smidm · January 19, 2014 21:45
diff --git a/strace frequent freezes.ipynb b/strace frequent freezes.ipynb
 {
 "metadata": {
  "name": "strace frequent freezes"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Strace of correct behaviour"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import matplotlib.pylab as pylab\n",
      "import pandas as pd\n",
      "import StringIO\n",
      "import matplotlib.pylab as plt\n",
      "import numpy as np\n",
      "pylab.rcParams['figure.figsize'] = 8, 8  # that's default image size for this interactive session\n",
      "pd.options.display.width = 200\n",
      "from datetime import datetime, timedelta"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_fwf('wc_normal.log', colspecs=[(0,14),(15,173)], header=None, parse_dates=[0])\n",
      "df[0] = pd.to_datetime(df[0])\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 1911 entries, 0 to 1910\n",
        "Data columns (total 2 columns):\n",
        "0    1911  non-null values\n",
        "1    1911  non-null values\n",
        "dtypes: datetime64[ns](1), object(1)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1911 entries, 0 to 1910\n",
        "Data columns (total 2 columns):\n",
        "0    1911  non-null values\n",
        "1    1911  non-null values\n",
        "dtypes: datetime64[ns](1), object(1)"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calls taking more than 1 second\n",
      "\n",
      "idxs = (df[0].diff() > np.timedelta64(1,'s')).values\n",
      "df.iloc[idxs.nonzero()[0] - 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>Int64Index([], dtype=int64)</td>\n",
        "      <td>Empty DataFrame</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 194,
       "text": [
        "Empty DataFrame\n",
        "Columns: [0, 1]\n",
        "Index: []"
       ]
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# overall time\n",
      "print df[0].max()-df[0].min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0:00:00.550690\n"
       ]
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[0].plot()\n",
      "plt.title('Calls vs timestamp')\n",
      "plt.ylabel('time (s)')\n",
      "# y axis max is 0.6 seconds\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 196,
       "text": [
        "<matplotlib.text.Text at 0x803c450>"
       ]
      },
      {
       "output_type": "display_data",
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nT1u7rpUpI/38szR4sFS8uN1ReVeg5A6Fw+tFfOLEiapXr55CAmlmCQBb7d4t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\nbt2qqKgo15+IiAhFR0f7MkZ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      }
     ],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 196
    }
   ],
   "metadata": {}
  }
 ]
 }
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