def grab_data(scnl, T1, T2, hostname, port, fill_value=0):
# scnl = list of station names (eg. ['PS4A.EHZ.AV.--','PVV.EHZ.AV.--','PS1A.EHZ.AV.--'])
# T1 and T2 are start/end obspy UTCDateTimes
# fill_value can be 0 (default), 'latest', or 'interpolate'
#
# returns stream of traces with gaps accounted for
#
# print('{} - {}'.format(T1.strftime('%Y.%m.%d %H:%M:%S'),T2.strftime('%Y.%m.%d %H:%M:%S')))
print('Grabbing data...')
st=Stream()
client = Client(hostname, int(port))
for sta in scnl:
try:
tr=client.get_waveforms(sta.split('.')[2], sta.split('.')[0],sta.split('.')[3],sta.split('.')[1],
T1, T2, cleanup=True)
if len(tr)>1:
if fill_value==0 or fill_value==None:
tr.detrend('demean')
tr.taper(max_percentage=0.01)
for sub_trace in tr:
# deal with error when sub-traces have different dtypes
if sub_trace.data.dtype.name != 'int32':
sub_trace.data=sub_trace.data.astype('int32')
if sub_trace.data.dtype!=np.dtype('int32'):
sub_trace.data=sub_trace.data.astype('int32')
# deal with rare error when sub-traces have different sample rates
if sub_trace.stats.sampling_rate!=np.round(sub_trace.stats.sampling_rate):
sub_trace.stats.sampling_rate=np.round(sub_trace.stats.sampling_rate)
print('Merging gappy data...')
tr.merge(fill_value=fill_value)
# deal where trace length is smaller than expected window length
if tr[0].stats.endtime - tr[0].stats.starttime < T2 - T1:
tr.detrend('demean')
tr.taper(max_percentage=0.01)
except:
tr=Stream()
# if no data, create a blank trace for that channel
if not tr:
from obspy import Trace
from numpy import zeros
tr=Trace()
tr.stats['station']=sta.split('.')[0]
tr.stats['channel']=sta.split('.')[1]
tr.stats['network']=sta.split('.')[2]
tr.stats['location']=sta.split('.')[3]
tr.stats['sampling_rate']=100
tr.stats['starttime']=T1
tr.data=zeros(int((T2-T1)*tr.stats['sampling_rate']),dtype='int32')
st+=tr
st.trim(T1,T2,pad=True, fill_value=0)
print('Detrending data...')
st.detrend('demean')
return st
xlim((1 / 10., 150.))
savefig('NOISE' + str(st[0].stats.starttime.year) + str(st[0].stats.starttime.julday) + \
str(st[0].stats.starttime.hour) + str(st[0].stats.starttime.minute) + \
st[0].stats.station + st[0].stats.location + st[0].stats.channel + \
st[1].stats.station + st[1].stats.location + st[1].stats.channel + \
st[2].stats.station + st[2].stats.location + st[2].stats.channel + \
'.jpg', format = 'jpeg', dpi=400)
savefig('NOISE' + str(st[0].stats.starttime.year) + str(st[0].stats.starttime.julday) + \
str(st[0].stats.starttime.hour) + str(st[0].stats.starttime.minute) + \
st[0].stats.station + st[0].stats.location + st[0].stats.channel + \
st[1].stats.station + st[1].stats.location + st[1].stats.channel + \
st[2].stats.station + st[2].stats.location + st[2].stats.channel + \
'.pdf', format = 'pdf', dpi=400)
clf()
st.detrend()
titlelegend = 'Time Series: ' + str(st[0].stats.starttime.year) + ' ' + str(st[0].stats.starttime.julday) + ' ' + \
str(st[0].stats.starttime.hour) + ':' + str(st[0].stats.starttime.minute) + ':' + str(st[0].stats.starttime.second) + \
' ' + str(st[0].stats.npts*delta) + ' seconds'
tval = np.arange(0, st[0].stats.npts / st[0].stats.sampling_rate,
st[0].stats.delta)
tseriesplot = figure(2)
title(titlelegend, fontsize=12)
subplot(311)
title(titlelegend, fontsize=12)
plot(tval,st[0].data,'r',label='TSeries ' + st[0].stats.station + ' ' + \
st[0].stats.location + ' ' + st[0].stats.channel )
legend(prop={'size': 12})
xlim((0, np.amax(tval)))
summary = []
summary.append("#" * 79)
summary.append("######## %s --- %s ########" % (T1, T2))
summary.append("#" * 79)
summary.append(st.__str__(extended=True))
if exceptions:
summary.append("#" * 33 + " Exceptions " + "#" * 33)
summary += exceptions
summary.append("#" * 79)
trig = []
mutt = []
if st:
# preprocessing, backup original data for plotting at end
st.merge(0)
st.detrend("linear")
for tr in st:
tr.data = tr.data * cosTaper(len(tr), 0.01)
#st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger = st.copy()
st.normalize(global_max=False)
# do the triggering
trig = coincidenceTrigger("recstalta", PAR.ON, PAR.OFF, st_trigger,
thr_coincidence_sum=PAR.MIN_STATIONS,
max_trigger_length=PAR.MAXLEN, trigger_off_extension=PAR.ALLOWANCE,
details=True, sta=PAR.STA, lta=PAR.LTA)
for t in trig:
chan = 'HHZ'
stime = UTCDateTime('2019-08-16 12:59:10')
etime = stime + 120
client = Client()
inv = client.get_stations(network=net,
station=sta,
starttime=stime,
endtime=etime,
channel=chan,
level="response")
st = Stream()
st += client.get_waveforms(net, sta, loc, chan, stime, etime)
st.detrend('constant')
st.merge(fill_value=0)
st.attach_response(inv)
st.remove_response(output="DISP")
#st.rotate(method="->ZNE",inventory=inv)
st.filter("bandpass", freqmin=.5, freqmax=5)
tr = st[0]
t = np.linspace(0, (tr.stats.npts - 1) / tr.stats.sampling_rate,
num=tr.stats.npts)
fig = plt.figure(1, figsize=(12, 12))
plt.ylabel('Displacement (mm)', fontsize=14)
plt.xlim([0, 120])
plt.ylim([-.02, .02])
plt.xlabel('seconds after origin', fontsize=14)
plt.title('%s-%s-%s-%s, 2019-08-16, Hutchinson, KS Earthquake' %
def getChannelWaveformFiles (network, station, location, channel, starttime, endtime, removeTrend, performInstrumentCorrection,applyScale,deconFilter1, deconFilter2, deconFilter3, deconFilter4, waterLevel, unit, client, fileTag, respdir = None, inventory = None):
debug = False
#
# stream holds the final stream
#
thisStartTime = UTCDateTime(starttime)
thisEndTime = UTCDateTime(endtime)
stream = Stream()
streamIn = Stream()
try:
#
# read in the files to a stream
#
print("[INFO] checking:",fileTag)
if performInstrumentCorrection:
streamIn = read(fileTag, starttime=thisStartTime, endtime=thisEndTime, nearest_sample=True, apply_calib=False )
else:
print("[INFO] Apply scaling")
streamIn = read(fileTag, starttime=thisStartTime, endtime=thisEndTime, nearest_sample=True, apply_calib=applyScale )
#print "STREAM IN",fileTag, starttime, endtime
except Exception as e:
print(str(e))
print("[ERRORnnelWaveformFile] ",network, station, location, channel, starttime, endtime)
return(None)
try:
#
# select the desire streams only
#
if location == "--":
streamOut = streamIn.select(network=network, station=station, location="", channel=channel)
else:
streamOut = streamIn.select(network=network, station=station, location=location, channel=channel)
for i in range(len(streamOut)):
if performInstrumentCorrection:
#
# get the network, station, location and channel information
#
(thisNSLC,thisTime,junk) = str(streamOut[i]).split('|')
(net,sta,loc,chan) = thisNSLC.strip().split('.')
if len(loc) == 0:
loc = "--"
#
# if respdir is defined, first look into user's respdir for stationXML files, if not found get it from FDSN
#
(start,end) = thisTime.split(' - ')
inv = None
if ( respdir is not None):
print("[INFO] Getting response from", respdir)
thisloc = loc
if loc == '--':
thisloc = ''
inventory, inv = getResponseFromFile (inventory, respdir, net, sta, thisloc, chan, starttime, debug)
if inv is not None:
if debug:
print("[INFO]: Attaching",inv)
streamOut[i].attach_response(inv)
stream += streamOut[i]
else:
thisStarttime = UTCDateTime(start.strip())
print("NO RESPONSE FILE:",net,sta,loc,chan,thisStarttime)
if (inv is None and client is not None):
#
# The FDSN webservices return StationXML metadata.
#
print("[INFO] Getting response from IRIS")
try:
thisStarttime = UTCDateTime(start.strip())
thisEndtime = UTCDateTime(end.strip())
inv = client.get_stations(network=net,station=sta,location=loc,channel=chan,starttime=thisStarttime,endtime=thisEndtime,level="response")
streamOut[i].attach_response(inv)
stream += streamOut[i]
if debug:
print("[INFO] Response attached:",inv)
except Exception as e:
print(str(e))
thisStarttime = UTCDateTime(start.strip())
print("NO RESPONSE:",net,sta,loc,chan,thisStarttime,thisEndtime)
continue
else:
print("[INFO] Response not removed")
stream += streamOut[i]
#
# print stream Gap information
#
# print "\n\nSTREAM GAP INFORMATION:\n"
#stream.printGaps()
if(removeTrend > 0):
stream.detrend("demean")
#
# remove the instrument response
#
if performInstrumentCorrection:
print("[INFO] PERFORM INSTRUMENT CORRECTION",unit)
if deconFilter1<=0 and deconFilter2<=0 and deconFilter3<=0 and deconFilter4<=0:
print("[INFO] NO DECON FILTER APPLIED")
stream.remove_response(output=unit,pre_filt=None, zero_mean=False, taper=False,water_level=waterLevel)
else:
stream.remove_response(output=unit,pre_filt=[deconFilter1,deconFilter2,deconFilter3,deconFilter4], zero_mean=False, taper=False,water_level=waterLevel)
except Exception as e:
print(str(e))
print("[ERROR] get_waveforms",network, station, location, channel, starttime, endtime)
return(None,None)
return(inventory,stream)
summary = []
summary.append("#" * 79)
summary.append("######## %s --- %s ########" % (T1, T2))
summary.append("#" * 79)
summary.append(st.__str__(extended=True))
if exceptions:
summary.append("#" * 33 + " Exceptions " + "#" * 33)
summary += exceptions
summary.append("#" * 79)
trig = []
mutt = []
if st:
# preprocessing, backup original data for plotting at end
st.merge(0)
st.detrend("linear")
for tr in st:
tr.data = tr.data * cosTaper(len(tr), 0.01)
#st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger = st.copy()
st.normalize(global_max=False)
# do the triggering
trig = coincidenceTrigger("recstalta", PAR.ON, PAR.OFF, st_trigger,
thr_coincidence_sum=PAR.MIN_STATIONS,
max_trigger_length=PAR.MAXLEN, trigger_off_extension=PAR.ALLOWANCE,
details=True, sta=PAR.STA, lta=PAR.LTA)
for t in trig:
def section_plot(self,
assoc_id,
files,
seconds_ahead=5,
record_length=100,
channel='Z',
scale_factor=2,
outfile=None):
"""
Plot a record section, with picks and waveforms ordered by distance
:param assoc_id: ID number for the association to plot
:param files: list of waveform files
:param seconds_ahead: time before origin_time to start plot
:param record_length: time after origin_time to end plot
:param channel: channel to plot (default='Z')
:param scale_factor: factor to scale wiggles by
:param outfile: save plot to this filename
"""
assert files, 'empty wavefile list'
station = self.assoc_db.query(Candidate.sta).\
filter(Candidate.assoc_id == assoc_id).all()
sta_list = []
for sta, in station:
sta_list.append(str(sta))
station_single = self.assoc_db.query(Pick.sta).\
filter(Pick.assoc_id == assoc_id).\
filter(Pick.locate_flag == None).all()
for sta, in station_single:
sta_list.append(str(sta))
# print sta_list
eve = self.assoc_db.query(Associated).\
filter(Associated.id == assoc_id).first()
# Earthquakes' epicenter
eq_lat = eve.latitude
eq_lon = eve.longitude
# Read the waveforms
ST = Stream()
for file in files:
st = read(file)
ST += st
# in case of some seismometer use channel code like BH1, BH2
# or BH3, resign the channel code as:
if channel == 'E' or channel == 'e':
Chan = 'E1'
elif channel == 'N' or channel == 'n':
Chan = 'N2'
elif channel == 'Z' or channel == 'z':
Chan = 'Z3'
else:
print('Please input component E, e, N, n, Z, or z,'
' the default is Z')
# Calculate distance from headers lat/lon
ST_new = Stream()
for tr in ST:
if tr.stats.channel[2] in Chan and tr.stats.station in sta_list:
if ((tr.stats.starttime.datetime < eve.ot)
and (tr.stats.endtime.datetime > eve.ot)):
tr.trim(
UTCDateTime(eve.ot - timedelta(seconds=seconds_ahead)),
UTCDateTime(eve.ot + timedelta(seconds=record_length)))
ST_new += tr
# print ST_new.__str__(extended=True)
# remove traces from same station, but different # samples
# X31A..B Z | 2011-01-18T01:40:30.325Z - 18T01:41:44.975Z | 2987 samps
# WMOK..B Z | 2011-01-18T01:40:30.325Z - 18T01:41:44.975Z | 2987 samps
# X31A..B Z | 2011-01-18T01:40:30.325Z - 18T01:42:10.325Z | 4001 samps
# WMOK..B Z | 2011-01-18T01:40:30.325Z - 18T01:42:10.325Z | 4001 samps
# Remove traces from same station with different # samples
while True:
ST_new_sta = []
for tr in ST_new:
ST_new_sta.append(tr.stats.station)
duplicate = list(
set([tr for tr in ST_new_sta if ST_new_sta.count(tr) > 1]))
if not duplicate:
break
index = [
i for (i, j) in enumerate(ST_new_sta) if j == duplicate[-1]
]
i = 0
while True:
if (ST_new[index[i]].stats.npts <
ST_new[index[i + 1]].stats.npts):
del ST_new[index[i]]
break
elif (ST_new[index[i]].stats.npts >=
ST_new[index[i + 1]].stats.npts):
del ST_new[index[i + 1]]
break
# print ST_new.__str__(extended=True)
ST_new.detrend('demean')
# ST_new.filter('bandpass', freqmin=0.1, freqmax=100)
segs, ticklocs, sta, circle_x, circle_y, segs_picks, ticklocs_picks, \
data_picks = self._make_section_arrays(ST_new, scale_factor,
channel, seconds_ahead,
record_length, assoc_id,
eve)
tick_min, tick_max = min(ticklocs), max(ticklocs)
offsets = np.zeros((len(ST_new), 2), dtype=float)
offsets[:, 0] = ticklocs
offsets_picks = np.zeros((len(segs_picks), 2), dtype=float)
offsets_picks[:, 0] = ticklocs_picks
lines = LineCollection(segs,
offsets=offsets,
transOffset=None,
linewidths=.25,
color='gray')
lines_picks = LineCollection(segs_picks,
offsets=offsets_picks,
transOffset=None,
linewidths=1,
color='k')
# Set up axis
fig = plt.figure(figsize=(15, 8))
ax1 = fig.add_subplot(111)
x1 = tick_max + (tick_max - tick_min) * 0.1
plt.ylim(0, record_length)
plt.xlim(0, x1)
# Plot data and picks
ax1.plot(circle_x, circle_y, 'o',
c='gray') # dots where picks cross the waveform
ax1.add_collection(lines) # Plot the data
ax1.add_collection(lines_picks) # Plot the picks
# Set axis labels and ticks
ax1.set_xticks(ticklocs)
ax1.set_xticklabels(sta)
ax1.invert_yaxis()
ax1.xaxis.tick_top()
plt.setp(plt.xticks()[1], rotation=45)
ax2 = ax1.secondary_xaxis('bottom')
ax2.set_xlabel('Offset(km)')
plt.ylabel('Record Length (s)', fontsize=18)
plt.title(
f'{channel}-channel Section Plot of Event at {tr.stats.starttime}')
# plt.tight_layout()
# Plot travel-time curves
ttable = self.tt_stations_db_1D.query(TTtable1D).\
filter(TTtable1D.d_km <= x1).all()
D = [x.d_km for x in ttable]
P = [x.p_tt + seconds_ahead for x in ttable]
S = [x.s_tt + seconds_ahead for x in ttable]
ax1.plot(D, P, 'b--', D, S, 'r--', linewidth=2)
if outfile:
plt.savefig(outfile)
else:
plt.show()
days = int(float(hours) / 24.) + 1
if days < 1:
days = 1
for day in range(days):
ctime = stime2 + 24. * 60. * 60. * day
string = '/msd/' + net + '_' + sta + '/' + str(ctime.year) + '/' + str(
ctime.julday).zfill(3) + '/'
#string = '/tr1/telemetry_days/' + net + '_' + sta + '/' + str(ctime.year) + '/*' + str(ctime.julday).zfill(3) + '/'
st += read(string + loc + '_LH*.seed')
if pcorr:
st += read(string + '30_LDO*.seed')
st.trim(starttime=stime2, endtime=stime2 + hours * 60. * 60.)
st.merge()
st.detrend('linear')
st.detrend('constant')
print(st)
st.merge()
print(st)
#st.decimate(10)
#st.decimate(10)
st.rotate(method="->ZNE", inventory=inventory)
for tr in st:
if tr.stats.channel == 'LHN':
tr.stats.channel = "LH1"
if tr.stats.channel == "LHE":
tr.stats.channel = "LH2"
def section_plot(self,
assoc_id,
files,
seconds_ahead=5,
record_length=100,
channel='Z'):
station = self.assoc_db.query(
Candidate.sta).filter(Candidate.assoc_id == assoc_id).all()
sta_list = []
for sta, in station:
sta_list.append(str(sta))
station_single = self.assoc_db.query(Pick.sta).filter(
Pick.assoc_id == assoc_id).filter(Pick.locate_flag == None).all()
for sta, in station_single:
sta_list.append(str(sta))
# print sta_list
eve = self.assoc_db.query(Associated).filter(
Associated.id == assoc_id).first()
# Earthquakes' epicenter
eq_lat = eve.latitude
eq_lon = eve.longitude
# Reading the waveforms
ST = Stream()
for file in files:
st = read(file)
ST += st
# in case of some seismometer use channel code like BH1, BH2 or BH3, resign the channel code as:
if channel == 'E' or channel == 'e':
Chan = 'E1'
elif channel == 'N' or channel == 'n':
Chan = 'N2'
elif channel == 'Z' or channel == 'z':
Chan = 'Z3'
else:
print(
'Please input component E, e, N, n, Z, or z, the default is Z')
# Calculating distance from headers lat/lon
ST_new = Stream() # ;print ST
for tr in ST:
if tr.stats.channel[2] in Chan and tr.stats.station in sta_list:
if tr.stats.starttime.datetime < eve.ot and tr.stats.endtime.datetime > eve.ot:
tr.trim(
UTCDateTime(eve.ot - timedelta(seconds=seconds_ahead)),
UTCDateTime(eve.ot + timedelta(seconds=record_length)))
ST_new += tr
# print ST_new.__str__(extended=True)
# remove the traces from same station, but different samples number (trace length)
# .X31A..B Z | 2011-01-18T01:40:30.325000Z - 2011-01-18T01:41:44.975000Z | 40.0 Hz, 2987 samples
# .WMOK..B Z | 2011-01-18T01:40:30.325000Z - 2011-01-18T01:41:44.975000Z | 40.0 Hz, 2987 samples
# .X31A..B Z | 2011-01-18T01:40:30.325000Z - 2011-01-18T01:42:10.325000Z | 40.0 Hz, 4001 samples
# .WMOK..B Z | 2011-01-18T01:40:30.325000Z - 2011-01-18T01:42:10.325000Z | 40.0 Hz, 4001 samples
while True:
ST_new_sta = []
for tr in ST_new:
ST_new_sta.append(tr.stats.station)
duplicate = list(
set([tr for tr in ST_new_sta if ST_new_sta.count(tr) > 1]))
if not duplicate:
break
index = [
i for (i, j) in enumerate(ST_new_sta) if j == duplicate[-1]
]
i = 0
while True:
if ST_new[index[i]].stats.npts < ST_new[index[i +
1]].stats.npts:
del ST_new[index[i]]
break
elif ST_new[index[i]].stats.npts >= ST_new[index[
i + 1]].stats.npts:
del ST_new[index[i + 1]]
break
# print ST_new.__str__(extended=True)
ST_new.detrend('demean')
# ST_new.filter('bandpass', freqmin=0.1, freqmax=100)
factor = 10
numRows = len(ST_new)
segs = []
ticklocs = []
sta = []
circle_x = []
circle_y = []
segs_picks = []
ticklocs_picks = []
for tr in ST_new:
dmax = tr.data.max()
dmin = tr.data.min()
data = tr.data / (dmax - dmin) * factor
t = np.arange(
0,
round(tr.stats.npts / tr.stats.sampling_rate / tr.stats.delta)
) * tr.stats.delta # due to the float point arithmetic issue, can not use "t=np.arange(0,tr.stats.npts/tr.stats.sampling_rate,tr.stats.delta)"
segs.append(np.hstack((data[:, np.newaxis], t[:, np.newaxis])))
lon, lat = self.tt_stations_db_1D.query(
Station1D.longitude, Station1D.latitude).filter(
Station1D.sta == tr.stats.station).first()
distance = int(
gps2dist_azimuth(lat, lon, eq_lat, eq_lon)[0] / 1000.
) # gps2DistAzimuth return in meters, convert to km by /1000
# distance=self.assoc_db.query(Candidate.d_km).filter(Candidate.assoc_id==assoc_id).filter(Candidate.sta==tr.stats.station).first()[0]#;print distance,tr.stats.station
ticklocs.append(distance)
sta.append(tr.stats.station)
# DOT plot where picks are picked, notice that for vertical trace plot p is queried from Pick table, s from PickModified table
# horizontal trace plot p and s queried from PickModified table
if channel == 'Z3':
picks_p = self.assoc_db.query(
Pick.time).filter(Pick.assoc_id == assoc_id).filter(
Pick.sta == tr.stats.station).filter(
Pick.chan == tr.stats.channel).filter(
Pick.phase == 'P').all()
if not picks_p:
picks_p = self.assoc_db.query(PickModified.time).filter(
PickModified.assoc_id == assoc_id).filter(
PickModified.sta == tr.stats.station).filter(
PickModified.phase == 'P').all()
picks_s = self.assoc_db.query(PickModified.time).filter(
PickModified.assoc_id == assoc_id).filter(
PickModified.sta == tr.stats.station).filter(
PickModified.phase == 'S').all()
# print picks_p,picks_s
else:
picks_p = self.assoc_db.query(PickModified.time).filter(
PickModified.assoc_id == assoc_id).filter(
PickModified.sta == tr.stats.station).filter(
PickModified.phase == 'P').all()
picks_s = self.assoc_db.query(PickModified.time).filter(
PickModified.assoc_id == assoc_id).filter(
PickModified.sta == tr.stats.station).filter(
PickModified.phase == 'S').all()
# print picks_p,picks_s
picks = picks_p + picks_s
# picks=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).all()
for pick, in picks:
index = int(
(pick - eve.ot +
timedelta(seconds=seconds_ahead)).total_seconds() /
tr.stats.delta) # ;print pick,eve.ot,index,len(data)
circle_x.append(distance + data[index])
circle_y.append(t[index])
# BAR plot where picks are picked
t_picks = np.array([t[index], t[index]])
data_picks = np.array([data.min(), data.max()])
segs_picks.append(
np.hstack(
(data_picks[:, np.newaxis], t_picks[:, np.newaxis])))
ticklocs_picks.append(distance)
tick_max = max(ticklocs)
tick_min = min(ticklocs)
offsets = np.zeros((numRows, 2), dtype=float)
offsets[:, 0] = ticklocs
offsets_picks = np.zeros((len(segs_picks), 2), dtype=float)
offsets_picks[:, 0] = ticklocs_picks
# lines=LineCollection(segs,offsets=offsets,transOffset=None,linewidths=.25,colors=[colorConverter.to_rgba(i) for i in ('b','g','r','c','m','y','k')]) #color='gray'
lines = LineCollection(segs,
offsets=offsets,
transOffset=None,
linewidths=.25,
color='gray')
# lines_picks=LineCollection(segs_picks,offsets=offsets_picks,transOffset=None,linewidths=1,color='r')
lines_picks = LineCollection(segs_picks,
offsets=offsets_picks,
transOffset=None,
linewidths=1,
color='k')
# print sta,ticklocs
fig = plt.figure(figsize=(15, 8))
ax1 = fig.add_subplot(111)
# ax1.plot(circle_x,circle_y,'o') # blue dots indicating where to cross the waveforms
ax1.plot(circle_x, circle_y, 'o', c='gray')
x0 = tick_min - (tick_max - tick_min) * 0.1
x1 = tick_max + (tick_max - tick_min) * 0.1
plt.ylim(0, record_length)
plt.xlim(0, x1)
ax1.add_collection(lines)
ax1.add_collection(lines_picks)
ax1.set_xticks(ticklocs)
ax1.set_xticklabels(sta)
ax1.invert_yaxis()
ax1.xaxis.tick_top()
# ax2 = ax1.twiny()
# ax2.xaxis.tick_bottom()
plt.setp(plt.xticks()[1], rotation=45)
# xlabel('Station (km)')
plt.xlabel('channel: ' + channel, fontsize=18)
plt.ylabel('Record Length (s)', fontsize=18)
# plt.title('Section Plot of Event at %s'%(tr.stats.starttime))
# plt.tight_layout()
Dist = self.tt_stations_db_1D.query(TTtable1D.d_km).all()
Ptime = self.tt_stations_db_1D.query(TTtable1D.p_tt).all()
Stime = self.tt_stations_db_1D.query(TTtable1D.s_tt).all()
D = []
P = []
S = []
for dist, in Dist:
D.append(dist)
for p, in Ptime:
P.append(p + seconds_ahead)
for s, in Stime:
S.append(s + seconds_ahead)
ax1.plot(D, P, 'k--', D, S, 'k--', linewidth=2)
plt.show()
for day in range(239, 340):
try:
stP += read("/msd/" + netp + "_" + stap + "/" + str(sday.year) + "/" +
str(day).zfill(3) + "/" + loc3 + "_" + chan3 + "*.seed")
except:
print('Day', day, 'has no data for test sensor')
print(Daynum)
# Rename data for rotations and organize in reverse
for tr in st:
if tr.stats.channel == 'LH1':
tr.stats.channel = 'LHN'
if tr.stats.channel == 'LH2':
tr.stats.channel = 'LHE'
st.detrend()
st.merge()
st.sort(reverse=True)
for tr in st2:
if tr.stats.channel == 'LH1':
tr.stats.channel = 'LHN'
if tr.stats.channel == 'LH2':
tr.stats.channel = 'LHE'
st2.detrend()
st2.merge(fill_value=0.)
st2.sort(reverse=True)
# Data is in ZNE format
if debug: