SXD: Add providers for computing Q histograms

[jl-wynen]

This should make the SXD workflow ready for live processing. Simply request the new Q2dHistogram and QHistogram. (See below.)

Once we can select ROI's in the dashboard, we should see if we can adapt this workflow for it.

You can use the workflow by adapting the bragg peak monitor docs notebook:

import scippnexus as snx

from ess.bifrost.data import (
    simulated_elastic_incoherent_with_phonon,
    tof_lookup_table_simulation,
)
from ess.bifrost.single_crystal import BifrostBraggPeakMonitorWorkflow
from ess.bifrost.single_crystal.types import *
from ess.bifrost.types import McStasDetectorData
from ess.reduce.time_of_flight import DetectorLtotal
from ess.spectroscopy.types import *

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

# --- New params ---
workflow[QRange] = (sc.scalar(2.7, unit='1/Å'), sc.scalar(2.84, unit='1/Å'))
workflow[SampleRotationBins] = sc.scalar(1, unit='deg')
workflow[QParallelBins] = sc.linspace('Q_parallel', -3, 3, 100, unit='1/Å')
workflow[QPerpendicularBins] = sc.linspace('Q_perpendicular', -3, 3, 100, unit='1/Å')
# ---

# hack to work with existing files:
workflow[NeXusDetectorName] = "309_channel_9_5_triplet"


def assemble_detector_data_flatten(
    detector: CalibratedBeamline[RunType],
    data: NeXusData[snx.NXdetector, RunType],
) -> McStasDetectorData[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 McStasDetectorData[RunType](monitor)


def detector_ltotal_from_straight_line_approximation(
    detector_beamline: DetectorData[RunType],
) -> 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]
    )


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

results = workflow.compute([Q2dHistogram[SampleRun], QHistogram[SampleRun]])

a, b = results.values()

a.plot()  # 2d Histogram
b.plot()  # 1d histogram

[SimonHeybrock]

This seems to be something quite specific, does it deserve a better name? QHistogram does not tell me much about what it is.

[SimonHeybrock]

Not sure about the naming, is there a better option? If not, feel free to ignore this comment.

[jl-wynen]

I changed the names. What do you think?
Note that the data does not yet represent intensities. But that is what it will ultimately be once we implement normalisation.

[SimonHeybrock]

Docstrings?

[SimonHeybrock]

Inconsistent camel-casing.