Metadata-Version: 2.1
Name: ashlarUC2
Version: 1.18.1
Summary: Alignment by Simultaneous Harmonization of Layer/Adjacency Registration
Home-page: https://github.com/openuc2/ashlar
Download-URL: https://github.com/openuc2/ashlar/archive/v1.18.1.tar.gz
Author: Jeremy Muhlich modified by Benedict Diederich
Author-email: benedictdied@gmail.com
License: MIT License
Keywords: scripts,microscopy,registration,stitching
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Visualization
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy>=1.18.1
Requires-Dist: pyjnius>=1.2.1
Requires-Dist: matplotlib>=3.1.2
Requires-Dist: networkx>=2.4
Requires-Dist: scipy>=1.4.1
Requires-Dist: scikit-image<0.20,>=0.19.2
Requires-Dist: scikit-learn>=0.21.1
Requires-Dist: tifffile>=2023.3.15
Requires-Dist: zarr>=2.11.3
Requires-Dist: blessed>=1.17



ASHLAR: Alignment by Simultaneous Harmonization of Layer/Adjacency Registration

Ashlar implements efficient combined stitching and registration of multi-channel
image mosaics collected using the Tissue-CycIF microscopy protocol [1]_. Although
originally developed for CycIF, it may also be applicable to other tiled and/or
cyclic imaging approaches. The package offers both a command line script for the
most common use cases as well as an API for building more specialized tools.

.. [1] Tissue-CycIF is multi-round immunofluorescence microscopy on large fixed
   tissue samples. See https://doi.org/10.1101/151738 for details.

