parseq 2026.6.0

ParSeq is a python software library for Parallel execution of Sequential data analysis

ParSeq

Package ParSeq is a python software library for Parallel execution of Sequential data analysis workflows. It implements a general analysis framework built around transformation nodes -- intermediate steps in a data pipeline used for visualization, cross-data operations (e.g., averaging), user interaction and status reporting -- and the transformations that connect these nodes.

ParSeq provides an adjustable data tree model that supports grouping, renaming, moving and drag-and-drop arrangement of datasets. It also includes customizable data format definitions, plotting tools for 1D, 2D, and 3D data, cross-data analysis routines and a flexible widget workspace suitable for both single- and multi-screen environments. ParSeq defines a structure for implementing specific analysis pipelines as lightweight Python packages.

ParSeq is primarily designed for synchrotron-based techniques, with a particular emphasis on spectroscopy.

An example application is ParSeq-XAS, an EXAFS analysis pipeline, shown in the screenshot below:

Main features

• ParSeq enables the creation of analysis pipelines as lightweight Python packages.

• Flexible use of screen space through detachable and dockable transformation nodes (components of the analysis pipeline).

• Two modes of applying GUI actions to multiple datasets: (a) simultaneous operations on multiple selected datasets, and (b) copying individual parameters or parameter groups from active datasets to subsequently selected datasets.

• Undo and redo support for most data processing steps.

• Ability to enter the analysis pipeline at any node, not only at its starting point.

• Creation of cross-data combinations (including average, sum, RMS, classical PCA, cumulative PCA, Target Transformation and MCR-ALS), with downstream propagation alongside the parent data. Parent datasets can optionally be terminated at any selected downstream node.

• General data correction routines for 1D data, including range deletion, scaling, spline replacement, spike removal, and jump correction.

• Parallel execution of data transformations using either multiprocessing or multithreading (configurable by the pipeline application).

• Optional curve-fitting solvers, also supporting parallel execution across multiple datasets.

• Informative error handling, providing alerts and stack traceback with the type and location of errors.

• Optional time profiling of the pipeline, controllable via a command-line option.

• Export of workflows to project files, as well as data export to various formats, accompanied by Python scripts that visualize the exported data in publication-quality plots.

• Support for container files (currently HDF5), which are represented as subfolders within the system file tree. The file tree, including containers, is lazy-loaded to efficiently handle large datasets.

• Integrated web viewer widget for each analysis node, displaying help pages automatically generated from docstrings using Sphinx at startup.

• The pipeline can be executed either through the GUI or via Python scripts (headless operation).

• Optional automatic loading of new data during ongoing measurements.

The mechanisms for creating nodes, transformations, and curve-fitting solvers, as well as for connecting them and developing Qt widgets for transformations and curve fitting, are demonstrated in separately distributed packages:

• ParSeq-XES-scan
• ParSeq-XAS

Running without installation

Download the ZIP archives of ParSeq and a ParSeq pipeline from GitHub. Extract their contents (e.g., parseq and parseq_XES_scan folders) into the same suitable directory. Install the [dependencies] (https://parseq.readthedocs.io/instructions.html), then run the pipeline starter.

One advantage of this approach is that a single ParSeq installation can be shared across multiple Python environments.

Running with installation

a) Install py pip: pip install parseq and e.g. pip install parseq-XAS. b) Alternatively, install from unzipped GitHub sources. Navigate to the directories containing pyproject.toml and run: python -m pip install ..

After installation, pipeline starters can be executed directly from the command line, for example: parseq-XAS.

Launch an example

Run the *_start.py module of a pipeline. If ParSeq and the corresponding pipeline are installed (rather than simply unpacked), these starters are also available as command-line commands.

You can invoke them with the --help option to explore the available parameters. A common usage pattern is to load a project (.pspj) file either from the GUI or directly via the command line at startup.

Documentation

See the documentation inside ParSeq or on https://parseq.readthedocs.io