notebook.ipynb 48.8 KB
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{
 "cells": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# X-Lap in Action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {
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    "collapsed": false,
    "deletable": true,
    "editable": true
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   },
   "outputs": [],
   "source": [
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    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
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    "from xlap.parse import evaluate, parse_config\n",
    "import xlap.analyse.jitter as jitter\n",
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    "from xlap.analyse.regress import linear as linear_regression\n",
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    "from xlap.analyse.trace import traces\n",
    "from xlap.analyse.util import extract_durations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Retrieval"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {
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    "collapsed": false,
    "deletable": true,
    "editable": true
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   },
   "outputs": [],
   "source": [
    "config = parse_config()\n",
    "data_files = config[\"data_files\"]\n",
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    "original = evaluate(data_files[\"sender\"], data_files[\"receiver\"], config=config, kind=0)"
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   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
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   "source": [
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    "## Traces"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {
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    "collapsed": false,
    "deletable": true,
    "editable": true
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
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McscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9wZ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\nRK5CYII=\n",
85
      "text/plain": [
86
       "<matplotlib.figure.Figure at 0x7fb3307090f0>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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    "traces(original, config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jitter Analysis"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {
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    "collapsed": false,
    "deletable": true,
    "editable": true
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "4018 / 4095 are no outliers.\n"
118
     ]
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "data": {
130
      "image/png": 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lZlbQIuPNwCclPQ78CLgLuB9oBd5nZvskXQTEgMuCNtPM7AxJ7wJuAM4FrgJe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\nTkSuQmCC\n",
131
      "text/plain": [
132
       "<matplotlib.figure.Figure at 0x7fb2eef53cc0>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    }
   ],
   "source": [
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    "df = jitter.prep(original, config=config)\n",
    "jitter.trace_jitter(df)"
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   ]
  },
  {
   "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": "3.5.2"
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  },
  "widgets": {
   "state": {
    "248cfbb900c24d1185033533c79ecf44": {
     "views": [
      {
       "cell_index": 6
      }
     ]
    }
   },
   "version": "1.2.0"
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}