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PKG-INFO
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Metadata-Version: 2.1
Name: parseq
Version: 2024.5.0
Summary: ParSeq is a python software library for Parallel execution of Sequential data analysis.
Home-page: http://parseq.readthedocs.io
Author: Konstantin Klementiev
Author-email: konstantin.klementiev@gmail.com
License: MIT License
Project-URL: Source, https://github.com/kklmn/ParSeq
Keywords: data-analysis pipeline framework gui synchrotron spectroscopy
Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: User Interfaces
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy>=1.8.0
Requires-Dist: scipy>=0.17.0
Requires-Dist: matplotlib>=2.0.0
Requires-Dist: sphinx>=1.6.2
Requires-Dist: sphinxcontrib-jquery
Requires-Dist: autopep8
Requires-Dist: h5py
Requires-Dist: silx>=1.1.0
Requires-Dist: hdf5plugin
Requires-Dist: psutil
Requires-Dist: pyqtwebengine
Requires-Dist: docutils
Requires-Dist: distro
Requires-Dist: colorama
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.
A screenshot of ParSeq-XAS (an EXAFS analysis pipeline) as an application
example:
.. image:: _images/XAS-foils.gif
:scale: 50 %
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, RMS or 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 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 -- 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 for
the user to tune their publication 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>`_
Dependencies
------------
- `silx <https://github.com/silx-kit/silx>`_ -- plotting, hdf5 handling, Qt
- `sphinx <https://github.com/sphinx-doc/sphinx>`_ -- building documentation
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.