{"cells": [{"cell_type": "markdown", "id": "61151cb3", "metadata": {}, "source": ["# R\u00e9partition, table de hashage\n", "\n", "Il est facile de r\u00e9partir des \u00e9l\u00e8ves en quatre groupe apr\u00e8s les avoir tri\u00e9s par ordre croissant, les \u00e9l\u00e8ves dont les noms de famille commencent par *A* dans le premier groupe et ainsi de suite... Cette r\u00e9partition peut poser un probl\u00e8me \u00e9thique parfois. \n", "On souhaite \u00e9crire une fonction de r\u00e9partition qui affecte les \u00e9tudiants dans un groupe parmi quatre :\n", "\n", "* Cette fonction prend comme entr\u00e9e le nom et le pr\u00e9nom.\n", "* Le r\u00e9sultat ne d\u00e9pend pas des autres noms pr\u00e9sents dans la classe.\n", "* Les groupes sont \u00e0 peu pr\u00e8s de taille identique."]}, {"cell_type": "code", "execution_count": 1, "id": "524109c6", "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, "id": "8a7f337b", "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "id": "de82ac75", "metadata": {}, "source": ["## Q1 - premi\u00e8re lettre\n", "\n", "Ecrire une fonction qui retourne la distribution la premi\u00e8re lettre de chaque personnage. Est-ce une distribution uniforme ?"]}, {"cell_type": "code", "execution_count": 3, "id": "57c7c276", "metadata": {}, "outputs": [], "source": ["personnages = \"\"\"\n", "Jean Valjean\n", "Cosette\n", "Fantine\n", "Marius Pontmercy\n", "Gavroche Th\u00e9nardier\n", "Antagonistes\n", "Javert\n", "Monsieur Th\u00e9nardier\n", "Madame Th\u00e9nardier\n", "Babet\n", "Claquesous\n", "Montparnasse\n", "Gueulemer\n", "Brujon\n", "Bamatabois\n", "Madame Victurnien\n", "Les Amis de l'ABC\n", "Enjolras\n", "Combeferre\n", "Courfeyrac\n", "Jean Prouvaire\n", "Feuilly\n", "Bahorel\n", "Lesgle\n", "Joly\n", "Grantaire\n", "Marius Pontmercy\n", "Personnages secondaires\n", "Amies de Fantine\n", "Favourite\n", "Dahlia\n", "Z\u00e9phine\n", "Amis de F\u00e9lix Tholomy\u00e8s\n", "Listolier\n", "Fameuil\n", "Blachevelle\n", "Autres\n", "Fauchelevent\n", "Mr Mabeuf\n", "Azelma Th\u00e9nardier\n", "Toussaint\n", "Luc-Esprit Gillenormand\n", "Georges Pontmercy\n", "\u00c9v\u00eaque Myriel\n", "Baptistine Myriel\n", "Mme Magloire\n", "Petit-Gervais\n", "\"\"\""]}, {"cell_type": "markdown", "id": "b82c6e09", "metadata": {}, "source": ["## Q2 - somme\n", "\n", "On \u00e9crit une fonction qui fait la somme des lettres (avec la fonction [ord](https://docs.python.org/3/library/functions.html?highlight=ord#ord) par exemple). On calcule ensuite la distribution du r\u00e9sultat modulo 26. Est-ce uniforme ?"]}, {"cell_type": "code", "execution_count": 4, "id": "d5de2740", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "efceff56", "metadata": {}, "source": ["## Q3 - pseudo g\u00e9n\u00e9rateur\n", "\n", "On s'inspire d'un [g\u00e9n\u00e9rateur congruentiel lin\u00e9aire](https://fr.wikipedia.org/wiki/G%C3%A9n%C3%A9rateur_congruentiel_lin%C3%A9aire).\n", "\n", "$X_{n+1} = (22695477 X_n + 1) \\mod 2^{32}$ qu'on transforme en $X_{n+1} = (22695477 (X_n + c_n) + 1) \\mod 2^{32}$ o\u00f9 $c_n$ est la n-\u00e9ni\u00e8me lettre du nom du personnage.\n", "\n", "On prendra comme r\u00e9sultat le dernier nombre de la s\u00e9rie $X_n >> 16$, le tout module 26. On calcule de nouveau la distribution."]}, {"cell_type": "code", "execution_count": 5, "id": "a27e2e08", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "0d09fd46", "metadata": {}, "source": ["## Q4 - hash\n", "\n", "On utilise un algorithme hashage impl\u00e9ment\u00e9 par la librairie [hashlib](https://docs.python.org/3/library/hashlib.html). On transforme chaque nom des personnages et on calcule la distribution de la premi\u00e8re lettre du *hash* obtenu."]}, {"cell_type": "code", "execution_count": 6, "id": "f5a37c91", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "dab17b45", "metadata": {}, "source": ["## Q5 - light hash\n", "\n", "On s'inspire de la page [Hash Functions](http://www.cse.yorku.ca/~oz/hash.html).\n", "\n", "```c\n", "    unsigned long\n", "    hash(unsigned char *str)\n", "    {\n", "        unsigned long hash = 5381;\n", "        int c;\n", "\n", "        while (c = *str++)\n", "            hash = ((hash << 5) + hash) + c; /* hash * 33 + c */\n", "\n", "        return hash;\n", "    }\n", "```\n", "\n", "De nouveau calculer la distribution du hash obtenu."]}, {"cell_type": "code", "execution_count": 7, "id": "0d30cd10", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "8ce85c58", "metadata": {}, "source": ["## Q6 - r\u00e9partition\n", "\n", "Imagine une fa\u00e7on de r\u00e9partir des \u00e9l\u00e8ves en 4 groupes."]}, {"cell_type": "code", "execution_count": 8, "id": "1ae07b3f", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "8f780015", "metadata": {}, "source": ["## Q7 - dictionnaire\n", "\n", "Les dictionnaires en python s'appuient sur une table de hashage et non sur un arbre de recherche dichotomique (voir aussi son code : [taille de hash](https://github.com/python/cpython/blob/main/Objects/dictobject.c#L38)). Quelle exp\u00e9rience pourrait-on mener pour s'assurer ? Le module [timeit](https://docs.python.org/3/library/timeit.html) pourrait vous mettre sur la voie."]}, {"cell_type": "code", "execution_count": 9, "id": "5e20ff30", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "id": "ea06a6e1", "metadata": {}, "source": ["## Quelques \u00e9l\u00e9ments de r\u00e9ponses"]}, {"cell_type": "markdown", "id": "90ed3f2a", "metadata": {}, "source": ["### Premi\u00e8re lettre"]}, {"cell_type": "code", "execution_count": 10, "id": "6ea0c84e", "metadata": {}, "outputs": [{"data": {"text/plain": ["{'J': 4,\n", " 'C': 4,\n", " 'F': 5,\n", " 'M': 8,\n", " 'G': 4,\n", " 'A': 5,\n", " 'B': 6,\n", " 'L': 4,\n", " 'E': 1,\n", " 'P': 2,\n", " 'D': 1,\n", " 'Z': 1,\n", " 'T': 1,\n", " '\u00c9': 1}"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["d = {}\n", "for m in personnages.split(\"\\n\"):\n", "    if len(m) == 0:\n", "        continue\n", "    if m[0] in d:\n", "        d[m[0]] += 1\n", "    else:\n", "        d[m[0]] = 1\n", "d"]}, {"cell_type": "code", "execution_count": 11, "id": "ff417ac6", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 576x144 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "import pandas\n", "\n", "def draw(*ds_legends):\n", "    fig, ax = plt.subplots(len(ds_legends) // 2, 1, figsize=(8, len(ds_legends)))\n", "    i = 0\n", "    while i < len(ds_legends):\n", "        d = ds_legends[i].copy()\n", "        for a in \"abcdefghijklmnopqrstuvwxyz\".upper():\n", "            if a not in d:\n", "                d[a] = 0\n", "        legend = ds_legends[i+1]\n", "        df = pandas.DataFrame(d.items(), columns=[\"L\", legend]).sort_values(\"L\").set_index(\"L\")\n", "        df.plot.bar(ax=ax[i // 2] if len(ds_legends) > 2 else ax)\n", "        i += 2\n", "    return ax\n", "    \n", "draw(d, \"lettre 1\");"]}, {"cell_type": "markdown", "id": "8d730fd8", "metadata": {}, "source": ["### Somme"]}, {"cell_type": "code", "execution_count": 12, "id": "bef8a69c", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 576x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["def somme(mot):\n", "    s = 0\n", "    for c in mot.lower():\n", "        s += ord(c) - ord(\"a\")\n", "    return chr(s % 26 + ord(\"A\"))\n", "\n", "def repartition(fct):\n", "    d = {}\n", "    for m in personnages.split(\"\\n\"):\n", "        if len(m) == 0:\n", "            continue\n", "        key = fct(m)\n", "        if key in d:\n", "            d[key] += 1\n", "        else:\n", "            d[key] = 1\n", "    return d\n", "\n", "d2 = repartition(somme)\n", "draw(d, \"lettre 1\", d2, \"somme\");"]}, {"cell_type": "markdown", "id": "80e4390b", "metadata": {}, "source": ["### Pseudo"]}, {"cell_type": "code", "execution_count": 13, "id": "2bb73aae", "metadata": {}, "outputs": [{"data": {"image/png": 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8EfU7w2LmsBToGT3+EzA6d1qM9on/7wTrp0+p8YHhwCJgQ8J2nwTGEXZ+Ho364JsTtJ9M6Pubk3yeomUuIxT03iX+zw3R30GEC1vd3Bqr2HoE/i/6LDbkjFuUN8+ixDl11psj4Yo5mnA3qNeBnxH2xF5NGKMx5/HypCuGsGX4zQLjTwfuiZnDIqBfgfH94744eS9wQ1vTSmkfc/ltbngAL5cQL9b/Xa5D9F5cAQwmHJr7I7BDCnFfizlfj6jzuTV6f/0nsF+Jy6wBLgZeJdzmdMcYbV4sZVrefInfx52UR+5GQQ/gTaA6QQ4XAU8SivoiPj6yuAfwZIz2izr6vkkrPmGj6MvAHcDfov73qJhtZ0Xr4UHgcsLRjtifCWB7wiH/RsIO1LWEDdwxMdvPK/UzUOg9GK2L6YR7JRxY7P1JOLKxW0f76s3iduabo4QVtA3hsMOvgPcJh3TGlrBySyliO0Ud7RPAj6Lhd8BTwKdi5tDm1nF70/Lm20D4nnoN4RD36pzn6zvYfnWM9ndQeKPkDMJhrJLf9N11AA4hfH0yJ2HnvSZn/a/Oey1aSsijF3AqYW/57ATt+hCK+KvAZQk7zvsJX/Pkjz8JmBMzxob8/z3JezJGHg+U8l4sqcOEg4BjgG1yxg0CDojzOnTie/TzhMsQF5vvi8BNhAI8J+pvt0m4rAcJe8G3EG78M4QE9yIAXgEuIDrKEI0bFvW/ifuYEtfXogLjPhfltiZmjA711flDWXxnXEj0vcJE4Cse73vKDYQCboTvQD5onUToQOOebPJ5wl4QwPPu/niCnNv8nqCk7xAyYGY7Ejq+fxAOjwL8K6EQHO3uf08Yr1v834XkfG9thP9/PeEDmMr31gny6AUcSTinoo7Qid7k7m/EaHsl8G+EvZmZ7r424bJ3Bu4DPgQWRqPrCZ+xY+LkkIY08sjpI2DTfqJLX8+OMLMlbH4uRR/gL4SNlXZPeDWzx4E7gdnu/k4H8jBgP8L3vp8h9JlvA0+5+6VF2tZ6dIe/AtO+6e4/KzWvuMzsaHe/v8D4HYAz3H16Z+ew2bLLtRh3R3kf9k0mkWCDoByY2RjChw2Sb5TknnzVm003jLpFp1cuzOw2Qkc3F7jb3ZcmbP8RYcOqhU078USvRd77YZm7P5Ykj7SUSx5ZiU68yuXAKncv1O90RT61wChCQR4P9HX37bPIpbtTMRYpY1Exbe1oSy6mImkxs2/z8R7xesLh5dZhicc4G1o2p8uziZQxdy+7awHIFq8O+AXhFyJ/zTiXiqE9YxERkYxltmfcr18/r6ury2rxIiIiXW7hwoVvuftmV2dMtRib2fbAfxNOOHHgNHd/qtC8dXV1LFiwIM3Fi4iIlDUz+3Oh8WnvGf8YeNDdj4suV9Y75fgiIiIVJ7VibGafJFwi7FQAd/8n0UXVRUREpG1p7hkPJFwZ6GYz25/ww/zJub9/M7NJhCu2MGBAh24u1Knqpv6m3elN04/sokxERGRLkGYx7km4rdW/u/vTZvZjwl01Lmmdwd1nEd1qrL6+Xqdxi4h0U+vXr6e5uZl169ZlnUpZqq6upra2lqqqeNd6SrMYNwPN7v509PxeQjEWEZEK09zcTE1NDXV1dVjs28ZvGdydVatW0dzczMCB8W41ndoFBdz9b4Sbju8VjTqMcJsrERGpMOvWraNv374qxAWYGX379k101CDts6n/HbgjOpP6FeDrKccXEZEyoULctqTrJtVi7O6NhDupiIiISEy6NrWIiHRYsV+hJLWl/WpFF6EXEZFu6f333+fII49k//33Z/Dgwdxzzz089thjDB8+nCFDhnDaaafxj3/8AwhXfZw2bRrDhg2jvr6ehoYGDj/8cHbffXduvPFGAJ544gkOPfRQjjrqKHbbbTemTp3KHXfcwciRIxkyZAgvv/wyACtXruTYY49lxIgRjBgxgieffLLD/4uKsYiIdEsPPvgg//Iv/8LixYtZunQp48aN49RTT+Wee+5hyZIltLS0cMMNN2ycf8CAATQ2NnLIIYdw6qmncu+99zJ//nwuvfTSjfMsXryYG2+8keXLl3P77bfz4osv8swzz3D66aczY8YMACZPnsy5557Ls88+y+zZszn99NM7/L/oMLWIiHRLQ4YM4fzzz+c//uM/GD9+PNtttx0DBw5k0KBBAJxyyinMnDmTc845B4AJEyZsbLd27VpqamqoqamhV69evPvuuwCMGDGCT3/60wDsvvvujB07dmOb3/72twA8+uijLFv28Y+FVq9ezdq1a9l2221L/l9UjEVEpFsaNGgQDQ0NzJ07l4svvpgxY8a0O3+vXr0A2GqrrTY+bn3e0tKyyTz58+XO89FHHzF//nyqq6tT+190mFpERLqlv/zlL/Tu3ZuTTjqJKVOm8NRTT9HU1MSKFSsAuP322zn00ENTX+7YsWM3HrIGaGxs7HBM7RmLiEi3tGTJEqZMmcJWW21FVVUVN9xwA++99x4TJ06kpaWFESNGcOaZZ6a+3Ouuu46zzjqLoUOH0tLSwujRozeeBFYqc8/mEtH19fVervcz1o0iRETat3z5cvbZZ5+s0yhrhdaRmS10982ux6HD1CIiIhlTMRYREcmYvjOWWHToXkSk82jPWERESpLVOUfdQdJ1o2IsIiKJVVdXs2rVKhXkAlrvZ5zkd8g6TC0iIonV1tbS3NzMypUrs06lLFVXV1NbWxt7fhVjERFJrKqqioEDB2adRsXQYWoREZGMqRiLiIhkTMVYREQkYyrGIiIiGVMxFhERyZiKsYiISMZSLcZm1sPMFpnZr9OMKyIiUsnS3jOeDCxPOaaIiEhFS60Ym1ktcCTw32nFFBER2RKkuWd8LXAh8FGKMUVERCpeKpfDNLPxwJvuvtDMPtfOfJOASQADBgzYZFqxW/SBbtMnIiKVKa0941HABDNrAu4GxpjZ/+TP5O6z3L3e3ev79++f0qJFRES6t1SKsbtPc/dad68Djgced/eT0ogtIiJS6fQ7YxERkYylfgtFd38CeCLtuCIiIpVKe8YiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikjEVYxERkYypGIuIiGRMxVhERCRjKsYiIiIZUzEWERHJWOqXw5TyUmm3piz2/3Sn/0VEurc0+yPtGYuIiGRMxVhERCRjKsYiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikjEVYxERkYypGIuIiGRMxVhERCRjKsYiIiIZS60Ym9kuZvZbM1tmZs+b2eS0YouIiFSyNG8U0QKc7+4NZlYDLDSzR9x9WYrLEBERqTip7Rm7+1/dvSF6vAZYDuycVnwREZFK1Sm3UDSzOmA48HTe+EnAJIABAwZ0xqLL5hZ7lXbrwjTotSlf5fLayObSeG26IkacOPrstS31E7jMbFtgNnCOu6/Onebus9y93t3r+/fvn/aiRUREuqVUi7GZVREK8R3ufl+asUVERCpVmmdTG/BzYLm7X51WXBERkUqX5p7xKOBrwBgza4yGI1KMLyIiUpFSO4HL3f8AWFrxREREthS6ApeIiEjGVIxFREQypmIsIiKSMRVjERGRjKkYi4iIZEzFWEREJGMqxiIiIhlTMRYREcmYirGIiEjGVIxFREQy1in3M5Z06N6f5aucXhvdi7h86bUpX+X22mjPWEREJGMqxiIiIhlTMRYREcmYirGIiEjGVIxFREQypmIsIiKSMRVjERGRjKkYi4iIZEzFWEREJGMqxiIiIhlLtRib2Tgze8HMVpjZ1DRji4iIVKrUirGZ9QBmAl8C9gVOMLN904ovIiJSqdLcMx4JrHD3V9z9n8DdwFEpxhcREalIaRbjnYHXc543R+NERESkHebu6QQyOw4Y5+6nR8+/Bhzo7mfnzDMJmBQ93Qt4oUjYfsBbHUxNMco3F8Uo31wUo3xzqaQY5ZRLV8XY1d37bzbW3VMZgIOBh3KeTwOmdTDmghTyUowyzUUxyjcXxSjfXCopRjnlknWMNA9TPwvsaWYDzewTwPHAnBTji4iIVKSeaQVy9xYzOxt4COgB3OTuz6cVX0REpFKlVowB3H0uMDfFkLMUI/UYacVRjPRjpBVHMdKPkVYcxeicON0+RmoncImIiEhpdDlMEZEyZGapHrmU8qZiLCJSnp7JOoFKZGZzzawu6zzylXUxNrPPmtnMLl7mHmY2qsD4UWa2e1fmIukyszVmtrqNYaWZzTezw7LOszszs2ozGxwN1Rnm0dvMhkZDrwyWP8LMPpXz/GQze8DMrjOzPnHDdFJ6iZjZzEJ9YglxzjGzkR3Z4zezAe1MOyRmmJuBh83sIjOr6kAuh7czbWLieOX2nbGZDQdOBCYCrwL3ufuMDsTrB6zymP+omf2a8PvoJXnjhwDfd/cvJ1x+fwB3X5mw3YXu/sPo8UR3/0XOtO+7+3dixhng7q8lWXZe+6OAWnefGT1/Gmj9wfqF7n5vqbHLSXRt9cHAHe4+uMi8u7j7621MG+/uv04hn3Pc/dqOxukqUQf7feA04M+EQrILoeO7yN3Xx4hRDZwJ7AEsAX7u7i0J86gCrgROJvQfBuwEzHD36WY2zN0bk8TMi78VcIK731FkvgbgC+7+tpmNJlwe+N+BYcA+7n5cjGU1A1e3Nd3d25yWF+e8/KaEC1P8wd1fjdF+MuGnqp8G/he4y90XxVl2XpyrgM8AexNe3yeBPwJ/dPe3Y8Z4BbgR+JG7b4jG7QT8CNjb3etjxtkWuAQYB9wOfNQ6LcF63QD8HjjJ3d/Im9bg7gfEidOqLL6TMLNBwAnR8BZwD2FD4fMJ4xwETAfeBq4grOR+wFZmdrK7PxgjzE75hRjA3ZfEPbRhZgZcCpxNOPpgZtZC6BC+F+ufCW/+H0aPpwG/yJk2DohVjIH7gQOivGa7+7Ex27W6MMqlVS9gBLANoaONVYzNbAahEyjI3b8dI8aaNmJYCOHbxcmljeVvABZHeRbziJmNc/emvPxOAy4COlyMgfOAa4vNZGbt/pbf3SfEiPHd9kP4FcViEApgDTDQ3ddEcbcDroqGyTFi3AqsB+bx8Q1n4rTL9SOgN+EqR5vkYWY3ED47A4sFidqcRbik7xzgEcLn+XxgMdBuMQZ65BSYrwCz3H02MNvMGmP+Lz2Aben4HnJNgXF1wEVmdpm7391eY3f/MfBjM9uV0BfcZGZbA3cRCvOLcZJw9wsAoutQ1BMK89eBWWb2rrvHubHQvxL6+MZoI2EI4bPyQ8IGWFz/BN4n9Gc15BTjBJ4D7gTmm9m5eTsmyV+zjl5xJI0hWhG/A/bIGfdKKVc/AcYS9qrfAQ6Kxu8NLIoZ46V2pq2IGeM8wod3YM643Qi/wT43ZoxFhR4Xel5qnJjtn817fn3O4/kJ4pySMzTlPT+lK99vHR2AI4AXgT1zxk0jbO3XprSM12POtxJoAKYAo4FDc4eYMc4vMFxC2MNdGzPGS0RH2vLG92jvM5U375Kcxz2BhhLW24p28tjYJ8SI8wBwC3AGYW/wiaiPGhaz/VKgZ/T4T8Do3GkxYyT+/xOuqz6lLgMYDiwCNpTQ9pOEjaIrgEejfvvmhDEmE+pGc9LPXLTsZYSi3rsD668h+juIcNGrm1vjlfTe7cwXO8E/dTThMM7rwM+Aw4BXS4jTmPN4ed60RTFj3AV8s8D404F7YsZYBPQrML5/gjwaCj1O+kK3Fydm+zY3QICXS3y9Y62Dch6i9+gKwqHtawmH23ZIMf5rMefrEXUut0bvu/8E9uvAcmuAiwmHeH8A7Biz3YulTMubr+T3eZp5RPPmbhj0AN4EqhO0v4hwGPaB6HVp/UpwD+DJmDEWpfV+SmMZhA2kLxOOCvwt6rOPStB+VrROHgQuJxz9SPSZAbYHfgo0Ena8riVsBI9JEGNeRz4jOXFy+9aehOL+AnBgSe/dzn6xE/5z2xC+L/4V4RDCDcDYEldOSR9swvdLfyRsCf8oGn4HPAV8KmaMNrd825uWN98GYDWwBmiJHrc+X59gnbQXZ3WM9ndQeOPkDMIhqg69ibvzABxC+FplTpKOOqf9mpzXY3Xea9NSQrxewKmEveWzE7btQyjkrwKXldBJ3g+cXGD8ScCcmDE25K+DJO/VGHk8kOD/SWPD4CDgGGCbnHGDgAPiviZpv2fz4n8eeDzGfF8EbiIU4DlRH71NCct7kLAXfAvhhkFDKHAUo0iMV4ALiI46ROOGRX12Sf1RB9bfogLjPhfluCZpvLI7gauVme1AONz8FXePdYZr9IX6+4Tj9VsDH7ROInSWsc+cM7PPE/Z6AJ5398cTtG3zy/tSvtjPkpntSOjg/kE4HArhe5tewNHu/vcSYnardZAv57trI6yH9YRC0uHvrkvMpxdwJOGcizpCh3mT551U0k77K4F/I+y5zHT3tSXksDNwH/AhsDAaXU/4HB4TN5eOSiuPnL4ENu1PMnmNO8LMlrD5uRZ9gL8QNlz+VKT944TvRme7+zsdzMWA/QjfF3+G0Me+DTzl7pfGaF/r7s1tTPumu/+sI/klYWZHu/v9BcbvAJzh7tMTxSvXYtyd5X2QN5lEwo2CcmFmYwgfIki4cRK1zz35qjebbih1q86tnJjZbYQObS5wt7svLSHGR4SNrRY27bQTvzZ575Nl7v5Y0nzSUC55lIPoxKtcTviFSaE+qkuYWS0wilCQxwN93X37rPIpByrGIt1YVEhbO9UOFVKRzmRm3+bjPeL1RD9rioYl7l7KGc0Voyx+2iQipXH3sr5wj0iOOsJPNM91979mnEvZ0Z6xiIhIxjLbM+7Xr5/X1dVltXgREZEut3DhwrfcvX/++FSLcXRJwQXAG+4+vr156+rqWLBgQZqLFxERKWtm9udC49P+vmkysDzlmCIiIhUttWIcnap+JPDfacUUERHZEqS5Z3wt4aYCW/Tp6SIiIkml8p2xmY0H3nT3hWb2uXbmm0S4DBoDBmx+W8r169fT3NzMunXr0kir4lRXV1NbW0tVVbe7ZohIt1I39TdF52mafmQXZFJZtF7bltYJXKOACWZ2BFANbGdm/+PuJ+XO5O6zCJfco76+frPfVDU3N1NTU0NdXR3hqmnSyt1ZtWoVzc3NDBxY9A5wIiLSjaRymNrdp7l7rbvXEe53+Xh+IY5j3bp19O3bV4W4ADOjb9++OmogIlKByu7qPSrEbdO6ERGpTKkXY3d/othvjCW47LLLuOqqq7JOQ0REMlbW16aO82V/ElvqiQEiIlLeyu4wddaamprYe++9+epXv8o+++zDcccdxwcffMDUqVPZd999GTp0KBdccAEAK1eu5Nhjj2XEiBGMGDGCJ598Eth8j3fw4ME0NTUB8F//9V8MGjSIz372s7zwwgsb52lsbOSggw5i6NChHHPMMbzzToduGyoiIt2IinEBL7zwAt/61rdYvnw52223HTNmzOCXv/wlzz//PM899xwXX3wxAJMnT+bcc8/l2WefZfbs2Zx++untxl24cCF33303jY2NzJ07l2effXbjtJNPPpkf/OAHPPfccwwZMoTLL7+8U/9HEREpH2V9mDoru+yyC6NGjQLgpJNO4uqrr6a6uppvfOMbjB8/nvHjw1fijz76KMuWLdvYbvXq1axdu7bNuPPmzeOYY46hd+/eAEyYMAGA9957j3fffZdDDz0UgFNOOYWJEyd2yv8mIiLlR8W4gPyzlquqqnjmmWd47LHHuPfee7n++ut5/PHH+eijj5g/fz7V1dWbzN+zZ08++ujjC5Hp50giItIeHaYu4LXXXuOpp54C4M4772TYsGG89957HHHEEVxzzTUsXrwYgLFjxzJjxoyN7RobG4FwR6qGhgYAGhoaePXVVwEYPXo0999/Px9++CFr1qzhV7/6FQCf/OQn2WGHHZg3bx4At99++8a9ZBERqXzaMy5gr732YubMmZx22mnsu+++XH755YwfP55169bh7lx99dUAXHfddZx11lkMHTqUlpYWRo8ezY033sixxx7Lbbfdxn777ceBBx7IoEGDADjggAP4yle+wv7778+OO+7IiBEjNi7z1ltv5cwzz+SDDz5gt9124+abb87kfxcRka5n7ptdlbJL1NfXe/79jJcvX84+++yTST6tmpqaGD9+PEuXLs00j7aUwzoSqXS6hnLn0HoFM1vo7vX543WYWkREJGMqxnnq6urKdq9YREQqk4qxiIhIxsquGGf1HXZ3oHUjIlKZyqoYV1dXs2rVKhWdAlrvZ5z/m2YREen+yuqnTbW1tTQ3N7Ny5cqsUylL1dXV1NbWZp2GiIikrKyKcVVVFQMHDsw6DRERkS5VVoepRUREtkQqxiIiIhkrq8PUkj5d8WZz5bJO0sqjWBy9vpvrqnVSLq9NGnmU03qtRNozFhERyZiKsYiISMZUjEVERDKmYiwiIpIxFWMREZGMqRiLiIhkTMVYREQkYyrGIiIiGVMxFhERyZiKsYiISMZUjEVERDKmYiwiIpIxFWMREZGMqRiLiIhkTMVYREQkYyrGIiIiGVMxFhERyZiKsYiISMZUjEVERDKWWjE2s13M7LdmtszMnjezyWnFFhERqWQ9U4zVApzv7g1mVgMsNLNH3H1ZissQERGpOKntGbv7X929IXq8BlgO7JxWfBERkUqV5p7xRmZWBwwHns4bPwmYBDBgwIDOWHQq6qb+pt3pTdOP7KJMRGRLp/5oU8XWB8RbJ+W2XlM/gcvMtgVmA+e4++rcae4+y93r3b2+f//+aS9aRESkW0q1GJtZFaEQ3+Hu96UZW0REpFKleTa1AT8Hlrv71WnFFRERqXRp7hmPAr4GjDGzxmg4IsX4IiIiFSm1E7jc/Q+ApRVPRERkS6ErcImIiGRMxVhERCRjKsYiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikjEVYxERkYypGIuIiGRMxVhERCRjKsYiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikjEVYxERkYypGIuIiGSsZ9YJVKq6qb8pOk/T9CM7PUY5Kfb/dKf/pdKk8dp0RYy4cUS6Qpp9mvaMRUREMqZiLCIikjEVYxERkYypGIuIiGRMxVhERCRjKsYiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikjEVYxERkYypGIuIiGRMxVhERCRjKsYiIiIZUzEWERHJmIqxiIhIxlSMRUREMqZiLCIikrFUi7GZjTOzF8xshZlNTTO2iIhIpUqtGJtZD2Am8CVgX+AEM9s3rfgiIiKVKs0945HACnd/xd3/CdwNHJVifBERkYqUZjHeGXg953lzNE5ERETaYe6eTiCz44Bx7n569PxrwIHufnbOPJOASdHTvYAXioTtB7zVwdQUo3xzUYzyzUUxyjeXSopRTrl0VYxd3b3/ZmPdPZUBOBh4KOf5NGBaB2MuSCEvxSjTXBSjfHNRjPLNpZJilFMuWcdI8zD1s8CeZjbQzD4BHA/MSTG+iIhIReqZViB3bzGzs4GHgB7ATe7+fFrxRUREKlVqxRjA3ecCc1MMOUsxUo+RVhzFSD9GWnEUI/0YacVRjM6J0+1jpHYCl4iIiJRGl8MUESlDZpbqkUspbyrGIiLl6ZmsE6hEZjbXzOqyziNfWRdjM/usmc3s4mXuYWajCowfZWa7d2Uuki4zW2Nmq9sYVprZfDM7LOs8uzMzqzazwdFQnWEevc1saDT0yiqPDrKsEwAws5mF+sQS4pxjZiM7ssdvZgPamXZIzDA3Aw+b2UVmVtWBXA5vZ9rExPHK7TtjMxsOnAhMBF4F7nP3GR2I1w9Y5TH/UTP7NeH30Uvyxg8Bvu/uX064/P4A7r4ySbu0mNkAd3+tA+2PAmrdfWb0/Gmg9QfrF7r7vSmkmbno2uqDgTvcfXCReXdx99fbmDbe3X+dQj7nuPu1HY3TVaIO9vvAacCfCYVkF0LHd5G7r48Roxo4E9gDWAL83N1bEuZRBVwJnEzoPwzYCZjh7tPNbJi7NyaJmRd/K+AEd7+jyHwjgNfd/W/R85OBYwnr5jJ3fzvGspqBq9ua7u5tTsuLc15+U8KFKf7g7q/GaD+Z8FPVTwP/C9zl7oviLDsvzlXAZ4C9Ca/vk8AfgT/GWR9RjFeAG4EfufuGaNxOwI+Avd29PmacbYFLgHHA7cBHrdMSrNcNwO+Bk9z9jbxpDe5+QJw4rcriOwkzGwScEA1vAfcQNhQ+nzDOQcB04G3gCsJK7gdsZWYnu/uDMcLslF+IAdx9SdxDG2ZmwKXA2YSjD2ZmLYQO4XsxY1zo7j+MHk9091/kTPu+u38nThzgfuCAqN1sdz82ZrtWFxI+iK16ASOAbQgdbaxibGYzCJ1AQe7+7Rgx1rQRw0II3y5OLm0sfwOwOMqzmEfMbJy7N+XldxpwEdDhYgycB1xbbCYza/e3/O4+IUaM77Yfwq8oFoNQAGuAge6+Joq7HXBVNEyOEeNWYD0wj49vOBOnXa4fAb0JVznaJA8zu4HQ+Q4sFiRqcxbhkr5zgEcIn+fzgcVAu8UY+CnwhSjWaEK/9O/AMMIZt8fF+F96ANvS8T3kmgLj6oCLzOwyd7+7vcbu/mPgx2a2K6EvuMnMtgbuIhTmF+Mk4e4XAETXoagnFOavA7PM7F13j3NjoX8lrMvGaCNhCOGz8kPCBlhc/wTeJ/RnNeQU4wSeA+4E5pvZuXk7Jslfs45ecSSNIVoRvwP2yBn3SilXPwHGEvaq3wEOisbvDSyKGeOldqatiBnjPMKHd2DOuN0Iv8E+N2aMhkKPCz0vEmdRoccJ2j+b9/z6nMfzE8Q5JWdoynt+SlbvvVIG4AjgRWDPnHHTCFv7tSkt4/WY860EGoApwGjg0NwhZozzCwyXEPbi1saM8RLRkba88T3a+0zlzbsk53HPJO/znHYr2sljY58QI84DwC3AGYS9wSeiPmpYzPaLcx7PJOwNtz5vjBkj8f+fcF31KXUZwHBgEbChhLafJGwUXQE8GvXbNyeMMZlQN5qTfuaiZS8jFPXeHVh/DdHfQYSLXt3cGq+k925nvtgJ/qmjCXd5eh34GXAY8GoJcRpzHi/Pm7YoZoy7gG8WGH86cE/MGIuAfgXG90+Qx6JCj5P8L/lvilI7t3amvVzi6x07/3IdovfoCsKh7WsJh9t2SDH+azHn6xF1LrdG77v/BPbrwHJrgIsJh3h/AOwYs92LpUzLm6/kjc4084jmzd0w6AG8CVQnaL8U6Bk9/hMwOndazBiL0no/pbEMwgbSlwlHBf4W9dlHJWg/i3Bo+kHgcsLRj0SfGWB7wlGHRsKO17WEjeAxCWLM68hnJCdObt/ak1DcXwAOLOW9WxaHqd39fuB+M9uGcNvFc4Ado8NKv3T3h2OGyj3U8GH+YmLGOAf4pZl9FVgYjasHPgEcEzNGlbtvdrFwd1+Z4IQBb+Nxoeft2d/MVhMOm2wdPYb4h3afNrNvuvvPckea2RmUfrZneZ2oUAJ3f8zMvk7YY/ojoTNYlyRGkcPuW8fMYwOhc3swOlHpBOAJM7vc3a9PkEsfwhGdrxIK+wHu/k7c9sCy6Kug2/LinkQoRnHsn/f+3DrnvRvnvVosj+Ux84BwuBzCgjeYWXPC1/cu4Hdm9hahL5oX5bEH8F7MGJ16MqGZfZ5wtKDYfF8kvK+OIHzm7wYmufv7CRc5gHBY+CXgDcJe7bsJYzQAPwHO8nA+wcNmNgz4iZn92d1PKBbA3eOe6FXMxkPRUS5TzexBwmu/+Y0gigWLqnrZMbMdCIebv+Lusd6U0Rfq7/NxZ/ZB6yTCVm3sM+eiN2rriTzPu/vjCdq2+eV93C/20/xfOsLMdiR87/wPwgcBwvc2vYCj3f3vJcRMfHJDOckpokZYD+uBDaTw3XWJ+fQCjiR0mHWE7zhv8ryTStppfyXwb4Q9l5nuvraEHHYG7iMUntyN2K2BY+Lm0lFp5ZHz+YNNP4OxX+PoHJZPAw+3Fq7o/Jht3b2h3cYpMrMlbL7R1wf4C3Cyu7e7sWRmjxO+G52dcAOtUCwD9iN8X/wZQh/7NvCUu18ao32tuze3MW2znYbOZGZHRzuS+eN3AM5w9+mJ4pVrMe7O8j7Im0yiCwtpmsxsDOFDBAk3TqL2uXuBvdl046LLC1ilMLPbCB3aXOBud19aQoyPCBtbLWzaaSd+bfLeJ8vc/bGk+aShXPIoB9GJV7mc8AuTpHu2qTGzWmAUoSCPB/q6+/ZZ5VMOVIxFurGokLZ2qh0qpCKdycy+zcd7xOuJftYUDUvcvZQzmitGWXxnLCKlcfeyvnCPSI464BeEX5T8NeNcyo72jEVERDKmrWoREZGMqRiLiIhkTMVYZAtiZol/tiQinU/FWEREJGMqxiIiIhlTMRYREcmYirGIiEjGVIxFREQypmIsIiKSMRVjkS1LbzNrzhnOyzohEdHlMEVERDKnPWMREZGMqRiLiIhkTMVYREQkYyrGIiIiGVMxFhERyZiKsYiISMZUjEVERDKmYiwiIpKx/w9TJXFiDgCa1AAAAABJRU5ErkJggg==\n", "text/plain": ["<Figure size 576x432 with 3 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["def pseudo(mot):\n", "    s = 0\n", "    for c in mot.lower():\n", "        n = ord(c) - ord(\"a\")\n", "        s = (22695477 * (s + n) + 1) % 2 ** 32\n", "    return chr(s % 26 + ord(\"A\"))\n", "\n", "d3 = repartition(pseudo)\n", "draw(d, \"lettre 1\", d2, \"somme\", d3, \"pseudo\");"]}, {"cell_type": "markdown", "id": "4547e7a0", "metadata": {}, "source": ["### Hash"]}, {"cell_type": "code", "execution_count": 14, "id": "0b447c75", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 576x576 with 4 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import hashlib\n", "\n", "def hash(mot):\n", "    m = hashlib.sha256()\n", "    m.update(mot.encode(\"utf-8\"))\n", "    h = m.digest()\n", "    s = h[0]\n", "    return chr(s % 26 + ord(\"A\"))\n", "\n", "d4 = repartition(hash)\n", "draw(d, \"lettre 1\", d2, \"somme\", d3, \"pseudo\", d4, \"hash\");"]}, {"cell_type": "markdown", "id": "c31db4b6", "metadata": {}, "source": ["### Light hash"]}, {"cell_type": "code", "execution_count": 15, "id": "1589301a", "metadata": {}, "outputs": [{"data": {"image/png": "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\n", "text/plain": ["<Figure size 576x720 with 5 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["def light_hash(mot):\n", "    mot = mot.encode(\"utf-8\")\n", "    x = 5381\n", "    for c in mot:\n", "        x = ((x << 5) + x) + c\n", "    return chr(x % 26 + ord(\"A\"))\n", "\n", "d5 = repartition(light_hash)\n", "draw(d, \"lettre 1\", d2, \"somme\", d3, \"pseudo\", d4, \"hash\", d5, \"light hash\");"]}, {"cell_type": "markdown", "id": "c587f86f", "metadata": {}, "source": ["### Q7\n", "\n", "Il faut mesurer l'acc\u00e8s \u00e0 des \u00e9l\u00e9ments d'un dictionnaire de diff\u00e9rentes tailles."]}, {"cell_type": "code", "execution_count": 16, "id": "770f0eae", "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "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": "3.10.5"}}, "nbformat": 4, "nbformat_minor": 5}