"""Functions to analyze and investigate FOOOF results - periodic components."""
import numpy as np
from fooof.core.items import PEAK_INDS
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[docs]def get_band_peak_fm(fm, band, select_highest=True, threshold=None, thresh_param='PW',
attribute='peak_params',):
"""Extract peaks from a band of interest from a FOOOF object.
Parameters
----------
fm : FOOOF
Object to extract peak data from.
band : tuple of (float, float)
Frequency range for the band of interest.
Defined as: (lower_frequency_bound, upper_frequency_bound).
select_highest : bool, optional, default: True
Whether to return single peak (if True) or all peaks within the range found (if False).
If True, returns the highest power peak within the search range.
threshold : float, optional
A minimum threshold value to apply.
thresh_param : {'PW', 'BW'}
Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
attribute : {'peak_params', 'gaussian_params'}
Which attribute of peak data to extract data from.
Returns
-------
1d or 2d array
Peak data. Each row is a peak, as [CF, PW, BW].
Examples
--------
Select an alpha peak from an already fit FOOOF object 'fm', selecting the highest power alpha:
>>> alpha = get_band_peak_fm(fm, [7, 14], select_highest=True) # doctest:+SKIP
Select beta peaks from a FOOOF object 'fm', extracting all peaks in the range:
>>> betas = get_band_peak_fm(fm, [13, 30], select_highest=False) # doctest:+SKIP
"""
return get_band_peak(getattr(fm, attribute + '_'), band,
select_highest, threshold, thresh_param)
[docs]def get_band_peak_fg(fg, band, threshold=None, thresh_param='PW', attribute='peak_params'):
"""Extract peaks from a band of interest from a FOOOFGroup object.
Parameters
----------
fg : FOOOFGroup
Object to extract peak data from.
band : tuple of (float, float)
Frequency range for the band of interest.
Defined as: (lower_frequency_bound, upper_frequency_bound).
threshold : float, optional
A minimum threshold value to apply.
thresh_param : {'PW', 'BW'}
Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
attribute : {'peak_params', 'gaussian_params'}
Which attribute of peak data to extract data from.
Returns
-------
2d array
Peak data. Each row is a peak, as [CF, PW, BW].
Each row represents an individual model from the input object.
Notes
-----
The returned array keeps track of which model each extracted peak comes from,
returning a [n_models, 3] array, with one peak returned per model.
- To do so, this function necessarily extracts and returns one peak per model fit.
- Each row reflects an individual model fit, in order, filled with nan if no peak was present.
If, instead, you wish to extract all peaks within a band, per model fit,
you can do something like:
>>> peaks = np.empty((0, 3))
>>> for f_res in fg: # doctest:+SKIP
... peaks = np.vstack((peaks, get_band_peak(f_res.peak_params, band, select_highest=False)))
Examples
--------
Extract alpha peaks from a FOOOFGroup object 'fg' that already has model results:
>>> alphas = get_band_peak_fg(fg, [7, 14]) # doctest:+SKIP
Extract peaks from a FOOOFGroup object 'fg', selecting those above a power threshold:
>>> betas = get_band_peak_fg(fg, [13, 30], threshold=0.1) # doctest:+SKIP
"""
return get_band_peak_group(fg.get_params(attribute), band, len(fg),
threshold, thresh_param)
[docs]def get_band_peak_group(peak_params, band, n_fits, threshold=None, thresh_param='PW'):
"""Extract peaks within a given band of interest, from peaks from a group fit.
Parameters
----------
peak_params : 2d array
Peak parameters, for a group fit, with shape of [n_peaks, 4].
band : tuple of (float, float)
Frequency range for the band of interest.
Defined as: (lower_frequency_bound, upper_frequency_bound).
n_fits : int
The number of model fits in the FOOOFGroup data.
threshold : float, optional
A minimum threshold value to apply.
thresh_param : {'PW', 'BW'}
Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
Returns
-------
band_peaks : 2d array
Peak data. Each row is a peak, as [CF, PW, BW].
Each row represents an individual model from the input array of all peaks.
Notes
-----
The returned array keeps track of which model each extracted peak comes from,
returning a [n_models, 3] array, with one peak returned per model.
- To do so, this function necessarily extracts and returns one peak per model fit.
- Each row reflects an individual model fit, in order, filled with nan if no peak was present.
"""
# Extracts an array per FOOOF fit, and extracts band peaks from it
band_peaks = np.zeros(shape=[n_fits, 3])
for ind in range(n_fits):
band_peaks[ind, :] = get_band_peak(peak_params[tuple([peak_params[:, -1] == ind])][:, 0:3],
band=band, select_highest=True,
threshold=threshold,
thresh_param=thresh_param)
return band_peaks
[docs]def get_band_peak(peak_params, band, select_highest=True, threshold=None, thresh_param='PW'):
"""Extract peaks within a given band of interest.
Parameters
----------
peak_params : 2d array
Peak parameters, with shape of [n_peaks, 3].
band : tuple of (float, float)
Frequency range for the band of interest.
Defined as: (lower_frequency_bound, upper_frequency_bound).
select_highest : bool, optional, default: True
Whether to return single peak (if True) or all peaks within the range found (if False).
If True, returns the highest peak within the search range.
threshold : float, optional
A minimum threshold value to apply.
thresh_param : {'PW', 'BW'}
Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
Returns
-------
band_peaks : 1d or 2d array
Peak data. Each row is a peak, as [CF, PW, BW].
"""
# Return nan array if empty input
if peak_params.size == 0:
return np.array([np.nan, np.nan, np.nan])
# Find indices of peaks in the specified range, and check the number found
peak_inds = (peak_params[:, 0] >= band[0]) & (peak_params[:, 0] <= band[1])
n_peaks = sum(peak_inds)
# If there are no peaks within the specified range, return nan
# Note: this also catches and returns if the original input was empty
if n_peaks == 0:
return np.array([np.nan, np.nan, np.nan])
band_peaks = peak_params[peak_inds, :]
# Apply a minimum threshold, if one was provided
if threshold:
band_peaks = threshold_peaks(band_peaks, threshold, thresh_param)
# If results > 1 and select_highest, then we return the highest peak
# Call a sub-function to select highest power peak in band
if n_peaks > 1 and select_highest:
band_peaks = get_highest_peak(band_peaks)
# Squeeze so that if there is only 1 result, return single peak in flat array
return np.squeeze(band_peaks)
[docs]def get_highest_peak(peak_params):
"""Extract the highest power peak.
Parameters
----------
peak_params : 2d array
Peak parameters, with shape of [n_peaks, 3].
Returns
-------
1d array
Peak data. The row is a peak, as [CF, PW, BW].
Notes
-----
This function returns the singular highest power peak from the input set, and as
such is defined to work on periodic parameters from a single model fit.
"""
# Catch & return NaN if empty
if len(peak_params) == 0:
return np.array([np.nan, np.nan, np.nan])
high_ind = np.argmax(peak_params[:, 1])
return peak_params[high_ind, :]
[docs]def threshold_peaks(peak_params, threshold, param='PW'):
"""Extract peaks that are above a given threshold value.
Parameters
----------
peak_params : 2d array
Peak parameters, with shape of [n_peaks, 3] or [n_peaks, 4].
threshold : float
A minimum threshold value to apply.
param : {'PW', 'BW'}
Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
Returns
-------
thresholded_peaks : 2d array
Peak parameters, with shape of [n_peaks, :].
Notes
-----
This function can be applied to periodic parameters from an individual model,
or a set or parameters from a group.
"""
# Catch if input is empty & return nan if so
if len(peak_params) == 0:
return np.array([np.nan, np.nan, np.nan])
# Otherwise, apply a mask to apply the requested threshold
thresh_mask = peak_params[:, PEAK_INDS[param]] > threshold
thresholded_peaks = peak_params[thresh_mask]
return thresholded_peaks