nan_common_denom

nan_common_denom(array: LabeledArray, sort: bool = True, trials_ax: int = 1, min_trials: int = 0, ch_ax: int = 0, crop_trials: bool = True, verbose: bool = False) → LabeledArray[source][source]

Remove trials with NaNs from all channels.

This function processes a LabeledArray to remove trials containing NaN
values, with options for sorting, specifying axes, and setting minimum

trial counts.

Parameters:

  • array (LabeledArray) – The input array to process.

  • sort (bool, optional) – Whether to sort trials by NaN presence, by default True.

  • trials_ax (int, optional) – The axis containing trials, by default 1.

  • min_trials (int, optional) – Minimum number of trials to keep, by default 0.

  • ch_ax (int, optional) – The axis containing channels, by default 0.

  • crop_trials (bool, optional) – Whether to crop trials to the minimum number, by default True.

  • verbose (bool, optional) – Whether to print verbose output, by default False.

Returns:

The processed array with NaN trials removed.

Return type:

LabeledArray

Examples

>>> import numpy as np
>>> from ieeg.arrays.label import LabeledArray
>>> data = np.array([[1, 2, np.nan], [4, 5, 6], [7, np.nan, 9]])
>>> labels = [['ch1', 'ch2', 'ch3'], ['trial1', 'trial2', 'trial3']]
>>> array = LabeledArray(data, labels)
>>> processed_array = nan_common_denom(array, sort=True, trials_ax=1,
... ch_ax=0, min_trials=3, crop_trials=True, verbose=True)
Lowest trials 2 at ch1
Channels excluded (too few trials): ['ch1', 'ch3']