From 5f29f28ded99426c8d304b9d6702e30fc8316d28 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 13 Jan 2016 10:32:59 +0100
Subject: [PATCH 01/24] back-reference from DataArrays to DataSets

---
 qcodes/data/data_array.py | 16 ++++++++++++++++
 qcodes/data/data_set.py   | 11 +++++++++++
 qcodes/data/format.py     |  4 ++--
 3 files changed, 29 insertions(+), 2 deletions(-)

diff --git a/qcodes/data/data_array.py b/qcodes/data/data_array.py
index a32d66c05cd..f14e415935c 100644
--- a/qcodes/data/data_array.py
+++ b/qcodes/data/data_array.py
@@ -37,6 +37,10 @@ def __init__(self, parameter=None, name=None, label=None, array_id=None,
         self.size = size
         self._preset = False
 
+        # store a reference up to the containing DataSet
+        # this also lets us make sure a DataArray is only in one DataSet
+        self._data_set = None
+
         self.data = None
         if preset_data is not None:
             self.init_data(preset_data)
@@ -47,6 +51,18 @@ def __init__(self, parameter=None, name=None, label=None, array_id=None,
         self.last_saved_index = None
         self.modified_range = None
 
+    @property
+    def data_set(self):
+        return self._data_set
+
+    @data_set.setter
+    def data_set(self, new_data_set):
+        if (self._data_set is not None and
+                new_data_set is not None and
+                self._data_set != new_data_set):
+            raise RuntimeError('A DataArray can only be part of one DataSet')
+        self._data_set = new_data_set
+
     def nest(self, size, action_index=None, set_array=None):
         '''
         nest this array inside a new outer loop
diff --git a/qcodes/data/data_set.py b/qcodes/data/data_set.py
index 221f91282ea..1bd39f014ad 100644
--- a/qcodes/data/data_set.py
+++ b/qcodes/data/data_set.py
@@ -208,12 +208,23 @@ def sync(self):
     def add_array(self, data_array):
         '''
         add one DataArray to this DataSet
+
+        note: DO NOT just set data_set.arrays[id] = data_array
+        because this will not check for overriding, nor set the
+        reference back to this DataSet. It would also allow you to
+        load the array in with different id than it holds itself.
+
         '''
+        # TODO: mask self.arrays so you *can't* set it directly
+
         if data_array.array_id in self.arrays:
             raise ValueError('array_id {} already exists in this '
                              'DataSet'.format(data_array.array_id))
         self.arrays[data_array.array_id] = data_array
 
+        # back-reference to the DataSet
+        data_array.data_set = self
+
     def _clean_array_ids(self, arrays):
         '''
         replace action_indices tuple with compact string array_ids
diff --git a/qcodes/data/format.py b/qcodes/data/format.py
index 0a6f78718a6..5de18936625 100644
--- a/qcodes/data/format.py
+++ b/qcodes/data/format.py
@@ -283,7 +283,7 @@ def read_one_file(self, data_set, f, ids_read):
                 set_array = DataArray(label=labels[i], array_id=array_id,
                                       set_arrays=set_arrays, size=set_size)
                 set_array.init_data()
-                arrays[array_id] = set_array
+                data_set.add_array(set_array)
 
             set_arrays = set_arrays + (set_array, )
             ids_read.add(array_id)
@@ -300,7 +300,7 @@ def read_one_file(self, data_set, f, ids_read):
                 data_array = DataArray(label=labels[i], array_id=array_id,
                                        set_arrays=set_arrays, size=size)
                 data_array.init_data()
-                arrays[array_id] = data_array
+                data_set.add_array(data_array)
             data_arrays.append(data_array)
             ids_read.add(array_id)
 

From 95875afc88f74687d88f99b2df07892366df61d5 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 13 Jan 2016 10:33:57 +0100
Subject: [PATCH 02/24] live plotting

---
 Qcodes example.ipynb         | 6552 ++++++++++++++++++++++++++++++++--
 qcodes/__init__.py           |    8 +
 qcodes/data/data_set.py      |   11 +-
 qcodes/plots.py              |  124 +
 qcodes/widgets/__init__.py   |    0
 qcodes/widgets/widgets.js    |   54 +
 qcodes/widgets/widgets.py    |   50 +
 test_multi_d/chan1_chan0.dat |    2 +-
 test_multi_d/chan1_chan2.dat |    2 +-
 9 files changed, 6546 insertions(+), 257 deletions(-)
 create mode 100644 qcodes/plots.py
 create mode 100644 qcodes/widgets/__init__.py
 create mode 100644 qcodes/widgets/widgets.js
 create mode 100644 qcodes/widgets/widgets.py

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 6b3e96fefba..6bbc5e43e25 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -6,13 +6,80 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/*\n",
+       " * Qcodes Jupyter/IPython widgets\n",
+       " */\n",
+       "require([\n",
+       "    'nbextensions/widgets/widgets/js/widget',\n",
+       "    'nbextensions/widgets/widgets/js/manager'\n",
+       "], function (widget, manager) {\n",
+       "\n",
+       "    var UpdateView = widget.DOMWidgetView.extend({\n",
+       "        render: function() {\n",
+       "            window.MYWIDGET = this;\n",
+       "            this._interval = 0;\n",
+       "            this.update();\n",
+       "        },\n",
+       "        update: function() {\n",
+       "            var me = this;\n",
+       "\n",
+       "            me.display(me.model.get('_message'));\n",
+       "            me.setInterval();\n",
+       "        },\n",
+       "        display: function(message) {\n",
+       "            /*\n",
+       "             * display method: override this for custom display logic\n",
+       "             */\n",
+       "            this.el.innerHTML = message;\n",
+       "        },\n",
+       "        remove: function() {\n",
+       "            this.setInterval(0);\n",
+       "        },\n",
+       "        setInterval: function(newInterval) {\n",
+       "            var me = this;\n",
+       "            if(newInterval===undefined) newInterval = me.model.get('interval');\n",
+       "            if(newInterval===me._interval) return;\n",
+       "\n",
+       "            me._interval = newInterval;\n",
+       "\n",
+       "            if(me._updater) clearInterval(me._updater);\n",
+       "\n",
+       "            if(me._interval) {\n",
+       "                me._updater = setInterval(function() {\n",
+       "                    me.send({myupdate: true});\n",
+       "                }, me._interval * 1000);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    manager.WidgetManager.register_widget_view('UpdateView', UpdateView);\n",
+       "\n",
+       "    var HiddenUpdateView = UpdateView.extend({\n",
+       "        display: function(message) {\n",
+       "            this.$el.parents('.widget-area').hide().css('background-color', '#fff');\n",
+       "        }\n",
+       "    });\n",
+       "    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\n",
+       "});\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "%matplotlib inline\n",
+    "%matplotlib nbagg\n",
     "import matplotlib.pyplot as plt\n",
+    "plt.ioff()\n",
     "import time\n",
-    "from IPython import display\n",
-    "import numpy.ma as ma\n",
+    "# from IPython import display\n",
+    "# import numpy.ma as ma\n",
     "\n",
     "# load the qcodes path, until we have this installed as a package\n",
     "import sys\n",
@@ -21,9 +88,9 @@
     "if qcpath not in sys.path:\n",
     "    sys.path.append(qcpath)\n",
     "\n",
-    "import qcodes as q\n",
+    "import qcodes as qc\n",
     "\n",
-    "q.set_mp_method('spawn')  # force Windows behavior on mac"
+    "qc.set_mp_method('spawn')  # force Windows behavior on mac"
    ]
   },
   {
@@ -50,7 +117,7 @@
     "from toymodel import AModel, MockGates, MockSource, MockMeter, AverageGetter, AverageAndRaw\n",
     "\n",
     "# no extra processes running yet\n",
-    "q.active_children()"
+    "qc.active_children()"
    ]
   },
   {
@@ -78,7 +145,7 @@
     "source = MockSource('source', model)\n",
     "meter = MockMeter('meter', model)\n",
     "\n",
-    "station = q.Station(gates, source, meter)\n",
+    "station = qc.Station(gates, source, meter)\n",
     "\n",
     "# could measure any number of things by adding arguments to this\n",
     "# function call, but here we're just measuring one, the meter amplitude\n",
@@ -94,7 +161,7 @@
     "\n",
     "# check that a DataServer is running (not yet implemented, but when we have\n",
     "# a monitor, defining a station will start the DataServer to run the monitor)\n",
-    "q.active_children()"
+    "qc.active_children()"
    ]
   },
   {
@@ -134,7 +201,7 @@
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
       "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-08 15:48:47\n"
+      "started at 2016-01-13 10:24:31\n"
      ]
     }
    ],
@@ -143,7 +210,7 @@
     "# the sweep values are defined by slicing the parameter object\n",
     "# but more complicated sweeps (eg nonlinear, or adaptive) can\n",
     "# easily be used instead\n",
-    "data = q.Loop(c0[-20:20:0.1], 0.03).run(location='testsweep')"
+    "data = qc.Loop(c0[-20:20:0.1], 0.03).run(location='testsweep')"
    ]
   },
   {
@@ -156,7 +223,7 @@
     {
      "data": {
       "text/plain": [
-       "[<MeasurementLoop-2, started daemon>, <DataServer-1, started daemon>]"
+       "[<DataServer-1, started daemon>, <MeasurementLoop-2, started daemon>]"
       ]
      },
      "execution_count": 6,
@@ -166,7 +233,7 @@
    ],
    "source": [
     "# now there should be two extra processes running, DataServer and a sweep\n",
-    "q.active_children()"
+    "qc.active_children()"
    ]
   },
   {
@@ -193,11 +260,11 @@
        "         0.016,  0.017,  0.018,  0.019,  0.02 ,  0.022,  0.023,  0.025,\n",
        "         0.027,  0.029,  0.031,  0.034,  0.037,  0.04 ,  0.044,  0.048,\n",
        "         0.053,  0.058,  0.064,  0.071,  0.077,  0.085,  0.092,  0.099,\n",
-       "         0.106,  0.111,  0.115,  0.117,  0.117,  0.117,  0.115,  0.111,\n",
-       "         0.106,  0.099,  0.092,  0.085,  0.077,  0.071,  0.064,  0.058,\n",
-       "         0.053,  0.048,  0.044,  0.04 ,  0.037,  0.034,  0.031,  0.029,\n",
-       "         0.027,  0.025,  0.023,  0.022,  0.02 ,  0.019,  0.018,  0.017,\n",
-       "         0.016,  0.015,  0.014,  0.013,    nan,    nan,    nan,    nan,\n",
+       "         0.106,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -242,11 +309,11 @@
        "        -13.7, -13.6, -13.5, -13.4, -13.3, -13.2, -13.1, -13. , -12.9,\n",
        "        -12.8, -12.7, -12.6, -12.5, -12.4, -12.3, -12.2, -12.1, -12. ,\n",
        "        -11.9, -11.8, -11.7, -11.6, -11.5, -11.4, -11.3, -11.2, -11.1,\n",
-       "        -11. , -10.9, -10.8, -10.7, -10.6, -10.5, -10.4, -10.3, -10.2,\n",
-       "        -10.1, -10. ,  -9.9,  -9.8,  -9.7,  -9.6,  -9.5,  -9.4,  -9.3,\n",
-       "         -9.2,  -9.1,  -9. ,  -8.9,  -8.8,  -8.7,  -8.6,  -8.5,  -8.4,\n",
-       "         -8.3,  -8.2,  -8.1,  -8. ,  -7.9,  -7.8,  -7.7,  -7.6,  -7.5,\n",
-       "         -7.4,  -7.3,  -7.2,  -7.1,  -7. ,  -6.9,  -6.8,   nan,   nan,\n",
+       "        -11. , -10.9, -10.8, -10.7, -10.6, -10.5, -10.4, -10.3,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -285,7 +352,7 @@
     }
    ],
    "source": [
-    "# bring the data into the main process and display it as numbers\n",
+    "# manually bring the data into the main process and display it as numbers\n",
     "data.sync()\n",
     "data.arrays"
    ]
@@ -300,9 +367,775 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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1wI4d3vWSMDfJS2w7yVV+TscAQlQwd+igc0U3brQdCUUBp2SQq2pqtINAxJvnY26Sl9h2\nkqv83CEDCFHBDHBokbzDKRnkKq9zc8AAXeTa1OTdc1J8se0kV7GHOQU/WOQVDiuSq7zOzfbtgS5d\nODpH3mDbSa5iwZyCO2WQVzisSK7yOjcB5id5Y+dO3Ymge3fvnpO5SV7hlIwU7GEmr3BYkVzlRy8J\n85O8kMxNr+bXA8xN8g57mFPwg0Ve2L4daGwEunXz7jkHDADq6vR5iYrh9ZA3wGFv8gZzk1zGgjkF\np2SQF/zoJWnXDujRQ4tmomJwSga5irlJLuOUjBTsYSYv+HUVyvwkL3BKBrnKj9zs1w9Yvx7Yu9fb\n56V42b5dz+no0sW/1whVwcyhG/KCH8OKAPOTimcMh73JXX7kZrt2ekgPR+eoGMnc9HLkuKVQFcx9\n+wIbNvBKlIrjx7AiwKFFKt62bdrge91LwtwkL7DtJFf5lZupQlUwt22rV6K1tbYjoTDjlAxyFXOT\nXMb8JFf5veAPCFnBDHBokYrHKRnkKr9ys18/YNMmoKHB++em+GDbSa7yKzdTha5g5tANFYvDiuQq\nv3KzTRugTx89IpuoEMn59QMGeP/cbDupWJySkQaHbqhYHFYkV/k5rMj8pGJs3gwccADQqZP3z83c\npGJxSkYaHLqhYhgDrF3rzweLuUnF8nNYkflJxWBukss4JSMNDt1QMTZuBDp2BDp08P65e/cGtm4F\ndu/2/rkpHvwcVmTbScVgbpLLOCUjDQ7dUDH8HLYpKQH699cebKJCcEoGuYq5Sa5Kjhz7Mb8+VegK\n5gED+MGiwvn9oWJ+UjH8zE/mJhXDz9zs3VtPaquv9+f5Kdo2bdK59e3b+/s6oSuY+/XjPsxUuHXr\nNIf8wvykQhnjb34yN6kYfuamiO7iwtP+qBB+f68nha5g7t1bV+vytD8qRG2tvx+s/v1ZlFBhtm3T\nY4L92IUAYG5ScWprNYf8wgs6KpTf3+tJoSuY27YFevTQI7KJ8uX3B4uNPhWKuUkuY36Sq1gwZ8AP\nFhWKjT65yu/c7NsXWL8eaGry7zUouth2kqtYMGfADxYVio0+ucrv3EweOrF5s3+vQdHFtpNcxYI5\nA36wqFBs9MlVQTT6zE8qxJ49uotFz57+vQZzkwrlVMEsIpNEZKmIfCQi17VynztFZJmIzBORw1Ju\n/6GIfCAiC0RkuoiUFhs0P1hUKBbM5CoWzOSqujrdxaLExy425iYVypmCWURKANwF4AwAhwK4UETG\ntLjPmQBGGmNGA5gK4O7E7UMBfAvA4caY8QDaAphSbND8YFEhGhv1pL8+ffx7DeYmFYoFM7mKuUku\nc6ZgBjARwDJjTKUxpgHADACTW9xnMoCHAMAY8w6AbiLSD8A2AHsAdBKRtgA6Aih663x+sKgQGzYA\n3bvr1l1+6dZNhy8/+cS/16BoYlFCrmJukstcKpjLAFSl/L4mcVum+1QDKDPGbAbwGwCrE7dtMca8\nVHi4ih8sKkQQHyoR3Y2A+Un5YlFCrmJukquM0SlDrhTMBROREQD+B8BQAAMBdBaRrxT7vPxgUSGC\nugplflIhWJSQq4LIzV699PCehgZ/X4eiZcsWPRLb72OxAZ1TnE01gCEpvw9K3NbyPoPT3OdkAG8Y\nYzYBgIg8CeA4AH9N90LTpk379N/l5eUoLy9PGxAbfSoEC2ZyGQtmclVtLTBokL+v0aaNFs11dUBZ\nyzFsolbk2m5WVFSgoqKiqNfKpWCeDWBUYgHfWuiivQtb3OdZAFcA+JuIHAOdelErIh8CuEFE2gPY\nDeCziedLK7VgzqRvX52P2tioHzKiXLBgJlft2KFDi507+/s6zE0qRG0tcOSR/r9OMj9ZMFOucv1e\nb9kJe9NNN+X9WlkLZmNMo4hcCeBF6BSOB4wxS0Rkqv7Z3GuMeV5EzhKR5QB2Avh64rHzReQhAO8D\naAQwF8C9eUfZQrt2QNeuuuNB377FPhvFBQtmctW6dZo3Iv6+Tr9++lpE+WDbSa5Ktp1ByKWHGcaY\nFwAc1OK2e1r8fmUrj/0VgF8VGmBrkh8sFsyUq9paYMyY7PcrVr9+wPLl/r8ORUeQBUldnfZm+12c\nU3SwYCZXBZWbQEhP+gP4waL8sdEnVwWVmx066BHZW7b4/1oUHWw7yVUsmHPADxbli40+uSrIRp/5\nSfnYu1cvsHr39v+1mJuULxbMOejfnx8syk9QHyzmJuUryEaf+Un5WL8e6NkzmAX2zE3KFwvmHAwa\nBFRVZb8fEaCn723YAAwY4P9rlZVpbhrj/2tRNFRVAYMHZ7+fF9h2Uj6Ym+SyIPMztAXz0KHA6tW2\no6CwWLNGi+W2OS1zLU7XrkBpqe7iQpSL1au1TQsC207KR2UlMGRI9vt5YehQfT2iXAXZdoa2YB4y\nhB8syt3q1cE1+gCLEspPkEUJ207KR5AFyaBBwNq1Om+aKJvt24Hdu/XAmyCEtmDmlSjlo7IyuEYf\nYH5S7pqadFiRvXjkoiDbztJSoE8foKYmmNejcEt2NAS1RWZoC+a+ffV0rF27bEdCYRB0wcxePMrV\n+vV6wl+nTsG8Hkc/KB9Bjn4AvKCj3AU5+gGEuGAuKdGJ3mz4KRfsYSZX2bqY46JUygXbTnJV0LkZ\n2oIZYC8e5Y5zmMlVQffgcVEq5SPoXrwhQ9h2Um6CbjtDXTDzSpRyxSkZ5KqgcxNg20m5CXpRFcDc\npNyxhzkP7MWjXDQ12elhZqNPuQi6Bw9g20m5CXpRFcC2k3LHOcx5YC8e5aKuLthFVUDzotSdO4N7\nTQqnoIcVAbadlBsbox/MTcoVp2TkgVeilAsbPXjJRak8tYqy4ZQMcpXN0Q8uSqVM9uzRzrCysuBe\nM/QFM4cVKRsbBQnAnhLKDadkkKtsjH5wUSrloro6uNN7k0JdMPNUIMqFrYKZvXiUjY1FVQBzk3LD\ntpNcZSM3Q10wl5YCvXvzVCDKLOgFf0ls9CmbZG4GuagK4OgH5cZW28n8pGxs5GaoC2ZAe5mrq21H\nQS5bs0bzJGhlZcxNysxWbvbtC2zbpr3bRK2xlZ/8XqdsbORm6AvmsjL2MFNmNTXBLgxIYm5SNrZy\ns6QE6N9fp7QRpdPUpPkxcGDwr822k7Kx0XaGvmAeOJAfLMqspsZOo8/cpGxs5SbA/KTMNm4EunQB\n2rcP/rWZm5SNjbYzEgUzh26oNU1NwLp1upo2aGz0KZvqarsFM9tOag0v5shlNtrOSBTM/GBRa9av\nB7p1Aw44IPjX7tUL2LUL+OST4F+bwoFFCbmqutrOdCGA6z8oO/YwF4BznSgTmwWJiPZsMz+pNbbm\nMANsOykzXsyRq5qagNra4EeOQ18wc1iRMrHZ6ANs+CkzTskgV9lsO3v00B1cdu608/rktro6oHt3\n3Vo4SJEomFmQUGtsDisCHFqk1jU26pSh/v3tvD7bTsrEZtspwvyk1tm6mAt9wdy9O9DQAOzYYTsS\nchF7mMlVdXVAz55Au3Z2Xp9TMigTtp3kKmvbcQb/kt7ilShlYnOOKMCihFpnczoGwCkZlBnbTnKV\nrbYz9AUzwIKZWsdeEnKV7dzs2lUXz2zfbi8Gcpft/GTbSa3hlIwicJ4otYa9eOQq2/PrRdh2UnoN\nDXpwSd++9mJg20mtsdV2RqJg5pUotYbDiuQq2z14ANtOSq+2FujTB2jb1l4MbDupNexhLgIbfUqn\noQHYvFkbfluS+zAbYy8GchMLZnIVc5NcxoK5CBy6oXTWrtUhxTZt7MXQpYu+/tat9mIgN9meLgSw\n7aT0mJvkMi76KwLn4VE6tqdjJDE/KR0X8pO5Sem4kJvJHmaOzlGqPXu0A8rG/PpIFMyDBgFr1tiO\nglxTVWW/0QeYn5ReVZXmhk3MTUrHhdzs1Ano0EEXHxIlrVmjUx1LLFSvkSmY164F9u61HQm5pLIS\nGDrUdhQaQ2Wl7SjIJdu2aU9Jr15242BuUjqutJ1DhjA/aV82czMSBXNpqS7s4gIBSrV6tTuN/urV\ntqMgl6xerXkhYjcO5ialk8xP24YOZX7SvmzmZiQKZoA9JbQ/V3pJmJvUkiu52bcvsGMHsHOn7UjI\nJa7kJ9tOaok9zB7gB4taqqx0p5eEuUmpXClISkqAwYPZi0fN9uwB6urs75IBcEoG7c/5gllEJonI\nUhH5SESua+U+d4rIMhGZJyKHpdzeTUQeF5ElIvKBiBztVfCpOLRILXFKBrnKlSFvgPlJ+6qu1kVV\nNg8tSeKUDGrJ6SkZIlIC4C4AZwA4FMCFIjKmxX3OBDDSGDMawFQAd6f8+XcAnjfGHAxgAoAlHsW+\nD/biUart24Hdu+0vqgK4KJX250oPM8C2k/bF3CSXud7DPBHAMmNMpTGmAcAMAJNb3GcygIcAwBjz\nDoBuItJPRLoCONEY82Dib3uNMdu8C78ZP1iUKjkdw/aiKoCLUml/LErIVS7lJqdkUKqmJt3y0Nke\nZgBlAKpSfl+TuC3TfaoTtw0HsEFEHhSROSJyr4h0KCbg1rDRp1QuNfoA85P25VJ+MjcplUu5yUWp\nlKquDujcWffotsHvWUptARwB4ApjzHsi8lsAPwBwY7o7T5s27dN/l5eXo7y8POcXSs7DM8aNXkWy\ny5X5y0mcJ0pJe/YA69e7sagKYG7SvlavBiZOtB2FSi5KraoCxozJfn+KtmLmL1dUVKCioqKo18+l\nYK4GkBrioMRtLe8zuJX7VBlj3kv8eyaAtIsGgX0L5nx17apD3xs3Ar17F/w0FBGu7JCRxF48Skqe\nVOXCoiqAuUn7qqwEvvxl21E0S07LYMFMxYx+tOyEvemmm/J+jlymZMwGMEpEhopIKYApAJ5tcZ9n\nAXwVAETkGABbjDG1xphaAFUicmDifp8FsDjvKHPEhp+SXBpWBJib1My13OSiVErlWn6y7aQk27mZ\ntWA2xjQCuBLAiwA+ADDDGLNERKaKyOWJ+zwP4GMRWQ7gHgDfSXmKqwBMF5F50F0ybvX4v+FT3IKG\nklybksHcpCTXcrO0VOeKclEqGWN3UVU6bDspyXbbmdOgoDHmBQAHtbjtnha/X9nKY+cDOKrQAPPB\nFbWU5NqUDOYmJbmWm0BzfroWFwUruaiqY0fbkTQbMgR4+WXbUZALKiuBPJa2eS4yJ/0BwIgRwIoV\ntqMg2z75BNiwAShruZeLRcOHA6tW6bY4FG8rVmhb5RK2nQQwN8lttvMzUgXz6NHAsmW2oyDbVqwA\nhg1zZ1EVoNvg9OihC74o3pYv17bKJaNHa1wUb8uWuZmb/F6npib9bh81yl4MkSuY2eiTiwUJwPwk\nxaKEXOVi29m/P1BfD2zZYjsSsqm6WjudOne2F0OkCubhw3XBQkOD7UjIJhcLEkCvjFmUxNvWrcCu\nXVoEuIQFMwGaAzZ78NIR0ZjY2RBvLnyvR6pgLi3Veasff2w7ErLJxUYfYFFCzbnp2uFKyYs5Y2xH\nQja5UJSkw84GcuF7PVIFM8CihNxt9Jmb5Gpu9ugBHHAAUFtrOxKyxRh385NtJ7mQmyyYKXJc+GCl\nw9wkV3MTYH7GXV2djtL26GE7kv0xN8mFtpMFM0XKrl16PPrgwdnvG7SRI3W6UGOj7UjIFhca/daw\n7Yw35ia5zIX8ZMFMkbJ8uS7+bNPGdiT769gR6N1bF6ZSPLm4C0ESd3GJNxcKktbwez3eGhu1s4lz\nmD3GD1a8udzoA1y8Encu5yfbznhzYVFVa/r21d2vNm2yHQnZUFUF9Opl/wTKyBXMw4YBa9cCu3fb\njoRscLkHD2AvXpxt3gzs2aNf/i5iwRxvLredImw748yV3Ixcwdyunc5f5dZy8eRyDx7AoiTOkrnp\n2pZyScm9brm1XDyx7SRXuZKbkSuYAQ57x5nLw4oAG/04cz03u3XTIc+1a21HQkEzxp1evNbwez2+\nXGk7I1kwsyiJL1euRFvD3Iwv13MTYH7G1bp1QIcOetHkKuZmfLnSdrJgpsjYsQPYsgUYNMh2JK0b\nORJYtQrYu9d2JBQ013vwAM4TjStXCpJM+L0eX67kJwtmiozly4ERI4ASh7O6fXtd9LV6te1IKGiu\nNPqZsO2MJ1eGvDNJ5ibn2MfL3r3ayTRypO1IWDBThIShIAGYn3EVhvxkbsZTGHKzd28tljdutB0J\nBWn1au0O4NajAAAgAElEQVRk6tDBdiQRLZiHDgVqa4H6etuRUJDC0OgDLEriaONGoKlJv/RdxtyM\npzC0ncmt5Zif8eJSbkayYG7bVovmFStsR0JBcumDlQkb/fhxfUu5pFGjtN1sarIdCQWJbSe5yqXc\njGTBDHDxShyFYVEVwNyMI5ca/Uy6dAG6dgVqamxHQkExRi+SwpCfbDvjx6Xv9cgWzNyzMX7CsHAF\nYC9JHC1fHo7cBJoPMKF4qKnRC6UuXWxHkh2/1+PHpe/1yBbMLEriZds2YPt2YOBA25FkN3y4LmTg\n1nLxEZYeZoBtZ9y4VJBkw9yMH5faThbMFAnLl+u2My5vKZfUvj3Qv79ulUPx4FKjnw3bzngJY25y\na7l42LtXO5dGjLAdiQpBeVGYMWOAJUtsR0FBWboUOOgg21HkjvkZH01NwIcfhic/mZvxEqa2s1cv\noLSUx7fHxYoVOmrcvr3tSFRkC+bBg4FPPgHWr7cdCQVhwQJgwgTbUeRu/Hhg4ULbUVAQKiuB7t2B\nHj1sR5Ib5ma8sO0kV7mWm5EtmEX4wYqTBQv0/++wGD9eY6boC1tuDh+u+0Zv3Wo7EgpC2PKTbWd8\nuJabkS2YAX6w4sS1D1Y2zM34CFtulpQAY8eysyEOamt1nmgYFksnse2MD9faThbMFHobN+oOGUOH\n2o4kd2PGAB9/zNMo48C1Rj8XbDvjIZmbrh+ok4q5GR+utZ0smCn0Fi4Exo0LV6NfWqorvhcvth0J\n+c21Rj8XbDvjIYy5ecghwEcfAXv22I6E/LR1q65Bc2WHDCDiBfPYsVqQcL/baAtjow+wKImDXbuA\nqirgwANtR5If5mY8hLHt7NABGDZMd56h6Fq0CDj0UKBNG9uRNIt0wdy5s87N4p6i0TZ/fvgafUBj\nnj/fdhTkp0WLdPpN27a2I8nPuHE6ctPUZDsS8hPbTnKVi7kZ6YIZYE9JHISxlwRgbsZBWHOze3eg\nZ0+dZ0/R1NCgvbSHHmo7kvyx7Yw+F9tOFswUao2NOu1m7FjbkeQv2UvCU6uiy8VGP1dsO6Ptww+B\nIUOAjh1tR5I/5mb0udh2smCmUFu+HOjXD+ja1XYk+RswQP933Tq7cZB/XGz0c8W2M9qYm+Sqpqbm\nxfwuYcFMoRbmRj95uA7zM5qMCXd+MjejLcy5OWQIsHMnT/KNqlWrmqeFuSTyBfOIEcCmTcCWLbYj\nIT+EudEHWJREWXW1bh/Yt6/tSArD3Iy2MLedPMk32lzNzcgXzDy1KtpcO2s+XxMmsCiJqrDn5ujR\nQE0NsGOH7UjID2HPT7ad0eVqbuZUMIvIJBFZKiIfich1rdznThFZJiLzROSwFn8rEZE5IvKsF0Hn\niz0l0eXqlWiumJvRFfbcbNsWOPhg4IMPbEdCXgvj6agtse2MLlfbzqwFs4iUALgLwBkADgVwoYiM\naXGfMwGMNMaMBjAVwN0tnuZqANbONOMHK5pcPAkoXzy1KrpcbfTzwbYzmsJ4OmpLzM3ocrXtzKWH\neSKAZcaYSmNMA4AZACa3uM9kAA8BgDHmHQDdRKQfAIjIIABnAbjfs6jzxA9WNLl4ElC+eGpVdLna\n6OeDbWc0RSE3eZJvNO3cCaxZ4+bpqLkUzGUAqlJ+X5O4LdN9qlPucweAawBY22123DgtrnhqVbRE\nodEHWJRE0e7dwMqVespfmDE3oykKbWfyJN/ly21HQl764AN3T0f1ddGfiJwNoNYYMw+AJH4Cl9ye\nZMUKG69Ofpk/3719Ggsxfjwwb57tKMhLixfrVKEDDrAdSXGSh+uwsyFa2HaSq1zOzVxq+GoAQ1J+\nH5S4reV9Bqe5z3kAzhGRswB0ANBFRB4yxnw13QtNmzbt03+Xl5ejvLw8h/Byc/TRwFtv6cpvioY3\n3wS+8Q3bURTv6KOBlNSnCHjzTeCYY2xHUbzevYFevYClS3W+PYXfzp16QXfEEbYjKV7ye33KFNuR\nkFf8ajsrKipQUVFR1HOIyXIur4i0AfAhgM8CWAvgXQAXGmOWpNznLABXGGPOFpFjAPzWGHNMi+c5\nGcD/M8ac08rrmGyxFOP3v9dhqPvu8+0lKECbNukK702bgHbtbEdTnB079LTCDRt0TjOF35QpwJln\nAl/7mu1IinfppcCxxwJTp9qOhLzwyivAj3+shUnYvfkmcOWVwJw5tiMhr4waBTz9tM5R95OIwBiT\n16yHrFMyjDGNAK4E8CKADwDMMMYsEZGpInJ54j7PA/hYRJYDuAfAd/KO3mcnnQS89prtKMgrb7yh\nV6FhL5YBnYs3dizw7ru2IyEvGKNtzUkn2Y7EG2w7oyVKufmZz+guQ1u32o6EvFBTA2ze7O5oVk5z\nmI0xLxhjDjLGjDbG3Ja47R5jzL0p97nSGDPKGDPBGLPf9Z4x5j+t9S4HYexYoK4OqK21FQF5adYs\n4MQTbUfhnRNP1P8mCr+VK/XApGHDbEfijRNP1CLLxwFAClCU2s7SUuCoo6LRW06amyecoO2nixwN\ny3tt2gDHH8+iJCqi1EsCsBcvSpK5GeY9blONGqVbd1VW2o6EirVnj45kHX+87Ui8w7YzOlz/Xo9N\nwQywFy8qdu7UjfePPtp2JN45/njg7be5p2gURKkHD9DCn21nNMyZA4wcqTtHRQVzMzpcbztjVTDz\nSjQa3n4bOOywaC2Q69VLFzHOnWs7EirWa6+53egXIjktg8LN9R68Qhx7rLabn3xiOxIqxqZNwKpV\nwOGH246kdbEqmI88Eli2jAsEws71q9BCsack/FxftFKok05ibkZBFNvOTp24aDoK3nhDR41dXsgf\nq4K5tBSYOJELBMJu1qzo9ZIALEqiwPVFK4UaO1YXTHPRdHg1NWlRErWCGWDbGQVh+F6PWLOeHYcW\nwy25aOW442xH4r1kDzNPVQuvMDT6hUgumn79dduRUKEWLdKDaPr3tx2J9/i9Hn5hmMoWu4KZV6Lh\nNmeOrtqP0qKVpLIyoFs3PVWNwikMjX6hWJSEW1Qv5gAd1eGi6fDauVMv6FxfyB+7gvmYY7hAIMyi\nXJAALErCLAyLVorBzoZwi3Lb2bMnF02H2dtvAxMmuL+QP3YFc6dOwLhxXCAQVlHuJQFYlIRZGBat\nFOPII3mqWlgZw7aT3BWW3IxdwQywFy+smpp0DmVUe0kAnqoWZlHcgSAVT1ULrxUronX6ZDr8Xg+v\nsIx+xLJg5pVoOC1aBPTtC/TrZzsS//BUtfCK4h63LXEv+3BK9uBF5fTJdE48UTtUuGg6XPbsAWbP\nDsfpk7EsmHmqWjiF5Sq0GMlT1ViUhEsUT59Mh3uFh1Mc2s7kouklS2xHQvl4/33tKOrWzXYk2cWy\nYOYCgXCK+pB3EouS8Ini6ZPp8FS1cGLbSa4KU27GsmAGOC0jbOKwaCWJuRk+YWr0i8FT1cInqqdP\npsO2M3zC9L0e24KZw97hEodFK0k8VS184jDkncS2M1yievpkOlw0HS5hW8gfg49QelwgEC5xWLSS\nxFPVwiVMi1a8wF68cAlTD16xuGg6XMK2kD+2BXNZmZ4Wt2iR7UgoFxUV4bkK9cJJJwGvvmo7CsrF\n7NnAyJHRPH0yneSpart3246EchGntlOEbWeYhC03Y1swA8C55wKPPWY7Csqmvh547jngnHNsRxKc\nyZOBJ57gTi5h8Le/AV/4gu0ogtOzJ3DEEcDzz9uOhLJZtEgPmvnMZ2xHEhx+r4dH2NrOWBfMF18M\nTJ/OaRmu+/vf9Qu6rMx2JME56CBg8GDglVdsR0KZNDRoo3/RRbYjCdbFFwOPPGI7Cspm+nTgK1+J\nx/zlpMmTdQSEa0DctnIlsHw5cPrptiPJXYw+RvubMAHo3FmPtCV3PfwwcMkltqMI3iWX6H87uevf\n/9bpGKNG2Y4kWOedB7z0ku6+QG5qatKCOW5tZ8eOWjQ/+qjtSCiTRx4BLrgAaNfOdiS5i3XBLMKe\nEtdt2KDznMI0bOOVCy7QqSg7dtiOhFrzyCPahsRN9+7aMzRzpu1IqDWvvabTZ8aOtR1J8Pi97jZj\nwtl2xrpgBnS4auZMLmBx1eOPA2edBXTtajuS4PXtqzsvPPOM7Ugone3bdR7v+efbjsSOiy/mCIjL\nwliQeOWUU3T/aZ7656bZs/V/jzrKbhz5in3BPHgwMH48F7C4Ks6NPsCeEpc9+aSuyO/d23Ykdpx5\nJrB4MbBqle1IqKX6es3PCy+0HYkdbdpoZ9j06bYjoXSS3+th2yY29gUzwKLEVStXAsuWhWtRgNe4\ngMVdcb+YKy0Fvvxl4K9/tR0JtRTHhdItcVG/m8K8UJoFM4AvfYkLWFw0fXr4FgV4rWNH3U5vxgzb\nkVCqmhrg/feBz3/ediR2JRem8mQ1t8T9Yg7QRf2dOgFvvmk7EkqVXCg9cqTtSPLHghnNC1gef9x2\nJJRkjH4Rx73RBzhX1EWPPqoLUTt0sB2JXcceq+s/5s61HQklbdyoC6W/+EXbkdiVXNTPttMtYf5e\nZ8GccMklnJbhkuSigIkT7cbhglNP5QIW17AHT3GnIfc89pjOL4/jQumWLrqIi/pdsm2brhe74ALb\nkRSGBXPCpElcwOKSsC4K8AMXsLhl0SLd7vDkk21H4oaLLtIed55K6QZezDXjon63PPUUUF4O9Opl\nO5LCsGBOKC3V7aG4gMW+MC8K8AsXsLgjjqenZcJTKd3BhdL74wiIO8J+MccmP0VyvhMXsNgV5kUB\nfuECFjfE9fS0bHgqpRu4UHp/XNTvhuRC6f/6L9uRFI4Fc4rkApY5c2xHEm9hXhTgFy5gcUOcT0/L\nhKdS2seF0ulxUb8b/vrX8C+UZsGcggtY7EsuCojr6WmZ8FRK+8I+pOgXnkpp3+zZWjRzofT++L1u\nXxTaThbMLVx8MRew2PTUU7qYKq6np2UyZAgwbhwXsNgS99PTsmFRYhcXSreOp1LatXChbncY9oXS\nLJhbOPBAYOhQ4OWXbUcST488wvmhmXD7Q3t4elpmPJXSnuRC6bD34PmFi/rtmj5dF/GHfaF0yMP3\nB3tK7IjCogC/cQGLPbyYy6xjRy2aeSpl8LhQOjsu6rcjuVA6ChdzLJjTmDIF+Oc/gQULbEcSL9Om\n6XB3mBcF+K17dy1KfvYz25HEyzvvAG+9pYtWqHXf/CZw++28oAtSQwNw88363lPrjj1Wezh5QRes\n++7TUbkoLJQW48jllogYV2IBgL/8BfjlL4H33mMBF4QnnwSuuUaP2OUJVZlt2AAcfjjwpz8Bp51m\nO5ro27ZN3+9f/YrHDefiqqt0WsaMGZxPG4Qf/1h3dvrHP/h+ZzNnjh5S9u67wLBhtqOJviVLgBNP\nBGbNAg4+2HY0+xIRGGPy+sSwYG6FMdrbOWIEcOuttqOJtu3bgVGjgGefBY4+2nY04fDKKzo9YOVK\n4IADbEcTbd/9ru5Mcu+9tiMJh/p64KijdMToS1+yHU20zZunC9rmzQP69bMdTTj8+tc6heVf/7Id\nSfQddxzwta8BU6fajmR/hRTMOU3JEJFJIrJURD4Sketauc+dIrJMROaJyGGJ2waJyCsi8oGILBSR\nq/IJziYRbfAffJA7Zvjtqae0UGaxnLtTTwVGj9apQ+Sf+nqdf/eTn9iOJDzatwd++EPggQdsRxJ9\nDz4I/Pd/s1jOx1VX6VqZykrbkUTbBx8Aq1dHa6pQ1oJZREoA3AXgDACHArhQRMa0uM+ZAEYaY0YD\nmArg7sSf9gL4vjHmUADHArii5WNdNmYMMGgQj3z1WxT2Z7SBi1P9949/6HSMQYNsRxIukyfrnG/u\nmOGfhgad9nLRRbYjCZfSUuDLX+aOGX575BE9O6BNG9uReCeXHuaJAJYZYyqNMQ0AZgCY3OI+kwE8\nBADGmHcAdBORfsaYdcaYeYnbdwBYAiBUmzJxGy9/1dTohvuf/7ztSMLnvPN0aHHLFtuRRBd3xihM\np07AOefoVmfkj5de0imDo0bZjiR8uGOGv5I7Y0St7cylYC4DUJXy+xrsX/S2vE91y/uIyDAAhwF4\nJ98gbbrgAp1bu3On7UiiacaM8B+XaUv37rrob+ZM25FE06ZNwKuvcqFfoTgC4i+OzBXuuOOATz7R\nud/kvVmzgB499KCtKGkbxIuISGcAMwFcnehpTmvatGmf/ru8vBzl5eW+x5ZNv37A5z4H/PznwC23\n2I4mWjZuBO64g71Qxbj8cuA739ELuy5dbEcTLTffrBdz3LWlMKeeqhcdTz8NnHuu7WiiZe5c4MUX\ngd/9znYk4SSibef113N3Ea81NgI33AB861u2I9lXRUUFKioqinqOrLtkiMgxAKYZYyYlfv8BAGOM\n+UXKfe4G8Kox5m+J35cCONkYUysibQH8HcA/jTGtfrxd2yUj1bp1Oo9xxozwH+3oCmO0527ECOA3\nv7EdTbh961vAnj26FSJ54/nndTHVvHlAz562owmvt97SYnnOHJ6Q6JWdO4Ejj9SFqF/5iu1owquh\nATjhBJ0DflVotiNw3y236EnJL73k9vxlX7aVE5E2AD4E8FkAawG8C+BCY8ySlPucBeAKY8zZiQL7\nt8aYYxJ/ewjABmPM97O8jrMFM9D8BTp/vg41UHHuuUd/3nqL26IVi1+g3qqtBQ47TEc+TjrJdjTh\nd8stunD63/92+ws0LC6/XLc55AVy8VasAI45Rgu88eNtRxN+yQvk9993f6G0b/swi8gkAL+Dznl+\nwBhzm4hMhfY035u4z10AJgHYCeBSY8xcETkewGsAFgIwiZ8fGWNeSPMaThfMAHD11UB1NfD44xzC\nKcbixdpT//rrwEEH2Y4mGubOBU4/XTfkHz7cdjTh1dQEnHWW7iP805/ajiYaGhuBU07R9/UHP7Ad\nTbjNnKnv4dy5nILllYcf1imX772nx7tTYbZu1ZH43/wmHCei8uASn9XX617B3/1utPYWDBLfQ//c\nfrtezM2aBbQNZHVC9NxxB/DYY8BrrwHt2tmOJjpWr9aLkOeeAyZOtB1NOFVV6UjS3//O99BLxuji\nya5dgT/+0XY04WSMTm3p2hW4++7s93cBC+YALF6sw7Svv677NFN+vvc97aV/7DH20nst2Ts6caIu\nWKP8sJfeX+wdLVxjoy6iPPNM9tL7Idk7evvtXKBaiDD20rNgDsjdd+v827ff5vzbfDz/PPDtb+tC\nKs4D90dygSrn3+aH88CDwQWqheE8cP+Faf6tS5YvB449Vhf5TZhgO5rcsWAOCHd4yB8LueDwwiR/\nl1+u04Ueesh2JNHGC5P8caeR4PDCJD8NDcDxx+t0jKuvth1NflgwB2jjRl1Jf//9wBln2I7GbU1N\nOpQ4cSIXUgWFU19y98QTwHXXcapAUObO1TbznXc49SUbThUIFqe+5OdHP9KOmTDuZc2COWCvvqpX\nVvPmAX372o7GXbffrvMXX3uNi9GCUl+v2yVdeSUXV2ZSVQV85jNcjBY0tgnZcTGaHWwTcvPKK5qf\nYa1/WDBbEOYrrCBwIZU9ye37Zs3iAtV02JtkDxeoZvfww8BttwGzZ4dnIVVUcIFqZlEYYWfBbEFy\nDs/FF/O0oJY4X9E+HhDTup/9TA8s4HxFO7iuoXU8UMM+LlBNLypruFgwW5Js3MK2StRvbHDsi0rj\n5rW33tLN9d9/nwupbOIC1f3xyGY3sMMnvbvvBu69N/ydMCyYLQrjPoR+4pCWO6IwfOYlLqRyCxeo\n7utHPwLmz9cDSvh+2JWcUvjOO9rpEHfJcyjeeCP8p/SyYLaICzSa8UQq93CBarOLL9aLuLh/Tl2R\nPP3zqquAyy6zHY1dr76q+Tl3Lj+nruAJqipqp/SyYLYsbGep+yG5kGrSJOCHP7QdDaVizxUXUrmK\nC1SbR4IeeEB7NckN3BZVXX21jgQ9/ng0vj8KKZhL/Aomjrp1A/76V+CSS4BevXTeU8ivAXI2bx4w\neLD+d7drB1x7re2IqKWbbgI2bwZ69gSGDQM+/NB2RMFobAQmT9bcvPJK4NFHWSy75pBDgFtvBY44\nQv9/uu022xEFZ8YMoE8fYOhQ4MILWSy7pqRE1+H86U+amxMnAtu3244qGJs26bqsXr2AZ58F7rsv\nGsVyodjD7IMdO4Bdu4D/+i/gq1/VL+ko27lT96285hotTLp3564Drtq7V0dCpk8H/vzn8C/cyMWt\nt+pOGI8/DnToAHTqZDsias2WLcD69TpP8rHHgBNPtB2Rv5ILxp95RueE9uwZ74LEZfX1+l137bV6\nEf7nP9uOyF/GAOedp8eE/+QnQOfO0fqu4JQMxyTPWH/lFWDcONvR+GfqVL1AePhh25FQrozRaUOj\nRwO/+pXtaPzzzjvAOefobhiDBtmOhnL1j38A3/lOtHfPaGjQC4IpU3ThI4XDzp06EjJtmo4IRNV9\n9wF/+APw9tvRKpSTWDA76KGHgF/+UudMduhgOxrvPfmk9izPnasLHik8NmzQOfdRnTO5bZv+9/36\n1/FdUxBmV18NrF2r+zRHsdf1+uu13eShV+EzZ46u03n3XZ3eFjVLlugoT5TXFLBgdpAxujtBu3b6\nv2PGAEOG2I6qOMYAr7+uw6ff/KbObTr6aNtRUSFeflmnDd13n06lOfbY8H95r1ypozv3369D3Hff\nbTsiKkR9vc4XPf98/d+JEzVHw6y+XtvOykrghhu0YO7Xz3ZUVIjf/AZ44gntaR44EBg71nZExZs3\nD6irA667Tkd4vvUt2xH5hwWzo7Zu1cTbtAlYulQbyT59bEdVuDvv1K12DjxQv8yisMVMnN15p+6c\nsXixrgL/+tdtR1S4NWt0S8Px43Vbrvvu4wK/MFuyBPjf/9UdJNq2BSoqwr2111e/qkXJgAG6T/0p\np9iOiArV1KRTaZYu1R7nF17QtTxh9Z//6Jzlww/Xn9tuC3/nSSYsmEPguuv0S+CZZ8KZjPPnA5/7\nnM5rGjnSdjTkpUWL9Av8jTf0YihsGhuB007TbQ1//GPb0ZCXmpr00J0TTgBuvNF2NIWZPl0vSN9/\nnwtPo+axx7TNmTNHF8eFzebNuqXhH/8InHWW7WiCwW3lQuCnP9Uhjy5dmn/OOku/7F30+uvaG56M\n9eijgTvuYLEcRWPHan6OH9/8/3f//vol4KI9e7TAT8batatehHL/7+gpKdH1IPfcs2/b+YMf2I6s\ndffdpzmZjPXb39YtDVksR8/55wPl5ft+Vx5yiI6MuKimBhg1qjnWAQO0dzkuxXKh2MNsQWOj7ioB\nNO9WcMop7vWKbdmiV5133KG9yoBuF8ch7mjbuVN79ADg6aeBn/3MzV6xa6/V4dBHHmkerenYkVsa\nRtmePcDu3frv7dt1zv0f/gCcfbbduFpKjta8+qrurwzoOpb27e3GRf4xRreUTbrxRuDjj3VhvEuj\nyU1Nusj7+ON1ulNS585uxek3TskIqepq3abm9NN1C6Wf/9xucfLSS1qELF6sPcq//729WMi+r31N\nF9GNHq3z1U84wV4sW7bo7gKbNwOvvabzQXv3thcP2fXaa9q7N2mS9phdf73dL/3779dRuVmzdFHf\npZfai4Xs2r1bL+gGDdL1FNdfDwwfbi+eDz/UHbtqarSwf/XVcK8HKBYL5hBbuFB78Z5+Wj9c995r\nJ47KSuCoo7Sx79UL+OIX2SsSdzt3Ak89BdTW6spwWyv7jWne93TSJD3wIapbHlHuKiqAVauA3/5W\npz1MnWonjpdf1ovLn/5U287Pfz5ePXa0v7VrgX/9Sy/s33pLL6batQs+jk8+0V1mzj5b28yzzw73\nxgNeYMEcAdu2aW/zf/+3nvwE6Byjk0/2p/Gtq9PDHZJ+8Qs9re+aa7x/LQq/G27QBZ9XXdV823HH\naYHgtaYm7QVJTl+aN0/35I3qnuZUnKVLdfTjzju1zQS0d+/ww/15vY8+aj5evrFRT3T985+bp68R\nJRmjRerQoc3zhNu0AT77WX8OBfnkEz0wLTm1buZM3dJwxgxexCWxYI6IOXOAm25qTvYFC/Royssu\n8/Z16ut1ykWvXs1TQEaO1C3jSrgclNJoaNBiec0a/X3rVr1t1izvh/fuuAO46y5dPAPo899yC3Do\nod6+DkXHE0/se2Tx22/rwSATJ3r7OlVVun3hxInNBchpp+17IUmUqq4O+O53mzsAKit1HvEf/+j9\na118sU6pLCvT37t2Bf7v/8K/j7mXWDBH1OLF2sPs9ak73/ueFj6PP86rTipMUxNw5pl64XXzzd49\n79y5Oqf/3XftzvujcJs5U3fSmDu3ude5WI2NunXhpEnckYUKt3Wrjn7cfjtw7rnePe/DD+s6qPfe\n4wL9TLitXEQdcghw663AuHFAaem+P0cfrcMvmTz3nPYgt3zsc8/pXGkWy1SokhLgL38BHnxw//zq\n0kWHBTPZvh2YMCF9Xt95J4tlKs555zUvpm6ZY9/4hg6VZ3Lrrfs/rn17/bn22mD+GyiaunXTvbkv\numj/HBs0CFi9OvPjP/xQ15K0fOwVV+j2hSyWvcce5hBpaNi3gTcGuOQS3Sv3zjvTP6a6WocOH398\n/+Or27ThFlzkjcbG/fcSf/VVnUaUaSeLSy/VHGw5LCliZ3EMRY8x2namqq/XuffXXKML9dKZNUt3\n4Jg9Wxdip2rXjh0N5I29e5unXybdfrtOJaqoSP8dndyB47LL9j++uqQk3rtf5IpTMmIoeULPZz6T\nfvHAggXABRfoYi2ioF1zjW5TePDB+/9t1y499XLOHPf2eKboW7hQp1acdlr6v//nPzoC59oezxR9\nyb2Sm5q0Q6ylNWuAnj119yJeuBWGBXNMrVix704XqTp0AM45hz3JZMeePTr1J3nYREsnnAAMGRJs\nTERJs2cDy5al/1v//lpQE9mweTPwwgvppw2VlOhuG127Bh9XVLBgJiIiIiLKgIv+iIiIiIg8xoKZ\niIiIiCgDFsxERERERBmwYCYiIiIiyoAFMxERERFRBiyYiYiIiIgyYMFMRERERJRBTgWziEwSkaUi\n8uEqd6wAAAW5SURBVJGIXNfKfe4UkWUiMk9EDsvnsURERERErspaMItICYC7AJwB4FAAF4rImBb3\nORPASGPMaABTAdyd62PJexUVFbZDiBS+n97i++kdvpfe4vvpLb6f3uF7aV8uPcwTASwzxlQaYxoA\nzAAwucV9JgN4CACMMe8A6CYi/XJ8LHmMHyxv8f30Ft9P7/C99BbfT2/x/fQO30v7cimYywBUpfy+\nJnFbLvfJ5bFERERERM7ya9FfXudzExERERG5Sowxme8gcgyAacaYSYnffwDAGGN+kXKfuwG8aoz5\nW+L3pQBOBjA822NTniNzIEREREREHjDG5NW52zaH+8wGMEpEhgJYC2AKgAtb3OdZAFcA+FuiwN5i\njKkVkQ05PLagwImIiIiIgpC1YDbGNIrIlQBehE7heMAYs0REpuqfzb3GmOdF5CwRWQ5gJ4CvZ3qs\nb/81REREREQeyzolg4iIiIgozqye9CcivxSRJYnDTp4Qka4pf/th4iCUJSJyus04w0JEzhORRSLS\nKCJHpNw+VER2icicxM8fbMYZFq29n4m/MT8LJCI3isialHycZDumMOKhUN4SkVUiMl9E5orIu7bj\nCRsReUBEakVkQcptPUTkRRH5UET+JSLdbMYYFq28l2w3CyQig0TkFRH5QEQWishVidvzyk/bR2O/\nCOBQY8xhAJYB+CEAiMghAM4HcDCAMwH8QUQ4xzm7hQC+AOA/af623BhzROLnOwHHFVZp308RORjM\nz2LdnpKPL9gOJmx4KJQvmgCUG2MON8ZMtB1MCD0IzcdUPwDwkjHmIACvIPEdT1mley8BtpuF2gvg\n+8aYQwEcC+CKRHuZV35aLZiNMS8ZY5oSv74NYFDi3+cAmGGM2WuMWQUtptmAZWGM+dAYswzpt/Vj\nQZenDO/nZDA/i8V8LA4PhfKewH4nUmgZY14HsLnFzZMB/CXx778AODfQoEKqlfcSYLtZEGPMOmPM\nvMS/dwBYAq0388pPlxqHbwB4PvHvlgeeVIMHnhRrWGIY51UROcF2MCHH/CzelYmpWPdzmLYgPBTK\newbAv0Vktoh8y3YwEdHXGFMLaNECoK/leMKO7WaRRGQYgMOgnbT98snPXLaVKza4fwPol3oTtGG6\n3hjzXOI+1wNoMMY86nc8YZfL+5lGDYAhxpjNibm4T4vIIYkrrVgr8P2kLDK9rwD+AOBmY4wRkVsA\n3A7gsuCjJNrH8caYtSLSB1o4L0n09JF3uMtA4dhuFklEOgOYCeBqY8yONOd/ZMxP3wtmY8xpmf4u\nIpcCOAvAqSk3VwMYnPL7oMRtsZft/WzlMQ1IDO8YY+aIyAoABwKY43F4oVPI+wnmZ1Z5vK/3AeCF\nSf6qAQxJ+Z05WCRjzNrE/64Xkaeg015YMBenVkT6Jc5l6A+gznZAYWWMWZ/yK9vNPIlIW2ix/LAx\n5pnEzXnlp+1dMiYBuAbAOcaY3Sl/ehbAFBEpFZHhAEYB4Krl/Hw610lEeicWCUFERkDfz5W2Agup\n1LljzM8iJBqmpC8CWGQrlhD79EApESmFHgr1rOWYQktEOiZ6nyAinQCcDuZlIQT7t5WXJv79NQDP\ntHwAtWqf95LtZtH+BGCxMeZ3KbfllZ9W92EWkWUASgFsTNz0dnIHBxH5IXS4oQHaff6inSjDQ0TO\nBfB7AL0BbAEwzxhzpoh8EcDNAPZAV4L/xBjzfOvPREDr72fib8zPAonIQ9A5ZE0AVgGYmpxHRrlL\ndDj8Ds2HQt1mOaTQSlz4PgUdkm0LYDrfz/yIyF8BlAPoBaAWwI0AngbwOHRErhLA+caYLbZiDItW\n3stTwHazICJyPIDXoDtfmcTPj6AdXY8hx/zkwSVERERERBm4tEsGEREREZFzWDATEREREWXAgpmI\niIiIKAMWzEREREREGbBgJiIiIiLKgAUzEREREVEGLJiJiIiIiDJgwUxERERElMH/ByRdCgLq5YS6\nAAAAAElFTkSuQmCC\n",
+      "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": [
-       "<matplotlib.figure.Figure at 0x105a3da90>"
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -310,20 +1143,8 @@
     }
    ],
    "source": [
-    "fig = plt.figure(figsize=(12, 5))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
-    "\n",
-    "# plot (can also be done repeatedly while the sweep is running)\n",
-    "# next up: create an active plot that will update itself automatically\n",
-    "# (via javascript, so the main python stays accessible) as long as the sweep is running\n",
-    "# as well as provide some nicer styling and labeling defaults\n",
-    "while data.is_live_mode:\n",
-    "    data.sync()\n",
-    "    ax.clear()\n",
-    "    ax.plot(data.chan0, data.amplitude)\n",
-    "    display.display(fig)\n",
-    "    display.clear_output(wait=True)\n",
-    "    time.sleep(1)"
+    "# live-updating plot, that syncs the data and stops updating when it's finished\n",
+    "plot = qc.Plot(x=data.chan0, y=data.amplitude)"
    ]
   },
   {
@@ -338,76 +1159,1560 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   chan0: chan0\n",
       "   amplitude_0: amplitude\n",
+      "   chan0: chan0\n",
       "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
-      "started at 2016-01-08 15:49:10\n"
+      "started at 2016-01-13 10:25:04\n"
      ]
-    }
-   ],
-   "source": [
-    "data2 = q.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
-    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
-    "    q.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
-    "    q.Wait(0.001),\n",
-    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
-    "    q.Task(c2.set, 0)\n",
-    "    ).run(location='test2d')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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dtVdRe+wbV5YBAGBCswwAABOaZQAAmFjWLFfVu6vqs1V17+HHG1cdCwAAVlj9B363jzFu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\nAMCEZhkAACY0ywAAMPH/Aepzu1Z7lMBjAAAAAElFTkSuQmCC\n",
+      "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": [
-       "<matplotlib.figure.Figure at 0x105a80710>"
+       "<IPython.core.display.Javascript object>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 5))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
-    "\n",
-    "# next up: create an active plot that will update itself automatically\n",
-    "# (via javascript, so the main python stays accessible) as long as the sweep is running\n",
-    "# as well as provide some nicer styling and labeling defaults\n",
-    "while True:\n",
-    "    data2.sync()\n",
-    "    ax.clear()\n",
-    "    ax.pcolormesh(ma.masked_invalid(data2.chan0),\n",
-    "                  ma.masked_invalid(data2.chan1),\n",
-    "                  ma.masked_invalid(data2.amplitude_0),\n",
-    "                  cmap=plt.cm.hot)\n",
-    "    display.display(fig)\n",
-    "    display.clear_output(wait=True)\n",
-    "    if not data2.is_live_mode:\n",
-    "        break\n",
-    "    time.sleep(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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\">"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105c7d470>"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -415,36 +2720,21 @@
     }
    ],
    "source": [
-    "# this plot shows the second array collected, amplitude_0 (at c2=1)\n",
-    "# note that these are acquired within the same 2D sweep\n",
-    "# as you can see if you run this cell and the above while the Loop is running\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "# pcolormesh needs NaN masked out or it barfs\n",
-    "plt.pcolormesh(ma.masked_invalid(data2.chan0),\n",
-    "               ma.masked_invalid(data2.chan1),\n",
-    "               ma.masked_invalid(data2.amplitude_3),\n",
-    "               cmap=plt.cm.hot)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# I obviously have a lot to figure out about matplotlib...\n",
-    "# calling the above redraws constantly to \"live update\"\n",
-    "# generates warnings after a while about too many open figures\n",
-    "plt.close('all')"
+    "data2 = qc.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
+    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
+    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
+    "    qc.Wait(0.001),\n",
+    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
+    "    qc.Task(c2.set, 0)\n",
+    "    ).run(location='test2d')\n",
+    "\n",
+    "plot2 = qc.Plot(x=data2.chan0, y=data2.chan1, z=data2.amplitude_0, cmap=plt.cm.hot)\n",
+    "plot2b = qc.Plot(x=data2.chan0, y=data2.chan1, z=data2.amplitude_3, cmap=plt.cm.hot)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -456,103 +2746,2337 @@
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
       "   amplitude_5_0: amplitude\n",
       "   amplitude_2: amplitude\n",
-      "   avg_amplitude: avg_amplitude\n",
+      "   chan1: chan1\n",
+      "   chan0: chan0\n",
       "   chan2: chan2\n",
+      "   avg_amplitude: avg_amplitude\n",
       "   amplitude_3_0: amplitude\n",
-      "   chan0: chan0\n",
-      "   chan1: chan1\n",
-      "started at 2016-01-08 15:49:54\n"
+      "started at 2016-01-13 10:25:37\n"
      ]
-    }
-   ],
-   "source": [
-    "data3 = q.Loop(c1[-15:15:1], 0.1).each(\n",
-    "    q.Task(c0.set, -10),\n",
-    "    q.Task(c2.set, 0),\n",
-    "    # a 1D measurement\n",
-    "    meter.amplitude,\n",
-    "    # a 2D sweep, .each is actually unnecessary bcs this is the default measurement\n",
-    "    q.Loop(c0[-15:15:1], 0.001).each(meter.amplitude),\n",
-    "    q.Task(c0.set, -10),\n",
-    "    # a 2D sweep with the same outer but different inner loop\n",
-    "    q.Loop(c2[-10:10:0.2], 0.001),\n",
-    "    AverageGetter(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
-    ").run(location='test_multi_d')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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gLs8yxjyG4eWyhm80xaMkSdLgeNlakiRJnXnZWpIkSZ1ZPEqSJKkzL1tLkiSpM0ceJUmS\n1Jkjj5IkSerMkUdJkiR1ZvHYj5MbxDi1QQyA0xvEaDWR7O82inPS29rEOfKeNnHe2SYMX28U54kG\nMZ5qEAOGlcdgLs8yxjwGc3mWMeYxDC+XR8fL1pIkSerMkUdJkiR1NsLicdNGd0CSJEknDkceJUmS\n+uI9j5IkSepshJetLR4lSZL64sijJEmSOnPkUZIkSZ1ZPA7XKQOKs7VBDGg3kSy7q0mYk0iTOFsb\nTWw7pN95K6368ppGcVoZYy6bx7MN7bxa/F0eYx7D8HJ5dLxsLUmSpM4ceZQkSVJnFo+SJEnqzMvW\nkiRJ6myEI4+9v54wyWVJHkjyYJJr+j6eJEnSYBxdx7KKLjVVkvcm2ZvkniTbprZvTvKxJPcn+esk\nPz3Z/oIkdyTZk+RTSTbPO6Vei8ckm4DrgFcDLwOuTvKSPo8pSZI0Nl1qqiSXA+dX1YXADuB9Ux//\nJ+D2qnop8E+A+yfb3w58pqouAj4LvGNeX/oeebwE2FtV+6rqMHALsL3nY0qSJA3DkXUsK+tSU20H\nPgRQVXcCm5NsSfJ84Ger6qbJZ09X1ben9rl58vPNwJXzTqnv4vEs4JGp9f2TbZIkSePXrnjsUlMt\nb3Ngsu3HgL9LclOSu5PckOQHJ23OrKpDAFV1EDhz3ikN4oGZb0/9fOpkkSRJ6mpxcZHFxcWN7saz\ndXjaevEgLB7qtRc/AFwMvLWq/jLJf2TpcvW18KzZ5ufOYt938XgAOHdq/ezJtu/z/AYHeqpBjFZx\nDjaIAXCk0az/rd5C0Ko/rb6fIf3OW2nVlz9uFOefNYozxlw2j2cb2nm1+H2NMY9heLm8XgsLCyws\nLHxvfdeuXRvXmWkdnrZeOGNpecauL6/YrEtNdQA4Z5U2j1TVX05+/u/AMw/cHEyypaoOJdkKfG1e\nf/u+bH0XcEGS85KcAlwF3NbzMSVJkoah3WXrLjXVbcDrAZJcCnyzqg5NLks/kuTHJ+1eAdw3tc8b\nJz+/Abh13in1OvJYVUeS7ATuYKlQvbGq7p+zmyRJ0jg0miR8tZoqyY6lj+uGqro9yRVJHgKeAN40\nFeI3gT9McjLwt1OfvQv4aJI3A/uA187rS+/3PFbVJ4GL+j6OJEnS4DScJHylmqqq3r9sfecq+/4V\n8FMrbH8ceOVa+jGIB2YkSZJGydcTSpIkqbMRvp7Q4lGSJKkvFo+SJEnqbISXrfueqkeSJEkjkqq5\nE4n324GkzpjfbK7TGsQAOL1BjAsbxIB2j6hvbRSn1USyexrF2dsozuMNYjzRIAYMK4/BXJ5ljHkM\n5vIsY8xjGF4uA3yhQW2ShKpqMyP7+vtQtabnmCf7fYYN7/ssXraWJEnqi/c8SpIkqbMR3vNo8ShJ\nktQXRx4lSZLUmSOPkiRJ6syRR0mSJHVm8ShJkqTOvGwtSZKkzhx5HK6nGsVpMUFuq4lSv94ozimN\n4rT6joc2mXGr82phSHkM5vIs5vFsY8zlMeYxDC+XR8fiUZIkSZ2N8LK177aWJElSZ448SpIk9cXL\n1pIkSepshJetLR4lSZL64sijJEmSOrN4lCRJUmdetpYkSVJnjjxKkiSpM4vHfhze6A5M+U6DGE82\niAHtZusf2tsMWn0/rfrTIv+GlMPQJo/BXJ5ljHncMk4rQ8rlMeYxDC+XR8fL1pIkSerMkUdJkiR1\nNsKRR19PKEmS1Jcj61hWkeSyJA8keTDJNau0eW+SvUnuSbJt2Webktyd5Lapbdcm2T/ZfneSy+ad\nkiOPkiRJfWl02TrJJuA64BXAV4G7ktxaVQ9MtbkcOL+qLkzy08D7gEunwvwWcB/w/GXhd1fV7q59\nceRRkiRp+C4B9lbVvqo6DNwCbF/WZjvwIYCquhPYnGQLQJKzgSuAD64QO2vpiMWjJElSX46uY1nZ\nWcAjU+v7J9tmtTkw1eY9wL8DaoXYOyeXuT+YZPO8U7J4lCRJ6kvDex7XK8kvAYeq6h6WRhmnRxqv\nB15cVduAg8Dcy9fe8yhJktSXDsXgYi0tcxwAzp1aP3uybXmbc1Zo82vAryS5AvhB4HlJPlRVr6+q\nx6bafwD4xLyOpGp+b/uUpE5rEOfkBjFaxmlhSH1paWiTEA9pkvAx5jEMrz8tjDGPW8YZYy4PqS8t\nDS2XAb7WoDZJQlWt6V6+1pKs60wCz+p7kpOAPSw9MPMo8Hng6qq6f6rNFcBbq+qXklwK/MequnRZ\nnJ8HfruqfmWyvrWqDk5+fhvwU1X1uln9c+RRkiSpJ62uQlfVkSQ7gTtYuu3wxqq6P8mOpY/rhqq6\nPckVSR5i6aVIb+oQ+t2TKX2OAg8DO+bt4MhjT3FaGFJfWhra/8t15LF/Q+tPC2PM45ZxxpjLQ+pL\nS0PLZRjXyON6Xv94Ks8eeRwSRx4lSZJ6MsIXzPT3tPV6ZiyXJEkakwE8bN1c3yOPa5qxXJIkaUzG\nOPLYd/E42Ov1kiRJfTsRRhLXqu9Jwtc0Y7kkSdKYjPGy9TEVj0k+neRLU8uXJ//9ZdYxY7kkSZKG\n7ZguW1fVqzo2nTlj+dPLOrSe6RCGNC3EKQ1iwNKj+i206s9TjeKsZ9qClbTqz5AMKY/BXJ7FPJ5t\njLk8xjyG8eTy4uIii4uLG9yLZxvjPY+9zfPYdcbyJPXDDY43pD9U/oM729D+UDnP4+rM5dWNMY9b\nxhljLo8xj2F4uQxwYETzPD66jv1+lOfuPI9rnrFckiRpTE6EexjXqrfisape31dsSZKkE8EYL1v7\nhhlJkqSeOPIoSZKkzhx5lCRJUmeOPEqSJKkzi0dJkiR15mVrSZIkdebIY09aTALbaiLZ5zWIcVqD\nGACnN4oztAlpH28U54lGcb7TKE4LQ8pjMJdnMY9nG2MujzGPYXi5PDZjLB6P6d3WkiRJem4ZxMij\nJEnSGHnPoyRJkjob42Vri0dJkqSeOPIoSZKkzhx5lCRJUmcWj5IkSerMy9aSJEnqzJHHAWs16WqL\niWQvbBAD4KJGcbY2inOwUZw9jeLsbRTnyQYxDjeIAcPKYzCXZxljHoO5PMsY8xiGl8tjY/EoSZKk\nzrxsLUmSpM7GOPLo6wklSZJ6cnQdy2qSXJbkgSQPJrlmlTbvTbI3yT1Jtk22nZrkziRfTPLXSf7D\nVPsXJLkjyZ4kn0qyed45WTxKkiQNXJJNwHXAq4GXAVcnecmyNpcD51fVhcAO4H0AVfUk8AtV9XLg\nHwP/IsnPTHZ7O/CZqroI+Czwjnl9sXiUJEnqyZF1LKu4BNhbVfuq6jBwC7B9WZvtwIcAqupOYHOS\nLZP1v5+0OZWl+u8bU/vcPPn5ZuDKeedk8ShJktSThsXjWcAjU+v7J9tmtTnwTJskm5J8kaUH9Rer\n6r5JmzOr6hBAVR0Ezpx3ThaPkiRJPWl5z+OxqKqjk8vWZwM/l+TnV2s6L5ZPW0uSJPWky9PWfw3c\nN7cVB4Bzp9bPnmxb3uacWW2q6ttJ/gfwk8CfA4eSbKmqQ0m2Al+b15FBFI8nN4hxaoMYAKc3iNFq\nItnfbRTnpLe1iXPkPW3ivLNNGL7eKM4TDWI81SAGDCuPwVyeZYx5DObyLGPMYxheLo9Nl+LxJZPl\nGX+0crO7gAuSnAc8ClwFXL2szW3AW4GPJLkU+OakKPwR4HBVfSvJDwKvAnZN7fNG4F3AG4Bb5/V3\nEMWjJEnSGLW6DF1VR5LsBO5g6bbDG6vq/iQ7lj6uG6rq9iRXJHmIpf9P+abJ7j8K3Jwkk33/a1X9\nz8ln7wI+muTNwD7gtfP6YvEoSZLUk5aThFfVJ1k2kF5V71+2vnOF/b4MXLxKzMeBV66lHxaPkiRJ\nPfH1hJIkSepsjK8ntHiUJEnqicWjJEmSOhvjZWsnCZckSVJnjjxKkiT1xMvWA3bKgOJsbRAD2k0k\ny+65bxrq5CTSJM7WRhPbDul33srQzslcXp15PNvQzqtFLo8xj2F4uTw2Fo+SJEnqbIz3PFo8SpIk\n9cSRR0mSJHXmyKMkSZI6G+PI4zFN1ZPk15Lcm+RIkouXffaOJHuT3J/kF4+tm5IkSSeeI+tYhu5Y\nRx6/DPwq8H0v5U7yUuC1wEuBs4HPJLmwqto8YiZJknQC8LL1MlW1ByDJ8vkCtgO3VNXTwMNJ9gKX\nAHcey/EkSZJOJCfCSOJa9XXP41nAX0ytH5hskyRJes54ThaPST4NbJneBBTwO1X1iRad+PbUz6dO\nFkmSpK4WFxdZXFzc6G48y3PysnVVvWodcQ8A50ytnz3ZtqLnr+MAyz3VIEarOAcbxAA40mjW/1Zv\nIWjVn1bfz5B+560M7ZzM5dWZx7MN7bxa/L7GmMcwvFxer4WFBRYWFr63vmvXro3rzMgd09PWy0z/\nr+E24KokpyT5MeAC4PMNjyVJkjR4Y3za+lin6rkyySPApcCfJvkzgKq6D/gocB9wO/AWn7SWJEnP\nNUfXsQzdsT5t/SfAn6zy2e8Dv38s8SVJkk5kJ8JI4lr5hhlJkqSeWDxKkiSpsxPhMvRaWTxKkiT1\nxJFHSZIkdWbxKEmSpM7GeNk6Gz2DTpI6o0Gc0xrEADi9QYwLG8QAuKhRnK2N4rSaSHZPozh7G8V5\nvEGMJxrEgGHlMZjLs4wxj8FcnmWMeQzDy2WALzSoTZJQVW1mZF9/H+pN69jvJtjwvs/iyKMkSVJP\nxjjyaPEoSZLUkzHe89jy9YSSJEnqSZLLkjyQ5MEk16zS5r1J9ia5J8nLp7bfmORQki8ta39tkv1J\n7p4sl83rh8WjJElST1q92zrJJuA64NXAy4Crk7xkWZvLgfOr6kJgB/Bfpj6+abLvSnZX1cWT5ZPz\nzsniUZIkqScN3219CbC3qvZV1WHgFmD7sjbbgQ8BVNWdwOYkWybrnwO+sUrsNT2cY/EoSZLUk1Yj\nj8BZwCNT6/sn22a1ObBCm5XsnFzm/mCSzfMaWzxKkiT1pEuxeBC4d2o5zq4HXlxV2yZd2T1vB5+2\nliRJ6kmXqXpeOFme8cDKzQ4A506tnz3ZtrzNOXPafJ+qemxq9QPAJ2Z2lhEVj081itNigtxWE6V+\nvVGcUxrFafUdD20y41bn1cKQ8hjM5VnM49nGmMtjzGMYXi6PTcOpeu4CLkhyHvAocBVw9bI2twFv\nBT6S5FLgm1V1aOrzsOz+xiRbq+qZOedfQ4fBz9EUj5IkSUPTapLwqjqSZCdwB0u3Hd5YVfcn2bH0\ncd1QVbcnuSLJQyzV8997wU2SDwMLwAuTfAW4tqpuAt6dZNukqw+z9JT2TBaPkiRJPWk5SfhkGp2L\nlm17/7L1navs+7pVtr9+rf2weJQkSerJGN8wY/EoSZLUE99tLUmSpM7GOPLoPI+SJEnqzJFHSZKk\nnoxx5NHiUZIkqSfe8yhJkqTOHHnsyeGN7sCU7zSI8WSDGNButv6hvc2g1ffTqj8t8m9IOQxt8hjM\n5VnGmMct47QypFweYx7D8HJ5bBx5lCRJUmeOPEqSJKkzi0dJkiR15mVrSZIkdebIoyRJkjqzeJQk\nSVJnXraWJElSZ448SpIkqTNHHnsytElpj1Wr8xnrhKtD+32PcZLwVszl1Q3tdz7WScJbaXFeY8xj\nGO/vXP0ZRPEoSZI0Rl62liRJUmcWj5IkSepsjPc8bjqWnZP8WpJ7kxxJcvHU9vOS/H2SuyfL9cfe\nVUmSpBPLkXUsQ3esI49fBn4VeP8Knz1UVRevsF0b4Eng1I3uxMgdAU7a6E6MnHncP/P4+DCXnztO\nhGJwrY6peKyqPQBJssLHK23TBvEPVf/8R7d/5nH/zOPjw1x+7vCy9dq8aHLJ+n8l+ec9HkeSJGmQ\nnpOXrZN8GtgyvQko4Heq6hOr7PZV4Nyq+sbkXsg/SfITVfXdY+6xJEnSCWKMI49zi8eqetVag1bV\nYeAbk5/vTvI3wI8Dd6/U/om1HkDrYuXePyfb7Z953D/z+Pgwl1e38t1wJ6R9T8B569mveU8aajlV\nz/d+00l+BHi8qo4meTFwAfC3K+1UVaPJEEmSpGdU1Ys2ug99ONapeq5M8ghwKfCnSf5s8tHPAV9K\ncjfwUWBHVX3z2LoqSZKkjZaq2ug+SJIk6QTR59PWMznBeP9W+44nn70jyd4k9yf5xY3q49gkuTbJ\n/qn8vWxvJw1cAAAC+UlEQVSj+zQWSS5L8kCSB5Ncs9H9GaMkDyf5qyRfTPL5je7PGCS5McmhJF+a\n2vaCJHck2ZPkU0k2b2Qfx2CV79m/xz3ZsOKRf5hg/M9X+Oyhqrp4srzlOPdrTFb8jpO8FHgt8FLg\ncuD6Vebq1PrsnsrfT250Z8YgySbgOuDVwMuAq5O8ZGN7NUpHgYWqenlVXbLRnRmJm1jK22lvBz5T\nVRcBnwXecdx7NT4rfc/g3+NebFjxWFV7qmovK08mbiHTwIzveDtwS1U9XVUPA3sB/6Fox/xt7xJg\nb1Xtm8zmcAtLeay2wsYOKoxOVX2OyewjU7YDN09+vhm48rh2aoRW+Z7Bv8e9GOofCScY79dZwCNT\n6wcm29TGziT3JPmgl6OaWZ6z+zFn+1DAp5PcleQ3NrozI3ZmVR0CqKqDwJkb3J8x8+9xD1pO1fMs\nTjDev3V+xzoGs75z4HrgnVVVSX4P2A38+vHvpbQuP1NVjyY5g6Ui8v7JiI765ZOr/fDvcU96LR6P\nxwTjz3Xr+Y5ZGmk8Z2r97Mk2dbCG7/wDgAV8GweAc6fWzdkeVNWjk/8+luTjLN0uYPHY3qEkW6rq\nUJKtwNc2ukNjVFWPTa3697ihoVy2/r4Jxic3xzNvgnGtyfR9H7cBVyU5JcmPsfQd+2RlA5N/CJ7x\nGuDejerLyNwFXDCZjeEU4CqW8liNJPmhJP9o8vNpwC9i/rYSnv03+I2Tn98A3Hq8OzRS3/c9+/e4\nP72OPM6S5ErgPwM/wtIE4/dU1eUsTTD+ziRPsfTknxOMr9Nq33FV3Zfko8B9LL2J7C3lhJ+tvDvJ\nNpZy92Fgx8Z2Zxyq6kiSncAdLP2f3hur6v4N7tbYbAE+nqRY+rfhD6vqjg3u0wkvyYeBBeCFSb4C\nXAv8AfCxJG9m6TV0r924Ho7DKt/zL/j3uB9OEi5JkqTOhnLZWpIkSScAi0dJkiR1ZvEoSZKkziwe\nJUmS1JnFoyRJkjqzeJQkSVJnFo+SJEnqzOJRkiRJnf1/CLJVID7qkzgAAAAASUVORK5CYII=\n",
+      "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": [
-       "<matplotlib.figure.Figure at 0x105c90278>"
+       "<IPython.core.display.Javascript object>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data3.sync()\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "# pcolormesh needs NaN masked out or it barfs\n",
-    "plt.pcolormesh(ma.masked_invalid(data3.chan0),\n",
-    "               ma.masked_invalid(data3.chan1),\n",
-    "               ma.masked_invalid(data3.amplitude_3_0),\n",
-    "               cmap=plt.cm.hot)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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S1WZmGn4z511mts/dr8vmSUcQAACgLQW3hgc/NHy8ZOvvpk3/WNIOd/909Et33/TS/83s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\nIAAAQFtmlvBImNk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\">"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105c797f0>"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data3.sync()\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "# pcolormesh needs NaN masked out or it barfs\n",
-    "plt.pcolormesh(ma.masked_invalid(data3.chan2),\n",
-    "               ma.masked_invalid(data3.chan1),\n",
-    "               ma.masked_invalid(data3.amplitude_5_0),\n",
-    "               cmap=plt.cm.hot)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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kdvfcIxun/etf2knOFc2pFJ4VWCF/5Ih2EnK7UaNkjiOL4uDUrSvH3bJl2knI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\nQmCC\n",
+      "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": [
-       "<matplotlib.figure.Figure at 0x10639f470>"
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -560,16 +5084,27 @@
     }
    ],
    "source": [
-    "data3.sync()\n",
-    "fig = plt.figure(figsize=(12, 5))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
-    "ax.plot(data3.chan1, data3.avg_amplitude)\n",
-    "plt.show()"
+    "data3 = qc.Loop(c1[-15:15:1], 0.1).each(\n",
+    "    qc.Task(c0.set, -10),\n",
+    "    qc.Task(c2.set, 0),\n",
+    "    # a 1D measurement\n",
+    "    meter.amplitude,\n",
+    "    # a 2D sweep, .each is actually unnecessary bcs this is the default measurement\n",
+    "    qc.Loop(c0[-15:15:1], 0.001).each(meter.amplitude),\n",
+    "    qc.Task(c0.set, -10),\n",
+    "    # a 2D sweep with the same outer but different inner loop\n",
+    "    qc.Loop(c2[-10:10:0.2], 0.001),\n",
+    "    AverageGetter(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
+    ").run(location='test_multi_d')\n",
+    "\n",
+    "plot3 = qc.Plot(x=data3.chan0, y=data3.chan1, z=data3.amplitude_3_0, cmap=plt.cm.hot)\n",
+    "plot3b = qc.Plot(x=data3.chan2, y=data3.chan1, z=data3.amplitude_5_0, cmap=plt.cm.hot)\n",
+    "plot3c = qc.Plot(x=data3.chan1, y=data3.avg_amplitude)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -579,63 +5114,1561 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DataSet: DataMode.PUSH_TO_SERVER, location='test_complex_param'\n",
+      "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
+      "   chan2: chan2\n",
       "   avg_amplitude: avg_amplitude\n",
-      "   amplitude: amplitude\n",
       "   chan1: chan1\n",
-      "   chan2: chan2\n",
-      "started at 2016-01-08 15:50:32\n"
+      "   amplitude: amplitude\n",
+      "started at 2016-01-13 10:26:07\n"
      ]
-    }
-   ],
-   "source": [
-    "# An example of a parameter that returns several values of different dimension\n",
-    "# This produces the last two arrays from data3, but only takes the data once.\n",
-    "data4 = q.Loop(c1[-15:15:1], 0.1).each(\n",
-    "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
-    ").run(location='test_complex_param', background=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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S1WZmGn4z511mts/dr8vmSUcQAACgLQW3hgc/NHy8ZOvvpk3/WNIOd/909Et33/TS/83s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\nAAAAAElFTkSuQmCC\n",
+      "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": [
-       "<matplotlib.figure.Figure at 0x10667c278>"
+       "<IPython.core.display.Javascript object>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.pcolormesh(ma.masked_invalid(data4.chan2),\n",
-    "               ma.masked_invalid(data4.chan1),\n",
-    "               ma.masked_invalid(data4.amplitude),\n",
-    "               cmap=plt.cm.hot)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
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AoDMMF58TRZ+rCmOAraPIP5Yvl7n11atrJxEtWwKJicDx49pJiKLr2DH5WW/ZUjuJqFFD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\nRqyVEauePbWTUKQVKAA88QT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\">"
+      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105cc1470>"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
@@ -643,11 +6676,24 @@
     }
    ],
    "source": [
-    "fig = plt.figure(figsize=(12, 5))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
-    "ax.plot(data4.chan1, data4.avg_amplitude)\n",
-    "plt.show()"
+    "# An example of a parameter that returns several values of different dimension\n",
+    "# This produces the last two arrays from data3, but only takes the data once.\n",
+    "data4 = qc.Loop(c1[-15:15:1], 0.1).each(\n",
+    "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
+    ").run(location='test_complex_param')\n",
+    "\n",
+    "plot4 = qc.Plot(x=data4.chan2, y=data4.chan1, z=data4.amplitude, cmap=plt.cm.hot)\n",
+    "plot4b = qc.Plot(x=data4.chan1, y=data4.avg_amplitude)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index f688e463633..ee124cdafcf 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -4,6 +4,14 @@
 from qcodes.station import Station
 from qcodes.loops import get_bg, halt_bg, Loop, Task, Wait
 
+# hack for code that should only be imported into the main (notebook) thread
+# see: http://stackoverflow.com/questions/15411967
+# in particular, importing matplotlib in the side processes takes a long
+# time and spins up other processes in order to try and get a front end
+import sys as _sys
+if 'ipy' in repr(_sys.stdout):
+    from qcodes.plots import Plot
+
 from qcodes.data.manager import get_data_manager
 from qcodes.data.data_set import DataMode, DataSet
 from qcodes.data.data_array import DataArray
diff --git a/qcodes/data/data_set.py b/qcodes/data/data_set.py
index 1bd39f014ad..99b689f9c94 100644
--- a/qcodes/data/data_set.py
+++ b/qcodes/data/data_set.py
@@ -168,6 +168,8 @@ def sync(self):
         '''
         synchronize this data set with a possibly newer version either
         in storage or on the DataServer, depending on its mode
+
+        returns: boolean, is this DataSet live on the server
         '''
         # TODO: sync implies bidirectional... and it could be!
         # we should keep track of last sync timestamp and last modification
@@ -179,7 +181,10 @@ def sync(self):
             # LOCAL DataSet - just read it in
             # TODO: compare timestamps to know if we need to read?
             self.read()
-            return
+            return False
+            # TODO - for remote live plotting, maybe set some timestamp
+            # threshold and call it static after it's been dormant a long time?
+            # I'm thinking like a minute, or ten? Maybe it's configurable?
 
         with self.data_manager.query_lock:
             if self.is_on_server:
@@ -198,12 +203,14 @@ def sync(self):
                     # but the DataSet is still on the server,
                     # so we got the data, and don't need to read.
                     self.mode = DataMode.LOCAL
-                return
+                    return False
+                return True
             else:
                 # this DataSet *thought* it was on the server, but it wasn't,
                 # so we haven't synced yet and need to read from storage
                 self.mode = DataMode.LOCAL
                 self.read()
+                return False
 
     def add_array(self, data_array):
         '''
diff --git a/qcodes/plots.py b/qcodes/plots.py
new file mode 100644
index 00000000000..e7806edfd8e
--- /dev/null
+++ b/qcodes/plots.py
@@ -0,0 +1,124 @@
+'''
+Live plotting in Jupyter notebooks
+using the nbagg backend
+
+Ironically, this also works best with interactive mode OFF
+(or maybe doesn't care?)
+'''
+import matplotlib.pyplot as plt
+from numpy.ma import masked_invalid as masked
+from IPython.display import display
+from collections import Mapping
+import warnings
+
+from qcodes.widgets.widgets import HiddenUpdateWidget
+
+
+class Plot(object):
+    '''
+    Plot x/y lines or x/y/z heatmap data
+
+    figsize: (width, height) tuple to pass to plt.figure
+        default (12, 5)
+    interval: period in seconds between update checks
+
+    subplots: either a sequence (args) or mapping (kwargs) to pass to
+        plt.subplots. default is a single simple subplot (1, 1)
+        you can use this to pass kwargs to the plt.figure constructor
+
+    kwargs: passed along to self.add() to add the first data trace
+    '''
+    def __init__(self, figsize=(12, 5), interval=1, subplots=(1, 1),
+                 **kwargs):
+        if isinstance(subplots, Mapping):
+            self.fig, self.subplots = plt.subplots(**subplots)
+        else:
+            self.fig, self.subplots = plt.subplots(*subplots)
+        if not hasattr(self.subplots, '__len__'):
+            self.subplots = (self.subplots,)
+
+        self.traces = []
+        self.data_updaters = set()
+
+        if kwargs:
+            self.add(**kwargs)
+
+        self.update_widget = HiddenUpdateWidget(self.update, interval)
+        display(self.update_widget)
+
+    def add(self, updater=None, **kwargs):
+        '''
+        adds one trace to this Plot.
+
+        updater: a callable (with no args) that updates the data in this trace
+            if omitted, we will look for DataSets referenced in this data, and
+            call their sync methods.
+
+        kwargs: with the following exceptions (mostly the data!), these are
+            passed directly to the matplotlib plotting routine.
+
+            `subplot`: the 1-based axes number to append to (default 1)
+
+            if kwargs include `z`, we will draw a heatmap (ax.pcolormesh):
+                `x`, `y`, and `z` are passed as positional args to pcolormesh
+
+            without `z` we draw a scatter/lines plot (ax.plot):
+                `x`, `y`, and `fmt` (if present) are passed as positional args
+        '''
+        # TODO some way to specify overlaid axes?
+
+        # TODO if we don't specify x (and/or y) take them from
+        # y (or z) setpoints
+        self.traces.append(kwargs)
+
+        if updater is not None:
+            self.data_updaters.add(updater)
+        else:
+            for part in ('x', 'y', 'z'):
+                data_array = kwargs.get(part, '')
+                if hasattr(data_array, 'data_set'):
+                    self.data_updaters.add(data_array.data_set.sync)
+
+        self.draw()
+
+    def update(self):
+        any_updates = False
+        for updater in self.data_updaters:
+            updates = updater()
+            if updates is not False:
+                any_updates = True
+
+        self.draw()
+
+        # once all updaters report they're finished (by returning exactly
+        # FALSE) we stop updating the plot.
+        if any_updates is False and hasattr(self, 'update_widget'):
+            self.update_widget.halt()
+
+    def draw(self):
+        for ax in self.subplots:
+            ax.clear()
+
+        for trace in self.traces:
+            if 'z' in trace:
+                self._draw_pcolormesh(**trace)
+            else:
+                self._draw_plot(**trace)
+
+        self.fig.show()
+
+    def _draw_plot(self, y, x=None, fmt=None, subplot=1, **kwargs):
+        ax = self.subplots[subplot - 1]
+        args = [arg for arg in [x, y, fmt] if arg is not None]
+        ax.plot(*args, **kwargs)
+
+    def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
+        ax = self.subplots[subplot - 1]
+
+        with warnings.catch_warnings():
+            # we get a warning about masking NaNs right at the beginning here.
+            # doesn't seem to be an issue so I'll ignore it.
+            warnings.simplefilter('ignore', UserWarning)
+            args = [masked(arg) for arg in [x, y, z] if arg is not None]
+
+            ax.pcolormesh(*args, **kwargs)
diff --git a/qcodes/widgets/__init__.py b/qcodes/widgets/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/qcodes/widgets/widgets.js b/qcodes/widgets/widgets.js
new file mode 100644
index 00000000000..50bd88dc1ed
--- /dev/null
+++ b/qcodes/widgets/widgets.js
@@ -0,0 +1,54 @@
+/*
+ * Qcodes Jupyter/IPython widgets
+ */
+require([
+    'nbextensions/widgets/widgets/js/widget',
+    'nbextensions/widgets/widgets/js/manager'
+], function (widget, manager) {
+
+    var UpdateView = widget.DOMWidgetView.extend({
+        render: function() {
+            window.MYWIDGET = this;
+            this._interval = 0;
+            this.update();
+        },
+        update: function() {
+            var me = this;
+
+            me.display(me.model.get('_message'));
+            me.setInterval();
+        },
+        display: function(message) {
+            /*
+             * display method: override this for custom display logic
+             */
+            this.el.innerHTML = message;
+        },
+        remove: function() {
+            this.setInterval(0);
+        },
+        setInterval: function(newInterval) {
+            var me = this;
+            if(newInterval===undefined) newInterval = me.model.get('interval');
+            if(newInterval===me._interval) return;
+
+            me._interval = newInterval;
+
+            if(me._updater) clearInterval(me._updater);
+
+            if(me._interval) {
+                me._updater = setInterval(function() {
+                    me.send({myupdate: true});
+                }, me._interval * 1000);
+            }
+        }
+    });
+    manager.WidgetManager.register_widget_view('UpdateView', UpdateView);
+
+    var HiddenUpdateView = UpdateView.extend({
+        display: function(message) {
+            this.$el.parents('.widget-area').hide().css('background-color', '#fff');
+        }
+    });
+    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);
+});
diff --git a/qcodes/widgets/widgets.py b/qcodes/widgets/widgets.py
new file mode 100644
index 00000000000..524cc8608b2
--- /dev/null
+++ b/qcodes/widgets/widgets.py
@@ -0,0 +1,50 @@
+import os
+
+from ipywidgets import widgets
+from traitlets import Unicode, Float
+from IPython.display import Javascript, display
+
+# load and display the javascript from this directory
+# some people use pkg_resources.resource_string for this, but that seems to
+# require an absolute path within the package, this way gives a relative path
+with open(os.path.join(os.path.split(__file__)[0], 'widgets.js')) as jsfile:
+    display(Javascript(jsfile.read()))
+
+
+class UpdateWidget(widgets.DOMWidget):
+    '''
+    Execute a callable periodically, and display its return in the output area
+
+    fn - the callable (with no parameters) to execute
+    interval - the period, in seconds
+        can be changed later by setting the interval attribute
+        interval=0 or the halt() method disables updates.
+    '''
+    _view_name = Unicode('UpdateView', sync=True)  # see widgets.js
+    _message = Unicode(sync=True)
+    interval = Float(sync=True)
+
+    def __init__(self, fn, interval, **kwargs):
+        super().__init__(**kwargs)
+
+        self._fn = fn
+        self.interval = interval
+
+        self.on_msg(self._handle_msg)
+
+        self._handle_msg({'init': True})
+
+    def _handle_msg(self, message=None):
+        self._message = str(self._fn())
+
+    def halt(self):
+        self.interval = 0
+
+
+class HiddenUpdateWidget(UpdateWidget):
+    '''
+    A variant on UpdateWidget that hides its section of the output area
+    Just lets the front end periodically execute code
+    that takes care of its own display.
+    '''
+    _view_name = Unicode('HiddenUpdateView', sync=True)  # see widgets.js
diff --git a/test_multi_d/chan1_chan0.dat b/test_multi_d/chan1_chan0.dat
index f66102672ce..dc0912eab98 100644
--- a/test_multi_d/chan1_chan0.dat
+++ b/test_multi_d/chan1_chan0.dat
@@ -478,7 +478,7 @@
 0	-6	0.009
 0	-5	0.006
 0	-4	0.009
-0	-3	0.027
+0	-3	0.014
 0	-2	0.027
 0	-1	0.064
 0	0	0.117
diff --git a/test_multi_d/chan1_chan2.dat b/test_multi_d/chan1_chan2.dat
index 23fc578160a..ccdf54e3864 100644
--- a/test_multi_d/chan1_chan2.dat
+++ b/test_multi_d/chan1_chan2.dat
@@ -2461,7 +2461,7 @@
 9	-3.4	0.531
 9	-3.2	0.498
 9	-3	0.468
-9	-2.8	0.442
+9	-2.8	0.419
 9	-2.6	0.419
 9	-2.4	0.399
 9	-2.2	0.382

From b6ad830fa923715b1c442c9a254b9718db3dda86 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 13 Jan 2016 12:14:47 +0100
Subject: [PATCH 03/24] infer setpoint data and dimensionality in Plot calls

---
 Qcodes example.ipynb         | 77 ++++++++++++++++--------------------
 qcodes/plots.py              | 51 ++++++++++++++++++++----
 test_multi_d/chan1_chan2.dat |  2 +-
 3 files changed, 78 insertions(+), 52 deletions(-)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 6bbc5e43e25..2c318b47439 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -199,9 +199,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-13 10:24:31\n"
+      "   chan0: chan0\n",
+      "started at 2016-01-13 12:10:18\n"
      ]
     }
    ],
@@ -254,13 +254,13 @@
        "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
        "         0.013,  0.012,  0.011,  0.011,  0.01 ,  0.01 ,  0.01 ,  0.009,\n",
        "         0.009,  0.008,  0.008,  0.008,  0.007,  0.007,  0.007,  0.007,\n",
-       "         0.006,  0.006,  0.006,  0.006,  0.006,  0.007,  0.007,  0.007,\n",
-       "         0.007,  0.008,  0.008,  0.008,  0.009,  0.009,  0.01 ,  0.01 ,\n",
-       "         0.01 ,  0.011,  0.011,  0.012,  0.013,  0.013,  0.014,  0.015,\n",
-       "         0.016,  0.017,  0.018,  0.019,  0.02 ,  0.022,  0.023,  0.025,\n",
-       "         0.027,  0.029,  0.031,  0.034,  0.037,  0.04 ,  0.044,  0.048,\n",
-       "         0.053,  0.058,  0.064,  0.071,  0.077,  0.085,  0.092,  0.099,\n",
-       "         0.106,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "         0.006,  0.006,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -304,12 +304,12 @@
        "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
        "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6, -16.5,\n",
        "        -16.4, -16.3, -16.2, -16.1, -16. , -15.9, -15.8, -15.7, -15.6,\n",
-       "        -15.5, -15.4, -15.3, -15.2, -15.1, -15. , -14.9, -14.8, -14.7,\n",
-       "        -14.6, -14.5, -14.4, -14.3, -14.2, -14.1, -14. , -13.9, -13.8,\n",
-       "        -13.7, -13.6, -13.5, -13.4, -13.3, -13.2, -13.1, -13. , -12.9,\n",
-       "        -12.8, -12.7, -12.6, -12.5, -12.4, -12.3, -12.2, -12.1, -12. ,\n",
-       "        -11.9, -11.8, -11.7, -11.6, -11.5, -11.4, -11.3, -11.2, -11.1,\n",
-       "        -11. , -10.9, -10.8, -10.7, -10.6, -10.5, -10.4, -10.3,   nan,\n",
+       "        -15.5, -15.4, -15.3, -15.2, -15.1, -15. ,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1144,7 +1144,7 @@
    ],
    "source": [
     "# live-updating plot, that syncs the data and stops updating when it's finished\n",
-    "plot = qc.Plot(x=data.chan0, y=data.amplitude)"
+    "plot = qc.Plot(data.amplitude)"
    ]
   },
   {
@@ -1159,11 +1159,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   amplitude_0: amplitude\n",
-      "   chan0: chan0\n",
       "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
-      "started at 2016-01-13 10:25:04\n"
+      "   amplitude_0: amplitude\n",
+      "   chan0: chan0\n",
+      "started at 2016-01-13 12:10:53\n"
      ]
     },
     {
@@ -2728,8 +2728,8 @@
     "    qc.Task(c2.set, 0)\n",
     "    ).run(location='test2d')\n",
     "\n",
-    "plot2 = qc.Plot(x=data2.chan0, y=data2.chan1, z=data2.amplitude_0, cmap=plt.cm.hot)\n",
-    "plot2b = qc.Plot(x=data2.chan0, y=data2.chan1, z=data2.amplitude_3, cmap=plt.cm.hot)"
+    "plot2 = qc.Plot(data2.amplitude_0, cmap=plt.cm.hot)\n",
+    "plot2b = qc.Plot(data2.amplitude_3, cmap=plt.cm.hot)"
    ]
   },
   {
@@ -2744,14 +2744,14 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   amplitude_5_0: amplitude\n",
       "   amplitude_2: amplitude\n",
-      "   chan1: chan1\n",
-      "   chan0: chan0\n",
       "   chan2: chan2\n",
+      "   chan0: chan0\n",
+      "   chan1: chan1\n",
       "   avg_amplitude: avg_amplitude\n",
+      "   amplitude_5_0: amplitude\n",
       "   amplitude_3_0: amplitude\n",
-      "started at 2016-01-13 10:25:37\n"
+      "started at 2016-01-13 12:11:47\n"
      ]
     },
     {
@@ -4297,7 +4297,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5097,9 +5097,9 @@
     "    AverageGetter(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
     ").run(location='test_multi_d')\n",
     "\n",
-    "plot3 = qc.Plot(x=data3.chan0, y=data3.chan1, z=data3.amplitude_3_0, cmap=plt.cm.hot)\n",
-    "plot3b = qc.Plot(x=data3.chan2, y=data3.chan1, z=data3.amplitude_5_0, cmap=plt.cm.hot)\n",
-    "plot3c = qc.Plot(x=data3.chan1, y=data3.avg_amplitude)"
+    "plot3 = qc.Plot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
+    "plot3b = qc.Plot(data3.amplitude_5_0, cmap=plt.cm.hot)\n",
+    "plot3c = qc.Plot(data3.avg_amplitude)"
    ]
   },
   {
@@ -5115,11 +5115,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   chan2: chan2\n",
-      "   avg_amplitude: avg_amplitude\n",
       "   chan1: chan1\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-13 10:26:07\n"
+      "   chan2: chan2\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-13 12:12:28\n"
      ]
     },
     {
@@ -6682,18 +6682,9 @@
     "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
     ").run(location='test_complex_param')\n",
     "\n",
-    "plot4 = qc.Plot(x=data4.chan2, y=data4.chan1, z=data4.amplitude, cmap=plt.cm.hot)\n",
-    "plot4b = qc.Plot(x=data4.chan1, y=data4.avg_amplitude)"
+    "plot4 = qc.Plot(data4.amplitude, cmap=plt.cm.hot)\n",
+    "plot4b = qc.Plot(data4.avg_amplitude)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/plots.py b/qcodes/plots.py
index e7806edfd8e..4e63fef0390 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -16,7 +16,10 @@
 
 class Plot(object):
     '''
-    Plot x/y lines or x/y/z heatmap data
+    Plot x/y lines or x/y/z heatmap data. The first trace may be included
+    in the constructor, other traces can be added with Plot.add()
+
+    args: shortcut to provide the x/y/z data. See Plot.add
 
     figsize: (width, height) tuple to pass to plt.figure
         default (12, 5)
@@ -26,10 +29,11 @@ class Plot(object):
         plt.subplots. default is a single simple subplot (1, 1)
         you can use this to pass kwargs to the plt.figure constructor
 
-    kwargs: passed along to self.add() to add the first data trace
+    kwargs: passed along to Plot.add() to add the first data trace
     '''
-    def __init__(self, figsize=(12, 5), interval=1, subplots=(1, 1),
+    def __init__(self, *args, figsize=(12, 5), interval=1, subplots=(1, 1),
                  **kwargs):
+
         if isinstance(subplots, Mapping):
             self.fig, self.subplots = plt.subplots(**subplots)
         else:
@@ -40,16 +44,22 @@ def __init__(self, figsize=(12, 5), interval=1, subplots=(1, 1),
         self.traces = []
         self.data_updaters = set()
 
-        if kwargs:
-            self.add(**kwargs)
+        if args or kwargs:
+            self.add(*args, **kwargs)
 
         self.update_widget = HiddenUpdateWidget(self.update, interval)
         display(self.update_widget)
 
-    def add(self, updater=None, **kwargs):
+    def add(self, *args, updater=None, **kwargs):
         '''
         adds one trace to this Plot.
 
+        args: a way to provide x/y/z data without keywords
+            The last one is the dependent data, and we look at its
+            dimensionality to determine if it's `y` or `z`.
+            If it's `y`, it may optionally be preceded by `x`.
+            If it's `z`, it may optionally be preceded by `x` and `y`.
+
         updater: a callable (with no args) that updates the data in this trace
             if omitted, we will look for DataSets referenced in this data, and
             call their sync methods.
@@ -67,8 +77,14 @@ def add(self, updater=None, **kwargs):
         '''
         # TODO some way to specify overlaid axes?
 
-        # TODO if we don't specify x (and/or y) take them from
-        # y (or z) setpoints
+        if args:
+            if hasattr(args[-1][0], '__len__'):
+                # 2D (or higher... but ignore this for now)
+                self._args_to_kwargs(args, kwargs, 'xyz', 'pcolormesh')
+            else:
+                # 1D
+                self._args_to_kwargs(args, kwargs, 'xy', 'plot')
+
         self.traces.append(kwargs)
 
         if updater is not None:
@@ -81,6 +97,18 @@ def add(self, updater=None, **kwargs):
 
         self.draw()
 
+    def _args_to_kwargs(self, args, kwargs, axletters, plot_func_name):
+        if len(args) not in (1, len(axletters)):
+            raise ValueError('{} needs either 1 or {} unnamed args'.format(
+                plot_func_name, len(axletters)))
+
+        axletters = axletters[-len(args):]
+
+        for arg, axletter in zip(args, axletters):
+            if axletter in kwargs:
+                raise ValueError(axletter + ' data provided twice')
+            kwargs[axletter] = arg
+
     def update(self):
         any_updates = False
         for updater in self.data_updaters:
@@ -109,11 +137,18 @@ def draw(self):
 
     def _draw_plot(self, y, x=None, fmt=None, subplot=1, **kwargs):
         ax = self.subplots[subplot - 1]
+        if x is None and hasattr(y, 'set_arrays'):
+            x = y.set_arrays[-1]
         args = [arg for arg in [x, y, fmt] if arg is not None]
         ax.plot(*args, **kwargs)
 
     def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
         ax = self.subplots[subplot - 1]
+        set_arrays = getattr(z, 'set_arrays', None)
+        if x is None and set_arrays:
+            x = set_arrays[-1]
+        if y is None and set_arrays:
+            y = set_arrays[-2]
 
         with warnings.catch_warnings():
             # we get a warning about masking NaNs right at the beginning here.
diff --git a/test_multi_d/chan1_chan2.dat b/test_multi_d/chan1_chan2.dat
index ccdf54e3864..23fc578160a 100644
--- a/test_multi_d/chan1_chan2.dat
+++ b/test_multi_d/chan1_chan2.dat
@@ -2461,7 +2461,7 @@
 9	-3.4	0.531
 9	-3.2	0.498
 9	-3	0.468
-9	-2.8	0.419
+9	-2.8	0.442
 9	-2.6	0.419
 9	-2.4	0.399
 9	-2.2	0.382

From d033e68e4f96c90673ede35e39eba174988c7d5c Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 13 Jan 2016 12:21:35 +0100
Subject: [PATCH 04/24] ion/ioff doesn't seem to matter

---
 Qcodes example.ipynb | 49 +++++++++++++++++++++-----------------------
 qcodes/plots.py      |  3 ---
 2 files changed, 23 insertions(+), 29 deletions(-)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 2c318b47439..1261d41f5f6 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -76,10 +76,7 @@
    "source": [
     "%matplotlib nbagg\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.ioff()\n",
     "import time\n",
-    "# from IPython import display\n",
-    "# import numpy.ma as ma\n",
     "\n",
     "# load the qcodes path, until we have this installed as a package\n",
     "import sys\n",
@@ -199,9 +196,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   amplitude: amplitude\n",
       "   chan0: chan0\n",
-      "started at 2016-01-13 12:10:18\n"
+      "   amplitude: amplitude\n",
+      "started at 2016-01-13 12:18:04\n"
      ]
     }
    ],
@@ -254,11 +251,11 @@
        "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
        "         0.013,  0.012,  0.011,  0.011,  0.01 ,  0.01 ,  0.01 ,  0.009,\n",
        "         0.009,  0.008,  0.008,  0.008,  0.007,  0.007,  0.007,  0.007,\n",
-       "         0.006,  0.006,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "         0.006,  0.006,  0.006,  0.006,  0.006,  0.007,  0.007,  0.007,\n",
+       "         0.007,  0.008,  0.008,  0.008,  0.009,  0.009,  0.01 ,  0.01 ,\n",
+       "         0.01 ,  0.011,  0.011,  0.012,  0.013,  0.013,  0.014,  0.015,\n",
+       "         0.016,  0.017,  0.018,  0.019,  0.02 ,  0.022,  0.023,  0.025,\n",
+       "         0.027,  0.029,  0.031,  0.034,  0.037,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -304,11 +301,11 @@
        "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
        "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6, -16.5,\n",
        "        -16.4, -16.3, -16.2, -16.1, -16. , -15.9, -15.8, -15.7, -15.6,\n",
-       "        -15.5, -15.4, -15.3, -15.2, -15.1, -15. ,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "        -15.5, -15.4, -15.3, -15.2, -15.1, -15. , -14.9, -14.8, -14.7,\n",
+       "        -14.6, -14.5, -14.4, -14.3, -14.2, -14.1, -14. , -13.9, -13.8,\n",
+       "        -13.7, -13.6, -13.5, -13.4, -13.3, -13.2, -13.1, -13. , -12.9,\n",
+       "        -12.8, -12.7, -12.6, -12.5, -12.4, -12.3, -12.2, -12.1, -12. ,\n",
+       "        -11.9, -11.8, -11.7, -11.6, -11.5,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1159,11 +1156,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   chan1: chan1\n",
-      "   amplitude_3: amplitude\n",
       "   amplitude_0: amplitude\n",
       "   chan0: chan0\n",
-      "started at 2016-01-13 12:10:53\n"
+      "   chan1: chan1\n",
+      "   amplitude_3: amplitude\n",
+      "started at 2016-01-13 12:18:27\n"
      ]
     },
     {
@@ -2744,14 +2741,14 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   amplitude_2: amplitude\n",
       "   chan2: chan2\n",
-      "   chan0: chan0\n",
-      "   chan1: chan1\n",
-      "   avg_amplitude: avg_amplitude\n",
+      "   amplitude_2: amplitude\n",
       "   amplitude_5_0: amplitude\n",
+      "   chan1: chan1\n",
+      "   chan0: chan0\n",
       "   amplitude_3_0: amplitude\n",
-      "started at 2016-01-13 12:11:47\n"
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-13 12:18:59\n"
      ]
     },
     {
@@ -5115,11 +5112,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   chan1: chan1\n",
-      "   amplitude: amplitude\n",
       "   chan2: chan2\n",
+      "   amplitude: amplitude\n",
+      "   chan1: chan1\n",
       "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-13 12:12:28\n"
+      "started at 2016-01-13 12:19:36\n"
      ]
     },
     {
diff --git a/qcodes/plots.py b/qcodes/plots.py
index 4e63fef0390..2137c221e20 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -1,9 +1,6 @@
 '''
 Live plotting in Jupyter notebooks
 using the nbagg backend
-
-Ironically, this also works best with interactive mode OFF
-(or maybe doesn't care?)
 '''
 import matplotlib.pyplot as plt
 from numpy.ma import masked_invalid as masked

From 69714cac102f641ca818da73c0b7dd4cd5692f04 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Thu, 14 Jan 2016 12:37:08 +0100
Subject: [PATCH 05/24] update instead of redraw plot

---
 Qcodes example.ipynb      |  86 +++++++++++++-------------
 qcodes/plots.py           | 123 ++++++++++++++++++++++++++++----------
 qcodes/widgets/widgets.js |   2 +-
 test_multi_d/chan1.dat    |  64 ++++++++++----------
 4 files changed, 167 insertions(+), 108 deletions(-)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 1261d41f5f6..10804d49965 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -59,7 +59,7 @@
        "\n",
        "    var HiddenUpdateView = UpdateView.extend({\n",
        "        display: function(message) {\n",
-       "            this.$el.parents('.widget-area').hide().css('background-color', '#fff');\n",
+       "            this.$el.hide();\n",
        "        }\n",
        "    });\n",
        "    manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);\n",
@@ -77,6 +77,7 @@
     "%matplotlib nbagg\n",
     "import matplotlib.pyplot as plt\n",
     "import time\n",
+    "import numpy as np\n",
     "\n",
     "# load the qcodes path, until we have this installed as a package\n",
     "import sys\n",
@@ -198,7 +199,7 @@
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
       "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-13 12:18:04\n"
+      "started at 2016-01-14 12:03:37\n"
      ]
     }
    ],
@@ -220,7 +221,7 @@
     {
      "data": {
       "text/plain": [
-       "[<DataServer-1, started daemon>, <MeasurementLoop-2, started daemon>]"
+       "[<MeasurementLoop-2, started daemon>, <DataServer-1, started daemon>]"
       ]
      },
      "execution_count": 6,
@@ -255,20 +256,20 @@
        "         0.007,  0.008,  0.008,  0.008,  0.009,  0.009,  0.01 ,  0.01 ,\n",
        "         0.01 ,  0.011,  0.011,  0.012,  0.013,  0.013,  0.014,  0.015,\n",
        "         0.016,  0.017,  0.018,  0.019,  0.02 ,  0.022,  0.023,  0.025,\n",
-       "         0.027,  0.029,  0.031,  0.034,  0.037,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "         0.027,  0.029,  0.031,  0.034,  0.037,  0.04 ,  0.044,  0.048,\n",
+       "         0.053,  0.058,  0.064,  0.071,  0.077,  0.085,  0.092,  0.099,\n",
+       "         0.106,  0.111,  0.115,  0.117,  0.117,  0.117,  0.115,  0.111,\n",
+       "         0.106,  0.099,  0.092,  0.085,  0.077,  0.071,  0.064,  0.058,\n",
+       "         0.053,  0.048,  0.044,  0.04 ,  0.037,  0.034,  0.031,  0.029,\n",
+       "         0.027,  0.025,  0.023,  0.022,  0.02 ,  0.019,  0.018,  0.017,\n",
+       "         0.016,  0.015,  0.014,  0.013,  0.013,  0.012,  0.011,  0.011,\n",
+       "         0.01 ,  0.01 ,  0.01 ,  0.009,  0.009,  0.008,  0.008,  0.008,\n",
+       "         0.007,  0.007,  0.007,  0.007,  0.006,  0.006,  0.006,  0.006,\n",
+       "         0.006,  0.007,  0.007,  0.007,  0.007,  0.008,  0.008,  0.008,\n",
+       "         0.009,  0.009,  0.01 ,  0.01 ,  0.01 ,  0.011,  0.011,  0.012,\n",
+       "         0.013,  0.013,  0.014,  0.015,  0.016,  0.017,  0.018,  0.019,\n",
+       "         0.02 ,  0.022,  0.023,  0.025,  0.027,  0.029,  0.031,  0.034,\n",
+       "         0.037,  0.04 ,  0.044,  0.048,  0.053,  0.058,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -305,19 +306,19 @@
        "        -14.6, -14.5, -14.4, -14.3, -14.2, -14.1, -14. , -13.9, -13.8,\n",
        "        -13.7, -13.6, -13.5, -13.4, -13.3, -13.2, -13.1, -13. , -12.9,\n",
        "        -12.8, -12.7, -12.6, -12.5, -12.4, -12.3, -12.2, -12.1, -12. ,\n",
-       "        -11.9, -11.8, -11.7, -11.6, -11.5,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "        -11.9, -11.8, -11.7, -11.6, -11.5, -11.4, -11.3, -11.2, -11.1,\n",
+       "        -11. , -10.9, -10.8, -10.7, -10.6, -10.5, -10.4, -10.3, -10.2,\n",
+       "        -10.1, -10. ,  -9.9,  -9.8,  -9.7,  -9.6,  -9.5,  -9.4,  -9.3,\n",
+       "         -9.2,  -9.1,  -9. ,  -8.9,  -8.8,  -8.7,  -8.6,  -8.5,  -8.4,\n",
+       "         -8.3,  -8.2,  -8.1,  -8. ,  -7.9,  -7.8,  -7.7,  -7.6,  -7.5,\n",
+       "         -7.4,  -7.3,  -7.2,  -7.1,  -7. ,  -6.9,  -6.8,  -6.7,  -6.6,\n",
+       "         -6.5,  -6.4,  -6.3,  -6.2,  -6.1,  -6. ,  -5.9,  -5.8,  -5.7,\n",
+       "         -5.6,  -5.5,  -5.4,  -5.3,  -5.2,  -5.1,  -5. ,  -4.9,  -4.8,\n",
+       "         -4.7,  -4.6,  -4.5,  -4.4,  -4.3,  -4.2,  -4.1,  -4. ,  -3.9,\n",
+       "         -3.8,  -3.7,  -3.6,  -3.5,  -3.4,  -3.3,  -3.2,  -3.1,  -3. ,\n",
+       "         -2.9,  -2.8,  -2.7,  -2.6,  -2.5,  -2.4,  -2.3,  -2.2,  -2.1,\n",
+       "         -2. ,  -1.9,  -1.8,  -1.7,  -1.6,  -1.5,  -1.4,  -1.3,  -1.2,\n",
+       "         -1.1,  -1. ,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1148,7 +1149,8 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
@@ -1156,11 +1158,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   amplitude_0: amplitude\n",
       "   chan0: chan0\n",
-      "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
-      "started at 2016-01-13 12:18:27\n"
+      "   chan1: chan1\n",
+      "   amplitude_0: amplitude\n",
+      "started at 2016-01-14 12:06:58\n"
      ]
     },
     {
@@ -2741,14 +2743,14 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
+      "   chan0: chan0\n",
       "   chan2: chan2\n",
-      "   amplitude_2: amplitude\n",
       "   amplitude_5_0: amplitude\n",
-      "   chan1: chan1\n",
-      "   chan0: chan0\n",
-      "   amplitude_3_0: amplitude\n",
       "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-13 12:18:59\n"
+      "   amplitude_3_0: amplitude\n",
+      "   amplitude_2: amplitude\n",
+      "   chan1: chan1\n",
+      "started at 2016-01-14 12:34:28\n"
      ]
     },
     {
@@ -5112,11 +5114,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   chan2: chan2\n",
+      "   avg_amplitude: avg_amplitude\n",
       "   amplitude: amplitude\n",
+      "   chan2: chan2\n",
       "   chan1: chan1\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-13 12:19:36\n"
+      "started at 2016-01-14 12:34:52\n"
      ]
     },
     {
diff --git a/qcodes/plots.py b/qcodes/plots.py
index 2137c221e20..5734973fe92 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -3,10 +3,11 @@
 using the nbagg backend
 '''
 import matplotlib.pyplot as plt
-from numpy.ma import masked_invalid as masked
+from matplotlib.transforms import Bbox
+import numpy as np
+from numpy.ma import masked_invalid, getmask
 from IPython.display import display
 from collections import Mapping
-import warnings
 
 from qcodes.widgets.widgets import HiddenUpdateWidget
 
@@ -82,7 +83,12 @@ def add(self, *args, updater=None, **kwargs):
                 # 1D
                 self._args_to_kwargs(args, kwargs, 'xy', 'plot')
 
-        self.traces.append(kwargs)
+        self._find_data_in_set_arrays(kwargs)
+
+        self.traces.append({
+            'config': kwargs,
+            'plot_object': self._draw_trace(kwargs)
+        })
 
         if updater is not None:
             self.data_updaters.add(updater)
@@ -92,19 +98,32 @@ def add(self, *args, updater=None, **kwargs):
                 if hasattr(data_array, 'data_set'):
                     self.data_updaters.add(data_array.data_set.sync)
 
-        self.draw()
-
     def _args_to_kwargs(self, args, kwargs, axletters, plot_func_name):
         if len(args) not in (1, len(axletters)):
             raise ValueError('{} needs either 1 or {} unnamed args'.format(
                 plot_func_name, len(axletters)))
 
-        axletters = axletters[-len(args):]
-
-        for arg, axletter in zip(args, axletters):
-            if axletter in kwargs:
-                raise ValueError(axletter + ' data provided twice')
-            kwargs[axletter] = arg
+        arg_axletters = axletters[-len(args):]
+
+        for arg, arg_axletters in zip(args, arg_axletters):
+            if arg_axletters in kwargs:
+                raise ValueError(arg_axletters + ' data provided twice')
+            kwargs[arg_axletters] = arg
+
+    def _find_data_in_set_arrays(self, kwargs):
+        axletters = 'xyz' if 'z' in kwargs else 'xy'
+        main_data = kwargs[axletters[-1]]
+        if hasattr(main_data, 'set_arrays'):
+            num_axes = len(axletters) - 1
+            # things will probably fail if we try to plot arrays of the
+            # wrong dimension... but we'll give it a shot anyway.
+            set_arrays = main_data.set_arrays[-num_axes:]
+            # for 2D: y is outer loop, which is earlier in set_arrays,
+            # and x is the inner loop... is this the right convention?
+            set_axletters = reversed(axletters[:-1])
+            for axletter, set_array in zip(set_axletters, set_arrays):
+                if axletter not in kwargs:
+                    kwargs[axletter] = set_array
 
     def update(self):
         any_updates = False
@@ -113,10 +132,49 @@ def update(self):
             if updates is not False:
                 any_updates = True
 
-        self.draw()
+        # matplotlib doesn't know how to autoscale to a pcolormesh after the
+        # first draw (relim ignores it...) so we have to do this ourselves
+        bboxes = dict(zip(self.subplots, [[] for p in self.subplots]))
+
+        for trace in self.traces:
+            config = trace['config']
+            plot_object = trace['plot_object']
+            if 'z' in config:
+                # pcolormesh doesn't seem to allow editing x and y data, only z
+                # so instead, we'll remove and re-add the data.
+                if plot_object:
+                    plot_object.remove()
+                plot_object = self._draw_pcolormesh(**config)
+                trace['plot_object'] = plot_object
+
+                if plot_object:
+                    bboxes[plot_object.axes].append(
+                        plot_object.get_datalim(plot_object.axes.transData))
+            else:
+                for axletter in 'xy':
+                    setter = 'set_' + axletter + 'data'
+                    if axletter in config:
+                        getattr(plot_object, setter)(config[axletter])
+
+        for ax in self.subplots:
+            if ax.get_autoscale_on():
+                ax.relim()
+                if bboxes[ax]:
+                    bbox = Bbox.union(bboxes[ax])
+                    if np.all(np.isfinite(ax.dataLim)):
+                        # should take care of the case of lines + heatmaps
+                        # where there's already a finite dataLim from relim
+                        ax.dataLim.set(Bbox.union(ax.dataLim, bbox))
+                    else:
+                        # when there's only a heatmap, relim gives inf bounds
+                        # so just completely overwrite it
+                        ax.dataLim = bbox
+                ax.autoscale()
+
+        self.fig.canvas.draw()
 
         # once all updaters report they're finished (by returning exactly
-        # FALSE) we stop updating the plot.
+        # False) we stop updating the plot.
         if any_updates is False and hasattr(self, 'update_widget'):
             self.update_widget.halt()
 
@@ -125,32 +183,31 @@ def draw(self):
             ax.clear()
 
         for trace in self.traces:
-            if 'z' in trace:
-                self._draw_pcolormesh(**trace)
-            else:
-                self._draw_plot(**trace)
+            self._draw_trace(trace['config'])
 
         self.fig.show()
 
+    def _draw_trace(self, config):
+        if 'z' in config:
+            return self._draw_pcolormesh(**config)
+        else:
+            return self._draw_plot(**config)
+
     def _draw_plot(self, y, x=None, fmt=None, subplot=1, **kwargs):
         ax = self.subplots[subplot - 1]
-        if x is None and hasattr(y, 'set_arrays'):
-            x = y.set_arrays[-1]
         args = [arg for arg in [x, y, fmt] if arg is not None]
-        ax.plot(*args, **kwargs)
+        return ax.plot(*args, **kwargs)[0]
 
     def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
         ax = self.subplots[subplot - 1]
-        set_arrays = getattr(z, 'set_arrays', None)
-        if x is None and set_arrays:
-            x = set_arrays[-1]
-        if y is None and set_arrays:
-            y = set_arrays[-2]
-
-        with warnings.catch_warnings():
-            # we get a warning about masking NaNs right at the beginning here.
-            # doesn't seem to be an issue so I'll ignore it.
-            warnings.simplefilter('ignore', UserWarning)
-            args = [masked(arg) for arg in [x, y, z] if arg is not None]
-
-            ax.pcolormesh(*args, **kwargs)
+
+        args = [masked_invalid(arg) for arg in [x, y, z]
+                if arg is not None]
+
+        for arg in args:
+            if np.all(getmask(arg)):
+                # if any entire array is masked, don't draw at all
+                # there's nothing to draw, and anyway it throws a warning
+                return False
+
+        return ax.pcolormesh(*args, **kwargs)
diff --git a/qcodes/widgets/widgets.js b/qcodes/widgets/widgets.js
index 50bd88dc1ed..5d8f6bcd86d 100644
--- a/qcodes/widgets/widgets.js
+++ b/qcodes/widgets/widgets.js
@@ -47,7 +47,7 @@ require([
 
     var HiddenUpdateView = UpdateView.extend({
         display: function(message) {
-            this.$el.parents('.widget-area').hide().css('background-color', '#fff');
+            this.$el.hide();
         }
     });
     manager.WidgetManager.register_widget_view('HiddenUpdateView', HiddenUpdateView);
diff --git a/test_multi_d/chan1.dat b/test_multi_d/chan1.dat
index db26202bc98..70acf2e9915 100644
--- a/test_multi_d/chan1.dat
+++ b/test_multi_d/chan1.dat
@@ -1,33 +1,33 @@
-# chan1	amplitude_2	avg_amplitude
-# "chan1"	"amplitude"	"Average: amplitude"
+# chan1	avg_amplitude	amplitude_2
+# "chan1"	"Average: amplitude"	"amplitude"
 # 30
--15	0.006	1.63366
--14	0.009	1.48254
--13	0.014	1.3513
--12	0.027	1.24736
--11	0.064	1.17375
--10	0.117	1.12666
--9	0.064	1.17375
--8	0.027	1.24736
--7	0.014	1.3513
--6	0.009	1.48254
--5	0.006	1.63366
--4	0.009	1.48254
--3	0.014	1.3513
--2	0.027	1.24736
--1	0.064	1.17375
-0	0.117	1.12666
-1	0.064	1.17375
-2	0.027	1.24736
-3	0.014	1.3513
-4	0.009	1.48254
-5	0.006	1.63366
-6	0.009	1.48254
-7	0.014	1.3513
-8	0.027	1.24736
-9	0.064	1.17375
-10	0.117	1.12666
-11	0.064	1.17375
-12	0.027	1.24736
-13	0.014	1.3513
-14	0.009	1.48254
+-15	1.63366	0.006
+-14	1.48254	0.009
+-13	1.3513	0.014
+-12	1.24736	0.027
+-11	1.17375	0.064
+-10	1.12666	0.117
+-9	1.17375	0.064
+-8	1.24736	0.027
+-7	1.3513	0.014
+-6	1.48254	0.009
+-5	1.63366	0.006
+-4	1.48254	0.009
+-3	1.3513	0.014
+-2	1.24736	0.027
+-1	1.17375	0.064
+0	1.12666	0.117
+1	1.17375	0.064
+2	1.24736	0.027
+3	1.3513	0.014
+4	1.48254	0.009
+5	1.63366	0.006
+6	1.48254	0.009
+7	1.3513	0.014
+8	1.24736	0.027
+9	1.17375	0.064
+10	1.12666	0.117
+11	1.17375	0.064
+12	1.24736	0.027
+13	1.3513	0.014
+14	1.48254	0.009

From 2d242d23e9841ee32df8125dc2b1997228609d6a Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Thu, 14 Jan 2016 13:59:40 +0100
Subject: [PATCH 06/24] Plot.draw() is no longer used

---
 qcodes/plots.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/qcodes/plots.py b/qcodes/plots.py
index 5734973fe92..5de877562df 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -178,15 +178,6 @@ def update(self):
         if any_updates is False and hasattr(self, 'update_widget'):
             self.update_widget.halt()
 
-    def draw(self):
-        for ax in self.subplots:
-            ax.clear()
-
-        for trace in self.traces:
-            self._draw_trace(trace['config'])
-
-        self.fig.show()
-
     def _draw_trace(self, config):
         if 'z' in config:
             return self._draw_pcolormesh(**config)

From e5328d2d9322e66a20dc0f5a8d3bd3d03957917a Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Thu, 14 Jan 2016 14:12:18 +0100
Subject: [PATCH 07/24] plot halt shortcut

---
 qcodes/plots.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/qcodes/plots.py b/qcodes/plots.py
index 5de877562df..9064da97d38 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -176,7 +176,10 @@ def update(self):
         # once all updaters report they're finished (by returning exactly
         # False) we stop updating the plot.
         if any_updates is False and hasattr(self, 'update_widget'):
-            self.update_widget.halt()
+            self.halt()
+
+    def halt(self):
+        self.update_widget.halt()
 
     def _draw_trace(self, config):
         if 'z' in config:

From edb72d0538af4c8baabbcca4db494a7d2dc86e3d Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Thu, 14 Jan 2016 14:15:04 +0100
Subject: [PATCH 08/24] plot docstrings

---
 qcodes/plots.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/qcodes/plots.py b/qcodes/plots.py
index 9064da97d38..1c361ebeba9 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -126,6 +126,11 @@ def _find_data_in_set_arrays(self, kwargs):
                     kwargs[axletter] = set_array
 
     def update(self):
+        '''
+        update the data in this plot, using the updaters given with
+        Plot.add() or in the included DataSets, then include this in
+        the plot
+        '''
         any_updates = False
         for updater in self.data_updaters:
             updates = updater()
@@ -179,6 +184,9 @@ def update(self):
             self.halt()
 
     def halt(self):
+        '''
+        stop automatic updates to this plot, by canceling its update widget
+        '''
         self.update_widget.halt()
 
     def _draw_trace(self, config):

From 3408bc73c3aba5378412736f6be447400bed5289 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Thu, 14 Jan 2016 15:08:24 +0100
Subject: [PATCH 09/24] colorbar

---
 Qcodes example.ipynb | 106 +++++++++++++++++++++----------------------
 qcodes/plots.py      |  14 +++++-
 2 files changed, 66 insertions(+), 54 deletions(-)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 10804d49965..c521cae0302 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -199,7 +199,7 @@
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
       "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-14 12:03:37\n"
+      "started at 2016-01-14 15:03:41\n"
      ]
     }
    ],
@@ -251,25 +251,25 @@
        "         0.037,  0.034,  0.031,  0.029,  0.027,  0.025,  0.023,  0.022,\n",
        "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
        "         0.013,  0.012,  0.011,  0.011,  0.01 ,  0.01 ,  0.01 ,  0.009,\n",
-       "         0.009,  0.008,  0.008,  0.008,  0.007,  0.007,  0.007,  0.007,\n",
-       "         0.006,  0.006,  0.006,  0.006,  0.006,  0.007,  0.007,  0.007,\n",
-       "         0.007,  0.008,  0.008,  0.008,  0.009,  0.009,  0.01 ,  0.01 ,\n",
-       "         0.01 ,  0.011,  0.011,  0.012,  0.013,  0.013,  0.014,  0.015,\n",
-       "         0.016,  0.017,  0.018,  0.019,  0.02 ,  0.022,  0.023,  0.025,\n",
-       "         0.027,  0.029,  0.031,  0.034,  0.037,  0.04 ,  0.044,  0.048,\n",
-       "         0.053,  0.058,  0.064,  0.071,  0.077,  0.085,  0.092,  0.099,\n",
-       "         0.106,  0.111,  0.115,  0.117,  0.117,  0.117,  0.115,  0.111,\n",
-       "         0.106,  0.099,  0.092,  0.085,  0.077,  0.071,  0.064,  0.058,\n",
-       "         0.053,  0.048,  0.044,  0.04 ,  0.037,  0.034,  0.031,  0.029,\n",
-       "         0.027,  0.025,  0.023,  0.022,  0.02 ,  0.019,  0.018,  0.017,\n",
-       "         0.016,  0.015,  0.014,  0.013,  0.013,  0.012,  0.011,  0.011,\n",
-       "         0.01 ,  0.01 ,  0.01 ,  0.009,  0.009,  0.008,  0.008,  0.008,\n",
-       "         0.007,  0.007,  0.007,  0.007,  0.006,  0.006,  0.006,  0.006,\n",
-       "         0.006,  0.007,  0.007,  0.007,  0.007,  0.008,  0.008,  0.008,\n",
-       "         0.009,  0.009,  0.01 ,  0.01 ,  0.01 ,  0.011,  0.011,  0.012,\n",
-       "         0.013,  0.013,  0.014,  0.015,  0.016,  0.017,  0.018,  0.019,\n",
-       "         0.02 ,  0.022,  0.023,  0.025,  0.027,  0.029,  0.031,  0.034,\n",
-       "         0.037,  0.04 ,  0.044,  0.048,  0.053,  0.058,    nan,    nan,\n",
+       "         0.009,  0.008,  0.008,  0.008,  0.007,  0.007,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -302,23 +302,23 @@
        "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
        "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6, -16.5,\n",
        "        -16.4, -16.3, -16.2, -16.1, -16. , -15.9, -15.8, -15.7, -15.6,\n",
-       "        -15.5, -15.4, -15.3, -15.2, -15.1, -15. , -14.9, -14.8, -14.7,\n",
-       "        -14.6, -14.5, -14.4, -14.3, -14.2, -14.1, -14. , -13.9, -13.8,\n",
-       "        -13.7, -13.6, -13.5, -13.4, -13.3, -13.2, -13.1, -13. , -12.9,\n",
-       "        -12.8, -12.7, -12.6, -12.5, -12.4, -12.3, -12.2, -12.1, -12. ,\n",
-       "        -11.9, -11.8, -11.7, -11.6, -11.5, -11.4, -11.3, -11.2, -11.1,\n",
-       "        -11. , -10.9, -10.8, -10.7, -10.6, -10.5, -10.4, -10.3, -10.2,\n",
-       "        -10.1, -10. ,  -9.9,  -9.8,  -9.7,  -9.6,  -9.5,  -9.4,  -9.3,\n",
-       "         -9.2,  -9.1,  -9. ,  -8.9,  -8.8,  -8.7,  -8.6,  -8.5,  -8.4,\n",
-       "         -8.3,  -8.2,  -8.1,  -8. ,  -7.9,  -7.8,  -7.7,  -7.6,  -7.5,\n",
-       "         -7.4,  -7.3,  -7.2,  -7.1,  -7. ,  -6.9,  -6.8,  -6.7,  -6.6,\n",
-       "         -6.5,  -6.4,  -6.3,  -6.2,  -6.1,  -6. ,  -5.9,  -5.8,  -5.7,\n",
-       "         -5.6,  -5.5,  -5.4,  -5.3,  -5.2,  -5.1,  -5. ,  -4.9,  -4.8,\n",
-       "         -4.7,  -4.6,  -4.5,  -4.4,  -4.3,  -4.2,  -4.1,  -4. ,  -3.9,\n",
-       "         -3.8,  -3.7,  -3.6,  -3.5,  -3.4,  -3.3,  -3.2,  -3.1,  -3. ,\n",
-       "         -2.9,  -2.8,  -2.7,  -2.6,  -2.5,  -2.4,  -2.3,  -2.2,  -2.1,\n",
-       "         -2. ,  -1.9,  -1.8,  -1.7,  -1.6,  -1.5,  -1.4,  -1.3,  -1.2,\n",
-       "         -1.1,  -1. ,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "        -15.5, -15.4,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1158,11 +1158,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
+      "   amplitude_0: amplitude\n",
       "   chan0: chan0\n",
-      "   amplitude_3: amplitude\n",
       "   chan1: chan1\n",
-      "   amplitude_0: amplitude\n",
-      "started at 2016-01-14 12:06:58\n"
+      "   amplitude_3: amplitude\n",
+      "started at 2016-01-14 15:04:16\n"
      ]
     },
     {
@@ -1932,7 +1932,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2708,7 +2708,7 @@
     {
      "data": {
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2744,13 +2744,13 @@
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
       "   chan0: chan0\n",
-      "   chan2: chan2\n",
+      "   chan1: chan1\n",
+      "   amplitude_2: amplitude\n",
+      "   amplitude_3_0: amplitude\n",
       "   amplitude_5_0: amplitude\n",
       "   avg_amplitude: avg_amplitude\n",
-      "   amplitude_3_0: amplitude\n",
-      "   amplitude_2: amplitude\n",
-      "   chan1: chan1\n",
-      "started at 2016-01-14 12:34:28\n"
+      "   chan2: chan2\n",
+      "started at 2016-01-14 15:04:49\n"
      ]
     },
     {
@@ -3520,7 +3520,7 @@
     {
      "data": {
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\">"
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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -4296,7 +4296,7 @@
     {
      "data": {
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5114,11 +5114,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   avg_amplitude: avg_amplitude\n",
       "   amplitude: amplitude\n",
-      "   chan2: chan2\n",
       "   chan1: chan1\n",
-      "started at 2016-01-14 12:34:52\n"
+      "   chan2: chan2\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-14 15:05:31\n"
      ]
     },
     {
@@ -5888,7 +5888,7 @@
     {
      "data": {
       "text/html": [
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/qcodes/plots.py b/qcodes/plots.py
index 1c361ebeba9..6a981a77bc8 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -212,4 +212,16 @@ def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
                 # there's nothing to draw, and anyway it throws a warning
                 return False
 
-        return ax.pcolormesh(*args, **kwargs)
+        pc = ax.pcolormesh(*args, **kwargs)
+
+        if getattr(ax, 'qcodes_colorbar', None):
+            # update_normal doesn't seem to work...
+            ax.qcodes_colorbar.update_bruteforce(pc)
+        else:
+            # TODO: what if there are several colormeshes on this subplot,
+            # do they get the same colorscale?
+            # We should make sure they do, and have it include
+            # the full range of both.
+            ax.qcodes_colorbar = self.fig.colorbar(pc, ax=ax)
+
+        return pc

From a22dfcabfd85154a7a9ec3de4c4f6c8a73c2a2cd Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Fri, 15 Jan 2016 11:51:21 +0100
Subject: [PATCH 10/24] automatic labels on plots

---
 qcodes/plots.py | 72 +++++++++++++++++++++++++++++++++++++++----------
 toymodel.py     |  3 +++
 2 files changed, 61 insertions(+), 14 deletions(-)

diff --git a/qcodes/plots.py b/qcodes/plots.py
index 6a981a77bc8..f42882d9132 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -42,6 +42,8 @@ def __init__(self, *args, figsize=(12, 5), interval=1, subplots=(1, 1),
         self.traces = []
         self.data_updaters = set()
 
+        self.title = self.fig.suptitle('')
+
         if args or kwargs:
             self.add(*args, **kwargs)
 
@@ -85,19 +87,60 @@ def add(self, *args, updater=None, **kwargs):
 
         self._find_data_in_set_arrays(kwargs)
 
+        ax = self._get_axes(kwargs)
+        if 'z' in kwargs:
+            plot_object = self._draw_pcolormesh(ax, **kwargs)
+        else:
+            plot_object = self._draw_plot(ax, **kwargs)
+
+        self._update_labels(ax, kwargs)
+        prev_default_title = self._get_default_title()
+
         self.traces.append({
             'config': kwargs,
-            'plot_object': self._draw_trace(kwargs)
+            'plot_object': plot_object
         })
 
+        if prev_default_title == self.title.get_text():
+            # in case the user has updated title, don't change it anymore
+            self.title.set_text(self._get_default_title())
+
         if updater is not None:
             self.data_updaters.add(updater)
         else:
-            for part in ('x', 'y', 'z'):
+            for part in 'xyz':
                 data_array = kwargs.get(part, '')
                 if hasattr(data_array, 'data_set'):
                     self.data_updaters.add(data_array.data_set.sync)
 
+    def _get_axes(self, config):
+        return self.subplots[config.get('subplot', 1) - 1]
+
+    def _get_default_title(self):
+        title_parts = []
+        for trace in self.traces:
+            config = trace['config']
+            for part in 'xyz':
+                data_array = config.get(part, '')
+                if hasattr(data_array, 'data_set'):
+                    location = data_array.data_set.location
+                    if location not in title_parts:
+                        title_parts.append(location)
+        return ', '.join(title_parts)
+
+    def set_title(self, title):
+        self.title.set_text(title)
+
+    def _update_labels(self, ax, config):
+        if 'x' in config and not ax.get_xlabel():
+            ax.set_xlabel(self._get_label(config['x']))
+        if 'y' in config and not ax.get_ylabel():
+            ax.set_ylabel(self._get_label(config['y']))
+
+    def _get_label(self, data_array):
+        return (getattr(data_array, 'label', '') or
+                getattr(data_array, 'name', ''))
+
     def _args_to_kwargs(self, args, kwargs, axletters, plot_func_name):
         if len(args) not in (1, len(axletters)):
             raise ValueError('{} needs either 1 or {} unnamed args'.format(
@@ -149,7 +192,9 @@ def update(self):
                 # so instead, we'll remove and re-add the data.
                 if plot_object:
                     plot_object.remove()
-                plot_object = self._draw_pcolormesh(**config)
+
+                ax = self._get_axes(config)
+                plot_object = self._draw_pcolormesh(ax, **config)
                 trace['plot_object'] = plot_object
 
                 if plot_object:
@@ -189,20 +234,12 @@ def halt(self):
         '''
         self.update_widget.halt()
 
-    def _draw_trace(self, config):
-        if 'z' in config:
-            return self._draw_pcolormesh(**config)
-        else:
-            return self._draw_plot(**config)
-
-    def _draw_plot(self, y, x=None, fmt=None, subplot=1, **kwargs):
-        ax = self.subplots[subplot - 1]
+    def _draw_plot(self, ax, y, x=None, fmt=None, subplot=1, **kwargs):
+        # subplot=1 is just there to strip this out of kwargs
         args = [arg for arg in [x, y, fmt] if arg is not None]
         return ax.plot(*args, **kwargs)[0]
 
-    def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
-        ax = self.subplots[subplot - 1]
-
+    def _draw_pcolormesh(self, ax, z, x=None, y=None, subplot=1, **kwargs):
         args = [masked_invalid(arg) for arg in [x, y, z]
                 if arg is not None]
 
@@ -224,4 +261,11 @@ def _draw_pcolormesh(self, z, x=None, y=None, subplot=1, **kwargs):
             # the full range of both.
             ax.qcodes_colorbar = self.fig.colorbar(pc, ax=ax)
 
+            # ideally this should have been in _update_labels, but
+            # the colorbar doesn't necessarily exist there.
+            # I guess we could create the colorbar no matter what,
+            # and just give it a dummy mappable to start, so we could
+            # put this where it belongs.
+            ax.qcodes_colorbar.set_label(self._get_label(z))
+
         return pc
diff --git a/toymodel.py b/toymodel.py
index ebc386dc45f..883e0117cfc 100644
--- a/toymodel.py
+++ b/toymodel.py
@@ -75,6 +75,7 @@ def __init__(self, name, model):
         for i in range(3):
             cmdbase = 'c{}'.format(i)
             self.add_parameter('chan{}'.format(i),
+                               label='Gate Channel {} (mV)'.format(i),
                                get_cmd=cmdbase + '?',
                                set_cmd=cmdbase + ' {:.4f}',
                                parse_function=float,
@@ -89,6 +90,7 @@ def __init__(self, name, model):
 
         # this parameter uses built-in sweeping to change slowly
         self.add_parameter('amplitude',
+                           label='Source Amplitude (\u03bcV)',
                            get_cmd='ampl?',
                            set_cmd='ampl {:.4f}',
                            parse_function=float,
@@ -102,6 +104,7 @@ def __init__(self, name, model):
         super().__init__(name, model=model)
 
         self.add_parameter('amplitude',
+                           label='Current (nA)',
                            get_cmd='ampl?',
                            parse_function=float)
 

From 57cd01a355fba8a01345b88cb04aea0980575acf Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Fri, 15 Jan 2016 12:03:10 +0100
Subject: [PATCH 11/24] wire up figsize correctly

---
 Qcodes example.ipynb               | 2539 ++--------------------------
 qcodes/plots.py                    |    6 +-
 test2d.dat                         |    2 +-
 test_complex_param/chan1.dat       |    2 +-
 test_complex_param/chan1_chan2.dat |    2 +-
 test_multi_d/chan1.dat             |   64 +-
 test_multi_d/chan1_chan0.dat       |    2 +-
 test_multi_d/chan1_chan2.dat       |    2 +-
 testsweep.dat                      |    2 +-
 9 files changed, 152 insertions(+), 2469 deletions(-)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index c521cae0302..003043a0367 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -197,9 +197,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-14 15:03:41\n"
+      "   chan0: chan0\n",
+      "started at 2016-01-15 12:00:48\n"
      ]
     }
    ],
@@ -221,7 +221,7 @@
     {
      "data": {
       "text/plain": [
-       "[<MeasurementLoop-2, started daemon>, <DataServer-1, started daemon>]"
+       "[<DataServer-1, started daemon>, <MeasurementLoop-2, started daemon>]"
       ]
      },
      "execution_count": 6,
@@ -246,12 +246,12 @@
      "data": {
       "text/plain": [
        "{'amplitude': DataArray[400]: amplitude\n",
-       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,  0.085,\n",
-       "         0.077,  0.071,  0.064,  0.058,  0.053,  0.048,  0.044,  0.04 ,\n",
-       "         0.037,  0.034,  0.031,  0.029,  0.027,  0.025,  0.023,  0.022,\n",
-       "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
-       "         0.013,  0.012,  0.011,  0.011,  0.01 ,  0.01 ,  0.01 ,  0.009,\n",
-       "         0.009,  0.008,  0.008,  0.008,  0.007,  0.007,    nan,    nan,\n",
+       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -297,12 +297,12 @@
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan]),\n",
        " 'chan0': DataArray[400]: chan0\n",
-       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,\n",
-       "        -19.1, -19. , -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,\n",
-       "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
-       "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6, -16.5,\n",
-       "        -16.4, -16.3, -16.2, -16.1, -16. , -15.9, -15.8, -15.7, -15.6,\n",
-       "        -15.5, -15.4,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1130,7 +1130,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1158,11 +1158,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   amplitude_0: amplitude\n",
       "   chan0: chan0\n",
       "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
-      "started at 2016-01-14 15:04:16\n"
+      "   amplitude_0: amplitude\n",
+      "started at 2016-01-15 12:01:05\n"
      ]
     },
     {
@@ -1932,7 +1932,7 @@
     {
      "data": {
       "text/html": [
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1940,6 +1940,43 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data2 = qc.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
+    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
+    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
+    "    qc.Wait(0.001),\n",
+    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
+    "    qc.Task(c2.set, 0)\n",
+    "    ).run(location='test2d')\n",
+    "\n",
+    "# use the subplot and add features of qc.Plot\n",
+    "plot2 = qc.Plot(data2.amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
+    "plot2.add(data2.amplitude_3, cmap=plt.cm.hot, subplot=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
+      "   amplitude_5_0: amplitude\n",
+      "   amplitude_3_0: amplitude\n",
+      "   chan1: chan1\n",
+      "   chan2: chan2\n",
+      "   amplitude_2: amplitude\n",
+      "   chan0: chan0\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-15 12:01:37\n"
+     ]
     },
     {
      "data": {
@@ -2708,7 +2745,7 @@
     {
      "data": {
       "text/html": [
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2716,42 +2753,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2 = qc.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
-    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
-    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
-    "    qc.Wait(0.001),\n",
-    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
-    "    qc.Task(c2.set, 0)\n",
-    "    ).run(location='test2d')\n",
-    "\n",
-    "plot2 = qc.Plot(data2.amplitude_0, cmap=plt.cm.hot)\n",
-    "plot2b = qc.Plot(data2.amplitude_3, cmap=plt.cm.hot)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   chan0: chan0\n",
-      "   chan1: chan1\n",
-      "   amplitude_2: amplitude\n",
-      "   amplitude_3_0: amplitude\n",
-      "   amplitude_5_0: amplitude\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "   chan2: chan2\n",
-      "started at 2016-01-14 15:04:49\n"
-     ]
     },
     {
      "data": {
@@ -3520,7 +3521,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3528,6 +3529,47 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data3 = qc.Loop(c1[-15:15:1], 0.1).each(\n",
+    "    qc.Task(c0.set, -10),\n",
+    "    qc.Task(c2.set, 0),\n",
+    "    # a 1D measurement\n",
+    "    meter.amplitude,\n",
+    "    # a 2D sweep, .each is actually unnecessary bcs this is the default measurement\n",
+    "    qc.Loop(c0[-15:15:1], 0.001).each(meter.amplitude),\n",
+    "    qc.Task(c0.set, -10),\n",
+    "    # a 2D sweep with the same outer but different inner loop\n",
+    "    qc.Loop(c2[-10:10:0.2], 0.001),\n",
+    "    AverageGetter(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
+    ").run(location='test_multi_d')\n",
+    "\n",
+    "# several plots updating simultaneously\n",
+    "plot3 = qc.Plot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
+    "plot3b = qc.Plot(data3.amplitude_5_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1,2))\n",
+    "plot3b.add(data3.avg_amplitude, subplot=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
+      "   amplitude: amplitude\n",
+      "   chan1: chan1\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "   chan2: chan2\n",
+      "started at 2016-01-15 12:02:19\n"
+     ]
     },
     {
      "data": {
@@ -4296,2375 +4338,7 @@
     {
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data3 = qc.Loop(c1[-15:15:1], 0.1).each(\n",
-    "    qc.Task(c0.set, -10),\n",
-    "    qc.Task(c2.set, 0),\n",
-    "    # a 1D measurement\n",
-    "    meter.amplitude,\n",
-    "    # a 2D sweep, .each is actually unnecessary bcs this is the default measurement\n",
-    "    qc.Loop(c0[-15:15:1], 0.001).each(meter.amplitude),\n",
-    "    qc.Task(c0.set, -10),\n",
-    "    # a 2D sweep with the same outer but different inner loop\n",
-    "    qc.Loop(c2[-10:10:0.2], 0.001),\n",
-    "    AverageGetter(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
-    ").run(location='test_multi_d')\n",
-    "\n",
-    "plot3 = qc.Plot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
-    "plot3b = qc.Plot(data3.amplitude_5_0, cmap=plt.cm.hot)\n",
-    "plot3c = qc.Plot(data3.avg_amplitude)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   amplitude: amplitude\n",
-      "   chan1: chan1\n",
-      "   chan2: chan2\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-14 15:05:31\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -6681,9 +4355,18 @@
     "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
     ").run(location='test_complex_param')\n",
     "\n",
-    "plot4 = qc.Plot(data4.amplitude, cmap=plt.cm.hot)\n",
-    "plot4b = qc.Plot(data4.avg_amplitude)"
+    "plot4 = qc.Plot(data4.amplitude, cmap=plt.cm.hot, subplots=(1,2), figsize=(12, 4.5))\n",
+    "plot4.add(data4.avg_amplitude, subplot=2)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/plots.py b/qcodes/plots.py
index f42882d9132..81a5a84c747 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots.py
@@ -29,13 +29,13 @@ class Plot(object):
 
     kwargs: passed along to Plot.add() to add the first data trace
     '''
-    def __init__(self, *args, figsize=(12, 5), interval=1, subplots=(1, 1),
+    def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
                  **kwargs):
 
         if isinstance(subplots, Mapping):
-            self.fig, self.subplots = plt.subplots(**subplots)
+            self.fig, self.subplots = plt.subplots(figsize=figsize, **subplots)
         else:
-            self.fig, self.subplots = plt.subplots(*subplots)
+            self.fig, self.subplots = plt.subplots(*subplots, figsize=figsize)
         if not hasattr(self.subplots, '__len__'):
             self.subplots = (self.subplots,)
 
diff --git a/test2d.dat b/test2d.dat
index f0ab5ac7f61..1ef2098ae43 100644
--- a/test2d.dat
+++ b/test2d.dat
@@ -1,5 +1,5 @@
 # chan1	chan0	amplitude_3	amplitude_0
-# "chan1"	"chan0"	"amplitude"	"amplitude"
+# "Gate Channel 1 (mV)"	"Gate Channel 0 (mV)"	"Current (nA)"	"Current (nA)"
 # 30	54
 -15	-15	0.009	0.003
 -15	-14.5	0.009	0.004
diff --git a/test_complex_param/chan1.dat b/test_complex_param/chan1.dat
index 95bee0aa8df..7d416d204ea 100644
--- a/test_complex_param/chan1.dat
+++ b/test_complex_param/chan1.dat
@@ -1,5 +1,5 @@
 # chan1	avg_amplitude
-# "chan1"	"Average: amplitude"
+# "Gate Channel 1 (mV)"	"Average: Current (nA)"
 # 30
 -15	1.63366
 -14	1.48254
diff --git a/test_complex_param/chan1_chan2.dat b/test_complex_param/chan1_chan2.dat
index dc37b2602a3..27a88c0745a 100644
--- a/test_complex_param/chan1_chan2.dat
+++ b/test_complex_param/chan1_chan2.dat
@@ -1,5 +1,5 @@
 # chan1	chan2	amplitude
-# "chan1"	"chan2"	"amplitude"
+# "Gate Channel 1 (mV)"	"Gate Channel 2 (mV)"	"Current (nA)"
 # 30	100
 -15	-10	3.362
 -15	-9.8	3.253
diff --git a/test_multi_d/chan1.dat b/test_multi_d/chan1.dat
index 70acf2e9915..7f788baae84 100644
--- a/test_multi_d/chan1.dat
+++ b/test_multi_d/chan1.dat
@@ -1,33 +1,33 @@
-# chan1	avg_amplitude	amplitude_2
-# "chan1"	"Average: amplitude"	"amplitude"
+# chan1	amplitude_2	avg_amplitude
+# "Gate Channel 1 (mV)"	"Current (nA)"	"Average: Current (nA)"
 # 30
--15	1.63366	0.006
--14	1.48254	0.009
--13	1.3513	0.014
--12	1.24736	0.027
--11	1.17375	0.064
--10	1.12666	0.117
--9	1.17375	0.064
--8	1.24736	0.027
--7	1.3513	0.014
--6	1.48254	0.009
--5	1.63366	0.006
--4	1.48254	0.009
--3	1.3513	0.014
--2	1.24736	0.027
--1	1.17375	0.064
-0	1.12666	0.117
-1	1.17375	0.064
-2	1.24736	0.027
-3	1.3513	0.014
-4	1.48254	0.009
-5	1.63366	0.006
-6	1.48254	0.009
-7	1.3513	0.014
-8	1.24736	0.027
-9	1.17375	0.064
-10	1.12666	0.117
-11	1.17375	0.064
-12	1.24736	0.027
-13	1.3513	0.014
-14	1.48254	0.009
+-15	0.006	1.63366
+-14	0.009	1.48254
+-13	0.014	1.3513
+-12	0.027	1.24736
+-11	0.064	1.17375
+-10	0.117	1.12666
+-9	0.064	1.17375
+-8	0.027	1.24736
+-7	0.014	1.3513
+-6	0.009	1.48254
+-5	0.006	1.63366
+-4	0.009	1.48254
+-3	0.014	1.3513
+-2	0.027	1.24736
+-1	0.064	1.17375
+0	0.117	1.12666
+1	0.064	1.17375
+2	0.027	1.24736
+3	0.014	1.3513
+4	0.009	1.48254
+5	0.006	1.63366
+6	0.009	1.48254
+7	0.014	1.3513
+8	0.027	1.24736
+9	0.064	1.17375
+10	0.117	1.12666
+11	0.064	1.17375
+12	0.027	1.24736
+13	0.014	1.3513
+14	0.009	1.48254
diff --git a/test_multi_d/chan1_chan0.dat b/test_multi_d/chan1_chan0.dat
index dc0912eab98..25d0d053de8 100644
--- a/test_multi_d/chan1_chan0.dat
+++ b/test_multi_d/chan1_chan0.dat
@@ -1,5 +1,5 @@
 # chan1	chan0	amplitude_3_0
-# "chan1"	"chan0"	"amplitude"
+# "Gate Channel 1 (mV)"	"Gate Channel 0 (mV)"	"Current (nA)"
 # 30	30
 -15	-15	0.003
 -15	-14	0.004
diff --git a/test_multi_d/chan1_chan2.dat b/test_multi_d/chan1_chan2.dat
index 23fc578160a..1b2e51fe76e 100644
--- a/test_multi_d/chan1_chan2.dat
+++ b/test_multi_d/chan1_chan2.dat
@@ -1,5 +1,5 @@
 # chan1	chan2	amplitude_5_0
-# "chan1"	"chan2"	"amplitude"
+# "Gate Channel 1 (mV)"	"Gate Channel 2 (mV)"	"Current (nA)"
 # 30	100
 -15	-10	3.362
 -15	-9.8	3.253
diff --git a/testsweep.dat b/testsweep.dat
index 6a8ad39a170..f394080b00d 100644
--- a/testsweep.dat
+++ b/testsweep.dat
@@ -1,5 +1,5 @@
 # chan0	amplitude
-# "chan0"	"amplitude"
+# "Gate Channel 0 (mV)"	"Current (nA)"
 # 400
 -20	0.117
 -19.9	0.117

From cbb6edc77820d86bbecbbf3cc268045637d7bcb3 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Fri, 15 Jan 2016 12:12:31 +0100
Subject: [PATCH 12/24] move Plot -> MatPlot so we can have different plotting
 backends

---
 Qcodes example.ipynb                     | 63 ++++++++++-------------
 qcodes/__init__.py                       |  2 +-
 qcodes/{plots.py => plots/matplotlib.py} | 12 ++---
 test_multi_d/chan1.dat                   | 64 ++++++++++++------------
 4 files changed, 66 insertions(+), 75 deletions(-)
 rename qcodes/{plots.py => plots/matplotlib.py} (96%)

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 003043a0367..e8d17b81ecd 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -197,9 +197,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   amplitude: amplitude\n",
       "   chan0: chan0\n",
-      "started at 2016-01-15 12:00:48\n"
+      "   amplitude: amplitude\n",
+      "started at 2016-01-15 12:10:14\n"
      ]
     }
    ],
@@ -221,7 +221,7 @@
     {
      "data": {
       "text/plain": [
-       "[<DataServer-1, started daemon>, <MeasurementLoop-2, started daemon>]"
+       "[<MeasurementLoop-2, started daemon>, <DataServer-1, started daemon>]"
       ]
      },
      "execution_count": 6,
@@ -246,11 +246,11 @@
      "data": {
       "text/plain": [
        "{'amplitude': DataArray[400]: amplitude\n",
-       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
-       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,  0.085,\n",
+       "         0.077,  0.071,  0.064,  0.058,  0.053,  0.048,  0.044,  0.04 ,\n",
+       "         0.037,  0.034,  0.031,  0.029,  0.027,  0.025,  0.023,  0.022,\n",
+       "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
+       "         0.013,  0.012,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -297,10 +297,10 @@
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan]),\n",
        " 'chan0': DataArray[400]: chan0\n",
-       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
-       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,\n",
+       "        -19.1, -19. , -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,\n",
+       "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
+       "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1142,7 +1142,7 @@
    ],
    "source": [
     "# live-updating plot, that syncs the data and stops updating when it's finished\n",
-    "plot = qc.Plot(data.amplitude)"
+    "plot = qc.MatPlot(data.amplitude)"
    ]
   },
   {
@@ -1158,11 +1158,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
+      "   amplitude_3: amplitude\n",
       "   chan0: chan0\n",
       "   chan1: chan1\n",
-      "   amplitude_3: amplitude\n",
       "   amplitude_0: amplitude\n",
-      "started at 2016-01-15 12:01:05\n"
+      "started at 2016-01-15 12:10:32\n"
      ]
     },
     {
@@ -1952,7 +1952,7 @@
     "    ).run(location='test2d')\n",
     "\n",
     "# use the subplot and add features of qc.Plot\n",
-    "plot2 = qc.Plot(data2.amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
+    "plot2 = qc.MatPlot(data2.amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
     "plot2.add(data2.amplitude_3, cmap=plt.cm.hot, subplot=2)"
    ]
   },
@@ -1968,14 +1968,14 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   amplitude_5_0: amplitude\n",
-      "   amplitude_3_0: amplitude\n",
-      "   chan1: chan1\n",
       "   chan2: chan2\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "   amplitude_5_0: amplitude\n",
       "   amplitude_2: amplitude\n",
       "   chan0: chan0\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-15 12:01:37\n"
+      "   amplitude_3_0: amplitude\n",
+      "   chan1: chan1\n",
+      "started at 2016-01-15 12:11:08\n"
      ]
     },
     {
@@ -3546,8 +3546,8 @@
     ").run(location='test_multi_d')\n",
     "\n",
     "# several plots updating simultaneously\n",
-    "plot3 = qc.Plot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
-    "plot3b = qc.Plot(data3.amplitude_5_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1,2))\n",
+    "plot3 = qc.MatPlot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
+    "plot3b = qc.MatPlot(data3.amplitude_5_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1,2))\n",
     "plot3b.add(data3.avg_amplitude, subplot=2)"
    ]
   },
@@ -3564,11 +3564,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
+      "   chan2: chan2\n",
       "   amplitude: amplitude\n",
-      "   chan1: chan1\n",
       "   avg_amplitude: avg_amplitude\n",
-      "   chan2: chan2\n",
-      "started at 2016-01-15 12:02:19\n"
+      "   chan1: chan1\n",
+      "started at 2016-01-15 12:11:27\n"
      ]
     },
     {
@@ -4355,18 +4355,9 @@
     "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
     ").run(location='test_complex_param')\n",
     "\n",
-    "plot4 = qc.Plot(data4.amplitude, cmap=plt.cm.hot, subplots=(1,2), figsize=(12, 4.5))\n",
+    "plot4 = qc.MatPlot(data4.amplitude, cmap=plt.cm.hot, subplots=(1,2), figsize=(12, 4.5))\n",
     "plot4.add(data4.avg_amplitude, subplot=2)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index ee124cdafcf..ef705e37b8d 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -10,7 +10,7 @@
 # time and spins up other processes in order to try and get a front end
 import sys as _sys
 if 'ipy' in repr(_sys.stdout):
-    from qcodes.plots import Plot
+    from qcodes.plots.matplotlib import MatPlot
 
 from qcodes.data.manager import get_data_manager
 from qcodes.data.data_set import DataMode, DataSet
diff --git a/qcodes/plots.py b/qcodes/plots/matplotlib.py
similarity index 96%
rename from qcodes/plots.py
rename to qcodes/plots/matplotlib.py
index 81a5a84c747..d2ee40b9968 100644
--- a/qcodes/plots.py
+++ b/qcodes/plots/matplotlib.py
@@ -12,12 +12,12 @@
 from qcodes.widgets.widgets import HiddenUpdateWidget
 
 
-class Plot(object):
+class MatPlot(object):
     '''
     Plot x/y lines or x/y/z heatmap data. The first trace may be included
-    in the constructor, other traces can be added with Plot.add()
+    in the constructor, other traces can be added with MatPlot.add()
 
-    args: shortcut to provide the x/y/z data. See Plot.add
+    args: shortcut to provide the x/y/z data. See MatPlot.add
 
     figsize: (width, height) tuple to pass to plt.figure
         default (12, 5)
@@ -27,7 +27,7 @@ class Plot(object):
         plt.subplots. default is a single simple subplot (1, 1)
         you can use this to pass kwargs to the plt.figure constructor
 
-    kwargs: passed along to Plot.add() to add the first data trace
+    kwargs: passed along to MatPlot.add() to add the first data trace
     '''
     def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
                  **kwargs):
@@ -52,7 +52,7 @@ def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
 
     def add(self, *args, updater=None, **kwargs):
         '''
-        adds one trace to this Plot.
+        adds one trace to this MatPlot.
 
         args: a way to provide x/y/z data without keywords
             The last one is the dependent data, and we look at its
@@ -171,7 +171,7 @@ def _find_data_in_set_arrays(self, kwargs):
     def update(self):
         '''
         update the data in this plot, using the updaters given with
-        Plot.add() or in the included DataSets, then include this in
+        MatPlot.add() or in the included DataSets, then include this in
         the plot
         '''
         any_updates = False
diff --git a/test_multi_d/chan1.dat b/test_multi_d/chan1.dat
index 7f788baae84..91ead1d6f75 100644
--- a/test_multi_d/chan1.dat
+++ b/test_multi_d/chan1.dat
@@ -1,33 +1,33 @@
-# chan1	amplitude_2	avg_amplitude
-# "Gate Channel 1 (mV)"	"Current (nA)"	"Average: Current (nA)"
+# chan1	avg_amplitude	amplitude_2
+# "Gate Channel 1 (mV)"	"Average: Current (nA)"	"Current (nA)"
 # 30
--15	0.006	1.63366
--14	0.009	1.48254
--13	0.014	1.3513
--12	0.027	1.24736
--11	0.064	1.17375
--10	0.117	1.12666
--9	0.064	1.17375
--8	0.027	1.24736
--7	0.014	1.3513
--6	0.009	1.48254
--5	0.006	1.63366
--4	0.009	1.48254
--3	0.014	1.3513
--2	0.027	1.24736
--1	0.064	1.17375
-0	0.117	1.12666
-1	0.064	1.17375
-2	0.027	1.24736
-3	0.014	1.3513
-4	0.009	1.48254
-5	0.006	1.63366
-6	0.009	1.48254
-7	0.014	1.3513
-8	0.027	1.24736
-9	0.064	1.17375
-10	0.117	1.12666
-11	0.064	1.17375
-12	0.027	1.24736
-13	0.014	1.3513
-14	0.009	1.48254
+-15	1.63366	0.006
+-14	1.48254	0.009
+-13	1.3513	0.014
+-12	1.24736	0.027
+-11	1.17375	0.064
+-10	1.12666	0.117
+-9	1.17375	0.064
+-8	1.24736	0.027
+-7	1.3513	0.014
+-6	1.48254	0.009
+-5	1.63366	0.006
+-4	1.48254	0.009
+-3	1.3513	0.014
+-2	1.24736	0.027
+-1	1.17375	0.064
+0	1.12666	0.117
+1	1.17375	0.064
+2	1.24736	0.027
+3	1.3513	0.014
+4	1.48254	0.009
+5	1.63366	0.006
+6	1.48254	0.009
+7	1.3513	0.014
+8	1.24736	0.027
+9	1.17375	0.064
+10	1.12666	0.117
+11	1.17375	0.064
+12	1.24736	0.027
+13	1.3513	0.014
+14	1.48254	0.009

From 1ec2f69c4e624b0f43999ea4d2f61b99fd910bc4 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Fri, 15 Jan 2016 12:16:15 +0100
Subject: [PATCH 13/24] comment update

---
 qcodes/plots/matplotlib.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index d2ee40b9968..d2ddcd64622 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -1,6 +1,6 @@
 '''
 Live plotting in Jupyter notebooks
-using the nbagg backend
+using the nbagg backend and matplotlib
 '''
 import matplotlib.pyplot as plt
 from matplotlib.transforms import Bbox

From 6cc56f0afb337f28c6c3f34b5a0028bf07592cbd Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Mon, 18 Jan 2016 12:05:38 +0100
Subject: [PATCH 14/24] break out BasePlot from MatPlot

---
 Qcodes example.ipynb       |  42 ++++-----
 qcodes/plots/base.py       | 174 +++++++++++++++++++++++++++++++++++++
 qcodes/plots/matplotlib.py | 122 +++-----------------------
 3 files changed, 209 insertions(+), 129 deletions(-)
 create mode 100644 qcodes/plots/base.py

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index e8d17b81ecd..6e7289906b8 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -197,9 +197,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   chan0: chan0\n",
       "   amplitude: amplitude\n",
-      "started at 2016-01-15 12:10:14\n"
+      "   chan0: chan0\n",
+      "started at 2016-01-18 12:00:54\n"
      ]
     }
    ],
@@ -246,11 +246,11 @@
      "data": {
       "text/plain": [
        "{'amplitude': DataArray[400]: amplitude\n",
-       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,  0.085,\n",
-       "         0.077,  0.071,  0.064,  0.058,  0.053,  0.048,  0.044,  0.04 ,\n",
-       "         0.037,  0.034,  0.031,  0.029,  0.027,  0.025,  0.023,  0.022,\n",
-       "         0.02 ,  0.019,  0.018,  0.017,  0.016,  0.015,  0.014,  0.013,\n",
-       "         0.013,  0.012,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       " array([ 0.117,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -297,10 +297,10 @@
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan]),\n",
        " 'chan0': DataArray[400]: chan0\n",
-       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,\n",
-       "        -19.1, -19. , -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,\n",
-       "        -18.2, -18.1, -18. , -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,\n",
-       "        -17.3, -17.2, -17.1, -17. , -16.9, -16.8, -16.7, -16.6,   nan,\n",
+       " array([-20. , -19.9,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -1158,11 +1158,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
+      "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
       "   chan0: chan0\n",
-      "   chan1: chan1\n",
       "   amplitude_0: amplitude\n",
-      "started at 2016-01-15 12:10:32\n"
+      "started at 2016-01-18 12:01:15\n"
      ]
     },
     {
@@ -1968,14 +1968,14 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
+      "   chan1: chan1\n",
       "   chan2: chan2\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "   amplitude_5_0: amplitude\n",
       "   amplitude_2: amplitude\n",
-      "   chan0: chan0\n",
+      "   amplitude_5_0: amplitude\n",
       "   amplitude_3_0: amplitude\n",
-      "   chan1: chan1\n",
-      "started at 2016-01-15 12:11:08\n"
+      "   chan0: chan0\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-18 12:01:56\n"
      ]
     },
     {
@@ -3564,11 +3564,11 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
-      "   chan2: chan2\n",
+      "   chan1: chan1\n",
       "   amplitude: amplitude\n",
+      "   chan2: chan2\n",
       "   avg_amplitude: avg_amplitude\n",
-      "   chan1: chan1\n",
-      "started at 2016-01-15 12:11:27\n"
+      "started at 2016-01-18 12:02:24\n"
      ]
     },
     {
diff --git a/qcodes/plots/base.py b/qcodes/plots/base.py
new file mode 100644
index 00000000000..e6ccdfd1114
--- /dev/null
+++ b/qcodes/plots/base.py
@@ -0,0 +1,174 @@
+'''
+Live plotting in Jupyter notebooks
+'''
+from IPython.display import display
+
+from qcodes.widgets.widgets import HiddenUpdateWidget
+
+
+class BasePlot(object):
+    '''
+    create an auto-updating plot connected to a Jupyter notebook
+
+    interval: period in seconds between update checks
+        default 1
+
+    data_keys: which keys in trace config can contain data
+        that we should look for updates in.
+        default 'xyz' (treated as a sequence) but add more if
+        for example marker size or color can contain data
+    '''
+    def __init__(self, interval=1, data_keys='xyz'):
+        self.data_keys = data_keys
+        self.traces = []
+        self.data_updaters = set()
+
+        self.update_widget = HiddenUpdateWidget(self.update, interval)
+        display(self.update_widget)
+
+    def add(self, *args, updater=None, **kwargs):
+        '''
+        add one trace to this plot
+
+        args: optional way to provide x/y/z data without keywords
+            If the last one is 1D, may be `y` or `x`, `y`
+            If the last one is 2D, may be `z` or `x`, `y`, `z`
+
+        updater: a callable (with no args) that updates the data in this trace
+            if omitted, we will look for DataSets referenced in this data, and
+            call their sync methods.
+
+        kwargs: after inserting info found in args and possibly in set_arrays
+            into `x`, `y`, and optionally `z`, these are passed along to
+            self.add_to_plot
+        '''
+        self.expand_trace(args, kwargs)
+        self.add_to_plot(**kwargs)
+        self.add_updater(updater, kwargs)
+
+    def add_to_plot(self, **kwargs):
+        '''
+        add a trace the plot itself (typically called by self.add,
+        which incorporates args into kwargs, so the subclass doesn't
+        need to worry about this). Data will be in `x`, `y`, and optionally
+        `z`
+        should be implemented by a subclass
+        '''
+        raise NotImplementedError
+
+    def add_updater(self, updater, plot_config):
+        if updater is not None:
+            self.data_updaters.add(updater)
+        else:
+            for key in self.data_keys:
+                data_array = plot_config.get(key, '')
+                if hasattr(data_array, 'data_set'):
+                    self.data_updaters.add(data_array.data_set.sync)
+
+    def get_default_title(self):
+        '''
+        the default title for a plot is just a list of DataSet locations
+        '''
+        title_parts = []
+        for trace in self.traces:
+            config = trace['config']
+            for part in self.data_keys:
+                data_array = config.get(part, '')
+                if hasattr(data_array, 'data_set'):
+                    location = data_array.data_set.location
+                    if location not in title_parts:
+                        title_parts.append(location)
+        return ', '.join(title_parts)
+
+    def get_label(self, data_array):
+        '''
+        look for a label, falling back on name.
+        '''
+        return (getattr(data_array, 'label', '') or
+                getattr(data_array, 'name', ''))
+
+    def expand_trace(self, args, kwargs):
+        '''
+        the x, y (and potentially z) data for a trace may be provided
+        as positional rather than keyword args. The allowed forms are
+        [y] or [x, y] if the last arg is 1D, and
+        [z] or [x, y, z] if the last arg is 2D
+
+        also, look in the main data array (`z` if it exists, else `y`)
+        for set_arrays that can provide the `x` (and potentially `y`) data
+
+        even if we allow data in other attributes (like marker size/color)
+        by providing a different self.data_keys, set_arrays will only
+        contribute x from y, or x & y from z, so we don't use data_keys here
+        '''
+        if args:
+            if hasattr(args[-1][0], '__len__'):
+                # 2D (or higher... but ignore this for now)
+                # this test works for both numpy arrays and bare sequences
+                axletters = 'xyz'
+                ndim = 2
+            else:
+                axletters = 'xy'
+                ndim = 1
+
+            if len(args) not in (1, len(axletters)):
+                raise ValueError('{}D data needs 1 or {} unnamed args'.format(
+                    ndim, len(axletters)))
+
+            arg_axletters = axletters[-len(args):]
+
+            for arg, arg_axletters in zip(args, arg_axletters):
+                if arg_axletters in kwargs:
+                    raise ValueError(arg_axletters + ' data provided twice')
+                kwargs[arg_axletters] = arg
+
+        # reset axletters, we may or may not have found them above
+        axletters = 'xyz' if 'z' in kwargs else 'xy'
+        main_data = kwargs[axletters[-1]]
+        if hasattr(main_data, 'set_arrays'):
+            num_axes = len(axletters) - 1
+            # things will probably fail if we try to plot arrays of the
+            # wrong dimension... but we'll give it a shot anyway.
+            set_arrays = main_data.set_arrays[-num_axes:]
+            # for 2D: y is outer loop, which is earlier in set_arrays,
+            # and x is the inner loop... is this the right convention?
+            set_axletters = reversed(axletters[:-1])
+            for axletter, set_array in zip(set_axletters, set_arrays):
+                if axletter not in kwargs:
+                    kwargs[axletter] = set_array
+
+    def update(self):
+        '''
+        update the data in this plot, using the updaters given with
+        MatPlot.add() or in the included DataSets, then include this in
+        the plot
+
+        this is a wrapper routine that the update widget calls,
+        inside this we call self.update() which should be subclassed
+        '''
+        any_updates = False
+        for updater in self.data_updaters:
+            updates = updater()
+            if updates is not False:
+                any_updates = True
+
+        self.update_plot()
+
+        # once all updaters report they're finished (by returning exactly
+        # False) we stop updating the plot.
+        if any_updates is False:
+            self.halt()
+
+    def update_plot(self):
+        '''
+        update the plot itself (typically called by self.update).
+        should be implemented by a subclass
+        '''
+        raise NotImplementedError
+
+    def halt(self):
+        '''
+        stop automatic updates to this plot, by canceling its update widget
+        '''
+        if hasattr(self, 'update_widget'):
+            self.update_widget.halt()
diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index d2ddcd64622..4998d8bab7a 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -6,13 +6,12 @@
 from matplotlib.transforms import Bbox
 import numpy as np
 from numpy.ma import masked_invalid, getmask
-from IPython.display import display
 from collections import Mapping
 
-from qcodes.widgets.widgets import HiddenUpdateWidget
+from .base import BasePlot
 
 
-class MatPlot(object):
+class MatPlot(BasePlot):
     '''
     Plot x/y lines or x/y/z heatmap data. The first trace may be included
     in the constructor, other traces can be added with MatPlot.add()
@@ -39,31 +38,17 @@ def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
         if not hasattr(self.subplots, '__len__'):
             self.subplots = (self.subplots,)
 
-        self.traces = []
-        self.data_updaters = set()
-
         self.title = self.fig.suptitle('')
 
+        super().__init__(interval)
+
         if args or kwargs:
             self.add(*args, **kwargs)
 
-        self.update_widget = HiddenUpdateWidget(self.update, interval)
-        display(self.update_widget)
-
-    def add(self, *args, updater=None, **kwargs):
+    def add_to_plot(self, **kwargs):
         '''
         adds one trace to this MatPlot.
 
-        args: a way to provide x/y/z data without keywords
-            The last one is the dependent data, and we look at its
-            dimensionality to determine if it's `y` or `z`.
-            If it's `y`, it may optionally be preceded by `x`.
-            If it's `z`, it may optionally be preceded by `x` and `y`.
-
-        updater: a callable (with no args) that updates the data in this trace
-            if omitted, we will look for DataSets referenced in this data, and
-            call their sync methods.
-
         kwargs: with the following exceptions (mostly the data!), these are
             passed directly to the matplotlib plotting routine.
 
@@ -77,16 +62,6 @@ def add(self, *args, updater=None, **kwargs):
         '''
         # TODO some way to specify overlaid axes?
 
-        if args:
-            if hasattr(args[-1][0], '__len__'):
-                # 2D (or higher... but ignore this for now)
-                self._args_to_kwargs(args, kwargs, 'xyz', 'pcolormesh')
-            else:
-                # 1D
-                self._args_to_kwargs(args, kwargs, 'xy', 'plot')
-
-        self._find_data_in_set_arrays(kwargs)
-
         ax = self._get_axes(kwargs)
         if 'z' in kwargs:
             plot_object = self._draw_pcolormesh(ax, **kwargs)
@@ -94,7 +69,7 @@ def add(self, *args, updater=None, **kwargs):
             plot_object = self._draw_plot(ax, **kwargs)
 
         self._update_labels(ax, kwargs)
-        prev_default_title = self._get_default_title()
+        prev_default_title = self.get_default_title()
 
         self.traces.append({
             'config': kwargs,
@@ -103,83 +78,25 @@ def add(self, *args, updater=None, **kwargs):
 
         if prev_default_title == self.title.get_text():
             # in case the user has updated title, don't change it anymore
-            self.title.set_text(self._get_default_title())
-
-        if updater is not None:
-            self.data_updaters.add(updater)
-        else:
-            for part in 'xyz':
-                data_array = kwargs.get(part, '')
-                if hasattr(data_array, 'data_set'):
-                    self.data_updaters.add(data_array.data_set.sync)
+            self.title.set_text(self.get_default_title())
 
     def _get_axes(self, config):
         return self.subplots[config.get('subplot', 1) - 1]
 
-    def _get_default_title(self):
-        title_parts = []
-        for trace in self.traces:
-            config = trace['config']
-            for part in 'xyz':
-                data_array = config.get(part, '')
-                if hasattr(data_array, 'data_set'):
-                    location = data_array.data_set.location
-                    if location not in title_parts:
-                        title_parts.append(location)
-        return ', '.join(title_parts)
-
     def set_title(self, title):
         self.title.set_text(title)
 
     def _update_labels(self, ax, config):
         if 'x' in config and not ax.get_xlabel():
-            ax.set_xlabel(self._get_label(config['x']))
+            ax.set_xlabel(self.get_label(config['x']))
         if 'y' in config and not ax.get_ylabel():
-            ax.set_ylabel(self._get_label(config['y']))
-
-    def _get_label(self, data_array):
-        return (getattr(data_array, 'label', '') or
-                getattr(data_array, 'name', ''))
-
-    def _args_to_kwargs(self, args, kwargs, axletters, plot_func_name):
-        if len(args) not in (1, len(axletters)):
-            raise ValueError('{} needs either 1 or {} unnamed args'.format(
-                plot_func_name, len(axletters)))
-
-        arg_axletters = axletters[-len(args):]
-
-        for arg, arg_axletters in zip(args, arg_axletters):
-            if arg_axletters in kwargs:
-                raise ValueError(arg_axletters + ' data provided twice')
-            kwargs[arg_axletters] = arg
-
-    def _find_data_in_set_arrays(self, kwargs):
-        axletters = 'xyz' if 'z' in kwargs else 'xy'
-        main_data = kwargs[axletters[-1]]
-        if hasattr(main_data, 'set_arrays'):
-            num_axes = len(axletters) - 1
-            # things will probably fail if we try to plot arrays of the
-            # wrong dimension... but we'll give it a shot anyway.
-            set_arrays = main_data.set_arrays[-num_axes:]
-            # for 2D: y is outer loop, which is earlier in set_arrays,
-            # and x is the inner loop... is this the right convention?
-            set_axletters = reversed(axletters[:-1])
-            for axletter, set_array in zip(set_axletters, set_arrays):
-                if axletter not in kwargs:
-                    kwargs[axletter] = set_array
-
-    def update(self):
+            ax.set_ylabel(self.get_label(config['y']))
+
+    def update_plot(self):
         '''
-        update the data in this plot, using the updaters given with
-        MatPlot.add() or in the included DataSets, then include this in
-        the plot
+        update the plot. The DataSets themselves have already been updated
+        in update, here we just push the changes to the plot.
         '''
-        any_updates = False
-        for updater in self.data_updaters:
-            updates = updater()
-            if updates is not False:
-                any_updates = True
-
         # matplotlib doesn't know how to autoscale to a pcolormesh after the
         # first draw (relim ignores it...) so we have to do this ourselves
         bboxes = dict(zip(self.subplots, [[] for p in self.subplots]))
@@ -223,17 +140,6 @@ def update(self):
 
         self.fig.canvas.draw()
 
-        # once all updaters report they're finished (by returning exactly
-        # False) we stop updating the plot.
-        if any_updates is False and hasattr(self, 'update_widget'):
-            self.halt()
-
-    def halt(self):
-        '''
-        stop automatic updates to this plot, by canceling its update widget
-        '''
-        self.update_widget.halt()
-
     def _draw_plot(self, ax, y, x=None, fmt=None, subplot=1, **kwargs):
         # subplot=1 is just there to strip this out of kwargs
         args = [arg for arg in [x, y, fmt] if arg is not None]
@@ -266,6 +172,6 @@ def _draw_pcolormesh(self, ax, z, x=None, y=None, subplot=1, **kwargs):
             # I guess we could create the colorbar no matter what,
             # and just give it a dummy mappable to start, so we could
             # put this where it belongs.
-            ax.qcodes_colorbar.set_label(self._get_label(z))
+            ax.qcodes_colorbar.set_label(self.get_label(z))
 
         return pc

From a3dfed6448efe096a65e7d16299a2f151d29bbba Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Mon, 18 Jan 2016 13:26:55 +0100
Subject: [PATCH 15/24] take out initial update call in HiddenUpdateWidget, so
 we plot init is cleaner

---
 qcodes/plots/matplotlib.py |  4 ++--
 qcodes/widgets/widgets.py  | 11 +++++++++--
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index 4998d8bab7a..6cc350b044d 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -31,6 +31,8 @@ class MatPlot(BasePlot):
     def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
                  **kwargs):
 
+        super().__init__(interval)
+
         if isinstance(subplots, Mapping):
             self.fig, self.subplots = plt.subplots(figsize=figsize, **subplots)
         else:
@@ -40,8 +42,6 @@ def __init__(self, *args, figsize=(8, 5), interval=1, subplots=(1, 1),
 
         self.title = self.fig.suptitle('')
 
-        super().__init__(interval)
-
         if args or kwargs:
             self.add(*args, **kwargs)
 
diff --git a/qcodes/widgets/widgets.py b/qcodes/widgets/widgets.py
index 524cc8608b2..b2f7a5d8c8d 100644
--- a/qcodes/widgets/widgets.py
+++ b/qcodes/widgets/widgets.py
@@ -19,12 +19,14 @@ class UpdateWidget(widgets.DOMWidget):
     interval - the period, in seconds
         can be changed later by setting the interval attribute
         interval=0 or the halt() method disables updates.
+    first_call - do we call the update function immediately, or only
+        after the first interval? default True
     '''
     _view_name = Unicode('UpdateView', sync=True)  # see widgets.js
     _message = Unicode(sync=True)
     interval = Float(sync=True)
 
-    def __init__(self, fn, interval, **kwargs):
+    def __init__(self, fn, interval, first_call=True, **kwargs):
         super().__init__(**kwargs)
 
         self._fn = fn
@@ -32,7 +34,8 @@ def __init__(self, fn, interval, **kwargs):
 
         self.on_msg(self._handle_msg)
 
-        self._handle_msg({'init': True})
+        if first_call:
+            self._handle_msg({'init': True})
 
     def _handle_msg(self, message=None):
         self._message = str(self._fn())
@@ -46,5 +49,9 @@ class HiddenUpdateWidget(UpdateWidget):
     A variant on UpdateWidget that hides its section of the output area
     Just lets the front end periodically execute code
     that takes care of its own display.
+    by default, first_call is False here, unlike UpdateWidget
     '''
     _view_name = Unicode('HiddenUpdateView', sync=True)  # see widgets.js
+
+    def __init__(self, *args, first_call=False, **kwargs):
+        super().__init__(*args, first_call=first_call, **kwargs)

From 10f562519de9ea30f0d0adcff1302bdbffe02c5a Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Mon, 18 Jan 2016 14:04:02 +0100
Subject: [PATCH 16/24] not using MatPlot.set_title anymore

---
 qcodes/plots/matplotlib.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index 6cc350b044d..b34c3155983 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -83,9 +83,6 @@ def add_to_plot(self, **kwargs):
     def _get_axes(self, config):
         return self.subplots[config.get('subplot', 1) - 1]
 
-    def set_title(self, title):
-        self.title.set_text(title)
-
     def _update_labels(self, ax, config):
         if 'x' in config and not ax.get_xlabel():
             ax.set_xlabel(self.get_label(config['x']))

From 537db9049277347fa846ad0abfe7b4d32e531882 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Mon, 18 Jan 2016 14:04:39 +0100
Subject: [PATCH 17/24] document BasePlot.traces requirement

---
 qcodes/plots/base.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/qcodes/plots/base.py b/qcodes/plots/base.py
index e6ccdfd1114..94d571b0b59 100644
--- a/qcodes/plots/base.py
+++ b/qcodes/plots/base.py
@@ -52,7 +52,9 @@ def add_to_plot(self, **kwargs):
         which incorporates args into kwargs, so the subclass doesn't
         need to worry about this). Data will be in `x`, `y`, and optionally
         `z`
-        should be implemented by a subclass
+
+        should be implemented by a subclass, and each call should append
+        a dictionary to self.traces, containing at least {'config': kwargs}
         '''
         raise NotImplementedError
 

From 20455ead1b02e41508e24e9d643ffafaa3803ccf Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 01:54:20 +0100
Subject: [PATCH 18/24] DataArray.__len__

---
 qcodes/data/data_array.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/qcodes/data/data_array.py b/qcodes/data/data_array.py
index f14e415935c..2f4cc2ced8f 100644
--- a/qcodes/data/data_array.py
+++ b/qcodes/data/data_array.py
@@ -1,8 +1,6 @@
 import numpy as np
 import collections
 
-import qcodes
-
 
 class DataArray(object):
     '''
@@ -178,6 +176,13 @@ def __getattr__(self, key):
 
         return getattr(self.data, key)
 
+    def __len__(self):
+        '''
+        must be explicitly delegated to, because len() will look for this
+        attribute to already exist
+        '''
+        return len(self.data)
+
     def _flat_index(self, indices, index_fill):
         indices = indices + index_fill[len(indices):]
         return np.ravel_multi_index(tuple(zip(indices)), self.size)[0]

From c4b5b2ca4a0aa3814faa327da614f2efe6ecb4ba Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 01:55:19 +0100
Subject: [PATCH 19/24] reset update timer when traces are added to a plot

---
 qcodes/plots/base.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/qcodes/plots/base.py b/qcodes/plots/base.py
index 94d571b0b59..335c1ebea96 100644
--- a/qcodes/plots/base.py
+++ b/qcodes/plots/base.py
@@ -23,6 +23,7 @@ def __init__(self, interval=1, data_keys='xyz'):
         self.traces = []
         self.data_updaters = set()
 
+        self.interval = interval
         self.update_widget = HiddenUpdateWidget(self.update, interval)
         display(self.update_widget)
 
@@ -67,6 +68,9 @@ def add_updater(self, updater, plot_config):
                 if hasattr(data_array, 'data_set'):
                     self.data_updaters.add(data_array.data_set.sync)
 
+        if self.data_updaters:
+            self.update_widget.interval = self.interval
+
     def get_default_title(self):
         '''
         the default title for a plot is just a list of DataSet locations

From 1374b877675de7f73f7ac6e266e54b7158b5370e Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 01:57:26 +0100
Subject: [PATCH 20/24] fix MatPlot docstring

---
 qcodes/plots/matplotlib.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index b34c3155983..f3d66a511d1 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -18,8 +18,8 @@ class MatPlot(BasePlot):
 
     args: shortcut to provide the x/y/z data. See MatPlot.add
 
-    figsize: (width, height) tuple to pass to plt.figure
-        default (12, 5)
+    figsize: (width, height) tuple in inches to pass to plt.figure
+        default (8, 5)
     interval: period in seconds between update checks
 
     subplots: either a sequence (args) or mapping (kwargs) to pass to

From a1ab3c1d39d9399075b16857f5ad38b1c339a388 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 01:58:16 +0100
Subject: [PATCH 21/24] some color constants copied in from plotly

---
 qcodes/plots/colors.py | 135 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 135 insertions(+)
 create mode 100644 qcodes/plots/colors.py

diff --git a/qcodes/plots/colors.py b/qcodes/plots/colors.py
new file mode 100644
index 00000000000..d6766793caf
--- /dev/null
+++ b/qcodes/plots/colors.py
@@ -0,0 +1,135 @@
+# default colors and colorscales, taken from plotly
+color_cycle = [
+    '#1f77b4',  # muted blue
+    '#ff7f0e',  # safety orange
+    '#2ca02c',  # cooked asparagus green
+    '#d62728',  # brick red
+    '#9467bd',  # muted purple
+    '#8c564b',  # chestnut brown
+    '#e377c2',  # raspberry yogurt pink
+    '#7f7f7f',  # middle gray
+    '#bcbd22',  # curry yellow-green
+    '#17becf'   # blue-teal
+]
+
+
+colorscales_raw = {
+    'Greys': [[0, 'rgb(0,0,0)'], [1, 'rgb(255,255,255)']],
+
+    'YIGnBu': [
+        [0, 'rgb(8, 29, 88)'], [0.125, 'rgb(37, 52, 148)'],
+        [0.25, 'rgb(34, 94, 168)'], [0.375, 'rgb(29, 145, 192)'],
+        [0.5, 'rgb(65, 182, 196)'], [0.625, 'rgb(127, 205, 187)'],
+        [0.75, 'rgb(199, 233, 180)'], [0.875, 'rgb(237, 248, 217)'],
+        [1, 'rgb(255, 255, 217)']],
+
+    'Greens': [
+        [0, 'rgb(0, 68, 27)'], [0.125, 'rgb(0, 109, 44)'],
+        [0.25, 'rgb(35, 139, 69)'], [0.375, 'rgb(65, 171, 93)'],
+        [0.5, 'rgb(116, 196, 118)'], [0.625, 'rgb(161, 217, 155)'],
+        [0.75, 'rgb(199, 233, 192)'], [0.875, 'rgb(229, 245, 224)'],
+        [1, 'rgb(247, 252, 245)']],
+
+    'YIOrRd': [
+        [0, 'rgb(128, 0, 38)'], [0.125, 'rgb(189, 0, 38)'],
+        [0.25, 'rgb(227, 26, 28)'], [0.375, 'rgb(252, 78, 42)'],
+        [0.5, 'rgb(253, 141, 60)'], [0.625, 'rgb(254, 178, 76)'],
+        [0.75, 'rgb(254, 217, 118)'], [0.875, 'rgb(255, 237, 160)'],
+        [1, 'rgb(255, 255, 204)']],
+
+    'Bluered': [[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']],
+
+    # modified RdBu based on
+    # www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf
+    'RdBu': [
+        [0, 'rgb(5, 10, 172)'], [0.35, 'rgb(106, 137, 247)'],
+        [0.5, 'rgb(190,190,190)'], [0.6, 'rgb(220, 170, 132)'],
+        [0.7, 'rgb(230, 145, 90)'], [1, 'rgb(178, 10, 28)']],
+    # Scale for non-negative numeric values
+    'Reds': [
+        [0, 'rgb(220, 220, 220)'], [0.2, 'rgb(245, 195, 157)'],
+        [0.4, 'rgb(245, 160, 105)'], [1, 'rgb(178, 10, 28)']],
+    # Scale for non-positive numeric values
+    'Blues': [
+        [0, 'rgb(5, 10, 172)'], [0.35, 'rgb(40, 60, 190)'],
+        [0.5, 'rgb(70, 100, 245)'], [0.6, 'rgb(90, 120, 245)'],
+        [0.7, 'rgb(106, 137, 247)'], [1, 'rgb(220, 220, 220)']],
+
+    'Picnic': [
+        [0, 'rgb(0,0,255)'], [0.1, 'rgb(51,153,255)'],
+        [0.2, 'rgb(102,204,255)'], [0.3, 'rgb(153,204,255)'],
+        [0.4, 'rgb(204,204,255)'], [0.5, 'rgb(255,255,255)'],
+        [0.6, 'rgb(255,204,255)'], [0.7, 'rgb(255,153,255)'],
+        [0.8, 'rgb(255,102,204)'], [0.9, 'rgb(255,102,102)'],
+        [1, 'rgb(255,0,0)']],
+
+    'Rainbow': [
+        [0, 'rgb(150,0,90)'], [0.125, 'rgb(0, 0, 200)'],
+        [0.25, 'rgb(0, 25, 255)'], [0.375, 'rgb(0, 152, 255)'],
+        [0.5, 'rgb(44, 255, 150)'], [0.625, 'rgb(151, 255, 0)'],
+        [0.75, 'rgb(255, 234, 0)'], [0.875, 'rgb(255, 111, 0)'],
+        [1, 'rgb(255, 0, 0)']],
+
+    'Portland': [
+        [0, 'rgb(12,51,131)'], [0.25, 'rgb(10,136,186)'],
+        [0.5, 'rgb(242,211,56)'], [0.75, 'rgb(242,143,56)'],
+        [1, 'rgb(217,30,30)']],
+
+    'Jet': [
+        [0, 'rgb(0,0,131)'], [0.125, 'rgb(0,60,170)'],
+        [0.375, 'rgb(5,255,255)'], [0.625, 'rgb(255,255,0)'],
+        [0.875, 'rgb(250,0,0)'], [1, 'rgb(128,0,0)']],
+
+    'Hot': [
+        [0, 'rgb(0,0,0)'], [0.3, 'rgb(230,0,0)'],
+        [0.6, 'rgb(255,210,0)'], [1, 'rgb(255,255,255)']],
+
+    'Blackbody': [
+        [0, 'rgb(0,0,0)'], [0.2, 'rgb(230,0,0)'],
+        [0.4, 'rgb(230,210,0)'], [0.7, 'rgb(255,255,255)'],
+        [1, 'rgb(160,200,255)']],
+
+    'Earth': [
+        [0, 'rgb(0,0,130)'], [0.1, 'rgb(0,180,180)'],
+        [0.2, 'rgb(40,210,40)'], [0.4, 'rgb(230,230,50)'],
+        [0.6, 'rgb(120,70,20)'], [1, 'rgb(255,255,255)']],
+
+    'Electric': [
+        [0, 'rgb(0,0,0)'], [0.15, 'rgb(30,0,100)'],
+        [0.4, 'rgb(120,0,100)'], [0.6, 'rgb(160,90,0)'],
+        [0.8, 'rgb(230,200,0)'], [1, 'rgb(255,250,220)']],
+
+    'Viridis': [
+        [0, '#440154'], [0.06274509803921569, '#48186a'],
+        [0.12549019607843137, '#472d7b'], [0.18823529411764706, '#424086'],
+        [0.25098039215686274, '#3b528b'], [0.3137254901960784, '#33638d'],
+        [0.3764705882352941, '#2c728e'], [0.4392156862745098, '#26828e'],
+        [0.5019607843137255, '#21918c'], [0.5647058823529412, '#1fa088'],
+        [0.6274509803921569, '#28ae80'], [0.6901960784313725, '#3fbc73'],
+        [0.7529411764705882, '#5ec962'], [0.8156862745098039, '#84d44b'],
+        [0.8784313725490196, '#addc30'], [0.9411764705882353, '#d8e219'],
+        [1, '#fde725']]
+}
+
+
+def make_rgba(colorscale):
+    return [(v, one_rgba(c)) for v, c in colorscale]
+
+
+def one_rgba(c):
+    '''
+    convert a single color value to (r, g, b, a)
+    input can be an rgb string 'rgb(r,g,b)', '#rrggbb'
+    if we decide we want more we can make more, but for now this is just
+    to convert plotly colorscales to pyqtgraph tuples
+    '''
+    if c[0] == '#' and len(c) == 7:
+        return (int(c[1:3], 16), int(c[3:5], 16), int(c[5:7], 16), 255)
+    if c[:4] == 'rgb(':
+        return tuple(map(int, c[4:-1].split(','))) + (255,)
+    raise ValueError('one_rgba only supports rgb(r,g,b) and #rrggbb colors')
+
+
+colorscales = {}
+for scale_name, scale in colorscales_raw.items():
+    colorscales[scale_name] = make_rgba(scale)

From 66a8526c5a3df30a8f147e7313ffc0c9880b6613 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 01:59:38 +0100
Subject: [PATCH 22/24] pyqtgraph

---
 Qcodes example.ipynb      | 3995 +------------------------------------
 qcodes/__init__.py        |    1 +
 qcodes/plots/pyqtgraph.py |  340 ++++
 3 files changed, 403 insertions(+), 3933 deletions(-)
 create mode 100644 qcodes/plots/pyqtgraph.py

diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 6e7289906b8..e3d601563c6 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -197,9 +197,9 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='testsweep'\n",
-      "   amplitude: amplitude\n",
       "   chan0: chan0\n",
-      "started at 2016-01-18 12:00:54\n"
+      "   amplitude: amplitude\n",
+      "started at 2016-01-20 01:47:30\n"
      ]
     }
    ],
@@ -246,7 +246,7 @@
      "data": {
       "text/plain": [
        "{'amplitude': DataArray[400]: amplitude\n",
-       " array([ 0.117,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
+       " array([ 0.117,  0.117,  0.115,  0.111,  0.106,  0.099,  0.092,  0.085,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
@@ -297,7 +297,7 @@
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan,\n",
        "           nan,    nan,    nan,    nan,    nan,    nan,    nan,    nan]),\n",
        " 'chan0': DataArray[400]: chan0\n",
-       " array([-20. , -19.9,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
+       " array([-20. , -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
        "          nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
@@ -362,787 +362,11 @@
     "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"
-    },
-    {
-     "data": {
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-       "<img 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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# live-updating plot, that syncs the data and stops updating when it's finished\n",
-    "plot = qc.MatPlot(data.amplitude)"
+    "# plot = qc.MatPlot(data.amplitude)\n",
+    "plotQ = qc.QtPlot(data.amplitude)"
    ]
   },
   {
@@ -1158,2377 +382,53 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
-      "   chan1: chan1\n",
       "   amplitude_3: amplitude\n",
       "   chan0: chan0\n",
+      "   chan1: chan1\n",
       "   amplitude_0: amplitude\n",
-      "started at 2016-01-18 12:01:15\n"
+      "started at 2016-01-20 01:47:52\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "data2 = qc.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
+    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
+    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
+    "    qc.Wait(0.001),\n",
+    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
+    "    qc.Task(c2.set, 0)\n",
+    "    ).run(location='test2d')\n",
+    "\n",
+    "# use the subplot and add features of qc.MatPlot\n",
+    "# plot2 = qc.MatPlot(data2.amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
+    "# plot2.add(data2.amplitude_3, cmap=plt.cm.hot, subplot=2)\n",
+    "\n",
+    "# the equivalent in QtPlot\n",
+    "plot2Q = qc.QtPlot(data2.amplitude_0, figsize=(1200, 500))\n",
+    "plot2Q.add(data2.amplitude_3, subplot=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": 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"
-    },
-    {
-     "data": {
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2 = qc.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
-    "    meter.amplitude, # first measurement, at c2=0 -> amplitude_0 bcs it's action 0\n",
-    "    qc.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
-    "    qc.Wait(0.001),\n",
-    "    meter.amplitude, # second measurement, at c2=1 -> amplitude_4 bcs it's action 4\n",
-    "    qc.Task(c2.set, 0)\n",
-    "    ).run(location='test2d')\n",
-    "\n",
-    "# use the subplot and add features of qc.Plot\n",
-    "plot2 = qc.MatPlot(data2.amplitude_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1, 2))\n",
-    "plot2.add(data2.amplitude_3, cmap=plt.cm.hot, subplot=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   chan1: chan1\n",
-      "   chan2: chan2\n",
-      "   amplitude_2: amplitude\n",
-      "   amplitude_5_0: amplitude\n",
-      "   amplitude_3_0: amplitude\n",
-      "   chan0: chan0\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-18 12:01:56\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
+      "   amplitude_3_0: amplitude\n",
+      "   chan2: chan2\n",
+      "   amplitude_2: amplitude\n",
+      "   amplitude_5_0: amplitude\n",
+      "   chan0: chan0\n",
+      "   chan1: chan1\n",
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-20 01:48:27\n"
+     ]
     }
    ],
    "source": [
@@ -3546,9 +446,12 @@
     ").run(location='test_multi_d')\n",
     "\n",
     "# several plots updating simultaneously\n",
-    "plot3 = qc.MatPlot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
-    "plot3b = qc.MatPlot(data3.amplitude_5_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1,2))\n",
-    "plot3b.add(data3.avg_amplitude, subplot=2)"
+    "# plot3 = qc.MatPlot(data3.amplitude_3_0, cmap=plt.cm.hot)\n",
+    "# plot3b = qc.MatPlot(data3.amplitude_5_0, cmap=plt.cm.hot, figsize=(12, 4.5), subplots=(1,2))\n",
+    "# plot3b.add(data3.avg_amplitude, subplot=2)\n",
+    "plot3Q = qc.QtPlot(data3.amplitude_3_0)\n",
+    "plot3bQ = qc.QtPlot(data3.amplitude_5_0, figsize=(1200, 500))\n",
+    "plot3bQ.add(data3.avg_amplitude, subplot=2)"
    ]
   },
   {
@@ -3564,788 +467,12 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_complex_param'\n",
+      "   avg_amplitude: avg_amplitude\n",
       "   chan1: chan1\n",
-      "   amplitude: amplitude\n",
       "   chan2: chan2\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "started at 2016-01-18 12:02:24\n"
+      "   amplitude: amplitude\n",
+      "started at 2016-01-20 01:49:11\n"
      ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -4355,8 +482,10 @@
     "    AverageAndRaw(meter.amplitude, c2[-10:10:0.2], 0.001)\n",
     ").run(location='test_complex_param')\n",
     "\n",
-    "plot4 = qc.MatPlot(data4.amplitude, cmap=plt.cm.hot, subplots=(1,2), figsize=(12, 4.5))\n",
-    "plot4.add(data4.avg_amplitude, subplot=2)"
+    "# plot4 = qc.MatPlot(data4.amplitude, cmap=plt.cm.hot, subplots=(1,2), figsize=(12, 4.5))\n",
+    "# plot4.add(data4.avg_amplitude, subplot=2)\n",
+    "plot4Q = qc.QtPlot(data4.amplitude, figsize=(1200, 500))\n",
+    "plot4Q.add(data4.avg_amplitude, subplot=2)"
    ]
   }
  ],
diff --git a/qcodes/__init__.py b/qcodes/__init__.py
index ef705e37b8d..87a6d419ec5 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -11,6 +11,7 @@
 import sys as _sys
 if 'ipy' in repr(_sys.stdout):
     from qcodes.plots.matplotlib import MatPlot
+    from qcodes.plots.pyqtgraph import QtPlot
 
 from qcodes.data.manager import get_data_manager
 from qcodes.data.data_set import DataMode, DataSet
diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
new file mode 100644
index 00000000000..93d73fb5ea8
--- /dev/null
+++ b/qcodes/plots/pyqtgraph.py
@@ -0,0 +1,340 @@
+'''
+Live plotting using pyqtgraph
+'''
+import numpy as np
+import pyqtgraph as pg
+import pyqtgraph.multiprocess as pgmp
+import warnings
+from collections import namedtuple
+
+from .base import BasePlot
+from .colors import color_cycle, colorscales
+
+
+TransformState = namedtuple('TransformState', 'translate scale revisit')
+
+
+class QtPlot(BasePlot):
+    '''
+    Plot x/y lines or x/y/z heatmap data. The first trace may be included
+    in the constructor, other traces can be added with QtPlot.add()
+
+    args: shortcut to provide the x/y/z data. See MatPlot.add
+
+    figsize: (width, height) tuple in pixels to pass to GraphicsWindow
+        default (1000, 600)
+    interval: period in seconds between update checks
+        default 0.25
+    theme: tuple of (foreground_color, background_color), where each is
+        a valid Qt color. default (dark gray, white), opposite the pyqtgraph
+        default of (white, black)
+
+    kwargs: passed along to QtPlot.add() to add the first data trace
+    '''
+    proc = None
+    rpg = None
+
+    def __init__(self, *args, figsize=(1000, 600), interval=0.25,
+                 theme=((60, 60, 60), 'w'), **kwargs):
+        super().__init__(interval)
+
+        if not self.__class__.proc:
+            self._init_qt()
+
+        self.theme = theme
+
+        self.win = self.rpg.GraphicsWindow()
+        self.win.setBackground(theme[1])
+        self.win.resize(*figsize)
+        self.subplots = [self.add_subplot()]
+
+        if args or kwargs:
+            self.add(*args, **kwargs)
+
+    def _init_qt(self):
+        # starting the process for the pyqtgraph plotting
+        # You do not want a new process to be created every time you start a
+        # run, so this only starts once and stores the process in the class
+        pg.mkQApp()
+        self.__class__.proc = pgmp.QtProcess()  # pyqtgraph multiprocessing
+        self.__class__.rpg = self.proc._import('pyqtgraph')
+
+    def add_subplot(self):
+        subplot_object = self.win.addPlot()
+
+        for side in ('left', 'bottom'):
+            ax = subplot_object.getAxis(side)
+            ax.setGrid(0.2)
+            ax.setPen(self.theme[0])
+            ax._qcodes_label = ''
+
+        return subplot_object
+
+    def add_to_plot(self, subplot=1, **kwargs):
+        if subplot > len(self.subplots):
+            for i in range(subplot - len(self.subplots)):
+                self.subplots.append(self.add_subplot())
+        subplot_object = self.subplots[subplot - 1]
+
+        if 'z' in kwargs:
+            plot_object = self._draw_image(subplot_object, **kwargs)
+        else:
+            plot_object = self._draw_plot(subplot_object, **kwargs)
+
+        self._update_labels(subplot_object, kwargs)
+        prev_default_title = self.get_default_title()
+
+        self.traces.append({
+            'config': kwargs,
+            'plot_object': plot_object
+        })
+
+        if prev_default_title == self.win.windowTitle():
+            self.win.setWindowTitle(self.get_default_title())
+
+    def _draw_plot(self, subplot_object, y, x=None, color=None, width=None,
+                   antialias=None, **kwargs):
+        if 'pen' not in kwargs:
+            if color is None:
+                cycle = color_cycle
+                color = cycle[len(self.traces) % len(cycle)]
+            if width is None:
+                width = 2
+
+            kwargs['pen'] = self.rpg.mkPen(color, width=width)
+
+        if antialias is None:
+            # looks a lot better antialiased, but slows down with many points
+            # TODO: dynamically update this based on total # of points
+            antialias = (len(y) < 1000)
+
+        return subplot_object.plot(*self._line_data(x, y), antialias=antialias,
+                                   **kwargs)
+
+    def _line_data(self, x, y):
+        return [self._clean_array(arg) for arg in [x, y] if arg is not None]
+
+    def _draw_image(self, subplot_object, z, x=None, y=None, cmap='Hot',
+                    **kwargs):
+        img = self.rpg.ImageItem()
+        subplot_object.addItem(img)
+
+        hist = self.rpg.HistogramLUTItem()
+        hist.setImageItem(img)
+        # TODO - ensure this goes next to the correct subplot?
+        self.win.addItem(hist)
+
+        plot_object = {
+            'image': img,
+            'hist': hist,
+            'histlevels': hist.getLevels(),
+            'cmap': cmap,
+            'scales': {
+                'x': TransformState(0, 1, True),
+                'y': TransformState(0, 1, True)
+            }
+        }
+
+        self._update_image(plot_object, {'x': x, 'y': y, 'z': z})
+
+        return plot_object
+
+    def _update_image(self, plot_object, config):
+        z = config['z']
+        img = plot_object['image']
+        hist = plot_object['hist']
+        scales = plot_object['scales']
+
+        # make sure z is a *new* numpy float array (pyqtgraph barfs on ints),
+        # and replace nan with minimum val bcs I can't figure out how to make
+        # pyqtgraph handle nans - though the source does hint at a way:
+        # http://www.pyqtgraph.org/documentation/_modules/pyqtgraph/widgets/ColorMapWidget.html
+        # see class RangeColorMapItem
+        z = np.asfarray(z).T
+        with warnings.catch_warnings():
+            warnings.simplefilter('error')
+            try:
+                z_range = (np.nanmin(z), np.nanmax(z))
+            except:
+                # we get a warning here when z is entirely NaN
+                # nothing to plot, so give up.
+                return
+        z[np.where(np.isnan(z))] = z_range[0]
+
+        self._update_cmap(plot_object)
+
+        hist_range = hist.getLevels()
+        if hist_range == plot_object['histlevels']:
+            plot_object['histlevels'] = z_range
+            hist.setLevels(*z_range)
+            hist_range = z_range
+
+        img.setImage(self._clean_array(z), levels=hist_range)
+
+        scales_changed = False
+        for axletter, axscale in scales.items():
+            if axscale.revisit:
+                axdata = config.get(axletter, None)
+                newscale = self._get_transform(axdata)
+                if (newscale.translate != axscale.translate or
+                        newscale.scale != axscale.scale):
+                    scales_changed = True
+                scales[axletter] = newscale
+
+        if scales_changed:
+            img.resetTransform()
+            img.translate(scales['x'].translate, scales['y'].translate)
+            img.scale(scales['x'].scale, scales['y'].scale)
+
+    def _update_cmap(self, plot_object):
+        gradient = plot_object['hist'].gradient
+        gradient.setColorMap(self._cmap(plot_object['cmap']))
+
+    def set_cmap(self, cmap, traces=None):
+        if isinstance(traces, int):
+            traces = (traces,)
+        elif traces is None:
+            traces = range(len(self.traces))
+
+        for i in traces:
+            plot_object = self.traces[i]['plot_object']
+            if not isinstance(plot_object, dict) or 'hist' not in plot_object:
+                continue
+
+            plot_object['cmap'] = cmap
+            self._update_cmap(plot_object)
+
+    def _get_transform(self, array):
+        '''
+        pyqtgraph seems to only support uniform pixels in image plots.
+
+        for a given setpoint array, extract the linear transform it implies
+        if the setpoint data is *not* linear (or close to it), or if it's not
+        uniform in any nested dimensions, issue a warning and return the
+        default transform of 0, 1
+
+        returns namedtuple TransformState(translate, scale, revisit)
+
+        in pyqtgraph:
+        translate means how many pixels to shift the image, away
+            from the bottom or left edge being at zero on the axis
+        scale means the data delta
+
+        revisit is True if we just don't have enough info to scale yet,
+        but we might later.
+        '''
+
+        if array is None:
+            return TransformState(0, 1, True)
+
+        # do we have enough confidence in the setpoint data we've seen
+        # so far that we don't have to repeat this as more data comes in?
+        revisit = False
+
+        # somewhat arbitrary - if the first 20% of the data or at least
+        # 10 rows is uniform, assume it's uniform thereafter
+        MINROWS = 10
+        MINFRAC = 0.2
+
+        # maximum setpoint deviation from linear to accept is 10% of a pixel
+        MAXPX = 0.1
+
+        if hasattr(array[0], '__len__'):
+            # 2D array: check that all (non-empty) elements are congruent
+            inner_len = max(len(row) for row in array)
+            collapsed = np.array([np.nan] * inner_len)
+            rows_before_trusted = max(MINROWS, len(array) * MINFRAC)
+            for i, row in enumerate(array):
+                for j, val in enumerate(row):
+                    if np.isnan(val):
+                        if i < rows_before_trusted:
+                            revisit = True
+                        continue
+                    if np.isnan(collapsed[j]):
+                        collapsed[j] = val
+                    elif val != collapsed[j]:
+                        warnings.warn(
+                            'nonuniform nested setpoint array passed to '
+                            'pyqtgraph. ignoring, using default scaling.')
+                        return TransformState(0, 1, False)
+        else:
+            collapsed = array
+
+        if np.isnan(collapsed).any():
+            revisit = True
+
+        indices_setpoints = list(zip(*((i, s) for i, s in enumerate(collapsed)
+                                     if not np.isnan(s))))
+        if not indices_setpoints:
+            return TransformState(0, 1, revisit)
+
+        indices, setpoints = indices_setpoints
+        npts = len(indices)
+        if npts == 1:
+            indices = indices + (indices[0] + 1,)
+            setpoints = setpoints + (setpoints[0] + 1,)
+
+        i0 = indices[0]
+        s0 = setpoints[0]
+        total_di = indices[-1] - i0
+        total_ds = setpoints[-1] - s0
+
+        if total_ds == 0:
+            warnings.warn('zero setpoint range passed to pyqtgraph. '
+                          'ignoring, using default scaling.')
+            return TransformState(0, 1, False)
+
+        for i, s in zip(indices[1:-1], setpoints[1:-1]):
+            icalc = i0 + (s - s0) * total_di / total_ds
+            if np.abs(i - icalc) > MAXPX:
+                warnings.warn('nonlinear setpoint array passed to pyqtgraph. '
+                              'ignoring, using default scaling.')
+                return TransformState(0, 1, False)
+
+        scale = total_ds / total_di
+        # extra 0.5 translation to get the first setpoint at the center of
+        # the first pixel
+        translate = s0 - (i0 + 0.5) * scale
+
+        return TransformState(translate, scale, revisit)
+
+    def _update_labels(self, subplot_object, config):
+        for axletter, side in (('x', 'bottom'), ('y', 'left')):
+            ax = subplot_object.getAxis(side)
+            if axletter in config and not ax._qcodes_label:
+                # pyqtgraph doesn't seem able to get labels, only set
+                # so we'll store it in the axis object and hope the user
+                # doesn't set it separately before adding all traces
+                label = self.get_label(config[axletter])
+                if label:
+                    ax._qcodes_label = label
+                    ax.setLabel(label)
+
+    def update_plot(self):
+        for trace in self.traces:
+            config = trace['config']
+            plot_object = trace['plot_object']
+            if 'z' in config:
+                self._update_image(plot_object, config)
+            else:
+                plot_object.setData(*self._line_data(config['x'], config['y']))
+
+    def _clean_array(self, array):
+        '''
+        we can't send a DataArray to remote pyqtgraph for some reason,
+        so send the plain numpy array
+        '''
+        if hasattr(array, 'data') and isinstance(array.data, np.ndarray):
+            return array.data
+        return array
+
+    def _cmap(self, scale):
+        if isinstance(scale, str):
+            if scale in colorscales:
+                values, colors = zip(*colorscales[scale])
+            else:
+                raise ValueError(scale + ' not found in colorscales')
+        elif len(scale) == 2:
+            values, colors = scale
+
+        return self.rpg.ColorMap(values, colors)

From 58ca8ad9c720385d82a41fb4b2d0e2f117d43646 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 02:45:41 +0100
Subject: [PATCH 23/24] comment update

---
 qcodes/plots/matplotlib.py | 2 +-
 qcodes/plots/pyqtgraph.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
index f3d66a511d1..e159c618d33 100644
--- a/qcodes/plots/matplotlib.py
+++ b/qcodes/plots/matplotlib.py
@@ -16,7 +16,7 @@ class MatPlot(BasePlot):
     Plot x/y lines or x/y/z heatmap data. The first trace may be included
     in the constructor, other traces can be added with MatPlot.add()
 
-    args: shortcut to provide the x/y/z data. See MatPlot.add
+    args: shortcut to provide the x/y/z data. See BasePlot.add
 
     figsize: (width, height) tuple in inches to pass to plt.figure
         default (8, 5)
diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 93d73fb5ea8..76f5d64ea87 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -19,7 +19,7 @@ class QtPlot(BasePlot):
     Plot x/y lines or x/y/z heatmap data. The first trace may be included
     in the constructor, other traces can be added with QtPlot.add()
 
-    args: shortcut to provide the x/y/z data. See MatPlot.add
+    args: shortcut to provide the x/y/z data. See BasePlot.add
 
     figsize: (width, height) tuple in pixels to pass to GraphicsWindow
         default (1000, 600)

From 8d303dde6904e54dde2529ac2be6badc2af83ed1 Mon Sep 17 00:00:00 2001
From: alexcjohnson <alex@plot.ly>
Date: Wed, 20 Jan 2016 10:05:06 +0100
Subject: [PATCH 24/24] pyqtgraph label colorscale, and tweak styling

---
 qcodes/plots/pyqtgraph.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
index 76f5d64ea87..abfe14aaf7e 100644
--- a/qcodes/plots/pyqtgraph.py
+++ b/qcodes/plots/pyqtgraph.py
@@ -64,7 +64,6 @@ def add_subplot(self):
 
         for side in ('left', 'bottom'):
             ax = subplot_object.getAxis(side)
-            ax.setGrid(0.2)
             ax.setPen(self.theme[0])
             ax._qcodes_label = ''
 
@@ -121,6 +120,8 @@ def _draw_image(self, subplot_object, z, x=None, y=None, cmap='Hot',
 
         hist = self.rpg.HistogramLUTItem()
         hist.setImageItem(img)
+        hist.axis.setPen(self.theme[0])
+        hist.axis.setLabel(self.get_label(z))
         # TODO - ensure this goes next to the correct subplot?
         self.win.addItem(hist)