rui-exp.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:af1479906b67bd78104e7bbb485d0b7f919b752fd25eb4554e16586cac6f0e1f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = 15\n",
      "n2 = n * n\n",
      "# points = np.array(\n",
      "#     [[2, 2], [8, 2], [14, 2], [4, 4], [8, 4], [12, 4], [6, 6], [8, 6], [10, 6], [2, 8], [4, 8], [6, 8],\n",
      "#      [8, 8], [10, 8], [12, 8], [14, 8], [6, 10], [8, 10], [10, 10], [4, 12], [8, 12, ], [12, 12], [2, 14], [8, 14], [14, 14]])\n",
      "points = np.array([[i,i] for i in range(n)])\n",
      "plist = possiblePointsList(n, points)\n",
      "\n",
      "listMat = [[] for i in range(n2)]\n",
      "vert = [set() for i in range(n)]\n",
      "valMat = [''] * n2\n",
      "\n",
      "ableList = list(range(n))\n",
      "shuffle(ableList)\n",
      "valMat[0], listMat[0] = ableList[0], ableList[1:]\n",
      "i = 1\n",
      "\n",
      "t = time()\n",
      "while i < n2:\n",
      "    if i % n ==  0:\n",
      "        vert = [set(valMat[k:i:n]) for k in range(n)]\n",
      "    if valMat[i]!='':\n",
      "        if not listMat[i]:\n",
      "            valMat[i] = ''\n",
      "            i -= 1\n",
      "        else:\n",
      "            valMat[i] = listMat[i].pop()\n",
      "            i += 1\n",
      "    else:\n",
      "        x = i % n\n",
      "        ableList = list(plist[valMat[i - 1]] - (vert[x] | set(valMat[i - x:i])))\n",
      "\n",
      "        if len(ableList) == 1:\n",
      "            valMat[i] = ableList[0]\n",
      "            i += 1\n",
      "        elif not ableList:\n",
      "            i -= 1\n",
      "        else:\n",
      "            #shuffle(ableList)\n",
      "            valMat[i], listMat[i] = ableList[0], ableList[1:]\n",
      "            i += 1\n",
      "\n",
      "print(np.array(valMat, dtype=np.object).reshape((n, n)))\n",
      "print(time() - t)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pandas as pd\n",
      "\n",
      "raw = pd.read_excel('D:\\\\Coding\\\\py\\\\py2\\\\rui\\\\model.xlsx', 'Sheet1', index_col=None, na_values=['NA'])\n",
      "valid = raw[pd.isnull(raw.bug)]\n",
      "\n",
      "valid.RT = valid.RT.astype(int)\n",
      "x = pd.DataFrame(valid.Location.apply(lambda x :x.split(' ')[0]))\n",
      "y = pd.DataFrame(valid.Location.apply(lambda x :x.split(' ')[1]))\n",
      "x.columns = [u'x']\n",
      "y.columns = [u'y']\n",
      "valid = pd.concat([valid, x],axis=1)\n",
      "valid = pd.concat([valid, y],axis=1)\n",
      "\n",
      "p = pd.pivot_table(valid, values = 'RT', rows = ['x','y'], cols = ['Structure'], aggfunc=np.mean)#\u4ee5A\u3001B\u4e3a\u884c\u6807\u7b7e\uff0c\u4ee5C\u4e3a\u5217\u6807\u7b7e\u5c06D\u5217\u7684\u503c\u6c47\u603b\u6c42\u548c\n",
      "p = pd.concat([p, pd.DataFrame(p.mean(1),columns=['MeanL'])], axis=1)# \u884c\u7684\u5e73\u5747\u503c\n",
      "\n",
      "p1 = p.reset_index()\n",
      "plt.scatter(list(p1.x),p1.y,s=p1.MeanL-600)\n",
      "\n",
      "# p = pd.concat([p, pd.DataFrame(p.mean(),columns=['MeanS']).T])# \u5217\u7684\u5e73\u5747\u503c\n",
      "# p = pd.concat([p, pd.DataFrame(p.mean(1),columns=['MeanL'])], axis=1)# \u884c\u7684\u5e73\u5747\u503c\n",
      "\n",
      "# pd.scatter_matrix(p)\n",
      "# print(valid.Location)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.collections.PathCollection at 0xdd254e0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZZ5/nxRdfxudrBLTDZsvH51vAWWf14amnHuKUU04xOybBYJD333+f8eMnsnbt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\nssTlaiiQH/HolDp1zpIZM2a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       "text": [
        "<matplotlib.figure.Figure at 0xd988588>"
       ]
      }
     ],
     "prompt_number": 34
    }
   ],
   "metadata": {}
  }
 ]
}