diff --git a/pylot/core/pick/run_autopicking.py b/pylot/core/pick/run_autopicking.py
new file mode 100755
index 00000000..c7b0a436
--- /dev/null
+++ b/pylot/core/pick/run_autopicking.py
@@ -0,0 +1,401 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Function to run automated picking algorithms using AIC,
+HOS and AR prediction. Uses object CharFuns and Picker and 
+function conglomerate utils.
+
+:author: MAGS2 EP3 working group / Ludger Kueperkoch
+"""
+
+from obspy.core import read
+import matplotlib.pyplot as plt
+import numpy as np
+from pylot.core.pick.CharFuns import *
+from pylot.core.pick.Picker import *
+from pylot.core.pick.CharFuns import *
+from pylot.core.pick import utils
+
+import pdb
+
+def run_autopicking(wfstream, pickparam):
+
+    '''
+    param: wfstream
+    :type: `~obspy.core.stream.Stream`
+
+    param: pickparam
+    :type: container of picking parameters from input file,
+           usually autoPyLoT.in
+    '''
+
+    # declaring pickparam variables (only for convenience)
+    # read your autoPyLoT.in for details!
+
+    #special parameters for P picking
+    algoP = pickparam.getParam('algoP')     
+    iplot = pickparam.getParam('iplot')
+    pstart = pickparam.getParam('pstart')
+    pstop = pickparam.getParam('pstop')
+    thosmw = pickparam.getParam('tlta')
+    hosorder = pickparam.getParam('hosorder')
+    tsnrz = pickparam.getParam('tsnrz')     
+    hosorder = pickparam.getParam('hosorder')
+    bpz1 = pickparam.getParam('bpz1')
+    bpz2 = pickparam.getParam('bpz2')
+    pickwinP = pickparam.getParam('pickwinP')
+    tsmoothP = pickparam.getParam('tsmoothP')
+    ausP = pickparam.getParam('ausP')
+    nfacP = pickparam.getParam('nfacP')
+    tpred1z = pickparam.getParam('tpred1z')
+    tdet1z = pickparam.getParam('tdet1z')
+    Parorder = pickparam.getParam('Parorder')
+    addnoise = pickparam.getParam('addnoise')
+    Precalcwin = pickparam.getParam('Precalcwin')
+    minAICPslope = pickparam.getParam('minAICPslope')
+    minAICPSNR = pickparam.getParam('minAICPSNR')
+    timeerrorsP = pickparam.getParam('timeerrorsP')
+    #special parameters for S picking
+    algoS = pickparam.getParam('algoS')      
+    sstart = pickparam.getParam('sstart')
+    sstop = pickparam.getParam('sstop')
+    bph1 = pickparam.getParam('bph1')
+    bph2 = pickparam.getParam('bph2')
+    tsnrh = pickparam.getParam('tsnrh')     
+    pickwinS = pickparam.getParam('pickwinS')
+    tpred1h = pickparam.getParam('tpred1h')
+    tdet1h = pickparam.getParam('tdet1h')
+    tpred2h = pickparam.getParam('tpred2h')
+    tdet2h = pickparam.getParam('tdet2h')
+    Sarorder = pickparam.getParam('Sarorder')
+    aictsmoothS = pickparam.getParam('aictsmoothS')
+    tsmoothS = pickparam.getParam('tsmoothS')
+    ausS = pickparam.getParam('ausS')
+    minAICSslope = pickparam.getParam('minAICSslope')
+    minAICSSNR = pickparam.getParam('minAICSSNR')
+    Srecalcwin = pickparam.getParam('Srecalcwin')
+    nfacS = pickparam.getParam('nfacS')
+    timeerrorsS = pickparam.getParam('timeerrorsS')
+    
+
+    # split components
+    zdat = wfstream.select(component="Z")
+    edat = wfstream.select(component="E")
+    ndat = wfstream.select(component="N")
+
+    if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
+        print '##########################################'
+        print 'run_autopicking: Working on P onset of station %s' % zdat[0].stats.station
+        print 'Filtering vertical trace ...'
+        print zdat
+        z_copy = zdat.copy()
+        #filter and taper data
+        tr_filt = zdat[0].copy()
+        tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1], zerophase=False)
+        tr_filt.taper(max_percentage=0.05, type='hann')
+        z_copy[0].data = tr_filt.data
+        ##############################################################
+        #check length of waveform and compare with cut times
+        Lc = pstop - pstart
+        Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
+        Ldiff = Lwf - Lc
+        if Ldiff < 0:
+           print 'run_autopicking: Cutting times are too large for actual waveform!' 
+           print 'Use entire waveform instead!'
+           pstart = 0
+           pstop = len(zdat[0].data) * zdat[0].stats.delta 
+        cuttimes = [pstart, pstop]
+        if algoP == 'HOS':
+           #calculate HOS-CF using subclass HOScf of class CharacteristicFunction
+           cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) #instance of HOScf
+        elif algoP == 'ARZ':
+           #calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
+           cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
+        ##############################################################
+        #calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
+        #class needs stream object => build it
+        tr_aic = tr_filt.copy()
+        tr_aic.data =cf1.getCF()
+        z_copy[0].data = tr_aic.data
+        aiccf = AICcf(z_copy, cuttimes) #instance of AICcf
+        ##############################################################
+        #get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
+        aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
+        ##############################################################
+        #go on with processing if AIC onset passes quality control
+        if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
+           print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
+                  (aicpick.getSlope(), aicpick.getSNR())
+           print 'Go on with refined picking ...'
+           #re-filter waveform with larger bandpass
+           print 'run_autopicking: re-filtering vertical trace ...'
+           z_copy = zdat.copy()
+           tr_filt = zdat[0].copy()
+           tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1], zerophase=False)
+           tr_filt.taper(max_percentage=0.05, type='hann')
+           z_copy[0].data = tr_filt.data
+           #############################################################
+           #re-calculate CF from re-filtered trace in vicinity of initial onset
+           cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])), \
+                        round(min([len(zdat[0].data) * zdat[0].stats.delta, \
+                        aicpick.getpick() + Precalcwin]))]
+           if algoP == 'HOS':
+              #calculate HOS-CF using subclass HOScf of class CharacteristicFunction
+              cf2 = HOScf(z_copy, cuttimes2, thosmw, hosorder) #instance of HOScf
+           elif algoP == 'ARZ':
+              #calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
+              cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
+           ##############################################################
+           #get refined onset time from CF2 using class Picker
+           refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, aicpick.getpick())
+           #############################################################
+           #quality assessment
+           #get earliest and latest possible pick and symmetrized uncertainty
+           [lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, refPpick.getpick(), iplot)
+
+           #get SNR
+           [SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, refPpick.getpick())
+
+           #weight P-onset using symmetric error
+           if Perror <= timeerrorsP[0]:
+              Pweight = 0
+           elif Perror > timeerrorsP[0] and Perror <= timeerrorsP[1]:
+              Pweight = 1
+           elif Perror > timeerrorsP[1] and Perror <= timeerrorsP[2]:
+              Pweight = 2
+           elif Perror > timeerrorsP[2] and Perror <= timeerrorsP[3]:
+              Pweight = 3
+           elif Perror > timeerrorsP[3]:
+              Pweight = 4
+
+           print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f' % (Pweight, SNRP, SNRPdB)
+       
+        else:
+           print 'Bad initial (AIC) P-pick, skip this onset!'
+           Pweight = 4
+    else:
+       print 'run_autopicking: No vertical component data available, skipping station!'
+       return
+
+    if edat is not None and ndat is not None and Pweight < 4:
+        print 'Go on picking S onset ...' 
+        print '##################################################'
+        print 'Working on S onset of station %s' % edat[0].stats.station
+        print 'Filtering horizontal traces ...'
+
+        #determine time window for calculating CF after P onset
+        #cuttimesh = [round(refPpick.getpick() + sstart), round(refPpick.getpick() + sstop)]
+        cuttimesh = [round(max([refPpick.getpick() + sstart, 0])), \
+                     round(min([refPpick.getpick() + sstop, Lwf]))]
+
+        if algoS == 'ARH':
+            print edat, ndat
+            #re-create stream object including both horizontal components
+            hdat = edat.copy()
+            hdat += ndat
+            h_copy = hdat.copy()
+            #filter and taper data
+            trH1_filt = hdat[0].copy()
+            trH2_filt = hdat[1].copy()
+            trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
+            trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
+            trH1_filt.taper(max_percentage=0.05, type='hann')
+            trH2_filt.taper(max_percentage=0.05, type='hann')
+            h_copy[0].data = trH1_filt.data
+            h_copy[1].data = trH2_filt.data
+        elif algoS == 'AR3':
+            print zdat, edat, ndat
+            #re-create stream object including both horizontal components
+            hdat = zdat.copy()
+            hdat += edat
+            hdat += ndat
+            h_copy = hdat.copy()
+            #filter and taper data
+            trH1_filt = hdat[0].copy()
+            trH2_filt = hdat[1].copy()
+            trH3_filt = hdat[2].copy()
+            trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
+            trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
+            trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
+            trH1_filt.taper(max_percentage=0.05, type='hann')
+            trH2_filt.taper(max_percentage=0.05, type='hann')
+            trH3_filt.taper(max_percentage=0.05, type='hann')
+            h_copy[0].data = trH1_filt.data
+            h_copy[1].data = trH2_filt.data
+            h_copy[2].data = trH3_filt.data
+        ##############################################################
+        if algoS == 'ARH':
+            #calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
+            arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
+        elif algoS == 'AR3':
+            #calculate ARH-CF using subclass AR3cf of class CharcteristicFunction
+            arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
+        ##############################################################
+        #calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
+        #class needs stream object => build it
+        tr_arhaic = trH1_filt.copy()
+        tr_arhaic.data = arhcf1.getCF()
+        h_copy[0].data = tr_arhaic.data
+        #calculate ARH-AIC-CF
+        haiccf = AICcf(h_copy, cuttimesh) #instance of AICcf
+        ##############################################################
+        #get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
+        aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None, aictsmoothS)
+        ###############################################################
+        #go on with processing if AIC onset passes quality control
+        if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
+           print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
+                   % (aicarhpick.getSlope(), aicarhpick.getSNR())
+           print 'Go on with refined picking ...'
+           #re-calculate CF from re-filtered trace in vicinity of initial onset
+           cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin), \
+                         round(aicarhpick.getpick() + Srecalcwin)]
+           #re-filter waveform with larger bandpass
+           print 'run_autopicking: re-filtering horizontal traces...'
+           h_copy = hdat.copy()
+           #filter and taper data
+           if algoS == 'ARH':
+               trH1_filt = hdat[0].copy()
+               trH2_filt = hdat[1].copy()
+               trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
+               trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
+               trH1_filt.taper(max_percentage=0.05, type='hann')
+               trH2_filt.taper(max_percentage=0.05, type='hann')
+               h_copy[0].data = trH1_filt.data
+               h_copy[1].data = trH2_filt.data
+               #############################################################
+               arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
+           elif algoS == 'AR3':
+               trH1_filt = hdat[0].copy()
+               trH2_filt = hdat[1].copy()
+               trH3_filt = hdat[2].copy()
+               trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
+               trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
+               trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
+               trH1_filt.taper(max_percentage=0.05, type='hann')
+               trH2_filt.taper(max_percentage=0.05, type='hann')
+               trH3_filt.taper(max_percentage=0.05, type='hann')
+               h_copy[0].data = trH1_filt.data
+               h_copy[1].data = trH2_filt.data
+               h_copy[2].data = trH3_filt.data
+               #############################################################
+               arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
+
+           #get refined onset time from CF2 using class Picker
+           refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, tsmoothS, aicarhpick.getpick())
+           #############################################################
+           #quality assessment
+           #get earliest and latest possible pick and symmetrized uncertainty
+           h_copy[0].data = trH1_filt.data
+           [lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
+           h_copy[0].data = trH2_filt.data
+           [lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
+           if algoS == 'ARH':
+              #get earliest pick of both earliest possible picks
+              epick = [epickS1, epickS2]
+              lpick = [lpickS1, lpickS2]
+              pickerr = [Serror1, Serror2]
+              ipick =np.argmin([epickS1, epickS2])
+           elif algoS == 'AR3':
+              [lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
+              #get earliest pick of all three picks
+              epick = [epickS1, epickS2, epickS3]
+              lpick = [lpickS1, lpickS2, lpickS3]
+              pickerr = [Serror1, Serror2, Serror3]
+              ipick =np.argmin([epickS1, epickS2, epickS3])
+           epickS = epick[ipick]
+           lpickS = lpick[ipick]
+           Serror = pickerr[ipick]
+
+           #get SNR
+           [SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, refSpick.getpick())
+
+           #weight S-onset using symmetric error
+           if Serror <= timeerrorsS[0]:
+              Sweight = 0
+           elif Serror > timeerrorsS[0] and Serror <= timeerrorsS[1]:
+              Sweight = 1
+           elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
+              Sweight = 2
+           elif Serror > timeerrorsS[2] and Serror <= timeerrorsS[3]:
+              Sweight = 3
+           elif Serror > timeerrorsS[3]:
+              Sweight = 4
+
+           print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (Sweight, SNRS, SNRSdB)
+
+        else:
+           print 'Bad initial (AIC) S-pick, skip this onset!'
+           Sweight = 4
+ 
+    else:
+       print 'run_autopicking: No horizontal component data available, skipping S picking!'
+       return
+
+    ##############################################################
+    if iplot > 0:
+       #plot vertical trace
+       plt.figure()
+       plt.subplot(3,1,1)
+       tdata = np.arange(0, tr_filt.stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
+       p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
+       p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
+       p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
+       p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
+       plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
+       plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
+       p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
+       plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
+       plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
+       plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
+       plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
+       plt.yticks([])
+       plt.ylim([-1.5, 1.5])
+       plt.ylabel('Normalized Counts')
+       plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (tr_filt.stats.station, \
+                  tr_filt.stats.channel, Pweight, SNRP, SNRPdB))
+       plt.suptitle(tr_filt.stats.starttime)
+       plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
+       #plot horizontal traces
+       plt.subplot(3,1,2)
+       th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
+       p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
+       p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
+       p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
+       p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
+       plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
+       plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
+       p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
+       plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
+       plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
+       plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
+       plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
+       plt.yticks([])
+       plt.ylim([-1.5, 1.5])
+       plt.ylabel('Normalized Counts')
+       plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
+                  Sweight, SNRS, SNRSdB))
+       plt.suptitle(trH1_filt.stats.starttime)
+       plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
+       plt.subplot(3,1,3)
+       th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
+       plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
+       plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
+       plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
+       plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
+       plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
+       plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
+       plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
+       plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
+       plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
+       plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
+       plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
+       plt.yticks([])
+       plt.ylim([-1.5, 1.5])
+       plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
+       plt.ylabel('Normalized Counts')
+       plt.title(trH2_filt.stats.channel)
+       plt.show()
+       raw_input()
+       plt.close()