Assignment2(1).ipynb

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    "# Assignment 2\n",
    "\n",
    "Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n",
    "\n",
    "An NOAA dataset has been stored in the file `data/C2A2_data/BinnedCsvs_d400/159e0ff350a61aecb1907fed80fbbeae59ccd681b28fbefe93ea7145.csv`. The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.\n",
    "\n",
    "Each row in the assignment datafile corresponds to a single observation.\n",
    "\n",
    "The following variables are provided to you:\n",
    "\n",
    "* **id** : station identification code\n",
    "* **date** : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)\n",
    "* **element** : indicator of element type\n",
    "    * TMAX : Maximum temperature (tenths of degrees C)\n",
    "    * TMIN : Minimum temperature (tenths of degrees C)\n",
    "* **value** : data value for element (tenths of degrees C)\n",
    "\n",
    "For this assignment, you must:\n",
    "\n",
    "1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.\n",
    "2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n",
    "3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.\n",
    "4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.\n",
    "\n",
    "The data you have been given is near **None, Texas, United States**, and the stations the data comes from are shown on the map below."
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplleaflet\n",
    "import pandas as pd\n",
    "\n",
    "def leaflet_plot_stations(binsize, hashid):\n",
    "\n",
    "    df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))\n",
    "\n",
    "    station_locations_by_hash = df[df['hash'] == hashid]\n",
    "\n",
    "    lons = station_locations_by_hash['LONGITUDE'].tolist()\n",
    "    lats = station_locations_by_hash['LATITUDE'].tolist()\n",
    "\n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.scatter(lons, lats, c='r', alpha=0.7, s=200)\n",
    "\n",
    "    return mplleaflet.display()\n",
    "\n",
    "leaflet_plot_stations(400,'159e0ff350a61aecb1907fed80fbbeae59ccd681b28fbefe93ea7145')"
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      "[ -1.7  -0.6  -1.7  -5.   -5.6  -5.6  -8.9  -8.9  -8.9  -1.1  -1.1   0.\n",
      "  -0.6  -0.6  -1.6  -3.3  -2.8  -1.6  -0.6   0.6   4.4   3.9  -1.7  -3.3\n",
      "  -1.7   0.    1.7   2.8   5.    3.9   4.4  -3.9  -3.9  -4.4  -0.6  -1.7\n",
      "  -1.7   1.1   5.    5.6   4.4   3.9   0.   -1.1  -0.6   1.1  -2.8  -3.3\n",
      "  -2.8  -0.6   0.6   6.7  -2.8  -3.9  -3.9  -3.3  -3.9  -3.8  -5.   -1.1\n",
      "  -2.2  -2.2  -3.9  -5.6  -6.7  -5.   -2.2   6.7   8.3   7.2   7.8   9.4\n",
      "  11.7   8.9   7.2   7.8  12.2  12.8  11.7  11.1   9.4   7.2   8.3  10.\n",
      "   4.4   2.8   4.4   7.8  12.2  15.6  16.1  17.2   9.4   8.3   8.3   9.4\n",
      "  10.   18.9  13.3   9.4   8.3  10.   10.   12.2  11.1  12.8  13.9  12.2\n",
      "   7.8   7.2   7.2  12.8  15.6  14.4  12.2  14.4  10.6   7.8   6.7   8.3\n",
      "  10.6  11.1  13.3  15.   15.   16.7  18.9  17.8  20.6  17.2  15.6  14.4\n",
      "  16.1  16.7  18.9  18.3  18.3  18.3  18.9  13.9  12.8  12.8  15.   18.3\n",
      "  16.7  15.6  16.1  16.7  18.3  15.6  14.4  13.9  16.1  18.3  19.4  20.6\n",
      "  20.6  20.6  21.1  21.7  21.7  22.8  23.3  22.8  22.2  22.8  22.2  22.2\n",
      "  21.7  21.7  21.7  21.7  21.7  21.7  22.2  22.2  21.7  19.4  18.9  19.4\n",
      "  20.6  20.6  21.1  21.7  22.2  24.4  24.4  24.4  23.9  21.7  21.7  21.7\n",
      "  22.8  22.8  21.7  22.2  22.2  23.3  23.3  23.9  21.7  23.3  23.9  23.3\n",
      "  21.7  22.2  22.2  23.3  23.9  22.2  22.8  22.8  21.1  19.4  20.6  21.7\n",
      "  23.9  25.   24.4  24.4  23.3  22.2  23.3  23.3  22.8  21.7  21.1  20.\n",
      "  21.1  22.8  18.3  17.8  17.8  20.   23.9  24.4  20.6  18.3  16.7  17.2\n",
      "  19.4  21.1  20.   20.   20.   20.   21.7  22.8  24.4  21.7  21.7  21.1\n",
      "  21.1  17.2  13.9  12.2  13.3  16.1  18.9  18.3  19.4  18.9  18.9  18.9\n",
      "  18.9  16.7  16.7  16.7  16.1  15.   16.1  18.9  17.8  14.4  11.1   8.9\n",
      "   8.9  12.2  12.2  13.9  15.   15.   13.9  12.8  12.8  11.1   9.4  10.\n",
      "  10.6  10.   10.    8.3   8.9  10.   16.1  18.3  13.3  12.8  12.8  11.7\n",
      "   9.4   8.9   9.4  13.9  12.2  12.2  12.2  13.9  14.4  12.8   8.9   5.\n",
      "   7.8  10.    6.7   6.7   4.4   6.1   7.8   8.9   6.1   5.    5.    7.8\n",
      "  -1.7  -2.8  -1.7   1.1   7.2   9.4   2.8   2.2   2.2   3.9   2.2   1.1\n",
      "   0.    0.6   0.    1.1   1.1   2.8   5.    5.6   5.6   6.7   5.    5.\n",
      "   6.7   0.   -1.   -1.1  -0.6   1.1   1.1   3.9   5.6   6.1   5.    5.\n",
      "   1.7  -1.   -1.   -1.7   0. ]\n"
     ]
    },
    {
     "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, fig.canvas.height);\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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 overriden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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": [
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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "Months_Names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec']\n",
    "Months_Length = [31,28,31,30,31,30,31,31,30,31,30,31]\n",
    "def read_weather_data():\n",
    "    w_pd = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/159e0ff350a61aecb1907fed80fbbeae59ccd681b28fbefe93ea7145.csv', index_col=None)\n",
    "    w_pd['Month'] = w_pd['Date'].map(lambda x : int(x[5:7]))\n",
    "    w_pd['Day'] = w_pd['Date'].map(lambda x : int(x[8:10]))\n",
    "    w_pd['Year'] = w_pd['Date'].map(lambda x : int(x[0:4]))\n",
    "    w_pd['Data_Value'] = w_pd['Data_Value'].map(lambda x : x/10)\n",
    "    w_pd = w_pd[~((w_pd['Month'] == 2) & (w_pd['Day'] == 29))]\n",
    "    w_pd = w_pd.drop(labels=['Date', 'ID'], axis=1)\n",
    "    w_tx = w_pd[((w_pd['Element'] == 'TMAX') & (w_pd['Year'] <2015))]\n",
    "    w_tx = w_tx.groupby(['Month', 'Day'], as_index=True).agg(['max'])\n",
    "    w_tn = w_pd[((w_pd['Element'] == 'TMIN')& (w_pd['Year'] <2015))]\n",
    "    w_tn = w_tn.groupby(['Month', 'Day'], as_index=True).agg(['min'])\n",
    "    wx_15 = w_pd[((w_pd['Element'] == 'TMAX') & (w_pd['Year'] == 2015))]\n",
    "    wx_15 = wx_15.groupby(['Month', 'Day'], as_index=True).agg(['max'])\n",
    "    wn_15 = w_pd[((w_pd['Element'] == 'TMIN') & (w_pd['Year'] == 2015))]\n",
    "    wn_15 = wn_15.groupby(['Month', 'Day'], as_index=True).agg(['min'])\n",
    "    return (w_tx, w_tn, wx_15, wn_15)\n",
    "def plot_weather():\n",
    "    w_tx, w_tn, wx_15, wn_15 = read_weather_data()\n",
    "    mx = np.array(w_tx.reset_index()['Data_Value']['max'])\n",
    "    mn = np.array(w_tn.reset_index()['Data_Value']['min'])\n",
    "    mx_15 = np.array(wx_15.reset_index()['Data_Value']['max'])\n",
    "    mn_15 = np.array(wn_15.reset_index()['Data_Value']['min'])\n",
    "    print(mn_15)\n",
    "    for i in range(len(mx_15)):\n",
    "        if(mx_15[i] <= mx[i]):\n",
    "            mx_15[i] = np.nan\n",
    "        if(mn_15[i] >= mn[i]):\n",
    "            mn_15[i] = np.nan\n",
    "    x = np.arange(0,365,1)\n",
    "    xi = [Months_Names[i] for i in range(0, 12)]\n",
    "    xl = []\n",
    "    sub_xl = []\n",
    "    for i in range(0,12):\n",
    "        if i == 0:\n",
    "            xl.append(Months_Length[0])\n",
    "            sub_xl.append(Months_Length[0]/2)\n",
    "        else:\n",
    "            xl.append(Months_Length[i] + xl[i-1])\n",
    "            sub_xl.append(Months_Length[i] + sub_xl[i-1])\n",
    "    axis = plt.figure(figsize=(10,8)).add_subplot(111, autoscale_on=False,xlim=(0,len(mx)),ylim=(-20,50))\n",
    "    axis.plot(x, mx, color = 'red', label='high temp')\n",
    "    axis.scatter(x, mx_15, s=35, color = 'green', alpha=0.9, label='high 2015 temp')\n",
    "    axis.plot(x, mn, color='blue', label='low temp')\n",
    "    axis.scatter(x, mn_15, s=35, color = 'black', alpha=0.9, label='low 2015 temp')\n",
    "    axis.fill_between(x, mn, mx, facecolor='cyan', alpha=0.5)\n",
    "    axis.set_xticks(xl)\n",
    "    axis.set_xticklabels(xi, minor=False, fontdict = {'fontsize': '9', 'horizontalalignment' : 'right'})\n",
    "    axis.set_ylabel('temperature in c')\n",
    "    axis.set_xlabel('time of the year')\n",
    "    axis.set_title('Texas, US High/Low Weather from 2005 to 2014')\n",
    "    plt.legend()\n",
    "    return''\n",
    "plot_weather()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
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