{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 1A.data - visualisation des donn\u00e9es - correction\n", "\n", "Correction."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt"]}, {"cell_type": "code", "execution_count": 3, "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": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 1 : \u00e9cart entre les mari\u00e9s\n", "\n", "On reprend d'abord le code qui permet de r\u00e9cup\u00e9rer les donn\u00e9es."]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["(246123, 16) Index(['ANAISH', 'DEPNAISH', 'INDNATH', 'ETAMATH', 'ANAISF', 'DEPNAISF',\n", "       'INDNATF', 'ETAMATF', 'AMAR', 'MMAR', 'JSEMAINE', 'DEPMAR', 'DEPDOM',\n", "       'TUDOM', 'TUCOM', 'NBENFCOM'],\n", "      dtype='object')\n"]}, {"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>ANAISH</th>\n", "      <th>DEPNAISH</th>\n", "      <th>INDNATH</th>\n", "      <th>ETAMATH</th>\n", "      <th>ANAISF</th>\n", "      <th>DEPNAISF</th>\n", "      <th>INDNATF</th>\n", "      <th>ETAMATF</th>\n", "      <th>AMAR</th>\n", "      <th>MMAR</th>\n", "      <th>JSEMAINE</th>\n", "      <th>DEPMAR</th>\n", "      <th>DEPDOM</th>\n", "      <th>TUDOM</th>\n", "      <th>TUCOM</th>\n", "      <th>NBENFCOM</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1982</td>\n", "      <td>75</td>\n", "      <td>1</td>\n", "      <td>1</td>\n", "      <td>1984</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>2012</td>\n", "      <td>01</td>\n", "      <td>1</td>\n", "      <td>29</td>\n", "      <td>99</td>\n", "      <td>9</td>\n", "      <td></td>\n", "      <td>N</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1956</td>\n", "      <td>69</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>1969</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>4</td>\n", "      <td>2012</td>\n", "      <td>01</td>\n", "      <td>3</td>\n", "      <td>75</td>\n", "      <td>99</td>\n", "      <td>9</td>\n", "      <td></td>\n", "      <td>N</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1982</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>1992</td>\n", "      <td>99</td>\n", "      <td>1</td>\n", "      <td>1</td>\n", "      <td>2012</td>\n", "      <td>01</td>\n", "      <td>5</td>\n", "      <td>34</td>\n", "      <td>99</td>\n", "      <td>9</td>\n", "      <td></td>\n", "      <td>N</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1985</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>1987</td>\n", "      <td>84</td>\n", "      <td>1</td>\n", "      <td>1</td>\n", "      <td>2012</td>\n", "      <td>01</td>\n", "      <td>4</td>\n", "      <td>13</td>\n", "      <td>99</td>\n", "      <td>9</td>\n", "      <td></td>\n", "      <td>N</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1968</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>1963</td>\n", "      <td>99</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>2012</td>\n", "      <td>01</td>\n", "      <td>6</td>\n", "      <td>26</td>\n", "      <td>99</td>\n", "      <td>9</td>\n", "      <td></td>\n", "      <td>N</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  ANAISH DEPNAISH INDNATH ETAMATH ANAISF DEPNAISF INDNATF ETAMATF  AMAR MMAR  \\\n", "0   1982       75       1       1   1984       99       2       1  2012   01   \n", "1   1956       69       2       4   1969       99       2       4  2012   01   \n", "2   1982       99       2       1   1992       99       1       1  2012   01   \n", "3   1985       99       2       1   1987       84       1       1  2012   01   \n", "4   1968       99       2       1   1963       99       2       1  2012   01   \n", "\n", "  JSEMAINE DEPMAR DEPDOM TUDOM TUCOM NBENFCOM  \n", "0        1     29     99     9              N  \n", "1        3     75     99     9              N  \n", "2        5     34     99     9              N  \n", "3        4     13     99     9              N  \n", "4        6     26     99     9              N  "]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["from urllib.error import URLError\n", "import pyensae.datasource\n", "from pyensae.datasource import dBase2df, DownloadDataException\n", "files = [\"etatcivil2012_nais2012_dbase.zip\",\n", "         \"etatcivil2012_dec2012_dbase.zip\",\n", "         \"etatcivil2012_mar2012_dbase.zip\" ]\n", "\n", "try:\n", "    pyensae.datasource.download_data(files[-1], \n", "                          website='http://telechargement.insee.fr/fichiersdetail/etatcivil2012/dbase/')\n", "except (DownloadDataException, URLError, TimeoutError):\n", "    # backup plan\n", "    pyensae.datasource.download_data(files[-1], website=\"xd\")\n", "\n", "df = dBase2df(\"mar2012.dbf\")\n", "print(df.shape, df.columns)\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Puis on effectue les op\u00e9rations sugg\u00e9r\u00e9es par l'\u00e9nonc\u00e9."]}, {"cell_type": "code", "execution_count": 5, "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>differenceHF</th>\n", "      <th>nb</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>-59</td>\n", "      <td>6</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>-56</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>97</th>\n", "      <td>50</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>98</th>\n", "      <td>56</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>99</th>\n", "      <td>59</td>\n", "      <td>1</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["    differenceHF  nb\n", "0            -59   6\n", "1            -56   1\n", "97            50   1\n", "98            56   1\n", "99            59   1"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["df[\"ANAISH\"] = df.apply (lambda r:  int(r[\"ANAISH\"]), axis=1)\n", "df[\"ANAISF\"] = df.apply (lambda r:  int(r[\"ANAISF\"]), axis=1)\n", "df[\"differenceHF\"] = df.ANAISH - df.ANAISF\n", "df[\"nb\"] = 1\n", "dist = df[[\"nb\",\"differenceHF\"]].groupby(\"differenceHF\", as_index=False).count()\n", "import pandas\n", "pandas.concat([dist.head(n=2), dist.tail(n=3)])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 2 : graphe de la distribution avec pandas\n", "\n", "L'exemple est sugg\u00e9r\u00e9 par le paragraphe : [bar plots](http://pandas.pydata.org/pandas-docs/stable/visualization.html#bar-plots)."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1152x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ax = dist.plot(kind=\"bar\", y=\"nb\", x=\"differenceHF\", figsize=(16,6), color=\"red\")\n", "ax.set_title(\"Distribution des \u00e9carts d'\u00e2ge entre mari et femme\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Mais on pouvait directement dessiner la distribution sans passer par un ``group by`` comme sugg\u00e9r\u00e9e par le paragraphe [histograms](http://pandas.pydata.org/pandas-docs/stable/visualization.html#histograms)."]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1152x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ax = df[\"differenceHF\"].hist(figsize=(16,6), bins=50)\n", "ax.set_title(\"Distribution des \u00e9carts d'\u00e2ge entre mari et femme\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Ou encore la distribution liss\u00e9e (voir [density plot](http://pandas.pydata.org/pandas-docs/stable/visualization.html#density-plot)) (cela prend une minute environ) :"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1152x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["ax = df[\"differenceHF\"].plot(figsize=(16,6), kind=\"kde\")\n", "ax.set_title(\"Distribution des \u00e9carts d'\u00e2ge entre mari et femme\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Le second graphique peut \u00eatre obtenu en \u00e9crivant :"]}, {"cell_type": "code", "execution_count": 9, "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": ["df[\"ageH\"] = -df.ANAISH + 2012\n", "df[\"ageF\"] = -df.ANAISF + 2012\n", "df.plot(x=\"ageH\", y=\"ageF\", kind=\"scatter\")\n", "ax.set_title(\"Nuage de points - \u00e2ge mari et femme\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Il y a trop de points pour que cela soit lisible. C'est pourquoi, on utilise souvent une [heatmap](http://pandas.pydata.org/pandas-docs/stable/visualization.html#hexagonal-bin-plot)."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 504x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df.plot(kind='hexbin', x=\"ageH\", y=\"ageF\", gridsize=25, figsize=(7,6))\n", "ax.set_title(\"Heatmap - \u00e2ge entre mari et femmes\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 3 : distribution des mariages par jour\n", "\n", "On veut obtenir un graphe qui contient l'histogramme de la distribution du nombre de mariages par jour de la semaine et d'ajouter une seconde courbe correspond avec un second axe \u00e0 la r\u00e9partition cumul\u00e9e.\n", "\n", "https://github.com/pydata/pandas/issues/11111"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["# ce code \u00e9choue pour pandas 0.17.rc1, prendre 0.16.2 ou 0.17.rc2\n", "df[\"nb\"] = 1\n", "dissem = df[[\"JSEMAINE\",\"nb\"]].groupby(\"JSEMAINE\",as_index=False).sum()\n", "total = dissem[\"nb\"].sum()\n", "repsem = dissem.cumsum() \n", "repsem[\"nb\"] /= total\n", "\n", "ax = dissem[\"nb\"].plot(kind=\"bar\", color=\"red\")\n", "repsem[\"nb\"].plot(ax=ax, secondary_y=True)\n", "ax.set_title(\"distribution des mariages par jour de la semaine\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 4 : dessin d'un graphe avec networkx\n", "\n", "On construit un graphe al\u00e9atoire, ses 20 arcs sont obtenus en tirant 20 fois deux nombres entiers entre 1 et 10.  Chaque arc doit avoir une \u00e9paisseur al\u00e9atoire. "]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"image/png": 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iRSBtOX36NMaPH4/s7Gy4uLhg37596Nevn77L0qsZuxLUv3WHBwzxdsGmYJqJTLhXU2Ca5NjQq66ZVF6jevl2CH0Si8V49OhRjUOnxcXFNR5raWmJFi1aKA1EDw8PWFrW/Uy6QCTh4u3gSMwp7A4dBLFYDLFYDKlUCjMzM5ibm8sflWFa258N/XUCgQBmZmbo378/EhMTMX78eJw5cwYDBw7EN998g08//RR8Na4FGzsut1CrLyNBxPCZXGCqcs2EMaBULMXSmGQA0HpoFhUVyRcAf/nx8OFDSKU1D101bNiwxrPE119/nbNfyvZCbn5kxowcjtU/L5J/zRiDRCKRB2hNf67tOVVeV1ZWhqKiIs7aq+kYsVgMHo+nEKBWVlYoLS1FeHg4vv32W7i6usLS0lLv4a7q6zQZFeByC7WZfTw1bouQujCpwLyR/gJLY1JUmmAAAKViGZbGpKCjm4NG10wYY8jJyalx6DQ7O7vGY18eOn35jLFhw4Zq16VK/RYlOeBpYZ3VqqFS30il0mpheurUKYSFhSE/Px9FRUX48ssv4e3tzUloi0QinXz4kEql8uBUNYBzWgagzKmtRn+vtIUa0TWTCszIM6kQSVSfYAAAIokUG86kvvKaiUQiQXp6eo2hWFRUVOOxFhYWaN68uTwEW7ZsqTB0KhQK1apdU2KxGAcOHMDKlSuRmHKvYpasBiesprbOqpmZWbWFFIKDg9GnTx+MHTsWly9fxtSpU7FixQrMmTPHaK7lymQy+YcBVc/CV18V4VaemuOxVdAWakSXTCYwubxmYsWX1jh0mpaWBomk5ut8Dg4O1YZMK88WXV1dOVmhhiv5+fnYsmUL1q1bh/T0dABAo0aN0MyiGOkyC6jzV8nj0TqrlZo2bYqzZ89i4cKFWLNmDebNm4e4uDhs377d4G6pUYbP54PP56s1KvBrznXcytN88hhtoUZ0yWQCk4trJmVlZfB5dyYyY3+q9XWurq41Xk90dHTUuA5te/jwIdauXYutW7eisLBiyMvLywvz589HcHAw7jwrU/t2AKHADCH9WnJdstGysLDA6tWr8cYbb2Dq1Kk4ePAgEhMTERUVZRQbb6vLq7E9LAVZGu0IQ1uoEV0zmcBMySrQ6D8nAMDMHCJLJ5ibmysMnVZ9NG/eHFZWVtwUrWPx8fFYuXIloqKi5BOM+vfvjwULFmDYsGHyiUM+7lYID/DC4sM3IOPV/Ueo4oZzL7p3TonAwED4+Phg9OjRuH79Onr06IG1a9dixowZRjNEq4ogXzesjr2jURumNrRP9M9kApOr2yEGvT0Cu4/8YFBDp5qQyWQ4evQoVq5cibi4OACAQCDAhAkTMH/+fHTp0kXpcYOaW2H2H9tg13cyeGYWtS6TR0ua1Y2npycuXryIuXPnYvPmzZg1axbi4uKwadMm2Nra6rs8Tmm8hRoN7RM9MJkbwLi6HaJxwwb1IixLSkqwceNGeHl5YeTIkYiLi4O9vT0+/vhj3L9/H7t3764xLAFg3bp1eJFwFNl7PkXJ3UtgUjGEAsUfJ6GAD0sBH0O8XbB/RncKyzoQCoXYtGkTdu3aBWtra+zZswddu3ZFUlKSvkvjXGi/lhAK1Pu/REP7RB9M5gyTrplUyM7ORkREBDZu3Ijc3FwAQLNmzTB37lxMmzYNdnavfn+FhYXYsGEDAKA8KxXPDn2HZavWwdE3oM7rrJLaBQcHo0uXLhg9ejSSkpLg7++PTZs2YeLEifoujTM+7g4ID/BScy1ZGtonumcygWnq10z++ecfrFq1Crt370Z5eTkAoGvXrliwYAECAwNVWhB8y5YtePHihfxrR0dHfDTjfdjY2HBdtknz9vbGX3/9hVmzZmH37t2YNGkS4uLisHbtWqO9Tv6yylEH2q2EGAOTGZKtvGYCpu4ZJjO6ayaMMcTGxmLYsGFo3749tm/fDrFYLB+CvXz5MsaMGaNSWJaXl2PVqlUK3wsLC6Ow1BIbGxvs3LkTP/74IywtLbFlyxb07NkTqamp+i6NM8HdPbB/RncM8XaBpYBPQ/vEYJnMGSYAWKfFQSZpCr656gsAyMRlkNyMAWO+Bj9rsby8HPv27cOqVatw48YNAICVlRWmTp2KuXPnolWrVmq3vXfvXmRmZsq/trKywuzZszWumdSMx+Phgw8+gL+/P4KCgpCYmIguXbpgx44dGDVqlL7L40RHNwdsCvaTb6H2T8YL7Dt4BKy8BJ8umIXRvu5G9WGV1E8ms1tJdHQ0goKCYNtpGBoOmAa+Rd1DU1YuwvM/t6Eo8QSmTZuGTZs2GeSehs+fP8fmzZuxfv16PH5ccVO4i4sLZs+ejVmzZsHJyUmj9mUyGdq3b4/k5GT598LCwrB+/XqN2iV1l5+fj2nTpiE6OhoAMGfOHPzwww+wsFB/f1JD1ahRI+Tm5iIrKwsuLi76LoeYkJp2KzGJIdlr167JJ0sUJZ7A8z+3QSYWgdWyDyRQsbgzE5fJwxIAtm3bhjFjxkAkEmm77Dq7f/8+PvroI7i7u+Ozzz7D48eP0a5dO2zfvh0PHz5EeHi4xmEJAMeOHVMISzMzMyxYsEDjdkndNWjQAAcOHMCaNWsgEAiwdu1a9OnTB48ePdJ3aZxzdXUFAGRkaL7oCCFcqPeB+fjxYwwfPhylpaXy7xUlnkD2nk/hxsut/ZpJOxesGPo6rDIVz7IPHTqEgIAAFBQU6OQ91OTSpUsICgpCq1atsH79ehQXF2PQoEH47bffcOvWLUydOlWlrbte5YcfflD4esyYMfDw8OCsfVI3PB4Pc+bMQVxcHNzd3XHlyhV07twZx48f13dpnKoMzKqXAAjRK8ZYjQ9fX19mzIqLi5mfnx9DxQRXhcc777zDpFIpe1YoYpvOprK5+66z9//7F5u77zrbdDaVPSsUydu5ffs2a9q0abU2unTpwrKzs3X6niQSCYuKimI9evSQ12Fubs4mT57Mbty4obV+z58/X+39X79+XWv9kbp59uwZCwgIkP+bfPbZZ0wsFuu7LE588MEHDACLjIzUdynExABIYEoysd4GplQqZaNHj1Yalj4+PqywsFCl9tLT05m3t3e1tlq3bs3S0tK09C7+p7CwkK1bt461aNFC3nfDhg3ZZ599xjIzM7Xe//DhwxXe95AhQ7TeJ6kbqVTKli1bxvh8PgPA+vbtyx4/fqzvsjT21VdfMQDs888/13cpxMSYXGAuWrRIaVi6uLiwhw8fqtXms2fPWLdu3aq16erqyv755x+O30GFzMxM9umnnzIHBwd5fy1atGDr169XOfTV9ffff1d7z3/++adO+iZ1d+bMGda4cWMGgL322mvsjz/+0HdJGtm6dSsDwCZPnqzvUoiJManA3LNnj9KwtLS0ZJcvX9ao7cLCQjZo0KBqbTs6OmrcdlWJiYls0qRJzNzcXN5Hz549WXR0NJNIJJz1UxeTJ09WeK/+/v5MJpPptAZSN1lZWWzAgAEMAOPz+eybb75hUqlU32Wp5cSJEwwAGzhwoL5LISbGZALz0qVLzNLSUmlg7t27l5M+RCIRGzNmTLX2bWxs2KlTp9RuVyaTsRMnTrA333xT3iafz2dBQUHs0qVLnNSuqvT0dCYQCBTeZ1RUlF5qIXUjkUjYokWLGI/Hkw+f5+Tk6Lssld28eZMBYF5eXvouhZgYkwjMhw8fMhcXF6VhuXjxYk77kkgkbObMmdX6MTc3Z7/88otKbYlEIrZt2zbWrl07hfD96KOP2L179zitW1Xz589XeH+tWrXS+RkuUc+JEyeYk5MTA8Dc3NzYhQsX9F2SSvLy8hgAZmtrq+9SiImp94FZWFjIfHx8lIbl6NGjtTIsJZPJ2BdffFGtPx6PxzZt2vTK43Nyctg333yjEPKvv/46+/7771leXh7n9aoqLy+P2draKry3zZs367ssooJHjx6xnj17MgBMIBCwlStXGs1wukwmY1ZWVgwAy8/P13c5xITU68CUSqVsxIgRSsPSz8+PFRcXa7X/1atXK+176dKlSn853b59m82aNUv+y6By5u7OnTtZWVmZVmtVxbffflttwlRpaam+yyIqKi8vZwsWLJD/O44cOZI9f/5c32XVSatWrRgAlpSUpO9SiAmp14H5ySefKA0sV1dXndxywRhjO3fuZGZmZtVqmDdvHpNKpUwmk7GzZ8+yESNGyK8tAWDDhg1jsbGxBvepv6SkhDk7Oyu8l++++07fZRENHDp0iDVo0IABYM2bN2cJCQn6LumV+vXrxwBoNDeAEFXV28DcsWOH0rC0trZmV69e1WktR48eZUKhsFotb7zxBuvSpYv8awsLCzZt2jSt3YrChY0bNyq8Bzs7O6M5KyE1u3fvnvxn0cLCgm3cuNHgPqxVNWHCBAaA7dixQ9+lEBNSU2Aa9dJ4cXFxmDFjhtLndu3ahS5duui0nrfffhunTp2Cvb29wvfj4uJw7do1ODo6YtGiRXj06BG2bt0Kb29vndZXV1KpFP/5z38Uvjdr1iw4ODjopyDCmRYtWuDChQv48MMPUV5ejg8//BDBwcEoKirSd2lKublV7D9L68kSQ2C0gXn//n28++67EIvF1Z5bunQpAgMD9VAV4OHhgZEjRyrdAqxt27aYP3++we+8EB0djXv37sm/Njc3x5w5c/RYEeGSUCjEhg0bsGfPHtjY2GDv3r3w9/fH33//re/SqqH1ZIkhMcrAzM/Px/Dhw5Gbm1vtueDgYHz22Wc6r+natWuYMGECWrRogZ07d4IxBqFQcQuxCxcuoF+/fsjKytJ5fXXFGMPy5csVvjdx4kT5Ly5Sf7z33ntISEhAu3btkJKSgq5du2Lnzp36LktB5RkmBSYxBEYXmBKJBOPGjUNSUlK153r27IktW7bobINnmUyGY8eOoX///vD19cXevXvBGMO4ceMQHx+P+/fvo0OHDgrH3LhxA71798aDBw90UqOq/vjjD1y7dk3+NY/Hw8cff6zHiog2eXl54cqVK5g0aRJKS0sxefJkTJ8+XWF3H32iLb6IQVF2YZMZ8KSfOXPmKJ3k07RpU53tHFJSUsI2b97M2rRpozApZv78+dXWqc3Ly5PfB1f10aRJE3bz5k2d1KuKqqsM4d9bEEj9J5PJ2LZt2+ST1jp27Mju3Lmj77JYRkYGAyrWxiVEV1AfZslu2rRJaVja2trqJHyys7PZ4sWLWaNGjeR9u7u7s//85z/sxYsXNR5XXFzMhg0bVq1uBwcHg1p95erVq9Vq1NeSfEQ/EhMT5fc+2tnZqbxqFdckEon8di2RSPTqAwjhgNEHZmxsrNL7HHk8Hjt69KhW+05KSmLTp09XWKPW19eX7d27l5WXl9epjfLycvbee+9Vq9/KyorFxMRotf66Gjt2rEJtffr00XdJRA/y8/MVtsabPXu2XhfUcHV1ZQDYgwcP9FYDMS1GHZi3b99W2N6q6mPlypVa6VMmk7E//vhDYXNeAGz48OHszJkzat27JpVKWVhYWLX3IBAIOFsYXl2pqany/RQrH8eOHdNrTUR/ZDIZW79+vXy3nK5du+pk31dlKrfUO3/+vF76J6bHaAMzNzdXPkT08mPatGmc33RdXl7Odu3axTp37izvRygUspkzZ7KUlBSN25fJZGzJkiVKz5TXr1/PwTtQz4cffqhQT/v27Q36hnaiG1euXGHNmjVjQMWG5doezVEmMDCQAWD79u3Ted/ENBllYJaXl8v39nv50bdvX06HiZ4/f86WL18uH/6pnGiwZMkS9vTpU876qRQZGamwRF7l48svv9R5UGVnZ1dboWjnzp06rYEYrtzcXPb222/LfzYWLlzIxGKxzvqfPXu2VkeTCHmZ0QWmTCZTun0WAObp6cmePXvGST8PHjxgc+bMUdiVo23btmzLli1aX2h879691faaBMDCwsJ0uunvyzuuNG3atM7XZolpkEqlbPny5fJ5BG+88YbO1mlevnw5AyrWZSZEF4wuMNeuXas0LBs0aMCSk5M1bv/y5cts9OjRCtftBgwYwI4fP67TsDpx4oTCriWVj/Hjx+tkokVhYSFr2LChQt9r1qzRer/EOJ09e5Y1adKEAWDOzs7s999/13qfu3fvZgDYmDFjtN4XIYwZWWDGxMRUm4ACgJmZmbGTJ0+q3a5EImEHDx5kvXr1UphwExwczK5fv87dG1DRxYsXq4UWADZ06FBWVFSk1b5XrVql0Kejo6PW+yTGLTs7mw0cOFB+7X3JkiVa3VT89OnTDADr1auX1vogpCqjCcy///6b2dnZKT27jIiIUKvNoqIiFhERwVq2bKlwpvrJJ5+w9PR0jt+Bem7duiX/5F710aNHD5abm6uVPsvKypibm5tCf4sWLdJKX6R+kUgkbPHixfLr8IMHD9bKtX7GGLt79y4DwJo1a6aV9gl5mVEE5tOnT1nz5s2VhmVoaKjK7T1+/Jh9/vnnzNHRUd5O8+bN2dq1a1lhYaEW3oFm7t+/rxDqVWesauN60X//+1+FfqysrLT2S4/UTydPnpQv5OHq6sri4uI476OkpIQBYObm5jq9XEJMl8EHpkgkYr1791YaloMGDVJpVt7NmzfZlClTmIWFhbyN7t27swMHDuh0dp86srKymI+PT7W/Aw8PD3b37l3O+pFKpaxdu3YafyghJD09XX6Zw8zMjK1YsYLzmd6VH3qzsrI4bZcQZQw6MGUyGZsyZYrSsGzTpk2dNi6WyWTs5MmTbPDgwfJjeTweCwwMNKjl5+rixYsXrE+fPtX+LlxcXDi71nr06NFq14fv37/PSdvE9JSXl7P/+7//k/88jRgxguXl5XHWfocOHRgAnW8KT0yTQQdm5bTxlx+Ojo6vPKsSiURsx44d8v9QAJi1tTULCwtjqampOqlfG0pKStjw4cOr/Z3Y29uzs2fPatz+y2fz48eP56BqYuoOHz4sX5XLw8ODxcfHc9Lu0KFDGQB25MgRTtojpDYGG5iHDx9WegO/QCBgp0+frvG43NxctnTpUoWJMk2aNGHLli3T2iQZXROLxWzy5MnV/m6EQiH79ddf1W73woUL1drU5yxhUr/cv3+f+fr6MgDMwsKCRUZGajxEO336dAaAbdiwgaMqCamZQQbm9evXmY2NjdKzyy1btig9JjU1lYWGhjJra2v5azt06MD++9//1svdDKRSKZs3b161vx8zMzP2008/qdXmiBEjFNoaPHgwx1UTUycSiVhISIj8Z2zcuHGsoKBA7fa+/PJLBoCFh4dzWCUhyuktMHMKRWzjmVQ2Z981NvW/f7E5+66xjWdSWdK9R8zd3V1pWM6fP1+hDZlMxs6fP8/effddhbPRIUOGsFOnTtX7NU9lMhlbtmyZ0r+rVatWqdRWUlJStTb++OMPLVVOTN3PP/8sX0WrdevWam/Dt2XLFgaATZ48mdsCCVGipsAUQEtupL9A5JlUnL2TAwAok8jkz1kKsvB9WRlK/SbCQnwA5Vl35c+99dZb+OGHHwAAEokEhw4dwsqVK3HlyhUAgIWFBSZMmID58+ejffv22irfoPB4PHz22WdwcnLCrFmzKj7p/Gv+/PnIzc3FN998Ax6P98q2VqxYofC1n58f+vfvz3nNhADAuHHj0LlzZwQFBeHvv/9Gt27dsGHDBkyZMkWldlxdXQEAmZmZWqiSkLrha6PR3ZfTMG7LZfyenI0yiUwhLIF/w9PMHNatusNlwnew7TQMANC+fXvs3bsXJSUlWLt2LVq1aoUxY8bgypUrcHR0RHh4OB4+fIjt27ebTFhWNWPGDPzyyy+wsLBQ+P7SpUvx4YcfQiqV1np8RkYGdu/erfC9hQsX1iloCVFXmzZtcOXKFUyZMgWlpaWYOnUq3n//fZSUlNS5jcrAzMjI0FaZhLwSr+rZysv8/PxYQkKCSg3uvpyGpTHJKBXLXv3if8nKRRDH78fB5fNw+PBhbN68Gfn5+QCAli1bYt68eZg8eTJsbGxUqqW+io2NxciRI1FcXKzw/aCgIOzevRuWlpYAgGdFZYi6moGUrAIUiCRITbqJG2djUHQzFrLSArRq1QrJyckwMzPTx9sgJmjHjh0ICQmBSCRChw4dcODAAbRp0+aVx+Xl5cHJyQl2dnYoKCjQQaXElPF4vKuMMb9q3+cyMG+kv8C4LZdRKq79TEdpgVIxsvd+itLM2wCA3r17Y8GCBRg+fDj9Qlfir7/+QkBAAHJzcxW+P2jQICyJ+C92XHmsdDhcJi4DeDyU3ktAaP+W+Gr2VJ3WTcjNmzcxevRo3LlzB7a2tti6dSvGjh1b6zGMMVhbW0MkEiE/Px/29vY6qpaYopoCk9Mh2cgzqRBJVA9LAJDxzGDbdZR8CDYuLg4jR46ksKxB165dERcXBzc3N4XvX8oRYPzWK/g9SflwON/cEnyBBaxbdce+nMbYfTlNh1UTAnTs2BHx8fEYM2YMioqKMG7cOISFhaGsrKzGY3g8nvxnna5jEn3hLDCfFZXh7J0c1HLCWisenw8H717YsG0nunbtylVZ9Vrbtm1x4cIF+ZCWbadhaDhwGmBmgVf9M/D4fIjEMiyNSabQJDpnb2+Pffv2ISIiAubm5oiMjETv3r3x4MEDpa9/VlQGG7+RcHp7PhYef4C5+69j09l7yC2qOWQJ4RpnQ7Kbzt7D6tg71c5oVCEU8DFvUGvM7OOpdhumKCcnB2+OnYbnXSaDby5U+XgrczPsn9EdHd0cuC+OkFeoPNtMS0uDg4MDfvrpJ4wYMQKA4mz78vJyMP7/JvYLBXwwAP3aOCOkb0v4uDvo5w2QekfrQ7IpWQUahSUAiCQypDwp5Kgi0+Hs7Ay/iZ+DL7B49YuVEEmk2HAmleOqCKkbf39/XLt2DcOHD8eLFy/wzjvv4JNPPsFPF+4pzLavGpZAxe+LMokMp5KyMW7LZRopIVrHWWAWiCQctSPmpB1T8qyoDOfvPwd46v1zMgacvp1Dw1tEbxo2bIgjR47ghx9+gJmZGTb+/je+OnILpWLpKy/zMAaUiqV0eYFoHWeBaS/kZg0Ee6E5J+2Ykqirmt+bxgMQdY3ucSP6w+Px8PHHH2Pb4Vg4vjkdzEy13wWlYhmWxqTgZsYL7RRITB5ngenV2B6WAs2aEwr48Gpix1FFpoOGw0l9cuG5LfgCS7WOpcsLRJs4C8wgX7dXv+gVGICgLpq3Y2poOJzUF/LZ9moeT5cXiDZxFpiNbC3Rt7Uz1F1ljccD+rdxhpOtep8sTRkNh5P6gi4vEEPG6cIFof1aQihQb6EBocAMIf1aclmOyaDhcFJfcHV5YWvUCWzcuBG3bt2CTKZZe4RU4jQwfdwdEB7gBStz1Zq1MucjPMCL7gNUEw2Hk/qCq8sLDx8/RUhICDp27AhHR0cMGzYM3377Lc6cOaPSou+EVMX59l7B3T0AAEtjUiCSvGJKOJPBysIc4QFe8uOI6iqHw39PzlZrpSUmk8HDopSGw4nelJWVISoqCpfOpAONO2jcnkz0v40J8vPz8dtvv+G3334DAAgEAnTu3Bm9evVC79690atXLzRu3FjjPkn9p5XtvYK7e2D/jO4Y4u0CSwEfwpeGC2XiMsgk5RA/uIo97/tRWHJAk+FwJi3HmQ2fYeXKlRxXRUjtMjIysGjRIjRt2hTBwcF4nBxfsUGABmTiMpTnpNX4vEQiQXx8PNasWYOgoCA0adIEnp6emDRpEn788Uf8888/NIxLlOJ8e6+X5RaVIepaBlKeFKJAJIa9UIDobeuQef4gZKUFOHXqFAYNGqRRH6SCulurPf9zG4oSTwAAFi1ahCVLltAemURrGGM4c+YMIiMjcfjwYYV9XPnWDeAWsgM8NVetAgCZpByZkVMgK1V/G7CGDRuiZ8+e6NWrF3r16gV/f39YWVmp3R4xLjUtjcf5kOzLnGwtq60NK7toj3W/V/wwR0dHU2ByRNXhcJmkXCEsAeCbb75Bfn4+Vq9eDT5fKwMQxEQVFhZi165diIyMRFJSktLXyEryUXIvATatu6u1chUPgF8TIaYtnI8LFy7g0qVLKCoqUrmd58+f4/jx4zh+/DgAwNzcHL6+vvIA7dWrF1577TWV2yXGTetnmMqcO3cOffv2BQC89tprePz4MW3jxaGbGS+w4UwqTt/OAQ8VswYrVS5Y3bdVI2T9+ROO/hSptI2pU6diy5Yt9O9CNJaSkoLIyEj89NNPKCyseXEMe3t7TJkyBQPHvI+FJ5+ota/uyxsJSKVS3Lp1C+fPn8eFCxdw4cIFpKenq/tWFLRq1UohQL28vGhkpp7QyQbSdSWVSvH666/j6dOnAICzZ8+iT58+nPdj6qoPh5vDq4kdgrq4wcnWElKpFCEhIfjxxx+VHh8UFIQ9e/bAwkL94TFimiQSCY4dO4bIyEjExsbW+tr27dsjNDQUwcHBsLW1BaDe5YWK2fZtXzkn4tGjR/LwvHDhAm7evMnJNUsnJyeFYVw/Pz8IharvHkT0z6ACEwBmzZqFzZs3AwA++ugjrF27Viv9kNoxxrBw4UKsWLFC6fNDhw5FdHQ0rK2tdVwZMUY5OTnYtm0bNm7ciEePHtX4OjMzMwQGBiI0NBR9+vRRemZWEZqvvrzA41Xcx63ubPuCggJcvnxZHqCXL19GcXHxqw98BQsLC/j5+SmchTZq1Ejjdon2GVxg/v777xg8eDAAwM3NDQ8fPqRrZnrCGMOyZcvwxRdfKH3+jTfewNGjR9GgQQMdV0aMxV9//YWIiAjs378f5eXlNb7OxcUFM2bMwMyZM+Hq6vrKdutyeaF/G2eE9GvJ2X3cEokEN27ckAfo+fPn8fjxY07abtOmjTw8e/fujVatWtEwrgEyuMAUi8VwcXHB8+fPAQBXrlxB165dtdIXqZv169fjo48+Uvqcr68vfvvtN/qETOREIhH279+PyMhIxMfH1/raXr16ITQ0FKNGjVJriP9Vlxe0iTGGhw8fKgzj3rp1C7X97qwrZ2dn+TBu79690aVLF1ha0v3Q+lZTYIIxVuPD19eXadOUKVMYKhaZYZ988olW+yJ1s2PHDsbn8+X/LlUf3t7eLCMjQ98lEj1LS0tjCxcuZE5OTkp/TiofVlZWbNq0aezatWv6Lplzz58/ZydOnGBffPEF69+/P7Oysqr176KuD0tLS9a7d2+2cOFCdvToUZabm6vvt2qSACQwJZmotzNMADh27BiGDx8OAPD09MTdu3dpeMIAREdHY/z48RCLq+9e0rx5c8TGxqJFixZ6qIzoC2MMsbGxiIyMxNGjR2udJNOiRQuEhIRg6tSpcHR01GGV+iMWi5GYmKgwjJuVlcVJ223btlVYlcjT05N+T2qZwQ3JAhXLYTk7O8unmicmJsLHx0dr/ZG6O3nyJN59912UlpZWe65JkyaIjY2Ft7e3HiojupSfn4+ffvoJGzZswO3bt2t8HY/Hw7BhwxAaGoqhQ4ea/HwExhgePHigEKD//PMPJ227uLigZ8+e8gDt3LkzzWTnmEEOyTLG2Pjx4+XDEYsWLdJ6f6Tu4uLimL29vdKhIycnJ5aQkKDvEomW3Lp1i82aNYvZ2NjUOoTo4ODA5s+fz+7evavvkg1eXl4eO378OPv8889Znz59mFAo5GQYVygUsj59+rDPP/+cHT9+nOXl5en7rRo9GOKQLFAx/BcUFAQA8Pb25uxTGOHGtWvXMGTIEDx79qzac3Z2djh27BjdQ1tPiMViHDlyBBERETh79mytr/Xx8UFYWBjee+89uuVITeXl5bh+/br8DPTChQvye9M11a5dO/kZaK9evdC8eXMaxlWBQQ7JAkBxcTGcnZ3lQ3/Jycnw8vLSap9ENcnJyXjzzTeVTq0XCoU4ePAghg0bpofKCBeysrKwZcsWbN68GZmZmTW+TiAQYPTo0QgNDUXPnj3pFzDHGGO4d++eQoAmJydz0nbjxo0VArRTp04wN6cN42tisIEJAIGBgTh06BAA4Ntvv0V4eLjW+ySqefDgAd58803cv3+/2nPm5ubYs2cPRo8erYfKiDoYY7h06RIiIiIQFRWldIJXpddffx2zZs3CBx98QNtg6Vhubi4uXbokD9D4+HiUlWm2mwsAWFtbo1u3bvIA7dGjB91nXYVBB+aePXsQHBwMAOjcuTOuXbum9T6J6h4/fozBgwcrHTbn8/nYunUrpk6dqofKSF2VlJTg559/RkREBBITE2t9bd++fREaGoqRI0fS2YiBKCsrw9WrVxXuCVV2uURVPB4PHTp0UFiVqFmzZiY7imDQgZmfnw9nZ2f5p9x79+7RbQsGKjc3F0OHDkVNPxdr1qzBnDlzdFwVeZV79+5h48aN2L59u3yxEGWsra0xadIkhISEoEMHzTdyJtrFGMOdO3cUArS22cyqcHV1VQhQHx8fCARa3+DKIBjsLNlKAQEB8llfK1as0Fm/RHX5+fmsT58+Nc7aW7JkCZPJZPou0+RJpVIWExPDAgICGI/Hq3WmZatWrdiaNWvY8+fP9V020dDTp0/Z4cOH2ccff8x69uzJLCwsOJmNa2NjwwYOHMgWL17MTp48yfLz8/X9VrUGNcySNZjA3LZtm/wfpnv37jrrl6inpKRE4UPOy48FCxZQaOpJXl4eW7lyJfP09Kz1FyCPx2MjRoxgJ0+eZFKpVN9lEy0pLS1lcXFx7Pvvv2fDhw9njo6OnAQon89nnTp1YqGhoWzv3r3s0aNH+n6rnKkpMA1iSBaoGOpzcXGR776enp4ONzc3nfRN1FNeXo6JEyfil19+Ufr8Bx98gI0bN9KemjqSmJiIyMhI7NmzR+mCE5WcnJwwffp0zJo1Cx4eHrorkBgEmUyG27dvKyyqkJqayknb7u7uCovLd+jQQSv//58VlSHqagZSsgpQIJLAXiiAV2N7jPblZm1hg76GWenNN9/EH3/8AaBiIfCwsDCd9U3UI5VKMXPmTGzbtk3p82PHjsWuXbto0oiWlJeX4+DBg4iIiMCFCxdqfa2vry/CwsIwduxYWFlZ6ahCYgyys7Nx8eJFeYBeu3at1pnTdWVnZ4fu3bvLA7Rbt27yPU/VcSP9BSLPpOLsnRwAQJmS3Wv6tXFGSN+W8HF3ULsfowjMjRs3IiQkBADQr18/nD59Wmd9E/UxxrBgwQKsXr1a6fNvvfUWDhw4QL+kOfT48WNs3rwZP/74Y61rllpYWGDs2LEIDQ1F165dTXbWI1FNaWkp4uPj5QF68eJFvHjxQuN2zczM4OPjo7A2bl22eQN0tz9qRRtGEJhZWVl4/fXXwRgDn89HVlYWnJ2dddY/UR9jDF9//TW++uorpc/37dsXR48ehZ2dnW4Lq0cYY4iLi0NERAQOHToEiURS42vd3d0xa9YsTJ8+Ha+99poOqyT1kUwmQ3JyssKiCsruyVZHs2bNFAK0Xbt21YZxK8IyGaXimhf9f5mVOR/hAW3VCk2jCEygYrPi8+fPAwB+/PFHfPDBBzrtn2hmzZo1mDdvntLn/P39ceLECTg5Oem4KuNWVFSEPXv2ICIiAn///Xetrx04cCBCQ0MxfPhwk7kFgOjHkydPcPHiRXmAXr9+vdYPcXVlb28v3yO0V69esHLzwtRdN1AqlqrclpW5GfbP6K7y5uJGE5hVf+EOGTIEv/32m077J5rbvn07PvjgA6VbQLVv3x6nTp1CkyZN9FCZcblz5w42bNiAHTt2oKCgoMbX2draYsqUKQgJCUHbtm11WCEh/1NcXIz4+Hh5gF66dAn5+fkat/vaqC9g5dkVUGMHHB4PGOLtgk3B1W+prP04IwnMR48eoVmzZgAq1q58+vQpGjZsqNMaiOZ++eUXTJgwQeknTk9PT8TGxtIMTSWkUiliYmIQERGBU6dO1fratm3bIiwsDBMnTqShbmJwpFIpkpKS5AF64cIFpKWlqdQG37oB3EJ2gCdQf/sySwEfFxcOUGn2rNEEJlAxdFfZ708//YRJkybpvAaiuZiYGIwaNQoikajac25ubvj9999pof1/5ebmYtu2bdi4cWOtv1T4fD5GjhyJ0NBQ9O/fnybxEKOSmZmpsCpRYmKi/FZCZey7BaJB7wngm6t/q4hQwMe8Qa0xs49nnY+pKTANcpfXUaNGyf8cHR2tx0qIJgICAvDbb78pPfvJyMhAnz59cP36dT1UZjgSEhIwdepUuLm5YeHChTWGpbOzM8LDw5GWlobo6GgMGDCAwpIYHVdXV4wZMwZr165FQkICXrx4gdjYWCxZsgSDBw+u9rvC3NlDo7AEAJFEhpQnhRq1UckgzzDv3r2L1q1bAwAsLS2Rk5NDQ05GLCEhAUOGDEFeXl615xo0aIDjx4+jV69eeqhMP8rKynDgwAFERETgypUrtb62e/fuCAsLQ1BQECwtNb8hmxBDJpVKcevWrf+dhZp1AM+to8btDvR6Ddsm+9f59UZ1htmqVSv5ws9lZWWIiYnRc0VEE35+fjh37pzSiT75+fkYPHgwfv/9dz1Uplvp6ekIDw+Hu7s7Jk6cWGNYWlpaYurUqUhISMClS5cwYcIECktiEszMzNCpUyeEhoZi7969ePftoZy0ay/kZuEUgwxMgIZl65t27dohLi5O6USfkpISvP322/I9UesTxhj+/PNPBAYGwsPDA8uWLUNOTo7S13p4eOCHH35AZmYmtm/fDl9fXx1XS4hh8WpsD0uBZjElFPDh1YSbEUqjCMyYmJha18YkxsHT0xPnz59XeutDeXk5Ro8ejZ07d+qhMu4VFBQgMjIS7dq1w8CBA3Ho0CGlt9kAFbdP/frrr0hNTcXHH39M96kS8q8gX83XE2cAgrpwsy65wQZmu3bt5Ncxi4uLXznFnhgHV1dXnD17Fl26dKn2nFQqxeTJkxEZGamHyriRnJyMsLAwuLq6IiwsDMnJyUpf16BBA8ydOxe3b9/Gb7/9huHDh9Mi9YS8pJGtJfq2doa689t4PKB/G2dOFmQHDDgweTweDcvWU87Ozvjzzz/Ru3dvpc+HhYXhu+++03FV6pNIJDh06BAGDhwIb29vREZGoqioSOlrO3TogM2bNyMzMxOrV6+WfygkhCgX2q8lhAL1PkwKBWYI6deSs1oMNjABxWHZX3/9FeXl5XqshnCpQYMGOHnyJIYMGaL0+c8//xyffvopapvFrW9Pnz7FsmXL0KJFCwQGBuLPP/9U+jqBQIAxY8bg3LlzuHHjBmbMmAEbGxsdV0uIcfJxd0B4gBeszFWLq4q1ZL1UXhavNgYdmF26dJFPEsnPz6/xFxIxTtbW1jhy5IjCB6Oqli9fjpCQkBqv/ekDYwyXL1/GxIkT4e7ujvDwcKSnpyt9bePGjfHll1/i4cOH2L9/P9544w26d5IQNQR390B4QFtYmZu9cniWx6tYQ1bdhddrY9CByePxEBgYKP+ahmXrH0tLS+zbtw9TpkxR+vymTZswceJETvbm00RpaSl27NgBf39/9OjRA7t3765xxKN3797Yt28fHj58iK+++gqvv/66jqslpP4J7u6B/TO6Y4i3CywFfAhfmj0rFPBhKeBjiLcL9s/oznlYAga6cEFVFy5ckF/ratSoEZ48eUK7MNRDMpkM8+bNw7p165Q+P2LECOzfvx9CoVCndaWlpWHjxo3YunWr0oUXKllZWSE4OBihoaHw8fHRYYWEmJ7cojJEXctAypNCFIjEsBeaw6uJHYK6uHEywceo1pKtSiaTwc3NDU+ePAEA/Pnnn+jfv79eayLawRjDl19+iW+++Ubp8wMHDsThw4c12rG9LmQyGWJjYxEREYFjx47Veh3V09MToaGhmDJlCm0SQEg9YVQr/VTF5/Px7rvvyr+mYdn6i8fj4euvv8aKFSuUPv/HH39g0KBBeP78uVb6f/HiBdasWQMvLy8MGTIER48eVRqWPB4Pb731Fk6cOIE7d+5g3rx5FJaEmACDP8MEKs4qBw4cCABo0qQJMjIywFdjbzRiPH788UfMmjVLaWB17NgRp06dgouLC54VlSHqagZSsgpQIJLAXiiAV2N7jPat+9DMrVu3EBkZiV27dqGkpKTG1zVs2BDTpk3Dhx9+iBYtWqj93gghhs1oh2SBivvcGjdujNzcXAAV1zV79uyp56qItv3888+YNGmS8j01uw5Aj2lf4q/0YgBAmeR/M2mFAj4YgH5tnBHStyV83B2qHS8Wi3Ho0CFERkbi3LlztdbRuXNnhIWFYdy4cbC2ttboPRFCDJ/RDskCFfexjRw5Uv41DcuahvHjx+PQoUPVFh637TQM5W+E4Ny9fJRJZAphCVRs51MmkeFUUjbGbbmM3ZfT5M89efIEX3/9NTw8PDB27Ngaw9Lc3BzvvfceLl68iKtXr+L999+nsCTExBnFGSYAnDhxAgEBAQCAZs2a4cGDB3RPm4k4ffo0RowYgaKiIth2GoaGA6eBb1732bJW5nyMbW2B28e3IioqSukZayVXV1fMmjULH3zwAVxcXLgonxBiZIz6DBOomCHZoEEDAMDDhw9NfuNhU9K/f3/ExsbCqXUXlcMSAErFMmxPfIGDZxJqDMt+/fohKioKDx48wBdffEFhSQipxmgC08LCAsOHD5d/TcOypqVbt25486MV4JlZqHU8z8wC9j1GK3zPxsYGH374IW7duoXTp09j1KhRMDfnZt88Qkj9YzSBCVTfI9OQ1xkl3HpWVIbrWWXgqTk7msfnw8rTH3wre7Rp0wbr1q1DZmYmNmzYgPbt23NcLSGkPjKqJXOGDBkCGxsbFBcX4/bt20hKSkK7du30XRbRgair6YCGH5DM+HzMW78fK94fQte/CSEqM6rAtLKyQkBAAA4cOACg4iyTArN+YIwhLy8PaWlp1R4PHz5ETssAWLZ5Q7M++AJIbRtTWBJC1GJUgQkAgYGBCoG5ePFiPVdE6oIxhtzc3BoDMS0trcY9JAHAufVITuooEOl3EXdCiPEyusB86623YGlpibKyMty8eROpqalo2ZK7DUKJehhjePbsmdJArAzF4uJitduXlal/bFX2QprUQwhRj9EFpp2dHQYPHoyjR48CqDjLXLhwoZ6rqv8YY8jJyak1EGtbVk5T4pw0yMRl4JurvxOBUMCHVxM7DqsihJgSowtMoGK2LAUmtxhjePr0aa2BWFpaqtUarK2t4eHhUe3RrFkz2L/mine23ay2qo8qGICgLm7cFUwIMSlGGZgjRoyAQCCARCJBfHw8Hj16hKZNm+q7LIPGGEN2dnatgSgSibRag42NjdJArAzFRo0a1Tohp2/rJ/g9OVutybI8HtC/jTMne+URQkyTUQZmw4YNMWDAAJw6dQoAcPDgQcydO1e/RemZTCZDVlaWfAKNskAsKyvTag22tra1BqKTk5NGM1RD+7VE3N1nKBVLVT5WKDBDSD+61k0IUZ9RBiZQMSxbGZjR0dH1PjBlMhmePHlSayCWl5drtQY7O7saA9HDwwMNGzbU6i0bPu4OCA/wwtKYZJSK6z40a2XOR3iAFzq6OWitNkJI/We0gTly5Eh8+OGHgNAON8UumPnfSxDzBGrth2gIpFIpnjx5onCbRdXHo0ePtB6I9vb2tQaig4OD3u9hDO7uAQBYGpMCkURa6/Asj1dxZhke4CU/jhBC1GW0gfmkzAJtpv0HxQ08wBjDydt58ueEgiysjr1T636IuiaVSvH48eNaA1Es1u49gg4ODmjWrFmtgWgMgrt7oKObAzacScXp2zngoWJLr0qV+2H2b+OMkH4t6cySEMIJo9neq6rdl9OwNCYFpWIJgJrPeHR5hiGVSpGZmVntZvyqgVjbtlJcaNiwYY2B2KxZM6MJRFXkFpUh6loGUp4UokAkhr3QHF5N7BDUxbhGGAghhqOm7b2M7gyzIiwrr2HVPjzIGFAqlmJpTDIAaBSaEomk1kBMT0/XeiA6OjrKw09ZIFZuf2ZKnGwtMbOPp77LIISYAKMKzBvpL/49s1TtXrxSsQxLY1LQ0c2hxuE5iUSCjIwMpUu2VQaiVKr67ExVODk51RqI9vb2Wu2fEEJIzYwqMCPPpEIkUS+0RBIpVhy/ifdbS5UGYkZGhtYDsVGjRgoB+HIg2tnRKjSEEGKojCYwnxWV4eydHLV3eGIMOJuai73zpkBWWsBtcf9ydnauNRBtbW210i8hhBDtM5rAjLqaoXkjjMGmw5so/OugWoe/9tpr1UKw6p9tbGw0r5EQQohBMprATMkq0GgdUQDgm1vCwtmjxuddXFxqDURra2uN+ieEEGK8jCYwC0TczEB1auKGQePGVQvFpk2bUiASQgipkdEEpr2Qm1ID3uyP1WPncdIWIYQQ08HXdwF15dXYHpYCzcql/RAJIYSoy2gCM8hX830MaT9EQggh6jKawGxka4m+rZ2h7trftB8iIYQQTRhNYAIV+yEKBWZqHUv7IRJCCNGEUQVm5X6IVuaqlU37IRJCCNGU0cySrUT7IRJCCNEHowtMgPZDJIQQontGGZgA0NHNAZuC/Wg/REIIITpR6wbSPB4vB8BD3ZVDCCGE6F0zxpjzy9+sNTAJIYQQUsGoZskSQggh+kKBSQghhNQBBSYhhBBSBxSYhBBCSB1QYBJCCCF18P9E+k7U0BpEWAAAAABJRU5ErkJggg==\n", "text/plain": ["<Figure size 576x288 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["import random\n", "import networkx as nx\n", "G=nx.Graph()\n", "edge_width = [ ]\n", "for i in range(20) :\n", "    G.add_edge ( random.randint(0,10),  random.randint(0,10) )\n", "    edge_width.append( random.randint( 1,5) )\n", "    \n", "import matplotlib.pyplot as plt\n", "f, ax = plt.subplots(figsize=(8,4))\n", "\n", "pos=nx.spring_layout(G) \n", "nx.draw_networkx_nodes(G,pos)\n", "nx.draw_networkx_edges(G,pos,width=edge_width,ax=ax);"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 14, "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": "3.9.5"}}, "nbformat": 4, "nbformat_minor": 2}