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

See also

clip()

Remove pixels outside of a certain range.