GeigerMeasurements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results from arduino radiation-board\n",
    "- Revived a forgotten Libelium/Cooking-hacks [radiation arduino shield](https://www.cooking-hacks.com/documentation/tutorials/geiger-counter-radiation-sensor-board-arduino-raspberry-pi-tutorial/).\n",
    "- The same board + *J305βγ tube* had produced these results ~5 years back: http://ankostis.blogspot.it/2011/07/blog-post.html\n",
    "- github: https://github.com/ankostis/radiationsensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "#%matplotlib inline \n",
    "#Plots FAIL with NO GUI!!\n",
    "import numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "from collections import OrderedDict\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ispra-201609-1-OFF.txt', 'Ispra-201609-2.txt', 'Ispra-201609-3.txt', 'Ispra-201609-4.txt', 'Ispra-201609-5.txt', 'IspraHome-20160910-1953.txt', 'IspraHome-20160911-0534.txt', 'IspraHome-20160911-0752.txt', 'IspraHome-20160911-1918.txt', 'IspraHome-Day-0630ToXXXX-20160908.txt', 'IspraHome-Day-1000ToXXXX-20160907.txt', 'IspraHome-Day-1000ToXXXX-20160910.txt', 'IspraHome-Day-1225ToXXXX-20160910.txt', 'IspraHome-Day-1300ToXXXX-20160910.txt', 'IspraHome-Night-0200ToXXXX-20160909.txt', 'IspraHome-Night-2100To1000-20160906.txt', 'IspraHome-Night-2100ToXXXX-20160908.txt', 'IspraHome-Night-2240ToXXXX-20160909.txt']\n"
     ]
    }
   ],
   "source": [
    "files=!ls *.txt \n",
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import io\n",
    "import re\n",
    "\n",
    "line_regexs = (\n",
    "    (re.compile(r'cpm = (?P<clicks>\\d+)'), lambda c: c / 6),\n",
    "    (re.compile(r'.*cpm=\\d+, clicks=(?P<clicks>\\d+).*'), None),\n",
    "    (re.compile(r'^(?P<clicks>\\d+),(?P<maxcpm>\\d+.\\d+)'), None),\n",
    ")\n",
    "def parse_click_line(l):\n",
    "    for r, click_fun in line_regexs:\n",
    "        m = r.search(l)\n",
    "        if m: \n",
    "            res = m.groupdict()\n",
    "            \n",
    "            clicks = res['clicks']\n",
    "            assert str(clicks) == str(int(clicks)), clicks\n",
    "            clicks = int(clicks)\n",
    "            if click_fun:\n",
    "                clicks = click_fun(clicks)\n",
    "                \n",
    "            maxcpm = res.get('maxcpm', 0.0)\n",
    "            if maxcpm:\n",
    "                maxcpm = float(maxcpm)\n",
    "            return clicks, maxcpm\n",
    "\n",
    "def read_clicks(file):\n",
    "    \"\"\":return: Nx1 or Nx2 matrix if 'maxcpm' exists\"\"\"\n",
    "    with io.open(file, 'rt') as fd:\n",
    "        lines = (parse_click_line(l) for l in fd if l)\n",
    "        return np.array([l for l in lines if l])\n",
    "\n",
    "# f='IspraHome-Night-0200ToXXXX-20160909.txt'\n",
    "# read_clicks(f)\n",
    "\n",
    "# file_clicks = {f: read_clicks(f) for f in files}\n",
    "# file_clicks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'IspraHome-20160910-1953.txt': datetime.datetime(2016, 9, 10, 19, 53),\n",
       " 'IspraHome-20160911-0534.txt': datetime.datetime(2016, 9, 11, 5, 34),\n",
       " 'IspraHome-20160911-0752.txt': datetime.datetime(2016, 9, 11, 7, 52),\n",
       " 'IspraHome-20160911-1918.txt': datetime.datetime(2016, 9, 11, 19, 18),\n",
       " 'IspraHome-Day-0630ToXXXX-20160908.txt': datetime.datetime(2016, 9, 8, 6, 30),\n",
       " 'IspraHome-Day-1000ToXXXX-20160907.txt': datetime.datetime(2016, 9, 7, 10, 0),\n",
       " 'IspraHome-Day-1000ToXXXX-20160910.txt': datetime.datetime(2016, 9, 10, 10, 0),\n",
       " 'IspraHome-Day-1225ToXXXX-20160910.txt': datetime.datetime(2016, 9, 10, 12, 25),\n",
       " 'IspraHome-Day-1300ToXXXX-20160910.txt': datetime.datetime(2016, 9, 10, 13, 0),\n",
       " 'IspraHome-Night-0200ToXXXX-20160909.txt': datetime.datetime(2016, 9, 9, 2, 0),\n",
       " 'IspraHome-Night-2100To1000-20160906.txt': datetime.datetime(2016, 9, 6, 21, 0),\n",
       " 'IspraHome-Night-2100ToXXXX-20160908.txt': datetime.datetime(2016, 9, 8, 21, 0),\n",
       " 'IspraHome-Night-2240ToXXXX-20160909.txt': datetime.datetime(2016, 9, 9, 22, 40)}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "fname_regexs = [\n",
    "    re.compile(r'-(?P<time>\\d{4})to.+-(?P<date>\\d{8})', re.I),\n",
    "    re.compile(r'-(?P<date>\\d{8})-(?P<time>\\d{4})', re.I),\n",
    "]\n",
    "def parse_fname_time(file):\n",
    "    for r in fname_regexs:\n",
    "        m = r.search(file)\n",
    "        if m:\n",
    "            tm = '%s%s' %(m.group('date'), m.group('time'))\n",
    "            tm = datetime.strptime(tm, '%Y%m%d%H%M')\n",
    "            return tm\n",
    "\n",
    "file_times = {f: parse_fname_time(f) for f in files}\n",
    "file_times = {k:v for k,v in file_times.items() if v}\n",
    "file_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def zipdict(*dcts):\n",
    "    for i in set(dcts[0]).intersection(*dcts[1:]):\n",
    "            yield (i, tuple(d[i] for d in dcts))\n",
    "\n",
    "# file_data = zipdict(file_times, file_clicks)\n",
    "# file_data = sorted(file_data, key=lambda v: v[1][0])\n",
    "\n",
    "def collect_data(files):\n",
    "    file_clicks = {f: read_clicks(f) for f in files}\n",
    "    #print([v.shape for v in file_clicks.values()])\n",
    "    \n",
    "    file_times = {f: parse_fname_time(f) for f in files}\n",
    "    file_times = {k:v for k,v in file_times.items() if v}\n",
    "\n",
    "    file_data = zipdict(file_times, file_clicks)\n",
    "    file_data = OrderedDict(sorted(file_data, key=lambda v: v[1][0]))\n",
    "\n",
    "    return file_data\n",
    "\n",
    "file_data = collect_data(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading  Ispra-201609-2.txt\n",
      "Loading  Ispra-201609-3.txt\n",
      "Loading  Ispra-201609-4.txt\n",
      "Loading  Ispra-201609-5.txt\n"
     ]
    }
   ],
   "source": [
    "fname2_regexs = [\n",
    "    re.compile(r'-\\d{6}-\\d{1,3}\\.txt', re.I),\n",
    "]\n",
    "def read_fname2_as_df(file):\n",
    "    for r in fname2_regexs:\n",
    "        m = r.search(file)\n",
    "        if m:\n",
    "            print('Loading ', file)\n",
    "            df = pd.read_csv(file, comment='#')\n",
    "            df.name = file\n",
    "            # df['tvalue'] = df.index\n",
    "            # df['tdelta'] = (df['tvalue']-df['tvalue'].shift()).fillna(0)\n",
    "            # df.rename({'clicks': 'total_clicks'})\n",
    "            # df['clicks'] = 60.0 * df['clicks'] / df['tdelta']\n",
    "            df.index = pd.to_datetime(df.index, unit='s')\n",
    "            #display(df.head())\n",
    "            df.columns = ['clicks', 'maxCPM']\n",
    "            return df#[['clicks', 'maxCPM']]\n",
    "\n",
    "file_dfs = {f: read_fname2_as_df(f) for f in files}\n",
    "file_dfs = {k: v for k, v in file_dfs.items() if v is not None}\n",
    "#file_dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ispra-201609-5.txt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ispra-201609-3.txt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ispra-201609-2.txt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ispra-201609-4.txt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clicks</th>\n",
       "      <th>maxCPM</th>\n",
       "      <th>clicks</th>\n",
       "      <th>maxCPM</th>\n",
       "      <th>clicks</th>\n",
       "      <th>maxCPM</th>\n",
       "      <th>clicks</th>\n",
       "      <th>maxCPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-19 10:15:37</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19 10:15:47</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19 10:15:57</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19 10:16:07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-19 10:16:17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Ispra-201609-5.txt        Ispra-201609-3.txt         \\\n",
       "                                clicks maxCPM             clicks maxCPM   \n",
       "2016-09-19 10:15:37                NaN    NaN                NaN    NaN   \n",
       "2016-09-19 10:15:47                NaN    NaN                NaN    NaN   \n",
       "2016-09-19 10:15:57                NaN    NaN                NaN    NaN   \n",
       "2016-09-19 10:16:07                NaN    NaN                NaN    NaN   \n",
       "2016-09-19 10:16:17                NaN    NaN                NaN    NaN   \n",
       "\n",
       "                    Ispra-201609-2.txt        Ispra-201609-4.txt         \n",
       "                                clicks maxCPM             clicks maxCPM  \n",
       "2016-09-19 10:15:37                6.0   71.0                NaN    NaN  \n",
       "2016-09-19 10:15:47                5.0   44.0                NaN    NaN  \n",
       "2016-09-19 10:15:57                4.0   54.0                NaN    NaN  \n",
       "2016-09-19 10:16:07               13.0  301.0                NaN    NaN  \n",
       "2016-09-19 10:16:17               10.0  344.0                NaN    NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def file_df(fname, stime, clicks):\n",
    "    ix = pd.date_range(stime, periods=clicks.shape[0], freq='10S')\n",
    "\n",
    "    df = pd.DataFrame(clicks, index=ix, columns=['clicks', 'maxCPM'])\n",
    "    df.name = fname\n",
    "    return df\n",
    "\n",
    "# file_data = collect_data(files)\n",
    "# dfs = OrderedDict((f, file_df(f, stime, clicks)) \n",
    "#          for f, (stime, clicks) in file_data.items())\n",
    "dfs = {}\n",
    "dfs.update(file_dfs)\n",
    "\n",
    "df = pd.concat(dfs.values(), axis=1, keys=dfs.keys())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def reject_outliers(sr, iq_range=0.5):\n",
    "    # from http://stackoverflow.com/a/16562028/548792\n",
    "    pcnt = (1 - iq_range) / 2\n",
    "    qlow, median, qhigh = sr.dropna().quantile([pcnt, 0.50, 1-pcnt])\n",
    "    iqr = qhigh - qlow\n",
    "    return sr[ (sr - median).abs() <= iqr]\n",
    "\n",
    "def resample_sr(sr, nmins: float=10, ewma_span=12):\n",
    "    sr = sr.dropna()\n",
    "    sr = sr.resample('%dmin' % nmins).sum() / float(nmins)\n",
    "    #sr = reject_outliers(sr)\n",
    "    sr2 = sr.ewm(span=ewma_span).mean()\n",
    "    return sr, sr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
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       "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",
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       "\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",
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       "                // Full images could contain transparency (where diff images\n",
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       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
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       "    titlebar.append(titletext)\n",
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       "    this.header = titletext[0];\n",
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       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\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",
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       "\n",
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       "    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",
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       "    canvas.addClass('mpl-canvas');\n",
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       "\n",
       "    this.canvas = canvas[0];\n",
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       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "\n",
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       "\n",
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       "\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    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"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1106b30f0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "def plot_clicks(sr, avg_sr):\n",
    "    ax = sr.plot(style='+', figsize=(12, 8))\n",
    "    avg_sr.plot(ax=ax, style='g-', lw=3, label='ewma12(CPM)')\n",
    "    ax.legend()\n",
    "    # From http://stackoverflow.com/a/13674286/548792\n",
    "    ax.xaxis.set_minor_locator(dates.AutoDateLocator())\n",
    "    ax.xaxis.set_minor_locator(dates.HourLocator(range(0, 24, 6)))\n",
    "    ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%m'))\n",
    "    ax.xaxis.set_major_locator(dates.HourLocator(range(0, 24, 12)))\n",
    "    ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%b %d\\n%Y'))\n",
    "    ax.xaxis.grid(True, which=\"minor\")\n",
    "    return ax\n",
    "\n",
    "sr = df.loc[:, (slice(None), 'clicks')].sum(axis=1)\n",
    "sr.name = 'CPM'\n",
    "sr, avg_sr = resample_sr(sr)\n",
    "plot_clicks(sr, avg_sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    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": [
    "def plot_maxcpm(df):\n",
    "    df = df.copy()\n",
    "    df[df < 10] = np.NaN\n",
    "    df = df.dropna(1, 'all')\n",
    "    ax = df.plot(figsize=(12, 10));\n",
    "    ax.legend(framealpha=0.2);\n",
    "    # From http://stackoverflow.com/a/13674286/548792\n",
    "    ax.xaxis.set_minor_locator(dates.AutoDateLocator())\n",
    "    ax.xaxis.set_minor_locator(dates.HourLocator(range(0, 24, 6)))\n",
    "    ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%m'))\n",
    "    ax.xaxis.set_major_locator(dates.HourLocator(range(0, 24, 12)))\n",
    "    ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%b %d\\n%Y'))\n",
    "    ax.xaxis.grid(True, which=\"minor\")\n",
    "\n",
    "\n",
    "#%matplotlib notebook\n",
    "plot_maxcpm(df.loc[:, (slice(None), 'maxCPM')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "ddsfds\n",
    "\n",
    "dsf\n",
    "\n",
    "ds\n",
    "\n",
    "dsf\n",
    "\n",
    "dsf\n",
    "\n",
    "sdf\n",
    "\n",
    "sf\n",
    "\n",
    "df\n",
    "\n",
    "dsf\n",
    "\n",
    "sdf\n",
    "\n",
    "ds\n",
    "\n",
    "fd\n",
    "\n",
    "fsdf\n",
    "\n",
    "sdf\n",
    "\n",
    "dsf\n",
    "\n",
    "dsf\n",
    "\n",
    "sd\n",
    "\n",
    "d"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}