Machine Vision

Rosow, Eric

doi:10.1016/b978-012226570-9/50101-0

Clinical Engineering Handbook 2004

DOI: 10.1016/b978-012226570-9/50101-0

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Machine Vision

Eric Rosow

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2022

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SAXSDOG: open software for real-time azimuthal integration of 2D scattering images

Burian¹,

Meisenbichler²,

Naumenko³

et al. 2022

J Appl Crystallogr

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In situ small- and wide-angle scattering experiments at synchrotrons often result in massive quantities of data within just seconds. Especially during such beamtimes, processing of the acquired data online, without appreciable delay, is key to obtaining feedback on the failure or success of the experiment. This had led to the development of SAXSDOG, a Python-based environment for real-time azimuthal integration of large-area scattering images. The software is primarily designed for dedicated data pipelines: once a scattering image is transferred from the detector onto the storage unit, it is automatically integrated and pre-evaluated using integral parameters within milliseconds. The control and configuration of the underlying server-based processes is achieved via a graphical user interface, SAXSLEASH, which visualizes the resulting 1D data together with integral classifiers in real time. SAXSDOG further includes a portable `take-home' version for users that runs on standalone computers, enabling its use in laboratories or at the preferred workspace.

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SAXSDOG: open software for real-time azimuthal integration of 2D scattering images

Burian¹,

Meisenbichler²,

Naumenko³

et al. 2022

J Appl Crystallogr

View full text Add to dashboard Cite

show abstract

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Machine Vision

Cited by 1 publication

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SAXSDOG: open software for real-time azimuthal integration of 2D scattering images

SAXSDOG: open software for real-time azimuthal integration of 2D scattering images

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