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huge structural rearrangement to resolve circular import problems

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[add] new feature added to QtPyLoT capable of automatically picking an event from overview window
This commit is contained in:
Sebastian Wehling-Benatelli
2015-07-10 09:22:58 +02:00
parent f704d8b258
commit ea976295d0
13 changed files with 128 additions and 162 deletions
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38
pylot/core/pick/Picker.py
View File

@@ -16,11 +16,11 @@ The picks with the above described algorithms are assumed to be the most likely
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
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import numpy as np
import matplotlib.pyplot as plt
from pylot.core.pick.utils import *
from pylot.core.pick.utils import getnoisewin, getsignalwin
from pylot.core.pick.CharFuns import CharacteristicFunction
import warnings
@@ -28,7 +28,7 @@ class AutoPicking(object):
'''
Superclass of different, automated picking algorithms applied on a CF determined
using AIC, HOS, or AR prediction.
'''
'''
warnings.simplefilter('ignore')
@@ -84,9 +84,9 @@ class AutoPicking(object):
PickWindow=self.getPickWindow(),
aus=self.getaus(),
Tsmooth=self.getTsmooth(),
Pick1=self.getpick1())
Pick1=self.getpick1())
def getTSNR(self):
return self.TSNR
@@ -116,7 +116,7 @@ class AutoPicking(object):
def getSNR(self):
return self.SNR
def getSlope(self):
return self.slope
@@ -140,12 +140,12 @@ class AICPicker(AutoPicking):
'''
Method to derive the onset time of an arriving phase based on CF
derived from AIC. In order to get an impression of the quality of this inital pick,
a quality assessment is applied based on SNR and slope determination derived from the CF,
a quality assessment is applied based on SNR and slope determination derived from the CF,
from which the AIC has been calculated.
'''
def calcPick(self):
print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
self.Pick = None
@@ -179,15 +179,15 @@ class AICPicker(AutoPicking):
#find NaN's
nn = np.isnan(diffcf)
if len(nn) > 1:
diffcf[nn] = 0
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
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 aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i]
break
@@ -198,7 +198,7 @@ class AICPicker(AutoPicking):
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
@@ -275,7 +275,7 @@ class AICPicker(AutoPicking):
if self.Pick == None:
print 'AICPicker: Could not find minimum, picking window too short?'
class PragPicker(AutoPicking):
'''
@@ -287,7 +287,7 @@ class PragPicker(AutoPicking):
if self.getpick1() is not None:
print 'PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
self.Pick = None
self.Pick = None
self.SNR = None
self.slope = None
pickflag = 0
@@ -333,7 +333,7 @@ class PragPicker(AutoPicking):
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]:
if cfpick1 >= self.cf[i]:
pick_r = self.Tcf[i]
self.Pick = pick_r
flagpick_l = 1
@@ -344,7 +344,7 @@ class PragPicker(AutoPicking):
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]:
if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i]
self.Pick = pick_l
flagpick_r = 1
@@ -360,7 +360,7 @@ class PragPicker(AutoPicking):
pickflag = 1
elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_l
pickflag = 1
pickflag = 1
else:
print 'PragPicker: Could not find reliable onset!'
self.Pick = None
@@ -372,7 +372,7 @@ class PragPicker(AutoPicking):
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
if pickflag > 0:
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
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)
@@ -380,7 +380,7 @@ class PragPicker(AutoPicking):
raw_input()
plt.close(p)
else:
else:
print 'PragPicker: No initial onset time given! Check input!'
self.Pick = None
return

32
pylot/core/pick/run_autopicking.py → pylot/core/pick/autopick.py
View File

@@ -11,9 +11,37 @@ function conglomerate utils.
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.pick.Picker import *
from pylot.core.pick.CharFuns import *
from pylot.core.pick.Picker import AICPicker, PragPicker
from pylot.core.pick.CharFuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker,\
getSNR, fmpicker, checkPonsets, wadaticheck
def autopickevent(data, param):
stations = []
all_onsets = {}
# get some parameters for quality control from
# parameter input file (usually autoPyLoT.in).
wdttolerance = param.getParam('wdttolerance')
mdttolerance = param.getParam('mdttolerance')
iplot = param.getParam('iplot')
for n in range(len(data)):
station = data[n].stats.station
if station not in stations:
stations.append(station)
else:
continue
for station in stations:
topick = data.select(station=station)
all_onsets[station] = run_autopicking(topick, param)
# quality control
# median check and jackknife on P-onset times
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
# check S-P times (Wadati)
return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
def run_autopicking(wfstream, pickparam):
"""

17
pylot/core/pick/utils.py
View File

@@ -12,24 +12,9 @@ import numpy as np
import scipy as sc
import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime
from pylot.core.pick.run_autopicking import run_autopicking
import warnings
import pdb
def autopickevent(data, param):
stations = []
for n in len(data):
station = data[n].stats.station
if station not in stations:
stations.append(station)
else:
continue
for station in stations:
topick = data.select(station=station)
stat_picks = run_autopicking(topick, param)
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
'''
@@ -538,7 +523,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio after check
cvpvsr = p2[0] + 1
print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
print 'wadatacheck: Skipped %d S pick(s).' % ibad
print 'wadatacheck: Skipped %d S pick(s).' % ibad
else:
print '###############################################'
print 'wadatacheck: Not enough checked S-P times available!'