###################################################################################################
# get_band_peak_group
# ~~~~~~~~~~~~~~~~~~~
#
# The :func:`get_band_peak_group` function can be used to select peaks within specific
# frequency ranges, from across a group of FOOOF fits.
#
###################################################################################################
# Generate some synthetic power spectra and fit a FOOOFGroup to use
freqs, spectra, _ = gen_group_power_spectra(
n_spectra=10,
freq_range=[3, 40],
aperiodic_params=param_sampler([[20, 2], [35, 1.5]]),
gauss_params=param_sampler([[], [10, 0.5, 2]]))
###################################################################################################
# Fit FOOOF models across the group of synthesized power spectra
fg = FOOOFGroup(peak_width_limits=[1, 8],
min_peak_height=0.05,
max_n_peaks=6,
verbose=False)
fg.fit(freqs, spectra)
###################################################################################################
# Get all alpha oscillations from a FOOOFGroup object
alphas = get_band_peak_group(fg.get_all_data('peak_params'), alpha_band,
# which can be used to iterate across parameters.
#
###################################################################################################
# param_sampler
# ~~~~~~~~~~~~~
#
# The :func:`param_sampler` function takes a list of parameter options and randomly selects from
# the parameters to create each power spectrum. You can optionally specify the
# probabilities with which to sample from the parameter options.
#
###################################################################################################
# Create a sampler to choose from two options for aperiodic parameters
ap_opts = param_sampler([[1, 1.25], [1, 1]])
# Create sampler to choose from two options for periodic parameters, and specify probabilities
gauss_opts = param_sampler([[10, 0.5, 1], [[10, 0.5, 1], [20, 0.25, 2]]],
[0.75, 0.25])
###################################################################################################
# Generate some power spectra, using the param samplers
fs, ps, syn_params = gen_group_power_spectra(10, [3, 40], ap_opts, gauss_opts)
###################################################################################################
# Plot some of the spectra that were generated
plot_spectra(fs, ps[0:4, :], log_powers=True)
# list of possible parameters, and creates an object that randomly samples from # them to generate power spectra. # # If you would like to generate single power spectra, you can use :func:`gen_power_spectrum`, # also in `fooof.synth.gen`. # ################################################################################################### # Settings for synthesizing power spectra n_spectra = 10 f_range = [3, 40] # Set some options for background parameters # Generated spectra will have an offset of either [20, 50, 35], and exponent of [2., 2.5, 1.5] ap_opts = param_sampler([[20, 2], [50, 2.5], [35, 1.5]]) # Set some options for peak parameters # Generated power spectra will have either no peaks, a 10 Hz peak, or a 10 Hz & 20 Hz peak gauss_opts = param_sampler([[], [10, 0.5, 2], [10, 0.5, 2, 20, 0.3, 4]]) ################################################################################################### # # We can now feed these settings into :func:`gen_group_power_spectra`, # that will generate a group of power spectra for us. # # Note that this function also returns a list of the parameters # used to generate each power spectrum. # ###################################################################################################
###################################################################################################
import numpy as np
# FOOOF imports
from fooof import FOOOFGroup
from fooof.funcs import fit_fooof_group_3d, combine_fooofs
from fooof.synth.gen import gen_group_power_spectra
from fooof.synth.params import param_sampler
###################################################################################################
# Settings for creating synthetic data
n_spectra = 10
freq_range = [3, 40]
ap_opts = param_sampler([[0, 1.0], [0, 1.5], [0, 2]])
gauss_opts = param_sampler([[], [10, 1, 1], [10, 1, 1, 20, 2, 1]])
###################################################################################################
# Generate some synthetic power spectra, and organize into a 3D matrix
spectra = []
for ind in range(3):
fs, ps, _ = gen_group_power_spectra(n_spectra, freq_range, ap_opts, gauss_opts)
spectra.append(ps)
spectra = np.array(spectra)
###################################################################################################
# Check the shape of the spectra
# This kind of 3D organization can be thought to represent [n_conditions, n_channels, n_freqs]