starts working on adding Freeman data

Snakefile

@@ -4530,23 +4530,28 @@ rule critical_scaling_pointplot:
                     crit_scaling.append(tmp)
                 crit_scaling = pd.concat(crit_scaling)
                 crit_scaling['model'] = crit_scaling['model'].map(fov.plotting.MODEL_PLOT)
-                crit_scaling = pd.concat([crit_scaling, fov.figures.wallis_critical_scaling()])
-                pal = fov.plotting.get_palette('model', fov.plotting.MODEL_PLOT.values())
-                crit_scaling.model = crit_scaling.model.apply(lambda x: x.replace(' model', ''))
-                pal = {k.replace(' model', ''): v for k, v in pal.items()}
-                # color copied from Freeman and Simoncelli, 2011's V2 purple
-                pal['Texture'] = (165/255, 109/255, 189/255)
                 # put dummy data in for this Luminance met vs met, since we
                 # can't actually fit
-                tmp = crit_scaling.query("model=='Luminance' & trial_type == 'metamer_vs_reference'")
+                tmp = crit_scaling.query("model=='Luminance model' & trial_type == 'metamer_vs_reference'")
                 tmp['trial_type']  = 'metamer_vs_metamer'
                 if wildcards.norm == 'True':
                     tmp['critical_scaling'] = 8*tmp.critical_scaling
                     assert wildcards.logscale == 'log', "if normalized, scale must be log!"
                 else:
                     tmp['critical_scaling'] = 24*tmp.critical_scaling
                 crit_scaling = pd.concat([crit_scaling, tmp])
-                g = sns.FacetGrid(crit_scaling, hue='model', palette=pal, height=fig_width)
+                crit_scaling.model = crit_scaling.model.apply(lambda x: f"{x}\n(this study)")
+                crit_scaling = pd.concat([crit_scaling, fov.figures.wallis_critical_scaling(),
+                                          fov.figures.freeman_critical_scaling()])
+                pal = fov.plotting.get_palette('model', fov.plotting.MODEL_PLOT.values())
+                crit_scaling.model = crit_scaling.model.apply(lambda x: x.replace(' model', ''))
+                pal = {k.replace(' model', ''): v for k, v in pal.items()}
+                # color copied from Freeman and Simoncelli, 2011's V2 purple
+                pal['Texture'] = (165/255, 109/255, 189/255)
+                pal = {k: pal[k.split()[0]] for k in crit_scaling.model.unique()}
+                print(crit_scaling)
+                g = sns.FacetGrid(crit_scaling, hue='model', palette=pal,
+                                  height=fig_width, aspect=1.5)
                 g.map_dataframe(fov.plotting.vertical_pointplot, x='model', y='critical_scaling',
                                 norm_y=wildcards.norm=='True')
                 ylabel = 'Critical scaling'

foveated_metamers/figures.py

@@ -2906,6 +2906,28 @@ def facet_compare(data, **kwargs):
     return g
 
 
+def freeman_critical_scaling():
+    """Return df with critical scaling values from Freeman and Simoncelli, 2011.
+
+    These are values for the texture and energy models, for the metamer_vs_metamer
+    comparison.
+
+    Numbers come from Freeman and Simoncelli, 2011, figure 5, extracted with
+    WebPlotDigitizer.
+
+    Returns
+    -------
+    df : pd.DataFrame
+        df with critical scaling values.
+
+    """
+    crit_scaling = [0.255, 0.483]
+    model = ['Energy model\n(Freeman)', 'Texture model\n(Freeman)']
+    return pd.DataFrame({'critical_scaling': crit_scaling,
+                         'model': model,
+                         'trial_type': 'metamer_vs_metamer'})
+
+
 def wallis_critical_scaling():
     """Return df with critical scaling values from Wallis et al., 2019.
 
@@ -2929,7 +2951,7 @@ def wallis_critical_scaling():
             'metamer_vs_metamer', 'metamer_vs_metamer']
     images = ['texture', 'scene', 'texture', 'scene']
     return pd.DataFrame({'critical_scaling': crit_scaling,
-                         'model': 'Texture model',
+                         'model': 'Texture model\n(Wallis)',
                          'trial_type': comp, 'image_type': images})
 
 

foveated_metamers/plotting.py

@@ -2127,7 +2127,7 @@ def vertical_pointplot(data, x, y, norm_y=False, **kwargs):
     - If there's more than two points contained in data, we groupby model and
       trial_type and take the mean.
 
-    - If the label == 'Luminance', we'll plot the point corresponding to
+    - If the label == 'Luminance\n(this study)', we'll plot the point corresponding to
       'metamer_vs_reference' as 'o' and the one corresponding to
       'metamer_vs_metamer' as '$\bigwedge$' (which looks like an up arrow) For
       all others, both points will be plotted as 'o'.
@@ -2167,7 +2167,7 @@ def vertical_pointplot(data, x, y, norm_y=False, **kwargs):
         data[y] = data[y] / data[y].min()
     # want the line to be under the points, so set zorder=0
     ax.plot(data[x].values, data[y].values, linewidth=lw, zorder=0, **kwargs)
-    if kwargs['label'] == 'Luminance':
+    if kwargs['label'] == 'Luminance\n(this study)':
         marker = {'metamer_vs_reference': 'o', 'metamer_vs_metamer': r'$\bigwedge$'}
     else:
         marker = {'metamer_vs_reference': 'o', 'metamer_vs_metamer': 'o'}