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1
.gitignore vendored
View File

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

39
.mailmap
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@ -1,39 +0,0 @@
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>

132
PyLoT.py
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@ -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, PylotParameterWidget, AutoPickDlg, CanvasWidget, AutoPickWidget, \
ComparisonWidget, TuneAutopicker, PylotParaBox, 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.parameterWidget = None
self.paraBox = None
self.array_map = None
self._metadata = Metadata(verbosity=0)
self._eventChanged = [False, False]
@ -188,6 +188,7 @@ 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:
@ -685,9 +686,10 @@ 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. If data were already loaded, try to select the event again in the eventbox.')
self.no_data_label = QLabel('No Data')
self.no_data_label.setStyleSheet('color: red')
self.no_data_label.setAlignment(Qt.AlignCenter)
# create central matplotlib figure canvas widget
self.init_wfWidget()
@ -716,14 +718,14 @@ 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()
@ -1011,7 +1013,7 @@ 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=False, merge_strategy=sld.merge_strategy)
self.load_data(filename, draw=False, event=event, ask_user=True, merge_strategy=sld.merge_strategy)
refresh = True
if not refresh:
return
@ -1020,8 +1022,8 @@ class MainWindow(QMainWindow):
self.fill_eventbox()
self.setDirty(True)
def load_data(self, fname=None, loc=False, draw=True, event=None, ask_user=True, merge_strategy='Overwrite',):
if ask_user:
def load_data(self, fname=None, loc=False, draw=True, event=None, ask_user=False, merge_strategy='Overwrite'):
if not ask_user:
if not self.okToContinue():
return
if fname is None:
@ -1030,24 +1032,8 @@ 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))
@ -1210,7 +1196,7 @@ 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 datapath. WILL ONLY USE SELECTED EVENTS out of {} events ' \
msg = 'Found file "eventlist.txt" in database path. 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]
@ -1235,34 +1221,49 @@ class MainWindow(QMainWindow):
# get path from first event in list and split them
path = eventlist[0]
try:
datapath = os.path.split(path)[0]
dirs = {
'datapath': datapath,
}
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)
}
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'])
settings.setValue("data/dataRoot", dirs['datapath']) # d irs['rootpath'])
settings.sync()
if not self.project.eventlist:
# init parameter object
self.setParameter(show=False)
# hide all parameter (show all needed parameter later)
self.parameterWidget.hide_parameter()
self.paraBox.hide_parameter()
for directory in dirs.keys():
# set parameter
box = self.parameterWidget.boxes[directory]
self.parameterWidget.setValue(box, dirs[directory])
box = self.paraBox.boxes[directory]
self.paraBox.setValue(box, dirs[directory])
# show needed parameter in box
self.parameterWidget.show_parameter(directory)
dirs_box = self.parameterWidget.get_groupbox_dialog('Directories')
self.paraBox.show_parameter(directory)
dirs_box = self.paraBox.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'):
@ -1271,6 +1272,7 @@ 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)
@ -1358,10 +1360,11 @@ class MainWindow(QMainWindow):
return True
def modify_project_path(self, new_rootpath):
self.project.datapath = new_rootpath
# TODO: change root to datapath
self.project.rootpath = new_rootpath
for event in self.project.eventlist:
event.datapath = new_rootpath
event.path = os.path.join(event.datapath, event.pylot_id)
event.rootpath = new_rootpath
event.path = os.path.join(event.rootpath, event.datapath, event.database, event.pylot_id)
event.path = event.path.replace('\\', '/')
event.path = event.path.replace('//', '/')
@ -1555,7 +1558,7 @@ class MainWindow(QMainWindow):
self.set_fname(self.get_data().getEventFileName(), type)
return self.get_fnames(type)
def saveData(self, event=None, directory=None, outformats=None):
def saveData(self, event=None, directory=None, outformats=['.xml', '.cnv', '.obs', '_focmec.in', '.pha']):
'''
Save event data to directory with specified output formats.
:param event: PyLoT Event, if not set current event will be used
@ -1563,8 +1566,6 @@ 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:
@ -1700,7 +1701,7 @@ class MainWindow(QMainWindow):
# WIP JG
def eventlistXml(self):
path = self._inputs['datapath']
path = self._inputs['rootpath'] + '/' + self._inputs['datapath'] + '/' + self._inputs['database']
outpath = self.project.location[:self.project.location.rfind('/')]
geteventlistfromxml(path, outpath)
return
@ -1976,6 +1977,7 @@ class MainWindow(QMainWindow):
self.dataPlot.activateObspyDMToptions(self.obspy_dmt)
if self.obspy_dmt:
self.prepareObspyDMT_data(eventpath)
self.dataPlot.activateCompareOptions(True)
def loadWaveformData(self):
'''
@ -2152,11 +2154,10 @@ 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(np.array(times), np.array(data),
pen=self.dataPlot.pen_linecolor,
skipFiniteCheck=True)
self.dataPlot.plotWidget.getPlotItem().plot(times, data,
pen=self.dataPlot.pen_linecolor)
if len(data_syn) > 0:
self.dataPlot.plotWidget.getPlotItem().plot(np.array(times_syn), np.array(data_syn),
self.dataPlot.plotWidget.getPlotItem().plot(times_syn, data_syn,
pen=self.dataPlot.pen_linecolor_syn)
self.dataPlot.reinitMoveProxy()
self.highlight_stations()
@ -2196,8 +2197,7 @@ class MainWindow(QMainWindow):
if event.pylot_autopicks:
self.drawPicks(picktype='auto')
if event.pylot_picks or event.pylot_autopicks:
if not self._inputs.get('extent') == 'global':
self.locateEventAction.setEnabled(True)
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.parameterWidget.params_to_gui()
self.paraBox.params_to_gui()
def getFilterOptions(self):
return self.filteroptions
@ -2632,6 +2632,7 @@ 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():
@ -3096,7 +3097,7 @@ class MainWindow(QMainWindow):
if self.pg:
if spe:
if not self.plot_method == 'fast' and picks['epp'] and picks['lpp']:
if 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'))
@ -3175,8 +3176,8 @@ class MainWindow(QMainWindow):
ttt = parameter['ttpatter']
outfile = parameter['outpatter']
eventname = self.get_current_event_name()
obsdir = os.path.join(self._inputs['datapath'], eventname)
self.saveData(event=self.get_current_event(), directory=obsdir, outformats=['.obs'])
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')
filename = 'PyLoT_' + eventname
locpath = os.path.join(locroot, 'loc', filename)
phasefile = os.path.join(obsdir, filename + '.obs')
@ -3590,7 +3591,7 @@ class MainWindow(QMainWindow):
def calc_magnitude(self):
self.init_metadata()
if not self.metadata:
return []
return None
wf_copy = self.get_data().getWFData().copy()
@ -3599,10 +3600,6 @@ 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'),
@ -3737,7 +3734,6 @@ 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()
@ -3855,13 +3851,13 @@ 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.parameterWidget:
self.parameterWidget = PylotParameterWidget(self._inputs, parent=self, windowflag=Qt.Window)
self.parameterWidget.accepted.connect(self._setDirty)
self.parameterWidget.accepted.connect(self.filterOptionsFromParameter)
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 show:
self.parameterWidget.params_to_gui()
self.parameterWidget.show()
self.paraBox.params_to_gui()
self.paraBox.show()
def deleteAllAutopicks(self):
qmb = QMessageBox(self, icon=QMessageBox.Question,
@ -3908,18 +3904,16 @@ 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.
@ -3929,6 +3923,8 @@ 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)

47
README.md
View File

@ -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, AlpArray and AdriaArray.
The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
## Installation
@ -27,44 +27,58 @@ Afterwards run (from the PyLoT main directory where the files *requirements.txt*
conda env create -f pylot.yml
or
conda create -c conda-forge --name pylot_311 python=3.11 --file requirements.txt
conda create --name pylot_38 --file requirements.txt
to create a new Anaconda environment called *pylot_311*.
to create a new Anaconda environment called "pylot_38".
Afterwards activate the environment by typing
conda activate pylot_311
conda activate pylot_38
#### Prerequisites:
In order to run PyLoT you need to install:
- Python 3
- cartopy
- joblib
- obspy
- pyaml
- pyqtgraph
- pyside2
- pyqtgraph
- cartopy
(the following are already dependencies of the above packages):
- scipy
- numpy
- matplotlib
- matplotlib <= 3.3.x
#### Some handwork:
Some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling
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
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 10/11.
## 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
PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
## Release notes
@ -73,7 +87,6 @@ An example dataset with waveform data, metadata and automatic picks in the obspy
- 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
@ -82,7 +95,7 @@ An example dataset with waveform data, metadata and automatic picks in the obspy
#### Known issues:
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.
We hope to solve these with the next release.
## Staff
@ -95,4 +108,4 @@ Others: A. Bruestle, T. Meier, W. Friederich
[ObsPy]: http://github.com/obspy/obspy/wiki
September 2024
April 2022

25
autoPyLoT.py
View File

@ -136,9 +136,11 @@ 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 = {'dpath': parameter.get('datapath'),}
dsfields = {'root': parameter.get('rootpath'),
'dpath': parameter.get('datapath'),
'dbase': parameter.get('database')}
exf = ['dpath']
exf = ['root', 'dpath', 'dbase']
if parameter['eventID'] != '*' and fnames == 'None':
dsfields['eventID'] = parameter['eventID']
@ -184,15 +186,15 @@ 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 in [None, 'None'] and parameter['eventID'] == '*':
if fnames == '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 in [None, 'None'] and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
elif fnames == 'None' and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
# single event processing
events = glob.glob(os.path.join(datapath, parameter['eventID']))
elif fnames in [None, 'None'] and type(parameter['eventID']) == list:
elif fnames == 'None' and type(parameter['eventID']) == list:
# multiple event processing
events = []
for eventID in parameter['eventID']:
@ -204,10 +206,12 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
locflag = 2
else:
# started in tune or interactive mode
datapath = parameter['datapath']
datapath = os.path.join(parameter['rootpath'],
parameter['datapath'])
events = []
for eventID in eventid:
events.append(os.path.join(datapath,
parameter['database'],
eventID))
if not events:
@ -234,15 +238,12 @@ 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:
if type(e) == FileNotFoundError:
print('Creating new event file.')
else:
print('Could not read event from file {}: {}'.format(filename, e))
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 in [None, 'None']:
data.setWFData(glob.glob(os.path.join(event_datapath, '*')))
if fnames == 'None':
data.setWFData(glob.glob(os.path.join(datapath, event_datapath, '*')))
# the following is necessary because within
# multiple event processing no event ID is provided
# in autopylot.in

77
docs/correlation.md
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@ -1,77 +0,0 @@
# 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

2
docs/gui.md
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@ -203,6 +203,8 @@ 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

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199
docs/tuning.md
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@ -6,123 +6,122 @@ 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. |
| *
@ -140,12 +139,12 @@ 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. |

7
inputs/pylot_global.in
View File

@ -4,8 +4,10 @@
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
#datapath# %data path
#eventID# %event ID for single event processing (* for all events found in datapath)
#rootpath# %project path
#datapath# %data path
#database# %name of data base
#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
@ -41,7 +43,6 @@ 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]

9
inputs/pylot_local.in
View File

@ -4,8 +4,10 @@
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/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 #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/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
@ -40,8 +42,7 @@ 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
P #taup_phases# %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
iasp91 #taup_model# %define TauPy model for traveltime estimation
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]

9
inputs/pylot_regional.in
View File

@ -4,8 +4,10 @@
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
#datapath# %data path
#eventID# %event ID for single event processing (* for all events found in datapath)
#rootpath# %project path
#datapath# %data path
#database# %name of data base
#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
@ -40,8 +42,7 @@ 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
P #taup_phases# %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
iasp91 #taup_model# %define TauPy model for traveltime estimation
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]

18
pylot.yml
View File

@ -1,12 +1,14 @@
name: pylot_311
name: pylot_38
channels:
- conda-forge
- defaults
dependencies:
- 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
- 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

2
pylot/__init__.py
View File

@ -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 Qt libraries to be installed first.
and the Qt4 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-

21
pylot/core/io/data.py
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@ -36,17 +36,8 @@ class Data(object):
loaded event. Container object holding, e.g. phase arrivals, etc.
"""
def __init__(self, parent=None, evtdata=None, picking_parameter=None):
def __init__(self, parent=None, evtdata=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:
@ -412,19 +403,23 @@ class Data(object):
not implemented: {1}'''.format(evtformat, e))
if fnext == '.cnv':
try:
velest.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
velest.export(picks_copy, fnout + fnext, 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:
focmec.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
parameter = PylotParameter()
logging.warning('Using default input parameter')
focmec.export(picks_copy, fnout + fnext, 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:
hypodd.export(picks_copy, fnout + fnext, self.picking_parameter, eventinfo=self.get_evt_data())
parameter = PylotParameter()
logging.warning('Using default input parameter')
hypodd.export(picks_copy, fnout + fnext, parameter, eventinfo=self.get_evt_data())
except KeyError as e:
raise KeyError('''{0} export format
not implemented: {1}'''.format(evtformat, e))

22
pylot/core/io/default_parameters.py
View File

@ -6,14 +6,24 @@ import numpy as np
Default parameters used for picking
"""
defaults = {'datapath': {'type': str,
'tooltip': 'path to eventfolders',
defaults = {'rootpath': {'type': str,
'tooltip': 'project path',
'value': '',
'namestring': 'Root path'},
'datapath': {'type': str,
'tooltip': 'data path',
'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 datapath)',
'value': '*',
'tooltip': 'event ID for single event processing (* for all events found in database)',
'value': '',
'namestring': 'Event ID'},
'extent': {'type': str,
@ -501,7 +511,7 @@ defaults = {'datapath': {'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': 'iasp91',
'value': None,
'namestring': 'TauPy model'},
'taup_phases': {'type': str,
@ -512,7 +522,9 @@ defaults = {'datapath': {'type': str,
settings_main = {
'dirs': [
'rootpath',
'datapath',
'database',
'eventID',
'invdir',
'datastructure',

36
pylot/core/io/inputs.py
View File

@ -1,7 +1,5 @@
#!/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
@ -53,16 +51,10 @@ 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)
@ -96,10 +88,10 @@ class PylotParameter(object):
return bool(self.__parameter)
def __getitem__(self, key):
if key in self.__parameter:
try:
return self.__parameter[key]
else:
logging.warning(f'{key} not found in PylotParameter')
except:
return None
def __setitem__(self, key, value):
try:
@ -426,28 +418,6 @@ 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):
'''

17
pylot/core/io/phases.py
View File

@ -278,6 +278,7 @@ 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)
@ -511,8 +512,8 @@ 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('datapath'), parameter.get('eventID')))
arrivals = chooseArrivals(arrivals)
(parameter.get('database'), parameter.get('eventID')))
arrivals = chooseArrivals(arrivals) # MP MP what is chooseArrivals? It is not defined anywhere
for key in arrivals:
# P onsets
if 'P' in arrivals[key]:
@ -664,8 +665,8 @@ 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('datapath'), parameter.get('eventID')))
arrivals = chooseArrivals(arrivals)
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
for key in arrivals:
# P onsets
if 'P' in arrivals[key] and arrivals[key]['P']['mpp'] is not None:
@ -756,11 +757,11 @@ 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) is False:
if isinstance(arrivals, dict) == False:
# convert pick object (PyLoT) into dictionary
evt = ope.Event(resource_id=eventinfo['resource_id'])
evt.picks = arrivals
arrivals = picksdict_from_picks(evt, parameter=parameter)
arrivals = picksdict_from_picks(evt)
# check for automatic and manual picks
# prefer manual picks
usedarrivals = chooseArrivals(arrivals)
@ -821,7 +822,7 @@ 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, parameter=parameter)
arrivals = picksdict_from_picks(evt)
# check for automatic and manual picks
# prefer manual picks
usedarrivals = chooseArrivals(arrivals)
@ -872,7 +873,7 @@ 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, parameter=parameter)
arrivals = picksdict_from_picks(evt)
# check for automatic and manual picks
# prefer manual picks
usedarrivals = chooseArrivals(arrivals)

67
pylot/core/pick/autopick.py
View File

@ -262,10 +262,6 @@ 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()
@ -447,15 +443,15 @@ class AutopickStation(object):
for arr in arrivals:
phases[identifyPhaseID(arr.phase.name)].append(arr)
# get first P and S onsets from arrivals list
arrival_time_p = 0
arrival_time_s = 0
estFirstP = 0
estFirstS = 0
if len(phases['P']) > 0:
arrP, arrival_time_p = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key=lambda t: t[1])
if len(phases['S']) > 0:
arrS, arrival_time_s = min([(arr, arr.time) for arr in phases['S']], key=lambda t: t[1])
arrS, estFirstS = 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(arrival_time_p, arrival_time_s))
return arrival_time_p, arrival_time_s
' origin time using TauPy'.format(estFirstP, estFirstS))
return estFirstP, estFirstS
def exit_taupy():
"""If taupy failed to calculate theoretical starttimes, picking continues.
@ -481,13 +477,10 @@ class AutopickStation(object):
raise AttributeError('No source origins given!')
arrivals = create_arrivals(self.metadata, self.origin, self.pickparams["taup_model"])
arrival_P, arrival_S = first_PS_onsets(arrivals)
self.estFirstP = (self.origin[0].time + arrival_P) - self.ztrace.stats.starttime
estFirstP, estFirstS = first_PS_onsets(arrivals)
# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
self.pickparams["pstart"] += self.estFirstP
self.pickparams["pstop"] += self.estFirstP
self.pickparams["pstart"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
self.pickparams["pstop"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
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
@ -498,10 +491,9 @@ 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.estFirstS
self.pickparams["sstop"] += self.estFirstS
self.pickparams["sstart"] += (self.origin[0].time + estFirstS) - trace_s_start
self.pickparams["sstop"] += (self.origin[0].time + estFirstS) - trace_s_start
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
@ -617,12 +609,6 @@ 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')
@ -727,15 +713,6 @@ 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])
@ -858,21 +835,14 @@ 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)
aiccf = AICcf(cf_stream, cuttimes)
z_copy[0].data = self.cf1.getCF()
aiccf = AICcf(z_copy, 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, ogstream=cut_ogstream)
linecolor=self.current_linecolor)
# save aicpick for plotting later
self.p_data.aicpick = aicpick
# add pstart and pstop to aic plot
@ -885,7 +855,7 @@ class AutopickStation(object):
label='P stop')
ax.legend(loc=1)
Pflag = self._pick_p_quality_control(aicpick, cf_stream, tr_filt)
Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt)
# go on with processing if AIC onset passes quality control
slope = aicpick.getSlope()
if not slope: slope = 0
@ -924,7 +894,7 @@ 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, ogstream=cut_ogstream)
self.current_linecolor)
# save PragPicker result for plotting
self.p_data.refPpick = refPpick
self.p_results.mpp = refPpick.getpick()
@ -1176,14 +1146,11 @@ 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, ogstream=ogstream)
linecolor=self.current_linecolor)
# save pick for later plotting
self.aicarhpick = aicarhpick

58
pylot/core/pick/charfuns.py
View File

@ -17,11 +17,7 @@ autoregressive prediction: application ot local and regional distances, Geophys.
:author: MAGS2 EP3 working group
"""
import numpy as np
try:
from scipy.signal import tukey
except ImportError:
from scipy.signal.windows import tukey
from scipy import signal
from obspy.core import Stream
from pylot.core.pick.utils import PickingFailedException
@ -60,7 +56,7 @@ class CharacteristicFunction(object):
self.setOrder(order)
self.setFnoise(fnoise)
self.setARdetStep(t2)
self.calcCF()
self.calcCF(self.getDataArray())
self.arpara = np.array([])
self.xpred = np.array([])
@ -212,15 +208,17 @@ class CharacteristicFunction(object):
data = self.orig_data.copy()
return data
def calcCF(self):
pass
def calcCF(self, data=None):
self.cf = data
class AICcf(CharacteristicFunction):
def calcCF(self):
def calcCF(self, data):
"""
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:
"""
@ -229,7 +227,7 @@ class AICcf(CharacteristicFunction):
ind = np.where(~np.isnan(xnp))[0]
if ind.size:
xnp[:ind[0]] = xnp[ind[0]]
xnp = tukey(len(xnp), alpha=0.05) * xnp
xnp = signal.tukey(len(xnp), alpha=0.05) * xnp
xnp = xnp - np.mean(xnp)
datlen = len(xnp)
k = np.arange(1, datlen)
@ -258,11 +256,13 @@ class HOScf(CharacteristicFunction):
"""
super(HOScf, self).__init__(data, cut, pickparams["tlta"], pickparams["hosorder"])
def calcCF(self):
def calcCF(self, data):
"""
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:
"""
@ -277,28 +277,47 @@ 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
@ -307,7 +326,10 @@ class HOScf(CharacteristicFunction):
first = ind[0]
LTA[:first] = LTA[first]
self.cf = LTA
if self.getOrder() > 2:
self.cf = LTA
else: # order 2 means STA/LTA!
self.cf = STA / LTA
self.xcf = x
@ -317,10 +339,12 @@ class ARZcf(CharacteristicFunction):
super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"],
fnoise=pickparams["addnoise"])
def calcCF(self):
def calcCF(self, data):
"""
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:
"""
@ -451,12 +475,14 @@ class ARHcf(CharacteristicFunction):
super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
fnoise=pickparams["addnoise"])
def calcCF(self):
def calcCF(self, data):
"""
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:
"""
@ -605,12 +631,14 @@ class AR3Ccf(CharacteristicFunction):
super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
fnoise=pickparams["addnoise"])
def calcCF(self):
def calcCF(self, data):
"""
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:
"""

29
pylot/core/pick/picker.py
View File

@ -37,8 +37,7 @@ class AutoPicker(object):
warnings.simplefilter('ignore')
def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None,
fig=None, linecolor='k', ogstream=None):
def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
"""
Create AutoPicker object
:param cf: characteristic function, on which the picking algorithm is applied
@ -60,15 +59,12 @@ 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()
@ -177,7 +173,7 @@ class AICPicker(AutoPicker):
nn = np.isnan(self.cf)
if len(nn) > 1:
self.cf[nn] = 0
# taper AIC-CF to get rid of side maxima
# taper AIC-CF to get rid off side maxima
tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.cf))
# smooth AIC-CF
@ -320,7 +316,16 @@ class AICPicker(AutoPicker):
plt.close(fig)
return
iislope = islope[0][0:imax + 1]
dataslope = self.Data[0].data[iislope]
# 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]
# calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope), 1)
try:
@ -331,7 +336,7 @@ 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 /= self.Data[0].data[icfmax]
self.slope /= aicsmooth[iaicmax]
except Exception as e:
print("AICPicker: Problems with data fitting! {}".format(e))
@ -351,12 +356,6 @@ 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)
@ -377,7 +376,7 @@ 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[iislope], datafit, 'g', linewidth=2,
ax2.plot(self.Tcf[pickindex: iaicmax], datafit, 'g', linewidth=2,
label='Slope') # MP MP changed temporarily!
if self.slope is not None:

23
pylot/core/pick/utils.py
View File

@ -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, common_range
from pylot.core.util.utils import get_bool, get_none, SetChannelComponents
def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
@ -828,22 +828,14 @@ 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
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!')
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]
# 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)
@ -882,10 +874,6 @@ 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]]],
@ -895,7 +883,6 @@ 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()

250
pylot/core/util/array_map.py
View File

@ -5,17 +5,14 @@ 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, QtGui
from PySide2 import QtWidgets
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
@ -27,10 +24,10 @@ matplotlib.use('Qt5Agg')
class MplCanvas(FigureCanvas):
def __init__(self, extern_axes=None, projection=None, width=15, height=5, dpi=100):
def __init__(self, parent=None, extern_axes=None, width=5, height=4, dpi=100):
if extern_axes is None:
self.fig = plt.figure(figsize=(width, height), dpi=dpi)
self.axes = self.fig.add_subplot(111, projection=projection)
self.axes = self.fig.add_subplot(111)
else:
self.fig = extern_axes.figure
self.axes = extern_axes
@ -62,30 +59,24 @@ 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.ax.get_xlim()
self.org_ylim = self.ax.get_ylim()
self.org_xlim = self.canvas.axes.get_xlim()
self.org_ylim = self.canvas.axes.get_ylim()
# initial map without event
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.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._style = None if not hasattr(parent, '_style') else parent._style
def init_map(self):
self.init_colormap()
self.connectSignals()
@ -98,24 +89,23 @@ class Array_map(QtWidgets.QWidget):
# initialize figure elements
if self.extern_plot_axes is None:
self.canvas = MplCanvas(projection=self.projection)
self.canvas = MplCanvas(self)
self.plotWidget = FigureCanvas(self.canvas.fig)
else:
self.canvas = MplCanvas(extern_axes=self.extern_plot_axes)
self.plotWidget = self.canvas
self.canvas = MplCanvas(self, extern_axes=self.extern_plot_axes)
self.plotWidget = FigureCanvas(self.canvas.fig)
# 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, 0)
self.main_box.addLayout(self.top_row, 1)
self.comboBox_phase = QtWidgets.QComboBox()
self.comboBox_phase.insertItem(0, 'P')
@ -134,8 +124,8 @@ 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 plasma as default
self.cmaps_box.setCurrentIndex(self.cmaps_box.findText('plasma'))
# try to set to viridis as default
self.cmaps_box.setCurrentIndex(self.cmaps_box.findText('viridis'))
self.top_row.addWidget(QtWidgets.QLabel('Select a phase: '))
self.top_row.addWidget(self.comboBox_phase)
@ -148,15 +138,14 @@ 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, 10)
self.main_box.addWidget(self.plotWidget, 1)
self.bot_row = QtWidgets.QHBoxLayout()
self.main_box.addLayout(self.bot_row, 0)
self.main_box.addLayout(self.bot_row, 0.3)
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):
@ -164,12 +153,14 @@ class Array_map(QtWidgets.QWidget):
self.init_lat_lon_grid()
def init_crtpyMap(self):
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)
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)
# parallels and meridians
self.add_merid_paral()
@ -177,8 +168,12 @@ class Array_map(QtWidgets.QWidget):
self.canvas.fig.tight_layout()
def add_merid_paral(self):
self.gridlines = self.ax.gridlines(draw_labels=False, alpha=0.6, color='gray',
linewidth=self.linewidth / 2, zorder=7, crs=ccrs.PlateCarree())
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
def remove_merid_paral(self):
if len(self.gridlines.xline_artists):
@ -186,24 +181,24 @@ class Array_map(QtWidgets.QWidget):
self.gridlines.yline_artists[0].remove()
def org_map_view(self):
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.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])
# parallels and meridians
#self.remove_merid_paral()
#self.add_merid_paral()
self.remove_merid_paral()
self.add_merid_paral()
self.canvas.draw_idle()
self.canvas.axes.figure.canvas.draw_idle()
def go2eq(self):
if self.eventLoc:
lats, lons = self.eventLoc
self.ax.set_xlim(lons - 10, lons + 10)
self.ax.set_ylim(lats - 5, lats + 5)
self.canvas.axes.set_xlim(lons - 10, lons + 10)
self.canvas.axes.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.draw_idle()
self.canvas.axes.figure.canvas.draw_idle()
else:
self.status_label.setText('No event information available')
@ -217,7 +212,6 @@ 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)
@ -226,32 +220,21 @@ class Array_map(QtWidgets.QWidget):
# set mouse events -----------------------------------------------------
def mouse_moved(self, event):
if not event.inaxes == self.ax:
if not event.inaxes == self.canvas.axes:
return
else:
cont, inds = self.sc.contains(event)
lat = event.ydata
lon = event.xdata
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)
self.status_label.setText('Latitude: {:3.5f}, Longitude: {:3.5f}'.format(lat, lon))
def mouse_scroll(self, event):
if not event.inaxes == self.ax:
if not event.inaxes == self.canvas.axes:
return
zoom = {'up': 1. / 2., 'down': 2.}
if event.button in zoom:
xlim = self.ax.get_xlim()
ylim = self.ax.get_ylim()
xlim = self.canvas.axes.get_xlim()
ylim = self.canvas.axes.get_ylim()
x, y = event.xdata, event.ydata
@ -263,24 +246,24 @@ class Array_map(QtWidgets.QWidget):
yb = y - 0.5 * ydiff
yt = y + 0.5 * ydiff
self.ax.set_xlim(xl, xr)
self.ax.set_ylim(yb, yt)
self.canvas.axes.set_xlim(xl, xr)
self.canvas.axes.set_ylim(yb, yt)
# parallels and meridians
#self.remove_merid_paral()
#self.add_merid_paral()
self.remove_merid_paral()
self.add_merid_paral()
self.ax.figure.canvas.draw_idle()
self.canvas.axes.figure.canvas.draw_idle()
def mouseLeftPress(self, event):
if not event.inaxes == self.ax:
if not event.inaxes == self.canvas.axes:
return
self.map_x = event.xdata
self.map_y = event.ydata
self.map_xlim = self.ax.get_xlim()
self.map_ylim = self.ax.get_ylim()
self.map_xlim = self.canvas.axes.get_xlim()
self.map_ylim = self.canvas.axes.get_ylim()
def mouseLeftRelease(self, event):
if not event.inaxes == self.ax:
if not event.inaxes == self.canvas.axes:
return
new_x = event.xdata
new_y = event.ydata
@ -288,13 +271,13 @@ class Array_map(QtWidgets.QWidget):
dx = new_x - self.map_x
dy = new_y - self.map_y
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)
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)
# parallels and meridians
#self.remove_merid_paral()
#self.add_merid_paral()
self.remove_merid_paral()
self.add_merid_paral()
self.ax.figure.canvas.draw_idle()
self.canvas.axes.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.'}
@ -374,6 +357,12 @@ class Array_map(QtWidgets.QWidget):
def get_max_from_stations(self, key):
return self._from_dict(max, key)
def get_min_from_picks(self):
return min(self.picks_rel.values())
def get_max_from_picks(self):
return max(self.picks_rel.values())
def current_picks_dict(self):
picktype = self.comboBox_am.currentText().split(' ')[0]
auto_manu = {'auto': self.autopicks_dict,
@ -418,34 +407,22 @@ class Array_map(QtWidgets.QWidget):
print('Cannot display pick for station {}. Reason: {}'.format(station_name, e))
return picks, uncertainties
def get_picks_rel(picks, func=min):
def get_picks_rel(picks):
picks_rel = {}
picks_utc = []
for pick in picks.values():
if type(pick) is UTCDateTime:
picks_utc.append(pick.timestamp)
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
picks_utc.append(pick)
if picks_utc:
self._reference_picktime = UTCDateTime(func(picks_utc))
self._earliest_picktime = min(picks_utc)
for st_id, pick in picks.items():
if type(pick) is UTCDateTime:
pick -= self._reference_picktime
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
pick -= self._earliest_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
@ -476,12 +453,11 @@ 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 picks_rel.items():
for st_id, pick in self.picks_rel.items():
picks.append(pick)
uncertainties.append(self.uncertainties.get(st_id))
latitudes.append(self.stations_dict[st_id]['latitude'])
@ -493,20 +469,13 @@ 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.ax.scatter(lon, lat, s=self.pointsize, edgecolors=color,
facecolors='none', zorder=12,
transform=ccrs.PlateCarree(), label='deleted'))
self.highlighted_stations.append(self.canvas.axes.scatter(lon, lat, s=self.pointsize, edgecolors=color,
facecolors='none', zorder=12,
transform=ccrs.PlateCarree(), label='deleted'))
def openPickDlg(self, ind):
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()
wfdata = self._parent.get_data().getWFData()
wfdata_comp = self._parent.get_data().getWFDataComp()
for index in ind:
network, station = self._station_onpick_ids[index].split('.')[:2]
pyl_mw = self._parent
@ -521,6 +490,7 @@ 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)
@ -548,19 +518,12 @@ class Array_map(QtWidgets.QWidget):
print(message, e)
print(traceback.format_exc())
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)
def draw_contour_filled(self, nlevel=50):
levels = np.linspace(self.get_min_from_picks(), self.get_max_from_picks(), nlevel)
self.contourf = self.ax.contourf(self.longrid, self.latgrid, self.picksgrid_active, levels,
linewidths=self.linewidth * 5, transform=ccrs.PlateCarree(),
alpha=0.4, zorder=8, cmap=self.get_colormap())
def get_residuals_absmax(self):
return np.max(np.absolute(list(self.picks_rel_mean_corrected.values())))
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())
def get_colormap(self):
return plt.get_cmap(self.cmaps_box.currentText())
@ -568,18 +531,18 @@ class Array_map(QtWidgets.QWidget):
def scatter_all_stations(self):
stations, lats, lons = self.get_st_lat_lon_for_plot()
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.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.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.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())
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())
def scatter_picked_stations(self):
picks, uncertainties, lats, lons = self.get_picks_lat_lon()
@ -592,13 +555,8 @@ class Array_map(QtWidgets.QWidget):
for uncertainty in uncertainties])
cmap = self.get_colormap()
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())
self.sc_picked = self.canvas.axes.scatter(lons, lats, s=sizes, edgecolors='white', cmap=cmap,
c=picks, zorder=11, label='Picked', transform=ccrs.PlateCarree())
def annotate_ax(self):
self.annotations = []
@ -616,20 +574,20 @@ class Array_map(QtWidgets.QWidget):
if st in self.marked_stations:
color = 'red'
self.annotations.append(
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.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.legend = self.ax.legend(loc=1, framealpha=1)
self.legend = self.canvas.axes.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.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)
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)
cbar.set_label(label)
cbax.yaxis.tick_left()
cbax.yaxis.set_label_position('left')
@ -674,9 +632,7 @@ 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 reference onset ({}) [s]'.format(self._reference_picktime)
)
self.cbar = self.add_cbar(label='Time relative to first onset ({}) [s]'.format(self._earliest_picktime))
self.comboBox_phase.setEnabled(True)
else:
self.comboBox_phase.setEnabled(False)

25
pylot/core/util/dataprocessing.py
View File

@ -27,10 +27,6 @@ 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):
@ -59,8 +55,6 @@ 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)
@ -268,6 +262,9 @@ 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')
@ -279,7 +276,7 @@ class Metadata(object):
continue
invtype, robj = self._read_metadata_file(os.path.join(path_to_inventory, fname))
try:
robj.get_coordinates(station_seed_id)
# robj.get_coordinates(station_seed_id) # TODO: Commented out, failed with Parser, is this needed?
self.inventory_files[fname] = {'invtype': invtype,
'data': robj}
if station_seed_id in self.seed_ids.keys():
@ -287,7 +284,6 @@ 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))
@ -652,8 +648,6 @@ def restitute_data(data, metadata, unit='VEL', force=False, ncores=0):
"""
# data = remove_underscores(data)
if not data:
return
# loop over traces
input_tuples = []
@ -661,14 +655,9 @@ def restitute_data(data, metadata, unit='VEL', force=False, ncores=0):
input_tuples.append((tr, metadata, unit, force))
data.remove(tr)
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()
pool = gen_Pool(ncores)
result = pool.imap_unordered(restitute_trace, input_tuples)
pool.close()
for tr, remove_trace in result:
if not remove_trace:

3
pylot/core/util/event.py
View File

@ -22,11 +22,14 @@ 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 = ''

18
pylot/core/util/gui.py
View File

@ -1,7 +1,6 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from functools import lru_cache
try:
import pyqtgraph as pg
@ -26,14 +25,14 @@ 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]
@ -81,7 +80,6 @@ def which(program, parameter):
return None
@lru_cache(maxsize=128)
def make_pen(picktype, phase, key, quality):
"""
Make PyQtGraph.QPen

8
pylot/core/util/utils.py
View File

@ -51,6 +51,7 @@ def readDefaultFilterInformation():
:rtype: dict
"""
pparam = PylotParameter()
pparam.reset_defaults()
return readFilterInformation(pparam)
@ -1075,7 +1076,7 @@ def check4rotated(data, metadata=None, verbosity=1):
return wfs_in
# check metadata quality
t_start = full_range(wfs_in)[0]
t_start = full_range(wfs_in)
try:
azimuths = []
dips = []
@ -1083,9 +1084,8 @@ 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.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)=}")
logging.error(f"{type(err)=} occurred: {err=} Rotating not possible, not all azimuth and dip information "
f"available in metadata. Stream remains unchanged.")
return wfs_in
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}")

26
pylot/core/util/widgets.py
View File

@ -8,7 +8,6 @@ import copy
import datetime
import getpass
import glob
import logging
import multiprocessing
import os
import subprocess
@ -1871,14 +1870,13 @@ 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, wftype=None):
event=None, filteroptions=None, model=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
@ -2271,8 +2269,8 @@ class PickDlg(QDialog):
arrivals = func[plot](source_origin.depth,
source_origin.latitude,
source_origin.longitude,
station_coords.get('latitude'),
station_coords.get('longitude'),
station_coords['latitude'],
station_coords['longitude'],
phases)
self.arrivals = arrivals
@ -3837,7 +3835,7 @@ class TuneAutopicker(QWidget):
self.stb_names = ['aicARHfig', 'refSpick', 'el_S1pick', 'el_S2pick']
def add_parameters(self):
self.paraBox = PylotParameterWidget(self.parameter, parent=self, windowflag=Qt.Widget)
self.paraBox = PylotParaBox(self.parameter, parent=self, windowflag=Qt.Widget)
self.paraBox.set_tune_mode(True)
self.update_eventID()
self.parameter_layout.addWidget(self.paraBox)
@ -4204,7 +4202,7 @@ class TuneAutopicker(QWidget):
self.qmb.show()
class PylotParameterWidget(QtWidgets.QWidget):
class PylotParaBox(QtWidgets.QWidget):
accepted = QtCore.Signal(str)
rejected = QtCore.Signal(str)
@ -4318,11 +4316,6 @@ class PylotParameterWidget(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'])
@ -4892,7 +4885,7 @@ class PropTab(QWidget):
def getValues(self):
return None
def resetValues(self, infile):
def resetValues(self, infile=None):
return None
@ -4989,7 +4982,12 @@ class InputsTab(PropTab):
else:
index = 2
datapath = para.get('datapath') if not para.get('datapath') is None else ''
values = {"data/dataRoot": self.dataDirEdit.setText("%s" % datapath),
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),
"user/FullName": self.fullNameEdit.text(),
"data/Structure": self.structureSelect.setCurrentIndex(index),
"tstart": self.tstartBox.setValue(0),

2
pylot/correlation/__init__.py
View File

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

95
pylot/correlation/parameters_adriaarray.yaml
View File

@ -1,95 +0,0 @@
############################# 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

1991
pylot/correlation/pick_correlation_correction.py
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40
pylot/correlation/submit_pick_corr_correction.sh
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@ -1,40 +0,0 @@
#!/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

23
pylot/correlation/submit_to_grid_engine.py
View File

@ -1,23 +0,0 @@
#!/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()))

61
pylot/correlation/utils.py
View File

@ -1,61 +0,0 @@
#!/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

19
requirements.txt
View File

@ -1,7 +1,12 @@
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
# 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

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tests/test_autopicker/PyLoT_20171010_063224.a_autopylot.xml
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tests/test_autopicker/PyLoT_20171010_063224.a_corrected_taup_times_0.01-0.2_SKS.xml
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tests/test_autopicker/PyLoT_20171010_063224.a_corrected_taup_times_0.03-0.5_P.xml
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tests/test_autopicker/PyLoT_20171010_063224.a_correlated_0.01-0.2_SKS.xml
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tests/test_autopicker/PyLoT_20171010_063224.a_correlated_0.03-0.5_P.xml
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tests/test_autopicker/PyLoT_20171010_063224.a_saved_from_GUI.xml
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99
tests/test_autopicker/pylot_alparray_mantle_corr_stack_0.03-0.5.in
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@ -1,99 +0,0 @@
%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

67
tests/test_autopicker/test_autopylot.py
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@ -1,67 +0,0 @@
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'

7
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
View File

@ -4,8 +4,10 @@
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/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 #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/metadata #invdir# %full path to inventory or dataless-seed file
PILOT #datastructure# %choose data structure
True #apverbose# %choose 'True' or 'False' for terminal output
@ -41,7 +43,6 @@ 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]

7
tests/test_autopickstation/autoPyLoT_global_taupy_true.in
View File

@ -4,8 +4,10 @@
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/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 #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/metadata #invdir# %full path to inventory or dataless-seed file
PILOT #datastructure# %choose data structure
True #apverbose# %choose 'True' or 'False' for terminal output
@ -41,7 +43,6 @@ 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]

72
tests/test_autopickstation/test_autopickstation.py
View File

@ -1,7 +1,6 @@
import os
import sys
import unittest
import pytest
import obspy
from obspy import UTCDateTime
@ -106,6 +105,7 @@ 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))
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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))
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['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)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('FIESA', station)
def test_autopickstation_gra1_z_comp_missing(self):
@ -272,8 +272,7 @@ class TestAutopickStation(unittest.TestCase):
with HidePrints():
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
metadata=(None, None))
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertEqual(expected, result)
self.assertEqual('GRA1', station)
def test_autopickstation_a106_taupy_enabled(self):
@ -291,9 +290,7 @@ class TestAutopickStation(unittest.TestCase):
with HidePrints():
result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertEqual(expected, result)
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
@ -314,37 +311,8 @@ class TestAutopickStation(unittest.TestCase):
with HidePrints():
result, station = autopickstation(wfstream=self.a005a, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
compare_dicts(expected=expected['P'], result=result['P'], hint='P-')
compare_dicts(expected=expected['S'], result=result['S'], hint='S-')
self.assertEqual(expected, result)
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()

76
tests/test_pick_correlation_correction/test_pick_correlation_correction.py
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@ -1,76 +0,0 @@
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)