jupyter/可视化.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASoAAAEFCAYAAABUwKrMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn6ampuHXKDs7G6fTyQM
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290aa5ab588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#林贻鑫\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.family']= 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "dim_num=6\n",
    "data=np.array([[0.50,0.32,0.35,0.30,0.30,0.88],\n",
    "[0.45,0.35,0.30,0.40,0.40,0.30],\n",
    "[0.43,0.99,0.30,0.28,0.22,0.30],\n",
    "[0.30,0.25,0.48,0.95,0.45,0.40],\n",
    "[0.20,0.38,0.87,0.45,0.32,0.28],\n",
    "[0.34,0.31,0.38,0.40,0.92,0.28]])\n",
    "angles=np.linspace(0, 2 * np.pi, dim_num, endpoint=False)\n",
    "angles=np.concatenate((angles,[angles[0]]))\n",
    "data=np.concatenate((data,[data[0]]))\n",
    "radar_labels=['研究型(I)','艺术型(A)','社会型(S)','企业型(E)','传统型©','现实型®']\n",
    "radar_labels=np.concatenate((radar_labels, [radar_labels[0]]))\n",
    "plt.polar(angles, data)\n",
    "plt.thetagrids(angles * 180/np.pi, labels=radar_labels)\n",
    "plt.fill(angles, data, alpha=0.25)\n",
    "plt.title('37')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfwAAAETCAYAAADTQLREAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nATExcu3RmEZYGEd6b9+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290ec66b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#刘征东\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x=np.linspace(0,12,50)\n",
    "y=np.sin(x)\n",
    "z=np.cos(x)\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.ploty(x,y,label=\"$sin(x)$\",color=\"red\",linewidth=2)\n",
    "plt.plot(x,z,\"b--\",label=\"$cos(x^2)$\")\n",
    "plt.xlabel(\"Time(s)\")\n",
    "plt.ylabel(\"Volt1\")\n",
    "plt.title(\"2-18\")\n",
    "plt.ylim(-1.2,1,2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAAEFCAYAAAC7AsHyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdcXfX9/5+fO9jzwuVyGYEEwkoCIWQREuKKK7Vqa93b\nauto1RjXr7ZaV6N26LdVq4211lFTW01trXGlMRsIATIICRD23hvu+vz+uAMIkMCFGBLv8/G4j+Se\ne+45n3O593Xen/fnPYSUEhcuXLiY7ihO9QBcuHDhYjy4xMqFCxenBS6xcuHCxWmBS6xcuHBxWuAS\nKxcuXJwWuMTKhQsXpwUusToNEUIEHOe184QQM5w87n1CiO8e5/V7hBAeQojNQoh5QogHhRB+Qog/\nCiEyT3DsBNu/s4QQ54xjLEohxNdCiKiJX4mLMxGXWE1jhBC+QohMIcRyIcRZQgiN7aV/CyGuF0J8\nVwjxHSHEpUKISCGEAvg14G3bP18IcVQIsV0IkSWEyDnBKRcA4cd5XQU8BpgAb+BqKWUncC5QfZzr\nuBj4pxBCABJ4XQjhdYKxXAh4SSkrTrCfi28JLrGa3rgBEcAGQAe427ZLoBKwAE8CLcAAcAvwb6AH\nmAdcABRKKZcDvwT2n+B8JsA82gtCCA+gFmgCAoGzgK02i8pHSnnUtp/7Me9TAE8A/09aKQM+AV48\nZr90IUS5EOKAECIf+ADQ2ATX/igQQhwUQpx1gutwcQbiEqtpjJSyRUr5HtAlpdwAnC+EWINVrAqA\nPUCnlHI7MAP4GVZB+gNW0VEDBtvhzgY+P8EpNYD/GK+5AZnAdcBCIAkQwJ2AQgixRwjRCBQKIXyH\nvG8t0CGl/NeQbT8DlgkhfmOztpBS7pJSRksp5wLPATuklDG2a1wmpZwvpUyRUs6RUm45wXW4OANx\nidU0RlgJA5RCCD3WqVc3VrFyA7SAWgihA/qAD4FVgC/wKuDBoDV2NvBzIcRvjnPKJCB1jNf6gKNA\nDbANaAfagBjgD1LKhcBmrFPDLtv4zwXuA+4YeiApZTdwDnAeVussfsg1RwNPDXnP2bbrdfEtxyVW\n0xsv4DUgFPgjVmHowWoxXQg8btv2W6AO61QvEbhRWpM+o4HzhBCRNjH5NdAw2olsjmw1MMcmfscS\nATQDD2EVrMexTkXVQJptnxlAqe14vsDrwM+BXUKIDiFEpxCiWghRDZQDrwBF2KaeNkH+BIgCPrJN\nByOA3bZpYKsQ4jsT+QBdnDkIVyLz9EcIUSSlTLCt8vUAH0gpzxFChALvSyntzvePgQ6sQrARiAW+\nD9RLKa8XQmwEfimlzBvlHM9gtWCagNlSynuOeT0NWA8EYL3JVQFG4GJgC1bx3CqlnDfkPW5SSoPt\n/88DNVLKl2zPNwHPSim32p6nYLUMXwQellJG2LaXAHOllP1CiL8A70opv5jEx+niNMVlWZ1GSCkr\nsfqJ2kd52QzkYXVMvwXsBFYD9wJaIcRdQMQYQhUL3IhVKNYDFxwbiiClzAWW284dBzwPHJZS2qef\nG7FOA4e+xzDk6Uoga8jzcIavIHZgdcL//jgfAbimhN9aVKd6AC7GhxBipe2/M4GvRtlFBWwHlgA/\nAv4HIKU8KIS4HzgAXDvKcUOwis0jUspm27Y7gH8IIVZLKYeGO4QBucAmIAH4hW37h8A6rL6m0cZ+\nKaCUUu4eslmPdTqJbZzlWC1CGOUmaltlDMK6YuniW4jLsprG2OKs7sAatnAf0IvVUnpnaIiAEEIJ\nBGO1Xr7CukL3HeBeIUQM8Gesvq9fCCGWDXnfCqzO8reklO/at0sp/wfcA/xPCPFzIYSP7aUSrGEU\n7lhX9OYIIb5n27YWa/zUkmOu4VKsvqk7bc/9bGPol1IOjHHpQ8MfVFj9YkVYfWL7TvjBuTgzkVK6\nHtP0gXXF7H2sq3QK4D9YRcQH2IXVV7QFq+DcPeR9NwGLgZ8Cu4Hltu1LgGKsK36hwBHgsuOcfyHw\nKdYpmxdWa+13QKjt9euAL4E42/OzsIZHeNmev4jVoksbcsyHbNtuPM553Yf8vwZrXJf6VP89XI9T\n+3A52E8jhBDzgQI5zj/aUAf3kG1qKaXR9n8x3mM5g21FsN9+PhcuJoNLrFy4cHFa4PJZuXDh4rTA\ntRrowsVpRm5ubohKpVoPzOXMMjgswAGTyfTDtLS0xmNfdImVCxenGSqVan1oaGiiVqttUygUZ4wf\nx2KxiKampqT6+vr1wIhSRWeSKrtw8W1hrlar7TyThApAoVBIrVbbgdViHPn6NzweFy5cTB7FmSZU\ndmzXNaouucTKhQsXpwUusXLhwsWkeOWVVzTvv//+WHXQpgyXg92FizOcd3ZXaP7vq+Lwpq4BN62v\nu+Gn586uuX5pVOtUHb+ystJNoVAYTrzn5HBZVi5cnMG8s7tC89R/CqMauwbcJNDYNeD21H8Ko97Z\nXaE54ZvHwGQy0dXV5dAOs9ks/Pz8zLb/09HRoTAapz5pwRXB7sLFaUZBQUF5SkpKM0D0I5+knWh/\nZylftzp3tO0HDx50v/baa2eqVCoJUFVV5e7l5WUJCgoyAgwMDCheffXVivT09F6VavTJm8lkQgiB\nUqkc8VpBQUFwSkpK9LHbXdNAFy5cTIg5c+YMFBQUFNmfp6enx4WHhxv+8Y9/lNu3Pfjgg/rzzz8/\n3i5ox2IymcTTTz9d9cADDzSP97wusXLhwoXT5Obmenh6elra2tqUBQUF7ikpKQMAL7zwQt0LL7xQ\nN5XnconVMdgqZKbIYypWCiE2AG9IKT8/Zrsea1ODa4FbpZSNQ7bvklJGfyMDdxJbd5lArMXwwgC9\nj49PuJubm4dSqXRXKBRuCoVCLYRQA9JisRiklAaz2Wwwm82Gnp6eNoPBUIO1BnwdUCel7D91VzRx\nbA0rXpVSjmi+ausmVCal/OibH9mJGWuqZsfusxowWRw+JneVwvLz7yRVTNbJ3tfXJ+66664ZL774\nYpW3t7fltttui968eXOxr6+vZTLHHQuXWI3kILBeCNEkpXx/yHYjg22t7OVP9mBtX9WMtX+fSQjx\nM6yVNCuYBlUtbX374oAFGo0mwcvLK0YIEWk0GvVCCK8ZM2aoNRqNjIyMFFFRUero6Gjv0NBQtbu7\nOyqVyvGw+xaMRiMmk8nx6OrqklVVVX3l5eV9FRUV5traWhEZGWkxGo1GlUrVqlAoas1mc3lra2tx\nf3//ASBXStlyCj+PT7CWgC4APsLaiAMgVgixBWsrsjuklDm2Gve/AL4SQnwf6+9lA9ZmsDNt76uV\nUj70DV7ChLAL0lSvBubl5XnccccdUddcc01Lenp6H8Add9zRuHLlyri33nqrbN68eWMVVnQal1gd\ng5SyRQhxBfA7IcSGseo9SSm7bBUvZwKPSSn/BiCEyML6hT7/Gxu0DbswCSHS9Hr9uVLKpZGRkUEJ\nCQkiMzPTNy4uziMsLAy9Xo9er8fL60RNkcd3WqyF+YYdTEpJS0tLRF1dXXJtbS21tbXs2bOnY8eO\nHQN6vd6sUqmKe3p6trS1te3gmxWwHwE3Y22McRBIByKBP0opL7Q1pXC3WZIP2R4WIAOrcLUDDwJr\nbMd7zbbPtOX6pVGtUxmq8Oqrr2qeeuqp8BdffLHiyiuv7LRvv/3229s8PT3l+eefH/f1118fjouL\nm9JwBtdq4BgcW5hOCPEOsF7aGmzahEEFJGNtqX451jrjJiHE/2Etw7tGShl7EscYKoQ4R6/Xnyel\nXKpSqYISExNZuXKl35IlSzwWLFhAYGDgyTq901gsFkpLS8nNzWXbtm0dO3bsGGhoaBgqYFuwNjk9\nKbE7QojtWLvxvIW1CqoSayegg8BsrEL2ONbWZl1YS0Z7Yq1aWojVUr3OdriNUsr5J2OcYzF0NfBU\nMDAwIHp6eoRGoxl1utfZ2anw8/Nzeio41mqgS6yGIIS4D/gh1qndrVjrjHth7agyE+t0rwtrjfC/\nATdg/RIHYm128AesjUV
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290ec686128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#唐圪菁\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']='SimHei'    #设置显示中文字体\n",
    "plt.rcParams['axes.unicode_minus']=False   #设置正常显示符号\n",
    "major=np.array(['网工','建智','计科','软工','自动化','通信'])\n",
    "number=np.array([[100,66,78],\n",
    "                 [60,50,40],\n",
    "                 [120,98,102],\n",
    "                [150,140,160],\n",
    "                [102,125,120],\n",
    "                [70,65,84]])\n",
    "data_length=len(number)  #数据长度\n",
    "#把圆周等分为data_length份\n",
    "angles=np.linspace(0,2*np.pi,data_length,endpoint=False)\n",
    "number = np.concatenate((number,[number[0]]))\n",
    "angles = np.concatenate((angles,[angles[0]]))\n",
    "#绘制雷达图\n",
    "plt.polar(angles,number,'o-',linewidth=3)\n",
    "#设置角度和网格标签\n",
    "plt.thetagrids(angles*180/np.pi,major,fontproperties='simhei',fontsize=12)   #将各major标签放到顶点上,并设置标签字体、大小\n",
    "plt.title('班级人数分布')   #设置雷达图标题\n",
    "plt.legend(['大一','大二','大三'],loc=(0.95,0.8),labelspacing=1)  #图列标题，图列位置，图列大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdgFGX6xz8zW9J7740EQkuAQAKEpmBFbFixoSJ2sdzp\nqWf5eXh6tvPsXbErcoCKoggIhBogCZDee6+72c22+f0ROJEksFk2JIH5/KPAvu+8szvznWee9ymC\nJEnIyMjIyAw+4mAvQEZGRkamG1mQZWRkZIYIsiDLyMjIDBFkQZaRkZEZIsiCLCMjIzNEkAVZRkZG\nZoggC7KMjIzMEEEWZBkZGZkhgizIMjIyMkMEZX8+7KtSSeGOjgO1liFBfqcGowRjfVwRhMFejYyM\nzOnA3gZNoyRJfif6XL8EOdzRkbSkJNtXNQzY1NLChZmZXBrjy+NJkYO9HBkZmdMA4Y3NZdZ8TnZZ\nHMMcLy8u8Vbx/J5S6joNg70cGRmZMwhZkHvh6RET6JLgkR3Fg70UGRmZMwhZkHsh1tmZu0PD+CS3\nlv0NHYO9HBkZmTMEWZD74C/h4fgoBe75OQOLXKJURkbmFCALch94qlQ8HR1LWruZbwsbBns5MjIy\nZwCyIB+HG4KCGO8s8vDvOehM5sFejoyMzGmOLMjHQSEI/Ct2HGVdEs/vKx/s5cjIyJzmyIJ8AmZ6\neXGFj4oX9pZRqdEP9nJkZGROY2RBtoL/GzERZ1EgffeBwV6KjIzMaUy/MvXOVCKcnMibmoqHRRZk\nGRmZgUO2kK3ESaEAwJyRMcgrkZGROV2RBbkfGFSJg70EGRmZ0xhZkG1AtpJlZGQGAlmQ+4lsJcvI\nyAwUsiDbgEGVKFvJMjIydkcW5JPgiCgbzZY//VdGRkbGFuSwNxsxqBJRGPaztriB3XUd1OsMOCpE\n3pgVN9hLk5GRGabIFrKNlOv1PFiiIy+7iHlhXjwyMZx2g5kfShsHe2kyMjLDFNlCtpFv6+uZ6RPH\n+a4VuIZ60agz4KpS4OOoGuylycjIDFNkQbaBL2tr2dzSwoPh4XSavShI20umdzBzw7yYGugx2MuT\nkZEZpsiCbANhjo6McHLircpKHEWRRr0Rn/Zyzpkph8TJyMjYjuxDtoGJbm4kuLmR29mJShRJ8R7F\n0kA1wS4Og700GRmZYYwsyDbgrFBwU1AQS0NCCFarmeHpiSAIGPbvRzqm3dOxf5aRkZHpC9llcRL4\nqlT/67enUybgZMpEEASqNF10msw4KEQCndWoFcIgr1RGRmY4IAvySeCvVuOvVgPd3UXaxXF8+dMu\nMh09EQQo6+hitJczL6WOGOSVysjIDAdkQbYTzUYj71ZVIZglFrt2ED1pPL5OKhb+fIhabReBsn9Z\nRkbmBMg+ZDuxtbUVhSBwU9hEJrspUYoCH2TXkBLgLscmy8jIWIUsyHbiq7o6Qh0cCFCrydSa2Zy2\nHyelyCXRvqgUolznQkZG5oTILgs7YJEkZnt58UVdHVkaDeGOAXQYKqmvbSe3pZPmLhMao5nXZ8bi\nrpa/chkZmd6RLWQ7IAoCtwUHc11gIBf5+qIWRapN3oR2NDMrxJPbRgcR7e7IWwerB3upMjIyQxjZ\nXLMTgiBwVUAAAAU6HVM9PDjfTYNvUzn6MeMwWSQi3OSNPRkZmb6RLWQ7s6+jgzcqK5nr7Y2r0wRy\nO81sqGhhrI8LV8T4D/byZGRkhjCyhWxnfJRKZnh68lF1NQFqNRaTidbmQvT+AXycW0u7wcQ4Hxfm\nhnkP9lJlZGSGGLKFbGdCHBw4z8eHg1otoiBQZvInT2dBKQpojWYsEjy5uxSNwTTYS5WRkRliyBay\nnVGKIvO8vZnn7Y3WbObD6moS/WKZJVXgN24CClEgu0VLuaaL0d7y1y8jI/MHsiIMIK9WVNBkNHJX\naChKYyVFu/axyS2IeWFejPZ2GezlycjIDDFkQR5AXBUKHEWRYp2O3M4wyjWFOLhYmB3sA3RXghME\nufAQgNZoprRdT7lGT6Wmi9pOA3U6A016E816I62mdjoMEp1GCb0ZDGYwWyQkQACUooBaAU5KARcV\nuDsIeCo98HZQ4eekItBZTZCLmjBXByLcHAlzdUApyh47maGFLMgDyDQPD5aXlpKp0TDWxYVYtcj5\nhgY8XUIBzkgxbtEbyWjUkNWk5VCzlrz2evJbLNRqe5Yp9XQQ8HUS8HYS8PR2J9xRibNaxFElolaK\nKEUBQehOzDGZJQxmCZ3BgqbLTIfeRF1zG9mtEg2dFrTGP8+tECDKQ2SkhyfxXi6M9XEhwceF0d4u\nqBWyUMsMDrIgDyBJ7u6sGD0aN6WSFqMRL1UEamPGGWMZmy0SB5o0bK1pY0dtO7sbGilq/SOF3MdJ\nYJS3yPmJAcQGOBHp40iEjyOhXg4EeqhRK+0njBq9iepWAxUtXZQ26ilu0FFQryOvrIkNlc10mbs/\np1ZAgp+CFL9ApgW5MyPIkxBXOX5c5tQg9KeA+kQ3NyktKWkAl3NmoDZmoEg8Pds9VXToWVfWzC8V\nzWyqbqJF3319hboJJMf5MCnCjYnhbiSEuRDgrh4SDyaT2UJhvY6MCg37yjTsya5hT63pf1b1CE+R\ns0MCOCfMm7lhXnL6u0y/Ed7YvFeSpBOKpyzIg4DamAFw2ohyXksn3xTWs6q0goz6blMz3F1g7vgA\n5oz0ZGacJ+E+joO8yv5hMlvIrNDye34rm/eWsbnCRIcBlCLMDFXyYkoCE/zcBnuZMsMEawVZftQP\nAgZV4v9EebjSpDfyeV4dnxSUsK/OjABMDVHwr4XRXDjeh/gg5yFh/dqKUiEyKdKNSZFuPHBOGEaT\nhR3F7azLamLtnio8PPPBRdH9Ye3p8WCVGXxkC3mQUBsz6DRLSOPGDatX4PT6dv6TVcU3hXV0mWFi\ngILrZ0dyRZI/IV5noK81/5gH61HirDeZ+SCnljhPJ+bJmZlnNLKFPMRpF8eRvDeNOe1FvDNn5GAv\n57hIksRvlS0s35/N5goTriq4ZWYwS2cGMz7MdbCXN7jEHWUd52eAS7dAl9eO4tXMSrQmMxsrW9AY\nzVwa7TdIi5QZLsiCPEg4KhSc5xvM69mVLBkTRJK/+2AvqVfSatp4ZHcW2yrNhLgKvHRlDLfOCMLd\nSb50enBEnPMzeC1nP+NDFNw4Vs2X+4PQm7qjS86UCBsZ25ADLgeRRyMj8VcJ3PFTJmaL9a6jU0Gl\nRs/Vv6WRumo/hS0W3lgUS9ELM3jgnDBZjE9Auc8oJC8/5p8zgS6ThMKpDFe3cqBn7Hmn0TwYS5QZ\nosiCPIh4KJX8c8Qo0jVmPsqtGezlAN0W3JsHqoj/YidrCo08eVEEhf9K5c45ITio5MvFGrYWtJEY\n5oqPq4oa/1FUOwWhEul2Zxx2aVgkiY2VLSzemMttm/LIbNQA3d+/zJmLfIcNMlf5+zPdXcHfthbQ\nrDeeeMAAUt9p4Pyf0rhrSwFTQ5QceiaZpy6OwsVBMajrGm4IAjRpu6v57SntoE1nZtL0hD9cGi4Z\nGBwzMFoknk2JZqKfK/9ILzs8VnZnnMnI756DjCAIvBw3gWnp6Ty+q4Q3Z8UNyjp21bZz2fr9NOsl\n3lwUy+2zgwdFHFq0RgrqdJQ06ilv1lPdaqC+w0CTxkibuR2NTkJvBKNJQpJAFAXUSnB2EHBzAi+V\nJ76uKgLd1YR6ORDh48gIfyei/RxRnqKUaKNJ4ve8VgrrdQjAFUl++Lmpu//xsCg75mdwbny3G2N3\nXRCjvZyp0nSR29LJR7k1nB3qxeL4oFOyXpmhgyzIQ4Bxrq4sCQnhnYNV3DY6iMRTnHCwqqiBa389\nRLCryM7HJpFwCiInJEmiormLHUXt7CltJ6O+loNlZupa/vzK7uIIAZ4iPu7d9SwCgxQ4OYgoFQKi\nCBYLGIwWOvUWOlrbKGtvIb1Coq5F4uhG3yoljAoVSQzyY3KkGynR7kwIdx0Qkb5xeiAKEfLrdJw3\n1pu82k4mRrjhrO4+liA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x290ec9dd438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#朱浩import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def calcu_elevation(x1, y1): # 计算高程\n",
    "\n",
    "  ele = (1-x1/2+x1**5+y1**3)*np.exp(-x1**2-y1**2)\n",
    "\n",
    "  return ele\n",
    "\n",
    "n = 256\n",
    "\n",
    "x = np.linspace(-3, 3, n)\n",
    "\n",
    "y = np.linspace(-3, 3, n)\n",
    "\n",
    "X, Y = np.meshgrid(x, y) # 将原始数据变为网格数据\n",
    "\n",
    "# 填充等高线的颜色, 8是等高线分为几部分\n",
    "\n",
    "plt.contourf(X, Y, calcu_elevation(X, Y), 8, alpha=0.75, cmap=plt.cm.hot) # 8表示要分几部分等高线\n",
    "\n",
    "# 画等高线\n",
    "\n",
    "C = plt.contour(X, Y, calcu_elevation(X, Y), 8, colors='black', linewidth=0.5)\n",
    "\n",
    "plt.clabel(C, inline=True, fontsize=10) # 添加文字标签 inlins表示等高线是穿过数字还是不穿过\n",
    "\n",
    "plt.xticks([])\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEmCAYAAABPkl3gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqrrc8eNjfO0ff0tTk2f
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd2ddbaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#王诗婷\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 解决 plt 中文显示的问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 极轴图 - 极坐标的柱状图\n",
    "\n",
    "plt.figure(figsize=(8,4))#创建宽为8英寸、高为4英寸的画布\n",
    "\n",
    "ax1= plt.subplot(111, projection='polar')\n",
    "#数字分别表示子图分布在第一行第一列、第一个索引位置，类型选为极点图\n",
    "ax1.set_title('Radar Map\\n')  # 创建标题\n",
    "ax1.set_rlim(0,13)#值域范围\n",
    "\n",
    "data = np.random.randint(1,140,5)\n",
    "\n",
    "theta=np.arange(0,2*np.pi,2*np.pi/5)#第一个参数为起点，第二个参数为终点，第三个参数为步长\n",
    "# 创建数据\n",
    "\n",
    "bar = ax1.bar(theta,data,alpha=0.2)#极坐标的柱状图,alpha表示不透明度\n",
    "for r,bar in zip(data, bar):\n",
    "    bar.set_facecolor(plt.cm.jet(r/10.))#通过plt.cm接口实现\n",
    "    # 设置颜色，jet的地方可以写其他的colormap\n",
    "plt.thetagrids(np.arange(0.0, 360.0, 90), [])#在极坐标设置网格线的theta位置\n",
    "# 设置网格、标签（这里是空标签，则不显示内容）\n",
    "\n",
    "plt.show\n",
    "\n",
    "# 输入产量与温度数据\n",
    "production = [1125, 1725, 2250, 2875, 2900, 3750, 4125]\n",
    "tem = [6, 8, 10, 13, 14, 16, 21]\n",
    "rain = [25, 40, 58, 68, 110, 98, 120]\n",
    "\n",
    "colors = np.random.rand(len(tem))  # 颜色数组\n",
    "size = production\n",
    "plt.scatter(tem, rain, s=size, c=colors, alpha=0.6)  # 画散点图, alpha=0.6 表示不透明度为 0.6\n",
    "plt.ylim([0, 150])  # 纵坐标轴范围\n",
    "plt.xlim([0, 30])   # 横坐标轴范围\n",
    "plt.xlabel('温度')  # 横坐标轴标题\n",
    "plt.ylabel('降雨量')  # 纵坐标轴标题\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQQAAADuCAYAAADfoHlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nejyeKymlwYIHPUv4hxI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd70a77ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#杨海坤\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "# 数据准备\n",
    "labels=np.array([u\"A\",\"B\",u\"C\",u\"D\",u\"E\",u\"F\"])\n",
    "stats=[83, 61, 95, 67, 76, 88]\n",
    "# 画图数据准备，角度、状态值\n",
    "angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)\n",
    "stats=np.concatenate((stats,[stats[0]]))\n",
    "angles=np.concatenate((angles,[angles[0]]))\n",
    "# 用Matplotlib画蜘蛛图\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "ax.plot(angles, stats, 'o-', linewidth=2)\n",
    "ax.fill(angles, stats, alpha=0.25)\n",
    "# 设置中文字体\n",
    "font = FontProperties(fname=r\"C:\\Windows\\Fonts\\simhei.ttf\", size=14)\n",
    "ax.set_thetagrids(angles * 180/np.pi, labels, FontProperties=font)\n",
    "#ax.set_thetagrids(angles * 180/np.pi, labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ymwy\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1297: UserWarning: findfont: Font family ['Microsoft YaHei'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEICAYAAAC6fYRZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr6XzHnNpxTPktXTeYy6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4397a5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def scatterplot(x_data, y_data, x_label=\"\", y_label=\"\", title=\"\", color = \"r\", yscale_log=False):\n",
    "  plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "  plt.rcParams['axes.unicode_minus'] = False\n",
    "  # Plot the data, set the size (s), color and transparency (alpha)\n",
    "  # of the points\n",
    "  plt.scatter(x_data, y_data, s = 10, color = color, alpha = 0.75)#散点图的制作\n",
    "\n",
    "  if yscale_log == True:\n",
    "      plt.set_yscale('log')\n",
    "\n",
    "  # Label the axes and provide a title\n",
    "  plt.title(title)\n",
    "  plt.xlabel(x_label)\n",
    "  plt.ylabel(y_label)\n",
    "\n",
    "height=[140,161,170,182,175,173,165,155,150,120]\n",
    "weight=[38,50,58,80,70,69,55,45,40,30]\n",
    "scatterplot(height,weight,\"身高\",\"体重\",\"Height-to-weight scale chart\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}