Metadata-Version: 2.1
Name: brighteyes-ism
Version: 0.1.0
Summary: A toolbox for analysing and simulating ISM images
Home-page: https://github.com/VicidominiLab/brighteyes-ism
Author: Alessandro Zunino
Author-email: Alessandro Zunino <alessandro.zunino@iit.it>
Project-URL: Homepage, https://github.com/VicidominiLab/brighteyes-ism
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-image
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: joblib
Requires-Dist: poppy
Requires-Dist: PyCustomFocus
Requires-Dist: tqdm
Requires-Dist: h5py
Requires-Dist: statsmodels
Provides-Extra: testing
Requires-Dist: tox ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'
Requires-Dist: pytest-qt ; extra == 'testing'
Requires-Dist: napari ; extra == 'testing'
Requires-Dist: pyqt5 ; extra == 'testing'

# BrightEyes-ISM

[![License](https://img.shields.io/pypi/l/napari-ISM.svg?color=green)](https://github.com/VicidominiLab/BrightEyes-ISM/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/napari-ISM.svg?color=green)](https://pypi.org/project/napari-ISM)
[![Python Version](https://img.shields.io/pypi/pyversions/napari-ISM.svg?color=green)](https://python.org)
<!--
[![tests](https://github.com/VicidominiLab/napari-ISM/workflows/tests/badge.svg)](https://github.com/VicidominiLab/napari-ISM/actions)
[![codecov](https://codecov.io/gh/VicidominiLab/napari-ISM/branch/main/graph/badge.svg)](https://codecov.io/gh/VicidominiLab/napari-ISM)
-->


A toolbox for analysing and simulating Image Scanning Microscopy (ISM) datasets.
The analysis module contains libraries for:

* Adaptive Pixel Reassignment (https://doi.org/10.1364/JOSAA.37.000154)
* Focus-ISM (https://doi.org/10.1101/2022.04.28.489892 )
* Image Deconvolution (https://doi.org/10.1038/s41592-018-0291-9)
* Fourier Ring Correlation (https://doi.org/10.1038/s41467-019-11024-z)

The simulation module contains libraries for:

* Generation of ISM point spread functions (https://doi.org/10.1016/j.cpc.2022.108315)
* Generation of tubulin phantom samples

The dataio module contains libraries for

* Reading the data and metadata from the MCS software (https://github.com/VicidominiLab/BrightEyes-MCS)

----------------------------------

## Installation

You can install `brighteyes-ism` via [pip] directly from GitHub:

    pip install git+https://github.com/VicidominiLab/BrightEyes-ISM

or using the version on [PyPI]:

    pip install brighteyes-ism

It requires the following Python packages

    numpy
	scipy
    matplotlib
	scikit-image
    scikit-learn
	poppy
	PyCustomFocus
    h5py
    tqdm
	statsmodels

## Documentation

You can read the manual of this package on Read the Docs:

https://brighteyes-ism.readthedocs.io/en/latest/autoapi/brighteyes_ism/index.html

## Contributing

Contributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.

## License

Distributed under the terms of the [GNU GPL v3.0] license,
"BrightEyes-ISM" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt

[file an issue]: https://github.com/VicidominiLab/brighteyes-ism/issues

[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
