little things

modelsusceptcontrasts.py

@@ -111,11 +111,17 @@ def plot_summary_contrasts(axs, s, cells):
     cdata = data[data['contrast'] == 0, :]
     nli_split = cdata['dsinorm']
     print(f'    mean SI = {np.mean(nli_split):.2f}, stdev = {np.std(nli_split):.2f}')
+    print(f'    triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}')
+    ntriangle = np.sum(cdata['triangle'] > 0.5)
+    print(f'    triangle cells: {ntriangle:2d} ({100*ntriangle/len(cdata):.1f}%)')
+    nborder = np.sum(cdata['border'] > 0.5)
+    print(f'    border cells:   {nborder:2d} ({100*nborder/len(cdata):.1f}%)')
+    nother = len(cdata) - ntriangle - nborder
+    print(f'    other cells:    {nother:2d} ({100*nother/len(cdata):.1f}%)')
     n = np.sum(nli_split > nli_thresh)
     print(f'    {n} cells ({100*n/len(nli_split):.1f}%) have SI > {nli_thresh:.1f}:')
     for name, cv in cdata[nli_split > nli_thresh, ['cell', 'cvbase']].row_data():
         print(f'        {name:<22} CV={cv:4.2f}')
-    print(f'    triangle cells have SI >= {np.min(nli_split[cdata["triangle"] > 0.5]):.2f}')
     print()
     for i, a in enumerate([0.01, 0.03, 0.1]):
         plot_si_cv(axs[1 + i], s, data, a, cells)
@@ -126,12 +132,12 @@ def plot_summary_contrasts(axs, s, cells):
         print(f'    correlation with split: r={r:.2f}, p={p:.1e}')
         print(f'    mean SI = {np.mean(nli):.2f}, stdev = {np.std(nli):.2f}')
         n = np.sum(nli > nli_thresh)
+        print(f'    triangle cells have SI >= {np.min(nli[cdata["triangle"] > 0.5]):.2f}')
         print(f'    {n} cells ({100*n/len(nli):.1f}%) have SI > {nli_thresh:.1f}:')
         for name, cv, respmod in cdata[nli > nli_thresh,
                                        ['cell', 'cvbase', 'respmod2']].row_data():
             print(f'        {name:<22} CV={cv:4.2f} '
                   f'response modulation={respmod:4.0f}Hz')
-        print(f'    triangle cells have SI >= {np.min(nli[cdata["triangle"] > 0.5]):.2f}')
         print()
     print('overall lowest baseline CV:',
           ' '.join([f'{cv:.2f}' for cv in np.unique(data['cvbase'])[:5]]))