BIFROST Bragg peak monitor

This notebook demonstrates how to process data for BIFROST’s Bragg peak monitor. It uses a dedicated single crystal diffraction workflow to construct a map in Q and an interactive slicer plot.

import scippnexus as snx

from ess.bifrost.data import (
    simulated_elastic_incoherent_with_phonon,
    tof_lookup_table_simulation,
)
from ess.bifrost.single_crystal import BifrostSimulationBraggPeakMonitorWorkflow, make_q_map
from ess.bifrost.single_crystal.types import *
from ess.bifrost.types import McStasRawDetector
from ess.reduce.time_of_flight import DetectorLtotal
from ess.spectroscopy.types import *

%matplotlib widget

Build a workflow for processing the data:

workflow = BifrostSimulationBraggPeakMonitorWorkflow()
workflow[Filename[SampleRun]] = simulated_elastic_incoherent_with_phonon()
workflow[TimeOfFlightLookupTableFilename] = tof_lookup_table_simulation()

Downloading file 'BIFROST_20240914T053723.h5' from 'https://public.esss.dk/groups/scipp/ess/bifrost/5/BIFROST_20240914T053723.h5' to '/home/runner/.cache/ess/bifrost'.
Downloading file 'BIFROST-simulation-tof-lookup-table.h5' from 'https://public.esss.dk/groups/scipp/ess/bifrost/5/BIFROST-simulation-tof-lookup-table.h5' to '/home/runner/.cache/ess/bifrost'.

Attention

The current tests data does not contain a bragg peak monitor. So we instead use a bank (‘triplet’) of the regular inelastic detector. This means that the workflow can run, but the results don’t show any bragg peaks.

workflow[NeXusDetectorName] = "309_channel_9_5_triplet"


def assemble_detector_data_flatten(
    detector: EmptyDetector[RunType],
    data: NeXusData[snx.NXdetector, RunType],
) -> McStasRawDetector[RunType]:
    from ess.reduce.nexus.workflow import assemble_detector_data as assemble
    base = assemble(detector, data)

    # The monitor only has one pixel, so combine all pixels from the selected bank:
    monitor = base.bins.concat()
    monitor.coords['detector_number'] = sc.index(0)
    # Halfway between the bank and the sample:
    monitor.coords['position'] = base.coords['position'].mean() / 2
    # Zero out the y-component because the monitor is in the sample plane:
    monitor.coords['position'].fields.y = sc.scalar(0.0, unit=monitor.coords['position'].unit)
    return McStasRawDetector[RunType](monitor)


def detector_ltotal_from_straight_line_approximation(
    detector_beamline: RawDetector[RunType],
    source_position: Position[snx.NXsource, RunType],
    sample_position: Position[snx.NXsample, RunType],
    gravity: GravityVector,
) -> DetectorLtotal[RunType]:
    from ess.reduce.time_of_flight import eto_to_tof
    return eto_to_tof.detector_ltotal_from_straight_line_approximation(
        detector_beamline,  # type: ignore[arg-type]

            source_position=source_position,
            sample_position=sample_position,
            gravity=gravity,
    )


workflow.insert(assemble_detector_data_flatten)
workflow.insert(detector_ltotal_from_straight_line_approximation)

workflow.visualize(CountsWithQMapCoords[SampleRun], graph_attr={"rankdir": "LR"})

Compute the data projected onto Q:

counts = workflow.compute(CountsWithQMapCoords[SampleRun])

Make an interactive figure. Use the slider to select a range in \(Q\) to integrate over and display in the 1D figure.

make_q_map(counts,
    q_parallel_bins=sc.linspace('Q_parallel', -3, 3, 100, unit='1/Å'),
    q_perpendicular_bins=sc.linspace('Q_perpendicular', -3, 3, 100, unit='1/Å'),
    sample_rotation_bins=sc.scalar(1.0, unit='deg'),
)