# WrightTools.data.Channel.trim¶

Channel.trim(neighborhood, method='ztest', factor=3, replace='nan', verbose=True)[source]

Remove outliers from the dataset.

Identifies outliers by comparing each point to its neighbors using a statistical test.

Parameters: neighborhood (list of integers) – Size of the neighborhood in each dimension. Length of the list must be equal to the dimensionality of the channel. method ({'ztest'} (optional)) – Statistical test used to detect outliers. Default is ztest. ztest Compare point deviation from neighborhood mean to neighborhood standard deviation. factor (number (optional)) – Tolerance factor. Default is 3. replace ({'nan', 'mean', number} (optional)) – Behavior of outlier replacement. Default is nan. nan Outliers are replaced by numpy nans. mean Outliers are replaced by the mean of its neighborhood. number Array becomes given number. Indicies of trimmed outliers. list of tuples

clip()