parseq 2026.1.0
ParSeq is a python software library for Parallel execution of Sequential data analysis.
License: MIT
Keywords: data-analysis pipeline framework gui synchrotron spectroscopy

Package ParSeq is a python software library for **Par**\ allel execution of
**Seq**\ uential data analysis. It implements a general analysis framework that
consists of transformation nodes -- intermediate stops along the data pipeline
for data visualization, cross-data operations (e.g. taking average), providing
user input and displaying status -- and transformations that connect the nodes.

It provides an adjustable data tree model (supports grouping, renaming, moving
and drag-and-drop arrangement), tunable data format definitions, plotters for
1D, 2D and 3D data, cross-data analysis routines and flexible widget work space
suitable for single- and multi-screen computers. It also defines a structure to
implement particular analysis pipelines as lightweight Python packages.

ParSeq is intended for synchrotron based techniques, first of all spectroscopy.

Main features
-------------

-  ParSeq allows creating analysis pipelines as lightweight Python packages.

-  Flexible use of screen area by detachable/dockable transformation nodes
   (parts of analysis pipeline).

-  Two ways of acting from GUI onto multiple data: (a) simultaneous work with
   multiply selected data and (b) copying a specific parameter or a group of
   parameters from active data items to later selected data items.

-  Undo and redo for most of treatment steps.

-  Entering into the analysis pipeline at any node, not only at the head of the
   pipeline.

-  Creation of cross-data combinations (e.g. averaging, PCA) and their
   propagation downstream the pipeline together with the parental data. The
   possibility of termination of the parental data at any selected downstream
   node.

-  General data correction routines for 1D data: range deletion, scaling,
   replacement by a spline, deletion of spikes and jump correction.

-  Parallel execution of data transformations with multiprocessing or
   multithreading (can be opted by the pipeline application).

-  Optional curve fitting solvers, also executed in parallel for multiple data
   items.

-  Informative error handling that provides alerts and stack traceback with the
   type and location of the occurred error.

-  Optional time profiling of the pipeline, as controlled by a command-line
   argument.

-  Export of the workflow into a project file. Export of data into various data
   formats with accompanied Python scripts that visualize the exported data in
   publication-quality plots.

-  ParSeq understands container files (presently only hdf5) and adds them to
   the system file tree as subfolders. The file tree, including hdf5
   containers, is lazy loaded thus enabling big data collections.

-  A web viewer widget near each analysis widget displays help pages generated
   from the analysis widget doc strings. The help pages are built by Sphinx at
   the startup time.

-  The pipeline can be operated by the GUI or from a Python script without GUI.

-  Optional automatic loading of new data during a measurement time.

The mechanisms for creating nodes, transformations and curve fitting solvers,
connecting them together and creating Qt widgets for the transformations and
and curve fits are exemplified by separately installed analysis packages:

- `ParSeq-XAS <https://github.com/kklmn/ParSeq-XAS>`_
- `ParSeq-XES-scan <https://github.com/kklmn/ParSeq-XES-scan>`_

Installation
------------

Install it by pip or conda **or** get ParSeq from
`GitHub <https://github.com/kklmn/ParSeq>`_ and use it with or without
installation.
The documentation is available online on
`Read the Docs <http://parseq.readthedocs.io>`_.

Launch an example
-----------------

Either install ParSeq and a ParSeq pipeline application by their installers to
the standard location or put them to any folder in their respective folders
(``parseq`` and e.g. ``parseq_XES_scan``) and run the ``*_start.py`` module of
the pipeline. You can try it with ``--help`` to explore the available options.
An assumed usage pattern is to load a project ``.pspj`` file from GUI or from
the starting command line.

Hosting and contact
-------------------

The ParSeq project is hosted on `GitHub <https://github.com/kklmn/ParSeq>`_.
Please use the project's Issues tab to get help or report an issue.

Citing ParSeq
-------------

Please cite ParSeq as: `K Klementiev, "ParSeq: Python software for comparative
data analysis pipelines", J. Phys.: Conf. Ser. 3010 (2025) 012126;
doi:10.1088/1742-6596/3010/1/012126
<https://doi.org/10.1088/1742-6596/3010/1/012126>`_.