Merge branch 'develop' of 134.147.164.251:/data/git/pylot into develop

pylot/core/pick/run_autopicking.py

@@ -74,6 +74,10 @@ def run_autopicking(wfstream, pickparam):
     minFMSNR = pickparam.getParam('minFMSNR')
     fmpickwin = pickparam.getParam('fmpickwin')
     minfmweight = pickparam.getParam('minfmweight')
+    # parameters for checking signal length
+    minsiglength = pickparam.getParam('minsiglength')
+    minpercent = pickparam.getParam('minpercent')
+    nfacsl = pickparam.getParam('noisefactor')
 
     # initialize output 
     Pweight = 4   # weight for P onset 
@@ -94,6 +98,7 @@ def run_autopicking(wfstream, pickparam):
 
     aicSflag = 0
     aicPflag = 0
+    Pflag = 0
     Sflag = 0
 
     # split components
@@ -152,9 +157,15 @@ def run_autopicking(wfstream, pickparam):
         # of class AutoPicking
         aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
         ##############################################################
+        # check signal length to detect spuriously picked noise peaks
+        z_copy[0].data = tr_filt.data
+        Pflag = checksignallength(z_copy, aicpick.getpick(), tsnrz, minsiglength, \
+                                          nfacsl, minpercent, iplot)
+        ##############################################################
         # go on with processing if AIC onset passes quality control
         if (aicpick.getSlope() >= minAICPslope and
-                aicpick.getSNR() >= minAICPSNR):
+                aicpick.getSNR() >= minAICPSNR and
+                Pflag == 1):
             aicPflag = 1
             print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
                   (aicpick.getSlope(), aicpick.getSNR())
@@ -190,7 +201,7 @@ def run_autopicking(wfstream, pickparam):
             mpickP = refPpick.getpick()
             #############################################################
             if mpickP is not None:
-            	# quality assessment
+           	# quality assessment
             	# get earliest and latest possible pick and symmetrized uncertainty
             	[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, mpickP, iplot)
 
@@ -227,8 +238,8 @@ def run_autopicking(wfstream, pickparam):
             Sflag = 0
 
     else:
-        print 'run_autopicking: No vertical component data available, ' \
-              'skipping station!'
+        print 'run_autopicking: No vertical component data availabler!, ' \
+              'Skip station!'
 
     if edat is not None and ndat is not None and len(edat) > 0 and len(
             ndat) > 0 and Pweight < 4:

pylot/core/pick/utils.py

@@ -9,6 +9,7 @@
 """
 
 import numpy as np
+import scipy as sc
 import matplotlib.pyplot as plt
 from obspy.core import Stream, UTCDateTime
 import warnings
@@ -47,14 +48,11 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
     x = X[0].data
     t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
                   X[0].stats.delta)
-    # get latest possible pick
-    # get noise window
     inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
     # get signal window
     isignal = getsignalwin(t, Pick1, TSNR[2])
     # remove mean
-    meanwin = np.hstack((inoise, isignal))
-    x = x - np.mean(x[meanwin])
+    x = x - np.mean(x[inoise])
     # calculate noise level
     nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
     # get time where signal exceeds nlevel
@@ -337,7 +335,7 @@ def getSNR(X, TSNR, t1):
         return
 
     # demean over entire snr window
-    x -= x[inoise[0]:isignal[-1]].mean()
+    x = x - np.mean(x[np.hstack([inoise, isignal])])
 
     # calculate ratios
     noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
@@ -514,3 +512,88 @@ def wadaticheck(pickdic, dttolerance, iplot):
         plt.close(iplot)
 
     return checkedonsets
+
+
+def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
+    '''
+    Function to detect spuriously picked noise peaks.
+    Uses envelope to determine, how many samples [per cent] after
+    P onset are below certain threshold, calculated from noise 
+    level times noise factor.
+
+    : param: X, time series (seismogram)
+    : type:  `~obspy.core.stream.Stream`
+
+    : param: pick, initial (AIC) P onset time
+    : type:  float 
+
+    : param: TSNR, length of time windows around initial pick [s]
+    : type:  tuple (T_noise, T_gap, T_signal)
+
+    : param: minsiglength, minium required signal length [s] to
+             declare pick as P onset
+    : type:  float
+
+    : param: nfac, noise factor (nfac * noise level = threshold)
+    : type:  float
+
+    : param: minpercent, minimum required percentage of samples
+             above calculated threshold
+    : type:  float
+
+    : param: iplot, if iplot > 1, results are shown in figure
+    : type:  int
+    '''
+    
+    assert isinstance(X, Stream), "%s is not a stream object" % str(X)
+
+    print 'Checking signal length ...'
+
+    x = X[0].data
+    t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
+                  X[0].stats.delta)
+
+    # generate envelope function from Hilbert transform
+    y = np.imag(sc.signal.hilbert(x))
+    e = np.sqrt(np.power(x, 2) + np.power(y, 2))
+    # get noise window
+    inoise = getnoisewin(t, pick, TSNR[0], TSNR[1])
+    # get signal window
+    isignal = getsignalwin(t, pick, TSNR[2])
+    # calculate minimum adjusted signal level
+    minsiglevel = max(e[inoise]) * nfac
+    # minimum adjusted number of samples over minimum signal level
+    minnum = len(isignal) * minpercent/100
+    # get number of samples above minimum adjusted signal level
+    numoverthr = len(np.where(e[isignal] >= minsiglevel)[0]) 
+
+    if numoverthr >= minnum:
+    	print 'checksignallength: Signal reached required length.'
+        returnflag = 1
+    else:
+        print 'checksignallength: Signal shorter than required minimum signal length!'
+        print 'Presumably picked picked noise peak, pick is rejected!'
+        returnflag = 0
+ 
+    if iplot == 2:
+        plt.figure(iplot)
+    	p1, = plt.plot(t,x, 'k')
+        p2, = plt.plot(t[inoise], e[inoise])
+        p3, = plt.plot(t[isignal],e[isignal], 'r') 
+        p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
+                        [minsiglevel, minsiglevel], 'g')
+        p5, = plt.plot([pick, pick], [min(x), max(x)], 'c')
+        plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
+                    'Envelope Signal Window', 'Minimum Signal Level', \
+                    'Onset'], loc='best')
+        plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
+        plt.ylabel('Counts')
+        plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
+        plt.yticks([])
+        plt.show()
+        raw_input()
+        plt.close(iplot)
+
+    return returnflag
+
+