def gapped_high_sample_stream(self):
"""
Create a stream which has two overlapping traces with high sampling
rates.
"""
# first trace
stats1 = {
"sampling_rate": 6000.0,
"starttime": UTCDateTime(2017, 9, 23, 18, 50, 29, 715100),
"endtime": UTCDateTime(2017, 9, 23, 18, 50, 31, 818933),
"network": "XI",
"station": "00037",
"location": "00",
"channel": "FL1",
}
data1 = np.random.rand(12624)
tr1 = obspy.Trace(data=data1, header=stats1)
# second trace
stat2 = {
"sampling_rate": 6000.0,
"delta": 0.00016666666666666666,
"starttime": UTCDateTime(2017, 9, 23, 18, 50, 31, 819100),
"endtime": UTCDateTime(2017, 9, 23, 18, 50, 31, 973933),
"npts": 930,
"calib": 1.0,
"network": "XI",
"station": "00037",
"location": "00",
"channel": "FL1",
}
data2 = np.random.rand(930)
tr2 = obspy.Trace(data=data2, header=stat2)
return obspy.Stream(traces=[tr1, tr2])
def clean_up(corr, sampling_rate, freqmin, freqmax):
data = []
st = obspy.Stream()
if corr.ndim == 2:
for ii in range(len(corr)):
tr = obspy.Trace(data=corr[ii, :])
tr.stats.sampling_rate = sampling_rate
st += tr
del tr
else:
tr = obspy.Trace(data=corr)
tr.stats.sampling_rate = sampling_rate
st += tr
del tr
st.detrend('constant')
st.detrend('simple')
percent = sampling_rate * 20 / st[0].stats.npts
st.taper(max_percentage=percent, max_length=20.)
st.filter('bandpass', freqmin=freqmin, freqmax=freqmax, zerophase=True)
for tr in st:
data.append(tr.data)
data = np.array(data)
if data.shape[0] == 1:
data = data.flatten()
return data
def test_calc_timeshift():
data_test = [0, 0, 1, 2, 1, 0, 0]
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='AAA', location='00', delta=0.1))
st_ref = Stream(tr_test)
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='BBB', location='00', delta=0.1))
st_ref.append(tr_test)
# Shift backwards by 0.2 s
dt = dict()
dt['AAA.00'] = 0.2
dt['BBB.00'] = -0.1
st_shift = st_ref.copy()
st_shift.shift_waveform(dt)
dt_res, CC = st_shift.calc_timeshift(st_ref)
npt.assert_allclose(dt_res['AAA.00'], dt['AAA.00'], rtol=1e-5)
npt.assert_allclose(dt_res['BBB.00'], dt['BBB.00'], rtol=1e-5)
# Shift backwards by 0.2 s, reverse order of traces in stream
dt = dict()
dt['AAA.00'] = 0.2
dt['BBB.00'] = -0.1
st_shift = st_ref.copy()
st_shift.shift_waveform(dt)
st_shift.sort(keys=['station'], reverse=True)
dt_res, CC = st_shift.calc_timeshift(st_ref)
npt.assert_allclose(dt_res['AAA.00'], dt['AAA.00'], rtol=1e-5)
npt.assert_allclose(dt_res['BBB.00'], dt['BBB.00'], rtol=1e-5)
def test_calc_amplitude_misfit():
data_test = [0.0, 0.0, 1.0, 2.0, 1.0, 0.0, 0.0]
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='AAA', location='00', delta=0.1))
st_ref = Stream(tr_test)
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='BBB', location='00', delta=0.1))
st_ref.append(tr_test)
st_mult = st_ref.copy()
st_mult.select(station='AAA')[0].data *= 2
st_mult.select(station='BBB')[0].data *= 0.5
dA = st_mult.calc_amplitude_misfit(st_ref)
npt.assert_almost_equal(dA['AAA.00'], 2.0, decimal=5)
npt.assert_almost_equal(dA['BBB.00'], 0.5, decimal=5)
# Check when order is mixed up
st_mult = st_ref.copy()
st_mult.select(station='AAA')[0].data *= 2
st_mult.select(station='BBB')[0].data *= 0.5
st_mult.sort(keys=['station'], reverse=True)
dA = st_mult.calc_amplitude_misfit(st_ref)
npt.assert_almost_equal(dA['AAA.00'], 2.0, decimal=5)
npt.assert_almost_equal(dA['BBB.00'], 0.5, decimal=5)
def test_shift_waveform():
data_test = [0, 0, 1, 2, 1, 0, 0]
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='AAA', location='00', delta=0.1))
st_ref = Stream(tr_test)
tr_test = obspy.Trace(np.array(data_test),
header=dict(station='BBB', location='00', delta=0.1))
st_ref.append(tr_test)
dt = dict()
dt['AAA.00'] = 0.2
dt['BBB.00'] = -0.1
st_shift = st_ref.copy()
st_shift.shift_waveform(dt)
# Shift backwards by 0.2 s
data_ref = [0, 0, 0, 0, 1, 2, 1]
npt.assert_allclose(st_shift.select(station='AAA')[0].data,
data_ref,
rtol=1e-5,
atol=1e-3,
err_msg='Shifted data not as expected')
# Shift forwards by 0.1 s
data_ref = [0, 1, 2, 1, 0, 0, 0]
npt.assert_allclose(st_shift.select(station='BBB')[0].data,
data_ref,
rtol=1e-5,
atol=1e-3,
err_msg='Shifted data not as expected')
def test_raise_unmatching_ids(self):
"""
Test error raised for multiple stream identifiers
"""
with NamedTemporaryFile() as tf1, NamedTemporaryFile() as tf2:
obspy.Trace(data=np.arange(10, dtype=np.int32),
header={
"starttime": obspy.UTCDateTime(0),
"network": "NL",
"station": "HGN",
"location": "02",
"channel": "BHZ"
}).write(tf1.name,
format="mseed",
encoding="STEIM1",
reclen=256)
obspy.Trace(data=np.arange(10, dtype=np.float32),
header={
"starttime": obspy.UTCDateTime(100),
"sampling_rate": 2.0,
"network": "BW",
"station": "ALTM",
"location": "00",
"channel": "EHE"
}).write(tf2.name,
format="mseed",
encoding="FLOAT32",
reclen=1024)
with self.assertRaises(ValueError) as e:
MSEEDMetadata([tf1.name, tf2.name])
self.assertEqual(e.exception.args[0],
"All traces must have the same SEED id and quality.")
def gappy_stream(self):
"""Create a very simple mseed with one gap, return it."""
stats = dict(
network="UU",
station="ELU",
location="01",
channel="ELZ",
sampling_rate=1,
starttime=self.start,
)
len1 = int(self.gap_start - self.start)
# create first trace
ar1 = np.random.rand(len1)
tr1 = obspy.Trace(data=ar1, header=stats)
assert tr1.stats.endtime <= self.gap_start
# create second trace
len2 = int(self.end - self.gap_end)
ar2 = np.random.rand(len2)
stats2 = dict(stats)
stats2.update({"starttime": self.gap_end})
tr2 = obspy.Trace(data=ar2, header=stats2)
# assemble traces make sure gap is there
assert tr2.stats.starttime >= self.gap_end
st = obspy.Stream(traces=[tr1, tr2])
gaps = st.get_gaps()
assert len(gaps) == 1
return st
def test_filter_bad_waveforms():
CC = dict()
tr = obspy.Trace(header={'station': 'AAA', 'location': '00'})
st = Stream(traces=tr)
code = '%s.%s' % (tr.stats.station, tr.stats.location)
CC[code] = 0.1
tr = obspy.Trace(header={'station': 'BBB', 'location': '00'})
st.append(tr)
code = '%s.%s' % (tr.stats.station, tr.stats.location)
CC[code] = 0.8
tr = obspy.Trace(header={'station': 'CCC', 'location': '00'})
st.append(tr)
code = '%s.%s' % (tr.stats.station, tr.stats.location)
CC[code] = -0.9
tr = obspy.Trace(header={'station': 'DDD', 'location': '00'})
st.append(tr)
code = '%s.%s' % (tr.stats.station, tr.stats.location)
CC[code] = 0.6
st_filter = st.filter_bad_waveforms(CC=CC, CClim=0.6)
npt.assert_equal(len(st_filter), 2)
npt.assert_string_equal(str(st_filter[0].stats.station), 'BBB')
npt.assert_string_equal(str(st_filter[1].stats.station), 'DDD')
def stack(stream, length=None, move=None):
"""
Stack traces in stream by correlation id
:param stream: |Stream| object with correlations
:param length: time span of one trace in the stack in seconds
(alternatively a string consisting of a number and a unit
-- ``'d'`` for days and ``'h'`` for hours -- can be specified,
i.e. ``'3d'`` stacks together all traces inside a three days time
window, default: None, which stacks together all traces)
:param move: define a moving stack, float or string,
default: None -- no moving stack,
if specified move usually is smaller than length to get an overlap
in the stacked traces
:return: |Stream| object with stacked correlations
"""
stream.sort()
stream_stack = obspy.Stream()
ids = {_corr_id(tr) for tr in stream}
ids.discard(None)
for id_ in ids:
traces = [tr for tr in stream if _corr_id(tr) == id_]
if length is None:
data = np.mean([tr.data for tr in traces], dtype=float, axis=0)
tr_stack = obspy.Trace(data, header=traces[0].stats)
tr_stack.stats.key = tr_stack.stats.key + '_s'
if 'num' in traces[0].stats:
tr_stack.stats.num = sum(tr.stats.num for tr in traces)
else:
tr_stack.stats.num = len(traces)
stream_stack.append(tr_stack)
else:
t1 = traces[0].stats.starttime
lensec = _time2sec(length)
movesec = _time2sec(move) if move else lensec
if (lensec % (24 * 3600) == 0
or isinstance(length, str) and 'd' in length):
t1 = UTC(t1.year, t1.month, t1.day)
elif (lensec % 3600 == 0
or isinstance(length, str) and 'm' in length):
t1 = UTC(t1.year, t1.month, t1.day, t1.hour)
t2 = max(t1, traces[-1].stats.endtime - lensec)
for t in IterTime(t1, t2, dt=movesec):
sel = [
tr for tr in traces
if -0.1 <= tr.stats.starttime - t <= lensec + 0.1
]
if len(sel) == 0:
continue
data = np.mean([tr.data for tr in sel], dtype=float, axis=0)
tr_stack = obspy.Trace(data, header=sel[0].stats)
key_add = '_s%s' % length + (move is not None) * ('m%s' % move)
tr_stack.stats.key = tr_stack.stats.key + key_add
tr_stack.stats.starttime = t
if 'num' in traces[0].stats:
tr_stack.stats.num = sum(tr.stats.num for tr in sel)
else:
tr_stack.stats.num = len(sel)
stream_stack.append(tr_stack)
return stream_stack
def test_extraction_of_basic_mseed_headers(self):
"""
Tests extraction of basic features.
"""
# Mixed files.
with NamedTemporaryFile() as tf1, NamedTemporaryFile() as tf2:
obspy.Trace(data=np.arange(10, dtype=np.int32),
header={"starttime": obspy.UTCDateTime(0),
"network": "BW", "station": "ALTM",
"location": "00", "channel": "EHE"}).write(
tf1.name, format="mseed", encoding="STEIM1", reclen=256)
obspy.Trace(data=np.arange(10, dtype=np.float32),
header={"starttime": obspy.UTCDateTime(100),
"sampling_rate": 2.0, "network": "BW",
"station": "ALTM", "location": "00",
"channel": "EHE"}).write(
tf2.name, format="mseed", encoding="FLOAT32", reclen=1024)
md = MSEEDMetadata([tf1.name, tf2.name], add_flags=True)
self.assertEqual(md.meta["network"], "BW")
self.assertEqual(md.meta["station"], "ALTM")
self.assertEqual(md.meta["location"], "00")
self.assertEqual(md.meta["channel"], "EHE")
self.assertEqual(md.meta["quality"], "D")
self.assertEqual(md.meta["start_time"], obspy.UTCDateTime(0))
self.assertEqual(md.meta["end_time"],
obspy.UTCDateTime(105))
self.assertEqual(md.meta["num_records"], 2)
self.assertEqual(md.meta["num_samples"], 20)
self.assertEqual(md.meta["sample_rate"], [1.0, 2.0])
self.assertEqual(md.meta["record_length"], [256, 1024])
self.assertEqual(md.meta["encoding"], ["FLOAT32", "STEIM1"])
def make_stream(dataset):
'''
input: hdf5 dataset
output: obspy stream
'''
data = np.array(dataset)
tr_E = obspy.Trace(data=data[:, 0])
tr_E.stats.starttime = UTCDateTime(dataset.attrs['trace_start_time'])
tr_E.stats.delta = 0.01
tr_E.stats.channel = dataset.attrs['receiver_type']+'E'
tr_E.stats.station = dataset.attrs['receiver_code']
tr_E.stats.network = dataset.attrs['network_code']
tr_N = obspy.Trace(data=data[:, 1])
tr_N.stats.starttime = UTCDateTime(dataset.attrs['trace_start_time'])
tr_N.stats.delta = 0.01
tr_N.stats.channel = dataset.attrs['receiver_type']+'N'
tr_N.stats.station = dataset.attrs['receiver_code']
tr_N.stats.network = dataset.attrs['network_code']
tr_Z = obspy.Trace(data=data[:, 2])
tr_Z.stats.starttime = UTCDateTime(dataset.attrs['trace_start_time'])
tr_Z.stats.delta = 0.01
tr_Z.stats.channel = dataset.attrs['receiver_type']+'Z'
tr_Z.stats.station = dataset.attrs['receiver_code']
tr_Z.stats.network = dataset.attrs['network_code']
stream = obspy.Stream([tr_E, tr_N, tr_Z])
return stream
def fix_cut(self):
"""
Checks to see if any trace is cut into multiple smaller traces based off their unique IDs
If there are multiple traces with the same ID, they will be added together
Any missing data between cut traces will be filled with the linear interpolation between the
start and end points of each trace
"""
toappend = []
newwf = self.copy()
i = 0
for it1, trace1 in enumerate(self):
item = []
idoi = trace1.id
for it2, trace2 in enumerate(newwf):
if trace1.id == trace2.id:
item.append(it2)
for tr in newwf.select(id=idoi):
tr.id = 'NA.NA..NA'
toappend.append(item)
newwf = op.Stream()
for item in toappend:
newtrace = op.Trace()
if len(item) > 1:
newtrace = self[item[0]].__add__(self[item[1]],fill_value='interpolate')
if len(item) > 2:
for i in range(2,len(item)):
newtrace += self[item[i]]
newwf += newtrace
elif len(item) > 0:
newwf += self[item[0]]
wf = newwf.copy()
newwf = op.Stream()
for t in wf:
if t.stats.npts != (self.seconds*self.sr + 1):
newst = t.stats.__deepcopy__()
newst.npts = self.seconds*self.sr + 1
newst.starttime = self.starttime
st1 = t.stats.__deepcopy__()
st1.starttime = self.starttime
st1.npts = 1
st2 = st1.__deepcopy__()
st2.starttime = self.endtime
t1 = op.Trace(data=np.ones(1,dtype=t.data.dtype),header=st1)
t2 = op.Trace(data=np.ones(1,dtype=t.data.dtype),header=st2)
newt = (t.__add__(t1,fill_value=0)).__add__(t2,fill_value=0)
newwf += newt
else:
newwf += t
self.clear()
self += newwf
def test_inplace(self):
src = 'test_src'
out = 'test_out'
inplace = True
data = {src: obspy.Trace(np.array([0, 0, 0]))}
transformed = obspy.Trace(np.array([1, 1, 1]))
tf = BaseTraceTransform(source=src, output=out, inplace=inplace)
data = tf.update(data, transformed)
assert len(data.keys()) == 1
assert data[src] == transformed
def test_continuous_segments_combined(self):
"""
Test continuous segments from traces in two files
that are continuous. Also test a continuous segment
that is continuous but has a different sampling rate
"""
tr_1 = obspy.Trace(data=np.arange(10, dtype=np.int32),
header={"starttime": obspy.UTCDateTime(0)})
tr_2 = obspy.Trace(data=np.arange(10, dtype=np.int32),
header={"starttime": obspy.UTCDateTime(10)})
tr_3 = obspy.Trace(data=np.arange(10, dtype=np.int32),
header={
"starttime": obspy.UTCDateTime(20),
"sampling_rate": 0.5
})
st = obspy.Stream(traces=[tr_1, tr_3])
st2 = obspy.Stream(traces=[tr_2])
with NamedTemporaryFile() as tf1, NamedTemporaryFile() as tf2:
st.write(tf1.name, format="mseed")
st2.write(tf2.name, format="mseed")
md = MSEEDMetadata(files=[tf1.name, tf2.name])
c_seg = md.meta["c_segments"]
self.assertEqual(len(c_seg), 2)
c = c_seg[0]
self.assertEqual(c["start_time"], obspy.UTCDateTime(0))
self.assertEqual(c["end_time"], obspy.UTCDateTime(20))
self.assertEqual(c["segment_length"], 20)
self.assertEqual(c["sample_min"], 0)
self.assertEqual(c["sample_max"], 9)
self.assertEqual(c["num_samples"], 20)
self.assertEqual(c["sample_median"], 4.5)
self.assertEqual(c["sample_lower_quartile"], 2.0)
self.assertEqual(c["sample_upper_quartile"], 7.0)
self.assertEqual(c["sample_rate"], 1.0)
# Not continuous because of different sampling_rate (0.5)
c = c_seg[1]
self.assertEqual(c["start_time"], obspy.UTCDateTime(20))
self.assertEqual(c["end_time"], obspy.UTCDateTime(40))
self.assertEqual(c["segment_length"], 20)
self.assertEqual(c["sample_min"], 0)
self.assertEqual(c["sample_max"], 9)
self.assertEqual(c["num_samples"], 10)
self.assertEqual(c["sample_median"], 4.5)
self.assertEqual(c["sample_lower_quartile"], 2.25)
self.assertEqual(c["sample_upper_quartile"], 6.75)
self.assertEqual(c["sample_rate"], 0.5)
def test_taper_signal():
tr_ref = obspy.Trace(data=np.ones(100), header={'delta': 0.1})
result_ref = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.00647486868104, 0.0257317790264, 0.0572719871734, 0.100278618298,
0.153637823245, 0.215967626634, 0.285653719298, 0.360891268042,
0.439731659872, 0.520132970055, 0.600012846888, 0.677302443521, 0.75,
0.816222687798, 0.874255374086, 0.922595042772, 0.959989721829,
0.985470908713, 0.998378654067, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.999291347838, 0.993634177361,
0.982383934407, 0.965668088726, 0.943676037528, 0.916656959541,
0.884916991715, 0.848815760567, 0.808762307473, 0.76521045406,
0.718653660229, 0.669619433059, 0.618663349936, 0.566362763642,
0.513310260719, 0.460106947223, 0.407355637956, 0.35565402633,
0.305587912263, 0.257724564834, 0.212606294893, 0.170744310467,
0.132612924568, 0.0986441810345, 0.0692229593031, 0.0446826135725,
0.0253011957658, 0.0112983050911, 0.00283259989493, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
])
taper_signal(tr_ref, t_begin=3, t_end=6)
npt.assert_allclose(tr_ref.data,
result_ref,
atol=1e-10,
err_msg='Tapering equal to reference')
tr_ref = obspy.Trace(data=np.ones(100), header={'delta': 0.1})
result_ref = np.array([
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.00647486868104, 0.0257317790264, 0.0572719871734, 0.100278618298,
0.153637823245, 0.215967626634, 0.285653719298, 0.360891268042,
0.439731659872, 0.520132970055, 0.600012846888, 0.677302443521, 0.75,
0.816222687798, 0.874255374086, 0.922595042772, 0.959989721829,
0.985470908713, 0.998378654067, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.999825770934,
0.998432666864, 0.995650341553, 0.991486549842, 0.985952896965,
0.979064806205, 0.970841475907, 0.961305825967, 0.950484433951,
0.938407461021
])
taper_signal(tr_ref, t_begin=3, t_end=9)
npt.assert_allclose(tr_ref.data,
result_ref,
atol=1e-10,
err_msg='Tapering equal to reference')
def create_stream(self,
starttime,
endtime,
seed_ids=None,
sampling_rate=None):
""" create a waveforms from random data """
t1 = obspy.UTCDateTime(starttime)
t2 = obspy.UTCDateTime(endtime)
sr = sampling_rate or self.sampling_rate
ar_len = int((t2.timestamp - t1.timestamp) * sr)
st = obspy.Stream()
for seed in seed_ids or self.seed_ids:
n, s, l, c = seed.split(".")
meta = {
"sampling_rate": sr,
"starttime": t1,
"network": n,
"station": s,
"location": l,
"channel": c,
}
data = np.random.randn(ar_len)
tr = obspy.Trace(data=data, header=meta)
st.append(tr)
return st
def __init__(self, file_name):
if file_name.endswith('.ascii'):
# Read in seismogram.
temp = np.loadtxt(file_name)
self.t, self.data = temp[:, 0], temp[:, 1]
self.fname = file_name
self.directory = os.path.dirname(file_name)
# Initialize obspy
self.tr = obspy.Trace(data=self.data)
self.tr.stats.delta = (self.t[1] - self.t[0])
self.tr.stats.sampling_rate = 1 / self.tr.stats.delta
self.tr.stats.network, self.tr.stats.station, \
self.tr.stats.channel = \
os.path.basename(self.fname).split('.')[:3]
self.tr.stats.channel = self.tr.stats.channel[2]
# Reverse X component to agree with LASIF
if self.tr.stats.channel == 'X':
self.data = self.data * (-1)
elif file_name.endswith('.mseed') or file_name.endswith('.sac'):
self.tr = obspy.read(file_name)[0]
self.fname = file_name
self.t = np.array([
x * self.tr.stats.delta for x in range(0, self.tr.stats.npts)
])
else:
raise SeismogramNotFoundError(
utils.print_red("Seismogram not "
"found."))
def correlate_traces(tr1, tr2, maxshift=3600, demean=True):
"""
Return trace of cross-correlation of two input traces
:param tr1,tr2: two |Trace| objects
:param maxsift: maximal shift in correlation in seconds
"""
n1, s1, l1, c1 = tr1.id.split('.')
n2, s2, l2, c2 = tr2.id.split('.')
sr = tr1.stats.sampling_rate
xdata = obscorr(tr1.data,
tr2.data,
int(round(maxshift * sr)),
demean=demean)
header = {
'network': s1,
'station': c1,
'location': s2,
'channel': c2,
'network1': n1,
'station1': s1,
'location1': l1,
'channel1': c1,
'network2': n2,
'station2': s2,
'location2': l2,
'channel2': c2,
'starttime': tr1.stats.starttime,
'sampling_rate': sr,
}
return obspy.Trace(data=xdata, header=header)
def test_create_Toeplitz_mult():
from stfinv.utils.inversion import _create_Toeplitz_mult
tr = obspy.Trace(data=np.array([0., 0., 0., 0., 1., 2., 1., 0., 0.]))
st = obspy.Stream(tr)
tr = obspy.Trace(data=np.array([0., 0., 1., 3., 2., 1., 0., 0., 0.]))
st.append(tr)
d = np.array([0., 0., 1., 1., 2., 1., 1., 0., 0.])
G = _create_Toeplitz_mult(st)
ref = [
np.convolve(st[0].data, d, 'same'),
np.convolve(st[1].data, d, 'same')
]
res = np.matmul(G, d).reshape(2, 9)
npt.assert_allclose(ref, res, atol=1e-7, rtol=1e-7)
def get_waveforms_for_event(self, event_id):
wf_ids = self._events[event_id]["waveform_ids"]
_t = self._dataframes["wfdisc"]
st = obspy.Stream()
for wf in wf_ids:
wf = _t[_t.id == wf].iloc[0]
with io.open(wf.filename, "rb") as fh:
data = fh.read(4 * wf.npts)
data = from_buffer(data, dtype=np.float32)
# Data is big-endian - we just want to work with little endian.
data.byteswap(True)
tr = obspy.Trace(data=data)
tr.stats.network = "LL"
tr.stats.station = wf.station
tr.stats.sampling_rate = wf.sampling_rate
tr.stats.starttime = wf.starttime
tr.stats.channel = wf.channel.upper()
tr.stats.calib = wf.calib
st.append(tr)
return st
def write_su_adjoint(conf, data, dt, events):
sta_list = {}
traces = {}
for event in events:
sta_list[event] = conf.get_station_list(event)
for comp in conf.simulation.comps:
traces[(event, comp)] = [None for s in sta_list[event]]
for sta in data.keys():
net, name, comp, event = sta.split(".")
tr = obspy.Trace(data[sta].astype(np.float32), {"delta": dt})
found = False
for i, s in enumerate(sta_list[event]):
if net == s["net"] and name == s["name"]:
found = True
break
if not found:
raise Exception("Stations could not be found: {}".format(sta)) # NOQA
traces[(event, comp)][i] = tr
for event in events:
for comp in conf.simulation.comps:
st = obspy.Stream(traces[(event, comp)])
st.write(conf.get_adjoint_su_filename(event, comp),
format="SU", byteorder="<")
def test_detrend():
ts = get_live_timeseries()
seis = get_live_seismogram()
tse = get_live_timeseries_ensemble(3)
seis_e = get_live_seismogram_ensemble(3)
detrend(ts, object_history=True, alg_id="0")
detrend(seis, object_history=True, alg_id="0")
detrend(tse, object_history=True, alg_id="0")
detrend(seis_e, object_history=True, alg_id="0")
detrend(ts, type="linear", object_history=True, alg_id="0")
detrend(ts, type="constant", object_history=True, alg_id="0")
detrend(ts, type="polynomial", order=2, object_history=True, alg_id="0")
detrend(ts,
type="spline",
order=2,
dspline=1000,
object_history=True,
alg_id="0")
# functionality verification testing
ts = get_live_timeseries()
tr = obspy.Trace()
tr.data = np.array(ts.data)
copy = np.array(ts.data)
tr.stats.sampling_rate = 20
tr.detrend(type="simple")
detrend(ts, "simple", object_history=True, alg_id="0")
assert all(abs(a - b) < 0.001 for a, b in zip(ts.data, tr.data))
assert not all(abs(a - b) < 0.001 for a, b in zip(ts.data, copy))
def calc_synthetic_from_grf6(self, st_data, tensor):
st_synth = Stream()
for tr in st_data:
stat = tr.stats.station
loc = tr.stats.location
data = (self.select(station=stat, location=loc,
channel='MTT')[0].data * tensor.m_tt +
self.select(station=stat, location=loc,
channel='MPP')[0].data * tensor.m_pp +
self.select(station=stat, location=loc,
channel='MRR')[0].data * tensor.m_rr +
self.select(station=stat, location=loc,
channel='MTP')[0].data * tensor.m_tp +
self.select(station=stat, location=loc,
channel='MRT')[0].data * tensor.m_rt +
self.select(station=stat, location=loc,
channel='MRP')[0].data * tensor.m_rp)
tr_synth = obspy.Trace(data=data, header=tr.stats)
# # Convolve with STF
# tr_synth.data = np.convolve(tr_synth.data, stf,
# mode='same')[0:tr.stats.npts]
st_synth += tr_synth
return st_synth
def create_stream(
self,
starttime: utc_able_type,
endtime: utc_able_type,
seed_ids: Optional[List[str]] = None,
sampling_rate: Optional[Union[float, int]] = None,
) -> obspy.Stream:
""" create a waveforms from random data """
t1 = to_utc(starttime)
t2 = to_utc(endtime)
sr = sampling_rate or self.sampling_rate
ar_len = int((t2.timestamp - t1.timestamp) * sr)
st = obspy.Stream()
for seed in seed_ids or self.seed_ids:
n, s, l, c = seed.split(".")
meta = {
"sampling_rate": sr,
"starttime": t1,
"network": n,
"station": s,
"location": l,
"channel": c,
}
data = np.random.randn(ar_len)
tr = obspy.Trace(data=data, header=meta)
st.append(tr)
return st
def _create_test_data():
"""
Test data used for some polarization tests.
:return:
"""
x = np.arange(0, 2048 / 20.0, 1.0 / 20.0)
x *= 2. * np.pi
y = np.cos(x)
trZ = obspy.Trace(data=y)
trZ.stats.sampling_rate = 20.
trZ.stats.starttime = obspy.UTCDateTime('2014-03-01T00:00')
trZ.stats.station = 'POLT'
trZ.stats.channel = 'HHZ'
trZ.stats.network = 'XX'
trN = trZ.copy()
trN.data *= 2.
trN.stats.channel = 'HHN'
trE = trZ.copy()
trE.stats.channel = 'HHE'
sz = obspy.Stream()
sz.append(trZ)
sz.append(trN)
sz.append(trE)
sz.sort(reverse=True)
return sz
def read_specfem_ascii_waveform_file(filename, network, station, channel):
"""
Reads SPECFEM ASCII files to a :class:`~obspy.core.stream.Stream` object.
:param filename: The filename.
:type filename: str
:param network: The network id of the data.
:type network: str
:param station: The station id of the data.
:type station: str
:param channel: The channel id of the data.
:type channel: str
"""
time_array, data = np.loadtxt(filename).T
# Try to get a reasonably accurate sample spacing.
dt = np.diff(time_array).mean()
tr = obspy.Trace(data=data)
tr.stats.network = network
tr.stats.station = station
tr.stats.channel = channel
tr.stats.delta = dt
tr.stats.starttime += time_array[0]
return obspy.Stream(traces=[tr])
def test_amplitude(self):
np.random.seed(0)
prob = 1.0
src = 'src'
out = 'out'
tf = Augment(augmentation_types=[AugmentationType.AMPLITUDE],
probability=1.0,
source=src,
output=out)
trace = obspy.Trace(np.random.normal(0, 1, 1000000))
orig_mean = trace.data.mean()
orig_std = trace.data.std()
data = {src: trace}
tf(data)
aug_mean = data[out].data.mean()
aug_std = data[out].data.std()
assert not np.allclose(data[src],
data[out]), 'output should differ from input'
assert np.allclose(orig_mean, aug_mean,
atol=1e-3), 'output and input mean should be close'
assert np.allclose(orig_std, aug_std,
atol=1e-1), 'output and input mean should be close'
def _mergeChannels(st):
"""
function to find longest continuous data chunck and discard the rest
"""
st1 = st.copy()
st1.merge(fill_value=0.0)
start = max([x.stats.starttime for x in st1])
end = min([x.stats.endtime for x in st1])
try:
st1.trim(starttime=start, endtime=end)
except ValueError: # if stream too factured end is larger than start
return obspy.Stream()
ar_len = min([len(x.data) for x in st1])
ar = np.ones(ar_len)
for tr in st1:
ar *= tr.data
trace = obspy.Trace(data=np.ma.masked_where(ar == 0.0, ar))
trace.stats.starttime = start
trace.stats.sampling_rate = st1[0].stats.sampling_rate
if (ar == 0.0).any():
try:
st2 = trace.split()
except Exception:
return obspy.Stream()
times = np.array([[x.stats.starttime, x.stats.endtime] for x in st2])
df = pd.DataFrame(times, columns=['start', 'stop'])
df['duration'] = df['stop'] - df['start']
max_dur = df[df.duration == df['duration'].max()].iloc[0]
st.trim(starttime=max_dur.start, endtime=max_dur.stop)
else:
st = st1
return st
def to_file(self, fname, ftype="su"):
if ftype != "su":
raise ValueError(f"ftype = {ftype} not recognized.")
stream = obspy.Stream()
rint = lambda x: int(round(x))
for sensor in self.sensors:
trace = obspy.Trace(np.array(sensor.amplitude, dtype=np.float32))
trace.stats.delta = sensor.dt
trace.stats.starttime = obspy.UTCDateTime(2020, 12, 18, 10, 0, 0)
if not hasattr(trace.stats, 'su'):
trace.stats.su = {}
trace.stats.su.trace_header = obspy.io.segy.segy.SEGYTraceHeader()
trace.stats.su.trace_header.scalar_to_be_applied_to_all_coordinates = -1000
trace.stats.su.trace_header.source_coordinate_x = rint(
self.source._x * 1000)
trace.stats.su.trace_header.source_coordinate_y = rint(
self.source._y * 1000)
trace.stats.su.trace_header.number_of_horizontally_stacked_traces_yielding_this_trace = rint(
sensor.nstacks - 1)
trace.stats.su.trace_header.delay_recording_time = rint(
sensor.delay * 1000)
trace.stats.su.trace_header.group_coordinate_x = rint(sensor.x *
1000)
trace.stats.su.trace_header.group_coordinate_y = rint(sensor.y *
1000)
trace.stats.su.trace_header.coordinate_units = 1
stream.append(trace)
stream.write(filename=fname, format="SU")
def test_invert_STF_dampened():
from stfinv.utils.inversion import invert_STF
tr = obspy.Trace(data=np.array([0., 0., 0., 0., 1., 2., 1., 0., 0.]))
st_synth = obspy.Stream(tr)
tr = obspy.Trace(data=np.array([0., 0., 1., 3., 2., 1., 0., 0., 0.]))
st_synth.append(tr)
stf_ref = np.array([0., 0., 1., 1., 0., 1., 1., 0., 0.])
tr = obspy.Trace(data=np.convolve(st_synth[0].data, stf_ref, 'same'))
st_data = obspy.Stream(tr)
tr = obspy.Trace(data=np.convolve(st_synth[1].data, stf_ref, 'same'))
st_data.append(tr)
stf = invert_STF(st_data, st_synth, method='dampened', eps=1e-4)
npt.assert_allclose(stf, stf_ref, rtol=1e-2, atol=1e-10)
