notebooks/Real_World_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "810c846e-10d2-496b-ab83-310c42c49bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from os.path import dirname, abspath\n",
    "d = os.path.abspath(os.path.join('..'))\n",
    "sys.path.append(d)\n",
    "from model_trial import Experiment\n",
    "from util.file_processor import FileProcessor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import tree\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 8, 'figure.figsize' : [1.5*3.125, 1.5*1.5],\n",
    "                           'legend.fontsize': 10, 'legend.fancybox': True,\n",
    "                           'font.family': 'serif', 'font.sans-serif': 'Times',\n",
    "                            'xtick.major.width': 0.2, 'xtick.minor.width': 2*0.2,\n",
    "                            'ytick.major.width': 0.2, 'ytick.minor.width': 2*0.2,\n",
    "                            'text.usetex': False, 'axes.labelsize': 7*2\n",
    "                           })\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import re\n",
    "import pathlib\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73e99b82-6453-491b-8c30-c7574855613e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run '../util/webrtc_reader.py'\n",
    "%run '../util/helper_functions.py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea4ca24b-4078-46e9-8f5a-9df4a5146af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = '13May'\n",
    "data_dir = '/scratch/datasets/vcqoe'\n",
    "cross_validations = 5\n",
    "tested_vcas = ['meet', 'teams']\n",
    "traces_available = False\n",
    "timestamps_available = False\n",
    "metrics = ['framesReceivedPerSecond', 'bitrate', 'frame_jitter', 'frameHeight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d97c716-a79a-40ff-8de6-974450e01aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name_lookup = {\n",
    "        'IP/UDP ML': '{}_ml_random_LSTATS-TSTATS_rf_full_video_{}_cv_{}_original',\n",
    "        'IP/UDP Heuristic': '{}_frame-lookback_random_LSTATS-TSTATS_rf_full_video_{}_cv_{}_original',\n",
    "        'RTP ML': '{}_rtp_ml_random_LSTATS-TSTATS_rf_full_video_{}_cv_{}_original',\n",
    "        'RTP Heuristic': '{}_rtp_random_LSTATS-TSTATS_rf_full_video_{}_cv_{}_original'\n",
    "}\n",
    "\n",
    "col_map = {\n",
    "        '{}_{}': 'Prediction',\n",
    "        '{}_gt': 'Ground Truth',\n",
    "        'throughput_mean_kbps': 'Throughput Mean (Kbps)',\n",
    "        'throughput_std_kbps': 'Throughput Jitter (Kbps)',\n",
    "        'latency_mean_ms': 'Latency Mean (ms)',\n",
    "        'latency_std_ms': 'Latency Jitter (ms)',\n",
    "        'MAE': 'Mean Absolute Error'\n",
    "}\n",
    "feature_map = {\n",
    "    'l_mean': 'Packet size mean', 'l_std': 'Packet size standard deviation', 'l_min': 'Packet size min', 'l_max': 'Packet size max', 'l_q1': 'Packet size 25th percentile',\n",
    "    'l_q2': 'Packet size 50th percentile', 'l_q3': 'Packet size 75th percentile', 'l_num_pkts': 'Total number of packets', 'l_num_bytes': 'Total number of bytes', \n",
    "    'l_num_unique': 'Total number of unique packet sizes', 'l_num_rtx': 'Number of packet retransmissions','t_mean': 'Packet IAT mean', \n",
    "    't_std': 'Packet IAT standard deviation', 't_min': 'Packet IAT min', 't_max': 'Packet IAT max', 't_q1': 'Packet IAT 25th percentile',\n",
    "    't_q2': 'Packet IAT 50th percentile', 't_q3': 'Packet IAT 75th percentile', \n",
    "    't_num_pkts': 'Total number of packets', 't_et': 'Interval end time', 'num_rtx': 'Number of retransmissions',\n",
    "    'vid_ts_unique': 'Number of unique RTP video timestamps', 'rtx_ts_unique': 'Number of unique RTP retransmission timestamps', 'vid_marker_sum': 'Marker bit sum for video packets', 'rtx_marker_sum': 'Marker bit sum for retransmitted packets',\n",
    "    'common_vid_rtx_ts_unique': 'Number of unique RTP timestamps shared by video and RTx streams', 'ooo_seqno_vid': 'Number of out of order video sequence numbers', 'buffer_time_mean': 'Mean of expected frame playback time', 'buffer_time_std': 'Stdev of expected frame playback time' , 'buffer_time_min': 'Min of expected frame playback time', 'buffer_time_max': 'Max of expected frame playback time', 'buffer_time_q1': 'Q1 of expected frame playback time', 'buffer_time_q2': 'Q2 of expected frame playback time', 'buffer_time_q3': 'Q3 of expected frame playback time', 'n_pkt_diff_mean': 'Mean number of packets between first and last packet of a frame',  'n_pkt_diff_std': 'Stdev of number of packets between first and last packet of a frame',\n",
    "    'n_pkt_diff_min': 'Min number of packets between first and last packet of a frame','n_pkt_diff_max': 'Max number of packets between first and last packet of a frame','n_pkt_diff_q1': 'Q1 of number of packets between first and last packet of a frame','n_pkt_diff_q2': 'Q2 of number of packets between first and last packet of a frame','n_pkt_diff_q3': 'Q3 of number of packets between first and last packet of a frame', 'rtp_lag_min': 'Min expected playback time lag','rtp_lag_max':'Max expected playback time lag',\n",
    "    'rtp_lag_q1': 'Q1 of expected playback time lag','rtp_lag_q2': 'Q2 of expected playback time lag','rtp_lag_q3': 'Q3 of expected playback time lag','rtp_lag_mean': 'Mean expected playback time lag','rtp_lag_std': 'Stdev of expected playback time lag'\n",
    "}\n",
    "suffix_map = {\n",
    "    'IP/UDP ML': 'ml',\n",
    "    'IP/UDP Heuristic': 'frame-lookback',\n",
    "    'RTP ML': 'rtp_ml',\n",
    "    'RTP Heuristic': 'rtp'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2feb7854-dd69-4d79-af9b-e6d036e7a685",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pkl(path):\n",
    "    with open(path, 'rb') as fd:\n",
    "        obj = pickle.load(fd)\n",
    "    return obj\n",
    "\n",
    "def get_call_logs(cv, vca):\n",
    "    pref = data_dir+'/'+dataset+'_cv_splits'\n",
    "    spl = load_pkl(pref + '/' +'cv_file_splits.pkl')\n",
    "    cols=['pcap_file', 'csv_file', 'webrtc_file', 'log_file', 'device_id']\n",
    "    if traces_available:\n",
    "        cols += ['trace_file']\n",
    "    if timestamps_available:\n",
    "        cols += ['timestamp_file']\n",
    "    df = pd.DataFrame(spl[cv-1][vca]['test'], columns=cols)\n",
    "    return df\n",
    "\n",
    "def load_intermediates(method):\n",
    "    suffix = f'_{suffix_map[method]}'\n",
    "    preds = []\n",
    "    f_imp = {}\n",
    "    for metric in metrics:\n",
    "        print('Metric:', metric)\n",
    "        f_imp[metric] = {}\n",
    "        if metric == 'frameHeight' and 'Heuristic' in method:\n",
    "            continue\n",
    "        for cv in range(1, cross_validations+1):\n",
    "            pref = model_name_lookup[method].format(metric, dataset, cv) + '/' + model_name_lookup[method].format(metric, dataset, cv)\n",
    "            experiment = pref+'.pkl'\n",
    "            model = pref+'_vca_model.pkl'\n",
    "            predictions = pref+'_predictions.pkl'\n",
    "            ddir = data_dir + '/' + dataset\n",
    "            exp = load_pkl(ddir+'_intermediates/'+experiment)\n",
    "            mod = load_pkl(ddir+'_intermediates/'+model)\n",
    "            pred = load_pkl(ddir+'_intermediates/'+predictions)\n",
    "            f_imp[metric][cv] = {}\n",
    "            for vca in tested_vcas:\n",
    "                df_logs = get_call_logs(cv, vca)\n",
    "                f_imp[metric][cv][vca] = (mod[vca].feature_importances if 'ML' in method else None)\n",
    "                for df in pred[vca]:\n",
    "                    pcap = df['file'].unique()[0]\n",
    "                    dfl = df_logs[df_logs['pcap_file'] == pcap]\n",
    "                    device_id = dfl['device_id'].iloc[0]\n",
    "                    rdr = WebRTCReader(dfl['webrtc_file'].unique()[0], dataset)\n",
    "                    df_webrtc = rdr.get_webrtc()\n",
    "                    df['cross_val'] = cv\n",
    "                    df['metric'] = metric\n",
    "                    df['method'] = method\n",
    "                    df['VCA'] = vca\n",
    "                    df['device_id'] = device_id\n",
    "                    df[f'deviation'] = df[metric+suffix] - df[f'{metric}_gt']\n",
    "                    df[f'abs_deviation'] = abs(df[f'deviation'])\n",
    "                    df_merged = pd.merge(df, df_webrtc, left_on='timestamp', right_on='ts')\n",
    "                    df_merged['csv_file'] = dfl['csv_file'].unique()[0]\n",
    "                    df_merged['webrtc_file'] = dfl['webrtc_file'].unique()[0]\n",
    "                    if traces_available:\n",
    "                        df_merged['trace_file'] = dfl['trace_file'].unique()[0]\n",
    "                    if timestamps_available:\n",
    "                        df_merged['timestamp_file'] = dfl['timestamp_file'].unique()[0]\n",
    "                    # df_merged['trace_profile'] = '-'.join(os.path.basename(dfl['csv_file'].unique()[0])[:-4].split('-')[2:8])\n",
    "                    df_merged = df_merged.rename(columns={'file': 'pcap_file', metric+suffix: 'Prediction', f'{metric}_gt': 'Ground Truth'})\n",
    "                    preds.append(df_merged)\n",
    "    df = pd.concat(preds, axis=0)\n",
    "    return df, f_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b83974a0-af01-4f15-812b-6715853970fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric: framesReceivedPerSecond\n",
      "Metric: bitrate\n",
      "Metric: frame_jitter\n",
      "Metric: frameHeight\n"
     ]
    }
   ],
   "source": [
    "rtp_ml, f_rtp_ml = load_intermediates('RTP ML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9d4575f3-2ae5-45c6-b7c8-2f04d5c5d610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric: framesReceivedPerSecond\n",
      "Metric: bitrate\n",
      "Metric: frame_jitter\n",
      "Metric: frameHeight\n"
     ]
    }
   ],
   "source": [
    "udp_ml, f_udp_ml = load_intermediates('IP/UDP ML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0aef62b2-cc41-448d-a644-b4c10e870371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric: framesReceivedPerSecond\n",
      "Metric: bitrate\n",
      "Metric: frame_jitter\n",
      "Metric: frameHeight\n"
     ]
    }
   ],
   "source": [
    "rtp_non_ml, f_rtp_non_ml = load_intermediates('RTP Heuristic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0c08ed8f-2a22-4bbd-bc11-0c21a6b1b1a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric: framesReceivedPerSecond\n",
      "Metric: bitrate\n",
      "Metric: frame_jitter\n",
      "Metric: frameHeight\n"
     ]
    }
   ],
   "source": [
    "udp_non_ml, f_udp_non_ml = load_intermediates('IP/UDP Heuristic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "cf794698-0185-4d17-8016-8e6b66fcc3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([rtp_ml, udp_ml, rtp_non_ml, udp_non_ml], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "601b0227-90fa-4a6f-ad3e-86e324dd9d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('netrics_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28beb17-22ca-4d69-a870-78ec7296604e",
   "metadata": {},
   "source": [
    "# Overall Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4070ac2-401b-40c5-aad4-5b1dc9c6c8bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('netrics_results.csv', index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "399903c6-f738-4510-972f-f620d9e6392b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FPS\n",
      "\\begin{tabular}{llr}\n",
      "\\toprule\n",
      "       &       &  abs\\_deviation \\\\\n",
      "method & VCA &                \\\\\n",
      "\\midrule\n",
      "IP/UDP Heuristic & meet &           3.38 \\\\\n",
      "       & teams &           1.85 \\\\\n",
      "IP/UDP ML & meet &           2.77 \\\\\n",
      "       & teams &           1.44 \\\\\n",
      "RTP Heuristic & meet &           1.78 \\\\\n",
      "       & teams &           1.21 \\\\\n",
      "RTP ML & meet &           2.68 \\\\\n",
      "       & teams &           1.28 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "Bitrate\n",
      "\\begin{tabular}{llr}\n",
      "\\toprule\n",
      "       &       &  abs\\_deviation \\\\\n",
      "method & VCA &                \\\\\n",
      "\\midrule\n",
      "IP/UDP Heuristic & meet &         140.94 \\\\\n",
      "       & teams &         117.03 \\\\\n",
      "IP/UDP ML & meet &          91.07 \\\\\n",
      "       & teams &         109.99 \\\\\n",
      "RTP Heuristic & meet &         125.43 \\\\\n",
      "       & teams &         128.71 \\\\\n",
      "RTP ML & meet &          90.75 \\\\\n",
      "       & teams &         115.42 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "Frame Jitter\n",
      "\\begin{tabular}{llr}\n",
      "\\toprule\n",
      "       &       &  abs\\_deviation \\\\\n",
      "method & VCA &                \\\\\n",
      "\\midrule\n",
      "IP/UDP Heuristic & meet &          94.96 \\\\\n",
      "       & teams &          47.84 \\\\\n",
      "IP/UDP ML & meet &           9.74 \\\\\n",
      "       & teams &           8.92 \\\\\n",
      "RTP Heuristic & meet &          23.86 \\\\\n",
      "       & teams &           7.54 \\\\\n",
      "RTP ML & meet &           7.63 \\\\\n",
      "       & teams &           7.86 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.loc[df['metric'] == 'bitrate', 'abs_deviation'] /= 1000\n",
    "df_fps = df[df['metric'] == 'framesReceivedPerSecond']\n",
    "dfg = df_fps.groupby(['method', 'VCA'])['abs_deviation'].mean()\n",
    "print('FPS')\n",
    "print(dfg.to_latex(float_format=\"%.2f\"))\n",
    "df_fps = df[df['metric'] == 'bitrate']\n",
    "dfg = df_fps.groupby(['method', 'VCA'])['abs_deviation'].mean()\n",
    "print('Bitrate')\n",
    "print(dfg.to_latex(float_format=\"%.2f\"))\n",
    "df_fps = df[df['metric'] == 'frame_jitter']\n",
    "dfg = df_fps.groupby(['method', 'VCA'])['abs_deviation'].mean()\n",
    "print('Frame Jitter')\n",
    "print(dfg.to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e347ebb6-05dc-4933-9199-3c3438226467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VCA\n",
       "meet    1471.48\n",
       "teams   1872.19\n",
       "Name: Ground Truth, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfb = df[df['metric'] == 'bitrate']\n",
    "dfb['Ground Truth'] /= 1000\n",
    "dfb.groupby('VCA')['Ground Truth'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b678d3-5864-4ecf-acad-a2e99fcdda9a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Deviations Across methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c22818a9-53c3-4986-9fe7-30257fb57840",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#023eff', '#ff7c00', '#1ac938', '#e8000b', '#8b2be2', '#9f4800', '#f14cc1', '#a3a3a3', '#ffc400', '#00d7ff']\n"
     ]
    }
   ],
   "source": [
    "print(sns.color_palette(\"bright\").as_hex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0fe0690a-a2ff-4a14-8ebf-657739e44a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fps(df_fps, vca):\n",
    "    labels = []\n",
    "    meet = df_fps[df_fps['VCA'] == vca]\n",
    "    llabels = {}\n",
    "    for txt in suffix_map.keys():\n",
    "        d = meet[meet['Method'] == txt]\n",
    "        mae = round(mean_absolute_error(d['Prediction'], d['Ground Truth']), 2)\n",
    "        d['Method'] = d['Method'].map({txt: txt + f\"; MAE = {mae}\"})\n",
    "        labels.append(d)\n",
    "        llabels[txt] =  txt + f\"; MAE = {mae}\"\n",
    "    meet = pd.concat(labels, axis=0)\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP ML')], x = 'deviation', ls=None, color='#023eff', ax=ax, label=llabels['RTP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP ML')], x = 'deviation', ls=None, color='#f14cc1', ax=ax, label=llabels['IP/UDP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP Heuristic')], x = 'deviation', ls='--', color='#023eff', ax=ax, label=llabels['RTP Heuristic'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP Heuristic')], x = 'deviation', ls='--', color='#f14cc1', ax=ax, label=llabels['IP/UDP Heuristic'])\n",
    "    plt.legend( loc='lower center', bbox_to_anchor=(0.5, 1.15), ncol=2, frameon=False, title=\"\")\n",
    "    plt.grid(visible=1)\n",
    "    plt.xlim([-10, 10])\n",
    "    plt.xlabel('Frame Rate Error [FPS]')\n",
    "    plt.ylabel('CDF')\n",
    "    plt.savefig(f'paper_plots/{vca}_fps_error_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "693b4973-d4fd-4ab9-8de6-6114402ffbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fps = df[(df['metric'] == 'framesReceivedPerSecond')]\n",
    "df_fps = df_fps.rename(columns={'method': 'Method'})\n",
    "plot_fps(df_fps, 'meet')\n",
    "plot_fps(df_fps, 'teams')\n",
    "# plot_fps(df_fps, 'webex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "93a9ed41-6bb8-40e9-95b5-6136fce0578e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.90823659480375\n",
      "80.2370275464446\n"
     ]
    }
   ],
   "source": [
    "df_meet = df_fps[(df_fps['Method'] == 'IP/UDP ML') & (df_fps['VCA'] == 'meet')]\n",
    "df_teams = df_fps[(df_fps['Method'] == 'IP/UDP ML') & (df_fps['VCA'] == 'teams')]\n",
    "df_webex = df_fps[(df_fps['Method'] == 'IP/UDP ML') & (df_fps['VCA'] == 'webex')]\n",
    "\n",
    "print(100*len(df_meet[(df_meet['deviation'] >= -2)&(df_meet['deviation'] <= 2)])/len(df_meet))\n",
    "print(100*len(df_teams[(df_teams['deviation'] >= -2)&(df_teams['deviation'] <= 2)])/len(df_teams))\n",
    "# print(100*len(df_webex[(df_webex['deviation'] >= -2)&(df_webex['deviation'] <= 2)])/len(df_webex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c9ecaab4-653a-4db4-a12e-70ba49341a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bitrate(df_bitrate, vca):\n",
    "    meet = df_bitrate[df_bitrate['VCA'] == vca]\n",
    "    labels = []\n",
    "    llabels = {}\n",
    "    for txt in suffix_map.keys():\n",
    "        d = meet[meet['Method'] == txt]\n",
    "        mae = round(mean_absolute_error(d['Prediction'], d['Ground Truth'])/1000, 2)\n",
    "        d['Method'] = d['Method'].map({txt: txt + f\"; MAE = {mae}\"})\n",
    "        labels.append(d)\n",
    "        llabels[txt] =  txt + f\"; MAE = {mae}\"\n",
    "    meet = pd.concat(labels, axis=0)\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP ML')], x = 'rel_deviation', ls=None, color='#023eff', ax=ax, label=llabels['RTP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP ML')], x = 'rel_deviation', ls=None, color='#f14cc1', ax=ax, label=llabels['IP/UDP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP Heuristic')], x = 'rel_deviation', ls='--', color='#023eff', ax=ax, label=llabels['RTP Heuristic'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP Heuristic')], x = 'rel_deviation', ls='--', color='#f14cc1', ax=ax, label=llabels['IP/UDP Heuristic'])\n",
    "    plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.15), ncol=2, frameon=False, title=\"\")\n",
    "    plt.grid(visible=1)\n",
    "    plt.xlim([-1, 1])\n",
    "    plt.xlabel('Relative Bitrate Error')\n",
    "    plt.ylabel('CDF')\n",
    "    plt.savefig(f'paper_plots/{vca}_bitrate_error_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ffe3c23-a0f2-4a0e-a870-e9d2d4e419fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tROC7IrIC94QTbaOOmXTEe2/mPCGdSlsthYqHD2/Tq27Fw4fviFfdTHra+bwpfa54LVNxk3+B8zYd4G6g4M7bjjw0zPet1BagTpxfpzQXAZf5MZD/iEjWuWxC7APMT2/oFgeDb9L+w8+NwLu4Wfx+lxkpbnbRXb0xF2CCiOytqm+Hkl2vqrNFZBTOJ902iHNBn63lcaWqPteOxjQnAwnfwjkM58PrQlW9hQ5cP94Angv8CGc4ZwMbcbMatuDuEf1xPrm+j3PgOK+D2nbalkc2voAbH0jzFnCGiETUuU0XYKz6KV8zETevwPdwYxXbS1uec7OVeaE6r7w1HUjeE72Jqqp+35d3LfCH9nYQkSpVXYRrQmf13hsic56QvkRPOJ+z8TbuYSLtuC99IwL3JJw2JCNx4wNp2noA2FNVzxHnTHQ68BDuZie+jH1V9c1Q+jcJGW5xjkb/qM69+4q2xKvzmH0XMEqdJ+2qjCQn4KaTDXzeKVwX2jaD+6q6WEQOFefAsiYj7qS2dHQEDU0m5VsPZd5wgLtWDgae893kb6a7rERktO8KA1ivqj/w4X8Afq3Oe/erPqwKN9Z4Xa76erTxkK29Zh6Ge1pIb39WVR/06SLAR1X1O+l9VfW/InIk8KiILMHN2HV7KO/0xCyXiEgS2A24Uf1c4xnxt+rWbsjTeXTUc+4PcOMV6e30E8IpwC2yk3oTBX4sIvN9WUvVzS+Av7H9gC3eXFvUzVYXAapFZIp/KmrNe2+6C2KreUJ2dnai87lYRM5V1TtFZAxbPPqmx/TuEJGbcTf4EWzpnr0Td05U41pFJ4jIE7jusVHp81hEynHn5D6+tXuQb0k34J6OZ+NaNwlxXbpzReRQoFJETlfnffgg3xpP4MbemsV56b1at53nPO2VuMbf6NM303B9z/f1ORzXunrL7z4EZ7Sf93XKvIYPxb2oUpN5PDtKa9dLKP6zbDlPTlfVP+O6cm/y1/Q4XFc54rzwPi8iY/1YzC/FzRfShJs1ck4o33E4D9lpb8C/UNVNHdbdRT0C3Yo/YIeo6j/zrSVXxLyJGhnszOdzPvEGsli3nv7V6CJ6hfHYmRHzJmoYnYK4uUse6qoxMmNrzHgYhmEYOdOjvzA3DMMweiZmPAzDMIycMeNhGIZh5IwZD8MwDCNnzHgYhmEYOWPGwzAMw8gZMx6GYRhGzpjxMAzDMHLGjIdhGIaRM2Y8DMMwjJwx42EYhmHkjBkPwzAMI2fMeBiGYRg5Y8bDMAzDyBkzHoZhGEbOmPEwDMMwcsaMh2EYhpEzZjwMwzCMnDHjYRiGYeSMGQ/DMAwjZ8x4GIZhGDljxsMwDMPIGTMehmEYRs70KuMhIlNFZEm+dRiGYfR2CvItoLMQkRjwHNCQJW4/4FRgDbBOVe/NkmZEel1VV3ShVMMwjJ2eXmM8VLUFWCAi2aKvAc5S1bUiMkNE7lPVVGt5VVZW6rBhw7pKarsEQUAkkt9GoWnoGRryXX6mBlUAQRUUUBUiESUiiqrQ1BIBSadzv0WxgGhEaUlGaEpGUAVRiPg8imMpCiJKMhBqW2KkAqFQAwRAoawwiUhAi0apScZoSQplpBBcPuVFSSIRpTGIsiEZQ4HyVArxGsriSUSUhiDK+mQMgP7JJK4mUFqUIpJSaiMFrEvFiKgyONWyuf4lhSneXvX+WlUd3OUHeyei1xiPdhipqmv9+iZgELAqnCDc2pg4cSJz587tRnlbU11dzZQpU/JWvmnoORraKr+pGdbUQKAwcqgLe3E2LFsNjc3Q3ALJJAwdCKcc7uK/cTOsq4UFy6G8BFauh+MPgeu/7m70oz8PqQCSKZc3wBlHLGLalaNYuzpgxOcjBAijpZFjY+sZJQkOmwz7lTSwNlnA+OcmkiTCxYWL+XRsHRMiDRTEIxQ1JWkpijJkzaEAPFf6GntFN21Vn/qKOLstOxCAx0ve4MCCuq3iV1WUssfy/QlUeKb0FcZHG7eKX1BWwbHr9kaA5wpfZoAkt4p/u7Qfp9XsAcCsghdRIIkQi7r4WbFKzt00kVKS3B99mwBBgeEFzQz45+GLO/B39Sn6ivFYLCKDvAEpBda2t4Nh9ARU4fX3Yf8JbvumP8MvH4Tla7ak2XsMzLrbrV/wfy59mIPGp9gtkSBS28wLM8qoa4CCCCxpijCmooVFzzXz+PvrQZU9G/sjAlXSyBHl66nUJPu+XkfdiUsoAq4s2ZXGIMI+0Xo+GV3vCpgDLUClwi/jwkBpoSqSYFikmQaNUpFIutZIIsXjJW9QJinKSbI2KCBAqJAkcVFiG5t4t2wGBSglkiKhQgSlAIgIDK3dxLry51o9VmPra1lQ9EKr8XtvquHd2IsApHyrR4DmFESB/YMaHou8ThIhpZDCGZeFzfGO/2F9iF5lPETkdKBSRM4G7gfuV9UTgR8CF4nIWuA3bXVZGUZP4JnX4e5HlPuenALA3B/VsGtZimWz4uxClBGVASOCBCM0wdg1Daw8cRWbNMrVyTjlpSnKJUWMgEKU+MoUZbcGADwL7o6pQCGQ8It/yP+oLCMmyipiDJUWWkRoxi210RhHxOtoikYpD1p4uWAQmwoKKA1SrInHUREKU8JGKWZ+SSXzI4JEhdJUC2sK4khMKI4La6JCRJWgIEqyIIJGBETc6zuh9QjQEotCRFiydDGjqqqcdoH6hDCoP0hU3C5RQSIgBQLRCBJlczgCLSmhstxlLRHZUkz6N+KzDq1HIj49wKPd9c/vPPQq46Gqfwb+HAo60Ye/AbyRF1GG0Q6aSBGsaSJYvIkXHm/gzBnDWdFcSJyA4wo2MDbSSMkti2mWgKvCOwpsJMo6KWRWpILSigiUFrE+EJrLhERpIVIgxKNKvDJKcU2CkphSu/8QIvEoI+94k4KNzS6vkiiRYXFKDxxI/EtVlAcKSUUKI5u7zgYBY/JwfACqq1cxZcrueSrdyEavMh6G0dMJ1jWRensjqUX1JN/aSDCvjvVBASu0iL2im9hLhWM1xtqCGPtE6llLIU0lMX5cugdDBgnDqwoZNzbC+L0KGTQkym7lsFsrZaUWb6LpnoVobZLUjNrN4WO/NxqJCM3J0eiGZmIHDyIyvHirfSUiUJj15RPDAMx4GEaXovVJmp9YScuTKwlWJqA52ByXKCzgd8FuXFk/igjKaEmQKCqgas8iPn8U7D9+MBuWT+fYo4/InrcqqcUNpObVESx0g8+peXUUnrIrsYMGEaxMkHxlPRQIhcePIDqpguikCt8PA4WfGNrl9Td6L2Y8DKOT0UBJvr6Blsc+JPnKOghABhfRPLyU2aX9eHhDP26dW0nKf6NbXgJnHgs//Vop/cq3zqt6lXvfVJsDki+vI/nmBqITKyg8Zhi6polN33xtq/SRqlKk0OVbsH9/yv96KBKPdn2ljT6HGQ/D6ERaqlfTePs82OTeyYh8YihP9B/BTTPLmfG2SzN6OEyqgncWwWlHwq8ugv4ho6GqiAjalGLYDKh/4FWCpVu+fQ0WbaLwmGFEhsSJf2M80T0qiAyJQ1GE8HdOEotArBsqbfRJzHgYRieg9S003j6f5PQ1UBih6OwxPBAdzo//GGXJKogVwLc/B+efDON3hSdfhVnz4OLTt+QR1DTT9JcltPx3JeV/OAiKosTqnDGIfXIYBfv2o2D/AUjplsu28FPDu7uqhgGY8TCMHSbY2EzDJW8SrGgk9unhBF8cw/m/iTLtUdhlMDxwFZxwCBTGYEOd+3bj6I+4BSBY30Tzv1fQ/NelAMiwOMHKBNExZSw9Rhk75SN5rJ1hZMeMh2HsABooiV/MJVidoOTqvXmjsD8nnOW+zv7GyXDDeRAvcmmTSfjUd+HgveCWb2/Zf9N330DXNhPdvz9Fp+xKwb7981Yfw+goZjwMYwdo/utSkq9toGjqaH49rz+X3g6VpXD3ZXDWp7ZOe/W98OpcuOBUaH56FbFDBiHxKPGpY5ChcQomVeSnEoaxHZjxMIztJPleLU1/WkTBRwZwT8GufOdmOGAi/OMa110V5tEZ8NN74MTD4JTlC0k8uJRg0SbiZ48hdsSQ/FTAMHaAXjWfh2F0F6pK4q4FyIBCfrfbBM67WThoD6j+5baGo6kZvnWLe8tq2uGraX5wKbFPDKHoy6Pzot0wOgMzHoaxHaRmbyR4v47FB+7Gd+4t5MTD4NnboCSLD731dVBcBHedWofc+T6RsWXEzx/v/C4Zxk6KGQ/D2A6a/74M6Rfj7BnDGDkU/vADiLbyLd7wgfDKncqBr32AlBVQcvke9uGesdNjYx6GkSNBTTPJNzawaP9deO2pKL+9FMpKsqf9YAVUlMCgfoJevQ+6sYVI/8LuFWwYXYC1PAwjR5LPrYGUcs3CoYwaBmcc23raL10F3/7aBlI1zc4VuBkOo5dgxsMwcqTl1fUkBhXztw9KueSL7uvxbLw4G9bNbeC2ltkkfpG/mSkNoysw42EYORBsbCH1Vg3PRwdQVgxfPLr1tLc+CFeWLaIgCsXfntB9Ig2jGzDjYRg5kJpdA0nlxoWD+drxUFGaPV1dAyx5oZbjWEvhSbsSGVjUrToNo6sx42EYOZB8YwMtsShvJMv44jGtp3trAXwrupRkvICiz7Y2XZNh7Lz0mretRCQGXA+8BUwGvqeqLT7uVuADoBkoVNVf5EunsXOTWlDPguJyBg2McMDE1tMdspey7rg4pcMrtvKCaxi9hd7U8jgZWKGq04AVwEmhuBeBKmA//LzmmYjIiPTStTKNnRVtSBJ8UM8zdeUctk/r6RqbAIRB3xxrrQ6j19KbHonGAMv8+od+O82FwBGqmhCRk9rLKAgCqqurO1tfh6mvr89r+aYhu4byRTA+iPDEpkoOqHiP6uqVWfe565Ex9F+qnHLOQiI7+C1gTzsGfVmDkYGq9ooFOBW42K9fApwaipsdWp/TXl4TJkzQfPLMM8/ktXzTkF1D4i+LdePx07Xf4S06f1n29M0tqt/8zIe68fjp2jJnY6eWny9MgyrwqvaA+1xPWnpTt9VDwAgRmQqMAGaJyF0+7lYR+b6IXAH8Iz/yjJ2d1IJ6VhfG6Te0gLG7ZE/z8rvwqaZVNJYVEh1fnj2RYfQCek23lbrB8Ysygs/xcXd0vyKjt5GaX8fLjeX8z1Gtp3nuiUbOi26EY0aa40OjV9ObWh6G0WVoXQu6uonXW8r4+L7Z0ySaIP78SiICFccP616BhtHNmPEwjA6Qml8PwBupcg7aM3saEeWzQzeycdIAIkOy+GY3jF5Er+m2MoyuJFjWAMDK0lKqWmlUFBUKI2+fjAbajcoMIz9Yy8MwOkBqWQP1RBmzewzJMpSxsR5+Og0WLAeJ2FiH0fsx42EYHaBlUQPvJks5eK/shuHRFwI+89eZBH9d3M3KDCM/mPEwjA7QsryRD4I4E0dmj3/vsY1URRLstl8rs0IZRi/DjIdhtINuShLb2My8oIS9Rm8bn2iCj72/lPqCGEUfG9D9Ag0jD5jxMIx2CFY0AjBPS7IOlj/zZIJDIzVsOGyEzU1u9BnMeBhGO6SNR21FMfEs03IUz1wNwNhTBnenLMPIK/aqrmG0Q2ppAymgbHT2bzcO//ZQUu/EiY228Q6j72DGwzDaIbVwE/ODEvYYn71LKjKwiMjhQ7pZlWHkF+u2Mox2aFm0iXdTJVQN3zbuX3fVcPmpK1j5YdD9wgwjj5jxMIy2CIB1TSwJ4ozOYjzKpy/nK4klDB5sHwYafQszHobRBoW1EEkp84Nidh+1dVzQHDBhYw3zBw8gWmDGw+hbmPEwjDaIb3C/C6WEXQZtHbfspTrKSZHcx77tMPoeZjwMow0KN7pfGRInmjFevvq5GgKFXQ+v7H5hhpFnzHgYRhsU1glJhIGjCreJGxhpYUVlOfvsG8uDMsPIL/aqrmG0QawWVgSFTKradkxj9OXj0EQKsY/KjT5IrzEeIhIDrgfeAiYD3/NT0yIi+wGHAXXAEFW9IV86jZ2L6PoIi4I4I4duHZ5ogveXwR6jor3nIjKMHMhrt5WIjPRLFqcPOXMysEJVpwErgJNCcTcCNUA/4IVWtIxIL52gxeglxDfAgqCEcbtsHb7iuvd5/RtzeGxmfnQZRr7J90PTm8AbwCXAqzuY1xhgmV//0G+nOQT4io9/TUQ+qqqp1jIKgoDq6uodlLP91NfX57V80+CItMDklghLgiIGL5lBdX1ic9wuswspoYJUzfNUVye7TEO+j4FpMFoj38ZjlqoeCSAizwCb5+9Mh+fAAiD9Jv5w4INQ3AqgRlVVRFqAQqAxvLOqrkivT5w4kSlTpuRYfOdRXV2d1/JNgyO1ZBObeI3lQRHXHn8QRX7MPNjYQn3iJebEK/jJp/fqUg35PgamwWiNfL9tFZ7seSpwNlCMayXkykPACBGZCowAZonIXT7uO8DlInI+cJ+qNmbPwjC2EKxyLY36iuLNhgMg9a57f7duN3tF1+i75LvlsRlVXQwgIo3p9TQiIqqq2ffcvH8LcFFG8Dk+7mHg4U6Ua/QB0q7YmwYWbxXe+G4dKYWB+5XnQ5Zh9Ajy3fLoKE/lW4DR99C1TTQSoXzI1s9YsVElrD5kV046cme5fAyj88l3y2O4iJwJhF+iH5YtrHtlGQYEKxMsDYoYPWLrbzyKjx7KxKPzJMowegj5Nh4TgXuyhGeGtdllZRhdQfPqZpZkfOOhDUnemhOwOFHICYeAmD9Eo4+S73b3dFWNtLcAz+ZZp9EHCVYnWB4UMSrU7m15bg2jr5jBVdc1muEw+jT5Nh6XdXI6w+gUtDFFQX0Li4M4I0LedFNz69goBfQfm31KWsPoK+TVeKjqjPC2iBSLyDARibeVzjC6mvSbVh8ExYwMzTCbXNLAO8lSPranNTuMvk2+Wx4AiMg5IvIWUA8sBzaJyJsicnaepRl9lGBFAwCLI0UMDU3X0bIywcJUnL3GtLKjYfQR8j1gjojcDXwZeAm4DagFKoGPAr8VkUNU9Wt5lGj0QYIV7gPBTWWyeWxDEykKNjazOIjz8dF5FGcYPYC8Gg8R+RxwLHCQqm7jYk5EDgIeFJFTVPXv3S7Q6LMEqxOsj8Qoqwz5rRKIf2cip6VK2aMqb9IMo0eQ726rrwFfzGY4YPNYx5eAc7tVldHnCVYmWKpxhlRucYYoRVEKjxzKfseUEcn3lWMYeSbfl8BgVZ3eVgJVrQaGtJXGMDqb1IpG3msqZrdBW9ygrX+5hp/8pJ5X38ujMMPoIeTbeNR1MF1tl6owjBDaEqDrmliicXYd2LA5vOXX89j95cXUNrSxs2H0EfI9YF4oIruxtSuSrOm6Q4xhgPOmKwofBHGm9F/rwlYnKF7fyIup4fx0Qp4FGkYPIN/G4yBgUTtpBHNPYnQjwUo3zrEgKOE0P+aRfGMDAMtG9KeyLG/SDKPHkG/j8SZwYTtpBPhF10sxDIeudgajpqiI8mL3tlVyTi0btYBhe5fkU5ph9BjybTzuaG/AHEBE7ugOMYYBruXRLBEqdy3c/I1Hy9w63olVsO8E+7LcMCD/xuOfInKzX79HVd8MR4rIncBVqnp790sz+irB6gQfUsSYXbcYioqfT+bY+iQyOI/CDKMHke+3rU7Dzfa3DueWJJMY8KKIVHWnKKNvk1reyHstxUwauSVMSgqIDImbJ13D8OTbeJwInKiq16jq2sxIVf0KcDlwxY4WJCJDRaRGRGxiKaNVVJXUikYWpIoZM8KFNT+9ivvPXcgFt9h7G4aRJt/Go0BVn2krgar+ERjbXkYiEhORm0VkqojcIiKxjCTfBObugFajD6C1LUSaA5ZonCr/mNHyzCrGfLie+oQ1OwwjTb6NR7L9JAC0dCDNycAKVZ0GrABOSkeIyMeB14HGrHu6NCPSSwc1Gb2QtCv2xUHcTQIVQPK9Ol5orjB/VoYRIt8D5nERKVLVptYSiEgRUNyBvMYAy/z6h34bEYkCn1HVi0Xkgo6ICoKA6urqjiTtEurr6/Nafl/WMOBdqCLCIoqY9041rGhAEmW8nipnn/o3qa7e0K16+ur/0BM1GFuTb+PxCHCniJyjqs2Zkb7r6XbgXx3IawEwyq8PBz7w63sDG0RkKjAMOE1E/qiq68I7q+qK9PrEiROZMmVKjlXpPKqrq/Nafl/WkFi+iARLiA4r4agjp/Dmb6oBeFvL+c0Xh1HWzZ959NX/oSdqMLYm38bj58DTwAIR+SfwHm5CqFJgd9yA+nw65lX3IeB6byRGAP8QkbtU9RxglogcjfvgMArUdG41jN5CsKKRlZE4I3d1PbqRFmgoLeTwI4q73XAYRk8mr8ZDVRMiciRwJc5AVIaiNwC/An6qqu2Oefg0F2UEnxOKfxKYuMOijV5NsCLBvJY4e/nJntbtA3t/+yB+mV9ZhtHjyHfLA1VNAJeKyGXAJKAfznDMVdUgn9qMvoWmlOSSTcxJDmPSKPfabnNzhIYElMTzrc4wehb5fttqM6oaqOq7qvqiqs4xw2F0N7o6QaQ54J3AzRQYLG9k0m8jHHF8HbM/aHd3w+hT9BjjYRj5JrXETdTxflDChF0hWLSJfo0BRTG2+trcMAwzHoaxmWDRJgBWlpbRrxySszZQR4QBe5VSkPcOXsPoWdglYRieYHWC9ZEYVaOiADS9XcvLyUoO28+esQwjE7sqDMMTLGvgg1Qxe46GoKaZyIoGnkv246gD8q3MMHoe1vIwDNybVclFm5jdMpiJuwHNAY2HDGNoQy37jc+3OsPoeVjLwzAAXd+MNKR4J1XG7lUQGRJn6GUTOOioNUTsKjGMbbDLwjCA1IJ6AN4JSpk4EtbO3sRfnlLqE9E8KzOMnol1WxkGECx0xuODwlKGa4KGy17jycZxTD6rNM/KDKNnYi0PwwBSCzbxYSzO8F0KCN7ZCMA70XLGD6/PszLD6JmY8TAMIDW/jreCcnavgtQ7G6mVAgrHlVEYM0cHhpENMx5GnydYlUDXNPHMpgr2HgPNs2p4qbmCoz5qMwcaRmuY8TD6PKk5rpvqhWQlBw5vQlYneDbVj0/sn2dhhtGDsQFzo8+TmldPSzTCnKCUfScrJVfuxSWxYobvDs8/n291htEzMeNh9HlS8+tYFi9lxBBhwACBAQPYLd+iDKOHY91WRp9GkwGpeXW8HFQyeVTAGz9awNkXNbK+Nt/KDKNnY8bD6NMESxugRXl6Qxmnla9l7KzlJJc10r8838oMo2fTa4yHiMRE5GYRmSoit4hILBR3h4icIyI3ichx+dRp9CxS77kmxmvJcg5YsZJFQZz9T+6P2ItWhtEmvcZ4ACcDK1R1GrACOCkU9zNVvQu4G/hitp1FZER66WqhRs8hNa+ORGEBCRWGrKjhoWAwp3/SLIdhtEdvGjAfAyzz6x/6bQBUdZFf/QJwTXsZBUFAdXV1J8vrOPX19Xktv89oUNjzZeH1aAUXVyxGFd4cFmfOW9XM6S4N7ZDv8k2D0Rq9yXgsAEb59eHA5lmnRaQQ+DZwD5D1k2FVXZFenzhxIlOmTOkyoe1RXV2d1/L7iobU0gY21b7Ks9EhTByWZEHxUM7+wnCmHDy82zS0R77LNw1Ga/SmbquHgBEiMhUYAcwSkbt83BPAAcDFwI15UWf0OJIvrgXgDzWDqD12FAfcMoHjDs6zKMPYSeg1LQ9VbQEuygg+x8cd0f2KjJ5Oy8x1bBxcyr4Ndcz+oJCGhFBanG9VhrFz0JtaHobRYYKVjQTv1zEzVcGfSt4lNnMNhbH29zMMw9FrWh6GkQvNT68iUBi3dgNLKOLsKwcRs6vBMDqMtTyMPocmUjQ9upKXIv0YJQn+PmkC++9pl4Jh5IJdMUafo/mxD2nc0MKhWsOtzbvyhQv751uSYex0mPEw+hTr1wXU/305ZRPKWBgr5YH+o5hgXhANI2fMeBh9AlW4/yk478z1XLp8NzadXMWhNfvx6SnRfEszjJ0SGyI0ejWpFNz7ONz79ySnLV3AXYWrWDmygqtfm0wzcLZ5OjOM7cKMh9Gruevf8N9fruHuknkMKEwSxKPscv3u/OVr8JnDYOLIfCs0jJ0T67YyehWq8Kcn4LYH3fbUAWuYVjKHAYXOK03Zj/bkh38pYt1GOO+k/Ok0jJ0da3kYvYIggH+9ANfeHbDPkpVMHJQiddJuRCsKIApEoPiSSTzZ2I9f/g2+8mk40uYoN4ztxoyHsdMzax6c9606ztblPFC4gQHFLUhFnOY/JWl5YCkyqJDS6yfzZk2c0y+ESSPhpvPzrdowdm7MeBg7HcvXwB3/gvG7wpmfhBHvr+bx2HsucpdiItEYwZIGWh5YSsHBg4j/71j+MbuIs66G0mJ45AboZzMFGsYOYcbD2ClQhZlz4G9PBqz51yqOLNhAsn8ReuxYBu5fTuOEcoI1CVjeSABExpRS+IVRzCwZyK9uEx54GvYcDX/+CVQNb7c4wzDawYyH0WNpSQpBAJEI/H5aAv6ykIsK1lFa7Aa/pX8J9d99HZ1XD0BkXBl8dhQvVw7mjy/FePQ6WFPjWhuXfgl+MhWKCvNXH8PoTZjxMHoEQQBPvQZPvOq6pV6ZowxYtR9rnlrBScXr+eTrtVTEkjTplilig+UNrBtWwfJxI/jTpsE8trySpb9wrRSACbvBj74MZxwLlWV5qphh9FLMeBjdyvpamDUf5i+Dd+YFrFuT4u5T6wjm1XH93RV8NKjliEgjlxbUUlWaYMlbRawrgSWl5WwoKOK+2kE8vaGMteqbEDXup2oY7DIITjkcPrE/HLwnDKzMWzUNo9djxsPoNLSuhWB9M9ocsGiZ8to8eO8D5YKqdcRjygVPVDLtwwFMiDSyZ6Se/aL1fCpax5K3GxkcaeHeWIQycV1SS4Mi/tMygJuaRvJGfQUAhTE4fF84IAKjh8NHd4dxu8Beo20A3DC6GzMeBhooNKTQphS0BGhTQNkyN+cFgNYn0Y3NJBY0sCRZxIcboL5OqW2AlXURDiiq50Cp5T9N/bmreRfiElBEwC6RJkZHErwwaxMfj21kamoDN1a8u7nclMJKLeRbiQnMSZWwx5AUUl5A2W5xhvaHMuZwzcEVTB4HQwfk6+gYhpENMx69AE0puikJDUm0Lok2B9AcEKxtIviwkUQSUgsbSNYmmdVQTM0GZW1LlNXNBRSlUuwaSXBSbB0A1yZGMScoYYBMREhRiDIu0sC5RR/yTqqYv7VUUCopdpEmhkaamRhtZmaigpmUEyPgn6Vvb6WtiQi3j9mdB4fty9jGWqhZyZqSEgrGlVI2uZLyigi/GwgDK9hmJr/q6lVMOXD37jqMhmHkQK8xHiISA64H3gImA9/z85ojIscCB+LcscxQ1f+2mVcAweoEBArqn8wD3HbgbtQphGRzQLJJaWqC4hIoLo6wanXAzNkBEaClBVJJZe065eRjowyuhNffCXj77STxRAsf1kXoF0vRr6GJyYfFqYwFzHw7YO4HuzDvlnlIoIgqEigHj2gm2hLwzuoozSpEUdL+YCMonyioAaA62Y8KkohACqFKEswJStg/WkcESKZgcqSR4ZFmUlGIFsCbqVKuTYxiE1HOLVzOqEjTVsfjtcJ+XH3UxyktFn7892c3l5uKCKmiAkYdXUTqMyOJp5IUzCwmNiJOZEAh0r+Q8gGF/EjSg9wVfjEMY2en1xgP4GRghapOE5FLgJOAv/q4K4HDAAGeB7YxHiIyIr0+uXIi9V+dmbWQ11NljJYE/SNJtx8QB55NVvLxgo0sSJZyVMGmbfZ76e1yDo7VUZss5/MFddvEr360gEJJUpEq45yCeki58JRCIMJby0rYSIwCAvaNbKKFCEmEZiKsDAq5s3IMBaL0q2ugIpmiRSIkFeZGy4jFhDuPO4xhA2HiG0up3dhIc5FQEg0oGRVn7/4FfOwzuxArgOSLRWgixZz332P38ZOIDC7iiOHFfGKIMwB6+qEQiyBR2aYOUAAn7ZL1uBmG0bvoTcZjDLDMr3/ot9OUq2oKQETaG1odOmvj3PWV/z5iYRdo7AjpzpuWTs31wR6gITdMQ/7LNw1byh+dp7J7LL3JeCwARvn14cAHobg6EYniGgq12XZW1RUA4rpY9k5vdzfpFlC+yjcNPUdDvss3DVuXb2xNb3LJ/hAwQkSmAiOAWSJyl4/7CXAZcDlwRT7EGYZh9CZ6TcvDD45flBF8jo/7L1nGOQzDMIztQzTty8EwDMMwOkhv6rYyDMMwugkzHoZhGEbO9GnjISJlInKliPwn31oMw8gvIjJVRJbkW8fOQq8ZMN9OKoG/AR/LFikiZwEDgKHAA6r6RrawHREgIqOAbwHzcN+j3BSKqwLuxL12XAJsUtXzRORvwFqf7KodfYWxLQ0+fpvyROS7QD0wEfg/VV3cVRpEZDfcm3IzcR97Xq2qC0XkDiA9aPcrVX2bHMjVK0Gungo6QcMdwKu4Y/yMqj7ij/t4v/sTqprbFzy5a7gWd74D3K+q1Z19HNop/99s+X5rX1U9OJumHSk/pOE5oCFL3H7AqcAaYJ2q3pstbEc17HSoap9egCrgsSzhUeBFvz4EeCRbWCeU/2vgI379YWBYKK4YGOrXvw2MD+1zBnA+UNiVGrKVh/uO5p8+7kDcjbsrj0MZMMivfwM4x6/fAJzpj03FdpT5eeBiv34JcGoo7iX/fxfgbpBZwzqh3m1pqPK/ewL3hc6Ds8PnQxdr+CFwFnBB+j/p7OPQwWOwP/CV1jR11gK8lyXsP6Hzb4av+zZhnaljZ1j6RMtDRIYC92UEr1TVM9rYbTD+KURVV/sn42xhO6QBGOR/AVbjPnRc6ctoBBpFJA6MVNV5Pt3lqlojImcAF+Juol2ioZXynsU9ccG2X/N3ugZVrQfqRaQE2Au42Ke71us6DLgOZ9xyIVevBLl4KthhDaq6yK9+AbjGr9/r61wCPAUc3JUagNt8ebsCvwc+Tecfh44cgzNxhqU1TV3JSFVNt7w34c7VbGGrulhHj6JPGA9VXQUcneNua3BdRYjIEGBxK2E7pEFEfgUMw108reV5NvA7n77Cp6vB3WQP6EoNrZS3CGdIYduv+Ttdg48fDkzFGY6hvsxxuG6d1V5HruTqlaBdTwWdqUFECnEtjHtwrjnB11lVG/x/06Ua2HKM1+KOO3T+cWirfERkb2CO+q6sVjR1JYtFZJA3FqW+3GxhfYo+/Z2Hv/jOwz3VfE9VHxWRQ4FjVPWK0PjGMOAvuvWYx+awHdSQ7uufD5Sp6k0icjpQoqq/8zeQn6nqd336wcCNwNPAQcBNqtrhm3euGoB/ZSuvC8c8smlIf+T5LG6MY4OqXiYi9wOP4frJ71PVV3IsM7Ov/VfAJap6Tka//suq+ni2sB2pcwc0TAdWABuBwar6WRG5Dme8BwIfqurvu1jDHbixprHAC+rGXTr1OLRVvo//OXCZqjb77W007Uj5IR2nAzcDPwDux42nnBga31iLG9+4J1tYZ2jYmejTxsMwDMPYPvr0q7qGYRjG9mHGwzAMw8gZMx6GYRhGzpjxMAzDMHLGjIdhGIaRM2Y8+ggiMlpEqkUkISKL/PoLIjJfRB70H1x1NK8LROQ9EVmUo4aTROSkjLC4iCwVkY/mkleO5Z4sIjNEREVklq/7GyLygYhcJSKRUNpTROT1jP0vFJHJnainn4hcISL9djCfg31d1P8f1RnLos5RbBjb0ic+EjRAVRcCU/wNZZqqXgGbv3V5Bfgz8PEO5vV/IrKR3GdlPMn/PhQKawHm0nkf3W2Dqv5DRN4AFgIXqveFJCLH41yhfAj8xidfD7yfkcWFuI8kZ3WSpH642S2n+Xy3C1V9CfefKnCdqk4Lx5vxMLoSa3n0cVS1Fvg3cFgnutzIpfyUqh6tqnPzUPa/gQ2EvnhX1WpV/UJ3a+kivpxvAUbvxYyHARDzv5u/GBWRcSLyqIi8LCLPi8htIlLaWga+K+ZuEZkpItNF5Dn/tX46/ufAp4BPhbpVikXkKRGp8d04xSLyiu+GeUVEDvb73icidSLyF79dKiJ3+K6nahH5l4iM3oG6Bz7fY0LdW1U+7CmcN4Hv+7LuEJGJoe6ir4rIX313mPp9PisiL4rIM/74/UJEinzc3rivlwHu9/mc5+MKRORnoa61J7enu0xEpojINFWd7rePCNXrcyLyTxGZ67svN3dBishZIvKIiKwRkWl+3/Ei8h8ReU1E3vb1L20v3+38L4ydiXx7ZrSlexeca4srQtujgOV4r60+rBDnJuSbfjsKPIhzypdOMxVYFNqehPMuGvPbH8e5bugXSjMN12WWqak6rQl3M18LnB+KHwA8G9r+A/AA3pMp8B2vN9ZGvatwxnFKKOx8oBk4Nku6qoxjNjVLnopzmxLH+Xl63YffD5wYqs9jwI/bKsOH/xR4Hoj77ZNxLaMB7fynGtYHTMk8zqEyp+EeGuPAS6H/siH0f38MuB0owvmZ+nGoLv/NOFdazdeW3r3kXYAt3fyHuxvhIn/DfgdI4HxKRUJppuL8VkVDYcfhntD7hdIsCsUX4t3Hh8I+BD4Z2p6WeVPz4dVsbdBuxflMSm+fjzcmwGiv46BQfJm/gZ3YRr3TN7lZvry5OJ9R38O76clIV5VxzKZmyVNbCd81I8//Dd9QWymj2N/Av5CR1xrg2+38pwq85+tV7es4rZX6T8my/1ScES3MCP+KDy8LhX3KH//R7eVrS+9ebMC8bzJNnePHApx79O/iXFtv8vF7454inxKR9D5FwBJgBNkHeZPAl2TL21QB0B/X5ZMr9wLfFJGJ6sZCvsCWwfa9cE/5N4tIc2ifRTi32O1xoW4ZMB+Je5I+EOfkbntYmiWsErhRnLPHZtwxKGonn3E4A3KJiHw9FF6DG2Bvj80D5iIyBWcQOqoXYJV6x4Mh9vLh9aGw+bjjvxfuBYT28jV6KWY8+jCqmhSRi3BdE18Hfh6K3qCqU3LI7ru4SXoO9Df89Ns+0tZOreh6RUTmAF8WkXuANaq6PiPZl3XL/CbbhaouEZHf4m70E1Q18y2rjpAKb/jxgKdx3XxnqGpKRKbS8TfTLlXVJ7ZDx2a8caxuJTqVY3hH2dH9jZ0MGzDv46jqcuBPwIW+JQLwNjBUQt8hiEhURO4RNzFVNg4HZunWb00VZqRJz0mR/r4jRuvcC3wJ98ZQeIrP2bhukonhxCLyw+0ZXMa1mKDtB6mw7jIJNceyMAk3H8lf1U+YRBvHwedZjnuiT7Btvf5XRI5po7xWEZHTtme/ELNx50FZKGws7vjP3sG8jZ0cMx4GwE24fvr0K6p/wnUDXR5Kcz5QpKqJVvJ4F9hL3EyBiMhBbNtltZotc0/fAhzbhqY/ek1n4qb8BDZ/r3If8D0RKfZlfQQ3Te6cNvLbBn9TPB03b3pbrwqHdc/ETf7TGouARuAoX0YUOCEjzVqcARkgIsOAp9XNGnkT8A0RGej3rcJNfpXT3Owhrt/O/dL8CTc514VeTwHu5YQ/+//B6Mvke9DFlu5ZcAPN1bin20XAvzPi/4ObRvPffnssbt722cAzuJkMK3zcBbgB2oTPswyowH1ouBj33cjNuAHz9/CDysAE4E3cpE4P457In8L16y8Crs7Q9ARwa5a6lOA+6pvrtT0CTGqj7ifj3gQLD5i/4Pf/GzDOpzsmlG4GcJgPP8nX40XcG1G7+DzS+d2QUV46/UzgH7jxpARQHUpzjT+2LwOf8WFR4Gq/73TgSeDgNup1aEjvAr8eXpp8ugMy6nVRKI+vZvyXR2aUMR54FHjN670TP4DeVr629P7FJoMyDMMwcsa6rQzDMIycMeNhGIZh5IwZD8MwDCNnzHgYhmEYOWPGwzAMw8gZMx6GYRhGzpjxMAzDMHLGjIdhGIaRM2Y8DMMwjJz5f7aP2yeOS84nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bitrate = df[(df['metric'] == 'bitrate')]\n",
    "df_bitrate['deviation'] /= 1000\n",
    "df_bitrate['rel_deviation'] = 1000 * df_bitrate['deviation'] / df_bitrate['Ground Truth']\n",
    "df_bitrate = df_bitrate.rename(columns={'method': 'Method'})\n",
    "plot_bitrate(df_bitrate, 'meet')\n",
    "plot_bitrate(df_bitrate, 'teams')\n",
    "# plot_bitrate(df_bitrate, 'webex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "537a50c4-2f4d-40e0-b4e3-97fc3aa97773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.45494748479823\n",
      "41.992312620115314\n"
     ]
    }
   ],
   "source": [
    "df_meet = df_bitrate[(df_bitrate['Method'] == 'IP/UDP ML') & (df_bitrate['VCA'] == 'meet')]\n",
    "df_teams = df_bitrate[(df_bitrate['Method'] == 'IP/UDP ML') & (df_bitrate['VCA'] == 'teams')]\n",
    "df_webex = df_bitrate[(df_bitrate['Method'] == 'IP/UDP ML') & (df_bitrate['VCA'] == 'webex')]\n",
    "print(100*len(df_meet[(df_meet['deviation'] >= -50)&(df_meet['deviation'] <= 50)])/len(df_meet))\n",
    "print(100*len(df_teams[(df_teams['deviation'] >= -50)&(df_teams['deviation'] <= 50)])/len(df_teams))\n",
    "# print(100*len(df_webex[(df_webex['deviation'] >= -50)&(df_webex['deviation'] <= 50)])/len(df_webex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "91a1c2bc-efb5-4240-80c7-7cdea4e294bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_jitter(df_jitter, vca):\n",
    "    meet = df_jitter[df_jitter['VCA'] == vca]\n",
    "    labels = []\n",
    "    llabels = {}\n",
    "    for txt in suffix_map.keys():\n",
    "        d = meet[meet['Method'] == txt]\n",
    "        mae = round(mean_absolute_error(d['Prediction'], d['Ground Truth']), 2)\n",
    "        d['Method'] = d['Method'].map({txt: txt + f\"; MAE = {mae}\"})\n",
    "        labels.append(d)\n",
    "        llabels[txt] =  txt + f\"; MAE = {mae}\"\n",
    "    meet = pd.concat(labels, axis=0)\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP ML')], x = 'deviation', ls=None, color='#023eff', ax=ax, label=llabels['RTP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP ML')], x = 'deviation', ls=None, color='#f14cc1', ax=ax, label=llabels['IP/UDP ML'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('RTP Heuristic')], x = 'deviation', ls='--', color='#023eff', ax=ax, label=llabels['RTP Heuristic'])\n",
    "    sns.ecdfplot(meet[meet['Method'].str.contains('IP/UDP Heuristic')], x = 'deviation', ls='--', color='#f14cc1', ax=ax, label=llabels['IP/UDP Heuristic'])\n",
    "    plt.legend( loc='lower center', bbox_to_anchor=(0.5, 1.15), ncol=2, frameon=False, title=\"\")\n",
    "    plt.grid(visible=1)\n",
    "    plt.xlim([-100, 100])\n",
    "    plt.xlabel('Frame Jitter Error [ms]')\n",
    "    plt.ylabel('CDF')\n",
    "    plt.savefig(f'paper_plots/{vca}_jitter_error_imc_zero_loss.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "91b36cf3-8dc1-4f0f-8029-7c575c3c2650",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_jitter = df[(df['metric'] == 'frame_jitter')]\n",
    "df_jitter = df_jitter.rename(columns={'method': 'Method'})\n",
    "plot_jitter(df_jitter, 'meet')\n",
    "plot_jitter(df_jitter, 'teams')\n",
    "# plot_jitter(df_jitter, 'webex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eb8032ac-8cf5-4a59-b207-9872cc8e9098",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 337.5x162 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 337.5x162 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "teams = df_jitter[df_jitter['VCA'] == 'teams']\n",
    "csv_file = list(teams['csv_file'].unique())[7]\n",
    "tdf = teams[(teams['csv_file'] == csv_file)&(teams['Method'].str.contains('IP/UDP ML'))].sort_values(by='ts')\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "tdf.plot('ts', 'Prediction', ax=ax, label=f'IP/UDP ML', color = '#023eff')\n",
    "tdf.plot('ts', 'Ground Truth', ax=ax, label='Ground Truth', color= '#f14cc1')\n",
    "plt.grid(visible=1)\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylabel('Frame Jitter [ms]')\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=3, frameon=False)\n",
    "# plt.ylim([0, 50])\n",
    "# plt.xlim([0, 20])\n",
    "# ax.spines[['right', 'top']].set_visible(False)\n",
    "plt.savefig('paper_plots/frame_jitter_ip_udp_ml_netrics.pdf', bbox_inches='tight')\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "tdf = teams[(teams['csv_file'] == csv_file)&(teams['Method'].str.contains('RTP ML'))].sort_values(by='ts')\n",
    "tdf.plot('ts', 'Prediction', ax=ax, label=f'RTP ML', color = '#023eff')\n",
    "tdf.plot('ts', 'Ground Truth', ax=ax, label='Ground Truth', color= '#f14cc1')\n",
    "plt.grid(visible=1)\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylabel('Frame Jitter [ms]')\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=3, frameon=False)\n",
    "# plt.ylim([0, 50])\n",
    "# plt.xlim([0, 20])\n",
    "# ax.spines[['right', 'top']].set_visible(False)\n",
    "plt.savefig('paper_plots/frame_jitter_rtp_ml.pdf', bbox_inches='tight')\n",
    "plt.figure()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "tdf = teams[(teams['csv_file'] == csv_file)&(teams['Method'].str.contains('IP/UDP Heuristic'))].sort_values(by='ts')\n",
    "tdf.plot('ts', 'Prediction', ax=ax, label=f'IP/UDP Heuristic', color = '#023eff')\n",
    "tdf.plot('ts', 'Ground Truth', ax=ax, label='Ground Truth', color= '#f14cc1')\n",
    "plt.grid(visible=1)\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylabel('Frame Jitter [ms]')\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=3, frameon=False)\n",
    "# plt.ylim([0, 50])\n",
    "# plt.xlim([0, 20])\n",
    "# ax.spines[['right', 'top']].set_visible(False)\n",
    "plt.savefig('paper_plots/frame_jitter_ip_udp_non_ml.pdf', bbox_inches='tight')\n",
    "plt.figure()\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "tdf = teams[(teams['csv_file'] == csv_file)&(teams['Method'].str.contains('RTP Heuristic'))].sort_values(by='ts')\n",
    "tdf.plot('ts', 'Prediction', ax=ax, label=f'RTP Heuristic', color = '#023eff')\n",
    "tdf.plot('ts', 'Ground Truth', ax=ax, label='Ground Truth', color= '#f14cc1')\n",
    "plt.grid(visible=1)\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylabel('Frame Jitter [ms]')\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=3, frameon=False)\n",
    "# plt.ylim([0, 50])\n",
    "# plt.xlim([0, 20])\n",
    "# ax.spines[['right', 'top']].set_visible(False)\n",
    "plt.savefig('paper_plots/frame_jitter_rtp_non_ml_netrics.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a1c84f32-6136-4fd1-848f-1f65a46ee5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/taveesh/vca-qoe-inference/venv/lib64/python3.6/site-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  app.launch_new_instance()\n",
      "/home/taveesh/vca-qoe-inference/venv/lib64/python3.6/site-packages/ipykernel_launcher.py:39: RuntimeWarning: invalid value encountered in true_divide\n",
      "/home/taveesh/vca-qoe-inference/venv/lib64/python3.6/site-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teams\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/taveesh/vca-qoe-inference/venv/lib64/python3.6/site-packages/ipykernel_launcher.py:39: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "df_frameHeight = df[(df['metric'] == 'frameHeight')]\n",
    "\n",
    "for vca in tested_vcas:\n",
    "    teams = df_frameHeight[df_frameHeight['VCA'] == vca]\n",
    "    print(vca)\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    udp = teams[teams['method'] == 'IP/UDP ML']\n",
    "    labels=sorted(list(udp['Ground Truth'].unique()))\n",
    "    ccounts = np.unique(udp['Ground Truth'], return_counts=True)[1]\n",
    "    cperc = ccounts / len(udp['Ground Truth'])\n",
    "    cm = confusion_matrix(udp['Prediction'], udp['Ground Truth'], labels=labels)\n",
    "    f1 = round(100*accuracy_score(udp['Prediction'], udp['Ground Truth']), 2)\n",
    "    cm_norm = 100*cm / cm.sum(axis=1, keepdims=True)\n",
    "    cm_norm[np.isnan(cm_norm)] = 0\n",
    "    total = cm.sum(axis=1, keepdims=True).flatten()\n",
    "    p = len(total)\n",
    "    formatted_text = np.asarray([\"{0:.2f}%\".format(a) for a in cm_norm.flatten()]).reshape(cm.shape[0], cm.shape[1])\n",
    "    sns.heatmap(cm_norm, annot=formatted_text, cmap='Blues', fmt='', xticklabels=labels, yticklabels=labels, cbar=False, ax=ax)\n",
    "    i = 0\n",
    "    for x in total:\n",
    "        plt.annotate(f\"  Total = {x}\", xy=(p, i+0.5))\n",
    "        i += 1\n",
    "    ax.tick_params(pad=1)\n",
    "    ax.set_title(f'Overall Accuracy = {f1}%')\n",
    "    ax.set_ylabel('Actual frame height')\n",
    "    ax.set_xlabel('Predicted frame height')\n",
    "    plt.savefig(f'paper_plots/{vca}_resolution_cm_netrics_ip.pdf', bbox_inches='tight')\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    rtp = teams[teams['method'] == 'RTP ML']\n",
    "    labels=sorted(list(rtp['Ground Truth'].unique()))\n",
    "    ccounts = np.unique(rtp['Ground Truth'], return_counts=True)[1]\n",
    "    cperc = ccounts / len(rtp['Ground Truth'])\n",
    "    cm = confusion_matrix(rtp['Prediction'], rtp['Ground Truth'], labels=labels)\n",
    "    f1 = round(100*accuracy_score(rtp['Prediction'], rtp['Ground Truth']), 2)\n",
    "    cm_norm = 100*cm / cm.sum(axis=1, keepdims=True)\n",
    "    cm_norm[np.isnan(cm_norm)] = 0\n",
    "    total = cm.sum(axis=1, keepdims=True).flatten()\n",
    "    formatted_text = np.asarray([\"{0:.2f}%\".format(a) for a in cm_norm.flatten()]).reshape(cm.shape[0], cm.shape[1])\n",
    "    sns.heatmap(cm_norm, annot=formatted_text, cmap='Blues', fmt='', xticklabels=labels, yticklabels=labels, cbar=False, ax=ax)\n",
    "    i = 0\n",
    "    for x in total:\n",
    "        plt.annotate(f\"  Total = {x}\", xy=(p, i+0.5))\n",
    "        i += 1\n",
    "    ax.tick_params(pad=1)\n",
    "    ax.set_title(f'Overall Accuracy = {f1}%')\n",
    "    ax.set_ylabel('Actual frame height')\n",
    "    ax.set_xlabel('Predicted frame height')\n",
    "    plt.savefig(f'paper_plots/{vca}_resolution_cm_netrics_rtp.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84275acf-100c-4d93-a1ec-50ec37196120",
   "metadata": {},
   "source": [
    "# Feature Importance Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9cd41008-3f40-4641-bae5-179784e93e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(f_rtp, f_udp, metric):\n",
    "    for vca in f_rtp[metric][1].keys():\n",
    "        print(vca)\n",
    "        scores_rtp = {}\n",
    "        for cv in f_rtp[metric].keys():\n",
    "            for feature in f_rtp[metric][cv][vca].keys():\n",
    "                if feature_map[feature] not in scores_rtp:\n",
    "                    scores_rtp[feature_map[feature]] = []\n",
    "                scores_rtp[feature_map[feature]].append(100*f_rtp[metric][cv][vca][feature])\n",
    "        scores_udp = {}\n",
    "        for cv in f_udp[metric].keys():\n",
    "            for feature in f_udp[metric][cv][vca].keys():\n",
    "                if feature_map[feature] not in scores_udp:\n",
    "                    scores_udp[feature_map[feature]] = []\n",
    "                scores_udp[feature_map[feature]].append(100*f_udp[metric][cv][vca][feature])\n",
    "                \n",
    "        mmap = {'framesReceivedPerSecond': 'Frames Per Second', 'bitrate': 'Bitrate', 'frame_jitter': 'Frame Jitter', 'frameHeight': 'FrameHeight'}\n",
    "        if vca == 'meet':\n",
    "            vc_name = 'Meet'\n",
    "        elif vca == 'teams':\n",
    "            vc_name = 'Teams'\n",
    "        elif vca == 'webex':\n",
    "            vc_name = 'Webex'\n",
    "        \n",
    "        fig, ax = plt.subplots(1, 1)\n",
    "        topk = pd.DataFrame(scores_rtp).mean().sort_values(ascending=False).head(10).sort_values()\n",
    "        plt.barh(topk.index, topk.values)\n",
    "        plt.xlabel(f'Feature Importance [%]')\n",
    "        plt.ylabel('Feature')\n",
    "        plt.xlim([0, 100])\n",
    "        plt.grid(visible=1)\n",
    "        # ax.spines[['right', 'top']].set_visible(False)\n",
    "        plt.savefig(f'paper_plots/{vca}_{metric}_rtp_ml_feature_importance_netrics.pdf', bbox_inches='tight')\n",
    "        \n",
    "        fig, ax = plt.subplots(1, 1)\n",
    "        topk = pd.DataFrame(scores_udp).mean().sort_values(ascending=False).head(10).sort_values()\n",
    "        plt.barh(topk.index, topk.values)\n",
    "        plt.xlabel(f'Feature Importance [%]')\n",
    "        plt.ylabel('Feature')\n",
    "        plt.xlim([0, 100])\n",
    "        plt.grid(visible=1)\n",
    "        # ax.spines[['right', 'top']].set_visible(False)\n",
    "        plt.savefig(f'paper_plots/{vca}_{metric}_ip_udp_ml_feature_importance_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "08ebb480-d467-486a-8f0a-3930f2bec60e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n",
      "teams\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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o+tTIniBoFyE1FwRBragmQD5gZl/Lu5DWQwZBEARBYag4QDYXHNO1vWtjThC0j+ak5kJmLgiCaqlGSadZJM2oRT1BEARB0F2oRijgf1u4vGX7TQmCIAiC7kM1PchBwD2Z1yzgJXzH+MubLxZUi6SNJd0naWAH1N1X0oxay79JGiDpckkXlpRtQvc1CIJ6pppJOueZ2QXliZLWA35SO5PqG0k34csfDgeuxlVubjGzxrJ803DZtLnldZjZy5Ke6wj7zOxdSY91QNVjgPvNbMm6RzO7U1Kre0yWNF+zogfVEFJzQRB0BNVM0lkmOKb0NyX9a+1MqnvytEi3kDQJeBvvtd+IL4z/lqQ7cNWa3sBawN1mdn95pZLWx2XaHsSVbwScgEuwzcQlAH9nZsMkfRs4GVe1GQPcDGwPPGNmF6cqd5W0OS5Ndwi+MH874F18feXlmfv1Bh4rBbD0vPfDxQY2woUFxqZrT5nZ7Izp60vaP93/Bnx95LeAbwBHAa+Q1kNKehN4gmV1YQ8EPgP645qtjzTX+M1JzdWrdFbRZL+K5g8Uz6ei+dMeqhUrL2d1YEdgjZpZVOfkaZEmrdOxaXPp+4CpeHC53szmJk3THXHZtv1wbdLyet9Iw5WPmtmtku4xM0vi4ZjZc5JeT8fTJR1tZhdIWhf4APghcBtQCpD3mtnvJf0cV7c5DQ+2i/Fh83eBRjwwzpC0csack4FTzGyOpOuBTWjSbs0GR4B5ZnZ1ksGbYWZjJH0Ll4772MyulLQ4lb2tGV3YjVLbTAf+L6dtWpWaq0eZOSie7FfR/IHi+VQ0f9pDNUOsjTlp83DxgAk514LmEd4LRFJP4BK8t9QfDz4tMS+9W3r/FOiVjntn8i2ld2pmiySt2IwtpfebzOw9Sa+a2ULfJIQPAczsn82UMypDmeM/APviQbhUh1Jb5OnCnotvrXYi8HvgugrvGQRB0GaqCZD3xFZXlSFpFEtrkZ4AHJ+GEKeb2XxJj+NbRb0L3AL8GA92QyVtjA/BjgOuSHWugW/99JmkD3Gt1c/jvc0zJM0D1pA0Dg+K/ZLe6ShgURL/7idpx5TWV9JgXP+0Edc7nSjpaaCnpNkp36qSHigLkKcBE5JO6rO4OPqxyc67zOzVdDwOWEvSwamuU1P5afgXq9LQfFb39XyW1YU9Jd3jb7goehAEQYdTTYD8z/KE9I1/Z+BPLez4sNxhZo+R0SJNE3Qay/Kc30zx0oSnrE4pZvYh/rtdiazW6bfTe54Wal6ZbFqJe9MrS14+zOwpUkDMsMwoQtqceZOc9LckXWJm76Xzct3Xcl3YH+fZEQRB0JFUEyCvJU3EyNAD/xA9FPi3WhkVFBdJBwBr43s/1pzQYg2CoFZUEyBVnpCG3b4Xa92CSjGzK7vahiAIgkpoMUBK+jeaeobDJE3NydYXWLPWhgVBWwgt1iAIakUlSjpq4WX4BIsQKw+CIAgKRYs9SDO7BZ9hiaRzzOzoTrGqoKTZqdcB++Qp6LSz7r747NAp5ao97ax3AL705J/A5OyawzbU1WH+B0EQ1JqKtVhbCo6S9quNOd0XSTdJOlLS85JOlnRenp6ppGnNaaia2cu4QEDNMbN3gY6UkDu8PcERqvO/mt+1JW1QerXVtiAIgnKqmaRTWou3HbA+S0/aOR64poZ2dUdCQi6p5KT1jb9M1z+PS8jdC5yDB+lhZnZiClhH42sex5rZ+IzfZ+EKPxfi0nKvAkOAO1Mb9JN0YKr7jFTvVmZ2WEsPKaTmujdF8weK51PR/GkP1UjN7YgPt34K9MEVTlbCg+UbHWFcdyIk5Jok5JII+T/M7ApJN+JLNr6ML+RfARiTAv8PgD+a2e2SHsjc57vA383sfEnDga/jgfttYFszO1fS66n+VYDNgAeAs5t5NiE1VycUzR8onk9F86c9VLPd1RnAN81sA1wKbJCZbYgvaP9Nh1hXf+RJyP0C7+mt0krZPAm50id9ixJyQCUSctOA68xsYUqvtYTcbkCftJvHSyzr72eSSn9vDwO7S1or3eedZN/FNA0Tm1zr7jNgLzyw/1bS6hXaFQRB0C6qGWI1M5uVjpVJfFDSibU1q3sSEnIuIZdYPf32vCMuIfd/qewBwOZ47/UC4GhJ/VO7XZH8Xxe4FVf+OQx4XNJhwKp4bxjgPXwI93+AfYA/48PCn1TyrIIgCNpLNQGyl6QVzOwzXFh6SPpNbl1gRAfZ160ICbmleMPMrmHp3553T+9ZMYAflZXL+l9qq1NybPn3zOlD6f2q8nxBEAQdRTUB8q/ArCQecAswO/WWNgf+2AG2Bd2U1JvtJ+lfzezprrYnS0jNBUFQK6oJkEfiE3LeA87DdVi/iPcWTq+5ZUG3JYmQD2s1YxAEQR1TcYA0sw/wWZMlzqaZWYVB0FWE1FwQBLWimlmsSBoi6RxJl6Tz/0hr7oIgCIKgUFQcICXtBPwFX6A+JiUvAH4jadcOsK1TkDSuSLuRSDotiRdUU6avpBl5ykDp+vaSHqmBeUEQBHVDNT3IU4DRZrYt8BaAmd0MfAk4rgNsqwpJoyW9LulAScdJmlxJufR7WiX1j5c0viytt6Spkn4rae2Utq6kdyStk84rkqjL1DlQ0rRKbGqGivzJ0ppMnZk9SNP6y1aRtL6kw1u43pg5Hi9pSKV1B0EQdBbVroN8tHScSXxXUo/amlU9ZjZL0gdJfUXA00mN5l9we18xs5sk7Q1sjKvbmJn9AiAtQL8RuAh4BF+f9wywLf4FYEzK96aZ3Zbu+XG6x/pm9k4yZX9gSio/iXyJuiXtJWkvXLGmB95D/xy+ZnIC8CuWlW8ryc1dAnwN+D6uYHMpLunWn7RWMMm5PYVL3k0EdgJ+nl4T8AX4P8Hl7LaibMlK+kIwHF/juGpK+xJLS9fdjm+mvXu6977J7j2BCyUdgYsgDAauTs+iJCN3b6ZdXwTOSm2wVToeU+5rnsi5Mhqs/QcNLr8cBEHQJqoJkH0krZHW7S0hLX5fq7ZmtZke6UO9Ly5z9ndck/RN4GDgJuBEM9sSQNLQVK43vsbuu2b2WpKQE94+C/AgUZJcKy1kX4YkLbcqPnnpz5J+lidRV1ZsXWAAHjyeTbaPNbOpklakTL4tyc0dY2YXSlqIryt8D1ejuVDSbsDWqe5XcJWdIbiO6e2S5uNartPwBf7TU3rerNQfmdnI5Nv3UtoZLC1d9wRwB9ALD5DHmNlbkk5K+Z+n6QvAv5vZmSUZuVTvzJRvF+BTM7ta0lvAD8zs+Bxf5zbX/hBarN2dovkDxfOpaP60h2oC5K/wtY9XAuumHs5wvMdwRkcY1wYWl4S3ASTdgotlvwiMz8lfUgRaDfg13ss6DO/lPGRmN0sagQfYkV6leiZ5tzy+g/dM9wJewPVXL2vF5hvwoHoEPnR9bbJ9BZrk286WNJYm+baSLN1C8mXmkLQVsIOZ7SPfXaRUdr6ZGbDAO9otkpehJF33nqRXzWyhpIvwdn7OzN4qy3+2mW0m6cv4siBYWo4v735ZmbtWfQ0t1vqhaP5A8Xwqmj/tocUAKak0GedJM5si6QPgJHyI8grgZWCimU3tWDNbR9JoYE1Je5nZDSn5d3jgeQYf1hsGTJZ0LP5h/GQanuuBi4xPlHQqLpF2ilwGbgAuefYM3gsdju9egqTe+DDgmqmtdgcOMLO3JX0CnCbpIbwHmJWoy7Iv8D7wD3w49C28F3kuvt50Kfk2SXOTL0Npmix1CLCnpP8CNsWfz1V4r/9gYAdgsVy2rp+kXczsj/jQ5aQUQEfgveXGjG0/l/Qz/DmvKWl7yqTrgLvM7FlJG+I7fCwlJADcL+lovLc5ILXpYynt7YwPhwI7S9of2BI4O/1Wu5Svkq5OAT4IgqBDUUufNfJdJG4Dzs0qpkhaDcDMKp64EQSdwYBNhtgKey+r9lev6yCL9m2+aP5A8Xwqmj+SHjWzrVvPuSytDbE+a2YT0k1eYunJOZu05YZB0JGE1FwQBLWitWUe2YA4KAXFlyM4BkEQBEWnmkk6JZYZk5XUz8xer4E9QdAuyqXm6nVoNQiCrqcqqbkWuLZG9QRBEARBt6C1HuQgST8uSxuYl1Y7k4IgCIKg62ktQK4PfDcnvTxtvdqYUz+kpQwnmVlDV9tSKWmZxoVtndHVWSSVndFmNr6rbQmCYPmltSHWB9PknBZfNO34XneoTjVcJU2UdGl6PSRpO0kNkq5NaSekfA1K4uXVaqp2Blpal/WncvWCqvRkJW1QetXaviAIll9a60Hm9R7bk6/bUa8arsC1ZjY32XyBmT2UAujTuNjAKynfGGBURkBhlSQb9xVgspk9XKpQ0mm4TN3NwI7AiakNyn06D1gHmI3Ly92TbRtgKq6h+iSu23o6rv36VeAWXFZuL1xztaTL+idgLC5jtwRJh+HasmsDL5jZb3Me4xLKpebqXTKraLJfRfMHiudT0fxpDy0GyDxh6Pbk68bUnYZrps2/AcxIxw8D9yf5tz8ktZuZwAoZdaGeuLD5C8DOqUyJO4GVzewySW/iCj0r5fg0HfhG0lVdBR9pyLbNvngAXQnXid0qldkmKTKtDYw0s5laWpf1ubJ2WRUPzmcB7+ABepkA2ZLUXL1KzJUo2qLtovkDxfOpaP60h7Ys8ygi9ajhWmKcmf1XOt7MzEr7Nr6P97pKuqc9zGwxSYtVLv6dq21a5mOeT2sCHwKYWbmmq1KZp8xsmqQBwMrpWp6uqqVecHPCsAtKzybpuQZBEHQKy32ArGMNVyR9FZcCLDE8pb0FvGZmT0jqjwfvKZKuSf6MomnotXyHlsGSDkrXT8B37Sj3qSGVHZh6sku1DS5sPyX1ygfiW3btiW/jNRQYle7ViPcwJ+PbZg0FxqVrQ/EAP1XSMfgw61/L2yAIgqCjaFGLNVi+SL9hNpjZpK61pO2Ua7HWu1BA0Ya7iuYPFM+novnTkVqswfLFOGCkpL5m9m5XG9MWQos1CIJaEQEyWIKZndR6riAIguWDCJBBoQgt1iAIakWttFiDIAiCoFBEgFxOkDQuq1oTBEEQtEwEyDqjK6TxWsg3pJI6O5qQmguCoCOI3yDrjK6QxkvnU3AZux2B72fybQSMxjVe/wtXINqfZuThJF2Kq+w8gq+RfATYFTjTzGZLOgt4ChdYmAj8P+BUXBrvLFyC74Xm2iek5ro3RfMHiudT0fxpDxEg65OukMYbDszBg9TCUj5gFnAvMA3Yhybt1ubk4aYDu5nZGZJmAUcBL+H6rLNxDdkVgSHAVmZ2XRJwPz/Z/XZ5Y4TUXP1QNH+geD4VzZ/2EAGyPukKabwJeC91Ki6FB4CZLZJ0FHA/8CgehFuThyvtKLLIzOZLWgSsKGkrYAcz20fSQGCVlO95YDdcGH2ZABkEQdARxG+QdUZWGi+TXJLGO5QyaTxJx+HycQ00SeNtm5HG+4qk/YE9gH/gw62jgdPKbn06LjX3OK6pOgYYI2k7vNf6KfATXJh8qqRjJB1O0oLN0IDL1I1Mto5O9xuFB/g+kg4GdgAaknTenvjOItdJ2rYt7RYEQVAt0YOsM8xsFq7Pmk27PHN6UXqfk1N8WHrfJpN2aFmeh1l6h4/SPQ5Ih1en9wk59ZZozLl3qZ5JOeVmZdJKCxd/mUkrDfcOb67eIAiCWhMBMigUITUXBEGtiCHWIAiCIMghepBBoQipuSAIakX0IIMgCIIghwiQyxEhNxcEQVA5ESDrkKLLzUn6aVIJCoIg6DLiN8g6pMhyc8BbwFjgSknbAycDlwBfA75vZnPL2yOrwdp/0ODqGzQIgiCHCJD1SyHl5szsdEnPpftPl3SMmV0oaSHwBWBuS40SWqzdm6L5A8XzqWj+tIcIkPVLIeXmcvLNS+8Lm7keWqx1RNH8geL5VDR/2kP8BlmHFFluTtLGwFBgXLK3X+r9lu4Vv00GQdApRA+yDlkO5Oa+kHM9e68gCIIOJwJkUChCai4IgloRQ6xBEARBkEMEyCAIgiDIIQJkEARBEOQQATIIgiAIcogAGQRBEAQ5xCzWoDAkybl3Jb3U1bbUiJIwwqddakXtKJo/UDyfiujPoLYWjgAZFI0RWWWdeqakMRv+dF+K5lNR/WkrMcQaBEEQBDlEgAyCIAiCHGRWLpMZBEEQBEH0IIMgCIIghwiQQRAEQZBDBMggCIIgyCECZBAEHYqkMyVd2tV2BMVH0nhJr9SqvlgHGRQCSSsCPwOeBLYEjjGzulrsLOmr+MbRLwHbAYcBpwDPA1sAk8zs/S4zsA1IGg5sBMxP56dTx/4ASDoSeBMYAtwE/Cd16pOkzYETgTuBUfj/0A+pw/+j9BlwL/BxJm2Zv7dq/gajBxkUhW8Cr5nZNOA1YI8utaZt/AU4xcyuAPoAWwFrmtlVwP3AQV1oW1s5AJgKIGlb6tyf9CXm87hCy8fAWtS3T3MBAavj/zMTqNP/IzP71MxeKJ3n/b1V+zcYATIoCpsAb6Tj19N5XWFmfzOzRZI2xb/hDqKOfZK0F3AL8FlKqvtnBIwAFprZNcA/8B5/Pfu0G/CEmZ0PfAf4J/XtT5a8v7eq/gYjQAZF4QVg/XTcD3ixC21pM5J2AL4ETMaHWuvZp03xYchdgWHAxtS3P+C9qvfT8TvAF6lvn9bDAz3481pMffuTJe8zoarPiQiQQVH4H2ADSeOBDdJ5XZGG736JD+Fdgf8DfyBpAvAF4PIuNK9qzOx0YAbQAx/G+1/q2J/EDcDq6e9sHHAg9e3TtcB2kr4LfAV/XnX7fyTpP4A1JU0ws9mUPZu8tBbrCyWdIAiCIFiW6EEGQRAEQQ4RIIMgCIIghwiQQRAEQZBDBMggCIIgyCECZBAEQRDkEAEyCLoYSYMkNUpaIGluOs6+3pDUUKN77SFpj1rU1Q4beiW/3k++3dKV9rSGpIGSJnW1HZWS2rb0d7RrSusl6VpJ90u6W9LAsjKnSppclrZ+quNxScvlcodY5hEE3QRJc4FpZjapLH1aSm+swT2mAZjZ+PbW1V4kNQJzu4MtLZG+nNxtZupiUyoitWtj9u9I0jHAdma2Z9KS/bKZ7Z6ubQzcBWxpZvNz6mugjvyvJdGDDILuz7m4eHQQtJXRuFADwB9xBaASZwGn5gXH5Z0IkEHQjZE018z+YmbvpvMfpSGveyTNlDQ2k7ePpKskPZyu3yvpC5nr5+Kyb7tmhm83TO9WGsaVdERpiC5T9rY0JPozSRen+j/LlNlH0mPJpvsl7d0GX8/IDA0em4YCn5P0NUlbSPpvSXMkXZApc4SkZ1O5oyTdKeklSddJWi2Tr2eq/ylJs5Od21Tg387Az1OeUpt9M50fKumhZOdsSRMlKV0blmnXgyTdIOkJSbdL6lvm9z6S/izpvtSGF0paJ3O92WdeBYto2r2pZzpH0heBgcCv2lBn8TGzeMUrXt3ghe+sMKk8LXN8APAc8Ll0vjWwANg0nQ8HHgRWTOdfxPVC+2TqmIYP15bf24CGzPkkfJgum6cReBXYOJ1fkO4xDngXGJDSNwY+AMa24m9juS3pvvOAMen8IFxc+ph0vhbwEfClTJnx+Ad+KU9v4FHg0kyeycATwOrpfH9cU3WdCvxr8I/KZey/H9giHa+a6t8vp11n4EGpBzAb+Enm+i74VmDD03kfXB+0oZJn3kK7lv8dHQLcjXeKfgJMT8ePAtu38pxy/V8eXtGDDILuxfhMT6Wx7NrJwJVm9h6AmT2Cb5F1aLr+IvBvlvbvM7N7gU/xvSVrxV1m9nKq/wfpHicB/21mr6T0l/FhvMPbeI83zWxmOr4PF9R+INX9D+Cv+FZgWQz4RcrzMXAZ8F1Jq0nqhe9xeLGZzUv5r8G3qzqsAv+a49tm9kTK+xHwB+CrOfluMLNFZrYY369wy8y1icCtZvZsqud9/DmXdpxo7ZlXyhV4G87ERcl/gG9t9VdgtqTTJc2S9BtJa1dZd2GJDZODoHsxzZaeXDE3va+O98z2U5qZmFgtvcB7UfuoaZbqZ8DnaNq9oBa8mpM2AhhQFtDXaiZvJbyeOf44J+0jYM2yMm+a2YLM+QvASsBgvB1WwbcQA7w7JOnFZHuWamzuL+kXwNr4F5GB+A4s5byWOZ4HrJE53xwPWksws2uh4mdeESk4n1g6l7QGcAIwBhdc3yEd/wTvOf9nNfUXlQiQQdCNMbOBZUnnmVlzOxAchffmtjWzObAkwLZl9mGPZtIXN5P+GzOb2Ib7VHqP8rSOmlHZnH9L39xnfv4Jn9xyZkqbhA9HtlSnUb3tLT3ztvJj4Coz+7ukXYCbzewzSTfQNJlnuSeGWIOgDkhDgy/j+youQdI3Je2TTscAj5eCY2KlsqpKmxcjaRVJK6bTefiu8iU2rMK8v+TYNVq+nKCzWFfSypnzwcBCvCf5PP673ZCMfcI3y/1LBXVn22wFSavivwX2Aq7P5Ctv60p4KmtXusfukkZV+MyrRtJQ4BvAOSmpJ01BPDuZZ7knAmQQ1A8/BfZVWuSdZkP+lKYP+b8Cm0taL13fnmWHV98CSrMof45PEgF4HNgxlVsP2KlKu74uaVQq3wufFDOnxVK1ZTHwvXT/3viklKvMbL6ZfQKcB3wvM7P1O/hknosqqPutVG9fYFt8otOzeG/wy+naKvgM4Wo5Hdhd0vBUz7rAFHxyFbT+zNvCFGBiZkh6Fk3Pe2w6DyBmscYrXl39AgbhMw8X4DNZG4ENm8l7JB4IZwH3AF/PXFsD+A3e6/gd/kH4Ov5hPj7l2RSfbTkTuBVYKaVvjX/o3odv2nw2Psvzd+n6Del8LvDHHLv+A1+r+UCybXwL/vZKPr6PT0a5JaVPTPW/j0+i2QyflWvpfbOUXrJjYio3Pp0fCNyRjq8DVsvcsydwBt5jm5383yZzvTX/rsa/RDwI7JDSDsF/c7w3lb8x1TEd74E3JtsfxwPPERn/pmfq/k7KMyvVtXOlz7yZ9m2kbBZr5touwD05z+M6fFbuPcAmZdcbWE5nsYaSThAEdY2k8XhAGNjFpnQLlKOk0876GgglnSAIgqAAvAHsoYwWa1tQ0mIFzsRHJZY7ogcZBEHdIukI/LfHgfjw524WkmlBjYgAGQRBEAQ5xBBrEARBEOQQATIIgiAIcogAGQRBEAQ5RIAMgiAIghwiQAZBEARBDv8fESvrVBL5X0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(f_rtp_ml, f_udp_ml, 'framesReceivedPerSecond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9603e761-b1ea-497e-aabb-d04049f6f9cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n",
      "teams\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(f_rtp_ml, f_udp_ml, 'bitrate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2151ae1d-aeee-496b-8141-af7c83216bc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n",
      "teams\n"
     ]
    },
    {
     "data": {
      "image/png": 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t3Eb0XBchFun3kJaz/WBDFk3SmsAniMD7T0ICbR3gxiLT1kOlpvg6tUm7D1ey/ZVQmBlL/Aj4FnBpUb55wPYVkj4IXGv7l5L+i1i4/yChmfoqsKOku4EdbG9cVGpWK/WPBL4HfNb2SxXbZvhVfG1I490kaRrw02LbVrb3b8xqJnRW69fs700+v4eKDSOA/yfpcmCU7X1LkB9TDYx1OqmtOhT0JbtR57LbfEp/upd2guO1tvcZKENq3EqoxgAzelPzA0+3ULYqe9YXzWTJHgNeItRZHuij3itt36HQAq3bNCNfCTgGnipSavMBHyaGplulpzab9F5gI9tfKL27hYuQ+YOSdiCEx3ur44dEcF6ZCFhV5mPmNVuYEFP/BnB8LV9Djm6W747t6ZLeYObOH61eswYn2F6rfB4f6iVPvc0B0VYdSM3UVunGBdnd5lP60720cye5T9I7bP+tfkLSb2xv1SmjbE+RNFbSXsSQ30hg99IzWpHoia0paRPb11WKXgF8VNKXgfWAEyRtQQwFfsT2VaX+F8tEmBmyZJI2Ag4ghh+vkfRqqXM9SbsTkmd7ADcBeyj2RRxZ1nrW5dP+xExZtLOAO8sw7mKE3Nt5wCklWG4MvCzpvPIMkl7aPYgYRj0bWFrSrsSQ65uEDNvpRM/tXZKWKeWWkbQ6MSQ5hdgR4z+B14kfAEsCFxTbliee5a5L9O6uBS5V7FhyWsWu2yXtT/SCG8OqN5ZrvAmwCSEtt25J2ww4qn7NGpVVPp93AzeUukcTQhOTgCck/Qe5vVqSJHORluXjFDtO7ABcRTw/e7NyeoLt1ZoWnEPKhI+9iC2abh6INvpoewJAVZR8qLZbetf72q739IY1kt5Znj2uD3zJ9r695e2kfNxQWOfYjb/iu82n9Gfoo4GWjyN6VHcCY8qrytLtNtwqZeumvQaq/t4owabRM6r36oZUu6VntTjRe+w29pd0F9HT/X5fGVM+LkmSTtFOcLzR9sebnVCsd+wqSlDaaTi0a3vSAJkz6NjedbBtSJJk3qPl4NhbYCzntu+MOUnSf+ZEPm4oDKMmSTJ06MgkhzLlPkmSJEm6gnZEAP6vj9PrzbkpSZIkSTI0aKfnOAa4pvK6DvgLMe2+GyeCDAqSVpV0fVnD2Om6+625Opt6V5F0uqRTG4o1Ss3UJEmGMe1MyDnJ9sn1REnLEwot8zySLiak4L4OnAMsA1xme0ot32Rgou1p9TpsP1IWyncc289I6q/mal9sCtxg++xKW1dKmu3+kHO6XCbl45IkGQjamZAzS2As6U+UBdwJHGD7IUnbEr3pV4D3KHbT+CfRU/8ZsSzhs5J+Syy676F3Wq9U0gqEdulNhNqMgIMJqbuphKTfL2yvIelzhPrNj4igdQmwIfBH2z8oVdY1Vz9EaLw+Q0i7nV5pb1Hg9oqa0LuBHQkpuncQQgebl3P32r6lYvoKRZBhQ+AiYA3gs8CngP0IqbmNS9knCE3aPYE/Eko+BwK7EEIQKwM/s31rbxd/TuTjhqJkVjdKeXWbT+lP99Ku8HidJQitziU7ZtEwpqzJrB4/V8QTNi+bQl9PKOY8CFxoe5qkRajonRJi5PV6/1GGKG+z/XNJ1xS1oIa26YOSHi/vL5C0v+2TJS0HPA/sSyjzNIJjXXP1aCLQvkkMkz9DKOrcbvtySQtVzDkcOKJoz15IaKE27KgGRoAXis7tZcDltjeV9FlCou8l22dKerOU/XW5ViK+l68AaxIB+FUiWP+pybXpiHzcUJCLq9ONC7K7zaf0p3tp504ypUnaC4QwwFxfDziMaeitNvRJ+9I7rfNC+duQNXqdkHqD6OE1aGibvgFMt/2GagLnFVsafy+2/aykR4v4OcROJdh+tZdyrckr9dSH/RXwJSIAN+pQuRYGbrZ9iWKLsieAE4mt0Q4BfklI7yVJkgwo7QTHa+bGdlXDnSJzNgrYmtBBPRg4qAwbXlDEue8EvkwEiB56p5JWJYZdtwAau18sSWilviXpX4QW6b8RvcxvS3oBWLLolL5Rzq9byrxRxMBHKXbqaKa5egBwqKT7gPkl3VLyLSbpxlpwPBrYSdIDwP3EbiEHFDuvsv1oeb8F8PaiAbs+sZ0VwGTiB1VjKP6PwK5EL/F7wBHF31WIIdsjSht/I7YWS5IkGXDaCY6fryeUX/sfBX5nuzN7BQ1zbN9OPFtrHE+h1uu23ZsAaGNi08a1/P8intM1WKPy/nPl74VNzjcrU01rcG15VWmWD9v3UoJhhVlGDhybGM+it2v7SUk/tP1sOf498PtKlt1rRf6zmR1JkiQDSTvB8VzKxIsKI4ib6O7Av3fKqKQ7kbQzscPKgIhGpLZqkiSdop3gqHpCGW7bI9ezJa1g+8zBtiFJkqQV+gyOkv6dmT3CNSSd1STbMsBSnTYsSdqlHW3V1FJNkqQvWlHIUR8vExMqUng8SZIk6Rr67DnavoyYTYmk79ref65Y1WEknURI3b1l+9TBtqdB2bvxNGDq7BRiJB0NvGF7Ygv1LkPMCp1UV+dpoewuwCa2J7RTrr+UWa2H2R4/N9pLkiRphZa1VfsKjJJ27Iw5PeqcIGlfSbtImljWvSHp45J2LOkXzq6ewntsnzw3A2Mrz2HL3o1TW6zyylbbtv0M0F+ZuD7bqfpVPqOx/WwHmDGrtd9IWrHxmpN6kiRJqrQlJ1LWn30AWIGeE3QOAn7cKaMkfRhYuxGQSw/ramBj278q0me7UruRS5oP+A5wD/De8n4tYCVJn7d9XiXvnsDLxOzJPxML3k8n1hd+l1hjtzuwELHMYSNC2mxV4DPANGBtQuLsMyX9VWKo+S5iXeEuxKL1fYBHgbHF5ruBk4l1ih+gEiArvcl6u1U/vwPcS0jPHUqsIVwd2I5Q4Dm/ZK3LxH2RmlSdpK8T8mxvAx4Abi1tjAZOIXqfV5e0cRW/riXk6ZA0klCwOZJY2nN1aWOc7Z2K5FyPa2b7LWpI2psQOlgdOMf2n0qv/2/Emswtgc/bfqReFtqTjxsOElndKOXVbT6lP91LO/JxHySGWF8HlibUSxYkAuU/OmzXBsTNH4gelqQ3JI20/ZTteyXtC9wo6ceVRepbAq8XybIngb1sHyTp8VpgXIwION8BngLeb/vAcnM+E9i1yKOtBSxm+zRJCxPPVrcEbiN+HCxCLPg/xPZ6pe5xDTk322dIWhP4BBF4/0mo4awD3Gj7R3XlmuLr1CbtPlzJ9ldgASLYvpdYH3lpkZR7wPYV5fOqy8Q9SEWqTtLdwA62N1ZI4jTWJY4kFuR/1vZLFdtm+FV8bcjG3SRpGvDTYttWtveXdHUpeniTa/b3WT92Hio2jAD+n2Kf0FG29y1Bfkw9MPZXPm4oysXV6UYpr27zKf3pXtrpOX4b+LTt6yRd3VDLkbQh0SvoJLcC2zQOSm9qfuBpSdvavtT2m0X5ZVHiZj8je/k7O2mzVypi2h8paY8Rup/jiF5UlWq9V9q+Q7F7xtO95SsBx8BTtieXnu2HgXfNxrZm9VFsfS+wke0vlN7dwrZfkvSgpB2A+/qooy+puvmYec0WJgTSvwEcX8tXlb7reSLUf95gpnxdq9eswQm21yqfx4d6yZMkSTLgtBMcbfu68l6VxJskHdJJo2xPkTRW0l7EkN9IYPfSM9pY0spE7+KKhtJK4Qrgo4qdINYDTigTPkZJ+ojtq0r9L0o6S9I3iaHVP0jaiFB+2Q24RlIj4K4naXdCAm0PYqeKPSTdCoy0fb2kYyUdUK7L3YRA9rPE0OxZwJ1lGHcxQgD8POCUEiw3Bl6WdF55Bkkv7R5EDKOeDSxdZNk2IsTCpxA9058C7yoTcprJxPWQqiME4y8oti0PXFraWIQYNr1U0vO2T6vYdbuk/YlecGNY9cZyjTcBNgHWV8jXjZK0GXBU/Zo1Kqt8Pu8Gbih1jybk4yYBT0j6D9rbezRJkmSOkN2adrSkm4key1uSrgF2dmzPtBwhFj1mQAyMCR97Aefavnkg2uij7QnQ/70G52a7pXe9r+16T29YI+md5dnj+sCXbO/bW95VVhvr+bbvTZmvJ8NhnWM3DnF1m0/pz9BH0m2239duuXZ6jn8ArlMIA1wG3KIQ0F6b6LENCI5toPYaqPp7owSbRs+o3qsbUu2WntXiRO+x29hf0l1ET/f7fWVM+bgkSTpFO8FxH2LyzbPAScSw5oeICSzHdNyyQaYEpbm+FVd/2rU9aYDMGXRs7zrYNiRJMu/RcnC0/TyxcW6DE8orSYYErcjHDYfh1CRJBp+2JjmUSTLflfTDcrxDmWKfJEmSJF1Dy8GxzDq8h3getmlJfgU4X9JWA2Bb1yNpwTJrdkILeY+WNLHFepeRdLmk8f2waRdJk9st118kbaHc1SVJkiFGOz3HIwjNzfcDTwLYvoRYt3fgANg2C5I2k/SEpM9V0taT9KKkT0haQNKQ2By3lRt+ysfNuXxckiTJQNDuOsfbGu8ric9IGtFZs3o14GpJ5wP7SbrU9ivAx4FHixLMhsTmy0dKOg1YDriZUIX5dFWyTCkf1xXycapoqq48ZvV6lUmSJP2ineC4tKQlbf+rmihpVeJGOLd4DjiDCJD3EAvXd4QZggQNdZYLgG1sHy/pPCqSZUr5uK6Rj6vSirbqcNKN7Eady27zKf3pXtoJjj8h1jaeCSwnaSdgTeBLhLTc3OR0orf1su3L477elBfK39eIYFIl5eN6Mizl49rVVh0OmqoNunFBdrf5lP50L33eSSQ1Jt7cbXuSpOeBw4ghxDOAR4BDbZ81sGbOsGcTQhZtDLAD8FxJW0rSx4ilJqPK8Op4QsZsHBHsxhN7HKZ8XMrHJUmS9Emf8nGSHidu5ifavq+SvjhET2HALRxElPJxg446LB83nNY5duOv+G7zKf0Z+miA5OPut71TaeAv9JyIs1qvpboApXzcUCHl45IkmevMLjhWg+EYAFW2q+pmUj5uaJDycUmSDAbtTMhpMMs4rKRRth/vgD1J0m9mJx83nIZUkyQZXDo1yeHcDtWTJEmSJIPO7HqOY5oozoxultY5k5IkSZJkcJldcFwB+EqT9Hra8p0xJ+kUZYnEYbbHD7YtSZIkw43ZBcebWpl8U1FCSTpMWTt4EbFo/+3AUrYPmV0521dKOqyF+ieU/JMraYsCpwJLAbvZfkrScsSG1++y/U9JFxNCDF8HzgGWAS6zPaUtB+eQlI9LkmQgmN06x9G2p822khbzJf1D0v221yyKO/cB/0Go7Bj4q+2LJW1PRd/V9ikKkfBtCLWb7xO6qXsCfySUcg5k5vKIi2z/utLmBGAF28eV428SKkML2p4oaazth0obnyN2aBlh++mS/yBgK+DnhCDBbwnd2p8VVaO6tu3lhKbt7cAatg9RCMwfTij7fBz4Wv17Vg2OY1Zb/e/7Hnlir9dxnZWWmt2lHlJMnz6dxRdffLDN6Cjd5lP6M/TZbLPNOr/OsdWAl4FxwBlRgtUywF6ELul7gCeAXYGLqem7lnKLEoo6X7H9mKTjCBWf+YlgtiZF9LwaGOtIWoRQ9zkBuEPS8bYfquax/Vyt2AXA+2yfKOl/iKB3A3CgpKuoadsSwfFvxCSxTSWtYPsCSd+0faqk1wg1oWm1dluWjxtO0nHQnQuyu82n9Kd76c9SjmTu82Zt2PMyYH9CjHxCk/wNTdPFgf8hdu7Yk+hp3mz7EknrEMF13ahS89vuTbX7i0SPdDuil7cj8KMW7G4oKFX1Vhsat3Vt208CS9s+QdLmhL4r9K2PmyRJMiBkcBziVLRjt7N9UUn+BbA3MTw6StIaQA991zLcOILYieNQSUcS22QdIWlJQrv02FLHrkQv8qDS5qKESs9SRV93G2Dn8qzxZeBoSTcTvbxRwNZED7XKeGBc0VgdR2iuUt6/DeihbUvs2rGTpJ2Jba3GK3b6GFV6wg3VoHPc17OAJEmSDpDBcYhj+zoiAFXTqjJxjWeG9V1EANYofzeopO1ey/P78qrW/xI9VXqmVs5dCFzYpI263ZMpQu/EcGiD/yl/T21SbJvy98wm9c91taIkSeZdMjgmXUNqqyZJ0ilyG6AkSZIkqZE9x6RrSG3VJEk6RfYckyRJkqRGBsd+ImmLsgB+2CBpQ0m3DrYds0PSLpImD7YdSZLMu8zzwVHSJpIeLzfkAyUd20o521e2WP+EhkRbJW1RSWdJ+l9JI0vacpKekrRsOb5Y0j6SHpJ0uKSTJI2v1DFe0rmSTpN0cElbWtIkSTs1/Cj5Jhabb2LmesMhQfUHhqSjigpQS9e2lFmx8RoI+5IkmTeZ55852r5O0vO2z2jIs0maSgvybACSlqBvebbG+rwnGio0tl8qbaxg+6liypeBSaX8ROCAIs+2LXA6RZ6tZv59wJPAX8vxrsANtn8m6QeSNijtr19ZJ7mwpD2AjwHH2p6xjEPS0cD7gEuADwKHlGtQ9+kkYFngFmBl4JrqtQHOImTf7gY+ABxDLMXYGrgM2JIQFHgvsY5xF+B3wOb0XMZBXWbO9v/O8iEW5hfsu05vOgah/jGcmD59+rCzeXZ0m0/pT/cyzwfHwnCUZ/s9EQhfk/QrSfcBqxESbQCPl+OpwHwVAYH5gdMIpZuP0nON45XAQrZ/JOkJYDdgwSY+XQB8yvZxkhYmBOqr1+ZLRPBcEHiWCIIXABvYnlR6y+vanirpcdtnlLIP1q7LYswqM9cjOKZ83PCi23xKf7qXDI7BcJRnW8t24/nhc8wU8F6hpI0CfkMEcCSNsP0mMN22i1ZpX3JsDR+b+bQU8C8A269Eh7tHOQP32p4saRVgoXKumRScS4+9RyUV6jJzSZIkA848HxyHsTzbmpK2JoZVH7N9l6RHKu0/Z/sWSSsTgXuSpB8Xf9Zn5nDrkrb/Val3dUlfLecPBt5s4tP4UraxG0uPawP8pLQ3gdgI+7vAZwg5uXHELh0AU4ie5bFEIB9H7NxBeT+SWWXmkiRJBpw+t6xK5i3KhJ/xticOriX9Y5XVxnq+7b/X6/nhts6xG4e4us2n9GfoI6nzW1Yl8xxbAOtKWsb2M4NtTLukfFySJJ0ig2MyA9uHDbYNSZIkQ4EMjknX0Jd83HAbUk2SZHCZ50UAkiRJkqROBsc2GY6ycQ2Gi+2Sjm6o+iRJkgwG82xwHAzZuD7yjW2lzlq5SUU67jRJd0taqdTVSNu1bkerts8tJI1WRUO1KPRAG/JxSZIkA8E8+8xxMGTjyvEkQvbtg8DXKvneAWxCaJ9+g1Do+TK9S6edbHtaUdf5lu2/l8X4txEL7G8v+ep2LCvp68DHga+VdYpIWpBQzlkIuBbYqPi0KrFGcRqwNnAEcB7wT0JJ6C3gmfL3bcADhEpPM8m55YCbi++fLraNk7RTSd8O6DEpSNIhwKPAWOBK29fWzs/QVF15zOokSZJ0gnk2OBYGQzZuTSKAfIdQipla0q8jgtJk4AvM1DRtKp3WCGrAzszUI72YmSo0NxeBg7odr9g+tSjkbEwEPYoM3VRgMdunFVm47Qkd1NuKf4sQAfBSYEnbJ0taHPit7Y3Lj4zVyrVsJjm3je3jJZ1HiBtMBTa3fVa5vo9XL5SkNYFPENqy/yzXoEdwrNKXtupw1IvsRp3LbvMp/ele5vXgOBiycTsRvdOzCJk4AGy/IWk/otd1GxGA+5ROk7QQMNr2AyXpnQ1JOUmvE8HMNTuaSbg1oyofd6XtO4r26dMl/V9NysxX8vcmOVdv28XWRrk6Bp4qMnTzAR+eJUOL2qrDTVcVunNBdrf5lP50L/NscBwM2bjCMUSP6U4iWDSGPRv5TwC+BVzB7KXTJhA9zQbji19vAb+w/Xyl3jUl/ab4Na7S7jnuKZO0nqTdCYm3PYCbgD0U+0COJIJ3o+wvbD8l6QLF7hnLE73KZtdjPCE5N46QhhsPXEj02k8khqhHSdqinFu31HNnqXsxoFfx9iRJkk6S8nHJDCoTdyYPriX9oy/5uOG4zrEbf8V3m0/pz9An5eOSOaJMyGn0CM+z/dogm9Q2KR+XJEmnyOCYADEhh3gemiRJMs8zz65zTJIkSZLeyOCYJEmSJDUyOCZJkiRJjQyOSZIkSVIjJ+QkXUFZf/qMpL8Mti0doiHQ8PqgWtFZus2n9GfoswAwpj8FMzgm3cQ6VcWc4UxDM7Zb/IHu8yn9GfpUtZfbJYdVkyRJkqRGBsckSZIkqZHycUmSJElSI3uOSZIkSVIjg2OSJEmS1MjgmCRJkiQ1MjgmSTKgSDpO0mmDbUfS3UiaIOmvnaov1zkmwx5JCwDHA3cD6wHftD3sFjJL2hrYBPgL8AFgT+AI4CHgPcBE288NmoH9QNKawDuA6eX4GIa3P/sATwBjgYuBzzO8/VkbOAS4ktjg/HhgX4bZ/1K5B1wLvFRJm+W71s73L3uOSTfwaeCxsknzY8C2g2pN/7kHOML2GcDSwHuBpWyfDdwAfHUQbesvOwNnAUh6P8PYn/Lj5d8I1ZWXgLczjP0pTAMELEH83+zEMPxfsv267T83jpt919r9/mVwTLqB1YB/lPePl+Nhh+2/2X5D0juJX7djGMZ+SdoOuAx4qyQN989pHeA12z8GniZ6+cPZH4BPAnfZ/h7wReBVhr9P0Py71tb3L4Nj0g38GVihvB8FPDyItswRkjYCPgwcSwyvDme/3kkMP24FrAGsyvD25zHgufL+KeBDDG9/AJYnAj3E5/Umw98naH5PaOs+kcEx6QYuBVaUNAFYsRwPO8qw3X8TQ3dnEP/Az0vaCdgYOH0QzWsb28cAlwMjiKG7/2MY+wNcBCxRvmdbALswvP0BOBf4gKSvAB8jPq9h+b8kaQdgKUk72b6F2mfTLK3P+lIhJ0mSJEl6kj3HJEmSJKmRwTFJkiRJamRwTJIkSZIaGRyTJEmSpEYGxyRJkiSpkcExSQYRSWMkTZH0iqRp5X319Q9J4zvU1raStu1EXXNgwyLFr+eKb5cNpj2zQ9JoSRMH245WKde28T3aqqQtIulcSTdIulrS6FqZIyUdW0tbodRxp6R5cklDLuVIkiGApGnAZNsTa+mTS/qUDrQxGcD2hDmta06RNAWYNhRs6Yvyw+Rq2xpkU1qiXNcp1e+RpG8CH7D9maIN+xHb25RzqwJXAevZnt6kvvEMI/87SfYck2RocyIhAp0k/WUTQoAB4ApC2afBd4AjmwXGeZ0MjkkyRJE0zfY9tp8px/9RhrmukTRV0uaVvEtLOlvS78v5ayVtXDl/IiHjtlVlyHal8teNoVtJezeG5Splf12GQY+X9INS/1uVMl+QdHux6QZJ2/fD129XhgMPKMN/D0r6uKT3SPqppAcknVwps7ek+0u5/SRdKekvks6TtHgl3/yl/nsl3VLs3KAF/z4K/FfJ07hmny7Hu0u6udh5i6RDJamcW6NyXb8q6SJJd0n6jaRlan5/QdIdkq4v1/BUSctWzvf6mbfBG8zcgWn+coykDwGjgZ/0o87ux3a+8pWvQX4RuyNMrKdV3u8MPAi8rRy/D3gFeGc5XhO4CVigHH+I0P9culLHZGKItt62gfGV44nE0Fw1zxTgUWDVcnxyaWML4BlglZK+KvA8sPls/J1St6W0+wKwaTn+KiEU/c1y/HbgReDDlTITiJt9I8+iwG3AaZU8xwJ3AUuU4y8TGqnLtuDf+LhNzmL/DcB7yvvFSv07NrmulxMBaQRwC/Ctyvktia281izHSxN6n+Nb+cz7uK7179FuwNVEZ+hbwAXl/W3AhrP5nJr6Py+8sueYJEOHCZUeypTaucOBM20/C2D7VmKLq93L+YeBf3fZe8/2tcDrxL6QneIq24+U+vcqbRwG/NT2X0v6I8TQ3df72cYTtqeW99cTwtg3lrqfBv5AbOVVxcApJc9LwI+Ar0haXNIixP6EP7D9Qsn/Y2LLqT1b8K83Pmf7rpL3ReBXwNZN8l1k+w3bbxL7Da5XOXco8HPb95d6niM+58bOEbP7zFvlDOIaTiXExfcitqb6A3CLpGMkXSfpfEkj26y7a8nNjpNk6DDZPSdSTCt/lyB6ZDuqzEAsLF5eEL2nL2jmbNS3gLcxcxeCTvBok7R1gFVqwfztveRthccr719qkvYisFStzBO2X6kc/xlYEFiduA4LE1uAAdENkvRwsb1KOzavLOkUYCTxI2Q0sYtKnccq718Alqwcr00ErBnYPhda/sxbogTmQxrHkpYEDgY2JcTTNyrvv0X0mD/fTv3dSgbHJBmi2B5dSzrJdm87CexH9OLeb/sBmBFc+zPLcEQv6W/2kn6+7UP70U6rbdTTBmrmZG/+9Ww8Znj+jpjIclxJm0gMQfZVp2nf9r4+8/7yn8DZtv8uaUvgEttvSbqImRN35nlyWDVJhjhlOPARYk/EGUj6tKQvlMNNgTsbgbGwYK2qxqbDSFpY0gLl8AViJ/gGK7Vh3j1N7NpEsWRgbrGcpIUqx6sDrxE9yIeI53RjK/aJ2Oj2nhbqrl6z+SQtRjz7WwS4sJKvfq1b4d6qXaWNbSSt3+Jn3jaSxgGfAr5bkuZnZgCvTtyZ58ngmCTDg6OAL6ks4C6zHo9i5g3+D8DakpYv5zdk1iHVJ4HGbMn/IiaEANwJfLCUWx7YrE27PiFp/VJ+EWICzAN9luosbwJ7lPYXJSagnG17uu2XgZOAPSozWL9ITNz5fgt1P1nqXQZ4PzGp6X6iF/iRcm5hYiZwuxwDbCNpzVLPcsAkYiIVzP4z7w+TgEMrw9DXMfPz3rwcJ5CzVfOVr8F8AWOIGYavEDNWpwAr9ZJ3HyIIXgdcA3yicm5J4Hyit/EL4ib4OHEjn1DyvJOYVTkV+DmwYEl/H3HDvZ7YbPkEYjbnL8r5i8rxNOCKJnbtQKzFvLHYNqEPfxcpPj5HTDy5rKQfWup/jpgwsxYx+9bl71olvWHHoaXchHK8C/Db8v48YPFKm/MD3yZ6arcU/zeonJ+df+cQPyBuAjYqabsRzxivLeV/Vuq4gOh5Tym230kEnb0r/l1QqfuLJc91pa6PtvqZ93J9p1CbrVo5tyVwTZPP4zxi9u01wGq18+OZR2erpkJOkiTDFsWO9RM96/PZeRI1UciZw/rGkwo5SZIkyTDnH8C2qmir9gcVbVXgOGI0Yp4je45JkgxLJO1NPGscTQx5ftIpg5Z0iAyOSZIkSVIjh1WTJEmSpEYGxyRJkiSpkcExSZIkSWpkcEySJEmSGhkckyRJkqTG/we7mW5T2cDSFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(f_rtp_ml, f_udp_ml, 'frame_jitter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2b03ed6a-91f6-4ed6-87a7-6e747ab51073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n",
      "teams\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(f_rtp_ml, f_udp_ml, 'frameHeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d20b7f-8325-450a-a3b5-df7410218c38",
   "metadata": {},
   "source": [
    "### Ground Truth Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b348cda9-d7d4-4f0d-a5e4-b81452981eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "df['bitrate_kbps'] = df['bitrate']/1000\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'meet'], x = 'bitrate_kbps', color = '#023eff', ax=ax, label='Meet')\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'teams'], x = 'bitrate_kbps', color='#f14cc1', ax=ax, label='Teams')\n",
    "# sns.ecdfplot(data=df[df['VCA'] == 'webex'], x = 'bitrate_kbps', color='#9f4800', ax=ax, label='Webex')\n",
    "plt.xlabel('Ground Truth Bitrate [kbps]')\n",
    "plt.grid(visible=1)\n",
    "plt.xlim([0, 3000])\n",
    "#add legend to plot\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=2, frameon=False)\n",
    "plt.savefig('paper_plots/gt_bitrate_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0f13e80a-f7cb-4856-94f3-c1c57f50a6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'meet'], x = 'framesReceivedPerSecond', color = '#023eff', ax=ax, label='Meet')\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'teams'], x = 'framesReceivedPerSecond', color='#f14cc1', ax=ax, label='Teams')\n",
    "# sns.ecdfplot(data=df[df['VCA'] == 'webex'], x = 'framesReceivedPerSecond', color='#9f4800', ax=ax, label='Webex')\n",
    "plt.legend(labels=['Meet', 'teams'])\n",
    "plt.xlabel('Ground Truth frame rate [frames/sec]')\n",
    "plt.grid(visible=1)\n",
    "plt.xlim([0, 35])\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=2, frameon=False, labels=['Meet', 'Teams'])\n",
    "plt.savefig('paper_plots/gt_fps_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "445020b4-ee65-4b5f-8225-c055b9c430fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nT3z46C7dLvaGVJyVR5ftofGrb0Kz80oqg0oovWgcVaePpKaifwxsLjRn8lB4eSq0/PSFlF9nEakGBnGg076fAid2c+pKYLzfH4Wr/nYwUFU7VtW8FXgsVbsyzcvvOqdE6RbAnhLb1UbLPSuJPLsVcKu1lH10POHjaqzTwzD6QI9faRE5HbgPODQxCtAeXOIx4DYRuQwYDfxORO5T1SuBu0XkBqAc+F1PbcoWTS3OMfhnzs/+vTWqtNyzgvYn/HCXkeUM+PosQmNtgLNhpINUyjV3A3cAtcAe9gufcKBD9aT4TpBrE4Kv9HH3pGBH1lm0Alrb4OQ52b1v2xP1tPx4BQBSU0r5Z6c6R93hPJ6wbBj9jFREcJOq3p0sQkQ+kSZ7+iWLlrvt3CwtkRd5bTvNP/IrulSGKbtgLKX/fKj5uTWMDJCKCL4oIlNVdXmSuMuBG9JkU7/j74ud0/JDD8nsfTSmtPxgKe21WwAIv3c4FVdNRQbYcE7DyBSpvF2jgBdE5HWgHojGxZ1FAYvgq0ud17hMjjuO1jXS+LlXAbdoacX1Mwkdau1+hpFpUhHBM4A/xB0XRd1sxx43SPpfzszcPYa+DY13OAEsu3Q8pR8ZZzM9DCNLpCKCT6jq5ckiROSuNNnT73jdV/6PnpH+a8d2ttF045tMWBfAgBADv3UEoWlV6b+RYRid0mMR7EwAfdzn02NO/2OJm43G7Eldp0uV6LLdNH5xEQC7xymjbznG/O0aRg5IaayFiMwQkftF5GX/uV9EpmfKuP7A8vUwsAJGDk3fNdv/0bBPAMuvmsqKD6sJoGHkiB6LoIicAbwBzASW+M8M4A0/kLogWbEBpo5JX6dI68NraP7W2wAMvOcYSs8clZ4LG4bRK1JpE/wmcIaqPhMfKCInA7cDf0unYf2F5etgVpqqwq0Pr6H1oTWEDq+m4gvTCEZVpOfChmH0mlSqw5oogD5wIT2bNpd3NLfCynqYNbHv12p7ot4J4MxBDPjWbBNAw+gnpCKCA0XkoOHCIjICtwRWwVG3EWIxmJo4WzpF2v++jZafrCCYVsWAr81CSmzam2H0F1KpDi8AXhWRX+BWhAGYAvwL3az/l6+s2ey24/owUyTy+g6ab30HqQoz4CuHmY8Pw+hnpDJE5vsishe4ERjng9cC31LV+zJhXK6p2+S2E3vZdxFduZemr70FlWEGfPtIgmFl6TPOMIy0kFKxRFXvBe4VkUp/nN4llvsZazdDONQ7nyKxjc00Xv0alAVU/mAuwUhrAzSM/kivGqdUdW+8AIrIvekzqf+wfD1MGg2hFBds1qYIjTe8AWUBFddMNwE0jH5MlyVBv9T9dlVdKCL3d5H0rLRa1U9Ysgamj+s+XSLNdy5Dt7cx4D9mEz4yD7y0G0YR011J8N+Bf/P7Z+MWTUj2KTiiUVcSnJGiCDb/eDmRF7ZRcvoIE0DDyAO6LAmq6ry4w/9T1U8mSyciv0qrVf2A9VuhPQKTx/T8nMiS3bT/eSPho4dS/qkpmTPOMIy0kUrHyE2JASJSClwBfK67k7vxOzwXOAm3bP8hqnp7kvOz6nd4bcfwmBE9Sx9raKXpukUwMEzFl2Yg5f3D85thGF2Tigg+APxTQpgC1Tj/wad1c35Xfoe/C/wCqAGe786QbPgd7vAzvLHuH9Q2NXWbftxfhJoAVp7Rxu6Xn0vpXoXmA7bQ8gOFl6dCy09f6NPIXV+S+46InNuD5F35HT4R+KSPf1VEjlHV+JWrs+53+FU/RvDCc46lurLrtLqnnT0/fJHwicM46vKZKd+r0HzAFlp+oPDyVGj56Qvd9Q5/AuhwojRHRJ5Kkmwo0NiDe3Xld7ge2KmqKiLtQCnQ3INrZoy1m6FqAN0KIEDb/9ZDRCm7cGzmDTMMI610VxKsAzoWTZgYt99BDNgC/HcP7vUYnfsdvga4UUTWAb9W1ZwKIMCy9TClB50i0VV7aX1oDcGUSoKpPVBMwzD6Fd31Dj+DFz4R2auqd/T2Rt34Hf4DB/ovyTnrNvdsjGDrQ2ugNHCdIeYXxDDyjlRmjHxXRF7JmCX9jE3bu58uF12xh8hLDZSeP4bQGPMMZxj5SCoi+IaqHp0xS/oRLa3Oy1xXIqhRpfmuZVAeWFugYeQxqYjgcj/W7yBE5DtpsqdfsGKD204a3XmayIvbiK1upOySCUhV0sdiGEYekMoQmT8DfxCRX+OGssQPYTmDAnK+3rGO4ISRyeNVldZf1iHDyyg9L4UpJYZh9DtSEcGf+e0ZSeIKann9dVvctrPZIrF1TcQ2NFN2+SQkZJ0hhpHPpFIdfkZVg2QfYGGmDMwFG7ZCEHTuZjPy6g4ASt4zLItWGYaRCVIRwU93EfexvhrSn6jf5jpFkq0jqFGl7X/WEUyuJDikPPvGGYaRVlJZXn8Z7HOxOdsHv6WqC+OntBUC9Q0wqpNSYPTdXejOdsqvmJxdowzDyAg9FkHvae63wHvY3wYoIvIc8CFV3ZoB+3LChq2dzxaJvNgAYSF8bCcqaRhGXpFKdfguYBkwEyjzn5k+7O70m5Y71m+FMcMPDteY0v7sVkKzqpEB5jXOMAqBVN7k6ao6NyFsKfCvIvJ6Gm3KKS2tsGtv8oHSkVe3o9vbKL180sGRhmHkJamUBLsaC1Iw40Q2bnfbEUlqu9HFuyAkhE+wXmHDKBRSEcF3ReRnIjJFRAL/meo9zb2bKQOzzQbfsjk2SXU48voOQjOqkNJeOekzDKMfksrb/AVcG+BSoM1/lviwz6fftNyw2Q0BPGiMYKyhldjqRsJH98IJsWEY/ZZUhshsAd4jIu8DZuF6iBeram2GbMsJWzqqwwmO4iIvNgAQPtp6hQ2jkEi5i1NVnwaezoAt/YKN291skeGDDwyPvLEDGVZKMN6WzDKMQiKlxi0R+YiIPCciDf7znIh8OFPG5YL1Ww6eLaIxJbp4F+EjhtjCqYZRYKQyWPqrwPW4FaA7fI1MBn4uIlNV9dsZsC/r1DfAmITO31hdI7onQujIwTmxyTCMzJFKdfgKYK6qrowPFJEpwJNAn0RQREbgOl1mqOqmvlyrL2zZcXDPcPTtXQCEZwzKgUWGYWSSVKrDaxMFEEBVVwCruztZREpE5PsicpmI3JlkgdarcCLY2fmjOz4p2JwyazcfvIRW+8vbkZHlyChbMMEwCo1USoLvisjRqnqAnxERmQe8End8r6p+Ksn5nTpfF5H3Aq8B7+2JIZlyvt7WHrBjz8m07l5Fbe1aACQKc94Qts6BV59JdLaXHgrNEXah5QcKL0+Flp++kIoIVgPPichLwBofNg6YAzwuIvf7sLM6OT+p83URCQHnq+qXROQLnd08G87X1/hK+PFHT2L+fDc1LvLadppiixl/zuFMOS4zYwQLzRF2oeUHCi9PhZafvpBKdXg+8DDOaXrUf1YDv8P5Hxa6nj63EuhYsD7e+fpsYIf3RzwSuEhEcjIieVOSMYKRd3ZDAOHZ1bkwyTCMDJNKSfAJVb28u0RxJcJEHqNz5+uLROQ0nIiGgJ0p2JU2OkQwfrZI9N3dBOMG2qoxhlGgpDJjpFsB7CpdV87XffyTwPSe2pMJ6re57Wg/REbbYkTf2UXpuRntizEMI4ekOlj6NBF5SkS2+s9TInJqpozLNvXb3CDpjupw9O1dEFFCswfn1C7DMDJHj0VQRD6Gq9JuBn7qP5tw1dpLMmJdlllVD4cO3z9bJLpkNwiED7fxgYZRqKTS0PVF4GhVXRIfKCIzgIf8J6+p2wiT45bVj65uJBhdgVSac3XDKFRSqQ63JgoggA9rTZ9JuWNdwrL60ZV7CCYMzJ1BhmFknFREsFxEpiUGishUIO+nUrRHXJvgeD9bJNbQim5pJTStKreGGYaRUVKpDt8BvCYijwMrfNgU4APAZ9JtWLbZvB1UYZTvGY6u3AtgImgYBU4qQ2R+JSJbgBuBM33wYpy7zb9lwrhskjhGMLrEDZIOTTERNIxCJpWltE4GGlV1fubMyR3rtrhtxzJa0cW7CE2tQspDnZ9kGEbek0qb4NNAQS2gGk/HQOlxI0AjMaIr9xKypbMMo+BJRQSfVdWrM2VIrtnY4MYH1gyC2KpGaIuZCBpGEZCKCL4tImOTRYjIn9NkT87YsM21B4ZCEF2+B4DQlMocW2UYRqZJpXd4D/C8iPwfsA63ikwHBw2dyTfWbXazRQAib+5EhpYiI/J+5I9hGN2Qigh+GlgETPSfeAanyZ6csW4LHDXdO1V6ayfheUPNqZJhFAGpiOALqnpOsggR+a802ZMTVF11+LyT3KIJuidCeJ75FzaMYqDLNkERKReRu0VkEzBLRL4mSYpHqvqRjFmYBXbuheZWGF0D0Xd2AxCaOzi3RhmGkRW66xj5BvBx4AXgTdxA6WT+Q/KaTQ1uO2Koaw8Mxg0gqC7NrVGGYWSF7kTwPGCeql6oqu8HTgcuzrxZ2WWtHyh9aE2M6NLdhGbZUvqGUSx0J4I7vUtNAFT1WaAsMZGI5PXcsrWb3XbS7t3QGiN85JCuTzAMo2DoTgSbk4S1JAl7vLsbdeV3WETuEZErReR7InJuJ+dnzO/whq0gAkM2+kVUzamSYRQN3fUOTxSRryWETUgW1oN7dep3GLhVVetE5HBcu+Mfu7pQuv0OP//aTEZUD2L7c6spHQyvvfp82q7dEwrNB2yh5QcKL0+Flp++0J0IjgQ+mSQ8MWxED+6V1O8wgKrW+d2LgVuSnZxJv8NffghmT1IGbSqh5IQa5s/Prr+nQvMBW2j5gcLLU6Hlpy90J4Ivqur7uruIiDzdg3utBMb7/Xi/w4hIKfB54Bc4H8ZZZWU9fPGoPbAiQviIwdm+vWEYOaS7NsFkpcDepnsMGB3nd3iRiNzn4/4GzAO+BHy3h/dMC43NsGMPzI3uAiBknSKGUVR0WRKMq6Z2SU/SdeV3WFVP6cl9MsEqX8meunMnwegKgiE2PtAwiomU/A4XIqs3wQCi1GzYSejIwbk2xzCMLFP0Ili3EU4PbyeIKiUnDsu1OYZhZJmiF8GVG+DUsp1QGhA63MYHGkaxUfQi+OYy5QMl2wjPHYKUFP3jMIyio6jfelUoWb2bwbF2wlYVNoyipKhFcNN2eH90M5FQQMnxNbk2xzCMHFDUIvjKu8rp4e00ThuCDEhlfVnDMAqFohbBbU82MCZoY+hZw3NtimEYOaKoRbB6SQNtCANOtvZAwyhWilYEdy5r5rTGzSweNxIJF+1jMIyipygbwlSVHd96hwoNqPrYuFybYxhGDinKItDeJ7dSs7OR39aM46gTDloo2zCMIqLoSoIr62JU3bWMzbEy5lxzKOZa2DCKm7wUwe17S7n1lxCJuk805tYEHF69/7gjbstOaGtz8U1b21hc+RLlorw4bzJXzjEFNIxiJy9FcNvuMr76s/3H4RAoEI3C8MHuOByCUAChEOzaCx8bvI2bq94BYPOZE7jyKusRNgwjT0VwTHkLy/99M0FMkZgS29KClIXQaAwiChEXpnsj6LZWYnuaYYc7t/wzU5h6Ttp9NRmGkafkpQgOb4/Q/sOlySND4j4B0BIjmFZF+PgagrEDKD1rFMGI8qzaahhG/yYvRbAxFFB5zzEQ9oIXFqQiDCWCWE+HYRgpkJciGAOC0RW5NsMwjAIgayLona3fBrwJzAGu835HEJEzgGNxldgXVfWvSc7f15A3eejMbJhsGEYRIKqanRuJfAQYp6rf887XV6vqoz7uBeAkQIDnVPX4JOd3iOAI4ElgdVYMzzwlftueUyvSR6HlBwovT4WYn7Gq2qv18LJZHe7U+TpQpapRABGpSnZyh/N13+Y3O94Zez7TIe6Wn/5LoeWpUPPTW7I5bW4lMNLvH+B8HdgjIiERCQO7s2iTYRhFTjZF8DE6d77+deArwI3AzVm0yTCMIidrbYKGYRj9kaJcRcYwDKMDE0HDMIoaE0HDMIoaE0EjbYjId0Tkp7m2wyhsROQyEVmbruvl1bS5rmad5BMicjZucPhq4Djg/+F6yFcARwI3q+rOnBnYC0RkBjAW2OuPbyG/83M1sBmYAvwPcAn5nZ9ZuNEXTwJH4d6ja8izd8lrwLNAU1zYQb+1VH5/+VYSvBCoV9UFQD1wQU6t6T1vAV9X1Z8Bg4G5QLWqPgD8Hbgyh7b1liuA+wFE5FjyOD/+n9Q83EyEJqCGPM6Ppw43I6sK995cTh6+S6rarqorO46T/dZS/f3lmwhOAjb5/cRZJ3mDqq5X1YiITMP9t5pIHudLRD4MPI5b2wLy/3uaDbSp6oNAA67Uns/5AXg/8Iaq/hC4FGgl//MEyX9rKf3+8k0Eu5p1kleIyAnAKcC3cdXifM7XNFy18SxgOjCe/M5PPbDT728D3kt+5wfcnPsGvz8NiJL/eYLkmpCSTuSbCD7GgbNOHsulMb3FV7fuxVW5fob7onaJyOXAe4D7uji936GqtwC/B0K4KtdT5HF+gEeBKv87Ow34V/I7PwC/Bo4TkU8CZ+K+r7x8l0Tko0C1iFyuqi+T8N0kC+vyejZjxDCMYibfSoKGYRhpxUTQMIyixkTQMIyixkTQMIyixkTQMIyixkQwAREpF5GviMgLIlIrIs+JyIsi8h8icniu7esKb2OdiNR2kabF56vW50tFZFFc2E4RmZAGW+b74RfxYQ+IyCYRWdCL610iIq+LyDMi8pqIjO+rjdnA2/rBuOM7ReTOuOPBInKziAxOOO+g55cB227233etiNyQwfvc6e/Rkuk89Ya8mjucaUSkAje3ci1wuqp2zIOdC/wVNyD44txZ2DWq+lURiQDzu0i2SVXnA3ixWw1craq1Pqw2TebM958FcfZ9spcCWIabkneWqtaKyEXsn53S31kGbI87Xp8QPxg3b3wB+wdoQ5LnlyEWdfweMoWqXg0gInWZvE9vMRE8kG8CE4BTVbWlI1BVXxeRf6drcckX7uwmfgEHvoz9gZFAGW7+K6r6m5xakwKqenHC8fdyZYuRHKsOe7yTp08Bj8cLYByPAr/waU+Jq0r+s4g8LiJLO/7TiUiliNwjIm+JyKsi8icRmeLj5sWdO8GH3RpfTRSR6rjqw/Ui8ksRedlX0Scm2H2liKzy1fafA5Vd5VNV7+wmfgEwwt9fReQKEXnUV5lVRG6Kr3LH2aoiMt+HXQdcBsyJq2bH2x2IyG2+artERM7szB4ROQXoEL1H/LXGxz2fL4nIg/HP1Fcln/ZpXhCRBfHVTRF5wlcDbxeRn4rIS/75ThaRD/v4FSLy8QRbDhGR34jIKyKyUER+JSLDurD9EX+fmzuei89vnT+eDTySkLfPdPX8RGSKt+8l/53/p4gM9HEXxX1PZ4vI70VkXSqle/9brPP3vN4/x+Uico6IHCki/+V/63clnHeJf4ZP+2d+a0/vmXNU1T5u1szhgAKf62H6CT79Atw/k3LgBR/3EPAXIOyPv46bv1iWcO6EuOstABYk3KMO+AdQ6Y//B/hFXPwJQAQ41h9Pxk0cr00xD/M7iVdcM0A5bjrcaz785sR7JF4nWZq4fG4HZvjjq4A1PbRzQkJ4HfAGMNQfPw6MA74DfN6HCW7a1P0J59bi5pgO98e/xq04cpE/PgvY0/HsfdizwPfiju8EFnZjey1uKaeO48uAuh7kLdkzLsUtuHGVPw4BvwUejEsz31/vW/54FPCnTmzr7Du62ef9ZH98pf9dXeePa4BG4BR/PNr/Dif54+FAQ5Lr1gGXZeod7u3HSoL7Gey3jSmet0BVY6raoqoniMgkXLvh91U14tN8HzgU+Ggv7PqD+rZJ3As1Jy7uczjh/QeAuiWG/taLe3TFQz5vqqpHpemar6vqEr+/EBgnIkN6ea3fqep2AFU9X1XXAj8A7vFhCvw3cHaSc59W1a1+/3ncOnu/9cfP4krVHSX4+bjVZOKrs/cB7xWRI3ppe6pcgmsa+AmAOl/d9wOXSkLHCvBzn2ajqp7Ti3ttVtWFfv953AIML/hrNgDv4JaAw8eFcIKOf6a9uWdOsDbB/ez02wOqkyIyD7jDhw9T1QkJ561LOD4cV/pY0RGgqntEZDNuiaZUiXeQvQcYFHc8E7c2YTxrcYKbLhLzlw7i89ThZ3oQsKMX10pmXxnwIxE5DGjD/YMbmSTdxrj9JmBrxz8uVW0UEYBqHz8bV8J6xIeDe/HX+Gu/2QvbU2U2rtbxf3E2lOG+89Ec2Jbb1+8t8dkkhjWy/9ksAn4JPOmr3o/gStZ5gYngfpbiXsgDhsGo6qtAx3CFB5KcF+3FvZKtWhHq5FrxYYoT2FSv3ReS2XTAPUQklIZrdpevVK71BO6f0PtUtdWX4p7uwbk9sesMVW1L1cg0skN70JvrS4l9obvfIvhn40vbHxeR23DV/VuA60TkGM2DFbitOuzxJYD7gAs6Gpp7ydt+O6UjQEQqgUPYX2rb47dVceeN6cW93uXgBSPH9eI6qbKH7m3fN4RFRErFDXPJOCJSAxwGPKaqrT64NA2Xfgv30k9LuN9dIjK6D9c9YKiPiFQlhsc9v7dwnVaD4+JCIvILESnvgw19QkTGiMgJqvq2ql6HK0iMBk7NlU2pYCJ4IF/DjZt7UET2VTt9e9VJ9KCUpaqrgIeBa8T1OIPz5bDeh+PbsNYCJ/rrz+DAtr6ecjdwgrjlxPE9iOf24jqpsgiYEdeOl6ytcwsw1O9fi1uTLxtsx/kG+SfZX2e8oK8XVTeO8lngJhEJAETkA8Axqlrf1bndsA0neENFZCRuLUZI/vwewnUu3Bh3/mdxHW7JRjRki6nAbXG/9wD3D2N57kxKgVz3zPS3D1ABfBV4GXjGb18HfgTM8WnmAS/iRPFF4NqEa1TiGuYXA6/iqmdTE9KcDSzx9/gurk1lE26R1RCuE6TFp7kEuMjvtxDXo4d7OVbhGq9/DdyFaxt6opt8XhiXh0X4HkcfN8bfvyPu9iTn/wjXhPC/wHlxaf/Zxw/DNaQ/j3uxa3Civcl/bsetQh3/HOckuc8pCWnuTPJ8Hkw45yT/nb2JWyz0Ln9+La797lH/jOpwAnNJ3LP9K0584vN/rr/ucNw/snd9nh4FRnfznGvxvcPAdfHfIft7/W/B/VZeAs7v7Pn58MnAH336p3EdIIN83Lne3o68XtKNbTdzcA/0Tf657AQexJWq45//YT684/nd5J/pA7h3pdZvP5nkfnX0w95hW1TVMNKMiFSr6i6//xxuiMq3c2zWQfjxi/M1wzNG4u5Xh/uHsCAb9+spVh02jPTzqIgM8e1003Cl0v7ITmCkZGnuMG4s4d5ukmcdKwkaRpoRkW8AHwKagadU9cs5NsnoAhNBwzCKGqsOG4ZR1JgIGoZR1JgIGoZR1JgIGoZR1JgIGoZR1Px/fmzQtVxQ+dYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'meet'], x = 'frame_jitter', color = '#023eff', ax=ax, label='Meet')\n",
    "sns.ecdfplot(data=df[df['VCA'] == 'teams'], x = 'frame_jitter', color='#f14cc1', ax=ax, label='Teams')\n",
    "plt.legend(labels=['Meet', 'Teams'])\n",
    "plt.xlabel('Ground Truth frame jitter [ms]')\n",
    "plt.grid(visible=1)\n",
    "plt.xlim([0, 100])\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=2, frameon=False, labels=['Meet', 'Webex'])\n",
    "plt.savefig('paper_plots/gt_interframe_delay_std_netrics.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d6d3d5-90bf-46e6-8afe-a7de56de5812",
   "metadata": {},
   "source": [
    "# Packet Size Distribution Meet vs Teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "77d2ad19-b5a4-479c-bc27-0cea14fcf2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Prediction', 'Ground Truth', 'timestamp', 'pcap_file', 'cross_val', 'metric', 'method', 'VCA', 'deviation', 'abs_deviation', 'ts', 'ssrc', 'lastPacketReceivedTimestamp', 'framesPerSecond', 'bitrate', '[codec]', 'packetsLost', 'framesDropped', 'framesReceived', 'framesReceivedPerSecond', 'frame_jitter', 'nackCount', 'packetsReceived', 'trackIdentifier', 'freezeCount', 'totalFreezesDuration', 'pauseCount', 'totalPausesDuration', 'jitterBufferDelay', 'framesDecodedPerSecond', 'jitterBufferEmittedCount', 'frameHeight', 'qpSum', 'duration', 'num_vals', 'csv_file', 'webrtc_file', 'bitrate_kbps'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d32dba3c-3e15-4ed6-9e0d-e177916d1768",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "mcsv = df[df['device_id'] == 'nm']\n",
    "mw = WebRTCReader('../../data/conext_data/logs_Meet_06-15_rush_hour/webrtc/3086_528_27783_259_53_1655334546.json', 'conext_data').get_webrtc()\n",
    "tw = WebRTCReader('../../data/conext_data/logs_Teams_06-15_rush_hour/webrtc/3085_999_198994_74624_50_1655406559.json', 'conext_data').get_webrtc()\n",
    "tcsv = '../../data/conext_data/logs_Teams_06-15_rush_hour/capture/3085_999_198994_74624_50_1655406559.csv'\n",
    "net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "m = pd.read_csv(mcsv, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii').sort_values(by='frame.time_relative')\n",
    "t = pd.read_csv(tcsv, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii').sort_values(by='frame.time_relative')\n",
    "m = m[m['udp.length'] > 304]\n",
    "t = t[t['udp.length'] > 304]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0ba967e9-3cb6-4d49-aeac-24cc80694df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.75x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m['ts'] = m['frame.time_epoch'].apply(int)\n",
    "t['ts'] = t['frame.time_epoch'].apply(int)\n",
    "m = m.groupby('ts')['udp.length'].sum().reset_index()\n",
    "t = t.groupby('ts')['udp.length'].sum().reset_index()\n",
    "mw['bitrate'] /= 1000\n",
    "m['udp.length'] *= (8/1000)\n",
    "tw['bitrate'] /= 1000\n",
    "t['udp.length'] *= (8/1000)\n",
    "m_ = pd.merge(m, mw, on='ts')\n",
    "t_ = pd.merge(t, tw, on='ts')\n",
    "m_['ts_rel'] = m_['ts']-m_['ts'].min()\n",
    "t_['ts_rel'] = t_['ts']-t_['ts'].min()\n",
    "fig, ax = plt.subplots(1,1)\n",
    "m_.groupby('ts_rel')[['udp.length']].max().plot(label='Meet', ax=ax)\n",
    "t_.groupby('ts_rel')[['udp.length']].max().plot(label='Teams', ax=ax)\n",
    "plt.xlabel('Time [sec]')\n",
    "plt.ylabel('Throughput [kbps]')\n",
    "plt.ylim([0, 3000])\n",
    "plt.grid(visible=1)\n",
    "plt.legend(labels=['Meet', 'Teams'], loc='lower center', bbox_to_anchor=(0.5, 1.0), ncol=2, frameon=False)\n",
    "plt.savefig('paper_plots/throughput_comparison.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "34845ca5-b2ac-4d99-8a3a-722f518d8182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.75x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "sns.ecdfplot(data=m, x = 'udp.length', color='#023eff')\n",
    "sns.ecdfplot(data=t, x = 'udp.length', color= '#f14cc1')\n",
    "plt.grid(visible=1)\n",
    "plt.legend(labels=['Meet', 'Teams'], loc='lower center', bbox_to_anchor=(0.5, 1.0), ncol=2, frameon=False)\n",
    "plt.xlabel('Packet length [Bytes]')\n",
    "plt.ylabel('CDF')\n",
    "ax.spines[['right', 'top']].set_visible(False)\n",
    "plt.savefig('paper_plots/udp_length_comparison.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d771bf05-e500-4756-bd94-b5bf12c0bf8f",
   "metadata": {},
   "source": [
    "# Lookback Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e02ddb3e-ddf7-4b71-9931-cdb5cc972cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run '../qoe_estimation/frame_lookback/model.py'\n",
    "%run '../config.py'\n",
    "# %run './lookback_tuning.py' #Generates the CSVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0d3b845c-50ba-4927-aff0-6a55614bcaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L\n",
      "1    4.61\n",
      "2    4.09\n",
      "3    4.00\n",
      "4    3.90\n",
      "5    3.70\n",
      "6    3.57\n",
      "7    3.51\n",
      "8    3.46\n",
      "9    3.39\n",
      "10   3.30\n",
      "Name: MAE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8e7feafb38>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 337.5x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# dfl1 = pd.read_csv('webex_lookback_imc_zero_loss.csv')\n",
    "# print(dfl1.groupby('L')['MAE'].mean())\n",
    "\n",
    "dfl2 = pd.read_csv('meet_lookback_netrics.csv')\n",
    "print(dfl2.groupby('L')['MAE'].mean())\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "# dfl1.groupby('L')['MAE'].mean().plot(marker='o', ax=ax, label='Webex')\n",
    "dfl2.groupby('L')['MAE'].mean().plot(marker='o', ax=ax, label='Meet')\n",
    "ax.set_ylabel('MAE')\n",
    "ax.grid(visible=1)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d0e77f27-4a5c-4e5c-bd4f-065821c6d654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['csv_file', 'frac_interleave', 'frac_coalesce', 'L', 'MAE'], dtype='object')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfl1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "deced168-f1f6-4e41-b94d-f43e2aadb65e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frac_coalesce</th>\n",
       "      <th>frac_interleave</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.09</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.93</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.37</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.54</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.01</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.10</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.68</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.54</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.71</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.75</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.86</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.05</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.33</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20.92</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21.80</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    frac_coalesce  frac_interleave\n",
       "L                                 \n",
       "1            6.09             1.12\n",
       "2            6.93             1.08\n",
       "3            8.37             1.06\n",
       "4            9.54             1.05\n",
       "5           11.01             1.04\n",
       "6           12.10             1.02\n",
       "7           13.68             1.01\n",
       "8           14.54             1.00\n",
       "9           15.71             0.99\n",
       "10          16.75             0.99\n",
       "11          17.86             0.98\n",
       "12          19.05             0.97\n",
       "13          20.33             0.96\n",
       "14          20.92             0.96\n",
       "15          21.80             0.95"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfl1.groupby('L')[['frac_coalesce', 'frac_interleave']].mean().mul(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "32217f03-e8bc-47c5-ae23-81af579c1fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frac_coalesce</th>\n",
       "      <th>frac_interleave</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.38</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.57</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.76</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.40</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.31</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.70</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.91</td>\n",
       "      <td>7.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.18</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.67</td>\n",
       "      <td>7.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frac_coalesce  frac_interleave\n",
       "L                                \n",
       "1           0.38             7.13\n",
       "2           0.57             7.12\n",
       "3           0.76             7.11\n",
       "4           1.40             7.11\n",
       "5           2.31             7.06\n",
       "6           2.70             7.04\n",
       "7           2.91             7.03\n",
       "8           3.18             7.02\n",
       "9           3.67             7.01"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfl2.groupby('L')[['frac_coalesce', 'frac_interleave']].mean().mul(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99813a37-b6cd-4de0-8ca2-c51e427fea77",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Coalesces and Interleaves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "93ad831c-401c-4599-a2b1-2f016f5b3e4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meet\n",
      "teams\n"
     ]
    }
   ],
   "source": [
    "df_files = pd.read_csv('conext_results.csv', index_col=None)\n",
    "vca_data = {'VCA': [], 'Interleaves': [], 'Coalesces': []}\n",
    "for vca in ['meet', 'teams']:\n",
    "    print(vca)\n",
    "    thresh = 1 if vca == 'meet' else 1\n",
    "    for file_tuple in df_files[df_files['VCA'] == vca].groupby(['pcap_file','csv_file', 'webrtc_file']).groups.keys():\n",
    "        pcap_file = file_tuple[0]\n",
    "        csv_file = file_tuple[1]\n",
    "        webrtc_file = file_tuple[2]\n",
    "        net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "        df = pd.read_csv(csv_file, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii')\n",
    "        dfg = dfn.groupby('rtp.timestamp').agg(max_size=('udp.length', max), min_size = ('udp.length', min)).reset_index()\n",
    "        if df['ip.proto'].dtype == object:\n",
    "            df = df[df['ip.proto'].str.contains(',') == False]\n",
    "        df = df[~df['ip.proto'].isna()]\n",
    "        df['ip.proto'] = df['ip.proto'].astype(int)\n",
    "        df = df[(df['ip.proto'] == 17) & (df['ip.dst'] == '192.168.1.107')]\n",
    "        src = df.groupby('ip.src').agg({'udp.length': sum, 'rtp.p_type': 'count'}).reset_index().sort_values(by='udp.length', ascending=False).head(1)['ip.src'].iloc[0]\n",
    "        df = df[df['ip.src'] == src]\n",
    "\n",
    "        df = df[df['udp.length'] > 306]\n",
    "        df = df.sort_values(by=['frame.time_relative'])\n",
    "        frame_id_assignment = [-1 for _ in range(df.shape[0])]\n",
    "        frame_id = 1\n",
    "        l = 1\n",
    "        for i in range(df.shape[0]):\n",
    "            found = False\n",
    "            s = df.iloc[i]['udp.length']\n",
    "            for j in range(max(0, i-l), i):\n",
    "                if abs(df.iloc[j]['udp.length'] - s) <= thresh:\n",
    "                    frame_id_assignment[i] = frame_id\n",
    "                    found = True\n",
    "                    break\n",
    "            if not found:\n",
    "                frame_id += 1\n",
    "                frame_id_assignment[i] = frame_id\n",
    "        df[\"frame_num\"] = frame_id_assignment\n",
    "        dfc = df.groupby(\"frame_num\").agg({\"rtp.timestamp\": (lambda x: len(set(x)))})\n",
    "        coal = len(dfc[dfc[\"rtp.timestamp\"] > 1]) / len(dfc)\n",
    "        dfi = df.groupby(\"rtp.timestamp\").agg({\"frame_num\": (lambda x: len(set(x)))})\n",
    "        il = len(dfi[dfi[\"frame_num\"] > 1]) / len(dfi)\n",
    "        vca_data[\"VCA\"].append(vca)\n",
    "        vca_data[\"Interleaves\"].append(il)\n",
    "        vca_data[\"Coalesces\"].append(coal)\n",
    "df_err = pd.DataFrame(vca_data)\n",
    "df_err.to_csv('coal_ileav.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "36e368f7-ee6b-4a9e-8c19-9df35743ad1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interleaves</th>\n",
       "      <th>Coalesces</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VCA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meet</th>\n",
       "      <td>7.13</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teams</th>\n",
       "      <td>1.13</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Interleaves  Coalesces\n",
       "VCA                          \n",
       "meet          7.13       0.38\n",
       "teams         1.13       2.86"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_err = pd.read_csv('coal_ileav.csv')\n",
    "df_err.groupby('VCA')[['Interleaves', 'Coalesces']].mean().mul(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32d8a4a-a136-4578-aa00-648ab4cd57f9",
   "metadata": {},
   "source": [
    "## Meet FPS Overestimation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "a35a6b4f-cbe6-4731-ad52-5107815ac18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file = \"../../data/conext_data/logs_Meet_06-15_rush_hour/capture/1010_78_3455_278_49_1655323428.csv\"\n",
    "net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "df = pd.read_csv(csv_file, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii')\n",
    "dfg = df.groupby('rtp.timestamp').agg(max_size=('udp.length', max), min_size = ('udp.length', min)).reset_index()\n",
    "if df['ip.proto'].dtype == object:\n",
    "    df = df[df['ip.proto'].str.contains(',') == False]\n",
    "df = df[~df['ip.proto'].isna()]\n",
    "df['ip.proto'] = df['ip.proto'].astype(int)\n",
    "df = df[(df['ip.proto'] == 17) & (df['ip.dst'] == '192.168.1.187')]\n",
    "src = df.groupby('ip.src').agg({'udp.length': sum, 'rtp.p_type': 'count'}).reset_index().sort_values(by='udp.length', ascending=False).head(1)['ip.src'].iloc[0]\n",
    "df = df[df['ip.src'] == src]\n",
    "\n",
    "df = df[df['udp.length'] > 306]\n",
    "df = df.sort_values(by=['frame.time_relative'])\n",
    "frame_id_assignment = [-1 for _ in range(df.shape[0])]\n",
    "frame_id = 1\n",
    "l = 10\n",
    "for i in range(df.shape[0]):\n",
    "    found = False\n",
    "    s = df.iloc[i]['udp.length']\n",
    "    for j in range(max(0, i-l), i):\n",
    "        if abs(df.iloc[j]['udp.length'] - s) <= 2:\n",
    "            frame_id_assignment[i] = frame_id\n",
    "            found = True\n",
    "            break\n",
    "    if not found:\n",
    "        frame_id += 1\n",
    "        frame_id_assignment[i] = frame_id\n",
    "df[\"frame_num\"] = frame_id_assignment\n",
    "dfc = df.groupby(\"frame_num\").agg({\"rtp.timestamp\": (lambda x: len(set(x)))})\n",
    "coal = len(dfc[dfc[\"rtp.timestamp\"] > 1]) / len(dfc)\n",
    "dfi = df.groupby(\"rtp.timestamp\").agg({\"frame_num\": (lambda x: len(set(x)))})\n",
    "il = len(dfi[dfi[\"frame_num\"] > 1]) / len(dfi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b9d053-c6c1-430a-aa95-e335da204e58",
   "metadata": {},
   "source": [
    "## Toy Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "b25d9490-05e5-4363-82cf-fcdfda4480de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.75x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "df['et'] = df['frame.time_relative'].apply(int)\n",
    "dfs = df[(df['et'] == 11)]\n",
    "rtps = dfs['rtp.timestamp'].tolist()\n",
    "fnums = dfs['frame_num'].tolist()\n",
    "times = dfs['frame.time_relative'].tolist()\n",
    "rtp_set = sorted(list(set(rtps)))\n",
    "fnum_set = sorted(list(set(fnums)))\n",
    "print(len(rtp_set), len(fnum_set))\n",
    "rtp_idx = {rtp_set[i]: i for i in range(len(rtp_set))}\n",
    "fnum_idx = {fnum_set[i]: i for i in range(len(fnum_set))}\n",
    "rel = [(i, fnum_idx[fnums[i]]) for i in range(len(rtps))]\n",
    "max_idx = defaultdict(lambda: -1)\n",
    "for a, b in rel:\n",
    "    max_idx[b] = max(max_idx[b], a)\n",
    "top_x = [max_idx[x] for x in max_idx.keys()]\n",
    "a_y_ = sorted(rtps)\n",
    "b_y_ = sorted(fnums)\n",
    "a_y = [rtp_idx[x] for x in a_y_]\n",
    "b_y = [fnum_idx[x] for x in b_y_]\n",
    "a_x = [1 for i in range(len(a_y))]\n",
    "b_x = [2 for i in range(len(fnum_idx))]\n",
    "plt.figure(figsize=(1.5*3.125, 1.5))\n",
    "plt.scatter(list(range(len(a_y))), a_x, c='r')\n",
    "plt.scatter(top_x, b_x, c='b')\n",
    "plt.margins(y=0.2)\n",
    "for i in range(len(rtps)):\n",
    "    plt.annotate(xy=(i-0.5, 0.83), text=f'{rtp_idx[rtps[i]]+1}', fontsize=8)\n",
    "for i in range(len(fnum_idx)):\n",
    "    plt.annotate(xy=(max_idx[i]-0.5, 2+0.045), text=f'{i+1}', fontsize=8)\n",
    "for i1, i2 in rel:\n",
    "    if i1 in [1, 2, 3]:\n",
    "        plt.annotate(xy=(i1,1), xytext=(max_idx[i2], 2), arrowprops=dict(arrowstyle='<-', ls = '--') , text='')\n",
    "    else:\n",
    "        plt.annotate(xy=(i1,1), xytext=(max_idx[i2], 2), arrowprops=dict(arrowstyle='<-') , text='')\n",
    "plt.text(x =3, y = 0.6, s = 'Packet RTP Timestamps', fontsize=14)\n",
    "plt.text(x =3, y = 2.3, s = 'Assigned frames', fontsize=14)\n",
    "plt.yticks([])\n",
    "plt.xticks([])\n",
    "plt.box(False)\n",
    "plt.savefig('paper_plots/coalesces.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "cfc888c4-cbf1-432a-86a8-a0c62512febc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame.time_relative</th>\n",
       "      <th>frame.time_epoch</th>\n",
       "      <th>ip.src</th>\n",
       "      <th>ip.dst</th>\n",
       "      <th>ip.proto</th>\n",
       "      <th>ip.len</th>\n",
       "      <th>udp.srcport</th>\n",
       "      <th>udp.dstport</th>\n",
       "      <th>udp.length</th>\n",
       "      <th>rtp.ssrc</th>\n",
       "      <th>rtp.timestamp</th>\n",
       "      <th>rtp.seq</th>\n",
       "      <th>rtp.p_type</th>\n",
       "      <th>rtp.marker</th>\n",
       "      <th>frame_num</th>\n",
       "      <th>et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>11.89</td>\n",
       "      <td>1655323442.94</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>1167.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>1147.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880642379.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>11.90</td>\n",
       "      <td>1655323442.95</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>1179.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>1159.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880642379.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>11.91</td>\n",
       "      <td>1655323442.96</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>1179.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>1159.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880642379.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>11.92</td>\n",
       "      <td>1655323442.97</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>1175.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>1155.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880642379.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>11.93</td>\n",
       "      <td>1655323442.98</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>416.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880646069.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>11.93</td>\n",
       "      <td>1655323442.98</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>570.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>550.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880650209.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>11.94</td>\n",
       "      <td>1655323442.99</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>603.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>583.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880653179.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>11.95</td>\n",
       "      <td>1655323443.00</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>918.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>898.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880657319.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>11.96</td>\n",
       "      <td>1655323443.01</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>587.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>567.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880660199.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>11.96</td>\n",
       "      <td>1655323443.01</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>637.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>617.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880664339.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>11.97</td>\n",
       "      <td>1655323443.02</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>590.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>570.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880668209.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>11.98</td>\n",
       "      <td>1655323443.03</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>1232.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>1212.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880671449.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>11.99</td>\n",
       "      <td>1655323443.04</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>508.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>488.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880675229.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>11.99</td>\n",
       "      <td>1655323443.05</td>\n",
       "      <td>142.250.82.185</td>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>17</td>\n",
       "      <td>561.00</td>\n",
       "      <td>3478.00</td>\n",
       "      <td>62887.00</td>\n",
       "      <td>541.00</td>\n",
       "      <td>0xcee588da</td>\n",
       "      <td>1880678469.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      frame.time_relative  frame.time_epoch          ip.src         ip.dst  ip.proto  ip.len  udp.srcport  udp.dstport  udp.length    rtp.ssrc  rtp.timestamp  rtp.seq rtp.p_type  rtp.marker  frame_num  et\n",
       "1066                11.89     1655323442.94  142.250.82.185  192.168.1.107        17 1167.00      3478.00     62887.00     1147.00  0xcee588da  1880642379.00     0.00         98        0.00          6  11\n",
       "1069                11.90     1655323442.95  142.250.82.185  192.168.1.107        17 1179.00      3478.00     62887.00     1159.00  0xcee588da  1880642379.00     1.00         98        0.00          7  11\n",
       "1071                11.91     1655323442.96  142.250.82.185  192.168.1.107        17 1179.00      3478.00     62887.00     1159.00  0xcee588da  1880642379.00     2.00         98        0.00          7  11\n",
       "1073                11.92     1655323442.97  142.250.82.185  192.168.1.107        17 1175.00      3478.00     62887.00     1155.00  0xcee588da  1880642379.00     3.00         98        1.00          8  11\n",
       "1078                11.93     1655323442.98  142.250.82.185  192.168.1.107        17  416.00      3478.00     62887.00      396.00  0xcee588da  1880646069.00     4.00         98        1.00          9  11\n",
       "1080                11.93     1655323442.98  142.250.82.185  192.168.1.107        17  570.00      3478.00     62887.00      550.00  0xcee588da  1880650209.00     5.00         98        1.00         10  11\n",
       "1086                11.94     1655323442.99  142.250.82.185  192.168.1.107        17  603.00      3478.00     62887.00      583.00  0xcee588da  1880653179.00     6.00         98        1.00         11  11\n",
       "1089                11.95     1655323443.00  142.250.82.185  192.168.1.107        17  918.00      3478.00     62887.00      898.00  0xcee588da  1880657319.00     7.00         98        1.00         12  11\n",
       "1092                11.96     1655323443.01  142.250.82.185  192.168.1.107        17  587.00      3478.00     62887.00      567.00  0xcee588da  1880660199.00     8.00         98        1.00         13  11\n",
       "1096                11.96     1655323443.01  142.250.82.185  192.168.1.107        17  637.00      3478.00     62887.00      617.00  0xcee588da  1880664339.00     9.00         98        1.00         14  11\n",
       "1098                11.97     1655323443.02  142.250.82.185  192.168.1.107        17  590.00      3478.00     62887.00      570.00  0xcee588da  1880668209.00    10.00         98        1.00         15  11\n",
       "1103                11.98     1655323443.03  142.250.82.185  192.168.1.107        17 1232.00      3478.00     62887.00     1212.00  0xcee588da  1880671449.00    11.00         98        1.00         16  11\n",
       "1110                11.99     1655323443.04  142.250.82.185  192.168.1.107        17  508.00      3478.00     62887.00      488.00  0xcee588da  1880675229.00    12.00         98        1.00         17  11\n",
       "1114                11.99     1655323443.05  142.250.82.185  192.168.1.107        17  561.00      3478.00     62887.00      541.00  0xcee588da  1880678469.00    13.00         98        1.00         18  11"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cc64b1d1-5fd8-4c45-ad8e-09dfe2f49e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='udp.length', ylabel='Proportion'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 468.75x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcsv = '../../data/IMC_Zero_Loss/2023-05-13_webex_1102_698_79_49_0_83/webex/captures/webex-chrome-1102_698_79_49_0_83-1684013286.csv'\n",
    "net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "m = pd.read_csv(wcsv, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii').sort_values(by='frame.time_relative')\n",
    "m = m[(~m['rtp.ssrc'].isna()) & (m['udp.length'] > 306)]\n",
    "dw = m.groupby('rtp.timestamp').agg({'udp.length': lambda x: max(x)-min(x)}).reset_index()\n",
    "sns.ecdfplot(data=dw, x='udp.length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "06f04664-d666-46ea-9f38-46b63def6080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rtp.p_type\n",
       "102.00   954.87\n",
       "Name: udp.length, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.groupby('rtp.p_type')['udp.length'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b9d02a1a-8d76-4b5a-ab26-4df045fcfa60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='udp.length', ylabel='Proportion'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.75x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcsv = '../../data/IMC_Zero_Loss/2023-05-14_meet_1102_698_79_49_0_83/meet/captures/meet-chrome-1102_698_79_49_0_83-1684053978.csv'\n",
    "net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "m = pd.read_csv(mcsv, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii').sort_values(by='frame.time_relative')\n",
    "m = m[(~m['rtp.ssrc'].isna()) & (m['udp.length'] > 306)]\n",
    "dw = m.groupby('rtp.timestamp').agg({'udp.length': lambda x: max(x)-min(x)}).reset_index()\n",
    "sns.ecdfplot(data=dw, x='udp.length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "79afa354-89c8-4413-8af7-4b9ba1fa5502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='udp.length', ylabel='Proportion'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.75x225 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcsv = '../../data/IMC_Zero_Loss/2023-05-14_teams_116_46_402_272_0_50/teams/captures/teams-chrome-116_46_402_272_0_50-1684097202.csv'\n",
    "net_columns = ['frame.time_relative','frame.time_epoch','ip.src','ip.dst','ip.proto','ip.len','udp.srcport','udp.dstport', 'udp.length','rtp.ssrc','rtp.timestamp','rtp.seq','rtp.p_type', 'rtp.marker']\n",
    "m = pd.read_csv(wcsv, header=None, sep='\\t', names=net_columns, lineterminator='\\n', encoding='ascii').sort_values(by='frame.time_relative')\n",
    "m = m[(~m['rtp.ssrc'].isna()) & (m['udp.length'] > 306)]\n",
    "dw = m.groupby('rtp.timestamp').agg({'udp.length': lambda x: max(x)-min(x), 'rtp.p_type': max}).reset_index()\n",
    "sns.ecdfplot(data=dw, x='udp.length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "296a31c3-bde9-4feb-a463-2000b48a47e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame.time_relative</th>\n",
       "      <th>frame.time_epoch</th>\n",
       "      <th>ip.src</th>\n",
       "      <th>ip.dst</th>\n",
       "      <th>ip.proto</th>\n",
       "      <th>ip.len</th>\n",
       "      <th>udp.srcport</th>\n",
       "      <th>udp.dstport</th>\n",
       "      <th>udp.length</th>\n",
       "      <th>rtp.ssrc</th>\n",
       "      <th>rtp.timestamp</th>\n",
       "      <th>rtp.seq</th>\n",
       "      <th>rtp.p_type</th>\n",
       "      <th>rtp.marker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5898</th>\n",
       "      <td>77.61</td>\n",
       "      <td>1684097280.69</td>\n",
       "      <td>52.113.18.245</td>\n",
       "      <td>192.168.1.187</td>\n",
       "      <td>17</td>\n",
       "      <td>493</td>\n",
       "      <td>3480.00</td>\n",
       "      <td>33125.00</td>\n",
       "      <td>473.00</td>\n",
       "      <td>0x0000a3ae</td>\n",
       "      <td>301015511.00</td>\n",
       "      <td>1401.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5900</th>\n",
       "      <td>77.61</td>\n",
       "      <td>1684097280.69</td>\n",
       "      <td>52.113.18.245</td>\n",
       "      <td>192.168.1.187</td>\n",
       "      <td>17</td>\n",
       "      <td>524</td>\n",
       "      <td>3480.00</td>\n",
       "      <td>33125.00</td>\n",
       "      <td>504.00</td>\n",
       "      <td>0x0000a3ae</td>\n",
       "      <td>301040836.00</td>\n",
       "      <td>1403.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5902</th>\n",
       "      <td>77.61</td>\n",
       "      <td>1684097280.69</td>\n",
       "      <td>52.113.18.245</td>\n",
       "      <td>192.168.1.187</td>\n",
       "      <td>17</td>\n",
       "      <td>464</td>\n",
       "      <td>3480.00</td>\n",
       "      <td>33125.00</td>\n",
       "      <td>444.00</td>\n",
       "      <td>0x0000a3ae</td>\n",
       "      <td>301061925.00</td>\n",
       "      <td>1405.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5989</th>\n",
       "      <td>79.59</td>\n",
       "      <td>1684097282.67</td>\n",
       "      <td>52.113.18.245</td>\n",
       "      <td>192.168.1.187</td>\n",
       "      <td>17</td>\n",
       "      <td>400</td>\n",
       "      <td>3480.00</td>\n",
       "      <td>33125.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>0x0000a3ae</td>\n",
       "      <td>301075796.00</td>\n",
       "      <td>1419.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5990</th>\n",
       "      <td>79.59</td>\n",
       "      <td>1684097282.67</td>\n",
       "      <td>52.113.18.245</td>\n",
       "      <td>192.168.1.187</td>\n",
       "      <td>17</td>\n",
       "      <td>432</td>\n",
       "      <td>3480.00</td>\n",
       "      <td>33125.00</td>\n",
       "      <td>412.00</td>\n",
       "      <td>0x0000a3ae</td>\n",
       "      <td>301097824.00</td>\n",
       "      <td>1420.00</td>\n",
       "      <td>103.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      frame.time_relative  frame.time_epoch         ip.src         ip.dst ip.proto ip.len  udp.srcport  udp.dstport  udp.length    rtp.ssrc  rtp.timestamp  rtp.seq  rtp.p_type  rtp.marker\n",
       "5898                77.61     1684097280.69  52.113.18.245  192.168.1.187       17    493      3480.00     33125.00      473.00  0x0000a3ae   301015511.00  1401.00      103.00        1.00\n",
       "5900                77.61     1684097280.69  52.113.18.245  192.168.1.187       17    524      3480.00     33125.00      504.00  0x0000a3ae   301040836.00  1403.00      103.00        1.00\n",
       "5902                77.61     1684097280.69  52.113.18.245  192.168.1.187       17    464      3480.00     33125.00      444.00  0x0000a3ae   301061925.00  1405.00      103.00        1.00\n",
       "5989                79.59     1684097282.67  52.113.18.245  192.168.1.187       17    400      3480.00     33125.00      380.00  0x0000a3ae   301075796.00  1419.00      103.00        1.00\n",
       "5990                79.59     1684097282.67  52.113.18.245  192.168.1.187       17    432      3480.00     33125.00      412.00  0x0000a3ae   301097824.00  1420.00      103.00        1.00"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[m['rtp.p_type'] == 103]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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