def default_group_params():
"""Create default parameters for generating a test group of power spectra."""
freq_range = [3, 50]
bgp_opts = param_sampler([[20, 2], [50, 2.5], [35, 1.5]])
gauss_opts = param_sampler([[10, 0.5, 2], [10, 0.5, 2, 20, 0.3, 4]])
return freq_range, bgp_opts, gauss_opts
###############################################################################
# Reload some data and fit a FOOOF model to use
freqs = np.load('dat/freqs_lfp.npy')
spectrum = np.load('dat/spectrum_lfp.npy')
fm = FOOOF(peak_width_limits=[2, 8])
fm.fit(freqs, spectrum, [3, 30])
###############################################################################
# Generate some synthetic power spectra and fit a FOOOFGroup to use
freqs, spectra, _ = gen_group_power_spectra(
n_spectra=10,
freq_range=[3, 40],
background_params=param_sampler([[20, 2], [35, 1.5]]),
gauss_params=param_sampler([[], [10, 0.5, 2]]))
fg = FOOOFGroup(peak_width_limits=[1, 8],
min_peak_amplitude=0.05,
max_n_peaks=6,
verbose=False)
fg.fit(freqs, spectra)
###############################################################################
# FOOOF Analysis Utilities
# ------------------------
#
# FOOOF is packaged with minimal analysis utility functions.
#
# The plan is for the FOOOF module to stay this way, as supporting further analysis of FOOOF-derived results is largely outside the scope of the current module.
#
# - Check your data, across the frequency range of interest, for what the aperiodic signal looks like. # - If it looks roughly linear (in log-log space), fit without a knee. # - This is likely across smaller frequency ranges, such as 3-30. # - Do not perform no-knee fits across a range in which this does not hold. # - If there is a clear knee, then use knee fits. # - This is likely across larger fitting ranges such as 1-150 Hz. # - Be wary of ambiguous ranges, where there may or may not be a knee. # - Trying to fit without a knee, when there is not a single consistent aperiodic signal, can lead to very bad fits. But it is also a known issue that trying to fit with a knee can lead to suboptimal fits when no knee is present. # - We therefore currently recommend picking frequency ranges in which the expected aperiodic signal process is relatively clear. ############################################################################### # Checking Fits Across a Group # ---------------------------- # Set the parameters options for aperiodic signal and Gaussian peaks bgp_opts = param_sampler([[20, 2], [50, 2.5], [35, 1.5]]) gauss_opts = param_sampler([[], [10, 0.5, 2], [10, 0.5, 2, 20, 0.3, 4]]) # Generate a group of power spectra freqs, power_spectra, syn_params = gen_group_power_spectra(10, [3, 50], bgp_opts, gauss_opts) ############################################################################### # Initialize a FOOOFGroup fg = FOOOFGroup(peak_width_limits=[1, 6]) ############################################################################### # Fit FOOOF and report on the group fg.report(freqs, power_spectra)