{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Exercice sur des données persos\n",
    "\n",
    "## Récupération des données\n",
    "\n",
    "Récupération d'un listing de notes persos au format markdown.\n",
    "\n",
    "```shell\n",
    "ls 20* -d > tree.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20200404193723_FirstNote.md',\n",
       " '20200404193815_SecondNote.md',\n",
       " '20200404193815_SecondNote.pdf',\n",
       " '20200406120140_PythonSciBasics.html',\n",
       " '20200406120140_PythonSciBasics.md',\n",
       " '20200406120140_PythonSciBasics.pdf',\n",
       " '20200406222521_OutilsPourMieuxCoder.md',\n",
       " '20200407101411_AnalyseSensibilite.md',\n",
       " '20200407102544_LTspice2latex.md',\n",
       " '20200407105048_latex2svg.md',\n",
       " '20200407171447_repetabiliteMesures.html',\n",
       " '20200407171447_repetabiliteMesures.md',\n",
       " '20200407172558_fideliteMesures.html',\n",
       " '20200407172558_fideliteMesures.md',\n",
       " '20200407172757_reproductibiliteMesures.html',\n",
       " '20200407172757_reproductibiliteMesures.md',\n",
       " '20200407174039_RechercheReproductible.md',\n",
       " '20200413171110_ExemplesCodesNonReproductibles.md',\n",
       " '20200413173245_GuideBonnesPratiquesNumeriquesBD.html',\n",
       " '20200413173245_GuideBonnesPratiquesNumeriquesBD.md',\n",
       " '20200413173245_GuideBonnesPratiquesNumeriquesBD.pdf',\n",
       " '20200501100000_DesignEcolo_EchangeEliseDiscord.md',\n",
       " '20200501115700_IAecolo_EchangeClementineDiscord.md',\n",
       " '20200502223100_MethodeKron.md',\n",
       " '20200502224900_PandocCitationCrossRef.md',\n",
       " '20200509142300_img/',\n",
       " '20200509142300_PredictionsClimat.md',\n",
       " '20200509173000_Retrobrighting.md',\n",
       " '20200515104700_img/',\n",
       " '20200515104700_ReparationPerforma400.md',\n",
       " '20200515142200_doc/',\n",
       " '20200515142200_ProgrammationMacOS7.md',\n",
       " '20200516161400_img/',\n",
       " '20200516161400_ReparationMacLC630.md',\n",
       " '20200519101800_ProgrammationScientifique.md',\n",
       " '20200526183300_Imprimante3D.md']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "from os import getcwd, listdir\n",
    "\n",
    "listFiles = []\n",
    "with open('tree.txt', 'r', encoding='utf8') as myFile:\n",
    "    myReader = csv.reader(myFile)\n",
    "    for row in myReader:\n",
    "        listFiles += row\n",
    "listFiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Séparation des notes et de leur dossier de pièces jointes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['20200509142300_img/', '20200515104700_img/', '20200515142200_doc/', '20200516161400_img/']\n",
      "['20200404193723_FirstNote.md', '20200404193815_SecondNote.md', '20200404193815_SecondNote.pdf', '20200406120140_PythonSciBasics.html', '20200406120140_PythonSciBasics.md', '20200406120140_PythonSciBasics.pdf', '20200406222521_OutilsPourMieuxCoder.md', '20200407101411_AnalyseSensibilite.md', '20200407102544_LTspice2latex.md', '20200407105048_latex2svg.md', '20200407171447_repetabiliteMesures.html', '20200407171447_repetabiliteMesures.md', '20200407172558_fideliteMesures.html', '20200407172558_fideliteMesures.md', '20200407172757_reproductibiliteMesures.html', '20200407172757_reproductibiliteMesures.md', '20200407174039_RechercheReproductible.md', '20200413171110_ExemplesCodesNonReproductibles.md', '20200413173245_GuideBonnesPratiquesNumeriquesBD.html', '20200413173245_GuideBonnesPratiquesNumeriquesBD.md', '20200413173245_GuideBonnesPratiquesNumeriquesBD.pdf', '20200501100000_DesignEcolo_EchangeEliseDiscord.md', '20200501115700_IAecolo_EchangeClementineDiscord.md', '20200502223100_MethodeKron.md', '20200502224900_PandocCitationCrossRef.md', '20200509142300_PredictionsClimat.md', '20200509173000_Retrobrighting.md', '20200515104700_ReparationPerforma400.md', '20200515142200_ProgrammationMacOS7.md', '20200516161400_ReparationMacLC630.md', '20200519101800_ProgrammationScientifique.md', '20200526183300_Imprimante3D.md']\n"
     ]
    }
   ],
   "source": [
    "notes = []\n",
    "directories = []\n",
    "for file in listFiles:\n",
    "    if file[-1]==\"/\":\n",
    "        directories += [file]\n",
    "    else:\n",
    "        notes += [file]\n",
    "\n",
    "print(directories)\n",
    "print(notes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Calcul du nombre de notes écrites par jour\n",
    "\n",
    "Pour ce faire on retrouve la date dans le nom de chaque note. Et on compte le nombre de notes écrite pour ce jour. Chaque jour depuis la première date est analysé, même sans notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "firstNote = notes[0]\n",
    "lastNote = notes[-1]\n",
    "\n",
    "# retrieve date from filename\n",
    "def findDate(filename):\n",
    "    year = int(filename[0:4])\n",
    "    month = int(filename[4:6])\n",
    "    day = int(filename[6:8])\n",
    "    return(datetime.datetime(year, month, day))\n",
    "    \n",
    "startDate = findDate(firstNote)\n",
    "endDate = findDate(lastNote)\n",
    "\n",
    "# build the days vector\n",
    "dayVect = np.arange(startDate, endDate + datetime.timedelta(days=1), datetime.timedelta(days=1)).astype(datetime.date)\n",
    "dayVectList = [startDate + datetime.timedelta(days=i) for i in range((endDate-startDate).days+1)]\n",
    "\n",
    "notesCount = np.zeros(len(dayVect))\n",
    "for note in notes:\n",
    "    curDate = findDate(note)\n",
    "    curInd = np.where(curDate == dayVect)\n",
    "    notesCount[curInd] += 1\n",
    "\n",
    "pjCount = np.zeros(len(dayVect))\n",
    "for mydir in directories:\n",
    "    curDate = findDate(mydir)\n",
    "    curInd = np.where(curDate == dayVect)\n",
    "    pjCount[curInd] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayVect == findDate(notes[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  0.,  4., 10.,  0.,  0.,  0.,  0.,  0.,  4.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  2.,  2.,  0.,  0.,  0.,  0.,  0.,  0.,  2.,  0.,  0.,  0.,\n",
       "        0.,  0.,  2.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        1.])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notesCount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Présentation des résultats\n",
    "\n",
    "Affichage sous forme de bar du nombre de notes écrites par jour.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "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",
    "import matplotlib.dates as mdates\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# grouped bar manual\n",
    "x = np.arange(len(dayVect))\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.bar(dayVect, notesCount, label='notes')\n",
    "ax.bar(dayVect, pjCount, label='dossiers PJ', color='#ff550099')\n",
    "plt.title('Nombre de notes écrites par jour sur les 2 derniers mois')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Notes/jour')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "Nombre moyen de notes écrit par jour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6792452830188679"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notesCount.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notesCount.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07547169811320754"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pjCount.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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