System Analysis (Micro).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System analysis (Micro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import analysis_utils\n",
    "import plot_utils\n",
    "from collections import defaultdict, OrderedDict\n",
    "import math\n",
    "from scipy.stats import pearsonr, spearmanr\n",
    "import numpy as np\n",
    "from scipy.stats import kendalltau\n",
    "import pandas as pd\n",
    "from scipy.stats import entropy\n",
    "from copy import deepcopy\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "debug=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Load data from BIN files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n3_title='n3'\n",
    "aida_title='aida'\n",
    "titles=[aida_title, n3_title]\n",
    "systems=['agdistis', 'spotlight', 'wat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Load data from disk\n",
    "system_data_articles={}\n",
    "for system in systems:\n",
    "    system_data_articles[system]={}\n",
    "    for data_title in titles:\n",
    "        with open('bin/%s_%s.bin' % (data_title, system), 'rb') as outfile:\n",
    "            system_data_articles[system][data_title]=pickle.load(outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [DEBUG-only] Store agdistis output to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if debug:\n",
    "    with open('debug/agdmag_aida_debug.tsv', 'w') as w:\n",
    "        w.write('collection\\tarticle_ID\\tbegin_index\\tend_index\\tmention\\tgold_link\\tsystem_link\\n')\n",
    "        for article in aida_agd_articles:\n",
    "            for mention in article.entity_mentions:\n",
    "                if mention.sys_link.startswith('http://aksw.org/'):\n",
    "                    mention.sys_link = '--NME--'\n",
    "\n",
    "                a_row=[article.collection, str(article.identifier), \n",
    "                       str(mention.begin_index), str(mention.end_index), \n",
    "                       mention.mention, mention.gold_link, \n",
    "                       mention.sys_link]\n",
    "                w.write('\\t'.join(a_row) + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.1. Pick dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "system_articles = {}\n",
    "\n",
    "for system in systems:\n",
    "    system_articles[system]=set()\n",
    "    for data_title in titles: \n",
    "        system_articles[system] |= system_data_articles[system][data_title]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.2. Normalize AGDISTIS NILs given as custom AKSW URIs **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for article in system_articles['agdistis']:\n",
    "    for mention in article.entity_mentions:\n",
    "        if mention.sys_link.startswith('http://aksw.org/'):\n",
    "            mention.sys_link = '--NME--'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.3.Normalize forms to lower-case (disabled for now)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if debug:\n",
    "    for system in systems:\n",
    "        for article in system_articles[system]:\n",
    "            for mention in article.entity_mentions:\n",
    "                mention.mention=mention.mention.lower()\n",
    "                print(mention.mention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Performance\n",
    "\n",
    "<span style=\"background-color:lightgreen\">Section 5.1. of the paper</span>\n",
    "\n",
    "#### 2.1. Overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_nils=True\n",
    "skip_nonnils=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "17222 9786 11769\n",
      "0.6376629146919431 0.5940464282018557 0.615082412185932\n",
      "0.615082412185932\n",
      "\n",
      "SPOTLIGHT\n",
      "21150 5505 7841\n",
      "0.7934721440630276 0.7295367527853471 0.7601624555224096\n",
      "0.7601624555224096\n",
      "\n",
      "WAT\n",
      "19027 2420 9964\n",
      "0.8871637058796102 0.6563071297989032 0.7544708354811849\n",
      "0.7544708354811849\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_sys_accs = defaultdict(dict)\n",
    "for system in systems:\n",
    "    print(system.upper())\n",
    "    sys_acc=analysis_utils.overall_performance_prf(system_articles[system], \n",
    "                                               skip_nils, \n",
    "                                               skip_nonnils)\n",
    "    all_sys_accs[system]['overall']=sys_acc\n",
    "#    scores.append(['overall', system, sys_acc])\n",
    "    print(sys_acc)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Only forms that are interpretted as NILs and not-NILs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if debug:\n",
    "    for system in systems:\n",
    "        interpretations, references = analysis_utils.get_interpretations_and_references(system_articles[system], skip_nils=False)\n",
    "        ambiguity={}\n",
    "        for form,meanings in interpretations.items():\n",
    "            ambiguity[form]=len(meanings)\n",
    "\n",
    "        nil_nonnil_forms=set()\n",
    "        for form, amb in ambiguity.items():\n",
    "            if amb>1:\n",
    "                if '--NME--' in interpretations[form]:\n",
    "                    nil_nonnil_forms.add(form)\n",
    "\n",
    "        nn_f_acc=analysis_utils.compute_prf_on_selection(system_articles[system], nil_nonnil_forms)\n",
    "        print(system, nn_f_acc)\n",
    "        all_sys_accs[system]['forms with nils & non-nils']=nn_f_acc\n",
    "    #    scores.append(['forms with nils & non-nils', system, nn_f_acc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3. Only ambiguous forms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4682 4869 5318\n",
      "0.4902104491676264 0.4682 0.4789524832489387\n",
      "agdistis 0.4789524832489387\n",
      "6502 3446 3498\n",
      "0.6535987133092079 0.6502 0.6518949268097053\n",
      "spotlight 0.6518949268097053\n",
      "6108 1665 3922\n",
      "0.785796989579313 0.6089730807577268 0.6861764871089142\n",
      "wat 0.6861764871089142\n"
     ]
    }
   ],
   "source": [
    "for system in systems:\n",
    "    interpretations, references = analysis_utils.get_interpretations_and_references(system_articles[system], skip_nils=True)\n",
    "    ambiguity={}\n",
    "    for form,meanings in interpretations.items():\n",
    "        ambiguity[form]=len(meanings)\n",
    "\n",
    "    ambiguous_forms=set()\n",
    "    for form, amb in ambiguity.items():\n",
    "        if amb>1:\n",
    "            ambiguous_forms.add(form)\n",
    "\n",
    "    amb_f_acc=analysis_utils.compute_prf_on_selection(system_articles[system], ambiguous_forms)\n",
    "    print(system, amb_f_acc)\n",
    "    all_sys_accs[system]['ambiguous forms']=amb_f_acc\n",
    "#    scores.append(['ambiguous forms', system, amb_f_acc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 2.4. Prepare and plot **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores=analysis_utils.prepare_scores_to_plot(all_sys_accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['overall', 'ambiguous forms']\n",
      "['AGDISTIS', 'SPOTLIGHT', 'WAT']\n",
      "width: 0.3\n",
      "evaluation: overall\n",
      "0.615082412185932\n",
      "0.7601624555224096\n",
      "0.7544708354811849\n",
      "evaluation: ambiguous forms\n",
      "0.4789524832489387\n",
      "0.6518949268097053\n",
      "0.6861764871089142\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebf5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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T+EupVKFCBZKTk83h7Oxs7O1z/tDdsmULffr0MYddXFzo0KEDAAEBAXmGixSfO9mh161b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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebf80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_utils.plot_scores(scores, title='Scores of systems')\n",
    "#plot_scores(scores, title='Scores of systems')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "baseline_articles = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "freq_system='x freq baseline'\n",
    "random_system='x random baseline'\n",
    "\n",
    "totals=analysis_utils.get_freq_totals(system_articles[systems[0]],\n",
    "                                      set(),\n",
    "                                      ambiguous_only=False\n",
    "                                     )\n",
    "freq_baseline_articles=deepcopy(system_articles['agdistis'])\n",
    "pr_baseline_articles=deepcopy(system_articles['agdistis'])\n",
    "random_baseline_articles=deepcopy(system_articles['agdistis'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "form_to_mostfreq=defaultdict(lambda: '--NME--')\n",
    "form_to_highest_pr = defaultdict(lambda: '--NME--')\n",
    "for form, dist in totals.items():\n",
    "    key_max = max(dist.keys(), key=(lambda k: dist[k]))\n",
    "    form_to_mostfreq[form]=key_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for article in freq_baseline_articles:\n",
    "    for mention in article.entity_mentions:\n",
    "        mention.sys_link=form_to_mostfreq[mention.mention]\n",
    "        \n",
    "for article in random_baseline_articles:\n",
    "    for mention in article.entity_mentions:\n",
    "        if mention.mention in totals.keys() and totals[mention.mention].keys():\n",
    "            key_random=random.choice(list(totals[mention.mention].keys()))\n",
    "        else:\n",
    "            key_random='--NME--'\n",
    "        mention.sys_link=key_random\n",
    "        \n",
    "baseline_articles[freq_system]=freq_baseline_articles\n",
    "baseline_articles[random_system]=random_baseline_articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27089 1902 1902\n",
      "0.9343934324445518 0.9343934324445518 0.9343934324445516\n",
      "0.9343934324445516\n",
      "22727 6264 6264\n",
      "0.783932944706978 0.783932944706978 0.783932944706978\n",
      "0.783932944706978\n"
     ]
    }
   ],
   "source": [
    "for baseline, articles in baseline_articles.items():\n",
    "    sys_acc=analysis_utils.overall_performance_prf(articles, skip_nils=True, skip_nonnils=False)\n",
    "    print(sys_acc)\n",
    "    all_sys_accs[baseline]['overall']=sys_acc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8092 2099 1902\n",
      "0.7940339515258561 0.8096858114868921 0.8017835026009412\n",
      "3730 6461 6264\n",
      "0.36600922382494355 0.37322393436061635 0.36958137230616794\n"
     ]
    }
   ],
   "source": [
    "for baseline, articles in baseline_articles.items():\n",
    "    amb_f_acc=analysis_utils.compute_prf_on_selection(articles, ambiguous_forms)\n",
    "    all_sys_accs[baseline]['ambiguous forms']=amb_f_acc\n",
    "    if debug: \n",
    "        nn_f_acc=analysis_utils.compute_prf_on_selection(articles, nil_nonnil_forms)\n",
    "        all_sys_accs[baseline]['forms with nils & non-nils']=nn_f_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Prepare the data needed to compare frequency/popularity and performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.1. Compute P, R and F1 per form and instance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_per_form_or_instance(articles, which='form', skip_nils=True, ambiguous_only=False, ambiguous_forms=None):\n",
    "    \"\"\"\n",
    "    Evaluate ranks for precision, recall, and F1-score.\n",
    "    \"\"\"\n",
    "    tps=defaultdict(int)\n",
    "    fns=defaultdict(int)\n",
    "    fps=defaultdict(int)\n",
    "   \n",
    "\n",
    "    all_cases=set()\n",
    "    for article in articles:\n",
    "        for mention in article.entity_mentions:\n",
    "                form=mention.mention\n",
    "                meaning=mention.gold_link\n",
    "                sys_meaning=mention.sys_link\n",
    "                if skip_nils and meaning=='--NME--':\n",
    "                    continue\n",
    "                if ambiguous_only and form not in ambiguous_forms:\n",
    "                    continue\n",
    "                if which=='form':\n",
    "                    factor_sys=form\n",
    "                    factor_gold=form\n",
    "                else:\n",
    "                    factor_gold=meaning\n",
    "                    factor_sys=sys_meaning\n",
    "                all_cases.add(factor_gold)\n",
    "                all_cases.add(factor_sys)\n",
    "                if sys_meaning==meaning:\n",
    "                    tps[factor_gold]+=1\n",
    "                else:\n",
    "                    if meaning and meaning!='--NME--':\n",
    "                        fns[factor_gold]+=1\n",
    "                    if sys_meaning and sys_meaning!='--NME--':\n",
    "                        fps[factor_sys]+=1\n",
    "\n",
    "    prec={}\n",
    "    recall={}\n",
    "    f1={}\n",
    "\n",
    "    for factor in all_cases:\n",
    "        if tps[factor]+fps[factor]>0:\n",
    "            prec[factor]=tps[factor]/(tps[factor]+fps[factor])\n",
    "        else:\n",
    "            prec[factor]=0.0\n",
    "        if tps[factor]+fns[factor]>0:\n",
    "            recall[factor]=tps[factor]/(tps[factor]+fns[factor])\n",
    "        else:\n",
    "            recall[factor]=0.0\n",
    "        if prec[factor]+recall[factor]>0:\n",
    "            f1[factor]=2*prec[factor]*recall[factor]/(prec[factor]+recall[factor])\n",
    "        else:\n",
    "            f1[factor]=0.0\n",
    "    return prec, recall, f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prec_by_form = {system: defaultdict(float) for system in systems}\n",
    "recall_by_form = {system: defaultdict(float) for system in systems}\n",
    "f1_by_form = {system: defaultdict(float) for system in systems}\n",
    "\n",
    "for system in systems:\n",
    "    prf = evaluate_per_form_or_instance(system_articles[system], 'form', \n",
    "                                        ambiguous_only=False, \n",
    "                                        ambiguous_forms=ambiguous_forms)\n",
    "    prec_by_form[system]=prf[0]\n",
    "    recall_by_form[system]=prf[1]\n",
    "    f1_by_form[system]=prf[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prec_by_instance = {system: defaultdict(float) for system in systems}\n",
    "recall_by_instance = {system: defaultdict(float) for system in systems}\n",
    "f1_by_instance = {system: defaultdict(float) for system in systems}\n",
    "\n",
    "for system in systems:\n",
    "    prf = evaluate_per_form_or_instance(system_articles[system], 'instance', \n",
    "                                        ambiguous_only=False, \n",
    "                                        ambiguous_forms=ambiguous_forms)\n",
    "    prec_by_instance[system]=prf[0]\n",
    "    recall_by_instance[system]=prf[1]\n",
    "    f1_by_instance[system]=prf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.2. Obtain frequency distributions for forms and instances**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "instance_frequencies, form_frequencies=analysis_utils.get_mention_counts(system_articles[systems[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.3. Obtain PageRank values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_zeros=True\n",
    "pagerank_frequency, pr_uniq, uri_pr = analysis_utils.get_pageranks(system_articles['spotlight'],\n",
    "                                                                     skip_zeros=skip_zeros,\n",
    "                                                                     ambiguous_only=False,\n",
    "                                                                      ambiguous_forms=ambiguous_forms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.4. Prepare entropy values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "form_entropies={}\n",
    "for form in ambiguous_forms:\n",
    "    counts=analysis_utils.get_form_counts(system_articles['spotlight'], form)\n",
    "    counts=list(counts)\n",
    "    form_entropies[form]=entropy(counts ,base=len(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Compute Correlations between frequency/popularity and performance\n",
    "\n",
    "<span style=\"background-color:lightgreen\">Section 5.2. of the paper</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 5.1. Compute correlation between form frequency and performance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: 0.2738635468207578 . Significance:  0.009003577920576223\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: 0.13211764600861006 . Significance:  0.21449224922784127\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: 0.4662792325483932 . Significance:  3.6244473600530232e-06\n"
     ]
    }
   ],
   "source": [
    "agg_per_frequency=defaultdict(set)\n",
    "for form, frequency in form_frequencies.items():\n",
    "    agg_per_frequency[frequency].add(form)\n",
    "\n",
    "for system in systems:\n",
    "    dist1=[]\n",
    "    dist2=[]\n",
    "    for frequency, forms in sorted(agg_per_frequency.items()):\n",
    "        f1_values=[]\n",
    "        for form in forms:\n",
    "            try:\n",
    "                f1_values.append(f1_by_form[system][form])\n",
    "            except KeyError:\n",
    "                continue\n",
    "        if f1_values:\n",
    "            dist1.append(frequency)\n",
    "            dist2.append(np.mean(f1_values))\n",
    "    correlation, significance = spearmanr(dist1, dist2)\n",
    "    print(system.upper())\n",
    "    print('The Spearman correlation between X and Y is:', correlation, '. Significance: ', significance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 5.2. Compute correlation between instance frequency and performance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: 0.3812486003427165 . Significance:  0.00014997013289569768\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: 0.1847205314484163 . Significance:  0.07469358495318532\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: 0.5049958458259581 . Significance:  2.0970549619133197e-07\n"
     ]
    }
   ],
   "source": [
    "agg_inst_per_frequency=defaultdict(set)\n",
    "for instance, frequency in instance_frequencies.items():\n",
    "    agg_inst_per_frequency[frequency].add(instance)\n",
    "\n",
    "for system in systems:\n",
    "    dist1=[]\n",
    "    dist2=[]\n",
    "    for frequency, instances in sorted(agg_inst_per_frequency.items()):\n",
    "        f1_values=[]\n",
    "        for instance in instances:\n",
    "            try:\n",
    "                f1_values.append(f1_by_instance[system][instance])\n",
    "            except KeyError:\n",
    "                continue\n",
    "        if f1_values:\n",
    "            dist1.append(frequency)\n",
    "            dist2.append(np.mean(f1_values))\n",
    "    correlation, significance = spearmanr(dist1, dist2)\n",
    "    print(system.upper())\n",
    "    print('The Spearman correlation between X and Y is:', correlation, '. Significance: ', significance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 5.3. Compute correlation between instance PageRank and performance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: 0.1464751663956799 . Significance:  0.0010446734961972902\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: 0.13565778335932957 . Significance:  0.0024153011696557186\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: 0.3163862093504307 . Significance:  4.857287198280011e-13\n"
     ]
    }
   ],
   "source": [
    "agg_inst_per_pr=defaultdict(set)\n",
    "for instance, i_pr in uri_pr.items():\n",
    "    agg_inst_per_pr[i_pr].add(instance)\n",
    "\n",
    "for system in systems:\n",
    "    dist1=[]\n",
    "    dist2=[]\n",
    "    for i_pr, instances in sorted(agg_inst_per_pr.items()):\n",
    "        f1_values=[]\n",
    "        for instance in instances:\n",
    "            try:\n",
    "                f1_values.append(f1_by_instance[system][instance])\n",
    "            except KeyError:\n",
    "                continue\n",
    "        if f1_values:\n",
    "            dist1.append(i_pr)\n",
    "            dist2.append(np.mean(f1_values))\n",
    "    correlation, significance = spearmanr(dist1, dist2)\n",
    "    print(system.upper())\n",
    "    print('The Spearman correlation between X and Y is:', correlation, '. Significance: ', significance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Entropy\n",
    "\n",
    "<span style=\"background-color:lightgreen\">Section 5.4. of the paper</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.1. Plot F1-scores per entropy class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forms_per_rentropy=defaultdict(set)\n",
    "for form, entr in form_entropies.items():\n",
    "    rounded_entropy=round(entr,1)\n",
    "    forms_per_rentropy[rounded_entropy].add(form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "1099 416 869\n",
      "0.7254125412541255 0.5584349593495935 0.6310651737008326\n",
      "845 250 275\n",
      "0.771689497716895 0.7544642857142857 0.762979683972912\n",
      "558 257 251\n",
      "0.6846625766871166 0.6897404202719407 0.687192118226601\n",
      "518 498 491\n",
      "0.5098425196850394 0.5133795837462835 0.5116049382716049\n",
      "376 575 587\n",
      "0.3953732912723449 0.3904465212876428 0.3928944618599791\n",
      "289 532 528\n",
      "0.3520097442143727 0.3537331701346389 0.35286935286935284\n",
      "256 782 778\n",
      "0.2466281310211946 0.24758220502901354 0.24710424710424708\n",
      "247 568 540\n",
      "0.3030674846625767 0.3138500635324015 0.3083645443196005\n",
      "220 406 405\n",
      "0.3514376996805112 0.352 0.3517186250999201\n",
      "274 585 594\n",
      "0.3189755529685681 0.315668202764977 0.3173132599884192\n",
      "SPOTLIGHT\n",
      "1542 409 426\n",
      "0.7903639159405433 0.7835365853658537 0.7869354427149784\n",
      "871 227 248\n",
      "0.7932604735883424 0.7783735478105451 0.7857465042850699\n",
      "638 158 172\n",
      "0.8015075376884422 0.7876543209876543 0.7945205479452055\n",
      "712 290 297\n",
      "0.7105788423153693 0.7056491575817642 0.7081054201889607\n",
      "554 414 407\n",
      "0.5723140495867769 0.5764828303850156 0.5743908761016071\n",
      "418 386 399\n",
      "0.5199004975124378 0.5116279069767442 0.5157310302282542\n",
      "524 481 511\n",
      "0.5213930348258706 0.506280193236715 0.5137254901960785\n",
      "378 404 410\n",
      "0.4833759590792839 0.4796954314720812 0.48152866242038217\n",
      "364 263 262\n",
      "0.580542264752791 0.5814696485623003 0.5810055865921788\n",
      "501 414 366\n",
      "0.5475409836065573 0.5778546712802768 0.5622895622895622\n",
      "WAT\n",
      "1770 92 199\n",
      "0.9505907626208379 0.898933468765871 0.9240407204385278\n",
      "887 65 232\n",
      "0.9317226890756303 0.7926720285969616 0.8565910188314825\n",
      "610 94 199\n",
      "0.8664772727272727 0.754017305315204 0.806345009914078\n",
      "622 160 387\n",
      "0.7953964194373402 0.6164519326065411 0.6945840312674484\n",
      "511 194 451\n",
      "0.724822695035461 0.5311850311850311 0.6130773845230955\n",
      "349 170 470\n",
      "0.6724470134874759 0.4261294261294261 0.5216741405082213\n",
      "434 203 603\n",
      "0.6813186813186813 0.4185149469623915 0.5185185185185185\n",
      "313 183 482\n",
      "0.6310483870967742 0.3937106918238994 0.48489542989930295\n",
      "256 159 380\n",
      "0.6168674698795181 0.4025157232704403 0.4871550903901047\n",
      "356 345 519\n",
      "0.5078459343794579 0.40685714285714286 0.4517766497461929\n",
      "X FREQ BASELINE\n",
      "1933 48 35\n",
      "0.9757698132256436 0.9822154471544715 0.9789820207647505\n",
      "1071 49 48\n",
      "0.95625 0.9571045576407506 0.9566770879857079\n",
      "746 69 63\n",
      "0.9153374233128835 0.9221260815822002 0.9187192118226601\n",
      "894 122 115\n",
      "0.8799212598425197 0.8860257680872151 0.8829629629629631\n",
      "785 194 176\n",
      "0.8018386108273748 0.8168574401664932 0.8092783505154638\n",
      "623 198 194\n",
      "0.758830694275274 0.762545899632803 0.7606837606837608\n",
      "698 357 336\n",
      "0.6616113744075829 0.6750483558994197 0.6682623264719962\n",
      "489 330 298\n",
      "0.5970695970695971 0.6213468869123253 0.6089663760896636\n",
      "394 259 231\n",
      "0.6033690658499234 0.6304 0.6165884194053208\n",
      "459 473 406\n",
      "0.4924892703862661 0.530635838150289 0.5108514190317195\n",
      "X RANDOM BASELINE\n",
      "748 1233 1220\n",
      "0.37758707723372037 0.3800813008130081 0.37883008356545966\n",
      "406 714 713\n",
      "0.3625 0.36282394995531725 0.36266190263510495\n",
      "294 521 515\n",
      "0.36073619631901843 0.36341161928306553 0.3620689655172414\n",
      "389 627 620\n",
      "0.3828740157480315 0.3855302279484638 0.3841975308641975\n",
      "310 669 651\n",
      "0.3166496424923391 0.3225806451612903 0.3195876288659794\n",
      "294 527 523\n",
      "0.35809987819732036 0.3598531211750306 0.358974358974359\n",
      "346 709 688\n",
      "0.32796208530805687 0.3346228239845261 0.33125897558640494\n",
      "276 543 511\n",
      "0.336996336996337 0.35069885641677256 0.3437110834371108\n",
      "290 363 335\n",
      "0.444104134762634 0.464 0.4538341158059468\n",
      "377 555 488\n",
      "0.40450643776824036 0.43583815028901735 0.41958820255982193\n"
     ]
    },
    {
     "data": {
      "image/png": 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DJycnHBwcKqXnrtRbKJgTJ05w7NixQtXNyMggMDAQQRA4ePAgISEh+d6rt/MRdOnShU6d\nOr33/NmJk7LDkQB0794dHR0dMjMzc9U3NTXFysqK8PBwUlJS8PPz4/Tp0+IbuqqqKr169SI6Opq/\n//67xLKE/fbbb4wcORIXFxexzNDQkDlz5pTp37tETsrcELRo0YLvvvuO77//HplMRu/evdHX1yev\nkEcFrXkPDAwsVvupqanvPdbW1pZq1arh7+/P8ePHOX/+PI0bN8bCwqLE3n4Lo6O0Ka4GExMTDA0N\nSU5OJjk5mejoaDHtoIaGhpjER01NrVD3SxnuRXF0xMTE5HjwFkR8fLz4GxcEgYcPH+aZJAbexJrK\nPm9MTEyhNQ0dOpThw4djYGBA/fr1ady4MZ999hkRERFkZWXlOE9QUBAPHjxAS0uL48ePY21tnSsb\nWGpqKrVr18bHx4dPPvlELE9MTBRfBgoqy+bVq1c8fPiQwMBArl69ipubW5716tSpQ2BgIBEREURE\nRIgGMHtF25MnT9DT08tx7OPHj3n58mWp/34+1t9oYSlzQ5CUlETLli3p27cv8GaM7/fff6datWo8\nfvxYrBcfHy8OH+VFcb2DixK0KTs9n7e3N1euXCE8PBwXF5dcST5KW0dpUVIa3u0tPH36lKdPnxa6\nt6AM96I4Oho0aFCooYyEhAQGDhyYoyw1NZWFCxfmOVdQ3PvRoEEDvv/+e65fv87169c5fPgwhw8f\nZsWKFaSnp4uJ4DMzMzEwMMDT0xMnJydCQ0OJiorK1WZgYCCGhobo6urm2Kevr4+Jicl7y7LR1tbG\n3NycBg0aIJfLsba2Fucr5s+fz9WrV8nKyiIlJQVvb29evHiBlZWVOEeQfT88PDxytZG9wq20fz8f\n62/0bXx9ffPdV+aG4NGjRwwbNoyjR4+io6PD6tWrcXV1pVatWuzduxdnZ2eSk5M5cuQIs2fPLmt5\nOZDJZNSpUwdra2v8/f05c+YM27Zto3bt2nTo0KFCLzktCkWZWzA2NsbExKRSzS14eXmJ2emyyczM\nLNG5Al9fX27evMnIkSNxdnbG2dmZ8ePH061bNy5evJhrjuBt7Ozs2LRpEykpKblCnV+9epUWLVqU\niEaA5s2bc+3aNdEQTJs2DYDo6Gi6detWYu1IFI0yNwS1a9dm1KhR9O3bl6ysLOzt7Zk5cyYKhYLI\nyEjc3NzIyMigX79+tGzZsqzl5YlMJqNJkyY0bNiQGzduiEtObW1tcXZ2rvBLTotKQXML2aEvNDQ0\nRKOQlZVVoT2c7969myvJ0OvXrwkICCixNgwNDVmzZg3NmjXDwcEBeDNskpKSgrOzM3/88Ue+xzZr\n1oyWLVvi4eHBnDlz0NPTIzMzk927dxMeHs6yZctKTOeECRMYNWoUNWrUoG3btshkMtLS0jh58qQU\n/qQcKRc/gsGDBzN48OBc5Xklv1AmFAoFrVu3FpecXrlyhbt372Jvb4+Tk1OlWHJaVArqLcTGxhIV\nFQVAZGSk+ACraOSVoL6kqVWrFqtWrWL58uXExcWhrq6Orq4uc+bMQU1N7b3HL1myhM2bN4t/l+np\n6djY2PD333+jq6tbKA3Lly/PYXCcnZ3x9PTMUadBgwZs3bqVVatWsWzZMuRyOenp6djZ2bFr164i\nXLFESSIlpvkAXr58iY+PD//995+YycnR0bFQS06VYcyxPDVkezi/jba2No0aNcp3ErW0UYbvRNKh\nnDqUQcOH6pAS05QSurq6dO3aFUdHR06fPo2Pjw/Xr1+nbdu22NvbV8olp4XlXQ9nDQ0NkpKSuHbt\nGqampjRs2FDKVSshUUZIg3IlgJGREX379mXkyJGYmJhw7NgxVq1axZ07d/JcFiuRE319fQwMDGjb\nti3169fnyZMn+Pj4EBQUlGtsXUJCouSRDEEJYm5uztChQxk0aBBqamrs3buX9evXc//+/fKWptRk\nezjL5XLq1KmDs7Mz1apVIzQ0lLNnzxITEyMZVAmJUkQyBCVM9pLT0aNH07NnT5KTk/nrr7/466+/\niI2NFevduXOnHFUqNxoaGjRv3pw2bdqgpqbGf//9x5UrV3jx4kV5S5OQqJBIcwSlRF5LTtetWycu\nOa1ImdJKC0NDQz777DMiIyMJCgri3Llz1KxZk3r16hVqJYyEhEThkAxBKfPuktPLly+LRiA8PBwr\nK6tK41hVHGQyGVZWVlSrVo3g4GDCw8OJiYnBxsYGS0tL6d5JSJQAkiEoIzQ0NJDL5TkmP7du3Qq8\ncbLr3bu3GGhMIjdqamo0btwYKysr7ty5g7+/P5GRkdja2mJgYFDe8iQkPmqkOYIypF27dsyaNYtZ\ns2YB0KNHDywsLLh//z6enp7s27eP8PBwaWK0AKpUqYKjoyPNmzcnLS2Nixcv4ufnR2pqanlLK1dG\njhzJli1bxO0HDx5gY2OTwys4ISEBW1tbXr58CcDChQuxtbUlLi5OrHPp0iXc3Nxwc3Nj2LBhtG7d\nWtw+evRomV2PRNki9QjKkaZNm9K0aVMePXok5kHw9/enatWq2NnZ0bRpU6mXkAcymQxzc3NMTU0J\nDQ3l/v37xMXFUbduXWrVqqXUoQrOnj0rpk0tSZycnLhy5QrDhg0D4MyZMzg7O3P69GkmTJgAvEkK\n1bx5c3R1dUlLS+PAgQN06tSJbdu2MXHiRCBnrP/s0C9SrP+Kj/L+xVRwsjOlwZuwzl26dGHChAm4\nubmhoaHB//73P7GXEBERIfUS8kChUFC/fn3atm2LoaEhgYGB+Pj48OjRo/KWli8+Pj6lcl4nJydu\n3LghBrc7c+YMo0aN4tWrV2IYj8uXL4tG6MiRI1haWjJs2DB27dpFSkpKqeiS+DiQegTlxLuZ0uBN\nMpBmzZrRrFkz4uPj8fX15fbt22Ivwd7enqZNm+aKEFnZ0dbWpmXLlsTHxxMQEFBm3sm3bt3i5s2b\nRT7u7SGcvEhOTubq1avAm2idTZs2fe85a9asiZ6eHsHBwVSvXp0HDx7QrFkznJycOHXqFMOGDePy\n5cti2OydO3fi5uZG48aNMTY2Zv/+/blCZUtUHqQegZJiamrKF198wfjx4+nevTvq6uqcOHECT09P\n9u/fT2RkpNRLeAdTU9Nc3snBwcF5ZugqSxITE8VkK4D4OTExsUTbcXJy4urVq5w7d442bdogl8tx\ndnbmwoULREdHA2BtbU1AQACBgYG4uroCb+aqvLy8pN9TJUbqESg5ampqNG/enObNmxMXFyf2Em7f\nvo2xsTH29vY0adJE6iX8HyoqKtSpUwdzc3MCAwMJCQkhKiqKhg0bUq1atRJdbpo9x1MUZs+eLS4W\nyI/iBhZzcnJi9+7dqKur06FDBwBat27Nzz//nGNYaMeOHSgUCnr37g28CYn96NEjzp07R9u2bYvc\nrsTHj9Qj+IgwMzPD1dWVCRMm0K1bN1RVVTl+/Dienp4cOHCAqKgo6a3u/9DU1MTOzg5HR8dK453c\nqlUrAgMDuXbtGp999hnw5j40bNiQbdu20bZtW168eMGRI0dYu3Ytp0+fFvNzd+/e/b1DVhIVF6lH\n8BGipqaGnZ0ddnZ2xMbG4uvri7+/P7du3cLExETsJWhoaJS31HLHyMhIzNsbHBzM+fPnsbKyKjfv\n5NJ849bQ0KBmzZpkZGTkyCHQtm1blixZQqtWrfj777+xtramdevWOY4dM2YMrq6u3Lt3T8weJlF5\nkPIRlBMlrSM9PR1/f3/+++8/YmJiUCgU2NraYm9vj7m5eZ5DIhX1XuRHeno6wcHBREREoKamRv36\n9bGwsBDvTWW7H5KOj0vDh+qQ8hFUAtTU1LC3t8fe3p7Y2Fhu3LjBnTt38PPzw9TUFDs7u1y9hDt3\n7ijFj7usyPZOtrS0JCAggNu3bxMRESF5J0tUesrFEJw8eZLff/8duVxOlSpVmDdvHubm5ixYsIAL\nFy6QmZnJ8OHDGTBgQHnI++ipVq0a3bp14/PPP8ff3x9fX1+OHTuGt7c3jRo1wsHBgerVq1fawHd6\neno4OjoSExPD3bt3uXjxIjVq1EBFRaVC506WkMiPMjcEqampTJo0iX///RcrKyu2bNnC3Llzadu2\nLRERERw+fJhXr17Rr18/GjVqRJMmTcpaYoVBXV0dBwcHHBwciImJydFLMDMzAyArK0upPXFLi7e9\nk0NCQrh//z6CIBAZGYmNjQ2CIEgB7SQqDWVuCDIzMxEEQYx38urVK9TV1fH29ubLL79EoVCgp6eH\nq6srBw8elAxBCVG9enW6d++OlpYWFy9eFOPL/Prrr8CbCcXSCH2g7ISFhREWFiZuy+VygoKCCA8P\nx8LCAgsLCynMh0SFp8wNgba2NrNnz6Z///7o6+uTlZXFzp07GT16NNWqVRPrmZmZERwcXNbyKjwu\nLi64uLjw5MkTVq1aBYClpSWtWrUqZ2Xlw7u5kw0MDDA0NCQqKoqQkBBCQkKoWrUqFhYWmJmZSXmo\nJSokZW4IgoODWbVqFUePHsXS0hIvLy9++OEHMUbK2xQ0ZBEYGFis9lNTU4t9bElSXjoEQSA1NZW/\n//4bbW1tsrKyiI6OZu3atTg5OZXL26+yfCeampq8ePECFRUVqlSpgqamJi9fviQxMZEnT54gl8vR\n1dVFV1cXdXX1UtOhLPdD0qFcGkpTR5kbggsXLmBnZ4elpSUAgwYNYsGCBbRq1YrHjx+L9eLj48Vx\n7LwozmqXhIQEPDw8WLRoEYaGhkUXX0KUhI7MzEySkpJ4+fIlL1684OXLl+K/7O23y98uyza6NWvW\nJDIykgULFnD69Gl8fHwYPHgwJiYmJXm570WZluY5ODjkKhcEgSdPnhAVFUVcXBzPnz9HT08PS0tL\nqlevjqqqaonrUJb7IelQHg0fqsPX1zfffWVuCBo2bMj27dt58uQJVatWxdvbmxo1atChQwf27t2L\ns7MzycnJHDlyhNmzZ5do25s2bSIsLIy1a9cycuRIZDIZKioqyGQy5HI5crk8z7Ls8pKaPPTy8iIs\nLAwvLy++++67fB/eBT3Uk5KSCmxDW1tbfHutUqUKxsbGVKlSBV1dXfz8/MRxcHgT9sDDw4Nr166x\nadMm+vfvT82aNUvkWisCMpkMY2NjjI2NSU9PJzo6mqioKPz9/QkICKB69epYWFhgaGgoTTBLfJSU\nuSFwdHRkxIgRDBkyBFVVVfT09Fi9ejW1atUiMjISNzc3MjIy6NevHy1btiyxdhMSEjh27BiCIHDy\n5ElOnjxZ5HPkZRwKY0jeLsvKyuLhw4cIgsC///4rxn7PC7lcjo6OjvgA19fXx8LCQny4v/t/dl0d\nHR0Uiry/2oSEBHbv3p0jEFtycjIzZ86kb9++qKurs23bNnr27EmjRo2KfI8qOmpqatSuXZtatWrx\n/PlzIiMjiYmJITo6Gm1tbSwsLKhRo0aZe3UHBAQwdOhQtmzZQuPGjQF4+vQpX375JTNmzMi1EMDD\nw4OLFy/m6pGuW7eOixcvMm/ePKpWrYqGhgaCIJCUlISDgwO//vor6urq+R7fu3dvhg4dCsDVq1dZ\nu3YtsbGxqKmpYWRkxLfffkuLFi3yvAYbGxvq1auHXC4nKysLmUzG2LFjMTc3z1GvV69epKamcuTI\nkRyGNywsjEWLFhEbGwu8WSY8btw4sZfXvn17VFVVc303s2bNws7ODhsbGy5fvpzrmqKjo+nfvz+3\nbt0iOjqaDh06MHfuXPr27SvW2bhxIyEhISxcuJB9+/Yxb948atSokev6Fi9enOe1lzfl4kcwaNAg\nBg0alKt8+vTppdaml5cXcrmczMxMVFRUsLW1pUOHDgiCIK5kysrKIisrK8fnopYVVDczM5N79+6J\nmuRyOfXq1aNz5845HurZn7W0tEp8aaeXl1eu+RiFQoG5uTm7d++mTp06NG/enD179pCUlFRpJ5Hf\nh0wmQ19fH319fRo2bEhsbCxRUVEEBQURHByMiYkJlpaWGBsb5/oOS8NXoVGjRkyaNIkff/yR/fv3\no62tzbhx4+jdu3e+q8GGDRvGiBEj8tzn4ODAuHHjxGGItLQ0BgwYwP79++nfv/97j/fx8eGXX35h\n+fLlNGvWDAA/Pz9++uknZs6cibOzc57Hbd26VXwQ3759m6FDh+Ll5SXuv337Nunp6aiqqnL+/Hmc\nnJzEfWPwL+FzAAAgAElEQVTHjmXcuHF07NgRgOvXrzN69GhOnTqFvr4+AEuXLhUNZXGRy+UsWrQI\nBwcHatWqlWcdBwcH/vzzzw9qpyypFJ7FCQkJHD9+XHwLzszMJCgoiJkzZ5bpXEFCQgIDBw4UA8Nl\nZWXx4MEDPvvsszLTcffu3Rx5k+FN9ElVVVXmz5/PkiVL+Pfff+nYsSPHjx/nxYsXuLi4SEMeeRAd\nHU1kZGSOMj09PdLS0nj06BHx8fHIZDLU1dXFnNUZGRkkJSWho6OTbwKd5ORknj17BrxZ0fXum2V+\n9O/fH19fX6ZNm4alpSV6enp88803H3aR/0diYiJJSUno6ekVqv7ixYuZOnWqaAQAmjVrxrRp01iy\nZEm+huBtnj17hqGhYY6VWjt27KBdu3YYGBiwdevWHIbg8ePHJCcni9stWrTgt99+K/GVXhoaGri7\nuzNhwgT+/vvvcolZVdJUCkOQ11twZmYmXl5ejBs3rlLpWL9+vfg5r4mnzZs3s3z5co4cOUKrVq24\ndOkSL1++xM3NTVo6WQhUVFTQ0tJCS0uL9PR00tLSSE1NzZFTWVtbu8QnmLOZPXs2PXr0IDAwkEOH\nDhVowLds2cLBgwfF7cGDB4vDHTdu3GDcuHHI5XKePXtGzZo1GT58OF26dMn3eHhjAMzMzAgNDc1z\nCKhNmzZ8//334oT7u3z11VfI5XKSk5OJiopizpw5Yo8qMTGRo0ePsmfPHvT19fH09CQ0NFTsWc2c\nOZPZs2ezZMkS7O3tadGiBV27ds0RgG/ixIk5hobU1NTYvXt3gfc0L8aMGcOlS5dYvnw5U6ZMybX/\nxo0buLm55SgbOnSoGPpb2agUhiC/t+CAgIBKqaMg9PT0mDVrFt7e3qxYsQJTU1MAkpKS6NevX6ku\nm/zYqFGjRqHe1lNSUsQJ5uTkZJKTk6latar41v4uH7Iy5MGDB7x69Yq0tDQCAgIKnGcrzNCQjY0N\nq1ev5tChQ2KOg/cd//z58/fqzC9Z0NtDQ2FhYQwZMoSJEyfSoEED9u3bR506dcToqG3atGHr1q2i\nU2TXrl3p2LEjvr6+XL9+nb1797JmzRr++ecf8XsqiaEheDM8tGTJEnr27Mmnn36aa780NKSEvO8t\nuKxo164dO3bsyNF91dLSYuTIkeWiJz9kMhkdO3akadOmLFq0iKCgIARBYMOGDQwdOjTHG5bE+9HU\n1KRu3brUqVOH2NhYQkNDiYqKIiIigipVqojLUD90iOHp06f88MMPTJ06lbS0NMaPH8/+/fsxNjYu\n9jnlcjnff/89N2/eZPr06axbt+69x+jp6WFtbc21a9fo1KkT8GY5uKmpKVeuXMHKyqpQQ6HW1ta0\naNGCwMBABEHg77//5vnz57Rv3x54Y2CvXbvG+PHjefr0Kfv372fixIm0adOGNm3a8OOPP+Lu7s6J\nEyfyNXgfQvXq1fnll1+YMmUKPXr0KPHzlyWVL8hMOTJ9+vQcRgDejAeX5iT5h2BiYsKSJUvo06cP\ngYGBxMfHs3r1ap48eVLe0j5KZDIZ1atXx8nJCRcXFzFv9Z07d/D29ubmzZs8efJE9FsIDQ0t9Lkz\nMzMZN24czs7OdO3ald69e/Ppp58yfvz4EknVOWvWLC5fvoy3t3eh6k+ZMoVFixbh5+cHvBkyGjRo\nEPPmzWPy5MmFOkdCQgI3b96kTp06XLx4kYSEBLy9vcWEOufPn8fY2Ji///6bqlWrsmvXLo4fPy4e\nn+0I2LBhw6JfcCHp0qULTk5ObN26tdTaKAsqRY9AWXh3YvF95cqAXC6nd+/etGjRgiVLlpCZmcmq\nVasYMGCAlMDkA1BTU6NmzZrUrFlTXIb68OFDHj58iKqqKlFRUUXKubF48WJSU1NzjFfPmjWLL7/8\nEk9PTyZNmvRBei0tLfn6669ZsGCBmP0srzmCpk2bMmfOHNq2bcvChQtZsWIFcXFxCIKAkZER1atX\n5+LFizg4OIgred4me44A3uSPGDVqFE2aNOGPP/7gyy+/zNEbVSgUjB49mt9//52RI0eydetWli1b\nxuLFi9HU1ERNTY0RI0bg6OgoHvPuHAHknBt5d/jL09OTunXrFnhvZsyYkctZK685AhUVFfbt21fg\nucoLKTFNGZGSkoKBgQFpaWm59hkYGBAfH19qE4j5UdR78fr1a7Zs2UJoaCjq6uq0bNlSTIBeljpK\ni/LUERwcTEhIiLitoqKCnZ2dOEdTHpTG/RAEgXPnztGyZctC59lWht+HMmj4UB0FPTeloaEyID09\nnb59+5KWlpZrHDh7VYa9vT3nz58vJ4WFQ6FQMHLkSPr37096ejrXr19n6dKleRo3iaJhY2ND165d\n6dq1Kzo6OqioqHD9+nUCAgJKZGhHWZDJZLRt27bQRkCibJAMQSmTmZnJkCFDxIThmzZtwsrKCplM\nhpWVFV5eXuzbt4/ExEScnJwYNmxYvuvLlYVmzZoxdepUVFVVefXqFVOmTCEoKKi8ZVUYjIyMxGGj\nBw8ecOnSJV69elXesiQqMJIhKEWysrL4+uuv2bVrF0uWLGH06NEMGjSI8PBwAgICCA8PZ9CgQfTs\n2ZPAwEA8PDzYsWOHuGRPmd8Eq1SpgoeHBzVq1MDAwIAVK1awZcuWXMtjJYqOQqGgXr162Nra4uDg\nQHJyMufPn+fhw4flLU2igiIZglJCEATGjRvH5s2bmTlzJhMnTiywvra2NgsWLOD27dvY2dnx3Xff\n0apVK65du1ZGiouOiooKw4cPp1WrVpiZmfHff/8xduxYpZ78/tgwMzPDyckJXV1dbt68ya1bt5T6\nBUHi40QyBKXEjBkzWLlyJT/99BO//PJLoY+rX78+3t7e7Ny5k5iYGFq3bs3o0aNJSEgoPbEfgEwm\no3Pnzri6umJkZISOjg7ffvst+/fvzzPHhETR0dTUxNHRkTp16hAVFcWFCxfEDH8SEiWBZAhKgYUL\nFzJ//ny+/vprli1bVuQ4PTKZjP79+xMUFMS4cePYuHEjNjY2bNiwQWkfrg4ODvTr148qVapgb2/P\nunXrmDx5co4cExLFRy6XU79+fVq1akV6ejrnz58nIiKCj3DRn4QSIhmCEuaPP/5g6tSpDBw4kDVr\n1nxQsLYqVarg6enJzZs3adCgAV9//TWffPIJN2/eLEHFJUf9+vX56quv0NbW5tNPPyU8PBx3d3dO\nnjwpPbBKCGNjYzFIob+/Pzdv3iQjI6O8ZUl85EiGoATZsmULP/zwA25ubmzZsqXEgrQ1btyYc+fO\nsXXrVu7fv4+DgwM//PADiYmJJXL+ksTCwoLhw4ejra1N8+bNqVu3LvPnz2f27NmFikFTEYmLi8s1\nlPPy5Uvi4uKKdT4NDQ1atWpF/fr1iY2N5fz581y5cgV7e3v8/f3Fek+fPsXFxYWzZ8/mOoeHhwef\nffYZbm5uuLm50a1bNzp06JAjHEs2vXr14osvvshlzG1sbFi+fHmOsuPHjzNkyBAA9u3bh729fY42\nhgwZwu3bt3Mcc/fuXUaPHk3Hjh1xc3NjwIABOTyYT506hY2NDStWrMhxnCAIdOjQga5du+Z5n2xs\nbOjWrZvYdvfu3fP0jM7v+sLCwhg1ahTdunXjxx9/ZPDgwdy4cUPc3759ezp16iReX/a///77T2z/\n6dOnudqLjo6mefPm4mcbG5tcge82btyIh4dHrvs4btw4sZ3CemgXCuEj5MaNG8U+9u7duyWo5P+z\na9cuQS6XCy4uLkJKSkqp6Xj27Jnw3XffCXK5XDA1NRW8vLyErKysYp2rtO6FIAjCixcvhDVr1ghz\n5swRVq9eLbi4uAi9evUSrly5UqY6ikJp6Xjx4oXg5+cnvHjxIs/tD9GRkJAgnDx5Ujh8+LCwcuVK\nwdnZWUhMTBQyMjKEIUOGCKtXr87zuClTpggbNmzIUfbw4UOhadOmQmhoqKjj1q1bgqurq9C9e3fB\nx8cnR/169eoJTZo0Ea5duyaWHTt2TBg8eLAgCIKwd+9eYdSoUTmOuXjxotCqVSshOjpaEARBuHXr\nlvDZZ58JZ86cEeuEhoYKrq6uws6dOwVBEISVK1cK7dq1Ezp06JDjXNeuXRPatGkjuLq65nmN9erV\nExISEsTtW7duCU2bNhXS0tJylOV3fV988YXwv//9T7wX165dE+zs7IRnz54JgiAIzs7Owu3bt/Ns\nO6/2s4mKihKaNWsmfq5fv75gb28v3L9/X6yzYcMGYcqUKYIg5LyPH/IbLei5KfUISoCjR48ycOBA\nHB0dOXDgQKlmp9LX1+ePP/7g+vXrWFlZMXToUNq1a8edO3dKrc3ioKuri7u7O1ZWVjx69Ijvv/8e\nPT09PDw88PT0JCUlpbwlfjAJCQkEBwe/919MTAwKhYJ79+5x+/Zt7t27h0KhICYmJs/6qamp4uf3\nLRIwNDTEyckJExMT0ffAw8ODZcuWFTkfQXYPRUdHRyzLjv/fvXv3POPp/PTTT0yaNKnQvb02bdrQ\nsWNHdu7cCcCKFSv45ptvciTPsba2ZvHixSxfvpz09HQA6tWrh5aWlvi2DbB//366d+9e6OvLzm/w\ndva+gq6vPPIbZF9vWSMZgg/k7Nmz9O7dmyZNmnDkyBG0tbXLpF07OzsuX77MunXruHPnDs2aNWPi\nxIlKtZpEXV2dQYMG0bhxY/z8/OjZsydffvklhw8fZuTIkQQEBJCQkICnp2eeXeiKhEKhQFVVlYyM\nDFRVVfNNJVoc1NTUcHBwoFGjRnTr1g1/f3+OHj3KwoUL35uPwM3NDRcXF1q1asWGDRtYu3atGNbi\n5cuXHD16lO7du9OtWzeuXLmSKxBe9+7dsbe35+effy603vr164uZ+v7777888xZkB4p7u70ePXqI\nqV1TUlLw9fUV4x7lx1dffYWbmxsdO3Zk9OjRfPPNN7nyG+R3fTNnzmTu3Ll8+umnLF68mG3bttG4\nceNc+Q3eHhZ6O31lURgzZgyampq5htqyyY5d9PbQ0N69e4vVVl4U+Gv08PBg4cKFwBvr27NnT3Ff\nnz592LNnT5EbPHDgAJs3bxa3X758SXx8PD4+Pqxdu5YLFy6QmZnJ8OHDGTBgQJHPX5ZcvXqVbt26\nUbt2bU6cOFHo7E0lhVwu5+uvv6Znz55MnTqVZcuWsXPnTpYvX07fvn2VIquYiooKPXv2RFdXl0uX\nLtGgQQOWLl3KkiVLGDt2LNbW1oSFhZV5kqCSwMjICCMjo0LVffnyJffv36datWo8fvyY6tWr5xvO\nOzAwEBsbmyJpkclk1KpVi0ePHpGWlkZ6ejrHjx+nV69e+f4OsvMJJCcn89NPPyGXy3M8lE+dOlVg\n/P9sZs+ejZubG7t37y50iPK3e835OSGmp6fn0J493j9jxgxOnjxJ+/bt3/t2nld+A2tra+zt7YuU\n3+DYsWNKkd+gtGIeFdgjCA4OFj+/nTcU8v/y3ke2Vf/333/Zs2cPxsbG/Pzzz5w4cYKIiAgOHz7M\nnj172Lp1a65JJWXi1q1bdO7cGVNTU06ePEnVqlXLTUvVqlVZv349ly9fxtTUlH79+vH555/n+P7K\nk+z8Bp06dSIwMJBbt27xxx9/0K5dO0JCQhAEgWPHjlXYXkG2EahduzbVq1endu3a3L9/v8R7b0+f\nPmXq1KnMmDGDYcOGsWDBAo4fP54jO1peaGlpsXjxYm7evCm+pAmCwIkTJ3j48CHt27enffv23Llz\nh4MHD4ppNLPR0dFh6dKlLFq0iPDw8PfqvHPnjvjwtbOz4+rVq7nq3L59G1VVVWrXri2WGRsb07Bh\nQ3x8fDhw4ECOF9PCkJ3fwNfXV8xvkN/1hYWFsXTpUtTV1WnTpg0DBw5k//791KtXjxMnThSp3cLy\ndn6Dd+9xaVOgIRDemkUX3plRL4m3zfXr12NoaEj//v3x9vamV69eKBQK9PT0cHV1zRXiVlkIDg7m\n888/R0dHB29vb6pXr17ekgBo3bo1169fZ+XKlVy/fp3GjRszbdq0XDkQyovWrVvTp08fHj58yD//\n/IOWlpb4Rpeens6mTZvKWWHp8OrVK2rXri2+Levq6lK7du0SjR/0dj6C7CEER0dHVq5cydmzZ98b\nv0pPT48pU6awatUq4uPjuXjxIs+fP883/v+7NG/eHHd3d1atWlVgOz4+Ppw9e5Z+/foBMGHCBDZs\n2ICPj49YJywsjKlTp/Ljjz/myojXo0cPNm/ezMuXL4scBj07v0Hjxo2l/AbvUOiBypIeZnj69Cmb\nN28W43PHxsZSrVo1cb+ZmZnSvNG+TXh4OC4uLgB4e3tTs2bN8hX0DioqKnz//ff07duXSZMmsWDB\nArZv386KFStwc3Mr9+GiRo0aoa2tzc6dO3n58iUaGhoYGxsTHh7O0aNHGTx4MGZmZuWqsaTJ63p0\ndXVLNNPbu/kIZDIZixcvpnfv3hw9epTXr19jbW2NjY2NOEb+Lt27d2f37t0sXLiQ9PR0Pv/88wLj\n/7/LmDFjuHz5co6yt+Pyy2QyTExM2Lhxo5g1rWHDhmzcuJEVK1Ywf/58VFRUqFKlCj/88AOdO3fO\n1YaLiwuzZs3ip59+KtR9ySu/gaOjI999912R8hvI5XJ0dXXLPb9Bamqq2F5J5jcoMB9Bz5492b9/\nf67PeW0XlbVr1xIeHi7OQXTq1IlFixbRrFkzAHbv3s358+f5/fffcx3r6+uLlpZWsdp9+0YWlceP\nHzN48GCeP3/Oli1bqF+/frHO86E6isKNGzf49ddfCQkJwcnJienTp2NhYVGmGvJi586dvH79GhUV\nFRQKhbjW3cjIiF9++aXEV2YUhvK8H6WpIysri4SEBF68eIG6ujqmpqaFyn1RUe/Hx6rhQ3UkJyfn\nm4+gwB5BVFSUuPzs7c/wxhHiQzh69CgzZswQt7Mn0bKJj48v8M2wuBMmxZ1sefLkCX369OHZs2d4\ne3vTqlWrYrX/oTqKSoMGDejfvz8rV65k1qxZdO/eHVdXV65fv050dDSWlpbMmzePQYMGlbqWt4mN\njSUqKoomTZqgUCioVasWERERJCQkcPDgQTw8PPJ9cy0tKkLykYKIiYnh9u3bxMTE0LRp0xw98LLU\nUVSUQYcyaPhQHe/2Mt6mQEPwdi7d7CTU+W0XhezUfNnedfCmC7V3716cnZ1JTk7myJEjzJ49u9ht\nlCTPnz+nU6dO3L9/n2PHjn2wEShrVFVVGT9+PP369aNPnz45upMRERGMGjUKoEyNwaBBg3KMC1tZ\nWWFlZYWOjg6HDx9GW1ubsWPHlvtQVkWievXq6Onp8d9//+Hr64uVlRUNGzYsl96XhHJRoCEoaFa+\nKIm13yUiIgJjY+Mc3dMBAwYQGRmJm5sbGRkZ9OvXj5YtWxa7jZLi1atXuLq64u/vz4EDB3I4vnxs\nmJubExsbm6s8OTmZ6dOnl6khaNeunXgvZ8+ezeDBgzl8+DCJiYl06dKFQ4cOoaOjw4gRI8pMU2VA\nW1ubTz75hKCgIO7fv8+zZ8+ws7PL4UQmUfko0BBER0ezfPlyDAwMmDBhApqamiQlJfH777+zY8eO\nYnuzNmnShJMnT+YUolDk6IEoA6mpqfTs2ZPLly/zzz//8MUXX5S3pA8mv1wB5Z1DwNramjFjxnDq\n1CmuXbuGk5MTR44cQUdHR1xhIlEyyOVyGjZsiJGREbdu3eL8+fPY2tqKc0cSlY8CB2GnTZuGgYEB\njx494s8//+TGjRt06dKFS5cusWHDhrLSWC5kZGTQv39/Tp48yaZNm+jTp095SyoRLC0t8ywvz4dA\n9nI8NTU1unTpgru7O4aGhjRt2pQzZ85w4MCBctNWkTE1NeWzzz5DX1+fW7ducfPmTSnDXCWlwB5B\nXFwcXl5epKam0qtXL3bt2sWwYcMYPnx4ibrIKxuZmZkMGzaMf//9lz/++IOvvvqqvCWVGPPmzWPU\nqFG5fAs++eSTclIEtra2ObYtLS355ptvOHPmDBcvXuTatWukp6fz5ZdflpPCioumpiatW7cmJCSE\ne/fukZiYiJ2dXZl7yUuULwX2CLKXaGpoaPD8+XMWL17MqFGjKrQREASBMWPGsGPHDhYsWMB3331X\n3pJKlEGDBrFu3TqsrKyQyWRYWlri4ODAnj17lCrPgUKhoGPHjri7u6OiokJgYCBr164lKSmpvKVV\nOGQyGfXq1aN169a8fv2aixcvEh4ejiAIPHny5IPmAyU+Dgq9Ps/IyCjPGBgVCUEQmDhxIuvXr2fa\ntGliPPCKxqBBgwgPDycgIICIiAiOHz+OsbExAwcOVBov5GysrKyYOHEiz58/JzY2lt9//51bt25J\niW5KgapVq+Lk5ETVqlW5c+cOUVFR3LhxQ+odVAIKNARvL92rDEvMZs+ejaenJ2PHjmXu3LnlLafM\nMDIywsvLi+DgYCZMmFDecnKhp6fHzz//THR0NE+fPuXAgQPs2LGj0ia6KU3Cw8PFcBQZGRm8fv2a\ngIAAoqOjlTZNqsSHU+AYT3BwMHZ2dsCbFTTZnwVBQCaT5YgN/rGzbNkyZs+ejbu7O8uXL69069c7\ndOjAxIkTWbJkCZ07dxbDAigLBgYGLFiwgLFjx6Kjo4NCoWD16tW4uLjg4OBQ6b6v0sLGxkaMfHru\n3DnU1dVJTU3Fz8+P4OBgateujaWlZaV4MaxMFGgI3l3iWVH5888/mThxIl9++SXr168vc49WZWHu\n3LmcOnWKESNG0LJly/d6npY1JiYmLF26lLFjx5KSkkL79u05evQoAQEBdOvWrdAhoSUKh4GBAZqa\nmlhbW/Po0SNCQ0MJCAggJCSE2rVrY2VlVahQFRLKT4FPPHNzc8zNzVFXVycwMJCgoCDU1NTE8orA\ntm3bGDNmDK6urvz111+V+k1HTU2N7du3k5yczFdffaWUQwE1atRg8eLFvHjxgjNnztChQwfi4+NZ\nu3YtFy9eVErNHysKhYI6deogk8kwNTWlTZs2ODo6oqenR1BQEKdOnSIwMJC0tLTylirxgbz31dfT\n0xMXFxdWr17Nb7/9xueff87SpUvLQlups3//foYNG0a7du3YvXs3ampq5S2p3Klfvz6//fYbJ0+e\nzJUsXFmoU6cOCxYsICEhgc2bNzN06FDq1KmDt7c3GzduJD4+vrwlVkhkMhlGRka0atWKzz77DGNj\nY8LCwjh16hT+/v5Kt9BAovAUaAj27t3LzZs3OXXqFPv27ePQoUMcP34cPz8/du/eXVYaS4X//e9/\n9O/fnxYtWnDw4EE0NTXLW5LS8PXXX+Pm5oaHhwd+fn7lLSdPbG1t+fXXX4mKimLevHl069aNPn36\n8Pz5c9atW8eZM2ck56hSRE9PD3t7e9q1a4e5uTmRkZGcOXOGmzdvKlW6VInCUaAh+Oeff1i2bFmO\nsVdTU1OWLl2aZ3KKj4Xz58/To0cPGjZsyNGjR6U4K+8gk8nYsGEDRkZGSrmkNBsHBwdmzJhBYGAg\ns2bNom7dunz77bfY2tpy7tw51q1b98FRciUKRkdHh6ZNm9K+fXtq1qxJXFwcPj4+XL9+vcyzbEkU\nnwINQXp6OiYmJrnKzczMSE9PLzVRpcH27dupWbMmjRo1om3btujr63PixAkMDAzKW5pSUrVqVbZu\n3UpgYCCTJk0qbzn54uTkxKRJk7hx4wZz585FXV2dnj17MnDgQNLS0ti4cSMnTpwgIyOjvKVWaDQ1\nNWnUqBEdOnSgbt26PH36lIsXL3L58mUeP34s+X0oOQUagoLeBD+mP6zt27czatQoIiIiEAQBQRBI\nTEysNKuiikvHjh2ZMGECq1ev5vDhw+UtJ186d+7Md999x/nz51m6dClZWVli78DBwYErV66wZs0a\nHjx4UN5SKzxqamrY2NjQoUMHGjRoQFJSElevXuXChQvExsZKBkFJKdAQ2Nra8u+//+YqP3DgAE2a\nNCk1USXN9OnTcxm1lJQUpYt2qozMmzePZs2a4e7uTlxcXHnLyZc+ffowbNgwjh8/zurVqxEEAXV1\ndVxdXfnqq6+QyWR4eXlx6NCh9yZzl/hwFAoF1tbWtG/fnsaNG5ORkYGvry8+Pj5ERUVJq7uUjAL9\nCH766SeGDBlCaGgoDg4OvH79mqtXr3L8+HH++eefstL4wShr6OWPAXV1dXbs2IGdnR3Dhg3j6NGj\nSutnMXToUJKSktizZw+6urpisMCaNWvyzTffcPbsWS5fvkxISAhdu3YtcvJziaKjoqKClZUVFhYW\nxMbGEhoayq1bt7h3757knKZEFPgXbWFhwbZt23j+/DlLly7lt99+Iy0tjV27dimds1FB5Bd6Ob9y\niZw0aNAAT09PTpw4wcqVK8tbTr7IZDLGjBlD586d2bJlC3v27BH3qaqq0rFjR0aMGIGmpiY7d+5k\n7969vHr1qhwVVx7kcjnm5uY4OTnRokULNDQ0CAgI4NSpU4SEhHxUQ80VkfeGEa1RowZz5swpCy2l\nRl6hl7W0tJg3b145qvq4+Oabbzh27BiTJ0/G2dlZaYcG5XI5EydO5NWrV6xatQptbW26dOki7jc3\nN2fUqFFcuHCBc+fOcf/+fbp06UKjRo24c+eOUuSlrchkO6eZmpqSkJBAaGgowcHBhIWFYWVlRe3a\ntVFXVy9vmSJZWVm8fv2azMxMnjx5QmJiInXq1ClvWSXOew3B77//TosWLXB0dARg8uTJWFpa8v33\n35e6uJIiOwXj9OnTiYyMLLeE7R8zMpmMjRs30qRJEwYOHMj169eV1vdCRUWFGTNmMH36dJYuXYq2\ntjZOTk459rdt25YGDRpw8OBB9u7di7+/P/fu3StH1ZUPIyMjjIyMeP78OaGhoYSFhfHgwQMsLCyw\ntrYWw+AXF0EQyMzMFIPnZWRk5PhcmLLMzEzxfFFRUbRo0eJDL1spKdAQrF+/nosXL9KjRw+xbPDg\nwcybNw9tbW3c3d2L1WhwcDBz587l5cuXyOVy5syZg62tLWvXruXAgQNkZmbSvXt3vv/++xILJjZo\n0DLLFXMAACAASURBVCAGDRpEYGCg9NZXTIyNjdm6dSudOnVi8uTJSj1MpKamxpw5c5g0aRK//vor\n8+fPz/VHbGJiwvDhw7l69SqnT58GkH4f5UC2c1pSUhJhYWFERkYSGRlJ9erVqV27NhkZGURERPD8\n+XNMTEwK9UDP/vy+VUpyuRyFQoGqqqr4v4aGBgqFgpcvX5KYmCjWzczM5PHjxxgZGVW4IIcFGoJD\nhw7x119/5YhH3qRJE1atWoW7u3uxDEFKSgojRoxg3rx5tG3bFm9vbyZOnMjUqVM5fvw4+/btQ0VF\nhREjRmBtbV0h8gRXJD7//HN++uknli9fTpcuXZT6+9HU1GTBggWMGzeOmTNnsmTJklzZ0M6dO4eP\nj4+4vWvXLgA+/fRTOnToUKZ6KzvZzmn16tXj/v37REZG8vDhwxx13l3goVAoxAe4qqoq6urqYnTa\ntx/u+X2Wy+WFeqifO3cOQRAICwsjKSmJ5s2bV6gEXQVeiYqKSp5JKapWrVrsmf6LFy9iYWFB27Zt\ngTfhj2vUqMG2bdvo2rWr2B3s1asXBw8eVOoHTWVl/vz5nDp1Cnd3d27fvo2pqWl5S8oXXV1dlixZ\nwtixY5k6dSrLly/PMcbbrl072rVrB7zJR+Ho6Mjly5e5d+8eTZo0wdjYuJyUV140NTVRKBQ5hmVq\n1aqFqalprgd5Wb2ZZ0diVVFRISAggEuXLtGiRQulHR4tKu9dB5iXB3F6enqx47g8ePAAY2Njpk2b\nRq9evXB3dyczM5PY2NgcK5HMzMyk4GFKioaGBjt27ODFixe4u7srvZOQoaEhS5cuRVNTk8mTJxcY\nduLzzz9n4MCBJCUlsW7dOnx9fZX++ioiNjY2dO3ala5du4ovo1WrVkVPTw8tLS3U1NTKdHgmOxJr\nrVq1aNGiBcnJyVy4cCHH0NHHjEwo4Fe+YsUKkpKSmDZtmnjTBUFgwYIFZGVlMWPGjCI3uGbNGtau\nXYuXlxdNmzbF29ubWbNmUbt2bfr374+rqysAly5dYtmyZezduzfXOXx9fYs9kZSamoqGhkaxji1J\nlEHHh2rYsWMHc+fOZdq0aQwePLjcdBSWuLg4PD09UVNTY/z48RgaGubY7+fnR7NmzYA3Q5jXrl0j\nPj6eGjVq4ODgUGbRaZXht6FMOl69ekV6enq5hoN5916kpaURFxdHZmYmJiYmZRav7EO+k+TkZOzt\n7fPeKRRAWlqaMGLECMHZ2VkYN26cMHbsWMHZ2VkYMWKEkJycXNCh+bJnzx6hR48eOcpatWolDB06\nVNi8ebNYtm/fPuHbb7/N8xw3btwoVtuCIAh3794t9rEliTLo+FANWVlZgqurq6Curi74+/uXm46i\ncO/ePcHV1VUYMmSI8PTp0wJ1ZGVlCefPnxfmzJkj/Pbbb0JkZGSZaFSG34YgSDrepyE1NVU4f/68\ncOjQISEkJETIysoqFx2FpaDnZoFDQ2pqamzYsIGFCxfSpEkTmjdvjqenJxs2bCj22JiTkxMPHz7k\nzp07AFy/fh2ZTMZXX33FwYMHSU5OJj09nX379uHi4lKsNiTKBplMxqZNm9DX12fAgAEfReiGunXr\nMn/+fB49esSUKVNISkrKt65MJuPTTz8VF0Vs3ryZ8+fPS0NFlYyEhAQ8PT15+vRpjnJ1dXUcHR2p\nXr06QUFB3Lp166MNnVGoWAEtW7bE3d2dr776iv/X3nmHNXX2//+dxQ57REB2AQX1Eal7UGWoiCgu\nnK2K1G2lWhWrVSt11KqttY+P1mpVhNYWxImKq6jFQbEuVPaQoYCsMEPu3x/8cr4iQ8CQBLhf15WL\n5OTknDcZ9+fc92fxeLz3qjduYGCAvXv3YuPGjRgzZgy2bNmCPXv2YPjw4XB3d8ekSZMwZswYODo6\n1glbpSgmhoaGOHz4MB49eoRVq1bJW06z6NmzJzZt2oSUlBQEBga+04CZmpri008/Rffu3XHlyhUc\nPXqU1tzvRBw5cgRJSUk4cuRIvec4HA569+4NW1tbZGZmIiYmpt1VZgbQ9NJQTk4O8fX1JdeuXSMi\nkYhMnTqVfPjhh2Tw4MHkwYMHrZ6ivC90aUjxNCxdupQAIOfOnZOrjpZw9epVMnz4cPLFF1+Q7Oxs\n4ufnR/Lz8xvdXywWk9jYWLJ582ayfft28vz58zbRpQjfDUKoDkIIycvLI25ubsTFxYW4urqS27dv\nk9LS0gb3zczMJGfPniWXL18mJSUlbaKnrZaGmgwf3bp1K4YNG4a+ffviwoULyM7OxrVr15CRkYEt\nW7bg8OHDsrFWFIVn27ZtuHLlChNS2lAfC0XDxcUFQqEQO3bsQEBAAHJycnDkyBF89tlnDe7PYrHg\n5OSErl274s8//8Tx48fRv39/jBgxokPFlFP+jx07djB1kEQiETPr1dHRQdeuXdG1a1eYmpoy9z/8\n8EPExcXhxo0b6NOnT7sJP27y25uYmIhdu3YBAP7++2+4urpCTU0NdnZ2ePXqlUwEUtoHKioqCAkJ\ngbOzM+bMmYPTp0+3i+xLT09PvHz5kpn2nzt3DrNmzaoXUfQmBgYG8PPzw8WLFxETE4O0tDRMmDCh\nTic/SvuGEIIDBw4gJiamznYulwtfX1/k5+cjMzMTt27dqtOJjc1mw9raGiNGjEBMTAwIITAxMYGZ\nmRn09PQUtnLvOxPKJMTFxWHZsmXMY9oPlvI2jo6OTPLWTz/9hEWLFslbUrMoLCwEm82GWCxGdXU1\nlixZgvXr18POzq7R13C5XIwePRpWVlaIiIjA/v37MXr0aPTq1UuGyiltQVlZGbZu3Yro6GiwWKx6\nwQElJSX44osv6jzOzMxEZmYmMjIykJGRgWvXrsHR0RHm5uY4c+YMrl+/DmVlZZiYmMDU1BRmZmYw\nNTVlZhN8Pv+duiRO623btjV5odIamjQEKioqyMnJQWlpKVJTU9G3b18AQFJSUrOEUzofixcvxvnz\n57FixQq4uLjAwcFB3pKaJD8/H5GRkXWiPbKysjB//nw4OTlh2rRpcHJyanR2Y29vjy5duiA8PBwn\nT55EcnIyRo8erVAVNCnNJz09HevXr0dGRgb09PSQn59f53mRSITHjx/X2cbn89GtW7d6NapqamoQ\nFxeHPn36wMnJCRkZGUhPT0diYiKio6PrfOe0tbXrGAbJkpOJiQmTv/Km07qx5cvW0qQhmD9/PsaN\nGweRSISZM2dCS0sLYWFh2LlzJ9avXy9VIZSOAYvFwqFDh9CjRw9MmzYNt2/fVoikpMY4cuRIvZA/\nLpcLW1tbpKamYsWKFbC1tcW0adMwePDgBkuraGlpYdasWfjrr7/w119/ITMzExMmTICxsbGs/g2K\nFLh58ya2bNkCLpeLHTt2oHfv3sxzrSlGyOFw4OzsjNTUVDx+/BgODg6YPXs2VFVVUV1djezsbGRk\nZNSZSdy5cweRkZHMMd4s2/3w4UMQQhAZGfnO5cuW0qQhcHFxwalTp/D69WtmmqytrY3vvvsO/fr1\nk5oISsfCyMgIhw4dwpgxYxAYGIidO3fKW1KjPHnypN4yp0gkQlVVFUJCQnDx4kWEhoZiw4YNMDU1\nha+vL9zc3OplGbPZbLi4uMDS0hJhYWE4ePAgXF1d0b9//3bhK+nMiMVi/Prrrzhy5AhsbW2xadMm\nqdbPsrCwgLq6OmJjY3Hjxg04OztDR0cHZmZmDTbHEgqFjHGQ/L137x5zwVJTUyP9WUFLQ5BCQ0Nb\nHb4kLWj4aPvQsHjxYgKAXLhwQa46mktjOkQiEbl69SqZN28ecXFxIRMmTCChoaFEKBQ2uL9QKCQh\nISFkw4YNJDg4uNFww5bqkDWdQUdJSQlZs2YNcXFxIVu2bCEVFRVtpqG4uJhcvnyZnD17lrx48aLZ\nr8vLyyPu7u7ExcWFuXl4eDQZ6twQrc4sbojQ0FDpWSFKh2b79u1wcHDAxx9/3K6jzDgcDlxcXPC/\n//0PO3bsgLm5Ofbt24cpU6bg4MGDdaJGgNrud1OmTMGoUaOQnJyMffv2ISUlRU7qKY2RkpKCBQsW\n4M6dO1i6dClWrVrVpr4dPp+PQYMGQVtbG//88w+eP3/erCz1hpYvJbMCadFiQ9Ac4RQKUFtO+Pjx\n43j9+jXmzp3b7r87LBYLffr0wXfffYf//ve/cHJyQnBwMHx9ffH9998jOzu7zr59+/aFn58fVFRU\ncOTIEVy+fLlOaWWK/Lh+/ToWLlwIoVCIXbt2Yfz48TJZwlNWVka/fv1gamqK58+f4/79++/8TjS2\nfPm20/p9aHEWzNuNPSiUpujZsye2bduGzz77DPv27cOCBQvkLUkq2NvbY+PGjUhPT8dvv/2GM2fO\n4NSpUxg+fDimTp0KKysrALXl1OfNm4fIyEjcuHEDqampmDBhArS1teX8H3ROampqcPDgQYSEhKB7\n9+7YsGGDzJO+OBwOevXqBQ0NDTx9+hRlZWVwdnZudDZy4MAB5n5bddBr8Yxg8+bNUhdB6dgsWbIE\nHh4eCAgIQHx8vLzlSBUzMzOsXLkSx48fx8SJE3Hz5k3MnTsXa9aswcOHDwHUFm8cO3YsJkyYgFev\nXmHfvn1SvZqjNI+ioiKsXr0aISEh8PLywq5du+SW+ctisWBjYwMnJycUFRXhxo0bKC4ulosWoBWG\ngEJpKWw2G4cPH4aGhgamTp2KyspKeUuSOgYGBliwYAFCQ0MxZ84cxMfHY+nSpViyZAn+/vtviMVi\nODo64tNPP4W+vj7++OMPnD59milfQGlbEhMTMX/+fPz7779YsWIFAgICZNZfoimMjY0xcOBAiMVi\n3Lp1Cy9fvpSLjnfmETTFvn37pCqG0nERCAQ4dOgQvLy8sHbtWuzYsUPektoETU1NzJw5E5MmTcL5\n8+fx22+/ITAwEBYWFpg6dSqGDx+O2bNn4+rVq7h58yYyMjIwYcIEhW732d6JiorCjh07wOfzsXv3\nbnTv3l3ekuqgra2NwYMH4+7du7hz5w4cHBxgaWkpUw1NzghcXV1x+/ZtuLi4wMPDo96NQmkJY8aM\nwcKFC/Hdd9/h0qVL8pbTpqioqGD8+PE4duwY0+Fvy5YtmDFjBiIiIjB48GDMnDkT5eXl+Pnnn3H3\n7l3GmS7p1UF5P0QiEfbu3YugoCDY2dnhf//7n8IZAQmqqqoYOHAgjIyM8PjxYzx8+FCmvQ2anBFM\nnDgRDx8+RH5+frupG0NRbL799ltcvXoVH3/8MR48eAB9fX15S2pTuFwu3Nzc4OrqipiYGBw/fhx7\n9uzBkSNH4OPjg+nTp+Py5cs4d+4ckpOTMXbsWDx58kTesgHUGqS2cEzKgtevX2Pjxo34999/4ePj\ngwULFih8hVgulwtnZ2fEx8cjOTkZZWVlcHJyAo/Ha/tzv2uHgIAAnD9/vs2FUDoHampqOH78OPr1\n6wc/Pz+Eh4fLW5JMYLFYGDBgAAYMGICHDx8iJCQEhw4dQkhICMaMGYOBAwciJiYGP/30E4Da5Qw+\nnw8OhwMOhwMul8vcb+hxY9s4HE6rwiLz8/Px5MkTFBQUSL3AWVvz9OlTrF+/HkVFRVizZg3c3d3l\nLanZsFgsdO/eHRoaGnj48CFu3ryJvn37trpHe3N5pyHQ0tKCr69vm4qgdC7+85//YMuWLfj888+h\nr6+P169fw8zMDEFBQZg+fbq85bU5PXr0QI8ePZCcnIzQ0FCEhYXBwsICZmZmTOvMmzdvSu18bDa7\nWYbjzccJCQkAattzBgQEtJsyGefPn8euXbugq6uLPXv2wNbWVt6SWoWZmRnU1NTqlKVoS4PcpCFY\nt24dvv76awBol1cGFMXF0NAQbDab6QOblpYGf39/AOgUxgAArKysEBgYiDlz5uD333/HmTNnUF1d\nDRcXF0RHR6NLly5QVlauM1Cz2ew6gzebzWa2sdlssFgsZhuLxapzA2oTQkUiEWpqapiWihLfBCEE\nVVVVdZKXSktLsWnTJhgYGGDAgAGwsrKClpaW7N+sd1BdXY29e/ciIiICTk5OWL9+vULqbAn6+voY\nNGgQ7t69i5iYGPTs2RMAkJeXh8LCQtjY2EjtXE0agjedVnPnzpXaNH7r1q2IjIxkPihLS0vs3r0b\n+/btw8mTJ1FTU4OxY8di8eLF7eZKhNIyvvzyy3rOsLKyMqxdu7bTGAIJAoEAS5cuRVlZGeNEF4vF\nIITA1NQUNTU1zOAtuVVVVdV53JJbc52QLi4uiI+Ph4mJCbhcLk6dOgUA0NPTg5WVFaytrWFhYSH3\nktv5+fnYsGEDHj16hClTpmDevHkNVoltj2hoaGDQoEGIjY3F/fv3oampiYyMDDg5OUn1PE0agjdL\nAkizPEBcXBx27txZ55+5fv06IiMjERYWBg6Hg7lz58La2hqjR4+W2nkpikN6enqLtnd08vPzcfXq\nVYjFYqSmpoIQgry8PPzwww9Sn4mLxWKIxeIGDUxeXh6WLl3K5Dfk5uYiNzcXAKCurg4zMzOw2WzE\nxsbi7t27YLFYMDU1hZWVFaysrGBiYiLTQfjRo0fYsGEDhEIh1q1bh+HDh8vs3LIiJSWF6YtQXFyM\n3r17Sz3IotludGldmVdVVeHJkyf45ZdfsGHDBpibm2PNmjW4dOkSxowZwzhFfHx8cOrUKWoIOihm\nZmZIS0urt72z1vB/s7BYamoqgDYqNwwwS0dcLrfe1fzRo0eZiz6JDi6Xi6FDh6Jnz56Ii4vDvXv3\nUFJSAi0tLZiZmYHFYiEjIwPXr1+HkpISLCwsmBmDnp5em8zqCSE4ffo09uzZA0NDQ2zfvp0p69HR\nsLOzY9oAREdHo7CwECYmJlI9R5OGQCwWo6ioCIQQ1NTUMPcltKZeSm5uLvr374+AgABYWlri4MGD\nWLhwIfT09DBgwABmP4FAwFyJUDoeQUFB8Pf3R1lZWZ3tlZWVSE5O7rA/6saQRWGxluqQGAKRSIT0\n9HSsW7cO3t7eEIvFSE5ORlxcHO7fv4/bt2+jsrIS2traMDc3h0gkwvPnzwHUJthJZgtWVlZQV1d/\nb41VVVXYvXs3zp8/j379+mHt2rWdpmOitrZ2myzFsUgTaz729vYN9uwEamcI0qgbQwhBnz590KtX\nL0ycOBGenp4AgFu3buG7777Dn3/+We81sbGxrQ6nqqioUIiOWYqgQ94azpw5g127diEnJwcCgQAT\nJkzAkSNHoKKigoMHD8La2lqmeuT9frRXHTU1NcjIyMCzZ8/w7NkzJCUlgcPhQFdXF8bGxtDQ0GD2\n1dLSgpGREQQCAfT19ZsV2/+mjoKCAhw4cABpaWkYNWoUPD09ZdIQvr19Jg1RVlaGPn36NPhck5/C\n06dPW3XCdx3z6dOnGDduHLONEAJjY+M6Netzc3MhEAgaPU5rE13aqnpfe9Qhbw3dunXDypUr6+jw\n9/eHm5sbZs+ejYsXL9ZpF9jWyPv9aM86HB0dMWrUKAC1V+zx8fG4f/8+/vnnH9y4cQOqqqrQ1dWF\niYkJioqK8Pz5c3A4HJiZmcHS0hLW1tYQCAQNDuonTpzApEmTcP/+fezYsQNVVVX4+uuvMXjwYKn+\nv00h0SBv3ue7ERsb2+hzMk+1Y7PZCAoKQp8+fdC1a1ccP34cdnZ2GDFiBH788UdMnjwZXC4XYWFh\n8PHxkbU8ipzp0aMHoqOjMWLECHz00Uc4d+4cBg4cKG9ZlBagpKSEXr16oVevXvj4449RUVGBR48e\nIS4uDv/88w8SExPB5/Ohr6+PqqoqpKSk4MqVK1BVVYWlpSWzjKSjo8MktklaSZqYmGDz5s0Ntnhs\nLRLnucSB3tD9J0+egBDSYaMYZW4IbG1t8eWXX2LBggWoqamBQCDAzp07YWxsjOfPn2PSpEmorq7G\niBEj6swaKJ2HDz74ADdu3MCIESPg5uaGiIgIuLq6ylsWpZWoqKjA2dkZzs7OAGpzEx4+fIh//vkH\ncXFxSE9Ph46ODvT19SEUCpkSGzo6OigpKQFQu1Q8ePBgODo64u7du7h9+3ajg3ZTA3pD95vLDz/8\nAGtra9jY2MDS0lLuYbPSRC7FN7y9veHt7V1v+/z5899Z8ZTSOTAzM0N0dDTc3Nzg6emJEydOYOzY\nsfKWRZECGhoaTLkNoLZPgGQZ6f79+8jLy6vnH5JEkyUlJUFdXb3BZDo2mw0ej8fcf/u5dz1++/7z\n588ZpzcAFBYWIjY2FrGxsWCz2TAzM4ONjQ1sbGxgaGjYrmcLil2FidKpEQgEuH79OkaOHAkfHx8c\nPXoUU6dOlbcsipTR0tLCsGHDMGzYMAC1mbNxcXE4evQoMjIy4OLighs3bsDT01PqobRN8aZjdePG\njfjqq68Yx3hCQgKSkpIQFRXF1IWytrbGBx98ACsrK4VwLLcEaggoCo2uri6ioqLg5eWF6dOnQygU\nws/PT96yKG2Ivr4+nJyc6vSsEIlEiIyMxKxZs+Ra6obD4cDCwgIWFhZwc3NDcXExkpKSkJiYyDjI\nJUl2ktlCly5dFH62QA0BReHR1NTE+fPnMXHiRMybNw8lJSVYvny5vGVR2hBZJtg1h8b6GGhqaqJ3\n797o3bs3xGIxMjMzkZiYiMTERFy9ehVXr16Furo641uwtrZu80qirYEaAkq7QE1NDSdPnsS0adMQ\nEBCAkpISrFu3TuGvtCito7HENnn1enZ0dHznPhK/gZmZGYYPHw6hUMjMFhISEvDgwQMAgImJCWMY\nTExMZJIH8S6oIaC0G5SUlBAaGgo/Pz989dVXKCkpwfbt26kx6IAcOHCAua8oeRUtRV1dHT179kTP\nnj0hFouRnZ3NzBaio6Px119/QUVFhTEKNjY2dZLvZAk1BJR2BZfLxS+//AINDQ3s2LEDJSUl+Omn\nnxTiqorSscjJyYG6unqd8hUlJSUQCoVNJrs2BJvNhomJCUxMTDBs2DCUl5cjKSmJmTFIZjoCgYBx\nOpuamtYr4NdWXeOoIaC0O9hsNvbs2QM+n4+tW7eitLQUhw8fVvhWhJT2hbq6OpKTk2FpaQlCCIqL\ni5GSkiKVOliqqqpwdHSEo6MjCCHIzc1lZgt///03bt68CSUlJVhZWTGzBS0trTZrY0p/OZR2iaQZ\nvKamJgIDAyEUChEaGtqhknwo8oXP58PCwgKJiYkghCAhIQGampqorq6GSCSS2oUHi8WCQCCAQCDA\n4MGDUVFRgZSUFMYwSEr9GBgYSOV8DUENAaVds2bNGmhoaGDp0qXw8vJCeHi4VCpcUigikQjZ2dlM\n0U1lZWUIhUIUFxcDqA1g0NTUhKamJtTV1aW2PKmiooJu3bqhW7duIITg3LlzuHfvHlOLbePGjQCA\nYcOGwcXFRSrnpIaA0u5ZsmQJNDQ04Ofnh5EjR+LMmTPtvk0hRb5UVlYiISEBlZWVTLZxTU0NrKys\nwGazUVxcjOLiYuTk5CAnJwdsNht8Pp8xDMrKylIJYmCxWPD09GSqMksS26QNNQSUDsHs2bOhoaGB\nadOmYcSIEYiMjJR6FydK50AoFCIxMRFisRgcDgfW1tbIzMyEqakp0yvD2NgYxsbGEIlEKCkpYQxD\nUVERgNoIN4lR4PP5Cu+/Umx1FEoLmDRpEtTV1TFhwgQMGzYMUVFR6NKli7xlUdoRhYWFSE5OBo/H\ng56eHrS0tJioIT6fDysrKwiFQmYbl8uFjo4OdHR0ANTOJCRGoaCgAHl5eQBqHc9vLiO1drbQWGLb\n+0INAaVDMXr0aJw/fx5eXl4YMmQIoqKiYGFhIW9ZlHbAq1evkJ6eDjU1NdjY2IDH49Xbh8/nN9kN\nTVlZGQYGBjAwMAAhhPEpFBcXIzs7G9nZ2fWWkVpSl6g5iW2tgRoCSofDxcUFUVFRGDlyJGMMJD1f\nKZS3IYQgKysLOTk50NLSgqWlZb34/dbAYrGgoaEBDQ0NhV9GooaA0iHp168frl+/Djc3NwwdOhQX\nL15Er1695C2LomCIxWKkpaWhoKAA+vr6MDMza7NM9TeXkQghzV5Gys3NlVpiW6PapHIUCkUB6dmz\nJ/766y+4urrCxcUF58+fR//+/eUti6IgiEQiJCUlobS0FCYmJjAyMpJZuRIWiwUVFRWoqKjA0NAQ\nhBCUlpbWW0bicDhQUVFBdnY205WtpKSEcVpLC5qXT+nQ2NnZITo6Gnp6enB1dcXVq1flLYmiAFRV\nVeHZs2cQCoWwsLCAQCCQa80qFosFPp8PExMTdOvWDb169YKlpSW0tbVRVVUFsViM1NRUlJeXM0ag\nKV9FS6GGgNLhsbCwQHR0NCwsLDB69GicPXtW3pIocqSsrAxPnz5FVVUVbGxsoKenJ29J9eByudDV\n1YWFhQV69OgBBwcHaGpqghACAwMDqRoBQM6GICoqCk5OTszjffv2YeTIkXBzc8OePXuYjD4K5X3p\n0qULrl27BgcHB4wbNw4nTpyQtySKHCguLsazZ88AAPb29tDU1JSzonfDYrFQXV2NsrIycLlcvHr1\niunlLC3kZghSU1Oxbds2ZrC/fv06IiMjERYWhjNnzuD27ds4f/68vORROiD6+vq4fPky+vfvD19f\nXxw6dEjekigyJC8vDwkJCVBWVoa9vT1UVVXlLalZvOkTkBSiS05OlqoxkIshKC8vx8qVK7F69Wpm\n26VLlzBmzBioqalBWVkZPj4+OHXqlDzkUTowWlpaiIyMhKurK+bMmYM9e/bIWxKljZGEh6alpYHP\n58POzg5KSkryltVshEJhHZ/Am4lt0kIuhmD9+vWYMmVKndju7OzsOlmgAoEAubm58pBH6eCoq6vj\n1KlTGD9+PJYuXYrJkyfDwsICDg4OsLCwQHBwsLwlUqQEIQRpaWnIzs6Grq4ubGxspJIjIEsEAkE9\nnwCfz5da6Cggh/DR4OBgcLlcTJw4EZmZmcz2hvwBTVXzi4+Pb9X5KyoqWv1aaaIIOhRBgzx1bNy4\nEampqXX8BWlpafDz80NWVhbGjBkjc00A/VykpUMSqy8Wi8HlclFeXs74B2SlQdq0lQ6ZG4LwXHWr\nEwAAH0ZJREFU8HBUVFTA29sb1dXVzP3u3bszZVYBIDc3t0mL19ouPYrS9k4RdMhTw5vdnyQ6pJ0k\n0xzy8/PrbauoqMDevXuxcuVKmel4E0X4brR3HdXV1UhISIBYLIa5ufl7FyBsz++FhNjY2Eafk/nS\n0B9//IEzZ84gIiIC+/fvh4qKCiIiIuDm5oZTp06hrKwMVVVVCAsLg6urq6zlUWSEpPuTxOElcYjJ\nupdARkZGg9vT0tJw4sQJlJaWylQP5f0pLy/H06dPUVlZCRsbG1qFthkoTGbx8OHD8fz5c0yaNAnV\n1dUYMWIExo0bJ5VjS7P3KEU68Pl8mJubIykpCQDaJEmmOZiZmSEtLa3edjabjcmTJ0NZWRkeHh7w\n8fGBl5cXdHV1ZaqP0jJKSkqQlJQEFosFOzs7qKmpyVtSu0CuhsDU1BRxcXHM4/nz52P+/PlSP4/k\n6lOSkt0WKdqUxhGLxaioqEB5eXmdW3V1NbMPl8uFWCwGIUSmGZ5BQUHw9/dHWVkZs01NTQ379u2D\nmZkZwsPDERYWhlOnToHD4cDFxQU+Pj4YN24cjI2NZaaT8m4KCgqQmpoKZWVl2NjY0LalLUBhZgRt\niSTcKiEhoU7v0bKyMtTU1EBJSQnKysrtLppA0SCEoKqqqt6AX1FRwewjqbHC5/PBZrNRUFAAoLbu\nS2JiItTV1WFsbCyzRJ/p06cDANauXYv09HSYmZkhKCiI2T5s2DDs2rULsbGxCAsLQ1hYGBYtWoRF\nixZhwIAB8PHxwfjx42FtbS0TvZT6SJq/v3jxAhoaGrC2tlb4RjCKRqd5tyR1xIuLi8HhcFBSUsKU\ngZXA4XCgrKzMGIY37yspKUmtJ2lHQCQS1Rvwy8vLIRaLmX2UlJSgqqoKbW1tqKqqQlVVFSoqKmCx\nWMyszMbGBpmZmTAxMUFSUhIqKiqQkJAADQ0NmJiYQENDo83/l+nTp2P69OmNOuJYLBacnZ3h7OyM\nb775BvHx8YxRWLlyJVauXImePXvCx8cHPj4+cHR0lGvdms4EIQQZGRl49eoVdHR0YGFhQX+nraDT\nGIKSkhImRRsArK2toaamhsrKSlRVVaGyspK5X15ejqKionohrTwer55xkBgMHo/3zh+/IvgqWqqh\nOcs6HA4Hqqqq0NPTYwZ8VVXVJmdYbyfJaGpqwsbGBqWlpeBwOMjOzsazZ8+gqakJY2NjhWpI361b\nN6xduxZr165Famoqs3y0ceNGbNiwATY2NoxR+PDDD+nA1EaIxWIkJyejqKgIRkZGMDExoQa4lXQK\nQ/CmT+Dt3qN8Pr/BQYYQgurq6npGorKyEiUlJcyShgQWiwUlJaUGjYSSkhK4XK5C+Coa02BpackY\nwbKysncu67w54DfHCL5NQ0bnze5P+vr6ePnyJXJycvD06VNoa2vD2NhY4coCWFhYYPny5Vi+fDly\nc3MRERGBsLAw7Ny5E9u3b4eJiQnGjRsHHx8fDB06tNElC0W4SGhPVFdXIykpCUKhEF27doWhoaG8\nJbVrOoUhaCpFu7EolTcH9oaWJ8RiMaqqqhjj8KbBKCwshEgkqrM/m81mjENCQgIAICEhAdra2kwN\n8jfP/baWhvS19rGuri4SExMZDcrKykhOTkZNTQ2zT1PLOrKAzWZDIBDAwMAAubm5yM3NRWFhIXR0\ndGBsbNyi9n6ywsjICP7+/vD390dhYSHOnDmD8PBw/PLLL9i7dy/09PQwduxY+Pj4wNXVtc7/oAgX\nCe2FiooKJCYmoqqqClZWVky/YErr6RSG4F1Xn62BzWYzjSUaoqampkEjUVVVBeD/MqkLCwvrPJY1\nLBYLXC633lW+ojjOORwOjI2NYWhoiNzcXLx8+RKvX7+Gnp4eunTporCRIdra2pgxYwZmzJiBsrIy\nXLhwgfErHDp0CBoaGhg9ejR8fHwwevRo8Pl8pKeno7S0FHFxcejRowcKCgpoV7W3KC0tRVJSEggh\nsLW1bXMfUnBwcKOBBB2JTmEI5IFk3fztpQzJlZ6EpmLn3zYO73rc3NeUlpYiPT2dubo3NjaWefx+\nS+FyuTAxMYGhoSFycnLw6tUrpr2gQCBQ6CJiampqGD9+PMaPH4+qqipcvXoV4eHhOHnyJB4/foxb\nt27Bzc2NCUcdMGAAgFqfVHR0NCwtLaGurg51dXWF/j/bmsLCQiQnJ0NJSQk2NjZtPisMDg6uE1qc\nlpYGf39/AOhwxoAaAhnyLl/F2zRniag1GjIyMmBtbd0sDYoGj8dD165dYWRkhJycHOTl5SEvLw8G\nBgYQCATg8XjyltgkPB4PAwYMQLdu3bBw4UJUV1dDLBbj3r17uH79OkaNGoWzZ89i7NixuHXrFszM\nzKCurs4YdB6PBw0NDcYwqKmpdUhn9Ns+k5cvXyIjIwM8Hg92dnYy+ZzXrl1bJ78EqG1qs3btWmoI\nKK2nNb6KjqhBGigpKcHMzAxGRkbIzs7Gy5cvkZeXB0NDQxgZGSlUHHlNTQ2Ki4tRWFiIoqIi1NTU\ngMViQUtLC9ra2tDS0sLChQuxZcsWfPHFF4iNjcX169exdetWrFmzBrdv30Z5eTlKS0shFAohFArx\n+vVrALUXB2pqaoxh0NDQ6BCzBonPRBLEkJGRARaLBXNz8zY3ApWVlbhw4UKDGedA7czgm2++gbu7\nO5ycnDqEIVacX0snoC18Fe1RgzRRVlZmes5mZWUhJycHL1++hEAggKGhodx8HSKRCIWFhSgsLERx\ncTEIIeBwONDW1oa2tjY0NTXrDCADBw7E6tWrmcJgsbGxWL16NQYMGAA2m80M9BKqq6vrGIZXr17h\n5cuXAGpnDRKj0N5mDWKxGNXV1WCxWDAwMEBiYiKTbW5jY9NmiYYikQhXr15FaGgowsLCUFhYCDab\nXScvRgKPx2PCh/X09ODm5gZ3d3e4u7vDxMSkTfS1NdQQUDoEKioqsLKyQnl5ObKyspCVlcVUsDU0\nNJTJQCiJGCssLGSK1SkpKcHAwADa2trQ0NBodHmvb9+++Pnnn+tsi42NRXx8PHx8fPDRRx/VeY7H\n40FHR4eJmCGEoKysjDEMpaWlTCACi8WCqqpqnSUlJSUlmcbcSwb4qqoqVFdXN3p7M3LtTYyMjKRu\nBMRiMf7++2+Ehobi999/x8uXL8Hn8zF+/HhMnToVL1++xIIFC+qVH9m/fz9cXV0RFRWFixcv4uLF\niwgNDQUAODg4wMPDA+7u7hg6dKjChTs3BjUElA6FqqoqrK2tIRQKkZWVhRcvXiA3NxddunSBvr6+\nVA0CIQQVFRV4/fo1CgsLUV5eDqDWKAkEAujo6EBVVbVZA25DpS4+++wzHDhwAO7u7vjvf/8LPz+/\nRl/PYrEanDVIjEJjswbJzEEya2hpPkNNTU2TA3tTAzyLxQKPxwOPx2PyUySPeTweqqqq8OLFC7BY\nLOTl5UFTU/O9Z66EENy/fx8hISH47bffkJ6eDhUVFYwZMwZTp07F6NGj6zihORxOo1FDkox0Qgge\nPnyIixcv4sKFC9i7dy927twJZWVlDB06lJkt9OjRQ2ET3likHXaIj42NRZ8+fVr12o5QV7wjaWhr\nHaWlpXjx4gVKS0vB4/EYg9DQD7I5OgghEAqFzJV/ZWUlgNo1bcmyz/tGs7ypo6ioCFOmTMGFCxew\nYsUKbN26tdXLXYSQer4GiX6g9mqXx+OhpKQEZmZmyMnJgb6+PrKysqCrqwsOh1Pvqr6hpZM3B/g3\nb0pKSnUeczicRgfGlgZWvItnz54hJCQEoaGhePbsGbhcLjw8PODr6wtvb+93HrMl39GysjJER0fj\nwoULuHjxIh4/fgygdllWYhTc3NxalQT3vv0IGhs36YyA0qHR0NCAra0tSkpKkJWVhfT0dOTk5MDY\n2Bi6urrNukITi8UoKSlhBn+RSAQWiwU+nw8jIyNoa2u3mQNTS0sLZ86cwWeffYYdO3bg+fPnCA4O\nblX8vMSx/GZpZsmsQTJzKCkpgVgsRmpqKgAwXQTz8vLqDPASo9HQrakBvrlII6ghPT0doaGhCAkJ\nwf3798FiseDi4oKAgABMmDABenp676WxMdTU1ODh4QEPDw8Ate/hpUuXcPHiRZw9exZHjhwBAPTu\n3Rvu7u7w8PDAwIED5ZoTQw0BpcPDYrGYZYXi4mK8ePECqampyMnJgZqaGvT09OqsP5eUlKCkpAQq\nKipMpI9YLAabza4T6SMrRzSXy8WPP/4Ie3t7LFu2DEOGDMHp06dhamr63sfm8XjMTAb4v1lDVlYW\nioqKoKury4TlSmOAby6tDWrIzc3FiRMnEBISglu3bgEA+vXrh127dmHy5MlyKR1uamqK2bNnY/bs\n2aipqUFcXBzjW/juu++wbds2qKmpwcXFhfEv2NnZ1Xmv2zqxjRoCSqdBErKpqamJwsJCZGVloaCg\nAAUFBTA2Nq5TyVKyYsrlcqGrqwttbW2mdLa8WLx4MWxsbDBlyhT07dsXp06dgrOzs1TPwWKxUFNT\nA6FQCC6Xi+LiYujr6yu00/P169cIDw9HSEgIrly5ArFYjB49eiAoKAi+vr4KVaaDw+EwlWwDAwNR\nUlKCa9euMctI586dA1DbMEmyjPT69WssX768TRPbqCGgdDpYLBZ0dHSgra2NgoICvHjxAllZWQBq\n2xzyeDxm8FdXV1coB9/IkSNx69YtjBkzBkOHDsWRI0cwceJEqR1f2mvzbYVQKMTp06cREhKCyMhI\nVFVVwdraGoGBgfD19YWDg4O8JTYLPp8PLy8veHl5AQBSUlKY2cKJEyfqRZJJkHpiG2mH3Lt3r9Wv\nffLkiRSVtB5F0KEIGgiRvw6xWEwSEhLIvXv3SGpqKhGLxXLV05z3Izc3lwwcOJAAIN98843UNGdn\nZ5Pi4uI6OoqLi0l2drZUjt8Sjh07RszNzQmLxSLm5ubk8OHDJCIigvj6+hI1NTUCgJiYmJCAgABy\n586dNv3c5PEdra6uJjdv3iQAGryxWKwWHa+pcVMuM4Jjx44hJCQELBYLXbt2xebNm6GtrY0tW7bg\nxo0bqKmpwZw5czB16lR5yKN0MiRRNFwuF4WFhdDV1VWoq9+GMDQ0xOXLl+Hn54fAwEA8ffoU+/fv\nf2+Ho6IkHDZU5+eTTz4BAOjp6WHWrFnw9fXFkCFD2k2yXEvhcrkYOHAgzM3NG8xyNjMzk965pHak\nZvLo0SP88ssviIiIAJ/Px7Zt2/D999/Dzs4OaWlpOHPmDIRCIaZMmQIHBwf07NlT1hIpnYj2shTS\nECoqKjh69Cjs7Oywfv16JCcnIzw8HPr6+vKW9t6sWbOmXp0foNYAZmZmKnxNKWnSWF/toKAgqZ1D\n5qbU0dERFy5cAJ/PR2VlJXJzc6GtrY2oqCj4+PiAy+VCS0sLnp6eOHXqlKzlUToZTYUptgdYLBbW\nrVuH0NBQ3Lt3D/369UN8fLy8ZbWawsJCbNq0CRkZGQ0+/+rVq05lBIBah/D+/fthbm7O1Fvav3+/\nVKOG5DKn4vF4iIqKwtChQ3H37l34+PggOzsbXbp0YfYRCATIycmRhzxKJ0IgENS78ufz+e2uK9iU\nKVNw7do1CIVCDBgwAJcuXZK3pBaRn5+PL7/8Eubm5vjqq68ajVKS5nJIe2L69OlITU3F48ePkZqa\nKvXqp3KLGnJ1dYWrqyt+//13zJ07t8FqkU2t/bX2qqeiokIhrpgUQYciaKA6pKdDU1MTx44dw6JF\nizBq1CisXbsWvr6+MtfREvLz83H48GGEhISgvLwcbm5umD9/PhITE7F+/fo6rVJVVFSwaNEiuXxG\n7f278U7e17PdUlJTU8ndu3eZxyKRiNjb25MZM2aQixcvMtv37NlDgoKCGjwGjRrqOBoIoTre5n11\nFBcXE09PTwKALFu2jIhEIrnoaIqsrCyyfPlyoqqqSthsNpk6dSp59OhRnX3ejho6duxYm+l5Fx3h\nu9HUuCnzpaFXr14hICCAaf5++vRpfPDBB3B3d8eff/4JkUiE4uJinD17Fq6urrKWR6G0e/h8PiIi\nIrB8+XJ8//33GDt2bJ2e2PIkIyMDixcvhqWlJX744QdMnjwZ8fHxOH78eL3Y/7ZeDqH8HzJfGnJ2\ndsb8+fMxa9YscDgcGBoaYu/evejSpQvS09Ph7e2N6upqJnuSQqG0HA6Hg507d8LW1haLFy/GoEGD\ncObMGZibm8tFT0pKCrZs2YLDhw+DEIJPPvkEa9asUais386MXHwE06ZNw7Rp0+ptX7t2rRzUUCgd\nl/nz58PGxgYTJ05E3759ERERgf79+8vs/AkJCfjmm29w9OhRcDgczJs3D6tWreq0Tl9FpWNmYlAo\nFAZXV1fExMRAQ0MDLi4uTBOVtiQ+Ph4zZsyAvb09QkNDsXjxYiQnJ2Pv3r3UCCgg1BBQKJ0Ae3t7\n3L59G3379sXUqVOxceNGprCeNHnw4AEmT54MBwcHnDx5Ep9//jlSU1Oxe/fudtvGsTNADQGF0knQ\n19fHpUuX8PHHH2PDhg2YPn16nfDM9yE2Nhbjx49Hr169EBkZiTVr1iA1NRXbt2+HkZGRVM5BaTto\n9VEKpROhrKyMQ4cOwd7enhmsw8PDWz1Yx8TE4Ouvv8a5c+egra2Nr776CsuWLWN6KVPaB3RGQKF0\nMlgsFlavXo0///wT9+/fR79+/fDo0aMWHSM6Ohru7u4YMGAAbt++jaCgIKSmpmLDhg3UCLRDqCGg\nUDopPj4+iI6ORlVVFQYOHIjz5883uT8hBFeuXIGLiwuGDh2Kf//9F99++y1SU1MRGBgILS0tGSmn\nSBtqCCiUTkyfPn1w584d2NjYYMyYMfjhhx8QHBwMCwsLODg4wMLCAsHBwYiMjMTgwYMxYsQIJCQk\nYPfu3UhJScGKFSta1T+ZolhQHwGF0skxNTXFX3/9hRkzZmDZsmXgcrkQiUQAavsAzJo1C2KxGF27\ndsXevXsxZ84cqKioyFk1RZpQQ0ChUKChoYGwsDDo6OjUK0chFouhq6uLxMREKCkpyUkhpS2hS0MU\nCgVAbbXfkpKSBp97/fo1NQIdGGoIKBQKQ2NZvzQbuGNDDQGFQmEICgqCmppanW3SbotIUTyoIaBQ\nKAyyaItIUTyos5hCodRh+vTpmD59OuLj49GtWzd5y6HIADojoFAolE4ONQQUCoXSyaGGgEKhUDo5\n1BBQKBRKJ4caAgqFQunksEhbtClqY2JjY+UtgUKhUNodffr0aXB7uzQEFAqFQpEedGmIQqFQOjnU\nEFAoFEonp0MagmvXrsHLywseHh5YunQpSktLG9yPEILVq1fj4MGDctMRERGBsWPHwtvbG76+vnj4\n8KFcdBw7dgyenp4YM2YMFixYgPz8fJlrkBAVFQUnJyepnr8lOrZu3QoXFxd4e3vD29sbn332mVx0\nPHv2DDNnzsS4cePg4+PT4naS0tBx8uRJ5n3w9vbG8OHD4eDggLy8PJnqAIBLly7By8sL3t7emDlz\nJtLT06Wqobk6jh49Cg8PD3h7eyMgIACFhYVS1/Gusaklv6fmnrBDkZ+fT/r3709SUlIIIYRs376d\nfPXVV/X2S0xMJDNnziQ9e/YkP//8s1x0JCUlkUGDBpHc3FxCCCHXrl0jw4YNk7mOhw8fko8++ogU\nFxcTQgjZunUrWbdunUw1SEhJSSGurq7kP//5j9TO31IdkydPJrGxsVI/f0t0lJWVkUGDBpFr164R\nQgi5dOkS8fDwkLmON6mqqiKTJ08mISEhMtdRXl5OevXqRVJTUwkhhBw6dIjMmzdP5jr+/vtvMmTI\nEJKdnU0IISQ8PJwsWbJEqjreNTa19HNrDh1uRnDjxg306NEDFhYWAICpU6fi9OnTIG/5xIODg+Hj\n44NRo0bJTYeSkhI2b94MQ0NDAICjoyPy8vJQVVUlUx2Ojo64cOEC+Hw+KisrkZubC21tbZlqAIDy\n8nKsXLkSq1evltq5W6qjqqoKT548wS+//IKxY8diyZIlyMrKkrmOmzdvomvXrhg2bBgAYMSIEdi9\ne7fMdbzJgQMHoKurC19fX5nrqKmpASGE6ZcgFAqhrKwscx2PHz/GwIEDIRAIAADu7u64cuWKVH+z\n7xqbWvq5NYcOZwhycnKYDwkABAIBSktLIRQK6+y3fv16jBs3Tq46TE1N4eLiAqB2KrhlyxYMHz5c\nqg1Amvt+8Hg8REVFYejQobh79y58fHxkrmH9+vWYMmUK7OzspHbulurIzc1F//79ERAQgIiICPTq\n1QsLFy58rx9Za3SkpKTAwMAAgYGB8PHxwezZs1FTUyM1Dc3VIaGgoACHDh1CYGCgVDU0V4e6ujo2\nbtwIX19fDB48GMHBwVixYoXMdfTs2RMxMTF48eIFACAsLAzV1dVSXR5619jUks+tuXQ4QyAWixvc\nzmbL9l9tiY6ysjIsW7YM6enp2Lx5s9x0uLq64vbt21iyZAnmzp3b6GvbQkNwcDC4XC4mTpwolXO2\nVkfXrl1x4MABWFlZgcViYe7cuUhPT0dmZqZMdYhEIly/fh1TpkxBWFgYZsyYAX9/f6leebbku/H7\n779jxIgR6Nq1q9TO3xIdz549w969e3Hu3DncuHED8+fPx5IlS6RqoJuj48MPP8SiRYuwePFi+Pj4\ngMViQVtbGzweT2o63kVbjHEdzhB06dIFr169Yh7n5uZCS0urXrMNRdGRlZUFX19fcDgcHDlyBJqa\nmjLXkZaWhnv37jGPJ0yYgKysLBQVFclMQ3h4OB4+fAhvb2/4+/ujoqIC3t7eyM3NlYqG5up4+vQp\nTp48Wed1hBCp/tCbo8PQ0BBWVlbo1asXgFojXVNTg4yMDJnqkHDu3DmpzhJbquPGjRtwcnJiOqVN\nnz4dCQkJeP36tUx1lJaWom/fvggPD0dYWBg8PDwAQKpLqdLQ2VI6nCEYPHgw/v33X6SmpgIAQkND\nMWLECIXUUVhYiBkzZsDd3R27du2CioqKXHS8evUKAQEBKCgoAACcPn0aH3zwAXR0dGSm4Y8//sCZ\nM2cQERGB/fv3Q0VFBRERETAyMpKKhubqYLPZCAoKYgbc48ePw87Ors5UXBY6hg4dihcvXjCRQnfv\n3gWLxYKpqalMdQBAUVER0tPT0bt3b6mdu6U6unfvjrt37zLRSlFRUTA1NYWurq5Mdbx8+RIzZ85k\nonR++ukneHp6gsViSU2HNHS2mPdyNSso165dI15eXmTkyJHE39+fvH79mjx48ICMHTu23r6rVq1q\nk6ih5uj46aefiL29PRk7dmydW0FBgUx1EEJIcHAw8fT0JGPHjiV+fn4kPT1d5hokZGRktEnUUHN1\nnDx5knh6epKRI0eSTz75hLx48UIuOu7cuUMmTpxIPD09yfjx48ndu3flouPff/8lrq6uUj93S3Uc\nO3aMjBw5knh5eZEZM2aQ58+fy0XH0aNHyciRI4m7uztZs2YNKS8vl7oOQuqOTW9raEjn+0BLTFAo\nFEonp8MtDVEoFAqlZVBDQKFQKJ0caggoFAqlk0MNAYVCoXRyqCGgUCiUTg5X3gIolLbEzs4Otra2\n9bIu9+7dC6C2hs/mzZsxadIk5rmDBw8iISEBgYGBmDlzJoDa7O/c3FxYWloCAAYOHIjp06fDzc0N\ntra2zGvLysogEAjwzTffMFm4BQUF2LlzJ27fvg1VVVWw2Wx4eXnhk08+AYfDQWZmZr3jEEIwa9Ys\nTJw4EZ9++imUlZXxww8/MM8LhUKMGzcO/v7+dbRTKK3ivYJPKRQFx9bWluTn5zf4XEZGBrG3tyd9\n+vQhycnJzPaff/6ZrFq1qs6+MTExxNPTs97r3853EIvFZNOmTWT58uWEEEKKioqIu7s7OXDgAKmu\nriaEEFJYWEiWLl3K7NPQcXJycoizszOJj48n+fn5ZNCgQeTkyZPM84GBgSQgIKAlbwWF0ih0aYjS\nqVFRUcHs2bPx+eefS6WOT2VlJV69egUtLS0AQEhICLp16wY/Pz9wubUTcC0tLWzfvh0xMTF48OBB\ng8cxMjKCubk5UlNToauri61btyIoKAi5ubm4evUq7t27h02bNr23XgoFoEtDlE7Axx9/XGdpyNTU\nlFkaAoAFCxbg1q1b2LVrF1atWtWiY0tqIonFYuTn50NLSwvu7u7w9/cHAMTFxWHIkCH1XqesrIw+\nffrgn3/+gaura73n4+LikJ6eztQaGjx4MHx8fBAYGIi0tDT8+OOPUFdXb5FWCqUxqCGgdHh+/fXX\nJmvSsNlsfPvttxg/fjwGDx7comNLaiIBQHR0NFauXIlBgwbVGaSrq6sbfO2bMxCJQQFqa+/r6Ojg\n22+/RZcuXZh9AgIC4OnpicmTJ8Pe3r5FOimUpqCGgEIBYGxsjA0bNmDVqlWt7lMxZMgQzJ49GwEB\nATh//jz4fD6cnJxw584dfPLJJ3X2FQqFePjwIRYtWgSgrkFpDCUlJQgEAqYCJ4UiLaiPgEL5/4wa\nNQpDhw7Fr7/+2upjzJkzB5qamkyEz7Rp05CUlIT9+/czjWWKioqwevVqODs7o2fPnlLRTqG8D3RG\nQOnwvO0jAGqXWaytrevt++WXXyI2NrbV5+LxeFi3bh38/PwwadIk2Nra4rfffsP333+P0aNHg8fj\ngcViwcvLC3PmzGn1eSgUaUKrj1IoFEonhy4NUSgUSieHGgIKhULp5FBDQKFQKJ0caggoFAqlk0MN\nAYVCoXRyqCGgUCiUTg41BBQKhdLJoYaAQqFQOjn/D7nxJH5onaLBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebf5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure()\n",
    "cnt=0\n",
    "c=['o', '^', '+', '3', 'x']\n",
    "clr=['0.0', '0.25', '0.5', '0.7', '0.8']\n",
    "\n",
    "\n",
    "for system in systems:\n",
    "    w=open('debug/entropy_%s.tsv' % system, 'w')\n",
    "    xs=[]\n",
    "    ys=[]\n",
    "    print(system.upper())\n",
    "    for rentropy, forms_set in sorted(forms_per_rentropy.items()):\n",
    "        f1=analysis_utils.compute_prf_on_selection(system_articles[system], forms_set)\n",
    "        fc=0\n",
    "        for frm in sorted(list(forms_set)):\n",
    "            interps=interpretations[frm]\n",
    "            if fc==0:\n",
    "                w.write('%f\\t%f\\t%s\\t%s\\n' % (rentropy, f1, frm, ','.join(interps)))\n",
    "            else:\n",
    "                w.write(' \\t \\t%s\\t%s\\n' % (frm, ','.join(interps)))\n",
    "            fc+=1\n",
    "        w.write('\\n')\n",
    "        xs.append(rentropy)\n",
    "        ys.append(f1*100.0)\n",
    "    plt.plot(xs, ys, \"%s-\" % c[cnt], color=clr[cnt], label=system.upper())\n",
    "    cnt+=1\n",
    "    w.close()\n",
    "    \n",
    "for baseline, articles in baseline_articles.items():    \n",
    "    xs=[]\n",
    "    ys=[]\n",
    "    print(baseline.upper())\n",
    "    for rentropy, forms_set in sorted(forms_per_rentropy.items()):\n",
    "        f1=analysis_utils.compute_prf_on_selection(articles, forms_set)\n",
    "        xs.append(rentropy)\n",
    "        ys.append(f1*100.0)\n",
    "    plt.plot(xs, ys, \"%s-\" % c[cnt], color=clr[cnt], label=baseline.upper())\n",
    "    cnt+=1\n",
    "    \n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel('ENTROPY')\n",
    "plt.ylabel('F1-SCORE')\n",
    "plt.xticks(xs)\n",
    " \n",
    "plt.show()\n",
    "\n",
    "save=True\n",
    "if save:\n",
    "    fig.savefig('img/entropy_f1.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.2. Compute correlation between form entropy and P/R/F1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: -0.1513011387144569 . Significance:  0.0006228346926715824\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: -0.23103931306007783 . Significance:  1.394701072498865e-07\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: -0.2614479059771961 . Significance:  2.1961874070858405e-09\n",
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: -0.1513011387144569 . Significance:  0.0006228346926715824\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: -0.23040660272681132 . Significance:  1.511749410116273e-07\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: -0.2889102155066703 . Significance:  3.185488442023032e-11\n",
      "AGDISTIS\n",
      "The Spearman correlation between X and Y is: -0.1513011387144569 . Significance:  0.0006228346926715824\n",
      "SPOTLIGHT\n",
      "The Spearman correlation between X and Y is: -0.23008360258617258 . Significance:  1.5750992154105367e-07\n",
      "WAT\n",
      "The Spearman correlation between X and Y is: -0.30580247227378793 . Significance:  1.855675912665002e-12\n"
     ]
    }
   ],
   "source": [
    "for metric in [prec_by_form, recall_by_form, f1_by_form]:\n",
    "    for system in systems:\n",
    "        print(system.upper())\n",
    "    \n",
    "        dist1, dist2 = analysis_utils.prepare_lists(metric[system], form_entropies)\n",
    "        correlation, significance = spearmanr(dist1, dist2)\n",
    "        print('The Spearman correlation between X and Y is:', correlation, '. Significance: ', significance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The average correlation across systems between F1-score and entropy is -0.20518622158**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Correlation between instance ranks (of a form) and performance\n",
    "\n",
    "<span style=\"background-color:lightgreen\">Section 5.4. of the paper</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.1. Collect interpretations for a form**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "interpretations=defaultdict(set)\n",
    "for article in system_articles['spotlight']:\n",
    "    for mention in article.entity_mentions:\n",
    "        form=mention.mention\n",
    "        meaning=mention.gold_link\n",
    "        if not skip_nils or meaning!='--NME--':\n",
    "            interpretations[form].add(meaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Marseille', 'Olympique_de_Marseille'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpretations['Marseille']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.2. Aggregate per rank**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rank_analysis(systems, system_articles, ambiguous_forms, uri_pr=None, ambiguous_only=True, factor='freq'):\n",
    "\n",
    "    print(factor)\n",
    "    fig, axs = plt.subplots(1,3, sharey=True, figsize=(14,3))\n",
    "    p={}\n",
    "    r={}\n",
    "    f1={}\n",
    "\n",
    "    for system in systems:\n",
    "        w=open('debug/%srank_%s.tsv' % (factor, system), 'w')\n",
    "        rank_tuples=analysis_utils.prepare_ranks(system_articles[system], \n",
    "                                                  ambiguous_forms,\n",
    "                                                  uri_pr=uri_pr,\n",
    "                                                  ambiguous_only=ambiguous_only, \n",
    "                                                  factor=factor)\n",
    "        for rank in sorted(rank_tuples.keys()):\n",
    "            w.write('%d\\n' % rank)\n",
    "            for t in rank_tuples[rank]:\n",
    "                w.write(' \\t%s\\t%s\\n' % (t[0], t[1]))\n",
    "            w.write('\\n')\n",
    "        w.close()\n",
    "        #print(rank_tuples)\n",
    "        p[system], r[system], f1[system] = analysis_utils.evaluate_ranks(system_articles[system], \n",
    "                                                          rank_tuples)\n",
    "        \n",
    "        print()\n",
    "\n",
    "    plot_utils.plot_prf(p, systems, axs[0], title=\"PRECISION\")\n",
    "    plot_utils.plot_prf(r, systems, axs[1], title=\"RECALL\")\n",
    "    plot_utils.plot_prf(f1, systems, axs[2], title=\"F1-SCORE\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    fig.savefig('img/accuracy_per_%s_rank.png' % factor, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freq\n",
      "tp defaultdict(<class 'int'>, {1: 4241, 2: 394, 3: 38, 6: 1, 5: 2})\n",
      "fp defaultdict(<class 'int'>, {1: 693, 2: 1114, 3: 405, 6: 165, 5: 68, 4: 1})\n",
      "fn defaultdict(<class 'int'>, {4: 78, 1: 3851, 2: 1103, 3: 248, 5: 19, 11: 1, 12: 1, 6: 8, 8: 2, 7: 3, 9: 2, 10: 2})\n",
      "precision {1: 0.8595460072963113, 2: 0.2612732095490716, 3: 0.08577878103837472, 4: 0.0, 5: 0.02857142857142857, 6: 0.006024096385542169, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.5240978744438952, 2: 0.2631930527722111, 3: 0.13286713286713286, 4: 0.0, 5: 0.09523809523809523, 6: 0.1111111111111111, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.6511592200214955, 2: 0.2622296173044925, 3: 0.10425240054869685, 4: 0.0, 5: 0.04395604395604396, 6: 0.011428571428571429, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "\n",
      "tp defaultdict(<class 'int'>, {2: 787, 1: 5525, 3: 139, 8: 2, 4: 34, 5: 8, 6: 1})\n",
      "fp defaultdict(<class 'int'>, {2: 763, 4: 218, 1: 410, 3: 336, 5: 35, 8: 3})\n",
      "fn defaultdict(<class 'int'>, {1: 2567, 2: 710, 4: 44, 3: 147, 11: 1, 9: 2, 5: 13, 6: 8, 7: 3, 12: 1, 10: 2})\n",
      "precision {1: 0.9309182813816343, 2: 0.5077419354838709, 3: 0.2926315789473684, 4: 0.1349206349206349, 5: 0.18604651162790697, 6: 1.0, 7: 0.0, 8: 0.4, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.6827731092436975, 2: 0.5257181028724115, 3: 0.486013986013986, 4: 0.4358974358974359, 5: 0.38095238095238093, 6: 0.1111111111111111, 7: 0.0, 8: 1.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.7877664504170528, 2: 0.5165736790285527, 3: 0.3653088042049934, 4: 0.20606060606060606, 5: 0.25, 6: 0.19999999999999998, 7: 0.0, 8: 0.5714285714285715, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "\n",
      "tp defaultdict(<class 'int'>, {1: 5452, 2: 530, 3: 65, 4: 19, 5: 4, 9: 1, 6: 1})\n",
      "fp defaultdict(<class 'int'>, {1: 483, 3: 137, 2: 481, 4: 30, 5: 9, 9: 1})\n",
      "fn defaultdict(<class 'int'>, {1: 2640, 2: 967, 3: 221, 8: 2, 4: 59, 5: 17, 6: 8, 9: 1, 7: 3, 11: 1, 12: 1, 10: 2})\n",
      "precision {1: 0.9186183656276327, 2: 0.5242334322453017, 3: 0.3217821782178218, 4: 0.3877551020408163, 5: 0.3076923076923077, 6: 1.0, 7: 0.0, 8: 0.0, 9: 0.5, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.6737518536826496, 2: 0.35404141616566465, 3: 0.22727272727272727, 4: 0.24358974358974358, 5: 0.19047619047619047, 6: 0.1111111111111111, 7: 0.0, 8: 0.0, 9: 0.5, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.7773579525201397, 2: 0.4226475279106858, 3: 0.26639344262295084, 4: 0.2992125984251968, 5: 0.23529411764705882, 6: 0.19999999999999998, 7: 0.0, 8: 0.0, 9: 0.5, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IiIjI6jFxISIiIiIiq2dViUtkJODrC/j5NYKvr9wmIiIiIiKyWFWxkkRGAiNHAqmpAGBA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1239f0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_analysis(systems, system_articles, ambiguous_forms, ambiguous_only=True, factor='freq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.3 PR ranks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pr\n",
      "tp defaultdict(<class 'int'>, {1: 4456, 2: 216, 3: 4})\n",
      "fp defaultdict(<class 'int'>, {1: 1934, 2: 346, 3: 166})\n",
      "fn defaultdict(<class 'int'>, {4: 119, 1: 2345, 2: 2161, 3: 550, 11: 5, 8: 1, 5: 103, 12: 1, 6: 17, 10: 5, 9: 3, 7: 2})\n",
      "precision {1: 0.697339593114241, 2: 0.38434163701067614, 3: 0.023529411764705882, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.6551977650345537, 2: 0.09087084560370215, 3: 0.007220216606498195, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.6756121598059283, 2: 0.1469887716910514, 3: 0.011049723756906077, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "\n",
      "tp defaultdict(<class 'int'>, {2: 1278, 3: 160, 1: 4902, 4: 49, 5: 87, 7: 2, 11: 5, 6: 13})\n",
      "fp defaultdict(<class 'int'>, {3: 219, 4: 209, 2: 621, 1: 536, 5: 97, 6: 80, 7: 3})\n",
      "fn defaultdict(<class 'int'>, {1: 1898, 2: 1102, 4: 70, 3: 392, 8: 1, 5: 16, 10: 5, 9: 3, 12: 1, 6: 4})\n",
      "precision {1: 0.9014343508642884, 2: 0.6729857819905213, 3: 0.42216358839050133, 4: 0.18992248062015504, 5: 0.47282608695652173, 6: 0.13978494623655913, 7: 0.4, 8: 0.0, 9: 0.0, 10: 0.0, 11: 1.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.7208823529411764, 2: 0.5369747899159664, 3: 0.2898550724637681, 4: 0.4117647058823529, 5: 0.8446601941747572, 6: 0.7647058823529411, 7: 1.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 1.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.8011112926948849, 2: 0.5973358261275998, 3: 0.34371643394199786, 4: 0.259946949602122, 5: 0.6062717770034843, 6: 0.23636363636363633, 7: 0.5714285714285715, 8: 0.0, 9: 0.0, 10: 0.0, 11: 1.0, 12: 0.0}\n",
      "\n",
      "\n",
      "tp defaultdict(<class 'int'>, {1: 5201, 2: 702, 3: 68, 4: 37, 5: 48, 10: 2, 6: 9, 7: 1})\n",
      "fp defaultdict(<class 'int'>, {2: 234, 1: 813, 3: 46, 6: 6, 4: 22, 5: 19, 7: 1})\n",
      "fn defaultdict(<class 'int'>, {1: 1597, 2: 1680, 5: 55, 3: 484, 4: 82, 10: 3, 6: 8, 11: 5, 7: 1, 9: 3, 12: 1, 8: 1})\n",
      "precision {1: 0.8648154306617891, 2: 0.75, 3: 0.5964912280701754, 4: 0.6271186440677966, 5: 0.7164179104477612, 6: 0.6, 7: 0.5, 8: 0.0, 9: 0.0, 10: 1.0, 11: 0.0, 12: 0.0}\n",
      "\n",
      "recall {1: 0.7650779641070903, 2: 0.2947103274559194, 3: 0.12318840579710146, 4: 0.31092436974789917, 5: 0.46601941747572817, 6: 0.5294117647058824, 7: 0.5, 8: 0.0, 9: 0.0, 10: 0.4, 11: 0.0, 12: 0.0}\n",
      "\n",
      "f1 {1: 0.8118950983453013, 2: 0.42314647377938513, 3: 0.2042042042042042, 4: 0.4157303370786517, 5: 0.5647058823529411, 6: 0.5625, 7: 0.5, 8: 0.0, 9: 0.0, 10: 0.5714285714285715, 11: 0.0, 12: 0.0}\n",
      "\n",
      "\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LxYisw6UTFwAYMQLIyQGWLVM7EiJSS7NmQK1a3OdiT/v2yf6i994Dhg4FTpwA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\nf/pJ3Tis5exZWZc+bJgcQGip0FC54WB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hk47Yv3ToIHuCuG+OqKhu3aTYhi2Xi+XmAq+/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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c7dadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_analysis(systems, system_articles, ambiguous_forms, uri_pr=uri_pr, ambiguous_only=True, factor='pr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Ambiguity and frequency together against accuracy\n",
    "\n",
    "<span style=\"background-color:lightgreen\">Section 5.3. of the paper</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def heatmap_me(AMB, FREQ, ACC, system, a, ylbl='', cbar=False, yticks=[]):\n",
    "    data = pd.DataFrame({'AMBIGUITY': AMB, 'FREQ': FREQ, 'ACC': ACC})\n",
    "    data_pivoted = data.pivot(\"FREQ\", \"AMBIGUITY\", \"ACC\")\n",
    "#    fig=plt.figure(figsize = (12,7))\n",
    "    ax = sns.heatmap(data_pivoted, annot=True, linewidths=.5, ax=a,cbar=cbar,fmt='g', yticklabels=yticks)\n",
    "    ax.set_ylabel(ylbl)\n",
    "    ax.set_title(system.upper())\n",
    "    for _, spine in ax.spines.items():\n",
    "        spine.set_edgecolor('darkblue')\n",
    "        spine.set_visible(True)\n",
    "#    plt.show()\n",
    "#    fig.savefig('img/%s_amb_frequency_acc.png' % system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def split_forms_per_amb_freq_pair(ambiguity, forms_by_log_count):\n",
    "    af_pairs=defaultdict(set)\n",
    "    for log_freq, forms in forms_by_log_count.items():\n",
    "        for form in forms:\n",
    "            amb=ambiguity[form]\n",
    "            rounded_freq=round(log_freq)\n",
    "            af_pairs[(amb, rounded_freq)].add(form)\n",
    "    return af_pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_af_pairs(af_pairs, articles, skip_nils=True):\n",
    "    \n",
    "    tp=defaultdict(int)\n",
    "    fn=defaultdict(int)\n",
    "    fp=defaultdict(int)\n",
    "    \n",
    "    counts=defaultdict(set)\n",
    "    for article in articles:\n",
    "        for mention in article.entity_mentions:\n",
    "            gold = mention.gold_link\n",
    "            syslink = mention.sys_link\n",
    "            form = mention.mention\n",
    "            if skip_nils and gold=='--NME--':\n",
    "                continue\n",
    "            this_pair=None\n",
    "            for pair, forms in af_pairs.items():\n",
    "                if form in forms:\n",
    "                    this_pair=pair\n",
    "            if not this_pair:\n",
    "                continue\n",
    "            else:\n",
    "                counts[this_pair].add(form)\n",
    "            if gold==syslink:\n",
    "                tp[this_pair]+=1\n",
    "            else:\n",
    "                if syslink!='--NME--':\n",
    "                    fp[this_pair]+=1\n",
    "                fn[this_pair]+=1\n",
    "    \n",
    "    agg_counts={}\n",
    "    for pair, forms in counts.items():\n",
    "        agg_counts[pair]=len(forms)\n",
    "    \n",
    "    prec=defaultdict(float)\n",
    "    recall=defaultdict(float)\n",
    "    f1=defaultdict(int)\n",
    "    \n",
    "    for pair in af_pairs.keys():\n",
    "        if tp[pair]+fp[pair]+fn[pair]==0:\n",
    "            continue\n",
    "        if tp[pair]+fp[pair]>0:\n",
    "            prec[pair]=tp[pair]/(tp[pair]+fp[pair])\n",
    "        else:\n",
    "            prec[pair]=0\n",
    "        if tp[pair]+fn[pair]>0:\n",
    "            recall[pair]=tp[pair]/(tp[pair]+fn[pair])\n",
    "        else:\n",
    "            recall[pair]=0\n",
    "        if prec[pair]+recall[pair]>0:\n",
    "            f1[pair]=int(100.0*2*prec[pair]*recall[pair]/(prec[pair]+recall[pair]))\n",
    "        else:\n",
    "            f1[pair]=0\n",
    "    \n",
    "    return f1, agg_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forms_by_count = analysis_utils.compute_counts_by_form(system_articles['wat'])\n",
    "forms_by_log_count=analysis_utils.counts_to_log_counts(forms_by_count)\n",
    "freq_intervals = analysis_utils.get_freq_intervals(forms_by_count)\n",
    "\n",
    "total_per_form = analysis_utils.get_freq_totals(system_articles[systems[0]], ambiguous_forms, ambiguous_only=False)\n",
    "af_pairs=split_forms_per_amb_freq_pair(ambiguity, forms_by_log_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_heatmap(data, cnt, freq_intervals, axs=None, system=''):\n",
    "    AMB=[]\n",
    "    FREQ=[]\n",
    "    ACC=[]\n",
    "    yticks=[]\n",
    "    for this_pair, val in data.items():\n",
    "\n",
    "        ACC.append(val)\n",
    "        AMB.append(this_pair[0])\n",
    "        mn=freq_intervals[this_pair[1]][0]\n",
    "        mx=freq_intervals[this_pair[1]][1]\n",
    "        FREQ.append((mn,mx))\n",
    "    if cnt>0:\n",
    "        ylbl=''\n",
    "    else:\n",
    "        ylbl='FREQUENCY'\n",
    "    \n",
    "    for cpl in sorted(list(set(FREQ))):\n",
    "        yticks.append('[ %d, %d ]' % (cpl[0], cpl[1]))\n",
    "    \n",
    "    heatmap_me(AMB, FREQ, ACC, system, axs[cnt], cbar=(cnt==2), ylbl=ylbl, yticks=yticks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+rThNvBNmzbh6urKzp07DefWHTt28PjxY06cOJGZ3RAiATMzs0Qvlmq12kSfpe7Vqxd5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\nEGb4P+15QVKq1sSoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127aa49e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 11})\n",
    "\n",
    "fig, axs = plt.subplots(1,4, sharey=True, figsize=(16,4), gridspec_kw={'width_ratios':[1,1,1.2,1]})\n",
    "cnt=0\n",
    "\n",
    "for system in systems:\n",
    "    \n",
    "    f1s, counts = evaluate_af_pairs(af_pairs, system_articles[system])\n",
    "    \n",
    "    plot_heatmap(f1s, cnt, freq_intervals, axs, system)\n",
    "\n",
    "    cnt+=1\n",
    "    \n",
    "plot_heatmap(counts, cnt, freq_intervals, axs, 'unique forms per bucket')\n",
    "    \n",
    "plt.show()\n",
    "fig.savefig('img/amb_frequency_acc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}