changed whole autoPyLoT figure structure to fig/ax structure to keep connections to figures in memory, iPlot now part of autoPyLoT function call parameters (not yet in argparser)
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6563b01293
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19
autoPyLoT.py
19
autoPyLoT.py
@ -29,7 +29,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
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__version__ = _getVersionString()
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def autoPyLoT(inputfile, fnames=None, savepath=None):
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def autoPyLoT(inputfile, fnames=None, savepath=None, iplot=0):
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"""
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Determine phase onsets automatically utilizing the automatic picking
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algorithms by Kueperkoch et al. 2010/2012.
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@ -152,7 +152,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
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print(data)
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##########################################################
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# !automated picking starts here!
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picks = autopickevent(wfdat, parameter)
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picks, mainFig = autopickevent(wfdat, parameter, iplot=iplot)
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##########################################################
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# locating
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if locflag == 1:
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@ -192,14 +192,14 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
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moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
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parameter.get('Qp'),
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parameter.get('rho'), True, \
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parameter.get('iplot'))
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iplot)
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# update pick with moment property values (w0, fc, Mo)
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for station, props in moment_mag.moment_props.items():
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picks[station]['P'].update(props)
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evt = moment_mag.updated_event()
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local_mag = RichterMagnitude(corr_dat, evt,
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parameter.get('sstop'), True,\
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parameter.get('iplot'))
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iplot)
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for station, amplitude in local_mag.amplitudes.items():
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picks[station]['S']['Ao'] = amplitude.generic_amplitude
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evt = local_mag.updated_event()
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@ -219,7 +219,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
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print("autoPyLoT: Number of maximum iterations reached, stop iterative picking!")
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break
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print("autoPyLoT: Starting with iteration No. %d ..." % nlloccounter)
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picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
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picks, _ = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
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# write phases to NLLoc-phase file
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nll.export(picks, phasefile, parameter)
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# remove actual NLLoc-location file to keep only the last
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@ -244,14 +244,14 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
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moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
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parameter.get('Qp'),
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parameter.get('rho'), True, \
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parameter.get('iplot'))
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iplot)
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# update pick with moment property values (w0, fc, Mo)
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for station, props in moment_mag.moment_props.items():
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picks[station]['P'].update(props)
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evt = moment_mag.updated_event()
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local_mag = RichterMagnitude(corr_dat, evt,
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parameter.get('sstop'), True, \
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parameter.get('iplot'))
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iplot)
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for station, amplitude in local_mag.amplitudes.items():
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picks[station]['S']['Ao'] = amplitude.generic_amplitude
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evt = local_mag.updated_event()
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@ -303,6 +303,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
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The Python picking and Location Tool\n
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************************************'''.format(version=_getVersionString())
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print(endsp)
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return picks, mainFig
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if __name__ == "__main__":
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@ -318,6 +319,8 @@ if __name__ == "__main__":
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parser.add_argument('-f', '-F', '--fnames', type=str,
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action='store',
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help='''optional, list of data file names''')
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# parser.add_argument('-p', '-P', '--plot', action='store',
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# help='show interactive plots')
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parser.add_argument('-s', '-S', '--spath', type=str,
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action='store',
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help='''optional, save path for autoPyLoT output''')
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@ -327,4 +330,4 @@ if __name__ == "__main__":
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cla = parser.parse_args()
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autoPyLoT(str(cla.inputfile), str(cla.fnames), str(cla.spath))
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picks, mainFig = autoPyLoT(str(cla.inputfile), str(cla.fnames), str(cla.spath))
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@ -1 +1 @@
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2628-dirty
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6563-dirty
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@ -203,20 +203,18 @@ class RichterMagnitude(Magnitude):
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# check for plot flag (for debugging only)
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if self.plot_flag > 1:
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st.plot()
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f = plt.figure(2)
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plt.plot(th, sqH)
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plt.plot(th[iwin], sqH[iwin], 'g')
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plt.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2)
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plt.title(
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(th, sqH)
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ax.plot(th[iwin], sqH[iwin], 'g')
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ax.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2)
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ax.title(
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'Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
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% (st[0].stats.station, wapp))
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plt.xlabel('Time [s]')
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plt.ylabel('Displacement [mm]')
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plt.show()
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raw_input()
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plt.close(f)
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ax.set_xlabel('Time [s]')
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ax.set_ylabel('Displacement [mm]')
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return wapp
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return wapp, fig
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def calc(self):
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for a in self.arrivals:
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@ -234,7 +232,7 @@ class RichterMagnitude(Magnitude):
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continue
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delta = degrees2kilometers(a.distance)
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onset = pick.time
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a0 = self.peak_to_peak(wf, onset)
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a0, self.p2p_fig = self.peak_to_peak(wf, onset)
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amplitude = ope.Amplitude(generic_amplitude=a0 * 1e-3)
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amplitude.unit = 'm'
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amplitude.category = 'point'
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@ -581,9 +579,6 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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plt.xlabel('Frequency [Hz]')
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plt.ylabel('Amplitude [m/Hz]')
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plt.grid()
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plt.show()
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raw_input()
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plt.close(f1)
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return w0, fc
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@ -685,7 +680,7 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
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"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
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if iplot > 1:
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plt.figure(iplot)
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plt.figure()#iplot)
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plt.loglog(f, S, 'k')
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plt.loglog([f[0], fc], [w0, w0], 'g')
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plt.loglog([fc, fc], [w0 / 100, w0], 'g')
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@ -694,7 +689,7 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
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plt.xlabel('Frequency [Hz]')
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plt.ylabel('Amplitude [m/Hz]')
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plt.grid()
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plt.figure(iplot + 1)
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plt.figure()#iplot + 1)
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plt.subplot(311)
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plt.plot(f[il:ir], STD, '*')
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plt.title('Common Standard Deviations')
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@ -707,8 +702,5 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
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plt.plot(f[il:ir], stdfc, '*')
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plt.title('Standard Deviations of Corner Frequencies')
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plt.xlabel('Corner Frequencies [Hz]')
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plt.show()
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raw_input()
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plt.close()
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return w0, fc
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@ -160,7 +160,7 @@ class AutoPickParameter(object):
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fid_out = open(fnout, 'w')
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lines = []
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for key, value in self.iteritems():
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lines.append('{key}\t{value}'.format(key=key, value=value))
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lines.append('{key}\t{value}\n'.format(key=key, value=value))
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fid_out.writelines(lines)
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@ -21,16 +21,16 @@ from pylot.core.util.utils import getPatternLine, gen_Pool
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from pylot.core.io.data import Data
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def autopickevent(data, param):
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def autopickevent(data, param, iplot=0):
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stations = []
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all_onsets = {}
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fig_dict = {}
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input_tuples = []
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# get some parameters for quality control from
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# parameter input file (usually autoPyLoT.in).
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wdttolerance = param.get('wdttolerance')
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mdttolerance = param.get('mdttolerance')
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iplot = param.get('iplot')
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apverbose = param.get('apverbose')
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for n in range(len(data)):
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station = data[n].stats.station
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@ -41,18 +41,27 @@ def autopickevent(data, param):
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for station in stations:
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topick = data.select(station=station)
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#all_onsets[station] = autopickstation(topick, param, verbose=apverbose)
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input_tuples.append((topick, param, apverbose))
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if not iplot:
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input_tuples.append((topick, param, apverbose))
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if iplot>0:
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all_onsets[station], fig_dict[station] = autopickstation(topick, param, verbose=apverbose, iplot=iplot)
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if iplot>0:
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print('iPlot Flag active: NO MULTIPROCESSING possible.')
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return all_onsets, fig_dict # changing structure of autopicking and figure generation MP MP
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pool = gen_Pool()
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result = pool.map(call_autopickstation, input_tuples)
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pool.close()
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for pick in result:
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for pick, fig_dict in result:
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station = pick['station']
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pick.pop('station')
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all_onsets[station] = pick
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return all_onsets, fig_dict # changing structure of autopicking and figure generation MP MP
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# quality control
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# median check and jackknife on P-onset times
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jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
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@ -62,10 +71,11 @@ def autopickevent(data, param):
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def call_autopickstation(input_tuple):
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wfstream, pickparam, verbose = input_tuple
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return autopickstation(wfstream, pickparam, verbose)
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#multiprocessing not possible with interactive plotting
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return autopickstation(wfstream, pickparam, verbose, iplot=0)
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def autopickstation(wfstream, pickparam, verbose=False):
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def autopickstation(wfstream, pickparam, verbose=False, iplot=0):
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"""
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:param wfstream: `~obspy.core.stream.Stream` containing waveform
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:type wfstream: obspy.core.stream.Stream
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@ -82,8 +92,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
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# read your autoPyLoT.in for details!
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# special parameters for P picking
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iplot = iplot
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algoP = pickparam.get('algoP')
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iplot = pickparam.get('iplot')
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pstart = pickparam.get('pstart')
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pstop = pickparam.get('pstop')
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thosmw = pickparam.get('tlta')
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@ -161,6 +172,8 @@ def autopickstation(wfstream, pickparam, verbose=False):
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Ao = None # Wood-Anderson peak-to-peak amplitude
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picker = 'auto' # type of picks
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fig_dict = {}
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# split components
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zdat = wfstream.select(component="Z")
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if len(zdat) == 0: # check for other components
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@ -223,6 +236,7 @@ def autopickstation(wfstream, pickparam, verbose=False):
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# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
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# of class AutoPicking
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aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
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fig_dict['aicFig'] = aicpick.fig
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##############################################################
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if aicpick.getpick() is not None:
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# check signal length to detect spuriously picked noise peaks
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@ -236,9 +250,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
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'Decreasing minsiglengh from {0} to ' \
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'{1}'.format(minsiglength, minsiglength / 2)
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if verbose: print(msg)
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2,
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nfacsl, minpercent, iplot)
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Pflag, fig_dict['slength'] = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2,
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nfacsl, minpercent, iplot)
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else:
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# filter and taper horizontal traces
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trH1_filt = edat.copy()
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@ -253,9 +267,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
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trH2_filt.taper(max_percentage=0.05, type='hann')
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zne += trH1_filt
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zne += trH2_filt
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength,
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nfacsl, minpercent, iplot)
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Pflag, fig_dict['slength'] = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength,
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nfacsl, minpercent, iplot)
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if Pflag == 1:
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# check for spuriously picked S onset
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@ -315,13 +329,18 @@ def autopickstation(wfstream, pickparam, verbose=False):
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algoP=algoP)
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refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
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aicpick.getpick())
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fig_dict['refPpick'] = refPpick.fig
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mpickP = refPpick.getpick()
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#############################################################
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if mpickP is not None:
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# quality assessment
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# get earliest/latest possible pick and symmetrized uncertainty
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[epickP, lpickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz,
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mpickP, iplot)
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if iplot:
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epickP, lpickP, Perror, fig_dict['el_Ppick'] = earllatepicker(z_copy, nfacP, tsnrz,
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mpickP, iplot)
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else:
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epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
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mpickP, iplot)
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# get SNR
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[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP)
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@ -342,7 +361,10 @@ def autopickstation(wfstream, pickparam, verbose=False):
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# get first motion of P onset
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# certain quality required
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if Pweight <= minfmweight and SNRP >= minFMSNR:
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FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
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if iplot:
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FM, fig_dict['fm_picker'] = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
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else:
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FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
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else:
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FM = 'N'
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@ -498,20 +520,31 @@ def autopickstation(wfstream, pickparam, verbose=False):
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# get refined onset time from CF2 using class Picker
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refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
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tsmoothS, aicarhpick.getpick())
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fig_dict['refSpick'] = refSpick.fig
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mpickS = refSpick.getpick()
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#############################################################
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if mpickS is not None:
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# quality assessment
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# get earliest/latest possible pick and symmetrized uncertainty
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h_copy[0].data = trH1_filt.data
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[epickS1, lpickS1, Serror1] = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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if iplot:
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epickS1, lpickS1, Serror1, fig_dict['el_S1pick'] = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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else:
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epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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h_copy[0].data = trH2_filt.data
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[epickS2, lpickS2, Serror2] = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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if iplot:
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epickS2, lpickS2, Serror2, fig_dict['el_S2pick'] = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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else:
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epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot)
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if epickS1 is not None and epickS2 is not None:
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if algoS == 'ARH':
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# get earliest pick of both earliest possible picks
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@ -603,62 +636,58 @@ def autopickstation(wfstream, pickparam, verbose=False):
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##############################################################
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if iplot > 0:
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# plot vertical trace
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plt.figure()
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plt.subplot(3, 1, 1)
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fig = plt.figure()
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ax1 = fig.add_subplot(311)
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tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
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tr_filt.stats.delta)
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# check equal length of arrays, sometimes they are different!?
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wfldiff = len(tr_filt.data) - len(tdata)
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if wfldiff < 0:
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tdata = tdata[0:len(tdata) - abs(wfldiff)]
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p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
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ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
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if Pweight < 4:
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p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
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'b')
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ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
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'b', label='CF1')
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if aicPflag == 1:
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p3, = plt.plot(cf2.getTimeArray(),
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cf2.getCF() / max(cf2.getCF()), 'm')
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p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
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'r')
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plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
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ax1.plot(cf2.getTimeArray(),
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cf2.getCF() / max(cf2.getCF()), 'm', label='CF2')
|
||||
ax1.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
|
||||
'r', label='Initial P Onset')
|
||||
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
||||
[1, 1], 'r')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
||||
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
||||
[-1, -1], 'r')
|
||||
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()],
|
||||
[-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
ax1.plot([refPpick.getpick(), refPpick.getpick()],
|
||||
[-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
|
||||
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'r', linewidth=2)
|
||||
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
|
||||
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
|
||||
plt.legend([p1, p2, p3, p4, p5],
|
||||
['Data', 'CF1', 'CF2', 'Initial P Onset',
|
||||
'Final P Pick'])
|
||||
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
|
||||
'Polarity: %s' % (tr_filt.stats.station,
|
||||
tr_filt.stats.channel,
|
||||
Pweight,
|
||||
SNRP,
|
||||
SNRPdB,
|
||||
FM))
|
||||
ax1.plot([lpickP, lpickP], [-1.1, 1.1], 'r--', label='lpp')
|
||||
ax1.plot([epickP, epickP], [-1.1, 1.1], 'r--', label='epp')
|
||||
ax1.set_title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
|
||||
'Polarity: %s' % (tr_filt.stats.station,
|
||||
tr_filt.stats.channel,
|
||||
Pweight,
|
||||
SNRP,
|
||||
SNRPdB,
|
||||
FM))
|
||||
else:
|
||||
plt.legend([p1, p2], ['Data', 'CF1'])
|
||||
plt.title('%s, P Weight=%d, SNR=None, '
|
||||
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
||||
ax1.set_title('%s, P Weight=%d, SNR=None, '
|
||||
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
||||
else:
|
||||
plt.title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
|
||||
ax1.set_title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
|
||||
tr_filt.stats.channel,
|
||||
Pweight))
|
||||
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(tr_filt.stats.starttime)
|
||||
ax1.legend()
|
||||
ax1.set_yticks([])
|
||||
ax1.set_ylim([-1.5, 1.5])
|
||||
ax1.set_ylabel('Normalized Counts')
|
||||
fig.suptitle(tr_filt.stats.starttime)
|
||||
|
||||
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1:
|
||||
# plot horizontal traces
|
||||
plt.subplot(3, 1, 2)
|
||||
ax2 = fig.add_subplot(312)
|
||||
th1data = np.arange(0,
|
||||
trH1_filt.stats.npts /
|
||||
trH1_filt.stats.sampling_rate,
|
||||
@ -667,50 +696,47 @@ def autopickstation(wfstream, pickparam, verbose=False):
|
||||
wfldiff = len(trH1_filt.data) - len(th1data)
|
||||
if wfldiff < 0:
|
||||
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
||||
p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k', label='Data')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
|
||||
ax2.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot(
|
||||
ax2.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g')
|
||||
plt.plot(
|
||||
[-1, 1], 'g', label='Initial S Onset')
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
plt.plot(
|
||||
ax2.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot(
|
||||
ax2.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
||||
ax2.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot(
|
||||
ax2.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25],
|
||||
['Data', 'CF1', 'CF2', 'Initial S Onset',
|
||||
'Final S Pick'])
|
||||
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
|
||||
ax2.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
||||
ax2.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
||||
ax2.set_title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
|
||||
trH1_filt.stats.channel,
|
||||
Sweight, SNRS, SNRSdB))
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
|
||||
ax2.set_title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
|
||||
trH1_filt.stats.channel, Sweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
ax2.legend()
|
||||
ax2.set_yticks([])
|
||||
ax2.set_ylim([-1.5, 1.5])
|
||||
ax2.set_ylabel('Normalized Counts')
|
||||
fig.suptitle(trH1_filt.stats.starttime)
|
||||
|
||||
plt.subplot(3, 1, 3)
|
||||
ax3 = fig.add_subplot(313)
|
||||
th2data = np.arange(0,
|
||||
trH2_filt.stats.npts /
|
||||
trH2_filt.stats.sampling_rate,
|
||||
@ -719,47 +745,41 @@ def autopickstation(wfstream, pickparam, verbose=False):
|
||||
wfldiff = len(trH2_filt.data) - len(th2data)
|
||||
if wfldiff < 0:
|
||||
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k', label='Data')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
|
||||
p22, = ax3.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot(
|
||||
ax3.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g')
|
||||
plt.plot(
|
||||
[-1, 1], 'g', label='Initial S Onset')
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
plt.plot(
|
||||
ax3.plot(
|
||||
[aicarhpick.getpick() - 0.5,
|
||||
aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot(
|
||||
ax3.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
|
||||
ax3.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot(
|
||||
ax3.plot(
|
||||
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25],
|
||||
['Data', 'CF1', 'CF2', 'Initial S Onset',
|
||||
'Final S Pick'])
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title(trH2_filt.stats.channel)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
ax3.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
|
||||
ax3.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
|
||||
ax3.legend()
|
||||
ax3.set_yticks([])
|
||||
ax3.set_ylim([-1.5, 1.5])
|
||||
ax3.set_xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
ax3.set_ylabel('Normalized Counts')
|
||||
ax3.set_title(trH2_filt.stats.channel)
|
||||
fig_dict['mainFig'] = fig
|
||||
##########################################################################
|
||||
# calculate "real" onset times
|
||||
if lpickP is not None and lpickP == mpickP:
|
||||
@ -806,7 +826,7 @@ def autopickstation(wfstream, pickparam, verbose=False):
|
||||
snrdb=SNRSdB, weight=Sweight, fm=None, picker=picker, Ao=Ao)
|
||||
# merge picks into returning dictionary
|
||||
picks = dict(P=ppick, S=spick, station=zdat[0].stats.station)
|
||||
return picks
|
||||
return picks, fig_dict
|
||||
|
||||
|
||||
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
|
||||
@ -884,7 +904,7 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
|
||||
print("zfac: %f => %f" % (zfac_old, pickparameter.get('zfac')))
|
||||
|
||||
# repick station
|
||||
newpicks = autopickstation(wf2pick, pickparameter)
|
||||
newpicks, fig = autopickstation(wf2pick, pickparameter)
|
||||
|
||||
# replace old dictionary with new one
|
||||
picks[badpicks[i][0]] = newpicks
|
||||
@ -899,4 +919,4 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
|
||||
pickparameter.setParam(noisefactor=noisefactor_old)
|
||||
pickparameter.setParam(zfac=zfac_old)
|
||||
|
||||
return picks
|
||||
return picks, fig
|
||||
|
||||
@ -72,7 +72,7 @@ class AutoPicker(object):
|
||||
self.setaus(aus)
|
||||
self.setTsmooth(Tsmooth)
|
||||
self.setpick1(Pick1)
|
||||
self.calcPick()
|
||||
self.fig = self.calcPick()
|
||||
|
||||
def __str__(self):
|
||||
return '''\n\t{name} object:\n
|
||||
@ -152,6 +152,7 @@ class AICPicker(AutoPicker):
|
||||
self.Pick = None
|
||||
self.slope = None
|
||||
self.SNR = None
|
||||
fig = None
|
||||
# find NaN's
|
||||
nn = np.isnan(self.cf)
|
||||
if len(nn) > 1:
|
||||
@ -225,18 +226,16 @@ class AICPicker(AutoPicker):
|
||||
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)
|
||||
fig = plt.figure() #self.iplot) ### WHY? MP MP
|
||||
ax = fig.add_subplot(111)
|
||||
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
|
||||
ax.plot(self.Tcf, x / max(x), 'k', legend='(HOS-/AR-) Data')
|
||||
ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', legend='Smoothed AIC-CF')
|
||||
ax.legend()
|
||||
ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
ax.set_yticks([])
|
||||
ax.set_title(self.Data[0].stats.station)
|
||||
return fig
|
||||
islope = islope[0][0:imax]
|
||||
dataslope = self.Data[0].data[islope]
|
||||
# calculate slope as polynomal fit of order 1
|
||||
@ -253,41 +252,36 @@ class AICPicker(AutoPicker):
|
||||
self.slope = None
|
||||
|
||||
if self.iplot > 1:
|
||||
p = plt.figure(self.iplot)
|
||||
fig = plt.figure()#self.iplot)
|
||||
ax1 = fig.add_subplot(211)
|
||||
x = self.Data[0].data
|
||||
p1, = plt.plot(self.Tcf, x / max(x), 'k')
|
||||
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
||||
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:
|
||||
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)
|
||||
|
||||
ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
|
||||
ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
ax1.set_yticks([])
|
||||
ax1.set_title(self.Data[0].stats.station)
|
||||
ax1.legend()
|
||||
|
||||
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)
|
||||
ax2 = fig.add_subplot(212)
|
||||
ax2.plot(self.Tcf, x, 'k', label='Data')
|
||||
ax2.plot(self.Tcf[inoise], self.Data[0].data[inoise], label='Noise Window')
|
||||
ax2.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r', label='Signal Window')
|
||||
ax2.plot(self.Tcf[islope], dataslope, 'g--', label='Slope Window')
|
||||
ax2.plot(self.Tcf[islope], datafit, 'g', linewidth=2, label='Slope')
|
||||
ax2.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
|
||||
self.SNR, self.slope))
|
||||
ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
ax2.set_ylabel('Counts')
|
||||
ax2.set_yticks([])
|
||||
ax2.legend()
|
||||
|
||||
if self.Pick == None:
|
||||
print('AICPicker: Could not find minimum, picking window too short?')
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
class PragPicker(AutoPicker):
|
||||
@ -296,7 +290,7 @@ class PragPicker(AutoPicker):
|
||||
'''
|
||||
|
||||
def calcPick(self):
|
||||
|
||||
|
||||
if self.getpick1() is not None:
|
||||
print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
|
||||
|
||||
@ -380,18 +374,17 @@ class PragPicker(AutoPicker):
|
||||
pickflag = 0
|
||||
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick, cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick, cfsmoothipick, 'r')
|
||||
fig = plt.figure()#self.getiplot())
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(Tcfpick, cfipick, 'k', label='CF')
|
||||
ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
|
||||
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)
|
||||
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)
|
||||
ax.legend()
|
||||
return fig
|
||||
|
||||
else:
|
||||
print('PragPicker: No initial onset time given! Check input!')
|
||||
|
||||
@ -105,35 +105,33 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, stealth_mode=False):
|
||||
PickError = symmetrize_error(diffti_te, diffti_tl)
|
||||
|
||||
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],
|
||||
fig = plt.figure()#iplot)
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(t, x, 'k', label='Data')
|
||||
ax.plot(t[inoise], x[inoise], label='Noise Window')
|
||||
ax.plot(t[isignal], x[isignal], 'r', label='Signal Window')
|
||||
ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', label='Noise Level')
|
||||
ax.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
|
||||
markersize=14, label='Zero Crossings')
|
||||
ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
|
||||
ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k', label='lpp')
|
||||
ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k', label='epp')
|
||||
ax.plot([Pick1 + PickError, Pick1 + PickError],
|
||||
[max(x) / 2, -max(x) / 2], 'r--', label='spe')
|
||||
ax.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(
|
||||
ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
|
||||
ax.set_yticks([])
|
||||
ax.set_title(
|
||||
'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
|
||||
X[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
ax.legend()
|
||||
|
||||
return EPick, LPick, PickError
|
||||
if iplot:
|
||||
return EPick, LPick, PickError, fig
|
||||
else:
|
||||
return Epick, LPick, PickError
|
||||
|
||||
|
||||
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
|
||||
@ -281,41 +279,37 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
|
||||
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)
|
||||
fig = plt.figure()#iplot)
|
||||
ax1 = fig.add_subplot(211)
|
||||
ax1.plot(t, xraw, 'k')
|
||||
ax1.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2, label='Pick')
|
||||
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[
|
||||
ax1.plot(t[islope1], xraw[islope1], label='Slope Window')
|
||||
ax1.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14, label='Zero Crossings')
|
||||
ax1.plot(t[islope1], datafit1, '--g', linewidth=2)
|
||||
ax1.legend()
|
||||
ax1.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
|
||||
ax1.set_yticks([])
|
||||
ax1.set_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',
|
||||
ax2=fig.add_subplot(212)
|
||||
ax2.set_title('First-Motion Determination, Filtered Data')
|
||||
ax2.plot(t, xfilt, 'k')
|
||||
ax2.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)
|
||||
ax2.plot(t[islope2], xfilt[islope2])
|
||||
ax2.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
|
||||
ax2.plot(t[islope2], datafit2, '--g', linewidth=2)
|
||||
ax2.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
|
||||
ax2.set_xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
|
||||
ax2.set_yticks([])
|
||||
|
||||
return FM
|
||||
if iplot:
|
||||
return FM, fig
|
||||
else:
|
||||
return FM
|
||||
|
||||
|
||||
def crossings_nonzero_all(data):
|
||||
@ -606,7 +600,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
|
||||
# plot results
|
||||
if iplot > 1:
|
||||
plt.figure(iplot)
|
||||
plt.figure()#iplot)
|
||||
f1, = plt.plot(Ppicks, SPtimes, 'ro')
|
||||
if wfitflag == 0:
|
||||
f2, = plt.plot(Ppicks, wdfit, 'k')
|
||||
@ -621,9 +615,6 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
|
||||
plt.ylabel('S-P Times [s]')
|
||||
plt.xlabel('P Times [s]')
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(iplot)
|
||||
|
||||
return checkedonsets
|
||||
|
||||
@ -700,25 +691,21 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
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)
|
||||
fig = plt.figure()#iplot)
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(t, rms, 'k', label='RMS Data')
|
||||
ax.plot(t[inoise], rms[inoise], 'c', label='RMS Noise Window')
|
||||
ax.plot(t[isignal], rms[isignal], 'r', label='RMS Signal Window')
|
||||
ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
|
||||
[minsiglevel, minsiglevel], 'g', linewidth=2, label='Minimum Signal Level')
|
||||
ax.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2, label='Onset')
|
||||
ax.legend()
|
||||
ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
|
||||
ax.set_ylabel('Counts')
|
||||
ax.set_title('Check for Signal Length, Station %s' % X[0].stats.station)
|
||||
ax.set_yticks([])
|
||||
|
||||
return returnflag
|
||||
return returnflag, fig
|
||||
|
||||
|
||||
def checkPonsets(pickdic, dttolerance, iplot):
|
||||
@ -808,8 +795,6 @@ def checkPonsets(pickdic, dttolerance, iplot):
|
||||
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
|
||||
|
||||
@ -962,22 +947,23 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
|
||||
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()
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(tz, z / max(z), 'k')
|
||||
ax.plot(tz[isignal], z[isignal] / max(z), 'r')
|
||||
ax.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
|
||||
ax.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
|
||||
ax.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
|
||||
ax.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
|
||||
ax.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
|
||||
[minsiglevel / max(z), minsiglevel / max(z)], 'g',
|
||||
linewidth=2, label='Minimum Signal Level')
|
||||
ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
|
||||
ax.set_ylabel('Normalized Counts')
|
||||
ax.set_yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
|
||||
ndat[0].stats.channel])
|
||||
ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
|
||||
ax.legend()
|
||||
|
||||
return returnflag
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user