ChunkDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chunk Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scapy.all import *\n",
    "from math import *\n",
    "\n",
    "GET_THRESH = 300 # bytes\n",
    "DOWN_THRESH = 300  # bytes\n",
    "VIDEO_CHUNK_GETSIZE = 700 # bytes\n",
    "AUDIO_CHUNK_GETSIZE = 600 # bytes\n",
    "NETINFO_NUM = 25\n",
    "\n",
    "class Chunk():\n",
    "    def __init__(self, start_time = 0, server_ip='',ttfb = 0, download_time = 0, slack_time = 0, get_size=0, chunk_size = 0, type=\"\", protocol=\"\"):\n",
    "        self.start_time = start_time\n",
    "        self.server_ip = server_ip\n",
    "        self.ttfb = ttfb\n",
    "        self.download_time = download_time\n",
    "        self.slack_time = slack_time\n",
    "        self.get_size = get_size\n",
    "        self.chunk_size = chunk_size\n",
    "        self.type = type\n",
    "        self.protocol = protocol\n",
    "    def __str__(self):\n",
    "        return f\"{self.start_time} {self.ttfb} {self.download_time} {self.slack_time} {self.get_size} {self.chunk_size} {self.type} {self.protocol}\"\n",
    "    def __repr__(self):\n",
    "        return f\"{self.start_time} {self.ttfb} {self.download_time} {self.slack_time} {self.get_size} {self.chunk_size} {self.type} {self.protocol}\"\n",
    "\n",
    "\n",
    "def isUplink(p):\n",
    "    return p[IP].src.startswith('192.168.')\n",
    "\n",
    "def detectAV(c):\n",
    "    if abs(c.get_size-VIDEO_CHUNK_GETSIZE) > abs(c.get_size-AUDIO_CHUNK_GETSIZE):\n",
    "        flag=0\n",
    "    else:\n",
    "        flag=1\n",
    "    if c.chunk_size<=80*1024:\n",
    "        flag=2\n",
    "    return flag\n",
    "\n",
    "def chunkDetection(filename):\n",
    "    a = rdpcap(filename)\n",
    "    meta_time = float(a[0].time)\n",
    "    chunk = {}\n",
    "    chunks = []\n",
    "    downFlag = {}\n",
    "    for p in a:\n",
    "        if p.haslayer(IP):\n",
    "            ipSrc=p[IP].src\n",
    "            ipDst=p[IP].dst\n",
    "            pLen=p[IP].len\n",
    "            pHdr=p[IP].ihl*4\n",
    "            ip_time=float(p.time)\n",
    "            if isUplink(p) and pLen-pHdr > GET_THRESH:\n",
    "                if ipDst in chunk:\n",
    "                    chunk[ipDst].slack_time = ip_time - chunk[ipDst].download_time\n",
    "                    avFlag=detectAV(chunk[ipDst])\n",
    "                    if avFlag==0:\n",
    "                        # chunk[ipDst].type='a'\n",
    "                        chunks.append(chunk[ipDst])\n",
    "                    elif avFlag==1:\n",
    "                        # chunk[ipDst].type='v'\n",
    "                        chunks.append(chunk[ipDst])\n",
    "                    else:\n",
    "                        chunk.pop(ipDst)\n",
    "                        downFlag.pop(ipDst)\n",
    "                chunk[ipDst] = Chunk(start_time=ip_time, get_size=pLen-pHdr, server_ip=ipDst)\n",
    "                downFlag[ipDst] = False\n",
    "            elif not isUplink(p) and pLen > DOWN_THRESH:\n",
    "                if ipSrc in chunk:\n",
    "                    if not downFlag[ipSrc]:\n",
    "                        chunk[ipSrc].ttfb = ip_time\n",
    "                        downFlag[ipSrc] = True\n",
    "                    chunk[ipSrc].download_time = ip_time\n",
    "                    chunk[ipSrc].chunk_size += pLen - pHdr\n",
    "                    chunk[ipSrc].protocol = p.proto\n",
    "    \n",
    "    for c in chunk.values():\n",
    "        avFlag=detectAV(c)\n",
    "        if avFlag==0:\n",
    "            # c.type='a'\n",
    "            chunks.append(c)\n",
    "        elif avFlag==1:\n",
    "            # c.type='v'\n",
    "            chunks.append(c)\n",
    "    return meta_time, chunks\n",
    "\n",
    "filename = 'RequetDataSet-master/A/PCAP_FILES/baseline_Jan17_exp_30.pcap'\n",
    "meta_time, chunks=chunkDetection(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1520313059.202668 1520313059.439442 1520313060.353278 0.00678706169128418 310 194840 v 17, 1520313060.360065 1520313060.385325 1520313061.821628 0.04106402397155762 683 194221 v 17, 1520313062.330472 1520313062.343013 1520313062.877641 0.0001430511474609375 685 215506 v 17, 1520313063.142399 1520313063.14423 1520313063.430298 0.012369871139526367 699 223216 v 17, 1520313063.442668 1520313063.466102 1520313063.663112 0.03597712516784668 621 82680 v 17, 1520313063.699089 1520313063.715391 1520313065.213543 0.009701013565063477 686 470989 v 17, 1520313065.223244 1520313065.261538 1520313065.995429 0.029619932174682617 638 210694 v 17, 1520313066.025049 1520313066.118452 1520313069.45236 0.002541065216064453 687 1560399 v 17, 1520313069.454901 1520313069.50511 1520313069.979387 1.1072678565979004 636 315414 v 17, 1520313071.086655 1520313071.115073 1520313074.160344 3.147207021713257 688 2007176 v 17, 1520313077.307551 1520313077.33479 1520313078.189515 4.121658802032471 623 457444 v 17, 1520313082.311174 1520313082.337852 1520313083.954479 8.356384038925171 705 1989004 v 17, 1520313092.310863 1520313092.344781 1520313092.787905 0.011300086975097656 624 540860 v 17, 1520313092.799205 1520313092.831873 1520313094.096282 9.213535070419312 707 1910803 v 17, 1520313103.309817 1520313103.361433 1520313106.149215 6.411860942840576 688 2069068 v 17, 1520313063.984615 1520313063.99153 1520313064.956154 48.14263892173767 444 149748 v 6, 1520313112.561076 1520313112.588789 1520313113.424822 0.008189916610717773 624 457430 v 17, 1520313113.433012 1520313113.460228 1520313115.181767 9.6282639503479 689 1927648 v 17, 1520313124.810031 1520313124.83626 1520313127.730527 0.010467052459716797 706 1924923 v 17, 1520313127.740994 1520313127.768029 1520313128.153662 6.408761024475098 640 449835 v 17, 1520313134.562423 1520313134.606375 1520313137.61598 4.698513984680176 689 1995285 v 17, 1520313142.314494 1520313142.33846 1520313143.036386 5.77267599105835 643 478613 v 17, 1520313148.809062 1520313148.835612 1520313151.643939 6.165313005447388 689 1875158 v 17, 1520313157.809252 1520313157.836374 1520313158.609696 0.013698101043701172 654 594432 v 17, 1520313158.623394 1520313159.166454 1520313161.515233 11.299829959869385 710 1928094 v 17, 1520313172.815063 1520313172.848326 1520313174.480534 3.4093728065490723 707 1786554 v 17, 1520313177.889907 1520313177.929157 1520313178.267178 7.044867992401123 624 476006 v 17, 1520313185.312046 1520313185.335833 1520313187.477964 5.33705997467041 690 2155709 v 17, 1520313192.815024 1520313192.895209 1520313193.260985 5.801269054412842 641 582016 v 17, 1520313199.062254 1520313199.106733 1520313201.040029 11.521905899047852 707 2168064 v 17, 1520313212.561935 1520313212.590218 1520313213.165075 0.055253028869628906 624 418912 v 17, 1520313213.220328 1520313213.496818 1520313216.365656 158.95534300804138 690 2198101 v 17, 1520313375.321373 1520313375.353289 1520313377.708069 0.0016148090362548828 976 1826368 v 17, 1520313377.709684 1520313377.731399 1520313378.252999 0.010574817657470703 639 550148 v 17, 1520313378.263574 1520313378.279267 1520313379.841987 0.03671908378601074 690 1782968 v 17, 1520313379.878706 1520313380.119109 1520313380.503551 0.0027980804443359375 623 592116 v 17, 1520313380.506349 1520313380.511316 1520313383.938212 0.004736185073852539 703 2050379 v 17, 1520313383.942948 1520313383.96445 1520313386.852794 0.0054149627685546875 689 1745707 v 17, 1520313386.858209 1520313386.875535 1520313387.276983 0.005238056182861328 638 542932 v 17, 1520313387.282221 1520313387.285634 1520313388.488073 0.002379894256591797 690 1824472 v 17, 1520313388.490453 1520313388.491334 1520313389.382056 0.04393601417541504 640 604778 v 17, 1520313389.425992 1520313389.539599 1520313392.476717 0.0011410713195800781 690 2018425 v 17, 1520313392.477858 1520313392.493836 1520313393.6663 2.7894973754882812e-05 691 2021292 v 17, 1520313393.666328 1520313393.666398 1520313394.002922 0.003983974456787109 640 468520 v 17, 1520313394.006906 1520313394.010387 1520313395.36779 0.0048389434814453125 706 2041545 v 17, 1520313395.372629 1520313395.380453 1520313397.392111 0.027642011642456055 690 2086894 v 17, 1520313397.419753 1520313397.423673 1520313397.701968 0.0007009506225585938 624 467603 v 17, 1520313397.702669 1520313397.7075 1520313399.063383 0.0012199878692626953 704 1692102 v 17, 1520313399.064603 1520313399.079973 1520313399.465443 0.0017180442810058594 625 610844 v 17, 1520313399.467161 1520313399.472691 1520313402.522065 17.798209190368652 704 2180684 v 17, 1520313420.320274 1520313420.394676 1520313422.147892 14.919119119644165 690 2035107 v 17, 1520313437.067011 1520313437.101821 1520313437.291343 4.526288032531738 644 352203 v 17]\n"
     ]
    }
   ],
   "source": [
    "print(chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['start_time', 'server_ip', 'type', 'ttfb', 'download_time', 'get_size', 'chunk_size', 'rel_start_time', 'rel_end_time']\n",
    "df_chunk=pd.DataFrame(columns=columns)\n",
    "\n",
    "\n",
    "for c in chunks:\n",
    "    start_time = c.start_time\n",
    "    server_ip = c.server_ip\n",
    "    type = c.type\n",
    "    ttfb = c.ttfb - c.start_time\n",
    "    download_time = c.download_time - c.start_time\n",
    "    get_size = c.get_size\n",
    "    chunk_size = c.chunk_size\n",
    "    rel_start_time = start_time-meta_time\n",
    "    rel_end_time = c.download_time - meta_time\n",
    "    s = pd.Series([start_time, server_ip, type, ttfb, download_time, get_size, chunk_size, rel_start_time, rel_end_time], index=columns)\n",
    "    df_chunk.loc[len(df_chunk)] = s\n",
    "\n",
    "av_getsize_gap = df_chunk.get_size.mean()\n",
    "\n",
    "for i in range(len(df_chunk)):\n",
    "    df_chunk.loc[i, 'type'] = 'a' if df_chunk.loc[i, 'get_size'] < av_getsize_gap else 'v'\n",
    "\n",
    "df_chunk.sort_values(by='start_time', inplace=True)\n",
    "df_chunk.to_csv('results/chunk_detection/A/baseline_Jan17_exp_30.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>start_time</th>\n",
       "      <th>server_ip</th>\n",
       "      <th>type</th>\n",
       "      <th>ttfb</th>\n",
       "      <th>download_time</th>\n",
       "      <th>get_size</th>\n",
       "      <th>chunk_size</th>\n",
       "      <th>rel_start_time</th>\n",
       "      <th>rel_end_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1516208887.77837</td>\n",
       "      <td>172.217.10.97</td>\n",
       "      <td>a</td>\n",
       "      <td>0.102662</td>\n",
       "      <td>14.326276</td>\n",
       "      <td>453</td>\n",
       "      <td>143372</td>\n",
       "      <td>1.795876</td>\n",
       "      <td>16.122152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1516208887.883347</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>a</td>\n",
       "      <td>0.022888</td>\n",
       "      <td>3.10512</td>\n",
       "      <td>1247</td>\n",
       "      <td>564594</td>\n",
       "      <td>1.900853</td>\n",
       "      <td>5.005973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1516208891.158829</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>a</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.312027</td>\n",
       "      <td>1255</td>\n",
       "      <td>284685</td>\n",
       "      <td>5.176335</td>\n",
       "      <td>5.488362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1516208891.471111</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>v</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.226336</td>\n",
       "      <td>1363</td>\n",
       "      <td>309178</td>\n",
       "      <td>5.488617</td>\n",
       "      <td>5.714953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1516208891.697607</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>a</td>\n",
       "      <td>0.019482</td>\n",
       "      <td>1.072918</td>\n",
       "      <td>1256</td>\n",
       "      <td>1075220</td>\n",
       "      <td>5.715113</td>\n",
       "      <td>6.788031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1516209299.832551</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>v</td>\n",
       "      <td>0.032158</td>\n",
       "      <td>1.769261</td>\n",
       "      <td>1367</td>\n",
       "      <td>2071300</td>\n",
       "      <td>413.850057</td>\n",
       "      <td>415.619318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1516209311.078533</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>v</td>\n",
       "      <td>0.012034</td>\n",
       "      <td>1.299998</td>\n",
       "      <td>1367</td>\n",
       "      <td>2126409</td>\n",
       "      <td>425.096039</td>\n",
       "      <td>426.396037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1516209321.089191</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>v</td>\n",
       "      <td>0.021621</td>\n",
       "      <td>1.865273</td>\n",
       "      <td>1367</td>\n",
       "      <td>2081301</td>\n",
       "      <td>435.106697</td>\n",
       "      <td>436.97197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1516209327.833738</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>a</td>\n",
       "      <td>0.017624</td>\n",
       "      <td>0.082237</td>\n",
       "      <td>1260</td>\n",
       "      <td>115337</td>\n",
       "      <td>441.851244</td>\n",
       "      <td>441.933481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1516209331.576823</td>\n",
       "      <td>173.194.184.139</td>\n",
       "      <td>v</td>\n",
       "      <td>0.015426</td>\n",
       "      <td>1.108346</td>\n",
       "      <td>1367</td>\n",
       "      <td>951604</td>\n",
       "      <td>445.594329</td>\n",
       "      <td>446.702675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time        server_ip type      ttfb download_time get_size  \\\n",
       "6    1516208887.77837    172.217.10.97    a  0.102662     14.326276      453   \n",
       "0   1516208887.883347  173.194.184.139    a  0.022888       3.10512     1247   \n",
       "1   1516208891.158829  173.194.184.139    a  0.000315      0.312027     1255   \n",
       "2   1516208891.471111  173.194.184.139    v  0.001053      0.226336     1363   \n",
       "3   1516208891.697607  173.194.184.139    a  0.019482      1.072918     1256   \n",
       "..                ...              ...  ...       ...           ...      ...   \n",
       "70  1516209299.832551  173.194.184.139    v  0.032158      1.769261     1367   \n",
       "71  1516209311.078533  173.194.184.139    v  0.012034      1.299998     1367   \n",
       "72  1516209321.089191  173.194.184.139    v  0.021621      1.865273     1367   \n",
       "73  1516209327.833738  173.194.184.139    a  0.017624      0.082237     1260   \n",
       "74  1516209331.576823  173.194.184.139    v  0.015426      1.108346     1367   \n",
       "\n",
       "   chunk_size rel_start_time rel_end_time  \n",
       "6      143372       1.795876    16.122152  \n",
       "0      564594       1.900853     5.005973  \n",
       "1      284685       5.176335     5.488362  \n",
       "2      309178       5.488617     5.714953  \n",
       "3     1075220       5.715113     6.788031  \n",
       "..        ...            ...          ...  \n",
       "70    2071300     413.850057   415.619318  \n",
       "71    2126409     425.096039   426.396037  \n",
       "72    2081301     435.106697    436.97197  \n",
       "73     115337     441.851244   441.933481  \n",
       "74     951604     445.594329   446.702675  \n",
       "\n",
       "[75 rows x 9 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    }
   ],
   "source": [
    "start_time = df_chunk['start_time'].min()\n",
    "end_time = df_chunk['start_time'].max()\n",
    "\n",
    "columns=['timestamp']+[str(i) for i in range(1,121)]\n",
    "\n",
    "df_120features=pd.DataFrame(columns=columns)\n",
    "\n",
    "for t_msec in range(int(start_time*1000), int(end_time*1000+100), 100):\n",
    "    s = [t_msec]\n",
    "    t = t_msec/1000\n",
    "    for w in range(1, 21):\n",
    "        period = w*10.0\n",
    "        if t-period<start_time:\n",
    "            total_number_of_chunks_v = -1\n",
    "            avg_chunk_size_v = -1\n",
    "            download_time_v = -1\n",
    "            total_number_of_chunks_a = -1\n",
    "            avg_chunk_size_a = -1\n",
    "            download_time_a = -1\n",
    "        else:\n",
    "            total_number_of_chunks_v = df_chunk[(df_chunk['type']=='v') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)].shape[0]\n",
    "            avg_chunk_size_v = df_chunk[(df_chunk['type']=='v') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)]['chunk_size'].mean()\n",
    "            download_time_v = df_chunk[(df_chunk['type']=='v') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)]['download_time'].sum()\n",
    "            total_number_of_chunks_a = df_chunk[(df_chunk['type']=='a') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)].shape[0]\n",
    "            avg_chunk_size_a = df_chunk[(df_chunk['type']=='a') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)]['chunk_size'].mean()\n",
    "            download_time_a = df_chunk[(df_chunk['type']=='a') & (df_chunk['start_time']>t-period) & (df_chunk['start_time']<t)]['download_time'].sum()\n",
    "        s += [total_number_of_chunks_v, avg_chunk_size_v, download_time_v, total_number_of_chunks_a, avg_chunk_size_a, download_time_a]\n",
    "    \n",
    "    df_120features.loc[len(df_120features)] = s\n",
    "\n",
    "df_120features.to_csv('results/120features/A/baseline_Jan17_exp_30.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/yin/Projects/requet/ChunkDetection.ipynb Cell 8'\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/yin/Projects/requet/ChunkDetection.ipynb#ch0000010?line=13'>14</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(df_audio_chunk)):\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/yin/Projects/requet/ChunkDetection.ipynb#ch0000010?line=14'>15</a>\u001b[0m     audio_chunk_prev[\u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mappend(audio_chunk_prev[\u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m][i] \u001b[39m+\u001b[39m df_audio_chunk\u001b[39m.\u001b[39mloc[i, \u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/yin/Projects/requet/ChunkDetection.ipynb#ch0000010?line=15'>16</a>\u001b[0m     audio_chunk_prev[\u001b[39m'\u001b[39;49m\u001b[39mdownload_time\u001b[39;49m\u001b[39m'\u001b[39;49m][i\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39mappend(audio_chunk_prev[\u001b[39m'\u001b[39m\u001b[39mdownload_time\u001b[39m\u001b[39m'\u001b[39m][i] \u001b[39m+\u001b[39m df_audio_chunk\u001b[39m.\u001b[39mloc[i, \u001b[39m'\u001b[39m\u001b[39mdownload_time\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/yin/Projects/requet/ChunkDetection.ipynb#ch0000010?line=17'>18</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(df_video_chunk)):\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/yin/Projects/requet/ChunkDetection.ipynb#ch0000010?line=18'>19</a>\u001b[0m     video_chunk_prev[\u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m][i\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mappend(video_chunk_prev[\u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m][i] \u001b[39m+\u001b[39m df_video_chunk\u001b[39m.\u001b[39mloc[i, \u001b[39m'\u001b[39m\u001b[39mchunk_size\u001b[39m\u001b[39m'\u001b[39m])\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "start_time = df_chunk['start_time'].min()\n",
    "end_time = df_chunk['start_time'].max()\n",
    "\n",
    "columns=['timestamp']+[str(i) for i in range(1,121)]\n",
    "\n",
    "df_120features=pd.DataFrame(columns=columns)\n",
    "\n",
    "df_audio_chunk=df_chunk[df_chunk.type=='a']\n",
    "df_video_chunk=df_chunk[df_chunk.type=='v']\n",
    "\n",
    "audio_chunk_prev = {'chunk_size':[0], 'download_time':[0]}\n",
    "video_chunk_prev = {'chunk_size':[0], 'download_time':[0]}\n",
    "\n",
    "for i in range(len(df_audio_chunk)):\n",
    "    audio_chunk_prev['chunk_size'].append(audio_chunk_prev['chunk_size'][i] + df_audio_chunk.loc[i, 'chunk_size'])\n",
    "    audio_chunk_prev['download_time'].append(audio_chunk_prev['download_time'][i] + df_audio_chunk.loc[i, 'download_time'])\n",
    "\n",
    "for i in range(len(df_video_chunk)):\n",
    "    video_chunk_prev['chunk_size'].append(video_chunk_prev['chunk_size'][i] + df_video_chunk.loc[i, 'chunk_size'])\n",
    "    video_chunk_prev['download_time'].append(video_chunk_prev['download_time'][i] + df_video_chunk.loc[i, 'download_time'])\n",
    "\n",
    "\n",
    "for t_msec in range(int(start_time*1000), int(end_time*1000+100), 100):\n",
    "    s = [t_msec]\n",
    "    t = t_msec/1000\n",
    "    for w in range(1, 21):\n",
    "        l_audio = 0\n",
    "        r_audio = 0\n",
    "        l_video = 0\n",
    "        r_video = 0\n",
    "        period = w * 10.0\n",
    "        l_t = t - period\n",
    "        if l_t<start_time:\n",
    "            total_number_of_chunks_v = -1\n",
    "            avg_chunk_size_v = -1\n",
    "            download_time_v = -1\n",
    "            total_number_of_chunks_a = -1\n",
    "            avg_chunk_size_a = -1\n",
    "            download_time_a = -1\n",
    "        else:\n",
    "            while df_audio_chunk.loc[l_audio, 'start_time'] < l_t:\n",
    "                l_audio += 1\n",
    "            while df_audio_chunk.loc[r_audio, 'start_time'] <= t:\n",
    "                r_audio += 1\n",
    "            while df_video_chunk.loc[l_video, 'start_time'] < l_t:\n",
    "                l_video += 1\n",
    "            while df_video_chunk.loc[r_video, 'start_time'] <= t:\n",
    "                r_video += 1\n",
    "            total_number_of_chunks_v = r_video - l_video \n",
    "            avg_chunk_size_v = video_chunk_prev['chunk_size'][r_video] - video_chunk_prev['chunk_size'][l_video]\n",
    "            download_time_v = video_chunk_prev['download_time'][r_video] - video_chunk_prev['download_time'][l_video]\n",
    "            total_number_of_chunks_a = r_audio - l_audio\n",
    "            avg_chunk_size_a = audio_chunk_prev['chunk_size'][r_audio] - audio_chunk_prev['chunk_size'][l_audio]\n",
    "            download_time_a = audio_chunk_prev['download_time'][r_audio-1] - audio_chunk_prev['download_time'][l_audio-1]\n",
    "        s += [total_number_of_chunks_v, avg_chunk_size_v, download_time_v, total_number_of_chunks_a, avg_chunk_size_a, download_time_a]\n",
    "    \n",
    "    df_120features.loc[len(df_120features)] = s\n",
    "\n",
    "df_120features.to_csv('results/120features/A/baseline_Jan17_exp_30.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}