clean-up to meet coding conventions

pylot/core/pick/utils.py

@@ -14,6 +14,8 @@ import matplotlib.pyplot as plt
 from obspy.core import Stream, UTCDateTime
 import warnings
 import pdb
+
+
 def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
     '''
     Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
@@ -432,7 +434,7 @@ def getResolutionWindow(snr):
     else:
         time_resolution = res_wins['HRW']
 
-    return time_resolution/2
+    return time_resolution / 2
 
 
 def wadaticheck(pickdic, dttolerance, iplot):
@@ -471,7 +473,6 @@ def wadaticheck(pickdic, dttolerance, iplot):
             Spicks.append(UTCSpick.timestamp)
             SPtimes.append(spt)
 
-
     if len(SPtimes) >= 3:
         # calculate slope
         p1 = np.polyfit(Ppicks, SPtimes, 1)
@@ -503,7 +504,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
                     checkedPpicks.append(checkedPpick.timestamp)
                     checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
                     checkedSpicks.append(checkedSpick.timestamp)
-                    checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
+                    checkedSPtime = pickdic[key]['S']['mpp'] - \
+                                    pickdic[key]['P']['mpp']
                     checkedSPtimes.append(checkedSPtime)
 
                 pickdic[key]['S']['marked'] = marker
@@ -526,7 +528,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
         print 'wadaticheck: Not enough S-P times available for reliable regression!'
         print 'Skip wadati check!'
         wfitflag = 1
-    iplot=2
+    iplot = 2
     # plot results
     if iplot > 1:
         plt.figure(iplot)
@@ -538,7 +540,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
             plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
                       'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
             plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \
-                           'Reliable S-Picks', 'Wadati 2'], loc='best')
+                                          'Reliable S-Picks', 'Wadati 2'],
+                       loc='best')
         else:
             plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
 
@@ -600,7 +603,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
     # calculate minimum adjusted signal level
     minsiglevel = max(e[inoise]) * nfac
     # minimum adjusted number of samples over minimum signal level
-    minnum = len(isignal) * minpercent/100
+    minnum = len(isignal) * minpercent / 100
     # get number of samples above minimum adjusted signal level
     numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
 
@@ -614,16 +617,17 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
 
     if iplot == 2:
         plt.figure(iplot)
-    	p1, = plt.plot(t,x, 'k')
+        p1, = plt.plot(t, x, 'k')
         p2, = plt.plot(t[inoise], e[inoise], 'c')
-        p3, = plt.plot(t[isignal],e[isignal], 'r') 
+        p3, = plt.plot(t[isignal], e[isignal], 'r')
         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]]], \
+        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)], 'b', linewidth=2)
         plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
-                    'Envelope Signal Window', 'Minimum Signal Level', \
+                                          'Envelope Signal Window',
+                                          'Minimum Signal Level', \
                                           'Onset'], loc='best')
         plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
         plt.ylabel('Counts')
@@ -667,7 +671,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
 
     # apply jackknife bootstrapping on variance of P onsets
     print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
-    [xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
+    [xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
     # get pseudo variances smaller than average variances
     # these picks passed jackknife test
     ij = np.where(PHI_pseudo <= xjack)
@@ -686,7 +690,8 @@ def checkPonsets(pickdic, dttolerance, iplot):
     badstations = np.array(stations)[ibad]
 
     print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \
-            + len(badjkstations), len(stations))
+                                                          + len(badjkstations),
+                                                          len(stations))
 
     goodmarker = 'goodPonsetcheck'
     badmarker = 'badPonsetcheck'
@@ -726,6 +731,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
 
     return checkedonsets
 
+
 def jackknife(X, phi, h):
     '''
     Function to calculate the Jackknife Estimator for a given quantity,
@@ -790,7 +796,7 @@ def jackknife(X, phi, h):
     return PHI_jack, PHI_pseudo, PHI_sub
 
 
-
 if __name__ == '__main__':
     import doctest
+
     doctest.testmod()