watch_temperature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "variable-retirement",
   "metadata": {},
   "source": [
    "# Watch a temperature : scan temperature *v* time\n",
    "\n",
    "*APS Training for Bluesky Data Acquisition*.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Measure the temperature for a short period and compute basics measures of the collected readings.\n",
    "\n",
    "## Start the `instrument` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acoustic-touch",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Tue-11:37:39 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/iocs/check_iocs.py\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/.logs/ipython_console.log\n",
      "Mode           : rotate\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Tue-11:37:39 - EPICS IOCs ready...\n",
      "I Tue-11:37:39 - #### Bluesky Framework ####\n",
      "I Tue-11:37:39 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/framework/check_python.py\n",
      "I Tue-11:37:39 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/framework/check_bluesky.py\n",
      "I Tue-11:37:40 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/framework/initialize.py\n",
      "I Tue-11:37:40 - using databroker catalog 'training'\n",
      "I Tue-11:37:40 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/framework/metadata.py\n",
      "I Tue-11:37:40 - #### Devices ####\n",
      "I Tue-11:37:40 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/area_detector.py\n",
      "I Tue-11:37:40 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/calculation_records.py\n",
      "I Tue-11:37:42 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/ioc_stats.py\n",
      "I Tue-11:37:42 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/motors.py\n",
      "I Tue-11:37:43 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/noisy_detector.py\n",
      "I Tue-11:37:43 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/scaler.py\n",
      "I Tue-11:37:45 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/shutter_simulator.py\n",
      "I Tue-11:37:45 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/devices/temperature_signal.py\n",
      "I Tue-11:37:45 - #### Callbacks ####\n",
      "I Tue-11:37:45 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/callbacks/spec_data_file_writer.py\n",
      "I Tue-11:37:45 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/20210413-113745.dat\n",
      "I Tue-11:37:45 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Tue-11:37:45 -    file will be created when bluesky ends its next scan\n",
      "I Tue-11:37:45 -    to change SPEC file, use command:   newSpecFile('title')\n",
      "I Tue-11:37:45 - #### Plans ####\n",
      "I Tue-11:37:45 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/plans/peak_finder_example.py\n",
      "I Tue-11:37:45 - #### Utilities ####\n",
      "I Tue-11:37:45 - /home/prjemian/Documents/projects/BCDA-APS/epics-bluesky-vm/instrument/utils/image_analysis.py\n",
      "I Tue-11:37:45 - #### Startup is complete. ####\n"
     ]
    }
   ],
   "source": [
    "from instrument.collection import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latin-intermediate",
   "metadata": {},
   "source": [
    "Add the name of this notebook as metadata to every run.  This is done by adding to the RunEngine's metadata dictionary (`RE.md`), content that will be added to the *start* document of every run.  The metadata is useful documentation about a run and can be used for several purposes, such as to record a general condition (such as the name of this notebook) or to identify these runs from a database search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "express-craps",
   "metadata": {},
   "outputs": [],
   "source": [
    "RE.md[\"notebook\"] = \"watch_temperature\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unknown-capability",
   "metadata": {},
   "source": [
    "## Describe\n",
    "\n",
    "Note the initial settings of the `temperature` (controller).  It's actually an EPICS *swait* record that simulates a temperature sensor and controller.  It has a setpoint, a readback, and a few other support attributes as shown.  A Random noise level is part of the simulation.  We'll compute that noise level from the standard deviation of the measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "jewish-manchester",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== ===== ==========================\n",
      "name                 value timestamp                 \n",
      "==================== ===== ==========================\n",
      "temperature_setpoint 25.0  2021-04-13 11:37:45.747165\n",
      "temperature          25.0  2021-04-13 11:37:45.748654\n",
      "==================== ===== ==========================\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pyRestTable.rest_table.Table at 0x7f66b6c934f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listdevice(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "happy-links",
   "metadata": {},
   "source": [
    "## Read\n",
    "\n",
    "The `temperature` setpoint can be used just as if it was a motor (it is based on the [`ophyd.PVPositioner`](https://blueskyproject.io/ophyd/positioners.html?highlight=pvpositioner#pvpositioner).  The simulator includes both noise and a limited ramp rate.  A move of more than a couple degrees will take a measureable time, simulating a physical system.\n",
    "\n",
    "Get the current temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "unnecessary-organ",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature.position = 25.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"{temperature.position = }\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informed-affect",
   "metadata": {},
   "source": [
    "Use the `.read()` method to get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "satellite-mouth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('temperature_setpoint',\n",
       "              {'value': 25.0, 'timestamp': 1618331865.747165}),\n",
       "             ('temperature', {'value': 25.0, 'timestamp': 1618331865.748654})])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "particular-score",
   "metadata": {},
   "source": [
    "## Move\n",
    "\n",
    "Change the temperature by 10 degrees (and measure how long this took).  Use the *relative* move magic command: `%movr`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "several-guyana",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "temperature:  19%|████▋                    | 1.872/10.0 [00:01<00:05,  1.42C/s]\n",
      "temperature:  41%|██████████▏              | 4.059/10.0 [00:03<00:04,  1.22C/s]\n",
      "temperature:  60%|███████████████          | 6.033/10.0 [00:05<00:03,  1.13C/s]\n",
      "temperature:  80%|████████████████████     | 8.011/10.0 [00:07<00:01,  1.09C/s]\n",
      "temperature: 100%|██████████████████████████| 10.0/10.0 [00:09<00:00,  1.07C/s]\n",
      "temperature [In progress. No progress bar available.]                          \n",
      "                                                                               \n",
      "time.time()-t0 = 9.348 s\n",
      "temperature.position = 35.240901808194096\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t0 = time.time()\n",
    "%movr temperature 10\n",
    "print(f\"{time.time()-t0 = :.3f} s\")\n",
    "print(f\"{temperature.position = }\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hydraulic-midnight",
   "metadata": {},
   "source": [
    "## Count\n",
    "Use the bluesky RunEngine, `RE`, and a standard plan, `bp.count()`, to count the temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "celtic-therapy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 46     Time: 2021-04-13 11:37:55\n",
      "Persistent Unique Scan ID: '1f7de125-d1a1-410d-8cef-cd7c6d2e7f60'\n",
      "New stream: 'baseline'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|   seq_num |       time | temperature_setpoint | temperature |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|         1 | 11:37:55.4 |             35.00000 |    35.24090 |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "generator count ['1f7de125'] (scan num: 46)\n"
     ]
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     "data": {
      "text/plain": [
       "('1f7de125-d1a1-410d-8cef-cd7c6d2e7f60',)"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RE(bp.count([temperature]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "personalized-senate",
   "metadata": {},
   "source": [
    "Take ten readings by adding the `num=10` keyword argument (a.k.a. *kwarg*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "organic-compound",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 47     Time: 2021-04-13 11:37:55\n",
      "Persistent Unique Scan ID: '586a30d3-ae48-4ff3-bf72-4cc3d4f03675'\n",
      "New stream: 'baseline'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|   seq_num |       time | temperature_setpoint | temperature |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|         1 | 11:37:55.9 |             35.00000 |    35.24090 |\n",
      "|         2 | 11:37:56.0 |             35.00000 |    35.24090 |\n",
      "|         3 | 11:37:56.1 |             35.00000 |    35.24090 |\n",
      "|         4 | 11:37:56.2 |             35.00000 |    35.24090 |\n",
      "|         5 | 11:37:56.2 |             35.00000 |    35.24090 |\n",
      "|         6 | 11:37:56.3 |             35.00000 |    35.24090 |\n",
      "|         7 | 11:37:56.4 |             35.00000 |    35.24090 |\n",
      "|         8 | 11:37:56.4 |             35.00000 |    35.24090 |\n",
      "|         9 | 11:37:56.5 |             35.00000 |    35.24090 |\n",
      "|        10 | 11:37:56.6 |             35.00000 |    35.24090 |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "generator count ['586a30d3'] (scan num: 47)\n"
     ]
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       "('586a30d3-ae48-4ff3-bf72-4cc3d4f03675',)"
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\n",
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acquired-australian",
   "metadata": {},
   "source": [
    "Do all the readings look the same?  Perhaps there are only two different values (first one, then the other)?  That's because our measurements were only *reading* the temperature without waiting for it to update.  (If there are two different values, we caught the moment when EPICS updated the calculation.)  The table above showed `temperature_scanning_rate 5` which means EPICS is only updating every 2 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "synthetic-malpractice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2 second'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature.scanning_rate.get(as_string=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reliable-abuse",
   "metadata": {},
   "source": [
    "## Adjust the measurement interval\n",
    "We need to slow down the measurement to this interval by adding the `delay=2` kwarg, sampling every 2 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "devoted-northern",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 48     Time: 2021-04-13 11:37:57\n",
      "Persistent Unique Scan ID: 'd212b165-bcb6-4dd6-96eb-8e024f461220'\n",
      "New stream: 'baseline'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|   seq_num |       time | temperature_setpoint | temperature |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|         1 | 11:37:57.3 |             35.00000 |    35.34834 |\n",
      "|         2 | 11:37:59.3 |             35.00000 |    34.73330 |\n",
      "|         3 | 11:38:01.3 |             35.00000 |    34.58548 |\n",
      "|         4 | 11:38:03.3 |             35.00000 |    34.68346 |\n",
      "|         5 | 11:38:05.3 |             35.00000 |    35.21107 |\n",
      "|         6 | 11:38:07.3 |             35.00000 |    35.37070 |\n",
      "|         7 | 11:38:09.3 |             35.00000 |    35.35594 |\n",
      "|         8 | 11:38:11.3 |             35.00000 |    34.99615 |\n",
      "|         9 | 11:38:13.3 |             35.00000 |    35.13532 |\n",
      "|        10 | 11:38:15.3 |             35.00000 |    34.55788 |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "generator count ['d212b165'] (scan num: 48)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('d212b165-bcb6-4dd6-96eb-8e024f461220',)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=10, delay=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-invitation",
   "metadata": {},
   "source": [
    "Let's reduce the update interval that EPICS uses, to the shortest interval EPICS provides, so our measurements do not take so long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "regulated-practice",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Passive', 'Event', 'I/O Intr', '10 second', '5 second', '2 second', '1 second', '.5 second', '.2 second', '.1 second')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2 second'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the list of possible values\n",
    "print(temperature.scanning_rate.enum_strs)\n",
    "\n",
    "# set the shortest interval, by name\n",
    "temperature.scanning_rate.put(\".1 second\")\n",
    "temperature.scanning_rate.get(as_string=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "executive-cooler",
   "metadata": {},
   "source": [
    "## Collect our measurements\n",
    "Repeat the measurement, this time with more observations.\n",
    "\n",
    "Also, we'll add a metadata key:value pair so that later we can find this data more easily in the database.  Most plans take a `md={}` kwarg where the value is a Python dictionary with the key:value pair(s) to add to the run's metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "worse-wings",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 49     Time: 2021-04-13 11:38:17\n",
      "Persistent Unique Scan ID: '798369e3-0f42-4666-8823-2485e1407e65'\n",
      "New stream: 'baseline'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|   seq_num |       time | temperature_setpoint | temperature |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|         1 | 11:38:17.9 |             35.00000 |    34.78574 |\n",
      "|         2 | 11:38:18.0 |             35.00000 |    34.86652 |\n",
      "|         3 | 11:38:18.1 |             35.00000 |    34.56160 |\n",
      "|         4 | 11:38:18.2 |             35.00000 |    35.23048 |\n",
      "|         5 | 11:38:18.3 |             35.00000 |    34.50099 |\n",
      "|         6 | 11:38:18.4 |             35.00000 |    35.21287 |\n",
      "|         7 | 11:38:18.5 |             35.00000 |    34.50694 |\n",
      "|         8 | 11:38:18.6 |             35.00000 |    35.33565 |\n",
      "|         9 | 11:38:18.7 |             35.00000 |    34.71675 |\n",
      "|        10 | 11:38:18.8 |             35.00000 |    34.97184 |\n",
      "|        11 | 11:38:18.9 |             35.00000 |    35.01128 |\n",
      "|        12 | 11:38:19.0 |             35.00000 |    35.47890 |\n",
      "|        13 | 11:38:19.1 |             35.00000 |    35.31827 |\n",
      "|        14 | 11:38:19.2 |             35.00000 |    34.70710 |\n",
      "|        15 | 11:38:19.3 |             35.00000 |    35.16488 |\n",
      "|        16 | 11:38:19.4 |             35.00000 |    34.93998 |\n",
      "|        17 | 11:38:19.5 |             35.00000 |    35.16937 |\n",
      "|        18 | 11:38:19.6 |             35.00000 |    34.81714 |\n",
      "|        19 | 11:38:19.7 |             35.00000 |    34.86599 |\n",
      "|        20 | 11:38:19.8 |             35.00000 |    34.74289 |\n",
      "|        21 | 11:38:19.9 |             35.00000 |    35.04186 |\n",
      "|        22 | 11:38:20.0 |             35.00000 |    35.35591 |\n",
      "|        23 | 11:38:20.1 |             35.00000 |    34.78096 |\n",
      "|        24 | 11:38:20.2 |             35.00000 |    34.62024 |\n",
      "|        25 | 11:38:20.3 |             35.00000 |    35.01919 |\n",
      "|        26 | 11:38:20.4 |             35.00000 |    34.59580 |\n",
      "|        27 | 11:38:20.5 |             35.00000 |    34.54564 |\n",
      "|        28 | 11:38:20.6 |             35.00000 |    34.65705 |\n",
      "|        29 | 11:38:20.7 |             35.00000 |    35.44003 |\n",
      "|        30 | 11:38:20.8 |             35.00000 |    34.73954 |\n",
      "|        31 | 11:38:20.9 |             35.00000 |    34.89568 |\n",
      "|        32 | 11:38:21.0 |             35.00000 |    35.26320 |\n",
      "|        33 | 11:38:21.1 |             35.00000 |    35.37366 |\n",
      "|        34 | 11:38:21.2 |             35.00000 |    34.98554 |\n",
      "|        35 | 11:38:21.3 |             35.00000 |    35.01705 |\n",
      "|        36 | 11:38:21.4 |             35.00000 |    35.32097 |\n",
      "|        37 | 11:38:21.5 |             35.00000 |    35.21392 |\n",
      "|        38 | 11:38:21.6 |             35.00000 |    35.12098 |\n",
      "|        39 | 11:38:21.7 |             35.00000 |    34.64199 |\n",
      "|        40 | 11:38:21.8 |             35.00000 |    35.35240 |\n",
      "|        41 | 11:38:21.9 |             35.00000 |    35.40088 |\n",
      "|        42 | 11:38:22.0 |             35.00000 |    34.71637 |\n",
      "|        43 | 11:38:22.1 |             35.00000 |    34.66887 |\n",
      "|        44 | 11:38:22.3 |             35.00000 |    34.56860 |\n",
      "|        45 | 11:38:22.4 |             35.00000 |    34.86159 |\n",
      "|        46 | 11:38:22.5 |             35.00000 |    35.00608 |\n",
      "|        47 | 11:38:22.6 |             35.00000 |    34.50230 |\n",
      "|        48 | 11:38:22.7 |             35.00000 |    35.22456 |\n",
      "|        49 | 11:38:22.8 |             35.00000 |    35.42502 |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|   seq_num |       time | temperature_setpoint | temperature |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "|        50 | 11:38:22.9 |             35.00000 |    34.72208 |\n",
      "+-----------+------------+----------------------+-------------+\n",
      "generator count ['798369e3'] (scan num: 49)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('798369e3-0f42-4666-8823-2485e1407e65',)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=50, delay=0.1, md={\"motive\": \"tscan\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-blink",
   "metadata": {},
   "source": [
    "## Get the data (from the database)\n",
    "Let's get the measured data from the database now.  It's the most recent bluesky run, so an index of `-1` will identify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "proprietary-center",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = db.v2[-1].primary.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "remarkable-economics",
   "metadata": {},
   "source": [
    "Let's get the values as a 1-D numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "floral-suicide",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = dataset[\"temperature\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vulnerable-response",
   "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": 15,
   "id": "brutal-andrew",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= ===================\n",
      "measure value              \n",
      "======= ===================\n",
      "max     35.478896772716865 \n",
      "mean    34.95966277561608  \n",
      "dev     0.29564720403532396\n",
      "min     34.50099183642328  \n",
      "#       50                 \n",
      "======= ===================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "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": "advised-southeast",
   "metadata": {},
   "source": [
    "Let's replot that data for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ongoing-adult",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f66b6087280>]"
      ]
     },
     "execution_count": 16,
     "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=\"o\", color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inappropriate-shelter",
   "metadata": {},
   "source": [
    "## Challenges\n",
    "\n",
    "Try these additional modifications or activities.\n",
    "\n",
    "1. Scan using the temperature as a positioner (`bp.scan()`)\n",
    "2. Record temperature values as an additional data stream during a scan."
   ]
  }
 ],
 "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
}