remove_intervals#

pypillometry.remove_intervals(intervals)#

Pipeline step for remove_intervals() (d | pp.remove_intervals(...)).

Remove the data in the given periods (e.g., breaks) and join the rest.

The samples in the periods are deleted. Gaps between the remaining data are closed: the data after a removed period are shifted back in time, so that they continue where the removed period started (the time vector has no gap). Removing data at the beginning or end does not change the times, so removing everything before start and after end is the same as crop(). Event onsets are shifted with the data; events within removed periods are deleted. Stored blinks and excluded periods are moved to the new positions. The removed periods are added to params["removed_intervals"], in the original time (the time before any periods were removed; a period across a gap closed by an earlier call is split at the gap). params["removed_shifts"] holds the shifts between the current and the original time.

To remove the periods marked with exclude_intervals(), use d.remove_intervals(d.get_excluded()).

Filters or interpolation applied after joining treat the data on both sides of a removed period as neighbors; apply them before, or use split_excluded() to process the parts separately.

Parameters:

  • intervals (Intervals, Epochs or list of tuple) – the periods to remove: an Intervals object (in time units or sample indices), Epochs or a list of (start, end) tuples (in ms, or with units, e.g., [(10 * u.min, 12 * u.min)]; "<start>" and "<end>" stand for the start and end of the recording); ends are exclusive

  • inplace (bool or None, optional) – if True, make change in-place and return the object; if False, make and return copy before making changes; if None, use the object-level setting (specified in the constructor)

Return type:

EyeData

Returns:

the object without the removed periods (self or a copy, depending on inplace)

Raises:

ValueError – if all samples would be removed

See also

crop

keep only the data between two times

split_excluded

split the data at the excluded periods

Examples

>>> breaks = d.get_intervals(start="break_start", end="break_end", units="ms")
>>> d = d.remove_intervals(breaks)
>>> d = d.remove_intervals(d.get_excluded())