reformat code (fix indentation and python 3.x issues)

pylot/core/pick/Picker.py

@@ -147,7 +147,7 @@ class AICPicker(AutoPicking):
 
     def calcPick(self):
 
-        print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
+        print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
 
         self.Pick = None
         self.slope = None
@@ -163,7 +163,7 @@ class AICPicker(AutoPicking):
         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!')
             return
         else:
             for i in range(1, len(aic)):
@@ -211,8 +211,8 @@ class AICPicker(AutoPicking):
             # get signal window
             isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
             # calculate SNR from CF
-           self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
+            self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
-                      - np.mean(aic[inoise])))
+                       max(abs(aic[inoise] - np.mean(aic[inoise])))
             # calculate slope from CF after initial pick
             # get slope window
             tslope = self.TSNR[3]  #slope determination window
@@ -222,8 +222,8 @@ class AICPicker(AutoPicking):
             # 'cause slope should be calculated up to first local minimum only!
             imax = np.argmax(self.Data[0].data[islope])
             if imax == 0:
-              print 'AICPicker: Maximum for slope determination right at the beginning of the window!'
+                print('AICPicker: Maximum for slope determination right at the beginning of the window!')
-              print 'Choose longer slope determination window!'
+                print('Choose longer slope determination window!')
                 if self.iplot > 1:
                     p = plt.figure(self.iplot)
                     x = self.Data[0].data
@@ -244,7 +244,7 @@ class AICPicker(AutoPicking):
             P = np.polyfit(xslope, dataslope, 1)
             datafit = np.polyval(P, xslope)
             if datafit[0] >= datafit[len(datafit) - 1]:
-              print 'AICPicker: Negative slope, bad onset skipped!'
+                print('AICPicker: Negative slope, bad onset skipped!')
                 return
             self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
 
@@ -273,9 +273,9 @@ class AICPicker(AutoPicking):
                 p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
                 p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
                 p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
-              plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
+                plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'],
                            loc='best')
-              plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station, \
+                plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
                                                                              self.SNR, self.slope))
                 plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
                 plt.ylabel('Counts')
@@ -286,7 +286,7 @@ class AICPicker(AutoPicking):
             plt.close(p)
 
         if self.Pick == None:
-           print 'AICPicker: Could not find minimum, picking window too short?'
+            print('AICPicker: Could not find minimum, picking window too short?')
 
 
 class PragPicker(AutoPicking):
@@ -297,7 +297,7 @@ class PragPicker(AutoPicking):
     def calcPick(self):
 
         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 ...')
 
             self.Pick = None
             self.SNR = None
@@ -307,7 +307,7 @@ class PragPicker(AutoPicking):
             ismooth = int(round(self.Tsmooth / self.dt))
             cfsmooth = np.zeros(len(self.cf))
             if len(self.cf) < ismooth:
-              print 'PragPicker: Tsmooth larger than CF!'
+                print('PragPicker: Tsmooth larger than CF!')
                 return
             else:
                 for i in range(1, len(self.cf)):
@@ -330,11 +330,11 @@ class PragPicker(AutoPicking):
             #check trend of CF, i.e. differences of CF and adjust aus regarding this trend
             #prominent trend: decrease aus
             #flat: use given aus
-           cfdiff = np.diff(cfipick);
+            cfdiff = np.diff(cfipick)
             i0diff = np.where(cfdiff > 0)
             cfdiff = cfdiff[i0diff]
-           minaus = min(cfdiff * (1 + self.aus));
+            minaus = min(cfdiff * (1 + self.aus))
-           aus1 = max([minaus, self.aus]);
+            aus1 = max([minaus, self.aus])
 
             #at first we look to the right until the end of the pick window is reached
             flagpick_r = 0
@@ -374,7 +374,7 @@ class PragPicker(AutoPicking):
                 self.Pick = pick_l
                 pickflag = 1
             else:
-           	print 'PragPicker: Could not find reliable onset!'
+                print('PragPicker: Could not find reliable onset!')
                 self.Pick = None
                 pickflag = 0
 
@@ -393,6 +393,6 @@ class PragPicker(AutoPicking):
                 plt.close(p)
 
         else:
-           print 'PragPicker: No initial onset time given! Check input!'
+            print('PragPicker: No initial onset time given! Check input!')
             self.Pick = None
             return