Merge pull request #290 from fooof-tools/paramshade

[ENH] - Add options to shade param reconstructions

specparam/plts/aperiodic.py

@@ -8,6 +8,7 @@
 from specparam.sim.gen import gen_freqs, gen_aperiodic
 from specparam.core.modutils import safe_import, check_dependency
 from specparam.plts.settings import PLT_FIGSIZES
+from specparam.plts.templates import plot_yshade
 from specparam.plts.style import style_param_plot, style_plot
 from specparam.plts.utils import check_ax, recursive_plot, savefig, check_plot_kwargs
 
@@ -62,6 +63,7 @@ def plot_aperiodic_params(aps, colors=None, labels=None, ax=None, **plot_kwargs)
 @style_plot
 @check_dependency(plt, 'matplotlib')
 def plot_aperiodic_fits(aps, freq_range, control_offset=False,
+                        average='mean', shade='sem', plot_individual=True,
                         log_freqs=False, colors=None, labels=None,
                         ax=None, **plot_kwargs):
     """Plot reconstructions of model aperiodic fits.
@@ -72,6 +74,15 @@ def plot_aperiodic_fits(aps, freq_range, control_offset=False,
         Aperiodic parameters. Each row is a parameter set, as [Off, Exp] or [Off, Knee, Exp].
     freq_range : list of [float, float]
         The frequency range to plot the peak fits across, as [f_min, f_max].
+    average : {'mean', 'median'}, optional, default: 'mean'
+        Approach to take to average across components.
+        If set to None, no average is plotted.
+    shade : {'sem', 'std'}, optional, default: 'sem'
+        Approach for shading above/below the average reconstruction
+        If set to None, no yshade is plotted.
+    plot_individual : bool, optional, default: True
+        Whether to plot individual component reconstructions.
+        If False, only the average component reconstruction is plotted.
     control_offset : boolean, optional, default: False
         Whether to control for the offset, by setting it to zero.
     log_freqs : boolean, optional, default: False
@@ -103,28 +114,28 @@ def plot_aperiodic_fits(aps, freq_range, control_offset=False,
 
         colors = colors[0] if isinstance(colors, list) else colors
 
-        avg_vals = np.zeros(shape=[len(freqs)])
-
-        for ap_params in aps:
+        all_ap_vals = np.zeros(shape=(len(aps), len(freqs)))
+        for ind, ap_params in enumerate(aps):
 
             if control_offset:
 
                 # Copy the object to not overwrite any data
                 ap_params = ap_params.copy()
                 ap_params[0] = 0
 
-            # Recreate & plot the aperiodic component from parameters
+            # Create & collect the aperiodic component model from parameters
             ap_vals = gen_aperiodic(freqs, ap_params)
+            all_ap_vals[ind, :] = ap_vals
 
-            ax.plot(plt_freqs, ap_vals, color=colors, alpha=0.35, linewidth=1.25)
-
-            # Collect a running average across components
-            avg_vals = np.nansum(np.vstack([avg_vals, ap_vals]), axis=0)
+            if plot_individual:
+                ax.plot(plt_freqs, ap_vals, color=colors, alpha=0.35, linewidth=1.25)
 
-        # Plot the average component
-        avg = avg_vals / aps.shape[0]
-        avg_color = 'black' if not colors else colors
-        ax.plot(plt_freqs, avg, linewidth=3.75, color=avg_color, label=labels)
+        # Plot the average across all components
+        if average is not False:
+            avg_color = 'black' if not colors else colors
+            plot_yshade(freqs, all_ap_vals, average=average, shade=shade,
+                        shade_alpha=plot_kwargs.pop('shade_alpha', 0.15),
+                        color=avg_color, linewidth=3.75, label=labels, ax=ax)
 
     # Add axis labels
     ax.set_xlabel('log(Frequency)' if log_freqs else 'Frequency')

specparam/plts/periodic.py

@@ -8,6 +8,7 @@
 from specparam.core.funcs import gaussian_function
 from specparam.core.modutils import safe_import, check_dependency
 from specparam.plts.settings import PLT_FIGSIZES
+from specparam.plts.templates import plot_yshade
 from specparam.plts.style import style_param_plot, style_plot
 from specparam.plts.utils import check_ax, recursive_plot, savefig, check_plot_kwargs
 
@@ -69,7 +70,8 @@ def plot_peak_params(peaks, freq_range=None, colors=None, labels=None, ax=None,
 
 @savefig
 @style_plot
-def plot_peak_fits(peaks, freq_range=None, colors=None, labels=None, ax=None, **plot_kwargs):
+def plot_peak_fits(peaks, freq_range=None, average='mean', shade='sem', plot_individual=True,
+                   colors=None, labels=None, ax=None, **plot_kwargs):
     """Plot reconstructions of model peak fits.
 
     Parameters
@@ -79,6 +81,15 @@ def plot_peak_fits(peaks, freq_range=None, colors=None, labels=None, ax=None, **
     freq_range : list of [float, float] , optional
         The frequency range to plot the peak fits across, as [f_min, f_max].
         If not provided, defaults to +/- 4 around given peak center frequencies.
+    average : {'mean', 'median'}, optional, default: 'mean'
+        Approach to take to average across components.
+        If set to None, no average is plotted.
+    shade : {'sem', 'std'}, optional, default: 'sem'
+        Approach for shading above/below the average reconstruction
+        If set to None, no yshade is plotted.
+    plot_individual : bool, optional, default: True
+        Whether to plot individual component reconstructions.
+        If False, only the average component reconstruction is plotted.
     colors : str or list of str, optional
         Color(s) to plot data.
     labels : list of str, optional
@@ -118,21 +129,22 @@ def plot_peak_fits(peaks, freq_range=None, colors=None, labels=None, ax=None, **
 
         colors = colors[0] if isinstance(colors, list) else colors
 
-        avg_vals = np.zeros(shape=[len(freqs)])
+        all_peak_vals = np.zeros(shape=(len(peaks), len(freqs)))
+        for ind, peak_params in enumerate(peaks):
 
-        for peak_params in peaks:
-
-            # Create & plot the peak model from parameters
+            # Create & collect the peak model from parameters
             peak_vals = gaussian_function(freqs, *peak_params)
-            ax.plot(freqs, peak_vals, color=colors, alpha=0.35, linewidth=1.25)
+            all_peak_vals[ind, :] = peak_vals
 
-            # Collect a running average average peaks
-            avg_vals = np.nansum(np.vstack([avg_vals, peak_vals]), axis=0)
+            if plot_individual:
+                ax.plot(freqs, peak_vals, color=colors, alpha=0.35, linewidth=1.25)
 
         # Plot the average across all components
-        avg = avg_vals / peaks.shape[0]
-        avg_color = 'black' if not colors else colors
-        ax.plot(freqs, avg, color=avg_color, linewidth=3.75, label=labels)
+        if average is not False:
+            avg_color = 'black' if not colors else colors
+            plot_yshade(freqs, all_peak_vals, average=average, shade=shade,
+                        shade_alpha=plot_kwargs.pop('shade_alpha', 0.15),
+                        color=avg_color, linewidth=3.75, label=labels, ax=ax)
 
     # Add axis labels
     ax.set_xlabel('Frequency')

specparam/plts/templates.py

@@ -150,14 +150,16 @@ def plot_yshade(x_vals, y_vals, average='mean', shade='std', scale=1., color=Non
         Data values to be plotted on the y-axis. `shade` must be provided if 1d.
     average : 'mean', 'median' or callable, optional, default: 'mean'
         Averaging approach for plotting the average. Only used if y_vals is 2d.
+        If set to None, no average line is plotted.
     shade : 'std', 'sem', 1d array or callable, optional, default: 'std'
         Approach for shading above/below the average.
+        If set to None, no shading is plotted.
     scale : float, optional, default: 1.
         Factor to multiply the plotted shade by.
     color : str, optional, default: None
         Color to plot.
     plot_function : callable, optional
-        xx
+        Function to use to create the plot.
     ax : matplotlib.Axes, optional
         Figure axes upon which to plot.
     **plot_kwargs
@@ -168,18 +170,21 @@ def plot_yshade(x_vals, y_vals, average='mean', shade='std', scale=1., color=Non
 
     shade_alpha = plot_kwargs.pop('shade_alpha', 0.25)
 
-    avg_data = compute_average(y_vals, average=average)
-    if plot_function:
-        plot_function(x_vals, avg_data, color=color, ax=ax, **plot_kwargs)
-    else:
-        ax.plot(x_vals, avg_data, color=color, **plot_kwargs)
+    avg_data = compute_average(y_vals, average=average if average else 'mean')
 
-    # Compute shade values and apply scaling
-    shade_vals = compute_dispersion(y_vals, shade) * scale
+    if average is not None:
 
-    # Plot +/- y-shading around spectrum
-    ax.fill_between(x_vals, avg_data - shade_vals, avg_data + shade_vals,
-                    alpha=shade_alpha, color=color)
+        if plot_function:
+            plot_function(x_vals, avg_data, color=color, ax=ax, **plot_kwargs)
+        else:
+            ax.plot(x_vals, avg_data, color=color, **plot_kwargs)
+
+    if shade is not None:
+
+        # Compute shade values, apply scaling & plot +/- y-shading
+        shade_vals = compute_dispersion(y_vals, shade) * scale
+        ax.fill_between(x_vals, avg_data - shade_vals, avg_data + shade_vals,
+                        alpha=shade_alpha, color=color)
 
 
 @check_dependency(plt, 'matplotlib')