def test_gaussian_filter(self):
from am_signal import gaussian_filter
from obspy.signal.freqattributes import cfrequency
import matplotlib.pyplot as plt
dt = 0.01
width = 1.0
gauss1, t1 = gaussian_filter(width, 1.0, dt)
gauss5, t5 = gaussian_filter(width, 5.0, dt)
npts1 = len(gauss1)
npts5 = len(gauss5)
self.assertEquals(npts1, npts5)
self.assertAlmostEquals(t1, t5)
self.assertAlmostEquals(np.argmax(gauss1), t1 / dt)
self.assertAlmostEquals(np.argmax(gauss5), t5 / dt)
data_trace5 = self.data_trace.copy()
data_trace5.data = np.real(
np.convolve(data_trace5.data, gauss5, 'same'))
cf5 = cfrequency(data_trace5.data, 1 / dt, 0, 0)
self.assertAlmostEquals(cf5, 5.0, 0)
def test_bp_filterbank(self):
from obspy.signal.freqattributes import cfrequency
st = Stream()
fst = Stream()
data_trace = self.data_trace.copy()
dt = data_trace.stats.delta
data_trace.data = np.zeros(400)
data_trace.data[200] = 1 * 2 * np.pi
freqmin = 1.0
freqmax = 4.0
freq_step = (freqmax - freqmin) / 2.0
n_bank = 5
for i in xrange(n_bank):
dtrace = data_trace.copy()
fr_min = freqmin + i * freq_step
fr_max = freqmax + i * freq_step
dtrace.filter('bandpass',
freqmin=fr_min,
freqmax=fr_max,
zerophase=True)
st.append(dtrace)
ftrace = self.data_trace.copy()
ftrace.data = np.real(np.convolve(ftrace.data, dtrace.data,
'same'))
cf = cfrequency(ftrace.data, 1 / dt, 0, 0)
self.assertTrue(cf > fr_min and cf < fr_max)
def test_cfrequency(self):
"""
"""
cfreq = freqattributes.cfrequency(self.data_win_bc, self.fs,
self.smoothie, self.fk)
rms = np.sqrt(np.sum((cfreq[0] - self.res[:, 18]) ** 2) /
np.sum(self.res[:, 18] ** 2))
self.assertEqual(rms < 1.0e-5, True)
rms = np.sqrt(np.sum((cfreq[1] - self.res[:, 19]) ** 2) /
np.sum(self.res[:, 19] ** 2))
self.assertEqual(rms < 1.0e-5, True)
def test_cfrequency(self):
"""
"""
cfreq = freqattributes.cfrequency(self.data_win_bc, self.fs,
self.smoothie, self.fk)
rms = np.sqrt(
np.sum(
(cfreq[0] - self.res[:, 18])**2) / np.sum(self.res[:, 18]**2))
self.assertEqual(rms < 1.0e-5, True)
rms = np.sqrt(
np.sum(
(cfreq[1] - self.res[:, 19])**2) / np.sum(self.res[:, 19]**2))
self.assertEqual(rms < 1.0e-5, True)
def test_bp_filterbank(self):
from obspy.signal.freqattributes import cfrequency
st=Stream()
fst=Stream()
data_trace=self.data_trace.copy()
dt=data_trace.stats.delta
data_trace.data=np.zeros(400)
data_trace.data[200]=1*2*np.pi
freqmin=1.0
freqmax=4.0
freq_step=(freqmax-freqmin)/2.0
n_bank=5
for i in xrange(n_bank):
dtrace=data_trace.copy()
fr_min=freqmin+i*freq_step
fr_max=freqmax+i*freq_step
dtrace.filter('bandpass',freqmin=fr_min, freqmax=fr_max, zerophase=True)
st.append(dtrace)
ftrace=self.data_trace.copy()
ftrace.data=np.real(np.convolve(ftrace.data,dtrace.data,'same'))
cf=cfrequency(ftrace.data,1/dt,0,0)
self.assertTrue(cf > fr_min and cf < fr_max)
def test_gaussian_filter(self):
from am_signal import gaussian_filter
from obspy.signal.freqattributes import cfrequency
import matplotlib.pyplot as plt
dt=0.01
width=1.0
gauss1, t1=gaussian_filter(width, 1.0, dt)
gauss5, t5=gaussian_filter(width, 5.0, dt)
npts1=len(gauss1)
npts5=len(gauss5)
self.assertEquals(npts1,npts5)
self.assertAlmostEquals(t1,t5)
self.assertAlmostEquals(np.argmax(gauss1),t1/dt)
self.assertAlmostEquals(np.argmax(gauss5),t5/dt)
data_trace5=self.data_trace.copy()
data_trace5.data=np.real(np.convolve(data_trace5.data,gauss5,'same'))
cf5=cfrequency(data_trace5.data,1/dt,0,0)
self.assertAlmostEquals(cf5,5.0,0)
def test_cfrequency_no_win(self):
cfreq = freqattributes.cfrequency(self.data_win_bc[0], self.fs,
self.smoothie, self.fk)
rms = (cfreq - self.res[0, 18]) / self.res[0, 18]
self.assertTrue(rms < 1.0e-5)
def test_cfrequency_no_win(self):
cfreq = freqattributes.cfrequency(self.data_win_bc[0], self.fs,
self.smoothie, self.fk)
rms = (cfreq - self.res[0, 18]) / self.res[0, 18]
self.assertTrue(rms < 1.0e-5)