diff --git a/fourier/04_signal_to_noise.py b/fourier/04_signal_to_noise.py
index 72f6083..1d4273e 100755
--- a/fourier/04_signal_to_noise.py
+++ b/fourier/04_signal_to_noise.py
@@ -216,7 +216,7 @@ if __name__ == "__main__":
 
         # plot the snrs
         fig, axs2 = plt.subplots()
-        fig.basefname="signal_to_noise"
+        fig.basefname="signal_to_noise_vs_N"
         axs2.set_xlabel("$N = T*f_s$")
         axs2.set_ylabel("SNR")
 
@@ -233,5 +233,24 @@ if __name__ == "__main__":
         
         axs2.legend()
 
+        # plot snrs vs T
+        fig, axs3 = plt.subplots()
+        fig.basefname="signal_to_noise_vs_T"
+        axs3.set_xlabel("time [us]")
+        axs3.set_ylabel("SNR")
+
+        for i, (f_sample, f_sine, t_lengths) in enumerate(fs_iter):
+            # plot the means
+            l = axs3.plot(t_lengths, np.mean(my_snrs[i], axis=-1), marker='o', ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black', markeredgewidth=1)
+
+            color = l[0].get_color()
+
+            for k, t_length in enumerate(t_lengths):
+                t_length = np.repeat(t_length , my_snrs.shape[-1])
+                axs3.plot(t_length, my_snrs[i,k], ls='none', color=color, marker='o', alpha=max(0.01, 1/my_snrs.shape[-1]))
+
+
+        axs3.legend()
+
     ### Save or show figures
     save_all_figs_to_path_or_show(args.fname, default_basename=__file__, default_extensions=default_extensions)