doc/source/examples/_TestMeasurement_nested_loops.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Run the HardwareSetup script to get access to ```hardware_setup``` and ```alazar_DAC```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11308"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%run HardwareSetup.ipynb\n",
    "\n",
    "import os\n",
    "os.getpid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start the atsaverage status window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import atsaverage.gui\n",
    "status_window = atsaverage.gui.ThreadedStatusWindow(alazar_DAC.card)\n",
    "status_window.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the example pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from qupulse.serialization import Serializer, FilesystemBackend\n",
    "from qupulse.pulses import PointPT, TablePT\n",
    "\n",
    "nested_loop_pulse = Serializer(FilesystemBackend('./serialized_pulses')).deserialize('complicated')\n",
    "\n",
    "trig_pulse = TablePT({'A': [(0, 0)], 'marker': [(0, 1), ('trig_len', 1)]})\n",
    "\n",
    "nested_loop_pulse = trig_pulse @ nested_loop_pulse\n",
    "\n",
    "parameters=dict(N_T=10, N_T_rep=1, N_rep=3, T=192, T_start=2*192, T_step=160, trig_len=192)\n",
    "\n",
    "assert nested_loop_pulse.measurement_names == {'M'}\n",
    "\n",
    "alazar_DAC.register_mask_for_channel('M', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "operations = [atsaverage.operations.Downsample()]\n",
    "operations[0].maskID = 'M'\n",
    "operations[0].identifier = 'DSM'\n",
    "\n",
    "alazar_DAC.register_operations('nested_loops', operations)\n",
    "\n",
    "# store raw data for later inspection\n",
    "alazar_DAC.config.rawDataMask = atsaverage.atsaverage.ChannelMask(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from qupulse.pulses.sequencing import Sequencer\n",
    "\n",
    "seq = Sequencer()\n",
    "seq.push(nested_loop_pulse, \n",
    "         parameters=parameters, channel_mapping=dict(A='TABOR_A', marker='TABOR_A_MARKER'))\n",
    "g = seq.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiply all voltages of ```TABOR_A``` with 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hardware_setup.rm_channel('TABOR_A')\n",
    "hardware_setup.set_channel('TABOR_A', PlaybackChannel(tchannelpair_AB, 0, voltage_transformation=lambda x: 0.5*x))\n",
    "hardware_setup.register_program('nested_loops', g, update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hardware_setup.arm_program('nested_loops')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tchannelpair_AB.run_current_program()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = alazar_DAC.card.extractNextScanline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw = results.rawData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
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       "    } 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",
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       "        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",
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       "\n",
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       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
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       "            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",
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       "            fig.send_message(\"refresh\", {});\n",
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       "\n",
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       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    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",
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       "            event.step = -1;\n",
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       "        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",
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       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
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       "\n",
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       "        canvas_div.focus();\n",
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       "\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",
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       "        return fig.toolbar_button_onclick(event['data']);\n",
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       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
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       "\n",
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       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "#plt.plot(raw)\n",
    "meas_rate = 10\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "to_plot = ((raw - np.mean(raw)) * (2. / (np.max(raw) - np.min(raw))))\n",
    "ax.plot(np.arange(len(to_plot)) * meas_rate,to_plot)\n",
    "\n",
    "import qupulse.pulses.plotting as qcplot\n",
    "\n",
    "fig = qcplot.plot(nested_loop_pulse, parameters, sample_rate=1, axes=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alazar_DAC.config.triggerConfiguration.triggerLevel1 = 129\n",
    "alazar_DAC.update_settings = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "type(fig.axes[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alazar_DAC.config.totalRecordSize = 4*1024\n",
    "alazar_DAC.update_settings = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": 1
}