diff --git a/Qcodes example.ipynb b/Qcodes example.ipynb
index 6b3e96fefba..e3d601563c6 100644
--- a/Qcodes example.ipynb	
+++ b/Qcodes example.ipynb	
@@ -6,13 +6,78 @@
    "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.hide();\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",
     "import time\n",
-    "from IPython import display\n",
-    "import numpy.ma as ma\n",
+    "import numpy as np\n",
     "\n",
     "# load the qcodes path, until we have this installed as a package\n",
     "import sys\n",
@@ -21,9 +86,9 @@
     "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 +115,7 @@
     "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 +143,7 @@
     "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 +159,7 @@
     "\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 +199,7 @@
       "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 2016-01-20 01:47:30\n"
      ]
     }
    ],
@@ -143,7 +208,7 @@
     "# 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')"
    ]
   },
   {
@@ -166,7 +231,7 @@
    ],
    "source": [
     "# now there should be two extra processes running, DataServer and a sweep\n",
-    "q.active_children()"
+    "qc.active_children()"
    ]
   },
   {
@@ -182,22 +247,22 @@
       "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,  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,    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",
@@ -233,20 +298,20 @@
        "           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, -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,   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",
@@ -285,7 +350,7 @@
     }
    ],
    "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"
    ]
@@ -297,40 +362,19 @@
     "collapsed": false,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1wI4d3vWSMDfJS2w7yVV+TscAQlQwd+igc0U3brQdCUUBp2SQq2pqtINAxJvnY26Sl9h2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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105a3da90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "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.MatPlot(data.amplitude)\n",
+    "plotQ = qc.QtPlot(data.amplitude)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
@@ -338,113 +382,35 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test2d'\n",
+      "   amplitude_3: amplitude\n",
       "   chan0: chan0\n",
-      "   amplitude_0: amplitude\n",
       "   chan1: chan1\n",
-      "   amplitude_3: amplitude\n",
-      "started at 2016-01-08 15:49:10\n"
+      "   amplitude_0: amplitude\n",
+      "started at 2016-01-20 01:47:52\n"
      ]
     }
    ],
    "source": [
-    "data2 = q.Loop(c1[-15:15:1], 0.1).loop(c0[-15:12:.5], 0.01).each(\n",
+    "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",
-    "    q.Task(c2.set, 1), # action 1 -> c2.set(1)\n",
-    "    q.Wait(0.001),\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",
-    "    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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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105a80710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 5))\n",
-    "ax = fig.add_subplot(1,1,1)\n",
+    "    qc.Task(c2.set, 0)\n",
+    "    ).run(location='test2d')\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": {
-      "image/png": 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yLq7Y/p5EGxdkJpK9PhGz+pJE0A2JmJMSMc8lYi6rjFi27vbqmM9VH+nOxCTN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\nP/Vza4LvKwXJAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105c7d470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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')"
+    "# 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": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -454,122 +420,43 @@
      "output_type": "stream",
      "text": [
       "DataSet: DataMode.PULL_FROM_SERVER, location='test_multi_d'\n",
-      "   amplitude_5_0: amplitude\n",
-      "   amplitude_2: amplitude\n",
-      "   avg_amplitude: avg_amplitude\n",
-      "   chan2: chan2\n",
       "   amplitude_3_0: amplitude\n",
+      "   chan2: chan2\n",
+      "   amplitude_2: amplitude\n",
+      "   amplitude_5_0: amplitude\n",
       "   chan0: chan0\n",
       "   chan1: chan1\n",
-      "started at 2016-01-08 15:49:54\n"
+      "   avg_amplitude: avg_amplitude\n",
+      "started at 2016-01-20 01:48:27\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",
+    "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",
-    "    q.Loop(c0[-15:15:1], 0.001).each(meter.amplitude),\n",
-    "    q.Task(c0.set, -10),\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",
-    "    q.Loop(c2[-10:10:0.2], 0.001),\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')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gLs8yxjyG4eWyhm80xaMkSdLgeNlakiRJnXnZWpIkSZ1ZPEqSJKkzL1tLkiSpM0ceJUmS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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105c90278>"
-      ]
-     },
-     "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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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105c797f0>"
-      ]
-     },
-     "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",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10639f470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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()"
+    ").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)\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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -579,74 +466,26 @@
      "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",
       "   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 2016-01-20 01:49:11\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",
+    "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', 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",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10667c278>"
-      ]
-     },
-     "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": {
-      "image/png": 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kdvfcIxun/etf2knOFc2pFJ4VWCF/5Ih2EnK7UaNkjiOL4uDUrSvH3bJl2knI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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105cc1470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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()"
+    ").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)\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 f688e463633..87a6d419ec5 100644
--- a/qcodes/__init__.py
+++ b/qcodes/__init__.py
@@ -4,6 +4,15 @@
 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.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
 from qcodes.data.data_array import DataArray
diff --git a/qcodes/data/data_array.py b/qcodes/data/data_array.py
index a32d66c05cd..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):
     '''
@@ -37,6 +35,10 @@ 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 +49,18 @@ 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
@@ -162,6 +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]
diff --git a/qcodes/data/data_set.py b/qcodes/data/data_set.py
index 221f91282ea..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:
@@ -198,22 +203,35 @@ 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):
         '''
         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)
 
diff --git a/qcodes/plots/base.py b/qcodes/plots/base.py
new file mode 100644
index 00000000000..335c1ebea96
--- /dev/null
+++ b/qcodes/plots/base.py
@@ -0,0 +1,180 @@
+'''
+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.interval = interval
+        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, and each call should append
+        a dictionary to self.traces, containing at least {'config': kwargs}
+        '''
+        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)
+
+        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
+        '''
+        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/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)
diff --git a/qcodes/plots/matplotlib.py b/qcodes/plots/matplotlib.py
new file mode 100644
index 00000000000..e159c618d33
--- /dev/null
+++ b/qcodes/plots/matplotlib.py
@@ -0,0 +1,174 @@
+'''
+Live plotting in Jupyter notebooks
+using the nbagg backend and matplotlib
+'''
+import matplotlib.pyplot as plt
+from matplotlib.transforms import Bbox
+import numpy as np
+from numpy.ma import masked_invalid, getmask
+from collections import Mapping
+
+from .base import BasePlot
+
+
+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 BasePlot.add
+
+    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
+        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 MatPlot.add() to add the first data trace
+    '''
+    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:
+            self.fig, self.subplots = plt.subplots(*subplots, figsize=figsize)
+        if not hasattr(self.subplots, '__len__'):
+            self.subplots = (self.subplots,)
+
+        self.title = self.fig.suptitle('')
+
+        if args or kwargs:
+            self.add(*args, **kwargs)
+
+    def add_to_plot(self, **kwargs):
+        '''
+        adds one trace to this MatPlot.
+
+        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?
+
+        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': 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())
+
+    def _get_axes(self, config):
+        return self.subplots[config.get('subplot', 1) - 1]
+
+    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 update_plot(self):
+        '''
+        update the plot. The DataSets themselves have already been updated
+        in update, here we just push the changes to the plot.
+        '''
+        # 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()
+
+                ax = self._get_axes(config)
+                plot_object = self._draw_pcolormesh(ax, **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()
+
+    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, ax, z, x=None, y=None, subplot=1, **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
+
+        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)
+
+            # 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/qcodes/plots/pyqtgraph.py b/qcodes/plots/pyqtgraph.py
new file mode 100644
index 00000000000..abfe14aaf7e
--- /dev/null
+++ b/qcodes/plots/pyqtgraph.py
@@ -0,0 +1,341 @@
+'''
+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 BasePlot.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.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)
+        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)
+
+        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)
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..5d8f6bcd86d
--- /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.hide();
+        }
+    });
+    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..b2f7a5d8c8d
--- /dev/null
+++ b/qcodes/widgets/widgets.py
@@ -0,0 +1,57 @@
+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.
+    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, first_call=True, **kwargs):
+        super().__init__(**kwargs)
+
+        self._fn = fn
+        self.interval = interval
+
+        self.on_msg(self._handle_msg)
+
+        if first_call:
+            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.
+    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)
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 db26202bc98..91ead1d6f75 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
+# "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
diff --git a/test_multi_d/chan1_chan0.dat b/test_multi_d/chan1_chan0.dat
index f66102672ce..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
@@ -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..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
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)