specparam.utils.interpolate_spectra

specparam.utils.interpolate_spectra(freqs, powers, interp_range, buffer=3)

Interpolate a frequency region across a group of power spectra.

Parameters:

freqs1d array

Frequency values for the power spectrum.

powers2d array

Power values for the power spectra.

interp_rangelist of float or list of list of float

Frequency range to interpolate, as [lowest_freq, highest_freq]. If a list of lists, applies each as it’s own interpolation range.

bufferint or list of int

The number of samples to use on either side of the interpolation range, that are then averaged and used to calculate the interpolation.

Returns:

freqs1d array

Frequency values for the power spectrum.

powers2d array

Power values, with interpolation, for the power spectra.

Notes

This function takes in, and returns, linearly spaced values.

This approach interpolates data linearly, in log-log spacing. This assumes a 1/f property of the data, and so should only be applied where this assumption is valid. This approach is intended for interpolating small frequency ranges, such as line noise regions.

The interpolation range is taken as the range from >= interp_range_low and <= interp_range_high. It does not round to below or above interp_range_low and interp_range_high, respectively.

To be more robust to noise, this approach takes a number of samples on either side of the interpolation range (the number of which is controlled by buffer) and averages these points to linearly interpolate between them. Setting buffer=1 is equivalent to a linear interpolation between the points adjacent to the interpolation range.

Examples

Using simulated spectra, interpolate away line noise peaks:

>>> from specparam.sim import sim_group_power_spectra
>>> freqs, powers = sim_group_power_spectra(5, [1, 75], [1, 1], [[10, 0.5, 1.0], [60, 2, 0.1]])
>>> freqs, powers = interpolate_spectra(freqs, powers, [58, 62])