goodvc78/vietnam-analysis-ipython-notebook

## vietnam-analysis-ipython-notebook
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 사용한 color 패턴 : u.make.me.happy (http://www.colourlovers.com/palette/360922/u.make.me.happy)\n",
    "* #5CACC4 #8CD19D #CEE879 #FCB653 #FF5254\n",
    "    \n",
    "<img src='./resource/color-pattern.png' style='height: 400px'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사진 데이터로 보는 다낭(베트남) 여행기 \n",
    "### 아이폰으로 찍은 사진의 시간, 위치 정보를 다낭 여행을 추억(재구성)해 보자 \n",
    "사진이란 나에게 중요한 순간을 기록하기 위한 가장 좋은 수단이다. \n",
    "특별한 여행 중이라면 순간순간의 감정과 상황을 기록하기 적극 활용 한다고 볼수 있다. \n",
    "\n",
    "이번에 마누라와 아들래미와 다녀온 4박 6일 베트남 다낭 여행에서도 나의 아이폰으로 만 250여장의 사직을 찍었다. \n",
    "그래서 이번에는 이번 여행에서 찍은 사진에 담겨진 데이터로 나의 다낭 가족 여행기를 작성해 보고자 한다. \n",
    "\n",
    "#### 우선 찍은 사진으로 부터 수집할수 있는 데이터를 정의해 보자 \n",
    "* 이미지에는 EXIF 메타 데이터에서 다음의 정보를 추출\n",
    " * 찍은 시간 \n",
    " * 찍은 위치 정보 \n",
    "* 공개된 Vision API로 얼굴 이미지 인식 \n",
    " * 나이 예측\n",
    " * 스마일 지수( 웃음 정도 ) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "이번에 아내와 아들래미 셋이서 4박 6일 일정으로 베트남 다낭을 다녀 온 후 사진을 정리하기 위해 \n",
    "아이폰으로 찍은 사진을 맥북의 iPhoto로 다운 받았다\n",
    "\n",
    "iPhoto에서 사진을 보던 중 \"장소\"라는 분류 항목이 있어 누르니 베트남의 위치에 사진이 생긴것을 볼수 있었다.\n",
    "아이폰은 사진을 찍을때 위치정보를 사진의 메타 정보에 입력하고 이것을 iPhoto가 보여준것이다. \n",
    "\n",
    "이것을 보고 있으니 사진의 메타 데이터 로 나의 다낭 여행을 추억해 보고자 한다. \n",
    "\n",
    "사진의 시간, 위치 정보 그리고 이미지 인식(Visison API) API를 통해 사진 등장 인물의 나이, 웃음정도(smiling)의 데이터 분석해 보고자 한다. \n",
    "</pre>\n",
    "\n",
    "<img src='./resource/screen-full-map.png' style='width: 600px'>\n",
    "<img src='./resource/screen-vietnam-map.png' style='height: 300px'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 이미지에서 EXIF 데이터 추출 및 가공 작업"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 추출할 이미지 리스트를 만들다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "import os\n",
    "from os.path import isfile, join\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(style=\"whitegrid\", palette=\"pastel\", color_codes=True, font_scale=1.4)\n",
    "rcParams['font.family'] = 'NanumGothic'\n",
    "\n",
    "imgpath = './resource/image'\n",
    "filenames = [f for f in listdir(imgpath) if isfile(join(imgpath, f)) and f.lower().endswith(\".jpg\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## file rename to uppercase\n",
    "for fn in filenames:\n",
    "    full_path = join(imgpath, fn)\n",
    "    os.rename(full_path,full_path.upper())\n",
    "filenames = [f for f in listdir(imgpath) if isfile(join(imgpath, f)) and f.lower().endswith(\".jpg\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 각각의 이미지에서 EXIF 정보를 추출 한다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "from PIL import Image\n",
    "from PIL.ExifTags import TAGS, GPSTAGS\n",
    " \n",
    "def get_exif(fn):\n",
    "    ret = {}\n",
    "    i = Image.open(fn)\n",
    "    info = i._getexif()\n",
    "    for tag, value in info.items():\n",
    "        decoded = TAGS.get(tag, tag)\n",
    "        if decoded == \"GPSInfo\":\n",
    "            gps_data = {}\n",
    "            for t in value:\n",
    "                sub_decoded = GPSTAGS.get(t, t)\n",
    "                ret[sub_decoded] = value[t]\n",
    "\n",
    "            ret[decoded] = gps_data\n",
    "        else:\n",
    "            ret[decoded] = value\n",
    "\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def toDecimal(gps_pos, direction ):\n",
    "    frac = lambda x : x[0]/x[1]\n",
    "    ret = frac(gps_pos[0]) + frac(gps_pos[1])/60 + frac(gps_pos[2])/3600\n",
    "    if direction in ['S','W']:\n",
    "        ret = ret*(-1)\n",
    "    return round(ret,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 필요한 정보(시간, 위치) 만 선택 및 가공 \n",
    "* 필요한 필드 : GPSLatitude, GPSLatitudeRef, GPSLongitude, GPSLongitudeRef, DateTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## lookup image file list\n",
    "image_exif = {}\n",
    "for fn in filenames:\n",
    "    image_exif[fn] = get_exif(join(imgpath,fn))\n",
    "    \n",
    "filtered_exif = {}\n",
    "for (fn, exif) in image_exif.items():\n",
    "    if 'GPSLatitude' not in exif:\n",
    "        continue\n",
    "    filtered_exif[fn] = {\n",
    "        'latitude': toDecimal(exif['GPSLatitude'],exif['GPSLatitudeRef']),\n",
    "        'longitude': toDecimal(exif['GPSLongitude'],exif['GPSLongitudeRef']),\n",
    "        'datetime' : datetime.strptime(exif['DateTime'], '%Y:%m:%d %H:%M:%S') }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPS위치 정보로 주요 여행지명 변환\n",
    "사진의 GPS 위치와 주요 여행지 대표 이미지의 GPS 위치 중 가장 가까운 거리의 여행지를 해당 사진의 여행지를 선택\n",
    "\n",
    "* 주요 다낭 여행지 \n",
    " * 인천공항 : IMG_1413\n",
    " * 다낭공항 : IMG_1437\n",
    " * 아라카르테 호텔(미케해변) : IMG_1444\n",
    " * 미케해변 : IMG_1445\n",
    " * 오행산 : IMG_1473\n",
    " * 호이안 : IMG_1579\n",
    " * 팜비치리조트(끄어다이해변) : IMG_1522\n",
    " * 다낭 병원 : IMG_1464\n",
    " * 빅씨마트 : IMG_1505\n",
    " \n",
    " <img src='./resource/screen-vietnam-map.png'  style='width: 400px'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['인천공항', 37.44715833, 126.45588611],\n",
       " ['다낭공항', 16.05361111, 108.20314722],\n",
       " ['아라카르테호텔', 16.06826667, 108.24470556],\n",
       " ['다낭병원', 16.05758611, 108.21488889],\n",
       " ['빅씨마트', 16.0666, 108.21109722],\n",
       " ['미케해변', 16.07009167, 108.24665833],\n",
       " ['오행산', 16.00338333, 108.26428889],\n",
       " ['호이안', 15.87625, 108.32641667],\n",
       " ['팜가든리조트\\n(끄어다이해변)', 15.90152222, 108.36000833]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_loc_tbl = \"인천공항:IMG_1413.JPG,다낭공항:IMG_1437.JPG,아라카르테호텔:IMG_1444.JPG,다낭병원:IMG_1464.JPG,빅씨마트:IMG_1505.JPG,\"\n",
    "base_loc_tbl += \"미케해변:IMG_1445.JPG,오행산:IMG_1473.JPG,호이안:IMG_1579.JPG,팜가든리조트\\n(끄어다이해변):IMG_1522.JPG\"\n",
    "base_loc_pos = []\n",
    "for loc in base_loc_tbl.split(','):\n",
    "    val = loc.split(':')\n",
    "    base_loc_pos.append([val[0], filtered_exif[val[1]]['latitude'], filtered_exif[val[1]]['longitude']])\n",
    "    \n",
    "base_loc_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image, HTML, display\n",
    "from glob import glob\n",
    "\n",
    "def drawImages(imagePathList, size='120px'):\n",
    "    imgtag = \"<img style='height: \"+size+\"; margin: 0px; float: left; border: 1px solid black;' src='%s' />\"\n",
    "    imagesList=''.join( [ imgtag % str(s) \n",
    "                         for s in imagePathList ])\n",
    "    \n",
    "    display(HTML(imagesList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "인천공항      다낭공항      아라카르테호텔      다낭병원      빅씨마트      미케해변      오행산      호이안      팜가든리조트(끄어다이해변)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1413.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1437.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1444.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1464.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1505.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1445.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1473.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1579.JPG' /><img style='height: 90px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1522.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_places = []\n",
    "base_images = []\n",
    "for loc in base_loc_tbl.split(','):\n",
    "    items = loc.split(':')\n",
    "    base_places.append(items[0].replace('\\n',''))\n",
    "    base_images.append(\"./resource/image/\"+items[1])\n",
    "print( \"      \".join(base_places ) )\n",
    "drawImages(base_images, \"90px\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import distance\n",
    "import numpy as np\n",
    "\n",
    "def nearest_loc(latitude, longitude, base_locs):\n",
    "    locs = [ loc[1:] for loc in base_locs]\n",
    "    distaces = distance.cdist(locs, [[latitude, longitude]])\n",
    "    nearest_idx = (np.argmin(distaces))\n",
    "    return (base_locs[nearest_idx][0], nearest_idx)\n",
    "\n",
    "for fn in filtered_exif.keys():\n",
    "    info = filtered_exif[fn]\n",
    "    (loc_name, idx) = nearest_loc( info['latitude'], info['longitude'], base_loc_pos )\n",
    "    filtered_exif[fn]['place'] = loc_name\n",
    "    filtered_exif[fn]['place_idx'] = idx\n",
    "\n",
    "    #nearest_loc(15.873514, 108.325981, base_loc_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import urllib.parse\n",
    "import urllib.request\n",
    "import json\n",
    "\n",
    "def face_detect(imgurl):\n",
    "    url = 'http://apius.faceplusplus.com/v2/detection/detect'\n",
    "    headers = { 'User-Agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.101 Safari/537.36',\n",
    "               'Origin':'http://www.faceplusplus.com', \n",
    "               'Accept-Language':'ko-KR,ko;q=0.8,en-US;q=0.6,en;q=0.4', \n",
    "               'Content-type':'application/x-www-form-urlencoded', \n",
    "               'Accept':'*/*', \n",
    "               'Referer':'http://www.faceplusplus.com/demo-landmark/', \n",
    "               'Connection':'keep-alive'\n",
    "              }\n",
    "    values = {'attribute' : 'none',\n",
    "              'url' : imgurl,\n",
    "              'api_key' : 'e6f2c06ac2b93b1ae6aad80b90d49eb0',\n",
    "              'api_secret' : '8o_amgigF4dV4EHZ2OOVEejDeXq4qFab',\n",
    "              'attribute':'glass,pose,gender,age,race,smiling'}\n",
    "\n",
    "    data = urllib.parse.urlencode(values)\n",
    "    data = data.encode(u'utf-8')\n",
    "    req = urllib.request.Request(url, data, headers)\n",
    "    response = urllib.request.urlopen(req)\n",
    "    response_data = response.read()\n",
    "    response.close()\n",
    "    return json.loads(response_data.decode('utf-8'))\n",
    "## sample\n",
    "face_detect('http://historie.kr/img/veitnam_trip/IMG_1643.JPG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "face_info = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## crawling facial detection \n",
    "import time\n",
    "\n",
    "def crawlingFacialInfo(filenames, face_info):   \n",
    "    for fn in filenames:\n",
    "        if fn in face_info:\n",
    "            continue\n",
    "        time.sleep(0.5)\n",
    "\n",
    "        img_url = 'http://historie.kr/img/veitnam_trip/{fn}'.format(fn=fn)\n",
    "        print(img_url)\n",
    "        try:\n",
    "            info = face_detect(img_url)\n",
    "        except:\n",
    "            print('error : ' + img_url)\n",
    "            continue \n",
    "        face_info[fn] = info\n",
    "    return face_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## save the facial information on pickle file\n",
    "import pickle\n",
    "\"\"\"\n",
    "## crawling all face info \n",
    "face_info = crawlingFacialInfo(filenames[:], face_info)\n",
    "\n",
    "## serialization face_info data \n",
    "with open('./resource/facial_info.pickle', 'wb') as handle:\n",
    "    pickle.dump(face_info, handle)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## load the fcial information from pickle file \n",
    "import pickle\n",
    "with open('./resource/facial_info.pickle', 'rb') as handle:\n",
    "    fi = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filtered_exif_tmp = filtered_exif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def classificationFace(age, gender):\n",
    "    if age<10:\n",
    "        return 'junior'\n",
    "    elif gender=='Female':\n",
    "        return 'wife'\n",
    "    return 'me'\n",
    "\n",
    "for fn in filtered_exif.keys():\n",
    "    if fn not in fi or len(fi[fn]['face']) == 0:\n",
    "        continue\n",
    "    person = 0\n",
    "    tmp_map = filtered_exif[fn] \n",
    "    for label in \"me_smiling me_age wife_smiling wife_age junior_smiling junior_age\".split():\n",
    "        tmp_map[label] = 0\n",
    "\n",
    "    for info in fi[fn]['face']:\n",
    "        person += 1\n",
    "        \n",
    "        smiling = info['attribute']['smiling']['value']\n",
    "        age = info['attribute']['age']['value']\n",
    "        gender = info['attribute']['gender']['value']\n",
    "        target = classificationFace(age,gender)\n",
    "        \n",
    "        tmp_map[target+\"_smiling\"] = smiling\n",
    "        tmp_map[target+\"_age\"] = age\n",
    "        \n",
    "        if person >= 3:\n",
    "            break\n",
    "    tmp_map['person'] = person\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'datetime': datetime.datetime(2016, 4, 19, 7, 43, 35),\n",
       " 'junior_age': 0,\n",
       " 'junior_smiling': 0,\n",
       " 'latitude': 37.36103333,\n",
       " 'longitude': 127.10181389,\n",
       " 'me_age': 27,\n",
       " 'me_smiling': 49.711,\n",
       " 'person': 1,\n",
       " 'place': '인천공항',\n",
       " 'place_idx': 0,\n",
       " 'wife_age': 0,\n",
       " 'wife_smiling': 0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_exif['IMG_1405.JPG']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 분석하기 용이한 데이터 pandas dataframe으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_meta_ds = pd.DataFrame.from_dict(filtered_exif, orient='index')\n",
    "image_meta_ds['hourofyear'] = image_meta_ds.datetime.apply(lambda x: x.dayofyear*24+x.hour*60+x.minute )\n",
    "image_meta_ds['ymd'] = image_meta_ds.datetime.apply(lambda x: x.strftime(\"%Y-%m-%d\") )\n",
    "image_meta_ds = image_meta_ds.reset_index()\n",
    "image_meta_ds['image_idx'] = image_meta_ds['index'].apply(lambda x : int(x.split('_')[1][:4]))\n",
    "image_meta_ds.latitude = image_meta_ds.latitude.apply(lambda x : round(x,4))\n",
    "image_meta_ds['imagepath'] = image_meta_ds['index'].apply(lambda x: './resource/image/%s' % x)\n",
    "image_meta_ds['smiling'] = image_meta_ds.apply(lambda x: (x.me_smiling+x.wife_smiling+x.junior_smiling)/x.person , axis=1)\n",
    "image_meta_ds['age'] = image_meta_ds.apply(lambda x: (x.me_age+x.wife_age+x.junior_age)/x.person , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>place</th>\n",
       "      <th>datetime</th>\n",
       "      <th>person</th>\n",
       "      <th>me_smiling</th>\n",
       "      <th>wife_smiling</th>\n",
       "      <th>junior_smiling</th>\n",
       "      <th>smiling</th>\n",
       "      <th>me_age</th>\n",
       "      <th>wife_age</th>\n",
       "      <th>junior_age</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> IMG_1404.JPG</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td>2016-04-19 07:43:35</td>\n",
       "      <td> 1</td>\n",
       "      <td> 10.92360</td>\n",
       "      <td>  0.000</td>\n",
       "      <td> 0</td>\n",
       "      <td> 10.92360</td>\n",
       "      <td> 25</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> IMG_1405.JPG</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td>2016-04-19 07:43:35</td>\n",
       "      <td> 1</td>\n",
       "      <td> 49.71100</td>\n",
       "      <td>  0.000</td>\n",
       "      <td> 0</td>\n",
       "      <td> 49.71100</td>\n",
       "      <td> 27</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> IMG_1406.JPG</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td>2016-04-19 07:43:40</td>\n",
       "      <td> 1</td>\n",
       "      <td>  6.50349</td>\n",
       "      <td>  0.000</td>\n",
       "      <td> 0</td>\n",
       "      <td>  6.50349</td>\n",
       "      <td> 43</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> IMG_1407.JPG</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td>2016-04-19 07:43:40</td>\n",
       "      <td> 1</td>\n",
       "      <td>  9.00829</td>\n",
       "      <td>  0.000</td>\n",
       "      <td> 0</td>\n",
       "      <td>  9.00829</td>\n",
       "      <td> 51</td>\n",
       "      <td>  0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> IMG_1408.JPG</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td>2016-04-19 07:43:55</td>\n",
       "      <td> 2</td>\n",
       "      <td> 45.15130</td>\n",
       "      <td> 55.404</td>\n",
       "      <td> 0</td>\n",
       "      <td> 50.27765</td>\n",
       "      <td> 43</td>\n",
       "      <td> 19</td>\n",
       "      <td> 0</td>\n",
       "      <td> 31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          index place            datetime  person  me_smiling  wife_smiling  \\\n",
       "0  IMG_1404.JPG  인천공항 2016-04-19 07:43:35       1    10.92360         0.000   \n",
       "1  IMG_1405.JPG  인천공항 2016-04-19 07:43:35       1    49.71100         0.000   \n",
       "2  IMG_1406.JPG  인천공항 2016-04-19 07:43:40       1     6.50349         0.000   \n",
       "3  IMG_1407.JPG  인천공항 2016-04-19 07:43:40       1     9.00829         0.000   \n",
       "4  IMG_1408.JPG  인천공항 2016-04-19 07:43:55       2    45.15130        55.404   \n",
       "\n",
       "   junior_smiling   smiling  me_age  wife_age  junior_age  age  \n",
       "0               0  10.92360      25         0           0   25  \n",
       "1               0  49.71100      27         0           0   27  \n",
       "2               0   6.50349      43         0           0   43  \n",
       "3               0   9.00829      51         0           0   51  \n",
       "4               0  50.27765      43        19           0   31  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_meta_ds[['index','place','datetime','person'\n",
    "               ,'me_smiling','wife_smiling','junior_smiling', 'smiling'\n",
    "               ,'me_age', 'wife_age', 'junior_age','age']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사진 전체 스냅 샷 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1404.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1407.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1410.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1413.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1416.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1419.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1422.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1425.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1428.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1431.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1434.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1438.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1441.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1444.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1451.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1454.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1458.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1461.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1464.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1467.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1470.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1473.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1477.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1480.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1483.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1486.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1489.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1494.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1512.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1517.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1525.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1531.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1537.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1542.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1554.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1558.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1562.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1569.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1573.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1579.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1588.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1592.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1597.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1611.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1630.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1637.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1640.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1643.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1647.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1654.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1671.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1676.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1680.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1685.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1692.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1699.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1706.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1718.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1721.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1724.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1737.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1780.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1783.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1786.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1789.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1796.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1803.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1806.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1810.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1813.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1818.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1821.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1826.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1832.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1835.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1842.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1847.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1850.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1862.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1865.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1868.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1871.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1874.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1877.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1880.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1883.JPG' /><img style='height: 70px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1889.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawImages(image_meta_ds[::3].imagepath.values, size='70px')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사진을 가장 많이 찍은 호이안과 팜가든 리조트! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mostSimiling(place, person=1, top=3):\n",
    "    tmp_ds = image_meta_ds[(image_meta_ds.place == place ) & (image_meta_ds.person >= person )].sort(['smiling'], ascending=False)    \n",
    "    drawImages(tmp_ds.imagepath[:top].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "호이안에서 가장 잘 웃는 사진 3장 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1596.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1580.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1649.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( \"호이안에서 가장 잘 웃는 사진 3장 \")\n",
    "mostSimiling('호이안', 0, 3) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "팜가든리조트에서 가장 잘 웃는 사진 3장 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1781.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1870.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1871.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( \"팜가든리조트에서 가장 잘 웃는 사진 3장 \")\n",
    "mostSimiling('팜가든리조트\\n(끄어다이해변)', 0, 3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "이번 여행에 한해서는 정답이다. 사진을 가장많이 찍은 호이안과 팜가든리조트가 여행중 가장 즐거웠던 순간이었다. \n",
    "\n",
    "그다음으로 많이 찍은 인천공항은 여행을 설레임을 갖고 출발해서 많이 찍은듯 하다. \n",
    "\n",
    "특이할 만한 것은 해변근처인 아라카르트 호텔은 2박이나 했음에도 불구하고 사진을 아주 적게 찍었는데 <br>\n",
    "그 이유는 그냥 아래 사진을 보면 바로 알것이다!!! 그렇다 우리방 전망이 공사중이라 사진찍을 생각이 전혀 안들었다. \n",
    "<img src='./resource/alacarte.JPG'  style='width: 600px'>\n",
    "\n",
    "결과적으로 사진을 많이 찍은 곳이 즐겨웠던 장소이고 순간이었듯 하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAGlpafD29jZaxtvbG8nJyWW9aSIiIptTpkF99OhRXL16Ff369QMAZGdnw9HR\n0Wg5tVqNnJycstw0ERGRTSqzLkSTkpIwd+5cfP7556hSpQqAgkDWaDRGy2o0GvnyOBERERWtTIL6\n33//xdtvv42pU6eiSZMm8nRvb2/cvn3baPmUlBTUqVOn2NdMSEgwa9sVdQn9119/RWZmZrm9PvfD\nfLawD0D570dJmfu/Jzpb2A9b2AeA+1FapQ7q3NxcTJgwAa+++ioCAwMN5rVs2RIREREG065cuQJX\nV1fUrFmz2Ndt1aqVWdt3d3c3e8Si0vD39y/XkY64H+azhX0Ayn8/SiIhIcHs/z2R2cJ+2MI+ANwP\nS16/KKW+Rx0eHo5atWph/PjxRvNat24NANi9ezeAgkveCxYsQEhISGk3S0REVCmU6ow6JSUFsbGx\naNSokfysNACoVCqsXbsWHh4eiIqKQnh4ONatW4f8/HwEBgZi8ODBpS6ciIioMihVUNepUweXL18u\ndhkvLy+sXr26NJshIiKqtDgoBxERkcAY1ERERAJjUBMREQmMQU1ERCQwBjUREZHAGNREREQCK7O+\nvolILFqtFomJiRatk5ycXNAzmwV8fHygVqstWoeIzMegJrJRiYmJmBB9AM4enpataEH3qTnpafhs\nQDfhukIlsiUMaiIb5uzhiaqedZUug4hKgfeoiYiIBMagJiIiEhiDmoiISGAMaiIiIoExqImIiATG\noCYiIhIYg5qIiEhgDGoiIiKBMaiJiIgExqAmIiISGIOaiIhIYAxqIiIigTGoiYiIBMagJiIiEhiD\nmoiISGAMaiIiIoExqImIiATGoCYiIhIYg5qIiEhgDGoiIiKBMaiJiIgExqAmIiISGIOaiIhIYAxq\nIiIigTGoiYiIBOagdAFEREXRarVITEy0eL3k5GS4u7ubvbyPjw/UarXF2yGqCAxqIhJWYmIiJkQf\ngLOHp+UrJ58za7Gc9DR8NqAb/Pz8LN8GUQVgUBOR0Jw9PFHVs67SZRAphveoiYiIBMagJiIiEhiD\nmoiISGC8R01EVM5K0nrd0pbrAFuv2yoGNRFROStx63UzW64DbL1uyxjUREQVgK3XqaR4j5qIiEhg\nDGoiIiKBMaiJiIgExnvURERklopovc6W68YY1EREZJbybr3OluumMaiJiMhsbL1e8XiPmoiISGAV\nckYdGxuLjRs3QqVSwc3NDfPmzUODBg0qYtNERERWrdyDOj4+Hps3b8aWLVvg6uqK+Ph4vPXWW9iz\nZw8bDBCsfEExAAAgAElEQVQRET1GuQd1dHQ0Jk6cCFdXVwBAhw4d0LhxY5w6dQovvvhieW+eiIhI\nVpKW64Cyrdcr5Ix6wYIFBtPatWuHkydPMqiJiKhClbjlOqBY6/VyDeoHDx7Azs4OTk5OBtPr1KmD\n48ePl+emiYiITLK2luvlGtTZ2dlwdHQ0mq5Wq/Hw4cNi123Xrp3RtNOnT5tcNmHZbKjsjBuwt5ww\n2+Ty5z6ba3J6UcsnLJuNfmud4eBg+Osqqh5TtT9u+by8PKTfzzHYj7KqX7+8pNMZ7EdZ1q9XeD/K\nun7AcB/Ko37gf/vQaqLpOktTv56k0wGvxJlVj56l+7tt2zbkpKeZVQ9QsuPN1OuXVf365R/dRlnW\nb+r1y7p+Pf12yrr+R1+/vOrXK8/325z0NPTr18/ovba4eiytv1+/fkbvtUXVA5Ts7/Xoe21x9ejr\nj4qKMjkfAFSSJElFzi2ljIwMdO/e3ajAw4cP46uvvsKqVatMrpeQkFBeJREREQmpVatWJqeX6xl1\njRo1oNVqkZOTA2dnZ3l6SkoKvL29i1yvqGKJiIgqm3Lt8ESlUiEgIMDojPqnn35iGBMREZmh3Hsm\nCw0NxfLly3Hv3j0AwKlTp/Dbb7/h1VdfLe9NExERWb1yfzyrS5cuSE1NxcCBA6FSqVC9enWsXr2a\nnZ0QERGZoVwbkxEREVHpcFAOIiIigTGoiYiIBFYpxqNOT0+Hh4eH0mVUiOTkZNy4cQNZWVlwd3eH\nr68v6tWrp3RZsmvXrqFRo0ZKl1FqleWYunLlCp566imlyyiWtR5TGzduhFarNZru6OiIkJAQ7N+/\nH0lJSWjevDnatGmDDz74AB999JEClZLe3bt3UaNGjQrfbqUI6iFDhmDfvn1Kl1FuNBoNdu7ciR07\ndsDNzQ1+fn5wc3NDVlYWrl27hszMTAQHByM4OFjxRnzvvvsu4uJM98hlTUJDQ61yP2bNmoW8vLxi\nlwkJCUGTJk0AAFOmTBF+P631mFKpVEY9cEmSBHt7ewDAxx9/jDfffBMRERHYtWsXLly4oESZxTLn\neAKAvn37omXLlujfvz9iYmIqoLLyMWjQIEWypFIE9ePay61cuRLjxo2roGrK1vnz57Fy5Ur897//\nRUxMDKpWrWq0zL///ovY2Fi89dZbCAsLQ4sWLSqsvszMTOTn5wMo+Dvk5eUhPT1dnu/m5gYHBwfc\nvn0bdnZ2xXaEY01EPaY6deqE3NxcHD58GE2aNIGbmxv27t2LAQMGQJIkqFQq1K5dW+kyi2Urx1RI\nSEix82vUqIF33nkHR44cqaCKLNepUyfk5eXJ77EqlUo+jiRJwvz58zFjxgz5b3D//n0lyy3WxYsX\nERAQAKDgPdPFxQXHjh1D586d5WWUanttk0Hdrl075OTkGEzT/wH0oqKi8PzzzwMADhw4IOSbqjku\nXbqEVatWmewbV8/FxQVDhgxBcHAwtm3bVqFBHRwcjNzcXINpffv2lb+fM2cOPDw8MG7cODg4OOCz\nzz7D008/XWH1mctWjqlXXnkFAHDr1i107NgRXl5euHDhAgIDAxWuzHy2ckx16dJFPqb0wQAAw4YN\nw1tvvaVkaWbTH0+PGjVqFL744gusWLHCao6t6dOnY//+/QAKzpy//vprREZGGgS1UmwyqIvq/Nxa\n/fDDD3jhhRdMzhs6dKjZr6NWqxEaGlpGVZnn4MGDAAr6fa9Zs6bJZWbMmIGFCxfCwcEBX375JSIj\nIyuyRLPY2jEFFJz9qFQqpcuwmK0cU99//738fZ8+fbB582aTV8REl5eXh59//hlubm74z3/+A6Dg\n5CA1NVXhykpOp9MpXYIBtvq2AhEREWXyOl999VWZvE5JFP5A8ejloytXrqB169Zo3rw5rly5UtGl\nlUpqaqp8GdZatGnTBlFRURg0aBC6du2KuLg4BAUFKV2WxWzpmGrUqBEcHBwQHx9vVffbU1JS0K9f\nP6xZswazZs3CiBEjoNFoUL9+fdy+fRvh4eFKl2gTbDaoJUnCsmXL0K5dO7Rt2xaLFy8W8g1169at\n8vfx8fEAgBUrVpi1brdu3fDSSy8V+/Xjjz/Ky3/55ZdlW7wZHjx4AADy737p0qV4/vnnERQUhKSk\nJAAwOKuzpjM8rVaLESNGWEUQFHbmzBlcunQJPj4+OH/+PH755RdoNBqlyzKbLR1TH3zwAQCgc+fO\nUKvVcHR0xNmzZxWuynwRERF45513sGbNGsTExKBFixZYt24dvLy8kJ6ejg4dOihdok2w2aBet24d\nrl+/jn379mHv3r24fv06vvjiC6XLMrJlyxb5+08++QQAsHfvXrPWjY6Oxvbt2xEYGIh58+bh888/\nR7NmzbBjxw5s374d27dvR5s2bcqlbnMFBwfL31+4cAFXrlzB8ePHMX36dHl/reVNtbCHDx9i7Nix\n6NmzJ5555hmlyym14to4iMaWjin9kL76IX8bNGiAmzdvGiyj/0Ai4onGn3/+aXBbbvDgwYiPj4e7\nuzvWr1+PmTNnYtOmTQpWaBtsNqh37tyJefPmwcPDA7Vr18bHH3+M3bt3K11WmapRowZq166NatWq\noUaNGvDw8ICTkxNq1aqF2rVro3bt2vKjHkop/OjG2bNn0adPH9jZ2aFDhw64c+cOAMDOzg5arRa5\nubmwMzEgvSguXbqEtLQ0nDx5En379sXzzz+P0aNHK11WmRsxYgSGDBmCIUOGICMjQ+lyjNjSMfUo\nd3d3ZGdnA/jfhycnJyc0a9YMTk5OSpZmFo1GA0dHR1SvXh116tRBp06dDD7IivhhwxpYz8doC92/\nfx/u7u7yz+7u7gadC/z222/yYwUiPzLwOL///jvS0tJw/fp13L17F3fv3sWdO3eEfCTFzs7OoJGG\n/vtOnTrhs88+g4ODQ5GN5kSwcOFC/PXXX0hNTcXIkSONHq9JS0uTjylrupT8qEmTJiE3NxcqlQpe\nXl5Kl1Msaz+m9PQfJuzt7fHw4UMAQGxsLABg165ditX1OO3bt8eyZcswbtw4aDQaREREoEePHnB0\ndETDhg0RGBgInU6H1q1bIzc3F35+fkqXbJVsNqjr1q2L33//XX4s4/Lly6hVq5Y8f+HChfKb0Ysv\nvqhQlaUXGRkJnU6H3bt3o2nTpgCA3r17y/e7lVb4smOHDh3wySefoGPHjjh27BgaNmwIABg9ejSm\nTZsGlUqFBQsWKFTp4+nv8Z85cwZffPEFBgwYgGXLlslhNnLkSPmYErk3r65duyI/Px9paWl46aWX\nAMDgg4W/v79SpZnFlo6pe/fuoXv37khOTkb37t2Rl5eH+vXrK12W2SZNmoTIyEi89NJLqFKlCgYN\nGoSgoCAkJCTIj9DZ2dlZxX33jIwMzJ8/HwDw999/Y/78+bh7967CVRWw2aCeOHEixo8fj/79+0On\n02HHjh3y/SsAWL9+vYLVlR19eLz++uvYvHmz/L0oNmzYIH/fpEkTvPjiiwgMDETDhg2xcOFCAAWX\n9pYvX65UiRZr06YN2rRpg+joaAwdOhRbt25FrVq1sGfPHqVLM8vOnTuNHj+pVq2aQtVYzpaOqQMH\nDiAzM1P+2c7ODp6engpWZBlnZ2fMnTsXc+fONZhuTbcb9CIiIpCSkgIAGD9+PFQqVZk9cVNaNhvU\nHTt2xIoVK+Q3z1WrVsnP+FVWStwf0p9t6rsuHT58OIYPH17hdZSHAQMGQKvVIiIiAosWLVK6HLMV\nviVkjWzpmHJxcZE7OilKcf0oiKpFixYV2rFSWdBfXSqOUg0TbTaoAeDpp58WskeiiqTvUUulUil6\nf+jrr79WbNvlKSQkxKrbOFgzWzimHjx4gKysLNSpU6fIZSIiIqwuqG3BH3/8YXRyp2+dX9FsOqit\nQUZGBt544w2oVCrcunULffr0MbgU9jipqanIy8tDbm4ubt++DUmS5MYogLg9agUFBdlMK3xrumxc\nuNtKvcLdVz6qatWq+O677yqitFKzxmMqISEBR48exaxZs5QupURWrVpV5KAcXl5e6Nu3L06cOIGj\nR4/iv//9r1U9Vx0WFiZ3Kar3xBNPKFKLTQZ1UcPHFUWJrjX19u7da/QIjCXDJ44aNQqSJMHBwUHu\nH/jJJ58s0xpLIz8/Hzk5OahWrZrBZaPCjZfy8/ORnJyM+vXrC3tv64033jDqX7qwjz/+GI0aNcKk\nSZNgb2+PRYsWCfk4TeFuK/W6d+9u9IYkMls5pgDDHtW++OIL7Nq1Cx06dMDMmTNRpUoVBSszj5OT\nE7RaLeLj4+Hr6wudTodr167hhRdeQJUqVXD69GnMmTMHoaGhmDdvHubNm6d43w6mFB7oBSj4u+h0\nOoPBXgqzt7ev0FtINhnUDx8+NGvoNT0l+3WtVauWQWt0U4o7IERvwNS9e3fk5ubi/v37cHJyQteu\nXQ3OHrRaLQYPHoyUlBR4enpi27ZtcHR0VLBi09atW4e8vDx5VKChQ4di8+bN8huth4cH1qxZg2ee\neQZVqlTBxo0bMWbMGIWrNnbixAmjN6ScnBwcO3bMaFkHBwd07NixIsszi60cUxkZGfIHjSNHjuDw\n4cN4//338dVXXyEqKgoTJ05UuMLH05/gaLVaeWS2I0eOyP0LjB8/Hh9//DHatWsHf39/rF+/Xsig\nNjXQC2A42Ethjo6OFfrh1iaDWsQ3yNKYPXu20iWUmL29Pb799lsABY+i/P333wbzN2zYAH9/f+zY\nsQPz58/HunXrMHbsWCVKLVZ2drb8j6z/tK2/RVGzZk3Y2dnh2LFjWLt2Lezs7DBs2DAhj8MDBw4Y\nNSps3749Dhw4YLSsqEFtK8fUgAED5C5E9+zZg/fffx8BAQFo3rw5+vfvbxVBXZipQV6uX7+Odu3a\nAShoYHbjxg0lSnss/UAvorLJoH5Ufn4+MjMzUbNmTaG7EwQKzu4zMjIMzrInTZpkcrDyc+fOGV05\nKDwebGGSJKFKlSqKtsR0dXWFq6urwbRvv/0WS5cuBVAwyMK4ceOEfFOdM2eOwe/a09NTfiSla9eu\nGDp0KLRarXy/2pIrOhXpo48+kr9fs2aNQc9qpo4b0VnzMaW/GiNJEpKSkuRn711cXKDT6TB58mTF\nxj+2RGhoKG7duoXvvvsOkiQhMzMTrq6uJn/n1rA/IrLpoN67dy82bNiA69evw8XFBffu3UOrVq3w\n9ttvo3Xr1kqXZ9Lp06fxzTff4OOPP5anFXVwb9261ehyzcmTJ4s8C1I6qE1JT0+Hj48PAKBOnToW\nNaSrSPpnd021BNUr/HcSbZi8wrKzs1G9enXExsZi9OjROHv2LObOnYu7d+/Cz88PkZGRxbZCFp21\nHFN6KpUKzs7O0Gq18iV6nU6H7t27AyjofVBkU6dOlT9YLF68GLm5uQgPD8fYsWMNPvhJkiR0e4Ep\nU6bg/PnzJt9vn3zySaxdu1aBqgrYbFBv2rQJcXFxeP/999G8eXOoVCrk5eXh5MmTCA8Px6xZs4Ro\ngTh9+nS89tpr8uMX+/fvR7du3cxa19Szu927dxe2o4cNGzbI3SHq/4Gt7Qxu0qRJiIuLw6lTp7Bm\nzRo88cQTmDZtGlxcXAzehETer4EDB+Kbb74BUHBv8aOPPsKqVatQr149HD58GLNnz8aaNWsUrtI8\n1nxM+fr6yt8/9dRTOH78OAIDA3H9+nW4urri5ZdfBmD6/1wk+p7sHB0d5d4R9Ro1aoT4+Hh06NAB\nCQkJQjV0fdSvv/6Kbdu2GX2YkCQJ/fv3V6iqAjYb1PrRowo/duLg4IDOnTvjySefxIwZM4QI6u++\n+w5nzpzBkiVL0LBhQ5w9e9bse9L6T6+FZWRkYMGCBUafCtVqNd59990yq7skgoOD5bOEYcOGASho\nNfrgwQNUrVoVDx8+FLal64QJE/DZZ58BALKysvDJJ59g4cKFSEhIwIIFCzBv3jy4ubnJDYQe14mF\nkgrfo75x4wZatGiBevXqAQBefvllLFu2TKnSLGbNx9Tq1atx/PhxAMCgQYMwbNgwnDx5EvHx8Zg5\nc6bC1ZWNkSNH4t1330WXLl3www8/yD3HicjBwaHIvu2Vboxos0ENoMg3y7p16+LevXsVXI1ptWrV\nwsKFCzFx4kS0bdsWgwYNMnvEq0aNGhk1DJo+fbp8RqG/56i/P620qlWromrVqgbT2rZti8OHD6Nn\nz544duwYWrVqpVB1xSs85vTFixfx3HPPoUmTJmjSpAneeOMNAAVdt86aNQsODg7o2bOnUqVaxNvb\nG9euXZN/Tk1NFeJYMZc1H1OFPfnkk1i9ejWOHDmCyMhItG3bVumSykRAQACWL1+OU6dOYenSpTYx\nJKwSbDao69Wrh5iYGJOXLJYvXy7UPWp/f3+EhYUhPDwcc+bMQWpqqsE41UUpHAbjx4/HihUrABSM\n4nTr1i00adJE6DM7oKAhyogRI3D16lXs379fsZ5/LOHs7Cx/0NNqtfIHq6CgIGRkZMDOzg59+vRR\nssRi9evXT/7e3d0dHTt2RN++fdGoUSMkJCRYbecbetZ0TFWrVk1uOKr/4GdtQkJCoNPpkJSUhCFD\nhiA/Px/Ozs7yfH9/f+EHehGdzQb13LlzMW7cOERHR6NNmzZwcXFBZmYm4uPj0aBBAyxZskTpEg30\n7NkT3377LY4cOYK2bdvCw8NDPhsujr7x2PXr1wEAW7Zswfbt2+Hr64vffvsNn3zyiaKfzsPCwoqd\n7+vri6VLl+Lo0aNYuHChsP2xDx48WP6+WbNmmDdvHqKiovDLL7/gtddek+dZQ5/T+kvE+isvY8aM\nQffu3ZGYmIipU6eiZs2aSpb3WLZyTAFAy5Yt0bJly2KXcXNzq6BqSmb69OkGV/ZUKhXq1q2rYEUl\no9VqER4ebvKJGaXHZVdJNt5e/uLFi/j1119x79491KhRAy1bthRqTNTCvULdvHkTY8aMwTfffGNw\n+bu4nqP087p3747t27djxIgRiImJgb29PRITEzFp0iTs3LmzQvbFEtbWG5Ze7969ERsbi4yMDOzd\nuxc+Pj5WO0xqRkaG8KFsCWs9pmyJNT7ip3fx4kX5hOdR3t7eirZpstkzar2AgAAEBASYnBceHo55\n8+ZVcEVFa9CgAZo1a4aDBw8iMDDQ4vVv376NgIAAOeR9fHwM+v0WyYcffqh0CSWib2Fcs2ZNDBky\nxGi+aMdUcWwppAHrO6b+/PNPi0a0c3BwkMfbFlWvXr2E7y2xKMVlhdJsNqj//PNPXLlypdhHnc6c\nOVOBFZmmH6pPb8iQIYiMjCxRUPv6+uLnn3+Wz5ROnTqF2rVrl1WpZUqkNgKWeLSh3qNEOKaK0717\nd4M+sR/HycnJZGc7IunZsyf27NljdcfUhx9+WGz/8Y+qUqUK1q9fX44VlV5R+2PNZ9oisNmgTkpK\nwpkzZ9CtWzdotVpcvnwZ//nPfwwaOYjg//7v/wx+9vf3h5eXF/Lz881q/V34LMLR0RHvvPMOBgwY\ngLy8PHh4eODTTz8t85otcejQoce+GT311FPy7YiZM2cKM1i7KTt27MCnn36KnJwc9O/fX+4C0lrE\nxsZadBYncgcVetYaDqKHrjke/eCXlpYmj+vctGlTLF68GO+88w6OHj2Kl156CUuWLDH7qZaKFBcX\nZ1FvglWqVEGPHj3KsSJDNhvUevfu3cPQoUPxzz//ACgYWUuke9QODsZ/gkefNSzuzVJ/FqHvialT\np044ePAgNBqN4s/+AQWD3hf+B5AkCYcPH0bXrl3laWq1Wv6b/PLLLxVeo7l+/PFHbN68GbGxsXBy\ncsK7776Lzz//HG+//bbSpZnt0KFDZge1g4NDhb4ZmctWwmH48OFGHzKKakCqVquxbt26iirNbFu3\nbjU6nvQfjhwdHfHll1+iRo0auHDhAiIiIvDll18K2eDywoULFl/dqMj/DZtsTLZjxw7UqVMHR48e\nhaenJ+7fv49JkyZh79692L9/v/wYk7U0Pnn48KGQQyaWVHG/99dffx1xcXEVXJF5wsLCMGDAALlR\nyV9//YWBAwfi1KlT8jKiH1OmOsnZtWuX/Cx4YVWqVMGkSZMqqjSzZWRkFBsOO3bswF9//YU5c+Yg\nIiICdevWFTIcbty4YfAhNikpCVu3bsWMGTMMrgboh7EVuVevwnbv3g21Wo3AwEC8+eabiIqKgqen\nJzIyMjB69Gh89dVXSpdoseLGbK8INnlGvXbtWvlZ0J9++knuNzswMBDLly+3aKxqEdhSSAP/e1M9\nd+4catasKXwDGb3Lly/LIwEBQMOGDeHk5IQbN26gfv36ClZmPlPBe/ToUUyfPl2BakrGVCO4wuGw\nf/9+REVFwd7eHmPHjsXo0aOFDOpHg9fBwQEuLi7y42Rnz55Fbm6uED0oWsLZ2Rnnzp1DYGAg7t+/\nD09PTwAFf7ecnByFq7Pc1atXMXHiRKxZs0a+clnRbDKo9fTPv+k7FFCpVLC3t5cHrVC6odXbb79t\n8eWWzz//vMzWr0g7d+6ERqNB//79sXXrVkiShLlz52Lx4sWK1FMS+fn5Rrch3N3dERwcLF9atYaW\n1CtWrMDJkyfh6+tr8MHDmllrOERFRcmjTDVo0MBgxKns7GwkJCRYXVDXr19f7kv+0TYCIrcZSE1N\nhZOTE6pXry6PDbFz507ExMRgwYIFioU0YONBrVKpUKtWLdy5cwf169eHTqdDfn4+zpw5A0mSStSy\nuiyNGjWq2KAtfOkLgFHXjo9b/1FKdA0ZGxuL3r17Y9OmTahXrx42b96MTz/9FNu2bUObNm2Eai/w\nOA4ODsjLyzNoV5Ceno49e/bIo03p+50W2f79+zF79mzcvHkT27dvx+3bt7Fp0yYMHTpU6dJKzFrD\nIS4uTg5nnU6HWbNmYceOHQAKzrj131uTWrVqyR2EODg4yJeNHzx4IGRbAb0pU6bg/v37yMzMRPXq\n1fH333/jqaeeQnR0tFE3tRXNJoO68Bi7zz//PNavX49Zs2YhJiZG7mtWhH/ex/VItH37dgQEBODp\np58u0foiWLNmDXr37o28vDysWrUKBw8exMiRI+Ht7W1196patGiB48ePo0uXLgAK+v+2t7e3uiEh\ndTod2rZti7Zt2yInJwcdOnTAhQsXMHbsWCxZskSIRoiWstZwKKxKlSoGt+Xc3d2Rnp6uYEUl4+jo\niH///RcA0KVLF2zYsAETJkzAli1b5FECRbR582b5+8zMTPz000/45ptvMHjwYISGhiraf7/4z16U\nwJtvvil/HxwcjD/++APPPvssNm7ciMmTJytYmWX+/fdfnDt3zqxls7KyEB8fj3379uHs2bPCdnTy\n6quvYtWqVcjIyEBSUhKAgh6B4uLisGfPHmRnZytcYdEGDx6MyMhInD17FpcuXcL06dMxZswYpcsq\nFRcXFzg7O2PRokVo2rQpxo8fL/RY2kUxFQ4AhA+H4jg7Owv7f1wcJycn+RgaNWoUjh8/jlatWuHI\nkSN46623FK7OPO7u7njppZfQrFkzqFQqHD58GMOHD8edO3cUqccmz6iB/10udnJywqZNm3Dnzh14\ne3sLcSZd2JgxYxAfHy+38uzatav8eJavry9OnjxZ7PoPHz7EJ598gsOHD+OZZ56Bm5sb/vnnH1y7\ndg2hoaEYNWpURexGkUw9ftayZUu89957mDJlCnbt2oU//vgD58+fh0qlQkhIiAJVmqdp06aYOnUq\n5s6di5ycHAwcONBggAtrpFar5bO4sWPH4sMPP8TixYsxZcoUhSuzzKPhMHz4cGzcuBGNGjUS8rEm\nPQcHB3z22Wfy+9I///wjP5ViScc0Snr0MbPc3Fy5rYaLiwu2b9+O9PR0ua2QqF577TXY2dnBy8sL\n3t7eOHHiBPr06YOtW7fCyckJhw8fVqy9g80GdaNGjeR7siqVStjLk4mJibhw4QIAIC8vz+DySoMG\nDR7bT3dERATs7e1x9OhRg17OsrOzMW3aNDg7OxsMKFHRiqo/MDAQ3377Lfbs2YM+ffoIPdpUYS+/\n/DJefvllpcsolfnz58vfN27cGB4eHvLP06dPN7gEKCpbCYf33nsPv/zyi3xiUbidgJOTk/zEisgm\nTZpk8JiZnZ0dfH195Z/1bYVEt2vXLmRlZSEtLQ2XLl3CP//8gwMHDqBu3bro06ePsv/3UiW2du1a\npUuQevToYfBzt27d5O/v3LkjBQcHF7v+q6++Kul0OpPz7t27J/Xu3bv0RZaBP/74w2jarVu3pOjo\naAWqKT8iHFOVwaVLl6Tz58/LXxcuXJCys7OVLqtcZGRkKF2CRUR5zykLt27dksaOHSsNHz5cyszM\nVKwOmz2jNseIESOULqFYzs7OuH//frHLSJJUZFeJkkB92TRq1Mhomo+Pj8nxwq2Z6MfUuXPnLB4I\nokWLFuVYUck0bdrU4Oc33nhDHjDF1gwaNEj4/tYLE/lxOEv5+Phg5cqV2Ldvn6Ld6VbqoBZR4YY8\njw7YYUqnTp0wY8YMvPfee3B3d5enp6am4r333lO0pSJgXjA0adIErq6uAIBFixZZVYM/a7NmzRqL\n+jR2cHDAqlWryrGismGt4XD9+nWkpKRApVLBz88P3t7eAAo6bRo5ciQAsT5wP6pXr15GHUglJSUV\n+ZiiWq3G119/XRGlWcTU+5T+BKhBgwZo27YtLl++LM+rWrUq/P39K6w+BrXCHh0UvkqVKvJQa5Ik\n4dVXXy12/WnTpmHJkiXo3r07atWqBVdXV2RmZuL+/fsYMmQIQkNDy6t0s5gTDGFhYfI+Hz16lEFd\njqwhdB/HVsIBAEJCQtCsWTPk5+cjMzMTMTExAAoG69EHtcjWrFnz2L4gCn/QUKIvB3MU9z7Vr18/\nrF27FtWqVZMf9fP09MQnn3xSYfXZZF/flZFWq0ViYiKys7NRo0YN+Pj4WM3zo4WJ3Nc3iSE1NdXi\ncPDy8qqI0ixWuG/4wsd+4emi9x9flDNnzuDWrVtW01C0OEr/DXhGbSP0I1BdvXrVavrOJmUsXLgQ\nF7JCeNgAAAupSURBVC9eNHlJtWHDhgZDp4qouNC1pXCwFvv27TPZy6OTkxNOnjzJv0UZYFALKigo\nCLt377Z4vYkTJwr56Xv37t1yH9/9+/c36JSGKtahQ4cQERFh0DhGpVJBp9Nh4sSJClZmPoaDOD7/\n/HOTf4sGDRogOTlZgYpKJioqyqizn2effRadO3dWqKL/YVArTKvVyo3G/v77b3mgEFOdHej7zdbL\nzMw0aAAhSRJ0Ol2R3Q7a29sbNDirKLGxsdixYwdmzpwJnU6HTz/9FDqdzuo7C7FWarW6yO5nq1Wr\nVsHVlIythIMtq169+mOfWhGJs7MzdDodNmzYgOHDh0Or1WLp0qUMairo4jQ2NhZAQcOS4h7DWL9+\nvUFQBwcHm7xX17dvX5PrOzo6KnK2vX79eqxevRp169YFACxduhSjR49mUFOZs7ZwIHEMGzYMQMHV\nP/2wqHv37lWyJBmDWmGFWxpa2q7v4MGDRc6TJAn5+fkmu/CsaHfv3pVDGgDq1Klj8GYaFxeH/Px8\nSJKErKwsJUokonJgyTP7VDTl38WpXHz//fe4dOmSEPcca9SogcTERHk819u3b8PFxUWef+HCBeTm\n5kKlUsmfZKn8aLVag/6l9SRJwt27dxWqquxYazhkZGRgwYIFyMvLM6sPBVHk5uaavBIoSRKys7Oh\n0+kU7SykLOhvSSqFQW3l5s+fj+nTpxtNf+KJJ4QZRnLEiBEYP348xo0bBwBYuXKlwSg6H3zwgVKl\nVUrvvfcerl69avIKTnh4uAIVWc5WwmHChAny95GRkbh58yZUKhV69OihYFWWGTx4MI4fP25y3siR\nI63i7wAUvC9JkoT09HSsWLECubm58hXJTZs2KVobg9rKnThxwmRQ169fHykpKQpUZCwoKAguLi7y\nvfhJkyYJ0UCjsurcubPV//5tJRwKN4jr1KkTOnXqZLSMaCP+PUrJQX/KUtWqVSFJEkaMGAGVSgWV\nSoUZM2YoXRYABrXNcnJyKrZTiIpmC6NOkThsJRwe9c8//xiNNGULvclZA31jMhFZx8dOKhF2Okdk\nXQoPc6n3xBNPKFAJiYRn1ApLS0vD5MmTodPpjJ6dzs/Px/z58+XGMRkZGRa9tk6ns5p7dUTED9dk\nGoNaYV988QXS0tIAwKCBFVAwAHuTJk2Qn58PlUplctABrVaLOXPmGP2DS5KEjIwMhjSRoNq1a2dy\n1C/9ADWPcnZ2xunTp8u7LBIQB+UQlLmdwF+4cAE3btwwmq5SqdCwYUM0b968PMojIqIKwqAW1Nmz\nZ9G6dWulyyAiIoXxuqhgevbsCQAMaaJK4NKlSzh06JDJq2JEerxHLRhLHqmKi4szGuz80bF4C6tS\npYpVdaRAZOumTp2KZ555BhcuXICDgwNCQkLQv39/ti0hA7z0raBu3bpBq9UaTEtLS4Onp6fJ5dVq\nNQ4cOCD//NFHHxkEuyRJOHDggMlGZ0BBULMXMCJxvP7664iLiwMAzJgxA3/99Rdyc3OxfPly1KtX\nT+HqSBQ8o1bQtm3bLOqX2N7e3uBnU6F75swZzJ07t9S1EVHFcnd3x7vvvovs7GwMHToUa9euha+v\nr9JlkQAY1AqqWbOm0iUQkSAcHR2h1WrRtWtXODk5YcyYMfi///s/gwFsqHLijRCFpaen4+bNm6Ua\n8efmzZuYM2cOgoKCkJiYiODgYCxZsgTp6ellWCkRlScnJyc8fPgQQEG/3z179kRERITCVZEIGNQK\nCw0Nxfjx4/Hcc89h3LhxiI+Pt2j9H374AQMHDkS9evUQGRmJI0eO4P333wcA9OnTB3/99Vc5VE1E\nZWHq1Kny982bN5eHggWAUaNGITs726jBKFU+bEymMH1jEo1Ggx9++AHr1q2Du7s7IiIiUKNGDbPW\nj4yMhL+/v9G87777DtHR0fjiiy/Ko3QiKmOSJAk/WhZVPJ5RC8LR0RFdu3ZFdHQ0OnXqhP79+yMp\nKemx62VkZJgMaQDo2LEjbt68WdalElE56dWrl9IlkIAY1IJRqVQYNGgQZs6cKV/6Ko6Xl1eR/f8e\nOHAAjRo1Ko8yiagciDQ0LYmDl74VVvg5ykdt3LgR58+fx7Jly4pc/+zZswgLC0P37t3Rpk0buLm5\n4e7duzhx4gROnjyJjRs38hEPIgGVth8FqjwY1ApbuXIlxo0bZ3KeJEkYOHAgFi9ejDp16hT5Gunp\n6YiLi8OlS5eQmZkJDw8PtG7dGq+99hqqVatWXqUTUSlkZGQU+7RHdnY2qlWrJvefYG9vz0c6KykG\ntSBGjBiBdevWGU1n4xKiymnBggVo3rw5XnnlFaVLIYXxHrUgbt++bXJ6WYR0eHh4qV+DiMre5cuX\nsW/fPuzbtw8HDx7E3bt35XkNGzbE77//rmB1JAr2TKag4cOHy41HUlJSMGTIEHleSEgIrl27hlOn\nTsln1b6+vkbdg6anp+P69euoWrUqatSogVq1asHR0dFgmf/X3v2Esv/HcQB/Tiaf0iyTfyORg0Ir\nJMVBorhNCcWBhijkz5WLg1xcNH8uTpJS4uqAUg5SCIlIqyVD1mxohn1+B/mU7+YX+fP+4Pk47f35\n1+tz2J7t8/7zWV9f//qbIaJ36+rqgslkgkajgcPhwN7eHrq6ugAAqamprw4Upb+FQS1QT09PwCjP\n53/QSUlJGB4eRm9vL8LCwiDLMrq7uwOu0dHRgaioKNzd3cHlcsHpdMLr9UKn0yE2NhYJCQnfci9E\n9H6hoaEYHBwEAKytrWFxcVHZFxcXh/Pzc1GlkYowqAXKyMjA5eUlDAaDsm1iYgLJyckwmUyQZRl5\neXnKvmBr/rrdbkxNTQVsv7q6wunpKex2OzY2Nr7mBojo0/zbzSVJEm5ubgRVQ2rCPmrBGhoaXrR1\nOh329/c/fN3IyEikp6ejtLT0w9ciou8nSRLnVRMA/qMWzmazwWw2K22Px4Pc3FyBFRGRCCEhITg4\nOMD8/DxkWYbX60VoKH+iiUEtXEJCAkZGRvA8S+7s7AxWq/XN55vNZvj9foSE8OEI0U9TXV2tfM7M\nzERmZiY2NzcBPAX3a2ss0N/CoBZMq9XCaDQqbb1eD4/H8+bzt7e3UVBQAL/fD0mSEB0djfj4eCQm\nJiIlJQWpqalfUTYRfYK6ujrlc3h4+Iu3aRE9Y1AL5na7lUddGo0G9/f3uL29BfA0eKympgZarRYA\ngo4APT4+Vl6N6fP5cHFxAYfDAbvdDpvNhuXl5e+7GSL6kJaWFoyPj4sug1SGQS2Y1+vFzs6O0vb7\n/cqygiMjI7Db7cpj8dfWAH4WFhYGo9EIo9GInJwcZXt5efkXVE5En41vu6NgGNSCxcTEoK+vT2nL\nsoyysjIAQFRUFNf2JfqlWltbA0Z1OxwONDY2Bj1eq9VibGzsO0ojlWFQCzYwMPCirdFo3tVP9e/0\nLiL6GZqamgKCuqmp6dXjn7vA6O9hUAuWlZUVsK2kpOTN57vdbjw8PPzvNI7KykoAT33Yk5OTsFgs\n7y+UiD5Vdna26BLoh+Dbs364nZ0dWK1WFBUVwWw2Q5KkgGOur68xNzeHpaUldHZ2wmQyCaiUiIKR\nZRkrKyvY2tqC0+lETEwM8vPzX4wzob+NQf0L+Hw+zM7OYmZmBhEREUhLS4NOp4Pb7cbh4SFcLheq\nq6tRVVUV8MIOIhLn5OQEzc3NMBgMKCwshF6vx/n5OZaXl6HX6zE6OsrvLDGof5uTkxMcHR3B4/FA\np9MhJSUFSUlJossioiAsFguKi4tRW1sbsG9oaAhXV1fo7+8XUBmpCYOaiEiQgoICrK6uBt13e3uL\niooKLCwsfHNVpDZcd5KISBBJknB2dhZ0n81m4/RMAsCgJiISxmKxoK6uDgsLC3A6nXh8fITT6cT8\n/Dza2trQ3t4uukRSAT76JiISaGVlBdPT09jd3YXL5YLBYEBOTg7q6+uDTt+kv4dBTUREpGJ89E1E\nRKRiDGoiIiIVY1ATERGpGIOaiIhIxRjUREREKvYfhC/MCJSXTAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae4c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "place_pictures_ds = image_meta_ds.groupby(['place']).count()[['datetime']].sort(['datetime'], ascending=False)\n",
    "\n",
    "place_pictures_ds.plot(kind='bar', legend=False, color='#5CACC4')\n",
    "plt.title('장소별 사진 찍은수')\n",
    "plt.xlabel('')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가장 많이 찍은 두 지역에서 가장 밝게 웃고 있는 사진은?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 언제 사진을 많이 찍었는가?\n",
    "\n",
    "날짜별로 호이안과 팜가든리조트 일정이 있었던 4/21,4/22일 가장을 가장 많이 찍었고, \n",
    "\n",
    "와이프가 항공 중이염으로 아팠던 4/20은 여행의 본격 시작 됐음에도 사진을 적게 찍었음을 알수 있었다. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5c+eaLtX2br31Vj300EOmy7Ak6GbGCxcu1Kmnnqq//vWvCg0NVUtLi5YsWSK3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae5c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_meta_ds.groupby(['ymd']).count()[['datetime']].plot(kind='bar', legend=False, color='#5CACC4')\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 이동경로 인덱스를 생성한다. \n",
    "route_idx = []\n",
    "old = ''\n",
    "current_idx = 0\n",
    "for idx, row in image_meta_ds.iterrows():\n",
    "    if old != row.place:\n",
    "        old = row.place\n",
    "        current_idx += 1\n",
    "    route_idx.append(current_idx)  \n",
    "image_meta_ds['route_idx'] = route_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>datetime</th>\n",
       "      <th>place</th>\n",
       "      <th>latitude</th>\n",
       "      <th>place_idx</th>\n",
       "      <th>longitude</th>\n",
       "      <th>me_age</th>\n",
       "      <th>junior_age</th>\n",
       "      <th>wife_smiling</th>\n",
       "      <th>junior_smiling</th>\n",
       "      <th>me_smiling</th>\n",
       "      <th>person</th>\n",
       "      <th>wife_age</th>\n",
       "      <th>hourofyear</th>\n",
       "      <th>ymd</th>\n",
       "      <th>image_idx</th>\n",
       "      <th>imagepath</th>\n",
       "      <th>smiling</th>\n",
       "      <th>age</th>\n",
       "      <th>route_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> IMG_1404.JPG</td>\n",
       "      <td>2016-04-19 07:43:35</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td> 37.361</td>\n",
       "      <td> 0</td>\n",
       "      <td> 127.101814</td>\n",
       "      <td> 25</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 10.9236</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3103</td>\n",
       "      <td> 2016-04-19</td>\n",
       "      <td> 1404</td>\n",
       "      <td> ./resource/image/IMG_1404.JPG</td>\n",
       "      <td> 10.9236</td>\n",
       "      <td> 25</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> IMG_1405.JPG</td>\n",
       "      <td>2016-04-19 07:43:35</td>\n",
       "      <td> 인천공항</td>\n",
       "      <td> 37.361</td>\n",
       "      <td> 0</td>\n",
       "      <td> 127.101814</td>\n",
       "      <td> 27</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 49.7110</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 3103</td>\n",
       "      <td> 2016-04-19</td>\n",
       "      <td> 1405</td>\n",
       "      <td> ./resource/image/IMG_1405.JPG</td>\n",
       "      <td> 49.7110</td>\n",
       "      <td> 27</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          index            datetime place  latitude  place_idx   longitude  \\\n",
       "0  IMG_1404.JPG 2016-04-19 07:43:35  인천공항    37.361          0  127.101814   \n",
       "1  IMG_1405.JPG 2016-04-19 07:43:35  인천공항    37.361          0  127.101814   \n",
       "\n",
       "   me_age  junior_age  wife_smiling  junior_smiling  me_smiling  person  \\\n",
       "0      25           0             0               0     10.9236       1   \n",
       "1      27           0             0               0     49.7110       1   \n",
       "\n",
       "   wife_age  hourofyear         ymd  image_idx                      imagepath  \\\n",
       "0         0        3103  2016-04-19       1404  ./resource/image/IMG_1404.JPG   \n",
       "1         0        3103  2016-04-19       1405  ./resource/image/IMG_1405.JPG   \n",
       "\n",
       "   smiling  age  route_idx  \n",
       "0  10.9236   25          1  \n",
       "1  49.7110   27          1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_meta_ds.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "route_grouped = image_meta_ds.groupby(['route_idx','place','place_idx'])\n",
    "route_infos = []\n",
    "for route,group in route_grouped:\n",
    "    route_infos.append( [route[0], route[1], route[2], group.shape[0], group.smiling.max(), \n",
    "                         group.datetime.min() + (group.datetime.max()-group.datetime.min())/2,\n",
    "                        group.datetime.max()-group.datetime.min()] )\n",
    "    \n",
    "trip_route_ds = pd.DataFrame(route_infos, \n",
    "                    columns=['seq','place', 'place_no', 'picture_cnt','smiling-max', 'timestamp', 'duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trip_route_ds.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['인천공항',\n",
       " '다낭공항',\n",
       " '아라카르테호텔',\n",
       " '다낭병원',\n",
       " '빅씨마트',\n",
       " '미케해변',\n",
       " '오행산',\n",
       " '호이안',\n",
       " '팜가든리조트\\n(끄어다이해변)']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## X bar list \n",
    "trip_list = {}\n",
    "cur_pos = 10\n",
    "for (idx, row) in trip_route_ds.iterrows():\n",
    "    if row.place_no not in trip_list:\n",
    "        trip_list[row.place_no] = []\n",
    "    trip_list[row.place_no].append((cur_pos, row.picture_cnt+5))\n",
    "    cur_pos += (row.picture_cnt+5)+3\n",
    "# Y label data\n",
    "y_ticks = []\n",
    "y_label = []\n",
    "for (idx,label) in enumerate(base_loc_pos):\n",
    "    y_ticks.append(15+idx*10)\n",
    "    y_label.append(label[0])\n",
    "    \n",
    "y_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 우리의 여행 이동 경로는?\n",
    "\n",
    "우리의 큰일정은 다음과 같았다. \n",
    "> 인천공항 --> 아라카르트 호텔(2박)+ 다낭시내 --> 팜가든리조트(2박) + 호이안 --> 인천공항 \n",
    "\n",
    "실제 사진의 시간순으로 여행 이동경로를 보자 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aDvR13qYRMpKU53WnhHWPy1Ssxf3oUjzEN9fLdrtUlH344XJul6Ex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\nD2mt1GgKNL1jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b2e1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot([idx+5 for idx in image_meta_ds.index.tolist()], (10 - image_meta_ds.place_idx).tolist(),\n",
    "        'o',ms=15, alpha=0.6 , drawstyle='steps', color='#5CACC4')\n",
    "ax.plot([idx+5 for idx in image_meta_ds.index.tolist()], (10 - image_meta_ds.place_idx).tolist(),\n",
    "        '-',ms=15,linewidth=2,  alpha=0.8 , drawstyle='steps', color='#FF5254')\n",
    "ax.set_ylim(1,11)\n",
    "ax.set_xlim(0,270)\n",
    "plt.xlabel('picture index')\n",
    "ax.set_yticks([ tick/10+0.5 for tick in y_ticks])\n",
    "ax.set_yticklabels(y_label[::-1])\n",
    "\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(6)\n",
    "\n",
    "fig.set_gid(False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이미지 분석 API로 얼마나 웃고 나이들어 보이는지 알아 보자 \n",
    "\n",
    "요즘 deep learning 발달로 이미지 분석 기술이 좋아 졌다. 그리고 좋은 기술들은 손쉽게 사용할 수 있도록 API를 제공해 주는데 \n",
    "나는 face plusplus에서 제공해 주는 face 분석 API를 통해 사진의 웃는 정도(smiling)와 나이(age) 분석값으로 \n",
    "내가 여행중 얼마나 웃고 즐기는지를 측정해 보고자 한다. \n",
    "\n",
    "<img src='./resource/screen-facepp.png'/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>                  IMG_1404.JPG</td>\n",
       "      <td>                  IMG_1405.JPG</td>\n",
       "      <td>                  IMG_1406.JPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <td>           2016-04-19 07:43:35</td>\n",
       "      <td>           2016-04-19 07:43:35</td>\n",
       "      <td>           2016-04-19 07:43:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>place</th>\n",
       "      <td>                          인천공항</td>\n",
       "      <td>                          인천공항</td>\n",
       "      <td>                          인천공항</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>                        37.361</td>\n",
       "      <td>                        37.361</td>\n",
       "      <td>                        37.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>place_idx</th>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>                      127.1018</td>\n",
       "      <td>                      127.1018</td>\n",
       "      <td>                      127.1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>me_age</th>\n",
       "      <td>                            25</td>\n",
       "      <td>                            27</td>\n",
       "      <td>                            43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>junior_age</th>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wife_smiling</th>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>junior_smiling</th>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>me_smiling</th>\n",
       "      <td>                       10.9236</td>\n",
       "      <td>                        49.711</td>\n",
       "      <td>                       6.50349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person</th>\n",
       "      <td>                             1</td>\n",
       "      <td>                             1</td>\n",
       "      <td>                             1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wife_age</th>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "      <td>                             0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hourofyear</th>\n",
       "      <td>                          3103</td>\n",
       "      <td>                          3103</td>\n",
       "      <td>                          3103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <td>                    2016-04-19</td>\n",
       "      <td>                    2016-04-19</td>\n",
       "      <td>                    2016-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image_idx</th>\n",
       "      <td>                          1404</td>\n",
       "      <td>                          1405</td>\n",
       "      <td>                          1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagepath</th>\n",
       "      <td> ./resource/image/IMG_1404.JPG</td>\n",
       "      <td> ./resource/image/IMG_1405.JPG</td>\n",
       "      <td> ./resource/image/IMG_1406.JPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smiling</th>\n",
       "      <td>                       10.9236</td>\n",
       "      <td>                        49.711</td>\n",
       "      <td>                       6.50349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>                            25</td>\n",
       "      <td>                            27</td>\n",
       "      <td>                            43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route_idx</th>\n",
       "      <td>                             1</td>\n",
       "      <td>                             1</td>\n",
       "      <td>                             1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            0                              1  \\\n",
       "index                            IMG_1404.JPG                   IMG_1405.JPG   \n",
       "datetime                  2016-04-19 07:43:35            2016-04-19 07:43:35   \n",
       "place                                    인천공항                           인천공항   \n",
       "latitude                               37.361                         37.361   \n",
       "place_idx                                   0                              0   \n",
       "longitude                            127.1018                       127.1018   \n",
       "me_age                                     25                             27   \n",
       "junior_age                                  0                              0   \n",
       "wife_smiling                                0                              0   \n",
       "junior_smiling                              0                              0   \n",
       "me_smiling                            10.9236                         49.711   \n",
       "person                                      1                              1   \n",
       "wife_age                                    0                              0   \n",
       "hourofyear                               3103                           3103   \n",
       "ymd                                2016-04-19                     2016-04-19   \n",
       "image_idx                                1404                           1405   \n",
       "imagepath       ./resource/image/IMG_1404.JPG  ./resource/image/IMG_1405.JPG   \n",
       "smiling                               10.9236                         49.711   \n",
       "age                                        25                             27   \n",
       "route_idx                                   1                              1   \n",
       "\n",
       "                                            2  \n",
       "index                            IMG_1406.JPG  \n",
       "datetime                  2016-04-19 07:43:40  \n",
       "place                                    인천공항  \n",
       "latitude                               37.361  \n",
       "place_idx                                   0  \n",
       "longitude                            127.1018  \n",
       "me_age                                     43  \n",
       "junior_age                                  0  \n",
       "wife_smiling                                0  \n",
       "junior_smiling                              0  \n",
       "me_smiling                            6.50349  \n",
       "person                                      1  \n",
       "wife_age                                    0  \n",
       "hourofyear                               3103  \n",
       "ymd                                2016-04-19  \n",
       "image_idx                                1406  \n",
       "imagepath       ./resource/image/IMG_1406.JPG  \n",
       "smiling                               6.50349  \n",
       "age                                        43  \n",
       "route_idx                                   1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_meta_ds.head(3).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 누가 가장 잘 웃고, 나이들어 보이나\n",
    "\n",
    "누가누가 잘웃나?\n",
    "\n",
    "wife의 웃음지수가 평균 60 이다. 원래 평소에는 내가 잘 웃는것으로 알고 있는데 의외로 wife의 웃음 지수가 높다. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 웃음지수 :  39.85\n",
      "Me 웃음지수 =  40.92\n",
      "Wife 웃음지수 =  60.54\n",
      "Junior 웃음지수 =  9.43\n"
     ]
    }
   ],
   "source": [
    "def getMean(target):\n",
    "    return round(image_meta_ds[image_meta_ds[target]>0][target].mean(),2)\n",
    "\n",
    "print( \"평균 웃음지수 : \", getMean('smiling') )\n",
    "print( \"Me 웃음지수 = \", getMean('me_smiling') )\n",
    "print( \"Wife 웃음지수 = \", getMean('wife_smiling') )\n",
    "print( \"Junior 웃음지수 = \", getMean('junior_smiling') )\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Me 평균 웃음지수 =  40.92\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1453.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1566.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1442.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.34   \t\t90.76   \t\t89.47\n",
      "\n",
      "**wife 평균 웃음지수 =  60.54\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1414.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1880.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1639.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.58   \t\t99.0   \t\t98.41\n",
      "\n",
      "**junior 평균 웃음지수 =  9.43\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1821.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1642.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1820.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.16   \t\t72.07   \t\t38.45\n"
     ]
    }
   ],
   "source": [
    "def mostSimilingByPeople(target, top=3, order = False):\n",
    "    tmp_ds = image_meta_ds[(image_meta_ds[target] > 0 )].sort([target], ascending=order)\n",
    "    drawImages(tmp_ds.imagepath[:top].values)\n",
    "    print(\"   \\t\\t\".join( tmp_ds[target].apply(lambda x: str(round(x,2))).values[:3]))\n",
    "    \n",
    "print( \"**Me 평균 웃음지수 = \", getMean('me_smiling') )\n",
    "mostSimilingByPeople('me_smiling')\n",
    "print( \"\\n**wife 평균 웃음지수 = \", getMean('wife_smiling') )\n",
    "mostSimilingByPeople('wife_smiling')\n",
    "print( \"\\n**junior 평균 웃음지수 = \", getMean('junior_smiling') )\n",
    "mostSimilingByPeople('junior_smiling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Me 평균 웃음지수 =  40.92\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1879.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1788.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1797.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.58   \t\t3.25   \t\t6.27\n",
      "\n",
      "**wife 평균 웃음지수 =  60.54\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1889.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1875.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1874.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.33   \t\t3.38   \t\t4.44\n",
      "\n",
      "**junior 평균 웃음지수 =  9.43\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1826.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1637.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1724.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21   \t\t0.36   \t\t0.42\n"
     ]
    }
   ],
   "source": [
    "print( \"**Me 평균 웃음지수 = \", getMean('me_smiling') )\n",
    "mostSimilingByPeople('me_smiling', order=True)\n",
    "print( \"\\n**wife 평균 웃음지수 = \", getMean('wife_smiling') )\n",
    "mostSimilingByPeople('wife_smiling', order=True)\n",
    "print( \"\\n**junior 평균 웃음지수 = \", getMean('junior_smiling') )\n",
    "mostSimilingByPeople('junior_smiling', order=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 언제 가장 많이 웃었는가?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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A/PzMlmQr7n0wBe6hGX8PdofVq1cnvTeF2JsO2Pvy/96F6mrIzuq4LZBdV0ev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\nIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b444dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "draw_ds = image_meta_ds\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "## draw path line\n",
    "ax.plot([idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist(),\n",
    "        '-', linewidth=2,  ms=15, alpha=0.7 , drawstyle='steps', color='#5CACC4')\n",
    "\n",
    "cmap = { 'Me':'#CEE879', 'Wife':'#FF5254', 'Junior':'#5CACC4' }\n",
    "## draw smiling index \n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Wife'], s=(draw_ds.wife_smiling.fillna(0)**1.7).tolist(), alpha=0.7\n",
    "          )\n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Me'], s=(draw_ds.me_smiling.fillna(0)**1.7).tolist(), alpha=0.7\n",
    "          )\n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Junior'], s=(draw_ds.junior_smiling.fillna(0)**1.7).tolist(), alpha=0.7\n",
    "          )\n",
    "\n",
    "\n",
    "ax.set_ylim(1,11)\n",
    "ax.set_xlim(0,draw_ds.index.max()+10)\n",
    "plt.xlabel('picture index')\n",
    "ax.set_yticks([ tick/10+0.5 for tick in y_ticks])\n",
    "ax.set_yticklabels(y_label[::-1])\n",
    "\n",
    "legend_list = []\n",
    "for (nm, c) in cmap.items():\n",
    "    legend_list.append(mlines.Line2D([], [], color=c, marker='o',markersize=15, label=nm+' 웃음크기', linewidth=0, alpha=0.8))\n",
    "plt.legend(handles=legend_list, loc='upper center')\n",
    "\n",
    "\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사진속 나이는 ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "me age =  36.3\n",
      "wife age =  29.58\n",
      "junior age =  2.82\n"
     ]
    }
   ],
   "source": [
    "def getMean(target):\n",
    "    return round(image_meta_ds[image_meta_ds[target]>0][target].mean(),2)\n",
    "\n",
    "print( \"me age = \", getMean('me_age') )\n",
    "print( \"wife age = \", getMean('wife_age') )\n",
    "print( \"junior age = \", getMean('junior_age') )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가장 나이들어 보일때는 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Me 평균 예측나이 = 36.30세 (실제 39세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1723.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1471.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1443.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  55세   \t\t  54세   \t\t  54세\n",
      "\n",
      "**wife 평균 예측나이 = 29.58세 (실제 39세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1850.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1472.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1835.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  61세   \t\t  59세   \t\t  50세\n",
      "\n",
      "**junior 평균 예측나이 = 2.82세 (실제 4세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1493.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1873.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1592.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9세   \t\t   8세   \t\t   7세\n"
     ]
    }
   ],
   "source": [
    "def mostAntiAgingByPeople(target, top=3, order=False):\n",
    "    tmp_ds = image_meta_ds[(image_meta_ds[target] > 0 )].sort([target], ascending=order)\n",
    "    drawImages(tmp_ds.imagepath[:top].values)\n",
    "    \n",
    "    \n",
    "    print(\"   \\t\\t\".join( tmp_ds[target].apply(lambda x: (\"%d세\" % (round(x))).rjust(5)).values[:3]))\n",
    "    \n",
    "    \n",
    "print( \"**Me 평균 예측나이 = %.2f세 (실제 39세)\" % getMean('me_age') )\n",
    "mostAntiAgingByPeople('me_age')\n",
    "print( \"\\n**wife 평균 예측나이 = %.2f세 (실제 39세)\" %  getMean('wife_age') )\n",
    "mostAntiAgingByPeople('wife_age')\n",
    "print( \"\\n**junior 평균 예측나이 = %.2f세 (실제 4세)\" %  getMean('junior_age') )\n",
    "mostAntiAgingByPeople('junior_age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가장 젊어 보이는 사진은 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Me 평균 예측나이 = 36.30세 (실제 39세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1875.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1834.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1797.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19세   \t\t  20세   \t\t  22세\n",
      "\n",
      "**wife 평균 예측나이 = 29.58세 (실제 39세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1777.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1421.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1420.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12세   \t\t  13세   \t\t  14세\n",
      "\n",
      "**junior 평균 예측나이 = 2.82세 (실제 4세)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1410.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1570.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1499.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1세   \t\t   1세   \t\t   1세\n"
     ]
    }
   ],
   "source": [
    "print( \"**Me 평균 예측나이 = %.2f세 (실제 39세)\" % getMean('me_age') )\n",
    "mostAntiAgingByPeople('me_age', order=True)\n",
    "print( \"\\n**wife 평균 예측나이 = %.2f세 (실제 39세)\" %  getMean('wife_age') )\n",
    "mostAntiAgingByPeople('wife_age', order=True)\n",
    "print( \"\\n**junior 평균 예측나이 = %.2f세 (실제 4세)\" %  getMean('junior_age') )\n",
    "mostAntiAgingByPeople('junior_age', order=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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63nrrLf7yl7/k9bcw+dxPPvkkL7zwAl6vl+9///s8+OCDXHXVVYm/tdl8//vf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\nQggxxkiwLoQQQgghhBBCjDH/H/6KFBWcnCH7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b450160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "draw_ds = image_meta_ds\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "## draw path line\n",
    "ax.plot([idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist(),\n",
    "        '-', linewidth=2,  ms=15, alpha=0.7 , drawstyle='steps', color='#5CACC4')\n",
    "\n",
    "cmap = { 'Me':'#CEE879', 'Wife':'#FF5254', 'Junior':'#5CACC4' }\n",
    "## draw smiling index \n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Wife'], s=(draw_ds.wife_age.fillna(0)**2).tolist(), alpha=0.7\n",
    "          )\n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Me'], s=(draw_ds.me_age.fillna(0)**2).tolist(), alpha=0.7\n",
    "          )\n",
    "ax.scatter( [idx+5 for idx in draw_ds.index.tolist()], (10 - draw_ds.place_idx).tolist()\n",
    "           , linewidth=0, c=cmap['Junior'], s=(draw_ds.junior_age.fillna(0)**2).tolist(), alpha=0.7\n",
    "          )\n",
    "\n",
    "\n",
    "ax.set_ylim(1,11)\n",
    "ax.set_xlim(0,draw_ds.index.max()+10)\n",
    "plt.xlabel('picture index')\n",
    "ax.set_yticks([ tick/10+0.5 for tick in y_ticks])\n",
    "ax.set_yticklabels(y_label[::-1])\n",
    "\n",
    "legend_list = []\n",
    "for (nm, c) in cmap.items():\n",
    "    legend_list.append(mlines.Line2D([], [], color=c, marker='o',markersize=15, label=nm+' 예측나이', linewidth=0, alpha=0.8))\n",
    "plt.legend(handles=legend_list, loc='upper center')\n",
    "\n",
    "\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 타입 테이블 만들기\n",
    "image_meta_ds['hour'] = image_meta_ds.datetime.apply(lambda x: x.strftime('%H H'))\n",
    "image_meta_ds['day'] = image_meta_ds.datetime.apply(lambda x: x.strftime('%m-%d'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  지역 날짜별 평균 웃음 지수 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "def drawHitTable(ds, title):\n",
    "    sns.set(style=\"whitegrid\", palette=\"pastel\", color_codes=True, font_scale=1.4)\n",
    "    rcParams['font.family'] = 'NanumGothic'\n",
    "    plt.figure(figsize=(10,8))\n",
    "    sns.heatmap(ds, annot=True, linewidths=.5)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel('')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ft99+K/S5cOEC5s6dC6VSCY1Go1PRA4ChQ4dizpw5iIiIgJ2dHeLi4uDg4IBX\nX30VQMV8N11+fj72Jx1A1MrlAIAP5y1Aalo6atfyMvHMTG/R0i8glUqQk/sQAW+2xX+6d9X6fPGy\nL6FSqVBQWIg6tbwwLmyEgZGsC79T4jBO4jBO4jFWj/E5dhVMeno6AgMDUalSJcyfPx9KpRKFhYUo\nLCxEUVER7t27h/T0dBQUFKB9+/Zwd3dHUlKSsL9EIoFardYaU6lUIjs7G4cOHQIAXLt2DVWrVtXq\nExYWht27d2POnDkoKiqCv78/1qxZI3xeEZdizyX/Cf82fsJ2544BOHX6jFX+h+Bpc96fKvweNnGK\nTmL34Ywpwu9zF32KlNQ01Kld66XNz1zxOyUO4yQO4yQeY2W5LD6x8/LyEhK1hw8fws7ODv3794dU\nKoWjoyNcXV3h4eGB5s2b693fw8MDKpVKaykWAPr3748JEyYAAKKjo7Vu0jh79iyKi4tRs2ZNDB8+\nHLm5ucjOzsaqVauQnp6OvLw89O3bt5zOuPzk5ObC2clJ2HZ2ckJKapoJZ2R+iooUcHoiRk/LzX2I\nB9nZcHNzfYmzMl/8TonDOInDOInHWD3GpVgz83TlKzc3FykpKSgsLETNmjW1PqtSpQpiY2MNjvXw\n4UM0bdpUq83V1RXbtm0zOgd3d3etmzKOHDkC4NF1djdv3kRGRgbCwsLQpEkTeHh4oHr16vj+++9F\nnZ85cXF2xrUbfwvb2Tk5cKng1wy+aKvXb0Do0EE67Wm3bmHN+o1IvnAJM6dMgKODgwlmZ374nRKH\ncRKHcRKPsbJcFTqxa9iwIRYvXoxVq1ZBJpMBABwcHODp6Yl27drBw8ND7355eXlYt24djhw5ApVK\nBY1GAwcHBwQGBmL58uUGj5ecnIz169fj2rVrQkJZv359hISEoE2bNkK/6dOnC78nJibi+PHj6Nq1\nK06cOCE8JiU/Px/t2rV73hC8VL5NmyAuYRuGD3mUuBw9/gvGhPJasRJx23bgtYavonnTJjqf1fL0\nxCfz5kCtVmP2/Ej4Nn4Nbq6s2vE7JQ7jJA7jJB5jZbkqdGIXGhqKESNGQCrV/zi+9PR0nTaNRoOw\nsDAEBARg8+bNcHR0BPDopomvvvoKU6dOFR5j8qSLFy9i2rRpiIyMhL+/v9B+8uRJzJkzB/Pnz0f7\n9u219ikuLoa7uztee+01AIC/v7+wLFyS8FUkVRwd0TOoB2bN+RgymQw+jRqibp3app6WWdi6cw8q\n2dsjsNtUS00fAAAgAElEQVRbRvvJZDIUFxdDpVIb7Wct+J0Sh3ESh3ESj7GyXBJNRbyKX6T09HSM\nGTNG62aIe/fu4e2338aZM2d0+qvVajRv3hx//vmnzl2r69atw507d/DRRx/p7BcXF4ebN2/qfNaq\nVSucPn1a79xu376Nf/75B2+88Uap56HIvV9qH2smd3IDABTcvWWS45/78wIi5keivX9bAI/+7zRh\ndBhecXm0rHH56l/4dut2VK5UCXn5/6LDm21LTQDLS6VqngD4nSpNyXeKcSodYyUO4yROSZxeppH+\n48tt7I0nvi63sQ2p0BW7Z1G1alU0aNAA0dHRGDZsGCpXrgzg0evE1qxZgy5duuh9FEm7du0wdepU\nnD59Gi1bthT6nD17FvHx8fj444919jH2MGQPDw+DS8VUsbzu2xRJ3yXotL//0Xx8tuBj+DR8FYvm\nfmiCmRERUWl480QFYmtrC7lcrtMeExODtWvXYvDgwVCr1dBoNKhUqRKCgoIQERGhd6zGjRtj6dKl\nWL9+PebPn4/i4mJIJBJ4e3tjwYIF8PPz09mnfv36CAoKEq7/e5qDgwMSEnQTArIMSyPnm3oKRERk\nZSx6KdZSsHRvnKmXYisSLsWKw2Uz8RgrcRgncUyxFDvqzYnlNnbMr6vLbWxD9N91QEREREQVjkUv\nxRIREREZY2nX2LFiR0RERGQhmNgRERERWQguxRIREZHV0veIs4qMFTsiIiIiC8GKHREREVkt3jxB\nRERERGaJFTsiIiKyWrzGjoiIiIjMEit2REREZLUkYMWOiIiIiMwQEzsiIiIiC8GlWCIiIrJaUsta\niWXFjoiIiMhSsGJHREREVouPOyEiIiIis8SKXQUgd3Iz9RQqhErVPE09hQqD3ylxGCfxGCtxGCfz\nY8pXihUWFiI6Oho//fQTNBoNCgsL8fHHH8Pf3x8AcP/+fcydOxfp6ekoLi5G7969MWbMGKNjMrEj\nIiIiq2WqpViVSoUxY8agTZs2SEhIgFwuBwCo1Wqhz+TJk9G/f3/06dMHCoUCEyZMgIuLC/r3729w\nXCZ2FUCzOh1NPQWzlpxyFACgyL1v4pmYv5JqAWNlHOMkHmMlDuMkjjVVNPfs2QMnJyeEh4drtctk\nMgDA1atXUVRUhD59+gAA5HI5Zs2ahYiICKOJHa+xIyIiInrJEhMTMXDgQIOf//rrr2jbtq1WW8OG\nDZGRkYGcnByD+7FiR0RERPSSXblyBXZ2dpg8eTJu3rwJFxcXjBo1Ch07Plqly8zMhIeHh85+NWrU\nwK1bt+Ds7Kx3XCZ2REREZLWkJnpX7IMHD7B69WrMnz8f3t7euH79OsaOHYvIyEj4+/sjNzcX9erV\n09lPLpejoKDA4LhciiUiIiJ6yaRSKUaNGgVvb28AQP369REWFoYdO3YAeJTAFRUV6exXVFQEe3t7\nw+OWz3SJiIiIzJ9EIim3H2Pc3Nx0KnJeXl548OABgEdLrrdv39bZLyMjAzVr1jQ4LhM7IiIislpS\niaTcfozx9fXFlStXtNpSUlJQp04dAMAbb7yBkydPan1+5coVVKlSBa6urobP5xnjQERERETPaMiQ\nIfjiiy+QmZkJALhx4wZiY2MxbNgwAECrVq0AALt37wbwaAn2s88+w4gRI4yOy5sniIiIyGqZ6sUT\n/v7+GDlyJEJCQiCVSlGpUiXMmzcP9evXF/qsWbMGH3/8MWJiYqBWqxEUFCQkfoYwsSMiIiIygf79\n+xt92HD16tWxdu3aMo3JpVgiIiIiC8GKHREREVmt0m5yqGhYsSMiIiKyEKzYERERkdWSmOjNE+WF\nFTsiIiIiC8GKHREREVmt0t4QUdGwYkdERERkISw+sRs3bhz++OOPUvstX74c+/btQ2FhIQIDA0WN\n/dFHH+Hnn38GAHTu3LlM8/rhhx8wd+7cMu1DREREL5apXilWXir0UuzRo0exePFiyOVyoU2hUKBb\nt254//33AQAqlQpqtRoAkJycjA8++EDoK5VKERgYiPDwcCiVSqjVaqjVahQVFWkd55tvvsG2bdsA\nADKZDJs3b4aTk5PQHwAKCgqE/tnZ2ejfvz8OHjyoNY6fnx9+//13AIBSqYRKpXpRoSAiIqJnYGEr\nsRU7sbt69SreeecdTJo0SWj7448/8MUXX+jt36xZMyQlJQnbp06dwooVKxAeHm70OKGhoQgNDRU9\nr+LiYiHhe1JhYSG+/vprYe729vaixyQiIiIqTYVO7DQajc5Fj1KpFBqNRtT+KSkpqFevnsHPs7Ky\nMGzYML3j9e3b1+jYmZmZ6Nmzp1abTCZDhw4doNFoIJVKcfPmTVHzNKW63rUwLOzx606avdEY/zd7\nKVr4+eK1pg2hVCohk0oROWc5iooUJpypedmfdAAHDv0ImVSG5s2aIjRkqKmnZLYYK3EYJ3EYJ/EY\nK8tUoRO7srp69SquXr0KhUKBvLw8/Pe//8XAgQMN9nd1dUViYiKAR8ur//zzD7y9vYWl34iICINJ\npLu7O/bt26fV1qJFC1SvXh0ajQbOzs4v6KzK180baYj8aDmAR3cOfREdidSb6Rg3dQQmhUUAAELf\nGwz/gFb46dCvppyq2cjPz8f+pAOIWvkobh/OW4DUtHTUruVl4pmZH8ZKHMZJHMZJPMbKcln8zRNP\nun//PlJTU5GVlYWcnBxcvHgRUVFRCAwMxM6dOw3uFxUVhUmTJmHr1q0YMGAATp8+DeBRxXD+/PkI\nDAxEXl5eqcdXKpVYtmwZli9fju+///6FndfL0i2oEw7/8DMe5ubhXmYW3Kq5ws5Ojppe1XHmVLKp\np2c2ziX/Cf82fsJ2544BOHX6jAlnZL4YK3EYJ3EYJ/EYq8d484SZM/Y8Gn9/f/j7+wMAZsyYgRkz\nZmD48OEAgE8//VTvPikpKfjxxx+xfft2AMCtW7cwbtw47Nu3DxKJBP/3f/+Hjh07om3btsI+Dg4O\nKCoq0lqKValUaNCgAT755BMAQGJiIo4fP/58J/uS9QzujmljPwIA7N6eiIEhfZDzIAfnTl/Aw9zS\nE1trkZObC2cnJ2Hb2ckJKalpJpyR+WKsxGGcxGGcxGOsLFeFT+yeXgotLi4udZ81a9agoKBASOqM\nSUtLQ4MGDYRtT09PPHjwwOh1fHZ2dvjll1+Mjiv2OkBz0abdG0j+4yJUKjVe9fFGxy5vYuXn6wAA\nXd4OQN+BQdi1NdHEszQPLs7OuHbjb2E7OycHLhVk6f1lY6zEYZzEYZzEY6we4yvFzIiXlxfi4+MR\nGBgo/EyZMgV169bV21+hUGDhwoU4e/YsVqxYIeoYjRs3xpkzZ3Dnzh0AwKFDh1C/fv3nflJ1RXvS\n9cDhfbH12z0AgKrVXPHk/w8URQp4eNUw0czMj2/TJvjt1O/C9tHjv6DVGy1MOCPzxViJwziJwziJ\nx1g9xqVYMxIUFISgoCDR/b/99ltUqlQJa9euhVQqLqd1dXXF3LlzMX78eCiVStSsWRNLliwRte/F\nixexfPlyxMTE6HzWpk0beHt7i567Kb3q4407tzORm/MQAHDi+Gm0bNMckcsioFAoYWdvhyXzvjTx\nLM1HFUdH9AzqgVlzPoZMJoNPo4aoW6e2qadllhgrcRgncRgn8Rgry1WhE7uyGjVq1DPtFxAQgICA\ngDLvp1QqoVDofwSIm5sb3Nzcnmk+L9tfl2/g0//7Sqtt1VLdZJUeC+zeDYHdu5l6GhUCYyUO4yQO\n4yQeY/VIBVtAK1WFXop9kWxtbWFjU7Y818bGxug+FW25lYiIiCo2i6/YlZZ8lZg+fTqAR68GE/tG\niIULFwq/V65cWefz6tWr48aNGzoPKn7S0qVL0ahRI1HHIyIiIjLG4hO7qKioMvWvVKmS8FDisjh8\n+LBOW40aNUq9O5aIiIjoRbH4xI6IiIjIEEu7bIqJHREREVktUz2WpLzw5gkiIiIiC8GKHREREVkt\nCyvYsWJHREREZClYsSMiIiKrxWvsiIiIiMgsMbEjIiIishBciiUiIiKrJQGXYomIiIjIDLFiR0RE\nRFbL0t48wYodERERkYVgxY6IiIisltSyCnas2BERERFZClbsiIiIyGrxGjsiIiIiMkus2FUAySlH\nTT2FCkHu5GbqKVQYjJU4jJN4jJU4jBOVNyZ2REREZLUsbSmWiV0FoMi9b+opmLWSfwEzTqVjrMRh\nnMRjrMRhnMRhRfP5MbEjIiIiq8XHnRARERGRWWLFjoiIiKwWr7EjIiIishAWltdxKZaIiIjIUjCx\nIyIiIrIQTOyIiIiILASvsSMiIiKrJbWwi+xYsSMiIiKyEKzYERERkdWSwLIqdkzsiIiIyGpZ2Eos\nl2KJiIiILAUrdkRERGS1ePMEEREREZklJnZEREREFoKJHREREZGFsOjEbty4cfjjjz9E94+MjMS+\nffvKdIy3334b2dnZZZ0aERERmQGJRFJuP6ZQYW+e+Oabb/Dvv/9i4sSJWm0FBQWYMGECAEClUkGt\nVgufFxUVoV+/fvj+++8BACEhIVi6dCmqV68OAFAqlVCpVEL/5ORkzJw5E3K5XGhTqVRo0qQJli5d\nKuzz5DEA4PDhw4iIiICrq6vB+X/66ado1qzZs54+ERERvQAWdu9ExU3sNBoNNBqNVltxcTGKi4sN\n7pOTkwOp9HGRMj8/H/b29gb7X79+HW3atMGCBQuEtjt37mDIkCFG55aRkYF+/frhgw8+KO00iIiI\niF6YCp3Ybd68GQcOHBDaHjx4gEGDBhncJzMzU6jOAUB2djacnJyMHufpUqpUKtVJKK3J/qQDOHDo\nR8ikMjRv1hShIUNNPSWzxDiJx1iJwziJwziJx1g9Yqol0/JSYRM7iUSCIUOGIDw8XGjbsGED/v33\nX4P7/Pnnn6hRowaAx0ndi/gDfTrR02g02LVrF37++We9/aVSKeLj4+Ho6Pjcx36Z8vPzsT/pAKJW\nLgcAfDhvAVLT0lG7lpeJZ2ZeGCfxGCtxGCdxGCfxGCvLVaETO6VSqdWmUCh0+j2ZdB09ehSXL1+G\nSqXCqVOncPfuXahUKtjYPF8Yhg4dCqlUipUrV+LVV1+FRCJB3759LW4p9lzyn/Bv4ydsd+4YgFOn\nz/A/BE9hnMRjrMRhnMRhnMRjrB6TWlbBruImdo0bN8aSJUtw+PBhoc3GxgZTp07V6ldSkUtLS0Na\nWhqCg4MRGxuL8+fPo1GjRti+fTsGDx78XHPZvHkz3NzchG0PDw989dVX+OmnnwzuM3v2bHTs2PG5\njvuy5eTmwvmJpWtnJyekpKaZcEbmiXESj7ESh3ESh3ESj7GyXBU2sWvTpg127dpltM+AAQNQp04d\nAMCSJUsQFhaG//znPxg6dCg0Gg3i4uIwePBgtGzZEg0bNtQ7hr5l1tJ07twZv/32m8gzqThcnJ1x\n7cbfwnZ2Tg5cnJ1NOCPzxDiJx1iJwziJwziJx1hZrgr/HLvhw4cjMDBQ78/69eshlUqRmJiIwsJC\nBAcHw97eHp6enhg5ciQqV66MyMhITJkyBXl5eTpj16hRAwcOHNAac9CgQfD29jbBmZqeb9Mm+O3U\n78L20eO/oNUbLUw4I/PEOInHWInDOInDOInHWD3G59iZmU2bNhn8LDQ0FH///TcCAgLQrl07AMCh\nQ4eQl5eHXr16AQB8fX0RHR2t90YGf39/nDx5snwmXgFVcXREz6AemDXnY8hkMvg0aoi6dWqbelpm\nh3ESj7ESh3ESh3ESj7GyXBU+sTNGJpMBAKpUqSK01a9fH4sXL9bqV6tWrRdyvNTUVIwbN07041Ak\nEgni4+PxyiuvvJDjvwyB3bshsHs3U0/D7DFO4jFW4jBO4jBO4jFWj1jY004sO7HTp169euU2du3a\ntZGYmFhu4xMREREZY9GJnbu7OypXriy6v62tLWxtbct0DDs7O6EySERERBWL1MJKdhad2D295Fqa\njz76qMzHSEpKKvM+REREROXBohM7IiIiImMs7ZViFf5xJ0RERET0CBM7IiIiIgvBpVgiIiKyWha2\nEsvEjoiIiMjUrl27hj59+mDcuHEIDw8HAHTr1g1yuVzriR3vvvsuhg0bZnAcJnZERERktczl5omF\nCxfizTffhEqlEtrUajWioqLK9CIFJnZERERktcwhr/v+++/h4eEBT09PrcTuWfDmCSIiIiITyc/P\nx+rVq/H++++/kPFYsSMiIiKrZeo3T6xevRrBwcFwc3PT+7nY98+XYGJHREREZALXr1/HsWPHsHv3\nbr2fSyQSREREID8/H1KpFJ06dcLYsWNhb29vcEwmdkREREQmEBkZiZkzZ8LGRn86tn37dri6ugIA\nsrKysHDhQixYsMDoK1N5jR0RERHRS3bw4EHY2NigY8eOQtvTy64lSV3J73PmzMEPP/xgdFxW7IiI\niMhqmeoSu/T0dKSlpaFPnz5C27179wAAx48fR2xsLBwdHbX2UavVkMlkRsdlYkdERERWy1TPsQsN\nDUVoaKhW26pVq6BWqzFlyhRoNBpkZGSgZs2aAB4txc6bNw/BwcFGx2ViR0RERGQGbGxshERToVBg\n2rRpyMvLg42NDaRSKfr27Wv0rRMAEzsiIiKyYubwgOIS48aNE363s7NDQkJCmcfgzRNEREREFoIV\nuwpA7qT/oYWkjXESj7ESh3ESj7ESh3EyP+byrtgXhRU7IiIiIgvBil0FoMi9b+opmLWSfwEzTqVj\nrMRhnMRjrMRhnMRhRfP5MbEjIiIiq2VhK7FciiUiIiKyFKzYERERkdWSWljJjhU7IiIiIgvBih0R\nERFZLQsr2LFiR0RERGQpWLEjIiIiq8UHFBMRERGRWWJiR0RERGQhuBRLREREVsvCVmJZsSMiIiKy\nFKzYERERkdXizRNEREREZJZYsSMiIiKrZWEFOyZ2REREZL24FEtEREREZum5Ers//vgD48ePL9M+\nWVlZ6NGjR5mP1aZNmzLvUxY3btxAWFgYACAsLAx///233n7vvfceunfvju7du+Ptt99GVlZWmY/V\nuXNnAEBsbCw2btz4zHMmIiIielKpS7FbtmxBTEwMCgoK4OPjgwULFsDT0xMAoFQqoVQqtfrHxcVh\n69atWm0qlQorVqyAj48P1Go1FAqF1ufHjh3D559/rtWmVCrx+eefw9fXFwBQUFCgM7fExER89dVX\nBuf+8OFDxMbGon79+gCAEydOYPHixVCpVEKfnj17YsKECVCpVMK5KJVKqNVqoc8ff/yB/Px8AMCw\nYcO0jnHx4kVoNBpIJBK0bNkSlStXRl5eHnr06IEqVaoI/Vq2bInIyEitc1Gr1VpzISIiInoeRhO7\nY8eOYcOGDYiPj0f16tURHx+PsWPHYv/+/QbXpIcNG6aT/MyePRs3btyAj4+P3n06dOiADh06aLVN\nnz4d//zzj5DY6RMUFISgoCCDn0+cOBEZGRlCYnfhwgV0794dkyZNMriPPidPnsS9e/e02kqSuSc1\naNAAlStXRnZ2NqpUqYKkpKQyHYeIiIheLgu7xM54Yrdlyxa8//77qF69OgBg6NChOHDgAI4dO4aO\nHTuKPsizXJh4/vx5hIeHl3m/J6nVaq1j60vGxChZbr5x4wbi4+Nx5coVFBQUwMvLC7169cJbb72l\ns8+tW7fQp08fFBcXAwDGjRuHo0eP4tq1a8jLy3vGMzK9/UkHcODQj5BJZWjerClCQ4aaekpmiXES\nj7ESh3ESh3ESj7GyTEYTuwsXLmDRokVabe3bt8e5c+cMJnaFhYW4cOGCkNBoNBr8888/kMlkBo9z\n4sQJnDt3DoWFhSgqKkJ2djZsbGzg7e1tdPLHjx/H7Nmz4erqqvdzBwcHoVqnj1KphFQqRUpKCtLT\n040e6/Lly3jvvffwwQcfYOrUqZDL5bhy5QoWLVqEGzduYMyYMVr9PT09sXv3bq22rl27QqVSoUuX\nLkaPZa7y8/OxP+kAolYuBwB8OG8BUtPSUbuWl4lnZl4YJ/EYK3EYJ3EYJ/EYq8cs7a5Yo4lddnY2\nnJ2dtdpcXFxw69YtYfvMmTMIDAxEnTp1EBUVha+++goXLlxAo0aNhD7NmjVD69athe3MzEwEBgbC\n1tYWe/bswSuvvIIGDRrA3t4ecrkcK1aswIgRI3TmExgYCIlEgk2bNqFq1apISUlB7969MWvWLNEn\nnJCQgEOHDgEA5HI5Fi5ciDVr1iAvL8/oH+6PP/6I9u3bay39NmvWDJMmTcKiRYu0EjupVCpcO6dW\nq5GdnY2UlBQUFRXB399f6KfRaETP2xycS/4T/m38hO3OHQNw6vQZq/wPgTGMk3iMlTiMkziMk3iM\n1WMWltcZT+zc3NyQk5OjVRHLzMyEu7u7sN2yZUusX79e2M7Ly0Pfvn3Rp08fg+O6u7trXX/m4+Mj\nXH938OBBKJVKDBo0SGe/p69Zk0gkZb75YNCgQTpLvF9++SX++usvLFiwwOB+fn5+mD17Nq5fvy5U\nAfPy8rBjxw6tZK3k/Ly8vBAUFAR7e3s4OTmhTp06aNWqFQAgNDQUAGBnZ6d1k4a5y8nNhbOTk7Dt\n7OSElNQ0E87IPDFO4jFW4jBO4jBO4jFWlstoYteyZUscO3ZMK0k7cuQIPvzwQ6ODPmsl6vjx41iy\nZAk2bdoEqbT0J7H4+vpi27ZtCAwMNNgnODgYo0ePLnWs0ubcunVrREREYPbs2Xj48CGkUimkUim6\ndeuGCRMmaPW1sbHBN998Y3Cs9957D4DuHbbmzsXZGdduPH4MTHZODlyequgS41QWjJU4jJM4jJN4\njNVjUgsr2RlN7EaOHIkpU6agYcOGqFu3Lr7++mu4uLigZcuWBveRSqUoLCzEv//+i4KCAhQUFOCf\nf/7BjRs3cPPmTQQHB+vso1AosG7dOuzduxfr168XHqdSmmbNmmHPnj2i+pbMraioCEVFRSgoKEB2\ndjZu3LgBJycnnSVnfbp164Zu3brh0KFDSEpKwrJly0rdp2/fvgarig8ePMCcOXOMJqbmxLdpE8Ql\nbMPwIY+qqUeP/4IxobpL5taOcRKPsRKHcRKHcRKPsbJcRhM7X19fREZGYuHChcjKykKbNm2wZs0a\nowO2a9cOq1evxubNm2FrawsHBwe4u7ujdu3aeP311+Hg4KCzz4wZM+Dk5ITvvvsOjo6Oz3dGRrRu\n3Rpz5szBTz/9BHt7e7i5uaF+/fpo3759mccSW5XctWuXwc+++OKLUm/aMCdVHB3RM6gHZs35GDKZ\nDD6NGqJundqmnpbZYZzEY6zEYZzEYZzEY6wes7CCHSSa57iC/+TJk1i3bp3WNXaluXv3LgYOHIjD\nhw8LbSqVCjY2xp+V3KxZMyQnJwMAUlNTMW7cONHJlUQiQXx8PF555RWDfa5evYqFCxfi22+/RUhI\nCObNm4cGDRpg0KBByMnJEXUcAIiIiNB6Jl+/fv1QWFio965gW1tbREREwM/PT+ezJyly74s+vjWS\nO7kBYJzEYKzEYZzEY6zEYZzEKYnTy3Twg6/Lbexun5bt7VwvQqlvnngZSkvqnla7dm0kJia+8DnI\n5XKd9oSEhOca9/r16zh//vxzjUFEREQkxnMldra2trC1tS3TPjKZTG8CVZpKlSqVeZ+y8Pb2RkxM\nDIBH51XWZNOQ+vXrIygoyOBz/Jo3by68aoyIiIheLkt7jt1zLcXSy8HSvXFc4hCPsRKHcRKPsRKH\ncRLHFEuxh2ZHldvYXZeMK7exDTGLpVgiIiIiU7Cwgh1Kf1gcEREREVUIrNgRERGR1ZJILatkx4od\nERERkYVgxY6IiIisFq+xIyIiIiKzxMSOiIiIyEJwKZaIiIislqU9oJgVOyIiIiILwYodERERWS0L\nK9ixYkdERERkKVixIyIiIqtladfYMbEjIiIiq2VheR2XYomIiIgsBRM7IiIiIgvBxI6IiIjIQvAa\nuwpA7uRm6ilUCIyTeIyVOIyTeIyVOIyTGbKwi+xYsSMiIiKyEKzYVQCK3PumnoJZK/kX8J7JX5l4\nJuav98pJAPidKk3Jd4pxKh1jJQ7jJI4pKpp83AkRERGRhbCwvI5LsURERESWghU7IiIisloSqWWV\n7FixIyIiIrIQTOyIiIiILASXYomIiMhq8eYJIiIiIjJLrNgRERGR1bK059ixYkdERERkIVixIyIi\nIqtlYQU7VuyIiIiILAUrdkRERGS1eI0dEREREZklJnZEREREFoJLsURERGS1LGwllhU7IiIiIkvB\nih0RERFZLd48YWU2bdqE7t27o3v37pg0aZLQHhoaipSUFDx48AD9+vUr05ht2rR50dMkIiIiss6K\n3YULFxAXF4ezZ89CpVJBJpPBx8cHQ4YMQdu2bbX6Dh8+HMOHD9cZQ6VSQalUCv+rz4gRI/D+++/D\n19dXq72goODFnQwRERE9OwsrcVldYnfo0CF8/vnnmD17NiIjI2FjY4Pi4mKcPHkSixYtQkhICAYM\nGACNRoN+/fpBpVLpjLF8+XLhd2Ml3Js3b6J69erlch6msj/pAA4c+hEyqQzNmzVFaMhQU0/JrKiL\ni7H1z4Ows5EjuEln7Lr0EySQ4F9lIXyq1cUbHo1MPUWzw++UOIyTOIyTeIzVI5a2FGt1id2XX36J\nRYsWoVWrVkKbVCqFv78/vv76a/Tt2xcDBgyARCLBrl27oFQqcf78eRQUFKBZs2ZwdnYW9tNoNNBo\nNHqP8/vvv+POnTv466+/4O7uXu7n9TLk5+djf9IBRK18lNh+OG8BUtPSUbuWl4lnZj4O3ziNVp6v\nIfmfawCAvo07CZ99fWonE7un8DslDuMkDuMkHmNlHnbt2oVNmzZBrVZDqVTC29sb06dPR/369QEA\n9+/fx9y5c5Geno7i4mL07t0bY8aMMTqmhRUgS1dQUAA7Ozu9n9nb26OgoEBI1rKyshAcHIzvvvsO\nx48fx9ChQ/Hrr78K/cPDw/Uu0wLAunXrMHr0aCxZsgSFhYU6n/fs2RM9e/bE/fv3X8BZvRznkv+E\nf5ThLzUAACAASURBVBs/YbtzxwCcOn3GhDMyL2czrsLL2R3VHFx0PlOqVahsq/97Z834nRKHcRKH\ncRKPsTIPbdu2xZYtW7B3714kJSWhQ4cOGDlyJBQKBQBg8uTJ6N69O/bu3YudO3fi5MmT2L59u9Ex\nrS6xGzJkCObOnYsrV65otd+8eRPTpk3DoEGDhLLs999/jx49euCTTz7Bhx9+iKVLlyI6OlrYZ/Xq\n1fj22291jrFlyxbk5uZixowZCA4ORnh4uPCHVGLfvn3Yt28f3NzcyuEsy0dObi6cnZyEbWcnJ2Tn\n5JhwRubjVu5dPCz6F69Vqwt9NdwD106iU703Xvq8zB2/U+IwTuIwTuIxVuahZs2asLe3F7YHDhwI\nFxcXXLp0CVevXkVRURH69OkDAJDL5Zg1axYSEhKMjml1S7FhYWFwdHTE+PHjoVar4ebmhuzsbBQW\nFiIkJATjx48X+taqVQvbtm2DQqGAXC7HmTNnULt2ba3xnl6KjY2NxebNm7Fp0yZIJBIh8+7Xrx9i\nYmIq9DV3Ls7OuHbjb2E7OycHLk8sTVuz8//8hQJlEXZe+glFKgVu5d7FibQL8K/VFMdunoOnUzXU\ncalp6mmaHX6nxGGcxGGcxGOsHjOnS+yKi4uRl5cHd3d3/PDDDzo3dDZs2BAZGRnIycnRujTsSVZX\nsQOAAQMG4PDhw0hISEBaWhrWr1+PX3/9VSupA4BOnTqhdevWGDp0KPr164crV65g1qxZAIBu3brB\n1dVVq39+fj6Sk5MRHx+vlcCNHTsWn332GapWrVr+J1eOfJs2wW+nfhe2jx7/Ba3eaGHCGZmPoIZv\nIrhJZ/Rr3Ak9Xm2Lui414V+rKX5N/RN2Mlu0qNnQ1FM0S/xOicM4icM4icdYmZ/U1FR8+OGH6N69\nOzw8PJCZmYkaNWro9KtRowZu3bplcByrq9g9qWbNmpDJZPDyMnyx6MiRIzFy5Eid9pJr64qLi7Fo\n0SIAgIODA5YtW6Z3nMaNGz//hE2siqMjegb1wKw5Hz96REyjhqhbp3bpO1oZiUQKqVSKlOwM/PT3\nH3itWh3svPQTAODtBm3gIK9k2gmaEX6nxGGcxGGcxGOsHjP1XbHR0dGIi4tDZmYmunTpggULFgAA\ncnNzUa9ePZ3+crnc6GPTJBpDt3VaibZt2+LYsWOQy+VG++3evRv79u1DTk4OiouLIZFIIJfL0bFj\nRwwfPhyVK1fW6t+rVy+sX79e7x2xP//8M9q3by96jorcinODhSnInR5dp7hn8lcmnon5673y0UO2\n+Z0yruQ7xTiVjrESh3ESpyROL9O5L3WvlX9RXp8SIrpvfn4+Nm3ahBMnTmDTpk1YsGABvL29MWzY\nMK1+ffv2RWRkJJo0aaJ3HKup2MXExGDHjh067a6urujdu7dOu5+fn5A1b9iwAWfOnMHnn3+utfya\nn5+PmJgYTJ8+HVFRUVr7lzy8WJ+yJHVERERk+RwcHDB+/Hjs378fV65cQY0aNXD79m2dfhkZGahZ\n0/A121aT2I0aNQqjRo16pn2lUilsbGx0yrVSqRS2traQyWQ6+0gkEoPPuCMiIiIzYU53T+BR0Uij\n0eCNN97AJ598ovXZlStXUKVKFZ1r/J9kNYnd8xg5ciSqVKmCqVOnIi8vDxqNRkj2OnXqpPe6uvr1\n6yM0NNTgM/N69OiBiRMnlvfUiYiIyAxpNBrcunVLuM6/oKAAy5Ytg5eXF3x8fITi0O7du9GnT5//\n196dx0VV7n8A/wwDAwqyKiJKQG5YKmYYl98VBXcgt9A09yW9Jtct01xSkhQrE26mppYL6sWLmrgl\n16t53ZAwtZvkcg0lQREQlFWWYeb8/vAyOQ7LgAxnmPm8Xy9fL85ztu/56oxfnvOc56C0tBSff/45\nJk6cWO1xWdhpKTg4GMHBwVpv/9VXHO9FRERElSsqKsL8+fORl5enmsvO399fNV+uRCLBxo0bsXz5\ncmzduhUKhQKBgYEaY+6ex8KOiIiIqIFZWVkhJiam2m1atmyJzZs31+q4LOyIiIjIaElM9GuM3Yti\nYUdERERGS8+enXhhRvnmCSIiIiJDxB47IiIiMlpiv3mivrHHjoiIiMhAsMeOiIiIjJaBddixx46I\niIjIULCwIyIiIjIQvBVLRERExsvA7sWyx46IiIjIQLDHjoiIiIyWob15gj12RERERAaCPXZERERk\ntAxsiB177IiIiIgMBXvsiIiIyHgZWJedRBAEQewgiIiIiMRw/dsYnR37lXdH6ezYVeGtWCIiIiID\nwVuxjcDliJ1ih6DXXn9/AgCgLD9H5Ej0n8zaAQBzVRPmSXvMlXYq8lSSkyFyJPrNwsGpwc9pYHdi\n2WNHREREZCjYY0dERERGixMUExEREZFeYo8dERERGS2JgQ2yY2FHRERExsuw6jreiiUiIiIyFCzs\niIiIiAwECzsiIiIiA8ExdkRERGS0DO3hCfbYERERERkI9tgRERGR0TK0HjsWdkRERGS8DOzepYFd\nDhEREZHxYo8dERERGS1DuxXLHjsiIiIiA8HCjoiIiMhAGPSt2BkzZmD69Ono3r07AKCkpAQjR45E\neXk5AGD69OkYPny4avtly5ahV69e6N+/v6otLi4O69atq/IcCoUCcXFxkEqlqrbMzExMnjwZgiAA\nAExNTRETE4OmTZsCAAYOHIiYmBjY2trW38USERGR0WvUhd3BgwexdetWPH78GHZ2dpgyZYpaoVZe\nXg6FQqFatrCwwJEjR6o8Xnl5OeRyuVpbQEAAAgICqtzH398fxcXFsLKyUrW1bNkSx44dq3IfuVyu\nFhcRERGJw9DG2DXawu7kyZPYvn07Nm7cCBcXF6Snp2P+/PmQSCQYNmyYxvbJycmYNWuWWpupqSkm\nTJiAkSNH1jmOoqIiWFpaqpaHDh2KsrKySreVSqXYuXNnnc9FRERE9cyw6rrGW9jFxsYiJCQELi4u\nAABnZ2csWLAAq1evrrSwa9euHeLi4tTaZsyYgdLSUq3P+eTJE8TExGDy5MkAgIKCAlhYWKhV+4cO\nHQIA3Lx5EwkJCVAqlfDy8oKnp6fasSpu0zYW28/9E+VKBUrL5WhlY49gr15QKpXYd+kMfs/OwIeB\n74gdol45Gnccx0/+AKmJFJ5dO2Py+LFih6S3mCvtME/aYZ7I2DXawq4ySqVS64Lp+PHjSElJwfr1\n61VtNe2bn5+PAwcOqAq7hw8fqo3Hq7Bt2zacPn0a48aNg4mJCTZt2oSOHTti7ty5qm3Gjx8PU1NT\nREZGol27dlrFLKbJvoNUP2/69xE8yM1Bem4OXnfrgNtZD0SMTP8UFRXhaNxxbFoXAQBYEhqG1LR7\neMmljciR6R/mSjvMk3aYp6opFAps/GYbbty6hY0RazTWh38RifLychSXlMDVxQUzpk5q8BjFIjEx\nrC67RlvYvf3224iIiEDnzp3h7OyMBw8eYM2aNRg9enSN+165cgXLli3Dt99+i4KCAowdOxaCIODh\nw4fo1auX1jG8/PLLWLZsmUZ7VFQUjh07prpF26dPH/j6+mLOnDmq3r3du3fDwcFB63Ppi8LSYuSX\nFMGmqRVa2Ta++BvCf64mwce7h2rZv7cvLl66zP9cKsFcaYd50g7zVLVzF36EX6+e+PXGjUrXL/lg\nnurnZStX4/fUNLi95NJQ4VE9arSFXe/evVFYWIgZM2agsLAQlpaWGD9+PEaMGFHtfseOHUN4eDic\nnJxw7tw5zJw5U/Wgw+LFi1XbXbp0qdKiTSKRVPowhbm5OQ4ePAgAcHNzw5kzZxAYGAgASExMRIsW\nLRr1AM3MvMfYd+kMfsu8jwn/1x9NZeZih6S38vLzYWNtrVq2sbbG3dQ0ESPSX8yVdpgn7TBPVfPz\n/bNW2+XnF+Dx41w0d7DXcUR6pBH/31yZRlvYAUBQUBCCgoK02jYtLQ1r165Famoqdu7ciTZt2uDz\nzz/HsGHDEBoaqpoSpYKXl5fGmDxtffHFF1i1ahW2bNkCExMTODk5qd3ybYxa2tjhr32HQalU4qsf\nDqKdozNsmlrVvKMRsrWxQfKdFNVybl4ebG1sRIxIfzFX2mGetMM81V3avfvY+O02/PLrNSycMwtW\nzzwUSI2L0UxQHB8fjz59+uDAgQN4+eWXIZPJ8NFHHyEiIgL29tX/ZpKSkoLQ0FAEBATA398ffn5+\nCA4OxubNmyt9ArZFixb429/+hoMHD+LAgQPYuHEj2rT541bAxx9/DJtG+mVjYmICpaBEuVIpdih6\nq0vnV/HjxZ9Uy2fOxcOr+2siRqS/mCvtME/aYZ7qzqVNa6z+eBmOxPwd3x//F3IePRI7JKqjRt1j\nBwArV66Ep6cnBg8erLFu9uzZcHd3BwDV2LvvvvsO7du3R9euXQEAbdu2VW3ft29f1fYVrl69irlz\n52Lu3Ln48MMPVZMMp6WlYdu2bRg3bhz27NmjNkEx8HQy5C1btuDcuXOqhzoEQUDHjh1VD040Fr9n\nZ+DY1USYm8lQXFYK75c7wcHqj9sdUhOj+f1AK82srDA4cBAWLl0OqVQKj44d4Ob6kthh6SXmSjvM\nk3aYpxcnlUqhUCpVE/kbAwO7E9v4C7vqJvutKN6e9fPPP0Mmk1W6rl+/fhptp0+fRmBgIIYMGaLW\n7uLigtDQUPj6+uL+/ft46SX1L4+FCxfC1dUVf//73yGTyVTtly5dQkhICKKjo+Hs7KzVNYrNrbkT\nZvYZWuX6DwNrfmDF2AQM6I+AAZpPTJMm5ko7zJN2mKfqVdapcOO/t7DrH3vRtGkTFBU9QX+/3mjp\n6ChCdFQfGn1hJ5FIajUnXG239/X1xQcffIAuXbqgd+/esLCwAAA8ePAAO3fuhKOjo9pt1mfPY2pq\nqvHAhKmpKUxNTWHCXi4iImpgG9Z+rvp5/pLlWLPyY3Tq2AHhoR+JF5TIGvODjZWRCI1tptzn7Nix\nA9u2batyzJqfnx/mz5+vWt62bRuioqJg/cyTU8/q3LkzVq9erdb222+/Yfv27bh8+bLqlWO2trbo\n168fpk6dCnNzzSdES0tLsWXLFpw9e1bjVuzEiRPRqVMnra/xcgTfVlGd19+fAAAoy88RORL9J7N+\nOkUNc1U95kl7zJV2KvJUkpMhciT6zcLBqcHP+ft3h3V2bLfgITVvVM8afWFnDFjYVY+Fnfb4n7B2\nmCftMVfaYWGnHVEKu9iq3yH/otyGa47/17VGfyuWiIiIqK4M7VYsB3oRERERGQgWdkREREQGgoUd\nERERkYHgGDsiIiIyXoY1xI49dkRERESGgj12REREZLQM7alYFnZERERktCQmhlXY8VYsERERkYFg\njx0REREZLwO7FcseOyIiIiIDwR47IiIiMlqG9vAEe+yIiIiIDAQLOyIiIiIDwVuxREREZLwM604s\ne+yIiIiIDAV77IiIiMhoGdoExRJBEASxgyAiIiISw/3jx3V27NYDB+rs2FVhjx0REREZLwOb7oSF\nXSNQlp8jdgh6TWbtAIB50kZFrt7rNUfkSPTb12e/BABcidwpciT6r/u8CQCA/TO/FDkS/TZi49PP\nHL+nqlfxHUV1x8KOiIiIjBYnKCYiIiIivcTCjoiIiMhA8FYsERERGS8Dm+6EPXZEREREBoI9dkRE\nRGS0DO3hCRZ2RERERCI4c+YM3n//fXz99dd44403VO39+/eHTCaDmZmZqm3EiBEYN25cjcdkYUdE\nRETGS6QOu+joaBw+fBitW7eGQqFQW6dQKLBp0ya4uLjU+rgNPsbu2rVrmDJlitbbP3r0CIMGDdJh\nRH+4cuUK3nvvvQY5FxEREYlPIpHo7E91pFIpoqKiYGNjU6/XU689dklJSViyZAmysrJgYWEBa2tr\nyOVypKenw83NDdu3b4dcLodcLlfts2HDBvzzn//UONaOHTvg4OAAhUKBsrIyjfVXr17FtGnT4Ojo\nWGkspqamiImJgUwmU7WNGzcOeXl5qmWlUgkXFxds2rQJADRiA4Dy8nIEBwdXGkN2djb69OmDzz77\nTKM9KCgI9vb2lcZWEcvYsWOrXE9ERESGa9SoUTo5br0Wdl26dMGRI0fwySefoEuXLhg2bBju37+P\nkJAQHDx4EACQlpamtk9ISAhCQkLU2gYPHozCwkI4OFT9apGsrCz07NkTa9eu1Tq+3bt3a7R5enpW\nu4+pqSkOHTqk0X7t2jXMmjULQUFBGusePXqE1q1b48CBA1rHRkRERFRBEIQ67aezMXYVAVUWWFJS\nEgICAuDs7IytW7eqrSstLUVWVhbatGlT4zmUSmW9xVkbhw8fxrp16xAZGVllYWhoT9kQERFRw5BI\nJFi8eDGKiopgYmICPz8/TJ8+HRYWFjXuq7PCrrrCpkuXLti1a1el686ePQsvLy9IpdJqj9+mTRv8\n8ssvCAgIqPL8UVFRaNGiRZXHKC8vr1UBdvfuXaxZswbnz59HYGAg3Nzcqtw2OTkZgwcPrnL9kiVL\n4OPjo/W5iYiISAf0cILiffv2qYZzPXr0CJ988gnCwsIQHh5e4771XtgJggClUgm5XI7CwkJkZmai\noKAA+/btQ35+Pry8vKrdd+vWrZgzZ45ae1ZWFgICAmBmZobDhw8DADw8PHDq1Klaxfbmm29CLpfD\nxOTpMyMSiQT9+/evcb9r165h165d+OWXXzB79mxERERg3759eOedd+Dl5YWAgAB4e3urjgsA7dq1\nw3fffVer+IiIiIieHaNvb2+PpUuXYtCgQQ1f2J08eRJr1qwBACQmJiImJgbNmjWDl5cXMjMz4eHh\nUe3+0dHRsLW11ejJcnR0RFxc3AvHl5qaiqtXr9Zqnw8++ACZmZl45513sGrVKuTm5uLx48cYO3Ys\nRo8ejQsXLuDo0aNo3749mjdvDgBwcHBAenp6lb2JABAUFIS//vWvL3Q9RERE9GIaw9AphUJR453M\nCvVa2PXr1w/9+vWrdptHjx5h4sSJGu2nT5/Gzp078Y9//KPa/S9duoRly5ZpHZO5ubnqwY26+Oyz\nz9SSeeTIEeTk5GD+/PmQSqXw9fWFr6+v2j4ODg5ISEio8zmJiIiogehZYScIAjIyMtCqVSsAT+um\n0NBQBAcHa7W/TsbYPXr0CCEhISgsLNRYJ5FIEBgYqCoABUFAVFQUYmJisHXrVtjZ2VV7bC8vL8TF\nxalu+WpbwVacq6SkBGVlZSguLkZhYSHu3r2LlJQUWFlZwd3dXWOf54/fGCp7IiIiahyef8OEXC7H\nvHnzUFhYCFNTU5iYmGD48OFavXUC0FFhl5aWBkEQcOTIEY11Fy9exPr16zFjxgwIgoAxY8bA0dER\nMTExsLa21vocsbGxuHnzJpYsWaL1PgMHDsTIkSMBAJaWlrCzs4OzszNefvlldO3aFQUFBTUeo66P\nHxMREZH+EbvD5vnZQWQyWY13L6ujk8JOEAS16vNZMplMVRxJJBJERESouhtrQ6FQaLyCoyZffPFF\ntesTExNVP5eUlCA4OLjKKVVOnjyptlxxLStWrEBubq7WMS1evBi9evXSensiIiKiqjT4u2IFQVCr\njutS1AG6r7AtLCzw/fff13q/PXv26CAa/XE07jiOn/wBUhMpPLt2xuTxfHtGZZin6jV3dsCg8QMA\nAAp5Ob7f8U8EThwEQVDCspklkn68hp9OXBY5Sv2w7dw/oVAqUCqXw8nWHiO8ekGpVGLfT2eQkp2B\nRUHviB2iXlAoldh//QeYm5phmIdflW30B35PGSadFHaOjo64detWpfO4FRYW1qqHSiqVqr0WrIKb\nmxsiIyNx8eLFKvedMmUKhg8frvW5zMzMquxpJKCoqAhH445j07oIAMCS0DCkpt3DSy41TyZtTJin\nmg2bPhjRX8TgSWGxqu0fkftUP7//1SwWdv8zxfePd2V/feoIHuTm4H5uDl5364DkrAciRqZfTv9+\nGa+38kBSVnK1bfQUv6cMl04KO2dnZ7Xbmi/C3t6+0nfJenl5IT4+vl7OUaF79+74+uuv6/WYhuQ/\nV5Pg491Dtezf2xcXL13mF8FzmKfquXZ6CY8f5mLo9Ddh3tQCt37+DRe+/1G13lRmiqL8JyJGqJ8K\nS4uRX1IEm6ZWaGVb9esWjdEvGb+hjbUjHJraPtN2S6ON/sDvqWfo4QTFL6LBb8VS45WXnw+bZx5w\nsbG2xt3UtGr2ME7MU/UcnOzh7O6EjYu+gaJcgdHzRiIzNQu3k+4AAIZMDcS/on8QOUr9kZn3GPt+\nOoPfMu9j/J/7o6nMXOyQ9Ep6wUMUlj2Bp1N7PC7Of6atGJ5OHVRtpI7fU38Q++GJ+sbCjrRma2OD\n5DspquXcvDzY2tiIGJF+Yp6qV1ZchhuX/gtF+dOHn67GJ+Glji64nXQHfUb6IfXWPaRc+13cIPVI\nSxs7/LXfMCiVSqw7eRDtHJ1h29RK7LD0RlJmMorLS3Ho5hmUKsqQnp+NGw9/x6uOL6u1Jd77Fd5t\nOosdrt7g95ThMql5k4a3cuXKSqdKqc7AgQNr9TQqAFy5cgXvvfeeVtsePnwYERFPxyLMnz8fV65c\n0fo8jx8/xqBBg2reUM916fwqfrz4k2r5zLl4eHV/TcSI9BPzVL3UW2lw83BVLbu/4ob7t++j17Ce\nKC0uxaUftP9sGRMTExMIghKKKp7UN1YD2/lgmIcfhnr0Rv+X/wRXWycs6TUZQz16q7WxqFPH76ln\nSCS6+yOCOvXYJSUl4fLly7C2tsb27dtV7RXzwT37pgdra2usW7cODg5/jAm5dOkSwsPDkZmZCXt7\neyxYsEDtgQq5XI7y8nLV8i+//IKFCxdqPETRs2dPfPjhh6p9np/+pKysDJ9//jnOnTsHiUQCb29v\nLF68GBYWFqp95HK52j7Pv9nio48+wp///GcoFArVts/HN2TIEHzzzTdo2bKlqm3ixIlYtGgROnXq\nhPLycpSVlanW7dy5E126dMFrrzWuD1EzKysMDhyEhUuXQyqVwqNjB7i5viR2WHqHeape/qMC3Pjp\nJqYsm4DSklLkPHgEeVk5Bozpi18TruOd95/ONXl46zEU5RWJHK24UrIzcOyXRFiYyVBcVoo3Xu4E\nB6s/bp+Zmujl7+aiMZFIYCIxqbGN+D1lyGpd2JWVlWH9+vXYsGEDTE1N8dZbb6nW+fj4IDIyElOn\nTlW1zZo1C7dv31YVdjk5OZg9ezYiIyPh7e2Na9euYfr06YiOjoarq6vG+QDgzp078PHxwccff1yr\nWCMjI5Gfn4+4uDhIJBKEhYUhPDwcYWFhVe5T8WaL2hAEQa3QAwClUom9e/fC0dERRUXq/zmNGTMG\nM2bMwIYNG2Bu3rjGywQM6I+AAf3FDkPvMU/Vi//+R8Q/88AEAHz09gqRotFf7s2dENJ3aJXrPwwa\n3YDR6D8bCysM9ehdYxs9xe+ppyTG/vDEd999Bz8/P5iaau6qVCph8txvkKWlpWjatKlq+ciRIwgI\nCIC3tzcA4NVXX8WkSZOwffv2agu32r7xoaysDAcPHkRcXJwqpsWLF8PPzw/z58+HTSVjCaZPn460\nNM3Bo23btkXfvn2rjW3q1KlqU6Xcu3cPI0eORNu2bfH48WMcO3ZMtc7U1BR9+/bFvn37tH5FCBER\nEVFNal3Y7d27F9HR0RrtZWVlaNKkiUZ7bm6u2vtfk5KS0L+/+m8Ivr6+WLRokWq5rq/tena/tLQ0\nNG/eHLa2fzzqLpPJ8Morr+D69evw8fHR2H/Lli0AnhajKSkpcHZ2Vr3mLDY2tsrzSiQSbNu2Dc7O\nzqq28ePHw97eHi1btqy0CB46dChGjx7Nwo6IiIjqTa0Ku/v378PKyqrSAu7hw4do3ry5RvujR4/U\n2nNzczV6y+zs7JCcnIyAgAAAT2/Xdu/evTahAQDGjh0LExMTfPnllygqKkKzZs00trGxscHjx4+r\nPMa5c+fwt7/9Dd26dcPVq1cxdOhQjBs3DoIg4MCBAzh9+jSysrIwfvx4tf0qK0Z3794NOzs7lJSU\naKxr2rQpbGxscP/+fbRu3brW10pERET1wJinO0lOTka7du0qXZeRkVHp68FKSkrUxpE5ODhoFFaZ\nmZl49dVXERMTAwAIDQ2tU69ddHS0aizf77//XulTstnZ2XB0dKzyGCtXrsSePXtgb28PhUKBYcOG\nYcCAAZBIJAgODsbChQsxe/ZstficnZ0xadIk1UMZFeeJjY2Fk5MTsrOz8fbbb2ucq23btrhz5w4L\nOyIiIqoXtSrsCgoKVLcmn5eWloY2bdRnrM7Ly9PY3svLC+fPn0dgYKCq7YcffsCf/vSnKs8rkUg0\nCr2SkhLcu3cPqamp8Pf319jHxcUFRUVFyMzMVD2tmp+fj+TkZHTuXPlj76WlpSgvL4e9vT2Ap68z\nc3d3R2pqKgD1XrlnJzTcvHlzlbE/v9+zbGxskJeXV+2+REREpDuGNkFxrZ4Bt7a2Rn5+5bN4//77\n73B3d1dre/DggdoUIAAQFBSE+Ph4HDp0CKWlpTh16hQOHTqECRMmVHleNzc3HD9+HAEBAQgKCsKb\nb76Jd955BxEREUhKStKY5gR4WpSNHz8eS5YsQUFBAQoLC7Fw4UKNnrVnmZubw8HBARcuXADwtFi9\nfv06PDw86jzurzp5eXlqYwCJiIiIXkSteuzc3d1VDxg877///S969uyp1paWlqb2QAEAWFpaYseO\nHVizZg02bNgAV1dX7NixQ22eu+d169YNiYmJlT51W513330XpqammDhxIgDgrbfeqvFhhTVr1mDF\nihVYvXo1LCwsEB4eDisrK60qem9v70rfkWtvb4/w8HCN9uTkZEybNk3LqyEiIqJ6Z2A9drUq7Fxc\nXJCbm4vCwkLExMRg//79auufndj3WQEBAejRo4dq/jh3d3ds3Lix1sHWpqirMGnSJEyaNEnr7V1d\nXbFt2zaNdm167IqLiyttl0qlGreaCwsLkZuby/F1REREVG9qPd3JqFGjEBsbi6lTp6pNRGzoQ9rp\nUAAAEqZJREFUzMzM1N58UVmhV5v79LGxsRg1alS9xEZERER1Y/QTFL/99tuYMWMGRo0apfGKr/pi\nZmamNtmvNszNzSGVSnV2nsGDB9e4X9u2bREYGFhlHBMnTsSIESNQVlaGU6dO1fjQBREREVFt1Lqw\nMzc3x9y5c7Fr1y6d9dh99NFHtd6ntq8BA4Du3bvj66+/rvV+a9eurbT9wIEDWu0fHR2N2bNn66ww\nJiIiIuNU68IOADw9PeHp6VnfsRiN2oz5IyIiIh0ysIcnav80AhERERHppTr12BEREREZBAPrsWNh\nR0REREbLqN88QURERET6iz12REREZLwMbB479tgRERERGQgWdkREREQGQiJo8xJUIiIiIgP0+NoV\nnR3b7tXuOjt2VTjGjoiIiIyWRGJYNy9Z2DUCZfk5Yoeg12TWDgCYJ21U5Gr7xDUiR6LfJkctAACc\nDf1G5Ej0X68V0wDw81cTfk9ppyJPDYrTnRARERGRPmKPHRERERktTlBMRERERHqJPXZERERkvDhB\nMRERERHpIxZ2RERERAaCt2KJiIjIaPHhCSIiIiLSS+yxIyIiIuPFHjsiIiIi0kfssSMiIiLjZWDv\nijWsqyEiIiIyYuyxIyIiIqMl4QTFRERERKSPWNgRERERGQjeiiUiIiLjxelODMP+/fsxYMAA1Z+9\ne/fW6TibNm3Cjh07arWPt7d3nc5FREREVB2j6rFLT09HcnIyJBIJWrZsiWXLlqmtP3fuHACgTZs2\ncHd3BwA8ePAAoaGhuH//PgBg1KhRmDBhgmqf8vJylJWVqR0nPj4eX375JdLT02FpaYmRI0di6tSp\nqteWFBcX6+waiYiISHuG9koxoyrs7t69izNnzqi1CYKg8Zfao0cPVWEXEhKCt99+G6NHj0ZhYSEm\nT54MBwcHBAUFVXqOGzduYPny5Vi/fj06deqE3NxcLF++HOvWrcOcOXN0c2FEREREMLLCzsfHBz4+\nPigpKUFMTAzOnz+P7Oxs2Nvb44033sCYMWPQrFkz1fZXr16FQqHA6NGjAQBWVlZYsmQJVq1aVWVh\nd/jwYYwePRqdOnUCANja2iI0NBSBgYEGUdgdjTuO4yd/gNRECs+unTF5/FixQ9JLzFP1JCYS+E4P\nhLy4DAlRJ/DKwNfh4NoSynIFJCYmSIg6AYW8XOwwRff3SyehUCpRqpCjZTM7DOn8f8gqyMWx6z8C\nAEylUgx+9f9g08RS5Ej1Bz972mOu/sfAJig2qsIOeNpDN23aNLRv3x6hoaFwcnJCdnY2oqOjMWHC\nBMTExEAmkwEArl27hh49eqjt361bN9y+fRsKhQJSqbTScyiVSo1lQRDU2gICAiCRSLBr1y44ODjU\n4xXqTlFREY7GHcemdREAgCWhYUhNu4eXXNqIHJl+YZ5q5jnEB8nnfoXbGx1h1kQG51dccTLyAACg\nc+AbcO7sirSfb4scpfjGevVT/bztxzhk5D/CwaTzGN9jACxlFiJGpp/42dMec/UHzmPXyGVkZODn\nn3/GsmXL0KZNG5iamsLJyQnvv/8+srOzcfPmTdW2BQUFsLKyUttfIpHAysoKubm5lR7/rbfewt69\ne1XHyc3Nxccff6zq9asQFxeHY8eONZqiDgD+czUJPt5/FLr+vX1x8dJlESPST8xT9dz/5IHsOw+Q\nl/EYACAvLsOT3EI0sbGE1MwUVg7WyPzvPZGj1C9FZSUoLH2CYnkp7Js2Q+zVc/g24Xucu50kdmh6\nhZ897TFXhsvoeuycnJzg7OyMPXv2YPTo0TAxMYEgCDh27BgAoH379qpt7ezskJ6errZ/eXk5CgoK\nYG9vX+nx27dvj/DwcCxfvhzZ2dmQyWQYNmwYpk2bpruLaiB5+fmwsbZWLdtYW+NuapqIEekn5qlq\n9i85oomNJVJ+vAmr5n/kKPncr/Do0w2lhcXI+u0+yp6Uihil/sgqyMWhX+NxJzsdo17zR3ZRHu7n\n5WCW73CYSqX4+6UT+O2hHdq3ML5elsrws6c95spwGV1hJ5FIsG3bNqxduxZRUVGQSqVQKBRo3749\nduzYgSZNmqi27d69O7799lu1/ePj4/H6669X+xSNt7d3nadP0We2NjZIvpOiWs7Ny4OtjY2IEekn\n5qlq7t4ekDU1h8/E/jCzkMHB1RGd+r2GJrZWuLL/6VPpL73eHu17dcFvZ9kb5djMFtN8gqBUKrEl\n4Si6tW6HV5xcYfq/YSCerdvh7qNMFnb/w8+e9pgrw2V0t2KBp9OZREZG4vjx43j48CGOHDmC9evX\no23btmrbtW3bFq6uroiMjIRCoUBaWho+++wzvPfeeyJFLq4unV/Fjxd/Ui2fORcPr+6viRiRfmKe\nqnZ531kkRJ1AQtQJXN5/Dpm/3UdexmO1+UEV8nK13jyC6s5C+xZtkJLzQNV+J+cBXGxbiBiZfuFn\nT3vM1TMkEt39EYHR9dg96/kHGioTGRmJtWvXYvjw4WjWrBmWLVsGLy+vGvebMmUKPvjgA7zyyisa\n6zZu3FineMXWzMoKgwMHYeHS5ZBKpfDo2AFuri+JHZbeYZ60IyiVUCqUSP/1dzh1bAPfaQFQlCsg\nNTNF4u4fxA5PdKmPM3Hi5mWYm5mhWF6G7i4d4GBpjVed3PDNhaMwNzWDg6UNOrbkv60K/Oxpj7ky\nXBJBm+rGAGzduhX79+/XaJdIJJUWeD169EBYWFiNx12/fj1kMhmmT5+u1j5+/HgsWLAAXbt2rXvQ\n/1OWn/PCxzBkMuunD6AwTzWryNX2iWtEjkS/TY5aAAA4G/qNyJHov14rno4f5uevevye0k5FnhrS\nk/SUmjeqo6bO7jo7dlWMpsdu6tSpmDp1aoOdr6qCkYiIiPQI57GjZ5mamqrmvXtWu3btMG/ePFha\nVj5x6KBBgxASEqLr8IiIiMiIsLB7QTNmzKi0ffny5Vi+fHkDR0NERES1wgmKiYiIiEgfsbAjIiIi\nMhAs7IiIiIgMBMfYERERkdGq7k1SjRELOyIiIjJeBjbdiWFdDREREZERY48dERERGS1DuxXLHjsi\nIiIiA8EeOyIiIjJeHGNHRERERPqIhR0RERGRgeCtWCIiIjJaEgN7VywLOyIiIiKRxMbGYseOHZBI\nJLCxsUFYWBhcXV3rfDwWdkRERGS8RJzuJCEhAbt27cLu3bvRrFkzJCQk4C9/+QsOHz4MmUxWp2Ny\njB0RERGRCPbs2YM5c+agWbNmAAAfHx906NABFy5cqPMxWdgRERGR0ZJITHT2pyYJCQnw9vZWa/P2\n9kZ8fHydr4eFHREREVEDe/LkCUxMTGBhYaHW3qpVK6SlpdX5uBxj1wjIrB3EDqFRYJ60Nzlqgdgh\nNAq9VkwTO4RGg58/7TBPekikMXb5+fkwNzfXaJfJZCgpKanzcVnYERERkdESq9iWyWQoLS3VaC8p\nKdHoxasN3oolIiIiamB2dnYoKytDcXGxWvuDBw/g5ORU5+OysCMiIiJqYBKJBF27dkViYqJa+8WL\nF/H666/X+bgs7IiIiIhEMGnSJKxbtw4FBQUAgAsXLuD69esYOHBgnY/JMXZEREREIvD390dmZibG\njBkDiUQCa2trbN68uc6TEwOARBAEoR5jJCIiIiKR8FYsERERkYFgYUdERERkIDjGzsjExsZix44d\nkEgksLGxQVhYGFxdXTW2mz9/PuLi4nD9+nWtjnv16lXMnDkT8+fPx/Dhw9XW3bt3D+Hh4bh//z6e\nPHmC3r17Y9GiRTA11e9/fvWdq4sXL2LLli3IysqCIAjo0KEDli5dCnt7e9U2T548wSeffIJff/0V\nSqUSvXr1wgcffACpVFrv11efxMgVAJSXlyMsLAznz5/HqVOn6vWadKGh86RQKPDFF18gISEBgiBA\nIpFg+vTpCAwM1Mn11Rcx/j3NmzcPt27dglQqRXl5OQYOHIiZM2fCzMys3q+vvoj1uasQGRmJzZs3\n49SpU3B2dq6Xa6J6IJDRuHDhgjB8+HAhPz9ftTxw4EChtLRUbbvExEThL3/5i+Dh4SEoFIoaj3vi\nxAlhyJAhwrhx44R9+/aprSsuLhb69esnnDhxQhAEQVAoFMLixYuFtWvX1tNV6YYucvWf//xHSE1N\nVS1/+umnwqxZs9S2WbBggbBhwwZBEJ7masmSJUJERER9XJLOiJWrgoIC4d133xUWL14s9OrVq56u\nRnfEyFN5ebnwr3/9S5DL5YIgCEJqaqrQs2dP4caNG/V1WfVOrH9Pt2/fVv2cl5cnvPvuu8KqVate\n9HJ0Rqw8VUhJSRGCg4MFPz8/tX1IfCzsjMisWbOE06dPa7T9+9//Vi3L5XJh+PDhQkpKitCxY0et\nCruDBw8Kubm5wqJFi4S9e/eqrTt27JgQEhKi1lZQUCD07NlTUCqVdb8YHdNVrp6Vn58vdO/eXbWc\nl5cn+Pn5qeUlNzdX8PX1rdtFNBAxciUIgpCRkSEcP35cuHfvXqMo7MTK0/NWrVolbN++vVbHbUj6\nkqekpCRhwIABtTpuQxI7T++++66QmJgo+Pv7s7DTMxxjZ0QSEhLg7e2t1ubt7Y34+HjV8u7du+Hj\n4wM3Nzetjzt06FDY2NhUui4tLQ0uLi5qbVZWVrCwsHihlxzrmq5y9azc3Fy19wQmJibC09MTkmfe\nW2hjY4MWLVrgxo0bdTpHQxAjVwDQsmVLDBgwAEIjebBfrDzVZRsx6Uue8vLyXmj2f10TM08nTpyA\npaUl3njjjTodl3SLhZ2RePLkCUxMTDTeP9eqVStVgfXw4UPs2bMHM2fOrLfz2tvb4969e2ptxcXF\nePjwIbKzs+vtPPWpoXIVHR2NYcOGqZazsrLQqlUrje2cnZ31tggWK1eNjb7kKScnB+fPn3+hyU91\nSR/yJJfLkZCQgDVr1mDu3Ll1PocuiZmnkpISREZG4sMPP6zzcUm39Hv0OtWb/Pz8Sn/zkslkKCkp\nAQB8/vnnmDFjBiwtLTW2u3XrFhYuXKhanjp1KgYPHlzjefv164fIyEicPn0avXv3RlFREVatWgWZ\nTAalUvkCV6Q7DZGrGzdu4OjRozh8+LDaeSublFImk2m8S1BfiJWrxkZf8hQWFoYxY8ZUORhebGLn\nacSIEUhJSUFZWRlWrFiB11577UUvSSfEzNOmTZsQGBhY6S+hpB9Y2BkJmUyG0tJSjfaSkhJYWFjg\n0qVLuHv3LtasWVPp/h06dMDBgwdrfV5bW1vs3LkTX375JSIiImBhYYEpU6bg559/hq2tba2P1xB0\nnau8vDzMmzcPK1euhJ2dndp58/PzNbYvLS1FkyZN6nAluidWrhobfcjTrl27kJ2djcjIyLpdRAMQ\nO0/79+8HAKSkpGD58uUAgLfeeqsul6JTYuUpNTUVcXFxjfqXLGPAW7FGws7ODmVlZRo9PxkZGXBy\ncsLKlSuxdOlSjf3qY/xS27ZtsW7dOhw+fBh79+5Fz549kZOTU+dxH7qmy1zJ5XLMmTMHwcHB6N27\nt9q6Vq1aIT09XWOf9PR0vR3rI1auGhux83T+/Hns3LkTX331FUxM9PdrX+w8VXB3d8eCBQuwe/fu\n2l1AAxErT6tXr8acOXM0egsbyzhXY8EeOyMhkUjQtWtXJCYmws/PT9WemJiIvn374syZMwgNDdXY\nLzg4GEOGDMGUKVPqLZbY2Fj4+vrq7Tx2uszV8uXL4ezsjGnTpmms69atG8LDw6FQKFTz1uXm5uLe\nvXvo1KnTi1+YDoiVq8ZGzDzdvHkTS5cuxTfffKO3t2Ar6NO/p8LCQr0dLiJWnrKzs7FlyxZs2bJF\n1ZaVlYX33nsPvr6+WLRo0YtdGNUP8R7IpYZ26tQptXmP4uPjhT59+mjMe1Shto/HVzbdiSA8nUtL\nEARBqVQKcXFxjeLxeF3k6quvvhLGjx+vmlOsMrNmzRLWr18vCMLTvC1evFj49NNP63gVDUOsXFVI\nS0trFNOdiJGnjIwMwd/fXzh79uyLBd+AxMhTTk6OUFhYqFq+c+eOEBQUVOn3mb4Q+3NXoTF8nxsb\n/ewyIZ3w9/dHZmYmxowZA4lEAmtra2zevLnSAfsAYG5urjb1Rk3MzMw0jvX48WNMmTIFEokEcrkc\nHh4eiIqK0pgCRd/Ud64EQcCmTZvQunVrjBgxQm3dp59+Cg8PDwDAqlWrEBYWhjfffBNKpRI+Pj54\n//336+/CdECsXFUwMzPT6+k7KoiRp/379yM3Nxdr167F2rVrVes9PT2xYsWK+rmweiZGni5fvoyI\niAhIpVKYmZmhadOmmDdvHvr27Vuv11afxP7cVTAzM9P7N+MYG4kg8OY4ERERkSHQ31G0RERERFQr\nLOyIiIiIDAQLOyIiIiIDwcKOiIiIyECwsCMiIiIyECzsiIiIiAwECzsiIiIiA/H/peBfh2N/gJcA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6e3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timetable = image_meta_ds.pivot_table(values='smiling', index='place', columns='day').fillna(0)\n",
    "\n",
    "drawHitTable(timetable, '여행지-날짜별 평균 웃음 지수')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 지역 날짜별 평균 예측 나이 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lam3YuRtvvt7W6OeDHuJnShzGSRzGSTzG6hFZKf7PEiRfsUtLS0N4eDjKly+P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\n7IiIiIhMxP8DZR2T/gcLD4EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b43c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timetable = image_meta_ds.pivot_table(values='age', index='place', columns='day').fillna(0)\n",
    "\n",
    "drawHitTable(timetable, '여행지-날짜별 평균 예측나이')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 오행상 돌산 사진들 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1499.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1493.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1473.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1471.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1469.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1475.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1477.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1479.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1481.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1483.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1485.JPG' /><img style='height: 150px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1487.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawImages(image_meta_ds[image_meta_ds.place=='오행산'].sort('age', ascending=True)[::2]['imagepath'].values, size='150px')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1694.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1691.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1684.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1679.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1673.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1663.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1648.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1626.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1605.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1699.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1706.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1862.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1819.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1814.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1811.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1807.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1787.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1782.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1721.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1707.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1559.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1466.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1462.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1459.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1455.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1450.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1439.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1434.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1425.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1416.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1558.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1555.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1552.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1538.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1517.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1512.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1487.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1484.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1481.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1478.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1470.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 인식 안된 사진들\n",
    "image_meta_ds = image_meta_ds.fillna(0)\n",
    "drawImages(image_meta_ds[image_meta_ds.person==0].sort('age', ascending=True)[1::3]['imagepath'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1431.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1777.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1410.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1409.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1878.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1877.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1880.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1421.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1881.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1499.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1440.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1875.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1647.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1821.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1842.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1793.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1576.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1489.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1872.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1643.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1873.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1804.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1422.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1570.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1442.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1525.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1789.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1847.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1418.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1443.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1717.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1851.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1590.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1867.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1871.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1408.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1852.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1531.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1850.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1588.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1833.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1596.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1562.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1488.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1788.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1472.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 잘웃지 않는 사진 \n",
    "drawImages(image_meta_ds[image_meta_ds.age>0].sort('age', ascending=True)[::3]['imagepath'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1431.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1430.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1464.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1777.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1592.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1832.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1410.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1797.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1876.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1409.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1724.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1830.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 나이 어린 사진\n",
    "drawImages(image_meta_ds[image_meta_ds.age>0].sort('age', ascending=True)[:12]['imagepath'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1472.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1471.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1407.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1788.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1406.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1584.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1488.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1837.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1473.JPG' /><img style='height: 120px; margin: 0px; float: left; border: 1px solid black;' src='./resource/image/IMG_1562.JPG' />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 잘웃지 않는 사진 \n",
    "drawImages(image_meta_ds[image_meta_ds.age>0].sort('age', ascending=False)[:10]['imagepath'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}