Switched off warnings.

This commit is contained in:
Ludger Küperkoch 2015-06-22 15:35:16 +02:00
parent 4a911a4ac9
commit f2510ff400

View File

@ -3,18 +3,18 @@
Created Dec 2014 to Feb 2015
Implementation of the automated picking algorithms published and described in:
Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group,
2010: Automated determination of P-phase arrival times at regional and local
distances using higher order statistics, Geophys. J. Int., 181, 1159-1170
Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
Automated determination of P-phase arrival times at regional and local distances
using higher order statistics, Geophys. J. Int., 181, 1159-1170
Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & Egelados
Working Group, 2012: Automated determination of S-phase arrival times using
autoregressive prediction: application ot local and regional distances,
Geophys. J. Int., 188, 687-702.
autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
188, 687-702.
The picks with the above described algorithms are assumed to be the most likely
picks. For each most likely pick the corresponding earliest and latest possible
picks are calculated after Diehl & Kissling (2009).
The picks with the above described algorithms are assumed to be the most likely picks.
For each most likely pick the corresponding earliest and latest possible picks are
calculated after Diehl & Kissling (2009).
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
@ -22,46 +22,43 @@ import numpy as np
import matplotlib.pyplot as plt
from pylot.core.pick.utils import *
from pylot.core.pick.CharFuns import CharacteristicFunction
import warnings
class AutoPicking(object):
'''
Superclass of different, automated picking algorithms applied on a CF
determined using AIC, HOS, or AR prediction.
Superclass of different, automated picking algorithms applied on a CF determined
using AIC, HOS, or AR prediction.
'''
def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None,
Pick1=None):
warnings.simplefilter('ignore')
def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
'''
:param cf: characteristic function, on which the picking algorithm is
applied
:type cf: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
:param: cf, characteristic function, on which the picking algorithm is applied
:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
:param TSNR: length of time windows for SNR determination - [s]
:type TSNR: tuple (T_noise, T_gap, T_signal)
:param: TSNR, length of time windows around pick used to determine SNR [s]
:type: tuple (T_noise, T_gap, T_signal)
:param PickWindow: length of pick window - [s]
:type PickWindow: float
:param: PickWindow, length of pick window [s]
:type: float
:param iplot: no. of figure window for plotting interims results
:type iplot: integer
:param: iplot, no. of figure window for plotting interims results
:type: integer
:param aus: aus ("artificial uplift of samples"), find local minimum at
i if aic(i-1)*(1+aus) >= aic(i)
:type aus: float
:param: aus ("artificial uplift of samples"), find local minimum at i if aic(i-1)*(1+aus) >= aic(i)
:type: float
:param Tsmooth: length of moving window to calculate smoothed CF - [s]
:type Tsmooth: float
:param: Tsmooth, length of moving smoothing window to calculate smoothed CF [s]
:type: float
:param Pick1: initial (prelimenary) onset time, starting point for
PragPicker
:type Pick1: float
:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker and
EarlLatePicker
:type: float
'''
assert isinstance(cf,
CharacteristicFunction), "%s is of wrong type" % str(
cf)
assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
self.cf = cf.getCF()
self.Tcf = cf.getTimeArray()
@ -89,6 +86,7 @@ class AutoPicking(object):
Tsmooth=self.getTsmooth(),
Pick1=self.getpick1())
def getTSNR(self):
return self.TSNR
@ -153,144 +151,132 @@ class AICPicker(AutoPicking):
self.Pick = None
self.slope = None
self.SNR = None
# find NaN's
#find NaN's
nn = np.isnan(self.cf)
if len(nn) > 1:
self.cf[nn] = 0
# taper AIC-CF to get rid off side maxima
self.cf[nn] = 0
#taper AIC-CF to get rid off side maxima
tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.cf))
# smooth AIC-CF
#smooth AIC-CF
ismooth = int(round(self.Tsmooth / self.dt))
aicsmooth = np.zeros(len(aic))
if len(aic) < ismooth:
print 'AICPicker: Tsmooth larger than CF!'
return
print 'AICPicker: Tsmooth larger than CF!'
return
else:
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[1: i])
# remove offset
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[1 : i])
#remove offset
offset = abs(min(aic) - min(aicsmooth))
aicsmooth = aicsmooth - offset
# get maximum of 1st derivative of AIC-CF (more stable!) as starting
# point
#get maximum of 1st derivative of AIC-CF (more stable!) as starting point
diffcf = np.diff(aicsmooth)
# find NaN's
#find NaN's
nn = np.isnan(diffcf)
if len(nn) > 1:
diffcf[nn] = 0
# taper CF to get rid off side maxima
diffcf[nn] = 0
#taper CF to get rid off side maxima
tap = np.hanning(len(diffcf))
diffcf = tap * diffcf * max(abs(aicsmooth))
icfmax = np.argmax(diffcf)
# find minimum in AIC-CF front of maximum
#find minimum in AIC-CF front of maximum
lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i]
break
# if no minimum could be found:
# search in 1st derivative of AIC-CF
if aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i]
break
#if no minimum could be found:
#search in 1st derivative of AIC-CF
if self.Pick is None:
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if diffcf[i - 1] >= diffcf[i]:
self.Pick = self.Tcf[i]
break
for i in range(icfmax -1, max([icfmax -lpickwindow, 2]), -1):
if diffcf[i -1] >= diffcf[i]:
self.Pick = self.Tcf[i]
break
# quality assessment using SNR and slope from CF
if self.Pick is not None:
# get noise window
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0],
self.TSNR[1])
# check, if these are counts or m/s, important for slope estimation!
# this is quick and dirty, better solution?
if max(self.Data[0].data < 1e-3):
self.Data[0].data *= 1000000
# get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
# calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
max(abs(aic[inoise] - np.mean(aic[inoise])))
# calculate slope from CF after initial pick
# get slope window
tslope = self.TSNR[3] # slope determination window
islope = np.where(
(self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)]))
and (self.Tcf >= self.Pick))
# find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only!
imax = np.argmax(self.Data[0].data[islope])
if imax == 0:
print 'AICPicker: Maximum for slope determination right at ' \
'the beginning of the window!'
print 'Choose longer slope determination window!'
return
islope = islope[0][0:imax]
dataslope = self.Data[0].data[islope]
# calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope), 1)
P = np.polyfit(xslope, dataslope, 1)
datafit = np.polyval(P, xslope)
if datafit[0] >= datafit[len(datafit) - 1]:
print 'AICPicker: Negative slope, bad onset skipped!'
return
# get noise window
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
# check, if these are counts or m/s, important for slope estimation!
# this is quick and dirty, better solution?
if max(self.Data[0].data < 1e-3):
self.Data[0].data = self.Data[0].data * 1000000
# get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
# calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
- np.mean(aic[inoise])))
# calculate slope from CF after initial pick
# get slope window
tslope = self.TSNR[3] #slope determination window
islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
& (self.Tcf >= self.Pick))
# find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only!
imax = np.argmax(self.Data[0].data[islope])
if imax == 0:
print 'AICPicker: Maximum for slope determination right at the beginning of the window!'
print 'Choose longer slope determination window!'
return
islope = islope[0][0 :imax]
dataslope = self.Data[0].data[islope]
# calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope), 1)
P = np.polyfit(xslope, dataslope, 1)
datafit = np.polyval(P, xslope)
if datafit[0] >= datafit[len(datafit) - 1]:
print 'AICPicker: Negative slope, bad onset skipped!'
return
self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0]
self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0]
else:
self.SNR = None
self.slope = None
self.SNR = None
self.slope = None
if self.iplot > 1:
p = plt.figure(self.iplot)
x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
if self.Pick is not None:
p3, = plt.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b',
linewidth=2)
plt.legend([p1, p2, p3],
['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
else:
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
p = plt.figure(self.iplot)
x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
if self.Pick is not None:
p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
else:
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
if self.Pick is not None:
plt.figure(self.iplot + 1)
p11, = plt.plot(self.Tcf, x, 'k')
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal],
'r')
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
plt.legend([p11, p12, p13, p14, p15],
['Data', 'Noise Window', 'Signal Window',
'Slope Window', 'Slope'],
loc='best')
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (
self.Data[0].stats.station,
self.SNR, self.slope))
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.ylabel('Counts')
ax = plt.gca()
plt.yticks([])
ax.set_xlim([self.Tcf[inoise[0][0]] - 5,
self.Tcf[isignal[0][len(isignal) - 1]] + 5])
if self.Pick is not None:
plt.figure(self.iplot + 1)
p11, = plt.plot(self.Tcf, x, 'k')
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
loc='best')
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
self.SNR, self.slope))
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.ylabel('Counts')
ax = plt.gca()
plt.yticks([])
ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5])
plt.show()
raw_input()
plt.close(p)
plt.show()
raw_input()
plt.close(p)
if self.Pick is None:
print 'AICPicker: Could not find minimum, picking window too short?'
if self.Pick == None:
print 'AICPicker: Could not find minimum, picking window too short?'
class PragPicker(AutoPicking):
@ -301,95 +287,90 @@ class PragPicker(AutoPicking):
def calcPick(self):
if self.getpick1() is not None:
print 'PragPicker: Get most likely pick from HOS- or AR-CF using ' \
'pragmatic picking algorithm ...'
print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
self.Pick = None
self.SNR = None
self.slope = None
# smooth CF
ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf))
if len(self.cf) < ismooth:
print 'PragPicker: Tsmooth larger than CF!'
return
else:
for i in range(1, len(self.cf)):
if i > ismooth:
ii1 = i - ismooth
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[
ii1]) / ismooth
else:
cfsmooth[i] = np.mean(self.cf[1: i])
self.Pick = None
self.SNR = None
self.slope = None
#smooth CF
ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf))
if len(self.cf) < ismooth:
print 'PragPicker: Tsmooth larger than CF!'
return
else:
for i in range(1, len(self.cf)):
if i > ismooth:
ii1 = i - ismooth;
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
else:
cfsmooth[i] = np.mean(self.cf[1 : i])
# select picking window
# which is centered around tpick1
ipick = np.where((self.Tcf >=
(self.getpick1() - self.PickWindow / 2)) and
(self.Tcf <=
(self.getpick1() + self.PickWindow / 2)))
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
Tcfpick = self.Tcf[ipick]
cfsmoothipick = cfsmooth[ipick] - np.mean(self.cf[ipick])
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
cfpick1 = 2 * self.cf[ipick1]
#select picking window
#which is centered around tpick1
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
Tcfpick = self.Tcf[ipick]
cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
cfpick1 = 2 * self.cf[ipick1]
# check trend of CF, i.e. differences of CF and adjust aus regarding this trend
# prominent trend: decrease aus
# flat: use given aus
cfdiff = np.diff(cfipick)
i0diff = np.where(cfdiff > 0)
cfdiff = cfdiff[i0diff]
minaus = min(cfdiff * (1 + self.aus))
aus1 = max([minaus, self.aus])
#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
#prominent trend: decrease aus
#flat: use given aus
cfdiff = np.diff(cfipick);
i0diff = np.where(cfdiff > 0)
cfdiff = cfdiff[i0diff]
minaus = min(cfdiff * (1 + self.aus));
aus1 = max([minaus, self.aus]);
# at first we look to the right until the end of the pick window is reached
flagpick_r = 0
flagpick_l = 0
flagpick = 0
lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(max(np.insert(ipick, 0, 2)),
min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_r = self.Tcf[i]
self.Pick = pick_r
flagpick_l = 1
cfpick_r = self.cf[i]
break
#at first we look to the right until the end of the pick window is reached
flagpick_r = 0
flagpick_l = 0
flagpick = 0
lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_r = self.Tcf[i]
self.Pick = pick_r
flagpick_l = 1
cfpick_r = self.cf[i]
break
# now we look to the left
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i]
self.Pick = pick_l
flagpick_r = 1
cfpick_l = self.cf[i]
break
#now we look to the left
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i]
self.Pick = pick_l
flagpick_r = 1
cfpick_l = self.cf[i]
break
# now decide which pick: left or right?
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
self.Pick = pick_l
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_r
#now decide which pick: left or right?
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
self.Pick = pick_l
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_r
if self.getiplot() > 1:
p = plt.figure(self.getiplot())
p1, = plt.plot(Tcfpick, cfipick, 'k')
p2, = plt.plot(Tcfpick, cfsmoothipick, 'r')
p3, = plt.plot([self.Pick, self.Pick],
[min(cfipick), max(cfipick)], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
plt.show()
raw_input()
plt.close(p)
if self.getiplot() > 1:
p = plt.figure(self.getiplot())
p1, = plt.plot(Tcfpick,cfipick, 'k')
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
plt.show()
raw_input()
plt.close(p)
else:
self.Pick = None
print 'PragPicker: No initial onset time given! Check input!'
self.Pick = None
print 'PragPicker: No initial onset time given! Check input!'
return