ParSeq : Python software for comparative data analysis pipelines
(2025) 15th International Conference on Synchrotron Radiation Instrumentation, SRI 2024 In Journal of Physics: Conference Series 3010.- Abstract
The package ParSeq is a Python software library for Parallel execution of Sequential data analysis. It implements a general analysis framework that consists of transformation nodes - intermediate stops along the analysis propagation to visualize data, display status and provide user input - and transformations that connect the nodes. ParSeq imports data into an adjustable data model - a collection of data objects that supports grouping, renaming and moving. ParSeq provides tunable data format definitions, plotters for 1D, 2D and 3D data, cross-data analysis routines and flexible widget workspace suitable for single- and multi-screen computers. ParSeq base classes are designed to implement analysis pipelines as relatively lightweight... (More)
The package ParSeq is a Python software library for Parallel execution of Sequential data analysis. It implements a general analysis framework that consists of transformation nodes - intermediate stops along the analysis propagation to visualize data, display status and provide user input - and transformations that connect the nodes. ParSeq imports data into an adjustable data model - a collection of data objects that supports grouping, renaming and moving. ParSeq provides tunable data format definitions, plotters for 1D, 2D and 3D data, cross-data analysis routines and flexible widget workspace suitable for single- and multi-screen computers. ParSeq base classes are designed to implement analysis pipelines as relatively lightweight Python packages. The usage of ParSeq is exemplified here by ParSeq-XAS - a data analysis pipeline for EXAFS spectra.
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- author
- Klementiev, K. LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 15th International Conference on Synchrotron Radiation Instrumentation (SRI 2024), 26 August to 30 August 2024
- series title
- Journal of Physics: Conference Series
- volume
- 3010
- article number
- 012126
- publisher
- IOP Publishing
- conference name
- 15th International Conference on Synchrotron Radiation Instrumentation, SRI 2024
- conference location
- Hamburg, Germany
- conference dates
- 2024-08-26 - 2024-08-30
- external identifiers
-
- scopus:105007975910
- ISSN
- 1742-6588
- DOI
- 10.1088/1742-6596/3010/1/012126
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © Published under licence by IOP Publishing Ltd.
- id
- 59f09c36-68d1-4670-87f8-3fc1bcb674af
- date added to LUP
- 2025-12-19 15:13:16
- date last changed
- 2025-12-19 15:14:02
@inproceedings{59f09c36-68d1-4670-87f8-3fc1bcb674af,
abstract = {{<p>The package ParSeq is a Python software library for Parallel execution of Sequential data analysis. It implements a general analysis framework that consists of transformation nodes - intermediate stops along the analysis propagation to visualize data, display status and provide user input - and transformations that connect the nodes. ParSeq imports data into an adjustable data model - a collection of data objects that supports grouping, renaming and moving. ParSeq provides tunable data format definitions, plotters for 1D, 2D and 3D data, cross-data analysis routines and flexible widget workspace suitable for single- and multi-screen computers. ParSeq base classes are designed to implement analysis pipelines as relatively lightweight Python packages. The usage of ParSeq is exemplified here by ParSeq-XAS - a data analysis pipeline for EXAFS spectra.</p>}},
author = {{Klementiev, K.}},
booktitle = {{15th International Conference on Synchrotron Radiation Instrumentation (SRI 2024), 26 August to 30 August 2024}},
issn = {{1742-6588}},
language = {{eng}},
publisher = {{IOP Publishing}},
series = {{Journal of Physics: Conference Series}},
title = {{ParSeq : Python software for comparative data analysis pipelines}},
url = {{http://dx.doi.org/10.1088/1742-6596/3010/1/012126}},
doi = {{10.1088/1742-6596/3010/1/012126}},
volume = {{3010}},
year = {{2025}},
}