{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scan Area Detector v. Motor\n",
    "\n",
    "<h3>Goals</h3>\n",
    "\n",
    "- [x] **Use EPICS motor(s):**\n",
    "  - [x] Record current value (readback)\n",
    "  - [x] Record commanded value (setpoint)\n",
    "- [x] **Use EPICS area detector:**\n",
    "  - [x] For each image\n",
    "    - [x] Write image to HDF5 file\n",
    "    - [x] Measure total counts in image\n",
    "    - [x] Measure maximum counts in image\n",
    "    - [x] Note the image's \"unique ID\"\n",
    "- [x] **Step scan motor and collect *n* image frame(s) at each step:**\n",
    "  - [x] Plan: count, just collect image(s)\n",
    "  - [x] Plan: scan with one motor\n",
    "  - [x] Plan: scan with two motors\n",
    "  - [x] Plan: grid_scan with two motors\n",
    "- [x] **Collect data in a SPEC data file.**\n",
    "\n",
    "## Start the `instrument` package\n",
    "\n",
    "Our instrument package is in the `bluesky` subdirectory, so we add that to the search path before importing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 Sat-13:18:30 - ############################################################ startup\n",
      "I Sat-13:18:30 - logging started\n",
      "I Sat-13:18:30 - logging level = 10\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/session_logs.py\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/collection.py\n",
      "I Sat-13:18:30 - CONDA_PREFIX = /home/prjemian/.conda/envs/bluesky_2024_2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Sat-13:18:30 - xmode exception level: 'Minimal'\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/mpl/notebook.py\n",
      "I Sat-13:18:30 - #### Bluesky Framework ####\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/framework/check_python.py\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/framework/check_bluesky.py\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/framework/initialize.py\n",
      "I Sat-13:18:30 - RunEngine metadata saved in directory: /home/prjemian/.config/Bluesky_RunEngine_md\n",
      "I Sat-13:18:30 - using databroker catalog 'training'\n",
      "I Sat-13:18:30 - using ophyd control layer: pyepics\n",
      "I Sat-13:18:30 - Using EPICS PV gp:gp:int20 for scan_id\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/framework/metadata.py\n",
      "I Sat-13:18:30 - #### Devices ####\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/devices/area_detector.py\n",
      "I Sat-13:18:30 - /home/prjemian/bluesky/instrument/devices/calculation_records.py\n",
      "I Sat-13:18:35 - /home/prjemian/bluesky/instrument/devices/fourc_diffractometer.py\n",
      "I Sat-13:18:35 - /home/prjemian/bluesky/instrument/devices/ioc_stats.py\n",
      "I Sat-13:18:35 - /home/prjemian/bluesky/instrument/devices/kohzu_monochromator.py\n",
      "I Sat-13:18:35 - /home/prjemian/bluesky/instrument/devices/motors.py\n",
      "I Sat-13:18:36 - /home/prjemian/bluesky/instrument/devices/noisy_detector.py\n",
      "I Sat-13:18:36 - /home/prjemian/bluesky/instrument/devices/scaler.py\n",
      "I Sat-13:18:37 - /home/prjemian/bluesky/instrument/devices/shutter_simulator.py\n",
      "I Sat-13:18:37 - /home/prjemian/bluesky/instrument/devices/simulated_fourc.py\n",
      "I Sat-13:18:37 - /home/prjemian/bluesky/instrument/devices/simulated_kappa.py\n",
      "I Sat-13:18:37 - /home/prjemian/bluesky/instrument/devices/slits.py\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/devices/sixc_diffractometer.py\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/devices/temperature_signal.py\n",
      "I Sat-13:18:38 - #### Callbacks ####\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/callbacks/spec_data_file_writer.py\n",
      "I Sat-13:18:38 - #### Plans ####\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/plans/lup_plan.py\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/plans/peak_finder_example.py\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/utils/image_analysis.py\n",
      "I Sat-13:18:38 - /home/prjemian/bluesky/instrument/plans/randomize_noisy_peak_plan.py\n",
      "I Sat-13:18:38 - #### Utilities ####\n",
      "I Sat-13:18:38 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/docs/source/howto/20240615-131838.dat\n",
      "I Sat-13:18:38 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Sat-13:18:38 -    file will be created when bluesky ends its next scan\n",
      "I Sat-13:18:38 -    to change SPEC file, use command:   newSpecFile('title')\n",
      "I Sat-13:18:38 - #### Startup is complete. ####\n"
     ]
    }
   ],
   "source": [
    "import pathlib, sys\n",
    "\n",
    "sys.path.append(str(pathlib.Path().home() / \"bluesky\"))\n",
    "from instrument.collection import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Create a custom area detector class** with our own IOC prefix, add ROI and STATS plugins, and connect with local directories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from apstools.devices import SingleTrigger_V34\n",
    "from ophyd.areadetector import ADComponent\n",
    "from ophyd.areadetector import DetectorBase\n",
    "from ophyd.areadetector.plugins import ImagePlugin_V34\n",
    "from ophyd.areadetector.plugins import PvaPlugin_V34\n",
    "from ophyd.areadetector.plugins import ROIPlugin_V34\n",
    "from ophyd.areadetector.plugins import StatsPlugin_V34\n",
    "import hdf5plugin  # needed to read LZ4-compressed image from HDF5 file\n",
    "\n",
    "from instrument.devices.area_detector import MyHDF5Plugin\n",
    "from instrument.devices.area_detector import SimDetectorCam_V34\n",
    "\n",
    "# for my IOC, these parameters apply:\n",
    "AD_IOC = \"kad:\"\n",
    "FILE_BASE_IOC = \"/tmp/\"\n",
    "FILE_BASE_BLUESKY = \"/mnt/iockad/tmp/\"\n",
    "\n",
    "class SimDetector_V34(SingleTrigger_V34, DetectorBase):\n",
    "    \"\"\"\n",
    "    ADSimDetector\n",
    "\n",
    "    SingleTrigger:\n",
    "\n",
    "    * stop any current acquisition\n",
    "    * sets image_mode to 'Multiple'\n",
    "    \"\"\"\n",
    "\n",
    "    cam = ADComponent(SimDetectorCam_V34, \"cam1:\")\n",
    "    hdf1 = ADComponent(\n",
    "        MyHDF5Plugin,\n",
    "        \"HDF1:\",\n",
    "        write_path_template=FILE_BASE_IOC,\n",
    "        read_path_template=FILE_BASE_BLUESKY,\n",
    "    )\n",
    "    image = ADComponent(ImagePlugin_V34, \"image1:\")\n",
    "    pva = ADComponent(PvaPlugin_V34, \"Pva1:\")\n",
    "    roi1 = ADComponent(ROIPlugin_V34, \"ROI1:\")\n",
    "    stats1 = ADComponent(StatsPlugin_V34, \"Stats1:\")\n",
    "\n",
    "simdet = SimDetector_V34(AD_IOC, name=\"simdet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup & connect with EPICS\n",
    "\n",
    "Search the ophyd object registry (`oregistry`) for the detector and motors. Verify that each is connected to EPICS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "obj.connected=True 'simdet'\n",
      "obj.connected=True 'm1'\n",
      "obj.connected=True 'm2'\n"
     ]
    }
   ],
   "source": [
    "for nm in \"simdet m1 m2\".split():\n",
    "    obj = oregistry.find(nm)\n",
    "    print(f\"{obj.connected=!r} {nm!r}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Configure the area detector** to:\n",
    "\n",
    "- Collect $n$ image frame(s) for each press of the *Acquire* button.\n",
    "- Save image(s) to HDF5.\n",
    "- Record total counts in the image.\n",
    "- Record maximum counts in the image.\n",
    "- Record the unique ID of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ad_setup(det):\n",
    "    det.wait_for_connection(timeout=3)\n",
    "    det.roi1.nd_array_port.put(\"SIM1\")\n",
    "    det.stats1.nd_array_port.put(\"ROI1\")\n",
    "    det.missing_plugins()\n",
    "\n",
    "    det.cam.stage_sigs[\"num_images\"] = 3\n",
    "\n",
    "    det.hdf1.kind = \"hinted\"\n",
    "    det.hdf1.create_directory.put(-5)\n",
    "    det.hdf1.stage_sigs[\"compression\"] = \"LZ4\"\n",
    "\n",
    "    det.stats1.kind = \"hinted\"\n",
    "    det.stats1.max_value.kind = \"hinted\"\n",
    "    det.stats1.total.kind = \"hinted\"\n",
    "    det.stats1.unique_id.kind = \"hinted\"\n",
    "\n",
    "    det.cam.stage_sigs[\"wait_for_plugins\"] = \"Yes\"\n",
    "    det.hdf1.stage_sigs[\"blocking_callbacks\"] = \"No\"\n",
    "    det.image.stage_sigs[\"blocking_callbacks\"] = \"No\"\n",
    "\n",
    "ad_setup(simdet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics (optional)\n",
    "\n",
    "These steps are optional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Show the data** from area detector `simdet`.\n",
    "This is the data collected by the `RE` during a run.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('simdet_stats1_unique_id',\n",
       "              {'value': 152858, 'timestamp': 1718408341.829117}),\n",
       "             ('simdet_stats1_max_value',\n",
       "              {'value': 164.0, 'timestamp': 1718408341.829133}),\n",
       "             ('simdet_stats1_total',\n",
       "              {'value': 11722735.0, 'timestamp': 1718408341.829158})])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simdet.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Show how** `simdet` is configured to collect $n$ frames per point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image frames per point: 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Image frames per point:\", simdet.cam.stage_sigs[\"num_images\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Show the staging configuration** for `simdet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'simdet': OrderedDict([('cam.acquire', 0), ('cam.image_mode', 1)])}\n",
      "{'simdet.cam': OrderedDict([('num_images', 3), ('wait_for_plugins', 'Yes')])}\n",
      "{'simdet.hdf1': OrderedDict([('enable', 1),\n",
      "                             ('blocking_callbacks', 'No'),\n",
      "                             ('parent.cam.array_callbacks', 1),\n",
      "                             ('create_directory', -3),\n",
      "                             ('auto_increment', 'Yes'),\n",
      "                             ('array_counter', 0),\n",
      "                             ('auto_save', 'Yes'),\n",
      "                             ('num_capture', 0),\n",
      "                             ('file_template', '%s%s_%6.6d.h5'),\n",
      "                             ('file_write_mode', 'Stream'),\n",
      "                             ('capture', 1),\n",
      "                             ('compression', 'LZ4')])}\n",
      "{'simdet.roi1': OrderedDict([('enable', 1),\n",
      "                             ('blocking_callbacks', 'Yes'),\n",
      "                             ('parent.cam.array_callbacks', 1)])}\n",
      "{'simdet.stats1': OrderedDict([('enable', 1),\n",
      "                               ('blocking_callbacks', 'Yes'),\n",
      "                               ('parent.cam.array_callbacks', 1)])}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "pprint({\"simdet\": simdet.stage_sigs})\n",
    "pprint({\"simdet.cam\": simdet.cam.stage_sigs})\n",
    "pprint({\"simdet.hdf1\": simdet.hdf1.stage_sigs})\n",
    "pprint({\"simdet.roi1\": simdet.roi1.stage_sigs})\n",
    "pprint({\"simdet.stats1\": simdet.stats1.stage_sigs})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Show how the area detector ports are connected** in a diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simdet.visualize_asyn_digraph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the Stats plugin signals\n",
    "\n",
    "- The plan below reads the signals from `simdet.stats1` multiple times. Since `simdet` itself is not included in the list of detectors, it will not be triggered (and thus will not produce new images). One can notice that the `unique_id` does not change. For clarity, the command is split into multiple lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 92     Time: 2024-06-15 13:18:40\n",
      "Persistent Unique Scan ID: 'ceaecdd7-e1af-411f-94f2-c14d5d3debab'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "device=MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']), exception=Data keys (field names) from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']) collide with those from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']). The colliding keys are {'m2', 'm2_user_setpoint'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------------------+---------------------+-------------------------+\n",
      "|   seq_num |       time | simdet_stats1_unique_id | simdet_stats1_total | simdet_stats1_max_value |\n",
      "+-----------+------------+-------------------------+---------------------+-------------------------+\n",
      "|         1 | 13:18:41.1 |                  152858 |            11722735 |                     164 |\n",
      "|         2 | 13:18:41.5 |                  152858 |            11722735 |                     164 |\n",
      "|         3 | 13:18:41.9 |                  152858 |            11722735 |                     164 |\n",
      "|         4 | 13:18:42.3 |                  152858 |            11722735 |                     164 |\n",
      "|         5 | 13:18:42.6 |                  152858 |            11722735 |                     164 |\n",
      "+-----------+------------+-------------------------+---------------------+-------------------------+\n",
      "generator count ['ceaecdd7'] (scan num: 92)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ceaecdd7-e1af-411f-94f2-c14d5d3debab',)"
      ]
     },
     "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(\n",
    "    bp.count(\n",
    "        [\n",
    "            simdet.stats1.unique_id, \n",
    "            simdet.stats1.total, \n",
    "            simdet.stats1.max_value\n",
    "            ], \n",
    "        num=5\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run=<BlueskyRun uid='ceaecdd7-e1af-411f-94f2-c14d5d3debab'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prjemian/.conda/envs/bluesky_2024_2/lib/python3.11/site-packages/databroker/intake_xarray_core/base.py:23: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
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       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 160B\n",
       "Dimensions:                  (time: 5)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Data variables:\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152858 152858 152858 152858 152858\n",
       "    simdet_stats1_total      (time) float64 40B 1.172e+07 ... 1.172e+07\n",
       "    simdet_stats1_max_value  (time) float64 40B 164.0 164.0 164.0 164.0 164.0</pre><div class='xr-wrap' style='display:none'><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-83c0b4f4-5061-4542-9444-a04c6ef6f9dc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83c0b4f4-5061-4542-9444-a04c6ef6f9dc' 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>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-239a76a3-1b74-4f89-9792-e320f8361709' class='xr-section-summary-in' type='checkbox'  checked><label for='section-239a76a3-1b74-4f89-9792-e320f8361709' 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'>1.718e+09 1.718e+09 ... 1.718e+09</div><input id='attrs-b4e0dbc2-4132-440c-a676-7c27b4d74f5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4e0dbc2-4132-440c-a676-7c27b4d74f5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d1277d-92e9-4bc9-a040-5050becf8582' class='xr-var-data-in' type='checkbox'><label for='data-63d1277d-92e9-4bc9-a040-5050becf8582' 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([1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7aa3551-de29-4cbb-b99e-6788296aef58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7aa3551-de29-4cbb-b99e-6788296aef58' class='xr-section-summary' >Data variables: <span>(3)</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>simdet_stats1_unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>152858 152858 152858 152858 152858</div><input id='attrs-873737b5-9998-4398-be80-351f081a98f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-873737b5-9998-4398-be80-351f081a98f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e5ae41-2cc8-4a0f-a59b-1dd9d0070cd0' class='xr-var-data-in' type='checkbox'><label for='data-e7e5ae41-2cc8-4a0f-a59b-1dd9d0070cd0' 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>simdet_stats1_unique_id</dd></dl></div><div class='xr-var-data'><pre>array([152858, 152858, 152858, 152858, 152858])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.172e+07 1.172e+07 ... 1.172e+07</div><input id='attrs-9f150ed4-0ccd-4335-8e84-893c52de946d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f150ed4-0ccd-4335-8e84-893c52de946d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8101fc6d-008e-4fcf-b742-64f3c9c595ec' class='xr-var-data-in' type='checkbox'><label for='data-8101fc6d-008e-4fcf-b742-64f3c9c595ec' 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>simdet_stats1_total</dd></dl></div><div class='xr-var-data'><pre>array([11722735., 11722735., 11722735., 11722735., 11722735.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_max_value</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>164.0 164.0 164.0 164.0 164.0</div><input id='attrs-84eea06e-793e-4319-8e44-7a850a14547c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84eea06e-793e-4319-8e44-7a850a14547c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed98ca34-daaa-44a9-8eb9-5e08068eda57' class='xr-var-data-in' type='checkbox'><label for='data-ed98ca34-daaa-44a9-8eb9-5e08068eda57' 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>simdet_stats1_max_value</dd></dl></div><div class='xr-var-data'><pre>array([164., 164., 164., 164., 164.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9ce164f-dd10-4e61-b3a8-d8133577418d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9ce164f-dd10-4e61-b3a8-d8133577418d' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-43535840-817e-444d-a6f1-b653962a05c3' class='xr-index-data-in' type='checkbox'/><label for='index-43535840-817e-444d-a6f1-b653962a05c3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1718475521.1052098,  1718475521.527617, 1718475521.9094555,\n",
       "       1718475522.3039458, 1718475522.6947227],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7a2fbe0-d67e-40c4-aeda-3468394e2b18' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7a2fbe0-d67e-40c4-aeda-3468394e2b18' 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> Size: 160B\n",
       "Dimensions:                  (time: 5)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Data variables:\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152858 152858 152858 152858 152858\n",
       "    simdet_stats1_total      (time) float64 40B 1.172e+07 ... 1.172e+07\n",
       "    simdet_stats1_max_value  (time) float64 40B 164.0 164.0 164.0 164.0 164.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume our run is the most recent one in the catalog\n",
    "run = cat[-1]\n",
    "print(f\"{run=!r}\")\n",
    "\n",
    "# Read the primary data from the most recent run\n",
    "dataset = run.primary.read()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take image(s) with the `bp.count` plan\n",
    "\n",
    "Collect 5 sets of images (3-frames each as configured above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 93     Time: 2024-06-15 13:18:44\n",
      "Persistent Unique Scan ID: 'f39f2eb2-fc04-4696-ac26-245cd574f7c2'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "device=MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']), exception=Data keys (field names) from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']) collide with those from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']). The colliding keys are {'m2', 'm2_user_setpoint'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------------------+-------------------------+---------------------+\n",
      "|   seq_num |       time | simdet_stats1_unique_id | simdet_stats1_max_value | simdet_stats1_total |\n",
      "+-----------+------------+-------------------------+-------------------------+---------------------+\n",
      "|         1 | 13:18:45.1 |                  152861 |                     164 |            11658834 |\n",
      "|         2 | 13:18:45.5 |                  152864 |                     164 |            11710598 |\n",
      "|         3 | 13:18:45.9 |                  152867 |                     163 |            11537287 |\n",
      "|         4 | 13:18:46.3 |                  152870 |                     159 |            11360372 |\n",
      "|         5 | 13:18:46.8 |                  152873 |                     166 |            11817560 |\n",
      "+-----------+------------+-------------------------+-------------------------+---------------------+\n",
      "generator count ['f39f2eb2'] (scan num: 93)\n",
      "uids=!r\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uids = RE(bp.count([simdet], num=5))\n",
    "print(f\"uids=!r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the collected data\n",
    "\n",
    "Let's examine the data from the most recent run in the catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_data.shape=(5, 3, 1024, 1024)\n"
     ]
    },
    {
     "data": {
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       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 16MB\n",
       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 16MB 10 9 ... 11\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152861 152864 152867 152870 152873\n",
       "    simdet_stats1_max_value  (time) float64 40B 164.0 164.0 163.0 159.0 166.0\n",
       "    simdet_stats1_total      (time) float64 40B 1.166e+07 ... 1.182e+07</pre><div class='xr-wrap' style='display:none'><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-32188741-d2f3-43bc-9cf2-b73c11a796e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32188741-d2f3-43bc-9cf2-b73c11a796e2' 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>: 5</li><li><span>dim_0</span>: 3</li><li><span>dim_1</span>: 1024</li><li><span>dim_2</span>: 1024</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b759b076-5748-459e-8fb5-dfb99831a734' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b759b076-5748-459e-8fb5-dfb99831a734' 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'>1.718e+09 1.718e+09 ... 1.718e+09</div><input id='attrs-044de852-17d8-4426-9f77-9f3858f91408' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-044de852-17d8-4426-9f77-9f3858f91408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31c03d83-becd-48f4-a779-eb4084278a83' class='xr-var-data-in' type='checkbox'><label for='data-31c03d83-becd-48f4-a779-eb4084278a83' 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([1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e83bff4-448f-4993-8fbb-8b11e6a63447' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e83bff4-448f-4993-8fbb-8b11e6a63447' class='xr-section-summary' >Data variables: <span>(4)</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>simdet_image</span></div><div class='xr-var-dims'>(time, dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>10 9 14 7 5 9 ... 7 9 10 13 10 11</div><input id='attrs-6a5105db-0e63-404c-aaae-e4748f087871' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a5105db-0e63-404c-aaae-e4748f087871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfc25c0e-3e33-4a91-998c-21af1d9fb5f5' class='xr-var-data-in' type='checkbox'><label for='data-bfc25c0e-3e33-4a91-998c-21af1d9fb5f5' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([[[[10,  9, 14, ...,  6, 11, 13],\n",
       "         [ 8,  8, 13, ..., 10,  6, 11],\n",
       "         [ 7,  8,  7, ..., 10, 12,  6],\n",
       "         ...,\n",
       "         [11,  5,  6, ...,  7,  7, 13],\n",
       "         [13,  5,  8, ...,  8, 14,  8],\n",
       "         [14, 10, 10, ...,  5, 13,  7]],\n",
       "\n",
       "        [[ 5,  8,  7, ...,  6,  6, 11],\n",
       "         [ 8,  5, 13, ...,  6, 14, 12],\n",
       "         [14, 14, 14, ...,  9,  7, 12],\n",
       "         ...,\n",
       "         [14,  9,  9, ...,  5,  8, 12],\n",
       "         [10, 13,  9, ..., 13,  8,  7],\n",
       "         [14, 13, 13, ...,  6,  5, 14]],\n",
       "\n",
       "        [[ 8, 11,  9, ...,  6, 14,  6],\n",
       "         [ 8, 14, 12, ..., 14, 14, 10],\n",
       "         [10,  5, 11, ..., 10,  8, 14],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [10,  6, 14, ..., 11, 11, 14],\n",
       "         [ 5, 11,  5, ..., 12, 12,  6],\n",
       "         [ 6,  7, 11, ...,  6,  7,  6]],\n",
       "\n",
       "        [[ 6,  8, 14, ..., 12,  5,  8],\n",
       "         [11, 10, 13, ...,  5,  7,  8],\n",
       "         [ 5,  5,  5, ..., 12,  5, 11],\n",
       "         ...,\n",
       "         [13, 12,  9, ...,  9,  9, 12],\n",
       "         [10, 12, 14, ...,  9,  8,  6],\n",
       "         [13,  7,  7, ...,  7, 10, 11]],\n",
       "\n",
       "        [[13,  7,  5, ..., 11,  5,  5],\n",
       "         [ 6,  9,  8, ...,  8,  7, 10],\n",
       "         [ 7,  6,  6, ..., 13, 12,  6],\n",
       "         ...,\n",
       "         [12, 14,  7, ...,  5, 11,  9],\n",
       "         [12, 13,  9, ..., 12, 14,  6],\n",
       "         [14,  7, 14, ..., 13, 10, 11]]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>152861 152864 152867 152870 152873</div><input id='attrs-e6f9267c-eb36-40be-bf2d-13f0239633d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6f9267c-eb36-40be-bf2d-13f0239633d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8732a11b-9cf7-4d63-9dc8-397f14245f42' class='xr-var-data-in' type='checkbox'><label for='data-8732a11b-9cf7-4d63-9dc8-397f14245f42' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([152861, 152864, 152867, 152870, 152873])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_max_value</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>164.0 164.0 163.0 159.0 166.0</div><input id='attrs-97aa71eb-8f5e-4f03-a830-2632611b306f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97aa71eb-8f5e-4f03-a830-2632611b306f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c56610b-dc4f-4f64-8565-f1edc877a852' class='xr-var-data-in' type='checkbox'><label for='data-0c56610b-dc4f-4f64-8565-f1edc877a852' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([164., 164., 163., 159., 166.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.166e+07 1.171e+07 ... 1.182e+07</div><input id='attrs-e73006a4-9f4b-4a0b-8592-87c2bb56eb64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e73006a4-9f4b-4a0b-8592-87c2bb56eb64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a49fad0-8781-4e59-8879-f478cbc2377d' class='xr-var-data-in' type='checkbox'><label for='data-3a49fad0-8781-4e59-8879-f478cbc2377d' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([11658834., 11710598., 11537287., 11360372., 11817560.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc1f7a84-5cb8-46c2-b9c1-af7f08f99f10' class='xr-section-summary-in' type='checkbox'  ><label for='section-fc1f7a84-5cb8-46c2-b9c1-af7f08f99f10' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8db6f19f-06ff-4fa6-9a05-25db516e817d' class='xr-index-data-in' type='checkbox'/><label for='index-8db6f19f-06ff-4fa6-9a05-25db516e817d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1718475525.1200914,  1718475525.555351,  1718475525.964354,\n",
       "        1718475526.390957, 1718475526.8042006],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-794a2e32-fa32-4067-9043-3c4e404fe649' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-794a2e32-fa32-4067-9043-3c4e404fe649' 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> Size: 16MB\n",
       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 16MB 10 9 ... 11\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152861 152864 152867 152870 152873\n",
       "    simdet_stats1_max_value  (time) float64 40B 164.0 164.0 163.0 159.0 166.0\n",
       "    simdet_stats1_total      (time) float64 40B 1.166e+07 ... 1.182e+07"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the dataset from the most recent run\n",
    "dataset = cat[uids[-1]].primary.read()\n",
    "\n",
    "#  Extract the image data\n",
    "image_data = dataset[\"simdet_image\"]\n",
    "\n",
    "# Print the shape of the image data\n",
    "print(f\"{image_data.shape=!r}\")\n",
    "\n",
    "# Display the dataset\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Display the first image frame** from the first *point*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fddf7c27d50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_data[0][0].plot.pcolormesh()  # Plot the first frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the HDF5 image file\n",
    "\n",
    "Information about the HDF5 file is saved as a `resource` document in the catalog.  This command shows the content of that document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Resource({'path_semantics': 'posix',\n",
       " 'resource_kwargs': {'frame_per_point': 3},\n",
       " 'resource_path': 'mnt/iockad/tmp/1245c370-4953-4ff3-8982_000000.h5',\n",
       " 'root': '/',\n",
       " 'run_start': 'f39f2eb2-fc04-4696-ac26-245cd574f7c2',\n",
       " 'spec': 'AD_HDF5',\n",
       " 'uid': '510c0d90-f922-41c2-a5ed-2b1013b82ab3'})]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run = cat[uids[-1]]\n",
    "resources = run.primary._resources\n",
    "resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with this `resources` object (a Python `list`), this command\n",
    "**reconstructs the image file name**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/iockad/tmp/1245c370-4953-4ff3-8982_000000.h5'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources[0][\"root\"] + resources[0][\"resource_path\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show that this file is recognized** on the file system available to Bluesky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "image_file = Path(resources[0][\"root\"] + resources[0][\"resource_path\"])\n",
    "image_file.exists()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The (1-D) `bp.scan()` plan with one motor\n",
    "\n",
    "Step scan a motor (m1); at each step:\n",
    "\n",
    "- Measure 3 images (as configured above during detector staging).\n",
    "- Record total counts.\n",
    "- Record maximum counts value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the interest of brevity here, further graphical data visualization will be turned off.  Refer to the section above for the visualization steps.  They are identical for each type of scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bec.disable_plots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run** the scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 94     Time: 2024-06-15 13:18:49\n",
      "Persistent Unique Scan ID: '03dde617-eb69-4b66-abe5-8595de04051d'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "device=MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']), exception=Data keys (field names) from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']) collide with those from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']). The colliding keys are {'m2', 'm2_user_setpoint'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|   seq_num |       time |         m1 | simdet_stats1_unique_id | simdet_stats1_max_value | simdet_stats1_total |\n",
      "+-----------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|         1 | 13:18:52.0 |    -1.0000 |                  152876 |                     160 |            11445712 |\n",
      "|         2 | 13:18:52.8 |    -0.5000 |                  152879 |                     163 |            11658331 |\n",
      "|         3 | 13:18:53.6 |     0.0000 |                  152882 |                     162 |            11535839 |\n",
      "|         4 | 13:18:54.4 |     0.5000 |                  152885 |                     165 |            11799101 |\n",
      "|         5 | 13:18:55.2 |     1.0000 |                  152888 |                     166 |            11824045 |\n",
      "+-----------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "generator scan ['03dde617'] (scan num: 94)\n",
      "image_data.shape=(5, 3, 1024, 1024)\n"
     ]
    },
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       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
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       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
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       "    simdet_stats1_total      (time) float64 40B 1.145e+07 ... 1.182e+07\n",
       "    m1                       (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m1_user_setpoint         (time) float64 40B -1.0 -0.5 0.0 0.5 1.0</pre><div class='xr-wrap' style='display:none'><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-d1783e76-0633-4ed8-bc8a-ad9098a9ab63' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1783e76-0633-4ed8-bc8a-ad9098a9ab63' 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>: 5</li><li><span>dim_0</span>: 3</li><li><span>dim_1</span>: 1024</li><li><span>dim_2</span>: 1024</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93bc7d08-9e3b-4bfd-8010-4d646d1abcd9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93bc7d08-9e3b-4bfd-8010-4d646d1abcd9' 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'>1.718e+09 1.718e+09 ... 1.718e+09</div><input id='attrs-d85205dd-597c-4323-9587-c3fba25c41bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d85205dd-597c-4323-9587-c3fba25c41bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a7875ad-b59b-47b4-a0af-07ea8ee22365' class='xr-var-data-in' type='checkbox'><label for='data-2a7875ad-b59b-47b4-a0af-07ea8ee22365' 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([1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7009529-18d4-44de-a264-7e281afa0789' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7009529-18d4-44de-a264-7e281afa0789' class='xr-section-summary' >Data variables: <span>(6)</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>simdet_image</span></div><div class='xr-var-dims'>(time, dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>8 10 7 5 7 8 5 ... 11 6 8 10 12 9 6</div><input id='attrs-282dc781-ee29-4b63-a1be-2295d778767d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-282dc781-ee29-4b63-a1be-2295d778767d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-481beffb-c8d0-451a-9c61-5f88b3e4341f' class='xr-var-data-in' type='checkbox'><label for='data-481beffb-c8d0-451a-9c61-5f88b3e4341f' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 8, 10,  7, ..., 10, 12, 13],\n",
       "         [ 6, 14,  5, ...,  8, 13, 12],\n",
       "         [ 5, 11, 10, ...,  9, 13,  5],\n",
       "         ...,\n",
       "         [10,  9, 14, ...,  7, 14,  9],\n",
       "         [14, 12, 10, ...,  5,  7, 12],\n",
       "         [10,  9, 10, ..., 10,  6,  7]],\n",
       "\n",
       "        [[13,  8, 13, ...,  6, 11, 11],\n",
       "         [14, 10, 13, ..., 10,  5,  6],\n",
       "         [ 9,  9, 14, ..., 11,  5,  9],\n",
       "         ...,\n",
       "         [ 6,  8,  7, ..., 12,  6, 11],\n",
       "         [ 7,  8, 13, ..., 13, 10,  9],\n",
       "         [ 5,  6, 14, ...,  5,  8,  6]],\n",
       "\n",
       "        [[ 9,  7, 13, ...,  7,  6,  7],\n",
       "         [14,  9, 10, ..., 12, 10,  7],\n",
       "         [ 7, 14, 11, ..., 11, 14, 12],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 6, 10,  6, ...,  6, 10, 10],\n",
       "         [11, 13,  5, ...,  7, 12,  8],\n",
       "         [ 5, 13,  7, ..., 10, 14, 13]],\n",
       "\n",
       "        [[13,  5, 13, ..., 11,  9, 12],\n",
       "         [12,  7,  9, ..., 10,  5, 14],\n",
       "         [ 8, 11, 12, ...,  7, 14, 10],\n",
       "         ...,\n",
       "         [13, 10, 12, ...,  7, 14, 12],\n",
       "         [ 8, 12, 10, ..., 12,  5,  6],\n",
       "         [ 5,  6, 14, ..., 12, 13, 12]],\n",
       "\n",
       "        [[13,  5,  7, ...,  9,  5,  5],\n",
       "         [ 8,  6,  7, ...,  7, 10, 10],\n",
       "         [ 9, 13, 11, ..., 13, 13, 10],\n",
       "         ...,\n",
       "         [10, 14,  8, ...,  5,  5,  9],\n",
       "         [ 6, 10, 11, ..., 13,  6,  9],\n",
       "         [12, 14, 10, ..., 12,  9,  6]]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>152876 152879 152882 152885 152888</div><input id='attrs-88b9397e-52dd-4566-a517-cc5b72d70d32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88b9397e-52dd-4566-a517-cc5b72d70d32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bfb0e54-2ef2-4c92-912e-8e7af3bd7ba8' class='xr-var-data-in' type='checkbox'><label for='data-7bfb0e54-2ef2-4c92-912e-8e7af3bd7ba8' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([152876, 152879, 152882, 152885, 152888])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_max_value</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>160.0 163.0 162.0 165.0 166.0</div><input id='attrs-7aa7b3bf-5de1-46fa-b16b-f2bb49df0b81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7aa7b3bf-5de1-46fa-b16b-f2bb49df0b81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b6d3311-c634-4c46-a113-c06f2bda179d' class='xr-var-data-in' type='checkbox'><label for='data-9b6d3311-c634-4c46-a113-c06f2bda179d' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([160., 163., 162., 165., 166.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.145e+07 1.166e+07 ... 1.182e+07</div><input id='attrs-bcccfed3-4467-425c-921d-c58a51457bf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcccfed3-4467-425c-921d-c58a51457bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-575082b8-e747-4d10-9f8f-33f7fbcb7424' class='xr-var-data-in' type='checkbox'><label for='data-575082b8-e747-4d10-9f8f-33f7fbcb7424' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([11445712., 11658331., 11535839., 11799101., 11824045.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.5 0.0 0.5 1.0</div><input id='attrs-62b31496-26de-4cac-a549-3fa0e8cac49b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62b31496-26de-4cac-a549-3fa0e8cac49b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e06a9e90-3eda-4e60-8383-f39abf091695' class='xr-var-data-in' type='checkbox'><label for='data-e06a9e90-3eda-4e60-8383-f39abf091695' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -0.5,  0. ,  0.5,  1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.5 0.0 0.5 1.0</div><input id='attrs-8b4614d1-788a-4ff8-a685-0bb67755cab8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b4614d1-788a-4ff8-a685-0bb67755cab8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30bc3ea5-000a-492d-b1a1-11110efb1bae' class='xr-var-data-in' type='checkbox'><label for='data-30bc3ea5-000a-492d-b1a1-11110efb1bae' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -0.5,  0. ,  0.5,  1. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4865efac-f6d2-49ea-8230-0ab956657b12' class='xr-section-summary-in' type='checkbox'  ><label for='section-4865efac-f6d2-49ea-8230-0ab956657b12' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dbd8c3ad-6908-4b40-9b1f-32eedab0cc8b' class='xr-index-data-in' type='checkbox'/><label for='index-dbd8c3ad-6908-4b40-9b1f-32eedab0cc8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1718475532.0808392,      1718475532.83,   1718475533.63973,\n",
       "       1718475534.4423587,  1718475535.242229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9a97f99-7fe4-4019-861b-31606f2bcd2f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9a97f99-7fe4-4019-861b-31606f2bcd2f' 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> Size: 16MB\n",
       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 16MB 8 10 ... 9 6\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152876 152879 152882 152885 152888\n",
       "    simdet_stats1_max_value  (time) float64 40B 160.0 163.0 162.0 165.0 166.0\n",
       "    simdet_stats1_total      (time) float64 40B 1.145e+07 ... 1.182e+07\n",
       "    m1                       (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m1_user_setpoint         (time) float64 40B -1.0 -0.5 0.0 0.5 1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uids = RE(bp.scan([simdet], m1, -1, 1, 5))\n",
    "dataset = cat[uids[-1]].primary.read()\n",
    "image_data = dataset[\"simdet_image\"]\n",
    "print(f\"{image_data.shape=!r}\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The (1-D) `bp.scan()` plan with two motors\n",
    "\n",
    "Step scan two motors together (m1 & m2) as in the previous section; at each step:\n",
    "\n",
    "- Measure 3 images (as configured above during detector staging).\n",
    "- Record total counts.\n",
    "- Record maximum counts value.\n",
    "\n",
    "For clarity, the `scan` arguments are split into multiple lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 95     Time: 2024-06-15 13:18:56\n",
      "Persistent Unique Scan ID: 'b0fc4001-c2e2-468a-ac82-1ccb19ddbdb3'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "device=MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']), exception=Data keys (field names) from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']) collide with those from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']). The colliding keys are {'m2', 'm2_user_setpoint'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|   seq_num |       time |         m1 |         m2 | simdet_stats1_unique_id | simdet_stats1_max_value | simdet_stats1_total |\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|         1 | 13:19:00.1 |    -1.0000 |     4.0000 |                  152891 |                     163 |            11659109 |\n",
      "|         2 | 13:19:00.9 |    -0.5000 |     3.3750 |                  152894 |                     159 |            11355578 |\n",
      "|         3 | 13:19:01.8 |     0.0000 |     2.7500 |                  152897 |                     164 |            11659418 |\n",
      "|         4 | 13:19:02.7 |     0.5000 |     2.1250 |                  152900 |                     162 |            11536310 |\n",
      "|         5 | 13:19:03.6 |     1.0000 |     1.5000 |                  152903 |                     163 |            11658922 |\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "generator scan ['b0fc4001'] (scan num: 95)\n",
      "uids=!r\n",
      "image_data.shape=(5, 3, 1024, 1024)\n"
     ]
    },
    {
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       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 16MB\n",
       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 16MB 8 6 ... 7 6\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152891 152894 152897 152900 152903\n",
       "    simdet_stats1_max_value  (time) float64 40B 163.0 159.0 164.0 162.0 163.0\n",
       "    simdet_stats1_total      (time) float64 40B 1.166e+07 ... 1.166e+07\n",
       "    m1                       (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m1_user_setpoint         (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m2                       (time) float64 40B 4.0 3.375 2.75 2.125 1.5\n",
       "    m2_user_setpoint         (time) float64 40B 4.0 3.375 2.75 2.125 1.5</pre><div class='xr-wrap' style='display:none'><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-59202596-1f72-4242-ad37-acf6cd6a8e3f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59202596-1f72-4242-ad37-acf6cd6a8e3f' 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>: 5</li><li><span>dim_0</span>: 3</li><li><span>dim_1</span>: 1024</li><li><span>dim_2</span>: 1024</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f7a72146-e6e8-44c5-a382-3c42a2c9049d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7a72146-e6e8-44c5-a382-3c42a2c9049d' 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'>1.718e+09 1.718e+09 ... 1.718e+09</div><input id='attrs-2b11d663-0d98-40c2-b437-4696e7cf6209' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b11d663-0d98-40c2-b437-4696e7cf6209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-649ee34c-2f76-4604-aa3a-c0fe198b4be5' class='xr-var-data-in' type='checkbox'><label for='data-649ee34c-2f76-4604-aa3a-c0fe198b4be5' 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([1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f03d8313-0e67-4614-ac84-8777ee0318ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f03d8313-0e67-4614-ac84-8777ee0318ef' class='xr-section-summary' >Data variables: <span>(8)</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>simdet_image</span></div><div class='xr-var-dims'>(time, dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>8 6 9 6 14 14 14 ... 14 7 11 11 7 6</div><input id='attrs-b7902ece-cb24-4afb-ae69-886bfb8ba50c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7902ece-cb24-4afb-ae69-886bfb8ba50c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62d135e8-3aba-45c1-8051-31b2f3d8217a' class='xr-var-data-in' type='checkbox'><label for='data-62d135e8-3aba-45c1-8051-31b2f3d8217a' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 8,  6,  9, ..., 13, 13, 11],\n",
       "         [13, 13,  5, ...,  8,  8, 13],\n",
       "         [ 6,  6, 13, ..., 12, 13,  5],\n",
       "         ...,\n",
       "         [12,  6,  5, ..., 13, 14, 12],\n",
       "         [ 5, 12,  5, ..., 13, 10,  8],\n",
       "         [13,  6, 10, ..., 14, 10,  8]],\n",
       "\n",
       "        [[ 5, 13, 11, ..., 13,  8,  7],\n",
       "         [ 6,  8, 12, ..., 12, 12,  7],\n",
       "         [10,  6, 10, ...,  8,  8,  7],\n",
       "         ...,\n",
       "         [ 7, 10,  7, ..., 10,  9,  5],\n",
       "         [ 6, 13, 10, ...,  9, 14, 13],\n",
       "         [12,  9,  8, ...,  8,  5, 11]],\n",
       "\n",
       "        [[ 6,  5, 11, ..., 14, 10,  8],\n",
       "         [ 8, 11, 12, ...,  5, 11,  7],\n",
       "         [ 7,  7,  7, ...,  6,  7, 10],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 6, 12, 14, ..., 11,  7, 14],\n",
       "         [10,  6,  9, ...,  8,  6, 13],\n",
       "         [10, 10,  8, ...,  6, 10,  6]],\n",
       "\n",
       "        [[14,  9, 10, ..., 11, 13,  5],\n",
       "         [12,  8, 13, ...,  5, 13,  7],\n",
       "         [13, 13, 12, ...,  6, 13,  8],\n",
       "         ...,\n",
       "         [ 9,  8,  7, ...,  6, 11, 10],\n",
       "         [ 7, 10,  7, ..., 14,  7, 10],\n",
       "         [ 8, 11,  8, ...,  9,  9,  6]],\n",
       "\n",
       "        [[ 8, 11,  8, ..., 11, 13,  9],\n",
       "         [11,  5,  5, ...,  7,  8,  9],\n",
       "         [13,  9,  7, ..., 12,  9,  8],\n",
       "         ...,\n",
       "         [14,  9,  6, ...,  7, 12, 14],\n",
       "         [ 7,  9, 13, ..., 12,  8,  6],\n",
       "         [12, 13, 11, ..., 11,  7,  6]]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>152891 152894 152897 152900 152903</div><input id='attrs-53994c44-b7c2-4523-88eb-c4d01099492a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53994c44-b7c2-4523-88eb-c4d01099492a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a456a8-5dad-4b06-902f-a5efa712f587' class='xr-var-data-in' type='checkbox'><label for='data-59a456a8-5dad-4b06-902f-a5efa712f587' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([152891, 152894, 152897, 152900, 152903])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_max_value</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>163.0 159.0 164.0 162.0 163.0</div><input id='attrs-60a9e99f-a818-4ab6-a347-0d34ed22f78b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60a9e99f-a818-4ab6-a347-0d34ed22f78b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab83b507-3818-4f47-b28b-0870eff0a3dc' class='xr-var-data-in' type='checkbox'><label for='data-ab83b507-3818-4f47-b28b-0870eff0a3dc' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([163., 159., 164., 162., 163.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.166e+07 1.136e+07 ... 1.166e+07</div><input id='attrs-1380e29b-9d46-414c-a7ad-7fcce24793a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1380e29b-9d46-414c-a7ad-7fcce24793a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc0f9414-32c6-4d51-84ed-e77ba28e7a6e' class='xr-var-data-in' type='checkbox'><label for='data-bc0f9414-32c6-4d51-84ed-e77ba28e7a6e' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([11659109., 11355578., 11659418., 11536310., 11658922.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.5 0.0 0.5 1.0</div><input id='attrs-eed0935b-c408-4a7a-a465-57dbc124cacd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eed0935b-c408-4a7a-a465-57dbc124cacd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac40ed7a-f64e-49b2-8f76-f22724bf5059' class='xr-var-data-in' type='checkbox'><label for='data-ac40ed7a-f64e-49b2-8f76-f22724bf5059' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -0.5,  0. ,  0.5,  1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.5 0.0 0.5 1.0</div><input id='attrs-562dcfce-0f66-4254-819d-d4295b6ea446' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-562dcfce-0f66-4254-819d-d4295b6ea446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436a0142-c341-498b-8a83-3aebe1f47251' class='xr-var-data-in' type='checkbox'><label for='data-436a0142-c341-498b-8a83-3aebe1f47251' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -0.5,  0. ,  0.5,  1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.0 3.375 2.75 2.125 1.5</div><input id='attrs-fca101ea-19cd-4d42-9aa4-09a955a80a14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca101ea-19cd-4d42-9aa4-09a955a80a14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6365ac08-2a1d-49be-8d51-8938c2742dff' class='xr-var-data-in' type='checkbox'><label for='data-6365ac08-2a1d-49be-8d51-8938c2742dff' 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>m2</dd></dl></div><div class='xr-var-data'><pre>array([4.   , 3.375, 2.75 , 2.125, 1.5  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m2_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.0 3.375 2.75 2.125 1.5</div><input id='attrs-2e03ac2c-9f04-4ecf-a42d-96b601e61381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e03ac2c-9f04-4ecf-a42d-96b601e61381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372ba54c-74a0-47d5-851d-205af1081770' class='xr-var-data-in' type='checkbox'><label for='data-372ba54c-74a0-47d5-851d-205af1081770' 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>m2</dd></dl></div><div class='xr-var-data'><pre>array([4.   , 3.375, 2.75 , 2.125, 1.5  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c37b0ef-8672-4cf7-8bde-b3178aede7e7' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c37b0ef-8672-4cf7-8bde-b3178aede7e7' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1de9eb6f-6367-4ff2-9f34-3ebf7703e626' class='xr-index-data-in' type='checkbox'/><label for='index-1de9eb6f-6367-4ff2-9f34-3ebf7703e626' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1718475540.1103883, 1718475540.9505434,  1718475541.860732,\n",
       "       1718475542.7624683,  1718475543.662016],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8f30c1f-efba-4220-bfc6-77b44fca1c13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8f30c1f-efba-4220-bfc6-77b44fca1c13' 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> Size: 16MB\n",
       "Dimensions:                  (time: 5, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 40B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 16MB 8 6 ... 7 6\n",
       "    simdet_stats1_unique_id  (time) int64 40B 152891 152894 152897 152900 152903\n",
       "    simdet_stats1_max_value  (time) float64 40B 163.0 159.0 164.0 162.0 163.0\n",
       "    simdet_stats1_total      (time) float64 40B 1.166e+07 ... 1.166e+07\n",
       "    m1                       (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m1_user_setpoint         (time) float64 40B -1.0 -0.5 0.0 0.5 1.0\n",
       "    m2                       (time) float64 40B 4.0 3.375 2.75 2.125 1.5\n",
       "    m2_user_setpoint         (time) float64 40B 4.0 3.375 2.75 2.125 1.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uids = RE(\n",
    "    bp.scan(\n",
    "        [simdet],\n",
    "        m1, -1, 1,\n",
    "        m2, 4, 1.5,\n",
    "        5,\n",
    "    )\n",
    ")\n",
    "print(f\"uids=!r\")\n",
    "\n",
    "dataset = cat[uids[-1]].primary.read()\n",
    "image_data = dataset[\"simdet_image\"]\n",
    "print(f\"{image_data.shape=!r}\")\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the sequence of motor positions\n",
    "\n",
    "Both motors move at the same time.  The same number of steps are taken by each\n",
    "motor.  Below is a plot showing the readback values of each motor during a\n",
    "two-motor `scan` versus the data point number:\n",
    "\n",
    "![grid_scan motors v point number](../_static/scan_two_motors.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The (2-D) `bp.grid_scan()` (mesh) plan\n",
    "\n",
    "Step scan two motors (m1 & m2) as in the previous section; at each step:\n",
    "\n",
    "- Measure 3 images (as configured aboveduring detector staging).\n",
    "- Record total counts.\n",
    "- Record maximum counts value.\n",
    "\n",
    "For clarity, the `grid_scan` arguments are split into multiple lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 96     Time: 2024-06-15 13:19:04\n",
      "Persistent Unique Scan ID: '5d5d851f-d953-4926-adb2-68e2cf658890'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "device=MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']), exception=Data keys (field names) from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']) collide with those from MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu']). The colliding keys are {'m2', 'm2_user_setpoint'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|   seq_num |       time |         m1 |         m2 | simdet_stats1_unique_id | simdet_stats1_max_value | simdet_stats1_total |\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "|         1 | 13:19:07.1 |    -1.0000 |    -0.5000 |                  152906 |                     164 |            11663011 |\n",
      "|         2 | 13:19:07.6 |    -1.0000 |    -0.1667 |                  152909 |                     160 |            11445949 |\n",
      "|         3 | 13:19:08.2 |    -1.0000 |     0.1667 |                  152912 |                     159 |            11355981 |\n",
      "|         4 | 13:19:08.8 |    -1.0000 |     0.5000 |                  152915 |                     164 |            11677059 |\n",
      "|         5 | 13:19:09.6 |    -0.5000 |     0.5000 |                  152918 |                     164 |            11708538 |\n",
      "|         6 | 13:19:10.2 |    -0.5000 |     0.1667 |                  152921 |                     166 |            11846736 |\n",
      "|         7 | 13:19:10.8 |    -0.5000 |    -0.1667 |                  152924 |                     164 |            11692952 |\n",
      "|         8 | 13:19:11.4 |    -0.5000 |    -0.5000 |                  152927 |                     164 |            11693749 |\n",
      "|         9 | 13:19:12.2 |     0.0000 |    -0.5000 |                  152930 |                     163 |            11657723 |\n",
      "|        10 | 13:19:12.8 |     0.0000 |    -0.1667 |                  152933 |                     162 |            11536782 |\n",
      "|        11 | 13:19:13.4 |     0.0000 |     0.1667 |                  152936 |                     164 |            11717110 |\n",
      "|        12 | 13:19:14.0 |     0.0000 |     0.5000 |                  152939 |                     160 |            11411453 |\n",
      "|        13 | 13:19:14.8 |     0.5000 |     0.5000 |                  152942 |                     161 |            11486128 |\n",
      "|        14 | 13:19:15.4 |     0.5000 |     0.1667 |                  152945 |                     164 |            11702804 |\n",
      "|        15 | 13:19:16.0 |     0.5000 |    -0.1667 |                  152948 |                     164 |            11728435 |\n",
      "|        16 | 13:19:16.6 |     0.5000 |    -0.5000 |                  152951 |                     164 |            11695619 |\n",
      "|        17 | 13:19:17.5 |     1.0000 |    -0.5000 |                  152954 |                     159 |            11388541 |\n",
      "|        18 | 13:19:18.1 |     1.0000 |    -0.1667 |                  152957 |                     160 |            11455055 |\n",
      "|        19 | 13:19:18.7 |     1.0000 |     0.1667 |                  152960 |                     160 |            11394404 |\n",
      "|        20 | 13:19:19.2 |     1.0000 |     0.5000 |                  152963 |                     165 |            11773811 |\n",
      "+-----------+------------+------------+------------+-------------------------+-------------------------+---------------------+\n",
      "generator grid_scan ['5d5d851f'] (scan num: 96)\n",
      "uids=!r\n",
      "image_data.shape=(20, 3, 1024, 1024)\n"
     ]
    },
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       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 63MB\n",
       "Dimensions:                  (time: 20, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 160B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 63MB 8 14 ... 8 6\n",
       "    simdet_stats1_unique_id  (time) int64 160B 152906 152909 ... 152960 152963\n",
       "    simdet_stats1_max_value  (time) float64 160B 164.0 160.0 ... 160.0 165.0\n",
       "    simdet_stats1_total      (time) float64 160B 1.166e+07 ... 1.177e+07\n",
       "    m1                       (time) float64 160B -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    m1_user_setpoint         (time) float64 160B -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    m2                       (time) float64 160B -0.5 -0.1667 ... 0.1667 0.5\n",
       "    m2_user_setpoint         (time) float64 160B -0.5 -0.1667 ... 0.1667 0.5</pre><div class='xr-wrap' style='display:none'><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-339bbca0-9d0a-4488-b5a5-99aa6ac36b10' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-339bbca0-9d0a-4488-b5a5-99aa6ac36b10' 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>: 20</li><li><span>dim_0</span>: 3</li><li><span>dim_1</span>: 1024</li><li><span>dim_2</span>: 1024</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-649e9464-aaf5-49cb-9cb1-5395c55446bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-649e9464-aaf5-49cb-9cb1-5395c55446bb' 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'>1.718e+09 1.718e+09 ... 1.718e+09</div><input id='attrs-1bdef301-deda-4b01-93b4-513d0d27da6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bdef301-deda-4b01-93b4-513d0d27da6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bfebcf1-6acc-499b-832d-ca85b97f780f' class='xr-var-data-in' type='checkbox'><label for='data-5bfebcf1-6acc-499b-832d-ca85b97f780f' 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([1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09,\n",
       "       1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09,\n",
       "       1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09,\n",
       "       1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09, 1.718476e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5761293f-fbc0-4391-8a34-a2a0835cf18b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5761293f-fbc0-4391-8a34-a2a0835cf18b' class='xr-section-summary' >Data variables: <span>(8)</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>simdet_image</span></div><div class='xr-var-dims'>(time, dim_0, dim_1, dim_2)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>8 14 13 12 13 5 6 ... 10 8 8 6 8 6</div><input id='attrs-5ce69aee-2620-4e5b-8c18-f56ba22e1c98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce69aee-2620-4e5b-8c18-f56ba22e1c98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21c9522f-84bd-40b5-ae7e-bd61c4aa3026' class='xr-var-data-in' type='checkbox'><label for='data-21c9522f-84bd-40b5-ae7e-bd61c4aa3026' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 8, 14, 13, ..., 11,  6,  5],\n",
       "         [ 9,  8, 12, ...,  9, 11, 13],\n",
       "         [ 9, 14, 12, ..., 14,  8,  9],\n",
       "         ...,\n",
       "         [ 8, 10, 11, ...,  7,  9,  8],\n",
       "         [11, 11, 14, ...,  5, 10, 12],\n",
       "         [ 5, 10,  6, ...,  7, 13,  6]],\n",
       "\n",
       "        [[11, 13,  9, ...,  5,  9, 12],\n",
       "         [ 8, 10, 11, ..., 13,  9, 14],\n",
       "         [10, 11, 11, ...,  5, 11,  9],\n",
       "         ...,\n",
       "         [11,  9,  7, ...,  7,  6,  5],\n",
       "         [ 9,  9,  6, ...,  6,  5,  9],\n",
       "         [ 5, 13,  5, ..., 10, 11, 10]],\n",
       "\n",
       "        [[12,  7,  8, ..., 11, 14, 14],\n",
       "         [ 5, 14,  5, ...,  9, 14, 11],\n",
       "         [ 5,  8,  6, ...,  6,  5, 14],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 5,  6,  9, ..., 10,  6,  9],\n",
       "         [ 7,  6, 13, ..., 14,  6,  9],\n",
       "         [ 5,  6, 10, ..., 10,  9,  9]],\n",
       "\n",
       "        [[11,  7,  5, ...,  8, 10, 11],\n",
       "         [ 8, 14, 14, ...,  8,  9, 13],\n",
       "         [ 6,  7, 12, ...,  5, 10, 11],\n",
       "         ...,\n",
       "         [11,  8, 12, ...,  9, 10, 14],\n",
       "         [ 8,  9, 12, ..., 13,  7,  6],\n",
       "         [ 7,  5,  7, ..., 14,  6,  5]],\n",
       "\n",
       "        [[ 6,  6,  9, ..., 11, 11,  9],\n",
       "         [11,  7,  9, ..., 13, 13, 13],\n",
       "         [10, 13,  9, ..., 14,  7,  8],\n",
       "         ...,\n",
       "         [11,  8, 14, ...,  5,  8, 13],\n",
       "         [ 7, 12,  7, ...,  7, 11,  9],\n",
       "         [10, 14,  5, ...,  6,  8,  6]]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_unique_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>152906 152909 ... 152960 152963</div><input id='attrs-092939ba-28de-44a6-8b13-32e4dcc97b20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-092939ba-28de-44a6-8b13-32e4dcc97b20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2caf5121-2be4-4877-a082-712d6bca3416' class='xr-var-data-in' type='checkbox'><label for='data-2caf5121-2be4-4877-a082-712d6bca3416' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([152906, 152909, 152912, 152915, 152918, 152921, 152924, 152927,\n",
       "       152930, 152933, 152936, 152939, 152942, 152945, 152948, 152951,\n",
       "       152954, 152957, 152960, 152963])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_max_value</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>164.0 160.0 159.0 ... 160.0 165.0</div><input id='attrs-af31b26b-5e57-48f9-aa54-ccba43f98c63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af31b26b-5e57-48f9-aa54-ccba43f98c63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd827f0f-caae-448e-920d-b0387e7c0610' class='xr-var-data-in' type='checkbox'><label for='data-dd827f0f-caae-448e-920d-b0387e7c0610' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([164., 160., 159., 164., 164., 166., 164., 164., 163., 162., 164.,\n",
       "       160., 161., 164., 164., 164., 159., 160., 160., 165.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simdet_stats1_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.166e+07 1.145e+07 ... 1.177e+07</div><input id='attrs-f43c7e46-f41a-4c83-a7e8-28be3dcce8d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f43c7e46-f41a-4c83-a7e8-28be3dcce8d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f64949f2-95c2-43d6-a80d-2907b54e7a4d' class='xr-var-data-in' type='checkbox'><label for='data-f64949f2-95c2-43d6-a80d-2907b54e7a4d' 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>simdet</dd></dl></div><div class='xr-var-data'><pre>array([11663011., 11445949., 11355981., 11677059., 11708538., 11846736.,\n",
       "       11692952., 11693749., 11657723., 11536782., 11717110., 11411453.,\n",
       "       11486128., 11702804., 11728435., 11695619., 11388541., 11455055.,\n",
       "       11394404., 11773811.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -1.0 -1.0 -1.0 ... 1.0 1.0 1.0</div><input id='attrs-158e5240-530f-4e59-b592-50aad90d74e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-158e5240-530f-4e59-b592-50aad90d74e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798778a5-6cd2-4e14-b259-d61a8e4f4f13' class='xr-var-data-in' type='checkbox'><label for='data-798778a5-6cd2-4e14-b259-d61a8e4f4f13' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -1. , -1. , -1. , -0.5, -0.5, -0.5, -0.5,  0. ,  0. ,  0. ,\n",
       "        0. ,  0.5,  0.5,  0.5,  0.5,  1. ,  1. ,  1. ,  1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m1_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -1.0 -1.0 -1.0 ... 1.0 1.0 1.0</div><input id='attrs-a92286fc-a36e-42f3-a907-6f00166d4ab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a92286fc-a36e-42f3-a907-6f00166d4ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42eb6e1d-99f9-44dd-afe8-9bd447420841' class='xr-var-data-in' type='checkbox'><label for='data-42eb6e1d-99f9-44dd-afe8-9bd447420841' 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>m1</dd></dl></div><div class='xr-var-data'><pre>array([-1. , -1. , -1. , -1. , -0.5, -0.5, -0.5, -0.5,  0. ,  0. ,  0. ,\n",
       "        0. ,  0.5,  0.5,  0.5,  0.5,  1. ,  1. ,  1. ,  1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5 -0.1667 0.1667 ... 0.1667 0.5</div><input id='attrs-129b2fc8-0b58-41fb-9347-23429edffd1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-129b2fc8-0b58-41fb-9347-23429edffd1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21ff222-79c1-4775-9214-811bb97db79f' class='xr-var-data-in' type='checkbox'><label for='data-f21ff222-79c1-4775-9214-811bb97db79f' 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>m2</dd></dl></div><div class='xr-var-data'><pre>array([-0.5   , -0.1667,  0.1667,  0.5   ,  0.5   ,  0.1667, -0.1667,\n",
       "       -0.5   , -0.5   , -0.1667,  0.1667,  0.5   ,  0.5   ,  0.1667,\n",
       "       -0.1667, -0.5   , -0.5   , -0.1667,  0.1667,  0.5   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m2_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5 -0.1667 0.1667 ... 0.1667 0.5</div><input id='attrs-cd529a3a-568f-46e4-82d2-d49fbeba4597' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd529a3a-568f-46e4-82d2-d49fbeba4597' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af0124f9-0aec-4585-8b73-c5c963db74f2' class='xr-var-data-in' type='checkbox'><label for='data-af0124f9-0aec-4585-8b73-c5c963db74f2' 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>m2</dd></dl></div><div class='xr-var-data'><pre>array([-0.5       , -0.16666667,  0.16666667,  0.5       ,  0.5       ,\n",
       "        0.16666667, -0.16666667, -0.5       , -0.5       , -0.16666667,\n",
       "        0.16666667,  0.5       ,  0.5       ,  0.16666667, -0.16666667,\n",
       "       -0.5       , -0.5       , -0.16666667,  0.16666667,  0.5       ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b1c3b30-fa35-4f3a-a94f-7683f98109a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b1c3b30-fa35-4f3a-a94f-7683f98109a3' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f54ebef1-af73-4636-bca0-10f0ac007b0d' class='xr-index-data-in' type='checkbox'/><label for='index-f54ebef1-af73-4636-bca0-10f0ac007b0d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1718475547.1016889, 1718475547.6889985, 1718475548.2715652,\n",
       "       1718475548.8735123, 1718475549.6955707,  1718475550.283441,\n",
       "       1718475550.8870528, 1718475551.4843397, 1718475552.2825255,\n",
       "       1718475552.8914993, 1718475553.4914536, 1718475554.0983245,\n",
       "        1718475554.898487, 1718475555.4984066,  1718475556.094555,\n",
       "       1718475556.6871016, 1718475557.5042434, 1718475558.1027176,\n",
       "        1718475558.702482, 1718475559.2953453],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-596d5917-c8bd-4ff3-a28a-635ef7e6b2a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-596d5917-c8bd-4ff3-a28a-635ef7e6b2a4' 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> Size: 63MB\n",
       "Dimensions:                  (time: 20, dim_0: 3, dim_1: 1024, dim_2: 1024)\n",
       "Coordinates:\n",
       "  * time                     (time) float64 160B 1.718e+09 ... 1.718e+09\n",
       "Dimensions without coordinates: dim_0, dim_1, dim_2\n",
       "Data variables:\n",
       "    simdet_image             (time, dim_0, dim_1, dim_2) uint8 63MB 8 14 ... 8 6\n",
       "    simdet_stats1_unique_id  (time) int64 160B 152906 152909 ... 152960 152963\n",
       "    simdet_stats1_max_value  (time) float64 160B 164.0 160.0 ... 160.0 165.0\n",
       "    simdet_stats1_total      (time) float64 160B 1.166e+07 ... 1.177e+07\n",
       "    m1                       (time) float64 160B -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    m1_user_setpoint         (time) float64 160B -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    m2                       (time) float64 160B -0.5 -0.1667 ... 0.1667 0.5\n",
       "    m2_user_setpoint         (time) float64 160B -0.5 -0.1667 ... 0.1667 0.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uids = RE(\n",
    "    bp.grid_scan(\n",
    "        [simdet], \n",
    "        m1, -1, 1, 5,\n",
    "        m2, -0.5, 0.5, 4,\n",
    "        snake_axes=True,\n",
    "    )\n",
    ")\n",
    "print(f\"uids=!r\")\n",
    "\n",
    "dataset = cat[uids[-1]].primary.read()\n",
    "image_data = dataset[\"simdet_image\"]\n",
    "print(f\"{image_data.shape=!r}\")\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the sequence of motor positions\n",
    "\n",
    "<details>\n",
    "\n",
    "Here is the sequence of how the motors are stepped during a `grid_scan`:\n",
    "\n",
    "- `m1` is stepped through its range; at each step of `m1`:\n",
    "  - At each step:\n",
    "    - `m2` is stepped through its range (`m1` does not move).\n",
    "      - With `snake_axes=True`, the `m2` changes direction each time `m1` is stepped.\n",
    "      - At each step:\n",
    "        - Measure 3 images (as configured aboveduring detector staging).\n",
    "        - Record total counts.\n",
    "        - Record maximum counts value.\n",
    "\n",
    "The `grid_scan` with `snake_axes=True` progresses through the sequence of (`m1`, `m2`) combinations as would a snake.\n",
    "\n",
    "</details>\n",
    "\n",
    "The second motor of the `grid_scan` (`m2`) is snaked.  It's direction changes with each subsequent increment of the first motor (`m1`).  Here's a plot of readback value of each motor from a `grid_scan` plotted *v*. data point number:\n",
    "\n",
    "![grid_scan motors v time-of-day](../_static/grid_scan_snake.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPEC data file\n",
    "\n",
    "All along, quietly in the background, the data has been saved both to a catalog\n",
    "and to a SPEC data file.  The total counts, max counts, and\n",
    "unique ID from all scans can be plotted from the SPEC file using PyMCA or NeXpy.\n",
    "\n",
    "The image content is not stored in the SPEC file, so images cannot be viewed\n",
    "from it. However, references to the images are saved as comments.  See the\n",
    "comment lines that match a search for\n",
    "\n",
    "`resource {'spec': 'AD_HDF5'`\n",
    "\n",
    "Here is an example:\n",
    "\n",
    "```\n",
    "#C Thu Jun 13 11:36:38 2024.  resource {'spec': 'AD_HDF5', 'root': '/', 'resource_path': 'mnt/iockad/tmp/d94f633b-12a7-4ff0-92af_000000.h5', 'resource_kwargs': {'frame_per_point': 3}, 'path_semantics': 'posix', 'uid': '5328206b-dd13-49f8-b108-17e0d867d470', 'run_start': 'e8c24278-656f-4c79-b3eb-d522310f7649'}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#F /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/docs/source/howto/20240615-131838.dat\n",
      "#E 1718475518\n",
      "#D Sat Jun 15 13:18:38 2024\n",
      "#C Bluesky  user = prjemian  host = arf.jemian.org\n",
      "#O0 dcm_m_theta  dcm_m_y  dcm_m_z  fourc_chi  fourc_omega  fourc_phi  fourc_tth  m1\n",
      "#O1 m10  m11  m12  m13  m14  m15  m16  m2\n",
      "#O2 m3  m4  m7  m8  m9  sixc_chi  sixc_delta  sixc_gamma\n",
      "#O3 sixc_mu  sixc_omega  sixc_phi\n",
      "#o0 dcm_m_theta  dcm_m_y  dcm_m_z  fourc_chi  fourc_omega  fourc_phi  fourc_tth  m1\n",
      "#o1 m10  m11  m12  m13  m14  m15  m16  m2\n",
      "#o2 m3  m4  m7  m8  m9  sixc_chi  sixc_delta  sixc_gamma\n",
      "#o3 sixc_mu  sixc_omega  sixc_phi\n",
      "\n",
      "#S 92  count(detectors=['simdet_stats1_unique_id', 'simdet_stats1_total', 'simdet_stats1_max_value'], num=5, delay=None)\n",
      "#D Sat Jun 15 13:18:40 2024\n",
      "#C Sat Jun 15 13:18:40 2024.  plan_type = generator\n",
      "#C Sat Jun 15 13:18:40 2024.  uid = ceaecdd7-e1af-411f-94f2-c14d5d3debab\n",
      "#MD uid = ceaecdd7-e1af-411f-94f2-c14d5d3debab\n",
      "#MD beamline_id = Bluesky_training\n",
      "#MD conda_prefix = /home/prjemian/.conda/envs/bluesky_2024_2\n",
      "#MD databroker_catalog = training\n",
      "#MD detectors = ['simdet_stats1_unique_id', 'simdet_stats1_total', 'simdet_stats1_max_value']\n",
      "#MD iconfig = {'ADSIM_IOC_PREFIX': 'ad:', 'GP_IOC_PREFIX': 'gp:', 'DATABROKER_CATALOG': 'training', 'RUNENGINE_METADATA': {'beamline_id': 'Bluesky_training', 'instrument_name': 'BCDA EPICS Bluesky training', 'proposal_id': 'training', 'databroker_catalog': 'training'}, 'RUN_ENGINE_SCAN_ID_PV': 'gp:gp:int20', 'AD_IMAGE_DIR': 'adsimdet/%Y/%m/%d', 'AD_MOUNT_PATH': '/tmp', 'BLUESKY_MOUNT_PATH': '/tmp/docker_ioc/iocad/tmp', 'ALLOW_AREA_DETECTOR_WARMUP': True, 'ENABLE_AREA_DETECTOR_IMAGE_PLUGIN': True, 'ENABLE_CALCS': True, 'USE_PROGRESS_BAR': False, 'WRITE_NEXUS_DATA_FILES': False, 'NEXUS_WARN_MISSING_CONTENT': False, 'NEXUS_FILE_EXTENSION': 'h5', 'WRITE_SPEC_DATA_FILES': True, 'RUNENGINE_MD_PATH': '/home/prjemian/.config/Bluesky_RunEngine_md', 'LOGGING': {'NUMBER_OF_PREVIOUS_BACKUPS': 9}, 'PV_READ_TIMEOUT': 15, 'PV_WRITE_TIMEOUT': 15, 'PV_CONNECTION_TIMEOUT': 15, 'XMODE_DEBUG_LEVEL': 'Minimal', 'MINIMUM_PYTHON_VERSION': [3, 8], 'MINIMUM_BLUESKY_VERSION': [1, 10], 'MINIMUM_OPHYD_VERSION': [1, 7], 'MINIMUM_DATABROKER_VERSION': [1, 2], 'ICONFIG_VERSION': '1.0.1'}\n",
      "#MD instrument_name = BCDA EPICS Bluesky training\n",
      "#MD login_id = prjemian@arf.jemian.org\n",
      "#MD num_intervals = 4\n",
      "#MD num_points = 5\n",
      "#MD pid = 154110\n",
      "#MD proposal_id = training\n",
      "#MD versions = {'apstools': '1.6.19', 'bluesky': '1.13.0a3', 'databroker': '1.2.5', 'epics': '3.5.2', 'h5py': '3.9.0', 'intake': '0.6.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'ophyd': '1.9.0', 'pyRestTable': '2020.0.8', 'spec2nexus': '2021.2.6'}\n",
      "#P0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n",
      "#P1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5\n",
      "#P2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
      "#P3 0.0 0.0 0.0\n",
      "#N 5\n",
      "#L Epoch_float  Epoch  simdet_stats1_unique_id  simdet_stats1_total  simdet_stats1_max_value\n",
      "0.15399622917175293 0 152858 11722735.0 164.0\n",
      "0.5764033794403076 1 152858 11722735.0 164.0\n",
      "0.9582419395446777 1 152858 11722735.0 164.0\n",
      "1.3527321815490723 1 152858 11722735.0 164.0\n",
      "1.74350905418396 2 152858 11722735.0 164.0\n",
      "#C Sat Jun 15 13:18:43 2024.  num_events_label_start_motor = 1\n",
      "#C Sat Jun 15 13:18:43 2024.  num_events_primary = 5\n",
      "#C Sat Jun 15 13:18:43 2024.  exit_status = success\n",
      "\n",
      "#S 93  count(detectors=['simdet'], num=5, delay=None)\n",
      "#D Sat Jun 15 13:18:44 2024\n",
      "#C Sat Jun 15 13:18:44 2024.  plan_type = generator\n",
      "#C Sat Jun 15 13:18:44 2024.  uid = f39f2eb2-fc04-4696-ac26-245cd574f7c2\n",
      "#MD uid = f39f2eb2-fc04-4696-ac26-245cd574f7c2\n",
      "#MD beamline_id = Bluesky_training\n",
      "#MD conda_prefix = /home/prjemian/.conda/envs/bluesky_2024_2\n",
      "#MD databroker_catalog = training\n",
      "#MD detectors = ['simdet']\n",
      "#MD iconfig = {'ADSIM_IOC_PREFIX': 'ad:', 'GP_IOC_PREFIX': 'gp:', 'DATABROKER_CATALOG': 'training', 'RUNENGINE_METADATA': {'beamline_id': 'Bluesky_training', 'instrument_name': 'BCDA EPICS Bluesky training', 'proposal_id': 'training', 'databroker_catalog': 'training'}, 'RUN_ENGINE_SCAN_ID_PV': 'gp:gp:int20', 'AD_IMAGE_DIR': 'adsimdet/%Y/%m/%d', 'AD_MOUNT_PATH': '/tmp', 'BLUESKY_MOUNT_PATH': '/tmp/docker_ioc/iocad/tmp', 'ALLOW_AREA_DETECTOR_WARMUP': True, 'ENABLE_AREA_DETECTOR_IMAGE_PLUGIN': True, 'ENABLE_CALCS': True, 'USE_PROGRESS_BAR': False, 'WRITE_NEXUS_DATA_FILES': False, 'NEXUS_WARN_MISSING_CONTENT': False, 'NEXUS_FILE_EXTENSION': 'h5', 'WRITE_SPEC_DATA_FILES': True, 'RUNENGINE_MD_PATH': '/home/prjemian/.config/Bluesky_RunEngine_md', 'LOGGING': {'NUMBER_OF_PREVIOUS_BACKUPS': 9}, 'PV_READ_TIMEOUT': 15, 'PV_WRITE_TIMEOUT': 15, 'PV_CONNECTION_TIMEOUT': 15, 'XMODE_DEBUG_LEVEL': 'Minimal', 'MINIMUM_PYTHON_VERSION': [3, 8], 'MINIMUM_BLUESKY_VERSION': [1, 10], 'MINIMUM_OPHYD_VERSION': [1, 7], 'MINIMUM_DATABROKER_VERSION': [1, 2], 'ICONFIG_VERSION': '1.0.1'}\n",
      "#MD instrument_name = BCDA EPICS Bluesky training\n",
      "#MD login_id = prjemian@arf.jemian.org\n",
      "#MD num_intervals = 4\n",
      "#MD num_points = 5\n",
      "#MD pid = 154110\n",
      "#MD proposal_id = training\n",
      "#MD versions = {'apstools': '1.6.19', 'bluesky': '1.13.0a3', 'databroker': '1.2.5', 'epics': '3.5.2', 'h5py': '3.9.0', 'intake': '0.6.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'ophyd': '1.9.0', 'pyRestTable': '2020.0.8', 'spec2nexus': '2021.2.6'}\n",
      "#P0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n",
      "#P1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5\n",
      "#P2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
      "#P3 0.0 0.0 0.0\n",
      "#N 7\n",
      "#L Epoch_float  Epoch  simdet_image  simdet_stats1_unique_id  simdet_stats1_max_value  simdet_stats1_total  simdet\n",
      "0.2595047950744629 0 0 152861 164.0 11658834.0 0\n",
      "#U 0 simdet_image 510c0d90-f922-41c2-a5ed-2b1013b82ab3/0\n",
      "0.6947643756866455 1 1 152864 164.0 11710598.0 0\n",
      "#U 1 simdet_image 510c0d90-f922-41c2-a5ed-2b1013b82ab3/1\n",
      "1.1037673950195312 1 2 152867 163.0 11537287.0 0\n",
      "#U 2 simdet_image 510c0d90-f922-41c2-a5ed-2b1013b82ab3/2\n",
      "1.5303704738616943 2 3 152870 159.0 11360372.0 0\n",
      "#U 3 simdet_image 510c0d90-f922-41c2-a5ed-2b1013b82ab3/3\n",
      "1.9436140060424805 2 4 152873 166.0 11817560.0 0\n",
      "#U 4 simdet_image 510c0d90-f922-41c2-a5ed-2b1013b82ab3/4\n",
      "#C Sat Jun 15 13:18:45 2024.  resource {'spec': 'AD_HDF5', 'root': '/', 'resource_path': 'mnt/iockad/tmp/1245c370-4953-4ff3-8982_000000.h5', 'resource_kwargs': {'frame_per_point': 3}, 'path_semantics': 'posix', 'uid': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'run_start': 'f39f2eb2-fc04-4696-ac26-245cd574f7c2'}\n",
      "#C Sat Jun 15 13:18:45 2024.  datum {'resource': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'datum_id': '510c0d90-f922-41c2-a5ed-2b1013b82ab3/0', 'datum_kwargs': {'point_number': 0}}\n",
      "#C Sat Jun 15 13:18:45 2024.  datum {'resource': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'datum_id': '510c0d90-f922-41c2-a5ed-2b1013b82ab3/1', 'datum_kwargs': {'point_number': 1}}\n",
      "#C Sat Jun 15 13:18:45 2024.  datum {'resource': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'datum_id': '510c0d90-f922-41c2-a5ed-2b1013b82ab3/2', 'datum_kwargs': {'point_number': 2}}\n",
      "#C Sat Jun 15 13:18:46 2024.  datum {'resource': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'datum_id': '510c0d90-f922-41c2-a5ed-2b1013b82ab3/3', 'datum_kwargs': {'point_number': 3}}\n",
      "#C Sat Jun 15 13:18:46 2024.  datum {'resource': '510c0d90-f922-41c2-a5ed-2b1013b82ab3', 'datum_id': '510c0d90-f922-41c2-a5ed-2b1013b82ab3/4', 'datum_kwargs': {'point_number': 4}}\n",
      "#C Sat Jun 15 13:18:47 2024.  num_events_label_start_motor = 1\n",
      "#C Sat Jun 15 13:18:47 2024.  num_events_primary = 5\n",
      "#C Sat Jun 15 13:18:47 2024.  exit_status = success\n",
      "\n",
      "#S 94  scan(detectors=['simdet'], num=5, args='['m1', -1, 1]', per_step='None')\n",
      "#D Sat Jun 15 13:18:49 2024\n",
      "#C Sat Jun 15 13:18:49 2024.  plan_type = generator\n",
      "#C Sat Jun 15 13:18:49 2024.  uid = 03dde617-eb69-4b66-abe5-8595de04051d\n",
      "#MD uid = 03dde617-eb69-4b66-abe5-8595de04051d\n",
      "#MD beamline_id = Bluesky_training\n",
      "#MD conda_prefix = /home/prjemian/.conda/envs/bluesky_2024_2\n",
      "#MD databroker_catalog = training\n",
      "#MD detectors = ['simdet']\n",
      "#MD iconfig = {'ADSIM_IOC_PREFIX': 'ad:', 'GP_IOC_PREFIX': 'gp:', 'DATABROKER_CATALOG': 'training', 'RUNENGINE_METADATA': {'beamline_id': 'Bluesky_training', 'instrument_name': 'BCDA EPICS Bluesky training', 'proposal_id': 'training', 'databroker_catalog': 'training'}, 'RUN_ENGINE_SCAN_ID_PV': 'gp:gp:int20', 'AD_IMAGE_DIR': 'adsimdet/%Y/%m/%d', 'AD_MOUNT_PATH': '/tmp', 'BLUESKY_MOUNT_PATH': '/tmp/docker_ioc/iocad/tmp', 'ALLOW_AREA_DETECTOR_WARMUP': True, 'ENABLE_AREA_DETECTOR_IMAGE_PLUGIN': True, 'ENABLE_CALCS': True, 'USE_PROGRESS_BAR': False, 'WRITE_NEXUS_DATA_FILES': False, 'NEXUS_WARN_MISSING_CONTENT': False, 'NEXUS_FILE_EXTENSION': 'h5', 'WRITE_SPEC_DATA_FILES': True, 'RUNENGINE_MD_PATH': '/home/prjemian/.config/Bluesky_RunEngine_md', 'LOGGING': {'NUMBER_OF_PREVIOUS_BACKUPS': 9}, 'PV_READ_TIMEOUT': 15, 'PV_WRITE_TIMEOUT': 15, 'PV_CONNECTION_TIMEOUT': 15, 'XMODE_DEBUG_LEVEL': 'Minimal', 'MINIMUM_PYTHON_VERSION': [3, 8], 'MINIMUM_BLUESKY_VERSION': [1, 10], 'MINIMUM_OPHYD_VERSION': [1, 7], 'MINIMUM_DATABROKER_VERSION': [1, 2], 'ICONFIG_VERSION': '1.0.1'}\n",
      "#MD instrument_name = BCDA EPICS Bluesky training\n",
      "#MD login_id = prjemian@arf.jemian.org\n",
      "#MD motors = ('m1',)\n",
      "#MD num_intervals = 4\n",
      "#MD num_points = 5\n",
      "#MD pid = 154110\n",
      "#MD plan_pattern = inner_product\n",
      "#MD plan_pattern_args = {'num': 5, 'args': [\"MyEpicsMotor(prefix='gp:m1', name='m1', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -1, 1]}\n",
      "#MD plan_pattern_module = bluesky.plan_patterns\n",
      "#MD proposal_id = training\n",
      "#MD versions = {'apstools': '1.6.19', 'bluesky': '1.13.0a3', 'databroker': '1.2.5', 'epics': '3.5.2', 'h5py': '3.9.0', 'intake': '0.6.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'ophyd': '1.9.0', 'pyRestTable': '2020.0.8', 'spec2nexus': '2021.2.6'}\n",
      "#P0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n",
      "#P1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5\n",
      "#P2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
      "#P3 0.0 0.0 0.0\n",
      "#N 9\n",
      "#L m1  Epoch_float  Epoch  simdet_image  simdet_stats1_unique_id  simdet_stats1_max_value  simdet_stats1_total  m1_user_setpoint  simdet\n",
      "-1.0 2.4708797931671143 2 0 152876 160.0 11445712.0 -1.0 0\n",
      "#U 0 simdet_image a833899a-286a-4eaf-94d8-8feaf24c9b52/0\n",
      "-0.5 3.2200405597686768 3 1 152879 163.0 11658331.0 -0.5 0\n",
      "#U 1 simdet_image a833899a-286a-4eaf-94d8-8feaf24c9b52/1\n",
      "0.0 4.029770612716675 4 2 152882 162.0 11535839.0 0.0 0\n",
      "#U 2 simdet_image a833899a-286a-4eaf-94d8-8feaf24c9b52/2\n",
      "0.5 4.832399368286133 5 3 152885 165.0 11799101.0 0.5 0\n",
      "#U 3 simdet_image a833899a-286a-4eaf-94d8-8feaf24c9b52/3\n",
      "1.0 5.632269620895386 6 4 152888 166.0 11824045.0 1.0 0\n",
      "#U 4 simdet_image a833899a-286a-4eaf-94d8-8feaf24c9b52/4\n",
      "#C Sat Jun 15 13:18:52 2024.  resource {'spec': 'AD_HDF5', 'root': '/', 'resource_path': 'mnt/iockad/tmp/d2ada5a8-8b64-475f-9c2c_000000.h5', 'resource_kwargs': {'frame_per_point': 3}, 'path_semantics': 'posix', 'uid': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'run_start': '03dde617-eb69-4b66-abe5-8595de04051d'}\n",
      "#C Sat Jun 15 13:18:52 2024.  datum {'resource': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'datum_id': 'a833899a-286a-4eaf-94d8-8feaf24c9b52/0', 'datum_kwargs': {'point_number': 0}}\n",
      "#C Sat Jun 15 13:18:52 2024.  datum {'resource': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'datum_id': 'a833899a-286a-4eaf-94d8-8feaf24c9b52/1', 'datum_kwargs': {'point_number': 1}}\n",
      "#C Sat Jun 15 13:18:53 2024.  datum {'resource': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'datum_id': 'a833899a-286a-4eaf-94d8-8feaf24c9b52/2', 'datum_kwargs': {'point_number': 2}}\n",
      "#C Sat Jun 15 13:18:54 2024.  datum {'resource': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'datum_id': 'a833899a-286a-4eaf-94d8-8feaf24c9b52/3', 'datum_kwargs': {'point_number': 3}}\n",
      "#C Sat Jun 15 13:18:55 2024.  datum {'resource': 'a833899a-286a-4eaf-94d8-8feaf24c9b52', 'datum_id': 'a833899a-286a-4eaf-94d8-8feaf24c9b52/4', 'datum_kwargs': {'point_number': 4}}\n",
      "#C Sat Jun 15 13:18:55 2024.  num_events_label_start_motor = 1\n",
      "#C Sat Jun 15 13:18:55 2024.  num_events_primary = 5\n",
      "#C Sat Jun 15 13:18:55 2024.  exit_status = success\n",
      "\n",
      "#S 95  scan(detectors=['simdet'], num=5, args='['m1', -1, 1, 'm2', 4, 1.5]', per_step='None')\n",
      "#D Sat Jun 15 13:18:56 2024\n",
      "#C Sat Jun 15 13:18:56 2024.  plan_type = generator\n",
      "#C Sat Jun 15 13:18:56 2024.  uid = b0fc4001-c2e2-468a-ac82-1ccb19ddbdb3\n",
      "#MD uid = b0fc4001-c2e2-468a-ac82-1ccb19ddbdb3\n",
      "#MD beamline_id = Bluesky_training\n",
      "#MD conda_prefix = /home/prjemian/.conda/envs/bluesky_2024_2\n",
      "#MD databroker_catalog = training\n",
      "#MD detectors = ['simdet']\n",
      "#MD iconfig = {'ADSIM_IOC_PREFIX': 'ad:', 'GP_IOC_PREFIX': 'gp:', 'DATABROKER_CATALOG': 'training', 'RUNENGINE_METADATA': {'beamline_id': 'Bluesky_training', 'instrument_name': 'BCDA EPICS Bluesky training', 'proposal_id': 'training', 'databroker_catalog': 'training'}, 'RUN_ENGINE_SCAN_ID_PV': 'gp:gp:int20', 'AD_IMAGE_DIR': 'adsimdet/%Y/%m/%d', 'AD_MOUNT_PATH': '/tmp', 'BLUESKY_MOUNT_PATH': '/tmp/docker_ioc/iocad/tmp', 'ALLOW_AREA_DETECTOR_WARMUP': True, 'ENABLE_AREA_DETECTOR_IMAGE_PLUGIN': True, 'ENABLE_CALCS': True, 'USE_PROGRESS_BAR': False, 'WRITE_NEXUS_DATA_FILES': False, 'NEXUS_WARN_MISSING_CONTENT': False, 'NEXUS_FILE_EXTENSION': 'h5', 'WRITE_SPEC_DATA_FILES': True, 'RUNENGINE_MD_PATH': '/home/prjemian/.config/Bluesky_RunEngine_md', 'LOGGING': {'NUMBER_OF_PREVIOUS_BACKUPS': 9}, 'PV_READ_TIMEOUT': 15, 'PV_WRITE_TIMEOUT': 15, 'PV_CONNECTION_TIMEOUT': 15, 'XMODE_DEBUG_LEVEL': 'Minimal', 'MINIMUM_PYTHON_VERSION': [3, 8], 'MINIMUM_BLUESKY_VERSION': [1, 10], 'MINIMUM_OPHYD_VERSION': [1, 7], 'MINIMUM_DATABROKER_VERSION': [1, 2], 'ICONFIG_VERSION': '1.0.1'}\n",
      "#MD instrument_name = BCDA EPICS Bluesky training\n",
      "#MD login_id = prjemian@arf.jemian.org\n",
      "#MD motors = ('m1', 'm2')\n",
      "#MD num_intervals = 4\n",
      "#MD num_points = 5\n",
      "#MD pid = 154110\n",
      "#MD plan_pattern = inner_product\n",
      "#MD plan_pattern_args = {'num': 5, 'args': [\"MyEpicsMotor(prefix='gp:m1', name='m1', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -1, 1, \"MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", 4, 1.5]}\n",
      "#MD plan_pattern_module = bluesky.plan_patterns\n",
      "#MD proposal_id = training\n",
      "#MD versions = {'apstools': '1.6.19', 'bluesky': '1.13.0a3', 'databroker': '1.2.5', 'epics': '3.5.2', 'h5py': '3.9.0', 'intake': '0.6.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'ophyd': '1.9.0', 'pyRestTable': '2020.0.8', 'spec2nexus': '2021.2.6'}\n",
      "#P0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n",
      "#P1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5\n",
      "#P2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
      "#P3 0.0 0.0 0.0\n",
      "#N 11\n",
      "#L m1  m2  Epoch_float  Epoch  simdet_image  simdet_stats1_unique_id  simdet_stats1_max_value  simdet_stats1_total  m1_user_setpoint  m2_user_setpoint  simdet\n",
      "-1.0 4.0 3.9975199699401855 4 0 152891 163.0 11659109.0 -1.0 4.0 0\n",
      "#U 0 simdet_image 46d1362c-9557-45a5-aadd-407e0bd98646/0\n",
      "-0.5 3.375 4.837675094604492 5 1 152894 159.0 11355578.0 -0.5 3.375 0\n",
      "#U 1 simdet_image 46d1362c-9557-45a5-aadd-407e0bd98646/1\n",
      "0.0 2.75 5.74786376953125 6 2 152897 164.0 11659418.0 0.0 2.75 0\n",
      "#U 2 simdet_image 46d1362c-9557-45a5-aadd-407e0bd98646/2\n",
      "0.5 2.125 6.649600028991699 7 3 152900 162.0 11536310.0 0.5 2.125 0\n",
      "#U 3 simdet_image 46d1362c-9557-45a5-aadd-407e0bd98646/3\n",
      "1.0 1.5 7.549147605895996 8 4 152903 163.0 11658922.0 1.0 1.5 0\n",
      "#U 4 simdet_image 46d1362c-9557-45a5-aadd-407e0bd98646/4\n",
      "#C Sat Jun 15 13:19:00 2024.  resource {'spec': 'AD_HDF5', 'root': '/', 'resource_path': 'mnt/iockad/tmp/6eafee88-ec97-4498-a6f5_000000.h5', 'resource_kwargs': {'frame_per_point': 3}, 'path_semantics': 'posix', 'uid': '46d1362c-9557-45a5-aadd-407e0bd98646', 'run_start': 'b0fc4001-c2e2-468a-ac82-1ccb19ddbdb3'}\n",
      "#C Sat Jun 15 13:19:00 2024.  datum {'resource': '46d1362c-9557-45a5-aadd-407e0bd98646', 'datum_id': '46d1362c-9557-45a5-aadd-407e0bd98646/0', 'datum_kwargs': {'point_number': 0}}\n",
      "#C Sat Jun 15 13:19:00 2024.  datum {'resource': '46d1362c-9557-45a5-aadd-407e0bd98646', 'datum_id': '46d1362c-9557-45a5-aadd-407e0bd98646/1', 'datum_kwargs': {'point_number': 1}}\n",
      "#C Sat Jun 15 13:19:01 2024.  datum {'resource': '46d1362c-9557-45a5-aadd-407e0bd98646', 'datum_id': '46d1362c-9557-45a5-aadd-407e0bd98646/2', 'datum_kwargs': {'point_number': 2}}\n",
      "#C Sat Jun 15 13:19:02 2024.  datum {'resource': '46d1362c-9557-45a5-aadd-407e0bd98646', 'datum_id': '46d1362c-9557-45a5-aadd-407e0bd98646/3', 'datum_kwargs': {'point_number': 3}}\n",
      "#C Sat Jun 15 13:19:03 2024.  datum {'resource': '46d1362c-9557-45a5-aadd-407e0bd98646', 'datum_id': '46d1362c-9557-45a5-aadd-407e0bd98646/4', 'datum_kwargs': {'point_number': 4}}\n",
      "#C Sat Jun 15 13:19:03 2024.  num_events_label_start_motor = 1\n",
      "#C Sat Jun 15 13:19:03 2024.  num_events_primary = 5\n",
      "#C Sat Jun 15 13:19:03 2024.  exit_status = success\n",
      "\n",
      "#S 96  grid_scan(detectors=['simdet'], args='['m1', -1, 1, 5, 'm2', -0.5, 0.5, 4, True]', per_step='None')\n",
      "#D Sat Jun 15 13:19:04 2024\n",
      "#C Sat Jun 15 13:19:04 2024.  plan_type = generator\n",
      "#C Sat Jun 15 13:19:04 2024.  uid = 5d5d851f-d953-4926-adb2-68e2cf658890\n",
      "#MD uid = 5d5d851f-d953-4926-adb2-68e2cf658890\n",
      "#MD beamline_id = Bluesky_training\n",
      "#MD conda_prefix = /home/prjemian/.conda/envs/bluesky_2024_2\n",
      "#MD databroker_catalog = training\n",
      "#MD detectors = ['simdet']\n",
      "#MD extents = ([-1, 1], [-0.5, 0.5])\n",
      "#MD iconfig = {'ADSIM_IOC_PREFIX': 'ad:', 'GP_IOC_PREFIX': 'gp:', 'DATABROKER_CATALOG': 'training', 'RUNENGINE_METADATA': {'beamline_id': 'Bluesky_training', 'instrument_name': 'BCDA EPICS Bluesky training', 'proposal_id': 'training', 'databroker_catalog': 'training'}, 'RUN_ENGINE_SCAN_ID_PV': 'gp:gp:int20', 'AD_IMAGE_DIR': 'adsimdet/%Y/%m/%d', 'AD_MOUNT_PATH': '/tmp', 'BLUESKY_MOUNT_PATH': '/tmp/docker_ioc/iocad/tmp', 'ALLOW_AREA_DETECTOR_WARMUP': True, 'ENABLE_AREA_DETECTOR_IMAGE_PLUGIN': True, 'ENABLE_CALCS': True, 'USE_PROGRESS_BAR': False, 'WRITE_NEXUS_DATA_FILES': False, 'NEXUS_WARN_MISSING_CONTENT': False, 'NEXUS_FILE_EXTENSION': 'h5', 'WRITE_SPEC_DATA_FILES': True, 'RUNENGINE_MD_PATH': '/home/prjemian/.config/Bluesky_RunEngine_md', 'LOGGING': {'NUMBER_OF_PREVIOUS_BACKUPS': 9}, 'PV_READ_TIMEOUT': 15, 'PV_WRITE_TIMEOUT': 15, 'PV_CONNECTION_TIMEOUT': 15, 'XMODE_DEBUG_LEVEL': 'Minimal', 'MINIMUM_PYTHON_VERSION': [3, 8], 'MINIMUM_BLUESKY_VERSION': [1, 10], 'MINIMUM_OPHYD_VERSION': [1, 7], 'MINIMUM_DATABROKER_VERSION': [1, 2], 'ICONFIG_VERSION': '1.0.1'}\n",
      "#MD instrument_name = BCDA EPICS Bluesky training\n",
      "#MD login_id = prjemian@arf.jemian.org\n",
      "#MD motors = ('m1', 'm2')\n",
      "#MD num_intervals = 19\n",
      "#MD num_points = 20\n",
      "#MD pid = 154110\n",
      "#MD plan_pattern = outer_product\n",
      "#MD plan_pattern_args = {'args': [\"MyEpicsMotor(prefix='gp:m1', name='m1', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -1, 1, 5, \"MyEpicsMotor(prefix='gp:m2', name='m2', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -0.5, 0.5, 4, True]}\n",
      "#MD plan_pattern_module = bluesky.plan_patterns\n",
      "#MD proposal_id = training\n",
      "#MD shape = (5, 4)\n",
      "#MD snaking = (False, True)\n",
      "#MD versions = {'apstools': '1.6.19', 'bluesky': '1.13.0a3', 'databroker': '1.2.5', 'epics': '3.5.2', 'h5py': '3.9.0', 'intake': '0.6.4', 'matplotlib': '3.8.4', 'numpy': '1.26.4', 'ophyd': '1.9.0', 'pyRestTable': '2020.0.8', 'spec2nexus': '2021.2.6'}\n",
      "#P0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n",
      "#P1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5\n",
      "#P2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n",
      "#P3 0.0 0.0 0.0\n",
      "#N 11\n",
      "#L m1  m2  Epoch_float  Epoch  simdet_image  simdet_stats1_unique_id  simdet_stats1_max_value  simdet_stats1_total  m1_user_setpoint  m2_user_setpoint  simdet\n",
      "-1.0 -0.5 2.472050189971924 2 0 152906 164.0 11663011.0 -1.0 -0.5 0\n",
      "#U 0 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/0\n",
      "-1.0 -0.16670000000000001 3.0593597888946533 3 1 152909 160.0 11445949.0 -1.0 -0.16666666666666669 0\n",
      "#U 1 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/1\n",
      "-1.0 0.16670000000000001 3.6419265270233154 4 2 152912 159.0 11355981.0 -1.0 0.16666666666666663 0\n",
      "#U 2 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/2\n",
      "-1.0 0.5 4.243873596191406 4 3 152915 164.0 11677059.0 -1.0 0.5 0\n",
      "#U 3 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/3\n",
      "-0.5 0.5 5.065932035446167 5 4 152918 164.0 11708538.0 -0.5 0.5 0\n",
      "#U 4 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/4\n",
      "-0.5 0.16670000000000001 5.653802394866943 6 5 152921 166.0 11846736.0 -0.5 0.16666666666666663 0\n",
      "#U 5 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/5\n",
      "-0.5 -0.16670000000000001 6.257414102554321 6 6 152924 164.0 11692952.0 -0.5 -0.16666666666666669 0\n",
      "#U 6 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/6\n",
      "-0.5 -0.5 6.854701042175293 7 7 152927 164.0 11693749.0 -0.5 -0.5 0\n",
      "#U 7 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/7\n",
      "0.0 -0.5 7.652886867523193 8 8 152930 163.0 11657723.0 0.0 -0.5 0\n",
      "#U 8 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/8\n",
      "0.0 -0.16670000000000001 8.261860609054565 8 9 152933 162.0 11536782.0 0.0 -0.16666666666666669 0\n",
      "#U 9 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/9\n",
      "0.0 0.16670000000000001 8.861814975738525 9 10 152936 164.0 11717110.0 0.0 0.16666666666666663 0\n",
      "#U 10 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/10\n",
      "0.0 0.5 9.468685865402222 9 11 152939 160.0 11411453.0 0.0 0.5 0\n",
      "#U 11 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/11\n",
      "0.5 0.5 10.268848419189453 10 12 152942 161.0 11486128.0 0.5 0.5 0\n",
      "#U 12 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/12\n",
      "0.5 0.16670000000000001 10.868767976760864 11 13 152945 164.0 11702804.0 0.5 0.16666666666666663 0\n",
      "#U 13 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/13\n",
      "0.5 -0.16670000000000001 11.464916229248047 11 14 152948 164.0 11728435.0 0.5 -0.16666666666666669 0\n",
      "#U 14 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/14\n",
      "0.5 -0.5 12.057462930679321 12 15 152951 164.0 11695619.0 0.5 -0.5 0\n",
      "#U 15 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/15\n",
      "1.0 -0.5 12.87460470199585 13 16 152954 159.0 11388541.0 1.0 -0.5 0\n",
      "#U 16 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/16\n",
      "1.0 -0.16670000000000001 13.473078966140747 13 17 152957 160.0 11455055.0 1.0 -0.16666666666666669 0\n",
      "#U 17 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/17\n",
      "1.0 0.16670000000000001 14.072843313217163 14 18 152960 160.0 11394404.0 1.0 0.16666666666666663 0\n",
      "#U 18 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/18\n",
      "1.0 0.5 14.665706634521484 15 19 152963 165.0 11773811.0 1.0 0.5 0\n",
      "#U 19 simdet_image 6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/19\n",
      "#C Sat Jun 15 13:19:07 2024.  resource {'spec': 'AD_HDF5', 'root': '/', 'resource_path': 'mnt/iockad/tmp/f23af069-cf36-4939-ac63_000000.h5', 'resource_kwargs': {'frame_per_point': 3}, 'path_semantics': 'posix', 'uid': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'run_start': '5d5d851f-d953-4926-adb2-68e2cf658890'}\n",
      "#C Sat Jun 15 13:19:07 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/0', 'datum_kwargs': {'point_number': 0}}\n",
      "#C Sat Jun 15 13:19:07 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/1', 'datum_kwargs': {'point_number': 1}}\n",
      "#C Sat Jun 15 13:19:08 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/2', 'datum_kwargs': {'point_number': 2}}\n",
      "#C Sat Jun 15 13:19:08 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/3', 'datum_kwargs': {'point_number': 3}}\n",
      "#C Sat Jun 15 13:19:09 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/4', 'datum_kwargs': {'point_number': 4}}\n",
      "#C Sat Jun 15 13:19:10 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/5', 'datum_kwargs': {'point_number': 5}}\n",
      "#C Sat Jun 15 13:19:10 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/6', 'datum_kwargs': {'point_number': 6}}\n",
      "#C Sat Jun 15 13:19:11 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/7', 'datum_kwargs': {'point_number': 7}}\n",
      "#C Sat Jun 15 13:19:12 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/8', 'datum_kwargs': {'point_number': 8}}\n",
      "#C Sat Jun 15 13:19:12 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/9', 'datum_kwargs': {'point_number': 9}}\n",
      "#C Sat Jun 15 13:19:13 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/10', 'datum_kwargs': {'point_number': 10}}\n",
      "#C Sat Jun 15 13:19:14 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/11', 'datum_kwargs': {'point_number': 11}}\n",
      "#C Sat Jun 15 13:19:14 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/12', 'datum_kwargs': {'point_number': 12}}\n",
      "#C Sat Jun 15 13:19:15 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/13', 'datum_kwargs': {'point_number': 13}}\n",
      "#C Sat Jun 15 13:19:16 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/14', 'datum_kwargs': {'point_number': 14}}\n",
      "#C Sat Jun 15 13:19:16 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/15', 'datum_kwargs': {'point_number': 15}}\n",
      "#C Sat Jun 15 13:19:17 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/16', 'datum_kwargs': {'point_number': 16}}\n",
      "#C Sat Jun 15 13:19:18 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/17', 'datum_kwargs': {'point_number': 17}}\n",
      "#C Sat Jun 15 13:19:18 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/18', 'datum_kwargs': {'point_number': 18}}\n",
      "#C Sat Jun 15 13:19:19 2024.  datum {'resource': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180', 'datum_id': '6b69e3a2-9d68-4ea7-beaa-8ea32f0c0180/19', 'datum_kwargs': {'point_number': 19}}\n",
      "#C Sat Jun 15 13:19:19 2024.  num_events_label_start_motor = 1\n",
      "#C Sat Jun 15 13:19:19 2024.  num_events_primary = 20\n",
      "#C Sat Jun 15 13:19:19 2024.  exit_status = success\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(specwriter.spec_filename) as f:\n",
    "    print(f.read())"
   ]
  }
 ],
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