Metadata-Version: 2.1
Name: eve_SMLM
Version: 0.2.0
Summary: Eve-SMLM: A Python package for single molecule localization microscopy from event-based sensors
Author: Laura M. Weber
Author-email: "Koen J.A. Martens" <koenjamartens@gmail.com>, Ulrike Endesfelder <endesfelder@uni-bonn.de>
Project-URL: Homepage, https://github.com/Endesfelder-Lab/EVE-software
Project-URL: Issues, https://github.com/Endesfelder-Lab/EVE-software/issues
Keywords: SMLM,molecule,EBS,neuromorphic,event,microscopy
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
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License-File: LICENCE
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## EVE - General-purpose software for eveSMLM localization

## About EVE
EVE is a user-interfaced software package that provides several methods to localize emitters from single molecule localization microscopy (SMLM) experiments performed on event-based sensors (eveSMLM).

For full information about EVE, please look at the corresponding scientific manuscript: "EVE is an open modular data analysis software for event-based localization microscopy" by Laura M. Weber$^‡$, Koen J.A. Martens$^‡$, Clément Cabriel, Joel J. Gates, Manon Albecq, Fredrik Vermeulen, Katharina Hein, Ignacio Izeddin, and Ulrike Endesfelder.

Event-based data differs fundamentally from conventional camera images. Unlike traditional sensors, event-based sensors only capture intensity changes, registering them as either positive (when intensity surpasses a predefined threshold) or negative events (when intensity drops below a predefined threshold). As a result, only a list of x and y pixel coordinate pairs is stored together with the detected event polarities and timestamps.

EVE is designed to quickly and directly process and analyse event-based single molecule data. The event-based data analysis is divided into two main parts:<br>  
1. **Candidate Finding:** The complete event-list is searched for characteristic event clusters that are generated by blinking fluorophores. Potential candidate clusters are then extracted and returned for further processing.<br>  
2. **Candidate Fitting:** The x,y,(z),t-localization is determined for each candidate cluster.<br>  
3. **Postprocessing and Evaluation:** This module includes various analytical routines to process and interpret the data.<br>  

