diff --git a/Plotting/PSD_muk.py b/Plotting/PSD_muk.py
index 5389006..64c4d49 100755
--- a/Plotting/PSD_muk.py
+++ b/Plotting/PSD_muk.py
@@ -1,104 +1,165 @@
 # Plots PSD, fits 1/f range and has notch filter to exclude power frequencies
 
 import numpy as np
 import scipy
 from scipy import signal
 import scipy.signal as sig
 import scipy.signal as sig
 import Functions as uf
 import LoadData
 import os
 import matplotlib
 import matplotlib.pyplot as plt
 import platform
 
 if platform.system() == 'Darwin':
     matplotlib.use('MacOSX')
 
 from tkinter import Tk
 from tkinter.filedialog import askopenfilenames
 from matplotlib.font_manager import FontProperties
 
 fontP = FontProperties()
 fontP.set_size('small')
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 matplotlib.rcParams['pdf.fonttype'] = 42
 matplotlib.rcParams['ps.fonttype'] = 42
 from matplotlib.ticker import EngFormatter
 
 root = Tk()
 root.withdraw()
 
 #file = '/Volumes/lben/lben-commun/2018 User Data/Michael/Axopatch/20181003/NIPm10_5nm_1MKCl_pH75_Noise_100kHz_0mV_1.dat'
 filenames = askopenfilenames() # show an "Open" dialog box and return the path to the selected file
 #colors=['k','r','b']
 
 root.update()
 
 cutoff_list = [50, 149.9, 249.8, 349.4, 449.37, 450.1, 549.4, 550, 649.3, 748.8, 848.4]
 Q_list = [100, 300, 200, 400, 1000, 1000, 2000, 1200, 500, 1200, 2000]
 
 # def func(ln_x, alpha, ln_beta):
 #     #return beta * x**(-alpha) # beta is I2A, x is frequency (A is the the low-frequency noise amplitude)
 #     return ln_beta - (alpha * ln_x) # Y = beta * x^-alpha, take log of this equation to linear fit and then back to real space
 
 for i,file in enumerate(filenames):
     fig = plt.figure(1, figsize=(10, 8))
     ax = fig.add_subplot(111)
     dat = LoadData.OpenFile(file)
     filename = str(os.path.split(file)[1][:-4])
     os.chdir(os.path.dirname(file))
     directory = (str(os.path.split(file)[0]) + os.sep + 'PSD' + '_SavedImages')
 
     if not os.path.exists(directory):
         os.makedirs(directory)
     # f, Pxx = sig.welch(dat['i1'], dat['samplerate'], nperseg=2**18)
     # ax.plot(f, Pxx*1e24, label = filename)
 
     #Notch filtering
     sample_rate = dat['samplerate']
     filtered_data = dat['i1']
     for cutoff, Q in zip(cutoff_list, Q_list):
         b, a = signal.iirnotch(cutoff, Q=Q, fs=sample_rate)
         filtered_data = signal.filtfilt(b, a, filtered_data)
 
     ff, Pxxf = sig.welch(filtered_data, fs=sample_rate, nperseg=2 ** 18)
     ax.plot(ff, Pxxf * 1e24, label=filename)  # , color = colors[i])
     plt.show()
 
     # Current Noise RMS calculation
     rms = np.sqrt(np.mean(dat['i1'] ** 2) * 1e12)
     print(rms)
 
 
     #1/f curve fitting
 
     index_restrict = np.where((ff < 100) & (ff>0))
     x_data = ff[index_restrict]
     y_data = Pxxf[index_restrict] * 1e24
 
 
     # y_data = y_data[:last_data]
     # popt = np.polyfit(np.log(x_data), np.log(y_data), 1)
 
 
     # popt, pcov = scipy.optimize.curve_fit(func, np.log(x_data), np.log(y_data)) # linear fitting with : log Y = log Beta - alpha (log x)
     # y_fit = np.exp(func(np.log(x_data), *popt)) #Y-data for line plot (returns Y from log Y)
 
     popt = np.polyfit(np.log(x_data), np.log(y_data), 1) #returns sorted according to degree
     polynomial_lin = np.poly1d(popt)
     y_fit = np.exp(polynomial_lin(np.log(x_data)))
 
     ax.plot(x_data, y_fit, label = "alpha = {}, beta(I2A) = {}".format(popt[0],np.exp(popt[1])))
 
     ax.set_xlabel('Frequency (Hz)')
     ax.set_ylabel(r'PSD ($\frac{pA^2}{Hz}$)')
     ax.set_yscale('log')
     ax.set_xscale('log')
     # plt.xlim(10, 1e3)
     ax.legend()
 
 plt.show()
 
 # fig.savefig(directory + os.sep + filename + '_PSD.pdf', transparent=True)
-# fig.savefig(directory + os.sep + filename + '_PSD.png', dpi=300)
\ No newline at end of file
+# fig.savefig(directory + os.sep + filename + '_PSD.png', dpi=300)
+
+import numpy as np
+import scipy
+from scipy import signal
+import scipy.signal as sig
+import scipy.signal as sig
+import Functions as uf
+import LoadData
+import os
+import matplotlib
+import matplotlib.pyplot as plt
+import platform
+
+if platform.system() == 'Darwin':
+    matplotlib.use('MacOSX')
+
+from tkinter import Tk
+from tkinter.filedialog import askopenfilenames
+from matplotlib.font_manager import FontProperties
+
+fontP = FontProperties()
+fontP.set_size('small')
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+from matplotlib.ticker import EngFormatter
+
+root = Tk()
+root.withdraw()
+
+#file = '/Volumes/lben/lben-commun/2018 User Data/Michael/Axopatch/20181003/NIPm10_5nm_1MKCl_pH75_Noise_100kHz_0mV_1.dat'
+filenames = askopenfilenames() # show an "Open" dialog box and return the path to the selected file
+
+
+root.update()
+
+### For normalizing the traces to zero:
+
+for i,file in enumerate(filenames):
+    fig = plt.figure(1, figsize=(10, 8))
+    ax = fig.add_subplot(111)
+    dat = LoadData.OpenFile(file)
+
+    filename = str(os.path.split(file)[1][:-4])
+    os.chdir(os.path.dirname(file))
+    directory = (str(os.path.split(file)[0]) + os.sep + 'PSD' + '_SavedImages')
+
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+    current_array = np.array(dat['i1'] * 1e9)
+    current_mean = np.mean(current_array)
+    current_array_normalized = current_array - current_mean
+
+    #
+    # print (len(current_array))
+    # print ((current_mean))
+    # print (len(current_array_normalized))
+
+ax.plot(current_array_normalized)
+