def _add_peaks_outline(fm, plt_log, ax, **plot_kwargs):
"""Add an outline of each peak.
Parameters
----------
fm : FOOOF
FOOOF object containing results from fitting.
plt_log : boolean
Whether to plot the frequency values in log10 spacing.
ax : matplotlib.Axes
Figure axes upon which to plot.
**plot_kwargs
Keyword arguments to pass into the plot call.
"""
defaults = {'color': PLT_COLORS['periodic'], 'alpha': 0.7, 'lw': 1.5}
plot_kwargs = check_plot_kwargs(plot_kwargs, defaults)
for peak in fm.gaussian_params_:
# Define the frequency range around each peak to plot - peak bandwidth +/- 3
peak_range = [peak[0] - peak[2] * 3, peak[0] + peak[2] * 3]
# Generate a peak reconstruction for each peak, and trim to desired range
peak_line = fm._ap_fit + gen_periodic(fm.freqs, peak)
peak_freqs, peak_line = trim_spectrum(fm.freqs, peak_line, peak_range)
# Plot the peak outline
peak_freqs = np.log10(peak_freqs) if plt_log else peak_freqs
ax.plot(peak_freqs, peak_line, **plot_kwargs)
def _add_peaks_shade(fm, plt_log, ax, **plot_kwargs):
"""Add a shading in of all peaks.
Parameters
----------
fm : FOOOF
FOOOF object containing results from fitting.
plt_log : boolean
Whether to plot the frequency values in log10 spacing.
ax : matplotlib.Axes
Figure axes upon which to plot.
**plot_kwargs
Keyword arguments to pass into the plot call.
"""
kwargs = check_plot_kwargs(plot_kwargs, {
'color': PLT_COLORS['periodic'],
'alpha': 0.25
})
for peak in fm.gaussian_params_:
peak_freqs = np.log10(fm.freqs) if plt_log else fm.freqs
peak_line = fm._ap_fit + gen_periodic(fm.freqs, peak)
ax.fill_between(peak_freqs, peak_line, fm._ap_fit, **kwargs)
def refit(fm, sig, fs, f_range, imf_kwargs=None, power_thresh=.2, energy_thresh=0., refit_ap=False):
"""Refit a power spectrum using EMD based parameter estimation.
Parameters
----------
fm : fooof.FOOOF
FOOOF object containing results from fitting.
sig : 1d array
Voltage time series.
fs : float
Sampling rate, in Hz.
f_range : tuple of [float, float]
Frequency range to restrict power spectrum to.
imf_kwargs : optional, default: None
Optional keyword arguments for compute_emd. Includes:
- max_imfs
- sift_thresh
- env_step_size
- max_iters
- energy_thresh
- stop_method
- sd_thresh
- rilling_thresh
power_thresh : float, optional, default: .2
IMF power threshold as the mean power above the initial aperiodic fit.
energy_thresh : float, optional, default: 0.
Normalized HHT energy threshold to define oscillatory frequencies. This aids the removal of
harmonic peaks if present.
refit_ap : bool, optional, default: None
Refits the aperiodic component when True. When False, the aperiodic component is defined
from the intial specparam fit.
Returns
-------
fm : fooof.FOOOF
Updated FOOOF fit.
imf : 2d array
Intrinsic modes functions.
pe_mask : 1d array
Booleans to mark imfs above aperiodic fit.
"""
fm_refit = fm.copy()
if imf_kwargs is None:
imf_kwargs = {'sd_thresh': .1}
# Compute modes
imf = compute_emd(sig, **imf_kwargs)
# Convert spectra of mode timeseries
_, powers_imf = compute_spectrum(imf, fs, f_range=f_range)
freqs = fm_refit.freqs
powers = fm_refit.power_spectrum
powers_imf = np.log10(powers_imf)
# Initial aperiodic fit
powers_ap = fm_refit._ap_fit
# Select superthreshold modes
pe_mask = select_modes(powers_imf, powers_ap, power_thresh=power_thresh)
# Refit periodic
if not pe_mask.any():
warnings.warn('No IMFs are above the intial aperiodic fit. '
'Returning the inital spectral fit.')
return fm_refit, imf, pe_mask
if energy_thresh > 0:
# Limit frequency ranges to fit using HHT
freqs_min, freqs_max = limit_freqs_hht(imf[pe_mask], freqs, fs,
energy_thresh=energy_thresh)
if freqs_min is None and freqs_max is None:
warnings.warn('No superthreshold energy in HHT. '
'Returning the inital spectral fit.')
return fm_refit, imf, np.zeros(len(pe_mask), dtype=bool)
limits = (freqs_min, freqs_max)
gauss_params = fit_gaussians(freqs, powers, powers_imf, powers_ap, pe_mask, limits)
else:
gauss_params = fit_gaussians(freqs, powers, powers_imf, powers_ap, pe_mask)
if gauss_params is not None:
fm_refit.peak_params_ = fm_refit._create_peak_params(gauss_params)
fm_refit._peak_fit = gen_periodic(freqs, gauss_params.flatten())
else:
fm_refit.peak_params_ = None
fm_refit._peak_fit = np.zeros_like(freqs)
# Refit aperiodic
if refit_ap:
ap_params, ap_fit = refit_aperiodic(freqs, powers, fm_refit._peak_fit)
fm_refit._ap_fit = ap_fit
fm_refit.aperiodic_params_ = ap_params
# Update attibutes
fm_refit.gaussian_params_ = gauss_params
fm_refit.fooofed_spectrum_ = fm_refit._peak_fit + fm_refit._ap_fit
fm_refit._calc_r_squared()
fm_refit._calc_error()
return fm_refit, imf, pe_mask