Stage 05 - Channel Wave Characterization

This stage evaluates the detected waves by deriving characteristic channel-wise measures.

config template

Input

A neo.Block and Segment object containing

a neo.Event object named ‘wavefronts’, containing

• labels: wave ids,

• array_annotations: channels, x_coords, y_coords.

Some blocks may require the additional AnalogSignal object called ‘optical_flow’ but containing the complex-valued optical flow values.

should pass check_input.py

Output

A table (pandas.DataFrame), containing * the characteristic measures per wave and channel, their unit, and if applicable their uncertainty as determined by the selected blocks * any annotations as selected via INCLUDE_KEYS or IGNORE_KEYS

Usage

In this stage, any number of blocks can be selected via the MEASURES parameter and are applied on the stage input (choose any). To include specific metadata in the output table, select the corresponding annotation keys with INCLUDE_KEYS, or to include all available metadata execept some specifiy only the corresponding annotations keys in IGNORE_KEYS.

Blocks

Utility Blocks (fixed)

check_input	Check whether the input data representation adheres to the stage's requirements.
merge_dataframes	Merge pandas DataFrames based on the values of selected columns.

Measure Blocks (choose any)

annotations	Extract the annotations of Neo objects and structure them in a DataFrame to complement a wave characterization.
direction_local	Calculate the wave directions per wave and channel, based on the spatial gradient of wave trigger times.
flow_direction_local	Calculate the wave directions per wave and channel, based on the optical flow at wave trigger times and locations.
inter_wave_interval_local	Calculate the period between two consecutive waves for each wave and channel.
velocity_local	Calculate the wave propagation velocity for each wave and channel.