Update naming background -> aperiodic

Author: TomDonoghue

fooof_mat/fooof.m

@@ -12,13 +12,13 @@
 %       settings.max_n_peaks
 %       settings.min_peak_amplitude
 %       settings.peak_threshold
-%       settings.background_mode
+%       settings.aperiodic_mode
 %       settings.verbose
-%   return_model    = boolean of whether to return actual model, optional 
+%   return_model    = boolean of whether to return actual model, optional
 %
 % Outputs:
 %   fooof_results   = fooof model ouputs, in a struct, including:
-%       fooof_results.background_params
+%       fooof_results.aperiodic_params
 %       fooof_results.peak_params
 %       fooof_results.gaussian_params
 %       fooof_results.error
@@ -27,12 +27,12 @@
 %            fooof_results.freqs
 %            fooof_results.power_spectrum
 %            fooof_results.fooofed_spectrum
-%            fooof_results.bg_fit
+%            fooof_results.ap_fit
 %
 % Notes
-%   Not all settings need to be set. Any settings that are not 
-%     provided as set to default values. To run with all defaults, 
-%     input settings as an empty struct. 
+%   Not all settings need to be set. Any settings that are not
+%     provided as set to default values. To run with all defaults,
+%     input settings as an empty struct.
 
 function fooof_results = fooof(freqs, power_spectrum, f_range, settings, return_model)
 
@@ -43,32 +43,32 @@
     freqs = py.numpy.array(freqs);
     power_spectrum = py.numpy.array(power_spectrum);
     f_range = py.list(f_range);
-    
+
     % Initialize FOOOF object
     fm = py.fooof.FOOOF(settings.peak_width_limits, ...
                         settings.max_n_peaks, ...
                         settings.min_peak_amplitude, ...
                         settings.peak_threshold, ...
-                        settings.background_mode, ...
+                        settings.aperiodic_mode, ...
                         settings.verbose);
-    
+
     % Run FOOOF fit
     fm.fit(freqs, power_spectrum, f_range)
 
     % Extract outputs
     fooof_results = fm.get_results();
     fooof_results = fooof_unpack_results(fooof_results);
-    
+
     % Also return the actual model fit, if requested
     %   This will default to not return model, if variable not set
     if exist('return_model', 'var') && return_model
-        
+
         % Get the model, and add outputs to foof_results
         model_out = fooof_get_model(fm);
         for field = fieldnames(model_out)'
             fooof_results.(field{1}) = model_out.(field{1});
         end
-        
+
     end
-    
+
 end

fooof_mat/fooof_check_settings.m

@@ -9,7 +9,7 @@
 %       settings.max_n_peaks
 %       settings.min_peak_amplitude
 %       settings.peak_threshold
-%       settings.background_mode
+%       settings.aperiodic_mode
 %       settings.verbose
 %
 % Returns:
@@ -18,11 +18,11 @@
 %       settings.max_n_peaks
 %       settings.min_peak_amplitude
 %       settings.peak_threshold
-%       settings.background_mode
+%       settings.aperiodic_mode
 %       settings.verbose
 
 % Notes:
-%   This is a helper function, probably not called directly by the user. 
+%   This is a helper function, probably not called directly by the user.
 %   Any settings not specified are set to default values
 
 function settings = fooof_check_settings(settings)
@@ -33,9 +33,9 @@
         'max_n_peaks', Inf, ...
         'min_peak_amplitude', 0.0, ...
         'peak_threshold', 2.0, ...
-        'background_mode', 'fixed', ...
+        'aperiodic_mode', 'fixed', ...
         'verbose', true);
-    
+
     % Overwrite any non-existent of nan settings with defaults
     for field = fieldnames(defaults)'
         if ~isfield(settings, field) || all(isnan(settings.(field{1})))

fooof_mat/fooof_get_model.m

@@ -11,19 +11,19 @@
 %       model_fit.freqs
 %       model_fit.power_spectrum
 %       model_fit.fooofed_spectrum
-%       model_fit.bg_fit
+%       model_fit.ap_fit
 %
 % Notes
-%   This function is mostly an internal function, but can be called 
+%   This function is mostly an internal function, but can be called
 %     directly by the user if you are interacting with FOOOF objects
-%     directly. 
+%     directly.
 
 % Get the actual fit model from a FOOOF object
 function model_fit = fooof_get_model(fm)
 
     model_fit = struct();
-    
+
     model_fit.freqs = double(py.array.array('d',fm.freqs));
     model_fit.power_spectrum = double(py.array.array('d', fm.power_spectrum));
     model_fit.fooofed_spectrum = double(py.array.array('d', fm.fooofed_spectrum_));
-    model_fit.bg_fit = double(py.array.array('d', py.getattr(fm, '_bg_fit')));
+    model_fit.ap_fit = double(py.array.array('d', py.getattr(fm, '_ap_fit')));

fooof_mat/fooof_group.m

@@ -12,12 +12,12 @@
 %       settings.max_n_peaks
 %       settings.min_peak_amplitude
 %       settings.peak_threshold
-%       settings.background_mode
+%       settings.aperiodic_mode
 %       settings.verbose
 %
 % Outputs:
 %   fooof_results   = fooof model ouputs, in a struct, including:
-%       fooof_results.background_params
+%       fooof_results.aperiodic_params
 %       fooof_results.peak_params
 %       fooof_results.gaussian_params
 %       fooof_results.error

fooof_mat/fooof_plot.m

@@ -23,37 +23,37 @@ function fooof_plot(fooof_results, log_freqs)
     else
         plt_freqs = fooof_results.freqs;
     end
-    
+
     % Plot settings
     lw = 2.5;
-    
+
     %% Create the Plots
-    
+
     figure()
     hold on
     % Plot the original data
     data = plot(plt_freqs, fooof_results.power_spectrum, 'black');
-    
+
     % Plot the full model fit
     model = plot(plt_freqs, fooof_results.fooofed_spectrum, 'red');
 
-    
-    % Plot the background fit
-    bg_fit = plot(plt_freqs, fooof_results.bg_fit, 'b--');
-    
+
+    % Plot the aperiodic fit
+    ap_fit = plot(plt_freqs, fooof_results.ap_fit, 'b--');
+
     %% Plot Settings
-    
+
     % Apply general plot settings
-    for plt = [data, model, bg_fit]
+    for plt = [data, model, ap_fit]
         set(plt, 'LineWidth', lw);
-        
+
     % Set alpha value for model - in a wonky way, because Matlab
-    %   Note: the '4' is magical and mysterious. No idea. 
+    %   Note: the '4' is magical and mysterious. No idea.
     model.Color(4) = 0.5;
-    
+
     grid on
     legend('Original Spectrum', 'Full Model Fit', 'Aperiodic Fit')
-    
+
     hold off
 
 end

fooof_mat/fooof_unpack_results.m

@@ -7,7 +7,7 @@
 %   fooof_results   = FOOOFResults object
 % Outputs:
 %   results_out     = fooof model results, in a struct, including:
-%       results_out.background_params
+%       results_out.aperiodic_params
 %       results_out.peak_params
 %       results_out.gaussian_params
 %       results_out.error
@@ -16,31 +16,31 @@
 % Notes:
 %   This function is mostly an internal function, and doesn't need to be
 %   called directly by the user - but can be if you are interacting
-%   directly with FOOOF objects. 
+%   directly with FOOOF objects.
 
 function results_out = fooof_unpack_results(results_in)
 
     results_out = struct();
 
-    results_out.background_params = ...
-        double(py.array.array('d', results_in.background_params));
-    
+    results_out.aperiodic_params = ...
+        double(py.array.array('d', results_in.aperiodic_params));
+
     temp = double(py.array.array('d', results_in.peak_params.ravel));
     results_out.peak_params = ...
         transpose(reshape(temp, 3, length(temp) / 3));
-    
+
     temp = double(py.array.array('d', results_in.gaussian_params.ravel));
     results_out.gaussian_params = ...
         transpose(reshape(temp, 3, length(temp) / 3));
-    
+
     results_out.error = ...
         double(py.array.array('d', py.numpy.nditer(results_in.error)));
-    
+
     % Note: r_squared seems to come out as float
     %   It's not clear why this value is different, but doesn't seem
     %   to require the type casting like other (code commented below)
     results_out.r_squared = results_in.r_squared;
     %results_out.r_squared = ...
     %    double(py.array.array('d', py.numpy.nditer(results_in.r_squared)));
-    
+
 end

fooof_mat/new_fooof_group.m

@@ -13,14 +13,14 @@
     freqs = py.numpy.array(freqs);
     psds = py.numpy.array(psds);
     f_range = py.list(f_range);
-    
+
     % Initialize FOOOF object
     fg = py.fooof.FOOOFGroup(settings.peak_width_limits, ...
                              settings.max_n_peaks, ...
                              settings.min_peak_amplitude, ...
                              settings.peak_threshold, ...
-                             settings.background_mode, ...
+                             settings.aperiodic_mode, ...
                              settings.verbose);
     fg.fit(freqs, psds, f_range)
-    
+
     out = 1