Grover-Search-Doptimal-ClosedForm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grover's search for D-optimal sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib notebook\n",
    "%config InlineBackend.figure_formats = {'png', 'retina'}\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 5.0)\n",
    "\n",
    "from quantum import *\n",
    "from math import log, ceil, floor\n",
    "from lib import paf, psd\n",
    "\n",
    "\n",
    "# search for D-optimal sequences length=3\n",
    "seq_len = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def equal(a, b):\n",
    "    EPSILON = 0.00001\n",
    "    return abs(a - b) < EPSILON\n",
    "\n",
    "\n",
    "def is_d_optimal(A, B):\n",
    "    # check PAF invariant\n",
    "    for s in range(1, (seq_len//2)+1):\n",
    "        if paf(A, s) + paf(B, s) != 2: return False\n",
    "    return True\n",
    "\n",
    "\n",
    "def bits_to_seq(bitstring):\n",
    "    return [-1 if i == '0' else 1 for i in bitstring]\n",
    "\n",
    "\n",
    "def irange(start, end):\n",
    "    return range(start, end+1)\n",
    "\n",
    "\n",
    "def int_to_seq_pair(bitstring):\n",
    "    bitmask = (2 ** seq_len) - 1 # 111...1 (length = N)\n",
    "    # get first half of bitstring\n",
    "    first_half = (bitstring >> seq_len) & bitmask\n",
    "    # get second half\n",
    "    second_half = bitstring & bitmask\n",
    "    seq_A = bits_to_seq(\"{:0{width}b}\".format(first_half, width=seq_len))\n",
    "    seq_B = bits_to_seq(\"{:0{width}b}\".format(second_half, width=seq_len))\n",
    "    return (seq_A, seq_B)\n",
    "\n",
    "\n",
    "def bitwise_and(integer):\n",
    "    \"\"\"\n",
    "    Performs AND on each individual bit of the binary representation of `integer`\n",
    "    \"\"\"\n",
    "    if integer == 0: return 0\n",
    "    result = 1\n",
    "    for i in range(int(log(integer, 2))):\n",
    "        bitmask = 0b1 << i\n",
    "        bit = (integer & bitmask) >> i\n",
    "        result = result & bit\n",
    "    return result\n",
    "\n",
    "\n",
    "def is_bitstring_doptimal(integer):\n",
    "    \"\"\"\n",
    "    Returns whether the given input is a binary encoding of two sequences that\n",
    "    are mutually D-optimal.\n",
    "    \n",
    "    :param: bitstring (int) length = seq_len\n",
    "    :returns: (bool)\n",
    "    \"\"\"\n",
    "    A, B = int_to_seq_pair(integer)\n",
    "    m = (seq_len - 1) // 2\n",
    "    pafs = (paf(A, s) + paf(B, s) for s in irange(1, m))\n",
    "    # PAF(A,s) + PAF(B,s) = 4k + 2   (for int k, for s = 1..m)\n",
    "    # PAF(A,s) + PAF(B,s) = 2        (for A,B D-optimal)\n",
    "    # subtract 2 (so that =0 for D-opt) and square to make non-negative\n",
    "    S = sum((p - 2)**2 for p in pafs)\n",
    "    # now we have a positive integer for non-D-optimal pairs\n",
    "    # NOT then bitwise AND yields 1 if we had 0 (D-opt), otherwise yields 0\n",
    "    x = np.uint32(S**2)\n",
    "    return bitwise_and(~x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found 100 out of 1024 d-optimal sequence pairs\n",
      "33, 34, 36, 40, 47, 48, 55, 59, 61, 62, 65, 66, 68, 72, 79, 80, 87, 91, 93, 94, 129, 130, 132, 136, 143, 144, 151, 155, 157, 158, 257, 258, 260, 264, 271, 272, 279, 283, 285, 286, 481, 482, 484, 488, 495, 496, 503, 507, 509, 510, 513, 514, 516, 520, 527, 528, 535, 539, 541, 542, 737, 738, 740, 744, 751, 752, 759, 763, 765, 766, 865, 866, 868, 872, 879, 880, 887, 891, 893, 894, 929, 930, 932, 936, 943, 944, 951, 955, 957, 958, 961, 962, 964, 968, 975, 976, 983, 987, 989, 990\n"
     ]
    }
   ],
   "source": [
    "all_bitstrings = range(2**(2*seq_len))\n",
    "dopt_bitstrings = [bits for bits in all_bitstrings if is_bitstring_doptimal(bits)]\n",
    "print('found {} out of {} d-optimal sequence pairs'.format(len(dopt_bitstrings), len(all_bitstrings)))\n",
    "print(', '.join(str(b) for b in dopt_bitstrings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sequence length: 5\n",
      "qubits: 10\n",
      "search space: 1024\n",
      "recall that the d-optimal bitstrings are\n",
      "33, 34, 36, 40, 47, 48, 55, 59, 61, 62, 65, 66, 68, 72, 79, 80, 87, 91, 93, 94, 129, 130, 132, 136, 143, 144, 151, 155, 157, 158, 257, 258, 260, 264, 271, 272, 279, 283, 285, 286, 481, 482, 484, 488, 495, 496, 503, 507, 509, 510, 513, 514, 516, 520, 527, 528, 535, 539, 541, 542, 737, 738, 740, 744, 751, 752, 759, 763, 765, 766, 865, 866, 868, 872, 879, 880, 887, 891, 893, 894, 929, 930, 932, 936, 943, 944, 951, 955, 957, 958, 961, 962, 964, 968, 975, 976, 983, 987, 989, 990\n",
      "\n",
      "peak probability: 1.000%\n",
      "significant qubit probabilities\n",
      " 33 (00000100001)    1.000%\n",
      " 34 (00000100010)    1.000%\n",
      " 36 (00000100100)    1.000%\n",
      " 40 (00000101000)    1.000%\n",
      " 47 (00000101111)    1.000%\n",
      " 48 (00000110000)    1.000%\n",
      " 55 (00000110111)    1.000%\n",
      " 59 (00000111011)    1.000%\n",
      " 61 (00000111101)    1.000%\n",
      " 62 (00000111110)    1.000%\n",
      " 65 (00001000001)    1.000%\n",
      " 66 (00001000010)    1.000%\n",
      " 68 (00001000100)    1.000%\n",
      " 72 (00001001000)    1.000%\n",
      " 79 (00001001111)    1.000%\n",
      " 80 (00001010000)    1.000%\n",
      " 87 (00001010111)    1.000%\n",
      " 91 (00001011011)    1.000%\n",
      " 93 (00001011101)    1.000%\n",
      " 94 (00001011110)    1.000%\n",
      "129 (00010000001)    1.000%\n",
      "130 (00010000010)    1.000%\n",
      "132 (00010000100)    1.000%\n",
      "136 (00010001000)    1.000%\n",
      "143 (00010001111)    1.000%\n",
      "144 (00010010000)    1.000%\n",
      "151 (00010010111)    1.000%\n",
      "155 (00010011011)    1.000%\n",
      "157 (00010011101)    1.000%\n",
      "158 (00010011110)    1.000%\n",
      "257 (00100000001)    1.000%\n",
      "258 (00100000010)    1.000%\n",
      "260 (00100000100)    1.000%\n",
      "264 (00100001000)    1.000%\n",
      "271 (00100001111)    1.000%\n",
      "272 (00100010000)    1.000%\n",
      "279 (00100010111)    1.000%\n",
      "283 (00100011011)    1.000%\n",
      "285 (00100011101)    1.000%\n",
      "286 (00100011110)    1.000%\n",
      "481 (00111100001)    1.000%\n",
      "482 (00111100010)    1.000%\n",
      "484 (00111100100)    1.000%\n",
      "488 (00111101000)    1.000%\n",
      "495 (00111101111)    1.000%\n",
      "496 (00111110000)    1.000%\n",
      "503 (00111110111)    1.000%\n",
      "507 (00111111011)    1.000%\n",
      "509 (00111111101)    1.000%\n",
      "510 (00111111110)    1.000%\n",
      "513 (01000000001)    1.000%\n",
      "514 (01000000010)    1.000%\n",
      "516 (01000000100)    1.000%\n",
      "520 (01000001000)    1.000%\n",
      "527 (01000001111)    1.000%\n",
      "528 (01000010000)    1.000%\n",
      "535 (01000010111)    1.000%\n",
      "539 (01000011011)    1.000%\n",
      "541 (01000011101)    1.000%\n",
      "542 (01000011110)    1.000%\n",
      "737 (01011100001)    1.000%\n",
      "738 (01011100010)    1.000%\n",
      "740 (01011100100)    1.000%\n",
      "744 (01011101000)    1.000%\n",
      "751 (01011101111)    1.000%\n",
      "752 (01011110000)    1.000%\n",
      "759 (01011110111)    1.000%\n",
      "763 (01011111011)    1.000%\n",
      "765 (01011111101)    1.000%\n",
      "766 (01011111110)    1.000%\n",
      "865 (01101100001)    1.000%\n",
      "866 (01101100010)    1.000%\n",
      "868 (01101100100)    1.000%\n",
      "872 (01101101000)    1.000%\n",
      "879 (01101101111)    1.000%\n",
      "880 (01101110000)    1.000%\n",
      "887 (01101110111)    1.000%\n",
      "891 (01101111011)    1.000%\n",
      "893 (01101111101)    1.000%\n",
      "894 (01101111110)    1.000%\n",
      "929 (01110100001)    1.000%\n",
      "930 (01110100010)    1.000%\n",
      "932 (01110100100)    1.000%\n",
      "936 (01110101000)    1.000%\n",
      "943 (01110101111)    1.000%\n",
      "944 (01110110000)    1.000%\n",
      "951 (01110110111)    1.000%\n",
      "955 (01110111011)    1.000%\n",
      "957 (01110111101)    1.000%\n",
      "958 (01110111110)    1.000%\n",
      "961 (01111000001)    1.000%\n",
      "962 (01111000010)    1.000%\n",
      "964 (01111000100)    1.000%\n",
      "968 (01111001000)    1.000%\n",
      "975 (01111001111)    1.000%\n",
      "976 (01111010000)    1.000%\n",
      "983 (01111010111)    1.000%\n",
      "987 (01111011011)    1.000%\n",
      "989 (01111011101)    1.000%\n",
      "990 (01111011110)    1.000%\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def grovers_plot(func, qubits, N=None, iterations=None):    \n",
    "    if N is None:\n",
    "        N = 2 ** qubits\n",
    "\n",
    "    # input: |00...0> (length n)\n",
    "    # output: |0>\n",
    "    state = Q(kron(*(zero for _ in range(qubits)), zero))\n",
    "\n",
    "    # Apply HX to the output register.\n",
    "    state = state.apply_gate(X, 0)\n",
    "    state = state.apply_gate(H, 0)\n",
    "\n",
    "    # Apply H^n to the input register.\n",
    "    # input register: qubits 1 to n\n",
    "    input_H = kron(*(H for _ in range(qubits)), I)\n",
    "    state = state.apply_unitary(input_H)\n",
    "\n",
    "    # First, we'll compute the diffusion operator.\n",
    "    # this definition of D (the diffusion matrix aka. inversion about the mean)\n",
    "    # is given in Grover's original paper\n",
    "    P = 1/N * np.ones((N, N))\n",
    "    D = kron(-np.eye(N) + 2*P, I)\n",
    "    \n",
    "    if iterations is None:\n",
    "        iterations = floor((np.pi/4) * sqrt(N).real)\n",
    "        \n",
    "    zgrid = np.zeros((iterations+1, 2**qubits))\n",
    "\n",
    "    np.set_printoptions(precision=2)\n",
    "    \n",
    "    # Now we perform the Grover iteration.\n",
    "    for iteration in range(1, iterations+1):\n",
    "        state = state.apply_func(func)\n",
    "        state = state.apply_unitary(D)\n",
    "        amplitudes = to_row(state.state)\n",
    "        probs = [(abs(a) ** 2).real for a in amplitudes]\n",
    "        \n",
    "        for qubit, prob in enumerate(probs):\n",
    "            bitstring = qubit >> 1\n",
    "            zgrid[iteration][bitstring] += prob\n",
    "            \n",
    "    print(\"peak probability: {:.3f}%\".format(100*zgrid.max()))\n",
    "    \n",
    "    print(\"significant qubit probabilities\")\n",
    "    amplitudes = to_row(state.state)\n",
    "    probs = [(abs(a) ** 2).real for a in amplitudes]\n",
    "    bitstrings = {}\n",
    "    for qubit, prob in enumerate(probs):\n",
    "        bitstring = qubit >> 1 # chop off output register\n",
    "        bitstrings[bitstring] = bitstrings.get(bitstring, 0) + prob\n",
    "    for bitstring, prob in bitstrings.items():\n",
    "        if prob < 0.0001: continue\n",
    "        print(\"{0:3} ({0:0{width}b})  {1:7.3f}%\".format(bitstring, 100*prob, width=2*seq_len+1))\n",
    "    \n",
    "\n",
    "    ax = plt.axes(projection='3d')\n",
    "    ax.set_xlabel('iterations')\n",
    "    ax.set_ylabel('qubit')\n",
    "    ax.set_zlabel('probability of measuring qubit')\n",
    "    ax.set_xlim(0, iterations)\n",
    "    ax.set_ylim(0, 2**qubits)\n",
    "    ax.set_zlim(0.0, zgrid.max())\n",
    "    \n",
    "    xscale = list(range(iterations+1))\n",
    "    yscale = list(range(2**qubits))\n",
    "    xmesh, ymesh = np.meshgrid(xscale, yscale)\n",
    "    ax.plot_surface(xmesh.T, ymesh.T, zgrid, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0)\n",
    "\n",
    "# we need 2 * N qubits to search through sequence pairs\n",
    "qubits = seq_len * 2\n",
    "space_size = 2 ** qubits\n",
    "\n",
    "print('sequence length:', seq_len)\n",
    "print('qubits:', qubits)\n",
    "print('search space:', space_size)\n",
    "\n",
    "print(\"recall that the d-optimal bitstrings are\")\n",
    "print(', '.join(str(b) for b in dopt_bitstrings))\n",
    "print()\n",
    "    \n",
    "def func(x):\n",
    "    return 1 if is_bitstring_doptimal(x) else 0\n",
    "    \n",
    "grovers_plot(func, qubits=qubits, iterations = 2)"
   ]
  }
 ],
 "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}