North Atlantic Hurricane Distribution

HurricaneDistribution.ipynb

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      "source": "import numpy as np\nhurricane = np.loadtxt('./hurricanesA.dat')\nprint(hurricane.shape)\nprint(np.max(hurricane[:, 1]))",
      "execution_count": 2,
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      "source": "# 4.1\nThe file hurricaneA.dat contains the number of hurricanes in the North-Atlantic from 1944 to 2012. \n- Estimate the total and average number of hurricanes observed over this period\n- Make an empirical distribution of the number of hurricanes\n- Plot empirical probability of observing x hurricanes per year\n- Estimate the mode of this empirical pdf. What does this value mean?\n- Estimate the median and compare it to the mean number of hurricanes per year.\n- Estimate the quintiles 95% and 99% and explain their meaning\n- Evaluate the variance and std of the empirical distribution"
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      "source": "import numpy as np\n\n# Total and average number of hurricanes\nhurricane_total = np.sum(hurricane[:, 1])\nhurricane_avg = np.average(hurricane[:, 1])\nprint('Total hurricanes = {:.0f}'.format(hurricane_total))\nprint('Average hurricanes = {:.2f}'.format(hurricane_avg))",
      "execution_count": 3,
      "outputs": [
        {
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          "text": "Total hurricanes = 174\nAverage hurricanes = 2.52\n",
          "name": "stdout"
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      "source": "import matplotlib.pyplot as plt\nimport numpy as np\n\n# Empirical distribution using histogram, Skewed to the left a bit\nunique, count = np.unique(hurricane[:, 1], return_counts=True)\n\nplt.figure()\nplt.bar(unique, count)\nplt.show()",
      "execution_count": 4,
      "outputs": [
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};\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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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\" width=\"640\">"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\nunique, count = np.unique(hurricane[:, 1], return_counts=True)\np_emp = count/len(hurricane[:, 0])\n\nplt.figure()\nplt.bar(unique, p_emp)\nplt.show()\n\nprint('Mode of the distribution = {:.2f}'.format(unique[np.argmax(count)]))",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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          "metadata": {}
        },
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          "data": {
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\" width=\"640\">"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "Mode of the distribution = 2.00\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The mode 2 indicates that over the observed period 2 hurricanes has been recorded most of the time, and thus to have a 2 hurricane is the highest in the probability distribution."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\n# Median vs. Mean hurricane\nhurricane_median = np.median(hurricane[:, 1])\nhurricane_avg = np.average(hurricane[:, 1])\n\nprint('Median hurricane = {:.2f}'.format(hurricane_median))\nprint('Mean hurricane = {:.2f}'.format(hurricane_avg))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Median hurricane = 2.00\nMean hurricane = 2.52\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Using mean statistics, the number of hurricane is higher compared to the median. This means the extreme occurance of large number of hurricanes shifted the means to the right, while the distribution itself is skewed to the left."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\n# 99 and 95 percent quantile\nhurricane_p99 = np.percentile(hurricane[:, 1], 99)\nhurricane_p95 = np.percentile(hurricane[:, 1], 95)\n\nprint('Hurricane 99 percentile = {:2f}'.format(hurricane_p99))\nprint('Hurricane 95 percentile = {:2f}'.format(hurricane_p95))",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Hurricane 99 percentile = 7.000000\nHurricane 95 percentile = 6.000000\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\n# variance and standard deviation\nhurricane_variance = np.var(hurricane[:, 1])\nhurricane_std = np.std(hurricane[:, 1])\n\nprint('Variance = {:.2f}'.format(hurricane_variance))\nprint('Std = {:.2f}'.format(hurricane_std))",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Variance = 2.86\nStd = 1.69\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# 4.2\nThe distribution of the number of intense hurricanes follows the Poisson statistics which describes the\noccurrence of rare events. The Poisson distribution gives the probability P of observing r events occurring with\nthe mean expected number λ :\n\\begin{equation}\nP(r;\\lambda) = \\frac{e^{-\\lambda}\\lambda^r}{r!}\n\\end{equation}\n\n- Compare the Poisson distribution with the empirical probability\n- Theoretical variance of the Poisson statistics V = λ ; Compare it to the empirical variance\n- Evaluate the quintiles 95% and 99% and compare them to the empirical estimates\n- What is the probability of observing more than 7 hurricanes in a year predicted by the empirical probability and by Poisson law?"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy.stats import poisson\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Empirical distribtuion\nunique, count = np.unique(hurricane[:, 1], return_counts=True)\np_emp = count/len(hurricane[:, 0])\n\n# Poission distribution parameters\nlamb = np.mean(hurricane[:, 1])\nR = np.arange(0, 11)\np_poisson = [poisson.pmf(r, lamb) for r in R]\n\nplt.figure()\nplt.bar(unique, p_emp, label='Empirical')\nplt.plot(R, p_poisson, 'r', label='Poisson')\nplt.xlabel('Number of Hurricanes per Year')\nplt.ylabel('Probability Mass Function')\nplt.legend()\nplt.show()",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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\" width=\"640\">"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Poission variance is the lambda value = 2.52, estimated empirical variance = 2.86 . Evidently the empirical variance is larger than the poission variance, indicating larger observed difference from them mean, which is expected given the shape of the distribution compared to the theoretical distribution."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 99 and 95 percentile\nlamb = np.mean(hurricane[:, 1])\npoisson_99p = poisson.ppf(q=0.99, mu=lamb)\npoisson_95p = poisson.ppf(q=0.95, mu=lamb)\n\nprint('Hurricane 99 percentile, empirical = {:.2f}, poisson = {:.2f}'.format(hurricane_p99, poisson_99p))\nprint('Hurricane 95 percentile, empirical = {:.2f}, poisson = {:.2f}'.format(hurricane_p95, poisson_95p))",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Hurricane 99 percentile, empirical = 7.00, poisson = 7.00\nHurricane 95 percentile, empirical = 6.00, poisson = 5.00\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Probability of having more than 7 hurricanes\n# Using empirical distribution it is zero, because we have not recorded any year with more than 7 cyclones\n# Using poission distribution\nlamb = np.mean(hurricane[:, 1])\nP = 1 - np.sum([poisson.pmf(r, lamb) for r in np.arange(0, 8)]) # Using sum\nP = 1 - poisson.cdf(7, lamb) # Using cumulative distributon funciton\nprint('Having a year with more than 7 hurricane = {:.4f}'.format(P))",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Having a year with more than 7 hurricane = 0.0045\n",
          "name": "stdout"
        }
      ]
    }
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