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WALL-E: Einmal aufräumen und zurück!

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This commit is contained in:
Sebastian Wehling-Benatelli
2016-03-30 08:14:58 +02:00
parent a2640e3126
commit d7cfd0d176
29 changed files with 1285 additions and 1105 deletions
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2
pylot/core/analysis/coinctimes.py
View File

@@ -5,9 +5,7 @@ from obspy.core import read
from obspy.signal.trigger import coincidenceTrigger
class CoincidenceTimes(object):
def __init__(self, st, comp='Z', coinum=4, sta=1., lta=10., on=5., off=1.):
_type = 'recstalta'
self.coinclist = self.createCoincTriggerlist(data=st, trigcomp=comp,

220
pylot/core/analysis/magnitude.py
View File

@@ -15,6 +15,7 @@ from scipy.optimize import curve_fit
from scipy import integrate, signal
from pylot.core.read.data import Data
class Magnitude(object):
'''
Superclass for calculating Wood-Anderson peak-to-peak
@@ -72,7 +73,6 @@ class Magnitude(object):
self.calcsourcespec()
self.run_calcMoMw()
def getwfstream(self):
return self.wfstream
@@ -108,7 +108,7 @@ class Magnitude(object):
def getrho(self):
return self.rho
def setvp(self, vp):
self.vp = vp
@@ -117,7 +117,7 @@ class Magnitude(object):
def setQp(self, Qp):
self.Qp = Qp
def getQp(self):
return self.Qp
@@ -154,6 +154,7 @@ class Magnitude(object):
def run_calcMoMw(self):
self.pickdic = None
class WApp(Magnitude):
'''
Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
@@ -207,10 +208,10 @@ class WApp(Magnitude):
class M0Mw(Magnitude):
'''
Method to calculate seismic moment Mo and moment magnitude Mw.
Requires results of class calcsourcespec for calculating plateau w0
and corner frequency fc of source spectrum, respectively. Uses
subfunction calcMoMw.py. Returns modified dictionary of picks including
Dc-value, corner frequency fc, seismic moment Mo and
Requires results of class calcsourcespec for calculating plateau w0
and corner frequency fc of source spectrum, respectively. Uses
subfunction calcMoMw.py. Returns modified dictionary of picks including
Dc-value, corner frequency fc, seismic moment Mo and
corresponding moment magntiude Mw.
'''
@@ -222,44 +223,45 @@ class M0Mw(Magnitude):
self.picdic = None
for key in picks:
if picks[key]['P']['weight'] < 4:
# select waveform
selwf = wfdat.select(station=key)
if len(key) > 4:
Ppattern = '%s ? ? ? P' % key
elif len(key) == 4:
Ppattern = '%s ? ? ? P' % key
elif len(key) < 4:
Ppattern = '%s ? ? ? P' % key
nllocline = getPatternLine(nllocfile, Ppattern)
# get hypocentral distance, station azimuth and
# angle of incidence from NLLoc-location file
delta = float(nllocline.split(None)[21])
az = float(nllocline.split(None)[22])
inc = float(nllocline.split(None)[24])
# call subfunction to estimate source spectrum
# and to derive w0 and fc
[w0, fc] = calcsourcespec(selwf, picks[key]['P']['mpp'], \
self.getinvdir(), self.getvp(), delta, az, \
inc, self.getQp(), self.getiplot())
if picks[key]['P']['weight'] < 4:
# select waveform
selwf = wfdat.select(station=key)
if len(key) > 4:
Ppattern = '%s ? ? ? P' % key
elif len(key) == 4:
Ppattern = '%s ? ? ? P' % key
elif len(key) < 4:
Ppattern = '%s ? ? ? P' % key
nllocline = getPatternLine(nllocfile, Ppattern)
# get hypocentral distance, station azimuth and
# angle of incidence from NLLoc-location file
delta = float(nllocline.split(None)[21])
az = float(nllocline.split(None)[22])
inc = float(nllocline.split(None)[24])
# call subfunction to estimate source spectrum
# and to derive w0 and fc
[w0, fc] = calcsourcespec(selwf, picks[key]['P']['mpp'], \
self.getinvdir(), self.getvp(), delta, az, \
inc, self.getQp(), self.getiplot())
if w0 is not None:
# call subfunction to calculate Mo and Mw
zdat = selwf.select(component="Z")
if len(zdat) == 0: # check for other components
zdat = selwf.select(component="3")
[Mo, Mw] = calcMoMw(zdat, w0, self.getrho(), self.getvp(), \
delta, self.getinvdir())
else:
Mo = None
Mw = None
if w0 is not None:
# call subfunction to calculate Mo and Mw
zdat = selwf.select(component="Z")
if len(zdat) == 0: # check for other components
zdat = selwf.select(component="3")
[Mo, Mw] = calcMoMw(zdat, w0, self.getrho(), self.getvp(), \
delta, self.getinvdir())
else:
Mo = None
Mw = None
# add w0, fc, Mo and Mw to dictionary
picks[key]['P']['w0'] = w0
picks[key]['P']['fc'] = fc
picks[key]['P']['Mo'] = Mo
picks[key]['P']['Mw'] = Mw
self.picdic = picks
# add w0, fc, Mo and Mw to dictionary
picks[key]['P']['w0'] = w0
picks[key]['P']['fc'] = fc
picks[key]['P']['Mo'] = Mo
picks[key]['P']['Mw'] = Mw
self.picdic = picks
def calcMoMw(wfstream, w0, rho, vp, delta, inv):
'''
@@ -271,7 +273,7 @@ def calcMoMw(wfstream, w0, rho, vp, delta, inv):
:param: w0, height of plateau of source spectrum
:type: float
:param: rho, rock density [kg/m³]
:type: integer
@@ -283,25 +285,24 @@ def calcMoMw(wfstream, w0, rho, vp, delta, inv):
'''
tr = wfstream[0]
delta = delta * 1000 # hypocentral distance in [m]
delta = delta * 1000 # hypocentral distance in [m]
print("calcMoMw: Calculating seismic moment Mo and moment magnitude Mw for station %s ..." \
% tr.stats.station)
% tr.stats.station)
# additional common parameters for calculating Mo
rP = 2 / np.sqrt(15) # average radiation pattern of P waves (Aki & Richards, 1980)
freesurf = 2.0 # free surface correction, assuming vertical incidence
rP = 2 / np.sqrt(15) # average radiation pattern of P waves (Aki & Richards, 1980)
freesurf = 2.0 # free surface correction, assuming vertical incidence
Mo = w0 * 4 * np.pi * rho * np.power(vp, 3) * delta / (rP * freesurf)
Mo = w0 * 4 * np.pi * rho * np.power(vp, 3) * delta / (rP * freesurf)
#Mw = np.log10(Mo * 1e07) * 2 / 3 - 10.7 # after Hanks & Kanamori (1979), defined for [dyn*cm]!
Mw = np.log10(Mo) * 2 / 3 - 6.7 # for metric units
# Mw = np.log10(Mo * 1e07) * 2 / 3 - 10.7 # after Hanks & Kanamori (1979), defined for [dyn*cm]!
Mw = np.log10(Mo) * 2 / 3 - 6.7 # for metric units
print("calcMoMw: Calculated seismic moment Mo = %e Nm => Mw = %3.1f " % (Mo, Mw))
return Mo, Mw
def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp, iplot):
'''
@@ -310,7 +311,7 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
source model. Has to be derived from instrument corrected displacement traces,
thus restitution and integration necessary! Integrated traces are rotated
into ray-coordinate system ZNE => LQT using Obspy's rotate modul!
:param: wfstream
:type: `~obspy.core.stream.Stream`
@@ -346,7 +347,7 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
Q = int(qu[0])
# A, i.e. power of frequency
A = float(qu[1])
delta = delta * 1000 # hypocentral distance in [m]
delta = delta * 1000 # hypocentral distance in [m]
fc = None
w0 = None
@@ -385,11 +386,11 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
# L: P-wave direction
# Q: SV-wave direction
# T: SH-wave direction
LQT=cordat_copy.rotate('ZNE->LQT',azimuth, incidence)
LQT = cordat_copy.rotate('ZNE->LQT', azimuth, incidence)
ldat = LQT.select(component="L")
if len(ldat) == 0:
# if horizontal channels are 2 and 3
# no azimuth information is available and thus no
# no azimuth information is available and thus no
# rotation is possible!
print("calcsourcespec: Azimuth information is missing, "
"no rotation of components possible!")
@@ -398,30 +399,30 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
# integrate to displacement
# unrotated vertical component (for copmarison)
inttrz = signal.detrend(integrate.cumtrapz(zdat[0].data, None, \
zdat[0].stats.delta))
zdat[0].stats.delta))
# rotated component Z => L
Ldat = signal.detrend(integrate.cumtrapz(ldat[0].data, None, \
ldat[0].stats.delta))
ldat[0].stats.delta))
# get window after P pulse for
# get window after P pulse for
# calculating source spectrum
if zdat[0].stats.sampling_rate <= 100:
winzc = zdat[0].stats.sampling_rate
elif zdat[0].stats.sampling_rate > 100 and \
zdat[0].stats.sampling_rate <= 200:
winzc = 0.5 * zdat[0].stats.sampling_rate
zdat[0].stats.sampling_rate <= 200:
winzc = 0.5 * zdat[0].stats.sampling_rate
elif zdat[0].stats.sampling_rate > 200 and \
zdat[0].stats.sampling_rate <= 400:
winzc = 0.2 * zdat[0].stats.sampling_rate
zdat[0].stats.sampling_rate <= 400:
winzc = 0.2 * zdat[0].stats.sampling_rate
elif zdat[0].stats.sampling_rate > 400:
winzc = zdat[0].stats.sampling_rate
winzc = zdat[0].stats.sampling_rate
tstart = UTCDateTime(zdat[0].stats.starttime)
tonset = onset.timestamp -tstart.timestamp
tonset = onset.timestamp - tstart.timestamp
impickP = tonset * zdat[0].stats.sampling_rate
wfzc = Ldat[impickP : impickP + winzc]
wfzc = Ldat[impickP: impickP + winzc]
# get time array
t = np.arange(0, len(inttrz) * zdat[0].stats.delta, \
zdat[0].stats.delta)
zdat[0].stats.delta)
# calculate spectrum using only first cycles of
# waveform after P onset!
zc = crossings_nonzero_all(wfzc)
@@ -441,14 +442,14 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
fny = zdat[0].stats.sampling_rate / 2
l = len(xdat) / zdat[0].stats.sampling_rate
# number of fft bins after Bath
n = zdat[0].stats.sampling_rate * l
n = zdat[0].stats.sampling_rate * l
# 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 = zdat[0].stats.delta * np.fft.fft(xdat, N)
Y = abs(y[: N/2])
Y = abs(y[: N / 2])
L = (N - 1) / zdat[0].stats.sampling_rate
f = np.arange(0, fny, 1/L)
f = np.arange(0, fny, 1 / L)
# remove zero-frequency and frequencies above
# corner frequency of seismometer (assumed
@@ -457,10 +458,10 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
F = f[fi]
YY = Y[fi]
# correction for attenuation
wa = 2 * np.pi * F #angular frequency
D = np.exp((wa * delta) / (2 * vp * Q*F**A))
YYcor = YY.real*D
# correction for attenuation
wa = 2 * np.pi * F # angular frequency
D = np.exp((wa * delta) / (2 * vp * Q * F ** A))
YYcor = YY.real * D
# get plateau (DC value) and corner frequency
# initial guess of plateau
@@ -477,24 +478,24 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
fc1 = optspecfit[1]
print ("calcsourcespec: Determined w0-value: %e m/Hz, \n"
"Determined corner frequency: %f Hz" % (w01, fc1))
# use of conventional fitting
# use of conventional fitting
[w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot)
# get w0 and fc as median of both
# source spectrum fits
# get w0 and fc as median of both
# source spectrum fits
w0 = np.median([w01, w02])
fc = np.median([fc1, fc2])
print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (w0, fc))
except TypeError as er:
raise TypeError('''{0}'''.format(er))
if iplot > 1:
f1 = plt.figure()
tLdat = np.arange(0, len(Ldat) * zdat[0].stats.delta, \
zdat[0].stats.delta)
plt.subplot(2,1,1)
zdat[0].stats.delta)
plt.subplot(2, 1, 1)
# show displacement in mm
p1, = plt.plot(t, np.multiply(inttrz, 1000), 'k')
p2, = plt.plot(tLdat, np.multiply(Ldat, 1000))
@@ -502,26 +503,26 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
if plotflag == 1:
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
plt.title('Seismogram and P Pulse, Station %s-%s' \
% (zdat[0].stats.station, zdat[0].stats.channel))
% (zdat[0].stats.station, zdat[0].stats.channel))
else:
plt.title('Seismogram, Station %s-%s' \
% (zdat[0].stats.station, zdat[0].stats.channel))
% (zdat[0].stats.station, zdat[0].stats.channel))
plt.xlabel('Time since %s' % zdat[0].stats.starttime)
plt.ylabel('Displacement [mm]')
if plotflag == 1:
plt.subplot(2,1,2)
plt.subplot(2, 1, 2)
p1, = plt.loglog(f, Y.real, 'k')
p2, = plt.loglog(F, YY.real)
p3, = plt.loglog(F, YYcor, 'r')
p4, = plt.loglog(F, fit, 'g')
plt.loglog([fc, fc], [w0/100, w0], 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
plt.legend([p1, p2, p3, p4], ['Raw Spectrum', \
'Used Raw Spectrum', \
'Q-Corrected Spectrum', \
'Fit to Spectrum'])
plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
% (w0, fc))
% (w0, fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
@@ -530,7 +531,7 @@ def calcsourcespec(wfstream, onset, inventory, vp, delta, azimuth, incidence, Qp
plt.close(f1)
return w0, fc
def synthsourcespec(f, omega0, fcorner):
'''
@@ -547,7 +548,7 @@ def synthsourcespec(f, omega0, fcorner):
:type: float
'''
#ssp = omega0 / (pow(2, (1 + f / fcorner)))
# ssp = omega0 / (pow(2, (1 + f / fcorner)))
ssp = omega0 / (1 + pow(2, (f / fcorner)))
return ssp
@@ -556,8 +557,8 @@ def synthsourcespec(f, omega0, fcorner):
def fitSourceModel(f, S, fc0, iplot):
'''
Calculates synthetic source spectrum by varying corner frequency fc.
Returns best approximated plateau omega0 and corner frequency, i.e. with least
common standard deviations.
Returns best approximated plateau omega0 and corner frequency, i.e. with least
common standard deviations.
:param: f, frequencies
:type: array
@@ -569,7 +570,7 @@ def fitSourceModel(f, S, fc0, iplot):
:type: float
'''
w0 = []
w0 = []
stdw0 = []
fc = []
stdfc = []
@@ -577,17 +578,17 @@ def fitSourceModel(f, S, fc0, iplot):
# get window around initial corner frequency for trials
fcstopl = fc0 - max(1, len(f) / 10)
il = np.argmin(abs(f-fcstopl))
il = np.argmin(abs(f - fcstopl))
fcstopl = f[il]
fcstopr = fc0 + min(len(f), len(f) /10)
ir = np.argmin(abs(f-fcstopr))
fcstopr = fc0 + min(len(f), len(f) / 10)
ir = np.argmin(abs(f - fcstopr))
fcstopr = f[ir]
iF = np.where((f >= fcstopl) & (f <= fcstopr))
# vary corner frequency around initial point
for i in range(il, ir):
for i in range(il, ir):
FC = f[i]
indexdc = np.where((f > 0 ) & (f <= FC))
indexdc = np.where((f > 0) & (f <= FC))
dc = np.mean(S[indexdc])
stddc = np.std(dc - S[indexdc])
w0.append(dc)
@@ -595,7 +596,7 @@ def fitSourceModel(f, S, fc0, iplot):
fc.append(FC)
# slope
indexfc = np.where((f >= FC) & (f <= fcstopr))
yi = dc/(1+(f[indexfc]/FC)**2)
yi = dc / (1 + (f[indexfc] / FC) ** 2)
stdFC = np.std(yi - S[indexfc])
stdfc.append(stdFC)
STD.append(stddc + stdFC)
@@ -607,31 +608,31 @@ def fitSourceModel(f, S, fc0, iplot):
elif len(STD) == 0:
fc = fc0
w0 = max(S)
print("fitSourceModel: best fc: %fHz, best w0: %e m/Hz" \
% (fc, w0))
% (fc, w0))
if iplot > 1:
plt.figure(iplot)
plt.loglog(f, S, 'k')
plt.loglog([f[0], fc], [w0, w0], 'g')
plt.loglog([fc, fc], [w0/100, w0], 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
plt.title('Calculated Source Spectrum, Omega0=%e m/Hz, fc=%6.2f Hz' \
% (w0, fc))
% (w0, fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.figure(iplot+1)
plt.figure(iplot + 1)
plt.subplot(311)
plt.plot(f[il:ir], STD,'*')
plt.plot(f[il:ir], STD, '*')
plt.title('Common Standard Deviations')
plt.xticks([])
plt.subplot(312)
plt.plot(f[il:ir], stdw0,'*')
plt.plot(f[il:ir], stdw0, '*')
plt.title('Standard Deviations of w0-Values')
plt.xticks([])
plt.subplot(313)
plt.plot(f[il:ir],stdfc,'*')
plt.plot(f[il:ir], stdfc, '*')
plt.title('Standard Deviations of Corner Frequencies')
plt.xlabel('Corner Frequencies [Hz]')
plt.show()
@@ -639,10 +640,3 @@ def fitSourceModel(f, S, fc0, iplot):
plt.close()
return w0, fc

6
pylot/core/analysis/trigger.py
View File

@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from obspy.signal.trigger import recSTALTA, triggerOnset
from obspy.signal.trigger import recursive_sta_lta, trigger_onset
def createSingleTriggerlist(st, station='ZV01', trigcomp='Z', stalta=(1, 10),
@@ -24,8 +24,8 @@ def createSingleTriggerlist(st, station='ZV01', trigcomp='Z', stalta=(1, 10),
tr = st.copy().select(component=trigcomp, station=station)[0]
df = tr.stats.sampling_rate
cft = recSTALTA(tr.data, int(stalta[0] * df), int(stalta[1] * df))
triggers = triggerOnset(cft, trigonoff[0], trigonoff[1])
cft = recursive_sta_lta(tr.data, int(stalta[0] * df), int(stalta[1] * df))
triggers = trigger_onset(cft, trigonoff[0], trigonoff[1])
trigg = []
for time in triggers:
trigg.append(tr.stats.starttime + time[0] / df)