diff --git a/functions.py b/functions.py
index 2fb7e27..952b570 100644
--- a/functions.py
+++ b/functions.py
@@ -1,70 +1,78 @@
 import scipy
 from scipy import signal
 import numpy as np
 import itertools
 
 def LowPass(data, ChimeraSettings, lowPass):
     """
     Function used to filter data with a digital Bessel filter of the 4th order.
 
     Specially useful for high bandwidth recordings.
 
     Parameters
     ----------
     data :
         Data to be filtered.
     samplerate : float
         Sampling frequency of the data aquisition.
     lowPass : float
         Cutoff frequency of the filter.
 
     Returns
     -------
     output: a list of the currents filtered
     newsamplerate : float of new sampling frequency corresponding to 2*Cutoff frequency (Nyquist-Shannon sampling theorem).
 
     """
     samplerate = ChimeraSettings.ADCSAMPLERATE
     Wn = round(2 * lowPass / samplerate, 4)  # [0,1] nyquist frequency
     b, a = scipy.signal.bessel(4, Wn, btype='low', analog=False)  # 4-th order digital filter
 
     z, p, k = signal.tf2zpk(b, a)
     eps = 1e-9
     r = np.max(np.abs(p))
     approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
     Filt_sig = (signal.filtfilt(b, a, data, method='gust', irlen=approx_impulse_len))
 
     ds_factor = np.ceil(samplerate / (2 * lowPass))
     output = {}
     output = scipy.signal.resample(Filt_sig, int(len(data) / ds_factor))
     output = output[100:len(output)-100]
     newsamplerate = samplerate / ds_factor
     return output, newsamplerate
 
 
 def LowPassFast(data, ChimeraSettings, displaysubsample, lowPass):
     downsampled = signal.resample(data, int(1 / displaysubsample * len(data)))
     effsamplerate = (ChimeraSettings.ADCSAMPLERATE / displaysubsample)
+
     Wn = round(2 * lowPass / effsamplerate, 4)
     b, a = scipy.signal.bessel(4, Wn, btype='low', analog=False)
-    output = signal.filtfilt(b, a, downsampled)
+
+    z, p, k = signal.tf2zpk(b, a)
+    eps = 1e-9
+    r = np.max(np.abs(p))
+    approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
+    Filt_sig = ()
+
+    output = signal.filtfilt(b, a, downsampled, method='gust', irlen=approx_impulse_len)
     output = output[100:len(output) - 100]
-    return output, ChimeraSettings.displaysubsample
+    return output, effsamplerate
 
 
 def EventDetection(inputtrace, Threshold=5, DwellTime=1):
 
     meanvalue = np.mean(inputtrace)
     stdeviation = np.std(inputtrace)
 
 
     limitdown = meanvalue - Threshold * stdeviation
 
     conditiondown = inputtrace < limitdown
 
     alldrops = [sum(1 for _ in group) for key, group in itertools.groupby(conditiondown) if key]
 
     downdrops = [item for item in alldrops if item>DwellTime]
     ndown = len(downdrops)
 
     return ndown