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Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop

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Sebastian Wehling-Benatelli
2015-10-20 12:02:01 +02:00
parent 0a7b02c04a 21ffbcabc8
commit 0064ff1889
10 changed files with 656 additions and 980 deletions
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15
pylot/core/pick/CharFuns.py
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@@ -25,7 +25,7 @@ class CharacteristicFunction(object):
'''
SuperClass for different types of characteristic functions.
'''
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None, stealthMode=False):
'''
Initialize data type object with information from the original
Seismogram.
@@ -62,6 +62,7 @@ class CharacteristicFunction(object):
self.calcCF(self.getDataArray())
self.arpara = np.array([])
self.xpred = np.array([])
self._stealthMode = stealthMode
def __str__(self):
return '''\n\t{name} object:\n
@@ -135,6 +136,9 @@ class CharacteristicFunction(object):
def getXCF(self):
return self.xcf
def _getStealthMode(self):
return self._stealthMode()
def getDataArray(self, cut=None):
'''
If cut times are given, time series is cut from cut[0] (start time)
@@ -219,7 +223,8 @@ class AICcf(CharacteristicFunction):
def calcCF(self, data):
#print 'Calculating AIC ...' ## MP MP output suppressed
#if self._getStealthMode() is False:
# print 'Calculating AIC ...'
x = self.getDataArray()
xnp = x[0].data
nn = np.isnan(xnp)
@@ -257,11 +262,13 @@ class HOScf(CharacteristicFunction):
if len(nn) > 1:
xnp[nn] = 0
if self.getOrder() == 3: # this is skewness
print 'Calculating skewness ...'
#if self._getStealthMode() is False:
# print 'Calculating skewness ...'
y = np.power(xnp, 3)
y1 = np.power(xnp, 2)
elif self.getOrder() == 4: # this is kurtosis
#print 'Calculating kurtosis ...' ## MP MP output suppressed
#if self._getStealthMode() is False:
# print 'Calculating kurtosis ...'
y = np.power(xnp, 4)
y1 = np.power(xnp, 2)

10
pylot/core/pick/utils.py
View File

@@ -15,7 +15,7 @@ from obspy.core import Stream, UTCDateTime
import warnings
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, 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,
@@ -44,7 +44,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
LPick = None
EPick = None
PickError = None
#print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
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,
@@ -75,8 +76,9 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
# if EPick stays NaN the signal window size will be doubled
while np.isnan(EPick):
if count > 0:
print("\nearllatepicker: 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))