sketch-rui.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resized to (806, 605, 3)\n",
      "(622, 1, 4) (622, 1) (622, 1) (622, 1)\n",
      "number of line segments detected (LSD): 622\n",
      "number of lines detected: 622\n"
     ]
    }
   ],
   "source": [
    "import os, math\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "SATURATE_TH = 0.09\n",
    "BG_HUE = 30   # read from gimp to be 60 deg\n",
    "\n",
    "# Try to get rid of lined paper background. Successful but would hinder recognition afterwards.\n",
    "# Perhaps should try fft-ifft method: https://stackoverflow.com/questions/29360025/removing-lines-from-an-image-a-notebook-for-digit-detection-python?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa\n",
    "def remove_nongray(imhsv):\n",
    "    \"\"\" Given an NxMx3 image, return gray image where non-gray pixels are set to gray\n",
    "    \"\"\"\n",
    "    h, s, v = imhsv[:,:,0], imhsv[:,:,1], imhsv[:,:,2]\n",
    "    #print(h.min(), h.max())\n",
    "    #print(s.min(), s.max())\n",
    "    #print(v.min(), v.max())\n",
    "    \n",
    "    nongray = s > SATURATE_TH * 255\n",
    "    s[nongray] = 0\n",
    "    v[nongray] = 255\n",
    "    return imhsv\n",
    "\n",
    "def nongray(s):\n",
    "    \"\"\" s is NxM s values in an hsv image.\n",
    "    \"\"\"\n",
    "    return s > SATURATE_TH * 255\n",
    "    \n",
    "\n",
    "def remove_rgb(imrgb, nongray, rgb):\n",
    "    \"\"\" Given an NxMx3 imrgb and a nongray mask of True/False, remove rgb from the nongray pixels.\n",
    "    \"\"\"\n",
    "    h, s, v = imhsv[:,:,0], imhsv[:,:,1], imhsv[:,:,2]\n",
    "    \n",
    "    nongray = s > SATURATE_TH * 255\n",
    "    s[nongray] = 0\n",
    "    v[nongray] = 255\n",
    "    return imhsv\n",
    "\n",
    "\n",
    "def get_lines(lines_in):\n",
    "    if cv2.__version__ < '3.0':\n",
    "        return lines_in[0]\n",
    "    return [l[0] for l in lines]\n",
    "\n",
    "#sketch_file=\"../typicaldrawings-jpg/1st_floor_interior_50.jpg\"\n",
    "#sketch_file=\"../typicaldrawings-jpg/1st_floor_interior_new_wing-50.jpg\"\n",
    "#sketch_file=\"../typicaldrawings-jpg/2nd_floor_interior-50.jpg\"\n",
    "sketch_file=\"../typicaldrawings-jpg/556DFC20-147D-43DA-9DC7-8A0DD26C3A57.JPG\"\n",
    "\n",
    "basename = os.path.splitext(sketch_file)[0]\n",
    "img = cv2.imread(sketch_file)\n",
    "\n",
    "img = cv2.resize(img, (0, 0), fx=.2, fy=.2)\n",
    "\n",
    "\n",
    "if 0:\n",
    "    imghsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    imghsv = remove_nongray(imghsv)\n",
    "    img = cv2.cvtColor(imghsv, cv2.COLOR_HSV2BGR)\n",
    "    cv2.imwrite(basename + '.1-unlind.jpg', img)\n",
    "    #plt.imshow(img)\n",
    "\n",
    "black = np.zeros_like(img)\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "print(\"resized to\", img.shape)\n",
    "cv2.imwrite(basename + '.1-gray-resize.jpg', gray)\n",
    "\n",
    "# a) adaptive thresholding\n",
    "#ngedges = cv2.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 71, 2)\n",
    "#ret,edges = cv2.threshold(ngedges,220,255,cv.THRESH_BINARY_INV)\n",
    "\n",
    "# b) simple thresholding\n",
    "#ret,edges = cv2.threshold(gray,80,255,cv2.THRESH_BINARY_INV)\n",
    "\n",
    "# c) just invert the image, and threshold with TOZERO\n",
    "edges = 255 - gray\n",
    "ret,edges = cv2.threshold(edges,170,255,cv2.THRESH_BINARY)\n",
    "#ret, edges = cv2.threshold(edges, 25, 255, cv2.THRESH_TOZERO)\n",
    "#edges = cv2.adaptiveThreshold(edges, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 71, 2)\n",
    "\n",
    "#plt.imshow(edges)\n",
    "cv2.imwrite(basename + '.2-edges.jpg', edges)\n",
    "#1/0\n",
    "\n",
    "# LSD\n",
    "lsd = cv2.createLineSegmentDetector(cv2.LSD_REFINE_ADV)  #  _scale[, _sigma_scale[, _quant[, _ang_th[, _log_eps[, _density_th[, _n_bins]]]]]]]\n",
    "lines, width, prec, nfa = lsd.detect(gray, )  # , _lines[, width[, prec[, nfa]\n",
    "print(lines.shape, width.shape, prec.shape, nfa.shape)\n",
    "print(\"number of line segments detected (LSD):\", len(lines))\n",
    "assert len(lines) > 0\n",
    "\n",
    "def write_lines(lines, background, color=(255, 255, 255)):\n",
    "    #black = np.zeros_like(img)\n",
    "    for line in get_lines(lines):\n",
    "        x1, y1, x2, y2 = line\n",
    "        cv2.line(background, (x1, y1), (x2, y2), color, 1)\n",
    "    return background\n",
    "\n",
    "_image = lsd.drawSegments(gray, lines)\n",
    "cv2.imwrite(basename + '.2-lsd-lines-on-gray.jpg', _image)\n",
    "\n",
    "lsdimage = write_lines(lines, np.zeros_like(img[:,:,0], ))\n",
    "cv2.imwrite(basename + '.2-lsd-lines-on-black.jpg', lsdimage)\n",
    "\n",
    "use_hough = False\n",
    "if use_hough:\n",
    "    # Hough\n",
    "    lsdimage = cv2.resize(lsdimage, (0, 0), fx=.5, fy=.5)\n",
    "    cv2.imwrite(basename + '.2-lsd-lines-33.jpg', lsdimage)\n",
    "\n",
    "    minLineLength = 30\n",
    "    maxLineGap = 10\n",
    "    lines = cv2.HoughLinesP(lsdimage,1,np.pi/180,threshold=50,minLineLength=minLineLength,maxLineGap=maxLineGap)\n",
    "    print(len(lines), 'lines')\n",
    "    if (len(lines) > 0):\n",
    "        for line in get_lines(lines):\n",
    "            x1, y1, x2, y2 = line\n",
    "            #print(x1, y1, x2, y2, \"len=\", int(math.hypot(x1-x2, y1-y2)))\n",
    "            cv2.line(img,(x1,y1),(x2,y2),(255,255,0),1)\n",
    "            cv2.line(black, (x1, y1), (x2, y2), (255,255,0), 1)\n",
    "\n",
    "        outfname = basename + '.3-lines.jpg'\n",
    "        cv2.imwrite(outfname, black)\n",
    "\n",
    "alllines = get_lines(lines)       \n",
    "print(\"number of lines detected:\", len(alllines))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    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",
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       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
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       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
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       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
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       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
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       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
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       "    }\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",
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       "        canvas_div.focus();\n",
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       "\n",
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       "\n",
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       "    nav_element.attr('style', 'width: 100%');\n",
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       "        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",
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       "        tooltip_span.addClass('ui-button-text');\n",
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       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all detected lines\n",
    "def plot_line(ax, line, *args, **kwds):\n",
    "    x1, y1, x2, y2 = line\n",
    "    ax.plot((x1, x2), (y1, y2), *args, **kwds)\n",
    "    \n",
    "def plot_lines(ax, lines, *args, **kwds):\n",
    "    for line in lines:\n",
    "        plot_line(ax, line, *args, **kwds)\n",
    "    ax.axis('image')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plot_lines(ax, alllines, '-k')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<rtree.index.Item object at 0x63e0d7728> 0 0\n",
      "length of alllines: 622\n",
      "(4, 2)\n",
      "[(281.0, 147.0), (213.0, 170.0), (239.0, 242.0), (307.0, 219.0)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(213.0, 170.0), (281.0, 147.0), (307.0, 219.0), (239.0, 242.0)]"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rtree import index\n",
    "from numpy.linalg import norm\n",
    "\n",
    "def pt2box(pt):\n",
    "    v = pt.ravel()\n",
    "    box = (v[0], v[1], v[0], v[1])\n",
    "    return box\n",
    "\n",
    "def boxaroundpoint(pt, d):\n",
    "    r = d / 2\n",
    "    box = (pt[0] - r, pt[1] - r, pt[0] + r, pt[1] + r)    # left, bottom, right, top\n",
    "    return box\n",
    "\n",
    "def length(seg):\n",
    "    return norm(vector(seg))\n",
    "\n",
    "def vector(seg):\n",
    "    ret = np.array((seg[2] - seg[0], seg[3] - seg[1]))\n",
    "    return ret\n",
    "\n",
    "def dist(line, xy):\n",
    "    \"\"\" line: (x1, y1, x2, y2)\n",
    "        xy:   (x, y)\n",
    "        -> distance between xy and line, can be positive or negative\n",
    "        \n",
    "        >> dist((0, 0, 2, 0), (1, 1))\n",
    "        1.0\n",
    "        \n",
    "        for the same line, dist from it can be positive or negative\n",
    "        if xys are located in both side of the the line\n",
    "    \"\"\"\n",
    "    line, xy = np.asarray(line), np.asarray(xy)\n",
    "    p1, p2 = line[:2], line[2:]\n",
    "    return np.cross(p2 - p1, xy - p1) / norm(p2 - p1)\n",
    "\n",
    "\n",
    "#https://stackoverflow.com/questions/41855695/sorting-list-of-two-dimensional-coordinates-by-clockwise-angle-using-python\n",
    "from scipy.spatial import distance\n",
    "\n",
    "# does NOT work for me\n",
    "def sortpts_clockwise(A):\n",
    "    # Sort A based on Y(col-2) coordinates\n",
    "    sortedAc2 = A[np.argsort(A[:,1]),:]\n",
    "\n",
    "    # Get top two and bottom two points\n",
    "    top2 = sortedAc2[0:2,:]\n",
    "    bottom2 = sortedAc2[2:,:]\n",
    "\n",
    "    # Sort top2 points to have the first row as the top-left one\n",
    "    sortedtop2c1 = top2[np.argsort(top2[:,0]),:]\n",
    "    top_left = sortedtop2c1[0,:]\n",
    "\n",
    "    # Use top left point as pivot & calculate sq-euclidean dist against\n",
    "    # bottom2 points & thus get bottom-right, bottom-left sequentially\n",
    "    sqdists = distance.cdist(top_left[None], bottom2, 'sqeuclidean')\n",
    "    rest2 = bottom2[np.argsort(np.max(sqdists,0))[::-1],:]\n",
    "\n",
    "    # Concatenate all these points for the final output\n",
    "    return np.concatenate((sortedtop2c1,rest2),axis =0)\n",
    "\n",
    "#https://gist.github.com/arthur-e/5cf52962341310f438e96c1f3c3398b8\n",
    "from functools import reduce\n",
    "def convex_hull_graham(points):\n",
    "    '''\n",
    "    Returns points on convex hull in CCW order according to Graham's scan algorithm. \n",
    "    By Tom Switzer <thomas.switzer@gmail.com>.\n",
    "    '''\n",
    "    TURN_LEFT, TURN_RIGHT, TURN_NONE = (1, -1, 0)\n",
    "\n",
    "    def cmp(a, b):\n",
    "        #print('a=', a, 'b=', b, 'type(a)=', type(a), 'type(b)=', type(b))\n",
    "        a = float(a)\n",
    "        return (a > b) - (a < b)\n",
    "\n",
    "    def turn(p, q, r):\n",
    "        return cmp((q[0] - p[0])*(r[1] - p[1]) - (r[0] - p[0])*(q[1] - p[1]), 0)\n",
    "\n",
    "    def _keep_left(hull, r):\n",
    "        while len(hull) > 1 and turn(hull[-2], hull[-1], r) != TURN_LEFT:\n",
    "            hull.pop()\n",
    "        if not len(hull) or hull[-1] != r:\n",
    "            hull.append(r)\n",
    "        return hull\n",
    "\n",
    "    points = sorted(points)\n",
    "    l = reduce(_keep_left, points, [])\n",
    "    u = reduce(_keep_left, reversed(points), [])\n",
    "    return l.extend(u[i] for i in range(1, len(u) - 1)) or l\n",
    "\n",
    "if 0:\n",
    "    # lineidx is index into alllines, obj is 0/1.\n",
    "    epindex = index.Index()\n",
    "    for lineidx, line in enumerate(alllines):\n",
    "        epindex.insert(lineidx, pt2box(line[:2]), obj=0)\n",
    "        epindex.insert(lineidx, pt2box(line[2:]), obj=1)\n",
    "\n",
    "\n",
    "line0 = alllines[0]\n",
    "line00 = line0[:2]\n",
    "pt2box(line00)\n",
    "\n",
    "ids = [id for id in epindex.intersection(boxaroundpoint(line00, 15), objects=True)]\n",
    "print(ids[0], ids[0].id, ids[0].object)\n",
    "\n",
    "# print(epindex.intersection(boxaroundpoint(line00, 15), objects=True)) # is a generator\n",
    "print('length of alllines:', len(alllines))\n",
    "\n",
    "\n",
    "A = np.array([[ 281.,  147.],\n",
    "       [ 213.,  170.],\n",
    "       [ 239.,  242.],\n",
    "       [ 307.,  219.]], dtype=np.float32)\n",
    "print(A.shape)\n",
    "\n",
    "sortpts_clockwise(A)\n",
    "#Out[86]: \n",
    "#array([[ 213.,  170.],\n",
    "#       [ 281.,  147.],\n",
    "#       [ 307.,  219.],\n",
    "#       [ 239.,  242.]], dtype=float32)\n",
    "print([(pt[0], pt[1]) for pt in A])\n",
    "convex_hull_graham([tuple(pt) for pt in A])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False - True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of alllines: 622\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "MAX_BOX_SIZE = 20\n",
    "LINE_THICKNESS = 2\n",
    "\n",
    "def length(seg):\n",
    "    return norm(vector(seg))\n",
    "\n",
    "def vector(seg):\n",
    "    return np.array((seg[2] - seg[0], seg[3] - seg[1]))\n",
    "\n",
    "class Curve:\n",
    "    \n",
    "    def __init__(self, lineindex, all_lines):\n",
    "        \"\"\" lineindex: init line index\n",
    "        \"\"\"\n",
    "        self.lineindices = [lineindex]\n",
    "        self.all_lines = all_lines\n",
    "        self.hotends = [0, 1]\n",
    "        initline = all_lines[lineindex]\n",
    "        self.nodes = [initline[:2], initline[2:]]\n",
    "    \n",
    "    def grow(self, spatial_index, part_of_a_curve):\n",
    "        \"\"\" uses alllines, spatial_index\n",
    "        \"\"\"\n",
    "        all_lines = self.all_lines\n",
    "        \n",
    "        # new lines added from last call\n",
    "        indices = self.lineindices[- len(self.hotends) :]  #; print(\"indices=\", indices)\n",
    "        endlines = [all_lines[idx] for idx in indices]\n",
    "        \n",
    "        # we always check both ends for new lines\n",
    "        \n",
    "        endlines += endlines\n",
    "        self.hotends += [zeroorone ^ 1 for zeroorone in self.hotends]\n",
    "        hotends = [line[:2] if lineend == 0 else line[2:]\n",
    "                   for line, lineend in zip(endlines, self.hotends)] \n",
    "            \n",
    "        #print('indices:', indices, 'self.hotends:', self.hotends, 'hotends:', hotends)\n",
    "        self.hotends.clear()\n",
    "        \n",
    "        d = 10   # a good value may be related to LSD parameters\n",
    "        searchboxes = [boxaroundpoint(e, d) for e in hotends]\n",
    "        for endline, box in zip(endlines, searchboxes):\n",
    "            nearbyends = spatial_index.intersection(box, objects=True)\n",
    "            nearbyends = [e for e in nearbyends\n",
    "                          if not part_of_a_curve[e.id] \n",
    "                          and e.id not in self.lineindices]  # end is on a different line seg\n",
    "        \n",
    "            nearbyendsids = [e.id for e in nearbyends]\n",
    "            #print('nearbyendsids:', nearbyendsids)\n",
    "                \n",
    "            # only those coherent to current curve\n",
    "            newends = [e for e in nearbyends if self.is_coherent(endline, all_lines[e.id])]\n",
    "            \n",
    "            for e in newends:           \n",
    "                lineseg = all_lines[e.id]\n",
    "                if e.id in self.lineindices:\n",
    "                    warnings.warn(\"line {} is already in self.lineindices\".format(e.id))\n",
    "                    continue\n",
    "                self.lineindices.append(e.id)\n",
    "                self.hotends.append({0:1, 1:0}[e.object])\n",
    "                part_of_a_curve[e.id] = True\n",
    "                print(\" {}:{},\".format(e.id, e.object), end=' ')\n",
    "            #print('self.hotends:', self.hotends)\n",
    "\n",
    "        # TODO: remove tail recursion\n",
    "        if len(self.hotends) > 0:\n",
    "            #print()\n",
    "            #print('self.hotends=', self.hotends)\n",
    "            #print('indices=', self.lineindices[- len(self.hotends) :])\n",
    "            #print()\n",
    "            self.grow(spatial_index, part_of_a_curve)\n",
    "            \n",
    "    def convexhull(self):\n",
    "        \"\"\" -> convex hull of all the end points of the line segments. array of Nx2\n",
    "        \"\"\"\n",
    "        lines = [self.all_lines[idx] for idx in self.lineindices]\n",
    "        if 0:\n",
    "            xs = [x for line in lines for x in line[0::2]]\n",
    "            ys = [y for line in lines for y in line[1::2]]\n",
    "            points = np.array((xs, ys)).T   # Nx2\n",
    "        \n",
    "            xymin, xymax = points.min(0), points.max(0)\n",
    "            xextremes = [xymin[0], xymax[0]]\n",
    "            yextremes = [xymin[1], xymax[1]]\n",
    "            extpts = np.array([pt for pt in points if pt[0] in xextremes or pt[1] in yextremes])\n",
    "        \n",
    "        # triangle is not welcome, need a convex\n",
    "        allpoints = [pt for line in lines for pt in (tuple(line[2:]), tuple(line[:2])) ]\n",
    "        convexhull = convex_hull_graham(allpoints)\n",
    "        return np.array(convexhull + convexhull[:1])   # return a closed curve\n",
    "                         \n",
    "    def borders(self, lines):\n",
    "        extpts = self.extreme_points()\n",
    "        # extpts should resolve to 4 points of a rectangle area\n",
    "        ls0, ls1 = self.project(extpts)\n",
    "        #print(\"extreme pts:\", extpts)\n",
    "        return ls0, ls1                 \n",
    "        \n",
    "    def project(self, xy):\n",
    "        \"\"\" return N points that is the projection of xy on to .line0 and .line1\n",
    "            xy - an Nx2 array or (2,) array\n",
    "        \"\"\"\n",
    "        pj0 = self._project(xy, self.line0)  # several points on .line0\n",
    "        #print(\"pj to line0:\\n\", pj0)\n",
    "        pj0 = pj0[pj0[:,0].argsort()]  # sort according to x\n",
    "        #print(\"pj to line0 (sorted):\\n\", pj0)\n",
    "\n",
    "        pt0, pt1 = pj0[0], pj0[-1]\n",
    "        lineseg0 = (pt0[0], pt0[1], pt1[0], pt1[1])\n",
    "        \n",
    "        pj1 = self._project(xy, self.line1)\n",
    "        pj1 = pj1[pj1[:,0].argsort()]  # sort according to x\n",
    "\n",
    "        #print(\"pj to line1:\\n\", pj1)\n",
    "        pt0, pt1 = pj1[0], pj1[-1]\n",
    "        lineseg1 = (pt0[0], pt0[1], pt1[0], pt1[1])\n",
    "        return lineseg0, lineseg1\n",
    "    \n",
    "    @staticmethod\n",
    "    def _project(xy, line):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        pts = np.array(xy)\n",
    "        x1, y1, x2, y2 = line\n",
    "        p1, p2 = np.array((x1, y1)), np.array((x2, y2))\n",
    "        \n",
    "        b = p2 - p1\n",
    "        b_hat = b / norm(b)\n",
    "        \n",
    "        p = pts - p1      \n",
    "        #print(\"p=\", p)\n",
    "        if len(p.shape) == 1:\n",
    "            p = p[np.newaxis, ...]\n",
    "        \n",
    "        #print(\"p=\", p, \"p.shape =\", p.shape)\n",
    "        res = np.array([pt.dot(b_hat) * b_hat + p1 for pt in p])\n",
    "        #print(res.shape)\n",
    "        return res\n",
    "    \n",
    "    @staticmethod\n",
    "    def is_coherent(line1, line0):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        vline0, vline1 = vector(line0), vector(line1)\n",
    "        result = np.cross(vline0, vline1)  \n",
    "        #print(\"cross product=\", result)\n",
    "        abberation = abs(result) / norm(vline0)   # line1's abberation from line0\n",
    "        sine = abberation / norm(vline1)\n",
    "        if 0:\n",
    "            print(\"is_parallel abberation={} sine={}\".format(abberation, sine))\n",
    "        #return sine < 0.5  # .5 sine(pi/6) == 1/2\n",
    "        return sine < 0.1 or (sine < 0.5 and abberation < 15)  \n",
    "    \n",
    "    \n",
    "    \n",
    "alllines = get_lines(lines)\n",
    "line = alllines[0]\n",
    "print('len of alllines:', len(alllines))\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.Curve object at 0x63e0558d0>\n",
      "sine= 0.0042273067\n",
      "envelope= [[ 87.33249  125.5708  ]\n",
      " [108.181725 261.20633 ]\n",
      " [121.42333  649.4066  ]\n",
      " [103.91705  680.63586 ]\n",
      " [ 87.33249  125.5708  ]] norms_sorted_order= [3 1 0 2]\n",
      "[inf, inf, 5117.5254, 5117.5254, 3148.8232, 355.87646, 299.57166, 254.83362, 21.793602, 16.010742, 12.285156, 0.2855835]\n",
      "simplied:\n",
      " [[ 87.33249  125.5708  ]\n",
      " [108.181725 261.20633 ]\n",
      " [121.42333  649.4066  ]\n",
      " [103.91705  680.63586 ]\n",
      " [ 87.33249  125.5708  ]]\n",
      "[101.63742 368.4781 ] 1.550765080671333\n"
     ]
    }
   ],
   "source": [
    "import cmath\n",
    "from polysimplify import VWSimplifier\n",
    "\n",
    "class Wall:\n",
    "    \n",
    "    def __init__(self, curve):\n",
    "        self.curve = curve\n",
    "        self.handle = None  # (x,y) of the center point\n",
    "        self.angle = None   # angle of the wall\n",
    "        envelope = self.simplify()\n",
    "        if not self.recognize(envelope):\n",
    "            raise RuntimeError(\"Curve does not seem a wall, no two parallel borders found.\")\n",
    "\n",
    "    \n",
    "    @staticmethod\n",
    "    def is_parallel(v0, v1):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        sine = np.cross(v0/norm(v0), v1/norm(v1))\n",
    "        print('sine=', sine)\n",
    "        return sine < 0.05\n",
    "        \n",
    "    def simplify(self): # , threshold=500):\n",
    "        chull = self.curve.convexhull()\n",
    "        assert np.allclose(chull[0], chull[-1]), \"convext hull is not closed.\"\n",
    "        \n",
    "        self.border = border = VWSimplifier(chull)\n",
    "        \n",
    "        #pts = border.from_threshold(threshold)      \n",
    "        pts = border.from_number(4 + 1)   # close curve has 1st and last point identical, they are not removed\n",
    "        return pts\n",
    "    \n",
    "    def recognize(self, envelope):\n",
    "        vlines = envelope[:-1] - envelope[1:]\n",
    "        norms = norm(vlines, axis=1)\n",
    "        norms_sorted_order = norms.argsort()[::-1]\n",
    "        vlines_sorted = vlines[norms_sorted_order]\n",
    "        \n",
    "        if self.is_parallel(vlines_sorted[0], vlines_sorted[1]):\n",
    "            print(\"envelope=\", envelope, \"norms_sorted_order=\", norms_sorted_order)\n",
    "            idx0, idx1 = norms_sorted_order[0], norms_sorted_order[1]\n",
    "            self.handle = envelope.mean(axis=0)\n",
    "            v = vlines_sorted[:2].mean(axis=0)\n",
    "            cv = v[0] + v[1] * 1j\n",
    "            self.angle = cmath.phase(cv)\n",
    "            return True  # usual wall\n",
    "        else:\n",
    "            return False\n",
    "     \n",
    "    \n",
    "print(sketch.curves[0])\n",
    "if 'sketch' in dir():\n",
    "    w = Wall(sketch.curves[0])\n",
    "    res = w.simplify()\n",
    "    chull = w.curve.convexhull()   # return a closed convex hull\n",
    "    print(w.handle, w.angle)\n",
    "    plt.plot(w.handle[0], w.handle[1], 'ro')  # start\n",
    "    plt.plot(res[:, 0], res[:, 1], \"g+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  20.849235  135.63553 ]\n",
      " [  13.241605  388.20027 ]\n",
      " [ -17.50628    31.22926 ]\n",
      " [ -16.58456  -555.06506 ]]\n",
      "[137.22859614 388.42604152  35.80134801 555.31276634]\n",
      "[3 1 0 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([101.637417, 368.478078])"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array( [[ 87.33249,  125.5708  ],\n",
    " [108.181725, 261.20633 ],\n",
    " [121.42333,  649.4066  ],\n",
    " [103.91705,  680.63586 ],\n",
    " [ 87.33249,  125.5708  ]])\n",
    "\n",
    "start = a[:-1]; end = a[1:]\n",
    "diff = end - start\n",
    "print(diff)\n",
    "\n",
    "norms = norm(diff, axis=1)\n",
    "print(norms)\n",
    "print(norms.argsort()[::-1])\n",
    "diff[norms.argsort()[::-1]]\n",
    "\n",
    "a.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(-0.00422731)"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.invert_yaxis()\n",
    "ax.plot(diff[:, 0], diff[:, 1], '+')\n",
    "ax.plot(a[:, 0], a[:, 1], '-')\n",
    "ax.axis('image')\n",
    "\n",
    "np.cross(diff[1]/norm(diff[1]), diff[3]/norm(diff[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 29:1,  21:0,  30:1,  17:0,  25:1,  28:1,  19:1,  18:0,  35:0,  14:1,  8:0, "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 14 is already in self.lineindices\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[20, 29, 21, 30, 17, 25, 28, 19, 18, 35, 14, 8]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg = Curve(20, alllines)\n",
    "lg.grow(epindex, np.zeros(len(alllines), dtype=bool))\n",
    "lg.lineindices\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sketch:\n",
    "    \n",
    "    def __init__(self, line_segments):\n",
    "        self.line_segments = line_segments\n",
    "        \n",
    "        # lineidx is index into alllines, obj is 0/1.\n",
    "        self.spatial_index = epindex = index.Index()\n",
    "        for lineidx, line in enumerate(line_segments):\n",
    "            epindex.insert(lineidx, pt2box(line[:2]), obj=0)\n",
    "            epindex.insert(lineidx, pt2box(line[2:]), obj=1)\n",
    "        \n",
    "        self.part_of_a_curve = np.zeros(len(self.line_segments), dtype=bool)\n",
    "        self.curves = []\n",
    "        \n",
    "    \n",
    "    def refresh_curves(self):\n",
    "        self.part_of_a_curve = np.zeros(len(self.line_segments), dtype=bool)\n",
    "        self.curves.clear()\n",
    "        \n",
    "        for idx in range(len(self.line_segments) - 1, -1, -1):\n",
    "    \n",
    "            if self.part_of_a_curve[idx]:\n",
    "                continue\n",
    "        \n",
    "            curve = Curve(idx, self.line_segments)\n",
    "            curve.grow(self.spatial_index, self.part_of_a_curve)\n",
    "            self.curves.append(curve)\n",
    "            print(\"curve created {}\".format(curve.lineindices))\n",
    "    \n",
    "            #for i in curve.lineindices:\n",
    "            #    part_of_a_curve[i] = True\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patches as patches\n",
    "\n",
    "def plot_lines_list(ax, lines, *args, **kwds):\n",
    "    for idx, line in enumerate(lines):\n",
    "        plot_line(ax, line, *args, **kwds)\n",
    "        start = line[:2]\n",
    "        ax.text(start[0], start[1], '%d' % idx)\n",
    "    ax.axis('image')\n",
    "    \n",
    "def plot_line_group(ax, lines, *args, **kwds):\n",
    "    plot_lines(ax, lines, '-k', alpha=0.7)\n",
    "    plot_line(ax, lines[0], '-r')\n",
    "    ax.axis('image')\n",
    "    \n",
    "def plot_line_sublist(ax, linelist, indices, *args, **kwds):\n",
    "    with_text = kwds.pop('with_text', True)\n",
    "    for idx in indices:\n",
    "        line = linelist[idx]\n",
    "        plot_line(ax, line, *args, **kwds)\n",
    "        start = line[:2]\n",
    "        if with_text:\n",
    "            ax.text(start[0], start[1], '%d' % idx)\n",
    "    plot_line(ax, linelist[indices[0]], '-r')\n",
    "    ax.axis('image')\n",
    "\n",
    "def plot_box(ax, box, *args, **kwds):\n",
    "    # a box is (left, bottom, right, top)\n",
    "    rect = patches.Rectangle(\n",
    "        (box[0], box[1]),    # bottom, left of box, exchanged as in image\n",
    "        box[3] - box[1],     # width\n",
    "        box[2] - box[0],     # height\n",
    "        fill=False     # remove background\n",
    "    )\n",
    "    ax.add_patch(rect)\n",
    "\n",
    "if 0:\n",
    "    linesthis = [alllines[i] for i in lg.lineindices]\n",
    "    print(len(linesthis), \"lines\")\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    plot_lines(ax, alllines, '-k', alpha=0.1)\n",
    "    plot_lines_list(ax, linesthis, '-k')\n",
    "    ax.invert_yaxis()\n",
    "    ax.axis('image')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 88.22394  706.49664  108.249695 704.2236  ]\n",
      "[204.36963 706.21936 188.1268  706.2652 ]\n"
     ]
    }
   ],
   "source": [
    "print(alllines[612])\n",
    "print(alllines[614])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curve created [621]\n",
      "curve created [620]\n",
      "curve created [619]\n",
      " 616:1,  617:0,  614:1,  612:0,  615:1,  610:0,  613:1,  611:0,  608:1,  602:0,  609:1,  607:0,  606:1,  605:0,  603:1,  598:0,  594:1,  600:1,  599:0,  593:1,  604:1, curve created [618, 616, 617, 614, 612, 615, 610, 613, 611, 608, 602, 609, 607, 606, 605, 603, 598, 594, 600, 599, 593, 604]\n",
      "curve created [601]\n",
      " 596:1, curve created [597, 596]\n",
      " 592:1, curve created [595, 592]\n",
      " 572:1,  571:0, curve created [591, 572, 571]\n",
      " 577:0, curve created [590, 577]\n",
      " 586:1,  539:0,  206:1,  207:1,  212:1,  545:0,  290:1,  237:1,  540:1,  541:0,  452:0,  208:0,  311:1,  249:1,  394:0,  247:0,  492:1,  275:1,  262:0,  443:1,  280:0,  422:1,  303:0,  402:1,  337:0,  380:1,  350:0, curve created [589, 586, 539, 206, 207, 212, 545, 290, 237, 540, 541, 452, 208, 311, 249, 394, 247, 492, 275, 262, 443, 280, 422, 303, 402, 337, 380, 350]\n",
      " 574:0,  581:1,  587:1,  578:1,  585:1,  580:1, curve created [588, 574, 581, 587, 578, 585, 580]\n",
      " 570:1, curve created [584, 570]\n",
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      " 565:0, curve created [576, 565]\n",
      "curve created [573]\n",
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      " 552:0,  537:0,  508:0,  499:0,  462:0,  455:0,  369:0,  314:0,  310:0,  298:0,  287:0,  302:0,  331:0,  227:1,  304:1,  417:0,  221:1,  226:0,  232:1,  313:1,  282:1,  299:0,  555:0,  220:1,  215:1,  269:1,  363:1,  243:0,  281:1,  209:1,  367:1,  362:1,  210:0,  342:1,  256:1,  370:0,  211:0,  252:1,  385:1,  390:0,  229:0, curve created [560, 552, 537, 508, 499, 462, 455, 369, 314, 310, 298, 287, 302, 331, 227, 304, 417, 221, 226, 232, 313, 282, 299, 555, 220, 215, 269, 363, 243, 281, 209, 367, 362, 210, 342, 256, 370, 211, 252, 385, 390, 229]\n",
      " 548:1,  546:0,  531:0,  542:1,  532:0, curve created [559, 548, 546, 531, 542, 532]\n",
      " 441:0,  556:1,  534:1,  438:0,  442:0,  445:0,  453:0,  448:0,  426:0,  411:1,  433:0,  550:0,  503:0,  400:0,  348:1,  450:1,  347:1,  418:0,  554:0,  352:0,  309:1,  332:0,  544:1,  323:0,  345:1,  325:1,  549:0,  285:1,  292:0,  318:0,  396:1,  288:1,  266:1,  276:0,  296:0,  265:1,  236:1,  242:0,  272:0,  257:1,  225:1,  228:0,  260:0,  205:1,  219:0,  218:1,  251:0,  204:1,  128:1,  217:0,  239:0,  82:1,  70:1,  107:0,  95:1, curve created [558, 441, 556, 534, 438, 442, 445, 453, 448, 426, 411, 433, 550, 503, 400, 348, 450, 347, 418, 554, 352, 309, 332, 544, 323, 345, 325, 549, 285, 292, 318, 396, 288, 266, 276, 296, 265, 236, 242, 272, 257, 225, 228, 260, 205, 219, 218, 251, 204, 128, 217, 239, 82, 70, 107, 95]\n",
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      "curve created [551]\n",
      "curve created [547]\n",
      " 526:1,  533:0,  528:1,  535:0,  511:1,  510:1,  515:1,  527:1, curve created [536, 526, 533, 528, 535, 511, 510, 515, 527]\n",
      " 529:0,  522:0, curve created [530, 529, 522]\n",
      " 523:1,  514:0,  512:0,  518:0, "
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     "name": "stderr",
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     "text": [
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 540 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 67 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 209 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 70 is already in self.lineindices\n"
     ]
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     "name": "stdout",
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      " 513:1,  520:0, curve created [521, 513, 520]\n",
      "curve created [519]\n",
      "curve created [516]\n",
      " 484:1,  500:0,  473:1,  468:1,  456:1, curve created [509, 484, 500, 473, 468, 456]\n",
      " 489:1,  506:0,  464:1,  478:0,  498:0,  458:1, curve created [507, 489, 506, 464, 478, 498, 458]\n",
      " 493:0,  467:0,  480:1, curve created [505, 493, 467, 480]\n",
      " 495:0,  496:1,  502:0, curve created [504, 495, 496, 502]\n",
      " 472:0,  485:1,  465:0, curve created [501, 472, 485, 465]\n",
      "curve created [497]\n",
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      " 483:0, curve created [491, 483]\n",
      "curve created [490]\n",
      " 476:0,  471:1,  479:0,  460:1,  470:0, curve created [488, 476, 471, 479, 460, 470]\n",
      " 408:0,  305:0,  297:0,  291:0,  286:0, curve created [486, 408, 305, 297, 291, 286]\n",
      " 475:1,  466:0,  481:0, curve created [482, 475, 466, 481]\n",
      " 474:1, curve created [477, 474]\n",
      " 461:1, curve created [469, 461]\n",
      " 457:1, curve created [459, 457]\n",
      "curve created [454]\n",
      " 440:0, curve created [451, 440]\n",
      " 446:1,  447:1, curve created [449, 446, 447]\n",
      "curve created [444]\n",
      "curve created [439]\n",
      " 428:1,  430:0,  427:1,  424:1,  421:0,  416:0, curve created [437, 428, 430, 427, 424, 421, 416]\n",
      "curve created [436]\n",
      " 432:1,  431:0, curve created [435, 432, 431]\n",
      " 423:1, curve created [429, 423]\n",
      " 420:0, curve created [425, 420]\n",
      "curve created [415]\n",
      " 403:1,  405:0, curve created [414, 403, 405]\n",
      "curve created [413]\n",
      "curve created [407]\n",
      "curve created [404]\n",
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      " 388:1, curve created [399, 388]\n",
      "curve created [398]\n",
      "curve created [393]\n",
      "curve created [392]\n",
      " 377:0,  383:1,  387:1,  378:0, curve created [389, 377, 383, 387, 378]\n",
      "curve created [384]\n",
      "curve created [382]\n",
      " 373:0, curve created [381, 373]\n",
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      " 371:1, curve created [374, 371]\n",
      "curve created [368]\n",
      " 361:0, curve created [366, 361]\n",
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      " 333:1, curve created [349, 333]\n",
      "curve created [346]\n",
      " 336:1, curve created [340, 336]\n",
      "curve created [330]\n",
      "curve created [327]\n",
      "curve created [326]\n",
      "curve created [324]\n",
      "curve created [322]\n",
      "curve created [321]\n",
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      "curve created [301]\n",
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      " 253:1,  254:1,  248:0,  250:0,  245:1, curve created [255, 253, 254, 248, 250, 245]\n",
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      " 223:0,  222:1,  213:0,  224:0, curve created [234, 223, 222, 213, 224]\n",
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      "curve created [230]\n",
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      " 190:1,  193:0,  184:1,  192:0, curve created [203, 190, 193, 184, 192]\n",
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      " 189:0,  180:0, curve created [200, 189, 180]\n",
      " 188:0,  183:0,  187:1, curve created [196, 188, 183, 187]\n",
      " 182:1, curve created [194, 182]\n",
      " 181:1, curve created [191, 181]\n",
      "curve created [179]\n",
      " 170:1, curve created [178, 170]\n",
      " 166:0,  174:1,  172:0,  167:1,  173:0,  165:1, curve created [177, 166, 174, 172, 167, 173, 165]\n",
      " 168:1, curve created [176, 168]\n",
      "curve created [175]\n",
      "curve created [171]\n",
      " 145:0, curve created [169, 145]\n",
      " 109:1,  89:0,  108:1,  81:0, curve created [164, 109, 89, 108, 81]\n",
      "curve created [163]\n",
      " 100:0,  72:0,  103:0,  98:1,  75:1,  116:0, curve created [161, 100, 72, 103, 98, 75, 116]\n",
      " 151:1,  144:1,  140:1, curve created [160, 151, 144, 140]\n",
      " 157:1,  139:0,  143:1, curve created [158, 157, 139, 143]\n",
      "curve created [156]\n",
      "curve created [155]\n",
      " 113:1,  88:1, curve created [153, 113, 88]\n",
      "curve created [152]\n",
      " 149:1,  148:1,  147:1,  136:1,  146:1, curve created [150, 149, 148, 147, 136, 146]\n",
      " 141:1, curve created [142, 141]\n",
      "curve created [138]\n"
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     "output_type": "stream",
     "text": [
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 421 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 261 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 98 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 43 is already in self.lineindices\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 134:0,  127:0,  122:0, curve created [137, 134, 127, 122]\n",
      " 131:0,  118:1,  125:0,  114:1,  111:0,  101:1,  99:0,  84:0, curve created [135, 131, 118, 125, 114, 111, 101, 99, 84]\n",
      " 119:1,  110:1,  92:1,  102:0,  112:1,  117:1, curve created [133, 119, 110, 92, 102, 112, 117]\n",
      "curve created [132]\n",
      "curve created [129]\n",
      "curve created [124]\n",
      "curve created [123]\n",
      " 105:1,  77:1,  76:1, curve created [115, 105, 77, 76]\n",
      "curve created [106]\n",
      " 73:0, curve created [104, 73]\n",
      " 85:1,  96:0, curve created [97, 85, 96]\n",
      "curve created [93]\n",
      " 86:1,  74:0,  90:1,  60:0,  66:1,  55:0,  62:1, curve created [91, 86, 74, 90, 60, 66, 55, 62]\n",
      " 80:0, curve created [83, 80]\n",
      "curve created [71]\n",
      " 58:1, curve created [69, 58]\n",
      " 57:1, curve created [65, 57]\n",
      "curve created [64]\n",
      " 54:0,  56:1,  46:0,  43:1,  38:0,  45:0,  41:1, curve created [61, 54, 56, 46, 43, 38, 45, 41]\n",
      " 50:0, curve created [59, 50]\n",
      " 51:1,  40:0,  52:1, curve created [53, 51, 40, 52]\n",
      " 44:1,  48:0, curve created [49, 44, 48]\n",
      "curve created [47]\n",
      "curve created [42]\n",
      "curve created [39]\n",
      "curve created [37]\n",
      " 13:1, curve created [36, 13]\n",
      " 28:1,  30:1,  17:1,  29:0,  21:1,  20:1,  25:1,  19:1,  18:0,  14:1,  8:0, curve created [35, 28, 30, 17, 29, 21, 20, 25, 19, 18, 14, 8]\n",
      " 16:0, curve created [34, 16]\n",
      " 32:1, curve created [33, 32]\n",
      " 26:1,  6:1, curve created [31, 26, 6]\n",
      " 11:0,  10:1,  24:0, curve created [27, 11, 10, 24]\n",
      " 15:1,  22:0,  9:1,  12:1, curve created [23, 15, 22, 9, 12]\n",
      "curve created [7]\n",
      "curve created [5]\n",
      "curve created [4]\n",
      "curve created [3]\n",
      "curve created [2]\n",
      "curve created [1]\n",
      "curve created [0]\n",
      "165 curves formed from 622 line segments\n",
      "0 : [558, 441, 556, 534, 438, 442, 445, 453, 448, 426, 411, 433, 550, 503, 400, 348, 450, 347, 418, 554, 352, 309, 332, 544, 323, 345, 325, 549, 285, 292, 318, 396, 288, 266, 276, 296, 265, 236, 242, 272, 257, 225, 228, 260, 205, 219, 218, 251, 204, 128, 217, 239, 82, 70, 107, 95]\n",
      "1 : [560, 552, 537, 508, 499, 462, 455, 369, 314, 310, 298, 287, 302, 331, 227, 304, 417, 221, 226, 232, 313, 282, 299, 555, 220, 215, 269, 363, 243, 281, 209, 367, 362, 210, 342, 256, 370, 211, 252, 385, 390, 229]\n",
      "2 : [561, 557, 543, 538, 372, 328, 329, 434, 283, 320, 419, 525, 268, 274, 307, 409, 198, 240, 270, 300, 365, 197, 259, 289, 354, 306, 235, 159, 233, 185, 120, 244, 162, 63, 126, 68, 87, 67, 94, 154]\n",
      "3 : [589, 586, 539, 206, 207, 212, 545, 290, 237, 540, 541, 452, 208, 311, 249, 394, 247, 492, 275, 262, 443, 280, 422, 303, 402, 337, 380, 350]\n",
      "4 : [618, 616, 617, 614, 612, 615, 610, 613, 611, 608, 602, 609, 607, 606, 605, 603, 598, 594, 600, 599, 593, 604]\n",
      "5 : [553, 463, 517, 410, 487, 360, 412, 308, 353, 406, 246, 214]\n",
      "6 : [35, 28, 30, 17, 29, 21, 20, 25, 19, 18, 14, 8]\n",
      "7 : [583, 579, 569, 582, 567, 562, 568, 566, 563, 564, 575]\n",
      "8 : [536, 526, 533, 528, 535, 511, 510, 515, 527]\n",
      "9 : [135, 131, 118, 125, 114, 111, 101, 99, 84]\n",
      "10 : [91, 86, 74, 90, 60, 66, 55, 62]\n",
      "11 : [61, 54, 56, 46, 43, 38, 45, 41]\n",
      "12 : [588, 574, 581, 587, 578, 585, 580]\n",
      "13 : [507, 489, 506, 464, 478, 498, 458]\n",
      "14 : [437, 428, 430, 427, 424, 421, 416]\n",
      "15 : [401, 397, 391, 395, 386, 379, 376]\n",
      "16 : [351, 343, 344, 339, 334, 338, 335]\n",
      "17 : [294, 238, 199, 130, 121, 78, 79]\n",
      "18 : [177, 166, 174, 172, 167, 173, 165]\n",
      "19 : [161, 100, 72, 103, 98, 75, 116]\n",
      "20 : [133, 119, 110, 92, 102, 112, 117]\n",
      "21 : [559, 548, 546, 531, 542, 532]\n",
      "22 : [509, 484, 500, 473, 468, 456]\n",
      "23 : [488, 476, 471, 479, 460, 470]\n",
      "24 : [486, 408, 305, 297, 291, 286]\n",
      "25 : [255, 253, 254, 248, 250, 245]\n",
      "26 : [150, 149, 148, 147, 136, 146]\n",
      "27 : [524, 523, 514, 512, 518]\n",
      "28 : [389, 377, 383, 387, 378]\n",
      "29 : [271, 264, 261, 263, 267]\n",
      "30 : [234, 223, 222, 213, 224]\n",
      "31 : [203, 190, 193, 184, 192]\n",
      "32 : [164, 109, 89, 108, 81]\n",
      "33 : [23, 15, 22, 9, 12]\n",
      "34 : [505, 493, 467, 480]\n",
      "35 : [504, 495, 496, 502]\n",
      "36 : [501, 472, 485, 465]\n",
      "37 : [482, 475, 466, 481]\n",
      "38 : [319, 316, 315, 312]\n",
      "39 : [284, 278, 277, 273]\n",
      "40 : [202, 195, 201, 186]\n",
      "41 : [196, 188, 183, 187]\n",
      "42 : [160, 151, 144, 140]\n",
      "43 : [158, 157, 139, 143]\n",
      "44 : [137, 134, 127, 122]\n",
      "45 : [115, 105, 77, 76]\n",
      "46 : [53, 51, 40, 52]\n",
      "47 : [27, 11, 10, 24]\n",
      "48 : [591, 572, 571]\n",
      "49 : [530, 529, 522]\n",
      "50 : [521, 513, 520]\n",
      "51 : [449, 446, 447]\n",
      "52 : [435, 432, 431]\n",
      "53 : [414, 403, 405]\n",
      "54 : [364, 356, 359]\n",
      "55 : [200, 189, 180]\n",
      "56 : [153, 113, 88]\n",
      "57 : [97, 85, 96]\n",
      "58 : [49, 44, 48]\n",
      "59 : [31, 26, 6]\n",
      "60 : [597, 596]\n",
      "61 : [595, 592]\n",
      "62 : [590, 577]\n",
      "63 : [584, 570]\n",
      "64 : [576, 565]\n",
      "65 : [491, 483]\n",
      "66 : [477, 474]\n",
      "67 : [469, 461]\n",
      "68 : [459, 457]\n",
      "69 : [451, 440]\n",
      "70 : [429, 423]\n",
      "71 : [425, 420]\n",
      "72 : [399, 388]\n",
      "73 : [381, 373]\n",
      "74 : [374, 371]\n",
      "75 : [366, 361]\n",
      "76 : [358, 341]\n",
      "77 : [349, 333]\n",
      "78 : [340, 336]\n",
      "79 : [295, 293]\n",
      "80 : [194, 182]\n",
      "81 : [191, 181]\n",
      "82 : [178, 170]\n",
      "83 : [176, 168]\n",
      "84 : [169, 145]\n",
      "85 : [142, 141]\n",
      "86 : [104, 73]\n",
      "87 : [83, 80]\n",
      "88 : [69, 58]\n",
      "89 : [65, 57]\n",
      "90 : [59, 50]\n",
      "91 : [36, 13]\n",
      "92 : [34, 16]\n",
      "93 : [33, 32]\n",
      "94 : [621]\n",
      "95 : [620]\n",
      "96 : [619]\n",
      "97 : [601]\n",
      "98 : [573]\n",
      "99 : [551]\n",
      "100 : [547]\n",
      "101 : [519]\n",
      "102 : [516]\n",
      "103 : [497]\n",
      "104 : [494]\n",
      "105 : [490]\n",
      "106 : [454]\n",
      "107 : [444]\n",
      "108 : [439]\n",
      "109 : [436]\n",
      "110 : [415]\n",
      "111 : [413]\n",
      "112 : [407]\n",
      "113 : [404]\n",
      "114 : [398]\n",
      "115 : [393]\n",
      "116 : [392]\n",
      "117 : [384]\n",
      "118 : [382]\n",
      "119 : [375]\n",
      "120 : [368]\n",
      "121 : [357]\n",
      "122 : [355]\n",
      "123 : [346]\n",
      "124 : [330]\n",
      "125 : [327]\n",
      "126 : [326]\n",
      "127 : [324]\n",
      "128 : [322]\n",
      "129 : [321]\n",
      "130 : [317]\n",
      "131 : [301]\n",
      "132 : [279]\n",
      "133 : [258]\n",
      "134 : [241]\n",
      "135 : [231]\n",
      "136 : [230]\n",
      "137 : [216]\n",
      "138 : [179]\n",
      "139 : [175]\n",
      "140 : [171]\n",
      "141 : [163]\n",
      "142 : [156]\n",
      "143 : [155]\n",
      "144 : [152]\n",
      "145 : [138]\n",
      "146 : [132]\n",
      "147 : [129]\n",
      "148 : [124]\n",
      "149 : [123]\n",
      "150 : [106]\n",
      "151 : [93]\n",
      "152 : [71]\n",
      "153 : [64]\n",
      "154 : [47]\n",
      "155 : [42]\n",
      "156 : [39]\n",
      "157 : [37]\n",
      "158 : [7]\n",
      "159 : [5]\n",
      "160 : [4]\n",
      "161 : [3]\n",
      "162 : [2]\n",
      "163 : [1]\n",
      "164 : [0]\n",
      "total 622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 20 is already in self.lineindices\n",
      "/Users/ywei/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:59: UserWarning: line 14 is already in self.lineindices\n"
     ]
    }
   ],
   "source": [
    "sketch = Sketch(alllines)\n",
    "sketch.refresh_curves()\n",
    "sketch.curves.sort(reverse=True, key=lambda curve: len(curve.lineindices))\n",
    "\n",
    "print(\"{} curves formed from {} line segments\".format(len(sketch.curves), len(alllines)))\n",
    "for idx, curve in enumerate(sketch.curves):\n",
    "    print(idx, \":\", curve.lineindices)\n",
    "\n",
    "nlines = [len(c.lineindices) for c in sketch.curves]\n",
    "print(\"total\", sum(nlines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx:0 lineindices:[558, 441, 556, 534, 438, 442, 445, 453, 448, 426, 411, 433, 550, 503, 400, 348, 450, 347, 418, 554, 352, 309, 332, 544, 323, 345, 325, 549, 285, 292, 318, 396, 288, 266, 276, 296, 265, 236, 242, 272, 257, 225, 228, 260, 205, 219, 218, 251, 204, 128, 217, 239, 82, 70, 107, 95]\n",
      "curve.convex_hull(): [[ 87.33249  125.5708  ]\n",
      " [ 91.66435  129.30656 ]\n",
      " [108.181725 261.20633 ]\n",
      " [114.08416  431.82806 ]\n",
      " [121.42333  649.4066  ]\n",
      " [120.29339  685.6468  ]\n",
      " [115.45391  686.8276  ]\n",
      " [108.48357  683.13617 ]\n",
      " [103.91705  680.63586 ]\n",
      " [ 97.07021  494.39716 ]\n",
      " [ 87.73168  143.21376 ]\n",
      " [ 87.33249  125.5708  ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0x63d9ec0b8>"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.invert_yaxis()\n",
    "plot_lines(ax, alllines, '-k', alpha=0.1)\n",
    "\n",
    "curves = sketch.curves\n",
    "idx = 0\n",
    "\n",
    "print('idx:{} lineindices:{}'.format(idx, sketch.curves[idx].lineindices))\n",
    "plot_line_sublist(ax, alllines, sketch.curves[idx].lineindices, '-k', alpha=0.5)\n",
    "\n",
    "print('curve.convex_hull():', curves[idx].convexhull())\n",
    "ax.add_patch(patches.Polygon(curves[idx].convexhull(), closed=True, fill=False, hatch='/'))\n",
    "#for curve in curves:\n",
    "#    if len(curve.lineindices) > 1:\n",
    "#        plot_line_group(ax, [alllines[i] for i in curve.lineindices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 2)\n",
      "[[476.9774  124.35653]\n",
      " [490.16425 123.11099]\n",
      " [492.7228  164.34644]\n",
      " [496.6745  282.05786]\n",
      " [498.48862 369.348  ]\n",
      " [500.9397  676.11835]\n",
      " [490.35873 679.37805]\n",
      " [486.6533  679.40985]\n",
      " [484.7266  598.11664]\n",
      " [478.4184  219.31613]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x63dd06978>]"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epts = sketch.curves[2].convexhull()\n",
    "print(epts.shape)\n",
    "print(epts)\n",
    "plt.plot(epts[:, 0], epts[:, 1], \"r+\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "206 0\n",
      "207 1\n",
      "212 1\n"
     ]
    }
   ],
   "source": [
    "# debugging endpoints\n",
    "box = boxaroundpoint(alllines[206][:2], 10)\n",
    "#print(box)\n",
    "plot_box(ax, box)\n",
    "\n",
    "nearbyends = sketch.spatial_index.intersection(box, objects=True)\n",
    "for e in nearbyends:\n",
    "    print(e.id, e.object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[476.9774  124.35653]\n",
      " [490.16425 123.11099]\n",
      " [492.7228  164.34644]\n",
      " [496.6745  282.05786]\n",
      " [498.48862 369.348  ]\n",
      " [500.9397  676.11835]\n",
      " [490.35873 679.37805]\n",
      " [486.6533  679.40985]\n",
      " [484.7266  598.11664]\n",
      " [478.4184  219.31613]]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-19.785618233680726, 559.1903819322586, 838.7176486492157, -26.71722807884217)"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort walls according to confidence, as the total number of the line segments\n",
    "sketch.curves.sort(reverse=True, key=lambda curve: len(curve.lineindices))\n",
    "print(sketch.curves[2].convexhull())\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.invert_yaxis()\n",
    "\n",
    "plot_lines(ax, alllines, '-k', alpha=0.1)\n",
    "\n",
    "for curve in sketch.curves:\n",
    "    if len(curve.lineindices) > 1:\n",
    "        # plot all segments\n",
    "        #plot_line_sublist(ax, alllines, curve.lineindices, '-k', alpha=0.5, with_text=False)\n",
    "        \n",
    "        poly = patches.Polygon(curve.convexhull(), closed=True, fill=False, hatch='/')\n",
    "        ax.add_patch(poly)\n",
    "\n",
    "ax.axis('image')\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 88.22394  706.49664 ]\n",
      " [108.249695 704.2236  ]\n",
      " [311.86914  697.8543  ]\n",
      " [390.62326  695.5099  ]\n",
      " [443.17096  694.0752  ]\n",
      " [510.60577  698.9311  ]\n",
      " [169.52254  709.3409  ]\n",
      " [ 91.83514  709.89685 ]]\n"
     ]
    }
   ],
   "source": [
    "print(sketch.curves[4].extreme_points())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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     "output_type": "display_data"
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}