ess.reflectometry.batch_processor#

ess.reflectometry.batch_processor(workflow, runs)[source]#

Creates a collection of sciline workflows from the provided runs.

Example:

from ess.reflectometry import amor, tools

workflow = amor.AmorWorkflow()

runs = {
    '608': {
        SampleRotationOffset[SampleRun]: sc.scalar(0.05, unit='deg'),
        Filename[SampleRun]: "file_608.hdf",
    },
    '609': {
        SampleRotationOffset[SampleRun]: sc.scalar(0.05, unit='deg'),
        Filename[SampleRun]: "file_609.hdf",
    },
    '610': {
        SampleRotationOffset[SampleRun]: sc.scalar(0.05, unit='deg'),
        Filename[SampleRun]: "file_610.hdf",
    },
    '611': {
        SampleRotationOffset[SampleRun]: sc.scalar(0.05, unit='deg'),
        Filename[SampleRun]: "file_611.hdf",
    },
}

batch = tools.batch_processor(workflow, runs)

results = batch.compute(ReflectivityOverQ)

Additionally, if a list of filenames is provided for Filename[SampleRun], the events from the files will be concatenated into a single event list before processing.

Example:

runs = {
    '608': {
        Filename[SampleRun]: "file_608.hdf",
    },
    '609+610': {
        Filename[SampleRun]: ["file_609.hdf", "file_610.hdf"],
    },
}
Parameters:

  • workflow (Pipeline) – The sciline workflow used to compute the targets for each of the runs.

  • runs (Mapping[Any, Mapping[type, Any]]) – The sciline parameters to be used for each run. Should be a mapping where the keys are the names of the runs and the values are mappings of type to value pairs. In addition, if one of the values for Filename[SampleRun] is a list or a tuple, then the events from the files will be concatenated into a single event list.

Return type:

BatchProcessor