diff --git a/step_response.py b/step_response.py
index a348f15..a9a19fa 100644
--- a/step_response.py
+++ b/step_response.py
@@ -23,10 +23,10 @@ datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
             #'2020-06-19-ab',   #-5Hz delta f, bad fit
             #'2020-06-22-aa',   #-5Hz delta f, bad fit
             #'2020-06-22-ab',   #-5Hz delta f, bad fit
-            '2020-06-22-ac',   #-15Hz delta f, good fit
-            '2020-06-22-ad',   #-15Hz delta f, horrible fit
-            '2020-06-22-ae',   #-15Hz delta f, maxfev way to high so horrible
-            '2020-06-22-af'   #-15Hz delta f, good fit
+            '2020-06-22-ac',    #-15Hz delta f, good fit
+            '2020-06-22-ad',    #-15Hz delta f, horrible fit
+            '2020-06-22-ae',    #-15Hz delta f, horrible fit
+            '2020-06-22-af'     #-15Hz delta f, good fit
             ]
 
 #dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
diff --git a/step_response_specto.py b/step_response_specto.py
new file mode 100644
index 0000000..fe50312
--- /dev/null
+++ b/step_response_specto.py
@@ -0,0 +1,66 @@
+import matplotlib.pyplot as plt
+import matplotlib as cm
+from matplotlib.colors import ListedColormap, LinearSegmentedColormap
+from matplotlib.mlab import specgram
+import os
+import glob
+import IPython
+import numpy as np
+import DataLoader as dl
+from IPython import embed
+from scipy.optimize import curve_fit
+from jar_functions import parse_dataset
+from jar_functions import parse_infodataset
+from jar_functions import mean_traces
+from jar_functions import mean_noise_cut
+from jar_functions import norm_function
+from jar_functions import step_response
+from jar_functions import sort_values
+from jar_functions import average
+
+base_path = 'D:\\jar_project\\JAR'
+
+#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
+datasets = [#'2020-06-19-aa',   #-5Hz delta f, horrible fit
+            #'2020-06-19-ab',   #-5Hz delta f, bad fit
+            #'2020-06-22-aa',   #-5Hz delta f, bad fit
+            #'2020-06-22-ab',   #-5Hz delta f, bad fit
+            '2020-06-22-ac',    #-15Hz delta f, good fit
+            #'2020-06-22-ad',    #-15Hz delta f, horrible fit
+            #'2020-06-22-ae',    #-15Hz delta f, horrible fit
+            #'2020-06-22-af'     #-15Hz delta f, good fit
+            ]
+
+#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
+#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
+
+time_all = []
+freq_all = []
+
+ID = []
+col = ['dimgrey', 'grey', 'darkgrey', 'silver', 'lightgrey', 'gainsboro', 'whitesmoke']
+labels = zip(ID, datasets)
+
+for infodataset in datasets:
+    infodataset = os.path.join(base_path, infodataset, 'info.dat')
+    i = parse_infodataset(infodataset)
+    identifier = i[0]
+    ID.append(identifier)
+
+
+for idx, dataset in enumerate(datasets):
+    datapath = os.path.join(base_path, dataset)
+    for info, key, time, data in dl.iload_traces(datapath, repro='Beats', before=0.0, after=0.0):
+        print( info[1]['RePro']  )
+        print(data.shape)
+        #plt.plot(time, data[0])  # V(t)
+        #plt.show()
+        nfft = 2**10
+        spec, freqs, times = specgram(data[0], Fs=1.0/(time[1]-time[0]), detrend='mean', NFFT=nfft, noverlap=nfft//10)
+        dbspec = 10.0*np.log10(spec)  # in dB
+        print(np.min(dbspec), np.max(dbspec))
+        plt.imshow(dbspec, cmap='jet', origin='lower', extent=(times[0], times[1], freqs[0], freqs[-1]), aspect='auto', vmin=-80, vmax=-30 )
+        # interpolation, vmin, vmax
+        # plot decibel as function of frequency for one time slot: wieso auflösung von frequenzen schlechter wenn nfft hoch?
+    plt.show()
+embed()
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