Merge branch 'develop' of 134.147.164.251:/data/git/pylot into add-save
This commit is contained in:
commit
92ed582c01
@ -117,8 +117,8 @@ class CharacteristicFunction(object):
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def getTimeArray(self):
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if self.getTime1():
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incr = self.getARdetStep()
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self.TimeArray = np.arange(0, len(self.getCF()) * incr[0], incr[0]) + self.getCut()[0] \
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incr = self.getARdetStep()[0]
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self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0] \
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+ self.getTime1() + self.getTime2()
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else:
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incr = self.getIncrement()
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@ -143,18 +143,31 @@ class CharacteristicFunction(object):
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cutting window
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'''
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if cut is not None:
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if self.cut[0] == 0:
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if len(self.orig_data) == 1:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = len(self.orig_data[0])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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if len(self.orig_data) == 1:
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zz = self.orig_data.copy()
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z1 = zz[0].copy()
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zz[0].data = z1.data[start:stop]
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data = zz
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return data
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elif len(self.orig_data) == 2:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = min([len(self.orig_data[0]), len(self.orig_data[1])])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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hh = self.orig_data.copy()
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h1 = hh[0].copy()
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h2 = hh[1].copy()
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@ -163,6 +176,15 @@ class CharacteristicFunction(object):
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data = hh
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return data
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elif len(self.orig_data) == 3:
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if self.cut[0] == 0 and self.cut[1] == 0:
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start = 0
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stop = min([len(self.orig_data[0]), len(self.orig_data[1]), len(self.orig_data[2])])
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elif self.cut[0] == 0 and self.cut[1] is not 0:
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start = 0
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stop = self.cut[1] / self.dt
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else:
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start = self.cut[0] / self.dt
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stop = self.cut[1] / self.dt
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hh = self.orig_data.copy()
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h1 = hh[0].copy()
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h2 = hh[1].copy()
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@ -195,6 +217,9 @@ class AICcf(CharacteristicFunction):
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print 'Calculating AIC ...'
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x = self.getDataArray()
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xnp = x[0].data
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nn = np.isnan(xnp)
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if len(nn) > 1:
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xnp[nn] = 0
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datlen = len(xnp)
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k = np.arange(1, datlen)
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cf = np.zeros(datlen)
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@ -224,6 +249,9 @@ class HOScf(CharacteristicFunction):
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x = self.getDataArray(self.getCut())
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xnp =x[0].data
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nn = np.isnan(xnp)
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if len(nn) > 1:
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xnp[nn] = 0
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if self.getOrder() == 3: # this is skewness
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print 'Calculating skewness ...'
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y = np.power(xnp, 3)
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@ -256,6 +284,9 @@ class HOScf(CharacteristicFunction):
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elif self.getOrder() == 4:
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LTA[j] = lta / np.power(lta1, 2)
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nn = np.isnan(LTA)
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if len(nn) > 1:
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LTA[nn] = 0
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self.cf = LTA
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@ -266,6 +297,9 @@ class ARZcf(CharacteristicFunction):
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print 'Calculating AR-prediction error from single trace ...'
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x = self.getDataArray(self.getCut())
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xnp = x[0].data
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nn = np.isnan(xnp)
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if len(nn) > 1:
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xnp[nn] = 0
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#some parameters needed
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#add noise to time series
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xnoise = xnp + np.random.normal(0.0, 1.0, len(xnp)) * self.getFnoise() * max(abs(xnp))
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@ -288,6 +322,9 @@ class ARZcf(CharacteristicFunction):
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#convert list to numpy array
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cf = np.asarray(cf)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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@ -376,6 +413,12 @@ class ARHcf(CharacteristicFunction):
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print 'Calculating AR-prediction error from both horizontal traces ...'
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xnp = self.getDataArray(self.getCut())
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n0 = np.isnan(xnp[0].data)
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if len(n0) > 1:
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xnp[0].data[n0] = 0
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n1 = np.isnan(xnp[1].data)
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if len(n1) > 1:
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xnp[1].data[n1] = 0
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#some parameters needed
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#add noise to time series
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@ -390,7 +433,7 @@ class ARHcf(CharacteristicFunction):
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cf = []
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 3, tend - lpred + 1, loopstep[1]):
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
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self.arDetH(Xnoise, self.getOrder(), i-ldet, i)
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#AR prediction of waveform using calculated AR coefficients
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self.arPredH(xnp, self.arpara, i + 1, lpred)
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@ -401,6 +444,9 @@ class ARHcf(CharacteristicFunction):
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#convert list to numpy array
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cf = np.asarray(cf)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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def arDetH(self, data, order, rind, ldet):
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@ -493,6 +539,15 @@ class AR3Ccf(CharacteristicFunction):
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print 'Calculating AR-prediction error from all 3 components ...'
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xnp = self.getDataArray(self.getCut())
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n0 = np.isnan(xnp[0].data)
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if len(n0) > 1:
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xnp[0].data[n0] = 0
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n1 = np.isnan(xnp[1].data)
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if len(n1) > 1:
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xnp[1].data[n1] = 0
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n2 = np.isnan(xnp[2].data)
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if len(n2) > 1:
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xnp[2].data[n2] = 0
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#some parameters needed
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#add noise to time series
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@ -508,7 +563,7 @@ class AR3Ccf(CharacteristicFunction):
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cf = []
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loopstep = self.getARdetStep()
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for i in range(ldet + self.getOrder() - 3, tend - lpred + 1, loopstep[1]):
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for i in range(ldet + self.getOrder() - 1, tend - lpred + 1, loopstep[1]):
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self.arDet3C(Xnoise, self.getOrder(), i-ldet, i)
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#AR prediction of waveform using calculated AR coefficients
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self.arPred3C(xnp, self.arpara, i + 1, lpred)
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@ -520,6 +575,9 @@ class AR3Ccf(CharacteristicFunction):
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#convert list to numpy array
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cf = np.asarray(cf)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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self.cf = cf
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def arDet3C(self, data, order, rind, ldet):
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@ -3,16 +3,16 @@
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Created Dec 2014
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Implementation of the picking algorithms published and described in:
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Küperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
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Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
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Automated determination of P-phase arrival times at regional and local distances
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using higher order statistics, Geophys. J. Int., 181, 1159-1170
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Küperkoch, L., Meier, T., Brüstle, A., Lee, J., Friederich, W., & Egelados
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Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & Egelados
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Working Group, 2012: Automated determination of S-phase arrival times using
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autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
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188, 687-702.
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:author: MAGS2 EP3 working group / Ludger Küperkoch
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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@ -23,7 +23,7 @@ class AutoPicking(object):
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Superclass of different, automated picking algorithms applied on a CF determined
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using AIC, HOS, or AR prediction.
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'''
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def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, peps=None, Tsmooth=None):
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def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, aus=None, Tsmooth=None, Pick1=None):
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'''
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:param: cf, characteristic function, on which the picking algorithm is applied
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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@ -41,11 +41,14 @@ class AutoPicking(object):
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:param: PickWindow, length of pick window [s]
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:type: float
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:param: peps, find local minimum at i if aic(i-1)*(1+peps) >= aic(i)
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:param: aus ("artificial uplift of samples"), find local minimum at i if aic(i-1)*(1+aus) >= aic(i)
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:type: float
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:param: Tsmooth, length of moving smoothing window to calculate smoothed CF [s]
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:type: float
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:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker
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:type: float
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'''
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#assert isinstance(cf, CharFuns), "%s is not a CharacteristicFunction object" % str(cf)
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@ -58,8 +61,9 @@ class AutoPicking(object):
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self.setaerr(aerr)
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self.setTSNR(TSNR)
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self.setPickWindow(PickWindow)
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self.setpeps(peps)
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self.setaus(aus)
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self.setTsmooth(Tsmooth)
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self.setpick1(Pick1)
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self.calcPick()
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def __str__(self):
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@ -68,15 +72,17 @@ class AutoPicking(object):
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aerr:\t{aerr}\n
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TSNR:\t\t\t{TSNR}\n
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PickWindow:\t{PickWindow}\n
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peps:\t{peps}\n
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aus:\t{aus}\n
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Tsmooth:\t{Tsmooth}\n
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Pick1:\t{Pick1}\n
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'''.format(name=type(self).__name__,
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Tslope=self.getTslope(),
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aerr=self.getaerr(),
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TSNR=self.getTSNR(),
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PickWindow=self.getPickWindow(),
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peps=self.getpeps(),
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Tsmooth=self.getTsmooth())
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aus=self.getaus(),
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Tsmooth=self.getTsmooth(),
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Pick1=self.getpick1())
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def getTslope(self):
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return self.Tslope
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@ -102,11 +108,11 @@ class AutoPicking(object):
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def setPickWindow(self, PickWindow):
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self.PickWindow = PickWindow
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def getpeps(self):
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return self.peps
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def getaus(self):
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return self.aus
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def setpeps(self, peps):
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self.peps = peps
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def setaus(self, aus):
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self.aus = aus
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def setTsmooth(self, Tsmooth):
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self.Tsmooth = Tsmooth
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@ -117,6 +123,12 @@ class AutoPicking(object):
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def getpick(self):
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return self.Pick
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def getpick1(self):
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return self.Pick1
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def setpick1(self, Pick1):
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self.Pick1 = Pick1
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def calcPick(self):
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self.Pick = None
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@ -128,7 +140,7 @@ class AICPicker(AutoPicking):
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def calcPick(self):
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print 'Get onset (pick) from AIC-CF ...'
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print 'Get onset time (pick) from AIC-CF ...'
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self.Pick = -1
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#taper AIC-CF to get rid off side maxima
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@ -155,4 +167,78 @@ class PragPicker(AutoPicking):
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def calcPick(self):
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print 'Get onset (pick) from HOS- or AR-CF using pragmatic picking algorithm ...'
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if self.getpick1() is not None:
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print 'Get onset time (pick) from HOS- or AR-CF using pragmatic picking algorithm ...'
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self.Pick = -1
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#smooth CF
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ismooth = round(self.Tsmooth / self.dt);
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cfsmooth = np.zeros(len(self.cf))
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if len(self.cf) < ismooth:
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print 'PragPicker: Tsmooth larger than CF!'
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return
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else:
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for i in range(1, len(self.cf)):
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if i > ismooth:
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ii1 = i - ismooth;
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cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
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else:
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cfsmooth[i] = np.mean(self.cf[1 : i])
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#select picking window
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#which is centered around tpick1
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ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
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& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
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cfipick = self.cf[ipick]
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Tcfpick = self.Tcf[ipick]
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cfsmoothipick = cfsmooth[ipick]
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ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
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cfpick1 = 2 * self.cf[ipick1]
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#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
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#prominent trend: decrease aus
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#flat: use given aus
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cfdiff = np.diff(cfipick);
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i0diff = np.where(cfdiff > 0)
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cfdiff = cfdiff[i0diff]
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minaus = min(cfdiff * (1 + self.aus));
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aus1 = max([minaus, self.aus]);
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#at first we look to the right until the end of the pick window is reached
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flagpick_r = 0
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flagpick_l = 0
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flagpick = 0
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_r = self.Tcf[i]
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self.Pick = pick_r
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flagpick_l = 1
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cfpick_r = self.cf[i]
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break
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#now we look to the left
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for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_l = self.Tcf[i]
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self.Pick = pick_l
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flagpick_r = 1
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cfpick_l = self.cf[i]
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break
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#now decide which pick: left or right?
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if flagpick_l > 0 and flagpick_r > 0:
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if cfpick_l <= cfpick_r:
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self.Pick = pick_l
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else:
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self.Pick = pick_r
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else:
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self.Pick = -1
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print 'PragPicker: No initial onset time given! Check input!'
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return
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@ -19,7 +19,7 @@ def run_makeCF(project, database, event, iplot, station=None):
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#parameters for CF calculation
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t2 = 7 #length of moving window for HOS calculation [sec]
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p = 4 #order of statistics
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cuttimes = [10, 40] #start and end time vor CF calculation
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cuttimes = [5, 40] #start and end time for CF calculation
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bpz = [2, 30] #corner frequencies of bandpass filter, vertical component
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bph = [2, 15] #corner frequencies of bandpass filter, horizontal components
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tdetz= 1.2 #length of AR-determination window [sec], vertical component
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@ -59,9 +59,6 @@ def run_makeCF(project, database, event, iplot, station=None):
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#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
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hoscf = HOScf(st_copy, cuttimes, t2, p) #instance of HOScf
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##############################################################
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#get onset time from HOS-CF using class Picker
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#hospick = PragPicker(hoscf, 2, 70, [1, 0.5, 0.2], 2, 0.001, 0.2)
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##############################################################
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#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
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#class needs stream object => build it
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tr_aic = tr_filt.copy()
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@ -69,9 +66,12 @@ def run_makeCF(project, database, event, iplot, station=None):
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st_copy[0].data = tr_aic.data
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aiccf = AICcf(st_copy, cuttimes, t2) #instance of AICcf
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##############################################################
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#get onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
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#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
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aicpick = AICPicker(aiccf, 2, 70, [1, 0.5, 0.2], 3)
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##############################################################
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#get refined onset time from HOS-CF using class Picker
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hospick = PragPicker(hoscf, 2, 70, [1, 0.5, 0.2], 2, 0.001, 0.2, aicpick.getpick())
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##############################################################
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#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
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#get stream object of filtered data
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st_copy[0].data = tr_filt.data
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@ -86,6 +86,9 @@ def run_makeCF(project, database, event, iplot, station=None):
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##############################################################
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#get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
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aicarzpick = AICPicker(araiccf, 2, 70, [1, 0.5, 0.2], 2)
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##############################################################
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#get refined onset time from ARZ-CF using class Picker
|
||||
arzpick = PragPicker(arzcf, 2, 70, [1, 0.5, 0.2], 2, 0, 0.2, aicarzpick.getpick())
|
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elif not wfzfiles:
|
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print 'No vertical component data found!'
|
||||
|
||||
@ -156,9 +159,15 @@ def run_makeCF(project, database, event, iplot, station=None):
|
||||
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'b')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'b')
|
||||
plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r--')
|
||||
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [1.3, 1.3], 'r')
|
||||
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [-1.3, -1.3], 'r')
|
||||
plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y--')
|
||||
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [1.2, 1.2], 'y')
|
||||
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [-1.2, -1.2], 'y')
|
||||
plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g--')
|
||||
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [1.4, 1.4], 'g')
|
||||
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [-1.4, -1.4], 'g')
|
||||
plt.yticks([])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.ylabel('Normalized Counts')
|
||||
@ -173,7 +182,7 @@ def run_makeCF(project, database, event, iplot, station=None):
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth +tpredh
|
||||
p21 = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
|
||||
p22 = plt.plot(tarhcf, arhcf.getCF()/max(arhcf.getCF()), 'r')
|
||||
p22 = plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
|
||||
p23 = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b--')
|
||||
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
|
||||
@ -185,7 +194,6 @@ def run_makeCF(project, database, event, iplot, station=None):
|
||||
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
|
||||
plt.subplot(212)
|
||||
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
|
||||
plt.plot(tarhcf, arhcf.getCF()/max(arhcf.getCF()), 'r')
|
||||
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b--')
|
||||
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user