Java DRA Algorithm Library

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Java DRA Algorithm Library

• For Opal Neutron Scattering

Data Analysis Team Jian Wang, Yang Fei, Paul
Hathaway contribute to this library
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Outline

• Introduction
• Data Reduction and analysis
• Library Design for DRA
• Global View of DRA Library
• Class UML Diagram and Control
• Summery

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Introduction

• Provide data reduction capability and support for
  data analysis tools to the users of the
  OPAL neutron beam instruments
• DRA library contains data reduction and analysis
  algorithm modules
• Provide graphic and non-graphic access interface
• Java is employed for working language. XML is
  used for configuration
• DRA visualisation is developed depending upon
  openGL.
• Data analysis package will offer various
  functions based on users inquiry
• Image process on two dimensional graphic
  environment.

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Experiment Review
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DRA Requirements

• Collected raw data should be corrected with
  detection efficiency due to detector and
  electronics sensitivity,
• Background should also be removed from data
  sample,
• Multiple scan data sample treatment in same
  experiment,
• Geometry should be corrected for difference
  type of detector.
• Mask process to reject bad regional data
• Should be easily to process regular and generic
  mask shape.
• Multiple masks can be applied in same data
  sample.
• Data integration in different 2theta region,
  also can be easily convert between 2theta and Q
  neutron wavelets.
• Curve fitting can be applied to selected
  distribution function.

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Detector and Data Sample
512 pixels in vertical
Horizontal with 128
Scattering pattern
corrected
geometry correction
Data sets stitching 128nScan x 512
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Algorithm Library design

• Data Reduction Algorithm
• Efficiency correction can be different with
  different detectors,
• Backgrounds should be considered all possible
  sources,
• Data set stitch is quite different for different
  instruments,
• Geometry correction is applied for pixel by pixel
  correction depending upon scattering theoretical
  prediction.
• Data Analysis Algorithm
• Multiple functions should be applied for curve
  fitting,
• Various data region shape integrations are
  considered,
• Wavelets Q and 2theta can be converted through
  graphic and non-graphic
• Visualisation should be applied at the any stage
  of DRA.
• Data integration with required region can be
  applied at the any stage

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Active Algorithm Diagram
Data in
Efficiency Correction
BG subtraction
Multi-Data sets stitching
Geometry correction
Mask process
Vis Library
Math Library
Stat Library
Multiple integrations
Meta para
Ctr para
Library
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Developing Status

• We have developed (or developing) algorithm for
  6 instruments.
• HRPD 12 algorithms have been full developed and
  tested
• Efficiency correction
• Background subtraction (simplified)
• Multiple data stitching
• Geometry correction
• Mask processor
• Integration multiple functions
• Regular data region integration, such as
  horizontal, vertical, box shape, oval
• Special data region integration, complex data
  region.
• Curve fitting
• Visualisation
• HIPD, RSD, TAS and SANS are full developed. HIPD
  and RSD are tested, TAS and SANS need slightly
  modification and discussion.
• Reflectometer are partly developed, need to
  discuss new requirement.

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Summery

• With Java platform independent characteristic,
  Java algorithm library can be applied to any
  platform.
• Library is designed module by module. It can be
  easily used.
• Graphic function is depend on openGL. This design
  offers good functionality to process mask
  application and data visualisation.
• Fitting is designed to use though graphic and
  non-graphic access. This is easy to use for any
  application
• Library modules can be full controlled by process
  manager input control parameters.