def continuous_signal(time, low, high, dt):
"""
Continuous source-signal
"""
sig = (np.random.rand(time.size) * 2 - 1)
tr = Trace()
tr.data = sig
tr.stats.delta = dt
tr.stats.starttime = 0
tr.filter('bandpass', freqmin=low, freqmax=high, corners=4)
tr.normalize()
return tr
def arrayProcessing(self, prewhiten, method):
np.random.seed(2348)
geometry = np.array([[0.0, 0.0, 0.0], [-5.0, 7.0,
0.0], [5.0, 7.0, 0.0],
[10.0, 0.0, 0.0], [5.0, -7.0, 0.0],
[-5.0, -7.0, 0.0], [-10.0, 0.0, 0.0]])
geometry /= 100 # in km
slowness = 1.3 # in s/km
baz_degree = 20.0 # 0.0 > source in x direction
baz = baz_degree * np.pi / 180.
df = 100 # samplerate
# SNR = 100. # signal to noise ratio
amp = .00001 # amplitude of coherent wave
length = 500 # signal length in samples
coherent_wave = amp * np.random.randn(length)
# time offsets in samples
dt = df * slowness * (np.cos(baz) * geometry[:, 1] +
np.sin(baz) * geometry[:, 0])
dt = np.round(dt)
dt = dt.astype('int32')
max_dt = np.max(dt) + 1
min_dt = np.min(dt) - 1
trl = list()
for i in xrange(len(geometry)):
tr = Trace(coherent_wave[-min_dt + dt[i]:-max_dt + dt[i]].copy())
# + amp / SNR * \
# np.random.randn(length - abs(min_dt) - abs(max_dt)))
tr.stats.sampling_rate = df
tr.stats.coordinates = AttribDict()
tr.stats.coordinates.x = geometry[i, 0]
tr.stats.coordinates.y = geometry[i, 1]
tr.stats.coordinates.elevation = geometry[i, 2]
# lowpass random signal to f_nyquist / 2
tr.filter("lowpass", freq=df / 4.)
trl.append(tr)
st = Stream(trl)
stime = UTCDateTime(1970, 1, 1, 0, 0)
etime = UTCDateTime(1970, 1, 1, 0, 0) + \
(length - abs(min_dt) - abs(max_dt)) / df
win_len = 2.
step_frac = 0.2
sll_x = -3.0
slm_x = 3.0
sll_y = -3.0
slm_y = 3.0
sl_s = 0.1
frqlow = 1.0
frqhigh = 8.0
semb_thres = -1e99
vel_thres = -1e99
args = (st, win_len, step_frac, sll_x, slm_x, sll_y, slm_y, sl_s,
semb_thres, vel_thres, frqlow, frqhigh, stime, etime)
kwargs = dict(prewhiten=prewhiten,
coordsys='xy',
verbose=False,
method=method)
out = array_processing(*args, **kwargs)
if 0: # 1 for debugging
print '\n', out[:, 1:]
return out
def array_processing(self, prewhiten, method):
np.random.seed(2348)
geometry = np.array([[0.0, 0.0, 0.0],
[-5.0, 7.0, 0.0],
[5.0, 7.0, 0.0],
[10.0, 0.0, 0.0],
[5.0, -7.0, 0.0],
[-5.0, -7.0, 0.0],
[-10.0, 0.0, 0.0]])
geometry /= 100 # in km
slowness = 1.3 # in s/km
baz_degree = 20.0 # 0.0 > source in x direction
baz = baz_degree * np.pi / 180.
df = 100 # samplerate
# SNR = 100. # signal to noise ratio
amp = .00001 # amplitude of coherent wave
length = 500 # signal length in samples
coherent_wave = amp * np.random.randn(length)
# time offsets in samples
dt = df * slowness * (np.cos(baz) * geometry[:, 1] + np.sin(baz) *
geometry[:, 0])
dt = np.round(dt)
dt = dt.astype(np.int32)
max_dt = np.max(dt) + 1
min_dt = np.min(dt) - 1
trl = list()
for i in range(len(geometry)):
tr = Trace(coherent_wave[-min_dt + dt[i]:-max_dt + dt[i]].copy())
# + amp / SNR * \
# np.random.randn(length - abs(min_dt) - abs(max_dt)))
tr.stats.sampling_rate = df
tr.stats.coordinates = AttribDict()
tr.stats.coordinates.x = geometry[i, 0]
tr.stats.coordinates.y = geometry[i, 1]
tr.stats.coordinates.elevation = geometry[i, 2]
# lowpass random signal to f_nyquist / 2
tr.filter("lowpass", freq=df / 4.)
trl.append(tr)
st = Stream(trl)
stime = UTCDateTime(1970, 1, 1, 0, 0)
etime = UTCDateTime(1970, 1, 1, 0, 0) + 4.0
# TODO: check why this does not work any more
# (length - abs(min_dt) - abs(max_dt)) / df
win_len = 2.
step_frac = 0.2
sll_x = -3.0
slm_x = 3.0
sll_y = -3.0
slm_y = 3.0
sl_s = 0.1
frqlow = 1.0
frqhigh = 8.0
semb_thres = -1e99
vel_thres = -1e99
args = (st, win_len, step_frac, sll_x, slm_x, sll_y, slm_y, sl_s,
semb_thres, vel_thres, frqlow, frqhigh, stime, etime)
kwargs = dict(prewhiten=prewhiten, coordsys='xy', verbose=False,
method=method)
out = array_processing(*args, **kwargs)
if False: # 1 for debugging
print('\n', out[:, 1:])
return out
tr = Trace(data=x)
tr.stats.station = "ABC"
tr.stats.sampling_rate = 20.0
tr.stats.starttime = UTCDateTime(2014, 2, 24, 15, 0, 0)
print(tr)
tr.plot()
# -
# - Use **`tr.filter(...)`** and apply a lowpass filter with a corner frequency of 1 Hertz.
# - Display the preview plot, there are a few seconds of zeros that we can cut off.
# + {"tags": ["exercise"]}
# + {"tags": ["solution"]}
tr.filter("lowpass", freq=1)
tr.plot()
# -
# - Use **`tr.trim(...)`** to remove some of the zeros at start and at the end
# - show the preview plot again
# + {"tags": ["exercise"]}
# + {"tags": ["solution"]}
tr.trim(tr.stats.starttime + 3, tr.stats.endtime - 5)
tr.plot()
# -
# - Scale up the amplitudes of the trace by a factor of 500
if os.path.exists(finpc1) and os.path.getsize(finpc1) > 0:
try:
st = read(finpc1, starttime=t1, endtime=t2)
if len(st) != 0:
st.merge(method=1, fill_value=0)
tc = st[0]
stat = tc.stats.station
chan = tc.stats.channel
tc.detrend("constant")
# 24h continuous trace starts 00 h 00 m 00.0s
trim_fill(tc, t1, t2)
tc.filter(
"bandpass",
freqmin=bandpass[0],
freqmax=bandpass[1],
zerophase=True,
)
# store detrended and filtered continuous data
# in a Stream
stream_df += Stream(traces=[tc])
except IOError:
pass
if len(stream_df) >= nch_min:
ntl = len(stt)
amaxat = np.empty(ntl)
# for each template event
# md=np.empty(ntl)
