From 41d43d060e7b69f219c8229ce3a0e79ae76efd43 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti <giulioungaretti@me.com>
Date: Mon, 7 Nov 2016 19:05:17 +0100
Subject: [PATCH 1/5] feature: Add png for jupyter nb and saving

---
 qcodes/plots/pyqtgraph.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 996140316d36..494123ccf85a 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -382,3 +382,19 @@ def _cmap(self, scale):
             values, colors = scale
 
         return self.rpg.ColorMap(values, colors)
+
+    def _repr_png_(self):
+        """
+        Create a png representation of the current window.
+        """
+        image = self.win.grab()
+        byte_array = self.rpg.QtCore.QByteArray()
+        buffer = self.rpg.QtCore.QBuffer(byte_array)
+        buffer.open(self.rpg.QtCore.QIODevice.ReadWrite)
+        image.save(buffer, 'PNG')
+        buffer.close()
+        return bytes(byte_array._getValue())
+    
+    def save(self, filename=""):
+        image = self.win.grab()
+        image.save(filename, "PNG", 0)

From eba6e95fa3b08692e26a7dbc694f1b13635861c1 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti <giulioungaretti@me.com>
Date: Tue, 8 Nov 2016 11:06:16 +0100
Subject: [PATCH 2/5] feature: Add save to all plotters

---
 qcodes/plots/base.py         |  9 +++++++++
 qcodes/plots/pyqtgraph.py    | 12 +++++++++++-
 qcodes/plots/qcmatplotlib.py | 13 +++++++++++++
 3 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/qcodes/plots/base.py b/qcodes/plots/base.py
index ad198980f46c..528b5baac88f 100644
--- a/qcodes/plots/base.py
+++ b/qcodes/plots/base.py
@@ -280,3 +280,12 @@ def halt(self):
         """
         if hasattr(self, 'update_widget'):
             self.update_widget.halt()
+
+    def save(self, filename=None):
+        """
+        Save current plot to filename
+
+        Args:
+            filename (Optional[str]): Location of the file
+        """
+        raise NotImplementedError
diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 494123ccf85a..e65878840c90 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -395,6 +395,16 @@ def _repr_png_(self):
         buffer.close()
         return bytes(byte_array._getValue())
     
-    def save(self, filename=""):
+    def save(self, filename=None):
+        """
+        Save current plot to filename, by default
+        to the location corresponding to the default 
+        title.
+
+        Args:
+            filename (Optional[str]): Location of the file
+        """
+        default = "{}.png".format(self.get_default_title())
+        filename = filename or default
         image = self.win.grab()
         image.save(filename, "PNG", 0)
diff --git a/qcodes/plots/qcmatplotlib.py b/qcodes/plots/qcmatplotlib.py
index ac034f62f8bf..135edac3c9e4 100644
--- a/qcodes/plots/qcmatplotlib.py
+++ b/qcodes/plots/qcmatplotlib.py
@@ -204,3 +204,16 @@ def _draw_pcolormesh(self, ax, z, x=None, y=None, subplot=1, **kwargs):
             ax.qcodes_colorbar.set_label(self.get_label(z))
 
         return pc
+
+    def save(self, filename=None):
+        """
+        Save current plot to filename, by default
+        to the location corresponding to the default 
+        title.
+
+        Args:
+            filename (Optional[str]): Location of the file
+        """
+        default = "{}.png".format(self.get_default_title())
+        filename = filename or default
+        self.fig.savefig(filename)

From 55eab2485ea5194b3f2938c6c6132267cd883446 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti <giulioungaretti@me.com>
Date: Tue, 8 Nov 2016 11:45:06 +0100
Subject: [PATCH 3/5] feature: Add end_task

Allow to programmatically do something after the loop is done.
F.ex save the plot.
---
 qcodes/loops.py | 31 ++++++++++++++++++++++++-------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/qcodes/loops.py b/qcodes/loops.py
index c04d0fed8554..56a9782c515d 100644
--- a/qcodes/loops.py
+++ b/qcodes/loops.py
@@ -180,6 +180,7 @@ def __init__(self, sweep_values, delay=0, station=None,
         self.actions = None
         self.then_actions = ()
         self.bg_task = None
+        self.end_task = None
         self.bg_min_delay = None
         self.progress_interval = progress_interval
 
@@ -249,9 +250,9 @@ def each(self, *actions):
         return ActiveLoop(self.sweep_values, self.delay, *actions,
                           then_actions=self.then_actions, station=self.station,
                           progress_interval=self.progress_interval,
-                          bg_task=self.bg_task, bg_min_delay=self.bg_min_delay)
+                          bg_task=self.bg_task, end_task=self.end_task, bg_min_delay=self.bg_min_delay)
 
-    def with_bg_task(self, task, min_delay=1):
+    def with_bg_task(self, task, end_task=None, min_delay=1):
         """
         Attaches a background task to this loop.
 
@@ -259,12 +260,15 @@ def with_bg_task(self, task, min_delay=1):
             task: A callable object with no parameters. This object will be
                 invoked periodically during the measurement loop.
 
+            end_task: A callable object with no parameters. This object will be
+                invoked at the end  of the measurement loop.
+
             min_delay (default 1): The minimum number of seconds to wait
                 between task invocations. Note that the actual time between
                 task invocations may be much longer than this, as the task is
                 only run between passes through the loop.
         """
-        return _attach_bg_task(self, task, min_delay)
+        return _attach_bg_task(self, task, end_task, min_delay)
 
     @staticmethod
     def validate_actions(*actions):
@@ -361,7 +365,7 @@ def _attach_then_actions(loop, actions, overwrite):
     return loop
 
 
-def _attach_bg_task(loop, task, min_delay):
+def _attach_bg_task(loop, task, end_task, min_delay):
     """Inner code for both Loop and ActiveLoop.bg_task"""
     if loop.bg_task is None:
         loop.bg_task = task
@@ -369,6 +373,9 @@ def _attach_bg_task(loop, task, min_delay):
     else:
         raise RuntimeError('Only one background task is allowed per loop')
 
+    if end_task:
+        loop.end_task = end_task
+
     return loop
 
 
@@ -390,7 +397,7 @@ class ActiveLoop(Metadatable):
 
     def __init__(self, sweep_values, delay, *actions, then_actions=(),
                  station=None, progress_interval=None, bg_task=None,
-                 bg_min_delay=None):
+                 end_task=None, bg_min_delay=None):
         super().__init__()
         self.sweep_values = sweep_values
         self.delay = delay
@@ -399,6 +406,7 @@ def __init__(self, sweep_values, delay, *actions, then_actions=(),
         self.then_actions = then_actions
         self.station = station
         self.bg_task = bg_task
+        self.end_task = end_task
         self.bg_min_delay = bg_min_delay
         self.data_set = None
 
@@ -441,7 +449,7 @@ def then(self, *actions, overwrite=False):
                           then_actions=self.then_actions, station=self.station)
         return _attach_then_actions(loop, actions, overwrite)
 
-    def with_bg_task(self, task, min_delay=1):
+    def with_bg_task(self, task, end_task=None, min_delay=1):
         """
         Attaches a background task to this loop.
 
@@ -449,12 +457,15 @@ def with_bg_task(self, task, min_delay=1):
             task: A callable object with no parameters. This object will be
                 invoked periodically during the measurement loop.
 
+            end_task: A callable object with no parameters. This object will be
+                invoked at the end  of the measurement loop.
+
             min_delay (default 1): The minimum number of seconds to wait
                 between task invocations. Note that the actual time between
                 task invocations may be much longer than this, as the task is
                 only run between passes through the loop.
         """
-        return _attach_bg_task(self, task, min_delay)
+        return _attach_bg_task(self, task, end_task, min_delay)
 
     def snapshot_base(self, update=False):
         """Snapshot of this ActiveLoop's definition."""
@@ -981,6 +992,12 @@ def _run_loop(self, first_delay=0, action_indices=(),
         for f in self._compile_actions(self.then_actions, ()):
             f()
 
+        # run the end_task from the bg_task:
+        if self.end_task is not None:
+            self.end_task()
+
+
+
     def _wait(self, delay):
         if delay:
             finish_clock = time.perf_counter() + delay

From dff292bb73f31f80e79f159a4b6131ab4242750c Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti <giulioungaretti@me.com>
Date: Tue, 8 Nov 2016 11:46:56 +0100
Subject: [PATCH 4/5] docs: Add save example, use faster refresh rate

The default was 1 second, but I think aiming for 100hz, and using
that as default is a bit closer to reality.

Then in the examples we can just use the default which makes for
less typing for the users!
---
 docs/examples/Tutorial.ipynb | 1081 +++++-----------------------------
 qcodes/loops.py              |    6 +-
 2 files changed, 157 insertions(+), 930 deletions(-)

diff --git a/docs/examples/Tutorial.ipynb b/docs/examples/Tutorial.ipynb
index 26d7e134abe4..97a4a71dc4a7 100644
--- a/docs/examples/Tutorial.ipynb
+++ b/docs/examples/Tutorial.ipynb
@@ -451,914 +451,144 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet:\n",
-      "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2016-10-10/#002_testsweep_10-08-32'\n",
-      "   <Type>   | <array_id>      | <array.name> | <array.shape>\n",
-      "   Setpoint | gates_chan0_set | chan0        | (201,)\n",
-      "   Measured | meter_amplitude | amplitude    | (201,)\n",
-      "started at 2016-10-10 10:08:40\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Notice that one can use an explicit location and `overwrite=True` here so that\n",
-    "# running this notebook over and over won't result in extra files.\n",
-    "# If you leave these out, you get a new timestamped DataSet each time.\n",
-    "\n",
-    "loop = qc.Loop(c0.sweep(0,20,0.1), delay=0.001).each(meter.amplitude)\n",
-    "data = loop.get_data_set(name='testsweep')\n",
-    "plot = qc.QtPlot()\n",
-    "plot.add(data.meter_amplitude)\n",
-    "_ = loop.with_bg_task(plot.update, 0.0005).run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Output of the loop\n",
-    "Notice the **\"DataSet\"**.  \n",
-    "A loop returns a dataset.  \n",
-    "The representation of the dataset shows what arrays it contains and where it is saved.   \n",
-    "The dataset initially starts out empty (filled with NAN's) and get's filled while the Loop get's executed. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the measurement is done, take a look at the file in finder/explorer (the dataset.location should give you the relative path). \n",
-    "Note also the snapshot that captures the settings of all instruments at the start of the Loop. \n",
-    "This metadata is also accesible from the dataset and captures a snapshot of each instrument listed in the station. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'__class__': 'toymodel.MockMeter',\n",
-       " 'functions': {},\n",
-       " 'name': 'meter',\n",
-       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
-       "   'instrument': 'toymodel.MockMeter',\n",
-       "   'instrument_name': 'meter',\n",
-       "   'label': 'IDN',\n",
-       "   'name': 'IDN',\n",
-       "   'ts': '2016-10-10 10:08:32',\n",
-       "   'units': '',\n",
-       "   'value': {'firmware': None,\n",
-       "    'model': 'MockMeter',\n",
-       "    'serial': 'meter',\n",
-       "    'vendor': None}},\n",
-       "  'amplitude': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
-       "   'instrument': 'toymodel.MockMeter',\n",
-       "   'instrument_name': 'meter',\n",
-       "   'label': 'Current (nA)',\n",
-       "   'name': 'amplitude',\n",
-       "   'ts': '2016-10-10 10:08:40',\n",
-       "   'units': '',\n",
-       "   'value': 0.117}}}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "meter.snapshot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting the loop  II\n",
-    "\n",
-    "QCodes supports both matplotlib inline plotting and pyqtgraph for plotting. \n",
-    "For a comparison see http://pyqtgraph.org/ (actually not that biased)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The same API works for plotting a measured dataset or an old  dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`DataSet` objects are not intended to be instantiated directly, but\n",
-    "rather through the helper functions:\n",
-    "- `load_data` for existing data sets, including the data currently\n",
-    "  being acquired.\n",
-    "- `new_data` to make an empty data set to be populated with new\n",
-    "  measurements or simulation data. `new_data` is called internally by\n",
-    "  `Loop.run()` so is also generally not needed directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:negative delay -0.001849 sec\n"
+     ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet:\n",
+      "   mode     = DataMode.LOCAL\n",
+      "   location = 'data/2016-11-08/#024_testsweep_13-29-04'\n",
+      "   <Type>   | <array_id>      | <array.name> | <array.shape>\n",
+      "   Setpoint | gates_chan0_set | chan0        | (201,)\n",
+      "   Measured | meter_amplitude | amplitude    | (201,)\n",
+      "started at 2016-11-08 13:29:23\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Notice that one can use an explicit location and `overwrite=True` here so that\n",
+    "# running this notebook over and over won't result in extra files.\n",
+    "# If you leave these out, you get a new timestamped DataSet each time.\n",
+    "\n",
+    "\n",
+    "loop = qc.Loop(c0.sweep(0,20,0.1), delay=0.001).each(meter.amplitude)\n",
+    "data = loop.get_data_set(name='testsweep')\n",
+    "plot = qc.QtPlot()\n",
+    "plot.add(data.meter_amplitude)\n",
+    "_ = loop.with_bg_task(plot.update, plot.save).run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Output of the loop\n",
+    "Notice the **\"DataSet\"**.  \n",
+    "A loop returns a dataset.  \n",
+    "The representation of the dataset shows what arrays it contains and where it is saved.   \n",
+    "The dataset initially starts out empty (filled with NAN's) and get's filled while the Loop get's executed. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the measurement is done, take a look at the file in finder/explorer (the dataset.location should give you the relative path). \n",
+    "Note also the snapshot that captures the settings of all instruments at the start of the Loop. \n",
+    "This metadata is also accesible from the dataset and captures a snapshot of each instrument listed in the station. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "<img src=\"data:image/png;base64,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\">"
-      ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "{'__class__': 'toymodel.MockMeter',\n",
+       " 'functions': {},\n",
+       " 'name': 'meter',\n",
+       " 'parameters': {'IDN': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'toymodel.MockMeter',\n",
+       "   'instrument_name': 'meter',\n",
+       "   'label': 'IDN',\n",
+       "   'name': 'IDN',\n",
+       "   'ts': '2016-11-08 13:29:02',\n",
+       "   'units': '',\n",
+       "   'value': {'firmware': None,\n",
+       "    'model': 'MockMeter',\n",
+       "    'serial': 'meter',\n",
+       "    'vendor': None}},\n",
+       "  'amplitude': {'__class__': 'qcodes.instrument.parameter.StandardParameter',\n",
+       "   'instrument': 'toymodel.MockMeter',\n",
+       "   'instrument_name': 'meter',\n",
+       "   'label': 'Current (nA)',\n",
+       "   'name': 'amplitude',\n",
+       "   'ts': '2016-11-08 13:29:23',\n",
+       "   'units': '',\n",
+       "   'value': 0.117}}}"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "meter.snapshot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting the loop  II\n",
+    "\n",
+    "QCodes supports both matplotlib inline plotting and pyqtgraph for plotting. \n",
+    "For a comparison see http://pyqtgraph.org/ (actually not that biased)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same API works for plotting a measured dataset or an old  dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`DataSet` objects are not intended to be instantiated directly, but\n",
+    "rather through the helper functions:\n",
+    "- `load_data` for existing data sets, including the data currently\n",
+    "  being acquired.\n",
+    "- `new_data` to make an empty data set to be populated with new\n",
+    "  measurements or simulation data. `new_data` is called internally by\n",
+    "  `Loop.run()` so is also generally not needed directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "loaded_data = qc.load_data(\"data/2016-10-10/#002_testsweep_10-08-32\")\n",
     "plot = qc.MatPlot(loaded_data.meter_amplitude)"
@@ -1373,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -1398,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2117,12 +1347,9 @@
        "    // 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",
+       "        // 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",
@@ -2171,7 +1398,7 @@
     {
      "data": {
       "text/html": [
-       "<img src=\"data:image/png;base64,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\">"
+       "<img src=\"data:image/png;base64,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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2184,7 +1411,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:root:negative delay -0.000553 sec\n"
+      "WARNING:root:negative delay -0.001320 sec\n"
      ]
     },
     {
@@ -2193,20 +1420,20 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2016-10-10/#003_2D_test_10-10-33'\n",
+      "   location = 'data/2016-11-08/#025_2D_test_13-29-23'\n",
       "   <Type>   | <array_id>        | <array.name> | <array.shape>\n",
       "   Setpoint | gates_chan1_set   | chan1        | (30,)\n",
       "   Setpoint | gates_chan0_set   | chan0        | (30, 54)\n",
       "   Measured | meter_amplitude_0 | amplitude    | (30, 54)\n",
       "   Measured | meter_amplitude_3 | amplitude    | (30, 54)\n",
-      "started at 2016-10-10 10:10:50\n"
+      "started at 2016-11-08 13:29:35\n"
      ]
     }
    ],
    "source": [
     "plot = qc.MatPlot(data.meter_amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
     "plot.add(data.meter_amplitude_3, cmap=plt.cm.hot, subplot=2)\n",
-    "data2 = loop.with_bg_task(plot.update, 0.0005).run()"
+    "data2 = loop.with_bg_task(plot.update, plot.save,  0.05).run()"
    ]
   },
   {
@@ -2229,13 +1456,13 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2016-10-10/#004_2D_test_10-11-02'\n",
+      "   location = 'data/2016-11-08/#026_2D_test_13-29-35'\n",
       "   <Type>   | <array_id>        | <array.name> | <array.shape>\n",
       "   Setpoint | gates_chan1_set   | chan1        | (30,)\n",
       "   Setpoint | gates_chan0_set   | chan0        | (30, 54)\n",
       "   Measured | meter_amplitude_0 | amplitude    | (30, 54)\n",
       "   Measured | meter_amplitude_3 | amplitude    | (30, 54)\n",
-      "started at 2016-10-10 10:11:16\n"
+      "started at 2016-11-08 13:29:57\n"
      ]
     }
    ],
@@ -2252,7 +1479,7 @@
     "plotQ = qc.QtPlot()\n",
     "plotQ.add(data.meter_amplitude_0, figsize=(1200, 500))\n",
     "plotQ.add(data.meter_amplitude_3, subplot=2)\n",
-    "data2 = loop.with_bg_task(plotQ.update, 0.0005).run()"
+    "data2 = loop.with_bg_task(plotQ.update, plotQ.save).run()"
    ]
   },
   {
@@ -2264,7 +1491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -2294,7 +1521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -2303,8 +1530,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:root:negative delay -0.000789 sec\n",
-      "WARNING:root:negative delay -0.000375 sec\n"
+      "/home/unga/src/Qcodes/qcodes/plots/pyqtgraph.py:292: UserWarning: nonuniform nested setpoint array passed to pyqtgraph. ignoring, using default scaling.\n",
+      "  'nonuniform nested setpoint array passed to '\n"
      ]
     },
     {
@@ -2313,7 +1540,7 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2016-10-10/#005_TwoD_different_inner_test_10-12-14'\n",
+      "   location = 'data/2016-11-08/#027_TwoD_different_inner_test_13-29-57'\n",
       "   <Type>   | <array_id>          | <array.name>  | <array.shape>\n",
       "   Setpoint | gates_chan1_set     | chan1         | (30,)\n",
       "   Measured | meter_amplitude_2   | amplitude     | (30,)\n",
@@ -2322,7 +1549,7 @@
       "   Setpoint | gates_chan2_set     | chan2         | (30, 100)\n",
       "   Measured | meter_amplitude_5_0 | amplitude     | (30, 100)\n",
       "   Measured | meter_avg_amplitude | avg_amplitude | (30,)\n",
-      "started at 2016-10-10 10:12:54\n"
+      "started at 2016-11-08 13:30:32\n"
      ]
     }
    ],
@@ -2331,7 +1558,7 @@
     "plotQ.add(data.meter_amplitude_3_0)\n",
     "plotQ.add(data.meter_amplitude_5_0, cmap='viridis', subplot=2)\n",
     "plotQ.add(data.meter_avg_amplitude, subplot=3)\n",
-    "data = loop3.with_bg_task(plotQ.update, 0.0005).run()"
+    "data = loop3.with_bg_task(plotQ.update, plotQ.save).run()"
    ]
   },
   {
@@ -2346,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -2358,13 +1585,13 @@
      "text": [
       "DataSet:\n",
       "   mode     = DataMode.LOCAL\n",
-      "   location = 'data/2016-10-10/#006_TwoD_average_test_10-13-01'\n",
+      "   location = 'data/2016-11-08/#028_TwoD_average_test_13-30-32'\n",
       "   <Type>   | <array_id>          | <array.name>  | <array.shape>\n",
       "   Setpoint | gates_chan1_set     | chan1         | (30,)\n",
       "   Measured | chan2               | chan2         | (30, 100)\n",
       "   Measured | meter_amplitude     | amplitude     | (30, 100)\n",
       "   Measured | meter_avg_amplitude | avg_amplitude | (30,)\n",
-      "started at 2016-10-10 10:13:12\n"
+      "started at 2016-11-08 13:30:49\n"
      ]
     }
    ],
@@ -2376,7 +1603,7 @@
     "plotQ = qc.QtPlot()\n",
     "plotQ.add(data4.meter_amplitude, figsize=(1200, 500), cmap='viridis')\n",
     "plotQ.add(data4.meter_avg_amplitude, subplot=2)\n",
-    "data4 = loop4.with_bg_task(plotQ.update, 0.005).run()"
+    "data4 = loop4.with_bg_task(plotQ.update, plotQ.save).run()"
    ]
   }
  ],
@@ -2397,7 +1624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/qcodes/loops.py b/qcodes/loops.py
index 56a9782c515d..95cf344441c5 100644
--- a/qcodes/loops.py
+++ b/qcodes/loops.py
@@ -252,7 +252,7 @@ def each(self, *actions):
                           progress_interval=self.progress_interval,
                           bg_task=self.bg_task, end_task=self.end_task, bg_min_delay=self.bg_min_delay)
 
-    def with_bg_task(self, task, end_task=None, min_delay=1):
+    def with_bg_task(self, task, end_task=None, min_delay=0.01):
         """
         Attaches a background task to this loop.
 
@@ -263,7 +263,7 @@ def with_bg_task(self, task, end_task=None, min_delay=1):
             end_task: A callable object with no parameters. This object will be
                 invoked at the end  of the measurement loop.
 
-            min_delay (default 1): The minimum number of seconds to wait
+            min_delay (default 0.01): The minimum number of seconds to wait
                 between task invocations. Note that the actual time between
                 task invocations may be much longer than this, as the task is
                 only run between passes through the loop.
@@ -449,7 +449,7 @@ def then(self, *actions, overwrite=False):
                           then_actions=self.then_actions, station=self.station)
         return _attach_then_actions(loop, actions, overwrite)
 
-    def with_bg_task(self, task, end_task=None, min_delay=1):
+    def with_bg_task(self, task, end_task=None, min_delay=0.01):
         """
         Attaches a background task to this loop.
 

From 125c4808d1a5f7b381f21a5eb762231aeb02cf71 Mon Sep 17 00:00:00 2001
From: Giulio Ungaretti <giulioungaretti@me.com>
Date: Wed, 9 Nov 2016 13:55:59 +0100
Subject: [PATCH 5/5] fix: Use better variable name, clarify docstring

---
 qcodes/loops.py | 47 ++++++++++++++++++++++++++---------------------
 1 file changed, 26 insertions(+), 21 deletions(-)

diff --git a/qcodes/loops.py b/qcodes/loops.py
index 95cf344441c5..7fd45d20d83a 100644
--- a/qcodes/loops.py
+++ b/qcodes/loops.py
@@ -180,7 +180,7 @@ def __init__(self, sweep_values, delay=0, station=None,
         self.actions = None
         self.then_actions = ()
         self.bg_task = None
-        self.end_task = None
+        self.bg_final_task = None
         self.bg_min_delay = None
         self.progress_interval = progress_interval
 
@@ -250,9 +250,9 @@ def each(self, *actions):
         return ActiveLoop(self.sweep_values, self.delay, *actions,
                           then_actions=self.then_actions, station=self.station,
                           progress_interval=self.progress_interval,
-                          bg_task=self.bg_task, end_task=self.end_task, bg_min_delay=self.bg_min_delay)
+                          bg_task=self.bg_task, bg_final_task=self.bg_final_task, bg_min_delay=self.bg_min_delay)
 
-    def with_bg_task(self, task, end_task=None, min_delay=0.01):
+    def with_bg_task(self, task, bg_final_task=None, min_delay=0.01):
         """
         Attaches a background task to this loop.
 
@@ -260,15 +260,19 @@ def with_bg_task(self, task, end_task=None, min_delay=0.01):
             task: A callable object with no parameters. This object will be
                 invoked periodically during the measurement loop.
 
-            end_task: A callable object with no parameters. This object will be
-                invoked at the end  of the measurement loop.
+            bg_final_task: A callable object with no parameters. This object will be
+                invoked to clean up after or otherwise finish the background
+                task work.
 
             min_delay (default 0.01): The minimum number of seconds to wait
-                between task invocations. Note that the actual time between
-                task invocations may be much longer than this, as the task is
-                only run between passes through the loop.
+                between task invocations.
+                Note that if a task is doing a lot of processing it is recommended
+                to increase min_delay.
+                Note that the actual time between task invocations may be much
+                longer than this, as the task is only run between passes
+                through the loop.
         """
-        return _attach_bg_task(self, task, end_task, min_delay)
+        return _attach_bg_task(self, task, bg_final_task, min_delay)
 
     @staticmethod
     def validate_actions(*actions):
@@ -365,7 +369,7 @@ def _attach_then_actions(loop, actions, overwrite):
     return loop
 
 
-def _attach_bg_task(loop, task, end_task, min_delay):
+def _attach_bg_task(loop, task, bg_final_task, min_delay):
     """Inner code for both Loop and ActiveLoop.bg_task"""
     if loop.bg_task is None:
         loop.bg_task = task
@@ -373,8 +377,8 @@ def _attach_bg_task(loop, task, end_task, min_delay):
     else:
         raise RuntimeError('Only one background task is allowed per loop')
 
-    if end_task:
-        loop.end_task = end_task
+    if bg_final_task:
+        loop.bg_final_task = bg_final_task
 
     return loop
 
@@ -397,7 +401,7 @@ class ActiveLoop(Metadatable):
 
     def __init__(self, sweep_values, delay, *actions, then_actions=(),
                  station=None, progress_interval=None, bg_task=None,
-                 end_task=None, bg_min_delay=None):
+                 bg_final_task=None, bg_min_delay=None):
         super().__init__()
         self.sweep_values = sweep_values
         self.delay = delay
@@ -406,7 +410,7 @@ def __init__(self, sweep_values, delay, *actions, then_actions=(),
         self.then_actions = then_actions
         self.station = station
         self.bg_task = bg_task
-        self.end_task = end_task
+        self.bg_final_task = bg_final_task
         self.bg_min_delay = bg_min_delay
         self.data_set = None
 
@@ -449,7 +453,7 @@ def then(self, *actions, overwrite=False):
                           then_actions=self.then_actions, station=self.station)
         return _attach_then_actions(loop, actions, overwrite)
 
-    def with_bg_task(self, task, end_task=None, min_delay=0.01):
+    def with_bg_task(self, task, bg_final_task=None, min_delay=0.01):
         """
         Attaches a background task to this loop.
 
@@ -457,15 +461,16 @@ def with_bg_task(self, task, end_task=None, min_delay=0.01):
             task: A callable object with no parameters. This object will be
                 invoked periodically during the measurement loop.
 
-            end_task: A callable object with no parameters. This object will be
-                invoked at the end  of the measurement loop.
+            bg_final_task: A callable object with no parameters. This object will be
+                invoked to clean up after or otherwise finish the background
+                task work.
 
             min_delay (default 1): The minimum number of seconds to wait
                 between task invocations. Note that the actual time between
                 task invocations may be much longer than this, as the task is
                 only run between passes through the loop.
         """
-        return _attach_bg_task(self, task, end_task, min_delay)
+        return _attach_bg_task(self, task, bg_final_task, min_delay)
 
     def snapshot_base(self, update=False):
         """Snapshot of this ActiveLoop's definition."""
@@ -992,9 +997,9 @@ def _run_loop(self, first_delay=0, action_indices=(),
         for f in self._compile_actions(self.then_actions, ()):
             f()
 
-        # run the end_task from the bg_task:
-        if self.end_task is not None:
-            self.end_task()
+        # run the bg_final_task from the bg_task:
+        if self.bg_final_task is not None:
+            self.bg_final_task()