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Merge branch 'improve_jackwada_plots' into develop

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This commit is contained in:
Darius Arnold 2017-09-15 18:32:26 +02:00
commit 864f035456
1 changed files with 23 additions and 12 deletions
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35
pylot/core/pick/utils.py
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@ -589,6 +589,7 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
Ppicks = [] Ppicks = []
Spicks = [] Spicks = []
SPtimes = [] SPtimes = []
stations = []
for key in list(pickdic.keys()): for key in list(pickdic.keys()):
if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4: if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
# calculate S-P time # calculate S-P time
@ -622,6 +623,7 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
ibad = 0 ibad = 0
for key in list(pickdic.keys()): for key in list(pickdic.keys()):
if 'SPt' in pickdic[key]: if 'SPt' in pickdic[key]:
stations.append(key)
wddiff = abs(pickdic[key]['SPt'] - wdfit[ii]) wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
ii += 1 ii += 1
# check, if deviation is larger than adjusted # check, if deviation is larger than adjusted
@ -682,12 +684,18 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
linecolor = 'k' linecolor = 'k'
plt_flag = 1 plt_flag = 1
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks') if ibad > 0:
ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
if wfitflag == 0: if wfitflag == 0:
ax.plot(Ppicks, wdfit, color=linecolor, linewidth=0.7, label='Wadati 1') ax.plot(Ppicks, wdfit, color=linecolor, linewidth=0.7, label='Wadati 1')
ax.plot(Ppicks, wdfit+dttolerance, color='0.9', linewidth=0.5, label='Wadati 1 Tolerance')
ax.plot(Ppicks, wdfit-dttolerance, color='0.9', linewidth=0.5)
ax.plot(checkedPpicks, wdfit2, 'g', label='Wadati 2')
ax.plot(checkedPpicks, checkedSPtimes, color=linecolor, ax.plot(checkedPpicks, checkedSPtimes, color=linecolor,
linewidth=0, marker='o', label='Reliable S-Picks') linewidth=0, marker='o', label='Reliable S-Picks')
ax.plot(checkedPpicks, wdfit2, 'g', label='Wadati 2') for Ppick, SPtime, station in zip(Ppicks, SPtimes, stations):
ax.text(Ppick, SPtime + 0.01, '{0}'.format(station))
ax.set_title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \ ax.set_title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr)) 'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
ax.legend(loc=1, numpoints=1) ax.legend(loc=1, numpoints=1)
@ -909,16 +917,19 @@ def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
plt_flag = 1 plt_flag = 1
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14) if len(badstations) > 0:
if len(badstations) < 1 and len(badjkstations) < 1: ax.plot(ibad, np.array(Ppicks)[ibad], marker ='o', markerfacecolor='orange', markersize=14,
ax.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14, label='Skipped P Picks') linestyle='None', label='Median Skipped P Picks')
else: if len(badjkstations) > 0:
ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks') ax.plot(badjk[0], np.array(Ppicks)[badjk], 'ro', markersize=14, label='Jackknife Skipped P Picks')
ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks')
linewidth=2, label='Median') ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', linewidth=2, label='Median')
for i in range(0, len(Ppicks)): ax.plot([0, len(Ppicks) - 1], [pmedian + dttolerance, pmedian + dttolerance], 'g--', linewidth=1.2,
ax.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i])) dashes=[25, 25], label='Median Tolerance')
ax.plot([0, len(Ppicks) - 1], [pmedian - dttolerance, pmedian - dttolerance], 'g--', linewidth=1.2,
dashes=[25, 25])
for index, pick in enumerate(Ppicks):
ax.text(index, pick + 0.01, '{0}'.format(stations[i]))
ax.set_xlabel('Number of P Picks') ax.set_xlabel('Number of P Picks')
ax.set_ylabel('Onset Time [s] from 1.1.1970') ax.set_ylabel('Onset Time [s] from 1.1.1970')
ax.legend(loc=1, numpoints=1) ax.legend(loc=1, numpoints=1)