scripts/tag_analysis_on_manual_tags.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retagging images that were manually tagged\n",
    "\n",
    "This code is adapted from tensorflow/tensorflow/models/image/imagenet/classify_image.py "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "import re\n",
    "import sys\n",
    "import tarfile\n",
    "import time\n",
    "import multiprocessing as mp\n",
    "import itertools\n",
    "import csv\n",
    "\n",
    "import tensorflow.python.platform\n",
    "from six.moves import urllib\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import h5py\n",
    "\n",
    "import glob\n",
    "import cPickle as pickle\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from tensorflow.python.platform import gfile\n",
    "from run_inference import predict_star, predict\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code to read in a graph + optimal parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def create_graph(pb_file):\n",
    "    \"\"\"\"Creates a graph from saved GraphDef file and returns a Graph object.\n",
    "    Returns:\n",
    "    Graph holding the trained Inception network.\n",
    "    \"\"\"\n",
    "    model_filename = pb_file\n",
    "    with gfile.FastGFile(model_filename, 'rb') as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "        tf.import_graph_def(graph_def, name='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#graph = create_graph\"/data/retrain_manualtags/output_graph_best.pb\")\n",
    "create_graph(\"/data/retrain_manualtags/output_graph_best.pb\")\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12,)\n",
      "(2048, 12)\n"
     ]
    }
   ],
   "source": [
    "params = pickle.load(open(\"/data/retrain_manualtags1/output_params700.pkl\", 'r'))\n",
    "fw = params[\"final_weights\"]\n",
    "fb = params[\"final_biases\"]\n",
    "print fb.shape\n",
    "print fw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['animals',\n",
       " 'objects',\n",
       " 'maps',\n",
       " 'text',\n",
       " 'nature',\n",
       " 'people',\n",
       " 'seals',\n",
       " 'miniatures',\n",
       " 'diagrams',\n",
       " 'architecture',\n",
       " 'decorations',\n",
       " 'landscape']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = []\n",
    "with open(\"/data/retrain_manualtags1/output_labels700.txt\", 'r') as ifile:\n",
    "    for line in ifile:\n",
    "        labels.append(line.rstrip())\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code to calculate scores on an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from http://www.socouldanyone.com/2013/03/converting-grayscale-to-rgb-with-numpy.html\n",
    "def to_rgb(im):\n",
    "    w, h = im.shape[:2]\n",
    "    ret = np.empty((w, h, 3), dtype=np.uint8)\n",
    "    ret[:, :, 0] = im\n",
    "    ret[:, :, 1] = im\n",
    "    ret[:, :, 2] = im\n",
    "    return ret\n",
    "\n",
    "\n",
    "# from https://gist.github.com/yusugomori/4462221\n",
    "def softmax(x):\n",
    "    e = np.exp(x - np.max(x))  # prevent overflow\n",
    "    if e.ndim == 1:\n",
    "        return e / np.sum(e, axis=0)\n",
    "    else:  \n",
    "        return e / np.array([np.sum(e, axis=1)]).T  # ndim = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_inference_on_image(image, fileType, visualize=True, verbose=True):\n",
    "    # Some useful tensors:\n",
    "    # 'softmax:0': A tensor containing the normalized prediction across\n",
    "    #   1000 labels.\n",
    "    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048\n",
    "    #   float description of the image.\n",
    "    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG\n",
    "    #   encoding of the image.\n",
    "    # Runs the softmax tensor by feeding the image_data as input to the graph.\n",
    "\n",
    "    softmax_tensor = sess.graph.get_tensor_by_name('pool_3/_reshape:0')\n",
    "    \n",
    "    if fileType=='jpg':\n",
    "        if not gfile.Exists(image):\n",
    "            tf.logging.fatal('File does not exist %s', image)\n",
    "        image_data = gfile.FastGFile(image).read()\n",
    "        predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data})\n",
    "    elif fileType=='arr':\n",
    "        image_data = [to_rgb(image)]\n",
    "        predictions = sess.run(softmax_tensor, {'ExpandDims:0': image_data})\n",
    "    else:\n",
    "        print \"Must be jpg or arr.\"\n",
    "        return\n",
    "\n",
    "    preds = softmax((np.dot(predictions, fw) + fb)[0])\n",
    "    top_k = preds.argsort()[-12:][::-1]\n",
    "    \n",
    "    for node_id in top_k:\n",
    "      score = preds[node_id]\n",
    "      if verbose:\n",
    "            print '%s (score = %.5f)' % (labels[int(node_id)], score)\n",
    "       \n",
    "    if visualize:\n",
    "        if fileType=='jpg': plt.imshow(mpimg.imread(image), cmap=mpl.cm.gray)\n",
    "        else:\n",
    "            plt.imshow(image, cmap=mpl.cm.gray)\n",
    "\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224)\n",
      "maps (score = 0.67245)\n",
      "text (score = 0.09778)\n",
      "diagrams (score = 0.07885)\n",
      "animals (score = 0.02738)\n",
      "nature (score = 0.02448)\n",
      "seals (score = 0.01955)\n",
      "objects (score = 0.01775)\n",
      "people (score = 0.01623)\n",
      "architecture (score = 0.01362)\n",
      "landscape (score = 0.01197)\n",
      "miniatures (score = 0.01077)\n",
      "decorations (score = 0.00917)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Oj4+LGmqaKHTtUQJRchG0oypLxkc1muCsVcq2aTbo7/i5dj1qTIS/NeMHeDAM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99d767b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = mpimg.imread(\"/data/reorg3/Maps/003055002_10_000599_1_.jpg\")\n",
    "print x.shape\n",
    "preds = run_inference_on_image(x, fileType=\"arr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in 1K images from our hdf5 file, and score their scores in a dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in hdf5 file\n",
    "image_hdf5 = h5py.File('/data/image_data.hdf5','r')\n",
    "(image_metadata, book_metadata, image_to_idx) = pickle.load(open(\"/data/all_metadata.pkl\", 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pictures in the tagged set\n",
    "tagged = set()\n",
    "for folder in glob.glob(\"/data/reorg3/*/\"):\n",
    "    for fn in glob.glob(folder + (\"/*.jpg\")):\n",
    "        tagged.add(os.path.basename(fn)[:-4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['002270696_0_000024_1_',\n",
       " '000744317_03_000239_2_',\n",
       " '002483175_09_000298_1_',\n",
       " '000459452_05_000382_1_',\n",
       " '003055019_02_000065_1_']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_to_idx.keys()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['001102875_03_000244_1_',\n",
       " '000082783_0_000113_1_',\n",
       " '003827942_01_000305_1_',\n",
       " '000890311_02_000381_1_',\n",
       " '000550576_02_000013_1_']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run on tagged images\n",
    "\n",
    "images_for_tagging = list(tagged)\n",
    "images_for_tagging[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "The history saving thread hit an unexpected error (OperationalError('database or disk is full',)).History will not be written to the database.\n",
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     ]
    }
   ],
   "source": [
    "images_to_scores = {}\n",
    "args = {\"images_to_scores\": images_to_scores, \n",
    "        \"image_to_idx\": image_to_idx, \n",
    "        \"image_hdf5\": image_hdf5, \n",
    "        \"fw\": fw, \n",
    "        \"fb\": fb, \n",
    "        \"sess\": sess}\n",
    "for i in range(len(images_for_tagging)):\n",
    "    if i % 10 == 0: print i\n",
    "    img = images_for_tagging[i]\n",
    "    try:\n",
    "        predict_star((img, args))\n",
    "    except:\n",
    "        continue\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Ground Truth and Prediction, and get Top 3 also\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 accuracy: 0.895833333333\n",
      "Top 5 accuracy: 0.96056547619\n"
     ]
    }
   ],
   "source": [
    "image_to_tags_ground = {}\n",
    "\n",
    "f = \"/home/ubuntu/ArtHistoryNet/analysis/20160228_image_to_manual_tags.csv\"\n",
    "with open(f, 'r') as ifile:\n",
    "    reader = csv.reader(ifile, delimiter='|')\n",
    "    reader.next()\n",
    "    for row in reader:\n",
    "        short_names = '_'.join(row[1].split('_')[:4]) + '_'\n",
    "        image_to_tags_ground[short_names] = row[2]\n",
    "\n",
    "num_intop3 = 0\n",
    "num_intop5 = 0\n",
    "num_total = 0\n",
    "image_to_tags = {}\n",
    "for img in images_to_scores:\n",
    "    image_to_tags[img] = labels[np.argmax(images_to_scores[img])]\n",
    "    top_3_tags = np.argsort(images_to_scores[img])[::-1][:3] \n",
    "    top_3_tags = [labels[x].capitalize() for x in top_3_tags]\n",
    "    top_5_tags = np.argsort(images_to_scores[img])[::-1][:5] \n",
    "    top_5_tags = [labels[x].capitalize() for x in top_5_tags]\n",
    "    \n",
    "    if img not in image_to_tags_ground: continue\n",
    "    #print image_to_tags_ground[img], top_3_tags\n",
    "    if image_to_tags_ground[img][1:] in top_3_tags:\n",
    "        num_intop3 += 1\n",
    "    if image_to_tags_ground[img][1:] in top_5_tags:\n",
    "        num_intop5 += 1\n",
    "    num_total += 1\n",
    "\n",
    "print \"Top 3 accuracy: {}\".format(float(num_intop3)/num_total)\n",
    "print \"Top 5 accuracy: {}\".format(float(num_intop5)/num_total)\n",
    "#print images_to_scores.keys()[:2]\n",
    "#print len(set(image_to_tags_ground.keys()) - set(images_to_scores.keys()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(12, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_y = []\n",
    "pred_y = []\n",
    "x = 0\n",
    "for img in image_to_tags_ground:\n",
    "    if img not in image_to_tags:\n",
    "        x+= 1 \n",
    "        continue     \n",
    "    else:\n",
    "        ground_y.append(image_to_tags_ground[img][1:])\n",
    "        pred_y.append(image_to_tags[img].capitalize())\n",
    "    \n",
    "print x\n",
    "confm = confusion_matrix(ground_y, pred_y)\n",
    "confm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animals</th>\n",
       "      <th>architecture</th>\n",
       "      <th>decorations</th>\n",
       "      <th>diagrams</th>\n",
       "      <th>landscape</th>\n",
       "      <th>maps</th>\n",
       "      <th>miniatures</th>\n",
       "      <th>nature</th>\n",
       "      <th>objects</th>\n",
       "      <th>people</th>\n",
       "      <th>seals</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>animals</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>architecture</th>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorations</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagrams</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>landscape</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maps</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miniatures</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nature</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objects</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>139</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seals</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              animals  architecture  decorations  diagrams  landscape  maps  \\\n",
       "animals            70             0            1         0          5     4   \n",
       "architecture        0            67            0         2         34     4   \n",
       "decorations         0             0           75         0          0     2   \n",
       "diagrams            0             0            0        70          0    11   \n",
       "landscape           1             9            0         0         51     2   \n",
       "maps                1             0            0         5          0    88   \n",
       "miniatures          0             0            5         1          0     4   \n",
       "nature              1             0           10         0         20     3   \n",
       "objects             1             3            1        10          4     7   \n",
       "people             13             3            0         1         11     9   \n",
       "seals               0             1            5         8          0     3   \n",
       "text                1             0            0         6          1     6   \n",
       "\n",
       "              miniatures  nature  objects  people  seals  text  \n",
       "animals                1       4        4       6      1     7  \n",
       "architecture           0       1        7       2      1     9  \n",
       "decorations            4       1        1       0      6     5  \n",
       "diagrams               0       0        7       0      1     8  \n",
       "landscape              0      23        3       1      1    12  \n",
       "maps                   0       1        1       0      0     3  \n",
       "miniatures            65       0        0       0     13     2  \n",
       "nature                 0      49        4       2      0     8  \n",
       "objects                0       1       64       3      5     3  \n",
       "people                 1      20       16     139      8     9  \n",
       "seals                  2       0        1       1     72     2  \n",
       "text                   3       1        0       1      4    84  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame(confm)\n",
    "data.columns = sorted(labels)\n",
    "data.index = sorted(labels)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DqlZ2rpUmOy/2Ny/17t6N3t27ATD5zbepUUT7QZjcQarQvFyKSsAeu6BtDJxj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\ncdanbKJ6uUpUKVuB1dv/4Y7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bIYQQwmVSsxVCCBG2IqRiK4WtEEKI8BUu9zYuLClshRBC\nhC3psxVCCCFEvkhhK4QQQrhMmpGFEEKErQhpRZbCVgghRPiKlD5bKWwjWKTdRjHz8CFP4jS+tnve\nMzlg6i2vexLnhldu9iROpSaNPYnjmQg5yAeVqlKtqLNQIJGyGQpU2Cqljuvr1Vr7ncmOEEIIEXny\nXdgqpVoDk7EeTxd8xmvwma7RzmdNCCHEqe5UvM72beBzrMfQpbqTHSGEECLynExhGw8M11rLM/mE\nEEJ4IkIqtid1ne3HwIVuZUQIIYSIVCdTsy0DfKyUmg9sD/1Aaz3A0VwJIYQQnJqX/vxu/wkhhBCe\niJCyNv+FrTw8XQghhNdOxZotSqlOwACgDrAVeFdrPdeFfAnbqDHjmbdgAdWqVmXW9HdcjTV/4WKe\nfPZ5AoEAfXr3ZOB11xbrOD3UNZQvV46oKB8loksw7bUXXYnj5vKceXE7GndqTiAQYPfmFH585Usq\n16lOx4EXEV2yBP6sLH5+81t2btied2L55MX28Wq/lt9PwRw5coQbhtxJRkYGWVlZdOvciVsGXu94\nnHChlDodmIl1KasPSARGAlWAm4Fke9ZhWuvZBYmR7wFSSqmbAI3VXzsLSALeV0p5c3uaU9RlvXrw\n6sRnXY/j9/t5fMIzvDrpOT6e+R5ff/Md/2zcWGzjgHV93pSJTzPjjVdcK2jdXJ6YyuVpdlEbPho2\nlQ8fepOoqCganteUc/p1YvmHP/PRsKks/3A+5/Tr7Eg88G77eLVfy++nYEqVKsWUF59Hv/0G+u03\nmL94Cat/X+d4nHChtV6vtW6ltW4NtAEOYQ0KBnhWa93a/itQQQsnV7N9AOimtf41OEEpNRP4CPDm\nvnMOUErFA7OBxcB5wDJgKvAoEAv0xzqzeQEoDRwGbtBa/6mUug7oA1QCagPvaa0fU0rFYJ2I1MG6\nwccYrfUHTuS3dcsWbEtyrtaSm9Vrf+e0evWoXasWAN0v7MrceT+TmJBQLOMABALg97t7pZrby+Pz\n+ShRpiQZh49QonRJDu05QMAfoFRMaQBKxZQmdc8BR2KBd9vHq/1afj8FV7aMde+iI3bttqgac4ug\nFfkC4G+t9WalFODMop/MpT/V+O8AKROo6kRGPNYAmKC1NoDGwNVa6/bAUGA4sA5or7VuAzwCjA/5\nbjusArcFcJV9Z63uwFb7zKg5VmFerCSnpFCzRtzR9zXi4khOSSm2ccD6kd5674P0H3Qbsz7/ypUY\nbi5P6t6D/PbVUvpPHMI1k28n/VAaW9f8y6Jp33NOvy70m3gr5/TrzJIZ8xyJB95un0gSib8fv9+P\num4gXXv24Zx2bWnWtIkrcfIS5fMV+u8k9QXeD3l/u1LqF6XUFKVUpQIvx0nMOx941q7FoZQqB0wA\nFhY0eBHaoLUOnjisBb63X6/GunlHZeBDpdRq4Dmgach352it92qt07Ca09vb3+umlBqvlGqvtXau\nqiEKbOrk53n/jZeZ9NQ4Zn78Kat+W1PUWToppWJKk9CmEdPvfJlpQ16kZOmSNPxfU5pe0JqF737H\n9DtfZuG739NpcI+izqqIQFFRUei33+DbTz9k9drf+XvDxqLOkuuUUiWB3kCwZfIlIFFr3RKrC7XA\nfRInU9jeglWb26eU2gHstd8PLmjwIpQe8tof8t4PlATGAD9orc8EenHsXtBgdaCHCmit/wRaYxW6\nY5VSI1zJtYviYmPZvn3H0fc7kpOJi40ttnEAYqtbTzmpWrkyXTq0Z826PxyP4eby1GmWwP7kvaQf\nSiMQCLBh+XpqNqrD6R2asXH5nwBsWGoS26CWI/HA2+0TSSLx9xNUvlw52rVpxYLFS1yNkxufr/B/\nJ+FiYIXWOgVAa50SctfE17FaNgsk34Wt1jpJa90Ra5RWL6C+1vp8rfW2ggYvQnmt/opYo60Bbsj2\nWTelVGWlVFngMmCBUqoWcFhrPR2rtt/aycwGAgECAXf7Hps1bcKmLVvYlpRERkYGs7/9ns4dOxTb\nOIfT0khNPWy9PnyYRcuW07B+guNx3Fyeg7v2E9ewNtElred81DkjgT1bd3FozwFqNa5nT4tnX9Ie\nR+KBd9sHvNmvvYoTab+fPXv3cuDgQQDS0tNZvHQ59ePjHY+THz6fr9B/J+FqQpqQlVI1Qz67HChw\n89gJB0gppXzBUj3ksXpb7b+j04rhI/YCubwOvn8KeMeuoX6Z7fOlWM3HdbAufVqplLoQmKCU8gNH\ngFudyugDI0azfOVK9u7bT7delzNk0ED69LrEqeSPio6OZtjQ+xh8+z34A3769O5JYv2EYhtn9+49\n3DtiND6fj6zMLC7u1pVzz2rreBw3lyfl7yQ2LDW54vEb8Gf52blxB+t++IWd/+7gfwMuwBcVRVZG\nJj9NcW6IgFfbx6v9Wn4/BbNz1y5GjBlPIBDA7/dzUdcudDjvHMfjhBO7i/QCYFDI5KeUUi2xWj03\nUsCpItkAACAASURBVIiWXN+JzviUUvu11hXt137+WzD5sJpRT4lH7Nmjkdtore8sTDpH9u305mEO\nEXIxeJBXD48vUbacJ3Ei7eHxeFBL9VSE/X78GRmexSpTraZjK++zuyYVesfq/cIdRb4x87r054yQ\n1/XdzIgQQggRqU5Y2GqtN4e8vUpr/XT2eZRS91KIEVrFidb6bazn+gohhPBApNyu8WRGI4/KZXqx\nG3krhBCiePB4NLJr8ryDlFKqi/0yWinVmeNH8iYCck2pEEIIV0RKzTY/t2t8w/6/DPBmyPQAsAO4\nw+lMCSGEEJEkz8JWa10fQCn1jjwkXgghhDh5J9Nn+6xSql7oBKVUPaVUC4fzJIQQQgCR02d7MoXt\nNKxbGYYqBbzrXHaEEEKIYzy+g5RrTqawPU1r/U/oBK3130CCozkSQgghIszJFLZb7MfJHWW/L473\nRhZCCFEMREoz8sk8PP454FOl1FPA31jPhL0fGOdGxiJauGx9h3h2G8UyMZ7E8cqAF671JM7CCR95\nEue8oVd4EifSpO1I8iRO6erF8+lNBXgebVg6maf+vA7cC1yC9WSbS4D7tNavuZQ3IYQQp7hTsWaL\n1voDjj1UVwghhBD5kNcj9q7VWr9rv74xt/m01m/m9pkQQghxqsurZns1xy7tya2DKcDxd5YSQggh\nHBEul+4UVl5P/ekR8rqz+9kRQgghjomQsjbPZuR8DaDSWvudyY4QQghxjC8qMkrbvJqRM7GaifMS\n7UBehBBCiIiUV2FbP+T1JcCVwHjgXyAeeBDw5iI+IYQQp5xTohlZa/1v8LVS6l7+n707j7Op/h84\n/rozljFjmcWMpTC2SCqEVChkqVBSbz9kKRVJ+aavFiGlkhYJ7XylUN6VFi2ovrLvVLZ2KhlmMDPG\nMma59/fHOaPhS5g557jufJ6Px3Dvufd+3uec+7nncz6f8zmfDzRW1TR70U8isgZYA7zi3ioeid8J\nOF9Vn/mH91QCXlRV+Yf3VAMuV9V3XFhNVyxZtoKx48YTCATo0rkj/fq4MxiCV3GulVsoHRVFWJiP\nYuHFmP76JMdjjBw9hoVLlxIXG8vsmW85nn5+Xu03AL/fT4/bB1AhPp4Xxzo3nsyhrMO8tWY+O9J3\n4/P56NOkPRt3bmXJbxsoU9IaTOSGC5tTv1L1k6R06kItX7sVZ/SLL7Fk9Vpio8vxzqQXAHh1+jss\nWrEaX1gYsdHlePS+QZSPiXEkHsDO5GQeeexJ9u5NxRfmo2vnTvTsdpNj6RdFp3OfbTkgEkjLtyzS\nXu46VZ0DzDnJe5KAExa0tupAD+C0ClsRCTsT16b9fj9PPfs8k1+eQHx8PN1796PVlS2okZh4VsYB\na0SYyROeo2yZMo6nneeGTtfSo1tXho16wrUY4O1+A5jx3gfUSEzkwAFnR+2a9e0C6leqTv/LO5Hr\n95OVk83GnVu5+rxLaFunsaOxIPTytZtxOl3dGul0LaPGTTiyrHfXGxhwS3cAZs35nMkzlYfu7l/o\nWHmKhYczdPAg6p5Xm4MHD9Kt7x1cfmkTqidWcyzGqSoSvZGPMQ34SkTGA38CVYB77eWFYtc25wIr\ngMuB1cBU4DEgHrgFqIdVs75HRKYC+4DGQAXgAVWdbafzqapeaD9+G+uEAGCQqq7AagavKyLr7HVP\ny0vXXpc5wLOqukhEMoDXgDbA3SKSCYwDooDdQF9V3SUi9wL9gWxgs6r2KOw+ybNh02aqVqlC5UqV\nAOjQrg0LFi52/GDhVRyAQAD8/lPpClBwjRpczI6kna7GAG/3267kFJYsX8XtvXsyfZZzY8scyj7M\nzynb6du0AwDhYWGUKlHSsfSPJ9TytZtxGlxwPknJyUctiyxV6sjjQ5mZ+MJOZ5j7kysfF0f5uDgr\nVmQkNRKrsStl9xkpbEPF6XxDDwATgG5YBc7/AZPs5U6oiVXI1QHqAt1VtTkwFBiG1VEr/xG6oqpe\nAXQCxuZbnveeZOBqVW1sr+tEe/lDwGJVbaSqLx7zmWNFActVtSGwyk6jq6o2wToZeMp+34NAA1Vt\nAAw4/U0/seSUFCpWSDjyvEJCAskpKU6G8DQOWNdg7hryID3vvJvZcz53JYZXvNxvz054ifvu7u/4\nmf7uA+mULlmKN1fN5Yn5b/P2mvlk5WQDsOCX9Yye/xZvrZ7HoazDjsUMtXztZT7I88rbM+l4a3/m\nLVxM/57/51qcv3Yk8eNPv3DRBee7FuOfFLnhGu0m1FftPzdsVdXN9uNNwNf24w0cfxq/j+z12iIi\nCcd5vTjwmog0AHKB2gVYpxxgtv24DlAf+FJEfFgnKnkzHn0HzBSRj/LWyzixqS+NJ758HHvT0rhr\nyINUr1aVhhfVP9OrFdQWLVtBXGwMdWvXYvW6bwkEnGsZ8PsD/JGaTPdGbUiMrcis9QuY+8MqWtVq\nSMd6l+Hz+fhowxLe++4bejdp71hco3Du6tWDu3r1YNr7H6JzPufOnt0cj3Hw4EHuHzaSB4fcQ2Tk\nmZkIpMg1I9sFzO1YtcR4Vb1IRFpi1TDVgXXJf9rsz/fcf4L1zP/+430b9wE77fUMBw6dIG4OR9fw\nI/I9zlTVvKOaD9ho16aPdR3QEugMPCIi9Z26vpsQH8/OnbuOPN+VnExCvPOzd3gVByC+vNU8FRsd\nTesWzdm45YeztrD1ar99u2EjC5csZ8nyVRzOOsyBgwcZPnoMT4x4uNBpx0SWJjayDImxFQG45Nza\nzP1hNWXyzbLUosZFvLTkw0LHyhNq+drL38+x2l/Zgn+NetLxwjYnJ4chw0bS8Zp2tGrZwtG0T0eI\nlLWn1Yz8ONAPeAOoai/bjtWE6oTC7NLjfbYckDd3VW/+vhc4A8jfM2cb0EBEfCJSBWh6gnR/BOJF\npBmAiBQTkXr2a1VVdSFWE3VZoHQhtuUo9eudzx/bt7MjKYns7Gzmzv/alYzvVZxDmZkcPGid9xw6\ndIjlq9dQq3qi43EAAoGAozXA4/Fqv93b/3bmzn6Xz96bwdOjRtC0UUNHClqAshFRxESWYVfGXgC2\nJP9BpbJxpOebOnH99p+pXK68I/Eg9PK123ECAesvz587/p6Wb+GKVVSvco5jsfKMfHIsNRMTuaXb\nzY6nXRSdTgepvkBDVd0tInm3+mwFaji0LoETPC7Ic4CXgQ9EpDdW56u8I8f3gF9E1gNvquqLIrIN\nq+l6C7D2eOmqaraI3ARMFJFyWIX3eBH5CZguImWxCucXVXXfP2/qqQsPD2fY0PvpP+g+/AE/XTp3\npEb1RKeS9zzO3r2pDBk+Cp/PR25OLte0bcNlTZ3v7frA8FGsWbeOtPR9tO10IwPv7EeXTtc5Hser\n/ea2bg1bMWXF5+QG/JSPKkffJh14d/1/+TMtGZ/PR1xkWW5p3NaxeKGWr92MM/zZF1i7YRPp+zLo\neGt/7uzRjaWr1/L7XzsICwujUkI8DzvYExlg/Xcb+Hzel9SuWQPp3Q+Ae++6k+aXXeponKLEd6pn\n/iKyA6ihqpkisldVY0WkDFbv2yqurmWIydq3x93qlsdCbvJ4j9qtcg9nehJn5YTPPIljJo8vmFCc\nPL5kTAXHfkSLH5tc6ONli0dvP+ON0afTjPwFME5ESsKRa7ijOcm9r4ZhGIZRUD6fr9B/weB0Ctv7\ngEpAOtb10P38PWSjYRiGYRgncErXbO1abHngZiAWq5D9U1XdHzXAMAzDKLKCpGJaaKdU2KpqQEQ2\nAGVUNRlrwAjDMAzDcFWoTLF3Os3I64Hz3FoRwzAMwwhVp3PrzzfAXBF5E2ts5Py3xfzH2dUyDMMw\njCLWjGy7Auu+2iuPWR4ATGFrGIZhGCdw0sJWRCKB4Vi9j9cBT6mqcyOSG4ZhGMYJBMutO4V1KjXb\nl7CmsvsC6IrVG/keN1fKMAzDMCB0mpFPpYNUB6Cdqj4AXAN0dHeVDMMwDMMSKoNanErNNkpVkwBU\n9U97XGCjMFweHP8Ir4YdPHSiCZWcVaxUlCdxMn75xZM4UdW8mYjbq2EUf5oxz5M4NW86ttuIO8KK\nnU6XloKLqFDJkzjZ+9I8iQNQMqaCZ7HOFqeSm4qJSCv+ngHn2Oeo6n/dWDnDMAzDCAWnUtgmc3Rv\n4z3HPA/g3Mw/hmEYhnFEkLQCF9pJC1tVTfRgPQzDMAzjfwTLNdfCOp0RpAzDMAzDKABvegAYhmEY\nRkGESJXQFLaGYRhG0DLNyIZhGIZhnBJTs7WJyPXAj6r6w5lel/xGjh7DwqVLiYuNZfbMt1yNtWTZ\nCsaOG08gEKBL547069PLkXQfHzeBJStXExsTzbuvTgRgX8Z+Hn7qGZKSU6hcIYGnH3mA0lHO3kfr\n1vYk79nL4y+/Rmr6PnxhPjq3vgrp0I59+w8wYsJL7Nq9m4rx5Xli8CBKR0Y6EjMrK4tbB95LdnY2\nubm5tG11FQP69XUk7WO5td/y+AMBHpj5OnFlyvJw5+5sS9nJ6//9jMzsbBLKRjO4w42UKlHC2Zh+\nPz1uH0CF+HheHPuko2kD7ExO5pHHnmTv3lR8YT66du5Ez243OR4H3P9+8kx//0M+/mI+Pp+P2jUS\nGTX0PooXL+5KrKLA1Gz/dgNwwel8QETCXVqXI27odC2vTRjndhj8fj9PPfs8r018gQ9nzeCLeV/x\n27ZtjqTdud3VTHzqsaOWvTnrfZo2vJjZU16h8cUXMfXd9x2JlcfN7QkPD+PeXj2Y8dwYXn98JLPn\nf8W2v3bw9ief0qT+Bbw77hkuuaAeb308x5F4ACVKlGDypPHotCnotCksWbGSDZu3OJZ+Hjf3W57P\n1q+gSlz8keevfDWHXs3bMu6WATStWZeP1i51NB7AjPc+oEZiouPp5ikWHs7QwYP48J23mP7GK7z7\nwYds3fa743G8+H4AUnbvYdZHc5j56gR08svk5OYyb8Eix+OcCp+v8H/BIGRrtiJSDWs85yXA5cB2\n4HqgF3AnUBz4xX7eEOgMtBSRR4CbgCnA/aq6TkTigDWqWl1E+gA3AqWxTlZaici/AQFKAB+q6tEl\nSyE0anAxO5J2OpXcCW3YtJmqVapQuZI1mk2Hdm1YsHCxIweoBvXrkbQr+ahlC5ev5PXnngKgY9vW\n9B86jHv69Sl0rDxubk9cdDRx0dEAREZEUO2cyqTs3cvitet4ecQwAK5t2Zy7R49hYPduhY6Xp1RE\nBABZdu3WjWOIm/sNYE/GPtZt+4WuTVowZ/1yAHak7uH8c6oCcHHVGjz+4XS6X9bKkXgAu5JTWLJ8\nFbf37sn0We85lm5+5ePiKB8XB0BkZCQ1EquxK2U31ROdHSXM7e8nv9zcXA5lZuILCyMz8zDxcbGO\nxzgV5prt2aEWMFFV6wPpWBMpfKCqTVW1IfAD0E9VlwOfAENVtZGq/nactPKPsdgQuFFVW4lIW6C2\nqja1lzcWkeZubpQbklNSqFgh4cjzCgkJJKekuBYvNS2duJgYAMrHxpCalu5o+l5tT1JKCj9v+4ML\natciNT2d2GhrNNO46GhS0/c5Gsvv9yN9+tGmYxeaNWlM/XrnO5o+uL/fpi6aR+/mbY+qbVQtn8Dq\nX38EYOnPm9i739n99uyEl7jv7v6eHbT/2pHEjz/9wkUXnH3fT5748nH0uvlGru3elw7SizKlo7j0\nkoaOxzkVoVKzDfXCdquqbrAfrwUSgQtFZJGIfA/04DSbjm1fqmpe6dAOaCsi67CmIKwD1C7cahc9\nZ+PZ68HMTB55YSL/6tOTyIgIOKau6fQmhYWFodOmMP/j99mwaTO/bt3mbACXrd36E+Uio6ieUPGo\n4cEHXt2ZL75bzQPvvMHh7CyKhTt3dWbRshXExcZQt3YtAoEAAZfHJT948CD3DxvJg0PuIdKh6/Vn\nQsb+/XyzbAWfzXyTefo2hw5l8sXXC87MyoRIaRuyzci2/PPu5gKlgDeBzqq60W4SPtGo5jn8fTIS\nccxrB/I99gFjVPWNwq/umZMQH8/OnbuOPN+VnExCfPw/fKJwYmOi2ZOaSlxMDLv3phIT7ez8Fm5v\nT05uLo+8MJH2La6gZeNLAIgtV5a9aVbtdk9aGjFlyzoWL7/SUVE0uaQhS1espGb1REfTdnO//bDj\nT9b89iPrtv5MVk4OmdmHmTDvQ+5t34WRN94CWE3Ka7f+7Eg8gG83bGThkuUsWb6Kw1mHOXDwIMNH\nj+GJEQ87FiNPTk4OQ4aNpOM17WjVsoXj6YN3v9OVa7/l3EoVKVe2DACtWlzOd5u2cE0b55r3i5pQ\nr9ke75SmNLBTRIoDPfMtzwDyHx23Ys3jC3DzP8SYB9wmIlEAIlJZRBzN/V6ckdevdz5/bN/OjqQk\nsrOzmTv/a0cPGNY2/P28ZbOmzJlvzV/x6Zf/5crLLnUsFri/PU+9NpnEcyvT7Zr2R5Y1v6QRny1a\nDMDni5bQonEjx+KlpqWRsX8/AJmHD7Ni1RqquzBrkJv7recVbXit3328cttghlzblfpVqnNv+y6k\nH7TOXf2BAB+sWky7CxufJKVTd2//25k7+10+e28GT48aQdNGDV0paAFGPjmWmomJ3NLtnw4XheN2\nvs5TsUI8G7b8wOGsLAKBAKvXfUf1qlUcjxNMRGSbiHwnIutFZJW9LEZE5ovIjyIyrzCz3oV6zfbY\nEioAjABWYU2wsBIoY7/2LvCGiNyD1UHqeUBF5A7gsxMFUNUvRaQusFxEwCq0bwEcuZDywPBRrFm3\njrT0fbTtdCMD7+xHl07XOZH0UcLDwxk29H76D7oPf8BPl84dqVE90ZG0HxnzHGu/30h6xj6uu+U2\n+vfqQd9uN/HQE2P5ZP5XVEqI5+lHHnAkVh43t+f7H39i/pJl1KxahT4PDcfn8zGg28306nwdw1+c\nxGffLKJC+fI8MfhuR+IB7N6zh+GjxxAIBPD7/bRv05oWlzdzLP08bu63E1ny40bmfr8agGa1zqf1\nBQ1cjeeG9d9t4PN5X1K7Zg2kdz8A7r3rTpo7fBLp1fdTv24d2rRsTvf+91AsPJy6tWpyY8drHI9z\nKnxhnjUD+4GrVDU137KHgK9U9RkReRB42F522nxu15iM/5WVvtubne7RtYrDe3d7EqdkbHlP4oTa\nfLZhHt0baeazLRhfuDdxvJzPNurcmo4dfL5/aUahj5cX3d3zpOsjIluBxqq6J9+yH4ArVXWXiFQE\nvlHVugVZh1BvRjYMwzCMUxEAvhSR1SJyu72sgqruAlDVnUDCCT99EqawNQzDMIKWz+cr9N8pukJV\nGwHXAneLSAuOfymyQExhaxiGYQQtr+78UdUk+/8U4COgKbBLRCoA2M3IySdO4Z+ZwtYwDMMo0kQk\nUkRK24+jsMZP2IA12FFf+219gI8LGsMUtoZhGEZRVwFYIiLrgRXAHFWdD4zFGrToR6AN8HRBA4T6\nrT+GYRjG2cyDuypUdSvwP/ecqepe4GonYpjC1jAMwwhaHt5n6ypT2BqGYRhBK0iGNi40c83WMAzD\nMFxmaraGYRhG8AqRqq2p2RqGYRiGy0zNNoT5s7M9iVMyJs6TOF6NwRxZ5VxP4vgPH/IkjldjI5/X\no50ncVaM+9CTOE3v7eRJnNysAyd/kwPCS0V5EsdpIVKxNYWtYRiGEbxCpTeyaUY2DMMwDJeZmq1h\nGIYRtE5jIoGgZgpbwzAMI3iFRllrmpENwzAMw22msDUMwzAMl5lmZMMwDCNomWu2ZwkRqQZ8qqoX\nHue114FxqvrDaaZ5MVBZVb9waDVPaOToMSxcupS42Fhmz3zLtThZWVncOvBesrOzyc3NpW2rqxjQ\nr6/jcdzcnsfHTWDJytXExkTz7qsTAdiXsZ+Hn3qGpOQUKldI4OlHHqB0lPP3G/r9fnrcPoAK8fG8\nOPZJx9P//c/tPPj4GHw+CATgr6Qk7rq1Nz263uB4rCXLVjB23HgCgQBdOnekX59ejsdwO18fzDrM\nW6vn8Vf6bnz46HtpB2rEVQJg/g+ref/bhbzQ5W6iSpZyJJ5Xvx+Aa+UWSkdFERbmo1h4Maa/Psnx\nGF5uz8mYwvbsEjjeQlW9s4DpNQAaA64Xtjd0upYe3boybNQTrsYpUaIEkyeNp1REBLm5ufQZMIgr\nLruUC+ud72gcN7enc7ur6XZ9Rx599oUjy96c9T5NG15MH+nKm7M+YOq773NPvz6Ox57x3gfUSEzk\nwAF3BiioVuVc3n3jJcAq2NvLLbRucYXjcfx+P089+zyTX55AfHw83Xv3o9WVLaiRmOhoHLfz9bvr\n/suFlaoz4IrO5Pr9ZOVaA7zsPZjB5p2/ExtV1tF4Xv1+AMJ8PiZPeI6yZco4nnYeL7fnpELkYmfI\nFbYiMgS4FauAnQJ8BBQXkelAI2Aj0FtVM0VkAXC/qq4TkbbAY0AJ4FfgVlU9KCJNgPFAFJAJtAMe\nByJE5ApgDLALeNGOGQBaqqojR91GDS5mR9JOJ5I6qVIREQBk2WezbpxPurk9DerXI2lX8lHLFi5f\nyevPPQVAx7at6T90mOOF7a7kFJYsX8XtvXsyfdZ7jqZ9PCvXrqdK5UpUTIh3PO0NmzZTtUoVKley\naoEd2rVhwcLFjhe2buaDQ9mH+SVlO7c1uwaA8LAwSoWVBEDXL+CmBlcyabHzo1B58fsBq2XD7z9u\n/cFRXm1PUREi5wwWEWkE9AGaAJcBtwMxQB1gkqrWAzKAgcd8Lg4YDrRR1cbAWmCIiBQH3gXuUdUG\nWJMIHwBGArNUtZGqvgf8Gxioqo2AFoA34/A5zO/3I3360aZjF5o1aUz9M3EW67DUtHTiYmIAKB8b\nQ2pauuMxnp3wEvfd3d+z5q55CxbSofVVrqSdnJJCxQoJR55XSEggOSXFlVhu2b0/ndIlSzF15ReM\nnvcWb62az+GcbL796xdiIstwbrTzJyng3e/H54O7hjxIzzvvZvacz12JAcFzPPD5fIX+CwYhVdgC\nzYEPVTXTrlnOxir8/lDVFfZ7ptvvy68ZUA9YKiLrgd5ANaxCeoeqrgNQ1f2qmnucuEuBF0TkHiBG\nVf1Ob5gXwsLC0GlTmP/x+2zYtJlft24706vkOKd/eIuWrSAuNoa6tWsRCAQIBNytcWTn5LBw2Uqu\nvqqFq3HOZv6Anz9Sk2lVuyEj2vemZLHizNm4jM83r+T6+n83vTv9TXn1+5n60njemfIKE595klkf\nfsz67ze6EqcoHA+8FGqF7bHyjqzH/q6Ofe4D5ts11YaqWl9V7zgmjRNS1bFAP6AUVoF9XmFW+kwr\nHRVFk0sasnTFyjO9KoUWGxPNntRUAHbvTSUmupyj6X+7YSMLlyznupt78vBjT7B6/bcMHz3G0Rj5\nLV25mnrn1SI2OtqV9BPi49m5c9eR57uSk0mId6cm6JaYyDLERJYhMbYiAI2q1OaP1F3sOZDOY3On\n8dCc10k7uJ8n5r3Nvkznr7G7/fuJL29N/BEbHU3rFs3ZuOW0+neetjN9PDA12+C0GLhBRCJEJAq4\nAVgEVBORS+339LDfl98K4AoRqQkgIpEiUhv4EagoIpfYy0uLSDhWU/SRHhYiUkNVN6nqM8BqoK6T\nG+VFjSk1LY2M/fsByDx8mBWr1lC9WjVXYrm5PVbafz9v2awpc+b/F4BPv/wvV1526Qk+WTD39r+d\nubPf5bP3ZvD0qBE0bdSQJ0Y87GiM/Ob+9xvat77KtfTr1zufP7ZvZ0dSEtnZ2cyd/zWtWrpTi3Yr\nH5SNiCI2sgw79+0FYMuuP6gaU4HnbxjImE538HSnO4mOLM2IDr0pG+FMz3Svfj+HMjM5eNC6SnXo\n0CGWr15DreqJjsfx8nhQVIRUBylVXS8ib2IVeAHgDSAN+AG4W0SmApuAV/N9LKCqu0WkL/COiJS0\nPztcVX8WkW7AJBEpBRzEum67AHhIRNZhdZBqISKtgFw7fcd6KT8wfBRr1q0jLX0fbTvdyMA7+9Gl\n03VOJX/E7j17GD56DIFAwOrt2qY1LS5v5ngcN7fnkTHPsfb7jaRn7OO6W26jf68e9O12Ew89MZZP\n5n9FpYR4nn7kAUdinQmHMjNZufZbRtw/2LUY4eHhDBt6P/0H3Yc/4KdL547UqJ7oeBy38/X/NWrN\n5BWfkev3E186mr5NOxz1ug/7HiqHePX72bs3lSHDR+Hz+cjNyeWatm24rGljx+N4tT2nJDgqpoXm\nc7vGFMxE5Hugk6r+7mXcrPTdnux0f06OF2EIK+bNOdvh1D2exCkWVdqTOIHsLE/iFCvt7G0uJ+TR\nsSTU5rP153iTD8KKlfAkDkBEXEXHisjf9KNCZ6wacsMZL7JDqmZ7OkRkPvCd1wWtYRiGcRqC5Jpr\nYRXZwlZV253pdTAMwzCKhlDrIGUYhmEYQafI1mwNwzCM4BcircimsDUMwzCCV7DcJ1tYprA1DMMw\ngldYaBS25pqtYRiGYbjM1GwNwzCMoBUqzcimZmsYhmEYLjM1W8MwDCN4hUbF1tRsDcMwDMNtpmZ7\nBmQm7/QkTonY8p7E8epGuJIxcZ7EObx3tydxfOEe/fw8GrM449dfPYlzoTUJl+uaNurmSZzlS//j\nSZycw96MwQyAgz/VULlmawpbwzAMI2j5QuTWH1PYGoZhGMErRGq25pqtYRiGYbjM1GwNwzCMoBUq\n12xNzdYwDMMwXGZqtoZhGEbwCo2KrSlsDcMwjOBleiMXYSLSB2isqve4kf7oF19iyeq1xEaX451J\nLwDw6vR3WLRiNb6wMGKjy/HofYMoHxPjWMysrCxuHXgv2dnZ5Obm0rbVVQzo19ex9PNbsmwFY8eN\nJxAI0KVzR/r16eV4jJGjx7Bw6VLiYmOZPfMtR9N+fNxElqxaTWx0NO++OgGArxYv5fXp77Ltz+28\n9eJz1K1d09GYADNnf8xHX8wDoMu1HejepbPjMdzcb8l79vL4y6+Rmr4PX5iPzq2vQjq0Y9/+A4yY\n8BK7du+mYnx5nhg8iNKRkQWO8+RLr7FkzTpio8sx44VnjizXz+bywdwvKRYezuWXNOTuXt1PEvvw\ntwAAIABJREFUO+1RzzzAla0vY8/uVG7qcBsAA4fcRqu2V+APBNibspfh/36aPSl7KVYsnBFj/s0F\nF9Yh1+/nmccmsnbldwXerjzT3/+Qj7+Yj8/no3aNREYNvY/ixYsXOt1jeZHfihJzzbbgXBspoNPV\nrZnw+IijlvXuegMzJ41jxoTnaN7kEibPVEdjlihRgsmTxqPTpqDTprBkxUo2bN7iaAwAv9/PU88+\nz2sTX+DDWTP4Yt5X/LZtm+Nxbuh0La9NGOd4ugCd27Vh4pOjjlpWK7Eaz418mEYXXuBKzF+3/c7H\nc+cz/aUXeefViSxesYrtSUmOx3Fzv4WHh3Fvrx7MeG4Mrz8+ktnzv2LbXzt4+5NPaVL/At4d9wyX\nXFCPtz6eU6g417W+khdHPnzUsrUbN7NkzTpmjH+GGeOfoef11xUo7Y/1Cwb0HnrUsqmvvsPN1/Sj\n27W3s2jBCgYM7gNA1+6dCAQC3NThNgbccj//Hj6wYBuUT8ruPcz6aA4zX52ATn6ZnNxc5i1YVOh0\nj+VVfjslPl/h/4JASNZsRaQaMBdYCzQCNgK9gXrAOCAK2A30VdVdItIAeAUoBfwK3Kaq6SKyAPgO\nuBIIt5evOSZWeeBVoIq96D5VXVaY9W9wwfkkJScftSyyVKkjjw9lZuILc/48qVREBABZdu3WjSy6\nYdNmqlapQuVKlQDo0K4NCxYupkZioqNxGjW4mB1J7ozU1aB+PZJ2Hf39JFY5F4CAS6M1bf3jT+rX\nrUOJElYNptFF9fnvkmX0vrmro3Hc3G9x0dHERUcDEBkRQbVzKpOydy+L167j5RHDALi2ZXPuHj2G\ngd0LPnpTg/PrkpScctSy2XO/pPeNnSkWHg5AdNmyBUp7/ZoNVDqnwlHLDh08dORxqVIRR/JAjdrV\nWLVsHQCpe9PJ2LefehfWYfOGHwsUO09ubu6RY0Bm5mHi42ILld7xeJXfToXpjRz86gCTVLUesA8Y\nBEwEuqpqE2Aq8JT93mnAUFVtgFUwP5ovnVKq2hC42/7MsV4ExqnqpcBNwGQ3Ngbglbdn0vHW/sxb\nuJj+Pf/P8fT9fj/Spx9tOnahWZPG1K93vuMxklNSqFgh4cjzCgkJJKek/MMnDICaidVYv3ET+zIy\nOJSZydJVq9mV7M2wkm5ISknh521/cEHtWqSmpxMbXQ6wCuTU9H2Ox/szKYn1m36g34MjGDhyNFt+\ncXZoyUH/7se8Zcq111/NS89bwy/+tPlXrrr6CsLCwjinSkXqXViHipUTTpLSP4svH0evm2/k2u59\n6SC9KFM6iksvaejEJhwl1PJbMAjlwvYPVV1hP54BtAcuAL4UkfXAI0BlESkLlFPVJfZ7pwEt86Xz\nDoCqLgbK2O/P72pgkp3mJ0BpESn4Bad/cFevHnw69TU6XNUSnfO54+mHhYWh06Yw/+P32bBpM79u\n3eZ4DKNgqletQl+5iYEPDmfwI6OoU7Mm4eFn58/3YGYmj7wwkX/16UlkRATHdjd1oyKTm+sn48AB\npowdzaBePXjk+QmOpj/puSm0v1z47KMv6dH3RgA+1M9J3rWbd+a8xr+HD2L9mg34c3MLFSdj/36+\nWbaCz2a+yTx9m0OHMvni6wVObMJRQim/BYuQbEY+gQxgk6pekX/hcQrPY+VvF/Txv9dqfcClqppd\n+FU8Ne2vbMG/Rj3JnT3dGSi9dFQUTS5pyNIVK6lZPdHRtBPi49m5c9eR57uSk0mIj3c0Rqjq3KEt\nnTu0BeCl/0yjwlm433Jyc3nkhYm0b3EFLRtbEwrElivL3jSrdrsnLY2YAjbx/pOE8nFc1awJAPVq\n1yTM5yM9I4NyZco4Gufzj7/i5Tef4ZXxb+L3+3lu9EtHXpv2wSS2bd1eqPRXrv2WcytVpFxZa71b\ntbic7zZt4Zo2rQqV7vEETX4Lkd7IoXyqUlVELrUf9wCWA/Ei0gxARIqJSD1V3QekikheIdwLWJgv\nnW72+5sDaaqacUyc+cDgvCcicrETKx8IHD1Zy587/u6csHDFKqpXOceJMEekpqWRsX8/AJmHD7Ni\n1RqqV6vmaAyA+vXO54/t29mRlER2djZz539Nq5YtHI8D1vVTt66hBgKBE/aQO/ErhZOalg5AUnIy\nC5Yup0Prq1yJ4+Z+e+q1ySSeW5lu17Q/sqz5JY34bNFiAD5ftIQWjRsVOk7gyD+WK5s2Zu2GTQD8\nsSOJnJzcAhe0Pp/vqOuIVar9/Vts3b4Fv/3yOwAlS5YgIqIkAM2aNyYnO4dtv/5RoJh5KlaIZ8OW\nHziclUUgEGD1uu+oXrXKyT9YAF7lt5PJ29+F+QsGoVyz/RG4W0SmApuwrtfOAyaKSDmsDk/jgc1A\nX+BVESkF/Abcmi+dTBFZh7Wv8i/PMxh4SUS+s9NcBBSq2+HwZ19g7YZNpO/LoOOt/bmzRzeWrl7L\n73/tICwsjEoJ8Tx8d//ChPgfu/fsYfjoMQQCAfx+P+3btKbF5c0cjQEQHh7OsKH303/QffgDfrp0\n7kiN6omOx3lg+CjWrFtHWvo+2na6kYF39qNLp4L1QD3WI08/z9rvN5CekcF1vfrRv1d3ypYuzbMv\nv07avgz+NfIJ6tSszoQnHj15Yqdh6ONPsi9jP8XCw3no3oGUjnL+aoWb++37H39i/pJl1KxahT4P\nDcfn8zGg28306nwdw1+cxGffLKJC+fI8MfjuQsUZ+cJE1m3cTHrGfq6/cxB3dLuJjm2u4olJr9Lz\nXw9QvHgxRg6+q0BpPz1hBI2bNSA6uizzlikvvzCVlq2bkVijKrm5uST9tYvRjzwPQGz5GF5961ly\n/X6Sd+5m2H1PFmq7AOrXrUObls3p3v8eioWHU7dWTW7seE2h0z0eL/LbKQmOsrLQfG6dwZ5Jdm/k\nT1X1wkKmswC4X1XXObNmln0/b/Rkp3s1n22YC/f4HZdHeTXU5rMtUS7akzhezWcbVtKb/Hbltfd5\nEser+WwDfu+O9aWr1nKsiExa8HWhV7xSqzZnvMgO5ZqtEzkr9M5EDMMwziLB0gxcWCFZ2Krq78BF\nDqTT2oHVMQzDMIKYiJwLvAVUAPzA66o6UUQeBe4A8m6sH6aqcwsSIyQLW8MwDCNEeNMbOQcYoqrf\nikhpYK2IfGm/Nk5VCz2smilsDcMwjCJNVXcCO+3H+0VkC5DXzdyR0t4UtoZhGEbQ8vqarYgkAg2A\nlUBzYJCI9ALWYHWYTS9IuqF8n61hGIZxtvNwIgK7Cfl9YLCq7gdeBmrYQ/nuxBpbv0BMzdYwDMMI\nWl7VbEWkGFZB+7aqfgygqvkHbn8DKPCUVKZmaxiGYRjwH2Czqr6Yt0BEKuZ7/UasiWoKxNRsDcMw\njCLNHq63J7DBnlQmAAwDethTsPqBbUCBh+4zha1hGIYRvDy49UdVl2INt3usAt1TezymsD0DIipU\n8iaQR8Mb5hw64EmcQLY3EyuVjPNmdhO/R9sT8BduWrdTVaZWLU/ieJWv13z/gSdxvnxslidxrh5x\nkydxnGZGkDIMwzAMt4VIYWs6SBmGYRiGy0zN1jAMwwhaPjN5vGEYhmEYp8LUbA3DMIzgZa7ZGoZh\nGIZxKkzN1jAMwwha5tYfwzAMw3CbKWyLNhF5FMhwYlLhk1mybAVjx40nEAjQpXNH+vXp5XiMkaPH\nsHDpUuJiY5k98y3H08/vWrmF0lFRhIX5KBZejOmvT3IlzvT3P+TjL+bj8/moXSORUUPvo3jx4o7H\n8eL7ycrK4taB95KdnU1ubi5tW13FgH59HY+zMzmZRx57kr17U/GF+ejauRM9u7kzGEKo5Wu3YiXv\nT+Xt9V9gTasaYM/BfXQ4rxnpmfvZtGsrxcLDiYssR/eLriaieElHYnqZD04mVHojm8I2yPn9fp56\n9nkmvzyB+Ph4uvfuR6srW1AjMdHRODd0upYe3boybNQTjqZ7PGE+H5MnPEfZMmVci5Gyew+zPprD\n7KmvUbx4cR4cPYZ5CxbRsV0bR+N49f2UKFGCyZPGUyoigtzcXPoMGMQVl13KhfXOdzROsfBwhg4e\nRN3zanPw4EG69b2Dyy9tQvXEao7GCcV87VashNIx3N+iBwD+QIDHv/4PF1asScqBVK6rewVhPh+f\n/rCUr35dQ8e6VzgS06t8UJQUycJWRCIBBc7BGg9zNPAr1lyFUcBuoK+q7hKR24E7geLAL0AvVc08\nJr17sQaozsaaNaKHU+u6YdNmqlapQuVK1hCPHdq1YcHCxY4flBo1uJgdSTsdTfNEAgHw+90fci83\nN5dDmZn4wsLIzDxMfFys4zG8+n4ASkVEAJBl127dON8vHxdH+bg4ACIjI6mRWI1dKbsdP8iGYr72\nItbPu/8gLrIcMaXKEFPq75PVatEV+X7nL47F8SofFCVFtTdyB+AvVW2oqhcB84CJQFdVbQJMBZ6y\n3/uBqjZV1YbAD0C/46T3INDAnmB4gJMrmpySQsUKCUeeV0hIIDkl5R8+Efx8PrhryIP0vPNuZs/5\n3JUY8eXj6HXzjVzbvS8dpBdlSkdx6SUNHY/j5ffj9/uRPv1o07ELzZo0pr7Dtdpj/bUjiR9/+oWL\nLnA+Tijmay+s3/EzjSqf9z/LV/25mbrxia7EdDMfnBIPJ493U5Gs2QIbgOdEZAzwGZAK1Ae+FBEf\n1knIDvu9F4nIaCAaq9Y77zjpfQfMFJGPgI/cXvmz3dSXxhNfPo69aWncNeRBqlerSsOL6jsaI2P/\nfr5ZtoLPZr5J6ahIHnjsKb74egHXtGnlaBwvhYWFodOmsP/AAf714CP8unUbNasnuhLr4MGD3D9s\nJA8OuYfIyEhXYhinJ9efy6bk3+hY9/Kjln/582rCwsK45Jw6jscMinwQJIVlYRXJmq2q/gw0wip0\nRwNdgY2q2siu7V6sqtfYb58KDLRrwI8DEcdJ8jpgkp3mahFxbL8mxMezc+euI893JSeTEO/NrDRu\niS9vNU/FRkfTukVzNm75wfEYK9d+y7mVKlKubBnCw8Np1eJyvtu0xfE4Z+L7KR0VRZNLGrJ0xUpX\n0s/JyWHIsJF0vKYdrVq2cCVGKOZrt21J+Z1zyyVQuuTfhd6qPzezJWUbvRq0dzyeF/ngVPh8vkL/\nBYMiWdiKSCXgkKrOBJ4DLgXiRaSZ/XoxEalnv700sFNEimNNLnw8VVV1IfAQUNb+jCPq1zufP7Zv\nZ0dSEtnZ2cyd/7VrGT8QCBBwefqyQ5mZHDx4yHp86BDLV6+hVvVEx+NUrBDPhi0/cDgri0AgwOp1\n31G9ahXH43j1/aSmpZGxfz8AmYcPs2LVGqpXc+f62cgnx1IzMZFbut3sSvoQevnai1jrd/xEo8p/\n1163JG9jwW/r6Ne4I8XCnW+k9CIfFCVFtRn5QuBZEfEDWcBdQA4wUUTKYXWaGg9sBkYCq4BkYCVw\nVBdaESkGTBeRslh9819U1X1OrWh4eDjDht5P/0H34Q/46dK5IzWqJzqV/BEPDB/FmnXrSEvfR9tO\nNzLwzn506XSd43H27k1lyPBR+Hw+cnNyuaZtGy5r2tjxOPXr1qFNy+Z0738PxcLDqVurJjd2vObk\nHzxNXn0/u/fsYfjoMQQCAfx+P+3btKbF5c0cj7P+uw18Pu9LatesgfS2uifce9edNL/sUkfjhFq+\ndjtWVm42P+3+g5svbH1k2YebFpIb8PPqSuvKVbXoitx0oTOXSbzKB6ckRG798Xl1xmf8LWvfHq9O\nsz0Jk5N50JM4Xk0eX7xstCdxvJo83qv7FH0u1K6OK8SOWaE4eXzJmAqOZbrUTesK/YXHXNDojJfY\nRbVmaxiGYZwFfL7QuNoZGlthGIZhGEHM1GwNwzCM4BUkvYkLyxS2hmEYRtAKllt3CssUtoZhGEbw\nCpHeyOaarWEYhmG4zBS2hmEYhuEy04xsGIZhBC1zzdYwDMMw3BYiha1pRjYMwzAMl5nhGs+ArPTd\n3uz0EDkjzJNzYL8ncYpFRnkSJ+fgAU/ihJUo6U2cYh41lHmVrz06NuZmHfYkzqyhMz2JA9D3zaGO\nfUn7ft1S6C+ibM3zz/jB0DQjG4ZhGEHLq7G93WaakQ3DMAzDZaZmaxiGYQSvELkcZmq2hmEYhuEy\nU7M1DMMwgpa5z9YwDMMw3BYi89mawtYwDMMIWqY3smEYhmEYp8TUbPMRkXJAD1V9pQCfrQZcrqrv\nOLlOI0ePYeHSpcTFxjJ75ltOJv0/lixbwdhx4wkEAnTp3JF+fXqd1XEy9h/g8WfH8evWbfjCwhj1\nwBAurHe+ozG8/H682J6srCxuHXgv2dnZ5Obm0rbVVQzo19fRGBCa+drLbfL7/fS4fQAV4uN5ceyT\njqVbr90l1G55IYEApG5PYenkL/Dn+gG4oENjGstVvHPPJLIOZDoWs6gwNdujxQADC/jZ6kAPB9cF\ngBs6XctrE8Y5nez/8Pv9PPXs87w28QU+nDWDL+Z9xW/btp21cQCenfgyzZs1ZfZbU5g1+VWqV6vq\neAyvvh/wZntKlCjB5Enj0WlT0GlTWLJiJRs2b3E8Tqjla/A2L8x47wNqJCY6mmap6CjOv7oRcx59\ni09GvElYWBjVL7VO5iJjSlP5gkT270l3NOYp8fkK/xcETM32aGOAGiKyDvgSSAEEKAF8qKqPiUhj\nYArQBCgOrLTfMwaoa392mqq+6MQKNWpwMTuSdjqR1D/asGkzVatUoXKlSgB0aNeGBQsXO/6D9irO\n/gMHWL9hI48/PBSAYsXCKV3M+WEYvfp+vNoegFIREQBk2bVbNw5VoZavwbtt2pWcwpLlq7i9d0+m\nz3rP0bR9YT6KlSxOdmYWxUoW42CaNURq0x6tWT3rG9oM7uJovFNapyApLAvL1GyP9hDwq6o2Ar4C\naqtqU6Ah0FhEmqvqGuBj4ElgLPCWqm62P7tYVRs5VdB6KTklhYoVEo48r5CQQHJKylkb56+knUSX\nK8ujTz9H9zsGMvq5F8g87M0YtG7wcnv8fj/Spx9tOnahWZPG1He4qdpLXuU3Lz074SXuu7u/44XQ\nobQDbJq7hpufH4C8cBdZBw+TtPl3qjSsxYE9GaRt3+1ovKLGFLYn1g5oa9dU1wF1gNr2a6OBtsAl\nwLNnZvWMf5Kbm8sPP/2C3NCZd954mYiSEUydOetMr1aBebk9YWFh6LQpzP/4fTZs2syvW7e5Esc4\nfYuWrSAuNoa6tWsRCARwciKZEpElqdKoFu/9+zX0X69QrERxalxej4s6Xsr6j5YceZ/n9UxfWOH/\ngoBpRj4xHzBGVd84zmvlgdJY+y8COOTlirkhIT6enTt3HXm+KzmZhPj4szZOhfh4KiTEc0Hd8wC4\n+soWvPnO2VvYnontKR0VRZNLGrJ0xUpqVk90NZZbvMpvXvl2w0YWLlnOkuWrOJx1mAMHDzJ89Bie\nGPFwodOuVK8a+1PSjnR++n3dz9RuXp/S5ctx/eN9wQdRMWXo9FhvPn1sOpkZBwsd85SYW39CUgZQ\nxn48D7hNRKIARKSyiJS3X3sVGA7MAJ45zmcd5fQZ7PHUr3c+f2zfzo6kJLKzs5k7/2tatWxx1saJ\ni42hQkI8v/+5HYBV69ZTI7Ga43HAm+/Hq+1JTUsjY791nS7z8GFWrFpD9Wpn737zKr/lcXub7u1/\nO3Nnv8tn783g6VEjaNqooSMFLcCBPfuIr1GZ8OLhAFSuV43f1/7MrMEv88EDb/DB0Dc4kJrBJyOn\neVfQhhAzn+0xRGQ6cBHwBbAduMN+KQO4BWgOdFbVm0UkDFgKPAwswSqgY4E3/+m67enMZ/vA8FGs\nWbeOtPR9xMXGMvDOfnTpdN2pffg0r+ksWbaCsc+Pxx/w06VzR27v2/u0Pu92nNOdz/bHX37l8Wdf\nICcnh3MrV2LUg/+mTOmTdyo6nflsC/P9nO58tgXdntOZz/bnX39l+OgxBAIB/H4/7du05o6+p3ar\nzOnMZ3tW5OvTPDYWdJsKOp/tmvXf8fa7esq3/pzKfLYXX3851S+tiz/Xz97fd7F06jwC9q0/AF2f\nuYM5j7190lt/nJzP9uCuPwpdSEVWqHrGq8emsD0DzOTxBWMmjy8YM3l8AZnJ4wvMycL2UPKfhf4i\nSiVUOeMHQ3PN1jAMwwheQdLBqbBMYWsYhmEErVC5z9YUtoZhGEbwCpGabWhshWEYhmEEMVPYGoZh\nGIbLTDOyYRiGEbS8ms9WRDoA47EqoVNUdayT6ZuarWEYhhG8PJj1xx4zYRLQHrgA6C4idZ3cDFPY\nGoZhGEVdU+BnVf1dVbOBd4HrnQxgmpENwzCMoOXzpjfyOcCf+Z5vxyqAHWMKW8MwDCN4mftsjYIq\nUa58aOQej5UoG3emV8FRJcqVP/mbDMMhfd8ceqZXoUBKlI3z4nj5F1A13/Nz7WWOMYWtYRiGUdSt\nBmqJSDUgCfg/oLuTAUwHKcMwDKNIU9VcYBAwH9gEvKuqW5yMYWb9MQzDMAyXmZqtYRiGYbjMFLaG\nYRiG4TJT2BqGYRiGy0xhaxiGYRguM4WtYRiGYbjMFLZBTERuFpEy9uPhIjJbRBq5FKu5iNxqP44X\nkepuxMkXL0xEyrqUdpQ9sDgicp6IdBaR4i7Fcn2/eZkP8sV07fux0/div3mWD+wYkW6lfZxYbv5+\nBp/KMuP0mMI2uI1Q1QwRaQ5cDUwBXnE6iIg8CjwIPGwvKg5MdyHOTBEpKyJRwEZgs4i4MazNIiBC\nRM7Bum+uF/Cm00G82m94lw88+X483G9e5YPLRWQz8IP9/GIRedmFOF79fvocZ1lfF+IUKaawDW65\n9v/XAa+r6mdACRfidAE6AwcAVHUHUMaFOPVUdR9wA/AFUB3rAOg0n6oeBG4EXlbVm7GmzXKaV/vN\nq3zg1ffj1X7zKh+8gDU12x4AVf0OaOlCHFe/HxHpLiJzgOoi8km+vwXAXqfiFFWmsA1uf4nIa0A3\n4HMRKYk731mWqgaAAFjNby7EAChuN+PdAHxiT2XlxqgqPhG5DOgJfGYvC3chjlf7zat84NX349V+\n8yofoKp/HrMo97hvLBy3v59lwPNYNfTn8/3dj3UyYRSCGRs5uAnQAXhOVdNEpBLgRrOR2gfzaBG5\nA7gNeMOFOK8B24DvgEX2OKT7XIjzL6wmyg9VdZOI1AAWuBDHq/3mVT7w6vvxar8Nxpt88KeIXA4E\n7MJwMODoUH82V78fVf0d+F1E+qnq5vyvichVwDdOxSqKzHCNQUhEYv/pdVV1vElHRNoC7QAfME9V\nv3Q6xgniFlPVHC9iuSHffgOY79Z+sztENceqySxV1XVuxDlOXFe+Hy/ym4jcrKrvnWyZA3HKAy9i\nXU/3YV0fHqyqe5yMc4LYjn8/IrIReAt4FogAngEaq+plTsYpakzNNjitxTqo+vL9nycA1HAqkIiE\nA1+paivA1QLWbv7sCiRydN573OE4jYFhx8ZR1YucjGPbAJTC+l42uJA+IjISuBmYbS+aKiLvqeoT\nDsepADwFVFbVa0SkHnAZVocsp2J4lt+warXHFqzHW1Yoqrobq6naFSIy5CRvGedwyEuBsVjNymWA\nGcAVDscockxhG4RU1dXbbo6JlSsifhEpp6rpLof7GEjHOpk47GKcGVjNrBsAv1tBROR2YCTwX6wT\nooki8riq/sfhUD2Bi1U10477NPAt4Ghhi9VTdyrwiP38J2AWDha2XuQ3EbkGuBY4R0Qm5HupLOBY\nLVBEJvIP10xV9V6HQrnReeyfZAOHsE4iI4Ctqura76ioMIVtkBORGKA2VqYHQFUXORxmP7BBRL7E\n7iFqx3HqYJHnXFXt4HCax5Oiqp94EGco0DCvuVBE4rBqA04Xtjuwvv9M+3lJHJ7Y2lZeVVVEHgZQ\n1RwRcaOjj9v5bQewBqvH89p8yzOA+xyKgR3Ddar6mBdx8lmNdWLcBCgPvCoiXe3e3EYBmcI2iNk1\np8HAuVg1mWbAcqC1w6Fm83cTpZuWiciFqupKc2s+j4rIZOBr8tWgVdXpbdyDdQDPk2Evc1o6sMku\nnAJAW2BVXq3NwULqgH3CkNdLuJkd22mu5jf71pvvRORD4IA9V2leE3ZJB+NMy//cHmQioKoZJ/hI\noYhIBNAP6/al/Cfftzkcqp+q5p1IJAHXi4gbt4AVKaawDW6Dsc4uV6hqKxGpi3VNzVHHHjRc1Bzo\nKyJbsQpBH9bByelrqbcCdbEGS8hr/grg/AH+F2CliHxsp3898H3eNTZVdepa2of2X55vHEr3WEOA\nT4CaIrIUiAducjqIh/ltPlanpf3281L2ssudDGL3EZiK1dzrE5E04DZVXfvPnzxtb2PdltMeq59D\nT1zo9ayqa+wBVGqr6lS7A9gSp+MUNaawDW6ZqpopIohISVX9QUTqOB3ELvz+59qTqjrWEct2jcPp\nnUgTVXV8Px3Hr/Zfno/t/x29xuZF4STWsIYRwJVAHawToR/tezmdjuVVfotQ1byCFlXdL+4Mqfgf\nYKCqLgZrKEqswtfpk8haqnqziFyvqtNEZCaw2OEYeSN8NcbKB1OxBlCZjukkVSimsA1u20UkGvgI\n+FJEUoHfXYjTON/jCKyer/94+1FBqOrvInIx0MJetNhu8nPaMhGpd+y9gk7z6lqaiNQGxgD1OLr5\n0LHCSVX9IvKSqjYENjmV7gl4kt+wmsUb5d0mJSKXYHX8cVpuXkELoKpLRMSN29nyTnzSRKQ+sBNI\ncCFOF6AhsA6sEb7EHpvbKDhT2AYxVe1iPxxlD5lWDpjrQpxjrzOOF5G1WD1tHSPWYOZ38Hdz7nQR\neV1VJzoZB+va9rduN1eLSDzwAP97Dc3pa+pTgUexhgVshdVM7sYIUl+LSFdgtj3Ckyu8ym9Yg5u8\nJyI7sPJARaxRuJy20B6k4x2sGns34Bv73mgcvCf6dbvD5Ais5v7SOL/PwB7hS0TcHuGrSDGFbZCz\nf1xVsDrfZAD1sc84HYyRfwaZMKyahxt5ox9wqaoesOOOxerw5XRh60WPZ7BuMZoFdATtUpM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ieHkRMAkjYB3k4nx2/tGrolZ/EHOzmLJwPX2t61ZjsppTRosmebkHQcsSJ5a+BeIgXhbcAfVW6q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\nCB8D8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21b381210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,6))\n",
    "ax = fig.add_subplot(111)\n",
    "sns.heatmap(data, annot=True, fmt='d')\n",
    "ax.set_xlabel(\"Ground truth\")\n",
    "ax.set_ylabel(\"Prediction\")\n",
    "plt.title(\"Confusion matrix on manually tagged data\")\n",
    "plt.savefig(\"confusion_matrix_manualtag.pdf\", format='pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}