5_Bell-Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello Quantum - Qiskit Edition\n",
    "## Chapter 5: Bell Tests for Quantum and Classical Variables\n",
    "\n",
    "If you don't know how to use Jupyter notebooks, see Chapter 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bell test for classical variables\n",
    "\n",
    "Here we'll investigate how quantum variables (based on qubits) differ from standard ones (based on bits).\n",
    "\n",
    "We'll do this by creating a pair of variables, which we will call `A` and `B`. We aren't going to put any conditions on what these can be, or how theu are initialized. So there are a lot of possibilities.\n",
    "\n",
    "* Could be any kind of variable, such as\n",
    "    * `int`\n",
    "    * `list`\n",
    "    * `dict`\n",
    "    * ...\n",
    "* Could be initialized by any kind of process, such as\n",
    "    * left empty\n",
    "    * filled with a given set of values\n",
    "    * generated by a given random process\n",
    "        * indepedently applied to `A` and `B`\n",
    "        * applied to `A` and `B` together, allowing for correlations between their randomness\n",
    "\n",
    "If the variables are initialized by a random process, it means they'll have different values every time we run our program. This is perfectly fine. The only rule we need to obey is that the process of generating the randomness is the same for every run.\n",
    "\n",
    "We'll use the function below to set up these variables. This currently has `A` and `B` defined as to be partially correlated random floating point numbers. But you can change it to whatever you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def setup_variables ():\n",
    "    \n",
    "    ### Replace this section with anything you want ###\n",
    "    \n",
    "    r = random.random()\n",
    "    \n",
    "    A = r*(2/3)\n",
    "    B = r*(1/3)\n",
    "    \n",
    "    ### End of section ###\n",
    "    \n",
    "    return A, B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next job is to define a hashing function. This simply needs to take one of the variables as input, and then give a bit value as an output.\n",
    "\n",
    "This function must also be capable of performing two different types of hash. So it needs to be able to be able to chew on a variable and spit out a bit in to different ways. Another input to the function is therefore the kind of hash we want to use.\n",
    "\n",
    "To be consistent with the rest of the program, the two possible hash types should be called `'H'` and `'V'`. Also, the output must be in the form of a single value bit string: either `'0'` or `'1'`.\n",
    "\n",
    "As an example, I created bits by comparing `A` and `B` to a certain value. The output is `'1'` if they are under that value, and `'0'` otherwise. The type of hash determines the value used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hash2bit ( variable, hash ):\n",
    "    \n",
    "    ### Replace this section with anything you want ###\n",
    "    \n",
    "    if hash=='V':\n",
    "        bit = (variable<0.5)\n",
    "    elif hash=='H':\n",
    "        bit = (variable<0.25)\n",
    "        \n",
    "    bit = str(int(bit))\n",
    "    \n",
    "    ### End of section ###\n",
    "        \n",
    "    return bit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once these are defined, there are four quantities we wish to calculate: `P['HH']`, `P['HV']`, `P['VH']` and `P['VV']`.\n",
    "\n",
    "Let's focus on `P['HV']` as an example. This is the probability that the bit value derived from an `'H'` type hash on `A` is the same as that from a `'V'` type has on `B`. We will estimate this probability by sampling many times and determining the fraction of samples for which the corresponding bit values agree.\n",
    "\n",
    "The other probabilities are defined similarly: `P['HH']` compares a `'H'` type hash on both `A` and `B`, `P['VV']` compares a `V` type hash on both, and `P['VH']` compares a `V` type hash on `A` with a `H` type has on `B`.\n",
    "\n",
    "These probabilities are calculated in the following function, which returns all the values of `P` in a dictionary. The parameter `shots` is the number of samples we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = 8192\n",
    "def calculate_P ( ):\n",
    "    \n",
    "    P = {}\n",
    "    for hashes in ['VV','VH','HV','HH']:\n",
    "        \n",
    "        # calculate each P[hashes] by sampling over `shots` samples\n",
    "        P[hashes] = 0\n",
    "        for shot in range(shots):\n",
    "\n",
    "            A, B = setup_variables()\n",
    "\n",
    "            a = hash2bit ( A, hashes[0] ) # hash type for variable `A` is the first character of `hashes`\n",
    "            b = hash2bit ( B, hashes[1] ) # hash type for variable `B` is the second character of `hashes`\n",
    "\n",
    "            P[hashes] += (a!=b) / shots\n",
    " \n",
    "    return P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's actually calculate these values for the method we have chosen to set up and hash the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'VV': 0.250244140625, 'VH': 0.0, 'HV': 0.6279296875, 'HH': 0.3765869140625}\n"
     ]
    }
   ],
   "source": [
    "P = calculate_P()\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values will vary slightly from one run to the next due to the fact that we only use a finite number of shots. To change them significantly, we need to change the way the variables are initiated, and/or the way the hash functions are defined.\n",
    "\n",
    "No matter how these functions are defined, there are certain restrictions that the values of `P` will always obey.\n",
    "\n",
    "For example, consider the case that `P['HV']`, `P['VH']` and `P['VV']` are all `0.0`. The only way that this can be possible is for `P['HH']` to also be `0.0`.\n",
    "\n",
    "To see why, we start by noting that `P['HV']=0.0` is telling us that `hash2bit ( A, H )` and `hash2bit ( B, V )` were never different in any of the runs. So this means we can always expect them to be equal.\n",
    "\n",
    "    hash2bit ( A, H ) = hash2bit ( B, V )        (1)\n",
    "    \n",
    "From `P['VV']=0.0` and `P['VH']=0.0` we can similarly get\n",
    "\n",
    "    hash2bit ( A, V ) = hash2bit ( B, V )        (2)\n",
    "    \n",
    "    hash2bit ( A, V ) = hash2bit ( B, H )        (3)\n",
    "    \n",
    "Putting (1) and (2) together implies that\n",
    "\n",
    "    hash2bit ( A, H ) = hash2bit ( A, V )        (4)\n",
    "    \n",
    "Combining this with (3) gives\n",
    "\n",
    "    hash2bit ( A, H ) = hash2bit ( B, H )        (5)\n",
    "\n",
    "And if these values are always equal, we'll never see a run in which they are different. This is exactly what we set out to prove: `P['HH']=0.0`.\n",
    "\n",
    "More generally, we can use the values of `P['HV']`, `P['VH']` and `P['VV']` to set an upper limit on what `P['HH']` can be. By adapting the [CHSH inequality](https://en.wikipedia.org/wiki/CHSH_inequality) we find that\n",
    "\n",
    "$\\,\\,\\,\\,\\,\\,\\,$ `P['HH']` $\\, \\leq \\,$ `P['HV'] + P['VH'] + P['VV']`\n",
    "\n",
    "This is not just a special property of `P['HH']`. It's also true for all the others: each of these probabilities cannot be greater than the sum of the others.\n",
    "\n",
    "To test whether this logic holds, we'll see how well the probabilities obey these inequalities. Note that we might get slight violations due to the fact that our the `P` values aren't exact, but are estimations made using a limited number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bell_test (P):\n",
    "    \n",
    "    sum_P = sum(P.values())\n",
    "    for hashes in P:\n",
    "        \n",
    "        bound = sum_P - P[hashes]\n",
    "        \n",
    "        print(\"The upper bound for P['\"+hashes+\"'] is \"+str(bound))\n",
    "        print(\"The value of P['\"+hashes+\"'] is \"+str(P[hashes]))\n",
    "        if P[hashes]<=bound:\n",
    "            print(\"The upper bound is obeyed :)\\n\")\n",
    "        else:\n",
    "            if P[hashes]-bound < 0.1:\n",
    "                print(\"This seems to have gone over the upper bound, but only by a little bit :S\\nProbably just rounding errors or statistical noise.\\n\")\n",
    "            else:\n",
    "                print(\"!!!!! This has gone well over the upper bound :O !!!!!\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The upper bound for P['VV'] is 1.0045166015625\n",
      "The value of P['VV'] is 0.250244140625\n",
      "The upper bound is obeyed :)\n",
      "\n",
      "The upper bound for P['VH'] is 1.2547607421875\n",
      "The value of P['VH'] is 0.0\n",
      "The upper bound is obeyed :)\n",
      "\n",
      "The upper bound for P['HV'] is 0.6268310546875\n",
      "The value of P['HV'] is 0.6279296875\n",
      "This seems to have gone over the upper bound, but only by a little bit :S\n",
      "Probably just rounding errors or statistical noise.\n",
      "\n",
      "The upper bound for P['HH'] is 0.878173828125\n",
      "The value of P['HH'] is 0.3765869140625\n",
      "The upper bound is obeyed :)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bell_test(P)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the initialization and hash functions provided in this notebook, the value of `P('HV')` should be pretty much the same as the upper bound. Since the numbers are estimated statistically, and therefore are slightly approximate due to statistical noise, you might even see it go a tiny bit over. But you'll never see it significantly surpass the bound.\n",
    "\n",
    "If you don't beleive me, try it for yourself. Change the way the variables are initialized, and how the hashes are calculated, and try to get one of the bounds to be significantly broken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bell test for quantum variables\n",
    "\n",
    "Now we are going to do the same thing all over again, except our variables `A` and `B` will be quantum variables. Specifically, they'll be the simplest kind of quantum variable: qubits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since you probably don't have a quantum computer handy, you can borrow from IBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import ClassicalRegister, QuantumRegister\n",
    "from qiskit import QuantumCircuit, execute\n",
    "from qiskit import register, available_backends, get_backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When writing quantum programs, we have to set up our qubits and bits before we can use them. This is done by the function below. It defines a register of two bits, and assigns them as our variables `A` and `B`. It then sets up a register of two bits to receive the outputs, and assigns them as `a` and `b`.\n",
    "\n",
    "Finally it uses these registers to set up an empty quantum program. This is called `qc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_program ():\n",
    "    \n",
    "    qubit = QuantumRegister(2)\n",
    "    A = qubit[0]\n",
    "    B = qubit[1]\n",
    "    \n",
    "    bit = ClassicalRegister(2)\n",
    "    a = bit[0]\n",
    "    b = bit[1]\n",
    "    \n",
    "    qc = QuantumCircuit(qubit, bit)\n",
    "    \n",
    "    return A, B, a, b, qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start writing the quantum program to set up our variables, let's think about what needs to happen at the end of the program. This will be where we define the different hash functions, which turn our qubits into bits.\n",
    "\n",
    "The simplest way to extract a bit from a qubit is through the `measure` command. This corresponds to the bottom circle of a qubit in the visualization we've been using. Let's use this as our `V` type hash.\n",
    "\n",
    "For the output that corresponds to the top circle, there is no direct means of access. However, we can do it indirectly by first doing an `h` to swap the top and bottom circles, and then using the `measure` command. This will be our `H` type hash.\n",
    "\n",
    "Note that this function has more inputs that its classical counterpart. We have to tell it the `bit` on which to write the result, and the quantum program, `qc`, on which we write the commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hash2bit  ( variable, hash, bit, qc ):\n",
    "    \n",
    "    if hash=='H':\n",
    "        qc.h( variable )\n",
    "        \n",
    "    qc.measure( variable, bit )\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now its time to set up the variables `A` and `B`. To write this program, you can use the grid below. You can either follow the suggested exercise, or do whatever you like. Once you are ready, just move on. The cell containing the `setup_variables()` function, will then use the program you wrote with the grid.\n",
    "\n",
    "Note that our choice of means that the probabilities `P['HH']`, `P['HV']`, `P['VH']` and `P['VV']` will explicitly correspond to circles on our grid. For example, the circle at the very top tells us how likely the two top outputs would be to disagree. If this is white, then `P['HH']=1` , if it is black then `P['HH']=0`.\n",
    "\n",
    "### Exercise\n",
    "* Make it so thatoutputs from the bottom circles of both qubits are most likely to disagree, whereas all other combinations of outputs are most likely to agree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"799.9\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "030b5b93a5f14b9082501f9261f0ee2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(ToggleButtons(options=('Choose gate', 'bloch', 'unbloch', 'x', 'z', 'h', 'cz', 'cx', 'ry(-pi/4)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Your quantum program so far\n",
      "\n",
      "qc.cx(B,A)\n",
      "qc.cz(A,B)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import engine\n",
    "initialize = []\n",
    "success_condition = {'ZZ':-0.7071,'ZX':-0.7071,'XZ':-0.7071,'XX':-0.7071}\n",
    "allowed_gates = {'0': {'bloch':0, 'x':0, 'z':0, 'h':0, 'cx':0, 'ry(pi/4)': 0, 'ry(-pi/4)': 0}, '1': {'bloch':0, 'x':0, 'z':0, 'h':0, 'cx':0, 'ry(pi/4)': 0, 'ry(-pi/4)': 0}, 'both': {'cz':0, 'unbloch':0}}\n",
    "vi = [[], True, True]\n",
    "qubit_names = {'0':'A', '1':'B'}\n",
    "writer = engine.run_game(initialize, success_condition, allowed_gates, vi, qubit_names, mode='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def setup_variables ( A, B, qc ):\n",
    "        \n",
    "    for line in writer.program:\n",
    "        eval(line)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of `P` are calculated in the function below. This is done by sending jobs to IBM and getting results which tell us how many of the samples gave each possible result. The results are given as a bit string, `string`, which IBM numbers from right to left. This means that the value of `a`, which corresponds to `bit[0]` is the first from the right\n",
    "\n",
    "    a = string[-1]\n",
    "    \n",
    "and the value of `b` is right next to it at the second from the right\n",
    "\n",
    "    b = string[-2]\n",
    "\n",
    "The number of samples for this bit string is provided by the dictionary of results, `stats`, as `stats[string]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = 8192\n",
    "def calculate_P ( backend ):\n",
    "    \n",
    "    P = {}\n",
    "    program = {}\n",
    "    for hashes in ['VV','VH','HV','HH']:\n",
    "\n",
    "        A, B, a, b, program[hashes] = initialize_program ()\n",
    "\n",
    "        setup_variables( A, B, program[hashes] )\n",
    "\n",
    "        hash2bit ( A, hashes[0], a, program[hashes])\n",
    "        hash2bit ( B, hashes[1], b, program[hashes])\n",
    "            \n",
    "    # submit jobs\n",
    "    job = execute( list(program.values()), backend, shots=shots )\n",
    "\n",
    "    # get the results\n",
    "    for hashes in ['VV','VH','HV','HH']:\n",
    "        stats = job.result().get_counts(program[hashes])\n",
    "        \n",
    "        P[hashes] = 0\n",
    "        for string in stats.keys():\n",
    "            a = string[-1]\n",
    "            b = string[-2]\n",
    "            \n",
    "            if a!=b:\n",
    "                P[hashes] += stats[string] / shots\n",
    "\n",
    "    return P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now its time to choose and set up the actually device we are going to use. By default, we'll use a simulator. But if you want to use the real device, just replace the first line below with `device='ibmqx4'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'local_qasm_simulator_py', 'operational': True, 'pending_jobs': 0}\n"
     ]
    }
   ],
   "source": [
    "device = 'local_qasm_simulator'\n",
    "backend = get_backend(device)\n",
    "print(backend.status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'VV': 0, 'VH': 0.5032958984375, 'HV': 0.4937744140625, 'HH': 0.4984130859375}\n"
     ]
    }
   ],
   "source": [
    "P = calculate_P( backend )\n",
    "print(P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The upper bound for P['VV'] is 1.4954833984375\n",
      "The value of P['VV'] is 0\n",
      "The upper bound is obeyed :)\n",
      "\n",
      "The upper bound for P['VH'] is 0.9921875\n",
      "The value of P['VH'] is 0.5032958984375\n",
      "The upper bound is obeyed :)\n",
      "\n",
      "The upper bound for P['HV'] is 1.001708984375\n",
      "The value of P['HV'] is 0.4937744140625\n",
      "The upper bound is obeyed :)\n",
      "\n",
      "The upper bound for P['HH'] is 0.9970703125\n",
      "The value of P['HH'] is 0.4984130859375\n",
      "The upper bound is obeyed :)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bell_test( P )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you prepared the state suggestion by the exercise, you will have found a significant violation of the upper bound for `P['HH']`. So what is going on here? The chain of logic we based the Bell test on obviously doesn't apply to quantum variables. But why?\n",
    "\n",
    "The answer is that there is a hidden assumption in that logic. To see why, let's revisit point (4).\n",
    "\n",
    "    hash2bit ( A, H ) = hash2bit ( A, V )        (4)\n",
    "    \n",
    "Here we compare the value we'd get from an `H` type of hash of the variable `A` with that for a `V` type hash.\n",
    "\n",
    "For classical variables, this is perfectly sensible. There is nothing stopping us from calculating both hashes and comparing the results. Even if calculating the hash of a variable changes the variable, that's not a problem. All we need to do is copy it beforehand and we can do both hashes without any issue.\n",
    "\n",
    "The same is not true for quantum variables. The result of the hashes is not known until we actually do them. It's only then that the qubit actually decides what bit value to give. And once it decides the value for one type of hash, we can never determine what it would have decided if we had used another type of hash. We can't get around this by copying the quantum variables either, because quantum variables [cannot be copied](https://en.wikipedia.org/wiki/No-cloning_theorem). This means there is no context in which the values `hash2bit(A,H)` and `hash2bit(A,V)` are well-defined at the same time, and so it is impossible to compare them.\n",
    "\n",
    "Another hidden assumption is that `hash2bit(A,hash)` depends only on the type of hash chosen for variable `A`, and not the one chosen for variable `B`. This is also perfectly sensible, since this exactly the way we set up the `hash2bit()` function. However, the very fact that the upper bound was violated does seem to imply that each variable knows what hash is  being done to the other, so they they can conspire to give very different behaviour when both have a `H` type hash.\n",
    "\n",
    "Even so, the effect is subtle. It is impossible to determine which variable is affecting which: You can change the order in which the hashes are done, or [effectively do them at the same time](https://en.wikipedia.org/wiki/Loopholes_in_Bell_test_experiments#Communication,_or_locality), and you'll get the same results. This means that it not clear whether we can say they affect each other (see [here](https://quantumcomputing.stackexchange.com/questions/2028/is-it-true-to-say-that-one-qubit-in-an-entangled-state-can-instantaneously-affec) for some discussion). But what we can say is that they are [contextual](https://en.wikipedia.org/wiki/Quantum_contextuality): to fully understand results from one variable, it is sometimes required to look at what was done to another.\n",
    "\n",
    "All this goes to show that quantum variables don't always follow the logic we are used to. They follow different rules, the rules of quantum mechanics, which will allow us to find ways of performing computation in very different ways than we are used to.\n",
    "\n",
    "To get more examples of this kind of effect, see [this notebook](https://github.com/QISKit/qiskit-tutorial/blob/master/reference/qis/entanglement_revisited.ipynb) in the QISKit tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}