{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Donn\u00e9es anonymis\u00e9es\n", "\n", "Ces donn\u00e9es ont \u00e9t\u00e9 g\u00e9n\u00e9r\u00e9es par une quarantaine de personnes et sont mises \u00e0 disposition une fois anonymis\u00e9es."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n)\n", "        a += \"    \";\n", "    return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", "    }\n", "    var i,t;\n", "    var text_menu = begin;\n", "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", "    var ind = \"\";\n", "    var memo_level = 1;\n", "    var href;\n", "    var tags = [];\n", "    var main_item = 0;\n", "    var format_open = 0;\n", "    for (i = 0; i <= llast; i++)\n", "        tags.push(\"h\" + i);\n", "\n", "    for (i = 0; i < anchors.length; i++) {\n", "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", "        var child = null;\n", "        for(t = 0; t < tags.length; t++) {\n", "            var r = anchors[i].getElementsByTagName(tags[t]);\n", "            if (r.length > 0) {\n", "child = r[0];\n", "break;\n", "            }\n", "        }\n", "        if (child == null) {\n", "            text_memo += \"null\\n\";\n", "            continue;\n", "        }\n", "        if (anchors[i].hasAttribute(\"id\")) {\n", "            // when converted in RST\n", "            href = anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Observations brutes"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>-a0</th>\n", "      <th>-a1</th>\n", "      <th>-a2</th>\n", "      <th>events</th>\n", "      <th>game</th>\n", "      <th>ml_french_qcm-0-a0</th>\n", "      <th>ml_french_qcm-0-a2</th>\n", "      <th>ml_french_qcm-0-a3</th>\n", "      <th>ml_french_qcm-0-b</th>\n", "      <th>ml_french_qcm-0-duration</th>\n", "      <th>...</th>\n", "      <th>simple_french_qcm-7-a1</th>\n", "      <th>simple_french_qcm-7-a2</th>\n", "      <th>simple_french_qcm-7-b</th>\n", "      <th>simple_french_qcm-7-duration</th>\n", "      <th>simple_french_qcm-7-nbvisit</th>\n", "      <th>simple_french_qcm-8-ANS</th>\n", "      <th>simple_french_qcm-8-b</th>\n", "      <th>simple_french_qcm-8-duration</th>\n", "      <th>simple_french_qcm-8-nbvisit</th>\n", "      <th>time</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2018-12-12 17:56:29.989</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>-a0,on</td>\n", "      <td>simple_french_qcm</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2018-12-12 17:56:34.255</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2018-12-12 17:56:34.302</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>-a2,on</td>\n", "      <td>simple_french_qcm</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2018-12-12 17:56:37.645</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2018-12-12 17:56:37.677</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 168 columns</p>\n", "</div>"], "text/plain": ["   -a0  -a1  -a2  events               game ml_french_qcm-0-a0  \\\n", "0  NaN  NaN  NaN     NaN                NaN                NaN   \n", "1  NaN  NaN  NaN  -a0,on  simple_french_qcm                NaN   \n", "2  NaN  NaN  NaN     NaN                NaN                NaN   \n", "3  NaN  NaN  NaN  -a2,on  simple_french_qcm                NaN   \n", "4  NaN  NaN  NaN     NaN                NaN                NaN   \n", "\n", "  ml_french_qcm-0-a2 ml_french_qcm-0-a3 ml_french_qcm-0-b  \\\n", "0                NaN                NaN               NaN   \n", "1                NaN                NaN               NaN   \n", "2                NaN                NaN               NaN   \n", "3                NaN                NaN               NaN   \n", "4                NaN                NaN               NaN   \n", "\n", "  ml_french_qcm-0-duration  ...  simple_french_qcm-7-a1  \\\n", "0                      NaN  ...                     NaN   \n", "1                      NaN  ...                     NaN   \n", "2                      NaN  ...                     NaN   \n", "3                      NaN  ...                     NaN   \n", "4                      NaN  ...                     NaN   \n", "\n", "  simple_french_qcm-7-a2 simple_french_qcm-7-b simple_french_qcm-7-duration  \\\n", "0                    NaN                   NaN                          NaN   \n", "1                    NaN                   NaN                          NaN   \n", "2                    NaN                   NaN                          NaN   \n", "3                    NaN                   NaN                          NaN   \n", "4                    NaN                   NaN                          NaN   \n", "\n", "  simple_french_qcm-7-nbvisit simple_french_qcm-8-ANS  simple_french_qcm-8-b  \\\n", "0                         NaN                     NaN                    NaN   \n", "1                         NaN                     NaN                    NaN   \n", "2                         NaN                     NaN                    NaN   \n", "3                         NaN                     NaN                    NaN   \n", "4                         NaN                     NaN                    NaN   \n", "\n", "  simple_french_qcm-8-duration simple_french_qcm-8-nbvisit  \\\n", "0                          NaN                         NaN   \n", "1                          NaN                         NaN   \n", "2                          NaN                         NaN   \n", "3                          NaN                         NaN   \n", "4                          NaN                         NaN   \n", "\n", "                      time  \n", "0  2018-12-12 17:56:29.989  \n", "1  2018-12-12 17:56:34.255  \n", "2  2018-12-12 17:56:34.302  \n", "3  2018-12-12 17:56:37.645  \n", "4  2018-12-12 17:56:37.677  \n", "\n", "[5 rows x 168 columns]"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["import pandas\n", "df = pandas.read_csv(\"logs/qcm100.txt\", encoding=\"utf-8\")\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Pr\u00e9paration des donn\u00e9es"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>1</th>\n", "      <th>3</th>\n", "      <th>5</th>\n", "      <th>7</th>\n", "      <th>9</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>-a0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>-a1</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>-a2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>events</th>\n", "      <td>-a0,on</td>\n", "      <td>-a2,on</td>\n", "      <td>-a2,on</td>\n", "      <td>-a2,on-a2,on-a3,on-a2,on</td>\n", "      <td>-a2,on</td>\n", "    </tr>\n", "    <tr>\n", "      <th>game</th>\n", "      <td>simple_french_qcm</td>\n", "      <td>simple_french_qcm</td>\n", "      <td>simple_french_qcm</td>\n", "      <td>simple_french_qcm</td>\n", "      <td>simple_french_qcm</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                        1                  3                  5  \\\n", "-a0                   NaN                NaN                NaN   \n", "-a1                   NaN                NaN                NaN   \n", "-a2                   NaN                NaN                NaN   \n", "events             -a0,on             -a2,on             -a2,on   \n", "game    simple_french_qcm  simple_french_qcm  simple_french_qcm   \n", "\n", "                               7                  9  \n", "-a0                          NaN                NaN  \n", "-a1                          NaN                NaN  \n", "-a2                          NaN                NaN  \n", "events  -a2,on-a2,on-a3,on-a2,on             -a2,on  \n", "game           simple_french_qcm  simple_french_qcm  "]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["df2 = df[df.qtime == 'end'].copy()\n", "df2.head().T.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Premier jeu : simple_french_qcm"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": ["person_id = ['person_id']"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["cols = person_id + [c for c in df2.columns if \"simple_french_qcm\" in c and '-a' in c]\n", "df_question = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"simple_french_qcm\" in c and '-b' in c]\n", "df_bouton = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"simple_french_qcm\" in c and '-nb' in c]\n", "df_visit = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"simple_french_qcm\" in c and '-ANS' in c]\n", "df_ans = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"simple_french_qcm\" in c and '-dur' in c]\n", "df_dur = df2[cols]"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/plain": ["(465, 31)"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["df_question.shape"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>1</th>\n", "      <th>3</th>\n", "      <th>5</th>\n", "      <th>7</th>\n", "      <th>9</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>person_id</th>\n", "      <td>c241c15008614ea67480</td>\n", "      <td>32606f02fa0df6aac111</td>\n", "      <td>32606f02fa0df6aac111</td>\n", "      <td>32606f02fa0df6aac111</td>\n", "      <td>32606f02fa0df6aac111</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-0-duration</th>\n", "      <td>0 days 00:00:04.266000000</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-1-duration</th>\n", "      <td>NaN</td>\n", "      <td>0 days 00:00:03.343000000</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-2-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>0 days 00:00:06.750000000</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1 days 00:00:00.000000000</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>0 days 00:00:09.688000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-7-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-8-duration</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                                                      1  \\\n", "person_id                          c241c15008614ea67480   \n", "simple_french_qcm-0-duration  0 days 00:00:04.266000000   \n", "simple_french_qcm-1-duration                        NaN   \n", "simple_french_qcm-2-duration                        NaN   \n", "simple_french_qcm-3-duration                        NaN   \n", "simple_french_qcm-4-duration                        NaN   \n", "simple_french_qcm-5-duration                        NaN   \n", "simple_french_qcm-6-duration                        NaN   \n", "simple_french_qcm-7-duration                        NaN   \n", "simple_french_qcm-8-duration                        NaN   \n", "\n", "                                                      3  \\\n", "person_id                          32606f02fa0df6aac111   \n", "simple_french_qcm-0-duration                        NaN   \n", "simple_french_qcm-1-duration  0 days 00:00:03.343000000   \n", "simple_french_qcm-2-duration                        NaN   \n", "simple_french_qcm-3-duration                        NaN   \n", "simple_french_qcm-4-duration                        NaN   \n", "simple_french_qcm-5-duration                        NaN   \n", "simple_french_qcm-6-duration                        NaN   \n", "simple_french_qcm-7-duration                        NaN   \n", "simple_french_qcm-8-duration                        NaN   \n", "\n", "                                                      5  \\\n", "person_id                          32606f02fa0df6aac111   \n", "simple_french_qcm-0-duration                        NaN   \n", "simple_french_qcm-1-duration                        NaN   \n", "simple_french_qcm-2-duration  0 days 00:00:06.750000000   \n", "simple_french_qcm-3-duration                        NaN   \n", "simple_french_qcm-4-duration                        NaN   \n", "simple_french_qcm-5-duration                        NaN   \n", "simple_french_qcm-6-duration                        NaN   \n", "simple_french_qcm-7-duration                        NaN   \n", "simple_french_qcm-8-duration                        NaN   \n", "\n", "                                                      7  \\\n", "person_id                          32606f02fa0df6aac111   \n", "simple_french_qcm-0-duration                        NaN   \n", "simple_french_qcm-1-duration                        NaN   \n", "simple_french_qcm-2-duration                        NaN   \n", "simple_french_qcm-3-duration  1 days 00:00:00.000000000   \n", "simple_french_qcm-4-duration                        NaN   \n", "simple_french_qcm-5-duration                        NaN   \n", "simple_french_qcm-6-duration                        NaN   \n", "simple_french_qcm-7-duration                        NaN   \n", "simple_french_qcm-8-duration                        NaN   \n", "\n", "                                                      9  \n", "person_id                          32606f02fa0df6aac111  \n", "simple_french_qcm-0-duration                        NaN  \n", "simple_french_qcm-1-duration                        NaN  \n", "simple_french_qcm-2-duration                        NaN  \n", "simple_french_qcm-3-duration                        NaN  \n", "simple_french_qcm-4-duration  0 days 00:00:09.688000000  \n", "simple_french_qcm-5-duration                        NaN  \n", "simple_french_qcm-6-duration                        NaN  \n", "simple_french_qcm-7-duration                        NaN  \n", "simple_french_qcm-8-duration                        NaN  "]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["df_dur.head().T"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"scrolled": false}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>simple_french_qcm-8-ANS</th>\n", "    </tr>\n", "    <tr>\n", "      <th>person_id</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0c3a2f07b272478e4cd8</th>\n", "      <td>Thal\u00e8s !</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0cf4e678b9586d5b0e37</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0fa2c1f9be1abc2c1d0b</th>\n", "      <td>Thal\u00e8s</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10fb62c55bc7a6a5cda2</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>14db30224001f714a266</th>\n", "      <td>Thales</td>\n", "    </tr>\n", "    <tr>\n", "      <th>26310ba7a08a6b652fad</th>\n", "      <td>T</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2c987776b9f528f4eeec</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2f2bc77792dacadebd46</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>30035b23562d5b2df36f</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>32606f02fa0df6aac111</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>33e5a65b534574acfb6e</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>33eada5fee0e4231d402</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3747fb5233006d3805da</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>43637590b1591346cfc7</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4a09796a11ccc59ef66a</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>4b2cc1988785a7526ca1</th>\n", "      <td>Ast\u00e9rix et Cl\u00e9op\u00e2tre</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50670df89db35a7ae946</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5102d6da7b6ed2c2690c</th>\n", "      <td>Thales</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5a24afa1f3a84660b6d7</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5a617c2783baa779527f</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>69fc4cf871b5db6fca3a</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6a5bf88f39630d05d66c</th>\n", "      <td>Thales</td>\n", "    </tr>\n", "    <tr>\n", "      <th>753a507a9205e4c196fa</th>\n", "      <td>Prout</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75a639141c325f39a368</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>75b801f977bc69f8a34b</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>8a8c40ad28eb1206efd5</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>8e09880e917ca37a12dd</th>\n", "      <td>Pythagore, il \u00e9tait p\u00e9t\u00e9 et il s'est exclam\u00e9 ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8f84e052a4513ed0dd80</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>93087555ec15d71b0da1</th>\n", "      <td>euler</td>\n", "    </tr>\n", "    <tr>\n", "      <th>afe4be0559208b09e577</th>\n", "      <td>Thal\u00e8s</td>\n", "    </tr>\n", "    <tr>\n", "      <th>b0316cecc64ba1c1bd87</th>\n", "      <td>Thales</td>\n", "    </tr>\n", "    <tr>\n", "      <th>b07297f205b22a5d42d5</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>b0d3a8fd2b67439fba05</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>c241c15008614ea67480</th>\n", "      <td>thal\u00e8s</td>\n", "    </tr>\n", "    <tr>\n", "      <th>c25deb2067e5a89b0223</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>d084aaa43b1793c01dd2</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>d228121baddcfea28ec2</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>d36d229a6a41414c6b26</th>\n", "      <td>Pythagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>dc1f7980a1e746469861</th>\n", "      <td></td>\n", "    </tr>\n", "    <tr>\n", "      <th>e2cf842295864a4d2620</th>\n", "      <td>Pytagore</td>\n", "    </tr>\n", "    <tr>\n", "      <th>eed744add9c2737812ad</th>\n", "      <td>Foucault</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                                                simple_french_qcm-8-ANS\n", "person_id                                                              \n", "0c3a2f07b272478e4cd8                                           Thal\u00e8s !\n", "0cf4e678b9586d5b0e37                                          Pythagore\n", "0fa2c1f9be1abc2c1d0b                                             Thal\u00e8s\n", "10fb62c55bc7a6a5cda2                                                   \n", "14db30224001f714a266                                             Thales\n", "26310ba7a08a6b652fad                                                  T\n", "2c987776b9f528f4eeec                                          Pythagore\n", "2f2bc77792dacadebd46                                                NaN\n", "30035b23562d5b2df36f                                                   \n", "32606f02fa0df6aac111                                                NaN\n", "33e5a65b534574acfb6e                                                   \n", "33eada5fee0e4231d402                                          Pythagore\n", "3747fb5233006d3805da                                                   \n", "43637590b1591346cfc7                                         Pythagore \n", "4a09796a11ccc59ef66a                                                   \n", "4b2cc1988785a7526ca1                               Ast\u00e9rix et Cl\u00e9op\u00e2tre\n", "50670df89db35a7ae946                                          Pythagore\n", "5102d6da7b6ed2c2690c                                             Thales\n", "5a24afa1f3a84660b6d7                                          Pythagore\n", "5a617c2783baa779527f                                                NaN\n", "69fc4cf871b5db6fca3a                                                NaN\n", "6a5bf88f39630d05d66c                                             Thales\n", "753a507a9205e4c196fa                                              Prout\n", "75a639141c325f39a368                                                   \n", "75b801f977bc69f8a34b                                                   \n", "8a8c40ad28eb1206efd5                                                   \n", "8e09880e917ca37a12dd   Pythagore, il \u00e9tait p\u00e9t\u00e9 et il s'est exclam\u00e9 ...\n", "8f84e052a4513ed0dd80                                          Pythagore\n", "93087555ec15d71b0da1                                              euler\n", "afe4be0559208b09e577                                             Thal\u00e8s\n", "b0316cecc64ba1c1bd87                                             Thales\n", "b07297f205b22a5d42d5                                          Pythagore\n", "b0d3a8fd2b67439fba05                                         Pythagore \n", "c241c15008614ea67480                                             thal\u00e8s\n", "c25deb2067e5a89b0223                                          Pythagore\n", "d084aaa43b1793c01dd2                                          Pythagore\n", "d228121baddcfea28ec2                                          Pythagore\n", "d36d229a6a41414c6b26                                          Pythagore\n", "dc1f7980a1e746469861                                                   \n", "e2cf842295864a4d2620                                          Pytagore \n", "eed744add9c2737812ad                                           Foucault"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["import numpy \n", "\n", "def aggnotnan_serie(values):\n", "    res = []\n", "    for v in values:\n", "        if isinstance(v, float) and numpy.isnan(v):\n", "            continue\n", "        if pandas.isnull(v):\n", "            continue\n", "        if v in ('ok', 'on'):\n", "            v = 1\n", "        elif v == 'skip':\n", "            v = 1000\n", "        res.append(v)\n", "    if len(res) > 0:        \n", "        if isinstance(res[0], str):\n", "            r = \",\".join(str(_) for _ in res)\n", "        else:\n", "            if len(res) == 1:\n", "                r = res[0]\n", "            else:\n", "                try:\n", "                    r = sum(res)\n", "                except:\n", "                    r = 0\n", "    else:\n", "        r = numpy.nan\n", "    return r\n", "\n", "\n", "def aggnotnan(values):\n", "    if isinstance(values, pandas.core.series.Series):\n", "        r = aggnotnan_serie(values)\n", "        return r\n", "    else:\n", "        res = []\n", "        for col in values.columns:\n", "            val = list(values[col])\n", "            res.append(aggnotnan_serie(val))\n", "        df = pandas.DataFrame(res, columns)\n", "        return df\n", "    \n", "gr_ans = df_ans.groupby(\"person_id\").agg(aggnotnan)\n", "gr_ans"]}, {"cell_type": "code", "execution_count": 10, "metadata": {"scrolled": false}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>person_id</th>\n", "      <th>0c3a2f07b272478e4cd8</th>\n", "      <th>0cf4e678b9586d5b0e37</th>\n", "      <th>0fa2c1f9be1abc2c1d0b</th>\n", "      <th>10fb62c55bc7a6a5cda2</th>\n", "      <th>14db30224001f714a266</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>simple_french_qcm-0-duration</th>\n", "      <td>0 days 00:00:05.094000000</td>\n", "      <td>1 days 00:00:00.000000000,1 days 00:00:00.0000...</td>\n", "      <td>0 days 00:00:03.546000000</td>\n", "      <td>0 days 00:00:04.912000000,0 days 00:00:07.5230...</td>\n", "      <td>0 days 00:00:08.688000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-1-duration</th>\n", "      <td>0 days 00:00:04.359000000</td>\n", "      <td>0 days 00:00:09.323000000</td>\n", "      <td>0 days 00:00:04.949000000</td>\n", "      <td>1 days 00:00:00.000000000,1 days 00:00:00.0000...</td>\n", "      <td>1 days 00:00:00.000000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-2-duration</th>\n", "      <td>0 days 00:00:26.656000000</td>\n", "      <td>0 days 00:00:05.733000000</td>\n", "      <td>0 days 00:00:58.690000000</td>\n", "      <td>0 days 00:00:31.779000000,0 days 00:00:34.1990...</td>\n", "      <td>0 days 00:00:15.349000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-duration</th>\n", "      <td>0 days 00:00:08.125000000</td>\n", "      <td>0 days 00:00:05.321000000</td>\n", "      <td>0 days 00:00:24.203000000</td>\n", "      <td>0 days 00:00:26.622000000,0 days 00:00:28.4660...</td>\n", "      <td>1 days 00:00:00.000000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-duration</th>\n", "      <td>0 days 00:01:29.203000000</td>\n", "      <td>0 days 00:00:07.680000000</td>\n", "      <td>0 days 00:00:09.975000000</td>\n", "      <td>0 days 00:00:13.814000000,0 days 00:00:15.0330...</td>\n", "      <td>0 days 00:01:08.922000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-duration</th>\n", "      <td>0 days 00:00:08.047000000</td>\n", "      <td>0 days 00:00:08.045000000</td>\n", "      <td>0 days 00:00:16.402000000</td>\n", "      <td>0 days 00:00:14.796000000,0 days 00:00:15.8870...</td>\n", "      <td>1 days 00:00:00.000000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-duration</th>\n", "      <td>0 days 00:00:18.359000000</td>\n", "      <td>0 days 00:00:04.377000000</td>\n", "      <td>0 days 00:01:13.287000000</td>\n", "      <td>0 days 00:00:26.275000000,0 days 00:00:28.6000...</td>\n", "      <td>0 days 00:00:34.538000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-7-duration</th>\n", "      <td>0 days 00:00:23.953000000</td>\n", "      <td>0 days 00:00:11.580000000</td>\n", "      <td>0 days 00:00:10.718000000</td>\n", "      <td>0 days 00:00:01.156000000</td>\n", "      <td>0 days 00:00:22.348000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-8-duration</th>\n", "      <td>0 days 00:00:07.641000000</td>\n", "      <td>0 days 00:00:06.796000000</td>\n", "      <td>0 days 00:00:08.438000000</td>\n", "      <td>0 days 00:00:01.813000000</td>\n", "      <td>0 days 00:00:47.971000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["person_id                          0c3a2f07b272478e4cd8  \\\n", "simple_french_qcm-0-duration  0 days 00:00:05.094000000   \n", "simple_french_qcm-1-duration  0 days 00:00:04.359000000   \n", "simple_french_qcm-2-duration  0 days 00:00:26.656000000   \n", "simple_french_qcm-3-duration  0 days 00:00:08.125000000   \n", "simple_french_qcm-4-duration  0 days 00:01:29.203000000   \n", "simple_french_qcm-5-duration  0 days 00:00:08.047000000   \n", "simple_french_qcm-6-duration  0 days 00:00:18.359000000   \n", "simple_french_qcm-7-duration  0 days 00:00:23.953000000   \n", "simple_french_qcm-8-duration  0 days 00:00:07.641000000   \n", "\n", "person_id                                                  0cf4e678b9586d5b0e37  \\\n", "simple_french_qcm-0-duration  1 days 00:00:00.000000000,1 days 00:00:00.0000...   \n", "simple_french_qcm-1-duration                          0 days 00:00:09.323000000   \n", "simple_french_qcm-2-duration                          0 days 00:00:05.733000000   \n", "simple_french_qcm-3-duration                          0 days 00:00:05.321000000   \n", "simple_french_qcm-4-duration                          0 days 00:00:07.680000000   \n", "simple_french_qcm-5-duration                          0 days 00:00:08.045000000   \n", "simple_french_qcm-6-duration                          0 days 00:00:04.377000000   \n", "simple_french_qcm-7-duration                          0 days 00:00:11.580000000   \n", "simple_french_qcm-8-duration                          0 days 00:00:06.796000000   \n", "\n", "person_id                          0fa2c1f9be1abc2c1d0b  \\\n", "simple_french_qcm-0-duration  0 days 00:00:03.546000000   \n", "simple_french_qcm-1-duration  0 days 00:00:04.949000000   \n", "simple_french_qcm-2-duration  0 days 00:00:58.690000000   \n", "simple_french_qcm-3-duration  0 days 00:00:24.203000000   \n", "simple_french_qcm-4-duration  0 days 00:00:09.975000000   \n", "simple_french_qcm-5-duration  0 days 00:00:16.402000000   \n", "simple_french_qcm-6-duration  0 days 00:01:13.287000000   \n", "simple_french_qcm-7-duration  0 days 00:00:10.718000000   \n", "simple_french_qcm-8-duration  0 days 00:00:08.438000000   \n", "\n", "person_id                                                  10fb62c55bc7a6a5cda2  \\\n", "simple_french_qcm-0-duration  0 days 00:00:04.912000000,0 days 00:00:07.5230...   \n", "simple_french_qcm-1-duration  1 days 00:00:00.000000000,1 days 00:00:00.0000...   \n", "simple_french_qcm-2-duration  0 days 00:00:31.779000000,0 days 00:00:34.1990...   \n", "simple_french_qcm-3-duration  0 days 00:00:26.622000000,0 days 00:00:28.4660...   \n", "simple_french_qcm-4-duration  0 days 00:00:13.814000000,0 days 00:00:15.0330...   \n", "simple_french_qcm-5-duration  0 days 00:00:14.796000000,0 days 00:00:15.8870...   \n", "simple_french_qcm-6-duration  0 days 00:00:26.275000000,0 days 00:00:28.6000...   \n", "simple_french_qcm-7-duration                          0 days 00:00:01.156000000   \n", "simple_french_qcm-8-duration                          0 days 00:00:01.813000000   \n", "\n", "person_id                          14db30224001f714a266  \n", "simple_french_qcm-0-duration  0 days 00:00:08.688000000  \n", "simple_french_qcm-1-duration  1 days 00:00:00.000000000  \n", "simple_french_qcm-2-duration  0 days 00:00:15.349000000  \n", "simple_french_qcm-3-duration  1 days 00:00:00.000000000  \n", "simple_french_qcm-4-duration  0 days 00:01:08.922000000  \n", "simple_french_qcm-5-duration  1 days 00:00:00.000000000  \n", "simple_french_qcm-6-duration  0 days 00:00:34.538000000  \n", "simple_french_qcm-7-duration  0 days 00:00:22.348000000  \n", "simple_french_qcm-8-duration  0 days 00:00:47.971000000  "]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["gr_dur = df_dur.groupby(\"person_id\").agg(aggnotnan)\n", "gr_dur.head().T"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: 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<th>simple_french_qcm-2-a3</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-a0</th>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-a2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-a3</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-a4</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-a0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-a1</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-a2</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-a3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-a0</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-a1</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-a2</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-a4</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-a0</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-a1</th>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-a2</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", 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<td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-7-a2</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["person_id               0c3a2f07b272478e4cd8  0cf4e678b9586d5b0e37  \\\n", "simple_french_qcm-0-a0                   1.0                   NaN   \n", "simple_french_qcm-0-a1                   NaN                   NaN   \n", "simple_french_qcm-0-a2                   NaN                   2.0   \n", "simple_french_qcm-1-a1                   NaN                   NaN   \n", "simple_french_qcm-1-a2                   1.0                   1.0   \n", "simple_french_qcm-2-a0                   NaN                   NaN   \n", "simple_french_qcm-2-a1                   NaN                   NaN   \n", "simple_french_qcm-2-a2                   NaN                   NaN   \n", "simple_french_qcm-2-a3                   1.0                   1.0   \n", "simple_french_qcm-3-a0                   NaN                   1.0   \n", "simple_french_qcm-3-a2                   NaN                   NaN   \n", "simple_french_qcm-3-a3                   1.0                   NaN   \n", "simple_french_qcm-3-a4                   NaN                   NaN   \n", "simple_french_qcm-4-a0                   NaN                   NaN   \n", "simple_french_qcm-4-a1                   NaN                   NaN   \n", "simple_french_qcm-4-a2                   1.0                   1.0   \n", "simple_french_qcm-4-a3                   NaN                   NaN   \n", "simple_french_qcm-5-a0                   1.0                   NaN   \n", "simple_french_qcm-5-a1                   1.0                   1.0   \n", "simple_french_qcm-5-a2                   1.0                   1.0   \n", "simple_french_qcm-5-a4                   1.0                   NaN   \n", "simple_french_qcm-6-a0                   1.0                   NaN   \n", "simple_french_qcm-6-a1                   NaN                   1.0   \n", "simple_french_qcm-6-a2                   1.0                   NaN   \n", "simple_french_qcm-6-a3                   NaN                   NaN   \n", "simple_french_qcm-6-a4                   NaN                   NaN   \n", "simple_french_qcm-6-a5                   NaN                   NaN   \n", "simple_french_qcm-7-a0                   NaN                   NaN   \n", "simple_french_qcm-7-a1                   NaN                   NaN   \n", "simple_french_qcm-7-a2                   1.0                   1.0   \n", "\n", "person_id               0fa2c1f9be1abc2c1d0b  10fb62c55bc7a6a5cda2  \\\n", "simple_french_qcm-0-a0                   NaN                   1.0   \n", "simple_french_qcm-0-a1                   1.0                   NaN   \n", "simple_french_qcm-0-a2                   NaN                   NaN   \n", "simple_french_qcm-1-a1                   NaN                   NaN   \n", "simple_french_qcm-1-a2                   1.0                   NaN   \n", "simple_french_qcm-2-a0                   NaN                   NaN   \n", "simple_french_qcm-2-a1                   NaN                   NaN   \n", "simple_french_qcm-2-a2                   1.0                   1.0   \n", "simple_french_qcm-2-a3                   NaN                   NaN   \n", "simple_french_qcm-3-a0                   NaN                   NaN   \n", "simple_french_qcm-3-a2                   1.0                   NaN   \n", "simple_french_qcm-3-a3                   NaN                   NaN   \n", "simple_french_qcm-3-a4                   NaN                   NaN   \n", "simple_french_qcm-4-a0                   NaN                   1.0   \n", "simple_french_qcm-4-a1                   1.0                   NaN   \n", "simple_french_qcm-4-a2                   NaN                   NaN   \n", "simple_french_qcm-4-a3                   NaN                   NaN   \n", "simple_french_qcm-5-a0                   1.0                   NaN   \n", "simple_french_qcm-5-a1                   1.0                   1.0   \n", "simple_french_qcm-5-a2                   1.0                   NaN   \n", "simple_french_qcm-5-a4                   NaN                   NaN   \n", "simple_french_qcm-6-a0                   NaN                   NaN   \n", "simple_french_qcm-6-a1                   NaN                   NaN   \n", "simple_french_qcm-6-a2                   NaN                   1.0   \n", "simple_french_qcm-6-a3                   1.0                   NaN   \n", "simple_french_qcm-6-a4                   NaN                   NaN   \n", "simple_french_qcm-6-a5                   NaN                   NaN   \n", "simple_french_qcm-7-a0                   NaN                   NaN   \n", "simple_french_qcm-7-a1                   NaN                   NaN   \n", "simple_french_qcm-7-a2                   1.0                   NaN   \n", "\n", "person_id               14db30224001f714a266  \n", "simple_french_qcm-0-a0                   NaN  \n", "simple_french_qcm-0-a1                   1.0  \n", "simple_french_qcm-0-a2                   NaN  \n", "simple_french_qcm-1-a1                   1.0  \n", "simple_french_qcm-1-a2                   NaN  \n", "simple_french_qcm-2-a0                   NaN  \n", "simple_french_qcm-2-a1                   NaN  \n", "simple_french_qcm-2-a2                   1.0  \n", "simple_french_qcm-2-a3                   NaN  \n", "simple_french_qcm-3-a0                   1.0  \n", "simple_french_qcm-3-a2                   NaN  \n", "simple_french_qcm-3-a3                   NaN  \n", "simple_french_qcm-3-a4                   NaN  \n", "simple_french_qcm-4-a0                   1.0  \n", "simple_french_qcm-4-a1                   NaN  \n", "simple_french_qcm-4-a2                   NaN  \n", "simple_french_qcm-4-a3                   NaN  \n", "simple_french_qcm-5-a0                   1.0  \n", "simple_french_qcm-5-a1                   NaN  \n", "simple_french_qcm-5-a2                   NaN  \n", "simple_french_qcm-5-a4                   NaN  \n", "simple_french_qcm-6-a0                   NaN  \n", "simple_french_qcm-6-a1                   NaN  \n", "simple_french_qcm-6-a2                   1.0  \n", "simple_french_qcm-6-a3                   NaN  \n", "simple_french_qcm-6-a4                   NaN  \n", "simple_french_qcm-6-a5                   NaN  \n", "simple_french_qcm-7-a0                   NaN  \n", "simple_french_qcm-7-a1                   NaN  \n", "simple_french_qcm-7-a2                   1.0  "]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["gr_question = df_question.groupby(\"person_id\").agg(aggnotnan)\n", "gr_question.head().T"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"text/plain": ["(41, 30)"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["gr_question.shape"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>person_id</th>\n", "      <th>0c3a2f07b272478e4cd8</th>\n", "      <th>0cf4e678b9586d5b0e37</th>\n", "      <th>0fa2c1f9be1abc2c1d0b</th>\n", "      <th>10fb62c55bc7a6a5cda2</th>\n", "      <th>14db30224001f714a266</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>simple_french_qcm-0-b</th>\n", "      <td>1.0</td>\n", "      <td>1001.0</td>\n", "      <td>1.0</td>\n", "      <td>1001.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-1-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>2000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-2-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>2000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>2000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>2000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1001.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>2000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-7-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-8-b</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1000.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["person_id              0c3a2f07b272478e4cd8  0cf4e678b9586d5b0e37  \\\n", "simple_french_qcm-0-b                   1.0                1001.0   \n", "simple_french_qcm-1-b                   1.0                   1.0   \n", "simple_french_qcm-2-b                   1.0                   1.0   \n", "simple_french_qcm-3-b                   1.0                   1.0   \n", "simple_french_qcm-4-b                   1.0                   1.0   \n", "simple_french_qcm-5-b                   1.0                   1.0   \n", "simple_french_qcm-6-b                   1.0                   1.0   \n", "simple_french_qcm-7-b                   1.0                   1.0   \n", "simple_french_qcm-8-b                   1.0                   1.0   \n", "\n", "person_id              0fa2c1f9be1abc2c1d0b  10fb62c55bc7a6a5cda2  \\\n", "simple_french_qcm-0-b                   1.0                1001.0   \n", "simple_french_qcm-1-b                   1.0                2000.0   \n", "simple_french_qcm-2-b                   1.0                2000.0   \n", "simple_french_qcm-3-b                   1.0                2000.0   \n", "simple_french_qcm-4-b                   1.0                2000.0   \n", "simple_french_qcm-5-b                   1.0                1001.0   \n", "simple_french_qcm-6-b                   1.0                2000.0   \n", "simple_french_qcm-7-b                   1.0                1000.0   \n", "simple_french_qcm-8-b                   1.0                1000.0   \n", "\n", "person_id              14db30224001f714a266  \n", "simple_french_qcm-0-b                   1.0  \n", "simple_french_qcm-1-b                   1.0  \n", "simple_french_qcm-2-b                   1.0  \n", "simple_french_qcm-3-b                   1.0  \n", "simple_french_qcm-4-b                   1.0  \n", "simple_french_qcm-5-b                   1.0  \n", "simple_french_qcm-6-b                   1.0  \n", "simple_french_qcm-7-b                   1.0  \n", "simple_french_qcm-8-b                   1.0  "]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["gr_bouton = df_bouton.groupby(\"person_id\").agg(aggnotnan)\n", "gr_bouton.head().T"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th>person_id</th>\n", "      <th>0c3a2f07b272478e4cd8</th>\n", "      <th>0cf4e678b9586d5b0e37</th>\n", "      <th>0fa2c1f9be1abc2c1d0b</th>\n", "      <th>10fb62c55bc7a6a5cda2</th>\n", "      <th>14db30224001f714a266</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>simple_french_qcm-0-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>2.0</td>\n", "      <td>1.0</td>\n", "      <td>3.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-1-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>2.0</td>\n", "      <td>1.0</td>\n", "      <td>4.0</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-2-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>3.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-3-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>3.0</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-4-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>3.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-5-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>3.0</td>\n", "      <td>1.5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-6-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>3.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-7-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>simple_french_qcm-8-nbvisit</th>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["person_id                    0c3a2f07b272478e4cd8  0cf4e678b9586d5b0e37  \\\n", "simple_french_qcm-0-nbvisit                   1.0                   2.0   \n", "simple_french_qcm-1-nbvisit                   1.0                   2.0   \n", "simple_french_qcm-2-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-3-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-4-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-5-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-6-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-7-nbvisit                   1.0                   1.0   \n", "simple_french_qcm-8-nbvisit                   1.0                   1.0   \n", "\n", "person_id                    0fa2c1f9be1abc2c1d0b  10fb62c55bc7a6a5cda2  \\\n", "simple_french_qcm-0-nbvisit                   1.0                   3.0   \n", "simple_french_qcm-1-nbvisit                   1.0                   4.0   \n", "simple_french_qcm-2-nbvisit                   1.0                   3.0   \n", "simple_french_qcm-3-nbvisit                   1.0                   3.0   \n", "simple_french_qcm-4-nbvisit                   1.0                   3.0   \n", "simple_french_qcm-5-nbvisit                   1.0                   3.0   \n", "simple_french_qcm-6-nbvisit                   1.0                   3.5   \n", "simple_french_qcm-7-nbvisit                   1.0                   1.5   \n", "simple_french_qcm-8-nbvisit                   1.0                   1.0   \n", "\n", "person_id                    14db30224001f714a266  \n", "simple_french_qcm-0-nbvisit                   1.0  \n", "simple_french_qcm-1-nbvisit                   1.5  \n", "simple_french_qcm-2-nbvisit                   1.0  \n", "simple_french_qcm-3-nbvisit                   1.5  \n", "simple_french_qcm-4-nbvisit                   1.0  \n", "simple_french_qcm-5-nbvisit                   1.5  \n", "simple_french_qcm-6-nbvisit                   1.0  \n", "simple_french_qcm-7-nbvisit                   1.0  \n", "simple_french_qcm-8-nbvisit                   1.0  "]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["gr_visit = df_visit.groupby(\"person_id\").agg(aggnotnan)\n", "gr_visit.head().T"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Histogrammes"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": ["nonan_question = gr_question.fillna(0)"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["(41, 30)"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["nonan_question.shape"]}, {"cell_type": "code", "execution_count": 17, "metadata": {"scrolled": false}, "outputs": [{"data": {"image/png": 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REW1WuwDYUdLDwOgmlvWAx4DxwKM1A4uIiGiz2gXA14E9gd8AtwP3ARtQBgBeVS+siIiIdqtdAOwJbEf5y39TytS/W4CPArMrxhUREdFqtQuAlYEbKQXAaOAJ4HFKa0DWAYiIiOiS2gXAOOBh4I/AapR4HgKeT8YAREREdE3tAmAlShGwM6UL4LPAkcAM4MGKcUVERLRa7Q137gIuBCZRFgO6s3n+S+CBinFFRES0Wu0WgFcAPwFOAhYClwO7A98Dzq4YV0RERKvV3gzocmDrmjFERESMRFULAEmnU0b7j6EUAus1L90FzLG9V63YIiIi2qx2F8DxzeP3KEv/fro5PpJsBhQREdE1tbsAzgeQNNb2xD4vfVPS/ZXCioiIaL3aswAWe1TSuySNan7eSVkfICIiIrqg9hiA+yljAFYCvgT8D6Dm5awDEBER0SW1uwDWWvxc0ir8o9//GtuP1IkqIiKi/ap3AUiaKGnd5gv/XmBLYAtJe1YOLSIiorWqFgCSjgbOB/4s6TDgLGAv4MfNT0RERHRB7RaA/6Js+/s34ERgHmVvgBuAdQa6WNIUSXMkzW2KiYiIiFgGtdcBWB34OmXA3wuB4yiDAgW8fGkXShoNnADsCcwHpks6zfZVXY04IiKiBWoXALcB76Fs/HMWcGjzuAdw9wDX7gzMtX09gKRTgP2AFAAREREDkO16Hy6tBLye8lf/Tylf6gcDNwIn2O53R0BJ+wNTbB/WHB8C7GL7iCe973Dg8OZwa2DOYP8eS9ADLBiCz1lRJB+dko9OyUen5KNT8tHp6eZjku1xS3qh9jTARcDJkjYDVrL9J+BPklYDNmTpWwJrCeeeUs3YngpMHYx4l5WkGbYnD+VnDmfJR6fko1Py0Sn56JR8dBrMfNSeBXCJpI8ApwFP9Hnpcco2wUszH5jQ53g8cMvgRhgREdFOtWcBjAXWpTTNXyjpPZI2sf0osPIA104HtpS0maSVgQMphUREREQMoPYgwLttv1/Sc4HfUhYBukTSHQwQm+1Fko4AzgZGA9Nsz+56xMtmSLscVgDJR6fko1Py0Sn56JR8dBq0fNQeBHiJ7Z0kPQv4AbBJ89JC4Crb+1cLLiIiosVqtwBcC2D7r8ALJa1JKUqyFXBEREQXVW0BAJC0DbAp8BfbC/ucn2L7rHqRRUREtFftWQCfB04HjgRmSzpF0umSPktZFXCg66dJul3Sld2OdVlkaeJOkuZJukLSLEkzasdTw5LuUUnrSTpX0nXN49iaMQ6lfvJxjKSbm/tklqS9a8Y4VCRNkHSepKslzZZ0VHN+JN8f/eVkpN4jq0q6WNJlTT6Obc4Pyj1SewzAw8CGtu+V9EPgZcDJlFUA32N73QGu340yXuAk29sP9Hk9PT3u7e1d7rgjIiJWBDNnzlwwLBcCohQg9zbPtwGeRVkR8CpglYEutn2BpN5l/bDe3l5mzBiRf4hGRMQIJOmG/l6rXQDcK+mjtj8OXEYpAl5JWQRo1cH4gL5LAU+cOHEw/snl1nv0Gcv83nnH7dPFSPrXphiHe3xQL8ZnakX43Z5JjM/091oR8jGUko9OwzUftRcCegmwnaS/AtsCF1FmBqwHvHUwPsD2VNuTbU8eN26JrSAREREjTu29AK4G3iBpD+Ah4FHKEr8PUgqCiIiI6ILaLQCLfZ6ymt8Otv9Oaf7/Vt2QIiIi2mu4FAAbAR8C/rM5HgNsNtBFkk6mdBtsLWm+pEO7F2JERER71B4EuNhqwLnAeEljgNcDiwa6yPZB3Q4sIiKijYZLC8D1wK7A9sDNzfP5VSOKiIhoseFSAPwQuBe4CfgwMBE4sWpEERERLTZcCoCfATtQxgJ8CTCw7BMnIyIi4mkZLgXAD4GvAWtT1gb4GmVJ4IiIiOiCqoMAJe3UPF0DmE1pBfg58CrKwMCIiIjogtqzAGY0P2tTlv+9Hehpng+X1omIiIjWqf0lewfwHMrGP6MoYwBGNz8RERHRJVULANsbAuMo/f0zKP3/N9nezPbmNWOLiIhos9pdANheCLxH0uuAM4EeSW9uXjupanAREREtVbUFQNJnm8cfAUdQWgNOAz4H7FsxtIiIiFarPQZg72bp332BlwG32X4T8FzKuICIiIjogtpdAGcBCyi7/90DrCHpPkDA6hXjioiIaLXagwA/YHsdYB5l+d93An8H5gLp/4+IiOiS2i0Ai+0BLLJ9oqSzKGsBLJDUY3tB5dgiIiJap/YYACTtDswB/ibpnOb05cDpwDn9XhgRERHPWPUCgDLi/3rb44CpwLnATsDWlLEAERERMchq7wXwZaAXeEjS6cDfgL8Av6GsBuh60UVERLRX7TEAbwPupAz4OxjYtTl/BzAB2LJSXBEREa1WuwCYDvyJshnQrym7As6jbAZ0FqVLICIiIgZZ7QLgAuA1lL0A5jfnxjfHX7d9XK3AIiIi2qx2AXAwsJ3txyRtDxxPWQ9gVeATkg7PpkARERGDr/YsgCeATSS9CriYsgTwIuBYygDA71WMLSIiorVqFwDvBn4LfB94EBgDbAb8P0qXwB7VIouIiGix2ksBnwVsRfmyXwDcD8wAvkSJbYN60UVERLRX7RYAbD8B/JnSCvAA5a//f6d0D7ylYmgRERGtVb0AaBwJrAY8BPwP8HNgW9t/rhlUREREW9WeBUCzAuDiFf/uaJ4/B/ixJGzvWy24iIiIlqpeAFCm/m0NfJrSCrCQMiDwGuDSinFFRES0VvUuANvnA0cA822vYXtD4BBgU+DFVYOLiIhoqeoFQON+YBVJmwPYvhB4FBhXNaqIiIiWqr0b4E7N04sp0wEvknQLZfrfGsAbasUWERHRZrXHAHz+ScdXA6sDNwMP2D576EOKiIhov6oFgO3dFz+X9E7gB7bvaY7HSvoP21+rFV9ERERb1W4BQNKzKDsCfgjYXNJ1wMm275b0DiAFQERExCCrOghQ0lHAiZTd/1anTAOcQBkLsAew8gDXT5E0R9JcSUd3PeCIiIiWqN0CcBiwo+3HJa0PvBE4CLgCOBWY1t+FkkYDJwB7UvYSmC7pNNtXdT/siIiIFdtwmAa4uAj5BGUp4H8HXk9ZDOiDS7luZ2Cu7ettPwqcAuzXzUAjIiLaonYLwDcpf7n/GdgNOMb2tyWNA061/fhSrt0UuKnP8Xxgl+6FGhER0R6yPfC7uvXh0maUJvy7gCuBWylTA3dujt9j++/9XPt64BW2D2uODwF2tn3kk953OHB4c7g1MKcLv8qT9VC2N44i+eiUfHRKPjolH52Sj05PNx+TbC9xUb3aLQA/BQTcbftlkr4J3Aa8Cngt8L/Aq/u5dj5lwOBi44Fbnvwm21OBqYMY84AkzbA9eSg/czhLPjolH52Sj07JR6fko9Ng5qP2GIBRwEbAVpLeC+xFaQ14HaUw6F3KtdOBLSVtJmll4EDgtO6GGxER0Q61WwAOpCwDLODllGmAa/V5vd8CxfYiSUcAZwOjgWm2Z3cx1oiIiNaovRLgHElfALYA5gIXAV+zfYekjYBnDXD9mcCZ3Y/0aRvSLocVQPLRKfnolHx0Sj46JR+dBi0fVQcBLo2kt9n+du04IiIi2qh2F0AHSfsA21FWBnyvpAm2P145rIiIiNapvRTw5X1+7gS+D3wSeCewNjCpZnxPV5Ym7iRpnqQrJM2SNKN2PDVImibpdklX9jm3nqRzJV3XPI6tGeNQ6icfx0i6ublPZknau2aMQ0XSBEnnSbpa0uxmafSRfn/0l5OReo+sKuliSZc1+Ti2OT8o90jtWQAbAm+mTPu7HdgR+CtlQZ+/0znN7ymW9H8mtfRZmngvYFvgIEnb1o1qWNjd9o4jeBrPd4ApTzp3NPBb21sCv22OR4rv8NR8AHyxuU92bMb2jASLgPfZfjbwQuCdzf9njOT7o7+cwMi8Rx4B9rD9XMr34xRJL2SQ7pHaCwF9C/i27Qsl/cX2Ls2qgK8FvgA8v/kF+7t+N2AhcJLt7Qf6vJ6eHvf29g5S9BEREcPbzJkzFwzLhYBsH9rn8FeS1gX+G7gEMPCNAa6/QFLvsn5eb28vM2aMyJboiIgYgSTd0N9rw2IQYNOk8T+27wdOlfR74Lm2fzcI//b/LQU8ceLE5f3nOvQefcYyv3fecfsM6mfHsuc/ue9f7uFOyUen5KPdao8BQNJE4ERgoYq3AccA35G03AWK7am2J9uePG7cEltBIiIiRpzqBQBlIR+5DEY4DtgH+AuwJlkAIiIioiuGQwEwCvirpHcB/wIcDKwPXAA8v2ZgERERbTUcCoCbKPP/d6UsAnQzZRrghwa6UNLJlOWDt5Y0X9KhA10TERERw2MQ4GHASZQNfc4FXkzZIfAU4L1Lu9D2QV2PLiIiooWGQwGwLaUFYB6l3/87wHxge9u/rRdWREREe9VeCvjTwIeB51AG/E20fartv1CWA46IiIguqN0C8CrgebYXSfoV8FNJn29eGyPpPttrV4wvIiKilWoPAlzJ9qLm+XHAzsD9wC+BW4Cv1AosIiKizWoXAH+V9JLFB7avpcwCmANsDOxeK7CIiIg2q10AvB64uHn+oKSVgVnAysDHgTUqxRUREdFqVQsA2w/Zfqg5fCelC+CHQA/wbOB1tWKLiIhos9qDAAGQdBhwFDCe0gLwQuAi23NrxhUREdFWtbsAkHQJ8EngAOAG27sDzwPuqBpYREREi1UvAICxgIGzgS0lvR+4D9i6alQREREtNhwKgLuBPwM7AD+iLP97PTBB0uE1A4uIiGir2isBbgasDrzF9j2UgYDnAFcA5wG71YsuIiKivWq3APyUMt//F83xl4B7gPdQioDVq0QVERHRcrULgFHAo5S+//cCewM38o8VATevGFtERERr1S4ADgRuBdYCXg3cRRn8txawFbBBtcgiIiJarOo6ALbnSHo78L/AiyhT/w6j7ANwPXBvxfAiIiJaq/YgwA8B3wB+TPnSPwH4GuWL/1zgiXrRRUREtFftlQAPBbaz/ZgkA/sCZwB/AD4CfLRmcBEREW1VuwB4Ajhf0r62PyepB/gAcDuldeKhpV4dERERz0jtQYDvBp4PfE/SVODtwE3A2sA7mp+IiIgYZLV3AzwLuIrS77+I8sV/C/Ab4Fhg/XrRRUREtFftFgAoi/4cR9kM6CRgHWAuMA8YUy+siIiI9qpeANj+PbArsAB4AJgJPAIcCVxeL7KIiIj2qj0IkGYFQICLKWv//xJYFfhucy4iIiIGWfUCgNLvvxOl6X8mZWtgKIVANgOKiIjoguFQAIwHFgK/Aw4B7rP9XgBJl9QMLCIioq2GQwGwm+2tACSdCPxB0q2UAYHVxyhERES00XAoACb2ef4pYDZl8N+bKJsCRURExCAbDgXAg5KmNGsCvAx4QbM08GGUfQIiIiJikA2HJvZ7gNUkvQ5YxfZjALa/SaYBRkREdMVwaAE4n7IJEMA1kt5BWQdgHYZHfBEREa1TvQXA9ttsv42y8t+6wCeB3YEPA1fXiywiIqK9qhcAfexPGQNwF3A68DpglaoRRUREtFT1AkDSL5qnDwGvAjYHXgtMoywQFBEREYOsegEATGoeZwD/BXwc2IUyDmC1pV0oaYqkOZLmSjq6u2FGRES0x3AoAAxg+z8A2f4UsCdlHYAb+rtI0mjgBGAvYFvgIEnbdj/ciIiIFd9wKACeK+k+SfcDO0raCNgbuAYYvZTrdgbm2r7e9qPAKcB+3Q83IiJixSfbA7+rmwH8YzfA/zsF/CfwBWBD20f1c93+wBTbhzXHhwC72D7iSe87HDi8OdwamDOI4fenh7K9cRTJR6fko1Py0Sn56JR8dHq6+Zhke9ySXhgO8+yPBc4EbgYeABZR/vJfDXhiKddpCeeeUs3YngpMXf4wl52kGbYnD+VnDmfJR6fko1Py0Sn56JR8dBrMfAyHLoDtKF/4BwHH2z4WuBv4BPDipVw3H5jQ53g8cEu3goyIiGiT6gWA7Rtt70+ZBnhm07RP06+/8lIunQ5sKWkzSSsDBwKndT3giIiIFhgOXQCL/RX4X8oUwPmS9mMp/Ry2F0k6Ajib0oIwzfbsIYl0YEPa5bACSD46JR+dko9OyUen5KPToOWj+iDAxSQ9C/gBsElzaj7wZttz60UVERHRTsOmAFhM0pqUuO6X9Gvbe9WOKSIiom2qdwFI2gnoBcYBV1LGAiBJlO6AiIiIGGTVBwFSlgD+HmUXwN8A3wU+DxxP2R2wX5KmSbpd0pXdDnJZZGniTpLmSbpC0ixJM2rHU8OS7lFJ60k6V9J1zePYmjEOpX7ycYykm5v7ZJakvWvGOFQkTZB0nqSrJc2WdFRzfiTfH/3lZKTeI6tKuljSZU0+jm3OD8o9Ur0LQNLDlAV8LpPUC/wU+J7tL0l61Ha/MwEk7QYsBE6yvf1An9XT0+Pe3t5BijwiImJ4mzlz5oLhvBDQHcDDALbnSXop8FNJkyiLA/XL9gVN0bBMent7mTFjRP4hGhERI5CkfvfUGQ4FwHX02fXP9kJJr6RsBzxxef/xvksBT5y43P/coOg9+oxlfu+84/ZZYT5rqC3r7zYYv1db89jW3wva/bsNleRwcAzXPA6HMQDn8aRlfW0vsv1mYLfl/cdtT7U92fbkceOW2AoSEREx4gyHFoD3A/8u6a/AyZRBgROAq23/sWpkERERLTUcCoBRwHOB5wH/BXwZuBVA0pdsH18xtoiIiFYaDl0AK9m+w/Y5wBrApsA7gYuA45Z2oaSTm/dtLWm+pEO7Hm1EREQLDIcWAEva1PbNlCl999o+TdIZwBVLvdA+aEgijIiIaJnhUAAcDpwj6VRgNvA7SWcB/0yZCRARERGDbDgUAO8BdgX+AvwamAk8Ahxp+5qagUVERLTVcBgDMAp4N6X//ybglubc3pLeWzGuiIiI1hoOLQAHAq+mLAb0IWDx0r+PAL+oE1JERES7VW8BsD3H9meB+4H9bY+1PRbYn9I1EBEREYOsegHQxy22/7D4wPaF9FkiOCIiIgZP9S4ASTs1Ty+W9L+U1QANvAHYoFpgERERLVa9AAAueNLxG/s8X30oA4mIiBgphkMB8ARl059nUfYBWEzAn6pEFBER0XLDoQB4DFgT+LDtnfq+IOn3VSKKiIhoueFQAJwDfB8YJ+nyPudFGQsQERERg6zqLABJXwHuAH4DPAz8rfmZTekOeFW96CIiItqr9jTAGZQC4FWUKX9bNz/7AVOA9euFFhER0V5VCwDb3wVeTxkIeAMwrvm5gdIF8N160UVERLRX7RYAKKP/VwJeY3t92+tTlgYe3bwWERERg2w4DAJ8lLIB0B8kXdycewFwMzCpWlQREREtNhwKgOmUXQDXBDajNP2fBzwALKgYV0RERGsNhy6AN1C+6J/HPwYB7gKsQhkfEBEREYNsOBQA9wMbNz9rUGKaADwfeF3FuCIiIlprOBQA3wP2aX7mANsAB1O6J75UMa6IiIjWGg5jAF5DmQb4VWBz4NeUFQA3pXQDRERExCAbDi0Ao4DrgTcBJwIPArc15xZVjCsiIqK1qrYASNoM+DnQA/wWuJuyIuA2lD0C5taLLiIior1qdwH81PbzJf3W9jhJ6wOynel/ERERXVS7ABgl6WPAVpLeu/ikpL2BM21/oV5oERER7VW7ADgQmAasBxzQnBOwA7CmpF7b76oVXERERFtVLQBsz5E0CfgL8B3Klz/A8cDXa8UVERHRdsNhFsC+lMF/rwR+0+wQeD/w9+Z5REREDLKqBYCkdwEnU3b+ezFwlqT3N3F9umZsERERbVa7BeAdwPNtvxrYFXiEsg/AhfyjOyAiIiIGWe0CYLTthQC25wEvBdYCbicFQERERNfULgBuk/QKSadI+gPwLuDVlIWBniPpFzWDi4iIaKvaBcCbgf8Cfg8cSdkR8HfAe4DdgEnVIouIiGixqgWA7fnAOrZPtD3L9pHAd4ELKPsBuGZ8ERERbVV7FsBHgDGSVpW0raRrgaOB9SmtAhsPcP0USXMkzZV0dPcjjoiIaIfaXQCvBb5JGfn/38BRtjejrA1wF3BlfxdKGg2cAOwFbAscJGnbrkccERHRArULAGx/0fb5wCa2f92cuxh4wvaeS7l0Z2Cu7ettPwqcAuzX/YgjIiJWfLULgM0lnSbpz8AWkrbp89oYSW9fyrWbAjf1OZ7fnIuIiIgByK43zk7SSyiLAT0HuBZ4PvBF4MfA/sChtnfq59rXA6+wfVhzfAiwczOQsO/7DgcObw63BuZ04Vd5sh4gWxr/Q/LRKfnolHx0Sj46JR+dnm4+Jtket6QXam8GdL6krwLPs71I0rrAD4HNbb9H0mFLuXw+MKHP8XjgliV8xlRg6iCGPSBJM2xPHsrPHM6Sj07JR6fko1Py0Sn56DSY+ahaAEg6Alil+fLfgrI18A7A5GZA38pLuXw6sKWkzYCbKVsLH9ztmCMiItqg9hiAfweubboCvgR80fa6wAGUkf3P7u9C24uAI4CzgauBH9ue3fWIIyIiWqBqC0Dz+a9vnm9g++cAtn8v6W7ghUu72PaZwJndDfEZGdIuhxVA8tEp+eiUfHRKPjolH50GLR+1BwF+ijJy/+PA+4FVgYuAiZRxAa+sFlxERESLVS0AACT9N2UfgMXdEQ9QFgES8G+2z6kVW0RERFvVHgMA8EpgG9srNz9jbT8LeBllXMAKI0sTd5I0T9IVkmZJmlE7nhokTZN0u6Qr+5xbT9K5kq5rHsfWjHEo9ZOPYyTd3NwnsyTtXTPGoSJpgqTzJF0tabako5rzI/n+6C8nI/UeWVXSxZIua/JxbHN+UO6R4dACcB2wI/CvwIspGwBdSFki+FLbWyzl2mmUAuJ229t3P9r+NUsTXwvsSZmiOB04yPZVNeOqSdI8YLLtETuHV9JuwELgpMX3qKTPAXfZPq4pFMfa/lDNOIdKP/k4Blho+/iasQ01SRsDG9u+RNJawEzKduhvZeTeH/3l5ABG5j0iYA3bCyWNoXw3HkVZRn+575HhUAD8J/BBYC5lRD+UL9GtgM/Z/sxSrn3K/5ksTU9Pj3t7e5c75oiIiBXBzJkzF/S3EFD1AgBA0jXAdygDAkX5C/qttrdZ2nXNtb3Ar5alAJg8ebJnzBiRLdERETECSZrZ38JBtacBLvZn4Pe2/wwgaRfKTIDl1ncp4IkTB+WfXG69R5+xzO+dd9w+XYykf0MZY/JRT1t/r2cq+eiUfAyO4ZrHqoMAmwFiV1F28btI0o2S7qEUBG9s+oOWi+2ptifbnjxu3BJbQSIiIkac2rMAXgncTtkA6LOUKYBfB3YFPg+cWC+0iIiI9qq9GdANktax/XEASW8GZgD32v64pFk144uIiGir2l0A5wFjmueHAKsAewE/ktR3caD+rj+ZsnLg1pLmSzq0yyFHRES0Qu1BgOOAn0laE3gXsJXtOyWtDlwCXLm0i20fNAQxRkREtE7tMQCPAf9reyFlPv8DzflHgMdt718tsoiIiBar3QLwHuAcSacCfwX+JsnAmsBlkta3fWfVCCMiIlqoaguA7d9TRvzfSln97wbgJOA1lFUBf1QtuIiIiBar3QKA7XuBr0s6zPYL+7z0W0mvrhRWREREq1UvAAAkjQMel3QpcHdzegPg1HpRRUREtFf1AkDS/cBqwCLKNMDHKV0ToiwH/LF60UVERLRT7VkA2F4LuMz2qsAVtleyPQq4wPbalcOLiIhopeHQArATMKZ5XCjpXcAdwOaSdrJ9Sd0IIyIi2qd6AUBZ8x/Kuv/PA7YHRlO6A74MvLhSXBEREa01HLoAdre9A3A9sJPttW2vAewIzK0aXEREREtVbQGQtB5wBHALsA2wr6TPAFcDn6YUARERETHIarcAfB9YA3g+sDFwMGUBoA2ASymFQERERAyy2mMANrG9tyQB+wLfBPZoXhsDvK1aZBERES1WuwVglKSxwFco6///3PZrKM3/K9t+uGp0ERERLVW7BeAzwBxgLHAY8J1mM6B/Bm6VtJ7tu2oGGBER0Ua1NwM6GegBDBwDTAK2ao43BGZUCy4iIqLFancBAHyQshuggTOAaZTlgD9ie/OagUVERLRV7WmALwf+Dfgr8GzgDcCDTVyz6kUWERHRbrVbAL4E/IvtPYAXNse3UnYE/ErNwCIiItqsdgGwEjBf0juAn1Km/e0OHApMkJRpgBEREV1QuwCYBkwHjqWs+z8aeFdzfF/zGBEREYOs9iyAzwCrU2YCfBEYD7y7ObcOZSZAREREDLLaLQAA6wI/pLQGzAPeD/wN+BZwZ7WoIiIiWqz2QkAAvwJ+Q/ni3wD4X+AhynoAv68XVkRERHtVbwGwfSjwTuAjlBkA2wEnAN+zfXDN2CIiItqqegHQ2Aj4H2Az2zcAv6BsExwRERFdMBy6AKAUAOOBeZKOAG6mbBMcERERXTBcCoB5wCJgPvB8yloA82sGFBER0WbDpQvgL8C+wBOUrYBvoQwMjIiIiC6oWgBI+pmkNwFHUwb/PUKZEngvZT2AiIiI6ILaXQC7UP7q/zLlL/7PAz+z/WjVqCIiIlqudhfA7bb3Bw4AXgR8G7hZ0i8knVY3tIiIiPaqXQC4efwUZTfAa4CtgbOAPWoFFRER0Xa1C4CFi5/Yvql5vMv2icBfq0UVERHRcrU3A9qteXqTpF0BS1pZ0vuBqwe6XtIUSXMkzZV0dFeDjYiIaJHagwAX+zfgO8DmwALgCuAHS7tA0mjKksF7UtYMmC7pNNtXdTfUiIiIFV/tLoDF3klZ+e8R4FTgWQw8BmBnYK7t65tZA6cA+3U1yoiIiJaQ7YHf1e0gpCuA5wKXUrYAPgj4pu1XLeWa/YEptg9rjg8BdrF9xJPedzhweHO4NTBn8H+Dp+ihtGREkXx0Sj46JR+dko9OyUenp5uPSbbHLemFql0Aki5vnj4LmAVsSWmVOJeyHfBSL1/CuadUM7anAlOfeZRPn6QZticP5WcOZ8lHp+SjU/LRKfnolHx0Gsx81O4CmAdcTtn9743AJykxjQIGWgdgPjChz/F4yhLCERERMYDaswD2pfT5j6dsBfwp4FbgYNsHDHD5dGBLSZtJWhk4kIGLhoiIiKB+CwC2fw7sDRwm6a/AWEqXwEDXLQKOAM6mTBn8se3Z3Yz1aRjSLocVQPLRKfnolHx0Sj46JR+dBi0fVQcBSnqR7T9KOpEyBfBCypf/hsBfbb+zWnAREREtVrsAmGn7+ZIeAlZ3E4ykUcAVtrerFlxERESL1V4I6DFJ36aM6J8m6f7m/FrUjy0iIqK1ao8BuBd4TvP8YMqqfns2zxcNdLGkaZJul3Rl90JcdlmauJOkeZKukDRL0oza8dSwpHtU0nqSzpV0XfM4tmaMQ6mffBwj6ebmPpklae+aMQ4VSRMknSfpakmzJR3VnB/J90d/ORmp98iqki6WdFmTj2Ob84Nyj9TuAnhJ8/RZLGHzH9vnD3D9bpQNhU6yvf1An9fT0+Pe3t5nEGlERMSKZ+bMmQuG5UJAlDX/j6DM378CeDewE2VU/6cHutj2BZJ6l/XDent7mTFjRP4hGhERI5CkG/p7rXYB8FNgY2AS8GXgUcpqfr2UpYFfXi2yiIiIFqtdAOwCvJ1SCNxoe9Nml7/XA98ajA/ouxfAxIkTB+OfXG69R5+xzO+dd9w+XYykf22KcTDiWxHysSIYyv9mz8SK8N95RYhxRZA81h8EuBJwDrApsKakXtuPU/YCGJTixPZU25NtTx43bondIBERESNO7RaAmcDNwEPA+4CTJK0CbEcZExARERFdULsF4KXA+4EZwFHAepTpf58EXjTQxZJOBi4CtpY0X9Kh3Qs1IiKiPaq2ANh+FPiapG/Yfqzva5J6gEcGuP6gbsYXERHRVlVbACTtLmk+cIukc540pe/ySmFFRES0Xu0ugM8Br2gWKZgKnCvphc1rPfXCioiIaLfagwB3Ai6StPh4FPBHSY8CY6pFFRER0XK1WwAWAbvaXrv5WZOyKNAcyoJAERER0QW1C4CfUFb8+z+251NmB1xYI6CIiIiRoPYsgDf1c/4eYLehjSYiImLkqD0LYIqkUc3POpK+LelaST+VtGHN2CIiItqs9iDAE4A1gScoK/9tB8wD/gX4JfDCfq+MiIiIZ6x2AbAxsDmwGjAX2Nb2HEmTKFsCR0RERBfULgBWAg4GBDwOXAtg+wb1mRsYERERg6v2LIA7gbUp3QDfoVn8R9ImwMP1woqIiGi32i0APwSOA55v+4+StpC0G3AvcGTd0CIiItqrdgvA4ZS//r8i6RDgTODVwBeBsRXjioiIaLXaLQBjgEuA9YFvUMYCTGoejwe+Ui+0iIiI9qrdAmBgX8oMgEeAg4CtgClkKeCIiIiuqV0A3AH8gDLn/yHg/cBbgP8E7q4YV0RERKvVLgC+BKwO/AdlBsALmucCflMxroiIiFarPQZgN+BHwLaUrYFXAq6hFAU3V4wrIiKi1Wq3APRS5v9/CdgGuAnYBHgx8MZqUUVERLRc7RaA5wBXUVYBHM0/Bv7dS5keGBEREV1QuwC4D1hEmQ54LbAqpQVgG+DSinFFRES0Wu0ugFuAVwAzge8Bu1I2BvpPYEHFuCIiIlqtdgFwG6X5/wDgdcCtlFaJvSjdAxEREdEFtbsA3kzpAjiZMu3vA8B2wKbAfhXjioiIaLXaLQA7U5r9NwdmA+MpAwCvInsBREREdE3tFoBXNY8CTgLObo5fQZkSGBEREV1QuwDYnzL1b3VKa8TiZv9RwBa1goqIiGi7ql0AtteyvTZwje1RtleyvRKlMJlTM7aIiIg2q9oCIGll4DHg95LOpsz9n0T56/+8mrFFRES0We1BgNOBdW0fQZkC+GZgImVhoAdqBhYREdFmtccAjLa9eNvfHYGjgIeBS4BfA0dXiisiIqLVahcA90naHnghsDXwGkqXwFcpewNEREREF9QuAP4N+AFlV8BfArsA5wN3ARvWCysiIqLdas8CuBzYCfgbcDHwNcpaAC+nLAYUERERXVC1AJD0Xkq//+XAIcBalJ0AzwbmVgwtIiKi1WrPAng/sA+lyf8XlEWBoHQH3DrQxZKmSJojaa6kDBiMiIhYRrXHADxCmft/AGVnwFOB79m+ZaALJY0GTgD2BOYD0yWdZjtdBxEREQOo3QJwt+33A28ANgA+Alwi6WJJvx/g2p2Bubavt/0ocArZQTAiImKZyPbA7+rWh0uX2N5J0l8o+wKcBkym/FV/su1+dwSUtD8wxfZhzfEhwC7NokJ933c4cHhzuDVDs8RwD7BgCD5nRZF8dEo+OiUfnZKPTslHp6ebj0m2xy3phdpdANcufmL7JknYfhw4S9KNA1yrJZx7SjVjeyowdfnCfHokzbA9eSg/czhLPjolH52Sj07JR6fko9Ng5qNqAWD7wObpTZJ2BdzsD/Au4OoBLp8PTOhzPB4YcOxARERE1J8G+C5JEygLAr0T2JTyxb5jc7w004EtJW3WFA0HUroQIiIiYgC1uwA+QVnv/6/AycC7bd+xLBfaXiTpCMqaAaOBabZndy3Sp2dIuxxWAMlHp+SjU/LRKfnolHx0GrR81B4EeAfli38isCWwGXAHZaDeX23/e7XgIiIiWqz2NMAHgJnA7cDdwH8CZ1G6AN5SL6yIiIh2q90CcKnt50k6D3i57cea82OA39h+SbXgIiIiWqx2C8AbmsdNKPsALLYmsNFAF0uaJul2SVd2I7inK0sTd5I0T9IVkmZJmlE7nhqWdI9KWk/SuZKuax77Xe+ibfrJxzGSbm7uk1mS9q4Z41CRNEHSeZKuljRb0lHN+ZF8f/SXk5F6j6zaLIx3WZOPY5vzg3KP1G4BWNf2PZLeBhwDnNe89BLgGNvfHeD63YCFwEm2tx/o83p6etzb27t8QUdERKwgZs6cuWC4LgS0oFny92TK6n/Pbs4fbfu2gS62fYGk3mX9sN7eXmbMGJF/iEZExAgk6Yb+XqtdAFwN/A9wEPDfwBWUVoAHJG1l+4Ll/YC+SwFPnDhxef+5Dr1Hn7HM75133D6D+tnd1tbfra2/V0TE01V7DMBjtn9F+dK/BXg+ZQGgc4EfD8YH2J5qe7LtyePGLbEVJCIiYsSpXQAsXs//KMomQHObvooXAP02W0RERMTyqV0A/KB5fNj2wwCSVrE9A1ilXlgRERHtVrUAsH1883S+pHWBXwDnSvoly7Cxj6STgYuArSXNl3Rot2KNiIhok6qDACVtDnwEmA0somwGNJ5SmLxtoOttH9TVACMiIlqqdhfAdyi7+o0FLgauoawB8EPK1MCIiIjogtoFwFq2vw7sCqxp+/O2bwJOBLatG1pERER71S4AnpC0FbA6sLqkyc35zfnHDIGIiIgYZLUXAvogcDqwMTAN+C9JOwAbApfVDCwiIqLNas8C+K3trYEtKJv/vBhYG/gVsG/N2CIiItqsdgsAktYEdgP+AvwRuA44x/YTVQOLiIhosdrTAA8APgBcCbwSuKuJaayk822/pmZ8ERERbVV7EOBHKNP+1gS+C0wAPk6ZDrhLxbgiIiJarXYBIOAhyhiA/wfI9ncpRcHaNQOLiIhos9pjAM4EzgJ6gHOA2yRtDzwMrFwzsIiIiDarWgDY/pCkvYG3Ulb+uws4jdIl8N6KoUVERLRa7UGAv7X9Mkn7Ar9udgTcXNJqlLUAIiIiogtqdwFsJ+mrwKHAQ5IWT/0bDbyBskBQREREDLLaBcD5wCuaOP4VWNCc7wHG1AoqIiKi7WqvBPgG21tSZgK8zfZE2xMpWwHfVzO2iIiINqs9BmBl4DHgVuC9kr5MGf1/G3BPxdAiIiJarfY6ANOBFwC/ByZSioHLgPHAA/XCioiIaLfaBcA44HvA+s3POcDPgd2BLSvGFRER0Wq1C4B1gDfZfjVwEfC85vyV1SKKiIgYAWoXAHcBv5f0AGXK31jK5kDzKMsER0RERBfULgDWAj4IvAW4ClgVOB44D1ilYlwRERGtVnsdgJuBU23fBvxU0u7AVOAQYKeqkUVERLRY7QIA4FOSHgY+StkF0MC5lBkBERER0QW1uwAWUeb730Rp9n8I+Bfgv8lCQBEREV1TuwB4wvb7bB8HrGv7s7ZvtP1xshBQRERE19TuAhgl6W3A/sAqkmYC1wEnUr84iYiIaK3aBYApC/58hlIE3Af8AfgU8HjFuCIiIlqt9l/Z42z/F2Db7wZebvtcYA/KlMCIiIjogtoFwFhJzwK+Imkn4FEA249QWgciIiKiC2p3AVwEzKEUIn8GbpB0OSWunpqBRUREtFnVAsD2yyRtBPwG2KdmLBERESNJ1QJA0r7A2ba3rxlHRETESFO7C+AXwBOS7qfM+7+/74u2d6gQU0REROvVHgR4FWUL4L80sWwKXANcAfy0YlwRERGtVrsAeNT2FcBaticB2wEXApsDHx7oYklTJM2RNFfS0V2ONSIiojVqFwBqHteQ9GLbt9n+MvA+YO5SL5RGAycAewHbAgdJ2rar0UZERLRE7TEA72keDwWmSVqHMv//XsqWwEuzMzDX9vUAkk4B9qN0K0RERMRS1J4G+PvmcSbwXElrA7J97zJcvillF8HF5gO7DHqQERERLVR7GuBdwG8pgwBPtH1fn9em2D5raZcv4dxTVg+UdDhweHO4UNKc5Qh5WfUACzri+OwQfGoly/C7PSUfK4Iu/jdbIfPRRclHp+SjU/LR6enmY1J/L8iut+KupL83T1cC1gF+DXzK9p8lXWJ7p6Vc+0/AMbZf0Rz/J4Dtz3Q57AFJmmF7cu04hovko1Py0Sn56JR8dEo+Og1mPmoPAlwbeJbt9YHdgB2Bn0i6HthkgGunA1tK2kzSysCBwGndDDYiIqItahcA2F7YPP4JeDYwGzifATYDsr0IOAI4G7ga+LHt2d2NNiIioh1qFwB3SNpx8UFTDLwSGA1sMNDFts+0vZXtZ9n+VPfCfNqm1g5gmEk+OiUfnZKPTslHp+Sj06Dlo/YYgPHAItu3LeG1F9n+Y4WwIiIiWq/2OgDvBU4FbpO0E/BiStP/H/PlHxER0T21uwAOAb4k6R5KX/52lCkO35b0kYEuljRN0u2SruxumMsmSxN3kjRP0hWSZkmaUTueGpZ0j0paT9K5kq5rHsfWjHEo9ZOPYyTd3NwnsyTtXTPGoSJpgqTzJF0tabako5rzI/n+6C8nI/UeWVXSxZIua/JxbHN+UO6R2l0Al9p+nqS5wA+A/Sn9/z8BDrb9rAGu3w1YCJxUe0vhZmnia4E9KYsSTQcOsj1iVyaUNA+YbHvEzuFd0j0q6XPAXbaPawrFsbY/VDPOodJPPo4BFto+vmZsQ03SxsDGti+RtBYwE3g18FZG7v3RX04OYGTeIwLWsL1Q0hjKXjlHAa9lEO6R2gXAJbZ3kvRrypflPZJ2oPwP4F9tr7EM/0Yv8KtlKQB6enrc29u7nFFHRESsGGbOnLnA9rglvVZ7DMAESV8GHgFmSzqXMgZgT+D0wf6w3t5eZswYkS3RERExAkm6ob/XahcAH6Z8+c8Eft7n/O8H6wP6LgU8ceLEwfpnI+IZ6D36jGV637zj9hmyz+r7ec/kmqG4bjDyEfU80/uj22pvBjQVQNJrgN8t3gRI0rrASwfxM6YCTJ48uV5/R0RExDBSdRaApCnN0481x9+SdDnwNeAT1QKLiIhoudrTAD/dPI4CPg/cCryKMoJ+wPZ6SScDFwFbS5ov6dBuBRoREdEmtccALDYD2Bf4J8o0wInA4wNdZPugLscVERHRSrULgA0kvRe4DlgZ+BEg4Bzg5pqBRUREtFntAuAbwFrN8y8C3wPuANYANq0VVERERNvVngWweFnDFwDTgLc1L90LvL1WXBEREW1XtQBomv8B3k/ZFOhvzfFmlIWANqkRV0RERNvVngWwVvMzBphCafbfFHgF9WOLiIhordpjABYv9/t24A/NsYG1gT1qBRUREdF2tQuAzzePPcDmNAsCUWYCbFQlooiIiBGgajO77d1t705ZEGgscD5lH4B1gU/WiywiIqLdarcAIGkb4M+UL/7Jzem3ARvWiikiIqLtas8CeBfwTuBqYEfgKNu/bF67BDirXnQRERHtVXuk/RGUOf/PB/4EHCPpqOa1ratFFRER0XK1C4DxwEeB5wCXAKsCr5X0BcpAwIiIiOiC2mMAHgdus30PcLykmcBU4AFgtZqBRUREtFntFoC/U77sAbB9HvA6YEvgvlpBRUREtF3tAmAUsL6k3y4+Yfty4GXALdWiioiIaLnaXQALgVcD20j6cJ/zAjauElFERMQIULsA2B6YQNkP4IPA6sBo4AkyCDAiIqJrancBPAa8FngNcBFwgO1RwL9QioCIiIjogtoFwCPAt4FDgd1pvvRt/56yKVBERER0Qe0ugIeBCylrAVwLfEXSjsCNwEMV44qIiGi12gXAAuA3wMnAs4BVgA8As4E7K8YVERHRarW7AG4DZtnexXaP7bUoOwHOATarGllERESL1W4BWAR8VtIjzfHqwNqUxYEerhZVREREy9UuAF5KmQGwEHgRcCRwKWVvgEfrhRUREdFutbsAHgYetH0+8EpgV9t7A8+jrAcQERERXVC7AJjbrP8PsJLtvwHYXgBcVy+siIiIdqvdBfBcSfdRVv1bRdJGtm+TtDJpAYiIiOiaqgWA7f6+5FcH1hjKWCIiIkaSqgWApNf29xJlNkBERER0Qe0ugB8Bf6cs+/sX4LnAROA+SitAREREdEHtQYAPA5+h7AT4AuD/AasCbydjACIiIrqmdgFwK3C67ZOBx2yf4uJ0YF7d0CIiItqrdgHwsO0bm+dfeNJri4Y6mIiIiJGidgFwgqQ1JX0X+OHik5J2ogwEjIiIiC6oPQ3wfwEk7WD7nj7nL5GUpYAjIiK6pHYLAJJeAfRI2qHPufWAcctw7RRJcyTNlXR0N+OMiIhok9rrAHyGsgnQVcAMSb8DpgOvZ4AxAJJGAycAewLzgemSTrN9VXejjoiIWPHVbgF4JbCH7VcAuwETgBcDrwXuHuDanSl7CVxv+1HgFGC/bgYbERHRFrJd78Olq20/u8/xRsD/AGsCW9neainX7g9MsX1Yc3wIsIvtI570vsOBw5vDrYE5g/pLLFkPsGAIPmdFkXx0Sj46JR+dko9OyUenp5uPSbaX2KVeeyXAv0p6CbAO8HlgE+B2oHcZrl3SLIGnVDO2pwJTlyPGp03SDNuTh/Izh7Pko1Py0Sn56JR8dEo+Og1mPmoXAK9vHv8MvBD4je3nSdodOHSAa+dTugwWGw/cMvghRkREtE/VMQC2H7L9EPAY8BJgvKTPA+sC/Tb/N6YDW0rarNk++EDgtG7GGxER0Ra1WwAW2xB4J3AZZVbA64BVlnaB7UWSjgDOpuwbMM327G4HuoyGtMthBZB8dEo+OiUfnZKPTslHp0HLR9VBgP8XhHQV8BxKv/4bKS0A/257m5pxRUREtNVwaQG4Bhhv+wbgu5ImUcYERERERBfUngZ4OmXk/njguYtPNz+P2x4uBUpERESr1F4I6HjK9L/VgUOA64GXAZ8FTh7oYknTJN0u6cquRrmMsjRxJ0nzJF0haZakGbXjqWFJ96ik9SSdK+m65nFszRiHUj/5OEbSzc19MkvS3jVjHCqSJkg6T9LVkmZLOqo5P5Lvj/5yMlLvkVUlXSzpsiYfxzbnB+UeGS5jAGbYnizpcts7NOf+ZHvXAa7bDVgInGR7+4E+p6enx729vYMSc0RExHA3c+bMBcNyISBJb7c9DXhQ0mbAOEkPA7cCDw90ve0LJPUu6+f19vYyY8aI/EM0IiJGIEk39Pda7T72I4BplOb/zwPHAWOBnSjLHS63vksBT5w4cTD+yRGh9+gzlvm9847bp4uR9G9ZYxzu8UFnjCtC7p+p4f7fLAZHm+/hZ2K45qN2AQCA7RskbWF7f0nr275T0qWD9G//31LAkydPrt/fERERMQzULgC2ljQVeATYRNL1wOPNyn6P1w0tIiKivWrPArgL+CMwA7gTOMz2lsBBwGo1A4uIiGiz2gXAA8APbH8XuNf27wBs/4ll2O5Q0snARZSWhPmSBtpAKCIiIqjfBXACMFPSL4CzJJ1KWRhoNWDWQBfbPqir0UVERLRU7QLg2cA9wGGUL/3RwDzgUcqiQBEREdEFtQuAV9veRNJLgJ8B42w/KmklYFBmAURERMRT1R4DsG7z+EVguu1HoWz1S2YBREREdE3tFoB7mlWKeoCVJF3enB8DTKoXVkRERLtVLQCa5v+NgLOBffu8tBplg6CIiIjogtotAAB/pyzV+3zKDIBbgIs9HHYpioiIaKnamwH9DdiEshLgoub0GGBlSTfb3rxacBERES1WuwVgEbAHsF9z/L3m8T+A/atEFBERMQLULgAA/gJ8zvaLFp+QdBTw1moRRUREtFztAmAaMB3YUNIxwLXABOBQ4O6KcUVERLRa7VkAn5H0S+DfgXcCq1Dm/98GvLJmbBEREW1WuwUA21cBRwJHNlMCH7J9b+WwIiIiWq3qSoCS1pE0VdJdkp6gTAG8TtJPJX2zZmwRERFtVnsp4OmUpv6HgHcDNwJTgV2BQ+qFFRER0W61C4BeypK/821/Gbjb9kea82MqxhUREdFqtQuAx4Bjgdsl7QpY0njg18ATVSOLiIhosdqDAN8CfIPy1/7elILkRkphcFjFuCIiIlqt9jTAn0q6CdgWuI+yBsBmwLm2f1UztoiIiDarvRfAycAWgIALgQOB0cCbJP3I9n/UjC8iIqKtao8BeA3wQuAzwLuAsZQZAWOBf60YV0RERKvVLgCw/TjwUeBh4CrbE4GXUbYGjoiIiC6oXQDcK+nTlF0Bvw6sK2kUsAalWyAiIiK6oPYsgM2BDwDbARsA44FHgHnANfXCioiIaLfaBcAi28dI+m9gZcp0wL2AdYAfVI0sIiKixWoXABcBOwEnAj8HXkzp+/+D7TtrBhYREdFmtccArCzpLcC+lIGAYygtAR+T9OuqkUVERLRY7QLg3yjTANcALgXWbH5mNecjIiKiC2qvBHghcKGknYFjbN8AIGkSsGrN2CIiItqs9kqAp1NW/lsEXC3pYsoYgF2Ay2vGFhER0Wa1BwFeTlnx727g78APgTnNa9+oFVRERETb1R4D8B7gOcCfgTcA7wXWt30+8EDNwCIiItqsdgEwCng5sCtlEaDPAf8t6ZuUtQAiIiKiC2p3AVxHKQDWBV7VnPsTsDewYaWYIiIiWq92AfAm4EHgd7a/tfikpDHAAdWiioiIaLmqXQC2L7N9HbCqpF5JY5uX1mQZugAkTZE0R9JcSUd3NdiIiIgWqVoASJoo6RTgS8C5wHRJt1N2BnznANeOBk6g7B2wLXCQpG27HHJEREQr1B4E+CPKHgBXAVvZ3gLYGPglsNkA1+4MzLV9ve1HgVOA/boZbERERFvUHgPQY/tHkiYDP5Z0ImUhoNdQtgVemk2Bm/ocz6csIBQREREDkO16H16a/+8CTgJeStkNcHVKy8Qdtl+/lGtfD7zC9mHN8SHAzraPfNL7DgcObw635h8LDXVTD7BgCD5nRZF8dEo+OiUfnZKPTslHp6ebj0m2xy3phdotAG8GDgWOofxFL8pf9acD3+r/MqD8xT+hz/F44JYnv8n2VGDqIMS6zCTNsD15KD9zOEs+OiUfnZKPTslHp+Sj02Dmo/YYgE/Y/jpwJuUv81GUv9LfD1w9wLXTgS0lbSZpZeBA4LRuBhsREdEWtQuAvZs5/5+mjPxfBOxO6RL43tIutL0IOAI4m1Is/Nj27O6GGxER0Q61uwDOovRlrEGZDbAGcAWlK2A14GNLu9j2mZTWg+FmSLscVgDJR6fko1Py0Sn56JR8dBq0fFQdBPh/QUh3AuOAnwK/A24GjrO9ddXAIiIiWqp2C8BiUyij//+LsiEQwFvqhRMREdFutVcC/L6kHtvTgRdRVgNcHXgunSP8VwhZmriTpHmSrpA0S9KM2vHUIGmapNslXdnn3HqSzpV0XfM4dmn/Rpv0k49jJN3c3CezJO1dM8ahImmCpPMkXS1ptqSjmvMj+f7oLycj9R5ZVdLFki5r8nFsc35Q7pHa6wBcYfs5zfM/AQcD51C2B/6t7ecu5doJlMGCGwFPAFNtf6n7Ufcbz2jgWmBPyhTF6cBBtq+qFVNtkuYBk22P2Dm8knYDFgIn2d6+Ofc54C7bxzWF4ljbH6oZ51DpJx/HAAttH18ztqEmaWNgY9uXSFoLmAm8GngrI/f+6C8nBzAy7xEBa9he2AyYvxA4Cngtg3CP1C4AHgceaA5XBx5qHh8EVrc9einXLvFGWdoXbk9Pj3t7ewcr/IiIiGFt5syZC4brQkDnUNb0/wRlD4AtgN2AM4A7l3ah7VuBW5vn90u6mrKYUL8FQG9vLzNmjMiW6IiIGIEk3dDfa1ULANt7SdoP+DJwD3AjsApwsu2zl/XfkdQLPA/4yxJe+7+lgCdOnLj8QY8QvUefsczvnXfcPl2MJCIiuqH2QkDYXrzz37eAdSn9PE/ny39N4FTg3bbvW8K/P9X2ZNuTx41bYitIRETEiFO9AJD0CuAE4F8o4wEulTRlGa8dQ/ny/4Htn3UvyoiIiHap2gUg6TRgZeA7lJHzUDb1eZekvWwftZRrRWk1uNr2F7oda0RERJvUbgF4JTAZ2AdYG7jI9inN8UDzPF8EHALsMdLmhkZERCyv2rMAHgHeAGwOvA/4tqSfA5cCDy/tQtsXUvYMiIiIiKepdgEwj7IT4FqULoCrKYs+vJF/rA8QERERg6x2AfCw7V0kbUSZwy9gvu3bJE2qHFtERERr1R4D8AFJsn2b7ZmUloA3Sppiu9/FCyIiImL51C4APk+Z+4+kDwCfAlYD3ifpMxXjioiIaLXaBcD6tu9unr+JMijw/cAawGuqRRUREdFytQuAdSVt3zzfADgNWA/4H8p6ABEREdEFtQcB3gj8QNJllD0AjgCeA+wAjNgtZCMiIrqtdgtAD3ABZRzAKOBE4GzKssAL64UVERHRbrVbAD7QPM6grOl/mu27m2mBp9ULKyIiot1qtwD8HHg25S/+RxcPCLR9G83sgIiIiBh8tQuAb1M2A/ojcJCkUyWd17z20mpRRUREtFztLoBXUVb/W9Q8LgTWkzQb2KZmYBEREW1WuwVgNPBL4HnAAcC9lD0AxlCKgoiIiOiC2gXAXcAVwNTm+MWULoHfAk/UCioiIqLtancBXAdcDvyOsvLfu4GHgH8iW/1GRER0Te0CYBbwMUpz/7nAmpR1AcZSugYiIiKiC2oXAP9M6f9fBbgV2JEy/e824KxqUS2j3qPPWOb3zjtuny5GsmJbEfK4IsT4TLT193qmko9OycdTLWtOVoR81C4A1qaM/B9D6fO/ijIuYRFZCjgiIqJrag8CHE/Z/W8rSt//cynbAX8U2LRiXBEREa1WuwBYZPurtv8K3Gh7ju3Hge+QaYARERFdU7sL4EFJXwO+C3xQ0ibABOCtwMM1A4uIiGiz2gXA3sBk4FhKk7+A+cCFwFEV44qIiGi1qgWA7YuAi4Cv1IwjIiJipKk6BkDSlD7P15X0LUkLJf1Q0oY1Y4uIiGiz2l0AP5d0XfN8PGXg3xjKToDXAutUiisiIqLVas8CeISyFPABlIWAdgZupywFPL9iXBEREa1WuwVgIfAocAawBnAD8JjtGyQ9XjWyiIiIFqvdAvANypf+ycD1wK+BlSVtRNknICIiIrqg9iyAY/seS3ou8E+2bwPeXCeqiIiI9qtaAEjaqXm6DnA4ZTMgS9oN+Ibt82rFFhER0Wa1xwB8vnl8HrAWMJuyGNAbKIsErVsnrIiIiHar3QWwO4Cku4AdbV/RHG8P/L5iaBEREa1WexDgYouA7SSNkjQK2JZsBhQREdE1tccA3A+YsgXwycD3m5dGkwIgIiKia6q2ANhey/balP7/9wKnNz/vbc4tlaQpkuZImivp6O5GGxER0R61BwECYPth4IuSvgFsBVzfnOuXpNHACcCelFUDp0s6zfZVXQ84IiJiBVd7M6Cv9Xn+YuAqysyAKyTNGODynYG5tq+3/ShwCrBf14KNiIhoEdmu9+HS1cAbm8P/Bb4IXEPZGOintldeyrX7A1NsH9YcHwLsYvuIJ73vcMoaAwBbA3MG9ZdYsh5gwRB8zooi+eiUfHRKPjolH52Sj05PNx+TbI9b0gu1uwC2AY6nzP3fBnhHn9cGik1LOPeUasb2VGDqMw3wmZA0w/bkofzM4Sz56JR8dEo+OiUfnZKPToOZj9oFgIFNgMea49favruZCvjIANfOByb0OR4P3DL4IUZERLRP7QLgPyhN8tc3xw80j+vxj1UC+zMd2FLSZsDNwIHAwd0IMiIiom1qrwR44pPPSVrf9gJgqdP6bC+SdARwNmXdgGm2Z3cn0qdtSLscVgDJR6fko1Py0Sn56JR8dBq0fNQeBHgc8FvgHkp3wM8piwI9Dhxg+/xqwUVERLRY7QLg78DfKC0R44DbKQsBvQrY2Pb4asFFRES0WO0C4GFgbUoBcB+wnu37JK0G3GV7tWrBRUREtFjtzYDuAH4FvLB5/nFJu1H6/x8a6GJJ0yTdLunK7oa5bLI0cSdJ8yRdIWnWMizs1EpLukclrSfpXEnXNY9ja8Y4lPrJxzGSbm7uk1mS9q4Z41CRNEHSeZKuljRb0lHN+ZF8f/SXk5F6j6wq6WJJlzX5OLY5Pyj3SO0WgL8AHwXeTlkCeCXK9L6zgLfY3mmA63cDFgIn2d5+oM/r6elxb2/v8oYdERGxQpg5c+aC4boQ0G62H6GM5P8/knqA3w10se0LJPUu64f19vYyY8aI/EM0IiJGIEk39Pda7WmA/7fYj6R9gO2AVfu85YohDyoiImIEqFoASNqBMqdxe8oqfqsD3wD2BzYCPj4In/F/ewFMnDhxef+5Dr1Hn7HM75133D6D+tnd1tbfra2/Fzyz3y35KFa03224S+47Ddd81B4E+DXgGGAepRDoAb4P/BOwymB8gO2ptifbnjxu3BK7QSIiIkac2gXAmrbPAh6wfTxlSeBzgecB/e4EGBEREcundgEgSesAv5K0LvD/KOsCnAc8sQwXnwxcBGwtab6kQ7sZbERERFvUngXwWeDZtj/RHJ8q6VfAFsC7B7rY9kFdjC0iIqK1as8C+GHf42Znv+cBV9l+R52oIiIi2q9qF4CkX/R5vh9l7v+3gdMkvbVSWBEREa1XuwvgVZLua56vQVn+dzXK9r7fAr5TKa6IiIhWqz0I8E7gF8CWwEzbawI3No+X1wwsIiKizWoXAOsDrwVuBnaStDFgSStTWgEiIiKiC2oPAhwNIGkUcATwY8pSwKsD/1oxtIiIiFarPQYAANtPSPolcBtld79HgWGxxW9EREQb1e4CAEDSO4E/At+0fSawK/CHulFFRES017AoAIBPAicAY5rjPwJb1wsnIiKi3YZLATDG9mcAN8eP1QwmIiKi7YZLAXCvpI8CoyTtCZwJ3F05poiIiNYaFoMAgbcB0yjx/ITSFfCjqhFFRES02HBpAXgtcCHlr/4zgBv4R3dAREREDLLaewFsJOnrwIGUZv8xwM7ANcCzasYWERHRZrW7AE4HeoFVgP+hxHMH8M/AmtWiioiIaLnaXQDPAV4AfBp4AHgLsDml+X/MUq6LiIiI5VC7BUDAfNufkLSK7VMl/QpYi7I/QERERHRB7QJgDjBX0gXAnZK+SNkg6KWUZYEjIiKiC2oXADMpYwB2AzYCbqc0/a8NnF0vrIiIiHarPQZgHrAasC5lHYBbgK8DV5ClgCMiIrqmdgvA/sCOwN8o2//eZPvjks6h7AcQERERXVC7AFhk+3FJq1JmAUjSWyjrAmQhoIiIiC6p3QXwqKTVKS0Ab6V0B+wIfJ9sCBQREdE1tQuA3Ww/CFwJrEoZ+f81yiyA0yvGFRER0Wq1C4A3No9HArtQZgRcDUwBPl4ppoiIiNarXQAcAdC0AmwMfBhYGXgX8KWKcUVERLRa7QIAAEm7AvsCH7D9BHA9sEPdqCIiItqrdgEwXtKXgZ8B9wB3Ati+jLIccERERHRB7QLgA5TVAB8APgSMhrJNMHBvxbgiIiJareo6ALa/CyDpVcB1wCJJKwNvAs6rGVtERESb1V4ICEmvAO4Hfgz0ULoCLgIOqBhWREREq1UtACRNB+6g7Ap4YHN6PPBm4KPAUZVCi4iIaLXaLQA7AGsCf7H9nsUnJf0IuJYUABEREV1RuwC4F7gbWFXSfYAoewCMpqwMGBEREV1QexbAFGA28BDwZ0rf/82UpYF3rhhXREREq9WeBXAJsEsz7W9TSgvAfNu3Lcv1kqZQVgwcDXzT9nFdCzYiIqJFqrYASHq7pInAw8DfKV/mf5V0qaStBrh2NHACsBewLXCQpG27HXNEREQb1O4C+ARwPqX5/+fAs4BTgPWBXw1w7c7AXNvX2360uW6/LsYaERHRGrJd78Olh4D1gNWB24GNbN8haQ1gge3VlnLt/sAU24c1x4cAu9g+4knvOxw4vDncmjLlsNt6gAVD8DkriuSjU/LRKfnolHx0Sj46Pd18TLI9bkkv1J4FMAb4LP9oibgXwPYDkjTAtUt6/SnVjO2pwNTlCfLpkjTD9uSh/MzhLPnolHx0Sj46JR+dko9Og5mP2gXARcALgFWA6cD3JJ0G7AP8bYBr5wMT+hyPB27pRpARERFtU7sA2B14PeUv959S+vUPBmYB7xjg2unAlpI2o0wdPLC5NiIiIgZQexrgIuBkSasC/wpsR1kAaBvgK8Dbl3atpCOAsynTAKfZnt39qJfJkHY5rACSj07JR6fko1Py0Sn56DRo+ag9CHAj4GPAnsAvKF/4D1DGBpxue6BWgIiIiHgGak8D/A5wFbAa8HLgEeBFwGeAV1eLKiIiouVqFwAb2v4KpQ9/XeA6YG3gB81jREREdEHtAmDx508Ffgz8P+A0SqvAHQNdLGmapNslXdm9EJedpCmS5kiaK+no2vHUJmmepCskzZI0o3Y8NSzpHpW0nqRzJV3XPI6tGeNQ6icfx0i6ublPZknau2aMQ0XSBEnnSbpa0mxJRzXnR/L90V9ORuo9sqqkiyVd1uTj2Ob8oNwjtccAfBz4nO2FTzq/BXCc7f0HuH43YCFwku3tB/q8np4e9/b2LkfEERERK46ZM2cuGK4LAd0DHN7Pmj9/Guhi2xdI6l3WD+vt7WXGjBH5h2hERIxAkm7o77XaBcDLm8cNKIv6LJ7Gty1wPfCF5f2AvksBT5w4cXn/uQ69R5+xzO+dd9w+g/rZERGxYhiu3xW1xwBc1PxsTFkGeGbzcy9le+DlZnuq7cm2J48bt8RWkIiIiBGn9kJAiwc0fBDY3vadzfGngX6bLSIiImL51O4CWOxB4DxJP6MsC/xa4KG6IUVERLRX7S6Axf6HsgTwLsALm+cD9v9LOpnShbC1pPmSDu1mkBEREW0xLFoAbH9K0q+BvSgxfZl/DAhc2nUHdTu2iIiINhoWBYCkfYHPUwb+3Q98FLiGsjlQREREDLKqXQDNik89wCcoTf6jgNMpAwDvqRhaREREq9VuARhne4Gkxyg7Ac6lzNlfFVhQNbKIiIgWq10APCZpU8pf+ysDMygbAQ24D0BEREQ8c7VnAbwHOAeYDlwOPBdYhbJC4HEV44qIiGi1qgWA7d8DuwI3Upr9/w78BTgQ+GK9yCIiItqtdgsAtu8FFlH++t/B9meBB4Bf1IwrIiKizWrPAlhJ0r9SpgCuCmzSrAfwMsoGQREREdEFtVsAvgfsCNxEWQTor8CxzbkJ1aKKiIhoudqzAHayvbWkzwFvbuJZC9gQeLxqZBERES1WuwXgbkmvB/6TMvXvCuBfgdspawJEREREF9RuAdgG+CzwDcqXvoB1gPMoMwEiIiKiC2oXAAuAW4HVgLWB+ZQiQMDDFeOKiIhotdpdAOOA0yh/8QtYnbIi4MuA8yvGFRER0Wq1C4BVgX8D7qWsAPhD4EzKOgCZBhgREdEltbsARgPPt32PpDttfxJA0ljgtrqhRUREtFftFoB7Kc3+2P5In/OTgEerRBQRETEC1G4BeC9wsaRFwEmUNQB2ATYHLq0ZWERERJvVLgB6KbMAdgBeBEzkH1/8W1SKKSIiovVqFwD7U5b9vRR4ATDe9n2SNqEsCxwRERFdULsAGA38CniCMgvgD5K2BgzcVTOwiIiINqtdAEwA/h9l7v/ngZnAN4ExwDH1woqIiGi32gXAaNunSvot8KDttwNI6gEOqxtaREREe9UuACTpY8BWwF8kvbfPa2MrxRQREdF6tQuAz1D6+1ei7AS4VnN+PWBepZgiIiJar2oBYPvjAJJm2P51zVgiIiJGkqoFgCQBrwcOlHQR8HxgP+AGYOyTVgeMiIiIQVK7C+AEyqY/LwO+SpkKeDqwN7AHkAIgIiKiC2oXAP9s+zmSLgf2Aja2/aiknwELKscWERHRWrU3A1rUPH6fEsshkt4OnEUWAoqIiOia2gXAbZLWtP054CDg2cB2lO6A+VUji4iIaLHaswD26vP8LEl/ArYEbgN+Vy2wiIiIlqvaAiDp+5J6JI2R9ApgNvBZ4E/AK2vGFhER0Wa1BwHuCsyijP4fA7zS9oXNUsA3AtMqxhYREdFatccAbAy81vY44Fbg25JeaHsBoIEuljRF0hxJcyUd3e1gIyIi2qJ2AfB34OvNyP/TgL8BP5f0O+C+pV0oaTRlHYG9gG2BgyRt2+V4IyIiWqF2AbAAeCewNWVDoEeAc4FJwOoDXLszMNf29bYfBU6hrCIYERERA6g9BuBo4CHbH+p7UtK6lMJgaTYFbupzPB/Y5clvknQ4cHhzuFDSnGcc7bLr4UkLGemzQ/Cpw9dT8jHCJR+dko9OyUenEZWPZfiueLr5mNTfC7WnAf7myeck/c72HsCnBrh8SWMEvITPmApMfWYRPjPN5kaTh/Izh7Pko1Py0Sn56JR8dEo+Og1mPmpvBnQ9sLA5HAVsAqwr6W7gVttL69OfD0zoczweuKUrgUZERLRM7TEA6wGXAwcAVwI/oCwC9BU6m/eXZDqwpaTNJK0MHEgZSBgREREDqD0G4HrgVEoT/Xjbm0vax/ZHJc1a2oW2F0k6AjgbGA1Msz276xEvmyHtclgBJB+dko9OyUen5KNT8tFp0PIh+ynd5kNG0nzgC5SFgD4EXADsZHu8pMtt71AtuIiIiBar3QLwDWCt5vkXgfOBbSRtRFkhMCIiIrqgagsAgKRRALafaPrytwfm2c52wBEREV1SezOgV1OWAL5F0unA3cDvgRskHbwM10+TdLukK7sa6DLK0sSdJM2TdIWkWZJm1I6nhiXdo5LWk3SupOuax7E1YxxK/eTjGEk3N/fJLEl714xxqEiaIOk8SVdLmi3pqOb8SL4/+svJSL1HVpV0saTLmnwc25wflHuk9hiASylL+f4EeAGwN2U54P8A/tX22gNcvxtlGuFJtrfvcrhL1SxNfC2wJ2WK4nTgINtX1YyrJknzgMnN3g4j0pLuUUmfA+6yfVxTKI598mJYbdVPPo4BFto+vmZsQ03SxsDGti+RtBYwE3g18FZG7v3RX04OYGTeIwLWsL1Q0hjgQuAo4LUMwj1SvQCw/TxJM4FV+n6JS3rA9hrL8G/0Ar9algKgp6fHvb29yxNyRETECmPmzJkLmg33nqL2IMDFYwDOA+5ePB4AeD3wwGB/Vm9vLzNmjMiW6IiIGIEk3dDfa7ULgK2AeyjL+q4BHNOcH03ZGGi59d0LYOLEiYPxT1bRe/QZy/zeecfts8J93lB5pr9XW/Mx1JY1j7Vy2Ob7Y0WIsa2Ga+5rrwT4iqaff5ztUbbHND+jbK82GB9ge6rtybYnjxu3xFaQiIiIEad2AfCl5vFPT35B0q+HOJaIiIgRo3YXwBhJvwR6JZ38pNd2G+ji5pqXAj3NqoIfs/2twQ8zIiKiXWoXANtRWiFWB3Z80mtjBrrY9kFdiCkiIqL1ahcAVwOvAVa3fVnfFyQNtBtgREREPEO1xwD8jbJQzGVLeO3IoQ4mIiJipKhdALwQeJ+kGyR9TtLzFr9g+xf1woqIiGi32gXAfOANwMnAPwPnSrpN0qclbVU3tIiIiPaqXQD0ACdS1gY38GPgdMrCPedVjCsiIqLVhkMBMMX2J4F/Aba1/Q5gJ+D2qpFFRES0WO0C4Hr+MRNhFWAtANs3sgzTACMiIuKZqV0ATAWmS5oKXAR8VdJ/SBoH3FU3tIiIiPaqvQ7AaODXwIbAL4D1gP8EVm2OIyIiogtqtwAcC/RSugIeAtakFCVrNT8RERHRBbULgBMprQBrUEb/v7F5/jbgrIpxRUREtFrtAuBltven7Ab4G+AnwE3AAcAXawYWERHRZrULAABs/xK4GZgI3E8ZA9BTNaiIiIgWqz0IcHNJpwGTgGcD44FZwNnA4xXjioiIaLXaLQC3Nz9jgb2Am2zvTlkUaE7NwCIiItqsdgEwBriHMg3wU8D6knpt/5EyODAiIiK6oHYBcLft9wNnAh8DbgGulXQHZTxAREREdEHtMQCjJa0HHNocvwJ4MfAi4IhqUUVERLRc7QLgOcAMQM3xSsDKwGNkL4CIiIiuqd0F8DDwEcq8/49SRv7/DVgEPFAxroiIiFar3QKwCDgaeBTYBJhLWRDo5ZR9ASIiIqILahcAY4B9gDuB+4Dn2r5D0uebcxEREdEFtQuA+ZQ1/39B6Q64RtJtlLiyHXBERESX1C4A7gL2A84A5gHXAncA44Dd64UVERHRbrULgN1sPyJpJdvbLz4pqQf4Y8W4IiIiWq32LIB3NF/2l0p6naQLJN0N/B64tG5oERER7VW7BeB44HDKYMC3UJr/7wG2BTarF1ZERES71S4AbgZe1Tw/Ddi3z2tnDX04ERERI0PtLoBTgGOBDYA/UJb/XR/YA/hrxbgiIiJarXYLwE+AU4E3UHb/GwW8C1gAHFQxroiIiFar3QLwc+BgYDvbK9teyfYqwOuAr9QNLSIior1qFwAb2/4L8NO+J23/GVijTkgRERHtV7sL4HZJC4BVJf2hObcaMAF4sF5YERER7Va7BWAL4FOUTYFGUcYB3Ax8HnhrvbAiIiLarWoLgO1HgS9K+n6zCdAatrMNcERERJdVbQGQNErS24CfS3oYuFvSKZIOlfS1Zbh+iqQ5kuZKOrr7EUdERLRD7S6AbwGTgLHA9ylbAH+LMgXw1Uu7UNJo4ARgL8rKgQdJ2rabwUZERLRF7QLg+baPARbaPgxY2/a5wD7AegNcuzMw1/b1TVfCKZSdBSMiImIAsl3vw6WZwAHAZ4FfUeb+r09ZDOj/2V5nKdfuD0xpCgckHQLsYvuIJ73vcMp+AwBbA3MG+/dYgh7KYkZRJB+dko9OyUen5KNT8tHp6eZjku1xS3qh9jTADwDnAY8CU4BHgPnA+cB3B7hWSzj3lGrG9lRg6vKF+fRImmF78lB+5nCWfHRKPjolH52Sj07JR6fBzEftWQC/kzQJWN/2063w5lPWC1hsPHDLoAUXERHRYrVbAABeCrxO0gRgZeAh4OvNWIClmQ5sKWkzytoBB1KWFY6IiIgB1J4GeC3wZuDPlH6NfwI2BE6XdMLSrrW9iLJ74NnA1cCPbc/ubsTLbEi7HFYAyUen5KNT8tEp+eiUfHQatHzUHgT4kO3Vmud/Ala2PVnSs4ArF78WERERg6v2NEBLWjzdb3XKksDY/mu9kCIiItqv9hiAVYEFkp6g7APwJgBJmwAP1wwsIiKizaq2ANgeRen7fyEw1vYPmpceBPYe6HpJ0yTdLunKLoa5zLI0cSdJ8yRdIWmWpBm146lhSfeopPUknSvpuuZxbM0Yh1I/+ThG0s3NfTJL0oD/228DSRMknSfpakmzJR3VnB/J90d/ORmp98iqki6WdFmTj2Ob84Nyj1QdA/BkktYEtgKut33PMrx/N2AhcJLt7Qd6f09Pj3t7e5c3zIiIiBXCzJkzFwzLhYCaDYC+D5xMWQToh8BfgS0k/avtM5d2ve0LJPUu6+f19vYyY8aI/EM0IiJGIEk39Pda7TEAALOAjwOTgVOBLwO3Az8GlloALIu+SwFPnDhxef+5QdF79BnL/N55x+3TxUj616YYByO+FSEfK4L8N4vBtiL8dx6uMdaeBfCE7a/afhFljf5ZwNeA31BW9ltutqfanmx78rhxS2wFiYiIGHFqFwCrSrpc0hXAZsA3bO9EGQC4pLX+IyIiYhDU7gL4FvDJPscLm8cFwL8PfTgREREjQ+1pgO+wfQPlC/8m2481L90NnDXQ9ZJOBi4CtpY0X9Kh3Ys2IiKiPWrPAhDwekorwE6SdgH2A/7WnN91adfbPqjrQUZERLRQ7S6AE4ANKBsAnQisApxOGQOwZcW4IiIiWq12AfDPtp/TbAT0SmAD249KugZ4VeXYIiIiWqt2AbCoeTwKOB/4bekVYGNgfq2gIiIi2q52AXCbpDVtT5e0DrA1Zfrf3cDP6oYWERHRXrVnAexle6GkdwJr2L7S9hWAgZ/WjC0iIqLNqhYAklZuZgK8w/Y9knaX9D5gByAj/CMiIrqk9kqA04F1gVGSPgB8ClgNeC+DtBRwREREPFXtMQCjbd8t6WzgP4E3UnYFfG7dsCIiItqtdgFwn6TtgQ9R5v7/G2VmwO+AZ9cMLCIios1qdwH8G/AD4PvAJcD2wL3A24DjK8YVERHRarVnAVwO7AQ8D7icshXw2ZTlgD9QMbSIiIhWq90FgO3HJT1i+7/7npeUpYAjIiK6pPZmQGdTFv/ZSNIfgR5gI+BRYHTN2CIiItqs9hiAPSjb/p4JvABYG7gFGAM8XjGuiIiIVqvdBfAQ8CPb50m6kTITQMA1lOWAIyIiogtqFwB/s31e8/wk21cufkHS7ZViioiIaL3aXQC/lLQmgO2PLD4paQvKWICIiIjogtotADsCP5Q0lrIGwFpk8F9ERETX1W4BmAJsQFkH4HrgYcq4gLlkEGBERETX1C4AHgbuB26mbAD0d8pKgIdR9gSIiIiILqhdAKxLmQK4AfAA8EfKboA7UVoBIiIioguqjgGw/QTwRUl/ouwGuKB5aQLwumqBRUREtFztQYCLXQm8HtgLuBP4f8BllIGBERERMciqdgFIeq2k11J2AjwQWAWY0TzfumZsERERbVa7BeBHlO2Ae4A9n/Ra7fEJERERrVW7ALgcOB7YEviK7UsAJD0fOLhmYBEREW1WuwB4N3Bf8/gTSbc05zcGDq0UU0REROvVngXwh+bpjZK2ofT7C7jG9mP1IouIiGi3qgWApInA4k1/DqVM/dsIuEfSHNtvqxZcREREi9UeaHdmE8P3KH3+OwMXAJOa5xEREdEFtQuAUbYfBLYAVgWut/1vQC8wsWZgERERbVZ7EOBNkvYAHgNuAsZL2p6yD8DKVSOLiIhosdoFwGHASZQ9Ae4CtgWmU+L6ar2wIiIi2q32LICbgN0lPRvYCvgGMB+Y3uwTEBEREV1QeyngXSStbftq4BxgB+AjwGckrbMM10+RNEfSXElHdzveiIiItqg9CPAk4BuS/gCcD4wFPgs8CFy9tAsljQZOoGwgtC1wkKRtuxtuREREO9QuADYFzgOOpIz8nwxcbftYyriApdkZmGv7etuPAqcA+3Uv1IiIiPaoXQAsAh6xPQs4A/g1cIGklwEe4NpNKTMHFpvfnIuIiIgByB7oe7aLHy5dTdn+d1dgAbBT87gusND2uKVc+3rgFbYPa44PAXa2feST3nc4cHhzuDUwZ5B/jSXpofweUSQfnZKPTslHp+SjU/LR6enmY1J/36W1pwFOBS4B/gPYnBLPfGAT4HMDXDsfmNDneDxwy5PfZHtq8zlDRtIM25OH8jOHs+SjU/LRKfnolHx0Sj46DWY+ak8D/CKApHGU7ohHgQdsXwrsOcDl04EtJW0G3AwcSLYQjoiIWCa1NwPaFfg4/1j690ZgnKTZwF627+3vWtuLJB0BnA2MBqbZnt39qCMiIlZ8tQcBngu80/YWwLeA1YB3UZYBnj7QxbbPtL2V7WfZ/lR3Q31ahrTLYQWQfHRKPjolH52Sj07JR6dBy0ftQYAP2V6teX4JZXOgHSWNAe63vWq14CIiIlqs9iDARZK+Rvlrfzzwq8rxREREjAi1uwAuA14J/Ddl9b9PN+c3Bf5WK6hnKksTd5I0T9IVkmZJmlE7nhokTZN0u6Qr+5xbT9K5kq5rHsfWjHEo9ZOPYyTd3NwnsyTtXTPGoSJpgqTzJF0tabako5rzI/n+6C8nI/UeWVXSxZIua/JxbHN+UO6Rql0A/WmW+V3F9oNLec8EylLCGwFPAFNtf2mIQlxSPKOBaymzF+ZTWjUOsn1VrZhqkzQPmGx7xM7hlbQbsBA4yfb2zbnPAXfZPq4pFMfa/lDNOIdKP/k4hrLux/E1YxtqkjYGNrZ9iaS1gJnAq4G3MnLvj/5ycgAj8x4RsIbthU3X+IXAUcD/Z+++w+Qqy/+Pvz+EhNBJ2FBTlhKaSA2EIr0YQAQREFBsYPT7BUWxkJ+iFBv6tSuWiAERAZFmlF5CUzDZhVBCCISQQEIJoSbUhNy/P56zsBN2d5bszD6bs5/XdeWamTNz5tz7MBdzz1Pu5zBq8BnJPQfgRuBc4MqIeOU9ntvmB6WjL9yGhoZobGzsSshmZmbLjObm5nk9tRDQPsBOwDmSbgX+AFxV1PbvUEQ8BTxV3J9fVBVcH2g3AWhsbKSpqVf2RJuZWS8kaVZ7z+VOAF4jJQCfAj4J/BlYTtI1wB8j4vrOvImkRmBb4L9tPPd2KeChQ4fWJupeoHHMVZ1+7cyzDqpjJGZmVg+5JwESEQ9ExNcjYm1gX9K4/j7A3ztzvqRVgMuAL0fEy228/9iIGBERIwYNandrATMzs14ldwKwuPWDiJgYEf8LrAl8uNrJxaSIy4C/RsTl9QnRzMysfHIPAYxu62CkmYm3dnRiMTvyT8DUiPhZHWIzMzMrrdybAV1YdOGPIu3stwh4BLg+IhZ3eDLsChwL3C9pcnHsmxFxdb3iNTMzK4vcmwEdCXydVPTnQFIC0AfoJ2luRAxp79yIuANQtwRqZmZWMrnnAJwK7AGsR/o13wfYEhiXMygzM7Oyy50AiLQUcEXgWmD5iJgVEf8DDMwamZmZWYnlngR4NemLvwG4Hpgh6UTgJfLHZmZmVlq5JwGeUmzqsD9wE3AP8F1gdVJNADMzM6uD7L+yi1n7V0samB7GZ3LHZGZmVnZZ5wBI2l/SxZLmksr4Tpf0hqQHJW2WMzYzM7Myyz0J8HLgCmBdYDzwT1JNgMeA2zPGZWZmVmq5hwCWj4i/AUjaB9ghIhZKugV4PWtkZmZmJZY7AVhU7Px3K7AyMEjSENLugK9ljczMzKzEcicAlwHDgROBlUgrAWYBtwB35wvLzMys3HIvA/xU68eSViMlBDMi4qw8UZmZmZVf7lUAF0g6WNL7Jf0GeBq4GXhS0s05YzMzMyuz3EMAo4ANgc1I+wD0Aa4B9gJ2yhiXmZlZqeVeBtiyFfDjpEmAD0fER4H1gRVyBmZmZlZmuXsA5gITSKV/FwHrSvp/wNZZozIzMyu53AnARcDewABgTnF7Gqln4t8Z4zIzMyu13KsATpG0c7obd0naCLgE+BFwac7YOqNxzFWdfu3Msw6qYyS9U2fbvxZt7//WtdGd/82Whv87V3J71EZPbcesCYCk8a3ut9zdGvhE8e/DGcIyMzMrvdxDAAcDbwLPkSr/ibQS4H05gzIzMyu73AnAK8DzwBpAP9KGQENJ8wLMzMysTnIvA3wYOAl4kLQD4KakHoAJwOczxmVmZlZquRMARcQVwD6k7X/7APOBA4A3cgZmZmZWZrmHACYARMQrwMmStgZ2johpwBlZIzMzMyux3MsATwaQNAIYQioGdEvOmMzMzHqD3MsAPwT8AXgJWBd4BlhL0gLgwIh4IGd8ZmZmZZV7DsBfgcuAW4FpQF/gkOKxdwM0MzOrk9xzAPpFxJeUqgDNBp6JiNuB2yW9ljk2MzOz0sqdALwm6S/ARGAQaStgJA0mf++EmZlZaeVOAL4E/Bb4KPAnYLikG4HNSZsCmZmZWR3kXgVwgaSLSCWA+wIbACsBcyLiqZyxmZmZlVlP6GZfHzgcmAxcBzQCu7TeKMjMzMxqK/cywDGkkr/rAl8H/o9UBXAnYEDG0MzMzEot9xyAY4EtgDuAHwCPRMRxklYm7RBoZmZmdZB7COCtiHgNuBdYDISk4cBZwKtZIzMzMyux3AnA3ZIuBNYi1QFYjzQPYDc6UQhI0ihJ0yRNL4YTzMzMrBNyDwEcDxwBBHApMBI4GngcOLujEyX1KV6zHyl5mCRpfEQ8WNeIzczMSiD3MsBFwEWSBgE/BN4H9CfNCxgF7N3B6TsC0yNiBoCki0llhJ0AmJmZVaGIyHdxaRXgG8DJpOGIfqQ9AZ4D7oyIUzo493BgVEQcXzw+FhgZEScu8brRwOji4abF+9dbAzCvG66zrHB7VHJ7VHJ7VHJ7VHJ7VHqv7TEsIga19UTuIYC/AlcAM4DzSMnAocCpwFFAuwkAqXjQkt6VzUTEWGBsF+N8TyQ1RcSI7rxmT+b2qOT2qOT2qOT2qOT2qFTL9sg9CbAxIs4DXo2InwErApsAvyTVBujIbGBIq8eDgSfrEaSZmVnZ5O4BeEXSB4DvSfoY8ADwNWA1YG6VcyeR9g7YAJhD6jE4pp7BmpmZlUXuBOALwDmkX/0PAJ+JiIeLSYFHd3RiRCySdCJp2WAfYFxETKl3wJ3UrUMOywC3RyW3RyW3RyW3RyW3R6WatUfuSYA/Ay6LiH9nC8LMzKwXyp0APAvMAgYBfwMuioh7iuc+ExHnZgvOzMysxHInAPdExLZF+d+jin99gIuAz0fEetmCMzMzK7HcqwA2k3QfcBmpIuBbpJhOoPoqACSNkzRX0gP1DbNzXJq4kqSZku6XNFlSU+54cmjrMyppoKQbJD1S3PaanS/baY/TJc0pPieTJR2YM8buImmIpAmSpkqaIumk4nhv/ny01ya99TPSX9JESfcW7XFGcbwmn5HcPQALgR2AF5Z8CvhPtR4ASbsDC4DzI2LLatdraGiIxsbGpYzWzMxs2dLc3DyvpxYCuhBYJSImL/mEpFuqnRwRt0lq7OzFGhsbaWrqlT9EzcysF5I0q73ncu8F8KmW+0UXxnDSXgAAv6/FNVqXAh46dGgt3vJtjWOu6vRrZ551UE2vXW9l/tuWhtujNjrbjrVow6X5b7a0/539+ajUne2xLLR9T40xdw8AAJJ+QNrIZz1gMrATMAXocrnD1qWAR4wYkW+8w8zMrAfJPQmwxVeA7YFZEbEXsB2wUd6QzMzMyqunJAAREa8DSFohIqbyzlCAmZmZ1VhPSQBelHQKMB64UdL9wEvVTpJ0EXAnsKmk2ZKOq3OcZmZmpdAj5gAA2wC/AvYmxXQ/MKraSRHR4X4BZmZm1rYekQBExFxSFUAkDYyI5zOHZGZmVmpZhwAknVrc/lnSSEkPA82SHpf0z5yxmZmZlVnuOQCHFbdbAd8BToqIDYDDgb2yRWVmZlZyuROAFssBQyLimuLxdNKmQGZmZlYHuecAbChpPLAYeL+kHwILgSOBeVkjMzMzK7HcCcAhre43AmuSEoDPkYYFzMzMrA5y7wVwa6uHty7x9O3dGYuZmVlvkjUBkLQDcCmwOnAZ0Exa//8QsHdEdHkvADMzM3u33EMAVwO3AS8AewCHAieQ9gXYMF9YZmZm5ZZ7FcAqEfFR0pj/qqTlf98l9QasmDMwMzOzMsvdA4Ck1UklgG+IiAmSPgpcAfTLG5mZmVl55e4BuIu09e9pEXEsQETcB3yKtDTQzMzM6iD3KoC9ACQtV9yulg7HHZIeyhmbmZlZmWUfAig8LmkBRY+EpMW8e1mgmZmZ1UhPSQA+ADwNvFU8FrBrvnDMzMzKrackAH0jYuPWByS9kisYMzOzsusJhYAOARZLegl4lrQHQD9SMSAzMzOrg9w9AP8pbhcB/Ulf/IOBFYpjZmZmVge5lwE+DmwMbEbaDGgz4Kri/hPZojIzMyu53D0AawJXkiYAttitODYwQzxmZma9Qu4EYEVgS9LWv8uRiv8sB2xNmgtgZmZmdZB7CGBF4BvAk8ApwNrAY8BBwF8yxmVmZlZqWROAiFgM/JH0y/8Y0iZAqwDXkXYHNDMzszrImgBIOhKYAPQBhgB9SVsDPwm8kTE0MzOzUss9BHAq6Zf+VsClpPkArwI3kpIBMzMzq4PckwAFvBYRr0r6MrBjRGwLIOmBrJGZmZmVWO4E4GrgfknLkwoAvSTpYuACUnJgZmZmdZA7AVgLuJc07r8e8ABwO2lFwDkZ4zIzMyu13AnA9hGxVcsDSZOAfwAfB24Bfp4pLjMzs1LLPQlwfUkjACSdALwf+BEwkbQc0MzMzOogdw/AS8Dlkl4HhgJHRcSVkjYlDQWYmZlZHeROAF4DdibtAnglMB4gIqZJejZjXGZmZqWWOwE4A5gE/Bn4N3CzpKeAQcC1OQMzMzMrs9wJwC7AK8CJwMukVQErkeJanDEuMzOzUss9CXA/YHtgY9JGQIMjYkdgJHBAtZMljZI0TdJ0SWPqG6qZmVl55O4BCGB/oJGUjERxfDEwsKMTJfUBziYlEbOBSZLGR8SDdYvWzMysJHInAPOBS0iTAQOYWFQC3IPqewHsCEyPiBkAxXmHAE4AzMzMqsg9BLAqcBBwKKkX4Dngg6QqgLOrnLs+8ESrx7OLY2ZmZlaFIqL6q+p1cem1iFhR0l8i4tiiW38ssBqwRUS8r4NzjwA+GBHHF4+PJW0m9MUlXjcaGF083BSYVo+/ZQkNwLxuuM6ywu1Rye1Rye1Rye1Rye1R6b22x7CIGNTWE7mHABZK+gGwi6TDimNXAUcDm1c5dzYwpNXjwcCTS74oIsaSkopuI6kpIkZ05zV7MrdHJbdHJbdHJbdHJbdHpVq2R+4E4FDgKGAN4OBWxxeQ5gZ0ZBIwXNIGwJzifY6pfYhmZmblkzUBiIibScV/3gS+FREvAUhaA9izyrmLJJ0IXAf0AcZFxJT6RmxmZlYOWROAVt3+B5ESgdZP/4RUHrhdEXE1cHVdguuabh1yWAa4PSq5PSq5PSq5PSq5PSrVrD1yTwI8t7j7UeAt4Obi8V5An4hYPUtgZmZmJZc1AXg7COlx4Brgx6R6AGNIM/yHZQ3MzMyspHLXAWgxH3gB+Btp8t880kTAZYpLE1eSNFPS/ZImS2rKHU8OksZJmivpgVbHBkq6QdIjxe2AnDF2p3ba43RJc4rPyWRJB+aMsbtIGiJpgqSpkqZIOqk43ps/H+21SW/9jPSXNFHSvUV7nFEcr8lnpKf0APwGGA5cROoBOIpU5e+LHZ7YgxQ1DB6mVWli4OjeXJpY0kxgRET02jW8knYnJbPnR8SWxbEfA89HxFlFojggIk7JGWd3aac9TgcWRMRPcsbW3SStC6wbEXdLWhVoJq2M+jS99/PRXpscSe/8jAhYOSIWSOoL3AGcBBxGDT4juecA9AGOJ63hf4W0IRDAbcD7IuJ7tbxeQ0NDNDY21vItzczMeqzm5uZ57RUCyp0AnEPa/nciKev9d0ScUDx3d0RsV8vrjRgxIpqaemVPtJmZ9UKSmtsrHJS7ENCuwL3At4AXgU9IOg4YD6yQMa5er3HMVZ1+7cyzDqpjJGZmVg+5JwE2AlcA60TE8GLZ3w+BrYENcwZmZmZWZrkTgIXASxHxVsuBiDgD+D+gf7aozMzMSi73EMDVwIclvcA7W/sOAbYD/p4tKjMzs5LLnQBcDuwD/B4YCIhUA6AJuDRjXGZmZqWWewjgAKAfaRvf1Uhr5x8DDifVAjAzM7M6yJ0AvACcQyr+s0VEfDQiPgq8L29YZmZm5ZY7ATgW+CWwP/AVSdsWx58BNskWlZmZWcnlngMwOyJGSPoL8BHg40Xpw1eAu/KGZmZmVl65E4AAiIhjJX0E2B1oIFUH3DlnYGZmZmWWOwFQq/t3A/Mj4kZJKwF9MsVkZmZWernnAOwGIOlzpGV/fyiOrw9c2dGJ7W0baWZmZtXlTgDeLMb8TyDtC7BY0leBjYG1qpy7CPhqRGwO7AScIGmLukZrZmZWErkTgEnAGsAbpD2O1wVWBL7KO1sDtykinoqIu4v784GppJ4DMzMzqyJ3AtAnIl4AbiUlAHOA/wILKCYIdoakRmDb4twlnxstqUlS07PPPluToM3MzJZ1uROAlyVtCYwhbQc8Ffg8cB3QqW9rSasAlwFfjoiXl3w+IsZGxIiIGDFo0KCaBW5mZrYsy50AfAH4K3AeaRXA+0nDAccBP6h2sqS+pC//v0bE5fUL08zMrFxyLwM8BtgP2J60KdAawN6kbYLndHRiMXnwT8DUiPhZfcM0MzMrl9w9AF8j/dJ/k5QEjIqIdYF9gZ9WOXdXUinhvSVNLv4dWNdozczMSiJ3D8BiYBjwT9Ls/9skPQ38rXjcroi4g8pCQmZmZtZJuROAt0gJwLHAB4ARwHrAl4rnzMzMrA5yDwEIGA0cCexJmgPwMHAWaTKgmZmZ1UHuHoCngR2BL0XEMwCS1gY+DUzOF5aZmVm55e4B2BZYE7hd0uuSFgEzgSNI9QDMzMysDnL3AJxR3PYH7gFeAAYBKwMTgKGZ4jIzMyu13D0AHwKaSTP+G0iFgOaSavoPyBiXmZlZqeVOAF6KiD8DrxWxbA8cTNoXYIWcgZmZmZVZ7iGArSS9DKxCWhHQUv2vD17jb2ZmVje5E4AnSMWA3iAtAbyc1BuwJ04AzMzM6iZ3AvDhiLgPQNI6wMji+Bntn9JzNI65qtOvnXnWQXWMpHfqbPvXou3937o2uvO/2dJYFv47d2eMS3utZSHG7tRTY8w9B+AeSdMlfRcYCLwMbAxsFhGP5Q3NzMysvHInAG8CnyziuA34F7A/8E1JP8wZmJmZWZnlTgCIiP9ExLdIBYBGAVOARtJ+AGZmZlYHuROAtyRtWdyfBzwQEScDw4Fn8oVlZmZWbrknAX4X+Kuke0kFgJok3QpsBZyaNTIzM7MSy5oARMSPJP2ENO6/CXAvMBs4OSJezBmbmZlZmeXuASAi3pI0HTge2AboB/xQEhGxYdbgzMzMSir3HIAWVwK7AWuTqgGuCdyZMyAzM7Myy94DUNgI2BK4IiJ2krQRcH/mmMzMzEqrpyQAbwAzgEckncg7ewKYmZlZHWRNACQdVty9AbgGuBY4krQM8MpOnD8K+CVp86BzIuKs+kRqZmZWLrl7AA4ubucX/7YCHi3+RUcnSuoDnA3sR1o5MEnS+Ih4sH7hmpmZlUPuBKCBKl/0HdgRmB4RMwAkXQwcAjgBMDMzq0IRS/v9W4OLS3sAO5D2A1iTtD3w88CKwJ0R8c0Ozj0cGBURxxePjwVGRsSJS7xuNDC6eLgpMK3Wf0cbGkiVDS1xe1Rye1Rye1Rye1Rye1R6r+0xLCIGtfVE7h6Aj5AKAPUFPlYcG0xKCD4FtJsAAGrj2LuymYgYC4ztWpjvjaSmiBjRndfsydweldweldweldweldwelWrZHrkTgAMjYhNJU4EnW3Xn/5fqv9RnA0NaPR4MPFmfMM3MzMoldwLwuqQdga8At0iaURzfhOpLAScBwyVtULz2KOCYukVqZmZWIrkTgE8DvwNWJf3iXxlYh/Tr/n86OjEiFhU1A64jLQMcFxFT6hpt53XrkMMywO1Rye1Rye1Rye1Rye1RqWbtkXsS4FDSLoBrAOsDHyZ15TcBf4yIRdmCMzMzK7HcewFcAywXEU+TCgDtCfQHDsVZn5mZWd3kTgA2AlYo7n+ctBpgKimug3IFZWZmVna5E4CFwLbF/ZWBT0fE90jJwGrVTpY0TtJcSQ/UMcZOkzRK0jRJ0yWNyR1PbpJmSrpf0mRJTbnjyaGtz6ikgZJukPRIcTsgZ4zdqZ32OF3SnOJzMlnSgTlj7C6ShkiaIGmqpCmSTiqO9+bPR3tt0ls/I/0lTZR0b9EeZxTHa/IZyT0HoAlYDLwObAb0A+4GBgKrR8QGVc7fHVgAnB8RW1a7XkNDQzQ2NnY1bDMzs2VCc3PzvJ5aCOizwF+Ap4ApwObAK6Rf/9+qdnJE3CapsbMXa2xspKmpV/4QNTOzXkjSrPaey5oARMR9krYD9iet/f8XaQngdRHxYs7YzMzMyiz3dsDzeXf5XgEhiYioOg+gE9d4ey+AoUOHdvXtKjSOuarTr5151rI1p7Gsf1tZ/y5YNv62zsbY0+ODZe/z0Z3cjpV6anvkngR4NrAh8Avgt8BzpLoAC4G/1uICETE2IkZExIhBg9ocBjEzM+t1cicAB0XEPOCDwEjgyIjYCNgZODZrZGZmZiWWOwHoK2l54C1SBcC7JS1H2iK4rd3+Kki6CLgT2FTSbEnH1TVaMzOzksi9CuBs4Grg18BppCV9bwBPk2r8dygijq5rdGZmZiWVexXAryXdT9r45zVgOmkVwBXAuTljMzMzK7PcPQCQSgFfD3wjIt5eryjps8C4bFGZmZmVWNY5AJJ+QCr4837gZklflHR+8fSJ+SIzMzMrt9w9ACcCt5KWAk4DTgUGSFqDtFGQmZmZ1UHuBKAv8BJwDqkgUB9SNcBVgeczxmVmZlZquZcB3gS8SBoGeCkibiatAPg3MCxjXGZmZqWWuwfgiOJ2TeDnkp4Blo+IUyX9LmNcZmZmpZa1ByAiXiNtBbw+cBGpN+BmSYqIOTljMzMzK7PcqwD2Bx4BTgcOJFUD3AR4tHjOzMzM6iD3HIDzSN3/GwGXAbuTCgL1x4WAzMzM6qbDOQCSNoiIx+p4/TWBocBKwL3AthHxqKTBwKN1vK6ZmVmvVq0H4FIASTfV6frPAdcCR5I2BBop6RTgn3gZoJmZWd1UWwWwnKTTgE0knbzkkxHxsy5ev4m07O8Q0kZAXwfuIi0DHN7F9zYzM7N2VOsBOIo0S395UnGeJf911SeAx4DxwObA94EhRVyfrsH7m5mZWRs67AGIiGnAjyTdFxHX1PriEfEy8MNWhy7lnWGHa4ADan1NMzMzqz4J8EvA5fX48i/ef7vi7hqkX/wbAv0AAVvV45pmZmZWfQ7Ad4Exkh4lFer5e0Q8W8PrTyJtBrQNMJfU/T8NWJf8SxTNzMxKq9qX7AxScZ7vAtsDD0q6VtKnJNViDsBU4PPAYxGxGfBoRGwXEeuSVgWYmZlZHVRLACIiFkfE9RFxHLAe8FtgFCk56KrTixgWFo+fknSQpG2BF2rw/mZmZtaGakMAav0gIhaSZuyPl7RiVy8eES0T/r4naXXgAuAXpK2BP9/V9zczM7O2VesB+Fh7TxQb+XSJpInFe/2LVAzoq6Qk4Flgi66+v5mZmbWtwwQgIh6u8/X7SjpS0hHAaOBMUnngS4CP1/naZmZmvVaHCYCk90u6S9ITksZKGtDquYk1uP4Q0hf9Z0lLAI8mVQfcGVi7Bu9vZmZmbag2BPA70kS99wMPA3dI2qh4rm8Nrr9a8d6bkWoBnBwR55PG/2uxysDMzMzaUG0S4CoRcW1x/yeSmoFrJR1LmqjXVQ9GxDYAkq6NiCeK4wtJJYLNzMysDqr1AKiYnQ9AREwAPgr8BRhWg+s/JWmV4r1HtTq+GmlzIDMzM6uDagnAj0ib9LwtIu4D9gEur8H1vxcRCyStsMTx+cCHavD+ZmZm1oZqqwAujIi72jj+eER8rgbX/1Vxe+cS7/9KRMytwfubmZlZG6ptBrRV8YsfSX2BU4AdgQdIv95f7eL1F0o6F1hf0q+WfDIivtTF9zczM7M2VBsCOK/V/bOAjYGfAisCv6/B9T8EXAe8DjS38c/MzMzq4L2UAt4H2CEiFkq6Dbi3qxePiHnAxZKmRkSX38/MzMw6p1oPwOqSPiLpo8AKxV4ARERQm2WAFO/3ri9/Sd+p1fubmZlZpWoJwK3Ah0ld9XdJWhtA0jrAvDrHdnyd39/MzKzX6nAIICI+087xp0lDAl0i6eX2niLNMzAzM7M6qDYHgKJQzyhS3f5FwCPA9RGxuAbXf5E0r+CZNq77xLtfbmZmZrVQbTOgI4EJpATgRNISwGOByZK2qsH1z6f9ioIXVjtZ0ihJ0yRNlzSmBvGYmZn1CtV6AE4FdoqIVyU1AH+NiA8WX/6/B3bpysUj4tQOnjulo3Ml9QHOBvYDZgOTJI2PiAe7EpOZmVlv0JllgK8V918B1oJUDljSarUMpNhqeDjQv+VYRNzWwSk7AtMjYkZx/sXAIYATADMzsyqUVvS186T0I2Ab0mqAA4BrIuIHkgYCt0fE+2oShHQ8cBIwGJgM7ATcGRF7d3DO4cCoiDi+eHwsMDIiTlzidaOB0cXDTYFptYi5igbqv0piWeL2qOT2qOT2qOT2qOT2qPRe22NYRAxq64lqqwBOkXQgsAVwZkTcUDz1IrDdewigmpOAHYC7ImIvSZsBZ1Q5R20ce1c2ExFjgbFdD7HzJDVFxIjuvGZP5vao5Pao5Pao5Pao5PaoVMv2qLoKICKuBq5e4thi4I1aBFB4PSJel4SkFSLiIUmbVjlnNmllQovBwJM1jMnMzKy0qq0CeF7SOZL2kdTWL+5amS1pDeBK4AZJ/6D6l/kkYLikDST1A44CxtcxRjMzs9Ko1gPwLGlM/kzgfEmXAhe1tUXw0pC0QUQ8FhEfKQ6dLmkCsDpwbUfnRsQiSSeSNhPqA4yLiCm1iKsGunXIYRng9qjk9qjk9qjk9qjk9qhUs/aoNgnw7ojYrrg/lPQr+yhgDeDiiPhmly4uNUfE9pJuioguVxY0MzOzzun0boAR8TjwY+DHxfj8UTW4/nKSTgM2kXTykk9GxM9qcA0zMzNbQrXNgCa0dTAipkVEtVn6nXEU8DopEVm1jX9mZmZWBx0mABHxrl/lNbYB8FnS8r03IuKM1v+U/Koo9XufpLeXHkqaKel+SZMlNdU5zk5xaeJKPfG/UXeTNE7SXEkPtDo2UNINkh4pbgfkjLE7tdMep0uaU3xOJhdLj0tP0hBJEyRNlTRF0knF8d78+WivTXrrZ6S/pImS7i3a44zieE0+I9XmAPwMuCwi/r104Xdw4VTK92HeKeX7EHA50HqHwCbgi8CBwEjglxExsjh/JjAiIjpdEKGhoSEaGxtrEb6ZmVmP19zcPG+pCgGRNv7ZXdIg4G+kFQD31Ciut0v5Svo9qbjQ8cDPgcOBicD6wPmRspS7JK0had2IeGppLtjY2EhTU6/8IWpmZr2QpFntPVdtDsDsouLQvsB84AJJD0k6TdImXYxrfaBly99dSF/8i4q5BTuTivy0fg2knoL1i/sBXC+puSj32yZJoyU1SWp69tlnuxiymZlZOVRLAAIgIh6JiO8Wtf+PJG3Yc3WHZ1bXurBQy4ZDiyStBywkzQ/oqNzvrsUSxQOAEyTt3uYfEDE2IkZExIhBg9rsBTEzM+t1qiUA7/oCjoj7IuL/RcTGXbx261K+/wI2Bm4E7gZmAhfRQbnfiGi5nQtcQRpSMDMzs06olgDsVsdrv13KF/gRcCjwQ2AYsFlEfIdU2veTxWqAnYCXIuIpSStLWhVA0srA/sADbV3EzMzM3q3aboALWu4XywyGk7r/W56/bWkv3F4pX0nXAP8Afk8aZjgQmA68CnymOH1t4Ipie4LlgQsjosPSwWZmZvaOqrsBAkg6nrRl72DS3gA7AXcCe3fx+qN4p+b/2pJ+TZoA+IikX0XEl4ATljwpImYAW3fx2mZmZr1WtSGAFicBOwCzImIvYFvSRkFddRgwkLTev7m4XVjcb67B+5uZmVkbOpsAvB4RrwNIWiEiHgI2rcH1NwfmkXoCboyIPwPzI+LPxX0zMzOrg04NAQCzJa0BXAncIOkFitn4XRER84EvS9qeVGPgKjqflJiZmdlS6lQCEBEfKe6eLmkCsDrvjN13WUQ0S9ob+F/gjlq9r5mZmbWtsz0AAEhaC3iseLgO8HitAinK/Z5d/DMzM7M66lR3u6QPS3qE9OV/a3F7TVcvXuz8dLGk2yV9U1LfVs9d2dX3NzMzs7Z1drz9u6Slfw9HxAakvQFqsUPgOOAW0o5/6wK3SlqzeG5YDd7fzMzM2tDZBGBhRDwHLCdpuYiYAGxTg+sPiojfR8TkiPgi8FvgNkkb8U7NfzMzM6uxzs4BeFHSKsDtwF8lzQUW1eD6fSX1b1liGBEXSHqaVB1w5Rq8v5mZmbWhsz0Ah5B27Psyafb/o8DBNbj+OcDI1gci4kbgCFzb38zMrG46uwzwFUnrkHbcex64rhgS6JKI+Hk7x+8B9uvq+5uZmVnbOrsK4HhgIql07+HAXZI+29WLSxolaZqkRyVdKem3ksa1+idJv5I0XdJ9krZr49zpksZ0NRYzM7PepLNDAF8Hto2IT0fEp4DtgVO6cmFJfUhr/g8A7iFtAvQh0jLDwcD84rnhxb/RwO/aOHcL4GhJW3QlHjMzs96kswnAbNIXcov5wBNdvPaOwPRiZ7+NgF8AfYs9AA4C3k+ae3B+JHcBa0hat/W5EfEmcHHxWjMzM+uEDucASDq5uDsH+K+kf5CW5x1CGhLoivV5J4lYSEoyFkvaEngaaARepTLRmF2ct34bxysmE5qZmVn7qk0CXLW4fbT41+IfNbi2Wt2/kVRsaG3g/uLY68DUNs6LJc5tffzdF5FGk4YPGDp06NLGamZmViodJgARcUYdrz0bGFLc3x/4O/AZ4CzSPgNvAA2tXgNpbsCTQL92jr9LRIwFxgKMGDHCxYXMzMyoMgdA0kqSviHp65L6S/qUpPGSflwUBuqKScAekjYg/aI/CliFVABoZ+ATwHjgk8VqgJ2AlyLiqeLc4ZI2kNSvOHd8F+MxMzPrNapNAjyP1C2/AXAVsAPwE9IX9u+6cuGIWESaW3AdaSa/SEMLuwDPAGsBVwMzgOnAH0nbBbece2Jx7lTgkoiY0pV4zMzMepNqcwA2iYgjJQl4Ctg3IkLS7cC9Nbj+AmBPYChpY6DPAV8BVgdmFFsEn9DWiRFxNSlBMDMzs/eos5UAQ9LVxRdyy+NajKevDjSTfv0HsFJEnFEML+xdg/c3MzOzNlQbAmhqGeuPiLcr/xW79c1v96zO24nUjX81aXjhC5LuA/5MMXPfzMzMaq/DBCAijo+IBW0cfxTYrQbXPw94kLSmfwJpw6EPAbcA36rB+5uZmVkbqlYClLS7pE2L+x+Q9DVJB7UMB3TR2hHx64g4C1gjIn4UEY9HxK+BYTV4fzMzM2tDtUqAvyCV3V1e0nXAPsA1wFck7RkRX+/i9VsnIOd38JyZmZnVULVJgPsBWwIrkpbsrR8Rr0o6i7SBT1cTgH9IWiUiFkTEqS0HJW0MPNzF9zYzM7N2VEsAopjxv7jlcXG7mBr8Qo+I77RzfDpp22EzMzOrg2oJwFXFmv/+wDnAJZLuAvYAbqtlIJIOAt5XXAuAiDizltcwMzOzpNpeAKdI2jndjbuK5X8fISUDl9YqCEm/B1YC9ire+3C6vtugmZmZtaNqIaCIuLPV/UdJpYBrbZeI2ErSfUUhoJ8Cl9fhOmZmZkb1zYCel3SOpH2KcsD18lpx+6qk9YCFpP0HzMzMrA6qTeR7FpgMnAnMlvTLYle+WvuXpDWA/wPuBmYCF9fhOmZmZkb1BOCViPhNROxK2qJ3DvBbSTMk/WBpLyppoKQbJD0i6QbgNxHxYkRcRioAtFlEfFvSKEnTJE2XNKbV+adLmiNpcvHvwKWNxczMrDeqlgC83e1fVOj7cURsBxwAvNGF644BboqI4cBNwN+KHgAi4g1gOUknAGcX19oCOFrSFq3e4+cRsU3xz7sCmpmZvQfVEoAJbR2MiGkRcUYXrnsIacMfitvdI+LFVu//AvAlYHpEzIiIN0lDAod04ZpmZmZWqLYZ0Ml1uu7aEfFUcY2nSKWG3+5tkNSHtCzwiVbnzAbWb/X4REn3SRonaUB7F5I0WlKTpKZnn322tn+FmZnZMqozmwF9UNJxkhqXOP7Zdk5pef5GSQ+08a+tX/GLSEWG9pG0N3ARafLhkloqEf4O2AjYBngK+Gl7cUTE2IgYEREjBg0a1FHIZmZmvUa1zYB+COxKmpn/TUm/KHbqAzgRGNfeuRGxbwfv+4ykdSPiKUnrArNIcwH+hzTv4HrgAaB1qeDBwJPFez/T6r3+CPyro7/DzMzMKqmjXX0l3Q9sGxGLikl6FwLTIuIrku6JiG2X6qLS/wHPkTYbugH4SESMXOI1y5M2BNqHtPpgEnBMRExpSR6K130FGBkRR3Xius+Sko16awDmdcN1lhVuj0puj0puj0puj0puj0rvtT2GRUSb3d/VEoCpEbF5q8d9gLHAasAWEfG+9xBE6/ddE7gE+ADpl/4qwFHAINKv/i8VL10H+AXQBxgXEd8vzv8Lqfs/SDUDPt+SEPQEkpoiYkTuOHoKt0clt0clt0clt0clt0elWrZHtVLAj0raIyJuBYiIt4DjJH0P+OjSXjQingP2kXQ4cBywCfCz4umFpDH9iIi9gXct8YuIY5f22mZmZlY9ATiirYMRcaqk33X14hFxKXCppG9HxHe7+n5mZmbWOdWWAb4WEa9J+oik1VuOF/MBdujqxSX1k6SWL39Je0n6qqRRXX3vzMbmDqCHcXtUcntUcntUcntUcntUqll7dDgH4O0XSZMjYpslji31JMBW73EvsGdEvCDp66Sthq8G9gCaI2JMh29gZmZmS6VqHYAOXld1K+FO6FNU/QP4GLBPRHyPVP7X9f3NzMzqpLMJQJOkn0naSNKGkn4ONNfg+i9L2rK4Pw/oX9xfvjOxFVUA50p6oAaxdFl7mxf1VpJmSrq/2LCpKXc8ObT1GV1yM6yOKlmWTTvt0Ss395I0RNIESVMlTZF0UnG8N38+2muT3voZ6S9poqR7i/Y4ozhek89IZ4cAVga+DbQU97ke+H5EvLI0F231vlsBfwHuLQ7tCtwKbAX8LCIurHL+7sAC4PyI2LKj19ZbsUTyYVJtg9mkugVHR8SDOePKSdJMYERE9No1vG19RiX9GHg+Is4qEsUBEXFKzji7SzvtcTqwICJ+kjO27lYUQVs3Iu6WtCrpR9WhwKfpvZ+P9trkSHrnZ0TAyhGxQFJf4A7gJOAwavAZ6VQCUE/FF+f+pKWAy5O+PK9rvTlQlfMbgX91JgFoaGiIxsbGpY7VzMxsWdLc3DyvvUJA1UoBXw5cDlwZEQvqEVxRW+AaSTcBm5GK+7xaj2s1NjbS1NQre6LNzKwXktRu9dtqE/lGAouBX0m6kbRJz1XF9rw1I+kg4PfAo6S9ADaQ9PmIuKYG7z0aGA0wdOjQrr5dhcYxV3X6tTPPOqim1663Mv9tZmZWfaLd3Ig4HBgG/BP4HDBH0rmS9q9hHD8F9oqIPSNiD2Av4Oe1eGPvBmhmZvZu1RKAAIiI+RHxl4g4ENgU+C9Qy1nucyNieqvHM4C5NXx/MzMza6XaEMC7xv0j4nlSd/3vu3pxSYcVd6dIupq0QVCQShBP6sT5FwF7Ag2SZgOnRcSfuhqXmZlZ2XWYAETE7nW+/sGt7j9DqgAI8CxQdV1jRBxdj6DMzMzKbqmr+UnaLyJu6MrFI+IzXTnfzMzMlk5Xyvn+CejStHpJv+ro+Yj4Ulfe38zMzNpWrQ7A+PaeAtaswfVbygnvCmwB/K14fAS1KTVsZmZmbajWA7Ab8AnePRlQwI5dvXhE/BlA0qdJywAXFo9/Tyo3bGZmZnVQLQG4C3g1Im5d8glJ02oYx3rAqsDzxeNVimNmZmZWB9VWARzQwXO1XCFwFnCPpAnF4z2A02v4/mZmZtbKe5oEKGk1YDgwIyJeqFUQEXGupGtIpYcBxkTE07V6fzMzM6vUYSVASRdIaijufxCYAvwImCzpiFoGUnzh3wQ8Abxey/c2MzOzStVKAW/dai/304DdImJfYHvg1K5eXNJvW93/APAgaV+A+yUd2NX3NzMzs7ZVSwCWK7r9Ie0K+DhAkRR0pYZAi51a3f8ucGhE7EWaA3BmDd7fzMzM2lDtS/wMYIKks4F/A3+X9A9gb+DaGseyWkTcDRARMyT1qfH7m5mZWaHaKoBLJN1N2gZ4k+L1OwMXRcR1Nbj+ZpLuI9UVaJQ0ICJekLQc0LcG729mZmZtqNqNX2zTe0qdrr95cbsiaeJfS8GhNYHv1emaZmZmvV6nxvEl9QeOA94H9G85HhGf7crFI2JW8f53AQcAm0sKYDbwJeDirry/mZmZta2zE/n+AjwEfJA0Oe/jwNQaxrEWcB/wKGk4YANgYQ3f38zMzFqptgqgxcYR8W3glaJ+/0HA+2sYxzrACRGxZ0TsAXwRlwI2MzOrm872ALT8Gn9R0pbA00BjDeN4CPi5pK8Xj9eltj0MZmZm1kpnE4CxkgaQiv+MJ23W852uXlzSYcXd/5ISijuKx7uQkgIzMzOrg04lABFxDoCkxcDZwP0RUYvteo8izf5/i9SrMIq0AuBFYGAN3t/MzMzaUG0vgImt7n8O+A3p1/9pksbU4PpbAF+IiM8Ac4FngB+Qfv1HDd7fzMzM2lCtB6B1MZ7RwH4R8ayknwB3kbbx7YrlIuLV4v6+wA4RsRi4QJJ3AzQzM6uTzuwFMEDSmoAi4lmAiHgFWFSD6z8hae/i/kxgCEBxPQ8BmJmZ1Um1HoDVgWbS2vyQtE5EPC1pleJYV+0CXC8JUpf/YZLeqtF7m5mZWTuq7QXQ2M5Ti4GP1OD6LwAbk37tt+w1MBuYBMyqwfubmZlZG5ZqS99i3P6xGlz/fGBYREwEphZbDw8n9TxcWIP3NzMzszZ0thJgvTQCMwAkfRCYAvwImAw0ZYvKzMys5JaqB6CGRkbEvOL+acBuETFTUgNwE/D3fKGZmZmVV+4EYGNJj5K6+1cAHgeIiHmScsdmZmZWWrm/ZGeSKgFuC2wIPC/pOqAPcG3GuMzMzEotdwLwAvAx4HjShkMNwGaknQAfyRiXmZlZqeVOABQRjwCnVBxMhQF2zxOSmZlZ+eVOAOZK+lUHz9/abZGYmZn1IrmXAV5IqjTYH9iO1O3/CLANaYdAMzMzq4PcPQBTgP8DNgd2BsYCO5KSgA9kjMvMzKzUcvcA/Bb4Mam08B3AHyJideC7wJY5AzMzMyuz3AlA34i4BvgWsDbwIUnnAb8A5lY7WdIoSdMkTZc0pq6RmpmZlUjuBOB1SfsDC4CngCeBK4CTgWc7OlFSH+Bs4ABgC+BoSVvUN1wzM7NyyD0H4Au8MwSwH/Al4ALSjoDHVTl3R2B6RLTsJXAxcAjwYN2iNTMzK4ncCcBvImI3SR8gFQH6EGkToI2BNaqcuz7wRKvHs4GRS75I0mhgdPFwgaRpXQ26ExqAea0P6EfdcNVMOvG3vas9ejm3RyW3RyW3RyW3R6X32h7D2nsidwKwfXH7K+Al4NCIuFvShsAlwNUdnKs2jsW7DkSMJa0u6DaSmiJiRHdesydze1Rye1Rye1Rye1Rye1SqZXvkTgBelzQTGET6Qj8vFQFEpF6AjswGhrR6PJg0h8DMzMyqyJ0A9AeeJk1GfAv4OPAyKQG4rsq5k4DhkjYA5gBHAcfUL1QzM7PyyJ0AbF7crgS8BjwZEW9KGgSc1tGJEbFI0omkRKEPMC4iptQ12s7r1iGHZYDbo5Lbo5Lbo5Lbo5Lbo1LN2kMR7xo273aS7gL2jYgFxeNVgOsjYpe8kZmZmZVT1h4ASasD/49U+39WMf4/F/gHsGq+yMzMzMotdyGgS4AXgPuA/SJiTWAv0pDAkI5ONDMzs6WXOwFojIgfAScAf5d0O/B34EDgxWonSxonaa6kB+obZue4NHElSTMl3S9psqSm3PHk0NZnVNJASTdIeqS4HZAzxu7UTnucLmlO8TmZLOnAnDF2F0lDJE2QNFXSFEknFcd78+ejvTbprZ+R/pImSrq3aI8ziuM1+YxknQMg6XrgRuDPwPPApsCawC7APhGxb5XzdyeVET4/IqpuHtTQ0BCNjY1dDdvMzGyZ0NzcPC8iBrX1XO5VAB8DxgC3kjYDClJZ4D8BR1Y7OSJuk9TY2Ys1NjbS1NQrf4iamVkvJGlWe8/lTgDOKG6vb3Xsk8CKwOmkvQG6pHUp4KFDh3b17So0jrmq06+dedZBNb12vS3N3+b2SJalv62sfxeU+2/r6dz2lXpqe+SeA/BJUnd/Q6t/y7e632URMTYiRkTEiEGD2uwFMTMz63VyJwBnAwtJScBOpC2BFwEjgFczxmVmZlZqWROAiPhWROxM+uKfXdy+SNokaHDG0MzMzEotdw9AiwHAfqRywHcAbwKN1U6SdBFwJ7CppNmSjqtnkGZmZmWRexJgi3+RNve5HHgEmEiqBtihiDi6znGZmZmVUk/pAdgTOJ5UFfDF4v7eGeMxMzMrtZ7SA9CfNPnvNtKOgM9I6pc5JjMzs9LKvRnQNsDvSeP944B5wGClXYFeyheZmZlZueUeAjgPOAnYGngd2IS0EdAbeDdAMzOzusk9BLB+RPxX0q4RsZOkVUj7E8yXND1zbGZmZqWVOwFA0lXA5pI+URwaIumTwLUZwzIzMyu13AnAjaTZ/gN55wt/ITAfGJIrKDMzs7LLmgBExNGS1gGuAz6cMxYzM7PeJOskQEkrkTYEugB4hlQP4NfACcBz+SIzMzMrt9xDABcA65Jm//+Q1P0/gxTX74Bj84VmZmZWXrmXAe4D/IG0DPBV4AfAocCzwAfzhWVmZlZuuROAfhFxXkTMBi4FPhwRjwCfAVbLG5qZmVl55R4CeFnSfqRZ/x8ANpT0JtCH/MmJmZlZaeX+kt0P+D5pC+CXgYeBFYGvANdnjMvMzKzUcvcANAIfAq4GdgeeAqaTEoEB+cIyMzMrt9wJwCXAINIEwDsBAU8C7wPWyhiXmZlZqeUeAlBEzCct9xtOqv53PSkxWJwzMDMzszLL3QOApB8Vd+cCu0XEGZJ2BL6QMSwzM7NSy50APAEcTxrvXwD8U9IbwFvAT3MGZmZmVma5hwDGABOB14CngUeBacWxXTPGZWZmVmq5NwO6FLhU0reB3wCLijkBZmZmVke5NwMaJul84GvAPGC6pBckXSCpb87YzMzMyiz3EMADwCURsTppY6C3gF8BuwCTcgZmZmZWZrkTgOUj4l/F/fcBT0fEaaTdATfLF5aZmVm55U4A3pA0RtJ6wErA7OL48qSiQGZmZlYHuROAI0krAaYA/YGtJI0jVQX8bc7AzMzMyiz3KoDrJa0J7E/q9l+e1AtwckS8mDM2MzOzMsu9CkDAR4FVSJP/JpPW/x8jKXfvhJmZWWnlrgT4B+ADwGDgr0CQqgOOBDYFTsoXmpmZWXnl/pV9FPBjYCtgIfBD4ABSNcCjM8ZlZmZWarkTgL4RcV5EzARuBw6KiEeATwOr5QzMzMyszHInAK9L2q+4fzbwfHF/LdJwgJmZmdVB7jkAJwHfl/R3UlXAzxbHVwC+my0qMzOzksueAETE9pLujogPtByMiFnADzLGZWZmVmq5E4CFks4F1pf0qyWe2zwi9mvrpBaSRgG/BPoA50TEWXWK08zMrFRyJwAfAvYF9gaal3ju4x2dKKkPad7AfqTiQZMkjY+IB+sRqJmZWZnkTgBuLm7fAL7a6rhIxYE6siMwPSJmAEi6GDgEcAJgZmZWRe5VAOsAdwDnAAcD1wLPAv/hnRUB7VmfVDSoxezimJmZmVWhiHyr7SQ9AdwKzAc2B+4HLiF16382IgZ3cO4RwAcj4vji8bHAjhHxxSVeNxoYXTzclFRkqN4agHndcJ1lhdujktujktujktujktuj0nttj2ERMaitJ3IPATwXEZ8o9gSYHRF7Fsdvl/ThKufOBoa0ejwYeHLJF0XEWGBsLYLtLElNETGiO6/Zk7k9Krk9Krk9Krk9Krk9KtWyPXIPASwnaQDwYWAVSY0AkoYBq1c5dxIwXNIGkvqRygqPr2ewZmZmZZG7B+CHwEPAQNIX+DmpM4DNgUUdnRgRiySdCFxHWgY4LiKm1DdcMzOzcsiaAETERZIuAZoj4jJJ/wC2AeaQvtirnX81cHV9o1wq3TrksAxwe1Rye1Rye1Rye1Rye1SqWXtknQQIIGkoaT3/DaQv8+1JScDmEXFovsjMzMzKK2sPgKQxwOdJ3f1rAz8jbQv8RnHfzMzM6iD3JMBjgS2AnUjj/utGxIqk9fxH5gxsaUgaJWmapOlFctOrSZop6X5JkyU15Y4nB0njJM2V9ECrYwMl3SDpkeJ2QM4Yu1M77XG6pDnF52SypANzxthdJA2RNEHSVElTJJ1UHO/Nn4/22qS3fkb6S5oo6d6iPc4ojtfkM5I7AXgrIl4DXgReA56TdHNEvNKZk9v6n0kurUoTH0BKao6WtEXeqHqEvSJim168jOc8YNQSx8YAN0XEcOCm4nFvcR7vbg+Anxefk22KuT29wSLgqxGxOelH0AnF/zN68+ejvTaB3vkZeQPYOyK2Jg2Nj5K0EzX6jOQuBPQ8qezvckAU/1YhFQZaLiLWqHL+7sAC4PyI2LLa9RoaGqKxsbGLUZuZmS0bmpub5/XUQkD/BtYALgdaVgD8Gfg9cH61kyPitpbaAZ3R2NhIU1Ov7Ik2M7NeSNKs9p7LnQCsRJrs9yvg0Yi4XNJPIuKbtbpA61LAQ4cOrdXbAtA45qpOv3bmWQfV9NqdtSzEuLQ6+7fV4u8qazuW9e+Ccv9t9o6l/e/sz0f+OQDrkjb9eQU4QtKtwMqStpO0XS0uEBFjI2JERIwYNKjNXhAzM7NeJ3cPwHdIkxfWB54CViRNCPwpaT7A3tkiMzMzK7HclQAvBS6V9G3gMWDDiDizKA60Ts7YzMzMyix3D0CL9YBdgB0lrQ7cDXwZ2KGjkyRdBOwJNEiaDZwWEX+qb6hmZmbLvp6SABwO3ENa0vcAqUDQBtVOioij6xyXmZlZKeWeBNhiZVIBneci4lzgk8UxMzMzq4OekgBMAa4F1pL0feBOoDlvSGZmZuWVezOgf5Jm+78KfAB4AjiCNCfgsYyhmZmZlVruOQA/aePYqqRSwGZmZlYnuROA3SLiewDFhg9XAn1J+wN8LGNcZmZmpZZ7DsDnWt3/NWkoYA3gZeB3OQIyMzPrDXInAK1r825H+tJfEzgNGJ4lIjMzs14g9xBAP0njSV3+qwJ/iYjFwBWScsdmZmZWWrl7AOYDC4E3gReAfgCS1i4em5mZWR3k/pX95Vb3W3oCKG7P6+5gzMzMeovcCcAlwG+B9wH9gTMlAfwH+GzGuMzMzEotdwIwFVgEPENa/gfwHKks8FzSvAAzMzOrsdxzANaJiI2Bp0iz/hcBvwQmAivmDMzMzKzMcvcALJZ0NLAJqSTw68Bfiuf2yRaVmZlZyeVOAH4C/JG089+zwGakYkBv4HLAZmZmdZN1CCAivkP69f81YCfg48BLwFvAmIyhmZmZlVru3QBHArNJewA8DexK2hFwMnBftsDMzMxKLvcQwK3Ai6Qv/42BxcWxg4FjgJWyRWZmZlZiuVcB9AO2jYhtgD6kROBcYANSXQAzMzOrg9wJQABHFfdfBE4GvgV8M1dAZmZmvUHuBGAacIak50nlf8cDGwKnkIYDzMzMrA5yzwE4mpSE7AFsT6oG+AjwN2DrjHGZmZmVWtYEICLuLe7eI2kAsCgiWtb/P5ApLDMzs9LLOgQgaUNJzZJeA+YBMyW9KunfktbIGZuZmVmZ5Z4DcCcwnTTzfwqpBsChpLgm5gvLzMys3HLPAVgtIj6mtAfwU8CciLhe0g3Aa5ljMzMzK63cPQCLJH0CWBd4DJjZ6jllicjMzKwXyJ0A/As4DLgeeBQ4sTi+HTAjV1BmZmZll3sVwNHtHG+WtEV3x2NmZtZb5J4DAICk/sCpwA5UlgDeI09EZmZm5dYjEgCgGVgZGAz8BxgJPJ41IjMzsxLLPQegxYbFv4URsXtxf0DekMzMzMqrpyQAb0XEYtKqgJHAG8BqmWMyMzMrrZ4yBDBJ0jDgD8AdLccyxmNmZlZqPaIHICL2iohZEfE1YDiwfUTsUu08SaMkTZM0XdKY+kdqZmZWDll7ACR9CbgCWBsYAiwCHomI+zpxbh/gbGA/YDapF2F8RDxYx5DNzMxKIfcQwA+AnwCvACuQuv9XlrQQODYinujg3B2B6RExA0DSxcAhgBMAMzOzKhQR+S4uvQo0AtsAx5E2ApoA3EcaBting3MPB0ZFxPHF42OBkRFx4hKvGw2MLh5uCkyr6R/RtgbS7oaWuD0quT0quT0quT0quT0qvdf2GBYRg9p6IncPgCJiLnC9pJtIE/9+CxxN9SJAbe0V8K5sJiLGAmO7Guh7IakpIkZ05zV7MrdHJbdHJbdHJbdHJbdHpVq2R+4E4FVJfwJuInXf3xIR4yXdSNoPoCOzSfMGWgwGnqxPmGZmZuWSexXAbqQqgLsANwJfL44HsH+VcycBwyVtIKkfcBQwvl6BmpmZlUnuzYAeBB6UtDawPrCVpCcj4hlgVpVzF0k6EbgO6AOMi4gpdQ+6c7p1yGEZ4Pao5Pao5Pao5Pao5PaoVLP2yD0JcB/gr0A/4H5Sl/56pFUBR0fE9dmCMzMzK7HcQwBXABcAPybV/v8DsAlwHvD3fGGZmZmVW+4egNcjon9x//GIGNrquTciYoVswZmZmZVY7h6A+ZKukvQx4GZJu0j6mKSrgPnVTpY0TtJcSQ/UP9TqXJq4kqSZku6XNFlSU+54cmjrMyppoKQbJD1S3PaanS/baY/TJc0pPieTJR2YM8buImmIpAmSpkqaIumk4nhv/ny01ya99TPSX9JESfcW7XFGcbwmn5HcPQBnAvcAHyRNAhRFWV/ggIg4vMr5uwMLgPMjYstq12toaIjGxsauhm1mZrZMaG5untdTCwE9FxFXSJobEf9e4rk/VTs5Im6T1NjZizU2NtLU1Ct/iJqZWS8kqd0VdbmHAP6nuP21pL6STpU0XtIPJK2UNTIzM7MSy90DsJakmcAgUhW/PsALpMJA/0NaGdAlrfcCGDp0aJVXd4/GMVd1+rUzzzqojpG0b1mIcWmU9e+Cpfvb3B7Jsva3dRe3YW301HbM3QMwC9gJmA7MBXYA9ga2B56uxQUiYmxEjIiIEYMGtTkMYmZm1uvkTgBWB3YGzgSWi4hHI2JWRMwC3swbmpmZWXnlHgKYDny4uN8s6a/A+4vjL1c7WdJFwJ5Ag6TZwGkRUXXyoJmZWW+XOwFoiIj9ASSdQ+r2/yZwGNW3AyYijq5veGZmZuWUewigtQ8A95G++J8Dts0bjpmZWXnl7gFYS9LJpK1/hwG/Bq4GDgD65wzMzMyszHL3APwRWJX0a/9HwLMR8RlgXzpRCtjMzMyWTtYegIhoqWt8UEScLulgSasBU+jEJEAzMzNbOrmHAJC0GfCUpPVJPQLNpPr+c7IGZmZmVmJZhwAkfQn4BxDAv4GngP2ATwENGUMzMzMrtdw9AJ8DRgCvAEOBy4CbgR+SdgY0MzOzOsg9CXANUtGfOcDWwGLgs6QSwavnC8vMzKzccvcArAEcCswA7iXtBfAo8DdSMSAzMzOrg9wJwCxgSkQ8LenxiJhWHP+opGkdnWhmZmZLL/cQwELSLoCQuv6RNFBSn+I5MzMzq4PcCcBvgAclTQEk6QagCXgCuDRrZGZmZiWWOwH4PHAEcDxwFXBGRGwIfIi0HNDMzMzqIPccgIERcb+kDUm1/2+UtDxpV8AFeUMzMzMrr9w9AOsVtxcCM4GfApsAtwAb5wnJzMys/HInAE9JWolU9GdRRHwrImYAp5FqApiZmVkd5B4C6A98hdTdv7GkRlJZ4I8Cz2eMy8zMrNRyJwArAV8lbQn8CnAr6Zd/X7wXgJmZWd3kHgI4jVQC+IGIWCMihkXEBhExGLg/c2xmZmallTsB+C7wX1L3/7ckDZL0A0nDSEMBZmZmVge5E4A3SAV/7if1BjwFjAEeAp7MGJeZmVmp5Z4DsAYwEPg9aR7AcNIWwQ8Cu+cLy8zMrNxyJwBTgXnAKODrETFH0ocjYgdJW2eOzczMrLRyJwBHRMTDkrYHLpB0Fe8MS/wTGJovNDMzs/LKnQCcKKnl/v3AQcDrkn5NGh4wMzOzOsidAHyGVAfgjeJxc6vnju7+cMzMzHqH3AnAFFIS8DzwJeDbwKHAw8Dr+cIyMzMrt9zLABcDPwEuAG4GrgUGkOoDTMsYl5mZWanlTgBWjIjLIuIiYGFEXAwMioh/khIBMzMzq4PcCUA/SQMlDQR+L2lNYKKkAcCKmWMzMzMrrdxzADYH7uadsr8nAOsD9wGr5QrKzMys7HL3AHyDVPb3w8UmQBsAsyNiSESsnjk2MzOz0sraAxARP5F0MfBLSWsAM4A1JY0rnv9szvjMzMzKKncPABExm7Qa4Fng2OL+YGB+zrjMzMzKLPccgBYbR8S2ku4nFQCaBlyXOSYzM7PSyt4DUFhY3L5Q3K4ObFntJEmjJE2TNF3SmLpFZ2ZmVjI9pQdgbLH079vAeGAVqsQmqQ9wNrAfMBuYJGl8RDxY72DNzMyWdVkTAEn3tXr4peJ2AfAKsEmV03cEpkfEjOK9LgYOAZwAmJmZVaGIqP6qel1cegb4NWn2/79JX+DbA48A/xsR63Vw7uHAqIg4vnh8LDAyIk5c4nWjgdHFw03pnhLDDcC8brjOssLtUcntUcntUcntUcntUem9tsewiBjU1hO5hwDmA3sBI4EDSF3/lwP7UH0zILVx7F3ZTESMBcZ2Lcz3RlJTRIzozmv2ZG6PSm6PSm6PSm6PSm6PSrVsj9wJwKsRsY+k5YE5wHoR8ZakC4B7q5w7GxjS6vFg4Mk6xWlmZlYquVcBLCepH9BI+vW/k6RVgBWAvlXOnQQMl7RB8R5HkSYQmpmZWRW5ewD+BbxI+rIXcHVx+xbwu45OjIhFkk4k1QvoA4yLiCl1jbbzunXIYRng9qjk9qjk9qjk9qjk9qhUs/bIPQnwHtLs/0dJXfhfJs0B2BbYNCIOzxacmZlZieVOAF6NiJUk3VTMBbg7IrYrnnswIrbIFpyZmVmJ5R4CeFPSTcCWkiYACyWdTJqb0JA3NDMzs/LKPQlwX9L4/erF7Y3AqsBA4B/VTpY0TtJcSQ/UNcpOcmniSpJmSrpf0mRJTbnjyaGtz6ikgZJukPRIcTsgZ4zdqZ32OF3SnOJzMlnSgTlj7C6ShkiaIGmqpCmSTiqO9+bPR3tt0ls/I/0lTZR0b9EeZxTHa/IZyToE8HYQ0gERcc1SnLc7qXLg+RFRde+AhoaGaGxsXIoIzczMlj3Nzc3zemQhIEkrAd8B3ifpIGBjYF1gAnBqRCzo6PyIuE1SY2ev19jYSFNTr/whamZmvZCkWe09l3sOwB3AmsDTwG7AE8A9wCeALYD9u3qB1qWAhw4d2tW3q4nGMVd1+rUzzzpombnWsmBp28PtWBudbcdcbej/znktC+3f0z/D70XuBGBzUgGgxcBTwHMR8UlJQ6lRzf7WpYBHjBiRf7zDzMysB8g9CRBg+UgTEVomABIRj+cNyczMrNxy9wBMBpol/QcYAfwIQNIOwJsZ4zIzMyu1rD0AEbEz8DHgeuDQiDi3OD4JWKPa+ZIuAu4ENpU0W9JxdQzXzMysNHKvAhgKPBoRU5ScAuxB2ujnu8Cijs6PiKO7IUwzM7PSyT0H4EGgZX3iP4FTgRWBLwK35wrKzMys7HLPAVg+Ip4o7u8FDIuIeUV9gOcyxmVmZlZquXsA3pR0ZHH/FVIJYICVSdsCm5mZWR3kTgCOA84tKhW9AjwoaQZpe+DzcgZmZmZWZlmHACLi75KuB04GtgGmAjOAsyNias7YzMzMyiz3HAAi4iXgtNxxmJmZ9SbZEwAASRNJsWwC9CNtDRwR0SPiMzMzK5vccwBabAkcCTxCWgZ4PDAva0RmZmYl1lMSgCCV/u0TEW8BN5M2CTIzM7M66Cld7DNIWwMvlDQFWBeYmzckMzOz8uopCcCHSGV/dwEOBW4FfpYzIDMzszLLOgQgaRRARMwiVQL8DrA/aU7A/IyhmZmZlVruOQC/AZD0W+DHwGrAQmBrYGLGuMzMzEotdwKwXnG7B7AC8BdgZ+AMYHCuoMzMzMoudwKwvKSTgbeAVYFvF8MBl5FWBpiZmVkd5J4EKOAUoG8Ryx2S3gRG4gTAzMysbnInAH9Z4vGVwEukXQG/0O3RmJmZ9RK5E4ABwP3Ai8XjjVs9d123R2NmZtZL5E4ADgEOIm0FvBC4FHgNOBi4LWNcZmZmpZZ7EuCbwFdJcwBWBj5NKgb0U2BYvrDMzMzKLXcCsDgifg3MJq39PxNYi5QA7JMzMDMzszLLnQC0+Atp6V9/4HzgCeCWnAGZmZmVWe45AP8CiIjvS7oG2K04fkxE3JMvLDMzs3LLnQBsBCDppojYB7g7czxmZma9Qu4EYIikc4BNioqAFSLCOwKamZnVQe4EYHlgFLAOcBipJsDTWSMyMzPrBXJPApwVEYOBz5MK/+wOHF08d1G2qMzMzEoudwLQUu//X0Aj8DhwJLA+cHummMzMzEovdwKg4vY8Ug/AehFxH/C/wNxcQZmZmZVd7gSgZdlfQ0RcAiwGiIhFpC2CzczMrA6yTgKMiAXF3eUkHQusJmkzYA3SroBmZmZWB1kTAEl7kMr+LgLOJc0JmFjEdUjG0MzMzEot9xDAL4ADImIksBlwK2kzoMOAr2eMy8zMrNRyJwD9gfnF/ceAjUlLAjcgrQQwMzOzOsidAKwD/F7SMcADpO2B/wvsRCcSAEmjJE2TNF3SmPqGamZmVh65KwE+RRrz3xlYCdgoIhZJugzYoaMTJfUBzgb2I20nPEnS+Ih4sM4xm5mZLfNy9wCsDTwEvAo08c6v/pWAhVXO3RGYHhEzIuJN4GI8cdDMzKxTFBHVX1Wvi0sPAwtIEwAnAduSEoLVgJ9HxB86OPdwYFREHF88PhYYGREnLvG60cDo4uGmwLRa/x1taADmdcN1lhVuj0puj0puj0puj0puj0rvtT2GRcSgtp7IPQTwTeA40hfzQFIp4OVIQwMfA9pNAHinimBr78pmImIsMLbLkb4HkpoiYkR3XrMnc3tUcntUcntUcntUcntUqmV75C4EdClwqaRvk8bzIyJe6OTps4EhrR4PBp6scYhmZmallHUOgKShki4GvkSa/T9J0lxJF0vap8rpk4DhkjaQ1A84Chhf55DNzMxKIfcQwN9IxYA+HhFvwduz+48ArgZWaO/EYrXAiaRNhPoA4yJiSt0j7pxuHXJYBrg9Krk9Krk9Krk9Krk9KtWsPXJPAnwROL+tp4D/jYg+3RuRmZlZ75B7GeDKwFbAC8Cjxb8XgC1I+wOYmZlZHeQeAriDtP5/JKkGgIAngMuADTPGZWZmVmq5ewA+CpwWEaMi4v0RsWVEHBARv42IDaqdLGlcMWnwgW6ItSqXJq4kaaak+yVNltSUO54c2vqMShoo6QZJjxS3A3LG2J3aaY/TJc0pPieTJR2YM8buImmIpAmSpkqaIumk4nhv/ny01ya99TPSX9JESfcW7XFGcbwmn5GscwAAJH2QtITvxoiY1er4ZyNiXJVzdycVEjo/Irasb6QdKyYvPkyr0sTA0b25NLGkmcCIiOi1RTza+oxK+jHwfEScVSSKAyLilJxxdpd22uN0YEFE/CRnbN1N0rrAuhFxt6RVgWbgUODT9N7PR3ttciS98zMiYOWIWCCpL6nX/CTSjrld/ozkngT4U9KM/1WA1YHXScWA/kHaJnjrTrxHI/CvziQADQ0N0djY2JWQzczMlhnNzc3zemolwNHAD4BzSV/+F5LmALxA2hK4phobG2lq6pU90WZm1gtJmtXec7kTgH4R8cOWB5IOJq1xHEEHNQDei9Z7AQwdOrQWb9lljWOu6vRrZ551UB0jsXpa2v/OZf58dPZv6y3tYZZT7kmAL0v6naS1AYpiQN8k7QvQrxYXiIixETEiIkYMGtRmL4iZmVmvkzsB2Iq0FfCtkp6X9DxwC2lpYNZJfWZmZmWWNQGIiKci4qsRsVlEDCz+bV7MZnyr2vmSLgLuBDaVNFvScXUP2szMrASyzgFotYnPnIi4SdIxwC7AVOAUoMNB+4g4uv5RmpmZlU/uSYD3kXohlpf0JtAXmA7sA6yTMzAzM7Myy50ADAf+B1hI2hXwJKClMMGfMsVkZmZWerknAb4GPARcSYplfET8mbRN8OKMcZmZmZVa7h6AHwJ/Jn35fwv4u6QZwE7A93MGZmZmVmZZE4CI+L6kc4uH80h1n0cC50bEf/JFZmZmVm65hwCIiCeBbUl7AJxHmhNwmaQDcsZlZmZWZtkTgMJ5wEukvQAuBF4Ezs8Yj5mZWanlngPQYmVgSES8DiDpLOC5vCGZmZmVV+5CQIcVd+cB4yVdSFoGeDTwVLbAzMzMSi53D8D3itsAduWdLYDXA57JEpGZmVkvkDsBeIP05f86lV3+0/KEY2Zm1jvkngT4ZeArwP3A+sCawGnA08D8fGGZmZmVW+4EYMWIuBU4hLQp0CqkAkAvAjtkjMvMzKzUcicAPyhuVwC+QNoT4GDg0eKYmZmZ1UHuOQAtHgAOI80F+DOwRXHfzMzM6iB3D8Bakk4Grif9+l8e2Bx4hFQZ0MzMzOogdw/AHcCOxf3xwATS5L/+wCdzBWVmZlZ2uROAlnX/AWxaPF4TWJfUK2BmZmZ1kDsB2BZ4C+hDmgcwD/gN8CHSlsBmZmZWB7kTgLdI+wDMII39DyElAxeTlgaamZlZHeSeBAgpCbkB6AucCTwE/BDolzMoMzOzMsvdA/Ac6Qu/D2lL4K+TtgV+k1QQyMzMzOogdwKwN7AAICKelLQGsC/weERMzBmYmZlZmeUeAngN+BnwN0nfBF6JiEsjYqKkK/OGZmZmVl65ewAuAdYGVgIGAIdIOjAingP2yxqZmZlZieXuAXg/8L/AZqQ5AOsDd0raCFDGuMzMzEotdwLQB7glIl6MiJ8Ax5J6A24hrQowMzOzOsidADxPmggIQERMAA4kVQaMXEGZmZmVXe4E4OukJOBtEXEf8AHSroBmZmZWB1knAUbEhe0cf1zSE90dj5mZWW+RNQGQ9E/a7+rfl1QZ0MzMzGos9zLAlvH/5UnDEQuLx31JEwTNzMysDnLPAXgO2BD4b0T0jYiVin99SeWAzczMrA5yJwDnA8OAQZI2bDkoaQOKEsFmZmZWe7knAZ4KIOkrwC2SZhRPNQIfzxWXmZlZ2eWeAwBARFwraQfSxL9+wOO8Mx+gXZJGAb8kzRc4JyLOqmugZmZmJdEjEgBJxwPfANYjfflvDEwHtujgnD7A2aQ9A2YDkySNj4gH6x+xmZnZsi33HIAW3weeAlYA/gu8BKxa5ZwdgekRMSMi3gQuBg6pa5RmZmYloYj8FXclvQqsArwCrFH8ezgiVu/gnMOBURFxfPH4WGBkRJy4xOtGA6OLh5sC02odfxsagHndcJ1lhdujktujktujktujktuj0nttj2ERMaitJ3rEEADwOrAa8AJwM+mPq9Y70dZuge/KZiJiLDC2qwG+F5KaImJEd16zJ3N7VHJ7VHJ7VHJ7VHJ7VKple/SUIYCLi9szScsCtwQur3LObGBIq8eDgSdrH5qZmVn55C4FvEaxFfD/Fod+L+laYLViU6COTAKGFzUD5gBHAcfUMVwzM7PSyD0EME/SLcBdwEOkOQBPAhOrnRgRiySdCFxHWgY4LiKm1DHW96JbhxyWAW6PSm6PSm6PSm6PSm6PSjVrj6yTACU9Rpr5DzAQeIY0D2BV4H8i4vpcsZmZmZVZ7gTgNWDziJgpaUXgYFJX/l7Ach2tAjAzM7OllzsBeB1YJSIWLXG8AZgSEWvniczMzKzccq8CuJlUwe8USccU/8YANwC/qHaypHGS5kp6oN6BdoakUZKmSZpe/B29mqSZku6XNFlSU+54cmjrMyppoKQbJD1S3A7IGWN3aqc9Tpc0p/icTJZ0YM4Yu4ukIZImSJoqaYqkk4rjvfnz0V6b9NbPSH9JEyXdW7THGcXxmnxGshcCkrQ5qYLf+qS1/bOBTpX0lbQ7adfA8yNiy2qvb2hoiMbGxq4FbGZmtoxobm6e1yMLAUlaiTTu/xbwddL4/1HAQElnRkSHWwJHxG2SGjt7vcbGRpqaeuUPUTMz64UkzWrvudzLAC8kreHvT1rO9zCwdXH7J+BjXb1A61LAQ4cO7erb1UTjmKs6/dqZZx1Ux0jaV6YYc8XX3Zbmv9my8N+5zHr6Z9ifj3dbmv9mPbUdcycAhwAzi/tDgKGkOscHk3YG7LLWpYBHjBiRf+MDMzOzHiD3JMAnSZvzfJg0jr8BMLu4fShrZGZmZiWWOwG4BvgS8B3gRUmrAiFpI2B+1sjMzMxKLGsCEBHHR8TDEXEEMIG0/G+liHgU2K3a+ZIuAu4ENpU0W9Jx9Y3YzMysHHLPAWjtMeDXwFBJnyyOnd/RCRFxdN2jMjMzK6EekQBIuo1UA2AT4Grgy8DzVEkAzMzMbOn0iAQA2AFYGbgnIj4jaW3S5EAzMzOrg9yTAFsE0BdYJGk14GVSbQAzMzOrg57SAzAZuAWYAjxC6g24P2M8ZmZmpdYjEoCI2EXSKGBfYDzQHBG/zxyWmZlZafWIBEDSR4CbI+LaYivgRZIOjYgrM4dmZmZWSrk3AzoA+C0wCNhN0gWksf8VgNeAK/NFZ2ZmVl65JwH+EDgQeBq4ETguIjYC9gMG5wzMzMyszHInAIsjYipwG9APeFbShqTd+17LGpmZmVmJ5Z4D8KKkzwPTSV/4E4AXgMfxZkBmZmZ1k7sH4FPAdqQKgB8HLiH1BCwHuK6/mZlZneTuATgPGAYs4p0xfwFrAitmisnMzKz0cvcA7Ebq7l8LGAAsBO4CniVNCjQzM7M6yN0DsDgi9pZ0BemX/37AW8B80lCAmZmZ1UHuHoCXJV0FbAP8GHiKtBPgC+RPTszMzEordwLwEHA2qfb//yMNA3wQ+B7wZMa4zMzMSi3rr+yI2B1A0jeBy4D3kyYG7gV8J19kZmZm5Za1B0DSppJuBfYkTQA8HVgdmI0LAZmZmdVN7iGA20jd/9uTvvQHAWeSvvz/lDEuMzOzUsudADSQxvqnk375PwbsAzwP9MkYl5mZWanlnmm/HHANafe/DwE3A/cAGxSPzczMrA5yJwCPA78GAvgl8E1SMrAyMDdjXGZmZqWWexXAMEnrFfeflHQ68BFgakTclTM2MzOzMsuaAEg6rNX9lUlVAF8C1pN0WERcni04MzOzEss9BHAkMIK0IdDyvLP0T8C1gBMAMzOzOsi9CmAt4HjgOtL2v38gLQX8A6k8sJmZmdVB7gRgzYi4BWiMiHHA7hHxCnAysF7WyMzMzEosdwLwrKRPABMlTQVC0qeAq4BX84ZmZmZWXrkTgM8CHwZ2JC37u5vU9X8haWjAzMzM6iD3MsDHSRMBzczMrBvlXgY4n1QEaDmgP2n2/yvF4+Ujon/G8MzMzEor6xBARKwaEasB84CfAg8Ag4GvAy9mDM3MzKzUcs8BaNEQEacAiyPi5Yj4HbBq7qDMzMzKKnchoBYLJX2FtApgOeDnwJuZYzIzMyutnpIAfJhU9a8BWEjq/q+6G6CkUaRNhPoA50TEWXWM0czMrDR6RAIQEbcDg4r9AJaLiPnVzpHUBzgb2A+YDUySND4iHqxvtGZmZsu+7HMAJB0n6U5JM4D7gOslHdeJU3cEpkfEjIh4E7gYOKSesZqZmZVF7mWAdwBbAf8CbigObwz8XNJnI2LXDk5fH3ii1ePZwMi6BGpmZlYyioh8F5feBFaOiIVLHO8HLIiIfh2cewTwwYg4vnh8LLBjRHxxideNBkYXDzcFptXwT2hPA2lpoyVuj0puj0puj0puj0puj0rvtT2GRcSgtp7IPQdgMXAAMH6J46NIBYI6MhsY0urxYODJJV8UEWOBsV2I8T2T1BQRI7rzmj2Z26OS26OS26OS26OS26NSLdsjdwLwNeASSYt4p/DPGqS4vlrl3EnAcEkbAHOAo4Bj6hOmmZlZueTeC+A3kn5L+sX/PlIp4AeA6yLirSrnLpJ0InAdaRnguIiYUu+YzczMyiD3JMDtSF/eTxf/VgQagd0kvRwRd3d0fkRcDVxd7ziXQrcOOSwD3B6V3B6V3B6V3B6V3B6VatYeuScBTiXN+l9ESkYWA2+QygBPi4jNsgVnZmZWYrnrACwC1gVuJH3xb1FsDrQnsFbGuMzMzEotdwLwVkTMI83mfzkiHgWIiFtJwwHLFEmjJE2TNF3SmNzx5CZppqT7JU2W1JQ7nhwkjZM0V9IDrY4NlHSDpEeK2wE5Y+xO7bTH6ZLmFJ+TyZIOzBljd5E0RNIESVMlTZF0UnG8N38+2muT3voZ6S9poqR7i/Y4ozhek89I7iGA8cAU4KPAKsDtwG3AZ4H1ImLdKuePI+0ZMDcitqxzuB0qShM/TKvSxMDRvbk0saSZwIgiyeuVJO0OLADOb/mMSvox8HxEnFUkigOK3TBLr532OJ1U9+MnOWPrbpLWBdaNiLslrQo0A4cCn6b3fj7aa5Mj6Z2fEZFq5SyQ1Be4AzgJOIwafEZyJwCrASeQJgIuJP1RQ4FHgU9ExMwq57/rfyYdaWhoiMbGxi5GbWZmtmxobm6e1yMLAUXEy8APASStCFwZEZ2u1BcRt0lq7OzrGxsbaWrqlT3RZmbWC0ma1d5zuZcBbgicCqwMbAusI+lxUkU/RcR+NbjG26WAhw4d2tW3q9A45qpOv3bmWQfV9Nr1tiz8bZ2NsRbxLQvtsSzozv9mS2NZ+O+8LMRolXrqf7PckwDPI42V70paETCfVBr4b8DOtbhARIyNiBERMWLQoDZ7QczMzHqd3AnAqhHxO1Ip31VIk/meiIg/keYFmJmZWR3kTgAWS9qEVAVwALCKpOGSzgdezRuamZlZeeVOAL4B/JO0Te/lwJrA/aRlgZ+vdrKki4A7gU0lzZZ0XB1jNTMzK43cqwBuIn35v01SA/BCtc2AivOPrldsZmZmZZZ7FcBKwMnAJqRf/luR9ga4XdKZEbEgZ3xmZmZllTUBIO0BsCHwPHA48DjwBKkS1ubAwdkiMzMzK7HcCcC2pHH/10gTAWdFxAclbUUa2zczM7M6yD0JEOC1SPWIr6XYATAi7qNnxGZmZlZKuXsAHgZukHQzaSjg7wCStgMW5wzMzMyszHKvAti62NZxC+DMiLiheGoyMDBbYGZmZiWXvZs9Iq4utnhsbtnTOCIWA7vnjczMzKy8ci8DHAr8GNgbeCkd0mrAzcBuwPoZwzMzMyut3HMAJgMzgLuWOD4cWKfbozEzM+slcicAqwOnAUsW/BGpRoCZmZnVQe45AM8AnwNeBx4p/r0OHElKAszMzKwOcvcA/BwYCfyZ1BsA8CLwIGlOgJmZmdVB7gTgu8D/kfYBWNI+3RyLmZlZr5E7AZgNXAnMB8YB2wCLgJlAn1xBmZmZlV3uOQBvALOAP5LG/p8nFQA6g/yxmZmZlVbuHoA3ImJesS1wX+C5ogjQFZI8CdDMzKxOcicAgyX9ClibtCvgomJfgEFAv6yRmZmZlVjubvavA82k7YBfBFYF3k9KCO7LF5aZmVm55U4ALgICUESsT5oUOJ40B2DJ4kBmZmZWI7mHAM4tYhgk6S/ACsC+pCGAbXMGZmZmVma5E4DDSNX/XgSOAh4DNiYlAF4GaGZmVie5hwAWAg8AHyMtCTwCmEOqB/B4vrDMzMzKLXcCcC9wCPAfUvGffwFrFY+fyxeWmZlZueUeAjgHWAPYCdidNBQwEHgCuDNbVGZmZiWXNQGIiD8DSDoUaAS2AHYG/gRcny0wMzOzkss9BNBiPWAwaRXAg8B+wEZZIzIzMyux3EMALf4DTCPVBHieVAjooawRmZmZlVhP6QEYAQwnrQD4Amk+QFtbBJuZmVkNZE8Aik1/+gDbAyLN/v83sGnOuMzMzMosawIgaX9SIaCBwKHAC6RSwPOBN/NFZmZmVm655wD8Etg3Ima2HJC0B2k44Ku5gjIzMyu73EMAy5M2AHpbRNwKnA/0zRKRmZlZL5C7B+Aq4CVJC0nbAl8MrEkqDbxCzsDMzMzKLHcPwAjgy8AvSF/4Z5HmA3wcmJctKjMzs5LLnQCsEhF/jIjTgb2Bb5B+/Q8k1QTokKRRkqZJmi5pTH1DNTMzK4/cQwCStDqwK/AH4FHgWWAC8FqVE/sAZ5OqBs4GJkkaHxEP1jdkMzOzZV/uHoAfAZsDPwP2iog9I2IEsBewuMq5OwLTI2JGRLxJmj9wSF2jNTMzKwlFVO1pr38Q0m0RsXurxwJubX2sjXMOB0ZFxPHF42OBkRFx4hKvGw2MLh5uSio5XG8NeA5Da26PSm6PSm6PSm6PSm6PSu+1PYZFxKC2nsg6BCDpK8AsYIqka4BXgQ2AVYGrq53exrF3ZTMRMRYY28VQ3xNJTUVPhuH2WJLbo5Lbo5Lbo5Lbo1It2yP3EMCpwMFAf2AIsBXwNKkK4EFVzp1dnNNiMPBkHWI0MzMrndyTAB+PiM8ASJoMbBsRC4shgHurnDsJGC5pA9ImQkcBx9QzWDMzs7LInQCsLukjpJ6IFSJiIUBEhKS1OjoxIhZJOhG4jrSZ0LiImFL3iDunW4cclgFuj0puj0puj0puj0puj0o1a4+skwAlnbvEoTER8YykdYCZEdE/R1xmZmZllzsBeLm9p4AVIyJ3D4WZmVkp5f6CfZFUAXB7YH3SLP4nSd369+cLy8zMrNxyrwK4B7gF2BNYCViZVASomeqTAJE0TtJcSQ/UMcZOc2niSpJmSrpf0mRJTbnjyaGtz6ikgZJukPRIcTsgZ4zdqZ32OF3SnOJzMlnSgTlj7C6ShkiaIGmqpCmSTiqO9+bPR3tt0ls/I/0lTZR0b9EeZxTHa/IZyT0EMI1UvOfFJY4PAP4bEZtUOX93YAFwfkRsWe16DQ0N0djYuPQBm5mZLUOam5vn9chCQKQdANvKQBbTdqGfChFxm6TGzl6ssbGRpqZe+UPUzMx6IUmz2nsudwIwFJgn6V7gdtJGQENJG/x8txYXaF0KeOjQobV4y7c1jrmq06+deVa1ukY9S1n/trL+XbB0f5vbI+lqe5S5HZdGd7bHstD2PTXG3HMA7gX2AJ4AjgVOBFYDPhIR59XiAhExNiJGRMSIQYPa7AUxMzPrdXL3AERE/Af4iKRhwIeAjYCrJc2OiJ3yhmdmZlZOuRMAAUj6HKmbfmBEbCTpd6Ttfc3MzKwOcg8B/F9xewKwK/AyQEQ8AvSrdrKki4A7gU0lzZZ0XL0CNTMzK5OsPQARcWFx942IeDPtAQSSlqft1QFLnn90HcMzMzMrrawJgKT7SV/060h6ChggaQawJvB6ztjMzMzKLPccgA8VtycAW5HmBAiYRuraNzMzszrIPQQwC0DSrhGxa3F/rYiYK+nfwJk54zMzMyurrJMAi3rGA4HVJB1Q3J8o6YOkegBmZmZWB7mHAOYBs0gz/q8kJSTLA+NJVQHNzMysDnIvA/wGabx/VESsQJr8NysiVoiIwXlDMzMzK6/ccwB+Iuli4OeSngCuAVaVtF9E3JAzNjMzszLLPQdgYkTMjogjgL6kYYB+wOmSxuSMzczMrMxyDwH0bXV/J2AzYGdgf+DjWSIyMzPrBXJPAuwj6QvAM6T1/7sBuwBTgUU5AzMzMyuz3AnAhsBPSD0RbwGfAC4CPggMyxiXmZlZqeVOAF4DDgX+C8wBDo6ItyRdBjTnDMzMzKzMcs8BGAD8CnisuP+B4vhiiq2CzczMrPZy9wA8TfriXw64AbhK0iLgDWDFnIGZmZmVWe4EoA9wfnH/ZeDfwEhgPWC7XEGZmZmVXe4hgHVIScAngVVJywLvBv4FzM8Yl5mZWanl7gF4BRhE+uJf8gs/uj8cMzOz3iF3AvB1Ui/A8qQeAEhf/AL+mCsoMzOzssudAPyGtCPgBODvwLakQkAPAj/IGJeZmVmp5Z4D8BJpzP9gUi2As4EGUingmfnCMjMzK7fcCcCKEXEA8DDwKjCKVBBoO9LQgJmZmdVB7gQASf1IY/4rAw9HxMnAPngvADMzs7rJPQfgUuAh0pf/m8AUSQtI+wBcnDMwMzOzMsuaAETEsZLWKx6+Sfrlvw1wc0TckC0wMzOzksuaAEgaFRHXShJwPPAFUk2AzSX1iYhrc8ZnZmZWVrnnALQs9fst8BVS1//BwETStsBmZmZWB7kTgBYjSUMAT0TErIj4AdA/c0xmZmallTsBWEvSycCawGoU5X8lDcoalZmZWcnlTgD+SCoBPInUA7COpO8Dd5KGAczMzKwOcq8COKPlvqTNSKsABBwcEVOzBWZmZlZyuXsAAJC0NmljoA8BF6ZDOi5vVGZmZuWVNQGQdGpx9zzgAWBvoBm4Gvh/mcIyMzMrvdw9AIcVtw3AvqRVABsARwLrZovKzMys5HInAC1eAYYALxePlwMW5wvHzMys3HInABtKGl/EsTmwkaR/A+cDT1c7WdIoSdMkTZc0ps6xmpmZlUbuzYAOaXW/D6kH4HXgBeDQjk6U1Ac4G9gPmA1MkjQ+Ih6sT6hmZmblkbUHICJuJU36GwRsDewMDAaeioizq5y+IzA9ImZExJuk3QMPqXKOmZmZkX8zoK8BHwPuBfYC5pMmBPaTdFBETOrg9PWBJ1o9nk0qKWxmZmZVKCLyXVx6DVgzIl6VdDap2/8Y0s6AH4yItTo494jiNccXj48FdoyILy7xutHA6OLhpsC0mv8h79YAzOuG6ywr3B6V3B6V3B6V3B6V3B6V3mt7DIuINsvr554DAPBacfsBYG5E3CrptlbH2zObtHKgxWDgySVfFBFjgbG1CLSzJDVFxIjuvGZP5vao5Pao5Pao5Pao5PaoVMv2yL0K4A3gbkl/BYYDfy+OD+jEuZOA4ZI2kNQPOAoYX58wzczMyiV3D8AVpF/uw4HbgT8Vx1cA/tPRiRGxSNKJwHWkFQTjImJKHWM1MzMrjdybAX2m5b6kgRTbAUfEU6SywNXOv5pUNrin6dYhh2WA26OS26OS26OS26OS26NSzdoj9yTAocCPSV/2LwF9Sd3/dwAnRMTMbMGZmZmVWO45AJNJwwDrAl8j9QBcAWwL3JQvLDMzs3LLnQCsFBF/i4i3gFOAvSPi08BWpLkByxSXJq4kaaak+yVNltSUO54cJI2TNFfSA62ODZR0g6RHitvOTHothXba43RJc4rPyWRJB+aMsbtIGiJpgqSpkqZIOqk43ps/H+21SW/9jPSXNFHSvUV7nFEcr8lnJHcC8Kqk30oaCawIvFHcPxN4tdrJbf3PJJdWpYkPALYAjpa0Rd6oeoS9ImKbXryM5zxg1BLHxgA3RcRwUk9Xb0oWz+Pd7QHw8+Jzsk0xt6c3WAR8NSI2B3YCTij+n9GbPx/ttQn0zs/IG6QfxlsD2wCjJO1EjT4juecAvAW8SRr7Xw54GJgJXAWMjoj3Vzl/d2ABcH5EbFnteg0NDdHY2NjFqM3MzJYNzc3N83pkIaCI6NPWcUlrAFW7jCPiNkmNnb1eY2MjTU29sifazMx6IUmz2nsudx0AACRtCPyS1OUTwJ3AV2r03m+XAh46dGgt3vJtjWOu6vRrZ551UE2vXW/Lwt/W2Rh7enyw7H0+llZ3/jfrzvb3f+tKbvtKPTXGrHMAJD0v6RzgX8AlpNUA65EqAl5Ui2tExNiIGBERIwYNarMXxMzMrNfJPQnwWdJSwCHAWcBPgRERcQFFUSAzMzOrvdxDAK8DF5KW/LV84Z8jaV0g+8x+MzOzssqdAGxGmuynJY6/BnS4AgBA0kXAnkCDpNnAaRHxp47PMjMzs9wJwNkRcfLSnhwRR9cyGDMzs94i6xyAiDhZ0u6SNgWQ9AFJX+stVZ7MzMxyydoDIOl3wEHAW5IuAD5JmhewgqR/RsQXc8ZnZmZWVrlXAXyCtNzvRlIpw6uB44ELgE9ljMvMzKzUcs8BWD4iTpHUHzgOODkiXpP0H9LugGZmZlYHuROA+ZLuBFYCFgL/lDQB2JdObAZkZmZmSyf3EMBJwKakQkDHkHYE/BSwNfD/MsZlZmZWark3A7pI0iWkOgCrAbcCjcCciHgqZ2xmZmZllnsvgKHAX4G5pMp/9wPXAz9/L7v8mZmZ2XuTew7AtcBA4L/AnOLYYFJ1v2uAzfOEZWZmVm65E4CNgU0iYmbrg5I2AKZlicjMzKwXyJ0ALAROkXQe8ERxbAjwGeCNXEGZmZmVXe4EYEVS4Z8jgDeBt4BXgJWBH2WMy8zMrNRyLwO8F/gocB+pFsCKwGPAZyLiezkDMzMzK7PcCUCQuvr/Slr7fyAwFThX0vScgZmZmZVZ7iGAwcC5wLPAqaQv/8HAncD2GeMyMzMrtdw9AKuSxvovIo39bwhMBG4H1swYl5mZWanlTgAUEb+MiLNIlQDfR6oKuDuwQtbIzMzMSiz3EMAiSXcDDwJXkgoD7UjqDeifMS4zM7NSy90D8DJpCOAq4CPAHyJideAU4MWMcZmZmZVa7gTgGaAJGAk0AB+XdBowHpiZMS4zM7NSyz0EsA6pBsDLpC7/XYERwBjgyYxxmZmZlVruHoABpFn/k4GDgFVIKwOeIyUHZmZmVge5ewACGBURb0haEyAi1gCQ9HrOwMzMzMosdwLwDPCCpNnAUGCmpJOBNcjfO2FmZlZaub9k/wxMInX9vwJcDawPHA1cnjEuMzOzUsvaAxARpwFIuh74aETMLx6fDvw9Y2hmZmallrUHQNKo4u5QoL+kP0m6D/gjsFG+yMzMzMot9xDAD4rbvwAPAcOBm4DdgDdzBWVmZlZ2uRMAACLi+8A84DLgMeBDwMKsQZmZmZVY7lUAaxWz/gWsXByL4nGPSE7MzMzKKHcC8EdS4Z89gL6kFQCvkYYEPARgZmZWJ7lXAZwBIGkqMCwiXi8enwXcnTM2MzOzMsvdA9BiJmkVwAjSdsCPAY9mjcjMzKzEsiYAkp4B/ga8QfrSD2AWsAVpVYCZmZnVQe6Jdq8DzcAVwAvAd4BfACcCA6udLGmUpGmSpksaU89AzczMyiT3EMCLwHhSIvLFiPhNyxOS/rejEyX1Ac4G9gNmA5MkjY+IB+sXrpmZWTnkTgBWJ/UACAhJ60TE05JWoXolwB2B6RExA0DSxcAhgBMAMzOzKhQR+S4ubdfOUysCV0TEWh2cezhpK+Hji8fHAiMj4sQlXjcaGF083BSY1uXAq2sgFTayxO1Rye1Rye1Rye1Rye1R6b22x7CIGNTWE7l7ACYBtwL9gBWKY2+QqgCuWuVctXHsXdlMRIwFxnYhxvdMUlNEjOjOa/Zkbo9Kbo9Kbo9Kbo9Kbo9KtWyP3JMAHyNN9lsTeKn410DqAXipyrmzgSGtHg8GnqxDjGZmZqWTuwegD/D9iKjY+lfSTsBFVc6dBAyXtAEwBzgKOKYuUZqZmZVM7gRg0ZJf/gARcZektzo6MSIWSToRuI6USIyLiCl1ivO96tYhh2WA26OS26OS26OS26OS26NSzdoj9yTAX5Fm+78J/A5YQOrW/ywwMCJ2yBacmZlZiWVNAAAkHUDa/OdO0sS+2aTaAN+LiPZWCZiZmVkX5B4CICKukfQkcDiwfES8ImlF3lkVYGZmZjWWexVAi9tIpYD/f3t3Hm/neO5//POVImaJHWMSG0WpKhp0VJRKo2gVh6pSHG1PtTqcVtr+eg6dpKo6He05KWkp5WjVMbWG1lSqSEgEEYIgpkhNiTlx/f64n81esYeIvfa197O+79crr7XWs9az17XvPMm61j1c9/2SDgX+Tu+rAAYclyZuJGm2pOmSpkqanB1PBkmTJM2VdGunY8MlXSbprup2WGaM/amb9jhG0oPVdTJV0rjMGPuLpFGSrpA0Q9Jtko6qjrfy9dFdm7TqNTJU0g2SplXt0bGDbp9cI+lDAACSrgd+DvwIOB24FPhRRGzRl+/T1tYW7e3tffkjzczMBqwpU6bMG6iFgF4REadL+kpEfAVA0vF9/R7t7e1MntySX0TNzKwFSbqvu+cGSgLwgKR3U/YDWA74ArB+ckxmZma1lZoASNq7unsBMAHYEHgMmAosTArL7DXax1+0xK+dPWH3JkZiZtY3snsA/hc4g1LD/+7qTwcvATQzM2uS7ATgFuAE4Fngp8A7KcnAdcD8xLjMzMxqLXsZ4BeBp4HfAWcD6wDrAr8HHs8Ly8zMrN5SE4CI+FtE3E9ZjvjbiFhY/TmdUhbYzMzMmiB7EuDw6u7Vkv6P0u2/HGUyYHbvhJmZWW1lzwGYQhnzX4uyIdDKwJPASsBLeWGZmZnVW/YQwAYRsSFwZ0QMA2ZUFYtWoyQHZmZm1gQDpZu949v+k5ImUhKA9rxwzMzM6i17DsDPqrtPS/ofShGgw4BPAP9IC8zMzKzmsucA7A1cCZwGqDq2A/DvWQGZmZm1guwhgM2AecCewPuBo4BVga2BC3s6sbttI83MzKx32ZMA50fEF4GRwI7AxZRhgHmUMsE9WQh8JSI2o1QQ/JykzZsXrZmZWX1k9wB0WBbYCHgQuDIivgus3tMJEfFwRNxU3Z8PzADWa3KcZmZmtTBQEoArgM8BdwKflHQgcNmSniypnTJscH0Xzx0habKkyY899lgfhWtmZja4Za8CmE8pBLQcsHx1eBElMXkZ+PoS/IyVgXOAL0bE04s/HxETgYkAY8aMib6J3MzMbHDLngOwSkSsCtxBSQBuB/YDNqd06fdI0rKUD/8zIuKPzYzVzMysTlITgKr+P8ALwIeANwNfBc6jlzkAkgScQqkeeGLzojQzM6uf7DkAYyR9GXgG+A1la+DNKcMAq/dy7nuAg4CdJU2t/oxrYqxmZma1kV0IaB1ge+AqyiS+ucDGwF3AsJ5OjIhreLV4kJmZmb0O2T0Ay1CqAf4HZQfADwBzgH0p9QDMzMysCbITgBnALsBDlAmAB1FWBLwT+FFiXGZmZrWWnQD8BzCeMt7/FPB5YGXgWODgvLDMzMzqLXsOwK8pdQCWpcwBGEIZ198uMygzM7O6y+4BOAnYkFL173lgaHV8BeDJpJjMzMxqLzsB2D0i5lEm/80AZkXEisA4YK3UyMzMzGosOwFYVtKbKOv+1wWekzQUWAMv8TMzM2ua7DkAJwF/onz7Xx14K7AAeA4vAzQzM2ua1AQgIn4uaTrwWWATyp4AzwCTga9lxrYk2sdftMSvnT1h9yZG0r2ljXEw/G5Lo66/V3+razvW9fdaWm6PvjFQ2zG7BwDKJkCXAl+LiPs6Dko6FJiUFpWZmVmNZW8G9H3gm8CngSslfb46Pgz4XmZsZmZmdZY9CXAPYGderQPwIUk/jogngNVSIzMzM6ux7ATgTRGxkFIA6DlKQrCqpHPIj83MzKy2sj9k75b0fuB04K/AIcC1wLaUPQHMzMysCbInAe4LEBFXSbqFsjGQgH8Fbs0MzMzMrM5SewAi4rmIeE7SBsCVEfHvwN3A1ZR5AWZmZtYEqT0Akr5c3f0S8DNJLwNfB1ai7Aw4Ois2MzOzOsueA3AssD0lEVmRshXwkOq+5wCYmZk1SXYC8FbKB/7zwG0RcSzwBDAVuC0xLjMzs1rLngNwf0TsA0wAfi1pHqXb/2hKcSAzMzNrguweAAAi4n+AtSnLAa+PiHdHxKzezpM0VtJMSbMkjW96oGZmZjUxIBIASWsBPwM2jYj3SNpc0mG9nDOEspvgh4DNgQMkbd78aM3MzAa/7FUA04EA2ilj/2tW9QAANgZO6eH07YBZEXFP9bPOAvYCbm9awGZmZjWhiMh7c2n96u4/gN9SSgHvBxwIHBwR6/Rw7j7A2Ig4vHp8ELB9RBy52OuOAI6oHm4KzOzTX6JrbcC8fnifwcLt0cjt0cjt0cjt0cjt0ej1tsf6ETGiqydSewA6tv+VtCLwA2CXiJgu6f+Az/Zyurr6kV28x0Rg4hsM9XWRNDkixvTnew5kbo9Gbo9Gbo9Gbo9Gbo9Gfdke2aWAOzwCXAGsL+laYBTwcC/nzKle12Ek8FBzwjMzM6uXATEJEPg45dv7Asp8gMerYz25EdhY0gaSlgP2B85vZpBmZmZ1MVB6AN4JvB94mdK1vwxwAHBTdydExEJJRwKXUIoJTYqIgVI8qF+HHAYBt0cjt0cjt0cjt0cjt0ejPmuP1EmArwQhTY2IrSS9lzK7fzpwfERsnRyamZlZLaUOAUi6oSMOSf8K/BewCnAMsG5WXGZmZnWXPQTQseXvJZRVAIcDTwJbApskxWRmZlZ72ZMAl5E0jPLhP58y8e9zwKWUWf6DiksTN5I0W9J0SVMlTc6OJ4OkSZLmSrq107Hhki6TdFd1Oywzxv7UTXscI+nB6jqZKmlcZoz9RdIoSVdImiHpNklHVcdb+frork1a9RoZKukGSdOq9ji2Ot4n10h2IaDZvDrxL4B3Ay8BLwDXRMRWffl+bW1t0d7e3pc/0szMbMCaMmXKvAFZCAg4CzghIuZJGgP8HVgELA98ta/frL29ncmTW/KLqJmZtSBJ93X3XHYCsHtEdHSV/xD4DHAHsAHwc+B/swIz66x9/EVL/NrZE3ZvYiRmZn0jOwFYVtKbgHGUWgDnAnOB9YEXMwMzMzOrs+xJgCcBfwJ+TNkO+BngYMrGQPcnxmVmZlZrqQlARPwc+D4wDPggpQbAeOBa4KnE0MzMzGotuweAiLiSUvL3fcDJlA/+t1JWA5iZmVkTZM8B6LAX8DzwJeBAYDXgrtSIzMzMamxAJAAR8Uynh6cCSLqfUhrYzMzM+lhqAiBpBrAOpSTw08BjHU8B62XFZWZmVnfZcwA2pHzL34eyH8BLwEHAHpQKgWZmZtYE2QnAAuDiiPhzRBxCWQ74O2AIZW8AMzMza4LsOQCPAK/U5o2I0yU9QukNeCEtKjMzs5rL7gE4Gdh+sWM3AfsCt7725a/qbtcoMzMz6112D8ANwMmSXgYOBb4LbESZFLhfL+cuBL4SETdJWgWYIumyiLi9qRGbmZnVQHYC8GPgJ8Bo4M/AnhFxjaRtKBsBbdzdiRHxMPBwdX9+taJgPcAJgJmZWS+yhwBGUwr/rASsDGwNEBE38TqWAUpqr869vu9DNDMzq5/sHoDVgJ0jYqGkG4EDJW0YEV+i1ALolaSVgXOAL0bE0108fwRwBMDo0aP7LnIblLytr5lZkd0DMA9YDiAizqCs/19V0p9Ygs2AJC1L+fA/IyL+2NVrImJiRIyJiDEjRozou8jNzMwGsewEYBqwbceDiFgUEYdRVgKs2dOJkgScAsyIiBObGqWZmVnNZCcA+wNvk3SxpFskTZd0LfBPYINezn0PpWrgzpKmVn/GNTtgMzOzOsieA3Az8BfgGOCjwOeAZ4DvV4936O7EiLiGJZwnYGZmZo2yE4BREfFZSb+g9AacANwHHEL5hm9mZmZNkJ0ALJK0L/B+4N6IOEbSMsBzwPtyQzMzM6uv7ARgGeBXwKoAkmZV96+nbBRkZmZmTZCdAFwLBKUewFbAMMo2wLsBM/PCMjMzq7fsBOA71e0xwKWUCYEAH6BUBjQzM7MmyE4ApgJfB95Fmf3/LmAucB7gMmxmZmZNkl0H4FrgCcoH/iGUhGAd4GCgLS0qMzOzmsvuAdicshnQssD5lAJATwDrZwZlZmZWd9k9AM8AFwKfoGzj+w5gH+AHwLOJcZmZmdVadg/A88AHgc8Aq1MqAz5K6Q14IC+sJeOd5cxenyX9N9MX/16W5t+n/033Dbdjo4HaHtkJwL8v9vj8iHhC0trAooyAzMzMWkFqAhARp3bcl7Q78K+ShlaHns+JyszMrP6yewCQtBtwIqUCYBtwa3V7eWZcZmZmdZY6CVDST4CjKEV/DgAeBH4E3AnskheZmZlZvWX3AIyLiE0kXR8R10iaB1wNnAvMT47NzMystrITgOclbQdcKGl14IfATZS4nsgMzMzMrM6yE4BDgF8Cq/Dq9r9PUnYC/HROSGZmZvWXOgcgIm6KiO2BD1Em/70JmAWcAdyWGZuZmVmdZVcC7HACZUvg71AmAW4G/G9qRGZmZjWWOgQgaSfgt8DalGV/90bEbOAKSS4FbGZm1iTZPQDHA7sBpwNXAJdJeqek7Sn7BPRI0lhJMyXNkjS+ybGamZnVRvYkwC2AMym7AX4SeJiyDPBNwAs9nShpCHASsCswB7hR0vkRcXtTIzYzM6uB7ATgLuBQ4LFOx9YGJtH7lsDbAbMi4h4ASWcBe1F2FTQzM7MeKCLy3ly6E9gXODEiPtDp+OrA5yLiez2cuw8wNiIOrx4fBGwfEUcu9rojgCOqh5sCM/v0l+haGzCvH95nsHB7NHJ7NHJ7NHJ7NHJ7NHq97bF+RIzo6onsHoB1gI8A20r68mLPPdfLueri2GuymYiYCExcquiWkqTJETGmP99zIHN7NHJ7NHJ7NHJ7NHJ7NOrL9shOAB6lJAErABvT9Yd6d+YAozo9Hgk81HehmZmZ1Vd2AjAeOAx4idI93zkB6G1s4kZgY0kbUDYR2h/4eDOCNDMzq5vUBCAi/iDpZsps/pnAQsrEwDMj4qlezl0o6UjgEmAIMCkiBkr1wH4dchgE3B6N3B6N3B6N3B6N3B6N+qw9sicBTgDeAVwFjAOmUjYB+ijwbxFxZVpwZmZmNZadAARwN6UWwLmU1QA7ShoNnBcRW6cFZ2ZmVmPZlQCfpywDXAb4A7BdVdFvGUpxIDMzM2uC7ARgHnAqMAJ4kbIp0JrAtTTO8O+SpEmS5kq6talRLiGXJm4kabak6ZKmSpqcHU+Grq5RScMlXSbprup2WGaM/amb9jhG0oPVdTJV0rjMGPuLpFGSrpA0Q9Jtko6qjrfy9dFdm7TqNTJU0g2SplXtcWx1vE+ukewhgJuBT1B2/7s1Iu6ojgvYISKu6uX8HYAFwGkRsUWz4+0lliHAnXQqTQwc0MqliSXNBsZERMsW8ejqGpV0PPB4REyoEsVhEXF0Zpz9pZv2OAZYEBEnZMbW3yStA6wTETdJWgWYQqmLcgite3101yb70ZrXiICVImKBpGWBa4CjgL3pg2skOwH4eET8brFjwyPi8dfxM9qBC5ckAWhra4v29vbXHaeZmdlgNGXKlHkDtRLgfZJmAC9T9gT4LrBRlensFxHX9eWbtbe3M3lyS/ZEm5lZC5J0X3fPZScAP6UsAfwgcB3wFKUewJ8p8wHe80bfoPNeAKNHj36jP65PtI+/aIlfO3vC7k2MpHt1irEv4hsM7TEY9Off2dIYDH/PgyFGazRQ/86yJwG+GfgHsANlR8ATgYOADYGN+uINImJiRIyJiDEjRnTZC2JmZtZyshOA5SPiNxExB/g0sGdE3AV8CmiZma9mZmb9LTsBmC2pYxvgRUDH5L8NKMMBPZJ0JmXoYFNJcyQd1pwwzczM6iV7DsC/ACdL2hSYTpkICPA0ZUJgjyLigCbGZmZmVlvZmwHdAmzX+ZikOyNiE+BnOVGZmZnVX2oCIGk+ZQtgUZYCAqxYHV8mIlZKC87MzKzGsucA3EQZ+78a+CelKuD9EbEKZTmgmZmZNUF2AjAc2B1YHvgN8C1gleo5JcVkZmZWe9kJwJCIuAbYhbIC4AVgZUkn4gTAzMysabITgEckbRURL0fEz4B9qj9twNtyQzMzM6uv7ATgt8ATHQ8i4uGIuDAiPkmpDmhmZmZNkJ0A/AKYIum3ksZVW+oCEBHXJsZlZmZWa9kJwFDgVspKgKOBhyT9t6T354ZlZmZWb9kJwPOUXf+WBbYAbqYkBcdLeiAzMDMzszrLLgUcwN+AlYBpwFuBzYCNKbUBzMzMrAmyE4CnKcWALgUeBO4CngPWBOYmxmVmZlZr2QnAMsA7IuJJAEmrUr79T6AkBWZmZtYE2QnAUGAY8KSk3YCTKSWAN6HMCzAzM7MmyE4AngJukPQcsDbwJOXDf0Xg2cS4zMzMai07AVgVOJ/yrf8I4FRKOeANKBUBzczMrAmylwE+A2wOPAScWd1/HBhCqQ1gZmZmTZDdA/AU0A58G7gT2JayMZAoqwLMzMysCbJ7AAK4nlIH4P2UfQFeAK7BPQBmZmZNk50APB8R44D3AC8Da1HmBYyiDAOYmZlZE2QnAE9K+jRwI+Wb/ynAJykf/m/ODMzMzKzOshOAg4FtgOUoH/h3Af8B3Au8lBiXmZlZrWUnAM8AXwfuAL4FnAvsDtwDTE+My8zMrNayVwE8QpkIKMpugEdQuv+FewDMzMyaJjsBuHuxx6IUAbonIRYzM7OWkZ0AdOz+dzKlDoAo2wOPBd6SGJeZmVmtZc8BOB3YlTL2/zdgBPBSRNwHHJcZmJmZWZ1lJwDfAN5KWf9/DXA5pQ4AlN4AMzMza4LsBGBIRDwcEc9ExMeBjwDPSPoCZXKgmZmZNUF2ArBI0kYdDyLir5TVAHtRegbMzMysCbITgLcBl0v6jqTNASJiPmUS4KGpkZmZmdVYdgJwC6XwzzLA+ZKmSRoPrBcRZ+SGZmZmVl/ZywC3BP7e6fEywLHA9yW9HBHZ8ZmZmdVS9gfsLRGxtaRvU6oC/pYy+/9ASnJgZmZmTZCdAPywut0tIrbvdPyXkq7PCMjMzKwVpM4BiIjfVXcXSTpQ0hBJbZIOBBb1dr6ksZJmSppVzR0wMzOzJZCaAEiaIKkN+DhlI6AXgUeBXwE/7eXcIcBJwIeAzYEDOlYSmJmZWc+yVwEcFhHzgG2Bl4F3RsQQYCvgq72cux0wKyLuiYgXgbMo9QPMzMysF4rIK7gn6XlgdcpKgBcj4p2dnpseEW/r4dx9gLERcXj1+CBg+4g4crHXHUHpXQDYFJjZp79E19qAef3wPoOF26OR26OR26OR26OR26PR622P9SNiRFdPZE8CvAaYT+mJWCjpReAlSly97QXQ1fOvyWYiYiIw8Q3G+bpImhwRY/rzPQcyt0cjt0cjt0cjt0cjt0ejvmyP7EmAu1B2A3wEuJ3y7fxq4AvASr2cPgcY1enxSOChJoRpZmZWO9k9AETElcB6HY8ltVXzAnpzI7CxpA2AB4H9KZMJzczMrBfZqwA+JOleSddI+rykR4A7JT0l6Tc9nRsRC4EjgUuAGcDZEXFb86NeIv065DAIuD0auT0auT0auT0auT0a9Vl7ZE8CnAocAHwfGAc8CfyCUglweES0pQVnZmZWY9kJwE0RsY2kW4BhwBMRsaWklYGHImLVtODMzMxqLHsOwJOSPk1ZCvgEsKqkbYCtgWUzAzMzM6uz7EJABwPbUFYB7ANcAVxPqfD3m95OljRJ0lxJtzYzyCXl0sSNJM2WNF3SVEmTs+PJ0NU1Kmm4pMsk3VXdDsuMsT910x7HSHqwuk6mShqXGWN/kTRK0hWSZki6TdJR1fFWvj66a5NWvUaGSrpB0rSqPY6tjvfJNZI6BNAVScsDQyPiqSV47Q7AAuC0iNiit9e3tbVFe3v7Gw/SzMxsEJgyZcq8gVoICIAqezkN2Aj4B2UoYLWI2LWn8yLiakntS/o+7e3tTJ7ckl9EzcysBUm6r7vnBkQCANxCKQK0EfAD4CDg7X3xgzuXAh49enRf/MhXtI+/aIlfO3vC7n363mbWs6X597m0/6b9f4ENRtlzADqsUVUFfDkiTqXs8De0L35wREyMiDERMWbEiC57QczMzFrOQOkBWCRpBcp+AFtQPvy9CsDMzKxJBkoC8CvgL8Ai4DpgReAnmQGZmZnV2YBIACLiy5LWAN5J2eXvH0uyH4CkM4EdgTZJc4D/jIhTmhqsmZlZDaQnAJK2AyIibpR0L/BtSk/An3s7NyIOaHZ8ZmZmdZSaAEi6GHgLMETSTGB9YF1ga0kzI6Ilij2YmZn1t+xVALsCN1Cq/r0X+D3wIvA7SoVAMzMza4LsBGBGFcNywB0R8Q3KhkDfopQHNjMzsybITgCeAT4J/B14RtI+AJJWA17ODMzMzKzOshOAHSLi2Yg4jzIcsD0wh1ID4ODUyMzMzGosexXASpJW6vT4uE73H+zvYMzMzFpFdgIwBQjK2v/RwBPV/dWBFygFgczMzKyPZScAH6tuvwGcCFxbPX4Pjb0BZmZm1oeyE4AbgauAbYE1gL07Pedv/2ZmZk2SPQlwBvBpyiqAvwKfAg6h7AvwQl5YZmZm9ZbdA/B94AhK1//alN6A1YG7gCPzwjIzM6u37B6Aj1QxrA1sBlwA7AFcQdnkx8zMzJogOwHYCrgf2BDYAdgH+AOwJS4FbGZm1jTZCcBGlG/+x1ASgQuB/wcMBzbOC8vMzKzesucAAPx7RCyQ9FREHA4g6a+UeQBmZmbWBNk9AE8CP5C0DDBN0pjq/h7As6mRmZmZ1Vh2D8COlIl/n6FsA3wIZROgfwJfTYvKzMys5rJ7ADajbAU8g7Iz4FTgYkoVwCPywjIzM6u37ATgG8B2wO2UWgBtwAOU1QHtaVGZmZnVXPYQwBrANGBlSk2A/wY+DizCpYDNzMyaJrsHYDiwG2UewB+AnYH1gPsoQwNmZmbWBNk9AEOA0ylbAq9I2Q8ggPWBlxLjMjMzq7XsBOBvwJ2UIYDHgdsokwDfC7wjMS4zM7Nayx4C+ALlw34H4DJKD8ChwFBcB8DMzKxpsnsAfgb8EvgHcBhwFLBHRPxT0s2pkZmZmdVYdgIwgjLmfxxl8t9qwGxJP6iOm5mZWRNkJwAbUMb6jwXmVMc+CnwXrwIwMzNrmuwE4AXgjIj4R6djP5d0DXBNUkxmZma1lz0J8B5gzWoDIACq+5sAt6ZFZWZmVnPZPQD7A6cCp0laVB1bBrixes7MzMyaIDsB+CJlE6BPAU8DAlYBPlk9d1RWYGZmZnWWPQQwDjiAMuP/LZSu/6DsBzAuMS4zM7Nay04AhlKq/+1IKQW8ErATZfx/aF5YZmZm9ZY9BPAyMBd4P68uAxwFPEKpEWBmZmZNkJ0AvAh8AFiBUghIlETgOWBybydLGgv8lLKp0MkRMaF5oZqZmdVHdgLwPeAm4FLK5L/26nZN4Os9nShpCHASsCslabhR0vkRcXszAzYzM6uD1DkAEXEqMAbYCNgWeBD4O2VewLa9nL4dMCsi7omIF4GzgL2aGK6ZmVltZPcAEBFPSFoPeEtEBLxSDGh6L6euBzzQ6fEcYPvmRGlmZlYvqQmApEMjYhIwE9hW0nHANpQKgff2dnoXx16zgZCkI4AjqocLJM18AyEvqTZgXkMcP+iHdx24XtMeLc7t0Si9PZbm3+fS/ptegvPS22OAcXs0er3tsX53T2T3AJwo6SOUXQD/DtwNTAXeTdkoqCdzKCsGOowEHlr8RRExEZjYB7EuMUmTI2JMf77nQOb2aOT2aOT2aOT2aOT2aNSX7ZGdAMwFflTdP5lXv6l3PO7JjcDGkjagzB3Yn1JAyMzMzHqRnQCsDnyM0p2/EnBLRDwBIOmFnk6MiIWSjgQuoSwDnBQRtzU3XDMzs3rITgC+B+xLGfcfCtwiKYCzgQt7Ozki/gT8qakRLp1+HXIYBNwejdwejdwejdwejdwejfqsPVRNvE8h6XLg2xFxpaS9gfcB/49SA2DNiDiixx9gZmZmSyU7AZgWEW/v9HhKRLyjuv9YRLgcsJmZWRNkbwb0mKRPSFpX0ueB2QCSRJkfMKhIGitppqRZeFyvJgAANzpJREFUksZnx5NN0mxJ0yVNldRraec6kjRJ0lxJt3Y6NlzSZZLuqm6HZcbYn7ppj2MkPVhdJ1MltcROoJJGSbpC0gxJt0k6qjreytdHd23SqtfIUEk3SJpWtcex1fE+uUayewDmU8b+lwEWAS9Q1vILWDEihvRy/iTgw8DciNiiyeH2qCpNfCedShMDB7RyaWJJs4ExEdGya3gl7QAsAE7ruEYlHQ88HhETqkRxWEQcnRlnf+mmPY4BFkTECZmx9TdJ6wDrRMRNklYBpgAfAQ6hda+P7tpkP1rzGhGwUkQskLQscA1wFLA3fXCNZCcA9wPbRsSjXTz3QESM6uK0zq95zX8mPWlra4v29valDdfMzGxQmTJlyrzuhtOzVwGcDXxb0g3AJOAbwLuAGcA5vZ0cEVdLal/SN2tvb2fy5JbsiTYzsxYk6b7unstOADan1PwfAxxU3f8BpRt9k754g86lgEePHt0XP/INax9/0RK/dvaE3ZsYSffqFGNfxDcY2mMw6M+/s6Xhv+e+4XZsNFDbIzsBWDcixkn6KLBXROwIIGk6cHNfvEHnUsBjxozJG+8wMzMbQLJXASxTzV78LrBSp+78IcDaaVGZmZnVXHYPwHHAHcBwSi3/k6tKgJsD/8wMzMzMrM5SewAi4kxgXeB04D3AvwH/BVwE/KW38yWdCVwHbCppjqTDmhiumZlZbWT3ABARi6pNfb4L/B8lKbkR+O0SnHtAc6MzMzOrp+w5AB0OAHYB1gEeBvYBWr6SnpmZWbMMlATgKGBb4L6I2AnYGlg/NyQzM7P6Sh0CkDS8uvsSsCIwRNLawGNAj1UAzczMbOllzwF4DLgPWIuy7n9V4F7gZfJjMzMzq63sD9l7gA9ExP0dByS9H1gNOCktKjMzs5rLTgB+AgwD7u90bHpEPC7JcwDMzMyaJDsBGBYR0wAkbU5ZBrhstQXiv2QGZmZmVmfZqwD27nT/h5TVAGdS9n7+cUpEZmZmLSC7B2CkpJ9V97cFxlF2BVwZeHNaVGZmZjWX3QPQBnwUeB8wFLiFsiRwCvBiYlxmZma1lp0A7AMsoOz8dwql+38+cDFlhYCZmZk1QfYQwIHAeZR6AF8HdgWWiYhHJa2cGpmZmVmNZScAG0XExyS9C5gLbAgskLQDrgRoZmbWNNlDAMtLWoZSD2A3YCbwVeCXlGJAZmZm1gTZPQBXAn8FNqPM/iciTpX0KHBRYlxmZma1lp0AvBn4X2AhZQfAjavNgLYAns4MzMzMrM6yhwA2p+wCeDVl/H8I8CBwMPBsYlxmZma1lt0DsCxlL4CNKDsB/gIYDexF6RUwMzOzJshOAG4HHgG2B7aJiPkAkt5DWR5oZmZmTZCdALyVUvZ3G+A4SadExM3ALPJjMzMzq63sD9k5ETFG0lzgY8B+1U6AC3EpYDMzs6bJTgCiun0Y+AKwFbAusB6wb1JMZmZmtZe9CuBNkoYD/w1MBEYCArajbA1sZmZmTZDdA7AqMJnyoT8a+Gx1fyhwNCUxMDMzsz6WnQBsQtn+95fA+cBzlAmBKwKr54VlZmZWb9lDADdSPui3pawI+B6wArAD8PG8sMzMzOotuwdgSEQ8IWke8EVgZ+B5yjDAOzMDMzMzq7PsHoCnJW0BHEAZCvg9cC6wJjAnMzAzM7M6y+4B+AxwBjCNsh/Ae4CrgHcB30mMy8zMrNZSewAi4hbKpL8zgSmUvQAuAXYBdsyLzMzMrN5SewCqGgAA11d/OgwBxvV/RGZmZq0hewjgMWA+ZdnfkOrYIspWwCtmBWVmZlZ32ZMAn6fUABgdEUMiYgilINBxwMupkZmZmdVYdgKwADgrIh7pOBARj0TED4Cn8sIyMzOrt+wEYBqwm6S1Og5IWkvS0cD03k6WNFbSTEmzJI1vZqBmZmZ1kp0A/AuwDnCTpGclPUNZDbAOsF9PJ0oaApwEfAjYHDhA0uZNjtfMzKwWsicB7gHsSdkH4MHq2Mjq+E3AaT2cux0wKyLuAZB0FrAXcHvTojUzM6sJRUTem0szgU9QdgX8R0Q8Ux0fBkyPiJE9nLsPMDYiDq8eHwRsHxFHLva6I4AjqoebAjP7/Bd5rTZgXj+8z2Dh9mjk9mjk9mjk9mjk9mj0ettj/YgY0dUT2T0Aw4DfAbcBp0g6KiLOo6wAaOvlXHVx7DXZTERMBCa+0UBfD0mTI2JMf77nQOb2aOT2aOT2aOT2aOT2aNSX7ZGdALxAWf//MHAncJKkrwFrA4/2cu4cYFSnxyOBh5oRpJmZWd1kTwKcD7yDUv//UUop4JWAi4Enejn3RmBjSRtIWg7YnzKXwMzMzHqR3QPwCLA+pQzwIxHxnKTjgd8Ab+vpxIhYKOlIyt4BQ4BJEXFbk+NdUv065DAIuD0auT0auT0auT0auT0a9Vl7ZE8CHAksBC4E3h0RL1bHlwOmRcRmacGZmZnVWGoPQETMAZD0JmALSe+lTOS7FngxMzYzM7M6y54D0GFl4I/AGpTZ/+dUx8zMzKwJBkoCAGUS4KeAQ6v7vY5NSJokaa6kW5sd3JJwaeJGkmZLmi5pqqTJ2fFk6OoalTRc0mWS7qpuh2XG2J+6aY9jJD1YXSdTJbXEVuCSRkm6QtIMSbdJOqo63srXR3dt0qrXyFBJN0iaVrXHsdXxPrlGUucAvBKE9GfgAMp8AFEm9Z0eER/u5bwdKBsKnRYRW/T2Pm1tbdHe3v7GAzYzMxsEpkyZMm9AFgKS9HNgTWAFSjGgG4D1gA2Av/Z2fkRcLal9Sd+vvb2dyZNb8ouomZm1IEn3dfdc9jLA9YAtKd/4b6KU6r0DWI382MzMzGor+0N2Y8qH/vKUmgA7RsRNklag1AZ4wzrvBTB69Oi++JGvaB9/0RK/dvaE3fv0vZutrr9bf/5eg6ENB0OMS2tpfrelbY86t6O9cQP1+sieBLgwIhYBHwCWo8z+h5IUrN8XbxAREyNiTESMGTGiy2EQMzOzlpOdALwoaUXgGEr9/yer4/dSEgIzMzNrguwEYIeIeJbSE/Bkp+PLAg/0drKkM4HrgE0lzZF0WHPCNDMzq5fsSoAvVHdnSfo6sJKktwOHA5cvwfkHNDM+MzOzuspeBrg58DPKsr/9gecoSwHvAHZNDM3MzKzWsocAJgGfi4iNgHcB50TE8sB/UbYGNjMzsybIXga4GfBDSR2Pd5B0fnV/p5yQzMzM6i+7B2Aa8BjwPLAJcCPwK+AZvBugmZlZ02QnAHsC/6TUATgH2DsiLgA+A9yfGZiZmVmdpSYAEfFkRHwNmAucEhHzq6eGA0PzIjMzM6u37FUAf6R88x8PXCnpnuqpdqryvWZmZtb3sicBbg+8DOxMWfd/LXA1cGunGgFmZmbWx7LnAMyNiH0odf/Po8wJuBz4naQPpkZmZmZWY9k9ABsARMR8SU9Tuv4vBHahLBHcPC80MzOz+spOADq//9HAzhFxr6Q24K9JMZmZmdVe9hDAnZ3uvyki7gWIiHmUuQFmZmbWBNk9AG+X9BIgAEkjI2KOpOUohYHMzMysCbJ7AC4HPg+MAX4JnC1pDWBFYLnMwMzMzOosuwdgO8oOgOOqx0OBeyklgbOTEzMzs9rK/pBdGVidMvv/EeDfgWOAt5Afm5mZWW1lf8g+R/ng/yTwAvBd4FTgw8CixLjMzMxqLXsIYFZE7Ffd/7ykT1AqAe4J3JIXlpmZWb1l9wAsK2mopFMlbQI8DZxJmRz4ltzQzMzM6iu7B+Bkyn4AO1V/LqcsCVy2ujUzM7MmSE0AIuLHAJLWA7aJiGnV462ByZmxmZmZ1Vl2D0CHF4GzJP0BCGC/6piZmZk1QWoCIOnL1d3nKPMR3kFJAACeTQnKzMysBWRPAlyl+nMlsCawIbAS0AbckBeWmZlZvaUmABFxbEQcC0wDpgLDgXOAjwGrJoZmZmZWa9lDACsCRwJHABsBM4FdgVHAaomhmZmZ1Vr2EMBvgLUoichFwArACZSNgNbLC8vMzKzeslcBbBIR+0l6GPg2pSzwppTaAJ4EaGZm1iTZPQAARMQJlBLA51ASgP8A5qUGZWZmVmPZPQCTJa0cEQsiYiyApDbK+P/83NDMzMzqKzsBOAeYLulB4PPA6cBQYHng4MzAzMzM6iw7ATgOGAesDvwF2J2yE+BvgTOAbdIiMzMzq7HsBGBd4LPV/eWAA4GDgJUpSwHNzMysCbInAa5KmfG/JvAksAawiDIM8EReWGZmZvWWnQBsRVn7/w5gD+BGSmng9YC9ejtZ0lhJMyXNkjS+mYGamZnVSXYp4DsiYktgf+DHwBDgoYjYPSJm9HSupCHAScCHgM2BAyRt3uyYzczM6iC7BwBJu1F6Aj5F2RXwmur4ob2cuh0wKyLuiYgXgbNYgl4DMzMzS04AJH0f+CbwNuCvwDIR8Ynq6SN7OX094IFOj+fg8sFmZmZLRBGR9+bSdGBrYAzwC2BLQB3PR8SQHs7dF9gtIg6vHh8EbBcRn1/sdUdQNhuCUmVwZl/+Dt1ow5UMO3N7NHJ7NHJ7NHJ7NHJ7NHq97bF+RIzo6onsZYBvioiFkv4L2A/4PXAzpUegvZdz59C4VHAk8NDiL4qIicDEPol2CUmaHBFj+vM9BzK3RyO3RyO3RyO3RyO3R6O+bI/sOQB3S3o/QETMAoZExKHAJZQspyc3AhtL2kDScpSJhOc3NVozM7OayO4B2Le6fbb6EJ8q6XjgYeD2nk6seg6OpCQLQ4BJEXFbU6M1MzOridQEICKeg1fG74cDv6ZUAlwf+O4SnP8n4E/NjHEp9euQwyDg9mjk9mjk9mjk9mjk9mjUZ+2ROgnwlSCkw4GjKOP4U4F3AtdFxM6ZcZmZmdXVQEkAZgK3UTYGeghYFhgWESunBmZmZlZT2ZMAO6xLqeoXwG7AKcCC1IiWgksTN5I0W9J0SVMlTc6OJ4OkSZLmSrq107Hhki6TdFd1Oywzxv7UTXscI+nB6jqZKmlcZoz9RdIoSVdImiHpNklHVcdb+frork1a9RoZKukGSdOq9ji2Ot4n18hA6QF4AtiAskZ/JmUjoPdFxPAezhkFnAasDbwMTIyIn/ZDuN3FMwS4E9iVskTxRuCAiOhxMmOdSZoNjImIll3DK2kHSjJ7WkRsUR07Hng8IiZUieKwiDg6M87+0k17HAMsiIgTMmPrb5LWAdaJiJskrQJMAT4CHELrXh/dtcl+tOY1ImCliFggaVlKpdyjgL3pg2tkoCQA1wLvA/4A3A88C3wsIjbt4ZwuL5SePnDb2tqivb29T2M3MzMbqKZMmTJvoBYC6vBF4AOU7OZtwJuBHrvQI+JhynJBImK+pBmUUsDdJgDt7e1MntySPdFmZtaCJN3X3XMDJQEYRykHvGlEbCJpXUpVwHOX5GRJ7ZSSwtd38dwrpYBHjx7dV/EC0D7+oiV+7ewJu/fpe5uZmb0RA2US4D6Usf+1JF0OnA68fUlOlLQycA7wxYh4evHnI2JiRIyJiDEjRnTZC2JmZtZyBkoCMBqYASwHHEuZRPdsbydVkyLOAc6IiD82NUIzM7MaGSgJwHxgO0o8mwAbA0/1dEI1O/IUYEZEnNj0CM3MzGokNQGQtHx1dw5lBcBDwFjgVHqP7T2UssE7t9raUDMzszcqexLgdcA2lO7+G4A9gJ8D/wp8qacTI+IaQM0O0MzMrI6yE4DlJB1M2fznA9Wxn1e32bGZmZnVVvaH7GeAA4HVKd/+oZQCvoRSFtgT+8zMzJogOwH4RXX7LPAOSpf+GtV9MzMza5LsBGA28DRwD6Xwz3zgb5Regb3ywjIzM6u31FUAEbEnZR3/l4ENImI28FJETAc+lhmbmZlZnaXXAYiIc4F7gR0lnU+ZGDiEUhTIzMzMmiB7CKDDxUA7cAFwN3BmdczMzMyaIL0HQNJo4PvA5cBHgXdRygJ/LTMuMzOzOsuuBDgeuIpSEOglYEPgNsrmQEclhmZmZlZr2T0AxwALgEXA/wBDKAWBlqX0CpiZmVkTZCcAiyj1/P9E6QFYFphCKQz0Yl5YZmZm9ZY9CfBFylj/bsAzwPXA+ZR6AJ9IjMvMzKzWsnsAZgPXUD7w7wP+mzIJ8FlgYV5YZmZm9ZbdA/Al4CTgMsoKgD8Dz1Hi+mViXGZmZrWW3QNwM3ApZSOglatjCyl1AI7NCsrMzKzusnsArgdmUb7tz6+OrQJsWz23aVJcZmZmtZadALQBdwL7AqsBZ1fHNwFGZQVVJ+3jL1ri186esPsbPm+gq+vvBf37u9W1Hev6ew0Wvob7V/YQwPOUD/qhwDzKdsDHA7+lLAk0MzOzJshOAJ6izAEYBawP/BtwFzAcrwIwMzNrmuwEoA14FPgrpTfgqurxv1GKBJmZmVkTZCcAjwBbA09SvvEvCzxAKQ+8Ql5YZmZm9ZY6CTAi3g4g6VLgrZQP/aHASrgOgJmZWdNkrwLosDlwL2UPgBcoicBLmQGZmZnVWfYQQIc1KLE8RVkKOJ+yN4CZmZk1wUBJAALYhTIRcBqwI2UowMzMzJpgoAwBPEkpBHQusD3wTkpvgJmZmTVBagIgaTrl2//TlKWA91MmAK4BzEwMzczMrNayewA+3On+CODtlITgFuCxlIjMzMxaQOocgIi4LyLuA3YF/giMAw4CzgM+kBmbmZlZnaUmAJKukNQGfBU4DngbcDdl/P+4zNjMzMzqLHsIYEREzJM0BzgMeFdE/FPSasCc5NjMzMxqKzsBWF3SMcCDwHbAlyS9BOyFCwGZmZk1TXYCcBll45/bqz+fBGZRlgTemBiXmZlZrWXvBfCpqrv/48AmVTxzgPMi4o7M2MzMzOosuweAiHiKLjb+kTQxIo7o6VxJY4GfAkOAkyNiQnOiNDMzq5fsVQDDqz9XSGrv9HhDynBAT+cOAU4CPkTZTOgASZv3Q9hmZmaDXnYPwGPAfcB6wBXVsQAELNfLudsBsyLiHgBJZ1EmD97enFDNzMzqQxGR9+bSXZSCP+cCH42I+6vj6wN3RUS3SYCkfYCxEXF49fggYPuIOHKx1x0BdAwlbEr/lBhuA+b1w/sMFm6PRm6PRm6PRm6PRm6PRq+3PdaPiBFdPZHdA/ATYBjwTeAaSVdVx3cAftXLueri2GuymYiYCEx8AzG+bpImR8SY/nzPgczt0cjt0cjt0cjt0cjt0agv2yN7FcBJ1d1pkrah7AIo4EsR0VuGMwcY1enxSOChvo/SzMysflInAQJIWlXSRhExLyIuBEZV1QG37OXUG4GNJW0gaTlgf+D8pgdsZmZWA9nbAZ8KfARYUM3qPxM4SNJQ4MuUb/VdioiFko4ELqEsA5wUEbc1P+ol0q9DDoOA26OR26OR26OR26OR26NRn7VH9iTARcCFwBTKh/1HgRWAHwKfiYh10oIzMzOrsewhgLsoNf9XAr4IbAksCzwBPJwXlpmZWb1lJwCPA0cDf6fsC/AeykS+vYC3JsZlZmZWa9kJwGcpwxDnAbsC2wMPAGOBQ3s7WdIkSXMl3drcMJeMpLGSZkqaJWl8djzZJM2WNF3SVEmTs+PJ0NU1WlW7vEzSXdXtsMwY+1M37XGMpAer62SqpHGZMfYXSaOqKqgzJN0m6ajqeCtfH921SateI0Ml3SBpWtUex1bH++QaSZ0D8EZJ2gFYAJwWEVv09vq2trZob29velxmZmYDwZQpU+YN1EJA3ZI0PyJW6ek1EXG1pPYl/Znt7e1MntySX0TNzKwFSbqvu+eylwF+pZunlgFW7M9YzMzMWkl2D8AJwCPdPNcn8xM67wUwevTovviRb1j7+IuW+LWzJ+zu9xoA6vy7LY26tkddf6/+NhjacTDE2GzZkwCfBw6LiHUW/wMs6os3iIiJETEmIsaMGNHlMIiZmVnLyU4Avgc8281zR/dnIGZmZq0kNQGIiO9GxJXdPPej3s6XdCZwHbCppDmSDuvjEM3MzGopexLgcOBISvGfU4BvUGoDnAl8PyKe6On8iDig6UGamZnVUPYkwBnA05TyvxOA+cBwYE/gE4D3AjAzM2uC7DkAqwDXA4cAAXwKeIoya39BXlhmZmb1lp0AzAJuB74DDAVmA88Bt1I2CTIzM7MmyB4COA74CSURmQ78DWgDbgGOzQvLzMys3lITgIg4U9LZlD0JFkraA/go8M2I8HbAZmZmTZLdA0BELJK0bHX/AuACAEnrR0S3NYzNzMxs6aXOAZC0k6Q5wEOSLl1sY5+7ksIyMzOrvewegFOBM4BHgS2BGyX9Drif/AmKZmZmtZX9ITsSeJGyHPBe4H+BA4ExmUGZmZnVXXYPwLPA3yLi0o4DkiYAF5KfnJiZmdVW9ofskcALnQ9ExBzg/ZTKgGZmZtYE2csAf9PN8ackeRKgmZlZk2SvAlhV0nGSfivp49WxO6unf5YYmpmZWa1lzwF4irIHAMAnJJ0BIGkR+cMTZmZmtZX9IRvARcC2QDtwAmVOwDvwXgBmZmZNk50APA38H/BDYC/g6OrYGTgBMDMza5rsBGAicB+wS/X4KmAh8BXgoaygzMzM6i57DkBHBcAtq8c3Ac8DOwG/zArKzMys7rITgCOBs4FRwAeBocATwI7AbODErMDMzMzqLHsI4JmIOBrYDlieUhnwL8DjlEmBZmZm1gTZCcBLktaj7AlwE/BIRHyKkhBkx2ZmZlZb2UMAXwIupSz9eyuwqqSrga0pvQFmZmbWBNmlgK+U9G7gfMqEwK2AbYC5lBUBZmZm1gTZPQBExFOUzX8AkNQOrBoRt6QFZWZmVnPZewFsKunXkr4raWVJFwLXASdViYCZmZk1QfZEuynArcACYBawBfBbYDgeAjAzM2ua7CGAIRHxIwBJxwKrR8Rzkr4BzM8NzczMrL6yewAWSdpd0raAgHdWxzepHpuZmVkTZPcAfBE4h1L+93LgAkkvAysAP0uMy8zMrNaylwGeLOnXlDLAmwCXAU8Bf4yIxzNjMzMzq7PsIQAo3/ZXosTyIjAHeDIzIDMzs7pL7QGQtB/wVeBeYBxlK+AhwHKSHouIkZnxmZmZ1VV2D8D/oxQBWhc4iPLhvwUwCYjEuMzMzGotOwEQ8BxlGOBi4E0RcV9EfJZSC8DMzMyaIHsVwJ8oH/xtlE2B7pF0JGUiYHZsZmZmtZXaAxARRwM/Bc4Fjgd2A94BfBTYpbfzJY2VNFPSLEnjmxqsmZlZjaR/y46IP1F6ApC0KvBfwD0R8URP50kaApwE7EpZOXCjpPMj4vYmh2xmZjboZa8COB24gfLNfzNgJPB49dznI+L3PZy+HTArIu6pXn8WsBfgBMDMzKwXisibbC/pcUrFv78AZwO/By6krA54c0Ss18O5+wBjI+Lw6vFBwPYRceRirzsCOKJ6uCkws69/jy60AfP64X0GC7dHI7dHI7dHI7dHI7dHo9fbHutHxIiunsgeAlgRODEinpZ0D+UD/ChJVwNP93JuV3sFvCabiYiJwMQ3HuqSkzQ5Isb053sOZG6PRm6PRm6PRm6PRm6PRn3ZHtnLAB8CrpV0KGU74I0kfRL4H8oWwT2ZA4zq9Hhk9fPMzMysF9k9AIcDpwMnAMsDNwHvokwKnNvLuTcCG0vaAHgQ2B/4ePNCNTMzq4/szYAul7QesEZELD6mcXYv5y6sagZcQqkgOCkibmtSqK9Xvw45DAJuj0Zuj0Zuj0Zuj0Zuj0Z91h6pkwDhlaV/IyLi7urx5RGxs6QtI+KW1ODMzMxqKnsVwH2UfQAWUib1dYzrzwQ2jogV0oIzMzOrsexJgKsD5wFbA/tRkoD5wB7A3XlhmZmZ1Vt2AnA/cAavjmm8F1iFUtDnxd5OljRJ0lxJtzYvxCXn0sSNJM2WNF3SVEmTs+PJ0NU1Kmm4pMsk3VXdDsuMsT910x7HSHqwuk6mShqXGWN/kTRK0hWSZki6TdJR1fFWvj66a5NWvUaGSrpB0rSqPY6tjvfJNZI9BPB3yjbAjwDfAd5M2QvgDuC9EbF8L+fvQFkueFpEbNHb+7W1tUV7e/sbDdvMzGxQmDJlyryBWgjos8AyEfEM8GVJb6csAzyFMiTQo4i4WlL7kr5Ze3s7kye35BdRMzNrQdVcuy5lJwAzgJc6PR4OrATsEhFn9MUbdC4FPHr06L74kSnax1+0xK+dPWH3JkYyuLkdbSBZ0uvR1+LgNlD/38meAzAV+IOkv0n6M/B9YAVKb8CdffEGETExIsZExJgRI7rsBTEzM2s52QnAaOAy4PPANpRVAL8EPkRjmV8zMzPrQ9kJgIBrImIqcDMwCbga2DgzKDMzs7rLTgAeBn4n6TRK7f+jgQcoycDzvZ0s6UzgOmBTSXMkHdbMYM3MzOoiexLgSZR5AEOBTYBplGqA3wP+o7eTI+KAZgZnZmZWV9mbAf2408M/A0haMyLmArvmRGVmZlZ/qQmApP/p4vABVdc+EfHpfg7JzMysJWQPAfwr8E9geWA54BlKHYCDKXMAnACYmZk1QfYkwK8BUyilf1eJiDUo+wOsQpkIaGZmZk2QmgBExAnA4cBmwE8lrQIEsDJlm2AzMzNrguweACJiDvAFYB/gHmAd4CZKVUAzMzNrguw5AABExK+rUsDvA9YGfh8RjySHZWZmVlupPQCSviRpFED1gT+PMhlw68y4zMzM6i57COAE4IZqM6A/Aj/g1c2AjssNzczMrL6yE4AXgfWA71C6/zcA3gv8DtgjMS4zM7Nay04AFgGbR8SllOWAmwO/AMYCb8kMzMzMrM6yJwE+APxB0jPAisBd1Z8VAG/sY2Zm1iTZCcCFlJr/04BlKT0SAt5GWQ5oZmZmTZCdAOwFvDUiXpL0XmDjakngusC1wITc8MzMzOopOwEQMEnSpsDjwLaSvgQ8SqkHYGZmZk2QnQA8RZnwtwplI6ChlERgK+DlvLDMzMzqLXsVwBBgLeBuygf+o8B4YEPyYzMzM6ut7A/ZZSLiZeDXlBUBUCYAXgo8kRaVmZlZzWUnANdJugE4CniYsipgU+C/KcMBZmZm1gTZCcAHgZuBX1KKAu0HvACsATybGJeZmVmtZU8CXAF4CNgC2Ay4Gvg3YD5eBWBmZtY02T0AL1AmAG4O/CdwFvBtyo6AQxLjMjMzq7XsHoCRwAFAO7Abry79uwbYOykmMzOz2svuAZgGHA38kLL2/z5gNvBm4Om0qMzMzGouuwfgamBIRHxP0nTgRGBdyrDAp1MjMzMzq7HsHoAPRsS06v6nga9GxIrA54Ev5IVlZmZWb9k9AG8CkDSUshJgV0l7VM9tlhaVmZlZzWX3APxB0m+AP1K6/fcDpgPbUfYJMDMzsyZITQAi4pvAlcD7gS2BYcC/UioC3p8XmZmZWb1l9wAQEb8BbouINuBaSi/ACcCozLjMzMzqLHsOAJJWBc6VNAz4FnA+sDJlPwAzMzNrgtQeAEn7AXcA/0Ip/vNsRGwYEWsCH86MzczMrM6yhwC+QfnwPxO4FbhE0mmS3gIoNTIzM7May04A1gJ+TvmwvwD4D2Bn4C/AmolxmZmZ1Vr2HIDVgR0jYmbHAUmnAucBO/Z2sqSxwE8pGwedHBETmhOmmZlZvWT3ADzCYtv+RsR8ylLAR3o6UdIQ4CTgQ5TdBA+QtHmT4jQzM6uV7B6AzwKnSLoLeKA6NpqyGdChvZy7HTArIu4BkHQWsBdwe5NiNTMzq43sBOD3QADr8OqkvwAWAWcDq/Zw7nq8mjQAzAG2b0KMZmZmtZOaAETEKpLeRJn89wilJPCmwBjKuH5PulolEK95kXQEcET1cIGkmYu/pgnagHn98D5d0g+y3rlbqe2xtJrYjoOyPZrI7dHoNe0xAP9N96eWuj6W4O/69bbH+t09kZoASDoE+BGwIrA38HfgXmAT4AXg+B5On0NjtcCRwEOLvygiJgIT+ybiJSNpckSM6c/3HMjcHo3cHo3cHo3cHo3cHo36sj2yJwF+hfKN/3bgHGA3YFfg+5RhgZ7cCGwsaQNJywH7U6oImpmZWS+y5wAsioh5kj4GzACuo3TjX0svmwFFxEJJRwKXUIYLJkXEbc0O2MzMrA6yE4D7JR0HrEL58L+ZMg9gF2CF3k6OiD8Bf2pqhEunX4ccBgG3RyO3RyO3RyO3RyO3R6M+aw9FvGbeXL+pNgL6HOVb/38BYymbAJ0NfCciHk4LzszMrMayE4BbFj9EmQA4EyAituz3oMzMzFpA9iTA2cAC4J+8OhyxCPgusEdSTEtN0lhJMyXNkjQ+O55skmZLmi5pqqTJ2fFkkDRJ0lxJt3Y6NlzSZZLuqm6HZcbYn7ppj2MkPVhdJ1MljcuMsb9IGiXpCkkzJN0m6ajqeCtfH921SateI0Ml3SBpWtUex1bH++QayU4A/kmZ7DcMmAWcCzxFWbe/Z28nd/WfSRaXJu7WThGxVQsv4/kNZWirs/HAXyNiY+Cv1eNW8Rte2x4AP66uk62quT2tYCHwlYjYDHgn8Lnq/4xWvj66axNozWvkBWDniHg7sBUwVtI76aNrJH0IICK2lLQS8B3gEOBZYCNganUR9HT+DpQehNMiYove3q+trS3a29vfcNxmZmaDwZQpU+ZFxIiunsteBfCSpI0i4m5JpwPvB34VES9I6jUziYirJbUv6Zu1t7czeXJL9kSbmVkLknRfd89lJwBfBa6Q9DywPHAMcLekPYCpffEGnUsBjx49ui9+5Cvax1+0xK+dPWH3Pn3vZuvP320wtONgiHEwWNJ27Is2XJq/s6X9e/b10cj/fzQaqDGmzgGIiMspdYp/ATwNnAgcS1kGuHYPp76e95gYEWMiYsyIEV32gpiZmbWc1ARA0p7AcsBhwLbAfRGxE7A18FhmbGZmZnWWPQTwv8Az1f2dASQtHxF3SNo0LywzM7N6y14GeAewMWUp4NHAm4F7JV1DFzv7LU7SmZQSwptKmiPpsGYGa2ZmVhfZPQAREU8A2wBIWhv4OmVXwG73MO508gHNDc/MzKyesnsA1PlBRDxCKW7wFuC9OSGZmZnVX3YPwAWS9u70WMBJkjri6nb9opmZmS297ATgW8BFwFuAv1XHVqLsAxCUrYHNzMysj2UnAPcAGwCrARdUx8Z1um9mZmZNkD0H4GDgamBFykY6n6ru7wF8ODEuMzOzWkvtAYiIa4Brqq1iLwZ+DDwWEZ/KjMvMzKzusnsAAIiIUyLiwYjYLyI2BJC0a3ZcZmZmdZVdCvhISW3V/TdLulrSE5KuB07LjM3MzKzOsicBfh/4oCSA7SnL/v4GtAFrJsZlZmZWa9kJwCqUD/pFlMl/LwFrUJYAqofzzMzM7A3IngPwIOWD/k7gXuB24GRgCPB8YlxmZma1lr0KYKSkTwE/BV4E9gfeA/wfpVfAzMzMmiC7B4CI+DUwF3hvRKwSEZtTJgCulRuZmZlZfWXPAUDSDpQu/0sl/RNYFVgZeDIzLjMzszrLXgb4E2ACZSLg6ZT5ABcDtwLD8iIzMzOrt+wegF2BLYC/AF8EjqAkJQspcwHMzMysCbITgIiIkHQ7sBNwKvAcsBzwQmpkZmZmNZadADwq6R5gJHAV8C7gOsoOgcMzAzMzM6uz7FUAVwN/pXzrHwnMB1agFAVyISAzM7MmSU0AIuLYiPhX4GnKEMC3KMv/HgOeyYzNzMyszrJXARxa3X2GMu6/L6/uAeA5AGZmZk2SPQRwZHV7BnAT8CzwY0otAA8BmJmZNUn2JMCVJc0BlgeWpSQBTwB7A9dmBmZmZlZn2T0AGwBXAmdRNv/5OHB9RNyMewDMzMyaJrsH4GHgsur+ZMqugKdKmgA8lRaVmZlZzWUnAHOBSyLikY4Dkj4AXAislBaVmZlZzWUnAOOBD0vaChhFKQF8F3AIsHteWGZmZvWWPQdgF0rN/38ALwH3AHcDv6YMB5iZmVkTZPcA7B4RbwOQdBZwVUR8VdIfgPuB36dGZ2ZmVlPZCcDyknakVAJcG1hF0jaUFQBDE+MyMzOrtewE4M3AxZQCQCtSuv1/RKkJ4GWAZmZmTZKdANxOmfAHMCsinux4QtIDGQGZmZm1guwE4BjKDoD3RMRLiz33jf4Px8zMrDVkrwL4J2U74IckXSqpvdNzX8oJyczMrP6yewB+CJwE3AisDvxD0t8o5YF7nQMgaSzwU2AIcHJETGhapGZmZjWSnQBsBGwFvBt4ErgVeC+wBjC6pxMlDaEkD7sCc4AbJZ0fEbc3MV4zM7NayE4AlgeOAuYBDwLrAutQSgGv3su521EmDt4Dr9QR2IsysdDMzMx6oIjIe3PpXmAfSgXA+4H1I+JxSWsCt0bEmj2cuw8wNiIOrx4fBGwfEUcu9rojgCOqh5sCM/v+N3mNNkpSY4Xbo5Hbo5Hbo5Hbo5Hbo9HrbY/1I2JEV09k9wD8jFLtbwjwTeD3ku4B3knp3u9JV3MEXpPNRMREYOIbjPN1kTQ5Isb053sOZG6PRm6PRm6PRm6PRm6PRn3ZHtmrAE4BLqJMApwHfAy4BDgMWKuXc+dQNhDqMBJ4qAkxmpmZ1U52AnA68Ex1uz8lIbggIm6g9AL05EZgY0kbSFquOv/8ZgZrZmZWF9kJwB7AGMqs/08CNwGXS1qjtxMjYiFwJKXHYAZwdkTc1sRYX49+HXIYBNwejdwejdwejdwejdwejfqsPbInAT4P7Ev59j4WuIayGmBHYKWIWD8tODMzsxrL7gF4EnguIg6kjOGfQdkVcCQwLDEuMzOzWsvuAbg5Irbu4vhqwEci4tSEsMzMzGovuwfgjMUPSDotIp5akg9/SZMkzZV0a3PCe30kjZU0U9IsSeOz48kmabak6ZKmSpqcHU+Grq5RScMlXSbpruq2ZXq7ummPYyQ9WF0nUyWNy4yxv0gaJekKSTMk3SbpqOp4K18f3bVJq14jQyXdIGla1R7HVsf75BrJ7gFYfNa+gJ2AywEiYs9ezt8BWACcFhFb9PZ+bW1t0d7evnTBmpmZDTJTpkyZN1ALAY2iFO+5Hvgj8AFKbf9ngF57ACLi6sV2EOxRe3s7kye35BdRMzNrQZLu6+657ATgLmAE8GFgG8ra/scpRX4OBC5+o2/QuRTw6NE97i/0urWPv2iJXzt7wu59+t62dOr8d9afv9tgaMeliXEw/F6Dgdux0UBtj+w5AJtExE7Au4C3V8cWAl/r9PgNiYiJETEmIsaMGNFlL4iZmVnLye4BACAi5kj6HfBn4OmICEl5kxPMzMxqLjsBmCxptWrW/6GSVgYelbQNMD85NjMzs9rKHgK4BrhH0p2SPgTcAvwAOI/edwNE0pnAdcCmkuZIOqyp0ZqZmdVEdg/AMcCWwHLANGDriLhb0lrAZcCZPZ0cEQc0PUIzM7Mayk4ARlM++P8MvATMBoiIRyUlhmVmZlZv2QnAfOC3lBn/Ap6WdCnwBPBwZmBmZmZ1lj0H4F5gLmVL39HAF4CNgXFAr5X9zMzMbOlkJwCjI+I44PmIWBARp0TEFhGxNvDu5NjMzMxqKzsBeFrS+sChkoZVGxwMlzQcLwM0MzNrmuw5ACdSyv1uCEyhzAPoENVxMzMz62PZPQDnUlYB/BM4mVIaeIOI2IBSE8DMzMyaIDsBmARcCRxNmQR4laQ1qoqAm2UGZmZmVmfZQwAjIuK/Jd1M2Q3wQOBqYC/KVsFmZmbWBNkJwLKShgKKiABOl/QIZV7AsrmhmZmZ1Vf2EMDJwPaU/QC+IGlZ4Crgj5RiQGZmZtYEqQlARPw4Iq4CPkNZ9/8gMAcYiQsBmZmZNU32EECHHwKfiYgnASQNA34EHJoZlJmZWV1lDwEgaTdgR2C1jmMR8QTwgayYzMzM6i41AZD0feCbwIrAlZI+Xx0fDqyZGZuZmVmdZfcA7AHsDHwFeBH4gqR/AH8HHs0MzMzMrM6yE4A3RcTCiDgN+CjwM0oJ4NnAM5mBmZmZ1Vl2AnC3pPdLGgEsT6kKuCswGVcCNDMza5rsVQDfosz2Hw20A49QhgKmAGPzwjIzM6u37B6AXwKfBS6jbAy0ckRsCFwDnJUZmJmZWZ1lJwArRMRMYPuI2BdYDiAifkpZGWBmZmZNkJ0A3C3pW8Dykn4EPAsgaZ3csMzMzOotOwE4FFiFMu6/B/CCpO9RdgT8dmZgZmZmdZY6CbAq/fs1AElvoVT/E7BnRMxIDM3MzKzWUhMASRdQ1v0v7oOSiIg9+zsmMzOzVpC9DPCE6nZvYG3g9OrxAZRiQGZmZtYE2UMAVwFI+k5E7NDpqQskXZ0UlpmZWe1lTwLsMELShh0PJG0AjEiMx8zMrNayhwA6fImyG+A91eN24NN54ZiZmdXbgEgAIuJiSRsDb6kO3RERL2TGZGZmVmfZqwD27uapjapVAH/s14DMzMxaRHYPwB7V7ZrAu4G/UuoA7ETZGdAJgJmZWRNkrwL4FICkC4HNI+Lh6vE6wEmZsZmZmdXZQFkF0N7x4V95FNgkKxgzM7O6GygJwJWSLpF0iKSDgYuAK3o7SdJYSTMlzZI0vvlhmpmZ1UP2HAAAIuLIakLg+6pDEyPi3J7OkTSEMkywKzAHuFHS+RFxe3OjNTMzG/wGRAIAdMz4fz2T/rYDZkXEPQCSzgL2ApwAmJmZ9WJADAFIeqekGyUtkPSipEWSnu7ltPWABzo9nlMdMzMzs14ooqvN+Po5CGkysD/we2AM8EngzRHxzR7O2RfYLSIOrx4fBGwXEZ9f7HVHAEdUDzcFZvb9b/AabcC8fnifwcLt0cjt0cjt0cjt0cjt0ej1tsf6EdFlaf2BNAQwS9KQiFgE/FrS33s5ZQ4wqtPjkcBDXfzcicDEvou0d5ImR8SY/nzPgczt0cjt0cjt0cjt0cjt0agv22OgJADPSloOmCrpeOBhYKVezrkR2LjaOOhBSg/Cx5sbppmZWT0MiDkAwEGUWI4EnqF8s/9YTydExMLq9ZcAM4CzI+K2JsdpZmZWCwOiByAi7qvuPi/plxExdwnP+xPwp+ZFttT6dchhEHB7NHJ7NHJ7NHJ7NHJ7NOqz9kidBChp+OKHgCnA1pTYHu//qMzMzOovOwF4GbhvscMjKRP8IiI27P+ozMzM6i97DsDXKMvy9oyIDSJiA2BOdX/Qffi7NHEjSbMlTZc0tVrq2XIkTZI0V9KtnY4Nl3SZpLuq22GZMfanbtrjGEkPVtfJVEnjMmPsL5JGSbpC0gxJt0k6qjreytdHd23SqtfIUEk3SJpWtcex1fE+uUbS6wBIGgn8mFLU5z+BaYP0w38IcCedShMDB7RyaWJJs4ExEdGya3gl7QAsAE6LiC2qY8cDj0fEhCpRHBYRR2fG2V+6aY9jgAURcUJmbP2t2vV0nYi4SdIqlOHPjwCH0LrXR3dtsh+teY0IWCkiFkhaFrgGOArYmz64RrJ7AIiIORGxL2Xzn8uAFZNDWlqvlCaOiBeBjtLE1sIi4mpg8bksewGnVvdPpfwH1xK6aY+WFBEPR8RN1f35lNVM69Ha10d3bdKSolhQPVy2+hP00TWSngB0iIgLgJ2AXbJjWUouTfxaAVwqaUpVkdGKtTq2v65u10yOZyA4UtIt1RBBy3R5d5DUTpn8fD2+PoDXtAm06DUiaYikqcBc4LKI6LNrZMAkAAAR8RxlXsBgpC6O5ddZzvWeiNgG+BDwuar712xxvwQ2AraiFAH7UWo0/UzSysA5wBcjorc9UFpCF23SstdIRCyKiK0oE+S3k7RFX/3s1DoAks5f/BCwk6TVASJiz34PauktUWniVhIRD1W3cyWdSxkmuTo3qgHhUUnrRMTD1ZjnEtW9qKuIeLTjvqRfARcmhtOvqnHdc4Azqh1RocWvj67apJWvkQ4R8aSkK4Gx9NE1kt0DMBJ4GjiRktH9CJjf6f5g8kpp4qqs8f7A4glOy5C0UjWJB0krAR8Ebu35rJZxPnBwdf9g4LzEWNJV/4F1+Cgtcp1UE7xOAWZExImdnmrZ66O7Nmnha2RExxdiSStQhsjvoI+ukew6AMtQZjSOA74aEVMl3TMYVwEAVEtTfgIMASZFxPdyI8ojaUPg3Orhm4DftWJ7SDoT2JGyg9ejlJUu/wecDYwG7gf2bZWiV920x46Urt0AZgOf7hjfrDNJ7wX+BkwHXq4Of4My5t2q10d3bXIArXmNbEmZ5DeE8oX97Ij4tqQ16INrJH0ZIDQsBXyUUhNgdHJIZmZmtTZQ9gKYA+wraXfKkICZmZk1UfYQwOoR8WRaAGZmZi0qexLgPEl/kXRYx0QHMzMza77sBGAGZdLczsDdks6TtH8129HMzMyaJDsBeCkiLoyIAylLAs+g1HyeI+l3uaGZmZnVV/YkwFeq51VVAM8Gzpa0Gi1U/9rMzKy/ZScAZ3R1MCKe4tWNDszMzKyPDYg6AGZmZta/svcCuIAeNswZZHsBmJmZDRrZQwAnVLd7A2sDp1ePD6CUezQzM7MmGBBDAJKujogdejtmZmZmfSN7GWCHEdXmMQBI2gAYkRiPmZlZrWUPAXT4EnClpHuqx+3Ap/PCMTMzq7cBMQQAIGl54C3Vwzsi4oXMeMzMzOpsQAwBSFoR+CpwZERMA0ZL+nByWGZmZrU1IBIA4NfAi8C7qsdzgO/mhWNmZlZvAyUB2CgijgdeglfKAqvnU8zMzGxpDZQE4MVqB8AAkLQR4DkAZmZmTTJQVgH8J3AxMFLSGcB7gENSIzIzM6uxgdID8BfgZEr1vzOBHYBnMwMyMzOrs4HSA/AL4GVglYi4UNIw4Bxg29ywzMzM6mmgJADbR8Q2km4GiIgnJC2XHZSZmVldDZQhgJckDeHVSYAjKD0CZmZm1gQDJQH4GXAusKak7wHXAN/PDcnMzKy+BlIp4LcAH6Cs//9rRMxIDsnMzKy2BkwCYGZmZv1noAwBmJmZWT9yAmBmZtaCnACYmZm1ICcAZmZmLcgJgJmZWQv6/+K6/g1xV6jXAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 576x2952 with 41 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots(nonan_question.shape[0], 1, \n", "                       figsize=(8, nonan_question.shape[0]))\n", "for i in range(0, nonan_question.shape[0]):\n", "    ax[i].set_ylabel(nonan_question.index[i])\n", "    ax[i].bar(list(range(nonan_question.shape[1])), \n", "              nonan_question.iloc[i,:])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### ACP"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"data": {"text/plain": ["(41, 30)"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["nonan_question.shape"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/plain": ["PCA(n_components=2, svd_solver='arpack')"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn.decomposition import PCA\n", "acp = PCA(n_components=2, svd_solver=\"arpack\")\n", "acp.fit(nonan_question)"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>X1</th>\n", "      <th>X2</th>\n", "      <th>cluster</th>\n", "    </tr>\n", "    <tr>\n", "      <th>person_id</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0c3a2f07b272478e4cd8</th>\n", "      <td>0.151681</td>\n", "      <td>0.892031</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0cf4e678b9586d5b0e37</th>\n", "      <td>0.809105</td>\n", "      <td>0.267815</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0fa2c1f9be1abc2c1d0b</th>\n", "      <td>-0.243414</td>\n", "      <td>0.005855</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10fb62c55bc7a6a5cda2</th>\n", "      <td>-0.830133</td>\n", "      <td>-0.506903</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>14db30224001f714a266</th>\n", "      <td>-0.433964</td>\n", "      <td>-1.207925</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                            X1        X2  cluster\n", "person_id                                        \n", "0c3a2f07b272478e4cd8  0.151681  0.892031        0\n", "0cf4e678b9586d5b0e37  0.809105  0.267815        0\n", "0fa2c1f9be1abc2c1d0b -0.243414  0.005855        0\n", "10fb62c55bc7a6a5cda2 -0.830133 -0.506903        0\n", "14db30224001f714a266 -0.433964 -1.207925        0"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["coord = acp.transform(nonan_question)\n", "data = pandas.DataFrame(data=coord, columns=['X1', 'X2'], index=nonan_question.index)\n", "data[\"cluster\"] = 0\n", "data.head()"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"image/png": 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1kgKqiy66EIminepQUqD9pDpoG2g9Sk9tVPWSAu0nQKXQPbbNqnjWXrUumUBL0D0W3VWxpED7CVA5pSQK26sl/ZOkUUl7JL0tIr7XZbs9kn4g6SeSjvTKdq1QxbP/PNSh/QRombLaKG6Q9HBEnCXp4fR9L2+IiFe2Okm0qWZf9fYToIVKaaOw/YSkiyJiv+1TJX0hIl7WZbs9ksYi4ruDfH6j2ijaWrNvyxUUUBFZbRRlXVGcHBH7JSl9flGP7ULSQ7Z32N6U9YG2N9mesj3VadKNY7M1+7lma/ZNNjIirV9PkgAqILc2CtvbJJ3SZdWNA3zM6yJin+0XSfqc7a9HxBe7bRgRmyVtlpIrioEDripq9gBKlluiiIiNvdbZ/o7tU+eUnp7u8Rn70uenbd8taYOkromisWZr9hMTyZXEzAw1ewCFKqv0dJ+k69LX10m6d/4Gtp9v+/jZ15Iuk/R4YRFWyfh40iaxbVvyPD5edkQAWqSs+yg+LOku2xOSvi3prZJk+8WS/jYirpR0sqS7bc/G+Y8R8UBJ8Zavivc8AGiFUhJFRByQdEmX5fskXZm+flLSeQWHBgCYh7GeAACZSBQAgEwkCgBAJhLFXJ2OtH07M70BwBwkilltGk8JAAZAopCYQxoAMpAopPaOpwQAfSBRSIynBAAZSBQScyCgHHSeQE2QKGYxnhKKROcJ1EgpExflrVETF6F52joZFSqtihMXAe1F5wnUDIkCKFqVOk/QToI+kCiAolWl8wTtJOgTbRRAWTqdpNw0Olp8kqCdBPNktVGUNXERgDIno5ptJ5mbKGbbSUgUmIfSE7AYda/tV6mdBJVHogAG1YTaflXaSVALtFEAg2habb/MdhJUCm0UwLA0rbZfZjsJaoPSEzAIavtoIRIFipFn42+RDcvU9tFCpSQK22+1vdP2c7a71sTS7S63/YTt3bZvKDJGDFGejb9lNCwzgCRappTGbNtnS3pO0q2Sfjcijml5tr1M0jckXSppWtJ2SeMR8V8LfT6N2RWSZ+Nv0xqWgRJVblDAiNgVEU8ssNkGSbsj4smIOCzpTklX5R8dhirPAfAYXA8oRJXbKE6TtHfO++l0WVe2N9mesj3VqetNUE2UZ+MvDctAIXJLFLa32X68y6PfqwJ3WdazThYRmyNiLCLGRig7VEeejb80LAOFyO0+iojYuMSPmJa0bs77tZL2LfEzm6UuN0uNj0sbN+YTa56fDUBStW+42y7pLNtnSPofSddI+vVyQ6qQyUlpYiKp0R8+nJxJV7n3TZ43dnHTGJCrsrrHvtn2tKQLJH3W9oPp8hfb3ipJEXFE0nskPShpl6S7ImJnGfFWTqeTJIlDh6SDB5PniYn6DlAHoNJKuaKIiLsl3d1l+T5JV855v1XS1gJDq4emDSMBoNKq3OsJvdDbB0CBSBR1RG8fAAWqcmN2PRXVE4nePshDXXrSoVBcUQxT0eMOjYxI69fzD43haMKETMgFExcNS5vGHeKss3na9P1FV5Ub66mR2jLuEGedzdSW7y8WhUQxLG3oicT9G83Vhu8vFo1EMSxt6InEWWdzteH7i0WjjWLYmly/p47dfE3+/iJTVhsF3WOHrcnjDs2edU5MJFcSMzOcdTZNk7+/WDQSBQbD/RtA65AoMDjOOoFWoTEbAJCJRAEAyESiAABkIlEAADKRKAAAmUgUAIBMJAqg7jodaft2xtxCbkgUQJ0xmi8KQKIA6orRfFEQEgVQV4zmi4KUkihsv9X2TtvP2e46WmG63R7b/2n7UdslDQcLVBRzSKAgZV1RPC7pLZK+2Me2b4iIV/Ya/hZoLeaQQEFKGRQwInZJku0yfj3QHIzmiwJUffTYkPSQ7ZB0a0Rs7rWh7U2SNknSS17ykoLCAyqA0XyRs9wShe1tkk7psurGiLi3z495XUTss/0iSZ+z/fWI6FquSpPIZimZ4W5RQQMAjpFbooiIjUP4jH3p89O275a0Qf21awAAhqSy3WNtP9/28bOvJV2mpBEcAFCgsrrHvtn2tKQLJH3W9oPp8hfb3ppudrKkf7X9mKSvSvpsRDxQRrwA0GZl9Xq6W9LdXZbvk3Rl+vpJSecVHBoAYB5HNK/d13ZH0lNlx9HDGknfLTuIARBvvog3X8Tbv9Mjomv3uUYmiiqzPVWnmweJN1/Emy/iHY7KNmYDAKqBRAEAyESiKF7Pu8srinjzRbz5It4hoI0CAJCJKwoAQCYSBQAgE4kiZwNM0nS57Sds77Z9Q5Exzotjte3P2f5m+nxij+1KnVRqof3lxF+l679m+9VFxzgvnoXivcj2wXR/Pmr7Q2XEmcZym+2nbXcdMqeC+3aheCuzb9N41tn+F9u70mPDe7tsU6l9rIjgkeND0tmSXibpC5LGemyzTNK3JP28pBWSHpN0Tknx/omkG9LXN0j64x7b7ZG0pqQYF9xfSu7wv1+SJZ0v6d9L/A70E+9Fkj5TVozzYrlQ0qslPd5jfWX2bZ/xVmbfpvGcKunV6evjJX2jyt/fiOCKIm8RsSsinlhgsw2SdkfEkxFxWNKdkq7KP7qurpJ0e/r6dkm/WlIcWfrZX1dJuiMSX5H0QtunFh1oqkp/3wVFMpT/MxmbVGnf9hNvpUTE/oh4JH39A0m7JJ02b7NK7WMSRTWcJmnvnPfTOvaLU5STI2K/lHyhJb2ox3azk0rtSCeNKlI/+6tK+7TfWC6w/Zjt+22fW0xoi1KlfduvSu5b26OSXiXp3+etqtQ+rvoMd7UwhEmaus0Jm1u/5ax4B/iYvieVykE/+6vQfbqAfmJ5RMlYOz+0faWkeySdlXdgi1SlfduPSu5b2z8n6VOS3hcR35+/usuPlLaPSRRDEEufpGla0ro579dK2rfEz+wpK17b37F9akTsTy91n+7xGWVOKtXP/ip0ny5gwVjmHigiYqvtj9leExFVHNCuSvt2QVXct7aXK0kS/xARn+6ySaX2MaWnatgu6SzbZ9heIekaSfeVFMt9kq5LX18n6ZgrogpMKtXP/rpP0jvS3iPnSzo4W1IrwYLx2j7FttPXG5T8bx4oPNL+VGnfLqhq+zaNZYukXRHx5z02q9Y+LrsHQNMfkt6s5Ozgx5K+I+nBdPmLJW2ds92VSno/fEtJyaqseE+S9LCkb6bPq+fHq6T3zmPpY2cZ8XbbX5Kul3R9+tqSPpqu/0/16HFWoXjfk+7LxyR9RdIvlhjrpKT9kmbS7+5ExfftQvFWZt+m8fySkjLS1yQ9mj6urPI+ZggPAEAmSk8AgEwkCgBAJhIFACATiQIAkIlEAQDIRKIAcpCOEPrftlen709M359u+wHb/2v7M2XHCfSDRAHkICL2SrpZ0ofTRR+WtDkinpL0p5KuLSs2YFAkCiA/fyHpfNvvU3KT1Z9JUkQ8LOkHJcYFDISxnoCcRMSM7d+T9ICkyyIZYhyoHa4ogHxdoWR4iZeXHQiwWCQKICe2XynpUiUzlL2/zIlngKUgUQA5SEcIvVnJXAPfVtKA/ZFyowIWh0QB5OO3JX07Ij6Xvv+YpF+w/XrbX5L0z5IusT1t+5dLixLoA6PHAgAycUUBAMhEogAAZCJRAAAykSgAAJlIFACATCQKAEAmEgUAINP/Azis6ZvW5RNUAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(1, 1)\n", "colors = ['red', 'blue', 'orange', 'green']\n", "for i in range(0, 1):\n", "    data[data.cluster==i].plot(x=\"X1\", y=\"X2\", \n", "                               kind=\"scatter\", \n", "                               ax=ax, label=\"c%d\" % i, \n", "                               color=colors[i])"]}, {"cell_type": "code", "execution_count": 22, "metadata": {"scrolled": false}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["data[\"weight\"] = 10\n", "# Plot miles per gallon against horsepower with other semantics\n", "fig, ax = plt.subplots(1, 1, figsize=(14, 4))\n", "ax.plot(data.loc[data.cluster == 0, 'X1'].values,\n", "        data.loc[data.cluster == 0, 'X2'].values, \"ro\", label=\"c0\")\n", "ax.plot(data.loc[data.cluster == 1, 'X1'].values,\n", "        data.loc[data.cluster == 1, 'X2'].values, \"bo\", label=\"c1\")\n", "ind = list(data.index)\n", "for i in range(0, data.shape[0]):\n", "    ax.text(data.iloc[i, 0], data.iloc[i, 1], ind[i])\n", "ax.legend();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Second jeu de donn\u00e9es"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": ["df2 = df[df.qtime == 'end'].copy()\n", "person_id = ['person_id']"]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": ["cols = person_id + [c for c in df2.columns if \"ml_french\" in c and '-a' in c]\n", "df2_question = df2[cols]"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1080x216 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import missingno as msno\n", "msno.matrix(df2_question, figsize=(15,3));"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": ["cols = person_id + [c for c in df2.columns if \"ml_french\" in c and '-b' in c]\n", "df2_bouton = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"ml_french\" in c and '-nb' in c]\n", "df2_visit = df2[cols]\n", "cols = person_id + [c for c in df2.columns if \"ml_french\" in c and '-dur' in c]\n", "df2_dur = df2[cols]"]}, {"cell_type": "code", "execution_count": 27, "metadata": {"scrolled": false}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th 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"      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-1-a2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-1-a3</th>\n", 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  <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-5-a1</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-5-a2</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-5-a4</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-6-a0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-6-a1</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-6-a2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-6-a3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-7-a3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-7-a4</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-7-a5</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-7-a6</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-8-a1</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-8-a2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-8-a3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-9-a0</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-9-a1</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-9-a2</th>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>ml_french_qcm-9-a3</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.0</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>...</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>53 rows \u00d7 41 columns</p>\n", "</div>"], "text/plain": ["person_id            0c3a2f07b272478e4cd8  0cf4e678b9586d5b0e37  \\\n", "ml_french_qcm-0-a0                    1.0                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    1.0                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   1.0                   NaN   \n", "ml_french_qcm-10-a3                   1.0                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   1.0                   NaN   \n", "ml_french_qcm-11-a2                   1.0                   NaN   \n", "ml_french_qcm-11-a3                   1.0                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   1.0                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   1.0                   NaN   \n", "ml_french_qcm-14-a1                   1.0                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    1.0                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    1.0                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    1.0                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    1.0                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    1.0                   NaN   \n", "ml_french_qcm-7-a6                    1.0                   NaN   \n", "ml_french_qcm-8-a1                    1.0                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    1.0                   NaN   \n", "ml_french_qcm-9-a1                    1.0                   NaN   \n", "ml_french_qcm-9-a2                    1.0                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            0fa2c1f9be1abc2c1d0b  10fb62c55bc7a6a5cda2  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    1.0                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    1.0                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   1.0                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   1.0                   NaN   \n", "ml_french_qcm-11-a3                   1.0                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   1.0                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   1.0                   NaN   \n", "ml_french_qcm-13-a3                   1.0                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    1.0                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    1.0                   NaN   \n", "ml_french_qcm-3-a4                    1.0                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    1.0                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    1.0                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    1.0                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    1.0                   NaN   \n", "ml_french_qcm-7-a4                    1.0                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    1.0                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    1.0                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            14db30224001f714a266  26310ba7a08a6b652fad  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   NaN   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            2c987776b9f528f4eeec  2f2bc77792dacadebd46  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   1.0   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   1.0   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   1.0   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   2.0   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   1.0   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   1.0   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   1.0   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   1.0   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   1.0   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   1.0   \n", "ml_french_qcm-5-a0                    NaN                   1.0   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   1.0   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   1.0   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   1.0   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   1.0   \n", "\n", "person_id            30035b23562d5b2df36f  32606f02fa0df6aac111  ...  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-0-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-0-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-1-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-1-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-1-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-10-a0                   NaN                   NaN  ...   \n", "ml_french_qcm-10-a1                   NaN                   NaN  ...   \n", "ml_french_qcm-10-a3                   NaN                   NaN  ...   \n", "ml_french_qcm-11-a0                   NaN                   NaN  ...   \n", "ml_french_qcm-11-a1                   NaN                   NaN  ...   \n", "ml_french_qcm-11-a2                   NaN                   NaN  ...   \n", "ml_french_qcm-11-a3                   NaN                   NaN  ...   \n", "ml_french_qcm-12-a0                   NaN                   NaN  ...   \n", "ml_french_qcm-12-a1                   NaN                   NaN  ...   \n", "ml_french_qcm-12-a2                   NaN                   NaN  ...   \n", "ml_french_qcm-12-a3                   NaN                   NaN  ...   \n", "ml_french_qcm-13-a0                   NaN                   NaN  ...   \n", "ml_french_qcm-13-a2                   NaN                   NaN  ...   \n", "ml_french_qcm-13-a3                   NaN                   NaN  ...   \n", "ml_french_qcm-14-a1                   NaN                   NaN  ...   \n", "ml_french_qcm-14-a3                   NaN                   NaN  ...   \n", "ml_french_qcm-2-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-2-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-2-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-3-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-3-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-3-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-3-a4                    NaN                   NaN  ...   \n", "ml_french_qcm-3-a5                    NaN                   NaN  ...   \n", "ml_french_qcm-4-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-4-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-4-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-4-a4                    NaN                   NaN  ...   \n", "ml_french_qcm-5-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-5-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-5-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-5-a4                    NaN                   NaN  ...   \n", "ml_french_qcm-6-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-6-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-6-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-6-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-7-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-7-a4                    NaN                   NaN  ...   \n", "ml_french_qcm-7-a5                    NaN                   NaN  ...   \n", "ml_french_qcm-7-a6                    NaN                   NaN  ...   \n", "ml_french_qcm-8-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-8-a2                    NaN                   NaN  ...   \n", "ml_french_qcm-8-a3                    NaN                   NaN  ...   \n", "ml_french_qcm-9-a0                    NaN                   NaN  ...   \n", "ml_french_qcm-9-a1                    NaN                   NaN  ...   \n", "ml_french_qcm-9-a2                    1.0                   NaN  ...   \n", "ml_french_qcm-9-a3                    NaN                   NaN  ...   \n", "\n", "person_id            b07297f205b22a5d42d5  b0d3a8fd2b67439fba05  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   NaN   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            c241c15008614ea67480  c25deb2067e5a89b0223  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   NaN   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            d084aaa43b1793c01dd2  d228121baddcfea28ec2  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   NaN   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            d36d229a6a41414c6b26  dc1f7980a1e746469861  \\\n", "ml_french_qcm-0-a0                    NaN                   NaN   \n", "ml_french_qcm-0-a2                    NaN                   NaN   \n", "ml_french_qcm-0-a3                    NaN                   NaN   \n", "ml_french_qcm-1-a1                    NaN                   NaN   \n", "ml_french_qcm-1-a2                    NaN                   NaN   \n", "ml_french_qcm-1-a3                    NaN                   NaN   \n", "ml_french_qcm-10-a0                   NaN                   NaN   \n", "ml_french_qcm-10-a1                   NaN                   NaN   \n", "ml_french_qcm-10-a3                   NaN                   NaN   \n", "ml_french_qcm-11-a0                   NaN                   NaN   \n", "ml_french_qcm-11-a1                   NaN                   NaN   \n", "ml_french_qcm-11-a2                   NaN                   NaN   \n", "ml_french_qcm-11-a3                   NaN                   NaN   \n", "ml_french_qcm-12-a0                   NaN                   NaN   \n", "ml_french_qcm-12-a1                   NaN                   NaN   \n", "ml_french_qcm-12-a2                   NaN                   NaN   \n", "ml_french_qcm-12-a3                   NaN                   NaN   \n", "ml_french_qcm-13-a0                   NaN                   NaN   \n", "ml_french_qcm-13-a2                   NaN                   NaN   \n", "ml_french_qcm-13-a3                   NaN                   NaN   \n", "ml_french_qcm-14-a1                   NaN                   NaN   \n", "ml_french_qcm-14-a3                   NaN                   NaN   \n", "ml_french_qcm-2-a0                    NaN                   NaN   \n", "ml_french_qcm-2-a2                    NaN                   NaN   \n", "ml_french_qcm-2-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a1                    NaN                   NaN   \n", "ml_french_qcm-3-a2                    NaN                   NaN   \n", "ml_french_qcm-3-a3                    NaN                   NaN   \n", "ml_french_qcm-3-a4                    NaN                   NaN   \n", "ml_french_qcm-3-a5                    NaN                   NaN   \n", "ml_french_qcm-4-a0                    NaN                   NaN   \n", "ml_french_qcm-4-a1                    NaN                   NaN   \n", "ml_french_qcm-4-a2                    NaN                   NaN   \n", "ml_french_qcm-4-a4                    NaN                   NaN   \n", "ml_french_qcm-5-a0                    NaN                   NaN   \n", "ml_french_qcm-5-a1                    NaN                   NaN   \n", "ml_french_qcm-5-a2                    NaN                   NaN   \n", "ml_french_qcm-5-a4                    NaN                   NaN   \n", "ml_french_qcm-6-a0                    NaN                   NaN   \n", "ml_french_qcm-6-a1                    NaN                   NaN   \n", "ml_french_qcm-6-a2                    NaN                   NaN   \n", "ml_french_qcm-6-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a3                    NaN                   NaN   \n", "ml_french_qcm-7-a4                    NaN                   NaN   \n", "ml_french_qcm-7-a5                    NaN                   NaN   \n", "ml_french_qcm-7-a6                    NaN                   NaN   \n", "ml_french_qcm-8-a1                    NaN                   NaN   \n", "ml_french_qcm-8-a2                    NaN                   NaN   \n", "ml_french_qcm-8-a3                    NaN                   NaN   \n", "ml_french_qcm-9-a0                    NaN                   NaN   \n", "ml_french_qcm-9-a1                    NaN                   NaN   \n", "ml_french_qcm-9-a2                    NaN                   NaN   \n", "ml_french_qcm-9-a3                    NaN                   NaN   \n", "\n", "person_id            e2cf842295864a4d2620  eed744add9c2737812ad  \n", "ml_french_qcm-0-a0                    NaN                   NaN  \n", "ml_french_qcm-0-a2                    NaN                   NaN  \n", "ml_french_qcm-0-a3                    NaN                   NaN  \n", "ml_french_qcm-1-a1                    NaN                   NaN  \n", "ml_french_qcm-1-a2                    NaN                   NaN  \n", "ml_french_qcm-1-a3                    NaN                   NaN  \n", "ml_french_qcm-10-a0                   NaN                   NaN  \n", "ml_french_qcm-10-a1                   NaN                   NaN  \n", "ml_french_qcm-10-a3                   NaN                   NaN  \n", "ml_french_qcm-11-a0                   NaN                   NaN  \n", "ml_french_qcm-11-a1                   NaN                   NaN  \n", "ml_french_qcm-11-a2                   NaN                   NaN  \n", "ml_french_qcm-11-a3                   NaN                   NaN  \n", "ml_french_qcm-12-a0                   NaN                   NaN  \n", "ml_french_qcm-12-a1                   NaN                   NaN  \n", "ml_french_qcm-12-a2                   NaN                   NaN  \n", "ml_french_qcm-12-a3                   NaN                   NaN  \n", "ml_french_qcm-13-a0                   NaN                   NaN  \n", "ml_french_qcm-13-a2                   NaN                   NaN  \n", "ml_french_qcm-13-a3                   NaN                   NaN  \n", "ml_french_qcm-14-a1                   NaN                   NaN  \n", "ml_french_qcm-14-a3                   NaN                   NaN  \n", "ml_french_qcm-2-a0                    NaN                   NaN  \n", "ml_french_qcm-2-a2                    NaN                   NaN  \n", "ml_french_qcm-2-a3                    NaN                   NaN  \n", "ml_french_qcm-3-a1                    NaN                   NaN  \n", "ml_french_qcm-3-a2                    NaN                   NaN  \n", "ml_french_qcm-3-a3                    NaN                   NaN  \n", "ml_french_qcm-3-a4                    NaN                   NaN  \n", "ml_french_qcm-3-a5                    NaN                   NaN  \n", "ml_french_qcm-4-a0                    NaN                   NaN  \n", "ml_french_qcm-4-a1                    NaN                   NaN  \n", "ml_french_qcm-4-a2                    NaN                   NaN  \n", "ml_french_qcm-4-a4                    NaN                   NaN  \n", "ml_french_qcm-5-a0                    NaN                   NaN  \n", "ml_french_qcm-5-a1                    NaN                   NaN  \n", "ml_french_qcm-5-a2                    NaN                   NaN  \n", "ml_french_qcm-5-a4                    NaN                   NaN  \n", "ml_french_qcm-6-a0                    NaN                   NaN  \n", "ml_french_qcm-6-a1                    NaN                   NaN  \n", "ml_french_qcm-6-a2                    NaN                   NaN  \n", "ml_french_qcm-6-a3                    NaN                   NaN  \n", "ml_french_qcm-7-a3                    NaN                   NaN  \n", "ml_french_qcm-7-a4                    NaN                   NaN  \n", "ml_french_qcm-7-a5                    NaN                   NaN  \n", "ml_french_qcm-7-a6                    NaN                   NaN  \n", "ml_french_qcm-8-a1                    NaN                   NaN  \n", "ml_french_qcm-8-a2                    NaN                   NaN  \n", "ml_french_qcm-8-a3                    NaN                   NaN  \n", "ml_french_qcm-9-a0                    NaN                   NaN  \n", "ml_french_qcm-9-a1                    NaN                   NaN  \n", "ml_french_qcm-9-a2                    NaN                   NaN  \n", "ml_french_qcm-9-a3                    NaN                   NaN  \n", "\n", "[53 rows x 41 columns]"]}, "execution_count": 28, "metadata": {}, "output_type": "execute_result"}], "source": ["gr2_question = df2_question.groupby(\"person_id\").agg(aggnotnan)\n", "gr2_question.T"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["msno.matrix(gr2_question, figsize=(10,4));"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### ACP"]}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [{"data": {"text/plain": ["(41, 53)"]}, "execution_count": 30, "metadata": {}, "output_type": "execute_result"}], "source": ["gr2_question.shape"]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": ["nonan2_question = gr2_question.fillna(0)"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [{"data": {"text/plain": ["PCA(n_components=2, svd_solver='arpack')"]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn.decomposition import PCA\n", "acp = PCA(n_components=2, svd_solver=\"arpack\")\n", "acp.fit(nonan2_question)"]}, {"cell_type": "code", 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<td>-0.002668</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                            X1        X2  cluster\n", "person_id                                        \n", "0c3a2f07b272478e4cd8  2.887376  0.633808        0\n", "0cf4e678b9586d5b0e37 -0.453284 -0.002668        0\n", "0fa2c1f9be1abc2c1d0b  2.192238  2.514812        0\n", "10fb62c55bc7a6a5cda2 -0.453284 -0.002668        0\n", "14db30224001f714a266 -0.453284 -0.002668        0\n", "26310ba7a08a6b652fad -0.453284 -0.002668        0\n", "2c987776b9f528f4eeec -0.453284 -0.002668        0\n", "2f2bc77792dacadebd46  2.600291 -1.841381        0\n", "30035b23562d5b2df36f -0.344912  0.188055        0\n", "32606f02fa0df6aac111 -0.453284 -0.002668        0\n", "33e5a65b534574acfb6e -0.453284 -0.002668        0\n", "33eada5fee0e4231d402 -0.453284 -0.002668        0\n", "3747fb5233006d3805da -0.453284 -0.002668        0\n", "43637590b1591346cfc7 -0.453284 -0.002668        0\n", "4a09796a11ccc59ef66a  1.761605 -1.720518        0\n", "4b2cc1988785a7526ca1 -0.453284 -0.002668        0\n", "50670df89db35a7ae946  2.517077  1.622700        0\n", "5102d6da7b6ed2c2690c -0.453284 -0.002668        0\n", "5a24afa1f3a84660b6d7 -0.453284 -0.002668        0\n", "5a617c2783baa779527f -0.105955  0.247710        0\n", "69fc4cf871b5db6fca3a -0.453284 -0.002668        0\n", "6a5bf88f39630d05d66c -0.453284 -0.002668        0\n", "753a507a9205e4c196fa -0.453284 -0.002668        0\n", "75a639141c325f39a368 -0.453284 -0.002668        0\n", "75b801f977bc69f8a34b -0.453284 -0.002668        0\n", "8a8c40ad28eb1206efd5 -0.453284 -0.002668        0\n", "8e09880e917ca37a12dd -0.453284 -0.002668        0\n", "8f84e052a4513ed0dd80 -0.453284 -0.002668        0\n", "93087555ec15d71b0da1  2.753499 -1.265432        0\n", "afe4be0559208b09e577  0.243858 -0.294390        0\n", "b0316cecc64ba1c1bd87 -0.453284 -0.002668        0\n", "b07297f205b22a5d42d5 -0.453284 -0.002668        0\n", "b0d3a8fd2b67439fba05 -0.453284 -0.002668        0\n", "c241c15008614ea67480 -0.453284 -0.002668        0\n", "c25deb2067e5a89b0223 -0.453284 -0.002668        0\n", "d084aaa43b1793c01dd2 -0.453284 -0.002668        0\n", "d228121baddcfea28ec2 -0.453284 -0.002668        0\n", "d36d229a6a41414c6b26 -0.453284 -0.002668        0\n", "dc1f7980a1e746469861 -0.453284 -0.002668        0\n", "e2cf842295864a4d2620 -0.453284 -0.002668        0\n", "eed744add9c2737812ad -0.453284 -0.002668        0"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["coord = acp.transform(nonan2_question)\n", "data = pandas.DataFrame(data=coord, columns=['X1', 'X2'], index=nonan_question.index)\n", "data[\"cluster\"] = 0\n", "data"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(1, 1)\n", "colors = ['red', 'blue', 'orange', 'green']\n", "for i in range(0, 1):\n", "    data[data.cluster==i].plot(x=\"X1\", y=\"X2\", \n", "                               kind=\"scatter\", \n", "                               ax=ax, label=\"c%d\" % i, \n", "                               color=colors[i])"]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2"}}, "nbformat": 4, "nbformat_minor": 2}