Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop
This commit is contained in:
Marcel Paffrath
commit
48c889129a
27
QtPyLoT.py
27
QtPyLoT.py
@ -386,8 +386,8 @@ class MainWindow(QMainWindow):
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else:
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raise DatastructureError('not specified')
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return self.fnames
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except DatastructureError, e:
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print e
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except DatastructureError as e:
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print(e)
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props = PropertiesDlg(self)
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if props.exec_() == QDialog.Accepted:
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return self.getWFFnames()
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@ -410,7 +410,7 @@ class MainWindow(QMainWindow):
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def saveData(self):
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def getSavePath(e):
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print 'warning: {0}'.format(e)
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print('warning: {0}'.format(e))
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directory = os.path.join(self.getRoot(), self.getEventFileName())
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file_filter = "QuakeML file (*.xml);;VELEST observation file format (*.cnv);;NonLinLoc observation file (*.obs)"
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fname = QFileDialog.getSaveFileName(self, 'Save event data ...',
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@ -513,7 +513,7 @@ class MainWindow(QMainWindow):
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def plotWaveformData(self):
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zne_text = {'Z': 'vertical', 'N': 'north-south', 'E': 'east-west'}
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comp = self.getComponent()
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title = 'overview: {0} components'.format(zne_text[comp])
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title = 'section: {0} components'.format(zne_text[comp])
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wfst = self.getData().getWFData().select(component=comp)
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self.getPlotWidget().plotWFData(wfdata=wfst, title=title)
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self.draw()
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@ -574,8 +574,8 @@ class MainWindow(QMainWindow):
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def getFilterOptions(self):
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try:
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return self.filteroptions[self.getSeismicPhase()]
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except AttributeError, e:
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print e
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except AttributeError as e:
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print(e)
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return FilterOptions(None, None, None)
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def getFilters(self):
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@ -592,12 +592,12 @@ class MainWindow(QMainWindow):
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settings = QSettings()
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if settings.value("filterdefaults",
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None) is None and not self.getFilters():
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for key, value in FILTERDEFAULTS.iteritems():
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for key, value in FILTERDEFAULTS.items():
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self.setFilterOptions(FilterOptions(**value), key)
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elif settings.value("filterdefaults", None) is not None:
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for key, value in settings.value("filterdefaults"):
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self.setFilterOptions(FilterOptions(**value), key)
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except Exception, e:
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except Exception as e:
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self.updateStatus('Error ...')
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emsg = QErrorMessage(self)
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emsg.showMessage('Error: {0}'.format(e))
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@ -636,8 +636,12 @@ class MainWindow(QMainWindow):
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if pickDlg.exec_():
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self.setDirty(True)
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self.updateStatus('picks accepted ({0})'.format(station))
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self.addPicks(station, pickDlg.getPicks())
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self.drawPicks(station)
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replot = self.addPicks(station, pickDlg.getPicks())
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if replot:
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self.plotWaveformData()
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self.drawPicks()
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else:
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self.drawPicks(station)
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else:
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self.updateStatus('picks discarded ({0})'.format(station))
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@ -667,6 +671,7 @@ class MainWindow(QMainWindow):
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def addPicks(self, station, picks):
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stat_picks = self.getPicksOnStation(station)
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rval = False
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if not stat_picks:
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stat_picks = picks
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else:
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@ -684,11 +689,13 @@ class MainWindow(QMainWindow):
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ret = msgBox.exec_()
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if ret == QMessageBox.Save:
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stat_picks = picks
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rval = True
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elif ret == QMessageBox.Cancel:
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pass
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else:
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raise Exception('FATAL: Should never occur!')
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self.getPicks()[station] = stat_picks
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return rval
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def updatePicks(self):
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evt = self.getData().getEvtData()
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@ -3,6 +3,7 @@
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<file>icons/pylot.ico</file>
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<file>icons/pylot.png</file>
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<file>icons/printer.png</file>
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<file>icons/delete.png</file>
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<file>icons/key_E.png</file>
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<file>icons/key_N.png</file>
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<file>icons/key_P.png</file>
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BIN
icons/delete.png
Executable file
BIN
icons/delete.png
Executable file
Binary file not shown.
|
After Width: | Height: | Size: 5.0 KiB |
File diff suppressed because one or more lines are too long
@ -25,7 +25,8 @@ class Magnitude(object):
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:type: float
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:param: pwin, pick window [To To+pwin] to get maximum
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peak-to-peak amplitude
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peak-to-peak amplitude (WApp) or to calculate
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source spectrum (DCfc)
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:type: float
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:param: iplot, no. of figure window for plotting interims results
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@ -40,6 +41,7 @@ class Magnitude(object):
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self.setpwin(pwin)
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self.setiplot(iplot)
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self.calcwapp()
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self.calcsourcespec()
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def getwfstream(self):
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@ -69,17 +71,22 @@ class Magnitude(object):
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def getwapp(self):
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return self.wapp
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def getw0(self):
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return self.w0
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def getfc(self):
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return self.fc
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def calcwapp(self):
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self.wapp = None
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def calcsourcespec(self):
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self.sourcespek = None
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class WApp(Magnitude):
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'''
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Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
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seismograph. Has to be derived from corrected traces!
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seismograph. Has to be derived from instrument corrected traces!
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'''
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def calcwapp(self):
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@ -110,6 +117,7 @@ class WApp(Magnitude):
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iwin = getsignalwin(th, self.getTo(), self.getpwin())
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self.wapp = np.max(sqH[iwin])
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print ("Determined Wood-Anderson peak-to-peak amplitude: %f mm") % self.wapp
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if self.getiplot() > 1:
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stream.plot()
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f = plt.figure(2)
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@ -128,10 +136,50 @@ class WApp(Magnitude):
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class DCfc(Magnitude):
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'''
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Method to calculate the source spectrum and to derive from that the plateau
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(the so-called DC-value) and the corner frequency assuming Aki's omega-square
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source model. Has to be derived from corrected traces!
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(so-called DC-value) and the corner frequency assuming Aki's omega-square
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source model. Has to be derived from instrument corrected displacement traces!
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'''
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def calcsourcespec(self):
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print ("Calculating source spectrum ....")
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self.w0 = None # DC-value
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self.fc = None # corner frequency
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stream = self.getwfstream()
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tr = stream[0]
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# get time array
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t = np.arange(0, len(tr) * tr.stats.delta, tr.stats.delta)
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iwin = getsignalwin(t, self.getTo(), self.getpwin())
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xdat = tr.data[iwin]
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# fft
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fny = tr.stats.sampling_rate / 2
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l = len(xdat) / tr.stats.sampling_rate
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n = tr.stats.sampling_rate * l # number of fft bins after Bath
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# find next power of 2 of data length
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m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
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N = int(np.power(m, 2))
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y = tr.stats.delta * np.fft.fft(xdat, N)
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Y = abs(y[: N/2])
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L = (N - 1) / tr.stats.sampling_rate
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f = np.arange(0, fny, 1/L)
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if self.getiplot() > 1:
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f1 = plt.figure(1)
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plt.subplot(2,1,1)
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plt.plot(t, np.multiply(tr, 1000), 'k') # show displacement in mm
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plt.plot(t[iwin], np.multiply(xdat, 1000), 'g') # show displacement in mm
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plt.title('Seismogram and P pulse, station %s' % tr.stats.station)
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plt.xlabel('Time since %s' % tr.stats.starttime)
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plt.ylabel('Displacement [mm]')
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plt.subplot(2,1,2)
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plt.semilogy(f, Y.real)
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plt.title('Source Spectrum from P Pulse')
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plt.xlabel('Frequency [Hz]')
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plt.ylabel('Amplitude [m/Hz]')
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plt.show()
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raw_input()
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plt.close(f1)
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@ -147,7 +147,7 @@ class AICPicker(AutoPicking):
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def calcPick(self):
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print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
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print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
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self.Pick = None
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self.slope = None
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@ -155,7 +155,7 @@ class AICPicker(AutoPicking):
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#find NaN's
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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self.cf[nn] = 0
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self.cf[nn] = 0
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#taper AIC-CF to get rid off side maxima
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tap = np.hanning(len(self.cf))
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aic = tap * self.cf + max(abs(self.cf))
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@ -163,15 +163,15 @@ class AICPicker(AutoPicking):
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ismooth = int(round(self.Tsmooth / self.dt))
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aicsmooth = np.zeros(len(aic))
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if len(aic) < ismooth:
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print 'AICPicker: Tsmooth larger than CF!'
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return
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print('AICPicker: Tsmooth larger than CF!')
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return
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else:
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for i in range(1, len(aic)):
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if i > ismooth:
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ii1 = i - ismooth
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aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
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else:
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aicsmooth[i] = np.mean(aic[1 : i])
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for i in range(1, len(aic)):
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if i > ismooth:
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ii1 = i - ismooth
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aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
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else:
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aicsmooth[i] = np.mean(aic[1 : i])
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#remove offset
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offset = abs(min(aic) - min(aicsmooth))
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aicsmooth = aicsmooth - offset
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@ -180,7 +180,7 @@ class AICPicker(AutoPicking):
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#find NaN's
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nn = np.isnan(diffcf)
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if len(nn) > 1:
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diffcf[nn] = 0
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diffcf[nn] = 0
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#taper CF to get rid off side maxima
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tap = np.hanning(len(diffcf))
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diffcf = tap * diffcf * max(abs(aicsmooth))
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@ -189,104 +189,104 @@ class AICPicker(AutoPicking):
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#find minimum in AIC-CF front of maximum
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if aicsmooth[i - 1] >= aicsmooth[i]:
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self.Pick = self.Tcf[i]
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break
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if aicsmooth[i - 1] >= aicsmooth[i]:
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self.Pick = self.Tcf[i]
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break
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#if no minimum could be found:
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#search in 1st derivative of AIC-CF
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if self.Pick is None:
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for i in range(icfmax -1, max([icfmax -lpickwindow, 2]), -1):
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if diffcf[i -1] >= diffcf[i]:
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self.Pick = self.Tcf[i]
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break
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for i in range(icfmax -1, max([icfmax -lpickwindow, 2]), -1):
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if diffcf[i -1] >= diffcf[i]:
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self.Pick = self.Tcf[i]
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break
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# quality assessment using SNR and slope from CF
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if self.Pick is not None:
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# get noise window
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inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
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# check, if these are counts or m/s, important for slope estimation!
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# this is quick and dirty, better solution?
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if max(self.Data[0].data < 1e-3):
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self.Data[0].data = self.Data[0].data * 1000000
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# get signal window
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isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
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# calculate SNR from CF
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self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
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- np.mean(aic[inoise])))
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# calculate slope from CF after initial pick
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# get slope window
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tslope = self.TSNR[3] #slope determination window
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islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
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& (self.Tcf >= self.Pick))
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# find maximum within slope determination window
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# 'cause slope should be calculated up to first local minimum only!
|
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imax = np.argmax(self.Data[0].data[islope])
|
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if imax == 0:
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print 'AICPicker: Maximum for slope determination right at the beginning of the window!'
|
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print 'Choose longer slope determination window!'
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if self.iplot > 1:
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p = plt.figure(self.iplot)
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x = self.Data[0].data
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
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plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
return
|
||||
islope = islope[0][0 :imax]
|
||||
dataslope = self.Data[0].data[islope]
|
||||
# calculate slope as polynomal fit of order 1
|
||||
xslope = np.arange(0, len(dataslope), 1)
|
||||
P = np.polyfit(xslope, dataslope, 1)
|
||||
datafit = np.polyval(P, xslope)
|
||||
if datafit[0] >= datafit[len(datafit) - 1]:
|
||||
print 'AICPicker: Negative slope, bad onset skipped!'
|
||||
return
|
||||
self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
|
||||
# get noise window
|
||||
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
|
||||
# check, if these are counts or m/s, important for slope estimation!
|
||||
# this is quick and dirty, better solution?
|
||||
if max(self.Data[0].data < 1e-3):
|
||||
self.Data[0].data = self.Data[0].data * 1000000
|
||||
# get signal window
|
||||
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
|
||||
# calculate SNR from CF
|
||||
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
|
||||
max(abs(aic[inoise] - np.mean(aic[inoise])))
|
||||
# calculate slope from CF after initial pick
|
||||
# get slope window
|
||||
tslope = self.TSNR[3] #slope determination window
|
||||
islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
|
||||
& (self.Tcf >= self.Pick))
|
||||
# find maximum within slope determination window
|
||||
# 'cause slope should be calculated up to first local minimum only!
|
||||
imax = np.argmax(self.Data[0].data[islope])
|
||||
if imax == 0:
|
||||
print('AICPicker: Maximum for slope determination right at the beginning of the window!')
|
||||
print('Choose longer slope determination window!')
|
||||
if self.iplot > 1:
|
||||
p = plt.figure(self.iplot)
|
||||
x = self.Data[0].data
|
||||
p1, = plt.plot(self.Tcf, x / max(x), 'k')
|
||||
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
||||
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
return
|
||||
islope = islope[0][0 :imax]
|
||||
dataslope = self.Data[0].data[islope]
|
||||
# calculate slope as polynomal fit of order 1
|
||||
xslope = np.arange(0, len(dataslope), 1)
|
||||
P = np.polyfit(xslope, dataslope, 1)
|
||||
datafit = np.polyval(P, xslope)
|
||||
if datafit[0] >= datafit[len(datafit) - 1]:
|
||||
print('AICPicker: Negative slope, bad onset skipped!')
|
||||
return
|
||||
self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
|
||||
|
||||
else:
|
||||
self.SNR = None
|
||||
self.slope = None
|
||||
self.SNR = None
|
||||
self.slope = None
|
||||
|
||||
if self.iplot > 1:
|
||||
p = plt.figure(self.iplot)
|
||||
x = self.Data[0].data
|
||||
p1, = plt.plot(self.Tcf, x / max(x), 'k')
|
||||
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
||||
if self.Pick is not None:
|
||||
p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
|
||||
else:
|
||||
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
p = plt.figure(self.iplot)
|
||||
x = self.Data[0].data
|
||||
p1, = plt.plot(self.Tcf, x / max(x), 'k')
|
||||
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
||||
if self.Pick is not None:
|
||||
p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
|
||||
else:
|
||||
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
|
||||
if self.Pick is not None:
|
||||
plt.figure(self.iplot + 1)
|
||||
p11, = plt.plot(self.Tcf, x, 'k')
|
||||
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
|
||||
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
|
||||
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
|
||||
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
|
||||
plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
|
||||
loc='best')
|
||||
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
|
||||
self.SNR, self.slope))
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.ylabel('Counts')
|
||||
plt.yticks([])
|
||||
if self.Pick is not None:
|
||||
plt.figure(self.iplot + 1)
|
||||
p11, = plt.plot(self.Tcf, x, 'k')
|
||||
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
|
||||
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
|
||||
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
|
||||
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
|
||||
plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'],
|
||||
loc='best')
|
||||
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
|
||||
self.SNR, self.slope))
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.ylabel('Counts')
|
||||
plt.yticks([])
|
||||
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
|
||||
if self.Pick == None:
|
||||
print 'AICPicker: Could not find minimum, picking window too short?'
|
||||
print('AICPicker: Could not find minimum, picking window too short?')
|
||||
|
||||
|
||||
class PragPicker(AutoPicking):
|
||||
@ -297,102 +297,102 @@ class PragPicker(AutoPicking):
|
||||
def calcPick(self):
|
||||
|
||||
if self.getpick1() is not None:
|
||||
print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
|
||||
print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
|
||||
|
||||
self.Pick = None
|
||||
self.SNR = None
|
||||
self.slope = None
|
||||
pickflag = 0
|
||||
#smooth CF
|
||||
ismooth = int(round(self.Tsmooth / self.dt))
|
||||
cfsmooth = np.zeros(len(self.cf))
|
||||
if len(self.cf) < ismooth:
|
||||
print 'PragPicker: Tsmooth larger than CF!'
|
||||
return
|
||||
else:
|
||||
for i in range(1, len(self.cf)):
|
||||
if i > ismooth:
|
||||
ii1 = i - ismooth;
|
||||
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
|
||||
else:
|
||||
cfsmooth[i] = np.mean(self.cf[1 : i])
|
||||
self.Pick = None
|
||||
self.SNR = None
|
||||
self.slope = None
|
||||
pickflag = 0
|
||||
#smooth CF
|
||||
ismooth = int(round(self.Tsmooth / self.dt))
|
||||
cfsmooth = np.zeros(len(self.cf))
|
||||
if len(self.cf) < ismooth:
|
||||
print('PragPicker: Tsmooth larger than CF!')
|
||||
return
|
||||
else:
|
||||
for i in range(1, len(self.cf)):
|
||||
if i > ismooth:
|
||||
ii1 = i - ismooth;
|
||||
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
|
||||
else:
|
||||
cfsmooth[i] = np.mean(self.cf[1 : i])
|
||||
|
||||
#select picking window
|
||||
#which is centered around tpick1
|
||||
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
|
||||
& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
|
||||
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
|
||||
Tcfpick = self.Tcf[ipick]
|
||||
cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
|
||||
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
|
||||
cfpick1 = 2 * self.cf[ipick1]
|
||||
#select picking window
|
||||
#which is centered around tpick1
|
||||
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
|
||||
& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
|
||||
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
|
||||
Tcfpick = self.Tcf[ipick]
|
||||
cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
|
||||
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
|
||||
cfpick1 = 2 * self.cf[ipick1]
|
||||
|
||||
#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
|
||||
#prominent trend: decrease aus
|
||||
#flat: use given aus
|
||||
cfdiff = np.diff(cfipick);
|
||||
i0diff = np.where(cfdiff > 0)
|
||||
cfdiff = cfdiff[i0diff]
|
||||
minaus = min(cfdiff * (1 + self.aus));
|
||||
aus1 = max([minaus, self.aus]);
|
||||
#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
|
||||
#prominent trend: decrease aus
|
||||
#flat: use given aus
|
||||
cfdiff = np.diff(cfipick)
|
||||
i0diff = np.where(cfdiff > 0)
|
||||
cfdiff = cfdiff[i0diff]
|
||||
minaus = min(cfdiff * (1 + self.aus))
|
||||
aus1 = max([minaus, self.aus])
|
||||
|
||||
#at first we look to the right until the end of the pick window is reached
|
||||
flagpick_r = 0
|
||||
flagpick_l = 0
|
||||
cfpick_r = 0
|
||||
cfpick_l = 0
|
||||
lpickwindow = int(round(self.PickWindow / self.dt))
|
||||
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_r = self.Tcf[i]
|
||||
self.Pick = pick_r
|
||||
flagpick_l = 1
|
||||
cfpick_r = self.cf[i]
|
||||
break
|
||||
#at first we look to the right until the end of the pick window is reached
|
||||
flagpick_r = 0
|
||||
flagpick_l = 0
|
||||
cfpick_r = 0
|
||||
cfpick_l = 0
|
||||
lpickwindow = int(round(self.PickWindow / self.dt))
|
||||
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_r = self.Tcf[i]
|
||||
self.Pick = pick_r
|
||||
flagpick_l = 1
|
||||
cfpick_r = self.cf[i]
|
||||
break
|
||||
|
||||
# now we look to the left
|
||||
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_l = self.Tcf[i]
|
||||
self.Pick = pick_l
|
||||
flagpick_r = 1
|
||||
cfpick_l = self.cf[i]
|
||||
break
|
||||
# now we look to the left
|
||||
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_l = self.Tcf[i]
|
||||
self.Pick = pick_l
|
||||
flagpick_r = 1
|
||||
cfpick_l = self.cf[i]
|
||||
break
|
||||
|
||||
# now decide which pick: left or right?
|
||||
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
|
||||
self.Pick = pick_l
|
||||
pickflag = 1
|
||||
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_r
|
||||
pickflag = 1
|
||||
elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
# now decide which pick: left or right?
|
||||
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
|
||||
self.Pick = pick_l
|
||||
pickflag = 1
|
||||
else:
|
||||
print 'PragPicker: Could not find reliable onset!'
|
||||
self.Pick = None
|
||||
pickflag = 0
|
||||
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_r
|
||||
pickflag = 1
|
||||
elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_l
|
||||
pickflag = 1
|
||||
else:
|
||||
print('PragPicker: Could not find reliable onset!')
|
||||
self.Pick = None
|
||||
pickflag = 0
|
||||
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick,cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
|
||||
if pickflag > 0:
|
||||
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick,cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
|
||||
if pickflag > 0:
|
||||
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
|
||||
else:
|
||||
print 'PragPicker: No initial onset time given! Check input!'
|
||||
self.Pick = None
|
||||
return
|
||||
print('PragPicker: No initial onset time given! Check input!')
|
||||
self.Pick = None
|
||||
return
|
||||
|
||||
@ -11,12 +11,13 @@ function conglomerate utils.
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from scipy import integrate
|
||||
from pylot.core.pick.Picker import AICPicker, PragPicker
|
||||
from pylot.core.pick.CharFuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
|
||||
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker,\
|
||||
getSNR, fmpicker, checkPonsets, wadaticheck
|
||||
getSNR, fmpicker, checkPonsets, wadaticheck, crossings_nonzero_all
|
||||
from pylot.core.read.data import Data
|
||||
from pylot.core.analysis.magnitude import WApp
|
||||
from pylot.core.analysis.magnitude import WApp, DCfc
|
||||
|
||||
def autopickevent(data, param):
|
||||
stations = []
|
||||
@ -309,6 +310,26 @@ def autopickstation(wfstream, pickparam):
|
||||
else:
|
||||
FM = 'N'
|
||||
|
||||
##############################################################
|
||||
# get DC value (w0) and corner frequency (fc) of source spectrum
|
||||
# from P pulse
|
||||
# restitute streams
|
||||
# initialize Data object
|
||||
data = Data()
|
||||
[corzdat, restflag] = data.restituteWFData(invdir, zdat)
|
||||
if restflag == 1:
|
||||
# integrate to displacement
|
||||
corintzdat = integrate.cumtrapz(corzdat[0], None, corzdat[0].stats.delta)
|
||||
# class needs stream object => build it
|
||||
z_copy = zdat.copy()
|
||||
z_copy[0].data = corintzdat
|
||||
# calculate source spectrum and get w0 and fc
|
||||
calcwin = 1 / bpz2[0] # largest detectable period == window length
|
||||
# around P pulse for calculating source spectrum
|
||||
specpara = DCfc(z_copy, mpickP, calcwin, iplot)
|
||||
w0 = specpara.getw0()
|
||||
fc = specpara.getfc()
|
||||
|
||||
print 'autopickstation: P-weight: %d, SNR: %f, SNR[dB]: %f, ' \
|
||||
'Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
|
||||
Sflag = 1
|
||||
|
||||
@ -45,7 +45,7 @@ class AutoPickParameter(object):
|
||||
self.__filename = fnin
|
||||
parFileCont = {}
|
||||
# read from parsed arguments alternatively
|
||||
for key, val in kwargs.iteritems():
|
||||
for key, val in kwargs.items():
|
||||
parFileCont[key] = val
|
||||
|
||||
if self.__filename is not None:
|
||||
@ -57,7 +57,7 @@ class AutoPickParameter(object):
|
||||
for line in lines:
|
||||
parspl = line.split('\t')[:2]
|
||||
parFileCont[parspl[0].strip()] = parspl[1]
|
||||
except Exception, e:
|
||||
except Exception as e:
|
||||
self._printParameterError(e)
|
||||
inputFile.seek(0)
|
||||
lines = inputFile.readlines()
|
||||
@ -65,7 +65,7 @@ class AutoPickParameter(object):
|
||||
if not line.startswith(('#', '%', '\n', ' ')):
|
||||
parspl = line.split('#')[:2]
|
||||
parFileCont[parspl[1].strip()] = parspl[0].strip()
|
||||
for key, value in parFileCont.iteritems():
|
||||
for key, value in parFileCont.items():
|
||||
try:
|
||||
val = int(value)
|
||||
except:
|
||||
@ -121,7 +121,7 @@ class AutoPickParameter(object):
|
||||
return len(self.__parameter.keys())
|
||||
|
||||
def iteritems(self):
|
||||
for key, value in self.__parameter.iteritems():
|
||||
for key, value in self.__parameter.items():
|
||||
yield key, value
|
||||
|
||||
def hasParam(self, parameter):
|
||||
@ -134,22 +134,22 @@ class AutoPickParameter(object):
|
||||
for param in args:
|
||||
try:
|
||||
return self.__getitem__(param)
|
||||
except KeyError, e:
|
||||
except KeyError as e:
|
||||
self._printParameterError(e)
|
||||
except TypeError:
|
||||
try:
|
||||
return self.__getitem__(args)
|
||||
except KeyError, e:
|
||||
except KeyError as e:
|
||||
self._printParameterError(e)
|
||||
|
||||
def setParam(self, **kwargs):
|
||||
for param, value in kwargs.iteritems():
|
||||
for param, value in kwargs.items():
|
||||
self.__setitem__(param, value)
|
||||
print self
|
||||
print(self)
|
||||
|
||||
@staticmethod
|
||||
def _printParameterError(errmsg):
|
||||
print 'ParameterError:\n non-existent parameter %s' % errmsg
|
||||
print('ParameterError:\n non-existent parameter %s' % errmsg)
|
||||
|
||||
def export2File(self, fnout):
|
||||
fid_out = open(fnout, 'w')
|
||||
|
||||
@ -187,7 +187,7 @@ class PickDlg(QDialog):
|
||||
try:
|
||||
data = parent.getData().getWFData().copy()
|
||||
self.data = data.select(station=station)
|
||||
except AttributeError, e:
|
||||
except AttributeError as e:
|
||||
errmsg = 'You either have to put in a data or an appropriate ' \
|
||||
'parent (PyLoT MainWindow) object: {0}'.format(e)
|
||||
raise Exception(errmsg)
|
||||
@ -239,6 +239,8 @@ class PickDlg(QDialog):
|
||||
zoom_icon.addPixmap(QPixmap(':/icons/zoom_in.png'))
|
||||
home_icon = QIcon()
|
||||
home_icon.addPixmap(QPixmap(':/icons/zoom_0.png'))
|
||||
del_icon = QIcon()
|
||||
del_icon.addPixmap(QPixmap(':/icons/delete.png'))
|
||||
|
||||
# create actions
|
||||
self.filterAction = createAction(parent=self, text='Filter',
|
||||
@ -251,9 +253,12 @@ class PickDlg(QDialog):
|
||||
slot=self.zoom, icon=zoom_icon,
|
||||
tip='Zoom into waveform',
|
||||
checkable=True)
|
||||
self.resetAction = createAction(parent=self, text='Home',
|
||||
self.resetZoomAction = createAction(parent=self, text='Home',
|
||||
slot=self.resetZoom, icon=home_icon,
|
||||
tip='Reset zoom to original limits')
|
||||
self.resetPicksAction = createAction(parent=self, text='Delete Picks',
|
||||
slot=self.delPicks, icon=del_icon,
|
||||
tip='Delete current picks.')
|
||||
|
||||
# create other widget elements
|
||||
self.selectPhase = QComboBox()
|
||||
@ -269,7 +274,9 @@ class PickDlg(QDialog):
|
||||
_dialtoolbar.addWidget(self.selectPhase)
|
||||
_dialtoolbar.addAction(self.zoomAction)
|
||||
_dialtoolbar.addSeparator()
|
||||
_dialtoolbar.addAction(self.resetAction)
|
||||
_dialtoolbar.addAction(self.resetZoomAction)
|
||||
_dialtoolbar.addSeparator()
|
||||
_dialtoolbar.addAction(self.resetPicksAction)
|
||||
|
||||
# layout the innermost widget
|
||||
_innerlayout = QVBoxLayout()
|
||||
@ -371,7 +378,7 @@ class PickDlg(QDialog):
|
||||
traceIDs = []
|
||||
for channel in channels:
|
||||
channel = channel.upper()
|
||||
for traceID, channelID in plotDict.iteritems():
|
||||
for traceID, channelID in plotDict.items():
|
||||
if channelID[1].upper().endswith(channel):
|
||||
traceIDs.append(traceID)
|
||||
return traceIDs
|
||||
@ -422,6 +429,13 @@ class PickDlg(QDialog):
|
||||
def getPicks(self):
|
||||
return self.picks
|
||||
|
||||
def resetPicks(self):
|
||||
self.picks = {}
|
||||
|
||||
def delPicks(self):
|
||||
self.resetPicks()
|
||||
self.resetPlot()
|
||||
|
||||
def setIniPick(self, gui_event):
|
||||
|
||||
trace_number = round(gui_event.ydata)
|
||||
@ -672,8 +686,21 @@ class PickDlg(QDialog):
|
||||
zoomx=self.getXLims(),
|
||||
zoomy=self.getYLims())
|
||||
self.setPlotLabels()
|
||||
self.drawPicks()
|
||||
self.draw()
|
||||
|
||||
def resetPlot(self):
|
||||
self.updateCurrentLimits()
|
||||
data = self.getWFData().copy()
|
||||
title = self.getPlotWidget().getAxes().get_title()
|
||||
self.getPlotWidget().plotWFData(wfdata=data, title=title,
|
||||
zoomx=self.getXLims(),
|
||||
zoomy=self.getYLims())
|
||||
self.setPlotLabels()
|
||||
self.drawPicks()
|
||||
self.draw()
|
||||
|
||||
|
||||
def setPlotLabels(self):
|
||||
|
||||
# get channel labels
|
||||
@ -711,7 +738,7 @@ class PickDlg(QDialog):
|
||||
else:
|
||||
# deal with something that should never happen
|
||||
scale_factor = 1
|
||||
print gui_event.button
|
||||
print(gui_event.button)
|
||||
|
||||
new_xlim = gui_event.xdata - \
|
||||
scale_factor * (gui_event.xdata - self.getXLims())
|
||||
@ -742,7 +769,7 @@ class PickDlg(QDialog):
|
||||
def apply(self):
|
||||
picks = self.getPicks()
|
||||
for pick in picks:
|
||||
print pick, picks[pick]
|
||||
print(pick, picks[pick])
|
||||
|
||||
def accept(self):
|
||||
self.apply()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user