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-0.4.0.tar.gz
(29.3 kB
view details)
Built Distribution
gridlib-0.4.0-py3-none-any.whl
(35.1 kB
view details)
File details
Details for the file gridlib-0.4.0.tar.gz
.
File metadata
- Download URL: gridlib-0.4.0.tar.gz
- Upload date:
- Size: 29.3 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 | 7d10fa98fc430e722bd6bb480e593a280f25fed185338388010dc18a2d15ec57 |
|
MD5 | 8412829799b766b078ca5e10168dcd36 |
|
BLAKE2b-256 | 8ec5d28c7a4146185de8a3457c25ab34343fc35f7dc17820ba834fa000a00431 |
File details
Details for the file gridlib-0.4.0-py3-none-any.whl
.
File metadata
- Download URL: gridlib-0.4.0-py3-none-any.whl
- Upload date:
- Size: 35.1 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 | 7d6182796e7b3a7ac32882cd03c6538b4d8ced6392c7ef4bcdf6230e73b48400 |
|
MD5 | 94db0f9f730425b654a431c4ab6e96fe |
|
BLAKE2b-256 | 9c3518bffb2467c11c6fe5076dec257a25636a51270bcb10db5d6546fa8937cf |