{
 "cells": [
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   "source": [
    "# Lineup a 1-D peak : scan detector *v* motor\n",
    "\n",
    "From *APS Python Training for Bluesky Data Acquisition*.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Line up a motor `m1` to a simulated diffraction peak `noisy`.  Also\n",
    "monitor a simulated `temperature` signal in a separate stream.\n",
    "\n",
    "## About the `noisy` peak\n",
    "\n",
    "In this lesson, alignment to a narrow diffraction peak is using\n",
    "capabilities provided by the [ophyd](https://blueskyproject.io/ophyd/)\n",
    "package.  The simulation consists of a simulated motor and simulated\n",
    "noisy detector.\n",
    "\n",
    "The noisy detector is configured to describe a narrow diffraction\n",
    "peak with Gaussian profile based on the value of the motor position.\n",
    "The peak is centered randomly somewhere between motor \n",
    "values -1 and +1.  The width is less than 0.05 in the same units.  The\n",
    "peak intensity is expected to be approximately 100,000 (counts/sec \n",
    "are typical units).\n",
    "\n",
    "The `noisy` peak simulation (computed using EPICS PV `gp:userCalc1`)\n",
    "has these parameters:\n",
    "\n",
    "parameter | description\n",
    ":--- | :---\n",
    "peak shape | Lorentzian as function of `m1.position`\n",
    "center | randomly-placed between -1 .. +1\n",
    "width | randomly-selected, at most 0.15 (motor units)\n",
    "scale (height) | randomly-selected, between 10,000 .. 100,000 (counts)\n",
    "counting noise| randomly-selected, between .01 .. .09 of scale (counts)\n",
    "\n",
    "The calculation updates `noisy` when a new `m1.position` (`gp:m1.RBV`) value is reported to `gp:userCalc1`.\n",
    "\n",
    "Since this detector is actually an EPICS calculation using the *swait* record, the concept of counting time was not built into the simulation.\n",
    "\n",
    "Since we know the peak is somewhere between -1 .. +1, we can limit the scan range.  To ensure the scan crosses the peak, we increase the scan range a bit, to -2 .. 2.\n",
    "\n",
    "Normally, we'd simulate a more narrow diffraction peak but the resolution of these motors is the limiting factor.\n",
    "\n",
    "## Start the `instrument` package\n",
    "\n",
    "Our instrument package is in the `bluesky` subdirectory here so we add that to the search path before importing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "909f38e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/prjemian/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/docs/source/howto/.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 Thu-17:00:57 - ############################################################ startup\n",
      "I Thu-17:00:57 - logging started\n",
      "I Thu-17:00:57 - logging level = 10\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/session_logs.py\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/collection.py\n",
      "I Thu-17:00:57 - 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 Thu-17:00:57 - xmode exception level: 'Minimal'\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/mpl/notebook.py\n",
      "I Thu-17:00:57 - #### Bluesky Framework ####\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/framework/check_python.py\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/framework/check_bluesky.py\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/framework/initialize.py\n",
      "I Thu-17:00:57 - RunEngine metadata saved in directory: /home/prjemian/Bluesky_RunEngine_md\n",
      "I Thu-17:00:57 - using databroker catalog 'training'\n",
      "I Thu-17:00:57 - using ophyd control layer: pyepics\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/framework/metadata.py\n",
      "I Thu-17:00:57 - /home/prjemian/bluesky/instrument/epics_signal_config.py\n",
      "I Thu-17:00:57 - Using RunEngine metadata for scan_id\n",
      "I Thu-17:00:58 - #### Devices ####\n",
      "I Thu-17:00:58 - /home/prjemian/bluesky/instrument/devices/area_detector.py\n",
      "I Thu-17:00:58 - /home/prjemian/bluesky/instrument/devices/calculation_records.py\n",
      "I Thu-17:01:00 - /home/prjemian/bluesky/instrument/devices/fourc_diffractometer.py\n",
      "I Thu-17:01:00 - /home/prjemian/bluesky/instrument/devices/ioc_stats.py\n",
      "I Thu-17:01:00 - /home/prjemian/bluesky/instrument/devices/kohzu_monochromator.py\n",
      "I Thu-17:01:00 - /home/prjemian/bluesky/instrument/devices/motors.py\n",
      "I Thu-17:01:00 - /home/prjemian/bluesky/instrument/devices/noisy_detector.py\n",
      "I Thu-17:01:01 - /home/prjemian/bluesky/instrument/devices/scaler.py\n",
      "I Thu-17:01:01 - /home/prjemian/bluesky/instrument/devices/shutter_simulator.py\n",
      "I Thu-17:01:01 - /home/prjemian/bluesky/instrument/devices/simulated_fourc.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/devices/simulated_kappa.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/devices/slits.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/devices/sixc_diffractometer.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/devices/temperature_signal.py\n",
      "I Thu-17:01:02 - #### Callbacks ####\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/callbacks/spec_data_file_writer.py\n",
      "I Thu-17:01:02 - #### Plans ####\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/plans/lup_plan.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/plans/peak_finder_example.py\n",
      "I Thu-17:01:02 - /home/prjemian/bluesky/instrument/utils/image_analysis.py\n",
      "I Thu-17:01:02 - #### Utilities ####\n",
      "I Thu-17:01:02 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/docs/source/howto/20230413-170102.dat\n",
      "I Thu-17:01:02 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Thu-17:01:02 -    file will be created when bluesky ends its next scan\n",
      "I Thu-17:01:02 -    to change SPEC file, use command:   newSpecFile('title')\n",
      "I Thu-17:01:02 - #### 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": "2456684c",
   "metadata": {},
   "source": [
    "Let's also monitor the simulated temperature during these scans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f04ab9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.monitors.append(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8b4cea",
   "metadata": {},
   "source": [
    "## 2-pass scan\n",
    "\n",
    "Find the peak of noisy v. m1 in the range of +/- 2.  This is a 2 scan procedure.  First scan passes through the full range.  Second scan is centered on the peak and width of the first scan.\n",
    "\n",
    "We know the peak of the simulated noisy detector is positioned somewhere between -1 to +1.  Overscan that range to find both sides of the peak.\n",
    "\n",
    "The plan used here comes from our instrument package, `instrument/plans/peak_finder_example.py`.  It's a custom plan that, among other activities, calls the standard bluesky plan for a scan relative to the current position.  The `bp.rel_scan()` is called twice in this custom plan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bdb6ae9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 937     Time: 2023-04-13 17:01:15\n",
      "Persistent Unique Scan ID: 'd3d48c63-299b-4105-b71c-d7f76d384610'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:01:18.2 |    -2.0000 |  157.59212 |\n",
      "|         2 | 17:01:18.7 |    -1.8182 |  198.25932 |\n",
      "|         3 | 17:01:19.2 |    -1.6364 |  247.01842 |\n",
      "|         4 | 17:01:19.7 |    -1.4546 |  338.79833 |\n",
      "|         5 | 17:01:20.2 |    -1.2727 |  463.20949 |\n",
      "|         6 | 17:01:20.7 |    -1.0909 |  664.81482 |\n",
      "|         7 | 17:01:21.2 |    -0.9091 | 1029.39208 |\n",
      "|         8 | 17:01:21.7 |    -0.7273 | 1876.34456 |\n",
      "|         9 | 17:01:22.2 |    -0.5454 | 4261.73803 |\n",
      "|        10 | 17:01:22.7 |    -0.3636 | 16701.77848 |\n",
      "|        11 | 17:01:23.2 |    -0.1818 | 82311.60621 |\n",
      "|        12 | 17:01:23.7 |     0.0000 | 10604.00156 |\n",
      "|        13 | 17:01:24.2 |     0.1818 | 3347.12140 |\n",
      "|        14 | 17:01:24.7 |     0.3636 | 1547.41780 |\n",
      "|        15 | 17:01:25.2 |     0.5454 |  901.41541 |\n",
      "|        16 | 17:01:25.7 |     0.7273 |  586.56298 |\n",
      "|        17 | 17:01:26.2 |     0.9091 |  404.69955 |\n",
      "|        18 | 17:01:26.7 |     1.0909 |  309.28576 |\n",
      "|        19 | 17:01:27.2 |     1.2727 |  239.25274 |\n",
      "|        20 | 17:01:27.7 |     1.4546 |  188.78387 |\n",
      "|        21 | 17:01:28.2 |     1.6364 |  155.08798 |\n",
      "|        22 | 17:01:28.7 |     1.8182 |  128.29261 |\n",
      "|        23 | 17:01:29.2 |     2.0000 |  107.78115 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['d3d48c63'] (scan num: 937)\n",
      "\n",
      "\n",
      "Transient Scan ID: 938     Time: 2023-04-13 17:01:32\n",
      "Persistent Unique Scan ID: '773ec7fe-cee1-42c5-9f9f-a71e5fbbba74'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:01:33.0 |    -0.4484 | 7904.46212 |\n",
      "|         2 | 17:01:33.1 |    -0.4246 | 9524.20099 |\n",
      "|         3 | 17:01:33.3 |    -0.4008 | 11769.89338 |\n",
      "|         4 | 17:01:33.5 |    -0.3770 | 14749.11678 |\n",
      "|         5 | 17:01:33.7 |    -0.3532 | 19493.29960 |\n",
      "|         6 | 17:01:33.9 |    -0.3294 | 25858.24056 |\n",
      "|         7 | 17:01:34.2 |    -0.3056 | 34818.95318 |\n",
      "|         8 | 17:01:34.3 |    -0.2818 | 47345.90678 |\n",
      "|         9 | 17:01:34.6 |    -0.2580 | 62548.96813 |\n",
      "|        10 | 17:01:34.8 |    -0.2343 | 80809.72645 |\n",
      "|        11 | 17:01:35.0 |    -0.2105 | 90355.73829 |\n",
      "|        12 | 17:01:35.2 |    -0.1867 | 84500.97257 |\n",
      "|        13 | 17:01:35.4 |    -0.1629 | 68930.86411 |\n",
      "|        14 | 17:01:35.5 |    -0.1391 | 48903.47788 |\n",
      "|        15 | 17:01:35.7 |    -0.1153 | 36644.26066 |\n",
      "|        16 | 17:01:35.9 |    -0.0915 | 27533.02424 |\n",
      "|        17 | 17:01:36.2 |    -0.0677 | 20668.16407 |\n",
      "|        18 | 17:01:36.3 |    -0.0439 | 16073.17176 |\n",
      "|        19 | 17:01:36.5 |    -0.0201 | 12742.72471 |\n",
      "|        20 | 17:01:36.7 |     0.0037 | 10125.59796 |\n",
      "|        21 | 17:01:37.0 |     0.0275 | 8580.01046 |\n",
      "|        22 | 17:01:37.2 |     0.0513 | 6969.20370 |\n",
      "|        23 | 17:01:37.3 |     0.0751 | 5982.47230 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['773ec7fe'] (scan num: 938)\n"
     ]
    },
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       "('d3d48c63-299b-4105-b71c-d7f76d384610',\n",
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(two_pass_scan(md={\"motive\": \"two_pass_scan\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c8e9dd",
   "metadata": {},
   "source": [
    "## Multi-pass scan\n",
    "\n",
    "Scan up to 4 times across the peak (in the same direction) starting \n",
    "with range of -2 .. +2, then reducing the range of the next scan \n",
    "based on the measured FWHM and centering on the measured center position (centroid).\n",
    "\n",
    "After all scans are complete, report the computed peak centroid and FWHM for each scan in a table.  The motor will be positioned at the last centroid after the last scan.\n",
    "\n",
    "The `findpeak_multipass` is also from our insturment package.  It calls `bp.rel_scan()` inside a loop for the desired number of iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1201e5be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 939     Time: 2023-04-13 17:01:55\n",
      "Persistent Unique Scan ID: '841f1016-f954-4f9d-9f1f-984417c01d69'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:01:57.7 |    -2.1000 |  141.70330 |\n",
      "|         2 | 17:01:58.2 |    -1.9091 |  180.10604 |\n",
      "|         3 | 17:01:58.7 |    -1.7182 |  220.01310 |\n",
      "|         4 | 17:01:59.2 |    -1.5273 |  297.27020 |\n",
      "|         5 | 17:01:59.7 |    -1.3364 |  399.82872 |\n",
      "|         6 | 17:02:00.2 |    -1.1455 |  580.57415 |\n",
      "|         7 | 17:02:00.7 |    -0.9546 |  925.49621 |\n",
      "|         8 | 17:02:01.2 |    -0.7636 | 1594.46591 |\n",
      "|         9 | 17:02:01.7 |    -0.5727 | 3625.38025 |\n",
      "|        10 | 17:02:02.2 |    -0.3818 | 14003.98883 |\n",
      "|        11 | 17:02:02.7 |    -0.1909 | 87128.55953 |\n",
      "|        12 | 17:02:03.2 |     0.0000 | 10781.74360 |\n",
      "|        13 | 17:02:03.7 |     0.1909 | 3151.01669 |\n",
      "|        14 | 17:02:04.2 |     0.3818 | 1474.58822 |\n",
      "|        15 | 17:02:04.7 |     0.5727 |  860.73083 |\n",
      "|        16 | 17:02:05.2 |     0.7636 |  557.77404 |\n",
      "|        17 | 17:02:05.7 |     0.9546 |  386.27086 |\n",
      "|        18 | 17:02:06.2 |     1.1455 |  278.80271 |\n",
      "|        19 | 17:02:06.7 |     1.3364 |  210.42662 |\n",
      "|        20 | 17:02:07.2 |     1.5273 |  175.16166 |\n",
      "|        21 | 17:02:07.7 |     1.7182 |  137.03429 |\n",
      "|        22 | 17:02:08.2 |     1.9091 |  115.49049 |\n",
      "|        23 | 17:02:08.7 |     2.1000 |   96.98126 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['841f1016'] (scan num: 939)\n",
      "\n",
      "\n",
      "Transient Scan ID: 940     Time: 2023-04-13 17:02:11\n",
      "Persistent Unique Scan ID: '9a544ec5-36b1-43ec-8364-2daccf62ab4f'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:02:12.5 |    -0.4602 | 7228.56248 |\n",
      "|         2 | 17:02:12.7 |    -0.4360 | 8710.12161 |\n",
      "|         3 | 17:02:12.9 |    -0.4117 | 10910.88638 |\n",
      "|         4 | 17:02:13.1 |    -0.3874 | 13381.24817 |\n",
      "|         5 | 17:02:13.4 |    -0.3632 | 17270.12555 |\n",
      "|         6 | 17:02:13.7 |    -0.3389 | 22573.96343 |\n",
      "|         7 | 17:02:14.0 |    -0.3146 | 30758.95023 |\n",
      "|         8 | 17:02:14.3 |    -0.2904 | 41349.69255 |\n",
      "|         9 | 17:02:14.5 |    -0.2661 | 57975.44508 |\n",
      "|        10 | 17:02:14.7 |    -0.2418 | 74679.24117 |\n",
      "|        11 | 17:02:14.9 |    -0.2176 | 91613.35872 |\n",
      "|        12 | 17:02:15.1 |    -0.1933 | 86654.05662 |\n",
      "|        13 | 17:02:15.3 |    -0.1691 | 74681.03816 |\n",
      "|        14 | 17:02:15.5 |    -0.1448 | 52992.45140 |\n",
      "|        15 | 17:02:15.7 |    -0.1205 | 38576.68641 |\n",
      "|        16 | 17:02:15.9 |    -0.0963 | 29362.35466 |\n",
      "|        17 | 17:02:16.1 |    -0.0720 | 22195.94876 |\n",
      "|        18 | 17:02:16.3 |    -0.0477 | 16870.37138 |\n",
      "|        19 | 17:02:16.5 |    -0.0235 | 12863.75950 |\n",
      "|        20 | 17:02:16.7 |     0.0008 | 10872.18860 |\n",
      "|        21 | 17:02:16.9 |     0.0251 | 8739.44338 |\n",
      "|        22 | 17:02:17.1 |     0.0493 | 7125.04495 |\n",
      "|        23 | 17:02:17.4 |     0.0736 | 6019.72705 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['9a544ec5'] (scan num: 940)\n",
      "\n",
      "\n",
      "Transient Scan ID: 941     Time: 2023-04-13 17:02:18\n",
      "Persistent Unique Scan ID: '2f671579-4ca2-499f-b6d6-61f9e981ee43'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:02:19.0 |    -0.3787 | 14399.84182 |\n",
      "|         2 | 17:02:19.1 |    -0.3632 | 17536.27942 |\n",
      "|         3 | 17:02:19.3 |    -0.3478 | 20784.29981 |\n",
      "|         4 | 17:02:19.5 |    -0.3323 | 24533.51947 |\n",
      "|         5 | 17:02:19.7 |    -0.3168 | 29518.74018 |\n",
      "|         6 | 17:02:19.9 |    -0.3014 | 34729.46943 |\n",
      "|         7 | 17:02:20.2 |    -0.2859 | 42741.67328 |\n",
      "|         8 | 17:02:20.5 |    -0.2705 | 54270.10986 |\n",
      "|         9 | 17:02:20.7 |    -0.2550 | 67470.90664 |\n",
      "|        10 | 17:02:20.9 |    -0.2395 | 79750.20383 |\n",
      "|        11 | 17:02:21.1 |    -0.2241 | 88410.00755 |\n",
      "|        12 | 17:02:21.3 |    -0.2086 | 94729.82146 |\n",
      "|        13 | 17:02:21.5 |    -0.1932 | 90374.69658 |\n",
      "|        14 | 17:02:21.7 |    -0.1777 | 77382.72138 |\n",
      "|        15 | 17:02:21.9 |    -0.1622 | 69850.84615 |\n",
      "|        16 | 17:02:22.1 |    -0.1468 | 55975.70555 |\n",
      "|        17 | 17:02:22.3 |    -0.1313 | 44631.50376 |\n",
      "|        18 | 17:02:22.5 |    -0.1159 | 38118.60161 |\n",
      "|        19 | 17:02:22.7 |    -0.1004 | 30890.33051 |\n",
      "|        20 | 17:02:22.9 |    -0.0849 | 24882.10213 |\n",
      "|        21 | 17:02:23.1 |    -0.0695 | 20989.25822 |\n",
      "|        22 | 17:02:23.4 |    -0.0540 | 18319.92818 |\n",
      "|        23 | 17:02:23.5 |    -0.0386 | 15567.73558 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['2f671579'] (scan num: 941)\n",
      "\n",
      "\n",
      "Transient Scan ID: 942     Time: 2023-04-13 17:02:24\n",
      "Persistent Unique Scan ID: 'ac664dc7-504f-4425-8fe4-489adbae7fdd'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'temperature_monitor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |         m1 |      noisy |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 17:02:24.8 |    -0.3578 | 18569.97283 |\n",
      "|         2 | 17:02:25.1 |    -0.3441 | 21728.44437 |\n",
      "|         3 | 17:02:25.3 |    -0.3305 | 25226.32175 |\n",
      "|         4 | 17:02:25.5 |    -0.3168 | 28941.51480 |\n",
      "|         5 | 17:02:25.7 |    -0.3031 | 34314.24493 |\n",
      "|         6 | 17:02:25.9 |    -0.2895 | 40531.42738 |\n",
      "|         7 | 17:02:26.1 |    -0.2758 | 49028.60617 |\n",
      "|         8 | 17:02:26.2 |    -0.2621 | 60024.30969 |\n",
      "|         9 | 17:02:26.5 |    -0.2485 | 68583.11363 |\n",
      "|        10 | 17:02:26.7 |    -0.2348 | 83210.76279 |\n",
      "|        11 | 17:02:26.9 |    -0.2211 | 88905.13543 |\n",
      "|        12 | 17:02:27.1 |    -0.2075 | 91254.82153 |\n",
      "|        13 | 17:02:27.3 |    -0.1938 | 91395.55793 |\n",
      "|        14 | 17:02:27.5 |    -0.1802 | 80290.89715 |\n",
      "|        15 | 17:02:27.8 |    -0.1665 | 71926.05508 |\n",
      "|        16 | 17:02:28.0 |    -0.1528 | 60632.25445 |\n",
      "|        17 | 17:02:28.2 |    -0.1392 | 50935.77565 |\n",
      "|        18 | 17:02:28.4 |    -0.1255 | 41194.65347 |\n",
      "|        19 | 17:02:28.6 |    -0.1118 | 35045.20416 |\n",
      "|        20 | 17:02:28.8 |    -0.0982 | 30070.29286 |\n",
      "|        21 | 17:02:29.0 |    -0.0845 | 25524.63241 |\n",
      "|        22 | 17:02:29.2 |    -0.0708 | 21599.11847 |\n",
      "|        23 | 17:02:29.4 |    -0.0572 | 18357.45299 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator rel_scan ['ac664dc7'] (scan num: 942)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Thu-17:02:30 - iterative results:\n",
      "======= ==================== ===================\n",
      "scan_id center               FWHM               \n",
      "======= ==================== ===================\n",
      "939     -0.19330745175234704 0.2224228216416789 \n",
      "940     -0.20861123128447245 0.14171756691442283\n",
      "941     -0.20747604249899584 0.12525759324435487\n",
      "942     -0.20662333630889465 0.12382359689785855\n",
      "======= ==================== ===================\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m1 now at -0.2066\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(findpeak_multipass(md={\"motive\": \"findpeak_multipass\"}))\n",
    "print(f\"{m1.name} now at {m1.position:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670ad449",
   "metadata": {},
   "source": [
    "Show how to re-plot the data from the catalog `cat`.  We'll show the plots of the last three runs.  The measured data is always in the `primary` stream."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ebf5b5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9c327f8d60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat[-3].primary.read().plot.scatter(x=\"m1\", y=\"noisy\", marker=\"x\")\n",
    "cat[-2].primary.read().plot.scatter(x=\"m1\", y=\"noisy\", marker=\"+\")\n",
    "cat[-1].primary.read().plot.scatter(x=\"m1\", y=\"noisy\", marker=\"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97c29db",
   "metadata": {},
   "source": [
    "Plot the monitored temperature *v* time.  (See the table of data printed during the scan, the `temperature_monitor` stream was listed near the top of the table.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa5af753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9c32681450>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temperature_monitor\n",
    "cat[-4].temperature_monitor.read().plot.scatter(x=\"time\", y=\"temperature\", marker=\"*\")\n",
    "cat[-3].temperature_monitor.read().plot.scatter(x=\"time\", y=\"temperature\", marker=\"x\")\n",
    "cat[-2].temperature_monitor.read().plot.scatter(x=\"time\", y=\"temperature\", marker=\"+\")\n",
    "cat[-1].temperature_monitor.read().plot.scatter(x=\"time\", y=\"temperature\", marker=\"o\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "60aa360bcd8d3c8cfbc4e726e53a455fcd5c15cdf29caaf63c7ca2494eba79e9"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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
}