{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Output scan(s) to a NeXus/HDF5 file\n",
"\n",
"**Objective**\n",
"\n",
"Demonstrate use of [NXWriter](https://bcda-aps.github.io/apstools/latest/api/_filewriters.html?highlight=nxwriter#nxwriter) (from [apstools.callbacks](https://bcda-aps.github.io/apstools/latest/api/_filewriters.html#apstools.callbacks.nexus_writer.NXWriter)) with [Bluesky](https://blueskyproject.io/bluesky) data acquisition. The `NXWriter` is used to export data from [databroker](https://blueskyproject.io/databroker). The `NXWriter` records data from a Bluesky measurement [run](https://blueskyproject.io/bluesky/multi_run_plans.html#definition-of-a-run) in a [NeXus](https://manual.nexusformat.org/user_manual.html) [HDF5](https://www.hdfgroup.org/solutions/hdf5) data file.\n",
"\n",
"**Contents**\n",
"\n",
"- [Use as callback](#Callback)\n",
"- [Export data to HDF5 file](#Export)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Callback\n",
"\n",
"To demonstrate how the `NXWriter` is used as a callback, it is necessary to have a data acquisition setup.\n",
"\n",
"This example scans a `sensor` in response to a `motor` position. The `NXWriter` is subscribed to the `RunEngine` so that during data collection, the `NXWriter` receives data updates. Once the acquisition ends (when a `stop` document is received), the HDF5 file is written.\n",
"\n",
"The data acquisition is a prebuilt [synApps xxx IOC](https://github.com/epics-modules/xxx) driver, packaged in a [docker](https://www.docker.com/) image\n",
"([prjemian/synapps](https://hub.docker.com/r/prjemian/prjemian/synapps/tags)). The [EPICS IOC](https://docs.epics-controls.org/projects/how-tos/en/latest/getting-started/creating-ioc.html) is started using prefix `gp:` by the [bash shell script](https://raw.githubusercontent.com/prjemian/epics-docker/main/resources/iocmgr.sh):\n",
"\n",
"
\n",
"$ iocmgr.sh start GP gp\n",
"
\n",
"\n",
"For the purposes of demonstration, the sensor is a random number generator (new values at 10 Hz). The random number generator is provided by a [userCalc](https://htmlpreview.github.io/?https://raw.githubusercontent.com/epics-modules/calc/R3-6-1/documentation/swaitRecord.html). The motor is a [software simulator of a stepping motor](https://github.com/epics-motor/motorMotorSim). There is no particular correlation between the `sensor` and the `motor` in this example, they are used only for purposes of illustration.\n",
"\n",
"After connecting with the EPICS PVs, the `RunEngine` is constructed and connected with a temporary databroker catalog.\n",
"\n",
"**Note**\n",
"\n",
"If you use ``NXWriter`` (or a subclass), you must wait for all data processing to finish before proceeding with the next acquisition or processing. (The `writer()` method is launched in a background thread to complete once all readable assets are available, potentially even after the run ends.) See the `NXWriter` [documentation](https://bcda-aps.github.io/apstools/latest/api/_filewriters.html#apstools.callbacks.nexus_writer.NXWriter) for details."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"from apstools.synApps import setup_random_number_swait\n",
"from apstools.synApps import SwaitRecord\n",
"from bluesky import RunEngine\n",
"from bluesky import SupplementalData\n",
"from bluesky import plans as bp\n",
"from bluesky.callbacks.best_effort import BestEffortCallback\n",
"from matplotlib import pyplot as plt\n",
"from ophyd import EpicsMotor\n",
"from ophyd import EpicsSignalRO\n",
"import databroker\n",
"\n",
"IOC = \"gp:\"\n",
"\n",
"# ophyd-level\n",
"motor = EpicsMotor(f\"{IOC}m10\", name=\"motor\")\n",
"calc10 = SwaitRecord(f\"{IOC}userCalc10\", name=\"calc10\")\n",
"sensor = EpicsSignalRO(calc10.calculated_value.pvname, name=\"sensor\")\n",
"motor.wait_for_connection()\n",
"sensor.wait_for_connection()\n",
"\n",
"# calc10 sets up the RNG, updating at 10Hz\n",
"calc10.wait_for_connection()\n",
"setup_random_number_swait(calc10)\n",
"\n",
"# bluesky-level\n",
"best_effort_callback = BestEffortCallback()\n",
"cat = databroker.temp().v2\n",
"plt.ion() # enables matplotlib graphics\n",
"RE = RunEngine({})\n",
"RE.subscribe(cat.v1.insert)\n",
"RE.subscribe(best_effort_callback) # LivePlot & LiveTable\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setup the `NXWriter` to create and write the scan data to an HDF5 file. We override the default HDF5 file name. The steps:\n",
"\n",
"1. import the Python structures\n",
"2. Define the file name. (A pathlib object provides an easy way to test if the\n",
" file exists.)\n",
"3. Create the `NXWriter` instance\n",
"4. Subscribe the writer's `receiver` to the RunEngine.\n",
"5. Configure the writer for file name and to suppress extra warnings in the example."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from apstools.callbacks import NXWriter\n",
"import pathlib\n",
"\n",
"h5_file = pathlib.Path(\"/tmp/nxwriter.h5\")\n",
"\n",
"nxwriter = NXWriter()\n",
"RE.subscribe(nxwriter.receiver)\n",
"nxwriter.file_name = str(h5_file)\n",
"nxwriter.warn_on_missing_content = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Collect data by scanning `sensor` *v*. ` motor`. A `LiveTable` and ` LivePlot` will be shown.\n",
"\n",
"The `sensor` updates automatically at 10 Hz. The `motor` moves slowly enough that the sensor updates before the next position is reached. The data itself is for the purpose of demonstrating the `NXWriter` callback.\n",
"\n",
"After the scan, show that the file exists."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Transient Scan ID: 1 Time: 2022-08-12 17:27:38\n",
"Persistent Unique Scan ID: 'c888d282-1094-403b-baff-3781057ff087'\n",
"New stream: 'primary'\n",
"+-----------+------------+------------+------------+\n",
"| seq_num | time | motor | sensor |\n",
"+-----------+------------+------------+------------+\n",
"| 1 | 17:27:40.1 | -0.50000 | 0.69377 |\n",
"| 2 | 17:27:40.5 | -0.25000 | 0.44727 |\n",
"| 3 | 17:27:41.0 | 0.00000 | 0.07127 |\n",
"| 4 | 17:27:41.5 | 0.25000 | 0.97433 |\n",
"| 5 | 17:27:42.0 | 0.50000 | 0.57342 |\n",
"+-----------+------------+------------+------------+\n",
"generator scan ['c888d282'] (scan num: 1)\n",
"\n",
"\n",
"\n",
"h5_file.exists()=True h5_file=PosixPath('/tmp/nxwriter.h5')\n"
]
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"RE(bp.scan([sensor], motor, -0.5, 0.5, 5))\n",
"print(f\"{h5_file.exists()=} {h5_file=}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the content of the NeXus HDF5 data file using [punx](https://punx.readthedocs.io), a program external to our Bluesky Python session."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"!!! WARNING: this program is not ready for distribution.\n",
"\n",
"/tmp/nxwriter.h5 : NeXus data file\n",
" @HDF5_Version = \"1.12.1\"\n",
" @NeXus_version = \"v2020.1\"\n",
" @creator = \"NXWriter\"\n",
" @default = \"entry\"\n",
" @file_name = \"/tmp/nxwriter.h5\"\n",
" @file_time = \"2022-08-12T17:27:42.294183\"\n",
" @h5py_version = \"3.7.0\"\n",
" entry:NXentry\n",
" @NX_class = \"NXentry\"\n",
" @default = \"data\"\n",
" @target = \"/entry\"\n",
" duration:NX_FLOAT64[] = \n",
" @units = \"s\"\n",
" end_time:NX_CHAR = b'2022-08-12T17:27:42.139426'\n",
" entry_identifier --> /entry/instrument/bluesky/metadata/run_start_uid\n",
" plan_name --> /entry/instrument/bluesky/metadata/plan_name\n",
" program_name:NX_CHAR = b'bluesky'\n",
" start_time:NX_CHAR = b'2022-08-12T17:27:38.779351'\n",
" title:NX_CHAR = b'scan-S0001-c888d28'\n",
" data:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"motor\"]\n",
" @signal = \"sensor\"\n",
" @target = \"/entry/data\"\n",
" EPOCH --> /entry/instrument/bluesky/streams/primary/sensor/time\n",
" motor --> /entry/instrument/bluesky/streams/primary/motor/value\n",
" motor_user_setpoint --> /entry/instrument/bluesky/streams/primary/motor_user_setpoint/value\n",
" sensor --> /entry/instrument/bluesky/streams/primary/sensor/value\n",
" instrument:NXinstrument\n",
" @NX_class = \"NXinstrument\"\n",
" @target = \"/entry/instrument\"\n",
" bluesky:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/bluesky\"\n",
" plan_name --> /entry/instrument/bluesky/metadata/plan_name\n",
" uid --> /entry/instrument/bluesky/metadata/run_start_uid\n",
" metadata:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/bluesky/metadata\"\n",
" detectors:NX_CHAR = b'- sensor\\n'\n",
" @target = \"/entry/instrument/bluesky/metadata/detectors\"\n",
" @text_format = \"yaml\"\n",
" hints:NX_CHAR = b'dimensions:\\n- !!python/tuple\\n - - motor\\n - primary\\n'\n",
" @target = \"/entry/instrument/bluesky/metadata/hints\"\n",
" @text_format = \"yaml\"\n",
" motors:NX_CHAR = b'!!python/tuple\\n- motor\\n'\n",
" @target = \"/entry/instrument/bluesky/metadata/motors\"\n",
" @text_format = \"yaml\"\n",
" num_intervals:NX_INT64[] = \n",
" @target = \"/entry/instrument/bluesky/metadata/num_intervals\"\n",
" num_points:NX_INT64[] = \n",
" @target = \"/entry/instrument/bluesky/metadata/num_points\"\n",
" plan_args:NX_CHAR = b\"args:\\n- EpicsMotor(prefix='gp:m10', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback',\\n 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity',\\n 'acceleration', 'motor_egu'])\\n- -0.5\\n- 0.5\\ndetectors:\\n- EpicsSignalRO(read_pv='gp:userCalc10.VAL', name='sensor', timestamp=1660343258.421057,\\n auto_monitor=False, string=False)\\nnum: 5\\nper_step: None\\n\"\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_args\"\n",
" @text_format = \"yaml\"\n",
" plan_name:NX_CHAR = b'scan'\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_name\"\n",
" plan_pattern:NX_CHAR = b'inner_product'\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_pattern\"\n",
" plan_pattern_args:NX_CHAR = b\"args:\\n- EpicsMotor(prefix='gp:m10', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback',\\n 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity',\\n 'acceleration', 'motor_egu'])\\n- -0.5\\n- 0.5\\nnum: 5\\n\"\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_pattern_args\"\n",
" @text_format = \"yaml\"\n",
" plan_pattern_module:NX_CHAR = b'bluesky.plan_patterns'\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_pattern_module\"\n",
" plan_type:NX_CHAR = b'generator'\n",
" @target = \"/entry/instrument/bluesky/metadata/plan_type\"\n",
" run_start_uid:NX_CHAR = b'c888d282-1094-403b-baff-3781057ff087'\n",
" @long_name = \"bluesky run uid\"\n",
" @target = \"/entry/instrument/bluesky/metadata/run_start_uid\"\n",
" versions:NX_CHAR = b'bluesky: 1.8.3\\nophyd: 1.6.4\\n'\n",
" @target = \"/entry/instrument/bluesky/metadata/versions\"\n",
" @text_format = \"yaml\"\n",
" streams:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/bluesky/streams\"\n",
" primary:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary\"\n",
" @uid = \"c808bd42-27b4-4498-819d-2279f9516608\"\n",
" motor:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"time\"]\n",
" @signal = \"value\"\n",
" @signal_type = \"positioner\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor\"\n",
" EPOCH:NX_FLOAT64[5] = [1660343259.975937, 1660343260.576943, 1660343261.078024, 1660343261.579225, 1660343262.080634]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 0.601006031036377, 1.1020870208740234, 1.603288173675537, 2.1046972274780273]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660343259.975937\n",
" @start_time_iso = \"2022-08-12T17:27:39.975937\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [-0.5, -0.25, 0.0, 0.25, 0.5]\n",
" @long_name = \"motor\"\n",
" @lower_ctrl_limit = -32000.0\n",
" @precision = 5\n",
" @signal_type = \"positioner\"\n",
" @source = \"PV:gp:m10.RBV\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor/value\"\n",
" @units = \"degrees\"\n",
" @upper_ctrl_limit = 32000.0\n",
" motor_user_setpoint:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"time\"]\n",
" @signal = \"value\"\n",
" @signal_type = \"other\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint\"\n",
" EPOCH:NX_FLOAT64[5] = [1660343258.784328, 1660343260.169698, 1660343260.644624, 1660343261.141399, 1660343261.645333]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 1.3853700160980225, 1.8602960109710693, 2.3570709228515625, 2.8610050678253174]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660343258.784328\n",
" @start_time_iso = \"2022-08-12T17:27:38.784328\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [-0.5, -0.25, 0.0, 0.25, 0.5]\n",
" @long_name = \"motor_user_setpoint\"\n",
" @lower_ctrl_limit = -32000.0\n",
" @precision = 5\n",
" @signal_type = \"other\"\n",
" @source = \"PV:gp:m10.VAL\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/motor_user_setpoint/value\"\n",
" @units = \"degrees\"\n",
" @upper_ctrl_limit = 32000.0\n",
" sensor:NXdata\n",
" @NX_class = \"NXdata\"\n",
" @axes = [\"time\"]\n",
" @signal = \"value\"\n",
" @signal_type = \"detector\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor\"\n",
" EPOCH:NX_FLOAT64[5] = [1660343260.021116, 1660343260.521089, 1660343261.021125, 1660343261.521131, 1660343262.021114]\n",
" @long_name = \"epoch time (s)\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor/EPOCH\"\n",
" @units = \"s\"\n",
" time:NX_FLOAT64[5] = [0.0, 0.4999730587005615, 1.0000090599060059, 1.5000150203704834, 1.9999980926513672]\n",
" @long_name = \"time since first data (s)\"\n",
" @start_time = 1660343260.021116\n",
" @start_time_iso = \"2022-08-12T17:27:40.021116\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor/time\"\n",
" @units = \"s\"\n",
" value:NX_FLOAT64[5] = [0.6937666895551995, 0.44727244983596554, 0.07127489127946898, 0.9743343251697566, 0.5734187838559548]\n",
" @long_name = \"sensor\"\n",
" @lower_ctrl_limit = 0.0\n",
" @precision = 5\n",
" @signal_type = \"detector\"\n",
" @source = \"PV:gp:userCalc10.VAL\"\n",
" @target = \"/entry/instrument/bluesky/streams/primary/sensor/value\"\n",
" @units = \"\"\n",
" @upper_ctrl_limit = 0.0\n",
" detectors:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/detectors\"\n",
" sensor:NXdetector\n",
" @NX_class = \"NXdetector\"\n",
" @target = \"/entry/instrument/detectors/sensor\"\n",
" data --> /entry/instrument/bluesky/streams/primary/sensor\n",
" positioners:NXnote\n",
" @NX_class = \"NXnote\"\n",
" @target = \"/entry/instrument/positioners\"\n",
" motor:NXpositioner\n",
" @NX_class = \"NXpositioner\"\n",
" @target = \"/entry/instrument/positioners/motor\"\n",
" value --> /entry/instrument/bluesky/streams/primary/motor\n",
" source:NXsource\n",
" @NX_class = \"NXsource\"\n",
" @target = \"/entry/instrument/source\"\n",
" name:NX_CHAR = b'Bluesky framework'\n",
" @short_name = \"bluesky\"\n",
" probe:NX_CHAR = b'x-ray'\n",
" type:NX_CHAR = b'Synchrotron X-ray Source'\n",
"\n"
]
}
],
"source": [
"from apstools.utils import unix\n",
"\n",
"for line in unix(f\"punx tree {nxwriter.file_name}\"):\n",
" print(line.decode().strip())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export\n",
"\n",
"It is possible to use the `NXWriter` to export the data from the databroker to a NeXus/HDF5 file. The bluesky community is preparing the [tiled](https://blueskyproject.io/tiled/) data access service to make such data readily available.\n",
"\n",
"This example shows an alternative method to export one *run* to one NeXus/HDF5 data file. While other variations are possible (such as a list of runs in one file), they are not shown here to keep the example simple.\n",
"\n",
"The data export is based on the [replay()](https://bcda-aps.github.io/apstools/latest/api/_utils.html?highlight=replay#apstools.utils.misc.replay) from [apstools](https://bcda-aps.github.io/apstools/latest/).\n",
"\n",
"We assume the *run* to be exported is identified by `scan_id = 1`, as the example above shows. This example uses the `cat` object created above. You should create this as shown in the comment.\n",
"\n",
"The steps:\n",
"\n",
"1. the Python imports\n",
"2. define the file to be read and the scan_id\n",
"3. create the `NXWriter()` instance\n",
"4. suppress the warnings we do not need to see\n",
"5. use `replay()` to get the data (note the `v1` is important) and send it to\n",
" the `receiver`\n",
"6. show the new HDF5 file exists"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"h5_file.exists()=True h5_file=PosixPath('/tmp/db_export.h5')\n"
]
}
],
"source": [
"from apstools.callbacks import NXWriter\n",
"from apstools.utils import replay\n",
"import databroker\n",
"\n",
"# This example uses 'cat' as defined above. You use this next line:\n",
"# cat = databroker.databroker[\"YOUR_CATALOG_NAME\"]\n",
"\n",
"h5_file = pathlib.Path(\"/tmp/db_export.h5\")\n",
"scan_id = 1 # TODO: you choose\n",
"\n",
"nxwriter = NXWriter()\n",
"nxwriter.file_name = str(h5_file)\n",
"nxwriter.warn_on_missing_content = False\n",
"replay(cat.v1[scan_id], nxwriter.receiver)\n",
"print(f\"{h5_file.exists()=} {h5_file=}\")"
]
}
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