ASTC25_PS3_Code

Untitled1.ipynb

{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-14T18:51:52.998622",
          "end_time": "2017-03-14T18:51:53.204055"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%pylab notebook",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "text": "Populating the interactive namespace from numpy and matplotlib\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-15T10:21:25.560776",
          "end_time": "2017-03-15T10:21:25.566329"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def tsettle(a_d=1e-4, R=1.496e13, rho_d=3, M_star=1.989e33):\n    G = 6.6740831e-8\n    Omega_K = np.sqrt(G*M_star/R**3)\n    Sigma = 2e3*(R/1.496e13)**-1.5\n    return (2/np.pi)*(Sigma/(Omega_K*rho_d*a_d))/np.e/31556926 \n\ntsettle()",
      "execution_count": 88,
      "outputs": [
        {
          "data": {
            "text/plain": "248475.67805789737"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 88
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-15T10:22:18.037340",
          "end_time": "2017-03-15T10:22:18.040839"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "grainSizes = np.logspace(-6, -1, 200)\ndistances = np.logspace(-2, 2, 200) * 1.496e13\n\nX, Y = np.meshgrid(grainSizes, distances)",
      "execution_count": 93,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T09:37:36.648134",
          "end_time": "2017-03-16T09:37:37.140412"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig = figure(figsize=(8,8))\nax = fig.add_subplot(111)\nim = ax.imshow(np.log(tsettle(X,Y[::-1]))/np.log(10), extent=[grainSizes.min(), grainSizes.max(), distances.min()/1.496e13, distances.max()/1.496e13], interpolation=\"lanczos\", cmap=\"magma\")\ncbar = fig.colorbar(im, label=r\"$t_{settle}$\")\nax.set_xscale('log')\nax.set_yscale('log')\nax.set_xlabel(r'$a_d$')\nax.set_ylabel(r'$AU$')\nax.set_title('Settling Time of a Dust Particle wrt.\\nDistance from the Star and Grain Size')\nplt.tight_layout()",
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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\" width=\"799.4621592461672\">"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:41:05.770980",
          "end_time": "2017-03-16T10:41:05.776092"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def tgrowth1(R_p=1e8, R=5.2*1.496e13, rho_d=3, M_star=1.989e33, F_g=5e3):\n    G = 6.6740831e-8\n    Omega_K = np.sqrt(G*M_star/(R**3))\n    Sigma = 0.04*(1.5e3*(R/1.496e13)**-1.5)\n    print(Omega_K, Sigma, \"{:.4e}\".format((4*rho_d*R_p)/(Sigma*Omega_K) / 31556926))\n    return ((4*rho_d*R_p)/(Sigma*Omega_K*F_g))/31556926",
      "execution_count": 519,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:41:05.975971",
          "end_time": "2017-03-16T10:41:05.981674"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "tgrowth1()",
      "execution_count": 520,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1.67923234418e-08 5.059950111386707 4.4754e+08\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "89507.520083605647"
          },
          "metadata": {},
          "execution_count": 520
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:41:10.975570",
          "end_time": "2017-03-16T10:41:10.984593"
        },
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def tgrowth2(R_i=1e6, R_f=1e8, R=5.2*1.496e13, rho_d=3, M_star=1.989e33, F_g=5e3):\n    G = 6.6740831e-8\n    Omega_K = np.sqrt(G*M_star/R**3)\n    Sigma = 0.04*(1.5e3*(R/1.496e13)**-1.5)\n    M_0 = 4*np.pi*rho_d*(R_i)**3/3\n    M_f = 4*np.pi*rho_d*(R_f)**3/3\n    avgmass = np.sqrt(M_0 * M_f)\n    avgradius = np.sqrt(R_i * R_f)\n    dispersion = np.sqrt(2*G*avgmass/(avgradius*F_g))\n    K = (1/3)*(Sigma*Omega_K*(np.pi**(2/3))*((3*rho_d/4)**(1/3))*(2*G/(dispersion**2)))\n    print(M_0, M_f, K, Omega_K, Sigma)\n    return ((M_0**(-1/3)-M_f**(-1/3))/K)/31556926",
      "execution_count": 521,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:41:11.133444",
          "end_time": "2017-03-16T10:41:11.156566"
        },
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "tgrowth2()",
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1.2566370614359173e+19 1.2566370614359174e+25 3.16754893106e-19 1.67923234418e-08 5.059950111386707\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "42600.420185354509"
          },
          "metadata": {},
          "execution_count": 522
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "ExecuteTime": {
          "start_time": "2017-03-16T09:42:18.274165",
          "end_time": "2017-03-16T09:42:18.278107"
        }
      },
      "cell_type": "code",
      "source": "import desolver as de",
      "execution_count": 215,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:12.410114",
          "end_time": "2017-03-16T10:44:12.412804"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel = de.OdeSystem()",
      "execution_count": 551,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:12.568553",
          "end_time": "2017-03-16T10:44:12.571172"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "initialGrainSize = 1e-4\ngrowthModel.add_equation([\"0.75*Omega_K**2*f*y_1*y_0/vbar\"], [4*3*np.pi*((initialGrainSize)**3)/3])",
      "execution_count": 552,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:12.727668",
          "end_time": "2017-03-16T10:44:12.730991"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "growthModel.show_system()",
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Equation 0\ny_0(0.0) = [[  1.25663706e-17]]\ndy_0 = 0.75*Omega_K**2*f*y_1*y_0/vbar\ny_0(0.0) = [[  1.25663706e-17]]\n\nThe time limits for this system are:\n t0 = 0.0, t1 = 0.0, t_current = 0.0, step_size = -1.0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:12.871558",
          "end_time": "2017-03-16T10:44:12.874095"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel.add_equation([\"-(3*y_0/(4*rho_d*pi))**(1/3)*Omega_K**2*h*sqrt(2*pi)*y_1/(Sigma*exp(-y_1**2/(2*h**2))*vbar)\"], [5*3e11])",
      "execution_count": 554,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.014610",
          "end_time": "2017-03-16T10:44:13.018537"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "growthModel.show_system()",
      "execution_count": 555,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Equation 0\ny_0(0.0) = [[  1.25663706e-17]]\ndy_0 = 0.75*Omega_K**2*f*y_1*y_0/vbar\ny_0(0.0) = [[  1.25663706e-17]]\n\nEquation 1\ny_1(0.0) = [[  1.50000000e+12]]\ndy_1 = -(3*y_0/(4*rho_d*pi))**(1/3)*Omega_K**2*h*sqrt(2*pi)*y_1/(Sigma*exp(-y_1**2/(2*h**2))*vbar)\ny_1(0.0) = [[  1.50000000e+12]]\n\nThe time limits for this system are:\n t0 = 0.0, t1 = 0.0, t_current = 0.0, step_size = -1.0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.131461",
          "end_time": "2017-03-16T10:44:13.142021"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel.add_constants(rho_d=3, Omega_K=1.99120422994e-07, h=3e11, Sigma=1000, vbar=1e5, f=1e-2)",
      "execution_count": 556,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.269452",
          "end_time": "2017-03-16T10:44:13.274988"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel.set_end_time(1000*31556926)",
      "execution_count": 557,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.408621",
          "end_time": "2017-03-16T10:44:13.411014"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel.set_step_size(31556926/10)",
      "execution_count": 558,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.541243",
          "end_time": "2017-03-16T10:44:13.550176"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "growthModel.record_trajectory(True)",
      "execution_count": 559,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:13.680211",
          "end_time": "2017-03-16T10:44:17.228342"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "growthModel.integrate()",
      "execution_count": 560,
      "outputs": [
        {
          "output_type": "stream",
          "text": "100%\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:17.229612",
          "end_time": "2017-03-16T10:44:17.234777"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "growthModel.show_system()",
      "execution_count": 561,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Equation 0\ny_0(0.0) = [[  1.25663706e-17]]\ndy_0 = 0.75*Omega_K**2*f*y_1*y_0/vbar\ny_0(31556926000.0) = [[ 24.24392507]]\n\nEquation 1\ny_1(0.0) = [[  1.50000000e+12]]\ndy_1 = -(3*y_0/(4*rho_d*pi))**(1/3)*Omega_K**2*h*sqrt(2*pi)*y_1/(Sigma*exp(-y_1**2/(2*h**2))*vbar)\ny_1(31556926000.0) = [[  1.53711788e+09]]\n\nThe constants that have been defined for this system are: \n{'rho_d': array([[3]]), 'h': array([[  3.00000000e+11]]), 'f': array([[ 0.01]]), 'vbar': array([[ 100000.]]), 'Sigma': array([[1000]]), 'Omega_K': array([[  1.99120423e-07]])}\nThe time limits for this system are:\n t0 = 0.0, t1 = 31556926000.0, t_current = 31556926000.0, step_size = 0.010372161865234375\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:44:17.236065",
          "end_time": "2017-03-16T10:44:17.377058"
        },
        "trusted": true,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "mass, height, time = growthModel.final_conditions()\ntime = np.array(time)",
      "execution_count": 562,
      "outputs": [
        {
          "output_type": "stream",
          "text": "y_0(31556926000.0) = [[ 24.24392507]]\ny_1(31556926000.0) = [[  1.53711788e+09]]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-16T10:45:24.331250",
          "end_time": "2017-03-16T10:45:24.685077"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "rho_d = 3\npi = np.pi\nh = 3e11\nfig = figure(figsize=(6,6))\nax1 = fig.add_subplot(211)\nax1.plot(time/31556926.0, height[:, 0]/h)\nax2 = fig.add_subplot(212)\nax2.plot(time/31556926.0, (mass[:, 0]/(4*rho_d*pi/3))**(1/3))\nax2.set_yscale('log')\nax1.set_xlabel(\"Time (Yrs)\")\nax2.set_xlabel(\"Time (Yrs)\")\nax1.set_ylabel(r\"$\\frac{z}{h}$\")\nax2.set_ylabel(r\"$\\log\\left(\\frac{a_d}{1\\mu m}\\right)$\")\nfig.tight_layout()",
      "execution_count": 564,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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\" 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-General-py",
      "display_name": "Python [conda env:General]",
      "language": "python"
    },
    "language_info": {
      "nbconvert_exporter": "python",
      "name": "python",
      "version": "3.5.2",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "mimetype": "text/x-python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "716691df78b29cc844d8db8ec9fe0e1a",
      "data": {
        "description": "ASTC25_PS3_Code",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/716691df78b29cc844d8db8ec9fe0e1a"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}