processing.modules.geomar_wildedit module

class processing.modules.geomar_wildedit.WildeditGEOMAR

Bases: ArrayModule

Flags outliers in a dataset via standard deviation.

Iterates over blocks of data, calculates mean and standard deviation and flags data outside a pre-set standard deviation window. In contrast to the standard SeaBird processing module, wild_edit, this module uses a sliding window around each data point instead of fixed blocks of data. Additionally, while the SeaBird variant terminates after two fixed cycles of flagging, here we iterate as long as there are bad values found.

transformation()

Selects the appropiate data columns and applies the flagging to each one individually.

Return type:

A numpy array, representing full cnv data with outliers removed.

processing.modules.geomar_wildedit.wildedit_geomar(data, flag=array([], shape=(0, 0), dtype=float64), std1=3.0, std2=10.0, window_size=50, minstd=0, **kwargs)

Wild editing filter, addapted from Gerd Krahmann.

Uses a sliding window and repeated flagging loops until no bad values are found.

Parameters:

• data (np.ndarray :) – The input data array.

• flag (np.ndarray :) – The data flag array.

• std1 (float :) –

  The standard deviation cutoff for the first flagging loop.

  (Default value = 3.0)

• std2 (float :) –

  The standard deviation cutoff for the all the following loops.

  (Default value = 10.0)

• window_size (int :) –

  The size of the sliding window.

  (Default value = 50)

• minstd (float :) –

  The minimum standard deviation threshold to flag data.

  (Default value = 0)

Return type:

A tuple of cleaned data and new data flags.