diff --git a/docs/examples/emulator/robustgasp/plot_scalargasp.py b/docs/examples/emulator/robustgasp/plot_scalargasp.py
index cbb5a6598d4a31bbf5601219b92dd3ef44674e9a..7e0b58992b629291d461f6c8a0d51b0e8ed3502d 100644
--- a/docs/examples/emulator/robustgasp/plot_scalargasp.py
+++ b/docs/examples/emulator/robustgasp/plot_scalargasp.py
@@ -30,8 +30,8 @@ emulator = ScalarGaSP(ndim=1)
 # %% md
 # 
 # Given training input points ``design`` and corresponding output values ``response``,
-# the emulator can be trained. Below we train an emulator using :math:`8` selected
-# points.
+# the emulator can be trained using :py:meth:`.ScalarGaSP.train`. Below we train
+# an emulator using :math:`8` selected points.
 
 import numpy as np
 
@@ -68,6 +68,7 @@ ax.set_ylim(np.min(y)-1,np.max(y)+1)
 _ = ax.plot([np.min(y)-1,np.max(y)+1], [np.min(y)-1,np.max(y)+1])
 _ = ax.errorbar(y, validation[:,0], validation[:,1], fmt='.', linestyle='', label='prediction and std')
 _ = plt.legend()
+plt.tight_layout()
 
 
 # %% md
@@ -89,6 +90,7 @@ plt.xlabel('x')
 plt.ylabel('emulator-predicted y')
 plt.xlim(testing_input[0], testing_input[-1])
 _ = plt.legend()
+plt.tight_layout()
 
 # %% md
 #
@@ -108,6 +110,7 @@ plt.xlabel('x')
 plt.ylabel('emulator-predicted y')
 plt.xlim(testing_input[0], testing_input[-1])
 _ = plt.legend()
+plt.tight_layout()
 
 
 # %% md