diff --git a/code/base_chirps.py b/code/base_chirps.py
index 1112d78..94307c0 100644
--- a/code/base_chirps.py
+++ b/code/base_chirps.py
@@ -58,7 +58,7 @@ axs[1,1].set_xlabel('Time [ms]')
 
 freq = list(df_map['-50Hz'])
 ls_mod = []
-beat_mods = []
+ls_beat = []
 for k in freq: 
 	e1 = eod[k]
 	zeit = np.asarray(e1[0])
@@ -69,14 +69,19 @@ for k in freq:
 		time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
 		eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
 		beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
-
+		
 		chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
-		beat_mod = np.std(beat_cut) #Std vom Bereich vor dem Chirp
-		ls_mod.append(chirp_mod)
-		beat_mods.append(beat_mod)
+		ls_mod.append(chirp_mod) #in die richtige Reihenfolge bringen?
+		#momentan nicht nach Chirp-Platz sortiert, sondern nacheinander
+
+		#erst beat_cuts auf die gleiche Länge bringen!
+		ls_beat.append(beat_cut)
 
-#Länge des Mods ist 160, 16 Wiederholungen mal 10 Chirps pro Trial
-#Verwendung der Std für die Amplitudenmodulation?
+#beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
+plt.figure()
+plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
+plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))/2, color = 'violet')
+plt.show()
 
 
 
diff --git a/code/base_spikes.py b/code/base_spikes.py
index 2fd0e9f..3984c0f 100644
--- a/code/base_spikes.py
+++ b/code/base_spikes.py
@@ -4,7 +4,7 @@ from utility import *
 #import nix_helpers as nh 
 import matplotlib.pyplot as plt
 import numpy as np
-from IPython import embed #Funktionen importieren
+from IPython import embed #Funktionen imposrtieren
 
 
 data_dir = "../data"
@@ -40,18 +40,44 @@ plt.show()
 
 chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
 df_map = map_keys(chirp_spikes)
-
-
+ls_rate = {}
 for i in df_map.keys():
 	freq = list(df_map[i])
+	ls_rate[i] = []
 	for k in freq:
-		spikes = chirp_spikes[k]
-		phase_map = map_keys(spikes)
-		for p in phase_map: 
-			spike_rate = 1./ np.diff(p)
+		for phase in chirp_spikes[k]:
+			spikes = chirp_spikes[k][phase]
+			rate = len(spikes)/ 1.2
+			ls_rate[i].append(rate)
+
+
+plt.figure()
+sort_df =  sorted(df_map.keys(),reverse = False)
+print(sort_df)
+
+for i in sort_df:
+	plt.plot(np.arange(0,len(ls_rate[i]),1),ls_rate[i], label = i)
+
+plt.vlines(10, ymin = 200, ymax = 300)
+plt.vlines(30, ymin = 200, ymax = 300)
+plt.vlines(50, ymin = 200, ymax = 300)
+plt.vlines(70, ymin = 200, ymax = 300)
+plt.vlines(90, ymin = 200, ymax = 300)
+plt.vlines(110, ymin = 200, ymax = 300)
+plt.vlines(130, ymin = 200, ymax = 300)
+plt.vlines(150, ymin = 200, ymax = 300)
+plt.legend()
+plt.show()
+
 
-print(spike_rate)
-#
-#	plt.plot(spikes, rate)
-#	plt.show()
 
+#mittlere Feuerrate einer Frequenz auf Frequenz
+
+plt.figure()
+ls_mean = []
+for i in sort_df:
+	mean = np.mean(ls_rate[i])
+	ls_mean.append(mean)
+
+plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
+plt.show()