spike_detection_vickers97.linear_interpolate_spikes

spike_detection_vickers97.linear_interpolate_spikes(data: ndarray, is_spike: ndarray, error_value: float) → ndarray[source]

Replace detected spikes with linear interpolation.

This function replaces spike values in a time series with linearly interpolated values using valid neighboring points. It handles both isolated spikes and consecutive sequences of spikes, as well as error values in the data.

Parameters:

  • data (numpy.ndarray) – Input data array containing the original time series with spikes

  • is_spike (numpy.ndarray) – Boolean array of same length as data, True where spikes were detected

  • error_value (float) – Special value indicating invalid or missing data points These points are skipped when finding valid neighbors for interpolation

Returns:

Copy of input data with spikes replaced by linear interpolation

Return type:

numpy.ndarray

Notes

The interpolation strategy is: 1. For each sequence of spikes, find valid (non-spike, non-error) points

before and after the sequence

  1. If both points exist: perform linear interpolation

  2. If only one exists: use that value (nearest neighbor)

  3. If neither exists: spikes remain unchanged

This implementation follows the EddyPro software’s approach but is vectorized for better performance in Python.

See also

spike_detection_vickers97

Main spike detection algorithm

Examples

>>> import numpy as np
>>> # Create sample data with spikes
>>> data = np.array([1.0, 10.0, 1.1, np.nan, 1.2])
>>> spikes = np.array([False, True, False, False, False])
>>> cleaned = linear_interpolate_spikes(data, spikes, np.nan)
>>> print(cleaned)  # [1.0, 1.05, 1.1, nan, 1.2]