sirex/frakcijos.ipynb

## frakcijos.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seimo narių pozicijų vizualizacija pagal balsavimus\n",
    "\n",
    "Žemiau rasite filmukus, kuriuose galima pamatyti kaip išsidėstę seimo nariai, kiekvienu laiko momentu.\n",
    "\n",
    "Grafikuose $x$ ir $y$ ašys neturi realios prasmės, grafikų esmė yra atstumas tarp taškų. Kiekvienas taškas yra seimo narys, taško spalva nurodo kokiai frakcijai priklauso seimo narys, spalvų reikšmes galima pamatyti grafiko legendoje.\n",
    "\n",
    "Kuo labiau taškai nutolę vienas nuo kito, tuo labiau išsiskiria seimo narių nuomonę.\n",
    "\n",
    "Seimo narių pozicija paskaičiuota imant trijų mėnesių balsavimų duomenis. Toks trijų mėnesių langas slenkamas laike ir kiekvienas vaizdo įrašo kadras rodo sekančios dienos, trijų mėnesių pozicija. Galutiniame rezultate matyti kaip kinta seimo narių  pozicija laike."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import timedelta\n",
    "from matplotlib import cm\n",
    "from sklearn import decomposition, manifold\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from matplotlib.ticker import NullFormatter\n",
    "from math import ceil\n",
    "from IPython.display import Image, YouTubeVideo\n",
    "\n",
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://atviriduomenys.lt/data/lrs/balsavimai/balsavimai.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5530115 entries, 0 to 5530114\n",
      "Data columns (total 9 columns):\n",
      "data                    5530115 non-null object\n",
      "laikas                  5530115 non-null object\n",
      "rezultatai.p_asm_id     5530115 non-null int64\n",
      "rezultatai.vardas       5530115 non-null object\n",
      "rezultatai.frakcija     5499323 non-null object\n",
      "rezultatai.už           1982019 non-null object\n",
      "rezultatai.prieš        326457 non-null object\n",
      "rezultatai.susilaikė    499121 non-null object\n",
      "key                     5530115 non-null object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 379.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pašalinam visus įrašus, kur Seimo narys nebalsavo. Mus domina tik tie įrašai, kur Seimo narys balsavo. Įtraukiant ir tuos, kurie nebalsavo, išsikraipys statistika, kadangi iš vis nebalsavosių yra didžioji dauguma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.dropna(how='all', subset=['rezultatai.už', 'rezultatai.prieš', 'rezultatai.susilaikė'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasitikriname ar už/prieš/susilaikė laukų reikšmės yra tvarkingos, pavyzdžiui ar nėra `+ ` (su tarpo simboliu) ir pan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "+    2807597\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([data['rezultatai.už'], data['rezultatai.prieš'], data['rezultatai.susilaikė']]).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matome, kad duomenys tvarkingi. Tokiu atveju verčiam už/prieš/susilaikė į skaitinę formą, nuo $-2$ (prieš) iki $2$ (už). Susilaikė verčiamas į $-1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['vote'] = (\n",
    "    (data['rezultatai.už'] == '+') * 2 +\n",
    "    (data['rezultatai.susilaikė'] == '+') * -1 +\n",
    "    (data['rezultatai.prieš'] == '+') * -2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kadangi vienas balsavimas identifikuojamas pagal balsavimo puslapio adresą, o adrese yra balsavimo id, ištraukiam balsavimo id į atskirą stulpelį, kad skaičiavimai veiktų greičiau."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://www.lrs.lt/sip/portal.show?p_r=15275&p_k=1&p_a=sale_bals&p_bals_id=-26066'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]['key']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['p_bals_id'] = data['key'].str.extract(r'p_bals_id=(-?\\d+)', expand=False).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apjungiame datos ir laiko stulpelius į vieną stulpelį ir konvertuojame naują stulpelį į datos ir laiko tipą."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['laikas'] = pd.to_datetime(data['data'] + ' ' + data['laikas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2807597 entries, 0 to 5530114\n",
      "Data columns (total 11 columns):\n",
      "data                    2807597 non-null object\n",
      "laikas                  2807597 non-null datetime64[ns]\n",
      "rezultatai.p_asm_id     2807597 non-null int64\n",
      "rezultatai.vardas       2807597 non-null object\n",
      "rezultatai.frakcija     2799827 non-null object\n",
      "rezultatai.už           1982019 non-null object\n",
      "rezultatai.prieš        326457 non-null object\n",
      "rezultatai.susilaikė    499121 non-null object\n",
      "key                     2807597 non-null object\n",
      "vote                    2807597 non-null int64\n",
      "p_bals_id               2807597 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(7)\n",
      "memory usage: 257.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Žemiau yra bandymas vizualizuoti, kaip keičiasi partijų išsidėstymas pagal balsavimus laike. Vizualizacijai naudojamas trijų mėnesių langas, kuris slenkamas laike, kas savaitę. Iš kiekvieno lango generuojamas paveiksliukas, taikant dimensijų mažinimo algoritmą.\n",
    "\n",
    "Išbandžiau keletą algoritmų tiek in [decomposition](http://scikit-learn.org/stable/modules/decomposition.html), tiek iš [manifold](http://scikit-learn.org/stable/modules/manifold.html) paketų, bet nei vienas iš jų netinka, tokio tipo vizualizacijai. Esminė problema, kad dimensijos yra klausimai, kurių reikšmė kinta, priklausomai nuo klausimo. Tai puikiai matosi ir vizualizacijoje, kadangi karts nuo karto vaizdas apsiverčia aukštyn kojom. Taip atsitinka todėl, kad frakcijos vieniems klausimams pritaria, kitiems ne. Nustatyti klausimų poliškumą nėra paprasta.\n",
    "\n",
    "Bet kokiu atveju, šis tas gavosi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_frames(data, window, interval):\n",
    "    if os.path.exists('frames'):\n",
    "        !rm -r frames\n",
    "\n",
    "    !mkdir frames\n",
    "\n",
    "    since = data['laikas'].min()\n",
    "    total = ceil((data['laikas'].max() - window - data['laikas'].min()) / interval)\n",
    "    no_fraction = 'Be frakcijos'\n",
    "\n",
    "    alg = decomposition.PCA(n_components=2, random_state=0)\n",
    "\n",
    "    for i in tqdm(range(total)):\n",
    "        frame = data[(since < data['laikas']) & (data['laikas'] < (since + window))]\n",
    "        frame = frame.set_index(['rezultatai.frakcija', 'rezultatai.vardas', 'p_bals_id'], drop=True)['vote'].unstack().fillna(0)\n",
    "\n",
    "        frakcijos = list(frame.index.levels[0]) + [no_fraction]\n",
    "\n",
    "        result = pd.DataFrame(alg.fit_transform(frame), index=frame.index).reset_index()\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(16, 9))\n",
    "\n",
    "        result['frakcija'] = result['rezultatai.frakcija'].fillna(no_fraction)\n",
    "        for label, color in zip(frakcijos, cm.jet(np.linspace(0, 1, len(frakcijos)), alpha=.4)):\n",
    "            fraction = result[result['frakcija'] == label]\n",
    "            if len(fraction):\n",
    "                ax.scatter(fraction[0], fraction[1], s=100, color=color, label=label)\n",
    "\n",
    "        ax.set_title(since.date().strftime('%Y-%m-%d'))\n",
    "        ax.set_xlabel('')\n",
    "        ax.set_ylabel('')\n",
    "        ax.xaxis.set_major_formatter(NullFormatter())\n",
    "        ax.yaxis.set_major_formatter(NullFormatter())\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-30, 30)\n",
    "\n",
    "        plt.legend()\n",
    "\n",
    "        fig.savefig('frames/%04d.png' % i, dpi=120)\n",
    "        ax.cla()\n",
    "        plt.close(fig)\n",
    "\n",
    "        since += interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 237/237 [01:50<00:00,  2.18it/s]\n"
     ]
    }
   ],
   "source": [
    "generate_frames(\n",
    "    data=data.sort_values('laikas'),\n",
    "    window=timedelta(days=90),\n",
    "    interval=timedelta(days=1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Galiausiai visus paveiksliukus suklijuojam į vientisą vaizdo failą."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3sHPnTtra2hgcHDyu60kqb9FSNyBJkiRJKr1VddAUh55B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\nZ/6en3b/6yzXe599f1ZSTy2Nl6wFAKAaBGAAgCob/yb58i/JxPWXR8yBV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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('frames/0000.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ffmpeg version 2.8.11-0ubuntu0.16.04.1 Copyright (c) 2000-2017 the FFmpeg developers\n",
      "  built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.4) 20160609\n",
      "  configuration: --prefix=/usr --extra-version=0ubuntu0.16.04.1 --build-suffix=-ffmpeg --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --cc=cc --cxx=g++ --enable-gpl --enable-shared --disable-stripping --disable-decoder=libopenjpeg --disable-decoder=libschroedinger --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-librtmp --enable-libschroedinger --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxvid --enable-libzvbi --enable-openal --enable-opengl --enable-x11grab --enable-libdc1394 --enable-libiec61883 --enable-libzmq --enable-frei0r --enable-libx264 --enable-libopencv\n",
      "  libavutil      54. 31.100 / 54. 31.100\n",
      "  libavcodec     56. 60.100 / 56. 60.100\n",
      "  libavformat    56. 40.101 / 56. 40.101\n",
      "  libavdevice    56.  4.100 / 56.  4.100\n",
      "  libavfilter     5. 40.101 /  5. 40.101\n",
      "  libavresample   2.  1.  0 /  2.  1.  0\n",
      "  libswscale      3.  1.101 /  3.  1.101\n",
      "  libswresample   1.  2.101 /  1.  2.101\n",
      "  libpostproc    53.  3.100 / 53.  3.100\n",
      "Input #0, image2, from 'frames/%04d.png':\n",
      "  Duration: 00:00:09.48, start: 0.000000, bitrate: N/A\n",
      "    Stream #0:0: Video: png, rgba(pc), 1920x1080 [SAR 4724:4724 DAR 16:9], 25 fps, 25 tbr, 25 tbn, 25 tbc\n",
      "\u001b[0;33mNo pixel format specified, yuv444p for H.264 encoding chosen.\n",
      "Use -pix_fmt yuv420p for compatibility with outdated media players.\n",
      "\u001b[0m\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0musing SAR=1/1\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mprofile High 4:4:4 Predictive, level 4.0, 4:4:4 8-bit\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0m264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=1000 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n",
      "Output #0, mp4, to 'output.mp4':\n",
      "  Metadata:\n",
      "    encoder         : Lavf56.40.101\n",
      "    Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv444p, 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 1000 kb/s, 30 fps, 15360 tbn, 30 tbc\n",
      "    Metadata:\n",
      "      encoder         : Lavc56.60.100 libx264\n",
      "Stream mapping:\n",
      "  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n",
      "Press [q] to stop, [?] for help\n",
      "frame=  237 fps= 27 q=-1.0 Lsize=     978kB time=00:00:07.83 bitrate=1023.2kbits/s    \n",
      "video:975kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.368449%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mframe I:2     Avg QP:26.25  size:  8067\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mframe P:61    Avg QP:29.02  size:  4689\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mframe B:174   Avg QP:36.54  size:  3996\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mconsecutive B-frames:  2.1%  0.0%  0.0% 97.9%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mmb I  I16..4: 14.1% 82.0%  4.0%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mmb P  I16..4:  0.5%  4.2%  2.0%  P16..4:  1.6%  0.4%  0.1%  0.0%  0.0%    skip:91.3%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mmb B  I16..4:  0.0%  0.8%  0.8%  B16..8:  5.8%  0.6%  0.1%  direct: 0.3%  skip:91.6%  L0:52.9% L1:46.2% BI: 1.0%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mfinal ratefactor: 29.92\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0m8x8 transform intra:62.6% inter:66.0%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mcoded y,u,v intra: 20.0% 18.6% 15.4% inter: 0.7% 0.8% 0.7%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mi16 v,h,dc,p: 71% 26%  2%  1%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19%  9% 59%  2%  2%  2%  2%  2%  2%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 16% 33%  5%  5%  5%  5%  4%  3%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mref P L0: 44.1%  6.0% 31.2% 18.7%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mref B L0: 75.1% 18.7%  6.2%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mref B L1: 90.0% 10.0%\n",
      "\u001b[1;36m[libx264 @ 0x180d7c0] \u001b[0mkb/s:1010.16\n"
     ]
    }
   ],
   "source": [
    "!avconv -y -r 30 -i frames/%04d.png -b:v 1000k output.mp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/Xz466FWNgMY\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fdc851dbb38>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('Xz466FWNgMY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kad vizualizacijos paveiksliukas būtų stabilus, bandome įtraukti tik tuos klausimus, kurių rezultatas buvo „už“."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(\n",
    "    data,\n",
    "    data.groupby('p_bals_id').agg({'vote': sum}).rename(columns={'vote': 'result'}),\n",
    "    how='left', left_on='p_bals_id', right_index=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7086/7086 [54:49<00:00,  1.86it/s]\n"
     ]
    }
   ],
   "source": [
    "generate_frames(\n",
    "    data=data[data['result'] > 0].sort_values('laikas'),\n",
    "    window=timedelta(days=90),\n",
    "    interval=timedelta(days=1),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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56hKSPtpTgtu+baWLd2oy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\nAlihUCgUCoVCoXhT7BckIZpf625WbCAo6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lXE6sxM9o0k9KuFAisPH1JcWyM7O4txSfd3\nMBolOztLYW6OlYcPiWQyV0viKxQKhUKhUCjeK5QAVigUCoVCoVAo3gSuI3J386mI3ZeTrlo7ERiJ\nHVUpu+2654GcCNjf+gOY+hE8Wzo/Tfh7/wYWf3NUO7u+evpYdF0icL53NNu3MxjTseVrwTA4raN9\nzADEAvI81UP5mu9JqrhRk6+/LH9BXmc2JwK41ZS0L4j0KhdFAp9HvSqfb818UJLsvePl+vJeMYze\n5zhfRba+y3xor+dtcIX64Ui1QCv8A8qZJnWrgc4uJlF0LDQ0fHw8GjjU5CnoJ84YFmkc5GdTnPHT\nAvQtsLCww+eff8vaWpGpqTTRaJBvVsKAhmFoaJrMiY7FgrRaDltbVVxnC+BcCWzoYIQPaEYfs84T\nmuziUsfDRcfAIIxFhjhjxLiJRd/RzlYWrPuQuge1JbBL4NugBWRmc2QCjO5+bjdxWadKFQcHHxON\nKCYjRLHo7eeOiYaBjtt+nF64di3Bs41D1vdruK3u3vNWy2VxcY/JSZnP/SbYnptjf3WV9NTUpfK3\ngxEMkp6a4mB1lcL8POP3z5nJrlAoFAqFQqH4YFACWKFQKBQKhUKheN3sF+DXPwd8SKZFtF5EICgJ\n2cIGrH4twhYkRfjZfWj89OI0YcCSium4J4Mtmw2RqZGYyF3DFGnsOEdzfTszfUH2990jSd1qiOgF\n2db3ZaZvRz5nclI1XcjDeOLUy3kxx3hvW2SwYUrq2fPOn2lst+DJosxGvd1tN6nitdCpL3ddMHuI\nxXV4U3OcG/XvRcr2e80V6ocjmXtEYkvYLGIQwKGOQwUPDx0djQBBUlhkCJHBJIKHTZV1oowQY/zt\nvNZjFApVHj5cYW2tyOxslmBQpKih++jtH6XH12cEgybXrsXZ2Cjz9dcFYrHgmXXQeuIJNz79ArJ5\namhYpAmRRcPAx8WhSo3n1FjnkFUG+IwYYycfxAhD/GqLGw5o8pgKTyizS5M6bju9qxPGIIPFGHFu\nEqOvSwkfxSSMQRWHJN3J0Q7xhMX4bD+VNZenf3dAKiai/Tyq1RaLi3uMj6d48OA22awsSKjX7dc2\nw9uu1SjMz6Np2qna58vobF+Yn+f6vXvfrZZdoVAoFAqFQvHOowSwQqFQKBQKhULxulmek0TuzGfy\n5+Mi9DwMQ6qc97ZgvgE/+VdHIvSyNOGdWVh6BP/0t1IFHbTgYAfKBxCOghmUD8cGzxGh67ki6gxd\nJAq6bOP7R0lkEGmsaRCOy+P5PqTSsv9OHq7fPJ1u7swxdl2RxOlBMM12EvmMdFe9KvI3Nw6/8+DN\nzXpVnE0sKYsHyiWZadsrr3uO835B7qvVebm+apV2Ytk8SqbfmpH7Ql1L7z891g8HrSzDDKMToMYG\nIYaRSegeGjo6FhZ96O1fjzjUqbJOmGEG+Oxk6vUtMTe3zerqPlNT6RfyFyAccrEsl3rDJBZ1Tuxj\nGDpDQ1F2dmrk82USkycFqpl8gpf+JanwJhF/hDgnZaCGSZAkQZIvzomHPMcpCXwFnlDhC3bIU0cD\n0lhkCWGg4eJTxWmr5xqrHPIZA4wRu/RxrxMlg8Vzaj0LYABj0ORTb5Ba8pDVpQMAcrk4yaSFYei4\nrkex2GBzswLA5GSaBw9uMz09cGI+cz5fplJp4Tg+pinp7FwuzsxMltnZwReyuFf2lpcp5/PEc1eb\nyR4bHuZwY4O9paXXV9euUCgUCoVCoXgnUAJYoVAoFAqFQqF4ndRrIqY0DdJDEtU6LkKDF6SafB+K\neyLOPv1p9/KqPyvi9PECPFuG0dsyh3f7OTSqckxau/bZbgFae6avL591X+qd7aYcb70iSWLdkPrn\naALical1jidlxqvriBDe2z57pu9A+5fVq19DcVfSmoGApDR97+QcY5Dk7+88gFvTPZxsxWth9I5I\n1GfLVxPAr3OO8+oC/PKhLLDQdMgMy6ziF7OxS3LcT5dkUYS6pj4Meqwf7gjLHb6gziY+YJHGJPoi\n7dqiSJN9AKKMnJ12fQvUajbz8wU0TTuVRh3MNEjFbbZ3Q6cEMEgSGCC/ccjNmymCAfm7HjogfO0L\nDt1NQs1xhjIXp1JNwsQZp8wa+davqCw3qeyFrpxufUKFX7LNJnVGiBJ+6VdTJlpbPQep47BOFQcP\n4FIJHMJgjDjr1KjjnHrsi6i3Bfe94WGu/+/jzM8XmJvbJp8vs7FxiOv6GIbI3IkJqXyemcmSzUZP\nzWceHo4zOpp8IY1LpSbLy/ssLe3x6NH2C2ncK81SiValQnL0ajPZQ6kU5XyeZqmLmewKhUKhUCgU\nivcaJYAVCoVCoVAoFIrXybNlkb2ZtgB9WYT6vghUK8yLLs9mTcSVpsHwDejLnp2UvYhb0/CjfwFr\n30glbtCC8Y9E0lYO289RbKeA25XUmiHJXMuS9KTnyferZfmzbkCyH1IZkcKJdrrYCkkC+LKZvgM5\nSWQW8rDwj1I1XdqHw4OTc4xvzUjaWaU13w3CEXlPni5JOjvcQ3Ltdc5xXl2An38O+TUYmzp9XKYu\nwrovc5Qqt9tzrZUE/jDooX44xhgBklRYo8waTXZpsnti3m2E68QZJ8b4O5H8BVhe3iOfL5PLnZ7j\nGwz65IYabO+GaLZ0rKB3aptk0uKg2GB7u8rIdUnhB/oe44fyVJ6McHMqQDdjZKuHPvnNKHuNr3jy\nDxWeP8pdKd16QJMv2GGTOuPECXBxrXwYk3HirFHmC3ZIEri0DvomMVY5ZJ1qV88BYOOxTpURoowT\noy9rcf/+OPfuXb+0zvms+czH0XWDTCZCJhN5URvdakmzRq8S2LVtfMdBv+JMdt0w8F0X1+5hJrtC\noVAoFAqF4r1ECWCFQqFQKBQKheJ1UilJJe3wsbTOcRFa2IBKUeSp54kEDlqSFh7IiQTdWpfH6ZWJ\nT2DyhyJlaxV5HrOduq2202KGKaKsU/WsAbbd/rN+NPO3WhbR6zjgtOTYRsKw+QxaTal9vmimb4do\nAoYMaNTgox/D2KSa1/o+0KkVf7IIt2dP13yfxeuc47xfkORvfq274wlHZbuVOdmvL6MWGHwPsejD\noo8Ud6myjkMVHwcNE5MoUUYwupw1+6YolZpUKi1GR8+uUL82VOd5Psz2TojcUB3TODljPhwOUCw2\nqNflZ75mNNBjTzgoafTFw1wbvvwYNvMV5r8usLNTJXGtyf/P3t01R5mv62G/ulutRlILARINaGYY\nGEBgRlpjx/Y4rKQq2VOuGJednX3ikyRVOfcXyLG/hD9Bjn2wTkxOXNlOvFJ21Y7NlgazBTJrXhCD\neJPQG3ppKQd/NDCMAEkIGJrfr4qChZ6n+xF0izW6nuu+h049ysDGxVTT2HW79b9kIdNPm787CWaT\npJ5qPktf7mQ5t7Lw2gD4cBr5Okezno3cyvy2LePnbbWMT6QnX+foLx6/p6eer746/utzltdy9epP\n+dOfZvOv//XNTE/P5+LFo6nXXx3M9vV1Z2yslfHxmVy5cjNDQ727Ggddq9dT6erKRrudWnX3O9k3\n2u1UarXU6m95JzsAAO+dABgAAN6m9bWn+0hf+KZw38Hk9MGyM/eX8so0AAAgAElEQVTh3TISeaNd\nmrAHepIjx0qotbmRTP+pPM5unTyXfH6utJAv/J0yivrW9RLoVqtJb395nkpKG3nu4bMQt91OslkC\n4u6uEkpvbpbP4/PzZax0Uq77wd1k6NjLd/o+7/lQ8M/+JyHch2JrrPjaaglRt2vcPu9t73He2qt9\n6sLOwuikHHfqQjnv5kTy9Tf7e018MGpp5GDOvu/L2JG1tXbW1zdTq20f9h1srufL84/T3qhk+qee\nHDv65BdN4Gq18nTzQPm9tfqPWX5yP83GYEYvlGn+r3JneiFXr/6U+w+Wc/x4Xw701FIdWM7S44dZ\nezi8q3brk7Tzp8ynkuxqNHOeO/5W5vNlDqWRV/9bszUqeqttnJQ9w33p+nnP8ELW8zArSZLP0rej\nPcMv7vm9fv1+bt58mEOHDuQ//sc7uXXrUYaHD+aTT/pz8OD2QXV3dy0XLgxmaupRJiZm8s03p3f8\n59AYGEh3s5mVubn0Du1+JP+T2dl0N5tpDLylnewAAPxmCIABAOBt6qqXELXdLk3bF9W7k2Pb7Mzd\n0n4aCnftoa3z/Ojeuz8kD2ZKwHt4KGmvlQC46+l/EvQfKu3ejY2kspk8eVLC5yOtMoa6qyvZTNkL\nfKC3BNhJcubLco23vyut5sYr2rtvOxTk7doam7y1czcpo837B57bufsO9jg/v1d7N+Ook2fHT00k\nv7ukbc5vXr1eS1dXJe32RqovucFm+NiTJMm3f3Mw9x82srmZHDq4lp4D69nc3ExSyfxiV/762+TI\nF4s5f3E5Z4+2cuI17d/5x6uZ+HYm9x8s55NP+lOrVbOx0pOuvsepdv963P/r2q0/ZjH3s5LBPbas\nj6SR+1nJD1nM2Rx87fGn0sxA6rn1dPD3/aeDv9vZSC3V9KSWT9Ob008Hf7+uWfzint/Bwd7U69Uc\nPdqbEyf6s7y8nrt3F3P37mJ+/HEuX37Z2nZ0d5Kfx0RPTMzk0qVPd7w/efDcufQPD+fhjRt7CoAX\n7tzJ4MhIBkfewk52AAB+UwTAAADwNjUHSjA6P1eC192any3nN/fY1jk3lvyn/yf5d1dKQ/focHL7\nVvlY13P/OdB9oITRq0+SRm/ZC7y5mfQ0k4HBMv45Se7+mNybLs3levezncYPZ0qo/OheCYHfdSjI\nu3HmYnkd35woTeCtMeZb7fV3scf5xb3auzV0Irl7u9wYcX5n+2PhfRkYaKTZ7M7c3EqGhnpfetzw\nsSdp9q1n+qee3P7pQGbn65l93JOlpfWsrXel3a5n5Gxy8dJ6Bs62c6z39Ttkb99+nHv3FnP8eN+z\nBvJmNalsJNVf7xtOXt1uXcx6ltNOKwd294fwVDNdeZCVLGZ9x+ccfjr4+8scyg9ZzGLWs57NdKWS\nvnTls/S9tk2cbL/n94cf5jI/v5pDh3pSq1XTbHan2ezOykoJgtvtu0ny0hD4xIlmbt9+nMnJB9uO\nmd5Ovbc3rdHRPJiczOriYrr7dn4TzOpi2cneGh1NvcfNLwAAnU4ADAAAb9PJc0lruIRWewmA798p\ngdrne2zrHGklracN442NEkSvLJcW7/Oq1fJ7T5aT5YUS4vb2l0B4fjZpPP3mdP9AMv+ojK3eai43\nB5JTI8kXX5ZQ+X2Egrw7R1plfPLvLpUQdWHu3e5x3m6v9m70Hyqv0b3s1YZ37Ny5wQwP9+fGjYev\nDICTMg764Nn5fPH5Qu7eO5DlJ7XcuDmbz0/253/5n4/nd18myz1d+Sm1bKadyiu+JbS60s7t6flU\nKpV0dz93XGWjhMAbL98/+7J263o2n7ZvK7v4E3imjG7eyHo2X3/wCxqp7ag1vJ2ZmcVcuXIzt27N\nZmysle7uEhgvL69nZaWdI0d++fWu0ejK8HB/pqfn8+23M2k2u7cdB33o0IFMT89nbm5lV9dzbGws\nd69ezYPr19MaG0ut+/Vj8Nurq3lw/XoGz59Pa3Sfd7IDAPCbJAAGAIC36fkxzMuLuxtZu1zaOjkz\nuvdAbXkpWZ5PPjldQrofbpZA99BgGfGcapKNZG0tWV99dl5zoFzr40fl+MNDZZR1o7eEyE+ejv/c\n2ul7ZjT5J/9rCY3fRyjIu3eg5/00aF+2V3unarVyc8Je9mrDO9bbW8/oaCuTkw+yuLj6c7j6Kt31\nzXw2vJzFxdU8WXiUv/inrfyDv1dC2LX0pZaerGcx3Xn5ZImZmcXMzj7JwMAv27rVxnI21rqzsfrq\nr+fbtVu7Ukkt1bSzmc1sZjarTwcyb6aaShqp5VC6U8/24XI7m6mlmq49Bsh7NT5+N1NTD3PhwuDP\n4W9S9ipvbGymWv319XZ1VXPsWF/u31/K9PT8tgFwrVZNu72ZtbX2rq6nr9XK2cuX015dzcz4eAYv\nXHhlE3h1cTEPrl/PodOnc/by5fS13IQFAPAxEAADAMDbdm4smbxagtKzY2V08utsBaufny+t2b3a\nGpd7ZjTp7i5jnRcel32/K0/K/65USlB78EhyuFUC45WVMhb6QE9pDC/OJwcPl6bwxkYJ0F6209dY\nXd6m1+3Vfp032asN78HY2LFcvXo3168/+EUD9VVWV9u5fv1Bzp8fzOjos8CvL5+mkaEs5cdXBsCl\n3br+q3Zrrfdx1ueOZn3u6Cuff7t2a9/T6PZGHmc57SxkLWvZ+DkArqeaZuoZSiNDOZDeF75ltZD1\n9KSWvnf4raylpbVMTMykUqn8Knyv1aqpVivZ2Nh4NiL7OY1GVzY3k+np+XzxxeFf/b212xup1Sqp\n13d/M8vRi2WNws0rV/Joquxk7x8eTmNgINVaLRvtdp7MzmbhTlm/MHj+fM5evvzzeQAAdD4BMAAA\nvG1HWiUgXVste1NPXXh1E/hlwepePD8ut6ueDJ8q+3p7+koQtrmRVKpJvZ70HSztyPm5sud3ab58\nfH29jILe3Cx7ftdWkunvSoPSTl/etfe9VxvesVarL5cvn83qajvj4zM/76B9mcXF1Vy//iCnTx/K\n5ctn02o9+/emlgPpz6ks5YesZzld2b7JW9qtSbX6rG1b6SpTItYeHc9me/sbKFZX27l7dzFLS6v5\n7ru5/NVfTWdgoJGRkcG0ezZzL8uZznIGUk9funIw9VSSbCZZSTuPspJHWc1MnuR0mhl8bl/ww6zk\n0/Tms+xiksYbunHjQaan57fd49vT05VGo5bl5fU0m9v/fQwMHMijR8u5e3cxn332yxHUs7NP0mx2\nZ2Dg1+3gnTh68WJ6h4YyMzGRu+PjmZ+ezuPbt7PZbqdSq6W72czgyEhao6NpjY5q/gIAfGQEwAAA\n8C5sBaR/vJL8WNo6GRouO3VrtRLGzs+Wnb9JCVb/3n9fQta/+rd7H6f84rjcRk85t7tRQrDt9A+U\nj8/Pln2/D2aSR/fLmOi1lfKd+s/PJZf+kZ2+vHvve682vAcXL5bG7ZUrNzM19ShJMjzcn4GBxtNR\nwhuZnX2SO3cWkiTnzw/m8uWzP5/3vGa+yONMZTE/pD+nU82vw9zSbk02NjZTq1WSajv1QzNZnzua\ntYfHf3X848cruX17PtPTjzM7+yTLy+t58GAp//bfVvPTTwtpjvZm87/uztJnG+k90JWBF0Y9V5L0\npCs96cpaNvIwq9nIfJJkMAeynPUkyen0p5E9jn/fg7m5lSwsrObkyV/fMHLsWDOHDh3IzMziSwPg\n3t6uzM09yfLyr0fO37mzkJGRwYyMDO75+vparZz+5pt8eulSHkxOZmVuLu21tdTq9TQGBjI4MpJ6\nj/ULAAAfIwEwAAC8K2culsDq5kRpAs9MJzO3yzjlaq0Esp+PJK1Pko3N5D/+3+WYpYWnIW5XOaY1\nXEY6nxt7ffj64rjcI8eS/kOlBfyyADhJGgeSxvFk4EiSSvLZ2eToiWT6T8nJkeR/+9+TQ0f280+H\nTvRkef93Qr/vvdrwnly8eDRDQ72ZmJjJ+PjdTE/P5/btx2m3S0jbbHZnZKSMfB4dbf2i+fu8Rg7n\naL7ORtYzn1vpy2e/agKXdmtXlpfX038oJfydP5LlH85n48kv27DT0/P59tuZ3Lu3lEqltF67u2s5\ncKCWCxeG0jjWnW9757LyU3L8zoH0fNWbhwMrGcqB1LbZ51tPNUNp5H5WcisL6Uo1d7Ocz9KX03nF\nv1tvwdpaO+vrm9uOeO7urmV4uD937y5mdbW97WjuMiJ6M+325i9+f3GxtKlHR1vp6XnzcfT1np4c\n/8r6BQAAnhEAAwDAu3SklXz9TfK7S9sHY+128lf/V2kJV6rJ0IkyvvnnlvBcaT5+N1n2Cr9u/PKL\n43K7u5Ojw8mDu2Uk9ev2Ea+tJIcGS+P34JFyrb//R8JfXu3hTHJjPJmaePObGLbzPvdqw3vUavXl\nm29O59KlTzM5+SBzcytZW2unXq/9PGp5J4FiM6eSJPfyH7KcMnmikcF0pS+V1DLUamTweDsL6z+l\n62BP1ueOZvmH81mf/WX7d3p6Pn/913dz//5Sjh3rS6NRvs10+/bjHDvWzCefHMzDU+0cPNWb+v3k\n/o+L6a8kza96c//gkxxJ4xdN4C21VHIk3bmflazmUcZyOF/naA5nb+OS96per6Wrq5J2eyPV6q8D\n3k8+OZgff5zPTz8t5JNP+n8VFG9sbKZarZQW9VMv288MAAD7SQAMAADvw4Ge5PwLbZ2pa8m/+9fJ\n9K3t9wR3VUuIe3jo2Z7gtdIiemkIvN243NYnyd0fk/s/lV/XXjFOc34uGTye9B8WnrEzU9eejTrf\nj5sYtvM+92rDb0BPTz1fffXrUcy70cyp1DOQhdzKfG5lJfezkvvZSDvVRi2tT6r56T8cSvXeF6kt\nffqr5u/jxyv59tuZ3L+/lOHh/nR1lfBzdbWdpIyorvZUM3d4JUnSV+1K45P+3P5uPrVaJYf/q2bm\nDpTRyH3pSiPVVFLJZjbzJBtZynqWsp5DqefvZSin3nH7N0kGBhppNrszN7eSoaHeX3384MFGvvzy\naNrtjdy+PZ/jx5u/aAIvLa2n0aj9HMq/aj8zAADsJwEwAAC8DbsdfftwpoRm07d21mjs6SvH3Rwv\n5x0e2j7U2m5cbt/B5MyXJYybuZ0MHd/++VZXUuZ5Hkm++xvhGa83dS35yz/s700ML7OXvdp7CZuh\ngzVyOI0czqF8mcX8kPUsZjPrqaQrvQerufbTo/x/3z7O2Fhvul/4Z+L27fncu1eav1vhb7u9kZ9+\nWsixY80MD/dnfqCdpb6N9CyVj3fVqjl+vJl73y3m5NGD+excb+5nJQtZy0LWs5GNVFNNPZUcSneO\n50AOpCvVbUZFvwvnzg1meLg/N2483DYATkrQnSTffnsv9+6VMfMDAwfS01P2/w4N9aZWS/76r+8m\nefV+ZgAA2C8CYAAA2LIf+0r3Ovr2xngJsU5d2Nk426Qcd+pCOe/mRBktvZ3txuUeHS4fm/o2eXSv\n/Lo5UD7PSjVZX02mvyvHrj4p1yw841Xe1k0Mr7LTvdpnRktz3c0LsK1aGjmYs7/4vSNHkn/0D+9l\nZelvMj4+kwsXBtPXV97Xq6vtTE8/TpKfxz6vrrbz008LGRrqzZdfHs3Bg43MdK9mvb6Zvvlno5Eb\nTxuy979fzN/6fDDD3b15lJWsZCMb2Uw1lTRS/Xnc83dZzGLW3/qfwXZ6e+sZHW1lcvJBFhdXf/78\nXzQ83J9ms/vnvcyzs09y795i5uZWMjDQyPLy+o72MwMAwH4RAAMAwH7tK93r6NvlpfLclcqrx9hu\nZ+v4qYmyV3i7oPpl43KPDpfPb2Y6uXe7tCXnZ0vzd/Fxcmgo+dv/TfIP/qHwjNd7mzcxvMrr9mrv\n5gYO4Be2WqpXrtzM1NSjJCXsnJ9fyaNHT9Lf352FhdXMzT1Jkhw71syXXx79uRW7UU02K0ll85eP\nOzBwILOzy7l7dyGffjaQo9n+PbqZzbSzkfVsbvvxd2Fs7FiuXr2b69cfZGys9YsRz887eLCRgwcb\n+eKLw/nxx7lcu3Y/Y2PH8o//8dmcOnVox/uZAQBgPwiAAQD4uO3XvtI3GX27vlZC2KHhvX0OQyeS\nu7fLNb64V3jLq8blnhpJjn+W/DBVguCNzeRv/d0Sqv03/1h4xuu97ZsYdmK7vdrAG7t48WiGhnoz\nMTGT8fG7mZ6ez+Tkg9y+PZ/BwZ709HT9PPJ5eLg/Bw82fj63ulHC3xdD4N6erszNPsny8qubve1s\nppZqut7TCOgkabX6cvny2ayutn/VhN7O2lo7jx6t5Pe//yx//ufnjXoGAOC9EAADAPDx2q99pW86\n+vazM6V1fOLk3j6P/kMlQF6Ye/VxOxmX+/f/zLhcdu/7G2//JgbgvWm1+vLNN6dz6dKnmZx8kH/z\nb25lbW0j588Ppre3nmPHmts2Y+ur1XStVbLW2EzjybMQt1qtZmNjM+32xiufdyHr6Uktfe/521cv\na0IPDDRSq1XTbm9kdvZJ7txZSGLPLwAA758AGACAj9N+7it909G3GxtPR05vP1bytWq1EuKur73+\nWONyeRsW5t7NTQzAe9XTU89XXx3P3NxKbt58mC++OJx6/eX/dvXP1dK7WM38QDuNJ8/2AG9sbKRa\nraRWq7703CR5mJV8mt58lve/M3e7JvTt24/Tbm+mVquk2ey25xcAgN8MATAAAB+n/dpXuh+jb+9+\nX35ut0vreLfaTxu8XbvYLWhcLvtpfe3d3cQAvHcDA400m92Zm1vJ0FDvS4/ralcy8Kgr8wPtrHdt\npmu9tICXltfTaNTS0/Pyb0stp4yHPp3+NLLHry377MUm9NzcStbW2qnXaxkYaNjzCwDAb4YAGACA\nj89uQ9u11eTh3eTJcgmpZqaT//f/TEb+dnL3hzcfffv9zfLr+bnSLN6t+dkyvrk5sLdrgDfVVU9q\nXeU98vjhs/dKtVZuNjhy7NU3WuzlJgbgvTl3bjDDw/25cePhKwPgJDn0sCuPBtfz+NB6Dj3sSnWj\nkrm5JznWaubYsea256xlIz9kMZ+lL6ez/THv01YTGgAAfqsEwAAAfHx2uq908XHZkTsznSzMJqsr\nZVzz2srT3blJjh5PHs282ejbrnrSOJDcn95bAHz/Thnd/PnI3q4B3lS7ncw9SP79d0l77dl7pVpN\nuhtJ81DSGk5anyR9B399vpsY4IPS21vP6Ggrk5MPsri4mr6+l9/gceBJNcM/dOfH6mZmj6ynMVNa\nwJ980p/6NnuDl7OeH7KYE+nJ1zmaw2nsz0W3l5OlyWRtLtlcSyr1pD6Q9I4kNasPAADoLAJgAAA+\nPjvZV3pvOpn6Nnl0rzSFmwPJwSMl0GqvJdPfJT9MJt9dL/uEj58s4dZu1WrlMY9/lkz/KVle3N0o\n6eXF8vOZUbt7eT+mriUT/77cFLEwl3z6xbP3ysZGsrJcGvQP7yZ3f0zOfJkcfeHmCzcxwAdnbOxY\nrl69m+vXH2RsrJXubcLcLQOz5dtPP5x4ku83F3P4woE0PzmQ9Wyklkra2cxC1vMwK0mSz9KXr3M0\np/aj/bsykyyMJwsTycp0sr6QbK4nla6kq5k0hpPmaNIcSxqtN38+AAD4DRAAAwDw8XndvtJ708nk\nXyez95PBY6XB+LxaPak3kuFT5bF+uJn8zX8qQfGLwdbrbI2+PTlSfv7T9eTs2M72Eq+tluM/P5+c\nHd3d8255spx8N1mCu/W10kZuDpQgTqDM60xdS/7yD8n0rfIavPWfy2t3671Vq5Vmb2+ztIIf3C2v\n+eTZe8VNDPBBarX6cvny2ayutjM+PpMLFwZf2QTuur2Rxf8wn3N/t5lTF4ZS6a/muyymnY3UUk1P\navk0vTmd/pxOc3+avwvXkvtXkqWppFJNGieSAyeTSi3ZbCfrc8nSjWRxMpm/mgxdTpoX3/x5AQDg\nPRMAAwDw8dnaV9puJ13VX35s8XFp/s7eLyNra9v8X+bNp6Ntq7XkYLPs8b13pwRfvc3tR9y+zNbo\n2+FTyclzJdS9OZ6cuvDqJvDyYgl/h08nv7+cHNlla+nhTHJjvOxCnpkujej2evl8e5vlcz8zmpwb\n2/1j83F4OJP88crT8HcsWX1S3jf3fypt+BdvsOhulNfVzNN2fW8z6T7w5jcxAO/NxYtHkyRXrtzM\n1NSjJMnwcH8GBhqp1apptzcyO/skd+4sJEm+/GIwl786mzMnjuSHLGYx61nPZrpSSV+68ln60sjL\nm8S7snAtufeHZPlW0nchqb3wb2qlmnQPlR/txWTxenJvtXxMCAwAwAdOAAwAwMenOVDCp/m5X+/c\nnbldxj4PHts+/E1Ka7a7kTR6kiPHkoHBZ+HXzHRyehcB8POjb7faj3+8kvw4VX49NJz0D5Qwrd0u\ngfH9O+Vjn58v4e+ZXX6jeuras+eoVEuAfeLkc88xV/YkfzeZTF7d23PQ+W6Ml9fQqQvl5od6dxnv\n3G6X99HQ8V832Wtd5b01ez/58b8kG+2938QA/CZcvHg0Q0O9mZiYyfj43UxPz+f27cdptzdTq1XS\nbHZnZGQwo6OtjI620mqVIPZsdvFv5W6tzJTm7/KtMtq5+pqpGrW+ctzCeDmvPmQcNAAAHzQBMAAA\nH5+T50oT8fsbvwyA11ZLgJv8euzz8xbmSvA7eKwEXK3hst90fa2Mj/70i52NcN5u9O2Zi+Wabk6U\nJvDMdAnTNp6Oiu5tlrD4zGhpTO42NHt+ZO92LeOuann+w0PPWsZrq8+uDZJkeam0xyuVX76GtsY6\nb+3PTsoNFwd6ys0GmxvlfTJ7v7y+/9t/mvx3/6PXFnzgWq2+fPPN6Vy69GkmJx9kbm4la2vt1Ou1\nDAw0MjIymJ6e+ru7oIXxMva578Lrw98t1e5y/NJU2Rfc+ObtXiMAALxFAmAAAD4+Pb0lQP1usoSc\nWwHWw7vJwmzSf+jl526FoUeHn4W8rU+Suz8m09+VYOvB3eT4Z6++hlft7z3SSr7+Jvndpf3dz/vi\nyN7XhdQ9feW4m+PlvMNDWpoU398oNycMbbPz+uhwuVFhZjq5d7u01udnk42no9O7G8nxz8sxY18L\nf6GD9PTU89VXx9/vRbSXSoBbqfx67PPrbB2/MJEcupTU7CUHAODDJAAGAODjdG6sjDf+0/VnYeiT\n5WR1JTl4ZPtz2u2y33TwWGn9buk7WEbfrq0mP9xMHj98dQC80/29B3qS81/t/XN80Ysje3ei3l2O\n/3GqtJK/1ogi5aaEpYUyOnw7fQfLKPRPvyg3RKwsP2uxN3qSgSPJ9zfL/wbYT0s3kpXppLHNDSo7\n0TiRrNxOliaT/n38NxgAAN4hATAAAB+nI60Svq6tlobrqQsloNpqKb5obbWEv4eGStjb98LuwqPD\nyeZmadnevV2aR29jf+9evWxk705sHT81UVrJe2kf89v2ZHl3bfP1taS9Xl7br1Lv3v5miM2N8n5b\nX9uf6wfYsjaXrC8kB15yg8rrdB1KnkyXxwEAgA+UABgAgI/XVvj6xyul4TozXYLe9lpSq5eQ6sly\nCcWS0vw98+WzPacvOtIqQfLIV0m1sv/7e9/Eq0b27sTQiRJsfze5v61k3q+HM6UZPjVRXh9LC0+D\n3a7yem0Nl9frubFfvl676uWYdrvsjd6t9tP3RNc73AkKfBw215LN9aSyxwkDlVqSdnkcAAD4QAmA\nAQD4uJ25WHbb3pwoQfDM7bLLt954tq/0yLES+raGf938fd78bAnJ/v6flaB3P/f3vqnXjex9nf5D\nJSBc0IjqGFPXnt38UKmWkP/Eyeca63PlxoHvJsu49Ocb682BEhDPz5X3z27Nz5bzmwP7+zkBVOpJ\npSvZbJevbbu12U5SK48DAAAfKAEwAAAcaZXdtiNfJZuV5IfJZPjUs32lg8d2tjP3/p0S8G6FvL+l\npuxOR/a+TK1mZG8nmbqW/OUfkulbpbX+4ljwrmoJdg8PPdtZvbZaPnbmYnLyXLkh4vsbewuAn3+v\nALSXy87dtbnSvK3Uk/pA0juS1HZ501R9IOlqJutzSfcevj6tz5bz625QAQDgwyUABgCALYcGk9//\nD8m/WUiOn9zdrtzlxfLzmdHf5o5cI3vZ8nCmNH+nbyVnx15/c0NPXznu5ng57/BQuWnizGhpBy8v\ndtZ7BXh3VmaShfFkYSJZmS67ezfXS4O3q5k0hpPmaNIcSxo7XJvQe66ct3RjbwHwyp0SPPe6QQUA\ngA/XHr7zAwAAHezcWPLpmV82Hl9nbbUc/8kXZbfvb9HzI3v3wsjeznFjvIx9PnVhZ832pBx36kJy\n+7+UcelJ575XgHdj4Vpy5/9I7v6rZOlm0jWQNC8mB/9O+blroIS4d/9VOW7h2s4et9ZbQuPNzaS9\nuLtr2jq+Obr75jEAAPyGaAADAMDzjrTKrtO11dJ43G487vO2xuMOny7nHdlhQ+ldM7KXJFleSqYm\nkkpld63d5NnxUxPJ7y517nsF3qHl5bVMTj7I3NxK1tbaqddrGRhoZGRkMD09HTxxYeFacu8PyfKt\npO9CUnvha0elWtq73UMllF28ntx7eqNJ8+LrH785lsxfLec1x5LqDm522Vgtx/eeLwEwAAB8wATA\nAADwojNPv7n8xyulKZkkQ8NJ/0DZhdtul0bs/TvlY5+fL4HWmR18U/p96ek1spdyA8DMdHk978XQ\nieTu7fI6Ov9VZ75X4B2YmVnM+PjdTEzMZHp6PgsLq1lf30xXVyXNZneGh/szOtrK2NixtFq7vFnj\nt25lJrl/pYS/Owlna33luIXxcl596PXjoButZOhyCY0XxrcPmZ+3FTL3nC7n7XTcNAAA/EYJgAEA\nYDtnLpam7M2J0m6cmU5mbicbT3fh9jZLG/bMaBll+yG0Gc+NJZNXSwtzJ7tfk2cjez8/b2RvJ1iY\nS5YWkhMn93Z+/6HyXlh4bpR4J75X4C26du1erly5mamph6lWKzlxoj8nTw6kVqum3d7I3NxKbtx4\nmMnJB7l69W4uXz6bixePvu/L3j8L48nSVAlld9LMTcpxfcsJB30AACAASURBVBfKeQsTSeOb15+z\n1RS+f6Wcl5TdwF0DSaWWbLaT9dmy8zcpzd+hyztrGAMAwG+cABgAAF7mSCv5+psy7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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('frames/0000.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ffmpeg version 2.8.11-0ubuntu0.16.04.1 Copyright (c) 2000-2017 the FFmpeg developers\n",
      "  built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.4) 20160609\n",
      "  configuration: --prefix=/usr --extra-version=0ubuntu0.16.04.1 --build-suffix=-ffmpeg --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --cc=cc --cxx=g++ --enable-gpl --enable-shared --disable-stripping --disable-decoder=libopenjpeg --disable-decoder=libschroedinger --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-librtmp --enable-libschroedinger --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxvid --enable-libzvbi --enable-openal --enable-opengl --enable-x11grab --enable-libdc1394 --enable-libiec61883 --enable-libzmq --enable-frei0r --enable-libx264 --enable-libopencv\n",
      "  libavutil      54. 31.100 / 54. 31.100\n",
      "  libavcodec     56. 60.100 / 56. 60.100\n",
      "  libavformat    56. 40.101 / 56. 40.101\n",
      "  libavdevice    56.  4.100 / 56.  4.100\n",
      "  libavfilter     5. 40.101 /  5. 40.101\n",
      "  libavresample   2.  1.  0 /  2.  1.  0\n",
      "  libswscale      3.  1.101 /  3.  1.101\n",
      "  libswresample   1.  2.101 /  1.  2.101\n",
      "  libpostproc    53.  3.100 / 53.  3.100\n",
      "Input #0, image2, from 'frames/%04d.png':\n",
      "  Duration: 00:04:43.44, start: 0.000000, bitrate: N/A\n",
      "    Stream #0:0: Video: png, rgba(pc), 1920x1080 [SAR 4724:4724 DAR 16:9], 25 fps, 25 tbr, 25 tbn, 25 tbc\n",
      "\u001b[0;33mNo pixel format specified, yuv444p for H.264 encoding chosen.\n",
      "Use -pix_fmt yuv420p for compatibility with outdated media players.\n",
      "\u001b[0m\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0musing SAR=1/1\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 AVX2 LZCNT BMI2\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mprofile High 4:4:4 Predictive, level 4.0, 4:4:4 8-bit\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0m264 - core 148 r2643 5c65704 - H.264/MPEG-4 AVC codec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n",
      "Output #0, mp4, to 'output_aye.mp4':\n",
      "  Metadata:\n",
      "    encoder         : Lavf56.40.101\n",
      "    Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv444p, 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 30 fps, 15360 tbn, 30 tbc\n",
      "    Metadata:\n",
      "      encoder         : Lavc56.60.100 libx264\n",
      "Stream mapping:\n",
      "  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))\n",
      "Press [q] to stop, [?] for help\n",
      "frame= 7086 fps= 28 q=-1.0 Lsize=   16256kB time=00:03:56.13 bitrate= 563.9kbits/s    \n",
      "video:16172kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.520091%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mframe I:29    Avg QP:16.28  size: 19498\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mframe P:1825  Avg QP:17.46  size:  4965\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mframe B:5232  Avg QP:29.32  size:  1325\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mconsecutive B-frames:  1.4%  0.2%  0.5% 97.9%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mmb I  I16..4: 22.3% 71.9%  5.8%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mmb P  I16..4:  0.5%  0.9%  0.9%  P16..4:  1.4%  0.8%  0.4%  0.0%  0.0%    skip:95.2%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mmb B  I16..4:  0.0%  0.1%  0.1%  B16..8:  2.5%  0.5%  0.1%  direct: 0.0%  skip:96.7%  L0:50.9% L1:48.0% BI: 1.1%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0m8x8 transform intra:52.3% inter:35.3%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mcoded y,u,v intra: 19.0% 15.8% 13.8% inter: 0.3% 0.2% 0.2%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mi16 v,h,dc,p: 84% 14%  2%  0%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20%  5% 73%  0%  0%  0%  0%  0%  0%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 15% 31%  5%  6%  6%  6%  5%  5%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mref P L0: 59.3%  5.7% 21.7% 13.3%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mref B L0: 82.2% 14.6%  3.1%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mref B L1: 93.7%  6.3%\n",
      "\u001b[1;36m[libx264 @ 0x16c7a60] \u001b[0mkb/s:560.85\n"
     ]
    }
   ],
   "source": [
    "!avconv -y -r 30 -i frames/%04d.png output_aye.mp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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      ],
      "text/plain": [
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('jXSnkhtvLYw')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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