Metadata-Version: 2.1
Name: Trapalyzer
Version: 0.0.5
Summary: Automatic feature detection and quantification for in-vitro NETosis experiments plugin for PartSeg
Home-page: https://github.com/Czaki/Neutrofile_analysis
Author: =?utf-8?q?Grzegorz_Bokota=2C_Micha=C5=82_Ciach?=
Author-email: g.bokota@uw.edu.pl, m_ciach@student.uw.edu.pl
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: License.txt
Requires-Dist: PartSeg (>=0.14.0)
Requires-Dist: SimpleITK
Requires-Dist: magicgui (>=0.4.0)
Requires-Dist: napari
Requires-Dist: nme (>=0.1.3)
Requires-Dist: numpy
Requires-Dist: pydantic

# Trapalyzer

Trapalyzer is a PartSeg and napari plugin for automatic feature detection and
quantification for in-vitro NET release studies.

## Installation

Trapalyzer is a plug-in for the PartSeg image processing. To use Trapalyzer,
you first need to [install PartSeg](https://github.com/4DNucleome/PartSeg).
If you use Windows, you can simply download and unpack the `PartSeg.zip` file -
no further installation needed. If you use Linux with a working Python
distribution, we recommend installing PartSeg by running
`pip install PartSeg[all]` in the command line.

After you have installed PartSeg, you can install Trapalyzer:

- On Windows, download the ZIP file with the plug-in (see the screenshot
   below). Unpack the .zip archive and move the `Trapalyzer` directory
   (located in `Trapalyzer-master/src` in the unpacked archive) to the
  `plugins` directory in the PartSeg folder.
- On Linux, we recommend installing via `pip install Trapalyzer`.

![](Tutorial/Figs/download.png)

## Usage examples

In the Tutorial directory you will find instructions on how to use
Trapalyzer to analyze an example data set of fluorescence microscopy images.
