[6, 0.2, 1, 10, 0.3, 1, 25, 0.15, 3]) ps1, ps2 = ps ################################################################################################### # The FOOOF plotting module has plots for plotting single or multiple # power spectra, options for plotting in linear or log space, and # plots for shading frequency regions of interest. # # Plotting in FOOOF uses matplotlib. Plotting functions can also take in any # matplotlib keyword arguments, that will be passed into the plot call. ################################################################################################### # Create a spectrum plot with a single power spectrum plot_spectrum(fs, ps2, log_powers=True) ################################################################################################### # Plot multiple spectra on the same plot plot_spectra(fs, ps, log_freqs=True, log_powers=True) ################################################################################################### # Plot a single power spectrum, with a shaded region covering alpha plot_spectrum_shading(fs, ps1, [8, 12], log_powers=True) ################################################################################################### # Plot multiple power spectra, with shades covering theta & beta ranges plot_spectra_shading(fs, ps, [[4, 8], [20, 30]], log_powers=True)
# for example, when examining power in particular frequency regions.
#
# The :func:`~.plot_spectra_shading` function takes in a power spectrum and one or more
# shaded regions, and plot the power spectrum with the indicated region shaded.
#
# The same can be done for multiple power spectra with :func:`~.plot_spectra_shading`.
#
# These functions take in an input designating one or more shade regions, each specified
# as [freq_low, freq_high] of the region to shade. They also take in an optional argument
# of `shade_colors` which can be used to control the color(s) of the shade regions.
#
###################################################################################################
# Plot a single power spectrum, with a shaded region covering alpha
plot_spectra_shading(freqs, powers1, [8, 12], log_powers=True)
###################################################################################################
# Plot multiple power spectra, with shades covering theta & beta ranges
plot_spectra_shading(freqs, [powers1, powers2], [[4, 8], [20, 30]],
log_powers=True,
shade_colors=['green', 'blue'])
###################################################################################################
# Put it all together
# -------------------
#
# Finally, we can put all these plotting tools together.
#
# To do so, note also that all plot functions also take in an optional `ax` argument
pe_g2 = [[2, 0.5, 1], [6, 0.3, 1], [10, 0.5, 1.5], [20, 0.15, 3], [40, 0.15, 3.5]]
# Set random seed, for consistency generating simulated data
set_random_seed(21)
###################################################################################################
# Simulate example power spectra for each group
freqs, g1_spectrum_bands = gen_power_spectrum(f_range, ap_params, pe_g1, nlv)
freqs, g2_spectrum_bands = gen_power_spectrum(f_range, ap_params, pe_g2, nlv)
###################################################################################################
# Plot the power spectra differences, representing the 'band-by-band' idea
plot_spectra_shading(freqs, [g1_spectrum_bands, g2_spectrum_bands],
log_powers=True, linewidth=3,
shades=bands.definitions, shade_colors=shade_cols,
labels=labels)
plt.xlim(f_range);
plt.title('Band-by-Band', t_settings);
###################################################################################################
# Flatten the Spectra
# ~~~~~~~~~~~~~~~~~~~
#
# Under the band-by-band idea, controlling for aperiodic activity and flattening
# the spectra should show specific differences in each band.
#
# It should also find no systematic difference in the aperiodic activity between groups.
#
# To check this, we can fit power spectrum models, and examine which parameters are
# changing in the data.
print('TBR difference from {:20} is \t {:1.3f}'.format(\
label, tbr - calc_band_ratio(freqs, spectrum, bands.theta, bands.beta)))
###################################################################################################
# Create figure of periodic changes
title_settings = {'fontsize': 16, 'fontweight': 'bold'}
fig, axes = plt.subplots(3, 3, figsize=(15, 14))
for ax, (label, spectrum) in zip(axes.flatten(), spectra.items()):
if spectrum is None: continue
plot_spectra_shading(freqs, [powers, spectrum], [bands.theta, bands.beta],
shade_colors=shade_color,
log_freqs=False,
log_powers=True,
ax=ax)
ax.set_title(label, **title_settings)
ax.set_xlim([0, 35])
ax.set_ylim([-1.75, 0])
ax.xaxis.label.set_visible(False)
ax.yaxis.label.set_visible(False)
# Turn off empty axes & space out axes
fig.subplots_adjust(hspace=.3, wspace=.3)
_ = [ax.axis('off') for ax in [axes[0, 0], axes[1, 1]]]
###################################################################################################
#
# - a change in alpha **power**, part of the periodic component
# - a change in alpha **center frequency**, part of the periodic component
# - a change in the **offset** of the aperiodic component
# - a change in the **exponent** of the aperiodic component
#
###################################################################################################
# Plot and compare all of our power spectra
fig, axes = plt.subplots(2, 2, figsize=(16, 12))
for ax, (title, powers) in zip(axes.reshape(-1), all_powers.items()):
# Create spectrum plot, with alpha band of interest shaded in
plot_spectra_shading(freqs, [powers_base, powers],
bands.alpha,
shade_colors=shade_color,
log_freqs=log_freqs,
log_powers=log_powers,
ax=ax)
# Add the title, and do some plot styling
ax.set_title(title, {'fontsize': 20})
ax.xaxis.label.set_visible(False)
ax.yaxis.label.set_visible(False)
###################################################################################################
# Comparing Power Spectra
# ~~~~~~~~~~~~~~~~~~~~~~~
#
# Now let's compare our different power spectra, in terms of band-specific power measures.
#
# To do so, we will first define a helper function that calculates the average power in
