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implied stealth mode

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This commit is contained in:
Marcel Paffrath 2015-10-12 13:00:09 +02:00
parent 0adc890aef
commit a19cdc4fee
1 changed files with 8 additions and 4 deletions
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12
pylot/core/pick/CharFuns.py
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@ -24,7 +24,7 @@ class CharacteristicFunction(object):
''' '''
SuperClass for different types of characteristic functions. 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 Initialize data type object with information from the original
Seismogram. Seismogram.
@ -61,6 +61,7 @@ class CharacteristicFunction(object):
self.calcCF(self.getDataArray()) self.calcCF(self.getDataArray())
self.arpara = np.array([]) self.arpara = np.array([])
self.xpred = np.array([]) self.xpred = np.array([])
self.stealthMode = stealthMode
def __str__(self): def __str__(self):
return '''\n\t{name} object:\n return '''\n\t{name} object:\n
@ -218,7 +219,8 @@ class AICcf(CharacteristicFunction):
def calcCF(self, data): def calcCF(self, data):
#print 'Calculating AIC ...' ## MP MP output suppressed if self.stealthMode is False:
print 'Calculating AIC ...'
x = self.getDataArray() x = self.getDataArray()
xnp = x[0].data xnp = x[0].data
nn = np.isnan(xnp) nn = np.isnan(xnp)
@ -256,11 +258,13 @@ class HOScf(CharacteristicFunction):
if len(nn) > 1: if len(nn) > 1:
xnp[nn] = 0 xnp[nn] = 0
if self.getOrder() == 3: # this is skewness if self.getOrder() == 3: # this is skewness
print 'Calculating skewness ...' if self.stealthMode is False:
print 'Calculating skewness ...'
y = np.power(xnp, 3) y = np.power(xnp, 3)
y1 = np.power(xnp, 2) y1 = np.power(xnp, 2)
elif self.getOrder() == 4: # this is kurtosis elif self.getOrder() == 4: # this is kurtosis
#print 'Calculating kurtosis ...' ## MP MP output suppressed if self.stealthMode is False:
print 'Calculating kurtosis ...'
y = np.power(xnp, 4) y = np.power(xnp, 4)
y1 = np.power(xnp, 2) y1 = np.power(xnp, 2)