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Simplified AIC-picking algorithm: Onset is definetly the minimum in front of maximum of AIC-CF! Smoothing of AIC-CF no more necessary.

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Ludger Küperkoch 2014-12-11 16:30:21 +01:00
parent 201c34a85b
commit 31273b384e
1 changed files with 14 additions and 33 deletions
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43
pylot/core/pick/Picker.py
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@ -23,7 +23,7 @@ class AutoPicking(object):
Superclass of different, automated picking algorithms applied on a CF determined Superclass of different, automated picking algorithms applied on a CF determined
using AIC, HOS, or AR prediction. using AIC, HOS, or AR prediction.
''' '''
def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, peps, Tsmooth): def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, peps=None, Tsmooth=None):
''' '''
:param: cf, characteristic function, on which the picking algorithm is applied :param: cf, characteristic function, on which the picking algorithm is applied
:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object :type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
@ -48,6 +48,9 @@ class AutoPicking(object):
:type: float :type: float
''' '''
#assert isinstance(cf, CharFuns), "%s is not a CharacteristicFunction object" % str(cf)
#wie kann man hier isinstance benutzen?
self.cf = cf.getCF() self.cf = cf.getCF()
self.Tcf = cf.getTimeArray() self.Tcf = cf.getTimeArray()
self.dt = cf.getIncrement() self.dt = cf.getIncrement()
@ -127,45 +130,23 @@ class AICPicker(AutoPicking):
print 'Get onset (pick) from AIC-CF ...' print 'Get onset (pick) from AIC-CF ...'
self.Pick = -1
#taper AIC-CF to get rid off side maxima #taper AIC-CF to get rid off side maxima
tap = np.hanning(len(self.cf)) tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.cf)) aic = tap * self.cf + max(abs(self.cf))
#get maximum of CF as starting point #get maximum of CF as starting point
icfmax = np.argmax(aic) icfmax = np.argmax(aic)
#smooth CF #find minimum in front of maximum
aicsmooth = np.zeros(len(aic))
ismooth = round(self.Tsmooth / self.dt)
if len(aic) < ismooth:
print 'AICPicker: Tsmooth larger than AIC function!'
self.Pick = -1
return self.Pick
else:
self.Pick = -1
for i in range(1, len(aic)):
if i > ismooth:
ii1 = i - ismooth
aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
else:
aicsmooth[i] = np.mean(aic[0:i])
#find common, local minimum in front of maximum
#of smoothed and unsmoothed AIC-CF
lpickwindow = int(round(self.PickWindow / self.dt)) lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1): for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if aic[i - 1] * (1 + self.peps) >= aic[i]: if aic[i - 1] >= aic[i]:
if aicsmooth[i - 1] * (1 + self.peps) >= aicsmooth[i]: self.Pick = self.Tcf[i]
self.Pick = self.Tcf[i] break
break if self.Pick == -1:
print 'AICPicker: Could not find minimum, picking window too short?'
#try again with larger peps if picking failed return self.Pick
if self.Pick < 0:
peps2 = self.peps + 0.01
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if aic[i - 1] * (1 + peps2) >= aic[i]:
if aicsmooth[i - 1] * (1 + peps2) >= aicsmooth[i]:
self.Pick = self.Tcf[i]
break
class PragPicker(AutoPicking): class PragPicker(AutoPicking):
''' '''