def corn_freq_2_paz(fc, damp):
from obspy.signal import cornFreq2Paz
paz_out = cornFreq2Paz(fc, damp)
return paz_out
def _resi(self, x, *args):
'''
:param numpy array x: Containing original guess for free period, damping, and sensitivity
:param tuple *args: tuple containing the step calibration input signal and output signal.
'''
f = x[0]
h = x[1]
sen = x[2]
trIN = args[0]
trOUT = args[1]
paz = cornFreq2Paz(f, h)
paz['zeros'] = [0.]
paz['sensitivity'] = sen
trINCP = trIN.copy()
trINCP.trim(trINCP.stats.starttime + 50, trINCP.stats.endtime - 50)
trINCP.detrend('constant')
trINCP.normalize()
trOUTsim = trOUT.copy()
trOUTsim.simulate(paz_remove=paz)
trOUTsim.trim(
trOUTsim.stats.starttime + 50, trOUTsim.stats.endtime - 50)
trOUTsim.detrend('constant')
trOUTsim.normalize()
comp = sum((trOUTsim.data - trINCP) ** 2)
return comp
def _resi(self, x, *args):
'''
:param numpy array x: Containing original guess for free period, damping, and sensitivity
:param tuple *args: tuple containing the step calibration input signal and output signal.
'''
f = x[0]
h = x[1]
sen = x[2]
trIN = args[0]
trOUT = args[1]
paz = cornFreq2Paz(f, h)
paz['zeros'] = [0.]
paz['sensitivity'] = sen
trINCP = trIN.copy()
trINCP.trim(trINCP.stats.starttime + 50, trINCP.stats.endtime - 50)
trINCP.detrend('constant')
trINCP.normalize()
trOUTsim = trOUT.copy()
trOUTsim.simulate(paz_remove=paz)
trOUTsim.trim(trOUTsim.stats.starttime + 50,
trOUTsim.stats.endtime - 50)
trOUTsim.detrend('constant')
trOUTsim.normalize()
comp = sum((trOUTsim.data - trINCP)**2)
return comp
def __set_paz_from_cornfreqs(self):
for _i, (f, h) in enumerate(zip(self.boxes_corn_freqs,
self.boxes_dampings)):
f = f.value()
h = h.value()
paz = cornFreq2Paz(f, h)
pole1 = paz['poles'][0]
pole2 = paz['poles'][1]
self.boxes_poles_real[_i*2].setValue(pole1.real)
self.boxes_poles_imag[_i*2].setValue(pole1.imag)
self.boxes_poles_real[_i*2+1].setValue(pole2.real)
self.boxes_poles_imag[_i*2+1].setValue(pole2.imag)
def __set_paz_from_cornfreqs(self):
for _i, (f, h) in enumerate(
zip(self.boxes_corn_freqs, self.boxes_dampings)):
f = f.value()
h = h.value()
paz = cornFreq2Paz(f, h)
pole1 = paz['poles'][0]
pole2 = paz['poles'][1]
self.boxes_poles_real[_i * 2].setValue(pole1.real)
self.boxes_poles_imag[_i * 2].setValue(pole1.imag)
self.boxes_poles_real[_i * 2 + 1].setValue(pole2.real)
self.boxes_poles_imag[_i * 2 + 1].setValue(pole2.imag)
def convolution_automatic(self,stream_data,parser_data):
#st=stream_data.values().copy()
#for tr in st and key in parser_data:
inst2hz = cornFreq2Paz(float(self.inst2hz.getvalue())) #what the hell is this?
waterLevel=float(self.water_level.getvalue())
parser_keys=parser_data.keys()
for key, st in stream_data.items():
for pr_key in parser_data.keys():
if pr_key.find(key[0:2]):
pr=parser_data.get(pr_key)
for tr in st:
paz=pr.getPAZ(tr.stats)
df = tr.stats.sampling_rate
tr.data = seisSim(tr.data, df, paz_remove=paz, paz_simulate=inst2hz,
water_level=waterLevel)
stream_data[key+'_converted']=tr.copy()
print "Try the manual option"
return stream_data
def computeStepCal(self):
# cal duration needs to be divided by 10000 for step cals only. This
# only applies for when you are reading the cal duration from the
# database.
if (self.dbconn is not None):
# divide by 10000 when getting the cal_duration from the database
duration = self.cal_duration / 10000.0
else:
duration = self.cal_duration
# Determine the type of sensor from the metadata
sensor = self._determineSensorType()
# ignores every location except for Z for triaxial STS-2s
if ((self.dbconn is not None) and ("Z" not in self.outChannel) and
(sensor == "STS-2HG" or sensor == "STS-4B" or sensor == "STS-2")):
print("Skipped " + str(self.outChannel) + ' ' + sensor)
# get the poles values for the sensor type
pz = self._pzvals(sensor)
# read data for the calibration
try:
stOUT = Stream()
stime = UTCDateTime(self.startdate) - 5 * 60
stOUT = read(self.dataOutLoc,
starttime=stime,
endtime=stime + duration + 5 * 60 + 900)
stOUT.merge()
stIN = read(self.dataInLoc,
starttime=stime,
endtime=stime + duration + 5 * 60 + 900)
stIN.merge()
trIN = stIN[0]
trOUT = stOUT[0]
trOUT.filter('lowpass', freq=.1)
trIN.filter('lowpass', freq=.1)
trIN.detrend('constant')
trIN.normalize()
trOUT.detrend('constant')
trOUT.normalize()
temp = trOUT.copy()
temp.trim(endtime=stime + int(duration / 2.))
if temp.max() < 0.0:
trOUT.data = -trOUT.data
except:
if (self.dbconn is not None):
self.stepcal_logger.error('Unable to read data for {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' +
str(self.location) + ', channel = ' +
str(self.outChannel) + '}')
else:
self.stepcal_logger.error('''(Manual Override) Unable read data
for manual input file ''' +
str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
# compute corner (cutoff) frequency
f = 1. / (2 * math.pi / abs(pz['poles'][0]))
# compute damping ratio
h = abs(pz['poles'][0].real) / abs(pz['poles'][0])
sen = 10.0
print('Using: h=' + str(h) + ' f=' + str(f) + ' sen = ' + str(sen))
x = numpy.array([f, h, sen])
try:
# compute best fit
bf = fmin(self._resi,
x,
args=(trIN, trOUT),
xtol=10**-8,
ftol=10**-3,
disp=False)
except:
bf = x
except:
if (self.dbconn is not None):
self.stepcal_logger.error('Unable to calculate {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' +
str(self.location) + ', channel = ' +
str(self.outChannel) + '}')
else:
self.stepcal_logger.error('''(Manual Override) Unable to
perform corner freq, damping ratio,
and best fit calculations for input
file ''' + str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
pazNOM = cornFreq2Paz(f, h)
pazNOM['zeros'] = [0. + 0.j]
pazPERT = cornFreq2Paz(bf[0], bf[1])
pazPERT['zeros'] = [0]
trOUTsimPert = trOUT.copy()
trOUTsimPert.simulate(paz_remove=pazPERT)
trOUTsimPert.trim(trOUTsimPert.stats.starttime + 50,
trOUTsimPert.stats.endtime - 50)
trOUTsimPert.detrend('constant')
trOUTsimPert.normalize()
trOUTsim = trOUT.copy()
trOUTsim.simulate(paz_remove=pazNOM)
trOUTsim.trim(trOUTsim.stats.starttime + 50,
trOUTsim.stats.endtime - 50)
trOUTsim.detrend('constant')
trOUTsim.normalize()
trIN.trim(trIN.stats.starttime + 50, trIN.stats.endtime - 50)
trIN.detrend('constant')
trIN.normalize()
compOUT = sum((trOUTsim.data - trIN.data)**2)
compOUTPERT = sum((trOUTsimPert.data - trIN.data)**2)
except:
if (self.dbconn is not None):
self.stepcal_logger.error('Unable to do calculation for {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' +
str(self.location) + ', channel = ' +
str(self.outChannel) + '}')
else:
self.stepcal_logger.error('''(Manual Override) Unable to
perform poles calculation or input
file ''' + str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
# create a plot for the step calibration and save it to the ./temp
# directory. This directory will be deleted when the program is
# finished running.
plt.clf()
t = numpy.arange(
0, trOUTsim.stats.npts / trOUTsim.stats.sampling_rate,
trOUTsim.stats.delta)
plt.plot(t, trIN.data, 'b', label='input')
plt.plot(t,
trOUTsim.data,
'k',
label='h=' + str(round(h, 6)) + ' f=' + str(round(f, 6)) +
' resi=' + str(round(compOUT, 6)))
plt.plot(t,
trOUTsimPert.data,
'g',
label='h=' + str(round(bf[1], 6)) + ' f=' +
str(round(bf[0], 6)) + ' resi=' +
str(round(compOUTPERT, 6)))
plt.xlabel('Time (s)')
plt.ylabel('Cnts normalized')
plt.title('Step Calibration ' + trOUT.stats.station + ' ' +
str(trOUT.stats.starttime.year) + ' ' +
str(trOUT.stats.starttime.julday).zfill(3))
plt.legend(prop={'size': 6})
plt.savefig('temp/' + str(trOUT.stats.station) +
str(self.outChannel) + str(self.location) +
str(self.startdate.year) + str(self.julianday) +
'step.png',
format="png",
dpi=400)
except:
if (self.dbconn is not None):
self.stepcal_logger.error('Unable to plot {' + 'network = ' +
self.network + ', station = ' +
self.station + ', sensor = ' +
str(sensor) + ', location = ' +
str(self.location) + ', channel = ' +
str(self.outChannel) + '}')
else:
self.stepcal_logger.error(
'(Manual Override) Unable to make plot for input file ' +
str(self.dataInLoc) + ' and output file ' +
str(self.dataOutLoc))
if (self.dbconn is not None):
try:
plt.close()
# insert results into the database
fin = open(
'temp/' + str(trOUT.stats.station) + str(self.outChannel) +
str(self.location) + str(self.startdate.year) +
str(self.julianday) + 'step.png', 'rb')
imgdata = fin.read()
cur = self.dbconn.cursor()
cur.execute(
'''INSERT INTO tbl_300calresults (fk_calibrationid,
nominal_cornerfreq, nominal_dampingratio, nominal_resi,
fitted_cornerfreq, fitted_dampingratio, fitted_resi,
outchannel, stepcal_img)
VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s)''', [
self.cal_id,
round(f, 6),
round(h, 6),
round(compOUT, 6),
round(bf[0], 6),
round(bf[1], 6),
round(compOUTPERT, 6),
str(self.outChannel),
psycopg2.Binary(imgdata)
])
self.dbconn.commit()
except:
self.stepcal_logger.error(
'Unable to insert into database for {' + 'network = ' +
self.network + ', station = ' + self.station +
', sensor = ' + str(sensor) + ', location = ' +
str(self.location) + ', channel = ' +
str(self.outChannel) + '}')
else:
try:
print('nominal corner freq = ' + str(round(f, 6)) +
', nominal damping ratio = ' + str(round(h, 6)) +
', nominal best fit = ' + str(round(compOUT, 6)) +
', fitted corner freq = ' + str(round(bf[0], 6)) +
', fitted damping ratio = ' + str(round(bf[1], 6)) +
', pert best fit ' + str(round(compOUTPERT, 6)))
plt.show()
plt.close()
except:
print(
'(Manual Override) Error displaying calculation results.')
def xcorrEvents(starttime,
endtime,
network_id='*',
station_id='*',
location_id='',
channel_id='EHZ',
phase='P',
time_window=(-1, 6),
method='manual',
merge=True):
"""
@param method: 'manual' or 'auto' or None.
"""
wildcard = "%s.%s.%s.%s" % (network_id, station_id, location_id,
channel_id)
# PAZ of instrument to simulate, 2.0Hz corner-frequency, 0.707 damping
inst = cornFreq2Paz(2.0)
# get all events between start and end time
client = Client("http://teide.geophysik.uni-muenchen.de:8080",
user="******",
password="******")
event_list = client.event.getList(datetime=(starttime, endtime),
localisation_method=method)
print "Fetching events ..."
networks = {}
for event in event_list:
id = event['resource_name']
print " EVENT:", str(event['datetime']), id
# request event resource
res = client.event.getXMLResource(id)
# fetch all picks with given phase
pick_list = res.xpath("/event/pick[phaseHint='%s']" % phase)
# cycle through picks
streams = []
for pick in pick_list:
temp = {}
try:
dt = UTCDateTime(str(pick.time.value))
except:
continue
sid = pick.waveform.attrib['stationCode']
nid = pick.waveform.attrib['networkCode'] or 'BW'
cid = pick.waveform.attrib['channelCode']
lid = pick.waveform.attrib['locationCode']
pid = '%s.%s.%s.%s' % (nid, sid, lid, cid)
print " PICK: %s - %s - %s" % (pid, phase, dt)
if not fnmatch.filter([pid], wildcard):
continue
# generate station/network list
networks.setdefault(nid, {})
networks[nid].setdefault(sid, [])
networks[nid][sid].append((event, dt))
print
print "Correlate events over each station ..."
# cycle through all networks/stations/events
for nid, stations in networks.iteritems():
for sid, events in stations.iteritems():
print " %s.%s:" % (nid, sid)
if len(events) < 2:
print " -> Skipping: Need at least 2 events per station"
print
continue
streams = []
for event in events:
id = event[0]['resource_name']
dt = event[1]
# get station PAZ for this date time
paz = client.station.getPAZ(nid, sid, dt, location_id,
channel_id)
if not paz:
print "!!! Missing PAZ for %s.%s for %s" % (nid, sid, dt)
continue
# get waveforms
try:
stream = client.waveform.getWaveform(
nid, sid, location_id, channel_id, dt + time_window[0],
dt + time_window[1])
except:
msg = "!!! Error fetching waveform for %s.%s.%s.%s for %s"
print msg % (nid, sid, location_id, channel_id, dt)
continue
if merge:
stream.merge()
for trace in stream:
# calculate zero mean
trace.data = trace.data - trace.data.mean()
# instrument correction
#trace.data = seisSim(trace.data, trace.stats.sampling_rate,
# paz, inst_sim=inst, water_level=50.0)
trace.data = bandpassZPHSH(trace.data,
2.0,
20.0,
df=trace.stats.sampling_rate,
corners=4)
print ' Got Trace:', trace
# append
streams.append((id, stream))
# cross correlation over all prepared streams
l = len(streams)
if l < 2:
print " -> Skipping: Need at least 2 events per station"
print
fp.close()
continue
# output file
filename = "%s.%s.txt" % (nid, sid)
fp = open(filename, "w")
# xcorr
for i in range(0, l - 1):
id1 = streams[i][0]
tr1 = streams[i][1][0]
for j in range(i + 1, l):
id2 = streams[j][0]
tr2 = streams[j][1][0]
# check sampling rate for both traces
if tr1.stats.sampling_rate != tr2.stats.sampling_rate:
print
print "!!! Sampling rate are not equal!"
continue
if tr1.stats.npts != tr2.stats.npts:
print
print "!!! Number of samples are not equal!"
continue
# divide by 2.0 as in eventcluster.c line 604
# remove last sample if npts is an odd number
delta = -1 * (tr1.stats.npts % 2)
winlen = int((tr1.stats.npts + delta) / 2.0)
shift, coe = xcorr(tr1.data[:delta].astype('float32'),
tr2.data[:delta].astype('float32'),
winlen)
fp.write("%d %d %.3f %d %s %s\n" %
(i + 1, j + 1, coe, shift, id1, id2))
print
fp.close()
import pickle, urllib
from obspy.core import UTCDateTime
from obspy.signal.array_analysis import sonic
from obspy.signal import cornFreq2Paz
# Load data
stream = pickle.load(urllib.urlopen("http://examples.obspy.org/agfa.dump"))
print stream
print stream[0].stats
#
# Instrument correction to 1Hz corner frequency
paz1hz = cornFreq2Paz(1.0, damp=0.707)
stream.simulate(paz_remove='self', paz_simulate=paz1hz)
# Execute sonic
arguments = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0,
slm_x=3.0,
sll_y=-3.0,
slm_y=3.0,
sl_s=0.03,
# sliding window propertieds
win_len=1.0,
win_frac=0.05,
# frequency properties
frqlow=1.0,
frqhigh=8.0,
prewhiten=0,
# restrict output
# merging
try:
st.merge(0)
except Exception, e:
summary.append("Error while merging:")
summary.append(str(e))
summary = "\n".join(summary)
summary += "\n" + "\n".join(("%s=%s" % (k, v) for k, v in PAR.items()))
open(SUMMARY, "at").write(summary + "\n")
continue
# preprocessing, keep original data for plotting at end
for tr in st:
tr.data = detrend(tr.data)
st.simulate(paz_remove="self",
paz_simulate=cornFreq2Paz(1.0),
remove_sensitivity=False)
st.sort()
st_trigger = st.copy()
st_trigger.filter("bandpass",
freqmin=PAR.LOW,
freqmax=PAR.HIGH,
corners=1,
zerophase=True)
st.trim(T1, T2)
st_trigger.trim(T1, T2)
st_trigger.trigger("recstalta", sta=PAR.STA, lta=PAR.LTA)
summary.append(str(st))
# do the triggering
trigger_list = []
import numpy as np
import matplotlib.pyplot as plt
from obspy.core import read
from obspy.signal import seisSim, cornFreq2Paz
from copy import deepcopy
onehzinst = cornFreq2Paz(1.0, damp=0.707) # 1Hz instrument
trace = read("http://examples.obspy.org/RJOB20090824.ehz")[0]
trace.data = trace.data - trace.data.mean()
sts2 = {
'gain':
60077000.0,
'poles': [(-0.037004000000000002 + 0.037016j),
(-0.037004000000000002 - 0.037016j), (-251.33000000000001 + 0j),
(-131.03999999999999 - 467.29000000000002j),
(-131.03999999999999 + 467.29000000000002j)],
'sensitivity':
2516778400.0,
'zeros': [0j, 0j]
}
data1 = deepcopy(trace.data)
trace.simulate(paz_remove=sts2, paz_simulate=onehzinst)
data2 = trace.data
# The plotting, plain matplotlib
t = np.arange(trace.stats.npts) / trace.stats.sampling_rate
plt.subplot(211)
plt.plot(t, data1, 'k')
plt.ylabel('STS-2 [counts]')
#
plt.subplot(212)
def get_PGMs(tr,args):
ta=Stream()
ta=tr.copy()
ts=tr.copy()
for i in range(len(ta)):
m_dis=0
m_vel=0
m_acc=0
#### Displacement
if abs(max(ta[i])) >= abs(min(ta[i])):
m_dis=abs(max(ta[i]))
else:
m_dis=abs(min(ta[i]))
#### Velocity
ta[i].data = np.gradient(ta[i].data,ta[i].stats['delta'])
if abs(max(ta[i])) >= abs(min(ta[i])):
m_vel=abs(max(ta[i]))
else:
m_vel=abs(min(ta[i]))
ts[i].data = ta[i].data
#### Acceleration
ta[i].data = np.gradient(ta[i].data,ta[i].stats['delta'])
if abs(max(ta[i])) >= abs(min(ta[i])):
m_acc=abs(max(ta[i]))
else:
m_acc=abs(min(ta[i]))
#store obtained pgms
tr[i].stats['max_dis'] = m_dis
tr[i].stats['max_vel'] = m_vel
tr[i].stats['max_acc'] = m_acc
#define vectrors for Hz, T and G
sa=args.sa.split(' ')
spa=[]
for l in range(len(sa)-1):
spa.append(0)
per=[]
for l in range(len(sa)-1):
per.append(0)
ges=[]
for l in range(len(sa)-1):
ges.append(0)
#now for each value of sa convolve with response of pendulum
for j in range(len(sa)):
#apply convolution
if j >= 1:
tu=ta.copy()
T=eval(sa[j])*1.0
D=eval(sa[0])
Ts = '%5.3f' % (1/T)
omega = (2 * 3.14159 * T)**2
paz_sa=cornFreq2Paz(T,damp=D)
paz_sa['sensitivity'] =omega
paz_sa['zeros'] = []
for n in range(len(tu)):
tu[n].simulate(paz_remove=None,paz_simulate=paz_sa,taper=True, simulate_sensitivity=True, taper_fraction=0.050000000000000003)
per[j-1] = Ts
# #now measure for each i
for i in range(len(tu)):
if abs(max(tu[i])) >= abs(min(tu[i])):
val=abs(max(tu[i]))
else:
val=abs(min(tu[i]))
g=val/9.80665*100
g='%10.3e' % (g)
val='%10.3e' % (val)
#here give spectral acceleration in standard units m/s^2
# and not in g (suitable only for shakemap, can be
# later converted
tr[i]=UpdatePsaHeader(tr[i],j,val)
for i in range(len(tr)):
tr[i].stats['Tsa'] = per
return tr
return int(sec * trace.stats.sampling_rate)
def trId(stats):
return stats.endtime, "%s%s%s%f" % (stats.network, stats.station,
stats.channel, stats.sampling_rate)
mseed_files = sys.argv[1:]
if mseed_files == []:
print __doc__
sys.exit(1)
client = Client()
inst = cornFreq2Paz(1.0)
nfft = 4194304 # next nfft of 5h
station_list = []
last_endtime, last_id = 0, "--"
for file in mseed_files:
print "\n", file,
try:
stream = read(file)
except:
continue
stream.merge(-1)
stream.sort()
stats = stream[0].stats
pick_file = "%s_%s_%s.picks" % (
stats.starttime.year, stats.starttime.strftime("%j"), stats.station)
if not stats.station in station_list:
def computeStepCal(self):
# cal duration needs to be divided by 10000 for step cals only. This
# only applies for when you are reading the cal duration from the
# database.
if(self.dbconn is not None):
# divide by 10000 when getting the cal_duration from the database
duration = self.cal_duration / 10000.0
else:
duration = self.cal_duration
# Determine the type of sensor from the metadata
sensor = self._determineSensorType()
# ignores every location except for Z for triaxial STS-2s
if((self.dbconn is not None) and ("Z" not in self.outChannel) and
(sensor == "STS-2HG" or sensor == "STS-4B" or sensor == "STS-2")):
print("Skipped " + str(self.outChannel) + ' ' + sensor)
# get the poles values for the sensor type
pz = self._pzvals(sensor)
# read data for the calibration
try:
stOUT = Stream()
stime = UTCDateTime(self.startdate) - 5 * 60
stOUT = read(
self.dataOutLoc, starttime=stime,
endtime=stime + duration + 5 * 60 + 900
)
stOUT.merge()
stIN = read(
self.dataInLoc, starttime=stime,
endtime=stime + duration + 5 * 60 + 900
)
stIN.merge()
trIN = stIN[0]
trOUT = stOUT[0]
trOUT.filter('lowpass', freq=.1)
trIN.filter('lowpass', freq=.1)
trIN.detrend('constant')
trIN.normalize()
trOUT.detrend('constant')
trOUT.normalize()
temp = trOUT.copy()
temp.trim(endtime=stime + int(duration / 2.))
if temp.max() < 0.0:
trOUT.data = -trOUT.data
except:
if(self.dbconn is not None):
self.stepcal_logger.error('Unable to read data for {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' + str(self.location) +
', channel = ' + str(self.outChannel) +
'}')
else:
self.stepcal_logger.error('''(Manual Override) Unable read data
for manual input file ''' +
str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
# compute corner (cutoff) frequency
f = 1. / (2 * math.pi / abs(pz['poles'][0]))
# compute damping ratio
h = abs(pz['poles'][0].real) / abs(pz['poles'][0])
sen = 10.0
print (
'Using: h=' + str(h) + ' f=' + str(f) + ' sen = ' + str(sen))
x = numpy.array([f, h, sen])
try:
# compute best fit
bf = fmin(self._resi, x, args=(trIN, trOUT),
xtol=10 ** -8, ftol=10 ** -3, disp=False)
except:
bf = x
except:
if(self.dbconn is not None):
self.stepcal_logger.error('Unable to calculate {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' + str(self.location) +
', channel = ' + str(self.outChannel) +
'}')
else:
self.stepcal_logger.error('''(Manual Override) Unable to
perform corner freq, damping ratio,
and best fit calculations for input
file ''' + str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
pazNOM = cornFreq2Paz(f, h)
pazNOM['zeros'] = [0. + 0.j]
pazPERT = cornFreq2Paz(bf[0], bf[1])
pazPERT['zeros'] = [0]
trOUTsimPert = trOUT.copy()
trOUTsimPert.simulate(paz_remove=pazPERT)
trOUTsimPert.trim(
trOUTsimPert.stats.starttime + 50, trOUTsimPert.stats.endtime - 50)
trOUTsimPert.detrend('constant')
trOUTsimPert.normalize()
trOUTsim = trOUT.copy()
trOUTsim.simulate(paz_remove=pazNOM)
trOUTsim.trim(
trOUTsim.stats.starttime + 50, trOUTsim.stats.endtime - 50)
trOUTsim.detrend('constant')
trOUTsim.normalize()
trIN.trim(trIN.stats.starttime + 50, trIN.stats.endtime - 50)
trIN.detrend('constant')
trIN.normalize()
compOUT = sum((trOUTsim.data - trIN.data) ** 2)
compOUTPERT = sum((trOUTsimPert.data - trIN.data) ** 2)
except:
if(self.dbconn is not None):
self.stepcal_logger.error('Unable to do calculation for {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' + str(self.location) +
', channel = ' + str(self.outChannel) +
'}')
else:
self.stepcal_logger.error('''(Manual Override) Unable to
perform poles calculation or input
file ''' + str(self.dataInLoc) +
' and output file ' +
str(self.dataOutLoc))
try:
# create a plot for the step calibration and save it to the ./temp
# directory. This directory will be deleted when the program is
# finished running.
plt.clf()
t = numpy.arange(
0, trOUTsim.stats.npts / trOUTsim.stats.sampling_rate, trOUTsim.stats.delta)
plt.plot(t, trIN.data, 'b', label='input')
plt.plot(t, trOUTsim.data, 'k', label='h=' + str(round(h, 6)) +
' f=' + str(round(f, 6)) + ' resi=' + str(round(compOUT, 6)))
plt.plot(t, trOUTsimPert.data, 'g', label='h=' + str(round(bf[1], 6)) + ' f=' + str(
round(bf[0], 6)) + ' resi=' + str(round(compOUTPERT, 6)))
plt.xlabel('Time (s)')
plt.ylabel('Cnts normalized')
plt.title('Step Calibration ' + trOUT.stats.station + ' ' + str(
trOUT.stats.starttime.year) + ' ' + str(trOUT.stats.starttime.julday).zfill(3))
plt.legend(prop={'size': 6})
plt.savefig('temp/' + str(trOUT.stats.station) + str(self.outChannel) + str(self.location) +
str(self.startdate.year) + str(self.julianday) + 'step.png', format="png", dpi=400)
except:
if(self.dbconn is not None):
self.stepcal_logger.error('Unable to plot {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' + str(self.location) +
', channel = ' + str(self.outChannel) +
'}')
else:
self.stepcal_logger.error('(Manual Override) Unable to make plot for input file ' + str(
self.dataInLoc) + ' and output file ' + str(self.dataOutLoc))
if(self.dbconn is not None):
try:
plt.close()
# insert results into the database
fin = open('temp/' + str(trOUT.stats.station) + str(self.outChannel) + str(
self.location) + str(self.startdate.year) + str(self.julianday) + 'step.png', 'rb')
imgdata = fin.read()
cur = self.dbconn.cursor()
cur.execute('''INSERT INTO tbl_300calresults (fk_calibrationid,
nominal_cornerfreq, nominal_dampingratio, nominal_resi,
fitted_cornerfreq, fitted_dampingratio, fitted_resi,
outchannel, stepcal_img)
VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s)''',
[self.cal_id, round(f, 6), round(h, 6),
round(compOUT, 6), round(bf[0], 6),
round(bf[1], 6), round(compOUTPERT, 6),
str(self.outChannel), psycopg2.Binary(imgdata)])
self.dbconn.commit()
except:
self.stepcal_logger.error('Unable to insert into database for {' +
'network = ' + self.network +
', station = ' + self.station +
', sensor = ' + str(sensor) +
', location = ' + str(self.location) +
', channel = ' + str(self.outChannel) +
'}')
else:
try:
print('nominal corner freq = ' + str(round(f, 6)) +
', nominal damping ratio = ' + str(round(h, 6)) +
', nominal best fit = ' + str(round(compOUT, 6)) +
', fitted corner freq = ' + str(round(bf[0], 6)) +
', fitted damping ratio = ' + str(round(bf[1], 6)) +
', pert best fit ' + str(round(compOUTPERT, 6)))
plt.show()
plt.close()
except:
print(
'(Manual Override) Error displaying calculation results.')
# merging
try:
st.merge(0)
except Exception, e:
summary.append("Error while merging:")
summary.append(str(e))
summary = "\n".join(summary)
summary += "\n" + "\n".join(("%s=%s" % (k, v) for k, v in PAR.items()))
open(SUMMARY, "at").write(summary + "\n")
continue
# preprocessing, keep original data for plotting at end
for tr in st:
tr.data = detrend(tr.data)
st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st_trigger = st.copy()
st_trigger.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger.trim(T1, T2)
st_trigger.trigger("recstalta", sta=PAR.STA, lta=PAR.LTA)
summary.append(str(st))
# do the triggering
trigger_list = []
for tr in st_trigger:
tr.stats.channel = "recstalta"
max_len = PAR.MAXLEN * tr.stats.sampling_rate
trigger_sample_list = triggerOnset(tr.data, PAR.ON, PAR.OFF, max_len=max_len)
for on, off in trigger_sample_list:
def xcorrEvents(starttime, endtime, network_id='*', station_id='*',
location_id='', channel_id='EHZ', phase='P',
time_window=(-1, 6), method='manual', merge=True):
"""
@param method: 'manual' or 'auto' or None.
"""
wildcard = "%s.%s.%s.%s" % (network_id, station_id,
location_id, channel_id)
# PAZ of instrument to simulate, 2.0Hz corner-frequency, 0.707 damping
inst = cornFreq2Paz(2.0)
# get all events between start and end time
client = Client("http://teide.geophysik.uni-muenchen.de:8080",
user="******", password="******")
event_list = client.event.getList(datetime=(starttime, endtime),
localisation_method=method)
print "Fetching events ..."
networks = {}
for event in event_list:
id = event['resource_name']
print " EVENT:", str(event['datetime']), id
# request event resource
res = client.event.getXMLResource(id)
# fetch all picks with given phase
pick_list = res.xpath("/event/pick[phaseHint='%s']" % phase)
# cycle through picks
streams = []
for pick in pick_list:
temp = {}
try:
dt = UTCDateTime(str(pick.time.value))
except:
continue
sid = pick.waveform.attrib['stationCode']
nid = pick.waveform.attrib['networkCode'] or 'BW'
cid = pick.waveform.attrib['channelCode']
lid = pick.waveform.attrib['locationCode']
pid = '%s.%s.%s.%s' % (nid, sid, lid, cid)
print " PICK: %s - %s - %s" % (pid, phase, dt)
if not fnmatch.filter([pid], wildcard):
continue
# generate station/network list
networks.setdefault(nid, {})
networks[nid].setdefault(sid, [])
networks[nid][sid].append((event, dt))
print
print "Correlate events over each station ..."
# cycle through all networks/stations/events
for nid, stations in networks.iteritems():
for sid, events in stations.iteritems():
print " %s.%s:" % (nid, sid)
if len(events) < 2:
print " -> Skipping: Need at least 2 events per station"
print
continue
streams = []
for event in events:
id = event[0]['resource_name']
dt = event[1]
# get station PAZ for this date time
paz = client.station.getPAZ(nid, sid, dt, location_id,
channel_id)
if not paz:
print "!!! Missing PAZ for %s.%s for %s" % (nid, sid, dt)
continue
# get waveforms
try:
stream = client.waveform.getWaveform(nid, sid, location_id,
channel_id,
dt + time_window[0],
dt + time_window[1])
except:
msg = "!!! Error fetching waveform for %s.%s.%s.%s for %s"
print msg % (nid, sid, location_id, channel_id, dt)
continue
if merge:
stream.merge()
for trace in stream:
# calculate zero mean
trace.data = trace.data - trace.data.mean()
# instrument correction
#trace.data = seisSim(trace.data, trace.stats.sampling_rate,
# paz, inst_sim=inst, water_level=50.0)
trace.data = bandpassZPHSH(trace.data,2.0,20.0,
df=trace.stats.sampling_rate,
corners=4)
print ' Got Trace:', trace
# append
streams.append((id, stream))
# cross correlation over all prepared streams
l = len(streams)
if l < 2:
print " -> Skipping: Need at least 2 events per station"
print
fp.close()
continue
# output file
filename = "%s.%s.txt" % (nid, sid)
fp = open(filename, "w")
# xcorr
for i in range(0, l - 1):
id1 = streams[i][0]
tr1 = streams[i][1][0]
for j in range(i + 1, l):
id2 = streams[j][0]
tr2 = streams[j][1][0]
# check sampling rate for both traces
if tr1.stats.sampling_rate != tr2.stats.sampling_rate:
print
print "!!! Sampling rate are not equal!"
continue
if tr1.stats.npts != tr2.stats.npts:
print
print "!!! Number of samples are not equal!"
continue
# divide by 2.0 as in eventcluster.c line 604
# remove last sample if npts is an odd number
delta = -1 * (tr1.stats.npts % 2)
winlen = int((tr1.stats.npts + delta) / 2.0)
shift, coe = xcorr(tr1.data[:delta].astype('float32'),
tr2.data[:delta].astype('float32'),
winlen)
fp.write("%d %d %.3f %d %s %s\n" % (i + 1, j + 1, coe,
shift, id1, id2))
print
fp.close()
import numpy as np
import matplotlib.pyplot as plt
from obspy.core import read
from obspy.signal import seisSim, cornFreq2Paz
onehzinst = cornFreq2Paz(1.0, damp=0.707) # 1Hz instrument
tr = read("http://examples.obspy.org/RJOB20090824.ehz")[0]
tr.data = tr.data - tr.data.mean()
sts2 = {'gain': 60077000.0,
'poles': [(-0.037004000000000002+0.037016j),
(-0.037004000000000002-0.037016j),
(-251.33000000000001+0j),
(-131.03999999999999-467.29000000000002j),
(-131.03999999999999+467.29000000000002j)],
'sensitivity': 2516778400.0,
'zeros': [0j, 0j]}
data2 = seisSim(tr.data,
tr.stats.sampling_rate, sts2,
inst_sim=onehzinst,water_level=600.0)
data2 = data2 / sts2["sensitivity"]
# The plotting, plain matplotlib
t = np.arange(tr.stats.npts) / tr.stats.sampling_rate
plt.subplot(211)
plt.plot(t, tr.data, 'k')
plt.ylabel('STS-2 [counts]')
#
plt.subplot(212)
plt.plot(t, data2, 'k')
plt.ylabel('1Hz Instrument [m/s]')
plt.xlabel('Time [s]')
from obspy.core import read
from obspy.signal import cornFreq2Paz
import numpy as np
import matplotlib.pyplot as plt
paz_sts2 = {
'poles': [-0.037004 + 0.037016j, -0.037004 - 0.037016j, -251.33 + 0j,
- 131.04 - 467.29j, -131.04 + 467.29j],
'zeros': [0j, 0j],
'gain': 60077000.0,
'sensitivity': 2516778400.0}
paz_1hz = cornFreq2Paz(1.0, damp=0.707) # 1Hz instrument
paz_1hz['sensitivity'] = 1.0
st = read()
# make a copy to keep our original data
st_orig = st.copy()
# Simulate instrument given poles, zeros and gain of
# the original and desired instrument
st.simulate(paz_remove=paz_sts2, paz_simulate=paz_1hz)
tr = st[0]
tr_orig = st_orig[0]
t = np.arange(tr.stats.npts) / tr.stats.sampling_rate
plt.subplot(211)
plt.plot(t, tr_orig.data, 'k')
plt.ylabel('STS-2 [counts]')
import pickle, urllib
from obspy.core import UTCDateTime
from obspy.signal.array_analysis import sonic
from obspy.signal import cornFreq2Paz
# Load data
stream = pickle.load(urllib.urlopen("http://examples.obspy.org/agfa.dump"))
print stream
print stream[0].stats
#
# Instrument correction to 1Hz corner frequency
paz1hz = cornFreq2Paz(1.0, damp=0.707)
stream.simulate(paz_remove='self', paz_simulate=paz1hz)
# Execute sonic
arguments = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0, slm_x=3.0, sll_y=-3.0, slm_y=3.0, sl_s=0.03,
# sliding window propertieds
win_len=1.0, win_frac=0.05,
# frequency properties
frqlow=1.0, frqhigh=8.0, prewhiten=0,
# restrict output
semb_thres=-1e9, vel_thres=-1e9, verbose=True, timestamp='mlabhour',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545")
)
# Perform beamforming with previously set arguments.
out = sonic(stream, **arguments)
from obspy.signal import cornFreq2Paz
import numpy as np
import matplotlib.pyplot as plt
paz_sts2 = {
'poles': [
-0.037004 + 0.037016j, -0.037004 - 0.037016j, -251.33 + 0j,
-131.04 - 467.29j, -131.04 + 467.29j
],
'zeros': [0j, 0j],
'gain':
60077000.0,
'sensitivity':
2516778400.0
}
paz_1hz = cornFreq2Paz(1.0, damp=0.707) # 1Hz instrument
paz_1hz['sensitivity'] = 1.0
st = read()
# make a copy to keep our original data
st_orig = st.copy()
# Simulate instrument given poles, zeros and gain of
# the original and desired instrument
st.simulate(paz_remove=paz_sts2, paz_simulate=paz_1hz)
tr = st[0]
tr_orig = st_orig[0]
t = np.arange(tr.stats.npts) / tr.stats.sampling_rate
import numpy as np
import matplotlib.pyplot as plt
from obspy.core import UTCDateTime
from obspy.arclink import Client
from obspy.signal import cornFreq2Paz, seisSim
# Retrieve data via ArcLink
# please provide a valid email address for the keyword user
client = Client(user="******")
t = UTCDateTime("2009-08-24 00:20:03")
st = client.getWaveform('BW', 'RJOB', '', 'EHZ', t, t + 30)
paz = client.getPAZ('BW', 'RJOB', '', 'EHZ', t)
paz = paz.values()[0]
# 1Hz instrument
one_hertz = cornFreq2Paz(1.0)
# Correct for frequency response of the instrument
res = seisSim(st[0].data.astype('float32'),
st[0].stats.sampling_rate,
paz,
inst_sim=one_hertz)
# Correct for overall sensitivity
res = res / paz['sensitivity']
# Plot the seismograms
sec = np.arange(len(res)) / st[0].stats.sampling_rate
plt.subplot(211)
plt.plot(sec, st[0].data, 'k')
plt.title("%s %s" % (st[0].stats.station, t))
plt.ylabel('STS-2')
plt.subplot(212)
def s2p(sec, trace):
"""Convert seconds to samples with the sampling rate of trace object"""
return int(sec * trace.stats.sampling_rate)
def trId(stats):
return stats.endtime, "%s%s%s%f" % (stats.network, stats.station,
stats.channel, stats.sampling_rate)
mseed_files = sys.argv[1:]
if mseed_files == []:
print __doc__
sys.exit(1)
client = Client()
inst = cornFreq2Paz(1.0)
nfft = 4194304 # next nfft of 5h
station_list = []
last_endtime, last_id = 0, "--"
for file in mseed_files:
print "\n", file,
try:
stream = read(file)
except:
continue
stream.merge(-1)
stream.sort()
stats = stream[0].stats
pick_file = "%s_%s_%s.picks" % (stats.starttime.year,
stats.starttime.strftime("%j"),
stats.station)
