ess.powder.grouping.focus_data_dspacing_and_two_theta#

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

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

The two-theta binning does not use TwoThetaBins but instead computes the two-theta bins from the ‘two_theta’ coordinate of the input data. This is necessary to ensure that we have sufficiently high wavelength resolution when performing a monitor normalization in a follow-up workflow step. If we were to use TwoThetaBins we would be influenced by and limited to the two-theta binning the user requests for the end result, which may not be sufficient.

Parameters:

  • data (CountsDspacing[TypeVar(RunType, ess.reduce.nexus.types.SampleRun (int), ess.reduce.nexus.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.

  • keep_events (KeepEvents[TypeVar(RunType, ess.reduce.nexus.types.SampleRun (int), ess.reduce.nexus.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:

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