diff --git a/fourier/04_signal_to_noise.py b/fourier/04_signal_to_noise.py
index 1d4273e..41f67fb 100755
--- a/fourier/04_signal_to_noise.py
+++ b/fourier/04_signal_to_noise.py
@@ -217,18 +217,35 @@ if __name__ == "__main__":
         # plot the snrs
         fig, axs2 = plt.subplots()
         fig.basefname="signal_to_noise_vs_N"
+        axs2.set_title("A: {:.2e}, $\\sigma$: {:.2e}".format(sine_amp, noise_sigma))
         axs2.set_xlabel("$N = T*f_s$")
         axs2.set_ylabel("SNR")
 
+        mycolors = {}
+        myshapes = { 250: '^', 500: 'v' }
         for i, (f_sample, f_sine, t_lengths) in enumerate(fs_iter):
+
+            if f_sine in mycolors.keys():
+                color = mycolors[f_sine]
+            else:
+                color = None
+
+            if f_sample in myshapes.keys():
+                marker = myshapes[f_sample]
+            else:
+                marker = 'x'
+
             # plot the means
-            l = axs2.plot(t_lengths*f_sample, np.mean(my_snrs[i], axis=-1), marker='*', ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black')
+            l = axs2.plot(t_lengths*f_sample, np.mean(my_snrs[i], axis=-1), color=color, marker=marker, ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black', mew=0.1)
 
             color = l[0].get_color()
+            mycolors[f_sine] = color
+            myshapes[f_sample] = l[0].get_marker()
 
-            for k, t_length in enumerate(t_lengths):
-                t_length = np.repeat(t_length * f_sample, my_snrs.shape[-1])
-                axs2.plot(t_length, my_snrs[i,k], ls='none', color=color, marker='o', alpha=max(0.01, 1/my_snrs.shape[-1]))
+            if True:
+                for k, t_length in enumerate(t_lengths):
+                    t_length = np.repeat(t_length * f_sample, my_snrs.shape[-1])
+                    axs2.plot(t_length, my_snrs[i,k], ls='none', color=color, marker='o', alpha=max(0.01, 1/my_snrs.shape[-1]))
 
         
         axs2.legend()
@@ -236,14 +253,30 @@ if __name__ == "__main__":
         # plot snrs vs T
         fig, axs3 = plt.subplots()
         fig.basefname="signal_to_noise_vs_T"
+        axs3.set_title("A: {:.2e}, $\\sigma$: {:.2e}".format(sine_amp, noise_sigma))
         axs3.set_xlabel("time [us]")
         axs3.set_ylabel("SNR")
 
+        #mycolors = {}
+        #myshapes = { 250: '^', 500: 'v' }
         for i, (f_sample, f_sine, t_lengths) in enumerate(fs_iter):
+
+            if f_sine in mycolors.keys():
+                color = mycolors[f_sine]
+            else:
+                color = None
+
+            if f_sample in myshapes.keys():
+                marker = myshapes[f_sample]
+            else:
+                marker = 'x'
+
             # 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)
+            l = axs3.plot(t_lengths, np.mean(my_snrs[i], axis=-1), color=color, marker=marker, ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black', mew=1)
 
             color = l[0].get_color()
+            mycolors[f_sine] = color
+            myshapes[f_sample] = l[0].get_marker()
 
             for k, t_length in enumerate(t_lengths):
                 t_length = np.repeat(t_length , my_snrs.shape[-1])