diff --git a/pylot/core/pick/utils.py b/pylot/core/pick/utils.py
index 1d620f47..5dc320ec 100644
--- a/pylot/core/pick/utils.py
+++ b/pylot/core/pick/utils.py
@@ -10,7 +10,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from obspy.core import Stream
+from obspy.core import Stream, UTCDateTime
 
 
 def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
@@ -66,9 +66,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
     #get earliest possible pick
 
     #determine all zero crossings in signal window
-    # remove mean from signal window
-    signal = x[isignal] - x[isignal].mean()
-    zc = crossings_nonzero_all(signal)
+    zc = crossings_nonzero_all(x[isignal])
     #calculate mean half period T0 of signal as the average of the
     T0 = np.mean(np.diff(zc)) * X[0].stats.delta  #this is half wave length!
     #T0/4 is assumed as time difference between most likely and earliest possible pick!
@@ -161,7 +159,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
         index1 = []
         i = 0
         for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
-            i += 1
+            i = i + 1
             if xraw[j - 1] <= 0 and xraw[j] >= 0:
                 zc1.append(t[ipick][i])
                 index1.append(i)
@@ -196,7 +194,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
         index2 = []
         i = 0
         for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
-            i += 1
+            i = i + 1
             if xfilt[j - 1] <= 0 and xfilt[j] >= 0:
                 zc2.append(t[ipick][i])
                 index2.append(i)
@@ -382,4 +380,117 @@ def getsignalwin(t, t1, tsignal):
 
     return isignal
 
+def wadaticheck(pickdic, dttolerance, iplot):
+    '''
+    Function to calculate Wadati-diagram from given P and S onsets in order
+    to detect S pick outliers. If a certain S-P time deviates from regression
+    of S-P time the S pick is marked and down graded.
 
+    : param: pickdic, dictionary containing picks and quality parameters
+    : type:  dictionary
+    
+    : param: dttolerance, maximum adjusted deviation of S-P time from 
+             S-P time regression
+    : type:  float
+
+    : param: iplot, if iplot > 1, Wadati diagram is shown
+    : type:  int
+    '''
+
+    checkedonsets = pickdic
+
+    # search for good quality picks and calculate S-P time
+    Ppicks = []
+    Spicks = []
+    SPtimes = []
+    vpvs = []
+    for key in pickdic:
+        if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
+           # calculate S-P time
+           spt = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(pickdic[key]['P']['mpp'])
+           # add S-P time to dictionary
+           pickdic[key]['SPt'] = spt
+           # add P onsets and corresponding S-P times to list
+           UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
+           UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
+           Ppicks.append(UTCPpick)
+           Spicks.append(UTCSpick)
+           SPtimes.append(spt)
+           vpvs.append(UTCPpick/UTCSpick)
+
+    # calculate average vp/vs ratio before check
+    vpvsr = np.mean(vpvs)
+    print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr
+
+    if len(SPtimes) >= 3:
+    	# calculate slope 
+    	p1 = np.polyfit(Ppicks, SPtimes, 1)
+    	wdfit = np.polyval(p1, Ppicks)
+        wfitflag = 0
+ 
+        checkedPpicks = []
+        checkedSpicks = []
+        checkedSPtimes = []
+        checkedvpvs = []
+        # calculate deviations from Wadati regression
+        for key in pickdic:
+            if pickdic[key].has_key('SPt'):
+                ii = 0
+            	wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])    	
+                ii += 1
+                # check, if deviation is larger than adjusted
+                if wddiff >= dttolerance:
+                    # mark onset and downgrade S-weight to 4 
+                    # (not used anymore)
+                    marker = 'badWadatiCheck'
+                    pickdic[key]['S']['weight'] = 4
+                else:
+                    marker = 'goodWadatiCheck'
+                    checkedPpick =  UTCDateTime(pickdic[key]['P']['mpp']) - \
+                                                 UTCDateTime(1970,1,1,0,0,0)
+                    checkedPpicks.append(checkedPpick)
+                    checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \
+                                                 UTCDateTime(1970,1,1,0,0,0)
+                    checkedSpicks.append(checkedSpick)
+                    checkedSPtime = UTCDateTime(pickdic[key]['S']['mpp']) - \
+                                                 UTCDateTime(pickdic[key]['P']['mpp'])
+                    checkedSPtimes.append(checkedSPtime)
+                    checkedvpvs.append(checkedPpick/checkedSpick)
+
+                pickdic[key]['S']['marked'] = marker
+
+        # calculate average vp/vs ratio after check
+        cvpvsr = np.mean(checkedvpvs)
+        print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
+
+    	# calculate new slope 
+    	p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
+    	wdfit2 = np.polyval(p2, checkedPpicks)
+
+        checkedonsets = pickdic
+ 
+    else:
+    	print 'wadaticheck: Not enough S-P times available for reliable regression!'
+        print 'Skip wadati check!'
+        wfitflag = 1
+
+    # plot results
+    iplot = 2
+    if iplot > 1:
+    	f = plt.figure(iplot)
+    	f1, = plt.plot(Ppicks, SPtimes, 'ro')
+        if wfitflag == 0:
+        	f2, = plt.plot(Ppicks, wdfit, 'k')
+                f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
+                f4, = plt.plot(checkedPpicks, wdfit2, 'g')
+        plt.ylabel('S-P Times [s]')
+        plt.xlabel('P Times [s]')
+        plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(old)=%5.2f, Vp/Vs(checked)=%5.2f' \
+                                                     % (len(SPtimes), vpvsr, cvpvsr))
+        plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', 'Reliable S-Picks', \
+                                       'Wadati 2'], loc='best')
+        plt.show()
+        raw_input()
+        plt.close(f)
+
+    return checkedonsets