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[reformat] code reformatting with PyCharm

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This commit is contained in:
Marcel Paffrath
2017-08-03 09:41:54 +02:00
parent 4107f0249d
commit 20b31a1c5c
49 changed files with 3255 additions and 3194 deletions
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58
pylot/core/pick/autopick.py
View File

@@ -11,14 +11,14 @@ function conglomerate utils.
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.io.data import Data
from pylot.core.io.inputs import PylotParameter
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
getSNR, fmpicker, checkPonsets, wadaticheck
from pylot.core.util.utils import getPatternLine, gen_Pool
from pylot.core.io.data import Data
def autopickevent(data, param, iplot=0, fig_dict=None):
@@ -43,10 +43,10 @@ def autopickevent(data, param, iplot=0, fig_dict=None):
if not iplot:
input_tuples.append((topick, param, apverbose))
if iplot>0:
if iplot > 0:
all_onsets[station] = autopickstation(topick, param, verbose=apverbose, iplot=iplot, fig_dict=fig_dict)
if iplot>0:
if iplot > 0:
print('iPlot Flag active: NO MULTIPROCESSING possible.')
return all_onsets
@@ -70,7 +70,7 @@ def autopickevent(data, param, iplot=0, fig_dict=None):
def call_autopickstation(input_tuple):
wfstream, pickparam, verbose = input_tuple
#multiprocessing not possible with interactive plotting
# multiprocessing not possible with interactive plotting
return autopickstation(wfstream, pickparam, verbose, iplot=0)
@@ -92,7 +92,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
# special parameters for P picking
iplot = iplot
algoP = pickparam.get('algoP')
pstart = pickparam.get('pstart')
pstop = pickparam.get('pstop')
@@ -291,7 +291,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
'Skipping control function checkZ4S.'
if verbose: print(msg)
else:
if iplot>1:
if iplot > 1:
if fig_dict:
fig = fig_dict['checkZ4s']
else:
@@ -364,7 +364,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
mpickP, iplot, fig=fig)
else:
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
mpickP, iplot)
mpickP, iplot)
# get SNR
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP)
@@ -392,7 +392,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
fig = None
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig)
else:
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
else:
FM = 'N'
@@ -567,15 +567,15 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
fig = fig_dict['el_S1pick']
else:
fig = None
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot,
fig=fig)
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot,
fig=fig)
else:
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
h_copy[0].data = trH2_filt.data
if iplot:
if fig_dict:
@@ -706,7 +706,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
[-1, -1], 'r')
ax1.plot([refPpick.getpick(), refPpick.getpick()],
[-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
[-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)
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
@@ -714,28 +714,28 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
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))
'Polarity: %s' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight,
SNRP,
SNRPdB,
FM))
else:
ax1.set_title('%s, P Weight=%d, SNR=None, '
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
else:
ax1.set_title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight))
tr_filt.stats.channel,
Pweight))
ax1.legend()
ax1.set_yticks([])
ax1.set_ylim([-1.5, 1.5])
ax1.set_ylabel('Normalized Counts')
#fig.suptitle(tr_filt.stats.starttime)
# fig.suptitle(tr_filt.stats.starttime)
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1:
# plot horizontal traces
ax2 = fig.add_subplot(3,1,2,sharex=ax1)
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
th1data = np.arange(0,
trH1_filt.stats.npts /
trH1_filt.stats.sampling_rate,
@@ -750,7 +750,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
if aicSflag == 1:
ax2.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
ax2.plot(
[aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g', label='Initial S Onset')
@@ -782,9 +782,9 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
ax2.set_yticks([])
ax2.set_ylim([-1.5, 1.5])
ax2.set_ylabel('Normalized Counts')
#fig.suptitle(trH1_filt.stats.starttime)
# fig.suptitle(trH1_filt.stats.starttime)
ax3 = fig.add_subplot(3,1,3, sharex=ax1)
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
th2data = np.arange(0,
trH2_filt.stats.npts /
trH2_filt.stats.sampling_rate,

5
pylot/core/pick/charfuns.py
View File

@@ -17,7 +17,6 @@ autoregressive prediction: application ot local and regional distances, Geophys.
:author: MAGS2 EP3 working group
"""
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream
@@ -466,7 +465,7 @@ class ARHcf(CharacteristicFunction):
# prediction error = CF
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2)) / (
2 * lpred))
2 * lpred))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
@@ -608,7 +607,7 @@ class AR3Ccf(CharacteristicFunction):
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2) \
+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (
3 * lpred))
3 * lpred))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0

18
pylot/core/pick/compare.py
View File

@@ -4,11 +4,10 @@
import copy
import operator
import os
import numpy as np
import glob
import matplotlib.pyplot as plt
from obspy import read_events
import matplotlib.pyplot as plt
import numpy as np
from obspy import read_events
from pylot.core.io.phases import picksdict_from_picks
from pylot.core.util.pdf import ProbabilityDensityFunction
from pylot.core.util.utils import find_in_list
@@ -334,7 +333,7 @@ class PDFDictionary(object):
axarr[l].set_title(phase)
if l is 0:
axann = axarr[l].annotate(station, xy=(.05, .5),
xycoords='axes fraction')
xycoords='axes fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey',
alpha=.7)
axann.set_bbox(bbox_props)
@@ -352,7 +351,6 @@ class PDFstatistics(object):
Takes a path as argument.
"""
def __init__(self, directory):
"""Initiates some values needed when dealing with pdfs later"""
self._rootdir = directory
@@ -449,7 +447,7 @@ class PDFstatistics(object):
else:
raise ValueError("for call to method {0} value has to be "
"defined but is 'None' ".format(method_options[
property.upper()]))
property.upper()]))
for pdf_dict in self:
# create worklist
@@ -459,7 +457,7 @@ class PDFstatistics(object):
return rlist
def writeThetaToFile(self,array,out_dir):
def writeThetaToFile(self, array, out_dir):
"""
Method to write array like data to file. Useful since acquiring can take
serious amount of time when dealing with large databases.
@@ -471,12 +469,12 @@ class PDFstatistics(object):
"""
fid = open(os.path.join(out_dir), 'w')
for val in array:
fid.write(str(val)+'\n')
fid.write(str(val) + '\n')
fid.close()
def main():
root_dir ='/home/sebastianp/Codetesting/xmls/'
root_dir = '/home/sebastianp/Codetesting/xmls/'
Insheim = PDFstatistics(root_dir)
Insheim.curphase = 'p'
qdlist = Insheim.get('qdf', 0.2)

55
pylot/core/pick/picker.py
View File

@@ -19,12 +19,13 @@ calculated after Diehl & Kissling (2009).
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import numpy as np
import matplotlib.pyplot as plt
from pylot.core.pick.utils import getnoisewin, getsignalwin
from pylot.core.pick.charfuns import CharacteristicFunction
import warnings
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.utils import getnoisewin, getsignalwin
class AutoPicker(object):
'''
@@ -212,14 +213,14 @@ class AICPicker(AutoPicker):
self.Data[0].data = self.Data[0].data * 1000000
# get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
ii = min([isignal[len(isignal)-1], len(self.Tcf)])
ii = min([isignal[len(isignal) - 1], len(self.Tcf)])
isignal = isignal[0:ii]
try:
aic[isignal]
aic[isignal]
except IndexError as e:
msg = "Time series out of bounds! {}".format(e)
print(msg)
return
msg = "Time series out of bounds! {}".format(e)
print(msg)
return
# calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
max(abs(aic[inoise] - np.mean(aic[inoise])))
@@ -242,7 +243,7 @@ class AICPicker(AutoPicker):
print("Choose longer slope determination window!")
if self.iplot > 1:
if not self.fig:
fig = plt.figure() #self.iplot) ### WHY? MP MP
fig = plt.figure() # self.iplot) ### WHY? MP MP
else:
fig = self.fig
ax = fig.add_subplot(111)
@@ -271,7 +272,7 @@ class AICPicker(AutoPicker):
if self.iplot > 1:
if not self.fig:
fig = plt.figure()#self.iplot)
fig = plt.figure() # self.iplot)
else:
fig = self.fig
ax1 = fig.add_subplot(211)
@@ -283,21 +284,25 @@ class AICPicker(AutoPicker):
ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax1.set_yticks([])
ax1.legend()
if self.Pick is not None:
ax2 = fig.add_subplot(2,1,2, sharex=ax1)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
ax2.plot(self.Tcf, x, 'k', label='Data')
ax1.axvspan(self.Tcf[inoise[0]],self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax1.axvspan(self.Tcf[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax1.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
ax2.axvspan(self.Tcf[inoise[0]],self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax2.axvspan(self.Tcf[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax2.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax1.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
label='Signal Window')
ax1.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
label='Slope Window')
ax2.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax2.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
label='Signal Window')
ax2.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
label='Slope Window')
ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2, label='Slope')
ax1.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
self.SNR, self.slope))
self.SNR, self.slope))
ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax2.set_ylabel('Counts')
ax2.set_yticks([])
@@ -307,7 +312,7 @@ class AICPicker(AutoPicker):
if self.Pick == None:
print('AICPicker: Could not find minimum, picking window too short?')
return
@@ -317,7 +322,7 @@ 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 ...')
@@ -402,7 +407,7 @@ class PragPicker(AutoPicker):
if self.getiplot() > 1:
if not self.fig:
fig = plt.figure()#self.getiplot())
fig = plt.figure() # self.getiplot())
else:
fig = self.fig
ax = fig.add_subplot(111)

155
pylot/core/pick/utils.py
View File

@@ -9,6 +9,7 @@
"""
import warnings
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream, UTCDateTime
@@ -41,11 +42,11 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
if verbosity == 2:
print('earllatepicker:')
print('earllatepicker:')
print('nfac:', nfac)
print('Init pick:', Pick1)
print('TSNR (T_noise, T_gap, T_signal):', TSNR)
LPick = None
EPick = None
PickError = None
@@ -69,14 +70,14 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
print('x_inoise:', x[inoise])
print('x_isignal:', x[isignal])
print('nlevel:', nlevel)
# get time where signal exceeds nlevel
ilup, = np.where(x[isignal] > nlevel)
ildown, = np.where(x[isignal] < -nlevel)
if not ilup.size and not ildown.size:
if verbosity:
print ("earllatepicker: Signal lower than noise level!\n"
"Skip this trace!")
print("earllatepicker: Signal lower than noise level!\n"
"Skip this trace!")
return LPick, EPick, PickError
il = min(np.min(ilup) if ilup.size else float('inf'),
np.min(ildown) if ildown.size else float('inf'))
@@ -118,7 +119,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
if iplot > 1:
if not fig:
fig = plt.figure()#iplot)
fig = plt.figure() # iplot)
ax = fig.add_subplot(111)
ax.plot(t, x, 'k', label='Data')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
@@ -131,9 +132,9 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
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')
[max(x) / 2, -max(x) / 2], 'r--', label='spe')
ax.plot([Pick1 - PickError, Pick1 - PickError],
[max(x) / 2, -max(x) / 2], 'r--')
[max(x) / 2, -max(x) / 2], 'r--')
ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
ax.set_yticks([])
ax.set_title(
@@ -173,7 +174,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
FM = None
if Pick is not None:
print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
print("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
xraw = Xraw[0].data
xfilt = Xfilt[0].data
@@ -212,15 +213,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
else:
li1 = index1[0]
if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
print("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
P1 = None
else:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
if imax1 == 0:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
if imax1 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
@@ -254,15 +255,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
else:
li2 = index2[0]
if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
print ("fmpicker: Onset on filtered trace too emergent for first motion determination!")
print("fmpicker: Onset on filtered trace too emergent for first motion determination!")
P2 = None
else:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
if imax2 == 0:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
if imax2 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
@@ -286,11 +287,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
elif P1[0] > 0 >= P2[0]:
FM = '+'
print ("fmpicker: Found polarity %s" % FM)
print("fmpicker: Found polarity %s" % FM)
if iplot > 1:
if not fig:
fig = plt.figure()#iplot)
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')
@@ -304,11 +305,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
ax1.set_title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
0].stats.station)
ax2=fig.add_subplot(2,1,2, sharex=ax1)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
ax2.set_title('First-Motion Determination, Filtered Data')
ax2.plot(t, xfilt, 'k')
ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
linewidth=2)
linewidth=2)
if P2 is not None:
ax2.plot(t[islope2], xfilt[islope2])
ax2.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
@@ -359,7 +360,7 @@ def getSNR(X, TSNR, t1, tracenum=0):
SNR = None
SNRdB = None
noiselevel = None
x = X[tracenum].data
npts = X[tracenum].stats.npts
sr = X[tracenum].stats.sampling_rate
@@ -372,7 +373,7 @@ def getSNR(X, TSNR, t1, tracenum=0):
# get signal window
isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print ("getSNR: Empty array inoise, check noise window!")
print("getSNR: Empty array inoise, check noise window!")
return SNR, SNRdB, noiselevel
# demean over entire waveform
@@ -380,13 +381,13 @@ def getSNR(X, TSNR, t1, tracenum=0):
# calculate ratios
noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
#signallevel = np.sqrt(np.mean(np.square(x[isignal])))
# signallevel = np.sqrt(np.mean(np.square(x[isignal])))
if np.size(isignal) < 1:
print ("getSNR: Empty array isignal, check signal window!")
print("getSNR: Empty array isignal, check signal window!")
return SNR, SNRdB, noiselevel
#noiselevel = np.abs(x[inoise]).max()
# noiselevel = np.abs(x[inoise]).max()
signallevel = np.abs(x[isignal]).max()
SNR = signallevel / noiselevel
@@ -418,9 +419,9 @@ def getnoisewin(t, t1, tnoise, tgap):
inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1:
inoise, = np.where((t>=t[0]) & (t<=t1))
inoise, = np.where((t >= t[0]) & (t <= t1))
if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!")
print("getnoisewin: Empty array inoise, check noise window!")
return inoise
@@ -444,7 +445,7 @@ def getsignalwin(t, t1, tsignal):
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
& (t >= t1))
if np.size(isignal) < 1:
print ("getsignalwin: Empty array isignal, check signal window!")
print("getsignalwin: Empty array isignal, check signal window!")
return isignal
@@ -473,7 +474,7 @@ def getResolutionWindow(snr, extent):
>>> getResolutionWindow(2)
2.5
"""
res_wins = {
'regional': {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.},
'local': {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.},
@@ -487,11 +488,11 @@ def getResolutionWindow(snr, extent):
time_resolution = res_wins[extent]['LRW']
elif snr < 3.:
time_resolution = res_wins[extent]['MRW']
elif snr >3.:
elif snr > 3.:
time_resolution = res_wins[extent]['HRW']
else:
time_resolution = res_wins[extent]['VLRW']
return time_resolution / 2
@@ -573,8 +574,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio before check
vpvsr = p1[0] + 1
print ("###############################################")
print ("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
print("###############################################")
print("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
checkedPpicks = []
checkedSpicks = []
@@ -611,23 +612,23 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio after check
cvpvsr = p2[0] + 1
print ("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print ("wadatacheck: Skipped %d S pick(s)" % ibad)
print("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print("wadatacheck: Skipped %d S pick(s)" % ibad)
else:
print ("###############################################")
print ("wadatacheck: Not enough checked S-P times available!")
print ("Skip Wadati check!")
print("###############################################")
print("wadatacheck: Not enough checked S-P times available!")
print("Skip Wadati check!")
checkedonsets = pickdic
else:
print ("wadaticheck: Not enough S-P times available for reliable regression!")
print ("Skip wadati check!")
print("wadaticheck: Not enough S-P times available for reliable regression!")
print("Skip wadati check!")
wfitflag = 1
# plot results
if iplot > 0:
plt.figure()#iplot)
plt.figure() # iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0:
f2, = plt.plot(Ppicks, wdfit, 'k')
@@ -645,12 +646,14 @@ def wadaticheck(pickdic, dttolerance, iplot):
return checkedonsets
def RMS(X):
'''
Function returns root mean square of a given array X
'''
return np.sqrt(np.sum(np.power(X, 2))/len(X))
return np.sqrt(np.sum(np.power(X, 2)) / len(X))
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None):
'''
Function to detect spuriously picked noise peaks.
@@ -684,7 +687,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Checking signal length ...")
print("Checking signal length ...")
if len(X) > 1:
# all three components available
@@ -714,21 +717,21 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
if numoverthr >= minnum:
print ("checksignallength: Signal reached required length.")
print("checksignallength: Signal reached required length.")
returnflag = 1
else:
print ("checksignallength: Signal shorter than required minimum signal length!")
print ("Presumably picked noise peak, pick is rejected!")
print ("(min. signal length required: %s s)" % minsiglength)
print("checksignallength: Signal shorter than required minimum signal length!")
print("Presumably picked noise peak, pick is rejected!")
print("(min. signal length required: %s s)" % minsiglength)
returnflag = 0
if iplot == 2:
if not fig:
fig = plt.figure()#iplot)
fig = plt.figure() # iplot)
ax = fig.add_subplot(111)
ax.plot(t, rms, 'k', label='RMS Data')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
[minsiglevel, minsiglevel], 'g', linewidth=2, label='Minimum Signal Level')
ax.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2, label='Onset')
@@ -771,8 +774,8 @@ def checkPonsets(pickdic, dttolerance, iplot):
stations.append(key)
# apply jackknife bootstrapping on variance of P onsets
print ("###############################################")
print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
print("###############################################")
print("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
# get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test
@@ -780,7 +783,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
# these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > 5 * xjack)
badjkstations = np.array(stations)[badjk]
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print(badjkstations)
# calculate median from these picks
@@ -793,9 +796,9 @@ def checkPonsets(pickdic, dttolerance, iplot):
goodstations = np.array(stations)[igood]
badstations = np.array(stations)[ibad]
print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
print("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
@@ -863,8 +866,8 @@ def jackknife(X, phi, h):
g = len(X) / h
if type(g) is not int:
print ("jackknife: Cannot divide quantity X in equal sized subgroups!")
print ("Choose another size for subgroups!")
print("jackknife: Cannot divide quantity X in equal sized subgroups!")
print("Choose another size for subgroups!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
# estimator of undisturbed spot check
@@ -932,7 +935,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Check for spuriously picked S onset instead of P onset ...")
print("Check for spuriously picked S onset instead of P onset ...")
returnflag = 0
@@ -949,7 +952,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
# get earliest time of all 3 traces
min_t = min(zdat[0].stats.starttime, edat[0].stats.starttime, ndat[0].stats.starttime)
# generate time arrays for all 3 traces
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
zdat[0].stats.delta)
@@ -961,11 +964,11 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
zdiff = (zdat[0].stats.starttime - min_t)
ndiff = (ndat[0].stats.starttime - min_t)
ediff = (edat[0].stats.starttime - min_t)
# get signal windows
isignalz = getsignalwin(tz, pick-zdiff, checkwin)
isignaln = getsignalwin(tn, pick-ndiff, checkwin)
isignale = getsignalwin(te, pick-ediff, checkwin)
isignalz = getsignalwin(tz, pick - zdiff, checkwin)
isignaln = getsignalwin(tn, pick - ndiff, checkwin)
isignale = getsignalwin(te, pick - ediff, checkwin)
# calculate RMS of traces
rmsz = RMS(zdat[0].data[isignalz])
@@ -978,9 +981,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
# vertical P-coda level must exceed horizontal P-coda level
# zfac times encodalevel
if rmsz < minsiglevel:
print ("checkZ4S: Maybe S onset? Skip this P pick!")
print("checkZ4S: Maybe S onset? Skip this P pick!")
else:
print ("checkZ4S: P onset passes checkZ4S test!")
print("checkZ4S: P onset passes checkZ4S test!")
returnflag = 1
if iplot > 1:
@@ -996,28 +999,28 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
'N': ndiff,
'E': ediff}
signal_dict = {'Z': isignalz,
'N': isignaln,
'E': isignale}
signal_dict = {'Z': isignalz,
'N': isignaln,
'E': isignale}
for i, key in enumerate(['Z', 'N', 'E']):
rms = rms_dict[key]
trace = traces_dict[key]
t = np.arange(diff_dict[key], trace.stats.npts / trace.stats.sampling_rate+diff_dict[key],
trace.stats.delta)
t = np.arange(diff_dict[key], trace.stats.npts / trace.stats.sampling_rate + diff_dict[key],
trace.stats.delta)
if i == 0:
ax1 = fig.add_subplot(3, 1, i+1)
ax1 = fig.add_subplot(3, 1, i + 1)
ax = ax1
ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
else:
ax = fig.add_subplot(3,1,i+1, sharex=ax1)
ax = fig.add_subplot(3, 1, i + 1, sharex=ax1)
ax.plot(t, abs(trace.data), color='b', label='abs')
ax.plot(t, trace.data, color='k')
name = str(trace.stats.channel) + ': {}'.format(rms)
ax.plot([pick, pick+checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
ax.plot([pick, pick + checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
ax.plot([pick, pick], ax.get_ylim(), 'm', label='Pick')
ax.set_ylabel('Normalized Counts')
ax.axvspan(pick, pick+checkwin, color='c', alpha=0.2,
ax.axvspan(pick, pick + checkwin, color='c', alpha=0.2,
lw=0)
ax.legend()
ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)

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

@@ -8,14 +8,14 @@
:author: Ludger Kueperkoch / MAGS2 EP3 working group
"""
import pdb
import numpy as np
import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime
import warnings
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream, UTCDateTime
def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode = False):
def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode=False):
'''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
as reasonable uncertainties. Latest possible pick is based on noise level,
@@ -45,7 +45,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode = False):
EPick = None
PickError = None
if stealthMode is False:
print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
print
'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
x = X[0].data
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
@@ -61,8 +62,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode = False):
ilup, = np.where(x[isignal] > nlevel)
ildown, = np.where(x[isignal] < -nlevel)
if not ilup.size and not ildown.size:
print ("earllatepicker: Signal lower than noise level!")
print ("Skip this trace!")
print("earllatepicker: Signal lower than noise level!")
print("Skip this trace!")
return LPick, EPick, PickError
il = min(np.min(ilup) if ilup.size else float('inf'),
np.min(ildown) if ildown.size else float('inf'))
@@ -70,20 +71,21 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode = False):
# get earliest possible pick
EPick = np.nan; count = 0
EPick = np.nan;
count = 0
pis = isignal
# if EPick stays NaN the signal window size will be doubled
while np.isnan(EPick):
if count > 0:
print("earllatepicker: Doubled signal window size %s time(s) "
"because of NaN for earliest pick." %count)
"because of NaN for earliest pick." % count)
if stealthMode is False:
print("\nearllatepicker: Doubled signal window size %s time(s) "
"because of NaN for earliest pick." %count)
"because of NaN for earliest pick." % count)
isigDoubleWinStart = pis[-1] + 1
isignalDoubleWin = np.arange(isigDoubleWinStart,
isigDoubleWinStart + len(pis))
isigDoubleWinStart + len(pis))
if (isigDoubleWinStart + len(pis)) < X[0].data.size:
pis = np.concatenate((pis, isignalDoubleWin))
else:
@@ -97,7 +99,6 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, stealthMode = False):
# T0/4 is assumed as time difference between most likely and earliest possible pick!
EPick = Pick1 - T0 / 2
# get symmetric pick error as mean from earliest and latest possible pick
# by weighting latest possible pick two times earliest possible pick
diffti_tl = LPick - Pick1
@@ -165,7 +166,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
FM = None
if Pick is not None:
print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
print("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
xraw = Xraw[0].data
xfilt = Xfilt[0].data
@@ -204,15 +205,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
else:
li1 = index1[0]
if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
print("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
P1 = None
else:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
if imax1 == 0:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
if imax1 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
@@ -246,15 +247,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
else:
li2 = index2[0]
if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
print ("fmpicker: Onset on filtered trace too emergent for first motion determination!")
print("fmpicker: Onset on filtered trace too emergent for first motion determination!")
P2 = None
else:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
if imax2 == 0:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
if imax2 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
@@ -278,7 +279,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
elif P1[0] > 0 >= P2[0]:
FM = '+'
print ("fmpicker: Found polarity %s" % FM)
print("fmpicker: Found polarity %s" % FM)
if iplot > 1:
plt.figure(iplot)
@@ -353,10 +354,10 @@ def getSNR(X, TSNR, t1):
# get signal window
isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print ("getSNR: Empty array inoise, check noise window!")
print("getSNR: Empty array inoise, check noise window!")
return
elif np.size(isignal) < 1:
print ("getSNR: Empty array isignal, check signal window!")
print("getSNR: Empty array isignal, check signal window!")
return
# demean over entire waveform
@@ -392,9 +393,9 @@ def getnoisewin(t, t1, tnoise, tgap):
# get noise window
inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0])))
& (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!")
print("getnoisewin: Empty array inoise, check noise window!")
return inoise
@@ -416,9 +417,9 @@ def getsignalwin(t, t1, tsignal):
# get signal window
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
& (t >= t1))
& (t >= t1))
if np.size(isignal) < 1:
print ("getsignalwin: Empty array isignal, check signal window!")
print("getsignalwin: Empty array isignal, check signal window!")
return isignal
@@ -457,7 +458,7 @@ def getResolutionWindow(snr):
else:
time_resolution = res_wins['HRW']
return time_resolution/2
return time_resolution / 2
def wadaticheck(pickdic, dttolerance, iplot):
@@ -485,17 +486,16 @@ def wadaticheck(pickdic, dttolerance, iplot):
SPtimes = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
# calculate S-P time
spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
# add S-P time to dictionary
pickdic[key]['SPt'] = spt
# add P onsets and corresponding S-P times to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
Ppicks.append(UTCPpick.timestamp)
Spicks.append(UTCSpick.timestamp)
SPtimes.append(spt)
# calculate S-P time
spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
# add S-P time to dictionary
pickdic[key]['SPt'] = spt
# add P onsets and corresponding S-P times to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
Ppicks.append(UTCPpick.timestamp)
Spicks.append(UTCSpick.timestamp)
SPtimes.append(spt)
if len(SPtimes) >= 3:
# calculate slope
@@ -505,8 +505,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio before check
vpvsr = p1[0] + 1
print ("###############################################")
print ("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
print("###############################################")
print("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
checkedPpicks = []
checkedSpicks = []
@@ -527,7 +527,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
ibad += 1
else:
marker = 'goodWadatiCheck'
checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
checkedPpicks.append(checkedPpick.timestamp)
checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
checkedSpicks.append(checkedSpick.timestamp)
@@ -543,18 +543,18 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio after check
cvpvsr = p2[0] + 1
print ("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print ("wadatacheck: Skipped %d S pick(s)" % ibad)
print("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print("wadatacheck: Skipped %d S pick(s)" % ibad)
else:
print ("###############################################")
print ("wadatacheck: Not enough checked S-P times available!")
print ("Skip Wadati check!")
print("###############################################")
print("wadatacheck: Not enough checked S-P times available!")
print("Skip Wadati check!")
checkedonsets = pickdic
else:
print ("wadaticheck: Not enough S-P times available for reliable regression!")
print ("Skip wadati check!")
print("wadaticheck: Not enough S-P times available for reliable regression!")
print("Skip wadati check!")
wfitflag = 1
# plot results
@@ -614,7 +614,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Checking signal length ...")
print("Checking signal length ...")
if len(X) > 1:
# all three components available
@@ -639,25 +639,25 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
# calculate minimum adjusted signal level
minsiglevel = max(rms[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level
minnum = len(isignal) * minpercent/100
minnum = len(isignal) * minpercent / 100
# get number of samples above minimum adjusted signal level
numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
if numoverthr >= minnum:
print ("checksignallength: Signal reached required length.")
print("checksignallength: Signal reached required length.")
returnflag = 1
else:
print ("checksignallength: Signal shorter than required minimum signal length!")
print ("Presumably picked noise peak, pick is rejected!")
print ("(min. signal length required: %s s)" % minsiglength)
print("checksignallength: Signal shorter than required minimum signal length!")
print("Presumably picked noise peak, pick is rejected!")
print("(min. signal length required: %s s)" % minsiglength)
returnflag = 0
if iplot == 2:
plt.figure(iplot)
p1, = plt.plot(t,rms, 'k')
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]]],
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',
@@ -698,22 +698,22 @@ def checkPonsets(pickdic, dttolerance, iplot):
stations = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4:
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
# apply jackknife bootstrapping on variance of P onsets
print ("###############################################")
print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
print("###############################################")
print("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
# get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test
ij = np.where(PHI_pseudo <= 2 * xjack)
# these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > 2 * xjack)
badjkstations = np.array(stations)[badjk]
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
# calculate median from these picks
pmedian = np.median(np.array(Ppicks)[ij])
@@ -725,9 +725,9 @@ def checkPonsets(pickdic, dttolerance, iplot):
goodstations = np.array(stations)[igood]
badstations = np.array(stations)[ibad]
print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
print("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
@@ -794,8 +794,8 @@ def jackknife(X, phi, h):
g = len(X) / h
if type(g) is not int:
print ("jackknife: Cannot divide quantity X in equal sized subgroups!")
print ("Choose another size for subgroups!")
print("jackknife: Cannot divide quantity X in equal sized subgroups!")
print("Choose another size for subgroups!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
# estimator of undisturbed spot check
@@ -863,7 +863,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Check for spuriously picked S onset instead of P onset ...")
print("Check for spuriously picked S onset instead of P onset ...")
returnflag = 0
@@ -876,10 +876,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
if len(ndat) == 0: # check for other components
ndat = X.select(component="1")
z = zdat[0].data
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
zdat[0].stats.delta)
zdat[0].stats.delta)
# calculate RMS trace from vertical component
absz = np.sqrt(np.power(z, 2))
@@ -904,16 +903,16 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
# vertical P-coda level must exceed horizontal P-coda level
# zfac times encodalevel
if zcodalevel < minsiglevel:
print ("checkZ4S: Maybe S onset? Skip this P pick!")
print("checkZ4S: Maybe S onset? Skip this P pick!")
else:
print ("checkZ4S: P onset passes checkZ4S test!")
print("checkZ4S: P onset passes checkZ4S test!")
returnflag = 1
if iplot > 1:
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
edat[0].stats.delta)
edat[0].stats.delta)
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')
plt.plot(tz[isignal], z[isignal] / max(z), 'r')
plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
@@ -955,9 +954,8 @@ def writephases(arrivals, fformat, filename):
:type: string
'''
if fformat == 'NLLoc':
print ("Writing phases to %s for NLLoc" % filename)
print("Writing phases to %s for NLLoc" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write('# EQEVENT: Label: EQ001 Loc: X 0.00 Y 0.00 Z 10.00 OT 0.00 \n')
@@ -969,34 +967,32 @@ def writephases(arrivals, fformat, filename):
onset = arrivals[key]['P']['mpp']
year = onset.year
month = onset.month
day =onset.day
day = onset.day
hh = onset.hour
mm = onset.minute
ss = onset.second
ss = onset.second
ms = onset.microsecond
ss_ms = ss + (ms / 1E06)
fid.write('%s ? ? ? P %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 1 \n' \
% (key, fm, year, month, day, hh, mm, ss_ms))
% (key, fm, year, month, day, hh, mm, ss_ms))
if arrivals[key]['S']['weight'] < 4:
fm = '?'
onset = arrivals[key]['S']['mpp']
year = onset.year
month = onset.month
day =onset.day
day = onset.day
hh = onset.hour
mm = onset.minute
ss = onset.second
ss = onset.second
ms = onset.microsecond
ss_ms = ss + (ms / 1E06)
fid.write('%s ? ? ? S %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 1 \n' \
% (key, fm, year, month, day, hh, mm, ss_ms))
% (key, fm, year, month, day, hh, mm, ss_ms))
fid.close()
if __name__ == '__main__':
import doctest
doctest.testmod()