merged 3 files
Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop Conflicts: pylot/core/active/activeSeismoPick.py pylot/core/active/seismicshot.py pylot/core/active/surveyPlotTools.py
This commit is contained in:
Marcel Paffrath
commit
195352a7ca
@ -8,11 +8,11 @@ import glob
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import matplotlib.pyplot as plt
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from obspy.core import read
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from pylot.core.util import _getVersionString
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from pylot.core.read.data import Data
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from pylot.core.read.inputs import AutoPickParameter
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from pylot.core.util.structure import DATASTRUCTURE
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from pylot.core.pick.autopick import autopickevent
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from pylot.core.util.version import get_git_version as _getVersionString
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__version__ = _getVersionString()
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@ -1,10 +1,10 @@
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<RCC>
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<qresource>
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<file>icons/pylot.ico</file>
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<file>icons/pylot.png</file>
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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_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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<file>icons/key_Q.png</file>
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@ -14,7 +14,7 @@
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<file>icons/key_U.png</file>
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<file>icons/key_V.png</file>
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<file>icons/key_W.png</file>
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<file>icons/key_Z.png</file>
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<file>icons/key_Z.png</file>
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<file>icons/filter.png</file>
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<file>icons/sync.png</file>
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<file>icons/zoom_0.png</file>
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@ -0,0 +1 @@
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# -*- coding: utf-8 -*-
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@ -1 +1,2 @@
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# -*- coding: utf-8 -*-
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__author__ = 'sebastianw'
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import sys
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import numpy as np
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from pylot.core.active import seismicshot
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@ -166,8 +167,8 @@ class Survey(object):
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def countAllTraces(self):
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numtraces = 0
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for line in self.getShotlist():
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for line in self.getReceiverlist():
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for shot in self.getShotlist():
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for rec in self.getReceiverlist():
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numtraces += 1
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return numtraces
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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def vgrids2VTK(inputfile = 'vgrids.in', outputfile = 'vgrids.vtk'):
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import sys
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from obspy import read
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from obspy import Stream
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import sys
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import numpy as np
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from scipy.interpolate import griddata
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import sys
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import numpy as np
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from scipy.interpolate import griddata
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@ -1,3 +1,6 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import numpy as np
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from obspy.core import read
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@ -31,8 +34,7 @@ class SeismicShot(object):
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self.snr = {}
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self.snrthreshold = {}
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self.timeArray = {}
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self.paras = {}
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self.paras['shotname'] = obsfile
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self.paras = {'shotname': obsfile}
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def removeEmptyTraces(self):
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traceIDs = []
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@ -154,7 +156,7 @@ class SeismicShot(object):
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def getPickError(self, traceID):
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pickerror = abs(self.getEarliest(traceID) - self.getLatest(traceID))
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if np.isnan(pickerror) == True:
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print "SPE is NaN for shot %s, traceID %s"%(self.getShotnumber(), traceID)
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print("SPE is NaN for shot %s, traceID %s"%(self.getShotnumber(), traceID))
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return pickerror
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def getStreamTraceIDs(self):
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@ -179,7 +181,7 @@ class SeismicShot(object):
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def getPickwindow(self, traceID):
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try:
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self.pickwindow[traceID]
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except KeyError, e:
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except KeyError as e:
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print('no pickwindow for trace %s, set to %s' % (traceID, self.getCut()))
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self.setPickwindow(traceID, self.getCut())
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return self.pickwindow[traceID]
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@ -262,17 +264,21 @@ class SeismicShot(object):
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return Stream(traces)
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else:
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self.setPick(traceID, None)
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print 'Warning: ambigious or empty traceID: %s' % traceID
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print('Warning: ambigious or empty traceID: %s' % traceID)
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#raise ValueError('ambigious or empty traceID: %s' % traceID)
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def pickTraces(self, traceID, windowsize, folm = 0.6, HosAic = 'hos'): ########## input variables ##########
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def pickTraces(self, traceID, pickmethod, windowsize, folm = 0.6, HosAic = 'hos'): ########## input variables ##########
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# LOCALMAX NOT IMPLEMENTED!
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'''
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Intitiate picking for a trace.
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:param: traceID
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:type: int
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:param: pickmethod, use either 'threshold' or 'localmax' method. (localmax not yet implemented 04_08_15)
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:type: string
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:param: cutwindow (equals HOScf 'cut' variable)
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:type: tuple
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@ -296,7 +302,13 @@ class SeismicShot(object):
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self.timeArray[traceID] = hoscf.getTimeArray()
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aiccftime, hoscftime = self.threshold(hoscf, aiccf, windowsize, self.getPickwindow(traceID), folm)
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if pickmethod == 'threshold':
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aiccftime, hoscftime = self.threshold(hoscf, aiccf, windowsize, self.getPickwindow(traceID), folm)
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#setpick = {'threshold':self.threshold,
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# 'localmax':self.localmax}
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#aiccftime, hoscftime = setpick[pickmethod](hoscf, aiccf, windowsize, pickwindow)
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setHosAic = {'hos': hoscftime,
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'aic': aiccftime}
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@ -497,6 +509,7 @@ class SeismicShot(object):
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:param: (tnoise, tgap, tsignal), as used in pylot SNR
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'''
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from pylot.core.pick.utils import getSNR
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tgap = self.getTgap()
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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import math
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import numpy as np
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@ -1,16 +1,69 @@
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import numpy as np
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from pylab import *
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startpos = []
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endpos = []
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def generateSurvey(obsdir, shotlist):
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from obspy.core import read
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from pylot.core.active import seismicshot
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shot_dict = {}
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for shotnumber in shotlist: # loop over data files
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# generate filenames and read manual picks to a list
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obsfile = obsdir + str(shotnumber) + '_pickle.dat'
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#obsfile = obsdir + str(shotnumber) + '.dat'
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if not obsfile in shot_dict.keys():
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shot_dict[shotnumber] = []
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shot_dict[shotnumber] = seismicshot.SeismicShot(obsfile)
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shot_dict[shotnumber].setParameters('shotnumber', shotnumber)
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return shot_dict
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def setParametersForShots(cutwindow, tmovwind, tsignal, tgap, receiverfile, sourcefile, shot_dict):
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for shot in shot_dict.values():
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shot.setCut(cutwindow)
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shot.setTmovwind(tmovwind)
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shot.setTsignal(tsignal)
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shot.setTgap(tgap)
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shot.setRecfile(receiverfile)
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shot.setSourcefile(sourcefile)
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shot.setOrder(order = 4)
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def removeEmptyTraces(shot_dict):
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filename = 'removeEmptyTraces.out'
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filename2 = 'updateTraces.out'
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outfile = open(filename, 'w')
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outfile2 = open(filename2, 'w')
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for shot in shot_dict.values():
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del_traceIDs = shot.updateTraceList()
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removed = shot.removeEmptyTraces()
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if removed is not None:
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outfile.writelines('shot: %s, removed empty traces: %s\n' %(shot.getShotnumber(), removed))
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outfile2.writelines('shot: %s, removed traceID(s) %s because they were not found in the corresponding stream\n' %(shot.getShotnumber(), del_traceIDs))
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print '\nremoveEmptyTraces, updateTraces: Finished! See %s and %s for more information of removed traces.\n' %(filename, filename2)
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outfile.close()
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outfile2.close()
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def readParameters(parfile, parameter):
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from ConfigParser import ConfigParser
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parameterConfig = ConfigParser()
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parameterConfig.read('parfile')
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value = parameterConfig.get('vars', parameter).split('#')[0]
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value = value.replace(" ", "")
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value = parameterConfig.get('vars', parameter).split('\t')[0]
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return value
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def setArtificialPick(shot_dict, traceID, pick):
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for shot in shot_dict.values():
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shot.setPick(traceID, pick)
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shot.setPickwindow(traceID, shot.getCut())
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def fitSNR4dist(shot_dict, shiftdist = 5):
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import numpy as np
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dists = []
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picks = []
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snrs = []
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@ -31,6 +84,7 @@ def fitSNR4dist(shot_dict, shiftdist = 5):
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plotFittedSNR(dists, snrthresholds, snrs)
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return fit_fn #### ZU VERBESSERN, sollte fertige funktion wiedergeben
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def plotFittedSNR(dists, snrthresholds, snrs):
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import matplotlib.pyplot as plt
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plt.interactive(True)
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@ -42,12 +96,84 @@ def plotFittedSNR(dists, snrthresholds, snrs):
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plt.legend()
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def setFittedSNR(shot_dict, shiftdist = 5, p1 = 0.004, p2 = -0.004):
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import numpy as np
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#fit_fn = fitSNR4dist(shot_dict)
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fit_fn = np.poly1d([p1, p2])
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for shot in shot_dict.values():
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for traceID in shot.getTraceIDlist(): ### IMPROVE
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shot.setSNRthreshold(traceID, 1/(fit_fn(shot.getDistance(traceID) + shiftdist)**2)) ### s.o.
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print "\nsetFittedSNR: Finished setting of fitted SNR-threshold"
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print "setFittedSNR: Finished setting of fitted SNR-threshold"
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#def linearInterp(dist_med, dist_start
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def exportFMTOMO(shot_dict, directory = 'FMTOMO_export', sourcefile = 'input_sf.in', ttFileExtension = '.tt'):
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count = 0
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fmtomo_factor = 1000 # transforming [m/s] -> [km/s]
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LatAll = []; LonAll = []; DepthAll = []
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srcfile = open(directory + '/' + sourcefile, 'w')
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srcfile.writelines('%10s\n' %len(shot_dict)) # number of sources
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for shotnumber in getShotlist(shot_dict):
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shot = getShotForShotnumber(shot_dict, shotnumber)
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ttfilename = str(shotnumber) + ttFileExtension
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(x, y, z) = shot.getSrcLoc() # getSrcLoc returns (x, y, z)
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srcfile.writelines('%10s %10s %10s\n' %(getAngle(y), getAngle(x), (-1)*z)) # lat, lon, depth
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LatAll.append(getAngle(y)); LonAll.append(getAngle(x)); DepthAll.append((-1)*z)
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srcfile.writelines('%10s\n' %1) #
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srcfile.writelines('%10s %10s %10s\n' %(1, 1, ttfilename))
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ttfile = open(directory + '/' + ttfilename, 'w')
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traceIDlist = shot.getTraceIDlist()
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traceIDlist.sort()
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ttfile.writelines(str(countPickedTraces(shot)) + '\n')
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for traceID in traceIDlist:
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if shot.getPick(traceID) is not None:
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pick = shot.getPick(traceID) * fmtomo_factor
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delta = shot.getPickError(traceID) * fmtomo_factor
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(x, y, z) = shot.getRecLoc(traceID)
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ttfile.writelines('%20s %20s %20s %10s %10s\n' %(getAngle(y), getAngle(x), (-1)*z, pick, delta))
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LatAll.append(getAngle(y)); LonAll.append(getAngle(x)); DepthAll.append((-1)*z)
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count += 1
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ttfile.close()
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srcfile.close()
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print 'Wrote output for %s traces' %count
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print 'WARNING: output generated for FMTOMO-obsdata. Obsdata seems to take Lat, Lon, Depth and creates output for FMTOMO as Depth, Lat, Lon'
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print 'Dimensions of the seismic Array, transformed for FMTOMO, are Depth(%s, %s), Lat(%s, %s), Lon(%s, %s)'%(
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min(DepthAll), max(DepthAll), min(LatAll), max(LatAll), min(LonAll), max(LonAll))
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def getShotlist(shot_dict):
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shotlist = []
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for shot in shot_dict.values():
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shotlist.append(shot.getShotnumber())
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shotlist.sort()
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return shotlist
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def getShotForShotnumber(shot_dict, shotnumber):
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for shot in shot_dict.values():
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if shot.getShotnumber() == shotnumber:
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return shot
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def getAngle(distance):
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'''
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Function returns the angle on a Sphere of the radius R = 6371 [km] for a distance [km].
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'''
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import numpy as np
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PI = np.pi
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R = 6371.
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angle = distance * 180 / (PI * R)
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return angle
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def countPickedTraces(shot):
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numtraces = 0
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for traceID in shot.getTraceIDlist():
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if shot.getPick(traceID) is not None:
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numtraces += 1
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print "countPickedTraces: Found %s picked traces in shot number %s" %(numtraces, shot.getShotnumber())
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return numtraces
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def countAllPickedTraces(shot_dict):
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traces = 0
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for shot in shot_dict.values():
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traces += countPickedTraces(shot)
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return traces
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def findTracesInRanges(shot_dict, distancebin, pickbin):
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'''
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@ -61,6 +187,7 @@ def findTracesInRanges(shot_dict, distancebin, pickbin):
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:param: pickbin
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:type: tuple, (t1[s], t2[s])
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'''
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shots_found = {}
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for shot in shot_dict.values():
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@ -72,3 +199,6 @@ def findTracesInRanges(shot_dict, distancebin, pickbin):
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shots_found[shot.getShotnumber()].append(traceID)
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return shots_found
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@ -0,0 +1 @@
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# -*- coding: utf-8 -*-
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@ -6,7 +6,7 @@ from obspy.signal.trigger import coincidenceTrigger
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class CoincidenceTimes():
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class CoincidenceTimes(object):
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def __init__(self, st, comp='Z', coinum=4, sta=1., lta=10., on=5., off=1.):
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_type = 'recstalta'
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@ -1,3 +1,4 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Created August/September 2015.
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@ -142,7 +143,7 @@ class DCfc(Magnitude):
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'''
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def calcsourcespec(self):
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print ("Calculating source spectrum ....")
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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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@ -1,3 +1,4 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Created Oct/Nov 2014
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@ -319,7 +320,7 @@ class ARZcf(CharacteristicFunction):
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cf = np.zeros(len(xnp))
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loopstep = self.getARdetStep()
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arcalci = ldet + self.getOrder() #AR-calculation index
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for i in range(ldet + self.getOrder(), tend - lpred - 1):
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for i in range(ldet + self.getOrder(), tend - lpred - 1):
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if i == arcalci:
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#determination of AR coefficients
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#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
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@ -366,7 +367,7 @@ class ARZcf(CharacteristicFunction):
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rhs = np.zeros(self.getOrder())
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for k in range(0, self.getOrder()):
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for i in range(rind, ldet+1):
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ki = k + 1
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ki = k + 1
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rhs[k] = rhs[k] + data[i] * data[i - ki]
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#recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
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@ -312,7 +312,7 @@ class PragPicker(AutoPicking):
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else:
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for i in range(1, len(self.cf)):
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if i > ismooth:
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ii1 = i - ismooth;
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ii1 = i - ismooth
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cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
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else:
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cfsmooth[i] = np.mean(self.cf[1 : i])
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@ -1 +1,2 @@
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# -*- coding: utf-8 -*-
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#
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@ -317,29 +317,29 @@ def autopickstation(wfstream, pickparam):
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data = Data()
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[corzdat, restflag] = data.restituteWFData(invdir, zdat)
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if restflag == 1:
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# integrate to displacement
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corintzdat = integrate.cumtrapz(corzdat[0], None, corzdat[0].stats.delta)
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# class needs stream object => build it
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z_copy = zdat.copy()
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z_copy[0].data = corintzdat
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# largest detectable period == window length
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# after P pulse for calculating source spectrum
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winzc = (1 / bpz2[0]) * z_copy[0].stats.sampling_rate
|
||||
impickP = mpickP * z_copy[0].stats.sampling_rate
|
||||
wfzc = z_copy[0].data[impickP : impickP + winzc]
|
||||
# calculate spectrum using only first cycles of
|
||||
# waveform after P onset!
|
||||
zc = crossings_nonzero_all(wfzc)
|
||||
if np.size(zc) == 0:
|
||||
print ("Something is wrong with the waveform, " \
|
||||
"no zero crossings derived!")
|
||||
print ("Cannot calculate source spectrum!")
|
||||
else:
|
||||
calcwin = (zc[3] - zc[0]) * z_copy[0].stats.delta
|
||||
# calculate source spectrum and get w0 and fc
|
||||
specpara = DCfc(z_copy, mpickP, calcwin, iplot)
|
||||
w0 = specpara.getw0()
|
||||
fc = specpara.getfc()
|
||||
# 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
|
||||
# largest detectable period == window length
|
||||
# after P pulse for calculating source spectrum
|
||||
winzc = (1 / bpz2[0]) * z_copy[0].stats.sampling_rate
|
||||
impickP = mpickP * z_copy[0].stats.sampling_rate
|
||||
wfzc = z_copy[0].data[impickP : impickP + winzc]
|
||||
# calculate spectrum using only first cycles of
|
||||
# waveform after P onset!
|
||||
zc = crossings_nonzero_all(wfzc)
|
||||
if np.size(zc) == 0:
|
||||
print ("Something is wrong with the waveform, " \
|
||||
"no zero crossings derived!")
|
||||
print ("Cannot calculate source spectrum!")
|
||||
else:
|
||||
calcwin = (zc[3] - zc[0]) * z_copy[0].stats.delta
|
||||
# calculate source spectrum and get w0 and fc
|
||||
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))
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
@ -495,9 +496,9 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
|
||||
|
||||
if len(SPtimes) >= 3:
|
||||
# calculate slope
|
||||
p1 = np.polyfit(Ppicks, SPtimes, 1)
|
||||
wdfit = np.polyval(p1, Ppicks)
|
||||
# calculate slope
|
||||
p1 = np.polyfit(Ppicks, SPtimes, 1)
|
||||
wdfit = np.polyval(p1, Ppicks)
|
||||
wfitflag = 0
|
||||
|
||||
# calculate vp/vs ratio before check
|
||||
@ -534,40 +535,40 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
pickdic[key]['S']['marked'] = marker
|
||||
|
||||
if len(checkedPpicks) >= 3:
|
||||
# calculate new slope
|
||||
p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
|
||||
wdfit2 = np.polyval(p2, checkedPpicks)
|
||||
# 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)
|
||||
# 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!")
|
||||
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 ("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')
|
||||
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')
|
||||
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.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
|
||||
|
||||
plt.ylabel('S-P Times [s]')
|
||||
plt.xlabel('P Times [s]')
|
||||
@ -614,7 +615,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
print ("Checking signal length ...")
|
||||
|
||||
if len(X) > 1:
|
||||
# all three components available
|
||||
# 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]
|
||||
@ -641,7 +642,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
|
||||
|
||||
if numoverthr >= minnum:
|
||||
print ("checksignallength: Signal reached required length.")
|
||||
print ("checksignallength: Signal reached required length.")
|
||||
returnflag = 1
|
||||
else:
|
||||
print ("checksignallength: Signal shorter than required minimum signal length!")
|
||||
@ -651,7 +652,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
|
||||
if iplot == 2:
|
||||
plt.figure(iplot)
|
||||
p1, = plt.plot(t,rms, 'k')
|
||||
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]]], \
|
||||
@ -731,27 +732,27 @@ def checkPonsets(pickdic, dttolerance, iplot):
|
||||
badjkmarker = 'badjkcheck'
|
||||
for i in range(0, len(goodstations)):
|
||||
# mark P onset as checked and keep P weight
|
||||
pickdic[goodstations[i]]['P']['marked'] = goodmarker
|
||||
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
|
||||
# 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
|
||||
|
||||
if iplot > 1:
|
||||
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
|
||||
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.text(i, Ppicks[i] + 0.2, stations[i])
|
||||
|
||||
plt.xlabel('Number of P Picks')
|
||||
plt.ylabel('Onset Time [s] from 1.1.1970')
|
||||
@ -791,37 +792,37 @@ def jackknife(X, phi, h):
|
||||
g = len(X) / h
|
||||
|
||||
if type(g) is not int:
|
||||
print ("jackknife: Cannot divide quantity X in equal sized subgroups!")
|
||||
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)
|
||||
# estimator of undisturbed spot check
|
||||
if phi == 'MEA':
|
||||
phi_sc = np.mean(X)
|
||||
elif phi == 'VAR':
|
||||
phi_sc = np.var(X)
|
||||
phi_sc = np.var(X)
|
||||
elif phi == 'MED':
|
||||
phi_sc = np.median(X)
|
||||
phi_sc = np.median(X)
|
||||
|
||||
# estimators of subgroups
|
||||
# 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)
|
||||
# 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)
|
||||
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)
|
||||
|
||||
@ -901,17 +902,17 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
|
||||
# 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!")
|
||||
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,
|
||||
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,
|
||||
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, 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')
|
||||
|
||||
@ -1 +1 @@
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
@ -208,8 +208,7 @@ class FilterOptions(object):
|
||||
|
||||
def parseFilterOptions(self):
|
||||
if self.getFilterType():
|
||||
robject = {'type':self.getFilterType()}
|
||||
robject['corners'] = self.getOrder()
|
||||
robject = {'type': self.getFilterType(), 'corners': self.getOrder()}
|
||||
if len(self.getFreq()) > 1:
|
||||
robject['freqmin'] = self.getFreq()[0]
|
||||
robject['freqmax'] = self.getFreq()[1]
|
||||
|
||||
@ -1 +1,2 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from pylot.core.util.version import get_git_version as _getVersionString
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
Created on 10.11.2014
|
||||
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
Created on 10.11.2014
|
||||
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import sys
|
||||
from PySide.QtCore import QThread, Signal
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys, time
|
||||
from PySide.QtGui import QApplication
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys
|
||||
import matplotlib
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys, time
|
||||
from PySide.QtGui import QApplication
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
|
||||
import sys, time
|
||||
from PySide.QtGui import QApplication
|
||||
@ -9,7 +11,7 @@ dialogs = [FilterOptionsDialog, PropertiesDlg, HelpForm]
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
for dlg in dialogs:
|
||||
win = dlg()
|
||||
win.show()
|
||||
time.sleep(1)
|
||||
win.destroy()
|
||||
win = dlg()
|
||||
win.show()
|
||||
time.sleep(1)
|
||||
win.destroy()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user