def filter_trace(tr, pre_filt):
"""
Perform a frequency domain taper mimicing the behavior during the
response removal, without a actual response removal.
:param tr: input trace
:param pre_filt: frequency array(Hz) in ascending order, to define
the four corners of filter, for example, [0.01, 0.1, 0.2, 0.5].
:type pre_filt: Numpy.array or list
:return: filtered trace
"""
if type(tr) != obspy.Trace:
raise ValueError("First Argument should be trace: %s" % type(tr))
if not check_array_order(pre_filt):
raise ValueError("Frequency band should be in ascending order: %s"
% pre_filt)
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:len(data)]
# assign processed data and store processing information
tr.data = data
def filter_trace(tr, pre_filt):
"""
Perform a frequency domain taper mimicing the behavior during the
response removal, without a actual response removal.
:param tr: input trace
:param pre_filt: frequency array(Hz) in ascending order, to define
the four corners of filter, for example, [0.01, 0.1, 0.2, 0.5].
:type pre_filt: Numpy.array or list
:return: filtered trace
"""
if not isinstance(tr, Trace):
raise TypeError("First Argument should be trace: %s" % type(tr))
if not check_array_order(pre_filt):
raise ValueError("Frequency band should be in ascending order: %s" %
pre_filt)
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:len(data)]
# assign processed data and store processing information
tr.data = data
def filter_synt(tr, pre_filt):
"""
Perform a frequency domain taper like during the response removal
just without an actual response...
:param tr:
:param pre_filt:
:return:
"""
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0 : len(data)]
# assign processed data and store processing information
tr.data = data
def process_function(st, inv):
st.detrend("linear")
st.detrend("demean")
st.taper(max_percentage=0.05, type="hann")
# Perform a frequency domain taper like during the response removal
# just without an actual response...
for tr in st:
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
from obspy.signal.util import _npts2nfft
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:len(data)]
# assign processed data and store processing information
tr.data = data
st.detrend("linear")
st.detrend("demean")
st.taper(max_percentage=0.05, type="hann")
st.interpolate(sampling_rate=sampling_rate,
starttime=starttime,
npts=npts)
components = [tr.stats.channel[-1] for tr in st]
if "N" in components and "E" in components:
station_latitude = inv[0][0].latitude
station_longitude = inv[0][0].longitude
_, baz, _ = gps2DistAzimuth(station_latitude, station_longitude,
event_latitude, event_longitude)
st.rotate(method="NE->RT", back_azimuth=baz)
# Convert to single precision to save space.
for tr in st:
tr.data = np.require(tr.data, dtype="float32")
return st
def process_function(st, inv):
st.detrend("linear")
st.detrend("demean")
st.taper(max_percentage=0.05, type="hann")
# Perform a frequency domain taper like during the response removal
# just without an actual response...
for tr in st:
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
from obspy.signal.util import _npts2nfft
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:len(data)]
# assign processed data and store processing information
tr.data = data
st.detrend("linear")
st.detrend("demean")
st.taper(max_percentage=0.05, type="hann")
st.interpolate(sampling_rate=sampling_rate, starttime=starttime,
npts=npts)
components = [tr.stats.channel[-1] for tr in st]
if "N" in components and "E" in components:
station_latitude = inv[0][0].latitude
station_longitude = inv[0][0].longitude
_, baz, _ = gps2DistAzimuth(station_latitude, station_longitude,
event_latitude, event_longitude)
st.rotate(method="NE->RT", back_azimuth=baz)
# Convert to single precision to save space.
for tr in st:
tr.data = np.require(tr.data, dtype="float32")
return st
def process_synthetics(self):
lowpass_freq = 1 / 60.
highpass_freq = 1 / 120.
freqmin = highpass_freq
freqmax = lowpass_freq
f2 = highpass_freq
f3 = lowpass_freq
f1 = 0.8 * f2
f4 = 1.2 * f3
pre_filt = (f1, f2, f3, f4)
self.tr.differentiate()
# self.tr.data = convolve_stf(self.tr.data)
self.tr.detrend("linear")
self.tr.detrend("demean")
self.tr.taper(max_percentage=0.05, type="hann")
# Perform a frequency domain taper like during the response removal
# just without an actual response...
data = self.tr.data.astype(np.float64)
orig_len = len(data)
# smart calculation of nfft dodging large primes
from obspy.signal.util import _npts2nfft
from obspy.signal.invsim import c_sac_taper
nfft = _npts2nfft(len(data))
fy = 1.0 / (self.tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:orig_len]
# assign processed data and store processing information
self.tr.data = data
def pre_filter(stream, pre_filt=(0.02, 0.05, 8.0, 10.0)):
"""
Applies the same filter as remove_response without actually removing the
response.
"""
for tr in stream:
data = tr.data.astype(np.float64)
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:tr.stats.npts]
# assign processed data and store processing information
tr.data = data
return stream
def pre_filter(stream, pre_filt=(0.02, 0.05, 8.0, 10.0)):
"""
Applies the same filter as remove_response without actually removing the
response.
"""
for tr in stream:
data = tr.data.astype(np.float64)
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:tr.stats.npts]
# assign processed data and store processing information
tr.data = data
return stream
def process_synt(datadir, station, event = None, stationxml_dir = None, period_band = None, npts=None,
sampling_rate=None, output_dir=None, suffix=None):
# ensemble the stream
#station_list = generate_station_list(datadir)
#for station in station_list:
zdatafile = station[0] + "." + station[1] + ".MXZ.sem.sac"
ndatafile = station[0] + "." + station[1] + ".MXN.sem.sac"
edatafile = station[0] + "." + station[1] + ".MXE.sem.sac"
zpath = os.path.join(datadir, zdatafile)
npath = os.path.join(datadir, ndatafile)
epath = os.path.join(datadir, edatafile)
st = read(zpath)
st2 = read(npath)
st3 = read(epath)
#print st
#print st2
st.append(st2[0])
st.append(st3[0])
#print "\nProcessing file: %s" %filename
# interpolation value
#npts = 3630
#sampling_rate = 1.0 # Hz
min_period = period_band[0]
max_period = period_band[1]
f2 = 1.0 / max_period
f3 = 1.0 / min_period
f1 = 0.8 * f2
f4 = 1.2 * f3
pre_filt = (f1, f2, f3, f4)
# fetch event information
event_name = get_event_name(event)
short_event_name = adjust_event_name(event_name)
origin = event.preferred_origin() or event.origins[0]
event_latitude = origin.latitude
event_longitude = origin.longitude
event_time = origin.time
event_depth = origin.depth
starttime = event_time
print "event_time:",event_time
#output_path = os.path.join(output_dir, short_event_name)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
nrecords = len(st)
# processing
for i, tr in enumerate(st):
#get component name
cmpname = tr.stats.channel
#print tr.stats
#tr.interpolate(sampling_rate=sampling_rate, starttime=starttime,
# npts=npts)
#tr.decimate(4, strict_length=False,no_filter=True)
print "Processing %d of total %d" %(i+1, nrecords)
print "Detrend, Demean and Taper..."
tr.detrend("linear")
tr.detrend("demean")
tr.taper(max_percentage=0.05, type="hann")
#print "response show:"
#print tr.stats.response
# Perform a frequency domain taper like during the response removal
# just without an actual response...
#for tr in st:
print "Filtering..."
data = tr.data.astype(np.float64)
# smart calculation of nfft dodging large primes
from obspy.signal.util import _npts2nfft
nfft = _npts2nfft(len(data))
fy = 1.0 / (tr.stats.delta * 2.0)
freqs = np.linspace(0, fy, nfft // 2 + 1)
# Transform data to Frequency domain
data = np.fft.rfft(data, n=nfft)
data *= c_sac_taper(freqs, flimit=pre_filt)
data[-1] = abs(data[-1]) + 0.0j
# transform data back into the time domain
data = np.fft.irfft(data)[0:len(data)]
# assign processed data and store processing information
tr.data = data
print "Detrend, Demean and Taper again..."
tr.detrend("linear")
tr.detrend("demean")
tr.taper(max_percentage=0.05, type="hann")
print "Interpolate..."
try:
tr.interpolate(sampling_rate=sampling_rate, starttime=starttime,
npts=npts)
except:
print "Error"
return
# rotate
station_latitude = st[0].stats.sac['stla']
station_longitude = st[0].stats.sac['stlo']
event_latitude = st[0].stats.sac['evla']
event_longitude = st[0].stats.sac['evlo']
#print station_latitude, station_longitude
#print station_latitude, station_longitude
_, baz, _ = gps2DistAzimuth(station_latitude, station_longitude,
event_latitude, event_longitude)
print st
st.rotate(method="NE->RT", back_azimuth=baz)
# write out
print st
for tr in st:
comp = tr.stats.channel
outfn = station[0] + "." + station[1] + "." + comp + ".sac"
if suffix is not None and suffix != "":
outfn = outfn + "." + suffix
outpath = os.path.join(output_dir, outfn)
print "file saved:", outpath
tr.stats.sac['b'] = 0
tr.stats.sac['iztype'] = 9
tr.write(outpath, format="SAC")
def correct_response(st, removeResp=False, removePAZ=False, simPAZ=False, pre_filt=None, cornFreq=0.0083):
"""
Correct the seismometer response.
Seismometer response is given in either a dictionary ``removeResp''
or a dictionary ``removePAZ''. ``removeResp has precedence. The
dictionaries have the following structure
removeResp: dictionary with Response information to be removed
has the following keys:
respfile: (str) filename of evalresp response file.
units: (str) Units to return response in. Can be either DIS, VEL or ACC
start_stage: (int) integer stage numbers of start stage (<0 causes
default evalresp bahaviour).
stop_stage: (int) integer stage numbers of stop stage
removePAZ: dictionary with poles and zeros to be removed has the following
keys:
poles: (list of complex numbers) location of poles
zeros: (list of complex numbers) location of zeros
gain: (float) gain
sensitivity: (float) sensitivity
It can easily be retrieved with obspy.arclink.client.Client.getPAZ
if ``removeResp'' is given the response of each trace must be present in
the respfile. If ``removePAZ'' is used the response is assumed to be the
same for all traces in the stream.
A filter specified in pre_filt can be applied in to avoid amplification of
noise.
The instrument to be simulated is either described in the dictionary simPAZ
or if simPAZ is False by the corner frequency ``cornFreq''. Response
correction is done in place and original data is overwritten.
The input stream ``st'' should be demeaned and tapered.
:type st: obspy.core.stream.Stream
:param st: data stream to be corrected
:type removeResp: dict
:param removeResp: Response information to be removed
:type removePAZ: dict
:param removePAZ: Response information to be removed
:type simPAZ: dict
:param simPAZ: Response information to be simulated
:type cornFreq: float
:param cornFreq: corner frequency of instrument to be simulated
:type pre_filt: list
:param pre_filt: 4 corners of the filter
"""
for tr in st:
starttime = tr.stats['starttime']
endtime = tr.stats['endtime']
network = tr.stats['network']
station = tr.stats['station']
channel = tr.stats['channel']
location = tr.stats['location']
length = tr.stats['npts']
sampling_rate = tr.stats['sampling_rate']
np2l = nextpow2(2.*length)
if not simPAZ:
simPAZ = cornFreq2Paz(cornFreq, damp=0.70716)
simresp, freqs = np.conj(pazToFreqResp(simPAZ['poles'], simPAZ['zeros'],
scale_fac=simPAZ['gain']*simPAZ['sensitivity'],
t_samp=1./sampling_rate,
nfft=np2l, freq=True)) #see Doc of pazToFreqResp for reason of conj()
if removeResp:
freqresp, freqs = evalresp(1./sampling_rate,np2l,removeResp['respfile'],
starttime, network=network, station=station,
channel=channel, locid=location,
start_stage=removeResp['start_stage'],
stop_stage=removeResp['stop_stage'],
units=removeResp['units'], freq=True)
else:
freqresp, freqs = np.conj(pazToFreqResp(removePAZ['poles'], removePAZ['zeros'],
scale_fac=removePAZ['gain']*removePAZ['sensitivity'],
t_samp=1./sampling_rate,
nfft=np2l, freq=True)) #see Doc of pazToFreqResp for reason of conj()
ftr = np.fft.rfft(tr.data,n=np2l)
ftr /= freqresp
ftr[0] = 0.j # correct the NaN in the DC component
ftr *= simresp
if pre_filt:
ftr *= c_sac_taper(freqs, flimit=pre_filt)
tr.data = np.fft.irfft(ftr)
tr.trim(starttime,endtime)
return
def correct_response(st,
removeResp=False,
removePAZ=False,
simPAZ=False,
pre_filt=None,
cornFreq=0.0083):
"""
Correct the seismometer response.
Seismometer response is given in either a dictionary ``removeResp''
or a dictionary ``removePAZ''. ``removeResp has precedence. The
dictionaries have the following structure
removeResp: dictionary with Response information to be removed
has the following keys:
respfile: (str) filename of evalresp response file.
units: (str) Units to return response in. Can be either DIS, VEL or ACC
start_stage: (int) integer stage numbers of start stage (<0 causes
default evalresp bahaviour).
stop_stage: (int) integer stage numbers of stop stage
removePAZ: dictionary with poles and zeros to be removed has the following
keys:
poles: (list of complex numbers) location of poles
zeros: (list of complex numbers) location of zeros
gain: (float) gain
sensitivity: (float) sensitivity
It can easily be retrieved with obspy.arclink.client.Client.getPAZ
if ``removeResp'' is given the response of each trace must be present in
the respfile. If ``removePAZ'' is used the response is assumed to be the
same for all traces in the stream.
A filter specified in pre_filt can be applied in to avoid amplification of
noise.
The instrument to be simulated is either described in the dictionary simPAZ
or if simPAZ is False by the corner frequency ``cornFreq''. Response
correction is done in place and original data is overwritten.
The input stream ``st'' should be demeaned and tapered.
:type st: obspy.core.stream.Stream
:param st: data stream to be corrected
:type removeResp: dict
:param removeResp: Response information to be removed
:type removePAZ: dict
:param removePAZ: Response information to be removed
:type simPAZ: dict
:param simPAZ: Response information to be simulated
:type cornFreq: float
:param cornFreq: corner frequency of instrument to be simulated
:type pre_filt: list
:param pre_filt: 4 corners of the filter
"""
for tr in st:
starttime = tr.stats['starttime']
endtime = tr.stats['endtime']
network = tr.stats['network']
station = tr.stats['station']
channel = tr.stats['channel']
location = tr.stats['location']
length = tr.stats['npts']
sampling_rate = tr.stats['sampling_rate']
np2l = nextpow2(2. * length)
if not simPAZ:
simPAZ = cornFreq2Paz(cornFreq, damp=0.70716)
simresp, freqs = np.conj(
pazToFreqResp(
simPAZ['poles'],
simPAZ['zeros'],
scale_fac=simPAZ['gain'] * simPAZ['sensitivity'],
t_samp=1. / sampling_rate,
nfft=np2l,
freq=True)) #see Doc of pazToFreqResp for reason of conj()
if removeResp:
freqresp, freqs = evalresp(1. / sampling_rate,
np2l,
removeResp['respfile'],
starttime,
network=network,
station=station,
channel=channel,
locid=location,
start_stage=removeResp['start_stage'],
stop_stage=removeResp['stop_stage'],
units=removeResp['units'],
freq=True)
else:
freqresp, freqs = np.conj(
pazToFreqResp(
removePAZ['poles'],
removePAZ['zeros'],
scale_fac=removePAZ['gain'] * removePAZ['sensitivity'],
t_samp=1. / sampling_rate,
nfft=np2l,
freq=True)) #see Doc of pazToFreqResp for reason of conj()
ftr = np.fft.rfft(tr.data, n=np2l)
ftr /= freqresp
ftr[0] = 0.j # correct the NaN in the DC component
ftr *= simresp
if pre_filt:
ftr *= c_sac_taper(freqs, flimit=pre_filt)
tr.data = np.fft.irfft(ftr)
tr.trim(starttime, endtime)
return
