andrei-tests.ipynb

{
 "cells": [
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
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   "cell_type": "code",
   "source": "from IPython.display import HTML\n\nHTML('''<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": "<script>\ncode_show=true; \nfunction code_toggle() {\n if (code_show){\n $('div.input').hide();\n } else {\n $('div.input').show();\n }\n code_show = !code_show\n} \n$( document ).ready(code_toggle);\n</script>\n<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>",
      "text/plain": "<IPython.core.display.HTML object>"
     },
     "metadata": {},
     "execution_count": 1
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
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     "marked": false,
     "old_gutter_background": "rgb(0, 255, 0)"
    },
    "trusted": true,
    "hide_output": false
   },
   "cell_type": "code",
   "source": "%matplotlib nbagg\nimport sys\nsys.path.append(\"../\")\nclear_output()",
   "execution_count": 12,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false,
     "old_gutter_background": "rgb(0, 255, 0)"
    },
    "trusted": true,
    "hide_output": false
   },
   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom IPython.display import clear_output, Latex, display, Image, display_html\nfrom dragons import meraxes, plotutils, nbody, munge\nfrom astropy.utils.console import ProgressBar\nimport pandas as pd\nimport seaborn as sns\nfrom aesthetics import models\nfrom collections import OrderedDict\nfrom astropy import cosmology\nfrom astropy import units as U\nfrom StringIO import StringIO\n\nsns.set(font_scale=1.3, rc={'lines.linewidth':3})\npd.set_option('mode.use_inf_as_null', True)\n\nclear_output()",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "run_control": {
     "marked": false,
     "old_gutter_background": "rgb(0, 255, 0)"
    },
    "hide_output": false
   },
   "cell_type": "markdown",
   "source": "# Ionising photons vs. ${\\rm M_{vir}}$\n\n> combining the f_ast plot with the halo mass functions, i would guess it is something like 10^9 — 10^10 Msun, but it is difficult to see..  it would be great to add a histogram of the number of ionizing photons produced as a function of halo mass...\n> <div style=\"float:right\">-- Andrei</div>\n\nLet's calculate this for the fiducial model at z=5 and 7.8..."
  },
  {
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     "marked": false,
     "old_gutter_background": "rgb(57, 65, 79)"
    },
    "trusted": true,
    "hide_output": false
   },
   "cell_type": "code",
   "source": "# read in the galaxies\nmodel = models[\"fiducial\"]\nfname = model[\"fname\"]\nmeraxes.set_little_h(fname)\nmodel[\"gals\"] = OrderedDict()\nfor redshift in \"5 7.8\".split():\n    snap = meraxes.check_for_redshift(fname, float(redshift))[0]\n    gals = meraxes.read_gals(fname, props=(\"GrossStellarMass\", \"FOFMvir\"), snapshot=snap)\n    gals[\"FOFMvir\"] = np.log10(gals[\"FOFMvir\"]*1e10)\n    model[\"gals\"][redshift] = gals.copy()\nclear_output()",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Ok - so the yaxis here is a little bit tricky to represent concisely!  Basically what is being plotted is the number of ionising photons emitted per Mvir bin, but then renormalised to form a PDF.  The peak positions are what is important though.  We are moving from low bias sources at z=7.8 ($x_{\\rm HI} \\approx 0.5$) to high bias at z=5.  However, there is still a peak of low bias sources in the z=5 distribution as well."
  },
  {
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    "collapsed": false,
    "run_control": {
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     "old_gutter_background": "rgb(0, 255, 0)"
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    "trusted": true,
    "hide_output": false
   },
   "cell_type": "code",
   "source": "# calculate the distributions\nlim, nbins = (6.,12.), 30\nfig, ax = plt.subplots(1, 1)\nfor redshift, gals in model[\"gals\"].iteritems():\n    ax.hist(gals[\"FOFMvir\"], range=lim, bins=nbins, weights=gals[\"GrossStellarMass\"],\n            alpha=0.8, normed=True, label=\"z=\"+redshift)\nsns.axlabel(r\"$\\log_{10}(M_{\\rm vir} / {\\rm M_{\\odot}})$\",\n            r\"${}^1/_{N_{\\gamma}}\\,{}^{d N_{\\gamma}}/_{d M_{\\rm vir}}$\")\nax.legend(loc=2)\nplt.tight_layout()\nplt.show()",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\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            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 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);\n        canvas.attr('height', height);\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'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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;\n    var y = canvas_pos.y;\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    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Ionising emmisivity vs. redshift\n\nAttempt to replicate the top panel of Figure 12 of Mesinger, McQuinn & Spergel (2012):"
  },
  {
   "metadata": {
    "collapsed": false,
    "run_control": {
     "marked": false,
     "old_gutter_background": "rgb(0, 255, 0)"
    },
    "trusted": true,
    "hide_output": false
   },
   "cell_type": "code",
   "source": "Image(filename='img/Screen Shot 2015-09-22 at 4.03.58 pm.png')",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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hcpZgp14oZSCQNiTEgY+3aMLEiar9WbNne6sLR2S6dI4KFSt6rf0RI0eqOSxc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\ne4+QICAICAKCQPIjAL33j2b8laSGX+9d20mgTagR6M7DxaCZvhj3EO/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Ddu1KXnwE/CHU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E0oaC4iFij71sWJvxrwIm92r7I6LDISTXCTxegCmYCqRn0gg4PuoUfAKNYqGMTnNtv6DZR8/jzp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EBAFBQBBolAgY9x+Acc/e0mt3cYGBFt0aOnYwk3wm++3bQeflVVrnPHKBHk3AqUtoP60VVt05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\nhPq6kWmYAAECBAgQIECAQAq8ARe6C41MiM6nAAAAAElFTkSuQmCC\n",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "run_control": {
     "marked": false,
     "old_gutter_background": "rgb(0, 255, 0)"
    },
    "hide_output": false
   },
   "cell_type": "markdown",
   "source": "First try to calculate this just at z=5 to check the methodology:"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "params = meraxes.read_input_params(fname)\nHII_eff_factor = params[\"TOCF_HII_eff_factor\"]\nvolume = params[\"Volume\"] * U.Unit('Mpc^3')\nsnaps, zlist, lbt = meraxes.read_snaplist(fname)\n\n# we've already read in the galaxies\nredshift = 5\ngals = model[\"gals\"][str(redshift)]\ntot_gross = gals[\"GrossStellarMass\"].sum() * U.Unit('1e10 Msun')\n\n# calculate the hubble time\ntiamat_cosmo = cosmology.FlatLambdaCDM(params[\"Hubble_h\"]*100., params[\"OmegaM\"],\n                                       Ob0=params[\"OmegaM\"]*params[\"BaryonFrac\"])\ndt = (1./tiamat_cosmo.H(redshift))\n#dt = (lbt[-2] - lbt[-1]) / 1000. #Gyr\n\n# total matter\nmtot = (tiamat_cosmo.Om(redshift) * tiamat_cosmo.critical_density(redshift)) * volume / (1+redshift)**3\n\n# calculate the result\nres = (tot_gross / mtot) * HII_eff_factor / dt\n\nclear_output()\nprint \"Volume =\", volume\nprint \"t_H =\", dt.to('Gyr')\nprint \"Mtot = 10^%.1f\" % np.log10(mtot / U.Msun)\nprint \"Mstellar = 10^%.1f\" % np.log10(tot_gross/ U.Msun)\ndisplay(Latex(r'$\\xi = %d$' % int(HII_eff_factor)))\ndisplay(Latex('$\\dot N = $ ' + res.to('1/Gyr')._repr_latex_()))\n# display(Latex(r'$\\Rightarrow N = $ '+res.decompose()._repr_latex_()))",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Volume = 1000000.0 Mpc3\nt_H = 1.75746965822 Gyr\nMtot = 10^16.6\nMstellar = 10^13.5\n",
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Latex object>",
      "text/latex": "$\\xi = 5841$"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.Latex object>",
      "text/latex": "$\\dot N = $ $2.5552889 \\; \\mathrm{\\frac{1}{Gyr}}$"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Looks good!  Now let's calculate it for every redshift and plot..."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "def ndot(ii):\n    snap, redshift = snaps[ii], zlist[ii]\n    try:\n        tot_gross = meraxes.read_gals(fname, snap, props=(\"GrossStellarMass\",), quiet=True)[\"GrossStellarMass\"].sum() * U.Unit('1e10 Msun')\n    except IndexError:\n        return 0.0\n    dt = (1./tiamat_cosmo.H(redshift))\n    mtot = (tiamat_cosmo.Om(redshift) * tiamat_cosmo.critical_density(redshift)) * volume / (1+redshift)**3\n    return ((tot_gross / mtot) * HII_eff_factor / dt).to(1/U.Gyr).value\n\nwith ProgressBar(snaps.size, ipython_widget=True) as bar:\n    vals = np.zeros(snaps.size)\n    for ii in xrange(snaps.size):\n        vals[ii] = ndot(ii)\n        bar.update()",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "text": "\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "# # obs data\n# obs_data = \"\"\"\n# McQuinn:\t\tBolton:\t\n# x\ty\tx\ty\n# 3.610\t1.310\t3.782\t2.322\n# 4.500\t1.686\t4.677\t2.205\n# 5.398\t2.090\t5.588\t2.069\n# 6.306\t2.355\t\t\n# \"\"\"\n# fd = StringIO(obs_data)\n# obs = {}\n# kwargs = dict(header=0, skiprows=2, names=[\"z\", \"N\"])\n# obs[\"McQuinn\"] = pd.read_table(fd, usecols=[0,1], **kwargs)\n# fd.seek(0)\n# obs[\"Bolton\"] = pd.read_table(fd, usecols=[2,3], **kwargs).dropna()\n# fd.close()",
   "execution_count": 9,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "# obs data\nobs_data = \"\"\"\nBolton:\nx\ty\n3.099\t2.330\n3.099\t2.655\n3.099\t2.024\n5.105\t2.052\n5.105\t2.396\n5.105\t1.736\n4.101\t2.196\n4.101\t2.521\n4.101\t1.917\n\nMcQuinn:\nx\ty\n2.902\t1.312\n2.902\t1.609\n2.902\t1.014\n3.897\t1.683\n3.897\t2.092\n3.897\t1.279\n4.895\t2.087\n4.895\t2.440\n4.895\t1.725\n5.915\t2.351\n5.915\t2.732\n5.915\t1.961\n\"\"\"\n\ndef reorder_obs(raw_data):\n    obsvals = raw_data[::3].copy()\n    obsvals['high'] = raw_data[1::3].N.values.copy()\n    obsvals['low'] = raw_data[2::3].N.values.copy()\n    obsvals['dhigh'] = obsvals.high - obsvals.N\n    obsvals['dlow'] = obsvals.N - obsvals.low\n    return obsvals\n\nfd = StringIO(obs_data)\nobs = {}\nkwargs = dict(header=0, names=[\"z\", \"N\"])\ntemp = pd.read_table(fd, skiprows=14, nrows=12, **kwargs)\nobs[\"McQuinn\"] = reorder_obs(temp)\nfd.seek(0)\ntemp = pd.read_table(fd, skiprows=2, nrows=9, **kwargs).dropna()\nobs[\"Bolton\"] = reorder_obs(temp)\nfd.close()",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig, ax = plt.subplots(1, 1)\nax.plot(zlist, vals, lw=4, **model[\"plot_args\"])\nobsvals = obs[\"Bolton\"]\nax.errorbar(obsvals.z, obsvals.N, yerr=(obsvals.dlow, obsvals.dhigh), marker='^', ms=16, ls='None', label='Bolton & Haenhelt (2007)', color='0.5')\nobsvals = obs[\"McQuinn\"]\nax.errorbar(obsvals.z, obsvals.N, yerr=(obsvals.dlow, obsvals.dhigh), marker='s', ms=16, ls='None', label='McQuinn et al. (2011)', color='0.5')\nax.set_xlabel(\"redshift\")\nax.set_ylabel(r\"$\\frac{dN}{dt}$ [photons per baryon Gyr$^{-1}$]\")\nax.set_xlim(2.5, 15)\nplt.legend(loc=1, markerfirst=False)\nplt.tight_layout()\nplt.setp(ax.get_xticklines(), visible=False)\nplt.setp(ax.get_yticklines(), visible=False)\nplt.show()",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\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            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 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);\n        canvas.attr('height', height);\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'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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;\n    var y = canvas_pos.y;\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    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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",
      "text/plain": "<IPython.core.display.Javascript object>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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\">"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We show a reasonable agreement with the data, however, there is no visible sign of a turn-over.  This agrees with the above plot that shows our low redshift ionising sources are reasonably biased.  According to the Mesinger et al. plot, a high source bias corresponds to a rapidly increasing emissivity at low redshift due to the increase in the ionising emissivity required to obtain a reasonable neutral fraction evolution."
  }
 ],
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