Merge branch 'refs/heads/develop' into correlation_picker

Certain tests fail on snrdb calculation which has to be examined (WIP)

.gitignore

@@ -2,3 +2,4 @@
 *~
 .idea
 pylot/RELEASE-VERSION
+/tests/test_autopicker/dmt_database_test/

PyLoT.py

@@ -1031,22 +1031,23 @@ class MainWindow(QMainWindow):
                 if not fname:
                     return
 
-        qmb = QMessageBox(self, icon=QMessageBox.Question,
-                          text='Do you want to overwrite this data?',)
-        overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole)
-        merge_button = qmb.addButton('Merge', QMessageBox.NoRole)
-
-        qmb.exec_()
-
-        if qmb.clickedButton() == overwrite_button:
-            merge_strategy = 'Overwrite'
-        elif qmb.clickedButton() == merge_button:
-            merge_strategy = 'Merge'
-        else:
-            return
-
         if not event:
             event = self.get_current_event()
+
+        if event.picks:
+            qmb = QMessageBox(self, icon=QMessageBox.Question,
+                              text='Do you want to overwrite the data?',)
+            overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole)
+            merge_button = qmb.addButton('Merge', QMessageBox.NoRole)
+            qmb.exec_()
+
+            if qmb.clickedButton() == overwrite_button:
+                merge_strategy = 'Overwrite'
+            elif qmb.clickedButton() == merge_button:
+                merge_strategy = 'Merge'
+            else:
+                return
+
         data = Data(self, event)
         try:
             data_new = Data(self, evtdata=str(fname))

autoPyLoT.py

@@ -184,15 +184,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
         if not input_dict:
             # started in production mode
             datapath = datastructure.expandDataPath()
-            if fnames == 'None' and parameter['eventID'] == '*':
+            if fnames in [None, 'None'] and parameter['eventID'] == '*':
                 # multiple event processing
                 # read each event in database
                 events = [event for event in glob.glob(os.path.join(datapath, '*')) if
                           (os.path.isdir(event) and not event.endswith('EVENTS-INFO'))]
-            elif fnames == 'None' and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
+            elif fnames in [None, 'None'] and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
                 # single event processing
                 events = glob.glob(os.path.join(datapath, parameter['eventID']))
-            elif fnames == 'None' and type(parameter['eventID']) == list:
+            elif fnames in [None, 'None'] and type(parameter['eventID']) == list:
                 # multiple event processing
                 events = []
                 for eventID in parameter['eventID']:
@@ -234,12 +234,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                 data.get_evt_data().path = eventpath
                 print('Reading event data from filename {}...'.format(filename))
             except Exception as e:
-                print('Could not read event from file {}: {}'.format(filename, e))
+                if type(e) == FileNotFoundError:
+                    print('Creating new event file.')
+                else:
+                    print('Could not read event from file {}: {}'.format(filename, e))
                 data = Data()
                 pylot_event = Event(eventpath)  # event should be path to event directory
                 data.setEvtData(pylot_event)
-            if fnames == 'None':
-                data.setWFData(glob.glob(os.path.join(datapath, event_datapath, '*')))
+            if fnames in [None, 'None']:
+                data.setWFData(glob.glob(os.path.join(event_datapath, '*')))
                 # the following is necessary because within
                 # multiple event processing no event ID is provided
                 # in autopylot.in

inputs/pylot_global.in

@@ -41,6 +41,7 @@ global                                             #extent#         %extent of a
 1150.0                                             #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
 True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
 iasp91                                             #taup_model#     %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
+P,Pdiff                                            #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
 0.05 0.5                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
 0.001 0.5                                          #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 0.05 0.5                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     

inputs/pylot_local.in

@@ -40,7 +40,8 @@ local                                              #extent#         %extent of a
 -0.5                                               #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
 10.0                                               #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
 False                                              #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
-iasp91                                             #taup_model#     %define TauPy model for traveltime estimation                                                        
+iasp91                                             #taup_model#     %define TauPy model for traveltime estimation
+P                                                  #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
 2.0 20.0                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
 2.0 30.0                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 2.0 10.0                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     

inputs/pylot_regional.in

@@ -40,7 +40,8 @@ local                                              #extent#         %extent of a
 -1.0                                               #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
 10.0                                               #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
 True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
-iasp91                                             #taup_model#     %define TauPy model for traveltime estimation                                                        
+iasp91                                             #taup_model#     %define TauPy model for traveltime estimation
+P                                                  #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
 2.0 10.0                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
 2.0 12.0                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 2.0 8.0                                            #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     

pylot/core/pick/autopick.py

@@ -262,6 +262,10 @@ class AutopickStation(object):
         self.metadata = metadata
         self.origin = origin
 
+        # initialize TauPy pick estimates
+        self.estFirstP = None
+        self.estFirstS = None
+
         # initialize picking results
         self.p_results = PickingResults()
         self.s_results = PickingResults()
@@ -443,15 +447,15 @@ class AutopickStation(object):
             for arr in arrivals:
                 phases[identifyPhaseID(arr.phase.name)].append(arr)
             # get first P and S onsets from arrivals list
-            estFirstP = 0
-            estFirstS = 0
+            arrival_time_p = 0
+            arrival_time_s = 0
             if len(phases['P']) > 0:
-                arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
+                arrP, arrival_time_p = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
             if len(phases['S']) > 0:
-                arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
+                arrS, arrival_time_s = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
             print('autopick: estimated first arrivals for P: {} s, S:{} s after event'
-                  ' origin time using TauPy'.format(estFirstP, estFirstS))
-            return estFirstP, estFirstS
+                  ' origin time using TauPy'.format(arrival_time_p, arrival_time_s))
+            return arrival_time_p, arrival_time_s
 
         def exit_taupy():
             """If taupy failed to calculate theoretical starttimes, picking continues.
@@ -477,10 +481,13 @@ class AutopickStation(object):
             raise AttributeError('No source origins given!')
 
         arrivals = create_arrivals(self.metadata, self.origin, self.pickparams["taup_model"])
-        estFirstP, estFirstS = first_PS_onsets(arrivals)
+        arrival_P, arrival_S = first_PS_onsets(arrivals)
+
+        self.estFirstP = (self.origin[0].time + arrival_P) - self.ztrace.stats.starttime
+
         # modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
-        self.pickparams["pstart"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
-        self.pickparams["pstop"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
+        self.pickparams["pstart"] += self.estFirstP
+        self.pickparams["pstop"] += self.estFirstP
         print('autopick: CF calculation times respectively:'
               ' pstart: {} s, pstop: {} s'.format(self.pickparams["pstart"], self.pickparams["pstop"]))
         # make sure pstart and pstop are inside the starttime/endtime of vertical trace
@@ -491,9 +498,10 @@ class AutopickStation(object):
             # for the two horizontal components take earliest and latest time to make sure that the s onset is not clipped
             # if start and endtime of horizontal traces differ, the s windowsize will automatically increase
             trace_s_start = min([self.etrace.stats.starttime, self.ntrace.stats.starttime])
+            self.estFirstS = (self.origin[0].time + arrival_S) - trace_s_start
             # modifiy sstart and sstop relative to estimated first S arrival (relative to station time axis)
-            self.pickparams["sstart"] += (self.origin[0].time + estFirstS) - trace_s_start
-            self.pickparams["sstop"] += (self.origin[0].time + estFirstS) - trace_s_start
+            self.pickparams["sstart"] += self.estFirstS
+            self.pickparams["sstop"] += self.estFirstS
             print('autopick: CF calculation times respectively:'
                   ' sstart: {} s, sstop: {} s'.format(self.pickparams["sstart"], self.pickparams["sstop"]))
             # make sure pstart and pstop are inside the starttime/endtime of horizontal traces
@@ -609,6 +617,12 @@ class AutopickStation(object):
             # plot tapered trace filtered with bpz2 filter settings
             ax1.plot(tdata, self.tr_filt_z_bpz2.data / max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7,
                      label='Data')
+            # plot pickwindows for P
+            pstart, pstop = self.pickparams['pstart'], self.pickparams['pstop']
+            if pstart is not None and pstop is not None:
+                ax1.axvspan(pstart, pstop, color='r', alpha=0.1, zorder=0, label='P window')
+            if self.estFirstP is not None:
+                ax1.axvline(self.estFirstP, ls='dashed', color='r', alpha=0.4, label='TauPy estimate')
             if self.p_results.weight < 4:
                 # plot CF of initial onset (HOScf or ARZcf)
                 ax1.plot(self.cf1.getTimeArray(), self.cf1.getCF() / max(self.cf1.getCF()), 'b', label='CF1')
@@ -713,6 +727,15 @@ class AutopickStation(object):
                         ax3.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
                         ax3.plot([self.s_results.lpp, self.s_results.lpp], [-1.1, 1.1], 'g--', label='lpp')
                         ax3.plot([self.s_results.epp, self.s_results.epp], [-1.1, 1.1], 'g--', label='epp')
+
+                # plot pickwindows for S
+                sstart, sstop = self.pickparams['sstart'], self.pickparams['sstop']
+                if sstart is not None and sstop is not None:
+                    for axis in [ax2, ax3]:
+                        axis.axvspan(sstart, sstop, color='b', alpha=0.1, zorder=0, label='S window')
+                        if self.estFirstS is not None:
+                            axis.axvline(self.estFirstS, ls='dashed', color='b', alpha=0.4, label='TauPy estimate')
+
                 ax3.legend(loc=1)
                 ax3.set_yticks([])
                 ax3.set_ylim([-1.5, 1.5])
@@ -835,14 +858,21 @@ class AutopickStation(object):
             self.cf1 = None
         assert isinstance(self.cf1, CharacteristicFunction), 'cf1 is not set correctly: maybe the algorithm name ({})' \
                                                              ' is corrupted'.format(self.pickparams["algoP"])
+        # get the original waveform stream from first CF class cut to identical length as CF for plotting
+        cut_ogstream = self.cf1.getDataArray(self.cf1.getCut())
+
+        # MP: Rename to cf_stream for further use of z_copy and to prevent chaos when z_copy suddenly becomes a cf
+        # stream and later again a waveform stream
+        cf_stream = z_copy.copy()
+        cf_stream[0].data = self.cf1.getCF()
+
         # calculate AIC cf from first cf (either HOS or ARZ)
-        z_copy[0].data = self.cf1.getCF()
-        aiccf = AICcf(z_copy, cuttimes)
+        aiccf = AICcf(cf_stream, cuttimes)
         # get preliminary onset time from AIC-CF
         self.set_current_figure('aicFig')
         aicpick = AICPicker(aiccf, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot,
                             Tsmooth=self.pickparams["aictsmooth"], fig=self.current_figure,
-                            linecolor=self.current_linecolor)
+                            linecolor=self.current_linecolor, ogstream=cut_ogstream)
         # save aicpick for plotting later
         self.p_data.aicpick = aicpick
         # add pstart and pstop to aic plot
@@ -855,7 +885,7 @@ class AutopickStation(object):
                           label='P stop')
                 ax.legend(loc=1)
 
-        Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt)
+        Pflag = self._pick_p_quality_control(aicpick, cf_stream, tr_filt)
         # go on with processing if AIC onset passes quality control
         slope = aicpick.getSlope()
         if not slope: slope = 0
@@ -894,7 +924,7 @@ class AutopickStation(object):
         refPpick = PragPicker(self.cf2, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot,
                               self.pickparams["ausP"],
                               self.pickparams["tsmoothP"], aicpick.getpick(), self.current_figure,
-                              self.current_linecolor)
+                              self.current_linecolor, ogstream=cut_ogstream)
         # save PragPicker result for plotting
         self.p_data.refPpick = refPpick
         self.p_results.mpp = refPpick.getpick()
@@ -1146,11 +1176,14 @@ class AutopickStation(object):
         # calculate AIC cf
         haiccf = self._calculate_aic_cf_s_pick(cuttimesh)
 
+        # get the original waveform stream cut to identical length as CF for plotting
+        ogstream = haiccf.getDataArray(haiccf.getCut())
+
         # get preliminary onset time from AIC cf
         self.set_current_figure('aicARHfig')
         aicarhpick = AICPicker(haiccf, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot,
                                Tsmooth=self.pickparams["aictsmoothS"], fig=self.current_figure,
-                               linecolor=self.current_linecolor)
+                               linecolor=self.current_linecolor, ogstream=ogstream)
         # save pick for later plotting
         self.aicarhpick = aicarhpick
 

pylot/core/pick/charfuns.py

@@ -17,7 +17,11 @@ autoregressive prediction: application ot local and regional distances, Geophys.
 :author: MAGS2 EP3 working group
 """
 import numpy as np
-from scipy import signal
+try:
+    from scipy.signal import tukey
+except ImportError:
+    from scipy.signal.windows import tukey
+
 from obspy.core import Stream
 
 from pylot.core.pick.utils import PickingFailedException
@@ -56,7 +60,7 @@ class CharacteristicFunction(object):
         self.setOrder(order)
         self.setFnoise(fnoise)
         self.setARdetStep(t2)
-        self.calcCF(self.getDataArray())
+        self.calcCF()
         self.arpara = np.array([])
         self.xpred = np.array([])
 
@@ -208,17 +212,15 @@ class CharacteristicFunction(object):
             data = self.orig_data.copy()
             return data
 
-    def calcCF(self, data=None):
-        self.cf = data
+    def calcCF(self):
+        pass
 
 
 class AICcf(CharacteristicFunction):
 
-    def calcCF(self, data):
+    def calcCF(self):
         """
         Function to calculate the Akaike Information Criterion (AIC) after Maeda (1985).
-        :param data: data, time series (whether seismogram or CF)
-        :type data: tuple
         :return: AIC function
         :rtype:
         """
@@ -227,7 +229,7 @@ class AICcf(CharacteristicFunction):
         ind = np.where(~np.isnan(xnp))[0]
         if ind.size:
             xnp[:ind[0]] = xnp[ind[0]]
-        xnp = signal.tukey(len(xnp), alpha=0.05) * xnp
+        xnp = tukey(len(xnp), alpha=0.05) * xnp
         xnp = xnp - np.mean(xnp)
         datlen = len(xnp)
         k = np.arange(1, datlen)
@@ -256,13 +258,11 @@ class HOScf(CharacteristicFunction):
         """
         super(HOScf, self).__init__(data, cut, pickparams["tlta"], pickparams["hosorder"])
 
-    def calcCF(self, data):
+    def calcCF(self):
         """
         Function to calculate skewness (statistics of order 3) or kurtosis
         (statistics of order 4), using one long moving window, as published
         in Kueperkoch et al. (2010), or order 2, i.e. STA/LTA.
-        :param data: data, time series (whether seismogram or CF)
-        :type data: tuple
         :return: HOS cf
         :rtype:
         """
@@ -277,47 +277,28 @@ class HOScf(CharacteristicFunction):
         elif self.getOrder() == 4:  # this is kurtosis
             y = np.power(xnp, 4)
             y1 = np.power(xnp, 2)
-        elif self.getOrder() == 2:  # this is variance, used for STA/LTA processing
-            y = np.power(xnp, 2)
-            y1 = np.power(xnp, 2)
 
         # Initialisation
         # t2: long term moving window
         ilta = int(round(self.getTime2() / self.getIncrement()))
-        ista = int(round((self.getTime2() / 10) / self.getIncrement()))  # TODO: still hard coded!!
         lta = y[0]
         lta1 = y1[0]
-        sta = y[0]
         # moving windows
         LTA = np.zeros(len(xnp))
-        STA = np.zeros(len(xnp))
         for j in range(0, len(xnp)):
             if j < 4:
                 LTA[j] = 0
-                STA[j] = 0
-            elif j <= ista and self.getOrder() == 2:
-                lta = (y[j] + lta * (j - 1)) / j
-                if self.getOrder() == 2:
-                    sta = (y[j] + sta * (j - 1)) / j
-            # elif j < 4:
             elif j <= ilta:
                 lta = (y[j] + lta * (j - 1)) / j
                 lta1 = (y1[j] + lta1 * (j - 1)) / j
-                if self.getOrder() == 2:
-                    sta = (y[j] - y[j - ista]) / ista + sta
             else:
                 lta = (y[j] - y[j - ilta]) / ilta + lta
                 lta1 = (y1[j] - y1[j - ilta]) / ilta + lta1
-                if self.getOrder() == 2:
-                    sta = (y[j] - y[j - ista]) / ista + sta
             # define LTA
             if self.getOrder() == 3:
                 LTA[j] = lta / np.power(lta1, 1.5)
             elif self.getOrder() == 4:
                 LTA[j] = lta / np.power(lta1, 2)
-            else:
-                LTA[j] = lta
-                STA[j] = sta
 
         # remove NaN's with first not-NaN-value,
         # so autopicker doesnt pick discontinuity at start of the trace
@@ -326,10 +307,7 @@ class HOScf(CharacteristicFunction):
             first = ind[0]
             LTA[:first] = LTA[first]
 
-        if self.getOrder() > 2:
-            self.cf = LTA
-        else:  # order 2 means STA/LTA!
-            self.cf = STA / LTA
+        self.cf = LTA
         self.xcf = x
 
 
@@ -339,12 +317,10 @@ class ARZcf(CharacteristicFunction):
         super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"],
                                     fnoise=pickparams["addnoise"])
 
-    def calcCF(self, data):
+    def calcCF(self):
         """
         function used to calculate the AR prediction error from a single vertical trace. Can be used to pick
         P onsets.
-        :param data:
-        :type data: ~obspy.core.stream.Stream
         :return: ARZ cf
         :rtype:
         """
@@ -475,14 +451,12 @@ class ARHcf(CharacteristicFunction):
         super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
                                     fnoise=pickparams["addnoise"])
 
-    def calcCF(self, data):
+    def calcCF(self):
         """
         Function to calculate a characteristic function using autoregressive modelling of the waveform of
         both horizontal traces.
         The waveform is predicted in a moving time window using the calculated AR parameters. The difference
         between the predicted and the actual waveform servers as a characteristic function.
-        :param data: wavefor stream
-        :type data: ~obspy.core.stream.Stream
         :return: ARH cf
         :rtype:
         """
@@ -631,14 +605,12 @@ class AR3Ccf(CharacteristicFunction):
         super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
                                      fnoise=pickparams["addnoise"])
 
-    def calcCF(self, data):
+    def calcCF(self):
         """
         Function to calculate a characteristic function using autoregressive modelling of the waveform of
         all three traces.
         The waveform is predicted in a moving time window using the calculated AR parameters. The difference
         between the predicted and the actual waveform servers as a characteristic function
-        :param data: stream holding all three traces
-        :type data: ~obspy.core.stream.Stream
         :return: AR3C cf
         :rtype:
         """

pylot/core/pick/picker.py

@@ -37,7 +37,8 @@ class AutoPicker(object):
 
     warnings.simplefilter('ignore')
 
-    def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
+    def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None,
+                 fig=None, linecolor='k', ogstream=None):
         """
         Create AutoPicker object
         :param cf: characteristic function, on which the picking algorithm is applied
@@ -59,12 +60,15 @@ class AutoPicker(object):
         :type fig: `~matplotlib.figure.Figure`
         :param linecolor: matplotlib line color string
         :type linecolor: str
+        :param ogstream: original stream (waveform), e.g. for plotting purposes
+        :type ogstream: `~obspy.core.stream.Stream`
         """
 
         assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
         self._linecolor = linecolor
         self._pickcolor_p = 'b'
         self.cf = cf.getCF()
+        self.ogstream = ogstream
         self.Tcf = cf.getTimeArray()
         self.Data = cf.getXCF()
         self.dt = cf.getIncrement()
@@ -173,7 +177,7 @@ class AICPicker(AutoPicker):
         nn = np.isnan(self.cf)
         if len(nn) > 1:
             self.cf[nn] = 0
-        # taper AIC-CF to get rid off side maxima
+        # taper AIC-CF to get rid of side maxima
         tap = np.hanning(len(self.cf))
         aic = tap * self.cf + max(abs(self.cf))
         # smooth AIC-CF
@@ -316,16 +320,7 @@ class AICPicker(AutoPicker):
                             plt.close(fig)
                     return
                 iislope = islope[0][0:imax + 1]
-            # MP MP change slope calculation
-            # get all maxima of aicsmooth
-            iaicmaxima = argrelmax(aicsmooth)[0]
-            # get first index of maximum after pickindex (indices saved in iaicmaxima)
-            aicmax = iaicmaxima[np.where(iaicmaxima > pickindex)[0]]
-            if len(aicmax) > 0:
-                iaicmax = aicmax[0]
-            else:
-                iaicmax = -1
-            dataslope = aicsmooth[pickindex: iaicmax]
+            dataslope = self.Data[0].data[iislope]
             # calculate slope as polynomal fit of order 1
             xslope = np.arange(0, len(dataslope), 1)
             try:
@@ -336,7 +331,7 @@ class AICPicker(AutoPicker):
                 else:
                     self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
                     # normalize slope to maximum of cf to make it unit independent
-                    self.slope /= aicsmooth[iaicmax]
+                    self.slope /= self.Data[0].data[icfmax]
             except Exception as e:
                 print("AICPicker: Problems with data fitting! {}".format(e))
 
@@ -356,6 +351,12 @@ class AICPicker(AutoPicker):
                 self.Tcf = self.Tcf[0:len(self.Tcf) - 1]
             ax1.plot(self.Tcf, cf / max(cf), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
             ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
+            # plot the original waveform also for evaluation of the CF and pick
+            if self.ogstream:
+                data = self.ogstream[0].data
+                if len(data) == len(self.Tcf):
+                    ax1.plot(self.Tcf, 0.5 * data / max(data), 'k', label='Seismogram', alpha=0.3, zorder=0,
+                             lw=0.5)
             if self.Pick is not None:
                 ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
             ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
@@ -376,7 +377,7 @@ class AICPicker(AutoPicker):
                             label='Signal Window')
                 ax2.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
                             label='Slope Window')
-                ax2.plot(self.Tcf[pickindex: iaicmax], datafit, 'g', linewidth=2,
+                ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2,
                          label='Slope')  # MP MP changed temporarily!
 
                 if self.slope is not None:

pylot/core/pick/utils.py

@@ -15,7 +15,7 @@ import numpy as np
 from obspy.core import Stream, UTCDateTime
 from scipy.signal import argrelmax
 
-from pylot.core.util.utils import get_bool, get_none, SetChannelComponents
+from pylot.core.util.utils import get_bool, get_none, SetChannelComponents, common_range
 
 
 def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
@@ -828,14 +828,22 @@ def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, line
     if len(X) > 1:
         # all three components available
         # make sure, all components have equal lengths
-        ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
-        x1 = X[0][0:ilen]
-        x2 = X[1][0:ilen]
-        x3 = X[2][0:ilen]
+        earliest_starttime = min(tr.stats.starttime for tr in X)
+        cuttimes = common_range(X)
+        X = X.slice(cuttimes[0], cuttimes[1])
+        x1, x2, x3 = X[:3]
+
+        if not (len(x1) == len(x2) == len(x3)):
+            raise PickingFailedException('checksignallength: unequal lengths of components!')
+
         # get RMS trace
         rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
+        ilen = len(rms)
+        dt = earliest_starttime - X[0].stats.starttime
+        pick -= dt
     else:
         x1 = X[0].data
+        x2 = x3 = None
         ilen = len(x1)
         rms = abs(x1)
 
@@ -874,6 +882,10 @@ def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, line
         fig._tight = True
         ax = fig.add_subplot(111)
         ax.plot(t, rms, color=linecolor, linewidth=0.7, label='RMS Data')
+        ax.plot(t, x1, 'k', alpha=0.3, lw=0.3, zorder=0)
+        if x2 is not None and x3 is not None:
+            ax.plot(t, x2, 'r', alpha=0.3, lw=0.3, zorder=0)
+            ax.plot(t, x3, 'g', alpha=0.3, lw=0.3, zorder=0)
         ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
         ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
         ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
@@ -883,6 +895,7 @@ def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, line
         ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
         ax.set_ylabel('Counts')
         ax.set_title('Check for Signal Length, Station %s' % X[0].stats.station)
+        ax.set_xlim(pickparams["pstart"], pickparams["pstop"])
         ax.set_yticks([])
         if plt_flag == 1:
             fig.show()

pylot/core/util/array_map.py

@@ -14,6 +14,8 @@ import obspy
 from PySide2 import QtWidgets, QtGui
 from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+from obspy import UTCDateTime
+
 from pylot.core.util.utils import identifyPhaseID
 from scipy.interpolate import griddata
 
@@ -60,6 +62,7 @@ class Array_map(QtWidgets.QWidget):
         self.parameter = parameter if parameter else parent._inputs
 
         self.picks_rel = {}
+        self.picks_rel_mean_corrected = {}
         self.marked_stations = []
         self.highlighted_stations = []
 
@@ -106,6 +109,7 @@ class Array_map(QtWidgets.QWidget):
         self.map_reset_button = QtWidgets.QPushButton('Reset Map View')
         self.save_map_button = QtWidgets.QPushButton('Save Map')
         self.go2eq_button = QtWidgets.QPushButton('Go to Event Location')
+        self.subtract_mean_cb = QtWidgets.QCheckBox('Subtract mean')
 
         self.main_box = QtWidgets.QVBoxLayout()
         self.setLayout(self.main_box)
@@ -152,6 +156,7 @@ class Array_map(QtWidgets.QWidget):
         self.bot_row.addWidget(self.map_reset_button, 2)
         self.bot_row.addWidget(self.go2eq_button, 2)
         self.bot_row.addWidget(self.save_map_button, 2)
+        self.bot_row.addWidget(self.subtract_mean_cb, 0)
         self.bot_row.addWidget(self.status_label, 5)
 
     def init_colormap(self):
@@ -212,6 +217,7 @@ class Array_map(QtWidgets.QWidget):
         self.map_reset_button.clicked.connect(self.org_map_view)
         self.go2eq_button.clicked.connect(self.go2eq)
         self.save_map_button.clicked.connect(self.saveFigure)
+        self.subtract_mean_cb.stateChanged.connect(self.toggle_subtract_mean)
 
         self.plotWidget.mpl_connect('motion_notify_event', self.mouse_moved)
         self.plotWidget.mpl_connect('scroll_event', self.mouse_scroll)
@@ -368,12 +374,6 @@ class Array_map(QtWidgets.QWidget):
     def get_max_from_stations(self, key):
         return self._from_dict(max, key)
 
-    def get_min_from_picks(self):
-        return min(self.picks_rel.values())
-
-    def get_max_from_picks(self):
-        return max(self.picks_rel.values())
-
     def current_picks_dict(self):
         picktype = self.comboBox_am.currentText().split(' ')[0]
         auto_manu = {'auto': self.autopicks_dict,
@@ -418,17 +418,17 @@ class Array_map(QtWidgets.QWidget):
                     print('Cannot display pick for station {}. Reason: {}'.format(station_name, e))
             return picks, uncertainties
 
-        def get_picks_rel(picks):
+        def get_picks_rel(picks, func=min):
             picks_rel = {}
             picks_utc = []
             for pick in picks.values():
-                if type(pick) is obspy.core.utcdatetime.UTCDateTime:
-                    picks_utc.append(pick)
+                if type(pick) is UTCDateTime:
+                    picks_utc.append(pick.timestamp)
             if picks_utc:
-                self._earliest_picktime = min(picks_utc)
+                self._reference_picktime = UTCDateTime(func(picks_utc))
                 for st_id, pick in picks.items():
-                    if type(pick) is obspy.core.utcdatetime.UTCDateTime:
-                        pick -= self._earliest_picktime
+                    if type(pick) is UTCDateTime:
+                        pick -= self._reference_picktime
                     picks_rel[st_id] = pick
             return picks_rel
 
@@ -437,6 +437,15 @@ class Array_map(QtWidgets.QWidget):
 
         self.picks, self.uncertainties = get_picks(self.stations_dict)
         self.picks_rel = get_picks_rel(self.picks)
+        self.picks_rel_mean_corrected = get_picks_rel_mean_corr(self.picks)
+
+    def toggle_subtract_mean(self):
+        if self.subtract_mean_cb.isChecked():
+            cmap = 'seismic'
+        else:
+            cmap = 'viridis'
+        self.cmaps_box.setCurrentIndex(self.cmaps_box.findText(cmap))
+        self._refresh_drawings()
 
     def init_lat_lon_dimensions(self):
         # init minimum and maximum lon and lat dimensions
@@ -467,11 +476,12 @@ class Array_map(QtWidgets.QWidget):
         return stations, latitudes, longitudes
 
     def get_picks_lat_lon(self):
+        picks_rel = self.picks_rel_mean_corrected if self.subtract_mean_cb.isChecked() else self.picks_rel
         picks = []
         uncertainties = []
         latitudes = []
         longitudes = []
-        for st_id, pick in self.picks_rel.items():
+        for st_id, pick in picks_rel.items():
             picks.append(pick)
             uncertainties.append(self.uncertainties.get(st_id))
             latitudes.append(self.stations_dict[st_id]['latitude'])
@@ -538,13 +548,20 @@ class Array_map(QtWidgets.QWidget):
                 print(message, e)
                 print(traceback.format_exc())
 
-    def draw_contour_filled(self, nlevel=50):
-        levels = np.linspace(self.get_min_from_picks(), self.get_max_from_picks(), nlevel)
+    def draw_contour_filled(self, nlevel=51):
+        if self.subtract_mean_cb.isChecked():
+            abs_max = self.get_residuals_absmax()
+            levels = np.linspace(-abs_max, abs_max, nlevel)
+        else:
+            levels = np.linspace(min(self.picks_rel.values()), max(self.picks_rel.values()), nlevel)
 
         self.contourf = self.ax.contourf(self.longrid, self.latgrid, self.picksgrid_active, levels,
                                          linewidths=self.linewidth * 5, transform=ccrs.PlateCarree(),
                                          alpha=0.4, zorder=8, cmap=self.get_colormap())
 
+    def get_residuals_absmax(self):
+        return np.max(np.absolute(list(self.picks_rel_mean_corrected.values())))
+
     def get_colormap(self):
         return plt.get_cmap(self.cmaps_box.currentText())
 
@@ -575,7 +592,12 @@ class Array_map(QtWidgets.QWidget):
                           for uncertainty in uncertainties])
 
         cmap = self.get_colormap()
-        self.sc_picked = self.ax.scatter(lons, lats, s=sizes, edgecolors='white', cmap=cmap,
+
+        vmin = vmax = None
+        if self.subtract_mean_cb.isChecked():
+            vmin, vmax = -self.get_residuals_absmax(), self.get_residuals_absmax()
+
+        self.sc_picked = self.ax.scatter(lons, lats, s=sizes, edgecolors='white', cmap=cmap, vmin=vmin, vmax=vmax,
                                          c=picks, zorder=11, label='Picked', transform=ccrs.PlateCarree())
 
     def annotate_ax(self):
@@ -652,7 +674,9 @@ class Array_map(QtWidgets.QWidget):
         if picks_available:
             self.scatter_picked_stations()
             if hasattr(self, 'sc_picked'):
-                self.cbar = self.add_cbar(label='Time relative to first onset ({}) [s]'.format(self._earliest_picktime))
+                self.cbar = self.add_cbar(
+                    label='Time relative to reference onset ({}) [s]'.format(self._reference_picktime)
+                )
             self.comboBox_phase.setEnabled(True)
         else:
             self.comboBox_phase.setEnabled(False)

pylot/core/util/dataprocessing.py

@@ -27,6 +27,10 @@ class Metadata(object):
         # saves which metadata files are from obspy dmt
         self.obspy_dmt_invs = []
         if inventory:
+            # make sure that no accidental backslashes mess up the path
+            if isinstance(inventory, str):
+                inventory = inventory.replace('\\', '/')
+                inventory = os.path.abspath(inventory)
             if os.path.isdir(inventory):
                 self.add_inventory(inventory)
             if os.path.isfile(inventory):

pylot/core/util/utils.py

@@ -1076,7 +1076,7 @@ def check4rotated(data, metadata=None, verbosity=1):
             return wfs_in
 
         # check metadata quality
-        t_start = full_range(wfs_in)
+        t_start = full_range(wfs_in)[0]
         try:
             azimuths = []
             dips = []

tests/test_autopicker/pylot_alparray_mantle_corr_stack_0.03-0.5.in

@@ -4,10 +4,8 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-                                                   #rootpath#       %project path                                                                                        
-                                                   #datapath#       %data path                                                                                           
-                                                   #database#       %name of data base                                                                                   
-20171010_063224.a                                  #eventID#        %event ID for single event processing (* for all events found in database)                           
+dmt_database_test                                  #datapath#       %data path
+20171010_063224.a                                  #eventID#        %event ID for single event processing (* for all events found in database)
                                                    #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        

tests/test_autopicker/test_autopylot.py

@@ -1,6 +1,8 @@
 import os
 import pytest
 
+from obspy import read_events
+
 from autoPyLoT import autoPyLoT
 
 
@@ -8,7 +10,11 @@ class TestAutopickerGlobal():
     def init(self):
         self.params_infile = 'pylot_alparray_mantle_corr_stack_0.03-0.5.in'
         self.test_event_dir = 'dmt_database_test'
+        self.fname_outfile_xml = os.path.join(
+            self.test_event_dir, '20171010_063224.a', 'PyLoT_20171010_063224.a_autopylot.xml'
+        )
 
+        # check if the input files exist
         if not os.path.isfile(self.params_infile):
             print(f'Test input file {os.path.abspath(self.params_infile)} not found.')
             return False
@@ -24,4 +30,38 @@ class TestAutopickerGlobal():
 
     def test_autopicker(self):
         assert self.init(), 'Initialization failed due to missing input files.'
-        #autoPyLoT(inputfile=self.params_infile, eventid='20171010_063224.a')
+        # check for output file in test directory and remove it if necessary
+        if os.path.isfile(self.fname_outfile_xml):
+            os.remove(self.fname_outfile_xml)
+        autoPyLoT(inputfile=self.params_infile, eventid='20171010_063224.a', obspyDMT_wfpath='processed')
+
+        # test for different known output files if they are identical or not
+        compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_autopylot.xml', True)
+        compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_saved_from_GUI.xml', True)
+        compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_corrected_taup_times_0.03-0.5_P.xml', False)
+
+
+def compare_pickfiles(pickfile1: str, pickfile2: str, samefile: bool = True) -> None:
+    """
+    Compare the pick times and errors from two pick files.
+
+    Parameters:
+        pickfile1 (str): The path to the first pick file.
+        pickfile2 (str): The path to the second pick file.
+        samefile (bool): A flag indicating whether the two files are expected to be the same. Defaults to True.
+
+    Returns:
+        None
+    """
+    cat1 = read_events(pickfile1)
+    cat2 = read_events(pickfile2)
+    picks1 = sorted(cat1[0].picks, key=lambda pick: str(pick.waveform_id))
+    picks2 = sorted(cat2[0].picks, key=lambda pick: str(pick.waveform_id))
+    pick_times1 = [pick.time for pick in picks1]
+    pick_times2 = [pick.time for pick in picks2]
+    pick_terrs1 = [pick.time_errors for pick in picks1]
+    pick_terrs2 = [pick.time_errors for pick in picks2]
+
+    # check if times and errors are identical or not depending on the samefile flag
+    assert (pick_times1 == pick_times2) is samefile, 'Pick times error'
+    assert (pick_terrs1 == pick_terrs2) is samefile, 'Pick time errors errors'

tests/test_autopickstation/autoPyLoT_global_taupy_false.in

@@ -41,6 +41,7 @@ global                                             #extent#         %extent of a
 875.0                                              #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
 False                                              #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
 IASP91                                             #taup_model#     %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
+P,Pdiff,S,Sdiff                                    #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
 0.01 0.1                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
 0.001 0.5                                          #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 0.01 0.5                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     

tests/test_autopickstation/autoPyLoT_global_taupy_true.in

@@ -41,6 +41,7 @@ global                                             #extent#         %extent of a
 875.0                                              #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
 True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
 IASP91                                             #taup_model#     %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
+P,Pdiff,S,Sdiff                                    #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
 0.01 0.1                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
 0.001 0.5                                          #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 0.01 0.5                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     

tests/test_autopickstation/test_autopickstation.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import unittest
+import pytest
 
 import obspy
 from obspy import UTCDateTime
@@ -105,7 +106,6 @@ class TestAutopickStation(unittest.TestCase):
         # show complete diff when difference in results dictionaries are found
         self.maxDiff = None
 
-    # @skip("Works")
     def test_autopickstation_taupy_disabled_gra1(self):
         expected = {
             'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
@@ -121,8 +121,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled,
                                               metadata=(None, None))
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_taupy_enabled_gra1(self):
@@ -140,8 +140,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_taupy_disabled_gra2(self):
@@ -157,8 +157,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled,
                                               metadata=(None, None))
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('GRA2', station)
 
     def test_autopickstation_taupy_enabled_gra2(self):
@@ -175,8 +175,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('GRA2', station)
 
     def test_autopickstation_taupy_disabled_ech(self):
@@ -190,8 +190,8 @@ class TestAutopickStation(unittest.TestCase):
                           'fm': None, 'spe': None, 'channel': u'LHE'}}
         with HidePrints():
             result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('ECH', station)
 
     def test_autopickstation_taupy_enabled_ech(self):
@@ -208,8 +208,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('ECH', station)
 
     def test_autopickstation_taupy_disabled_fiesa(self):
@@ -224,8 +224,8 @@ class TestAutopickStation(unittest.TestCase):
                           'fm': None, 'spe': None, 'channel': u'LHE'}}
         with HidePrints():
             result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('FIESA', station)
 
     def test_autopickstation_taupy_enabled_fiesa(self):
@@ -242,8 +242,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
-        self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('FIESA', station)
 
     def test_autopickstation_gra1_z_comp_missing(self):
@@ -272,7 +272,8 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
                                               metadata=(None, None))
-        self.assertEqual(expected, result)
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
         self.assertEqual('GRA1', station)
 
     def test_autopickstation_a106_taupy_enabled(self):
@@ -290,7 +291,9 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertEqual(expected, result)
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
+
 
     def test_autopickstation_station_missing_in_metadata(self):
         """This station is not in the metadata, but Taupy is enabled. Taupy should exit cleanly and modify the starttime
@@ -311,8 +314,37 @@ class TestAutopickStation(unittest.TestCase):
         with HidePrints():
             result, station = autopickstation(wfstream=self.a005a, pickparam=self.pickparam_taupy_enabled,
                                               metadata=self.metadata, origin=self.origin)
-        self.assertEqual(expected, result)
+        compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
+        compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
 
 
+def run_dict_comparison(result, expected):
+    for key, expected_value in expected.items():
+        if isinstance(expected_value, dict):
+            run_dict_comparison(result[key], expected[key])
+        else:
+            res = result[key]
+            if isinstance(res, UTCDateTime) and isinstance(expected_value, UTCDateTime):
+                res = res.timestamp
+                expected_value = expected_value.timestamp
+            assert expected_value == pytest.approx(res), f'{key}: {expected_value} != {res}'
+
+
+def compare_dicts(result, expected, hint=''):
+    try:
+        run_dict_comparison(result, expected)
+    except AssertionError:
+        raise AssertionError(f'{hint}Dictionaries not equal.'
+                             f'\n\n<<Expected>>\n{pretty_print_dict(expected)}'
+                             f'\n\n<<Result>>\n{pretty_print_dict(result)}')
+
+
+def pretty_print_dict(dct):
+    retstr = ''
+    for key, value in sorted(dct.items(), key=lambda x: x[0]):
+        retstr += f"{key} : {value}\n"
+
+    return retstr
+
 if __name__ == '__main__':
     unittest.main()