def _regenerate_model(self): """Regenerate model fit from parameters.""" self._ap_fit = gen_aperiodic(self.freqs, self.aperiodic_params_) self._peak_fit = gen_peaks(self.freqs, np.ndarray.flatten(self._gaussian_params)) self.fooofed_spectrum_ = self._peak_fit + self._ap_fit
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 fit(self, freqs=None, power_spectrum=None, freq_range=None): """Fit the full power spectrum as a combination of periodic and aperiodic components. Parameters ---------- freqs : 1d array, optional Frequency values for the power spectrum, in linear space. power_spectrum : 1d array, optional Power values, which must be input in linear space. freq_range : list of [float, float], optional Frequency range to restrict power spectrum to. If not provided, keeps the entire range. Notes ----- Data is optional if data has been already been added to FOOOF object. """ # If freqs & power_spectrum provided together, add data to object. if freqs is not None and power_spectrum is not None: self.add_data(freqs, power_spectrum, freq_range) # If power spectrum provided alone, add to object, and use existing frequency data # Note: be careful passing in power_spectrum data like this: # It assumes the power_spectrum is already logged, with correct freq_range. elif isinstance(power_spectrum, np.ndarray): self.power_spectrum = power_spectrum # Check that data is available if self.freqs is None or self.power_spectrum is None: raise ValueError('No data available to fit - can not proceed.') # Check and warn about width limits (if in verbose mode) if self.verbose: self._check_width_limits() # In rare cases, the model fails to fit. Therefore it's in a try/except # Cause of failure: RuntimeError, failure to find parameters in curve_fit try: # Fit the aperiodic component self.aperiodic_params_ = self._robust_ap_fit( self.freqs, self.power_spectrum) self._ap_fit = gen_aperiodic(self.freqs, self.aperiodic_params_) # Flatten the power_spectrum using fit aperiodic fit self._spectrum_flat = self.power_spectrum - self._ap_fit # Find peaks, and fit them with gaussians self._gaussian_params = self._fit_peaks( np.copy(self._spectrum_flat)) # Calculate the peak fit # Note: if no peaks are found, this creates a flat (all zero) peak fit. self._peak_fit = gen_peaks( self.freqs, np.ndarray.flatten(self._gaussian_params)) # Create peak-removed (but not flattened) power spectrum. self._spectrum_peak_rm = self.power_spectrum - self._peak_fit # Run final aperiodic fit on peak-removed power spectrum # Note: This overwrites previous aperiodic fit self.aperiodic_params_ = self._simple_ap_fit( self.freqs, self._spectrum_peak_rm) self._ap_fit = gen_aperiodic(self.freqs, self.aperiodic_params_) # Create full power_spectrum model fit self.fooofed_spectrum_ = self._peak_fit + self._ap_fit # Convert gaussian definitions to peak parameters self.peak_params_ = self._create_peak_params(self._gaussian_params) # Calculate R^2 and error of the model fit. self._calc_r_squared() self._calc_rmse_error() # Catch failure, stemming from curve_fit process except RuntimeError: # Clear any interim model results that may have run # Partial model results shouldn't be interpreted in light of overall failure self._reset_data_results(clear_freqs=False, clear_spectrum=False, clear_results=True) # Print out status if self.verbose: print('Model fitting was unsuccessful.')
################################################################################################### # # The FOOOF object stores most of the intermediate steps internally. # # For this notebook, we will first fit the full model, as normal, but then step through, # and visualize each step the algorithm takes to come to that final fit. # # Fit the FOOOF model fm.fit(freqs, spectrum, [3, 40]) ################################################################################################### # Do an initial aperiodic signal fit - a robust fit, that excludes outliers # This recreates an initial fit that isn't ultimately stored in the FOOOF object) init_ap_fit = gen_aperiodic(fm.freqs, fm._robust_ap_fit(fm.freqs, fm.power_spectrum)) # Plot the initial aperiodic fit _, ax = plt.subplots(figsize=(12, 10)) plot_spectrum(fm.freqs, fm.power_spectrum, plt_log, label='Original Power Spectrum', ax=ax) plot_spectrum(fm.freqs, init_ap_fit, plt_log, label='Initial Aperiodic Fit', ax=ax) ###################################################################################################