[bugfix] explicitly pass parameters to "picksdict_from_picks" to calculate pick weights. Otherwise no weights could be calculated. Closes #40

[minor] Commented out search for network name in metadata filename considered being unsafe

.gitignore

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

.mailmap

@@ -0,0 +1,39 @@
+Darius Arnold <Darius.Arnold@ruhr-uni-bochum.de> <Darius_A@web.de>
+Darius Arnold <Darius.Arnold@ruhr-uni-bochum.de> <darius.arnold@rub.de>
+Darius Arnold <Darius.Arnold@ruhr-uni-bochum.de> <darius.arnold@ruhr-uni-bochum.de>
+Darius Arnold <Darius.Arnold@ruhr-uni-bochum.de> <mail@dariusarnold.de>
+
+Dennis Wlecklik <dennisw@minos02.geophysik.ruhr-uni-bochum.de>
+
+Jeldrik Gaal <jeldrikgaal@gmail.com>
+
+Kaan Coekerim <kaan.coekerim@ruhr-uni-bochum.de>
+Kaan Coekerim <kaan.coekerim@ruhr-uni-bochum.de> <kaan.coekerim@rub.de>
+
+Ludger Kueperkoch <kueperkoch@igem-energie.de> <kueperkoch@bestec-for-nature.com>
+Ludger Kueperkoch <kueperkoch@igem-energie.de> <ludger@quake2.(none)>
+Ludger Kueperkoch <kueperkoch@igem-energie.de> <ludger@sauron.bestec-for-nature>
+
+Marc S. Boxberg <marc.boxberg@rub.de>
+
+Marcel Paffrath <marcel.paffrath@ruhr-uni-bochum.de> <marcel.paffrath@rub.de> 
+Marcel Paffrath <marcel.paffrath@ruhr-uni-bochum.de> <marcel@minos01.geophysik.ruhr-uni-bochum.de>
+Marcel Paffrath <marcel.paffrath@ruhr-uni-bochum.de> <marcel@minos02.geophysik.ruhr-uni-bochum.de>
+Marcel Paffrath <marcel.paffrath@ruhr-uni-bochum.de> <marcel@minos25.geophysik.ruhr-uni-bochum.de>
+Marcel Paffrath <marcel.paffrath@ruhr-uni-bochum.de> <marcel@email.com>
+
+Sally Zimmermann <sally.zimmermann@ruhr-uni-bochum.de>
+
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastianw@minos01.geophysik.ruhr-uni-bochum.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastianw@minos02.geophysik.ruhr-uni-bochum.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastianw@minos22.geophysik.ruhr-uni-bochum.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastian.wehling-benatelli@scisys.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastian.wehling@rub.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <sebastian.wehling@rub.de>
+Sebastian Wehling-Benatelli <sebastian.wehling-benatelli@cgi.com> <DarkBeQst@users.noreply.github.com>
+
+Thomas Moeller <thomas.moeller@rub.de>
+
+Ann-Christin Koch <ann-christin.koch@ruhr-uni-bochum.de> <Ann-Christin.Koch@ruhr-uni-bochum.de>
+
+Sebastian Priebe <sebastian.priebe@rub.de>

PyLoT.py

@@ -83,7 +83,7 @@ from pylot.core.util.event import Event
 from pylot.core.io.location import create_creation_info, create_event
 from pylot.core.util.widgets import FilterOptionsDialog, NewEventDlg, \
     PylotCanvas, WaveformWidgetPG, PropertiesDlg, HelpForm, createAction, PickDlg, \
-    ComparisonWidget, TuneAutopicker, PylotParaBox, AutoPickDlg, CanvasWidget, AutoPickWidget, \
+    ComparisonWidget, TuneAutopicker, PylotParameterWidget, AutoPickDlg, CanvasWidget, AutoPickWidget, \
     CompareEventsWidget, ProgressBarWidget, AddMetadataWidget, SingleTextLineDialog, LogWidget, PickQualitiesFromXml, \
     SpectrogramTab, SearchFileByExtensionDialog
 from pylot.core.util.array_map import Array_map
@@ -136,7 +136,7 @@ class MainWindow(QMainWindow):
         self.project.parameter = self._inputs
         self.tap = None
         self.apw = None
-        self.paraBox = None
+        self.parameterWidget = None
         self.array_map = None
         self._metadata = Metadata(verbosity=0)
         self._eventChanged = [False, False]
@@ -188,7 +188,6 @@ class MainWindow(QMainWindow):
         self.table_headers = ['', 'Event', 'Time', 'Lat', 'Lon', 'Depth', 'Ml', 'Mw', '[N] MP', '[N] AP', 'Tuning Set',
                               'Test Set', 'Notes']
 
-        # TODO: refactor rootpath to datapath
         while True:
             try:
                 if settings.value("user/FullName", None) is None:
@@ -686,10 +685,9 @@ class MainWindow(QMainWindow):
         # add scroll area used in case number of traces gets too high
         self.wf_scroll_area = QtWidgets.QScrollArea(self)
         self.wf_scroll_area.setVisible(False)
-        self.no_data_label = QLabel('No Data')
+        self.no_data_label = QLabel('No Data. If data were already loaded, try to select the event again in the eventbox.')
         self.no_data_label.setStyleSheet('color: red')
         self.no_data_label.setAlignment(Qt.AlignCenter)
-
         # create central matplotlib figure canvas widget
         self.init_wfWidget()
 
@@ -718,14 +716,14 @@ class MainWindow(QMainWindow):
         self.tabs.addTab(wf_tab, 'Waveform Plot')
         self.tabs.addTab(array_tab, 'Array Map')
         self.tabs.addTab(events_tab, 'Eventlist')
-        self.tabs.addTab(spectro_tab, 'Spectro')
+        #self.tabs.addTab(spectro_tab, 'Spectro')
 
         self.wf_layout.addWidget(self.no_data_label)
         self.wf_layout.addWidget(self.wf_scroll_area)
         self.wf_scroll_area.setWidgetResizable(True)
         self.init_array_tab()
         self.init_event_table()
-        self.init_spectro_tab()
+        #self.init_spectro_tab()
         self.tabs.setCurrentIndex(0)
 
         self.eventLabel = QLabel()
@@ -1013,7 +1011,7 @@ class MainWindow(QMainWindow):
         for event in events:
             for filename in filenames:
                 if os.path.isfile(filename) and event.pylot_id in filename:
-                    self.load_data(filename, draw=False, event=event, ask_user=True, merge_strategy=sld.merge_strategy)
+                    self.load_data(filename, draw=False, event=event, ask_user=False, merge_strategy=sld.merge_strategy)
                     refresh = True
         if not refresh:
             return
@@ -1022,8 +1020,8 @@ class MainWindow(QMainWindow):
         self.fill_eventbox()
         self.setDirty(True)
 
-    def load_data(self, fname=None, loc=False, draw=True, event=None, ask_user=False, merge_strategy='Overwrite'):
-        if not ask_user:
+    def load_data(self, fname=None, loc=False, draw=True, event=None, ask_user=True, merge_strategy='Overwrite',):
+        if ask_user:
             if not self.okToContinue():
                 return
         if fname is None:
@@ -1032,8 +1030,24 @@ class MainWindow(QMainWindow):
                 fname = self.filename_from_action(action)
                 if not fname:
                     return
+
         if not event:
             event = self.get_current_event()
+
+        if event.picks and ask_user:
+            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))
@@ -1196,7 +1210,7 @@ class MainWindow(QMainWindow):
                 with open(eventlist_file, 'r') as infile:
                     eventlist_subset = [os.path.join(basepath, filename.split('\n')[0]) for filename in
                                         infile.readlines()]
-                    msg = 'Found file "eventlist.txt" in database path. WILL ONLY USE SELECTED EVENTS out of {} events ' \
+                    msg = 'Found file "eventlist.txt" in datapath. WILL ONLY USE SELECTED EVENTS out of {} events ' \
                           'contained in this subset'
                     print(msg.format(len(eventlist_subset)))
                     eventlist = [eventname for eventname in eventlist if eventname in eventlist_subset]
@@ -1221,49 +1235,34 @@ class MainWindow(QMainWindow):
         # get path from first event in list and split them
         path = eventlist[0]
         try:
-            system_name = platform.system()
-            if system_name in ["Linux", "Darwin"]:
-                dirs = {
-                    'database': path.split('/')[-2],
-                    'datapath': os.path.split(path)[0],  # path.split('/')[-3],
-                    'rootpath': '/' + os.path.join(*path.split('/')[:-3])
-                }
-            elif system_name == "Windows":
-                rootpath = path.split('/')[:-3]
-                rootpath[0] += '/'
-                dirs = {
-                    # TODO: Arrange path to meet Win standards
-                    'database': path.split('/')[-2],
-                    'datapath': path.split('/')[-3],
-                    'rootpath': os.path.join(*rootpath)
-                }
+            datapath = os.path.split(path)[0]
+            dirs = {
+                'datapath': datapath,
+            }
         except Exception as e:
             dirs = {
-                'database': '',
                 'datapath': '',
-                'rootpath': ''
             }
             print('Warning: Could not automatically init folder structure. ({})'.format(e))
 
         settings = QSettings()
-        settings.setValue("data/dataRoot", dirs['datapath'])  # d irs['rootpath'])
+        settings.setValue("data/dataRoot", dirs['datapath'])
         settings.sync()
 
         if not self.project.eventlist:
             # init parameter object
             self.setParameter(show=False)
             # hide all parameter (show all needed parameter later)
-            self.paraBox.hide_parameter()
+            self.parameterWidget.hide_parameter()
             for directory in dirs.keys():
                 # set parameter
-                box = self.paraBox.boxes[directory]
-                self.paraBox.setValue(box, dirs[directory])
+                box = self.parameterWidget.boxes[directory]
+                self.parameterWidget.setValue(box, dirs[directory])
                 # show needed parameter in box
-                self.paraBox.show_parameter(directory)
-            dirs_box = self.paraBox.get_groupbox_dialog('Directories')
+                self.parameterWidget.show_parameter(directory)
+            dirs_box = self.parameterWidget.get_groupbox_dialog('Directories')
             if not dirs_box.exec_():
                 return
-            self.project.rootpath = dirs['rootpath']
             self.project.datapath = dirs['datapath']
         else:
             if hasattr(self.project, 'datapath'):
@@ -1272,7 +1271,6 @@ class MainWindow(QMainWindow):
                                         'Datapath missmatch to current project!')
                     return
             else:
-                self.project.rootpath = dirs['rootpath']
                 self.project.datapath = dirs['datapath']
 
         self.project.add_eventlist(eventlist)
@@ -1360,11 +1358,10 @@ class MainWindow(QMainWindow):
             return True
 
     def modify_project_path(self, new_rootpath):
-        # TODO: change root to datapath
-        self.project.rootpath = new_rootpath
+        self.project.datapath = new_rootpath
         for event in self.project.eventlist:
-            event.rootpath = new_rootpath
-            event.path = os.path.join(event.rootpath, event.datapath, event.database, event.pylot_id)
+            event.datapath = new_rootpath
+            event.path = os.path.join(event.datapath, event.pylot_id)
             event.path = event.path.replace('\\', '/')
             event.path = event.path.replace('//', '/')
 
@@ -1558,7 +1555,7 @@ class MainWindow(QMainWindow):
             self.set_fname(self.get_data().getEventFileName(), type)
         return self.get_fnames(type)
 
-    def saveData(self, event=None, directory=None, outformats=['.xml', '.cnv', '.obs', '_focmec.in', '.pha']):
+    def saveData(self, event=None, directory=None, outformats=None):
         '''
         Save event data to directory with specified output formats.
         :param event: PyLoT Event, if not set current event will be used
@@ -1566,6 +1563,8 @@ class MainWindow(QMainWindow):
         :param outformats: str/list of output formats
         :return:
         '''
+        if outformats is None:
+            outformats = ['.xml', '.cnv', '.obs', '_focmec.in', '.pha']
         if not event:
             event = self.get_current_event()
         if not type(outformats) == list:
@@ -1701,7 +1700,7 @@ class MainWindow(QMainWindow):
 
     # WIP JG
     def eventlistXml(self):
-        path = self._inputs['rootpath'] + '/' + self._inputs['datapath'] + '/' + self._inputs['database']
+        path = self._inputs['datapath']
         outpath = self.project.location[:self.project.location.rfind('/')]
         geteventlistfromxml(path, outpath)
         return
@@ -1977,7 +1976,6 @@ class MainWindow(QMainWindow):
         self.dataPlot.activateObspyDMToptions(self.obspy_dmt)
         if self.obspy_dmt:
             self.prepareObspyDMT_data(eventpath)
-            self.dataPlot.activateCompareOptions(True)
 
     def loadWaveformData(self):
         '''
@@ -2154,10 +2152,11 @@ class MainWindow(QMainWindow):
         self.wf_scroll_area.setVisible(len(plots) > 0)
         self.no_data_label.setVisible(not len(plots) > 0)
         for times, data, times_syn, data_syn in plots:
-            self.dataPlot.plotWidget.getPlotItem().plot(times, data,
-                                                        pen=self.dataPlot.pen_linecolor)
+            self.dataPlot.plotWidget.getPlotItem().plot(np.array(times), np.array(data),
+                                                        pen=self.dataPlot.pen_linecolor,
+                                                        skipFiniteCheck=True)
             if len(data_syn) > 0:
-                self.dataPlot.plotWidget.getPlotItem().plot(times_syn, data_syn,
+                self.dataPlot.plotWidget.getPlotItem().plot(np.array(times_syn), np.array(data_syn),
                                                             pen=self.dataPlot.pen_linecolor_syn)
         self.dataPlot.reinitMoveProxy()
         self.highlight_stations()
@@ -2197,7 +2196,8 @@ class MainWindow(QMainWindow):
         if event.pylot_autopicks:
             self.drawPicks(picktype='auto')
         if event.pylot_picks or event.pylot_autopicks:
-            self.locateEventAction.setEnabled(True)
+            if not self._inputs.get('extent') == 'global':
+                self.locateEventAction.setEnabled(True)
             self.qualities_action.setEnabled(True)
             self.eventlist_xml_action.setEnabled(True)
 
@@ -2427,7 +2427,7 @@ class MainWindow(QMainWindow):
         filterS = filteroptions['S']
         minP, maxP = filterP.getFreq()
         minS, maxS = filterS.getFreq()
-        self.paraBox.params_to_gui()
+        self.parameterWidget.params_to_gui()
 
     def getFilterOptions(self):
         return self.filteroptions
@@ -2632,7 +2632,6 @@ class MainWindow(QMainWindow):
                           picks=self.getPicksOnStation(station, 'manual'),
                           autopicks=self.getPicksOnStation(station, 'auto'),
                           metadata=self.metadata, event=event,
-                          model=self.inputs.get('taup_model'),
                           filteroptions=self.filteroptions, wftype=wftype,
                           show_comp_data=self.dataPlot.comp_checkbox.isChecked())
         if self.filterActionP.isChecked():
@@ -3097,7 +3096,7 @@ class MainWindow(QMainWindow):
 
                 if self.pg:
                     if spe:
-                        if picks['epp'] and picks['lpp']:
+                        if not self.plot_method == 'fast' and picks['epp'] and picks['lpp']:
                             pen = make_pen(picktype, phaseID, 'epp', quality)
                             self.drawnPicks[picktype][station].append(pw.plot([epp, epp], ylims,
                                                                               alpha=.25, pen=pen, name='EPP'))
@@ -3176,8 +3175,8 @@ class MainWindow(QMainWindow):
         ttt = parameter['ttpatter']
         outfile = parameter['outpatter']
         eventname = self.get_current_event_name()
-        obsdir = os.path.join(self._inputs['rootpath'], self._inputs['datapath'], self._inputs['database'], eventname)
-        self.saveData(event=self.get_current_event(), directory=obsdir, outformats='.obs')
+        obsdir = os.path.join(self._inputs['datapath'], eventname)
+        self.saveData(event=self.get_current_event(), directory=obsdir, outformats=['.obs'])
         filename = 'PyLoT_' + eventname
         locpath = os.path.join(locroot, 'loc', filename)
         phasefile = os.path.join(obsdir, filename + '.obs')
@@ -3591,7 +3590,7 @@ class MainWindow(QMainWindow):
     def calc_magnitude(self):
         self.init_metadata()
         if not self.metadata:
-            return None
+            return []
 
         wf_copy = self.get_data().getWFData().copy()
 
@@ -3600,6 +3599,10 @@ class MainWindow(QMainWindow):
         for station in np.unique(list(self.getPicks('manual').keys()) + list(self.getPicks('auto').keys())):
             wf_select += wf_copy.select(station=station)
 
+        if not wf_select:
+            logging.warning('Empty Stream in calc_magnitude. Return.')
+            return []
+
         corr_wf = restitute_data(wf_select, self.metadata)
         # calculate moment magnitude
         moment_mag = MomentMagnitude(corr_wf, self.get_data().get_evt_data(), self.inputs.get('vp'),
@@ -3734,6 +3737,7 @@ class MainWindow(QMainWindow):
                 if self.project.parameter:
                     # do this step to update default parameter on older PyLoT projects
                     self.project.parameter.reinit_default_parameters()
+                    PylotParameter.check_deprecated_parameters(self.project.parameter)
 
                     self._inputs = self.project.parameter
                     self.updateFilteroptions()
@@ -3851,13 +3855,13 @@ class MainWindow(QMainWindow):
 
     def setParameter(self, checked=0, show=True):
         if checked: pass  # dummy argument to receive trigger signal (checked) if called by QAction
-        if not self.paraBox:
-            self.paraBox = PylotParaBox(self._inputs, parent=self, windowflag=Qt.Window)
-            self.paraBox.accepted.connect(self._setDirty)
-            self.paraBox.accepted.connect(self.filterOptionsFromParameter)
+        if not self.parameterWidget:
+            self.parameterWidget = PylotParameterWidget(self._inputs, parent=self, windowflag=Qt.Window)
+            self.parameterWidget.accepted.connect(self._setDirty)
+            self.parameterWidget.accepted.connect(self.filterOptionsFromParameter)
         if show:
-            self.paraBox.params_to_gui()
-            self.paraBox.show()
+            self.parameterWidget.params_to_gui()
+            self.parameterWidget.show()
 
     def deleteAllAutopicks(self):
         qmb = QMessageBox(self, icon=QMessageBox.Question,
@@ -3904,16 +3908,18 @@ class Project(object):
     Pickable class containing information of a PyLoT project, like event lists and file locations.
     '''
 
-    # TODO: remove rootpath
     def __init__(self):
         self.eventlist = []
         self.location = None
-        self.rootpath = None
         self.datapath = None
         self.dirty = False
         self.parameter = None
         self._table = None
 
+    @property
+    def rootpath(self):
+        return self.datapath
+
     def add_eventlist(self, eventlist):
         '''
         Add events from an eventlist containing paths to event directories.
@@ -3923,8 +3929,6 @@ class Project(object):
             return
         for item in eventlist:
             event = Event(item)
-            event.rootpath = self.parameter['rootpath']
-            event.database = self.parameter['database']
             event.datapath = self.parameter['datapath']
             if not event.path in self.getPaths():
                 self.eventlist.append(event)

README.md

@@ -11,7 +11,7 @@ PILOT has originally been developed in Mathworks' MatLab. In order to distribute
 problems, it has been decided to redevelop the software package in Python. The great work of the ObsPy group allows easy
 handling of a bunch of seismic data and PyLoT will benefit a lot compared to the former MatLab version.
 
-The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
+The development of PyLoT is part of the joint research project MAGS2, AlpArray and AdriaArray.
 
 ## Installation
 
@@ -27,58 +27,44 @@ Afterwards run (from the PyLoT main directory where the files *requirements.txt*
     conda env create -f pylot.yml
 or
     
-    conda create --name pylot_38 --file requirements.txt
+    conda create -c conda-forge --name pylot_311 python=3.11 --file requirements.txt
 
-to create a new Anaconda environment called "pylot_38".
+to create a new Anaconda environment called *pylot_311*.
 
 Afterwards activate the environment by typing
 
-    conda activate pylot_38
+    conda activate pylot_311
 
 #### Prerequisites:
 
 In order to run PyLoT you need to install:
 
 - Python 3
-- obspy
-- pyside2
-- pyqtgraph
 - cartopy
+- joblib
+- obspy
+- pyaml
+- pyqtgraph
+- pyside2
 
 (the following are already dependencies of the above packages):
 - scipy
 - numpy
-- matplotlib <= 3.3.x
+- matplotlib
 
 #### Some handwork:
 
-PyLoT needs a properties folder on your system to work. It should be situated in your home directory
-(on Windows usually C:/Users/*username*):
-
-    mkdir ~/.pylot
-
-In the next step you have to copy some files to this directory:
-
-*for local distance seismicity*
-
-    cp path-to-pylot/inputs/pylot_local.in ~/.pylot/pylot.in
-
-*for regional distance seismicity*
-
-    cp path-to-pylot/inputs/pylot_regional.in ~/.pylot/pylot.in
-
-*for global distance seismicity*
-
-    cp path-to-pylot/inputs/pylot_global.in ~/.pylot/pylot.in
-
-and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling
+Some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling
 relation
 
     cp path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
 
 You may need to do some modifications to these files. Especially folder names should be reviewed.
 
-PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
+PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10/11.
+
+## Example Dataset
+An example dataset with waveform data, metadata and automatic picks in the obspy-dmt dataset format for testing the teleseismic picking can be found at https://zenodo.org/doi/10.5281/zenodo.13759803
 
 ## Release notes
 
@@ -87,6 +73,7 @@ PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
 - event organisation in project files and waveform visualisation
 - consistent manual phase picking through predefined SNR dependant zoom level
 - consistent automatic phase picking routines using Higher Order Statistics, AIC and Autoregression
+- pick correlation correction for teleseismic waveforms
 - interactive tuning of auto-pick parameters
 - uniform uncertainty estimation from waveform's properties for automatic and manual picks
 - pdf representation and comparison of picks taking the uncertainty intrinsically into account
@@ -95,7 +82,7 @@ PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
 
 #### Known issues:
 
-We hope to solve these with the next release.
+Current release is still in development progress and has several issues. We are currently lacking manpower, but hope to assess many of the issues in the near future.
 
 ## Staff
 
@@ -108,4 +95,4 @@ Others: A. Bruestle, T. Meier, W. Friederich
 
 [ObsPy]: http://github.com/obspy/obspy/wiki
 
-April 2022
+September 2024

autoPyLoT.py

@@ -136,11 +136,9 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
     if parameter.hasParam('datastructure'):
         # getting information on data structure
         datastructure = DATASTRUCTURE[parameter.get('datastructure')]()
-        dsfields = {'root': parameter.get('rootpath'),
-                    'dpath': parameter.get('datapath'),
-                    'dbase': parameter.get('database')}
+        dsfields = {'dpath': parameter.get('datapath'),}
 
-        exf = ['root', 'dpath', 'dbase']
+        exf = ['dpath']
 
         if parameter['eventID'] != '*' and fnames == 'None':
             dsfields['eventID'] = parameter['eventID']
@@ -186,15 +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']:
@@ -206,12 +204,10 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                 locflag = 2
         else:
             # started in tune or interactive mode
-            datapath = os.path.join(parameter['rootpath'],
-                                    parameter['datapath'])
+            datapath = parameter['datapath']
             events = []
             for eventID in eventid:
                 events.append(os.path.join(datapath,
-                                           parameter['database'],
                                            eventID))
 
         if not events:
@@ -238,12 +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

docs/correlation.md

@@ -0,0 +1,77 @@
+# Pick-Correlation Correction
+
+## Introduction
+Currently, the pick-correlation correction algorithm is not accessible from they PyLoT GUI. The main file *pick_correlation_correction.py* is located in the directory *pylot\correlation*.
+The program only works for an obspy dmt database structure.
+
+The basic workflow of the algorithm is shown in the following diagram. The first step **(1)** is the normal (automatic) picking procedure in PyLoT. Everything from step **(2)** to **(5)** is part of the correlation correction algorithm.
+
+*Note: The first step is not required in case theoretical onsets are used instead of external picks when the parameter use_taupy_onsets is set to True. However, an existing event quakeML (.xml) file generated by PyLoT might be required for each event in case not external picks are used.*
+
+![images/workflow_stacking.png](images/workflow_stacking.png)
+
+A detailed description of the algorithm can be found in the corresponding publication:
+
+*Paffrath, M., Friederich, W., and the AlpArray and AlpArray-SWATH D Working Groups: Teleseismic P waves at the AlpArray seismic network: wave fronts, absolute travel times and travel-time residuals, Solid Earth, 12, 1635–1660, https://doi.org/10.5194/se-12-1635-2021, 2021.*
+
+## How to use
+To use the program you have to call the main program providing two mandatory arguments: a path to the obspy dmt database folder *dmt_database_path* and the path to the PyLoT infile *pylot.in* for picking of the beam trace:
+
+```python pick_correlation_correction.py dmt_database_path pylot.in```
+
+By default, the parameter file *parameters.yaml* is used. You can use the command line option *--params* to specify a different parameter file and other optional arguments such as *-pd* for plotting detailed information or *-n 4* to use 4 cores for parallel processing:
+
+```python pick_correlation_correction.py dmt_database_path pylot.in --params parameters_adriaarray.yaml -pd -n 4```
+
+## Cross-Correlation Parameters
+
+The program uses the parameters in the file *parameters.yaml* by default. You can use the command line option *--params* to specify a different parameter file. An example of the parameter file is provided in the *correlation\parameters.yaml* file.
+
+In the top level of the parameter file the logging level *logging* can be set, as well as a list of pick phases *pick_phases* (e.g. ['P', 'S']).
+
+For each pick phase the different parameters can be set in the first sub-level of the parameter file, e.g.:
+
+```yaml
+logging: info
+pick_phases: ['P', 'S']
+
+P:
+ min_corr_stacking: 0.8
+ min_corr_export: 0.6
+ [...]
+
+S:
+ min_corr_stacking: 0.7
+ [...]
+```
+
+The following parameters are available:
+
+
+| Parameter Name                 | Description                                                                                        | Parameter Type |
+|--------------------------------|----------------------------------------------------------------------------------------------------|----------------|
+| min_corr_stacking              | Minimum correlation coefficient for building beam trace                                            | float          |
+| min_corr_export                | Minimum correlation coefficient for pick export                                                    | float          |
+| min_stack                      | Minimum number of stations for building beam trace                                                 | int            |
+| t_before                       | Correlation window before reference pick                                                           | float          |
+| t_after                        | Correlation window after reference pick                                                            | float          |
+| cc_maxlag                      | Maximum shift for initial correlation                                                              | float          |
+| cc_maxlag2                     | Maximum shift for second (final) correlation (also for calculating pick uncertainty)               | float          |
+| initial_pick_outlier_threshold | Threshold for excluding large outliers of initial (AIC) picks                                      | float          |
+| export_threshold               | Automatically exclude all onsets which deviate more than this threshold from corrected taup onsets | float          |
+| min_picks_export               | Minimum number of correlated picks for export                                                      | int            |
+| min_picks_autopylot            | Minimum number of reference auto picks to continue with event                                      | int            |
+| check_RMS                      | Do RMS check to search for restitution errors (very experimental)                                  | bool           |
+| use_taupy_onsets               | Use taupy onsets as reference picks instead of external picks                                      | bool           |
+| station_list                   | Use the following stations as reference for stacking                                               | list[str]      |
+| use_stacked_trace              | Use existing stacked trace if found (spare re-computation)                                         | bool           |
+| data_dir                       | obspyDMT data subdirectory (e.g. 'raw', 'processed')                                               | str            |
+| pickfile_extension             | Use quakeML files (PyLoT output) with the following extension                                      | str            |
+| dt_stacking                    | Time difference for stacking window (in seconds)                                                   | list[float]    |
+| filter_options                 | Filter for first correlation (rough)                                                               | dict           |
+| filter_options_final           | Filter for second correlation (fine)                                                               | dict           |
+| filter_type                    | Filter type (e.g. bandpass)                                                                        | str            |
+| sampfreq                       | Sampling frequency (in Hz)                                                                         | float          |
+
+## Example Dataset
+An example dataset with waveform data, metadata and automatic picks in the obspy-dmt dataset format for testing can be found at https://zenodo.org/doi/10.5281/zenodo.13759803

docs/gui.md

@@ -203,8 +203,6 @@ The meaning of the header entries is:
 PyLoT GUI starts with an empty project. To add events, use the add event data button. Select one or multiple folders
 containing events.
 
-[//]: <> (TODO: explain _Directories: Root path, Data path, Database path_)
-
 ### Saving projects
 
 Save the current project from the menu with File->Save project or File->Save project as. PyLoT uses ``.plp`` files to

docs/tuning.md

@@ -6,122 +6,123 @@ A description of the parameters used for determining automatic picks.
 
 Parameters applied to the traces before picking algorithm starts.
 
-| Name                | Description                                                                                                                                                                                                             |
-|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| *P Start*, *P
-Stop* | Define time interval relative to trace start time for CF calculation on vertical trace. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue.    |
-| *S Start*, *S
-Stop* | Define time interval relative to trace start time for CF calculation on horizontal traces. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue. |
-| *Bandpass
-Z1*       | Filter settings for Butterworth bandpass applied to vertical trace for calculation of initial P pick.                                                                                                                   |
-| *Bandpass
-Z2*       | Filter settings for Butterworth bandpass applied to vertical trace for calculation of precise P pick.                                                                                                                   |
-| *Bandpass
-H1*       | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of initial S pick.                                                                                                                |
-| *Bandpass
-H2*       | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of precise S pick.                                                                                                                |
+| Name          | Description                                                                                                                                                                                                             |
+|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| *P Start*, *P |                                                                                                                                                                                                                         |
+| Stop*         | Define time interval relative to trace start time for CF calculation on vertical trace. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue.    |
+| *S Start*, *S |                                                                                                                                                                                                                         |
+| Stop*         | Define time interval relative to trace start time for CF calculation on horizontal traces. Value is relative to theoretical onset time if 'Use TauPy' option is enabled in main settings of 'Tune Autopicker' dialogue. |
+| *Bandpass     |                                                                                                                                                                                                                         |
+| Z1*           | Filter settings for Butterworth bandpass applied to vertical trace for calculation of initial P pick.                                                                                                                   |
+| *Bandpass     |                                                                                                                                                                                                                         |
+| Z2*           | Filter settings for Butterworth bandpass applied to vertical trace for calculation of precise P pick.                                                                                                                   |
+| *Bandpass     |                                                                                                                                                                                                                         |
+| H1*           | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of initial S pick.                                                                                                                |
+| *Bandpass     |                                                                                                                                                                                                                         |
+| H2*           | Filter settings for Butterworth bandpass applied to horizontal traces for calculation of precise S pick.                                                                                                                |
 
 ## Inital P pick
 
 Parameters used for determination of initial P pick.
 
-| Name         | Description                                                                                                                  |
-|--------------|------------------------------------------------------------------------------------------------------------------------------|
-| *
-tLTA*       | Size of gliding LTA window in seconds used for calculation of HOS-CF.                                                        |
-| *pickwin
-P*  | Size of time window in seconds in which the minimum of the AIC-CF in front of the maximum of the HOS-CF is determined.       |
-| *
-AICtsmooth* | Average of samples in this time window will be used for smoothing of the AIC-CF.                                             |
-| *
-checkwinP*  | Time in front of the global maximum of the HOS-CF in which to search for a second local extrema.                             |
-| *minfactorP* | Used with *
-checkwinP*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorP*. |
-| *
-tsignal*    | Time window in seconds after the initial P pick used for determining signal amplitude.                                       |
-| *
-tnoise*     | Time window in seconds in front of initial P pick used for determining noise amplitude.                                      |
-| *tsafetey*   | Time in seconds between *tsignal* and *
-tnoise*.                                                                              |
-| *
-tslope*     | Time window in seconds after initial P pick in which the slope of the onset is calculated.                                   |
+| Name                                                                                                              | Description                                                                                                            |
+|-------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
+| *                                                                                                                 |                                                                                                                        |
+| tLTA*                                                                                                             | Size of gliding LTA window in seconds used for calculation of HOS-CF.                                                  |
+| *pickwin                                                                                                          |                                                                                                                        |
+| P*                                                                                                                | Size of time window in seconds in which the minimum of the AIC-CF in front of the maximum of the HOS-CF is determined. |
+| *                                                                                                                 |                                                                                                                        |
+| AICtsmooth*                                                                                                       | Average of samples in this time window will be used for smoothing of the AIC-CF.                                       |
+| *                                                                                                                 |                                                                                                                        |
+| checkwinP*                                                                                                        | Time in front of the global maximum of the HOS-CF in which to search for a second local extrema.                       |
+| *minfactorP*                                                                                                      | Used with *                                                                                                            |
+| checkwinP*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorP*. |                                                                                                                        |
+| *                                                                                                                 |                                                                                                                        |
+| tsignal*                                                                                                          | Time window in seconds after the initial P pick used for determining signal amplitude.                                 |
+| *                                                                                                                 |                                                                                                                        |
+| tnoise*                                                                                                           | Time window in seconds in front of initial P pick used for determining noise amplitude.                                |
+| *tsafetey*                                                                                                        | Time in seconds between *tsignal* and *                                                                                |
+| tnoise*.                                                                                                          |                                                                                                                        |
+| *                                                                                                                 |                                                                                                                        |
+| tslope*                                                                                                           | Time window in seconds after initial P pick in which the slope of the onset is calculated.                             |
 
 ## Inital S pick
 
 Parameters used for determination of initial S pick
 
-| Name          | Description                                                                                                                  |
-|---------------|------------------------------------------------------------------------------------------------------------------------------|
-| *
-tdet1h*      | Length of time window in seconds in which AR params of the waveform are determined.                                          |
-| *
-tpred1h*     | Length of time window in seconds in which the waveform is predicted using the AR model.                                      |
-| *
-AICtsmoothS* | Average of samples in this time window is used for smoothing the AIC-CF.                                                     |
-| *
-pickwinS*    | Time window in which the minimum in the AIC-CF in front of the maximum in the ARH-CF is determined.                          |
-| *
-checkwinS*   | Time in front of the global maximum of the ARH-CF in which to search for a second local extrema.                             |
-| *minfactorP*  | Used with *
-checkwinS*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorS*. |
-| *
-tsignal*     | Time window in seconds after the initial P pick used for determining signal amplitude.                                       |
-| *
-tnoise*      | Time window in seconds in front of initial P pick used for determining noise amplitude.                                      |
-| *tsafetey*    | Time in seconds between *tsignal* and *
-tnoise*.                                                                              |
-| *
-tslope*      | Time window in seconds after initial P pick in which the slope of the onset is calculated.                                   |
+| Name                                                                                                              | Description                                                                                         |
+|-------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
+| *                                                                                                                 |                                                                                                     |
+| tdet1h*                                                                                                           | Length of time window in seconds in which AR params of the waveform are determined.                 |
+| *                                                                                                                 |                                                                                                     |
+| tpred1h*                                                                                                          | Length of time window in seconds in which the waveform is predicted using the AR model.             |
+| *                                                                                                                 |                                                                                                     |
+| AICtsmoothS*                                                                                                      | Average of samples in this time window is used for smoothing the AIC-CF.                            |
+| *                                                                                                                 |                                                                                                     |
+| pickwinS*                                                                                                         | Time window in which the minimum in the AIC-CF in front of the maximum in the ARH-CF is determined. |
+| *                                                                                                                 |                                                                                                     |
+| checkwinS*                                                                                                        | Time in front of the global maximum of the ARH-CF in which to search for a second local extrema.    |
+| *minfactorP*                                                                                                      | Used with *                                                                                         |
+| checkwinS*. If a second local maximum is found, it has to be at least as big as the first maximum * *minfactorS*. |                                                                                                     |
+| *                                                                                                                 |                                                                                                     |
+| tsignal*                                                                                                          | Time window in seconds after the initial P pick used for determining signal amplitude.              |
+| *                                                                                                                 |                                                                                                     |
+| tnoise*                                                                                                           | Time window in seconds in front of initial P pick used for determining noise amplitude.             |
+| *tsafetey*                                                                                                        | Time in seconds between *tsignal* and *                                                             |
+| tnoise*.                                                                                                          |                                                                                                     |
+| *                                                                                                                 |                                                                                                     |
+| tslope*                                                                                                           | Time window in seconds after initial P pick in which the slope of the onset is calculated.          |
 
 ## Precise P pick
 
 Parameters used for determination of precise P pick.
 
-| Name         | Description                                                                                                                                                                                                             |
-|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| *Precalcwin* | Time window in seconds for recalculation of the HOS-CF. The new CF will be two times the size of *
-Precalcwin*, since it will be calculated from the initial pick to +/- *Precalcwin*.                                   |
-| *
-tsmoothP*   | Average of samples in this time window will be used for smoothing the second HOS-CF.                                                                                                                                    |
-| *
-ausP*       | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed HOS-CF is found when the previous sample is larger or equal to the current sample times (1+*
-ausP*). |
+| Name                                                                                | Description                                                                                                                                                                                                      |
+|-------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| *Precalcwin*                                                                        | Time window in seconds for recalculation of the HOS-CF. The new CF will be two times the size of *                                                                                                               |
+| Precalcwin*, since it will be calculated from the initial pick to +/- *Precalcwin*. |                                                                                                                                                                                                                  |
+| *                                                                                   |                                                                                                                                                                                                                  |
+| tsmoothP*                                                                           | Average of samples in this time window will be used for smoothing the second HOS-CF.                                                                                                                             |
+| *                                                                                   |                                                                                                                                                                                                                  |
+| ausP*                                                                               | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed HOS-CF is found when the previous sample is larger or equal to the current sample times (1+* |
+| ausP*).                                                                             |                                                                                                                                                                                                                  |
 
 ## Precise S pick
 
 Parameters used for determination of precise S pick.
 
-| Name         | Description                                                                                                                                                                                                             |
-|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| *
-tdet2h*     | Time window for determination of AR coefficients.                                                                                                                                                                       |
-| *
-tpred2h*    | Time window in which the waveform is predicted using the determined AR parameters.                                                                                                                                      |
-| *Srecalcwin* | Time window for recalculation of ARH-CF. New CF will be calculated from initial pick +/- *
-Srecalcwin*.                                                                                                                  |
-| *
-tsmoothS*   | Average of samples in this time window will be used for smoothing the second ARH-CF.                                                                                                                                    |
-| *
-ausS*       | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed ARH-CF is found when the previous sample is larger or equal to the current sample times (1+*
-ausS*). |
-| *
-pickwinS*   | Time window around initial pick in which to look for a precise pick.                                                                                                                                                    |
+| Name         | Description                                                                                                                                                                                                      |
+|--------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| *            |                                                                                                                                                                                                                  |
+| tdet2h*      | Time window for determination of AR coefficients.                                                                                                                                                                |
+| *            |                                                                                                                                                                                                                  |
+| tpred2h*     | Time window in which the waveform is predicted using the determined AR parameters.                                                                                                                               |
+| *Srecalcwin* | Time window for recalculation of ARH-CF. New CF will be calculated from initial pick +/- *                                                                                                                       |
+| Srecalcwin*. |                                                                                                                                                                                                                  |
+| *            |                                                                                                                                                                                                                  |
+| tsmoothS*    | Average of samples in this time window will be used for smoothing the second ARH-CF.                                                                                                                             |
+| *            |                                                                                                                                                                                                                  |
+| ausS*        | Controls artificial uplift of samples during precise picking. A common local minimum of the smoothed and unsmoothed ARH-CF is found when the previous sample is larger or equal to the current sample times (1+* |
+| ausS*).      |                                                                                                                                                                                                                  |
+| *            |                                                                                                                                                                                                                  |
+| pickwinS*    | Time window around initial pick in which to look for a precise pick.                                                                                                                                             |
 
 ## Pick quality control
 
 Parameters used for checking quality and integrity of automatic picks.
 
-| Name                                       | Description                                                                                                                                                                                                                                                                    |
-|--------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| *
-minAICPslope*                             | Initial P picks with a slope lower than this value will be discared.                                                                                                                                                                                                           |
-| *
-minAICPSNR*                               | Initial P picks with a SNR below this value will be discarded.                                                                                                                                                                                                                 |
-| *
-minAICSslope*                             | Initial S picks with a slope lower than this value will be discarded.                                                                                                                                                                                                          |
-| *
-minAICSSNR*                               | Initial S picks with a SNR below this value will be discarded.                                                                                                                                                                                                                 |
-| *minsiglength*, *noisefacor*. *minpercent* | Parameters for checking signal length. In the time window of size *
+| Name                                       | Description                                                           |
+|--------------------------------------------|-----------------------------------------------------------------------|
+| *                                          |                                                                       |
+| minAICPslope*                              | Initial P picks with a slope lower than this value will be discared.  |
+| *                                          |                                                                       |
+| minAICPSNR*                                | Initial P picks with a SNR below this value will be discarded.        |
+| *                                          |                                                                       |
+| minAICSslope*                              | Initial S picks with a slope lower than this value will be discarded. |
+| *                                          |                                                                       |
+| minAICSSNR*                                | Initial S picks with a SNR below this value will be discarded.        |
+| *minsiglength*, *noisefacor*. *minpercent* | Parameters for checking signal length. In the time window of size *   |
+
 minsiglength* after the initial P pick *
 minpercent* of samples have to be larger than the RMS value.                                                                                                        |
 | *
@@ -139,12 +140,12 @@ wdttolerance*                             | Maximum allowed deviation of S onset
 
 Parameters for discrete quality classes.
 
-| Name             | Description                                                                                                                                                                                                           |
-|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| *
-timeerrorsP*    | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for P onsets.                                                                           |
-| *
-timeerrorsS*    | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for S onsets.                                                                           |
-| *nfacP*, *nfacS* | For determination of latest possible onset time. The time when the signal reaches an amplitude of *
-nfac* * mean value of the RMS amplitude in the time window *tnoise* corresponds to the latest possible onset time. |
+| Name                                                                                                               | Description                                                                                                                                 |
+|--------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
+| *                                                                                                                  |                                                                                                                                             |
+| timeerrorsP*                                                                                                       | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for P onsets. |
+| *                                                                                                                  |                                                                                                                                             |
+| timeerrorsS*                                                                                                       | Width of the time windows in seconds between earliest and latest possible pick which represent the quality classes 0, 1, 2, 3 for S onsets. |
+| *nfacP*, *nfacS*                                                                                                   | For determination of latest possible onset time. The time when the signal reaches an amplitude of *                                         |
+| nfac* * mean value of the RMS amplitude in the time window *tnoise* corresponds to the latest possible onset time. |                                                                                                                                             |
 

inputs/pylot_global.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                                                                                   
-                                                   #eventID#        %event ID for single event processing (* for all events found in database)                           
+                                                   #datapath#       %data path
+                                                   #eventID#        %event ID for single event processing (* for all events found in datapath)
                                                    #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
@@ -43,6 +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

@@ -4,10 +4,8 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-/DATA/Insheim                                      #rootpath#       %project path                                                                                        
-EVENT_DATA/LOCAL                                   #datapath#       %data path                                                                                           
-2018.02_Insheim                                    #database#       %name of data base                                                                                   
-e0006.038.18                                       #eventID#        %event ID for single event processing (* for all events found in database)                           
+/DATA/Insheim/EVENT_DATA/LOCAL/2018.02_Insheim     #datapath#       %data path
+e0006.038.18                                       #eventID#        %event ID for single event processing (* for all events found in datapath)
 /DATA/Insheim/STAT_INFO                            #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
@@ -42,7 +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

@@ -4,10 +4,8 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-                                                   #rootpath#       %project path                                                                                        
-                                                   #datapath#       %data path                                                                                           
-                                                   #database#       %name of data base                                                                                   
-                                                   #eventID#        %event ID for single event processing (* for all events found in database)                           
+                                                   #datapath#       %data path
+                                                   #eventID#        %event ID for single event processing (* for all events found in datapath)
                                                    #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
@@ -42,7 +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.yml

@@ -1,14 +1,12 @@
-name: pylot_38
+name: pylot_311
 channels:
   - conda-forge
   - defaults
 dependencies:
-  - cartopy=0.20.2
-  - matplotlib-base=3.3.4
-  - numpy=1.22.3
-  - obspy=1.3.0
-  - pyqtgraph=0.12.4
-  - pyside2>=5.13.2
-  - python=3.8.12
-  - qt>=5.12.9
-  - scipy=1.8.0
+  - cartopy=0.23.0=py311hcf9f919_1
+  - joblib=1.4.2=pyhd8ed1ab_0
+  - obspy=1.4.1=py311he736701_3
+  - pyaml=24.7.0=pyhd8ed1ab_0
+  - pyqtgraph=0.13.7=pyhd8ed1ab_0
+  - pyside2=5.15.8=py311h3d699ce_4
+  - pytest=8.3.2=pyhd8ed1ab_0

pylot/__init__.py

@@ -9,7 +9,7 @@ PyLoT - the Python picking and Localization Tool
 
 This python library contains a graphical user interfaces for picking
 seismic phases. This software needs ObsPy (http://github.com/obspy/obspy/wiki)
-and the Qt4 libraries to be installed first.
+and the Qt libraries to be installed first.
 
 PILOT has been developed in Mathworks' MatLab. In order to distribute
 PILOT without facing portability problems, it has been decided to re-

pylot/core/io/data.py

@@ -36,8 +36,17 @@ class Data(object):
     loaded event. Container object holding, e.g. phase arrivals, etc.
     """
 
-    def __init__(self, parent=None, evtdata=None):
+    def __init__(self, parent=None, evtdata=None, picking_parameter=None):
         self._parent = parent
+
+        if not picking_parameter:
+            if hasattr(parent, '_inputs'):
+                picking_parameter = parent._inputs
+            else:
+                logging.warning('No picking parameters found! Using default input parameters!!!')
+                picking_parameter = PylotParameter()
+        self.picking_parameter = picking_parameter
+
         if self.getParent():
             self.comp = parent.getComponent()
         else:
@@ -403,23 +412,19 @@ class Data(object):
                                      not implemented: {1}'''.format(evtformat, e))
             if fnext == '.cnv':
                 try:
-                    velest.export(picks_copy, fnout + fnext, eventinfo=self.get_evt_data())
+                    velest.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
                 except KeyError as e:
                     raise KeyError('''{0} export format
                                      not implemented: {1}'''.format(evtformat, e))
             if fnext == '_focmec.in':
                 try:
-                    parameter = PylotParameter()
-                    logging.warning('Using default input parameter')
-                    focmec.export(picks_copy, fnout + fnext, parameter, eventinfo=self.get_evt_data())
+                    focmec.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
                 except KeyError as e:
                     raise KeyError('''{0} export format
                                      not implemented: {1}'''.format(evtformat, e))
             if fnext == '.pha':
                 try:
-                    parameter = PylotParameter()
-                    logging.warning('Using default input parameter')
-                    hypodd.export(picks_copy, fnout + fnext, parameter, eventinfo=self.get_evt_data())
+                    hypodd.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
                 except KeyError as e:
                     raise KeyError('''{0} export format
                                      not implemented: {1}'''.format(evtformat, e))

pylot/core/io/default_parameters.py

@@ -6,24 +6,14 @@ import numpy as np
 Default parameters used for picking
 """
 
-defaults = {'rootpath': {'type': str,
-                         'tooltip': 'project path',
-                         'value': '',
-                         'namestring': 'Root path'},
-
-            'datapath': {'type': str,
-                         'tooltip': 'data path',
+defaults = {'datapath': {'type': str,
+                         'tooltip': 'path to eventfolders',
                          'value': '',
                          'namestring': 'Data path'},
 
-            'database': {'type': str,
-                         'tooltip': 'name of data base',
-                         'value': '',
-                         'namestring': 'Database path'},
-
             'eventID': {'type': str,
-                        'tooltip': 'event ID for single event processing (* for all events found in database)',
-                        'value': '',
+                        'tooltip': 'event ID for single event processing (* for all events found in datapath)',
+                        'value': '*',
                         'namestring': 'Event ID'},
 
             'extent': {'type': str,
@@ -511,7 +501,7 @@ defaults = {'rootpath': {'type': str,
 
             'taup_model': {'type': str,
                            'tooltip': 'Define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6',
-                           'value': None,
+                           'value': 'iasp91',
                            'namestring': 'TauPy model'},
 
             'taup_phases': {'type': str,
@@ -522,9 +512,7 @@ defaults = {'rootpath': {'type': str,
 
 settings_main = {
     'dirs': [
-        'rootpath',
         'datapath',
-        'database',
         'eventID',
         'invdir',
         'datastructure',

pylot/core/io/inputs.py

@@ -1,5 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+import logging
+import os
 
 from pylot.core.io import default_parameters
 from pylot.core.util.errors import ParameterError
@@ -51,10 +53,16 @@ class PylotParameter(object):
         self.__parameter = {}
         self._verbosity = verbosity
         self._parFileCont = {}
+
         # io from parsed arguments alternatively
         for key, val in kwargs.items():
             self._parFileCont[key] = val
         self.from_file()
+
+        # if no filename or kwargs given, use default values
+        if not fnin and not kwargs:
+            self.reset_defaults()
+
         if fnout:
             self.export2File(fnout)
 
@@ -88,10 +96,10 @@ class PylotParameter(object):
         return bool(self.__parameter)
 
     def __getitem__(self, key):
-        try:
+        if key in self.__parameter:
             return self.__parameter[key]
-        except:
-            return None
+        else:
+            logging.warning(f'{key} not found in PylotParameter')
 
     def __setitem__(self, key, value):
         try:
@@ -418,6 +426,28 @@ class PylotParameter(object):
             line = value + name + ttip
             fid.write(line)
 
+    @staticmethod
+    def check_deprecated_parameters(parameters):
+        if parameters.hasParam('database') and parameters.hasParam('rootpath'):
+            parameters['datapath'] = os.path.join(parameters['rootpath'], parameters['datapath'],
+                                                  parameters['database'])
+            logging.warning(
+                f'Parameters database and rootpath are deprecated. '
+                f'Tried to merge them to now path: {parameters["datapath"]}.'
+            )
+
+        remove_keys = []
+        for key in parameters:
+            if not key in default_parameters.defaults.keys():
+                remove_keys.append(key)
+                logging.warning(f'Removing deprecated parameter: {key}')
+
+        for key in remove_keys:
+            del parameters[key]
+
+        parameters._settings_main = default_parameters.settings_main
+        parameters._settings_special_pick = default_parameters.settings_special_pick
+
 
 class FilterOptions(object):
     '''

pylot/core/io/phases.py

@@ -278,7 +278,6 @@ def picksdict_from_picks(evt, parameter=None):
         weight = phase.get('weight')
         if not weight:
             if not parameter:
-                logging.warning('Using ')
                 logging.warning('Using default input parameter')
                 parameter = PylotParameter()
             pick.phase_hint = identifyPhase(pick.phase_hint)
@@ -512,8 +511,8 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         fid = open("%s" % filename, 'w')
         # write header
         fid.write('# EQEVENT: %s Label: EQ%s  Loc:  X 0.00  Y 0.00  Z 10.00  OT 0.00 \n' %
-                  (parameter.get('database'), parameter.get('eventID')))
-        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
+                  (parameter.get('datapath'), parameter.get('eventID')))
+        arrivals = chooseArrivals(arrivals)
         for key in arrivals:
             # P onsets
             if 'P' in arrivals[key]:
@@ -665,8 +664,8 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
         print("Writing phases to %s for HYPOSAT" % filename)
         fid = open("%s" % filename, 'w')
         # write header
-        fid.write('%s, event %s \n' % (parameter.get('database'), parameter.get('eventID')))
-        arrivals = chooseArrivals(arrivals)  # MP MP what is chooseArrivals? It is not defined anywhere
+        fid.write('%s, event %s \n' % (parameter.get('datapath'), parameter.get('eventID')))
+        arrivals = chooseArrivals(arrivals)
         for key in arrivals:
             # P onsets
             if 'P' in  arrivals[key] and arrivals[key]['P']['mpp'] is not None:
@@ -757,11 +756,11 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             cns, eventsource['longitude'], cew, eventsource['depth'], eventinfo.magnitudes[0]['mag'], ifx))
         n = 0
         # check whether arrivals are dictionaries (autoPyLoT) or pick object (PyLoT)
-        if isinstance(arrivals, dict) == False:
+        if isinstance(arrivals, dict) is False:
             # convert pick object (PyLoT) into dictionary
             evt = ope.Event(resource_id=eventinfo['resource_id'])
             evt.picks = arrivals
-            arrivals = picksdict_from_picks(evt)
+            arrivals = picksdict_from_picks(evt, parameter=parameter)
         # check for automatic and manual picks
         # prefer manual picks
         usedarrivals = chooseArrivals(arrivals)
@@ -822,7 +821,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             # convert pick object (PyLoT) into dictionary
             evt = ope.Event(resource_id=eventinfo['resource_id'])
             evt.picks = arrivals
-            arrivals = picksdict_from_picks(evt)
+            arrivals = picksdict_from_picks(evt, parameter=parameter)
         # check for automatic and manual picks
         # prefer manual picks
         usedarrivals = chooseArrivals(arrivals)
@@ -873,7 +872,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
             # convert pick object (PyLoT) into dictionary
             evt = ope.Event(resource_id=eventinfo['resource_id'])
             evt.picks = arrivals
-            arrivals = picksdict_from_picks(evt)
+            arrivals = picksdict_from_picks(evt, parameter=parameter)
         # check for automatic and manual picks
         # prefer manual picks
         usedarrivals = chooseArrivals(arrivals)

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

@@ -5,14 +5,17 @@ import traceback
 
 import cartopy.crs as ccrs
 import cartopy.feature as cf
+from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
 import matplotlib
 import matplotlib.patheffects as PathEffects
 import matplotlib.pyplot as plt
 import numpy as np
 import obspy
-from PySide2 import QtWidgets
+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
 
@@ -24,10 +27,10 @@ matplotlib.use('Qt5Agg')
 
 class MplCanvas(FigureCanvas):
 
-    def __init__(self, parent=None, extern_axes=None, width=5, height=4, dpi=100):
+    def __init__(self, extern_axes=None, projection=None, width=15, height=5, dpi=100):
         if extern_axes is None:
             self.fig = plt.figure(figsize=(width, height), dpi=dpi)
-            self.axes = self.fig.add_subplot(111)
+            self.axes = self.fig.add_subplot(111, projection=projection)
         else:
             self.fig = extern_axes.figure
             self.axes = extern_axes
@@ -59,24 +62,30 @@ 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 = []
 
         # call functions to draw everything
+        self.projection = ccrs.PlateCarree()
         self.init_graphics()
+        self.ax = self.canvas.axes
+        self.ax.set_adjustable('datalim')
+
         self.init_stations()
         self.init_crtpyMap()
         self.init_map()
+
         # set original map limits to fall back on when home button is pressed
-        self.org_xlim = self.canvas.axes.get_xlim()
-        self.org_ylim = self.canvas.axes.get_ylim()
+        self.org_xlim = self.ax.get_xlim()
+        self.org_ylim = self.ax.get_ylim()
+
         # initial map without event
-        self.canvas.axes.set_xlim(self.org_xlim[0], self.org_xlim[1])
-        self.canvas.axes.set_ylim(self.org_ylim[0], self.org_ylim[1])
+        self.ax.set_xlim(self.org_xlim[0], self.org_xlim[1])
+        self.ax.set_ylim(self.org_ylim[0], self.org_ylim[1])
 
         self._style = None if not hasattr(parent, '_style') else parent._style
 
-
     def init_map(self):
         self.init_colormap()
         self.connectSignals()
@@ -89,23 +98,24 @@ class Array_map(QtWidgets.QWidget):
         # initialize figure elements
 
         if self.extern_plot_axes is None:
-            self.canvas = MplCanvas(self)
-            self.plotWidget = FigureCanvas(self.canvas.fig)
+            self.canvas = MplCanvas(projection=self.projection)
         else:
-            self.canvas = MplCanvas(self, extern_axes=self.extern_plot_axes)
-            self.plotWidget = FigureCanvas(self.canvas.fig)
+            self.canvas = MplCanvas(extern_axes=self.extern_plot_axes)
+
+        self.plotWidget = self.canvas
 
         # initialize GUI elements
         self.status_label = QtWidgets.QLabel()
         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)
 
         self.top_row = QtWidgets.QHBoxLayout()
-        self.main_box.addLayout(self.top_row, 1)
+        self.main_box.addLayout(self.top_row, 0)
 
         self.comboBox_phase = QtWidgets.QComboBox()
         self.comboBox_phase.insertItem(0, 'P')
@@ -124,8 +134,8 @@ class Array_map(QtWidgets.QWidget):
         self.cmaps_box = QtWidgets.QComboBox()
         self.cmaps_box.setMaxVisibleItems(20)
         [self.cmaps_box.addItem(map_name) for map_name in sorted(plt.colormaps())]
-        # try to set to viridis as default
-        self.cmaps_box.setCurrentIndex(self.cmaps_box.findText('viridis'))
+        # try to set to plasma as default
+        self.cmaps_box.setCurrentIndex(self.cmaps_box.findText('plasma'))
 
         self.top_row.addWidget(QtWidgets.QLabel('Select a phase: '))
         self.top_row.addWidget(self.comboBox_phase)
@@ -138,14 +148,15 @@ class Array_map(QtWidgets.QWidget):
         self.top_row.addWidget(self.auto_refresh_box)
         self.top_row.addWidget(self.refresh_button)
 
-        self.main_box.addWidget(self.plotWidget, 1)
+        self.main_box.addWidget(self.plotWidget, 10)
 
         self.bot_row = QtWidgets.QHBoxLayout()
-        self.main_box.addLayout(self.bot_row, 0.3)
+        self.main_box.addLayout(self.bot_row, 0)
         self.bot_row.addWidget(QtWidgets.QLabel(''), 5)
         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):
@@ -153,14 +164,12 @@ class Array_map(QtWidgets.QWidget):
         self.init_lat_lon_grid()
 
     def init_crtpyMap(self):
-        self.canvas.axes.cla()
-        self.canvas.axes = plt.axes(projection=ccrs.PlateCarree())
-        self.canvas.axes.add_feature(cf.LAND)
-        self.canvas.axes.add_feature(cf.OCEAN)
-        self.canvas.axes.add_feature(cf.COASTLINE, linewidth=1, edgecolor='gray')
-        self.canvas.axes.add_feature(cf.BORDERS, alpha=0.7)
-        self.canvas.axes.add_feature(cf.LAKES, alpha=0.7)
-        self.canvas.axes.add_feature(cf.RIVERS, linewidth=1)
+        self.ax.add_feature(cf.LAND)
+        self.ax.add_feature(cf.OCEAN)
+        self.ax.add_feature(cf.COASTLINE, linewidth=1, edgecolor='gray')
+        self.ax.add_feature(cf.BORDERS, alpha=0.7)
+        self.ax.add_feature(cf.LAKES, alpha=0.7)
+        self.ax.add_feature(cf.RIVERS, linewidth=1)
 
         # parallels and meridians
         self.add_merid_paral()
@@ -168,12 +177,8 @@ class Array_map(QtWidgets.QWidget):
         self.canvas.fig.tight_layout()
 
     def add_merid_paral(self):
-        self.gridlines = self.canvas.axes.gridlines(draw_labels=False, alpha=0.6, color='gray',
-                                                    linewidth=self.linewidth / 2, zorder=7)
-        # TODO: current cartopy version does not support label removal. Devs are working on it.
-        #  Should be fixed in coming cartopy versions
-        # self.gridlines.xformatter = LONGITUDE_FORMATTER
-        # self.gridlines.yformatter = LATITUDE_FORMATTER
+        self.gridlines = self.ax.gridlines(draw_labels=False, alpha=0.6, color='gray',
+                                           linewidth=self.linewidth / 2, zorder=7, crs=ccrs.PlateCarree())
 
     def remove_merid_paral(self):
         if len(self.gridlines.xline_artists):
@@ -181,24 +186,24 @@ class Array_map(QtWidgets.QWidget):
             self.gridlines.yline_artists[0].remove()
 
     def org_map_view(self):
-        self.canvas.axes.set_xlim(self.org_xlim[0], self.org_xlim[1])
-        self.canvas.axes.set_ylim(self.org_ylim[0], self.org_ylim[1])
+        self.ax.set_xlim(self.org_xlim[0], self.org_xlim[1])
+        self.ax.set_ylim(self.org_ylim[0], self.org_ylim[1])
         # parallels and meridians
-        self.remove_merid_paral()
-        self.add_merid_paral()
+        #self.remove_merid_paral()
+        #self.add_merid_paral()
 
-        self.canvas.axes.figure.canvas.draw_idle()
+        self.canvas.draw_idle()
 
     def go2eq(self):
         if self.eventLoc:
             lats, lons = self.eventLoc
-            self.canvas.axes.set_xlim(lons - 10, lons + 10)
-            self.canvas.axes.set_ylim(lats - 5, lats + 5)
+            self.ax.set_xlim(lons - 10, lons + 10)
+            self.ax.set_ylim(lats - 5, lats + 5)
             # parallels and meridians
-            self.remove_merid_paral()
-            self.add_merid_paral()
+            #self.remove_merid_paral()
+            #self.add_merid_paral()
 
-            self.canvas.axes.figure.canvas.draw_idle()
+            self.canvas.draw_idle()
 
         else:
             self.status_label.setText('No event information available')
@@ -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)
@@ -220,21 +226,32 @@ class Array_map(QtWidgets.QWidget):
 
     # set mouse events -----------------------------------------------------
     def mouse_moved(self, event):
-        if not event.inaxes == self.canvas.axes:
+        if not event.inaxes == self.ax:
             return
+        else:
+            cont, inds = self.sc.contains(event)
         lat = event.ydata
         lon = event.xdata
-        self.status_label.setText('Latitude: {:3.5f}, Longitude: {:3.5f}'.format(lat, lon))
+        text = f'Longitude: {lon:3.3f}, Latitude: {lat:3.3f}'
+
+        if cont:
+            indices = inds['ind']
+            text += ' | Station: ' if len(indices) == 1 else ' | Stations: '
+            text += ' - '.join([self._station_onpick_ids[index] for index in indices[:5]])
+            if len(indices) > 5:
+                text += '...'
+
+        self.status_label.setText(text)
 
     def mouse_scroll(self, event):
-        if not event.inaxes == self.canvas.axes:
+        if not event.inaxes == self.ax:
             return
 
         zoom = {'up': 1. / 2., 'down': 2.}
 
         if event.button in zoom:
-            xlim = self.canvas.axes.get_xlim()
-            ylim = self.canvas.axes.get_ylim()
+            xlim = self.ax.get_xlim()
+            ylim = self.ax.get_ylim()
 
             x, y = event.xdata, event.ydata
 
@@ -246,24 +263,24 @@ class Array_map(QtWidgets.QWidget):
             yb = y - 0.5 * ydiff
             yt = y + 0.5 * ydiff
 
-            self.canvas.axes.set_xlim(xl, xr)
-            self.canvas.axes.set_ylim(yb, yt)
+            self.ax.set_xlim(xl, xr)
+            self.ax.set_ylim(yb, yt)
             # parallels and meridians
-            self.remove_merid_paral()
-            self.add_merid_paral()
+            #self.remove_merid_paral()
+            #self.add_merid_paral()
 
-            self.canvas.axes.figure.canvas.draw_idle()
+            self.ax.figure.canvas.draw_idle()
 
     def mouseLeftPress(self, event):
-        if not event.inaxes == self.canvas.axes:
+        if not event.inaxes == self.ax:
             return
         self.map_x = event.xdata
         self.map_y = event.ydata
-        self.map_xlim = self.canvas.axes.get_xlim()
-        self.map_ylim = self.canvas.axes.get_ylim()
+        self.map_xlim = self.ax.get_xlim()
+        self.map_ylim = self.ax.get_ylim()
 
     def mouseLeftRelease(self, event):
-        if not event.inaxes == self.canvas.axes:
+        if not event.inaxes == self.ax:
             return
         new_x = event.xdata
         new_y = event.ydata
@@ -271,13 +288,13 @@ class Array_map(QtWidgets.QWidget):
         dx = new_x - self.map_x
         dy = new_y - self.map_y
 
-        self.canvas.axes.set_xlim((self.map_xlim[0] - dx, self.map_xlim[1] - dx))
-        self.canvas.axes.set_ylim(self.map_ylim[0] - dy, self.map_ylim[1] - dy)
+        self.ax.set_xlim((self.map_xlim[0] - dx, self.map_xlim[1] - dx))
+        self.ax.set_ylim(self.map_ylim[0] - dy, self.map_ylim[1] - dy)
         # parallels and meridians
-        self.remove_merid_paral()
-        self.add_merid_paral()
+        #self.remove_merid_paral()
+        #self.add_merid_paral()
 
-        self.canvas.axes.figure.canvas.draw_idle()
+        self.ax.figure.canvas.draw_idle()
 
     def onpick(self, event):
         btn_msg = {1: ' in selection. Aborted', 2: ' to delete a pick on. Aborted', 3: ' to display info.'}
@@ -357,12 +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,
@@ -407,22 +418,34 @@ 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
 
+        def get_picks_rel_mean_corr(picks):
+            return get_picks_rel(picks, func=np.nanmean)
+
         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
@@ -453,11 +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'])
@@ -469,13 +493,20 @@ class Array_map(QtWidgets.QWidget):
         stat_dict = self.stations_dict['{}.{}'.format(network, station)]
         lat = stat_dict['latitude']
         lon = stat_dict['longitude']
-        self.highlighted_stations.append(self.canvas.axes.scatter(lon, lat, s=self.pointsize, edgecolors=color,
-                                                                  facecolors='none', zorder=12,
-                                                                  transform=ccrs.PlateCarree(), label='deleted'))
+        self.highlighted_stations.append(self.ax.scatter(lon, lat, s=self.pointsize, edgecolors=color,
+                                                         facecolors='none', zorder=12,
+                                                         transform=ccrs.PlateCarree(), label='deleted'))
 
     def openPickDlg(self, ind):
-        wfdata = self._parent.get_data().getWFData()
-        wfdata_comp = self._parent.get_data().getWFDataComp()
+        try:
+            wfdata = self._parent.get_data().getWFData()
+        except AttributeError:
+            QtWidgets.QMessageBox.warning(
+                self, "PyLoT Warning",
+                "No waveform data found. Check if they were already loaded in Waveform plot tab."
+            )
+            return
+        wfdata_comp = self._parent.get_data().getAltWFdata()
         for index in ind:
             network, station = self._station_onpick_ids[index].split('.')[:2]
             pyl_mw = self._parent
@@ -490,7 +521,6 @@ class Array_map(QtWidgets.QWidget):
                                   picks=self._parent.get_current_event().getPick(station),
                                   autopicks=self._parent.get_current_event().getAutopick(station),
                                   filteroptions=self._parent.filteroptions, metadata=self.metadata,
-                                  model=self.parameter.get('taup_model'),
                                   event=pyl_mw.get_current_event())
             except Exception as e:
                 message = 'Could not generate Plot for station {st}.\n {er}'.format(st=station, er=e)
@@ -518,12 +548,19 @@ 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.canvas.axes.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())
+        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())
@@ -531,18 +568,18 @@ class Array_map(QtWidgets.QWidget):
     def scatter_all_stations(self):
         stations, lats, lons = self.get_st_lat_lon_for_plot()
 
-        self.sc = self.canvas.axes.scatter(lons, lats, s=self.pointsize * 3, facecolor='none', marker='.',
-                                           zorder=10, picker=True, edgecolor='0.5', label='Not Picked',
-                                           transform=ccrs.PlateCarree())
+        self.sc = self.ax.scatter(lons, lats, s=self.pointsize * 3, facecolor='none', marker='.',
+                                  zorder=10, picker=True, edgecolor='0.5', label='Not Picked',
+                                  transform=ccrs.PlateCarree())
 
         self.cid = self.plotWidget.mpl_connect('pick_event', self.onpick)
         self._station_onpick_ids = stations
         if self.eventLoc:
             lats, lons = self.eventLoc
-            self.sc_event = self.canvas.axes.scatter(lons, lats, s=5 * self.pointsize, facecolor='red', zorder=11,
-                                                     label='Event (might be outside map region)', marker='*',
-                                                     edgecolors='black',
-                                                     transform=ccrs.PlateCarree())
+            self.sc_event = self.ax.scatter(lons, lats, s=5 * self.pointsize, facecolor='red', zorder=11,
+                                            label='Event (might be outside map region)', marker='*',
+                                            edgecolors='black',
+                                            transform=ccrs.PlateCarree())
 
     def scatter_picked_stations(self):
         picks, uncertainties, lats, lons = self.get_picks_lat_lon()
@@ -555,8 +592,13 @@ class Array_map(QtWidgets.QWidget):
                           for uncertainty in uncertainties])
 
         cmap = self.get_colormap()
-        self.sc_picked = self.canvas.axes.scatter(lons, lats, s=sizes, edgecolors='white', cmap=cmap,
-                                                  c=picks, zorder=11, label='Picked', transform=ccrs.PlateCarree())
+
+        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):
         self.annotations = []
@@ -574,20 +616,20 @@ class Array_map(QtWidgets.QWidget):
             if st in self.marked_stations:
                 color = 'red'
             self.annotations.append(
-                self.canvas.axes.annotate(' %s' % st, xy=(x + 0.003, y + 0.003), fontsize=self.pointsize / 4.,
-                                          fontweight='semibold', color=color, alpha=0.8,
-                                          transform=ccrs.PlateCarree(), zorder=14,
-                                          path_effects=[PathEffects.withStroke(
-                                              linewidth=self.pointsize / 15., foreground='k')]))
+                self.ax.annotate(f'{st}', xy=(x + 0.003, y + 0.003), fontsize=self.pointsize / 4.,
+                                 fontweight='semibold', color=color, alpha=0.8,
+                                 transform=ccrs.PlateCarree(), zorder=14,
+                                 path_effects=[PathEffects.withStroke(
+                                     linewidth=self.pointsize / 15., foreground='k')]))
 
-        self.legend = self.canvas.axes.legend(loc=1, framealpha=1)
+        self.legend = self.ax.legend(loc=1, framealpha=1)
         self.legend.set_zorder(100)
         self.legend.get_frame().set_facecolor((1, 1, 1, 0.95))
 
     def add_cbar(self, label):
-        self.cbax_bg = inset_axes(self.canvas.axes, width="6%", height="75%", loc=5)
-        cbax = inset_axes(self.canvas.axes, width='2%', height='70%', loc=5)
-        cbar = self.canvas.axes.figure.colorbar(self.sc_picked, cax=cbax)
+        self.cbax_bg = inset_axes(self.ax, width="6%", height="75%", loc=5)
+        cbax = inset_axes(self.ax, width='2%', height='70%', loc=5)
+        cbar = self.ax.figure.colorbar(self.sc_picked, cax=cbax)
         cbar.set_label(label)
         cbax.yaxis.tick_left()
         cbax.yaxis.set_label_position('left')
@@ -632,7 +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):
@@ -55,6 +59,8 @@ class Metadata(object):
         :type path_to_inventory: str
         :return: None
         """
+        path_to_inventory = path_to_inventory.replace('\\', '/')
+        path_to_inventory = os.path.abspath(path_to_inventory)
         assert (os.path.isdir(path_to_inventory)), '{} is no directory'.format(path_to_inventory)
         if path_to_inventory not in self.inventories:
             self.inventories.append(path_to_inventory)
@@ -262,9 +268,6 @@ class Metadata(object):
         if not fnames:
             # search for station name in filename
             fnames = glob.glob(os.path.join(path_to_inventory, '*' + station + '*'))
-        if not fnames:
-            # search for network name in filename
-            fnames = glob.glob(os.path.join(path_to_inventory, '*' + network + '*'))
         if not fnames:
             if self.verbosity:
                 print('Could not find filenames matching station name, network name or seed id')
@@ -276,7 +279,7 @@ class Metadata(object):
                     continue
             invtype, robj = self._read_metadata_file(os.path.join(path_to_inventory, fname))
             try:
-                # robj.get_coordinates(station_seed_id)  # TODO: Commented out, failed with Parser, is this needed?
+                robj.get_coordinates(station_seed_id)
                 self.inventory_files[fname] = {'invtype': invtype,
                                                'data': robj}
                 if station_seed_id in self.seed_ids.keys():
@@ -284,6 +287,7 @@ class Metadata(object):
                 self.seed_ids[station_seed_id] = fname
                 return True
             except Exception as e:
+                logging.warning(e)
                 continue
         print('Could not find metadata for station_seed_id {} in path {}'.format(station_seed_id, path_to_inventory))
 
@@ -648,6 +652,8 @@ def restitute_data(data, metadata, unit='VEL', force=False, ncores=0):
     """
 
     # data = remove_underscores(data)
+    if not data:
+        return
 
     # loop over traces
     input_tuples = []
@@ -655,9 +661,14 @@ def restitute_data(data, metadata, unit='VEL', force=False, ncores=0):
         input_tuples.append((tr, metadata, unit, force))
         data.remove(tr)
 
-    pool = gen_Pool(ncores)
-    result = pool.imap_unordered(restitute_trace, input_tuples)
-    pool.close()
+    if ncores == 0:
+        result = []
+        for input_tuple in input_tuples:
+            result.append(restitute_trace(input_tuple))
+    else:
+        pool = gen_Pool(ncores)
+        result = pool.imap_unordered(restitute_trace, input_tuples)
+        pool.close()
 
     for tr, remove_trace in result:
         if not remove_trace:

pylot/core/util/event.py

@@ -22,14 +22,11 @@ class Event(ObsPyEvent):
         :param path: path to event directory
         :type path: str
         """
-        # TODO: remove rootpath and database
         self.pylot_id = path.split('/')[-1]
         # initialize super class
         super(Event, self).__init__(resource_id=ResourceIdentifier('smi:local/' + self.pylot_id))
         self.path = path
-        self.database = path.split('/')[-2]
         self.datapath = os.path.split(path)[0]  # path.split('/')[-3]
-        self.rootpath = '/' + os.path.join(*path.split('/')[:-3])
         self.pylot_autopicks = {}
         self.pylot_picks = {}
         self.notes = ''

pylot/core/util/gui.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 import os
+from functools import lru_cache
 
 try:
     import pyqtgraph as pg
@@ -25,14 +26,14 @@ def pick_linestyle_pg(picktype, key):
     :return: Qt line style parameters
     :rtype:
     """
-    linestyles_manu = {'mpp': (QtCore.Qt.SolidLine, 2.),
-                       'epp': (QtCore.Qt.DashLine, 1.),
-                       'lpp': (QtCore.Qt.DashLine, 1.),
-                       'spe': (QtCore.Qt.DashLine, 1.)}
-    linestyles_auto = {'mpp': (QtCore.Qt.DotLine, 2.),
-                       'epp': (QtCore.Qt.DashDotLine, 1.),
-                       'lpp': (QtCore.Qt.DashDotLine, 1.),
-                       'spe': (QtCore.Qt.DashDotLine, 1.)}
+    linestyles_manu = {'mpp': (QtCore.Qt.SolidLine, 2),
+                       'epp': (QtCore.Qt.DashLine, 1),
+                       'lpp': (QtCore.Qt.DashLine, 1),
+                       'spe': (QtCore.Qt.DashLine, 1)}
+    linestyles_auto = {'mpp': (QtCore.Qt.DotLine, 2),
+                       'epp': (QtCore.Qt.DashDotLine, 1),
+                       'lpp': (QtCore.Qt.DashDotLine, 1),
+                       'spe': (QtCore.Qt.DashDotLine, 1)}
     linestyles = {'manual': linestyles_manu,
                   'auto': linestyles_auto}
     return linestyles[picktype][key]
@@ -80,6 +81,7 @@ def which(program, parameter):
     return None
 
 
+@lru_cache(maxsize=128)
 def make_pen(picktype, phase, key, quality):
     """
     Make  PyQtGraph.QPen

pylot/core/util/utils.py

@@ -51,7 +51,6 @@ def readDefaultFilterInformation():
     :rtype: dict
     """
     pparam = PylotParameter()
-    pparam.reset_defaults()
     return readFilterInformation(pparam)
 
 
@@ -1076,7 +1075,7 @@ 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 = []
@@ -1084,8 +1083,9 @@ def check4rotated(data, metadata=None, verbosity=1):
                 azimuths.append(metadata.get_coordinates(tr_id, t_start)['azimuth'])
                 dips.append(metadata.get_coordinates(tr_id, t_start)['dip'])
         except (KeyError, TypeError) as err:
-            logging.error(f"{type(err)=} occurred: {err=} Rotating not possible, not all azimuth and dip information "
-                  f"available in metadata. Stream remains unchanged.")
+            logging.warning(f"Rotating not possible, not all azimuth and dip information "
+                            f"available in metadata. Stream remains unchanged.")
+            logging.debug(f"Rotating not possible, {err=}, {type(err)=}")
             return wfs_in
         except Exception as err:
             print(f"Unexpected {err=}, {type(err)=}")

pylot/core/util/widgets.py

@@ -8,6 +8,7 @@ import copy
 import datetime
 import getpass
 import glob
+import logging
 import multiprocessing
 import os
 import subprocess
@@ -1870,13 +1871,14 @@ class PickDlg(QDialog):
 
     def __init__(self, parent=None, data=None, data_compare=None, station=None, network=None, location=None, picks=None,
                  autopicks=None, rotate=False, parameter=None, embedded=False, metadata=None, show_comp_data=False,
-                 event=None, filteroptions=None, model=None, wftype=None):
+                 event=None, filteroptions=None, wftype=None):
         super(PickDlg, self).__init__(parent, Qt.Window)
         self.orig_parent = parent
         self.setAttribute(Qt.WA_DeleteOnClose)
 
         # initialize attributes
         self.parameter = parameter
+        model = self.parameter.get('taup_model')
         self._embedded = embedded
         self.showCompData = show_comp_data
         self.station = station
@@ -2269,8 +2271,8 @@ class PickDlg(QDialog):
         arrivals = func[plot](source_origin.depth,
                               source_origin.latitude,
                               source_origin.longitude,
-                              station_coords['latitude'],
-                              station_coords['longitude'],
+                              station_coords.get('latitude'),
+                              station_coords.get('longitude'),
                               phases)
         self.arrivals = arrivals
 
@@ -3835,7 +3837,7 @@ class TuneAutopicker(QWidget):
         self.stb_names = ['aicARHfig', 'refSpick', 'el_S1pick', 'el_S2pick']
 
     def add_parameters(self):
-        self.paraBox = PylotParaBox(self.parameter, parent=self, windowflag=Qt.Widget)
+        self.paraBox = PylotParameterWidget(self.parameter, parent=self, windowflag=Qt.Widget)
         self.paraBox.set_tune_mode(True)
         self.update_eventID()
         self.parameter_layout.addWidget(self.paraBox)
@@ -4202,7 +4204,7 @@ class TuneAutopicker(QWidget):
         self.qmb.show()
 
 
-class PylotParaBox(QtWidgets.QWidget):
+class PylotParameterWidget(QtWidgets.QWidget):
     accepted = QtCore.Signal(str)
     rejected = QtCore.Signal(str)
 
@@ -4316,6 +4318,11 @@ class PylotParaBox(QtWidgets.QWidget):
         grid = QtWidgets.QGridLayout()
 
         for index1, name in enumerate(parameter_names):
+            if name in ['rootpath', 'database']:
+                logging.warning(
+                    f'Deprecated parameter loaded: {name}. Check if datapath is still correct in parameter widget.'
+                )
+                continue
             default_item = self.parameter.get_defaults()[name]
             tooltip = default_item['tooltip']
             tooltip += ' | type: {}'.format(default_item['type'])
@@ -4885,7 +4892,7 @@ class PropTab(QWidget):
     def getValues(self):
         return None
 
-    def resetValues(self, infile=None):
+    def resetValues(self, infile):
         return None
 
 
@@ -4982,12 +4989,7 @@ class InputsTab(PropTab):
         else:
             index = 2
         datapath = para.get('datapath') if not para.get('datapath') is None else ''
-        rootpath = para.get('rootpath') if not para.get('rootpath') is None else ''
-        database = para.get('database') if not para.get('database') is None else ''
-        if isinstance(database, int):
-            database = str(database)
-        path = os.path.join(os.path.expanduser('~'), rootpath, datapath, database)
-        values = {"data/dataRoot": self.dataDirEdit.setText("%s" % path),
+        values = {"data/dataRoot": self.dataDirEdit.setText("%s" % datapath),
                   "user/FullName": self.fullNameEdit.text(),
                   "data/Structure": self.structureSelect.setCurrentIndex(index),
                   "tstart": self.tstartBox.setValue(0),

pylot/correlation/__init__.py

@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+#

pylot/correlation/parameters_adriaarray.yaml

@@ -0,0 +1,95 @@
+#############################  correlation parameters  #####################################
+# min_corr_stacking: minimum correlation coefficient for building beam trace
+# min_corr_export: minimum correlation coefficient for pick export
+# min_stack: minimum number of stations for building beam trace
+# t_before: correlation window before pick
+# t_after: correlation window after pick#
+# cc_maxlag: maximum shift for initial correlation
+# cc_maxlag2: maximum shift for second (final) correlation (also for calculating pick uncertainty)
+# initial_pick_outlier_threshold: (hopefully) threshold for excluding large outliers of initial (AIC) picks
+# export_threshold: automatically exclude all onsets which deviate more than this threshold from corrected taup onsets
+# min_picks_export: minimum number of correlated picks for export
+# min_picks_autopylot: minimum number of reference auto picks to continue with event
+# check_RMS: do RMS check to search for restitution errors (very experimental)
+# use_taupy_onsets: use taupy onsets as reference picks instead of external picks
+# station_list: use the following stations as reference for stacking
+# use_stacked_trace: use existing stacked trace if found (spare re-computation)
+# data_dir: obspyDMT data subdirectory (e.g. 'raw', 'processed')
+# pickfile_extension: use quakeML files (PyLoT output) with the following extension, e.g. '_autopylot' for pickfiles
+#                     such as 'PyLoT_20170501_141822_autopylot.xml'
+# dt_stacking: time shift for stacking (e.g. [0, 250] for 0 and 250 seconds shift)
+# filter_options: filter for first correlation (rough)
+# filter_options_final: filter for second correlation (fine)
+# filter_type: e.g. 'bandpass'
+# sampfreq: sampling frequency of the data
+
+logging: info
+pick_phases: ['P', 'S']
+
+# P-phase
+P:
+  min_corr_stacking: 0.8
+  min_corr_export: 0.6
+  min_stack: 20
+  t_before: 30.
+  t_after: 50.
+  cc_maxlag: 50.
+  cc_maxlag2: 5.
+  initial_pick_outlier_threshold: 30.
+  export_threshold: 2.5
+  min_picks_export: 100
+  min_picks_autopylot: 50
+  check_RMS: True
+  use_taupy_onsets: False
+  station_list: ['HU.MORH', 'HU.TIH',  'OX.FUSE', 'OX.BAD']
+  use_stacked_trace: False
+  data_dir: 'processed'
+  pickfile_extension: '_autopylot'
+  dt_stacking: [250, 250]
+
+  # filter for first correlation (rough)
+  filter_options:
+    freqmax: 0.5
+    freqmin: 0.03
+  # filter for second correlation (fine)
+  filter_options_final:
+    freqmax: 0.5
+    freqmin: 0.03
+
+  filter_type: bandpass
+  sampfreq: 20.0
+
+# S-phase
+S:
+  min_corr_stacking: 0.7
+  min_corr_export: 0.6
+  min_stack: 20
+  t_before: 60.
+  t_after: 60.
+  cc_maxlag: 100.
+  cc_maxlag2: 25.
+  initial_pick_outlier_threshold: 30.
+  export_threshold: 5.0
+  min_picks_export: 200
+  min_picks_autopylot: 50
+  check_RMS: True
+  use_taupy_onsets: False
+  station_list: ['HU.MORH','HU.TIH',  'OX.FUSE', 'OX.BAD']
+  use_stacked_trace: False
+  data_dir: 'processed'
+  pickfile_extension: '_autopylot'
+  dt_stacking: [250, 250]
+
+  # filter for first correlation (rough)
+  filter_options:
+    freqmax: 0.1
+    freqmin: 0.01
+
+  # filter for second correlation (fine)
+  filter_options_final:
+    freqmax: 0.2
+    freqmin: 0.01
+
+  filter_type: bandpass
+  sampfreq: 20.0
+

pylot/correlation/submit_pick_corr_correction.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+#ulimit -s 8192
+#ulimit -v $(ulimit -v | awk '{printf("%d",$1*0.95)}')
+#ulimit -v
+
+#655360
+
+source /opt/anaconda3/etc/profile.d/conda.sh
+conda activate pylot_311
+NSLOTS=20
+
+#qsub -l low -cwd -l "os=*stretch" -pe smp 40 submit_pick_corr_correction.sh
+#$ -l low
+#$ -l h_vmem=6G
+#$ -cwd
+#$ -pe smp 20
+#$ -N corr_pick
+
+
+export PYTHONPATH="$PYTHONPATH:/home/marcel/git/pylot_tools/"
+export PYTHONPATH="$PYTHONPATH:/home/marcel/git/"
+export PYTHONPATH="$PYTHONPATH:/home/marcel/git/pylot/"
+
+#export MKL_NUM_THREADS=${NSLOTS:=1}
+#export NUMEXPR_NUM_THREADS=${NSLOTS:=1}
+#export OMP_NUM_THREADS=${NSLOTS:=1}
+
+#python pick_correlation_correction.py '/data/AlpArray_Data/dmt_database_mantle_M5.8-6.0' '/home/marcel/.pylot/pylot_alparray_mantle_corr_stack_0.03-0.5.in' -pd -n ${NSLOTS:=1} -istart 0 -istop 100
+#python pick_correlation_correction.py '/data/AlpArray_Data/dmt_database_mantle_M5.8-6.0' '/home/marcel/.pylot/pylot_alparray_mantle_corr_stack_0.03-0.5.in' -pd -n ${NSLOTS:=1} -istart 100 -istop 200
+#python pick_correlation_correction.py '/data/AlpArray_Data/dmt_database_mantle_M6.0-6.5' '/home/marcel/.pylot/pylot_alparray_mantle_corr_stack_0.03-0.5.in' -pd -n ${NSLOTS:=1} -istart 0 -istop 100
+#python pick_correlation_correction.py '/data/AlpArray_Data/dmt_database_mantle_M5.8-6.0' '/home/marcel/.pylot/pylot_alparray_mantle_corr_stack_0.03-0.5.in' -pd -n ${NSLOTS:=1} -istart 100 -istop 200
+#python pick_correlation_correction.py 'H:\sciebo\dmt_database' 'H:\Sciebo\dmt_database\pylot_alparray_mantle_corr_S_0.01-0.2.in' -pd -n 4 -t
+
+pylot_infile='/home/marcel/.pylot/pylot_alparray_syn_fwi_mk6_it3.in'
+#pylot_infile='/home/marcel/.pylot/pylot_adriaarray_corr_P_and_S.in'
+
+# THIS SCRIPT SHOLD BE CALLED BY "submit_to_grid_engine.py" using the following line:
+python pick_correlation_correction.py $1 $pylot_infile -pd -n ${NSLOTS:=1} -istart $2 --params 'parameters_fwi_mk6_it3.yaml'
+#--event_blacklist eventlist.txt

pylot/correlation/submit_to_grid_engine.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+
+import subprocess
+
+fnames = [
+    ('/data/AlpArray_Data/dmt_database_synth_model_mk6_it3_no_rotation', 0),
+          ]
+
+#fnames = [('/data/AlpArray_Data/dmt_database_mantle_0.01-0.2_SKS-phase', 0),
+#          ('/data/AlpArray_Data/dmt_database_mantle_0.01-0.2_S-phase', 0),]
+
+####
+script_location = '/home/marcel/VersionCtrl/git/code_base/correlation_picker/submit_pick_corr_correction.sh'
+####
+
+for fnin, istart in fnames:
+    input_cmds = f'qsub -q low.q@minos15,low.q@minos14,low.q@minos13,low.q@minos12,low.q@minos11 {script_location} {fnin} {istart}'
+
+    print(input_cmds)
+    print(subprocess.check_output(input_cmds.split()))
+
+
+

pylot/correlation/utils.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import glob
+import json
+
+from obspy import read_events
+
+from pylot.core.util.dataprocessing import Metadata
+from pylot.core.util.obspyDMT_interface import qml_from_obspyDMT
+
+
+def get_event_obspy_dmt(eventdir):
+    event_pkl_file = os.path.join(eventdir, 'info', 'event.pkl')
+    if not os.path.exists(event_pkl_file):
+        raise IOError('Could not find event path for event: {}'.format(eventdir))
+    event = qml_from_obspyDMT(event_pkl_file)
+    return event
+
+
+def get_event_pylot(eventdir, extension=''):
+    event_id = get_event_id(eventdir)
+    filename = os.path.join(eventdir, 'PyLoT_{}{}.xml'.format(event_id, extension))
+    if not os.path.isfile(filename):
+        return
+    cat = read_events(filename)
+    return cat[0]
+
+
+def get_event_id(eventdir):
+    event_id = os.path.split(eventdir)[-1]
+    return event_id
+
+
+def get_picks(eventdir, extension=''):
+    event_id = get_event_id(eventdir)
+    filename = 'PyLoT_{}{}.xml'
+    filename = filename.format(event_id, extension)
+    fpath = os.path.join(eventdir, filename)
+    fpaths = glob.glob(fpath)
+    if len(fpaths) == 1:
+        cat = read_events(fpaths[0])
+        picks = cat[0].picks
+        return picks
+    elif len(fpaths) == 0:
+        print('get_picks: File not found: {}'.format(fpath))
+        return
+    print(f'WARNING: Ambiguous pick file specification. Found the following pick files {fpaths}\nFilemask: {fpath}')
+    return
+
+
+def write_json(object, fname):
+    with open(fname, 'w') as outfile:
+        json.dump(object, outfile, sort_keys=True, indent=4)
+
+
+def get_metadata(eventdir):
+    metadata_path = os.path.join(eventdir, 'resp')
+    metadata = Metadata(inventory=metadata_path, verbosity=0)
+    return metadata

requirements.txt

@@ -1,12 +1,7 @@
-# This file may be used to create an environment using:
-# $ conda create --name <env> --file <this file>
-# platform: win-64
-cartopy=0.20.2
-matplotlib-base=3.3.4
-numpy=1.22.3
-obspy=1.3.0
-pyqtgraph=0.12.4
-pyside2=5.13.2
-python=3.8.12
-qt=5.12.9
-scipy=1.8.0
+Cartopy==0.23.0
+joblib==1.4.2
+obspy==1.4.1
+pyaml==24.7.0
+pyqtgraph==0.13.7
+PySide2==5.15.8
+pytest==8.3.2

tests/test_autopicker/pylot_alparray_mantle_corr_stack_0.03-0.5.in

@@ -0,0 +1,99 @@
+%This is a parameter input file for PyLoT/autoPyLoT.
+%All main and special settings regarding data handling
+%and picking are to be set here!
+%Parameters are optimized for %extent data sets!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#main settings#
+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                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#NLLoc settings#
+None                                               #nllocbin#       %path to NLLoc executable                                                                            
+None                                               #nllocroot#      %root of NLLoc-processing directory                                                                  
+None                                               #phasefile#      %name of autoPyLoT-output phase file for NLLoc                                                       
+None                                               #ctrfile#        %name of autoPyLoT-output control file for NLLoc                                                     
+ttime                                              #ttpatter#       %pattern of NLLoc ttimes from grid                                                                   
+AUTOLOC_nlloc                                      #outpatter#      %pattern of NLLoc-output file                                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#parameters for seismic moment estimation#
+3530.0                                             #vp#             %average P-wave velocity                                                                             
+2500.0                                             #rho#            %average rock density [kg/m^3]                                                                       
+300.0 0.8                                          #Qp#             %quality factor for P waves (Qp*f^a); list(Qp, a)                                                    
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#settings local magnitude#
+1.0 1.0 1.0                                        #WAscaling#      %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm]                           If zeros are set, original Richter magnitude is calculated!
+1.0 1.0                                            #magscaling#     %Scaling relation for derived local magnitude [a*Ml+b].                            If zeros are set, no scaling of network magnitude is applied!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#filter settings#
+0.03 0.03                                          #minfreq#        %Lower filter frequency [P, S]                                                                       
+0.5 0.5                                            #maxfreq#        %Upper filter frequency [P, S]                                                                       
+4 4                                                #filter_order#   %filter order [P, S]                                                                                 
+bandpass bandpass                                  #filter_type#    %filter type (bandpass, bandstop, lowpass, highpass) [P, S]                                          
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#common settings picker#
+global                                             #extent#         %extent of array ("local", "regional" or "global")                                                   
+-100.0                                             #pstart#         %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)     
+50.0                                               #pstop#          %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)       
+-50.0                                              #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
+50.0                                               #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                                 
+ak135                                              #taup_model#     %Define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
+P,Pdiff,S,SKS                                      #taup_phases#    %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
+0.03 0.5                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
+0.01 0.5                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
+0.03 0.5                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     
+0.01 0.5                                           #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
+#special settings for calculating CF#
+%!!Edit the following only if you know what you are doing!!%
+#Z-component#
+HOS                                                #algoP#          %choose algorithm for P-onset determination (HOS, ARZ, or AR3)                                       
+300.0                                              #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
+4                                                  #hosorder#       %for HOS-picker, order of Higher Order Statistics                                                    
+2                                                  #Parorder#       %for AR-picker, order of AR process of Z-component                                                   
+16.0                                               #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
+10.0                                               #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
+12.0                                               #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
+6.0                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
+0.001                                              #addnoise#       %add noise to seismogram for stable AR prediction                                                    
+60.0 5.0 20.0 12.0                                 #tsnrz#          %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]     
+50.0                                               #pickwinP#       %for initial AIC pick, length of P-pick window [s]                                                   
+30.0                                               #Precalcwin#     %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)                        
+2.0                                                #aictsmooth#     %for HOS/AR, take average of samples for smoothing of AIC-function [s]                               
+2.0                                                #tsmoothP#       %for HOS/AR, take average of samples in this time window for smoothing CF [s]                        
+0.006                                              #ausP#           %for HOS/AR, artificial uplift of samples (aus) of CF (P)                                            
+2.0                                                #nfacP#          %for HOS/AR, noise factor for noise level determination (P)                                          
+#H-components#
+ARH                                                #algoS#          %choose algorithm for S-onset determination (ARH or AR3)                                             
+12.0                                               #tdet1h#         %for HOS/AR, length of AR-determination window [s], H-components, 1st pick                           
+6.0                                                #tpred1h#        %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick                              
+8.0                                                #tdet2h#         %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick                            
+4.0                                                #tpred2h#        %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick                              
+4                                                  #Sarorder#       %for AR-picker, order of AR process of H-components                                                  
+100.0                                              #Srecalcwin#     %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)                             
+195.0                                              #pickwinS#       %for initial AIC pick, length of S-pick window [s]                                                   
+60.0 10.0 30.0 12.0                                #tsnrh#          %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]    
+22.0                                               #aictsmoothS#    %for AIC-picker, take average of samples in this time window for smoothing of AIC-function [s]       
+20.0                                               #tsmoothS#       %for AR-picker, take average of samples for smoothing CF [s] (S)                                     
+0.001                                              #ausS#           %for HOS/AR, artificial uplift of samples (aus) of CF (S)                                            
+2.0                                                #nfacS#          %for AR-picker, noise factor for noise level determination (S)                                       
+#first-motion picker#
+1                                                  #minfmweight#    %minimum required P weight for first-motion determination                                            
+3.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
+10.0                                               #fmpickwin#      %pick window [s] around P onset for calculating zero crossings                                       
+#quality assessment#
+0.1 0.2 0.4 0.8                                    #timeerrorsP#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P                           
+4.0 8.0 16.0 32.0                                  #timeerrorsS#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S                           
+0.005                                              #minAICPslope#   %below this slope [counts/s] the initial P pick is rejected                                          
+1.1                                                #minAICPSNR#     %below this SNR the initial P pick is rejected                                                       
+0.002                                              #minAICSslope#   %below this slope [counts/s] the initial S pick is rejected                                          
+1.3                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
+20.0                                               #minsiglength#   %length of signal part for which amplitudes must exceed noiselevel [s]                               
+1.0                                                #noisefactor#    %noiselevel*noisefactor=threshold                                                                    
+10.0                                               #minpercent#     %required percentage of amplitudes exceeding threshold                                               
+0.1                                                #zfac#           %P-amplitude must exceed at least zfac times RMS-S amplitude                                         
+100.0                                              #mdttolerance#   %maximum allowed deviation of P picks from median [s]                                                
+50.0                                               #wdttolerance#   %maximum allowed deviation from Wadati-diagram                                                       
+25.0                                               #jackfactor#     %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor

tests/test_autopicker/test_autopylot.py

@@ -0,0 +1,67 @@
+import os
+import pytest
+
+from obspy import read_events
+
+from autoPyLoT import autoPyLoT
+
+
+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
+
+        if not os.path.exists(self.test_event_dir):
+            print(
+                f'Test event directory not found at location "{os.path.abspath(self.test_event_dir)}". '
+                f'Make sure to load it from the website first.'
+            )
+            return False
+
+        return True
+
+    def test_autopicker(self):
+        assert self.init(), 'Initialization failed due to missing input files.'
+        # 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

@@ -4,10 +4,8 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-/home/darius                                       #rootpath#       %project path                                                                                        
-alparray                                           #datapath#       %data path                                                                                           
-waveforms_used                                     #database#       %name of data base                                                                                   
-e0093.173.16                                       #eventID#        %event ID for single event processing (* for all events found in database)                           
+/home/darius/alparray/waveforms_used               #datapath#       %data path
+e0093.173.16                                       #eventID#        %event ID for single event processing (* for all events found in datapath)
 /home/darius/alparray/metadata                     #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
@@ -43,6 +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

@@ -4,10 +4,8 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-/home/darius                                       #rootpath#       %project path                                                                                        
-alparray                                           #datapath#       %data path                                                                                           
-waveforms_used                                     #database#       %name of data base                                                                                   
-e0093.173.16                                       #eventID#        %event ID for single event processing (* for all events found in database)                           
+/home/darius/alparray/waveforms_used               #datapath#       %data path
+e0093.173.16                                       #eventID#        %event ID for single event processing (* for all events found in datapath)
 /home/darius/alparray/metadata                     #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
@@ -43,6 +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()

tests/test_pick_correlation_correction/test_pick_correlation_correction.py

@@ -0,0 +1,76 @@
+import pytest
+from obspy import read, Trace, UTCDateTime
+
+from pylot.correlation.pick_correlation_correction import XCorrPickCorrection
+
+
+class TestXCorrPickCorrection():
+    def setup(self):
+        self.make_test_traces()
+        self.make_test_picks()
+        self.t_before = 2.
+        self.t_after = 2.
+        self.cc_maxlag = 0.5
+
+    def make_test_traces(self):
+        # take first trace of test Stream from obspy
+        tr1 = read()[0]
+        # filter trace
+        tr1.filter('bandpass', freqmin=1, freqmax=20)
+        # make a copy and shift the copy by 0.1 s
+        tr2 = tr1.copy()
+        tr2.stats.starttime += 0.1
+
+        self.trace1 = tr1
+        self.trace2 = tr2
+
+    def make_test_picks(self):
+        # create an artificial reference pick on reference trace (trace1) and another one on the 0.1 s shifted trace
+        self.tpick1 = UTCDateTime('2009-08-24T00:20:07.7')
+        # shift the second pick by 0.2 s, the correction should be around 0.1 s now
+        self.tpick2 = self.tpick1 + 0.2
+
+    def test_slice_trace_okay(self):
+
+        self.setup()
+        xcpc = XCorrPickCorrection(UTCDateTime(), Trace(), UTCDateTime(), Trace(),
+                                   t_before=self.t_before, t_after=self.t_after, cc_maxlag=self.cc_maxlag)
+
+        test_trace = self.trace1
+        pick_time = self.tpick2
+
+        sliced_trace = xcpc.slice_trace(test_trace, pick_time)
+        assert ((sliced_trace.stats.starttime == pick_time - self.t_before - self.cc_maxlag / 2)
+                and (sliced_trace.stats.endtime == pick_time + self.t_after + self.cc_maxlag / 2))
+
+    def test_slice_trace_fails(self):
+        self.setup()
+
+        test_trace = self.trace1
+        pick_time = self.tpick1
+
+        with pytest.raises(ValueError):
+            xcpc = XCorrPickCorrection(UTCDateTime(), Trace(), UTCDateTime(), Trace(),
+                                       t_before=self.t_before + 20, t_after=self.t_after, cc_maxlag=self.cc_maxlag)
+            xcpc.slice_trace(test_trace, pick_time)
+
+        with pytest.raises(ValueError):
+            xcpc = XCorrPickCorrection(UTCDateTime(), Trace(), UTCDateTime(), Trace(),
+                                       t_before=self.t_before, t_after=self.t_after + 50, cc_maxlag=self.cc_maxlag)
+            xcpc.slice_trace(test_trace, pick_time)
+
+    def test_cross_correlation(self):
+        self.setup()
+
+        # create XCorrPickCorrection object
+        xcpc = XCorrPickCorrection(self.tpick1, self.trace1, self.tpick2, self.trace2, t_before=self.t_before,
+                                   t_after=self.t_after, cc_maxlag=self.cc_maxlag)
+
+        # execute correlation
+        correction, cc_max, uncert, fwfm = xcpc.cross_correlation(False, '', '')
+
+        # define awaited test result
+        test_result = (-0.09983091718314982, 0.9578431835689154, 0.0015285160561610929, 0.03625786256084631)
+
+        # check results
+        assert pytest.approx(test_result, rel=1e-6) == (correction, cc_max, uncert, fwfm)