Stage 05 - Wave Characterization

This stage evaluates the detected waves by deriving characteristic wave-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 wave-wise characteristic measures, 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_planar	Calculate the wave directions by either interpolating trigger times and locations or by averaging the corresponding optical flow values.
duration	Calculate the time from the first to the last trigger in each wave.
inter_wave_interval	Calculate the period between two consecutive waves for each wave.
label_planar	Calculate the planarity each waves.
number_of_triggers	Calculate the number of triggers involved in each wave.
time_stamp	Calculate the timing of each wave.
velocity_planar	Calculate the wave propagation velocity for each wave.