Python package to perform GRID analysis on fluorescence survival time distributions.
Project description
GRIDLib
Python package to perform GRID analysis on fluorescence survival time distributions.
- GitHub repo: https://github.com/boydcpeters/gridlib.git
- Documentation: https://gridlib.readthedocs.io
- Free software: GNU General Public License v3
Features
- TODO
Quickstart
TODO
References
The GRID fitting procedure implemented in this package is based on the following paper:
@article{reisser2020inferring,
title={Inferring quantity and qualities of superimposed reaction rates from single molecule survival time distributions},
author={Reisser, Matthias and Hettich, Johannes and Kuhn, Timo and Popp, Achim P and Gro{\ss}e-Berkenbusch, Andreas and Gebhardt, J Christof M},
journal={Scientific reports},
volume={10},
number={1},
pages={1--13},
year={2020},
publisher={Nature Publishing Group}
}
Credits
This package was created with Cookiecutter and the fedejaure/cookiecutter-modern-pypackage project template.
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
gridlib-0.3.1.tar.gz
(24.9 kB
view details)
Built Distribution
gridlib-0.3.1-py3-none-any.whl
(30.3 kB
view details)
File details
Details for the file gridlib-0.3.1.tar.gz
.
File metadata
- Download URL: gridlib-0.3.1.tar.gz
- Upload date:
- Size: 24.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.2.2 CPython/3.10.6 Windows/10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 916cfac104dc75fce79dd191e1524e1d0b5cb3d79c55977f98f52f65c4b418f5 |
|
MD5 | 32f58f790f5cfe9316934464ae645901 |
|
BLAKE2b-256 | a37448d008cb5a55dcbdfbf5755f4663775865cb4528dce52a8b95edd103edff |
File details
Details for the file gridlib-0.3.1-py3-none-any.whl
.
File metadata
- Download URL: gridlib-0.3.1-py3-none-any.whl
- Upload date:
- Size: 30.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.2.2 CPython/3.10.6 Windows/10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 737da17f20b55d00d780f0cf512288d00a4e9d96c093e59c6eac814c96a3c3a9 |
|
MD5 | 80b33a90becb405592345f2f62ff9b3e |
|
BLAKE2b-256 | 94051d1f4a878454afa6f0fe44b95fa4335bfe728048c9bcdc49a426749e7b0b |