{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from texenv import texenv\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Informations sur le devoir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '\\\\premiereS', 'date': '4 mai 2015', 'titre': 'DM 7'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_name = \"DM_0504\"\n",
    "classe = \"1S\"\n",
    "\n",
    "latex_info = {}\n",
    "latex_info['titre'] = 'DM 7'\n",
    "latex_info['classe'] = \"\\\\premiereS\"\n",
    "latex_info['date'] = '4 mai 2015'\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import et premiers traitements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "notes = pd.ExcelFile(\"./../../../notes_\"+classe+\".xls\")\n",
    "notes.sheet_names\n",
    "notes = notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_0504', 'a_arrondi', 'Malus', 'Exercice 1', '1.1.a (domaines)',\n",
       "       '1.1.b (Dérivée)', '1.1.c (Variations)', '1.2.a (domaines)',\n",
       "       '1.2.b (Dérivée)', '1.2.c (Variations)', '1.3.a (Courbe)',\n",
       "       '1.3.b (Intersection)', '1.3.c (Resolution graphique)', '1 Malus',\n",
       "       'Exercice 2', '2.1 (variations)', '2.2.a (Equation tangente)',\n",
       "       '2.2.b (Position tangente)', '2.2.c (Courbe)', 'Exercice 3',\n",
       "       '3.1 (domaines)', '3.2 (variations)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.drop('a_arrondi', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]\n",
    "#notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supression des notes inutiles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liste des exercices (donc notés)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def toRepVal(val):\n",
    "    if pd.isnull(val):\n",
    "        return \"\\\\NoRep\"\n",
    "    elif val == 0:\n",
    "        return \"\\\\RepZ\"\n",
    "    elif val == 1:\n",
    "        return \"\\\\RepU\"\n",
    "    elif val == 2:\n",
    "        return \"\\\\RepD\"\n",
    "    elif val == 3:\n",
    "        return \"\\\\RepT\"\n",
    "    else:\n",
    "        return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_0504', 'Malus', 'Exercice 1', '1.1.a (domaines)', '1.1.b (Dérivée)',\n",
       "       '1.1.c (Variations)', '1.2.a (domaines)', '1.2.b (Dérivée)',\n",
       "       '1.2.c (Variations)', '1.3.a (Courbe)', '1.3.b (Intersection)',\n",
       "       '1.3.c (Resolution graphique)', '1 Malus', 'Exercice 2',\n",
       "       '2.1 (variations)', '2.2.a (Equation tangente)',\n",
       "       '2.2.b (Position tangente)', '2.2.c (Courbe)', 'Exercice 3',\n",
       "       '3.1 (domaines)', '3.2 (variations)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.1.a (domaines)',\n",
       " '1.1.b (Dérivée)',\n",
       " '1.1.c (Variations)',\n",
       " '1.2.a (domaines)',\n",
       " '1.2.b (Dérivée)',\n",
       " '1.2.c (Variations)',\n",
       " '1.3.a (Courbe)',\n",
       " '1.3.b (Intersection)',\n",
       " '1.3.c (Resolution graphique)',\n",
       " '2.1 (variations)',\n",
       " '2.2.a (Equation tangente)',\n",
       " '2.2.b (Position tangente)',\n",
       " '2.2.c (Courbe)',\n",
       " '3.1 (domaines)',\n",
       " '3.2 (variations)']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_avec_note = list_exo + [ds_name, \"1 Malus\", \"Malus\"]\n",
    "sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
    "sous_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/pandas/core/frame.py:2142: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "notes[sous_exo] = notes[sous_exo].applymap(toRepVal)\n",
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(notes.T.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation du fichier .tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
    "with open(\"./bilan.tex\",\"w\") as f:\n",
    "    f.write(bilan.render(eleves = notes, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Un peu de statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30.000000\n",
       "mean     11.016667\n",
       "std       5.414672\n",
       "min       0.000000\n",
       "25%       8.375000\n",
       "50%      12.250000\n",
       "75%      14.500000\n",
       "max      19.500000\n",
       "Name: DM_0504, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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