notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downlink Max Throughput "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# increase font size\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "def get_throughputs(dict_output):\n",
    "    array = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array.append(item[\"throughput\"])\n",
    "    return np.array(array)\n",
    "\n",
    "def get_packet_loss(dict_output):\n",
    "    array = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array.append(item[\"loss\"])\n",
    "    return np.array(array)\n",
    "\n",
    "def get_soft_loads(dict_output):\n",
    "    matriz = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array = []\n",
    "        ids = []\n",
    "        for i, cpu_load in enumerate(item[\"mpstat\"][\"sysstat\"][\"hosts\"][0][\"statistics\"][0][\"cpu-load\"]):\n",
    "            array.append(cpu_load[\"soft\"])\n",
    "            # It will override each iteraction. \n",
    "            ids.append(cpu_load[\"cpu\"])\n",
    "        matriz.append(array)\n",
    "    return np.array(matriz), np.array(ids)\n",
    "\n",
    "\n",
    "reports_path = \"tests/reports\"\n",
    "img_path = os.path.join(reports_path, \"img\")\n",
    "\n",
    "filename = os.path.join(reports_path, \"DownlinkMaxThoughtput.json\")\n",
    "test_title = 'Downlink Max Throughput'\n",
    "# filename = os.path.join(reports_path, \"UplinkMaxThoughtput.json\")\n",
    "# test_title = 'Uplink Max Throughput'\n",
    "\n",
    "with open(filename,\"r\") as report:\n",
    "    dict_output = json.load(report)\n",
    "    y = get_throughputs(dict_output)\n",
    "    y_loads, ids = get_soft_loads(dict_output)\n",
    "    loss_array = get_packet_loss(dict_output)\n",
    "\n",
    "x = np.arange(1, len(y_loads) + 1, 1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, 'o', ls='-', ms=4)\n",
    "\n",
    "ax.set(xlabel='Number of cores', ylabel='Packet rate (Mpps)',\n",
    "       title=test_title)\n",
    "ax.grid()\n",
    "plt.savefig(os.path.join(img_path, test_title + \".svg\"), bbox_inches='tight', format='svg')\n",
    "\n",
    "n_tests , _= y_loads.shape\n",
    "for i, y_load in enumerate(y_loads, 1):   \n",
    "    fig2, ax = plt.subplots()    \n",
    "    # Skip cpu \"all\"\n",
    "    y = y_load[1:]\n",
    "    x = ids[1:]\n",
    "    ax.bar(x, y)\n",
    "    mean = round(np.mean(y[:i]),2)\n",
    "    pkt_loss = round(loss_array[i-1]*100, 2)\n",
    "    plt.axhline(mean, color=\"red\", linestyle=\"--\")\n",
    "    # ax.text(0, mean + 1, \"μ = {}\".format(mean),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    ax.text(0, 40, \"cpu load = {}%\".format(mean),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    ax.text(0, 20, \"pkt loss = {}%\".format(pkt_loss),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    load_title = 'Load per Core - {} Rx Queue'.format(i) \n",
    "    ax.set(xlabel='# Core', ylabel='Load',\n",
    "       title=load_title)\n",
    "    ax.yaxis.grid()\n",
    "    plt.savefig(os.path.join(img_path, test_title + load_title + \".svg\"),  bbox_inches='tight', format='svg')\n",
    "\n",
    "\n",
    "print (y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uplink Max Throughput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# increase font size\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "def get_throughputs(dict_output):\n",
    "    array = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array.append(item[\"throughput\"])\n",
    "    return np.array(array)\n",
    "\n",
    "def get_packet_loss(dict_output):\n",
    "    array = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array.append(item[\"loss\"])\n",
    "    return np.array(array)\n",
    "\n",
    "def get_soft_loads(dict_output):\n",
    "    matriz = []\n",
    "    for item in dict_output[\"items\"]:\n",
    "        array = []\n",
    "        ids = []\n",
    "        for i, cpu_load in enumerate(item[\"mpstat\"][\"sysstat\"][\"hosts\"][0][\"statistics\"][0][\"cpu-load\"]):\n",
    "            array.append(cpu_load[\"soft\"])\n",
    "            # It will override each iteraction. \n",
    "            ids.append(cpu_load[\"cpu\"])\n",
    "        matriz.append(array)\n",
    "    return np.array(matriz), np.array(ids)\n",
    "\n",
    "\n",
    "reports_path = \"tests/reports\"\n",
    "img_path = os.path.join(reports_path, \"img\")\n",
    "\n",
    "# filename = os.path.join(reports_path, \"DownlinkMaxThoughtput.json\")\n",
    "# test_title = 'Downlink Max Throughput'\n",
    "filename = os.path.join(reports_path, \"UplinkMaxThoughtput.json\")\n",
    "test_title = 'Uplink Max Throughput'\n",
    "\n",
    "with open(filename,\"r\") as report:\n",
    "    dict_output = json.load(report)\n",
    "    y = get_throughputs(dict_output)\n",
    "    y_loads, ids = get_soft_loads(dict_output)\n",
    "    loss_array = get_packet_loss(dict_output)\n",
    "\n",
    "x = np.arange(1, len(y_loads) + 1, 1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, 'o', ls='-', ms=4)\n",
    "\n",
    "ax.set(xlabel='Number of cores', ylabel='Packet rate (Mpps)',\n",
    "       title=test_title)\n",
    "ax.grid()\n",
    "plt.savefig(os.path.join(img_path, test_title + \".svg\"), bbox_inches='tight', format='svg')\n",
    "\n",
    "n_tests , _= y_loads.shape\n",
    "for i, y_load in enumerate(y_loads, 1):   \n",
    "    fig2, ax = plt.subplots()    \n",
    "    # Skip cpu \"all\"\n",
    "    y = y_load[1:]\n",
    "    x = ids[1:]\n",
    "    ax.bar(x, y)\n",
    "    mean = round(np.mean(y[:i]),2)\n",
    "    pkt_loss = round(loss_array[i-1]*100, 2)\n",
    "    plt.axhline(mean, color=\"red\", linestyle=\"--\")\n",
    "    # ax.text(0, mean + 1, \"μ = {}\".format(mean),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    ax.text(0, 40, \"cpu load = {}%\".format(mean),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    ax.text(0, 20, \"pkt loss = {}%\".format(pkt_loss),  bbox=dict(boxstyle=\"round\", ec=(1., 0.5, 0.5), fc=(1., 0.8, 0.8),))\n",
    "    load_title = 'Load per Core - {} Rx Queue'.format(i) \n",
    "    ax.set(xlabel='# Core', ylabel='Load',\n",
    "       title=load_title)\n",
    "    ax.yaxis.grid()\n",
    "    plt.savefig(os.path.join(img_path, test_title + load_title + \".svg\"),  bbox_inches='tight', format='svg')\n",
    "\n",
    "\n",
    "print (y)\n"
   ]
  }
 ],
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