Metadata-Version: 2.1
Name: dynflatfield
Version: 1.0.0
Summary: This package implements the dynamic flat-field correction
Author: Sarlota Birnsteinova, Egor Sobolev
License: BSD-3-Clause
Project-URL: Homepage, https://github.com/sarlotabirnsteinova/OnlineVisualization
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: h5py
Requires-Dist: matplotlib
Requires-Dist: numba
Requires-Dist: numpy
Requires-Dist: psutil
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: threadpoolctl

# Dynamic Flat-Field Correction

This package implements the dynamic flat-field correction.

## Brief description

The method described here consists of two separate steps:

1. Initially, reference flat-fields and dark-fields are
acquired and PCA is used to obtain the most relevant principal
components of the flat-field dataset.

2. During data acquisition with a sample, the effecitve flat-
field is computed for each individual frame as a weighted sum
of principal components, while the weights subject to minimize
the total variance of the corrected image.

## How to cite

S. Birnsteinova *et. al.* Online dynamic flat-field correction
for MHz microscopy data at European XFEL (2023). J. Synchrotron
Rad. 30, 1030-1037. DOI: [10.1107/S1600577523007336](
http://dx.doi.org/10.1107/S1600577523007336)
