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    "# Accessing data later, after the measurement\n",
    "\n",
    "From *APS Python Training for Bluesky Data Acquisition*.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Get the data from previous *run(s)* and work with it.\n",
    "\n",
    "## Skip the `instrument` package\n",
    "\n",
    "There is no need to import the instrument package since we won't measure anything in this notebook.\n",
    "\n",
    "## Access the database\n",
    "\n",
    "In the instrument package (in file `instrument/framework/initialize.py`), we connected databroker with the MongoDB server using the `class_2021_03` *catalog*. Get that catalog now.  We'll need to import some tools first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28528e86",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "catalog: training\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scan_id</th>\n",
       "      <th>time</th>\n",
       "      <th>plan_name</th>\n",
       "      <th>detectors</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2021-08-19 14:03:33</td>\n",
       "      <td>scan</td>\n",
       "      <td>[vsc16]</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <td>38</td>\n",
       "      <td>2021-08-19 11:45:29</td>\n",
       "      <td>scan</td>\n",
       "      <td>[scaler1]</td>\n",
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       "      <th>6</th>\n",
       "      <td>37</td>\n",
       "      <td>2021-08-19 11:40:40</td>\n",
       "      <td>scan</td>\n",
       "      <td>[noisy]</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36</td>\n",
       "      <td>2021-08-19 11:39:38</td>\n",
       "      <td>scan</td>\n",
       "      <td>[noisy]</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>2021-08-19 11:04:45</td>\n",
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       "      <td>scan</td>\n",
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       "      <td>scan</td>\n",
       "      <td>[vsc16]</td>\n",
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       "      <th>11</th>\n",
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       "      <td>scan</td>\n",
       "      <td>[vsc16]</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-08-19 10:57:09</td>\n",
       "      <td>scan</td>\n",
       "      <td>[vsc16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-08-19 10:53:19</td>\n",
       "      <td>scan</td>\n",
       "      <td>[vsc16]</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35</td>\n",
       "      <td>2021-04-13 12:19:15</td>\n",
       "      <td>count</td>\n",
       "      <td>[temperature]</td>\n",
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       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>2021-04-13 12:18:54</td>\n",
       "      <td>count</td>\n",
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       "      <td>count</td>\n",
       "      <td>[temperature]</td>\n",
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       "      <th>18</th>\n",
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       "      <th>19</th>\n",
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       "      <td>2021-04-11 14:14:58</td>\n",
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      "text/plain": [
       "    scan_id                 time plan_name      detectors\n",
       "0         4  2021-08-19 14:03:33      scan        [vsc16]\n",
       "1         3  2021-08-19 14:03:10      scan        [vsc16]\n",
       "2         2  2021-08-19 14:02:58      scan        [vsc16]\n",
       "3         1  2021-08-19 14:02:41      scan        [vsc16]\n",
       "4        39  2021-08-19 13:39:12      scan        [noisy]\n",
       "5        38  2021-08-19 11:45:29      scan      [scaler1]\n",
       "6        37  2021-08-19 11:40:40      scan        [noisy]\n",
       "7        36  2021-08-19 11:39:38      scan        [noisy]\n",
       "8         4  2021-08-19 11:04:45      scan        [vsc16]\n",
       "9         3  2021-08-19 11:04:08      scan        [vsc16]\n",
       "10        2  2021-08-19 11:00:56      scan        [vsc16]\n",
       "11        1  2021-08-19 10:58:39      scan        [vsc16]\n",
       "12        2  2021-08-19 10:57:09      scan        [vsc16]\n",
       "13        1  2021-08-19 10:53:19      scan        [vsc16]\n",
       "14       35  2021-04-13 12:19:15     count  [temperature]\n",
       "15       34  2021-04-13 12:18:54     count  [temperature]\n",
       "16       33  2021-04-13 12:18:51     count  [temperature]\n",
       "17       32  2021-04-13 12:18:51     count  [temperature]\n",
       "18       31  2021-04-11 14:15:20     count  [temperature]\n",
       "19       30  2021-04-11 14:14:58     count  [temperature]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from apstools.utils import listruns, quantify_md_key_use\n",
    "import databroker\n",
    "from databroker.queries import TimeRange\n",
    "\n",
    "db = databroker.catalog[\"training\"]\n",
    "listruns()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4295908",
   "metadata": {},
   "source": [
    "Our temperature count measurement is one of those listed, but which one?  Recall how we started our run: `RE(bp.count([temperature], num=50, delay=0.1, md={\"motive\": \"tscan\"}))`.  \n",
    "\n",
    "In case some other type of run is more recent, we'll search for the most recent run with this metadata.  First, find any recent (since Feb 2021) runs with this metadata.  We need to do this search with the `v2` version of databroker catalogs.  The result of the search is another catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3bcb5b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search for runs with motive='tscan' metadata found 5 run(s).\n"
     ]
    }
   ],
   "source": [
    "cat = db.v2.search(TimeRange(since=\"2021-02-02\")).search({\"motive\": \"tscan\"})\n",
    "print(f\"Search for runs with motive='tscan' metadata found {len(cat)} run(s).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de33ff9f",
   "metadata": {},
   "source": [
    "Show the run's metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d2836de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'start': Start({'beamline_id': 'APS EPICS/Python/Bluesky training VM',\n",
       " 'detectors': ['temperature'],\n",
       " 'hints': {'dimensions': [[['time'], 'primary']]},\n",
       " 'instrument_name': 'training',\n",
       " 'login_id': 'apsu@apsu-sim',\n",
       " 'motive': 'tscan',\n",
       " 'notebook': 'watch_temperature',\n",
       " 'num_intervals': 49,\n",
       " 'num_points': 50,\n",
       " 'pid': 3432,\n",
       " 'plan_args': {'detectors': [\"MyPvPositioner(prefix='gp:userCalc8', \"\n",
       "                             \"name='temperature', settle_time=0.0, \"\n",
       "                             \"timeout=None, read_attrs=['setpoint', \"\n",
       "                             \"'readback'], configuration_attrs=['calculation', \"\n",
       "                             \"'description', 'max_change', 'noise', \"\n",
       "                             \"'previous_value_pv', 'scanning_rate', \"\n",
       "                             \"'tolerance'], limits=(-20, 255), egu='C')\"],\n",
       "               'num': 50},\n",
       " 'plan_name': 'count',\n",
       " 'plan_type': 'generator',\n",
       " 'proposal_id': 'training-2021-04',\n",
       " 'scan_id': 35,\n",
       " 'time': 1618334355.5878208,\n",
       " 'uid': 'c0bbb846-43ae-46c2-b9e4-319a4c162812',\n",
       " 'versions': {'apstools': '1.5.0',\n",
       "              'bluesky': '1.6.7',\n",
       "              'databroker': '1.2.3',\n",
       "              'epics': '3.4.3',\n",
       "              'h5py': '2.10.0',\n",
       "              'intake': '0.6.2',\n",
       "              'matplotlib': '3.3.4',\n",
       "              'numpy': '1.19.2',\n",
       "              'ophyd': '1.6.1',\n",
       "              'pyRestTable': '2020.0.3',\n",
       "              'spec2nexus': '2021.1.8'}}),\n",
       " 'stop': Stop({'exit_status': 'success',\n",
       " 'num_events': {'baseline': 2, 'primary': 50},\n",
       " 'reason': '',\n",
       " 'run_start': 'c0bbb846-43ae-46c2-b9e4-319a4c162812',\n",
       " 'time': 1618334361.5295637,\n",
       " 'uid': '38f1deba-2c64-4faf-a0b7-30a50c6869ca'}),\n",
       " 'catalog_dir': None}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if len(cat):\n",
    "    run = cat[-1]\n",
    "else:\n",
    "    raise KeyError(\"No runs found with `motive='tscan'`\")\n",
    "\n",
    "run.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d052e7",
   "metadata": {},
   "source": [
    "## Read the primary data stream of the run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b5a1f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = run.primary.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faeca1ca",
   "metadata": {},
   "outputs": [
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       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (time: 50)\n",
       "Coordinates:\n",
       "  * time                  (time) float64 1.618e+09 1.618e+09 ... 1.618e+09\n",
       "Data variables:\n",
       "    temperature_setpoint  (time) float64 35.0 35.0 35.0 35.0 ... 35.0 35.0 35.0\n",
       "    temperature           (time) float64 34.89 35.01 35.19 ... 35.02 34.62 35.38</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d51a71b1-d4f3-4106-95ba-bbae05f4f72e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d51a71b1-d4f3-4106-95ba-bbae05f4f72e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc2aecd3-f5ed-4390-bccb-df13db15eb4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc2aecd3-f5ed-4390-bccb-df13db15eb4c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.618e+09 1.618e+09 ... 1.618e+09</div><input id='attrs-51ed0f83-0a3d-4b7a-8963-0440220bc421' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51ed0f83-0a3d-4b7a-8963-0440220bc421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0288218f-d5ae-42a9-bc29-52395912ffe6' class='xr-var-data-in' type='checkbox'><label for='data-0288218f-d5ae-42a9-bc29-52395912ffe6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09,\n",
       "       1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09, 1.618334e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eeecbd08-0bec-43e3-b8c7-3325ddbaa148' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eeecbd08-0bec-43e3-b8c7-3325ddbaa148' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temperature_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.0 35.0 35.0 ... 35.0 35.0 35.0</div><input id='attrs-c8dd7f1e-06b2-4c49-bce3-6f233ee6fce6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8dd7f1e-06b2-4c49-bce3-6f233ee6fce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d614dd63-4df2-43b8-a92c-d9c66f515c0b' class='xr-var-data-in' type='checkbox'><label for='data-d614dd63-4df2-43b8-a92c-d9c66f515c0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>temperature</dd></dl></div><div class='xr-var-data'><pre>array([35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35.,\n",
       "       35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35.,\n",
       "       35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35.,\n",
       "       35., 35., 35., 35., 35., 35., 35., 35., 35., 35., 35.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.89 35.01 35.19 ... 34.62 35.38</div><input id='attrs-a896b679-868c-4232-a86f-699b474e7013' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a896b679-868c-4232-a86f-699b474e7013' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e5e2809-d3f4-41a2-9d1f-af807ded91e8' class='xr-var-data-in' type='checkbox'><label for='data-6e5e2809-d3f4-41a2-9d1f-af807ded91e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>temperature</dd></dl></div><div class='xr-var-data'><pre>array([34.8896849 , 35.01305409, 35.19233234, 35.0216907 , 35.43205158,\n",
       "       34.5056611 , 35.37074083, 34.51771572, 34.85019455, 34.82137026,\n",
       "       35.3455024 , 34.82780957, 35.21680781, 34.54200809, 35.08982223,\n",
       "       34.87608911, 35.03902495, 35.00504311, 35.42178225, 34.76303502,\n",
       "       34.91893645, 34.9121767 , 34.5496376 , 34.78567941, 34.63224994,\n",
       "       35.42848096, 35.0320058 , 35.24485389, 35.22823682, 35.06241703,\n",
       "       34.86455329, 34.54406806, 35.24770733, 34.91025406, 34.60228122,\n",
       "       35.48713664, 34.94979019, 35.21024643, 34.94386969, 35.1342565 ,\n",
       "       35.12812238, 34.58931106, 34.60415808, 35.36431678, 34.6698024 ,\n",
       "       34.99550622, 35.29185168, 35.02181277, 34.61917296, 35.3817731 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cbc48871-480c-4bf9-a8b8-6ac7d2a84815' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cbc48871-480c-4bf9-a8b8-6ac7d2a84815' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (time: 50)\n",
       "Coordinates:\n",
       "  * time                  (time) float64 1.618e+09 1.618e+09 ... 1.618e+09\n",
       "Data variables:\n",
       "    temperature_setpoint  (time) float64 35.0 35.0 35.0 35.0 ... 35.0 35.0 35.0\n",
       "    temperature           (time) float64 34.89 35.01 35.19 ... 35.02 34.62 35.38"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b64a13",
   "metadata": {},
   "source": [
    "Let's get the values as a 1-D numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2b41a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = dataset[\"temperature\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f8f94b",
   "metadata": {},
   "source": [
    "## Compute the statistics\n",
    "Use the methods of numpy arrays to compute maximum, mean, standard deviation, and minimum.  Looks nicer in a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d19bbd9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= ===================\n",
      "measure value              \n",
      "======= ===================\n",
      "max     35.48713664454108  \n",
      "mean    34.98192172121767  \n",
      "dev     0.28183495874527975\n",
      "min     34.50566109712368  \n",
      "#       50                 \n",
      "======= ===================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyRestTable\n",
    "\n",
    "table = pyRestTable.Table()\n",
    "table.addLabel(\"measure\")\n",
    "table.addLabel(\"value\")\n",
    "table.addRow((\"max\", T.max()))\n",
    "table.addRow((\"mean\", T.mean()))\n",
    "table.addRow((\"dev\", T.std()))\n",
    "table.addRow((\"min\", T.min()))\n",
    "table.addRow((\"#\", len(T)))\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aae4d45",
   "metadata": {},
   "source": [
    "Let's replot that data for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "025839fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb18671baf0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[\"temperature\"].plot(marker=\"+\", color=\"purple\")"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}