{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "invisible-desire",
   "metadata": {},
   "source": [
    "# Custom bluesky plan\n",
    "\n",
    "*APS Training for Bluesky Data Acquisition*.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Build a custom plan for an up-down scan using `m2` and `scaler1` channels `I0` and `diode`.  Make the counting time configurable.  Accept user metadata.\n",
    "\n",
    "## Preparation\n",
    "\n",
    "First, start the instrument package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "negative-unemployment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/_iconfig.py\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/.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 Fri-15:54:55 - ############################################################ startup\n",
      "I Fri-15:54:55 - logging started\n",
      "I Fri-15:54:55 - logging level = 10\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/session_logs.py\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/collection.py\n",
      "I Fri-15:54:55 - CONDA_PREFIX = /home/prjemian/.conda/envs/bluesky_2023_2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Fri-15:54:55 - xmode exception level: 'Minimal'\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/mpl/notebook.py\n",
      "I Fri-15:54:55 - #### Bluesky Framework ####\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/framework/check_python.py\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/framework/check_bluesky.py\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/framework/initialize.py\n",
      "I Fri-15:54:55 - RunEngine metadata saved in directory: /home/prjemian/Bluesky_RunEngine_md\n",
      "I Fri-15:54:55 - using databroker catalog 'training'\n",
      "I Fri-15:54:55 - using ophyd control layer: pyepics\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/framework/metadata.py\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/epics_signal_config.py\n",
      "I Fri-15:54:55 - Using RunEngine metadata for scan_id\n",
      "I Fri-15:54:55 - #### Devices ####\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/area_detector.py\n",
      "I Fri-15:54:55 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/calculation_records.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/fourc_diffractometer.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/ioc_stats.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/kohzu_monochromator.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/motors.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/noisy_detector.py\n",
      "I Fri-15:54:58 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/scaler.py\n",
      "I Fri-15:54:59 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/shutter_simulator.py\n",
      "I Fri-15:54:59 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/simulated_fourc.py\n",
      "I Fri-15:54:59 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/simulated_kappa.py\n",
      "I Fri-15:54:59 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/slits.py\n",
      "I Fri-15:54:59 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/sixc_diffractometer.py\n",
      "I Fri-15:55:00 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/devices/temperature_signal.py\n",
      "I Fri-15:55:00 - #### Callbacks ####\n",
      "I Fri-15:55:00 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/callbacks/spec_data_file_writer.py\n",
      "I Fri-15:55:00 - #### Plans ####\n",
      "I Fri-15:55:00 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/plans/lup_plan.py\n",
      "I Fri-15:55:00 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/plans/peak_finder_example.py\n",
      "I Fri-15:55:00 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/instrument/utils/image_analysis.py\n",
      "I Fri-15:55:00 - #### Utilities ####\n",
      "I Fri-15:55:00 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/bluesky/20230414-155500.dat\n",
      "I Fri-15:55:00 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Fri-15:55:00 -    file will be created when bluesky ends its next scan\n",
      "I Fri-15:55:00 -    to change SPEC file, use command:   newSpecFile('title')\n",
      "I Fri-15:55:00 - #### Startup is complete. ####\n"
     ]
    }
   ],
   "source": [
    "import pathlib, sys\n",
    "sys.path.append(str(pathlib.Path.home() / \"bluesky\"))\n",
    "from instrument.collection import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggressive-peeing",
   "metadata": {},
   "source": [
    "## Select scaler channels\n",
    "The scaler channels are selected by calling its `.select_channels()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "national-rebel",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler1.select_channels([\"I0\", \"diode\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjacent-vintage",
   "metadata": {},
   "source": [
    "## Count time\n",
    "The count time is in the `.preset_time` attribute.  The way to set the scaler with this time is to use ophyd's staging process.  Add `preset_time` to the `.stage_sigs` dictionary with the desired count time as the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "steady-democrat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('preset_time', 1)])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler1.stage_sigs[\"preset_time\"] = 1\n",
    "scaler1.stage_sigs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promotional-bracket",
   "metadata": {},
   "source": [
    "# Construct the plan\n",
    "Create a plan that scans up, then scans down (2 scans total).  Add `temperature` just to get some other interesting data.  We'll add a metadata key for the scan direction.\n",
    "\n",
    "We'll do this in small steps starting with the innermost part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stretch-johnston",
   "metadata": {},
   "outputs": [],
   "source": [
    "def up_down_once(lo, hi, num, md={}):\n",
    "    dets = [scaler1, temperature]\n",
    "    _md = {}\n",
    "    _md.update(md)\n",
    "    _md[\"direction\"] = \"down\"\n",
    "    yield from bp.scan(dets, m2, lo, hi, num, md=_md)\n",
    "    _md[\"direction\"] = \"down\"\n",
    "    yield from bp.scan(dets, m2, hi, lo, num, md=_md)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "associate-reading",
   "metadata": {},
   "source": [
    "Verify that this code will process through the RunEngine by testing it without running it.  Use the `summarize_plan()` function imported from the `bluesky` package.  This will show the steps of the measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "proved-projector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== Open Run ===================================\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n"
     ]
    }
   ],
   "source": [
    "summarize_plan(up_down_once(0, 1, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lesbian-thermal",
   "metadata": {},
   "source": [
    "Next, build a plan that runs this twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "inner-component",
   "metadata": {},
   "outputs": [],
   "source": [
    "def up_down_twice(lo, hi, num, md={}):\n",
    "    for i in range(2):\n",
    "        yield from up_down_once(lo, hi, num, md=md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "chinese-mainland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== Open Run ===================================\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n"
     ]
    }
   ],
   "source": [
    "summarize_plan(up_down_twice(0, 1, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "middle-portable",
   "metadata": {},
   "source": [
    "Finish the custom plan by setting the count time.  Give a default of 1.0 s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "automatic-greensboro",
   "metadata": {},
   "outputs": [],
   "source": [
    "def customScan(lo=0, hi=1, num=5, ct=1, md={}):\n",
    "    scaler1.stage_sigs[\"preset_time\"] = ct\n",
    "    _md = {}\n",
    "    _md.update(md)\n",
    "    _md[\"count_time\"] = ct\n",
    "    _md[\"example\"] = \"customPlan\"\n",
    "\n",
    "    scaler1.select_channels([\"I0\", \"diode\"])\n",
    "    yield from up_down_twice(lo, hi, num, md=_md)\n",
    "    scaler1.select_channels()  # reset for all named channels\n",
    "    del scaler1.stage_sigs[\"preset_time\"]  # remove our custom staging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "external-pavilion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== Open Run ===================================\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n",
      "=================================== Open Run ===================================\n",
      "m2 -> 1.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.75\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.5\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.25\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "m2 -> 0.0\n",
      "  Read ['scaler1', 'temperature', 'm2']\n",
      "================================== Close Run ===================================\n"
     ]
    }
   ],
   "source": [
    "summarize_plan(customScan(md=dict(demo=\"concise custom plan example\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "irish-national",
   "metadata": {},
   "source": [
    "## Run\n",
    "Run the `customScan()` plan, using the RunEngine object: `RE()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "romance-moral",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 971     Time: 2023-04-14 15:55:48\n",
      "Persistent Unique Scan ID: '3533adeb-2796-47c7-9c34-c174063ca1bd'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|   seq_num |       time |         m2 | temperature |         I0 |      diode |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|         1 | 15:55:50.2 |     0.0000 |    25.18394 |          4 |          4 |\n",
      "|         2 | 15:55:52.0 |     0.2500 |    24.85393 |          5 |          3 |\n",
      "|         3 | 15:55:53.8 |     0.5000 |    24.50531 |          5 |          6 |\n",
      "|         4 | 15:55:55.7 |     0.7500 |    25.21157 |          6 |          5 |\n",
      "|         5 | 15:55:57.6 |     1.0000 |    25.49052 |          5 |          5 |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "generator scan ['3533adeb'] (scan num: 971)\n",
      "\n",
      "\n",
      "Transient Scan ID: 972     Time: 2023-04-14 15:55:58\n",
      "Persistent Unique Scan ID: '2f6e0c34-f250-4344-8e5d-776b4a8c12f9'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|   seq_num |       time |         m2 | temperature |         I0 |      diode |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|         1 | 15:55:59.5 |     1.0000 |    25.49052 |          5 |          5 |\n",
      "|         2 | 15:56:01.3 |     0.7500 |    25.38635 |          6 |          4 |\n",
      "|         3 | 15:56:03.1 |     0.5000 |    24.53601 |          5 |          5 |\n",
      "|         4 | 15:56:05.0 |     0.2500 |    25.03256 |          4 |          5 |\n",
      "|         5 | 15:56:06.9 |     0.0000 |    24.64768 |          3 |          5 |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "generator scan ['2f6e0c34'] (scan num: 972)\n",
      "\n",
      "\n",
      "Transient Scan ID: 973     Time: 2023-04-14 15:56:07\n",
      "Persistent Unique Scan ID: '539e7792-04da-4d3c-bb41-937b1779e40f'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|   seq_num |       time |         m2 | temperature |         I0 |      diode |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|         1 | 15:56:09.0 |     0.0000 |    24.72637 |          4 |          3 |\n",
      "|         2 | 15:56:10.8 |     0.2500 |    24.81574 |          5 |          7 |\n",
      "|         3 | 15:56:12.7 |     0.5000 |    24.84084 |          5 |          6 |\n",
      "|         4 | 15:56:14.6 |     0.7500 |    25.38981 |          5 |          4 |\n",
      "|         5 | 15:56:16.5 |     1.0000 |    25.42090 |          6 |          5 |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "generator scan ['539e7792'] (scan num: 973)\n",
      "\n",
      "\n",
      "Transient Scan ID: 974     Time: 2023-04-14 15:56:17\n",
      "Persistent Unique Scan ID: 'a697d94a-c398-4285-aeb6-d54d369e051b'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|   seq_num |       time |         m2 | temperature |         I0 |      diode |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "|         1 | 15:56:18.5 |     1.0000 |    25.45387 |          4 |          4 |\n",
      "|         2 | 15:56:20.5 |     0.7500 |    25.05831 |          5 |          4 |\n",
      "|         3 | 15:56:22.5 |     0.5000 |    25.20127 |          5 |          5 |\n",
      "|         4 | 15:56:24.5 |     0.2500 |    24.76677 |          6 |          5 |\n",
      "|         5 | 15:56:26.5 |     0.0000 |    25.30977 |          6 |          7 |\n",
      "+-----------+------------+------------+-------------+------------+------------+\n",
      "generator scan ['a697d94a'] (scan num: 974)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('3533adeb-2796-47c7-9c34-c174063ca1bd',\n",
       " '2f6e0c34-f250-4344-8e5d-776b4a8c12f9',\n",
       " '539e7792-04da-4d3c-bb41-937b1779e40f',\n",
       " 'a697d94a-c398-4285-aeb6-d54d369e051b')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(customScan(md=dict(demo=\"concise custom plan example\")))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "changing-actor",
   "metadata": {},
   "source": [
    "## Challenges\n",
    "\n",
    "Make these additions or improvements to the plans:\n",
    "\n",
    "1. Let the caller specify how many *up*-*down* iterations.\n",
    "1. Wait between *up* and *down* scans for some time (caller-specified). (hint: `yield from bps.sleep(seconds)`)\n",
    "1. Use `logger` to report at various places.\n",
    "\n",
    "   _Hint_: `logger.info(\"text: %s  value: %g\", s1, v2, ...)`\n",
    "\n",
    "   Consider examples as used in `instrument/plans/peak_finder_example.py`.\n",
    "   See the [Python logging tutorial](https://docs.python.org/3/howto/logging.html) for more information.\n",
    "1. Control the `shutter` (hint: `yield from bps.mv(shutter, \"open\")` or `close`)\n",
    "1. Change the temperature before *up* scan and then before *down* scan.  (hint: `yield from bps.mv(temperature, value)`)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bluesky_2023_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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}