notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# X-Lap in Action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.parse import evaluate, parse_config\n",
    "config = parse_config()\n",
    "data_files = {\n",
    "    \"sender\": \"rtn2018/20180417_testbed/\",\n",
    "    \"receiver\": \"rtn2018/20180417_testbed/\"\n",
    "}\n",
    "df_1GHz = evaluate(data_files[\"sender\"] + \"sender-1000000.csv\", data_files[\"receiver\"] + \"receiver-1000000.csv\", config=config, kind=0)\n",
    "df_1GHz.name = \"1GHz\"\n",
    "df_2GHz = evaluate(data_files[\"sender\"] + \"sender-2000000.csv\", data_files[\"receiver\"] + \"receiver-2000000.csv\", config=config, kind=0)\n",
    "df_2GHz.name = \"2GHz\"\n",
    "df_3GHz = evaluate(data_files[\"sender\"] + \"sender-3000000.csv\", data_files[\"receiver\"] + \"receiver-3000000.csv\", config=config, kind=0)\n",
    "df_3GHz.name = \"3GHz\"\n",
    "dfs = [df_1GHz, df_2GHz, df_3GHz]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual Packet Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.trace import traces\n",
    "traces(df_1GHz, config, global_xaxis=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trace Jitter Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.jitter import multi_trace_jitter\n",
    "multi_trace_jitter(dfs, config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latency Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.cdf import multi_cdf\n",
    "from xlap.analyse.util import colors\n",
    "import copy\n",
    "cfg = copy.deepcopy(config)\n",
    "list(map(cfg[\"durations\"].pop, (\"Decoding\",\n",
    "                                \"ReceiverIPC\",\n",
    "                                \"HandlePacket\", \n",
    "                                \"Feedback\", \n",
    "                                \"SenderIPC\",\n",
    "                                \"SenderEnqueued\",\n",
    "                                \"Enqueue\")))\n",
    "multi_cdf(dfs, cfg, colors=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation with E2E Latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.correlation import multi_correlation\n",
    "multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\Delta$elta\n",
    "## Latency Criticality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.latency import analyse\n",
    "d = analyse(df_1GHz, config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.corr.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Control Flow Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.cfg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timing Behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlap.analyse.timing import timing_behaviour\n",
    "timing_behaviour(df_1GHz, df_2GHz, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timing_behaviour(df_1GHz, df_3GHz, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timing_behaviour(df_2GHz, df_3GHz, config)"
   ]
  }
 ],
 "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": "3.6.8"
  },
  "widgets": {
   "state": {
    "df95aa8c42974dfeaf8e0a2c05100645": {
     "views": [
      {
       "cell_index": 6
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}