brighteyes-ism 1.5.1

A toolbox for analysing and simulating ISM images

BrightEyes-ISM

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

• Adaptive Pixel Reassignment (https://doi.org/10.1038/s41592-018-0291-9)
• Focus-ISM (https://doi.org/10.1038/s41467-022-35333-y)
• Image Deconvolution (https://doi.org/10.1088/1361-6420/accdc5)
• Fourier Ring Correlation (https://doi.org/10.1038/s41467-019-11024-z)
• Image and ISM datasets visualization
• Miscellaneous tools

The simulation module contains libraries for:

• Generation of ISM point spread functions (https://doi.org/10.48550/arXiv.2502.03170)
• Generation of tubulin phantom samples

The dataio module contains libraries for:

• Reading data and metadata from the MCS software (https://doi.org/10.21105/joss.07125).

MCS data format support has been moved to brighteyes-mcs-reader. Legacy MCS reading is available from brighteyes_mcs_reader.reader_legacy.

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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
scikit-image
scikit-learn
matplotlib
joblib
tqdm
h5py
statsmodels
matplotlib-scalebar
torch
torchvision
zernikepy
psf-generator

Documentation

You can find some examples of usage here:

https://github.com/VicidominiLab/BrightEyes-ISM/tree/main/examples

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

https://brighteyes-ism.readthedocs.io

Citation

If you find BrightEyes-ISM useful for your research, please cite it as:

Zunino, A., Slenders, E., Fersini, F. et al. Open-source tools enable accessible and advanced image scanning microscopy data analysis. Nat. Photon. (2023). https://doi.org/10.1038/s41566-023-01216-x

License

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

Contributing

You want to contribute? Great! Contributing works best if you creat a pull request with your changes.

1. Fork the project.
2. Create a branch for your feature: git checkout -b cool-new-feature
3. Commit your changes: git commit -am 'My new feature'
4. Push to the branch: git push origin cool-new-feature
5. Submit a pull request!

If you are unfamilar with pull requests, you find more information on pull requests in the github help

Issues

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