Merging picker.py did not work correctly
This commit is contained in:
Darius Arnold
parent
5258a7e9b4
commit
b1a1e8924a
@ -23,9 +23,9 @@ import warnings
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.signal import argrelmax
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from pylot.core.pick.charfuns import CharacteristicFunction
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from pylot.core.pick.utils import getnoisewin, getsignalwin, real_Bool, real_None, set_NaNs_to, taper_cf, cf_positive, \
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smooth_cf, check_counts_ms, getSNR, getslopewin, calcSlope
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from pylot.core.pick.utils import getnoisewin, getsignalwin
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class AutoPicker(object):
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@ -132,13 +132,6 @@ class AutoPicker(object):
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return self.iplot
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def setiplot(self, iplot):
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try:
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iplot = int(iplot)
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except ValueError:
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if real_Bool(iplot):
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self.iplot = 2
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else:
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self.iplot = 0
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self.iplot = iplot
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def getpick1(self):
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@ -159,79 +152,71 @@ class AICPicker(AutoPicker):
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from which the AIC has been calculated.
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"""
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def prepareCF(self):
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"""
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data pre processing: remove invalid entries, taper cf, remove offset
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"""
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def calcPick(self):
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self.cf = set_NaNs_to(self.cf, 0)
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self.cf = taper_cf(self.cf)
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self.cf = cf_positive(self.cf)
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# smooth AIC-CF
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print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
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self.Pick = None
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self.slope = None
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self.SNR = None
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plt_flag = 0
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try:
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self.aicsmooth = smooth_cf(self.cf, self.Tsmooth, self.dt)
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except ValueError as e:
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iplot = int(self.iplot)
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except:
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if self.iplot == True or self.iplot == 'True':
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iplot = 2
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else:
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iplot = 0
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# find NaN's
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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self.cf[nn] = 0
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# taper AIC-CF to get rid off side maxima
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tap = np.hanning(len(self.cf))
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aic = tap * self.cf + max(abs(self.cf))
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# smooth AIC-CF
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ismooth = int(round(self.Tsmooth / self.dt))
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aicsmooth = np.zeros(len(aic))
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if len(aic) < ismooth:
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print('AICPicker: Tsmooth larger than CF!')
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raise e
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def findMinimum(self):
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"""
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Find minimum representing the preliminary onset, sets Pick to minimum
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"""
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# get maximum of HOS/AR-CF as starting point for searching
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return
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else:
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for i in range(1, len(aic)):
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if i > ismooth:
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ii1 = i - ismooth
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aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
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else:
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aicsmooth[i] = np.mean(aic[1: i])
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# remove offset in AIC function
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offset = abs(min(aic) - min(aicsmooth))
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aicsmooth = aicsmooth - offset
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# get maximum of HOS/AR-CF as startimg point for searching
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# minimum in AIC function
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icfmax = np.argmax(self.Data[0].data)
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# find minimum in AIC-CF front of maximum of HOS/AR-CF
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if self.aicsmooth[i - 1] >= self.aicsmooth[i]:
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if aicsmooth[i - 1] >= aicsmooth[i]:
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self.Pick = self.Tcf[i]
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break
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# if no minimum could be found:
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# search in 1st derivative of AIC-CF
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if self.Pick is None:
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diffcf = np.diff(self.aicsmooth)
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diffcf = set_NaNs_to(diffcf, 0)
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diffcf = np.diff(aicsmooth)
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# find NaN's
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nn = np.isnan(diffcf)
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if len(nn) > 1:
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diffcf[nn] = 0
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# taper CF to get rid off side maxima
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diffcf = taper_cf(diffcf)
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diffcf = cf_positive(diffcf)
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tap = np.hanning(len(diffcf))
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diffcf = tap * diffcf * max(abs(aicsmooth))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if diffcf[i - 1] >= diffcf[i]:
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self.Pick = self.Tcf[i]
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break
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if self.Pick is None:
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raise ValueError
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def calcPick(self):
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"""
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Calculate pick using cf derived from AIC
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:return:
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:rtype: None
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"""
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print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
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self.Pick = None
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self.slope = -1
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self.SNR = -1
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plt_flag = 0
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iplot = self.iplot
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try:
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self.prepareCF()
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except ValueError:
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return
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try:
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self.findMinimum()
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except ValueError:
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print('Could not determine pick on AIC CF')
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return
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# quality assessment using SNR and slope from CF
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if self.Pick is not None:
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# get noise window
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inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
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@ -269,29 +254,9 @@ class AICPicker(AutoPicker):
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# find maximum within slope determination window
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# 'cause slope should be calculated up to first local minimum only!
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try:
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self.slope, iislope, datafit = calcSlope(self.Data, self.aicsmooth, self.Tcf, self.Pick, self.TSNR)
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except Exception:
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if self.iplot > 1:
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if real_None(self.fig) is None:
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fig = plt.figure()
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plt_flag = 1
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else:
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fig = self.fig
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ax = fig.add_subplot(111)
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x = self.Data[0].data
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ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
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ax.plot(self.Tcf, self.aicsmooth / max(self.aicsmooth), 'r', label='Smoothed AIC-CF')
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ax.legend(loc=1)
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ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax.set_yticks([])
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ax.set_title(self.Data[0].stats.station)
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if plt_flag == 1:
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fig.show()
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try:
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input()
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except SyntaxError:
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pass
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plt.close(fig)
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dataslope = self.Data[0].data[islope[0][0:-1]]
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except IndexError:
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print("Slope Calculation: empty array islope, check signal window")
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return
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if len(dataslope) < 2:
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print('No or not enough data in slope window found!')
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@ -344,13 +309,11 @@ class AICPicker(AutoPicker):
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self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
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else:
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self.SNR = -1
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self.slope = -1
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self.SNR = None
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self.slope = None
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if iplot > 1:
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inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
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isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
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if real_None(self.fig) is None:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.iplot)
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plt_flag = 1
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else:
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@ -361,14 +324,14 @@ class AICPicker(AutoPicker):
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if len(self.Tcf) > len(self.Data[0].data): # why? LK
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self.Tcf = self.Tcf[0:len(self.Tcf)-1]
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ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
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ax1.plot(self.Tcf, self.aicsmooth / max(self.aicsmooth), 'r', label='Smoothed AIC-CF')
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ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
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if self.Pick is not None:
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ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
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ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax1.set_yticks([])
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ax1.legend(loc=1)
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if self.Pick is not None and self.SNR is not None:
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if self.Pick is not None:
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ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
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ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data')
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ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
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@ -415,7 +378,13 @@ class PragPicker(AutoPicker):
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def calcPick(self):
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iplot = self.getiplot()
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try:
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iplot = int(self.getiplot())
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except:
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if self.getiplot() == True or self.getiplot() == 'True':
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iplot = 2
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else:
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iplot = 0
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if self.getpick1() is not None:
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print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
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@ -426,7 +395,19 @@ class PragPicker(AutoPicker):
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pickflag = 0
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plt_flag = 0
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# smooth CF
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cfsmooth = smooth_cf(self.cf, self.Tsmooth, self.dt)
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ismooth = int(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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@ -437,7 +418,7 @@ class PragPicker(AutoPicker):
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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 ("artificial uplift
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# check trend of CF, i.e. differences of CF and adjust aus ("artificial uplift
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# of picks") regarding this trend
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# prominent trend: decrease aus
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# flat: use given aus
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@ -501,7 +482,7 @@ class PragPicker(AutoPicker):
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pickflag = 0
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if iplot > 1:
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if real_None(self.fig) is None:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.getiplot())
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plt_flag = 1
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else:
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