ess.powder.grouping.focus_data_dspacing_and_two_theta#

ess.powder.grouping.focus_data_dspacing_and_two_theta(data, dspacing_bins, two_theta_bins, keep_events)[source]#

Reduce the pixel-based data to d-spacing and two-theta dimensions.

The two-theta binning is finer than TwoThetaBins and covers the full two-theta range of the detector, not only the requested range. Both are necessary to have sufficient wavelength resolution when performing a monitor normalization in a follow-up workflow step. The bins are nevertheless aligned with TwoThetaBins such that group_two_theta() can produce the requested binning exactly, see _focussing_two_theta_bins().

Parameters:

  • data (CorrectedDetector[TypeVar(RunType, ess.reduce.nexus.types.SampleRun (int), ess.powder.types.VanadiumRun (int), ess.powder.types.EmptyCanRun (int))]) – The input data to be reduced, which must have ‘wavelength’, ‘dspacing’, ‘two_theta’ coordinates.

  • dspacing_bins (ess.powder.types.DspacingBins (scipp.Variable)) – The bins to use for the d-spacing dimension.

  • two_theta_bins (ess.powder.types.TwoThetaBins (types.scipp._scipp.core.Variable | None)) – The two-theta bins requested for the final result, or None if the data will not be grouped by two-theta.

  • keep_events (KeepEvents[TypeVar(RunType, ess.reduce.nexus.types.SampleRun (int), ess.powder.types.VanadiumRun (int), ess.powder.types.EmptyCanRun (int))]) – Whether to keep the events in the output. If False, the output will be histogrammed instead of binned.

Returns:

CorrectedDspacing[TypeVar(RunType, ess.reduce.nexus.types.SampleRun (int), ess.powder.types.VanadiumRun (int), ess.powder.types.EmptyCanRun (int))] – The reduced data with ‘dspacing’ and ‘two_theta’ dimensions.