on_halotools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Populating dark matter haloes with HOD models\n",
    "\n",
    "We are going to populate the millimil dark matter haloes with a halo occupation distribution (HOD) model, using [halotools](http://halotools.readthedocs.io/en/latest/overview.html#halotools-science-overview). The millimil has been run in a cubic box of comovin side of $62.5 {\\rm Mpc}/h$ with dark matter particles with a minnimum mass of $0.86*10^9 {\\rm M}_{\\odot}/h$. The assumed cosmological parameters in this simulation are: $\\Omega_0 = 0.25$, $\\Lambda _0 = 0.75$, $\\Omega _b = 0.045$ and $h_0 = 0.73$. \n",
    "\n",
    "Halotools is written in python and the source code is available in [github](https://github.com/astropy/halotools). The easiest way to install it is through 'pip' or 'conda'. If you install it fresh and encounter a problem related to the C compilers try: \"sudo apt-get install python-dev\". \n",
    "\n",
    "The steps we are going to follow are: 1) Get dark matter haloes at one redshift, 2) Choose an HOD model and 3) Populate the dark matter haloes with galaxies, following the chosen HOD model.\n",
    "\n",
    "Let's start importing the python packages used in this notebook. Note that halotools does not work on the jupyter notebook, so the corresponding code is quoted so you can copy and paste it on a script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/violeta/Desktop/Programs/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import eagleSqlTools as sql\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import os.path\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HOD model: connecting galaxies to their host dark matter haloes\n",
    "\n",
    "The probability of finding a certian type of galaxy hosted by a halo of a given mass can be established using semi-analytical and hydrodynamical models. Below you can find the code to get the mean halo occupation distribution (HOD) for all galaxies with masses M$>10^{10}M_{\\odot}h^{-1}$ at redshift 0 for galaxies from the [De Lucia et al 2007](http://galaxy-catalogue.dur.ac.uk:8080/Millennium/Help?page=databases/millimil/delucia2006a) model, that has been used to populate the millimil dark matter haloes.\n",
    "\n",
    "#### Q.1 What is the difference between central and satellite galaxies? What is the column storing this infomation in the millimil..DeLucia2006a table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con = sql.connect(\"xyz\", \"abc\", url=\"http://virgodb.dur.ac.uk:8080/Millennium\")\n",
    "\n",
    "the_query =\"\"\" select .1*(.5+floor((log10(h.m_Crit200)+10)/.1)) as logx, \n",
    "        SUM(CASE WHEN g.type=0 THEN 1 ELSE 0 END) as Haloes,\n",
    "        SUM(CASE WHEN g.stellarmass > 1 THEN 1 ELSE 0 END) as Galaxies,\n",
    "        SUM(CASE WHEN g.stellarmass > 1 AND g.type=0  THEN 1 ELSE 0 END) as Centrals,\n",
    "        SUM(CASE WHEN g.stellarmass > 1 AND g.type!=0 THEN 1 ELSE 0 END) as Satellites\n",
    "        from millimil..mpahalo h, millimil..DeLucia2006a g\n",
    "        where g.redshift = 0. and h.m_Crit200 > 0. and h.haloID = g.haloID\n",
    "        group by .1*(.5+floor((log10(h.m_Crit200)+10)/.1))\n",
    "        order by .1*(.5+floor((log10(h.m_Crit200)+10)/.1)); \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q.2 Where is the condition M$>10^{10}M_{\\odot}h^{-1}$ imposed in the above SQL query? Does the variable \"Haloes\" include empty haloes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            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",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var 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",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\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",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the SQL query\n",
    "data = con.execute_query(the_query)\n",
    "\n",
    "# Discard bins with no haloes\n",
    "inhaloes = data[\"Haloes\"] ; ind = np.where(inhaloes>0.)\n",
    "haloes = 1.*inhaloes[ind]\n",
    "logx = data[\"logx\"][ind]\n",
    "tot = 1.*data[\"Galaxies\"][ind]\n",
    "cen = 1.*data[\"Centrals\"][ind]\n",
    "sat = 1.*data[\"Satellites\"][ind]\n",
    "\n",
    "# Plot\n",
    "ytit ='log<N_{\\\\rm galaxies}>' ; xtit ='log$_{10}({\\\\rm M_{\\\\rm halo}}/M_{\\odot}h^{-1}$'\n",
    "\n",
    "fig = plt.figure(2)\n",
    "plt.xlabel(xtit) ; plt.ylabel(ytit)\n",
    "plt.xlim(10.,15.) ; plt.ylim(-2,2.)\n",
    "\n",
    "idx = np.where(tot>0.)\n",
    "plt.plot(logx[idx],np.log10(tot[idx]/haloes[idx]),'k',label='All galaxies with M$_S>10^{10}M_{\\odot}h^{-1}$')\n",
    "\n",
    "idx = np.where(cen>0.)\n",
    "plt.plot(logx[idx],np.log10(cen[idx]/haloes[idx]),'r-',label='Central galaxies')\n",
    "\n",
    "idx = np.where(sat>0.)\n",
    "plt.plot(logx[idx],np.log10(sat[idx]/haloes[idx]),'b:',label='Satellite galaxies')\n",
    "\n",
    "leg = plt.legend(loc=2) ; leg.draw_frame(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q.3 Looking to the above figure, what is ratio between the typical mass of a halo hosting only a central galaxy with M$_S>10^{10}M_{\\odot}h^{-1}$  and a halo hosting a satellite and a central galaxy? Is that reasonable?\n",
    "\n",
    "There are different empirical equations used to describe the above distributions. Here are those from [[Zheng et al. 2005](https://arxiv.org/abs/astro-ph/0408564):\n",
    "$\\langle N \\rangle_{\\rm central} = 0.5 \\left( 1 + erf \\left(  \\frac{\\rm log_{10}(M_{S})- log_{10}(M_{min})}{\\sigma} \\right) \\right)$\n",
    "\n",
    "$\\langle N \\rangle_{\\rm satellite} = \\left( {\\rm M_{S}} - \\frac{\\rm M_{cut}}{\\rm M'_{min}} \\right) ^{\\alpha _{\\rm sat}}$\n",
    "\n",
    "The above equations establish the probability of finding in a halo of a given mass a certain type of galaxies split into centrals and satellites. However, these equations do not give any information on how to distribute several satellite galaxies within one sinlge halo. Such information can be provided through subhaloes and/or particle information from the simulation. When this information is not available a model is needed to establish how satellites are distributed within a halo. \n",
    "\n",
    "Typically, satellite galaxies are assumed to follow the density profile of the dark matter. This has been described by empirical equations derived from N-body simulations, such as the  [NFW](https://arxiv.org/abs/astro-ph/9611107) profile:\n",
    "\n",
    "$\\frac{\\rho(r)}{\\rho_{crit}}=\\frac{\\delta_{crit}}{(r/r_S)(1+r/r_S)^2}$\n",
    "\n",
    "Where $r_S$ is a scale radius and $\\delta_{crit}$ is a characteristic overdensity. These quantitites can be related to how concentrated is the dark matter in a halo. If the halo concentration is unkown, a model (for example [Dutton & Maccio 2014](https://arxiv.org/abs/1402.7073))can be used to derive it from known properties, such as the halo mass.\n",
    "\n",
    "Halotools provide several [prebuilt HOD models](http://halotools.readthedocs.io/en/latest/quickstart_and_tutorials/tutorials/model_building/preloaded_models/index.html). Let's choose the one described in [Zheng et al. 2007](https://arxiv.org/abs/astro-ph/0703457), assuming the default [NFW](https://arxiv.org/abs/astro-ph/9611107) profile applied using the approximations from [Dutton & Maccio 2014](https://arxiv.org/abs/1402.7073):\n",
    "\n",
    "```python\n",
    "from halotools.empirical_models import PrebuiltHodModelFactory\n",
    "model = PrebuiltHodModelFactory('zheng07', conc_mass_model='dutton_maccio14')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dark matter haloes\n",
    "\n",
    "#### Q.4 Using a SQL query download from the millimil table [MPAHalo](http://galaxy-catalogue.dur.ac.uk:8080/Millennium/Help?page=databases/millimil/mpahalo) the following properties of haloes at $z=0$: positions, peculiar velocities, number of particles, mass,  the half mass radius of the halo, the maximum rotational velocity of the halo, the unique ID of the halo and the id of the halo at the center of the friend-of-friends group to which this halo belongs. \n",
    "\n",
    "This is the header of the **haloes_mpahalo_z0.dat** file generated following the above instructions:\n",
    "```\n",
    "# mpahalo, snapnum=63 \n",
    "# x,y,z,velx,vely,velz,np,m_Crit200,halfmassRadius,vmax,haloID,firstHaloInFOFgroupId \n",
    "6.575790405273437500e+00 1.308603954315185547e+01 2.533813095092773438e+01 -8.194823455810546875e+01 -3.502460861206054688e+01 2.041479949951171875e+02 5.198400000000000000e+04 3.600987792968750000e+03 2.840324640274047852e-01 5.876464843750000000e+02 0.000000000000000000e+00 0.000000000000000000e+00\n",
    "```\n",
    "\n",
    "Let's have a look to the distribution of downloaded haloes in the simulation box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00123051460832 62.4997062683 0.000606290181167 62.4988937378 0.00131582748145 62.4995422363\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            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",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var 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",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\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",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f89dd434f10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the file with halo information we have downloaded:\n",
    "x,y,z = np.loadtxt('haloes_mpahalo_z0.dat',usecols=(0,1,2),unpack=True)\n",
    "print min(x),max(x),min(y),max(y),min(z),max(z)\n",
    "\n",
    "# Plot\n",
    "fig = plt.figure(1) ; ax = fig.add_subplot(111,projection='3d')\n",
    "xtit ='x (Mpc/h)' ; ytit ='y (Mpc/h)'; ztit ='z (Mpc/h)'\n",
    "ax.set_xlabel(xtit) ; ax.set_ylabel(ytit) ; ax.set_zlabel(ztit)\n",
    "ax.scatter(xs=x,ys=y,zs=z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The halo catalogue we have generated needs to be modify in order to be used by [halotos](http://halotools.readthedocs.io/en/latest/api/halotools.sim_manager.UserSuppliedHaloCatalog.html#halotools.sim_manager.UserSuppliedHaloCatalog). Below you have the python lines for halotools to use a halo catalogue stored in 'new_catalogue.txt'. In this catalogue the mass is in  $M_{\\odot}h^{-1}$ units, 'ids' is an integer array and 'upid' is an integer array of zeros and -1 for those haloes at the center of the friend-of-friend group they belong to.\n",
    "\n",
    "#### Q.4 Generate the 'new_catalogue.txt' file that can be used in the code below.  What are the keys in the halotools halo_catalog?\n",
    "\n",
    "```python\n",
    "    from halotools.sim_manager import UserSuppliedHaloCatalog\n",
    "\n",
    "    xm, ym, zm, vx, vy, vz, \\\n",
    "        nparticles, mass, r50, vmax, ids, upids = np.loadtxt('new_catalogue.txt',unpack='True') \n",
    "\n",
    "    halo_catalog = UserSuppliedHaloCatalog(simname='millimil',\\\n",
    "                                       redshift = 0.0,\\\n",
    "                                       Lbox = 62.5,\\\n",
    "                                       particle_mass = 8.6e8,\\\n",
    "                                       halo_x = xm,\\\n",
    "                                       halo_y = ym,\\\n",
    "                                       halo_z = zm,\\\n",
    "                                       halo_vx = vx,\\\n",
    "                                       halo_vy = vy,\\\n",
    "                                       halo_vz = vz,\\\n",
    "                                       halo_rvir = r50,\\\n",
    "                                       halo_mvir = mass,\\\n",
    "                                       halo_id = ids,\\\n",
    "                                       halo_upid = upids)\n",
    "\n",
    "    # The halo catalog itself is stored in the halo_table attribute, with columns accessed as follows\n",
    "    halos = halo_catalog.halo_table\n",
    "    print(halos.keys())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Populating with galaxies the dark matter halo catalogue\n",
    "\n",
    "Halotools uses the **populate_mock** command to populate a halo catalogue, here the millimil one that has been stored before as **halo_catalog**, using the Zheng et al. model, previously defined as **model**:\n",
    "\n",
    "```python\n",
    "model.populate_mock(halocat = halo_catalog)\n",
    "```\n",
    "\n",
    "To see the list of output properties:\n",
    "\n",
    "```python\n",
    "print model.mock.galaxy_table.keys()\n",
    "```\n",
    "\n",
    "#### Q.5 Do model galaxies have stellar masses or luminosities associated? \n",
    "\n",
    "Halotools provides many functions and [examples](http://halotools.readthedocs.io/en/latest/quickstart_and_tutorials/index.htm) on how to use them. Using some of these built in functions, let's calculate here the two-point correlation function and decompose it into the 1-halo and 2-halo terms:\n",
    "\n",
    "```python\n",
    "from halotools.mock_observables import tpcf_one_two_halo_decomp\n",
    "from halotools.mock_observables import tpcf\n",
    "\n",
    "x = model.mock.galaxy_table['x']\n",
    "y = model.mock.galaxy_table['y']\n",
    "z = model.mock.galaxy_table['z']\n",
    "pos = zip(x, y, z)\n",
    "\n",
    "lbox = model.mock.Lbox\n",
    "halo_hostid = model.mock.galaxy_table['halo_id']\n",
    "\n",
    "# Define bins in separation\n",
    "rbins = np.logspace(np.log10(0.01), np.log10(lbox/5.), 10)\n",
    "rbin_centers = (rbins[1:] + rbins[:-1])/2.\n",
    "\n",
    "# Obtain the two point correlation functions\n",
    "xi_all = tpcf(pos, rbins, period = lbox, num_threads = 'max')\n",
    "xi_1h, xi_2h = tpcf_one_two_halo_decomp(pos, halo_hostid, rbins, \\\n",
    "                                        period = lbox)\n",
    "\n",
    "```\n",
    "\n",
    "#### Q.6 Use the code above and plot the 3D two point correlation function as shown below.\n",
    "\n",
    "<img src=\"images/xi.jpg\">\n",
    "\n",
    "#### Q.7 Why the 1-halo and 2-halo terms do not overlap in the plot above? Try changing the binning to explore what is happening. Are these lines showing the behaviour you expected?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}