[ENH] - Add average_reconstructions function

doc/api.rst

@@ -52,6 +52,7 @@ Functions to manipulate, examine and analyze FOOOF objects, and related utilitie
 
    compare_info
    average_fg
+   average_reconstructions
    combine_fooofs
 
 .. currentmodule:: fooof

fooof/objs/utils.py

@@ -65,18 +65,15 @@ def average_fg(fg, bands, avg_method='mean', regenerate=True):
         If there are no model fit results available to average across.
     """
 
-    if avg_method not in ['mean', 'median']:
-        raise ValueError("Requested average method not understood.")
     if not fg.has_model:
         raise NoModelError("No model fit results are available, can not proceed.")
 
-    if avg_method == 'mean':
-        avg_func = np.nanmean
-    elif avg_method == 'median':
-        avg_func = np.nanmedian
+    avg_funcs = {'mean' : np.nanmean, 'median' : np.nanmedian}
+    if avg_method not in avg_funcs.keys():
+        raise ValueError("Requested average method not understood.")
 
     # Aperiodic parameters: extract & average
-    ap_params = avg_func(fg.get_params('aperiodic_params'), 0)
+    ap_params = avg_funcs[avg_method](fg.get_params('aperiodic_params'), 0)
 
     # Periodic parameters: extract & average
     peak_params = []
@@ -90,15 +87,15 @@ def average_fg(fg, bands, avg_method='mean', regenerate=True):
         # Check if there are any extracted peaks - if not, don't add
         #   Note that we only check peaks, but gauss should be the same
         if not np.all(np.isnan(peaks)):
-            peak_params.append(avg_func(peaks, 0))
-            gauss_params.append(avg_func(gauss, 0))
+            peak_params.append(avg_funcs[avg_method](peaks, 0))
+            gauss_params.append(avg_funcs[avg_method](gauss, 0))
 
     peak_params = np.array(peak_params)
     gauss_params = np.array(gauss_params)
 
     # Goodness of fit measures: extract & average
-    r2 = avg_func(fg.get_params('r_squared'))
-    error = avg_func(fg.get_params('error'))
+    r2 = avg_funcs[avg_method](fg.get_params('r_squared'))
+    error = avg_funcs[avg_method](fg.get_params('error'))
 
     # Collect all results together, to be added to FOOOF object
     results = FOOOFResults(ap_params, peak_params, r2, error, gauss_params)
@@ -116,6 +113,41 @@ def average_fg(fg, bands, avg_method='mean', regenerate=True):
     return fm
 
 
+def average_reconstructions(fg, avg_method='mean'):
+    """Average across model reconstructions for a group of power spectra.
+
+    Parameters
+    ----------
+    fg : FOOOFGroup
+        Object with model fit results to average across.
+    avg : {'mean', 'median'}
+        Averaging function to use.
+
+    Returns
+    -------
+    freqs : 1d array
+        Frequency values for the average model reconstruction.
+    avg_model : 1d array
+        Power values for the average model reconstruction.
+        Note that power values are in log10 space.
+    """
+
+    if not fg.has_model:
+        raise NoModelError("No model fit results are available, can not proceed.")
+
+    avg_funcs = {'mean' : np.nanmean, 'median' : np.nanmedian}
+    if avg_method not in avg_funcs.keys():
+        raise ValueError("Requested average method not understood.")
+
+    models = np.zeros(shape=fg.power_spectra.shape)
+    for ind in range(len(fg)):
+        models[ind, :] = fg.get_fooof(ind, regenerate=True).fooofed_spectrum_
+
+    avg_model = avg_funcs[avg_method](models, 0)
+
+    return fg.freqs, avg_model
+
+
 def combine_fooofs(fooofs):
     """Combine a group of FOOOF and/or FOOOFGroup objects into a single FOOOFGroup object.
 

fooof/plts/__init__.py

@@ -1,4 +1,3 @@
 """Plots sub-module for FOOOF."""
 
-from .spectra import plot_spectra
-from .spectra import plot_spectra as plot_spectrum
+from .spectra import plot_spectrum, plot_spectra

fooof/plts/spectra.py

@@ -77,6 +77,10 @@ def plot_spectra(freqs, power_spectra, log_freqs=False, log_powers=False,
     style_spectrum_plot(ax, log_freqs, log_powers)
 
 
+# Alias `plot_spectrum` to `plot_spectra` for backwards compatibility
+plot_spectrum = plot_spectra
+
+
 @savefig
 @check_dependency(plt, 'matplotlib')
 def plot_spectra_shading(freqs, power_spectra, shades, shade_colors='r',

fooof/tests/objs/test_utils.py

@@ -45,6 +45,13 @@ def test_average_fg(tfg, tbands):
     with raises(NoModelError):
         average_fg(ntfg, tbands)
 
+def test_average_reconstructions(tfg):
+
+    freqs, avg_model = average_reconstructions(tfg)
+    assert isinstance(freqs, np.ndarray)
+    assert isinstance(avg_model, np.ndarray)
+    assert freqs.shape == avg_model.shape
+
 def test_combine_fooofs(tfm, tfg):
 
     tfm2 = tfm.copy()