def test_highpassVsPitsa(self):
"""
Test Butterworth highpass filter against Butterworth highpass 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
freq = 10
corners = 4
# filter trace
datcorr = highpass(data, freq, df=samp_rate, corners=corners)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_highpass.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_highpassVsPitsa(self):
"""
Test Butterworth highpass filter against Butterworth highpass 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
freq = 10
corners = 4
# filter trace
datcorr = highpass(data, freq, df=samp_rate, corners=corners)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_highpass.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_highpassZPHSHVsPitsa(self):
"""
Test Butterworth zero-phase highpass filter against Butterworth
zero-phase highpass 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
freq = 10
corners = 2
# filter trace
datcorr = highpass(data, freq, df=samp_rate, corners=corners,
zerophase=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_highpassZPHSH.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_highpassZPHSHVsPitsa(self):
"""
Test Butterworth zero-phase highpass filter against Butterworth
zero-phase highpass 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
freq = 10
corners = 2
# filter trace
datcorr = highpass(data, freq, df=samp_rate, corners=corners,
zerophase=True)
# load pitsa file
file = os.path.join(self.path, 'rjob_20051006_highpassZPHSH.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 plot_FilteredResult(x, Fs, c, Fr, bw, level, spec=1):
from SK_process import raylinv
from SK_grid import nextpow2
sig = np.median(np.abs(c)) / np.sqrt(np.pi / 2.)
threshold = sig * raylinv(np.array([
.999,
]), np.array([
1,
]))
t = np.arange(len(x)) / float(Fs)
tc = np.linspace(t[0], t[-1], len(c))
fig = plt.figure()
ax1 = plt.subplot(2 + spec, 1, 1)
Fr *= Fs
filt = highpass(x, Fr - bw / 2, Fs, corners=2)
filt = lowpass(filt, Fr + bw / 2, Fs, corners=2)
filt /= np.max(filt)
plt.plot(t, x, 'k', label='Original Signal')
plt.plot(t, filt, label='Obspy Filtered Signal')
plt.plot(tc, np.abs(c), 'r', label='filtered Signal')
plt.legend()
plt.grid(True)
plt.subplot(2 + spec, 1, 2, sharex=ax1)
plt.plot(tc, np.abs(c), 'k')
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.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.tight_layout()
plt.show()
