diff --git a/step_response_specto.py b/step_response_specto.py
index fe50312..ae6d9ab 100644
--- a/step_response_specto.py
+++ b/step_response_specto.py
@@ -55,12 +55,29 @@ for idx, dataset in enumerate(datasets):
         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)
+        nfft = 50000
+        nfft1 = 5000
+        spec, freqs, times = specgram(data[0], Fs=1.0/(time[1]-time[0]), detrend='mean', NFFT=nfft, noverlap=nfft//2)
+        spec1, freqs1, times1 = specgram(data[0], Fs=1.0 / (time[1] - time[0]), detrend='mean', NFFT=nfft1, noverlap=nfft1 //2)
+
         dbspec = 10.0*np.log10(spec)  # in dB
+        dbspec1 = 10.0 * np.log10(spec1)  # 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 )
+        #plt.imshow(dbspec, cmap='jet', origin='lower', extent=(times[0], times[-1], freqs[0], freqs[-1]), aspect='auto', vmin=-80, vmax=-30)
+        plt.imshow(dbspec1, cmap='jet', origin='lower', extent=(times1[0], times1[-1], freqs1[0], freqs1[-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()
\ No newline at end of file
+        # wird frequenzauflösung besser bei höherem nfft, auch da bei nfft hoch df klein und somit hohe auflösung?
+        # zeitliche auflösung schlechter mit größerem nfft
+
+        #for k in range(len(times)):
+        #plt.plot(freqs, dbspec[:,100], label = '0')
+        #plt.plot(freqs1, dbspec1[:, 100], label = '1')
+        df = freqs[1] - freqs[0]
+        df1 = freqs1[1] - freqs1[0]
+        print(df)
+        print(df1)
+        plt.legend()
+        plt.show()
+        embed()
+