# 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', 'exclusive_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, including itself.

exclusive_mean

Outilers are replaced by the mean of its neighborhood, not including itself.

number

Array becomes given number.

Returns

Indicies of trimmed outliers.

Return type

list of tuples

clip()