{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-notebook\")\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "from extract import extract_flat_marks, get_class_ws, list_classes\n",
    "from df_marks_manip import digest_flat_df, students_pov, round_half_point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_eval(evaluation):\n",
    "    ws = get_class_ws(clsW.value)\n",
    "    flat = extract_flat_marks(ws)\n",
    "    _,_,eval_m = digest_flat_df(flat)\n",
    "    print(\"Devoir sur \",eval_m[(eval_m[\"Nom\"]==evaluation) & (eval_m[\"Mark\"] > 0)][\"Bareme\"].iloc[0])\n",
    "    print(eval_m[eval_m[\"Nom\"]==evaluation][\"Mark\"].describe())\n",
    "def select_classe(classe):\n",
    "    ws = get_class_ws(classe)\n",
    "    evals = extract_flat_marks(ws)[\"Nom\"].unique()\n",
    "    evalW.options = list(evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classes = list_classes()\n",
    "clsW = widgets.Select(options=classes)\n",
    "cls_init = clsW.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ws = get_class_ws(cls_init)\n",
    "evals = extract_flat_marks(ws)[\"Nom\"].unique()\n",
    "evalW = widgets.Dropdown(options = list(evals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devoir sur  22.0\n",
      "count    30.000000\n",
      "mean     10.200000\n",
      "std       3.786546\n",
      "min       0.000000\n",
      "25%       8.625000\n",
      "50%      10.750000\n",
      "75%      12.375000\n",
      "max      15.500000\n",
      "Name: Mark, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "j = widgets.interactive(print_eval, evaluation=evalW)\n",
    "i = widgets.interactive(select_classe, classe=clsW)\n",
    "display(i,j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quand on change de classe, l'évaluation n'est pas mis à jour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solution 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classes = list_classes()\n",
    "clsW = widgets.Select(options=classes)\n",
    "cls_init = clsW.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ws = get_class_ws(cls_init)\n",
    "evals = extract_flat_marks(ws)[\"Nom\"].unique()\n",
    "evalW = widgets.Select(options = list(evals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def update_evals(*args):\n",
    "    classe = clsW.value\n",
    "    ws = get_class_ws(classe)\n",
    "    evals = extract_flat_marks(ws)[\"Nom\"].unique()\n",
    "    evalW.options = list(evals)\n",
    "    \n",
    "clsW.observe(update_evals, \"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def resume(classe, evaluation):\n",
    "    ws = get_class_ws(classe)\n",
    "    flat = extract_flat_marks(ws)\n",
    "    _,_,eval_m = digest_flat_df(flat)\n",
    "    print(\"Devoir sur \",eval_m[(eval_m[\"Nom\"]==evaluation) & (eval_m[\"Mark\"] > 0)][\"Bareme\"].iloc[0])\n",
    "    print(eval_m[eval_m[\"Nom\"]==evaluation][\"Mark\"].describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devoir sur  22.0\n",
      "count    29.000000\n",
      "mean      7.879310\n",
      "std       4.787736\n",
      "min       0.000000\n",
      "25%       5.500000\n",
      "50%       8.500000\n",
      "75%      11.500000\n",
      "max      15.500000\n",
      "Name: Mark, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.resume>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(resume, classe=clsW, evaluation=evalW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  },
  "widgets": {
   "state": {
    "849ab02f915e4783b04221c9ed128d96": {
     "views": [
      {
       "cell_index": 13
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}