Merge remote-tracking branch 'origin/develop' into darius

this is probably just a workaround. Why was the program trying to access picks that were removed from the pick-dictionary?

.gitignore

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

QtPyLoT.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, QLineEdit, QListView, QAbstractItemView, \
+    QActionGroup, QListWidget, QDockWidget, 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('PyLoT: Could not import pyqtgraph. {}'.format(e))
+    print('QtPyLoT: 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
+from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty, removePicksAbove
 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 DatastructureError, \
+from pylot.core.util.errors import FormatError, 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, transform_colors_mpl, transform_colors_mpl_str
+    check4rotated
 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, \
@@ -87,8 +87,6 @@ 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:
@@ -112,7 +110,6 @@ 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!")
@@ -127,7 +124,6 @@ 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
@@ -144,6 +140,12 @@ 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
@@ -172,8 +174,6 @@ 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:
@@ -210,8 +210,6 @@ class MainWindow(QMainWindow):
         except:
             self.startTime = UTCDateTime()
 
-        self.init_styles()
-
         pylot_icon = QIcon()
         pylot_icon.addPixmap(QPixmap(':/icons/pylot.png'))
 
@@ -553,13 +551,6 @@ 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()
@@ -576,24 +567,21 @@ class MainWindow(QMainWindow):
         self.eventBox.activated.connect(self.refreshEvents)
 
         # add main tab widget
-        self.tabs = QTabWidget(self)
+        self.tabs = QTabWidget()
         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)
+        self.wf_scroll_area = QtGui.QScrollArea()
 
         # create central matplotlib figure canvas widget
+        self.pg = pg
         self.init_wfWidget()
 
         # init main widgets for main tabs
-        wf_tab = QtGui.QWidget(self)
-        array_tab = QtGui.QWidget(self)
-        events_tab = QtGui.QWidget(self)
+        wf_tab = QtGui.QWidget()
+        array_tab = QtGui.QWidget()
+        events_tab = QtGui.QWidget()
 
         # init main widgets layouts
         self.wf_layout = QtGui.QVBoxLayout()
@@ -636,8 +624,8 @@ class MainWindow(QMainWindow):
             self.dataPlot = PylotCanvas(parent=self, connect_events=False, multicursor=True)
             self.dataPlot.updateWidget(xlab, None, plottitle)
         else:
-            self.pg = pg
-            self.dataPlot = WaveformWidgetPG(parent=self,
+            self.pg = True
+            self.dataPlot = WaveformWidgetPG(parent=self, xlabel=xlab, ylabel=None,
                                              title=plottitle)
         self.dataPlot.setCursor(Qt.CrossCursor)
         self.wf_scroll_area.setWidget(self.dataPlot)
@@ -648,7 +636,7 @@ class MainWindow(QMainWindow):
         '''
         Initiate/create buttons for assigning events containing manual picks to reference or test set.
         '''
-        self.ref_event_button = QtGui.QPushButton('Tune')
+        self.ref_event_button = QtGui.QPushButton('Ref')
         self.test_event_button = QtGui.QPushButton('Test')
         self.ref_event_button.setToolTip('Set manual picks of current ' +
                                          'event as reference picks for autopicker tuning.')
@@ -656,8 +644,8 @@ 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_border_color(self.ref_event_button, self._style['ref']['rgba'])
-        self.set_button_border_color(self.test_event_button, self._style['test']['rgba'])
+        self.set_button_color(self.ref_event_button, self._colors['ref'])
+        self.set_button_color(self.test_event_button, self._colors['test'])
         self.ref_event_button.clicked.connect(self.toggleRef)
         self.test_event_button.clicked.connect(self.toggleTest)
         self.ref_event_button.setEnabled(False)
@@ -675,86 +663,6 @@ 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
@@ -806,8 +714,12 @@ 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):
-        self.load_data(fname, loc=True)
+        type = getDataType(self)
+        self.load_data(fname, type=type, loc=True)
 
     def load_pilotevent(self):
         filt = "PILOT location files (*LOC*.mat)"
@@ -822,8 +734,10 @@ 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)
+        self.load_data(fname_dict, type=type)
 
     def load_multiple_data(self):
         if not self.okToContinue():
@@ -888,27 +802,20 @@ class MainWindow(QMainWindow):
     def add_recentfile(self, event):
         self.recentfiles.insert(0, event)
 
-    def set_button_border_color(self, button, color=None):
+    def set_button_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:
-            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'])
+        elif type(color) == str or not color:
+            button.setStyleSheet("background-color: {}".format(color))
 
     def getWFFnames(self):
         try:
@@ -964,7 +871,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event(self, eventbox=None):
         '''
-        Return event (type PyLoT.Event) currently selected in eventbox.
+        Return event (type QtPylot.Event) currently selected in eventbox.
         '''
         if not eventbox:
             eventbox = self.eventBox
@@ -973,7 +880,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event_path(self, eventbox=None):
         '''
-        Return event path of event (type PyLoT.Event) currently selected in eventbox.
+        Return event path of event (type QtPylot.Event) currently selected in eventbox.
         '''
         event = self.get_current_event(eventbox)
         if event:
@@ -981,7 +888,7 @@ class MainWindow(QMainWindow):
 
     def get_current_event_name(self, eventbox=None):
         '''
-        Return event path of event (type PyLoT.Event) currently selected in eventbox.
+        Return event path of event (type QtPylot.Event) currently selected in eventbox.
         '''
         path = self.get_current_event_path(eventbox)
         if path:
@@ -1122,7 +1029,7 @@ class MainWindow(QMainWindow):
         '''
 
         # if pick widget is open, refresh tooltips as well
-        if self.apw:
+        if hasattr(self, 'apw'):
             self.apw.refresh_tooltips()
         if hasattr(self, 'cmpw'):
             self.cmpw.refresh_tooltips()
@@ -1184,9 +1091,9 @@ class MainWindow(QMainWindow):
             item_ref = QtGui.QStandardItem()  # str(event_ref))
             item_test = QtGui.QStandardItem()  # str(event_test))
             if event_ref:
-                item_ref.setBackground(self._ref_test_colors['ref'])
+                item_ref.setBackground(self._colors['ref'])
             if event_test:
-                item_test.setBackground(self._ref_test_colors['test'])
+                item_test.setBackground(self._colors['test'])
             item_notes = QtGui.QStandardItem(event.notes)
 
             openIcon = self.style().standardIcon(QStyle.SP_DirOpenIcon)
@@ -1342,11 +1249,10 @@ class MainWindow(QMainWindow):
         if len(eventdict) < 1:
             return
 
-
         # init event selection options for autopick
-        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.compareoptions =[('tune events', self.get_ref_events),
+                              ('test events', self.get_test_events),
+                              ('all (picked) events', self.get_manu_picked_events)]
 
         self.cmpw = CompareEventsWidget(self, self.compareoptions, eventdict, comparisons)
         self.cmpw.start.connect(self.compareMulti)
@@ -1354,9 +1260,7 @@ class MainWindow(QMainWindow):
         self.cmpw.show()
 
     def compareMulti(self):
-        if not self.compareoptions:
-            return
-        for key, func, color in self.compareoptions:
+        for key, func in self.compareoptions:
             if self.cmpw.rb_dict[key].isChecked():
                 # if radio button is checked break for loop and use func
                 break
@@ -1370,10 +1274,7 @@ class MainWindow(QMainWindow):
     def buildMultiCompareWidget(self, eventlist):
         global_comparison = Comparison(eventlist=eventlist)
         compare_widget = ComparisonWidget(global_comparison, self)
-        for events_name, rb in self.cmpw.rb_dict.items():
-            if rb.isChecked():
-                break
-        compare_widget.setWindowTitle('Histograms for {}'.format(events_name))
+        compare_widget.setWindowTitle('Histograms for all selected events')
         compare_widget.hideToolbar()
         compare_widget.setHistboxChecked(True)
         return compare_widget
@@ -1531,8 +1432,7 @@ class MainWindow(QMainWindow):
         '''
         if load:
             self.wfd_thread = Thread(self, self.loadWaveformData,
-                                     progressText='Reading data input...',
-                                     pb_widget=self.mainProgressBarWidget)
+                                     progressText='Reading data input...')
         if load and plot:
             self.wfd_thread.finished.connect(self.plotWaveformDataThread)
 
@@ -1562,7 +1462,7 @@ class MainWindow(QMainWindow):
         check4gaps(wfdat)
         check4doubled(wfdat)
         # check for stations with rotated components
-        wfdat = check4rotated(wfdat, self.metadata, verbosity=0)
+        wfdat = check4rotated(wfdat, self.metadata)
         # 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]
@@ -1622,8 +1522,7 @@ class MainWindow(QMainWindow):
         self.getPlotWidget().updateWidget()
         plots = self.wfp_thread.data
         for times, data in plots:
-            self.dataPlot.plotWidget.getPlotItem().plot(times, data,
-                                                        pen=self.dataPlot.pen_linecolor)
+            self.dataPlot.plotWidget.getPlotItem().plot(times, data, pen='k')
         self.dataPlot.reinitMoveProxy()
         self.dataPlot.plotWidget.showAxis('left')
         self.dataPlot.plotWidget.showAxis('bottom')
@@ -1633,7 +1532,7 @@ class MainWindow(QMainWindow):
         if self.pg:
             self.finish_pg_plot()
         else:
-            self._max_xlims = self.dataPlot.getXLims(self.dataPlot.axes[0])
+            self._max_xlims = self.dataPlot.getXLims()
         plotWidget = self.getPlotWidget()
         plotDict = plotWidget.getPlotDict()
         pos = plotDict.keys()
@@ -1671,22 +1570,10 @@ class MainWindow(QMainWindow):
         if event.pylot_picks and event.pylot_autopicks:
             for station in event.pylot_picks:
                 if station in event.pylot_autopicks:
-                    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:
@@ -1707,7 +1594,7 @@ class MainWindow(QMainWindow):
             self.dataPlot.plotWidget.hideAxis('bottom')
             self.dataPlot.plotWidget.hideAxis('left')
         else:
-            self.dataPlot.axes[0].cla()
+            self.dataPlot.getAxes().cla()
         self.loadlocationaction.setEnabled(False)
         self.auto_tune.setEnabled(False)
         self.auto_pick.setEnabled(False)
@@ -1729,8 +1616,7 @@ class MainWindow(QMainWindow):
         '''
         self.clearWaveformDataPlot()
         self.wfp_thread = Thread(self, self.plotWaveformData,
-                                 progressText='Plotting waveform data...',
-                                 pb_widget=self.mainProgressBarWidget)
+                                 progressText='Plotting waveform data...')
         self.wfp_thread.finished.connect(self.finishWaveformDataPlot)
         self.wfp_thread.start()
 
@@ -1866,27 +1752,27 @@ 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
@@ -2020,35 +1906,26 @@ class MainWindow(QMainWindow):
             'el_S1pick',
             'el_S2pick',
             'refSpick',
-            'aicARHfig',
-            'plot_style'
+            'aicARHfig'
         ]
         for key in self.fig_keys:
-            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:
-            if not key == 'plot_style':
-                self.canvas_dict[key] = PylotCanvas(self.fig_dict[key], parent=self)
+            self.canvas_dict[key] = PylotCanvas(self.fig_dict[key])
 
     def init_fig_dict_wadatijack(self, eventIDs):
         self.fig_dict_wadatijack = {}
         self.fig_keys_wadatijack = [
             'jackknife',
-            'wadati',
-            'plot_style'
+            'wadati'
         ]
         for eventID in eventIDs:
             self.fig_dict_wadatijack[eventID] = {}
             for key in self.fig_keys_wadatijack:
-                if key == 'plot_style':
-                    fig = self._style
-                else:
                 fig = Figure()
                 self.fig_dict_wadatijack[eventID][key] = fig
 
@@ -2057,9 +1934,7 @@ class MainWindow(QMainWindow):
         for eventID in self.fig_dict_wadatijack.keys():
             self.canvas_dict_wadatijack[eventID] = {}
             for key in self.fig_keys_wadatijack:
-                if not key == 'plot_style':
-                    self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key],
-                                                                            parent=self)
+                self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key])
 
     def tune_autopicker(self):
         '''
@@ -2092,14 +1967,6 @@ 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()
@@ -2108,13 +1975,12 @@ class MainWindow(QMainWindow):
                                 "No autoPyLoT output declared!")
             return
 
-        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.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)]
 
         self.listWidget = QListWidget()
         self.setDirty(True)
@@ -2126,9 +1992,7 @@ class MainWindow(QMainWindow):
         self.apw.show()
 
     def start_autopick(self):
-        if not self.pickoptions:
-            return
-        for key, func, _ in self.pickoptions:
+        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
@@ -2188,6 +2052,7 @@ 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)
@@ -2314,7 +2179,7 @@ class MainWindow(QMainWindow):
         if self.pg:
             pw = self.getPlotWidget().plotWidget
         else:
-            ax = self.getPlotWidget().axes[0]
+            ax = self.getPlotWidget().axes
         ylims = np.array([-.5, +.5]) + plotID
 
         stat_picks = self.getPicks(type=picktype)[station]
@@ -2355,7 +2220,6 @@ 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,
@@ -2372,6 +2236,7 @@ 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))
@@ -2544,8 +2409,7 @@ 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...',
-                          pb_widget=self.mainProgressBarWidget)
+        self.amt = Thread(self, self.array_map.init_map, arg=None, progressText='Generating map...')
         self.amt.finished.connect(self.finish_array_map)
         self.amt.start()
 
@@ -2572,7 +2436,7 @@ 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().getAutopicks())
+            self.array_map.refresh_drawings(self.get_current_event().getPicks())
         self._eventChanged[1] = False
 
     def init_event_table(self, tabindex=2):
@@ -2636,7 +2500,7 @@ class MainWindow(QMainWindow):
             self.events_layout.removeWidget(self.event_table)
 
         # init new qtable
-        self.event_table = QtGui.QTableWidget(self)
+        self.event_table = QtGui.QTableWidget()
         self.event_table.setColumnCount(12)
         self.event_table.setRowCount(len(eventlist))
         self.event_table.setHorizontalHeaderLabels(['',
@@ -2690,8 +2554,8 @@ class MainWindow(QMainWindow):
             item_notes = QtGui.QTableWidgetItem()
 
             # manipulate items
-            item_ref.setBackground(self._ref_test_colors['ref'])
-            item_test.setBackground(self._ref_test_colors['test'])
+            item_ref.setBackground(self._colors['ref'])
+            item_test.setBackground(self._colors['test'])
             item_path.setText(event.path)
             if hasattr(event, 'origins'):
                 if event.origins:
@@ -2747,8 +2611,7 @@ 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...',
-                                pb_widget=self.mainProgressBarWidget)
+        self.rm_thread = Thread(self, read_metadata, arg=fninv, progressText='Reading metadata...')
         self.rm_thread.finished.connect(self.set_metadata)
         self.rm_thread.start()
 
@@ -2766,7 +2629,7 @@ class MainWindow(QMainWindow):
     def get_new_metadata(self):
         self.init_metadata(new=True)
 
-    def init_metadata(self, new=False, ask_default=True):
+    def init_metadata(self, new=False):
         def set_inv(settings):
             fninv, _ = QFileDialog.getOpenFileName(self, self.tr(
                 "Select inventory..."), self.tr("Select file"))
@@ -2795,7 +2658,7 @@ class MainWindow(QMainWindow):
             settings.setValue("inventoryFile", self.project.inv_path)
 
         fninv = settings.value("inventoryFile", None)
-        if fninv and ask_default:
+        if fninv:
             ans = QMessageBox.question(self, self.tr("Use default metadata..."),
                                        self.tr(
                                            "Do you want to use the default value for metadata?\n({})".format(fninv)),
@@ -2806,8 +2669,6 @@ 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()
@@ -2820,7 +2681,7 @@ 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) ## MP MP missing parameter wascaling in function call!
+                                       verbosity=True)
             return local_mag.updated_event()
         elif type == 'Mw':
             moment_mag = MomentMagnitude(corr_wf, self.get_data().get_evt_data(), self.inputs.get('vp'),
@@ -2916,7 +2777,7 @@ class MainWindow(QMainWindow):
         if not self.okToContinue():
             return
         if not fnm:
-            dlg = QFileDialog(parent=self)
+            dlg = QFileDialog()
             fnm = dlg.getOpenFileName(self, 'Open project file...', filter='Pylot project (*.plp)')
             if not fnm:
                 return
@@ -2931,12 +2792,10 @@ class MainWindow(QMainWindow):
             self.setDirty(False)
             if hasattr(self.project, 'metadata'):
                 if self.project.metadata:
-                    self.init_metadata(ask_default=False)
-                    #self.init_array_map(index=0)
+                    self.init_array_map(index=0)
                     return
             if hasattr(self.project, 'inv_path'):
-                self.init_metadata(ask_default=False)
-                #self.init_array_map(index=0)
+                self.init_array_map(index=0)
                 return
 
             self.init_array_tab()
@@ -2945,7 +2804,7 @@ class MainWindow(QMainWindow):
         '''
         Save back project to new pickle file.
         '''
-        dlg = QFileDialog(self)
+        dlg = QFileDialog()
         fnm = dlg.getSaveFileName(self, 'Create a new project file...', filter='Pylot project (*.plp)')
         filename = fnm[0]
         if not len(fnm[0]):
@@ -3007,7 +2866,7 @@ class MainWindow(QMainWindow):
 
     def setParameter(self, show=True):
         if not self.paraBox:
-            self.paraBox = PylotParaBox(self._inputs, parent=self, windowflag=1)
+            self.paraBox = PylotParaBox(self._inputs)
             self.paraBox.accepted.connect(self._setDirty)
             self.paraBox.accepted.connect(self.filterOptionsFromParameter)
         if show:
@@ -3025,16 +2884,16 @@ class MainWindow(QMainWindow):
 
     def helpHelp(self):
         if checkurl():
-            form = HelpForm(self,
+            form = HelpForm(
                 'https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/wiki')
         else:
-            form = HelpForm(self, ':/help.html')
+            form = HelpForm(':/help.html')
         form.show()
 
 
 class Project(object):
     '''
-    Pickable class containing information of a PyLoT project, like event lists and file locations.
+    Pickable class containing information of a QtPyLoT project, like event lists and file locations.
     '''
 
     def __init__(self):
@@ -3099,7 +2958,7 @@ class Project(object):
                 print(e, datetime, filename)
                 continue
             for event in self.eventlist:
-                if eventID in str(event.resource_id) or event.origins:
+                if eventID in str(event.resource_id) or eventID in event.origins:
                     if event.origins:
                         origin = event.origins[0]  # should have only one origin
                         if origin.time == datetime:
@@ -3219,10 +3078,9 @@ def create_window():
     if app is None:
         app = QApplication(sys.argv)
         app_created = True
-    # 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.setOrganizationName("QtPyLoT");
+    app.setOrganizationDomain("rub.de");
+    app.setApplicationName("RUB");
     app.references = set()
     # app.references.add(window)
     # window.show()
@@ -3231,7 +3089,6 @@ 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:
@@ -3251,15 +3108,20 @@ def main(args=None):
 
     # create the main window
     pylot_form = MainWindow(infile=pylot_infile)
-    pylot_form.setWindowIcon(app_icon)
+    icon = QIcon()
+    pylot_form.setWindowIcon(icon)
     pylot_form.setIconSize(QSize(60, 60))
 
-    # set other App information
-    pylot_app.setApplicationVersion(pylot_form.__version__)
-    pylot_app.setWindowIcon(app_icon)
+    splash.showMessage('Loading. Please wait ...')
     pylot_app.processEvents()
 
-    splash.showMessage('Loading. Please wait ...')
+    # set Application Information
+    pylot_app.setOrganizationName("Ruhr-University Bochum / BESTEC")
+    pylot_app.setOrganizationDomain("rub.de")
+    pylot_app.processEvents()
+    pylot_app.setApplicationName("PyLoT")
+    pylot_app.setApplicationVersion(pylot_form.__version__)
+    pylot_app.setWindowIcon(app_icon)
     pylot_app.processEvents()
 
     # Show main window and run the app

README.md

@@ -1,6 +1,6 @@
 # PyLoT
 
-version: 0.2
+version: 0.1a
 
 The Python picking and Localisation Tool
 
@@ -14,7 +14,7 @@ to redevelop the software package in Python. The great work of the ObsPy
 group allows easy handling of a bunch of seismic data and PyLoT will
 benefit a lot compared to the former MatLab version.
 
-The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
+The development of PyLoT is part of the joint research project MAGS2.
 
 ## Installation
 
@@ -25,7 +25,7 @@ Best way to install is to clone the repository and add the path to your Python p
 
 In order to run PyLoT you need to install:
 
-- python 2 or 3
+- python
 - scipy
 - numpy
 - matplotlib
@@ -34,24 +34,21 @@ In order to run PyLoT you need to install:
 
 #### 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*):
+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:
 
-*for local distance seismicity*
+    cp path-to-pylot/inputs/pylot.in ~/.pylot/
 
-    cp path-to-pylot/inputs/pylot_local.in ~/.pylot/pylot.in
+for local distance seismicity
 
-*for regional distance seismicity*
+    cp path-to-pylot/inputs/autoPyLoT_local.in ~/.pylot/autoPyLoT.in
 
-    cp path-to-pylot/inputs/pylot_regional.in ~/.pylot/pylot.in
+for regional distance seismicity
 
-*for global distance seismicity*
-
-    cp path-to-pylot/inputs/pylot_global.in ~/.pylot/pylot.in
+    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
 
@@ -59,52 +56,35 @@ and some extra information on error estimates (just needed for reading old PILOT
 
 You may need to do some modifications to these files. Especially folder names should be reviewed.
 
-PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
+PyLoT has been tested on Mac OSX (10.11) and Debian Linux 8.
 
 
 ## 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)
+- 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)
 
-##### Platform support:
-- Python 3 support
-- Windows support
+#### Known issues:
 
-##### Performance:
-- multiprocessing for automatic picking and restitution of multiple stations
-- use pyqtgraph library for better performance on main waveform plot
+- Magnitude estimation from manual PyLoT takes some time (instrument correction)
 
-##### 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
+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, M. Paffrath, K. Olbert,
-M. Bischoff, C. Wollin, M. Rische
+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
 
-September 2017
+October 2016

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=int(cla.iplot))
+                      ncores=cla.ncores, iplot=str(cla.iplot))

git_out

@@ -0,0 +1,8 @@
+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

@@ -2,8 +2,6 @@
     <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

@@ -0,0 +1,100 @@
+%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

@@ -0,0 +1,99 @@
+%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

@@ -0,0 +1,100 @@
+%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

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

inputs/pylot.in

@@ -4,19 +4,19 @@
 %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                                                       
+/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                                                       
 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                                                     
+/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                                                     
 ttime                                              #ttpatter#       %pattern of NLLoc ttimes from grid                                                                   
 AUTOLOC_nlloc                                      #outpatter#      %pattern of NLLoc-output file                                                                        
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -37,64 +37,62 @@ bandpass bandpass                                  #filter_type#    %filter type
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #common settings picker#
 global                                             #extent#         %extent of array ("local", "regional" or "global")                                                   
--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)       
+50.0                                               #pstart#         %start time [s] for calculating CF for P-picking                                                     
+600.0                                              #pstop#          %end time [s] for calculating CF for P-picking                                                       
 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                                 
-iasp91                                             #taup_model#     %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
+True                                               #use_taup#       %use estimated traveltimes from taupy for calculating windows for CF                                 
 0.05 0.5                                           #bpz1#           %lower/upper corner freq. of first band pass filter Z-comp. [Hz]                                     
-0.001 0.5                                          #bpz2#           %lower/upper corner freq. of second band pass filter Z-comp. [Hz]                                    
+0.01 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]                                     
-0.001 0.5                                          #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
+0.01 0.5                                           #bph2#           %lower/upper corner freq. of second band pass filter z-comp. [Hz]                                    
 #special settings for calculating CF#
 %!!Edit the following only if you know what you are doing!!%
 #Z-component#
 HOS                                                #algoP#          %choose algorithm for P-onset determination (HOS, ARZ, or AR3)                                       
-150.0                                              #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
+15.0                                               #tlta#           %for HOS-/AR-AIC-picker, length of LTA window [s]                                                    
 4                                                  #hosorder#       %for HOS-picker, order of Higher Order Statistics                                                    
 2                                                  #Parorder#       %for AR-picker, order of AR process of Z-component                                                   
-16.0                                               #tdet1z#         %for AR-picker, length of AR determination window [s] for Z-component, 1st pick                      
-10.0                                               #tpred1z#        %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick                         
-12.0                                               #tdet2z#         %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick                      
-6.0                                                #tpred2z#        %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick                         
+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                         
 0.001                                              #addnoise#       %add noise to seismogram for stable AR prediction                                                    
-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)                        
+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)                        
 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)                                             
-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                              
+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                              
 4                                                  #Sarorder#       %for AR-picker, order of AR process of H-components                                                  
-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)                                            
+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)                                            
 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                                            
-3.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
-10.0                                               #fmpickwin#      %pick window around P onset for calculating zero crossings                                           
+2.0                                                #minFMSNR#       %miniumum required SNR for first-motion determination                                                
+0.2                                                #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                                          
-1.3                                                #minAICSSNR#     %below this SNR the initial S pick is rejected                                                       
+1.5                                                #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                                               
-1.2                                                #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
+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                                                       

inputs/pylot_local.in

@@ -1,100 +0,0 @@
-%This is a parameter input file for PyLoT/autoPyLoT.
-%All main and special settings regarding data handling
-%and picking are to be set here!
-%Parameters are optimized for %extent data sets!
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#main settings#
-                                                   #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

@@ -1,100 +0,0 @@
-%This is a parameter input file for PyLoT/autoPyLoT.
-%All main and special settings regarding data handling
-%and picking are to be set here!
-%Parameters are optimized for %extent data sets!
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#main settings#
-                                                   #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

@@ -158,29 +158,23 @@ def buildPyLoT(verbosity=None):
 
 
 def installPyLoT(verbosity=None):
-    files_to_copy = {'pylot_local.in': ['~', '.pylot'],
-                     'pylot_regional.in': ['~', '.pylot'],
-                     'pylot_global.in': ['~', '.pylot']}
+    files_to_copy = {'autoPyLoT_local.in': ['~', '.pylot'],
+                     'autoPyLoT_regional.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, 2=global) :') or 0
+                '([0]=local, 1=regional) :') or 0
     if not isinstance(ans, int):
         ans = int(ans)
-    if ans == 0:
-        ans = 'local'
-    elif ans == 1:
-        ans = 'regional'
-    elif ans == 2:
-        ans = 'global'
+    ans = 'local' if ans is 0 else 'regional'
     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('pylot.in')
+            link_dest.append('autoPyLoT.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

@@ -0,0 +1,240 @@
+#!/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

@@ -240,7 +240,7 @@ 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
+                        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]
@@ -442,6 +442,7 @@ 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

@@ -273,6 +273,26 @@ 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,
@@ -455,7 +475,9 @@ settings_special_pick = {
         'aictsmooth',
         'tsmoothP',
         'ausP',
-        'nfacP'],
+        'nfacP',
+        'checkwindowP',
+        'minfactorP'],
     'h': [
         'algoS',
         'tdet1h',
@@ -469,7 +491,9 @@ settings_special_pick = {
         'aictsmoothS',
         'tsmoothS',
         'ausS',
-        'nfacS'],
+        'nfacS',
+        'checkwindowS',
+        'minfactorS'],
     'fm': [
         'minfmweight',
         'minFMSNR',

pylot/core/io/phases.py

@@ -865,7 +865,7 @@ 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)
+                    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
@@ -907,14 +907,14 @@ 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
+                        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
+                        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]):

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 pylot.in).
+    # parameter input file (usually autoPyLoT.in).
     wdttolerance = param.get('wdttolerance')
     mdttolerance = param.get('mdttolerance')
     jackfactor = param.get('jackfactor')
@@ -64,11 +64,8 @@ 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_str))
+          'stations on {} cores.'.format(len(input_tuples), ncores))
 
     pool = gen_Pool(ncores)
     result = pool.map(call_autopickstation, input_tuples)
@@ -113,7 +110,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
     :type wfstream: obspy.core.stream.Stream
 
     :param pickparam: container of picking parameters from input file,
-           usually pylot.in
+           usually autoPyLoT.in
     :type pickparam: PylotParameter
     :param verbose:
     :type verbose: bool
@@ -121,7 +118,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
     """
 
     # declaring pickparam variables (only for convenience)
-    # read your pylot.in for details!
+    # read your autoPyLoT.in for details!
     plt_flag = 0
 
     # special parameters for P picking
@@ -180,6 +177,10 @@ 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
@@ -228,10 +229,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
                                          data=str(zdat))
         if verbose: print(msg)
         z_copy = zdat.copy()
-        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 = zdat[0].copy()
         tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1],
                        zerophase=False)
         tr_filt.taper(max_percentage=0.05, type='hann')
@@ -327,11 +326,10 @@ 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
-            linecolor = 'k'
-        aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig, linecolor=linecolor)
+        aicpick = AICPicker(aiccf, tsnrz, pickwinP, checkwindow=checkwindowP, minfactor=minfactorP,
+                            iplot=iplot, Tsmooth=tsmoothP, fig=fig)
         # add pstart and pstop to aic plot
         if fig:
             for ax in fig.axes:
@@ -354,21 +352,16 @@ 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, linecolor)
+                                          fig)
             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)
@@ -381,14 +374,12 @@ 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, linecolor)
+                                          fig)
 
             if Pflag == 1:
                 # check for spuriously picked S onset
@@ -401,12 +392,10 @@ 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, linecolor)
+                                     tsnrz[2], iplot, fig)
                     if Pflag == 0:
                         Pmarker = 'SinsteadP'
                         Pweight = 9
@@ -429,7 +418,6 @@ 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')
@@ -459,12 +447,10 @@ 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
-                linecolor = 'k'
-            refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
-                                  aicpick.getpick(), fig, linecolor)
+            refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot=iplot, aus=ausP, Tsmooth=tsmoothP,
+                                  Pick1 = aicpick.getpick(), fig=fig)
             mpickP = refPpick.getpick()
             #############################################################
             if mpickP is not None:
@@ -473,13 +459,10 @@ 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,
-                                                            linecolor=linecolor)
+                                                            mpickP, iplot, fig=fig)
                 else:
                     epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
                                                             mpickP, iplot)
@@ -509,10 +492,9 @@ 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, linecolor)
+                        FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig)
                     else:
                         FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
                 else:
@@ -592,8 +574,6 @@ 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],
@@ -612,9 +592,6 @@ 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],
@@ -652,12 +629,11 @@ 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
-            linecolor = 'k'
-        aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
-                               aictsmoothS, fig=fig, linecolor=linecolor)
+        aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, checkwindow=checkwindowS,
+                               minfactor=minfactorS, iplot=iplot, Tsmooth=aictsmoothS,
+                               fig=fig)
         ###############################################################
         # go on with processing if AIC onset passes quality control
         slope = aicarhpick.getSlope()
@@ -682,8 +658,6 @@ 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],
@@ -699,9 +673,6 @@ 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],
@@ -721,12 +692,10 @@ 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
-                linecolor = 'k'
-            refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
-                                  tsmoothS, aicarhpick.getpick(), fig, linecolor)
+            refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot=iplot, aus=ausS,
+                                  Tsmooth=tsmoothS, Pick1=aicarhpick.getpick(), fig=fig)
             mpickS = refSpick.getpick()
             #############################################################
             if mpickS is not None:
@@ -736,15 +705,12 @@ 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,
-                                                               linecolor=linecolor)
+                                                               fig=fig)
                 else:
                     epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
@@ -754,15 +720,12 @@ 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,
-                                                               linecolor=linecolor)
+                                                               fig=fig)
                 else:
                     epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
                                                                tsnrh,
@@ -854,7 +817,7 @@ 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!')
 
     ##############################################################
@@ -871,11 +834,8 @@ 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)
@@ -883,7 +843,7 @@ 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), color=linecolor, linewidth=0.7, label='Data')
+        ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
         if Pweight < 4:
             ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
                      'b', label='CF1')
@@ -942,7 +902,7 @@ 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), color=linecolor, linewidth=0.7, label='Data')
+            ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k', label='Data')
             if Pweight < 4:
                 ax2.plot(arhcf1.getTimeArray(),
                          arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
@@ -991,7 +951,7 @@ 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), color=linecolor, linewidth=0.7, label='Data')
+            ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k', 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):
+    def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None, stealthMode=False):
         '''
         Initialize data type object with information from the original
         Seismogram.
@@ -63,6 +63,7 @@ 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
@@ -136,6 +137,9 @@ 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)
@@ -220,11 +224,13 @@ class AICcf(CharacteristicFunction):
 
     def calcCF(self, data):
 
+        # if self._getStealthMode() is False:
+        #    print 'Calculating AIC ...'
         x = self.getDataArray()
         xnp = x[0].data
-        ind = np.where(~np.isnan(xnp))[0]
-        if ind.size:
-            xnp[:ind[0]] = xnp[ind[0]]
+        nn = np.isnan(xnp)
+        if len(nn) > 1:
+            xnp[nn] = 0
         datlen = len(xnp)
         k = np.arange(1, datlen)
         cf = np.zeros(datlen)
@@ -258,9 +264,13 @@ 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)
 
@@ -335,7 +345,6 @@ class ARZcf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        if np.size(ino):
         cf[io] = cf[ino[0][0]]
 
         self.cf = cf
@@ -468,7 +477,6 @@ class ARHcf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        if np.size(ino):
         cf[io] = cf[ino[0][0]]
 
         self.cf = cf
@@ -611,7 +619,6 @@ class AR3Ccf(CharacteristicFunction):
         cf = tap * cf
         io = np.where(cf == 0)
         ino = np.where(cf > 0)
-        if np.size(ino):
         cf[io] = cf[ino[0][0]]
 
         self.cf = cf

pylot/core/pick/compare.py

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

pylot/core/pick/picker.py

@@ -23,9 +23,8 @@ 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
+from pylot.core.pick.utils import getnoisewin, getsignalwin, get_maximum_index
 
 
 class AutoPicker(object):
@@ -36,7 +35,7 @@ class AutoPicker(object):
 
     warnings.simplefilter('ignore')
 
-    def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
+    def __init__(self, cf, TSNR, PickWindow, checkwindow=None, minfactor=None, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
         '''
         :param: cf, characteristic function, on which the picking algorithm is applied
         :type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
@@ -63,8 +62,7 @@ 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()
@@ -76,6 +74,8 @@ class AutoPicker(object):
         self.setTsmooth(Tsmooth)
         self.setpick1(Pick1)
         self.fig = fig
+        self.setCheckWindow(checkwindow)
+        self.minfactor = minfactor
         self.calcPick()
 
     def __str__(self):
@@ -91,6 +91,10 @@ 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
@@ -190,7 +194,8 @@ class AICPicker(AutoPicker):
         aicsmooth = aicsmooth - offset
         # get maximum of HOS/AR-CF as startimg point for searching
         # minimum in AIC function
-        icfmax = np.argmax(self.Data[0].data)
+        icfmax = get_maximum_index(self.Data[0].data, self.checkwindow, self.minfactor,
+                                   int(self.TSNR[1]/self.Data[0].stats.delta))
 
         # find minimum in AIC-CF front of maximum of HOS/AR-CF
         lpickwindow = int(round(self.PickWindow / self.dt))
@@ -254,12 +259,8 @@ class AICPicker(AutoPicker):
             if len(dataslope) < 1:
                 print('No data in slope window found!')
                 return
-            imaxs, = argrelmax(dataslope)
-            if imaxs.size:
-                imax = imaxs[0]
-            else:
             imax = np.argmax(dataslope)
-            iislope = islope[0][0:imax + 1]
+            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!")
@@ -270,13 +271,13 @@ class AICPicker(AutoPicker):
                     print("Choose longer slope determination window!")
                     if self.iplot > 1:
                         if self.fig == None or self.fig == 'None':
-                            fig = plt.figure()
+                            fig = plt.figure()  # self.iplot) ### WHY? MP MP
                             plt_flag = 1
                         else:
                             fig = self.fig
                         ax = fig.add_subplot(111)
                         x = self.Data[0].data
-                        ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
+                        ax.plot(self.Tcf, x / max(x), 'k', 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)
@@ -309,12 +310,11 @@ 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), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
+            ax1.plot(self.Tcf, x / max(x), 'k', 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, color=self._linecolor, linewidth=0.7, label='Data')
+                ax2.plot(self.Tcf, x, 'k', 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')
@@ -479,12 +479,11 @@ class PragPicker(AutoPicker):
                     plt_flag = 1
                 else:
                     fig = self.fig
-                fig._tight = True
                 ax = fig.add_subplot(111)
-                ax.plot(Tcfpick, cfipick, color=self._linecolor, linewidth=0.7, label='CF')
+                ax.plot(Tcfpick, cfipick, 'k', label='CF')
                 ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
                 if pickflag > 0:
-                    ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], self._pickcolor_p, linewidth=2, label='Pick')
+                    ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', 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

@@ -13,9 +13,10 @@ 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, linecolor='k'):
+def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
     '''
     Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
     as reasonable uncertainties. Latest possible pick is based on noise level,
@@ -94,7 +95,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
 
     # get earliest possible pick
 
-    EPick = np.nan
+    EPick = np.nan;
     count = 0
     pis = isignal
 
@@ -130,18 +131,17 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
         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, color=linecolor, linewidth=0.7, label='Data')
+        ax.plot(t, x, 'k', 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], color=linecolor, linewidth=0.7, linestyle='dashed', label='Noise Level')
+        ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', 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], color=linecolor, linewidth=0.7, linestyle='dashed')
+        ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
         ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
-        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([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([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, linecol
     return EPick, LPick, PickError
 
 
-def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
+def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
     '''
     Function to derive first motion (polarity) of given phase onset Pick.
     Calculation is based on zero crossings determined within time window pickwin
@@ -324,9 +324,8 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
         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, color=linecolor, linewidth=0.7)
+        ax1.plot(t, xraw, 'k')
         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')
@@ -340,7 +339,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
 
         ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
         ax2.set_title('First-Motion Determination, Filtered Data')
-        ax2.plot(t, xfilt, color=linecolor, linewidth=0.7)
+        ax2.plot(t, xfilt, 'k')
         ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
                  linewidth=2)
         if P2 is not None:
@@ -591,10 +590,7 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
     Ppicks = []
     Spicks = []
     SPtimes = []
-    stations = []
-    ibad = 0
-
-    for key in list(pickdic.keys()):
+    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']
@@ -624,19 +620,17 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
         badstations = []
         # calculate deviations from Wadati regression
         ii = 0
-        for key in list(pickdic.keys()):
+        ibad = 0
+        for key in pickdic:
             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:
-                    # 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
+                    # mark onset and downgrade S-weight to 9
+                    # (not used anymore)
+                    marker = 'badWadatiCheck'
+                    pickdic[key]['S']['weight'] = 9
                     badstations.append(key)
                     ibad += 1
                 else:
@@ -649,7 +643,6 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
                     checkedSPtimes.append(checkedSPtime)
 
                 pickdic[key]['S']['marked'] = marker
-                #pickdic[key]['S']['marked'] = marker
         print("wadaticheck: the following stations failed the check:")
         print(badstations)
 
@@ -680,28 +673,19 @@ 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)
-        if ibad > 0:
         ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
         if wfitflag == 0:
-            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(Ppicks, wdfit, 'k', label='Wadati 1')
+            ax.plot(checkedPpicks, checkedSPtimes, 'ko', label='Reliable S-Picks')
             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, numpoints=1)
+            ax.legend(loc=1)
         else:
             ax.set_title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
 
@@ -720,7 +704,7 @@ 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, linecolor='k'):
+def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None):
     '''
     Function to detect spuriously picked noise peaks.
     Uses RMS trace of all 3 components (if available) to determine,
@@ -805,9 +789,8 @@ 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, color=linecolor, linewidth=0.7, label='RMS Data')
+        ax.plot(t, rms, 'k', 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]]],
@@ -882,9 +865,9 @@ 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'
@@ -893,21 +876,15 @@ 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)):
-        # remove pick from dictionary
-        pickdic.pop(badstations[i])
+        # 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)):
-        # 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
+        # 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
 
@@ -920,22 +897,19 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
             plt_flag = 1
         ax = fig.add_subplot(111)
 
-        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(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')
-        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.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]))
+
         ax.set_xlabel('Number of P Picks')
-        ax.set_ylabel('Onset Time [s] from 1.1.1970') # MP MP Improve this?
-        ax.legend(loc=1, numpoints=1)
+        ax.set_ylabel('Onset Time [s] from 1.1.1970')
+        ax.legend(loc=1)
         ax.set_title('Jackknifing and Median Tests on P Onsets')
         if plt_flag:
             fig.show()
@@ -967,8 +941,9 @@ def jackknife(X, phi, h):
     PHI_sub = None
 
     # determine number of subgroups
+    g = int(len(X) / h)
 
-    if len(X) % h:
+    if (len(X) / h) % 1 != 0:
         print("jackknife: Cannot divide quantity X in equal sized subgroups!")
         print("Choose another size for subgroups!")
         return PHI_jack, PHI_pseudo, PHI_sub
@@ -985,7 +960,7 @@ def jackknife(X, phi, h):
         # estimators of subgroups
         PHI_pseudo = []
         PHI_sub = []
-        for i in range(0, g - 1):
+        for i in range(0, g):
             # subgroup i, remove i-th sample
             xx = X[:]
             del xx[i]
@@ -1007,7 +982,7 @@ def jackknife(X, phi, h):
     return PHI_jack, PHI_pseudo, PHI_sub
 
 
-def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
+def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
     '''
     Function to compare energy content of vertical trace with
     energy content of horizontal traces to detect spuriously
@@ -1134,9 +1109,8 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
                     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=linecolor, linewidth=0.7)
+            ax.plot(t, trace.data, color='k')
             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')
@@ -1180,6 +1154,55 @@ 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

@@ -0,0 +1,998 @@
+#!/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.ruhr-uni-bochum.de/trac/PyLoT/'):
+def checkurl(url='https://ariadne.geophysik.rub.de/trac/PyLoT'):
     try:
         urlopen(url, timeout=1)
         return True

pylot/core/util/thread.py

@@ -5,6 +5,41 @@ 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)
 
@@ -96,6 +131,7 @@ 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 ()))
@@ -103,7 +139,6 @@ 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

@@ -14,7 +14,6 @@ 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
@@ -22,7 +21,7 @@ from PySide import QtCore, QtGui
 try:
     import pyqtgraph as pg
 except Exception as e:
-    print('PyLoT: Could not import pyqtgraph. {}'.format(e))
+    print('QtPyLoT: Could not import pyqtgraph. {}'.format(e))
     pg = None
 
 def _pickle_method(m):
@@ -72,8 +71,6 @@ 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
 
@@ -580,22 +577,36 @@ def modify_rgba(rgba, modifier, intensity):
 
 
 def base_phase_colors(picktype, phase):
-    phasecolors = style_settings.phasecolors
-    return phasecolors[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]
 
-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):
     """
@@ -691,7 +702,7 @@ def get_stations(data):
     return stations
 
 
-def check4rotated(data, metadata=None, verbosity=1):
+def check4rotated(data, metadata=None):
 
     def rotate_components(wfstream, metadata=None):
         """rotates components if orientation code is numeric.
@@ -700,13 +711,11 @@ def check4rotated(data, metadata=None, verbosity=1):
             # indexing fails if metadata is None
             metadata[0]
         except:
-            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,))
-            if verbosity:
             msg = 'Warning: could not rotate traces since no metadata was given\nCheck inventory directory!'
             print(msg)
             return wfstream

pylot/core/util/widgets.py

@@ -16,6 +16,11 @@ 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
@@ -48,18 +53,23 @@ 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, \
-    check4rotated, check4doubled, check4gaps, remove_underscores
+    demeanTrace, isSorted, findComboBoxIndex, clims, pick_linestyle_plt, pick_color_plt
 from autoPyLoT import autoPyLoT
 from pylot.core.util.thread import Thread
 
 if sys.version_info.major == 3:
-    import icons_rc_3 as icons_rc
+    pass
 elif sys.version_info.major == 2:
-    import icons_rc_2 as icons_rc
+    pass
 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:",
@@ -89,7 +99,6 @@ 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
 
@@ -263,11 +272,9 @@ 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']
@@ -340,7 +347,6 @@ 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),
@@ -362,26 +368,16 @@ 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)
-                _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()
+                axes_dict[phase]['exp'].set_xlabel('expectation [s]')
+                axes_dict[phase]['std'].set_xlabel('standard deviation [s]')
 
             for ax in axes_dict['P'].values():
-                ax.set_ylabel('Frequency [-]')
+                ax.set_ylabel('number of picks [-]')
 
             self.canvas.draw()
         else:
@@ -439,32 +435,29 @@ class PlotWidget(FigureCanvas):
 
 
 class WaveformWidgetPG(QtGui.QWidget):
-    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
+    def __init__(self, parent=None, xlabel='x', ylabel='y', title='Title'):
+        QtGui.QWidget.__init__(self, parent)  # , 1)
+        self.setParent(parent)
+        self._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 = self.pg.PlotWidget(self.parent(), title=title, autoDownsample=True)
+        self.plotWidget = pg.PlotWidget(title=title, autoDownsample=True)
         self.main_layout.addWidget(self.plotWidget)
         self.main_layout.addWidget(self.label)
-        self.plotWidget.showGrid(x=False, y=True, alpha=0.3)
+        self.plotWidget.showGrid(x=False, y=True, alpha=0.2)
         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 = self.pg.SignalProxy(self.plotWidget.scene().sigMouseMoved, rateLimit=60, slot=self.mouseMoved)
+        self._proxy = pg.SignalProxy(self.plotWidget.scene().sigMouseMoved, rateLimit=60, slot=self.mouseMoved)
 
     def reinitMoveProxy(self):
-        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.vLine = pg.InfiniteLine(angle=90, movable=False)
+        self.hLine = pg.InfiniteLine(angle=0, movable=False)
         self.plotWidget.addItem(self.vLine, ignoreBounds=True)
         self.plotWidget.addItem(self.hLine, ignoreBounds=True)
 
@@ -474,10 +467,10 @@ 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.orig_parent.getWFID(y)
-            station = self.orig_parent.getStationName(wfID)
+            wfID = self._parent.getWFID(y)
+            station = self._parent.getStationName(wfID)
             abstime = self.wfstart + x
-            if self.orig_parent.get_current_event():
+            if self._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())
@@ -491,6 +484,12 @@ 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):
@@ -611,6 +610,7 @@ 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
@@ -618,19 +618,17 @@ class PylotCanvas(FigureCanvas):
 
         self.axes = figure.axes
         self.figure = figure
-        self.figure.set_facecolor(parent._style['background']['rgba_mpl'])
+        self.figure.set_facecolor((1., 1., 1.))
         # 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=parent._style['multicursor']['rgba_mpl'], lw=1)
+                                           color='m', lw=1)
 
         # initialize panning attributes
         self.press = None
@@ -745,7 +743,7 @@ 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'):
@@ -886,6 +884,12 @@ 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):
@@ -918,9 +922,6 @@ 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]
@@ -941,13 +942,11 @@ 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, color=linecolor, linewidth=0.7)
+                ax.plot(times, data, 'k', linewidth=0.7)
                 if noiselevel is not None:
                     for level in noiselevel:
                         ax.plot([time_ax[0], time_ax[-1]],
-                                            [level, level],
-                                color = linecolor,
-                                linestyle = 'dashed')
+                                            [level, level], '--k')
                 self.setPlotDict(n, (station, channel, network))
         if iniPick:
             ax.vlines(iniPick, ax.get_ylim()[0], ax.get_ylim()[1],
@@ -1110,8 +1109,7 @@ 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, 1)
-        self.orig_parent = parent
+        super(PickDlg, self).__init__(parent)
 
         # initialize attributes
         self.parameter = parameter
@@ -1131,7 +1129,6 @@ 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
@@ -1270,8 +1267,8 @@ class PickDlg(QDialog):
         self.plot_arrivals_button.setCheckable(True)
 
         # create accept/reject button
-        self.accept_button = QPushButton('&Accept')
-        self.reject_button = QPushButton('&Reject')
+        self.accept_button = QPushButton('&Accept Picks')
+        self.reject_button = QPushButton('&Reject Picks')
         self.disable_ar_buttons()
 
         # add hotkeys
@@ -1295,20 +1292,10 @@ 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
@@ -1410,7 +1397,7 @@ class PickDlg(QDialog):
             return
         ax = self.multicompfig.axes[0]
         if not textOnly:
-            ylims = self.getGlobalLimits(ax, 'y')
+            ylims = self.getGlobalLimits('y')
         else:
             ylims = self.multicompfig.getYLims(ax)
         stime = self.getStartTime()
@@ -1538,46 +1525,36 @@ class PickDlg(QDialog):
             self.leave_picking_mode()
 
     def init_p_pick(self):
-        self.set_button_border_color(self.p_button, 'yellow')
+        self.set_button_color(self.p_button, 'yellow')
         self.activatePicking()
         self.currentPhase = str(self.p_button.text())
 
     def init_s_pick(self):
-        self.set_button_border_color(self.s_button, 'yellow')
+        self.set_button_color(self.s_button, 'yellow')
         self.activatePicking()
         self.currentPhase = str(self.s_button.text())
 
     def getPhaseID(self, phase):
         return identifyPhaseID(phase)
 
-    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)
-        '''
+    def set_button_color(self, button, color=None):
         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:
-            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'])
+        elif type(color) == str or not color:
+            button.setStyleSheet("background-color: {}".format(color))
 
     def reset_p_button(self):
-        self.set_button_border_color(self.p_button)
+        self.set_button_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_border_color(self.s_button)
+        self.set_button_color(self.s_button)
         self.s_button.setEnabled(True)
         self.s_button.setChecked(False)
         self.s_button.setText('S')
@@ -1627,7 +1604,6 @@ 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):
@@ -1662,8 +1638,8 @@ class PickDlg(QDialog):
     def setYLims(self, limits):
         self.cur_ylim = limits
 
-    def getGlobalLimits(self, ax, axis):
-        return self.multicompfig.getGlobalLimits(ax, axis)
+    def getGlobalLimits(self, axis):
+        return self.multicompfig.getGlobalLimits(axis)
 
     def getWFData(self):
         return self.data
@@ -1717,10 +1693,10 @@ class PickDlg(QDialog):
         self.cidpress = self.multicompfig.connectPressEvent(self.setPick)
 
         if self.getPhaseID(self.currentPhase) == 'P':
-            self.set_button_border_color(self.p_button, 'green')
+            self.set_button_color(self.p_button, 'green')
             self.setIniPickP(gui_event, wfdata, trace_number)
         elif self.getPhaseID(self.currentPhase) == 'S':
-            self.set_button_border_color(self.s_button, 'green')
+            self.set_button_color(self.s_button, 'green')
             self.setIniPickS(gui_event, wfdata)
 
         self.zoomAction.setEnabled(False)
@@ -1955,14 +1931,13 @@ class PickDlg(QDialog):
         self.drawPicks(picktype='manual')
         self.drawPicks(picktype='auto')
 
-    def drawPicks(self, phase=None, picktype='manual', textOnly=False, picks=None):
+    def drawPicks(self, phase=None, picktype='manual', textOnly=False):
         # plotting picks
         ax = self.multicompfig.axes[0]
         if not textOnly:
             ylims = self.multicompfig.getGlobalLimits(ax, 'y')
         else:
             ylims = ax.get_ylim()
-        if not picks:
         if self.getPicks(picktype):
             if phase is not None and not phase == 'SPt':
                 if (type(self.getPicks(picktype)[phase]) is dict
@@ -2260,20 +2235,9 @@ class MultiEventWidget(QWidget):
 
         self.start_button = QtGui.QPushButton('Start')
 
-        for index, (key, func, color) in enumerate(self.options):
+        for index, (key, func) 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
@@ -2288,7 +2252,7 @@ class MultiEventWidget(QWidget):
         self.main_layout.insertLayout(0, self.rb_layout)
 
     def refresh_tooltips(self):
-        for key, func, color in self.options:
+        for key, func in self.options:
             eventlist = func()
             if not type(eventlist) == list:
                 eventlist = [eventlist]
@@ -2324,7 +2288,6 @@ 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()
@@ -2392,9 +2355,6 @@ 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()
@@ -2473,18 +2433,17 @@ class TuneAutopicker(QWidget):
     QWidget used to modifiy and test picking parameters for autopicking algorithm.
 
     :param: parent
-    :type: PyLoT Mainwindow
+    :type: QtPyLoT Mainwindow
     '''
 
     def __init__(self, parent):
         QtGui.QWidget.__init__(self, parent, 1)
-        self._style = parent._style
+        self.parent = parent
+        self.setParent(parent)
         self.setWindowTitle('PyLoT - Tune Autopicker')
-        self.parameter = self.parent()._inputs
-        self.fig_dict = self.parent().fig_dict
+        self.parameter = parent._inputs
+        self.set_fig_dict(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()
@@ -2496,9 +2455,8 @@ class TuneAutopicker(QWidget):
         self.add_log()
         self.set_stretch()
         self.resize(1280, 720)
-        self._manual_pick_plots = []
-        if hasattr(self.parent(), 'metadata'):
-            self.metadata = self.parent().metadata
+        if hasattr(parent, 'metadata'):
+            self.metadata = self.parent.metadata
         else:
             self.metadata = None
             # self.setWindowModality(QtCore.Qt.WindowModality.ApplicationModal)
@@ -2506,7 +2464,6 @@ class TuneAutopicker(QWidget):
 
     def set_fig_dict(self, fig_dict):
         for key, value in fig_dict.items():
-            if key is not 'mainFig':
             value._tight = True
         self.fig_dict = fig_dict
 
@@ -2521,7 +2478,7 @@ 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)
@@ -2540,18 +2497,13 @@ 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():
@@ -2565,7 +2517,7 @@ 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()._ref_test_colors['ref'])
+                item.setBackground(self.parent._colors['ref'])
             model.appendRow(item)
 
     def init_figure_tabs(self):
@@ -2580,7 +2532,7 @@ class TuneAutopicker(QWidget):
         self.stb_names = ['aicARHfig', 'refSpick', 'el_S1pick', 'el_S2pick']
 
     def add_parameters(self):
-        self.paraBox = PylotParaBox(self.parameter, parent=self, windowflag=0)
+        self.paraBox = PylotParaBox(self.parameter)
         self.paraBox.set_tune_mode(True)
         self.update_eventID()
         self.parameter_layout.addWidget(self.paraBox)
@@ -2589,7 +2541,6 @@ 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)
@@ -2607,7 +2558,7 @@ 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]
@@ -2623,7 +2574,10 @@ 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
 
     def get_current_station(self):
         return str(self.stationBox.currentText()).split('.')[-1]
@@ -2632,66 +2586,51 @@ 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():
-            if self.pdlg_widget:
-                self.pdlg_widget.setParent(None)
-            self.pdlg_widget = None
+            self.pickDlg = 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
-        self.pickDlg = PickDlg(self, data=data.select(station=station),
+        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)
-        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)
+        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()
         hl = QtGui.QHBoxLayout()
-        self.pdlg_widget.setLayout(hl)
-        hl.addWidget(self.pickDlg)
+        self.pickDlg.setLayout(hl)
+        hl.addWidget(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()
-
-    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))
+                self.parent.drawPicks(station)
+            self.parent.draw()
 
     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
@@ -2710,48 +2649,47 @@ 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_pick_to_ax(ax=ax, picks=picks, phase='P',
-                                        starttime=starttime, quality=qualityPpick)
+            self.plot_manual_Ppick_to_ax(ax, (picks['P']['mpp'] - starttime))
         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_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)
+            self.plot_manual_Spick_to_ax(ax, (picks['S']['mpp'] - starttime))
 
+    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]
-        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.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')
         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.pdlg_widget, 'Traces Plot')
+        id0 = self.figure_tabs.insertTab(0, self.pickDlg, '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')
@@ -2794,12 +2732,12 @@ 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
@@ -2818,7 +2756,7 @@ 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(
@@ -2840,7 +2778,6 @@ class TuneAutopicker(QWidget):
                 'locflag': 0,
                 'savexml': False}
         for key in self.fig_dict.keys():
-            if not key == 'plot_style':
             self.fig_dict[key].clear()
         self.ap_thread = Thread(self, autoPyLoT, arg=args,
                                 progressText='Picking trace...',
@@ -2882,8 +2819,8 @@ 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):
@@ -2891,11 +2828,10 @@ class TuneAutopicker(QWidget):
         self.tune_layout.setStretch(1, 1)
 
     def clear_all(self):
-        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, 'pickDlg'):
+            if self.pickDlg:
+                self.pickDlg.setParent(None)
+            del (self.pickDlg)
         if hasattr(self, 'overview'):
             self.overview.setParent(None)
         if hasattr(self, 'p_tabs'):
@@ -2924,7 +2860,7 @@ class PylotParaBox(QtGui.QWidget):
     accepted = QtCore.Signal(str)
     rejected = QtCore.Signal(str)
 
-    def __init__(self, parameter, parent=None, windowflag=1):
+    def __init__(self, parameter, parent=None):
         '''
         Generate Widget containing parameters for PyLoT.
 
@@ -2932,7 +2868,7 @@ class PylotParaBox(QtGui.QWidget):
         :type: PylotParameter (object)
 
         '''
-        QtGui.QWidget.__init__(self, parent, windowflag)
+        QtGui.QWidget.__init__(self, parent)
         self.parameter = parameter
         self.tabs = QtGui.QTabWidget()
         self.layout = QtGui.QVBoxLayout()
@@ -3475,6 +3411,9 @@ 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)
 
@@ -3494,7 +3433,6 @@ 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
@@ -3795,29 +3733,14 @@ 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")
@@ -3834,12 +3757,6 @@ 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')
@@ -3851,10 +3768,6 @@ 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()}
@@ -4302,23 +4215,15 @@ class LoadDataDlg(QDialog):
 
 
 class HelpForm(QDialog):
-    def __init__(self, parent=None,
-                 page=QUrl('https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/')):
-        super(HelpForm, self).__init__(parent, 1)
+    def __init__(self, page=QUrl('https://ariadne.geophysik.rub.de/trac/PyLoT'),
+                 parent=None):
+        super(HelpForm, self).__init__(parent)
         self.setAttribute(Qt.WA_DeleteOnClose)
         self.setAttribute(Qt.WA_GroupLeader)
 
-        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 = QAction(QIcon(":/back.png"), "&Back", self)
         backAction.setShortcut(QKeySequence.Back)
-        homeAction = QAction(home_icon, "&Home", self)
+        homeAction = QAction(QIcon(":/home.png"), "&Home", self)
         homeAction.setShortcut("Home")
         self.pageLabel = QLabel()
 
@@ -4331,21 +4236,21 @@ class HelpForm(QDialog):
 
         layout = QVBoxLayout()
         layout.addWidget(toolBar)
-        layout.addWidget(self.webBrowser)
+        layout.addWidget(self.webBrowser, 1)
         self.setLayout(layout)
 
-        backAction.triggered.connect(self.webBrowser.back)
-        homeAction.triggered.connect(self.home)
-        self.webBrowser.urlChanged.connect(self.updatePageTitle)
+        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)
 
-        self.resize(1280, 720)
+        self.resize(400, 600)
         self.setWindowTitle("{0} Help".format(QApplication.applicationName()))
 
-    def home(self):
-        self.webBrowser.load(self.home_page)
-
     def updatePageTitle(self):
-        self.pageLabel.setText(self.webBrowser.title())
+        self.pageLabel.setText(self.webBrowser.documentTitle())
 
 
 if __name__ == '__main__':

pylot/styles/__init__.py

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

pylot/styles/bright.qss

@@ -1,259 +0,0 @@
-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

@@ -1,258 +0,0 @@
-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

@@ -1,70 +0,0 @@
-# -*- 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

@@ -0,0 +1,13 @@
+#!/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

@@ -0,0 +1,20 @@
+#!/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

@@ -0,0 +1,13 @@
+#!/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

@@ -0,0 +1,19 @@
+#!/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

@@ -0,0 +1,23 @@
+# -*- 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

@@ -0,0 +1,17 @@
+# -*- 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

@@ -0,0 +1,311 @@
+#!/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

@@ -0,0 +1,8 @@
+#!/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

@@ -0,0 +1,16 @@
+#!/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

@@ -0,0 +1,29 @@
+#!/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

@@ -0,0 +1,25 @@
+#!/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

@@ -0,0 +1,41 @@
+#!/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

@@ -0,0 +1,38 @@
+#!/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

@@ -0,0 +1,15 @@
+#!/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

@@ -0,0 +1,32 @@
+#!/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

@@ -0,0 +1,314 @@
+#!/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='0.2',
+    version='0.1a1',
     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', 'matplotlib', 'numpy'],
+    requires=['obspy', 'PySide'],
     url='dummy',
     license='LGPLv3',
     author='Sebastian Wehling-Benatelli',