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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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Marcel Paffrath 2017-05-08 15:38:41 +02:00
parent 6563b01293
commit c784d46521
7 changed files with 290 additions and 296 deletions
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19
autoPyLoT.py
View File

@ -29,7 +29,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString() __version__ = _getVersionString()
def autoPyLoT(inputfile, fnames=None, savepath=None): def autoPyLoT(inputfile, fnames=None, savepath=None, iplot=0):
""" """
Determine phase onsets automatically utilizing the automatic picking Determine phase onsets automatically utilizing the automatic picking
algorithms by Kueperkoch et al. 2010/2012. algorithms by Kueperkoch et al. 2010/2012.
@ -152,7 +152,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
print(data) print(data)
########################################################## ##########################################################
# !automated picking starts here! # !automated picking starts here!
picks = autopickevent(wfdat, parameter) picks, mainFig = autopickevent(wfdat, parameter, iplot=iplot)
########################################################## ##########################################################
# locating # locating
if locflag == 1: if locflag == 1:
@ -192,14 +192,14 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'), moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'), parameter.get('Qp'),
parameter.get('rho'), True, \ parameter.get('rho'), True, \
parameter.get('iplot')) iplot)
# update pick with moment property values (w0, fc, Mo) # update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items(): for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props) picks[station]['P'].update(props)
evt = moment_mag.updated_event() evt = moment_mag.updated_event()
local_mag = RichterMagnitude(corr_dat, evt, local_mag = RichterMagnitude(corr_dat, evt,
parameter.get('sstop'), True,\ parameter.get('sstop'), True,\
parameter.get('iplot')) iplot)
for station, amplitude in local_mag.amplitudes.items(): for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude picks[station]['S']['Ao'] = amplitude.generic_amplitude
evt = local_mag.updated_event() evt = local_mag.updated_event()
@ -219,7 +219,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
print("autoPyLoT: Number of maximum iterations reached, stop iterative picking!") print("autoPyLoT: Number of maximum iterations reached, stop iterative picking!")
break break
print("autoPyLoT: Starting with iteration No. %d ..." % nlloccounter) print("autoPyLoT: Starting with iteration No. %d ..." % nlloccounter)
picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter) picks, _ = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
# write phases to NLLoc-phase file # write phases to NLLoc-phase file
nll.export(picks, phasefile, parameter) nll.export(picks, phasefile, parameter)
# remove actual NLLoc-location file to keep only the last # remove actual NLLoc-location file to keep only the last
@ -244,14 +244,14 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'), moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'), parameter.get('Qp'),
parameter.get('rho'), True, \ parameter.get('rho'), True, \
parameter.get('iplot')) iplot)
# update pick with moment property values (w0, fc, Mo) # update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items(): for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props) picks[station]['P'].update(props)
evt = moment_mag.updated_event() evt = moment_mag.updated_event()
local_mag = RichterMagnitude(corr_dat, evt, local_mag = RichterMagnitude(corr_dat, evt,
parameter.get('sstop'), True, \ parameter.get('sstop'), True, \
parameter.get('iplot')) iplot)
for station, amplitude in local_mag.amplitudes.items(): for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude picks[station]['S']['Ao'] = amplitude.generic_amplitude
evt = local_mag.updated_event() evt = local_mag.updated_event()
@ -303,6 +303,7 @@ def autoPyLoT(inputfile, fnames=None, savepath=None):
The Python picking and Location Tool\n The Python picking and Location Tool\n
************************************'''.format(version=_getVersionString()) ************************************'''.format(version=_getVersionString())
print(endsp) print(endsp)
return picks, mainFig
if __name__ == "__main__": if __name__ == "__main__":
@ -318,6 +319,8 @@ if __name__ == "__main__":
parser.add_argument('-f', '-F', '--fnames', type=str, parser.add_argument('-f', '-F', '--fnames', type=str,
action='store', action='store',
help='''optional, list of data file names''') help='''optional, list of data file names''')
# parser.add_argument('-p', '-P', '--plot', action='store',
# help='show interactive plots')
parser.add_argument('-s', '-S', '--spath', type=str, parser.add_argument('-s', '-S', '--spath', type=str,
action='store', action='store',
help='''optional, save path for autoPyLoT output''') help='''optional, save path for autoPyLoT output''')
@ -327,4 +330,4 @@ if __name__ == "__main__":
cla = parser.parse_args() cla = parser.parse_args()
autoPyLoT(str(cla.inputfile), str(cla.fnames), str(cla.spath)) picks, mainFig = autoPyLoT(str(cla.inputfile), str(cla.fnames), str(cla.spath))

2
pylot/RELEASE-VERSION
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@ -1 +1 @@
2628-dirty 6563-dirty

32
pylot/core/analysis/magnitude.py
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@ -203,20 +203,18 @@ class RichterMagnitude(Magnitude):
# check for plot flag (for debugging only) # check for plot flag (for debugging only)
if self.plot_flag > 1: if self.plot_flag > 1:
st.plot() st.plot()
f = plt.figure(2) fig = plt.figure()
plt.plot(th, sqH) ax = fig.add_subplot(111)
plt.plot(th[iwin], sqH[iwin], 'g') ax.plot(th, sqH)
plt.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2) ax.plot(th[iwin], sqH[iwin], 'g')
plt.title( ax.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2)
ax.title(
'Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \ 'Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
% (st[0].stats.station, wapp)) % (st[0].stats.station, wapp))
plt.xlabel('Time [s]') ax.set_xlabel('Time [s]')
plt.ylabel('Displacement [mm]') ax.set_ylabel('Displacement [mm]')
plt.show()
raw_input()
plt.close(f)
return wapp return wapp, fig
def calc(self): def calc(self):
for a in self.arrivals: for a in self.arrivals:
@ -234,7 +232,7 @@ class RichterMagnitude(Magnitude):
continue continue
delta = degrees2kilometers(a.distance) delta = degrees2kilometers(a.distance)
onset = pick.time onset = pick.time
a0 = self.peak_to_peak(wf, onset) a0, self.p2p_fig = self.peak_to_peak(wf, onset)
amplitude = ope.Amplitude(generic_amplitude=a0 * 1e-3) amplitude = ope.Amplitude(generic_amplitude=a0 * 1e-3)
amplitude.unit = 'm' amplitude.unit = 'm'
amplitude.category = 'point' amplitude.category = 'point'
@ -581,9 +579,6 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
plt.xlabel('Frequency [Hz]') plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]') plt.ylabel('Amplitude [m/Hz]')
plt.grid() plt.grid()
plt.show()
raw_input()
plt.close(f1)
return w0, fc return w0, fc
@ -685,7 +680,7 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0)) "fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
if iplot > 1: if iplot > 1:
plt.figure(iplot) plt.figure()#iplot)
plt.loglog(f, S, 'k') plt.loglog(f, S, 'k')
plt.loglog([f[0], fc], [w0, w0], 'g') plt.loglog([f[0], fc], [w0, w0], 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g') plt.loglog([fc, fc], [w0 / 100, w0], 'g')
@ -694,7 +689,7 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
plt.xlabel('Frequency [Hz]') plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]') plt.ylabel('Amplitude [m/Hz]')
plt.grid() plt.grid()
plt.figure(iplot + 1) plt.figure()#iplot + 1)
plt.subplot(311) plt.subplot(311)
plt.plot(f[il:ir], STD, '*') plt.plot(f[il:ir], STD, '*')
plt.title('Common Standard Deviations') plt.title('Common Standard Deviations')
@ -707,8 +702,5 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
plt.plot(f[il:ir], stdfc, '*') plt.plot(f[il:ir], stdfc, '*')
plt.title('Standard Deviations of Corner Frequencies') plt.title('Standard Deviations of Corner Frequencies')
plt.xlabel('Corner Frequencies [Hz]') plt.xlabel('Corner Frequencies [Hz]')
plt.show()
raw_input()
plt.close()
return w0, fc return w0, fc

2
pylot/core/io/inputs.py
View File

@ -160,7 +160,7 @@ class AutoPickParameter(object):
fid_out = open(fnout, 'w') fid_out = open(fnout, 'w')
lines = [] lines = []
for key, value in self.iteritems(): for key, value in self.iteritems():
lines.append('{key}\t{value}'.format(key=key, value=value)) lines.append('{key}\t{value}\n'.format(key=key, value=value))
fid_out.writelines(lines) fid_out.writelines(lines)

258
pylot/core/pick/autopick.py
View File

@ -21,16 +21,16 @@ from pylot.core.util.utils import getPatternLine, gen_Pool
from pylot.core.io.data import Data from pylot.core.io.data import Data
def autopickevent(data, param): def autopickevent(data, param, iplot=0):
stations = [] stations = []
all_onsets = {} all_onsets = {}
fig_dict = {}
input_tuples = [] input_tuples = []
# get some parameters for quality control from # get some parameters for quality control from
# parameter input file (usually autoPyLoT.in). # parameter input file (usually autoPyLoT.in).
wdttolerance = param.get('wdttolerance') wdttolerance = param.get('wdttolerance')
mdttolerance = param.get('mdttolerance') mdttolerance = param.get('mdttolerance')
iplot = param.get('iplot')
apverbose = param.get('apverbose') apverbose = param.get('apverbose')
for n in range(len(data)): for n in range(len(data)):
station = data[n].stats.station station = data[n].stats.station
@ -41,18 +41,27 @@ def autopickevent(data, param):
for station in stations: for station in stations:
topick = data.select(station=station) topick = data.select(station=station)
#all_onsets[station] = autopickstation(topick, param, verbose=apverbose)
input_tuples.append((topick, param, apverbose)) if not iplot:
input_tuples.append((topick, param, apverbose))
if iplot>0:
all_onsets[station], fig_dict[station] = autopickstation(topick, param, verbose=apverbose, iplot=iplot)
if iplot>0:
print('iPlot Flag active: NO MULTIPROCESSING possible.')
return all_onsets, fig_dict # changing structure of autopicking and figure generation MP MP
pool = gen_Pool() pool = gen_Pool()
result = pool.map(call_autopickstation, input_tuples) result = pool.map(call_autopickstation, input_tuples)
pool.close() pool.close()
for pick in result: for pick, fig_dict in result:
station = pick['station'] station = pick['station']
pick.pop('station') pick.pop('station')
all_onsets[station] = pick all_onsets[station] = pick
return all_onsets, fig_dict # changing structure of autopicking and figure generation MP MP
# quality control # quality control
# median check and jackknife on P-onset times # median check and jackknife on P-onset times
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot) jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
@ -62,10 +71,11 @@ def autopickevent(data, param):
def call_autopickstation(input_tuple): def call_autopickstation(input_tuple):
wfstream, pickparam, verbose = input_tuple wfstream, pickparam, verbose = input_tuple
return autopickstation(wfstream, pickparam, verbose) #multiprocessing not possible with interactive plotting
return autopickstation(wfstream, pickparam, verbose, iplot=0)
def autopickstation(wfstream, pickparam, verbose=False): def autopickstation(wfstream, pickparam, verbose=False, iplot=0):
""" """
:param wfstream: `~obspy.core.stream.Stream` containing waveform :param wfstream: `~obspy.core.stream.Stream` containing waveform
:type wfstream: obspy.core.stream.Stream :type wfstream: obspy.core.stream.Stream
@ -82,8 +92,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
# read your autoPyLoT.in for details! # read your autoPyLoT.in for details!
# special parameters for P picking # special parameters for P picking
iplot = iplot
algoP = pickparam.get('algoP') algoP = pickparam.get('algoP')
iplot = pickparam.get('iplot')
pstart = pickparam.get('pstart') pstart = pickparam.get('pstart')
pstop = pickparam.get('pstop') pstop = pickparam.get('pstop')
thosmw = pickparam.get('tlta') thosmw = pickparam.get('tlta')
@ -161,6 +172,8 @@ def autopickstation(wfstream, pickparam, verbose=False):
Ao = None # Wood-Anderson peak-to-peak amplitude Ao = None # Wood-Anderson peak-to-peak amplitude
picker = 'auto' # type of picks picker = 'auto' # type of picks
fig_dict = {}
# split components # split components
zdat = wfstream.select(component="Z") zdat = wfstream.select(component="Z")
if len(zdat) == 0: # check for other components if len(zdat) == 0: # check for other components
@ -223,6 +236,7 @@ def autopickstation(wfstream, pickparam, verbose=False):
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker # get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
# of class AutoPicking # of class AutoPicking
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP) aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
fig_dict['aicFig'] = aicpick.fig
############################################################## ##############################################################
if aicpick.getpick() is not None: if aicpick.getpick() is not None:
# check signal length to detect spuriously picked noise peaks # check signal length to detect spuriously picked noise peaks
@ -236,9 +250,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
'Decreasing minsiglengh from {0} to ' \ 'Decreasing minsiglengh from {0} to ' \
'{1}'.format(minsiglength, minsiglength / 2) '{1}'.format(minsiglength, minsiglength / 2)
if verbose: print(msg) if verbose: print(msg)
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz, Pflag, fig_dict['slength'] = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength / 2, minsiglength / 2,
nfacsl, minpercent, iplot) nfacsl, minpercent, iplot)
else: else:
# filter and taper horizontal traces # filter and taper horizontal traces
trH1_filt = edat.copy() trH1_filt = edat.copy()
@ -253,9 +267,9 @@ def autopickstation(wfstream, pickparam, verbose=False):
trH2_filt.taper(max_percentage=0.05, type='hann') trH2_filt.taper(max_percentage=0.05, type='hann')
zne += trH1_filt zne += trH1_filt
zne += trH2_filt zne += trH2_filt
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz, Pflag, fig_dict['slength'] = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength, minsiglength,
nfacsl, minpercent, iplot) nfacsl, minpercent, iplot)
if Pflag == 1: if Pflag == 1:
# check for spuriously picked S onset # check for spuriously picked S onset
@ -315,13 +329,18 @@ def autopickstation(wfstream, pickparam, verbose=False):
algoP=algoP) algoP=algoP)
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
aicpick.getpick()) aicpick.getpick())
fig_dict['refPpick'] = refPpick.fig
mpickP = refPpick.getpick() mpickP = refPpick.getpick()
############################################################# #############################################################
if mpickP is not None: if mpickP is not None:
# quality assessment # quality assessment
# get earliest/latest possible pick and symmetrized uncertainty # get earliest/latest possible pick and symmetrized uncertainty
[epickP, lpickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, if iplot:
mpickP, iplot) epickP, lpickP, Perror, fig_dict['el_Ppick'] = earllatepicker(z_copy, nfacP, tsnrz,
mpickP, iplot)
else:
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
mpickP, iplot)
# get SNR # get SNR
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP) [SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP)
@ -342,7 +361,10 @@ def autopickstation(wfstream, pickparam, verbose=False):
# get first motion of P onset # get first motion of P onset
# certain quality required # certain quality required
if Pweight <= minfmweight and SNRP >= minFMSNR: if Pweight <= minfmweight and SNRP >= minFMSNR:
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot) if iplot:
FM, fig_dict['fm_picker'] = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
else:
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
else: else:
FM = 'N' FM = 'N'
@ -498,20 +520,31 @@ def autopickstation(wfstream, pickparam, verbose=False):
# get refined onset time from CF2 using class Picker # get refined onset time from CF2 using class Picker
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
tsmoothS, aicarhpick.getpick()) tsmoothS, aicarhpick.getpick())
fig_dict['refSpick'] = refSpick.fig
mpickS = refSpick.getpick() mpickS = refSpick.getpick()
############################################################# #############################################################
if mpickS is not None: if mpickS is not None:
# quality assessment # quality assessment
# get earliest/latest possible pick and symmetrized uncertainty # get earliest/latest possible pick and symmetrized uncertainty
h_copy[0].data = trH1_filt.data h_copy[0].data = trH1_filt.data
[epickS1, lpickS1, Serror1] = earllatepicker(h_copy, nfacS, if iplot:
tsnrh, epickS1, lpickS1, Serror1, fig_dict['el_S1pick'] = earllatepicker(h_copy, nfacS,
mpickS, iplot) tsnrh,
mpickS, iplot)
else:
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
h_copy[0].data = trH2_filt.data h_copy[0].data = trH2_filt.data
[epickS2, lpickS2, Serror2] = earllatepicker(h_copy, nfacS, if iplot:
tsnrh, epickS2, lpickS2, Serror2, fig_dict['el_S2pick'] = earllatepicker(h_copy, nfacS,
mpickS, iplot) tsnrh,
mpickS, iplot)
else:
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
if epickS1 is not None and epickS2 is not None: if epickS1 is not None and epickS2 is not None:
if algoS == 'ARH': if algoS == 'ARH':
# get earliest pick of both earliest possible picks # get earliest pick of both earliest possible picks
@ -603,62 +636,58 @@ def autopickstation(wfstream, pickparam, verbose=False):
############################################################## ##############################################################
if iplot > 0: if iplot > 0:
# plot vertical trace # plot vertical trace
plt.figure() fig = plt.figure()
plt.subplot(3, 1, 1) ax1 = fig.add_subplot(311)
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate, tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
tr_filt.stats.delta) tr_filt.stats.delta)
# check equal length of arrays, sometimes they are different!? # check equal length of arrays, sometimes they are different!?
wfldiff = len(tr_filt.data) - len(tdata) wfldiff = len(tr_filt.data) - len(tdata)
if wfldiff < 0: if wfldiff < 0:
tdata = tdata[0:len(tdata) - abs(wfldiff)] tdata = tdata[0:len(tdata) - abs(wfldiff)]
p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k') ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
if Pweight < 4: if Pweight < 4:
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
'b') 'b', label='CF1')
if aicPflag == 1: if aicPflag == 1:
p3, = plt.plot(cf2.getTimeArray(), ax1.plot(cf2.getTimeArray(),
cf2.getCF() / max(cf2.getCF()), 'm') cf2.getCF() / max(cf2.getCF()), 'm', label='CF2')
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], ax1.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
'r') 'r', label='Initial P Onset')
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
[1, 1], 'r') [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') [-1, -1], 'r')
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], ax1.plot([refPpick.getpick(), refPpick.getpick()],
[-1.3, 1.3], 'r', linewidth=2) [-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
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) [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) [-1.3, -1.3], 'r', linewidth=2)
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--') ax1.plot([lpickP, lpickP], [-1.1, 1.1], 'r--', label='lpp')
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--') ax1.plot([epickP, epickP], [-1.1, 1.1], 'r--', label='epp')
plt.legend([p1, p2, p3, p4, p5], ax1.set_title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
['Data', 'CF1', 'CF2', 'Initial P Onset', 'Polarity: %s' % (tr_filt.stats.station,
'Final P Pick']) tr_filt.stats.channel,
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f ' Pweight,
'Polarity: %s' % (tr_filt.stats.station, SNRP,
tr_filt.stats.channel, SNRPdB,
Pweight, FM))
SNRP,
SNRPdB,
FM))
else: else:
plt.legend([p1, p2], ['Data', 'CF1']) ax1.set_title('%s, P Weight=%d, SNR=None, '
plt.title('%s, P Weight=%d, SNR=None, ' 'SNRdB=None' % (tr_filt.stats.channel, Pweight))
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
else: 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, tr_filt.stats.channel,
Pweight)) Pweight))
ax1.legend()
plt.yticks([]) ax1.set_yticks([])
plt.ylim([-1.5, 1.5]) ax1.set_ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts') ax1.set_ylabel('Normalized Counts')
plt.suptitle(tr_filt.stats.starttime) fig.suptitle(tr_filt.stats.starttime)
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1: if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1:
# plot horizontal traces # plot horizontal traces
plt.subplot(3, 1, 2) ax2 = fig.add_subplot(312)
th1data = np.arange(0, th1data = np.arange(0,
trH1_filt.stats.npts / trH1_filt.stats.npts /
trH1_filt.stats.sampling_rate, trH1_filt.stats.sampling_rate,
@ -667,50 +696,47 @@ def autopickstation(wfstream, pickparam, verbose=False):
wfldiff = len(trH1_filt.data) - len(th1data) wfldiff = len(trH1_filt.data) - len(th1data)
if wfldiff < 0: if wfldiff < 0:
th1data = th1data[0:len(th1data) - abs(wfldiff)] 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: if Pweight < 4:
p22, = plt.plot(arhcf1.getTimeArray(), ax2.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b') arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
if aicSflag == 1: if aicSflag == 1:
p23, = plt.plot(arhcf2.getTimeArray(), ax2.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm') arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
p24, = plt.plot( ax2.plot(
[aicarhpick.getpick(), aicarhpick.getpick()], [aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g') [-1, 1], 'g', label='Initial S Onset')
plt.plot( ax2.plot(
[aicarhpick.getpick() - 0.5, [aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5], aicarhpick.getpick() + 0.5],
[1, 1], 'g') [1, 1], 'g')
plt.plot( ax2.plot(
[aicarhpick.getpick() - 0.5, [aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5], aicarhpick.getpick() + 0.5],
[-1, -1], 'g') [-1, -1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], ax2.plot([refSpick.getpick(), refSpick.getpick()],
[-1.3, 1.3], 'g', linewidth=2) [-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
plt.plot( ax2.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[1.3, 1.3], 'g', linewidth=2) [1.3, 1.3], 'g', linewidth=2)
plt.plot( ax2.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[-1.3, -1.3], 'g', linewidth=2) [-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--') ax2.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--') ax2.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
plt.legend([p21, p22, p23, p24, p25], ax2.set_title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
['Data', 'CF1', 'CF2', 'Initial S Onset',
'Final S Pick'])
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
trH1_filt.stats.channel, trH1_filt.stats.channel,
Sweight, SNRS, SNRSdB)) Sweight, SNRS, SNRSdB))
else: else:
plt.legend([p21, p22], ['Data', 'CF1']) ax2.set_title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
trH1_filt.stats.channel, Sweight)) trH1_filt.stats.channel, Sweight))
plt.yticks([]) ax2.legend()
plt.ylim([-1.5, 1.5]) ax2.set_yticks([])
plt.ylabel('Normalized Counts') ax2.set_ylim([-1.5, 1.5])
plt.suptitle(trH1_filt.stats.starttime) 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, th2data = np.arange(0,
trH2_filt.stats.npts / trH2_filt.stats.npts /
trH2_filt.stats.sampling_rate, trH2_filt.stats.sampling_rate,
@ -719,47 +745,41 @@ def autopickstation(wfstream, pickparam, verbose=False):
wfldiff = len(trH2_filt.data) - len(th2data) wfldiff = len(trH2_filt.data) - len(th2data)
if wfldiff < 0: if wfldiff < 0:
th2data = th2data[0:len(th2data) - abs(wfldiff)] 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: if Pweight < 4:
p22, = plt.plot(arhcf1.getTimeArray(), p22, = ax3.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b') arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
if aicSflag == 1: if aicSflag == 1:
p23, = plt.plot(arhcf2.getTimeArray(), ax3.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm') arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
p24, = plt.plot( ax3.plot(
[aicarhpick.getpick(), aicarhpick.getpick()], [aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g') [-1, 1], 'g', label='Initial S Onset')
plt.plot( ax3.plot(
[aicarhpick.getpick() - 0.5, [aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5], aicarhpick.getpick() + 0.5],
[1, 1], 'g') [1, 1], 'g')
plt.plot( ax3.plot(
[aicarhpick.getpick() - 0.5, [aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5], aicarhpick.getpick() + 0.5],
[-1, -1], 'g') [-1, -1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], ax3.plot([refSpick.getpick(), refSpick.getpick()],
[-1.3, 1.3], 'g', linewidth=2) [-1.3, 1.3], 'g', linewidth=2, label='Final S Pick')
plt.plot( ax3.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[1.3, 1.3], 'g', linewidth=2) [1.3, 1.3], 'g', linewidth=2)
plt.plot( ax3.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[-1.3, -1.3], 'g', linewidth=2) [-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--') ax3.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--') ax3.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
plt.legend([p21, p22, p23, p24, p25], ax3.legend()
['Data', 'CF1', 'CF2', 'Initial S Onset', ax3.set_yticks([])
'Final S Pick']) ax3.set_ylim([-1.5, 1.5])
else: ax3.set_xlabel('Time [s] after %s' % tr_filt.stats.starttime)
plt.legend([p21, p22], ['Data', 'CF1']) ax3.set_ylabel('Normalized Counts')
plt.yticks([]) ax3.set_title(trH2_filt.stats.channel)
plt.ylim([-1.5, 1.5]) fig_dict['mainFig'] = fig
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()
########################################################################## ##########################################################################
# calculate "real" onset times # calculate "real" onset times
if lpickP is not None and lpickP == mpickP: 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) snrdb=SNRSdB, weight=Sweight, fm=None, picker=picker, Ao=Ao)
# merge picks into returning dictionary # merge picks into returning dictionary
picks = dict(P=ppick, S=spick, station=zdat[0].stats.station) picks = dict(P=ppick, S=spick, station=zdat[0].stats.station)
return picks return picks, fig_dict
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter): 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'))) print("zfac: %f => %f" % (zfac_old, pickparameter.get('zfac')))
# repick station # repick station
newpicks = autopickstation(wf2pick, pickparameter) newpicks, fig = autopickstation(wf2pick, pickparameter)
# replace old dictionary with new one # replace old dictionary with new one
picks[badpicks[i][0]] = newpicks picks[badpicks[i][0]] = newpicks
@ -899,4 +919,4 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
pickparameter.setParam(noisefactor=noisefactor_old) pickparameter.setParam(noisefactor=noisefactor_old)
pickparameter.setParam(zfac=zfac_old) pickparameter.setParam(zfac=zfac_old)
return picks return picks, fig

99
pylot/core/pick/picker.py
View File

@ -72,7 +72,7 @@ class AutoPicker(object):
self.setaus(aus) self.setaus(aus)
self.setTsmooth(Tsmooth) self.setTsmooth(Tsmooth)
self.setpick1(Pick1) self.setpick1(Pick1)
self.calcPick() self.fig = self.calcPick()
def __str__(self): def __str__(self):
return '''\n\t{name} object:\n return '''\n\t{name} object:\n
@ -152,6 +152,7 @@ class AICPicker(AutoPicker):
self.Pick = None self.Pick = None
self.slope = None self.slope = None
self.SNR = None self.SNR = None
fig = None
# find NaN's # find NaN's
nn = np.isnan(self.cf) nn = np.isnan(self.cf)
if len(nn) > 1: 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('AICPicker: Maximum for slope determination right at the beginning of the window!')
print('Choose longer slope determination window!') print('Choose longer slope determination window!')
if self.iplot > 1: 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 x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k') ax.plot(self.Tcf, x / max(x), 'k', legend='(HOS-/AR-) Data')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r') ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', legend='Smoothed AIC-CF')
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF']) ax.legend()
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([]) ax.set_yticks([])
plt.title(self.Data[0].stats.station) ax.set_title(self.Data[0].stats.station)
plt.show() return fig
raw_input()
plt.close(p)
return
islope = islope[0][0:imax] islope = islope[0][0:imax]
dataslope = self.Data[0].data[islope] dataslope = self.Data[0].data[islope]
# calculate slope as polynomal fit of order 1 # calculate slope as polynomal fit of order 1
@ -253,41 +252,36 @@ class AICPicker(AutoPicker):
self.slope = None self.slope = None
if self.iplot > 1: if self.iplot > 1:
p = plt.figure(self.iplot) fig = plt.figure()#self.iplot)
ax1 = fig.add_subplot(211)
x = self.Data[0].data x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k') ax1.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r') ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
if self.Pick is not None: if self.Pick is not None:
p3, = plt.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2) ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick']) ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
else: ax1.set_yticks([])
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF']) ax1.set_title(self.Data[0].stats.station)
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) ax1.legend()
plt.yticks([])
plt.title(self.Data[0].stats.station)
if self.Pick is not None: if self.Pick is not None:
plt.figure(self.iplot + 1) ax2 = fig.add_subplot(212)
p11, = plt.plot(self.Tcf, x, 'k') ax2.plot(self.Tcf, x, 'k', label='Data')
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise]) ax2.plot(self.Tcf[inoise], self.Data[0].data[inoise], label='Noise Window')
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r') ax2.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r', label='Signal Window')
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--') ax2.plot(self.Tcf[islope], dataslope, 'g--', label='Slope Window')
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2) ax2.plot(self.Tcf[islope], datafit, 'g', linewidth=2, label='Slope')
plt.legend([p11, p12, p13, p14, p15], ax2.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], self.SNR, self.slope))
loc='best') ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, ax2.set_ylabel('Counts')
self.SNR, self.slope)) ax2.set_yticks([])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) ax2.legend()
plt.ylabel('Counts')
plt.yticks([])
plt.show()
raw_input()
plt.close(p)
if self.Pick == None: if self.Pick == None:
print('AICPicker: Could not find minimum, picking window too short?') print('AICPicker: Could not find minimum, picking window too short?')
return fig
class PragPicker(AutoPicker): class PragPicker(AutoPicker):
@ -296,7 +290,7 @@ class PragPicker(AutoPicker):
''' '''
def calcPick(self): def calcPick(self):
if self.getpick1() is not None: if self.getpick1() is not None:
print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...') print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
@ -380,18 +374,17 @@ class PragPicker(AutoPicker):
pickflag = 0 pickflag = 0
if self.getiplot() > 1: if self.getiplot() > 1:
p = plt.figure(self.getiplot()) fig = plt.figure()#self.getiplot())
p1, = plt.plot(Tcfpick, cfipick, 'k') ax = fig.add_subplot(111)
p2, = plt.plot(Tcfpick, cfsmoothipick, 'r') ax.plot(Tcfpick, cfipick, 'k', label='CF')
ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
if pickflag > 0: if pickflag > 0:
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2) ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2, label='Pick')
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick']) ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime) ax.set_yticks([])
plt.yticks([]) ax.set_title(self.Data[0].stats.station)
plt.title(self.Data[0].stats.station) ax.legend()
plt.show() return fig
raw_input()
plt.close(p)
else: else:
print('PragPicker: No initial onset time given! Check input!') print('PragPicker: No initial onset time given! Check input!')

174
pylot/core/pick/utils.py
View File

@ -105,35 +105,33 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, stealth_mode=False):
PickError = symmetrize_error(diffti_te, diffti_tl) PickError = symmetrize_error(diffti_te, diffti_tl)
if iplot > 1: if iplot > 1:
p = plt.figure(iplot) fig = plt.figure()#iplot)
p1, = plt.plot(t, x, 'k') ax = fig.add_subplot(111)
p2, = plt.plot(t[inoise], x[inoise]) ax.plot(t, x, 'k', label='Data')
p3, = plt.plot(t[isignal], x[isignal], 'r') ax.plot(t[inoise], x[inoise], label='Noise Window')
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k') ax.plot(t[isignal], x[isignal], 'r', label='Signal Window')
p5, = plt.plot(t[isignal[zc]], np.zeros(len(zc)), '*g', ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', label='Noise Level')
markersize=14) ax.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
plt.legend([p1, p2, p3, p4, p5], markersize=14, label='Zero Crossings')
['Data', 'Noise Window', 'Signal Window', 'Noise Level', ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
'Zero Crossings'], ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
loc='best') ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k', label='lpp')
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k') ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k', label='epp')
plt.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2) ax.plot([Pick1 + PickError, Pick1 + PickError],
plt.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k') [max(x) / 2, -max(x) / 2], 'r--', label='spe')
plt.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k') ax.plot([Pick1 - PickError, Pick1 - PickError],
plt.plot([Pick1 + PickError, Pick1 + PickError],
[max(x) / 2, -max(x) / 2], 'r--') [max(x) / 2, -max(x) / 2], 'r--')
plt.plot([Pick1 - PickError, Pick1 - PickError], ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
[max(x) / 2, -max(x) / 2], 'r--') ax.set_yticks([])
plt.xlabel('Time [s] since %s' % X[0].stats.starttime) ax.set_title(
plt.yticks([])
plt.title(
'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % 'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
X[0].stats.station) X[0].stats.station)
plt.show() ax.legend()
raw_input()
plt.close(p)
return EPick, LPick, PickError if iplot:
return EPick, LPick, PickError, fig
else:
return Epick, LPick, PickError
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None): 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) print ("fmpicker: Found polarity %s" % FM)
if iplot > 1: if iplot > 1:
plt.figure(iplot) fig = plt.figure()#iplot)
plt.subplot(2, 1, 1) ax1 = fig.add_subplot(211)
plt.plot(t, xraw, 'k') ax1.plot(t, xraw, 'k')
p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2) ax1.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2, label='Pick')
if P1 is not None: if P1 is not None:
p2, = plt.plot(t[islope1], xraw[islope1]) ax1.plot(t[islope1], xraw[islope1], label='Slope Window')
p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14) ax1.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14, label='Zero Crossings')
p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2) ax1.plot(t[islope1], datafit1, '--g', linewidth=2)
plt.legend([p1, p2, p3, p4], ax1.legend()
['Pick', 'Slope Window', 'Zero Crossings', 'Slope'], ax1.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
loc='best') ax1.set_yticks([])
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14) ax1.set_title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
ax = plt.gca()
plt.yticks([])
plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
0].stats.station) 0].stats.station)
plt.subplot(2, 1, 2) ax2=fig.add_subplot(212)
plt.title('First-Motion Determination, Filtered Data') ax2.set_title('First-Motion Determination, Filtered Data')
plt.plot(t, xfilt, 'k') ax2.plot(t, xfilt, 'k')
p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b', ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
linewidth=2) linewidth=2)
if P2 is not None: if P2 is not None:
p2, = plt.plot(t[islope2], xfilt[islope2]) ax2.plot(t[islope2], xfilt[islope2])
p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14) ax2.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2) ax2.plot(t[islope2], datafit2, '--g', linewidth=2)
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14) ax2.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
ax = plt.gca() ax2.set_xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime) ax2.set_yticks([])
plt.yticks([])
plt.show()
raw_input()
plt.close(iplot)
return FM if iplot:
return FM, fig
else:
return FM
def crossings_nonzero_all(data): def crossings_nonzero_all(data):
@ -606,7 +600,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
# plot results # plot results
if iplot > 1: if iplot > 1:
plt.figure(iplot) plt.figure()#iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro') f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0: if wfitflag == 0:
f2, = plt.plot(Ppicks, wdfit, 'k') f2, = plt.plot(Ppicks, wdfit, 'k')
@ -621,9 +615,6 @@ def wadaticheck(pickdic, dttolerance, iplot):
plt.ylabel('S-P Times [s]') plt.ylabel('S-P Times [s]')
plt.xlabel('P Times [s]') plt.xlabel('P Times [s]')
plt.show()
raw_input()
plt.close(iplot)
return checkedonsets return checkedonsets
@ -700,25 +691,21 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
returnflag = 0 returnflag = 0
if iplot == 2: if iplot == 2:
plt.figure(iplot) fig = plt.figure()#iplot)
p1, = plt.plot(t, rms, 'k') ax = fig.add_subplot(111)
p2, = plt.plot(t[inoise], rms[inoise], 'c') ax.plot(t, rms, 'k', label='RMS Data')
p3, = plt.plot(t[isignal], rms[isignal], 'r') ax.plot(t[inoise], rms[inoise], 'c', label='RMS Noise Window')
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]], ax.plot(t[isignal], rms[isignal], 'r', label='RMS Signal Window')
[minsiglevel, minsiglevel], 'g', linewidth=2) ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
p5, = plt.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2) [minsiglevel, minsiglevel], 'g', linewidth=2, label='Minimum Signal Level')
plt.legend([p1, p2, p3, p4, p5], ['RMS Data', 'RMS Noise Window', ax.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2, label='Onset')
'RMS Signal Window', 'Minimum Signal Level', ax.legend()
'Onset'], loc='best') ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.xlabel('Time [s] since %s' % X[0].stats.starttime) ax.set_ylabel('Counts')
plt.ylabel('Counts') ax.set_title('Check for Signal Length, Station %s' % X[0].stats.station)
plt.title('Check for Signal Length, Station %s' % X[0].stats.station) ax.set_yticks([])
plt.yticks([])
plt.show()
raw_input()
plt.close(iplot)
return returnflag return returnflag, fig
def checkPonsets(pickdic, dttolerance, iplot): 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'], plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'],
loc='best') loc='best')
plt.title('Check P Onsets') plt.title('Check P Onsets')
plt.show()
raw_input()
return checkedonsets return checkedonsets
@ -962,22 +947,23 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
edat[0].stats.delta) 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) ndat[0].stats.delta)
plt.plot(tz, z / max(z), 'k') fig = plt.figure()
plt.plot(tz[isignal], z[isignal] / max(z), 'r') ax = fig.add_subplot(111)
plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k') ax.plot(tz, z / max(z), 'k')
plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r') ax.plot(tz[isignal], z[isignal] / max(z), 'r')
plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k') ax.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r') ax.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
plt.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]], ax.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
[minsiglevel / max(z), minsiglevel / max(z)], 'g', ax.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
linewidth=2) ax.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime) [minsiglevel / max(z), minsiglevel / max(z)], 'g',
plt.ylabel('Normalized Counts') linewidth=2, label='Minimum Signal Level')
plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel, ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
ndat[0].stats.channel]) ax.set_ylabel('Normalized Counts')
plt.title('CheckZ4S, Station %s' % zdat[0].stats.station) ax.set_yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
plt.show() ndat[0].stats.channel])
raw_input() ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
ax.legend()
return returnflag return returnflag