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Merging picker.py did not work correctly

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This commit is contained in:
Darius Arnold 2018-07-06 10:51:17 +02:00
parent 5258a7e9b4
commit b1a1e8924a
1 changed files with 77 additions and 96 deletions
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171
pylot/core/pick/picker.py
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@ -23,9 +23,9 @@ import warnings
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from scipy.signal import argrelmax
from pylot.core.pick.charfuns import CharacteristicFunction from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.utils import getnoisewin, getsignalwin, real_Bool, real_None, set_NaNs_to, taper_cf, cf_positive, \ from pylot.core.pick.utils import getnoisewin, getsignalwin
smooth_cf, check_counts_ms, getSNR, getslopewin, calcSlope
class AutoPicker(object): class AutoPicker(object):
@ -132,13 +132,6 @@ class AutoPicker(object):
return self.iplot return self.iplot
def setiplot(self, iplot): def setiplot(self, iplot):
try:
iplot = int(iplot)
except ValueError:
if real_Bool(iplot):
self.iplot = 2
else:
self.iplot = 0
self.iplot = iplot self.iplot = iplot
def getpick1(self): def getpick1(self):
@ -159,79 +152,71 @@ class AICPicker(AutoPicker):
from which the AIC has been calculated. from which the AIC has been calculated.
""" """
def prepareCF(self): def calcPick(self):
"""
data pre processing: remove invalid entries, taper cf, remove offset
"""
self.cf = set_NaNs_to(self.cf, 0) print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
self.cf = taper_cf(self.cf)
self.cf = cf_positive(self.cf) self.Pick = None
# smooth AIC-CF self.slope = None
self.SNR = None
plt_flag = 0
try: try:
self.aicsmooth = smooth_cf(self.cf, self.Tsmooth, self.dt) iplot = int(self.iplot)
except ValueError as e: except:
if self.iplot == True or self.iplot == 'True':
iplot = 2
else:
iplot = 0
# 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
tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.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!') print('AICPicker: Tsmooth larger than CF!')
raise e return
else:
def findMinimum(self): for i in range(1, len(aic)):
""" if i > ismooth:
Find minimum representing the preliminary onset, sets Pick to minimum ii1 = i - ismooth
""" aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
else:
# get maximum of HOS/AR-CF as starting point for searching aicsmooth[i] = np.mean(aic[1: i])
# remove offset in AIC function
offset = abs(min(aic) - min(aicsmooth))
aicsmooth = aicsmooth - offset
# get maximum of HOS/AR-CF as startimg point for searching
# minimum in AIC function # minimum in AIC function
icfmax = np.argmax(self.Data[0].data) icfmax = np.argmax(self.Data[0].data)
# find minimum in AIC-CF front of maximum of HOS/AR-CF # find minimum in AIC-CF front of maximum of HOS/AR-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 self.aicsmooth[i - 1] >= self.aicsmooth[i]: if aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i] self.Pick = self.Tcf[i]
break break
# if no minimum could be found: # if no minimum could be found:
# search in 1st derivative of AIC-CF # search in 1st derivative of AIC-CF
if self.Pick is None: if self.Pick is None:
diffcf = np.diff(self.aicsmooth) diffcf = np.diff(aicsmooth)
diffcf = set_NaNs_to(diffcf, 0) # find NaN's
nn = np.isnan(diffcf)
if len(nn) > 1:
diffcf[nn] = 0
# taper CF to get rid off side maxima # taper CF to get rid off side maxima
diffcf = taper_cf(diffcf) tap = np.hanning(len(diffcf))
diffcf = cf_positive(diffcf) diffcf = tap * diffcf * max(abs(aicsmooth))
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1): for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if diffcf[i - 1] >= diffcf[i]: if diffcf[i - 1] >= diffcf[i]:
self.Pick = self.Tcf[i] self.Pick = self.Tcf[i]
break break
if self.Pick is None:
raise ValueError
def calcPick(self):
"""
Calculate pick using cf derived from AIC
:return:
:rtype: None
"""
print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
self.Pick = None
self.slope = -1
self.SNR = -1
plt_flag = 0
iplot = self.iplot
try:
self.prepareCF()
except ValueError:
return
try:
self.findMinimum()
except ValueError:
print('Could not determine pick on AIC CF')
return
# quality assessment using SNR and slope from CF
if self.Pick is not None: if self.Pick is not None:
# get noise window # get noise window
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1]) inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
@ -269,29 +254,9 @@ class AICPicker(AutoPicker):
# find maximum within slope determination window # find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only! # 'cause slope should be calculated up to first local minimum only!
try: try:
self.slope, iislope, datafit = calcSlope(self.Data, self.aicsmooth, self.Tcf, self.Pick, self.TSNR) dataslope = self.Data[0].data[islope[0][0:-1]]
except Exception: except IndexError:
if self.iplot > 1: print("Slope Calculation: empty array islope, check signal window")
if real_None(self.fig) is None:
fig = plt.figure()
plt_flag = 1
else:
fig = self.fig
ax = fig.add_subplot(111)
x = self.Data[0].data
ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
ax.plot(self.Tcf, self.aicsmooth / max(self.aicsmooth), 'r', label='Smoothed AIC-CF')
ax.legend(loc=1)
ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax.set_yticks([])
ax.set_title(self.Data[0].stats.station)
if plt_flag == 1:
fig.show()
try:
input()
except SyntaxError:
pass
plt.close(fig)
return return
if len(dataslope) < 2: if len(dataslope) < 2:
print('No or not enough data in slope window found!') print('No or not enough data in slope window found!')
@ -344,13 +309,11 @@ class AICPicker(AutoPicker):
self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0]) self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
else: else:
self.SNR = -1 self.SNR = None
self.slope = -1 self.slope = None
if iplot > 1: if iplot > 1:
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1]) if self.fig == None or self.fig == 'None':
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
if real_None(self.fig) is None:
fig = plt.figure() # self.iplot) fig = plt.figure() # self.iplot)
plt_flag = 1 plt_flag = 1
else: else:
@ -361,14 +324,14 @@ class AICPicker(AutoPicker):
if len(self.Tcf) > len(self.Data[0].data): # why? LK if len(self.Tcf) > len(self.Data[0].data): # why? LK
self.Tcf = self.Tcf[0:len(self.Tcf)-1] self.Tcf = self.Tcf[0:len(self.Tcf)-1]
ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data') ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
ax1.plot(self.Tcf, self.aicsmooth / max(self.aicsmooth), 'r', label='Smoothed AIC-CF') ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
if self.Pick is not None: if self.Pick is not None:
ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick') ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime) ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax1.set_yticks([]) ax1.set_yticks([])
ax1.legend(loc=1) ax1.legend(loc=1)
if self.Pick is not None and self.SNR is not None: if self.Pick is not None:
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1) ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data') ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data')
ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window') ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
@ -415,7 +378,13 @@ class PragPicker(AutoPicker):
def calcPick(self): def calcPick(self):
iplot = self.getiplot() try:
iplot = int(self.getiplot())
except:
if self.getiplot() == True or self.getiplot() == 'True':
iplot = 2
else:
iplot = 0
if self.getpick1() is not None: 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 ...')
@ -426,7 +395,19 @@ class PragPicker(AutoPicker):
pickflag = 0 pickflag = 0
plt_flag = 0 plt_flag = 0
# smooth CF # smooth CF
cfsmooth = smooth_cf(self.cf, self.Tsmooth, self.dt) 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 # select picking window
# which is centered around tpick1 # which is centered around tpick1
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \ ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
@ -501,7 +482,7 @@ class PragPicker(AutoPicker):
pickflag = 0 pickflag = 0
if iplot > 1: if iplot > 1:
if real_None(self.fig) is None: if self.fig == None or self.fig == 'None':
fig = plt.figure() # self.getiplot()) fig = plt.figure() # self.getiplot())
plt_flag = 1 plt_flag = 1
else: else: