Implementation of 2D and 3D scientific image deconvolution
Project description
SDeconv
SDeconv is a python framework to develop scientific image deconvolution algorithms. This library has been developed for microscopy 2D and 3D images, but can be use to any image deconvolution application.
System Requirements
Software Requirements
OS Requirements
The SDeconv
development version is tested on Windows 10, MacOS and Linux operating systems.
The developmental version of the package has been tested on the following systems:
- Linux: 20.04.4
- Mac OSX: Mac OS Catalina 10.15.7
- Windows: 10
install
Library installation from PyPI
- Install an Anaconda distribution of Python -- Choose Python 3.9 and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
- Open an anaconda prompt / command prompt with
conda
for python 3 in the path - Create a new environment with
conda create --name sdeconv python=3.9
. - To activate this new environment, run
conda activate sdeconv
- To install the
SDeconv
library, runpython -m pip install sdeconv
.
if you need to update to a new release, use:
python -m pip install sdeconv --upgrade
Library installation from source
This installation is for developers or people who want the last features in the main
branch.
- Install an Anaconda distribution of Python -- Choose Python 3.9 and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
- Open an anaconda prompt / command prompt with
conda
for python 3 in the path - Create a new environment with
conda create --name sdeconv python=3.9
. - To activate this new environment, run
conda activate sdeconv
- Pull the source code from git with `git pull https://github.com/sylvainprigent/sdeconv.git
- Then install the
SDeconv
library from you local dir with:python -m pip install -e ./sdeconv
.
Use SDeconv with napari
The SDeconv library is embedded in a napari plugin that allows using SDeconv
with a graphical interface.
Please refer to the SDeconv
napari plugin documentation to install and use it.
SDeconv documentation
The full documentation with tutorial and docstring is available here
Project details
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 sdeconv-1.0.4.tar.gz
.
File metadata
- Download URL: sdeconv-1.0.4.tar.gz
- Upload date:
- Size: 33.9 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | cb2130faefdbf188e8ba38e75f9f7640ec664b4d9814b54e7c772253982e86e9 |
|
MD5 | b0c7020f60b056f3263b80a4ebc8ad9d |
|
BLAKE2b-256 | 8fac557ac7b412ccb939d4601ef20c68985ead64c154651fe1ab1870d15d01e1 |
File details
Details for the file sdeconv-1.0.4-py3-none-any.whl
.
File metadata
- Download URL: sdeconv-1.0.4-py3-none-any.whl
- Upload date:
- Size: 34.0 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 44f8e897e072c9583156d49d180504c4532c6fd6ee9903cdbf4574d06f0ec777 |
|
MD5 | 4f32cc432d2c79d6e6b3f94d4c42e3b5 |
|
BLAKE2b-256 | 6641e749ab860a0bdf1687e787b6d26543917a8c9923532afba73ef150cd919e |