A toolbox for image reconstruction in super resolution microscopy
Project description
s²ISM
This python package implements s²ISM (Super-resolution Sectioning Image Scanning Microscopy), a computational technique to reconstruct images with enhanced resolution, optical sectioning, signal-to-noise ratio and sampling from a conventional ISM dataset acquired by a laser scanning microscope equipped with a detector array.
The ISM dataset should be a numpy array in the format (x, y, time, channel), where the temporal dimension is not mandatory and the channel dimension is the flattened 2D dimension of the detector array.
This package also contains a module for simulating instrument-specific PSFs by retrieving the relavant parameters automatically from the raw dataset using a minimization procedure. Important: the current implementation of the automatic PSF generation works under the assumption that the detectors of the array are arranged in a squared fashion. If this is not the case for your detector (e.g. AiryScan), you need to provide the PSFs manually.
Installation
You can install s2ism
via pip directly from GitHub:
pip install git+https://github.com/VicidominiLab/s2ISM
or using the version on PyPI:
pip install s2ism
It requires the following Python packages
numpy
matplotlib
scipy
scikit-image
brighteyes-ism
torch
tqdm
Documentation
You can find examples of usage here:
https://github.com/VicidominiLab/s2ISM/tree/main/examples
Citation
If you find s²ISM useful for your research, please cite it as:
_
License
Distributed under the terms of the GNU GPL v3.0 license, "s2ISM" is free and open source software
Contributing
You want to contribute? Great! Contributing works best if you creat a pull request with your changes.
- Fork the project.
- Create a branch for your feature:
git checkout -b cool-new-feature
- Commit your changes:
git commit -am 'My new feature'
- Push to the branch:
git push origin cool-new-feature
- 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file s2ism-0.1.0.tar.gz
.
File metadata
- Download URL: s2ism-0.1.0.tar.gz
- Upload date:
- Size: 53.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.10.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5700dfa87790c1925c5a69a0073f295c6efaf4e27de5689c06dfdd9068c535d9 |
|
MD5 | 877d7ec6516187852e57bd56b2071fdd |
|
BLAKE2b-256 | 6198595a9f80acac54308eccb409ba2d7f6e2896321ce8db41c2de38ae0534f2 |
File details
Details for the file s2ism-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: s2ism-0.1.0-py3-none-any.whl
- Upload date:
- Size: 40.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.10.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 790bc78bbe61d7220775252fb4bbe05f2f4643678861d0e2fd68f5ddd493108f |
|
MD5 | f3bd72c98e1d911ee9fe535409f9605b |
|
BLAKE2b-256 | f5787326de88cc847e8bddd021664bc89a85be4c7732534aaaf2f7367160e630 |