Reb Temperatures vs Trigger Count

REBTempsVsTriggerCount.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from string import Template\n",
    "import fnmatch\n",
    "import datetime\n",
    "from IPython.core.display import display, Javascript\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pytz\n",
    "import requests\n",
    "\n",
    "DEFAULT_REST_URL = \"https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/rest/\"\n",
    "DEFAULT_SITE = \"ir2\"\n",
    "\n",
    "class Channel:\n",
    "    ''' Represents a single channel as read from the trending tree. A Channel may\n",
    "    represent a folder with no actual data, or a leaf node with associated trending\n",
    "    data '''\n",
    "    def __init__(self, restURL, path, node):\n",
    "        self.restURL = restURL\n",
    "        self.path = path\n",
    "        self.text = node['text']\n",
    "        self.id = node['id']\n",
    "        self.data = int(node.get('data')) if 'data' in node else None\n",
    "        self.hasChildren = node['children']\n",
    "        self.children = {}\n",
    "\n",
    "    def full_path(self):\n",
    "        return self.path + '/' + self.text if self.path else self.text\n",
    "\n",
    "    def __repr__(self):\n",
    "        return \"Channel (%s (%s) %s)\" % (self.full_path(), self.hasChildren, self.data)\n",
    "\n",
    "    def find(self, name):\n",
    "        if self.hasChildren and not self.children:\n",
    "            self.__load_children()\n",
    "        tokens = name.split('/', 1)\n",
    "        child = self.children[tokens[0]]\n",
    "        if len(tokens) == 1:\n",
    "            return child\n",
    "        else:\n",
    "            return child.find(tokens[1])\n",
    "\n",
    "    def find_all(self, path, leaf_only=True):\n",
    "        if self.hasChildren and not self.children:\n",
    "            self.__load_children()\n",
    "        tokens = path.split('/', 1)\n",
    "        result = []\n",
    "        for key, child in self.children.items():\n",
    "            if fnmatch.fnmatchcase(key, tokens[0]):\n",
    "                if len(tokens) == 1:\n",
    "                    if not leaf_only or not child.hasChildren:\n",
    "                        result.append(child)\n",
    "                else:\n",
    "                    result += child.find_all(tokens[1], leaf_only)\n",
    "        return result\n",
    "\n",
    "    def ls(self, recursive=False):\n",
    "        if self.hasChildren and not self.children:\n",
    "            self.__load_children()\n",
    "        if recursive:\n",
    "            l = []\n",
    "            for key, value in self.children. items():\n",
    "                l.append(key)\n",
    "                if value.hasChildren:\n",
    "                    l.append(value.ls(True))\n",
    "            return l\n",
    "        else:\n",
    "            return self.children.keys()\n",
    "\n",
    "    def __load_children(self):\n",
    "        if self.hasChildren and not self.children:\n",
    "            url = self.restURL\n",
    "            if id != 0:\n",
    "                url += \"?id=%d\" % self.id\n",
    "            r = requests.get(url)\n",
    "            r.raise_for_status()\n",
    "            json = r.json()\n",
    "            full_path = self.full_path()\n",
    "            for node in json:\n",
    "                c = Channel(self.restURL, full_path, node)\n",
    "                self.children[c.text] = c\n",
    "\n",
    "class ChannelMapHelper:\n",
    "    def __init__(self, path, cm):\n",
    "        self.channels = cm.find_all(path, leaf_only=True)\n",
    "        if not self.channels:\n",
    "            raise RuntimeError(\"Path did not match any leaf nodes: \"+path)\n",
    "        elif len(self.channels) == 1:\n",
    "            fp = self.channels[0].full_path()\n",
    "            self.suggested_title = fp[:-1]\n",
    "            self.suggested_names = fp[-1:]\n",
    "        else:\n",
    "            matches = self.channels[0].full_path().split('/')\n",
    "            for channel in self.channels[1:]:\n",
    "                fp = channel.full_path()\n",
    "                tokens = fp.split('/')\n",
    "                for i, token in enumerate(tokens):\n",
    "                    if token != matches[i]:\n",
    "                        matches[i] = \"*\"\n",
    "            self.suggested_title = '/'.join(matches)\n",
    "            names = [\"\"] * len(self.channels)\n",
    "            for channel_index, channel in enumerate(self.channels):\n",
    "                fp = channel.full_path()\n",
    "                tokens = fp.split('/')\n",
    "                channel_name = []\n",
    "                for i, token in enumerate(tokens):\n",
    "                    if matches[i] == \"*\":\n",
    "                        channel_name.append(token)\n",
    "                names[channel_index] += '/'.join(channel_name)\n",
    "            self.suggested_names = names\n",
    "\n",
    "class ChannelMap:\n",
    "    def __init__(self, site=DEFAULT_SITE, restURL=DEFAULT_REST_URL):\n",
    "        self.root = Channel(restURL+site+\"/channels/\", \"\", {'text': '', 'id': 0, 'children': True})\n",
    "\n",
    "    def find(self, name):\n",
    "        return self.root.find(name)\n",
    "\n",
    "    def find_all(self, path, leaf_only=False):\n",
    "        return self.root.find_all(path, leaf_only)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return self.root.__repr__()\n",
    "\n",
    "    def ls(self, recursive=False):\n",
    "        return self.root.ls(recursive)\n",
    "\n",
    "class ChannelDataReader:\n",
    "\n",
    "    def __init__(self, site=DEFAULT_SITE, restURL=DEFAULT_REST_URL):\n",
    "        self.restURL = restURL+site\n",
    "\n",
    "    def read_data(self, ids, names, timePeriod, nBins=100):\n",
    "\n",
    "        url = \"%s?n=%d\" % (self.restURL, nBins)\n",
    "        url += \"&t1=%d&t2=%d\" % timePeriod.as_millis()\n",
    "        for id in ids:\n",
    "            url += \"&key=%d\" % id\n",
    "        r = requests.get(url)\n",
    "        r.raise_for_status()\n",
    "        json = r.json()\n",
    "        data = np.array(json['data'])\n",
    "        df = pd.DataFrame(data=data[:, 1:], index=data[:, 0], columns=names)\n",
    "        df.index = pd.to_datetime(df.index, unit='ms')\n",
    "        return df\n",
    "\n",
    "\n",
    "class TimePeriod:\n",
    "    epoch = pytz.utc.localize(datetime.datetime.utcfromtimestamp(0))\n",
    "    @staticmethod\n",
    "    def to_millis(dt):\n",
    "        return int((dt-TimePeriod.epoch).total_seconds()*1000)\n",
    "\n",
    "    def as_millis(self):\n",
    "        pass\n",
    "\n",
    "    def as_ccs_string(self):\n",
    "        pass\n",
    "\n",
    "    @staticmethod\n",
    "    def for_range(period):\n",
    "        if isinstance(period, datetime.timedelta):\n",
    "            return DeltaTimePeriod(period)\n",
    "        elif isinstance(period, tuple):\n",
    "            return StartEndTimePeriod(period[0], period[1])\n",
    "        else:\n",
    "            raise RuntimeError(\"Unsupported argument type for for_range \"+period)\n",
    "\n",
    "\n",
    "class StartEndTimePeriod(TimePeriod):\n",
    "    def __init__(self, start, end):\n",
    "        self.start = start\n",
    "        self.end = end\n",
    "\n",
    "    def as_millis(self):\n",
    "        return (TimePeriod.to_millis(self.start), TimePeriod.to_millis(self.end))\n",
    "\n",
    "    def as_ccs_string(self):\n",
    "        return \"{start: new Date(%d), end: new Date(%d)}\" % (TimePeriod.to_millis(self.start), TimePeriod.to_millis(self.end))\n",
    "\n",
    "class DeltaTimePeriod(TimePeriod):\n",
    "    def __init__(self, delta):\n",
    "        self.delta = delta\n",
    "\n",
    "    def as_millis(self):\n",
    "        now = pytz.utc.localize(datetime.datetime.utcnow())\n",
    "        return (TimePeriod.to_millis(now-self.delta), TimePeriod.to_millis(now))\n",
    "\n",
    "    def as_ccs_string(self):\n",
    "        return \"%d\" % self.delta.total_seconds()\n",
    "\n",
    "class CCSTrending:\n",
    "    \"\"\"A simple jupyter interface to CCS trending\"\"\"\n",
    "\n",
    "    def __init__(self, title=\"Trending Plot\", data=None, range=datetime.timedelta(days=1), site=DEFAULT_SITE, restURL=DEFAULT_REST_URL):\n",
    "        self.plots = []\n",
    "        self.title = title\n",
    "        self.range = range\n",
    "        self.restURL = restURL+site\n",
    "        self.cm = ChannelMap(site=site, restURL=restURL)\n",
    "        self.dr = ChannelDataReader(site=site, restURL=restURL)\n",
    "\n",
    "        if data:\n",
    "            if isinstance(data, str):\n",
    "                cmh = ChannelMapHelper(data, self.cm)\n",
    "                # TODO: Dont replace user supplied title\n",
    "                self.title = cmh.suggested_title\n",
    "                for channel, name in zip(cmh.channels, cmh.suggested_names):\n",
    "                    self.add_channel(channel, name)\n",
    "            elif isinstance(data, dict):\n",
    "                for key, value in data.items():\n",
    "                    self.add_channel(key, value)\n",
    "            else:\n",
    "                raise RuntimeError(\"Unsupported argument type for data \"+data)\n",
    "\n",
    "    @property\n",
    "    def range(self):\n",
    "        return self.__range\n",
    "\n",
    "    @range.setter\n",
    "    def range(self, range):\n",
    "        self.__range = TimePeriod.for_range(range)\n",
    "\n",
    "    def add_channel(self, id, key=None):\n",
    "        if isinstance(id, int):\n",
    "            self.plots.append([id, key])\n",
    "        elif isinstance(id, Channel):\n",
    "            self.plots.append([id.data, key if key else id.full_path()])\n",
    "        elif isinstance(id, str):\n",
    "            channel = self.cm.find(id)\n",
    "            self.plots.append([channel.data, key if key else channel.text])\n",
    "        else:\n",
    "            raise RuntimeError('Unsuppored argument type for add_channel '+ id)\n",
    "\n",
    "    def add_all(self, channels):\n",
    "        for channel in channels:\n",
    "            self.add_channel(channel)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return str(self.plots)\n",
    "\n",
    "    def as_dataframe(self):\n",
    "        ids = []\n",
    "        keys = []\n",
    "        for id, key in self.plots:\n",
    "            ids.append(id)\n",
    "            keys.append(key)\n",
    "        return self.dr.read_data(ids, keys, self.__range)\n",
    "\n",
    "    def plot(self):\n",
    "        code = Template('''\n",
    "            $$('<style>.dygraph-legend {left: 70px !important;}</style>').appendTo(element);\n",
    "            var div = $$('<div style=\"width:100%;height:400px\"></div>').appendTo(element)\n",
    "            setTimeout(function(){\n",
    "                var g = new CCSTrendingPlot(\n",
    "                        div[0], {\n",
    "                    title: '${title}',\n",
    "                    restURL: '${restURL}',\n",
    "                    range: ${range},\n",
    "                    data: { ${data} }\n",
    "                });\n",
    "                var zoomDict = {'hour': 3600, '3 hour': 3*3600, '6 hour': 6*3600, '12 hour': 12*3600,\n",
    "                   'day': 86400, 'week': 604800, 'month': 30 * 86400 };\n",
    "                var errorBarDict = { 'none': 'NONE', 'minmax': 'MINMAX', 'rms': 'RMS'}\n",
    "                function zoom(event) { g.zoom(event.data); }\n",
    "                function setErrorBars(event) { g.setErrorBars(event.data); }\n",
    "\n",
    "                $$('<b>Zoom:&nbsp;</b>').appendTo(element);\n",
    "                for (var key in zoomDict) {\n",
    "                  var a = $$('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
    "                  a.click(zoomDict[key],zoom);\n",
    "                  element.append('&nbsp;');\n",
    "                }\n",
    "                $$('<b>Error Bars:&nbsp;</b>').appendTo(element);\n",
    "                for (var key in errorBarDict) {\n",
    "                  var a = $$('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
    "                  a.click(errorBarDict[key],setErrorBars);\n",
    "                  element.append('&nbsp;');\n",
    "                }\n",
    "              },0);\n",
    "           ''')\n",
    "        def output(plots):\n",
    "            result = \"\"\n",
    "            for id, key in plots:\n",
    "                result += \"%d: {title: '%s'},\" % (id, key)\n",
    "            return result\n",
    "\n",
    "        js = Javascript(code.substitute(title=self.title, data=output(self.plots), range=self.range.as_ccs_string(), restURL=self.restURL),\n",
    "                        lib=[\"//cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js\",\n",
    "                             \"https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/CCSTrending.js\"])\n",
    "        display(js)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Plotting example\n",
    "\n",
    "This example shows how to create a simple plot, based on a \"glob pattern\" within the tree of available data. By default the data is plotted over the previous 24 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = CCSTrending(title=\"REB temperatures\", data=\"focal-plane/*/Reb1/Temp6\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the resulting data is simple. Note that the produced plot is fully interactive, you can zoom in by dragging on the plot and can pan by shift-dragging. The plot contains a live connection to the CCS trending data, so as you zoom and pan more data will be fetched as necessary. You can also use the links below the plot to update the plot time range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$.getScript(\"//cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js\", function () {\n",
       "$.getScript(\"https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/CCSTrending.js\", function () {\n",
       "\n",
       "            $('<style>.dygraph-legend {left: 70px !important;}</style>').appendTo(element);\n",
       "            var div = $('<div style=\"width:100%;height:400px\"></div>').appendTo(element)\n",
       "            setTimeout(function(){\n",
       "                var g = new CCSTrendingPlot(\n",
       "                        div[0], {\n",
       "                    title: 'focal-plane/*/Reb1/Temp6',\n",
       "                    restURL: 'https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/rest/ir2',\n",
       "                    range: 86400,\n",
       "                    data: { 15643: {title: 'R10'},15615: {title: 'R22'}, }\n",
       "                });\n",
       "                var zoomDict = {'hour': 3600, '3 hour': 3*3600, '6 hour': 6*3600, '12 hour': 12*3600,\n",
       "                   'day': 86400, 'week': 604800, 'month': 30 * 86400 };\n",
       "                var errorBarDict = { 'none': 'NONE', 'minmax': 'MINMAX', 'rms': 'RMS'}\n",
       "                function zoom(event) { g.zoom(event.data); }\n",
       "                function setErrorBars(event) { g.setErrorBars(event.data); }\n",
       "\n",
       "                $('<b>Zoom:&nbsp;</b>').appendTo(element);\n",
       "                for (var key in zoomDict) {\n",
       "                  var a = $('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
       "                  a.click(zoomDict[key],zoom);\n",
       "                  element.append('&nbsp;');\n",
       "                }\n",
       "                $('<b>Error Bars:&nbsp;</b>').appendTo(element);\n",
       "                for (var key in errorBarDict) {\n",
       "                  var a = $('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
       "                  a.click(errorBarDict[key],setErrorBars);\n",
       "                  element.append('&nbsp;');\n",
       "                }\n",
       "              },0);\n",
       "           });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying time range\n",
    "\n",
    "You can control the range over which the data is plotted programatically as well, either by specifying a delta time, which is interpreted as the time period ending now over which your wish to view data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also specify an absolute time range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$.getScript(\"//cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js\", function () {\n",
       "$.getScript(\"https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/CCSTrending.js\", function () {\n",
       "\n",
       "            $('<style>.dygraph-legend {left: 70px !important;}</style>').appendTo(element);\n",
       "            var div = $('<div style=\"width:100%;height:400px\"></div>').appendTo(element)\n",
       "            setTimeout(function(){\n",
       "                var g = new CCSTrendingPlot(\n",
       "                        div[0], {\n",
       "                    title: 'focal-plane/*/Reb1/Temp6',\n",
       "                    restURL: 'https://lsst-camera-dev.slac.stanford.edu/CCSWebTrending/rest/ir2',\n",
       "                    range: {start: new Date(1559883600000), end: new Date(1560193200000)},\n",
       "                    data: { 15643: {title: 'R10'},15615: {title: 'R22'}, }\n",
       "                });\n",
       "                var zoomDict = {'hour': 3600, '3 hour': 3*3600, '6 hour': 6*3600, '12 hour': 12*3600,\n",
       "                   'day': 86400, 'week': 604800, 'month': 30 * 86400 };\n",
       "                var errorBarDict = { 'none': 'NONE', 'minmax': 'MINMAX', 'rms': 'RMS'}\n",
       "                function zoom(event) { g.zoom(event.data); }\n",
       "                function setErrorBars(event) { g.setErrorBars(event.data); }\n",
       "\n",
       "                $('<b>Zoom:&nbsp;</b>').appendTo(element);\n",
       "                for (var key in zoomDict) {\n",
       "                  var a = $('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
       "                  a.click(zoomDict[key],zoom);\n",
       "                  element.append('&nbsp;');\n",
       "                }\n",
       "                $('<b>Error Bars:&nbsp;</b>').appendTo(element);\n",
       "                for (var key in errorBarDict) {\n",
       "                  var a = $('<a href=\"#\">'+key+'</a>').appendTo(element);\n",
       "                  a.click(errorBarDict[key],setErrorBars);\n",
       "                  element.append('&nbsp;');\n",
       "                }\n",
       "              },0);\n",
       "           });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "t1 = parser.parse(\"Jun 06 2019 22:00 PST\")\n",
    "t2 = parser.parse(\"Jun 10 2019 12:00 PST\")\n",
    "plot.range = (t1,t2)\n",
    "plot.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting plot data\n",
    "\n",
    "The plot data can easily be exported as a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = plot.as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2019-01-19 17:47:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.515686</td>\n",
       "      <td>17.766271</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 17:52:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.515686</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>17.991637</td>\n",
       "      <td>17.670392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 17:57:30</th>\n",
       "      <td>17.822007</td>\n",
       "      <td>17.350505</td>\n",
       "      <td>17.725180</td>\n",
       "      <td>17.991636</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:02:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.460626</td>\n",
       "      <td>17.834757</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.725180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:07:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.901105</td>\n",
       "      <td>18.108699</td>\n",
       "      <td>17.991637</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:12:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.515684</td>\n",
       "      <td>18.108699</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:17:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.680864</td>\n",
       "      <td>18.163487</td>\n",
       "      <td>18.212094</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:22:30</th>\n",
       "      <td>18.043124</td>\n",
       "      <td>17.790984</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:27:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.552392</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>18.046751</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:32:30</th>\n",
       "      <td>17.822007</td>\n",
       "      <td>17.322976</td>\n",
       "      <td>17.725180</td>\n",
       "      <td>18.046751</td>\n",
       "      <td>17.697786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:37:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.680866</td>\n",
       "      <td>17.670392</td>\n",
       "      <td>17.936523</td>\n",
       "      <td>17.670392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:42:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.570746</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.046750</td>\n",
       "      <td>17.725180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:47:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.956165</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.046751</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:52:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.625804</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 18:57:30</th>\n",
       "      <td>18.208961</td>\n",
       "      <td>17.515684</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.212094</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:02:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.625804</td>\n",
       "      <td>17.971728</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:07:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.625805</td>\n",
       "      <td>17.971728</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:12:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.405566</td>\n",
       "      <td>17.779968</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:17:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.735925</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>18.046751</td>\n",
       "      <td>17.670392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:22:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.405565</td>\n",
       "      <td>17.779968</td>\n",
       "      <td>18.101865</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:27:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.570745</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:32:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.625805</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>18.053910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:37:30</th>\n",
       "      <td>18.264241</td>\n",
       "      <td>17.735925</td>\n",
       "      <td>17.944333</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:42:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.790984</td>\n",
       "      <td>17.944333</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:47:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.735925</td>\n",
       "      <td>17.834757</td>\n",
       "      <td>17.991637</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-19 19:52:30</th>\n",
       "      <td>17.711449</td>\n",
       "      <td>17.460626</td>\n",
       "      <td>17.697786</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 14:57:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.804750</td>\n",
       "      <td>18.040213</td>\n",
       "      <td>18.322322</td>\n",
       "      <td>17.971728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:02:30</th>\n",
       "      <td>18.043124</td>\n",
       "      <td>17.690043</td>\n",
       "      <td>18.063042</td>\n",
       "      <td>18.138609</td>\n",
       "      <td>17.926071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:07:30</th>\n",
       "      <td>17.822007</td>\n",
       "      <td>17.529449</td>\n",
       "      <td>17.560815</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.971728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:12:30</th>\n",
       "      <td>17.822007</td>\n",
       "      <td>17.506508</td>\n",
       "      <td>17.926071</td>\n",
       "      <td>18.000822</td>\n",
       "      <td>17.697786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:17:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.515684</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>17.991637</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:22:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.680865</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:27:30</th>\n",
       "      <td>18.208961</td>\n",
       "      <td>17.790985</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>18.053910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:32:30</th>\n",
       "      <td>18.208961</td>\n",
       "      <td>17.515684</td>\n",
       "      <td>18.108699</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:37:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.625804</td>\n",
       "      <td>17.944333</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:42:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.460626</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:47:30</th>\n",
       "      <td>17.766728</td>\n",
       "      <td>17.295445</td>\n",
       "      <td>17.779968</td>\n",
       "      <td>18.156980</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:52:30</th>\n",
       "      <td>17.766728</td>\n",
       "      <td>17.598276</td>\n",
       "      <td>17.903242</td>\n",
       "      <td>17.977859</td>\n",
       "      <td>17.903242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 15:57:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.644159</td>\n",
       "      <td>17.834757</td>\n",
       "      <td>18.138609</td>\n",
       "      <td>17.926071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:02:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.667100</td>\n",
       "      <td>17.944333</td>\n",
       "      <td>18.322322</td>\n",
       "      <td>17.903242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:07:30</th>\n",
       "      <td>18.043124</td>\n",
       "      <td>17.781809</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.267208</td>\n",
       "      <td>18.063042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:12:30</th>\n",
       "      <td>17.987845</td>\n",
       "      <td>17.625804</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:17:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.680865</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:22:30</th>\n",
       "      <td>17.877286</td>\n",
       "      <td>17.405565</td>\n",
       "      <td>17.779968</td>\n",
       "      <td>18.101865</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:27:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.625806</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.046751</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:32:30</th>\n",
       "      <td>18.043124</td>\n",
       "      <td>17.405565</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:37:30</th>\n",
       "      <td>18.264241</td>\n",
       "      <td>17.680865</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.834757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:42:30</th>\n",
       "      <td>18.319520</td>\n",
       "      <td>17.625804</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.212094</td>\n",
       "      <td>17.944333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:47:30</th>\n",
       "      <td>18.208961</td>\n",
       "      <td>17.667100</td>\n",
       "      <td>17.999122</td>\n",
       "      <td>18.184537</td>\n",
       "      <td>17.971728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:52:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.690041</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.092680</td>\n",
       "      <td>18.017385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 16:57:30</th>\n",
       "      <td>17.932565</td>\n",
       "      <td>17.667100</td>\n",
       "      <td>18.053910</td>\n",
       "      <td>18.101866</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 17:02:30</th>\n",
       "      <td>17.711449</td>\n",
       "      <td>17.690042</td>\n",
       "      <td>17.725180</td>\n",
       "      <td>17.936522</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 17:07:30</th>\n",
       "      <td>17.766728</td>\n",
       "      <td>17.295446</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>17.881408</td>\n",
       "      <td>17.999122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 17:12:30</th>\n",
       "      <td>18.098403</td>\n",
       "      <td>17.735925</td>\n",
       "      <td>17.944333</td>\n",
       "      <td>18.101865</td>\n",
       "      <td>17.889545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 17:17:30</th>\n",
       "      <td>18.264241</td>\n",
       "      <td>17.680866</td>\n",
       "      <td>17.889545</td>\n",
       "      <td>18.046752</td>\n",
       "      <td>18.053910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20 17:22:30</th>\n",
       "      <td>18.153682</td>\n",
       "      <td>17.460624</td>\n",
       "      <td>18.108699</td>\n",
       "      <td>18.212094</td>\n",
       "      <td>18.053910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           AUX      ENTRY    MAIN_NE    MAIN_NW    MAIN_SE\n",
       "2019-01-19 17:27:30  18.319520  17.735925  17.944333  18.212094  17.944333\n",
       "2019-01-19 17:32:30  18.264241  17.680865  18.053910  18.156980  17.944333\n",
       "2019-01-19 17:37:30  18.153682  17.956165  17.944333  18.267208  18.053910\n",
       "2019-01-19 17:42:30  17.932565  17.570745  17.971728  18.156980  18.053910\n",
       "2019-01-19 17:47:30  17.877286  17.515686  17.766271  18.156980  17.834757\n",
       "2019-01-19 17:52:30  17.877286  17.515686  17.889545  17.991637  17.670392\n",
       "2019-01-19 17:57:30  17.822007  17.350505  17.725180  17.991636  17.889545\n",
       "2019-01-19 18:02:30  17.877286  17.460626  17.834757  18.156980  17.725180\n",
       "2019-01-19 18:07:30  18.098403  17.901105  18.108699  17.991637  17.889545\n",
       "2019-01-19 18:12:30  18.153682  17.515684  18.108699  18.156980  17.999122\n",
       "2019-01-19 18:17:30  18.153682  17.680864  18.163487  18.212094  17.999122\n",
       "2019-01-19 18:22:30  18.043124  17.790984  17.999122  18.101866  17.999122\n",
       "2019-01-19 18:27:30  17.877286  17.552392  17.889545  18.046751  17.834757\n",
       "2019-01-19 18:32:30  17.822007  17.322976  17.725180  18.046751  17.697786\n",
       "2019-01-19 18:37:30  17.877286  17.680866  17.670392  17.936523  17.670392\n",
       "2019-01-19 18:42:30  17.932565  17.570746  18.053910  18.046750  17.725180\n",
       "2019-01-19 18:47:30  17.987845  17.956165  17.999122  18.046751  17.944333\n",
       "2019-01-19 18:52:30  18.098403  17.625804  17.999122  18.101866  17.999122\n",
       "2019-01-19 18:57:30  18.208961  17.515684  18.053910  18.212094  17.889545\n",
       "2019-01-19 19:02:30  18.098403  17.625804  17.971728  18.156980  17.944333\n",
       "2019-01-19 19:07:30  17.932565  17.625805  17.971728  18.156980  17.889545\n",
       "2019-01-19 19:12:30  17.877286  17.405566  17.779968  18.046752  17.834757\n",
       "2019-01-19 19:17:30  17.932565  17.735925  17.889545  18.046751  17.670392\n",
       "2019-01-19 19:22:30  17.932565  17.405565  17.779968  18.101865  17.834757\n",
       "2019-01-19 19:27:30  17.987845  17.570745  17.999122  18.156980  17.834757\n",
       "2019-01-19 19:32:30  18.098403  17.625805  17.999122  18.101866  18.053910\n",
       "2019-01-19 19:37:30  18.264241  17.735925  17.944333  18.156980  17.944333\n",
       "2019-01-19 19:42:30  18.153682  17.790984  17.944333  18.046752  17.999122\n",
       "2019-01-19 19:47:30  17.877286  17.735925  17.834757  17.991637  17.889545\n",
       "2019-01-19 19:52:30  17.711449  17.460626  17.697786  18.046752  17.834757\n",
       "...                        ...        ...        ...        ...        ...\n",
       "2019-01-20 14:57:30  18.098403  17.804750  18.040213  18.322322  17.971728\n",
       "2019-01-20 15:02:30  18.043124  17.690043  18.063042  18.138609  17.926071\n",
       "2019-01-20 15:07:30  17.822007  17.529449  17.560815  18.046752  17.971728\n",
       "2019-01-20 15:12:30  17.822007  17.506508  17.926071  18.000822  17.697786\n",
       "2019-01-20 15:17:30  17.932565  17.515684  17.889545  17.991637  17.834757\n",
       "2019-01-20 15:22:30  18.153682  17.680865  17.999122  18.046752  17.999122\n",
       "2019-01-20 15:27:30  18.208961  17.790985  18.053910  18.156980  18.053910\n",
       "2019-01-20 15:32:30  18.208961  17.515684  18.108699  18.156980  17.999122\n",
       "2019-01-20 15:37:30  17.987845  17.625804  17.944333  18.101866  17.999122\n",
       "2019-01-20 15:42:30  17.987845  17.460626  18.053910  18.046752  17.944333\n",
       "2019-01-20 15:47:30  17.766728  17.295445  17.779968  18.156980  17.889545\n",
       "2019-01-20 15:52:30  17.766728  17.598276  17.903242  17.977859  17.903242\n",
       "2019-01-20 15:57:30  17.987845  17.644159  17.834757  18.138609  17.926071\n",
       "2019-01-20 16:02:30  17.987845  17.667100  17.944333  18.322322  17.903242\n",
       "2019-01-20 16:07:30  18.043124  17.781809  18.053910  18.267208  18.063042\n",
       "2019-01-20 16:12:30  17.987845  17.625804  18.053910  18.046752  17.944333\n",
       "2019-01-20 16:17:30  17.932565  17.680865  17.889545  18.046752  17.834757\n",
       "2019-01-20 16:22:30  17.877286  17.405565  17.779968  18.101865  17.834757\n",
       "2019-01-20 16:27:30  17.932565  17.625806  17.999122  18.046751  17.834757\n",
       "2019-01-20 16:32:30  18.043124  17.405565  17.889545  18.101866  17.889545\n",
       "2019-01-20 16:37:30  18.264241  17.680865  17.999122  18.101866  17.834757\n",
       "2019-01-20 16:42:30  18.319520  17.625804  18.053910  18.212094  17.944333\n",
       "2019-01-20 16:47:30  18.208961  17.667100  17.999122  18.184537  17.971728\n",
       "2019-01-20 16:52:30  18.153682  17.690041  18.053910  18.092680  18.017385\n",
       "2019-01-20 16:57:30  17.932565  17.667100  18.053910  18.101866  17.999122\n",
       "2019-01-20 17:02:30  17.711449  17.690042  17.725180  17.936522  17.889545\n",
       "2019-01-20 17:07:30  17.766728  17.295446  17.889545  17.881408  17.999122\n",
       "2019-01-20 17:12:30  18.098403  17.735925  17.944333  18.101865  17.889545\n",
       "2019-01-20 17:17:30  18.264241  17.680866  17.889545  18.046752  18.053910\n",
       "2019-01-20 17:22:30  18.153682  17.460624  18.108699  18.212094  18.053910\n",
       "\n",
       "[288 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9BVbpgnUVVRugoijy725Wzi/Eix8vJbJSFEvMSToK3/wwnHoIbvlNeMN/Aat2yD35Hfja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\nMqckj6t+tYm/vlo95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i7jK6uXcd7ySs0Eq6RLvTOsvx9aj8SeKQ6tXxvIuGj5RFrKLbGX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\nKZX2NChWSimllFJp7/8DZ8oOrcf9hx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6d996518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "ax = df.plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = pd.read_csv(\"/home/tonyj/Downloads/images.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "images['OBSDATE']=pd.to_datetime(images['OBSDATE'])\n",
    "images=images.sort_values(\"OBSDATE\")\n",
    "images.index=pd.DatetimeIndex(images.OBSDATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "images['ACTIVE']=images['DARKTIME']+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TELCODE</th>\n",
       "      <th>SEQNUM</th>\n",
       "      <th>DAYOBS</th>\n",
       "      <th>CONTROLLER</th>\n",
       "      <th>DARKTIME</th>\n",
       "      <th>EXPOSURETIME</th>\n",
       "      <th>FILELOCATION</th>\n",
       "      <th>IMAGETAG</th>\n",
       "      <th>IMGTYPE</th>\n",
       "      <th>OBSDATE</th>\n",
       "      <th>RAFTMASK</th>\n",
       "      <th>RUNNUMBER</th>\n",
       "      <th>TESTTYPE</th>\n",
       "      <th>TSEQNUM</th>\n",
       "      <th>TSTAND</th>\n",
       "      <th>ACTIVE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBSDATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:01:03.498</th>\n",
       "      <td>MC</td>\n",
       "      <td>45</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.267000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926863498</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:01:03.498</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.267000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:01:13.293</th>\n",
       "      <td>MC</td>\n",
       "      <td>46</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926873293</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:01:13.293</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:01:24.237</th>\n",
       "      <td>MC</td>\n",
       "      <td>47</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926884237</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:01:24.237</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:01:32.278</th>\n",
       "      <td>MC</td>\n",
       "      <td>48</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926892278</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:01:32.278</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:01:40.791</th>\n",
       "      <td>MC</td>\n",
       "      <td>49</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926900791</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:01:40.791</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:02:19.518</th>\n",
       "      <td>MC</td>\n",
       "      <td>50</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>17.621000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559926939518</td>\n",
       "      <td>SPOT</td>\n",
       "      <td>2019-06-07 10:02:19.518</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>SPOT_FLAT</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>19.621000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:03:27.726</th>\n",
       "      <td>MC</td>\n",
       "      <td>51</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.053000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927007726</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:03:27.726</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:03:33.934</th>\n",
       "      <td>MC</td>\n",
       "      <td>52</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927013934</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:03:33.934</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:03:39.997</th>\n",
       "      <td>MC</td>\n",
       "      <td>53</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927019997</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:03:39.997</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:03:44.666</th>\n",
       "      <td>MC</td>\n",
       "      <td>54</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927024666</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:03:44.666</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:03:49.235</th>\n",
       "      <td>MC</td>\n",
       "      <td>55</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927029235</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:03:49.235</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:04:16.775</th>\n",
       "      <td>MC</td>\n",
       "      <td>56</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>14.824000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927056775</td>\n",
       "      <td>SPOT</td>\n",
       "      <td>2019-06-07 10:04:16.775</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>SPOT_FLAT</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>16.824000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:04.607</th>\n",
       "      <td>MC</td>\n",
       "      <td>57</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.056000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927224607</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:07:04.607</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.056000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:09.805</th>\n",
       "      <td>MC</td>\n",
       "      <td>58</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927229805</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:07:09.805</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:14.460</th>\n",
       "      <td>MC</td>\n",
       "      <td>59</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927234460</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:07:14.460</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:19.206</th>\n",
       "      <td>MC</td>\n",
       "      <td>60</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927239206</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:07:19.206</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:24.836</th>\n",
       "      <td>MC</td>\n",
       "      <td>61</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927244836</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:07:24.836</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:07:43.677</th>\n",
       "      <td>MC</td>\n",
       "      <td>62</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>12.778000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927263677</td>\n",
       "      <td>SPOT</td>\n",
       "      <td>2019-06-07 10:07:43.677</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>SPOT_FLAT</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>14.778000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:14:44.492</th>\n",
       "      <td>MC</td>\n",
       "      <td>63</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927684492</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:14:44.492</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.041000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:14:50.409</th>\n",
       "      <td>MC</td>\n",
       "      <td>64</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927690409</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:14:50.409</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:14:59.449</th>\n",
       "      <td>MC</td>\n",
       "      <td>65</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927699449</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:14:59.449</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:15:04.068</th>\n",
       "      <td>MC</td>\n",
       "      <td>66</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927704068</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:15:04.068</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:15:09.212</th>\n",
       "      <td>MC</td>\n",
       "      <td>67</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927709212</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:15:09.212</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:15:27.659</th>\n",
       "      <td>MC</td>\n",
       "      <td>68</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>12.762000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559927727659</td>\n",
       "      <td>SPOT</td>\n",
       "      <td>2019-06-07 10:15:27.659</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>SPOT_FLAT</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>14.762000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:24:02.948</th>\n",
       "      <td>MC</td>\n",
       "      <td>69</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928242948</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:24:02.948</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:24:08.322</th>\n",
       "      <td>MC</td>\n",
       "      <td>70</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928248322</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:24:08.322</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:24:13.389</th>\n",
       "      <td>MC</td>\n",
       "      <td>71</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928253389</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:24:13.389</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:24:19.862</th>\n",
       "      <td>MC</td>\n",
       "      <td>72</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928259862</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:24:19.862</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:24:24.864</th>\n",
       "      <td>MC</td>\n",
       "      <td>73</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928264864</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-07 10:24:24.864</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 10:25:09.005</th>\n",
       "      <td>MC</td>\n",
       "      <td>74</td>\n",
       "      <td>20190607</td>\n",
       "      <td>C</td>\n",
       "      <td>12.770000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1559928309005</td>\n",
       "      <td>SPOT</td>\n",
       "      <td>2019-06-07 10:25:09.005</td>\n",
       "      <td>4128</td>\n",
       "      <td>(null)</td>\n",
       "      <td>SPOT_FLAT</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>14.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:48:59.361</th>\n",
       "      <td>MC</td>\n",
       "      <td>2787</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192539361</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:48:59.361</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:04.939</th>\n",
       "      <td>MC</td>\n",
       "      <td>2788</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192544939</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:04.939</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:09.876</th>\n",
       "      <td>MC</td>\n",
       "      <td>2789</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.032000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192549876</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:09.876</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.032000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:14.811</th>\n",
       "      <td>MC</td>\n",
       "      <td>2790</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192554811</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:14.811</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>5</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.027000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:22.001</th>\n",
       "      <td>MC</td>\n",
       "      <td>2791</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192562001</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:22.001</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>6</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:41.314</th>\n",
       "      <td>MC</td>\n",
       "      <td>2792</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192581314</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:41.314</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>7</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.027000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:46.278</th>\n",
       "      <td>MC</td>\n",
       "      <td>2793</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192586278</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:46.278</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>8</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.028000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:49:51.463</th>\n",
       "      <td>MC</td>\n",
       "      <td>2794</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192591463</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:49:51.463</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>9</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:50:00.474</th>\n",
       "      <td>MC</td>\n",
       "      <td>2795</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192600474</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:50:00.474</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.028000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:55:06.132</th>\n",
       "      <td>MC</td>\n",
       "      <td>2796</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.029999</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192906132</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 11:55:06.132</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 11:55:12.117</th>\n",
       "      <td>MC</td>\n",
       "      <td>2797</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560192912117</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 11:55:12.117</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.027000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:00:17.060</th>\n",
       "      <td>MC</td>\n",
       "      <td>2798</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.022003</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560193217060</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 12:00:17.060</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.022003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:00:22.399</th>\n",
       "      <td>MC</td>\n",
       "      <td>2799</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.311000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560193222399</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:00:22.399</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.311000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:05:27.022</th>\n",
       "      <td>MC</td>\n",
       "      <td>2800</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.110992</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560193527022</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 12:05:27.022</td>\n",
       "      <td>4128</td>\n",
       "      <td>6695D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>5</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.110992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:36:25.180</th>\n",
       "      <td>MC</td>\n",
       "      <td>2801</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195385180</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:36:25.180</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.078000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:36:44.399</th>\n",
       "      <td>MC</td>\n",
       "      <td>2802</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195404399</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:36:44.399</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:36:55.628</th>\n",
       "      <td>MC</td>\n",
       "      <td>2803</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195415628</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:36:55.628</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.019000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:37:00.649</th>\n",
       "      <td>MC</td>\n",
       "      <td>2804</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195420649</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:37:00.649</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.028000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:37:09.982</th>\n",
       "      <td>MC</td>\n",
       "      <td>2805</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195429982</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:37:09.982</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:37:24.232</th>\n",
       "      <td>MC</td>\n",
       "      <td>2806</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195444232</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:37:24.232</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>5</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:38:20.063</th>\n",
       "      <td>MC</td>\n",
       "      <td>2807</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195500063</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:38:20.063</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>6</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.042000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:38:25.588</th>\n",
       "      <td>MC</td>\n",
       "      <td>2808</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195505588</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:38:25.588</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>7</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:39:19.051</th>\n",
       "      <td>MC</td>\n",
       "      <td>2809</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195559051</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:39:19.051</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>8</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:39:24.956</th>\n",
       "      <td>MC</td>\n",
       "      <td>2810</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195564956</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:39:24.956</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>9</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:39:29.818</th>\n",
       "      <td>MC</td>\n",
       "      <td>2811</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195569818</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:39:29.818</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>0</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:44:35.400</th>\n",
       "      <td>MC</td>\n",
       "      <td>2812</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.028015</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195875400</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 12:44:35.400</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>1</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.028015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:44:40.716</th>\n",
       "      <td>MC</td>\n",
       "      <td>2813</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560195880716</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:44:40.716</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:49:45.342</th>\n",
       "      <td>MC</td>\n",
       "      <td>2814</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.024994</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560196185342</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 12:49:45.342</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>3</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.024994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:49:50.279</th>\n",
       "      <td>MC</td>\n",
       "      <td>2815</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560196190279</td>\n",
       "      <td>BIAS</td>\n",
       "      <td>2019-06-10 12:49:50.279</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>4</td>\n",
       "      <td>BOT</td>\n",
       "      <td>2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10 12:54:56.025</th>\n",
       "      <td>MC</td>\n",
       "      <td>2816</td>\n",
       "      <td>20190610</td>\n",
       "      <td>C</td>\n",
       "      <td>300.023987</td>\n",
       "      <td>300.0</td>\n",
       "      <td>/gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...</td>\n",
       "      <td>1560196496025</td>\n",
       "      <td>DARK</td>\n",
       "      <td>2019-06-10 12:54:56.025</td>\n",
       "      <td>4128</td>\n",
       "      <td>6696D</td>\n",
       "      <td>DARK</td>\n",
       "      <td>5</td>\n",
       "      <td>BOT</td>\n",
       "      <td>302.023987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13897 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TELCODE  SEQNUM    DAYOBS CONTROLLER    DARKTIME  \\\n",
       "OBSDATE                                                                    \n",
       "2019-06-07 10:01:03.498      MC      45  20190607          C    0.267000   \n",
       "2019-06-07 10:01:13.293      MC      46  20190607          C    0.019000   \n",
       "2019-06-07 10:01:24.237      MC      47  20190607          C    0.021000   \n",
       "2019-06-07 10:01:32.278      MC      48  20190607          C    0.023000   \n",
       "2019-06-07 10:01:40.791      MC      49  20190607          C    0.024000   \n",
       "2019-06-07 10:02:19.518      MC      50  20190607          C   17.621000   \n",
       "2019-06-07 10:03:27.726      MC      51  20190607          C    0.053000   \n",
       "2019-06-07 10:03:33.934      MC      52  20190607          C    0.021000   \n",
       "2019-06-07 10:03:39.997      MC      53  20190607          C    0.023000   \n",
       "2019-06-07 10:03:44.666      MC      54  20190607          C    0.022000   \n",
       "2019-06-07 10:03:49.235      MC      55  20190607          C    0.020000   \n",
       "2019-06-07 10:04:16.775      MC      56  20190607          C   14.824000   \n",
       "2019-06-07 10:07:04.607      MC      57  20190607          C    0.056000   \n",
       "2019-06-07 10:07:09.805      MC      58  20190607          C    0.022000   \n",
       "2019-06-07 10:07:14.460      MC      59  20190607          C    0.022000   \n",
       "2019-06-07 10:07:19.206      MC      60  20190607          C    0.020000   \n",
       "2019-06-07 10:07:24.836      MC      61  20190607          C    0.022000   \n",
       "2019-06-07 10:07:43.677      MC      62  20190607          C   12.778000   \n",
       "2019-06-07 10:14:44.492      MC      63  20190607          C    0.041000   \n",
       "2019-06-07 10:14:50.409      MC      64  20190607          C    0.021000   \n",
       "2019-06-07 10:14:59.449      MC      65  20190607          C    0.023000   \n",
       "2019-06-07 10:15:04.068      MC      66  20190607          C    0.018000   \n",
       "2019-06-07 10:15:09.212      MC      67  20190607          C    0.018000   \n",
       "2019-06-07 10:15:27.659      MC      68  20190607          C   12.762000   \n",
       "2019-06-07 10:24:02.948      MC      69  20190607          C    0.050000   \n",
       "2019-06-07 10:24:08.322      MC      70  20190607          C    0.019000   \n",
       "2019-06-07 10:24:13.389      MC      71  20190607          C    0.021000   \n",
       "2019-06-07 10:24:19.862      MC      72  20190607          C    0.023000   \n",
       "2019-06-07 10:24:24.864      MC      73  20190607          C    0.024000   \n",
       "2019-06-07 10:25:09.005      MC      74  20190607          C   12.770000   \n",
       "...                         ...     ...       ...        ...         ...   \n",
       "2019-06-10 11:48:59.361      MC    2787  20190610          C    0.024000   \n",
       "2019-06-10 11:49:04.939      MC    2788  20190610          C    0.020000   \n",
       "2019-06-10 11:49:09.876      MC    2789  20190610          C    0.032000   \n",
       "2019-06-10 11:49:14.811      MC    2790  20190610          C    0.027000   \n",
       "2019-06-10 11:49:22.001      MC    2791  20190610          C    0.022000   \n",
       "2019-06-10 11:49:41.314      MC    2792  20190610          C    0.027000   \n",
       "2019-06-10 11:49:46.278      MC    2793  20190610          C    0.028000   \n",
       "2019-06-10 11:49:51.463      MC    2794  20190610          C    0.022000   \n",
       "2019-06-10 11:50:00.474      MC    2795  20190610          C    0.028000   \n",
       "2019-06-10 11:55:06.132      MC    2796  20190610          C  300.029999   \n",
       "2019-06-10 11:55:12.117      MC    2797  20190610          C    0.027000   \n",
       "2019-06-10 12:00:17.060      MC    2798  20190610          C  300.022003   \n",
       "2019-06-10 12:00:22.399      MC    2799  20190610          C    0.311000   \n",
       "2019-06-10 12:05:27.022      MC    2800  20190610          C  300.110992   \n",
       "2019-06-10 12:36:25.180      MC    2801  20190610          C    0.078000   \n",
       "2019-06-10 12:36:44.399      MC    2802  20190610          C    0.019000   \n",
       "2019-06-10 12:36:55.628      MC    2803  20190610          C    0.019000   \n",
       "2019-06-10 12:37:00.649      MC    2804  20190610          C    0.028000   \n",
       "2019-06-10 12:37:09.982      MC    2805  20190610          C    0.021000   \n",
       "2019-06-10 12:37:24.232      MC    2806  20190610          C    0.021000   \n",
       "2019-06-10 12:38:20.063      MC    2807  20190610          C    0.042000   \n",
       "2019-06-10 12:38:25.588      MC    2808  20190610          C    0.040000   \n",
       "2019-06-10 12:39:19.051      MC    2809  20190610          C    0.022000   \n",
       "2019-06-10 12:39:24.956      MC    2810  20190610          C    0.020000   \n",
       "2019-06-10 12:39:29.818      MC    2811  20190610          C    0.023000   \n",
       "2019-06-10 12:44:35.400      MC    2812  20190610          C  300.028015   \n",
       "2019-06-10 12:44:40.716      MC    2813  20190610          C    0.018000   \n",
       "2019-06-10 12:49:45.342      MC    2814  20190610          C  300.024994   \n",
       "2019-06-10 12:49:50.279      MC    2815  20190610          C    0.020000   \n",
       "2019-06-10 12:54:56.025      MC    2816  20190610          C  300.023987   \n",
       "\n",
       "                         EXPOSURETIME  \\\n",
       "OBSDATE                                 \n",
       "2019-06-07 10:01:03.498           0.0   \n",
       "2019-06-07 10:01:13.293           0.0   \n",
       "2019-06-07 10:01:24.237           0.0   \n",
       "2019-06-07 10:01:32.278           0.0   \n",
       "2019-06-07 10:01:40.791           0.0   \n",
       "2019-06-07 10:02:19.518          10.0   \n",
       "2019-06-07 10:03:27.726           0.0   \n",
       "2019-06-07 10:03:33.934           0.0   \n",
       "2019-06-07 10:03:39.997           0.0   \n",
       "2019-06-07 10:03:44.666           0.0   \n",
       "2019-06-07 10:03:49.235           0.0   \n",
       "2019-06-07 10:04:16.775          10.0   \n",
       "2019-06-07 10:07:04.607           0.0   \n",
       "2019-06-07 10:07:09.805           0.0   \n",
       "2019-06-07 10:07:14.460           0.0   \n",
       "2019-06-07 10:07:19.206           0.0   \n",
       "2019-06-07 10:07:24.836           0.0   \n",
       "2019-06-07 10:07:43.677          10.0   \n",
       "2019-06-07 10:14:44.492           0.0   \n",
       "2019-06-07 10:14:50.409           0.0   \n",
       "2019-06-07 10:14:59.449           0.0   \n",
       "2019-06-07 10:15:04.068           0.0   \n",
       "2019-06-07 10:15:09.212           0.0   \n",
       "2019-06-07 10:15:27.659          10.0   \n",
       "2019-06-07 10:24:02.948           0.0   \n",
       "2019-06-07 10:24:08.322           0.0   \n",
       "2019-06-07 10:24:13.389           0.0   \n",
       "2019-06-07 10:24:19.862           0.0   \n",
       "2019-06-07 10:24:24.864           0.0   \n",
       "2019-06-07 10:25:09.005          10.0   \n",
       "...                               ...   \n",
       "2019-06-10 11:48:59.361           0.0   \n",
       "2019-06-10 11:49:04.939           0.0   \n",
       "2019-06-10 11:49:09.876           0.0   \n",
       "2019-06-10 11:49:14.811           0.0   \n",
       "2019-06-10 11:49:22.001           0.0   \n",
       "2019-06-10 11:49:41.314           0.0   \n",
       "2019-06-10 11:49:46.278           0.0   \n",
       "2019-06-10 11:49:51.463           0.0   \n",
       "2019-06-10 11:50:00.474           0.0   \n",
       "2019-06-10 11:55:06.132         300.0   \n",
       "2019-06-10 11:55:12.117           0.0   \n",
       "2019-06-10 12:00:17.060         300.0   \n",
       "2019-06-10 12:00:22.399           0.0   \n",
       "2019-06-10 12:05:27.022         300.0   \n",
       "2019-06-10 12:36:25.180           0.0   \n",
       "2019-06-10 12:36:44.399           0.0   \n",
       "2019-06-10 12:36:55.628           0.0   \n",
       "2019-06-10 12:37:00.649           0.0   \n",
       "2019-06-10 12:37:09.982           0.0   \n",
       "2019-06-10 12:37:24.232           0.0   \n",
       "2019-06-10 12:38:20.063           0.0   \n",
       "2019-06-10 12:38:25.588           0.0   \n",
       "2019-06-10 12:39:19.051           0.0   \n",
       "2019-06-10 12:39:24.956           0.0   \n",
       "2019-06-10 12:39:29.818           0.0   \n",
       "2019-06-10 12:44:35.400         300.0   \n",
       "2019-06-10 12:44:40.716           0.0   \n",
       "2019-06-10 12:49:45.342         300.0   \n",
       "2019-06-10 12:49:50.279           0.0   \n",
       "2019-06-10 12:54:56.025         300.0   \n",
       "\n",
       "                                                              FILELOCATION  \\\n",
       "OBSDATE                                                                      \n",
       "2019-06-07 10:01:03.498  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:01:13.293  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:01:24.237  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:01:32.278  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:01:40.791  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:02:19.518  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:03:27.726  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:03:33.934  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:03:39.997  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:03:44.666  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:03:49.235  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:04:16.775  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:04.607  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:09.805  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:14.460  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:19.206  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:24.836  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:07:43.677  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:14:44.492  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:14:50.409  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:14:59.449  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:15:04.068  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:15:09.212  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:15:27.659  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:24:02.948  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:24:08.322  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:24:13.389  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:24:19.862  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:24:24.864  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-07 10:25:09.005  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "...                                                                    ...   \n",
       "2019-06-10 11:48:59.361  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:04.939  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:09.876  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:14.811  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:22.001  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:41.314  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:46.278  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:49:51.463  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:50:00.474  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:55:06.132  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 11:55:12.117  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:00:17.060  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:00:22.399  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:05:27.022  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:36:25.180  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:36:44.399  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:36:55.628  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:37:00.649  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:37:09.982  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:37:24.232  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:38:20.063  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:38:25.588  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:39:19.051  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:39:24.956  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:39:29.818  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:44:35.400  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:44:40.716  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:49:45.342  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:49:50.279  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "2019-06-10 12:54:56.025  /gpfs/slac/lsst/fs3/g/data/rawData/focal-plane...   \n",
       "\n",
       "                              IMAGETAG IMGTYPE                 OBSDATE  \\\n",
       "OBSDATE                                                                  \n",
       "2019-06-07 10:01:03.498  1559926863498    BIAS 2019-06-07 10:01:03.498   \n",
       "2019-06-07 10:01:13.293  1559926873293    BIAS 2019-06-07 10:01:13.293   \n",
       "2019-06-07 10:01:24.237  1559926884237    BIAS 2019-06-07 10:01:24.237   \n",
       "2019-06-07 10:01:32.278  1559926892278    BIAS 2019-06-07 10:01:32.278   \n",
       "2019-06-07 10:01:40.791  1559926900791    BIAS 2019-06-07 10:01:40.791   \n",
       "2019-06-07 10:02:19.518  1559926939518    SPOT 2019-06-07 10:02:19.518   \n",
       "2019-06-07 10:03:27.726  1559927007726    BIAS 2019-06-07 10:03:27.726   \n",
       "2019-06-07 10:03:33.934  1559927013934    BIAS 2019-06-07 10:03:33.934   \n",
       "2019-06-07 10:03:39.997  1559927019997    BIAS 2019-06-07 10:03:39.997   \n",
       "2019-06-07 10:03:44.666  1559927024666    BIAS 2019-06-07 10:03:44.666   \n",
       "2019-06-07 10:03:49.235  1559927029235    BIAS 2019-06-07 10:03:49.235   \n",
       "2019-06-07 10:04:16.775  1559927056775    SPOT 2019-06-07 10:04:16.775   \n",
       "2019-06-07 10:07:04.607  1559927224607    BIAS 2019-06-07 10:07:04.607   \n",
       "2019-06-07 10:07:09.805  1559927229805    BIAS 2019-06-07 10:07:09.805   \n",
       "2019-06-07 10:07:14.460  1559927234460    BIAS 2019-06-07 10:07:14.460   \n",
       "2019-06-07 10:07:19.206  1559927239206    BIAS 2019-06-07 10:07:19.206   \n",
       "2019-06-07 10:07:24.836  1559927244836    BIAS 2019-06-07 10:07:24.836   \n",
       "2019-06-07 10:07:43.677  1559927263677    SPOT 2019-06-07 10:07:43.677   \n",
       "2019-06-07 10:14:44.492  1559927684492    BIAS 2019-06-07 10:14:44.492   \n",
       "2019-06-07 10:14:50.409  1559927690409    BIAS 2019-06-07 10:14:50.409   \n",
       "2019-06-07 10:14:59.449  1559927699449    BIAS 2019-06-07 10:14:59.449   \n",
       "2019-06-07 10:15:04.068  1559927704068    BIAS 2019-06-07 10:15:04.068   \n",
       "2019-06-07 10:15:09.212  1559927709212    BIAS 2019-06-07 10:15:09.212   \n",
       "2019-06-07 10:15:27.659  1559927727659    SPOT 2019-06-07 10:15:27.659   \n",
       "2019-06-07 10:24:02.948  1559928242948    BIAS 2019-06-07 10:24:02.948   \n",
       "2019-06-07 10:24:08.322  1559928248322    BIAS 2019-06-07 10:24:08.322   \n",
       "2019-06-07 10:24:13.389  1559928253389    BIAS 2019-06-07 10:24:13.389   \n",
       "2019-06-07 10:24:19.862  1559928259862    BIAS 2019-06-07 10:24:19.862   \n",
       "2019-06-07 10:24:24.864  1559928264864    BIAS 2019-06-07 10:24:24.864   \n",
       "2019-06-07 10:25:09.005  1559928309005    SPOT 2019-06-07 10:25:09.005   \n",
       "...                                ...     ...                     ...   \n",
       "2019-06-10 11:48:59.361  1560192539361    BIAS 2019-06-10 11:48:59.361   \n",
       "2019-06-10 11:49:04.939  1560192544939    BIAS 2019-06-10 11:49:04.939   \n",
       "2019-06-10 11:49:09.876  1560192549876    BIAS 2019-06-10 11:49:09.876   \n",
       "2019-06-10 11:49:14.811  1560192554811    BIAS 2019-06-10 11:49:14.811   \n",
       "2019-06-10 11:49:22.001  1560192562001    BIAS 2019-06-10 11:49:22.001   \n",
       "2019-06-10 11:49:41.314  1560192581314    BIAS 2019-06-10 11:49:41.314   \n",
       "2019-06-10 11:49:46.278  1560192586278    BIAS 2019-06-10 11:49:46.278   \n",
       "2019-06-10 11:49:51.463  1560192591463    BIAS 2019-06-10 11:49:51.463   \n",
       "2019-06-10 11:50:00.474  1560192600474    BIAS 2019-06-10 11:50:00.474   \n",
       "2019-06-10 11:55:06.132  1560192906132    DARK 2019-06-10 11:55:06.132   \n",
       "2019-06-10 11:55:12.117  1560192912117    BIAS 2019-06-10 11:55:12.117   \n",
       "2019-06-10 12:00:17.060  1560193217060    DARK 2019-06-10 12:00:17.060   \n",
       "2019-06-10 12:00:22.399  1560193222399    BIAS 2019-06-10 12:00:22.399   \n",
       "2019-06-10 12:05:27.022  1560193527022    DARK 2019-06-10 12:05:27.022   \n",
       "2019-06-10 12:36:25.180  1560195385180    BIAS 2019-06-10 12:36:25.180   \n",
       "2019-06-10 12:36:44.399  1560195404399    BIAS 2019-06-10 12:36:44.399   \n",
       "2019-06-10 12:36:55.628  1560195415628    BIAS 2019-06-10 12:36:55.628   \n",
       "2019-06-10 12:37:00.649  1560195420649    BIAS 2019-06-10 12:37:00.649   \n",
       "2019-06-10 12:37:09.982  1560195429982    BIAS 2019-06-10 12:37:09.982   \n",
       "2019-06-10 12:37:24.232  1560195444232    BIAS 2019-06-10 12:37:24.232   \n",
       "2019-06-10 12:38:20.063  1560195500063    BIAS 2019-06-10 12:38:20.063   \n",
       "2019-06-10 12:38:25.588  1560195505588    BIAS 2019-06-10 12:38:25.588   \n",
       "2019-06-10 12:39:19.051  1560195559051    BIAS 2019-06-10 12:39:19.051   \n",
       "2019-06-10 12:39:24.956  1560195564956    BIAS 2019-06-10 12:39:24.956   \n",
       "2019-06-10 12:39:29.818  1560195569818    BIAS 2019-06-10 12:39:29.818   \n",
       "2019-06-10 12:44:35.400  1560195875400    DARK 2019-06-10 12:44:35.400   \n",
       "2019-06-10 12:44:40.716  1560195880716    BIAS 2019-06-10 12:44:40.716   \n",
       "2019-06-10 12:49:45.342  1560196185342    DARK 2019-06-10 12:49:45.342   \n",
       "2019-06-10 12:49:50.279  1560196190279    BIAS 2019-06-10 12:49:50.279   \n",
       "2019-06-10 12:54:56.025  1560196496025    DARK 2019-06-10 12:54:56.025   \n",
       "\n",
       "                         RAFTMASK RUNNUMBER   TESTTYPE  TSEQNUM TSTAND  \\\n",
       "OBSDATE                                                                  \n",
       "2019-06-07 10:01:03.498      4128    (null)       BIAS        0    BOT   \n",
       "2019-06-07 10:01:13.293      4128    (null)       BIAS        1    BOT   \n",
       "2019-06-07 10:01:24.237      4128    (null)       BIAS        2    BOT   \n",
       "2019-06-07 10:01:32.278      4128    (null)       BIAS        3    BOT   \n",
       "2019-06-07 10:01:40.791      4128    (null)       BIAS        4    BOT   \n",
       "2019-06-07 10:02:19.518      4128    (null)  SPOT_FLAT        0    BOT   \n",
       "2019-06-07 10:03:27.726      4128    (null)       BIAS        0    BOT   \n",
       "2019-06-07 10:03:33.934      4128    (null)       BIAS        1    BOT   \n",
       "2019-06-07 10:03:39.997      4128    (null)       BIAS        2    BOT   \n",
       "2019-06-07 10:03:44.666      4128    (null)       BIAS        3    BOT   \n",
       "2019-06-07 10:03:49.235      4128    (null)       BIAS        4    BOT   \n",
       "2019-06-07 10:04:16.775      4128    (null)  SPOT_FLAT        0    BOT   \n",
       "2019-06-07 10:07:04.607      4128    (null)       BIAS        0    BOT   \n",
       "2019-06-07 10:07:09.805      4128    (null)       BIAS        1    BOT   \n",
       "2019-06-07 10:07:14.460      4128    (null)       BIAS        2    BOT   \n",
       "2019-06-07 10:07:19.206      4128    (null)       BIAS        3    BOT   \n",
       "2019-06-07 10:07:24.836      4128    (null)       BIAS        4    BOT   \n",
       "2019-06-07 10:07:43.677      4128    (null)  SPOT_FLAT        0    BOT   \n",
       "2019-06-07 10:14:44.492      4128    (null)       BIAS        0    BOT   \n",
       "2019-06-07 10:14:50.409      4128    (null)       BIAS        1    BOT   \n",
       "2019-06-07 10:14:59.449      4128    (null)       BIAS        2    BOT   \n",
       "2019-06-07 10:15:04.068      4128    (null)       BIAS        3    BOT   \n",
       "2019-06-07 10:15:09.212      4128    (null)       BIAS        4    BOT   \n",
       "2019-06-07 10:15:27.659      4128    (null)  SPOT_FLAT        0    BOT   \n",
       "2019-06-07 10:24:02.948      4128    (null)       BIAS        0    BOT   \n",
       "2019-06-07 10:24:08.322      4128    (null)       BIAS        1    BOT   \n",
       "2019-06-07 10:24:13.389      4128    (null)       BIAS        2    BOT   \n",
       "2019-06-07 10:24:19.862      4128    (null)       BIAS        3    BOT   \n",
       "2019-06-07 10:24:24.864      4128    (null)       BIAS        4    BOT   \n",
       "2019-06-07 10:25:09.005      4128    (null)  SPOT_FLAT        0    BOT   \n",
       "...                           ...       ...        ...      ...    ...   \n",
       "2019-06-10 11:48:59.361      4128     6695D       BIAS        2    BOT   \n",
       "2019-06-10 11:49:04.939      4128     6695D       BIAS        3    BOT   \n",
       "2019-06-10 11:49:09.876      4128     6695D       BIAS        4    BOT   \n",
       "2019-06-10 11:49:14.811      4128     6695D       BIAS        5    BOT   \n",
       "2019-06-10 11:49:22.001      4128     6695D       BIAS        6    BOT   \n",
       "2019-06-10 11:49:41.314      4128     6695D       BIAS        7    BOT   \n",
       "2019-06-10 11:49:46.278      4128     6695D       BIAS        8    BOT   \n",
       "2019-06-10 11:49:51.463      4128     6695D       BIAS        9    BOT   \n",
       "2019-06-10 11:50:00.474      4128     6695D       DARK        0    BOT   \n",
       "2019-06-10 11:55:06.132      4128     6695D       DARK        1    BOT   \n",
       "2019-06-10 11:55:12.117      4128     6695D       DARK        2    BOT   \n",
       "2019-06-10 12:00:17.060      4128     6695D       DARK        3    BOT   \n",
       "2019-06-10 12:00:22.399      4128     6695D       DARK        4    BOT   \n",
       "2019-06-10 12:05:27.022      4128     6695D       DARK        5    BOT   \n",
       "2019-06-10 12:36:25.180      4128     6696D       BIAS        0    BOT   \n",
       "2019-06-10 12:36:44.399      4128     6696D       BIAS        1    BOT   \n",
       "2019-06-10 12:36:55.628      4128     6696D       BIAS        2    BOT   \n",
       "2019-06-10 12:37:00.649      4128     6696D       BIAS        3    BOT   \n",
       "2019-06-10 12:37:09.982      4128     6696D       BIAS        4    BOT   \n",
       "2019-06-10 12:37:24.232      4128     6696D       BIAS        5    BOT   \n",
       "2019-06-10 12:38:20.063      4128     6696D       BIAS        6    BOT   \n",
       "2019-06-10 12:38:25.588      4128     6696D       BIAS        7    BOT   \n",
       "2019-06-10 12:39:19.051      4128     6696D       BIAS        8    BOT   \n",
       "2019-06-10 12:39:24.956      4128     6696D       BIAS        9    BOT   \n",
       "2019-06-10 12:39:29.818      4128     6696D       DARK        0    BOT   \n",
       "2019-06-10 12:44:35.400      4128     6696D       DARK        1    BOT   \n",
       "2019-06-10 12:44:40.716      4128     6696D       DARK        2    BOT   \n",
       "2019-06-10 12:49:45.342      4128     6696D       DARK        3    BOT   \n",
       "2019-06-10 12:49:50.279      4128     6696D       DARK        4    BOT   \n",
       "2019-06-10 12:54:56.025      4128     6696D       DARK        5    BOT   \n",
       "\n",
       "                             ACTIVE  \n",
       "OBSDATE                              \n",
       "2019-06-07 10:01:03.498    2.267000  \n",
       "2019-06-07 10:01:13.293    2.019000  \n",
       "2019-06-07 10:01:24.237    2.021000  \n",
       "2019-06-07 10:01:32.278    2.023000  \n",
       "2019-06-07 10:01:40.791    2.024000  \n",
       "2019-06-07 10:02:19.518   19.621000  \n",
       "2019-06-07 10:03:27.726    2.053000  \n",
       "2019-06-07 10:03:33.934    2.021000  \n",
       "2019-06-07 10:03:39.997    2.023000  \n",
       "2019-06-07 10:03:44.666    2.022000  \n",
       "2019-06-07 10:03:49.235    2.020000  \n",
       "2019-06-07 10:04:16.775   16.824000  \n",
       "2019-06-07 10:07:04.607    2.056000  \n",
       "2019-06-07 10:07:09.805    2.022000  \n",
       "2019-06-07 10:07:14.460    2.022000  \n",
       "2019-06-07 10:07:19.206    2.020000  \n",
       "2019-06-07 10:07:24.836    2.022000  \n",
       "2019-06-07 10:07:43.677   14.778000  \n",
       "2019-06-07 10:14:44.492    2.041000  \n",
       "2019-06-07 10:14:50.409    2.021000  \n",
       "2019-06-07 10:14:59.449    2.023000  \n",
       "2019-06-07 10:15:04.068    2.018000  \n",
       "2019-06-07 10:15:09.212    2.018000  \n",
       "2019-06-07 10:15:27.659   14.762000  \n",
       "2019-06-07 10:24:02.948    2.050000  \n",
       "2019-06-07 10:24:08.322    2.019000  \n",
       "2019-06-07 10:24:13.389    2.021000  \n",
       "2019-06-07 10:24:19.862    2.023000  \n",
       "2019-06-07 10:24:24.864    2.024000  \n",
       "2019-06-07 10:25:09.005   14.770000  \n",
       "...                             ...  \n",
       "2019-06-10 11:48:59.361    2.024000  \n",
       "2019-06-10 11:49:04.939    2.020000  \n",
       "2019-06-10 11:49:09.876    2.032000  \n",
       "2019-06-10 11:49:14.811    2.027000  \n",
       "2019-06-10 11:49:22.001    2.022000  \n",
       "2019-06-10 11:49:41.314    2.027000  \n",
       "2019-06-10 11:49:46.278    2.028000  \n",
       "2019-06-10 11:49:51.463    2.022000  \n",
       "2019-06-10 11:50:00.474    2.028000  \n",
       "2019-06-10 11:55:06.132  302.029999  \n",
       "2019-06-10 11:55:12.117    2.027000  \n",
       "2019-06-10 12:00:17.060  302.022003  \n",
       "2019-06-10 12:00:22.399    2.311000  \n",
       "2019-06-10 12:05:27.022  302.110992  \n",
       "2019-06-10 12:36:25.180    2.078000  \n",
       "2019-06-10 12:36:44.399    2.019000  \n",
       "2019-06-10 12:36:55.628    2.019000  \n",
       "2019-06-10 12:37:00.649    2.028000  \n",
       "2019-06-10 12:37:09.982    2.021000  \n",
       "2019-06-10 12:37:24.232    2.021000  \n",
       "2019-06-10 12:38:20.063    2.042000  \n",
       "2019-06-10 12:38:25.588    2.040000  \n",
       "2019-06-10 12:39:19.051    2.022000  \n",
       "2019-06-10 12:39:24.956    2.020000  \n",
       "2019-06-10 12:39:29.818    2.023000  \n",
       "2019-06-10 12:44:35.400  302.028015  \n",
       "2019-06-10 12:44:40.716    2.018000  \n",
       "2019-06-10 12:49:45.342  302.024994  \n",
       "2019-06-10 12:49:50.279    2.020000  \n",
       "2019-06-10 12:54:56.025  302.023987  \n",
       "\n",
       "[13897 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.loc[t1:t2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2 = images.loc[t1:t2].groupby(pd.Grouper(freq='5Min'))[[\"ACTIVE\"]].count().plot(ax=ax, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4f6c955208>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZuBNhOYzjSo7L2D4Joj+w5sAazCyZieLsYnx26DNMcUzBiNwRfd0sYpAw4O2IhEJ9mxcG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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f6c4de438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "images.loc[t1:t2].groupby(pd.Grouper(freq='5Min'))[[\"ACTIVE\"]].count().plot(ax=ax2, figsize=(14,8), color='g')\n",
    "ax2.set_ylabel(\"# triggers\")\n",
    "ax1.set_ylabel(\"REB Temperature\")\n",
    "df.plot(ax=ax1)\n",
    "ax1.legend(loc='upper left')"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}