diff --git a/FourierTrafoAndRMSNoiseAnalysis.py b/FourierTrafoAndRMSNoiseAnalysis.py
index 2333d88..840167e 100644
--- a/FourierTrafoAndRMSNoiseAnalysis.py
+++ b/FourierTrafoAndRMSNoiseAnalysis.py
@@ -10,7 +10,6 @@ __email__ = "l.hoenen@posteo.de", "lennart.hoenen@stud.uni-heidelberg.de"
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.fft import rfft, rfftfreq
-from scipy.signal.windows import blackman
 from scipy.integrate import simps
 import scipy.io.wavfile as wavfile
 import json
@@ -37,7 +36,7 @@ def Average_Fourier_Uniform(Data, Samplingrate):
     return xf, yf_av
 
 
-def Calc_Power_Noise_RMS(LSD, maxFreq):
+def Calc_Power_Noise_RMS(Frequencies, LSD, maxFreq):
     """
     Calculate the RMS Noise Power over a specified frequency range.
     As the impedance of the coils acts as a low pass filter, the current and thus the magnetic field cannot follow
@@ -52,24 +51,24 @@ def Calc_Power_Noise_RMS(LSD, maxFreq):
     The desired max. frequency might not be part of the list of discrete frequencies in the space of the DFT. Therefore
     a closest match is found and selected.
     """
-    res = max(LSD)
-    index = len(LSD)
+    res = max(Frequencies)
+    index = len(Frequencies)
 
-    for i in range(len(LSD)):
-        if res > np.abs(maxFreq - LSD[i]):
-            res = np.abs(maxFreq - LSD[i])
+    for i in range(len(Frequencies)):
+        if res > np.abs(maxFreq - Frequencies[i]):
+            res = np.abs(maxFreq - Frequencies[i])
             index = i
 
-    print("You chose the maximum Frequency ", maxFreq, "Hz. The closest matching frequency contained in the DFT is ", LSD[index], "Hz with index ", index, ".")
-    PowerNoiseRMS = simps(y_Final_av[0:index]**2, x_Final_av[0:index])
+    print("You chose the maximum Frequency ", maxFreq, "Hz. The closest matching frequency contained in the DFT is ", Frequencies[index], "Hz with index ", index, ".")
+    PowerNoiseRMS = simps(LSD[0:index]**2, Frequencies[0:index])
 
     return PowerNoiseRMS
 
-def Calc_Voltage_Noise_RMS(LSD, maxFreq):
+def Calc_Voltage_Noise_RMS(Frequencies, LSD, maxFreq):
     """
     See explanation of Calc_Power_Noise_RMS() and Lenny's Thesis 3.4.
     """
-    PowerNoiseRMS = Calc_Power_Noise_RMS(LSD, maxFreq)
+    PowerNoiseRMS = Calc_Power_Noise_RMS(Frequencies, LSD, maxFreq)
     VoltageNoiseRMS = np.sqrt(PowerNoiseRMS)
 
     return VoltageNoiseRMS
@@ -106,30 +105,38 @@ data = data.T
 """
 EXAMPLE:
 """
-samplingrateFinal, dataFinal = wavfile.read("Z:/Users/Lenny/Power Supply Mk.2/Noise Measurements/NoiseDensityHHCoilsFinalPowerSupplyWithPIBatteryInput.wav")
+samplingrateFinal, dataFinal = wavfile.read("Z:/Users/Lenny/Power Supply Mk.2/Noise Measurements/NoiseDensityHHCoil2ndPatchedChannelBatteryInput.wav")
 dataFinal = dataFinal*0.4/2**16 #Correct conversion from bit values to physical data, for the used Voltae range and precision.
 dataFinalshaped = np.reshape(dataFinal, [5,4000000]) #Shape one long continuous measurement into 5 shorter measurements to be averaged
 
-samplingrateshielding, datashielding = wavfile.read("Z:/Users/Lenny/Power Supply/TimeSeriesOP549/FinalMeasurements/NoiseDensitySingleTestCoilWithPIBatteryInputWithShielding.wav")
-datashielding = datashielding*0.4/2**16
-datashieldingshaped = np.reshape(datashielding, [10,2000000])
+samplingrateprototype, dataprototype = wavfile.read("Z:/Users/Lenny/Power Supply Mk.2/Noise Measurements/NoiseDensityHHCoilPrototypeBatteryInput.wav")
+dataprototype = dataprototype*0.4/2**16
+dataprototypeshaped = np.reshape(dataprototype, [5,4000000])
+
+samplingratebackground, databackground = wavfile.read("Z:/Users/Lenny/Power Supply Mk.2/Noise Measurements/NoiseDensityHHCoilsFinalPowerSupplyWithPIBatteryInputBUTEVERYTHINGTURNEDOFF.wav")
+databackground = databackground*0.4/2**16
+databackgroundshaped = np.reshape(databackground, [5,4000000])
 
 x_Final_av, y_Final_av = Average_Fourier_Uniform(dataFinalshaped, samplingrateFinal)
-x_shielding_av, y_shielding_av = Average_Fourier_Uniform(datashieldingshaped, samplingrateshielding)
-print(Calc_Voltage_Noise_RMS(x_Final_av, 25000))
+x_prototype_av, y_prototype_av = Average_Fourier_Uniform(dataprototypeshaped, samplingrateprototype)
+x_background_av, y_background_av = Average_Fourier_Uniform(databackgroundshaped, samplingratebackground)
+print(Calc_Voltage_Noise_RMS(x_Final_av, y_Final_av, 30000))
+print(Calc_Voltage_Noise_RMS(x_prototype_av, y_prototype_av, 30000))
+print(Calc_Voltage_Noise_RMS(x_background_av, y_background_av, 30000))
 
 plt.rcParams["figure.figsize"] = [7,5]
 plt.rcParams["font.size"] = 12
-plt.figure(1, figsize=[12,7])
-plt.loglog(x_Final_av, y_Final_av, linewidth=1, label="Noise Final Powersupply in Carton Box", alpha=0.7)
-plt.loglog(x_shielding_av, y_shielding_av, linewidth=1, label="Noise Prototype with Shielding", alpha=0.7)
+plt.figure(2, figsize=[12,7])
+plt.loglog(x_background_av, y_background_av, linewidth=1, label="Background Noise in Lab", alpha=0.7)
+plt.loglog(x_prototype_av, y_prototype_av, linewidth=1, label="Noise Prototype in carton box", alpha=0.7)
+plt.loglog(x_Final_av, y_Final_av, linewidth=1, label="Noise Final Powersupply in carton box", alpha=0.7)
 #plt.loglog(x_DC1V_av, y_DC1V_av, linewidth=1, label="Noise with FreqGen and Shielding", alpha=0.7)
 plt.legend()
 plt.grid(which="major")
 plt.grid(which="minor", alpha=0.2)
 plt.xlabel("Frequency [Hz]")
 plt.ylabel(r"Voltage Noise Spectrum [$V/ \sqrt{Hz}$]")
-plt.title("2 Measurements of 10M Samples at 6.52MHz averaged")
+plt.title("5 Measurements of 4M Samples at 6.52MHz averaged")
 plt.show()
 plt.close()