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[refactor] automatic code reformatting (Pycharm)

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Marcel Paffrath 2022-03-09 14:41:34 +01:00
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34
PyLoT.py
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@ -24,18 +24,16 @@ https://www.iconfinder.com/iconsets/flavour
"""
import argparse
import matplotlib
import json
import os
import platform
import shutil
import sys
import copy
import traceback
import json
from datetime import datetime
import matplotlib
matplotlib.use('Qt5Agg')
from PySide2 import QtGui, QtCore, QtWidgets
@ -44,8 +42,8 @@ from PySide2.QtCore import QCoreApplication, QSettings, Signal, QFile, \
from PySide2.QtGui import QIcon, QKeySequence, QPixmap, QStandardItem
from PySide2.QtWidgets import QMainWindow, QInputDialog, QFileDialog, \
QWidget, QHBoxLayout, QVBoxLayout, QStyle, QLabel, QFrame, QAction, \
QDialog, QErrorMessage, QApplication, QMessageBox, QSplashScreen, \
QActionGroup, QListWidget, QLineEdit, QListView, QAbstractItemView, \
QDialog, QApplication, QMessageBox, QSplashScreen, \
QActionGroup, QListWidget, QListView, QAbstractItemView, \
QTreeView, QComboBox, QTabWidget, QPushButton, QGridLayout, QTableWidgetItem, QTableWidget
import numpy as np
from obspy import UTCDateTime, Stream
@ -60,7 +58,6 @@ try:
from matplotlib.backends.backend_qt5agg import FigureCanvas
except ImportError:
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
from pylot.core.analysis.magnitude import LocalMagnitude, MomentMagnitude
@ -68,8 +65,8 @@ from pylot.core.io.data import Data
from pylot.core.io.inputs import FilterOptions, PylotParameter
from autoPyLoT import autoPyLoT
from pylot.core.pick.compare import Comparison
from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty, getPickQuality, get_quality_class
from pylot.core.io.phases import picksdict_from_picks, picks_from_picksdict
from pylot.core.pick.utils import getQualityFromUncertainty
from pylot.core.io.phases import picksdict_from_picks
import pylot.core.loc.nll as nll
from pylot.core.util.errors import DatastructureError, \
OverwriteError
@ -79,7 +76,7 @@ from pylot.core.util.utils import fnConstructor, getLogin, \
full_range, readFilterInformation, pick_color_plt, \
pick_linestyle_plt, identifyPhaseID, excludeQualityClasses, \
transform_colors_mpl, transform_colors_mpl_str, getAutoFilteroptions, check_all_obspy, \
check_all_pylot, get_Bool, get_None, SetChannelComponents
check_all_pylot, get_Bool, get_None
from pylot.core.util.gui import make_pen
from pylot.core.util.event import Event
from pylot.core.io.location import create_creation_info, create_event
@ -493,7 +490,8 @@ class MainWindow(QMainWindow):
self.eventlist_xml_action = self.createAction(parent=self, text='Create Eventlist from XML',
slot=self.eventlistXml, shortcut='Alt+X',
icon=eventlist_xml_icon, tip='Create an Eventlist from a XML File')
icon=eventlist_xml_icon,
tip='Create an Eventlist from a XML File')
self.eventlist_xml_action.setEnabled(False)
printAction = self.createAction(self, "&Print event ...",
@ -561,7 +559,8 @@ class MainWindow(QMainWindow):
' the complete project on grid engine.')
self.auto_pick_sge.setEnabled(False)
pickActions = (self.auto_tune, self.auto_pick, self.compare_action, self.qualities_action, self.eventlist_xml_action)
pickActions = (
self.auto_tune, self.auto_pick, self.compare_action, self.qualities_action, self.eventlist_xml_action)
# pickToolBar = self.addToolBar("PickTools")
# pickToolActions = (selectStation, )
@ -602,7 +601,6 @@ class MainWindow(QMainWindow):
self.autoPickMenu = self.pickMenu.addMenu(self.autopicksicon_small, 'Automatic picking')
self.autoPickMenu.setEnabled(False)
autoPickActions = (self.auto_pick, self.auto_pick_local, self.auto_pick_sge)
self.helpMenu = self.menuBar().addMenu('&Help')
@ -960,7 +958,6 @@ class MainWindow(QMainWindow):
self.recentProjectsMenu.addAction(action)
@property
def inputs(self):
return self._inputs
@ -1179,7 +1176,8 @@ class MainWindow(QMainWindow):
eventlist_file = os.path.join(basepath, 'eventlist.txt')
if os.path.isfile(eventlist_file):
with open(eventlist_file, 'r') as infile:
eventlist_subset = [os.path.join(basepath, filename.split('\n')[0]) for filename in infile.readlines()]
eventlist_subset = [os.path.join(basepath, filename.split('\n')[0]) for filename in
infile.readlines()]
msg = 'Found file "eventlist.txt" in database path. WILL ONLY USE SELECTED EVENTS out of {} events ' \
'contained in this subset'
print(msg.format(len(eventlist_subset)))
@ -1544,6 +1542,7 @@ class MainWindow(QMainWindow):
event = self.get_current_event()
if not type(outformats) == list:
outformats = [outformats]
def getSavePath(event, directory, outformats):
if not directory:
title = 'Save event data as {} to directory ...'.format(outformats)
@ -3457,7 +3456,6 @@ class MainWindow(QMainWindow):
if event == current_event:
set_background_color(item_list, QtGui.QColor(*(0, 143, 143, 255)))
def set_metadata(self):
self.project.inventories = self.metadata.inventories
if self.metadata.inventories:
@ -3763,7 +3761,6 @@ class MainWindow(QMainWindow):
self.plotWaveformDataThread()
self.refreshTabs()
def PyLoTprefs(self):
if not self._props:
self._props = PropertiesDlg(self, infile=self.infile,
@ -3792,6 +3789,7 @@ 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 = []

22
README.md
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@ -4,22 +4,19 @@ version: 0.2
The Python picking and Localisation Tool
This python library contains a graphical user interfaces for picking
seismic phases. This software needs [ObsPy][ObsPy]
This python library contains a graphical user interfaces for picking seismic phases. This software needs [ObsPy][ObsPy]
and the PySide Qt4 bindings for python to be installed first.
PILOT has originally been developed in Mathworks' MatLab. In order to
distribute PILOT without facing portability 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.
PILOT has originally been developed in Mathworks' MatLab. In order to distribute PILOT without facing portability
problems, it has been decided to redevelop the software package in Python. The great work of the ObsPy group allows easy
handling of a bunch of seismic data and PyLoT will benefit a lot compared to the former MatLab version.
The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
## Installation
At the moment there is no automatic installation procedure available for PyLoT.
Best way to install is to clone the repository and add the path to your Python path.
At the moment there is no automatic installation procedure available for PyLoT. Best way to install is to clone the
repository and add the path to your Python path.
#### Prerequisites:
@ -53,7 +50,8 @@ In the next step you have to copy some files to this directory:
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
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/
@ -61,7 +59,6 @@ You may need to do some modifications to these files. Especially folder names sh
PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
## Release notes
#### Features:
@ -85,8 +82,7 @@ We hope to solve these with the next release.
Original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
Developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
C. Wollin, M. Rische, M. Paffrath
Developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff, C. Wollin, M. Rische, M. Paffrath
Others: A. Bruestle, T. Meier, W. Friederich

1
autoPyLoT.py
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@ -8,6 +8,7 @@ import datetime
import glob
import os
import traceback
from obspy import read_events
from obspy.core.event import ResourceIdentifier

259
docs/gui.md
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@ -44,6 +44,7 @@ This section describes how to use PyLoT graphically to view waveforms and create
After opening PyLoT for the first time, the setup routine asks for the following information:
Questions:
1. Full Name
2. Authority: Enter authority/institution name
3. Format: Enter output format (*.xml, *.cnv, *.obs)
@ -52,7 +53,8 @@ Questions:
## Main Screen
After entering the [information](#first-start), PyLoTs main window is shown. It defaults to a view of the [Waveform Plot](#waveform-plot), which starts empty.
After entering the [information](#first-start), PyLoTs main window is shown. It defaults to a view of
the [Waveform Plot](#waveform-plot), which starts empty.
<img src=images/gui/pylot-main-screen.png alt="Tune autopicks button" title="Tune autopicks button">
@ -61,24 +63,21 @@ Add trace data by [loading a project](#projects-and-events) or by [adding event
### Waveform Plot
The waveform plot shows a trace list of all stations of an event.
Click on any trace to open the stations [picking window](#picking-window), where you can review automatic and manual picks.
Click on any trace to open the stations [picking window](#picking-window), where you can review automatic and manual
picks.
<img src=images/gui/pylot-waveform-plot.png alt="A Waveform Plot showing traces of one event">
Above the traces the currently displayed event can be selected.
In the bottom bar information about the trace under the mouse cursor is shown. This information includes the station name (station), the absolute UTC time (T) of the point under the mouse cursor and the relative time since the first trace start in seconds (t) as well as a trace count.
Above the traces the currently displayed event can be selected. In the bottom bar information about the trace under the
mouse cursor is shown. This information includes the station name (station), the absolute UTC time (T) of the point
under the mouse cursor and the relative time since the first trace start in seconds (t) as well as a trace count.
#### Mouse view controls
Hold left mouse button and drag to pan view.
Hold right mouse button and
Direction | Result
--- | ---
Move the mouse up | Increase amplitude scale
Move the mouse down | Decrease amplitude scale
Move the mouse right | Increase time scale
Move the mouse left | Decrease time scale
Hold right mouse button and Direction | Result --- | --- Move the mouse up | Increase amplitude scale Move the mouse
down | Decrease amplitude scale Move the mouse right | Increase time scale Move the mouse left | Decrease time scale
Press right mouse button and click "View All" from the context menu to reset the view.
@ -108,21 +107,31 @@ Press right mouse button and click "View All" from the context menu to reset the
### Array Map
The array map will display a color diagram to allow a visual check of the consistency of picks across multiple stations. This works by calculating the time difference of every onset to the earliest onset. Then isolines are drawn between stations with the same time difference and the areas between isolines are colored.
The result should resemble a color gradient as the wavefront rolls over the network area. Stations where picks are earlier/later than their neighbours can be reviewed by clicking on them, which opens the [picking window](#picking-window).
The array map will display a color diagram to allow a visual check of the consistency of picks across multiple stations.
This works by calculating the time difference of every onset to the earliest onset. Then isolines are drawn between
stations with the same time difference and the areas between isolines are colored.
The result should resemble a color gradient as the wavefront rolls over the network area. Stations where picks are
earlier/later than their neighbours can be reviewed by clicking on them, which opens
the [picking window](#picking-window).
Above the Array Map the picks that are used to create the map can be customized.
The phase of picks that should be used can be selected, which allows checking the consistency of the P- and S-phase separately.
Additionally the pick type can be set to manual, automatic or hybrid, meaning display only manual picks, automatic picks or only display automatic picks for stations where there are no manual ones.
Above the Array Map the picks that are used to create the map can be customized. The phase of picks that should be used
can be selected, which allows checking the consistency of the P- and S-phase separately. Additionally the pick type can
be set to manual, automatic or hybrid, meaning display only manual picks, automatic picks or only display automatic
picks for stations where there are no manual ones.
![Array Map](images/gui/arraymap-example.png "Array Map")
*Array Map for an event at the Northern Mid Atlantic Ridge, between North Africa and Mexico (Lat. 22.58, Lon. -45.11). The wavefront moved from west to east over the network area (Alps and Balcan region), with the earliest onsets in blue in the west.*
*Array Map for an event at the Northern Mid Atlantic Ridge, between North Africa and Mexico (Lat. 22.58, Lon. -45.11).
The wavefront moved from west to east over the network area (Alps and Balcan region), with the earliest onsets in blue
in the west.*
To be able to display an array map PyLoT needs to load an inventory file, where the metadata of seismic stations is kept. For more information see [Metadata](#adding-metadata). Additionally automatic or manual picks need to exist for the current event.
To be able to display an array map PyLoT needs to load an inventory file, where the metadata of seismic stations is
kept. For more information see [Metadata](#adding-metadata). Additionally automatic or manual picks need to exist for
the current event.
### Eventlist
The eventlist displays event parameters. The displayed parameters are saved in the .xml file in the event folder. Events can be deleted from the project by pressing the red X in the leftmost column of the corresponding event.
The eventlist displays event parameters. The displayed parameters are saved in the .xml file in the event folder. Events
can be deleted from the project by pressing the red X in the leftmost column of the corresponding event.
<img src="images/gui/eventlist.png" alt="Eventlist">
@ -144,22 +153,32 @@ The eventlist displays event parameters. The displayed parameters are saved in t
### Projects and Events
PyLoT uses projects to categorize different seismic data. A project consists of one or multiple events. Events contain seismic traces from one or multiple stations. An event also contains further information, e.g. origin time, source parameters and automatic as well as manual picks.
Projects are used to group events which should be analysed together. A project could contain all events from a specific region within a timeframe of interest or all recorded events of a seismological experiment.
PyLoT uses projects to categorize different seismic data. A project consists of one or multiple events. Events contain
seismic traces from one or multiple stations. An event also contains further information, e.g. origin time, source
parameters and automatic as well as manual picks. Projects are used to group events which should be analysed together. A
project could contain all events from a specific region within a timeframe of interest or all recorded events of a
seismological experiment.
### Event folder structure
PyLoT expects the following folder structure for seismic data:
* Every event should be in it's own folder with the following naming scheme for the folders:
``e[id].[doy].[yy]``, where ``[id]`` is a four-digit numerical id increasing from 0001, ``[doy]`` the three digit day of year and ``[yy]`` the last two digits of the year of the event. This structure has to be created by the user of PyLoT manually.
``e[id].[doy].[yy]``, where ``[id]`` is a four-digit numerical id increasing from 0001, ``[doy]`` the three digit day
of year and ``[yy]`` the last two digits of the year of the event. This structure has to be created by the user of
PyLoT manually.
* These folders should contain the seismic data for their event as ``.mseed`` or other supported filetype
* All automatic and manual picks should be in an ``.xml`` file in their event folder. PyLoT saves picks in this file. This file does not have to be added manually unless there are picks to be imported. The format used to save picks is QUAKEML.
* All automatic and manual picks should be in an ``.xml`` file in their event folder. PyLoT saves picks in this file.
This file does not have to be added manually unless there are picks to be imported. The format used to save picks is
QUAKEML.
Picks are saved in a file with the same filename as the event folder with ``PyLoT_`` prepended.
* The file ``notes.txt`` is used for saving analysts comments. Everything saved here will be displayed in the 'Notes' column of the eventlist.
* The file ``notes.txt`` is used for saving analysts comments. Everything saved here will be displayed in the 'Notes'
column of the eventlist.
### Loading event information from CSV file
Event information can be saved in a ``.csv`` file located in the rootpath. The file is made from one header line, which is followed by one or multiple data lines. Values are separated by comma, while a dot is used as a decimal separator.
Event information can be saved in a ``.csv`` file located in the rootpath. The file is made from one header line, which
is followed by one or multiple data lines. Values are separated by comma, while a dot is used as a decimal separator.
This information is then shown in the table in the [Eventlist tab](#Eventlist).
One example header and data line is shown below.
@ -181,36 +200,50 @@ The meaning of the header entries is:
### Adding events to project
PyLoT GUI starts with an empty project. To add events, use the add event data button. Select one or multiple folders containing events.
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 save project information. This file format is not interchangeable between different versions of Python interpreters.
Saved projects contain the automatic and manual picks. Seismic trace data is not included into the ``.plp`` file, but read from its location used when saving the file.
Save the current project from the menu with File->Save project or File->Save project as. PyLoT uses ``.plp`` files to
save project information. This file format is not interchangeable between different versions of Python interpreters.
Saved projects contain the automatic and manual picks. Seismic trace data is not included into the ``.plp`` file, but
read from its location used when saving the file.
### Adding metadata
[//]: <> (TODO: Add picture of metadata "manager" when it is done)
PyLoT can handle ``.dless``, ``.xml``, ``.resp`` and ``.dseed`` file formats for Metadata. Metadata files stored on disk can be added to a project by clicking *Edit*->*Manage Inventories*. This opens up a window where the folders which contain metadata files can be selected. PyLoT will then search these files for the station names when it needs the information.
PyLoT can handle ``.dless``, ``.xml``, ``.resp`` and ``.dseed`` file formats for Metadata. Metadata files stored on disk
can be added to a project by clicking *Edit*->*Manage Inventories*. This opens up a window where the folders which
contain metadata files can be selected. PyLoT will then search these files for the station names when it needs the
information.
# Picking
PyLoTs automatic and manual pick determination works as following:
* Using certain parameters, a first initial/coarse pick is determined. The first manual pick is determined by visual review of the whole waveform and selection of the most likely onset by the analyst. The first automatic pick is determined by calculation of a characteristic function (CF) for the seismic trace. When a wave arrives, the CFs properties change, which is determined as the signals onset.
* Afterwards, a refined set of parameters is applied to a small part of the waveform around the initial onset. For manual picks this means a closer view of the trace, for automatic picks this is done by a recalculated CF with different parameters.
* Using certain parameters, a first initial/coarse pick is determined. The first manual pick is determined by visual
review of the whole waveform and selection of the most likely onset by the analyst. The first automatic pick is
determined by calculation of a characteristic function (CF) for the seismic trace. When a wave arrives, the CFs
properties change, which is determined as the signals onset.
* Afterwards, a refined set of parameters is applied to a small part of the waveform around the initial onset. For
manual picks this means a closer view of the trace, for automatic picks this is done by a recalculated CF with
different parameters.
* This second picking phase results in the precise pick, which is treated as the onset time.
## Manual Picking
To create manual picks, you will need to open or create a project that contains seismic trace data (see [Adding events to projects](#adding-events-to-project)). Click on a trace to open the [Picking window](#picking-window).
To create manual picks, you will need to open or create a project that contains seismic trace data (
see [Adding events to projects](#adding-events-to-project)). Click on a trace to open
the [Picking window](#picking-window).
### Picking window
Open the picking window of a station by leftclicking on any trace in the waveform plot. Here you can create manual picks for the selected station.
Open the picking window of a station by leftclicking on any trace in the waveform plot. Here you can create manual picks
for the selected station.
<img src="images/gui/picking/pickwindow.png" alt="Picking window">
@ -243,105 +276,167 @@ Access the Filter options by pressing Ctrl+f on the Waveform plot or by the menu
<img src=images/gui/pylot-filter-options.png>
Here you are able to select filter type, order and frequencies for the P and S pick separately. These settings are used in the GUI for displaying the filtered waveform data and during manual picking. The values used by PyLoT for automatic picking are displayed next to the manual values. They can be changed in the [Tune Autopicker dialog](#tuning).
A green value automatic value means the automatic and manual filter parameter is configured the same, red means they are configured differently.
By toggling the "Overwrite filteroptions" checkmark you can set whether the manual precise/second pick uses the filter settings for the automatic picker (unchecked) or whether it uses the filter options in this dialog (checked).
To guarantee consistent picking results between automatic and manual picking it is recommended to use the same filter settings for the determination of automatic and manual picks.
Here you are able to select filter type, order and frequencies for the P and S pick separately. These settings are used
in the GUI for displaying the filtered waveform data and during manual picking. The values used by PyLoT for automatic
picking are displayed next to the manual values. They can be changed in the [Tune Autopicker dialog](#tuning).
A green value automatic value means the automatic and manual filter parameter is configured the same, red means they are
configured differently. By toggling the "Overwrite filteroptions" checkmark you can set whether the manual
precise/second pick uses the filter settings for the automatic picker (unchecked) or whether it uses the filter options
in this dialog (checked). To guarantee consistent picking results between automatic and manual picking it is recommended
to use the same filter settings for the determination of automatic and manual picks.
### Export and Import of manual picks
#### Export
After the creation of manual picks they can either be saved in the project file (see [Saving projects](#saving-projects)). Alternatively the picks can be exported by pressing the <img src="../icons/savepicks.png" alt="Save event information button" title="Save picks button" height=24 width=24> button above the waveform plot or in the menu File->Save event information (shortcut Ctrl+p). Select the event directory in which to save the file. The filename will be ``PyLoT_[event_folder_name].[filetype selected during first startup]``.
You can rename and copy this file, but PyLoT will then no longer be able to automatically recognize the correct picks for an event and the file will have to be manually selected when loading.
After the creation of manual picks they can either be saved in the project file (
see [Saving projects](#saving-projects)). Alternatively the picks can be exported by pressing
the <img src="../icons/savepicks.png" alt="Save event information button" title="Save picks button" height=24 width=24>
button above the waveform plot or in the menu File->Save event information (shortcut Ctrl+p). Select the event directory
in which to save the file. The filename will be ``PyLoT_[event_folder_name].[filetype selected during first startup]``
.
You can rename and copy this file, but PyLoT will then no longer be able to automatically recognize the correct picks
for an event and the file will have to be manually selected when loading.
#### Import
To import previously saved picks press the <img src="../icons/openpick.png" alt="Load event information button" width="24" height="24"> button and select the file to load. You will be asked to save the current state of your project if you have not done so before. You can continue without saving by pressing "Discard". This does not delete any information from your project, it just means that no project file is saved before the changes of importing picks are applied.
PyLoT will automatically load files named after the scheme it uses when saving picks, described in the paragraph above. If it can't find any matching files, a file dialogue will open and you can select the file you wish to load.
To import previously saved picks press
the <img src="../icons/openpick.png" alt="Load event information button" width="24" height="24"> button and select the
file to load. You will be asked to save the current state of your project if you have not done so before. You can
continue without saving by pressing "Discard". This does not delete any information from your project, it just means
that no project file is saved before the changes of importing picks are applied. PyLoT will automatically load files
named after the scheme it uses when saving picks, described in the paragraph above. If it can't find any matching files,
a file dialogue will open and you can select the file you wish to load.
If you see a warning "Mismatch in event identifiers" and are asked whether to continue loading the picks, this means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have either been saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and you can continue loading them. Or they could be picks from a different event, in which case loading them is not recommended.
If you see a warning "Mismatch in event identifiers" and are asked whether to continue loading the picks, this means
that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have either been
saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and
you can continue loading them. Or they could be picks from a different event, in which case loading them is not
recommended.
## Automatic Picking
The general workflow for automatic picking is as following:
- After setting up the project by loading waveforms and optionally metadata, the right parameters for the autopicker have to be determined
- After setting up the project by loading waveforms and optionally metadata, the right parameters for the autopicker
have to be determined
- This [tuning](#tuning) is done for single stations with immediate graphical feedback of all picking results
- Afterwards the autopicker can be run for all or a subset of events from the project
For automatic picking PyLoT discerns between tune and test events, which the user has to set as such. Tune events are used to calibrate the autopicking algorithm, test events are then used to test the calibration. The purpose of that is controlling whether the parameters found during tuning are able to reliably pick the "unknown" test events.
If this behaviour is not desired and all events should be handled the same, dont mark any events. Since this is just a way to group events to compare the picking results, nothing else will change.
For automatic picking PyLoT discerns between tune and test events, which the user has to set as such. Tune events are
used to calibrate the autopicking algorithm, test events are then used to test the calibration. The purpose of that is
controlling whether the parameters found during tuning are able to reliably pick the "unknown" test events.
If this behaviour is not desired and all events should be handled the same, dont mark any events. Since this is just a
way to group events to compare the picking results, nothing else will change.
### Tuning
Tuning describes the process of adjusting the autopicker settings to the characteristics of your data set. To do this in PyLoT, use the <img src=../icons/tune.png height=24 alt="Tune autopicks button" title="Tune autopicks button"> button to open the Tune Autopicker.
Tuning describes the process of adjusting the autopicker settings to the characteristics of your data set. To do this in
PyLoT, use the <img src=../icons/tune.png height=24 alt="Tune autopicks button" title="Tune autopicks button"> button to
open the Tune Autopicker.
<img src=images/gui/tuning/tune_autopicker.png>
View of a station in the Tune Autopicker window.
1. Select the event to be displayed and processed.
2. Select the station from the event.
3. To pick the currently displayed trace, click the <img src=images/gui/tuning/autopick_trace_button.png alt="Pick trace button" title="Autopick trace button" height=16> button.
4. These tabs are used to select the current view. __Traces Plot__ contains a plot of the stations traces, where manual picks can be created/edited. __Overview__ contains graphical results of the automatic picking process. The __P and S tabs__ contain the automatic picking results of the P and S phase, while __log__ contains a useful text output of automatic picking.
5. These buttons are used to load/save/reset settings for automatic picking. The parameters can be saved in PyLoT input files, which have the file ending *.in*. They are human readable text files, which can also be edited by hand. Saving the parameters allows you to load them again later, even on different machines.
6. These menus control the behaviour of the creation of manual picks from the Tune Autopicker window. Picks allows to select the phase for which a manual pick should be created, Filter allows to filter waveforms and edit the filter parameters. P-Channels and S-Channels allow to select the channels that should be displayed when creating a manual P or S pick.
7. This menu is the same as in the [Picking Window](#picking-window-settings), with the exception of the __Manual Onsets__ options. The __Manual Onsets__ buttons accepts or reject the manual picks created in the Tune Autopicker window, pressing accept adds them to the manual picks for the event, while reject removes them.
3. To pick the currently displayed trace, click
the <img src=images/gui/tuning/autopick_trace_button.png alt="Pick trace button" title="Autopick trace button" height=16>
button.
4. These tabs are used to select the current view. __Traces Plot__ contains a plot of the stations traces, where manual
picks can be created/edited. __Overview__ contains graphical results of the automatic picking process. The __P and S
tabs__ contain the automatic picking results of the P and S phase, while __log__ contains a useful text output of
automatic picking.
5. These buttons are used to load/save/reset settings for automatic picking. The parameters can be saved in PyLoT input
files, which have the file ending *.in*. They are human readable text files, which can also be edited by hand. Saving
the parameters allows you to load them again later, even on different machines.
6. These menus control the behaviour of the creation of manual picks from the Tune Autopicker window. Picks allows to
select the phase for which a manual pick should be created, Filter allows to filter waveforms and edit the filter
parameters. P-Channels and S-Channels allow to select the channels that should be displayed when creating a manual P
or S pick.
7. This menu is the same as in the [Picking Window](#picking-window-settings), with the exception of the __Manual
Onsets__ options. The __Manual Onsets__ buttons accepts or reject the manual picks created in the Tune Autopicker
window, pressing accept adds them to the manual picks for the event, while reject removes them.
8. The traces plot in the centre allows creating manual picks and viewing the waveforms.
9. The parameters which influence the autopicking result are in the Main settings and Advanced settings tabs on the left side. For a description of all the parameters see the [tuning documentation](tuning.md).
9. The parameters which influence the autopicking result are in the Main settings and Advanced settings tabs on the left
side. For a description of all the parameters see the [tuning documentation](tuning.md).
### Production run of the autopicker
After the settings used during tuning give the desired results, the autopicker can be used on the complete dataset. To invoke the autopicker on the whole set of events, click the <img src=../icons/autopylot_button.png alt="Autopick" title="Autopick" height=32> button.
After the settings used during tuning give the desired results, the autopicker can be used on the complete dataset. To
invoke the autopicker on the whole set of events, click
the <img src=../icons/autopylot_button.png alt="Autopick" title="Autopick" height=32> button.
### Evaluation of automatic picks
PyLoT has two internal consistency checks for automatic picks that were determined for an event:
1. Jackknife check
2. Wadati check
#### 1. Jackknife check
The jackknife test in PyLoT checks the consistency of automatically determined P-picks by checking the statistical variance of the picks. The variance of all P-picks is calculated and compared to the variance of subsets, in which one pick is removed.
The idea is, that picks that are close together in time should not influence the estimation of the variance much, while picks whose positions deviates from the norm influence the variance to a greater extent. If the estimated variance of a subset with a pick removed differs to much from the estimated variance of all picks, the pick that was removed from the subset will be marked as invalid.
The factor by which picks are allowed to skew from the estimation of variance can be configured, it is called *jackfactor*, see [here](tuning.md#Pick-quality-control).
The jackknife test in PyLoT checks the consistency of automatically determined P-picks by checking the statistical
variance of the picks. The variance of all P-picks is calculated and compared to the variance of subsets, in which one
pick is removed.
The idea is, that picks that are close together in time should not influence the estimation of the variance much, while
picks whose positions deviates from the norm influence the variance to a greater extent. If the estimated variance of a
subset with a pick removed differs to much from the estimated variance of all picks, the pick that was removed from the
subset will be marked as invalid.
The factor by which picks are allowed to skew from the estimation of variance can be configured, it is called *
jackfactor*, see [here](tuning.md#Pick-quality-control).
Additionally, the deviation of picks from the median is checked. For that, the median of all P-picks that passed the Jackknife test is calculated. Picks whose onset times deviate from the mean onset time by more than the *mdttolerance* are marked as invalid.
Additionally, the deviation of picks from the median is checked. For that, the median of all P-picks that passed the
Jackknife test is calculated. Picks whose onset times deviate from the mean onset time by more than the *mdttolerance*
are marked as invalid.
<img src=images/gui/jackknife_plot.png title="Jackknife/Median test diagram">
*The result of both tests (Jackknife and Median) is shown in a diagram afterwards. The onset time is plotted against a running number of stations. Picks that failed either the Jackknife or the median test are colored red. The median is plotted as a green line.*
*The result of both tests (Jackknife and Median) is shown in a diagram afterwards. The onset time is plotted against a
running number of stations. Picks that failed either the Jackknife or the median test are colored red. The median is
plotted as a green line.*
The Jackknife and median check are suitable to check for picks that are outside of the expected time window, for example, when a wrong phase was picked. It won't recognize picks that are in close proximity to the right onset which are just slightly to late/early.
The Jackknife and median check are suitable to check for picks that are outside of the expected time window, for
example, when a wrong phase was picked. It won't recognize picks that are in close proximity to the right onset which
are just slightly to late/early.
#### 2. Wadati check
The Wadati check checks the consistency of S picks. For this the SP-time, the time difference between S and P onset is plotted against the P onset time. A line is fitted to the points, which minimizes the error. Then the deviation of single picks to this line is checked. If the deviation in seconds is above the *wdttolerance* parameter ([see here](tuning.md#Pick-quality-control)), the pick is marked as invalid.
The Wadati check checks the consistency of S picks. For this the SP-time, the time difference between S and P onset is
plotted against the P onset time. A line is fitted to the points, which minimizes the error. Then the deviation of
single picks to this line is checked. If the deviation in seconds is above the *wdttolerance*
parameter ([see here](tuning.md#Pick-quality-control)), the pick is marked as invalid.
<img src=images/gui/wadati_plot.png title="Output diagram of Wadati check">
*The Wadati plot in PyLoT shows the SP onset time difference over the P onset time. A first line is fitted (black). All picks which deviate to much from this line are marked invalid (red). Then a second line is fitted which excludes the invalid picks. From this lines slope, the ratio of P and S wave velocity is determined.*
*The Wadati plot in PyLoT shows the SP onset time difference over the P onset time. A first line is fitted (black). All
picks which deviate to much from this line are marked invalid (red). Then a second line is fitted which excludes the
invalid picks. From this lines slope, the ratio of P and S wave velocity is determined.*
### Comparison between automatic and manual picks
Every pick in PyLoT consists of an earliest possible, latest possible and most likely onset time.
The earliest and latest possible onset time characterize the uncertainty of a pick.
This approach is described in Diel, Kissling and Bormann (2012) - Tutorial for consistent phase picking at local to regional distances.
These times are represented as a Probability Density Function (PDF) for every pick.
The PDF is implemented as two exponential distributions around the most likely onset as the expected value.
Every pick in PyLoT consists of an earliest possible, latest possible and most likely onset time. The earliest and
latest possible onset time characterize the uncertainty of a pick. This approach is described in Diel, Kissling and
Bormann (2012) - Tutorial for consistent phase picking at local to regional distances. These times are represented as a
Probability Density Function (PDF) for every pick. The PDF is implemented as two exponential distributions around the
most likely onset as the expected value.
To compare two single picks, their PDFs are cross correlated to create a new PDF.
This corresponds to the subtraction of the automatic pick from the manual pick.
To compare two single picks, their PDFs are cross correlated to create a new PDF. This corresponds to the subtraction of
the automatic pick from the manual pick.
<img src=images/gui/comparison/comparison_pdf.png title="Comparison between automatic and manual pick">
*Comparison between an automatic and a manual pick for a station in PyLoT by comparing their PDFs.*
*The upper plot shows the difference between the two single picks that are shown in the lower plot.*
*The difference is implemented as a cross correlation between the two PDFs. and results in a new PDF, the comparison PDF.*
*The expected value of the comparison PDF corresponds to the time distance between the automatic and manual picks most likely onset.*
*The difference is implemented as a cross correlation between the two PDFs. and results in a new PDF, the comparison
PDF.*
*The expected value of the comparison PDF corresponds to the time distance between the automatic and manual picks most
likely onset.*
*The standard deviation corresponds to the combined uncertainty.*
To compare the automatic and manual picks between multiple stations of an event, the properties of all the comparison PDFs are shown in a histogram.
To compare the automatic and manual picks between multiple stations of an event, the properties of all the comparison
PDFs are shown in a histogram.
<img src=images/gui/comparison/compare_widget.png title="Comparison between picks of an event">
@ -350,11 +445,13 @@ To compare the automatic and manual picks between multiple stations of an event,
*The bottom left plot shows the expected values of the comparison PDFs for P picks.*
*The top right plot shows the standard deviation of the comparison PDFs for S picks.*
*The bottom right plot shows the expected values of the comparison PDFs for S picks.*
*The standard deviation plots show that most P picks have an uncertainty between 1 and 2 seconds, while S pick uncertainties have a much larger spread between 1 to 15 seconds.*
*The standard deviation plots show that most P picks have an uncertainty between 1 and 2 seconds, while S pick
uncertainties have a much larger spread between 1 to 15 seconds.*
*This means P picks have higher quality classes on average than S picks.*
*The expected values are largely negative, meaning that the algorithm tends to pick earlier than the analyst with the applied settings (Manual - Automatic).*
*The number of samples mentioned in the plots legends is the amount of stations that have an automatic and a manual P pick.*
*The expected values are largely negative, meaning that the algorithm tends to pick earlier than the analyst with the
applied settings (Manual - Automatic).*
*The number of samples mentioned in the plots legends is the amount of stations that have an automatic and a manual P
pick.*
### Export and Import of automatic picks
@ -367,7 +464,11 @@ To be added.
# FAQ
Q: During manual picking the error "No channel to plot for phase ..." is displayed, and I am unable to create a pick.
A: Select a channel that should be used for the corresponding phase in the Pickwindow. For further information read [Picking Window settings](#picking-window-settings).
A: Select a channel that should be used for the corresponding phase in the Pickwindow. For further information
read [Picking Window settings](#picking-window-settings).
Q: I see a warning "Mismatch in event identifiers" when loading picks from a file.
A: This means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have been saved under a different installation of PyLoT but with the same waveform data, which means they are still compatible and you can continue loading them or they could be the picks of a different event, in which case loading them is not recommended.
A: This means that PyLoT doesn't recognize the picks in the file as belonging to this specific event. They could have
been saved under a different installation of PyLoT but with the same waveform data, which means they are still
compatible and you can continue loading them or they could be the picks of a different event, in which case loading them
is not recommended.

143
docs/tuning.md
View File

@ -8,12 +8,18 @@ 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. |
| *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
@ -21,15 +27,24 @@ 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. |
| *
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
@ -37,16 +52,26 @@ 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. |
| *
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
@ -54,9 +79,13 @@ 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*). |
| *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
@ -64,12 +93,19 @@ 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. |
| *
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
@ -77,15 +113,27 @@ 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 *minsiglength* after the initial P pick *minpercent* of samples have to be larger than the RMS value. |
| *zfac* | To recognize misattributed S picks, the RMS amplitude of vertical and horizontal traces are compared. The RMS amplitude of the vertical traces has to be at least *zfac* higher than the RMS amplitude on the horizontal traces for the pick to be accepted as a valid P pick. |
| *jackfactor* | A P pick is removed if the jackknife pseudo value of the variance of his subgroup is larger than the variance of all picks multiplied with the *jackfactor*. |
| *mdttolerance* | Maximum allowed deviation of P onset times from the median. Value in seconds. |
| *wdttolerance* | Maximum allowed deviation of S onset times from the line during the Wadati test. Value in seconds. |
| *
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. |
| *
zfac* | To recognize misattributed S picks, the RMS amplitude of vertical and horizontal traces are compared. The RMS amplitude of the vertical traces has to be at least *
zfac* higher than the RMS amplitude on the horizontal traces for the pick to be accepted as a valid P pick. |
| *
jackfactor* | A P pick is removed if the jackknife pseudo value of the variance of his subgroup is larger than the variance of all picks multiplied with the *
jackfactor*. |
| *
mdttolerance* | Maximum allowed deviation of P onset times from the median. Value in seconds. |
| *
wdttolerance* | Maximum allowed deviation of S onset times from the line during the Wadati test. Value in seconds. |
## Pick quality determination
@ -93,7 +141,10 @@ 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. |
| *
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. |

9
pylot/core/io/data.py
View File

@ -3,16 +3,16 @@
import copy
import os
from PySide2.QtWidgets import QMessageBox
from obspy import read_events
from obspy.core import read, Stream, UTCDateTime
from obspy.core.event import Event as ObsPyEvent
from obspy.io.sac import SacIOError
from PySide2.QtWidgets import QMessageBox
import pylot.core.loc.velest as velest
import pylot.core.loc.focmec as focmec
import pylot.core.loc.hypodd as hypodd
import pylot.core.loc.velest as velest
from pylot.core.io.phases import readPILOTEvent, picks_from_picksdict, \
picksdict_from_pilot, merge_picks, PylotParameter
from pylot.core.util.errors import FormatError, OverwriteError
@ -503,7 +503,8 @@ class Data(object):
real_or_syn_data[synthetic] += read(fname, format='GSE2', starttime=self.tstart, endtime=self.tstop)
except Exception as e:
try:
real_or_syn_data[synthetic] += read(fname, format='SEGY', starttime=self.tstart, endtime=self.tstop)
real_or_syn_data[synthetic] += read(fname, format='SEGY', starttime=self.tstart,
endtime=self.tstop)
except Exception as e:
warnmsg += '{0}\n{1}\n'.format(fname, e)
except SacIOError as se:

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

@ -8,14 +8,10 @@
Edited for use in PyLoT
JG, igem, 01/2022
"""
import pdb
import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
import glob
from obspy.core.event import read_events
from pyproj import Proj
import glob
"""
Creates an eventlist file summarizing all events found in a certain folder. Only called by pressing UI Button eventlis_xml_action
@ -24,14 +20,15 @@ Creates an eventlist file summarizing all events found in a certain folder. Only
:param path: Path to root folder where single Event folder are to found
"""
def geteventlistfromxml(path, outpath):
p = Proj(proj='utm', zone=32, ellps='WGS84')
# open eventlist file and write header
evlist = outpath + '/eventlist'
evlistobj = open(evlist, 'w')
evlistobj.write('EventID Date To Lat Lon EAST NORTH Dep Ml NoP NoS RMS errH errZ Gap \n')
evlistobj.write(
'EventID Date To Lat Lon EAST NORTH Dep Ml NoP NoS RMS errH errZ Gap \n')
# data path
dp = path + "/e*/*.xml"
@ -52,7 +49,8 @@ def geteventlistfromxml(path, outpath):
NoP = []
NoS = []
except IndexError:
print ('Insufficient data found for event (not localised): ' + names.split('/')[-1].split('_')[-1][:-4] + ' Skipping event for eventlist.' )
print('Insufficient data found for event (not localised): ' + names.split('/')[-1].split('_')[-1][
:-4] + ' Skipping event for eventlist.')
continue
for i in range(len(cat.events[0].origins[0].arrivals)):
@ -72,8 +70,10 @@ def geteventlistfromxml(path, outpath):
# write into eventlist
evlistobj.write('%s %s %s %9.6f %9.6f %13.6f %13.6f %8.6f %3.1f %d %d NaN %d %d %d\n' % (evID, \
Date, To, Lat, Lon, EAST, NORTH, Dep, Ml, len(NoP), len(NoS), errH, errZ, Gap))
Date, To, Lat, Lon,
EAST, NORTH, Dep, Ml,
len(NoP), len(NoS),
errH, errZ, Gap))
print('Adding Event ' + names.split('/')[-1].split('_')[-1][:-4] + ' to eventlist')
print('Eventlist created and saved in: ' + outpath)
evlistobj.close()

11
pylot/core/io/getQualitiesfromxml.py
View File

@ -9,14 +9,14 @@
Edited for usage in PyLoT: Jeldrik Gaal, igem, 01/2022
"""
import argparse
import numpy as np
import matplotlib.pyplot as plt
from obspy.core.event import read_events
import glob
def getQualitiesfromxml(path):
import matplotlib.pyplot as plt
import numpy as np
from obspy.core.event import read_events
def getQualitiesfromxml(path):
# uncertainties
ErrorsP = [0.02, 0.04, 0.08, 0.16]
ErrorsS = [0.04, 0.08, 0.16, 0.32]
@ -136,4 +136,3 @@ def getQualitiesfromxml(path):
plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
plt.legend([p1, p2], ['P-Weights', 'S-Weights'])
plt.show()

6
pylot/core/io/phases.py
View File

@ -1,12 +1,13 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import glob
import os
import warnings
import matplotlib.pyplot as plt
import numpy as np
import obspy.core.event as ope
import os
import scipy.io as sio
import warnings
from obspy.core import UTCDateTime
from obspy.core.event import read_events
from obspy.core.util import AttribDict
@ -1005,6 +1006,7 @@ def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
fid1.close()
fid2.close()
def chooseArrival(arrivals):
"""
takes arrivals and returns the manual picks if manual and automatic ones are there

3
pylot/core/loc/nll.py
View File

@ -4,11 +4,12 @@
import glob
import os
import subprocess
from obspy import read_events
from pylot.core.io.phases import writephases
from pylot.core.util.utils import getPatternLine, runProgram
from pylot.core.util.gui import which
from pylot.core.util.utils import getPatternLine, runProgram
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()

93
pylot/core/pick/autopick.py
View File

@ -9,21 +9,21 @@ function conglomerate utils.
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import copy
import traceback
import matplotlib.pyplot as plt
import numpy as np
import traceback
from obspy import Trace
from obspy.taup import TauPyModel
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
getSNR, fmpicker, checkPonsets, wadaticheck, get_pickparams, get_quality_class
getSNR, fmpicker, checkPonsets, wadaticheck, get_quality_class
from pylot.core.util.utils import getPatternLine, gen_Pool, \
get_Bool, identifyPhaseID, get_None, correct_iplot
from obspy.taup import TauPyModel
from obspy import Trace
def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None, ncores=0, metadata=None, origin=None):
"""
@ -337,7 +337,8 @@ class AutopickStation(object):
for key in self.channelorder:
waveform_data[key] = self.wfstream.select(component=key) # try ZNE first
if len(waveform_data[key]) == 0:
waveform_data[key] = self.wfstream.select(component=str(self.channelorder[key])) # use 123 as second option
waveform_data[key] = self.wfstream.select(
component=str(self.channelorder[key])) # use 123 as second option
return waveform_data['Z'], waveform_data['N'], waveform_data['E']
def get_traces_from_streams(self):
@ -588,9 +589,11 @@ class AutopickStation(object):
plt_flag = 0
fig._tight = True
ax1 = fig.add_subplot(311)
tdata = np.linspace(start=0, stop=self.ztrace.stats.endtime-self.ztrace.stats.starttime, num=self.ztrace.stats.npts)
tdata = np.linspace(start=0, stop=self.ztrace.stats.endtime - self.ztrace.stats.starttime,
num=self.ztrace.stats.npts)
# 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')
ax1.plot(tdata, self.tr_filt_z_bpz2.data / max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7,
label='Data')
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')
@ -631,23 +634,28 @@ class AutopickStation(object):
if self.horizontal_traces_exist() and self.s_data.Sflag == 1:
# plot E trace
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
th1data = np.linspace(0, self.etrace.stats.endtime-self.etrace.stats.starttime, self.etrace.stats.npts)
th1data = np.linspace(0, self.etrace.stats.endtime - self.etrace.stats.starttime,
self.etrace.stats.npts)
# plot filtered and tapered waveform
ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7, label='Data')
ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7,
label='Data')
if self.p_results.weight < 4:
# plot initial CF (ARHcf or AR3Ccf)
ax2.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
ax2.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b',
label='CF1')
if self.s_data.aicSflag == 1 and self.s_results.weight <= 4:
aicarhpick = self.aicarhpick
refSpick = self.refSpick
# plot second cf, used for determing precise onset (ARHcf or AR3Ccf)
ax2.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm', label='CF2')
ax2.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm',
label='CF2')
# plot preliminary onset time, calculated from CF1
ax2.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g', label='Initial S Onset')
ax2.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
ax2.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
# plot precise onset time, calculated from CF2
ax2.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
ax2.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2,
label='Final S Pick')
ax2.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
ax2.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
ax2.plot([self.s_results.lpp, self.s_results.lpp], [-1.1, 1.1], 'g--', label='lpp')
@ -667,15 +675,19 @@ class AutopickStation(object):
# plot N trace
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
th2data= np.linspace(0, self.ntrace.stats.endtime-self.ntrace.stats.starttime, self.ntrace.stats.npts)
th2data = np.linspace(0, self.ntrace.stats.endtime - self.ntrace.stats.starttime,
self.ntrace.stats.npts)
# plot trace
ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7, label='Data')
ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7,
label='Data')
if self.p_results.weight < 4:
p22, = ax3.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b', label='CF1')
p22, = ax3.plot(self.arhcf1.getTimeArray(), self.arhcf1.getCF() / max(self.arhcf1.getCF()), 'b',
label='CF1')
if self.s_data.aicSflag == 1:
aicarhpick = self.aicarhpick
refSpick = self.refSpick
ax3.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm', label='CF2')
ax3.plot(self.arhcf2.getTimeArray(), self.arhcf2.getCF() / max(self.arhcf2.getCF()), 'm',
label='CF2')
ax3.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g', label='Initial S Onset')
ax3.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
ax3.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
@ -716,7 +728,8 @@ class AutopickStation(object):
if aicpick.getpick() is None:
msg = "Bad initial (AIC) P-pick, skipping this onset!\nAIC-SNR={0}, AIC-Slope={1}counts/s\n " \
"(min. AIC-SNR={2}, min. AIC-Slope={3}counts/s)"
msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"],
self.pickparams["minAICPslope"])
self.vprint(msg)
return 0
# Quality check initial pick with minimum signal length
@ -732,8 +745,10 @@ class AutopickStation(object):
minsiglength = minsiglength / 2
else:
# filter, taper other traces as well since signal length is compared on all traces
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.pickparams["bph1"][0], freqmax=self.pickparams["bph1"][1])
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.pickparams["bph1"][0], freqmax=self.pickparams["bph1"][1])
trH1_filt, _ = self.prepare_wfstream(self.estream, freqmin=self.pickparams["bph1"][0],
freqmax=self.pickparams["bph1"][1])
trH2_filt, _ = self.prepare_wfstream(self.nstream, freqmin=self.pickparams["bph1"][0],
freqmax=self.pickparams["bph1"][1])
zne += trH1_filt
zne += trH2_filt
minsiglength = minsiglength
@ -819,15 +834,18 @@ class AutopickStation(object):
# 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)
Tsmooth=self.pickparams["aictsmooth"], fig=self.current_figure,
linecolor=self.current_linecolor)
# save aicpick for plotting later
self.p_data.aicpick = aicpick
# add pstart and pstop to aic plot
if self.current_figure:
# TODO remove plotting from picking, make own plot function
for ax in self.current_figure.axes:
ax.vlines(self.pickparams["pstart"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
ax.vlines(self.pickparams["pstop"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
ax.vlines(self.pickparams["pstart"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed',
label='P start')
ax.vlines(self.pickparams["pstop"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed',
label='P stop')
ax.legend(loc=1)
Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt)
@ -841,7 +859,8 @@ class AutopickStation(object):
error_msg = 'AIC P onset slope to small: got {}, min {}'.format(slope, self.pickparams["minAICPslope"])
raise PickingFailedException(error_msg)
if aicpick.getSNR() < self.pickparams["minAICPSNR"]:
error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(), self.pickparams["minAICPSNR"])
error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(),
self.pickparams["minAICPSNR"])
raise PickingFailedException(error_msg)
self.p_data.p_aic_plot_flag = 1
@ -849,7 +868,8 @@ class AutopickStation(object):
'autopickstation: re-filtering vertical trace...'.format(aicpick.getSlope(), aicpick.getSNR())
self.vprint(msg)
# refilter waveform with larger bandpass
tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.pickparams["bpz2"][0], freqmax=self.pickparams["bpz2"][1])
tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.pickparams["bpz2"][0],
freqmax=self.pickparams["bpz2"][1])
# save filtered trace in instance for later plotting
self.tr_filt_z_bpz2 = tr_filt
# determine new times around initial onset
@ -864,22 +884,26 @@ class AutopickStation(object):
'corrupted'.format(self.pickparams["algoP"])
self.set_current_figure('refPpick')
# get refined onset time from CF2
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)
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)
# save PragPicker result for plotting
self.p_data.refPpick = refPpick
self.p_results.mpp = refPpick.getpick()
if self.p_results.mpp is None:
msg = 'Bad initial (AIC) P-pick, skipping this onset!\n AIC-SNR={}, AIC-Slope={}counts/s\n' \
'(min. AIC-SNR={}, min. AIC-Slope={}counts/s)'
msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"],
self.pickparams["minAICPslope"])
self.vprint(msg)
self.s_data.Sflag = 0
raise PickingFailedException(msg)
# quality assessment, get earliest/latest pick and symmetrized uncertainty
# todo quality assessment in own function
self.set_current_figure('el_Ppick')
elpicker_results = earllatepicker(z_copy, self.pickparams["nfacP"], self.pickparams["tsnrz"], self.p_results.mpp,
elpicker_results = earllatepicker(z_copy, self.pickparams["nfacP"], self.pickparams["tsnrz"],
self.p_results.mpp,
self.iplot, fig=self.current_figure, linecolor=self.current_linecolor)
self.p_results.epp, self.p_results.lpp, self.p_results.spe = elpicker_results
snr_results = getSNR(z_copy, self.pickparams["tsnrz"], self.p_results.mpp)
@ -887,7 +911,8 @@ class AutopickStation(object):
# weight P-onset using symmetric error
self.p_results.weight = get_quality_class(self.p_results.spe, self.pickparams["timeerrorsP"])
if self.p_results.weight <= self.pickparams["minfmweight"] and self.p_results.snr >= self.pickparams["minFMSNR"]:
if self.p_results.weight <= self.pickparams["minfmweight"] and self.p_results.snr >= self.pickparams[
"minFMSNR"]:
# if SNR is high enough, try to determine first motion of onset
self.set_current_figure('fm_picker')
self.p_results.fm = fmpicker(self.zstream, z_copy, self.pickparams["fmpickwin"], self.p_results.mpp,
@ -1115,7 +1140,8 @@ class AutopickStation(object):
# 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)
Tsmooth=self.pickparams["aictsmoothS"], fig=self.current_figure,
linecolor=self.current_linecolor)
# save pick for later plotting
self.aicarhpick = aicarhpick
@ -1126,8 +1152,10 @@ class AutopickStation(object):
# get refined onset time from CF2
self.set_current_figure('refSpick')
refSpick = PragPicker(arhcf2, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot, self.pickparams["ausS"],
self.pickparams["tsmoothS"], aicarhpick.getpick(), self.current_figure, self.current_linecolor)
refSpick = PragPicker(arhcf2, self.pickparams["tsnrh"], self.pickparams["pickwinS"], self.iplot,
self.pickparams["ausS"],
self.pickparams["tsmoothS"], aicarhpick.getpick(), self.current_figure,
self.current_linecolor)
# save refSpick for later plotitng
self.refSpick = refSpick
self.s_results.mpp = refSpick.getpick()
@ -1151,7 +1179,6 @@ class AutopickStation(object):
self.current_linecolor = plot_style['linecolor']['rgba_mpl']
def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None, metadata=None, origin=None):
"""
Main function to calculate picks for the station.

11
pylot/core/pick/charfuns.py
View File

@ -18,8 +18,8 @@ autoregressive prediction: application ot local and regional distances, Geophys.
"""
import numpy as np
from scipy import signal
from obspy.core import Stream
from scipy import signal
class CharacteristicFunction(object):
@ -313,7 +313,8 @@ class HOScf(CharacteristicFunction):
class ARZcf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"], fnoise=pickparams["addnoise"])
super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"],
fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""
@ -448,7 +449,8 @@ class ARZcf(CharacteristicFunction):
class ARHcf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""
@ -600,7 +602,8 @@ class ARHcf(CharacteristicFunction):
class AR3Ccf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"],
fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""

6
pylot/core/pick/compare.py
View File

@ -2,10 +2,11 @@
# -*- coding: utf-8 -*-
import copy
import matplotlib.pyplot as plt
import numpy as np
import operator
import os
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import AttribDict
from pylot.core.util.pdf import ProbabilityDensityFunction
@ -400,6 +401,7 @@ class PDFstatistics(object):
This object can be used to get various statistic values from probability density functions.
Takes a path as argument.
"""
# TODO: change root to datapath
def __init__(self, directory):

3
pylot/core/pick/picker.py
View File

@ -19,9 +19,10 @@ calculated after Diehl & Kissling (2009).
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import warnings
import matplotlib.pyplot as plt
import numpy as np
import warnings
from scipy.signal import argrelmax, argrelmin
from pylot.core.pick.charfuns import CharacteristicFunction

17
pylot/core/pick/utils.py
View File

@ -9,10 +9,11 @@
"""
import warnings
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import argrelmax
from obspy.core import Stream, UTCDateTime
from scipy.signal import argrelmax
from pylot.core.util.utils import get_Bool, get_None, SetChannelComponents
@ -539,6 +540,7 @@ def getslopewin(Tcf, Pick, tslope):
slope = np.where((Tcf <= min(Pick + tslope, Tcf[-1])) & (Tcf >= Pick))
return slope[0]
def getResolutionWindow(snr, extent):
"""
Produce the half of the time resolution window width from given SNR value
@ -1329,6 +1331,7 @@ def get_quality_class(uncertainty, weight_classes):
quality = len(weight_classes)
return quality
def set_NaNs_to(data, nan_value):
"""
Replace all NaNs in data with nan_value
@ -1344,6 +1347,7 @@ def set_NaNs_to(data, nan_value):
data[nn] = nan_value
return data
def taper_cf(cf):
"""
Taper cf data to get rid off of side maximas
@ -1355,6 +1359,7 @@ def taper_cf(cf):
tap = np.hanning(len(cf))
return tap * cf
def cf_positive(cf):
"""
Shifts cf so that all values are positive
@ -1365,6 +1370,7 @@ def cf_positive(cf):
"""
return cf + max(abs(cf))
def smooth_cf(cf, t_smooth, delta):
"""
Smooth cf by taking samples over t_smooth length
@ -1393,6 +1399,7 @@ def smooth_cf(cf, t_smooth, delta):
cf_smooth -= offset # remove offset from smoothed function
return cf_smooth
def check_counts_ms(data):
"""
check if data is in counts or m/s
@ -1475,8 +1482,10 @@ def get_pickparams(pickparam):
:rtype: (dict, dict, dict, dict)
"""
# Define names of all parameters in different groups
p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(' ')
s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(' ')
p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(
' ')
s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(
' ')
first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
# Get list of values from pickparam by name
@ -1494,6 +1503,7 @@ def get_pickparams(pickparam):
return p_params, s_params, first_motion_params, signal_length_params
def getQualityFromUncertainty(uncertainty, Errors):
# set initial quality to 4 (worst) and change only if one condition is hit
quality = 4
@ -1517,6 +1527,7 @@ def getQualityFromUncertainty(uncertainty, Errors):
return quality
if __name__ == '__main__':
import doctest

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

@ -1,27 +1,22 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import os
import matplotlib
from PySide2 import QtCore, QtGui, QtWidgets
from PySide2.QtCore import Qt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib.patheffects as PathEffects
import traceback
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy.feature as cf
from cartopy.mpl.gridliner import LongitudeFormatter, LatitudeFormatter
import traceback
import obspy
import matplotlib
import matplotlib.patheffects as PathEffects
import matplotlib.pyplot as plt
import numpy as np
import obspy
from PySide2 import QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from scipy.interpolate import griddata
from pylot.core.util.widgets import PickDlg
from pylot.core.pick.utils import get_quality_class
from pylot.core.util.widgets import PickDlg
matplotlib.use('Qt5Agg')
@ -173,7 +168,8 @@ class Array_map(QtWidgets.QWidget):
self.canvas.fig.tight_layout()
def add_merid_paral(self):
self.gridlines = self.canvas.axes.gridlines(draw_labels=False, alpha=0.6, color='gray', linewidth=self.linewidth/2, zorder=7)
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

5
pylot/core/util/dataprocessing.py
View File

@ -2,9 +2,10 @@
# -*- coding: utf-8 -*-
import glob
import numpy as np
import os
import sys
import numpy as np
from obspy import UTCDateTime, read_inventory, read
from obspy.io.xseed import Parser
@ -211,6 +212,7 @@ class Metadata(object):
self.stations_dict[st_id] = {'latitude': station[0].latitude,
'longitude': station[0].longitude,
'elevation': station[0].elevation}
read_stat = {'xml': stat_info_from_inventory,
'dless': stat_info_from_parser}
@ -380,6 +382,7 @@ def check_time(datetime):
except ValueError:
return False
# TODO: change root to datapath
def get_file_list(root_dir):
"""

1
pylot/core/util/event.py
View File

@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
import os
from obspy import UTCDateTime
from obspy.core.event import Event as ObsPyEvent
from obspy.core.event import Origin, ResourceIdentifier

6
pylot/core/util/generate_array_maps.py
View File

@ -3,6 +3,7 @@
# small script that creates array maps for each event within a previously generated PyLoT project
import os
num_thread = "16"
os.environ["OMP_NUM_THREADS"] = num_thread
os.environ["OPENBLAS_NUM_THREADS"] = num_thread
@ -15,6 +16,7 @@ import multiprocessing
import sys
import glob
import matplotlib
matplotlib.use('Qt5Agg')
sys.path.append(os.path.join('/'.join(sys.argv[0].split('/')[:-1]), '../../..'))
@ -52,7 +54,8 @@ def array_map_worker(input_dict):
print('Working on event: {} ({}/{})'.format(eventdir, input_dict['index'] + 1, input_dict['nEvents']))
xml_picks = glob.glob(os.path.join(eventdir, f'*{input_dict["f_ext"]}.xml'))
if not len(xml_picks):
print('Event {} does not have any picks associated with event file extension {}'. format(eventdir, input_dict['f_ext']))
print('Event {} does not have any picks associated with event file extension {}'.format(eventdir,
input_dict['f_ext']))
return
# check for picks
manualpicks = event.getPicks()
@ -92,4 +95,3 @@ if __name__ == '__main__':
for infile in args.infiles:
main(os.path.join(args.dataroot, infile), f_ext='_correlated_0.03-0.1', ncores=args.ncores)

2
pylot/core/util/gui.py
View File

@ -11,7 +11,6 @@ try:
except Exception as e:
print('Warning: Could not import module QtCore.')
from pylot.core.util.utils import pick_color
@ -101,4 +100,3 @@ def make_pen(picktype, phase, key, quality):
linestyle, width = pick_linestyle_pg(picktype, key)
pen = pg.mkPen(rgba, width=width, style=linestyle)
return pen

1
pylot/core/util/obspyDMT_interface.py
View File

@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
import os
from obspy import UTCDateTime

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

@ -1,8 +1,9 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import warnings
import numpy as np
from obspy import UTCDateTime
from pylot.core.util.utils import fit_curve, clims

5
pylot/core/util/thread.py
View File

@ -1,6 +1,9 @@
# -*- coding: utf-8 -*-
import sys, os, traceback
import multiprocessing
import os
import sys
import traceback
from PySide2.QtCore import QThread, Signal, Qt, Slot, QRunnable, QObject
from PySide2.QtWidgets import QDialog, QProgressBar, QLabel, QHBoxLayout, QPushButton

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

@ -2,12 +2,13 @@
# -*- coding: utf-8 -*-
import hashlib
import numpy as np
import os
import platform
import re
import subprocess
import warnings
import numpy as np
from obspy import UTCDateTime, read
from obspy.core import AttribDict
from obspy.signal.rotate import rotate2zne

2
pylot/core/util/version.py
View File

@ -35,9 +35,9 @@ from __future__ import print_function
__all__ = "get_git_version"
import inspect
# NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)
import os
import inspect
from subprocess import Popen, PIPE
# NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)

12
pylot/core/util/widgets.py
View File

@ -8,15 +8,16 @@ Created on Wed Mar 19 11:27:35 2014
import copy
import datetime
import getpass
import matplotlib
import multiprocessing
import numpy as np
import os
import subprocess
import sys
import time
import traceback
import matplotlib
import numpy as np
matplotlib.use('QT5Agg')
from matplotlib.figure import Figure
@ -36,7 +37,7 @@ from PySide2.QtWidgets import QAction, QApplication, QCheckBox, QComboBox, \
QGridLayout, QLabel, QLineEdit, QMessageBox, \
QTabWidget, QToolBar, QVBoxLayout, QHBoxLayout, QWidget, \
QPushButton, QFileDialog, QInputDialog
from PySide2.QtCore import QSettings, Qt, QUrl, Signal, Slot
from PySide2.QtCore import QSettings, Qt, QUrl, Signal
from PySide2.QtWebEngineWidgets import QWebEngineView as QWebView
from obspy import Stream, Trace, UTCDateTime
from obspy.core.util import AttribDict
@ -65,19 +66,20 @@ else:
raise ImportError(f'Python version {sys.version_info.major} of current interpreter not supported.'
f'\nPlease use Python 3+.')
# workaround to prevent PyCharm from deleting icons_rc import when optimizing imports
# icons_rc = icons_rc
icons_rc = icons_rc
class QSpinBox(QtWidgets.QSpinBox):
''' Custom SpinBox, insensitive to Mousewheel (prevents accidental changes when scrolling through parameters) '''
def wheelEvent(self, event):
event.ignore()
class QDoubleSpinBox(QtWidgets.QDoubleSpinBox):
''' Custom DoubleSpinBox, insensitive to Mousewheel (prevents accidental changes when scrolling through parameters) '''
def wheelEvent(self, event):
event.ignore()

2
tests/testPickingResults.py
View File

@ -1,6 +1,8 @@
import unittest
from pylot.core.pick.autopick import PickingResults
class TestPickingResults(unittest.TestCase):
def setUp(self):

7
tests/test_Metadata/test_Metadata.py
View File

@ -1,5 +1,6 @@
import os
import unittest
from obspy import UTCDateTime
from obspy.io.xseed import Parser
from obspy.io.xseed.utils import SEEDParserException
@ -197,7 +198,8 @@ class TestMetadataMultipleTime(unittest.TestCase):
def setUp(self):
self.seed_id = 'LE.ROTT..HN'
path = os.path.dirname(__file__) # gets path to currently running script
metadata = os.path.join('test_data', 'dless_multiple_times', 'MAGS2_LE_ROTT.dless') # specific subfolder of test data
metadata = os.path.join('test_data', 'dless_multiple_times',
'MAGS2_LE_ROTT.dless') # specific subfolder of test data
metadata_path = os.path.join(path, metadata)
self.m = Metadata(metadata_path)
self.p = Parser(metadata_path)
@ -299,7 +301,8 @@ Channels:
def setUp(self):
self.seed_id = 'KB.TMO07.00.HHZ'
path = os.path.dirname(__file__) # gets path to currently running script
metadata = os.path.join('test_data', 'dless_multiple_instruments', 'MAGS2_KB_TMO07.dless') # specific subfolder of test data
metadata = os.path.join('test_data', 'dless_multiple_instruments',
'MAGS2_KB_TMO07.dless') # specific subfolder of test data
metadata_path = os.path.join(path, metadata)
self.m = Metadata(metadata_path)
self.p = Parser(metadata_path)

163
tests/test_autopickstation/test_autopickstation.py
View File

@ -1,12 +1,13 @@
import unittest
from unittest import skip
import obspy
from obspy import UTCDateTime
import os
import sys
from pylot.core.pick.autopick import autopickstation
from pylot.core.io.inputs import PylotParameter
import unittest
import obspy
from obspy import UTCDateTime
from pylot.core.io.data import Data
from pylot.core.io.inputs import PylotParameter
from pylot.core.pick.autopick import autopickstation
from pylot.core.util.utils import trim_station_components
@ -96,7 +97,8 @@ class TestAutopickStation(unittest.TestCase):
self.xml_file = os.path.join(os.path.dirname(__file__), self.event_id, 'PyLoT_' + self.event_id + '.xml')
self.data = Data(evtdata=self.xml_file)
# create origin for taupy testing
self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0, time=UTCDateTime("2016-01-24T10:30:30.0"))]
self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0,
time=UTCDateTime("2016-01-24T10:30:30.0"))]
# mocking metadata since reading it takes a long time to read from file
self.metadata = MockMetadata()
@ -105,39 +107,87 @@ class TestAutopickStation(unittest.TestCase):
# @skip("Works")
def test_autopickstation_taupy_disabled_gra1(self):
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000),
'w0': None, 'spe': 0.93333333333333235, 'network': u'GR',
'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000),
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905,
'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000),
'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665,
'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled,
metadata=(None, None))
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('GRA1', station)
def test_autopickstation_taupy_enabled_gra1(self):
expected = {'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000),
'w0': None, 'spe': 0.93333333333333235, 'network': u'GR',
'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000),
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905,
'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000),
'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665,
'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('GRA1', station)
def test_autopickstation_taupy_disabled_gra2(self):
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None,
'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None,
'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000),
'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None,
'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000),
'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled,
metadata=(None, None))
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('GRA2', station)
def test_autopickstation_taupy_enabled_gra2(self):
expected = {'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000), 'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000),
'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000),
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294,
'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000),
'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667,
'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin = self.origin)
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('GRA2', station)
def test_autopickstation_taupy_disabled_ech(self):
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'fm': None, 'spe': None, 'channel': u'LHE'}}
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None,
'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None,
'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41),
'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None,
'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57),
'fm': None, 'spe': None, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
@ -146,16 +196,32 @@ class TestAutopickStation(unittest.TestCase):
def test_autopickstation_taupy_enabled_ech(self):
# this station has a long time of before the first onset, so taupy will help during picking
expected = {'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None, 'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None, 'spe': 3.3333333333333335, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None,
'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29),
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577,
'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None,
'spe': 3.3333333333333335, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('ECH', station)
def test_autopickstation_taupy_disabled_fiesa(self):
# this station has a long time of before the first onset, so taupy will help during picking
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'fm': None, 'spe': None, 'channel': u'LHE'}}
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None,
'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None,
'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42),
'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None,
'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58),
'fm': None, 'spe': None, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
@ -164,9 +230,18 @@ class TestAutopickStation(unittest.TestCase):
def test_autopickstation_taupy_enabled_fiesa(self):
# this station has a long time of before the first onset, so taupy will help during picking
expected = {'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None, 'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2), 'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None,
'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None,
'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333),
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849,
'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2),
'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
self.assertEqual('FIESA', station)
@ -176,7 +251,8 @@ class TestAutopickStation(unittest.TestCase):
wfstream = self.gra1.copy()
wfstream = wfstream.select(channel='*E') + wfstream.select(channel='*N')
with HidePrints():
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
metadata=(None, None))
self.assertIsNone(result)
self.assertEqual('GRA1', station)
@ -184,17 +260,36 @@ class TestAutopickStation(unittest.TestCase):
"""Picking on a stream without horizontal traces should still pick the P phase on the vertical component"""
wfstream = self.gra1.copy()
wfstream = wfstream.select(channel='*Z')
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None, 'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None, 'fm': 'N', 'channel': None}}
expected = {
'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None,
'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None,
'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000),
'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D',
'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None,
'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None,
'fm': 'N', 'channel': None}}
with HidePrints():
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled,
metadata=(None, None))
self.assertEqual(expected, result)
self.assertEqual('GRA1', station)
def test_autopickstation_a106_taupy_enabled(self):
"""This station has invalid values recorded on both N and E component, but a pick can still be found on Z"""
expected = {'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None, 'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None, 'spe': None, 'fm': None, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None,
'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None,
'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30),
'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None,
'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [],
'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None,
'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None,
'spe': None, 'fm': None, 'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertEqual(expected, result)
def test_autopickstation_station_missing_in_metadata(self):
@ -202,10 +297,22 @@ class TestAutopickStation(unittest.TestCase):
relative to the theoretical onset to one relative to the traces starttime, eg never negative.
"""
self.pickparam_taupy_enabled.setParamKV('pstart', -100) # modify starttime to be relative to theoretical onset
expected = {'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None, 'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000), 'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None, 'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000), 'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None, 'channel': u'LHE'}}
expected = {
'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None,
'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000),
'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000),
'fm': None, 'channel': u'LHZ'},
'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None,
'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None,
'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000),
'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None,
'channel': u'LHE'}}
with HidePrints():
result, station = autopickstation(wfstream = self.a005a, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
result, station = autopickstation(wfstream=self.a005a, pickparam=self.pickparam_taupy_enabled,
metadata=self.metadata, origin=self.origin)
self.assertEqual(expected, result)
if __name__ == '__main__':
unittest.main()

2
tests/test_get_quality_class.py
View File

@ -1,4 +1,5 @@
import unittest
from pylot.core.pick.utils import get_quality_class
@ -52,5 +53,6 @@ class TestQualityClassFromUncertainty(unittest.TestCase):
# Error exactly in class 3
self.assertEqual(3, get_quality_class(5.6, self.error_classes))
if __name__ == '__main__':
unittest.main()

1
tests/utils.py
View File

@ -33,6 +33,7 @@ class HidePrints:
def silencer(*args, **kwargs):
with HidePrints():
func(*args, **kwargs)
return silencer
def __init__(self, hide_prints=True):