Merge branch 'github-master'

release notes:
==============
Features:
- centralize all functionalities of PyLoT and control them from within the main GUI
- handling multiple events inside GUI with project files (save and load work progress)
- GUI based adjustments of pick parameters and I/O
- interactive tuning of parameters from within the GUI
- call automatic picking algorithm from within the GUI
- comparison of automatic with manual picks for multiple events using clear differentiation of manual picks into 'tune' and 'test-set' (beta)
- manual picking of different (user defined) phase types
- phase onset estimation with ObsPy TauPy

- interactive zoom/scale functionalities in all plots (mousewheel, pan, pan-zoom)
- array map to visualize stations and control onsets (beta feature, switch to manual picks not implemented)

Platform support:
- python 3 support
- Windows support

Performance:
- multiprocessing for automatic picking and restitution of multiple stations
- use pyqtgraph library for better performance on main waveform plot

Visualization:
- pick uncertainty (quality classes) visualization with gradients
- pick color unification for all plots
- new icons and stylesheets

Known Issues:

.gitignore

@@ -1,4 +0,0 @@
-
-\.idea/
-
-*.pyc

PyLoT.py

@@ -39,7 +39,7 @@ from PySide.QtCore import QCoreApplication, QSettings, Signal, QFile, \
 from PySide.QtGui import QMainWindow, QInputDialog, QIcon, QFileDialog, \
     QWidget, QHBoxLayout, QVBoxLayout, QStyle, QKeySequence, QLabel, QFrame, QAction, \
     QDialog, QErrorMessage, QApplication, QPixmap, QMessageBox, QSplashScreen, \
-    QActionGroup, QListWidget, QDockWidget, QLineEdit, QListView, QAbstractItemView, \
+    QActionGroup, QListWidget, QLineEdit, QListView, QAbstractItemView, \
     QTreeView, QComboBox, QTabWidget, QPushButton, QGridLayout
 import numpy as np
 from obspy import UTCDateTime
@@ -49,7 +49,7 @@ from obspy.core.util import AttribDict
 try:
     import pyqtgraph as pg
 except Exception as e:
-    print('QtPyLoT: Could not import pyqtgraph. {}'.format(e))
+    print('PyLoT: Could not import pyqtgraph. {}'.format(e))
     pg = None
 
 try:
@@ -64,18 +64,18 @@ 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, removePicksAbove
+from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty
 from pylot.core.io.phases import picksdict_from_picks
 import pylot.core.loc.nll as nll
 from pylot.core.util.defaults import FILTERDEFAULTS, SetChannelComponents
-from pylot.core.util.errors import FormatError, DatastructureError, \
+from pylot.core.util.errors import DatastructureError, \
     OverwriteError
 from pylot.core.util.connection import checkurl
 from pylot.core.util.dataprocessing import read_metadata, restitute_data
 from pylot.core.util.utils import fnConstructor, getLogin, \
     full_range, readFilterInformation, trim_station_components, check4gaps, make_pen, pick_color_plt, \
     pick_linestyle_plt, remove_underscores, check4doubled, identifyPhaseID, excludeQualityClasses, has_spe, \
-    check4rotated
+    check4rotated, transform_colors_mpl, transform_colors_mpl_str
 from pylot.core.util.event import Event
 from pylot.core.io.location import create_creation_info, create_event
 from pylot.core.util.widgets import FilterOptionsDialog, NewEventDlg, \
@@ -87,6 +87,8 @@ from pylot.core.util.structure import DATASTRUCTURE
 from pylot.core.util.thread import Thread, Worker
 from pylot.core.util.version import get_git_version as _getVersionString
 
+from pylot.styles import style_settings
+
 if sys.version_info.major == 3:
     import icons_rc_3 as icons_rc
 elif sys.version_info.major == 2:
@@ -110,6 +112,7 @@ class MainWindow(QMainWindow):
             print('Using default input file {}'.format(infile))
         if os.path.isfile(infile) == False:
             infile = QFileDialog().getOpenFileName(caption='Choose PyLoT-input file')
+
             if not os.path.exists(infile[0]):
                 QMessageBox.warning(self, "PyLoT Warning",
                                     "No PyLoT-input file declared!")
@@ -124,6 +127,7 @@ class MainWindow(QMainWindow):
         self.project = Project()
         self.project.parameter = self._inputs
         self.tap = None
+        self.apw = None
         self.paraBox = None
         self.array_map = None
         self._metadata = None
@@ -140,12 +144,6 @@ class MainWindow(QMainWindow):
         # default factor for dataplot e.g. enabling/disabling scrollarea
         self.height_factor = 12
 
-        # default colors for ref/test event
-        self._colors = {
-            'ref': QtGui.QColor(200, 210, 230, 255),
-            'test': QtGui.QColor(200, 230, 200, 255)
-        }
-
         # UI has to be set up before(!) children widgets are about to show up
         self.createAction = createAction
         # read settings
@@ -174,6 +172,8 @@ class MainWindow(QMainWindow):
         self.fnames = None
         self._stime = None
         structure_setting = settings.value("data/Structure", "PILOT")
+        if not structure_setting:
+            structure_setting = 'PILOT'
         self.dataStructure = DATASTRUCTURE[structure_setting]()
         self.seismicPhase = str(settings.value("phase", "P"))
         if settings.value("data/dataRoot", None) is None:
@@ -210,6 +210,8 @@ class MainWindow(QMainWindow):
         except:
             self.startTime = UTCDateTime()
 
+        self.init_styles()
+
         pylot_icon = QIcon()
         pylot_icon.addPixmap(QPixmap(':/icons/pylot.png'))
 
@@ -551,6 +553,13 @@ class MainWindow(QMainWindow):
         self.addActions(toolbars["autoPyLoT"], pickActions)
         self.addActions(toolbars["LocationTools"], locationToolActions)
 
+        # init pyqtgraph
+        self.pg = pg
+
+        # init style
+        settings = QSettings()
+        style = settings.value('style')
+        self.set_style(style)
 
         # add event combo box and ref/test buttons
         self.eventBox = self.createEventBox()
@@ -567,21 +576,24 @@ class MainWindow(QMainWindow):
         self.eventBox.activated.connect(self.refreshEvents)
 
         # add main tab widget
-        self.tabs = QTabWidget()
+        self.tabs = QTabWidget(self)
         self._main_layout.addWidget(self.tabs)
         self.tabs.currentChanged.connect(self.refreshTabs)
 
+        # add progressbar
+        self.mainProgressBarWidget = QtGui.QWidget()
+        self._main_layout.addWidget(self.mainProgressBarWidget)
+
         # add scroll area used in case number of traces gets too high
-        self.wf_scroll_area = QtGui.QScrollArea()
+        self.wf_scroll_area = QtGui.QScrollArea(self)
 
         # create central matplotlib figure canvas widget
-        self.pg = pg
         self.init_wfWidget()
 
         # init main widgets for main tabs
-        wf_tab = QtGui.QWidget()
-        array_tab = QtGui.QWidget()
-        events_tab = QtGui.QWidget()
+        wf_tab = QtGui.QWidget(self)
+        array_tab = QtGui.QWidget(self)
+        events_tab = QtGui.QWidget(self)
 
         # init main widgets layouts
         self.wf_layout = QtGui.QVBoxLayout()
@@ -624,8 +636,8 @@ class MainWindow(QMainWindow):
             self.dataPlot = PylotCanvas(parent=self, connect_events=False, multicursor=True)
             self.dataPlot.updateWidget(xlab, None, plottitle)
         else:
-            self.pg = True
-            self.dataPlot = WaveformWidgetPG(parent=self, xlabel=xlab, ylabel=None,
+            self.pg = pg
+            self.dataPlot = WaveformWidgetPG(parent=self,
                                              title=plottitle)
         self.dataPlot.setCursor(Qt.CrossCursor)
         self.wf_scroll_area.setWidget(self.dataPlot)
@@ -636,7 +648,7 @@ class MainWindow(QMainWindow):
         '''
         Initiate/create buttons for assigning events containing manual picks to reference or test set.
         '''
-        self.ref_event_button = QtGui.QPushButton('Ref')
+        self.ref_event_button = QtGui.QPushButton('Tune')
         self.test_event_button = QtGui.QPushButton('Test')
         self.ref_event_button.setToolTip('Set manual picks of current ' +
                                          'event as reference picks for autopicker tuning.')
@@ -644,8 +656,8 @@ class MainWindow(QMainWindow):
                                           'event as test picks for autopicker testing.')
         self.ref_event_button.setCheckable(True)
         self.test_event_button.setCheckable(True)
-        self.set_button_color(self.ref_event_button, self._colors['ref'])
-        self.set_button_color(self.test_event_button, self._colors['test'])
+        self.set_button_border_color(self.ref_event_button, self._style['ref']['rgba'])
+        self.set_button_border_color(self.test_event_button, self._style['test']['rgba'])
         self.ref_event_button.clicked.connect(self.toggleRef)
         self.test_event_button.clicked.connect(self.toggleTest)
         self.ref_event_button.setEnabled(False)
@@ -663,6 +675,86 @@ class MainWindow(QMainWindow):
         if event.key() == QtCore.Qt.Key.Key_Shift:
             self._shift = False
 
+    def init_styles(self):
+        self._styles = {}
+        styles = ['default', 'dark', 'bright']
+        stylecolors = style_settings.stylecolors
+        for style in styles:
+            if style in stylecolors.keys():
+                self._styles[style] = stylecolors[style]
+
+        self._phasecolors = style_settings.phasecolors
+        styles_dir = os.path.dirname(style_settings.__file__)
+
+        for style, stylecolors in self._styles.items():
+            stylesheet = stylecolors['stylesheet']['filename']
+            if stylesheet:
+                stylesheet_file = open(os.path.join(styles_dir, stylesheet), 'r')
+                stylesheet = stylesheet_file.read()
+                stylesheet_file.close()
+            else:
+                stylesheet = self.styleSheet()
+
+            bg_color = stylecolors['background']['rgba']
+            line_color = stylecolors['linecolor']['rgba']
+            multcursor_color = stylecolors['multicursor']['rgba']
+
+            # transform to 0-1 values for mpl and update dict
+            stylecolors['background']['rgba_mpl'] = transform_colors_mpl(bg_color)
+            stylecolors['linecolor']['rgba_mpl'] = transform_colors_mpl(line_color)
+            multcursor_color = stylecolors['multicursor']['rgba_mpl'] = transform_colors_mpl(multcursor_color)
+
+            stylecolors['stylesheet'] = stylesheet
+
+    def set_style(self, stylename=None):
+        if not stylename:
+            stylename = 'default'
+        if not stylename in self._styles:
+            qmb = QMessageBox.warning(self, 'Could not find style',
+                                      'Could not find style with name {}. Using default.'.format(stylename))
+            self.set_style('default')
+            return
+
+        style = self._styles[stylename]
+        self._style = style
+        self._stylename = stylename
+        self.setStyleSheet(style['stylesheet'])
+
+        # colors for ref/test event
+        self._ref_test_colors = {
+            'ref': QtGui.QColor(*style['ref']['rgba']),
+            'test': QtGui.QColor(*style['test']['rgba']),
+        }
+
+        # plot colors
+        bg_color = style['background']['rgba']
+        bg_color_mpl_na = transform_colors_mpl_str(bg_color, no_alpha=True)
+        line_color = style['linecolor']['rgba']
+        line_color_mpl_na = transform_colors_mpl_str(line_color, no_alpha=True)
+
+        for param in matplotlib.rcParams:
+            if 'color' in param and matplotlib.rcParams[param] in ['k', 'black']:
+                matplotlib.rcParams[param] = line_color_mpl_na
+
+        matplotlib.rc('axes',
+                      edgecolor=line_color_mpl_na,
+                      facecolor=bg_color_mpl_na,
+                      labelcolor=line_color_mpl_na)
+        matplotlib.rc('xtick',
+                      color=line_color_mpl_na)
+        matplotlib.rc('ytick',
+                      color=line_color_mpl_na)
+        matplotlib.rc('figure',
+                      facecolor=bg_color_mpl_na)
+
+        if self.pg:
+            pg.setConfigOption('background', bg_color)
+            pg.setConfigOption('foreground', line_color)
+
+        settings = QSettings()
+        settings.setValue('style', stylename)
+        settings.sync()
+
     @property
     def metadata(self):
         return self._metadata
@@ -714,12 +806,8 @@ class MainWindow(QMainWindow):
         settings = QSettings()
         return settings.value("data/dataRoot")
 
-    def load_autopicks(self, fname=None):
-        self.load_data(fname, type='auto')
-
     def load_loc(self, fname=None):
-        type = getDataType(self)
-        self.load_data(fname, type=type, loc=True)
+        self.load_data(fname, loc=True)
 
     def load_pilotevent(self):
         filt = "PILOT location files (*LOC*.mat)"
@@ -734,10 +822,8 @@ class MainWindow(QMainWindow):
                                                   filter=filt, dir=loc_dir)
         fn_phases = fn_phases[0]
 
-        type = getDataType(self)
-
         fname_dict = dict(phasfn=fn_phases, locfn=fn_loc)
-        self.load_data(fname_dict, type=type)
+        self.load_data(fname_dict)
 
     def load_multiple_data(self):
         if not self.okToContinue():
@@ -802,20 +888,27 @@ class MainWindow(QMainWindow):
     def add_recentfile(self, event):
         self.recentfiles.insert(0, event)
 
-    def set_button_color(self, button, color=None):
+    def set_button_border_color(self, button, color=None):
         '''
         Set background color of a button.
         button: type = QtGui.QAbstractButton
         color: type = QtGui.QColor or type = str (RGBA)
         '''
         if type(color) == QtGui.QColor:
+            button.setStyleSheet({'QPushButton{background-color:transparent}'})
             palette = button.palette()
             role = button.backgroundRole()
             palette.setColor(role, color)
             button.setPalette(palette)
             button.setAutoFillBackground(True)
-        elif type(color) == str or not color:
-            button.setStyleSheet("background-color: {}".format(color))
+        elif type(color) == str:
+            button.setStyleSheet('QPushButton{border-color: %s}'
+                                 'QPushButton:checked{background-color: rgba%s}'% (color, color))
+        elif type(color) == tuple:
+            button.setStyleSheet('QPushButton{border-color: rgba%s}'
+                                 'QPushButton:checked{background-color: rgba%s}' % (str(color), str(color)))
+        elif not color:
+            button.setStyleSheet(self.orig_parent._style['stylesheet'])
 
     def getWFFnames(self):
         try:
@@ -871,7 +964,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event(self, eventbox=None):
         '''
-        Return event (type QtPylot.Event) currently selected in eventbox.
+        Return event (type PyLoT.Event) currently selected in eventbox.
         '''
         if not eventbox:
             eventbox = self.eventBox
@@ -880,7 +973,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event_path(self, eventbox=None):
         '''
-        Return event path of event (type QtPylot.Event) currently selected in eventbox.
+        Return event path of event (type PyLoT.Event) currently selected in eventbox.
         '''
         event = self.get_current_event(eventbox)
         if event:
@@ -888,7 +981,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event_name(self, eventbox=None):
         '''
-        Return event path of event (type QtPylot.Event) currently selected in eventbox.
+        Return event path of event (type PyLoT.Event) currently selected in eventbox.
         '''
         path = self.get_current_event_path(eventbox)
         if path:
@@ -1029,7 +1122,7 @@ class MainWindow(QMainWindow):
         '''
 
         # if pick widget is open, refresh tooltips as well
-        if hasattr(self, 'apw'):
+        if self.apw:
             self.apw.refresh_tooltips()
         if hasattr(self, 'cmpw'):
             self.cmpw.refresh_tooltips()
@@ -1091,9 +1184,9 @@ class MainWindow(QMainWindow):
             item_ref = QtGui.QStandardItem()  # str(event_ref))
             item_test = QtGui.QStandardItem()  # str(event_test))
             if event_ref:
-                item_ref.setBackground(self._colors['ref'])
+                item_ref.setBackground(self._ref_test_colors['ref'])
             if event_test:
-                item_test.setBackground(self._colors['test'])
+                item_test.setBackground(self._ref_test_colors['test'])
             item_notes = QtGui.QStandardItem(event.notes)
 
             openIcon = self.style().standardIcon(QStyle.SP_DirOpenIcon)
@@ -1249,10 +1342,11 @@ class MainWindow(QMainWindow):
         if len(eventdict) < 1:
             return
 
+
         # init event selection options for autopick
-        self.compareoptions =[('tune events', self.get_ref_events),
-                              ('test events', self.get_test_events),
-                              ('all (picked) events', self.get_manu_picked_events)]
+        self.compareoptions =[('tune events', self.get_ref_events, self._style['ref']['rgba']),
+                              ('test events', self.get_test_events, self._style['test']['rgba']),
+                              ('all (picked) events', self.get_manu_picked_events, None)]
 
         self.cmpw = CompareEventsWidget(self, self.compareoptions, eventdict, comparisons)
         self.cmpw.start.connect(self.compareMulti)
@@ -1260,7 +1354,9 @@ class MainWindow(QMainWindow):
         self.cmpw.show()
 
     def compareMulti(self):
-        for key, func in self.compareoptions:
+        if not self.compareoptions:
+            return
+        for key, func, color in self.compareoptions:
             if self.cmpw.rb_dict[key].isChecked():
                 # if radio button is checked break for loop and use func
                 break
@@ -1274,7 +1370,10 @@ class MainWindow(QMainWindow):
     def buildMultiCompareWidget(self, eventlist):
         global_comparison = Comparison(eventlist=eventlist)
         compare_widget = ComparisonWidget(global_comparison, self)
-        compare_widget.setWindowTitle('Histograms for all selected events')
+        for events_name, rb in self.cmpw.rb_dict.items():
+            if rb.isChecked():
+                break
+        compare_widget.setWindowTitle('Histograms for {}'.format(events_name))
         compare_widget.hideToolbar()
         compare_widget.setHistboxChecked(True)
         return compare_widget
@@ -1432,7 +1531,8 @@ class MainWindow(QMainWindow):
         '''
         if load:
             self.wfd_thread = Thread(self, self.loadWaveformData,
-                                     progressText='Reading data input...')
+                                     progressText='Reading data input...',
+                                     pb_widget=self.mainProgressBarWidget)
         if load and plot:
             self.wfd_thread.finished.connect(self.plotWaveformDataThread)
 
@@ -1462,7 +1562,7 @@ class MainWindow(QMainWindow):
         check4gaps(wfdat)
         check4doubled(wfdat)
         # check for stations with rotated components
-        wfdat = check4rotated(wfdat, self.metadata)
+        wfdat = check4rotated(wfdat, self.metadata, verbosity=0)
         # trim station components to same start value
         trim_station_components(wfdat, trim_start=True, trim_end=False)
         self._stime = full_range(self.get_data().getWFData())[0]
@@ -1522,7 +1622,8 @@ class MainWindow(QMainWindow):
         self.getPlotWidget().updateWidget()
         plots = self.wfp_thread.data
         for times, data in plots:
-            self.dataPlot.plotWidget.getPlotItem().plot(times, data, pen='k')
+            self.dataPlot.plotWidget.getPlotItem().plot(times, data,
+                                                        pen=self.dataPlot.pen_linecolor)
         self.dataPlot.reinitMoveProxy()
         self.dataPlot.plotWidget.showAxis('left')
         self.dataPlot.plotWidget.showAxis('bottom')
@@ -1532,7 +1633,7 @@ class MainWindow(QMainWindow):
         if self.pg:
             self.finish_pg_plot()
         else:
-            self._max_xlims = self.dataPlot.getXLims()
+            self._max_xlims = self.dataPlot.getXLims(self.dataPlot.axes[0])
         plotWidget = self.getPlotWidget()
         plotDict = plotWidget.getPlotDict()
         pos = plotDict.keys()
@@ -1570,10 +1671,22 @@ class MainWindow(QMainWindow):
         if event.pylot_picks and event.pylot_autopicks:
             for station in event.pylot_picks:
                 if station in event.pylot_autopicks:
-                    autopick_p = event.pylot_autopicks[station]['P']['spe']
-                    manupick_p = event.pylot_picks[station]['P']['spe']
-                    autopick_s = event.pylot_autopicks[station]['S']['spe']
-                    manupick_s = event.pylot_picks[station]['S']['spe']
+                    try:
+                        autopick_p = event.pylot_autopicks[station]['P']['spe']
+                    except KeyError:
+                        autopick_p = None
+                    try:
+                        manupick_p = event.pylot_picks[station]['P']['spe']
+                    except KeyError:
+                        manupick_p = None
+                    try:
+                        autopick_s = event.pylot_autopicks[station]['S']['spe']
+                    except KeyError:
+                        autopick_s = None
+                    try:
+                        manupick_s = event.pylot_picks[station]['S']['spe']
+                    except KeyError:
+                        manupick_s = None
                     if autopick_p and manupick_p:
                         return True
                     elif autopick_s and manupick_s:
@@ -1594,7 +1707,7 @@ class MainWindow(QMainWindow):
             self.dataPlot.plotWidget.hideAxis('bottom')
             self.dataPlot.plotWidget.hideAxis('left')
         else:
-            self.dataPlot.getAxes().cla()
+            self.dataPlot.axes[0].cla()
         self.loadlocationaction.setEnabled(False)
         self.auto_tune.setEnabled(False)
         self.auto_pick.setEnabled(False)
@@ -1616,7 +1729,8 @@ class MainWindow(QMainWindow):
         '''
         self.clearWaveformDataPlot()
         self.wfp_thread = Thread(self, self.plotWaveformData,
-                                 progressText='Plotting waveform data...')
+                                 progressText='Plotting waveform data...',
+                                 pb_widget=self.mainProgressBarWidget)
         self.wfp_thread.finished.connect(self.finishWaveformDataPlot)
         self.wfp_thread.start()
 
@@ -1752,27 +1866,27 @@ class MainWindow(QMainWindow):
 
         self.checkFilterOptions()
 
-    def updateFilterOptions(self):
-        try:
-            settings = QSettings()
-            if settings.value("filterdefaults",
-                              None) is None and not self.getFilters():
-                for key, value in FILTERDEFAULTS.items():
-                    self.setFilterOptions(FilterOptions(**value), key)
-            elif settings.value("filterdefaults", None) is not None:
-                for key, value in settings.value("filterdefaults"):
-                    self.setFilterOptions(FilterOptions(**value), key)
-        except Exception as e:
-            self.update_status('Error ...')
-            emsg = QErrorMessage(self)
-            emsg.showMessage('Error: {0}'.format(e))
-        else:
-            self.update_status('Filter loaded ... '
-                               '[{0}: {1} Hz]'.format(
-                self.getFilterOptions().getFilterType(),
-                self.getFilterOptions().getFreq()))
-        if self.filterAction.isChecked():
-            self.filterWaveformData()
+    # def updateFilterOptions(self):
+    #     try:
+    #         settings = QSettings()
+    #         if settings.value("filterdefaults",
+    #                           None) is None and not self.getFilters():
+    #             for key, value in FILTERDEFAULTS.items():
+    #                 self.setFilterOptions(FilterOptions(**value), key)
+    #         elif settings.value("filterdefaults", None) is not None:
+    #             for key, value in settings.value("filterdefaults"):
+    #                 self.setFilterOptions(FilterOptions(**value), key)
+    #     except Exception as e:
+    #         self.update_status('Error ...')
+    #         emsg = QErrorMessage(self)
+    #         emsg.showMessage('Error: {0}'.format(e))
+    #     else:
+    #         self.update_status('Filter loaded ... '
+    #                            '[{0}: {1} Hz]'.format(
+    #             self.getFilterOptions().getFilterType(),
+    #             self.getFilterOptions().getFreq()))
+    #     if self.filterAction.isChecked():
+    #         self.filterWaveformData()
 
     def getSeismicPhase(self):
         return self.seismicPhase
@@ -1906,27 +2020,36 @@ class MainWindow(QMainWindow):
             'el_S1pick',
             'el_S2pick',
             'refSpick',
-            'aicARHfig'
+            'aicARHfig',
+            'plot_style'
         ]
         for key in self.fig_keys:
-            fig = Figure()
+            if key == 'plot_style':
+                fig = self._style
+            else:
+                fig = Figure()
             self.fig_dict[key] = fig
 
     def init_canvas_dict(self):
         self.canvas_dict = {}
         for key in self.fig_keys:
-            self.canvas_dict[key] = PylotCanvas(self.fig_dict[key])
+            if not key == 'plot_style':
+                self.canvas_dict[key] = PylotCanvas(self.fig_dict[key], parent=self)
 
     def init_fig_dict_wadatijack(self, eventIDs):
         self.fig_dict_wadatijack = {}
         self.fig_keys_wadatijack = [
             'jackknife',
-            'wadati'
+            'wadati',
+            'plot_style'
         ]
         for eventID in eventIDs:
             self.fig_dict_wadatijack[eventID] = {}
             for key in self.fig_keys_wadatijack:
-                fig = Figure()
+                if key == 'plot_style':
+                    fig = self._style
+                else:
+                    fig = Figure()
                 self.fig_dict_wadatijack[eventID][key] = fig
 
     def init_canvas_dict_wadatijack(self):
@@ -1934,7 +2057,9 @@ class MainWindow(QMainWindow):
         for eventID in self.fig_dict_wadatijack.keys():
             self.canvas_dict_wadatijack[eventID] = {}
             for key in self.fig_keys_wadatijack:
-                self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key])
+                if not key == 'plot_style':
+                    self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key],
+                                                                            parent=self)
 
     def tune_autopicker(self):
         '''
@@ -1967,6 +2092,14 @@ class MainWindow(QMainWindow):
         self.tap.fill_tabs(picked=True)
         for canvas in self.canvas_dict.values():
             canvas.setZoomBorders2content()
+        if self.tap.pylot_picks:
+            station = self.tap.get_current_station()
+            p_pick = self.tap.pylot_picks[station]['P']
+            s_pick = self.tap.pylot_picks[station]['S']
+            self.tap.pickDlg.autopicks['P_tuning'] = p_pick
+            self.tap.pickDlg.autopicks['S_tuning'] = s_pick
+            self.tap.pickDlg.drawPicks(phase='P_tuning', picktype='auto', picks=p_pick)
+            self.tap.pickDlg.drawPicks(phase='S_tuning', picktype='auto', picks=s_pick)
 
     def autoPick(self):
         autosave = self.get_current_event_path()
@@ -1975,24 +2108,27 @@ class MainWindow(QMainWindow):
                                 "No autoPyLoT output declared!")
             return
 
-        # init event selection options for autopick
-        self.pickoptions =[('current event', self.get_current_event),
-                           ('tune events', self.get_ref_events),
-                           ('test events', self.get_test_events),
-                           ('all (picked) events', self.get_manu_picked_events),
-                           ('all events', self.get_all_events)]
+        if not self.apw:
+            # init event selection options for autopick
+            self.pickoptions =[('current event', self.get_current_event, None),
+                               ('tune events', self.get_ref_events, self._style['ref']['rgba']),
+                               ('test events', self.get_test_events, self._style['test']['rgba']),
+                               ('all (picked) events', self.get_manu_picked_events, None),
+                               ('all events', self.get_all_events, None)]
 
-        self.listWidget = QListWidget()
-        self.setDirty(True)
-        self.apw = AutoPickWidget(self, self.pickoptions)
-        self.apw.insert_log_widget(self.listWidget)
-        self.apw.refresh_tooltips()
+            self.listWidget = QListWidget()
+            self.setDirty(True)
+            self.apw = AutoPickWidget(self, self.pickoptions)
+            self.apw.insert_log_widget(self.listWidget)
+            self.apw.refresh_tooltips()
 
-        self.apw.start.connect(self.start_autopick)
+            self.apw.start.connect(self.start_autopick)
         self.apw.show()
 
     def start_autopick(self):
-        for key, func in self.pickoptions:
+        if not self.pickoptions:
+            return
+        for key, func, _ in self.pickoptions:
             if self.apw.rb_dict[key].isChecked():
                 # if radio button is checked break for loop and use func
                 break
@@ -2052,7 +2188,6 @@ class MainWindow(QMainWindow):
     def finalizeAutoPick(self, result):
         self.apw.enable(True)
         if result:
-            result = removePicksAbove(result, 3)
             self.init_canvas_dict_wadatijack()
             for eventID in result.keys():
                 event = self.get_event_from_id(eventID)
@@ -2179,7 +2314,7 @@ class MainWindow(QMainWindow):
         if self.pg:
             pw = self.getPlotWidget().plotWidget
         else:
-            ax = self.getPlotWidget().axes
+            ax = self.getPlotWidget().axes[0]
         ylims = np.array([-.5, +.5]) + plotID
 
         stat_picks = self.getPicks(type=picktype)[station]
@@ -2220,6 +2355,7 @@ class MainWindow(QMainWindow):
                         pen = make_pen(picktype, phaseID, 'lpp', quality)
                         pw.plot([lpp, lpp], ylims,
                                 alpha=.25, pen=pen, name='LPP')
+                    pen = make_pen(picktype, phaseID, 'mpp', quality)
                     if spe:
                         # pen = make_pen(picktype, phaseID, 'spe', quality)
                         # spe_l = pg.PlotDataItem([mpp - spe, mpp - spe], ylims, pen=pen,
@@ -2236,7 +2372,6 @@ class MainWindow(QMainWindow):
                         #     fb = pw.addItem(fill)
                         # except:
                         #     print('Warning: drawPicks: Could not create fill for symmetric pick error.')
-                        pen = make_pen(picktype, phaseID, 'mpp', quality)
                         pw.plot([mpp, mpp], ylims, pen=pen, name='{}-Pick'.format(phase))
                     else:
                         pw.plot([mpp, mpp], ylims, pen=pen, name='{}-Pick (NO PICKERROR)'.format(phase))
@@ -2409,7 +2544,8 @@ class MainWindow(QMainWindow):
         Start modal thread to init the array_map object.
         '''
         # Note: basemap generation freezes GUI but cannot be threaded as it generates a Pixmap.
-        self.amt = Thread(self, self.array_map.init_map, arg=None, progressText='Generating map...')
+        self.amt = Thread(self, self.array_map.init_map, arg=None, progressText='Generating map...',
+                          pb_widget=self.mainProgressBarWidget)
         self.amt.finished.connect(self.finish_array_map)
         self.amt.start()
 
@@ -2436,7 +2572,7 @@ class MainWindow(QMainWindow):
                 lon = event.origins[0].longitude
                 self.array_map.eventLoc = (lat, lon)
         if self.get_current_event():
-            self.array_map.refresh_drawings(self.get_current_event().getPicks())
+            self.array_map.refresh_drawings(self.get_current_event().getAutopicks())
         self._eventChanged[1] = False
 
     def init_event_table(self, tabindex=2):
@@ -2500,7 +2636,7 @@ class MainWindow(QMainWindow):
             self.events_layout.removeWidget(self.event_table)
 
         # init new qtable
-        self.event_table = QtGui.QTableWidget()
+        self.event_table = QtGui.QTableWidget(self)
         self.event_table.setColumnCount(12)
         self.event_table.setRowCount(len(eventlist))
         self.event_table.setHorizontalHeaderLabels(['',
@@ -2554,8 +2690,8 @@ class MainWindow(QMainWindow):
             item_notes = QtGui.QTableWidgetItem()
 
             # manipulate items
-            item_ref.setBackground(self._colors['ref'])
-            item_test.setBackground(self._colors['test'])
+            item_ref.setBackground(self._ref_test_colors['ref'])
+            item_test.setBackground(self._ref_test_colors['test'])
             item_path.setText(event.path)
             if hasattr(event, 'origins'):
                 if event.origins:
@@ -2611,7 +2747,8 @@ class MainWindow(QMainWindow):
         self.tabs.setCurrentIndex(tabindex)
 
     def read_metadata_thread(self, fninv):
-        self.rm_thread = Thread(self, read_metadata, arg=fninv, progressText='Reading metadata...')
+        self.rm_thread = Thread(self, read_metadata, arg=fninv, progressText='Reading metadata...',
+                                pb_widget=self.mainProgressBarWidget)
         self.rm_thread.finished.connect(self.set_metadata)
         self.rm_thread.start()
 
@@ -2629,7 +2766,7 @@ class MainWindow(QMainWindow):
     def get_new_metadata(self):
         self.init_metadata(new=True)
 
-    def init_metadata(self, new=False):
+    def init_metadata(self, new=False, ask_default=True):
         def set_inv(settings):
             fninv, _ = QFileDialog.getOpenFileName(self, self.tr(
                 "Select inventory..."), self.tr("Select file"))
@@ -2658,7 +2795,7 @@ class MainWindow(QMainWindow):
             settings.setValue("inventoryFile", self.project.inv_path)
 
         fninv = settings.value("inventoryFile", None)
-        if fninv:
+        if fninv and ask_default:
             ans = QMessageBox.question(self, self.tr("Use default metadata..."),
                                        self.tr(
                                            "Do you want to use the default value for metadata?\n({})".format(fninv)),
@@ -2669,6 +2806,8 @@ class MainWindow(QMainWindow):
                     return None
             elif ans == QMessageBox.Yes:
                 self.read_metadata_thread(fninv)
+        if fninv and not ask_default:
+            self.read_metadata_thread(fninv)
 
     def calc_magnitude(self, type='ML'):
         self.init_metadata()
@@ -2681,7 +2820,7 @@ class MainWindow(QMainWindow):
         #     raise ProcessingError('Restitution of waveform data failed!')
         if type == 'ML':
             local_mag = LocalMagnitude(corr_wf, self.get_data().get_evt_data(), self.inputs.get('sstop'),
-                                       verbosity=True)
+                                       verbosity=True) ## MP MP missing parameter wascaling in function call!
             return local_mag.updated_event()
         elif type == 'Mw':
             moment_mag = MomentMagnitude(corr_wf, self.get_data().get_evt_data(), self.inputs.get('vp'),
@@ -2777,7 +2916,7 @@ class MainWindow(QMainWindow):
         if not self.okToContinue():
             return
         if not fnm:
-            dlg = QFileDialog()
+            dlg = QFileDialog(parent=self)
             fnm = dlg.getOpenFileName(self, 'Open project file...', filter='Pylot project (*.plp)')
             if not fnm:
                 return
@@ -2792,10 +2931,12 @@ class MainWindow(QMainWindow):
             self.setDirty(False)
             if hasattr(self.project, 'metadata'):
                 if self.project.metadata:
-                    self.init_array_map(index=0)
+                    self.init_metadata(ask_default=False)
+                    #self.init_array_map(index=0)
                     return
             if hasattr(self.project, 'inv_path'):
-                self.init_array_map(index=0)
+                self.init_metadata(ask_default=False)
+                #self.init_array_map(index=0)
                 return
 
             self.init_array_tab()
@@ -2804,7 +2945,7 @@ class MainWindow(QMainWindow):
         '''
         Save back project to new pickle file.
         '''
-        dlg = QFileDialog()
+        dlg = QFileDialog(self)
         fnm = dlg.getSaveFileName(self, 'Create a new project file...', filter='Pylot project (*.plp)')
         filename = fnm[0]
         if not len(fnm[0]):
@@ -2866,7 +3007,7 @@ class MainWindow(QMainWindow):
 
     def setParameter(self, show=True):
         if not self.paraBox:
-            self.paraBox = PylotParaBox(self._inputs)
+            self.paraBox = PylotParaBox(self._inputs, parent=self, windowflag=1)
             self.paraBox.accepted.connect(self._setDirty)
             self.paraBox.accepted.connect(self.filterOptionsFromParameter)
         if show:
@@ -2884,16 +3025,16 @@ class MainWindow(QMainWindow):
 
     def helpHelp(self):
         if checkurl():
-            form = HelpForm(
+            form = HelpForm(self,
                 'https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/wiki')
         else:
-            form = HelpForm(':/help.html')
+            form = HelpForm(self, ':/help.html')
         form.show()
 
 
 class Project(object):
     '''
-    Pickable class containing information of a QtPyLoT project, like event lists and file locations.
+    Pickable class containing information of a PyLoT project, like event lists and file locations.
     '''
 
     def __init__(self):
@@ -2958,7 +3099,7 @@ class Project(object):
                 print(e, datetime, filename)
                 continue
             for event in self.eventlist:
-                if eventID in str(event.resource_id) or eventID in event.origins:
+                if eventID in str(event.resource_id) or event.origins:
                     if event.origins:
                         origin = event.origins[0]  # should have only one origin
                         if origin.time == datetime:
@@ -3078,9 +3219,10 @@ def create_window():
     if app is None:
         app = QApplication(sys.argv)
         app_created = True
-    app.setOrganizationName("QtPyLoT");
-    app.setOrganizationDomain("rub.de");
-    app.setApplicationName("RUB");
+    # set aplication/organization name, domain (important to do this BEFORE setupUI is called for correct QSettings)
+    app.setOrganizationName("Ruhr-University Bochum / BESTEC")
+    app.setOrganizationDomain("rub.de")
+    app.setApplicationName("PyLoT")
     app.references = set()
     # app.references.add(window)
     # window.show()
@@ -3089,6 +3231,7 @@ def create_window():
 
 def main(args=None):
     project_filename = None
+    #args.project_filename = 'C:/Shared/AlpArray/alparray_data/project_alparray_test.plp'
     pylot_infile = None
     if args:
         if args.project_filename:
@@ -3108,22 +3251,17 @@ def main(args=None):
 
     # create the main window
     pylot_form = MainWindow(infile=pylot_infile)
-    icon = QIcon()
-    pylot_form.setWindowIcon(icon)
+    pylot_form.setWindowIcon(app_icon)
     pylot_form.setIconSize(QSize(60, 60))
 
-    splash.showMessage('Loading. Please wait ...')
-    pylot_app.processEvents()
-
-    # set Application Information
-    pylot_app.setOrganizationName("Ruhr-University Bochum / BESTEC")
-    pylot_app.setOrganizationDomain("rub.de")
-    pylot_app.processEvents()
-    pylot_app.setApplicationName("PyLoT")
+    # set other App information
     pylot_app.setApplicationVersion(pylot_form.__version__)
     pylot_app.setWindowIcon(app_icon)
     pylot_app.processEvents()
 
+    splash.showMessage('Loading. Please wait ...')
+    pylot_app.processEvents()
+
     # Show main window and run the app
     pylot_form.showMaximized()
     pylot_app.processEvents()

README.md

@@ -1,90 +1,110 @@
-# PyLoT
-
-version: 0.1a
-
-The Python picking and Localisation Tool
-
-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.
-
-The development of PyLoT is part of the joint research project MAGS2.
-
-## 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.
-
-#### Prerequisites:
-
-In order to run PyLoT you need to install:
-
-- python
-- scipy
-- numpy
-- matplotlib
-- obspy
-- pyside
-
-#### Some handwork:
-
-PyLoT needs a properties folder on your system to work. It should be situated in your home directory:
-
-    mkdir ~/.pylot
-
-In the next step you have to copy some files to this directory:
-
-    cp path-to-pylot/inputs/pylot.in ~/.pylot/
-
-for local distance seismicity
-
-    cp path-to-pylot/inputs/autoPyLoT_local.in ~/.pylot/autoPyLoT.in
-
-for regional distance seismicity
-
-    cp path-to-pylot/inputs/autoPyLoT_regional.in ~/.pylot/autoPyLoT.in
-
-and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling relation
-
-    cp path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
-
-You may need to do some modifications to these files. Especially folder names should be reviewed.
-
-PyLoT has been tested on Mac OSX (10.11) and Debian Linux 8.
-
-
-## Release notes
-
-#### Features:
-
-- consistent manual phase picking through predefined SNR dependant zoom level
-- uniform uncertainty estimation from waveform's properties for automatic and manual picks
-- pdf representation and comparison of picks taking the uncertainty intrinsically into account 
-- Richter and moment magnitude estimation
-- location determination with external installation of [NonLinLoc](http://alomax.free.fr/nlloc/index.html)
-
-#### Known issues:
-
-- Magnitude estimation from manual PyLoT takes some time (instrument correction)
-
-We hope to solve these with the next release.
-
-## Staff
-
-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
-
-Others: A. Bruestle, T. Meier, W. Friederich
-
-
-[ObsPy]: http://github.com/obspy/obspy/wiki
-
-October 2016
+# PyLoT
+
+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]
+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.
+
+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.
+
+#### Prerequisites:
+
+In order to run PyLoT you need to install:
+
+- python 2 or 3
+- scipy
+- numpy
+- matplotlib
+- obspy
+- pyside
+
+#### Some handwork:
+
+PyLoT needs a properties folder on your system to work. It should be situated in your home directory 
+(on Windows usually C:/Users/*username*):
+
+    mkdir ~/.pylot
+
+In the next step you have to copy some files to this directory:
+
+*for local distance seismicity*
+
+    cp path-to-pylot/inputs/pylot_local.in ~/.pylot/pylot.in
+
+*for regional distance seismicity*
+
+    cp path-to-pylot/inputs/pylot_regional.in ~/.pylot/pylot.in
+
+*for global distance seismicity*
+
+    cp path-to-pylot/inputs/pylot_global.in ~/.pylot/pylot.in
+
+and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling relation
+
+    cp path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
+
+You may need to do some modifications to these files. Especially folder names should be reviewed.
+
+PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
+
+
+## Release notes
+
+#### Features:
+
+- centralize all functionalities of PyLoT and control them from within the main GUI
+- handling multiple events inside GUI with project files (save and load work progress)
+- GUI based adjustments of pick parameters and I/O
+- interactive tuning of parameters from within the GUI
+- call automatic picking algorithm from within the GUI
+- comparison of automatic with manual picks for multiple events using clear differentiation of manual picks into 'tune' and 'test-set' (beta)
+- manual picking of different (user defined) phase types
+- phase onset estimation with ObsPy TauPy
+- interactive zoom/scale functionalities in all plots (mousewheel, pan, pan-zoom)
+- array map to visualize stations and control onsets (beta feature, switch to manual picks not implemented)
+
+##### Platform support:
+- Python 3 support
+- Windows support
+
+##### Performance:
+- multiprocessing for automatic picking and restitution of multiple stations
+- use pyqtgraph library for better performance on main waveform plot
+
+##### Visualization:
+- pick uncertainty (quality classes) visualization with gradients
+- pick color unification for all plots
+- new icons and stylesheets
+
+#### Known Issues:
+- some Qt related errors might occur at runtime
+- filter toggle not working in pickDlg
+- PyLoT data structure requires at least three parent directories for waveform data directory
+
+## Staff
+
+Original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
+
+Developer(s): S. Wehling-Benatelli, L. Kueperkoch, M. Paffrath, K. Olbert,
+M. Bischoff, C. Wollin, M. Rische
+
+Others: A. Bruestle, T. Meier, W. Friederich
+
+
+[ObsPy]: http://github.com/obspy/obspy/wiki
+
+September 2017

autoPyLoT.py

@@ -311,7 +311,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                         # calculate seismic moment Mo and moment magnitude Mw
                         moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
                                                      parameter.get('Qp'),
-                                                     parameter.get('rho'), True, \
+                                                     parameter.get('rho'), True,
                                                      iplot)
                         # update pick with moment property values (w0, fc, Mo)
                         for stats, props in moment_mag.moment_props.items():
@@ -374,7 +374,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                             for key in picks:
                                 if picks[key]['P']['weight'] >= 4 or picks[key]['S']['weight'] >= 4:
                                     badpicks.append([key, picks[key]['P']['mpp']])
-                            print("autoPyLoT: After iteration No. %d: %d bad onsets found ..." % (nlloccounter, \
+                            print("autoPyLoT: After iteration No. %d: %d bad onsets found ..." % (nlloccounter,
                                                                                                   len(badpicks)))
                             if len(badpicks) == 0:
                                 print("autoPyLoT: No more bad onsets found, stop iterative picking!")
@@ -384,7 +384,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
                             # calculate seismic moment Mo and moment magnitude Mw
                             moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
                                                          parameter.get('Qp'),
-                                                         parameter.get('rho'), True, \
+                                                         parameter.get('rho'), True,
                                                          iplot)
                             # update pick with moment property values (w0, fc, Mo)
                             for stats, props in moment_mag.moment_props.items():
@@ -502,4 +502,4 @@ if __name__ == "__main__":
 
     picks = autoPyLoT(inputfile=str(cla.inputfile), fnames=str(cla.fnames),
                       eventid=str(cla.eventid), savepath=str(cla.spath),
-                      ncores=cla.ncores, iplot=str(cla.iplot))
+                      ncores=cla.ncores, iplot=int(cla.iplot))

git_out

@@ -1,8 +0,0 @@
-git pull
-Entferne qrc_resources.py
-KONFLIKT (ändern/löschen): pylot/core/pick/getSNR.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/getSNR.py wurde im Arbeitsbereich gelassen.
-KONFLIKT (ändern/löschen): pylot/core/pick/fmpicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/fmpicker.py wurde im Arbeitsbereich gelassen.
-KONFLIKT (ändern/löschen): pylot/core/pick/earllatepicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/earllatepicker.py wurde im Arbeitsbereich gelassen.
-Automatisches Zusammenfügen von icons.qrc
-Automatischer Merge fehlgeschlagen; beheben Sie die Konflikte und committen Sie dann das Ergebnis.
-

icons.qrc

@@ -2,6 +2,8 @@
     <qresource>
         <file>icons/pylot.ico</file>
         <file>icons/pylot.png</file>
+        <file>icons/back.png</file>
+        <file>icons/home.png</file>
         <file>icons/newfile.png</file>
         <file>icons/open.png</file>
         <file>icons/openproject.png</file>

inputs/autoPyLoT.in

@@ -1,100 +0,0 @@
-%This is a parameter input file for autoPyLoT.
-%All main and special settings regarding data handling
-%and picking are to be set here!
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-#main settings#
-/DATA/Insheim                       #rootpath#     %project path
-EVENT_DATA/LOCAL		    #datapath#     %data path
-2013.02_Insheim                     #database#     %name of data base
-e0019.048.13                        #eventID#      %certain evnt ID for processing
-True                                #apverbose#
-PILOT				    #datastructure#  %choose data structure
-0                                   #iplot#        %flag for plotting: 0 none, 1, partly, >1 everything
-AUTOPHASES_AIC_HOS4_ARH             #phasefile#    %name of autoPILOT output phase file 
-AUTOLOC_AIC_HOS4_ARH                #locfile#      %name of autoPILOT output location file 
-AUTOFOCMEC_AIC_HOS4_ARH.in          #focmecin#     %name of focmec input file containing polarities
-HYPOSAT                             #locrt#        %location routine used ("HYPOINVERSE" or "HYPOSAT")
-6                                   #pmin#         %minimum required P picks for location
-4                                   #p0min#        %minimum required P picks for location if at least
-                                                   %3 excellent P picks are found
-2                                   #smin#         %minimum required S picks for location
-/home/ludger/bin/run_HYPOSAT4autoPILOT.csh #cshellp# %path and name of c-shell script to run location routine
-7.6 8.5                             #blon#         %longitude bounding for location map
-49 49.4                             #blat#         %lattitude bounding for location map
-#parameters for moment magnitude estimation#
-5000                                #vp#           %average P-wave velocity
-2800                                #vs#           %average S-wave velocity
-2200                                #rho#          %rock density [kg/m^3]
-300                                 #Qp#           %quality factor for P waves
-100                                 #Qs#           %quality factor for S waves
-#common settings picker#
-15                                  #pstart#       %start time [s] for calculating CF for P-picking
-40                                  #pstop#        %end time [s] for calculating CF for P-picking
--1.0                                #sstart#       %start time [s] after or before(-) P-onset for calculating CF for S-picking
-7                                   #sstop#        %end time [s] after P-onset for calculating CF for S-picking
-2 20                                #bpz1#         %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
-2 30                                #bpz2#         %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
-2 15                                #bph1#         %lower/upper corner freq. of first band pass filter H-comp. [Hz]
-2 20                                #bph2#         %lower/upper corner freq. of second band pass filter z-comp. [Hz]
-#special settings for calculating CF#
-%!!Be careful when editing the following!!
-#Z-component#
-HOS                                 #algoP#        %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
-7                                   #tlta#         %for HOS-/AR-AIC-picker, length of LTA window [s] 
-4                                   #hosorder#     %for HOS-picker, order of Higher Order Statistics
-2                                   #Parorder#     %for AR-picker, order of AR process of Z-component
-1.2                                 #tdet1z#       %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
-0.4                                 #tpred1z#      %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
-0.6                                 #tdet2z#       %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
-0.2                                 #tpred2z#      %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
-0.001                               #addnoise#     %add noise to seismogram for stable AR prediction
-3 0.1 0.5 0.1                       #tsnrz#        %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-3                                   #pickwinP#     %for initial AIC pick, length of P-pick window [s]
-8                                   #Precalcwin#   %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
-0                                   #peps4aic#     %for HOS/AR, artificial uplift of samples of AIC-function (P)
-0.2                                 #aictsmooth#   %for HOS/AR, take average of samples for smoothing of AIC-function [s]
-0.1                                 #tsmoothP#     %for HOS/AR, take average of samples for smoothing CF [s]
-0.001                               #ausP#         %for HOS/AR, artificial uplift of samples (aus) of CF (P)
-1.3                                 #nfacP#        %for HOS/AR, noise factor for noise level determination (P)
-#H-components#
-ARH                                 #algoS#        %choose algorithm for S-onset determination (ARH or AR3)
-0.8                                 #tdet1h#       %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
-0.4                                 #tpred1h#      %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
-0.6                                 #tdet2h#       %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
-0.3                                 #tpred2h#      %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
-4                                   #Sarorder#     %for AR-picker, order of AR process of H-components
-6                                   #Srecalcwin#   %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
-3                                   #pickwinS#     %for initial AIC pick, length of S-pick window [s]
-2 0.2 1.5 0.5                       #tsnrh#        %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-0.05                                #aictsmoothS#  %for AIC-picker, take average of samples for smoothing of AIC-function [s]
-0.02                                #tsmoothS#     %for AR-picker, take average of samples for smoothing CF [s] (S)
-0.2                                 #pepsS#        %for AR-picker, artificial uplift of samples of CF (S) 
-0.4                                 #ausS#         %for HOS/AR, artificial uplift of samples (aus) of CF (S)
-1.5                                 #nfacS#        %for AR-picker, noise factor for noise level determination (S)
-%first-motion picker%
-1                                   #minfmweight#  %minimum required p weight for first-motion determination
-2                                   #minFMSNR#     %miniumum required SNR for first-motion determination
-0.2                                 #fmpickwin#    %pick window around P onset for calculating zero crossings
-%quality assessment%
-#inital AIC onset#
-0.01 0.02 0.04 0.08                 #timeerrorsP#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
-0.04 0.08 0.16 0.32                 #timeerrorsS#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
-80                                  #minAICPslope#  %below this slope [counts/s] the initial P pick is rejected
-1.2                                 #minAICPSNR#    %below this SNR the initial P pick is rejected
-50                                  #minAICSslope#  %below this slope [counts/s] the initial S pick is rejected
-1.5                                 #minAICSSNR#    %below this SNR the initial S pick is rejected
-#check duration of signal using envelope function#
-1.5                                 #prepickwin#   %pre-signal window length [s] for noise level estimation 
-0.7                                 #minsiglength# %minimum required length of signal [s]
-0.2                                 #sgap#         %safety gap between noise and signal window [s]
-2                                   #noisefactor#  %noiselevel*noisefactor=threshold
-60                                  #minpercent#   %per cent of samples required higher than threshold
-#check for spuriously picked S-onsets#
-3.0                                 #zfac#         %P-amplitude must exceed zfac times RMS-S amplitude 
-#jackknife-processing for P-picks#
-3                                   #thresholdweight#%minimum required weight of picks
-3                                   #dttolerance#  %maximum allowed deviation of P picks from median [s]
-4                                   #minstats#     %minimum number of stations with reliable P picks
-3                                   #Sdttolerance# %maximum allowed deviation from Wadati-diagram
-

inputs/autoPyLoT_local.in

@@ -1,99 +0,0 @@
-%This is a parameter input file for autoPyLoT.
-%All main and special settings regarding data handling
-%and picking are to be set here!
-%Parameters are optimized for local data sets!
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#main settings#
-/DATA/Insheim                       #rootpath#     %project path
-EVENT_DATA/LOCAL		    #datapath#     %data path
-2016.08_Insheim                     #database#     %name of data base
-e0007.224.16                        #eventID#      %event ID for single event processing
-/DATA/Insheim/STAT_INFO             #invdir#       %full path to inventory or dataless-seed file
-PILOT				    #datastructure#%choose data structure
-0                                   #iplot#        %flag for plotting: 0 none, 1 partly, >1 everything
-True                                #apverbose#    %choose 'True' or 'False' for terminal output
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#NLLoc settings#
-/home/ludger/NLLOC                  #nllocbin#     %path to NLLoc executable
-/home/ludger/NLLOC/Insheim          #nllocroot#    %root of NLLoc-processing directory
-AUTOPHASES.obs                      #phasefile#    %name of autoPyLoT-output phase file for NLLoc
-                                                   %(in nllocroot/obs)
-Insheim_min1d032016_auto.in         #ctrfile#      %name of autoPyLoT-output control file for NLLoc
-                                                   %(in nllocroot/run)
-ttime                               #ttpatter#     %pattern of NLLoc ttimes from grid
-                                                   %(in nllocroot/times)
-AUTOLOC_nlloc                       #outpatter#    %pattern of NLLoc-output file
-                                                   %(returns 'eventID_outpatter')
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#parameters for seismic moment estimation#
-3530                                #vp#           %average P-wave velocity
-2500                                #rho#          %average rock density [kg/m^3]
-300 0.8                             #Qp#           %quality factor for P waves ([Qp, ap], Qp*f^a)
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-AUTOFOCMEC_AIC_HOS4_ARH.in          #focmecin#     %name of focmec input file containing derived polarities
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#common settings picker#
-15.0                                #pstart#       %start time [s] for calculating CF for P-picking
-60.0                                #pstop#        %end time [s] for calculating CF for P-picking
--1.0                                #sstart#       %start time [s] relative to P-onset for calculating CF for S-picking
-10.0                                #sstop#        %end time [s] after P-onset for calculating CF for S-picking
-2 20                                #bpz1#         %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
-2 30                                #bpz2#         %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
-2 15                                #bph1#         %lower/upper corner freq. of first band pass filter H-comp. [Hz]
-2 20                                #bph2#         %lower/upper corner freq. of second band pass filter z-comp. [Hz]
-#special settings for calculating CF#
-%!!Edit the following only if you know what you are doing!!%
-#Z-component#
-HOS                                 #algoP#        %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
-7.0                                 #tlta#         %for HOS-/AR-AIC-picker, length of LTA window [s] 
-4                                   #hosorder#     %for HOS-picker, order of Higher Order Statistics
-2                                   #Parorder#     %for AR-picker, order of AR process of Z-component
-1.2                                 #tdet1z#       %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
-0.4                                 #tpred1z#      %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
-0.6                                 #tdet2z#       %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
-0.2                                 #tpred2z#      %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
-0.001                               #addnoise#     %add noise to seismogram for stable AR prediction
-3 0.1 0.5 0.5                       #tsnrz#        %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-3.0                                 #pickwinP#     %for initial AIC pick, length of P-pick window [s]
-6.0                                 #Precalcwin#   %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
-0.2                                 #aictsmooth#   %for HOS/AR, take average of samples for smoothing of AIC-function [s]
-0.1                                 #tsmoothP#     %for HOS/AR, take average of samples for smoothing CF [s]
-0.001                               #ausP#         %for HOS/AR, artificial uplift of samples (aus) of CF (P)
-1.3                                 #nfacP#        %for HOS/AR, noise factor for noise level determination (P)
-#H-components#
-ARH                                 #algoS#        %choose algorithm for S-onset determination (ARH or AR3)
-0.8                                 #tdet1h#       %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
-0.4                                 #tpred1h#      %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
-0.6                                 #tdet2h#       %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
-0.3                                 #tpred2h#      %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
-4                                   #Sarorder#     %for AR-picker, order of AR process of H-components
-5.0                                 #Srecalcwin#   %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
-3.0                                 #pickwinS#     %for initial AIC pick, length of S-pick window [s]
-2 0.2 1.5 0.5                       #tsnrh#        %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-0.5                                 #aictsmoothS#  %for AIC-picker, take average of samples for smoothing of AIC-function [s]
-0.7                                 #tsmoothS#     %for AR-picker, take average of samples for smoothing CF [s] (S)
-0.9                                 #ausS#         %for HOS/AR, artificial uplift of samples (aus) of CF (S)
-1.5                                 #nfacS#        %for AR-picker, noise factor for noise level determination (S)
-%first-motion picker%
-1                                   #minfmweight#  %minimum required P weight for first-motion determination
-2                                   #minFMSNR#     %miniumum required SNR for first-motion determination
-0.2                                 #fmpickwin#    %pick window around P onset for calculating zero crossings
-%quality assessment%
-#inital AIC onset#
-0.05 0.10 0.20 0.40                 #timeerrorsP#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
-0.10 0.20 0.40 0.80                 #timeerrorsS#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
-4                                   #minAICPslope#  %below this slope [counts/s] the initial P pick is rejected
-1.2                                 #minAICPSNR#    %below this SNR the initial P pick is rejected
-2                                   #minAICSslope#  %below this slope [counts/s] the initial S pick is rejected
-1.5                                 #minAICSSNR#    %below this SNR the initial S pick is rejected
-#check duration of signal using envelope function#
-3                                   #minsiglength# %minimum required length of signal [s]
-1.0                                 #noisefactor#  %noiselevel*noisefactor=threshold
-40                                  #minpercent#   %required percentage of samples higher than threshold
-#check for spuriously picked S-onsets#
-2.0                                 #zfac#         %P-amplitude must exceed at least zfac times RMS-S amplitude 
-#check statistics of P onsets#
-2.5                                 #mdttolerance#  %maximum allowed deviation of P picks from median [s]
-#wadati check#
-1.0                                 #wdttolerance# %maximum allowed deviation from Wadati-diagram
-

inputs/autoPyLoT_regional.in

@@ -1,100 +0,0 @@
-%This is a parameter input file for autoPyLoT.
-%All main and special settings regarding data handling
-%and picking are to be set here!
-%Parameters are optimized for regional data sets!
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-#main settings#
-/DATA/Egelados                      #rootpath#     %project path
-EVENT_DATA/LOCAL		    #datapath#     %data path
-2006.01_Nisyros                     #database#     %name of data base
-e1412.008.06                        #eventID#      %event ID for single event processing
-/DATA/Egelados/STAT_INFO            #invdir#       %full path to inventory or dataless-seed file
-PILOT				    #datastructure#  %choose data structure
-0                                   #iplot#        %flag for plotting: 0 none, 1, partly, >1 everything
-True                                #apverbose#    %choose 'True' or 'False' for terminal output
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#NLLoc settings#
-/home/ludger/NLLOC                  #nllocbin#     %path to NLLoc executable
-/home/ludger/NLLOC/Insheim          #nllocroot#    %root of NLLoc-processing directory
-AUTOPHASES.obs                      #phasefile#    %name of autoPyLoT-output phase file for NLLoc
-                                                   %(in nllocroot/obs)
-Insheim_min1d2015_auto.in           #ctrfile#      %name of autoPyLoT-output control file for NLLoc
-                                                   %(in nllocroot/run)
-ttime                               #ttpatter#     %pattern of NLLoc ttimes from grid
-                                                   %(in nllocroot/times)
-AUTOLOC_nlloc                       #outpatter#    %pattern of NLLoc-output file
-                                                   %(returns 'eventID_outpatter')
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#parameters for seismic moment estimation#
-3530                                #vp#           %average P-wave velocity
-2700                                #rho#          %average rock density [kg/m^3]
-1000f**0.8                          #Qp#           %quality factor for P waves (Qp*f^a)
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-AUTOFOCMEC_AIC_HOS4_ARH.in          #focmecin#     %name of focmec input file containing derived polarities
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#common settings picker#
-20                                  #pstart#       %start time [s] for calculating CF for P-picking
-100                                 #pstop#        %end time [s] for calculating CF for P-picking
-1.0                                 #sstart#       %start time [s] after or before(-) P-onset for calculating CF for S-picking
-100                                 #sstop#        %end time [s] after P-onset for calculating CF for S-picking
-3 10                                #bpz1#         %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
-3 12                                #bpz2#         %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
-3 8                                 #bph1#         %lower/upper corner freq. of first band pass filter H-comp. [Hz]
-3 6                                 #bph2#         %lower/upper corner freq. of second band pass filter H-comp. [Hz]
-#special settings for calculating CF#
-%!!Be careful when editing the following!!
-#Z-component#
-HOS                                 #algoP#        %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
-7                                   #tlta#         %for HOS-/AR-AIC-picker, length of LTA window [s] 
-4                                   #hosorder#     %for HOS-picker, order of Higher Order Statistics
-2                                   #Parorder#     %for AR-picker, order of AR process of Z-component
-1.2                                 #tdet1z#       %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
-0.4                                 #tpred1z#      %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
-0.6                                 #tdet2z#       %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
-0.2                                 #tpred2z#      %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
-0.001                               #addnoise#     %add noise to seismogram for stable AR prediction
-5 0.2 3.0 1.5                       #tsnrz#        %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-3                                   #pickwinP#     %for initial AIC and refined pick, length of P-pick window [s]
-8                                   #Precalcwin#   %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
-1.0                                 #aictsmooth#   %for HOS/AR, take average of samples for smoothing of AIC-function [s]
-0.3                                 #tsmoothP#     %for HOS/AR, take average of samples for smoothing CF [s]
-0.3                                 #ausP#         %for HOS/AR, artificial uplift of samples (aus) of CF (P)
-1.3                                 #nfacP#        %for HOS/AR, noise factor for noise level determination (P)
-#H-components#
-ARH                                 #algoS#        %choose algorithm for S-onset determination (ARH or AR3)
-0.8                                 #tdet1h#       %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
-0.4                                 #tpred1h#      %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
-0.6                                 #tdet2h#       %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
-0.3                                 #tpred2h#      %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
-4                                   #Sarorder#     %for AR-picker, order of AR process of H-components
-10                                  #Srecalcwin#   %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
-25                                  #pickwinS#     %for initial AIC and refined pick, length of S-pick window [s]
-5 0.2 3.0 3.0                       #tsnrh#        %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
-3.5                                 #aictsmoothS#  %for AIC-picker, take average of samples for smoothing of AIC-function [s]
-1.0                                 #tsmoothS#     %for AR-picker, take average of samples for smoothing CF [s] (S)
-0.2                                 #ausS#         %for HOS/AR, artificial uplift of samples (aus) of CF (S)
-1.5                                 #nfacS#        %for AR-picker, noise factor for noise level determination (S)
-%first-motion picker%
-1                                   #minfmweight#  %minimum required p weight for first-motion determination
-2                                   #minFMSNR#     %miniumum required SNR for first-motion determination
-6.0                                 #fmpickwin#    %pick window around P onset for calculating zero crossings
-%quality assessment%
-#inital AIC onset#
-0.04 0.08 0.16 0.32                 #timeerrorsP#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
-0.04 0.08 0.16 0.32                 #timeerrorsS#   %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
-3                                   #minAICPslope#  %below this slope [counts/s] the initial P pick is rejected
-1.2                                 #minAICPSNR#    %below this SNR the initial P pick is rejected
-5                                   #minAICSslope#  %below this slope [counts/s] the initial S pick is rejected
-2.5                                 #minAICSSNR#    %below this SNR the initial S pick is rejected
-#check duration of signal using envelope function#
-30                                  #minsiglength# %minimum required length of signal [s]
-2.5                                 #noisefactor#  %noiselevel*noisefactor=threshold
-60                                  #minpercent#   %required percentage of samples higher than threshold
-#check for spuriously picked S-onsets#
-0.5                                 #zfac#         %P-amplitude must exceed at least zfac times RMS-S amplitude 
-#check statistics of P onsets#
-45                                  #mdttolerance#  %maximum allowed deviation of P picks from median [s]
-#wadati check#
-3.0                                 #wdttolerance# %maximum allowed deviation from Wadati-diagram
-

inputs/filter.in

@@ -1,2 +0,0 @@
-P bandpass 4 2.0 20.0
-S bandpass 4 2.0 15.0

inputs/pylot_global.in

@@ -4,19 +4,19 @@
 %Parameters are optimized for %extent data sets!
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #main settings#
-/home/marcel/marcel_scratch                        #rootpath#       %project path                                                                                        
-alparray                                           #datapath#       %data path                                                                                           
-waveforms                                          #database#       %name of data base                                                                                   
-e0006.036.13                                       #eventID#        %event ID for single event processing (* for all events found in database)                           
-None                                               #invdir#         %full path to  inventory or dataless-seed file                                                       
+                                                   #rootpath#       %project path                                                                                        
+                                                   #datapath#       %data path                                                                                           
+                                                   #database#       %name of data base                                                                                   
+                                                   #eventID#        %event ID for single event processing (* for all events found in database)                           
+                                                   #invdir#         %full path to  inventory or dataless-seed file                                                       
 PILOT                                              #datastructure#  %choose data structure                                                                               
 True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #NLLoc settings#
-/progs/bin                                         #nllocbin#       %path to NLLoc executable                                                                            
-/home/ludger/NLLOC/Insheim                         #nllocroot#      %root of NLLoc-processing directory                                                                  
-AUTOPHASES.obs                                     #phasefile#      %name of autoPyLoT-output phase file for NLLoc                                                       
-Insheim_min1d032016_auto.in                        #ctrfile#        %name of autoPyLoT-output control file for NLLoc                                                     
+None                                               #nllocbin#       %path to NLLoc executable                                                                            
+None                                               #nllocroot#      %root of NLLoc-processing directory                                                                  
+None                                               #phasefile#      %name of autoPyLoT-output phase file for NLLoc                                                       
+None                                               #ctrfile#        %name of autoPyLoT-output control file for NLLoc                                                     
 ttime                                              #ttpatter#       %pattern of NLLoc ttimes from grid                                                                   
 AUTOLOC_nlloc                                      #outpatter#      %pattern of NLLoc-output file                                                                        
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -37,62 +37,64 @@ bandpass bandpass                                  #filter_type#    %filter type
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #common settings picker#
 global                                             #extent#         %extent of array ("local", "regional" or "global")                                                   
-50.0                                               #pstart#         %start time [s] for calculating CF for P-picking                                                     
-600.0                                              #pstop#          %end time [s] for calculating CF for P-picking                                                       
+-150.0                                             #pstart#         %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)     
+600.0                                              #pstop#          %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)       
 200.0                                              #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
 1150.0                                             #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
-True                                               #use_taup#       %use estimated traveltimes from taupy for calculating windows for CF                                 
+True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
+iasp91                                             #taup_model#     %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
 0.05 0.5                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
-0.01 0.5                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
+0.001 0.5                                          #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
 0.05 0.5                                           #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     
-0.01 0.5                                           #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
+0.001 0.5                                          #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
 #special settings for calculating CF#
 %!!Edit the following only if you know what you are doing!!%
 #Z-component#
 HOS                                                #algoP#          %choose algorithm for P-onset determination (HOS, ARZ, or AR3)                                       
-15.0                                               #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
+150.0                                              #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
 4                                                  #hosorder#       %for HOS-picker, order of Higher Order Statistics                                                    
 2                                                  #Parorder#       %for AR-picker, order of AR process of Z-component                                                   
-6.0                                                #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
-2.0                                                #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
-3.0                                                #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
-1.0                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
+16.0                                               #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
+10.0                                               #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
+12.0                                               #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
+6.0                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
 0.001                                              #addnoise#       %add noise to seismogram for stable AR prediction                                                    
-60.0 10.0 150.0 3.0                                #tsnrz#          %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]     
-10.0                                               #pickwinP#       %for initial AIC pick, length of P-pick window [s]                                                   
-20.0                                               #Precalcwin#     %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)                        
+60.0 10.0 40.0 10.0                                #tsnrz#          %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]     
+150.0                                              #pickwinP#       %for initial AIC pick, length of P-pick window [s]                                                   
+35.0                                               #Precalcwin#     %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)                        
 6.0                                                #aictsmooth#     %for HOS/AR, take average of samples for smoothing of AIC-function [s]                               
 4.0                                                #tsmoothP#       %for HOS/AR, take average of samples for smoothing CF [s]                                            
 0.001                                              #ausP#           %for HOS/AR, artificial uplift of samples (aus) of CF (P)                                            
 1.1                                                #nfacP#          %for HOS/AR, noise factor for noise level determination (P)                                          
 #H-components#
 ARH                                                #algoS#          %choose algorithm for S-onset determination (ARH or AR3)                                             
-6.0                                                #tdet1h#         %for HOS/AR, length of AR-determination window [s], H-components, 1st pick                           
-4.0                                                #tpred1h#        %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick                              
-6.0                                                #tdet2h#         %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick                            
-3.0                                                #tpred2h#        %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick                              
+12.0                                               #tdet1h#         %for HOS/AR, length of AR-determination window [s], H-components, 1st pick                           
+6.0                                                #tpred1h#        %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick                              
+8.0                                                #tdet2h#         %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick                            
+4.0                                                #tpred2h#        %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick                              
 4                                                  #Sarorder#       %for AR-picker, order of AR process of H-components                                                  
-5.0                                                #Srecalcwin#     %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)                             
-15.0                                               #pickwinS#       %for initial AIC pick, length of S-pick window [s]                                                   
-100.0 10.0 40.0 6.0                                #tsnrh#          %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]    
-2.0                                                #aictsmoothS#    %for AIC-picker, take average of samples for smoothing of AIC-function [s]                           
-3.0                                                #tsmoothS#       %for AR-picker, take average of samples for smoothing CF [s] (S)                                     
-0.9                                                #ausS#           %for HOS/AR, artificial uplift of samples (aus) of CF (S)                                            
+30.0                                               #Srecalcwin#     %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)                             
+195.0                                              #pickwinS#       %for initial AIC pick, length of S-pick window [s]                                                   
+100.0 10.0 45.0 10.0                               #tsnrh#          %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]    
+22.0                                               #aictsmoothS#    %for AIC-picker, take average of samples for smoothing of AIC-function [s]                           
+10.0                                               #tsmoothS#       %for AR-picker, take average of samples for smoothing CF [s] (S)                                     
+0.001                                              #ausS#           %for HOS/AR, artificial uplift of samples (aus) of CF (S)                                            
 1.2                                                #nfacS#          %for AR-picker, noise factor for noise level determination (S)                                       
 #first-motion picker#
 1                                                  #minfmweight#    %minimum required P weight for first-motion determination                                            
-2.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
-0.2                                                #fmpickwin#      %pick window around P onset for calculating zero crossings                                           
+3.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
+10.0                                               #fmpickwin#      %pick window around P onset for calculating zero crossings                                           
 #quality assessment#
 1.0 2.0 4.0 8.0                                    #timeerrorsP#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P                           
 4.0 8.0 16.0 32.0                                  #timeerrorsS#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S                           
 0.5                                                #minAICPslope#   %below this slope [counts/s] the initial P pick is rejected                                          
 1.1                                                #minAICPSNR#     %below this SNR the initial P pick is rejected                                                       
 1.0                                                #minAICSslope#   %below this slope [counts/s] the initial S pick is rejected                                          
-1.5                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
+1.3                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
 5.0                                                #minsiglength#   %length of signal part for which amplitudes must exceed noiselevel [s]                               
 1.0                                                #noisefactor#    %noiselevel*noisefactor=threshold                                                                    
 10.0                                               #minpercent#     %required percentage of amplitudes exceeding threshold                                               
-1.5                                                #zfac#           %P-amplitude must exceed at least zfac times RMS-S amplitude                                         
-6.0                                                #mdttolerance#   %maximum allowed deviation of P picks from median [s]                                                
-1.0                                                #wdttolerance#   %maximum allowed deviation from Wadati-diagram                                                       
+1.2                                                #zfac#           %P-amplitude must exceed at least zfac times RMS-S amplitude                                         
+25.0                                               #mdttolerance#   %maximum allowed deviation of P picks from median [s]                                                
+50.0                                               #wdttolerance#   %maximum allowed deviation from Wadati-diagram                                                       
+5.0                                                #jackfactor#     %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor

inputs/pylot_local.in

@@ -0,0 +1,100 @@
+%This is a parameter input file for PyLoT/autoPyLoT.
+%All main and special settings regarding data handling
+%and picking are to be set here!
+%Parameters are optimized for %extent data sets!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#main settings#
+                                                   #rootpath#       %project path                                                                                        
+                                                   #datapath#       %data path                                                                                           
+                                                   #database#       %name of data base                                                                                   
+                                                   #eventID#        %event ID for single event processing (* for all events found in database)                           
+                                                   #invdir#         %full path to  inventory or dataless-seed file                                                       
+PILOT                                              #datastructure#  %choose data structure                                                                               
+True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#NLLoc settings#
+None                                               #nllocbin#       %path to NLLoc executable                                                                            
+None                                               #nllocroot#      %root of NLLoc-processing directory                                                                  
+None                                               #phasefile#      %name of autoPyLoT-output phase file for NLLoc                                                       
+None                                               #ctrfile#        %name of autoPyLoT-output control file for NLLoc                                                     
+ttime                                              #ttpatter#       %pattern of NLLoc ttimes from grid                                                                   
+AUTOLOC_nlloc                                      #outpatter#      %pattern of NLLoc-output file                                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#parameters for seismic moment estimation#
+3530.0                                             #vp#             %average P-wave velocity                                                                             
+2500.0                                             #rho#            %average rock density [kg/m^3]                                                                       
+300.0 0.8                                          #Qp#             %quality factor for P waves (Qp*f^a); list(Qp, a)                                                    
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#settings local magnitude#
+1.11 0.0009 -2.0                                   #WAscaling#      %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm]                           If zeros are set, original Richter magnitude is calculated!
+1.0382 -0.447                                      #magscaling#     %Scaling relation for derived local magnitude [a*Ml+b].                            If zeros are set, no scaling of network magnitude is applied!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#filter settings#
+1.0 1.0                                            #minfreq#        %Lower filter frequency [P, S]                                                                       
+10.0 10.0                                          #maxfreq#        %Upper filter frequency [P, S]                                                                       
+2 2                                                #filter_order#   %filter order [P, S]                                                                                 
+bandpass bandpass                                  #filter_type#    %filter type (bandpass, bandstop, lowpass, highpass) [P, S]                                          
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#common settings picker#
+local                                              #extent#         %extent of array ("local", "regional" or "global")                                                   
+15.0                                               #pstart#         %start time [s] for calculating CF for P-picking                                                     
+60.0                                               #pstop#          %end time [s] for calculating CF for P-picking                                                       
+-1.0                                               #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
+10.0                                               #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
+True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
+iasp91                                             #taup_model#     %define TauPy model for traveltime estimation                                                        
+2.0 10.0                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
+2.0 12.0                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
+2.0 8.0                                            #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     
+2.0 10.0                                           #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
+#special settings for calculating CF#
+%!!Edit the following only if you know what you are doing!!%
+#Z-component#
+HOS                                                #algoP#          %choose algorithm for P-onset determination (HOS, ARZ, or AR3)                                       
+7.0                                                #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
+4                                                  #hosorder#       %for HOS-picker, order of Higher Order Statistics                                                    
+2                                                  #Parorder#       %for AR-picker, order of AR process of Z-component                                                   
+1.2                                                #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
+0.4                                                #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
+0.6                                                #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
+0.2                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
+0.001                                              #addnoise#       %add noise to seismogram for stable AR prediction                                                    
+3.0 0.1 0.5 1.0                                    #tsnrz#          %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]     
+3.0                                                #pickwinP#       %for initial AIC pick, length of P-pick window [s]                                                   
+6.0                                                #Precalcwin#     %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)                        
+0.2                                                #aictsmooth#     %for HOS/AR, take average of samples for smoothing of AIC-function [s]                               
+0.1                                                #tsmoothP#       %for HOS/AR, take average of samples for smoothing CF [s]                                            
+0.001                                              #ausP#           %for HOS/AR, artificial uplift of samples (aus) of CF (P)                                            
+1.3                                                #nfacP#          %for HOS/AR, noise factor for noise level determination (P)                                          
+#H-components#
+ARH                                                #algoS#          %choose algorithm for S-onset determination (ARH or AR3)                                             
+0.8                                                #tdet1h#         %for HOS/AR, length of AR-determination window [s], H-components, 1st pick                           
+0.4                                                #tpred1h#        %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick                              
+0.6                                                #tdet2h#         %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick                            
+0.3                                                #tpred2h#        %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick                              
+4                                                  #Sarorder#       %for AR-picker, order of AR process of H-components                                                  
+5.0                                                #Srecalcwin#     %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)                             
+4.0                                                #pickwinS#       %for initial AIC pick, length of S-pick window [s]                                                   
+2.0 0.3 1.5 1.0                                    #tsnrh#          %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]    
+1.0                                                #aictsmoothS#    %for AIC-picker, take average of samples for smoothing of AIC-function [s]                           
+0.7                                                #tsmoothS#       %for AR-picker, take average of samples for smoothing CF [s] (S)                                     
+0.9                                                #ausS#           %for HOS/AR, artificial uplift of samples (aus) of CF (S)                                            
+1.5                                                #nfacS#          %for AR-picker, noise factor for noise level determination (S)                                       
+#first-motion picker#
+1                                                  #minfmweight#    %minimum required P weight for first-motion determination                                            
+2.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
+0.2                                                #fmpickwin#      %pick window around P onset for calculating zero crossings                                           
+#quality assessment#
+0.02 0.04 0.08 0.16                                #timeerrorsP#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P                           
+0.04 0.08 0.16 0.32                                #timeerrorsS#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S                           
+0.8                                                #minAICPslope#   %below this slope [counts/s] the initial P pick is rejected                                          
+1.1                                                #minAICPSNR#     %below this SNR the initial P pick is rejected                                                       
+1.0                                                #minAICSslope#   %below this slope [counts/s] the initial S pick is rejected                                          
+1.5                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
+1.0                                                #minsiglength#   %length of signal part for which amplitudes must exceed noiselevel [s]                               
+1.0                                                #noisefactor#    %noiselevel*noisefactor=threshold                                                                    
+10.0                                               #minpercent#     %required percentage of amplitudes exceeding threshold                                               
+1.5                                                #zfac#           %P-amplitude must exceed at least zfac times RMS-S amplitude                                         
+6.0                                                #mdttolerance#   %maximum allowed deviation of P picks from median [s]                                                
+1.0                                                #wdttolerance#   %maximum allowed deviation from Wadati-diagram                                                       
+5.0                                                #jackfactor#     %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor

inputs/pylot_regional.in

@@ -0,0 +1,100 @@
+%This is a parameter input file for PyLoT/autoPyLoT.
+%All main and special settings regarding data handling
+%and picking are to be set here!
+%Parameters are optimized for %extent data sets!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#main settings#
+                                                   #rootpath#       %project path                                                                                        
+                                                   #datapath#       %data path                                                                                           
+                                                   #database#       %name of data base                                                                                   
+                                                   #eventID#        %event ID for single event processing (* for all events found in database)                           
+                                                   #invdir#         %full path to  inventory or dataless-seed file                                                       
+PILOT                                              #datastructure#  %choose data structure                                                                               
+True                                               #apverbose#      %choose 'True' or 'False' for terminal output                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#NLLoc settings#
+None                                               #nllocbin#       %path to NLLoc executable                                                                            
+None                                               #nllocroot#      %root of NLLoc-processing directory                                                                  
+None                                               #phasefile#      %name of autoPyLoT-output phase file for NLLoc                                                       
+None                                               #ctrfile#        %name of autoPyLoT-output control file for NLLoc                                                     
+ttime                                              #ttpatter#       %pattern of NLLoc ttimes from grid                                                                   
+AUTOLOC_nlloc                                      #outpatter#      %pattern of NLLoc-output file                                                                        
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#parameters for seismic moment estimation#
+3530.0                                             #vp#             %average P-wave velocity                                                                             
+2500.0                                             #rho#            %average rock density [kg/m^3]                                                                       
+300.0 0.8                                          #Qp#             %quality factor for P waves (Qp*f^a); list(Qp, a)                                                    
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#settings local magnitude#
+1.11 0.0009 -2.0                                   #WAscaling#      %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm]                           If zeros are set, original Richter magnitude is calculated!
+1.0382 -0.447                                      #magscaling#     %Scaling relation for derived local magnitude [a*Ml+b].                            If zeros are set, no scaling of network magnitude is applied!
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#filter settings#
+1.0 1.0                                            #minfreq#        %Lower filter frequency [P, S]                                                                       
+10.0 10.0                                          #maxfreq#        %Upper filter frequency [P, S]                                                                       
+2 2                                                #filter_order#   %filter order [P, S]                                                                                 
+bandpass bandpass                                  #filter_type#    %filter type (bandpass, bandstop, lowpass, highpass) [P, S]                                          
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#common settings picker#
+local                                              #extent#         %extent of array ("local", "regional" or "global")                                                   
+15.0                                               #pstart#         %start time [s] for calculating CF for P-picking                                                     
+60.0                                               #pstop#          %end time [s] for calculating CF for P-picking                                                       
+-1.0                                               #sstart#         %start time [s] relative to P-onset for calculating CF for S-picking                                 
+10.0                                               #sstop#          %end time [s] after P-onset for calculating CF for S-picking                                         
+True                                               #use_taup#       %use estimated traveltimes from TauPy for calculating windows for CF                                 
+iasp91                                             #taup_model#     %define TauPy model for traveltime estimation                                                        
+2.0 10.0                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
+2.0 12.0                                           #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
+2.0 8.0                                            #bph1#           %lower/upper corner freq. of first band pass filter H-comp. [Hz]                                     
+2.0 10.0                                           #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
+#special settings for calculating CF#
+%!!Edit the following only if you know what you are doing!!%
+#Z-component#
+HOS                                                #algoP#          %choose algorithm for P-onset determination (HOS, ARZ, or AR3)                                       
+7.0                                                #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
+4                                                  #hosorder#       %for HOS-picker, order of Higher Order Statistics                                                    
+2                                                  #Parorder#       %for AR-picker, order of AR process of Z-component                                                   
+1.2                                                #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
+0.4                                                #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
+0.6                                                #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
+0.2                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
+0.001                                              #addnoise#       %add noise to seismogram for stable AR prediction                                                    
+3.0 0.1 0.5 1.0                                    #tsnrz#          %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]     
+3.0                                                #pickwinP#       %for initial AIC pick, length of P-pick window [s]                                                   
+6.0                                                #Precalcwin#     %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)                        
+0.2                                                #aictsmooth#     %for HOS/AR, take average of samples for smoothing of AIC-function [s]                               
+0.1                                                #tsmoothP#       %for HOS/AR, take average of samples for smoothing CF [s]                                            
+0.001                                              #ausP#           %for HOS/AR, artificial uplift of samples (aus) of CF (P)                                            
+1.3                                                #nfacP#          %for HOS/AR, noise factor for noise level determination (P)                                          
+#H-components#
+ARH                                                #algoS#          %choose algorithm for S-onset determination (ARH or AR3)                                             
+0.8                                                #tdet1h#         %for HOS/AR, length of AR-determination window [s], H-components, 1st pick                           
+0.4                                                #tpred1h#        %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick                              
+0.6                                                #tdet2h#         %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick                            
+0.3                                                #tpred2h#        %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick                              
+4                                                  #Sarorder#       %for AR-picker, order of AR process of H-components                                                  
+5.0                                                #Srecalcwin#     %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)                             
+4.0                                                #pickwinS#       %for initial AIC pick, length of S-pick window [s]                                                   
+2.0 0.3 1.5 1.0                                    #tsnrh#          %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]    
+1.0                                                #aictsmoothS#    %for AIC-picker, take average of samples for smoothing of AIC-function [s]                           
+0.7                                                #tsmoothS#       %for AR-picker, take average of samples for smoothing CF [s] (S)                                     
+0.9                                                #ausS#           %for HOS/AR, artificial uplift of samples (aus) of CF (S)                                            
+1.5                                                #nfacS#          %for AR-picker, noise factor for noise level determination (S)                                       
+#first-motion picker#
+1                                                  #minfmweight#    %minimum required P weight for first-motion determination                                            
+2.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
+0.2                                                #fmpickwin#      %pick window around P onset for calculating zero crossings                                           
+#quality assessment#
+0.02 0.04 0.08 0.16                                #timeerrorsP#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P                           
+0.04 0.08 0.16 0.32                                #timeerrorsS#    %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S                           
+0.8                                                #minAICPslope#   %below this slope [counts/s] the initial P pick is rejected                                          
+1.1                                                #minAICPSNR#     %below this SNR the initial P pick is rejected                                                       
+1.0                                                #minAICSslope#   %below this slope [counts/s] the initial S pick is rejected                                          
+1.5                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
+1.0                                                #minsiglength#   %length of signal part for which amplitudes must exceed noiselevel [s]                               
+1.0                                                #noisefactor#    %noiselevel*noisefactor=threshold                                                                    
+10.0                                               #minpercent#     %required percentage of amplitudes exceeding threshold                                               
+1.5                                                #zfac#           %P-amplitude must exceed at least zfac times RMS-S amplitude                                         
+6.0                                                #mdttolerance#   %maximum allowed deviation of P picks from median [s]                                                
+1.0                                                #wdttolerance#   %maximum allowed deviation from Wadati-diagram                                                       
+5.0                                                #jackfactor#     %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor

makePyLoT.py

@@ -158,23 +158,29 @@ def buildPyLoT(verbosity=None):
 
 
 def installPyLoT(verbosity=None):
-    files_to_copy = {'autoPyLoT_local.in': ['~', '.pylot'],
-                     'autoPyLoT_regional.in': ['~', '.pylot']}
+    files_to_copy = {'pylot_local.in': ['~', '.pylot'],
+                     'pylot_regional.in': ['~', '.pylot'],
+                     'pylot_global.in': ['~', '.pylot']}
     if verbosity > 0:
         print('starting installation of PyLoT ...')
     if verbosity > 1:
         print('copying input files into destination folder ...')
     ans = input('please specify scope of interest '
-                '([0]=local, 1=regional) :') or 0
+                '([0]=local, 1=regional, 2=global) :') or 0
     if not isinstance(ans, int):
         ans = int(ans)
-    ans = 'local' if ans is 0 else 'regional'
+    if ans == 0:
+        ans = 'local'
+    elif ans == 1:
+        ans = 'regional'
+    elif ans == 2:
+        ans = 'global'
     link_dest = []
     for file, destination in files_to_copy.items():
         link_file = ans in file
         if link_file:
             link_dest = copy.deepcopy(destination)
-            link_dest.append('autoPyLoT.in')
+            link_dest.append('pylot.in')
             link_dest = os.path.join(*link_dest)
         destination.append(file)
         destination = os.path.join(*destination)

pylot/core/analysis/magnitude.py

@@ -617,9 +617,9 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
             p3, = plt.loglog(F, YYcor, 'r')
             p4, = plt.loglog(F, fit, 'g')
             plt.loglog([fc, fc], [w0 / 100, w0], 'g')
-            plt.legend([p1, p2, p3, p4], ['Raw Spectrum', \
-                                          'Used Raw Spectrum', \
-                                          'Q-Corrected Spectrum', \
+            plt.legend([p1, p2, p3, p4], ['Raw Spectrum',
+                                          'Used Raw Spectrum',
+                                          'Q-Corrected Spectrum',
                                           'Fit to Spectrum'])
             plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
                       % (w0, fc))

pylot/core/analysis/magnitude.py.orig

@@ -1,240 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-Created August/September 2015.
-
-:author: Ludger Küperkoch / MAGS2 EP3 working group
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-from obspy.core import Stream
-from pylot.core.pick.utils import getsignalwin
-from scipy.optimize import curve_fit
-
-
-class Magnitude(object):
-    '''
-    Superclass for calculating Wood-Anderson peak-to-peak
-    amplitudes, local magnitudes and moment magnitudes.
-    '''
-
-    def __init__(self, wfstream, To, pwin, iplot):
-        '''
-        :param: wfstream
-        :type: `~obspy.core.stream.Stream
-
-        :param: To, onset time, P- or S phase
-        :type:  float
-
-        :param: pwin, pick window [To To+pwin] to get maximum
-                peak-to-peak amplitude (WApp) or to calculate
-                source spectrum (DCfc)
-        :type:  float
-
-        :param: iplot, no. of figure window for plotting interims results
-        :type: integer
-
-        '''
-
-        assert isinstance(wfstream, Stream), "%s is not a stream object" % str(wfstream)
-
-        self.setwfstream(wfstream)
-        self.setTo(To)
-        self.setpwin(pwin)
-        self.setiplot(iplot)
-        self.calcwapp()
-        self.calcsourcespec()
-
-    def getwfstream(self):
-        return self.wfstream
-
-    def setwfstream(self, wfstream):
-        self.wfstream = wfstream
-
-    def getTo(self):
-        return self.To
-
-    def setTo(self, To):
-        self.To = To
-
-    def getpwin(self):
-        return self.pwin
-
-    def setpwin(self, pwin):
-        self.pwin = pwin
-
-    def getiplot(self):
-        return self.iplot
-
-    def setiplot(self, iplot):
-        self.iplot = iplot
-
-    def getwapp(self):
-        return self.wapp
-
-    def getw0(self):
-        return self.w0
-
-    def getfc(self):
-        return self.fc
-
-    def calcwapp(self):
-        self.wapp = None
-
-    def calcsourcespec(self):
-        self.sourcespek = None
-
-
-class WApp(Magnitude):
-    '''
-    Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
-    seismograph. Has to be derived from instrument corrected traces!
-    '''
-
-    def calcwapp(self):
-        print("Getting Wood-Anderson peak-to-peak amplitude ...")
-        print("Simulating Wood-Anderson seismograph ...")
-
-        self.wapp = None
-        stream = self.getwfstream()
-
-        # poles, zeros and sensitivity of WA seismograph
-        # (see Uhrhammer & Collins, 1990, BSSA, pp. 702-716)
-        paz_wa = {
-            'poles': [5.6089 - 5.4978j, -5.6089 - 5.4978j],
-            'zeros': [0j, 0j],
-            'gain': 2080,
-            'sensitivity': 1}
-
-        stream.simulate(paz_remove=None, paz_simulate=paz_wa)
-
-        trH1 = stream[0].data
-        trH2 = stream[1].data
-        ilen = min([len(trH1), len(trH2)])
-        # get RMS of both horizontal components
-        sqH = np.sqrt(np.power(trH1[0:ilen], 2) + np.power(trH2[0:ilen], 2))
-        # get time array
-        th = np.arange(0, len(sqH) * stream[0].stats.delta, stream[0].stats.delta)
-        # get maximum peak within pick window
-        iwin = getsignalwin(th, self.getTo(), self.getpwin())
-        self.wapp = np.max(sqH[iwin])
-        print("Determined Wood-Anderson peak-to-peak amplitude: %f mm") % self.wapp
-
-        if self.getiplot() > 1:
-            stream.plot()
-            f = plt.figure(2)
-            plt.plot(th, sqH)
-            plt.plot(th[iwin], sqH[iwin], 'g')
-            plt.plot([self.getTo(), self.getTo()], [0, max(sqH)], 'r', linewidth=2)
-            plt.title('Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
-                      % (stream[0].stats.station, self.wapp))
-            plt.xlabel('Time [s]')
-            plt.ylabel('Displacement [mm]')
-            plt.show()
-            raw_input()
-            plt.close(f)
-
-
-class DCfc(Magnitude):
-    '''
-    Method to calculate the source spectrum and to derive from that the plateau
-    (so-called DC-value) and the corner frequency assuming Aki's omega-square
-    source model. Has to be derived from instrument corrected displacement traces!
-    '''
-
-    def calcsourcespec(self):
-        print("Calculating source spectrum ....")
-
-        self.w0 = None  # DC-value
-        self.fc = None  # corner frequency
-
-        stream = self.getwfstream()
-        tr = stream[0]
-
-        # get time array
-        t = np.arange(0, len(tr) * tr.stats.delta, tr.stats.delta)
-        iwin = getsignalwin(t, self.getTo(), self.getpwin())
-        xdat = tr.data[iwin]
-
-        # fft
-        fny = tr.stats.sampling_rate / 2
-        l = len(xdat) / tr.stats.sampling_rate
-        n = tr.stats.sampling_rate * l  # number of fft bins after Bath
-        # find next power of 2 of data length
-        m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
-        N = int(np.power(m, 2))
-        y = tr.stats.delta * np.fft.fft(xdat, N)
-        Y = abs(y[: N / 2])
-        L = (N - 1) / tr.stats.sampling_rate
-        f = np.arange(0, fny, 1 / L)
-
-        # remove zero-frequency and frequencies above
-        # corner frequency of seismometer (assumed
-        # to be 100 Hz)
-        fi = np.where((f >= 1) & (f < 100))
-        F = f[fi]
-        YY = Y[fi]
-        # get plateau (DC value) and corner frequency
-        # initial guess of plateau
-        DCin = np.mean(YY[0:100])
-        # initial guess of corner frequency
-        # where spectral level reached 50% of flat level
-        iin = np.where(YY >= 0.5 * DCin)
-        Fcin = F[iin[0][np.size(iin) - 1]]
-        fit = synthsourcespec(F, DCin, Fcin)
-        [optspecfit, pcov] = curve_fit(synthsourcespec, F, YY.real, [DCin, Fcin])
-        self.w0 = optspecfit[0]
-        self.fc = optspecfit[1]
-        print("DCfc: Determined DC-value: %e m/Hz, \n" \
-              "Determined corner frequency: %f Hz" % (self.w0, self.fc))
-
-        # if self.getiplot() > 1:
-        iplot = 2
-        if iplot > 1:
-            print("DCfc: Determined DC-value: %e m/Hz, \n"
-                  "Determined corner frequency: %f Hz" % (self.w0, self.fc))
-
-        if self.getiplot() > 1:
-            f1 = plt.figure()
-            plt.subplot(2, 1, 1)
-            # show displacement in mm
-            plt.plot(t, np.multiply(tr, 1000), 'k')
-            plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
-            plt.title('Seismogram and P pulse, station %s' % tr.stats.station)
-            plt.xlabel('Time since %s' % tr.stats.starttime)
-            plt.ylabel('Displacement [mm]')
-
-            plt.subplot(2, 1, 2)
-            plt.loglog(f, Y.real, 'k')
-            plt.loglog(F, YY.real)
-            plt.loglog(F, fit, 'g')
-            plt.title('Source Spectrum from P Pulse, DC=%e m/Hz, fc=%4.1f Hz' \
-                      % (self.w0, self.fc))
-            plt.xlabel('Frequency [Hz]')
-            plt.ylabel('Amplitude [m/Hz]')
-            plt.grid()
-            plt.show()
-            raw_input()
-            plt.close(f1)
-
-
-def synthsourcespec(f, omega0, fcorner):
-    '''
-    Calculates synthetic source spectrum from given plateau and corner
-    frequency assuming Akis omega-square model.
-
-    :param: f, frequencies
-    :type:  array
-
-    :param: omega0, DC-value (plateau) of source spectrum
-    :type:  float
-
-    :param: fcorner, corner frequency of source spectrum
-    :type:  float
-    '''
-
-    # ssp = omega0 / (pow(2, (1 + f / fcorner)))
-    ssp = omega0 / (1 + pow(2, (f / fcorner)))
-
-    return ssp

pylot/core/io/data.py

@@ -240,8 +240,8 @@ class Data(object):
                     mstation = picks[i].waveform_id.station_code
                     mstation_ext = mstation + '_'
                     for k in range(len(picks_copy)):
-                        if ((picks_copy[k].waveform_id.station_code == mstation) or \
-                                    (picks_copy[k].waveform_id.station_code == mstation_ext)) and \
+                        if ((picks_copy[k].waveform_id.station_code == mstation) or
+                                (picks_copy[k].waveform_id.station_code == mstation_ext)) and \
                                 (picks_copy[k].method_id == 'auto'):
                             del picks_copy[k]
                             break
@@ -442,7 +442,6 @@ class Data(object):
                 else:
                     if self.get_evt_data().picks:
                         raise OverwriteError('Existing picks would be overwritten!')
-                        break
                     else:
                         picks = picks_from_picksdict(picks)
                         break

pylot/core/io/default_parameters.py

@@ -273,26 +273,6 @@ defaults = {'rootpath': {'type': str,
                       'value': 1.5,
                       'namestring': 'Noise factor S'},
 
-            'checkwindowP': {'type': float,
-                      'tooltip': 'time window before HOS/AR-maximum to check for smaller maxima [s]',
-                      'value': 10.0,
-                      'namestring': 'Check Window P'},
-
-            'minfactorP': {'type': float,
-                      'tooltip': 'Second maximum must be at least minfactor * first maximum [-]',
-                      'value': 0.7,
-                      'namestring': 'Minimum Factor P'},
-
-            'checkwindowS': {'type': float,
-                             'tooltip': 'time window before AR-maximum to check for smaller maxima [s]',
-                             'value': 10.0,
-                             'namestring': 'Check Window S'},
-
-            'minfactorS': {'type': float,
-                           'tooltip': 'Second maximum must be at least minfactor * first maximum [-]',
-                           'value': 0.7,
-                           'namestring': 'Minimum Factor S'},
-
             'minfmweight': {'type': int,
                             'tooltip': 'minimum required P weight for first-motion determination',
                             'value': 1,
@@ -475,9 +455,7 @@ settings_special_pick = {
         'aictsmooth',
         'tsmoothP',
         'ausP',
-        'nfacP',
-        'checkwindowP',
-        'minfactorP'],
+        'nfacP'],
     'h': [
         'algoS',
         'tdet1h',
@@ -491,9 +469,7 @@ settings_special_pick = {
         'aictsmoothS',
         'tsmoothS',
         'ausS',
-        'nfacS',
-        'checkwindowS',
-        'minfactorS'],
+        'nfacS'],
     'fm': [
         'minfmweight',
         'minFMSNR',

pylot/core/io/phases.py

@@ -865,8 +865,8 @@ def merge_picks(event, picks):
             if p.waveform_id.station_code == station\
                     and p.waveform_id.network_code == network\
                     and p.phase_hint == phase\
-                    and (str(p.method_id) in str(method)\
-                    or str(method) in str(p.method_id)):
+                    and (str(p.method_id) in str(method)
+                         or str(method) in str(p.method_id)):
                 p.time, p.time_errors, p.waveform_id.network_code, p.method_id = time, err, network, method
         del time, err, phase, station, network, method
     return event
@@ -907,15 +907,15 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
                 for mpick in arrivals_copy:
                     phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
                     if phase == 'P':
-                        if ((mpick.waveform_id.station_code == mstation) or \
-                                    (mpick.waveform_id.station_code == mstation_ext)) and \
+                        if ((mpick.waveform_id.station_code == mstation) or
+                                (mpick.waveform_id.station_code == mstation_ext)) and \
                                 ((mpick.method_id).split('/')[1] == 'auto') and \
                                 (mpick.time_errors['uncertainty'] <= ErrorsP[3]):
                             del mpick
                             break
                     elif phase == 'S':
-                        if ((mpick.waveform_id.station_code == mstation) or \
-                                    (mpick.waveform_id.station_code == mstation_ext)) and \
+                        if ((mpick.waveform_id.station_code == mstation) or
+                                (mpick.waveform_id.station_code == mstation_ext)) and \
                                 ((mpick.method_id).split('/')[1] == 'auto') and \
                                 (mpick.time_errors['uncertainty'] <= ErrorsS[3]):
                             del mpick

pylot/core/pick/autopick.py

@@ -38,7 +38,7 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
 
 
     # get some parameters for quality control from
-    # parameter input file (usually autoPyLoT.in).
+    # parameter input file (usually pylot.in).
     wdttolerance = param.get('wdttolerance')
     mdttolerance = param.get('mdttolerance')
     jackfactor = param.get('jackfactor')
@@ -64,8 +64,11 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
         print('iPlot Flag active: NO MULTIPROCESSING possible.')
         return all_onsets
 
+    # rename str for ncores in case ncores == 0 (use all cores)
+    ncores_str = ncores if ncores != 0 else 'all available'
+
     print('Autopickstation: Distribute autopicking for {} '
-          'stations on {} cores.'.format(len(input_tuples), ncores))
+          'stations on {} cores.'.format(len(input_tuples), ncores_str))
 
     pool = gen_Pool(ncores)
     result = pool.map(call_autopickstation, input_tuples)
@@ -110,7 +113,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
     :type wfstream: obspy.core.stream.Stream
 
     :param pickparam: container of picking parameters from input file,
-           usually autoPyLoT.in
+           usually pylot.in
     :type pickparam: PylotParameter
     :param verbose:
     :type verbose: bool
@@ -118,7 +121,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
     """
 
     # declaring pickparam variables (only for convenience)
-    # read your autoPyLoT.in for details!
+    # read your pylot.in for details!
     plt_flag = 0
 
     # special parameters for P picking
@@ -177,10 +180,6 @@ def autopickstation(wfstream, pickparam, verbose=False,
     # parameter to check for spuriously picked S onset
     zfac = pickparam.get('zfac')
     # path to inventory-, dataless- or resp-files
-    checkwindowP = pickparam.get('checkwindowP')
-    minfactorP = pickparam.get('minfactorP')
-    checkwindowS = pickparam.get('checkwindowS')
-    minfactorS = pickparam.get('minfactorS')
 
     # initialize output
     Pweight = 4  # weight for P onset
@@ -229,8 +228,10 @@ def autopickstation(wfstream, pickparam, verbose=False,
                                          data=str(zdat))
         if verbose: print(msg)
         z_copy = zdat.copy()
-        # filter and taper data
         tr_filt = zdat[0].copy()
+        #remove constant offset from data to avoid unwanted filter response
+        tr_filt.detrend(type='demean')
+        # filter and taper data
         tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1],
                        zerophase=False)
         tr_filt.taper(max_percentage=0.05, type='hann')
@@ -326,10 +327,11 @@ def autopickstation(wfstream, pickparam, verbose=False,
         key = 'aicFig'
         if fig_dict:
             fig = fig_dict[key]
+            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
         else:
             fig = None
-        aicpick = AICPicker(aiccf, tsnrz, pickwinP, checkwindow=checkwindowP, minfactor=minfactorP,
-                            iplot=iplot, Tsmooth=tsmoothP, fig=fig)
+            linecolor = 'k'
+        aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig, linecolor=linecolor)
         # add pstart and pstop to aic plot
         if fig:
             for ax in fig.axes:
@@ -352,16 +354,21 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 key = 'slength'
                 if fig_dict:
                     fig = fig_dict[key]
+                    linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                 else:
                     fig = None
+                    linecolor = 'k'
                 Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
                                           minsiglength / 2,
                                           nfacsl, minpercent, iplot,
-                                          fig)
+                                          fig, linecolor)
             else:
                 # filter and taper horizontal traces
                 trH1_filt = edat.copy()
                 trH2_filt = ndat.copy()
+                # remove constant offset from data to avoid unwanted filter response
+                trH1_filt.detrend(type='demean')
+                trH2_filt.detrend(type='demean')
                 trH1_filt.filter('bandpass', freqmin=bph1[0],
                                  freqmax=bph1[1],
                                  zerophase=False)
@@ -374,12 +381,14 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 zne += trH2_filt
                 if fig_dict:
                     fig = fig_dict['slength']
+                    linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                 else:
                     fig = None
+                    linecolor = 'k'
                 Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
                                           minsiglength,
                                           nfacsl, minpercent, iplot,
-                                          fig)
+                                          fig, linecolor)
 
             if Pflag == 1:
                 # check for spuriously picked S onset
@@ -392,10 +401,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
                     if iplot > 1:
                         if fig_dict:
                             fig = fig_dict['checkZ4s']
+                            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                         else:
                             fig = None
+                            linecolor = 'k'
                     Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
-                                     tsnrz[2], iplot, fig)
+                                     tsnrz[2], iplot, fig, linecolor)
                     if Pflag == 0:
                         Pmarker = 'SinsteadP'
                         Pweight = 9
@@ -418,6 +429,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
             # re-filter waveform with larger bandpass
             z_copy = zdat.copy()
             tr_filt = zdat[0].copy()
+            tr_filt.detrend(type='demean')
             tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
                            zerophase=False)
             tr_filt.taper(max_percentage=0.05, type='hann')
@@ -447,10 +459,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 algoP=algoP)
             if fig_dict:
                 fig = fig_dict['refPpick']
+                linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
             else:
                 fig = None
-            refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot=iplot, aus=ausP, Tsmooth=tsmoothP,
-                                  Pick1 = aicpick.getpick(), fig=fig)
+                linecolor = 'k'
+            refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
+                                  aicpick.getpick(), fig, linecolor)
             mpickP = refPpick.getpick()
             #############################################################
             if mpickP is not None:
@@ -459,10 +473,13 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 if iplot:
                     if fig_dict:
                         fig = fig_dict['el_Ppick']
+                        linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                     else:
                         fig = None
+                        linecolor = 'k'
                     epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
-                                                            mpickP, iplot, fig=fig)
+                                                            mpickP, iplot, fig=fig,
+                                                            linecolor=linecolor)
                 else:
                     epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
                                                             mpickP, iplot)
@@ -492,9 +509,10 @@ def autopickstation(wfstream, pickparam, verbose=False,
                     if iplot:
                         if fig_dict:
                             fig = fig_dict['fm_picker']
+                            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                         else:
                             fig = None
-                        FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig)
+                        FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig, linecolor)
                     else:
                         FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
                 else:
@@ -574,6 +592,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
             # filter and taper data
             trH1_filt = hdat[0].copy()
             trH2_filt = hdat[1].copy()
+            trH1_filt.detrend(type='demean')
+            trH2_filt.detrend(type='demean')
             trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
                              zerophase=False)
             trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
@@ -592,6 +612,9 @@ def autopickstation(wfstream, pickparam, verbose=False,
             trH1_filt = hdat[0].copy()
             trH2_filt = hdat[1].copy()
             trH3_filt = hdat[2].copy()
+            trH1_filt.detrend(type='demean')
+            trH2_filt.detrend(type='demean')
+            trH3_filt.detrend(type='demean')
             trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
                              zerophase=False)
             trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
@@ -629,11 +652,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
         # of class AutoPicking
         if fig_dict:
             fig = fig_dict['aicARHfig']
+            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
         else:
             fig = None
-        aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, checkwindow=checkwindowS,
-                               minfactor=minfactorS, iplot=iplot, Tsmooth=aictsmoothS,
-                               fig=fig)
+            linecolor = 'k'
+        aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
+                               aictsmoothS, fig=fig, linecolor=linecolor)
         ###############################################################
         # go on with processing if AIC onset passes quality control
         slope = aicarhpick.getSlope()
@@ -658,6 +682,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
             if algoS == 'ARH':
                 trH1_filt = hdat[0].copy()
                 trH2_filt = hdat[1].copy()
+                trH1_filt.detrend(type='demean')
+                trH2_filt.detrend(type='demean')
                 trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
                                  zerophase=False)
                 trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
@@ -673,6 +699,9 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 trH1_filt = hdat[0].copy()
                 trH2_filt = hdat[1].copy()
                 trH3_filt = hdat[2].copy()
+                trH1_filt.detrend(type='demean')
+                trH2_filt.detrend(type='demean')
+                trH3_filt.detrend(type='demean')
                 trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
                                  zerophase=False)
                 trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
@@ -692,10 +721,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
             # get refined onset time from CF2 using class Picker
             if fig_dict:
                 fig = fig_dict['refSpick']
+                linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
             else:
                 fig = None
-            refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot=iplot, aus=ausS,
-                                  Tsmooth=tsmoothS, Pick1=aicarhpick.getpick(), fig=fig)
+                linecolor = 'k'
+            refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
+                                  tsmoothS, aicarhpick.getpick(), fig, linecolor)
             mpickS = refSpick.getpick()
             #############################################################
             if mpickS is not None:
@@ -705,12 +736,15 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 if iplot:
                     if fig_dict:
                         fig = fig_dict['el_S1pick']
+                        linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                     else:
                         fig = None
+                        linecolor = 'k'
                     epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
                                                                mpickS, iplot,
-                                                               fig=fig)
+                                                               fig=fig,
+                                                               linecolor=linecolor)
                 else:
                     epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
@@ -720,12 +754,15 @@ def autopickstation(wfstream, pickparam, verbose=False,
                 if iplot:
                     if fig_dict:
                         fig = fig_dict['el_S2pick']
+                        linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
                     else:
                         fig = None
+                        linecolor = ''
                     epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
                                                                mpickS, iplot,
-                                                               fig=fig)
+                                                               fig=fig,
+                                                               linecolor=linecolor)
                 else:
                     epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
@@ -817,7 +854,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
             hdat += ndat
  
     else:
-        print('autopickstation: No horizontal component data available or ' \
+        print('autopickstation: No horizontal component data available or '
               'bad P onset, skipping S picking!')
 
     ##############################################################
@@ -834,8 +871,11 @@ def autopickstation(wfstream, pickparam, verbose=False,
         if fig_dict == None or fig_dict == 'None':
             fig = plt.figure()
             plt_flag = 1
+            linecolor = 'k'
         else:
             fig = fig_dict['mainFig']
+            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
+        fig._tight = True
         ax1 = fig.add_subplot(311)
         tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
                           tr_filt.stats.delta)
@@ -843,7 +883,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
         wfldiff = len(tr_filt.data) - len(tdata)
         if wfldiff < 0:
             tdata = tdata[0:len(tdata) - abs(wfldiff)]
-        ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
+        ax1.plot(tdata, tr_filt.data / max(tr_filt.data), color=linecolor, linewidth=0.7, label='Data')
         if Pweight < 4:
             ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
                      'b', label='CF1')
@@ -902,7 +942,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
             wfldiff = len(trH1_filt.data) - len(th1data)
             if wfldiff < 0:
                 th1data = th1data[0:len(th1data) - abs(wfldiff)]
-            ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k', label='Data')
+            ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), color=linecolor, linewidth=0.7, label='Data')
             if Pweight < 4:
                 ax2.plot(arhcf1.getTimeArray(),
                          arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
@@ -951,7 +991,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
             wfldiff = len(trH2_filt.data) - len(th2data)
             if wfldiff < 0:
                 th2data = th2data[0:len(th2data) - abs(wfldiff)]
-            ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k', label='Data')
+            ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), color=linecolor, linewidth=0.7, label='Data')
             if Pweight < 4:
                 p22, = ax3.plot(arhcf1.getTimeArray(),
                                 arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')

pylot/core/pick/charfuns.py

@@ -26,7 +26,7 @@ class CharacteristicFunction(object):
     SuperClass for different types of characteristic functions.
     '''
 
-    def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None, stealthMode=False):
+    def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
         '''
         Initialize data type object with information from the original
         Seismogram.
@@ -63,7 +63,6 @@ class CharacteristicFunction(object):
         self.calcCF(self.getDataArray())
         self.arpara = np.array([])
         self.xpred = np.array([])
-        self._stealthMode = stealthMode
 
     def __str__(self):
         return '''\n\t{name} object:\n
@@ -137,9 +136,6 @@ class CharacteristicFunction(object):
     def getXCF(self):
         return self.xcf
 
-    def _getStealthMode(self):
-        return self._stealthMode()
-
     def getDataArray(self, cut=None):
         '''
         If cut times are given, time series is cut from cut[0] (start time)
@@ -224,13 +220,11 @@ class AICcf(CharacteristicFunction):
 
     def calcCF(self, data):
 
-        # if self._getStealthMode() is False:
-        #    print 'Calculating AIC ...'
         x = self.getDataArray()
         xnp = x[0].data
-        nn = np.isnan(xnp)
-        if len(nn) > 1:
-            xnp[nn] = 0
+        ind = np.where(~np.isnan(xnp))[0]
+        if ind.size:
+            xnp[:ind[0]] = xnp[ind[0]]
         datlen = len(xnp)
         k = np.arange(1, datlen)
         cf = np.zeros(datlen)
@@ -264,13 +258,9 @@ class HOScf(CharacteristicFunction):
         if len(nn) > 1:
             xnp[nn] = 0
         if self.getOrder() == 3:  # this is skewness
-            # if self._getStealthMode() is False:
-            #    print 'Calculating skewness ...'
             y = np.power(xnp, 3)
             y1 = np.power(xnp, 2)
         elif self.getOrder() == 4:  # this is kurtosis
-            # if self._getStealthMode() is False:
-            #    print 'Calculating kurtosis ...'
             y = np.power(xnp, 4)
             y1 = np.power(xnp, 2)
 
@@ -345,7 +335,8 @@ class ARZcf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        cf[io] = cf[ino[0][0]]
+        if np.size(ino):
+            cf[io] = cf[ino[0][0]]
 
         self.cf = cf
         self.xcf = x
@@ -477,7 +468,8 @@ class ARHcf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        cf[io] = cf[ino[0][0]]
+        if np.size(ino):
+            cf[io] = cf[ino[0][0]]
 
         self.cf = cf
         self.xcf = xnp
@@ -619,7 +611,8 @@ class AR3Ccf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        cf[io] = cf[ino[0][0]]
+        if np.size(ino):
+            cf[io] = cf[ino[0][0]]
 
         self.cf = cf
         self.xcf = xnp

pylot/core/pick/compare.py

@@ -118,8 +118,8 @@ class Comparison(object):
         """
         compare_pdfs = dict()
 
-        pdf_a = self.get(self.names[0]).generate_pdf_data(type)
-        pdf_b = self.get(self.names[1]).generate_pdf_data(type)
+        pdf_a = self.get('auto').generate_pdf_data(type)
+        pdf_b = self.get('manu').generate_pdf_data(type)
 
         for station, phases in pdf_a.items():
             if station in pdf_b.keys():

pylot/core/pick/picker.py

@@ -23,8 +23,9 @@ import warnings
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.signal import argrelmax
 from pylot.core.pick.charfuns import CharacteristicFunction
-from pylot.core.pick.utils import getnoisewin, getsignalwin, get_maximum_index
+from pylot.core.pick.utils import getnoisewin, getsignalwin
 
 
 class AutoPicker(object):
@@ -35,7 +36,7 @@ class AutoPicker(object):
 
     warnings.simplefilter('ignore')
 
-    def __init__(self, cf, TSNR, PickWindow, checkwindow=None, minfactor=None, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
+    def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
         '''
         :param: cf, characteristic function, on which the picking algorithm is applied
         :type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
@@ -62,7 +63,8 @@ class AutoPicker(object):
         '''
 
         assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
-
+        self._linecolor = linecolor
+        self._pickcolor_p = 'b'
         self.cf = cf.getCF()
         self.Tcf = cf.getTimeArray()
         self.Data = cf.getXCF()
@@ -74,8 +76,6 @@ class AutoPicker(object):
         self.setTsmooth(Tsmooth)
         self.setpick1(Pick1)
         self.fig = fig
-        self.setCheckWindow(checkwindow)
-        self.minfactor = minfactor
         self.calcPick()
 
     def __str__(self):
@@ -91,10 +91,6 @@ class AutoPicker(object):
                    aus=self.getaus(),
                    Tsmooth=self.getTsmooth(),
                    Pick1=self.getpick1())
-    def setCheckWindow(self, checkwindow):
-        '''convert checkwindow to samples'''
-        if checkwindow:
-            self.checkwindow = int(checkwindow / self.Data[0].stats.delta)
 
     def getTSNR(self):
         return self.TSNR
@@ -193,9 +189,8 @@ class AICPicker(AutoPicker):
         offset = abs(min(aic) - min(aicsmooth))
         aicsmooth = aicsmooth - offset
         # get maximum of HOS/AR-CF as startimg point for searching
-        # minimum in AIC function
-        icfmax = get_maximum_index(self.Data[0].data, self.checkwindow, self.minfactor,
-                                   int(self.TSNR[1]/self.Data[0].stats.delta))
+        # minimum in AIC function 
+        icfmax = np.argmax(self.Data[0].data)
 
         # find minimum in AIC-CF front of maximum of HOS/AR-CF
         lpickwindow = int(round(self.PickWindow / self.dt))
@@ -259,8 +254,12 @@ class AICPicker(AutoPicker):
             if len(dataslope) < 1:
                 print('No data in slope window found!')
                 return
-            imax = np.argmax(dataslope)
-            iislope = islope[0][0:imax+1]
+            imaxs, = argrelmax(dataslope)
+            if imaxs.size:
+                imax = imaxs[0]
+            else:
+                imax = np.argmax(dataslope)
+            iislope = islope[0][0:imax + 1]
             if len(iislope) < 2:
                 # calculate slope from initial onset to maximum of AIC function
                 print("AICPicker: Not enough data samples left for slope calculation!")
@@ -271,13 +270,13 @@ class AICPicker(AutoPicker):
                     print("Choose longer slope determination window!")
                     if self.iplot > 1:
                         if self.fig == None or self.fig == 'None':
-                            fig = plt.figure()  # self.iplot) ### WHY? MP MP
+                            fig = plt.figure()
                             plt_flag = 1
                         else:
                             fig = self.fig
                         ax = fig.add_subplot(111)
                         x = self.Data[0].data
-                        ax.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
+                        ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
                         ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
                         ax.legend(loc=1)
                         ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
@@ -310,11 +309,12 @@ class AICPicker(AutoPicker):
                 plt_flag = 1
             else:
                 fig = self.fig
+            fig._tight = True
             ax1 = fig.add_subplot(211)
             x = self.Data[0].data
             if len(self.Tcf) > len(self.Data[0].data): # why? LK
                 self.Tcf = self.Tcf[0:len(self.Tcf)-1]
-            ax1.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
+            ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
             ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
             if self.Pick is not None:
                 ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
@@ -324,7 +324,7 @@ class AICPicker(AutoPicker):
 
             if self.Pick is not None:
                 ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
-                ax2.plot(self.Tcf, x, 'k', label='Data')
+                ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data')
                 ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
                 ax1.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
                             label='Signal Window')
@@ -479,11 +479,12 @@ class PragPicker(AutoPicker):
                     plt_flag = 1
                 else:
                     fig = self.fig
+                fig._tight = True
                 ax = fig.add_subplot(111)
-                ax.plot(Tcfpick, cfipick, 'k', label='CF')
+                ax.plot(Tcfpick, cfipick, color=self._linecolor, linewidth=0.7, label='CF')
                 ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
                 if pickflag > 0:
-                    ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2, label='Pick')
+                    ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], self._pickcolor_p, linewidth=2, label='Pick')
                 ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
                 ax.set_yticks([])
                 ax.set_title(self.Data[0].stats.station)

pylot/core/pick/utils.py

@@ -13,10 +13,9 @@ import warnings
 import matplotlib.pyplot as plt
 import numpy as np
 from obspy.core import Stream, UTCDateTime
-from scipy.signal import argrelextrema
 
 
-def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
+def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
     '''
     Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
     as reasonable uncertainties. Latest possible pick is based on noise level,
@@ -95,7 +94,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
 
     # get earliest possible pick
 
-    EPick = np.nan;
+    EPick = np.nan
     count = 0
     pis = isignal
 
@@ -131,17 +130,18 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
         if fig == None or fig == 'None':
             fig = plt.figure()  # iplot)
             plt_flag = 1
+        fig._tight = True
         ax = fig.add_subplot(111)
-        ax.plot(t, x, 'k', label='Data')
+        ax.plot(t, x, color=linecolor, linewidth=0.7, label='Data')
         ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
         ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
-        ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', label='Noise Level')
+        ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], color=linecolor, linewidth=0.7, linestyle='dashed', label='Noise Level')
         ax.plot(t[pis[zc]], np.zeros(len(zc)), '*g',
                 markersize=14, label='Zero Crossings')
-        ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
+        ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], color=linecolor, linewidth=0.7, linestyle='dashed')
         ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
-        ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k', label='lpp')
-        ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k', label='epp')
+        ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='lpp')
+        ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='epp')
         ax.plot([Pick1 + PickError, Pick1 + PickError],
                 [max(x) / 2, -max(x) / 2], 'r--', label='spe')
         ax.plot([Pick1 - PickError, Pick1 - PickError],
@@ -161,7 +161,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
     return EPick, LPick, PickError
 
 
-def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
+def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
     '''
     Function to derive first motion (polarity) of given phase onset Pick.
     Calculation is based on zero crossings determined within time window pickwin
@@ -324,8 +324,9 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
         if fig == None or fig == 'None':
             fig = plt.figure()  # iplot)
             plt_flag = 1
+        fig._tight = True
         ax1 = fig.add_subplot(211)
-        ax1.plot(t, xraw, 'k')
+        ax1.plot(t, xraw, color=linecolor, linewidth=0.7)
         ax1.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2, label='Pick')
         if P1 is not None:
             ax1.plot(t[islope1], xraw[islope1], label='Slope Window')
@@ -339,7 +340,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
 
         ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
         ax2.set_title('First-Motion Determination, Filtered Data')
-        ax2.plot(t, xfilt, 'k')
+        ax2.plot(t, xfilt, color=linecolor, linewidth=0.7)
         ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
                  linewidth=2)
         if P2 is not None:
@@ -590,7 +591,10 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
     Ppicks = []
     Spicks = []
     SPtimes = []
-    for key in pickdic:
+    stations = []
+    ibad = 0
+
+    for key in list(pickdic.keys()):
         if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
             # calculate S-P time
             spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
@@ -620,17 +624,19 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
         badstations = []
         # calculate deviations from Wadati regression
         ii = 0
-        ibad = 0
-        for key in pickdic:
+        for key in list(pickdic.keys()):
             if 'SPt' in pickdic[key]:
+                stations.append(key)
                 wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
                 ii += 1
                 # check, if deviation is larger than adjusted
                 if wddiff > dttolerance:
-                    # mark onset and downgrade S-weight to 9
-                    # (not used anymore)
-                    marker = 'badWadatiCheck'
-                    pickdic[key]['S']['weight'] = 9
+                    # remove pick from dictionary
+                    pickdic.pop(key)
+                    # # mark onset and downgrade S-weight to 9
+                    # # (not used anymore)
+                    # marker = 'badWadatiCheck'
+                    # pickdic[key]['S']['weight'] = 9
                     badstations.append(key)
                     ibad += 1
                 else:
@@ -642,7 +648,8 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
                     checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
                     checkedSPtimes.append(checkedSPtime)
 
-                pickdic[key]['S']['marked'] = marker
+                    pickdic[key]['S']['marked'] = marker
+                #pickdic[key]['S']['marked'] = marker
         print("wadaticheck: the following stations failed the check:")
         print(badstations)
 
@@ -673,19 +680,28 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
     if iplot > 0:
         if fig_dict:
             fig = fig_dict['wadati']
+            linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
             plt_flag = 0
         else:
             fig = plt.figure()
+            linecolor = 'k'
             plt_flag = 1
         ax = fig.add_subplot(111)
-        ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
+        if ibad > 0:
+            ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
         if wfitflag == 0:
-            ax.plot(Ppicks, wdfit, 'k', label='Wadati 1')
-            ax.plot(checkedPpicks, checkedSPtimes, 'ko', label='Reliable S-Picks')
+            ax.plot(Ppicks, wdfit, color=linecolor, linewidth=0.7, label='Wadati 1')
+            ax.plot(Ppicks, wdfit+dttolerance, color='0.9', linewidth=0.5, label='Wadati 1 Tolerance')
+            ax.plot(Ppicks, wdfit-dttolerance, color='0.9', linewidth=0.5)
             ax.plot(checkedPpicks, wdfit2, 'g', label='Wadati 2')
+            ax.plot(checkedPpicks, checkedSPtimes, color=linecolor,
+                    linewidth=0, marker='o', label='Reliable S-Picks')
+            for Ppick, SPtime, station in zip(Ppicks, SPtimes, stations):
+                ax.text(Ppick, SPtime + 0.01, '{0}'.format(station), color='0.25')
+
             ax.set_title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
                       'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
-            ax.legend(loc=1)
+            ax.legend(loc=1, numpoints=1)
         else:
             ax.set_title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
 
@@ -704,7 +720,7 @@ def RMS(X):
     return np.sqrt(np.sum(np.power(X, 2)) / len(X))
 
 
-def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None):
+def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None, linecolor='k'):
     '''
     Function to detect spuriously picked noise peaks.
     Uses RMS trace of all 3 components (if available) to determine,
@@ -789,8 +805,9 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
         if fig == None or fig == 'None':
             fig = plt.figure()  # iplot)
             plt_flag = 1
+        fig._tight = True
         ax = fig.add_subplot(111)
-        ax.plot(t, rms, 'k', label='RMS Data')
+        ax.plot(t, rms, color=linecolor, linewidth=0.7, label='RMS Data')
         ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
         ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
         ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
@@ -865,9 +882,9 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
     badstations = np.array(stations)[ibad]
 
     print("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
+    print(badstations)
     print("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
                                                             + len(badjkstations), len(stations)))
-    print(badstations)
 
     goodmarker = 'goodPonsetcheck'
     badmarker = 'badPonsetcheck'
@@ -876,15 +893,21 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
         # mark P onset as checked and keep P weight
         pickdic[goodstations[i]]['P']['marked'] = goodmarker
     for i in range(0, len(badstations)):
-        # mark P onset and downgrade P weight to 9
-        # (not used anymore)
-        pickdic[badstations[i]]['P']['marked'] = badmarker
-        pickdic[badstations[i]]['P']['weight'] = 9
+        # remove pick from dictionary
+        pickdic.pop(badstations[i])
     for i in range(0, len(badjkstations)):
-        # mark P onset and downgrade P weight to 9
-        # (not used anymore)
-        pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
-        pickdic[badjkstations[i]]['P']['weight'] = 9
+        # remove pick from dictionary
+        pickdic.pop(badjkstations[i])
+    # for i in range(0, len(badstations)):
+    #     # mark P onset and downgrade P weight to 9
+    #     # (not used anymore)
+    #     pickdic[badstations[i]]['P']['marked'] = badmarker
+    #     pickdic[badstations[i]]['P']['weight'] = 9
+    # for i in range(0, len(badjkstations)):
+    #     # mark P onset and downgrade P weight to 9
+    #     # (not used anymore)
+    #     pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
+    #     pickdic[badjkstations[i]]['P']['weight'] = 9
 
     checkedonsets = pickdic
 
@@ -897,19 +920,22 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
             plt_flag = 1
         ax = fig.add_subplot(111)
 
-        ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14)
-        if len(badstations) < 1 and len(badjkstations) < 1:
-            ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14, label='Skipped P Picks')
-        else:
-            ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks')
-            ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
-                       linewidth=2, label='Median')
-        for i in range(0, len(Ppicks)):
-            ax.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i]))
-
+        if len(badstations) > 0:
+            ax.plot(ibad, np.array(Ppicks)[ibad], marker ='o', markerfacecolor='orange', markersize=14,
+                    linestyle='None', label='Median Skipped P Picks')
+        if len(badjkstations) > 0:
+            ax.plot(badjk[0], np.array(Ppicks)[badjk], 'ro', markersize=14, label='Jackknife Skipped P Picks')
+        ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks')
+        ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', linewidth=2, label='Median')
+        ax.plot([0, len(Ppicks) - 1], [pmedian + dttolerance, pmedian + dttolerance], 'g--', linewidth=1.2,
+                dashes=[25, 25], label='Median Tolerance')
+        ax.plot([0, len(Ppicks) - 1], [pmedian - dttolerance, pmedian - dttolerance], 'g--', linewidth=1.2,
+                dashes=[25, 25])
+        for index, pick in enumerate(Ppicks):
+            ax.text(index, pick + 0.01, '{0}'.format(stations[index]), color='0.25')
         ax.set_xlabel('Number of P Picks')
-        ax.set_ylabel('Onset Time [s] from 1.1.1970')
-        ax.legend(loc=1)
+        ax.set_ylabel('Onset Time [s] from 1.1.1970') # MP MP Improve this?
+        ax.legend(loc=1, numpoints=1)
         ax.set_title('Jackknifing and Median Tests on P Onsets')
         if plt_flag:
             fig.show()
@@ -941,9 +967,8 @@ def jackknife(X, phi, h):
     PHI_sub = None
 
     # determine number of subgroups
-    g = int(len(X) / h)
 
-    if (len(X) / h) % 1 != 0:
+    if len(X) % h:
         print("jackknife: Cannot divide quantity X in equal sized subgroups!")
         print("Choose another size for subgroups!")
         return PHI_jack, PHI_pseudo, PHI_sub
@@ -960,7 +985,7 @@ def jackknife(X, phi, h):
         # estimators of subgroups
         PHI_pseudo = []
         PHI_sub = []
-        for i in range(0, g):
+        for i in range(0, g - 1):
             # subgroup i, remove i-th sample
             xx = X[:]
             del xx[i]
@@ -982,7 +1007,7 @@ def jackknife(X, phi, h):
     return PHI_jack, PHI_pseudo, PHI_sub
 
 
-def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
+def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
     '''
     Function to compare energy content of vertical trace with
     energy content of horizontal traces to detect spuriously
@@ -1109,8 +1134,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
                     fig = plt.figure()  # self.iplot) ### WHY? MP MP
                     plt_flag = 1
                 ax = fig.add_subplot(3, 1, i + 1, sharex=ax1)
+            fig._tight = True
             ax.plot(t, abs(trace.data), color='b', label='abs')
-            ax.plot(t, trace.data, color='k')
+            ax.plot(t, trace.data, color=linecolor, linewidth=0.7)
             name = str(trace.stats.channel) + ': {}'.format(rms)
             ax.plot([pick, pick + checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
             ax.plot([pick, pick], ax.get_ylim(), 'm', label='Pick')
@@ -1154,55 +1180,6 @@ def getQualityFromUncertainty(uncertainty, Errors):
 
     return quality
 
-def removePicksAbove(pickDic, minWeight):
-    '''remove picks from pick dicitonary with a weight > minweight'''
-    newdic = {}
-    for event in pickDic.keys():
-        newdic[event] = {}
-
-    for eventKey, eventDic in pickDic.items():
-        for station, phases in eventDic.items():
-            if phases['P']['weight'] < minWeight or phases['S']['weight'] < minWeight:
-                # dont append stations that will be empty to output dict
-                newdic[eventKey][station] = {}
-                if len(phases) > 2:
-                    # copy over other values beside P/S information
-                    additional_info = phases.copy()
-                    if 'P' in phases.keys():
-                        additional_info.pop('P')
-                    if 'S' in phases.keys():
-                        additional_info.pop('S')
-                    newdic[eventKey][station].update(additional_info)
-            for phasename, phaseinfo in phases.items():
-                if phasename in ('P', 'S') and phaseinfo['weight'] < minWeight:
-                    newdic[eventKey][station].update({phasename: phaseinfo})
-    return newdic
-
-
-def get_maximum_index(data, checkwindow, minfactor, safetygap):
-    '''get maximum of CF as starting point, then check for highest local maximum
-    in front of it.
-    return second maximum if its larger than first maximum * minfactor, else
-    return first maximum.
-    checkwindow and safetygap are given in samples'''
-    icfmax1 = np.argmax(data)
-    imax_local = argrelextrema(data[icfmax1 - checkwindow:icfmax1 - safetygap], np.greater)[0] # indices of local maxima
-    if imax_local.size > 0:
-        imax_local = imax_local + icfmax1 - checkwindow
-        local_maxima = (imax_local, data[imax_local])
-        largest_local_max = np.where(local_maxima[1] == max(local_maxima[1]))
-        icfmax2 = local_maxima[0][largest_local_max]
-        if data[icfmax2] > data[icfmax1] * minfactor:
-            print("Found valid local maximum in front of first maximum")
-            return icfmax2[0]
-        else:
-            print("First maximum is the largest: {}>{}".format(data[icfmax1],
-                                                               data[icfmax2]))
-            return icfmax1
-    else:
-        print("No local maxima found in check window")
-        return icfmax1
-
 if __name__ == '__main__':
     import doctest
 

pylot/core/pick/utils.py.orig

@@ -1,998 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# -*- coding: utf-8 -*-
-"""
-   Created Mar/Apr 2015
-   Collection of helpful functions for manual and automatic picking.
-
-   :author: Ludger Kueperkoch / MAGS2 EP3 working group
-"""
-import warnings
-
-import matplotlib.pyplot as plt
-import numpy as np
-from obspy.core import Stream, UTCDateTime
-
-
-def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode=False):
-    '''
-    Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
-    as reasonable uncertainties. Latest possible pick is based on noise level,
-    earliest possible pick is half a signal wavelength in front of most likely
-    pick given by PragPicker or manually set by analyst. Most likely pick
-    (initial pick Pick1) must be given.
-
-    :param: X, time series (seismogram)
-    :type:  `~obspy.core.stream.Stream`
-
-    :param: nfac (noise factor), nfac times noise level to calculate latest possible pick
-    :type: int
-
-    :param: TSNR, length of time windows around pick used to determine SNR [s]
-    :type: tuple (T_noise, T_gap, T_signal)
-
-    :param: Pick1, initial (most likely) onset time, starting point for earllatepicker
-    :type: float
-
-    :param: iplot, if given, results are plotted in figure(iplot)
-    :type: int
-    '''
-
-    assert isinstance(X, Stream), "%s is not a stream object" % str(X)
-
-    LPick = None
-    EPick = None
-    PickError = None
-    if stealthMode is False:
-        print
-        'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
-
-    x = X[0].data
-    t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
-                  X[0].stats.delta)
-    inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
-    # get signal window
-    isignal = getsignalwin(t, Pick1, TSNR[2])
-    # remove mean
-    x = x - np.mean(x[inoise])
-    # calculate noise level
-    nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
-    # get time where signal exceeds nlevel
-    ilup, = np.where(x[isignal] > nlevel)
-    ildown, = np.where(x[isignal] < -nlevel)
-    if not ilup.size and not ildown.size:
-        print("earllatepicker: Signal lower than noise level!")
-        print("Skip this trace!")
-        return LPick, EPick, PickError
-    il = min(np.min(ilup) if ilup.size else float('inf'),
-             np.min(ildown) if ildown.size else float('inf'))
-    LPick = t[isignal][il]
-
-    # get earliest possible pick
-
-    EPick = np.nan;
-    count = 0
-    pis = isignal
-
-    # if EPick stays NaN the signal window size will be doubled
-    while np.isnan(EPick):
-        if count > 0:
-            print("earllatepicker: Doubled signal window size %s time(s) "
-                  "because of NaN for earliest pick." % count)
-            if stealthMode is False:
-                print("\nearllatepicker: Doubled signal window size %s time(s) "
-                      "because of NaN for earliest pick." % count)
-            isigDoubleWinStart = pis[-1] + 1
-            isignalDoubleWin = np.arange(isigDoubleWinStart,
-                                         isigDoubleWinStart + len(pis))
-            if (isigDoubleWinStart + len(pis)) < X[0].data.size:
-                pis = np.concatenate((pis, isignalDoubleWin))
-            else:
-                print("Could not double signal window. Index out of bounds.")
-                break
-        count += 1
-        # determine all zero crossings in signal window (demeaned)
-        zc = crossings_nonzero_all(x[pis] - x[pis].mean())
-        # calculate mean half period T0 of signal as the average of the
-        T0 = np.mean(np.diff(zc)) * X[0].stats.delta  # this is half wave length
-        # T0/4 is assumed as time difference between most likely and earliest possible pick!
-        EPick = Pick1 - T0 / 2
-
-    # get symmetric pick error as mean from earliest and latest possible pick
-    # by weighting latest possible pick two times earliest possible pick
-    diffti_tl = LPick - Pick1
-    diffti_te = Pick1 - EPick
-    PickError = (diffti_te + 2 * diffti_tl) / 3
-
-    if iplot > 1:
-        p = plt.figure(iplot)
-        p1, = plt.plot(t, x, 'k')
-        p2, = plt.plot(t[inoise], x[inoise])
-        p3, = plt.plot(t[isignal], x[isignal], 'r')
-        p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
-        p5, = plt.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
-                       markersize=14)
-        plt.legend([p1, p2, p3, p4, p5],
-                   ['Data', 'Noise Window', 'Signal Window', 'Noise Level',
-                    'Zero Crossings'],
-                   loc='best')
-        plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
-        plt.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2)
-        plt.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k')
-        plt.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k')
-        plt.plot([Pick1 + PickError, Pick1 + PickError],
-                 [max(x) / 2, -max(x) / 2], 'r--')
-        plt.plot([Pick1 - PickError, Pick1 - PickError],
-                 [max(x) / 2, -max(x) / 2], 'r--')
-        plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
-        plt.yticks([])
-        plt.title(
-            'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
-            X[0].stats.station)
-        plt.show()
-        raw_input()
-        plt.close(p)
-
-    return EPick, LPick, PickError
-
-
-def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
-    '''
-    Function to derive first motion (polarity) of given phase onset Pick.
-    Calculation is based on zero crossings determined within time window pickwin
-    after given onset time.
-
-    :param: Xraw, unfiltered time series (seismogram)
-    :type:  `~obspy.core.stream.Stream`
-
-    :param: Xfilt, filtered time series (seismogram)
-    :type:  `~obspy.core.stream.Stream`
-
-    :param: pickwin, time window after onset Pick within zero crossings are calculated
-    :type: float
-
-    :param: Pick, initial (most likely) onset time, starting point for fmpicker
-    :type: float
-
-    :param: iplot, if given, results are plotted in figure(iplot)
-    :type: int
-    '''
-
-    warnings.simplefilter('ignore', np.RankWarning)
-
-    assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
-    assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
-
-    FM = None
-    if Pick is not None:
-        print("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
-
-        xraw = Xraw[0].data
-        xfilt = Xfilt[0].data
-        t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate,
-                      Xraw[0].stats.delta)
-        # get pick window
-        ipick = np.where(
-            (t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
-        # remove mean
-        xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
-        xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
-
-        # get zero crossings after most likely pick
-        # initial onset is assumed to be the first zero crossing
-        # first from unfiltered trace
-        zc1 = []
-        zc1.append(Pick)
-        index1 = []
-        i = 0
-        for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
-            i = i + 1
-            if xraw[j - 1] <= 0 <= xraw[j]:
-                zc1.append(t[ipick][i])
-                index1.append(i)
-            elif xraw[j - 1] > 0 >= xraw[j]:
-                zc1.append(t[ipick][i])
-                index1.append(i)
-            if len(zc1) == 3:
-                break
-
-        # if time difference betweeen 1st and 2cnd zero crossing
-        # is too short, get time difference between 1st and 3rd
-        # to derive maximum
-        if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
-            li1 = index1[1]
-        else:
-            li1 = index1[0]
-        if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
-            print("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
-            P1 = None
-        else:
-            imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
-            if imax1 == 0:
-                imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
-                if imax1 == 0:
-                    print("fmpicker: Zero crossings too close!")
-                    print("Skip first motion determination!")
-                    return FM
-
-            islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
-            # calculate slope as polynomal fit of order 1
-            xslope1 = np.arange(0, len(xraw[islope1]), 1)
-            P1 = np.polyfit(xslope1, xraw[islope1], 1)
-            datafit1 = np.polyval(P1, xslope1)
-
-        # now using filterd trace
-        # next zero crossings after most likely pick
-        zc2 = []
-        zc2.append(Pick)
-        index2 = []
-        i = 0
-        for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
-            i = i + 1
-            if xfilt[j - 1] <= 0 <= xfilt[j]:
-                zc2.append(t[ipick][i])
-                index2.append(i)
-            elif xfilt[j - 1] > 0 >= xfilt[j]:
-                zc2.append(t[ipick][i])
-                index2.append(i)
-            if len(zc2) == 3:
-                break
-
-        # if time difference betweeen 1st and 2cnd zero crossing
-        # is too short, get time difference between 1st and 3rd
-        # to derive maximum
-        if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
-            li2 = index2[1]
-        else:
-            li2 = index2[0]
-        if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
-            print("fmpicker: Onset on filtered trace too emergent for first motion determination!")
-            P2 = None
-        else:
-            imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
-            if imax2 == 0:
-                imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
-                if imax2 == 0:
-                    print("fmpicker: Zero crossings too close!")
-                    print("Skip first motion determination!")
-                    return FM
-
-            islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
-            # calculate slope as polynomal fit of order 1
-            xslope2 = np.arange(0, len(xfilt[islope2]), 1)
-            P2 = np.polyfit(xslope2, xfilt[islope2], 1)
-            datafit2 = np.polyval(P2, xslope2)
-
-        # compare results
-        if P1 is not None and P2 is not None:
-            if P1[0] < 0 and P2[0] < 0:
-                FM = 'D'
-            elif P1[0] >= 0 > P2[0]:
-                FM = '-'
-            elif P1[0] < 0 <= P2[0]:
-                FM = '-'
-            elif P1[0] > 0 and P2[0] > 0:
-                FM = 'U'
-            elif P1[0] <= 0 < P2[0]:
-                FM = '+'
-            elif P1[0] > 0 >= P2[0]:
-                FM = '+'
-
-        print("fmpicker: Found polarity %s" % FM)
-
-    if iplot > 1:
-        plt.figure(iplot)
-        plt.subplot(2, 1, 1)
-        plt.plot(t, xraw, 'k')
-        p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2)
-        if P1 is not None:
-            p2, = plt.plot(t[islope1], xraw[islope1])
-            p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14)
-            p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2)
-            plt.legend([p1, p2, p3, p4],
-                       ['Pick', 'Slope Window', 'Zero Crossings', 'Slope'],
-                       loc='best')
-            plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
-            ax = plt.gca()
-        plt.yticks([])
-        plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
-            0].stats.station)
-
-        plt.subplot(2, 1, 2)
-        plt.title('First-Motion Determination, Filtered Data')
-        plt.plot(t, xfilt, 'k')
-        p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
-                       linewidth=2)
-        if P2 is not None:
-            p2, = plt.plot(t[islope2], xfilt[islope2])
-            p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
-            p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2)
-            plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
-            ax = plt.gca()
-        plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
-        plt.yticks([])
-        plt.show()
-        raw_input()
-        plt.close(iplot)
-
-    return FM
-
-
-def crossings_nonzero_all(data):
-    pos = data > 0
-    npos = ~pos
-    return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
-
-
-def getSNR(X, TSNR, t1):
-    '''
-    Function to calculate SNR of certain part of seismogram relative to
-    given time (onset) out of given noise and signal windows. A safety gap
-    between noise and signal part can be set. Returns SNR and SNR [dB] and
-    noiselevel.
-
-    :param: X, time series (seismogram)
-    :type:  `~obspy.core.stream.Stream`
-
-    :param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
-    :type: tuple (T_noise, T_gap, T_signal)
-
-    :param: t1, initial time (onset) from which noise and signal windows are calculated
-    :type: float
-    '''
-
-    assert isinstance(X, Stream), "%s is not a stream object" % str(X)
-
-    x = X[0].data
-    t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
-                  X[0].stats.delta)
-
-    # get noise window
-    inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
-
-    # get signal window
-    isignal = getsignalwin(t, t1, TSNR[2])
-    if np.size(inoise) < 1:
-        print("getSNR: Empty array inoise, check noise window!")
-        return
-    elif np.size(isignal) < 1:
-        print("getSNR: Empty array isignal, check signal window!")
-        return
-
-    # demean over entire waveform
-    x = x - np.mean(x[inoise])
-
-    # calculate ratios
-    noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
-    signallevel = np.sqrt(np.mean(np.square(x[isignal])))
-    SNR = signallevel / noiselevel
-    SNRdB = 10 * np.log10(SNR)
-
-    return SNR, SNRdB, noiselevel
-
-
-def getnoisewin(t, t1, tnoise, tgap):
-    '''
-    Function to extract indeces of data out of time series for noise calculation.
-    Returns an array of indeces.
-
-    :param: t, array of time stamps
-    :type:  numpy array
-
-    :param: t1, time from which relativ to it noise window is extracted
-    :type: float
-
-    :param: tnoise, length of time window [s] for noise part extraction
-    :type: float
-
-    :param: tgap, safety gap between t1 (onset) and noise window to
-            ensure, that noise window contains no signal
-    :type: float
-    '''
-
-    # get noise window
-    inoise, = np.where((t <= max([t1 - tgap, 0])) \
-                       & (t >= max([t1 - tnoise - tgap, 0])))
-    if np.size(inoise) < 1:
-        print("getnoisewin: Empty array inoise, check noise window!")
-
-    return inoise
-
-
-def getsignalwin(t, t1, tsignal):
-    '''
-    Function to extract data out of time series for signal level calculation.
-    Returns an array of indeces.
-
-    :param: t, array of time stamps
-    :type:  numpy array
-
-    :param: t1, time from which relativ to it signal window is extracted
-    :type: float
-
-    :param: tsignal, length of time window [s] for signal level calculation
-    :type: float
-    '''
-
-    # get signal window
-    isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
-                        & (t >= t1))
-    if np.size(isignal) < 1:
-        print("getsignalwin: Empty array isignal, check signal window!")
-
-    return isignal
-
-
-def getResolutionWindow(snr):
-    """
-    Number -> Float
-    produce the half of the time resolution window width from given SNR
-    value
-          SNR >= 3    ->  2 sec    HRW
-      3 > SNR >= 2    ->  5 sec    MRW
-      2 > SNR >= 1.5  -> 10 sec    LRW
-    1.5 > SNR         -> 15 sec   VLRW
-    see also Diehl et al. 2009
-
-    >>> getResolutionWindow(0.5)
-    7.5
-    >>> getResolutionWindow(1.8)
-    5.0
-    >>> getResolutionWindow(2.3)
-    2.5
-    >>> getResolutionWindow(4)
-    1.0
-    >>> getResolutionWindow(2)
-    2.5
-    """
-
-    res_wins = {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.}
-
-    if snr < 1.5:
-        time_resolution = res_wins['VLRW']
-    elif snr < 2.:
-        time_resolution = res_wins['LRW']
-    elif snr < 3.:
-        time_resolution = res_wins['MRW']
-    else:
-        time_resolution = res_wins['HRW']
-
-    return time_resolution / 2
-
-
-def wadaticheck(pickdic, dttolerance, iplot):
-    '''
-    Function to calculate Wadati-diagram from given P and S onsets in order
-    to detect S pick outliers. If a certain S-P time deviates by dttolerance
-    from regression of S-P time the S pick is marked and down graded.
-
-    : param: pickdic, dictionary containing picks and quality parameters
-    : type:  dictionary
-
-    : param: dttolerance, maximum adjusted deviation of S-P time from
-             S-P time regression
-    : type:  float
-
-    : param: iplot, if iplot > 1, Wadati diagram is shown
-    : type:  int
-    '''
-
-    checkedonsets = pickdic
-
-    # search for good quality picks and calculate S-P time
-    Ppicks = []
-    Spicks = []
-    SPtimes = []
-    for key in pickdic:
-        if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
-            # calculate S-P time
-            spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
-            # add S-P time to dictionary
-            pickdic[key]['SPt'] = spt
-            # add P onsets and corresponding S-P times to list
-            UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
-            UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
-            Ppicks.append(UTCPpick.timestamp)
-            Spicks.append(UTCSpick.timestamp)
-            SPtimes.append(spt)
-
-    if len(SPtimes) >= 3:
-        # calculate slope
-        p1 = np.polyfit(Ppicks, SPtimes, 1)
-        wdfit = np.polyval(p1, Ppicks)
-        wfitflag = 0
-
-        # calculate vp/vs ratio before check
-        vpvsr = p1[0] + 1
-        print("###############################################")
-        print("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
-
-        checkedPpicks = []
-        checkedSpicks = []
-        checkedSPtimes = []
-        # calculate deviations from Wadati regression
-        ii = 0
-        ibad = 0
-        for key in pickdic:
-            if pickdic[key].has_key('SPt'):
-                wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
-                ii += 1
-                # check, if deviation is larger than adjusted
-                if wddiff > dttolerance:
-                    # mark onset and downgrade S-weight to 9
-                    # (not used anymore)
-                    marker = 'badWadatiCheck'
-                    pickdic[key]['S']['weight'] = 9
-                    ibad += 1
-                else:
-                    marker = 'goodWadatiCheck'
-                    checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
-                    checkedPpicks.append(checkedPpick.timestamp)
-                    checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
-                    checkedSpicks.append(checkedSpick.timestamp)
-                    checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
-                    checkedSPtimes.append(checkedSPtime)
-
-                pickdic[key]['S']['marked'] = marker
-
-        if len(checkedPpicks) >= 3:
-            # calculate new slope
-            p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
-            wdfit2 = np.polyval(p2, checkedPpicks)
-
-            # calculate vp/vs ratio after check
-            cvpvsr = p2[0] + 1
-            print("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
-            print("wadatacheck: Skipped %d S pick(s)" % ibad)
-        else:
-            print("###############################################")
-            print("wadatacheck: Not enough checked S-P times available!")
-            print("Skip Wadati check!")
-
-        checkedonsets = pickdic
-
-    else:
-        print("wadaticheck: Not enough S-P times available for reliable regression!")
-        print("Skip wadati check!")
-        wfitflag = 1
-
-    # plot results
-    if iplot > 1:
-        plt.figure(iplot)
-        f1, = plt.plot(Ppicks, SPtimes, 'ro')
-        if wfitflag == 0:
-            f2, = plt.plot(Ppicks, wdfit, 'k')
-            f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
-            f4, = plt.plot(checkedPpicks, wdfit2, 'g')
-            plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
-                      'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
-            plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1',
-                                          'Reliable S-Picks', 'Wadati 2'], loc='best')
-        else:
-            plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
-
-        plt.ylabel('S-P Times [s]')
-        plt.xlabel('P Times [s]')
-        plt.show()
-        raw_input()
-        plt.close(iplot)
-
-    return checkedonsets
-
-
-def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
-    '''
-    Function to detect spuriously picked noise peaks.
-    Uses RMS trace of all 3 components (if available) to determine,
-    how many samples [per cent] after P onset are below certain
-    threshold, calculated from noise level times noise factor.
-
-    : param: X, time series (seismogram)
-    : type:  `~obspy.core.stream.Stream`
-
-    : param: pick, initial (AIC) P onset time
-    : type:  float
-
-    : param: TSNR, length of time windows around initial pick [s]
-    : type:  tuple (T_noise, T_gap, T_signal)
-
-    : param: minsiglength, minium required signal length [s] to
-             declare pick as P onset
-    : type:  float
-
-    : param: nfac, noise factor (nfac * noise level = threshold)
-    : type:  float
-
-    : param: minpercent, minimum required percentage of samples
-             above calculated threshold
-    : type:  float
-
-    : param: iplot, if iplot > 1, results are shown in figure
-    : type:  int
-    '''
-
-    assert isinstance(X, Stream), "%s is not a stream object" % str(X)
-
-    print("Checking signal length ...")
-
-    if len(X) > 1:
-        # all three components available
-        # make sure, all components have equal lengths
-        ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
-        x1 = X[0][0:ilen]
-        x2 = X[1][0:ilen]
-        x3 = X[2][0:ilen]
-        # get RMS trace
-        rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
-    else:
-        x1 = X[0].data
-        rms = np.sqrt(np.power(2, x1))
-
-    t = np.arange(0, ilen / X[0].stats.sampling_rate,
-                  X[0].stats.delta)
-
-    # get noise window in front of pick plus saftey gap
-    inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
-    # get signal window
-    isignal = getsignalwin(t, pick, minsiglength)
-    # calculate minimum adjusted signal level
-    minsiglevel = max(rms[inoise]) * nfac
-    # minimum adjusted number of samples over minimum signal level
-    minnum = len(isignal) * minpercent / 100
-    # get number of samples above minimum adjusted signal level
-    numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
-
-    if numoverthr >= minnum:
-        print("checksignallength: Signal reached required length.")
-        returnflag = 1
-    else:
-        print("checksignallength: Signal shorter than required minimum signal length!")
-        print("Presumably picked noise peak, pick is rejected!")
-        print("(min. signal length required: %s s)" % minsiglength)
-        returnflag = 0
-
-    if iplot == 2:
-        plt.figure(iplot)
-        p1, = plt.plot(t, rms, 'k')
-        p2, = plt.plot(t[inoise], rms[inoise], 'c')
-        p3, = plt.plot(t[isignal], rms[isignal], 'r')
-        p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
-                       [minsiglevel, minsiglevel], 'g', linewidth=2)
-        p5, = plt.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2)
-        plt.legend([p1, p2, p3, p4, p5], ['RMS Data', 'RMS Noise Window',
-                                          'RMS Signal Window', 'Minimum Signal Level',
-                                          'Onset'], loc='best')
-        plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
-        plt.ylabel('Counts')
-        plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
-        plt.yticks([])
-        plt.show()
-        raw_input()
-        plt.close(iplot)
-
-    return returnflag
-
-
-def checkPonsets(pickdic, dttolerance, iplot):
-    '''
-    Function to check statistics of P-onset times: Control deviation from
-    median (maximum adjusted deviation = dttolerance) and apply pseudo-
-    bootstrapping jackknife.
-
-    : param: pickdic, dictionary containing picks and quality parameters
-    : type:  dictionary
-
-    : param: dttolerance, maximum adjusted deviation of P-onset time from
-             median of all P onsets
-    : type:  float
-
-    : param: iplot, if iplot > 1, Wadati diagram is shown
-    : type:  int
-    '''
-
-    checkedonsets = pickdic
-
-    # search for good quality P picks
-    Ppicks = []
-    stations = []
-    for key in pickdic:
-        if pickdic[key]['P']['weight'] < 4:
-            # add P onsets to list
-            UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
-            Ppicks.append(UTCPpick.timestamp)
-            stations.append(key)
-
-    # apply jackknife bootstrapping on variance of P onsets
-    print("###############################################")
-    print("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
-    [xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
-    # get pseudo variances smaller than average variances
-    # (times safety factor), these picks passed jackknife test
-    ij = np.where(PHI_pseudo <= 2 * xjack)
-    # these picks did not pass jackknife test
-    badjk = np.where(PHI_pseudo > 2 * xjack)
-    badjkstations = np.array(stations)[badjk]
-    print("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
-
-    # calculate median from these picks
-    pmedian = np.median(np.array(Ppicks)[ij])
-    # find picks that deviate less than dttolerance from median
-    ii = np.where(abs(np.array(Ppicks)[ij] - pmedian) <= dttolerance)
-    jj = np.where(abs(np.array(Ppicks)[ij] - pmedian) > dttolerance)
-    igood = ij[0][ii]
-    ibad = ij[0][jj]
-    goodstations = np.array(stations)[igood]
-    badstations = np.array(stations)[ibad]
-
-    print("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
-    print("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
-                                                            + len(badjkstations), len(stations)))
-
-    goodmarker = 'goodPonsetcheck'
-    badmarker = 'badPonsetcheck'
-    badjkmarker = 'badjkcheck'
-    for i in range(0, len(goodstations)):
-        # mark P onset as checked and keep P weight
-        pickdic[goodstations[i]]['P']['marked'] = goodmarker
-    for i in range(0, len(badstations)):
-        # mark P onset and downgrade P weight to 9
-        # (not used anymore)
-        pickdic[badstations[i]]['P']['marked'] = badmarker
-        pickdic[badstations[i]]['P']['weight'] = 9
-    for i in range(0, len(badjkstations)):
-        # mark P onset and downgrade P weight to 9
-        # (not used anymore)
-        pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
-        pickdic[badjkstations[i]]['P']['weight'] = 9
-
-    checkedonsets = pickdic
-
-    if iplot > 1:
-        p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
-        p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
-        p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
-                       linewidth=2)
-        for i in range(0, len(Ppicks)):
-            plt.text(i, Ppicks[i] + 0.2, stations[i])
-
-        plt.xlabel('Number of P Picks')
-        plt.ylabel('Onset Time [s] from 1.1.1970')
-        plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'],
-                   loc='best')
-        plt.title('Check P Onsets')
-        plt.show()
-        raw_input()
-
-    return checkedonsets
-
-
-def jackknife(X, phi, h):
-    '''
-    Function to calculate the Jackknife Estimator for a given quantity,
-    special type of boot strapping. Returns the jackknife estimator PHI_jack
-    the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
-
-    : param: X, given quantity
-    : type:  list
-
-    : param: phi, chosen estimator, choose between:
-             "MED" for median
-             "MEA" for arithmetic mean
-             "VAR" for variance
-    : type:  string
-
-    : param: h, size of subgroups, optinal, default = 1
-    : type:  integer
-    '''
-
-    PHI_jack = None
-    PHI_pseudo = None
-    PHI_sub = None
-
-    # determine number of subgroups
-    g = len(X) / h
-
-    if type(g) is not int:
-        print("jackknife: Cannot divide quantity X in equal sized subgroups!")
-        print("Choose another size for subgroups!")
-        return PHI_jack, PHI_pseudo, PHI_sub
-    else:
-        # estimator of undisturbed spot check
-        if phi == 'MEA':
-            phi_sc = np.mean(X)
-        elif phi == 'VAR':
-            phi_sc = np.var(X)
-        elif phi == 'MED':
-            phi_sc = np.median(X)
-
-        # estimators of subgroups
-        PHI_pseudo = []
-        PHI_sub = []
-        for i in range(0, g - 1):
-            # subgroup i, remove i-th sample
-            xx = X[:]
-            del xx[i]
-            # calculate estimators of disturbed spot check
-            if phi == 'MEA':
-                phi_sub = np.mean(xx)
-            elif phi == 'VAR':
-                phi_sub = np.var(xx)
-            elif phi == 'MED':
-                phi_sub = np.median(xx)
-
-            PHI_sub.append(phi_sub)
-            # pseudo values
-            phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
-            PHI_pseudo.append(phi_pseudo)
-        # jackknife estimator
-        PHI_jack = np.mean(PHI_pseudo)
-
-    return PHI_jack, PHI_pseudo, PHI_sub
-
-
-def checkZ4S(X, pick, zfac, checkwin, iplot):
-    '''
-    Function to compare energy content of vertical trace with
-    energy content of horizontal traces to detect spuriously
-    picked S onsets instead of P onsets. Usually, P coda shows
-    larger longitudal energy on vertical trace than on horizontal
-    traces, where the transversal energy is larger within S coda.
-    Be careful: there are special circumstances, where this is not
-    the case!
-
-    : param: X, fitered(!) time series, three traces
-    : type:  `~obspy.core.stream.Stream`
-
-    : param: pick, initial (AIC) P onset time
-    : type:  float
-
-    : param: zfac, factor for threshold determination,
-             vertical energy must exceed coda level times zfac
-             to declare a pick as P onset
-    : type:  float
-
-    : param: checkwin, window length [s] for calculating P-coda
-             energy content
-    : type:  float
-
-    : param: iplot, if iplot > 1, energy content and threshold
-             are shown
-    : type:  int
-    '''
-
-    assert isinstance(X, Stream), "%s is not a stream object" % str(X)
-
-    print("Check for spuriously picked S onset instead of P onset ...")
-
-    returnflag = 0
-
-    # split components
-    zdat = X.select(component="Z")
-    edat = X.select(component="E")
-    if len(edat) == 0:  # check for other components
-        edat = X.select(component="2")
-    ndat = X.select(component="N")
-    if len(ndat) == 0:  # check for other components
-        ndat = X.select(component="1")
-
-    z = zdat[0].data
-    tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
-                   zdat[0].stats.delta)
-
-    # calculate RMS trace from vertical component
-    absz = np.sqrt(np.power(z, 2))
-    # calculate RMS trace from both horizontal traces
-    # make sure, both traces have equal lengths
-    lene = len(edat[0].data)
-    lenn = len(ndat[0].data)
-    minlen = min([lene, lenn])
-    absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
-                    + np.power(ndat[0].data[0:minlen - 1], 2))
-
-    # get signal window
-    isignal = getsignalwin(tz, pick, checkwin)
-
-    # calculate energy levels
-    zcodalevel = max(absz[isignal])
-    encodalevel = max(absen[isignal])
-
-    # calculate threshold
-    minsiglevel = encodalevel * zfac
-
-    # vertical P-coda level must exceed horizontal P-coda level
-    # zfac times encodalevel
-    if zcodalevel < minsiglevel:
-        print("checkZ4S: Maybe S onset? Skip this P pick!")
-    else:
-        print("checkZ4S: P onset passes checkZ4S test!")
-        returnflag = 1
-
-    if iplot > 1:
-        te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
-                       edat[0].stats.delta)
-        tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
-                       ndat[0].stats.delta)
-        plt.plot(tz, z / max(z), 'k')
-        plt.plot(tz[isignal], z[isignal] / max(z), 'r')
-        plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
-        plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
-        plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
-        plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
-        plt.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
-                 [minsiglevel / max(z), minsiglevel / max(z)], 'g',
-                 linewidth=2)
-        plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime)
-        plt.ylabel('Normalized Counts')
-        plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
-                               ndat[0].stats.channel])
-        plt.title('CheckZ4S, Station %s' % zdat[0].stats.station)
-        plt.show()
-        raw_input()
-
-    return returnflag
-
-
-def writephases(arrivals, fformat, filename):
-    '''
-    Function of methods to write phases to the following standard file
-    formats used for locating earthquakes:
-
-    HYPO71, NLLoc, VELEST, HYPOSAT, HYPOINVERSE and hypoDD
-
-    :param: arrivals
-    :type: dictionary containing all phase information including
-           station ID, phase, first motion, weight (uncertainty), 
-           ....
-
-    :param: fformat
-    :type:  string, chosen file format (location routine),
-            choose between NLLoc, HYPO71, HYPOSAT, VELEST, 
-            HYPOINVERSE, and hypoDD
-
-    :param: filename, full path and name of phase file
-    :type: string
-    '''
-
-    if fformat == 'NLLoc':
-        print("Writing phases to %s for NLLoc" % filename)
-        fid = open("%s" % filename, 'w')
-        # write header
-        fid.write('# EQEVENT:  Label: EQ001  Loc:  X 0.00  Y 0.00  Z 10.00  OT 0.00 \n')
-        for key in arrivals:
-            if arrivals[key]['P']['weight'] < 4:
-                # write phase information to NLLoc-phase file
-                # see the NLLoc tutorial at www.alomax.free.fr/nlloc/
-                fm = arrivals[key]['P']['fm']
-                onset = arrivals[key]['P']['mpp']
-                year = onset.year
-                month = onset.month
-                day = onset.day
-                hh = onset.hour
-                mm = onset.minute
-                ss = onset.second
-                ms = onset.microsecond
-                ss_ms = ss + (ms / 1E06)
-                fid.write('%s ? ? ? P   %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 1 \n' \
-                          % (key, fm, year, month, day, hh, mm, ss_ms))
-            if arrivals[key]['S']['weight'] < 4:
-                fm = '?'
-                onset = arrivals[key]['S']['mpp']
-                year = onset.year
-                month = onset.month
-                day = onset.day
-                hh = onset.hour
-                mm = onset.minute
-                ss = onset.second
-                ms = onset.microsecond
-                ss_ms = ss + (ms / 1E06)
-                fid.write('%s ? ? ? S   %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 1 \n' \
-                          % (key, fm, year, month, day, hh, mm, ss_ms))
-
-        fid.close()
-
-
-if __name__ == '__main__':
-    import doctest
-
-    doctest.testmod()

pylot/core/util/connection.py

@@ -7,7 +7,7 @@ except:
     from urllib.request import urlopen
 
 
-def checkurl(url='https://ariadne.geophysik.rub.de/trac/PyLoT'):
+def checkurl(url='https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/'):
     try:
         urlopen(url, timeout=1)
         return True

pylot/core/util/thread.py

@@ -5,41 +5,6 @@ from PySide.QtCore import QThread, Signal, Qt, Slot, QRunnable, QObject
 from PySide.QtGui import QDialog, QProgressBar, QLabel, QHBoxLayout, QPushButton
 
 
-class AutoPickThread(QThread):
-    message = Signal(str)
-    finished = Signal()
-
-    def __init__(self, parent, func, infile, fnames, eventid, savepath):
-        super(AutoPickThread, self).__init__()
-        self.setParent(parent)
-        self.func = func
-        self.infile = infile
-        self.fnames = fnames
-        self.eventid = eventid
-        self.savepath = savepath
-
-    def run(self):
-        sys.stdout = self
-
-        picks = self.func(None, None, self.infile, self.fnames, self.eventid, self.savepath)
-
-        print("Autopicking finished!\n")
-
-        try:
-            for station in picks:
-                self.parent().addPicks(station, picks[station], type='auto')
-        except AttributeError:
-            print(picks)
-        sys.stdout = sys.__stdout__
-        self.finished.emit()
-
-    def write(self, text):
-        self.message.emit(text)
-
-    def flush(self):
-        pass
-
-
 class Thread(QThread):
     message = Signal(str)
 
@@ -131,7 +96,6 @@ class Worker(QRunnable):
         try:
             result = self.fun(self.args)
         except:
-            #traceback.print_exc()
             exctype, value = sys.exc_info ()[:2]
             print(exctype, value, traceback.format_exc())
             self.signals.error.emit ((exctype, value, traceback.format_exc ()))
@@ -139,6 +103,7 @@ class Worker(QRunnable):
             self.signals.result.emit(result)
         finally:
             self.signals.finished.emit('Done')
+        sys.stdout = sys.__stdout__
 
     def write(self, text):
         self.signals.message.emit(text)

pylot/core/util/utils.py

@@ -14,6 +14,7 @@ from obspy.signal.rotate import rotate2zne
 from obspy.io.xseed.utils import SEEDParserException
 
 from pylot.core.io.inputs import PylotParameter
+from pylot.styles import style_settings
 
 from scipy.interpolate import splrep, splev
 from PySide import QtCore, QtGui
@@ -21,7 +22,7 @@ from PySide import QtCore, QtGui
 try:
     import pyqtgraph as pg
 except Exception as e:
-    print('QtPyLoT: Could not import pyqtgraph. {}'.format(e))
+    print('PyLoT: Could not import pyqtgraph. {}'.format(e))
     pg = None
 
 def _pickle_method(m):
@@ -71,6 +72,8 @@ def gen_Pool(ncores=0):
     if ncores == 0:
         ncores = multiprocessing.cpu_count()
 
+    print('gen_Pool: Generated multiprocessing Pool with {} cores\n'.format(ncores))
+
     pool = multiprocessing.Pool(ncores)
     return pool
 
@@ -577,36 +580,22 @@ def modify_rgba(rgba, modifier, intensity):
 
 
 def base_phase_colors(picktype, phase):
-    phases = {
-        'manual':
-            {
-            'P':
-                {
-                'rgba': (0, 0, 255, 255),
-                'modifier': 'g'
-                },
-            'S':
-                {
-                'rgba': (255, 0, 0, 255),
-                'modifier': 'b'
-                }
-            },
-        'auto':
-            {
-            'P':
-                {
-                'rgba': (140, 0, 255, 255),
-                'modifier': 'g'
-                },
-            'S':
-                {
-                'rgba': (255, 140, 0, 255),
-                'modifier': 'b'
-                }
-            }
-    }
-    return phases[picktype][phase]
+    phasecolors = style_settings.phasecolors
+    return phasecolors[picktype][phase]
 
+def transform_colors_mpl_str(colors, no_alpha=False):
+    colors = list(colors)
+    colors_mpl = tuple([color / 255. for color in colors])
+    if no_alpha:
+        colors_mpl = '({}, {}, {})'.format(*colors_mpl)
+    else:
+        colors_mpl = '({}, {}, {}, {})'.format(*colors_mpl)
+    return colors_mpl
+
+def transform_colors_mpl(colors):
+    colors = list(colors)
+    colors_mpl = tuple([color / 255. for color in colors])
+    return colors_mpl
 
 def remove_underscores(data):
     """
@@ -702,7 +691,7 @@ def get_stations(data):
     return stations
 
 
-def check4rotated(data, metadata=None):
+def check4rotated(data, metadata=None, verbosity=1):
 
     def rotate_components(wfstream, metadata=None):
         """rotates components if orientation code is numeric.
@@ -711,13 +700,15 @@ def check4rotated(data, metadata=None):
             # indexing fails if metadata is None
             metadata[0]
         except:
-            msg = 'Warning: could not rotate traces since no metadata was given\nset Inventory file!'
-            print(msg)
+            if verbosity:
+                msg = 'Warning: could not rotate traces since no metadata was given\nset Inventory file!'
+                print(msg)
             return wfstream
         if metadata[0] is None:
             # sometimes metadata is (None, (None,))
-            msg = 'Warning: could not rotate traces since no metadata was given\nCheck inventory directory!'
-            print(msg)
+            if verbosity:
+                msg = 'Warning: could not rotate traces since no metadata was given\nCheck inventory directory!'
+                print(msg)
             return wfstream
         else:
             parser = metadata[1]

pylot/core/util/widgets.py

@@ -16,11 +16,6 @@ import time
 
 import numpy as np
 
-try:
-    import pyqtgraph as pg
-except:
-    pg = None
-
 from matplotlib.figure import Figure
 from pylot.core.util.utils import find_horizontals, identifyPhase, loopIdentifyPhase, trim_station_components, \
     identifyPhaseID, check4rotated
@@ -53,23 +48,18 @@ from pylot.core.pick.compare import Comparison
 from pylot.core.util.defaults import OUTPUTFORMATS, FILTERDEFAULTS, \
     SetChannelComponents
 from pylot.core.util.utils import prepTimeAxis, full_range, scaleWFData, \
-    demeanTrace, isSorted, findComboBoxIndex, clims, pick_linestyle_plt, pick_color_plt
+    demeanTrace, isSorted, findComboBoxIndex, clims, pick_linestyle_plt, pick_color_plt, \
+    check4rotated, check4doubled, check4gaps, remove_underscores
 from autoPyLoT import autoPyLoT
 from pylot.core.util.thread import Thread
 
 if sys.version_info.major == 3:
-    pass
+    import icons_rc_3 as icons_rc
 elif sys.version_info.major == 2:
-    pass
+    import icons_rc_2 as icons_rc
 else:
     raise ImportError('Could not determine python version.')
 
-if pg:
-    pg.setConfigOption('background', 'w')
-    pg.setConfigOption('foreground', 'k')
-    pg.setConfigOptions(antialias=True)
-    # pg.setConfigOption('leftButtonPan', False)
-
 
 def getDataType(parent):
     type = QInputDialog().getItem(parent, "Select phases type", "Type:",
@@ -99,6 +89,7 @@ def plot_pdf(_axes, x, y, annotation, bbox_props, xlabel=None, ylabel=None,
         _axes.set_ylabel(ylabel)
     _anno = _axes.annotate(annotation, xy=(.05, .5), xycoords='axes fraction')
     _anno.set_bbox(bbox_props)
+    _anno.draggable()
 
     return _axes
 
@@ -272,9 +263,11 @@ class ComparisonWidget(QWidget):
 
         _gs = gridspec.GridSpec(3, 2)
         self.clf()
+        self.canvas.figure._tight = True
         _axes = self.canvas.figure.add_subplot(_gs[0:2, :])
         _ax1 = self.canvas.figure.add_subplot(_gs[2, 0])
         _ax2 = self.canvas.figure.add_subplot(_gs[2, 1])
+        self.canvas.figure.tight_layout()
 
         # _axes.cla()
         station = self.plotprops['station']
@@ -347,6 +340,7 @@ class ComparisonWidget(QWidget):
                 if wname != name:
                     self.widgets[wname].setEnabled(False)
             self.canvas.figure.clf()
+            self.canvas.figure._tight = True
             _axPstd, _axPexp = self.canvas.figure.add_subplot(221), self.canvas.figure.add_subplot(223)
             _axSstd, _axSexp = self.canvas.figure.add_subplot(222), self.canvas.figure.add_subplot(224)
             axes_dict = dict(P=dict(std=_axPstd, exp=_axPexp),
@@ -368,16 +362,26 @@ class ComparisonWidget(QWidget):
                                  "number of samples: {nsamples}".format(phase=phase, nsamples=len(std))
                 _anno_std = axes_dict[phase]['std'].annotate(std_annotation, xy=(.05, .8), xycoords='axes fraction')
                 _anno_std.set_bbox(bbox_props)
+                _anno_std.draggable()
                 exp_annotation = "Distribution curve for {phase} differences'\n" \
                                  "expectations (all stations)\n" \
                                  "number of samples: {nsamples}".format(phase=phase, nsamples=len(exp))
                 _anno_exp = axes_dict[phase]['exp'].annotate(exp_annotation, xy=(.05, .8), xycoords='axes fraction')
                 _anno_exp.set_bbox(bbox_props)
-                axes_dict[phase]['exp'].set_xlabel('expectation [s]')
-                axes_dict[phase]['std'].set_xlabel('standard deviation [s]')
+                _anno_exp.draggable()
+                axes_dict[phase]['exp'].set_xlabel('Time [s]')
+
+                # add colors (early, late) for expectation
+                ax = axes_dict[phase]['exp']
+                xlims = ax.get_xlim()
+                ylims = ax.get_ylim()
+                ax.fill_between([xlims[0], 0], ylims[0], ylims[1], color=(0.9, 1.0, 0.9, 0.5), label='earlier than manual')
+                ax.fill_between([0, xlims[1]], ylims[0], ylims[1], color=(1.0, 0.9, 0.9, 0.5), label='later than manual')
+            legend = ax.legend()
+            legend.draggable()
 
             for ax in axes_dict['P'].values():
-                ax.set_ylabel('number of picks [-]')
+                ax.set_ylabel('Frequency [-]')
 
             self.canvas.draw()
         else:
@@ -435,29 +439,32 @@ class PlotWidget(FigureCanvas):
 
 
 class WaveformWidgetPG(QtGui.QWidget):
-    def __init__(self, parent=None, xlabel='x', ylabel='y', title='Title'):
-        QtGui.QWidget.__init__(self, parent)  # , 1)
-        self.setParent(parent)
-        self._parent = parent
+    def __init__(self, parent, title='Title'):
+        QtGui.QWidget.__init__(self, parent=parent)
+        self.pg = self.parent().pg
+        # added because adding widget to scrollArea will set scrollArea to parent
+        self.orig_parent = parent
         # attribute plotdict is a dictionary connecting position and a name
         self.plotdict = dict()
         # create plot
         self.main_layout = QtGui.QVBoxLayout()
         self.label = QtGui.QLabel()
         self.setLayout(self.main_layout)
-        self.plotWidget = pg.PlotWidget(title=title, autoDownsample=True)
+        self.plotWidget = self.pg.PlotWidget(self.parent(), title=title, autoDownsample=True)
         self.main_layout.addWidget(self.plotWidget)
         self.main_layout.addWidget(self.label)
-        self.plotWidget.showGrid(x=False, y=True, alpha=0.2)
+        self.plotWidget.showGrid(x=False, y=True, alpha=0.3)
         self.plotWidget.hideAxis('bottom')
         self.plotWidget.hideAxis('left')
         self.wfstart, self.wfend = 0, 0
+        self.pen_multicursor = self.pg.mkPen(self.parent()._style['multicursor']['rgba'])
+        self.pen_linecolor = self.pg.mkPen(self.parent()._style['linecolor']['rgba'])
         self.reinitMoveProxy()
-        self._proxy = pg.SignalProxy(self.plotWidget.scene().sigMouseMoved, rateLimit=60, slot=self.mouseMoved)
+        self._proxy = self.pg.SignalProxy(self.plotWidget.scene().sigMouseMoved, rateLimit=60, slot=self.mouseMoved)
 
     def reinitMoveProxy(self):
-        self.vLine = pg.InfiniteLine(angle=90, movable=False)
-        self.hLine = pg.InfiniteLine(angle=0, movable=False)
+        self.vLine = self.pg.InfiniteLine(angle=90, movable=False, pen=self.pen_multicursor)
+        self.hLine = self.pg.InfiniteLine(angle=0, movable=False, pen=self.pen_multicursor)
         self.plotWidget.addItem(self.vLine, ignoreBounds=True)
         self.plotWidget.addItem(self.hLine, ignoreBounds=True)
 
@@ -467,10 +474,10 @@ class WaveformWidgetPG(QtGui.QWidget):
             mousePoint = self.plotWidget.getPlotItem().vb.mapSceneToView(pos)
             x, y, = (mousePoint.x(), mousePoint.y())
             # if x > 0:# and index < len(data1):
-            wfID = self._parent.getWFID(y)
-            station = self._parent.getStationName(wfID)
+            wfID = self.orig_parent.getWFID(y)
+            station = self.orig_parent.getStationName(wfID)
             abstime = self.wfstart + x
-            if self._parent.get_current_event():
+            if self.orig_parent.get_current_event():
                 self.label.setText("station = {}, T = {}, t = {} [s]".format(station, abstime, x))
             self.vLine.setPos(mousePoint.x())
             self.hLine.setPos(mousePoint.y())
@@ -484,12 +491,6 @@ class WaveformWidgetPG(QtGui.QWidget):
     def clearPlotDict(self):
         self.plotdict = dict()
 
-    def getParent(self):
-        return self._parent
-
-    def setParent(self, parent):
-        self._parent = parent
-
     def plotWFData(self, wfdata, title=None, zoomx=None, zoomy=None,
                    noiselevel=None, scaleddata=False, mapping=True,
                    component='*', nth_sample=1, iniPick=None, verbosity=0):
@@ -610,7 +611,6 @@ class WaveformWidgetPG(QtGui.QWidget):
 class PylotCanvas(FigureCanvas):
     def __init__(self, figure=None, parent=None, connect_events=True, multicursor=False,
                  panZoomX=True, panZoomY=True):
-        self._parent = parent
         if not figure:
             figure = Figure()
             # create axes
@@ -618,17 +618,19 @@ class PylotCanvas(FigureCanvas):
 
         self.axes = figure.axes
         self.figure = figure
-        self.figure.set_facecolor((1., 1., 1.))
+        self.figure.set_facecolor(parent._style['background']['rgba_mpl'])
         # attribute plotdict is a dictionary connecting position and a name
         self.plotdict = dict()
         # initialize super class
         super(PylotCanvas, self).__init__(self.figure)
+        self.setParent(parent)
+        self.orig_parent = parent
 
         if multicursor:
             # add a cursor for station selection
             self.multiCursor = MultiCursor(self.figure.canvas, self.axes,
                                            horizOn=True, useblit=True,
-                                           color='m', lw=1)
+                                           color=parent._style['multicursor']['rgba_mpl'], lw=1)
 
         # initialize panning attributes
         self.press = None
@@ -743,7 +745,7 @@ class PylotCanvas(FigureCanvas):
     def saveFigure(self):
         if self.figure:
             fd = QtGui.QFileDialog()
-            fname, filter = fd.getSaveFileName(self._parent, filter='Images (*.png)')
+            fname, filter = fd.getSaveFileName(self.parent(), filter='Images (*.png)')
             if not fname:
                 return
             if not fname.endswith('.png'):
@@ -884,12 +886,6 @@ class PylotCanvas(FigureCanvas):
     def clearPlotDict(self):
         self.plotdict = dict()
 
-    def getParent(self):
-        return self._parent
-
-    def setParent(self, parent):
-        self._parent = parent
-
     def plotWFData(self, wfdata, title=None, zoomx=None, zoomy=None,
                    noiselevel=None, scaleddata=False, mapping=True,
                    component='*', nth_sample=1, iniPick=None, verbosity=0):
@@ -922,6 +918,9 @@ class PylotCanvas(FigureCanvas):
         nsc.sort()
         nsc.reverse()
 
+        style = self.orig_parent._style
+        linecolor = style['linecolor']['rgba_mpl']
+
         for n, (network, station, channel) in enumerate(nsc):
             st = st_select.select(network=network, station=station, channel=channel)
             trace = st[0]
@@ -942,11 +941,13 @@ class PylotCanvas(FigureCanvas):
                     trace.normalize(np.max(np.abs(trace.data)) * 2)
                 times = [time for index, time in enumerate(time_ax) if not index % nth_sample]
                 data = [datum + n for index, datum in enumerate(trace.data) if not index % nth_sample]
-                ax.plot(times, data, 'k', linewidth=0.7)
+                ax.plot(times, data, color=linecolor, linewidth=0.7)
                 if noiselevel is not None:
                     for level in noiselevel:
                         ax.plot([time_ax[0], time_ax[-1]],
-                                            [level, level], '--k')
+                                            [level, level],
+                                color = linecolor,
+                                linestyle = 'dashed')
                 self.setPlotDict(n, (station, channel, network))
         if iniPick:
             ax.vlines(iniPick, ax.get_ylim()[0], ax.get_ylim()[1],
@@ -1109,7 +1110,8 @@ class PickDlg(QDialog):
     def __init__(self, parent=None, data=None, station=None, network=None, picks=None,
                  autopicks=None, rotate=False, parameter=None, embedded=False, metadata=None,
                  event=None, filteroptions=None, model='iasp91'):
-        super(PickDlg, self).__init__(parent)
+        super(PickDlg, self).__init__(parent, 1)
+        self.orig_parent = parent
 
         # initialize attributes
         self.parameter = parameter
@@ -1129,6 +1131,7 @@ class PickDlg(QDialog):
         pylot_user = getpass.getuser()
         self._user = settings.value('user/Login', pylot_user)
         self._dirty = False
+        self._style = parent._style
         if picks:
             self.picks = copy.deepcopy(picks)
             self._init_picks = picks
@@ -1267,8 +1270,8 @@ class PickDlg(QDialog):
         self.plot_arrivals_button.setCheckable(True)
 
         # create accept/reject button
-        self.accept_button = QPushButton('&Accept Picks')
-        self.reject_button = QPushButton('&Reject Picks')
+        self.accept_button = QPushButton('&Accept')
+        self.reject_button = QPushButton('&Reject')
         self.disable_ar_buttons()
 
         # add hotkeys
@@ -1292,10 +1295,20 @@ class PickDlg(QDialog):
         _dialtoolbar.addSeparator()
         _dialtoolbar.addAction(self.resetPicksAction)
         if self._embedded:
+            manu_label = QLabel('Manual Onsets:')
+            manu_label.setStyleSheet('QLabel {'
+                                     'padding:2px;'
+                                     'padding-left:5px}')
+            _dialtoolbar.addWidget(manu_label)
             _dialtoolbar.addWidget(self.accept_button)
             _dialtoolbar.addWidget(self.reject_button)
         else:
             _dialtoolbar.addWidget(self.nextStation)
+        est_label = QLabel('Estimated onsets:')
+        est_label.setStyleSheet('QLabel {'
+                                   'padding:2px;'
+                                   'padding-left:5px}')
+        _dialtoolbar.addWidget(est_label)
         _dialtoolbar.addWidget(self.plot_arrivals_button)
 
         # layout the innermost widget
@@ -1397,7 +1410,7 @@ class PickDlg(QDialog):
             return
         ax = self.multicompfig.axes[0]
         if not textOnly:
-            ylims = self.getGlobalLimits('y')
+            ylims = self.getGlobalLimits(ax, 'y')
         else:
             ylims = self.multicompfig.getYLims(ax)
         stime = self.getStartTime()
@@ -1525,36 +1538,46 @@ class PickDlg(QDialog):
             self.leave_picking_mode()
 
     def init_p_pick(self):
-        self.set_button_color(self.p_button, 'yellow')
+        self.set_button_border_color(self.p_button, 'yellow')
         self.activatePicking()
         self.currentPhase = str(self.p_button.text())
 
     def init_s_pick(self):
-        self.set_button_color(self.s_button, 'yellow')
+        self.set_button_border_color(self.s_button, 'yellow')
         self.activatePicking()
         self.currentPhase = str(self.s_button.text())
 
     def getPhaseID(self, phase):
         return identifyPhaseID(phase)
 
-    def set_button_color(self, button, color=None):
+    def set_button_border_color(self, button, color=None):
+        '''
+        Set background color of a button.
+        button: type = QtGui.QAbstractButton
+        color: type = QtGui.QColor or type = str (RGBA)
+        '''
         if type(color) == QtGui.QColor:
+            button.setStyleSheet({'QPushButton{background-color:transparent}'})
             palette = button.palette()
             role = button.backgroundRole()
             palette.setColor(role, color)
             button.setPalette(palette)
             button.setAutoFillBackground(True)
-        elif type(color) == str or not color:
-            button.setStyleSheet("background-color: {}".format(color))
+        elif type(color) == str:
+            button.setStyleSheet('QPushButton{border-color: %s}' % color)
+        elif type(color) == tuple:
+            button.setStyleSheet('QPushButton{border-color: rgba%s}' % str(color))
+        elif not color:
+            button.setStyleSheet(self.orig_parent._style['stylesheet'])
 
     def reset_p_button(self):
-        self.set_button_color(self.p_button)
+        self.set_button_border_color(self.p_button)
         self.p_button.setEnabled(True)
         self.p_button.setChecked(False)
         self.p_button.setText('P')
 
     def reset_s_button(self):
-        self.set_button_color(self.s_button)
+        self.set_button_border_color(self.s_button)
         self.s_button.setEnabled(True)
         self.s_button.setChecked(False)
         self.s_button.setText('S')
@@ -1604,6 +1627,7 @@ class PickDlg(QDialog):
         return self.station
 
     def getChannelID(self, key):
+        if key < 0: key = 0
         return self.multicompfig.getPlotDict()[int(key)][1]
 
     def getTraceID(self, channels):
@@ -1638,8 +1662,8 @@ class PickDlg(QDialog):
     def setYLims(self, limits):
         self.cur_ylim = limits
 
-    def getGlobalLimits(self, axis):
-        return self.multicompfig.getGlobalLimits(axis)
+    def getGlobalLimits(self, ax, axis):
+        return self.multicompfig.getGlobalLimits(ax, axis)
 
     def getWFData(self):
         return self.data
@@ -1693,10 +1717,10 @@ class PickDlg(QDialog):
         self.cidpress = self.multicompfig.connectPressEvent(self.setPick)
 
         if self.getPhaseID(self.currentPhase) == 'P':
-            self.set_button_color(self.p_button, 'green')
+            self.set_button_border_color(self.p_button, 'green')
             self.setIniPickP(gui_event, wfdata, trace_number)
         elif self.getPhaseID(self.currentPhase) == 'S':
-            self.set_button_color(self.s_button, 'green')
+            self.set_button_border_color(self.s_button, 'green')
             self.setIniPickS(gui_event, wfdata)
 
         self.zoomAction.setEnabled(False)
@@ -1931,26 +1955,27 @@ class PickDlg(QDialog):
         self.drawPicks(picktype='manual')
         self.drawPicks(picktype='auto')
 
-    def drawPicks(self, phase=None, picktype='manual', textOnly=False):
+    def drawPicks(self, phase=None, picktype='manual', textOnly=False, picks=None):
         # plotting picks
         ax = self.multicompfig.axes[0]
         if not textOnly:
             ylims = self.multicompfig.getGlobalLimits(ax, 'y')
         else:
             ylims = ax.get_ylim()
-        if self.getPicks(picktype):
-            if phase is not None and not phase == 'SPt':
-                if (type(self.getPicks(picktype)[phase]) is dict
-                    or type(self.getPicks(picktype)[phase]) is AttribDict):
-                    picks = self.getPicks(picktype)[phase]
-            elif phase is None:
-                for phase in self.getPicks(picktype):
-                    self.drawPicks(phase, picktype, textOnly)
-                return
+        if not picks:
+            if self.getPicks(picktype):
+                if phase is not None and not phase == 'SPt':
+                    if (type(self.getPicks(picktype)[phase]) is dict
+                        or type(self.getPicks(picktype)[phase]) is AttribDict):
+                        picks = self.getPicks(picktype)[phase]
+                elif phase is None:
+                    for phase in self.getPicks(picktype):
+                        self.drawPicks(phase, picktype, textOnly)
+                    return
+                else:
+                    return
             else:
                 return
-        else:
-            return
 
         # get quality classes
         if self.getPhaseID(phase) == 'P':
@@ -2235,9 +2260,20 @@ class MultiEventWidget(QWidget):
 
         self.start_button = QtGui.QPushButton('Start')
 
-        for index, (key, func) in enumerate(self.options):
+        for index, (key, func, color) in enumerate(self.options):
             rb = QtGui.QRadioButton(key)
             rb.toggled.connect(self.check_rb_selection)
+            if color:
+                color = 'rgba{}'.format(color)
+            else:
+                color = 'transparent'
+            rb.setStyleSheet('QRadioButton{'
+                             'background-color: %s;'
+                             'border-style:outset;'
+                             'border-width:1px;'
+                             'border-radius:5px;'
+                             'padding:5px;'
+                             '}' % str(color))
             if index == 0:
                 rb.setChecked(True)
             self.rb_dict[key] = rb
@@ -2252,7 +2288,7 @@ class MultiEventWidget(QWidget):
         self.main_layout.insertLayout(0, self.rb_layout)
 
     def refresh_tooltips(self):
-        for key, func in self.options:
+        for key, func, color in self.options:
             eventlist = func()
             if not type(eventlist) == list:
                 eventlist = [eventlist]
@@ -2288,6 +2324,7 @@ class AutoPickWidget(MultiEventWidget):
 
     def __init__(self, parent, options):
         MultiEventWidget.__init__(self, options, parent, 1)
+        self.events2plot = {}
         self.connect_buttons()
         self.init_plot_layout()
         self.init_log_layout()
@@ -2355,6 +2392,9 @@ class AutoPickWidget(MultiEventWidget):
         self.main_layout.setStretch(1, 1)
 
     def reinitEvents2plot(self):
+        for eventID, eventDict in self.events2plot.items():
+            for widget_key, widget in eventDict.items():
+                widget.setParent(None)
         self.events2plot = {}
         self.eventbox.clear()
         self.refresh_plot_tabs()
@@ -2433,17 +2473,18 @@ class TuneAutopicker(QWidget):
     QWidget used to modifiy and test picking parameters for autopicking algorithm.
 
     :param: parent
-    :type: QtPyLoT Mainwindow
+    :type: PyLoT Mainwindow
     '''
 
     def __init__(self, parent):
         QtGui.QWidget.__init__(self, parent, 1)
-        self.parent = parent
-        self.setParent(parent)
+        self._style = parent._style
         self.setWindowTitle('PyLoT - Tune Autopicker')
-        self.parameter = parent._inputs
-        self.set_fig_dict(parent.fig_dict)
+        self.parameter = self.parent()._inputs
+        self.fig_dict = self.parent().fig_dict
         self.data = Data()
+        self.pdlg_widget = None
+        self.pylot_picks = None
         self.init_main_layouts()
         self.init_eventlist()
         self.init_figure_tabs()
@@ -2455,8 +2496,9 @@ class TuneAutopicker(QWidget):
         self.add_log()
         self.set_stretch()
         self.resize(1280, 720)
-        if hasattr(parent, 'metadata'):
-            self.metadata = self.parent.metadata
+        self._manual_pick_plots = []
+        if hasattr(self.parent(), 'metadata'):
+            self.metadata = self.parent().metadata
         else:
             self.metadata = None
             # self.setWindowModality(QtCore.Qt.WindowModality.ApplicationModal)
@@ -2464,7 +2506,8 @@ class TuneAutopicker(QWidget):
 
     def set_fig_dict(self, fig_dict):
         for key, value in fig_dict.items():
-            value._tight = True
+            if key is not 'mainFig':
+                value._tight = True
         self.fig_dict = fig_dict
 
     def init_main_layouts(self):
@@ -2478,7 +2521,7 @@ class TuneAutopicker(QWidget):
         self.setLayout(self.main_layout)
 
     def init_eventlist(self):
-        self.eventBox = self.parent.createEventBox()
+        self.eventBox = self.parent().createEventBox()
         self.eventBox.setMaxVisibleItems(20)
         self.fill_eventbox()
         self.trace_layout.addWidget(self.eventBox)
@@ -2497,13 +2540,18 @@ class TuneAutopicker(QWidget):
         self.stationBox.activated.connect(self.fill_tabs)
 
     def fill_stationbox(self):
-        fnames = self.parent.getWFFnames_from_eventbox(eventbox=self.eventBox)
+        fnames = self.parent().getWFFnames_from_eventbox(eventbox=self.eventBox)
         self.data.setWFData(fnames)
         wfdat = self.data.getWFData()  # all available streams
+        # remove possible underscores in station names
+        wfdat = remove_underscores(wfdat)
+        # rotate misaligned stations to ZNE
+        # check for gaps and doubled channels
+        check4gaps(wfdat)
+        check4doubled(wfdat)
+        wfdat = check4rotated(wfdat, self.parent().metadata, verbosity=0)
         # trim station components to same start value
         trim_station_components(wfdat, trim_start=True, trim_end=False)
-        # rotate misaligned stations to ZNE
-        wfdat = check4rotated(wfdat, self.parent.metadata)
         self.stationBox.clear()
         stations = []
         for trace in self.data.getWFData():
@@ -2517,7 +2565,7 @@ class TuneAutopicker(QWidget):
         for network, station in stations:
             item = QtGui.QStandardItem(network + '.' + station)
             if station in self.get_current_event().pylot_picks:
-                item.setBackground(self.parent._colors['ref'])
+                item.setBackground(self.parent()._ref_test_colors['ref'])
             model.appendRow(item)
 
     def init_figure_tabs(self):
@@ -2532,7 +2580,7 @@ class TuneAutopicker(QWidget):
         self.stb_names = ['aicARHfig', 'refSpick', 'el_S1pick', 'el_S2pick']
 
     def add_parameters(self):
-        self.paraBox = PylotParaBox(self.parameter)
+        self.paraBox = PylotParaBox(self.parameter, parent=self, windowflag=0)
         self.paraBox.set_tune_mode(True)
         self.update_eventID()
         self.parameter_layout.addWidget(self.paraBox)
@@ -2541,6 +2589,7 @@ class TuneAutopicker(QWidget):
 
     def add_buttons(self):
         self.pick_button = QtGui.QPushButton('Pick Trace')
+        self.pick_button.setStyleSheet('QPushButton{border-color: rgba(110, 200, 0, 255)}')
         self.pick_button.clicked.connect(self.call_picker)
         self.close_button = QtGui.QPushButton('Close')
         self.close_button.clicked.connect(self.hide)
@@ -2558,7 +2607,7 @@ class TuneAutopicker(QWidget):
 
     def get_current_event(self):
         path = self.eventBox.currentText()
-        return self.parent.project.getEventFromPath(path)
+        return self.parent().project.getEventFromPath(path)
 
     def get_current_event_name(self):
         return self.eventBox.currentText().split('/')[-1]
@@ -2574,10 +2623,7 @@ class TuneAutopicker(QWidget):
     def get_current_event_autopicks(self, station):
         event = self.get_current_event()
         if event.pylot_autopicks:
-            if station in event.pylot_autopicks:
-                return event.pylot_autopicks[station]
-            else:
-                return
+            return event.pylot_autopicks[station]
 
     def get_current_station(self):
         return str(self.stationBox.currentText()).split('.')[-1]
@@ -2586,51 +2632,66 @@ class TuneAutopicker(QWidget):
         widget = QtGui.QWidget()
         v_layout = QtGui.QVBoxLayout()
         v_layout.addWidget(canvas)
-        v_layout.addWidget(NavigationToolbar2QT(canvas, self))
         widget.setLayout(v_layout)
         return widget
 
     def gen_pick_dlg(self):
         if not self.get_current_event():
-            self.pickDlg = None
+            if self.pdlg_widget:
+                self.pdlg_widget.setParent(None)
+            self.pdlg_widget = None
             return
         station = self.get_current_station()
         data = self.data.getWFData()
-        metadata = self.parent.metadata
+        metadata = self.parent().metadata
         event = self.get_current_event()
-        filteroptions = self.parent.filteroptions
-        pickDlg = PickDlg(self, data=data.select(station=station),
-                          station=station, parameter=self.parameter,
-                          picks=self.get_current_event_picks(station),
-                          autopicks=self.get_current_event_autopicks(station),
-                          metadata=metadata, event=event, filteroptions=filteroptions,
-                          embedded=True)
-        pickDlg.update_picks.connect(self.picks_from_pickdlg)
-        pickDlg.update_picks.connect(self.fill_eventbox)
-        pickDlg.update_picks.connect(self.fill_stationbox)
-        pickDlg.update_picks.connect(lambda: self.parent.setDirty(True))
-        pickDlg.update_picks.connect(self.parent.enableSaveEventAction)
-        self.pickDlg = QtGui.QWidget()
+        filteroptions = self.parent().filteroptions
+        self.pickDlg = PickDlg(self, data=data.select(station=station),
+                               station=station, parameter=self.parameter,
+                               picks=self.get_current_event_picks(station),
+                               autopicks=self.get_current_event_autopicks(station),
+                               metadata=metadata, event=event, filteroptions=filteroptions,
+                               embedded=True)
+        self.pickDlg.update_picks.connect(self.picks_from_pickdlg)
+        self.pickDlg.update_picks.connect(self.fill_eventbox)
+        self.pickDlg.update_picks.connect(self.fill_stationbox)
+        self.pickDlg.update_picks.connect(lambda: self.parent().setDirty(True))
+        self.pickDlg.update_picks.connect(self.parent().enableSaveEventAction)
+        self.pickDlg.update_picks.connect(self.plot_manual_picks_to_figs)
+        self.pdlg_widget = QtGui.QWidget(self)
         hl = QtGui.QHBoxLayout()
-        self.pickDlg.setLayout(hl)
-        hl.addWidget(pickDlg)
+        self.pdlg_widget.setLayout(hl)
+        hl.addWidget(self.pickDlg)
 
     def picks_from_pickdlg(self, picks=None):
         station = self.get_current_station()
-        replot = self.parent.addPicks(station, picks)
+        replot = self.parent().addPicks(station, picks)
         self.get_current_event().setPick(station, picks)
-        if self.get_current_event() == self.parent.get_current_event():
+        if self.get_current_event() == self.parent().get_current_event():
             if replot:
-                self.parent.plotWaveformDataThread()
-                self.parent.drawPicks()
+                self.parent().plotWaveformDataThread()
+                self.parent().drawPicks()
             else:
-                self.parent.drawPicks(station)
-            self.parent.draw()
+                self.parent().drawPicks(station)
+            self.parent().draw()
+
+    def clear_plotitem(self, plotitem):
+        if type(plotitem) == list:
+            for item in plotitem:
+                self.clear_plotitem(item)
+            return
+        try:
+            plotitem.remove()
+        except Exception as e:
+            print('Warning could not remove item {}: {}'.format(plotitem, e))
 
     def plot_manual_picks_to_figs(self):
         picks = self.get_current_event_picks(self.get_current_station())
         if not picks:
             return
+        for plotitem in self._manual_pick_plots:
+            self.clear_plotitem(plotitem)
+        self._manual_pick_plots = []
         st = self.data.getWFData()
         tr = st.select(station=self.get_current_station())[0]
         starttime = tr.stats.starttime
@@ -2649,47 +2710,48 @@ class TuneAutopicker(QWidget):
             ('refSpick', 0),
             ('el_S1pick', 0),
             ('el_S2pick', 0)]
+        qualityPpick = getQualityFromUncertainty(picks['P']['spe'], self.parameter['timeerrorsP'])
+        qualitySpick = getQualityFromUncertainty(picks['S']['spe'], self.parameter['timeerrorsS'])
         for p_ax in p_axes:
-            axes = self.parent.fig_dict[p_ax[0]].axes
+            axes = self.parent().fig_dict[p_ax[0]].axes
             if not axes:
                 continue
             ax = axes[p_ax[1]]
-            self.plot_manual_Ppick_to_ax(ax, (picks['P']['mpp'] - starttime))
+            self.plot_manual_pick_to_ax(ax=ax, picks=picks, phase='P',
+                                        starttime=starttime, quality=qualityPpick)
         for s_ax in s_axes:
-            axes = self.parent.fig_dict[s_ax[0]].axes
+            axes = self.parent().fig_dict[s_ax[0]].axes
             if not axes:
                 continue
             ax = axes[s_ax[1]]
-            self.plot_manual_Spick_to_ax(ax, (picks['S']['mpp'] - starttime))
+            self.plot_manual_pick_to_ax(ax=ax, picks=picks, phase='S',
+                                        starttime=starttime, quality=qualitySpick)
+        for canvas in self.parent().canvas_dict.values():
+            canvas.draw()
+
+    def plot_manual_pick_to_ax(self, ax, picks, phase, starttime, quality):
+        mpp = picks[phase]['mpp'] - starttime
+        color = pick_color_plt('manual', phase, quality)
 
-    def plot_manual_Ppick_to_ax(self, ax, pick):
         y_top = 0.9 * ax.get_ylim()[1]
         y_bot = 0.9 * ax.get_ylim()[0]
-        ax.vlines(pick, y_bot, y_top,
-                  color='teal', linewidth=2, label='manual P Onset')
-        ax.plot([pick - 0.5, pick + 0.5],
-                [y_bot, y_bot], linewidth=2, color='teal')
-        ax.plot([pick - 0.5, pick + 0.5],
-                [y_top, y_top], linewidth=2, color='teal')
-        ax.legend(loc=1)
-
-    def plot_manual_Spick_to_ax(self, ax, pick):
-        y_top = 0.9 * ax.get_ylim()[1]
-        y_bot = 0.9 * ax.get_ylim()[0]
-        ax.vlines(pick, y_bot, y_top,
-                  color='magenta', linewidth=2, label='manual S Onset')
-        ax.plot([pick - 0.5, pick + 0.5],
-                [y_bot, y_bot], linewidth=2, color='magenta')
-        ax.plot([pick - 0.5, pick + 0.5],
-                [y_top, y_top], linewidth=2, color='magenta')
+        self._manual_pick_plots.append(ax.vlines(mpp, y_bot, y_top,
+                                                 color=color, linewidth=2,
+                                                 label='manual {} Onset (quality: {})'.format(phase, quality)))
+        self._manual_pick_plots.append(ax.plot([mpp - 0.5, mpp + 0.5],
+                                               [y_bot, y_bot], linewidth=2,
+                                               color=color))
+        self._manual_pick_plots.append(ax.plot([mpp - 0.5, mpp + 0.5],
+                                               [y_top, y_top], linewidth=2,
+                                               color=color))
         ax.legend(loc=1)
 
     def fill_tabs(self, event=None, picked=False):
         self.clear_all()
-        canvas_dict = self.parent.canvas_dict
         self.gen_pick_dlg()
+        canvas_dict = self.parent().canvas_dict
         self.overview = self.gen_tab_widget('Overview', canvas_dict['mainFig'])
-        id0 = self.figure_tabs.insertTab(0, self.pickDlg, 'Traces Plot')
+        id0 = self.figure_tabs.insertTab(0, self.pdlg_widget, 'Traces Plot')
         id1 = self.figure_tabs.insertTab(1, self.overview, 'Overview')
         id2 = self.figure_tabs.insertTab(2, self.p_tabs, 'P')
         id3 = self.figure_tabs.insertTab(3, self.s_tabs, 'S')
@@ -2732,12 +2794,12 @@ class TuneAutopicker(QWidget):
         self.init_tab_names()
 
     def fill_eventbox(self):
-        project = self.parent.project
+        project = self.parent().project
         if not project:
             return
         # update own list
-        self.parent.fill_eventbox(eventBox=self.eventBox, select_events='ref')
-        index_start = self.parent.eventBox.currentIndex()
+        self.parent().fill_eventbox(eventBox=self.eventBox, select_events='ref')
+        index_start = self.parent().eventBox.currentIndex()
         index = index_start
         if index == -1:
             index += 1
@@ -2756,7 +2818,7 @@ class TuneAutopicker(QWidget):
         if not index == index_start:
             self.eventBox.activated.emit(index)
         # update parent
-        self.parent.fill_eventbox()
+        self.parent().fill_eventbox()
 
     def update_eventID(self):
         self.paraBox.boxes['eventID'].setText(
@@ -2778,7 +2840,8 @@ class TuneAutopicker(QWidget):
                 'locflag': 0,
                 'savexml': False}
         for key in self.fig_dict.keys():
-            self.fig_dict[key].clear()
+            if not key == 'plot_style':
+                self.fig_dict[key].clear()
         self.ap_thread = Thread(self, autoPyLoT, arg=args,
                                 progressText='Picking trace...',
                                 pb_widget=self.pb_widget,
@@ -2819,8 +2882,8 @@ class TuneAutopicker(QWidget):
 
     def params_from_gui(self):
         parameters = self.paraBox.params_from_gui()
-        if self.parent:
-            self.parent._inputs = parameters
+        if self.parent():
+            self.parent()._inputs = parameters
         return parameters
 
     def set_stretch(self):
@@ -2828,10 +2891,11 @@ class TuneAutopicker(QWidget):
         self.tune_layout.setStretch(1, 1)
 
     def clear_all(self):
-        if hasattr(self, 'pickDlg'):
-            if self.pickDlg:
-                self.pickDlg.setParent(None)
-            del (self.pickDlg)
+        if hasattr(self, 'pdlg_widget'):
+            if self.pdlg_widget:
+                self.pdlg_widget.setParent(None)
+                # TODO: removing widget by parent deletion raises exception when activating stationbox:
+                # RuntimeError: Internal C++ object (PylotCanvas) already deleted.
         if hasattr(self, 'overview'):
             self.overview.setParent(None)
         if hasattr(self, 'p_tabs'):
@@ -2860,7 +2924,7 @@ class PylotParaBox(QtGui.QWidget):
     accepted = QtCore.Signal(str)
     rejected = QtCore.Signal(str)
 
-    def __init__(self, parameter, parent=None):
+    def __init__(self, parameter, parent=None, windowflag=1):
         '''
         Generate Widget containing parameters for PyLoT.
 
@@ -2868,7 +2932,7 @@ class PylotParaBox(QtGui.QWidget):
         :type: PylotParameter (object)
 
         '''
-        QtGui.QWidget.__init__(self, parent)
+        QtGui.QWidget.__init__(self, parent, windowflag)
         self.parameter = parameter
         self.tabs = QtGui.QTabWidget()
         self.layout = QtGui.QVBoxLayout()
@@ -3411,9 +3475,6 @@ class SubmitLocal(QWidget):
 
         self.main_layout.addWidget(self.button)
 
-    def start(self):
-        print('subprocess Popen')
-
     def start(self, pp_export, ncores):
         self.execute_command(pp_export, ncores)
 
@@ -3433,6 +3494,7 @@ class SubmitLocal(QWidget):
 class PropertiesDlg(QDialog):
     def __init__(self, parent=None, infile=None, inputs=None):
         super(PropertiesDlg, self).__init__(parent)
+        self._pylot_mainwindow = self.parent()
 
         self.infile = infile
         self.inputs = inputs
@@ -3733,14 +3795,29 @@ class PhasesTab(PropTab):
 class GraphicsTab(PropTab):
     def __init__(self, parent=None):
         super(GraphicsTab, self).__init__(parent)
+        self.pylot_mainwindow = parent._pylot_mainwindow
         self.init_layout()
         self.add_pg_cb()
         self.add_nth_sample()
+        self.add_style_settings()
         self.setLayout(self.main_layout)
 
     def init_layout(self):
         self.main_layout = QGridLayout()
 
+    def add_style_settings(self):
+        styles = self.pylot_mainwindow._styles
+        active_stylename = self.pylot_mainwindow._stylename
+        label = QtGui.QLabel('Application style (might require Application restart):')
+        self.style_cb = QComboBox()
+        for stylename, style in styles.items():
+            self.style_cb.addItem(stylename, style)
+        index_current_style = self.style_cb.findText(active_stylename)
+        self.style_cb.setCurrentIndex(index_current_style)
+        self.main_layout.addWidget(label, 2, 0)
+        self.main_layout.addWidget(self.style_cb, 2, 1)
+        self.style_cb.activated.connect(self.set_current_style)
+
     def add_nth_sample(self):
         settings = QSettings()
         nth_sample = settings.value("nth_sample")
@@ -3757,6 +3834,12 @@ class GraphicsTab(PropTab):
         self.main_layout.addWidget(self.spinbox_nth_sample, 1, 1)
 
     def add_pg_cb(self):
+        try:
+            import pyqtgraph as pg
+            pg = True
+        except:
+            pg = False
+
         text = {True: 'Use pyqtgraphic library for plotting',
                 False: 'Cannot use library: pyqtgraphic not found on system'}
         label = QLabel('PyQt graphic')
@@ -3768,6 +3851,10 @@ class GraphicsTab(PropTab):
         self.main_layout.addWidget(label, 0, 0)
         self.main_layout.addWidget(self.checkbox_pg, 0, 1)
 
+    def set_current_style(self):
+        selected_style = self.style_cb.currentText()
+        self.pylot_mainwindow.set_style(selected_style)
+
     def getValues(self):
         values = {'nth_sample': self.spinbox_nth_sample.value(),
                   'pyqtgraphic': self.checkbox_pg.isChecked()}
@@ -4215,15 +4302,23 @@ class LoadDataDlg(QDialog):
 
 
 class HelpForm(QDialog):
-    def __init__(self, page=QUrl('https://ariadne.geophysik.rub.de/trac/PyLoT'),
-                 parent=None):
-        super(HelpForm, self).__init__(parent)
+    def __init__(self, parent=None,
+                 page=QUrl('https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/')):
+        super(HelpForm, self).__init__(parent, 1)
         self.setAttribute(Qt.WA_DeleteOnClose)
         self.setAttribute(Qt.WA_GroupLeader)
 
-        backAction = QAction(QIcon(":/back.png"), "&Back", self)
+        self.home_page = page
+
+        back_icon = QIcon()
+        back_icon.addPixmap(QPixmap(':/icons/back.png'))
+
+        home_icon = QIcon()
+        home_icon.addPixmap(QPixmap(':/icons/home.png'))
+
+        backAction = QAction(back_icon, "&Back", self)
         backAction.setShortcut(QKeySequence.Back)
-        homeAction = QAction(QIcon(":/home.png"), "&Home", self)
+        homeAction = QAction(home_icon, "&Home", self)
         homeAction.setShortcut("Home")
         self.pageLabel = QLabel()
 
@@ -4236,21 +4331,21 @@ class HelpForm(QDialog):
 
         layout = QVBoxLayout()
         layout.addWidget(toolBar)
-        layout.addWidget(self.webBrowser, 1)
+        layout.addWidget(self.webBrowser)
         self.setLayout(layout)
 
-        self.connect(backAction, Signal("triggered()"),
-                     self.webBrowser, Slot("backward()"))
-        self.connect(homeAction, Signal("triggered()"),
-                     self.webBrowser, Slot("home()"))
-        self.connect(self.webBrowser, Signal("sourceChanged(QUrl)"),
-                     self.updatePageTitle)
+        backAction.triggered.connect(self.webBrowser.back)
+        homeAction.triggered.connect(self.home)
+        self.webBrowser.urlChanged.connect(self.updatePageTitle)
 
-        self.resize(400, 600)
+        self.resize(1280, 720)
         self.setWindowTitle("{0} Help".format(QApplication.applicationName()))
 
+    def home(self):
+        self.webBrowser.load(self.home_page)
+
     def updatePageTitle(self):
-        self.pageLabel.setText(self.webBrowser.documentTitle())
+        self.pageLabel.setText(self.webBrowser.title())
 
 
 if __name__ == '__main__':

pylot/styles/__init__.py

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

pylot/styles/bright.qss

@@ -0,0 +1,259 @@
+QMainWindow{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
+color: rgba(0, 0, 0, 255);
+}
+
+QWidget{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(235, 235, 235, 255), stop:1 rgba(230, 230, 230, 255));
+color: rgba(0, 0, 0, 255);
+}
+
+QToolBar QWidget:checked{
+background-color: transparent;
+border-color: rgba(230, 230, 230, 255);
+border-width: 2px;
+border-style:inset;
+}
+
+QComboBox{
+background-color: rgba(255, 255, 255, 255);
+color: rgba(0, 0, 0, 255);
+min-height: 1.5em;
+
+selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
+}
+
+QComboBox *{
+background-color: rgba(255, 255, 255, 255);
+color: rgba(0, 0, 0, 255);
+
+selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
+selection-color: rgba(255, 255, 255, 255);
+}
+
+QMenuBar{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:2, stop:0 rgba(240, 240, 240, 255), stop:1 rgba(230, 230, 230, 255));
+padding:1px;
+}
+
+QMenuBar::item{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:2, stop:0 rgba(240, 240, 240, 255), stop:1 rgba(230, 230, 230, 255));
+color: rgba(0, 0, 0, 255);
+padding:3px;
+padding-left:5px;
+padding-right:5px;
+}
+
+QMenu{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(230, 230, 230 255));
+color: rgba(0, 0, 0, 255);
+padding:0;
+}
+
+*::item:selected{
+color: rgba(0, 0, 0, 255);
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
+}
+
+QToolBar{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
+border-style:solid;
+border-color:rgba(200, 200, 200, 150);
+border-width:1px;
+}
+
+QToolBar *{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
+}
+
+QMessageBox{
+background-color: rgba(255, 255, 255, 255);
+color: rgba(0, 0, 0, 255);
+}
+
+QTableWidget{
+background-color: rgba(255, 255, 255, 255);
+color:rgba(0, 0, 0, 255);
+border-color:rgba(0, 0, 0, 255);
+selection-background-color: rgba(200, 210, 230, 255);
+}
+
+QTableCornerButton::section{
+border: none;
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
+}
+
+QHeaderView::section{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
+border:none;
+border-top-style:solid;
+border-width:1px;
+border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
+color:rgba(0, 0, 0, 255);
+padding:5px;
+}
+
+QHeaderView::section:checked{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
+border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
+}
+
+QHeaderView{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
+
+border:none;
+border-top-style:solid;
+border-width:1px;
+border-top-color:rgba(230, 230, 230, 255);
+color:rgba(0, 0, 0, 255);
+}
+
+QListWidget{
+background-color:rgba(230, 230, 230, 255);
+color:rgba(0, 0, 0, 255);
+}
+
+QStatusBar{
+background-color:rgba(255, 255, 255, 255);
+color:rgba(0, 0, 0, 255);
+}
+
+QPushButton{
+background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(245, 245, 245, 255));
+color:rgba(0, 0, 0, 255);
+border-style: outset;
+border-width: 1px;
+border-color: rgba(100, 100, 120, 255);
+min-width: 6em;
+padding: 4px;
+padding-left:5px;
+padding-right:5px;
+border-radius: 2px;
+}
+
+QPushButton:pressed{
+background-color: rgba(230, 230, 230, 255);
+border-style: inset;
+}
+
+QPushButton:checked{
+background-color: rgba(230, 230, 230, 255);
+border-style: inset;
+}
+
+*:disabled{
+color:rgba(100, 100, 120, 255);
+}
+
+QTabBar{
+background-color:transparent;
+}
+
+QTabBar::tab{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(210, 210, 210, 255));
+color: rgba(0, 0, 0, 255);
+border-style:solid;
+border-color:rgba(210, 210, 210 255);
+border-bottom-color: transparent;
+border-width:1px;
+padding:5px;
+}
+
+QTabBar::tab:selected{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(245, 245, 245, 255));
+color: rgba(0, 0, 0, 255);
+border-style:solid;
+border-color:rgba(245, 245, 245, 255);
+border-bottom-color: transparent;
+border-width:1px;
+padding:5px;
+}
+
+QTabBar::tab:disabled{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(210, 210, 210, 255));
+color: rgba(100, 100, 120, 255);
+}
+
+QTabWidget{
+background-color:transparent;
+}
+
+QTabWidget::pane{
+background-color:rgba(0, 0, 0, 255);
+border-style:solid;
+border-color:rgba(245, 245, 245, 255);
+border-width:1px;
+}
+
+QTabWidget::tab{
+background-color:rgba(255, 255, 255, 255);
+}
+
+QTabWidget > QWidget{
+background-color: rgba(245, 245, 245, 255);
+color: rgba(0, 0, 0, 255);
+}
+
+QScrollArea{
+background: transparent;
+}
+
+QScrollArea>QWidget>QWidget{
+background: transparent;
+}
+
+QLabel{
+color: rgba(0, 0, 0, 255);
+background-color: transparent;
+}
+
+QTextEdit{
+color: rgba(0, 0, 0, 255);
+background-color: rgba(255, 255, 255, 255);
+}
+
+QSpinBox{
+color: rgba(0, 0, 0, 255);
+background-color: rgba(255, 255, 255, 255);
+}
+
+QDoubleSpinBox{
+color: rgba(0, 0, 0, 255);
+background-color: rgba(255, 255, 255, 255);
+}
+
+QCheckBox{
+background-color:transparent;
+border:none;
+}
+
+QLineEdit{
+background-color: rgba(255, 255, 255, 255);
+border: 1px inset;
+border-radius:0;
+border-color: rgba(100, 100, 120, 255);
+}
+
+QLineEdit:disabled{
+background-color: rgba(255, 255, 255, 255);
+border: 1px inset;
+border-radius:0;
+border-color: rgba(200, 200, 200, 255);
+}
+
+QListWidget{
+background-color:rgba(255, 255, 255, 255)
+}
+
+QProgressBar{
+background-color:rgba(230, 230, 230, 255);
+}
+
+QProgressBar::chunk{
+background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0.5, y2:0, stop:0 transparent, stop:1 rgba(0, 70, 180, 150));
+}
+
+QStatusBar{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(235, 235, 235, 255), stop:1 rgba(230, 230, 230, 255));
+color: rgba(0, 0, 0, 255);
+}

pylot/styles/dark.qss

@@ -0,0 +1,258 @@
+QMainWindow{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+}
+
+QWidget{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+}
+
+QToolBar QWidget:checked{
+background-color: transparent;
+border-color: rgba(100, 100, 120, 255);
+border-width: 2px;
+border-style:inset;
+}
+
+QComboBox{
+background-color: rgba(90, 90, 100, 255);
+color: rgba(255, 255, 255, 255);
+min-height: 1.5em;
+
+selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
+}
+
+QComboBox *{
+background-color: rgba(90, 90, 100, 255);
+color: rgba(255, 255, 255, 255);
+
+selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
+selection-color: rgba(255, 255, 255, 255);
+}
+
+QMenuBar{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+padding:1px;
+}
+
+QMenuBar::item{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+padding:3px;
+padding-left:5px;
+padding-right:5px;
+}
+QMenu{
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+padding:0;
+}
+
+*::item:selected{
+color: rgba(255, 255, 255, 255);
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
+}
+
+QToolBar{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+border-style:solid;
+border-color:rgba(80, 80, 90, 255);
+border-width:1px;
+}
+
+QToolBar *{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+}
+
+QMessageBox{
+background-color: rgba(60, 60, 70, 255);
+color: rgba(255, 255, 255, 255);
+}
+
+QTableWidget{
+background-color: rgba(80, 80, 90, 255);
+color:rgba(255, 255, 255, 255);
+border-color:rgba(255, 255, 255, 255);
+selection-background-color: rgba(200, 210, 230, 255);
+}
+
+QTableCornerButton::section{
+border: none;
+background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
+}
+
+QHeaderView::section{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
+border:none;
+border-top-style:solid;
+border-width:1px;
+border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
+color:rgba(255, 255, 255, 255);
+padding:5px;
+}
+
+QHeaderView::section:checked{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 120, 150, 255), stop:1 rgba(0, 150, 190, 255));
+border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 120, 150, 255), stop:1 rgba(0, 150, 190, 255));
+}
+
+QHeaderView{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
+
+border:none;
+border-top-style:solid;
+border-width:1px;
+border-top-color:rgba(70, 70, 80, 255);
+color:rgba(255, 255, 255, 255);
+}
+
+QListWidget{
+background-color:rgba(200, 200, 200, 255);
+color:rgba(255, 255, 255, 255);
+}
+
+QStatusBar{
+background-color:rgba(60, 60, 70, 255);
+color:rgba(255, 255, 255, 255);
+}
+
+QPushButton{
+background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color:rgba(255, 255, 255, 255);
+border-style: outset;
+border-width: 2px;
+border-color: rgba(50, 50, 60, 255);
+min-width: 6em;
+padding: 4px;
+padding-left:5px;
+padding-right:5px;
+border-radius: 2px;
+}
+
+QPushButton:pressed{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
+border-style: inset;
+}
+
+QPushButton:checked{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
+border-style: inset;
+}
+
+*:disabled{
+color: rgba(130, 130, 130, 255);
+}
+
+QTabBar{
+background-color:transparent;
+}
+
+QTabBar::tab{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+border-style:solid;
+border-color:rgba(70, 70, 80, 255);
+border-bottom-color: transparent;
+border-width:1px;
+padding:5px;
+}
+
+QTabBar::tab:selected{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
+color: rgba(255, 255, 255, 255);
+border-style:solid;
+border-color:rgba(70, 70, 80, 255);
+border-bottom-color: transparent;
+border-width:1px;
+padding:5px;
+}
+
+QTabBar::tab:disabled{
+background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(100, 100, 120, 255);
+}
+
+QTabWidget{
+background-color:transparent;
+}
+
+QTabWidget::pane{
+background-color:rgba(70, 70, 80, 255);
+border-style:solid;
+border-color:rgba(70, 70, 80, 255);
+border-width:1px;
+}
+
+QTabWidget::tab{
+background-color:rgba(70, 70, 80, 255);
+}
+
+QTabWidget > QWidget{
+background-color: rgba(70, 70, 80, 255);
+color: rgba(255, 255, 255, 255);
+}
+
+QScrollArea{
+background: transparent;
+}
+
+QScrollArea>QWidget>QWidget{
+background: transparent;
+}
+
+QLabel{
+color: rgba(255, 255, 255, 255);
+background-color: transparent;
+}
+
+QTextEdit{
+color: rgba(255, 255, 255, 255);
+background-color: rgba(90, 90, 100, 255);
+}
+
+QSpinBox{
+color: rgba(255, 255, 255, 255);
+background-color: rgba(90, 90, 100, 255);
+}
+
+QDoubleSpinBox{
+color: rgba(255, 255, 255, 255);
+background-color: rgba(90, 90, 100, 255);
+}
+
+QCheckBox{
+background-color:transparent;
+border:none;
+}
+
+QLineEdit{
+background-color: rgba(90, 90, 100, 255);
+border: 1px inset;
+border-radius:0;
+border-color: rgba(100, 100, 120, 255);
+}
+
+QLineEdit:disabled{
+background-color: rgba(90, 90, 100, 255);
+border: 1px inset;
+border-radius:0;
+border-color: rgba(200, 200, 200, 255);
+}
+
+QListWidget{
+background-color:rgba(60, 60, 70, 255)
+}
+
+QProgressBar{
+background-color:rgba(60, 60, 70, 255);
+}
+
+QProgressBar::chunk{
+background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0.5, y2:0, stop:0 transparent, stop:1 rgba(0, 150, 190, 255));
+}
+
+QStatusBar{
+background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
+color: rgba(255, 255, 255, 255);
+}

pylot/styles/style_settings.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+
+# Set base phase colors for manual and automatic picks
+# together with a modifier (r, g, or b) used to alternate
+# the base color
+phasecolors = {
+    'manual': {
+        'P':{
+            'rgba': (0, 0, 255, 255),
+            'modifier': 'g'},
+        'S':{
+            'rgba': (255, 0, 0, 255),
+            'modifier': 'b'}
+    },
+    'auto':{
+        'P':{
+            'rgba': (140, 0, 255, 255),
+            'modifier': 'g'},
+        'S':{
+            'rgba': (255, 140, 0, 255),
+            'modifier': 'b'}
+    }
+}
+
+# Set plot colors and stylesheet for each style
+stylecolors = {
+    'default':{
+        'linecolor':{
+            'rgba': (0, 0, 0, 255)},
+        'background': {
+            'rgba': (255, 255, 255, 255)},
+        'multicursor': {
+            'rgba': (255, 190, 0, 255)},
+        'ref': {
+            'rgba': (200, 210, 230, 255)},
+        'test': {
+            'rgba': (200, 230, 200, 255)},
+        'stylesheet': {
+            'filename': None}
+    },
+    'dark': {
+        'linecolor': {
+            'rgba': (230, 230, 230, 255)},
+        'background': {
+            'rgba': (50, 50, 60, 255)},
+        'multicursor': {
+            'rgba': (0, 150, 190, 255)},
+        'ref': {
+            'rgba': (80, 110, 170, 255)},
+        'test': {
+            'rgba': (130, 190, 100, 255)},
+        'stylesheet': {
+            'filename': 'dark.qss'}
+    },
+    'bright': {
+        'linecolor': {
+            'rgba': (0, 0, 0, 255)},
+        'background': {
+            'rgba': (255, 255, 255, 255)},
+        'multicursor': {
+            'rgba': (100, 100, 190, 255)},
+        'ref': {
+            'rgba': (200, 210, 230, 255)},
+        'test': {
+            'rgba': (200, 230, 200, 255)},
+        'stylesheet': {
+            'filename': 'bright.qss'}
+    }
+}
+

pylot/testing/testHelpForm.py

@@ -1,13 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import sys
-
-from PySide.QtGui import QApplication
-from pylot.core.util.widgets import HelpForm
-
-app = QApplication(sys.argv)
-
-win = HelpForm()
-win.show()
-app.exec_()

pylot/testing/testPickDlg.py

@@ -1,20 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import sys
-
-import matplotlib
-
-matplotlib.use('Qt4Agg')
-matplotlib.rcParams['backend.qt4'] = 'PySide'
-
-from PySide.QtGui import QApplication
-from obspy.core import read
-from pylot.core.util.widgets import PickDlg
-
-app = QApplication(sys.argv)
-
-data = read()
-win = PickDlg(data=data)
-win.show()
-app.exec_()

pylot/testing/testPropDlg.py

@@ -1,13 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import sys
-
-from PySide.QtGui import QApplication
-from pylot.core.util.widgets import PropertiesDlg
-
-app = QApplication(sys.argv)
-
-win = PropertiesDlg()
-win.show()
-app.exec_()

pylot/testing/testUIcomponents.py

@@ -1,19 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-
-import sys
-import time
-
-from PySide.QtGui import QApplication
-from pylot.core.util.widgets import FilterOptionsDialog, PropertiesDlg, HelpForm
-
-dialogs = [FilterOptionsDialog, PropertiesDlg, HelpForm]
-
-app = QApplication(sys.argv)
-
-for dlg in dialogs:
-    win = dlg()
-    win.show()
-    time.sleep(1)
-    win.destroy()

pylot/testing/testUtils.py

@@ -1,23 +0,0 @@
-# -*- coding: utf-8 -*-
-'''
-Created on 10.11.2014
-
-@author: sebastianw
-'''
-import unittest
-
-
-class Test(unittest.TestCase):
-    def setUp(self):
-        pass
-
-    def tearDown(self):
-        pass
-
-    def testName(self):
-        pass
-
-
-if __name__ == "__main__":
-    # import sys;sys.argv = ['', 'Test.testName']
-    unittest.main()

pylot/testing/testWidgets.py

@@ -1,17 +0,0 @@
-# -*- coding: utf-8 -*-
-'''
-Created on 10.11.2014
-
-@author: sebastianw
-'''
-import unittest
-
-
-class Test(unittest.TestCase):
-    def testName(self):
-        pass
-
-
-if __name__ == "__main__":
-    # import sys;sys.argv = ['', 'Test.testName']
-    unittest.main()

pylot/testing/test_autopick.py

@@ -1,311 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-"""
-   Script to run autoPyLoT-script "makeCF.py".
-   Only for test purposes!
-"""
-
-import argparse
-import glob
-
-from obspy.core import read
-from pylot.core.pick.charfuns import *
-from pylot.core.pick.picker import *
-
-
-def run_makeCF(project, database, event, iplot, station=None):
-    # parameters for CF calculation
-    t2 = 7  # length of moving window for HOS calculation [sec]
-    p = 4  # order of HOS
-    cuttimes = [10, 50]  # start and end time for CF calculation
-    bpz = [2, 30]  # corner frequencies of bandpass filter, vertical component
-    bph = [2, 15]  # corner frequencies of bandpass filter, horizontal components
-    tdetz = 1.2  # length of AR-determination window [sec], vertical component
-    tdeth = 0.8  # length of AR-determination window [sec], horizontal components
-    tpredz = 0.4  # length of AR-prediction window [sec], vertical component
-    tpredh = 0.4  # length of AR-prediction window [sec], horizontal components
-    addnoise = 0.001  # add noise to seismogram for stable AR prediction
-    arzorder = 2  # chosen order of AR process, vertical component
-    arhorder = 4  # chosen order of AR process, horizontal components
-    TSNRhos = [5, 0.5, 1, 0.1]  # window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
-    # from HOS-CF [noise window, safety gap, signal window, slope determination window]
-    TSNRarz = [5, 0.5, 1, 0.5]  # window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
-    # from ARZ-CF
-    # get waveform data
-    if station:
-        dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
-        dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
-        dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
-        # dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
-        # dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
-        # dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
-    else:
-        # dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_z.gse' % (project, database, event)
-        # dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_e.gse' % (project, database, event)
-        # dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_n.gse' % (project, database, event)
-        dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
-        dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
-        dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
-    wfzfiles = glob.glob(dpz)
-    wfefiles = glob.glob(dpe)
-    wfnfiles = glob.glob(dpn)
-    if wfzfiles:
-        for i in range(len(wfzfiles)):
-            print
-            'Vertical component data found ...'
-            print
-            wfzfiles[i]
-            st = read('%s' % wfzfiles[i])
-            st_copy = st.copy()
-            # filter and taper data
-            tr_filt = st[0].copy()
-            tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
-            tr_filt.taper(max_percentage=0.05, type='hann')
-            st_copy[0].data = tr_filt.data
-            ##############################################################
-            # calculate HOS-CF using subclass HOScf of class CharacteristicFunction
-            hoscf = HOScf(st_copy, cuttimes, t2, p)  # instance of HOScf
-            ##############################################################
-            # calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_aic = tr_filt.copy()
-            tr_aic.data = hoscf.getCF()
-            st_copy[0].data = tr_aic.data
-            aiccf = AICcf(st_copy, cuttimes)  # instance of AICcf
-            ##############################################################
-            # get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
-            aicpick = AICPicker(aiccf, None, TSNRhos, 3, 10, None, 0.1)
-            ##############################################################
-            # get refined onset time from HOS-CF using class Picker
-            hospick = PragPicker(hoscf, None, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
-            # get earliest and latest possible picks
-            hosELpick = EarlLatePicker(hoscf, 1.5, TSNRhos, None, 10, None, None, hospick.getpick())
-            ##############################################################
-            # calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
-            # get stream object of filtered data
-            st_copy[0].data = tr_filt.data
-            arzcf = ARZcf(st_copy, cuttimes, tpredz, arzorder, tdetz, addnoise)  # instance of ARZcf
-            ##############################################################
-            # calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_arzaic = tr_filt.copy()
-            tr_arzaic.data = arzcf.getCF()
-            st_copy[0].data = tr_arzaic.data
-            araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz)  # instance of AICcf
-            ##############################################################
-            # get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
-            aicarzpick = AICPicker(araiccf, 1.5, TSNRarz, 2, 10, None, 0.1)
-            ##############################################################
-            # get refined onset time from ARZ-CF using class Picker
-            arzpick = PragPicker(arzcf, 1.5, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
-            # get earliest and latest possible picks
-            arzELpick = EarlLatePicker(arzcf, 1.5, TSNRarz, None, 10, None, None, arzpick.getpick())
-    elif not wfzfiles:
-        print
-        'No vertical component data found!'
-
-    if wfefiles and wfnfiles:
-        for i in range(len(wfefiles)):
-            print
-            'Horizontal component data found ...'
-            print
-            wfefiles[i]
-            print
-            wfnfiles[i]
-            # merge streams
-            H = read('%s' % wfefiles[i])
-            H += read('%s' % wfnfiles[i])
-            H_copy = H.copy()
-            # filter and taper data
-            trH1_filt = H[0].copy()
-            trH2_filt = H[1].copy()
-            trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            trH1_filt.taper(max_percentage=0.05, type='hann')
-            trH2_filt.taper(max_percentage=0.05, type='hann')
-            H_copy[0].data = trH1_filt.data
-            H_copy[1].data = trH2_filt.data
-
-            ##############################################################
-            # calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
-            arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise)  # instance of ARHcf
-            ##############################################################
-            # calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_arhaic = trH1_filt.copy()
-            tr_arhaic.data = arhcf.getCF()
-            H_copy[0].data = tr_arhaic.data
-            # calculate ARH-AIC-CF
-            arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth)  # instance of AICcf
-            ##############################################################
-            # get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
-            aicarhpick = AICPicker(arhaiccf, 1.5, TSNRarz, 4, 10, None, 0.1)
-            ###############################################################
-            # get refined onset time from ARH-CF using class Picker
-            arhpick = PragPicker(arhcf, 1.5, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
-            # get earliest and latest possible picks
-            arhELpick = EarlLatePicker(arhcf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
-
-            # create stream with 3 traces
-            # merge streams
-            AllC = read('%s' % wfefiles[i])
-            AllC += read('%s' % wfnfiles[i])
-            AllC += read('%s' % wfzfiles[i])
-            # filter and taper data
-            All1_filt = AllC[0].copy()
-            All2_filt = AllC[1].copy()
-            All3_filt = AllC[2].copy()
-            All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
-            All1_filt.taper(max_percentage=0.05, type='hann')
-            All2_filt.taper(max_percentage=0.05, type='hann')
-            All3_filt.taper(max_percentage=0.05, type='hann')
-            AllC[0].data = All1_filt.data
-            AllC[1].data = All2_filt.data
-            AllC[2].data = All3_filt.data
-            # calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
-            ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise)  # instance of AR3Ccf
-            # get earliest and latest possible pick from initial ARH-pick
-            ar3cELpick = EarlLatePicker(ar3ccf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
-            ##############################################################
-            if iplot:
-                # plot vertical trace
-                plt.figure()
-                tr = st[0]
-                tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
-                p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
-                p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
-                p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF() / max(aiccf.getCF()), 'b')
-                p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF() / max(arzcf.getCF()), 'g')
-                p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF() / max(araiccf.getCF()), 'y')
-                plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
-                plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
-                plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [1.3, 1.3], 'r')
-                plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [-1.3, -1.3], 'r')
-                plt.plot([hosELpick.getLpick(), hosELpick.getLpick()], [-1.1, 1.1], 'r--')
-                plt.plot([hosELpick.getEpick(), hosELpick.getEpick()], [-1.1, 1.1], 'r--')
-                plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
-                plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [1.2, 1.2], 'y')
-                plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [-1.2, -1.2], 'y')
-                plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
-                plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [1.4, 1.4], 'g')
-                plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [-1.4, -1.4], 'g')
-                plt.plot([arzELpick.getLpick(), arzELpick.getLpick()], [-1.2, 1.2], 'g--')
-                plt.plot([arzELpick.getEpick(), arzELpick.getEpick()], [-1.2, 1.2], 'g--')
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.xlabel('Time [s]')
-                plt.ylabel('Normalized Counts')
-                plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station, \
-                                                                     tr.stats.channel, aicpick.getSNR(),
-                                                                     aicpick.getSlope()))
-                plt.suptitle(tr.stats.starttime)
-                plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
-                # plot horizontal traces
-                plt.figure(2)
-                plt.subplot(2, 1, 1)
-                tsteph = tpredh / 4
-                th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
-                th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
-                tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth + tpredh
-                p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
-                p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
-                p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
-                plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
-                plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
-                plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
-                plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()], \
-                         [-0.2, 0.2], 'r--')
-                plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()], \
-                         [-0.2, 0.2], 'r--')
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
-                plt.suptitle(trH1_filt.stats.starttime)
-                plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
-                plt.subplot(2, 1, 2)
-                plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
-                plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
-                plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
-                plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
-                plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
-                plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
-                plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()], \
-                         [-0.2, 0.2], 'r--')
-                plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()], \
-                         [-0.2, 0.2], 'r--')
-                plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.xlabel('Time [s]')
-                plt.ylabel('Normalized Counts')
-                # plot 3-component window
-                plt.figure(3)
-                plt.subplot(3, 1, 1)
-                p31, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
-                p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
-                plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
-                plt.yticks([])
-                plt.xticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([tr.stats.station, tr.stats.channel])
-                plt.suptitle(trH1_filt.stats.starttime)
-                plt.legend([p31, p32], ['Data', 'AR3C-CF'])
-                plt.subplot(3, 1, 2)
-                plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
-                plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
-                plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
-                plt.yticks([])
-                plt.xticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
-                plt.subplot(3, 1, 3)
-                plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
-                plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
-                plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
-                plt.yticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
-                plt.xlabel('Time [s]')
-                plt.show()
-                raw_input()
-                plt.close()
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
-    parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
-    parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
-    parser.add_argument('--iplot', help='anything, if set, figure occurs')
-    parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
-    args = parser.parse_args()
-
-    run_makeCF(args.project, args.database, args.event, args.iplot, args.station)

pylot/testing/test_pdf.py

@@ -1,8 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from pylot.core.util.pdf import ProbabilityDensityFunction
-
-pdf = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
-pdf2 = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
-diff = pdf - pdf2

scripts/pylot-noisewindow.py

@@ -1,16 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import argparse
-
-import numpy
-from pylot.core.pick.utils import getnoisewin
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
-    parser.add_argument('--t1', type=float, help='time from which relativ to it noise window is extracted')
-    parser.add_argument('--tnoise', type=float, help='length of time window [s] for noise part extraction')
-    parser.add_argument('--tgap', type=float, help='safety gap between signal (t1=onset) and noise')
-    args = parser.parse_args()
-    getnoisewin(args.t, args.t1, args.tnoise, args.tgap)

scripts/pylot-pick-earliest-latest.py

@@ -1,29 +0,0 @@
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-"""
-   Created Mar 2015
-   Transcription of the rezipe of Diehl et al. (2009) for consistent phase
-   picking. For a given inital (the most likely) pick, the corresponding earliest
-   and latest possible pick is calculated based on noise measurements in front of
-   the most likely pick and signal wavelength derived from zero crossings.
-
-   :author: Ludger Kueperkoch / MAGS2 EP3 working group
-"""
-
-import argparse
-
-import obspy
-from pylot.core.pick.utils import earllatepicker
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--X', type=~obspy.core.stream.Stream,
-                        help='time series (seismogram) read with obspy module read')
-    parser.add_argument('--nfac', type=int,
-                        help='(noise factor), nfac times noise level to calculate latest possible pick')
-    parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
-                        [s] (Tnoise, Tgap, Tsignal)')
-    parser.add_argument('--Pick1', type=float, help='Onset time of most likely pick')
-    parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
-    args = parser.parse_args()
-    earllatepicker(args.X, args.nfac, args.TSNR, args.Pick1, args.iplot)

scripts/pylot-pick-firstmotion.py

@@ -1,25 +0,0 @@
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-"""
-   Created Mar 2015
-   Function to derive first motion (polarity) for given phase onset based on zero crossings.
-
-   :author: MAGS2 EP3 working group / Ludger Kueperkoch
-"""
-
-import argparse
-
-import obspy
-from pylot.core.pick.utils import fmpicker
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--Xraw', type=obspy.core.stream.Stream,
-                        help='unfiltered time series (seismogram) read with obspy module read')
-    parser.add_argument('--Xfilt', type=obspy.core.stream.Stream,
-                        help='filtered time series (seismogram) read with obspy module read')
-    parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
-    parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
-    parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
-    args = parser.parse_args()
-    fmpicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)

scripts/pylot-reasses-pilot-db.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import argparse
-
-from pylot.core.io.phases import reassess_pilot_db
-from pylot.core.util.version import get_git_version as _getVersionString
-
-__version__ = _getVersionString()
-__author__ = 'S. Wehling-Benatelli'
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='reassess old PILOT event data base in terms of consistent '
-                    'automatic uncertainty estimation',
-        epilog='Script written by {author} belonging to PyLoT version'
-               ' {version}\n'.format(author=__author__,
-                                     version=__version__)
-    )
-
-    parser.add_argument(
-        'root', type=str, help='specifies the root directory'
-    )
-    parser.add_argument(
-        'db', type=str, help='specifies the database name'
-    )
-    parser.add_argument(
-        '--output', '-o', type=str, help='path to the output directory',
-        dest='output'
-    )
-    parser.add_argument(
-        '--parameterfile', '-p', type=str,
-        help='full path to the parameterfile', dest='parfile'
-    )
-    parser.add_argument(
-        '--verbosity', '-v', action='count', help='increase output verbosity',
-        default=0, dest='verbosity'
-    )
-
-    args = parser.parse_args()
-    reassess_pilot_db(args.root, args.db, args.output, args.parfile, args.verbosity)

scripts/pylot-reasses-pilot-event.py

@@ -1,38 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import argparse
-
-from pylot.core.io.phases import reassess_pilot_event
-from pylot.core.util.version import get_git_version as _getVersionString
-
-__version__ = _getVersionString()
-__author__ = 'S. Wehling-Benatelli'
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='reassess old PILOT event data in terms of consistent '
-                    'automatic uncertainty estimation',
-        epilog='Script written by {author} belonging to PyLoT version'
-               ' {version}\n'.format(author=__author__,
-                                     version=__version__)
-    )
-
-    parser.add_argument(
-        'root', type=str, help='specifies the root directory'
-    )
-    parser.add_argument(
-        'db', type=str, help='specifies the database name'
-    )
-    parser.add_argument(
-        'id', type=str, help='PILOT event identifier'
-    )
-    parser.add_argument(
-        '--output', '-o', type=str, help='path to the output directory', dest='output'
-    )
-    parser.add_argument(
-        '--parameterfile', '-p', type=str, help='full path to the parameterfile', dest='parfile'
-    )
-
-    args = parser.parse_args()
-    reassess_pilot_event(args.root, args.db, args.id, args.output, args.parfile)

scripts/pylot-signalwindow.py

@@ -1,15 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import argparse
-
-import numpy
-from pylot.core.pick.utils import getsignalwin
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
-    parser.add_argument('--t1', type=float, help='time from which relativ to it signal window is extracted')
-    parser.add_argument('--tsignal', type=float, help='length of time window [s] for signal part extraction')
-    args = parser.parse_args()
-    getsignalwin(args.t, args.t1, args.tsignal)

scripts/pylot-snr.py

@@ -1,32 +0,0 @@
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-"""
-   Created Mar/Apr 2015
-   Function to calculate SNR of certain part of seismogram relative
-   to given time. Returns SNR and SNR [dB].
-
-   :author: Ludger Kueperkoch /MAGS EP3 working group
-"""
-
-import argparse
-
-import obspy
-from pylot.core.pick.utils import getSNR
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', '-d', type=obspy.core.stream.Stream,
-                        help='time series (seismogram) read with obspy module '
-                             'read',
-                        dest='data')
-    parser.add_argument('--tsnr', '-s', type=tuple,
-                        help='length of time windows around pick used to '
-                             'determine SNR [s] (Tnoise, Tgap, Tsignal)',
-                        dest='tsnr')
-    parser.add_argument('--time', '-t', type=float,
-                        help='initial time from which noise and signal windows '
-                             'are calculated',
-                        dest='time')
-    args = parser.parse_args()
-    print
-    getSNR(args.data, args.tsnr, args.time)

scripts/run_makeCF.py

@@ -1,314 +0,0 @@
-#!/usr/bin/python
-# -*- coding: utf-8 -*-
-
-"""
-   Script to run autoPyLoT-script "run_makeCF.py".
-   Only for test purposes!
-"""
-
-import argparse
-import glob
-
-from obspy.core import read
-from pylot.core.pick.utils import *
-
-
-def run_makeCF(project, database, event, iplot, station=None):
-    # parameters for CF calculation
-    t2 = 7  # length of moving window for HOS calculation [sec]
-    p = 4  # order of HOS
-    cuttimes = [10, 50]  # start and end time for CF calculation
-    bpz = [2, 30]  # corner frequencies of bandpass filter, vertical component
-    bph = [2, 15]  # corner frequencies of bandpass filter, horizontal components
-    tdetz = 1.2  # length of AR-determination window [sec], vertical component
-    tdeth = 0.8  # length of AR-determination window [sec], horizontal components
-    tpredz = 0.4  # length of AR-prediction window [sec], vertical component
-    tpredh = 0.4  # length of AR-prediction window [sec], horizontal components
-    addnoise = 0.001  # add noise to seismogram for stable AR prediction
-    arzorder = 2  # chosen order of AR process, vertical component
-    arhorder = 4  # chosen order of AR process, horizontal components
-    TSNRhos = [5, 0.5, 1, .6]  # window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
-    # from HOS-CF [noise window, safety gap, signal window, slope determination window]
-    TSNRarz = [5, 0.5, 1, 1.0]  # window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
-    # from ARZ-CF
-    # get waveform data
-    if station:
-        dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
-        dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
-        dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
-        # dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
-        # dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
-        # dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
-    else:
-        dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
-        dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
-        dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
-    wfzfiles = glob.glob(dpz)
-    wfefiles = glob.glob(dpe)
-    wfnfiles = glob.glob(dpn)
-    if wfzfiles:
-        for i in range(len(wfzfiles)):
-            print
-            'Vertical component data found ...'
-            print
-            wfzfiles[i]
-            st = read('%s' % wfzfiles[i])
-            st_copy = st.copy()
-            # filter and taper data
-            tr_filt = st[0].copy()
-            tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
-            tr_filt.taper(max_percentage=0.05, type='hann')
-            st_copy[0].data = tr_filt.data
-            ##############################################################
-            # calculate HOS-CF using subclass HOScf of class CharacteristicFunction
-            hoscf = HOScf(st_copy, cuttimes, t2, p)  # instance of HOScf
-            ##############################################################
-            # calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_aic = tr_filt.copy()
-            tr_aic.data = hoscf.getCF()
-            st_copy[0].data = tr_aic.data
-            aiccf = AICcf(st_copy, cuttimes)  # instance of AICcf
-            ##############################################################
-            # get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
-            aicpick = AICPicker(aiccf, TSNRhos, 3, 10, None, 0.1)
-            ##############################################################
-            # get refined onset time from HOS-CF using class Picker
-            hospick = PragPicker(hoscf, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
-            #############################################################
-            # get earliest and latest possible picks
-            st_copy[0].data = tr_filt.data
-            [lpickhos, epickhos, pickerrhos] = earllatepicker(st_copy, 1.5, TSNRhos, hospick.getpick(), 10)
-            #############################################################
-            # get SNR
-            [SNR, SNRdB] = getSNR(st_copy, TSNRhos, hospick.getpick())
-            print
-            'SNR:', SNR, 'SNR[dB]:', SNRdB
-            ##########################################################
-            # get first motion of onset
-            hosfm = fmpicker(st, st_copy, 0.2, hospick.getpick(), 11)
-            ##############################################################
-            # calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
-            arzcf = ARZcf(st, cuttimes, tpredz, arzorder, tdetz, addnoise)  # instance of ARZcf
-            ##############################################################
-            # calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_arzaic = tr_filt.copy()
-            tr_arzaic.data = arzcf.getCF()
-            st_copy[0].data = tr_arzaic.data
-            araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz)  # instance of AICcf
-            ##############################################################
-            # get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
-            aicarzpick = AICPicker(araiccf, TSNRarz, 2, 10, None, 0.1)
-            ##############################################################
-            # get refined onset time from ARZ-CF using class Picker
-            arzpick = PragPicker(arzcf, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
-            # get earliest and latest possible picks
-            st_copy[0].data = tr_filt.data
-            [lpickarz, epickarz, pickerrarz] = earllatepicker(st_copy, 1.5, TSNRarz, arzpick.getpick(), 10)
-    elif not wfzfiles:
-        print
-        'No vertical component data found!'
-
-    if wfefiles and wfnfiles:
-        for i in range(len(wfefiles)):
-            print
-            'Horizontal component data found ...'
-            print
-            wfefiles[i]
-            print
-            wfnfiles[i]
-            # merge streams
-            H = read('%s' % wfefiles[i])
-            H += read('%s' % wfnfiles[i])
-            H_copy = H.copy()
-            # filter and taper data
-            trH1_filt = H[0].copy()
-            trH2_filt = H[1].copy()
-            trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            trH1_filt.taper(max_percentage=0.05, type='hann')
-            trH2_filt.taper(max_percentage=0.05, type='hann')
-            H_copy[0].data = trH1_filt.data
-            H_copy[1].data = trH2_filt.data
-
-            ##############################################################
-            # calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
-            arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise)  # instance of ARHcf
-            ##############################################################
-            # calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
-            # class needs stream object => build it
-            tr_arhaic = trH1_filt.copy()
-            tr_arhaic.data = arhcf.getCF()
-            H_copy[0].data = tr_arhaic.data
-            # calculate ARH-AIC-CF
-            arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth)  # instance of AICcf
-            ##############################################################
-            # get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
-            aicarhpick = AICPicker(arhaiccf, TSNRarz, 4, 10, None, 0.1)
-            ###############################################################
-            # get refined onset time from ARH-CF using class Picker
-            arhpick = PragPicker(arhcf, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
-            # get earliest and latest possible picks
-            H_copy[0].data = trH1_filt.data
-            [lpickarh1, epickarh1, pickerrarh1] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
-            H_copy[0].data = trH2_filt.data
-            [lpickarh2, epickarh2, pickerrarh2] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
-            # get earliest pick of both earliest possible picks
-            epick = [epickarh1, epickarh2]
-            lpick = [lpickarh1, lpickarh2]
-            pickerr = [pickerrarh1, pickerrarh2]
-            ipick = np.argmin([epickarh1, epickarh2])
-            epickarh = epick[ipick]
-            lpickarh = lpick[ipick]
-            pickerrarh = pickerr[ipick]
-
-            # create stream with 3 traces
-            # merge streams
-            AllC = read('%s' % wfefiles[i])
-            AllC += read('%s' % wfnfiles[i])
-            AllC += read('%s' % wfzfiles[i])
-            # filter and taper data
-            All1_filt = AllC[0].copy()
-            All2_filt = AllC[1].copy()
-            All3_filt = AllC[2].copy()
-            All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
-            All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
-            All1_filt.taper(max_percentage=0.05, type='hann')
-            All2_filt.taper(max_percentage=0.05, type='hann')
-            All3_filt.taper(max_percentage=0.05, type='hann')
-            AllC[0].data = All1_filt.data
-            AllC[1].data = All2_filt.data
-            AllC[2].data = All3_filt.data
-            # calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
-            ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise)  # instance of AR3Ccf
-            ##############################################################
-            if iplot:
-                # plot vertical trace
-                plt.figure()
-                tr = st[0]
-                tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
-                p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
-                p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
-                p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF() / max(aiccf.getCF()), 'b')
-                p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF() / max(arzcf.getCF()), 'g')
-                p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF() / max(araiccf.getCF()), 'y')
-                plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
-                plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
-                plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [1.3, 1.3], 'r')
-                plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [-1.3, -1.3], 'r')
-                plt.plot([lpickhos, lpickhos], [-1.1, 1.1], 'r--')
-                plt.plot([epickhos, epickhos], [-1.1, 1.1], 'r--')
-                plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
-                plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [1.2, 1.2], 'y')
-                plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [-1.2, -1.2], 'y')
-                plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
-                plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [1.4, 1.4], 'g')
-                plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [-1.4, -1.4], 'g')
-                plt.plot([lpickarz, lpickarz], [-1.2, 1.2], 'g--')
-                plt.plot([epickarz, epickarz], [-1.2, 1.2], 'g--')
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.xlabel('Time [s]')
-                plt.ylabel('Normalized Counts')
-                plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station,
-                                                                     tr.stats.channel, aicpick.getSNR(),
-                                                                     aicpick.getSlope()))
-                plt.suptitle(tr.stats.starttime)
-                plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
-                # plot horizontal traces
-                plt.figure(2)
-                plt.subplot(2, 1, 1)
-                tsteph = tpredh / 4
-                th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
-                th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
-                tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth + tpredh
-                p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
-                p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
-                p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
-                plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
-                plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
-                plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
-                plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
-                plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
-                plt.suptitle(trH1_filt.stats.starttime)
-                plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
-                plt.subplot(2, 1, 2)
-                plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
-                plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
-                plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
-                plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
-                plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
-                plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
-                plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
-                plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
-                plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
-                plt.yticks([])
-                plt.ylim([-1.5, 1.5])
-                plt.xlabel('Time [s]')
-                plt.ylabel('Normalized Counts')
-                # plot 3-component window
-                plt.figure(3)
-                plt.subplot(3, 1, 1)
-                p31, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
-                p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.yticks([])
-                plt.xticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([tr.stats.station, tr.stats.channel])
-                plt.suptitle(trH1_filt.stats.starttime)
-                plt.legend([p31, p32], ['Data', 'AR3C-CF'])
-                plt.subplot(3, 1, 2)
-                plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
-                plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.yticks([])
-                plt.xticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
-                plt.subplot(3, 1, 3)
-                plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
-                plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
-                plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
-                plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
-                plt.yticks([])
-                plt.ylabel('Normalized Counts')
-                plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
-                plt.xlabel('Time [s]')
-                plt.show()
-                raw_input()
-                plt.close()
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
-parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
-parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
-parser.add_argument('--iplot', help='anything, if set, figure occurs')
-parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
-args = parser.parse_args()
-
-run_makeCF(args.project, args.database, args.event, args.iplot, args.station)

setup.py

@@ -4,11 +4,11 @@ from distutils.core import setup
 
 setup(
     name='PyLoT',
-    version='0.1a1',
+    version='0.2',
     packages=['pylot', 'pylot.core', 'pylot.core.loc', 'pylot.core.pick',
               'pylot.core.io', 'pylot.core.util', 'pylot.core.active',
               'pylot.core.analysis', 'pylot.testing'],
-    requires=['obspy', 'PySide'],
+    requires=['obspy', 'PySide', 'matplotlib', 'numpy'],
     url='dummy',
     license='LGPLv3',
     author='Sebastian Wehling-Benatelli',