Stage 04 - Wave Detection

This stage detects individual propagating waves based on the local transition times and optionally complements the wave description with additionally derived properties.

config template

Input

A neo.Block and Segment object containing

an AnalogSignal object with all signal channels with

• array_annotations: x_coords and y_coords specifying the integer position on the channel grid;

an Event object named ‘transitions’ with

• times: time stamps where a potential wavefront, i.e., state transition, was detected,

• labels: UP (DOWN or other are ignored),

• array_annotations: channels, x_coords, y_coords

should pass check_input.py

Output

The same input data object, but extended with a neo.Event object named ‘wavefronts’, containing

• times: UP transitions times from ‘transitions’ event,

• labels: wave ids,

• annotations: parameters of clustering algorithm, copy of transitions event annotations,

• array_annotations: channels, x_coords, y_coords

eventually additional AnalogSignal and Event objects from the blocks specified as ADDITIONAL_PROPERTIES

• such as an AnalogSignal object called ‘optical_flow’ equivalent to the primary AnalogSignal object, but containing the complex-valued optical flow values.

The output neo.Block is stored in {output_path}/{profile}/stage04_wave_detection/waves.{NEO_FORMAT}

The intermediate results and plots of each processing block are stored in the {output_path}/{profile}/stage04_wave_detection/{block_name}/

Usage

In this stage offers alternative wave detection methods (choose one), which can be selected via the DETECTION_BLOCK parameter. There are blocks to add additional properties, to be selected (choose any) via the ADDITIONAL_PROPERTIES parameter.

Blocks

Utility Blocks (fixed)

check_input	Check whether the input data representation adheres to the stage's requirements.
merge_wave_definitions	Combine the AnalogSignal and Event objects from different wave analysis blocks into the same Neo Block.
plot_clustering	Visualize the wave detection by means of clustering the detected trigger in (time,x,y) space.

Detection Blocks (choose one)

trigger_clustering

Detect waves by clustering triggers that are close to each other in time and space.

Additional Properties Blocks (choose any)

critical_points	Detect and characterize critical points in the optical flow vector field at each time point.
optical_flow	Calculate the optical flow (vector field) signal using the Horn Schunck algorithm.
wave_mode_clustering	Cluster similar waves into modes.