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DetectorEfficiencyVariation-v1.rst

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.. algorithm::


.. summary::


.. alias::


.. properties::

Description

It is intended that the input white beam vanadium workspaces are from the same instrument and were collected before and after an experimental run of interest. First the ratios of the total number of counts in corresponding histograms from each input workspace are calculated and then the median ratio is calculated. Each ratio is compared to the median and a histogram will fail when any of the following conditions are true:

  • (sum1/sum2)/median(sum1/sum2) > Variation
  • (sum1/sum2)/median(sum1/sum2) < 1/Variation

where sum1 is the sum of the counts in a histogram in the workspace WhiteBeamBase and sum2 is the sum of the counts in the equivalent histogram in WhiteBeamCompare. The above equations only make sense for identifying bad detectors if Variation > 1. If a value of less than one is given for Variation then Variation will be set to the reciprocal.

The output workspace contains a MaskWorkspace where those spectra that fail the tests are masked and those that pass them are assigned a single positive value.

Child algorithms used

Uses the :ref:`algm-Integration` algorithm to sum the spectra.

Usage

Example:

.. testcode:: ExDetEffVariation

    import numpy as np

    wsBase=CreateSampleWorkspace()
    ws=CreateSampleWorkspace()
    noisyY =  np.array(ws.readY(0))
    noisyY[0]=1e20
    ws.setY(50,noisyY)

    (wsOut, numFailures) = DetectorEfficiencyVariation(WhiteBeamBase='wsBase', WhiteBeamCompare='ws')

    print "%i spectra has been masked in wsOut" % numFailures

Output:

.. testoutput:: ExDetEffVariation

    1 spectra has been masked in wsOut



.. categories::