+rVACSeMZtOdZ0NeqGdzaIIethNG4/RK1GXUuL4Qv0BsmeqPt0MmNn1/iFtkTlCx7F7IcH+Ma0Gf5yC4DXOvT2rfb8W7C4QrLhKmN2JwhCft8XQ3U4WlgTj3pWtBdsbBcH2Fherx9XUVCzeQJ+PUw0T7iotBVun9vUanHtVCqt1ePv7PdYR6onX1cNysbVgdxLp99MYyDkOVld1iNLtasOwT37HJoi3JH2Iej1+AIiuhx38t7BPmog4GoxfhzlDJEjWSF3t2AsJ9kAPJfxoGzEicM++HrUxrQW7k7gPHcJfU4MpOxtTv34dX6BQeeddKttumdrn9zdXO0YhU6RpQvqYEcrjNyeo6s1IkJKHpY+qCnUfOhiZz9DEFNLrxb1fPUmGItjGtDRMOTlIhwPPkSORNq/b0ILdSVq2EO1U/LpwjXrtStjAlgoJmeB1QF1xVItnmv49+gWqz/KmdqzUviMIgXmwerrwFBUjPZ7IfI4mZnAXHkG63Zj65WJMSgrpnmBYJBhK6Qlowe4krVLYOoqULQS7i42RMpozRaI1ecbf2Ihz924wGrHbAml2kQqHBDDkjMSU4AWfL+rVnZrI4wqIrm34iJDvsQU2Hl17tGD3eoIx24TJnRDssl1QXwpJfdVA3a6Q3pyLbQ6EZjzHjnVtzQ7i2LYdfD5sI0diKAvMpozUhmOQrBFYkgJx7MOFkf0sTdRpyhAZEfrvizUYx9Yedu/GV1uLu2A/wmzGOmZMxxcoCIzIHHpO19PeWqT2GdPTEQkJ+Gtr8dXUdG3dDtAUDpk4vrkYqL3p710lc2RzLnahzhTp6TQJdgfqHawjel5qnxbsTuAI9CiwjRmD4YSJFyGxX82jY9h5XTemxcajEAJL//4AuI8c7fraIdL4ZSD/enAa+NyQNRrsfSL7oZnDmzJFPIXaw+7pnEqwa1w1PLnpSX63/ncU1bfuo2PNHwwmE57CI/gbGrrJ0sgSsmALIe4UQnzY/pU9H+e2rQDYJk7o+M2uOji8BhAwpBPl7CeSfkLxzMABAHiOds/OuPT7mzNE0gO/FOFu+NQW6UMwB0MiB3WmSE/G73KpfHuDAcuQIU3HGzwNXPPuNfxj+z94YdcLXPfedZQ0NLcqEBYL1iFDQEpcgQyTeCckwRZCDAJuiKwp8YNjixJs+4SJHb9597vgc6nNxsSMrhsTzMWuOgh+H5b+SrDdhd0j2K6CAvy1tZhyczHXq05qYW/41BYmK5Z+WQC4Dx2I/Odpoob7gEpbtQwc2Krf/OIvF3Oo9hCDUwYzJmMMpY2lPLru0Vb39rQ4dqge9h+A+yNpSLwgpWzqs2uf0Ik84x2vqtdx3wiPQdYkSMpRoYiaI5gHqJBId+WeOr4IbL5OmRLRhk9tYclXFW/uopIeVRyhaY2r4OSCmQpHBa/seQWAx896nKfOeQq7yc6KIyvYWr616bqgYDt7SKZIu4IthLga2AK02fZKCHGrEGKjEGJjeZSaDnUnnmNF+I4fx5iWhnnAgI7dXF8GBR+pKSxjLg2fUS02Hi0DVbMldzeFRBq/UFkh9hEDoLECErOavf4IY8gdicnuA69Pd+3rwTRXODaXpC/duxS33805A85hZPpIshOyuXLUlQD8Z/d/mq5rLlHvJYINfBU4F3gZmCqE+EHLk1LKZ6SU06SU07KysiJhY0zRFL+eML7jBTPr/wZ+D4y4CJLC+G/VQrDNgU1HTzeFRJo87GyVsRGxhk9tkTG0KVPEozNFeixNHvYwJdhSSt4+8DYAl428rOm6K0ZeAcCHhz+k0aP6YNtahESklN1mc6RoV7CllFdLKecCVwKbpJSLI29W7NLp+LWrDjY8q96f8cPwGtWiCZQ5Lw+EwFMc+QpAT2kZnqNHMSQmYhWBjb9I51+3JHM45qbUPp0p0lMJCrYlINjbKrZxuPYwGbYMZuU2/7zlJeUxMWsiLp+Lz499DoApNxdDcjK+qip8FRXdb3yY0Wl9HaTT8euPHwFHpfJAwy1qTfMdCzBYLKqniN8f8QpARzCdb9IkxNHAhPQIVzi2ImNYcxMoXTzTI/EHe4GYTFgHDwbgf4f/B8BF+RdhMrQexHveQJUq+2GhSmgTQjTFvntCiXrIgi2lPCSlDEPicPwiPR6cOwJTm08zU+4kti6BtX9WseuLFoU/ZNBCsIFuyxRp3BQQ7AmjoWIPmGxqek53kdwPS5qa1OM+UNB9n6vpNlz71cxSy6BBiEDNw2dHPwPgnIHnnHT9uYPObbrG61dPX9aRwbBI/Lda1R52B3Dt24d0OjEPHIipTzuFIV4X7PsfvHYbvHqLOnber6BfBAStz2D1x6C6ELyubsvFblyvvOqEvEDxUN5UMHWikKizGAxY+qtyfJ3a1zNxFQQKZgLhkGP1x9hfs59EcyKTsk/+XRqQPIDBKYOp99SzvWI70CKOvSf+Kx5N7V+iCRKscLRPOE3BjKMKPn0cvvgXuGrVMWGEcx8Mf+w6iMmqRnFVHYLKg80edgRT+7yVlbj27EFYrdgTAhka3Rm/DmAeMhzYiKeoBOn3tzs6ShNfuE9I6fv8qIpNn9HvDMyGtrtBzsydyaHaQ6wrXsek7Ek9Khdb/3R3AMfW4IbjKcIhdSXwzNmwZrES677j4KyfwO3rYO6PIps9kRHIUT1egCXgYUdyGkvjOpVznTB1CobiLk7O6QLGfqMw2nxIjw9vaWm3f74msjSl9AU87E2lKo205WbjiczMVfso60rUz2hQ7F3798d9vr4W7A4QTOlr08P2uuDlb6uKw5zxcOsn8L1VcPb9Xe/IFwot4tjB8l33/siFCRrWBgR7+tTAMGEB/TsxeaerZA5vbgKlNx57HM1FM+rne0v5FoA2wyFBZuTMQCDYXLYZh9eBMSUFU24u0uWK+58RLdgh4qutxVWwH8xmrKNHn3zBmj/BsY2QOgCueS0yserT0aIvtmXwYBACd2Eh0u2OyMc1rlUTcxKHpKrc8r5jwZ4Wkc86LRlDm9us6lzsHoW/oUG1CjabsQwcSHljOUUNRSSaExmaOvSU96VaUxmVPgqP38OXZapOoKcU0GjBDhHHl1+ClNjHjWvVzwBQoZBPH1fvFz4V3qKYUGnysPdjsNlUAY3PF5H8ZE9xMe7DhzEkJmKzRi9+DQRS+/QE9Z5IsJzcOnQowmxu8q7HZ47HaDCe9t7pOeppLyjYth4Sx9aCHSKNG1XsLGHa1JNPrnsaPA0w8mIYGoYOfJ2hSbADu+qBsIgrAmGRpmnx06cjjgXzr6Mk2LZUzBmJAHj2x/cvo6Y1zl2qG4Yt8EQbFOyJWe0XrQWv2VKm7gn2xnbti++fES3YIdK4KdAzY+oJgu1uhE3Pqfdzf9S9RrUkJQ9MdmgoB0c1lqHqkdF9IPxtJes/+xSAxDPOgCPBYcJREmzAMii4yarbrPYknLt2AZ0T7GCMe2vFVnx+X49pAtWrBFtKifT7O3yf3+XCuW0bCKG60rVk5xuqgrHflOhsugUxGFoMM9iPdWhkPGzp8dDw+SoAksbngbMGUvpDWgcbYYURyxD1y+guKusR/SI0CtfOgGCPGY3H52FHhSpam5DVfh/67IRschNzafA0sL9mf/MwgyNH8Dc2RtDqyNIrBNvvdFLym0fYO3Uae6dNp+TXD3VoAoVz61akx4N1xAiMKSmtT25bol6nXNt9TY9ORcuufU0hkfB62I7Nm/HX1WHJz8fiDlQXDur+dL6WGPuPxmj1Id1evGU9v2Nkb0B6PM1TZkaNYnflbtx+N/mp+aRaU0NaY1KW8rK3lG9Rwwzy89Uwg4L4rYrt8YItvV6O3PY9qv79b/yNjfgbG6n6z38ovOVW/A5HSGs0xWynnTCnsL4cDqwEgym87VI7S4vUPmswJHLwYKeeKk5F/SefAJB01llwUL0n/8ywrd8pMoc3ZYrorn09A9f+/UiPB/OggRiTkthcrqYahRIOCTIxW127uUzd2xMKaHq8YB//299oXLsWY1Ymg5cuJf/11zDl5uL44gtKH/1tSGvUf6Z6FyTOm9v6xM7XQfrUMN2E9HCb3nFaCLYxJQVT375IpzOsMw/rP1Hx66R5c+GQ+nch/6ywrd8pMnTXvp6Gc0dww1ENue5I/DpI0MMODjRoimNrwY5NvFVVHP+bammat2gR9nFjsY0axYC//gVhsVD9yivUfXj6MZXeykqc27cjLBYSZ5wwSWXH6+p13LciYX7HCQqtR8opAAAgAElEQVR2hXqUtAUmujt3tjl7osN4jh3DtW8fhsREEvLMKn6dNgj6DArL+p2mzyAsKeopIhKbrJrux7E9MOh6bOcFe0T6CGxGG4dqD1HlrGqRix2/TaB6tGBX/uMf+BsbSZw3j8TZzXFW28iRZN9zDwDFv/wV3qqqU67R8NlnICUJ06ZhSEhoPuGsURkSwggjLojYd+gQLQXb728SbEegw2BXqf1AtbVMnDcPcXS1Ojgkyt41gNGMJUc94bgLdkXZGE04cAQ6QSZMmUJpQyklDSUkmZMYmnbqgpkTMRvMjMlQvwNby7diGxlI7duzJ243p3usYPudTqpeURuCWbd//6Tzfa75NgnTpuGrqKD0N4+ccp2ad98FIOmcE1o5HvgE/F41vzAaFX5tkZAOybngdUD1oSbvJFwedt3y5QCkXHhBi/h1DAg2YBmovHxd7Rj/+Gprce3bhzCbsY0b1+RdT8iagEF0TLKCcewt5VvUMIPUVHxVVXhL4nOkXI8V7Lrly/HX1GAbOxb7pJPLxIXBQO4jv0HYbNS+/XaboRHv8eMqhc1kIuUrF7U+WRC4fliMtQjPDpTNl+1qERLZ1WWPwlNcjGPzZoTNRtIZs+DwGnVi8LwurRsuLEOV9+Qpqohb70mjcGzeDFJiGzsWg9XaqXBIkKYCmvItCCGwjxunPiMwiCTe6LGCXb10GQBpV1x+ymssAweSfdddQNuhkZo33wKfj6S5czGlt9hUlDKGBVuJNGU7MfXtizE9HX9NDZ5jRV1atu5/KhySdOaZGMo2KS++7zhI7ttVi8OCcdBYDBY/fpcH3/Hj0TZH0wWCVcX2qarmIRyCva1iG16/F9t4JdjOnirYQgiTEGKJEGKVEOIf3WFUV/GWl9O4cSPCbCblootOe22fa76NfdpUFRp55NGm436Xi8rnngMg7fLLWt9Uvhtqj0FiNuS0n8TfrbTwsIUQLbzsrsWxa99X4ZDkC85XgxkAhp/fpTXDSsaw5q59OlMkrgmmjibOmo3b52bncRXSG5/VwbF8QKY9k7ykPBxeBwXVBdgDk6Ic27aHz+BuJBQP+1Jgi5RyDpArhOjmNnQdp+7DD0FKEufOxZicfNprhcFAv98EQiNvvUX5n/+MlJKyRY/hLS3FOnIkSfPnt76pybs+V1UYxhJBD7s0kBY1diwAji1bOr2k++hRHF98ocIhZ82HfUq8Y2azFSCjZZtVHceOVzzHjuHaswdDQgIJM2ewq3IXHr+HoalDSbGktL9AG7TsKxIc7efcvj2s9QndRShq8z7whBDCBKQBtZE1qevULv8ACHiDIWAZNIjch34NQlDxx6fYO2MmVS++CEYjub/+1clTTAo+Uq9Dzw2n2eEhayQgVBMor5uEwGOlI/CY2RlqXn8DgOTzF2B0FkPlAbD3iW4p/okkZWPpowYouQt2R9kYTWep+3glAIlz52KwWJqKXoKbh52hZRzbnJ2NqW9f/PX1cdndsV3BllLWSykbgVVAqZSyVXMKIcStQoiNQoiN5eXRLwv2NzaqRk1CkHyiZ3waUr/2NfKefFLFfOvqMKSm0v8P/4d94gk/KB4HHA6ktEWrM9/psCSqGY9+LxwvwD5lChgMOHbsCLmysyXS76fmdZVvnvb1rzd718POg3ZaXHYrQmDJzQTAowU7JnEdOEjZE09S9sSTpywPr33rLQCSz1VZWcH2qJOzJ3f6c1tmigBNcezgBKl4IpQYdoYQwgqcAfQRQrRSKSnlM1LKaVLKaVlZUegDfQKNmzaBx4Nt3DiMaR1Lt0u58AKGrfyYIe+9y/DPPiX5vDY2FAvXgM8FuRMhMTNMVoeZvioMQsk2jElJqtuZ19upsEjjxo14jh7FlJtLwsyZsPd9dWJ4DIVDApgHDQZ0DDsWqVvxMQcvuYTjzzzD8Wee4cDXv0Hl88+3yuhx7tyJY8sWDElJJC9YgJQyLII9oo8qoCmsK6TSWYl9vNp3csZhHDuUkMjdwGVSSh/QCNgja1LXaFit0s1aFsp0BEOgSYzBcorp3/tXqNeh57R9PhYITrspUj/swR4oDYE5jB2heulSAFIvvQThqIJDq1TvlGGxFw6yDFd/qNzFOrUvlvCUlFB0771Ij4fkiy4k9dJLweOh9NHfUnTfffidTrVv9PsnAEj75jcwJCQ0CWy6LZ2ByQM7/flmg5mxmepnY2v51qaZrI7Nm7v+5bqZUAT7T8BNQog1wHFgeWRN6hoNawKCfUaEOsjt/1i9DonBcEiQfgFvJCDYiXPOAJp330PFU1xM7bvvgcFA2je/CbvfUr1ThpwdG71TTsA4aAwGsx+/04uvoiLa5mgClC9ejL+hgaRzzyXviSfo99tHyfvjHxAJCdS++RaHrriSo7d9j4ZVqzAkJZHx3e8CzeGQKdlTEF3shNkyjm2fMAGMRpy7duGrr+/al+tmQolhH5NSniOlnC2lvCbgacck3uPHce3ejbBasU/u/CPUKakrgdLtalBAFBv2t0u/QM/u4i3g85IwcybCbse1cxeeDkwWr3z+X+D1knLhhVj694cdr6kTY2OgM2EbiMwRWFNVpki428pqOof3+HFq33wLhKDvvfc0CW/K+ecz+KWXMOfl4dqzh/pPPkGYzeT9/vGmmoegYJ9u4G6otBRsQ2KiKqDx+3F88UWX1+5OYiwnrWs0JdxPmXzy3MVwcGCleh08F0wRWD9cJKSrpkxeB1TswWC1qukwQH1gF749vFVVVL/yCgDpN98EDcfh4GcqHDLyK5GyvGtkDMWa4gHAtXdPlI3RANQuX450u0k8c54aDt0C28gR5L/xBrm/+Q3Z997LkLffUm17A2ws2QgoD7urBAV7e8V2vH4vCTNUhlPjhg1dXrs76VGCHdxUS4iEdw3xEb8Okhf4IT+mPIjkQC+U2nfeCen2ij//palxln3s2BbhkPkxGQ4BwJKINUfl3bt2xF98sidSF0ixTb344jbPG5MSSfvmN8i4+SYsg5q7Ph6rP0ZhXSHJ5mRGZ4zush0Z9gz6J/XH4XWwr2ofCdOVYDesX9/ltbuTHinYJ6XihQO/vzl+HQ+C3RTHDgj2Becj7HYaN2xot7DEffgwVS+/DEKQfc/d6uCW/6rXsV+PlMVhwZI/GAD3Pp3aF228x48rD9ZsJunsju35rClSe1EzcmdgMpjCYk/L9L5guqtz+44OTZ+KNj1GsKXHg3O7StOxTYhAuXjZDmgoU93wskaGf/1wkxeYjnNEeRDGpCRSzleFRME+K20hfT6KH3gQPB5SL71UtaQ8vh8KV4M5AcZcEnHTu4J1lMqxdR0u1pkiUab+k0/B7ydx9qx2K45PZHWRqnWYnRu+5IGWcWxjUpLKx/b5OpU9FS16jGA7d+9BulxYBg/G1KdP+D9gbyA5Zui50Z/dGAp5U8FohdId0FgJQNqVVwBQ9eKLp+wBXvnPf9K4fj3GjIxm73rzi+p1zKVg7dgvXndjGjkNg9mPr8GFr7Iy2ub0ahrWKi85ae7cdq5sjdvnbvKwz+h3RtjsCQ7vDRbQBOPl9R9/HLbPiDQ9RrCbwiFttFINC3tUX2xGnr6ZVMxgtgVKx2VTZWbC5MkknjkPf2MjFU8tPumWuhUfU/bEkwD0e+Q3mDIywO+DzS+pCyZ/u7us7zSi71isKYFMkX3xO2w13pFS0hichTqrYxlVq4tWU++pZ1T6KAakDAibTSP6jMBusnOk7gilDaVN+zp1K1fGTV+RHijYEYhf15XAsU1gssVmOfqpGDxHvR5e1XQo+847wWik6j//ofo1VXIu/X6qXnmFo3fcAX4/md//fvNu/f4VUFekyt0HzenmL9AJMoZhSVWZp6694RncoOk47v378ZaXY8zMxDp8eIfuXX5IPc1eMDi81bRmg5npOWqzcV3JOqwjR2LKzcVXXoEzTFOZIk3PE+xIbDjueU+9DpmvenXEC4MDj6LBdETANnp083i0++/n0JVXcWDhQkoe/AV4PKTfcAOZP/xB8xpr/6xep1wfH6EgkwVrPxUSc2+PrxzbnkRjYMRX4ozpTbnXR+qO8MLOF1i2dxnVzuo276t11/JRoWqudv6g8LfvnZWrvP01RWsQQpB89nwA6j76KOyfFQl6hGD7qqvxFBYibLYO/zUPid1vq9dYzT8+FQNmgSUZynZCVXNmSMaNN9D3wQcQCQk4Nm/GXbAfY1Ymub99lL4/ua+5qqxsl/KwTXaYekN0vkMnsA7JB8C1L36nY8c7wbF0tkDfjtf2vcbC1xfyuw2/45drfslXXv1KkzC35NW9r+LwOpiZO5OBKZ0vRz8VQcFeW7wWKSXJgY342rffiYtN6h4h2M5davCqbdQohKmNFKC6UljzZ1j+M9j0PDg70CG2vkyl8xlMMKrtXNKYxWSBYYEUxL2tOwqkX301w1d+TP+//oWBzz3HsA8/JO3SEyoYP31MvU66OnZzr9vAOlqJhOtIfM7t6wk0Cfbo0Wwq3cQvVv8Cr9/LgkELmJ4znTpPHT/6+Ef8ZfNf8EsVP652VvPs9mcBuG7MdRGxa1jaMDLtmVQ4KthfvZ+E6dMx9e2LJ9DzPdbpGYId/OEY00aC/e534KmpsPx+WLMY3vp/sHh6cxFMe2xfpgpGhi2I3e58p2NEYJM0uGnaAmNKCsnz55M4a+bJlaGlO9R3N1pg7p3dYGj4MI2egcHkx1fvxqszRbod6fHg2qMqTa2jR/Hw2oeRSG4edzNPzH+Cv5//d+6aehcCwZ+3/Jn/t+L/sbpoNXeuvJMaVw0zcmYwLy8ys0KFEM1hkeI1CKOR1IVfA6DmjTcj8pnhpIcIdsDDDozDauLAJ/DK9eCuU4J77i9Uult9CbzwLdj+6ukXlhK+fEG9n3hFBCzvBkZcAAazmnJe1wGPc8XD6nXqDZAWvp367kD0HdvcU2SPLlHvblwHDiLdbsz9+7OufjsF1QVkJ2Tz/UnfB5Ro3jjuRv583p9JtiTzydFP+O7/vsvG0o2k29J5eM7DXW72dDqCgh3M9U5duBCA2vfew9/YGLHPDQc9RLCVh20d3cLDbqyEZd8Bvwdm3gbfXgLz7oKb/wdz7lBe87KbYdvSUy+8f4Vq9pTUN/7i10ES0pVoSz9seTm0e/Z9qDxycyLMuzuy9kWC1AFYM9Rb55bOT9rRdA7nruAT7xj+vevfAFw16iosxtYti+fmzWXZ15ZxxcgrGJsxlq8N+Rr/ufg/5CblRtS+OXlzEAjWFa+jwdOAdfhw7BMn4q+ro+aNNyL62V0l7gXb39CgRv2Yza03HJf/TFUmDjwDLnikOcPBYITzfgVn3adE7NVbm7vQtURK+FzlJDPzu7Hd7Kk9JgXyp7/4l8qrPh3OWnhHTZJn/k8gOSeytkUCgwHbIDXN3bklvnpF9ASCDpR32EBWH1uNxWDhW8O/1ea1uUm5/HzWz3n5qy/zyLxHyEvKi7h9mfZMJmdPxuP38NnRzwBIv17FzCv//UJM52THvWA79+wBKbEOG9Y8dKB0B2x5ScVfL1l88igrIWD+/XDmvcrTXnoz7DwhfrVtKRz6DGypMO2m7vkykWL4AkgdCJX7YWc7HsQ7d0P1YTUNfuZt3WNfBLCNUQ3rXXt18Ux34wqEKLel1yORzM2bS5qtY9OfIs05A9VmfDBTJXnBAkx9++I+cICGVatOd2tUiX/BbopftwiHrHwUkEpoM4a2faMQcPbPYO5dSrSXXA8fPaQySna+qTYnQXnj9giUuncnRjPM/ZF6/8ki8Hnavm7Nn2DbK6pnyLf+obJM4hTr5DkgJK7iavwuV7TN6TVIv78pa+sdk3oNdwFMODh3oJqY9OnRT3H5XAizmT7XqCfR48/+PZqmnZYeINjB9KHAhmPVYdj1diC74a7T3ywEnPsgnPUTFQL57HH4/Qh45VrwNMKka+Iq//i0TL5GVSuW74JPHz/5/JcvwPKfqvdf+yNkRiCfvRsx5E/HkuQFvzzlwFdN+PEcOYK/oQFDZgarXDuwGCzMHzA/2madRP/k/oxOH02jt5GPC1UvkT5XXokhKYnGdes6Nf+0O+g5gh3MENn4d0CqNqDJfdtfQAg4+3648V01+sqSrITtgkdg4VPxUd0XCiYrLAz0D/nkd7DuGfB5wVEF7/4Y3rhdnVvwEEy4LHp2hovMkdjSVSzStTX282t7CsHfx7r8bACm5UwjwZwQTZNOyTeGfwOApXtV4oExOZk+V10FwPFnn42aXacjlKnpQgjxvBBirRDiTSFEeJrThgG/2628JyGwjRyhBOjLQGe56bd0bLFBZ8B1r8NPj8IdW2D27WCI+79nrcmfB+f9EpDw3r3w24Hw2DBY/zQIA3zlcZjz/6JsZJgwmrAOUHnzzi/XRtmY3kMwRLk/W/2xDGe3vXBz8ZCLsRltrCtZR2FtIQDp112LsFio+9+HMTlmLhRFmgOYpJSzgBQg/AX+ncS1dx94vVjy8zEkJqpc48YKyBgO/adF27zYZO6dKj6dPhQ8DSprZMh8uHUlzOjgH7kYxzZqBADO3buibEnvIehhr0lWs0NjWbCTLcmcP1jJ2X/3qAEdpqwsUr+hhnQc//s/ombbqQhFsEuBPwTeuyNoS4dpyvcM5l8HC2HGfbPnhDIiwbhvwg83wX2H4KdFcN0bkBuBpllRxjZZFUi4DpfFRZ+IeEdK2bThuLVPHZn2TIalDYuyVafn6tFXA7Bk7xKqnKpHfMZNN4HBQM1bb+EpLo6meScRytT0fVLK9UKIrwMWoFVTCiHErUKIjUKIjeXl5ZGys01axa+9Ltj1ljox7hvdakdcIoTKfrHEZnwxHJhGz8Fo9eF3+fAcPRptc3o83tJSfJWV+JLslKfC5OzJEa1YDAdjM8YyN28uDq+Df+9URT6WgQNJufBC8HiofO75KFvYmpCCtEKIhcAdwNeklK0qL6SUz0gpp0kpp2VlZUXCxlPiapnSd+ATcNVA33HxMcJLE3myx2BLVz+uzi164zHSBOPXFQOSQQgmZUVomEiY+e6E7wLw4q4XKW9UTmfGLd8BoGrJklNOZ4oGoWw65gD3AhdLKesib1JoSJ9PFc0QCInsD7RqjJeJMJrIY7JiG5AKgHPdyuja0gsIPvHuzlKR00nZ8SHYk7InMX/AfBq9jTy5SVU320aPJnHePGRjI1Uv/ifKFjYTiod9PZALLBdCfC6EiImyP/ehQ0inE1O/XIxpafE10VzTbdjHqKctx7ZtUbak5xMU7M1ptViNVkant9E9M0b58fQfYzFYeOvAW2ws2Qi08LL//e+YaQoVSgz7d1LKYVLKuYH/YmLrtKnCcfQYqDkGFXvAkhSYY6jRKGzTzwTAebA4pntE9ASCG44HcwRjM8ZiNpqjbFHoDEgewE3jlS/6qzW/wuVzkTB9OvaJE/HV1MRMU6i4TTQOpmrZRo2CAwHvevBcVYat0QQwjz8bk92H3+XHffBgtM3psXirqvAWF+OzmijuEz/hkJbcMv4W8lPzOVR7iKe3PI0Qgj7XXQtA1Usvx0SmUdwKtmtXiw1HHQ7RnIrMEdiz1C+aY/3nUTam5xIMhxT3syEN8bPh2BKL0cKvzvgVAsE/t/+TPZV7SFmwAGNGBq69e2NiIk1cCraUsjkkMnJks4c9JI4mmmu6B4MBW36g1eqGT6NsTM8lKNi7MlSjrXj0sEGlIl4x8gq80suDqx/EZzKQ9i3VGrbqpRD7yUeQuBRsb2kpvupqDKmpmAwV0HgcUvrHfcMiTWSwT1AzHh07d0fZkp5L8Im3INvH4JTB9LHFb4fLH039ETmJOew8vpMXdr5An8svA4OB2uXL8R4/3ul1/W43NW+8QdH9P6Xs//4PT2lph9eIS8Fu3nAcjTiwUh0cOl9XN2raxDbrPABcR6qQ7pgq1u0xOHcoD/tAjmBiVnxXzSaaE3lg1gMA/GnznyhJ9pN01lng8VC9rJ2xgqfAU1bGoSuvpOi+n1Dz2msc/+vTHFx4CY1fftmhdeJTsFuWpAeH6epwiOYUGEfOw5LsRfokzj3ayw43vro63IcP4zMZOJqpwgrxzpn9z+Qr+V/B6XOyaMMi+lytuvhVv/wy0tfO1KYT8LvdHL3te7h27sLcvz/ZP/4xiXPm4Kup4eht38NTVBTyWnEp2K7d6pfONnwIFK4FhBZszalJysKWo7KHnKs/jLIxPQ/njh0AHMkx4jOKuI1fn8i90+/FbrKz8uhKCoYnYh4wAE9REfWffdahdSoW/wnnzp2YBwxg8Cv/JeOmGxnw9F9JPHMevpoain/+QMhrxaVgN4VEUp3gc0HuBEjMiLJVmljGPnwgAI4vdKvVcBMsStqT7SXFkkJ+an6ULQoPmfZMrhl9DQB/3LKYtCsuB6DqpZdCXsNVUMDxv/8dhKDf736LKT0dAGEy0e83v8GYmkrD6tUhrxd3gu2rqcFz7BjCasXiUn/ZdTqfpj1sk1RBlXPPgShb0vNwble/hwW5yrs2iLiTlVNyw7gbSLYks6FkA/vmDEBYLDR8+hnuEJuJlf3+CfD5SLvichKmTGl1zpSVRdY9d3fInrj7l3XuVv1DrCNHIg4H0rR0OETTDrYzLlAzHksb8Dc0RNucHoUz4GEfyBU9In7dkhRLCjeNUxWQTx14nuQLLwQpqf7vf9u9t2H9euo//hhDQgJZP/hBm9ekff3rWIeH3oI2/gQ7uOE4dBCUbgeTHQbOirJVkcXhdbC7cjcHaw7GRLVVPGIYNA1bHy9IcHyxLtrm9Bi8lZV4iopwWQwczSAuC2ba4+pRV9PH2odtFds4et5YAKqXLsN/mowjKSVlj6nZqenfuRlTZmab1wmTiex77gnZlvgT7GAP7IxAX4jBc9S8wh7Iccdxfrn6l8x7eR6XvXUZC19fyHlLz+Ol3S/h83dsp7rXY7ZhH5wGgOOTd6NsTM+hybvuKzGazIzLHBdli8JPgjmB68deD8BizwdYR4/CV1VF3fLlp7yn7r33cG7bhjErk4wbbmg6Xums5MuyLylpKGk6lnTWWSHbEn+CvW07ADZrYBJEDw2H7Di+g8vfupxl+5bh8rnIT80n055JWWMZj6x7hDs+voNGT2x0EIsXEiYo76gxBkqMYwnH1q0U3vwd9p19Dkd/dCfuo8dCvrdx4yYA9vaDMeljsJlskTIzqlw56kpSralsrthC9VfUE/3xvz3bZkMxv8tF2eO/ByDrhz/EkJBAtbOa+z69j7NfOZvr3ruOBUsXcM8n91DjqumQHXEl2L7aWtwHDyLMZmyNqgViT9xw3Fy2mRvfv5EyRxlTsqfw5qVv8ualb7LishU8Mf8JUq2pfHL0E+5ceScevyfa5sYN9jlqfp9jf6nu3BegftUqDl9zLQ2rVuEtLqbu/fc5dPnluEJslNW4Uf0e7hzYc9L52iLRnMh1Y64D4C/9dmHKycG1dy91H3xw0rWVzz2Pp6gI68iRpH3zm1Q6K7nh/Rt49+C7GISBMRljsBqtLD+0nFs+uKVDoh1Xgu3crrxr6/DBCEcJJOVAdvz03A2FA9UHuP2j23F4HVw85GKePf/ZpjQpIQQLBi3g3xf9m3RbOquLVvPYhseibHH8YJ5yIaYEr+rct1cP5vVWVFB09z1It5udc/J46o7BlIzJxldZyZHv3IKv7vTzSvwOB47t2/EL2N1fMCV7ymmvj3euGnUVyZZk1lZsouFqNSildNEifPXNm9iugwep+OtfAej7k/vwCj8/XPFD9tfsZ1jaMN7++tv896v/5fVLXmdg8kB2Ve7ivk/vC9mGuBJsRyAcYu9nVweGzO9R5eilDaV898PvUuuuZX7/+Tw85+E2ewrnp+bz1DlPYTKYeGn3S6woXBEFa+OQxAwS8tQje+PKt6NsTPQp/+NT+Kqr2Zpv4FfzSvgs4Sj3XnScwlwTnmPHKFu06LT3OzZvBo+Hw30FLruR6bk9uxd9siW5KS/7D3k7sI0di7eomJJf/wrp9+Orq+PYXXcjHQ5SLr6YxNmzWfzlYraWb6VvQl/+dv7fyEvKA6B/cn+ePf9Z0qxprCpaFbINcSbYWwGwJ1aoAz0oHFLrruW2D2+jpKGEiVkTWXTWIkwG0ymvn5A1gTun3AnAg6sf5Lij801pehP2MUMBcKwPvVihJ+Lav5/qpUvxCfj7AsE3RnyLf130LyYOnMmTX5V4jVC9ZCkN69efco36T1XF37ZBMC5jHCmWlO4yP2pcO+ZaUq2pbKz4gpIffQths1H75lscuvwKDl5yKa5duzAPGEDOL3/B6mOr+cf2f2AQBhaduYhMe+tMkdykXH4z9zcd+vxQh/CahRBvdWjlCODcqnakbf7A4+yQ+VGzJZw0ehq5/cPbKaguID81n8XnLMZusrd737VjrmVW7ixqXDX8fuPvu8HS+Mc+S02gcew+HGVLokvlc8+B38/HEwUjJ53NL2b/gsnZk3nqnKdIGTGGV2craSh96CGk5+R9EikldR+pOaobhhuY1a9np9YGSbYk851xanTYE9XLyPvTYoxpaTi3b1dx6+HDGfjPf1JlcnH/5/cD8P2J32dK37bDRWf2P5NLh10a8ueHMoTXDmwCFoS8agTwlJbiLSvDkGjHktCgpqMn942mSa2oclbx/sH3eW77c7yy5xV2VOzAL9vf2HJ4Hdy18i42l28mJzGHp897mjRbWkifKYTgwVkPYjVaeevAW6wt1mXX7WGbewnC6Mdd6cJbXh5tc6KCt7KSqsDIq4/OUJ3pRCC0mGBO4LGzHuO9OVZK0sC1r4DKf/37pDVc+/bhKSykLtHA3jyY029Ot36HaHLlqCvJTshmd+VuPsgqYejy98n74x8Y8Oyz5L+6DGO/HH7y6U+odFYyI2cG3xn/ndOud8+0MOZhSykdUsoJQGi1mBHCsTUQDumfosLWQ+ZH05wmgrnS57xyDvd+ei+/3/R7Hlr7EFe+cyUXv3oxz1ZhY4IAAB0gSURBVG57lvLGtoWhrLGM7yz/DquKVpFuS+eZBc+Qm5Tboc8fkDKA2ybeBsDDax/G7dPtQ0+HyByCPVuJk+PT3pmPXfPa6wi3h01DBQvOvIHshOxW5welDOLGKd/l7+creShfvBhPcXGra2rfUf92G4ZKMhKz4r6lakewmWzcPVWVlD++8XFKjPWknH8+SXPnIMxmFm9ezLqSdaTb0nl03qMYDcbTrpdqTQ35s7scwxZC3CqE2CiE2FgeQY/FsUnlztpSa9WBGIhfby7bzNff+DrL9i1DIpmdO5trRl/DwqEL6ZvQl6P1R/nDF39gwdIF3LHiDt4+8Db7qvaxvWI7f9nyFy59/VK2VmylX2I//nHBPzrdNOf6MdeTn5rP4drD/Gvnv8L8LXsYQmAf0R+AxlW9s3Nf2WtLAFg11c61Y65t85qbxt1E7ZShrB0pkA4HpY/+tumc3+Wieola4+MJBs4deG67ohQRao7Bp4/Bf6+BpTfD+r+Bo6pbPvqi/Is4e8DZ1Hvque1/t1HaUIpf+nl6y9M8u+1ZDMLA42c9ftIfw65y6l2tEJFSPgM8AzBt2rSI1U03blIJ+gn2o6ocfVB0H8GWH1rOTz/7KW6/m5k5M/nZrJ+1Elyf38fqotUs27eMlUdWsuLIClYcOTmbY17ePH4959cnbUh0BLPRzE9n/pRbPriFp7c8zcX5F3fYU+9NJM6azfFPltLwZe9L7XPu2QMFh6izQf/zF55yo9BsNHP/jPu5/8gtTDrggw8+oO7jj0k++2wqn/8XvspKjuaY2dPfz72Dujla6vPCqidh5e+gZR3C9qWw4mG44BGY/O2ImiCE4OG5D3Pj+zeyt2ovX33tq6RYUyhrLEMg+OXsXzI9J/xZM10W7O7A39CgStINAnumG/IXgLnzFVUev4dqZzXJluQOV2ZJKXl+x/P8fpPa5Lt8xOXcP/P+kzI6jAYj8/rPY17/eVQ4Knh7/9tsLN1IYV0hRmFkTMYYFg5dyIycGU3xw64wK3cWFw6+kPcPvc+iDYt48uwnu7xmT8V+/hWIx5bgKq7HW1nZ1PKyN1D5+msArB4tuHT0t0577ex+s5ky/nyWzF3OtR/7Kbr7HlIu/grVr70OwHNn+RiYMjgiwnRK3A3w8reb57iOuQRGfRU8Dtj6Chz+HN74PhRvgQsfhQh6/imWFJ5Z8Ay/XPNLVh5ZibPRSXZCNj+b+TPOGRiZCEBcCHbj5s3g82HLS8RoljCsc3/R1xav5bntz7GuZB1evxeDMDCyz0guHnIxC4cubHcOncfn4dH1j7Jkr3ocvHPqndw49sZ2BTfTnskN427ghnE3dMruULl72t18cvQTPiz8kFXHVjEnr/dsBHUEQ7+x2HMEjUXQ+MEyUq68JdomdQvS5+P4G69hAgrPyGdM+ph27/nx9B9zyZHPGFjewFnbG6leshSAT89MZ+uQWn4y+urua6fqboQXvgWFqyExC77xTOvQ6JTr4MsX4O07Yf3TStwXPgWGyNmXYc/gqXOeorShlHpPPYNTBkc0PBTyN5FSht4DMMw4guGQ1Gp1YNi5Hbrf5XPx889/zi0f3MKqolV4/V76WPtgwMCuyl08vvFxzl1yLg+seoCCqoI21yhpKOGW/93Ckr1LsBgsPHbmY9w07qaweMfhIicxh+9N/B4Aj65/VG9AngohSByrwlcNK0/dwKen0bBmLabKWor7wPTzvh3Sz25OYg53TLuTP33VwP9dZuP4t85i5Q9ms/iMGvKS8rhsxGXdYDkgpfKcC1dDci7c+N7J+1hCwJRr4brXVdh08wvw3r3q3gjTN7EvQ9OGRjyWHx8e9gbVryAhvQ7Sh0DG0JDvrXHVcPtHt7OlfAs2o41bJtzC5SMuJ82WhsPrYPWx1Szdt5RVx1bxesHrvF7wOnPz5nLZiMsY0WcE9Z56Pjz8If/a+S8cXgdZ9iz+eM4fY7Yr2TWjr+H1gtc5UHOA53c8zy0Teof32FESzjoX/vcsjdv2RduUbqN4mZqUsma8mR8MuTjk+64adRV7q/ayTCxjNaoqz2Qw88jcR7AYLRGx9SQ+fQx2vAaWZLj2NcgcfuprB8+Fq16C/1wBG/5/e2ceJUV17/HPrerqdaanZ99gZkCEcUHFACrGp4AYHooxruCWRUXzYvRE8eSZEE1iPHl5MYaoWTQmmijEJeoTcAFEJZoclSgosqgMDDPDALNP9/T0WnXfH9XMDOsszEx3D/U5p05Vd1VX/eZO1bd+93fv/d3HzRQW5941PHYOMWIw8ytPnjxZ/juRDGawMIJBPjvzLIjHGX9JPeo5C2DOkYfM7iMUD7Fg1QI2NG6g2FPMwzMeZkLOhEMeW+Ov4a+b/8rL214mrIcPecys8lncPfVu8t35A/57hoP3d7/PjatuxKk6efmSlynJKEm2SSmHbKvn8y9Px4grjFuzEq20LNkmDSlGMMjmaWeiRuK8eP/5/PCyh/v3e2nw3GfPsXz7cjLtmdxyyi3Dl+xpy3KzJwgCrn4Wxn+lH7+7DpDw1d8NeUPk0SCE+FBKObm341J+aHrw/Q8gFsNVAKpDwglz+/S7mBHjzrfv7BqQ8tf//OthxRqgzFvGojMXsfry1dw26TZOLzidIk8RFd4K5o6dyxNfeYIHz3sw5cUa4IziM5hdMbtrxmeLgxG+EtyjzTzqwWUHDwwZabSvXoUaibO1FC44+/p+/14RCvMq57FkzhL+cP4fhk+sGz+Dl8xxBsz6Sd/FGkytmJNIjrb8NtiW/t04Uz4kEnzXzFfgyW8Hdy6UndWn3/3q37/inV3v4HP4eHTWoxR5ivr0O5/Tx02n3JT2oYSFkxeytm4ta2rWsGL7Ci4ae1GyTUo5MqaeQseODwmseQPft3+YbHOGlJrnnsIOfDo5j0sKe3XkUoNwOzxzNUQ74OTLYdpt/T/H1JugvQ7+uRievR6++SqUpG8a2JT2sKWUXQlmMoojMGEOqL2/Y17b8RpLtizBpth4eMbDjM0aO9SmphyFnkK+P8VM2/iz935GbaA2yRalHhlfNQeNBLfuwQiFkmzN0BHbuxdt/RZiKoy65KqUaig/LHoM/v4taN4GhRPN3h4DtXvmvTDxSogFYckV0Fo9qKYOJykt2NHqamJ1dahOgTM71qdwSFVbFff+617A7JI0kpOq98alx1/KrPJZBGNBFq5dSCg+ckVpIGinzcKZK5FxCL7W+6Sq6crul55FSPhonMJFp16VbHN6x9Dh/75thjDcuTDvabC7B34+RYGv/hbGnAvBBnj6MgimZ3bLlBbswKrVAHgKgwhPTq/TgQVjQb739vcIxUPMGTOHeRPmDYeZKYsQgnvPupfSjFI2N29m0buL+pSQ6phBUcg43exx1PHqi0k2ZmiQUtLwgjluoHn6xNRvg4kE4G/zYePzYM+Aa/4O2RVHf16bHa56ykwa17wN/jbP7NedZqS0YPtXvg6AtywEE68wC/0wSCn58b9+zI72HYzzjePes+5Nj6rfEJPlyOKRGY+QoWWwaucqfrnul9bM6z3InHslAIF1XyAjkSRbM/h0btqEp7YJvwvOvOTbyTbn8MTC5kjF302DL1aCK9sU69JBnMXGmWWe0zsK6j6AF28yvfk0ImUbHaM7dxLZvAVFk3iKInDa1Uc8funWpbxe/Tpum5sHz3sQt3YUVagRxrjscTxw7gPc+uatPL3laQxp8P2p3x++EWopjHPmNTiyf06kVdDx0hNkzrsl2SYNKp/9+Te4gA2nebmp/JzkGFG/3uxiV/sBdOw1RyBqLtDcoNjMYeXN27rzghSdAlc82a/xFn3GWwzXvgB/vgC2roAXboCvPQo2x+BfawhI2Se27UUz50FmaQil+EQoPnz6xvUN63lg3QMA/PTsnw44691I5uzSs1l83mI0RWPp1qXc9uZt/Z6xeUSi2sg6ZyIA7S88k2RjBhe9vR3banNmnax5Vw7/C3rvJvjLxfDYefDOr6D6HWj6HPy7TIHe8wnUfwSNW8CIm8/43N/ATW8NjVjvo6AS5j8LDq85GOfpy8weKWlASnrYMh6n/UUzpugb2wlTFxy2hbixs5E73r6DuIxz3YnX8ZWKfvTTPMY4d/S5PDLzEe5aexdr69Zy+fLLWTh5IReUXzCo4aNQPERdoI6aQA21/loaQg2E4qGuRk+n6sRlc5HnyqPAXUBJRgnjfOP6lRd4MPFedysNy26mY9Me4g312ApGxkCjbUv+iBYz2DhGYfa5NwzfhaWEf/4G3rzPFGJ7pjloZex0yC43Y9OxkNldTxpgc4JvtBmyGC7Kz4JvvAJLLjdfJI9NT8S4Txo+GwZASgp2YM2bxBsbsWfGcJV74dRDNx7G9Bh3rr2TplATU4qmcMeX7hhmS9OPaSXTeG7uc9y19i42Nm1k4dqFTMybyPzK+cwsm9nnUFIwFqQ2UEuNv8YU5h7bDZ0NA7KtyFPEhOwJnF54OlOLplKZU3nEeS0HC23iuXjGOAjuiNL20I/J+9ljQ37NocaIRAg8tQQPELj4nD7PYnTU6DF45Q74KJGXfcqNMONH4Bqm6/eH4lPghlXwt6uhYRP8cSZc+Csz/Jqi7V8pNzRdSkn15VcQ3rSJwtPbyFlwO5z334c8btE/F7GsahmF7kKevehZcl25R3XtYwnd0Hnhixd4ZP0jtEbMpO+aojGpYBLjs8dTklGCy+ZCFSodsQ5aw63UddSxK7CLuo46WsIthz23TbExKmMUozNHU+Yto8hdhFtzd81TGdbDBKNBGkONNHQ2UBuopaqt6qCUABlaBueMOofZFbP5cumXhzRvRXDJL6i570lUt2Dcvz5CcQ48fW8qsO3xh4g98Ht2FghOWbGaEm/p0F807Ifnvw5Vb5pe86V/hBMvHvrrHi3RTnjlTvh4qfn5+AvgwgdNr3+Y6OvQ9JQT7I61a6m9+RZUh864KyIod24wW4wPYPGHi/nTp3/CZXPxxFee4KS81K7KpCqheIhXt7/Ki1+8yMamjUj6dj84VIcpyt7RlGWWUZZZ1rVd5Cnqt2esGzq1gVo2NW9i3Z51fLDng/0G+2RoGcweM5v5lfMZnz2+X+fuCzIeZcd/nEqkBQq/Ppucu9M3n7jR2clH08/C0x7l3e+czU3ffXzoL9q+yxyU0rAJ3Hkw/xkYPYx5so8WKWHDUnj9boi0m2Gb6T80awhH6J02WKSlYMtolO1z5xLdWUPBae3k3vYDmHbrQcf9aeOfWPzRYmzCxsMzH2Zq4TS+aAjw+d4ALcEYkbiOz2WnOMvJSaVeCjLT21saLlrDraxvWM/29u00djYS1sPEjTiZ9kyy7FmUZJQwKnMUozJGke/OH/JGrFp/LSt3rmRl9Uq2tmzt+n5y4WTmV85nRtmMQQ2ZBB6/h7oHnkd1wnFvvI2alzqTPPeHD++5Hfdzq9hRrDB5xVsUeAZ3mqqDqF9v9p0O7IbccXDN82ZWzXQksAdeXWj2agHIOQ5m/RQqLxycMImUZl9zI26+FBIvg7QU7IZfL6b50UexZ8YY+41CxM1v7fd2k1Ly0PqHeHyj6THcMOFu9tafzIpPdhMIxw973lKfi+mV+Zx/QiFnHZeLw5aE+ecsjoqqtiqe2foMy6qW0Rk3BzwUe4qZXzmfS4+/dFAaLKUep+aCSXTuipM1ZTQlT6066nMON3ve/wdN37wZYcDm/7meKy65e2gvuPHv8PKtEA9B2TSYtwTcI2AGn89eg1WLzN4sAKPPgLNvh/Gz+zeLjb8eqt/tXlqrQfbo++3KgbzxiBtXpZdgB958i7rv/BcgKZvpx7NoJRRN7Nrvj/q555/3sKZmDQIFV/s17K3vDoNU5Lo5qSSL/EwHDptCW2eMnS1BPt3lpyPSLeYeu8q5E0zxnlFZgM89sOqOlJKmjij1bSE6InF0Q2JIidtuw+fW8Lk0sj12NDVle06mJR3RDl6uepmlW5ZSE6gBwGVzcfFxF3P1CVcfdd6YyHsr2HHDQqQuKP7O5fi+e99gmD0sBHfV8OmlF+Ftj/HB9CKu/e0bQ5dQP7DHFLSN5ihKJl1rxn3TpD9zn9Bj8OGT8PbPoTMxlN1XDidfZqbJKJoIqtZ9vJTQsh3q/m1OVVb9rvn5wNPa3BiKhhoLokhTm8RP/Okj2IFVq9l1x/eQcZ38iX7yfrQYTjFnspBSsvyLlfzvul/SHm9A6g5C9fPROyop8jr52umlXDqplOMLMw95bsOQfFrfzhtbGli9eS9bdvu79qmK4Etl2Uwq93FySRYlPhcFmQ5URWBISVtnjMaOCI3+CLvaQtS3hahvD7GrNUR9e5hovPdh3jkeO3kZdvIzHeRnOMjPdJDXY60qgqhuENcl4ZjetYRiOqGoQSimE4nruO0qXqeGz60xKttNWY6bEp8LVRnc1mzdkDQEwtS3hdjVFibY42UkJTg1Baem4tJUXHZzneG0keXSyHJpuDR1WEaYGtLg3V3v8tTmp3hv93td308pmsKs8lnMGD2DQs/AQhqt9y9gz1PvgJCU3HE9WTf9YLDMHjJat21myzevIbsxzLZyO5OffYVi36j+ncQwzKq6UEwv8sD/Y9hvhj82vQQf/w3iYXPwywX3weQbUrZnxUDQDUlbZ5RAOI6IdpCx5Rm8G/6I1qNdRaoOpK8M4fAiYkFkYDfigP7cIcXNp+qJ/CNWydrIBLbKMqLsE3lJIa2MVXbzzM+/f/SCLYRwAn8HRgOfANfLI/ygv4Id272bxl/cS/vrZka+7OODFC5ahDhjAdsam3ny4xW8UfciQWG+pfRQKTRcy+zKk7ns9FGcOTa334JV19rJG5v3snrLXt7f3kLcGPgLy+fWKPW58Do1VEUgBHRGddpDMdo6o7QEoxzF6XtFUwWjc9yMzfMwJs9DRWI9Js9DttuOU9vfu9INSXMwQmMgQkPPl1BbiPq2MLvaQuz1h4+qTGyKIMul4U0sPpdGttusbeS47fgS62yPRrbbTo7Hjs+tHVWYalvrNpZsXcLyquVE9O7h5eOzxzOpYBKnFZzGhOwJlHvL+9bTREoabp1L85oqAHzTKsi759doFZUDtnGoiIaCrPv9fbj+sgxXRFJTbKPiyb8wofwQQ7pDrdBclVi2QUtiu2UHRANmn+ieCMUciShUU8CjHfvvP2GuGd8d5nh1XDcIhOO0h2K0h2L4w+Y6FNUxpMSQdDkYXX9KQiYEoutzJKbTEozSknhWmzvM9b7vDlQ6BYMzlC1cqLzH2cqnjFH2HmRbo8xig3Ec64wJvGecyCZZgY55b2c4bJTnusnx2BFCEIrGaQhE2NMe5vP75wyKYN8ITJZS3iKEWAE8JKU8bGDvlJNPlMufexJ0A0MaSMPAkDpIkNEIcm8dYnctsqoKuXkbxvZWc5+QBKboLJt1IesMjYZIFXFtO0IxYz1SdzOKr3HDqfOZc3IpHsfgNDS1h2K8v72ZT3e1s3l3gMZAmKaOKLohEQKyXFqXJ1zic1Lic1GaWEp8rl7t0A1JSzBKYyBCU4cplI0dEZr2rTsiGAZoNgVNETg11fRe7YrpwWoqTruKXVUIRXX84RgtwRi1LZ3sbAmy13/k3Bd2VcHtUNENSUw3iMSNPk1vl5fhoNTnpDjLRZZLQ1EEqmLe7JG4TihmEIqaNYHOaJxAON710IRjA0su5bGreBy2Lq/9QC/eoXWXict+8H6npiKVTj7zv8+G5nfY2PI+UWP/OS1VoVKSUUpZZhl5rnwK3PnkuwrIc+WSoWWQYffgsXtw29zYhUbo/tvwv/QhGAIUiXtcFhmTJmIffyLamErU7HyENxfhzUHYNFBtSFVFJCZ9NQwj4bUaIA2koSMTawzDzOkiDbMqbehI3QCMxL7E7zCQukEkHqEz2kGwuYGOhnpat20ltvlzCjbU4Q6b/9TNJ2Uw5e6FjPJkQrAR0bYT2nZC605oqUKEDt8Vcx9SsYE0EIdIEiZVhzlKcMx09IlXoOefgGFA3DAwDNCl7KqN6UaPRUqMxDoSMwjHdCLx/dfhuEEk8TmSqGHuE2RzieNPbPcMcQ4lPreG16l1ib2U5ssilnienHqQbL0JpxEkJB20kIWaWUCxz0VZjpvyXA8VeW7KcjxU9BDqAzEMiaoqgyLYS4EXpJQvCCHuAPKllIdtxTjZ6ZLPV1T0WhD7iCvwwQTBs+co7M494A+RAo8Yw5cL53D7mVcx2peCHe+TTCiqU90cpLopyPamIDuazO3q5k78oRhRff+HTgjI9di7QjIlWeaLp8Tn7HoJFWU5D/LM+0MkruMPxXs8aFFagzFaE15Ma2eM1oQHY9ZCzH36YFdFRAzVWYfqrkZx1aA6GhBaC0L07zqjGyVXvmMw5XOJkqI5s2oLJfppIc73taEdocLZKR3skEWJpZhqw9yulkW0kYGBAPadQKJiYENHwUBDJ4izy1tMJkKA16nhdXWH4rxODZddRRUCRQiURI0Xes7BK/f7rKkKOR77fktuhrnOdvev/Wnf/TvQEGVfe4n05qrmAvuCMn7goDm2hBALgAUAlS4nARdIkSiafWtAV8HvBr9bsCdHUFWs8nGFg4DbiSYyyVG8ZDtyqMiqYFLhCcwZP418tzUQ5ki47ConFHs5odh7yP2mB6yjKgJNFdhVBdsQN4I6bCr5mSr5mX1vfJJSEojECUV1c0l4WOEe2/s8+p6x/f3j/XrXd93He+kMjScelCDBIIpuawJbM9LmR6h+UP0IWwcoYRBRhBIBJYIQOgiDmhyDBy6R5AR0TqvWGbvboLAN8vwSZwwcUXDEQJEgEsuBJWwACNNRh/2fj0N9Jw/zGQEhJ4TckphHYuQaFOXEOdUdR6KyQ5bSLj34pYcWMqmT+V1LtSyiAR/dgtwDsd+q60sDlWhCoMP7H4rAFCdFCGyKSNTChCmYibWqdC+KMI932NSudhCHTcGRWDt7rJ2JY7wJMe65eF0amQ4byiC33Rwtg92WdDh6E+wmYF9/qazE5/2QUj4GPAZmDHvqIE/CazFw9oVYUh0hhOkxObXeDx5ipJRHbDTtbT+ATIRBhHqIhrshpG+T4FmkM725W2uACxLbM4C3htYcC4vk0psY96UHjFAUhM02onpNWKQGvQn2EqBUCPEJ0IIp4BYWFhYWSeCIIREpZQSwptu2sLCwSAGsYXgWFhYWaYIl2BYWFhZpgiXYFhYWFmmCJdgWFhYWaYIl2BYWFhZpgiXYFhYWFmmCJdgWFhYWaYIl2BYWFhZpwqBOYCCEaAR2DtoJB04eh8h7coxilUU3Vll0Y5VFN6lQFuVSyvzeDhpUwU4VhBD/7kuqwmMBqyy6scqiG6ssukmnsrBCIhYWFhZpgiXYFhYWFmnCSBXsx5JtQAphlUU3Vll0Y5VFN2lTFiMyhm1hYWExEhmpHraFhYXFiOOYEGwhxPeEEG8k245kIkz+IoR4TwixTAgxOFPPpzhCCKcQYoUQ4mMhxFOiL1PGjGCO1fvgcKSbNox4wRZClAPfSLYdKcDZgE1KeSbgpXvqt5HOtUCdlPJUIBuYlWR7ks2xeh8cRDpqw4gXbOA3wN3JNiIF2ItZFgDRZBoyzMwAVie23wSmJ9GWVOBYvQ8ORdppw4iuDgkhrgY+BjYn25ZkI6X8AkAI8TXADqxMrkXDRi7Qntj2AxOSaEvSOYbvg/1IV20Y6R72RcBM4BngS0KIW5NsT1IRQlwM3A7MlVLqybZnmGgCshLbWSR/CHLSOUbvgwNJS204Jrr1CSEqgMellOcn2ZSkIYQoAp4HZkspg8m2Z7gQQnwLOENKebMQ4hXg11LKtGlkGmyO1fvgcKSbNox0D9uim68DxcBKIcS7CSE7FlgClAohPgFagDVJtifZHKv3wYjgmPCwLSwsLEYClodtYWFhkSZYgm1hYWGRJliCbWFhYZEmWIJtYWFhkSZYgm1hYWGRJliCbWFhYZEmWIJtYWFhkSb8Pwgi+f70gBVWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "with open('images/ch9/PDOS.csv', 'r', encoding='utf-8') as file:\n",
    "    ls = [line.strip().split(',') for line in file]  # 逐行读取文件中的数据创建二维列表\n",
    "for i in range(0, 8, 2):                # i取偶数0，2，4，6，i+1的值分别为1，3，5，7\n",
    "    x_ls = [float(x[i]) for x in ls]    # 列表x_ls为ls子列表中序号为i的元素的浮点类型\n",
    "    y_ls = [float(x[i+1]) for x in ls]  # 列表y_ls为ls子列表中序号为i+1的元素的浮点类型\n",
    "    plt.plot(x_ls, y_ls,  linewidth=2)  # 绘制当前数据的曲线，颜色通过索引获得\n",
    "    \n",
    "plt.xlim(-5, 5)                         # 设置x轴的上下限，获得区域放大的曲线\n",
    "plt.ylim(-0.5, 8)                       # 设置y轴的上下限，获得区域放大的曲线\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"images/ch9/9.5 PDOS.csv\" target=\"_blank\">9.5 PDOS.csv</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    cl = ['cyan', 'green', 'red', 'blue']  # 创建颜色列表\n",
    "    sty = ['-', '--', ':', '-.']\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        plt.plot(x, y, color=cl[i], linestyle=sty[i],linewidth=3)  # cl[i]根据i值取颜色\n",
    "\n",
    "\n",
    "def draw_label():  # 加图名和轴标签\n",
    "    plt.title('态密度（Density of States）曲线')\n",
    "    plt.xlabel('能量/eV')\n",
    "    plt.ylabel('分波态密度/eV')\n",
    "    \n",
    "    \n",
    "def zoom_area():\n",
    "    \"\"\"只显示横坐标在-5到5之间的区域，局部放大，纵坐标根据曲线的最大值来设定为-0.5到8\"\"\"\n",
    "    plt.xlim(-5, 5)                         # 设置x轴的上下限，获得区域放大的曲线\n",
    "    plt.ylim(-0.5, 8)                       # 设置y轴的上下限，获得区域放大的曲线\n",
    "\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    draw_label()\n",
    "    zoom_area()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 numpy读文件，利用数组绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "[[-5.85574280e+01  0.00000000e+00 -5.85574280e+01 ...  0.00000000e+00\n",
      "  -5.85574280e+01  0.00000000e+00]\n",
      " [-5.85469054e+01  0.00000000e+00 -5.85469054e+01 ...  0.00000000e+00\n",
      "  -5.85469054e+01  0.00000000e+00]\n",
      " [-5.74735926e+01  1.10663000e-04 -5.71684350e+01 ...  1.02657000e-04\n",
      "  -5.74735926e+01  1.11050000e-04]\n",
      " ...\n",
      " [ 1.81739122e+01  2.81961870e-02  1.77003919e+01 ...  1.02179000e-04\n",
      "   1.61640814e+01  6.77418280e-01]\n",
      " [ 1.81844349e+01  2.56184650e-02  1.77109145e+01 ...  9.39000000e-05\n",
      "   1.61746041e+01  6.83354996e-01]\n",
      " [ 1.81949576e+01  2.32660780e-02  1.77214372e+01 ...  0.00000000e+00\n",
      "   1.61851268e+01  6.90765294e-01]]\n"
     ]
    },
    {
     "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": [
    "# 利用numpy读文件绘制数据曲线\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = np.loadtxt('images/ch9/9.5 PDOS.csv',delimiter=',')\n",
    "\n",
    "print(len(data[0]))\n",
    "print(data)\n",
    "for i in range(4):\n",
    "    plt.plot(data[0:, 2*i], data[0:, 2*i+1])  # 切片方法获取两列数据\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 pandas读文件，利用dataframe绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j9CIi4kJ9wi90lGLYO8U+biC0TmQlxfjeg5XnlWWhlGqpTaW1Jr91G2YK33+nchHRnocuuRhJGyOZ9DcuYtWfxfZecQ4eZOLLX+bA7966qOP0IiLiQn10rSfeS3ZK6Im7LXjvuhQU1YQTmy2MxLP3/oDic8+hTA8VReJ+KqHOB5WcSRuVTAEsanUfb5GRuDvpV4xqSXVcMCLiQl3ClX0IhawFqXadiht44q7lT9TFGYmHIq4sC4zW3uEUnnwCgDUvK0ff9FDEvUDEVSqJCiLxxRT81CzG3ATu1OIuAr2MiLhQn8gT78HslJIv4l4rJnXLoZ1i+xkvLYzEy4cOY61eTXoo7xcWKY0u+uIdZq0Y6RRGIgEsTsR1aXEi7mVFxJtFRFyoj64V8V4q9nGcwDduYSSO3fpI3Mvl/LU1SzOQHAp6pPhiG0biRiqFSgSReCHX/LkKi+u9IotSNI+IuFCXk+yUFmdRdBJeUEXpmb6Ie+X4MnNCO0OZfiTeyhRDL5/DSKcADelhP6UxqOCMIvFMOrJTvPxM0+fSi7RTvOmpymPxxReEiLhQn8hOCaPR3onEQxHXQfGK2wo7xbbBUC3tnaJzeYxUkHmSWeZH4sH5w8UijExfNLGpCwsT8eqUxMV2QaxOUdSLjOp7DRFxoT56ViTeQyLulnw7xQtFPMYl1KLsC8tGGaq1dko+j0oGVZTpET8SL4V2SjDBmulDBUKvFxiJ1ywwsdhIvGpi0ysU8HI5Srt3L+qYvYKIuFAXrT1QumpRiB4S8cATx/Yn/NwYm0PpkyLxFrS6DfDyeYxkcBFOj/hphoHwRotFZPpQibT/3EJFvCpiXnQknq2NxA/+0fvZ8erXSMrhPBARF+oTikuw2n0veeJRR78gEndi7DSoq7NTjFZ74nmMRCDimeV+JB6mGIaeeN8gRioQ8QUKcfVk5mJyzAG8qolNr1BgOmiqVT46tqjj9gIi4kJ9PM9PSwtEvJc88cg+CVLvvDhXpA8nNu0EqsWRuM7lMBLBVzwdeOJBZOtl/eIa1T9Q5YkvLDulJhJfbJ541cRm9XHdyYlFHbcXEBEX6hO2ojV6z04Js1FUYKc4MUbL0co6dgLa4IkbdvAVjyY2AztlxrcvjL4hVDrj719YWCdDL0Y7xZvOosKLZrWIT4iIz4WIuFAXrbXfoCm0U3pIxENPXCWCFMNY7ZT2ROLacdClEirsDhvZKcGk7Ywf+aq+IVTSF/GwEGje56i2UxadYjiNNTp68nFzzeeu9wqLEnGl1LuVUt+PazBCB6G9QMSDBli95IkHoq2SwcRmnNkpVSKOYVTy8WMmKuYJVwxIj4ChKyKem0GZGpXqjyLxhab2hRkusLhqT/BTDEMRr4nwJd1wTpoWcaXUJuBd8Q1F6Cg8HZRqB4sh9FDvFC8QOiOoZIwzEo888UQySDFskYjnAhE3g+NnlvmReFiNmsv62xIZVKrPf660MMEMLRTHAHeBVsxJ460W8aroe7GVoL3AYiLxjwN/GNdAhM7ipIWSe6gVrQ4i77BFa5zFPtGiDFYCTKN1dko+yD4xg7GHE5uB3+/lZlCWB4l+jHQg4k1G4tkUFHPN9z7xikV0ucz3ZrYC4B47XvU+RMTnoikRV0q9HXgMeKrB9luUUluVUlvHxlqTIvSTP/ggD//F37fk2AL+xGa1ndJDnni4+o4Z2Skt8MQTCVQb7BSlimClITkwS8TzQSTehwpFfIFNrMJIPJsGdxERszfl+/O7LD9jxqnSDInE56bZSPz1wHXAl4BLlVK/Xb1Ra32b1nqL1nrLaHCLFDfLvnE7/f/0dy05tgDowE4Js1Pa3E+8dHSMY/f8oK3nDPHKoYi3wE6JJk1TKMOoVMbGTOSJ6xz0rQArFSwHF7QUKPiLJGNnIJkG9IJzvaNIPA3eIrJTwuZXE/3+z9VLvYknPjdNLZSstX47gFJqM/CPWmtR09MNb3aKYXvtlAd/572seOwB+h58iNTQQFvPrZ0yLgorKJSJM0+8dmKzDZ64l4XMMrCSfiReCCLxQiGIxPtR2kOZi4jEUwqdXcyCEn4kPtEHHhKJLxRJMRTqoqPslLDYp70iPvCE748e2bmvrecFPzvFMwxsOxTxGO2U8IJgJ1Gm4atWC/BCTzy3D4Y3gWGiTBVl3uhCMZrYjAS+uLAS9+pInAW+FmDqzrvY9ba34Rz3PfBcSlGya0VcL3LCtBdYlIhrrXdrrV8Z12Dmi9dDS4UtGV7giS9R75RwfcuZyfb3mdaug6NM7KCXerwTm6EnnmppimG0/FruAJzxcv+cphG1T/CKxYqdYiZQZqUQaL64hTwekEvSlIiPf+YzFB57nPyjj0FwnJIF5aNHK+8jxjbApytdGYmXCtIUp+UEnriKin3aG4l7gY1TXOQCvM2gHRdXGdihneLGKCRBJK4SST/zp0VV92GanrI0nP8m/7FpRpG4Vyz71ZyGWYnESwtbKLmcy1KyfeE1mhDbsAVAccfzAMwkIZ8EHfzOSxY4i6wE7QW6UsTLVdVhukUTQ72OXuLsFDdYnr2YXYKKPcfBrbFTYiy7j+yUMBKP7dA1RJ54ph/6/eQCZVlR0ZYuOaiwOZZhYZhV6Y/zpJzLUrKgZCmMsrtgy83o87NiSjt2An4kXrAr2yczUCzIij9z0ZUiXspXRNxp4jZOmAdaA6rKTlmaSLyUbX61mWbRroOrzJZE4qFQqmTgibcsOyXsUtgfPadsIxJxr+hghm1qlUKZCm+BkbiTz1G0oRyupb3A7BYj5TfeKu3ahWtAIeH/AyibflReKkrZ/Vx0pYiXq/5YxFppEVHF5tKIuA4qRZ0lKPbQrounDGzLf+9enA2w3LC5VgrMVkbiOZSlUOmh6Dk/Etdoz8MruhjpRGWb6UfnC8HJz1AM7BRYuIhXR/65lGLdwHoKtv97n0mCY4K7yAWYe4GuFPFSVfRdHZULMRLYKUu9KIS7wOgwDlS5TNm0MFuQnUJ1dophxh6Il/btY9cvvY3C40/4nneyKj3TskD7Je4ARjoZbVKWUbF65olbKPh2SmCBLDjPfKZylzWThHX96ygEQ8ol/XJ+WW9zbprKE19qylXRd1FSkFqCnt0Aa4l6p3gL9GnjQJVLlA0LK4rEY04xNDTKSrRkYnP6u9+l8PjjANhDJqQGo20qzLY5cQIAI5OqbDONBWeCePmcP7EZXOcXGolXi3g2pVnbvzbyxHNhJF6WIG0uujISr/bBy3m5UrcErf3mV1F2SnsnkFUQoi7F8lyqXMIxLQwz9MTjFfGoErYFdoo7MRk9NiyvJhJX4ZqhQY9usy9T2WZVcsjni84XKNgKK+pHvsDeKzUirvxIPHB4smmFY6oFZ8z0Il0p4tXZKaVF9jEWGuD5Gr7UDbCWJhIvUzZtzCAS1zFmp+C6/lqXBJ+tVrFmWDlHj0SPDdODZFUkHvRHdwIRr570NGyzsizdPNH5PEUbUn3+ORYTiedSzBJxf3JzobnrvUhXirhTFZ2VRcRbQmSfLFHFZshSiLhRLuNYFRH3YpwPiCJxACv+C6QTWCUAhunMisSDhl7HjvnbB6oyVyxzwRcrlS9StCGTGQYWJuK6VKqZ2JxK+yKeT/gfTj7h2ykLTXvsRbpSxN1CtZ0iIt4SPO2vxh5YCu2OxCt2ytJ44o5lY0SReJwi7kbfuvAuJ06h8mYqKXnKcCBVyU4xUsFyc0FZu9FfFaXb5oLz4VWhRDEBff2+iC9kdR83iMLNFSsAP01xJDWCk/FnNm0XXBPUAu8OepGuFPFqT1zyxFuD9sKKzaXJTjHD8y1FJO6U8Uwb02qBJ+66/rJsgAovkDH6/tULKmjHqLFTjLQ/kekcPuz/PFCdfrjwSNwolijY0N+/zD/fAqorw4tN4sLzANi+XjGSHOHoRv/uYN/GNNqyKtk8QkO6UsTdqj96Vzyz1uB5fiS+RNkppud/edt9O13Yvp3lR/bi2ZVIPNZePY6LCiPxKNKP727Sm5khec7ZAKRHizV2SiTiRw4BYA6NRNtUwka78/fmZx56CLPo4CQskhn/HAuJxEM//MPDP+EH//ONbD3XYCAxwPhZy3nPr5s89tJVaMtExZneeZrSlSJe7Ym7RfHMWoLWKLV0FZtW2Pfaae/vd8+vvhOAcjKNZcffS127LsoMTHHLn2hc7Erx1Xi5HOlLLuWMf/hzVpyfrUkxNDJpAMphJD60LNqmbAvtzE/EvVyOve+8CYDCQCIS8WJ+/qv7hCKeS8DdiR0MpIYwDZOBxAD7RxXDqRE828CIc1L5NKUrRbwmEpdigJaggy6GylgaT9wKIvF22inacaJVZkzDwDRbUbFZ5YmHIh5jLrQ3M4ORyZDa4K+pWROJpwIRP3gIM+Gh0rXph9qdXy+i0r5Ke+Byf4pkcJxSbv59TkIRLyQV209sZyTp3xUMJPxjDSYH0baFIZ74nHSliFdXcTki4q3B83zvdgmyU5xSGTOsgmljK9LqPtaYRuSJxxuJexVPPDx+TCKuXRedz2NkMlD0L0Y1nnjQcMqdnMZMurUCH+SQz8eDdo5WPqfj6wdJ9TUfieeDlMKRlC/i/bbviS9PLQfLwojxAnq60p0iXmWhSCTeGnSUndJ+O6U691+10U4JJwWPLFvD/S9/M6blfz3ijMSpEnECEV/oijqNCBdpMPr6oBgIao2IV4p7zKS/SHKICtYT9eaRKOAFVdJffveLyG4YIZXxJ0jLTUTioYgPJ/0MlzASX55eDrYldso86EoRd6tusaW3QovwAk88tFPa2PK3poCrnSIeiODXL7sRPTgSReJxtuHVjuuvck+lgjIuOyUURiOTgUJQuVntiVcV95hJD9LVE5uJYCxzf5/CdS+PpRwyVoZMagAPKC+gBUZkp8yKxK3g763P7kPZNoaurAsq1KcrRby6FNuTic2WoLUfiSuz/YtCVDc1U21MMQsnGHdNewymrUrFZqyeuI4mNkNPnJgyrLxcIOJ9mapIvMr3TvVh2P57sTMNRHweQVG4CPO0KtJn95FJ9PnLqi0gEi9nfbtndiT+86t/HoBzhs+BoAGZFPycmq4U8eroO85FbIUqPB144mE02k4Rr2RrtNVOCc5bMm1StokVikjs2SnB1y6c2IzJTgkjZCOd9j1xwwar0uQKMxGV/Cf6HUgPR5tUwi+ymU/VpQ7ulKZUgYyVIW2lySXBnZ6/J16YnsAxwLH8C9qylJ8pc/WGq7njjXdw9YarowpTEfFT05UirsVOaTn6pAZY7RPxcpUva7TxVnrihB8dFkybmaIbZafEa6d4qCB1MbJTnJgi8UDEVTLl2ympQSo1/oCVinLUEyMWWFWtaJOBiOfnjqbD8+wuHiJjZ8jYGXJJ8KbnH4mXpifJJ2B132oABhO+7WMog3NHzkUpVWU3iYifiq5sRVv9S5VfcIvwNBhGJcWwjcU+5SWKxKcmZ1CAZyf47WvPjvqJE6ed4nhRJB7ZKXFF4kEUrdxsIOLDtTtYCVZdPMnx3avIbOqv2WQk/Yhd5+deSakw4zfQCkvlM1aGbAqGFiDi5elJ8kn4zRf9Jvun9/PaM1970j6RiEugdkq6VsQ9FAZ6wUtKCfNDa3w7JYpG2zexWS6WoltEI85FiueglMuTBP70rZdwxgo/Hc9DxWunOB5GeHGw5z+ZOK9jByJu3PEOGNWw+qLaHawUgxsLDJ5fhL4VNZvUAkT8xORRHBtQiuHkMBk7w0xKoabnv5SaM5OlkIANAxt48zlvrruPsQCfvpfpSjuFcpmiJX5ZS1nCBlhhZ0pHGW0VcSdYXDhd1d3PUyrW+QDt6qjcPir2ic1OCSJxU4NX63kDlXTD7GEYWF2zSQXrXXqFuUW8NDMVLcl27si5pMwUuRQYM/PPTnGzWXKJSkphPSKLZ5EiPvPgQxz56McWvHJRt9CVIq7LZRzTwlGGiHir8DTKMKoaYLVPxMMulQU71VYRLwcinupLR895yoi52Eejgkg8zAiJqyo1zK4xzOCuqaqDIVAbfQ+uq9mkEv57nk8k7ubzlGz47Ks+y2WrL0MpRTFjY83Mv32Azs6QS6pTirgZ3h0sUsSP/K//xfF/+RfyjzyyqON0Kl0p4jgOjmHiGNLlrFVoTU1g1b3cAAAgAElEQVQkHufCBXMRdqYsWknMNoq4G6TOpfpniXiskTgVEY/K7mOKxItVkTjAwJraHTKnEPF0IOLzyPX2igWKFqwfWB89V84ksHOled+1qGyOXKpSoVkPM8iYWejandXoUonis88CUHhme9PH6WSaEnHl8zml1ANKqa8rpdrrrZfLuIaFY5htb5DUM2g/Eo/slDZmibjBl7aUSGG287z5AmVlkqlae9JTMXviro4m7LADuyAuT7wwS8T7V9XuUJUXzvCGmk0qlQmOMbevrfMFSjakrcrFzulPoTR42flNbhq5ArnkqUXcCCwep8l1dL1Cgedf9ero5/LhQ00dp9NpNhK/ErC01pcDg8AN8Q1pbpTj4JoWrmktSb/pXkB7BMU+Jigda5rdXIQphuVkqr2ReKFAybRJh90LAW0Ysb53z6tkXUR50DEFImG+uXHpr8DVfwSX3lS7g1H1dV91Qe2mZBiJz22J6IIfiWesShl/ud8XXLdqZaGGr9caa6ZIOZPANMyG+1nBmErzSHusR27rNpxDh8C2MZcti3q+uJOTnLj99tPGI29WxI8AHw8et33qWDllX8QNs60NknqKMBInSDVuY1/nsB+Ok0xHfcXbgS4UKM4ScQ8V2yLRWmvw1EkiHldrgbD3i1q2Hq5+X23kHfLy98L6y2DVhTVPq3SQjTOPSJxiiXLCwDbt6Cl30Bf0cBHmRpz4ylfY+brXY7gebl/ylPtaQdfF0jwmW+tR2rkTgHN+cC+JzZtxjh4FYPxTn+bwn3yQ6bvvaeq4nUZTIq61fk5r/ZBS6heABHBX9Xal1C1Kqa1Kqa1j1Z3hYkI5ZTzTxDWttuYR9xK+Jx72TG1v/4qwCZOXTGG18Q4gFPGUXflaaMOIbVWjcIIumtCMO8Uwn0UZGpVqPFnIte+HX/9+tNhHSCjiujR3JK6KJVy79vV60LdF6om4m81y5CMfobR3L0f/4i8jcS0sa2ylANgpf0yleUy21sOb8SN4c2AAa+XKSMTzjz4KQPH555o6bqfRtJetlHojcCvwBq11zV+51vo24DaALVu2xD4jphwHz7RxzXJbGyT1FIGdAvhVfu0U8UDsvHSmrXYKpSJlK+E3/grHoozYin1CvzlcdV4lguyLuOyUXNb3w5OnFsd6qHR/MMa5/WejWMYdqpUONeSnL9YT8ey993L8c/9Kad9+vKrS/NKKoZP2rcZO+9F9uclI3M1mUYkEKpHAWjlK9r770OUyhe3+BGd1S91uptmJzdXAHwCv01rPv2FCTBiOg2f5nriSDmctQVfbKUabI/FAxHU609ZInKIv4tXoGLNTwpL2sE9JVLEZkzfr5Wd8EU80IeIZX4Tn44kbJQedsGueS4wsB+qLePG55wHI3nMPaE3fVVdxdGWS7FmrTtq35phJPxJvemJzZgaj3/8s7NVr0LkcMw88iA6ykOayfrqFZj3xm4A1wF1KqR8rpW6OcUxzolxfxLVhip3SKpbSTglth3QaE43TpnkPVSzi2LNE3IhTxP2IMhRxwkg8rjzx3IyfI548hZ3SgEjEi3MLplly0MlaER9ZsR5PQfH4sZP2d44cqfl57cc+ykduXU2mf/ikfatJZnwBblrEs1UivsYvbpr46h0AJDZvntckbDfQlJ2itf4Y8LGYxzJvDLeMY/YFnrhMbLYETU0kHmf/kLnwAlFTwUo0pUIx6ijYSoxyEdeunWzz88Rj8sQLoYj7F4q4s1O8Qr75SDzZhzL0vNb7tEoeOlV7sVvZv4psChLjJ6fxVUe8xuAg5sgI4/lxf/WeU5AILJ5ycf7l/NV42Wy0mpG12hfx6e/cSfrii7FWLKe0e3dTx+00urJ3iuG6aMtCW1ZbK/p6CT/FMIjEjXhzpec8dzCxaWZ8T7RUKJIZ6Gv5eY1SEa+/NoqN104JRDyoRAyrJOOyU3Qo4k144iRCET91YY3WGrvsRe8hZFVmFdNpyBw76t9JOY6/OAV+Sl/missZuOYaUhdeyHR5mqJbZDQzespzpQIRdwvNFft4MzOYgYjbayqFTwPXX09x5w7cicmmjttpdGXFpuk6aMvCM0XEW8asSDzOhRHmPHW5TFmZGCk/Ki43+SVeKGa5hDdLnLShUHFF4oFVEfYEIWpFG5eIF/zrbhOROKaNMsGbo6Ni1CkxVXvHsjKzkuk0lE+c4MDv3sqO11S6EroTE5jDwyx75zvJXHIJh7KHoteciqSdxjHAnYfFUw93JhvZKdbKyrkGrn8l5uDQgvqfdzJdGYn7Im77kXhORLwVaE1lGTFDtdVOoVzCMcxo8d5yvj2lCFa5hK7niceVnVIMs1NSNf/HZqcUi0EkvnBPHECZc/cpCVf1CRtmhYxmRsmmFd7kJNnH/NQ958QJrJER3MlJzGHf//7ZsZ/xG3f9BhCs3nMKUmaKKcsv828GLzuDscGEQ4+j1lzE2r/4c5zxYyQ2bMAcHEAXCnilUtQtsVvpWhHHsvBMWyLxVlEViWOotlZsUnZwTCv6cpUX0TtjIVhOCT1LnOLsnRLmLRtB6lwo4rHZKcUiptWcJw6gLBVZWQ3PEWSvmKlMzfODiUFyGRN7Z8WiKB84iDk05Iv40BBaaz784IeZLk9z1bqrOHP4zFOeK2klcczme6d4MzMYh34Kn/k83HwXQ294Q7TNSPhppN7UFMaKFY0O0RV0pZ1iBSKubQujneLSQ/jFPkHLVEO12U4p4RoWZpDGVp7HCuxxYDvlSuZIiGGgYloQQwdrUBp9QUtYO4jEY5pv8IpllAnY6Tn3rYcy5+4KGjYJMzO151BK4Q31kchWBNcZH/N7qXgen9//n7zj2+/g8bHH+aOX/BGffOUnMdSp5SdpJilZixDx7DRGKcgFf+IrlQ1Hn8H80Qf99zM11dSxO4nuFHHPATuBMq32FoP0CNp1QatKtz0DcNvXxZCg1XBFxFsfiWvPI+GWYZbX69cixJTHHUTiqs+3O5Rp+X1p4orEy2X/d1a9JFvAvql95MqnzvJQ1twiXp7xfWSrr060P1JbvOMeOxaJ5C49xuPjjzOUHOLGs2485TlCUlbK71vexJyILpfRxRKGFVyA92+tbDz4CGbCf96d7P7Jze4UcddBJRJoS0S8FUQTcGG3PcNo60LJYW8cM7BTnELrawGiVXFStRGmZ1qYcYlsGIn3B5G4Uv4FMq7jlxyMxMkO6bH8Md74tTfyJz/9k1O+3rAMdLnxXUHh6aeZfvIxAKzMydlC5vLalEFnbDwSyVLG5tOv/DRffO0XydiZk15bj4SRIJdSDD61j/Hb/mFB7ZC9GT8TyLQ1bLgcju8Mbi+BsWcwbf/v2as3ubl/G+S7J4e860Tc8zwSngMJ2+9OJnZK/JQCEbfCSLy9E5uqXMa1bMxkIOJtsFPCle6NkyLx+OZdvHwwsdlfiVhVjIVUnuNGJf3VPHXsKRzP4a7dd9V5VQVlm3gNCqu01uz6hTcz+acfASCROXnyNDVaqcB0DCiOHYlEMjW8nCvXXcnGwY3zfj9KKWYyBsmJHGN//dcUnnxy3q91s76IG7YH6y6B4lRFmMeewYgi8Vl2yrEd8I/Xwjdunfe5lpquE/HwC23YCbBsicRbwOxIXBnxLlE2F6pcwrWqIvGYFhI+FYUgcjNmT2xaFqYbU0VlkCdu9FVVKhqgY+oQqcseRvLkTIvtJyqLIUyXGqfVKdtuGIl7M7VWTLL/5L4nfasqC00cHoHswb2RndI3cup0wkYU+ip3FsXnd8z7ddEksq1h7SX+kyd2+f8ffRoz4Ufl7tQsO2XXD/3/n/paU+NdCrpOxIv5IE81kQDbam9vjR4h7EtN2NvDNNq6ULJRLuFaCaxAkNw2ROL5KV+kwgKjEM+0Y1uYIkrPG6iIuDLjKbvXWqMdXclBr+LZ489Gj/dO7214DCNl45Xqv9epsf01P9cT8aF1Z0SPjw8oCocPRpH4wPLVJ+0/Hwacyp1F+cCBhvvpUqnmjia0U4ykBaMvAKA4vgdKMzCxB7PP/z17J8ZrDxR658qALgkQu07ES4GIG8kESuyUllAvEm/nxKbh+HUAVhCJu21Y7Twf3H7bs7IuvBjnXfz0PI3qqwigYYIuxSDiYb+ZqmIlT3uM58d59sSzrO7zRfRg9mDDYxipBF6p/u/5+JE9NT+n6/Q9WbH2LH54oeKfrzc4PgAcHacclNwPLV930v7z4ZkrK0vAucePN9xv72/cwp6bKotghCsMmf2D6CF/FaMv3PVjGPPvStQ5V6NMD3f8cO2BDj3u/689yNb2fOlUuk/EgzxVI5kAK4GtXbw23ur3Ajr0xMPCF7O9E5umU8Kz7SgSd4qtn9gshCLeNysSj9Gy8/J5lAmqqlOisoyGPvRCCPO3qydm/2rrX3HN7dewY3IH1264FphLxJN45foinj1WK3bp4SC3umqycWVmJX//BpM7t/gibp3IMjN+GE/BimXNifihF6/j/3qvyYFl4DQQcV0qkXvwQfJbt+EETa1CETcGBxlzUkzrNGpqP0ee2+a/6KxrMBMa93hVO9rCFIw94y+YYVgw0x2tartPxMNIPJHECFLg2pVH3CtEpdWBiCujvXaK6Th4dgI71b5IvDjti3hilohry4ptdSFdLGLMSh5RloolEo+smio76Nu7vh09vmLtFQzYAxzINrYkjEyKRm81N14blfYPLPMffPcD8IlLoVxgNF3phaJGV2B4mvzO58klYUV/c574hSsuxDUVk31QGK8fGZer+oKXdvi+uRuK+NAIO8ZyHNArWK/GObL9QUgMwMaXYiY8vMmqLJSn/hO8Mrzur+EDY7D2xU2Nud10XcVmKOJmMhHNxJcKJZLp1KleJiyEYHWXaPEC04htibL5YDplCCJxB/BiELm5KGZzWECy/+RI3IorEi+WULOSRwzbRMcQiYdLsxl9fWw/vh2lFOP5cc4cOpNNg5u4Yu0VrO1fe+pIPJMBT6ELhZPK6gsnaqPSgcSAH4Xf/3f+E/sexD7zFVyy8hI2DW7COPE0MIb32M+Y6IOV6eaqIm++8Ga01kx99RMUx2pFvLB9O8o0a3K9izt2ktmyJZqINYaWs2Msyxq9gk3WMZyjWfS6F6IG12LYXm2e+GNfhhXnwoafr5tr36l0nYiXCwUUvoiHvTWcgkTicVKZ2Ky2U9p3fssto+0EdipJgUp/8VZSDkQwOatbYpy1CF6hiGHX3vwq22g4mbigY0/7YjSZsvmlb/4SXvAL+9OX/ikvXulHlGv717Jvel/DY4RdB73p45iptTXbyoFN8aPzFdk0vDsxCNmjlR0OPQpnvoLPveZzaK35i+n3A09iTExzfLPigvSpOxY2ImEmuH7T9Xwz8wn0oYrgaq3Z886bUIbBqg+8vzLOYPIzslOGV7JzbAZDjfIKczvlssOjziVcnBrCTCrK09Nw70fgnOth70/hqv/eVQIOXWinhB3trFQyut0vtanLXa+go0g8sFNMs61l95ZbhkQCO8i08GJaNOFUlHO+HZGaFYlrM74MKC9XxEzOWtvStmJJMfQm/cUYfmblIgEHOG/5edHjdf3rOJA90LBoxggKeLzJk71nd3KCXAI+caPJZ28w/SXsxitZLxx5KnqolGL5urM4HMx9HhuE5elT9w4/FcvSy5jKgDE9E2WgOGNjeJOTuCdOUHzGn6zMJTMUIhGfRlkeuwppdoxlKfStxXJypFWJ2w8sZ6bkYvYl8SaOww8/Cv94nT+Zee6rmx7nUtF1Iu6EIp5MVcqy2xCp9RJhJK6CBRJUwkY77bNTbNeBRIJE4Im3w05xZnwRzwzMKidPJLBcZ0HVgo1w8yWMdK2fYtgW2ln8BdKb8oX3gOl/F4aTw7xq86tImpWUw3X968g7eSaK9ZclCxdQCC8INUxOk8uY3P762/ny67/sPzce5J+PngdHn6rZfd3AOn74Ql9eDmzqrxnHQhlKDDHdZ6B0ZYGJ4jPPRNtnHniAnJVkR/8qxnf4KZTu5HFMS/P5x6b46Y5x1PCGaP8fl87izicPY/T3UZ6xKE0HF1Y7A2svbnqcS0UXirj/R2qlEhihiIudEiu6XDuxaSQTDSe8WoHlOpBIkgjmOZptRboQ3MBOSYd2iuvAU18nYYOBjmWhaK/gYKRrxUzZFrq8eBHXU77dsdfMc/bw2dz1lrv46FUfrdlnbb9vkTTyxY1gQQxv+mSRV1MzFPpszlt+HucvP99/cuxZv2Pi2df5UXnVHculKy/lP69QfOStBgde8XPzeg9fe/QAPzt4ci8T0zBxh4Ol2o75F5jC0xURLzzxBOOpIcbSI7hH/Cwa58RxjITHcT1A2dWYqy+I9ncHNvKdJw+RXOvfHWTHR8BMwCU3MVP2+OXb7ufbT5y8QlGn0nUiXg4mLOy+TNSq1GlTq9JeQYfl4UG6mpFMoNsk4m7ZwdYuRiJBuj+Ni0LPNLc814LOm8/jocj0Byl6T9wOt/8qm/J+qXezq8tU4xU9jEzthKFK2ngx5OCHwrubaTYNbiJjZ7BmpcKs6/fT/BplqBiDI/6x6tgpVjZPuX9WND2+HVacAyvPA6cAJ3ZHm0Yzo1y1+RoeOdvghatfVH/Q3/0AfOd9AGw/PM2tX3qUm/754fr7Bs213OPHmb7nXsb+5m/8hR6Cdsnj6SGOZoZJTx5Huy7FEycwbc0E/oVp5Vkvhld9BG76Jq9+4Vrue26cvss2sv6qY4z86s1w6+Nww5/x2L4JHth5nPf9x+P1x9GBdJ2Il4IJi8xQfyTi4onHixc2ahrwvzgqlUJ7KrZue6ciO+GXaRt9fRiGQd5O4eVmWn5enc9TNG0yYQOp/b6YDHp+RV9+HqvAz4Vb0lGlYIhhx2NVedmwW+BEw/4ka/r9JcoaRuJD/uRjPTslmS3hDsxqcTv2LKx4gS/icJKl8mcv/TPefem7ufnCOuuoO0X46SfgwU/D1EEe2u1fOMazRSZyJ99Z28v97JbyocMceM97ADCveCnGKr+I6VhqiIEN6zA9F2f8GOXJSQzbw+pfjqHgko0jcMVvwRlX8doXrqbkeDyRuYSBDS7q/NfC4BowbZ457FeYThcdvDZmZC2GrhNxJ4jKMoODJPr8P6pStvWRWi+hA9EMKwvDUm490/q2ndkTvhhZ/b6tUUykINf636/OTpNLpDGMIDPhyM8AyHi+TZGbXtwYdLmMdhRGf232i4rpLifMTpm2HTYNbKq7z2BikIFE41xxYyQQ8amTO/ilcw56KJgv8Dz43p/A9EE/l3o0sEuOPl3zmuHUMDdfeDMjZgq23+m/rlyAwmRta9h9D/HInso5nz40DQ/eBn97MZzwK0UzK3yxzj30ELpQYOSmm7h12dU8WfbvbMb7l7HpPH+RiWM79/jrayY0f/K2V/Dj913LysHKHdAlG0dYOZDkn6d+Hv7H3hoffPvhSm+Zg5PNLQvXbrpPxIPKusxwP+kRf/o7d7z7ewJ3EmHkawSl1Ubav1h6M61voJ8LInE7mGAsJVIY+daLuJqeJp8MBFbrSJAy2heXwonFvXf3qD/hFi5TFmKk/QKbxXYydI8fQ1sa11RsGqwv4uBbKgdn/Ei87JU5mqukCaphvwvhbBH3SiX68ho1FLQLePY78JOP+1H4hW+BRB+MbD4pEo/4wUfh398Gj/wrfOUm+Ovz4bEvVrYf2MZ/7T3BxRv9z2b7gXH4/of89rGPfgGAgdG1eAqyP/4xAL8zcwZPnHAoBRKmV69lzTn++z64fRcqV8CwPdav38Da4do7CMNQvOTM5fzX3hP+2Kt45sg04XV851jr7wDjoOtE3A2+0H2D/fQv9/+oihP1Z9uF5tAFPwIxgkZNoTeus62/WOYm/EgoOeiLeDmZbouIG7kZSungCz25H684xfj6S7FsP0zOTyxOxJ29zwNgrarNvzaHhgCFd/xwnVfNH3diknLKv/0/VbvXzYObefrY02w/vp03f+3NXPeV6/jwgx9Ga421yn9dWLoeMn3Mn+SzRoIqzefv9ic0f/MnEFZirjwfjj5DXXbe6/9/1wfg2TuhlIVH/g3WvAjWXUpp71Z2H8vx6gtWs7wvQeLZb0HZF1Bvz/3+4ftWMZUBd2yMvJlgaznDr5nfwV3vZ5Z451/IxvPPBmBixx5UsYRnG5jJk/ueA7xo/RCHJgscna7YZJ6nefbwNK+50Leddo2LiLcEPZOjZFjYyQT9y/2JmNLsnsDCooj6Xg/4n2+4JqTOtf5zLkyFIu4vnOAk01hNrna+EOxclnK40MHRp/nbkSGutcd4vs8XieLE4i5gzkG/Daq1en3N88awnyHhHm7cXXBex5/KkksrUmaqpvx9Ntdtuo6x/Bi/+I1fZKY8ww2bbuDfn/l37t13Lyqdxkho3BO173XiiF8gZIeLPux9ADZeDmZVuuTK8+DYc1CadcHNT8DhJyA9AmEb3LA17Nmv5GfqHMr7trFJHeZXnvktvqr+gOsOforSwAa+5F5Lce828DzyhQyTwXTC7sE1vOt8xQftz/MrL7iXP3rtb/A7pQ+zfve/MZHsx3luO8rVOKnGaY0XrfcDlCf2V97r3uM58mWXq85ZQV/CZOdYtuHrO4nuE/HJCbIpP0obXOGLjDPVuEeysHC8qWkwNCpo/G8M+n/w7rHWd3XLjfmTav2jftTnpTLYbRDxVG4a1ZeGf7iO8hffyu0DA2jge0P+XUhhkYGCe9hv5WquPaPmeXO5L7juWONy+HkdP5tjKmMwkljDnmON71xeufGVvOWct3D1hqv54uu+yMde/jE2D27mU499yo/GMwpnovb7NBF0MOwfXQvlvN8kanY+9RkvB8+BnT/wfe98cHe8936/iObGT8Lmq+CVH4Jf+hy87N1kL/lv/MveFfSpIj9Mvoe+sUfZ5OxilXeUewffxDbvbNLeDNmDT/OJ7x5lfMj3OY6NruODGx4F/OLKv+y/jdXTT2Lc/SGmhpYzvMO3ddzB+lE4wIXrBjEUPFYl4k8f8n/HP7dmkLNXDUSTnJ1O14m4OXmCXCAumf4MJcPCOw0WO+0knIlJrFRltXtrre81Oocbl2zHRf6w79GObvLT4dzhYfpbfAfglcsM5iZZ3XcCDmzl/r4Bpk2DwcQg9434Ip4dX5xlV9rjR+L22RcxNl3kln/dyn//ymN4w4GIH1ucneKcyLN/ULH38AC/+Omf1s3wALAMiw+99EN84tpPsLpvNZZhcfOFN/PM8Wd48PCDWP1J3MnaCHRy93MArNj0An/CV7uwZlZzqE1XQnIIHv8SfObl8JfnwlNfh133gZmEs66Fd30TXvZuGN4Ir/wQ336+xNdLW5ha81I4+5Wo336Yb1/9LX6r9Lv8zq7LeQK/D/idd34dd8Zgx2pfxDPnrIWtn4Uzr6E4ci5nGZWc7r4RxcC0bweVl6+iEZmExTkrB3h8f+X3+uTBSSxD8XOrB3jhukF+dnCqKzJUFiziSqmUUuqbSqnHlFKfV6q9jQaSk8cpVjXVn+gfgcPdk5jfDbiTM5gZi+lCmSf2T2Ku82f9ncP753jl4imPHaVsmIys9m/drbVrGSjOMNXCyeuxXfsw0WxU2yluvJx/vejVLEuO8MErPsi+AYVWUDy4uEi5tGcfdj8wuIL33P4o333qCP+xbT9fOeinNDr7djV9bC+fx5nx2DcMbmE9J3Jlbv6Xh3nj3/2YKz5yN5/54Q7+/t7nefKA/xk+fWiKf39oL9sPT6O15nVnvo7lqeV87mefwx4dpnSsNp0yv2c3noI1G8/2vWyA9Vtq9tGGxeTmV/sr4oxvZ9zNoL/yLnjgk7ibXsoD+2YYz9amAn/p4b2sGx1h4JZvwzvugOGNbD73Ir7tXU7JU9z4ylcwRR9b9n6W+8w/5XtbFJ+65kxuXHa7H4K/+qMkXxgsuvyqj8DIGawerOS4mxvP8semNeU6qzNdtH6Ih3cd58iU/35/uuMY568dJGWbvHDdENmiw84u8MWbaYD1DmC/1vr1SqlvAtcD3413WPUpFYqMnjjMnvNe5GcQaM306Fr62xAh9hKlsWnM0QFe8/Efsf9Enl++cISblKa8Z8/cL14k5q4djA2v8qskc8fp2+SXS+/a+iQvuuHKlpzz+fseZhnw/LJJPpxZwf7DD/K+y97Hy9e/nIRpkh10ULufb/r4ulwmt2OMxPpB3vOtf+Ph3J2cf4nHgH4hH3vsDL5hagpPN8jsmAf5H30LgD0r4X9c/VomJlbziXueZ+VAkmV9CT7yHX/C8eN3P8erLljNt584hBtEmGev7OfGF63lVRt+kS8+9xmObD6H1CNHKO/8GfaZfpWjfvo5xlZYXPCP10D2MGx6GV7fKnaNZbl/xzF+9NwYO8ZmyB59GX9uP8UPuJRvGNfyMe9/83LzCT5w6OV86bYHADhztI/VgymW9yf5r70T/PHrz6c6DjxvzQA/t3qAXeMz3Hjxep569BVcPum31D1D9XHw4uexjpzg2Fu/QrlvGGPLuzCWn8nUhsvoyx5izXOfZi9+Tnny4pfwjR3f4PNPfZ5nTzzLVeuv4k1nv4lNA5voT/Rz6VkmX3kkx0s+fDfXvGCUR/ZO8Puv2shdu+9i28xPsIcMvvroet57Q4NipQ5BLbQnhFLqi8AdWus7lFLvAUa11n/YaP8tW7borVu3NtrckK//1e8weMf3MT0wXf9fugS2A5/9BXj6LADNldsMbrxXMzYMbnBf0ejWQDV4q009H56kap/ovPWei/P5BmOM41yGhlQZvnCNxdcuS2OZBmXX439+cZozD8HYUOV1SgePZ/2vdGVc0T6c/Nzs/QD6inDnFsW/X6fQQH8OPv5Jj6INU5m5zzN7TI32ibYDmSKc6IPf+n9NNi07kz98yR9x+ZrLAXjvd/5vNv7L/Vz3mObIssrxon/huWY9Z1T9bDtgu/A3N5rcf77CZpCzlq3lmePPgDb549tLXMiKHYgAAAdNSURBVLDLY2xE1RwHTj5uvfOnC1BIwu//P+u4+5bvYhiKR/ZN8IJVA2QSJnuP57BNg/f+x+P8dMc4b710Aze/7Awe3n2crz8aFNoYOQbO/CTrJsf4q39wyaX9Y5oujEzD8xeXueAFSf5BvYUf6os55qQoBj1f1g2nOXO0j6tfsJIV/QlevGGY/qTF7335UX703Bhrh9L83vXnMjZd5In9kxyeKvDckWlevHGYf7rpMlJ2bVOwiVyJqbzDxuUZ9h06ykN3/iuveP07uefhD/JnYz/mVCQ9j5vu9dizSvHdCy00ms2Dm9myegv37L2H44WTq1FNkpTLaSzTwzOCYjNl4GkPrQ1WZUZJWglM5Tf+Ug2/jSfz5nPezE0X3DT3jnVQSm3TWm+Zc78mRPwu4C+01t9XSv06cJnW+r/N2ucW4BaAjRs3XrqniQjurn/+/8j9n//AM8E1VfRvctRm1xWDYFgoZaAKBc675wQjx2bl2VZ9zrrB89XoRq5Qw+Ocev+TP1XV8Px69vPqpAcn71PzehXtrevtcyrHq/r9BfvlMjbffcVlbF6/nKG0zZ5jM+hndvOqn/yMZMmLxqKrjh2eVytVta1yDo2q/V/5G6LxKl+0yymDw1evQPenwU6hSjkGHjvCmsdnMDxd+7q6Y1BVY6neVjuucBwohVaKo5cuZ931b+O1F7wd26hkXUwWJ/mn//w9Vn7tEfqyXjB2VXNurRr8H50Djqwc4r7LX8JL1l7B77701SQtm+dOPMeXn/4azz62jRt+8Az9eRetKr+HmuOh/EWVq/9XgDLQBjxz0Zm8/df/inOW12a/zKbkeCSsWhf1wESeH24f45EDB3g29z1e/MQPuWi7X6nqmgq3P0nqsqt4ZM0vo80UpqGwTcU5qwa4dNMIZ67oo5GremSqwHDGJmnVCrXWuuFrGuFpj2/t/BaHZw7Tn+gnYSRwtYunPfrsPqZL0xwaf5pjRx7HHFzHulUXcdnqy7h45cUYyqDslnli/AmO5o6SLWfxtMdMeYax/BhjMyfI2EnW9K/m0lWXcvHKi/nxvkf49o57sBNTOJ7fBM3VC8vnv2bjNbz+zNcv6DUhrRTxLwBfDSLx3weWaa3f32j/ZiNxQRCEXma+It5MdsrdwA3B42uBe5s4hiAIghADzYj4F4B1SqnHgeP4oi4IgiAsAQvOTtFaF4HmTB5BEAQhVrqu2EcQBEGoICIuCILQxYiIC4IgdDEi4oIgCF2MiLggCEIXs+BinwWfQKkxYD4lmyuA8ZYOpjk6cVydOCbozHF14phAxrUQOnFM0PpxbdJaN24OH9ByEZ8vSqmt86lOajedOK5OHBN05rg6cUwg41oInTgm6JxxiZ0iCILQxYiIC4IgdDGdJOK3LfUAGtCJ4+rEMUFnjqsTxwQyroXQiWOCDhlXx3jigiAIwsLppEhcEARBWCAdJeJKqfcqpX6klPqOUiqhlFoR/PyEUuqjSzSmVyul9iulfhz8e8FSrzNaNbZ3K6W+HzzuhM/KUkp9RSn1E6XUPwfPLelnpXw+p5R6QCn19WCMnfL7s5VS36j6eUnG1Smfx6wxRZ9Np4yvzt9SfyeMq2NEXCl1JnCB1voq4DvAeuD3gG8BLwJeo5Q6d4mG9ymt9cuCf9uprDP6ImAEf53RtqKU2gS8q+qpTvis3gQ8prW+ElijlHoxS/9ZXQlYWuvLgUH8XvhLPSaUUmlg26xzL9W4lvzzqKbOZ9Mp45v9t3RzJ4yrY0QcuA4YUUrdB1wF7MJfdOJ7WmsP+CFwzRKN7S1KqYeUUncEV9trge8F2+5ZonF9HKhe27QTPqs7gb9WSlnAMDDF0n9WR/A/K4BS8P9SjwmtdV5rfRGwv+rppRrXkn8e1dT5bDplfLP/lj5EB4yrk0R8FBjTWr8cPwp/GbAcmAy2TwHLlmBcO4A/1lr/PLAGeMVSj0sp9XbgMaB6ifQl/6y01lmtdQ74CXBEa71zqceltX5Oa/2QUuoXgARw11KP6RQs1bg69fMI6Yjx1flb2tYJ4+okEZ8CtgePdwLr8Etah4Lnhlia0tvjwPeDx7uBlSz9uF6Pf+fyJeBSpdRvd8CYUEotV0olgZfi31Vd0yHjeiNwK/AGrbXbCWNqwFKNq1M/j5COGV/13xJwtBPG1Ukivg24LHh8Nr6Q3w3coJQy8CPgpVjP8z3ALwdjuBB4kiVeZ1Rr/Xat9cuAXwa2aa3/js74rH4feGsglDkgzRJ/Vkqp1cAfAK/TWk8HT3fqOrFLNa5O/TxCOmJ8df6WOmJcHSPiWuv7gXGl1MPAdq31Q8DfAq8FHge+pbV+fgmG9nfArwEPAv9Ha/0UnbnOaCd8Vn8P3KyUuh84hm9dLPVndRO+DXZXkF10cweMqRFLNa5O/TxCOmV8NX9LgN0J45JiH0EQhC6mYyJxQRAEYeGIiAuCIHQxIuKCIAhdjIi4IAhCFyMiLgiC0MWIiAuCIHQxIuKCIAhdzP8PYLoBxulKUWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用pandas读文件绘制数据曲线\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('images/ch9/9.5 PDOS.csv')\n",
    "for i in range(4):\n",
    "    plt.plot(data.iloc[0:, 2*i], data.iloc[0:, 2*i+1])  # 切片方法获取两列数据\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "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 matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        plt.subplot(211)  # 分2x1，小于10的数可合成3位数\n",
    "        plt.plot(x, y)\n",
    "    plt.title('态密度（Density of States）曲线')\n",
    "    plt.ylabel('分波态密度/eV')\n",
    "\n",
    "\n",
    "def zoom_in_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    sty = ['-', '--', ':', '-.']\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst if -5 < float(ls[2 * i]) < 5]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst if -5 < float(ls[2 * i]) < 5]  # 生成y的列表\n",
    "        plt.subplot(212)  # 分2x1，小于10的数可合成3位数\n",
    "        plt.plot(x, y, linestyle=sty[i])  # cl[i]根据i值取颜色\n",
    "    plt.xlabel('能量/eV')\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    zoom_in_dos(dos_in_lst)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/ch9/19.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zoom_in_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    sty = ['-', '--', ':', '-.']\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        plt.subplot(212)  # 分2x1，小于10的数可合成3位数\n",
    "        plt.plot(x, y, color=cl[i], linestyle=sty[i])  # cl[i]根据i值取颜色\n",
    "    plt.xlim(-5, 5)  # 设置横轴的上下限\n",
    "    plt.ylim(-0.5, 7.5)  # 设置纵轴的上下限\n",
    "    plt.xlabel('能量/eV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 两列数据分段绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例 9.7 两列数据绘制多条曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有一个数据文件band.txt，其中包括两列数据，分别代表坐标x,y的值，数据间用制表符分隔（'\\t'），其中x值数据从0变化到1时表示一条曲线，整个文件包含多组这样的数据，请读取这个文件的数据并绘制相应的曲线。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "0\t-17.0916786423819\n",
    "0.0211944262814081\t-17.0751964353257\n",
    "0.0423888474761543\t-17.0264812561439\n",
    "0.0635832737575624\t-16.9477206279502\n",
    "0.0847777000389705\t-16.8426201969397\n",
    "0.105972121233717\t-16.7164274062928\n",
    "......\n",
    "0.933251465270729\t-16.1766441729898\n",
    "0.955500978923773\t-16.0688718562406\n",
    "0.977750486346955\t-15.9960071184942\n",
    "1\t-15.9701767127752\n",
    "0\t-16.1625663637734\n",
    "0.0211944262814081\t-16.1595137907805\n",
    "0.0423888474761543\t-16.1505503610793\n",
    "0.0635832737575624\t-16.1361511854246\n",
    "0.0847777000389705\t-16.1171364149128\n",
    "......\n",
    "0.933251465270729\t-16.0104068383765\n",
    "0.955500978923773\t-15.9889860373421\n",
    "0.977750486346955\t-15.9750252370744\n",
    "1\t-15.9701758964337\n",
    "0\t-15.9087935461524\n",
    "0.0211944262814081\t-15.916396678796\n",
    "0.0423888474761543\t-15.9373388315959\n",
    "0.0635832737575624\t-15.9665271220299\n",
    "0.0847777000389705\t-15.9972065962757\n",
    "......\n",
    "0.933251465270729\t17.4571661619832\n",
    "0.955500978923773\t17.68681499974\n",
    "0.977750486346955\t17.7927935420937\n",
    "1\t17.811444496409"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[9.7 band.txt](images/ch9/9.7 band.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考前述方法，将一条曲线的数据附加到一组列表中。  \n",
    "因每条曲线的x值都是从0逐渐增加到1为止，因此可查看保存x值数据的列表的最后一个元素值是否是1，当该元素值为1时，利用列表中的数据绘制一条曲线。  然后清空列表，读取并附加下一组数据。  \n",
    "重复操作，直至所有数据读取完毕。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split('\\t') for line in fr]  # 数据转列表\n",
    "        # data_lst = [line.strip().split() for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_label():  # 加图名和轴标签\n",
    "    plt.title('能带曲线')\n",
    "    plt.ylabel('Energy/eV')\n",
    "\n",
    "\n",
    "def draw_band(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    x, y = [], []\n",
    "    for xy in data_lst:\n",
    "        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中\n",
    "        y.append(float(xy[1]))\n",
    "        if x[-1] == 1:  # 列表最后一个元素为1时\n",
    "            plt.plot(x, y, '-',linewidth=2)  # 绘制曲线\n",
    "            x, y = [], []  # 清空列表，准备接收下一条曲线的数据\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.7 band.txt'\n",
    "    data_in_lst = read_csv(file)\n",
    "    draw_band(data_in_lst)\n",
    "    draw_label()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split() for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    \"\"\"加图名和轴标签\"\"\"\n",
    "    plt.title('能带曲线')\n",
    "    plt.ylabel('Energy/eV')\n",
    "\n",
    "\n",
    "def draw_band(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    x, y = [], []\n",
    "    for xy in data_lst:\n",
    "        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中\n",
    "        y.append(float(xy[1]))\n",
    "        if x[-1] == 1:  # 列表最后一个元素为1时\n",
    "            plt.plot(x, y, '-')  # 绘制曲线\n",
    "            x, y = [], []  # 清空列表，准备接收下一条曲线的数据\n",
    "    \n",
    "def draw_line():\n",
    "    \"\"\"绘制区间线\"\"\"\n",
    "    xtick = [0, 0.25, 0.43, 0.69, 1]\n",
    "    plt.xticks(xtick)  # 横轴刻度\n",
    "    for i in xtick:    # 在刻度处绘制纵向破折线\n",
    "        plt.axvline(i, linestyle='--', c='gray')\n",
    "    plt.axhline(0, linestyle='--', c='gray')  # 绘制水平线\n",
    "    \n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.7 band.txt'\n",
    "    data_in_lst = read_csv(file)\n",
    "    plt.subplot(121)        # 一行两列第一个位置绘图\n",
    "    draw_band(data_in_lst)  # 绘制完整数据图\n",
    "    draw_label()\n",
    "    draw_line()\n",
    "    plt.subplot(122)        # 一行两列第二个位置绘图\n",
    "    draw_band(data_in_lst)  # 绘制完整数据图\n",
    "    plt.ylim(-5, 5)         # 显示纵坐标值在-5到5之间部分\n",
    "    draw_label()\n",
    "    draw_line()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split() for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    \"\"\"加图名和轴标签\"\"\"\n",
    "    plt.title('能带曲线')\n",
    "    plt.ylabel('Energy/eV')\n",
    "\n",
    "\n",
    "def draw_band(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    x, y = [], []\n",
    "    for xy in data_lst:\n",
    "        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中\n",
    "        y.append(float(xy[1]))\n",
    "        if x[-1] == 1:  # 列表最后一个元素为1时\n",
    "            plt.plot(x, y, '-')  # 绘制曲线\n",
    "            x, y = [], []  # 清空列表，准备接收下一条曲线的数据\n",
    "    \n",
    "def draw_line():\n",
    "    \"\"\"绘制区间线\"\"\"\n",
    "    xtick = [0, 0.25, 0.43, 0.69, 1]\n",
    "    plt.xticks(xtick)  # 横轴刻度\n",
    "    for i in xtick:    # 在刻度处绘制纵向破折线\n",
    "        plt.axvline(i, linestyle='--', c='gray')\n",
    "    plt.axhline(0, linestyle='--', c='gray')  # 绘制水平线\n",
    "    \n",
    "def zoom_area(m,n,data_lst):\n",
    "    \"\"\"接收二维列表为参数，只绘制满足区间条件的数据曲线，避免绘制的曲线不完整。\"\"\"\n",
    "    lsx, lsy = [], []\n",
    "    for x,y in data_lst:\n",
    "        if m<=float(y)<=n:\n",
    "            lsx.append(float(x))  # 将数据转为浮点型附加到列表中\n",
    "            lsy.append(float(y))\n",
    "            if lsx[-1] == 1:  # 列表最后一个元素为1时\n",
    "                plt.plot(lsx, lsy, '-')  # 绘制曲线\n",
    "                lsx, lsy = [], []  # 清空列表，准备接收下一条曲线的数据\n",
    "    \n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.7 band.txt'\n",
    "    data_in_lst = read_csv(file)\n",
    "    plt.subplot(121)        # 一行两列第一个位置绘图\n",
    "    draw_band(data_in_lst)  # 绘制完整数据图\n",
    "    draw_label()\n",
    "    draw_line()\n",
    "    plt.subplot(122)        # 一行两列第二个位置绘图\n",
    "    zoom_area(-5,5,data_in_lst)\n",
    "    draw_label()\n",
    "    draw_line()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/ch9/20.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 网络数据绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实例9.8 TIOBE 程序语言排行榜曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TIOBE排行榜 是根据互联网上有经验的程序员、课程和第三方厂商的数量，并使用搜索引擎（如Google、Bing、Yahoo!）以及Wikipedia、Amazon、YouTube和Baidu（百度）统计出排名数据，只是反映某个编程语言的热门程度，并不能说明一门编程语言好不好，或者一门语言所编写的代码数量多少。  \n",
    "TIOBE开发语言排行榜每月更新一次，依据的指数是基于世界范围内的资深软件工程师和第三方供应商提供，其结果作为当前业内程序开发语言的流行使用程度的有效指标。  \n",
    "该指数可以用来检阅开发者的编程技能能否跟上趋势，或是否有必要作出战略改变，以及什么编程语言是应该及时掌握的。观察认为，该指数反应的虽并非当前最流行或应用最广的语言，但对世界范围内开发语言的走势仍具有重要参考意义。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/ch9/21.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网页上右键查看网页源代码，搜索“series: ”，可看到数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    series: [\n",
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[Date.UTC(2019, 5, 9), 1.42], [Date.UTC(2019, 6, 6), 1.12], [Date.UTC(2019, 7, 5), 0.89], [Date.UTC(2019, 8, 9), 1.10], [Date.UTC(2019, 9, 5), 1.36], [Date.UTC(2019, 10, 3), 1.65], [Date.UTC(2019, 11, 6), 1.49], [Date.UTC(2020, 0, 5), 1.80], [Date.UTC(2020, 1, 4), 1.46], [Date.UTC(2020, 2, 4), 1.24], [Date.UTC(2020, 3, 2), 1.52], [Date.UTC(2020, 4, 2), 1.79], [Date.UTC(2020, 5, 1), 1.46], [Date.UTC(2020, 6, 4), 1.43], [Date.UTC(2020, 7, 2), 1.42], [Date.UTC(2020, 8, 6), 1.38], [Date.UTC(2020, 9, 4), 1.09], [Date.UTC(2020, 10, 3), 1.35], [Date.UTC(2020, 11, 3), 1.22], [Date.UTC(2021, 0, 2), 1.43], [Date.UTC(2021, 1, 6), 1.13], [Date.UTC(2021, 2, 4), 0.95], [Date.UTC(2021, 3, 4), 1.19], [Date.UTC(2021, 4, 2), 1.14], [Date.UTC(2021, 5, 5), 1.10], [Date.UTC(2021, 6, 4), 1.07], [Date.UTC(2021, 7, 3), 0.98], [Date.UTC(2021, 8, 11), 1.07], [Date.UTC(2021, 9, 6), 1.11], [Date.UTC(2021, 10, 6), 1.43], [Date.UTC(2021, 11, 5), 1.76]]}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[tiobe202208.txt](images/ch9/tiobe202208.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "复制方括号中的部分放到双引号中，转为字符串类型\n",
    "替换掉'name : '、',data '、'Date.UTC'为空字符串，再用 eval()函数将字符串写为元素为字典的列表，每个字典中只有一个元素。\n",
    "替换后的数据如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiobe = [\n",
    "{name : 'Python',data : [[Date.UTC(2001, 5, 30), 1.25], [Date.UTC(2001, 6, 30), 1.13], [Date.UTC(2001, 7, 30), 1.20], [Date.UTC(2001, 8, 28), 1.17], [Date.UTC(2001, 9, 26), 1.28], [Date.UTC(2001, 10, 28), 1.23], [Date.UTC(2001, 11, 31), 1.04], [Date.UTC(2002, 0, 30), 1.02], [Date.UTC(2002, 1, 27), 0.99], [Date.UTC(2002, 2, 29), 0.99], [Date.UTC(2002, 3, 29), 1.07], [Date.UTC(2002, 4, 29), 1.06], [Date.UTC(2002, 5, 29), 1.13], [Date.UTC(2002, 6, 31), 1.08], [Date.UTC(2002, 7, 29), 1.22], [Date.UTC(2002, 8, 29), 1.08], [Date.UTC(2002, 9, 30), 1.19], [Date.UTC(2002, 10, 29), 1.00], [Date.UTC(2002, 11, 31), 1.00], [Date.UTC(2003, 0, 31), 1.03], [Date.UTC(2003, 1, 28), 0.97], [Date.UTC(2003, 2, 31), 0.99], [Date.UTC(2003, 3, 30), 1.01], [Date.UTC(2003, 4, 30), 1.16], [Date.UTC(2003, 5, 30), 1.28], [Date.UTC(2003, 6, 31), 1.30], [Date.UTC(2003, 7, 31), 1.42], [Date.UTC(2003, 8, 30), 1.49], [Date.UTC(2003, 9, 31), 1.77], [Date.UTC(2003, 10, 30), 1.10], [Date.UTC(2003, 11, 31), 1.13], [Date.UTC(2004, 0, 31), 1.01], [Date.UTC(2004, 1, 29), 1.02], [Date.UTC(2004, 2, 31), 1.01], [Date.UTC(2004, 3, 30), 4.57], [Date.UTC(2004, 4, 30), 6.58], [Date.UTC(2004, 5, 30), 4.96], [Date.UTC(2004, 6, 31), 4.70], [Date.UTC(2004, 7, 30), 5.60], [Date.UTC(2004, 8, 30), 4.44], [Date.UTC(2004, 9, 31), 5.20], [Date.UTC(2004, 10, 30), 3.03], [Date.UTC(2004, 11, 31), 2.84], [Date.UTC(2005, 0, 31), 2.51], [Date.UTC(2005, 1, 28), 2.42], [Date.UTC(2005, 2, 31), 2.70], [Date.UTC(2005, 3, 30), 2.48], [Date.UTC(2005, 4, 31), 2.80], [Date.UTC(2005, 5, 30), 2.52], [Date.UTC(2005, 6, 31), 2.88], [Date.UTC(2005, 7, 31), 3.03], [Date.UTC(2005, 8, 30), 2.88], [Date.UTC(2005, 11, 3), 2.68], [Date.UTC(2006, 0, 4), 2.60], [Date.UTC(2006, 1, 2), 2.67], [Date.UTC(2006, 2, 1), 3.09], [Date.UTC(2006, 3, 2), 2.76], [Date.UTC(2006, 4, 1), 3.04], [Date.UTC(2006, 5, 1), 3.46], [Date.UTC(2006, 6, 2), 3.02], [Date.UTC(2006, 7, 2), 3.07], [Date.UTC(2006, 8, 2), 3.14], [Date.UTC(2006, 9, 1), 3.47], [Date.UTC(2006, 10, 2), 3.64], [Date.UTC(2006, 11, 1), 3.76], [Date.UTC(2007, 0, 2), 3.50], [Date.UTC(2007, 1, 3), 3.57], [Date.UTC(2007, 2, 3), 3.90], [Date.UTC(2007, 3, 1), 3.81], [Date.UTC(2007, 4, 5), 3.78], [Date.UTC(2007, 5, 2), 3.16], [Date.UTC(2007, 6, 2), 3.02], [Date.UTC(2007, 7, 5), 2.75], [Date.UTC(2007, 8, 2), 3.03], [Date.UTC(2007, 9, 4), 3.43], [Date.UTC(2007, 10, 4), 4.23], [Date.UTC(2007, 11, 3), 4.70], [Date.UTC(2008, 0, 3), 5.54], [Date.UTC(2008, 1, 7), 4.76], [Date.UTC(2008, 5, 1), 4.90], [Date.UTC(2008, 6, 2), 4.97], [Date.UTC(2008, 7, 3), 4.98], [Date.UTC(2008, 8, 3), 5.01], [Date.UTC(2008, 9, 6), 4.56], [Date.UTC(2008, 10, 2), 5.14], [Date.UTC(2008, 11, 3), 4.17], [Date.UTC(2009, 0, 2), 4.73], [Date.UTC(2009, 1, 1), 4.57], [Date.UTC(2009, 2, 5), 5.18], [Date.UTC(2009, 3, 7), 6.08], [Date.UTC(2009, 4, 1), 5.55], [Date.UTC(2009, 5, 4), 4.76], [Date.UTC(2009, 6, 2), 4.43], [Date.UTC(2009, 7, 1), 4.49], [Date.UTC(2009, 8, 5), 3.93], [Date.UTC(2009, 9, 2), 3.90], [Date.UTC(2009, 10, 2), 4.67], [Date.UTC(2009, 11, 2), 5.19], [Date.UTC(2010, 0, 5), 4.45], [Date.UTC(2010, 1, 7), 4.31], [Date.UTC(2010, 2, 7), 4.23], [Date.UTC(2010, 3, 5), 4.21], [Date.UTC(2010, 4, 15), 4.10], [Date.UTC(2010, 6, 6), 4.22], [Date.UTC(2010, 6, 30), 4.22], [Date.UTC(2010, 8, 11), 4.58], [Date.UTC(2010, 9, 2), 4.86], [Date.UTC(2010, 10, 3), 5.68], [Date.UTC(2010, 11, 7), 6.48], [Date.UTC(2011, 0, 2), 6.26], [Date.UTC(2011, 1, 8), 7.04], [Date.UTC(2011, 2, 8), 5.74], [Date.UTC(2011, 3, 3), 4.93], [Date.UTC(2011, 4, 2), 4.58], [Date.UTC(2011, 5, 5), 3.90], [Date.UTC(2011, 5, 27), 3.90], [Date.UTC(2011, 6, 8), 3.58], [Date.UTC(2011, 7, 3), 3.41], [Date.UTC(2011, 8, 10), 4.00], [Date.UTC(2011, 9, 9), 3.94], [Date.UTC(2011, 10, 7), 3.62], [Date.UTC(2011, 11, 4), 3.49], [Date.UTC(2012, 0, 8), 3.21], [Date.UTC(2012, 1, 5), 3.15], [Date.UTC(2012, 2, 11), 3.29], [Date.UTC(2012, 3, 8), 3.66], [Date.UTC(2012, 4, 9), 3.82], [Date.UTC(2012, 5, 10), 3.85], [Date.UTC(2012, 6, 4), 4.00], [Date.UTC(2012, 7, 10), 3.88], [Date.UTC(2012, 8, 2), 3.86], [Date.UTC(2012, 9, 5), 3.90], [Date.UTC(2012, 10, 4), 4.06], [Date.UTC(2012, 11, 2), 3.85], [Date.UTC(2013, 0, 5), 4.17], [Date.UTC(2013, 1, 8), 4.95], [Date.UTC(2013, 2, 11), 4.39], [Date.UTC(2013, 3, 7), 4.44], [Date.UTC(2013, 4, 8), 4.32], [Date.UTC(2013, 5, 9), 4.18], [Date.UTC(2013, 6, 7), 4.03], [Date.UTC(2013, 6, 12), 4.03], [Date.UTC(2013, 7, 4), 3.60], [Date.UTC(2013, 8, 11), 3.17], [Date.UTC(2013, 9, 10), 3.11], [Date.UTC(2013, 10, 9), 3.11], [Date.UTC(2013, 11, 6), 2.21], [Date.UTC(2014, 0, 1), 2.37], [Date.UTC(2014, 1, 8), 2.16], [Date.UTC(2014, 2, 3), 2.02], [Date.UTC(2014, 3, 10), 1.99], [Date.UTC(2014, 4, 7), 3.06], [Date.UTC(2014, 5, 8), 2.71], [Date.UTC(2014, 6, 6), 2.66], [Date.UTC(2014, 7, 11), 3.12], [Date.UTC(2014, 8, 1), 2.78], [Date.UTC(2014, 9, 3), 2.33], [Date.UTC(2014, 10, 8), 2.59], [Date.UTC(2014, 11, 7), 2.29], [Date.UTC(2015, 0, 6), 2.61], [Date.UTC(2015, 1, 5), 2.88], [Date.UTC(2015, 2, 7), 2.61], [Date.UTC(2015, 3, 13), 2.69], [Date.UTC(2015, 4, 13), 3.72], [Date.UTC(2015, 5, 6), 4.00], [Date.UTC(2015, 6, 12), 4.26], [Date.UTC(2015, 7, 6), 4.07], [Date.UTC(2015, 8, 5), 3.66], [Date.UTC(2015, 9, 4), 4.51], [Date.UTC(2015, 10, 7), 3.77], [Date.UTC(2015, 11, 4), 4.43], [Date.UTC(2016, 0, 2), 3.85], [Date.UTC(2016, 1, 2), 4.18], [Date.UTC(2016, 2, 3), 4.26], [Date.UTC(2016, 3, 7), 3.33], [Date.UTC(2016, 4, 6), 3.79], [Date.UTC(2016, 5, 5), 3.90], [Date.UTC(2016, 6, 4), 4.17], [Date.UTC(2016, 7, 6), 4.40], [Date.UTC(2016, 8, 8), 4.30], [Date.UTC(2016, 9, 7), 3.77], [Date.UTC(2016, 10, 5), 3.57], [Date.UTC(2016, 11, 4), 4.24], [Date.UTC(2017, 0, 7), 3.46], [Date.UTC(2017, 1, 8), 4.04], [Date.UTC(2017, 2, 7), 3.92], [Date.UTC(2017, 3, 9), 3.46], [Date.UTC(2017, 4, 6), 3.55], [Date.UTC(2017, 5, 3), 4.33], [Date.UTC(2017, 6, 7), 3.54], [Date.UTC(2017, 7, 2), 3.69], [Date.UTC(2017, 8, 6), 2.98], [Date.UTC(2017, 9, 5), 3.80], [Date.UTC(2017, 10, 12), 4.48], [Date.UTC(2017, 11, 9), 3.78], [Date.UTC(2018, 0, 3), 4.68], [Date.UTC(2018, 1, 8), 5.17], [Date.UTC(2018, 2, 7), 5.87], [Date.UTC(2018, 3, 1), 5.80], [Date.UTC(2018, 4, 6), 5.19], [Date.UTC(2018, 5, 10), 5.76], [Date.UTC(2018, 6, 7), 6.36], [Date.UTC(2018, 7, 1), 6.99], [Date.UTC(2018, 8, 3), 7.65], [Date.UTC(2018, 9, 5), 7.16], [Date.UTC(2018, 10, 8), 7.68], [Date.UTC(2018, 11, 2), 8.38], [Date.UTC(2019, 0, 4), 8.29], [Date.UTC(2019, 1, 6), 7.57], [Date.UTC(2019, 2, 2), 8.26], [Date.UTC(2019, 3, 7), 8.17], [Date.UTC(2019, 4, 4), 7.83], [Date.UTC(2019, 5, 9), 8.53], [Date.UTC(2019, 6, 6), 9.26], [Date.UTC(2019, 7, 5), 10.02], [Date.UTC(2019, 8, 9), 9.87], [Date.UTC(2019, 9, 5), 9.09], [Date.UTC(2019, 10, 3), 9.84], [Date.UTC(2019, 11, 6), 10.31], [Date.UTC(2020, 0, 5), 9.70], [Date.UTC(2020, 1, 4), 9.35], [Date.UTC(2020, 2, 4), 10.11], [Date.UTC(2020, 3, 2), 9.31], [Date.UTC(2020, 4, 2), 9.12], [Date.UTC(2020, 5, 1), 8.36], [Date.UTC(2020, 6, 4), 9.09], [Date.UTC(2020, 7, 2), 9.69], [Date.UTC(2020, 8, 6), 10.47], [Date.UTC(2020, 9, 4), 11.28], [Date.UTC(2020, 10, 3), 12.12], [Date.UTC(2020, 11, 3), 12.21], [Date.UTC(2021, 0, 2), 11.72], [Date.UTC(2021, 1, 6), 10.86], [Date.UTC(2021, 2, 4), 10.31], [Date.UTC(2021, 3, 4), 11.03], [Date.UTC(2021, 4, 2), 11.87], [Date.UTC(2021, 5, 5), 11.84], [Date.UTC(2021, 6, 4), 10.96], [Date.UTC(2021, 7, 3), 11.86], [Date.UTC(2021, 8, 11), 11.67], [Date.UTC(2021, 9, 6), 11.27], [Date.UTC(2021, 10, 6), 11.77], [Date.UTC(2021, 11, 5), 12.90], [Date.UTC(2022, 0, 1), 13.58], [Date.UTC(2022, 1, 2), 15.33], [Date.UTC(2022, 2, 2), 14.26], [Date.UTC(2022, 3, 5), 13.92], [Date.UTC(2022, 4, 3), 12.74], [Date.UTC(2022, 5, 4), 12.20], [Date.UTC(2022, 6, 2), 13.44], [Date.UTC(2022, 7, 2), 15.42]]}, \n",
    "{name : 'C',data : [[Date.UTC(2001, 5, 30), 20.24], [Date.UTC(2001, 6, 30), 20.77], [Date.UTC(2001, 7, 30), 20.75], [Date.UTC(2001, 8, 28), 20.77], [Date.UTC(2001, 9, 26), 19.75], [Date.UTC(2001, 10, 28), 19.21], [Date.UTC(2001, 11, 31), 20.14], [Date.UTC(2002, 0, 30), 18.83], [Date.UTC(2002, 1, 27), 19.89], [Date.UTC(2002, 2, 29), 19.85], [Date.UTC(2002, 3, 29), 19.82], [Date.UTC(2002, 4, 29), 19.99], [Date.UTC(2002, 5, 29), 19.57], [Date.UTC(2002, 6, 31), 19.25], [Date.UTC(2002, 7, 29), 18.16], [Date.UTC(2002, 8, 29), 18.70], [Date.UTC(2002, 9, 30), 18.73], [Date.UTC(2002, 10, 29), 17.50], [Date.UTC(2002, 11, 31), 17.26], [Date.UTC(2003, 0, 31), 18.25], [Date.UTC(2003, 1, 28), 18.54], [Date.UTC(2003, 2, 31), 17.21], [Date.UTC(2003, 3, 30), 18.02], [Date.UTC(2003, 4, 30), 18.50], [Date.UTC(2003, 5, 30), 18.52], [Date.UTC(2003, 6, 31), 17.97], [Date.UTC(2003, 7, 31), 17.78], [Date.UTC(2003, 8, 30), 18.31], [Date.UTC(2003, 9, 31), 17.00], [Date.UTC(2003, 10, 30), 18.47], [Date.UTC(2003, 11, 31), 18.60], [Date.UTC(2004, 0, 31), 18.20], [Date.UTC(2004, 1, 29), 18.93], [Date.UTC(2004, 2, 31), 17.75], [Date.UTC(2004, 3, 30), 17.95], [Date.UTC(2004, 4, 30), 18.29], [Date.UTC(2004, 5, 30), 17.14], [Date.UTC(2004, 6, 31), 16.33], [Date.UTC(2004, 7, 30), 17.12], [Date.UTC(2004, 8, 30), 18.17], [Date.UTC(2004, 9, 31), 17.99], [Date.UTC(2004, 10, 30), 19.70], [Date.UTC(2004, 11, 31), 20.71], [Date.UTC(2005, 0, 31), 19.82], [Date.UTC(2005, 1, 28), 19.47], [Date.UTC(2005, 2, 31), 18.63], [Date.UTC(2005, 3, 30), 18.52], [Date.UTC(2005, 4, 31), 19.37], [Date.UTC(2005, 5, 30), 19.85], [Date.UTC(2005, 6, 31), 19.47], [Date.UTC(2005, 7, 31), 19.16], [Date.UTC(2005, 8, 30), 18.77], [Date.UTC(2005, 11, 3), 19.63], [Date.UTC(2006, 0, 4), 19.01], [Date.UTC(2006, 1, 2), 18.33], [Date.UTC(2006, 2, 1), 17.79], [Date.UTC(2006, 3, 2), 17.69], [Date.UTC(2006, 4, 1), 17.69], [Date.UTC(2006, 5, 1), 18.25], [Date.UTC(2006, 6, 2), 17.83], [Date.UTC(2006, 7, 2), 17.43], 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7), 4.00], [Date.UTC(2015, 3, 13), 3.01], [Date.UTC(2015, 4, 13), 2.72], [Date.UTC(2015, 5, 6), 2.87], [Date.UTC(2015, 6, 12), 2.89], [Date.UTC(2015, 7, 6), 2.73], [Date.UTC(2015, 8, 5), 2.53], [Date.UTC(2015, 9, 4), 2.56], [Date.UTC(2015, 10, 7), 3.25], [Date.UTC(2015, 11, 4), 2.79], [Date.UTC(2016, 0, 2), 2.71], [Date.UTC(2016, 1, 2), 2.77], [Date.UTC(2016, 2, 3), 2.77], [Date.UTC(2016, 3, 7), 2.99], [Date.UTC(2016, 4, 6), 2.99], [Date.UTC(2016, 5, 5), 3.23], [Date.UTC(2016, 6, 4), 3.27], [Date.UTC(2016, 7, 6), 3.17], [Date.UTC(2016, 8, 8), 2.85], [Date.UTC(2016, 9, 7), 2.74], [Date.UTC(2016, 10, 5), 3.13], [Date.UTC(2016, 11, 4), 2.92], [Date.UTC(2017, 0, 7), 2.56], [Date.UTC(2017, 1, 8), 3.07], [Date.UTC(2017, 2, 7), 3.01], [Date.UTC(2017, 3, 9), 3.38], [Date.UTC(2017, 4, 6), 2.69], [Date.UTC(2017, 5, 3), 2.77], [Date.UTC(2017, 6, 7), 3.09], [Date.UTC(2017, 7, 2), 2.29], [Date.UTC(2017, 8, 6), 2.21], [Date.UTC(2017, 9, 5), 2.79], [Date.UTC(2017, 10, 12), 1.90], [Date.UTC(2017, 11, 9), 1.59], [Date.UTC(2018, 0, 3), 2.53], [Date.UTC(2018, 1, 8), 3.42], [Date.UTC(2018, 2, 7), 4.01], [Date.UTC(2018, 3, 1), 4.22], [Date.UTC(2018, 4, 6), 3.32], [Date.UTC(2018, 5, 10), 2.88], [Date.UTC(2018, 6, 7), 2.83], [Date.UTC(2018, 7, 1), 2.93], [Date.UTC(2018, 8, 3), 2.78], [Date.UTC(2018, 9, 5), 2.79], [Date.UTC(2018, 10, 8), 2.38], [Date.UTC(2018, 11, 2), 2.44], [Date.UTC(2019, 0, 4), 2.68], [Date.UTC(2019, 1, 6), 2.27], [Date.UTC(2019, 2, 2), 2.42], [Date.UTC(2019, 3, 7), 2.24], [Date.UTC(2019, 4, 4), 2.49], [Date.UTC(2019, 5, 9), 2.57], [Date.UTC(2019, 6, 6), 2.17], [Date.UTC(2019, 7, 5), 2.08], [Date.UTC(2019, 8, 9), 1.86], [Date.UTC(2019, 9, 5), 1.91], [Date.UTC(2019, 10, 3), 1.72], [Date.UTC(2019, 11, 6), 2.05], [Date.UTC(2020, 0, 5), 2.41], [Date.UTC(2020, 1, 4), 2.02], [Date.UTC(2020, 2, 4), 2.02], [Date.UTC(2020, 3, 2), 2.37], [Date.UTC(2020, 4, 2), 2.49], [Date.UTC(2020, 5, 1), 2.26], [Date.UTC(2020, 6, 4), 1.90], [Date.UTC(2020, 7, 2), 2.24], [Date.UTC(2020, 8, 6), 2.49], [Date.UTC(2020, 9, 4), 2.09], [Date.UTC(2020, 10, 3), 1.79], [Date.UTC(2020, 11, 3), 2.12], [Date.UTC(2021, 0, 2), 1.99], [Date.UTC(2021, 1, 6), 1.75], [Date.UTC(2021, 2, 4), 2.07], [Date.UTC(2021, 3, 4), 1.84], [Date.UTC(2021, 4, 2), 1.86], [Date.UTC(2021, 5, 5), 2.21], [Date.UTC(2021, 6, 4), 2.58], [Date.UTC(2021, 7, 3), 2.19], [Date.UTC(2021, 8, 11), 1.85], [Date.UTC(2021, 9, 6), 2.10], [Date.UTC(2021, 10, 6), 1.81], [Date.UTC(2021, 11, 5), 1.50], [Date.UTC(2022, 0, 1), 1.40], [Date.UTC(2022, 1, 2), 1.79], [Date.UTC(2022, 2, 2), 1.92], [Date.UTC(2022, 3, 5), 1.64], [Date.UTC(2022, 4, 3), 1.52], [Date.UTC(2022, 5, 4), 1.25], [Date.UTC(2022, 6, 2), 1.20], [Date.UTC(2022, 7, 2), 1.39]]}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "遍历列表，将日期部分拼接为一个字符串，根据日期和当月热度可绘制折线图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:\n",
    "        tiobe = fr.read()  # 读文件到字符会串\n",
    "tiobe = eval(tiobe.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))\n",
    "\n",
    "# print(tiobe)\n",
    "for line in tiobe:\n",
    "    lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串\n",
    "    data = line[lan_name]  # 当前处理的语言的热度数据列表\n",
    "    date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]\n",
    "    rank = [x[1] for x in data]\n",
    "    plt.plot(date_of_lan, rank, label=lan_name)\n",
    "    plt.legend(loc='lower left')\n",
    "plt.xticks(\n",
    "    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],\n",
    "    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小\n",
    "plt.rcParams['figure.dpi'] = 300          # default for me was 75\n",
    "\n",
    "def read_txt():\n",
    "    \"\"\"读文本文件中的数据为字符串，替换转为字典，返回字典\"\"\"\n",
    "    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:\n",
    "        tiobe_str = fr.read()  # 读文件到字符会串\n",
    "    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))\n",
    "    return tiobe_dic\n",
    "\n",
    "\n",
    "def plot_ranking_curve(tiobe_dic):\n",
    "    for line in tiobe_dic:               # 遍历字典，逐条绘制\n",
    "        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串\n",
    "        data = line[lan_name]            # 当前处理的语言的热度数据列表\n",
    "        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]  # 格式化日期，月和日用2位数，不足时补0\n",
    "        rank = [x[1] for x in data]\n",
    "        plt.plot(date_of_lan, rank, label=lan_name, linewidth=2)\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    plt.title('TIOBE Programming Community index')\n",
    "    plt.xlabel('year')\n",
    "    plt.ylabel('Ratings(%)')\n",
    "    plt.legend(loc='lower left')\n",
    "    plt.xticks(\n",
    "    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],\n",
    "    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45\n",
    "    \n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    tiobe_dict = read_txt()\n",
    "    plot_ranking_curve(tiobe_dict)\n",
    "    draw_label()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.xticks(date_of_lan[::12],[x[:4] for x in date_of_lan[::12]],rotation=-45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 修改x刻度\n",
    "plt.xticks(\n",
    "    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],\n",
    "    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45\n",
    "\n",
    "# 改变图例位置\n",
    "plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8,\n",
    "           ncol=5, mode=\"expand\", borderaxespad=0.)\n",
    "# 增加横向主网格\n",
    "plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小\n",
    "plt.rcParams['figure.dpi'] = 300          # default for me was 75\n",
    "\n",
    "def read_txt():\n",
    "    \"\"\"读文本文件中的数据为字符串，替换转为字典，返回字典\"\"\"\n",
    "    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:\n",
    "        tiobe_str = fr.read()  # 读文件到字符会串\n",
    "    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))\n",
    "    return tiobe_dic\n",
    "\n",
    "\n",
    "def plot_ranking_curve(tiobe_dic):\n",
    "    for line in tiobe_dic:\n",
    "        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串\n",
    "        data = line[lan_name]  # 当前处理的语言的热度数据列表\n",
    "        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]\n",
    "        rank = [x[1] for x in data]\n",
    "        plt.plot(date_of_lan, rank, label=lan_name, linewidth=2)\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    plt.title('TIOBE Programming Community index')\n",
    "    plt.xlabel('year')\n",
    "    plt.ylabel('Ratings(%)')\n",
    "\n",
    "    # 修改x刻度\n",
    "    plt.xticks(\n",
    "    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],\n",
    "    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45\n",
    "    \n",
    "    # 改变图例位置\n",
    "    plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8, ncol=5, mode=\"expand\", borderaxespad=0.)\n",
    "    \n",
    "    # 增加横向主网格\n",
    "    plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    tiobe_dict = read_txt()\n",
    "    plot_ranking_curve(tiobe_dict)\n",
    "    draw_label()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小\n",
    "plt.rcParams['figure.dpi'] = 300          # default for me was 75\n",
    "\n",
    "def read_txt():\n",
    "    \"\"\"读文本文件中的数据为字符串，替换转为字典，返回字典\"\"\"\n",
    "    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:\n",
    "        tiobe_str = fr.read()  # 读文件到字符会串\n",
    "    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))\n",
    "    return tiobe_dic\n",
    "\n",
    "\n",
    "def plot_ranking_curve(tiobe_dic):\n",
    "    \"\"\"逐条绘制排名前4的程序设计语言曲线，Python语言曲线宽度设为4，突出显示\"\"\"    \n",
    "    for line in tiobe_dic[:4]:  # 只绘制排名前4的语言\n",
    "        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串\n",
    "        if lan_name == 'Python':\n",
    "            width = 4\n",
    "        else:\n",
    "            width = 2\n",
    "        data = line[lan_name]  # 当前处理的语言的热度数据列表\n",
    "        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]\n",
    "        rank = [x[1] for x in data]\n",
    "        plt.plot(date_of_lan, rank, label=lan_name, linewidth=width)\n",
    "\n",
    "\n",
    "def draw_label():\n",
    "    plt.title('TIOBE Programming Community index')\n",
    "    plt.xlabel('year')\n",
    "    plt.ylabel('Ratings(%)')\n",
    "\n",
    "    # 修改x刻度\n",
    "    plt.xticks(\n",
    "    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],\n",
    "    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45\n",
    "    \n",
    "    # 改变图例位置\n",
    "    plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8, ncol=5, mode=\"expand\", borderaxespad=0.)\n",
    "    \n",
    "    # 增加横向主网格\n",
    "    plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    tiobe_dict = read_txt()\n",
    "    plot_ranking_curve(tiobe_dict)\n",
    "    draw_label()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 面向对象方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 matplotlib.figure.Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, **kwargs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该函数的功能就是为了创建一个图形figure，或者激活一个已经存在的图形figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. num：int or str, optional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合法值为整数或者一个字符串，可选。该关键字是一个figure独一无二的ID标识，当num是整数的时候，该整数即为Figure.number属性的值，如果是字符串，则Figure.number属性的值自动加1。下面看一个例子：\n",
    " 该例子展示了plt.figure()中关键字num的作用，当存在多个figure的时候，就可以通过调用plt.figure()，传入对应的ID标识即可激活对应的figure，其中figure是有对应的编号的，可以传入数字来激活。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(-np.pi, np.pi, 256)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    " \n",
    "fig1 = plt.figure(num='first')\n",
    "fig1.suptitle('first figure')\n",
    "plt.plot(x, y1)\n",
    " \n",
    "fig2 = plt.figure(num='second')\n",
    "fig2.suptitle('second figure')\n",
    "plt.plot(x, y2)\n",
    " \n",
    "plt.figure(num=1)  #plt.figure(num='first')\n",
    "plt.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. figsize：(float, float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合法值为一个元组(float, float), default: rcParams[\"figure.figsize\"] (default: [6.4, 4.8])，分别表示宽和高，整型会被转化为浮点型float。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1 = plt.figure(figsize=(2,1))\n",
    "fig1.suptitle('fig1')\n",
    "plt.plot([1,2,3,4])\n",
    " \n",
    "fig2 = plt.figure(figsize=(4,2))\n",
    "fig2.suptitle('fig2')\n",
    "plt.plot([1,2,3,4])\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. facecolor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "背景色（background color），default: rcParams[\"figure.facecolor\"] (default: 'white')，默认白色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1 = plt.figure(figsize=(2,1),facecolor='b')\n",
    "fig1.suptitle('fig1')\n",
    "plt.plot([1,2,3,4])\n",
    " \n",
    "fig2 = plt.figure(figsize=(4,2),facecolor='g')\n",
    "fig2.suptitle('fig2')\n",
    "plt.plot([1,2,3,4])\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合法值为bool类型，default: False，主要是用来清除已经存在的figure。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 返回值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "返回一个图形figure句柄。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将图形保存为文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savfig('my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib支持许多图形格式，具体格式由操作系统已安装的图形显示接口决定，可以通过canvas对象的方法查看系统支持的文件格式。需要注意的是，当你保存图形文件时，不需要使用plt.show()了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 matplotlib.axes.Axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.axes相关属性设置（绘图方式、坐标轴、坐标刻度、文本等）,其继承关系如下："
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:320415ff-ff96-4f5c-879e-826af191002f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在绘制子图过程中，对于每一个子图的不同设置，ax 可以直接实现对于单个子图的设定，因此掌握必要的 ax 设置命令尤为重要！\n",
    "\n",
    "参数传递，返回新的 AXES 对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.axes.Axes（fig，rect，*，facecolor = None，frameon = True，sharex = None，sharey = None，label = ‘’，xscale = None，yscale = None，box_aspect = None，** kwargs）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 基本绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes.plot\t将y对x绘制为线条或标记。\n",
    "Axes.errorbar\t将y与x绘制为带有错误栏的线和/或标记。\n",
    "Axes.scatter\ty与y的散点图\n",
    "Axes.plot_date\t绘制强制轴以将浮点数视为日期的图。\n",
    "Axes.step\t绘制一个阶梯图。\n",
    "Axes.loglog\t在x轴和y轴上使用对数缩放绘制图。\n",
    "Axes.semilogx\t在x轴上绘制具有对数比例的图。\n",
    "Axes.semilogy\t用y轴上的对数比例绘制图。\n",
    "Axes.fill_between\t填充两条水平曲线之间的区域。\n",
    "Axes.fill_betweenx\t填充两条垂直曲线之间的区域。\n",
    "Axes.bar\t绘制条形图。\n",
    "Axes.barh\t绘制水平条形图。\n",
    "Axes.bar_label\t标记条形图。\n",
    "Axes.stem\t创建一个茎图。\n",
    "Axes.eventplot\t在给定位置绘制相同的平行线。\n",
    "Axes.pie\t绘制饼图。\n",
    "Axes.stackplot\t绘制堆积面积图。\n",
    "Axes.broken_barh\t绘制矩形的水平序列。\n",
    "Axes.vlines\t在每个x上绘制从ymin到ymax的垂直线。\n",
    "Axes.hlines\t在从xmin到xmax的每个y上绘制水平线。\n",
    "Axes.fill\t绘制填充的多边形。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 跨度，光谱，填充，2D数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes.axhline\t在轴上添加一条水平线。\n",
    "Axes.axhspan\t在轴上添加水平跨度（矩形）。\n",
    "Axes.axvline\t在轴上添加一条垂直线。\n",
    "Axes.axvspan\t在轴上添加垂直跨度（矩形）。\n",
    "Axes.axline\t添加无限长的直线。\n",
    "\n",
    "Axes.acorr\t绘制x的自相关。\n",
    "Axes.angle_spectrum\t绘制角度光谱。\n",
    "Axes.cohere\t绘制x和y之间的相干性。\n",
    "Axes.csd\t绘制交叉光谱密度。\n",
    "Axes.magnitude_spectrum\t绘制幅度谱。\n",
    "Axes.phase_spectrum\t绘制相位谱。\n",
    "Axes.psd\t绘制功率谱密度。\n",
    "Axes.specgram\t绘制频谱图。\n",
    "Axes.xcorr\t绘制x和y之间的互相关。\n",
    "\n",
    "Axes.clabel\t标注等高线图。\n",
    "Axes.contour\t绘制轮廓线。\n",
    "Axes.contourf\t绘制填充轮廓。\n",
    "\n",
    "Axes.imshow\t将数据显示为图像，即在2D常规栅格上。\n",
    "Axes.matshow\t将2D矩阵或数组的值绘制为颜色编码的图像。\n",
    "Axes.pcolor\t创建具有非规则矩形网格的伪彩色图。\n",
    "Axes.pcolorfast\t创建具有非规则矩形网格的伪彩色图。\n",
    "Axes.pcolormesh\t创建具有非规则矩形网格的伪彩色图。\n",
    "Axes.spy\t绘制2D阵列的稀疏模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 坐标轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes.axis\t获取或设置某些轴属性的便捷方法。\n",
    "Axes.set_axis_off\t关闭x和y轴。\n",
    "Axes.set_axis_on\t开启x和y轴。\n",
    "Axes.set_frame_on\t设置是否绘制轴矩形补丁。\n",
    "Axes.get_frame_on\t获取是否绘制了轴矩形补丁。\n",
    "Axes.set_axisbelow\t设置轴刻度线和网格线是在图上方还是下方。\n",
    "Axes.get_axisbelow\t获取轴刻度和网格线是在图上方还是下方。\n",
    "Axes.grid\t增加网格线。\n",
    "Axes.get_facecolor\t获取轴的表面色。\n",
    "Axes.set_facecolor\t设置轴的表面色。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.4 轴的范围、方向、标签、标题、图例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes.invert_xaxis\t反转x轴。\n",
    "Axes.xaxis_inverted\t返回x轴是否沿“反”方向定向。\n",
    "Axes.invert_yaxis\t反转y轴。\n",
    "Axes.yaxis_inverted\t返回y轴是否沿“反”方向定向。\n",
    "Axes.set_xlim\t设置x轴范围。\n",
    "Axes.get_xlim\t返回x轴范围。\n",
    "Axes.set_ylim\t设置y轴范围。\n",
    "Axes.get_ylim\t返回y轴范围。\n",
    "Axes.set_xbound\t设置x轴的上下边界。\n",
    "Axes.get_xbound\t以递增顺序返回x轴的上下边界。\n",
    "Axes.set_ybound\t设置y轴的上下边界。\n",
    "Axes.get_ybound\t以递增顺序返回y轴的上下边界。\n",
    "\n",
    "Axes.set_xlabel\t设置x轴的标签。\n",
    "Axes.get_xlabel\t获取xlabel文本字符串。\n",
    "Axes.set_ylabel\t设置y轴的标签。\n",
    "Axes.get_ylabel\t获取ylabel文本字符串。\n",
    "Axes.set_title\t为轴设置标题。\n",
    "Axes.get_title\t获取轴标题。\n",
    "Axes.legend\t在轴上放置一个图例。\n",
    "Axes.get_legend\t返回Legend实例，如果未定义图例，则返回None。\n",
    "Axes.get_legend_handles_labels\t返回图例的句柄和标签\n",
    "\n",
    "\n",
    "Axes.set_xscale\t设置x轴比例。\n",
    "Axes.get_xscale\t返回xaxis的比例尺（以str表示）。\n",
    "Axes.set_yscale\t设置y轴比例。\n",
    "Axes.get_yscale\t返回yaxis的比例尺（以str表示）。\n",
    "\n",
    "\n",
    "Axes.set_xticks\t设置xaxis的刻度位置。\n",
    "Axes.get_xticks\t返回数据坐标中xaxis的刻度位置。\n",
    "Axes.set_xticklabels\t使用字符串标签列表设置xaxis的标签。\n",
    "Axes.get_xticklabels\t获取xaxis的刻度标签。\n",
    "Axes.get_xmajorticklabels\t返回xaxis的主要刻度标签，作为的列表Text。\n",
    "Axes.get_xminorticklabels\t返回xaxis的次刻度标签，作为的列表Text。\n",
    "Axes.get_xgridlines\t返回xaxis的网格线作为Line2Ds的列表。\n",
    "Axes.get_xticklines\t以x的列表形式返回xaxis的刻度线Line2D。\n",
    "Axes.xaxis_date\t设置轴刻度和标签，以将沿x轴的数据视为日期。\n",
    "Axes.set_yticks\t设置yaxis的刻度位置。\n",
    "Axes.get_yticks\t返回数据坐标中yaxis的刻度位置。\n",
    "Axes.set_yticklabels\t使用字符串标签列表设置yaxis标签。\n",
    "Axes.get_yticklabels\t获取yaxis的刻度标签。\n",
    "Axes.get_ymajorticklabels\t返回yaxis的主要刻度标签，作为的列表Text。\n",
    "Axes.get_yminorticklabels\t返回yaxis的次要刻度标签，作为的列表Text。\n",
    "Axes.get_ygridlines\t返回yaxis的网格线作为Line2Ds的列表。\n",
    "Axes.get_yticklines\t返回yaxis的刻度线作为Line2Ds的列表。\n",
    "Axes.yaxis_date\t设置轴刻度和标签，以将沿y轴的数据视为日期。\n",
    "Axes.minorticks_off\t去除轴上的细小滴答声。\n",
    "Axes.minorticks_on\t在轴上显示较小的刻度。\n",
    "Axes.ticklabel_format\t配置ScalarFormatter默认情况下用于线性轴。\n",
    "Axes.tick_params\t更改刻度线，刻度线标签和网格线的外观。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.5 投影"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Axes.get_xaxis_transform\t获取用于绘制x轴标签，刻度线和网格线的转换。\n",
    "Axes.get_yaxis_transform\t获取用于绘制y轴标签，刻度线和网格线的转换。\n",
    "Axes.get_data_ratio\t返回缩放数据的纵横比。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.多子图绘制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 matplotlib.pyplot api 绘制子图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    cl = ['r', 'g', 'b', 'purple']\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        plt.subplot(eval(f'22{i+1}'))  # 分2x2，小于10的数可合成3位数\n",
    "        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 面向对象方式绘制子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    my_dpi = 80\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(720 / my_dpi, 480 / my_dpi), dpi=my_dpi,\n",
    "                            sharex=False,  # x轴刻度值共享开启\n",
    "                            sharey=False,  # y轴刻度值共享关闭                        \n",
    "                            )\n",
    "    cl = ['r', 'g', 'b', 'purple']\n",
    "\n",
    "    x0 = [float(ls[0]) for ls in data_lst]  # 生成x的列表\n",
    "    y0 = [float(ls[1]) for ls in data_lst]  # 生成y的列表\n",
    "    axs[0][0].plot(x0, y0, color=cl[0])\n",
    "    \n",
    "    x1 = [float(ls[2]) for ls in data_lst]  # 生成x的列表\n",
    "    y1 = [float(ls[3]) for ls in data_lst]  # 生成y的列表\n",
    "    axs[0][1].plot(x1, y1, color=cl[1])\n",
    "    \n",
    "    x2 = [float(ls[4]) for ls in data_lst]  # 生成x的列表\n",
    "    y2 = [float(ls[5]) for ls in data_lst]  # 生成y的列表\n",
    "    axs[1][0].plot(x2, y2, color=cl[2])\n",
    "    \n",
    "    x3 = [float(ls[6]) for ls in data_lst]  # 生成x的列表\n",
    "    y3 = [float(ls[7]) for ls in data_lst]  # 生成y的列表\n",
    "    axs[1][1].plot(x3, y3, color=cl[3])\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 matplotlib.pyplot add_subplot方式添加子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    cl = ['r', 'g', 'b', 'purple']\n",
    "    my_dpi = 80\n",
    "    fig = plt.figure(figsize=(640/my_dpi,480/my_dpi),dpi=my_dpi)\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        fig.add_subplot(eval(f'22{i+1}'))\n",
    "        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 matplotlib.gridspec.GridSpec方式添加子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    cl = ['r', 'g', 'b', 'purple']\n",
    "    fig = plt.figure(dpi=100,constrained_layout=True)  # 类似于tight_layout，使得各子图之间的距离自动调整【类似excel中行宽根据内容自适应】\n",
    "    gs = GridSpec(3, 3, figure=fig)  # GridSpec将fiure分为3行3列，每行三个axes，gs为一个matplotlib.gridspec.GridSpec对象，可灵活的切片figure\n",
    "\n",
    "    x0 = [float(ls[0]) for ls in data_lst]  # 生成x的列表\n",
    "    y0 = [float(ls[1]) for ls in data_lst]  # 生成y的列表\n",
    "    ax1 = fig.add_subplot(gs[0, 0:1])\n",
    "    plt.plot(x0, y0, color=cl[0])\n",
    "\n",
    "\n",
    "    x1 = [float(ls[2]) for ls in data_lst]  # 生成x的列表\n",
    "    y1 = [float(ls[3]) for ls in data_lst]  # 生成y的列表\n",
    "    ax2 = fig.add_subplot(gs[0, 1:3])#gs[0, 0:3]中0选取figure的第一行，0:3选取figure第二列和第三列\n",
    "    plt.plot(x1, y1, color=cl[1])\n",
    "    plt.xlim(-5, 5)\n",
    "\n",
    "    x2 = [float(ls[4]) for ls in data_lst]  # 生成x的列表\n",
    "    y2 = [float(ls[5]) for ls in data_lst]  # 生成y的列表\n",
    "    ax3 = fig.add_subplot(gs[1, 0:2])\n",
    "    plt.plot(x2, y2, color=cl[2])\n",
    "    plt.xlim(-5, 5)\n",
    "\n",
    "    x3 = [float(ls[6]) for ls in data_lst]  # 生成x的列表\n",
    "    y3 = [float(ls[7]) for ls in data_lst]  # 生成y的列表\n",
    "    ax4 = fig.add_subplot(gs[1:3, 2:3])\n",
    "    plt.plot(x3, y3, color=cl[3])\n",
    "    plt.xlim(-5, 5)\n",
    "\n",
    "    ax5 = fig.add_subplot(gs[2, 0:1])\n",
    "    plt.scatter([1, 2, 3], [4, 5, 6], marker='*')\n",
    "\n",
    "    ax6 = fig.add_subplot(gs[2, 1:2])\n",
    "    plt.bar([1, 2, 3], [4, 5, 6])\n",
    "    fig.suptitle(\"GridSpec\", color='r')\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 任意位置绘制子图(plt.axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "\n",
    "def read_csv(filename):\n",
    "    \"\"\"接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。\"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as fr:\n",
    "        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表\n",
    "    return data_lst\n",
    "\n",
    "\n",
    "def draw_dos(data_lst):\n",
    "    \"\"\"接收二维列表为参数，绘制数据曲线。\"\"\"\n",
    "    cl = ['r', 'g', 'b', 'purple']\n",
    "    loc = [[0.15, 0.7, 0.2, 0.15],[0.7, 0.7, 0.2, 0.15],[0.15, 0.3, 0.2, 0.15],[0.7, 0.3, 0.2, 0.15]]\n",
    "    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线\n",
    "        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表\n",
    "        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表\n",
    "        plt.subplot(eval(f'22{i+1}'))  # 分2x2，小于10的数可合成3位数\n",
    "        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色\n",
    "        plt.axes(loc[i])   ## [left, bottom, width, height]四个参数(fractions of figure)可以非常灵活的调节子图中子图的位置\n",
    "        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色\n",
    "        plt.xlim(-10,10)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    file = 'images/ch9/9.5 PDOS.csv'\n",
    "    dos_in_lst = read_csv(file)\n",
    "    draw_dos(dos_in_lst)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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