diff --git a/sin_response_fit.py b/sin_response_fit.py
index f8068a6..54fa7c6 100644
--- a/sin_response_fit.py
+++ b/sin_response_fit.py
@@ -5,6 +5,7 @@ import pylab
 from IPython import embed
 from scipy.optimize import curve_fit
 from jar_functions import sin_response
+from jar_functions import mean_noise_cut
 
 def take_second(elem):      # function for taking the names out of files
     return elem[1]
@@ -33,6 +34,15 @@ for i, d in enumerate(data):
     print(dd)
 
     time = np.load('%s time.npy' %dd)       # time file
+    for float(d[1]) = 0.001:        #ähnlich wie das, einfach passenstens n verwenden?
+        n = int((len(jar) / time[-1]) * (1 / 15))
+        print(n)
+        cutf, cutt = mean_noise_cut(jar, time, n = 2)
+    plt.plot(cutt, cutf, label='cut amfreq')
+    plt.plot(time, jar, label='spec')
+    plt.legend()
+    plt.show()
+    #embed()
 
     b, a = signal.butter(4, (float(d[1]) / 2) / 10000, 'high', analog=True)     # high pass filtering so our fit gets a bit better
     y = signal.filtfilt(b, a, jm)
@@ -115,5 +125,5 @@ embed()
 # AM-frequency / envelope-frequency scale title?
 
 # bevor fit noch filtern mit 15Hz damit AM-Modulation rausgefiltert wird und nur noch envelope übrig bleibt.
-# Dazu running average mit n wobei n über samplingrate und delta f bestimmt wird
+# Dazu running average mit n wobei n über samplingrate von spectogram und delta f bestimmt wird
 # samplingrate über overlap muss dabei aber größer sein als samplingrate die noch übrig bleibt wenn ich mit delta f frequency gefiltert hab
\ No newline at end of file
diff --git a/sin_response_specto.py b/sin_response_specto.py
index dec10a1..da36942 100644
--- a/sin_response_specto.py
+++ b/sin_response_specto.py
@@ -23,10 +23,6 @@ from jar_functions import import_amfreq
 
 base_path = 'D:\\jar_project\\JAR\\sin\\2020lepto16'
 
-#nicht: -5Hz delta f, 19-aa, 22-ae, 22-ad (?)
-
-#dat = glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')
-#infodat = glob.glob('D:\\jar_project\\JAR\\2020*\\info.dat')
 datasets = ['2020-08-04-ab',
             '2020-08-04-ac',
             '2020-08-04-ad',
@@ -82,7 +78,7 @@ for idx, dataset in enumerate(datasets):
         if float(amfreq) < 0.01:
             spec, freqs, times = specgram(dat, Fs=1/dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.8)
         else:
-            spec, freqs, times = specgram(dat, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
+            spec, freqs, times = specgram(dat, Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.96)
 
         dbspec = 10.0*np.log10(spec)  # in dB
         power = dbspec[:, 50]
@@ -111,22 +107,8 @@ for idx, dataset in enumerate(datasets):
         np.save('%s time' % file_name, cut_times)
         np.save('%s' % file_name, jar4)
 
-        cutf, cutt = mean_noise_cut(jar4, cut_times, 40000 * (1/15))
-        plt.plot(cutt, cutf)
-        plt.show()
-        embed()
-
-        #plt.plot(cut_times, jm, '-k')
-
-        #cf, ct = mean_noise_cut(jar4, cut_times, n = int(round(len(jar4)/((times[-1] - times [0]) * amfreq))))
-        #plt.plot(ct, cf, '-k')
-
         #plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
 
-        #plt.legend()
-        #plt.ylim(lim0, lim1)
-        #plt.legend()
-        #plt.show()
         np.save('files.npy', files)
 #embed()