Introduction to the Neurotorch API

Neurotorch is shipped with an API and plugin system. Popular use cases may be:

• Automate the image analysis

• Add custom filters

• Interact with the UI and add custom features (e.g. as the shipped plugins for Fiji/ImageJ and TraceSelector)

Here you find the documentation for those interfaces.

Interact with Neurotorch

Besides the common approach to start Neurotorch via a shortcut or importing the module like you can also import Neurotorch from any Python script

import neurotorchmz
session = neurotorchmz.start_background(headless=False)

The start_background() function returns a Session object which holds references to all objects associated with Neurotorch. It is your main connection point to the API. If you do not need the GUI, launch with headless=False. Please note that the session is returned immediately while you need to wait for the GUI to be ready before you should interact with the API.

Important classes

If you want to use the Neurotorch API, you should have a look at the following classes first

• neurotorchmz.core.session.Session: An object of this class is returned when starting Neurotorch. It allows you to interact with the API and GUI

• neurotorchmz.utils.image.ImageObject: This class is used to efficiently work with videos (called images for history reasons) by caching views (e.g. the spatial) or the delta video.

• neurotorchmz.utils.synapse_detection: This module contains code to detect ROIs and Synapses in an ImageObject. It includes the algorithms and classes to describe Synapses and ROIs

• neurotorchmz.utils.signal_detection: This module contains the code to detect signals (a generic term for stimulation) in ImageObjects.

• neurotorchmz.core.events.Event: If you want to develop a plugin, the event library may be helpful. For example, the GUI fires a WindowLoadedEvent as well as a ImageObjectChangedEvent