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Modified plot output.

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This commit is contained in:
Ludger Küperkoch 2017-05-05 14:07:35 +02:00
parent f4a76680ad
commit 2ef647251e
1 changed files with 27 additions and 12 deletions
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39
pylot/core/pick/utils.py
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@ -9,7 +9,6 @@
""" """
import warnings import warnings
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from obspy.core import Stream, UTCDateTime from obspy.core import Stream, UTCDateTime
@ -411,7 +410,9 @@ def getnoisewin(t, t1, tnoise, tgap):
inoise, = np.where((t <= max([t1 - tgap, 0])) \ inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0]))) & (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1: if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!") inoise, = np.where((t>=t[0]) & (t<=t1))
if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!")
return inoise return inoise
@ -605,7 +606,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
wfitflag = 1 wfitflag = 1
# plot results # plot results
if iplot > 1: if iplot > 0:
plt.figure(iplot) plt.figure(iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro') f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0: if wfitflag == 0:
@ -756,11 +757,12 @@ def checkPonsets(pickdic, dttolerance, iplot):
[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 # get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test # (times safety factor), these picks passed jackknife test
ij = np.where(PHI_pseudo <= 2 * xjack) ij = np.where(PHI_pseudo <= 5 * xjack)
# these picks did not pass jackknife test # these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > 2 * xjack) badjk = np.where(PHI_pseudo > 5 * xjack)
badjkstations = np.array(stations)[badjk] badjkstations = np.array(stations)[badjk]
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations)) print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print(badjkstations)
# calculate median from these picks # calculate median from these picks
pmedian = np.median(np.array(Ppicks)[ij]) pmedian = np.median(np.array(Ppicks)[ij])
@ -795,19 +797,22 @@ def checkPonsets(pickdic, dttolerance, iplot):
checkedonsets = pickdic checkedonsets = pickdic
if iplot > 1: if iplot > 0:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14) p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14)
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14) if len(badstations) < 1 and len(badjkstations) < 1:
p2, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14)
else:
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
linewidth=2) linewidth=2)
for i in range(0, len(Ppicks)): for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.2, stations[i]) plt.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i]))
plt.xlabel('Number of P Picks') plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970') plt.ylabel('Onset Time [s] from 1.1.1970')
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'],
loc='best') loc='best')
plt.title('Check P Onsets') plt.title('Jackknifing and Median Tests on P Onsets')
plt.show() plt.show()
raw_input() raw_input()
@ -943,8 +948,18 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
isignal = getsignalwin(tz, pick, checkwin) isignal = getsignalwin(tz, pick, checkwin)
# calculate energy levels # calculate energy levels
zcodalevel = max(absz[isignal]) try:
encodalevel = max(absen[isignal]) zcodalevel = max(absz[isignal])
except:
ii = np.where(isignal <= len(absz))
isignal = isignal[ii]
zcodalevel = max(absz[isignal - 1])
try:
encodalevel = max(absen[isignal])
except:
ii = np.where(isignal <= len(absen))
isignal = isignal[ii]
encodalevel = max(absen[isignal - 1])
# calculate threshold # calculate threshold
minsiglevel = encodalevel * zfac minsiglevel = encodalevel * zfac