def test_evalresp_file_like_object(self):
"""
Test evalresp with file like object
"""
rawf = os.path.join(self.path, "CRLZ.HHZ.10.NZ.SAC")
respf = os.path.join(self.path, "RESP.NZ.CRLZ.10.HHZ")
tr1 = read(rawf)[0]
tr2 = read(rawf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {
"filename": respf,
"date": date,
"units": "VEL",
"network": "NZ",
"station": "CRLZ",
"location": "10",
"channel": "HHZ",
}
tr1.data = simulate_seismometer(tr1.data, tr1.stats.sampling_rate, seedresp=seedresp)
with open(respf, "rb") as fh:
stringio = io.BytesIO(fh.read())
seedresp["filename"] = stringio
tr2.data = simulate_seismometer(tr2.data, tr2.stats.sampling_rate, seedresp=seedresp)
self.assertEqual(tr1, tr2)
def test_evalresp_file_like_object(self):
"""
Test evalresp with file like object
"""
rawf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC')
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
tr1 = read(rawf)[0]
tr2 = read(rawf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {
'filename': respf,
'date': date,
'units': 'VEL',
'network': 'NZ',
'station': 'CRLZ',
'location': '10',
'channel': 'HHZ'
}
tr1.data = simulate_seismometer(tr1.data,
tr1.stats.sampling_rate,
seedresp=seedresp)
with open(respf, 'rb') as fh:
stringio = io.BytesIO(fh.read())
seedresp['filename'] = stringio
tr2.data = simulate_seismometer(tr2.data,
tr2.stats.sampling_rate,
seedresp=seedresp)
self.assertEqual(tr1, tr2)
def remove_response_paz(st, paz_path, pre_filt):
"""
remove response using paz file
"""
for trace in st:
# get key
network = trace.stats.network
station = trace.stats.station
channel = trace.stats.channel
key = f"{network}.{station}.{channel}"
paz_path_used = join(paz_path, key)
# try to read paz
try:
attach_paz(trace, paz_path_used)
except: # pylint: disable=bare-except
return None
# ndarray
data = simulate_seismometer(trace.data,
trace.stats.sampling_rate,
paz_remove=trace.stats.paz,
water_level=6e9,
zero_mean=False,
taper=False,
pre_filt=pre_filt,
sacsim=True)
trace.data = data
return st
def get_peak_spectrals(data, samp_rate,periods):
"""Calculate peak pseudo-spectral parameters.
Compute 5% damped PSA at input periods seconds.
Data must be an acceleration Trace.
:param data:
Obspy Trace object, containing acceleration data to convolve with pendulum at freq.
:param delta:
sample rate (samples per sec).
:returns:
Dictionary containing keys of input periods, and values of corresponding spectral accelerations.
"""
D = 0.05 # 5% damping
psadict = {}
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq) ** 2
paz_sa = corn_freq_2_paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = simulate_seismometer(data.data, samp_rate, paz_remove=None, paz_simulate=paz_sa,
taper=True, simulate_sensitivity=True, taper_fraction=0.05)
if abs(max(dd)) >= abs(min(dd)):
psa = abs(max(dd))
else:
psa = abs(min(dd))
psadict[T] = psa
return psadict
def test_seis_sim_vs_pitsa1(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - LE3D seismometer.
"""
# load test file
filename = os.path.join(self.path, 'rjob_20051006.gz')
with gzip.open(filename) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_le3d = {'poles': [-4.21 + 4.66j,
-4.21 - 4.66j,
-2.105 + 0.0j],
'zeros': [0.0 + 0.0j] * 3,
'sensitivity': 1.0,
'gain': 0.4}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(
data, samp_rate, paz_remove=paz_le3d, paz_simulate=paz,
water_level=600.0, zero_mean=False, nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rjob_20051006_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) /
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1.1e-05)
def test_simulate(self):
"""
Tests if calling simulate of trace gives the same result as using
simulate_seismometer manually.
"""
tr = read()[0]
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_le3d1s = {'poles': [-4.440 + 4.440j, -4.440 - 4.440j,
- 1.083 + 0.0j],
'zeros': [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j],
'gain': 0.4,
'sensitivity': 1.0}
data = simulate_seismometer(
tr.data, tr.stats.sampling_rate, paz_remove=paz_sts2,
paz_simulate=paz_le3d1s, remove_sensitivity=True,
simulate_sensitivity=True)
tr.simulate(paz_remove=paz_sts2, paz_simulate=paz_le3d1s)
# There is some strange issue on Win32bit (see #2188). Thus we just
# use assert_allclose() here instead of testing for full equality.
np.testing.assert_allclose(tr.data, data)
def calculate_damped_spectral_acc(data, delta, freq, damp, ty):
samp_rate = 1.0 / delta
t = freq * 1.0
d = damp
omega = (2 * math.pi * t)**2
paz_sa = corn_freq_2_paz(t, damp=d)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
if ty == 'displacement':
data = np.gradient(data, delta)
data = np.gradient(data, delta)
elif ty == 'velocity':
data = np.gradient(data, delta)
data = simulate_seismometer(data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
return data
def test_simulate(self):
"""
Tests if calling simulate of trace gives the same result as using
simulate_seismometer manually.
"""
tr = read()[0]
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_le3d1s = {
'poles': [-4.440 + 4.440j, -4.440 - 4.440j, -1.083 + 0.0j],
'zeros': [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j],
'gain': 0.4,
'sensitivity': 1.0
}
data = simulate_seismometer(tr.data,
tr.stats.sampling_rate,
paz_remove=paz_sts2,
paz_simulate=paz_le3d1s,
remove_sensitivity=True,
simulate_sensitivity=True)
tr.simulate(paz_remove=paz_sts2, paz_simulate=paz_le3d1s)
np.testing.assert_array_equal(tr.data, data)
def test_seis_sim_vs_pitsa_2(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - STS-2 seismometer.
"""
# load test file
file = os.path.join(self.path, 'rotz_20081028.gz')
with gzip.open(file) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_sts2 = {'poles': [-0.03736 - 0.03617j,
-0.03736 + 0.03617j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1.5}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(
data, samp_rate, paz_remove=paz_sts2, paz_simulate=paz,
water_level=600.0, zero_mean=False, nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rotz_20081028_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) /
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1e-04)
def test_simulate(self):
"""
Tests if calling simulate of trace gives the same result as using
simulate_seismometer manually.
"""
tr = read()[0]
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_le3d1s = {
'poles': [-4.440 + 4.440j, -4.440 - 4.440j, -1.083 + 0.0j],
'zeros': [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j],
'gain': 0.4,
'sensitivity': 1.0
}
data = simulate_seismometer(tr.data,
tr.stats.sampling_rate,
paz_remove=paz_sts2,
paz_simulate=paz_le3d1s,
remove_sensitivity=True,
simulate_sensitivity=True)
tr.simulate(paz_remove=paz_sts2, paz_simulate=paz_le3d1s)
# There is some strange issue on Win32bit (see #2188). Thus we just
# use assert_allclose() here instead of testing for full equality.
np.testing.assert_allclose(tr.data, data)
def test_sac_instrument_correction(self):
# SAC recommends to taper the transfer function if a pure
# deconvolution is done instead of simulating a different
# instrument. This test checks the difference between the
# result from removing the instrument response using SAC or
# ObsPy. Visual inspection shows that the traces are pretty
# much identical but differences remain (rms ~ 0.042). Haven't
# found the cause for those, yet. One possible reason is the
# floating point arithmetic of SAC vs. the double precision
# arithmetic of Python. However differences still seem to be
# too big for that.
pzf = os.path.join(self.path, 'SAC_PZs_KARC_BHZ')
sacf = os.path.join(self.path, 'KARC.LHZ.SAC.asc.gz')
testsacf = os.path.join(self.path, 'KARC_corrected.sac.asc.gz')
plow = 160.
phigh = 4.
fl1 = 1.0 / (plow + 0.0625 * plow)
fl2 = 1.0 / plow
fl3 = 1.0 / phigh
fl4 = 1.0 / (phigh - 0.25 * phigh)
# Uncomment the following to run the sac-commands
# that created the testing file
# if 1:
# import subprocess as sp
# p = sp.Popen('sac',shell=True,stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s"%sacf, file=cd1)
# print("rmean", file=cd1)
# print("rtrend", file=cd1)
# print("taper type cosine width 0.03", file=cd1)
# print("transfer from polezero subtype %s to none \
# freqlimits %f %f %f %f" % (pzf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over ./data/KARC_corrected.sac", file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
stats = {'network': 'KA', 'delta': 0.99999988079072466,
'station': 'KARC', 'location': 'S1',
'starttime': UTCDateTime(2001, 2, 13, 0, 0, 0, 993700),
'calib': 1.00868e+09, 'channel': 'BHZ'}
with gzip.open(sacf) as f:
tr = Trace(np.loadtxt(f), stats)
attach_paz(tr, pzf, tovel=False)
tr.data = simulate_seismometer(
tr.data, tr.stats.sampling_rate, paz_remove=tr.stats.paz,
remove_sensitivity=False, pre_filt=(fl1, fl2, fl3, fl4))
with gzip.open(testsacf) as f:
data = np.loadtxt(f)
# import matplotlib.pyplot as plt
# plt.plot(tr.data)
# plt.plot(data)
# plt.show()
rms = np.sqrt(np.sum((tr.data - data) ** 2) /
np.sum(tr.data ** 2))
self.assertTrue(rms < 0.0421)
def test_evalrespVsObsPy(self):
"""
Compare results from removing instrument response using
evalresp in SAC and ObsPy. Visual inspection shows that the traces are
pretty much identical but differences remain (rms ~ 0.042). Haven't
found the cause for those, yet.
"""
evalrespf = os.path.join(self.path, "CRLZ.HHZ.10.NZ.SAC_resp")
rawf = os.path.join(self.path, "CRLZ.HHZ.10.NZ.SAC")
respf = os.path.join(self.path, "RESP.NZ.CRLZ.10.HHZ")
fl1 = 0.00588
fl2 = 0.00625
fl3 = 30.0
fl4 = 35.0
# #Set the following if-clause to True to run
# #the sac-commands that created the testing file
# if False:
# import subprocess as sp
# p = sp.Popen('sac', stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s" % rawf, file=cd1)
# print("rmean", file=cd1)
# print("taper type cosine width 0.05", file=cd1)
# print("transfer from evalresp fname %s to vel freqlimits\
# %f %f %f %f" % (respf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over %s" % evalrespf, file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
tr = read(rawf)[0]
trtest = read(evalrespf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {
"filename": respf,
"date": date,
"units": "VEL",
"network": "NZ",
"station": "CRLZ",
"location": "10",
"channel": "HHZ",
}
tr.data = simulate_seismometer(
tr.data,
tr.stats.sampling_rate,
paz_remove=None,
pre_filt=(fl1, fl2, fl3, fl4),
seedresp=seedresp,
taper_fraction=0.1,
pitsasim=False,
sacsim=True,
)
tr.data *= 1e9
rms = np.sqrt(np.sum((tr.data - trtest.data) ** 2) / np.sum(trtest.data ** 2))
self.assertTrue(rms < 0.0094)
def test_evalresp_vs_obspy(self):
"""
Compare results from removing instrument response using
evalresp in SAC and ObsPy. Visual inspection shows that the traces are
pretty much identical but differences remain (rms ~ 0.042). Haven't
found the cause for those, yet.
"""
evalrespf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC_resp')
rawf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC')
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
fl1 = 0.00588
fl2 = 0.00625
fl3 = 30.
fl4 = 35.
# #Set the following if-clause to True to run
# #the sac-commands that created the testing file
# if False:
# import subprocess as sp
# p = sp.Popen('sac', stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s" % rawf, file=cd1)
# print("rmean", file=cd1)
# print("taper type cosine width 0.05", file=cd1)
# print("transfer from evalresp fname %s to vel freqlimits\
# %f %f %f %f" % (respf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over %s" % evalrespf, file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
tr = read(rawf)[0]
trtest = read(evalrespf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {
'filename': respf,
'date': date,
'units': 'VEL',
'network': 'NZ',
'station': 'CRLZ',
'location': '10',
'channel': 'HHZ'
}
tr.data = simulate_seismometer(tr.data,
tr.stats.sampling_rate,
paz_remove=None,
pre_filt=(fl1, fl2, fl3, fl4),
seedresp=seedresp,
taper_fraction=0.1,
pitsasim=False,
sacsim=True)
tr.data *= 1e9
rms = np.sqrt(
np.sum((tr.data - trtest.data)**2) / np.sum(trtest.data**2))
self.assertTrue(rms < 0.0094)
def remove_response(st, pre_filt=None, inv=None):
for index, item in enumerate(st):
pz_vir_file = io.StringIO()
inv_tr = inv.select(channel=item.stats.channel)
inv_tr.write(pz_vir_file, format="SACPZ")
pz_vir_file.seek(0)
obspy.io.sac.sacpz.attach_paz(item, pz_vir_file)
data = simulate_seismometer(
item.data, item.stats.sampling_rate, paz_remove=item.stats.paz, water_level=6e9, zero_mean=False, taper=False, pre_filt=pre_filt, sacsim=True)
st[index].data = data
return st
def removeInstrument(st,args):
if(args.sim == 'PZs'):
# prefilters
f = args.flim.split()
f0 = eval(f[0])
f1 = eval(f[1])
f2 = eval(f[2])
f3 = eval(f[3])
toPurge= [] # station to purge if no Paz found
for i in range(len(st)):
# attach poles and zeros instrument
if(args.dva=='1'):
try:
attach_paz(st[i], st[i].stats.PZs_file,todisp=False)
except:
print "No appropriate PZs file found for station " + st[i].stats.station,st[i].stats.channel,st[i].stats.network
toPurge.append(st[i].stats.station)
else:
try:
attach_paz(st[i], st[i].stats.PZs_file,tovel=True)
except:
print "No appropriate PZs file found for station " + st[i].stats.station,st[i].stats.channel,st[i].stats.network
toPurge.append(st[i].stats.station)
# remove stations if len(toPurge>0)
if len(toPurge) > 0:
st = purgeListStation(st,toPurge,'r')
print "Check if station/channel/network/location of the PZs files and the same string within loaded binary files "
print "do correspond. It may occour for instance that the headers strings of the waveform files (e.g. sac, fseed) "
print "do not agrees with the same strings of the PZs name files. For instance the name of the network. "
print "If these strings do not correspond, modify the name of the PZs files or the header values of the waveforms"
print "You may also choose to remove this station using the option --purge (see help for details)"
# now do remove
for i in range(len(st)):
# remove instrument to displacement
# st[i].data=detrend(st[i].data)
st[i].data = simulate_seismometer(st[i].data,st[i].stats.sampling_rate,paz_remove=st[i].stats.paz, \
taper=True, taper_fraction=0.050, pre_filt=(f0,f1,f2,f3)) #,water_level=60.0)
# from meters to centimeters
st[i].data = st[i].data * 100
return st
def test_evalresp_file_like_object(self):
"""
Test evalresp with file like object
"""
rawf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC')
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
tr1 = read(rawf)[0]
tr2 = read(rawf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {'filename': respf, 'date': date, 'units': 'VEL',
'network': 'NZ', 'station': 'CRLZ', 'location': '10',
'channel': 'HHZ'}
tr1.data = simulate_seismometer(
tr1.data, tr1.stats.sampling_rate, seedresp=seedresp)
with open(respf, 'rb') as fh:
stringio = io.BytesIO(fh.read())
seedresp['filename'] = stringio
tr2.data = simulate_seismometer(tr2.data, tr2.stats.sampling_rate,
seedresp=seedresp)
self.assertEqual(tr1, tr2)
def get_peak_spectrals(data, samp_rate, periods):
"""Calculate peak pseudo-spectral parameters.
Compute 5% damped PSA at input periods seconds.
Data must be an acceleration Trace.
:param data:
Obspy Trace object, containing acceleration data to convolve with pendulum at freq.
:param delta:
sample rate (samples per sec).
:returns:
Dictionary containing keys of input periods, and values of corresponding spectral accelerations.
"""
D = 0.05 # 5% damping
psadict = {}
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq)**2
paz_sa = corn_freq_2_paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = simulate_seismometer(data.data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
if abs(max(dd)) >= abs(min(dd)):
psa = abs(max(dd))
else:
psa = abs(min(dd))
psadict[T] = psa
return psadict
def test_simulate(self):
"""
Tests if calling simulate of trace gives the same result as using
simulate_seismometer manually.
"""
tr = read()[0]
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_le3d1s = {'poles': [-4.440 + 4.440j, -4.440 - 4.440j,
- 1.083 + 0.0j],
'zeros': [0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j],
'gain': 0.4,
'sensitivity': 1.0}
data = simulate_seismometer(
tr.data, tr.stats.sampling_rate, paz_remove=paz_sts2,
paz_simulate=paz_le3d1s, remove_sensitivity=True,
simulate_sensitivity=True)
tr.simulate(paz_remove=paz_sts2, paz_simulate=paz_le3d1s)
np.testing.assert_array_equal(tr.data, data)
def test_seis_sim_vs_pitsa_2(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - STS-2 seismometer.
"""
# load test file
file = os.path.join(self.path, 'rotz_20081028.gz')
with gzip.open(file) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_sts2 = {
'poles': [-0.03736 - 0.03617j, -0.03736 + 0.03617j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1.5
}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(data,
samp_rate,
paz_remove=paz_sts2,
paz_simulate=paz,
water_level=600.0,
zero_mean=False,
nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rotz_20081028_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(
np.sum((datcorr - data_pitsa)**2) / np.sum(data_pitsa**2))
self.assertTrue(rms < 1e-04)
def test_seis_sim_vs_pitsa1(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - LE3D seismometer.
"""
# load test file
filename = os.path.join(self.path, 'rjob_20051006.gz')
with gzip.open(filename) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_le3d = {
'poles': [-4.21 + 4.66j, -4.21 - 4.66j, -2.105 + 0.0j],
'zeros': [0.0 + 0.0j] * 3,
'sensitivity': 1.0,
'gain': 0.4
}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(data,
samp_rate,
paz_remove=paz_le3d,
paz_simulate=paz,
water_level=600.0,
zero_mean=False,
nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rjob_20051006_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(
np.sum((datcorr - data_pitsa)**2) / np.sum(data_pitsa**2))
self.assertTrue(rms < 1.1e-05)
from cut_traces3 import cut_traces
from resp import respN
from obspy.signal.invsim import seisSim,simulate_seismometer
units = 'DIS'
station = 'GUA'
pre_filt = (0.2, 0.3, 20.0, 25.0)
dic,head=cut_traces('01-2250-03L.S201403',0)
tr=dic[station][0]
tr2 = tr.copy()
tr1 = tr.copy()
seedrespN=respN(station,units)
tr1.simulate(paz_remove=None, pre_filt=pre_filt, seedresp=seedrespN)
seedrespN['date']=tr2.stats.starttime
seedrespN['network']=tr2.stats.network
seedrespN['station']=tr2.stats.station
seedrespN['location']=tr2.stats.location
seedrespN['network']=tr2.stats.network
seedrespN['channel']=tr2.stats.channel
tr2.data=simulate_seismometer(tr2.data, 100, remove_sensitivity=False, simulate_sensitivity=False,
water_level=0, seedresp=seedrespN, pre_filt=None )
tr.plot()
#tr1.plot()
tr2.plot()
def simulate(data, params, remove_sensitivity=True):
"""Remove instrumental response"""
data -= np.mean(data)
data = simulate_seismometer(data, params['df'], paz_remove=params['paz'], \
pre_filt=params['pre_filt'], remove_sensitivity=remove_sensitivity)
return data
def test_sac_instrument_correction(self):
# SAC recommends to taper the transfer function if a pure
# deconvolution is done instead of simulating a different
# instrument. This test checks the difference between the
# result from removing the instrument response using SAC or
# ObsPy. Visual inspection shows that the traces are pretty
# much identical but differences remain (rms ~ 0.042). Haven't
# found the cause for those, yet. One possible reason is the
# floating point arithmetic of SAC vs. the double precision
# arithmetic of Python. However differences still seem to be
# too big for that.
pzf = os.path.join(self.path, 'SAC_PZs_KARC_BHZ')
sacf = os.path.join(self.path, 'KARC.LHZ.SAC.asc.gz')
testsacf = os.path.join(self.path, 'KARC_corrected.sac.asc.gz')
plow = 160.
phigh = 4.
fl1 = 1.0 / (plow + 0.0625 * plow)
fl2 = 1.0 / plow
fl3 = 1.0 / phigh
fl4 = 1.0 / (phigh - 0.25 * phigh)
# Uncomment the following to run the sac-commands
# that created the testing file
# if 1:
# import subprocess as sp
# p = sp.Popen('sac',shell=True,stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s"%sacf, file=cd1)
# print("rmean", file=cd1)
# print("rtrend", file=cd1)
# print("taper type cosine width 0.03", file=cd1)
# print("transfer from polezero subtype %s to none \
# freqlimits %f %f %f %f" % (pzf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over ./data/KARC_corrected.sac", file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
stats = {
'network': 'KA',
'delta': 0.99999988079072466,
'station': 'KARC',
'location': 'S1',
'starttime': UTCDateTime(2001, 2, 13, 0, 0, 0, 993700),
'calib': 1.00868e+09,
'channel': 'BHZ'
}
with gzip.open(sacf) as f:
tr = Trace(np.loadtxt(f), stats)
attach_paz(tr, pzf, tovel=False)
tr.data = simulate_seismometer(tr.data,
tr.stats.sampling_rate,
paz_remove=tr.stats.paz,
remove_sensitivity=False,
pre_filt=(fl1, fl2, fl3, fl4))
with gzip.open(testsacf) as f:
data = np.loadtxt(f)
# import matplotlib.pyplot as plt
# plt.plot(tr.data)
# plt.plot(data)
# plt.show()
rms = np.sqrt(np.sum((tr.data - data)**2) / np.sum(tr.data**2))
self.assertTrue(rms < 0.0421)
def peak_ground_motion(data, delta, freq, damp=0.1):
"""
Peak ground motion parameters
Compute the maximal displacement, velocity, acceleration and the peak
ground acceleration at a certain frequency (standard frequencies for
ShakeMaps are 0.3/1.0/3.0 Hz).
Data must be displacement
:type data: :class:`~numpy.ndarray`
:param data: Data in displacement to convolve with pendulum at freq.
:type delta: float
:param delta: Sampling interval
:type freq: float
:param freq: Frequency in Hz.
:type damp: float
:param damp: damping factor. Default is set to 0.1
:rtype: (float, float, float, float)
:return: Peak Ground Acceleration, maximal displacement, velocity,
acceleration
"""
data = data.copy()
# Displacement
if abs(max(data)) >= abs(min(data)):
m_dis = abs(max(data))
else:
m_dis = abs(min(data))
# Velocity
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_vel = abs(max(data))
else:
m_vel = abs(min(data))
# Acceleration
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_acc = abs(max(data))
else:
m_acc = abs(min(data))
samp_rate = 1.0 / delta
T = freq * 1.0
D = damp
omega = (2 * 3.14159 * T) ** 2
paz_sa = corn_freq_2_paz(T, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
data = simulate_seismometer(data, samp_rate, paz_remove=None,
paz_simulate=paz_sa, taper=True,
simulate_sensitivity=True, taper_fraction=0.05)
if abs(max(data)) >= abs(min(data)):
pga = abs(max(data))
else:
pga = abs(min(data))
return (pga, m_dis, m_vel, m_acc)
import obspy
from obspy.clients.arclink import Client
from obspy.signal.invsim import corn_freq_2_paz, simulate_seismometer
# Retrieve data via ArcLink
# please provide a valid email address for the keyword user
client = Client(user="******")
t = obspy.UTCDateTime("2009-08-24 00:20:03")
st = client.get_waveforms('BW', 'RJOB', '', 'EHZ', t, t + 30)
paz = client.get_paz('BW', 'RJOB', '', 'EHZ', t)
# 1Hz instrument
one_hertz = corn_freq_2_paz(1.0)
# Correct for frequency response of the instrument
res = simulate_seismometer(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)
plt.plot(sec, res, 'k')
plt.xlabel('Time [s]')
plt.ylabel('1Hz CornerFrequency')
plt.show()
import obspy
from obspy.clients.arclink import Client
from obspy.signal.invsim import corn_freq_2_paz, simulate_seismometer
# Retrieve data via ArcLink
# please provide a valid email address for the keyword user
client = Client(user="******")
t = obspy.UTCDateTime("2009-08-24 00:20:03")
st = client.get_waveforms('BW', 'RJOB', '', 'EHZ', t, t + 30)
paz = client.get_paz('BW', 'RJOB', '', 'EHZ', t)
# 1Hz instrument
one_hertz = corn_freq_2_paz(1.0)
# Correct for frequency response of the instrument
res = simulate_seismometer(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)
plt.plot(sec, res, 'k')
plt.xlabel('Time [s]')
plt.ylabel('1Hz CornerFrequency')
plt.show()
def peak_ground_motion(data, delta, freq, damp=0.1):
"""
Peak ground motion parameters
Compute the maximal displacement, velocity, acceleration and the peak
ground acceleration at a certain frequency (standard frequencies for
ShakeMaps are 0.3/1.0/3.0 Hz).
Data must be displacement
:type data: :class:`~numpy.ndarray`
:param data: Data in displacement to convolve with pendulum at freq.
:type delta: float
:param delta: Sampling interval
:type freq: float
:param freq: Frequency in Hz.
:type damp: float
:param damp: damping factor. Default is set to 0.1
:rtype: (float, float, float, float)
:return: Peak Ground Acceleration, maximal displacement, velocity,
acceleration
"""
data = data.copy()
# Displacement
if abs(max(data)) >= abs(min(data)):
m_dis = abs(max(data))
else:
m_dis = abs(min(data))
# Velocity
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_vel = abs(max(data))
else:
m_vel = abs(min(data))
# Acceleration
data = np.gradient(data, delta)
if abs(max(data)) >= abs(min(data)):
m_acc = abs(max(data))
else:
m_acc = abs(min(data))
samp_rate = 1.0 / delta
t = freq * 1.0
d = damp
omega = (2 * 3.14159 * t)**2
paz_sa = corn_freq_2_paz(t, damp=d)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
data = simulate_seismometer(data,
samp_rate,
paz_remove=None,
paz_simulate=paz_sa,
taper=True,
simulate_sensitivity=True,
taper_fraction=0.05)
if abs(max(data)) >= abs(min(data)):
pga = abs(max(data))
else:
pga = abs(min(data))
return (pga, m_dis, m_vel, m_acc)
seedrespN['station']=trN.stats.station
seedrespN['location']=trN.stats.location
seedrespN['network']=trN.stats.network
seedrespN['channel']=trN.stats.channel
seedrespE['date']=trE.stats.starttime
seedrespE['network']=trE.stats.network
seedrespE['station']=trE.stats.station
seedrespE['location']=trE.stats.location
seedrespE['network']=trE.stats.network
seedrespE['channel']=trE.stats.channel
#"""
amp_t.append(trN.max())
trN.data=simulate_seismometer(trN.data, 100, paz_remove=None, paz_simulate=None, remove_sensitivity=True, simulate_sensitivity=True, water_level=wt, taper=True, taper_fraction=tpf, pre_filt=pre_filt, seedresp=seedrespN, nfft_pow2=False)
#trN.simulate(paz_remove=None, pre_filt=pre_filt, seedresp=seedrespN)
trE.data=simulate_seismometer(trE.data, 100, paz_remove=None, paz_simulate=None, remove_sensitivity=True, simulate_sensitivity=True, water_level=wt, taper=True, taper_fraction=tpf, pre_filt=pre_filt, seedresp=seedrespN, nfft_pow2=False)
#trE.simulate(paz_remove=None, pre_filt=pre_filt, seedresp=seedrespE)
# trN.plot(outfile='espectros/Swave_%s_%s.png' % (units,trN.stats.station))
#trN.plot()
#==================================================================================================================
#========= #Calculando la transformada de fourier ==============================================================
#==================================================================================================================
#spectrum=mper(trN.data,200,np.size(trN.data))
n=np.size(trN.data)
#dt=0.01
t=np.linspace(0,n-1,n)*dt
