[cleanup] removed unused old code and output

This commit is contained in:
Marcel Paffrath 2017-09-21 14:38:56 +02:00
parent 238998e626
commit 2a987cbdfa
7 changed files with 0 additions and 405298 deletions

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git pull
Entferne qrc_resources.py
KONFLIKT (ändern/löschen): pylot/core/pick/getSNR.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/getSNR.py wurde im Arbeitsbereich gelassen.
KONFLIKT (ändern/löschen): pylot/core/pick/fmpicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/fmpicker.py wurde im Arbeitsbereich gelassen.
KONFLIKT (ändern/löschen): pylot/core/pick/earllatepicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/earllatepicker.py wurde im Arbeitsbereich gelassen.
Automatisches Zusammenfügen von icons.qrc
Automatischer Merge fehlgeschlagen; beheben Sie die Konflikte und committen Sie dann das Ergebnis.

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created August/September 2015.
:author: Ludger Küperkoch / MAGS2 EP3 working group
"""
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream
from pylot.core.pick.utils import getsignalwin
from scipy.optimize import curve_fit
class Magnitude(object):
'''
Superclass for calculating Wood-Anderson peak-to-peak
amplitudes, local magnitudes and moment magnitudes.
'''
def __init__(self, wfstream, To, pwin, iplot):
'''
:param: wfstream
:type: `~obspy.core.stream.Stream
:param: To, onset time, P- or S phase
:type: float
:param: pwin, pick window [To To+pwin] to get maximum
peak-to-peak amplitude (WApp) or to calculate
source spectrum (DCfc)
:type: float
:param: iplot, no. of figure window for plotting interims results
:type: integer
'''
assert isinstance(wfstream, Stream), "%s is not a stream object" % str(wfstream)
self.setwfstream(wfstream)
self.setTo(To)
self.setpwin(pwin)
self.setiplot(iplot)
self.calcwapp()
self.calcsourcespec()
def getwfstream(self):
return self.wfstream
def setwfstream(self, wfstream):
self.wfstream = wfstream
def getTo(self):
return self.To
def setTo(self, To):
self.To = To
def getpwin(self):
return self.pwin
def setpwin(self, pwin):
self.pwin = pwin
def getiplot(self):
return self.iplot
def setiplot(self, iplot):
self.iplot = iplot
def getwapp(self):
return self.wapp
def getw0(self):
return self.w0
def getfc(self):
return self.fc
def calcwapp(self):
self.wapp = None
def calcsourcespec(self):
self.sourcespek = None
class WApp(Magnitude):
'''
Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
seismograph. Has to be derived from instrument corrected traces!
'''
def calcwapp(self):
print("Getting Wood-Anderson peak-to-peak amplitude ...")
print("Simulating Wood-Anderson seismograph ...")
self.wapp = None
stream = self.getwfstream()
# poles, zeros and sensitivity of WA seismograph
# (see Uhrhammer & Collins, 1990, BSSA, pp. 702-716)
paz_wa = {
'poles': [5.6089 - 5.4978j, -5.6089 - 5.4978j],
'zeros': [0j, 0j],
'gain': 2080,
'sensitivity': 1}
stream.simulate(paz_remove=None, paz_simulate=paz_wa)
trH1 = stream[0].data
trH2 = stream[1].data
ilen = min([len(trH1), len(trH2)])
# get RMS of both horizontal components
sqH = np.sqrt(np.power(trH1[0:ilen], 2) + np.power(trH2[0:ilen], 2))
# get time array
th = np.arange(0, len(sqH) * stream[0].stats.delta, stream[0].stats.delta)
# get maximum peak within pick window
iwin = getsignalwin(th, self.getTo(), self.getpwin())
self.wapp = np.max(sqH[iwin])
print("Determined Wood-Anderson peak-to-peak amplitude: %f mm") % self.wapp
if self.getiplot() > 1:
stream.plot()
f = plt.figure(2)
plt.plot(th, sqH)
plt.plot(th[iwin], sqH[iwin], 'g')
plt.plot([self.getTo(), self.getTo()], [0, max(sqH)], 'r', linewidth=2)
plt.title('Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
% (stream[0].stats.station, self.wapp))
plt.xlabel('Time [s]')
plt.ylabel('Displacement [mm]')
plt.show()
raw_input()
plt.close(f)
class DCfc(Magnitude):
'''
Method to calculate the source spectrum and to derive from that the plateau
(so-called DC-value) and the corner frequency assuming Aki's omega-square
source model. Has to be derived from instrument corrected displacement traces!
'''
def calcsourcespec(self):
print("Calculating source spectrum ....")
self.w0 = None # DC-value
self.fc = None # corner frequency
stream = self.getwfstream()
tr = stream[0]
# get time array
t = np.arange(0, len(tr) * tr.stats.delta, tr.stats.delta)
iwin = getsignalwin(t, self.getTo(), self.getpwin())
xdat = tr.data[iwin]
# fft
fny = tr.stats.sampling_rate / 2
l = len(xdat) / tr.stats.sampling_rate
n = tr.stats.sampling_rate * l # number of fft bins after Bath
# find next power of 2 of data length
m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
N = int(np.power(m, 2))
y = tr.stats.delta * np.fft.fft(xdat, N)
Y = abs(y[: N / 2])
L = (N - 1) / tr.stats.sampling_rate
f = np.arange(0, fny, 1 / L)
# remove zero-frequency and frequencies above
# corner frequency of seismometer (assumed
# to be 100 Hz)
fi = np.where((f >= 1) & (f < 100))
F = f[fi]
YY = Y[fi]
# get plateau (DC value) and corner frequency
# initial guess of plateau
DCin = np.mean(YY[0:100])
# initial guess of corner frequency
# where spectral level reached 50% of flat level
iin = np.where(YY >= 0.5 * DCin)
Fcin = F[iin[0][np.size(iin) - 1]]
fit = synthsourcespec(F, DCin, Fcin)
[optspecfit, pcov] = curve_fit(synthsourcespec, F, YY.real, [DCin, Fcin])
self.w0 = optspecfit[0]
self.fc = optspecfit[1]
print("DCfc: Determined DC-value: %e m/Hz, \n" \
"Determined corner frequency: %f Hz" % (self.w0, self.fc))
# if self.getiplot() > 1:
iplot = 2
if iplot > 1:
print("DCfc: Determined DC-value: %e m/Hz, \n"
"Determined corner frequency: %f Hz" % (self.w0, self.fc))
if self.getiplot() > 1:
f1 = plt.figure()
plt.subplot(2, 1, 1)
# show displacement in mm
plt.plot(t, np.multiply(tr, 1000), 'k')
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
plt.title('Seismogram and P pulse, station %s' % tr.stats.station)
plt.xlabel('Time since %s' % tr.stats.starttime)
plt.ylabel('Displacement [mm]')
plt.subplot(2, 1, 2)
plt.loglog(f, Y.real, 'k')
plt.loglog(F, YY.real)
plt.loglog(F, fit, 'g')
plt.title('Source Spectrum from P Pulse, DC=%e m/Hz, fc=%4.1f Hz' \
% (self.w0, self.fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.show()
raw_input()
plt.close(f1)
def synthsourcespec(f, omega0, fcorner):
'''
Calculates synthetic source spectrum from given plateau and corner
frequency assuming Akis omega-square model.
:param: f, frequencies
:type: array
:param: omega0, DC-value (plateau) of source spectrum
:type: float
:param: fcorner, corner frequency of source spectrum
:type: float
'''
# ssp = omega0 / (pow(2, (1 + f / fcorner)))
ssp = omega0 / (1 + pow(2, (f / fcorner)))
return ssp

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# -*- coding: utf-8 -*-
"""
Created Mar/Apr 2015
Collection of helpful functions for manual and automatic picking.
:author: Ludger Kueperkoch / MAGS2 EP3 working group
"""
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):
'''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
as reasonable uncertainties. Latest possible pick is based on noise level,
earliest possible pick is half a signal wavelength in front of most likely
pick given by PragPicker or manually set by analyst. Most likely pick
(initial pick Pick1) must be given.
:param: X, time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
:type: int
:param: TSNR, length of time windows around pick used to determine SNR [s]
:type: tuple (T_noise, T_gap, T_signal)
:param: Pick1, initial (most likely) onset time, starting point for earllatepicker
:type: float
:param: iplot, if given, results are plotted in figure(iplot)
:type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
LPick = None
EPick = None
PickError = None
if stealthMode is False:
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,
X[0].stats.delta)
inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, Pick1, TSNR[2])
# remove mean
x = x - np.mean(x[inoise])
# calculate noise level
nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
# 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:
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'))
LPick = t[isignal][il]
# get earliest possible pick
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)
if stealthMode is False:
print("\nearllatepicker: Doubled signal window size %s time(s) "
"because of NaN for earliest pick." % count)
isigDoubleWinStart = pis[-1] + 1
isignalDoubleWin = np.arange(isigDoubleWinStart,
isigDoubleWinStart + len(pis))
if (isigDoubleWinStart + len(pis)) < X[0].data.size:
pis = np.concatenate((pis, isignalDoubleWin))
else:
print("Could not double signal window. Index out of bounds.")
break
count += 1
# determine all zero crossings in signal window (demeaned)
zc = crossings_nonzero_all(x[pis] - x[pis].mean())
# calculate mean half period T0 of signal as the average of the
T0 = np.mean(np.diff(zc)) * X[0].stats.delta # this is half wave length
# 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
diffti_te = Pick1 - EPick
PickError = (diffti_te + 2 * diffti_tl) / 3
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],
[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(
'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
X[0].stats.station)
plt.show()
raw_input()
plt.close(p)
return EPick, LPick, PickError
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0):
'''
Function to derive first motion (polarity) of given phase onset Pick.
Calculation is based on zero crossings determined within time window pickwin
after given onset time.
:param: Xraw, unfiltered time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: Xfilt, filtered time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: pickwin, time window after onset Pick within zero crossings are calculated
:type: float
:param: Pick, initial (most likely) onset time, starting point for fmpicker
:type: float
:param: iplot, if given, results are plotted in figure(iplot)
:type: int
'''
warnings.simplefilter('ignore', np.RankWarning)
assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
FM = None
if Pick is not None:
print("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
xraw = Xraw[0].data
xfilt = Xfilt[0].data
t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate,
Xraw[0].stats.delta)
# get pick window
ipick = np.where(
(t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
# remove mean
xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
# get zero crossings after most likely pick
# initial onset is assumed to be the first zero crossing
# first from unfiltered trace
zc1 = []
zc1.append(Pick)
index1 = []
i = 0
for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
i = i + 1
if xraw[j - 1] <= 0 <= xraw[j]:
zc1.append(t[ipick][i])
index1.append(i)
elif xraw[j - 1] > 0 >= xraw[j]:
zc1.append(t[ipick][i])
index1.append(i)
if len(zc1) == 3:
break
# if time difference betweeen 1st and 2cnd zero crossing
# is too short, get time difference between 1st and 3rd
# to derive maximum
if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
li1 = index1[1]
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!")
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!")
return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
# calculate slope as polynomal fit of order 1
xslope1 = np.arange(0, len(xraw[islope1]), 1)
P1 = np.polyfit(xslope1, xraw[islope1], 1)
datafit1 = np.polyval(P1, xslope1)
# now using filterd trace
# next zero crossings after most likely pick
zc2 = []
zc2.append(Pick)
index2 = []
i = 0
for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
i = i + 1
if xfilt[j - 1] <= 0 <= xfilt[j]:
zc2.append(t[ipick][i])
index2.append(i)
elif xfilt[j - 1] > 0 >= xfilt[j]:
zc2.append(t[ipick][i])
index2.append(i)
if len(zc2) == 3:
break
# if time difference betweeen 1st and 2cnd zero crossing
# is too short, get time difference between 1st and 3rd
# to derive maximum
if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
li2 = index2[1]
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!")
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!")
return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
# calculate slope as polynomal fit of order 1
xslope2 = np.arange(0, len(xfilt[islope2]), 1)
P2 = np.polyfit(xslope2, xfilt[islope2], 1)
datafit2 = np.polyval(P2, xslope2)
# compare results
if P1 is not None and P2 is not None:
if P1[0] < 0 and P2[0] < 0:
FM = 'D'
elif P1[0] >= 0 > P2[0]:
FM = '-'
elif P1[0] < 0 <= P2[0]:
FM = '-'
elif P1[0] > 0 and P2[0] > 0:
FM = 'U'
elif P1[0] <= 0 < P2[0]:
FM = '+'
elif P1[0] > 0 >= P2[0]:
FM = '+'
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)
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[
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',
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)
return FM
def crossings_nonzero_all(data):
pos = data > 0
npos = ~pos
return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
def getSNR(X, TSNR, t1):
'''
Function to calculate SNR of certain part of seismogram relative to
given time (onset) out of given noise and signal windows. A safety gap
between noise and signal part can be set. Returns SNR and SNR [dB] and
noiselevel.
:param: X, time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
:type: tuple (T_noise, T_gap, T_signal)
:param: t1, initial time (onset) from which noise and signal windows are calculated
:type: float
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
x = X[0].data
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
X[0].stats.delta)
# get noise window
inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print("getSNR: Empty array inoise, check noise window!")
return
elif np.size(isignal) < 1:
print("getSNR: Empty array isignal, check signal window!")
return
# demean over entire waveform
x = x - np.mean(x[inoise])
# calculate ratios
noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
signallevel = np.sqrt(np.mean(np.square(x[isignal])))
SNR = signallevel / noiselevel
SNRdB = 10 * np.log10(SNR)
return SNR, SNRdB, noiselevel
def getnoisewin(t, t1, tnoise, tgap):
'''
Function to extract indeces of data out of time series for noise calculation.
Returns an array of indeces.
:param: t, array of time stamps
:type: numpy array
:param: t1, time from which relativ to it noise window is extracted
:type: float
:param: tnoise, length of time window [s] for noise part extraction
:type: float
:param: tgap, safety gap between t1 (onset) and noise window to
ensure, that noise window contains no signal
:type: float
'''
# get noise window
inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1:
print("getnoisewin: Empty array inoise, check noise window!")
return inoise
def getsignalwin(t, t1, tsignal):
'''
Function to extract data out of time series for signal level calculation.
Returns an array of indeces.
:param: t, array of time stamps
:type: numpy array
:param: t1, time from which relativ to it signal window is extracted
:type: float
:param: tsignal, length of time window [s] for signal level calculation
:type: float
'''
# get signal window
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
& (t >= t1))
if np.size(isignal) < 1:
print("getsignalwin: Empty array isignal, check signal window!")
return isignal
def getResolutionWindow(snr):
"""
Number -> Float
produce the half of the time resolution window width from given SNR
value
SNR >= 3 -> 2 sec HRW
3 > SNR >= 2 -> 5 sec MRW
2 > SNR >= 1.5 -> 10 sec LRW
1.5 > SNR -> 15 sec VLRW
see also Diehl et al. 2009
>>> getResolutionWindow(0.5)
7.5
>>> getResolutionWindow(1.8)
5.0
>>> getResolutionWindow(2.3)
2.5
>>> getResolutionWindow(4)
1.0
>>> getResolutionWindow(2)
2.5
"""
res_wins = {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.}
if snr < 1.5:
time_resolution = res_wins['VLRW']
elif snr < 2.:
time_resolution = res_wins['LRW']
elif snr < 3.:
time_resolution = res_wins['MRW']
else:
time_resolution = res_wins['HRW']
return time_resolution / 2
def wadaticheck(pickdic, dttolerance, iplot):
'''
Function to calculate Wadati-diagram from given P and S onsets in order
to detect S pick outliers. If a certain S-P time deviates by dttolerance
from regression of S-P time the S pick is marked and down graded.
: param: pickdic, dictionary containing picks and quality parameters
: type: dictionary
: param: dttolerance, maximum adjusted deviation of S-P time from
S-P time regression
: type: float
: param: iplot, if iplot > 1, Wadati diagram is shown
: type: int
'''
checkedonsets = pickdic
# search for good quality picks and calculate S-P time
Ppicks = []
Spicks = []
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)
if len(SPtimes) >= 3:
# calculate slope
p1 = np.polyfit(Ppicks, SPtimes, 1)
wdfit = np.polyval(p1, Ppicks)
wfitflag = 0
# calculate vp/vs ratio before check
vpvsr = p1[0] + 1
print("###############################################")
print("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
checkedPpicks = []
checkedSpicks = []
checkedSPtimes = []
# calculate deviations from Wadati regression
ii = 0
ibad = 0
for key in pickdic:
if pickdic[key].has_key('SPt'):
wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
ii += 1
# check, if deviation is larger than adjusted
if wddiff > dttolerance:
# mark onset and downgrade S-weight to 9
# (not used anymore)
marker = 'badWadatiCheck'
pickdic[key]['S']['weight'] = 9
ibad += 1
else:
marker = 'goodWadatiCheck'
checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
checkedPpicks.append(checkedPpick.timestamp)
checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
checkedSpicks.append(checkedSpick.timestamp)
checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
checkedSPtimes.append(checkedSPtime)
pickdic[key]['S']['marked'] = marker
if len(checkedPpicks) >= 3:
# calculate new slope
p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
wdfit2 = np.polyval(p2, checkedPpicks)
# calculate vp/vs ratio after check
cvpvsr = p2[0] + 1
print("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print("wadatacheck: Skipped %d S pick(s)" % ibad)
else:
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!")
wfitflag = 1
# plot results
if iplot > 1:
plt.figure(iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0:
f2, = plt.plot(Ppicks, wdfit, 'k')
f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
f4, = plt.plot(checkedPpicks, wdfit2, 'g')
plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1',
'Reliable S-Picks', 'Wadati 2'], loc='best')
else:
plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
plt.ylabel('S-P Times [s]')
plt.xlabel('P Times [s]')
plt.show()
raw_input()
plt.close(iplot)
return checkedonsets
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
'''
Function to detect spuriously picked noise peaks.
Uses RMS trace of all 3 components (if available) to determine,
how many samples [per cent] after P onset are below certain
threshold, calculated from noise level times noise factor.
: param: X, time series (seismogram)
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: TSNR, length of time windows around initial pick [s]
: type: tuple (T_noise, T_gap, T_signal)
: param: minsiglength, minium required signal length [s] to
declare pick as P onset
: type: float
: param: nfac, noise factor (nfac * noise level = threshold)
: type: float
: param: minpercent, minimum required percentage of samples
above calculated threshold
: type: float
: param: iplot, if iplot > 1, results are shown in figure
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print("Checking signal length ...")
if len(X) > 1:
# all three components available
# make sure, all components have equal lengths
ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
x1 = X[0][0:ilen]
x2 = X[1][0:ilen]
x3 = X[2][0:ilen]
# get RMS trace
rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
else:
x1 = X[0].data
rms = np.sqrt(np.power(2, x1))
t = np.arange(0, ilen / X[0].stats.sampling_rate,
X[0].stats.delta)
# get noise window in front of pick plus saftey gap
inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, pick, minsiglength)
# calculate minimum adjusted signal level
minsiglevel = max(rms[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level
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.")
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)
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)
return returnflag
def checkPonsets(pickdic, dttolerance, iplot):
'''
Function to check statistics of P-onset times: Control deviation from
median (maximum adjusted deviation = dttolerance) and apply pseudo-
bootstrapping jackknife.
: param: pickdic, dictionary containing picks and quality parameters
: type: dictionary
: param: dttolerance, maximum adjusted deviation of P-onset time from
median of all P onsets
: type: float
: param: iplot, if iplot > 1, Wadati diagram is shown
: type: int
'''
checkedonsets = pickdic
# search for good quality P picks
Ppicks = []
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)
# 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)
# 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))
# calculate median from these picks
pmedian = np.median(np.array(Ppicks)[ij])
# find picks that deviate less than dttolerance from median
ii = np.where(abs(np.array(Ppicks)[ij] - pmedian) <= dttolerance)
jj = np.where(abs(np.array(Ppicks)[ij] - pmedian) > dttolerance)
igood = ij[0][ii]
ibad = ij[0][jj]
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)))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
badjkmarker = 'badjkcheck'
for i in range(0, len(goodstations)):
# mark P onset as checked and keep P weight
pickdic[goodstations[i]]['P']['marked'] = goodmarker
for i in range(0, len(badstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badstations[i]]['P']['marked'] = badmarker
pickdic[badstations[i]]['P']['weight'] = 9
for i in range(0, len(badjkstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
pickdic[badjkstations[i]]['P']['weight'] = 9
checkedonsets = pickdic
if iplot > 1:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
linewidth=2)
for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.2, stations[i])
plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970')
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
def jackknife(X, phi, h):
'''
Function to calculate the Jackknife Estimator for a given quantity,
special type of boot strapping. Returns the jackknife estimator PHI_jack
the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
: param: X, given quantity
: type: list
: param: phi, chosen estimator, choose between:
"MED" for median
"MEA" for arithmetic mean
"VAR" for variance
: type: string
: param: h, size of subgroups, optinal, default = 1
: type: integer
'''
PHI_jack = None
PHI_pseudo = None
PHI_sub = None
# determine number of subgroups
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!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
# estimator of undisturbed spot check
if phi == 'MEA':
phi_sc = np.mean(X)
elif phi == 'VAR':
phi_sc = np.var(X)
elif phi == 'MED':
phi_sc = np.median(X)
# estimators of subgroups
PHI_pseudo = []
PHI_sub = []
for i in range(0, g - 1):
# subgroup i, remove i-th sample
xx = X[:]
del xx[i]
# calculate estimators of disturbed spot check
if phi == 'MEA':
phi_sub = np.mean(xx)
elif phi == 'VAR':
phi_sub = np.var(xx)
elif phi == 'MED':
phi_sub = np.median(xx)
PHI_sub.append(phi_sub)
# pseudo values
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
PHI_pseudo.append(phi_pseudo)
# jackknife estimator
PHI_jack = np.mean(PHI_pseudo)
return PHI_jack, PHI_pseudo, PHI_sub
def checkZ4S(X, pick, zfac, checkwin, iplot):
'''
Function to compare energy content of vertical trace with
energy content of horizontal traces to detect spuriously
picked S onsets instead of P onsets. Usually, P coda shows
larger longitudal energy on vertical trace than on horizontal
traces, where the transversal energy is larger within S coda.
Be careful: there are special circumstances, where this is not
the case!
: param: X, fitered(!) time series, three traces
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: zfac, factor for threshold determination,
vertical energy must exceed coda level times zfac
to declare a pick as P onset
: type: float
: param: checkwin, window length [s] for calculating P-coda
energy content
: type: float
: param: iplot, if iplot > 1, energy content and threshold
are shown
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print("Check for spuriously picked S onset instead of P onset ...")
returnflag = 0
# split components
zdat = X.select(component="Z")
edat = X.select(component="E")
if len(edat) == 0: # check for other components
edat = X.select(component="2")
ndat = X.select(component="N")
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)
# calculate RMS trace from vertical component
absz = np.sqrt(np.power(z, 2))
# calculate RMS trace from both horizontal traces
# make sure, both traces have equal lengths
lene = len(edat[0].data)
lenn = len(ndat[0].data)
minlen = min([lene, lenn])
absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
+ np.power(ndat[0].data[0:minlen - 1], 2))
# get signal window
isignal = getsignalwin(tz, pick, checkwin)
# calculate energy levels
zcodalevel = max(absz[isignal])
encodalevel = max(absen[isignal])
# calculate threshold
minsiglevel = encodalevel * zfac
# 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!")
else:
print("checkZ4S: P onset passes checkZ4S test!")
returnflag = 1
if iplot > 1:
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
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()
return returnflag
def writephases(arrivals, fformat, filename):
'''
Function of methods to write phases to the following standard file
formats used for locating earthquakes:
HYPO71, NLLoc, VELEST, HYPOSAT, HYPOINVERSE and hypoDD
:param: arrivals
:type: dictionary containing all phase information including
station ID, phase, first motion, weight (uncertainty),
....
:param: fformat
:type: string, chosen file format (location routine),
choose between NLLoc, HYPO71, HYPOSAT, VELEST,
HYPOINVERSE, and hypoDD
:param: filename, full path and name of phase file
:type: string
'''
if fformat == 'NLLoc':
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')
for key in arrivals:
if arrivals[key]['P']['weight'] < 4:
# write phase information to NLLoc-phase file
# see the NLLoc tutorial at www.alomax.free.fr/nlloc/
fm = arrivals[key]['P']['fm']
onset = arrivals[key]['P']['mpp']
year = onset.year
month = onset.month
day = onset.day
hh = onset.hour
mm = onset.minute
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))
if arrivals[key]['S']['weight'] < 4:
fm = '?'
onset = arrivals[key]['S']['mpp']
year = onset.year
month = onset.month
day = onset.day
hh = onset.hour
mm = onset.minute
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))
fid.close()
if __name__ == '__main__':
import doctest
doctest.testmod()

319295
pylot/os

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