def gen_aperiodic(freqs, aperiodic_params, aperiodic_mode=None):
"""Generate aperiodic values.
Parameters
----------
freqs : 1d array
Frequency vector to create aperiodic component for.
aperiodic_params : list of float
Parameters that define the aperiodic component.
aperiodic_mode : {'fixed', 'knee'}, optional
Which kind of aperiodic component to generate.
If not provided, is inferred from the parameters.
Returns
-------
ap_vals : 1d array
Aperiodic values, in log10 spacing.
"""
if not aperiodic_mode:
aperiodic_mode = infer_ap_func(aperiodic_params)
ap_func = get_ap_func(aperiodic_mode)
ap_vals = ap_func(freqs, *aperiodic_params)
return ap_vals
def _robust_ap_fit(self, freqs, power_spectrum):
"""Fit the aperiodic component of the power spectrum robustly, ignoring outliers.
Parameters
----------
freqs : 1d array
Frequency values for the power spectrum, in linear scale.
power_spectrum : 1d array
Power values, in log10 scale.
Returns
-------
aperiodic_params : 1d array
Parameter estimates for aperiodic fit.
"""
# Do a quick, initial aperiodic fit
popt = self._simple_ap_fit(freqs, power_spectrum)
initial_fit = gen_aperiodic(freqs, popt)
# Flatten power_spectrum based on initial aperiodic fit
flatspec = power_spectrum - initial_fit
# Flatten outliers - any points that drop below 0
flatspec[flatspec < 0] = 0
# Use percential threshold, in terms of # of points, to extract and re-fit
perc_thresh = np.percentile(flatspec, self._ap_percentile_thresh)
perc_mask = flatspec <= perc_thresh
freqs_ignore = freqs[perc_mask]
spectrum_ignore = power_spectrum[perc_mask]
# Second aperiodic fit - using results of first fit as guess parameters
# See note in _simple_ap_fit about warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
freqs_ignore,
spectrum_ignore,
p0=popt,
maxfev=5000,
bounds=self._ap_bounds)
return aperiodic_params
def _simple_ap_fit(self, freqs, power_spectrum):
"""Fit the aperiodic component of the power spectrum.
Parameters
----------
freqs : 1d array
Frequency values for the power_spectrum, in linear scale.
power_spectrum : 1d array
Power values, in log10 scale.
Returns
-------
aperiodic_params : 1d array
Parameter estimates for aperiodic fit.
"""
# Set guess params for lorentzian aperiodic fit, guess params set at init
guess = np.array(
([power_spectrum[0]]
if not self._ap_guess[0] else [self._ap_guess[0]]) +
([self._ap_guess[1]] if self.aperiodic_mode == 'knee' else []) +
[self._ap_guess[2]])
# Ignore warnings that are raised in curve_fit
# A runtime warning can occur while exploring parameters in curve fitting
# This doesn't effect outcome - it won't settle on an answer that does this
# It happens if / when b < 0 & |b| > x**2, as it leads to log of a negative number
with warnings.catch_warnings():
warnings.simplefilter("ignore")
aperiodic_params, _ = curve_fit(get_ap_func(self.aperiodic_mode),
freqs,
power_spectrum,
p0=guess,
maxfev=5000,
bounds=self._ap_bounds)
return aperiodic_params
