"""Utilities for working with data and models."""
import numpy as np
###################################################################################################
###################################################################################################
[docs]def trim_spectrum(freqs, power_spectra, f_range):
"""Extract a frequency range from power spectra.
Parameters
----------
freqs : 1d array
Frequency values for the power spectrum.
power_spectra : 1d or 2d array
Power spectral density values.
f_range: list of [float, float]
Frequency range to restrict to, as [lowest_freq, highest_freq].
Returns
-------
freqs_ext : 1d array
Extracted frequency values for the power spectrum.
power_spectra_ext : 1d or 2d array
Extracted power spectral density values.
Notes
-----
This function extracts frequency ranges >= f_low and <= f_high.
It does not round to below or above f_low and f_high, respectively.
Examples
--------
Using a simulated spectrum, extract a frequency range:
>>> from fooof.sim import gen_power_spectrum
>>> freqs, powers = gen_power_spectrum([1, 50], [1, 1], [10, 0.5, 1.0])
>>> freqs, powers = trim_spectrum(freqs, powers, [3, 30])
"""
# Create mask to index only requested frequencies
f_mask = np.logical_and(freqs >= f_range[0], freqs <= f_range[1])
# Restrict freqs & spectra to requested range
# The if/else is to cover both 1d or 2d arrays
freqs_ext = freqs[f_mask]
power_spectra_ext = power_spectra[f_mask] if power_spectra.ndim == 1 \
else power_spectra[:, f_mask]
return freqs_ext, power_spectra_ext
[docs]def interpolate_spectrum(freqs, powers, interp_range, buffer=3):
"""Interpolate a frequency region in a power spectrum.
Parameters
----------
freqs : 1d array
Frequency values for the power spectrum.
powers : 1d array
Power values for the power spectrum.
interp_range : list of float
Frequency range to interpolate, as [lowest_freq, highest_freq].
buffer : 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
-------
freqs : 1d array
Frequency values for the power spectrum.
powers : 1d array
Power values, with interpolation, for the power spectrum.
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 a simulated spectrum, interpolate away a line noise peak:
>>> from fooof.sim import gen_power_spectrum
>>> freqs, powers = gen_power_spectrum([1, 75], [1, 1], [[10, 0.5, 1.0], [60, 2, 0.1]])
>>> freqs, powers = interpolate_spectrum(freqs, powers, [58, 62])
"""
# Get the set of frequency values that need to be interpolated
interp_mask = np.logical_and(freqs >= interp_range[0], freqs <= interp_range[1])
interp_freqs = freqs[interp_mask]
# Get the indices of the interpolation range
ii1, ii2 = np.flatnonzero(interp_mask)[[0, -1]]
# Extract & log the requested range of data to use around interpolated range
xs1 = np.log10(freqs[ii1-buffer:ii1])
xs2 = np.log10(freqs[ii2:ii2+buffer])
ys1 = np.log10(powers[ii1-buffer:ii1])
ys2 = np.log10(powers[ii2:ii2+buffer])
# Linearly interpolate, in log-log space, between averages of the extracted points
vals = np.interp(np.log10(interp_freqs),
[np.median(xs1), np.median(xs2)],
[np.median(ys1), np.median(ys2)])
powers[interp_mask] = np.power(10, vals)
return freqs, powers