def main():
## Individual Model Plot
# Download examples data files needed for this example
freqs = load_fooof_data('freqs.npy', folder='data')
spectrum = load_fooof_data('spectrum.npy', folder='data')
# Initialize and fit an example power spectrum model
fm = FOOOF(peak_width_limits=[1, 6],
max_n_peaks=6,
min_peak_height=0.2,
verbose=False)
fm.fit(freqs, spectrum, [3, 40])
# Save out the report
fm.save_report('FOOOF_report.png', 'img')
## Group Plot
# Download examples data files needed for this example
freqs = load_fooof_data('group_freqs.npy', folder='data')
spectra = load_fooof_data('group_powers.npy', folder='data')
# Initialize and fit a group of example power spectrum models
fg = FOOOFGroup(peak_width_limits=[1, 6],
max_n_peaks=6,
min_peak_height=0.2,
verbose=False)
fg.fit(freqs, spectra, [3, 30])
# Save out the report
fg.save_report('FOOOFGroup_report.png', 'img')
## Make the icon plot
# Simulate an example power spectrum
fs, ps = gen_power_spectrum([4, 35], [0, 1],
[[10, 0.3, 1], [22, 0.15, 1.25]],
nlv=0.01)
def custom_style(ax, log_freqs, log_powers):
"""Custom styler-function for the icon plot."""
# Set the top and right side frame & ticks off
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Set linewidth of remaining spines
ax.spines['left'].set_linewidth(10)
ax.spines['bottom'].set_linewidth(10)
ax.set_xticks([], [])
ax.set_yticks([], [])
# Create and save out the plot
plot_spectrum(fs,
ps,
log_freqs=False,
log_powers=True,
lw=12,
alpha=0.8,
color='grey',
plot_style=custom_style,
ax=check_ax(None, [6, 6]))
plt.tight_layout()
plt.savefig('img/spectrum.png')
## Clean Up
# Remove the data folder
shutil.rmtree('data')
###################################################################################################
# Fitting Multiple Spectra
# ------------------------
#
# So far, we have explored using the :class:`~fooof.FOOOF` object to fit individual power spectra.
#
# However, many potential analyses will including many power spectra that need to be fit.
#
# To support this, here we will introduce the :class:`~fooof.FOOOFGroup` object, which
# applies the model fitting procedure across multiple power spectra.
#
###################################################################################################
# Load examples data files needed for this example
freqs = load_fooof_data('group_freqs.npy', folder='data')
spectra = load_fooof_data('group_powers.npy', folder='data')
###################################################################################################
#
# For parameterizing a group of spectra, we can use a 1d array of frequency values
# corresponding to a 2d array for power spectra.
#
# This is the organization of the data we just loaded.
#
###################################################################################################
# Check the shape of the loaded data
print(freqs.shape)
print(spectra.shape)
# all frequencies, but rather a 'bend' in the aperiodic component. For these cases, fitting
# should be done using an extra parameter to capture this, using the 'knee' mode.
#
###################################################################################################
# Fitting with an Aperiodic 'Knee'
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# Let's explore fitting power spectrum models across a broader frequency range,
# using some local field potential data.
#
###################################################################################################
# Load example data files needed for this example
freqs = load_fooof_data('freqs_lfp.npy', folder='data')
spectrum = load_fooof_data('spectrum_lfp.npy', folder='data')
###################################################################################################
# Initialize a FOOOF object, setting the aperiodic mode to use a 'knee' fit
fm = FOOOF(peak_width_limits=[2, 8], aperiodic_mode='knee')
###################################################################################################
# Fit a power spectrum model
# Note that this time we're specifying an optional parameter to plot in log-log
fm.report(freqs, spectrum, [2, 70], plt_log=True)
###################################################################################################
# A note on interpreting the 'knee' parameter