def test_bandpassVsPitsa(self):
"""
Test Butterworth bandpass filter against Butterworth bandpass filter
of PITSA. Note that the corners value is twice the value of the filter
sections in PITSA. The rms of the difference between ObsPy and PITSA
tends to get bigger with higher order filtering.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# parameters for the test
samp_rate = 200.0
freq1 = 5
freq2 = 10
corners = 4
# filter trace
datcorr = bandpass(data, freq1, freq2, df=samp_rate, corners=corners)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_bandpass.gz')
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.assertEqual(rms < 1.0e-05, True)
def test_bandpassZPHSHVsPitsa(self):
"""
Test Butterworth zero-phase bandpass filter against Butterworth
zero-phase bandpass filter of PITSA. Note that the corners value is
twice the value of the filter sections in PITSA. The rms of the
difference between ObsPy and PITSA tends to get bigger with higher
order filtering.
Note: The Zero-Phase filters deviate from PITSA's zero-phase filters
at the end of the trace! The rms for the test is calculated omitting
the last 200 samples, as this part of the trace is assumed to
generally be of low interest/importance.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# parameters for the test
samp_rate = 200.0
freq1 = 5
freq2 = 10
corners = 2
# filter trace
datcorr = bandpass(data, freq1, freq2, df=samp_rate,
corners=corners, zerophase=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_bandpassZPHSH.gz')
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr[:-200] - data_pitsa[:-200]) ** 2) /
np.sum(data_pitsa[:-200] ** 2))
self.assertEqual(rms < 1.0e-05, True)
def test_bandpassVsPitsa(self):
"""
Test Butterworth bandpass filter against Butterworth bandpass filter
of PITSA. Note that the corners value is twice the value of the filter
sections in PITSA. The rms of the difference between ObsPy and PITSA
tends to get bigger with higher order filtering.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# parameters for the test
samp_rate = 200.0
freq1 = 5
freq2 = 10
corners = 4
# filter trace
datcorr = bandpass(data, freq1, freq2, df=samp_rate, corners=corners)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_bandpass.gz')
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.assertEqual(rms < 1.0e-05, True)
def filter(self):
""" Filter data for each band.
"""
n_bands = self._N_bands()
LEN = self.tr.stats.npts
df = self.tr.stats.sampling_rate
# create zeros 2D array for BF
BF = np.zeros(shape = (n_bands,LEN))
for j in range(n_bands):
octave_high = (self.freqmin + self.freqmin * 2.0) / 2.0 * (2**j)
octave_low = octave_high / 2.0
BF[j] = bandpass(self.tr.data, octave_low, octave_high, df, corners = self.cnr, zerophase = False)
BF[j] = cosTaper(LEN, self.perc_taper) * BF[j]
return BF
def test_bandpassZPHSHVsPitsa(self):
"""
Test Butterworth zero-phase bandpass filter against Butterworth
zero-phase bandpass filter of PITSA. Note that the corners value is
twice the value of the filter sections in PITSA. The rms of the
difference between ObsPy and PITSA tends to get bigger with higher
order filtering.
Note: The Zero-Phase filters deviate from PITSA's zero-phase filters
at the end of the trace! The rms for the test is calculated omitting
the last 200 samples, as this part of the trace is assumed to
generally be of low interest/importance.
"""
# load test file
file = os.path.join(self.path, 'rjob_20051006.gz')
f = gzip.open(file)
data = np.loadtxt(f)
f.close()
# parameters for the test
samp_rate = 200.0
freq1 = 5
freq2 = 10
corners = 2
# filter trace
datcorr = bandpass(data,
freq1,
freq2,
df=samp_rate,
corners=corners,
zerophase=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_bandpassZPHSH.gz')
f = gzip.open(file)
data_pitsa = np.loadtxt(f)
f.close()
# calculate normalized rms
rms = np.sqrt(
np.sum((datcorr[:-200] - data_pitsa[:-200])**2) /
np.sum(data_pitsa[:-200]**2))
self.assertEqual(rms < 1.0e-05, True)
def filter(self):
""" Filter data for each band.
"""
n_bands = self._N_bands()
LEN = self.tr.stats.npts
df = self.tr.stats.sampling_rate
# create zeros 2D array for BF
BF = np.zeros(shape=(n_bands, LEN))
for j in range(n_bands):
octave_high = (self.freqmin + self.freqmin * 2.0) / 2.0 * (2**j)
octave_low = octave_high / 2.0
BF[j] = bandpass(self.tr.data,
octave_low,
octave_high,
df,
corners=self.cnr,
zerophase=False)
BF[j] = cosTaper(LEN, self.perc_taper) * BF[j]
return BF
str(stime.julday).zfill(3) + '/' + locs[0] + '_' + sr +
'H2.512.seed')
st2 += read('/tr1/telemetry_days/' + stas[1] + '/2016/2016_' +
str(stime.julday).zfill(3) + '/' + locs[1] + '_' + sr +
'H2.512.seed')
st2 += read('/tr1/telemetry_days/' + stas[2] + '/2016/2016_' +
str(stime.julday).zfill(3) + '/' + locs[2] + '_' + sr +
'H2.512.seed')
st2.trim(starttime=stime, endtime=etime)
st2.detrend('constant')
for tr in st1:
#tr.data /= paz['sensitivity']
tr.data = bandpass(tr.data, 1. / 10., 1., 1. / delta, 4, False)
tr.taper(0.05)
for tr in st2:
#tr.data /= paz['sensitivity']
tr.data = bandpass(tr.data, 1. / 10., 1., 1. / delta, 4, False)
tr.taper(0.05)
#Sensor 1 and 2
def rot1(theta):
theta = theta % 360.
cosd = np.cos(np.deg2rad(theta))
sind = np.sin(np.deg2rad(theta))
data1 = cosd * st1[0].data - sind * st2[0].data
data2 = sind * st1[0].data + cosd * st2[0].data
resi = abs(
sum(data1 * st1[1].data) /
)[1] != last_id or tr.stats.starttime - last_endtime > 1.0 / df:
data_buf = np.array([], dtype='float64')
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data,
df,
paz_remove=tr.stats.paz,
paz_simulate=inst,
remove_sensitivity=True)
# XXX is removed in seisSim... ?!
# XXX data /= (paz['sensitivity'] / 1e9) #V/nm/s correct for overall sensitivity
data = bandpass(data, LOW, HIGH, df)
data = recStalta(data, s2p(STA, tr), s2p(LTA, tr))
picked_values = triggerOnset(data, ON, OFF, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
trigger_list.append(
(begin.timestamp, end.timestamp, tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
last_endtime, last_id = trId(tr.stats)
###############################################################################
# start of coincidence part
def Find_wav_kurt(x,h,g,h1,h2,h3,nlevel,Sc,Fr,opt,Fs=1):
# [c,Bw,fc,i] = Find_wav_kurt(x,h,g,h1,h2,h3,nlevel,Sc,Fr,opt2)
# Sc = -log2(Bw)-1 with Bw the bandwidth of the filter
# Fr is in [0 .5]
#
# -------------------
# J. Antoni : 12/2004
# -------------------
level = np.fix((Sc))+ ((Sc%1) >= 0.5) * (np.log2(3)-1)
Bw = 2**(-level-1)
freq_w = np.arange(0,2**(level-1)) / 2**(level+1) + Bw/2.
J = np.argmin(np.abs(freq_w-Fr))
fc = freq_w[J]
i = int(np.round(fc/Bw-1./2))
if level % 1 == 0:
acoeff = binary(i, level)
bcoeff = np.array([])
temp_level = level
else:
i2 = np.fix((i/3.))
temp_level = np.fix((level))-1
acoeff = binary(i2,temp_level)
bcoeff = np.array([i-i2*3,])
acoeff = acoeff[::-1]
c = K_wpQ_filt(x,h,g,h1,h2,h3,acoeff,bcoeff,temp_level)
kx = kurt(c,opt)
print "kx", kx
sig = np.median(np.abs(c))/np.sqrt(np.pi/2.)
print sig
threshold = sig*raylinv(np.array([.999,]),np.array([1,]))
print "threshold", threshold
#~ spec = int(raw_input(' Do you want to see the envelope spectrum (yes = 1 ; no = 0): '))
spec = 1
t = np.arange(len(x))/Fs
tc = np.linspace(t[0],t[-1],len(c))
fig = plt.figure()
ax1 = plt.subplot(2+spec,1,1)
plt.plot(t,x,'k',label='Original Signal')
#~ plt.plot(tc,(c/np.max(c))*np.max(np.abs(x)),c='r',label='Filtered Signal')
from obspy.signal import bandpass, envelope
plt.plot(t,bandpass(x,Fr,Fr+50./32,Fs,corners=2),label='Obspy Filterded Signal')
plt.legend()
plt.grid(True)
plt.subplot(2+spec,1,2,sharex=ax1)
plt.plot(tc,np.abs(c),'k')
#~ plt.plot(tc,envelope(c),'k')
#~ plt.plot(tc,threshold*np.ones(len(c)),'--r')
plt.axhline(threshold,c='r')
for ti in tc[ np.where(np.abs(c) >= threshold)[0]]:
plt.axvline(ti,c='g',zorder=-1)
ax1.axvline(ti,c='g',zorder=-1)
#~ plt.title('Envlp of the filtr sgl, Bw=Fs/2^{'+(level+1)+'}, fc='+(Fs*fc)+'Hz, Kurt='+(np.round(np.abs(10*kx))/10)+', \alpha=.1%']
plt.xlabel('time [s]')
plt.grid(True)
if spec == 1:
#~ print nextpow2(len(c))
nfft = int(nextpow2(len(c)))
env = np.abs(c)**2
S = np.abs(np.fft.fft(env.ravel()-np.mean(env)*np.hanning(len(env))/len(env),nfft))
f = np.linspace(0, 0.5*Fs/2**level,nfft/2)
plt.subplot(313)
plt.plot(f,S[:nfft/2],'k')
plt.title('Fourier transform magnitude of the squared envelope')
plt.xlabel('frequency [Hz]')
plt.grid(True)
plt.show()
return [c,Bw,fc]
overlap = s2p(30.0, tr)
olap = overlap
samp = 0
df = tr.stats.sampling_rate
if trId(tr.stats)[1] != last_id or tr.stats.starttime - last_endtime > 1.0 / df:
data_buf = np.array([], dtype='float64')
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data, df, paz_remove=tr.stats.paz, paz_simulate=inst, remove_sensitivity=True)
# XXX is removed in seisSim... ?!
# XXX data /= (paz['sensitivity'] / 1e9) #V/nm/s correct for overall sensitivity
data = bandpass(data, LOW, HIGH, df)
data = recStalta(data, s2p(STA, tr), s2p(LTA, tr))
picked_values = triggerOnset(data, ON, OFF, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
trigger_list.append((begin.timestamp, end.timestamp, tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
last_endtime, last_id = trId(tr.stats)
###############################################################################
# start of coincidence part
###############################################################################