Protein Folding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\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 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",
       "\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'] / 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",
       "\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 * 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",
       "\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",
       "    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",
       "\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 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",
       "\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\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",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\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 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",
       "\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'] / 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",
       "\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 * 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",
       "\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",
       "    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",
       "\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 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",
       "\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": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# Protein Folding Monte Carlo by Bhareth Kachroo\n",
    "# Using as simple a model as possible to reproduce a funneled energy landscape.\n",
    "# 20 Amino acid type as points, with randomized interaction energies and characteristic length\n",
    "# scales, where energy is calculated with a Lennard-Jones type exponential potential.\n",
    "# Vary absolute angle instead of acid positions randomly, leads to much faster convergence,\n",
    "# as bond length energy outweighs any other interactions.\n",
    "\n",
    "# CONSTANTS\n",
    "N = 15 # the length of the chain\n",
    "num_amino_acids = 20 # types of amino acid\n",
    "R = 1 # constant distance between acids\n",
    "T = 1 # temperature for Boltzmann factor\n",
    "potential_radius = 0.2 # units of bond length\n",
    "steps = 200\n",
    "\n",
    "# GEOMETRY functions: ----------\n",
    "\n",
    "# output: absolute positions of each acid based on spherical \n",
    "# angles between each acid in the chain, using spherical\n",
    "# to cartesian transform\n",
    "# time: O(N)\n",
    "def positions(first_position, z_angles, a_angles, R):\n",
    "    length = len(z_angles)\n",
    "    result_positions = np.zeros((length, 3))\n",
    "    result_positions[0] = first_position\n",
    "    \n",
    "    # loop over spherical to cartesian transform\n",
    "    for i in range(1, length):\n",
    "        result_positions[i, 0] = (result_positions[i-1,0] + \n",
    "        R*np.cos(a_angles[i-1])*np.cos(z_angles[i-1]) ) # x-coord\n",
    "        result_positions[i, 1] = (result_positions[i-1,1] + \n",
    "        R*np.sin(a_angles[i-1])*np.cos(z_angles[i-1]) ) #y-coord\n",
    "        result_positions[i, 2] = (result_positions[i-1,2] + \n",
    "        R*np.sin(z_angles[i-1]) ) # z-coord\n",
    "    \n",
    "    return result_positions\n",
    "\n",
    "# output: random angles in radians (pi, -pi), uniform dist.\n",
    "def random_angle():\n",
    "    return np.random.rand()*2*np.pi - np.pi\n",
    "\n",
    "# output: distance between two points\n",
    "def distance(a, b):\n",
    "    return np.linalg.norm(b-a)\n",
    "\n",
    "# output: the relative angle given spherical angles\n",
    "# using great circle distance formula\n",
    "def relative_angle(z_angles, a_angles):\n",
    "    theta_0 = z_angles[0] - np.pi\n",
    "    phi_0 = a_angles[0] - np.pi\n",
    "    theta_1 = z_angles[1]\n",
    "    phi_1 = a_angles[1]\n",
    "    return np.arccos(np.sin(theta_0)* np.sin(theta_1) \n",
    "                     + np.cos(theta_0)*np.cos(theta_1)*np.cos(phi_1-phi_0))\n",
    "\n",
    "# output: reduced angle to the proper interval\n",
    "def reduce_angle(angle):\n",
    "    if angle > np.pi:\n",
    "        return angle - 2*np.pi\n",
    "    if angle < -np.pi:\n",
    "        return angle + 2*np.pi\n",
    "    else:\n",
    "        return angle\n",
    "    \n",
    "# END GEOMETRY functions -------------\n",
    "\n",
    "# MONTE-CARLO functions: --------------\n",
    "    \n",
    "# output: given distance and and randomized variables\n",
    "# return potential energy\n",
    "def potential(r, m, n):\n",
    "    return (n*np.exp(-r/potential_radius) - \n",
    "            np.exp(-r/(potential_radius*m)))\n",
    "\n",
    "# output: total energy of acid-acid interactions given\n",
    "# the positions\n",
    "def energy(positions):\n",
    "    E = 0 # sum the energy\n",
    "    # loop over all unique interactions\n",
    "    for i in range(N):\n",
    "        for k in range(i+2, N): # no nearest-neighbour interactions\n",
    "            r = distance(positions[i], positions[k])\n",
    "            E += potential(r, 25, 8)\n",
    "    return E\n",
    "\n",
    "# output: return two new angles that respect relative\n",
    "# angle boundaries > pi/2\n",
    "def try_angles(z_angles, a_angles, index): \n",
    "    rel_angle_1 = 0\n",
    "    rel_angle_2 = 0\n",
    "    # endpoints have no second angle\n",
    "    if index == 0:\n",
    "        rel_angle_1 = np.pi\n",
    "    if index == N-1:\n",
    "        rel_angle_2 = np.pi \n",
    "    # keep trying random angles until a pair is found that are\n",
    "    # not too acute\n",
    "    while(np.abs(rel_angle_1) < np.pi/2 or \n",
    "          np.abs(rel_angle_2) < np.pi/2):\n",
    "        temp_z = reduce_angle(z_angles[index] + random_angle())\n",
    "        temp_a = reduce_angle(a_angles[index] + random_angle())\n",
    "        if index != 0:\n",
    "            # find relative angle between previous and current acid\n",
    "            rel_angle_1 = relative_angle([z_angles[index-1], temp_z], \n",
    "                                     [a_angles[index-1], temp_a])\n",
    "        if index != N-1:\n",
    "            # find relative angle between current and next acid\n",
    "            rel_angle_2 = relative_angle([temp_z, z_angles[index+1]], \n",
    "                                     [temp_a, a_angles[index+1]])\n",
    "    \n",
    "    return temp_z, temp_a\n",
    "\n",
    "# output: given a current state, return a new\n",
    "# state using the Metropolis algorithm\n",
    "def metropolis(z_angles, a_angles, current_E):\n",
    "    # pick a random position\n",
    "    index = np.random.randint(0, high=N)\n",
    "    # get a random angle that's not too acute\n",
    "    new_z, new_a = try_angles(z_angles, a_angles, index)\n",
    "    old_z = z_angles[index]\n",
    "    old_a = a_angles[index]\n",
    "    z_angles[index] = new_z\n",
    "    a_angles[index] = new_a\n",
    "    # recalculate the positions\n",
    "    pos = positions(initial_position, z_angles, a_angles, R)\n",
    "    # calculate the energy\n",
    "    new_E = energy(pos)\n",
    "    # do metropolis decision\n",
    "    if (new_E <= current_E): # P = 1\n",
    "        return z_angles, a_angles, new_E\n",
    "    # otherwise, get probability\n",
    "    p = np.exp(-(new_E - current_E)/T)\n",
    "    if (np.random.random() > p): # if no\n",
    "        z_angles[index] = old_z # change back\n",
    "        a_angles[index] = old_a\n",
    "        new_E = current_E\n",
    "    \n",
    "    # otherwise yes, return new\n",
    "    return z_angles, a_angles, new_E\n",
    "\n",
    "# END MONTE-CARLO functions ----------\n",
    "\n",
    "# INITIALIZATION\n",
    "# straight line to start\n",
    "initial_position = np.zeros(3) # anchor at origin\n",
    "zenith_angles = np.zeros(N) # measured from x-axis\n",
    "azim_angles = np.zeros(N) # parallel to xy-plane\n",
    "initial_E = energy(positions(initial_position, zenith_angles,\n",
    "                            azim_angles, R))\n",
    "energy_record = np.zeros(steps)\n",
    "#best_E_record = np.zeros(steps)\n",
    "#energy_record2 = np.zeros(steps) # The 2s are for stability comparison\n",
    "\n",
    "# MAIN LOOP\n",
    "\n",
    "z = zenith_angles\n",
    "a = azim_angles\n",
    "E = initial_E\n",
    "#z2 = np.copy(z)\n",
    "#a2 = np.copy(a)\n",
    "#E2 = initial_E\n",
    "for i in range(steps):\n",
    "    z, a, E = metropolis(z, a, E)\n",
    "    energy_record[i] = E\n",
    "    #z2, a2, E2 = metropolis(z2, a2, E2)\n",
    "    #energy_record2[i] = E2\n",
    "    #if i == 0:\n",
    "     #   best_E_record[i] = E\n",
    "    #else:\n",
    "     #   if E < best_E_record[i-1]:\n",
    "      #      best_E_record[i] = E\n",
    "       # else: \n",
    "        #    best_E_record[i] = best_E_record[i-1]\n",
    "            \n",
    "pos = positions(initial_position, z, a, R)\n",
    "#pos2 = positions(initial_position, z2, a2, R)\n",
    "\n",
    "# PLOTS\n",
    "fig = plt.figure(1)\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_xlim3d(-4, 4)\n",
    "ax.set_ylim3d(-4, 4)\n",
    "ax.set_zlim3d(-4, 4)\n",
    "ax.plot(pos[:,0], pos[:,1], pos[:,2]) # line plot\n",
    "ax.plot(pos[:,0], pos[:,1], pos[:,2], 'o') # ball plot\n",
    "plt.title('Step %d' % steps)\n",
    "\n",
    "'''fig = plt.figure(3)\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_xlim3d(-4, 4)\n",
    "ax.set_ylim3d(-4, 4)\n",
    "ax.set_zlim3d(-4, 4)\n",
    "ax.plot(pos2[:,0], pos2[:,1], pos2[:,2]) # line plot\n",
    "ax.plot(pos2[:,0], pos2[:,1], pos2[:,2], 'o') # ball plot '''\n",
    "\n",
    "fig1 = plt.figure(2)\n",
    "plt.plot(range(steps), energy_record, label='chain 1')\n",
    "#plt.plot(range(steps), best_E_record)\n",
    "#plt.plot(range(steps), energy_record2, label='chain 2')\n",
    "#plt.legend()\n",
    "plt.xlabel('Steps')\n",
    "plt.ylabel('Energy')\n",
    "plt.title('Energy over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          0.          0.        ]\n",
      " [ 0.75        0.4330127   0.5       ]\n",
      " [ 1.6160254   0.9330127   0.5       ]\n",
      " [ 0.98728112  0.57000702 -0.1876827 ]\n",
      " [ 0.76251474  0.44023809 -1.15341564]\n",
      " [-0.09195953 -0.05309286 -1.3161986 ]\n",
      " [ 0.63465277  0.36641695 -0.77209744]\n",
      " [ 1.17002608  0.67551487  0.01392667]\n",
      " [ 2.03605149  1.17551487  0.01392667]\n",
      " [ 1.46892575  0.84808467 -0.7418232 ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5366094185273218"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# testing positions()\n",
    "zenith_angles[0] = np.pi/6\n",
    "\n",
    "for i in range(N):\n",
    "    azim_angles[i] = np.pi/6\n",
    "    \n",
    "pos = positions(initial_position, zenith_angles, azim_angles, R)\n",
    "print(pos)\n",
    "# expecting a sideways motion, then vertical through\n",
    "\n",
    "# relative angle\n",
    "\n",
    "relative_angle([np.pi/6, -np.pi/3], [np.pi/6, np.pi/9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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