test_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "solving problem with no guess\n",
      "initial state [-0.1736486   0.53573545  1.46896263  2.91795523 -1.40528599 -0.62649066]\n",
      "goal state [ 2.99741224  0.08305251  0.1         0.08305251 -1.33218322  0.        ]\n",
      "flight time 1.54388718787\n",
      "num time steps 30\n",
      "exact traj False\n",
      "dt 0.0514629062623\n",
      "SolutionResult.kSolutionFound\n",
      "expected remaining fuel consumption 173.145995436\n",
      "took 0.0313169956207 seconds\n",
      "goal state [ 2.99741224  0.08305251  0.1         0.08305251 -1.33218322  0.        ]\n",
      "end state [ 2.98197341  0.06761364  0.11543887  0.09849139 -1.31674433 -0.01543887]\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(29,)\n",
      "took 0.0557289123535 seconds\n"
     ]
    }
   ],
   "source": [
    "# credits due to 6.832 Spring 2018 course\n",
    "# with modifications by Hao Shen\n",
    "# for 6.832 final project\n",
    "\n",
    "# Invokes trajectory optimization, given starting state, target end state, and flight_time \n",
    "# useful for debugging \n",
    "\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from drone_sim import DroneSim\n",
    "drone = DroneSim()\n",
    "\n",
    "# specify starting state\n",
    "x0 = np.array([-0.1736486,   0.53573545,  1.46896263,  2.91795523, -1.40528599, -0.62649066])\n",
    "# specify goal state\n",
    "goal = np.array([ 2.99741224,  0.08305251,  0.1,         0.08305251, -1.33218322,  0.      ])   \n",
    "# specify flight time\n",
    "flight_time = 1.543887187869\n",
    "\n",
    "start_time = time.time()\n",
    "traj, u_traj, time_array = drone.compute_trajectory(x0, goal, flight_time)\n",
    "drone.plot_trajectory_with_boosters(traj, u_traj, goal)\n",
    "print(\"took %s seconds\" % (time.time() - start_time ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define tracker\n",
    "def getGoalState(t):\n",
    "    # trajectory 1\n",
    "#     x = t\n",
    "#     y = 0.0\n",
    "#     dx = 1.0\n",
    "#     dy = 0.0\n",
    "    \n",
    "    # trajectory 2\n",
    "    x = 5*np.sin(t/1.5)\n",
    "    y = 4*np.cos(t/1.5)\n",
    "    dx = 5./1.5*np.cos(t/1.5)\n",
    "    dy = -4./1.5*np.sin(t/1.5)\n",
    "    \n",
    "    # trajectory 3\n",
    "#     x = 4*np.sin(t/5.+4)\n",
    "#     y = 2*np.sin(t)\n",
    "#     dx = 4./5*np.cos(t/5.+4)\n",
    "#     dy = 2*np.cos(t)\n",
    "    state = np.array([x, y, 0.1, \\\n",
    "                      dx, dy, 0.0])\n",
    "    return state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.10587049  4.69386399  7.15433026 -1.44771275 -0.53031557  2.32362604]\n",
      "Reached goal! sim time: 3.65\n",
      "total cost 347.638006161\n",
      "took 2.21221852303 seconds\n",
      "DONE!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from drone_sim import DroneSim\n",
    "drone = DroneSim()\n",
    "\n",
    "# calculate earliest point when we hit the target\n",
    "# traj - current trajectory\n",
    "# dest - destination state\n",
    "# time array - time associated with traj\n",
    "# sim_time current simulation time\n",
    "def timeToLand(traj, dest, time_array, sim_time):\n",
    "    assert len(traj) == len(time_array)\n",
    "    min_dist = None\n",
    "    \n",
    "    if time_array[-1] < 1.0: \n",
    "        #if the flight time is small, don't worry about finding a shorter time\n",
    "        dist_vec = traj[-1,:3]-dest[:3]\n",
    "        vel_vec = traj[-1,-3:]-dest[-3:]\n",
    "        if dist_vec.dot(dist_vec) < 0.01:\n",
    "            return time_array[-1], len(traj)-1, dist_vec.dot(dist_vec), True\n",
    "\n",
    "    for i in range(len(traj)):\n",
    "#         dest = getGoalState(sim_time + time_array[i]) # maybe this is a better estimate of where the destination is?\n",
    "        dist_vec = traj[i,:3]-dest[:3]\n",
    "        vel_vec = traj[i,-3:]-dest[-3:]\n",
    "\n",
    "        if dist_vec.dot(dist_vec) < 0.01:\n",
    "            return time_array[i], i, dist_vec.dot(dist_vec), True\n",
    "        if min_dist is None:\n",
    "            min_dist = dist_vec.dot(dist_vec) \n",
    "            min_i = i\n",
    "        elif min_dist > dist_vec.dot(dist_vec):\n",
    "            min_dist = dist_vec.dot(dist_vec)\n",
    "            min_i = i\n",
    "            \n",
    "    return time_array[min_i], min_i, min_dist, False\n",
    "\n",
    "# add a numpy array to a 1 dimension higher list of arrays\n",
    "def addElementTo(tracker, element):\n",
    "    if tracker is None:\n",
    "        tracker = np.expand_dims(element, axis=0)\n",
    "    else:\n",
    "        tracker = np.vstack((tracker, np.expand_dims(element, axis=0)))\n",
    "    return tracker\n",
    "\n",
    "# global variables to keep track of\n",
    "plot_traj = []\n",
    "plot_booster = None\n",
    "plot_goal = None\n",
    "plot_pos = None\n",
    "plot_vel = None\n",
    "plot_land_goal = None\n",
    "total_cost = 0\n",
    "total_time = 0\n",
    "    \n",
    "# high level planner, simulating the drone flying frome start to end with the trajectory calculated at every step\n",
    "# \n",
    "# start - initial state of drone\n",
    "# end - goal state of landing target\n",
    "# flight_time - estimated time to land\n",
    "# time_step - time step to forward simulate system\n",
    "# prev - length two length with past traj, and forces over time\n",
    "# exact - whether or not to look for an exact trajector\n",
    "def SimulateDrone(sim_time, start, end, flight_time, time_step, prev = None, exact = False, verbose = False):\n",
    "    global plot_traj, plot_booster, plot_goal, plot_land_goal, plot_pos, plot_vel, total_cost, total_time\n",
    "    \n",
    "    cur_pos = start[:3]\n",
    "    cur_vel = start[-3:]\n",
    "    if verbose:\n",
    "        print '========================'\n",
    "        print 'time is', sim_time\n",
    "        print 'flight time is', flight_time\n",
    "        print 'position', cur_pos\n",
    "        print 'velocity', cur_vel\n",
    "    \n",
    "    dist_vec = cur_pos-getGoalState(sim_time)[:3]\n",
    "    vel_vec = cur_vel-getGoalState(sim_time)[-3:]\n",
    "    \n",
    "    # if within landing target, we are done\n",
    "    if dist_vec.dot(dist_vec) < 0.02 and vel_vec.dot(vel_vec) < 0.1 :\n",
    "        print 'Reached goal! sim time:', sim_time\n",
    "        return sim_time\n",
    "    \n",
    "    # compute landing trajectory\n",
    "    start_time = time.time()\n",
    "    traj, u_traj, time_array = drone.compute_trajectory(start, end, flight_time, exact = exact, verbose=verbose)\n",
    "    total_time += time.time() - start_time\n",
    "    \n",
    "    # use previous found path if nan\n",
    "    if np.isnan(u_traj[0,0]):\n",
    "        if verbose:\n",
    "            print 'size of previous traj', len(prev[1])\n",
    "\n",
    "        if prev is None:\n",
    "            traj = np.array([start])\n",
    "            u_traj = np.array([[0, 0, 9.8]])\n",
    "        else:\n",
    "            traj = prev[0]\n",
    "            u_traj = prev[1]\n",
    "        ttl = flight_time*1.1\n",
    "        exact_landing = False\n",
    "        time_index = len(time_array)\n",
    "        if verbose:\n",
    "            print '***********GOT NAN***********'\n",
    "            print 'using past trajectory inputs'\n",
    "    else:\n",
    "        # find the estimated time to land \n",
    "        ttl, time_index, min_dist, exact_landing = timeToLand(traj, end, time_array, sim_time)\n",
    "\n",
    "        if verbose:\n",
    "            print 'goal', end[:3]\n",
    "        if not exact_landing:\n",
    "            if verbose:\n",
    "                if time_index != len(traj)-1:\n",
    "                    print 'trying to land in', time_array[time_index], 'but not exact'\n",
    "                else:\n",
    "                    print 'cannot land in time, extending search horizon'\n",
    "                    print 'traj end', traj[-1,:3]\n",
    "            # if our trajectory is nowhere near the target\n",
    "            if time_index == len(traj)-1 or min_dist > 0.1:\n",
    "                ttl = flight_time*1.1\n",
    "        else:\n",
    "            if verbose:\n",
    "                print 'closest point', traj[time_index][:3] \n",
    "                \n",
    "    if verbose:\n",
    "        print 'boosting', u_traj[0,:]\n",
    "        print time_array\n",
    "        print 'time to land', ttl\n",
    "    \n",
    "    if ttl < 0.5:\n",
    "        exact_landing = True\n",
    "        \n",
    "    # PLOTTING\n",
    "    plot_goal = addElementTo(plot_goal, getGoalState(sim_time)[:3])\n",
    "    plot_land_goal = addElementTo(plot_land_goal, end[:3])\n",
    "    plot_pos = addElementTo(plot_pos, cur_pos)\n",
    "    plot_vel = addElementTo(plot_vel, cur_vel)\n",
    "    plot_booster = addElementTo(plot_booster, u_traj[0,:])\n",
    "    plot_traj.append(traj)\n",
    "        \n",
    "#     plot everything\n",
    "#     drone.plot_trajectory_with_boosters(traj, u_traj, getGoalState(sim_time)[:2])\n",
    "    \n",
    "    \n",
    "    # forward simulate\n",
    "    next_state = drone.get_next_state(start, u_traj[0,:], time_step)\n",
    "    total_cost += np.sum(u_traj[0,:]**2)*time_step\n",
    "    \n",
    "    # stop simulation if it takes too long\n",
    "    if sim_time > 8.0:\n",
    "        return sim_time\n",
    "\n",
    "    look_ahead_time = min(ttl, 2.0) # can only anticipate 2 second into the future \n",
    "    return SimulateDrone(sim_time+time_step, next_state, getGoalState(sim_time+look_ahead_time), ttl, \\\n",
    "              time_step, prev = [traj, u_traj], exact = exact_landing, verbose = verbose)\n",
    "\n",
    "all_start = []\n",
    "all_cost = []\n",
    "all_time = []\n",
    "all_flight_time = []\n",
    "for i in range(1):\n",
    "    #reset everything\n",
    "    plot_traj = []\n",
    "    plot_booster = None\n",
    "    plot_goal = None\n",
    "    plot_pos = None\n",
    "    plot_vel = None\n",
    "    plot_land_goal = None\n",
    "    total_cost = 0\n",
    "    total_time = 0\n",
    "    \n",
    "    # randomize initial conditions\n",
    "    randx, randy = np.random.random_sample((2,))*10-5\n",
    "    randz = np.random.random_sample(1)*5+5\n",
    "    randdx, randdy, randdz = np.random.random_sample((3,))*5-2.5\n",
    "    start = np.array([randx, randy, randz, randdx, randdy, randdz])\n",
    "    \n",
    "    print start\n",
    "    end = getGoalState(0.0)\n",
    "    start_time = time.time()\n",
    "    sim_time = SimulateDrone(0.0, start, end, 5.0, 0.05, verbose = False)\n",
    "    print 'total cost', total_cost\n",
    "    print(\"took %s seconds\" % (total_time))\n",
    "    \n",
    "    #put them in\n",
    "    all_start.append(start)\n",
    "    all_cost.append(total_cost)\n",
    "    all_time.append(total_time)\n",
    "    all_flight_time.append(sim_time)\n",
    "    \n",
    "print 'DONE!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start at [-1.86131611  0.91483854  6.74442984  2.07362823  0.57176071 -1.12953109]\n",
      "landing in  2.45\n",
      "took 0.040708065033 seconds\n",
      "total fuel 273.344412142\n",
      "ending errors:\n",
      "0.00847984774788\n",
      "0.00543576452508\n",
      "start at [ 2.06439398 -0.60755293  7.18404547  1.69113401 -1.58793138 -0.29532038]\n",
      "landing in  2.5\n",
      "took 0.0397419929504 seconds\n",
      "total fuel 282.746049597\n",
      "ending errors:\n",
      "0.00979327568553\n",
      "0.0084931668698\n",
      "start at [-2.01808317 -1.08419094  9.52889447  1.07893487  0.4721049  -2.17072207]\n",
      "landing in  2.6\n",
      "took 0.0393130779266 seconds\n",
      "total fuel 316.491620189\n",
      "ending errors:\n",
      "0.00702705240313\n",
      "0.00802627470617\n",
      "start at [-4.69500422  3.2318462   6.55858657 -1.22205074  2.21956633  2.4811747 ]\n",
      "landing in  2.95\n",
      "took 0.0411398410797 seconds\n",
      "total fuel 336.540980781\n",
      "ending errors:\n",
      "0.00607663533512\n",
      "0.00887667012576\n",
      "start at [-1.80919921 -4.49099216  7.69106017  1.07838107  0.47795786 -1.61856946]\n",
      "landing in  2.55\n",
      "took 0.0383529663086 seconds\n",
      "total fuel 303.771958421\n",
      "ending errors:\n",
      "0.00702279605553\n",
      "0.00689340989215\n",
      "start at [-4.53845582  4.20847807  5.65543252 -0.47748324  1.31871512  1.66443993]\n",
      "landing in  2.8\n",
      "took 0.0394170284271 seconds\n",
      "total fuel 313.368032376\n",
      "ending errors:\n",
      "0.0073259534619\n",
      "0.00679016607515\n",
      "start at [-3.417743   -3.96801021  8.17132103  2.49207242  0.58908514 -1.25949674]\n",
      "landing in  2.55\n",
      "took 0.0410540103912 seconds\n",
      "total fuel 301.419326343\n",
      "ending errors:\n",
      "0.00804307812606\n",
      "0.00639212957595\n",
      "start at [ 4.52582647 -3.53603203  9.43546775  1.36383007  1.22200522  1.4343214 ]\n",
      "landing in  2.8\n",
      "took 0.045089006424 seconds\n",
      "total fuel 320.654332436\n",
      "ending errors:\n",
      "0.00952309674298\n",
      "0.00836914800736\n",
      "start at [ 2.13777132  1.06912877  7.47388643  0.76506481  0.22678928 -0.29148013]\n",
      "landing in  2.5\n",
      "took 0.0405368804932 seconds\n",
      "total fuel 277.316570344\n",
      "ending errors:\n",
      "0.0093406940223\n",
      "0.00828000487829\n",
      "start at [-3.29218801  2.05329279  7.72453743 -0.91953764  1.98830013  0.74542934]\n",
      "landing in  2.75\n",
      "took 0.0379860401154 seconds\n",
      "total fuel 322.800522692\n",
      "ending errors:\n",
      "0.00771584578272\n",
      "0.00848114600911\n",
      "start at [-3.38948892  4.27323016  9.43527719  2.05866406 -0.87805401  1.05741869]\n",
      "landing in  2.75\n",
      "took 0.0418560504913 seconds\n",
      "total fuel 311.894615944\n",
      "ending errors:\n",
      "0.00621375448643\n",
      "0.0062506389545\n",
      "start at [-0.01387213  1.50692478  7.94940957 -1.11170629  2.44958635 -2.39788491]\n",
      "landing in  2.55\n",
      "took 0.0399889945984 seconds\n",
      "total fuel 325.675078241\n",
      "ending errors:\n",
      "0.00920926065604\n",
      "0.00834836293044\n",
      "start at [ 4.45963784 -0.64962082  5.38272364  1.4325478  -0.75884125  0.44340512]\n",
      "landing in  2.3\n",
      "took 0.043927192688 seconds\n",
      "total fuel 262.760211186\n",
      "ending errors:\n",
      "0.0111671653135\n",
      "0.00936502088181\n",
      "start at [ 4.69455267 -3.42190146  9.33160525 -0.43897453  0.81098672 -1.64759447]\n",
      "landing in  2.55\n",
      "took 0.0392019748688 seconds\n",
      "total fuel 315.031841552\n",
      "ending errors:\n",
      "0.0067312251808\n",
      "0.00784966941284\n",
      "start at [-4.38444822  0.5014198   9.62823533 -1.53918914 -1.57221825  0.34843782]\n",
      "landing in  3.0\n",
      "took 0.0454168319702 seconds\n",
      "total fuel 355.66070133\n",
      "ending errors:\n",
      "0.00518675767217\n",
      "0.00606621760308\n",
      "start at [-0.65886723 -4.17517522  7.04123145  2.31675605 -0.24409495 -0.49812125]\n",
      "landing in  2.5\n",
      "took 0.0391139984131 seconds\n",
      "total fuel 287.373278389\n",
      "ending errors:\n",
      "0.0102684042252\n",
      "0.00870787478401\n",
      "start at [ 2.46407028 -0.17585096  6.63508451 -0.92655462  2.28366943 -1.33794468]\n",
      "landing in  2.4\n",
      "took 0.0393249988556 seconds\n",
      "total fuel 277.033562424\n",
      "ending errors:\n",
      "0.00750087088994\n",
      "0.00749994529991\n",
      "start at [-3.00421546 -4.00474026  8.52216353  0.56277322  1.80974033  1.12976943]\n",
      "landing in  2.7\n",
      "took 0.039412021637 seconds\n",
      "total fuel 300.90337102\n",
      "ending errors:\n",
      "0.00870708696936\n",
      "0.00773355907521\n",
      "start at [ 0.63038926 -2.88381065  6.8532678   0.17678761 -0.7250317  -1.14450433]\n",
      "landing in  2.5\n",
      "took 0.0381410121918 seconds\n",
      "total fuel 286.725831946\n",
      "ending errors:\n",
      "0.00941822123558\n",
      "0.00826718648267\n",
      "start at [ 3.08573772  4.27624056  5.65814987  1.89186873  0.90992574  1.04810983]\n",
      "landing in  2.55\n",
      "took 0.0423650741577 seconds\n",
      "total fuel 285.225891211\n",
      "ending errors:\n",
      "0.0113751832319\n",
      "0.00919887327988\n",
      "start at [-1.38976982  0.91098042  6.15673234 -0.13613997  1.11864231 -0.72299947]\n",
      "landing in  2.45\n",
      "took 0.0352399349213 seconds\n",
      "total fuel 274.336933215\n",
      "ending errors:\n",
      "0.00974424830125\n",
      "0.00781289815047\n",
      "start at [ 4.62721509 -4.02633822  8.08845008  0.98767228 -0.06438831  1.58500389]\n",
      "landing in  2.75\n",
      "took 0.0456120967865 seconds\n",
      "total fuel 307.016816176\n",
      "ending errors:\n",
      "0.00928386370007\n",
      "0.00824174181079\n",
      "start at [-2.83063438 -3.89138785  6.29393563 -0.26163343  0.03674103 -0.27833592]\n",
      "landing in  2.55\n",
      "took 0.0404970645905 seconds\n",
      "total fuel 293.818069134\n",
      "ending errors:\n",
      "0.00768075607487\n",
      "0.00716017944949\n",
      "start at [-2.97624111 -3.37049124  6.09307999 -0.66629992  0.40468689  1.33368718]\n",
      "landing in  2.7\n",
      "took 0.0412578582764 seconds\n",
      "total fuel 287.249108069\n",
      "ending errors:\n",
      "0.00410619978184\n",
      "0.00887175456857\n",
      "start at [ 2.60419689 -3.00857899  5.08688155 -1.69151904 -1.59889056  1.55452786]\n",
      "landing in  2.55\n",
      "took 0.0441980361938 seconds\n",
      "total fuel 265.247722596\n",
      "ending errors:\n",
      "0.0113316477046\n",
      "0.00920857137414\n",
      "start at [ 1.13670498 -2.86173649  9.75561042 -0.3440178   0.8787588  -0.55238777]\n",
      "landing in  2.7\n",
      "took 0.041668176651 seconds\n",
      "total fuel 313.057293832\n",
      "ending errors:\n",
      "0.00597673597094\n",
      "0.00785568809638\n",
      "start at [ 2.59749921 -1.24864387  8.13340956  0.02899635  2.46694194 -0.79936203]\n",
      "landing in  2.55\n",
      "took 0.0438091754913 seconds\n",
      "total fuel 292.294949576\n",
      "ending errors:\n",
      "0.00925778302464\n",
      "0.00820763410907\n",
      "start at [-2.20621293  4.14073344  7.25071574  0.61408491 -0.85124352 -2.3628826 ]\n",
      "landing in  2.5\n",
      "took 0.0502541065216 seconds\n",
      "total fuel 305.68168111\n",
      "ending errors:\n",
      "0.0078741259755\n",
      "0.00866418104205\n",
      "start at [-0.30134143  2.48272496  6.91387388  1.45745521  0.16355911 -2.14729561]\n",
      "landing in  2.45\n",
      "took 0.0481581687927 seconds\n",
      "total fuel 288.53941581\n",
      "ending errors:\n",
      "0.00748612814026\n",
      "0.00862451081031\n",
      "start at [-4.89835805  3.59647285  5.15138334 -1.7974687  -0.68050922  2.31230126]\n",
      "landing in  2.85\n",
      "took 0.0581841468811 seconds\n",
      "total fuel 313.278124737\n",
      "ending errors:\n",
      "0.010315137128\n",
      "0.00908260488866\n",
      "DONE!!\n"
     ]
    }
   ],
   "source": [
    "opt_cost_fixed_time = []\n",
    "\n",
    "# for all trials, take the total flight time, recalculate the trajectory using that flight time\n",
    "# this is 'optimal' trajectory taking that flight time\n",
    "for i in range(len(all_start)):\n",
    "    start = all_start[i]\n",
    "    t = all_flight_time[i]\n",
    "\n",
    "    start_time = time.time()\n",
    "    traj, u_traj, total_fuel = drone.compute_strict_trajectory(start, getGoalState(t), t, False)\n",
    "    # check if found a path in the time\n",
    "    dist_vec = traj[-1,:3]-getGoalState(t)[:3]\n",
    "    vel_vec = traj[-1,-3:]-getGoalState(t)[-3:]\n",
    "\n",
    "    # plot\n",
    "    # drone.plot_trajectory_with_boosters(traj, u_traj, getGoalState(t))\n",
    "\n",
    "    print 'start at', start\n",
    "    print 'landing in ', t\n",
    "    print(\"took %s seconds\" % (time.time() - start_time ))\n",
    "    print \"total fuel\", total_fuel\n",
    "\n",
    "    opt_cost_fixed_time.append(total_fuel)\n",
    "    \n",
    "print 'DONE!!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "took 0.234031915665 seconds\n",
      "319.163766735\n",
      "2.31298230428\n",
      "took 0.23840713501 seconds\n",
      "292.459211402\n",
      "2.0592063129\n",
      "took 0.221725940704 seconds\n",
      "330.39993276\n",
      "2.53556506773\n",
      "took 0.286036968231 seconds\n",
      "346.062993599\n",
      "2.63226603783\n",
      "took 0.304740905762 seconds\n",
      "258.943762014\n",
      "2.08741996073\n"
     ]
    }
   ],
   "source": [
    "# nonlinear trajectory optimizations using dt as a decision variable\n",
    "from drone_sim import DroneSim\n",
    "drone = DroneSim()\n",
    "import time\n",
    "\n",
    "# to keep track of everything to compare at the end\n",
    "opt_cost = []\n",
    "opt_flight_time = []\n",
    "for i in range(len(all_start)):\n",
    "    start_time = time.time()\n",
    "    start = all_start[i]\n",
    "    traj, u_traj, time_array, total_fuel = drone.compute_opt_trajectory(start, getGoalState, False)\n",
    "\n",
    "    dist_vec = traj[-1,:3]-getGoalState(time_array[-1])[:3]\n",
    "    vel_vec = traj[-1,-3:]-getGoalState(time_array[-1])[-3:]\n",
    "\n",
    "    if dist_vec.dot(dist_vec) < 0.02 and vel_vec.dot(vel_vec) < 0.02:\n",
    "        print(\"took %s seconds\" % (time.time() - start_time ))\n",
    "        opt_cost.append(total_fuel)\n",
    "        opt_flight_time.append(time_array[-1])\n",
    "        print total_fuel\n",
    "        print time_array[-1]\n",
    "# plot in case we want to\n",
    "#     drone.plot_trajectory_with_boosters(traj, u_traj, getGoalState(time_array[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([-4.32394818, -0.19741878,  6.31119975, -0.64044535,  0.40688603,\n",
      "       -1.88790573])]\n",
      "[1.9530075949243664, 2.1074251469604173, 2.1518857637060886, 2.677419487599312, 2.1303027118162881, 2.4390969676520697, 2.1601307272467896, 2.5219669443871857, 2.0824071268771629, 2.4258453913201596, 2.3976180658981789, 2.1094261066240487, 2.1425545547103253, 2.2312184993585502, 2.6464293756225614, 2.1577524557432493, 1.9504144565228736, 2.3568657125764374, 2.0491288372164558, 2.2756216134926461, 1.9724196057247996, 2.443684299597936, 2.1584589310964302, 2.2906699541517774, 2.201434914259659, 2.2519042701171861, 2.1107327575924524, 2.0589746440101675, 1.957488937763358, 2.7242683511594836]\n",
      "[257.18858443827804, 278.27644980892234, 305.79052944862246, 346.02399348293619, 294.81217359639976, 315.79090936959795, 295.08512516952658, 328.63236960563387, 271.32502373444828, 326.29650716824705, 314.69293285958491, 312.96060027207079, 267.36906581018826, 314.11474204637074, 360.03635519875911, 284.89891865839945, 263.08358436979483, 303.69031022866233, 275.26048723642577, 290.0103024961237, 259.64806326112307, 314.91930977517802, 288.17551298803477, 285.51521939340563, 264.93017614733856, 307.6963045280404, 284.62775629122461, 292.75338261827687, 270.88911949858345, 319.7154515038464]\n",
      "[2.6999999999999984]\n",
      "[330.28072316747171]\n",
      "(54, 3)\n",
      "(54, 3)\n"
     ]
    }
   ],
   "source": [
    "print all_start\n",
    "print opt_flight_time\n",
    "print opt_cost\n",
    "print all_flight_time\n",
    "print all_cost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# post process velocity and force vectors for plotting\n",
    "\n",
    "row_norms = np.linalg.norm(plot_vel, axis=1)\n",
    "normalized_plot_vel =  plot_vel / row_norms[:, np.newaxis]\n",
    "\n",
    "scaled_plot_vel = plot_vel/5.\n",
    "scaled_plot_booster = plot_booster/5.\n",
    "\n",
    "# post process the past values\n",
    "past_pos = []\n",
    "for i in range(1, len(plot_pos)+1):    \n",
    "    past_pos.append(plot_pos[:i,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation\n",
    "\n",
    "# First set up the figure, the axis, and the plot element we want to animate\n",
    "fig = plt.figure(figsize=(8,6), dpi=120)\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.set_xlim([-5,5])\n",
    "ax.set_ylim([-5,5])\n",
    "ax.set_zlim([0,10])\n",
    "drone_plot, = ax.plot([], [], [], 'o')\n",
    "goal, = ax.plot([], [], [], 'o')\n",
    "land_goal, = ax.plot([],[],[],'o')\n",
    "past_drone, = ax.plot([], [], [])\n",
    "future_drone, = ax.plot([], [], [], 'c--')\n",
    "past_x = []\n",
    "past_y = []\n",
    "past_z = []\n",
    "\n",
    "# for quivers\n",
    "def compute_segs(i):\n",
    "    x = np.array([[[plot_pos[i,0],plot_pos[i,0]]]])\n",
    "    y = np.array([[[plot_pos[i,1],plot_pos[i,1]]]])\n",
    "    z2 = np.array([[[plot_pos[i,2],plot_pos[i,2]]]])\n",
    "    u = np.array([[[plot_pos[i,0]+scaled_plot_vel[i,0],plot_pos[i,0]+scaled_plot_booster[i,0]]]])\n",
    "    v = np.array([[[plot_pos[i,1]+scaled_plot_vel[i,1],plot_pos[i,1]+scaled_plot_booster[i,1]]]])\n",
    "    w = np.array([[[plot_pos[i,2]+scaled_plot_vel[i,2],plot_pos[i,2]+scaled_plot_booster[i,2]]]])\n",
    "    return x,y,z2,u,v,w\n",
    "\n",
    "segs = compute_segs(0)\n",
    "quivers = ax.quiver(*segs, length=0.1, colors = ['r','b'])\n",
    "\n",
    "# initialization function: plot the background of each frame\n",
    "def init():\n",
    "    drone_plot.set_data([], [])\n",
    "    drone_plot.set_3d_properties([])\n",
    "    goal.set_data([], [])\n",
    "    goal.set_3d_properties([])\n",
    "    land_goal.set_data([], [])\n",
    "    land_goal.set_3d_properties([])\n",
    "    \n",
    "    past_x = []\n",
    "    past_y = []\n",
    "    past_z = []\n",
    "    past_drone.set_data([], [])\n",
    "    past_drone.set_3d_properties([])\n",
    "    \n",
    "    future_drone.set_data([], [])\n",
    "    future_drone.set_3d_properties([])\n",
    "    \n",
    "    segs = compute_segs(0)\n",
    "    quivers = ax.quiver(*segs, length=0.1, colors = ['r','b'])\n",
    "    return drone_plot, goal, land_goal, past_drone, future_drone, quivers\n",
    "\n",
    "# animation function.  This is called sequentially\n",
    "def animate(i):\n",
    "    drone_x, drone_y, drone_z = plot_pos[i,:]\n",
    "    goal_x, goal_y, goal_z = plot_goal[i,:]\n",
    "    drone_plot.set_data(drone_x, drone_y)\n",
    "    drone_plot.set_3d_properties(drone_z)\n",
    "    goal.set_data(goal_x, goal_y)\n",
    "    goal.set_3d_properties(goal_z)\n",
    "    land_goal_x, land_goal_y, land_goal_z = plot_land_goal[i,:3]\n",
    "    land_goal.set_data(land_goal_x, land_goal_y)\n",
    "    land_goal.set_3d_properties(land_goal_z) \n",
    "    \n",
    "    past_drone.set_data(past_pos[i][:,0], past_pos[i][:,1])\n",
    "    past_drone.set_3d_properties(past_pos[i][:,2])\n",
    "    future_drone.set_data(plot_traj[i][:,0], plot_traj[i][:,1])\n",
    "    future_drone.set_3d_properties(plot_traj[i][:,2])\n",
    "    \n",
    "    segs = np.array(compute_segs(i)).reshape(6,-1)\n",
    "    new_segs = [[[x,y,z],[u,v,w]] for x,y,z,u,v,w in zip(*segs.tolist())]\n",
    "    quivers.set_segments(new_segs)\n",
    "    return drone_plot, goal, land_goal, past_drone, quivers\n",
    "\n",
    "# call the animator.  blit=True means only re-draw the parts that have changed.\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=len(plot_pos), interval=100, blit=True, repeat=True)\n",
    "plt.show()\n",
    "# Writer = animation.writers['ffmpeg']\n",
    "# writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
    "# anim.save('path1.mp4', writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 1\n",
    "x=[1.9530075949243664, 2.1074251469604173, 2.1518857637060886, 2.677419487599312, 2.1303027118162881, 2.4390969676520697, 2.1601307272467896, 2.5219669443871857, 2.0824071268771629, 2.4258453913201596, 2.3976180658981789, 2.1094261066240487, 2.1425545547103253, 2.2312184993585502, 2.6464293756225614, 2.1577524557432493, 1.9504144565228736, 2.3568657125764374, 2.0491288372164558, 2.2756216134926461, 1.9724196057247996, 2.443684299597936, 2.1584589310964302, 2.2906699541517774, 2.201434914259659, 2.2519042701171861, 2.1107327575924524, 2.0589746440101675, 1.957488937763358, 2.7242683511594836]\n",
    "# x =[257.18858443827804, 278.27644980892234, 305.79052944862246, 346.02399348293619, 294.81217359639976, 315.79090936959795, 295.08512516952658, 328.63236960563387, 271.32502373444828, 326.29650716824705, 314.69293285958491, 312.96060027207079, 267.36906581018826, 314.11474204637074, 360.03635519875911, 284.89891865839945, 263.08358436979483, 303.69031022866233, 275.26048723642577, 290.0103024961237, 259.64806326112307, 314.91930977517802, 288.17551298803477, 285.51521939340563, 264.93017614733856, 307.6963045280404, 284.62775629122461, 292.75338261827687, 270.88911949858345, 319.7154515038464]\n",
    "y=[2.4499999999999993, 2.499999999999999, 2.5999999999999988, 2.9499999999999975, 2.549999999999999, 2.799999999999998, 2.549999999999999, 2.799999999999998, 2.499999999999999, 2.7499999999999982, 2.7499999999999982, 2.549999999999999, 2.3, 2.549999999999999, 2.9999999999999973, 2.499999999999999, 2.3999999999999995, 2.6999999999999984, 2.499999999999999, 2.549999999999999, 2.4499999999999993, 2.7499999999999982, 2.549999999999999, 2.6999999999999984, 2.549999999999999, 2.6999999999999984, 2.549999999999999, 2.499999999999999, 2.4499999999999993, 2.849999999999998]\n",
    "# y=[280.2486689270097, 293.62191085846047, 316.32680069430927, 334.31627005130514, 313.17204748605064, 317.8690462816607, 308.53237699527386, 330.32904193435644, 285.25515805464744, 322.46827030796317, 317.6436967581617, 334.61363855152911, 278.13104358195045, 321.17804034122366, 356.57435224272479, 296.61880074784608, 280.29396163135135, 308.26842163708733, 294.119623284505, 303.09743539335079, 276.87978520203751, 319.78936760449636, 297.31947911057097, 294.08546489879893, 271.62857593350651, 313.03706594743136, 300.54120534930064, 314.21128899333735, 299.52672682776517, 311.72526972588605]\n",
    "\n",
    "# 2\n",
    "# x=[2.7007072822032576, 2.291796352505675, 2.2315453408978665, 2.3381497581103137, 2.2982354596151509, 2.4600181738201554, 2.9364297705342191, 2.4835968333515215, 2.6695532633683938, 2.6045189659608141, 2.032983844747275, 2.5780842986807193, 2.4905841250553471, 2.0383120341466632, 3.6405592395976334, 1.9567362994796162, 3.5437019507777068, 2.7260354734940657, 3.2932697339315693, 2.3303671163249731, 2.4226114183500123, 2.6621743327368046, 2.186956260862595, 2.6585026740560416, 2.1312665378857134, 2.7284604034162427, 2.33735780006459, 2.1150473712470186, 2.6865504263479409, 2.1326677689247266]\n",
    "# x =[362.21020637643522, 298.17635560304802, 265.20020851221864, 317.00037753299966, 324.1658899688008, 306.22458772063101, 417.73705919140252, 307.28191204246599, 340.76624042705379, 382.91351382100459, 279.64202285616983, 336.21194662965803, 321.11359374246223, 261.03502887303745, 451.71935694943016, 251.84049642880072, 450.68923646490123, 400.76641637758343, 437.93516048234619, 293.10280303932859, 307.02979795032741, 372.01406131145495, 319.55748052096811, 346.2217560536057, 277.73981428997911, 377.78723035819411, 289.61046768248411, 257.28649227340361, 330.86741971277127, 288.14196645918611]\n",
    "# y=[3.699999999999995, 3.649999999999995, 3.599999999999995, 3.649999999999995, 3.649999999999995, 3.649999999999995, 3.699999999999995, 3.649999999999995, 3.699999999999995, 3.649999999999995, 3.5499999999999954, 3.699999999999995, 3.699999999999995, 3.5499999999999954, 3.699999999999995, 3.5499999999999954, 3.699999999999995, 3.699999999999995, 3.699999999999995, 3.649999999999995, 3.649999999999995, 3.699999999999995, 3.599999999999995, 3.699999999999995, 3.5499999999999954, 3.699999999999995, 3.649999999999995, 3.649999999999995, 3.649999999999995, 3.599999999999995]\n",
    "# y= [384.84440730382812, 356.50367449193197, 343.29064919432074, 376.64678706840056, 402.05019684465094, 349.51125736167751, 435.1248019300449, 355.77325472306217, 366.26538452946062, 432.94820965164075, 387.22031745157301, 373.35695142846214, 360.97901974277693, 357.23836866606223, 438.43185245606531, 360.6006410661397, 446.74828978553541, 440.84661601896534, 453.43691069570707, 347.6864836437357, 350.36806171230739, 395.73165916715749, 398.05253477907178, 370.06348780357513, 358.13844765470333, 418.56052817300326, 345.60204707879029, 342.644176138837, 381.16681460786123, 373.1054336311596]\n",
    "\n",
    "# 3\n",
    "# x= [2.2008509021061373, 2.4694325801419255, 2.8653046911651527, 2.8133125447590617, 2.2569573926992308, 2.4757542613278831, 2.1615783399547728, 2.1240129779067751, 1.7171977884066725, 2.2133758743931993, 2.2921175430827061, 2.264402038317864, 2.6936748831730917, 1.8348608470132464, 2.3165757289146076, 2.7532788281420415, 2.6893245848635416, 2.4616855376643416, 2.2415397683635798, 2.2344339795445034, 2.6493352554639271, 2.0536991209014981, 2.471554481281105, 2.4314335653590948, 2.2911174060391906, 1.9790098584560423, 2.7927662660640276, 2.5622004986709488, 2.4882807811291183, 2.377199805599663]\n",
    "# x = [294.30404001285081, 322.800429400415, 376.82732355977026, 385.70321721080052, 323.95080875165633, 326.93833507772871, 283.2807448640695, 293.87372469017123, 226.70546245364844, 296.29412011605814, 284.13626505069442, 332.82946621987475, 357.57093823724102, 236.68841048829952, 291.80286977333918, 361.88986989805119, 342.38073796239837, 313.05910260410405, 300.58975483383307, 267.98929307724734, 358.89091886899843, 248.70410398800135, 347.66869003632667, 319.84664362346535, 313.297859202388, 265.31159812470833, 368.8850652931809, 331.15692267026787, 328.85454117497659, 289.36314547898309]\n",
    "# y=[2.5999999999999988, 2.799999999999998, 2.9499999999999975, 2.849999999999998, 2.5999999999999988, 2.5999999999999988, 2.499999999999999, 2.5999999999999988, 2.25, 2.499999999999999, 2.6499999999999986, 2.6499999999999986, 2.8999999999999977, 2.3999999999999995, 2.6499999999999986, 2.849999999999998, 2.7499999999999982, 2.5999999999999988, 2.5999999999999988, 2.5999999999999988, 2.849999999999998, 2.6499999999999986, 2.5999999999999988, 2.849999999999998, 2.5999999999999988, 2.3999999999999995, 2.9999999999999973, 2.7499999999999982, 2.6999999999999984, 2.5999999999999988]\n",
    "# y = [313.64366168237956, 323.0813799767086, 388.85814611525831, 384.52876000242435, 341.18821879703239, 328.05448093326436, 298.2523150999524, 306.77239661539107, 256.38962042074519, 309.46290119490425, 295.65043941311637, 348.49009797698102, 357.05057349020649, 272.94302719706513, 293.02765066038523, 371.15480354927462, 341.86818784938185, 316.7114287774375, 310.58335718056236, 275.97900896039954, 353.23401169889809, 268.57207628424561, 358.07446395627932, 317.53478966977889, 328.46912331230203, 297.24658503253102, 371.07168815416014, 336.27402914767362, 327.55958183438435, 291.77937873899043]\n",
    "# now plot both limits against eachother\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x, y, alpha=0.5)\n",
    "\n",
    "lims = [\n",
    "    np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes\n",
    "    np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes\n",
    "]\n",
    "ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)\n",
    "ax.set_xlim(lims)\n",
    "ax.set_ylim(lims)\n",
    "plt.show()\n",
    "plt.savefig('time1.png')"
   ]
  }
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