py/create_chipper_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.0\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "with open('../CMakeLists.txt', 'r') as cmake_file:\n",
    "    content = cmake_file.read()\n",
    "major_version = re.search('CHIPPER_VERSION_MAJOR (\\d+)', content).group(1)\n",
    "minor_version = re.search('CHIPPER_VERSION_MINOR (\\d+)', content).group(1)\n",
    "patch_version = re.search('CHIPPER_VERSION_PATCH (\\d+)', content).group(1)\n",
    "version = \"%s.%s.%s\" % (major_version, minor_version, patch_version)\n",
    "print version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_dir=/workspace/chipper_data/chipper-0.4.0-data/\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Note: This notebook uses blast. To install on MacOS, run `brew install blast`.\n",
    "# Set this to the location of your Swiss-Prot database\n",
    "swiss_db_path = \"/workspace/blast/swiss/swissprot\"\n",
    "# Maximum length of the IEDB epitopes used\n",
    "max_epitope_len = 18\n",
    "# Minimum length of the IEDB epitopes used\n",
    "min_epitope_len = 9\n",
    "# Total length of the samples used to create the model. The cleave location is at offset = generated_sample_len/2.\n",
    "generated_sample_len = 20\n",
    "\n",
    "data_dir = \"/workspace/chipper_data/chipper-%s-data/\" % (version)\n",
    "src_dir = \"../src/\"\n",
    "models_dir = \"../models/\"\n",
    "\n",
    "# Path to IEDB (http://iedb.org/) epitope export file\n",
    "epitope_csv = data_dir + \"epitope_table_export_1478815050.csv\"\n",
    "\n",
    "# Paths for models and predictions\n",
    "LR_model_file = models_dir + \"lr.model\"\n",
    "LR_predictions_file = data_dir + \"LR_predictions.txt\"\n",
    "SVC_model_file = models_dir + \"svc.model\"\n",
    "SVC_predictions_file = data_dir + \"SVC_predictions.txt\"\n",
    "# File to save raw training data set to\n",
    "raw_data_csv = data_dir + \"training_set.csv\"\n",
    "\n",
    "print \"data_dir=%s\" % (data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2881: DtypeWarning: Columns (3,4,5,6,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 88489 IEDB MHC-1 epitopes from 31164 unique proteins (antigens).\n",
      "blastdbcmd -db /workspace/blast/swiss/swissprot -entry_batch /workspace/chipper_data/chipper-0.4.0-data//protein_ids.txt -outfmt %a|%s -out /workspace/chipper_data/chipper-0.4.0-data//blast_output.txt\n",
      "Found 11293 IEDB proteins in Swiss-Prot sequence database\n"
     ]
    }
   ],
   "source": [
    "import os, sys, argparse\n",
    "import subprocess\n",
    "\n",
    "# Reads protein_ids from file and returns a DataFrame with two columns: assession_id, sequence\n",
    "def __create_swiss_prot_dataframe(output_path, swiss_db_path, protein_id_file):\n",
    "    blast_output = output_path + \"/\" + \"blast_output.txt\"\n",
    "    delimiter=\"|\"\n",
    "    cmd = [\n",
    "        \"blastdbcmd\", \"-db\", swiss_db_path, \"-entry_batch\", protein_id_file,\n",
    "        \"-outfmt\", \"%%a%s%%s\" % (delimiter), \"-out\", blast_output\n",
    "    ]\n",
    "    print \" \".join(cmd)\n",
    "    with open(os.devnull, \"w+\") as devnull:\n",
    "        subprocess.call(cmd, stdout=devnull, stderr=subprocess.STDOUT)\n",
    "    \n",
    "    with open(blast_output, 'r') as blast:\n",
    "        data = []\n",
    "        for line in blast.readlines():\n",
    "            (protein_id, protein_sequence) = line.split(delimiter)\n",
    "            data.append((protein_id.strip(), protein_sequence.strip()))\n",
    "   \n",
    "    return pd.DataFrame(data, columns=['protein_id', 'protein_sequence'])\n",
    "    \n",
    "# Converts the merged IEDB/Swiss-Prot table into training data\n",
    "def __create_training_set(output_dir, iedb_swiss_merged, generated_sample_len):\n",
    "    data = []\n",
    "    for (protein_id,\n",
    "         row_ids) in iedb_swiss_merged.groupby(\"protein_id\").groups.items():\n",
    "        epitope_rows = iedb_swiss_merged.loc[row_ids]\n",
    "        # Grab the protein sequence from the first row\n",
    "        protein_sequence = epitope_rows['protein_sequence'].iloc[0]\n",
    "        # Sort by the C-terminus ('end')\n",
    "        sorted_epitopes = epitope_rows.sort_values(by=\"end\")\n",
    "        protein_sequence_len = len(protein_sequence)\n",
    "        current_loc = 0\n",
    "        for (i, epitope_sequence, start, end) in \\\n",
    "                sorted_epitopes[[\"epitope_sequence\", \"start\", \"end\"]].itertuples():\n",
    "            epitope_start = int(start) - 1\n",
    "            epitope_end = int(end) - 1\n",
    "\n",
    "            half_sample_len = generated_sample_len / 2\n",
    "            sample_start_end = lambda pos: (pos - half_sample_len + 1, pos + half_sample_len + 1)\n",
    "            \n",
    "            epitope_sequence_len = len(epitope_sequence)\n",
    "            epitope_midpoint = epitope_end - epitope_sequence_len / 2\n",
    "            \n",
    "            cleaved_sample_pos = sample_start_end(epitope_end)\n",
    "            uncleaved_sample_pos = sample_start_end(epitope_midpoint)\n",
    "            \n",
    "            # Check if our samples are off the protein sequence or overlap (TODO: handle these)\n",
    "            if (uncleaved_sample_pos[0] < current_loc or\n",
    "                    cleaved_sample_pos[1] > protein_sequence_len):\n",
    "                continue\n",
    "\n",
    "            current_loc = cleaved_sample_pos[1]\n",
    "\n",
    "            # Double check that the start and end positions are correct\n",
    "            assert protein_sequence[epitope_start:epitope_end + 1] == epitope_sequence,\\\n",
    "                     \"Epitope failed to align to protein\"\n",
    "\n",
    "            fetch_seq = lambda pos: protein_sequence[pos[0]:pos[1]]\n",
    "            data.append((fetch_seq(cleaved_sample_pos), 1))\n",
    "            data.append((fetch_seq(uncleaved_sample_pos), 0))\n",
    "                        \n",
    "    cleavage_data = pd.DataFrame(data)\n",
    "    cleavage_data.columns = ['sequence', 'is_cleaved']\n",
    "    return cleavage_data\n",
    "\n",
    "\n",
    "def create_iedb_swiss_dataset(epitope_csv, swiss_db_path, output_dir,\n",
    "                              max_epitope_len, min_epitope_len,\n",
    "                              generated_sample_len):\n",
    "    # Load the CSV from IEDB (skipping the first line, [2:])\n",
    "    epi = pd.DataFrame.from_csv(epitope_csv)[2:]\n",
    "    # Rename the columns to remove whitespace\n",
    "    epi.columns = [\n",
    "        'type', 'epitope_sequence', 'start', 'end', 'chebi', 'syn', 'protein',\n",
    "        'protein_id', 'organism', 'oid', 'comments'\n",
    "    ]\n",
    "    # Remove GI entries that start with prefix \"SRC\"\n",
    "    epi = epi[epi.protein_id.str.startswith(\"SRC\") == False]\n",
    "    # Remove entries with '+' notation (note: looking into this, e.g. \"PLNISLGDVVLY + DEAM(N3)\")\n",
    "    epi = epi[epi.epitope_sequence.str.find('+') == -1]\n",
    "    # Remove the \"GI:\" prefix from the GIs provided by IEDB\n",
    "    epi[\"protein_id\"] = epi.protein_id.str.replace(\"GI:\", \"\")\n",
    "    # Drop any epitopes that are not the desired length\n",
    "    iedb_epitopes = epi[(epi.epitope_sequence.str.len() >= min_epitope_len) \\\n",
    "                         & (epi.epitope_sequence.str.len() <= max_epitope_len)]\\\n",
    "             .loc[:, [\"epitope_sequence\", \"protein_id\", \"start\", \"end\"]]\n",
    "    # Create a file with a list of unique protein (antigen) IDs for use with BLAST\n",
    "    antigen_ids = iedb_epitopes[\"protein_id\"].unique()\n",
    "    protein_id_file = output_dir + \"/\" + \"protein_ids.txt\"\n",
    "    np.savetxt(protein_id_file, antigen_ids, fmt=\"%s\")\n",
    "    num_iedb_epitopes = iedb_epitopes.shape[0]\n",
    "    num_iedb_proteins = antigen_ids.shape[0]\n",
    "    print \"There are %d IEDB MHC-1 epitopes from %d unique proteins (antigens).\" % (\n",
    "        num_iedb_epitopes, num_iedb_proteins)\n",
    "    swiss_prot_db = __create_swiss_prot_dataframe(output_dir, swiss_db_path,\n",
    "                                                  protein_id_file)\n",
    "    print \"Found %d IEDB proteins in Swiss-Prot sequence database\" % (\n",
    "        swiss_prot_db.shape[0])\n",
    "    \n",
    "    # Merge the IEDB epitops with the SWISS protein data and drop any NaN values (proteins not in SWISSprot)\n",
    "    iedb_swiss_merged = pd.merge(\n",
    "        iedb_epitopes, swiss_prot_db, on=\"protein_id\", how=\"left\").dropna()\n",
    "    # Since we're going to sort on position, convert to numeric columns\n",
    "    iedb_swiss_merged[\"start\"] = pd.to_numeric(iedb_swiss_merged[\n",
    "        \"start\"]).astype(int)\n",
    "    iedb_swiss_merged[\"end\"] = pd.to_numeric(iedb_swiss_merged[\"end\"]).astype(\n",
    "        int)\n",
    "    # Save the raw merged data for reference\n",
    "    iedb_swiss_merged.to_csv(output_dir + \"/\" + \"merged_iedb_swiss.csv\")\n",
    "    return __create_training_set(output_dir, iedb_swiss_merged, generated_sample_len)\n",
    "\n",
    "raw_training_data = create_iedb_swiss_dataset(epitope_csv, swiss_db_path, data_dir,\n",
    "                              max_epitope_len, min_epitope_len,\n",
    "                              generated_sample_len)\n",
    "raw_training_data = raw_training_data.drop_duplicates(subset=[\"sequence\"], keep=False)\n",
    "raw_training_data.to_csv(raw_data_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110aa8790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HO/NZPaoOWNVQDMdb5TYAZQyt1A2QC48OWZ92WxTCfs76sK7/AkmohV5ik4+o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\n/we1Ss4qZKBe0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8ebf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import log\n",
    "\n",
    "aa_names = {\"A\": \"Alanine\",\n",
    "            \"C\": \"Cysteine\",\n",
    "            \"D\": \"Aspartic Acid\",\n",
    "            \"E\": \"Glutamic Acid\",\n",
    "            \"F\": \"Phenylalanine\",\n",
    "            \"G\": \"Glycine\",\n",
    "            \"H\": \"Histidine\",\n",
    "            \"I\": \"Isoleucine\",\n",
    "            \"K\": \"Lysine\",\n",
    "            \"L\": \"Leucine\",\n",
    "            \"M\": \"Methionine\",\n",
    "            \"N\": \"Asparagine\",\n",
    "            \"P\": \"Proline\",\n",
    "            \"Q\": \"Glutamine\",\n",
    "            \"R\": \"Arginine\",\n",
    "            \"S\": \"Serine\",\n",
    "            \"T\": \"Threonine\",\n",
    "            \"U\": \"Selenocysteine\",\n",
    "            \"V\": \"Valine\",\n",
    "            \"W\": \"Tryptophan\",\n",
    "            \"Y\": \"Tyrosine\"}\n",
    "\n",
    "aa_alphabet = \"ACDEFGHIKLMNPQRSTUVWY\"\n",
    "\n",
    "def alphabet_dict():\n",
    "    d = {}\n",
    "    for letter in aa_alphabet:\n",
    "        d[letter] = 0\n",
    "    return d\n",
    "\n",
    "def init_counters():\n",
    "    return [alphabet_dict() for i in range(20)]\n",
    "\n",
    "counters = [init_counters(), init_counters()]\n",
    "for (rowid, seq, is_cleaved) in raw_training_data.itertuples():\n",
    "    for (i, aa) in enumerate(seq):\n",
    "        counters[is_cleaved][i][aa] += 1\n",
    "\n",
    "def prob(is_cleaved, pos, aa):\n",
    "    counter = counters[is_cleaved][pos]\n",
    "    total = 0\n",
    "    for count in counter.values():\n",
    "        total += count\n",
    "    if counter[aa] == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return float(counter[aa])/total\n",
    "\n",
    "def safe_log(x):\n",
    "    if x <= 0:\n",
    "        return float(\"-inf\")\n",
    "    return log(x, 2.0)\n",
    "    \n",
    "result = [alphabet_dict() for i in range(20)]\n",
    "for i in range(20):\n",
    "    for letter in aa_alphabet:\n",
    "        result[i][letter] = prob(1, i, letter) * safe_log( prob(1, i, letter) / max(0.00000001, prob(0, i, letter)))\n",
    "\n",
    "r = pd.DataFrame(result).transpose()\n",
    "vmin = r.min().min()\n",
    "vmax = r.max().max()\n",
    "center = (vmax - vmin)/2\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "ax = sns.heatmap(r, linewidths=.5, \n",
    "            yticklabels=[\"%s (%s)\" % (aa_names[code], code) for code in r.index],\n",
    "            xticklabels=[\"%d\" %(i) for i in range(10,0,-1)] + [\"%d'\" % (i) for i in range(1, 11)], \n",
    "            vmin=vmin, vmax=vmax, center=center, cmap=\"YlGnBu\", alpha=0.8)\n",
    "ax.set_title('Training Data Kullback-Leibler Information, $I(i) = \\sum_{L=0}^{N}p_{i}^{L}\\log_2(p_{i}^{L}/q_{i}^{L})$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read and return the Saxon test set \n",
    "def fetch_testing_data():\n",
    "    df = pd.DataFrame.from_csv(data_dir + \"saxova_in_vivo_mhc_1_ligands_dataset.csv\")\n",
    "    df = df[[\"Sequences\", \"Activity\"]]\n",
    "    df.columns = [\"sequence\", \"is_cleaved\"]\n",
    "    df.is_cleaved[df.is_cleaved == -1] = 0\n",
    "    return df\n",
    "\n",
    "raw_testing_data = fetch_testing_data()\n",
    "if generated_sample_len != 28:\n",
    "    # Testing data is 28 residues in length, cut off excess residues\n",
    "    trim_residues = (28 - generated_sample_len) / 2\n",
    "    raw_testing_data[\"sequence\"] = raw_testing_data.sequence.str[trim_residues:-trim_residues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 52363 training samples before removing the Saxova sequences.\n",
      "There are 52335 training samples after filtering out Saxova.\n",
      "Final training set has 52332 samples of 52335 raw samples.\n",
      "Final testing set has 416 samples of 419 raw samples.\n"
     ]
    }
   ],
   "source": [
    "from aa_props import seq_to_aa_props\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# We need to remove the Saxova samples from our training set\n",
    "print \"There are %d training samples before removing the Saxova sequences.\" % (\n",
    "    raw_training_data.shape[0])\n",
    "raw_training_data = raw_training_data[raw_training_data.sequence.isin(\n",
    "    raw_testing_data.sequence) == False]\n",
    "print \"There are %d training samples after filtering out Saxova.\" % (\n",
    "    raw_training_data.shape[0])\n",
    "\n",
    "# Filter to check for selenocysteine (TODO) and an invalid \"'\"\n",
    "train_seq_validator = lambda seq: seq.find(\"U\") == -1 and seq.find(\"'\") == -1\n",
    "# Testing set has two sample which are not the correct len, filter them out too\n",
    "test_seq_validator = lambda seq: train_seq_validator(seq) and len(seq) == generated_sample_len\n",
    "\n",
    "process_raw_data = lambda data, is_valid: [(seq_to_aa_props(seq), is_cleaved)\n",
    "             for (i, seq, is_cleaved) in data.itertuples()\n",
    "             if is_valid(seq)]\n",
    "\n",
    "# Filter AA seqs and expand to AA features\n",
    "training_X_y = process_raw_data(raw_training_data, train_seq_validator)\n",
    "testing_X_y = process_raw_data(raw_testing_data, test_seq_validator)\n",
    "\n",
    "(training_X, training_y) = zip(*training_X_y)\n",
    "(testing_X, testing_y) = zip(*testing_X_y)\n",
    "\n",
    "# Scale the data\n",
    "scaler = MinMaxScaler()\n",
    "training_X = scaler.fit_transform(training_X)\n",
    "testing_X = scaler.transform(testing_X)\n",
    "\n",
    "print \"Final training set has %d samples of %d raw samples.\" % (\n",
    "    training_X.shape[0], raw_training_data.shape[0])\n",
    "print \"Final testing set has %d samples of %d raw samples.\" % (\n",
    "    testing_X.shape[0], raw_testing_data.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_file = data_dir + \"training_data.ll\"\n",
    "test_file = data_dir + \"testing_data.ll\"\n",
    "\n",
    "def create_liblinear_files():\n",
    "    for (outpath, rows) in [(train_file, zip(training_y, training_X)),\n",
    "                            (test_file, zip(testing_y, testing_X))]:\n",
    "        with open(outpath, 'w') as out:\n",
    "            for (is_cleaved, features) in rows:\n",
    "                out.write(\"%+d \" % (is_cleaved))\n",
    "                for (feature_id, feature_value) in enumerate(features):\n",
    "                    out.write(\"%d:%s \" % (feature_id + 1, feature_value))\n",
    "                out.write(\"\\n\")\n",
    "\n",
    "create_liblinear_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing parameter search with 5-fold cross validation.\n",
      "log2c= -25.00\trate=80.1001\n",
      "log2c= -24.00\trate=80.1498\n",
      "log2c= -23.00\trate=80.211\n",
      "log2c= -22.00\trate=80.3333\n",
      "log2c= -21.00\trate=80.4995\n",
      "log2c= -20.00\trate=80.6657\n",
      "log2c= -19.00\trate=80.6849\n",
      "log2c= -18.00\trate=80.8874\n",
      "log2c= -17.00\trate=81.0078\n",
      "log2c= -16.00\trate=81.2849\n",
      "log2c= -15.00\trate=81.6823\n",
      "log2c= -14.00\trate=82.2594\n",
      "log2c= -13.00\trate=83.1193\n",
      "log2c= -12.00\trate=83.9869\n",
      "log2c= -11.00\trate=84.9251\n",
      "log2c= -10.00\trate=85.7563\n",
      "log2c=  -9.00\trate=86.4519\n",
      "log2c=  -8.00\trate=87.0347\n",
      "log2c=  -7.00\trate=87.5258\n",
      "log2c=  -6.00\trate=87.9022\n",
      "log2c=  -5.00\trate=88.1659\n",
      "log2c=  -4.00\trate=88.2959\n",
      "log2c=  -3.00\trate=88.3265\n",
      "log2c=  -2.00\trate=88.4239\n",
      "log2c=  -1.00\trate=88.4239\n",
      "log2c=   0.00\trate=88.4239\n",
      "log2c=   1.00\trate=88.4239\n",
      "Best C = 0.25  CV accuracy = 88.4239%\n"
     ]
    }
   ],
   "source": [
    "# Find the best C parameter for the LR model\n",
    "LR_findC = !train -C -s 0 \"$train_file\"\n",
    "print \"\\n\".join(LR_findC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing parameter search with 5-fold cross validation.\n",
      "log2c= -26.00\trate=80.3161\n",
      "log2c= -25.00\trate=80.3466\n",
      "log2c= -24.00\trate=80.5091\n",
      "log2c= -23.00\trate=80.6715\n",
      "log2c= -22.00\trate=80.7059\n",
      "log2c= -21.00\trate=80.8798\n",
      "log2c= -20.00\trate=81.0097\n",
      "log2c= -19.00\trate=81.2868\n",
      "log2c= -18.00\trate=81.69\n",
      "log2c= -17.00\trate=82.3072\n",
      "log2c= -16.00\trate=83.2473\n",
      "log2c= -15.00\trate=84.2066\n",
      "log2c= -14.00\trate=85.206\n",
      "log2c= -13.00\trate=86.0754\n",
      "log2c= -12.00\trate=86.7385\n",
      "log2c= -11.00\trate=87.3213\n",
      "log2c= -10.00\trate=87.7073\n",
      "log2c=  -9.00\trate=88.0685\n",
      "log2c=  -8.00\trate=88.2175\n",
      "log2c=  -7.00\trate=88.3284\n",
      "log2c=  -6.00\trate=88.3284\n",
      "log2c=  -5.00\trate=88.3284\n",
      "log2c=  -4.00\trate=88.4163\n",
      "log2c=  -3.00\trate=88.4163\n",
      "log2c=  -2.00\trate=88.4163\n",
      "log2c=  -1.00\trate=88.4163\n",
      "Best C = 0.0625  CV accuracy = 88.4163%\n"
     ]
    }
   ],
   "source": [
    "# Find the best C parameter for the SVC model\n",
    "SVC_findC = !train -C -s 2 \"$train_file\"\n",
    "print \"\\n\".join(SVC_findC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def create_model_and_predictions(search_output, solver, model_file, predictions_file, probability_estimates):\n",
    "    # Grab C from the last line...\n",
    "    bestC = float(search_output[-1].split(\" \")[3])\n",
    "    cmd = \"train -c %f -s %d '%s' '%s'\" % (bestC, solver, train_file, model_file)\n",
    "    print \"> \" + cmd\n",
    "    createModel = !{cmd}\n",
    "    print \"\\n\".join(createModel)\n",
    "    cmd = \"predict -b %d '%s' '%s' '%s'\" % (probability_estimates, test_file, model_file, predictions_file)\n",
    "    print \"> \" + cmd\n",
    "    predict = !{cmd}\n",
    "    print \"\\n\".join(predict)\n",
    "    print \"\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> train -c 0.250000 -s 0 '/workspace/chipper_data/chipper-0.4.0-data/training_data.ll' '../models/lr.model'\n",
      "iter  1 act 4.149e+03 pre 3.625e+03 delta 2.284e+00 f 9.068e+03 |g| 5.401e+03 CG   5\n",
      "cg reaches trust region boundary\n",
      "iter  2 act 8.879e+02 pre 7.290e+02 delta 2.937e+00 f 4.919e+03 |g| 1.706e+03 CG   9\n",
      "cg reaches trust region boundary\n",
      "iter  3 act 3.079e+02 pre 2.619e+02 delta 3.621e+00 f 4.031e+03 |g| 7.669e+02 CG  11\n",
      "cg reaches trust region boundary\n",
      "iter  4 act 1.063e+02 pre 9.789e+01 delta 3.960e+00 f 3.723e+03 |g| 2.180e+02 CG  15\n",
      "iter  5 act 2.148e+01 pre 2.088e+01 delta 3.960e+00 f 3.617e+03 |g| 5.875e+01 CG  40\n",
      "> predict -b 1 '/workspace/chipper_data/chipper-0.4.0-data/testing_data.ll' '../models/lr.model' '/workspace/chipper_data/chipper-0.4.0-data/LR_predictions.txt'\n",
      "Accuracy = 77.6442% (323/416)\n",
      "\n",
      "\n",
      "> train -c 0.062500 -s 2 '/workspace/chipper_data/chipper-0.4.0-data/training_data.ll' '../models/svc.model'\n",
      "iter  1 act 1.875e+03 pre 1.813e+03 delta 1.012e+00 f 3.271e+03 |g| 5.401e+03 CG   5\n",
      "cg reaches trust region boundary\n",
      "iter  2 act 1.753e+02 pre 1.530e+02 delta 1.208e+00 f 1.396e+03 |g| 1.341e+03 CG   9\n",
      "cg reaches trust region boundary\n",
      "iter  3 act 6.347e+01 pre 5.636e+01 delta 1.396e+00 f 1.221e+03 |g| 2.575e+02 CG  11\n",
      "cg reaches trust region boundary\n",
      "iter  4 act 2.194e+01 pre 2.084e+01 delta 1.478e+00 f 1.157e+03 |g| 1.104e+02 CG  29\n",
      "iter  5 act 1.766e+00 pre 1.759e+00 delta 1.478e+00 f 1.135e+03 |g| 6.355e+01 CG  28\n",
      "> predict -b 0 '/workspace/chipper_data/chipper-0.4.0-data/testing_data.ll' '../models/svc.model' '/workspace/chipper_data/chipper-0.4.0-data/SVC_predictions.txt'\n",
      "Accuracy = 78.6058% (327/416)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "create_model_and_predictions(LR_findC, 0, LR_model_file, LR_predictions_file, 1)\n",
    "create_model_and_predictions(SVC_findC, 2, SVC_model_file, SVC_predictions_file, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** Logistic Regression Report, cutoff= 0.36 (MCC=0.620) **\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.75      0.79       208\n",
      "          1       0.77      0.87      0.82       208\n",
      "\n",
      "avg / total       0.81      0.81      0.81       416\n",
      "\n",
      "** Support Vector Classification Report (MCC=0.572) ***\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.79      0.78      0.78       208\n",
      "          1       0.78      0.79      0.79       208\n",
      "\n",
      "avg / total       0.79      0.79      0.79       416\n",
      "\n",
      "LR: sensitivity(recall)=87.0, specificity=74.5, precision=77.4\n",
      "SVC: sensitivity(recall)=79.3, specificity=77.9, precision=78.2\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, matthews_corrcoef, confusion_matrix\n",
    "\n",
    "def fetch_LR_probabilities(filepath):\n",
    "    predictions = []\n",
    "    with open(filepath, \"r\") as input:\n",
    "        for line in input.readlines()[1:]:\n",
    "            predictions.append(float(line.split(\" \")[1]))\n",
    "    return predictions\n",
    "\n",
    "def fetch_SVC_predictions(filepath):\n",
    "    predictions = []\n",
    "    with open(filepath, \"r\") as input:\n",
    "        for line in input.readlines():\n",
    "            predictions.append(int(line))\n",
    "    return predictions\n",
    "\n",
    "SVC_predictions = fetch_SVC_predictions(SVC_predictions_file)\n",
    "LR_probabilities = fetch_LR_probabilities(LR_predictions_file)\n",
    "\n",
    "LR_classification_vector = lambda cutoff: [1 if pred >= cutoff else 0 for pred in LR_probabilities]\n",
    "\n",
    "def find_best_mcc():\n",
    "    best_mcc = 0.0\n",
    "    best_cutoff = 0.0\n",
    "    for i in range(1, 100):\n",
    "        pred_cutoff = i/100.0\n",
    "        mcc = matthews_corrcoef(testing_y, LR_classification_vector(pred_cutoff))\n",
    "        if (mcc > best_mcc):\n",
    "            best_mcc = mcc\n",
    "            best_cutoff = pred_cutoff\n",
    "    return (best_cutoff, best_mcc)\n",
    "\n",
    "(best_cutoff, best_mcc) = find_best_mcc()\n",
    "print \"** Logistic Regression Report, cutoff= %.2f (MCC=%.3f) **\" % (best_cutoff, best_mcc)\n",
    "print classification_report(testing_y, LR_classification_vector(best_cutoff))\n",
    "\n",
    "print \"** Support Vector Classification Report (MCC=%.3f) ***\" % (matthews_corrcoef(testing_y, SVC_predictions))\n",
    "print classification_report(testing_y, SVC_predictions)\n",
    "\n",
    "def print_metrics(name, actual, predicted):\n",
    "    ((tn, fp), (fn, tp)) = confusion_matrix(actual, predicted)\n",
    "    sensitivity = 100.0 * tp / (tp + fn)\n",
    "    specificity = 100.0 * tn / (tn + fp)\n",
    "    precision = 100.0 * tp / (tp + fp)\n",
    "    print \"%s: sensitivity(recall)=%.1f, specificity=%.1f, precision=%.1f\" % (name, sensitivity, specificity, precision)\n",
    "\n",
    "print_metrics(\"LR\", testing_y, LR_classification_vector(best_cutoff))\n",
    "print_metrics(\"SVC\", testing_y, SVC_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x145a2ff50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, metrics\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(testing_y, LR_probabilities, pos_label=1)\n",
    "roc_auc = metrics.roc_auc_score(testing_y, LR_probabilities, average='macro', sample_weight=None)\n",
    "plt.title('Logistic Regression ROC Curve')\n",
    "plt.plot(fpr, tpr, 'b', label='AUC = %0.2f'% (roc_auc))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.2])\n",
    "plt.ylim([-0.1,1.2])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def read_LR_model_weights():\n",
    "    weights=[]\n",
    "    with open(LR_model_file, \"r\") as model:\n",
    "        for line in model.readlines()[6:]:\n",
    "            weights.append(float(line))\n",
    "    return weights\n",
    "\n",
    "num_hydrophobic_metrics = 18\n",
    "num_steric_metrics = 17\n",
    "num_electronic_metrics = 15\n",
    "num_aa_properties = num_hydrophobic_metrics + num_steric_metrics + num_electronic_metrics\n",
    "\n",
    "w = read_LR_model_weights()\n",
    "\n",
    "h_mean = []\n",
    "h_std = []\n",
    "s_mean = []\n",
    "s_std = []\n",
    "e_mean = []\n",
    "e_std = []\n",
    "\n",
    "for chunk in [w[i:i+50] for i in range(0, generated_sample_len * num_aa_properties, num_aa_properties)]:\n",
    "    start = 0\n",
    "    end = num_hydrophobic_metrics\n",
    "    h = chunk[start:end]\n",
    "    start = end\n",
    "    end = start + num_steric_metrics\n",
    "    s = chunk[start:end]\n",
    "    start = end\n",
    "    e = chunk[start:]\n",
    "    h_mean.append(np.mean(h))\n",
    "    h_std.append(np.std(h))\n",
    "    s_mean.append(np.mean(s))\n",
    "    s_std.append(np.std(s))\n",
    "    e_mean.append(np.mean(e))\n",
    "    e_std.append(np.std(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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TxzHm/bNd/PNN588Z84cc1mbqXKt9lCZ6vtckCTJB02fyZMnt/f09Cxevnz5WPPQin5r75JisfhBc3lu/zyoXgprLi98h4jM/8yZYU2l1DtxHE/o6OgwBmPlvXwD7/kLw7CmASOiWXEcm+q3MT/GBJnL8Mx9ZSeZ+x77Yuhby6Zy1D+uKIoeMn7S5Ib5vVKqnYjM2t0yCIJiPU5RFC03FbCenp7rjEnr6uq61vQPw9Bc7reNTQWsfxxhGD6tlPp2nwEzDxsx+1Qcx8YYmnVmvrz4uLks1jyMJkmSfdMqYIVC4SBzyeIAA/ZWqVTatlAomMuGV2PXxzJvnw+IFwSGMwEYsOGsPuYOAoNMwMaAdXZ2ftp8u09EM/u+ZQ/D8OdENC9Jkm9PnDhxk2KxaC5BPGagATOH8XK5/Bel1CfNN9LmQFooFB4vlUrbm0MsEV1YKBS2/stf/rIgiqILicjcx/OlegbMVJaUUuYStR26urrmRVFkqly3BkGwqbmvS2t9UG9v7w5z585dHIbhzUqpF+I4/m7foaqtrW15qVR6oLe39wNVI3mM1nq3JEnMfWU1fzo7O3crl8s3E9F/J0liKlsVI2Gektfb27tFsVhcQymVxHE8pnr5YFy9z+2mzTfffONCoTCzVCrtbQ6h9QxYGIbmHiFzCeKhHR0d7UEQ/I6IvhfHcaVSOPBnwoQJa7e3t7/e29v7nrlz575VraQ8rJTaPQiCub29vS8kSbKm6TfAgJn3GR/H8ee32mqrtZcvX/40Ed1Qx4DVfTrfgEsQHyei+03FpBrXH4noTHMfWrFYfMFUWAy3KIoM4xlxHJtLzbqrl2may+5WqQ6FYfh7pdRtcRxfOnHixLXa2tp+p7W+uFgs/q7evAZcgvhHpdRV5vBu+AdB8KdSqTT5pZdeMvdgVX763/NUi2+KAfupUmpJHMfTzSWXQRA8qLX+dVtb20U9PT1J9RLCh80liIVC4b6NNtpo/IIFC8wTJc2lnpWK7oA4+l8ieJS53yxJko9V86xv7T1T62mZ5l4kpdQJcRx39I1ZZTZPa/01rfV8XwYsCIKf1FvLffeImhiq9+C9EARBaNZoX1xRFJmc7ioWi0fV4rTmmmtuvGjRomXmy5ve3l5zyeAB5pLanp4e1d7ebi4bNmss9RLE/nyjKDpLax2NGzfuoEcffdTco3e1MjedBsEJ5XL52Z6enu2rlyCaNX6tqU4P+HKgp7e3d7xp0+9LHHOJ9g+VUjvFcfyvjo6OQ4MgMPdYbtZnVgeaVxiwQf4gw/AgMAgEYMAGASqGBAEQWEHA8imIQRRF5pv3A/sukaveP2S+GTeX071SvfTnJ1UDtsqTFavm4Dyt9ejqvWbmEH5X9XH3xgD9WyllHuQwe8SIEUdU77cwl+IZA1B5DL150EXfv83lTebAo7UumIcKKKWONU+nqx7Cp5iHdpj7nIjo8WKx+I3Zs2cvD8Nwbrlc/oy5gb6jo+NzQRCYKpm5p2lBb2/voVUzZi5FMo9GX+3x1dX7Sr5NRGtVb7xfEATBt42prN4HZO5RWbe9vX3nnp6e8eVy+fvmXjqllPm2/9IkSa4eeKjv/+/qpXfmckDzEAfzKO2fm8vNqt/Ym4P7NgNzNgxD8+h4Uwn8l/mfebCH1vqmYrH4YL1KkbmfbvTo0Vdorc37GENi+v65zj1g5n6h/k/T63sc/f8z9/v1PfK/+nAM8xAOU/lpU0rdbExv1ejsrpQ6xzyMxDzYwTzK3VSOqvfUmUvmzINVzBMlV2ptDH1bW9sPtdbmUj7zUI+fGnM34F67VYxlfwNm7kkMguCHRLShufzNPHSi1uPfgyCoO79yuWweWLGyctQ/f8yXCqVSyXwhYS5nKyqlHorj2FwiV6re92UulV3D3GtUKpW+OWfOnCfNvYbGKFUvhzP3Jq40guaBHkT0kimWEpF5eMRhc+bM+Xu1yWprb4DBMI9tv6q7u/uK/r83uaGU2qP6sBPDtvI0y4EVsCiKXjOPfR94CeKAKo6pQpkvMi6ut5YHvLepnI3rq2z3M2DGVN5dLBY3XrZs2QQz74Gc+u1Hb4ZheIFSyjyJ1Twop9usO1cDZircI0aMuICIPlrdL8ylokeZx9xX77cz+4+55LNHKXWyqUD31zoMw1+YJ0uWSqV9giC4u28PMQ/w0Fqbh3iYNfG6Uupr5gEeURStZFXlvcq/8bkDAiCQHwJiDJh5apH5RjZJksqNuf0+QMzlOeZ6crOBXTfwUoT8oEakIAACtQh0dnbuZG46b/ZvFdUwECvvNZNAPooiYwbM37la+ZQ6IXHd7fI32STEjBj8EBistecnOowCAiAAAq1LQIQBC8PQfHN3iFJqcRzHO/XhNk8zWrBgwV/K5fK2a6211jsLFy78g3lca3d39+utKwlmBgLDh4C5Ad9cQmb+BlIcx+ZSM28/1QqY098b8/bmNQYy8SRJ8tO+e4cG871sx64+4t087MRcwoafYURgMNfeMMKIqYIACIAAi4AUA2ae+GWennVjfwNWvQ/ivCRJPmlmZx6ba/4AYnd3922s2aITCIAACIAACIAACIAACIAACAwhAREGzMy/ep3/z/obsCiKzE30R8ZxbJ4KZAzYmUEQ/K3vyUVDyA1vDQIgAAIgAAIgAAIgAAIgAALOBEQbMFMB01qbJ3WZR01XKmDmxve+G+edZ4sOIAACIAACIAACIAACIAACIDCEBKQZsJ+bv6nTx6P6F+1nL1++fIcxY8aYvyr/RKFQmGoeKd2IWU9Pry4WzUOx8AMCIAACIAACIAACIAACIAACg0vA/BkK23ewbmg7ILdd/0sQwzA8SCll/uDpNWEYmurXGeZxrFrrHw98HG6t93vttYXmj1au9rP++mvS668v4oY4qP0QGx8v2IEdnwC/J/IO7PgEeD0l55yZkeT4JMcGdrz10NdLsraIja+tZHb1ZrXBBmOtfZV1Qz7C7HvCgPllLn0RSI5Pcmz40G9unUjWVnJsyDt+3kFXsOMT4PdE3rUmO+jK1xUGrA4BGDC/SYVFyucJdmDHJ8DvibxrTXbQtTV1xZcSfF3Bjs8O+wmfHQwYDJj/7KkxIhYpHzPYgR2fAL8n8q412UHX1tQVJoKvK9jx2WE/4bODAYMB8589MGBemWKD4+MEO7DjE+D3lJx3kmPDQZifc2AHds0R4PXGfsLj1qgX7gHDQzi8ZhUWKR8n2IEdnwC/J/KuNdlB19bUFQaMryvY8dlhP+GzQwUMFTD/2YMKmFem2OD4OMEO7PgE+D0l553k2HAQ5ucc2IFdcwR4vc1+sskm5UrnmTOX8AYZxF7S97taU0cFDBUwr0tC+iKQHJ/k2PCh39wykayt5NiQd/y8g65gxyfA74m8a012MGB8XVEBQwXMf/agAuaVKT64+DjBDuz4BPg9Jeed5NhgrPk5B3Zg1xwBXu/BMGDPPjuT7rzzNjrzzHNWBnXFFf9Lm246gfbcc6+Gge699x50110PrGzjut/9858L6IwzTqYrr7zOCsjA9zOd7r//Hho7di3aeeePWI0xsBEqYKiAsRKnXifXReD1zS0Gkxyf5NjwoW+RXA2aSNZWcmzIO37eQVew4xPg90TetSa7wTJgd911O82YcTbDgH2C7rrr100ZsBkzTqErrrjWSrC99171/aw6pTSCAYMB85FH7EXg9c0tBpP84SA5NhyELZILBqw5SHV6S14XiI0vOdiBHZ8AvyfybnV2M2aMoLvvLq72wvz5qt/vFJVKuvLvQmHVpuPGrfh9/5+pU3tpxoxlDYWqVwF74IH76AtfmEb77vsZWrRoER1zzNfommtuoO9977v0t7/No3Hj3kd//OMTdN99D9M555xJb731Jr3zzhI655yL6frrr6Hnn/8zKaVo9933oM985sBKG601vfbaq/TOO+/QqaeeSe3t7XT00V+lzTbroH/96/XK/5944ilkKmPnnvsdKpVKlTGOOeYE2myzzWnPPT9GO+zwIXr11X/S5puHdNxxJ9K1115F6633Htp77/3okkvOpxdfnE2lUi9NmzadPvzhXVKTFAYMBiw1SVwaSN7cYCJclFy9rWRtJceGvEPeNUeA1xtrgsdN+nqVHh/yLn95N5QG7PTTT6L3v39iBZoxSQsWzKdp044gUxkzlwfefvsvqbe3hzbYYEP6/e8fpdNO+27FBB100P7029/+oWKujCH6+tdNn/vpvvt+RWeddT719vbS179+eMVU/fznN1VM25e+dBg9+eQf6K67bqsYq8MP/yL9/Oe30+jRY+jAA/elK6+8ni688NzK5Y/mssLu7oTOO++sivn72Md2rrQ1cZxxxkm0++57UpJ0VQzYOuusS7/97UOVSt7ixYvpF7+4ib785empiQADBgOWmiQuDbD5utBatS3YgR2fAL8n8q412UHX1tQVBoyvK9jx2WV1CeKVV/6ANtlkU3rwwfvp6KOPp/PO+y6dd94lFUM2atSoSkXL/Bx44H4VQ2QM2Mc+tjtNnboHXXbZD6lYLNJnP3tQpc1ll11EW265NT311BM0deo+tNVWUyoGafr0L9FFF12+yj1gX/nKtIqBOuGEo+lHP7qW1lhjjcoYffd+HXTQfvSzn91e+d0tt/ysUiFbuvQdWnfd9WjRooU0cuTIlbHZUoYBgwGzzRWrdvjQt8JUsxHYgR2fAL8n8q412UHX1tQVJoKvK9jx2WVlwPoewjFixEh67LFHSOsynXnmuZXq18MPr6gymUsGTcXqN795vGLAPv7xPehTn9q9WgG7m84667xKBeyrX51G3/rWKfTLX/6cwjCqGKQnnni8UiU78shvrmLApk8/tPI+P/jBpbTHHp+sXELY3R1Xxr/uupvp4x//MN1yy10Vw3XqqSdWDN3s2bMqFTDzv0ceeahSnTMGz1T1Lr748lTYMGAwYKlJ4tIAH/outFZtC3ZgxyfA74m8a0120LU1dYWJ4OsKdnx2WRmwvgrY7rt/gvbZZ08644yzaPvtd6gEfvHF51Ecd9GGG76XXnzxBbr11rtXMWCvv76IfvCDy2jWrOcqBsxUxj73uUMqbf7zn39TT08PlctlOuWUGZXx+j+EY0UFzDyNUVcuOzRtzf1c3/zmtygMO2n//feiyZO3qtxHtvXWU+hrXzt6lXvALr30AkqSuDK+uYTygx/cMRU2DBgMWGqSuDTAh74LLRgwPi2wAztfBPjjSN7vJMeGgzA/58AO7JojwOs9GAasUSRLly6lo46aTldf/ROrgBvtd31VMhtTZPVmnhrBgMGAeUqlFcPgQ5+PE+zAjk+A3xN515rsoGtr6ir9cxZ515p5l6WuL7zwPF1wwTmVpwnuuutHrYDCgFlhktXoNRgwr4JkuUg5gUuOT3Js+NDnZNu7fSRrKzk25B0/76Ar2PEJ8Hsi71qTHXTl61qvJypgMGBeswqLlI8T7MCOT4DfE3nXmuyga2vqii8l+LqCHZ8d9hM+OxiwOgRQAfObVFikfJ5gB3Z8AvyeyLvWZAddW1NXmAi+rmDHZ4f9hM8OBgwGzH/21BgRi5SPGezAjk+A3xN515rsoGtr6goTwdcV7PjszH5S2mTTygD/mfkCf6BB6il9v6s1bVyCiEsQvS4H6YtAcnySY8MHV3PLRLK2kmND3vHzDrqCHZ8AvyfyrjXZwYDxdUUFDBUw/9mDCphXpvjg4uMEO7DjE+D3lJx3kmODsebnHNiBXXMEeL0Hy4D99KfX0zPP/F/l73YVCoXK39dqayvSokWLaMqUbayCPfXUb9GVV/6IzN8By9MPKmCogHnNV3zo83GCHdjxCfB7Iu9akx10bU1dYcD4uoIdn91gGLB58/5K5533XfrRj66tBDZnTjedddYZtMsuu9G6665H++yzv3XA0ve7WhOBAYMBs05wm4bSF4Hk+CTHhg8um+yv30aytpJjQ97x8w66gh2fAL8n8i5/7MbMOJVG3H3naoEH8/+x8neKiHSptOLfhcIqbcvj3rda32VT93qpr8wAACAASURBVKElM85qCONf/3qdjjjiSzRt2hG044470Xvesz7985//pKOOOoLa2tro9NO/S+YPMl911Q8r1bH3vW88HX/8SfTQQ7+me+/9FWmtK32/851T6YknnqBHH32SLr/84srv119/fTr99LOovb2dL8gg94QBgwHzmmLYfPk4wQ7s+AT4PZF3rckOuramrvhSgq8r2NVmN1QGzETT3R3Trbf+onIZ4qhRo+jww79Kc+e+VKmA7b33fnTQQftVKmRrr702XXPNFbThhu+lYrFIjz32KJ177oWVCe299yfoiSf+QHvtNZXOPPNc2mSTTSsGLQwj6uiImkuYQewNAwYD5jW98KHPxwl2YMcnwO+JvGtNdtC1NXWFieDrCnZ8doNxCeI//vFKJSBT2TI/cdxFxx13FO2++ydowoT30y67fJQOOGAf6uycVKlqLV++nLbffodKe3P54le/etQqBmynnXaiu+56gD/JjHvCgMGAeU05fOjzcYId2PEJ8Hsi71qTHXRtTV1hIvi6gh2f3WAYsN/97hH61a/uoPPOu7hS1VqyZDEddtgXaI89PlmpeO299/50yCEH0FVXXUejR4+hxx9/jEaPHk2vvvpPevnlv9H06V9fxYB9+tP70He+c27FoN10008qlbCPfGQ3/qQHuScMGAyY1xTDhz4fJ9iBHZ8AvyfyrjXZQdfW1BUmgq8r2PHZDYYBM9HceON19PDDD1WMldZlOvjgL1bu9/rhD79Pxx57YuXpiNddd3XltTFj1qBTT/0OPfnk4zUN2O9//xRdfvklFAQBrbfee+jUU8+sGDupPzBgMGBecxMf+nycYAd2fAL8nsi71mQHXVtTV5gIvq5gx2c3WAaMH9GqPaXvd7XmCQMGA+Yr/yvjSF8EkuOTHJt0bcGOv4zBrjXZQdfW1BV7MV9XsOOzw37CZ1evJwwYDJjXrMIi5eMEO7DjE+D3RN61Jjvo2pq6wkTwdQU7PjvsJ3x2MGB1CLwGA+Y1q7BI+TjBDuz4BPg9kXetyQ66tqauMBF8XcGOzw77CZ8dDBgMmP/sqTEiFikfM9iBHZ8AvyfyrjXZQdfW1BUmgq8r2PHZYT/hs4MBgwHznz0wYF6ZYoPj4wQ7sOMT4PeUnHeSY8NBmJ9zYAd2zRHg9Tb7ySYXb1rpPPOQF3iDDGIv6ftdranjHjBcguh1SUhfBJLjkxwbPvSbWyaStZUcG/KOn3fQFez4BPg9kXetyQ4GjK8rKmCogPnPHlTAvDLFBxcfJ9iBHZ8Av6fkvJMcG4w1P+fADuyaI8DrPRgG7NlnZ9Lpp59E73//RNJak1KK1lprbXrzzTfohBNOrvwhZduf5557isaP36zy97+4P/fffw+NHbsW7bzzR7hDOPVDBQwVMKeESWuMD/00QvVfBzuw4xPg98xb3m277ZjKZGfOXMKftKeektlJjg0morkElKyt5NiQd/y8GywDdtddt9OMGWevEthRR013NmDHHvs1OuaYE51MG5+Gn54wYDBgfjKpOgo2Xz5OsAM7PgF+z7zlHQyYndZ509VuVtm0Ajs+Z7DLH7sZT5xKd79052qBz1/8j1V+V9Klyr8LqrDK78et8b7V+k7dbB+asdNZDWGYCtidd95GZ555Tk0Dtt5669G5536XFi1aWHn96KOPp4kTN6N77rmT7rzzdiqXy/ThD+9CkyZtQd/97uk0fvwmdNpp36FTTjmB1l57Hdpxx51p++0/SJdccgEVCgVqbx9BJ554SqXfjBmn0IYbbkivvPIKbbHFlnTccSfStddeVamg7b33fnTJJefTiy/OplKpl6ZNm155H98/MGAwYF5zCpsvHyfYgR2fAL9n3vIOBsxO67zpajerbFqBHZ8z2OWP3VAasIGXIBrT9Mc//oGOP/4kuvfeX9FGG42jffbZn1555e90zjln0tlnX0Bf/eqX6cYbf0FtbW105ZU/oC9+8ct0yinHVSpg5neHH/5FuvPO+yum67DDvkAnnXQ6bbbZ5vT447+jX//6PjryyGPosMMOoVtvvYfa29vpgAP2oauuup7uuOPWigFbZ5116be/fahSmVu8eDH94hc30Ze/PJ0vbJ2eMGAwYF6TCpsvHyfYgR2fAL9n3vIOBsxO67zpajerbFqBHZ8z2LUmuywvQfzGN75SMWCXX34xvfnmmzRq1KjKPWLm3rBvf/t0uvnmn1SMWP+fvksQjaEyps4YKvOz776fpDvuuK/y34sWLaqYtwsvvIzOOONkuvLK6yq//8pXptGMGefQfff9qmLATMVt5MiR9JnPHMgX06InDBgMmEWa2DfB5mvPamBLsAM7PgF+z7zlHQyYndZ509VuVtm0Ajs+Z7BrTXaDZcAaXYL4q1/dQZ2dk+jjH9+D3njjDbrnnrto6tR96Mgjj6Drr7+ZisUinXrqiXTMMcfT9753Jh111PE0YsSIVczV4Yd/oWLaTAXsscceJfOgjaOPPm6VNtOnH0pnnnnuSgNmTNgjjzxEp5323UoFzBi6iy++nC9snZ4wYDBgXpMKmy8fJ9iBHZ8Av2fe8g4GzE7rvOlqN6tsWoEdnzPYtSa7wTJgphI1YcL7V0Izla7ly5fTKafMoLXXXrtyD9jixYvo7bffpmnTjqg8odCYKGPcgkDRzjvvQp///Jfoppt+TI8++rvKwzsuvvh8uuKKaytjdncndNllF1YqaMawffvbp1WetmjuAetrM7ACZu4Bu/TSCyhJ4sr9YuZ9P/jBHfnCwoDVJvAaDJjXpMLmy8cJdmDHJ8Dvmbe8gwGz0zpvutrNKptWYMfnDHatyW4wDBif1Oo9peddrbmiAgYD5nMNkPRFIDk+ybGZJJEcn+TYwK65LWagtjBgdjyxJuw41WoFdmDHJ8DvKTnvJMcm/TO2XkbAgMGA8XeLGj2xSPk4wQ7s+AT4PfOWdzBgdlrnTVe7WWXTCuz4nMGuNdlBV76uMGB1COASRL9JhUXK5wl2YMcnwO+Zt7yDAbPTOm+62s0qm1Zgx+cMdq3JDrrydYUBgwHznz2ogHllig2OjxPshg87GDA7rbEm7DjVagV2YMcnwO8pOe8kx2aIS4+vVlbgEkRcgsjfLWDAwM4rAf5g0jdfyfFJjq3WBysMmN06yZuudrPKphXY8TmDXWuyg658XVEBQwXMf/bAgHllig2OjxPshg87GDA7rbEm7DihAsbnBHbDhx32E79am9FQAUMFzGtWYZHycYId2PEJ8HvmLe9gwOy0zpuudrPKphXY8TmDXWuyg658XVEBQwXMf/agAuaVKTY4Pk6wGz7sYMDstMaasOOEKg6fE9gNH3bYT/xqjQoYEeEpiH6TCouUzxPswI5PgN8zb3kHA2andd50tZtVNq3Ajs8Z7FqTHXTl64oKGCpg/rMHFTCvTLHB8XGC3fBhBwNmpzXWhB0nVHH4nMBu+LDDfuJXa1TAUAHznlFYpHykYAd2fAL8nnnLOxgwO63zpqvdrLJpBXZ8zmDXmuygK19XVMBQAfOfPaiAeWWKDY6PE+yGDzsYMDutsSbsOKGKw+cEdsOHHfYTv1qjAoYKmPeMwiLlIwU7sOMT4PfMW97BgNlpnTdd7WaVTSuw43MGu9ZkB135uqIChgqY/+xBBcwrU2xwfJxgN3zYwYDZaY01YccJVRw+J7AbPuywn/jVGhUwVMC8ZxQWKR8p2IEdnwC/Z97yDgbMTuu86Wo3q2xagR2fM9i1Jjvoyte1VSpgKoqiHxLRFK31UiI6LEmSuX2T6+zsPFhrfSwR9ZbL5eu6u7uvSEOGx9CnEXJ7HYvUjVf/1mAHdnwC/J55yzsYMDut86ar3ayyaQV2fM5g15rsoCtf15YwYGEY7quUmhrH8bQwDHcgopOSJNmnb3JhGM7v7e2dtO666769aNGiF3t6erabO3fuW42wwYD5TSosUj5PsAM7PgF+z7zlHQyYndZ509VuVtm0Ajs+Z7BrTXbQla9rqxiwi5RST8VxfIuZUBRFr8RxPL6fAbs/CIKvlkql/wRBMLNcLm/b3d29EAbMf+LUGxGLlM8a7MCOT4DfM295BwNmp3XedLWbVTatwI7PGexakx105evaKgbs6iAIbu3q6nrATCgMw3lJkkwkonLVkF2otT5UKbVYa317kiTfTEOGClgaIbfXsUjdePVvDXZgxyfA75m3vIMBs9M6b7razSqbVmDH5wx2rckOuvJ1bRUDdlEQBE92dXXdWjVcL8dxvIn5787Ozq3K5fItbW1t28+ePXtJFEU3lcvl27q7u29rhA0GzG9SYZHyeYId2PEJ8HvmLe9gwOy0zpuudrPKphXY8TmDXWuyg658XVvFgO2nlNrL3APW0dGxYxAEp8Vx/CkzuYkTJ27S1tZ2x5prrrnjM8880xOG4aVKqRfiOL6mEbaenl5dLBb8k8WIIAACIAAC3glMmLBiyHnzvA+NAUEABEAABEAgEwJKKWX7RtYNbQdktKs8BVFrvbXpWygUDi2VStsqpcYYoxWG4XSl1DQiWkZEL2200UaHP/roo72N3gcVMIYKDbrgWxI+T7ADOz4Bfs+85R0qYHZa501Xu1ll0wrs+JzBrjXZQVe+rvV6brDBWGtfZd3Qf5iDNyIMmF+2WKR8nmAHdnwC/J55yzsYMDut86ar3ayyaQV2fM5g15rsoCtfVxiwOgRgwPwmFRYpnyfYgR2fAL9n3vIOBsxO67zpajerbFqBHZ8z2LUmO+jK1xUGDAbMf/bUGBGLlI8Z7MCOT4DfM295BwNmp3XedLWbVTatwI7PGexakx105esKAwYD5j97YMC8MsUGx8cJdsOHHQyYndZYE3acarUCO7DjE+D3lJx3kmMzxKXHVysrcA/Yawt13jZgyYkmOTbpixTsWvODC3nH17UWOxgwO57YT+w45e3zH/sJX1ew47PDfsJnhwoYKmD+swcVMK9MscHxcYLd8GEHA2anNdaEHScYMD4nsBs+7LCf+NXajIYKGCpgXrMKi5SPE+zAjk+A3zNveQcDZqd13nS1m1U2rcCOzxnsWpMddOXrigoYKmD+swcVMK9MscHxcYLd8GEHA2anNdaEHSdUcficwG74sMN+4ldrVMCICI+h95tUWKR8nmAHdnwC/J55yzsYMDut86ar3ayyaQV2fM5g15rsoCtfV1TAUAHznz2ogHllig2OjxPshg87GDA7rbEm7DihisPnBHbDhx32E79aowKGCpj3jMIi5SMFO7DjE+D3zFvewYDZaZ03Xe1mlU0rsONzBrvWZAdd+bqiAoYKmP/sQQXMK1NscHycYDd82MGA2WmNNWHHCVUcPiewGz7ssJ/41RoVMFTAvGcUFikfKdiBHZ8Av2fe8g4GzE7rvOlqN6tsWoEdnzPYtSY76MrXFRUwVMD8Zw8qYF6ZYoPj4wS74cMOBsxOa6wJO06o4vA5gd3wYYf9xK/WqIChAuY9o7BI+UjBDuz4BPg985Z3MGB2WudNV7tZZdMK7Picwa412UFXvq6ogKEC5j97UAHzyhQbHB8n2A0fdjBgdlpjTdhxQhWHzwnshg877Cd+tUYFDBUw7xmFRcpHCnZgxyfA75m3vIMBs9M6b7razSqbVmDH5wx2rckOuvJ1RQUMFTD/2YMKmFem2OD4OMFu+LCDAbPTGmvCjhOqOHxOYDd82GE/8as1KmCogHnPKCxSPlKwAzs+AX7PvOUdDJid1nnT1W5W2bQCOz5nsGtNdtCVrysqYKiA+c8eVMC8MsUGx8cJdsOHHQyYndZYE3acUMXhcwK74cMO+4lfrVEBQwXMe0ZhkfKRgh3Y8Qnwe+Yt72DA7LTOm652s8qmFdjxOYNda7KDrnxdUQFDBcx/9qAC5pUpNjg+TrAbPuxgwOy0xpqw44QqDp8T2A0fdthP/GqNChgqYN4zCouUjxTswI5PgN8zb3kHA2andd50tZtVNq3Ajs8Z7FqTHXTl64oKGCpg/rMHFTCvTLHB8XGC3fBhBwNmpzXWhB0nVHH4nMBu+LDDfuJXa1TAUAHznlFYpHykYAd2fAL8nnnLOxgwO63zpqvdrLJpBXZ8zmDXmuygK19XVMBQAfOfPaiAeWWKDY6PE+yGDzsYMDutsSbsOKGKw+cEdsOHHfYTv1qjAoYKmPeMwiLlIwU7sOMT4PfMW97BgNlpnTdd7WaVTSuw43MGu9ZkB135uqIChgqY/+xBBcwrU2xwfJxgN3zYwYDZaY01YccJVRw+J7AbPuywn/jVGhUwVMC8ZxQWKR8p2IEdnwC/Z97yDgbMTuu86Wo3q2xagR2fM9i1JjvoytcVFTBUwPxnDypgXplig+PjBLvhww4GzE5rrAk7Tqji8DmB3fBhh/3Er9aogKEC5j2jsEj5SMEO7PgE+D3zlncwYHZa501Xu1ll0wrs+JzBrjXZQVe+rqiAoQLmP3tQAfPKFBscHyfYDR92MGB2WmNN2HFCFYfPCeyGDzvsJ361RgUMFTDvGYVFykcKdmDHJ8Dvmbe8gwGz0zpvutrNKptWYMfnDHatyQ668nVFBQwVMP/ZgwqYV6bY4Pg4wW74sIMBs9Maa8KOE6o4fE5gN3zYYT/xqzUqYKiAec8oLFI+UrADOz4Bfs+85R0MmJ3WedPVblbZtAI7Pmewa0120JWvKypgqID5zx5UwLwyxQbHxwl2w4cdDJid1lgTdpxQxeFzArvhww77iV+tUQFDBcx7RmGR8pGCHdjxCfB75i3vYMDstM6brnazyqYV2PE5g11rsoOufF1RAUMFzH/2oALmlSk2OD5OsBs+7GDA7LTGmrDjhCoOnxPYDR922E/8ao0KGCpg3jMKi5SPFOzAjk+A3zNveQcDZqd13nS1m1U2rcCOzxnsWpMddOXrigoYKmD+swcVMK9MscHxcYLd8GEHA2anNdaEHSdUcficwG74sMN+4ldrVMBQAfOeUVikfKRgB3Z8Avyeecs7GDA7rfOmq92ssmkFdnzOYNea7KArX1dUwFAB8589qIB5ZYoNjo8T7IYPOxgwO62xJuw4oYrD5wR2w4cd9hO/WqMChgqY94zCIuUjBTuw4xPg98xb3sGA2WmdN13tZpVNK7Djcwa71mQHXfm6ogKGCpj/7EEFzCtTbHCNcW5745aVBjMPeWG1hmDHT8W8sYMBs9M6b7razSqbVmDH5wx2rckOuvJ1hQGDAfOfPTBgXplig4MB85pQloPlLe9gwOyEzZuudrPKphXY8TmDXWuyg658XWHAYMD8Zw8MmFem2OBgwLwmlOVgecs7GDA7YfOmq92ssmkFdnzOYNea7KArX1cYMBgw/9kDA+aVKTY4GDCvCWU5WN7yDgbMTti86Wo3q2xagR2fM9i1JjvoytcVBgwGzH/2wIB5ZYoNDgbMa0JZDpa3vIMBsxM2b7razSqbVmDH5wx2rckOuvJ1hQGDAfOfPTBgXplig4MB85pQloPlLe9gwOyEzZuudrPKphXY8TmDXWuyg658XWHAYMD8Zw8MmFem2OBgwLwmlOVgecs7GDA7YfOmq92ssmkFdnzOYNea7KArX1cYMBgw/9kDA+aVKTY4GDCvCWU5WN7yDgbMTti86Wo3q2xagR2fM9i1JjvoytcVBgwGzH/2wIB5ZYoNDgbMa0JZDpa3vIMBsxM2b7razSqbVmDH5wx2rckOuvJ1hQGDAfOfPTBgXplig4MB85pQloPlLe9gwOyEzZuudrPKphXY8TmDXWuyg658XWHAYMD8Zw8MmFem2OBgwLwmlOVgecs7GDA7YfOmq92ssmkFdnzOYNea7KArX1cYMBgw/9kDA+aVKTY4GDCvCWU5WN7yDgbMTti86Wo3q2xagR2fM9i1JjvoytcVBgwGzH/2wIB5ZYoNDgbMa0JZDpa3vIMBsxM2b7razSqbVmDH5wx2rckOuvJ1hQGDAfOfPTBgXplig4MB85pQloPlLe9gwOyEzZuudrPKphXY8TmDXWuyg658XWHAYMD8Zw8MmFem2OBgwLwmlOVgecs7GDA7YfOmq92ssmkFdnzOYNea7KArX9dWMWAqiqIfEtEUrfVSIjosSZK5fZOLomh7IrrI/Ftr/c+2trbPz549e3kjbK+9tlDXel1ysiE2/kIAu/yy2/bGLSvBzzzkhdUmIVlXE6zk+CTHVosdDJjdGs6brnazyqYV2PE5g11rsoOufF1bwoCFYbivUmpqHMfTwjDcgYhOSpJkn34G7Fmt9f7GlHV2dk4LguD3L774YjcMmP/EqTciFimfNdg1ZgcDxs+tRj3zlncwYHZ5kDdd7WaVTSuw43MGu9ZkB135uraKAbtIKfVUHMe3mAlFUfRKHMfjzX9PmjQpLJVKpjrWpZTaUmt9T5IkF6YhQwUsjZDb61ikbrz6twY7GDB+9vB75i3vYMDstM6brnazyqYV2PE5g11rsoOufF1bxYBdHQTBrV1dXQ+YCYVhOC9JkolEVO7s7NxJa/1QEATbbLjhhnPnz59/TxAE3+vq6noUFTD/iYMKmH+m2OBgwPxnVfqIecs7GLB0TU2LvOlqN6tsWoEdnzPYtSY76MrXtVUM2EVBEDzZ1dV1q5lQFEUvx3G8ifnvzs7OqFwu35IkyZTqa8dorYtpVbCenl5dLBb8k8WIIAACXglMuHRCZbx5x8zzOi4GyxeBCSvSgOYhDfIlHKIFARAAARBYSUAppWxxWDe0HdC1XRiG+yml9jL3gHV0dOwYBMFpcRx/yoyz3XbbtS1atKhLa727uQcsiqLbiOiaOI7vb/Q+uATRVYXG7fEtCZ8n2DVmh3vA+LnVqGfe8g4VMLs8yJuudrPKphXY8TmDXWuyg658Xev13GCDsda+yrqh/zDfNYzmKYha663NbwqFwqGlUmlbpdSYOI6v6ezs3E1rfZ55TWv9RJIk30yLBQYsjZDb61ikbrz6twY7GDB+9vB75i3vYMDstM6brnazyqYV2PE5g11rsoOufF1bxYB5JwAD5hcpFimfJ9jBgPGzh98zb3kHA2andd50tZtVNq3Ajs8Z7FqTHXTl6woDVocADJjfpMIi5fMEOxgwfvbwe+Yt72DA7LTOm652s8qmFdjxOYNda7KDrnxdYcBgwPxnT40RsUj5mMEOBoyfPfyeEvLO5f4+GDA7rSXo2ihSyfFJjs0wlRyf5NjAzm7vqNUKuvLZwYDBgPnPHhgwr0yxwcGAeU0oy8Ek5B0MmKVYDs0k6AoD5iCYQ1PJ2kqODQbMIckGNIWufHYwYDBg/rMHBswrUwkbnMtB2OvkLQaTHFta+BK0rRejhNhctEUFLC3bVrwuQVcYMDutXFtJ1lZybNLXhWR2kmOTrisMGAyY6x7Pao9FysIm5sDkchDmz5TXU3JsaTOSvC4kxOaiLQxYWrbBgNkRqt9KwpqAeW1Wxdr9JWuL2PiaS2YHAwYDxs9sh57SF4Hk+CTE5nIQdkgLL00lx5Y2QQna1otRQmwu2sKApWUbDJgdIRiwZjnV6i9hP4F59a8sdPXPNG9/B8w7ATwF0S9SLFI+TwnsXA7C/JnyekqOLW1GErSFAUtTyf116OrOrK8H2IEdnwC/J/KOx04yNzMj6fHVog4D9tpCnbdvcSQnmuTYpC9SCewkmxzJsaV9pEnQFgYsTSX316GrOzMYMD4zsAO75gnwRpC810k/29UjDgMGA8ZbjXV6YZHycUpgJ9nkSI4tTXUJ2sKApank/jp0dWcGE8FnBnZg1zwB3giS9zoYMJ6mQ94LlyD6lQCLlM9TAjvJJkdybGmqS9AWBixNJffXoas7M5gIPjOwA7vmCfBGkLzXwYDxNB3yXjBgfiXAIuXzlMBOsslxjQ0Pa7DLxbzlHXTNj66NIpWQd5K/lAA7uzx3bYW8cyW2or1kbnmIrxZ1XIKISxB5q7FOLyxSPk4J7FxNDn+27j1dY8NB3Y5x3vIOuuZHV5gIO61cW0lYszCvrqqlt4eu6YzymncwYDUIoALGT/haPSVvINK/JZHAztXk+M2exqO5xoaDup06ecs76JofXWHA7LRybSVhzeb1IAx2rtm2or1kbnmIDwYMBoy38hx6YZE6wBrQVAI7V5PDn617T9fYcFC3Y5y3vIOu+dEVBsxOK9dWEtYsDJirauntoWs6o7zmHQwYDBg/uy17St5ApH9LIoGdq8mxTAsvzVxjw0HdDnve8g665kdXGDA7rVxbSVizeT0Ig51rtq1oL5lbHuKDAYMB4608h15YpA6wBjSVwM7V5PBn697TNTYc1O0Y5y3voGt+dIUBs9PKtZWENQsD5qpaenvoms4or3kHAwYDxs9uy56SNxDp35JIYOdqcizTwksz19hwULfDnre8g6750RUGzE4r11YS1mxeD8Jg55ptK9pL5paH+GDAYMB4K8+hFxapA6wBTSWwczU5/Nm693SNDQd1O8Z5yzvomh9dYcDstHJtJWHNwoC5qpbeHrqmM8pr3sGAwYDxs9uyp+QNRPq3JBLYuZocy7Tw0sw1NhzU7bDnLe+ga350hQGz08q1lYQ1m9eDMNi5ZtuK9pK55SE+GDAYMN7Kc+iFReoAa0BTCexcTQ5/tu49XWPDQd2Ocd7yDrrmR1cYMDutXFtJWLMwYK6qpbeHrumM8pp3MGAwYPzstuwpeQOR/i2JBHauJscyLbw0c40NB3U77HnLO+iaH11hwOy0cm0lYc3m9SAMdq7ZtqK9ZG55iA8GrAUM2LrbbkmFQNHrT8/iraJB7oVFygcsgZ2ryeHP1r2na2w4qNsxzlveQdf86AoDZqeVaysJaxYGzFW19PbQNZ1RXvMOBgwGjJ/dlj0lbyDSvyWRwM7V5FimhZdmrrHhoG6HXXLe1frCCbrmR1cYMDutXFtJWLN5PQiDnWu2rWgvmVse4oMBgwHjrTyHXlikDrAGNJXAztXk8Gfr3tM1NhzU7RhLzjsYMDsNa7WSoCsMGF8/sAO7wSHAeK6L/wAAIABJREFUGxX7CY9bo14bbDBW2Y5q3dB2QAntXnttoc7ThxcuQWwuayRvIhJiczU5zanh1ts1NhgwO76S8w4GzE7DPH2G9cUqIe/q0ZUcm/Rv+8GuNdcsdOXrWq8nDFiLGDAph808LlKwe3d7cDU5/rek+iO6xiZFVxyY0rOknrYwYOnsYCL4jMAO7PwT4I8o+fwkOTbpn7EwYHUItEoFTMphM4+LFOxgwPgfmXY9Ja8LCbHBgNnlkUsrCbo2ildyfJJjk37YBDuXVbpqW8nsJMcmfU3AgMGA8XcFh555XKQwYDBgDinOaip5XUiIDQaMlVYNO0nQFQbMv67SD5vIO77mktlJjk36moABgwHj7woOPfO4SGHAYMAcUpzVVPK6kBAbDBgrrWDA/GOrjChhTcC8Do64krVFbHzNJbODAYMB42e2Q0/pi6BWfDBgMGAOKc5qKnldSIgNBoyVVjBg/rHBgDXJVMJ+AvPapIg1ukNX/0zxEA48hMNrVuVxkcKAwYB5XQQ5+/CSsGZhwPxnoARdcRD2r6v0Ch3yjq+5ZHaSY5O+JlABQwWMvys49MzjIoUBgwFzSHFWU8nrQkJsMGCstEIFzD82VMCaZCphP4Hxb1LEnH2JCAPmX+9MRsRTEP1izuPmm6UBc32Uul910keTHJ9rbFnqmkZW8rqQEBsMWFoGub8uQVcchN11s+khWVvJsUk/qEtmJzk26bqiAoYKmM2+3nSbPC7SLA/qriaiaUEcB5Acn2tsWeqahlnyupAQGwxYWga5vy5BVxgwd91sekjWVnJs0g/qktlJjk26rjBgMGA2+3rTbfK4SLM8qLuaiKYFcRxAcnyusWWpaxpmyetCQmwwYGkZ5P66BF1hwNx1s+khWVvJsUk/qEtmJzk26brCgMGA2ezrTbfJ4yLN8qDuaiKaFsRxAMnxucaWpa5pmCWvCwmxwYClZZD76xJ0hQFz182mh2RtJccm/aAumZ3k2KTrCgMGA2azrzfdJo+LNMuDuquJaFoQxwEkx+caW5a6pmGWvC4kxAYDlpZB7q9L0BUGzF03mx6StZUcm/SDumR2kmOTrisMGAyYzb7edJs8LtIsD+quJqJpQRwHkByfa2xZ6pqGWfK6kBAbDFhaBrm/LkFXGDB33Wx6SNZWcmzSD+qS2UmOTbquMGAwYDb7etNt8rhIszyou5qIpgVxHEByfK6xZalrGmbJ60JCbDBgaRnk/roEXWHA3HWz6SFZW8mxST+oS2YnOTbpusKAwYDZ7OtNt8njIs3yoO5qIpoWxHEAyfG5xpalrmmYJa8LCbHBgKVlkPvrEnSFAXPXzaaHZG0lxyb9oC6ZneTYpOsKAwYDZrOvN90mj4s0y4O6q4loWhDHASTH5xpblrqmYZa8LiTEBgOWlkHur0vQFQbMXTebHpK1lRyb9IO6ZHaSY5OuKwwYDJjNvt50mzwu0iwP6q4momlBHAeQHJ9rbFnqmoZZ8rqQEBsMWFoGub8uQVcYMHfdbHpI1lZybNIP6pLZSY5Nuq4wYDBgNvt6023yuEizPKi7moimBXEcQHJ8rrFlqWsaZsnrQkJsMGBpGeT+ugRdYcDcdbPpIVlbybFJP6hLZic5Num6woDBgNns6023qbVIpR+Es4zP1UQ0LYjjAJLjc40tS13TMEv+8JIQGwxYWga5vy5BVxgwd91sekjWVnJs0g/qktlJjk26rjBgMGA2+3rTbWDAGiN0NRFNC+I4gGt8WZocybGlYZb84SUhNhiwtAxyf12CrjBg7rrZ9JCsreTYpB/UJbOTHJt0XWHAYMBs9vWm2qy77ZZUCBS9/vSsVcbJ8pCeNoGhNoiuJiJtPr5fd40vS20lx5amg+QPLwmxwYClZZD76xJ0hQFz182mh2RtJccm/aAumZ3k2KTrCgMGA2azrzfVBgYsHZ+riUgf0W8L1/hgwOz4S/7wkhAbDJhdHrm0kqArDJiLYvZtJWsrOTbpB3XJ7CTHJl1XGDAYMPvdndkSBiwdnKvBSR/RbwvX+GDA7PhL/vCSEBsMmF0eubSSoCsMmIti9m0lays5NukHdcnsJMcmXVcYMBgw+92d2RIGLB2cq8FJH9FvC9f4YMDs+Ev+8JIQGwyYXR65tJKgKwyYi2L2bSVrKzk26Qd1yewkxyZdVxgwGDD73Z3ZEgYsHZyrwUkf0W8L1/hgwOz4S/7wkhAbDJhdHrm0kqArDJiLYvZtJWsrOTbpB3XJ7CTHJl1XGDAYMPvdndkSBiwdnKvBSR/RbwvX+GDA7PhL/vCSEBsMmF0eubSSoCsMmIti9m0lays5NukHdcnsJMcmXVcYMBgw+92d2RIGLB2cq8FJH9FvC9f4YMDs+Ev+8JIQGwyYXR65tJKgKwyYi2L2bSVrKzk26Qd1yewkxyZdVxgwGDD73Z3ZEgYsHZyrwUkf0W8L1/hgwOz4S/7wkhAbDJhdHrm0kqArDJiLYvZtJWsrOTbpB3XJ7CTHJl1XGDAYMPvdndlSigFrJRPBlILdrZXYZWkO04BL/vCSEBsMWFoGub8uQVcYMHfdbHpI1lZybNIP6pLZSY5Nuq4wYDBgNvt6U21gwNLxuRqc9BH9tnCNL0uTIzm2NBUkf3hJiA0GLC2D3F+XoCsMmLtuNj0kays5NukHdcnsJMcmXVcYMBgwm329qTYwYOn4XE1E+oh+W7jGBwNmx1/yh5eE2GDA7PLIpZUEXWHAXBSzbytZW8mxST+oS2YnOTbpuraKAVNRFP2QiKZorZcS0WFJkswdOLkoiq4kon/HcXxy2pb22msLda02UpNNismpxUxKbK1kItLy1/frrcQuS3OYpoPU/UTKBxcMWFoGub8uOeek5F09qmDnnm99PcCuNdlBV76uLWHAwjDcVyk1NY7jaWEY7kBEJyVJsk//yYVhOJ2IvqCU+h0MmP+EaTQiDFg6b1eDkz6i3xau8WVpciTHlqaC5A8vCbHBgKVlkPvrEnRFBcxdN5sekrWVHBuMv0121W4DXfnsWsWAXaSUeiqO41vMhKIoeiWO4/F9k9t8880/FATBl4noMaVUJwyY/4SBAWuOqauJaO7d3Hu7xgcDZsdY8oeXhNhgwOzyyKWVBF1hwFwUs28rWVvJscGA2efYwJbQlc+uVQzY1UEQ3NrV1fWAmVAYhvOSJJlIROXJkye/t6en5/rly5fv097efoBSKoIB858wMGDNMXU1OM29m3tv1/hgwOwYS/7wkhAbDJhdHrm0kqArDJiLYvZtJWsrOTYYMPscgwHjs7LtucEGY5VtW+uGtgO6tgvD8KIgCJ7s6uq61fSNoujlOI43qZqxo8ylh0S0iIg2IqJRRHR6kiQ3NHof3APmqkL99rgEMZ2lq8FJH9FvC9f4YMDs+Es+lEiIDQbMLo9cWknQFQbMRTH7tpK1lRwbDJh9jsGA8VnZ9sybAdtPKbWXuQeso6NjxyAITovj+FMDJxuG4RdtK2A9Pb26WCzY8hr6dhMmrIhh3rxVYqnz62zjFRLbhEtXMJp3zKqM6sHIkp1rbNkKSOQan2R2WcaWtU6t9n51866GiNC11dTHfEAABEBg+BFQSlkXtqwbDiLGylMQtdZbm/coFAqHlkqlbZVSY+I4vqbvfV0MGCpg/tRCBSydpWuFKX1Evy1c40MFzI6/5G+FJcSGCphdHrm0kqBro3glxyc5NsNUcnySYwM7lx1k1bbQlc+uXs9cVcD8T59IqgFzOZAYLlkehOvpAAOWnqGuBid9RL8tXOPLMu8kx5amguQPLwmxuex3WeYcdE0jwH9dQt7Vi15ybDAR/JwDOz47rAk+OxiwOgRgwPwlFQxYOktXE5E+ot8WrvFleRiWHFuaCkP94eXKLm0+vl+HAfNNVHaVBAfh5vQe6v2kUfSSY0Pe8fMOuvLZwYDBgPnPngEjwoClI87rQbjeBxcMWLrmEj7085p3tfaULHMuTV3JhxLJsUlYEzARadnNex15x+OGNcHnJp0dDBgMWHPZbdEbBiwdkutBOOvDpuT4JMeWpvxQH0pc2aXNx/frqID5JooKWDNEh3q9psUuOT7JsUk/qEtmJzk26brCgMGApe3pTb8OA5aO0PUgDAP2LlPp7CR/o+7KLj2T/baAAfPLMw8HEskHOsmxSdcW7PhrWTI7ybFJXxMwYDBg/F3BsicMWDoo14MwDBgMWHpWpbdwzbv0Ef22gAHzyzMPBxLJBzrJsUnXFuz4a1kyO8mxSV8TMGAwYPxdwbInDFg6KNeDMAwYDFh6VqW3cM279BH9toAB4/GUrmujWUk+0EmOTfphE+x4axm68rlJZwcDBgPWXHZb9IYBS4fkemCSYsAkaCudneTDpiu79Ez22wIGjMdTuq6S10ReY5N+2IQB461l6MrnJp0dDBgMWHPZbdFbwiHdhOl6KMnS5EiOrRE7CdpKZyf5QOfKzmK5e20i2YBJZic5trQEkXxQlxyb9MMm2KVlfv3XJbOTHJv0NQEDBgPG3xUse0o4pMOAWYpVp5nLQbjCetsxlZFmzlzS3Btb9HY9bGYZW1r4Q/3h5coubT6+X3fJu6x1lcxOcmxpOTLUa0LyFyZgl0aA/zryjsdOMjcYMJ6mQ94Lf4jZnwQwYOksXQ9MUg6bErSVzk7ygc6VXXom+20BA8bjKV1XyWsir7FJP2zioM5by9CVz006u3oz22CDscp21tYNbQeU0A4GzJ8KEg7pZjauh5IsTY7k2Bqxk6CtdHaSD3Su7PztCnYjwYDZcRrYSrqurmsiy73YNTaeQoPTS7LJkRyb9IO6ZHaSY5OuKwxYHQIwYP4+ICQc0mHAmtPT5SBcYY1LEK2AD/WHl/SDukveZZlznP3EKiE8NZKuq6vJyVrbevEN9XpNSw/J8UmOTfpBXTI7ybFJ1xUGDAYsbU9v+nUYsHSErgemrA8kLgdhGLB0vftaDPWHl2ve2c/MT0uXvJOyJiR86EvXFQbMz/oYOMpQ7yeuug4OBd6oYNd63CTsxRyquATxtYW6FrihXqQuB5KsD8L1Eg0GLH0Juh6YpBw2JWgrnZ3kQ4kru/RM9tvCZb+TsiYkfOhL19V1TWStbb34hvrzP211SY5PcmwS1qzrmkjLhaxeh67+ScOAwYB5yyoJh/SKGb1xy8qcZh7ywmpzq7WJZPmhLzm2RuwkaCudneQPVld23jYFy4FgwCxBDWgmXVfXNZHlXuwaG0+hwekl+TAsOTYYMH4+Qlc+u3o9YcBgwLxllYRDOgxYc3K6HIQrrHEPmBXwof7wkn5Qd8m7LHOOs59YJYSnRtJ1dTU5WWtbL76hXq9p6SE5PsmxwYClZVb916Ernx0MWB0CeAiHv6SCAUtn6XpgyvpA4nIQhgFL17uvxVB/eLnmnf3M/LR0yTspa0LCYU66rjBgftbHwFGGej9x1XVwKPBGBbvW4yZhL+ZQRQUMFTBO3tTsAwOWjtL1wCTlsClBW+nsJB9KXNmlZ7LfFjBgPJ7SdXVdE1nvd/Xik3xIl37YBDveWoaufG7S2dWbGQwYDFhzWd+vt4RDeqUqg3vA2Jq6HIQrrHEJohXroT6UuK4Jq0l5bOSSd1nmHGc/8YgldSjpusKApUrIajDU+4mrrqxJDlInsOOBlcwNBoyn6ZD3wiWI/iSAAUtn6XpgknLYlKCtdHaSDyWu7NIz2W8LGDAeT+m6uq6JrPe7evHhsMnLxzwchCVri9haN+9qzQwVMFTA+Bk/oKeEQzrnG+ssP/RdD0xZxtaInQRtpbNzPWx6W3gWA7mysxjSaxMYMB5O6bq6roms9zsYMF7euerq/134I8Lk8NhJ5pYH4w8DVoMAKmC8xVirl4RDOgxYc3q6HIQrrHEJohXwof7wkn5Qd8m7LHOOs59YJYSnRtJ1dT2oZ60tDJinROw3zFDvdWkzkhwfYktTr/7rktnVixoVMFTA+Bk/oCcMWDpK1wNT1gcSl4MwDFi63n0thvrDwVVX+5n5aekSn5Q1IeFbV9f9xI9afkYZ6r/J6GoO/czazyhDvZ+AnR8dB44CXflcJbODAatDABUwfsIP7AkDls7S9cAk5bApQVvp7CQfSlwMTnoW+2/hEp+UNQED1lwewIDx+Uk+bEqOTcKalfw5kdfYpOsKAwYDxt/tLXtKOKSbUCUf1CXH1oidBG2ls5P84eVicCyXu9dmLvHBgL2LvtXWRNba1ktimAj+8ga71mQHXfm6woDBgPnPngEjSjikw4A1J7PLQbjCGveAWQEf6g8vV12tJuWxkUt8WeYcZz/xiCV1KBiwVESsBkO9XtOClhyf5NikV0oks5Mcm3RdYcBgwNL29KZfhwFLRyj9wORyEIYBS9e7r8VQf3i56mo/Mz8tXeKDAXuXufT9pFF24BJE/toZ6v3EVVf+TP33BDseU8ncYMB4mg55L9wD5k8CGLB0ltIPTC4HYRiwdL1hwOwYueQdDBgMmF1W8VvhsAl2fAL8npLzTnJsMGD8nBvSnjBg/vDDgKWzhAFLZ1SvhXR2kr8VdjE4fIX4PV3ik2LA6u13fAruPVttTWStbT3iOGy656KUL5vSIpesLWJLU6/+65LZ1Ysaj6HHY+j5GT+gJwxYOkrpByaXgzAqYOl6SzmUuOpqPzM/LV3iy/qQ7hKbHxr2o+R1P6n3jXXW2sKA2eeabUvpB2HJ8SE22yxbvZ1kdjBgdQigAsZP+IE9pRswCfHl9cAEds2tk6H+cJBsIipG/sYtK4BnHvLCKqBr5V3Wh3SX2JrLEvfeed1PJBgwV3bu6gxej6HeTxrNTHJs9fJu8JRyG1kyO8mxSdcVBgwGzG0nYLSWcEh3PcxV2uNJfivVdj1sgp3dQhnqDy9XXe1m5a+VS3xZ5hxnP/FHJX0kVxMhhR0MWLq2eTU5Q73XpZGVHB9iS1Ov/uuS2cGAwYDxM9uyJwxYOqi8HpgkaCudneQDk4vBSc9i/y1c4pNiInAPWHoeSF6zrrGlzza7FpIPm5Jjk14pkcxOcmzSdYUBgwEb9E8HCYd0zjfWWR7oXD/0s4wN7AZviQz1h5eLwRk8CvVHdolP+prIkp8Lt8r6zrDa32g/QQWsuSwZ6v1E8pdNaWTBLo1Q7dclc4MB42k65L1wD5g/CWDA0lnCgKUzqtdCOjvJhxLXgzpfJV5Pl/ikmAjJFTDpe7FkAyZB17RVJPkwLDk26Qd1yewkxyZdV1TAUAFL29Obfl36h76E+KSbCJeDcNbfqEtnBwPG30Jc8g4G7F3OLtyyXq95rYDBgPHXcR4OwpKNBGLj555kdjBgMGD8zLbsKcHgNPrQlxCfdBMh+UAnnR0MmOVGUaOZS97BgMGA8TONz87He/oaQ/JhU3Js0g2iZHaSY5OuKwwYDJivvb/uOBIMTl4NGNilpycMWDqjei1cDA7/Xfg9XeKDAeObCCns6h2YsozPJeeGonoo+QudvMYm/aAu2eRIjk26rjBgMGD805FlT5iIdFDSP/QlxwcDlp5fMGB8RnlkJ3m9NvoyDAasuTyVfBiWHJv0g7pkdpJjk67rkBqwrbbaap1Zs2a90dyWMzi98RAOf1xhwNJZ5vXAJEFbGLD0/MqjiWh0UMcfYm6seV73Exgw/lqWftjEQZ2vrWR2kmOTviaGxICFYfhfRPRzpdRorfWHlFK/K5VKn50zZ86f+CnqtycMmD+eEg7proe5SvsMH82c1wOTBG1hwPhr1TXv+O/E6+kSX5brlbOf8Ajwerlwy3qvQwWMp6lNL8mHYcmxST+oS2YnOTbpug6JAYui6DGt9XSl1M1xHG/T0dGxu1Lq7CRJPmizyWTRBgbMH2UJh3TOgSnLA11eD0wStIUB469V17zjvxOvp0t8Wa5Xzn7CI8Dr5cINBmxVxtLZNcoIyYdhybFJP6hLZic5Num6DokBC8PwmSRJtoui6FljwEwQYRg+lyTJFN7Hjf9eMGD+mEo4pHMOTFke6KR/6EuODwaMv1ZddeW/E6+nS3xZrlfOfsIjwOvlwg0GDAaMl2VuvXBQd+PVv7VkdpJjgwGrkXNRFD2otT5BKXVdHMcf6OzsPLhcLh+WJMlH+SnqtycMmD+eMGDpLPN6YJKgLQxYen7Va+Gad/x34vV0iQ8G7F3GLtxgwGDAeKvTrRcO6m68YMD4vPLCrt4MN9hgrLKdvXXDvgE7Ojo2U0r9hIi2V0q9o7XuDoLg811dXbHtmw52Oxgwf4QlHNIrh4wbt6xMauYhL6wyOQnxSY4tr+zqffuV9UG90Uoa6kOJa9752xXsRnKJL2tdXWKzm62/Vq6xSWEnYc22Gjt/WdXcSEO916VFLzk+xJamXv3XJbMbEgPW96ZTpkwZ8/bbbxe6u7sX8vEOTk8YMB7XWh9eEgxOXk0E2KXnISpg6YzqtXA9bPLfidfTJT4pJqLemuUR4PVy4VbZGzN84FCjvRgGLF1v1/0ufcRsWkg/CEuOD7Hxc1QyuyExYGEYPkJEeuCbJ0nyMT5mvz1hwHg8YcD8cTMjwYCl83Q9kGR92Kw3AxzU+driMfSN2cGApeeWry8lst5PXPc7Pgm/PaUfhCXHh9j4uSiZ3ZAYsI6Ojl373lgp1aaU2ltr/UaSJKfzMfvtCQPG4wkD5o8bDJgdS9cDSdYHJhgwOx1rtXIxElnr6hIbnwCvp2tsUtiZ2dY6MGUZX6ux42WQ/17SD8KS40Ns/HyUzG5IDFitN42i6Kk4jnfgY/bbEwaMxxMGzB83GDA7ljBgdpyaNTimf5YH4cr7Ody3KTk2vkK8ni7cJOkKA5aut+t+lz5iNi2kH4Qlx4fY+Dkqmd2QGLCJEydu0vfGI0eOVL29vZOVUt+P43hzPma/PWHAeDxhwPxxgwGzY+l6IMn6oF5vFrgEMV1fFyORta4usaXP1G8L19iksIMBS88D1/0ufcRsWkg/CEuOD7Hxc1QyuyExYGEY/rXfG2ul1OtENCOO4/v5mP32hAHj8YQB88cNBsyOpeuBJOvDJgyYnY61WrkYiax1dYmNT4DX0zU2KexgwNL1dt3v0kfMpoX0g7Dk+BAbP0clsxsSA8ZHmV1PGDAeaxgwf9xgwOxYuh5Isj5swoDZ6QgDxuc0sCcMGJ9lXtlJqKg3oi79ICw5PsTGX8+S2WVqwKIouq7W0w/7gojjeBofs9+eMGA8njBg/rjBgNmxhAGz49SswTH9szavLodhybHxFeL1dOEmSVcTCx7C0VhzV215GeS/l/SDsOT4EBs/HyWzy9SAhWH4xUYYkyQxf5xZxA8MGE8GGDB/3GDA7Fi2mgHL0ki4HuayjK1iDPAQDrtFMKCVCzcYsFXh5ZUdKmCspbKyk+SDOmLjayuZXaYGrP+bTZ48ed1ly5aNKZfLqq2trUBE74/j+Ld8zH57woDxeMKA+eMGA2bHEgbMjlOtVq102JRsDvkK8XrmVVczW1TAGmvuqi0vg/z3kn4QlhwfYuPno2R2Q2LAoig6h4i+TkRtRPQvInofET2Dx9CnJ5nr5ivhUII/JtyaukoxiDBg6flVr0Ue95N6eSdhr2u0JvgquffMq64wYOlau2qbPmI2LaQfhCXHh9j4OSqZ3ZAYMPMURK31lCAILiuXy2cVCoVNtNbHxXG8Fx+z356ogPF4ogLmj5sUg2PicP3Qz/IwDAPGyznpurrGl2XOucbGV4jXU/J6bcQOBixdb1dt00fMpoX0g7Dk+BAbP0clsxsSAxZF0RNxHO8UhuFxRPTXJEluD8Pw6SRJtmdiVlEU/ZCIpmitlxLRYUmSzO0bKwzDg5RSR2ute4hoVpIkX0t7n1oGTMI11q6br4RDCSpgadkm2+BwDptZ5h0MWHp+1WuRx/2k3hcTWeYcZ03wVXLvmVddYcDStXbVNn3EbFpIPwhLjg+x8XNUMrshMWBhGN6vlLpZKfV3rfVR5XL5giAIfsr9Q8xhGO6rlJpqnqIYhuEORHRSkiT7mMlNmDBh5IgRI2aVy+Utu7u7l0VRdHO5XL65u7v7nkaSwoDxEh4VMH/c6h00K4e/bcdU3mjmzCW8N3Ts5fqhn2V80g1YHtnl8UuTLHOOY8CyjE9yzjViBwOWvjG7aps+YjYtpB+EJceH2Pg5KpndkBiwzs7OceVy+aAkSS4Kw/AiIvo4EZ2bJMnPOZjNGEqpp+I4vsX0j6LolTiOx1fHUh0dHe/p7u42f+yZwjA0ba5KkuQ3MGAc2o37wIDxmLp+qGZ5mMvrYVPCYS6v7GDA0tex5DUrOTYYsPTcatTCVdvm3s1fb+kHYcnxITZ+Hkpml6kBC8PwyBEjRtw0a9asN/g4V+8ZhuHVQRDc2tXV9UDVZM1LkmQiEZX7tw7D8Cil1CfiOP5U2vujApZGqPbrMGD+uJmR8ngQrhywMqzQoQLGyznp5tA1vixzzjU2KWtC+n4i4UsTV4MjPe/4u4PfntIPwpLjQ2z8XJTMLlMDFkXRtURkzM9vy+Xytd3d3Q/xsb7b01TAgiB4squr61bz2yiKXo7jeJN+Y5t7xM7XWndorQ8wlyKmvS8MWBohGDAeIXtuMGB2hGHA7DjVatVKh03pB+Es48urrjBg6WvZVdv0EbNpIf0gLDk+xMbPUcnsMjVg5s023njjUaNGjTL3Z31BKWUM0U+LxeJ1L7744t+4iMMw3E8ptZe5B6yjo2PHIAhO61/lMhUypdQ7cRx/w/Y9enp6dbFo/jxZv58JE1b8Y94822G8t5tw6YoY5h0zIIY6sWUdcs34JMdmYAqIL5e6gp3V+pasreTYKunlsN+J2OuwJppbE3V6Z6mtS841kNuKA6eRa3yc90AfEACBoSOglFK2727dcOCAEydO3LCtre1zWuvPENGiJEk+YfumA9pVnoKotd7a/L5QKBxaKpW2VUqZP/Q8MwiCp7XWv6/20Uqpy+I4vqvRe6ECxlMClyD642ZGkn7JkIT4XL8RzrISYTSUHJ/k2FwomsMRAAAgAElEQVTZQdd395686mpmUOsb6yy1zSs7CU9pbvTpJ70SITk+xMY7V9XbT/ijZdNzgw3GWvsq64YDQ99iiy027e3tPZiI9iOi15Mk2TOb6aW/CwxYOqNaLWDA/HGDAbNj2WoHJhw2eUYiS26u5rDSXsB9kRK+MGnEDgYsfc9z3e/SR8ymhWQTIf2gLpmd5Nik61pv5Q2aAYui6D1EdAARHay1Xo+IfqK1vqG7u/uVbLYBu3eBAbPjNLAVDJg/bjBgdixdDyRZHoRxULfTsF4rF22hK8+4Zm0OYcCyWxPNvZPf3jio83lKZic5NhiwfjkXhuGvlVLm73TdobW+PkmSx/gpObg9N75oEz3zkBdWeRMJJX6XA4mUD1bp37pKiC+PukoxiGDH3wtbiR0MGAwYfyXkn52E80kj/jio87NTMjvJscGA9cu5zs7OaYVC4ZbZs2cv5qdiNj1hwHicUQHzx02KwUEVh6dpXy/JJkdybK55BwOWfxNR78CUpbattCaa27n89sZBnc9TMjvJscGA8XNuSHvCgPHww4D54wYDZsey1Q5MOGzyjESW3FzNYaU97gFbKazkPx3RavuJ3S46+K1wUOczlsxOcmwwYPycG9KeMGA8/DBg/rjBgNmxbLUDEw7qMGB2mV+/VV7XBCpg6cq7aps+YjYtcFDnc5bMTkJsrl/o8JXIpuegPYTDhL/VVlutM2vWrDf6T8U8EbGZvwXmGwsMGI8oDJg/bjBgdixdDyRZGhxUSuw0rNfKRVvoyjOulRzNsDrXaE3AgKWvF5c1kT5adi0kHNQbzVZyfIitcZ7CgFms480333zjcrms2tra7iuVSnua/zbdisVikYjuS5Kk02KYTJrAgPEww4D54wYDZsfS9UAi5bCJh7+k6+uiLXSFAUvPqPQWLjknybziIRzp2sKANceoVm8J5hAGzELXKIqu1Vp/lIjGEdH8vi5KqV6t9T1JknzTYphMmsCA8TDDgPnjBgNmx7LVDkxZGok8s1swX9FO4+auTJJ//EOR1kTjxmkKglVzZ+bMJXbJ5NBKMjvJsVVMy41bVkgPfNKw+R3+EHPjJHTV1iGlB7WphIM6DJh/iSXo6rqf+Kfgd8RBvQQxiqIT4zg+z2/IfkeDAePxhAHzxw0GzI6l64EkS4PT6LCJCli6vo20HWjAXnnFGDBFa66pae219SqDw4CtwCEh52DA0vO+UQvX/a65d/PXW8JBHQbMn559I0nQFQbMQVdzD9jy5cs/Zz4PiKhyGaL5ieP4Ow7DDGpTGDAeXhgwf9zycGCScKBzPZDAgL2bo63C7j//IersXKPycTJ2rKZnn11Ma67JW4u2vSSzkxwbDJhthtVu56ptc+/mr7eEgzoMmD89YcD8s+wbcbArYA8R0Vta6xeUUiu/qozj+MzBm5LbyDBgbrz6WsOA+eMGA2bH0vVAAgPWegbsiiva6PTTR1KxqKm3V9Hppy+lI4/ssUsgZivJeSc5NhgwZsJVu7lq29y7+esNA8ZnKZmdhNhQAXPIrTAMZyVJspVDl8ybwoDxkMOA+eMGA7Y6y7786v/KK4v+XvnnuDXeR4F69+afYP4/aN6t4+n1p2etMhAMWGsZMHPf14c/PJq6uwN673s1LV6saMwYTc88s4RGjOCtR5tergfhLPNOcmwwYDbZVb+Nq7bNvZu/3hIO6o1mIzk+xNY4D2HAHNZpGIY3KKUujOP4eYdumTaFAePhhgHzxw0GLN2ALVq+kN5c9mal4doj1qE129+97gwGLD0XXQ9zWZqIRgf1/pe+/vGPBfr0p0fT6NGa1ltP06c/3Us/+EE7XXzxUvr85wevCiaZneTYYMDS12WjFq7aNvdu/npLNhFmlpLjQ2wwYPUIrLyHy3apRlH0JyKaorV+lYiWVu8D00mSTLQdY7DbwYDxCLeSAdtmmzGkFNGf/uT/CWoD6bp+qEo8CPef02DHp7Wm854+my5+5nwqqAKVdZk2HrspPfW5Z6kQFCqhSLg/zdZEZMlulfeq8zS6PLH7+tdH0i9/2Ubrr1+mkSOJ7r33bdpuuzE0frymP/xhCRVWpIP3H8lrVnJsMGDNpaJ0bevNTrKJgAHj56QEXVEBc9DP/NHlWs3xh5jTIUrffPNqwAY+UW3xYqI33ljx3YJ5rPXAQ5zvp6rlUdehMjnGbJ36+Il0zawracLY99Oy0jJauHwhLelZTD/e4waautk+MGDpW0mlRd7zrvuhWbT11mvQRhtp6umhyhcmZm0ee+wI+ulP2+nHP36Hpk7ttaTh1kwyO8mxwYC55VnevqyDAWtO31q9JZgcybrCgDnmXEdHx+eCIJi8aNGis9dYY43PJElyg+MQg9ocFTAe3rwbsHKZ6M03FS1ZYsyXeT6MokJB0/rra2pre5cJDNgKFllXSnrLvXTMI1+nW+Kf0aR1t6Bbpt5Jn7z949RT7qF/LllA2264Hd2338OklMo8tnorRvJhWHJsNgbx3OlddPLJI+m005bR9devWKBmbb70kqKddhpDU6aU6YEH3q4YM98/ktlJjg0GrLlMlKxtng/CMDm8vJTALc95V4v6oD4FMQzD7xHRePMFrNZ6B6XUXUT0pyRJjuOlgP9eLgZssC+36j87yZtvvQ/WrA/p3IPw0798gaZNG0UvvFCgrbcu0euvK3r7baK33goqf1vouuveoV12KflPthaoRGTxoAtT6TriwUPp/r/eQx/YYFv62V630Toj111ZxZm83pb063n30d37Pkg7bLQjDJhFpuZxP+kz/oFSNHnMSzRnTkB//vMS+sQnRq80YOY/pk0bSffc00a33fY2feQj/tetZHaSY4MBs1iYDZpI1jbPB2EJRqKe7Iit8ZrJc95lbsCiKHo2juMPmHvB4jjeZrfddisuWLDg+TiOt2hua/LXGwaMxzKvFbB7l+5Ohyy7mhYuVHTIIcvp7LOX0c47j6lAOPnkZXT00SPJVMfMjf0HHuj/kibJH6o2lYjBNmCLexbTl+4/mB575RH6yPt2pZ/seTOtUX3gRh+7//3vK2nvO/ekT75/Kl2/500wYBZLOM9593TPB+hD/7yDPv3pHrrmmqU08IuwZ58NaI89xtCuu/bSL3/5jgUNtyaS2UmODQbMLc8GtpasbZ4PwjA5vLyUwC3PeTcUBmxmHMfbRVFk/v8DU6ZMGbN06dKn4jjekpcC/nvBgPGY5s2A9fYSXRrdQOcv+jqNHKnp/PPfNVj9D3RPPlmgL35xVOXyxOOPX0YnnLDc62VNkj9Uh9qAvbn0DTro3s/QzFefpk9M+CRd9f+up5HFkSsTtI/dM5+fRXvcuhs99/qf6cmD/0TbfWxvKgQKj6HP6bfpaXl3+H8uoOuWfJZuueVt2m230moGzPTff/9R9PvfF+k3v1lCW29d5m1qdXpJXrOSY0vTdajXLNjxl0meD8ISjAQqYLzcy3PeDYUBO1FrvS0RfVApdSkRHaK1vi1JknN4+P33ggHjMc2TAXvtNUVf+cpIevzxIm1WnEdXPbA+bbXVu4e0gd+oz5mj6MADR9PLLwf0P//TQ5dcspTa23mc8vSt5lAemF59+1U64O596cV/v0D7d3yWvv+xH1Fbod/NeAMu37yj+1aa/tA0OnTLw+jqbz4IA5aSnnk9bBa2+RCNn/80rb/xCPq//1tCQUA1DdgjjxTogANG095799DVV5sH7vr7kcxOcmxDuZ/YqP//2bsO8Kaqt/+7WU03tKVllA1J2bTMMgSUIVNBERREZauICE6G4kYBcaEMUZA9RZClDBllCLRAGU3YUMrooitp5v2ec27TJpA2uWmSXv5fzvPkuUnuufe+933P+p13+XjnDJfs13mUF8I+AOaa3IXAt0e53XkdgJEHxsTE9GJZtjvLsmKWZfdevHjxL9fE75mrfADMNb4+KgDs2DExxoyR484dEQb478KyiHdgOJlg89L2fPuIX9iLL/ojMVGMTp2M+PVXLSpVco1X1lf5Jv2HeXgz7wae3TIAV3OuUED1Zec5NomWLVdY844E6Wi7ogUyCzNwbWkYIg0SnwasjOb5qLa7dcpZmJD9OaZNI+bBevqG9vorSdLcvXsAzp0T4fDhAtSrRwLruKcImXdCps0HwMrX/oQs20d5ISwEIFFay/DRVnafeZTbndcAWIMGDeIuXbqUqFAoHrP3ULVafaB8Q5P7rvYBMNd4KXQARhZkYzUqfPKJH/XpmjZNjw9/U0Aidt5UjQTmeO01ObZvl0KhMGHVKi1q1Srfwk7Ik2pFLJguZqsxeMtTSCu4hUlxb+ODdjNoZEN75UHeLTj9Iz5MmIpPToRiRnIlHwD7HwNgpA/3jL6BswYlTiUXIiqK63ulBUPavFmCsWP9MWKEHnPm6Fwb2OxcJeQ+K2TaKmI84SN0H+/4cMu27qO8EPaBHNfkLgS+PcrtzmsATKlULlKpVGMVCsU+Ow8liZgfd60JuP8qHwBzjadCBmCxi9sja+3X0J7pS5O2LlpUiI4dTS4FazCZgJkz/bBwoYzea+VKLVq2dN3HxDfpl7S35PTTGPLXQGRoMzAj/hO8ETupzMb4IO/y9Llo+XtjBNzPx/X10cg9dtbmem9GLfUtNl0bRyxX2esXluAaA/13YuH1jsUPKE2uxM8zPj4Qd+4wOHGioBiwlY8yYedQ840nrkvXxzv3847cUQgL9bLeTMj0+Wgru036ABjPPlu/fv3Iy5cv36tevXpAQEBA9UuXLl3ieQuPVvcBMNfYK1QAduGCCE88ewfG9Ppo185Io6ZZds7LEyb/l1+kmD7dD3I5sHChFr16uRbu2jfpc+3t6O0jGLZtMPL1eZjd5VuMaPKKw4Zoj3czD0/HT6e+x5JD4ei/6qoPgJXCxUex3U2Z4ofly2XYEfkSWp390SEAIxVIjrB335Vj4kQdpk/nTBbLW4TMOyHT5tuUKF/LE7Js+S6Evb0Z5gNg5Wt79q4WAjjk2+7czwX33tHTecDeYBjmFRIBsXHjxrWNRuNOAPPUavUi976G63fzATDXeCdEALZhgwRvvy2HRsMguMsipKx63iapcnkAGOHSzp1ijBvnD50ONHz9qFEG3swT8qTqrQXT3hv/4JWdw2lS5flPLMLAhs86xUd7vLuVl4o2yxpDmSPF3qkZNuaL3p70hSxbIdNmr93l5wNNmwYhQncLV2o8hqwTp50CYFotZ6Ko1zNISspHcLBTTavMSkLmnZBp89Z44qqEfbxzlXOla4XJHe0t1L09FvsAmOuyLe1KHwBzP089CsCUSuVZuVze7vTp0wWEdKIFCwoKOqZWq5u5/1Vcu6MPgLnGNyEBMAKIPvzQD7/9JkNQEAu/Z15FQPMdOPmirUlaeQEY4RQxixo+3B/p6SKMG6fHzJk6iMXO8/D/+6S/5dIfeHX3aIgZMZb0+h096jzpNPNK492kyVWxqoEGa/ptxOO1eji1UHf6oTwqClm2QqbN3kJ9+XIppkyRY2bIXHxU+Qcb/z5Hi7nvvpPh88/98OGHhZgwgf8myYMiFzLvhEybD4DxGDzsVBWybIVMmyOuCwFICBnkCJk2nwbMUeu2Oq9QKFTVq1dv8u+//9KMtiQRc1paWqJarW7O4zYereoDYK6xVygALDWVwejRXLTCRo1MNFrhc4eb0JfyBAAj971xg8ELL/hDrRajTx8DfvqpEAEBzvFR6BOXJ+lbdWE5Jv/7BgIkgVjRZy061OjkHNOKapVG2/WeCrR5+g4ei+6GDQP+9AGwR2wxZ2+h3rNnAM6cEeFKVHvUlt3lBcBycoDY2CAEBrLUF8zPj1cze6iyJ/tE+SgTtn+aD4CVT7q+dlc+/gkZSPhoc022PgDGg28KheIrhmHiWZZdV3TZIIZhDqtUquk8buPRqj4A5hp7hQDA9u4V0yiFWVlcvi6SXDkw0DuLkvv3gVde8UdCggStWpnw++9aVKniOEKikCdVTy6YLBELw+RhWNNvE1pGxvFueGXxrkefu9hXTYe9zyWgaQSnYHekKeFNgIMLhCxbIdP2YLtLThbhiScC0auXEdvO138ox5szcv34Yz/Mny/DN98UYvjw8mnBhMw7IdPmyfHEHX3XxzvXuSh03pX1Zj4NmGtyFwLffACMp+xiYmKeZVm2C8uyBoZhDqhUqs08b+HR6j4A5hp7KxKAkbDyc+fKMGeOjPp4EX+sESMMsEQv99bkoNcDb70lx/r1UtSqZcaaNRo0aFA2CPMWba5J1f3glWVZfH38C8w98RWqBlbD+v5/QhkW4xJ5ZfFuV61C9O1xD4MVQzG/O+di6sxC3SVCSrlIyLIVMm0PLtTffdcPS5fKsGKFBkM/aOQSACOREFu3DkR0NIuEhAJeZsIPilfIvBMybT4AVr7RRciyFTJtjrguBCBRGo0+2sqWng+AOWrdAHx5wJxg0iO8m17axOoOHytHnMvKIrm5/LF3rwTR0WYsWaJFbKxtWHhvTg4kV9HXX8swd64fKlVisWyZFvHxpUdI9CZtjnhp77w76TOzZsw49D4WJy9A7ZA62DBgCz26WsqijREBjSb740rOZZwcfhbVgqr7AJgVo90pV1flV9Z1FvoODDqLZs2CEBzM4uTJAkS2a+oSACPPmjzZDytWyOgY0b8/tYJ3qQiZd0KmzQfAXGpuxRcJWbZCps0R130gxxGH7J8XAt98AMwJ2fnygDnBJB8A480kEgBj1Ch/pKaK8PjjRvz0kxZhYQ/fpiImhzVrJJg8WQ6RCPj++0IMGmR/wVcRtPFhtLvoM5qNeGvfBKxVrUJMWCOq+YoKrMqHlIfqOqJt3rK3qY/ZG7FvYUb8xz4A9ggCsCniFEya5I8pU3R47z293dx9zmo2L19m0KFDIFq0MGPXLk2xhpxvI3TU7tKPJ9vc0ln6+NLh6Q0Td9Dz4D18vHOdqz7euc67sq4UApAojT4h0FZauxMybYSfQqCPb4v1SBREhUKxXK1Wv6hUKkepVKolfInyZn2fCaJr3PamCSLRMpH8PjNm+MFgAN59V4+33tJTsCOkRcmBA2LqF5aXx2DaNB0mTtQ/tOgT8qRKeOkO+nQmHcb9PRLbr25FXGQrrOq3AWHycNcaGg8QcfPIccQtb0LD2ye9eA5d4jnARzQp3iju4J2n6BQybdbtLnLVFSQmiqjMiPmgPa06H4AzcqQcf/0lxcaNGnTu/L+Xu+9RkaunAiKVp7/4eOc694TOOx8Ac79shQBwfBowJ+SqUCiuMQzzGcuyJNjGhw9eolarf3fiNl6p4gNgrrHZWwCsoAB45x05NmyQIizMjJ9/LkS3bmUvpCpyciCJoIcN47R0w4fr8dVXOptcZBVJmzOSLi99BYYCvLzjBexP3YdONR7D771XI0jmhmRMToLDOcdnUZ+zzzrOwsKR7/gAWJHQyytXZ9pOeeoQ+vRpMbg7dxeeeMKI1au19HblBWBEa96rVyC6dDFi/XrunnyLkHknZNrctaHDV17O1vfxzllOPVxP6LzzATD3y9YHwFznaWlXekQDplQqe7MsS7KrPs0wzJYHHs6qVKqR7n8V1+7oA2Cu8c0bAOzSJQYjR/ojJUWMuDgTfvlFS3fFHZWKnhzu3mUoCDtzRoyuXY3UB8WSELaiafMk7+4XZuOFbYNx4u5/6FWnNxb3XAa5RO7okU6fd4Z3mdpMxP7eCJEBUTB/pwbDSnwaMCfBq7Ug+GiZnBZgGRWJbLM3fYz8hJexdKkWffpwJrzlBWDkHoMG+ePQIQl27y5A8+a2/qLO0O5Mu6so3gmZNh8Ac6Z1lV5HyLIVMm2OuC4EIFEajUKgzWeC6KgFue+8RwAYCT+vVqvfUygUb6jV6h/cR6777+QDYK7x1NMAbOtWCd58U478fALC9Pj4Y53T+XyEMDnk5wPjxvnjn38kND8Z2dGvXp11i4mfaxJz7ipXebfj4FU8t/VpnM88i2caPofvH/8ZUrHUuYc6WctZ2t7Z/xaWnVuC8D1rEHBtsA+APQIALHZJa6R98h8iQ4KRmFhQrDV2BwDbt0+MIUMC8NRTBixeXOhkayup5my7s1zhTfAqZNp8AIx3U7O5QMiyFTJtjrhe0SBH6GZ0PgDmqAW577ynANg1AKMB/ARgFADGmmS1Wn3Afa9Qvjv5AJhr/PMUACM+Xp9+6ocFC2QICGAxd24hnnmGXwQzoUwORiMwdSoXUrtqVTNWrtTi5VONKcOF6BPh6oLJGHQd/mN70AiELzcZhVmPzYWIhCV0c3FWrpfvX0SHVa0hvdcWkVsTkHhS42ZK7N/OWfp8C/WH+Rfz5ofIWj0PkybpMHWqvriCOwAY8SHt3j0A586JcPhwAerVc6xFt6bQJ1fXu4+Pdz7e0XmlVSA373nJH7csrvsAWNlt8lEEYKVF4Ha993nnSk8BsDEAhgJoC+DEA6/CqtXqx73zeo6f4gNgjnlkr4YnABjJ3TNmjBzHjknQoIEJv/5aiJiYR9tkiCz+fv5Zipkz5QgMZOH/wkvwj9n/PwPAmj+eivQne8EUdBNvxk3B1HYfgrEkZHOtaZV6FZ/F3IjtQ7Hz2nZEbj2AsztaupkSHwBzN0Oj212A/mpbHD+ej9q1SwCSOwAYoXXzZgnGjvXHiBF6zJmj40U+n3bn7cWmkGlzdUPHWwt1H+94dQObykLnnQ+AuV+2FQ1cXRlPXOeCd670CACzkK5UKmeoVKpPvfMqrj3lkQNgcU0gZs3I3LILIKt7kpWYZTHwKTkYsPhjk6b4P8s5y5Gcf/A/7h4svQfDcvcqrmP9nTwHpA53ftLe1yEys/im6w/F9YPemQSxWIT0pAuwjjzhzO5XQoKYgq+MDBE1FZo3rxBBQa7JVIiTAzGpfP11OQr1JlQeNB2qn6bZvJw3cqg5w00+vEtOP40evw+C2T8d09t/jIlxbznzCJfr8KHtaNphDNj8JPyvPYXrXy93+Zl8LuRD3//nhbqFTxbeau5FIROXgJyaqFw7FQHSAIjAoHamEdGX7mL1sWiknzhbLApnxpMH5Ua00fHxgSCbPCdOFCAqynktmE+ufHqBbV0f73y88/ZY54jjFQ0kHkUTRKFomIQ8njhqd/bOexSANWzY0I9hmCkMwyjNZvMbIpFokkQimXXu3LkSGxNXqHbjNUIGYKK0W7i8rgb3tiYjmLx8MHm5tvacbuSFu27FSqUwVw6DxWkrLY1Bx+pX7JofEDz3ww8yfPGFjIaVnzlThzFjDC7n7HFll8SVBZ0rvDp+XIT+zxXCXBCOiRM5UytLKP1HDYAdu30Uw7YNRq4uF5UT5kO1argrLOF1DZ/Bl2VZ1PykO/QRJ3B02EnUq9SA17NcqUzos+mz5CZmM/2P2GCbqkRyGxNFQi+rX7jy/LKu4cM7Ty+YrAEYkVPaTX+YK6uLyZeagB6XgZFJQN+LgNwImKpVAyScT6GrfCOpLN59V0773vTpzk9BQuLdgzIWMm1CHouFTpvQ6RN6uytrLPQBsLJnFz7WTe6epxzd71Fud14HYAqFYjHDMOkABuTl5bUNCgpaQPzBSI4wR4z21nnBA7AVVTjQpdHQRRzZtyUmXmb/AI5FRd51Gg2DLQFDMXCQiVvgkf+JKZhIBLboyKEa7j/LOXpkGLDW1xD/HetrrO9TdO67pG/A5uVg6unQYlExOTkcOSxL6WSDgsCGVkLaHbFdAJaTA7zxhhw7d0qpjxRxkG/XzrVcPdbtRcidtPm8vshYvBTGjHoYONCA774rhFxuP9qbpxfC9vqYM7zbe2M3Xtk5jObbCtmzFIFXnveKbX9ZtN1OY9Ch+pXiVyLA/laldWCffR5B519F5SM/2ryuJ3wR7AEwJjsbovy8ErrIN6kUrEyG+xo/jKiyHb+drF+8WeGpcc8Zudr0IU/6bLAsRFevQHriP+w9swkDq+9CbzXw8zZgdTNgZTPgbBRHTWgh8Mx5YFgy8FhuGJiAIJcBmFbL+aLodAySkvIREuIctwXFuwdIFjJtPhDhXPsqrZaQZStk2hxx/UEA5q0NWAtdPg2YIwmVfv5RbndeB2BKpTJRpVLFKZXKJJVKFUtW/0qlMlmlUjV1XQTuvVKQAMxsxntTG2D0/jx0uMz5KxgVSmjHvY6Ab76GWCJG+vFkG0YIYRCxaHHu/7AQQW+/CYlaBVNUVbxu/A7b/Z/BycSSYAjJySKMGuWPa9dE6NzZiAULClGlivNmQWW1AiF3UkKbqaASov86if/+k6BdOyOWLdOiQY+mEIsYQcqV8Noi21/XfIrx/4yCmBHjl17L8MEzJNuEd5yry5Lr9VsyxEeoUFgIusDW6wGWMQETGwCB91B97XWIdSXJoD0FwCgvXuTM5aRHEhD6dB9ALAYTGgrNwGchPX0KkrNnwBA0UFSIxtjYqAmMLWJhbBkLY4uWMMY0BmQytw12Fdon8vMhPZVIAZfkxH+QnjwOUWYmzAzQZgyQWB1YuiIahTVyMHTETzC2aYtkSSY2XlyHzQd/QGoQtylTIxcYbG6CTXsWwXy/nUvBVb77TobPP/fDjBk6vPGGc1qwCuWdgxYgZNp8AKx83VfIshUybWW1O3LOB8DKbpc+DVj5+i2fqz1qgqhUKk9KJJJ4o9F4lACxhg0bVmEYZq9arW7Gh0hP1hUUACsogHzNSvgv/hmSK5fpa+u7Pg7N+Ndh6NadaqWEbKpmQ5tej4Afv0XAvNlgdDrskfdBy0NfwVyrNlavluC99+QoLGRo1LP33tOTNarbipAnBwttCYPPYuJEOTZvlqJePTN2aLtAIbspaAC2TFmAMZ2z4S8JwIo+a9GxRmevRreylivx6Tl9WoSEBAmOzTmKBF0baNgirTCAJk1MuHJFBG2L74An38L7badjcut33dbG7N3Ipt0VFCCsazxEN2/AHFEFYn95iWyNRojVKnw+8ByaG07iBcV/kJw7S/tJMSiTyWBs0rQIlMXB0Po5nO0AACAASURBVLwlTMoYG99KPi/jtT7BshBfuQTJieOQ0s9/EF84B4b6kHLFVLMWDK3bYF2cH14yrQKTPARRDW9DWuXaQ8FpKrVqgoTqevw6Oh5br27BfRl3n8Cs+niz9zAMajgYtUJqO80KonWPjQ2iAXGIL5ifn+NLvcY7x6Q8VEPItPkAmAsCtbpEyLIVMm0+AOb+dufzASsfT0u72tMAjJgajmFZtiGAtQzDDGQY5uOUlJRfPfM6/O8qBABGfET8lyyCfPlvEN2/D9bPD2taSLDksWCsfrfEN4K83SMDwIpEQRZj57u8g066PdRsck3MDIxIehtBoWL8+KMWvXqV3+TwQakLeXKwpo2sSYnv2/ff+yFClIktkWPQ4Mwym9cRgmaTELR8RC1MaX8flf0qY02/TYiNakXp9BZ9JhPQ8uuhKLwUj7aF7+PIETHNEWcpjaVqxI+og44dTejQwYiwMCAuLhB37+fDMKEWKgX54czIs25NDF1Wuwuc+g4CflkIzetvwm/LH461mwYDxCkXID2dBMmpJEjI8fxZMCQvQ1Fh5XIYmzSjWjID1ZbFwdRQQTVsjoqn+gSTnwdJ4kmq1SrWbmVl2dLcIhaG1m3px9i6DcxRVWEwGdB+eVvczLuO1seScbdXL3pNWekZdAU5ODR3JDbc+wd/KQCdhHtM26rt8YziOTzVYCDC5CVaztJ48vHHfpg/X4ZvvinE8OEl/C2tvqd450hmzpwXMm1lLYSFMI/5eOdMC7Nf51HlHXmbR00D5q051iJpnwbM9X7B90qPAjBCTMOGDRuLRKJuLMuKGIbZr1KpzvAl0pP1KxKAkUWW/4L58PtzExijEeaICGhfGQPty6MRt7ObwwWJNV+E3ElbxQVgQN5KfJI/BWGmDKTIW8D80zxU6dfaI6IV8uRgj7bff5fivbclkDJGzP+FRf/+JXnPKlquJEjC7ONfYs6JWaimEWPtyMOICWtULDdP0UfAaUqKCIcOiennyBEJcnJKAFf9+mZ07GhEp04m9J3ZCdWlmXa1h0SpdK/F+0DH2fim63wMb+w591OLbM/U+xmVBvaFsaEC2XsOIaxja8cAzF5P0OkgSTlfAsgIMEs5T8eKYlAWEABj0+YwUNPFIlBWv0FxoI+yJlVyjtdCmGi3LhPt1n8l2i1Cj7V2q1Ztqt3iwFZbGBs3tWtKufz8Ukz5dyJwfDwWDvgGn+XHOD3eTW30N6YXjMTuRrlY/ngVHAjOAPE6lYgkeKJWD5oIvGed3jSaor1CIiG2bh2I6GgWCQkFDvHrozae8JarR0Zh7qY+3rnOXB/v3M87HwBzzFOhADALHYRiM2tGgaEA93XZYMAgOrimzYsQJca1DdEVbkHkmLu2NTwNwIjP13gA3VmWFYtEon0pKSk/EH7yJdRT9b0OwEwmyHbtgP/C+ZAdSaCvZYxpBO34CSgcNBg0IoPAJ67S6COLudRbYsRHXQJZIxKthdHIIC+Pi24fhizMlbyLl42/wgwGvwe9in6nPgAbUhLIwx1yfhQnrmONJ+D5zPkoQCCNBDl+PBcJ0lMApzQ+22joWDM+TPgAi878jHq5EvyzKxLB+1JsLnUXfaR9XLzIAS6SkuDwYTEyM0uSOdeqZUZm1fXwa3AE+977DNWqlZ0rirbRokASzTpex/aGClSTKXBq7BGP5ikL0JlxboEYotSbuL99N4xxrfmBHEcdoLCQasYsWjLpqSSIVRdsQJA5MAjG5i1KfMpaxiL24FNgRQyv/HNB5lwc/vZAie9W4gmIsrNLwJ+/P6eJK9JuGVq1ARtVFD2jjPfQGrVotyIWd3KyUel3NZIPh6LDOs4t2JkE5USuNYzXcSD6eUiPH8P1RtFY9m4/rC84jLMZ3P5eoDQIfev1p2Csc3QXCs6sy+TJflixQoYlS7Q2Gx72yH4UxxNewNpRmyvHeR/vXGeej3fe4Z275jBnqS1NrvbAofU85gm/ZWfHu4owQSR8MrEm5OvzkW/IoyCMFB8Ac7KlKZXK2cT8UCQS/cqyLMOy7CsArqnV6klO3sLj1bwGwPLzIV+zAgGLfob42lX6XvrHu0MzfgIMXbpxUQetilAHX7JQzshg0G3hMJiyamJ87bm4cYPB9esi3DqcihvG6jDAXvAAFpUrszS3V9vCA/gy+1U0NKbAVLUa8j//Gvp+Ax7igTPCt94lsdRPzbtZvAgTMyKIRGIaNEKSkYkTf1UDs/cMpGIupLW3B7iy5JpsbILe7F+4c0eEkSP1+OwzHdq1C6Q0envwPTbsFCb/+wbWpKykGq+/5+egZqHUbTtMpB1dvcpQHy4CuAjwunevBHBVr040XCZ06mSkx1q1WJd307ds0aD156/C1HQFlnbfiD6KHs40Ld51iGw/25iFEYfzoXlzCgqmfUTv4fHFsEYDydlkSM4kgQAyolknPmY0Z19RyZEzOBstQ2yv8cUmjObadRDWuhmnnTt2GuJLFyE5yfltXV9zAgrDOYhoPFOumGrV4bRbbay0WySsPs8yP+l7fHxkOnDoPbza8BN8/LGOl2yLF0xH7yPw6y/g//03gESCghkf49TgJ7Dx4noawONm3g1KWWRAFAY2eIaaKbaoEksB+OXLDDp0CESLFmbs2qUpM+2FUMdiOnYtdx64enusI8+L+70J5bczwNrb9Amdd0KmT8i08e0XPgBmO4ALQQN2I/c6Fpz+EasuLIfGqEElv0oY2Wws1lxYCbFILMjxhOc0yM1NkSG2C/8ybuJ0Rcs9FArFabVaTaIfUujatWtXSVpaWrJarS6xYXKFajde42kAJrqVCv9fFkK+fClEuTkgfhyFg4dCO/Y1zqm+lFKRAxzRWBFAdeMG+TBFx5LvJOS9vRIluoe6klRU79cMRGNBFs01a5ppsAnippKYWFBymU7HBen4dg4NPqDr+STyv5wDc81avKT7IADTGjXI0GY4vEeYPAxV/CPp4uzkgWoQaaMwaXQYIgPIf5HF58L9Ix7aPXd48zIqOJLr6T/P4oUX/HH+vBg9expx7hwBkN4FYMTssEVkLLZd2YLYyDis7rcRDTp2cc2MzooXN28SwCXGwYMc6EpLKwFcVaqY0bmziYItYlpYty770KLYEe/Kigw6+esLWBHUDrWM3XBi4p/lEWGp1745owFWLbxHNdrZ/xwoDi3vcQBmjyISefDsGUhOJUJy+hRSD2xCg/QS00VyCU09YSa6aO67tSkh+feY32NoMTaO898i2q3IyHLzLVeXgzYrmiM33wzT3KtI2CNDw4ZmXkDiwQWT9N+9CHltDEQZ6XQcyfvuZ5jCKuO/O8ewUb0OWy5tQraO09zVr9SAasUIGPtkUmNs2ybFxo0a2vaEOBY7Ynh5+oSje/M9T8AWSU2hN+mhN+vokfwmpXpgdYittJBCMBkSEu/s8VrI9AmZNsJLPvQJBYBVyDxhp+HxAWDu5t3ZjGT8mPQt/ry0iWq/agRF49UWE/BC4xEIkgbxkittB55Mp8J3gLRT36MATKlUnpNIJLGWxMt16tSR+/n5nfj/EIZekniCmhn6bdkMxmSCuUoktCPHQPvSKLAREQ5Fx2cA4dvQSKjumzc5QGUPaN2/bx9ghYSwRcDKjAMFSyEJu4n5z75fDLSiO/MPpS6+fBFB706G7OB+sAEBKHh3GrRjX6U72nxLVmEmOq9uh3TtPUT6R2JVvw24p7mLdE06/S/3l7m4F2DCzU6t6G9y7r7ufpmPIepuAtYIUIsg4Mw/ElUIQLP6Ts6R3xHyCLo7U1ZxRq4EAI8c6Y/9+yWQSlkanv/UKSvwypcxPOrH/t6YAlidqRCdajyG33uvRpAs2CUtzu3bTLFJ4aFDEgrkLSUsjNNwcVouE12EP6AEfohqZ3hnfZH14JufDyi/eBqG6L1Y0zUBjzd2byBWkquvsHVtVM0xIW/Xfmr+V/yurfj3Cx4ic6oq4V3o1VScWhsBhsTo1+vBGPTF/mSsREJzk0HmR4MApaXL0LHGVbdrXmcd+xTfnJwN7P4C8aZ38OefXDh+PrK1N6kyd+8i5PWxkB3YB1O16shbsASG+I703gQI7Lu5BxvVa7Hz6nYUmgrp/zFBbZCybgTiQwbhz1XBpfKRD218x2KnhFdGpYqijZgDXb5/CUn3TuLUvUScupeEk3ePU188e4VYIET4V4FMzFlH+ACYY8lXlGwdU8avv3q7T7hjPHGGB67WEbJcS+OdJ8Eh2fBNSDtIgRfJMUpKo7AmmBD7Jp5u8IyttZLANf5824RHAZhCoZjKMEw/lmVXE8IYhnmeZdm/1Gr1F3wJ9VR9t2rAiH/X9r8QsHA+pP8dpSQTJ3QSRl438FleyVbL00mJ/1VamkVzxR2tgdbduyULYWu+yuWcxoporjgNFve9dm3ue6VKJbXdukvCsvBbvwZBH02l+YEMTZsjf8631H+GTxn798vYfGkTQmWVEOIX4pSamizO2nQtgDngDr5ZeB33NPcoOEvXEIDGfacgTpuOHCfAGtGYcZq1IqBWpGWrElCFgrg39oyHiBFh+6DdMLJGGM0GGMxGBAx5CncygZFRG8CKDGBhRJ7GCJ3BCIgMEEkMYCQGQGQEIzYAYgMaNdFDLDNALCVHI8RSA0RSA8QScuTqcR8jrU/uA7ERLGOgn8N39gMMZ1dNBkFS9GYuN5Jc7I8I/4hifylnFkz37jHUd4vz45Lg8uWSdhYayiI+nguaQUBXo0ZmqtnjU8rTJ8hzpi/bjUUFg1D7/jAcn/ozn0c7rBs0ZSL8ly/Ftz1CMGxlqk19T05eDgkrqlAq72IbQ8wA6YnnbW7liZ1D0p/armgB6IOg+fIy5n8rwuDBnFaOj2xLpc1shv8P8xA46zPqdKp5+31o3nrHJkpknj4X265spZqxg7f2c34FZjHahj+Bl1sNRu+6/RAo5cx+LYUPbfRdvLjr6g3ayNiQmn+TAq0kCrYScTr9FAgvLYUArEbhTajGnJh5kmNMWGO0W9kSefo85Opz4C/xx889lqB33b4ubeg429adrecN3jlLi716QqZPyLS5bTwpj/DKuPZR5J0n5jCT2YTtV7dS4EXGFVI6VO+EN2In4fFaPez6agudd3ybjEcBGCFGqVT2BvA4iYIIgOQA28aXSE/WdwcAI7vf8lXL4b94IcQ3rlFydT160cTJhs5dyuXbZM92/nYagw7Vr9DnkHUziVRNFr/kOzH1I8EviKvig0UsZlGjhi24sgCt2rU5TYuzi2K3ArAiQpmsTAR+8iH8Vy0HyzDQjhoLzQczwAaHOGwCRGU95u+X0TqqLe4U3PaI30GhsRAZ2nQOnBWBNO47p2XjgBoBb+l0wfFIFbMYMEu4z+XukJ2cQsEcI9OAkWohMmRg3FUxMOYl+PsDAQEsPX76qQx6PYMaNcxQqUq0fyTPUnw8Z05IQFfTpmaH0eYc8au8g6/BaEa92R2gC1JjZdtz6NGumqNHOnVeunc3Kg0dhPPVpOg/qSqOvnLO5jpPTF5OEWZVqby84/s8e/WnHnwHvyQvRND+HyFJeg2nT+fTNuTuBZPk2FGEjB8J8a1U6Dt2Rt7Pv8Bc9WFZ39Xcxewdm/B74nqgxglKR4AkAE/W7YtnFc+hS/TjdPdVCLwrjf+eoI0A5VP3TlqBraSHzLobVGqIlpFxFGiRY9OI5hRgPVgs9H3S8Uu8vnsMSACWD+M/xfTRC3EnTVQ8j5E5i+Qmz85m6BxE5qkHi7v9YD3BO3f0E8s9hEyfkGlz93jiTpnypY3W9+KGTmn0uXMOI+uotapV+OnU97iac4UG1ehTrz/VeLWKalMmu4Xe7vi2FY8BsGbNmlXW6/VilUpFHXJiYmK6mkymcxcvXkznS6Qn65cHgJEkq9S/a8UyiPJywfr7o/C5F6gJHc3PU45SVkMjAKxd1BUUFADEH8tgKAFbIhFLrfcsHwK6fvmlkGqwyKTmgmWf3bfwBACzPEh6+BCC3pkEyUU1F6Tji9nQ9+1fKpAlC6kua9rRyX3fcwkYvPVpequKdPwmtLRfGUvtmM2sCSazmX637BpzixWmyOyOgaigAGPUQTC8OBpSkYT6S0hFUsz/IRCMWYL33jFDIpJCwkggYiRgjVKYDFKYDRKYDTIY9BKY9FL6MeilMOok0BfKYNBJYCiUQV8ogU4rhV4rhU4rQ6FGAp1GBm0B971QK6ILoDvZuTAb5ICRi8bpbGEYFsSCjWhRSXJb8t3G58/ZG5VRzx2D70d/rsDPt15DtStv49TsDx2aPToim8m5j8qPtYco/R56T4zA+RqyCm13pdHrDt454kVZ54lTdfyqOIQyNZAxU4Wxo0CDzLiy2HRmQcJkZyF40gT47fgL5vBw5P24EPonej5EItm06t49AGfvXMTIb5ZhT/paXMvlgiQRDfBTDQZh6+U/IRPJkDjifw9YE60+0WZZa7du5dtqcGsG16Igy6LZalGlJUL8nItca93uzqSfwvDtQ+gG2UhVIMavb4hewSfpHMblILfMYywiIrgNHuviA2AcN9y5GHa1T1f0eOKIbj70OTOeOHoen/N8aCP3FQJ97mhz9wuzsfTcEiw+s4BuVJMxdUjMC3it5RuoX4mkCnZchM47x29gW8MjAKxhw4axDMNsZxjmFZVKtZM8UqlUfs6y7MsMw/QWUi4wVwDY6YX7uPxd27ZQ/y5TVFUUjhoL7YhXwIY5TgTqjJDsNTSNBjgYNw0rNM/gH/1jMJsZ6iNEgjUcOyamC9+kJO/4CnkSgFH+WIJ0zJtNfVZ0vXpzQTqibfM/EPOYl3Y8j53XtuOLTl9jdPPx/+92rJ1pT87Wscj1vxfOUkCm1TIg7U7W/2ncuBeAkeGbqabV8iE71sR3q3r1h4NmCHHBRHbfGvzYlAYJmN9AjcEDHt61d5ZXpF7Qm6/Bf/UKFLw7FY2iVtBLKxL4l0Z7RU9cE/aMwzrVajS6sAwX1o7AgQMFiIkpyUbChz6nFyQsC/mvixD00TQ6hmhem4iCqR8+lJds82YJxo71x4gReuxt3oCa4WoMBTT6liX0MeErMbMjH2JCTD5ijRbvJYfCb/JMmgCa+IqS4/NP1YSoMByJJzhzXk8WPnzTGDRoO0ANfZUTePzFIzidnkT9uKwL8dOyaLU4c8I4EPNpV8uD9J29cRsvbhuKW2wScLULsG4ToA1Dq1YmDBhgwPffy5CVxdANnNWrtVR77qnCh3dCWQgTOtyxGC4vT/+XeOf0eFJephVd/yjyrjxtLi3/Fhacng+S+7HAkI9gWQheaTIaY5qPR1RgVV5cFTrveL2Mp6IgKhSKPSKR6NOUlJR/rQmKiYnpZTab31Gr1d35Euqp+k4DMKMRHzb7G6PzvkUrPeffRXyVtMS/6+ln7CYbLQ/NloZ2fNhZHDkixrp1UmzdKkF+PrdLSCas554z4KmnDAgL+9/ZJXmQZzRIxztvQXboANiAQBS8Nw3aMeOLg3SQMOkT975KA0ZsGLCFLoyE3EmFTBttVwJ2cnUXbdP+/hqLL32Gysfm4cyiUXTjwpUi+2cnQoc9B0OzFri/cy9areECb/gAmC03L2SeR9e18agf3BiX3j6NNq2Bbds0NpX4yJbvgkmSfBrBY16G5MplGOJaIXfhbyAh+C2F+MzGxweCJGgOf78NxCGckQbZ3CEBOzK1XKJnAr6IFtvZEiILRWV5ZYRTcBaOMP+iYxFQI/+FF/1Xmf4Xxjviaml8C2rTBOfDDdg39/1i7ZYq64IN/YQ+Eu00tkocd4yMo1HHSNh4dxVCnykvHJMCDtP5i0Q/NYs1wMARQONNCEcD/PL4OnSMaVA0rwWCBIkiSddJhgMSoTIuzjNpQ/m0OW7OrZiUIL7xhH9r5CNbn1xt+euuzXVVVgr17yIpQYxmI6oGVsO45q9jRJOXKQhzpfCRa0X0Wb7v5BENmFKpTFSpVHH2iFEqladUKlVLvoR6qr4jAMbk5kC+cjn8f1kA8U0urwzRxpDEyYYOnVzy73LmXZrPHQDNyUEISXkdqalctILoaDNG5P6Il4L+QOVTW2wXMAKYHMqzS1ImT0iQjnWruSAdWVl0wZs/9ztcrx+JLmvj6aJi/5AjqBVSm5vE/x+ACGfakCt1/j/wLlObiaZLGsOUE4UZlZPxxmv2I7eVxT/mfjYqd24HUVYmDTlvatzE1+5KYdgIoqG+ug1PazZh89cD8f33WgwdahsSn0+7c2XBxOTn0Wir8g1rYQ4JRd68H6Dvz5kqk7J0qRTvvivHxIk6TJ9uq7mypo2AsgJjAbK0mTA92wPZ/mZcmf05/U2isGYVZmHt1hyY5Zmo1/Re0X+ZxeHYHfXJUL9KxZo0i0btQaBmDeh6ru9Cb0lSRZREJEzEubST0FkFkiUmz8RP68LudpBltMa2RU1QN7Q+3bDyRCE+ydu2SfDh4kToLrcDWM4/tHVrTtM17OcuWByfglnNc2mOn1+fXEE30SyyJbnhRo+WIzQU2LxZQ4P2uLvwaXN0XhHAHEvo8Ng86yyDtVoMWBSLKtfu4I9d4dQKCGYTYDKDKcinhqTmSpVpNFXq88AwNChYx+pX3B5VtTSS+cjW23Il6RrIhk7SCNvARxUu1yJmlheAHb19BD8mzsPf16nxGxpWUmBC7CQMUgyGn9jF3c4yaBNEn3C27zxQzyMATKFQkFxfLSz5v6yeKVIqlWdVKlVjF+l1+2WlAjCTEZr+T1PwJcrPoyHSfxe9hF+DJmLDmRpup4PcMCuLTDZSrF8vxcmT3IQVFMTSCeu554xo396EiDYVH9KaTkZ2QI6nBxAmkwTpmEFNvswiBj3erYG98lR80/UHDG/8UrFM+Ay+3p5YhUxbaXIVygDnTt69+fdbWH1pCfz/WoPE5X0RHs4PhAW/Phby9WtQ8MEMLtKeD/jbHROP3zmGvpt6oHVUO9yYmUB9Dc+cyUdAgG11PrItz4LJb81KBL8/BYxGQ1OC5H/yBYizETG3JffV6RgkJeUjxGqDlg9t9sYTCtoM+cgkAK0IqJHv2YVZFKBlarkj+ZD/aL3CTLpr7GqRmYBXLgZBOf5L6r9FkqlLRBKPgoi7dznQRTRdxGqDmMiTIqtzAjNGNUW/fsbi4BqWueKH5e9jyr8T6YL068fmYd6Lr9FriPnymjUSTJzoj8hIM7Zu1dDcgO4s5ZWrO2mxdy+v0UdyAWZn0zx6xJeVHBnynf4u+hT9ZsjvgnynX53mGpTJkKOTY0zEJvx0ogntb54ufHhXnvHE0XsQOkj/J/nwSGoXYgJvSYNB+qNUJINMJIVULIM8PRM319dEWfksHT3PHeddWdsdP5GHXdd2UI0XGfNJaVO1Hd6IfQs96zzpto0ePnL19trOFd57CoD9CCBTrVZ/ZE2UUqn8kGXZBmq1eoQrxHrimtpTxOzldVaAymymOUqYorDcrFgMNigIbGAQ0u5K3L6LQ1Ly7N4twbp1Evzzj4QG1CCBNGSK/QhotRGJn39hs1jxNMhxlseudFJ3+QRJEw5i5aKXMaFdOp684YfVnZbAYBWkQ8idVMi0/X8CEVfuX0L8qlZgU9vgFfMBfDXLeX8d2c7tCB0xFIYWsbi/Y0+xOayQZVsRtJGFx8A/++Jw2iFMrfYPvhjXHSNH6jFrVknwDct4w4e+8i6YxBfVCCEmiefPwtioCXIXL4VJocR338nw+ed+mDFDhzfeKGkPfGhz16RPeEcC9tgCtUzMSHgfJtZMfdNIcB8S7IeUAEkgzbFFPyIZxLfTcG1DtMcXc9agi6SgYFkOdLVpw2m6ftJ1haTy7TLNco+kJeDlHS/QZNnByZMRenwWEk9wudoWL5Zi2jQ5DSJFQFi1au4DYRUhV2fn13KPxVotB6CKQVUGB6oooCIgK6MEaGVmcFqsMgrJE2iOqEI/JI/p1ryjyAgSY9hj78BcpQrYKty5kBHPQwwWeZPfo6l4yEd8nYsMTQq5j7FFSxjatIehLfdxR4L3B0nnI9vyjif22EYCzey/uQ/vHZhMAZe1L6mlPon+92DevEitCI0VXam2umlEMzQJb4bne7YAw0oqVHtY2rozro0EBfVXImLAbFy8r6av1qtOb7weOwntq8Xzae5O1eUjV3eNxU4R5mIljwCwmJiYYLPZTIJwkNi/x1luVCYmifekUumAc+fOZblIr9svewiAGQwQ3bnNqdHDwqnmyxIqzV1qdILtkpJE1K+LOIBnZXFmII0amTBkiAHPPGNEn7+b0P+EaP9d2uTgLXB4Lecq9SmRGcxI/sGE6CwDdE/2odESSZAOIXdSIdNW7knf7b3T9obu5t2L257HruvbIFp6AAdWxEGhcGzmRFIl0KiH97ORvfsgTDGNiol0N33uZGdF0EaSag79axC61+oJZvU2usG0d28BTUlQ4QsmrZaaNPsvXULHeDJ23O0zHLFxwTTFwokTBZAXBQKtCN45K/uKoI2Arr/+KtF0WUBX27ZGDBhgpJouEpSHz3hyJecyhm97DpfuX4T8en+c/2whgqRB9B5z58rw1Vd+UChMNHE3X211abysCN45K9dSeWc0IqxlI4jNJuRNfrcIYGVQUJW8NwsR5nuo5XfXKS0VMcU1R0SALQJW5iqR9DcFWhRUkd8EWEWADa0E6xw1fHhHkqRP65qE1rrDeFlxEJIzp4sTwJP3NNWpWwzGCCAjmyFO58MphaF86HMHAMvX59GNJgK6DqT+C1V2SjFlJC/oY9Fd0aVmN3rs/0cveu7E8GTczLuBsxnJOJtxBuqN3+FMmB7Xgm3BMGOUQ5rdDEO6NEHj8KYUnDUJb4IgWenJ4/m0M2fG4gfXdmRzaNm53/D57p9hCkyj2vVnFUPwWsuJVNvuqcJHrrQPedlsmO97ewSAFRHBKJXKbizLkoiIZpZlT6jV6oN8CXygPrnnTwBasCxLtshGq9VqLiEWsTVt2LA/wzAzSHwMhmF+U6lUvzh6EOoSiQAAIABJREFUnl0TRJKcVCxC+omzNpeXV5ipqQw2bJBSbdelS5yJYZUqZgq4SEAN64WJ0BtaRWnAyE4S2VUnu6Y/dV+MIaI4GrJelnCQC9Lx/jQ0k/8Mk5gRJHh9FOVK2qm3wHVZ/dXdvCO26gPIZHjhafTIWo+VKzltQlklePwoyDetR/70j6Gd+Jbt+ODzPSzmB+mnPTd0BQk9vrpLAoY9EY+WLc3YudM2+IblAj6yLe84bC002dY/EfzWBIhyc1A4aDCmhv2Meb+EY+7cQrz4ooFW5UObtyd9b9FmAV1btkhw9GiJpqtduxLQZU87xYc+Eqa62acjoauxh+78r+izFjWCo2nE1Y8+8sOCBTK0aGGigTmsTUQd9dnSzvOhzdtytbQ7Yo1zeU01kMgkjFYLplALxmx/o4hA3rvi6ghvRECVBUgVgSgCqMh/FlAVHkFyhrjKuvL1CY0G0lOJVDsmIVqy4/9BlHO/ZOyoVAmG1m1hLNKQGVrGkcSTvGjlI1tXxhNiHpx49yQOpO7D/tR9OHn3eLHJMMkl2L56B5pHkACuxuFNbALbOKLt4sEDOJ95joKys5nJ2HjoPAyVzwJibjyylLqh9YrAGAFlzej3aoHVyx1Ep6y13dl//6Fh5Ek4eZLrlNEHIUg1Fgdnj0H1IM+45li/syPeVbT5Jq9G6qkoiHyJcLa+QqEYyDBMf5VKNVKhULQD8IFaraae1F27dpXcvn37gtlsbhUaGqrNzc1NYFm2r6O8Y46CcNgI3wU0nZ8PultItF0kAhTZLSS5knr35kBXly4mu7m5hN7QKgqALTw9HzMSPkCfuv3x25MruMGGBOlYuwpBM6fRIB3J0VJ88GwYfpt60aZp/S+CCGf7jrP1hNzu3E0bMfN6cuPjSLqbCPygwroFNdC1a+mmOLK/tiB05HAYWrXG/a1/F5seugIivL2gczfvHLUnS2L0QQ2fRb2kFZgzxw/z5hVi2DDbRYQrvHNlwVQWvaIb1xEybiSkJ49DV6s+Hktbi4xasUhIKKDJw73NO0e89daCxB7oIvn+2rYl5oWcpsuRSSBf3sW1liE7fiIKGi1CZEAUlvdeg9ioVhSETZ7sh5UrZWjf3og1a7R81+QPsZUvbe5ud2XJmcnIwKyZrdHrZBa6X2FsXCJAfLaI5oikvCE+VqSRikRIuyNGxxpXvWKq5lbemc0Qq1XFJovUbPEal4uPFGq22LyFrdliVFSZ3YQPfc7IlcwVRDtLAdfNfUhIO1Sc05MEsmlZJbZIw9UNrau2LTPYRGm0hcY1R3JaBPpWOQZiEUpjm5iB3FwGrEiPoDoXYI48BXPUaZgizsAYcQqs3NaYjATuIZsXTSgg40AZCYJBEso7W+zRl9FNiXnNc7GskYGm6SDpKsY2fxXLJkyESF/ZK22OzpkC3uR0lr/W9TypAXOFnjKvUSgUcxmGOaZSqdaRikqlMlWlUkWT7zExMc3MZvNXarW6D/mtUCi+YVk24eLFixvLuqknABjpOAcOcKHjt2+X0FxKpMTHE9BlRP/+Boc7eLG/c3FK/lcj5bjSOC5mq/HEuk4IlAbiwND/HspPQ4J0BH08HfI1K2FiAN3o8dB8MANsEKeq9wEwx1wX8gDnCdosQAHHx6PRlR+xd6+GrmceLGRBFPZYWzB5ecjem2A30bon6HMsMedqeJM2g8mAzmva4kbedRx47jiefbw5DSuenJyPIM6q7KHChz5nFkzOccWqlsGAwFmfIeCHeTCIZJhino2Wv4xG/wEmQU/6fPhGFzAONhFJKH6yYUg0XSS3JNkwJKCrXbsS0FW1qvN+WK7QR/xixv86Bx8enkr92X58YiEGNBhIF6TjxsmxZYsUTzxhxLJlWpovzNXiCm3kWe7yZX6QbtHVK/DbuR2yHX9RMGLRdBljGkH3ZF/oe/eFsUUswto0h1jEeNy/ryy+epp3xGxRevwYB8qOH4Xk9Clbs8XadWzNFpUxLptIltYn7mnu4WDqv9SkkICutIJbxSwh2qfHoruhS3Q3dKrRGZXklUtlV0EBQKKC3r0rQno6gylbZ8GUVwX9IscV/c/QY8Y9FmbYmXxKvTMLhKTCr04S5HVOg6l6Cvqw09DIL9tcIWFkqOevQLNgBeKC66KFpDqamiJRWWMCk5tL5zQS7ZtEihXl5mLvuU0IydGi420ZjlUzYXa8CX/EAMSRqEEmMOU/CUZckEHOyJCVJ8GLVXZi+eGqYINdCy3Pp/96ut3xocUddR81ALZYJBJtSElJ2UVeXqFQXFOr1fWIu5ZSqewIYIJKpXq+6NzHIpHoekpKyq9lMcqdAOzCBc6va+NGCe7c4fy66tY1U03Xs88aULu2/YmLNCqyw6Iz6Yoj5ZBdBlKiAqpSx2pLISYJ3nCudtS4vK0BIyr/fpt6IPHeSfzaawX61R9QKokTPmqALzZkoX66EaZq1WkCZ32ffj4A5kioAt9h8sTgS9pVu5UtkZaTDtPsm5jzaSBGjHhYS0PySMn/3IT8mZ9D+9obdjnpCfqcEJlTVbxJG0m4SSLbvdRkFHrqv8ewYQE0yfGcOQ8H37AQz4c+RyDCKYaUUkm6dzcCx4+F9H4G/g3pjybHvker7VyodyH64/LhG3kHe7wrDXSRqLtE09W3rxF8QJc1a8tD3z/XdmLsPyNpBMkP2s7ApFZv0yBVI0b4Y+9eCc2BuWBBod0NE2faQHloc+b+DuuwLCRnTlHA5bdjGyQXuLDkLMPA2KYdvopMwd9N/bFxssrmVv8vNxK1Ws5s8dgR+2aLocRssU2x2WLblHEolImc6rOWPnHwaDqO3k7A/pv/UrPC85klLihEs9S5RokfV3RQbWRmcsCJaIrJ8d49kc1vArjI/5bcraW1B+JzGhXFovqtE6guuYfQF3rS3yT6Z7WwQnw8WYcgcy4+npyOgtt5KLybB0NGLkzZeUBOLpCXC2lBLqSFeQhhcyD2y8KdqHu4EZWNS1ULcL6qAclRsElLQWipkw20vFPyaXEXqF1kCbqjIfB1R2B/UbrE1reA9xKAp9UiSIz2TWDN4eHUn89Uuy53pJ96MNetC3NklFtSNlV4n3XYqflV8AgAq1evXq2yyLhy5QqXUItnIRowkUh0JCUlZQO5VKlU3lCpVPRZRAPGsuwslUrVl/wmGjAAh9Rq9aayHlN7Xm322qSSKD20bp2iVnfN9n97f9+9C6xeDfz+OwmswT2pUiVg6FBgxAigfXv77c5kNiHpThL2XNmDmftnQmfUPRQRh9yLJP+sGlS1JEFnaioFYHCCNp7s5VW9zrccj2x4x4NvvB4G4MuDX2Lq3ql4odkLWDloZZmXE9r8DCxUBa8AX34JkFCTTz0FnDjBmY1VIO/s8o1nm+PLOz71hUyfp2j79ui3eGvXW5Ae/BSVk6fj4kXYaqjXrQOGDAE6dCCqbc7kx07xFH185FdaXW/RpjVo0fCHhsjSZuHSxEt47cXq+PNPojUA4uxmhuQo5kNfKcOMO9jE3eP2bZxpMRzN0/eisEo0hg814EQ9ue1YJ5A+y4dv1iQnJJAkx8D69QD5Tkz8iDV3587A4MHAM88A1UgIrXIWV+mzDNFn7p5B/9X9cSPnBoY3H45f+v8Ck94PTz4JHDwIjB4NLFrk2tquvLS5xBqDAdi/n+SbAe0YqancbUjOrB49gKefBvr1A6KiePWJMpqjS2Q6uqhCeGdNFLHNu3CBa7yWz+USzY9BBJyLlqHloNeAjh25D2nQVoMHWYOdvH0SvV7dDW31f8BGH4bexG18y0R+iAnojHpsD0Tkdgdzqymy0gzIvl2I+3cKkZuug9RcCD/oIEfJ0fLdH4WICNahSnAhwoMKERaoQ2V5IULlOiSl7YKfIQMDbpsRAC0kMHIdkGQgJ4WsUcj7leLr50g2JHK3MSAEenkotLIQ5ItDkSUKRnIwcCpUh/OV8nApPAu3I+9AG2ibUkCkDQEKK8FcuWiJfrk7RKdHQpxeB73v5yNyWA9Eh2lQX3QVtQyXsXvhZdQ1X8bLj10GQ/hPOi5p4w8W4sNXrx5Qv/7Dn9q1QbOuO1EqvN05QSOfKgyPrPecHZ0TRalUkqAb2QByiqpbX8sWaa2cuJNtFYVCMYhhmH7EB6xhw4btRSLRDAvgIj5gaWlp5/R6fbvAwECNwWA4LBaL+1+4cOF2WQ9yRQNGfAN27eL8uvbuFcNkYiCRsOje3YjBg43o0cP4kH8r0XBdybmE/an/4mDqfiTcOoD7uhLH00ZhTfBYdBd0ju6C+Ood0XVtB+Tqc5Gjuw9FZSW2DNwJkpBTCLtfhJ/e1ICdyziLnhu60Pc/MPQoKsvDymw7hDbqvEzSC5ColtlZYHQE4JKJmoG5chhYYrtSAUkihb6DI2T6PEUbiWDV8vfGMOnkKPj8Gt58ncG0adxEzNy7x5kearXI3nsIpvoNS217nqKP90Bp5wJv0TY/6Xt8fGQ6zf8yus4niIsLRJMmZuzebT/4hoVUPvR5UgNmoSfxOIuDfb/Hx/gIrMiMb3qF4pVfr9mAbyGMxWXx7XYagw7Vi2NU0Ve7dYspWtdZpmSWrv39/VkcPKihO+/uLHzkSucVOyaSxBTspR1DcfLuCbSt2h5Le6+CzBCBQYMCcOaMGK++qsfMmTpLsGKnyXcHbc48jJh2Ec0q0XLJdv9dHHDCXKkS9D2epOaF7e5+AI1fSVJskjfqbsEdevtgWTAYRgQRiDmoCOKsbGz4twqMSzfSSJHBshBap0Nbbl70lImk9bt6i3fO8NdSx9psMWX7IjRN1UNmpawhwORyqAl/1wP+qSfG/romZBelJGNYQHnbHx2vBKDLFSk63AAqGfXF4EqCssP086HTUV1CJ23MxL+PEUGrE+HPwKF4arg/2JAQsMHBYEkES3IMDin6jzuaibuFVeTusp5VWMii9eIu0Gsl0JtMMDIFMMqyYJbeB9LaQHZ2JNjU9jDnRcCUHw6YSwdJZP1LHhsoN6K26CbqmC5j0XsXqC+f+OoVehSRj50ccuR9zTVqWmnNrDVodUky3OLXEGK7cyTPss57RAOmVConARgIIA/AWoZhNqekpJDv5S00CiLLss3JjcRi8Ssmk6kVwzCBJOKhQqEg2i+Se4xhWXbJxYsXFzh6IB8A1rRpIAoKGLoBThwjSWnZ0kRNDJ9+2oiICNvJiwygxH6YfAjosrYhrhlci0bIIYCrU40uiAyItCHV0tD61XsKP5/+Aa2i2mDjgK2Ibt+2wu2/6UTppUTMZEeq14ZuOJeZjFV916N7bS6Ea1nFBoCRiiwLRlMAJiuLOi9bCjH1IE4EeXo/vBe2CJ/tbAxzrdqubac6IqrovNAHECHT50naPjnyIU0iGbp/EQoPj6YBGGrVNCPkleHw274V+Z/NgnYslyS2tOJJ+pxsXhVKW64uB21WNKd5qk4MP4Nff4rCrFl+mD27EC+9ZD/4hoVgPrzzBgAjdA0a5A8cOoI1QT1RI78Q+s5dkffTIpijqlKyHyUARjbYiR9eXh4ZAUtAF1k0WRS6nli485ErnVdK8VEj+c4m7X0Nf1zaiFohdbCyzzqEmxvhqaf8cfGiGO+/r8Pkyc7n8ittDnOXXAkY8Pt7BzUvlB34FwyxwiCBM6Jr0pQp+t79YGjfoXjn3+KGQHJGkQ0hS7JeXv3eJINIH4LoqCAES4MpKLN8Aq1/0+8hCJIFIaj4fw7EEUAXKA2CWFS2L5K75Mrr/XhUJvT5X0/FwU1h2FdVg93ROuypbcJVK1ctYm7X4zLQ4wrQ8aoUQRp/6IhOi5FTvZae8UNETRnE/n4QB/lBGiSDOFBOtZUs2bUoPsrp7+L/ZH5gSYRJmQysnxyQ+4El/5HvfjIM/nso9BIGfwzZU/wfORfWPpaLvn082eZNvTXeWR4a9zsXtdHa5LpyXFOkpIWiBwkCYuYChJAPGVMs2nNLOgoLGPvzTw0aNzaXbIyQdVhGBsTXOED24IekUrBXSCoEas5Ytx5+ytmB6xESzBy2hpo3ksie5AFCGIt5NM/iqh4BYJa7E1NEiUTyHAASqTCdYZg1Go1my82bNx3He3blbVy4pjQA9uDuIdEOp6dzO1RicRHaD2Sp5tQycZEFCImOY3HcVGeX2G4TG2ICtCygq05I3TLDhVoGuOPDz+D13WOx8eI69KjdC1s+Og85U/Gd1FsAbNZ/n+GbE19jWKMRmNeN5Pd2XEqdHEh6AaMB+RMmQXIqCZLTSZBcso2UaA4Lo47OhpaxMLaIg7FlLMzVqrsNlD0KExfh8P+qv0tprSct/xZar2iGCKYh7sw4h4EDjVjaazlCxo+Cvn0H5Gze7jA3jZBl6w3aZh37FN+cnI1p7T7CG7FT0KZNIPWTIME3gh2krOFDn7cWJPv2iTFkSABqNFmJXzEGPc9paV6k3B8XwvB4d0FM+s7wjWi9SPCK//6TUEsNkkcrObnA8UDqhhrO0Gf9mLJkS6xIZh//EnNOzKIAYnHPpYiR9ED//gG4eVOEL74oxOjRZQN9m2e5OaKa+PJFyLZvg9+OvyA5ebw4cqGxcVPoevelfsjGps0fmkvIumF1ygr8enYxruZwGst21eJxKVsNiUhK35PkXcoz5CFPnwfT7JkokLG4O+IF+pv75OJIogZmaR4io3NoXQLkHkz066xICQij4E0ajOu51+hahUT7YyCCiGFQYODaT4iMC7xg2Xpm8nIx5VwICsZPoH7tlrJgIac9GTdOb/O/hT4bOq2us/7f6u/i9yLP+O3cw9mG7L670R+BV+MgU/UGc+UJSLVKdO0pwd5D/vDzFyEx8dHrE87Kk089V9Z2+/cXYPduLnjPnj0lgefq1zfThOz9+xupJQTZ8y615OfTpN22wKxIe5Z6026ycLqJTiyZDAYwIhFMNWhMvuLirty9fPjHp65HAZg1IQ0aNGgiEokWkRxearW6lFhYfEh3T11nARhB/USYRN4kySSNfi4uhC7yMIZN30XDk55KTyrOeG7JBUEcNx+r2RVNwpvSAczZYt0JiBboxe1DsO/mHrysDsSvCeHIOO7eHGXO0mWp50on5bvDmnT3JPps6k5zW+wfeoROus4UPpM+if7zWrwazfUn8XbXo9TRlwwC1oXkTuEAWSyMsXEwtIgDG2mrsXSGLlKHD220vgupD5ylxV49IdPnadpe2z0GG9RrUe/IVmh2tcL1oCaQmnXI2ncY5rok1k/ZxdP0OXp+Wec9TRsxE2u7ogWNUPrf8NM4djAEQ4cGYNgwPebNKz34RlnjCTlnb2fTW32CLPi6dw9A8lmg6ntdcTGoHwI/ngHGYIBmwiT4bd5Y4TvWjuS6+svzmDBBjqwsEZ5+2oDjx8XUsonvWOxq23NEnyu7/RvV6zBp3+s079Jnnb5Ct8BxGDAggAZB+OEHLYYMMTpFbrlpM5shSTrJmRbu3AaJmttwZUUiqt0iUQuJeaG5dpFP+QNUqbJSsCR5Idap1kBjLIBcLMeghoMxqvk4NItoXu65guTi0xgKkG/ILwZpFsCWXwTQ6O8iYEdAC/nfGtSRazO1GS4DOacE4alKLCDOqwtW1Q/mU8OBtFaAyIBBfvvQ5/uu1GWEaIC9NZ64MtYJZQ3AR8NEoj5awBg5WqKA16tXAsZIztsywdiDbYK4kqTexMQlPVD3cgY++S+Ii4pZ9GGK1HBEw2xd/l8DsJo1a/rL5fI+DMMMBtCWYZi/AaxTqVR7PdXn+N6XjwliXGs59OGJGPvlThxI3Y//bh8pNhUgmcDjIltTk0ISmjQuqrVN9EK+dD04OZCB8Jk/+yHpXiI+OB2CtxYWOe8W3VgIgwifTuqIH4XGQnRf3xlEi0hMLwlfnS3lnViZ7Cwa9pZoyKRFmjJx6k2bx5uq1+AAWctYGIqOLMnL8ggv0ulg7+ZdYUf84HPe07QlZ5yhaQ5aBHXFJ28HYwC2Iu+L2SgcPc4pMj1Nn1NElFLJ07RNPfgOfkleiFmPzcXIpmPwyitybNsmxc6dBYiLsx81y5pUPvR5c6zbvFmCsWP9ERi/Alf/fIqOCcFjX4Hk6hXqR8pUqoTs1RvBBgSADQwCGxiI1l0iYWbEXgE5pfEtOK4lPsx9B7Nzx8PPj8Wnn+qoGWjr1oGU7Y8yACP0H79zDC/teAEZ2nSMajYWz1f+Gs8OCkFuLrBkSSGN3Oio8GlzloWwjNXhxJwd8NuxnYIu8V3OT4v194e+y+PQ9elH/brYcPtzAQn88Pf1nbSvEEsZUqKDauLlpqMxvPEI6udsKa7Q5ynZkk1ga3A2dOtACsrmd19MyWWKjPuDXxsDCQPc/4nTSln+Hz+eJH1msHAhF2jC8r/1d+t4BNbnrVfq9q8rkfT9bBEOHRJj8R9XYTw9GDAEUq0vye9INiBe+LI1KksKbMz8vDmeCH2OLY0+V9d2BIwRjRjRjBEwptFwKjASHZxoxkiUVT5gzG6fYFmEEesmiRjpJypWMeFozHnwvEc0YEqlcgjLss8CaM0wDAkZT0AXGW0cz8R836Cc9csCYPf+O4PL90ngjH3Uh2tHykGwfiWBMxqHN6XA4LEaXOCMIJkDOxsetNpraBnaDDz1rQIXQ434vNNXGNP81ZLBWgCaElc7qT22zDw8HT+d+p5Orl92nsODc54BESQYg/RMUonpYlIixPfu2tBlqlWnRFNGNWYtqbOsdRHSpGqPqUKmzxu0PbNlAF0YJS0Asu90xcWf/8LAZ5wbtrxBH6+OYFXZk7TdyL2O+FVxqBZUA4efP4HsDD/ExgZCqTTTvGrO7HTyoc+bCyaywVqzWRZM96OQGtUJVcXpNDoZ2aQRaUoPLFLIyCGrXALKigEaBWqBHFizfA8gvwPBFh1h9d3yn+U8NbdxINfUVAavtVfhqL4VyK7z4sVaNGvGtWFv8s7Ti03S7ohlyIWs83i8Vne8VmUZRgypRjfFV6zQlplUnQ9txEpCtucf/DtpJ7oV7kAwy7mzE3N1fc/e0PXuB32Xblzwg1LK/cJsrLywHL+dXUzz45HSsXpnjG4+Hr3q9C6JcuylPuvqOGK5jk9/9XS7y84G3ez54w8JEhLEMJsZgDHDr/5RfD4+Dv36GRBWFLerIjXqQuSds2sAd6ztyHBpAWP//FMCxurUKQFjZJwqa74QUrsrbx8i13sKgJHR/ibLsgcYhiFbUTbRKUgUQ3cQ7457PAjA7hTcRuLIDthboxD/tAjF7YK04seI8+pAnvYEvpnQkfpzVQmo4g4S7N6jtIaW0yUGnfvfxd0AMxb0WIKBDQnOFcbE6o5OSt7l6O0jeOqPJ1EntC72PpdAzZr4FG91UtGd25AkJUJyOpHTlJ1KhCjLNjO9sX6DYk2ZsWUc2p8dTaNdCdHHis+ipHgy8SLw94Zc955ag6GHx2LYWTGO/6mGJqoODcjhXxQtq6x26A36+PQD67qepG3CnnFYp1qNn7ovxrOKIfj+exk++8wPX35ZiFGjnPPJ4UOft0GE8tUvkb3xC7wbPB9fVPqaYyvL0kirxPTFHBgEsGYu2I+Zha7QjAuyFmhWNxeMRgOmIB9MQQGNxFreQp39rbRtZzVXoIEe8elyavp2w1QDO3VPIAPhqI/LeKrSv/ATGzn/RZEId9PF6FbtAg4lFsWdLy9BDq7nI1dX5jHi+zT271ew58Y/iAlrhDcjN2DSS41oYJH16zVo06b0zZMyaTObkDdxCvXnkiYcpGanpNwQ10XEaBJEoy9NBPwgIH6QHeczz1EzQ2LaTAKJ+Ev88axiKN1YbBzepEzueZp35RF9RdOWnw/s3CnB5s1SEF9Nkh+OlNatTRg40ID5+s4Qh9xzap719nhS0bxzJHd79LlrbWd5tgWMbd0qwd9/l4Cx2rVLwFjz5g+DMaHzzhFvHzzvEQCmUCheKosQtVq9jC+hnqpfY040+3mnr3Hw1r84cPNfXLyvLn5UuDycarg6k2iFNbrgmceb0nPeMN8oq6GdDTeg83MFKDRqsarvBnSp2U0QO5vu6KTEubfb2g50l3DL07vQtlo73qKvsE5KFmWpN6mWjPiSWQJ9iHIt2RgAsjl3KVKKWl0Hl2jLmjRDWKc2go1uSQTgDtnyFuQDF3hcriyL4OGD0arh31BHijAs8yKW/VAP06bp8OabjiOseZy+cjDQU7RdyDyPrmvjERPWGPuGkIRSIrRrF0gTkJ45k49QWwVwqW/Ahz5vL5hif22F258lIJCJQFJSfnGOON59wmjkorFag7Ki76DHgiKwpgGjIaCNHIv+s5ynv0vuoc/JgtzIP3Q8AXLmSpXBVq5cdAyDuXJlsJUqc8fKVr9JvbAwWs/ZENcWQfOR6/+xdx5QUlRZH7/VPXmIIqgERXS6RxAwS1jFBCISFAOKgV3T6uqueZPrrulTd1dXN+iurBmzgpIUc1ZAQASF6R6SrgIiOQ2Tur7zqplhZujuqne7uvpW++9zPAhdr/rW79736v7rvnqPI8BUG/Uu2C2f3ETjF/yb9izek65o+yz98xdHUufSTfTY/auprNMmCmzZRMbmzY3/BbZsphc/e4jabNhGpy0vjO/D1LD3Un1989Vy8/PJLC6hNVtL6KjO/6O581JvqaDsmbH8VUt4fbzyQwuFWrlRTc0dW34+tStqshRfij7tBTvukJIN26qq4u8WqWnBqoKyY0dcdPXuraYX1lkbc++7b7wv6Njn9XiiYxu3T3D9moyd9linYYASY2pjdSXG1PZODdMUlRgbMSI+TbFv37gYk85O47KtQzMlwK4qLCx8euHChWovMNmfW3ZV59T84sJgIRVvq6Z3Z+xNe7++uNnCGV52VLtAe+WVB2nM1NMpP1hAk097lX42dKBn4jCTnfS3H1xvrQZ15SFX058G3M6KHTt2nBe/WYaoRkqULV9G+WrFxfmf04LXx9PB/6umVk0KA40rSBkG1e/ZMb4p5846vNcvkYp1wpMFAAAgAElEQVRi57EAK3zuaWrzqyto/Jhy+vlBFXRZz2to4hX3Wjf6mTO32e6R9GNkd+Fr59KM5dPpqWHP05Dup9AHHwTpzDNLaMyYWvrnP3duLOqg8+iw83IcbhjrNr91JW167dd0883V9MtfxsV4JpMSB8gaE83Y2i7U49XpVPnFDuq17ya6+w/r6dA/nE5qIfHNt/wfGRs3WJV59eeMZ7ZQ+9g6+knPtWRs2ECBjRvI2LixccU+u99V771Zwm2nIEso2BoEXLv2NOzds2ljSYA+vPirZqv/JWN3xGHF1MrcQh9MXdVELDURT1s2U8ASUjv/rfHvm+mhrivp6v4bKBgjenQy0XnNV/NOeWnWGNyw99JOAWZdpyp975z2aTcWr6taR08vfoIe//IR+nZr/J3hY7seT5f0/rm1grHd0u4tDdTpE14n6l7Zplbvf//9IL38cj699lqetQ2Q+pSVxUWXqnYdeODuDyB07MvGeKKuwU+zYLwa65TIbirGGvy9775KjNXRi3lnUEG3BTTvwubvenlln934qPt9RgRYOBx+lIjUnlzvxGKxRysrK9/UNcyz428hUy2lqlYgKggWxpdbXfkdrXipa1b3Y3AygExdOpkuef1C6lC8J+U98RHlbznQk+pcpgTY+/97l86aOorC7cvpzbM+oKI89eKu/scJu6Zn9XIAVrblffstVT67V3xvmJrqnX/WND51VVOJrHc/Skpo5dpCGthleVb9KinZzNSNS/X59sccbQnm1e+8T4e8O5Sq66vphkAl/fG3HemCC2ro3ntTTyGTHndus5uzera1SqnaHHfq6a9bY+ellxbR5Mn5NG3aNjrqKGfvziUbT5LFnZf9tcG2WFUb2nT3AiopMWnOnG2ktvmRcNM/6NfX0frn7yGzqi2deWYt/eUvO6x9S7VsU4JDCZqdgszauH6DEmYbKKD+VO+7NfxdCbb16/WFmwJpTYMMxv9U4556PlWgqk+xXdUnNY1Tc8hXS1E3bEj7xoEBOmfgt7Q5v54ur+hJvZ4/lqi0LY3+WTG17rpz01q1YW3rtnT2B+NoS1GApv10FplqA1tlF0NYq8V7HlnwEE2qfNFalKskr5TGlJ9LFx/8cwrtEda8ml2H/9jGk4YrV/tLffJJ0Kp0TZuWTxs2xCNCJeFqIQ0lvOyWNNdhl43xxO2xmB1kCRp6MQXRib1KjL37bnwBD1UZaxBjwT3+Rz8/Zy9rEY9DD41XxrTGOyc/7tExGRFgyna1AmJxcbHa/+tCwzDKTNN8Ki8v77FFixbF30AV8tFZBdHLjup0AHnsy4fpNx9cR8HNPWivaR/S/I/cWwgklYvc7qRqL5RBz/cn9Q7ejDPeob6dDmVHiFN2DT8gwq+H9aJgbQ1VDR1OhVNfbnyX7OtgD3ql9Fy64NXTqD7Ev5k7helLdocfnN70TdOktueMpoJ336Ytf/sn7Th/nLX3nNqD7tb+d9Ezv7qRKisD9Pbb260bf7LPj4md2n9n9OTh1hSrKafNoH6dB9DatQb17VtKau+X9993tvhGYx/UWH3Ty/7aVByOWFpJDz5YQPfeu4MuuKA2qzd99ezmttsKafz4AjLyq+i+vxKde25d4wvsniQkajGSTRubVNJ2VdoaBNuMuU9S+w3b6bjvCnaJLNVup+Ot2oUSPjurT9W1AfqwaDAdM7yUTCWU2rRtFFdm69bxf2vd1vqz8T/1Dt5O8aROp5Z3P//Vs639q3rGzqRFdz5JB+xXSGpj2E6ddlVL0umvtfW19OryqdZqhrNWfWpdjdrb01qRsfx8alPocO5tioE5Hfucjvfc49y2Tc3+nDMnYL3TNXlyXuOeq3vtpURXnSW81GqqThb0adpnnVSZsjWeOLHNuhYP37NOxs6T8SRFMKp9eJUYu+LvM6jqq5PIrI7nud26xWj48Do6f+JZ1L/oC1o7J7ubWOv2p4wJsKaG9OjRY6/8/PyxO1dG3BKNRofqGpqp4/0uwBSXP8/+P7p3zp8pf+2hVPHbqY73y0qHqdsC7Jp3rqRnKibQ9Uf8hn5z1E3pmCZ6nrCjG1dtLRW8/w4VTnyR6idNp1IzvkFk7cF9qHr0WVR9+hkUa7HhYFrAmjR2ZF/T4z28OWTKtqKnnqDW1/2Sao4/kTY9N8lKBtfvWEeHPtmT9izuSHd3XkjnnduGjj22jl58sSppEpAp+9zwrdu2vfvN2zRm2ul00r5D6JnhL1kmPvBAPt16axHdcccOuuwyZ4tv+EmATR/8lbWMe9euprUwS8ej0hT+TMeuWGFYS+PPnx+kvL0qac8Lr6AFN77c7GzZTphS+jUW2yVe1bLRTbJqt5JNNR3wpzPGWuJor9oj6fu/T6Fe+3WkV17Z3vheIqdP1BetoQv//gA98dWjjQt0qRUY1TTDE/YdrLXXp537U9m3aqVBAzrHN21WHyXI1XuX6tOmTXyf0p3rrlh/Pv98FbVta1rfqQppE71qZ0bC7zns1Imavj+vXrtbuDBgTS9Uouvbb3dWIPeIJ9Wnn15H/frVWwuq6H507HMr5pzaqGObOqcE+ySNJ2ZtId3ZeT5NmZJvVca2bNlZIQ1+S8Mu7WRVxg4/fOc7Yx7mJ0793/Q4TwRYz54996urqzuPiEYT0Q/RaPQUjrGZaJMLAkw9je5x9Q20rfy/1mIhKiFS77Jl8uOmAHtjxWt0/qtjqPeefem1M95Oa/80a8DSeJru9QCna9tPDiU6acdU+tsRT1HBO281rsZV029AXIyNOC3pnjMc/+va5+XNIRO2Bf73DbUf1N/KSDZ8MJNinbs0YlOVZVVh/u+Qx+mZm86znsA9/fR2Gjy4PmNJCcdnTtq4yU5t8DrkpeNowQ/z6e2zP7I2jVXJ1IABpaSWQFeLb7R3ts5Ao+k69nkZcy3Hk2uvLaSnny6gRx6ponG3lKdXeXXiuBbHTJuWR9dcU0SbNxvWe3Yf9e1NgcIqX71Poi7Ji4ROTSG+/r1fWSt0ltZ1o23/nUpH7nswvfDCdrXSv9Z9Yv6aeXTaXY/Q9h7PEwVrqFV+azq3/Dy6qPeldEC7MoYn7ZvYCbD++ywjtbCmSkAbFqSwP6vSu3ERpsSY+q91a/Vn/O/x/4//Xf1/g2hr3XrX8er7417uTUbAZMVdNKpEV3wFw6VL46JL/dawYfF3uo45pp7y851cSfJj/DKeNL0CL/qEE6pu5nZOfk/nmJa2qfh/770gvf7z12hK1WDabLaxTtelS1zEqzgrKCCaNy/+EFvaJ2MCLBwO70lEY4joPNM01e6CT5im+WRlZWXzHYSzTCQXBJhCeNgRhbTuhLOpqvtkGnXAaHpoyKOuPo1r6Sa3OqmqNBz7XD/asGO99d6X3dK8TsJFZ/C1EiwPn5KkY5t6F6Nw2hQqnPQi5X/ykfXivJmXZ1VuLDF28jCy7qxpfNKxL42fddTUddvU1MMzR1HBh+/R5n/8m6rPUc+Idn2WbVpK/Z8+jA7pdCjd3+t9Ov74UmtfJTW9LlGC4Lp9jqg4O8hN26YseZkueWMcjS47k/4zWL3uG39n47TTSqz3kB580PniGw3W69jnZX+1xocmD3SWLDFo4MBSa1Wuz9b2oLyg4cm7wirRuOWWQnrkkQLrPbS7795B55xTpyUivB7rWrJrGqleJZvq4eQ/5v2N/m/WrZRX34rqnn+WBu0z1NonbMALO1c1viDxC/3fzZxLU5e+Yk0znPv9Z5b5eRvDdPuIS2hMeKyr+34m6sXJ+kSrww6ll6pG0N173UOLFsXLQwMG1FEkErCqRX//+w5LoDf8t2ULWf+/aZNhiTW1WbX6e/z/1Z9qdqjmm3dGjIzCrdS5Q6tmIm7PT6ZT+8Bmyr/o7EYRpwTbzTcXklrJv0sXs9Hm4mKTTj5ZTS+soxNOqLPeq3TrI3k8SXaNXvUJO8Zu5XZ2v8P5PpVfV3xXSP32qLBWUVTvj5lmPKbVAwc1a6Hlx4vVzO2uMSMCLBQKzTAMQ60f/rJpmo9Ho9EP7AzJ1ve5IsBUUhILVlG33w2hmas+saZEqOX1m+4u7yZjtzrp5W9eRJMqX6I/9LuFfnXYda6YqDP4ep2UuGWbWjSi8JVJcTG2YL7FTS3aUX3yKVQ9+mxLlFmPfjQ/btmn+bOODnfbtqLHH6HWv76WqgefTJufeqHZVKgGg8a9NpZeWz7Nes9p4n3H0xNPFCTd38pt+xxBcXiQW7apd1+Oee4oa5uIj879jHq0PcCy4PLLi2jSJDWVaDv175+4QpjKVB37sinA1DX87GdF1savb3Q8lwaXfJpxAbZ8eXzK4RdfBKm8vJ7++98d1ibX1tgluNovyT61YNWVb11GO+p2EL1+Lw3rcCUtGFRORrB+typOzU8OoocP2koPDiyiNdu/J7U6slrF8POHrqbC706ieXOrHPa69A5r6dtNm4iefLKAHrlzA62s35uCQdNaovuKK2rokENi7AeJqnq9bVtclMX/i1fVEom4BuH2XuUciu1oTfvkHdTYxpmIMy2hVVoa/1NNhcxEIqzTL7weT5JFRTIBll4U6bd2K7fT/2X7FnZV4YZpuSqm1Xtj69YZ1nRcJfx/NAKsvLz8omAw+MJXX3211R5pdo/IJQGmSL798bc06pVTaPH6RXTT0X+iqw+/PiOA3eik6unixa9fSIfvdaS1ilpeIM8VW3UGX78KsKaggksqLSGm/stbttT6KtauHVWPOJ2qzziLavsNcDzp/8fCLvD1CtpjUH8y8/Npw4ezKLb3PgljT20KPvLlk2no/qfSvUc8R/36lVJ+vmktS9+uXfMmPwZ2ExY9bk3rGtfrYvrroPssAGrv8T59WlmrlH38sd7iGw0Eddh5nTC1tG3evAANHVpKJxV+QG/ufWFGBZhaAezaa4ushHjs2Bq6885qaysuDjevxzpJAkzZoqYRnj/9HFpTtZpozmVUvGYQdTjr9zRv3EJSlTJV5VLVrqkVL1JtkKhNQVs696Dzrf279m/bgy1wuDe1hrh75bivrMVWnnoq31oJrpWxlS5p9Tyd/+6Yxn2vvPZtyz7RIOICA0+iJd+3obF7vm5tq9awvdrGjfFqROfO5m7vn0GAxSMEAsy+p+jcJ7zuE/bW735ERipg6mfKysqGG4axKBqNLguHw2o1xItN0/y8c+fOt7333nt1HGMz0SbXBJgazFZtXUmnThps7UVy//EP0NiDLnAdXboCbM32NTTouaNJbbz8ztkf04Ht3ZtHL7mTZtQ206S8Lz63Fu8ofGUiBb9fbfm9fp/OVH3aGZYYq+vdN2Glxw8JnWvsYjFqe8YIKvj4Q9r8wHiqPuucpP1DJWbDJp1I876fS5+OnUvTJ/Sk228vpMsvr6Hbbmu+LL1r9rneW92plFTVVVG/pw+ljdUbaNZ582nv0rhofeihfLr55iK69dYddMUVeotvcOIu2wJM2Tx6dDF99FEezd37VOq24LlmHnPDPvX09o9/LKTHH49POfzrX3fQWWftftuUHHPSBJiyZ+XW72js1DG0aMMComUnUMl3w+kvfyqlhxf+h+b/8Lnlx54b8umXi1vTyY99Sa3yd03pdsOvOl27953n0pb3LqXqhSOpvt6gvfeO0aWX1tLVjx5BHfK2ZFT429mZS3HntV9Tse3YsTX98MMWO/wZ/T7d3C6TxkmPO91rz4gAC4VCN6j3v0zTHBcIWGUNtU7r1aZp9lSLzkaj0Wt0Dc3U8bkowBSryg1RGj5pMG2u2UyPn/IMndzd3XVP0umkKqn96YzzrKlddwy8my7r+wtX3Su5k3pmW3299Z5Y4csvUeHUyRTYtNFiXHdgWfx9sdFnUn2PA3fj7pl9DI+7ZVvRIw9R69/dSNVDT6XNTzyTUpAqMxveefppr4vptqPvo5/8pJRWrTLoww+3UY8e7ixrzcCh1cQNdg98/g+69dM/0C8PvZZu7n+r9fvqCfcxx5TQihUB+uKLbdShw+5TPZwYqmOf1wlTItvefTdIY8aU0Nkl0+hfKwa5KsCWLTPokkuK6csvg3TQQfX08MM7qKws8fYHOtwsQeTh+66SBFgDJ2WTWkRm3dbNtIM2E6n3RIx4zBbnFVuLaxSvWUtfv9QtKwJHVY3eeitI//53AX38cXxGSM+e9dY0Q7UqoJpVLuFdoVyKO6/7BASYkztC4mOkx53ulWVKgH2xdevW/itXrtweCoXuNgxjv0gkcq56Hy4UCqmq2EG6hmbq+FwVYIqX2ij1jCkjrOkVL42cSkfto17Lc+eTjgBTq1Jd9fbPaUDnn9CkUdNcXyxEcifNim3V1dYKitY0xddfJUM9XlfL2h9yaFyMnXZG4xS8rNjnMCTdsC2wfBntcfwAMgsLaf0Hs8ncay/bX6+L1VmVnx+q1tC8CxbRx2/uZSXIw4bV0uOP71pwwg37bI1hHpCubWqfviOf6kP1ZozmnL+A2hXFlzmcOTNII0eWWKuXPfSQ/uIbDZejY5/XCVMi25TwHNr1a/qi9iD6ZGZVcyGehshRq3Zdd12RNdVMbf59xx3VVFyc3Ok63CDAdnFU98Rv128gqi4lWnAhtW5bR+36T7QOUO/Xrnipq6cCTA3JL72UT//+dz5VVsYX1igKvU+tjxtPX97+n2ZbX0CA2Q+COv3C6/EklwSYvSfcPULHr9kY73SvNiMCLBwOz49EIocoY8Lh8CzTNB+MRqNPqL+HQqHFEGD2bnIr0N76+nW64NVzqHVBa5p6+hsU3qPc/scdHMEVYGoKiFr1sN6sp/fGfEL7tenu4Nf0DnGLnd6vOjs627YZW7dQwavTrMpYwXvvkFFfT6ZhUO1PjrXE2ICtd9OmkgBreWFnBPhHpc1OTT08bRgVzPyENv/nEet6nX7Gf/Eg/eHj31p71F13+G9o+PAS+uyzoLWv0IAB8UUn0rbPqTGM49K17e5Zt9Pf5v51t/dKr7qqiF54IZ8mTtxuLR/N/ejY53XClMy2tw76LY1d9wBdeGEN3XPPrumoHPvUql1/+EMhTZhQYC1QoDZ7Hj3afqa+DrdsJCSS7VO21a3rRuajH9Dq1QG6774ddN553m6wrRYJePzxfHrkkXxauzZgvV+q/K6mOF84T00Yotwci7kDhcN2OnHH6a8OzdA+zG9TELUvMM0GOn7Nxnine3kZEWChUGhOTU3NSfn5+a0Mw1gWDAb3W7x48aqd+4FNi0ajvXUNzdTxuVwBa2D2XMXT9Kt3rqDOpV1o+ug3qUvrrmnj5AiwOXO20jnTRtO7/3ub7hn0d7qw18/StiPRCSR3Ukm2GWvXUuGUl6lIraQ4e6aFsjpI9F55MfW/5t9UPXgoNbzxnwtPXYvHP0it/vBbqj51JG1+dILt1MOmsbW1ZgsdOqEX5Qfyad4FX9GiBSXWQgx9+tTTG29st14sl+Tblv0iHdvU+5pHPdWXSvNLafb5X1h/qs/GjfHFN/bZx6RPP92W1uauOvZ5nTAls63NYX2p5+p36bvgvjRnzjbaa6/4VDZd+9TS9qqiqpYU79VLTTmsogMOcDaVU4cbx7Z0B2jJ9jXY9sxRi2jUqGLasMGg8eN30M9uy/z+bmqa6UMPFdBzz+VTVZVhLeU+blwNXXJJrdWfLF8JXuFSsm267HT7a7p9IlV7qQJM2SzZNgn5CScuMiLAysvLz4zFYvcYhpFnmuaUaDT6i3A4fDYR3UlEt0YikQkcYzPR5scgwBS3f35+P93+6R8p3L6cppw+g9oX7ZEWTo4Au/rJf9EN719Nx3c7kZ4bPsnTJfLVxUropFJvXGpD4sKXJ9I3j9xBPVfFF1KIlbaimmHDrffFWt14LQWDAU+n5bgpIoJLK6n9CT+xluq3ph527Kgd/7d/+if65+f30X3H/YvO63khXXFFEU2cmE//+EeV+D2Z0om73394o7U63N3H3mutCNfwefjhfPr974vo5pur6Ze/rNHm2bSBjn1eJ0ypbBu/7QL6xfo76Fe/qqY//CHOQMe+l17KoxtuKLL2rlEJ+O23V2vth6TDTde2tBy6s7Fk+5raNn9+gEaPLrE2Np7U9qc0vPT9jIx1s2cH6MEHC+i11/KsfYq6dYvRz39eQ2PH1u62haNf2LWMk0SJuk6fyGTcJRISXtsGAcb3sOQ+wbmqjAgwZUh5eXln0zT3jEQiC9TfQ6HQsEAgsL2iouI9jqGZavNjEWBqzvsfP/4dPbTgQTpy76PpxRGTqSS/yXrGmoB1BVhdq+W0+fxDKGgE6YNzZlLnVl00f9H54ZI7qWTbrARtwsFUvuBbmjGpFRnbt1lTFNVHPZNVe8rV79mRqLCwsXq0cqVBAzsvy8heLq4JsNmbqd3IoZT/2Sza9PATVDPydOfB1ORItbro4U8dbO199cE5s2jld0EaMKCU2rWLV4COnZR6Y9cfPlvY7He9vPFz4+7ldxdR/2cOo31adaFPzp1DBcH43nLqHajjjiuhysoAzZ+/jTp1claxSQZexz4vuTX0CfXn3AQb9tZQEe1btZiqqw36/POt1uazTuzbvp3oppsK6emnC6hVK5P+9rcd1oa0uh8dbhBgzem2ZPfpp2phlWIyqnfQ650upPKF8U3GGz5O/JrIf2oIVYJLCa85c+Lvdx1ySD394hc1NHx4HeUl2X1Fsm+T2aauDQJMtxfvOl5ylUmCbcnISni4zvF6xgQYx5hstPmxCDDFVq3+9Iu3LrE2PlarIj429Gn23ls6Auyww4vph2EnUfU+79O/TnyIzg6r9Vgy9/HjjUvKAKLYqZfQl77QJZ5l19TEhdjWrRTfyYXIDATILC4ms7iEVq4vpoFdlosWYIsuvpta3XIT7Rg1mrb89/G0Ak9t5vpi9Dl65tQX6aT9Tqa77y6gv/2tkG64oZqe3ydknTtRoh4MGBl5ou70Yrh9ov9fzie1aM6DJ/2XzgyNafy5OXMCNGxYKY0YUUuPPMJffKMxudWYbsVNhJ2yanmcHbubzovQnXcWNlYC7eyLRgN06aVFtHhxkHr3VhsrN1/EQ8dOO9uyKfrVdUi2L5FtagXCC8fmU7Gxgya9EaC+fXetPmnn15Z+U5sbqymGaqqhWiVUfU4+uc4SXv361TdbWCORz/3GruEaIMB0enDzYyWInGRxJ8E2CDB+bIls+WMSYMoBNfU1dN70s+j9b9+lseUX0H3H/4s1FVBHgIUufIQ29ruWTtl/OD0+9GnW7+kEjx9vXJIEWEIRcVgvCtbWUNWpI6lg2hQKrvnecsnGQHt6s2gEDRl/KtUMOiFeHcvQh+PXA2or6J0Nh5HZug2t/3A2mR06pGXdl2sX0gkvDKRjugyiiaOm0tatZG3OrDbKbXfj0ZTX9vucEWA17b+kNaMPofI9etK7Yz5utlrpNdcU0jPPFNALL2yn447jL76RCwJsyVsL6dBDW1n7dal3wQYOjL8jl2iD2eefz6Pf/CY+5fCii2rollv0phzqikMIsOTdPdl4ohZXOW/dP2mPDkSTJ1dRKBQXYU4F2PffG/Too/nWHm7qvbLCQpPOPruWLr+8Nul2AhBgaQ3LuzXWqdA59au7FiY+mwSRAwHmhafjv4EK2L37mrn0xNrJrvJqQYHTJw+nL374nK49/Ab63dF/1I44pwJsyYZKGjhhIBl1pbTg8lnUqaST9m/pNuAk6skSJt3ftjtesm1WkuGkEhGLUd7sWVQ47RXa+MhU6lz/rXXZsdZtqGbIUKoecRrVHH8ipVw/2w5Ugu8d2dak3VGHFdKkNcfQoTWzadNjT1PNqSMYv7p7kzOnjKIPvn2X3j77I+q9Zx96+ul8uvbaIio54kXqcO4NOSPA1p50OlXtN4WeGvY8DWmyj+DmzfHFN/bc06TZs9NbfCMXBJgSObfcUmhNMVOrF95/f3yaZtOxWFVD1Ptyzz6bT61bm3T//TtoxAj9KYcQYK504bigSjHWPbrtXLps/d20zz4xmjp1O+27r2krwCKRAP3nP/n04ov5VFNj0B57xOhnP6uliy6qpY4d9afo6o53XgoJHYGjI17d8q6ufW79brrngQDjEZTyAFvXegiwH6EAU0Hyw/YfaPjLg2n5pmV050/+Qpf0uVwrdpwIsPpYPQ1/eQjN/f4z6vD287R4orubQScz2I83LikDiC67Iw4rpkNqZtOzZzxHhdOnUPCbry23mCWlVD3k5LgYO2EwUWm8KpDOR9e2h8seoN9t+h3tGH0WbfnPI+n8dLO273zzJp0z7QxrSp6amqfe8TjppBL66qsg7XXNqbTw9881O16Cb3XZHTx0Ia0ZOZCO2rsfTT399WZV68cey7eqOL//fTVdc016i2/kigBbvdqgI44opa5dTTVr15pe1iDAKiriUw4jkSD17VtP48dX0f776yfkiQJY169eJul2Ikf6tNxbf1ZBt9xSRN27x0XYsGHxd6abCms1S/vjj4OW+H7rrfjLXPvvH7OWkR8zprZhEVnW2CPZt7oCR0rcKUdIEDnJAkKCbaiAsborqxEE2I9UgKloWbFpOZ06aTCtrfqBxg95jEYdONpxEDkRYP+Ydx/dMfNPVLL0HOrw3tOevCfk95u+r6cMmSblLZhPhVMnW8vbB1csj4ux4mKqOXEIVY8YRTWDTyazVWvHcdb0QJ2EJFixmEqPPYY2BfagwKJPyVTziVz6qAVtBj3fj5ZsrKQ55y+0FpT54IMgnXlmCRX2mEnffNrL1xunquvb9+ZRVN35PZpy2gzq13lAIzmVcJ54YgktXhxffKNh6fV00er4Vkoy11JYX3ttfFGNDh1iVuKtEvXnnotPOVRLjV96aQ398Y/Vrs7S1eFmjY1pbBLN8bFk+5zY1vCO50EH1dOmTQYFg3G/1tYSTZ0aX1hjwYL4whpHHaXe76q13vNSx6X7cWJfs/HRQ99CgKXr3cTtIcB4XCU85ORYDgH2IxZgKmAW/vAFjXplGNXUV9OzwyfSMehtevMAACAASURBVF0HOYojOwG2aN1XNOTFQdSuqD3lPfQlBWv2gABzOsWviQd8mzCZJgW/+tKaplg45RXKW1IZF2OFhVRz/ElxMXbyKWS2aeso3rSEdW0ttRt2EuV/8TldvOfLdPeiEx3/htMDn1k8ga5590q68pCr6U8Dbrea7XvcTNqxaDA9+miVtbpZw0fCzUEnmXv3m7dpzLTTqeh/p9A3dz3fDIlarnvIkFI65ZRaeuKJ9BffaDi5jn1S+4Taz0u9/6VWtVOrQg4cWG9tUq32eFJTDpvGhNM4sztOhxsEWHOaTtipBw5qtcqHHy6gggLTmnarlo0fP76AvvsuQIGASaeeWkdXXFFDRxyxa8EOO785+d6JfRBgiUnqCkQn/vDiGAgwHmUJ91iO5RBgP3IBpoLmw2/fp3OnnUEFwUKafNqr1LtjX9tYSiXAVs6cR0MnnkAL135hvT/yuzPOtM7n5P002x92cIAfb1xSBpCMsFNiLFJBhVNfocJpkylv8aK4GMvPp5rjTohPU1RirH3qvemc2lZy31+p9K7b6aWSC+i6Do9nJO6q66vpsCd7kfpz/oWLqFVBa+pz70ha/dc3qPu+Qfrww22NlQ4JvnXKTq2UOuSl42jBD/Npr5fn0sI3y5r1uOuvL6QJEwro2We304knpr/4Ri4JMHUtP/tZEU2fnk/BoEn19QYdemh8yuF++7kz5bDl8OfUr42cPaySWILPyTulTS7KS3GdyrZVKw0a0HlZfJwyidavN6yFUxo+hmFas6rV+3xKcGfi3uZHdoqPhFUQU6UJEkROMvsk2IYpiA6STJcOgQCDALNCacqSl+nSN35KexZ3pOmj36TubfdPGWKpBNiND5xL98y521pl8f4THsC0l6YJhuCExKuEKVgZtYRYwdTJlP+ltU0gmXl5VHvMIEuMVZ8yPOFqhU4SElV1az9kEMU67EmHBBfSpkD7jCRHyub75vyV7pp9O90+8C76ed8rrWRzwyt/oq0fXkS33rqDrrgivqG1nwSYGgcueWNcwmnDasXH3r1bUfv2Jn322TZXpln5XYA1TdTVtaiNfNesiS85rvb3UnvEqffBGj5uJ+pO+kTTgdxLgePVeMLNhZwKsAYR9t13hiXG2raNi6+m0wzd9qtf2UGAcaMx3g4CjMdPwj2WYzkEGARYY9w8svAh+t2HN1L3NvvTtNFvplyxMJkAm9+xho4e9QPtXboPvT/mU2pT2BYCDAIs6dgUWLaUCqdNsaYq5s//PC7GgkGqHXCMNU2xetgIMjvFV860TTY/mUfthp5A+Qu/oE3PvEi9fxN/pzETyZE67/od6+jQJ3taDy1mnTefjn76EKrf3pa23/sFxWJEs2Ztow4dTN8IsLpYHR3z3FH09eYVtOdzX1H+lgObsZswIZ+uv76Ifv3rarrhBncW38g1Aaau59tvDUt0demye9XL7Vi07RNZ3PzbUZ/Non1gx0kZ4210p/h5LfxTXZkEkZPMPgm2oQLG7xe6LSHAIMCaxcxds26j++beQ306HkKvjJpuTa1K9EnUSUuO6kVHjVpNi9rX0osjJtOgbsfHB2tMe2lEiJt+8iEq8M3XcTE29RXKn/tZXIwZBtX2G2CJsRN23E/ft81Lusz7tjHnUelf7qSqc8+nrX9/0JO4+80H19FjXz5M4wc/RrfP/JNl82XbI3TzzUV08cU1dNdd1b4RYE8teoKue++XNK7XxfTOdeN3E69DhpTQggUBmjdvG3Xu7O6UOp1+gfFkVx/S4YaxuPnYA3a66aJ93KkjMAWRz1WCAJMsDpPZhgoYP+ay2vLHthGzHWy1AppKwp5e/CQd2/V4eubUF6kgGN/Xpukn0c3rL5d3oXv6bKGLDr6U7j723l2iAwIMAswu8Fp8H1j5nbWsvTVNcdanZKi5P0Q0p3sBlV90C1UPH0Wxrt2sf7MG37paMn/4gWKd9qIN739KZtt2ngiwZZuWUv+nD6NDOh1qbe1gGAZ9OuZLOvbYUvr6a4Pef3879Tu3J0lfcvubT2dTv6cPpY3VG6xq3qnHHthMgC1cGKATTyylIUPq6KmnqjS9aX+4TjIMAWafCEtJSHT86rVAlGybxULwdHVUwOzHNM4REGAcavE20oV/oitDBQwVsN3iQk1FumjG+TRjxas0uuxMevCkhylgxN9raPi0HIBnr5pFIyYNph5b8uit6/9Hpfm79n1CwoSEiT+sEgW+X00F06fSgod/T/2WVlNwZ/Gl9rDDqXr4aVQ8/kEKrltLam3ojc9NotoTTrJ+zqu4++lr59Gry6dSp+JOVJhXZFXoXnstj8aNK6bBg+votYoDxAuwW/97Gd3yyU30y0OvpZv737obu1//upAef7yAJkzYTief7N7iG8nGk4Z/TyQkvPIrxzYv4056ki7dPskCx6/s/JAIQ+Tw7saSufkh7iDAEhBABSxxZ9xeu53OnnoazV49ky7rcwXdPvDuZpuxNr15bavdRie8MJBWbFxGH7y6F4Wnx5ccb0xgUAFLKlxTJZpeJ3N+uOl3XPItffZcOzK2byeq3kFN1jegWGlps/2+Vq40aGDnZRl7B6zBd7NWzaQRLw+h4rxi630wJcBUwW706GL6+OM8euGF7XTWWSX0ww9bstYvUiWbWwtN2v/irVRvxmjO+QusrSOaipxt24j69GllLSqh3mFSq765/dFJhiHA8EDHjfjTiTmMxc2JowLmRgTufg7JIkeybRBgmYnHjJ8VAiw54g071tPIl4dSZEMF/aHfrfSrw65NKCR+/+GN9PDCh+jaha3pb3P2IF9vJpzhiMNNnw9YsVNTE5e+0CV+kvp6MqqqyNiw3hJi9V26EgV2VWq9EmBq2u6wSSfS3O/nWIvPLBgXscxT0/ZOOqmEystjtHBhkNavlynAbjl8E91xyCa66eg/0dWHX2/Z3lTkPPtsHl19dTFdd101/fa37i6+0fiQRmO6FQQYBBh/FAG7TLLzQyIsWUjANn50Ygoin13WWkKApUa/cut3dOqkwfTd1m/pHyf8m84pPy+eoO1MmO4//gE6Y8oICrUP07z7tlOpGYAAS4EUAozf1SWxa7Cl4WpUxXjdjrXWX0vySqg4r4SK8opo4/P30rbPzqbx44lOO02eAKsdeBCFzl5JJW060uzzv2icOtxU5JxySgnNmxegOXO2Ubdu7i6+AQHG7w9Nx2FVdW36wTtg9lwljSeJrJVsHypg9vHFOQICjEMt3gYCjM8uay0hwOzRR9dHaPjLg2lLzRZ68pRnaXD3oZYAU5u2GmTQqm0r6dXRb9GJw8Zl/V0X6UmJ5Jsq2Nn3hZaCoeHvqgqmHlKY1FygFJntaMe0P9Oea86kWe/lU+smi4p6WclJFne3XdmZ/tVrq7Vojlo8p/H6dk4bnjChio47rpROPLGOnn3W/cU3IMCcx5zfknSMJ7nrWwiw9HybrDUEGJ8rBBifXdZaQoA5Q//Z6ll05pSR1sETR06ln795kbUPknr/67rDb6TfHn2ziOW2cdN35s9kR0kWiJJta4g7JcSeHPYcTV82hV5dNpUWr18URx0LUrf6QXTVicNp2P7Daa/SvT1bJCRZn/hm89fU/4ne1HV7Hn14w+pmq502iMOTT66jhx8uoMceq6JTT61LL7hStNbxrZfCFeNJei7X8avF2sP3hSXb5te4U3ZLT4Qhcnh9WjI3P8RdIupYBRGrIDrujW+seI3GvTaW2hS0sZ70b6zeSL069KbXz3zXSt4w7cUeJW769oz8KA6TJUxLN1bSyxVT6b4Z06m2U3xvM1U1PnLvoyk6+UwqXnEaffF+fKPpTH4Sxd1Vb/+cXog8S0+934GGPL+82c+rRFhtJr11q0GFhSZ9/vk2ys/PnIU6/cLLJN2viTDGYvtY1Yk5r8WhX+POD4mwZCEB2+z7bbIjpAt/CLAEBFABsw/4pu+7bK3dSmpxjobPXiV7Nz45VwskrHipK94BS4EUN337eMslAdZwLTNmtKYLf/kN9Tl7IrU6chLNXPWJNYVXfXrv2ZdO7TGCTu0xksJ7lPMBacTd4nWL6Ljn+1PvdXn0+ZR9aN1nzd8hUiJHrX64fn2Arr66mm66KTOLbzSYrNMvIMB2OVqHG0RE8w4CdvyhBlMQ+exStYQA43OFAOOzy1pLCDB79C0XHNhcvYk21WyynuR3bR3fDFd9IMCcs8RL8/asWh7h14RJXUeHDq3p8MPraf78IE2fvo269/qefnLxW1TVfRLV7/cO1cZqrcstaxeyhJgSZH06HtJs6wd9YskT9QtfO5dmLJ9Or7zRkUZ9V5Lwocn33xtUU2PQ7NlbqXv3zCy+AQGWjldlb9ZrCT6N1S29FoiSbfMrO2W39EQYIoc35kjm5oe4S0QdUxAxBZHVGw954iAKBoLWnkcNH0x7sUeJm749o2RH+JVdw81h6tTtNHJkiSXEXn11Ox1xRHyz8nc++Y7e/Pp1mr5sKr3zzZtUVRdf7KJrq26NlTE1ZVH1N+6nKbs5q2fTsEkn0VF796OP/+87ygvsvnJpnz6ltHp1gAYNqqMXX8zc4hsQYFyPxtv5tU9IuFeAHT/2UAHjs0vVUrLIkWwbBFhm4jHjZ0UFjIc40QAs4aYqPSnBTZ8Xb9L9msq+pjeHiy8uoqlT8+mhh6rojjsKLRhqc+OGj1rO/t3/vW0t4vHGihm0uWaT9ZXa4PmU/YfT8B4jaWCXY5otmOGEaEPczTl/IY2ePJw+XvkhTTltBg0bcZm1cum3Hy2kDRsMWr/esP4877xi2r7doIcfrqKRIzO3+AYEmBPvJT8G4wmfH9i5z84PibBkIQHb+DEpvfKa6MpQAUMFjBXxEGAsbHhizcPm66f9TZOS5csNOuaYUurUySTTjO8b3VSANcVTU19DH333vlUZe235NFpbFd9nrG1hOxqy31BrquJx3U6gkvySxmb19USbNlGjmNq4MS6qbn7zrxTb3p6OOrAHvbX3cGq/9hTq8t5U2rR4Na2rb0fbKV6Na/4xqWtXkwy1y3WTTzJ703CtVr/AO2C7SENE8KMO7NxnBwHGZ5qMXXpndK+1ZHHoh7iDAEtAABUwXgeFAHOPmzoTqof2PP2aMLW8OdxySyE9+GABtW0bozZtEgswJc7UIhiqGqX+W7s+RnO+n0mfbpxMC2on05bAtxawQH0Jtfl+KOUvOZ1qvxpOm9a0JdNsoZga0BoxokuPJOo8j+g/n1PJ5r60Z/V31KZ+I1UW9rLEoPovGCTasoUs4dWly+7vfkGAxYFK6LN+7RNg5+/xDlMQ7f3HOUKyyJFsGwQYJ9oEtIEA4zkBAsw9blKSOWWH5IROsm2p2KnvjjyyNcXUuu5qS7AY0apVhvWnEjklJfF/U/+pClaHDqYlutQCGIk/JlHnuUQHTSTqOZGoQ6V1mFFfQG3XnUhdt5xGITqV9mnTkdq3N2mPPUz6vy+upto9FtDmtjPplG5n0fghj1BhoQwRoRt3qIDtigq/9gkIMPv7h2TfQoDZ+49zhGSRI9k2CDBOtAloAwHGcwIEmHvcIMCcsZSckOgIMHXs1q2quhXY7cINw6T99ouLpnbtzEYBpYRU0//U9+rv7drF6LvaCnp1+RRrquKXaxdY5wwYAeq/z0BrEY9hPUbQ8ElDaPW2VWoTMvro3M+oR9sDrOMkJMIQYM7iP9FRfu0TEuIO7NyPOz8kwpKFBGzjxyTeAeOzy1pLCDAeeggw97j5NRG2EufD4+8OZWJaWkvCfk2YEiUlaorhwQeXWhWwV17ZTu3akSW48vJ4cdXQasWm5TT4pUFUVbudamK7791Vmt+K9ijao/FH/Lh1hJcxpysO0Seax6/kPivZNr/GHQRYeuM3BBifHwQYn13WWkKA8dBDgLnHDQLMGUu/JkxeJyUNnOpjdbS9roqq6rZTdX21BblzaWcKBnapPAgw+9iTHHeSbfOriJBQnfMrO6/HOvveu/sREDkcaon3d+OdKTOtIMAywzWjZ9URYBk1pMXJ/XhjxY3LPkL86FcpAtGv7CQkJYc8eRCRSTR/3OJmQerHPosK2C4X+rVPSIg7sLO/XyU7Au+A8dmlaglxyOcKAcZnl7WWEGA89KiAucdNisDx61NXCclcKnYSBFguJZsQYBBgvNG3eatc6hPW+CNgOriEsc4uNiBy7Agl/l4yNz/EXSKq2AdMYx8wXtjyWvnx5iA9EZZgnx/9KkUg+pWdhJuDX9kl6rNeJpp4KMG7fzW0khx3km2THne6VRyv+6yufelFuXutJYscybYlu8e655nMnAkCDAKMFVmogLGwiV7mXfpN368JEwSYfV/R8a3XyZyObVIqERIeNmE8sY/7VEdIjjtdgeN1n9W1Lz1PuddassiRbBsEmHsx6OmZMAWRhxsCzD1uUipMSJh4PrV72g8BZs9VJ9n0OpnTsQ0CrLmvwc4+9pMdIZmdrsDxus/q2sf3krstJYscybZBgLkbh56dDQKMhxoCzD1uEGDOWEpPSCS/mO5XdpiCmLpv5JJfIV79I151BQ4EmLN7nGSRI9k2CDBn8SXuKAgwnksgwNzjBgHmjKVfk01UwOz9q+Nbr5M5HdsgIvwjIiT71YqjCQdbMOde8GUzqFKmlybr1dJXo5MsJGCb/b1CJ+74Z/OmJd4BwztgrEiDAGNhE39TlXzTl2xbqoQJAsy+r+j4FgJsF08dbl6LQ+kiAuzs+yXnCAgwDrV4Gwiw3GSX7KogwBIIMAkdwY83BylP5iSzk2wbEib+4A8Blhl2mIKYmivGE37cgR2fXaqWEGB8rhBgucnO9wKse/fuRYWFhU8RUSci2hwIBMYtXrx4XdMLC4fD1xLRGLK2HKVXI5HI7XbuTDQFEQLMjlri6REQYDxuqhXY5S47VMDc9S0qYKiA2UeU/REQYPaMOEdAgHGooQLGpyafne8F2E5x1ToSidwWDofHmKbZPxqNXtNwYeFweH/TNJ+PRqNHqX8LhUIfBYPByxcvXtx8AnULEhBgvLDHFET3uEGAOWPp14QJAszevzq+hQCDALOPKPsjdGJOnQ1xZ89UwlhnZyWqTHaEEn8vmVuyuONdqXetfDMFMRwOTzRN88/RaHR2WVlZm0Ag8EkkEom/pRr/BHv16tX2q6++Wq/+Eg6HZ9XX15+3ZMmSJalwQoDxgg0CzD1uEGDOWPo1YZKQlPiVHaYgpu4bueRXr0UO2Dkbd3WPQgVMl9iu4yWLHMm2QYDxY263luXl5RfFYjE1nVBNJVQfwzCM1YZhXFVRURFRfw+Hw19HIpF9E/1sOBz+KxG1ikQiV9iZBQFmRyjx9xBg7nGDAHPG0q8JEwSYvX91fItKxC6eOty8FjjW7wleyU+ybdLZperREGD2412yIySLHMm2QYDxY85RS1UBMwzjroqKijmqAmYYxkfRaLRP08ZlZWWFgUDgUdM0N0Wj0SubCLikv7HfffuZK65Z4cgGLw/qfn936+d2s617/N9pRXObk/xzxkxOaJ9k2xQJAfb50q9g56gfJfVtktZe9tlcijsvuVmhL3gslmwb2DkaNpIeJN23OlfndZ/VsQ3HgkC2CKjKktPfdnyg0xPqHBcOh6/bWdW6LRQKnUNEx+wUWY2nCYfDr5um+VY0GlUVMEcfVMAcYdrtIFTA3OOmzoRFOOx5+vWJtbqybD8V9is7TEFM3S/86lf0Cf+Pd8muINtjnR1ZyZUc2GbnveTfS2aXzGrfvAPWrVu34uLi4ieIaB8iqq6vrx+7dOnSNWpxjlgsVhkIBPKI6BnTNGeqKYo7q1+/i0ajs1K5FAKMF/AQYO5xgwBzxhLJpjNOiY7yKzsIMAgwftTnJjspD+sgwNyPTMkiQrJtyR7ouO8hd8/oGwHm7mXvOhsEGI8sBJh73CDAnLH0q4jA0357/+r4Fu+A7eKpw021ksIOfYLfJySwS2U9KmD2vtURr/yzudsSAsxdnupsEGDYiJkVVRBgLGyiX0q3EjS8NM9zbAp2EhImyX7VjTspIkJCJUK6XyUn6tLZJbNPwnii61f2oJqBhpKFBGzjO1wyu2RXBQEGAcaKeAgwFjbRAkc3Efb6iToSJl7MSferrn0QYP6pgOkm6l76FuMJfzzR9Wtmfol3VsmJOmzj+TTZQwn+2bxpCQEGAcaKNAgwFjYIMB42qxUSJj48v7LDO2CpfS7dr7qJOgSYvbhGBYw/DkpP1CHA+L6VzA4VsCQE8A4YL+AhwNzjps4kYTqTdJEjPdmUPGXIr+wgwCDAeCOtfSu/9gkIMHvf6gr/9M7oXmvJIkKybdKFNQQYBJh7o0SSaoRfRYSrYGxO5tebvgTf+pWdhITJr+wgwCDAMjU++7VPSBhP/CpwpCfqkkWOZNuk+xUCDALM1ftYLlXAXAUDAZYxnEiY+Gj9yg4CDAKMH/X+Zie5og4BlpmolCxyJNsGAZaZeMz4WTEFkYcYAsw9bupMEipMyg7Jibpk21Kxk/DE2q/sIMD8LSJ0E3W8A7aLmK4A492R3G+FRJ3PVDI7ybZBgPFjLqstIcB4+CHA3OMGAeaMpV9FBASYvX91fOtlko6HEva+4x6R7f2idGLOioPDS61LnTt3G/eStdpBgGnhcnywZCEB2xy7cbcDJbNLdlVYBRGrILIiHgKMhU10hQnJJs+nDa10EyYvEzrJtunGnZfcdG3zOlGXLiJS9SgIsNTjjW6fTW/0cq+19ERYsn2wjR+HktlBgCUhgAoYL+AhwNzjps6EKYj2PKUnm7oJk5dCQrJtuiLHS266tkGA2ffjhiMgwCDAnEeLe0dKTtRhG9/PktlBgEGA8SM7QUsIMB5Ov4oICQLRr+xUpEhNNiXYpityIMB2jT3S+wQqYLz7RKo+kazP8n/J3ZbSE2HJ9sE2fixKZgcBBgHGj2wIMNfYSU+YJNsn2TZOwuSlkEAFjN+FJcedZNvsiEt9KCHhYRNnPLHj7dX30hNhyfbBNn6USmYHAZaEwJo1m81EX2XbmdJvrKiA8QYKP/pVXamEpMSv7CRUmSDAeP1VtzpnHe/hYg3S+wQqYO7HHSpgfKZgx2eX7ZzYznLp9iWy/0e/CAcEmF1YJ/4eAsw9blIEDpJNnk8bWkkWOZJt0407LwWOrm0QYM77ECpgqVnp9lnn5DN7pPREWLJ9sI0fm5LZoQKGChg/shO0hADj4ZT+xFqyfZJtS5WoJ3vq6qWQ0E3mvLRNV+RItg0CzPm4CAEGAeY8Wtw7UnKiDtv4fpbMDgIMAowf2RBgrrHzq4jAFET7EJAsciTbBgFmH1vJjpA+nqS6MggwCDB+5PNbSk7UYVtu+hUCDAKMH9kQYK6xk54wSbZPsm2ogKXXRXR8iwrYLtY63LyuztlFBAQYBJhdjGTie4gcHlXJ3NQVSbcvEXW8A4ZFOFi9EVMQWdiwETMPm9VKerIpucok2TZd30KAQYClMYw0Ns218cQNJm6cQ3oiLNk+2MaPQMnsUAFDBYwf2aiAucbOrzd9TEG0DwHJIkeybRBg9rGV7Ajp40mqK0MFjO93ycmmZNukV0oks5Nsm3S/QoBBgPFHewgw19hJT5gk2yfZtlQiItnNwctKDgQYvwtLjjvJttkRhwCzI5T8e8nJsGTbpCfqktlJtk26XyHAIMD4oz0EmGvspCdMku2TbBsEWHpdRMe3XgpX3eqcdTz2AXMUDBBgjjAlPEhyMizZNumJumR2km2T7lcIMAgw/mgPAeYaO51E0+tkDslmem6WXGWSbJtu3HkpcHRt87rPSh9PUvUoCDD+eCM5GZZsm/REXTI7ybZJ9ysEGAQYf7SHAHONnfSESbJ9km1DBSy9LqLjWwiwXax1uHktDu0iAgLMjlDy7yUnw5Jtk56oS2Yn2TbpfoUAgwDjj/YQYK6xk54wSbZPsm0QYOl1ER3fQoBBgKUXbfHWOjHnB/HqBhM3zoFEnU9RMjvJtkGA8WMuqy3XYBl6Fv9ENy8JK+VxbqwsAMxG0m/6ku2TbBsEGLND7Gym41sIMAiw9KINAswNfsnOgUSdT1cyO8m2QYDxYy6rLSHAePghwNzjps7kV/HqZTKsk6Rn44m15PesJNum+9DEy5jTtc3ruJPeJ1KNkpiCyLuHSE82kajDr3wC/JbS4y7RlWEjZlTAWBEPAcbCJn7ai+SETrJtqIDx+kNDKx3fQoDlbgUsvSjSa60Tc14La7srkZxsSrYN4tUuspJ/D7/y2SVrCQEGAcaKKggwFjYIMB42q5X0hElylUmybbq+hQCDAEtjGGlsKn080a0eusHEjXMgUedTlMxOsm3ShTUEWBICmILIGywgwNzjps6EKYj2PKUnTJJFjmTbIMDsYz/ZEdL7hGQRAXb8uJPsV7urkiwkYJud9/xboUtkOSpgqICxIh4CjIXNt1UcCQJResIkWeRItg0CjDeW6HKzjvdwk2i7q8p2sil9PPGryMm2X6XHHfxq5yHe99LjDgIsAQFUwHjBDgHmHjdUwJyxlJ4wSRY5km3TFRJeiwjJcSfZNrtene2ECezsPMT7Ptt+tbNasn2wzc57qIDxCQlsCQHGc0qym1e2V7ZKlcyp77I9wOne9Hne4bfStc/LZFiybZy4k8AuWZ/w0jYIsNzsr3ZX5bex2Os+gUqJXQTxvs923MGvPL/ZtZLs12S2YwoipiDaxXXC7yHAWNi0pyDyfoXfSrLIkWwbBBg/5qQLsGRXhmm56fk82wmT9PEEiXp68ZWsdbbjDn798fkVAiwJAVTAeJ0BAsxdbsmSOd6v8FtJTkok2wYBxo85CDA+O+l9QnKyCXb8uJPsV7urggCzI5T4e8nclMXS7UtEFRUwVMBYvRECjIUNFTAeNquV9IRJ8ntWkm3T9a2UqWCogKXRmQUkTNLHE7+KHOmJsGT7YBt/TJHMDhUwVMD4kZ2gJQQYD6fuTZ/3K/xWuvZ5mQxLtg0VMH7MWhJkCAAAIABJREFUQYDx2UnvE5JFBNjx406yX+2uSnKiDtvsvJf8e8nsIMAgwPiRDQHmGjvdm75rP+zwRLr2QYDtAiu5yiTZNggwh51TYxyWUJ2zu6psJ0ySxzrp7CDA7DzE+z7bfQJ+5fmN2wpTEDEFkRU7qICxsGlPo+P9Cr+V5KREsm2pRIT6Lturg0KA8ftEspYSRI70PiE5oQM79/tEsrEuM7/EOytETu5x80PcJaIOAQYBxuqNkgVYLt30Wc5Jo5HkpESybRBgaQSd5vt9XlZdU12VHwVYel5yt3W2E2Hp44nk+5hfbZOeqGe7T8Cv7o5xdmeDAIMAs4uRhN9DgLGwoQLGw2a1kp4wSa4ySbZN17cQYLs6kW6fSKP7ud4028mmLjspcQcRkV4oZjvu/CpyJHOT3ieS+RwCDAKMNZpBgLGwaYsI3q/wW0lOSiTbhgoYP+YgwNJjl6i1lG0tJCeb0h9KSGbnV9ukJ+qSRY5k26T7FQIsCQHsA8a7+UOAuctNygAiWeRItg0CjNcfGlrp+FZKJULCFEQkwvy4gwDjs0Pc/fjYQYC573NUwFABY0UVBBgLW9IKGASYPU+dJN0SRIeXWiedO3eb/cldOEJyQifZNr9WwJL1Wa/jLlnoImFK3aml9wm/ihzEHf9mIpmdZNuk5E+6nocAgwDTjRnreAgwFjYIMB62lDEnpRIhOaGTbBsEWBqdIkVTJEwQYJmJrNRnRdzxqUtmJ9k2CDB+zGW1JaYg8vBDgLnLTcoAIrnKJNm2VCJCQqUEAozXX+1aZXt7Ab9WSSSMd9L7hF99i0TdbtRI/r1kdpJtkzCecLyOChgqYJy4QQWMRS155VDKACJZ5Ei2DQKM2SF2NtPxrZQpfhKEtV+TdAnjHQRYen02WWsk6nyuktlJtk3CeMLxOgQYBBgnbrQEGOsHMtQo24OI7k0/QxiSnlYnEbZEh8fvWSUyHFMQ7aNEN+689qtO3Hltm67IkWJftsc6u6jMtn3S+4Ru3Nnx9ur7bPvV7jol2wfb7Lznz+phMqshwCDAWBGvMwWR9QMZapTtAU73pp8hDBBgGQCr61svE3XJtllCfsLBlkfmXvBlM88kEtdecrMLE0xBtCMkN2GS3icgwPixBXbus8t27mR3RdLtS2Q/BBgEmF1cJ/weAoyFDYtw8LClbIUKmD1U6ckmBJi9D3WPkJ6QZNs+6X0CIkI34p0dn+24g1+d+Un3KMl+TXYtEGAQYLpxbh0PAcbCBgHGwwYBliY36ckmBFiaDk7QXHpCkm37pPcJJOru9wl1xmzHHfz64/MrBFgSAlgFkdcZIMDc5SblxqCTCFtCHO+ANQaC5IROsm2pHuhgCiJvnJEynkhONqX3Ccns/Gqb9H4BcZi7412iK0MFDBUwVsRDgLGwoQLGw4YKWJrcpCebOsJfguhvcAfeAeMHZraTTel9wq8iJ9t+tYtIyfbBNjvvJf9eMjtUwFAB40d2gpYQYDycujd93q/wW+kkwqiANees61svhYRk21AB4/dXvybpEioR0vuEX30rPRGWbB9s44+FktlBgEGA8SMbAsw1dro3fdd+2OGJIMAcgtLoE8mSTQiwXRB14s5LbnbRgAqYHSG5T6x1x2Lpccf3hLstpSfCku2DbfxYlMwOAgwCjB/ZGsmm9E6Qbft0b/quOs3ByXQSYVTAUAFzEFKODtHpF9ITYSn2ZXuss3N8tu3TiTkpY10D02yz82t1LtnDMLtY9ep7+JVPWjI7CDAIMH5kQ4C5xk73pu/aDzs8EQSYQ1AafSLZTd/LRF037ry0zUpuk+wDloid17bpJptS7JOekGTbPp2YgwBzPi5m2692lkq2D7bZeS/595LZ+V6Ade/evaiwsPApIupERJsDgcC4xYsXr0twYUYoFJpORK9Eo9Hxdu702yqIEpK5VAmT9E6Qbft0b/p28ev29xBgfKK6vvUyUZdsGwQYP+Z0xWFmfol3Vr+NxV72Vzui2WaHuLPzEO97+JXHLVlezD+bNy19swpiOBy+lohaRyKR28Lh8BjTNPtHo9FrWmIKh8P/R0THm6b5OARY5oIIi3Dw2Oomwrxf4bfSFWD8X3KvJTZitmepG3deJ5s69nltm26yKcU+ycmchIRJJ+ashwQCttxoiEXJvpVsm4S40x1P7Ed3b46AX93n7CcBNtE0zT9Ho9HZZWVlbQKBwCeRSOTgpkjKysrOMAyjr2EYdaZprvazANPtpF7fHCDAeJ1R96bP+xV+Kwgw99klu+l72Wd1485L26zkFlMQ+YGXpCUSptRIdWIOAsx5eCLunLNqeaRkdpJtky6sk0WESAFWXl5+USwWUxUvc6fhhmEYqw3DuKqioiJCREY4HP46Eons23BhBx54YK9gMHhbJBI5MxwO/9E0zVUQYPyBwK4lBJgdocTf6970eb/CbwUB5j47CDB7pjr9wmtxKP1hWDL7kDBBgNn3PPePQNzxmUpmJ9k2CDB+zDlqGQ6HJxqGcVdFRcUcVQEzDOOjaDTap6FxKBT6s2EYx5qmuYOIuhNRtWmav6qsrHwj1Q9IfQdM+k0fAsxR2O52kE6iyfuF9FpBgPH56frWSyEh2TZFXMc+L7nZRUOipESKfUiYIMDs4jcT3yPu+FQls5NsGwQYP+YctQyHw9cRUSv1DlgoFDqHiI6JRqNXJmocDof/5LQCVltbZ+blBR3ZIPmg7kpyEtGKFd5Y2f3++A+uuMajH/TmsjL+K9K5JbXP6wDT8UQS27w2Wde3Xton2Tblah37vOSmE4YNx0q3j3NNudhGJ+asGPX4HpuLzHFNIAACuwioqX1OeTg+0OkJdY7r1q1bcXFx8RNEtI+qbtXX149dunTpGrU4RywWq6ysrJzWcD4dAYYKmI4Xdh2LCpi73NTZJDxhQgWM51fVSqeKYx3v4Uv9km3TZeclN7toQAXMjlDy77M93knvE6nIZpudX22Tcp9Nxg9+9e94wrFc5DtgnAvhtoEA45GDAHOXm5QbAwQYz6+6IgICrDlnnWRYugDjR5C7LSUncxLGO52Y87q/2kWCZN9Ktk1C3PlVvMKvdr1S/3sIsDWbGxb6aEZPcrBJeOoKAabf2ThJOu9X+K0gwNxnl+ym76WQkJ5s6tjnJTe7aPDbfcLuerz8PtvsdGIOAsx5ZGTbr3aWSrYPttl5L/n3ktklsxoCDAKMFfEQYCxs2tPUeL/CbwUB5j47CDB7pjrJMASYPU/pT/ql2yfhIScqJc7iXPcoyYk6bNP15q7jJbODAEtCAFMQeQEPAcbjJv2mCgHG96uOiPD6ibpk2ywWObQPGD+C3G0pPSGRbB8EGD8WJfvVj8Kf7wl3W8Kv7vJUZ0MFDBUwVlRBgLGwpWwkYYCDAOP7VUdEQIA156zDDhUwZzEqYTyR/sApmX0QYM5iLNFRiLvcZAe/8v2KChgqYK5GDwSYqzitk0kY4CDA+H7VEREQYBBg/Ehz1lLCeAIB5sxXukdJ9q1k26TcZ3WEv25sZOp4+NV9sqiAoQLGiioIMBY2VMDcx5ZUvHpdKYEA4ztXh53Xfs0lEcH3kPstJSd0qIDx/S3ZrxBg8CufgPstIcAgwFhRBQHGwgYB5j62pAIsQz+V9LQ6IkKdxEshIdk2iwXeAXM9XJEI85FCgLnLjn8291tK7hewje9vyeySXRUEGAQYK+IhwFjYIMDcxwYB5oCpjsDxWhxCgDlwIOMQ6QmJZPsgwBgBt7OJZL+iAga/8gm43xICDAKMFVUQYCxsEGDuY4MAc8AUAswBJMYhkpNNybb5MRH2smJtF4qSfSvZNj/GnV0sePU9/Oo+aQgwCDBWVEGAsbBBgLmPDQLMAVMIMAeQGIdITkok2+bHRBgCzFkHQdw545ToKMnsJNsmfTxJFhEQYBBgrNECAoyFDQLMfWwQYA6YShdgqS6h5Y0fibADhwtZVVXHr86uypujMAWRzxmJem6yg1/5foUAS0IgVzZidj80Up8RAsx94hIGOD8uQy/l6ZdkkSPZNrueBAFmRyjx9xLGEwgwnu/sWkn2rWTbpNwrkvlXMjvJtkn3KwQYBJjdmK71PQSYFi5HB0sY4CDAHLkq4UGSRY5k2+yIQ4DZEYIA4xFK3goVMD5RCfexXBL+fE+42xJ+dZenOhumIObIFET3QwMVMK+ZShjgIMAy4/VsJ3QQYN75NTO/pH9WCeNJLiXCmPrqLAYRd844JTpKMjvJtqECxo+5rLbEFEQeflTAeNykJyQQYO77NdnNwcuEDgLMO79m5pf0z4qESZ9ZQ4tsPzCxs1yybyXbJj1Rl8xOsm3S/ZqsP6MChgqY3Vif8HsIMBa2lI0kDHAQYO77FQIsPaaYgsjjJ2E8kf7AKZl9EGC8mPNDIiy5X8C23I27RFcGAQYBxop4CDAWNggw97FZZ5R844IAS8/pEGA8fn7sE7wrdb8VBBifKeIuN9nBr3y/ogKWhACmIPKCCgKMx036E2FUwNz3KwRYekwhwHj8kDDxuEnor3aWS/atZNukP6yTzE6ybdL9CgEGAWY3pmt9DwGmhcvRwRIGOAgwR67SPijbT9TxDpi2yxw1kNBnkxkq2TbpCRPYOQr/hAeBXW6yg1/5foUAgwBzNXogwFzFaZ1MwgAHAea+XyU8UYcA886vmfkl/bNKGE9SWS3ZPsm2SblXQPjr90m7FpLjTrJt0vsEBBgEmF3f1/oeAkwLl6ODJQxwEGCOXKV9ECpg2sgaG2AKIo+dhPEEAoznO7tWkn0r2TbpibpkdpJtk+5XCDAIMLsxXet7CDAtXI4OljDAQYA5cpX2QRBg2sggwPjIrJYSxhMIsDSdmKS5ZN9Ktk16v5DMTrJt0v0KAQYB5uqdAALMVZxiEiYIMPf9muzmgH3AnLFGBcwZp5ZHIWHicfNDMifZt5Jtk+5byewk2ybdrxBgEGD8u1GClhBgruKEAEsTpx9vDhBgzpwOAeaMEwQYj1OiVn4cT9y7+vTOBHZ8fpLZSbYNAowfc1ltiWXoefghwHjcUrWSMMChAua+X1EBS48pBBiPn4TxRPp4l8w+sOPFnB8SYcm+hW25G3eJrgwbMWMjZlbEQ4CxsKVsJGHwhQBz368QYOkxhQDj8ZMwnkCA8Xxn10qybyXbJl0gSmYn2Tbpfk3WnyHAIMDsxvqE30OAsbBBgLmPzTqjH28OmILoLBggwJxxanmUH/sE70rdbwV2fKZgl5vs4Fe+XyHAkhDAFEReUEGA8bhJfyKMCpj7fkUFLD2mEGA8fkiYeNz8+kCHf7XutkTc8XlKZifZNj/02URRgQoYKmCs0QICjIUNFTD3saEC5oApNmJ2AIlxiOSkRLJt0hMmsGN0hp1NwC432cGvfL+iAoYKmKvRAwHmKk4xIgIVMPf9igpYekxRAePxQ8LE4yZdHEq3D3GXm3EHv/L9CgEGAeZq9ECAuYpTjABLdlV7HH4wBQMG/fDZQvcv3IUz+vHmgHfAnDkeAswZp5ZH+bFP8K7U/VZgx2cKdrnJDn7l+xUCDALM1eiBAHMVJwRYmjj9eHOAAHPmdAgwZ5wgwHicErXy43ji3tWndyaw4/OTzE6ybYq4dPsSRQXeAcM7YKzRAgKMhS1lI8kDCCpg6fk7kW8hwJwxhQBzxgkCjMcJAsw9bn5IhCXfZ2EbPxYls0MFDBUwfmQnaAkB5ipOVMDSxCl98IUA4zsYAozHzo99gnel7rcCOz5TsMtNdvAr368QYBBgrkYPBJirOCHA0sTpx5sDKmDOnA4B5owTKmA8TqiAuccNFbD0WEq+j0m2zQ9xlygyMAURUxDTGzFatEYn5eOUzA5TEPl+TXZzgABzxhQCzBknCDAeJwgw97j5IRGWfJ+FbfxYlMwOFTBUwPiRrdFSeieQbJ9k26TfWP3IDgLM2cACAeaMEwQYjxMEmHvcpN8npNsn+T4m2TbpfoUAgwBzd5RNcjZ0Uj5msPtxsYMAc+ZvCDBnnCDAeJwgwNzj5odEWPJ9FrbxY1EyOwgwCDB+ZGu0lN4JJNsn2TbpN1awS91Jk72zmcyvXopDu+EFAsyOUOLv0Sd43KSPddLtQ9zlZtzBr3y/QoBBgLkfPQnOiE7Kxwx2YMcnAAGWKXapziu5z0q2DSIivWiV7FvJtiHu+HEHv/LZQYBBgLkfPRBgrjLFAMfHCXbuCjC+J9xviQoYjyn6BI+b9CRdun2Iu9yMO/iV71cIMAgw96MHAsxVphjg+DjBDgKMHz38lpLjTrJtEBH8mAM7sEuPAK81xhMet1StsAw9lqF3NarQSfk4wQ7s+AQgwDLFLtV5JfdZybZBRKQXrZJ9K9k2xB0/7uBXPjtUwFABcz96UAFzlSkGOD5OsIMA40cPv6XkuJNsGxJhfsyBHdilR4DXGuMJjxsqYCkIrEEFzNWoQifl4wQ7sOMTgADLFDtUwDJDVvJ4J9k2CLD04lGyb2Eb37eS2aEChgoYP7I1WkrvBJLtk2wbbvoanUBgZVh3Gfr0rtbd1liEg8cT4wmPm/SxTrp9iLvcjDv4le9XCDAIMPejR2CiaXeRkgcRybbhpm8XWam/z7ZvIcDS81+y1tn2q1+rcxhP0otHxB2fH9jx2EnmJn08gQCDAOP1Os1W6KSawJocDnZgxyeQumUuCbBMMeKcV3KflWyb9IQJ7Di9Id4G7HKTHfzK9ysEGASY+9GT4IzopHzMYAd2fAIQYJlil+q8kvusZNukJ+pgx+9NYJeb7OBXvl8hwCDA3I8eCDBXmWKA4+MEOwgwfvTwW0qOO8m2QYDxYw7swC49ArzWGE943FK1wj5gWAXR1ahCJ+XjBDuw4xOAAMsUO1TAMkNW8ngn2TYIsPTiUbJvYRvft5LZoQKGChg/sjVaSu8Eku2TbBtu+hqdQGBlGO+Apee/ZK0l91nJtmE8SS8eJftWsm2IO37cwa98dhBgEGDuR4/ARNPuIiUPIpJtw43LLrJSf59t30KApec/CDD3+WW7T/i1somxOL1YRNzx+EnmJr1PQIBBgPF6nWYrdFJNYE0OBzuw4xNI3RICLDNkJfdZybZJT5jAjt9fwC432cGvfL9CgEGAuR89qIC5yhQDHB8n2EGA8aOH31Jy3Em2DQKMH3NgB3bpEeC1xnjC45aqFRbhwCIcrkYVOikfJ9iBHZ8ABFim2KU6r+Q+K9k2iIj0olWybyXbhrjjxx38ymeHChgqYO5HDypgrjLFAMfHCXYQYPzo4beUHHeSbUMizI85sAO79AjwWmM84XHLiQpY9+7diwoLC58iok5EtDkQCIxbvHjxuqYXFw6HTyGiP6p/M01zbjQavcoO2RpUwOwQaX2PTqqFq9nBYAd2fAIQYJlil+q8kvusZNsgItKLVsm+lWwb4o4fd/Arn12ylr6ZghgOh68lotaRSOS2cDg8xjTN/tFo9JqGC+vVq1er2traT/Pz8wd99dVX60Oh0A3BYPCxliKtJQgIMHeDCp2UzxPswI5PAAIsU+wgwDJDVvJ4J9k2iIj04lGyb2Eb37eS2eWCAJtomuafo9Ho7LKysjaBQOCTSCRycMOFlZWVDQkEAj8lohoi6kFE/41EIhPs3AkBZkdI73vpnUCyfZJtw01frx+0PDrbvsUqiOn5L1nrbPvVr+IQ40l68Yi44/MDOx47ydykjye+EmDl5eUXxWIxVfEydxpuGIax2jCMqyoqKiJEZITD4a8jkci+TQTYWMMw7jFNs29JScn2HTt2fFhfX3/2kiVLlqQKNwgwXmf0Y0IivZNigOPHItihAsaPHn5LyXEn2TaMxfyYAzuwS48ArzXGEx63VK38NAVxomEYd1VUVMxRFTDDMD6KRqN9Gi6uvLz85Fgs9otoNDpK/VsoFLo/EAh8VFFR8VIqALW1dWZeXtB9sjgjCIAACAgh0P3+7pYlK65ZIcQimAECIAACIAACP14CqrLk9OodH+j0hDrHhcPh64iolXoHLBQKnUNEx0Sj0SsbzlFWVtYxEAjMzMvLO7Jjx46bV61a9VEsFruosrJyUarfQQVMxwv2x+IpiT2jZEeAHdjxCaRuiSmImSEruc9Ktk15Q7J9km0Du/T6smTfwja+byWzS3ZVvqmAdevWrbi4uPgJItqHiKrr6+vHLl26dI1anCMWi1VWVlZOC4fDZ5um+eudUxefj0aj99i5EwLMjpDe99I7gWT7JNuGm75eP2h5dLZ9CwGWnv/8+NAk2zFnR1yyfZJtw1hsF1mpv5fsW9jG961kdr4XYHy3pG4JAeYuWemdQLJ9km3DTT+9fpJt30KApec/CDD3+WW7T6S6Ism2YSxOLxYl+xa28X0rmR0EWBICEGD8gE/UUnonkGyfZNtw00+vn2TbtxBg6fkPAsx9ftnuExBg7vtU+n1Cun3oE/yYlMwOAgwCjB/ZGi2ldwLJ9km2DTcujU6Q4NBs+xYCLD3/QYC5zy/bfQICzH2fSr9PSLcPfYIfk5LZQYBBgPEjW6Ol9E4g2T7JtuHGpdEJIMDSg9WiteR+Adv4rgY7sOMT4LdE3PHYSeYmPT+BAIMA4/U6zVbopJrAmhwOdmDHJ5C6JSpgmSEruc9Ktk16wgR2/P4CdrnJDn7l+xUCDALM/egR+KTf7iIlDyKSbUPCZBdZqb/Ptm8hwNLzX7LW2fZrqquSbBvGk/TiUbJvJduGuOPHHfzKZwcBBgHmfvRAgLnKFAMcHyfYoQLGjx5+S8lxJ9k2JML8mAM7sEuPAK81xhMet1StfLMPmPuXHj8jVkF0lyw6KZ8n2IEdnwAEWKbYpTqv5D4r2TaIiPSiVbJvJduGuOPHHfzKZ5esJQTYms1mIjiSgw228TsC2IEdnwC/ZbbjDlMQ+b6DAAO7zBDgnzXb44lf+wQEWG7GnHS/QoAlIYAKGL9D+k24Su+kkm+qYJdeP8m2byHA0vNfstbZ9isS4R+fXzEWp+dz9FkeP8ncpPcJCDAIMF6v02yFTqoJrMnhYAd2fAKpW0KAZYas5D4r2TbpCRPY8fsL2OUmO/iV71cIMAgw96MnwRnRSfmYwQ7s+AQgwDLFzq9VJown/IgAO7DjE+C3lBx3km2T/kAHAgwCjD8qaLREJ9WA1eJQsAM7PgEIsEyxgwDLDFnJ451k26Qnm2DH7y+S2Um2TXqfgACDAOOPChot0Uk1YEGA8WGBnRY7TEHUwuX4YMnjnWTbpCdMYOe4C+x2INjlJjv4le9XCDAIMPejJ8EZ0Un5mMEO7PgEUAHLFDtUwDJDVvJ4J9k2iNf04lGyb2Eb37eS2UGAQYDxI1ujpfROINk+ybbhpq/RCQQ+mEAFLD3/JWstuc9Ktg3jSXrxKNm3km1D3PHjDn7ls4MAgwBzP3oEJpp2Fyl5EJFsG25cdpGV+vts+xYCLD3/QYC5zy/bfcKvlU2MxenFIuKOx08yN+l9AgIMAozX6zRboZNqAmtyONiBHZ9A6pYQYJkhK7nPSrZNesIEdvz+Ana5yQ5+5fsVAgwCzP3oQQXMVaYY4Pg4wQ4CjB89/JaS406ybRBg/JgDO7BLjwCvNcYTHrdUrTp1amM4PavjA52eUMJxa9ZsNhPZITnYYBs/csAO7PgE+C2zHXeogPF9l6pltv3qV9sgItKLR8Qdnx/Y8dhJ5iZ9PElGHAIMAozXG5O0Qifl4wQ7sOMTSN0SAiwzZCX3Wcm2SU+YwI7fX8AuN9nBr3y/QoAlIYAKmLtBhU7K5wl2YMcnAAGWKXZ+rTJhPOFHBNiBHZ8Av6XkuJNsm/QHOhBgEGD8UUGjJTqpBqwWh4Id2PEJQIBlih0EWGbISh7vJNsmPdkEO35/kcxOsm3S+wQEGAQYf1TQaIlOqgELAowPC+y02GEKohYuxwdLHu8k2yY9YQI7x11gtwPBLjfZwa98v0KAQYC5Hz0JzohOyscMdmDHJ4AKWKbYoQKWGbKSxzvJtkG8phePkn0L2/i+lcwOAgwCjB/ZGi2ldwLJ9km2DTd9jU4g8MEEKmDp+S9Za8l9VrJtGE/Si0fJvpVsG+KOH3fwK58dBBgEmPvRIzDRtLtIyYOIZNtw47KLrNTfZ9u3EGDp+Q8CzH1+2e4Tfq1sYixOLxYRdzx+krlJ7xMQYBBgvF6n2QqdVBNYk8PBDuz4BFK3hADLDFnJfVaybdITJrDj9xewy0128CvfrxBgEGDuRw8qYK4yxQDHxwl2EGD86OG3lBx3km2DAOPHHNiBXXoEeK0xnvC4pWqFjZixEbOrUYVOyscJdmDHJwABlil2qc4ruc9Ktg0iIr1olexbybYh7vhxB7/y2aEChgqY+9GDCpirTDHA8XGCHQQYP3r4LSXHnWTbkAjzYw7swC49ArzWGE943FABS0FgDSpgrkYVOikfJ9iBHZ8ABFim2KEClhmyksc7ybZBgKUXj5J9C9v4vpXMDhUwVMD4ka3RUnonkGyfZNtw09foBD6rDCPu+L6VzE6ybRhP+DEHdmCXHgFea4wnPG6ogKEC5n7kJDkjOikfNdiBHZ8AvyXiLjfZwa+56VcIML5fwY7PDuMJnx0qYKiAuR89PnvSj8E3vRCQPABLtg1xh7hLjwCvNfoEj5v0/irdPsRdbsYd/Mr3KwQYBJj70QMB5ipTDHB8nGAHdnwC/JaS406ybRAR/JgDO7BLjwCvNcYTHrdUrbAMPRbhcDWq0En5OMEO7PgE+C0Rd7nJDn7NTb9CgPH9CnZ8dhhP+OxQAUMFzP3oQQXMVaYY4Pg4wQ7s+AT4LSXHnWTbkAjzYw7swC49ArzWGE943FABS0EAy9C7G1TopHyeYAd2fAL8loi73GQHv+amXyHA+H4FOz47jCd8dqiAoQLmfvSgAuYqUwxwfJxgB3Z8AvyWkuNOsm1IhPkxB3Zglx4BXmuMJzxuqIChAuZ+5CQ5IzopHzXYgR2fAL8l4i432cGvuelXCDC+X8GOzw7jCZ8dKmCWgTokAAAWIklEQVSogLkfPaiAucoUAxwfJ9iBHZ8Av6XkuJNsGxJhfsyBHdilR4DXGuMJjxsqYKiAuR85qIC5zhQDHB8p2IEdnwC/peS4k2wbRAQ/5sAO7NIjwGuN8YTHDQIMAsz9yIEAc50pBjg+UrADOz4BfkvJcSfZNogIfsyBHdilR4DXGuMJjxsEGASY+5EDAeY6UwxwfKRgB3Z8AvyWkuNOsm0QEfyYAzuwS48ArzXGEx43CDAIMPcjBwLMdaYY4PhIwQ7s+AT4LSXHnWTbICL4MQd2YJceAV5rjCc8bhBgEGDuRw4EmOtMMcDxkYId2PEJ8FtKjjvJtkFE8GMO7MAuPQK81hhPeNwgwCDA3I8cCDDXmWKA4yMFO7DjE+C3lBx3km2DiODHHNiBXXoEeK0xnvC4QYBBgLkfORBgrjPFAMdHCnZgxyfAbyk57iTbBhHBjzmwA7v0CPBaYzzhcYMAgwBzP3IgwFxnigGOjxTswI5PgN9SctxJtg0igh9zYAd26RHgtcZ4wuMGAQYB5n7kQIC5zhQDHB8p2IEdnwC/peS4k2wbRAQ/5sAO7NIjwGuN8YTHDQIMAsz9yIEAc50pBjg+UrADOz4BfkvJcSfZNogIfsyBHdilR4DXGuMJjxsEGASY+5EDAeY6UwxwfKRgB3Z8AvyWkuNOsm0QEfyYAzuwS48ArzXGEx43CDAIMPcjBwLMdaYY4PhIwQ7s+AT4LSXHnWTbICL4MQd2YJceAV5rjCc8bhBgEGDuRw4EmOtMMcDxkYId2PEJ8FtKjjvJtkFE8GMO7MAuPQK81hhPeNwgwCDA3I8cCDDXmWKA4yMFO7DjE+C3lBx3km2DiODHHNiBXXoEeK0xnvC4QYBBgLkfORBgrjPFAMdHCnZgxyfAbyk57iTbBhHBjzmwA7v0CPBaYzzhcYMAgwBzP3IgwFxnigGOjxTswI5PgN9SctxJtg0igh9zYAd26RHgtcZ4wuOWEwKse/fuRYWFhU8RUSci2hwIBMYtXrx4XdOLC4VC1xPRuYZh1BPRXZFI5BU7ZGvWbDYTHSM52GCbnVeTfw92YMcnwG+JuAM7PgFeS8kxBxHB82lDK8m+lWwb4o4fd/Arn12ylp06tTGcntXxgU5PqHNcOBy+lohaRyKR28Lh8BjTNPtHo9FrGs7Ro0ePtvn5+QsikUiP7t27ty4oKJgfjUa72/0GBJgdIb3v0Un1eDU9GuzAjk+A3xJxl5vs4Nfc9CtEBN+vYMdnh/GEzy4XBNhE0zT/HI1GZ5eVlbUJBAKfRCKRgxsu7LjjjstbtWrV29XV1aPy8/NbBQKB9yORyAF2yCDA7AjpfY9OqscLAozPC+zAzh0C/LNIHu8k24ZEmB9zYAd26RHgtcZ4wuOWqpXIClh5eflFsVhMVbwapgcahmGsNgzjqoqKiggRGeFw+OtIJLJvCwH2hGmaxxuGETAM466Kioq/2yGDALMjpPc9OqkeL4gIPi+wAzt3CPDPInm8k2wbRAQ/5sAO7NIjwGuN8YTHzXcCLJHB4XB44k5RNUdVwAzD+CgajfZpOLasrGyEYRjXtGnTZmhVVZVRV1f3hmEYN1RUVMxxHxvOCAIgAAIgAAIgAAIgAAIgAAKZJZDtd8CuI6JW6h2wUCh0DhEdE41Gr2y45HA4/BMi+m0kEhmu/i0UCqkFOP4VjUbfyiwWnB0EQAAEQAAEQAAEQAAEQAAE3CeQVQHWrVu34uLi4ieIaB8iqq6vrx+7dOnSNWpxjlgsVllZWTktFArdYhjGUNM01SqIqkL2G/cx4IwgAAIgAAIgAAIgAAIgAAIgkHkCWRVgmb88/AIIgAAIgAAIgAAIgAAIgAAIyCEAASbHF7AEBEAABEAABEAABEAABEAgxwlAgOW4g3F5IAACIAACIAACIAACIAACcghAgMnxBSwBARAAARAAARAAARAAARDIcQI5LcBCodDRRHR3NBo9vqys7ADDMB43DCNmmuaXTVdbzKaPe/XqVVBXV/cYEfUwTXOTaZpXVlZWLs2mTQ2/vXMjbLUPW3fDMOoCgcClixcvjkqwTdkQCoXGEdFP1d5yhmEUE1HfWCy2d2Vl5WYJNobD4d8S0UjTNPNN03ywsrJS+VnEJxwOz1XxttOY5dFo9GIRhjUx4oADDuiUl5c3JxAInCQo7gKhUOi/RBRWY0ksFru8srJykRR2qs+uXLnyUcMwuhNRQSwW+7/KysqpUuzb2W8bx2WpdoVCoeV1dXWDli1b9k22bUzkU8Mw/iHEvmb9ob6+/opAIDBNiG2NrmsYSwzDGByLxWZIsa/pOGwYxgq156kU2xS8pvewQCDw71gsdrME+xLc+w8hoh21tbV9pfTZVatWNeZOhmFcJiXuWuacsVjsqkAg8Ga2/WqXr5eVlQ0yDOMWlc9ne0x26/dzVoCFQqEbiegCwzC2RiKRAaFQaDIR3RONRj8Mh8P/JqIZkUhE/VtWP+FwWC273zsSiVx+0EEHherr6/8RjUaHZtWonT9eXl4+MhaLjY1Go+eEQqGTDMO4PBKJnCnBtpY2hEKhfxmG8XkkEnlEgn07B4vrotHoqL59+5bu2LHjerXdghDbCgOBwCeRSORwCfYksmGn+H/BNM2ewWBwpBQBFg6HR5mmOSIajV6y08fXRqPR06RwDIVC6oFEn2g0el3v3r3b19TUzI9EIvsJsq/ZuCzVrnA4/EF1dfWQFStW7Mi2jU192r1793aFhYVfENGK6urqk7NtX6L+YBhGewm2Nfit6VgSCARU//2vBN+WlZXtNg5LirsE97AbiOhECeya9smd9/75RHShFNua5k7hcPhEIrqCiDpK6BdNc86ePXuW1dXV/VM9wM6mbU7ydcMwKtQDgGg0en62x2S3fj+XBdjppmkuCAQCE5QAC4fD30Yika4KnOocpmkOjkQiv3QLJPc8oVDoAcMw3mgQg+Fw+GspCVM4HC4nojsikchZZWVlowOBwBmRSGQs91oz1a68vPyIWCz2l2g0ekKmfkP3vOFw+E5VmTNNsxcRtY7FYjcuWbJknu55MnF8KBQ6ioieNAzja9M0g0R0UzQanZWJ3+KeMxQK3W+a5nTDMH4XDAYvlyLAdl5PgIhi6imsYRjH/X97dxskR1HGAfx5ZnflLSBWDMhJEjgy3XsxkUIiJgKBFEREEkQQSsXCQqCgyhIsoEBECS8l4EsMAh94UaAAFQSJkRc/qMmhpYCUUAmmbp/eq0swhhBRwVSFS7Kz/Vh9zlLLeUnuru6ls/e/b7mbme7+9ezM/NPdsyJy/nDbOdL7tbW17TtlyhRevXr11o6Ojsne+xdEZMZIlzPc4xlj3nVdHu5xRnq//vVK03RKtVp9Y6TLGc7xBupTIporIv8czvFGYZ93fR6891fFYhfa2v9akmXZmzHUb6DrsKr2xFC34DbQPYyZN8RSv/xZ7p17f0yf2YGenbz3l8VgN9AzJxEdPZ7Xk/7X3509r4d7WldX179G4Ro2Lods2QAWNGfOnDm9Xq//LB8B2+ic+2D+oV3gvT/fOXfeuKg3FWqMuYiIjnHOXZSm6Vxm/oNz7j3h4X2865am6aFJkqxQ1UlENFlVF1Wr1efHu179y7fW/sJ7f3u1Wn02lrpZa+9R1WltbW2LNm/e3F6v13/lnAuBdtx/Ojo6ZnnvPxZGC8P/gNXr9V+LiAmhYtwr978HpjCK0+acu9kYs6pQKFwcWQALDycPENEZqvrZGL8Yvlwuh9C/gpnvFpFHY+jXRh2ar8uo1+AFYu7TWD8PMV9LYr8Ox3wPa3xqYrz3h7rF/OwU6zPnnvC8Pvir9eC2nDABzFq7QUSm5gEsjICdLCKXDo5pVLcqGGO+x8xzVPWPzLxAROaOaomDPLgxZikzbxORa40xIbyuKpVKs9auXbtjkIcY9c3a29vfWywWwxd0zx71woZQgDHmFmb+h4gsC7tZa8MUiXDOjfv/Woc54Fu3bk0a05estS+o6pnOuY1DaOKobWqtfVZV+8IgM4e5/ZJl2enhS9pHrdBhHDisKykUCn/u7e3t2LBhQ+8wDjEqu8yYMWNqkiRPENGdzrnwRfdR/SCADb07Yu/T0KIYPw8xX0tivw7HfA8L51us9/5Qt8ifnaJ85my+L0T8vD70i/cu9pgwASxfA7bUOff7fA3YShF5bEQ1h3GwfNRrsnPu6XK5fLSqhrVCUUzzM8Zcy8w1EfluWMfU29v7Sm9v74diethM03QxM5/knPvaMPhHbRdjzGnMfKmInFIul9u8953OORvJyOYlzDw7vIgm1E1Vfysis2IZAWvulNhGwKy1Yf75oSJya5qmByRJ8rL3fma1Wt0+aifTEA7c3t5+cKlUWsXMX6lUKquGsOuYbZrfaB8RkXljVuggCoq1XjH3aeyfh5ivJWmaRn0djvkeFvo11nt/HsCifXaK9Zmz3whYeD9DdM/rg7iNDGmTCRPA8qlW94Y30hFRV5jyF8PDcL5O4xEi2o+I3iwWixesXbv29SH14ihtnIeu8Ea1Q4gouN0W23QmY0xYGLzDOXf7KDEM+7DGmFuJKKxLC5+za2KZqjZnzpzSli1bwhsZp+dv8rs6xqml+Y1sZUxrwMJ6nEmTJt3PzB9Q1aKq3lKtVp8a9kkywjuG9S5EdA4RVfLzLqxDPDWWgBiaixGwoXV6zH0a++ehXwCL6lqyJ1yHY72H5feGaO/9MT87xfrM2XxfiPV5fWhX7t1v3dIBbPfNxxYQgAAEIAABCEAAAhCAAATGTgABbOysURIEIAABCEAAAhCAAAQgMMEFEMAm+AmA5kMAAhCAAAQgAAEIQAACYyeAADZ21igJAhCAAAQgAAEIQAACEJjgAghgE/wEQPMhAAEIQAACEIAABCAAgbETQAAbO2uUBAEIQAACEIAABCAAAQhMcAEEsAl+AqD5EIAABCAAAQhAAAIQgMDYCSCAjZ01SoIABCAwYQQ6Ojpmee/XqOpZzrnlg224MeYGVX1xpL5fzRjzIhG95pz79O7qYK19SUQ+0n87Y8y6LMtO6Onp+Vvjb2mansDMTzFzNf/d3qr613322ef81atXb91dWc1/N8ZcHL6X0jl3j7X2vnq9vqS7u3uDtfapYrF4YSzfDTmUNmFbCEAAAhDYuQACGM4OCEAAAhAYcQFr7fdVdSozHygip4x4AYM4YAiB9Xr9LiI6lIiOdc5tHMRu/7eJMaYny7ITBwhgS5xz4cvW+36stT9R1dedc1cMp5ywz0Bhb7jHwn4QgAAEIBCnAAJYnP2CWkEAAhDYkwUK1tqN9Xr9uCRJnmPmY0RkXQgXRPQoMy8iopqqXktEVzDzDGa+olKpPG6tvV9VVxWLxWfr9fryMKpEREcx8+vbt28/e/369W+labooSZKbiCjcw3q89xdXq9U3+oMZY5Yy83+IaLKqvuWcWxK2mT179vt27NjxYyIqE9E2Zr68Uql0Wmu9iCT53x9W1RDcuohofpZlc3cXwNI0vSRJktNEZPHO6hiCKRGdpKp1Zl4hIjdZa/vqFeqiqjeGUbVisTi/Vqv9JR95C6Nht+X7eWZ+WES+G0bhkiT5hqq+TUQdRLSmra3tC52dndmefPKg7hCAAARaXQABrNV7GO2DAAQgMMYC1tow3e8aEZlrrb1HVf/tnPt6HsCWOufuDFPtiOgIEVlgjDmOmZeJyNH9AlhPCF8issZa+zgRrfLe/zxJkpfr9fq8ME3PGHMlER3jnDunuZknnnhicdOmTRuZeb6qvp+IHhGR6UTkjTF3EtFW59zV+SjZ3c65Y621dREJ4fEOVX3TOXedMeZ4Zu6s1WqH7yqAHXnkkftt27btCSJa6b2/b6A6Zll2ZalUekZEZqVpuhcz31sqlS7MsuyaMAVRRG4MRt77+Xnb+kbeSqXSaUR0soicNXXq1H323XffTiK63nv/NjM/WSgUbFdX1yZr7QshwDnnnh7jLkdxEIAABCAwBAEEsCFgYVMIQAACENi9gDFmeZIknZVK5Ydpmi5MkuSh/ffff+qWLVuc935hd3d3dxj1UdVCCDkzZ86cnmXZKudce3MAy7LsOedcWyjRGHM9M9dV9SVm/nIII+H3aZoewMzrnHOTm2tmrT2DiJaIyFFhpMxa+yoRfVVEVhhjXikUCp/v6uoKo2vv/DQCWPi79/5z3d3da8MfrbXVWq120i7WgLGqFonod6VS6aparbZwgDr2OOcOMsY8T0Q7wvqxWq3203DMfATsnQDWWG/WmPpYLBaXquqD1Wr1ydziUmae5r1/MkmSW0Tk43k9Hwh1EJGHdt9L2AICEIAABMZLAAFsvORRLgQgAIEWFEjTdAoz/52ZN6tqmAqXENEhRPQlIrq5sZaqOXTsIoD1hbI8XIRpeuq9fzlJkvNF5Mzw+8MOO+zAvfbaa72IHNjMaYz5JREdT0RhCiKHtWiq+rxz7tTwso0kSc5rBLByuWwrlYqz1mZhBMwYsyZJknMrlcoreeCpZFn2id1NQWyUn6bp4l3UMSmXy2FU7lOq+sUw2lUoFM5tHgHrH8BKpdIy7/0DjQBWLpcv894frqrLmfmddWiN8Oqce7AFTy00CQIQgEDLCCCAtUxXoiEQgAAExl/AWnt5WKskImHaXN+PtfY6VV1ARGGkq+9lFoMMYJ3OucObA1iWZXcVCoWXsyyblx/nKlWd55z7TKO89vb2g0ul0rpisdjeeIOgtTYElgozl1U1jCBtFZFvWmvDv5/JR9/61oAZY34Q1qiFKYrW2o8S0XO1Wq19sAHsiCOOOGigOhLRDUR0h3PuhDAV0lr7G1W9jZnnNAJYGG1T1VOccz2NF3KUSqXFRLRARM5pa2vbe9KkSZ3M/G3v/VsIYON/zqMGEIAABIYqgAA2VDFsDwEIQAACOxUwxqwO67+cc880NgqjYkmSrA8vwmgKTn0jWmHdU78RsPtUtTO8hKMxLbE5gOXrpE5j5ptUtcTMr9ZqtQt6eno2NwW+y/NQdnZzRY0xj4UXXHjvb2XmHzGzUdVakiSXVSqVPzWmIJbL5f1V9QFVNURUIaIPZ1m2cLABLJRpjBmwjsaY7xDR6WENGjOH195fYq39VtMI2DIiOjVJkk9671eGwDpt2rTXNm3aFKYhhjcuhqmODznnbs5fhd88AtZnhxEwfEAhAAEIxC2AABZ3/6B2EIAABCAAAQhAAAIQgEALCSCAtVBnoikQgAAEIAABCEAAAhCAQNwCCGBx9w9qBwEIQAACEIAABCAAAQi0kAACWAt1JpoCAQhAAAIQgAAEIAABCMQtgAAWd/+gdhCAAAQgAAEIQAACEIBACwkggLVQZ6IpEIAABCAAAQhAAAIQgEDcAghgcfcPagcBCEAAAhCAAAQgAAEItJAAAlgLdSaaAgEIQAACEIAABCAAAQjELYAAFnf/oHYQgAAEIAABCEAAAhCAQAsJIIC1UGeiKRCAAAQgAAEIQAACEIBA3AIIYHH3D2oHAQhAAAIQgAAEIAABCLSQAAJYC3UmmgIBCEAAAhCAAAQgAAEIxC2AABZ3/6B2EIAABCAAAQhAAAIQgEALCfwXBqBWldgyIzsAAAAASUVORK5CYII=\">"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "n_groups = generated_sample_len\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.15\n",
    "\n",
    "ax1 = ax.errorbar(index + bar_width, h_mean, h_std, bar_width, label=\"Hydrophobic\", color='b')\n",
    "ax2 = ax.errorbar(index, s_mean, s_std, bar_width, label=\"Steric\", color='r')\n",
    "ax3 = ax.errorbar(index - bar_width, e_mean, e_std, bar_width, label=\"Electronic\", color='g')\n",
    "\n",
    "ax.set_xlim(-bar_width,len(index)+bar_width)\n",
    "\n",
    "plt.xlabel('Amino Acid Position')\n",
    "plt.ylabel('SVM Coefficient Value')\n",
    "plt.title('Hydrophobic, Steric, and Electronic Effect by Amino Acid Position')\n",
    "plt.xticks(index, range (n_groups/2, 0, -1) + [str(i)+\"'\" for i in range (1, n_groups/2+1)])\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sys import stdout as out\n",
    "from aa_props import all_aa_props\n",
    "\n",
    "scale = np.array(scaler.scale_[0:50])\n",
    "\n",
    "scaled_aa_props = {}\n",
    "for (key, values) in all_aa_props.viewitems():\n",
    "    scaled_aa_props[key] = np.multiply(scale, values)\n",
    "\n",
    "def write_aa_props(srcfile, hdrfile):\n",
    "    generated_warning = \"\"\"\n",
    "/*** THIS IS AUTO-GENERATED SOURCE! DO NOT EDIT! ***/\n",
    "\"\"\"\n",
    "    header_content = \"\"\"\n",
    "#ifndef CHIPPER_H_DEFINED\n",
    "#define CHIPPER_H_DEFINED\n",
    "\n",
    "#include \"linear.h\"\n",
    "#include \"output.h\"\n",
    "\n",
    "#define NUM_AA_PROPERTIES 50\n",
    "#define TRAINING_SET_SIZE %s\n",
    "#define BEST_CUTOFF_VALUE %.2f\n",
    "#define BEST_CUTOFF_MCC %.3f\n",
    "#define GENERATED_SAMPLE_LEN %d\n",
    "#define NUM_FEATURES (GENERATED_SAMPLE_LEN * NUM_AA_PROPERTIES)\n",
    "\n",
    "int get_aa_properties(char aa, struct feature_node *features);\n",
    "int predict_cleavage(const char *fasta_input, const char *output, chipper_output_format format, const char *model_file, int output_probabilities, int cutoff_provided, double cutoff);\n",
    "\n",
    "#endif\n",
    "\"\"\" % (training_X.shape[0], best_cutoff, best_mcc, generated_sample_len)\n",
    "    \n",
    "    with open(hdrfile, 'w') as out:\n",
    "        out.write(generated_warning + header_content)\n",
    "     \n",
    "    aa_props_src_header = generated_warning + \"\"\"\n",
    "#include \"chipper.h\"\n",
    "#include <stdio.h>\n",
    "#include <stdlib.h>\n",
    "#include <string.h>\n",
    "\n",
    "/* Amino acid properties from https://www.ncbi.nlm.nih.gov/pubmed/15895431\n",
    "   Scaled to match the current model's input data. */\n",
    "\"\"\"\n",
    "    aa_props_src_footer = \"\"\"\n",
    "\n",
    "int get_aa_properties(char aa, struct feature_node *features) {\n",
    "    int i;\n",
    "    int target_aa = aa;\n",
    "    /* If lowercase, make upper case */\n",
    "    if ('a' <= target_aa && target_aa <= 'z') {\n",
    "        target_aa -= ('a'-'A');\n",
    "    }\n",
    "    /* Shift to make 'A' equal to zero offset */\n",
    "    target_aa -= 'A';\n",
    "    if (0 <= target_aa &&  target_aa <= 25) {\n",
    "        for(i = 0; i < NUM_AA_PROPERTIES; i++) {\n",
    "            if (features[i].index == -1) {\n",
    "                fprintf(stderr, \"End of features reached before AA properties for '%c' could be set\", target_aa);\n",
    "                exit(EXIT_FAILURE);\n",
    "            }\n",
    "            features[i].value = aa_props[target_aa][i];\n",
    "        }\n",
    "        return aa_props[target_aa][0] == -1? 0: NUM_AA_PROPERTIES;\n",
    "    } else {\n",
    "        return 0;\n",
    "    }\n",
    "}\n",
    "\"\"\"    \n",
    "    with open(srcfile, \"w\") as out:\n",
    "        out.write(aa_props_src_header)\n",
    "        out.write(\"static const float aa_props[26][NUM_AA_PROPERTIES] = {\\n\")\n",
    "        for key in range(ord('A'), ord('Z')+1):\n",
    "            out.write(\"   {\")\n",
    "            try:\n",
    "                values = scaled_aa_props[chr(key)] \n",
    "                out.write(\",\".join([str(i) for i in values]))\n",
    "            except KeyError:\n",
    "                # We scaled our values from zero to one. Mark misses with -1\n",
    "                out.write(\",\".join([str(-1) for i in range(0, 50)]))\n",
    "            out.write(\"}\")\n",
    "            if key != ord('Z'):\n",
    "                out.write(\",\")\n",
    "            out.write(\"\\n\")\n",
    "        out.write(\"};\")\n",
    "        out.write(aa_props_src_footer)\n",
    "        \n",
    "aa_props_src = src_dir + \"aa_props.c\"\n",
    "aa_props_header = src_dir + \"chipper.h\"\n",
    "write_aa_props(aa_props_src, aa_props_header)\n",
    "index_src = !gindent -linux {aa_props_src} {aa_props_header}"
   ]
  }
 ],
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