{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# X-Lap in Action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from xlap.parse import evaluate, evaluate_side, parse_config\n",
    "import xlap.analyse.jitter as jitter\n",
    "from xlap.analyse.cdf import multi_cdf\n",
    "from xlap.analyse.regress import linear as linear_regression\n",
    "from xlap.analyse.trace import traces\n",
    "from xlap.analyse.correlation import correlation, multi_correlation\n",
    "from xlap.analyse.latency import analyse\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import copy\n",
    "%matplotlib inline\n",
    "colors = [\"#E69F00\", \"#009E73\", \"#56B4E9\",  \"#CC79A7\", \"#D55E00\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = parse_config()\n",
    "data_files = {\n",
    "    \"sender\": \"rtn2018/20180417_testbed/\",\n",
    "    \"receiver\": \"rtn2018/20180417_testbed/\"\n",
    "}\n",
    "original1 = evaluate(data_files[\"sender\"] + \"sender-1000000.csv\", data_files[\"receiver\"] + \"receiver-1000000.csv\", config=config, kind=0)\n",
    "original1.name = \"1GHz\"\n",
    "original2 = evaluate(data_files[\"sender\"] + \"sender-2000000.csv\", data_files[\"receiver\"] + \"receiver-2000000.csv\", config=config, kind=0)\n",
    "original2.name = \"2GHz\"\n",
    "original3 = evaluate(data_files[\"sender\"] + \"sender-3000000.csv\", data_files[\"receiver\"] + \"receiver-3000000.csv\", config=config, kind=0)\n",
    "original3.name = \"3GHz\"\n",
    "dfs = [original1, original2, original3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces(original1, config, global_xaxis=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jitter Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_trace_jitter(dfs, config):\n",
    "    for df in dfs:\n",
    "        print(\"############################ {} ############################\".format(df.name))\n",
    "        jitter.trace_jitter(df, config=config, threshold=200)\n",
    "        \n",
    "multi_trace_jitter(dfs, config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = copy.deepcopy(config)\n",
    "d = cfg[\"durations\"]\n",
    "\n",
    "l=(\"Decoding\",\"ReceiverIPC\",\"HandlePacket\", \"Feedback\", \"SenderIPC\",\"SenderEnqueued\",\"Enqueue\")\n",
    "for e in l:\n",
    "    if e in l:\n",
    "        del d[e]\n",
    "\n",
    "multi_cdf(dfs, cfg, colors=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_correlation(dfs, config, colors=colors, figsize=(3.0,2.0), cols=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latency Criticality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = analyse(original1, 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": [
    "# Kolmogorov\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from xlap.analyse.util import extract_durations\n",
    "import numpy as np\n",
    "\n",
    "def timing_behaviour(df1, df2, config, confidence=0.9):\n",
    "    durations = [x + \"_D\" for x in extract_durations(config)]\n",
    "    \n",
    "    norm = lambda x: x / np.max(x)\n",
    "    \n",
    "    for duration in durations:\n",
    "        rvs1 = norm(df1[duration])\n",
    "        rvs2 = norm(df2[duration])\n",
    "        stat, pvalue = stats.ks_2samp(rvs1, rvs2)\n",
    "        result = \"CANNOT REJECT\"\n",
    "        if pvalue < 1 - confidence:\n",
    "            result = \"REJECT\"\n",
    "        print(duration.ljust(20), \"{:.6f}\".format(pvalue), result, sep=\"\\t\\t\")\n",
    "\n",
    "timing_behaviour(original1, original2, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timing_behaviour(original1, original3, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timing_behaviour(original2, original3, config)"
   ]
  }
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