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 date2time(sta_sdate, sta_edate):
logger = logging.getLogger(__name__)
smonth = f'0{sta_sdate.month}' if sta_sdate.month < 10 else f'{sta_sdate.month}'
emonth = f'0{sta_edate.month}' if sta_edate.month < 10 else f'{sta_edate.month}'
sday = f'0{sta_sdate.day}' if sta_sdate.day < 10 else f'{sta_sdate.day}'
eday = f'0{sta_edate.day}' if sta_edate.day < 10 else f'{sta_edate.day}'
stime = f'{sta_sdate.year}-{smonth}-{sday}'
etime = f'{sta_edate.year}-{emonth}-{eday}'
return UTC(stime), UTC(etime)
def _filter_starttime_endtime(df, starttime=None, endtime=None):
""" Filter dataframe on starttime and endtime. """
bool_index = np.ones(len(df), dtype=bool)
t1 = UTC(starttime).timestamp if starttime is not None else -1 * np.inf
t2 = UTC(endtime).timestamp if endtime is not None else np.inf
# get time columns
start_col = getattr(df, "starttime", getattr(df, "start_date", None))
end_col = getattr(df, "endtime", getattr(df, "end_date", None))
in_time = ~((end_col < t1) | (start_col > t2))
return np.logical_and(bool_index, in_time.values)
def _trim_time_period(stream, time_period):
"""Restrict traces of stream to given time period"""
if time_period is None:
return
starttime, endtime = time_period
traces = [
tr for tr in stream
if (starttime is None or tr.stats.starttime >= UTC(starttime)) and (
endtime is None or tr.stats.starttime < UTC(endtime))
]
stream.traces = traces
def custom_get_waveforms(network, station, location, channel, starttime,
endtime, quality=None, minimumlength=None,
longestonly=None, filename=None, attach_response=False,
**kwargs):
with pyasdf.ASDFDataSet('/g/data/ha3/Passive/_ANU/7X(2009-2011)/ASDF/7X(2009-2011).h5', mode='r') as asdfDataSet:
st = Stream()
# ignoring channel for now as all the 7D network waveforms have only BH? channels
filteredList = [i for i in asdfDataSet.waveforms[network + '.' + station].list() if
'raw_recording' in i and
UTC(i.split("__")[1]) < starttime and
UTC(i.split("__")[2]) > endtime]
for t in filteredList:
st += asdfDataSet.waveforms[network + '.' + station][t]
return st
def test_downsample_and_shift(self):
tr = read()[0]
t = tr.stats.starttime = UTC('2018-01-01T00:00:10.000000Z')
# decimate
tr2 = _downsample_and_shift(tr.copy(), 50.)
self.assertEqual(tr2.stats.sampling_rate, 50)
# interpolate
tr2 = _downsample_and_shift(tr.copy(), 40.)
self.assertEqual(tr2.stats.sampling_rate, 40)
# decimate and time shift
tr2 = tr.copy()
tr2.stats.starttime += 0.002
tr2 = _downsample_and_shift(tr2, 50.)
self.assertEqual(tr2.stats.sampling_rate, 50)
self.assertEqual(tr2.stats.starttime, t)
tr2 = tr.copy()
tr2.stats.starttime -= 0.002
tr2 = _downsample_and_shift(tr2, 50.)
self.assertEqual(tr2.stats.sampling_rate, 50)
self.assertEqual(tr2.stats.starttime, t)
# interpolate and time shift
tr2 = tr.copy()
tr2.stats.starttime += 0.002
tr2 = _downsample_and_shift(tr2, 40.)
self.assertEqual(tr2.stats.sampling_rate, 40)
self.assertEqual(tr2.stats.starttime - tr2.stats.delta, t)
tr2 = tr.copy()
tr2.stats.starttime -= 0.002
tr2 = _downsample_and_shift(tr2, 40.)
self.assertEqual(tr2.stats.sampling_rate, 40)
self.assertEqual(tr2.stats.starttime, t)
def test_preprocess(self):
stream = read()
day = UTC('2018-01-02')
for tr in stream:
tr.stats.starttime = day
tr = stream[1]
tr.id = 'GR.FUR..BH' + tr.stats.channel[-1]
tr.stats.sampling_rate = 80.
tr = stream[2]
tr.id = 'GR.WET..BH' + tr.stats.channel[-1]
tr.stats.sampling_rate = 50.
stream = stream.cutout(day + 0.01, day + 10)
stream = stream.cutout(day + 14, day + 16.05)
norm = ('clip', 'spectral_whitening', 'mute_envelope', '1bit')
# see https://docs.scipy.org/doc/numpy-1.13.0/release.html#
# assigning-to-slices-views-of-maskedarray
ignore_msg = r'setting an item on a masked array which has a shared'
with np.warnings.catch_warnings():
np.warnings.filterwarnings('ignore', ignore_msg)
preprocess(stream, day=day, inventory=read_inventory(),
remove_response=True,
filter=None,
normalization=norm,
time_norm_options=None,
spectral_whitening_options=None,
decimate=5)
for tr in stream:
self.assertEqual(tr.stats.sampling_rate, 10)
for tr in stream:
self.assertEqual(set(tr.data._data), {-1, 0, 1})
mask = np.ma.getmask(tr.data)
np.testing.assert_equal(tr.data[mask]._data, 0)
self.assertGreater(np.count_nonzero(mask), 0)
self.assertEqual(len(stream), 3)
def test_write_pha_minimal(self):
ori = Origin(time=UTC(0), latitude=42, longitude=43, depth=10000)
pick = Pick(time=UTC(10),
phase_hint='S',
waveform_id=WaveformStreamID(station_code='STA'))
del ori.latitude_errors
del ori.longitude_errors
del ori.depth_errors
cat = Catalog([Event(origins=[ori], picks=[pick])])
with NamedTemporaryFile() as tf:
tempfile = tf.name
with self.assertWarnsRegex(UserWarning, 'Missing mag'):
cat.write(tempfile, 'HYPODDPHA')
cat2 = read_events(tempfile)
self.assertEqual(len(cat2), 1)
self.assertEqual(len(cat2[0].picks), 1)
def test_shift(self):
tr = read()[0]
dt = tr.stats.delta
t = tr.stats.starttime = UTC('2018-01-01T00:00:10.000000Z')
tr2 = tr.copy()
_downsample_and_shift(tr2)
self.assertEqual(tr2, tr)
tr2 = tr.copy()
tr2.stats.starttime = t + 0.1 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
tr2 = tr.copy()
tr2.stats.starttime = t - 0.1 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
tr2 = tr.copy()
tr2.stats.starttime = t - 0.49 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
tr2 = tr.copy()
tr2.stats.starttime = t - 0.0001 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
# shift cumulatively by +1 sample
tr2 = tr.copy()
tr2.stats.starttime += 0.3 * dt
_downsample_and_shift(tr2)
tr2.stats.starttime += 0.3 * dt
_downsample_and_shift(tr2)
tr2.stats.starttime += 0.4 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
np.testing.assert_allclose(tr2.data[201:-200], tr.data[200:-201],
rtol=1e-2, atol=1)
cc = correlate(tr2.data, tr.data, 1000)
shift, cc_max = xcorr_max(cc)
self.assertEqual(shift, 1)
self.assertGreater(cc_max, 0.995)
# shift cumulatively by -1 sample
tr2 = tr.copy()
tr2.stats.starttime -= 0.3 * dt
_downsample_and_shift(tr2)
tr2.stats.starttime -= 0.3 * dt
_downsample_and_shift(tr2)
tr2.stats.starttime -= 0.4 * dt
_downsample_and_shift(tr2)
self.assertEqual(tr2.stats.starttime, t)
np.testing.assert_allclose(tr2.data[200:-201], tr.data[201:-200],
rtol=1e-2, atol=2)
cc = correlate(tr2.data, tr.data, 1000)
shift, cc_max = xcorr_max(cc)
self.assertEqual(shift, -1)
self.assertGreater(cc_max, 0.995)
def get_cmap(extend=False):
bounds = [b.matplotlib_date for b in get_bounds()]
if extend:
bounds[-1] = UTC('2018-05-26').matplotlib_date
colors = get_colors()[:len(bounds) - 1]
cmap = ListedColormap(colors, name='AccentL')
norm = BoundaryNorm(bounds, ncolors=len(colors))
return cmap, norm
def _create_event(ser):
""" create an event from a row from the event dataframe """
event = oe.Event(
resource_id=rid(ser.evid),
creation=oe.CreationInfo(agency_id=ser.auth,
creation_time=UTC(ser.lddate)),
preferred_origin_id=str(ser.prefor),
)
return event
def get_waveforms(
stream: Stream,
network: str = "*",
station: str = "*",
location: str = "*",
channel: str = "*",
starttime: Optional[UTC] = None,
endtime: Optional[UTC] = None,
):
"""
A subset of the Client.get_waveforms method.
Simply makes successive calls to Stream.select and Stream.trim under the
hood. Matching is available on all str parameters.
Parameters
----------
network
The network code
station
The station code
location
Location code
channel
Channel code
starttime
Starttime for query
endtime
Endtime for query
Returns
-------
Stream
"""
stream = stream.copy()
st = stream.select(network=network,
station=station,
location=location,
channel=channel)
st = st.trim(starttime=UTC(starttime or SMALL_UTC),
endtime=UTC(endtime or BIG_UTC))
return st
def _create_pick(ser):
""" create picks """
ser = ser[(ser != -1) & ~(ser.isnull())]
co = oe.CreationInfo(
agencey_idr=ser.get('auth'),
creation_time=UTC(ser.get('lddate')),
)
seed_str = _get_seed_str(ser)
wid = oe.WaveformStreamID(seed_string=seed_str)
pick = oe.Pick(
time=UTC(ser.time),
resource_id=rid(ser.arid),
creation_info=co,
waveform_id=wid,
)
return pick
def create_config(conf='conf.json', tutorial=False, less_data=False):
"""Create JSON config file and download tutorial data if requested"""
shutil.copyfile(resource_filename('yam', 'conf_example.json'), conf)
temp_dir = os.path.join(tempfile.gettempdir(), 'yam_example_data')
template = os.path.join(temp_dir, 'example_data')
station_template = os.path.join(temp_dir, 'example_inventory')
try:
num_files = (len([name for name in os.listdir(template)]),
len([name for name in os.listdir(station_template)]))
except FileNotFoundError:
num_files = (0, 0)
if tutorial and (num_files[0] <
(9 if less_data else 54) or num_files[1] < 3):
print('Download example data from Geofon')
from obspy import UTCDateTime as UTC
from obspy.clients.fdsn.mass_downloader import (GlobalDomain,
Restrictions,
MassDownloader)
domain = GlobalDomain()
restrictions = Restrictions(
starttime=UTC('2010-02-04' if less_data else '2010-02-01'),
endtime=UTC('2010-02-06' if less_data else '2010-02-15'),
network='CX',
station='PATCX',
location=None,
channel_priorities=["BH[ZN]"],
chunklength_in_sec=86400,
reject_channels_with_gaps=False,
minimum_length=0.5)
mdl = MassDownloader(providers=['GFZ'])
kw = dict(threads_per_client=1, download_chunk_size_in_mb=200)
mdl.download(domain, restrictions, template, station_template, **kw)
restrictions.station = 'PB06'
if not less_data:
restrictions.endtime = UTC('2010-02-12')
mdl.download(domain, restrictions, template, station_template, **kw)
restrictions.station = 'PB01'
restrictions.endtime = UTC('2010-02-04 08:00:00')
restrictions.channel_priorities = ["BHZ"]
mdl.download(domain, restrictions, template, station_template, **kw)
if not less_data:
restrictions.starttime = UTC('2010-02-08 00:00:00')
restrictions.endtime = UTC('2010-02-09 23:55:00')
restrictions.channel_priorities = ["BHZ"]
mdl.download(domain, restrictions, template, station_template,
**kw)
if tutorial:
dest_dir = os.path.dirname(conf)
dest_dir_data = os.path.join(dest_dir, 'example_data')
dest_dir_inv = os.path.join(dest_dir, 'example_inventory')
if not os.path.exists(dest_dir_data):
if less_data:
ignore = shutil.ignore_patterns('*2010020[123]T000000Z__*',
'*2010020[6-9]T000000Z__*',
'*2010021?T000000Z__*')
else:
ignore = None
shutil.copytree(template, dest_dir_data, ignore=ignore)
if not os.path.exists(dest_dir_inv):
shutil.copytree(station_template, dest_dir_inv)
def _download_bingham(self):
""" Use obspy's mass downloader to get station/waveforms data. """
bank = WaveBank(self.waveform_path)
domain = CircularDomain(
self.latitude,
self.longitude,
minradius=0,
maxradius=kilometers2degrees(self.max_dist),
)
chan_priorities = ["HH[ZNE]", "BH[ZNE]", "EL[ZNE]", "EN[ZNE]"]
cat = obspy.read_events(str(self.source_path / "events.xml"))
df = events_to_df(cat)
for _, row in df.iterrows():
starttime = row.time - self.time_before
endtime = row.time + self.time_after
restrictions = Restrictions(
starttime=UTC(starttime),
endtime=UTC(endtime),
minimum_length=0.90,
minimum_interstation_distance_in_m=100,
channel_priorities=chan_priorities,
location_priorities=["", "00", "01", "--"],
)
kwargs = dict(
domain=domain,
restrictions=restrictions,
mseed_storage=str(self.waveform_path),
stationxml_storage=str(self.station_path),
)
MassDownloader(providers=[self._download_client]).download(
**kwargs)
# ensure data were downloaded
bank.update_index()
assert not bank.read_index(starttime=starttime,
endtime=endtime).empty
# update wavebank
WaveBank(self.waveform_path).update_index()
def multi_download(client,inv,net,stn,slat,slon,elat,elon,evdp,evtime,em,emt,fcat,stalons,stalats,staNetNames,phase='P',locations=[""]):
logger = logging.getLogger(__name__)
strm = None
j=0
msg = None
model = TauPyModel('iasp91')
arrivals = model.get_travel_times_geo(float(evdp),slat,slon,float(elat),float(elon),phase_list=[phase])
if phase=='P':
t1 = UTC(str(evtime)) + int(arrivals[0].time - 50)
t2 = UTC(str(evtime)) + int(arrivals[0].time + 110)
# t1 = UTC(str(evtime)) + int(arrivals[0].time - 25)
# t2 = UTC(str(evtime)) + int(arrivals[0].time + 75)
elif phase=='SKS':
t1 = UTC(str(evtime)) + int(arrivals[0].time - 80)
t2 = UTC(str(evtime)) + int(arrivals[0].time + 80)
# sel_inv = inv.select(network=net).select(station=stn)[0][0]
# if not sel_inv.is_active(starttime=t1, endtime=t2):
# # logger.warning(f"------> Station not active during {evtime}")
# msg = f"Station not active during {evtime}"
# return strm, 0, msg
# process_id = os.getpid()
pharr = UTC(str(evtime)) + arrivals[0].time
while not strm:
client_local = Client(client[j])
stats_args = {"_format":'H5', "onset" : UTC(str(evtime)) + arrivals[0].time, "event_latitude": elat, "event_longitude": elon,"event_depth":evdp, "event_magnitude":em,"event_time":UTC(str(evtime)),"phase":phase,"station_latitude":slat,"station_longitude":slon,"inclination":arrivals[0].incident_angle,"slowness":arrivals[0].ray_param_sec_degree}
if phase=='P':
for loc in locations:
with Timeout(5):
strm = retrieve_waveform(client_local,net,stn,t1,t2,stats_dict=stats_args,cha="BHE,BHN,BHZ",loc=loc,pharr = pharr, phasenm = phase)
if strm:
break
elif phase=='SKS':
for loc in locations:
# print(f"Location: {loc}")
with Timeout(5):
strm = retrieve_waveform(client_local,net,stn,t1,t2,stats_dict=stats_args,cha="BHE,BHN,BHZ",attach_response=True,loc=loc,pharr = pharr, phasenm = phase)
if strm:
break #break the locations loop
if strm:
fcat.write('{} | {:9.4f}, {:9.4f} | {:5.1f} | {:5.1f} {:4s} | {}\n'.format(evtime,elat,elon,evdp,em,emt,client[j]))
stalons.append(slon)
stalats.append(slat)
staNetNames.append(f"{net}_{stn}")
# print("stream obtained\n")
msg = f"Data {evtime}"
res = 1
break
elif j == len(client)-1:
res = 0
msg = f"No data {evtime}"
break
j+=1
return strm, res, msg
def main():
base_dir = '/g/data/ha3/Passive/_ANU/7B(1993-1995)'
asdf_file = os.path.join(base_dir, 'ASDF', '7B(1993-1995).h5')
out_dir = os.path.join(base_dir, 'small_mseed_DATA')
inv = read_inventory(os.path.join(base_dir, '7B.xml'))
asdf = ASDFDataSet(asdf_file, mode='r')
for sta in inv.networks[0].stations:
if asdf.waveforms.__contains__(inv.networks[0].code + '.' + sta.code):
for i in asdf.waveforms[inv.networks[0].code + '.' +
sta.code].list():
if i.endswith('raw_recording'):
start_time = UTC(i.split("__")[1])
st = asdf.waveforms[inv.networks[0].code + '.' +
sta.code][i]
medn = np.median(st[0].data)
while (abs(st[0].data[np.argmax(st[0].data)]) > 1e8
or abs(st[0].data[np.argmin(st[0].data)]) > 1e8):
if abs(st[0].data[np.argmax(st[0].data)]) > 1e8:
st[0].data[np.argmax(
st[0].data)] = abs(medn) if st[0].data[
np.argmax(st[0].data)] > 0 else -abs(medn)
if abs(st[0].data[np.argmin(st[0].data)]) > 1e8:
st[0].data[np.argmin(
st[0].data)] = abs(medn) if st[0].data[
np.argmin(st[0].data)] > 0 else -abs(medn)
while (start_time + 86400 < UTC(i.split("__")[2])):
tr = st[0].copy()
create_chunk(out_dir, tr, start_time,
start_time + 86400, sta)
start_time += 86400
if start_time < UTC(i.split("__")[2]):
tr = st[0].copy()
create_chunk(out_dir, tr, start_time,
UTC(i.split("__")[2]), sta)
def _create_origin(ser):
""" create an origin and attach to event """
event = get_object(ser.evid)
origin = oe.Origin(
resource_id=rid(ser.orid),
time=UTC(ser.time),
latitude=ser.lat,
longitude=ser.lon,
depth=ser.depth * 1000, # convert to m
)
# temporarily attach event reference to origin
origin.__dict__['event'] = event
event.origins.append(origin)
def test_stretch(self):
h = {'sampling_rate': 100}
# TODO: allow to call stretch without these headers
h['network1'] = h['network2'] = 'NET'
h['station1'] = h['station2'] = h['network'] = h['location'] = 'STA'
h['location1'] = h['location2'] = ''
h['channel1'] = h['channel2'] = h['location'] = h['channel'] = 'HHZ'
h['dist'] = h['azi'] = h['baz'] = 0
vel_changes = [0, 1, -1]
traces = []
dt = 24 * 3600
t0 = UTC()
for i, v in enumerate(vel_changes):
mul = 1 + v / 100
# there is a small difference, because the routines from MIIC
# use the following approximation for stretching:
# mul = np.exp(v / 100)
t = np.linspace(-10 * mul, 10 * mul, 10001)
data = np.cos(2 * np.pi * t)
h['starttime'] = t0 + i * dt
tr = Trace(data, header=h)
traces.append(tr)
d = stretch(Stream(traces),
reftr=traces[0],
str_range=1.1,
nstr=2201,
time_windows=[[1], 4],
sides='both')
expect = np.array(vel_changes)[:, np.newaxis]
np.testing.assert_allclose(d['velchange_vs_time'], expect, atol=0.008)
# routine from miic uses approximation exp(dv/v) = 1 + dv/v
corrected = -np.log(d['velchange_vs_time'] / -100 + 1) * 100
np.testing.assert_allclose(corrected, expect, rtol=1e-4)
# test writing and reading
with tempfile.TemporaryDirectory(prefix='yam_') as tmpdir:
fname = os.path.join(tmpdir, 'stretch.h5')
d['attrs']['key'] = 'test'
write_dict(d, fname)
d2 = read_dicts(fname)[0]
for key in d:
if key == 'sim_mat':
np.testing.assert_allclose(d2[key], d[key], rtol=1e-3)
elif isinstance(d2[key], np.ndarray):
np.testing.assert_equal(d2[key], d[key])
else:
self.assertEqual(d2[key], d[key])
d2['attrs']['key'] = 'test2'
write_dict(d2, fname)
def test_stretch(self):
h = {'sampling_rate': 100}
h['network1'] = h['network2'] = 'NET'
h['station1'] = h['station2'] = h['network'] = h['location'] = 'STA'
h['location1'] = h['location2'] = ''
h['channel1'] = h['channel2'] = h['location'] = h['channel'] = 'HHZ'
h['dist'] = h['azi'] = h['baz'] = 0
vel_changes = [0, 1, -1]
traces = []
dt = 24 * 3600
t0 = UTC()
for i, v in enumerate(vel_changes):
mul = 1 + v / 100
t = np.linspace(-10 * mul, 10 * mul, 10001)
data = np.cos(2 * np.pi * t)
h['starttime'] = t0 + i * dt
tr = Trace(data, header=h)
traces.append(tr)
d = stretch(Stream(traces),
max_stretch=1.1,
num_stretch=2201,
tw=(1, 5),
sides='both',
reftr=traces[0])
expect = np.array(vel_changes)
np.testing.assert_allclose(d['velchange_vs_time'], expect)
np.testing.assert_allclose(d['corr_vs_time'], (1, 1, 1))
self.assertAlmostEqual(d['velchange_values'][-1], 1.1)
self.assertEqual(len(d['velchange_values']), 2201)
# test writing and reading
with tempfile.TemporaryDirectory(prefix='yam_') as tmpdir:
fname = os.path.join(tmpdir, 'stretch.h5')
d['attrs']['key'] = 'test'
write_dict(d, fname)
d2 = read_dicts(fname)[0]
for key in d:
if key == 'sim_mat':
np.testing.assert_allclose(d2[key], d[key], rtol=1e-3)
elif isinstance(d2[key], np.ndarray):
np.testing.assert_equal(d2[key], d[key])
else:
self.assertEqual(d2[key], d[key])
d2['attrs']['key'] = 'test2'
write_dict(d2, fname)
# Copyright 2013-2016 Tom Eulenfeld, MIT license
from obspy import read_events, read_inventory, Stream, UTCDateTime as UTC
from obspy.clients.fdsn import Client
from rf.rfstream import obj2stats, rfstats
from rf import RFStream
evname = './example_events.xml'
invname = './example_inventory.xml'
wavname = './example_data.mseed'
wavname2 = './minimal_example.sac'
lon, lat = -70, -21
t1, t2 = UTC('2011-02-01'), UTC('2011-06-01')
seedid = 'CX.PB01..BH?'
def get_events():
try:
return read_events(evname)
except:
pass
client = Client()
events = client.get_events(starttime=t1,
endtime=t2,
latitude=lat,
longitude=lon,
minradius=30,
maxradius=90,
minmagnitude=6.,
maxmagnitude=6.5)
def iter_matching_event(lat, lon, ddeg, time):
for a in ev:
if (abs(a['lat'] - lat) < ddeg and abs(a['lon'] - lon) < ddeg
and abs(time - UTC(a['time'])) < 5):
yield a
# Date: 2005/02/05 Time: 18:43:30.3100 GMT
# Lat= 37.40 Lon= -121.48 Depth= 7.20
# Moment Tensor Depth= 11.00
# Expo= +22 -0.193150 -2.045900 2.239000 0.137150 0.253480 0.837600
# Md= 4.25 Ml= 4.42 Mw= 4.18 Scalar Moment= 2.317000E+22
# Fault plane: strike= 56 dip=88 slip= 6
# Fault plane: strike=326 dip=84 slip= 178
pattern = (r'Date:\s*([\d/]+)\s*Time:\s*([\d:.]+).*?'
r'Lat=\s*([.\d-]+)\s*Lon=\s*([.\d-]+).+?Mw=\s*([\d.]+)')
for match in re.findall(pattern, text1, flags=re.DOTALL):
date, time, lat, lon, mag = match
lat = float(lat)
lon = float(lon)
mag = float(mag)
utc = UTC(date.replace('/', '_') + ' ' + time)
for a in iter_matching_event(lat, lon, 0.5, utc):
v = (a['time'], 0, dic(a, 'Berkeley', mag))
events.append(v)
# 2005/01/28 22:37:07 34.71 -111.00 3 4.0 Arizona Y 20050128223707 Mechanism
for line in text2.split('\n'):
if line.startswith('#'):
continue
date, time, lat, lon, dep, mag, *bla = line.split()
utc = UTC(date.replace('/', '_') + ' ' + time)
lat = float(lat)
try:
lon = float(lon)
except:
lon = float(lon + dep)
return st
# ---+----------Main---------------------------------
if __name__ == '__main__':
# we use centre of Australia to calculate radius and gather events from 15 to 90 degrees
lonlat = [133.88, -23.69]
# Change parameters below
data = os.path.join('DATA', '')
invfile = data + '7X-inventory.xml'
datafile = data + '7X-event_waveforms_for_rf.h5'
start_time = '2009-12-01 00:00:00'
end_time = '2011-04-01 00:00:00'
inventory = read_inventory(invfile)
# ----------------- End ----------------------
catalog = get_events(lonlat, UTC(start_time), UTC(end_time))
stream = RFStream()
with tqdm() as pbar:
for s in iter_event_data(catalog, inventory, custom_get_waveforms, pbar=pbar):
for trace in s:
stream.extend(s)
stream.write(datafile, 'H5')
def obtain_events(self,
catalogxmlloc,
catalogtxtloc,
minmagnitude=5.5,
maxmagnitude=9.5):
## Check for the station information
if os.path.exists(self.inventorytxtfile):
invent_df = pd.read_csv(self.inventorytxtfile,
sep="|",
keep_default_na=False,
na_values=[""])
total_stations = invent_df.shape[0]
if invent_df.shape[0] == 0:
self.logger.error("No data available, exiting...")
sys.exit()
else:
self.logger.error("No data available, exiting...")
sys.exit()
tot_evnt_stns = 0
if not self.inv:
self.logger.info(
"Reading station inventory to obtain events catalog")
try:
# Read the station inventory
self.inv = read_inventory(self.inventoryfile,
format="STATIONXML")
except Exception as exception:
self.logger.error("No available data", exc_info=True)
sys.exit()
# list all the events during the station active time
self.staNamesNet, staLats, staLons = [], [], []
count = 1
for net in self.inv:
for sta in net:
network = net.code #network name
station = sta.code #station name
print("\n")
self.logger.info(
f"{count}/{total_stations} Retrieving event info for {network}-{station}"
)
count += 1
self.staNamesNet.append(f"{network}_{station}")
sta_lat = sta.latitude #station latitude
staLats.append(sta_lat)
sta_lon = sta.longitude #station longitude
staLons.append(sta_lon)
sta_sdate = sta.start_date #station start date
sta_edate = sta.end_date #station end date
# sta_edate_str = sta_edate
if not sta_edate:
sta_edate = UTC("2599-12-31T23:59:59")
# sta_edate_str = "2599-12-31T23:59:59"
stime, etime = date2time(
sta_sdate, sta_edate) #station start and end time in UTC
catalogxml = catalogxmlloc + f'{network}-{station}-{sta_sdate.year}-{sta_edate.year}-{self.method}-{self.method}_events.xml' #xml catalog
# self.allcatalogxml.append(catalogxml)
catalogtxt = catalogtxtloc + f'{network}-{station}-{sta_sdate.year}-{sta_edate.year}-events-info-{self.method}.txt' #txt catalog
if not os.path.exists(catalogxml) and not os.path.exists(
catalogtxt):
self.logger.info(
f"Obtaining catalog: {self.method}: {network}-{station}-{sta_sdate.year}-{sta_edate.year}"
)
kwargs = {
'starttime': stime,
'endtime': etime,
'latitude': sta_lat,
'longitude': sta_lon,
'minradius': self.minradius,
'maxradius': self.maxradius,
'minmagnitude': minmagnitude,
'maxmagnitude': maxmagnitude
}
client = Client('IRIS')
try:
catalog = client.get_events(**kwargs)
except:
self.logger.warning(
"ConnectionResetError while obtaining the events from the client - IRIS"
)
continue
catalog.write(catalogxml, 'QUAKEML') #writing xml catalog
tot_evnt_stns += len(catalog)
evtimes,evlats,evlons,evdps,evmgs,evmgtps=[],[],[],[],[],[]
self.logger.info("Writing the event data into a text file")
with open(catalogtxt, 'w') as f:
f.write('evtime,evlat,evlon,evdp,evmg\n')
for cat in catalog:
try:
try:
evtime, evlat, evlon, evdp, evmg, evmgtp = cat.origins[
0].time, cat.origins[
0].latitude, cat.origins[
0].longitude, cat.origins[
0].depth / 1000, cat.magnitudes[
0].mag, cat.magnitudes[
0].magnitude_type
except:
evtime, evlat, evlon, evdp, evmg, evmgtp = cat.origins[
0].time, cat.origins[
0].latitude, cat.origins[
0].longitude, cat.origins[
0].depth / 1000, cat.magnitudes[
0].mag, "Mww"
evtimes.append(str(evtime))
evlats.append(float(evlat))
evlons.append(float(evlon))
evdps.append(float(evdp))
evmgs.append(float(evmg))
evmgtps.append(str(evmgtp))
f.write(
'{},{:.4f},{:.4f},{:.1f},{:.1f}\n'.format(
evtime, evlat, evlon, evdp,
evmg)) #writing txt catalog
except Exception as exception:
self.logger.warning(
f"Unable to write for {evtime}")
self.logger.info(
"Finished writing the event data into a text and xml file"
)
else:
self.logger.info(
f"{catalogxml.split('/')[-1]} and {catalogtxt.split('/')[-1]} already exists!"
)
class TestGetGaps:
""" test that the get_gaps method returns info about gaps """
start = UTC("2017-09-18")
end = UTC("2017-09-28")
sampling_rate = 1
gaps = [
(UTC("2017-09-18T18-00-00"), UTC("2017-09-18T19-00-00")),
(UTC("2017-09-18T20-00-00"), UTC("2017-09-18T20-00-15")),
(UTC("2017-09-20T01-25-35"), UTC("2017-09-20T01-25-40")),
(UTC("2017-09-21T05-25-35"), UTC("2017-09-25T10-36-42")),
]
durations = np.array([y - x for x, y in gaps])
overlap = 0
def _make_gappy_archive(self, path):
""" Create the gappy archive defined by params in class. """
ArchiveDirectory(
path,
self.start,
self.end,
self.sampling_rate,
gaps=self.gaps,
overlap=self.overlap,
).create_directory()
return path
# fixtures
@pytest.fixture(scope="class")
def gappy_dir(self, class_tmp_dir):
""" create a directory that has gaps in it """
self._make_gappy_archive(join(class_tmp_dir, "temp1"))
return class_tmp_dir
@pytest.fixture(scope="class")
def gappy_bank(self, gappy_dir):
""" init a sbank on the gappy data """
bank = WaveBank(gappy_dir)
# make sure index is updated after gaps are introduced
if os.path.exists(bank.index_path):
os.remove(bank.index_path)
bank._index_cache = obsplus.bank.utils._IndexCache(bank, 5)
bank.update_index()
return bank
@pytest.fixture()
def gappy_and_contiguous_bank(self, tmp_path):
""" Create a directory with gaps and continuous data """
# first create directory with gaps
self._make_gappy_archive(tmp_path)
# first write data with no gaps
st = obspy.read()
for num, tr in enumerate(st):
tr.stats.station = "GOOD"
tr.write(str(tmp_path / f"good_{num}.mseed"), "mseed")
return WaveBank(tmp_path).update_index()
@pytest.fixture(scope="class")
def empty_bank(self):
""" create a Sbank object initated on an empty directory """
with tempfile.TemporaryDirectory() as td:
bank = WaveBank(td)
yield bank
@pytest.fixture(scope="class")
def gap_df(self, gappy_bank):
""" return a gap df from the gappy bank"""
return gappy_bank.get_gaps_df()
@pytest.fixture(scope="class")
def uptime_df(self, gappy_bank):
""" return the uptime dataframe from the gappy bank """
return gappy_bank.get_uptime_df()
@pytest.fixture()
def uptime_default(self, default_wbank):
""" return the uptime from the default stream bank. """
return default_wbank.get_uptime_df()
# tests
def test_gaps_length(self, gap_df):
""" ensure each of the gaps shows up in df """
assert isinstance(gap_df, pd.DataFrame)
assert not gap_df.empty
group = gap_df.groupby(["network", "station", "location", "channel"])
for gnum, df in group:
assert len(df) == len(self.gaps)
dif = abs(df.gap_duration - self.durations)
assert (dif < (1.5 * self.sampling_rate)).all()
def test_gappy_uptime_df(self, uptime_df):
""" ensure the uptime df is of correct type and accurate """
assert isinstance(uptime_df, pd.DataFrame)
gap_duration = sum([x[1] - x[0] for x in self.gaps])
duration = self.end - self.start
uptime_percent = (duration - gap_duration) / duration
assert (abs(uptime_df["availability"] - uptime_percent) < 0.001).all()
def test_uptime_default(self, uptime_default):
"""
Ensure the uptime of the basic bank (no gaps) has expected times/channels.
"""
df = uptime_default
st = obspy.read()
assert not df.empty, "uptime df is empty"
assert len(df) == len(st)
assert {tr.id for tr in st} == set(obsplus.utils.get_nslc_series(df))
assert (df["gap_duration"] == 0).all()
def test_empty_directory(self, empty_bank):
""" ensure an empty bank get_gaps returns and empty df with expected
columns """
gaps = empty_bank.get_gaps_df()
assert not len(gaps)
assert set(WaveBank.gap_columns).issubset(set(gaps.columns))
def test_kemmerer_uptime(self, kem_fetcher):
""" ensure the kemmerer bank returns an uptime df"""
bank = kem_fetcher.waveform_client
df = bank.get_uptime_df()
assert (df["uptime"] == df["duration"]).all()
def test_gappy_and_contiguous_uptime(self, gappy_and_contiguous_bank):
"""
Ensure when there are gappy streams and continguous streams
get_uptime still returns correct results.
"""
wbank = gappy_and_contiguous_bank
index = wbank.read_index()
uptime = wbank.get_uptime_df()
# make sure the same seed ids are in the index as uptime df
seeds_from_index = set(obsplus.utils.get_nslc_series(index))
seeds_from_uptime = set(obsplus.utils.get_nslc_series(uptime))
assert seeds_from_index == seeds_from_uptime
assert not uptime.isnull().any().any()
class TestSummarizeStreams:
"""tests for summarizing streams."""
start = UTC("2017-09-20T01-00-00")
end = UTC("2017-09-20T02-00-00")
gap_start = UTC("2017-09-20T01-25-35")
gap_end = UTC("2017-09-20T01-25-40")
def clean_dataframe(self, df):
"""Function to fix some common issues with the dataframe."""
for id_code in NSLC:
df[id_code] = (
df[id_code].astype(str).str.replace("b'", "").str.replace("'", "")
)
for time_col in ["starttime", "endtime"]:
df[time_col] = df[time_col].astype("datetime64[ns]")
return df[sorted(df.columns)]
@pytest.fixture
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
@pytest.fixture
def gappy_mseed_path(self, gappy_stream, tmp_path):
"""Return a path to the saved mseed file with gaps."""
out_path = tmp_path / "out.mseed"
gappy_stream.write(str(out_path), format="mseed")
return out_path
def test_summarize_mseed(self, gappy_stream, gappy_mseed_path):
"""
Summarize mseed should return the same answer as the generic
summary function.
"""
summary_1 = summarize_mseed(str(gappy_mseed_path))
df1 = self.clean_dataframe(pd.DataFrame(summary_1))
summary_2 = summarize_generic_stream(str(gappy_mseed_path))
df2 = self.clean_dataframe(pd.DataFrame(summary_2))
assert len(df1) == len(df2)
assert (df1 == df2).all().all()
inv = read_inventory(os.path.join(base_dir, '7B.xml'))
asdf = ASDFDataSet(asdf_file, mode='r')
def create_chunk(trace, st_time, end_time, sta):
trace.trim(starttime=st_time, endtime=end_time)
st_out = Stream(traces=[trace, ])
dest_dir = os.path.join(out_dir, str(trace.stats.starttime.timetuple().tm_year), str(tr.stats.starttime.timetuple().tm_yday))
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
st_out.write(os.path.join(dest_dir, sta.code+'_'+trace.stats.channel+'_'+str(st_time)+'_'+str(end_time)+'.ms'), format='MSEED')
for sta in inv.networks[0].stations:
if asdf.waveforms.__contains__(inv.networks[0].code+'.'+sta.code):
for i in asdf.waveforms[inv.networks[0].code+'.'+sta.code].list():
if i.endswith('raw_recording'):
start_time = UTC(i.split("__")[1])
st = asdf.waveforms[inv.networks[0].code+'.'+sta.code][i]
medn = np.median(st[0].data)
while (abs(st[0].data[np.argmax(st[0].data)]) > 1e8 or abs(st[0].data[np.argmin(st[0].data)]) > 1e8):
if abs(st[0].data[np.argmax(st[0].data)]) > 1e8:
st[0].data[np.argmax(st[0].data)] = abs(medn) if st[0].data[np.argmax(st[0].data)] > 0 else -abs(medn)
if abs(st[0].data[np.argmin(st[0].data)]) > 1e8:
st[0].data[np.argmin(st[0].data)] = abs(medn) if st[0].data[np.argmin(st[0].data)] > 0 else -abs(medn)
while (start_time+86400<UTC(i.split("__")[2])):
tr = st[0].copy()
create_chunk(tr, start_time, start_time+86400, sta)
start_time += 86400
if start_time < UTC(i.split("__")[2]):
tr=st[0].copy()
create_chunk(tr, start_time, UTC(i.split("__")[2]), sta)
def test_correlate(self):
stream = read()
stream2 = stream.copy()
stream3 = stream.copy()
for tr in stream2:
tr.id = 'GR.FUR..BH' + tr.stats.channel[-1]
tr.stats.sampling_rate = 80.
for tr in stream3:
tr.id = 'GR.WET..BH' + tr.stats.channel[-1]
tr.stats.sampling_rate = 50.
stream = stream + stream2 + stream3
day = UTC('2018-01-02')
for tr in stream:
tr.stats.starttime = day
# create some gaps
stream = stream.cutout(day + 0.01, day + 10)
stream = stream.cutout(day + 14, day + 16.05)
# prepare mock objects for call to yam_correlate
def data(starttime, endtime, **kwargs):
return stream.select(**kwargs).slice(starttime, endtime)
io = {'data': data, 'data_format': None, 'inventory': read_inventory()}
res = yam_correlate(io, day, 'outkey', keep_correlations=True)
self.assertEqual(len(res['corr']), 6)
# by default only 'ZZ' combinations
for tr in res['corr']:
self.assertEqual(tr.stats.station[-1], 'Z')
self.assertEqual(tr.stats.channel[-1], 'Z')
if len(set(tr.id.split('.'))) == 2: # autocorr
np.testing.assert_allclose(xcorr_max(tr.data), (0, 1.))
res = yam_correlate(
io, day, 'outkey',
station_combinations=('GR.FUR-GR.WET', 'RJOB-RJOB'),
component_combinations=('ZZ', 'NE', 'NR'),
keep_correlations=True,
stack='1d', njobs=self.njobs)
self.assertEqual(len(res['corr']), 7)
self.assertEqual(len(res['stack']), 7)
ids = ['RJOB.EHE.RJOB.EHN', 'RJOB.EHZ.RJOB.EHZ',
'FUR.BHE.WET.BHN', 'FUR.BHN.WET.BHE',
'FUR.BHR.WET.BHN', 'FUR.BHN.WET.BHR',
'FUR.BHZ.WET.BHZ']
for tr in res['corr']:
self.assertIn(tr.id, ids)
if len(set(tr.id.split('.'))) == 2: # autocorr
np.testing.assert_allclose(xcorr_max(tr.data), (0, 1.))
res = yam_correlate(
io, day, 'outkey', only_auto_correlation=True,
station_combinations=('GR.FUR-GR.WET', 'RJOB-RJOB'),
component_combinations=['ZN', 'RT'], njobs=self.njobs,
keep_correlations=True,
remove_response=True)
self.assertEqual(len(res['corr']), 1)
tr = res['corr'][0]
self.assertEqual(tr.stats.station[-1], 'N')
self.assertEqual(tr.stats.channel[-1], 'Z')
stream.traces = [tr for tr in stream if tr.stats.channel[-1] != 'N']
res = yam_correlate(
io, day, 'outkey',
station_combinations=('GR.FUR-GR.WET', 'RJOB-RJOB'),
component_combinations=('NT', 'NR'), discard=0.0,
keep_correlations=True)
self.assertEqual(res, None)
def main(inventory_file,
waveform_database,
event_catalog_file,
event_trace_datafile,
start_time,
end_time,
taup_model,
distance_range,
magnitude_range,
catalog_only=False):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
waveform_db_is_web = is_url(
waveform_database
) or waveform_database in obspy.clients.fdsn.header.URL_MAPPINGS
if not waveform_db_is_web:
assert os.path.exists(
waveform_database), "Cannot find waveform database file {}".format(
waveform_database)
log.info("Using waveform data source: {}".format(waveform_database))
min_dist_deg = distance_range[0]
max_dist_deg = distance_range[1]
min_mag = magnitude_range[0]
max_mag = magnitude_range[1]
inventory = read_inventory(inventory_file)
log.info("Loaded inventory {}".format(inventory_file))
# Compute reference lonlat from the inventory.
channels = inventory.get_contents()['channels']
lonlat_coords = []
for ch in channels:
coords = inventory.get_coordinates(ch)
lonlat_coords.append((coords['longitude'], coords['latitude']))
lonlat_coords = np.array(lonlat_coords)
lonlat = np.mean(lonlat_coords, axis=0)
log.info("Inferred reference coordinates {}".format(lonlat))
# If start and end time not provided, infer from date range of inventory.
if not start_time:
start_time = inventory[0].start_date
for net in inventory:
start_time = min(start_time, net.start_date)
log.info("Inferred start time {}".format(start_time))
# end if
if not end_time:
end_time = inventory[0].end_date
if end_time is None:
end_time = UTC.now()
for net in inventory:
end_time = max(end_time, net.end_date)
log.info("Inferred end time {}".format(end_time))
# end if
start_time = UTC(start_time)
end_time = UTC(end_time)
event_catalog_file = timestamp_filename(event_catalog_file, start_time,
end_time)
event_trace_datafile = timestamp_filename(event_trace_datafile, start_time,
end_time)
assert not os.path.exists(event_trace_datafile), \
"Output file {} already exists, please remove!".format(event_trace_datafile)
log.info("Traces will be written to: {}".format(event_trace_datafile))
exit_after_catalog = catalog_only
catalog = get_events(lonlat, start_time, end_time, event_catalog_file,
(min_dist_deg, max_dist_deg), (min_mag, max_mag),
exit_after_catalog)
if waveform_db_is_web:
log.info("Use fresh query results from web")
client = Client(waveform_database)
waveform_getter = client.get_waveforms
else:
# Form closure to allow waveform source file to be derived from a setting (or command line input)
asdf_dataset = FederatedASDFDataSet(waveform_database, logger=log)
def closure_get_waveforms(network, station, location, channel,
starttime, endtime):
return asdf_get_waveforms(asdf_dataset, network, station, location,
channel, starttime, endtime)
waveform_getter = closure_get_waveforms
# end if
with tqdm(smoothing=0) as pbar:
stream_count = 0
for s in iter_event_data(catalog,
inventory,
waveform_getter,
tt_model=taup_model,
pbar=pbar):
# Write traces to output file in append mode so that arbitrarily large file
# can be processed. If the file already exists, then existing streams will
# be overwritten rather than duplicated.
# Check first if rotation for unaligned *H1, *H2 channels to *HN, *HE is required.
if not s:
continue
# end if
if s.select(component='1') and s.select(component='2'):
try:
s.rotate('->ZNE', inventory=inventory)
except ValueError as e:
log.error('Unable to rotate to ZNE with error:\n{}'.format(
str(e)))
continue
# end try
# end if
# Order the traces in ZNE ordering. This is required so that normalization
# can be specified in terms of an integer index, i.e. the default of 0 in rf
# library will normalize against the Z component.
s.traces = sorted(s.traces, key=zne_order)
# Assert the ordering of traces in the stream is ZNE.
assert s[0].stats.channel[-1] == 'Z'
assert s[1].stats.channel[-1] == 'N'
assert s[2].stats.channel[-1] == 'E'
# Iterator returns rf.RFStream. Write traces from obspy.Stream to decouple from RFStream.
grp_id = '.'.join(s.traces[0].id.split('.')[0:3])
event_time = str(s.traces[0].meta.event_time)[0:19]
pbar.set_description("{} -- {}".format(grp_id, event_time))
out_stream = obspy.Stream([tr for tr in s])
assert out_stream[0].stats.channel[-1] == 'Z'
assert out_stream[1].stats.channel[-1] == 'N'
assert out_stream[2].stats.channel[-1] == 'E'
write_h5_event_stream(event_trace_datafile, out_stream, mode='a')
stream_count += 1
# end for
if stream_count == 0:
log.warning("No traces found!")
else:
log.info("Wrote {} streams to output file".format(stream_count))
