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 = seisSim(tr1.data, tr1.stats.sampling_rate, seedresp=seedresp)
with open(respf, "rb") as fh:
stringio = io.BytesIO(fh.read())
seedresp["filename"] = stringio
tr2.data = seisSim(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 = seisSim(tr1.data,
tr1.stats.sampling_rate,
seedresp=seedresp)
with open(respf, 'rb') as fh:
stringio = compatibility.BytesIO(fh.read())
seedresp['filename'] = stringio
tr2.data = seisSim(tr2.data,
tr2.stats.sampling_rate,
seedresp=seedresp)
self.assertEqual(tr1, tr2)
def test_seisSimVsPitsa1(self):
"""
Test seisSim seismometer simulation against seismometer simulation
of Pitsa - LE3D seismometer.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# 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.iteritems():
# simulate instrument
datcorr = seisSim(data, samp_rate, paz_remove=PAZ_LE3D,
paz_simulate=paz, water_level=600.0,
zero_mean=False, nfft_pow2=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_%s.gz' % id)
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# 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_seisSimVsPitsa2(self):
"""
Test seisSim seismometer simulation against seismometer simulation of
Pitsa - STS-2 seismometer.
"""
# load test file
file = os.path.join(self.path, 'rotz_20081028.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# 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.iteritems():
# simulate instrument
datcorr = seisSim(data, samp_rate, paz_remove=PAZ_STS2,
paz_simulate=paz, water_level=600.0,
zero_mean=False, nfft_pow2=True)
# load pitsa file
file = os.path.join(self.path, 'rotz_20081028_%s.gz' % id)
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) / \
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1e-04)
def test_seisSimVsPitsa2(self):
"""
Test seisSim seismometer simulation against seismometer simulation of
Pitsa - STS-2 seismometer.
"""
# load test file
file = os.path.join(self.path, 'rotz_20081028.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# 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.iteritems():
# simulate instrument
datcorr = seisSim(data, samp_rate, paz_remove=PAZ_STS2,
paz_simulate=paz, water_level=600.0,
zero_mean=False, nfft_pow2=True)
# load pitsa file
file = os.path.join(self.path, 'rotz_20081028_%s.gz' % id)
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) /
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1e-04)
def test_seisSimVsPitsa1(self):
"""
Test seisSim seismometer simulation against seismometer simulation
of Pitsa - LE3D seismometer.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# 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.iteritems():
# simulate instrument
datcorr = seisSim(data, samp_rate, paz_remove=PAZ_LE3D,
paz_simulate=paz, water_level=600.0,
zero_mean=False, nfft_pow2=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_%s.gz' % id)
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# 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_SacInstCorrection(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 >>cd1, "r %s"%sacf
# print >>cd1, "rmean"
# print >>cd1, "rtrend"
# print >>cd1, "taper type cosine width 0.03"
# print >>cd1, "transfer from polezero subtype %s to none \
# freqlimits %f %f %f %f" % (pzf, fl1, fl2, fl3, fl4)
# print >>cd1, "w over ./data/KARC_corrected.sac"
# print >>cd1, "quit"
# 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'}
tr = Trace(np.loadtxt(sacf), stats)
attach_paz(tr, pzf, tovel=False)
tr.data = seisSim(tr.data, tr.stats.sampling_rate,
paz_remove=tr.stats.paz, remove_sensitivity=False,
pre_filt=(fl1, fl2, fl3, fl4))
data = np.loadtxt(testsacf)
# 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_SacInstCorrection(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 >>cd1, "r %s"%sacf
# print >>cd1, "rmean"
# print >>cd1, "rtrend"
# print >>cd1, "taper type cosine width 0.03"
# print >>cd1, "transfer from polezero subtype %s to none \
# freqlimits %f %f %f %f" % (pzf, fl1, fl2, fl3, fl4)
# print >>cd1, "w over ./data/KARC_corrected.sac"
# print >>cd1, "quit"
# 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'}
tr = Trace(np.loadtxt(sacf), stats)
attach_paz(tr, pzf, tovel=False)
tr.data = seisSim(tr.data, tr.stats.sampling_rate,
paz_remove=tr.stats.paz, remove_sensitivity=False,
pre_filt=(fl1, fl2, fl3, fl4))
data = np.loadtxt(testsacf)
# 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 >>cd1, "r %s" % rawf
# print >>cd1, "rmean"
# print >>cd1, "taper type cosine width 0.05"
# print >>cd1, "transfer from evalresp fname %s to vel freqlimits\
# %f %f %f %f" % (respf, fl1, fl2, fl3, fl4)
# print >>cd1, "w over %s" % evalrespf
# print >>cd1, "quit"
# 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 = seisSim(
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_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.
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 >>cd1, "r %s" % rawf
# print >>cd1, "rmean"
# print >>cd1, "taper type cosine width 0.05"
# print >>cd1, "transfer from evalresp fname %s to vel freqlimits\
# %f %f %f %f" % (respf, fl1, fl2, fl3, fl4)
# print >>cd1, "w over %s" % evalrespf
# print >>cd1, "quit"
# 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 = seisSim(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_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'}
tr1.data = seisSim(tr1.data, tr1.stats.sampling_rate,
seedresp=seedresp)
with open(respf) as fh:
stringio = StringIO(fh.read())
seedresp['filename'] = stringio
tr2.data = seisSim(tr2.data, tr2.stats.sampling_rate,
seedresp=seedresp)
self.assertEqual(tr1, tr2)
def smPSA(data, samp_rate):
"""
ShakeMap pseudo-spectral parameters
Compute 5% damped PSA at 0.3, 1.0, and 3.0 seconds.
Data must be an acceleration Trace
:type data: :class:`obspy.trace`
:param data: Data in acceleration to convolve with pendulum at freq.
:type delta: float
:param delta: sample rate (samples per sec)
:rtype: (float, float, float)
:return: PSA03, PSA10, PSA30
"""
D = 0.05 # 5% damping
out = []
periods = [0.3, 1.0, 3.0]
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq)**2
paz_sa = cornFreq2Paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = seisSim(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))
out.append(psa)
return out
def smPSA(data, samp_rate):
"""
ShakeMap pseudo-spectral parameters
Compute 5% damped PSA at 0.3, 1.0, and 3.0 seconds.
Data must be an acceleration Trace
:type data: :class:`obspy.trace`
:param data: Data in acceleration to convolve with pendulum at freq.
:type delta: float
:param delta: sample rate (samples per sec)
:rtype: (float, float, float)
:return: PSA03, PSA10, PSA30
"""
D = 0.05 # 5% damping
out = []
periods = [0.3, 1.0, 3.0]
for T in periods:
freq = 1.0 / T
omega = (2 * 3.14159 * freq) ** 2
paz_sa = cornFreq2Paz(freq, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
dd = seisSim(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))
out.append(psa)
return out
seedrespN['network']=trN.stats.network
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
trN.data=seisSim(trN.data, 100, paz_remove=None, paz_simulate=None, remove_sensitivity=True, simulate_sensitivity=True, water_level=wt, zetro_mean=True, taper=True, taper_fraction=tpf, pre_filt=pre_filt, seedresp=seedrespN, nfft_pow2=False)
# trN.filter("highpass", freq=0.5)
trE.data=seisSim(trE.data, 100, paz_remove=None, paz_simulate=None, remove_sensitivity=True, simulate_sensitivity=True, water_level=wt, zetro_mean=True, taper=True, taper_fraction=tpf, pre_filt=pre_filt, seedresp=seedrespN, nfft_pow2=False)
# 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
df=1/(n*dt)
def pgm(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 = cornFreq2Paz(T, damp=D)
paz_sa["sensitivity"] = omega
paz_sa["zeros"] = []
data = seisSim(
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)
def pgm(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 dispalcement 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 = cornFreq2Paz(T, damp=D)
paz_sa['sensitivity'] = omega
paz_sa['zeros'] = []
data = seisSim(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)