use_case_profits_fr.ipynb

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   "source": [
    "[In English](https://marieevelb.github.io/jupyter-notebook/use_case_profits_en/)  \n",
    "\n",
    "![ECCC logo](https://eccc-msc.github.io/open-data/img_eccc-logo.png)  \n",
    "\n",
    "[TdM](https://eccc-msc.github.io/open-data/readme_fr/) > [Aperçu de l'utilisation](https://eccc-msc.github.io/open-data/usage/readme_fr/) > Requêtes WMS en Python\n",
    "\n",
    "# Cas d'utilisation : profits anticipés selon la probabilité de précipitations\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Les données des services web géospatiaux [GeoMet du SMC](https://eccc-msc.github.io/open-data/msc-geomet/readme_fr/) peuvent aisément être utilisées pour prévoir la demande associée à certains produits ou services. Cet exemple de cas d'utilisation vous montrera comment extraire et traiter les données météorologiques brutes à l'aide du langage de programmation [Python](https://www.python.org/) afin de créer un outil qui permet de calculer les profits anticipés de la vente d'un produit dont la demande est affectée par les conditions météorologiques. Il vous permettra d'apprendre comment :\n",
    "* Accéder aux données météorologiques de GeoMet du SMC et les interroger;\n",
    "* Effectuer des requêtes pour obtenir les données d'une couche spécifique à un lieu précis; \n",
    "* Effectuer des requêtes temporelles;\n",
    "* Afficher les résultats sous forme de graphique et de tableau.\n",
    "\n",
    "Pour accéder à la version interactive du code source de ce Jupyter Notebook, cliquez sur la bannière ci-dessous.\n",
    "\n",
    "![badge](https://img.shields.io/badge/Version%20interactive-binder-F5A252.svg?logo=data:image/png;base64,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)\n",
    "\n",
    "## Création d'un outil de calcul des profits anticipés\n",
    "\n",
    "Prenons la situation suivante : un propriétaire de kiosque de souvenirs à Vancouver souhaite calculer les profits qu'il anticipe de faire grâce à la vente de parapluies à l'aide des prévisions météorologiques d'Environnement et Changement climatique Canada. En effet, ses ventes de parapluies augmentent drastiquement les jours où la probabilité qu'il y ait un minimum de 5 mm de précipitations est de 30% et plus. Considérant que le kiosque est ouvert de 9:00 le matin à 9:00 le soir, celui-ci souhaite calculer les profits qu'il prévoit faire dans les prochains jours selon les prévisions météorologiques obtenues avec les services web géospatiaux GeoMet du SMC.\n",
    "\n",
    "Afin de déterminer le nombre de parapluies qui devraient être vendus, la première étape est d'effectuer une requête avec le [standard Web Map Service (WMS)](https://eccc-msc.github.io/open-data/msc-geomet/web-services_fr/#web-map-service-wms) du SMC afin d'obtenir la probabilité qu'il y ait 5 mm ou plus de précipitations pour l'heure, la date et le lieu désiré à l'aide du langage de programmation Python. Pour réaliser cette étape, il faut tout d'abord importer les modules Python nécessaires et déterminer les paramètres de la requête comme la couche de données désirée, le lieu et le fuseau horaire local. La couche qui sera utilisée est celle du [Système régional de prévision d'ensemble (SRPE)](https://eccc-msc.github.io/open-data/msc-data/nwp_reps/readme_reps_fr/) qui contient la probabilité en pourcentage que la quantité de précipitations soit égale ou supérieure à 5 mm (``REPS.DIAG.3_PRMM.ERGE5``)."
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importation des modules Python \n",
    "from datetime import datetime, timedelta\n",
    "import re\n",
    "\n",
    "# Les modules suivants doivent avoir été préalablement installés sur \n",
    "# votre ordinateur pour utiliser le code hors de Jupyter Notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy\n",
    "from owslib.wms import WebMapService\n",
    "import pandas\n",
    "from tabulate import tabulate\n",
    "\n",
    "# Choix des paramètres\n",
    "# Couche de données désirée :\n",
    "layer = 'REPS.DIAG.3_PRMM.ERGE5'\n",
    "# Coordonnées de l'endroit pour lequel on souhaite obtenir des prévisions :\n",
    "y, x = 49.288, -123.116\n",
    "# Fuseau horaire local. Dans cet exemple, le fuseau horaire est UTC-07:00 :\n",
    "time_zone = -7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une fois les paramètres sélectionnés, il faut calculer les valeurs du paramètre ``bbox`` de la requête (lieu visé par la requête)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcul du paramètre bbox\n",
    "min_x, min_y, max_x, max_y = x - 0.25, y - 0.25, x + 0.25, y + 0.25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il faut ensuite se connecter au service WMS afin d'accéder aux métadonnées de la couche de données désirée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connexion au service WMS\n",
    "wms = WebMapService('https://geo.weather.gc.ca/geomet?SERVICE=WMS' +\n",
    "                    '&REQUEST=GetCapabilities',\n",
    "                    version='1.3.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le temps de début et de fin des prévisions disponibles ainsi que l'intervalle de temps entre les prévisions peuvent être récupérés grâce à une requête ``GetCapabilities``. Dans cette requête, ``dimensions`` fait référence à la section du même nom dans les métadonnées qui contient les informations liées au temps. ``dimensions`` étant un dictionnaire python, il est possible de récupérer les informations recherchées en utilisant les clés du dictionnaire et en utilisant la méthode ``split`` pour séparer les chaînes de caractères situées de part et d'autre du caractère ``/``. Pour l'intervalle de temps, la méthode ``re.sub`` permet de récupérer uniquement les chiffres de l'expression présente dans les métadonnées en remplaçant tout les autres caractères par un espace vide.\n",
    "\n",
    "Par la suite, une boucle ``while`` va permettre de générer la date et l'heure de toutes les prévisions disponibles pour le fuseau horaire UTC±00:00 entre le temps de début et le temps de fin des prévisions. Ces informations sont nécessaires pour réaliser la requête WMS. Cette même boucle ``while`` sera également utilisée pour obtenir la date et l'heure locale de chaque prévision disponible. Cela va permettre d'afficher les résultats obtenus aux heures locales pour faciliter l'interprétation des résultats."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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   "source": [
    "# Extraction des informations liées au temps\n",
    "# des prévisions disponibles à partir des métadonnées\n",
    "def time_parameters(layer):\n",
    "    start_time, end_time, interval = (wms[layer]\n",
    "                                      .dimensions['time']['values'][0]\n",
    "                                      .split('/')\n",
    "                                      )\n",
    "    iso_format = '%Y-%m-%dT%H:%M:%SZ'\n",
    "    start_time = datetime.strptime(start_time, iso_format)\n",
    "    end_time = datetime.strptime(end_time, iso_format)\n",
    "    interval = int(re.sub(r'\\D', '', interval))\n",
    "    return start_time, end_time, interval\n",
    "\n",
    "\n",
    "start_time, end_time, interval = time_parameters(layer)\n",
    "\n",
    "# Afin d'utiliser une heure de début et de fin locale spécifique,\n",
    "# enlevez le # au début des lignes suivantes et remplacez\n",
    "# les valeurs de start_time et end_time par les valeurs désirées :\n",
    "# start_time = 'AAAA-MM-JJThh:00'\n",
    "# end_time = 'AAAA-MM-JJThh:00'\n",
    "# fmt = '%Y-%m-%dT%H:%M'\n",
    "# start_time = datetime.strptime(start_time, fmt) - timedelta(hours=time_zone)\n",
    "# end_time = datetime.strptime(end_time, fmt) - timedelta(hours=time_zone)\n",
    "\n",
    "# Calcul de la date et de l'heure de chaque prévision\n",
    "# pour le fuseau horaire UTC±00:00 (variable time) \n",
    "# et pour le fuseau horaire local (variable local_time)\n",
    "time = [start_time]\n",
    "local_time = [start_time + timedelta(hours=time_zone)]\n",
    "while time[-1] < end_time:\n",
    "    time.append(time[-1] + timedelta(hours=interval))\n",
    "    local_time.append(time[-1] + timedelta(hours=time_zone))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La requête peut maintenant être envoyée au service WMS. Pour obtenir la probabilité qu'il y ait un minimum de 5 mm de précipitations à chaque date et heure désirée, une boucle ``for`` est utilisée afin de refaire la requête pour chaque heure de prévision déterminée précédemment. Comme le résultat de chacune de ces requêtes est une réponse contenant différentes informations, il faut ensuite extraire la probabilité qu'il y ait un minimum de 5 mm de précipitations de la réponse obtenue. Pour y arriver, il est possible d'utiliser les expressions régulières (module ``re``) afin de retrouver une chaîne de caractères. Les expressions régulières, souvent appelées regex, sont des expressions décrivant des ensembles de chaînes de caractères possibles. Dans cet exemple, les regex utilisées sont ``\\s`` (espace blanc), ``\\d`` (caractère numérique), ``*`` (0 ou plusieurs caractères du type mentionné juste avant) et ``+`` (1 ou plusieurs caractères du type mentionné juste avant). Ainsi, la méthode ``re.findall`` permet de trouver toutes les chaînes de caractères correspondant à l'expression qui lui est fournie. Les méthodes ``re.sub`` et ``strip`` sont également utilisées pour retirer les éléments qui entourent notre valeur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Boucle pour effectuer les requêtes et extraire les probabilités\n",
    "def request(layer): \n",
    "    info = []\n",
    "    pixel_value = []\n",
    "    for timestep in time:\n",
    "        # Requête GetFeatureInfo\n",
    "        info.append(wms.getfeatureinfo(layers=[layer],\n",
    "                                       srs='EPSG:4326',\n",
    "                                       bbox=(min_x, min_y, max_x, max_y),\n",
    "                                       size=(100, 100),\n",
    "                                       format='image/jpeg',\n",
    "                                       query_layers=[layer],\n",
    "                                       info_format='text/plain',\n",
    "                                       xy=(50, 50),\n",
    "                                       feature_count=1,\n",
    "                                       time=str(timestep.isoformat()) + 'Z'\n",
    "                                       ))\n",
    "        # Extraction des probabilités des résultats de la requête\n",
    "        text = info[-1].read().decode('utf-8')\n",
    "        pixel_value.append(str(re.findall(r'value_0\\s+\\d*.*\\d+', text)))\n",
    "        pixel_value[-1] = float(\n",
    "            re.sub('value_0 = \\'', '', pixel_value[-1])\n",
    "            .strip('[\"\"]')\n",
    "        )\n",
    "    \n",
    "    return pixel_value\n",
    "\n",
    "pixel_value = request(layer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les probabilités obtenues peuvent être visualisées sous la forme d'un graphique interactif facile à exporter grâce au module ``matplotlib``. Sur Jupyter Notebook, le côté interactif du graphique peut être activé avec la commande «magic» ``%matplotlib notebook``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prochaine ligne à retirer si le code est utilisé hors de Jupyter Notebook\n",
    "%matplotlib notebook\n",
    "\n",
    "\n",
    "# Création d'une fonction permettant de mieux aligner deux axes y ensemble\n",
    "def align_yaxis(ax, ax2):\n",
    "    y_lims = numpy.array([ax.get_ylim() for ax in [ax, ax2]])\n",
    "\n",
    "    # Normalisation des limites des axes y\n",
    "    y_magnitudes = (y_lims[:,1] - y_lims[:,0]).reshape(len(y_lims),1)\n",
    "    y_lims_normalized = y_lims / y_magnitudes\n",
    "\n",
    "    # Obtention de l'étendue combinée des axes y\n",
    "    y_new_lims_normalized = numpy.array([numpy.min(y_lims_normalized), \n",
    "                                         numpy.max(y_lims_normalized)])\n",
    "\n",
    "    # Dénormalisation de l'étendue combinée pour obtenir \n",
    "    # les nouvelles limites d'axe\n",
    "    new_lim1, new_lim2 = y_new_lims_normalized * y_magnitudes\n",
    "    ax2.set_ylim(new_lim2)\n",
    "\n",
    "    \n",
    "# Création d'une fonction pour faire le graphique\n",
    "def fig(x, y, title, xlabel, ylabel, ylim, color = 'black', y2 = '', y2label = ''):\n",
    "    # Taille du graphique et des polices de caractères\n",
    "    params = {'legend.fontsize': '14',\n",
    "              'figure.figsize': (8, 6),\n",
    "              'axes.labelsize': '14',\n",
    "              'axes.titlesize': '16',\n",
    "              'xtick.labelsize': '12',\n",
    "              'ytick.labelsize': '12'}\n",
    "    plt.rcParams.update(params)\n",
    "\n",
    "    # Création du graphique et de son style\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(x, y, marker='o')\n",
    "\n",
    "    # Titres\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel, color=color)\n",
    "\n",
    "    # Étendue de l'axe des y\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.tick_params(axis='y', labelcolor=color)\n",
    "    \n",
    "    # Grille\n",
    "    plt.grid(True, which='both')\n",
    "    \n",
    "    # Ajout d'un deuxième jeu de données\n",
    "    if y2 is not None:\n",
    "        ax2 = plt.twinx()\n",
    "        ax2.plot(x, y2, marker='o', color='tab:red')\n",
    "        # Titre du deuxième axe y\n",
    "        ax2.set_ylabel(y2label, color='tab:red')\n",
    "        # Étendue et marques du deuxième axe y\n",
    "        ax2.set_ylim(0, (max(y2) * 1.1))\n",
    "        ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "        align_yaxis(ax, ax2)\n",
    "\n",
    "    # Format de la date sur l'axe des x\n",
    "    plt.gcf().autofmt_xdate()\n",
    "    my_format = mdates.DateFormatter('%m/%d %H:%M')\n",
    "    plt.gca().xaxis.set_major_formatter(my_format)\n",
    "\n",
    "    # Graduation de l'axe des x selon le nombre de points sur le graphique\n",
    "    # Variable contenant les heures pour lesquelles il y aura des étiquettes: \n",
    "    hour = []\n",
    "    for timestep in x:\n",
    "        hour.append(int(timestep.strftime('%#H')))\n",
    "\n",
    "    # Détermination de la fréquence des marques et étiquette sur l'axe des x\n",
    "    if len(hour) < 8:\n",
    "        # Graduation plus précise si peu de valeurs\n",
    "        ax.xaxis.set_major_locator(mdates.HourLocator(byhour=hour))\n",
    "    elif len(hour) > 8 and len(hour) <25:\n",
    "        ax.xaxis.set_major_locator(mdates.HourLocator(byhour=hour, interval=2))\n",
    "        ax.xaxis.set_minor_locator(mdates.HourLocator(byhour=hour))\n",
    "    else:\n",
    "        # Graduation plus grossière s'il y a beaucoup de valeurs\n",
    "        ax.xaxis.set_major_locator(mdates.DayLocator())\n",
    "        ax.xaxis.set_minor_locator(mdates.HourLocator(byhour=(0, 12)))\n",
    "    \n",
    "    return fig\n",
    "\n",
    "# Ajout de la quantité de précipitation au graphique\n",
    "# Vérification de la compatibilité des paramètres temporels :\n",
    "start_time1, end_time1, interval1 = time_parameters('REPS.DIAG.3_PRMM.ERMEAN')\n",
    "\n",
    "if (start_time1 == start_time\n",
    "        and end_time1 == end_time\n",
    "        and interval1 == interval):\n",
    "    # Requête GetFeatureInfo\n",
    "    y2 = request(layer = 'REPS.DIAG.3_PRMM.ERMEAN')\n",
    "    y2label = 'Quantité de précipitations (mm)'\n",
    "else:\n",
    "    y2 = None\n",
    "    y2label = None\n",
    "\n",
    "# Utilisation de la fonction fig pour générer le graphique\n",
    "fig(x = local_time,\n",
    "    y = pixel_value,\n",
    "    title = ('Probabilité que la quantité de précipitations ' +\n",
    "             f'soit de 5 mm et plus\\nentre {local_time[0]} et ' +\n",
    "             f'{local_time[-1]} (heure locale)'),\n",
    "    xlabel = '\\nDate et heure',\n",
    "    ylabel = 'Probabilité qu\\'il y ait 5 mm\\net plus de précipitations (%)',\n",
    "    ylim = (-10, 110),\n",
    "    y2 = y2,\n",
    "    y2label = y2label\n",
    "    )\n",
    "\n",
    "# Affichage du graphique\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une fois les probabilités obtenues, le propriétaire doit calculer les profits que rapporte la vente de parapluie. Comme mentionné plus tôt, il sait qu'en moyenne, le kiosque vend 3 parapluies par jour lorsque la probabilité qu'il y ait un minimum de 5 mm de pluie est inférieure à 30%. À ce nombre s'ajoutent en moyenne 10 parapluies par heure lorsque la probabilité est de 30% et le nombre de parapluies continue à augmenter de manière linéaire pour atteindre une moyenne de 30 parapluies par heure lorsque la probabilité est de 100%. Le nombre de parapluies vendus par heure si la probabilité de précipitation est de 30% et plus peut être calculé à l'aide des équations suivantes:\n",
    "\n",
    "$$ m = \\frac{y_2 - y_1}{x_2 - x_1} = \\frac{30 - 10}{100 - 30}$$\n",
    "\n",
    "$$ n_{parapluies} = 10 + m * (probabilité - 30)$$\n",
    "\n",
    "Sachant que chaque parapluie lui rapporte 10,00$, il est possible de calculer les profits pour les trois prochains jours avec le code qui suit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nombre de parapluies vendus lorsqu'il y a moins de 30% \n",
    "# de probabilité qu'il y ait 5 mm et plus de précipitations \n",
    "base = 3\n",
    "\n",
    "# Profit par parapluie\n",
    "umbrella_profit = 10.00\n",
    "\n",
    "# Données pour le calcul de la pente\n",
    "# Lorsque la probabilité est de 30%...\n",
    "x1 = 30\n",
    "# ... 10 parapluies sont vendus chaque heure\n",
    "y1 = 10\n",
    "# Lorsque la probabilité est de 100%...\n",
    "x2 = 100\n",
    "# ... 30 parapluies sont vendus chaque heure\n",
    "y2 = 30\n",
    "\n",
    "# Calcul de la pente\n",
    "slope = ((y2-y1)/(x2-x1))\n",
    "\n",
    "# Heures d'ouverture\n",
    "opening = '09:00:00'\n",
    "opening = datetime.strptime(opening, '%H:%M:%S')\n",
    "closing = '21:00:00'\n",
    "closing = datetime.strptime(closing, '%H:%M:%S')\n",
    "\n",
    "# Obtention des heures de prévision incluses dans les heures d'ouverture\n",
    "open_hours = []\n",
    "for timestep in local_time:\n",
    "    if (timestep.time() > opening.time() \n",
    "            and timestep.time() < closing.time()):\n",
    "        open_hours.append(timestep)\n",
    "\n",
    "# Calcul du nombre de parapluies vendus chaque jour, peu importe \n",
    "# les conditions météorologiques\n",
    "opening_interval = (opening + timedelta(hours=interval)).time()\n",
    "umbrella = []\n",
    "for timestep in local_time:\n",
    "    new_day = timestep.time() < opening_interval\n",
    "    if (umbrella == [] or new_day) and timestep in open_hours:\n",
    "        umbrella.append(base)      \n",
    "    else:\n",
    "        umbrella.append(0)\n",
    "\n",
    "# Calcul du nombre de parapluies vendus chaque jour selon les conditions\n",
    "# météorologiques et des profits cumulatifs anticipés de la vente de parapluies\n",
    "cumulative_profit = []\n",
    "for index, probability in enumerate(pixel_value):\n",
    "    # Équation pour obtenir le nombre de parapluies vendus par heure\n",
    "    eq = y1 + round((probability - 30) * slope)\n",
    "    # Équation pour obtenir le nombre de parapluies vendus entre 2 prévisions\n",
    "    eq2 = eq * interval \n",
    "    if local_time[index] in open_hours and probability > 30:\n",
    "        if index == 0:\n",
    "            umbrella[index] = umbrella[index] + eq2\n",
    "        else: \n",
    "            umbrella[index] = umbrella[index] + umbrella[index - 1] + eq2\n",
    "    elif index != 0:\n",
    "        umbrella[index] = umbrella[index] + umbrella[index-1]                            \n",
    "    cumulative_profit.append(umbrella[index] * umbrella_profit)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les résultats obtenus peuvent alors être visualisés sous la forme d'un graphique grâce à la fonction ``fig`` créée plus tôt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Création du graphique\n",
    "fig(x=local_time,\n",
    "    y=pixel_value,\n",
    "    title=('Profits anticipés de la vente de parapluies\\n' +\n",
    "           'selon la probabilité de précipitations'),\n",
    "    xlabel='\\nDate et heure',\n",
    "    ylabel='Probabilité qu\\'il y ait 5 mm\\net plus de précipitations (%)',\n",
    "    ylim=(-10, 110),\n",
    "    y2=cumulative_profit,\n",
    "    y2label='Profits cumulatifs anticipés ($)'\n",
    "    )\n",
    "\n",
    "# Affichage du graphique\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les profits cumulatifs anticipés peuvent également être visualisés sous la forme d'un tableau synthèse et exportés en format CSV si désiré."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tableau 1 : Profits cumulatifs anticipés selon la probabilité de précipitations\n",
      "+----------------------+-----------------+----------------------------------+\n",
      "| Date et heure locale | Probabilité (%) | Profits cumulatifs anticipés ($) |\n",
      "+----------------------+-----------------+----------------------------------+\n",
      "| 2020-05-21 20:00:00  |       0.0       |               30.0               |\n",
      "| 2020-05-22 11:00:00  |       0.0       |               60.0               |\n",
      "| 2020-05-22 14:00:00  |       0.0       |               60.0               |\n",
      "| 2020-05-22 17:00:00  |       0.0       |               60.0               |\n",
      "| 2020-05-22 20:00:00  |       0.0       |               60.0               |\n",
      "| 2020-05-23 11:00:00  |       0.0       |               90.0               |\n",
      "| 2020-05-23 14:00:00  |       0.0       |               90.0               |\n",
      "| 2020-05-23 17:00:00  |       0.0       |               90.0               |\n",
      "| 2020-05-23 20:00:00  |       0.0       |               90.0               |\n",
      "| 2020-05-24 11:00:00  |       0.0       |              120.0               |\n",
      "| 2020-05-24 14:00:00  |       5.0       |              120.0               |\n",
      "| 2020-05-24 17:00:00  |       0.0       |              120.0               |\n",
      "+----------------------+-----------------+----------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# Obtention des probabilités et des profits cumulatifs\n",
    "# uniquement pour les heures d'ouverture du kiosque\n",
    "probability = []\n",
    "profit = []\n",
    "for index, timestep in enumerate(local_time):\n",
    "    if timestep in open_hours:\n",
    "        probability.append(pixel_value[index])\n",
    "        profit.append(cumulative_profit[index])\n",
    "\n",
    "# Création du tableau\n",
    "profit_df = pandas.DataFrame({'Date et heure locale':  open_hours,\n",
    "                              'Probabilité (%)': probability,\n",
    "                              'Profits cumulatifs anticipés ($)': profit\n",
    "                              })\n",
    "\n",
    "# Affichage du tableau\n",
    "print('Tableau 1 : Profits cumulatifs anticipés' +\n",
    "      ' selon la probabilité de précipitations')\n",
    "print(tabulate(profit_df,\n",
    "               headers='keys',\n",
    "               tablefmt='pretty',\n",
    "               showindex=False))\n",
    "\n",
    "# Sauvegarde du tableau en format CSV\n",
    "# Enlever le # au début des lignes suivantes:\n",
    "# profit_df.to_csv('profit.csv',\n",
    "#                 sep=';',\n",
    "#                 index=False,\n",
    "#                 encoding='utf-8-sig')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Grâce à cet outil, Arthur a maintenant un aperçu des profits provenant de la vente de parapluies qu'il peut s'attendre à faire dans les prochains jours. Suite à la lecture de cet exemple de cas d'utilisation, vous devriez être en mesure de savoir comment accéder à un WMS, effectuer des requêtes incluant des requêtes temporelles et afficher les résultats obtenus sous forme de graphique et de tableau. En changeant les paramètres de la requête et les étapes de traitement des données obtenues, il est possible d'adapter cet exemple pour répondre à vos besoins."
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