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

autoPyLoT.py

@@ -357,8 +357,15 @@ if __name__ == "__main__":
     # parse arguments
     parser = argparse.ArgumentParser(
         description='''autoPyLoT automatically picks phase onset times using higher order statistics,
-                       autoregressive prediction and AIC''')
+                       autoregressive prediction and AIC followed by locating the seismic events using 
+                       NLLoc''')
 
+    #parser.add_argument('-d', '-D', '--input_dict', type=str,
+    #                    action='store',
+    #                    help='''optional, dictionary containing processing parameters''')
+    #parser.add_argument('-p', '-P', '--parameter', type=str,
+    #                    action='store',
+    #                    help='''parameter file, default=None''')
     parser.add_argument('-i', '-I', '--inputfile', type=str,
                         action='store',
                         help='''full path to the file containing the input
@@ -369,17 +376,18 @@ if __name__ == "__main__":
     parser.add_argument('-e', '-E', '--eventid', type=str,
                         action='store',
                         help='''optional, event path incl. event ID''')
-    # parser.add_argument('-p', '-P', '--plot', action='store',
-    #                     help='show interactive plots')
     parser.add_argument('-s', '-S', '--spath', type=str,
                         action='store',
                         help='''optional, save path for autoPyLoT output''')
-    parser.add_argument('-v', '-V', '--version', action='version',
+    #parser.add_argument('-v', '-V', '--version', action='version',
-                        version='autoPyLoT ' + __version__,
+    #                    version='autoPyLoT ' + __version__,
-                        help='show version information and exit')
+    #                    help='show version information and exit')
 
     cla = parser.parse_args()
     
-    picks, mainFig = autoPyLoT(inputfile=str(cla.inputfile),
+    try:
-                               fnames=str(cla.fnames), eventid=str(cla.eventid), 
+        picks, mainFig = autoPyLoT(inputfile=str(cla.inputfile), fnames=str(cla.fnames), 
-                               savepath=str(cla.spath))
+                                   eventid=str(cla.eventid), savepath=str(cla.spath))
+    except ValueError:
+        print("autoPyLoT was running in production mode.")
+                             

pylot/core/io/data.py

@@ -5,6 +5,7 @@ import copy
 import os
 from obspy import read_events
 from obspy.core import read, Stream, UTCDateTime
+from obspy.io.sac import SacIOError
 from obspy.core.event import Event
 from pylot.core.io.phases import readPILOTEvent, picks_from_picksdict, \
     picksdict_from_pilot, merge_picks
@@ -230,6 +231,8 @@ class Data(object):
                     self.wfdata += read(fname, format='GSE2')
                 except Exception as e:
                     warnmsg += '{0}\n{1}\n'.format(fname, e)
+            except SacIOError as se:
+                    warnmsg += '{0}\n{1}\n'.format(fname, se)
         if warnmsg:
             warnmsg = 'WARNING: unable to read\n' + warnmsg
             print(warnmsg)

pylot/core/pick/picker.py

@@ -241,8 +241,12 @@ class AICPicker(AutoPicker):
                     ax.set_title(self.Data[0].stats.station)
                 return
 
-            islope = islope[0][0:imax]
+            iislope = islope[0][0:imax]
-            dataslope = self.Data[0].data[islope]
+            if len(iislope) <= 3:
+                # calculate slope from initial onset to maximum of AIC function
+                imax = np.argmax(aicsmooth[islope])
+                iislope = islope[0][0:imax]
+            dataslope = self.Data[0].data[iislope]
             # calculate slope as polynomal fit of order 1
             xslope = np.arange(0, len(dataslope), 1)
             P = np.polyfit(xslope, dataslope, 1)
@@ -276,12 +280,12 @@ class AICPicker(AutoPicker):
                 ax2.plot(self.Tcf, x, 'k', 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[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
-                ax1.axvspan(self.Tcf[islope[0]],self.Tcf[islope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
+                ax1.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
                 
                 ax2.axvspan(self.Tcf[inoise[0]],self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
                 ax2.axvspan(self.Tcf[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
-                ax2.axvspan(self.Tcf[islope[0]],self.Tcf[islope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')                
+                ax2.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')                
-                ax2.plot(self.Tcf[islope], datafit, 'g', linewidth=2, label='Slope')
+                ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2, label='Slope')
                 
                 ax1.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
                                                                                 self.SNR, self.slope))