diff --git a/code/base_chirps.py b/code/base_chirps.py
index aa8f838..dfdfe37 100644
--- a/code/base_chirps.py
+++ b/code/base_chirps.py
@@ -5,39 +5,71 @@ import numpy as np
 from IPython import embed
 
 
+
 data_dir = "../data"
-dataset = "2018-11-09-ad-invivo-1"
-#data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
-spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
+dataset = "2018-11-09-ad-invivo-1" 
+data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
+
+
+
+#for dataset in data:
 eod = read_chirp_eod(os.path.join(data_dir, dataset))
 times = read_chirp_times(os.path.join(data_dir, dataset))
 
 
-df_map = {}
-for k in spikes.keys():
+
+df_map = {} #Keys werden nach df sortiert ausgegeben
+for k in eod.keys():
 	df = k[1]
+	ch = k[3] 
 	if df in df_map.keys():
 		df_map[df].append(k)
 	else:
 		df_map[df] = [k]
-print(df_map.keys())
-
-e1 = eod[0, '-50Hz', '20%', '100Hz']
-#plt.plot(e1[1])
-#plt.show()
-plt.title('EOD chirps')
-plt.xlabel('Frequency')
-plt.ylabel('Amplitude')
-plt.plot(e1[0],e1[1])
-plt.show()
 
-ct = times[0, '-50Hz', '20%', '100Hz']
-#plt.scatter(ct*1000, np.ones(len(ct)))
-plt.scatter(np.asarray(ct)*1000, np.ones(len(ct)))
+print(ch) #die Chirphöhe wird ausgegeben, um zu bestimmen, ob Chirps oder Chirps large benutzt wurde
+
+
+
+#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
+#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz in 4 Subplots ab
+for idx in df_map.keys():
+	freq = list(df_map[idx]) 
+	fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
+
+	for idx, k in enumerate(freq):
+		ct = times[k]
+		e1 = eod[k]
+		zeit = e1[0]
+		eods = e1[1]
+	
+		if idx <= 3:
+			axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+		elif 4<= idx <= 7:
+			axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+		elif 8<= idx <= 11:
+			axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+		else: 
+			axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
+			axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
+
+
+fig.suptitle('EOD for chirps', fontsize = 16)
 plt.show()
 
-#plt.scatter(spikes[0, '-50Hz', '20%', '100Hz'][0, 0.614])
 
-#print(len(spikes))
-#print(len(eod))
-#print(len(times))
+
+#Problem: axs hat keine label-Funktion, also müsste axes nochmal definiert werden. Momentan erscheint Schrift nur auf einem der Subplots
+
+#ax = plt.gca()
+#ax.set_ylabel('Time [ms]')
+#ax.set_xlabel('Amplitude [mV]')
+#ax.label_outer()
+
+
+
+
+#next Step: relative Amplitudenmodulation berechnen, Max und Min der Amplitude bestimmen, EOD und Chirps zuordnen, Unterschied berechnen
diff --git a/code/code_cw.py b/code/code_cw.py
deleted file mode 100644
index 23f2af2..0000000
--- a/code/code_cw.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-freq  = 800
-freq2 = 820
-dt = 0.00001
-x = np.arange(0.0, 1.0, dt)
-eod = np.sin(x * 2 * np.pi * freq) + np.sin(x * 2 * np.pi * freq * 2) * 0.5
-eod2 = np.sin(x * 2 * np.pi * freq2) + np.sin(x * 2 * np.pi * freq2 * 2) * 0.5
-  
-fig = plt.figure(figsize=(5., 7.5))
-ax= fig.add_subplot(311)
-ax.plot(x, eod, color="darkgreen", linewidth = 1.0)
-ax.set_xlim(0.0, 0.1)
-ax.set_ylabel("voltage [mV]")
-
-
-ax= fig.add_subplot(312)
-ax.plot(x, eod2, color="crimson", linewidth = 1.0)
-ax.set_xlim(0.0, 0.1)
-ax.set_ylabel("voltage [mV]")
-
-ax= fig.add_subplot(313)
-ax.plot(x, eod + eod2 * 0.05, color="lightblue", linewidth = 1.0)
-ax.set_xlim(0.0, 0.1)
-ax.set_xlabel("time [s]")
-ax.set_ylabel("voltage [mV]")
-
-plt.tight_layout()
-plt.savefig("eods.pdf")
-plt.show()
-
-