qpep_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QPEP analysis\n",
    "\n",
    "This notebook looks at the results from the qpep performance analysis in the opensand testbed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle, os\n",
    "import re\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib\n",
    "\n",
    "plt.rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each bottleneck bandwidth, I want to plot throughput (i.e. sent bps) against: delays or plr (on two different plots). On the same plot, we have qpep and plain for the 3 different file sizes. Put confidence intervals around the datapoints of one of the file sizes.\n",
    "\n",
    "I should create a dictionnary with\n",
    "```bash\n",
    "bandwidths\n",
    "|-plr\n",
    "|--delay\n",
    "|---filesize [10kbps,100kbps,10mbps]\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_percentages = [0,0.01,0.5,1] #that is in packet error\n",
    "file_sizes = [1e4,1e5,1e7]#in bytes\n",
    "bandwidths = [7e2,1e4,4e5]#in kbps\n",
    "delays = [1,10,50,100,250]#Added delay on the satellite link (=RTT/4)\n",
    "\n",
    "def average_bps(file_path):\n",
    "    try:\n",
    "        data = np.loadtxt(file_path)\n",
    "        # only take values after time is more than 100\n",
    "        return np.average(data[data[:,0] > 100])\n",
    "    except: \n",
    "        return 0\n",
    "\n",
    "def get_error(file_path):\n",
    "    match = re.search(r\"_([.\\d]*)plr_\", file_path)\n",
    "    if not match: \n",
    "        print(f\"Error: Error not found in {file_path}\")\n",
    "        return None\n",
    "    return match.group(1)\n",
    "\n",
    "def get_delay(file_path):\n",
    "    match = re.search(r\"_(\\d+?[\\.\\d]*?)ms\",file_path)\n",
    "    if not match: \n",
    "        print(f\"Error: Delay not found in {file_path}\")\n",
    "        return None\n",
    "    return match.group(1)\n",
    "\n",
    "def get_bandwidth(file_path):\n",
    "    match = re.search(r\"_(\\d+?.\\d+?kbps)_\",file_path)\n",
    "    if not match: \n",
    "        print(f\"Error: Bandwidth not found in {file_path}\")\n",
    "        return None\n",
    "    return match.group(1)\n",
    "\n",
    "def filesize_to_number(name):\n",
    "    match = re.search(r\"iperf_(.*?)mb\",name)\n",
    "    if not match:\n",
    "        print(f\"Error: number not found in {name}\")\n",
    "        return None\n",
    "    return match.group(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_throughput(ax, results = {}, bw = '400000kbps',rtt = None, error = None, scenario=\"\",filesize=\"\",colour='red',linestyle='',errorbar=True):\n",
    "    \"\"\"Plot the qpep experiments results\n",
    "    Specify either one error to plot against rtt or one rtt to plot against error\n",
    "    \"\"\"\n",
    "    # Maybe assert that it is a valid rtt\n",
    "    # assert(results[bw][].keys)\n",
    "    # Points is an array containing the points to plot\n",
    "    # every row is a point, which has: an x-value (the error rate), a y-value(the send bps median), a lower error, an upper error\n",
    "    if rtt is not None:\n",
    "        points = np.zeros((len(results[bw].keys()),4)) #we have this many different errors\n",
    "        for j,error in enumerate(results[bw].keys()):\n",
    "            points[j,:] = float(error)/10 , np.average(results[bw][error][rtt][filesize]), np.min(results[bw][error][rtt][filesize]), np.max(results[bw][error][rtt][filesize])\n",
    "        \n",
    "    elif error is not None:\n",
    "        points = np.zeros((len(results[bw][error].keys()),4)) #we have this many different rtts\n",
    "        for j,rtt in enumerate(results[bw][error].keys()):\n",
    "            points[j,:] = 10+float(rtt)*4 , np.average(results[bw][error][rtt][filesize]), np.min(results[bw][error][rtt][filesize]), np.max(results[bw][error][rtt][filesize])\n",
    "    #reorder the points by x-value\n",
    "    points = points[points[:,0].argsort()]\n",
    "    if errorbar:\n",
    "        ax.errorbar(points[:,0],points[:,1], yerr=[abs(points[:,1]-points[:,2]), abs(points[:,3]-points[:,1])], fmt='none', capsize=3,alpha=0.5,color=colour)\n",
    "    ax.plot(points[:,0],points[:,1],label=f'{scenario}',marker='x', markerfacecolor=\"none\", markersize = '4',linestyle=linestyle,color=colour)\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cca = \"Opensand emulation\"\n",
    "\n",
    "\n",
    "def get_results_from_folder_path(folder_path):\n",
    "    # Get a list of all files in the folder\n",
    "    file_list = os.listdir(folder_path)\n",
    "    # Create dictionary to store the average for each error, rtt, and run\n",
    "    results = {}\n",
    "    for i,filename in enumerate(file_list):\n",
    "        bandwidth = get_bandwidth(f'{folder_path}{filename}')\n",
    "        error = get_error(f'{folder_path}{filename}')\n",
    "        rtt = get_delay(f'{folder_path}{filename}')\n",
    "        if bandwidth not in results:\n",
    "            results[bandwidth] = {}\n",
    "        if error not in results[bandwidth]:\n",
    "            results[bandwidth][error] = {}\n",
    "        if rtt not in results[bandwidth][error]:\n",
    "            results[bandwidth][error][rtt] = {}\n",
    "        file = open(f'{folder_path}{filename}','rb')\n",
    "        data = pickle.load(file)\n",
    "        for filesize in data.keys():\n",
    "            #remove throughputs that are zero\n",
    "            data[filesize]['received_bps'] = [x for x in data[filesize]['received_bps'] if x > 1]\n",
    "            results[bandwidth][error][rtt][filesize_to_number(filesize)] = np.array(data[filesize]['received_bps'])/1e6\n",
    "        file.close()\n",
    "    return results\n",
    "\n",
    "# sort the dictionary by error key\n",
    "# results[bw] = dict(sorted(results[bw].items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = '0'\n",
    "my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,6))\n",
    "bw = '700.0kbps'\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep',filesize='0.0',colour = my_colours[1], linestyle='--')\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,error=error, scenario ='plain',filesize='0.0',colour = my_colours[2],linestyle=':')\n",
    "# bw = '10000.0kbps'\n",
    "# results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep',filesize='0.0',colour = my_colours[3],  linestyle='--')\n",
    "# results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario ='plain',filesize='0.0',colour = my_colours[4],linestyle=':')\n",
    "# bw = '400000.0kbps'\n",
    "# results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep',filesize='0.0',colour = my_colours[5],  linestyle='--')\n",
    "# results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario ='plain',filesize='0.0',colour = my_colours[6],linestyle=':')\n",
    "ax.text(0.6, 0.95, f\"plr = {error}\", transform=ax.transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "plt.tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "ax.legend(title=f\"transfer file size\",loc='upper left',fontsize=\"small\", markerscale=0.6)\n",
    "ax.set_xlabel(\"RTT\")\n",
    "ax.set_ylabel(\"Throughput(Mbps)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_figure = True\n",
    "filename = \"qpep_throughput_vs_rtt_27_08_23\"\n",
    "if save_figure:\n",
    "    plt.rcParams.update(plt.rcParamsDefault)\n",
    "    matplotlib.use(\"pgf\")\n",
    "    matplotlib.rcParams.update(\n",
    "        {\n",
    "            # Adjust to your LaTex-Engine\n",
    "            \"pgf.texsystem\": \"pdflatex\",\n",
    "            \"font.family\": \"serif\",\n",
    "            \"text.usetex\": True,\n",
    "            \"pgf.rcfonts\": False,\n",
    "            \"axes.unicode_minus\": False,\n",
    "        }\n",
    "    )\n",
    "    plt.close('all')\n",
    "else:\n",
    "    plt.rcParams.update(plt.rcParamsDefault)\n",
    "    matplotlib.use(\"GTK3Agg\")\n",
    "\n",
    "error_percentages = ['0','0.01','1']\n",
    "#my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "my_colours = ['#0f5b12','#35b539','#009877','#5e03fc','#c26df7','#ca03fc']\n",
    "props = dict(boxstyle='round', facecolor='white', alpha=0.7)\n",
    "fig, ax = plt.subplots(3,1,figsize=(5,5),sharex=True)\n",
    "bandwidths = ['400000.0kbps','10000.0kbps','700.0kbps']\n",
    "for j,bw in enumerate(bandwidths):\n",
    "    results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "    for i,error in enumerate(error_percentages):\n",
    "        plot_throughput(ax[j],results,bw=bw,error=error, scenario = f'qpep, plr={error}%',filesize='0.0',colour =my_colours[i], linestyle='--')\n",
    "    results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "    for i,error in enumerate(error_percentages):\n",
    "        plot_throughput(ax[j],results,bw=bw,error=error, scenario =f'plain, plr={error}%',filesize='0.0',colour = my_colours[i+3],linestyle=':')\n",
    "ax[0].text(0.25, 0.95, f\"bottleneck bandwidth = 400Mbps\", transform=ax[0].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "ax[1].text(0.18, 0.26, f\"bottleneck bandwidth = 10Mbps\", transform=ax[1].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "ax[2].text(0.06, 0.12, f\"bottleneck bandwidth = 0.7Mbps\", transform=ax[2].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "for i in range(3):\n",
    "    ax[i].set_ylabel(\"Throughput(Mbps)\")\n",
    "    ax[i].tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "    ax[i].grid()\n",
    "ax[0].legend(title=f\"Scenario\",loc='upper right',fontsize=\"small\", markerscale=0.6)\n",
    "ax[2].set_xlabel(\"RTT of satellite link(ms)\")\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.06)\n",
    "if save_figure:\n",
    "    plt.savefig(f\"../Thesis/plots/qpep/{filename}.pgf\")\n",
    "else:\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_figure = True\n",
    "filename = \"qpep_throughput_vs_rtt_29_08_23\"\n",
    "if save_figure:\n",
    "    plt.rcParams.update(plt.rcParamsDefault)\n",
    "    matplotlib.use(\"pgf\")\n",
    "    matplotlib.rcParams.update(\n",
    "        {\n",
    "            # Adjust to your LaTex-Engine\n",
    "            \"pgf.texsystem\": \"pdflatex\",\n",
    "            \"font.family\": \"serif\",\n",
    "            \"text.usetex\": True,\n",
    "            \"pgf.rcfonts\": False,\n",
    "            \"axes.unicode_minus\": False,\n",
    "        }\n",
    "    )\n",
    "    plt.close('all')\n",
    "else:\n",
    "    plt.rcParams.update(plt.rcParamsDefault)\n",
    "    matplotlib.use(\"GTK3Agg\")\n",
    "\n",
    "error_percentages = ['0','0.01','1']\n",
    "#my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "my_colours = ['#0f5b12','#35b539','#009877','#5e03fc','#c26df7','#ca03fc']\n",
    "props = dict(boxstyle='round', facecolor='white', alpha=0.7)\n",
    "fig, ax = plt.subplots(3,1,figsize=(5,5),sharex=True)\n",
    "bandwidths = ['400000.0kbps','10000.0kbps','700.0kbps']\n",
    "for j,bw in enumerate(bandwidths):\n",
    "    results = get_results_from_folder_path(\"./qpep_results/29.08.23/qpep_iperf/\")\n",
    "    for i,error in enumerate(error_percentages):\n",
    "        plot_throughput(ax[j],results,bw=bw,error=error, scenario = f'qpep, plr={error}%',filesize='0.0',colour =my_colours[i], linestyle='--')\n",
    "    results = get_results_from_folder_path(\"./qpep_results/29.08.23/plain_iperf/\")\n",
    "    for i,error in enumerate(error_percentages):\n",
    "        plot_throughput(ax[j],results,bw=bw,error=error, scenario =f'plain, plr={error}%',filesize='0.0',colour = my_colours[i+3],linestyle=':')\n",
    "ax[0].text(0.25, 0.95, f\"bottleneck bandwidth = 400Mbps\", transform=ax[0].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "ax[1].text(0.18, 0.26, f\"bottleneck bandwidth = 10Mbps\", transform=ax[1].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "ax[2].text(0.06, 0.12, f\"bottleneck bandwidth = 0.7Mbps\", transform=ax[2].transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "for i in range(3):\n",
    "    ax[i].set_ylabel(\"Throughput(Mbps)\")\n",
    "    ax[i].tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "    ax[i].grid()\n",
    "ax[0].legend(title=f\"Scenario\",loc='upper right',fontsize=\"small\", markerscale=0.6)\n",
    "ax[2].set_xlabel(\"RTT of satellite link(ms)\")\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.06)\n",
    "if save_figure:\n",
    "    plt.savefig(f\"../Thesis/plots/qpep/{filename}.pgf\")\n",
    "else:\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delay = '30'\n",
    "my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "bw = '700.0kbps'\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,rtt=delay, scenario = 'qpep,btlnbw=700Kbps',filesize='0.0',colour = my_colours[1])\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,rtt = delay, scenario ='plain,btlnbw=700Kbps',filesize='0.0',colour = my_colours[2])\n",
    "bw = '10000.0kbps'\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,rtt=delay, scenario = 'qpep,btlnbw=10Mbps',filesize='0.0',colour = my_colours[3])\n",
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/plain_iperf/\")\n",
    "plot_throughput(ax,results,bw=bw,rtt = delay, scenario ='plain,btlnbw=10Mbps',filesize='0.0',colour = my_colours[4])\n",
    "ax.text(0.6, 0.95, f\"rtt = {float(delay)*4}ms\", transform=ax.transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "plt.tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "ax.legend(title=f\"transfer file size\",loc='upper left',fontsize=\"small\", markerscale=0.6)\n",
    "ax.set_xlabel(\"plr\")\n",
    "ax.set_ylabel(\"Throughput(Mbps)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'700.0kbps': {'0.01': {'100': {'0.0': array([0.676164, 0.680327, 0.690911])}, '10': {'0.0': array([0.689929, 0.690125, 0.654938])}, '250': {'0.0': array([0.674102, 0.690812, 0.690812])}, '175': {'0.0': array([0.681014, 0.680327, 0.690812])}, '30': {'0.0': array([0.690518, 0.690812, 0.65611 ])}, '1': {'0.0': array([0.690714, 0.689929, 0.654642])}, '50': {'0.0': array([0.689634, 0.690812, 0.690812])}}, '1': {'50': {'0.0': array([0.610333, 0.657814, 0.649753])}, '100': {'0.0': array([0.437613, 0.453803, 0.476738])}, '250': {'0.0': array([0.229734, 0.185758, 0.218204])}, '175': {'0.0': array([0.360413, 0.243135, 0.281475])}, '30': {'0.0': array([0.677509, 0.672659, 0.677803])}, '1': {'0.0': array([0.680425, 0.679345, 0.679247])}, '10': {'0.0': array([0.679345, 0.679149, 0.678952])}}, '0': {'1': {'0.0': array([0.690812, 0.690027, 0.65481 ])}, '50': {'0.0': array([0.689733, 0.690812, 0.65864 ])}, '10': {'0.0': array([0.689929, 0.690027, 0.655141])}, '100': {'0.0': array([0.686649, 0.690714, 0.66094 ])}, '30': {'0.0': array([0.690616, 0.690812, 0.656141])}, '175': {'0.0': array([0.681574, 0.661499, 0.666229])}, '250': {'0.0': array([0.674986, 0.667444, 0.661372])}}}, '400000.0kbps': {'1': {'30': {'0.0': array([1.22713, 1.28122, 1.27831])}, '250': {'0.0': array([0.208369, 0.161576, 0.166396])}, '175': {'0.0': array([0.27258])}, '100': {'0.0': array([0.57645 , 0.513045, 0.411958])}, '10': {'0.0': array([3.49342, 3.6145 , 3.51148])}, '50': {'0.0': array([1.18092 , 0.798389, 0.824898])}, '1': {'0.0': array([22.4553, 22.446 , 22.6781])}}, '0.01': {'10': {'0.0': array([34.273 , 35.7205, 37.2723])}, '1': {'0.0': array([50.7677, 51.2296, 47.3054])}, '30': {'0.0': array([18.3045, 17.5354, 15.6178])}, '100': {'0.0': array([8.40725, 5.8863 , 3.73286])}, '50': {'0.0': array([9.96526, 9.0393 , 7.34071])}, '175': {'0.0': array([6.09271, 2.58368, 2.66069])}, '250': {'0.0': array([4.62762, 2.38495, 3.6607 ])}}, '0': {'100': {'0.0': array([18.4452, 17.4348, 18.3626])}, '175': {'0.0': array([15.5148, 16.1505, 16.1513])}, '50': {'0.0': array([22.6456, 22.7214, 18.9848])}, '250': {'0.0': array([10.7008, 11.3243, 11.3242])}, '10': {'0.0': array([39.3028, 39.5327, 48.5164])}, '1': {'0.0': array([43.4537, 46.3085, 57.6828])}, '30': {'0.0': array([29.3514, 32.826 , 33.0597])}}}, '10000.0kbps': {'0': {'175': {'0.0': array([9.28962, 9.65574, 9.65584])}, '250': {'0.0': array([9.05504, 9.59787, 9.59846])}, '1': {'0.0': array([9.62818, 9.64401, 9.66498])}, '50': {'0.0': array([9.59381, 9.67487, 9.69614])}, '100': {'0.0': array([9.50186, 9.68893, 9.68847])}, '10': {'0.0': array([9.63545, 9.6636 , 9.67438])}, '30': {'0.0': array([9.6308 , 9.68546, 9.69594])}}, '0.01': {'10': {'0.0': array([9.60449, 9.60108, 9.61147])}, '30': {'0.0': array([9.44661, 9.60681, 9.4472 ])}, '175': {'0.0': array([2.40464, 2.78786, 3.90945])}, '250': {'0.0': array([4.39664, 3.23905, 1.86201])}, '50': {'0.0': array([8.8194 , 8.63823, 8.39656])}, '1': {'0.0': array([9.59692])}, '100': {'0.0': array([7.18995, 5.26503, 3.81072])}}, '1': {'50': {'0.0': array([0.943059, 0.867529, 0.863434])}, '30': {'0.0': array([1.53075, 1.22657, 1.12899])}, '100': {'0.0': array([0.443219, 0.472252, 0.502658])}, '250': {'0.0': array([0.301102, 0.175833, 0.170844])}, '10': {'0.0': array([3.43847, 3.3811 , 3.53681])}, '175': {'0.0': array([0.266826, 0.313914, 0.315652])}, '1': {'0.0': array([9.55622, 9.5545 , 9.55797])}}}}\n"
     ]
    }
   ],
   "source": [
    "results = get_results_from_folder_path(\"./qpep_results/23.08.23/qpep_iperf/\")\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'700.0kbps': {'1': {'50': {'0.0': array([0.67849])}, '100': {'0.0': array([0.680326])}, '250': {'0.0': array([0.682163])}, '1': {'0.0': array([0.682261])}, '10': {'0.0': array([0.680523])}}, '0': {'1': {'0.0': array([0.690812])}, '50': {'0.0': array([0.690714])}, '10': {'0.0': array([0.691107])}, '100': {'0.0': array([0.690812])}, '250': {'0.0': array([0.691009])}}}, '400000.0kbps': {'1': {'250': {'0.0': array([22.3657])}, '100': {'0.0': array([22.8445])}, '10': {'0.0': array([22.0561])}, '50': {'0.0': array([18.6784])}, '1': {'0.0': array([22.6466])}}, '0': {'100': {'0.0': array([36.6398])}, '50': {'0.0': array([32.4265])}, '250': {'0.0': array([36.7544])}, '10': {'0.0': array([37.5312])}, '1': {'0.0': array([39.1649])}}}, '10000.0kbps': {'1': {'50': {'0.0': array([9.55982])}, '100': {'0.0': array([9.56621])}, '250': {'0.0': array([9.56874])}, '10': {'0.0': array([9.55652])}, '1': {'0.0': array([9.55991])}}, '0': {'250': {'0.0': array([9.62913])}, '1': {'0.0': array([9.62864])}, '50': {'0.0': array([9.63031])}, '100': {'0.0': array([9.63274])}, '10': {'0.0': array([9.63001])}}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_delay(file_path):\n",
    "    match = re.search(r\"_(\\d+?[\\.\\d]*?)ms\",file_path)\n",
    "    if not match: \n",
    "        print(f\"Error: Delay not found in {file_path}\")\n",
    "        return None\n",
    "    return match.group(1)\n",
    "\n",
    "delay = '100'\n",
    "my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
    "bw = '700.0kbps'\n",
    "filesize = '0.0'\n",
    "results = get_results_from_folder_path(\"./qpep_results/21.08.23/qpep_iperf_time/\")\n",
    "print(results)\n",
    "plot_throughput(ax,results,bw=bw,rtt=delay, scenario = 'qpep,btlnbw=700Kbps',filesize=filesize,colour = my_colours[1])\n",
    "results = get_results_from_folder_path(\"./qpep_results/21.08.23/plain_iperf_time/\")\n",
    "plot_throughput(ax,results,bw=bw,rtt = delay, scenario ='plain,btlnbw=700Kbps',filesize=filesize,colour = my_colours[2])\n",
    "ax.text(0.6, 0.95, f\"rtt = {float(delay)*4}ms\", transform=ax.transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "plt.tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "ax.legend(title=f\"transfer file size\",loc='upper left',fontsize=\"small\", markerscale=0.6)\n",
    "ax.set_xlabel(\"plr\")\n",
    "ax.set_ylabel(\"Throughput(Mbps)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = '0'\n",
    "my_colours = list(mcolors.TABLEAU_COLORS.values())\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,6))\n",
    "bw = '700.0kbps'\n",
    "filesize ='0.0'\n",
    "results = get_results_from_folder_path(\"./qpep_results/21.08.23/qpep_iperf_time/\")\n",
    "plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep,bltnbw=700Kbps',filesize=filesize,colour = my_colours[1], linestyle='--')\n",
    "results = get_results_from_folder_path(\"./qpep_results/21.08.23/plain_iperf_time/\")\n",
    "plot_throughput(ax,results,bw=bw,error=error, scenario ='plain,bltnbw=700Kbps',filesize=filesize,colour = my_colours[2],linestyle=':')\n",
    "# bw = '10000.0kbps'\n",
    "# results = get_results_from_folder_path(\"./qpep_results/18.08.23/qpep_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep,bltnbw=10Mbps',filesize='10.0',colour = my_colours[3])\n",
    "# results = get_results_from_folder_path(\"./qpep_results/18.08.23/plain_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario ='plain,bltnbw=10Mbps',filesize='10.0',colour = my_colours[4])\n",
    "# bw = '400000.0kbps'\n",
    "# results = get_results_from_folder_path(\"./qpep_results/18.08.23/qpep_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario = 'qpep,bltnbw=400Mbps',filesize='10.0',colour = my_colours[5])\n",
    "# results = get_results_from_folder_path(\"./qpep_results/18.08.23/plain_iperf/\")\n",
    "# plot_throughput(ax,results,bw=bw,error=error, scenario ='plain,bltnbw=400Mbps',filesize='10.0',colour = my_colours[6])\n",
    "ax.text(0.6, 0.95, f\"plr = {error}\", transform=ax.transAxes, fontsize=\"small\", verticalalignment='top', bbox=props)\n",
    "plt.tick_params(axis='both', direction='in',labelsize=\"small\", grid_alpha=0.5, grid_linestyle=':')\n",
    "ax.legend(title=f\"transfer file size\",loc='upper left',fontsize=\"small\", markerscale=0.6)\n",
    "ax.set_xlabel(\"RTT\")\n",
    "ax.set_ylabel(\"Throughput(Mbps)\")\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qpepcca",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}