Elionix log parser draft

log-parser.py

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab  inline\n",
      "import pandas as pd\n",
      "import style"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "old_files =  !ls Giullio/ *.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scale = 1000000\n",
      "format_date = \"%b %d %H:%M:%S %Y\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_files = []\n",
      "for name in old_files:\n",
      "    if \".csv\" in name:\n",
      "        if \"Reg\" not in name:\n",
      "            all_files.append(name)\n",
      "        if \"reg\" not in name:\n",
      "            all_files.append(name)\n",
      "        if len(all_files) != 0:\n",
      "            all_files.append(name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates = []\n",
      "averages_x = []\n",
      "averages_y = []"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for filez in all_files:\n",
      "    try:\n",
      "        file_csv = open(filez,'r')\n",
      "        list_lines = file_csv.readlines()\n",
      "        date_string = list_lines[5]\n",
      "        date = datetime.datetime.strptime(date_string[20:-2],format_date)\n",
      "        header = list_lines[7]\n",
      "        header = header.split(', ')\n",
      "        list_data = array(list_lines[8:-6])\n",
      "        list_list = []\n",
      "        for i in range(len(list_data)):\n",
      "            line = list_data[i].split(', ') \n",
      "            values = [word.strip('\\r\\n') for word in line]\n",
      "            temp = [float(i) for i in values[1:]]\n",
      "            list_list.append(temp)\n",
      "        list_list = array(list_list)\n",
      "        x_drift = list_list[:, 2] - list_list[:,4] / scale\n",
      "        y_drift =  list_list[:, 3] - list_list[:,5] / scale\n",
      "        avgx = mean(x_drift)\n",
      "        avgy = mean(y_drift)\n",
      "        dates.append(date)\n",
      "        averages_x.append(avgx)\n",
      "        averages_y.append(avgy)\n",
      "    except:\n",
      "        continue"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig,ax = subplots()\n",
      "plt.xticks(rotation=70)\n",
      "ax.plot(array(dates),array(averages_x),'o')\n",
      "style.rstyle(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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89ttvcHFxQZcuXUTLJfVsUs+XlpaGY8eOwdbWFllZWfj999/RvHlz/PLLL9i4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\nmJHhxs8YY0bm/wMRqkSEoGbRDwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4b3d550>"
       ]
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.xticks(rotation=70)\n",
      "plot( averages_y, 'o') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 188,
       "text": [
        "[<matplotlib.lines.Line2D at 0x648b610>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x5b290d0>"
       ]
      }
     ],
     "prompt_number": 188
    }
   ],
   "metadata": {}
  }
 ]
}