diff --git a/response_discriminability.py b/response_discriminability.py
index 4e08050..a25f8a9 100644
--- a/response_discriminability.py
+++ b/response_discriminability.py
@@ -234,7 +234,7 @@ def get_chirp_metadata(block):
     return trial_duration, dt, chirp_size, chirp_duration, chirp_times
 
 
-def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
+def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name="", store_roc=False):
     detection_performances = [] 
 
     for contrast in all_contrasts:
@@ -285,12 +285,15 @@ def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, ke
         score = np.hstack((valid_distances_baseline, valid_distances_comparison))
         fpr, tpr, _ = roc_curve(group, score, pos_label=1)
         auc = roc_auc_score(group, score)
-        detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
+        if store_roc:
+            detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
+        else:
+            detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
     print("\n")
     return detection_performances
 
 
-def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
+def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name="", store_roc=False):
     detection_performances = []
 
     for contrast in all_contrasts:
@@ -361,13 +364,18 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
         score = np.hstack((valid_no_other_distances, valid_self_vs_alone_distances))
         fpr, tpr, _ = roc_curve(group, score, pos_label=1)
         auc = roc_auc_score(group, score)
-        detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
-        
+        if store_roc:
+            detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
+        else:
+            detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
         group = np.hstack((baseline_temp, other_vs_baseline_temp))
         score = np.hstack((valid_baseline_distances, valid_other_vs_baseline_distances))
         fpr, tpr, _ = roc_curve(group, score, pos_label=1)
         auc = roc_auc_score(group, score)
-        detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
+        if store_roc:
+            detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
+        else:
+            detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
 
     print("\n")
     return detection_performances
@@ -414,7 +422,7 @@ def plot_detection_results(data_frame, df, kernel_width, cell):
             auc_ax.set_xlabel("contrast [%]")
             auc_ax.set_ylim([0.25, 1.0])
             auc_ax.set_ylabel("discriminability")
-        auc_ax.legend(ncol=2, fontsize=6)
+        auc_ax.legend(ncol=2, fontsize=6, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25)
         auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls"--",)
     fig.savefig("discrimination.pdf")