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

pylot/core/analysis/magnitude.py

@@ -6,6 +6,7 @@ Revised/extended summer 2017.
 
 :author: Ludger Küperkoch / MAGS2 EP3 working group
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 import obspy.core.event as ope
@@ -219,7 +220,7 @@ class LocalMagnitude(Magnitude):
         sqH = np.sqrt(power_sum)
 
         # get time array
-        th = np.arange(0, len(sqH) * dt, dt)
+        th=np.arange(0, st[0].stats.npts/st[0].stats.sampling_rate, st[0].stats.delta)
         # get maximum peak within pick window
         iwin = getsignalwin(th, t0 - stime, self.calc_win)
         ii = min([iwin[len(iwin) - 1], len(th)])

pylot/core/pick/autopick.py

@@ -100,9 +100,9 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
 
     # quality control
     # median check and jackknife on P-onset times
-    jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, jackfactor, 1, fig_dict_wadatijack)
+    jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, jackfactor, iplot, fig_dict_wadatijack)
     # check S-P times (Wadati)
-    wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, 1, fig_dict_wadatijack)
+    wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, iplot, fig_dict_wadatijack)
     return wadationsets
 
 
@@ -333,7 +333,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
             return
 
         Ldiff = Lwf - abs(Lc)
-        if Ldiff < 0 or pstop <= pstart:
+        if Ldiff <= 0 or pstop <= pstart or pstop - pstart <= thosmw:
             msg = 'autopickstation: Cutting times are too large for actual ' \
                   'waveform!\nUsing entire waveform instead!'
             if verbose: print(msg)
@@ -564,7 +564,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
                                                                      SNRPdB,
                                                                      FM)
                 print(msg)
-                msg = 'autopickstation: Refined P-Pick: {} s | P-Error: {} s'.format(mpickP, Perror)
+                msg = 'autopickstation: Refind P-Pick: {} s | P-Error: {} s'.format(zdat[0].stats.starttime \
+                                                                             + mpickP, Perror)
                 print(msg)
                 Sflag = 1
 
@@ -843,7 +844,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
                     lpickS = lpick[ipick]
                     Serror = pickerr[ipick]
 
-                    msg = 'autopickstation: Refined S-Pick: {} s | S-Error: {} s'.format(mpickS, Serror)
+                    msg = 'autopickstation: Refined S-Pick: {} s | S-Error: {} s'.format(hdat[0].stats.starttime \
+                                                                                  + mpickS, Serror)
                     print(msg)
 
                     # get SNR

pylot/core/pick/picker.py

@@ -239,7 +239,7 @@ class AICPicker(AutoPicker):
                 print(msg)
                 return
             # calculate SNR from CF
-            self.SNR = max(abs(self.Data[0].data[isignal] - np.mean(self.Data[0].data[isignal]))) / \
+            self.SNR = max(abs(self.Data[0].data[isignal])) / \
                        max(abs(self.Data[0].data[inoise] - np.mean(self.Data[0].data[inoise])))
             # calculate slope from CF after initial pick
             # get slope window
@@ -253,13 +253,15 @@ class AICPicker(AutoPicker):
             except IndexError:
                 print("Slope Calculation: empty array islope, check signal window")
                 return
-            if len(dataslope) <= 1:
+            if len(dataslope) < 2:
                 print('No or not enough data in slope window found!')
                 return
+            try:
                 imaxs, = argrelmax(dataslope)
-            if imaxs.size:
+                imaxs.size
                 imax = imaxs[0]
-            else:
+            except ValueError as e:
+                print(e, 'picker: argrelmax not working!')
                 imax = np.argmax(dataslope)
             iislope = islope[0][0:imax + 1]
             if len(iislope) < 2:
@@ -298,7 +300,7 @@ class AICPicker(AutoPicker):
             datafit = np.polyval(P, xslope)
             if datafit[0] >= datafit[-1]:
                 print('AICPicker: Negative slope, bad onset skipped!')
-                return
+            else:
                 self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
 
         else:
@@ -340,8 +342,11 @@ class AICPicker(AutoPicker):
                             label='Slope Window')
                 ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2, label='Slope')
 
+                if self.slope is not None:
                     ax1.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
                                                                                      self.SNR, self.slope))
+                else:
+                    ax1.set_title('Station %s, SNR=%7.2f' % (self.Data[0].stats.station, self.SNR))
                 ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
                 ax2.set_ylabel('Counts')
                 ax2.set_yticks([])