notebooks/confidence_intervals.ipynb

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    "# Confidence Intervals    \n",
    "\n",
    "```bash\n",
    "by Jeffrey Chijioke-Uche, MSIS, MSIT, CPDS (IBM Sr. Solution Architect, Hybrid Cloud & Multicloud)\n",
    "IBM Information Technology PhD Scholar at Harvard University & Walden University \n",
    "```   \n",
    "\n",
    "## Table of Contents\n",
    "\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Standard-confidence-intervals-for-normal-distribution\" data-toc-modified-id=\"Standard-confidence-intervals-for-normal-distribution-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Standard confidence intervals for normal distribution</a></div><div class=\"lev1 toc-item\"><a href=\"#Bootstrapped-confidence-intervals\" data-toc-modified-id=\"Bootstrapped-confidence-intervals-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Bootstrapped confidence intervals</a></div><div class=\"lev1 toc-item\"><a href=\"#Bayesian-estimates\" data-toc-modified-id=\"Bayesian-estimates-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Bayesian estimates</a></div>"
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   "source": [
    "import math\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "VERBOSE = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is just for my reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "collapsed": true
   },
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   "source": [
    "def phi(x,mu=0,sigma=1):\n",
    "    \"\"\" Cumulative distribution function for normal distribution \"\"\"\n",
    "    return (1.0 + math.erf((x - mu)/ (sigma*math.sqrt(2.0)))) / 2.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some parameters for playing with confidence intervals"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "mu = 500\n",
    "std = 15\n",
    "sample_size=100\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard confidence intervals for normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I've gone ahead and created my own confidence_interval function for pedagological purposes"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    "collapsed": true
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   "source": [
    "def confidence_interval(mu,std,sample_size,critical_val=1.96):\n",
    "    \"\"\"Compute a confidence interval for a normal distribution. If population sigma (standard deviation) is known,\n",
    "    then the critical_val should be the z_score. Otherwise, critical_val should use Student's t distribution to \n",
    "    calculate the interval. (t is z as sample size approaches infinity; when sample_size >= 30, people often use Z\n",
    "    rather than t) \"\"\"\n",
    "    interval = critical_val*(std/math.sqrt(sample_size))\n",
    "    if VERBOSE:\n",
    "        print(round(mu - critical_val*(std/math.sqrt(sample_size)),2),round(mu + critical_val*(std/math.sqrt(sample_size)),2))\n",
    "    return mu-interval,mu+interval\n",
    "        \n"
   ]
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   "execution_count": 5,
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     "text": [
      "497.06 502.94\n"
     ]
    },
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     "data": {
      "text/plain": [
       "(497.06, 502.94)"
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   "source": [
    "confidence_interval(mu=mu,std=std,sample_size=sample_size,critical_val=1.96)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confidence interval is built into the scipy.stats package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(499.9059398330852, 500.0940601669148)"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.interval(alpha, loc=mu, scale=std/math.sqrt(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bootstrapped confidence intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I'll be calculating the basic bootstrap, but there are other methods to consider.\n",
    "See https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#Methods_for_bootstrap_confidence_intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "okay, now we actually need a full sample, rather than just sample parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "501.610832001 13.89028112\n"
     ]
    }
   ],
   "source": [
    "sample = np.random.normal(loc=mu, scale=std, size=sample_size)\n",
    "if VERBOSE: print (np.mean(sample),np.std(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we resample the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "501.644671043 13.8784181426\n"
     ]
    }
   ],
   "source": [
    "repeats = 100 #not sure how to choose the number of bootstrapping steps\n",
    "\n",
    "means = []\n",
    "stds = []\n",
    "for r in range(repeats):\n",
    "    boot = np.random.choice(sample,sample_size,replace=True) #bootstrapping draws with replacement\n",
    "    means.append(np.mean(boot))\n",
    "    stds.append(np.std(boot))\n",
    "if VERBOSE: print(np.mean(means),np.mean(stds))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we compute the quantiles that correspond to the confidence intervals we want. Here, we'll calculate confidence intervals for the estimate of our mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 498.2925181   504.40913121]\n"
     ]
    }
   ],
   "source": [
    "#to compute the confidence interval, one simply takes the empirical quantiles from the bootstrap distribution of the parameter\n",
    "confi = np.percentile(means,[(alpha/2)*100,(1-(alpha/2))*100])\n",
    "if VERBOSE: print (confi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "498.283808682 504.564949685\n"
     ]
    }
   ],
   "source": [
    "#This is essentially the logical behind percentile, although the built-in function uses interpolation\n",
    "#again, this is just for learning purposes\n",
    "means = sorted(means)\n",
    "start = int(math.floor((alpha/2)*sample_size))\n",
    "finish = int(math.ceil((1-(alpha/2))*sample_size))\n",
    "if VERBOSE: print (means[start],means[finish])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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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 overriden (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        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e95e748>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_confidence_interval_histogram(dist, myrange,bins=30):\n",
    "    fig,ax = plt.subplots()\n",
    "    ax.hist(dist,bins)\n",
    "    #ax.axhline(y=1,xmin=myrange[0],xmax=myrange[1],color='r',linewidth=4)\n",
    "    plt.plot([myrange[0], myrange[1]], [1, 1],color='r',linewidth=2)\n",
    "    plt.show()\n",
    "    \n",
    "plot_confidence_interval_histogram(means,confi)\n",
    "plt.title('bootstrapped mean values')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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        this.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 overriden (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        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ea6a780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_confidence_interval_histogram(sample,confi)\n",
    "plt.title('sample distribution with 95% confidence interval around mean')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information about how the Bayesian interval is calculated by scipy: http://scholarsarchive.byu.edu/facpub/278/\n",
    "The model prior is appropriately non-informative (Jeffery's prior), but I haven't carefully read about the appropriate usage,\n",
    "except that it makes the frequent assumption that all data has the same mean and variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Mean(statistic=501.6108320011233, minmax=(501.52306951058137, 501.69859449166523)),\n",
       " Variance(statistic=198.90712329114456, minmax=(194.46519376252789, 197.97264565986478)),\n",
       " Std_dev(statistic=14.067142029709027, minmax=(13.945077761078561, 14.07027525174489)))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.bayes_mvs(sample, alpha=alpha)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
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    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
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   },
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    "width": "252px"
   },
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   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": true,
   "toc_section_display": "block",
   "toc_window_display": true,
   "widenNotebook": false
  }
 },
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 "nbformat_minor": 2
}