def test_obspy_snuffle(self):
fn = common.test_data_file('test1.mseed')
stream = obspy.read(fn)
stream.snuffle(launch_hook=close_win)
trace = stream[0]
trace.snuffle(launch_hook=close_win)
def test_obspy_snuffle(self):
fn = common.test_data_file('test1.mseed')
stream = obspy.read(fn)
stream.snuffle(launch_hook=close_win)
trace = stream[0]
trace.snuffle(launch_hook=close_win)
def forward(rundir_or_config_path, event_names):
if not event_names:
return
if op.isdir(rundir_or_config_path):
rundir = rundir_or_config_path
config = read_config(op.join(rundir, 'config.yaml'))
problem, xs, misfits, _ = load_problem_info_and_data(
rundir, subset='harvest')
gms = problem.combine_misfits(misfits)
ibest = num.argmin(gms)
xbest = xs[ibest, :]
ds = config.get_dataset(problem.base_source.name)
problem.set_engine(config.engine_config.get_engine())
for target in problem.targets:
target.set_dataset(ds)
payload = [(problem, xbest)]
else:
config = read_config(rundir_or_config_path)
payload = []
for event_name in event_names:
ds = config.get_dataset(event_name)
event = ds.get_event()
problem = config.get_problem(event)
xref = problem.preconstrain(
problem.pack(problem.base_source))
payload.append((problem, xref))
all_trs = []
events = []
for (problem, x) in payload:
ds.empty_cache()
results = problem.evaluate(x)
event = problem.get_source(x).pyrocko_event()
events.append(event)
for result in results:
if isinstance(result, WaveformMisfitResult):
result.filtered_obs.set_codes(location='ob')
result.filtered_syn.set_codes(location='sy')
all_trs.append(result.filtered_obs)
all_trs.append(result.filtered_syn)
markers = []
for ev in events:
markers.append(pmarker.EventMarker(ev))
trace.snuffle(all_trs, markers=markers, stations=ds.get_stations())
def forward(env, show='filtered'):
payload = []
if env.have_rundir():
env.setup_modelling()
history = env.get_history(subset='harvest')
xbest = history.get_best_model()
problem = env.get_problem()
ds = env.get_dataset()
payload.append((ds, problem, xbest))
else:
for event_name in env.get_selected_event_names():
env.set_current_event_name(event_name)
env.setup_modelling()
problem = env.get_problem()
ds = env.get_dataset()
xref = problem.preconstrain(problem.get_reference_model())
payload.append((ds, problem, xref))
all_trs = []
events = []
stations = {}
for (ds, problem, x) in payload:
results = problem.evaluate(x)
event = problem.get_source(x).pyrocko_event()
events.append(event)
for result in results:
if isinstance(result, WaveformMisfitResult):
if show == 'filtered':
result.filtered_obs.set_codes(location='ob')
result.filtered_syn.set_codes(location='sy')
all_trs.append(result.filtered_obs)
all_trs.append(result.filtered_syn)
elif show == 'processed':
result.processed_obs.set_codes(location='ob')
result.processed_syn.set_codes(location='sy')
all_trs.append(result.processed_obs)
all_trs.append(result.processed_syn)
else:
raise ValueError('Invalid argument for show: %s' % show)
for station in ds.get_stations():
stations[station.nsl()] = station
markers = []
for ev in events:
markers.append(pmarker.EventMarker(ev))
trace.snuffle(all_trs, markers=markers, stations=list(stations.values()))
def manual_qc(options, conf, event_names):
conf = conf['iris_pull_config']
if not event_names:
sys.exit('need event name')
for event_name in event_names:
conf.event_name = event_name
event = _get_event_infos(conf)
stations = _get_stations(conf)
traces = _get_prepared_traces(conf, stations)
fn = conf.path('manual_blacklist_path')
nsl_blacklist = read_manual_blacklist(fn)
retval, markers = trace.snuffle(traces,
events=[event],
stations=stations,
want_markers=True)
for m in markers:
if type(m) is gui_util.Marker:
try:
nslc = m.one_nslc()
nsl_blacklist.add(nslc[:3])
except gui_util.MarkerOneNSLCRequired:
pass
update_manual_blacklist(nsl_blacklist, fn)
def snuffle(stream_or_trace, inventory=None, catalog=None, **kwargs):
'''
Explore ObsPy data with Snuffler.
:param stream_or_trace:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` or
:py:class:`obspy.Trace <obspy.core.trace.Trace>` object
:param inventory:
:py:class:`obspy.Inventory <obspy.core.inventory.inventory.Inventory>`
object
:param catalog:
:py:class:`obspy.Catalog <obspy.core.event.Catalog>` object
:param kwargs:
extra arguments passed to :meth:`pyrocko.trace.Trace.snuffle`.
:returns:
``(return_tag, markers)``, where ``return_tag`` is the a string to flag
how the Snuffler window has been closed and ``markers`` is a list of
:py:class:`pyrocko.gui.marker.Marker` objects.
This function displays an ObsPy stream object in Snuffler. It returns to
the caller once the window has been closed. The ``return_tag`` returned by
the function can be used as a primitive way to communicate a user decision
to the calling script. By default it returns the key pressed to close the
window (if any), either ``'q'`` or ``'x'``, but the value could be
customized when the exit is triggered from within a Snuffling.
See also :py:func:`fiddle` for a variant of this function returning
an interactively modified ObsPy stream object.
'''
from pyrocko import trace
import obspy
obspy_inventory = inventory
obspy_catalog = catalog
if isinstance(stream_or_trace, obspy.Trace):
obspy_stream = obspy.core.stream.Stream(traces=[stream_or_trace])
else:
obspy_stream = stream_or_trace
events = to_pyrocko_events(obspy_catalog)
stations = to_pyrocko_stations(obspy_inventory)
return trace.snuffle(
to_pyrocko_traces(obspy_stream),
events=events,
stations=stations,
want_markers=True,
**kwargs)
def snuffle(stream_or_trace, inventory=None, catalog=None, **kwargs):
'''
Explore ObsPy data with Snuffler.
:param stream_or_trace:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` or
:py:class:`obspy.Trace <obspy.core.trace.Trace>` object
:param inventory:
:py:class:`obspy.Inventory <obspy.core.inventory.inventory.Inventory>`
object
:param catalog:
:py:class:`obspy.Catalog <obspy.core.event.Catalog>` object
:param kwargs:
extra arguments passed to :meth:`pyrocko.trace.Trace.snuffle`.
:returns:
``(return_tag, markers)``, where ``return_tag`` is the a string to flag
how the Snuffler window has been closed and ``markers`` is a list of
:py:class:`pyrocko.gui.marker.Marker` objects.
This function displays an ObsPy stream object in Snuffler. It returns to
the caller once the window has been closed. The ``return_tag`` returned by
the function can be used as a primitive way to communicate a user decision
to the calling script. By default it returns the key pressed to close the
window (if any), either ``'q'`` or ``'x'``, but the value could be
customized when the exit is triggered from within a Snuffling.
See also :py:func:`fiddle` for a variant of this function returning
an interactively modified ObsPy stream object.
'''
from pyrocko import trace
import obspy
obspy_inventory = inventory
obspy_catalog = catalog
if isinstance(stream_or_trace, obspy.Trace):
obspy_stream = obspy.core.stream.Stream(traces=[stream_or_trace])
else:
obspy_stream = stream_or_trace
events = to_pyrocko_events(obspy_catalog)
stations = to_pyrocko_stations(obspy_inventory)
return trace.snuffle(to_pyrocko_traces(obspy_stream),
events=events,
stations=stations,
want_markers=True,
**kwargs)
def iload(filename, load_data=True):
for gse in readgse(filename, load_data=load_data):
for wv in gse.waveforms:
yield wv.trace()
def detect(first512):
lines = first512.lstrip().splitlines()
if len(lines) >= 2:
if lines[0].startswith('WID2 '):
return True
if lines[0].startswith('BEGIN GSE2'):
return True
return False
if __name__ == '__main__':
all_traces = []
for fn in sys.argv[1:]:
if detect(open(fn).read(512)):
all_traces.extend(iload(fn))
trace.snuffle(all_traces)
def plot_snuffler(self):
for i in xrange(self.Nwav):
manifold = self.seismo[i]
# trace.snuffle(manifold.traces)
trace.snuffle(manifold.syn+manifold.traces,events=manifold.events)
def model(
engine,
store_id,
magnitude_min, magnitude_max,
moment_tensor,
stress_drop_min, stress_drop_max,
rupture_velocity_min, rupture_velocity_max,
depth_min, depth_max,
distance_min, distance_max,
measures,
nsources=400,
nreceivers=1,
apply_source_response_via_spectra=True,
debug=True):
d2r = math.pi / 180.
components = set()
for measure in measures:
if not measure.components:
raise Exception('no components given in measurement rule')
for component in measure.components:
components.add(component)
components = list(components)
data = []
nerrors = 0
traces_debug = []
markers_debug = []
for isource in xrange(nsources):
magnitude = num.random.uniform(
magnitude_min, magnitude_max)
stress_drop = num.random.uniform(
stress_drop_min, stress_drop_max)
rupture_velocity = num.random.uniform(
rupture_velocity_min, rupture_velocity_max)
radius = (pmt.magnitude_to_moment(magnitude) * 7./16. /
stress_drop)**(1./3.)
duration = 1.5 * radius / rupture_velocity
if moment_tensor is None:
mt = pmt.MomentTensor.random_dc(magnitude=magnitude)
else:
mt = copy.deepcopy(moment_tensor)
mt.magnitude = magnitude
depth = num.random.uniform(depth_min, depth_max)
if apply_source_response_via_spectra:
source = gf.MTSource(
m6=mt.m6(),
depth=depth)
extra_responses = [
wmeasure.BruneResponse(duration=duration)]
else:
source = gf.MTSource(
m6=mt.m6(),
depth=depth,
stf=gf.HalfSinusoidSTF(effective_duration=duration))
extra_responses = []
for ireceiver in xrange(nreceivers):
angle = num.random.uniform(0., 360.)
distance = num.exp(num.random.uniform(
math.log(distance_min), math.log(distance_max)))
targets = []
for comp in components:
targets.append(gf.Target(
quantity='displacement',
codes=('', '%i_%i' % (isource, ireceiver), '', comp),
north_shift=distance*math.cos(d2r*angle),
east_shift=distance*math.sin(d2r*angle),
depth=0.,
store_id=store_id))
resp = engine.process(source, targets)
amps = []
for measure in measures:
comp_to_tt = {}
for (source, target, tr) in resp.iter_results():
comp_to_tt[target.codes[-1]] = (target, tr)
targets, trs = zip(*(
comp_to_tt[c] for c in measure.components))
try:
result = wmeasure.evaluate(
engine, source, targets, trs,
extra_responses,
debug=debug)
if not debug:
amps.append(result)
else:
amp, trs, marker = result
amps.append(amp)
traces_debug.extend(trs)
markers_debug.append(marker)
except wmeasure.AmplitudeMeasurementFailed:
nerrors += 1
amps.append(None)
data.append([magnitude, duration, depth, distance] + amps)
if debug:
trace.snuffle(traces_debug, markers=markers_debug)
return num.array(data, dtype=num.float)
request_response = fdsn.station(
site='geofon', selection=selection, level='response')
# save the response in YAML and StationXML format
request_response.dump(filename='responses_geofon.yaml')
request_response.dump_xml(filename='responses_geofon.xml')
# Loop through retrieved waveforms and request meta information
# for each trace
traces = io.load('traces.mseed')
displacement = []
for tr in traces:
polezero_response = request_response.get_pyrocko_response(
nslc=tr.nslc_id,
timespan=(tr.tmin, tr.tmax),
fake_input_units='M')
# *fake_input_units*: required for consistent responses throughout entire
# data set
# deconvolve transfer function
restituted = tr.transfer(
tfade=2.,
freqlimits=(0.01, 0.1, 1., 2.),
transfer_function=polezero_response,
invert=True)
displacement.append(restituted)
# Inspect waveforms using Snuffler
trace.snuffle(displacement)
def command_view(args):
def setup(parser):
parser.add_option('--extract',
dest='extract',
metavar='start:stop[:step|@num],...',
help='specify which traces to show')
parser.add_option('--show-phases',
dest='showphases',
default=None,
metavar='[phase_id1,phase_id2,...|all]',
help='add phase markers from ttt')
parser.add_option('--qt5',
dest='gui_toolkit_qt5',
action='store_true',
default=False,
help='use Qt5 for the GUI')
parser.add_option('--qt4',
dest='gui_toolkit_qt4',
action='store_true',
default=False,
help='use Qt4 for the GUI')
parser.add_option('--opengl',
dest='opengl',
action='store_true',
default=False,
help='use OpenGL for drawing')
parser, options, args = cl_parse('view', args, setup=setup)
if options.gui_toolkit_qt4:
config.override_gui_toolkit = 'qt4'
if options.gui_toolkit_qt5:
config.override_gui_toolkit = 'qt5'
gdef = None
if options.extract:
try:
gdef = gf.meta.parse_grid_spec(options.extract)
except gf.meta.GridSpecError as e:
die(e)
store_dirs = get_store_dirs(args)
alpha = 'abcdefghijklmnopqrstxyz'.upper()
markers = []
traces = []
try:
for istore, store_dir in enumerate(store_dirs):
store = gf.Store(store_dir)
if options.showphases == 'all':
phasenames = [pn.id for pn in store.config.tabulated_phases]
elif options.showphases is not None:
phasenames = options.showphases.split(',')
ii = 0
for args in store.config.iter_extraction(gdef):
gtr = store.get(args)
loc_code = ''
if len(store_dirs) > 1:
loc_code = alpha[istore % len(alpha)]
if gtr:
sta_code = '%04i (%s)' % (
ii, ','.join('%gk' % (x/1000.) for x in args[:-1]))
tmin = gtr.deltat * gtr.itmin
tr = trace.Trace(
'',
sta_code,
loc_code,
'%02i' % args[-1],
ydata=gtr.data,
deltat=gtr.deltat,
tmin=tmin)
if options.showphases:
for phasename in phasenames:
phase_tmin = store.t(phasename, args[:-1])
if phase_tmin:
m = marker.PhaseMarker(
[('',
sta_code,
loc_code,
'%02i' % args[-1])],
phase_tmin,
phase_tmin,
0,
phasename=phasename)
markers.append(m)
traces.append(tr)
ii += 1
except (gf.meta.GridSpecError, gf.StoreError, gf.meta.OutOfBounds) as e:
die(e)
trace.snuffle(traces, markers=markers, opengl=options.opengl)
def polarization(ds,
store,
timing,
fmin,
fmax,
ffactor,
time_factor_pre=2.,
time_factor_post=2.,
distance_min=None,
distance_max=None,
depth_min=None,
depth_max=None,
size_factor=0.05,
nsl_to_time=None,
output_filename=None,
output_format=None,
output_dpi=None):
event = ds.get_event()
stations = ds.get_stations()
source = gf.Source.from_pyrocko_event(event)
trs = []
for station in stations:
nsl = station.nsl()
dist = source.distance_to(station)
if distance_min is not None and dist < distance_min:
continue
if distance_max is not None and distance_max < dist:
continue
if depth_min is not None and station.depth < depth_min:
continue
if depth_max is not None and depth_max < station.depth:
continue
if nsl_to_time is None:
tp = event.time + store.t(timing, source, station)
else:
if nsl not in nsl_to_time:
continue
tp = nsl_to_time[nsl]
for component in 'ZNE':
tmin = tp - time_factor_pre / fmin
tmax = tp + time_factor_post / fmin
nslc = nsl + (component, )
freqlimits = [fmin / ffactor, fmin, fmax, fmax * ffactor]
tfade = 1.0 / (fmin / ffactor)
try:
trs_projected, trs_restituted, trs_raw, _ = \
ds.get_waveform(
nslc,
tmin=tmin,
tmax=tmax,
tfade=tfade,
freqlimits=freqlimits,
debug=True)
for tr in trs_projected:
tr.shift(-tp)
trs.extend(trs_projected)
except dataset.NotFound as e:
logger.warn(str(e))
continue
trace.snuffle(trs, stations=stations)
plot_polarizations(stations,
trs,
event=event,
size_factor=size_factor,
output_filename=output_filename,
output_format=output_format,
output_dpi=output_dpi)
# depthmin=2*km,
# depthmax=600*km,
# magmin=4.9)
candidate_fn = '../candidates2013.pf'
candidates = [
m.get_event() for m in gui_util.Marker.load_markers(candidate_fn)
]
event_selector = EventCollection(events=candidates)
ag = AutoGain(data_pile,
stations=stations,
reference_nsl=reference_id,
event_selector=event_selector,
component='Z')
ag.set_phaser(phases)
ag.set_window(window)
ag.process(fband, taper)
ag.save_mean(candidate_fn.replace('candidates', 'gains'))
optics = Optics(ag)
optics.plot()
plt.show()
for s in ag.get_sections():
scaled_traces = s.get_gained_traces()
unscaled_traces = s.get_ungained_traces()
scaled_traces.extend(unscaled_traces)
trace.snuffle(scaled_traces, events=candidates)
import time
import numpy as num
from pyrocko import trace
from pyrocko.io import save
now = time.time()
ydata = num.random.random(1000) - 0.5
tr = [trace.Trace('', 'sta1', '', 'N',
deltat=0.1,
tmin=now,
ydata=ydata),
trace.Trace('', 'sta2', '', 'E',
deltat=0.1,
tmin=now,
ydata=ydata)]
trace.snuffle(tr)
c=0
for t in tr:
c=c+1
save(tr,'/home/djamil/Workspace/Python/Course-Boot-Camp/git/tipb-exercise/tmp_file_'+str(c)+".txt",'text')
if True:
traces, tmins = heart.seis_synthetics(
engine, patches, targets,
arrival_times=arrival_times,
wavename='any_P',
arrival_taper=arrival_taper,
filterer=filterer,
outmode='stacked_traces')
all_traces = traces + synth_traces_nn_t + synth_traces_ml_t+ synth_traces_nn + synth_traces_ml
# display to check
trace.snuffle(
all_traces,
stations=sc.wavemaps[0].stations, events=[event])
if False:
from pyrocko.io import save
save(all_traces, 'traces_%s.yaff' % tshift_str, format='yaff')
if False:
traces1, tmins = heart.seis_synthetics(
engine, [patches[0]], targets, arrival_times=ats,
wavename='any_P', arrival_taper=arrival_taper,
filterer=filterer, outmode='stacked_traces')
gfs.set_stack_mode('numpy')
def _test_homogeneous_scenario(
self,
config_type_class,
component_scheme,
discretized_source_class):
if config_type_class.short_type == 'C' \
or component_scheme.startswith('poro'):
assert False
store_id = 'homogeneous_%s_%s' % (
config_type_class.short_type, component_scheme)
vp = 5.8 * km
vs = 3.46 * km
mod = cake.LayeredModel.from_scanlines(cake.read_nd_model_str('''
0. %(vp)g %(vs)g 2.6 1264. 600.
20. %(vp)g %(vs)g 2.6 1264. 600.'''.lstrip() % dict(vp=vp/km, vs=vs/km)))
store_type = config_type_class.short_type
params = dict(
id=store_id,
sample_rate=1000.,
modelling_code_id='ahfullgreen',
component_scheme=component_scheme,
earthmodel_1d=mod)
if store_type in ('A', 'B'):
params.update(
source_depth_min=1.*km,
source_depth_max=2.*km,
source_depth_delta=0.5*km,
distance_min=4.*km,
distance_max=6.*km,
distance_delta=0.5*km)
if store_type == 'A':
params.update(
receiver_depth=3.*km)
if store_type == 'B':
params.update(
receiver_depth_min=2.*km,
receiver_depth_max=3.*km,
receiver_depth_delta=0.5*km)
if store_type == 'C':
params.update(
source_depth_min=1.*km,
source_depth_max=2.*km,
source_depth_delta=0.5*km,
source_east_shift_min=1.*km,
source_east_shift_max=2.*km,
source_east_shift_delta=0.5*km,
source_north_shift_min=2.*km,
source_north_shift_max=3.*km,
source_north_shift_delta=0.5*km)
config = config_type_class(**params)
config.validate()
store_dir = mkdtemp(prefix=store_id)
self.tempdirs.append(store_dir)
gf.store.Store.create_editables(store_dir, config=config)
store = gf.store.Store(store_dir, 'r')
store.make_ttt()
store.close()
fomosto_ahfullgreen.build(store_dir, nworkers=1)
store = gf.store.Store(store_dir, 'r')
dsource_type = discretized_source_class.__name__
params = {}
if dsource_type == 'DiscretizedMTSource':
params.update(
m6s=num.array([
[1., 2., 3., 4., 5., 6.],
[1., 2., 3., 4., 5., 6.]]))
elif dsource_type == 'DiscretizedExplosionSource':
params.update(
m0s=num.array([2., 2.]))
elif dsource_type == 'DiscretizedSFSource':
params.update(
forces=num.array([[1., 2., 3.], [1., 2., 3.]]))
elif dsource_type == 'DiscretizedPorePressureSource':
params.update(
pp=num.array([3., 3.]))
snorth = 2.0*km
seast = 2.0*km
sdepth = 1.0*km
rnorth = snorth + 3.*km
reast = seast + 4.*km
rdepth = 3.0*km
t0 = 10.0 * store.config.deltat
dsource = discretized_source_class(
times=num.array([t0, t0+5.*store.config.deltat]),
north_shifts=num.array([snorth, snorth]),
east_shifts=num.array([seast, seast]),
depths=num.array([sdepth, sdepth]),
**params)
receiver = gf.Receiver(
north_shift=rnorth,
east_shift=reast,
depth=rdepth)
components = gf.component_scheme_to_description[
component_scheme].provided_components
for seismogram in (store.seismogram, store.seismogram_old):
for interpolation in ('nearest_neighbor', 'multilinear'):
trs1 = []
for component, gtr in seismogram(
dsource, receiver, components,
interpolation=interpolation).items():
tr = gtr.to_trace('', 'STA', '', component)
trs1.append(tr)
trs2 = _make_traces_homogeneous(
dsource, receiver,
store.config.earthmodel_1d.require_homogeneous(),
store.config.deltat, '', 'STA', 'a')
tmin = max(tr.tmin for tr in trs1+trs2)
tmax = min(tr.tmax for tr in trs1+trs2)
for tr in trs1+trs2:
tr.chop(tmin, tmax)
assert tr.data_len() > 2
trs1.sort(key=lambda tr: tr.channel)
trs2.sort(key=lambda tr: tr.channel)
denom = 0.0
for t1, t2 in zip(trs1, trs2):
assert t1.channel == t2.channel
denom += num.sum(t1.ydata**2) + num.sum(t2.ydata**2)
ds = []
for t1, t2 in zip(trs1, trs2):
ds.append(2.0 * num.sum((t1.ydata - t2.ydata)**2) / denom)
ds = num.array(ds)
if component_scheme == 'elastic8':
limit = 1e-2
else:
limit = 1e-6
if not num.all(ds < limit):
print(ds)
trace.snuffle(trs1+trs2)
assert num.all(ds < limit)
def fiddle(stream_or_trace, inventory=None, catalog=None, **kwargs):
'''
Manipulate ObsPy stream object interactively.
:param stream_or_trace:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` or
:py:class:`obspy.Trace <obspy.core.trace.Trace>` object
:param inventory:
:py:class:`obspy.Inventory <obspy.core.inventory.inventory.Inventory>`
object
:param catalog:
:py:class:`obspy.Catalog <obspy.core.event.Catalog>` object
:param kwargs:
extra arguments passed to :meth:`pyrocko.trace.Trace.snuffle`.
:returns: :py:class:`obspy.Stream <obspy.core.stream.Stream>` object with
changes applied interactively (or :py:class:`obspy.Trace
<obspy.core.trace.Trace>` if called with a trace as first argument).
This function displays an ObsPy stream object in Snuffler like
:py:func:`snuffle`, but additionally adds a Snuffling panel to apply some
basic ObsPy signal processing to the contained traces. The applied changes
are handed back to the caller as a modified copy of the stream object.
.. code::
import obspy
from pyrocko import obspy_compat
obspy_compat.plant()
stream = obspy.read()
stream_filtered = stream.fiddle() # returns once window has been
# closed
'''
from pyrocko import trace
import obspy
obspy_inventory = inventory
obspy_catalog = catalog
if isinstance(stream_or_trace, obspy.Trace):
obspy_stream = obspy.core.stream.Stream(traces=[stream_or_trace])
else:
obspy_stream = stream_or_trace
events = to_pyrocko_events(obspy_catalog)
stations = to_pyrocko_stations(obspy_inventory)
snuffling_loader = ObsPyStreamSnufflingLoader(obspy_stream)
launch_hook = kwargs.pop('launch_hook', [])
if not isinstance(launch_hook, list):
launch_hook = [launch_hook]
launch_hook.append(snuffling_loader)
trace.snuffle(
[],
events=events,
stations=stations,
controls=False,
launch_hook=launch_hook,
**kwargs)
new_obspy_stream = snuffling_loader.get_snuffling().get_obspy_stream()
if isinstance(obspy_stream, obspy.Trace):
return new_obspy_stream[0]
else:
return new_obspy_stream
def snuffle(self):
trace.snuffle(self.traces, events=[self.event], stations=self.stations)
lon=11.,
depth=10000.,
strike=20.,
dip=40.,
rake=60.,
magnitude=4.)
# Processing that data will return a pyrocko.gf.Reponse object.
response = engine.process(source_dc, targets)
# This will return a list of the requested traces:
synthetic_traces = response.pyrocko_traces()
# In addition to that it is also possible to extract interpolated travel times
# of phases which have been defined in the store's config file.
store = engine.get_store(store_id)
markers = []
for t in targets:
dist = t.distance_to(source_dc)
depth = source_dc.depth
arrival_time = store.t('begin', (depth, dist))
m = PhaseMarker(tmin=arrival_time,
tmax=arrival_time,
phasename='P',
nslc_ids=(t.codes,))
markers.append(m)
# Finally, let's scrutinize these traces.
trace.snuffle(synthetic_traces, markers=markers)
def process(self, fband, taper, twd, debug):
no_events = len(self.events)
for i_ev, event in enumerate(self.events):
tr_nslc_ids = []
self.logs.info('Processing event %s of %s' % (i_ev, no_events))
section = Section(event, self.stations)
skipped = 0
unskipped = 0
for i_s, s in enumerate(self.stations):
dist = distance_accurate50m(event, s)
arrival = self.arrT[i_ev, i_s]
if num.isnan(arrival):
skipped += 1
self.logs.warning('skipped %s.%s %s' %
(s.network, s.station, event.time))
continue
else:
unskipped += 1
selector = lambda tr: (s.network, s.station, self.component)\
== (tr.network, tr.station, tr.channel)
tr_generator = self.data_pile.chopper(tmin=arrival - twd[0],
tmax=arrival + twd[1],
trace_selector=selector,
load_data=True)
if self.method == 'syn_comp':
tr_syn_generator = self.syn_data_pile.chopper(
tmin=arrival - twd[0],
tmax=arrival + twd[1],
trace_selector=selector,
load_data=True)
for tr in tr_generator:
if not len(tr) > 1 and tr:
tr = tr[0]
if len(tr.ydata) > 0 and num.max(
num.abs(tr.get_ydata())) != 0:
dtype = type(tr.ydata[0])
tr.ydata -= dtype(tr.get_ydata().mean())
# make SNR threshold here!
st_s = num.argmax(num.abs(tr.ydata)) - 10
snr = num.mean([y*y for y in tr.ydata[st_s:st_s+60]])/\
num.mean([y*y for y in tr.ydata[0:60]])
if snr < self.snr_thresh:
continue
# mean(A*A_signal)/mean(A*A_noise)
tr.highpass(fband['order'], fband['corner_hp'])
tr.taper(taper, chop=False)
tr.lowpass(fband['order'], fband['corner_lp'])
if debug is True:
self.logs.debug('SNR %s' % snr)
self.logs.debug('arrival time %s' %
util.time_to_str(arrival))
trace.snuffle(tr,
markers=[
pm.Marker(
nslc_ids=[tr.nslc_id],
tmin=arrival,
tmax=arrival + 3)
])
if num.max(num.abs(tr.get_ydata())) != 0:
section.max_tr[tr.nslc_id] = num.max(
num.abs(tr.get_ydata()))
tr_nslc_ids.append(tr.nslc_id)
else:
for t in tr:
tt = t #[0]
if len(tt.ydata) > 0 and num.max(
num.abs(tt.get_ydata())) != 0:
dtype = type(tt.ydata[0])
# print(tr.ydata, type(tr.ydata))
tt.ydata -= dtype(tt.get_ydata().mean())
st_s = num.argmax(num.abs(tt.ydata)) - 10
snr = num.mean([y*y for y in tt.ydata[st_s:st_s+60]])/\
num.mean([y*y for y in tt.ydata[0:60]])
# print('SNR', snr)
if snr < self.snr_thresh:
continue
tt.highpass(fband['order'], fband['corner_hp'])
tt.taper(taper, chop=False)
tt.lowpass(fband['order'], fband['corner_lp'])
if debug is True:
self.logs.debug('SNR %s' % snr)
self.logs.debug('arrival time %s' %
util.time_to_str(arrival))
trace.snuffle(
tt,
markers=[
pm.Marker(nslc_ids=[tt.nslc_id],
tmin=arrival,
tmax=arrival + 3)
])
if num.max(num.abs(tt.get_ydata())) != 0:
section.max_tr[tt.nslc_id] = num.max(
num.abs(tt.get_ydata()))
tr_nslc_ids.append(tt.nslc_id)
if self.method == 'syn_comp':
for tr in tr_syn_generator:
if not len(tr) > 1 and tr:
tr = tr[0]
if len(tr.ydata) > 0 and num.max(
num.abs(tr.get_ydata())) != 0:
dtype = type(tr.ydata[0])
tr.ydata -= dtype(tr.get_ydata().mean())
st_s = num.argmax(num.abs(tr.ydata)) - 10
snr = num.mean([y*y for y in tr.ydata[st_s:st_s+60]])/\
num.mean([y*y for y in tr.ydata[0:60]])
tr.highpass(fband['order'], fband['corner_hp'])
tr.taper(taper, chop=False)
tr.lowpass(fband['order'], fband['corner_lp'])
if debug is True:
self.logs.debug('SNR %s' % snr)
self.logs.debug('arrival time %s' %
util.time_to_str(arrival))
trace.snuffle(
tr,
markers=[
pm.Marker(nslc_ids=[tr.nslc_id],
tmin=arrival,
tmax=arrival + 3)
])
if num.max(num.abs(tr.get_ydata())) != 0:
section.max_tr_syn[
tr.nslc_id[0:2]] = num.max(
num.abs(tr.get_ydata()))
# tr_nslc_ids_syn.append(tr.nslc_id)
else:
for t in tr:
tt = t #[0]
if len(tt.ydata) > 0 and num.max(
num.abs(tt.get_ydata())) != 0:
dtype = type(tt.ydata[0])
# print(tr.ydata, type(tr.ydata))
tt.ydata -= dtype(tt.get_ydata().mean())
st_s = num.argmax(num.abs(tt.ydata)) - 10
snr = num.mean([y*y for y in tt.ydata[st_s:st_s+60]])/\
num.mean([y*y for y in tt.ydata[0:60]])
# print('SNR', snr)
if snr < self.snr_thresh:
continue
tt.highpass(fband['order'],
fband['corner_hp'])
tt.taper(taper, chop=False)
tt.lowpass(fband['order'],
fband['corner_lp'])
if debug is True:
self.logs.debug('SNR %s' % snr)
self.logs.debug(
'arrival time %s' %
util.time_to_str(arrival))
trace.snuffle(
tt,
markers=[
pm.Marker(
nslc_ids=[tt.nslc_id],
tmin=arrival,
tmax=arrival + 3)
])
if num.max(num.abs(tt.get_ydata())) != 0:
section.max_tr_syn[
tt.nslc_id[0:2]] = num.max(
num.abs(tt.get_ydata()))
#tr_nslc_ids_syn.append(tt.nslc_id)
#else:
# print('no trace', s.network, s.station, tr, util.time_to_str(event.time))
#break
# print(i_s)
self.logs.debug('skipped %s/%s' % (skipped, unskipped))
section.finish(self.method, fband, taper, i_ev)
self.all_nslc_ids.update(tr_nslc_ids)
gc.collect()
self.sections.append(section)
if self.method == 'median_all_avail' and i_ev == no_events - 1:
self.handle_median_stats_option()
taper = trace.CosFader(xfrac=0.25)
#event_selector = EventSelector(distmin=1000*km,
# distmax=20000*km,
# depthmin=2*km,
# depthmax=600*km,
# magmin=4.9)
candidate_fn = '../candidates2013.pf'
candidates = [m.get_event() for m in gui_util.Marker.load_markers(candidate_fn)]
event_selector = EventCollection(events=candidates)
ag = AutoGain(data_pile, stations=stations,
reference_nsl=reference_id, event_selector=event_selector, component='Z')
ag.set_phaser(phases)
ag.set_window(window)
ag.process(fband, taper)
ag.save_mean(candidate_fn.replace('candidates', 'gains'))
optics = Optics(ag)
optics.plot()
plt.show()
for s in ag.get_sections():
scaled_traces = s.get_gained_traces()
unscaled_traces = s.get_ungained_traces()
scaled_traces.extend(unscaled_traces)
trace.snuffle(scaled_traces, events=candidates)
from pyrocko import io, trace, pile
from pyrocko.example import get_example_data
get_example_data('test.mseed')
traces = io.load('test.mseed')
traces[0].snuffle() # look at a single trace
trace.snuffle(traces) # look at a bunch of traces
# do something with the traces:
new_traces = []
for tr in traces:
new = tr.copy()
new.whiten()
# to allow the viewer to distinguish the traces
new.set_location('whitened')
new_traces.append(new)
trace.snuffle(traces + new_traces)
# it is also possible to 'snuffle' a pile:
p = pile.make_pile(['test.mseed'])
p.snuffle()
def snuffle(self):
trace.snuffle(self.traces, events=[self.event], stations=self.stations)
deltat = 0.001
n = int(num.round(tlen / deltat))
out_x = num.zeros(n)
out_y = num.zeros(n)
out_z = num.zeros(n)
import pylab as lab
tau = 0.01
t = num.arange(1000) * deltat
lab.plot(t, num.exp(-t**2 / tau**2))
#lab.show()
add_seismogram(vp, vs, 1.0, 1.0, 1.0, x, f, m6, 'displacement', deltat,
0.0, out_x, out_y, out_z, Gauss(tau))
trs = []
for out, comp in zip([out_x, out_y, out_z], 'NED'):
tr = trace.Trace('',
'Naja!',
'',
comp,
deltat=deltat,
tmin=0.0,
ydata=out)
trs.append(tr)
trace.snuffle(trs)
def off_test_synthetic(self):
from pyrocko import gf
km = 1000.
nstations = 10
edepth = 5*km
store_id = 'crust2_d0'
swin = 2.
lwin = 9.*swin
ks = 1.0
kl = 1.0
kd = 3.0
engine = gf.get_engine()
snorths = (num.random.random(nstations)-1.0) * 50*km
seasts = (num.random.random(nstations)-1.0) * 50*km
targets = []
for istation, (snorths, seasts) in enumerate(zip(snorths, seasts)):
targets.append(
gf.Target(
quantity='displacement',
codes=('', 's%03i' % istation, '', 'Z'),
north_shift=float(snorths),
east_shift=float(seasts),
store_id=store_id,
interpolation='multilinear'))
source = gf.DCSource(
north_shift=50*km,
east_shift=50*km,
depth=edepth)
store = engine.get_store(store_id)
response = engine.process(source, targets)
trs = []
station_traces = defaultdict(list)
station_targets = defaultdict(list)
for source, target, tr in response.iter_results():
tp = store.t('any_P', source, target)
t = tp - 5 * tr.deltat + num.arange(11) * tr.deltat
if False:
gauss = trace.Trace(
tmin=t[0],
deltat=tr.deltat,
ydata=num.exp(-((t-tp)**2)/((2*tr.deltat)**2)))
tr.ydata[:] = 0.0
tr.add(gauss)
trs.append(tr)
station_traces[target.codes[:3]].append(tr)
station_targets[target.codes[:3]].append(target)
station_stalta_traces = {}
for nsl, traces in station_traces.iteritems():
etr = None
for tr in traces:
sqr_tr = tr.copy(data=False)
sqr_tr.ydata = tr.ydata**2
if etr is None:
etr = sqr_tr
else:
etr += sqr_tr
autopick.recursive_stalta(swin, lwin, ks, kl, kd, etr)
etr.set_codes(channel='C')
station_stalta_traces[nsl] = etr
trace.snuffle(trs + station_stalta_traces.values())
deltat = trs[0].deltat
nnorth = 50
neast = 50
size = 400*km
north = num.linspace(-size/2., size/2., nnorth)
north2 = num.repeat(north, neast)
east = num.linspace(-size/2., size/2., neast)
east2 = num.tile(east, nnorth)
depth = 5*km
def tcal(target, i):
try:
return store.t(
'any_P',
gf.Location(
north_shift=north2[i],
east_shift=east2[i],
depth=depth),
target)
except gf.OutOfBounds:
return 0.0
nsls = sorted(station_stalta_traces.keys())
tts = num.fromiter((tcal(station_targets[nsl][0], i)
for i in xrange(nnorth*neast)
for nsl in nsls), dtype=num.float)
arrays = [
station_stalta_traces[nsl].ydata.astype(num.float) for nsl in nsls]
offsets = num.array(
[int(round(station_stalta_traces[nsl].tmin / deltat))
for nsl in nsls], dtype=num.int32)
shifts = -num.array(
[int(round(tt / deltat))
for tt in tts], dtype=num.int32).reshape(nnorth*neast, nstations)
weights = num.ones((nnorth*neast, nstations))
print shifts[25*neast + 25] * deltat
print offsets.dtype, shifts.dtype, weights.dtype
print 'stack start'
mat, ioff = parstack(arrays, offsets, shifts, weights, 1)
print 'stack stop'
mat = num.reshape(mat, (nnorth, neast))
from matplotlib import pyplot as plt
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1, aspect=1.0)
axes.contourf(east/km, north/km, mat)
axes.plot(
g(targets, 'east_shift')/km,
g(targets, 'north_shift')/km, '^')
axes.plot(source.east_shift/km, source.north_shift/km, 'o')
plt.show()
yield trace.Trace('',
sta,
'',
cha,
tmin=tmin,
tmax=tmax,
deltat=deltat,
ydata=ydata)
except EOF:
pass
def detect(first512):
lines = first512.splitlines()
if len(lines) >= 5 and \
lines[0].startswith(b'XW01') and lines[2].startswith(b'WID1') and \
lines[4].startswith(b'DAT1'):
return True
return False
if __name__ == '__main__':
all_traces = []
for fn in sys.argv[1:]:
all_traces.extend(iload(fn))
trace.snuffle(all_traces)
def run_extract(config, debug=False):
engine = config.get_engine()
output_path = config.expand_path(config.output_path)
util.ensuredir(output_path)
output_measures_path = op.join(output_path, 'measures.txt')
output_config_path = op.join(output_path, 'config.yaml')
wconfig.write_config(config, output_config_path)
with open(output_measures_path, 'w') as out:
out.write('# event station %s\n' %
' '.join(measure.name for measure in config.measures))
event_names = config.get_event_names()
for event_name in event_names:
logger.info('processing event %s' % event_name)
try:
ds = config.get_dataset(event_name)
except OSError as e:
logger.warn('could not get dataset for event %s' % event_name)
continue
event = ds.get_event()
source = gf.DCSource.from_pyrocko_event(event)
stations = ds.get_stations()
debug_infos = []
for station in stations:
values = []
try:
for measure in config.measures:
targets = [
gf.Target(quantity='velocity',
codes=station.nsl() + (component, ),
store_id=config.store_id,
lat=station.lat,
lon=station.lon,
depth=station.depth,
elevation=station.elevation)
for component in measure.components
]
value, debug_info = measure.evaluate(engine,
source,
targets,
ds,
debug=debug)
values.append(value)
debug_infos.append(debug_info)
out.write('%s %s %s\n' %
(event.name, '.'.join(x for x in station.nsl()),
' '.join('%g' % value for value in values)))
except (wmeasure.FeatureMeasurementFailed, dataset.NotFound,
gf.OutOfBounds) as e:
logger.warn(
'feature extraction failed for %s, %s:\n %s' %
(event.name, '.'.join(x for x in station.nsl()), e))
if debug:
traces = []
markers = []
for traces_this, markers_this in debug_infos:
traces.extend(traces_this)
markers.extend(markers_this)
trace.snuffle(traces,
markers=markers,
events=[event],
stations=stations)
def command_view(args):
def setup(parser):
parser.add_option('--extract',
dest='extract',
metavar='start:stop[:step|@num],...',
help='specify which traces to show')
parser.add_option('--show-phases',
dest='showphases',
default=None,
metavar='[phase_id1,phase_id2,...|all]',
help='add phase markers from ttt')
parser.add_option('--qt5',
dest='gui_toolkit_qt5',
action='store_true',
default=False,
help='use Qt5 for the GUI')
parser.add_option('--qt4',
dest='gui_toolkit_qt4',
action='store_true',
default=False,
help='use Qt4 for the GUI')
parser.add_option('--opengl',
dest='opengl',
action='store_true',
default=False,
help='use OpenGL for drawing')
parser, options, args = cl_parse('view', args, setup=setup)
if options.gui_toolkit_qt4:
config.override_gui_toolkit = 'qt4'
if options.gui_toolkit_qt5:
config.override_gui_toolkit = 'qt5'
gdef = None
if options.extract:
try:
gdef = gf.meta.parse_grid_spec(options.extract)
except gf.meta.GridSpecError as e:
die(e)
store_dirs = get_store_dirs(args)
alpha = 'abcdefghijklmnopqrstxyz'.upper()
markers = []
traces = []
try:
for istore, store_dir in enumerate(store_dirs):
store = gf.Store(store_dir)
if options.showphases == 'all':
phasenames = [pn.id for pn in store.config.tabulated_phases]
elif options.showphases is not None:
phasenames = options.showphases.split(',')
ii = 0
for args in store.config.iter_extraction(gdef):
gtr = store.get(args)
loc_code = ''
if len(store_dirs) > 1:
loc_code = alpha[istore % len(alpha)]
if gtr:
sta_code = '%04i (%s)' % (ii, ','.join('%gk' % (x / 1000.)
for x in args[:-1]))
tmin = gtr.deltat * gtr.itmin
tr = trace.Trace('',
sta_code,
loc_code,
'%02i' % args[-1],
ydata=gtr.data,
deltat=gtr.deltat,
tmin=tmin)
if options.showphases:
for phasename in phasenames:
phase_tmin = store.t(phasename, args[:-1])
if phase_tmin:
m = marker.PhaseMarker([
('', sta_code, loc_code, '%02i' % args[-1])
],
phase_tmin,
phase_tmin,
0,
phasename=phasename)
markers.append(m)
traces.append(tr)
ii += 1
except (gf.meta.GridSpecError, gf.StoreError, gf.meta.OutOfBounds) as e:
die(e)
trace.snuffle(traces, markers=markers, opengl=options.opengl)
def fiddle(stream_or_trace, inventory=None, catalog=None, **kwargs):
'''
Manipulate ObsPy stream object interactively.
:param stream_or_trace:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` or
:py:class:`obspy.Trace <obspy.core.trace.Trace>` object
:param inventory:
:py:class:`obspy.Inventory <obspy.core.inventory.inventory.Inventory>`
object
:param catalog:
:py:class:`obspy.Catalog <obspy.core.event.Catalog>` object
:param kwargs:
extra arguments passed to :meth:`pyrocko.trace.Trace.snuffle`.
:returns: :py:class:`obspy.Stream <obspy.core.stream.Stream>` object with
changes applied interactively (or :py:class:`obspy.Trace
<obspy.core.trace.Trace>` if called with a trace as first argument).
This function displays an ObsPy stream object in Snuffler like
:py:func:`snuffle`, but additionally adds a Snuffling panel to apply some
basic ObsPy signal processing to the contained traces. The applied changes
are handed back to the caller as a modified copy of the stream object.
.. code::
import obspy
from pyrocko import obspy_compat
obspy_compat.plant()
stream = obspy.read()
stream_filtered = stream.fiddle() # returns once window has been
# closed
'''
from pyrocko import trace
import obspy
obspy_inventory = inventory
obspy_catalog = catalog
if isinstance(stream_or_trace, obspy.Trace):
obspy_stream = obspy.core.stream.Stream(traces=[stream_or_trace])
else:
obspy_stream = stream_or_trace
events = to_pyrocko_events(obspy_catalog)
stations = to_pyrocko_stations(obspy_inventory)
snuffling_loader = ObsPyStreamSnufflingLoader(obspy_stream)
launch_hook = kwargs.pop('launch_hook', [])
if not isinstance(launch_hook, list):
launch_hook = [launch_hook]
launch_hook.append(snuffling_loader)
trace.snuffle([],
events=events,
stations=stations,
controls=False,
launch_hook=launch_hook,
**kwargs)
new_obspy_stream = snuffling_loader.get_snuffling().get_obspy_stream()
if isinstance(obspy_stream, obspy.Trace):
return new_obspy_stream[0]
else:
return new_obspy_stream
def snuffle(self):
trace.snuffle(self.pyrocko_traces())
def _test_homogeneous_scenario(self, config_type_class, component_scheme,
discretized_source_class):
if config_type_class.short_type == 'C' \
or component_scheme.startswith('poro'):
assert False
store_id = 'homogeneous_%s_%s' % (config_type_class.short_type,
component_scheme)
vp = 5.8 * km
vs = 3.46 * km
mod = cake.LayeredModel.from_scanlines(
cake.read_nd_model_str('''
0. %(vp)g %(vs)g 2.6 1264. 600.
20. %(vp)g %(vs)g 2.6 1264. 600.'''.lstrip() % dict(vp=vp / km, vs=vs / km)))
store_type = config_type_class.short_type
params = dict(id=store_id,
sample_rate=1000.,
modelling_code_id='ahfullgreen',
component_scheme=component_scheme,
earthmodel_1d=mod)
if store_type in ('A', 'B'):
params.update(source_depth_min=1. * km,
source_depth_max=2. * km,
source_depth_delta=0.5 * km,
distance_min=4. * km,
distance_max=6. * km,
distance_delta=0.5 * km)
if store_type == 'A':
params.update(receiver_depth=3. * km)
if store_type == 'B':
params.update(receiver_depth_min=2. * km,
receiver_depth_max=3. * km,
receiver_depth_delta=0.5 * km)
if store_type == 'C':
params.update(source_depth_min=1. * km,
source_depth_max=2. * km,
source_depth_delta=0.5 * km,
source_east_shift_min=1. * km,
source_east_shift_max=2. * km,
source_east_shift_delta=0.5 * km,
source_north_shift_min=2. * km,
source_north_shift_max=3. * km,
source_north_shift_delta=0.5 * km)
config = config_type_class(**params)
config.validate()
store_dir = mkdtemp(prefix=store_id)
self.tempdirs.append(store_dir)
gf.store.Store.create_editables(store_dir, config=config)
store = gf.store.Store(store_dir, 'r')
store.make_ttt()
store.close()
fomosto_ahfullgreen.build(store_dir, nworkers=1)
store = gf.store.Store(store_dir, 'r')
dsource_type = discretized_source_class.__name__
params = {}
if dsource_type == 'DiscretizedMTSource':
params.update(m6s=num.array([[1., 2., 3., 4., 5., 6.],
[1., 2., 3., 4., 5., 6.]]))
elif dsource_type == 'DiscretizedExplosionSource':
params.update(m0s=num.array([2., 2.]))
elif dsource_type == 'DiscretizedSFSource':
params.update(forces=num.array([[1., 2., 3.], [1., 2., 3.]]))
elif dsource_type == 'DiscretizedPorePressureSource':
params.update(pp=num.array([3., 3.]))
snorth = 2.0 * km
seast = 2.0 * km
sdepth = 1.0 * km
rnorth = snorth + 3. * km
reast = seast + 4. * km
rdepth = 3.0 * km
t0 = 10.0 * store.config.deltat
dsource = discretized_source_class(
times=num.array([t0, t0 + 5. * store.config.deltat]),
north_shifts=num.array([snorth, snorth]),
east_shifts=num.array([seast, seast]),
depths=num.array([sdepth, sdepth]),
**params)
receiver = gf.Receiver(north_shift=rnorth,
east_shift=reast,
depth=rdepth)
components = gf.component_scheme_to_description[
component_scheme].provided_components
for seismogram in (store.seismogram, store.seismogram_old):
for interpolation in ('nearest_neighbor', 'multilinear'):
trs1 = []
for component, gtr in seismogram(
dsource, receiver, components,
interpolation=interpolation).iteritems():
tr = gtr.to_trace('', 'STA', '', component)
trs1.append(tr)
trs2 = make_traces_homogeneous(
dsource, receiver,
store.config.earthmodel_1d.require_homogeneous(),
store.config.deltat, '', 'STA', 'a')
tmin = max(tr.tmin for tr in trs1 + trs2)
tmax = min(tr.tmax for tr in trs1 + trs2)
for tr in trs1 + trs2:
tr.chop(tmin, tmax)
assert tr.data_len() > 2
trs1.sort(key=lambda tr: tr.channel)
trs2.sort(key=lambda tr: tr.channel)
denom = 0.0
for t1, t2 in zip(trs1, trs2):
assert t1.channel == t2.channel
denom += num.sum(t1.ydata**2) + num.sum(t2.ydata**2)
ds = []
for t1, t2 in zip(trs1, trs2):
ds.append(2.0 * num.sum((t1.ydata - t2.ydata)**2) / denom)
ds = num.array(ds)
if component_scheme == 'elastic8':
limit = 1e-2
else:
limit = 1e-6
if not num.all(ds < limit):
print ds
trace.snuffle(trs1 + trs2)
assert num.all(ds < limit)
def ccs_allstats_one_event(i_ev,
ev,
stat_list,
all_stations,
p_obs,
p_syn,
out_dir,
bp,
arrT_array,
cc_thresh,
debug_mode=False):
"""
for one event: call cc_single_stat_single_event for each station,
collect optimal time shifts
return list with timeshift, fixed order of stations!
"""
ev_t_str = util.time_to_str(ev.time).replace(' ', '_')
#p_obs = pile.make_pile(datapath+ev_t_str, show_progress=False)
#p_syn = pile.make_pile(syndatapath+ev_t_str, show_progress=False)
tshift_list = []
if p_obs and p_syn:
for i_st, st in enumerate(stat_list):
try:
s = st.station
n = st.network
l = 'not_set'
except:
n, s, l, c = st
i_ast = [
i_ast for i_ast, ast in enumerate(all_stations)
if ast.network == n and ast.station == s
]
if len(i_ast) >= 1:
ii_ast = i_ast[0]
tmin = arrT_array[i_ev, ii_ast] - 30
elif len(i_ast) == 0:
logging.warning('station %s.%s not in all station list' %
(n, s))
continue
if l != 'not_set':
tr_obs = p_obs.all(
trace_selector=lambda tr: tr.network == n and tr.station ==
s and tr.location == l and tr.channel == 'Z',
tmin=tmin,
tmax=tmin + 300,
want_incomplete=True)
else:
tr_obs = p_obs.all(trace_selector=lambda tr: tr.network == n
and tr.station == s and tr.channel == 'Z',
tmin=tmin,
tmax=tmin + 300,
want_incomplete=True)
tr_syn = p_syn.all(trace_selector=lambda tr: tr.network == n and tr
.station == s and tr.channel == 'Z',
tmin=tmin,
tmax=tmin + 300,
want_incomplete=True)
if len(tr_obs) != 0 and len(tr_syn) != 0:
tr_syn = tr_syn[0]
tr_obs = tr_obs[0]
tr_obs.bandpass(bp[0], bp[1], bp[2])
tr_syn.bandpass(bp[0], bp[1], bp[2])
c = trace.correlate(tr_syn,
tr_obs,
mode='same',
normalization='normal')
t, coef = c.max()
if debug_mode is True:
logging.debug('%s %s' % (t, coef))
trace.snuffle([tr_syn, tr_obs])
trace.snuffle([c])
if coef > cc_thresh:
tshift_list.append(t)
else:
tshift_list.append(num.nan)
else:
tshift_list.append(num.nan)
return tshift_list
def check(
config,
event_names=None,
target_string_ids=None,
show_waveforms=False,
n_random_synthetics=10,
stations_used_path=None):
markers = []
stations_used = {}
erroneous = []
for ievent, event_name in enumerate(event_names):
ds = config.get_dataset(event_name)
event = ds.get_event()
trs_all = []
try:
problem = config.get_problem(event)
_, nfamilies = problem.get_family_mask()
logger.info('Problem: %s' % problem.name)
logger.info('Number of target families: %i' % nfamilies)
logger.info('Number of targets (total): %i' % len(problem.targets))
if target_string_ids:
problem.targets = [
target for target in problem.targets
if util.match_nslc(target_string_ids, target.string_id())]
logger.info(
'Number of targets (selected): %i' % len(problem.targets))
check_problem(problem)
results_list = []
sources = []
if n_random_synthetics == 0:
x = problem.get_reference_model()
sources.append(problem.base_source)
results = problem.evaluate(x)
results_list.append(results)
else:
for i in range(n_random_synthetics):
x = problem.get_random_model()
sources.append(problem.get_source(x))
results = problem.evaluate(x)
results_list.append(results)
if show_waveforms:
engine = config.engine_config.get_engine()
times = []
tdata = []
for target in problem.targets:
tobs_shift_group = []
tcuts = []
for source in sources:
tmin_fit, tmax_fit, tfade, tfade_taper = \
target.get_taper_params(engine, source)
times.extend((tmin_fit-tfade*2., tmax_fit+tfade*2.))
tobs, tsyn = target.get_pick_shift(engine, source)
if None not in (tobs, tsyn):
tobs_shift = tobs - tsyn
else:
tobs_shift = 0.0
tcuts.append(target.get_cutout_timespan(
tmin_fit+tobs_shift, tmax_fit+tobs_shift, tfade))
tobs_shift_group.append(tobs_shift)
tcuts = num.array(tcuts, dtype=num.float)
tdata.append((
tfade,
num.mean(tobs_shift_group),
(num.min(tcuts[:, 0]), num.max(tcuts[:, 1]))))
tmin = min(times)
tmax = max(times)
tmax += (tmax-tmin)*2
for (tfade, tobs_shift, tcut), target in zip(
tdata, problem.targets):
store = engine.get_store(target.store_id)
deltat = store.config.deltat
freqlimits = list(target.get_freqlimits())
freqlimits[2] = 0.45/deltat
freqlimits[3] = 0.5/deltat
freqlimits = tuple(freqlimits)
try:
trs_projected, trs_restituted, trs_raw, _ = \
ds.get_waveform(
target.codes,
tmin=tmin+tobs_shift,
tmax=tmax+tobs_shift,
tfade=tfade,
freqlimits=freqlimits,
deltat=deltat,
backazimuth=target.
get_backazimuth_for_waveform(),
debug=True)
except NotFound as e:
logger.warn(str(e))
continue
trs_projected = copy.deepcopy(trs_projected)
trs_restituted = copy.deepcopy(trs_restituted)
trs_raw = copy.deepcopy(trs_raw)
for trx in trs_projected + trs_restituted + trs_raw:
trx.shift(-tobs_shift)
trx.set_codes(
network='',
station=target.string_id(),
location='')
for trx in trs_projected:
trx.set_codes(location=trx.location + '2_proj')
for trx in trs_restituted:
trx.set_codes(location=trx.location + '1_rest')
for trx in trs_raw:
trx.set_codes(location=trx.location + '0_raw')
trs_all.extend(trs_projected)
trs_all.extend(trs_restituted)
trs_all.extend(trs_raw)
for source in sources:
tmin_fit, tmax_fit, tfade, tfade_taper = \
target.get_taper_params(engine, source)
markers.append(pmarker.Marker(
nslc_ids=[('', target.string_id(), '*_proj', '*')],
tmin=tmin_fit, tmax=tmax_fit))
markers.append(pmarker.Marker(
nslc_ids=[('', target.string_id(), '*_raw', '*')],
tmin=tcut[0]-tobs_shift, tmax=tcut[1]-tobs_shift,
kind=1))
else:
for itarget, target in enumerate(problem.targets):
nok = 0
for results in results_list:
result = results[itarget]
if not isinstance(result, gf.SeismosizerError):
nok += 1
if nok == 0:
sok = 'not used'
elif nok == len(results_list):
sok = 'ok'
try:
s = ds.get_station(target)
stations_used[s.nsl()] = s
except (NotFound, InvalidObject):
pass
else:
sok = 'not used (%i/%i ok)' % (nok, len(results_list))
logger.info('%-40s %s' % (
(target.string_id() + ':', sok)))
except GrondError as e:
logger.error('Event %i, "%s": %s' % (
ievent,
event.name or util.time_to_str(event.time),
str(e)))
erroneous.append(event)
if show_waveforms:
trace.snuffle(trs_all, stations=ds.get_stations(), markers=markers)
if stations_used_path:
stations = list(stations_used.values())
stations.sort(key=lambda s: s.nsl())
model.dump_stations(stations, stations_used_path)
if erroneous:
raise GrondError(
'Check failed for events: %s'
% ', '.join(ev.name for ev in erroneous))
lon=11.,
depth=10000.,
strike=20.,
dip=40.,
rake=60.,
magnitude=4.)
# Processing that data will return a pyrocko.gf.Reponse object.
response = engine.process(source_dc, targets)
# This will return a list of the requested traces:
synthetic_traces = response.pyrocko_traces()
# In addition to that it is also possible to extract interpolated travel times
# of phases which have been defined in the store's config file.
store = engine.get_store(store_id)
markers = []
for t in targets:
dist = t.distance_to(source_dc)
depth = source_dc.depth
arrival_time = store.t('any_P', (depth, dist))
m = PhaseMarker(tmin=arrival_time,
tmax=arrival_time,
phasename='P',
nslc_ids=(t.codes, ))
markers.append(m)
# Finally, let's scrutinize these traces.
trace.snuffle(synthetic_traces, markers=markers)
def search(config,
override_tmin=None,
override_tmax=None,
show_detections=False,
show_movie=False,
show_window_traces=False,
force=False,
stop_after_first=False,
nparallel=6,
save_imax=False,
bark=False):
fp = config.expand_path
run_path = fp(config.run_path)
# if op.exists(run_path):
# if force:
# shutil.rmtree(run_path)
# else:
# raise common.LassieError(
# 'run directory already exists: %s' %
# run_path)
util.ensuredir(run_path)
write_config(config, op.join(run_path, 'config.yaml'))
ifm_path_template = config.get_ifm_path_template()
detections_path = config.get_detections_path()
events_path = config.get_events_path()
figures_path_template = config.get_figures_path_template()
config.setup_image_function_contributions()
ifcs = config.image_function_contributions
grid = config.get_grid()
receivers = config.get_receivers()
norm_map = gridmod.geometrical_normalization(grid, receivers)
data_paths = fp(config.data_paths)
for data_path in fp(data_paths):
if not op.exists(data_path):
pass
p = pile.make_pile(data_paths, fileformat='detect')
if p.is_empty():
raise common.LassieError('no usable waveforms found')
for ifc in ifcs:
ifc.prescan(p)
shift_tables = []
tshift_minmaxs = []
for ifc in ifcs:
shift_tables.append(ifc.get_table(grid, receivers))
tshift_minmaxs.append(num.nanmin(shift_tables[-1]))
tshift_minmaxs.append(num.nanmax(shift_tables[-1]))
fsmooth_min = min(ifc.get_fsmooth() for ifc in ifcs)
tshift_min = min(tshift_minmaxs)
tshift_max = max(tshift_minmaxs)
if config.detector_tpeaksearch is not None:
tpeaksearch = config.detector_tpeaksearch
else:
tpeaksearch = (tshift_max - tshift_min) + 1.0 / fsmooth_min
tpad = max(ifc.get_tpad() for ifc in ifcs) + \
(tshift_max - tshift_min) + tpeaksearch
tinc = (tshift_max - tshift_min) * 10. + 3.0 * tpad
tavail = p.tmax - p.tmin
tinc = min(tinc, tavail - 2.0 * tpad)
if tinc <= 0:
raise common.LassieError('available waveforms too short \n'
'required: %g s\n'
'available: %g s\n' % (2. * tpad, tavail))
blacklist = set(tuple(s.split('.')) for s in config.blacklist)
whitelist = set(tuple(s.split('.')) for s in config.whitelist)
distances = grid.distances(receivers)
distances_to_grid = num.min(distances, axis=0)
distance_min = num.min(distances)
distance_max = num.max(distances)
station_index = dict(
(rec.codes, i) for (i, rec) in enumerate(receivers)
if rec.codes not in blacklist and (
not whitelist or rec.codes in whitelist) and (
config.distance_max is None
or distances_to_grid[i] <= config.distance_max))
check_data_consistency(p, config)
deltat_cf = max(p.deltats.keys())
assert deltat_cf > 0.0
while True:
if not all(ifc.deltat_cf_is_available(deltat_cf * 2) for ifc in ifcs):
break
deltat_cf *= 2
logger.info('CF lassie sampling interval (rate): %g s (%g Hz)' %
(deltat_cf, 1.0 / deltat_cf))
ngridpoints = grid.size()
logger.info('number of grid points: %i' % ngridpoints)
logger.info('minimum source-receiver distance: %g m' % distance_min)
logger.info('maximum source-receiver distance: %g m' % distance_max)
logger.info('minimum travel-time: %g s' % tshift_min)
logger.info('maximum travel-time: %g s' % tshift_max)
idetection = 0
tmin = override_tmin or config.tmin or p.tmin + tpad
tmax = override_tmax or config.tmax or p.tmax - tpad
events = config.get_events()
twindows = []
if events is not None:
for ev in events:
if tmin <= ev.time <= tmax:
twindows.append(
(ev.time + tshift_min - (tshift_max - tshift_min) *
config.event_time_window_factor,
ev.time + tshift_min + (tshift_max - tshift_min) *
config.event_time_window_factor))
else:
twindows.append((tmin, tmax))
for iwindow_group, (tmin_win, tmax_win) in enumerate(twindows):
nwin = int(math.ceil((tmax_win - tmin_win) / tinc))
logger.info('start processing time window group %i/%i: %s - %s' %
(iwindow_group + 1, len(twindows),
util.time_to_str(tmin_win), util.time_to_str(tmax_win)))
logger.info('number of time windows: %i' % nwin)
logger.info('time window length: %g s' % (tinc + 2.0 * tpad))
logger.info('time window payload: %g s' % tinc)
logger.info('time window padding: 2 x %g s' % tpad)
logger.info('time window overlap: %g%%' % (100.0 * 2.0 * tpad /
(tinc + 2.0 * tpad)))
iwin = -1
for trs in p.chopper(
tmin=tmin_win,
tmax=tmax_win,
tinc=tinc,
tpad=tpad,
want_incomplete=config.fill_incomplete_with_zeros,
trace_selector=lambda tr: tr.nslc_id[:3] in station_index):
iwin += 1
trs_ok = []
for tr in trs:
if tr.ydata.size == 0:
logger.warn('skipping empty trace: %s.%s.%s.%s' %
tr.nslc_id)
continue
if not num.all(num.isfinite(tr.ydata)):
logger.warn('skipping trace because of invalid values: '
'%s.%s.%s.%s' % tr.nslc_id)
continue
trs_ok.append(tr)
trs = trs_ok
if not trs:
continue
logger.info('processing time window %i/%i: %s - %s' %
(iwin + 1, nwin, util.time_to_str(
trs[0].wmin), util.time_to_str(trs[0].wmax)))
wmin = trs[0].wmin
wmax = trs[0].wmax
if config.fill_incomplete_with_zeros:
trs = zero_fill(trs, wmin - tpad, wmax + tpad)
t0 = math.floor(wmin / deltat_cf) * deltat_cf
iwmin = int(round((wmin - tpeaksearch - t0) / deltat_cf))
iwmax = int(round((wmax + tpeaksearch - t0) / deltat_cf))
lengthout = iwmax - iwmin + 1
pdata = []
trs_debug = []
parstack_params = []
for iifc, ifc in enumerate(ifcs):
dataset = ifc.preprocess(trs, wmin - tpeaksearch,
wmax + tpeaksearch,
tshift_max - tshift_min, deltat_cf)
if not dataset:
continue
nstations_selected = len(dataset)
nsls_selected, trs_selected = zip(*dataset)
for tr in trs_selected:
tr.meta = {'tabu': True}
trs_debug.extend(trs + list(trs_selected))
istations_selected = num.array(
[station_index[nsl] for nsl in nsls_selected],
dtype=num.int)
arrays = [tr.ydata.astype(num.float) for tr in trs_selected]
offsets = num.array([
int(round((tr.tmin - t0) / deltat_cf))
for tr in trs_selected
],
dtype=num.int32)
w = ifc.get_weights(nsls_selected)
weights = num.ones((ngridpoints, nstations_selected))
weights *= w[num.newaxis, :]
weights *= ifc.weight
shift_table = shift_tables[iifc]
ok = num.isfinite(shift_table[:, istations_selected])
bad = num.logical_not(ok)
shifts = -num.round(shift_table[:, istations_selected] /
deltat_cf).astype(num.int32)
weights[bad] = 0.0
shifts[bad] = num.max(shifts[ok])
pdata.append((list(trs_selected), shift_table, ifc))
parstack_params.append((arrays, offsets, shifts, weights))
if config.stacking_blocksize is not None:
ipstep = config.stacking_blocksize
frames = None
else:
ipstep = lengthout
frames = num.zeros((ngridpoints, lengthout))
twall_start = time.time()
frame_maxs = num.zeros(lengthout)
frame_argmaxs = num.zeros(lengthout, dtype=num.int)
ipmin = iwmin
while ipmin < iwmin + lengthout:
ipsize = min(ipstep, iwmin + lengthout - ipmin)
if ipstep == lengthout:
frames_p = frames
else:
frames_p = num.zeros((ngridpoints, ipsize))
for (arrays, offsets, shifts, weights) in parstack_params:
frames_p, _ = parstack(arrays,
offsets,
shifts,
weights,
0,
offsetout=ipmin,
lengthout=ipsize,
result=frames_p,
nparallel=nparallel,
impl='openmp')
if config.sharpness_normalization:
frame_p_maxs = frames_p.max(axis=0)
frame_p_means = num.abs(frames_p).mean(axis=0)
frames_p *= (frame_p_maxs / frame_p_means)[num.newaxis, :]
frames_p *= norm_map[:, num.newaxis]
if config.ifc_count_normalization:
frames_p *= 1.0 / len(ifcs)
frame_maxs[ipmin-iwmin:ipmin-iwmin+ipsize] = \
frames_p.max(axis=0)
frame_argmaxs[ipmin-iwmin:ipmin-iwmin+ipsize] = \
pargmax(frames_p)
ipmin += ipstep
del frames_p
twall_end = time.time()
logger.info('wallclock time for stacking: %g s' %
(twall_end - twall_start))
tmin_frames = t0 + iwmin * deltat_cf
tr_stackmax = trace.Trace('',
'SMAX',
'',
'',
tmin=tmin_frames,
deltat=deltat_cf,
ydata=frame_maxs)
tr_stackmax.meta = {'tabu': True}
trs_debug.append(tr_stackmax)
if show_window_traces:
trace.snuffle(trs_debug)
ydata_window = tr_stackmax.chop(wmin, wmax,
inplace=False).get_ydata()
logger.info('CF stats: min %g, max %g, median %g' %
(num.min(ydata_window), num.max(ydata_window),
num.median(ydata_window)))
if nstations_selected != 17:
logger.info(
'Warning, station outage detected! Nr of station operable: %s'
% nstations_selected)
detector_threshold_seiger = config.detector_threshold - (
(17 - nstations_selected) * 4
) # 17 is maximum number of seiger stations, 4 is a mean baseline for noise
if nstations_selected != 17:
logger.info(
'Warning, station outage detected! Nr of station operable: %s, threshold now: %s'
% (nstations_selected, detector_threshold_seiger))
tpeaks, apeaks = list(
zip(*[(tpeak, apeak) for (tpeak, apeak) in zip(
*tr_stackmax.peaks(detector_threshold_seiger, tpeaksearch))
if wmin <= tpeak and tpeak < wmax])) or ([], [])
tr_stackmax_indx = tr_stackmax.copy(data=False)
tr_stackmax_indx.set_ydata(frame_argmaxs.astype(num.int32))
tr_stackmax_indx.set_location('i')
for (tpeak, apeak) in zip(tpeaks, apeaks):
iframe = int(round((tpeak - tmin_frames) / deltat_cf))
imax = frame_argmaxs[iframe]
latpeak, lonpeak, xpeak, ypeak, zpeak = \
grid.index_to_location(imax)
idetection += 1
detection = Detection(id='%06i' % idetection,
time=tpeak,
location=geo.Point(lat=float(latpeak),
lon=float(lonpeak),
x=float(xpeak),
y=float(ypeak),
z=float(zpeak)),
ifm=float(apeak))
if bark:
common.bark()
logger.info('detection found: %s' % str(detection))
f = open(detections_path, 'a')
f.write(
'%06i %s %g %g %g %g %g %g\n' %
(idetection,
util.time_to_str(tpeak, format='%Y-%m-%d %H:%M:%S.6FRAC'),
apeak, latpeak, lonpeak, xpeak, ypeak, zpeak))
f.close()
ev = detection.get_event()
f = open(events_path, 'a')
model.dump_events([ev], stream=f)
f.close()
if show_detections or config.save_figures:
fmin = min(ifc.fmin for ifc in ifcs)
fmax = min(ifc.fmax for ifc in ifcs)
fn = figures_path_template % {
'id': util.tts(t0).replace(" ", "T"),
'format': 'png'
}
util.ensuredirs(fn)
if frames is not None:
frames_p = frames
tmin_frames_p = tmin_frames
iframe_p = iframe
else:
iframe_min = max(
0, int(round(iframe - tpeaksearch / deltat_cf)))
iframe_max = min(
lengthout - 1,
int(round(iframe + tpeaksearch / deltat_cf)))
ipsize = iframe_max - iframe_min + 1
frames_p = num.zeros((ngridpoints, ipsize))
tmin_frames_p = tmin_frames + iframe_min * deltat_cf
iframe_p = iframe - iframe_min
for (arrays, offsets, shifts, weights) \
in parstack_params:
frames_p, _ = parstack(arrays,
offsets,
shifts,
weights,
0,
offsetout=iwmin +
iframe_min,
lengthout=ipsize,
result=frames_p,
nparallel=nparallel,
impl='openmp')
if config.sharpness_normalization:
frame_p_maxs = frames_p.max(axis=0)
frame_p_means = num.abs(frames_p).mean(axis=0)
frames_p *= (frame_p_maxs /
frame_p_means)[num.newaxis, :]
frames_p *= norm_map[:, num.newaxis]
if config.ifc_count_normalization:
frames_p *= 1.0 / len(ifcs)
try:
plot.plot_detection(grid,
receivers,
frames_p,
tmin_frames_p,
deltat_cf,
imax,
iframe_p,
xpeak,
ypeak,
zpeak,
tr_stackmax,
tpeaks,
apeaks,
detector_threshold_seiger,
wmin,
wmax,
pdata,
trs,
fmin,
fmax,
idetection,
tpeaksearch,
movie=show_movie,
show=show_detections,
save_filename=fn,
event=ev)
except:
pass
del frames_p
if stop_after_first:
return
tr_stackmax.chop(wmin, wmax)
tr_stackmax_indx.chop(wmin, wmax)
if save_imax is True:
io.save([tr_stackmax, tr_stackmax_indx], ifm_path_template)
del frames
logger.info('end processing time window group: %s - %s' %
(util.time_to_str(tmin_win), util.time_to_str(tmax_win)))
cat = Catalog()
files = glob("%s/../figures/*qml*" % run_path)
files.sort(key=os.path.getmtime)
for file in files:
cat_read = read_events(file)
for event in cat_read:
cat.append(event)
cat.write("%s/../all_events_stacking.qml" % run_path, format="QUAKEML")
synth_traces_nn_t.append(tr)
synth_traces_ml_t = []
for i, target in enumerate(targets):
tr = trace.Trace(
ydata=synthetics_ml_t[i, :],
tmin=gfs.reference_times[i],
deltat=gfs.deltat)
#print 'trace tmin synthst', tr.tmin
tr.set_codes(*target.codes)
tr.set_location('ml_t')
synth_traces_ml_t.append(tr)
# display to check
trace.snuffle(
traces + synth_traces_nn + synth_traces_ml + synth_traces_nn_t + synth_traces_ml_t,
stations=sc.wavemaps[0].stations, events=[event])
traces1, tmins = heart.seis_synthetics(
engine, [patches[0]], targets, arrival_times=ats,
wavename='any_P', arrival_taper=arrival_taper,
filterer=filterer, outmode='stacked_traces')
gfs.set_stack_mode('numpy')
synth_traces_ml1 = []
for i in range(1):
synthetics_ml1 = gfs.stack_all(
targetidxs=targetidxs,
patchidxs=[i],
starttimes=starttimes[0],
def prep_orient(datapath,
st,
loc,
catalog,
dir_ro,
v_rayleigh,
bp,
dt_start,
dt_stop,
ccmin=0.80,
plot_heatmap=False,
plot_distr=False,
debug=False):
"""
Perform orientation analysis using Rayleigh waves, main function.
time wdw: 20s before 4.0km/s arrival and 600 s afterwards
(Stachnik et al. 2012)
- compute radial component for back values of 0 to 360 deg
- for each c-c of hilbert(R) with Z comp.
- call plotting functions and/or write results to file
:param datapath: path to rrd data
:param st: current station (pyrocko station object)
:param catalog: list of pyrocko events used for analysis
:param dir_ro: output directory
:param plot_heatmap: bool, optional
:param plot_distr: bool, optional
"""
logs = logging.getLogger('prep_orient')
st_data_pile = pile.make_pile(datapath,
regex='%s_%s_' % (st.network, st.station),
show_progress=False)
n_ev = len(catalog)
if st_data_pile.tmin is not None and st_data_pile.tmax is not None:
# calculate dist between all events and current station
r_arr_by_ev = num.empty(n_ev)
ev_lats = num.asarray([ev.lat for ev in catalog])
ev_lons = num.asarray([ev.lon for ev in catalog])
dists = distance_accurate50m_numpy(a_lats=ev_lats,
a_lons=ev_lons,
b_lats=st.lat,
b_lons=st.lon,
implementation='c')
r_arr_by_ev = (dists / 1000.) / v_rayleigh
cc_i_ev_vs_rota = num.empty((n_ev, 360))
rot_angles = range(-180, 180, 1)
for i_ev, ev in enumerate(catalog):
arrT = ev.time + r_arr_by_ev[i_ev]
start_twd1 = ev.time
end_twd1 = arrT + 1800
trZ = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'Z')
trR = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'R')
trT = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'T')
start_twd2 = ev.time + r_arr_by_ev[i_ev] - dt_start
end_twd2 = arrT + dt_stop
if len(trZ) == 1 and len(trR) == 1 and len(trT) == 1:
trZ = trZ[0]
trR = trR[0]
trT = trT[0]
# debugging - window selection:
if debug is True:
trace.snuffle([trZ, trR, trT],
markers=[
pm.Marker(nslc_ids=[
trZ.nslc_id, trR.nslc_id, trT.nslc_id
],
tmin=start_twd2,
tmax=end_twd2),
pm.Marker(nslc_ids=[
trZ.nslc_id, trR.nslc_id, trT.nslc_id
],
tmin=arrT,
tmax=arrT + 3)
])
else:
cc_i_ev_vs_rota[i_ev, :] = num.nan
continue
try:
trZ.bandpass(bp[0], bp[1], bp[2])
trZ.chop(tmin=start_twd2, tmax=end_twd2)
except trace.NoData:
logs.warning('no data %s %s %s' % (trZ, trR, trT))
continue
for i_r, r in enumerate(rot_angles):
print('rotation angle [deg]: %5d' % r, end='\r')
rot_2, rot_3 = trace.rotate(traces=[trR, trT],
azimuth=r,
in_channels=['R', 'T'],
out_channels=['2', '3'])
rot_2_y = rot_2.ydata
rot_2_hilb = num.imag(trace.hilbert(rot_2_y, len(rot_2_y)))
rot_2_hilb_tr = trace.Trace(deltat=rot_2.deltat,
ydata=rot_2_hilb,
tmin=rot_2.tmin)
# problem: rot_2 and rot_2_hilb look exactly the same!
# --> no phase shift. why? should be num.imag!!!
# trace.snuffle([rot_2, rot_2_hilb_tr])
rot_2_hilb_tr.bandpass(bp[0], bp[1], bp[2])
rot_2_hilb_tr.chop(tmin=start_twd2, tmax=end_twd2)
# if st.station == 'RORO' and r == 0:
# trace.snuffle([rot_2_hilb_tr, trZ])
# normalize traces
trZ.ydata /= abs(max(trZ.ydata))
rot_2_hilb_tr.ydata /= abs(max(rot_2_hilb_tr.ydata))
c = trace.correlate(trZ,
rot_2_hilb_tr,
mode='valid',
normalization='normal')
t, coef = c.max()
t2, coef2 = max_or_min(c)
'''
if st.station == 'MATE' and r == 0:
print(i_ev, ev.name, ev.depth)
print(r, t, coef, t2, coef2)
trace.snuffle([trZ, trR, rot_2_hilb_tr])
'''
cc_i_ev_vs_rota[i_ev, i_r] = coef
'''
if st.station == 'MATE':
for i_ev in range(n_ev):
print(num.argmax(cc_i_ev_vs_rota[i_ev,:]),
num.max(cc_i_ev_vs_rota[i_ev,:]))
'''
if plot_heatmap is True:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 2))
cax = ax.imshow(cc_i_ev_vs_rota,
interpolation='nearest',
vmin=-1.0,
vmax=1.0,
aspect='auto',
extent=[-180, 180, n_ev, 0],
cmap='binary')
ax.set_ylabel('i_ev')
ax.set_xlabel('Correction angle (deg)')
ax.set_title('%s %s' % (st.network, st.station))
cbar = fig.colorbar(cax,
ticks=[0, 0.5, 1.0],
orientation='horizontal',
fraction=0.05,
pad=0.5)
cbar.ax.set_xticklabels(['0', '0.5', '1.0'])
plt.tight_layout()
# plt.show(fig)
fig.savefig(
os.path.join(
dir_ro, '%s_%s_%s_rot_cc_heatmap.png' %
(st.network, st.station, loc)))
plt.close()
if plot_distr is True:
plot_ccdistr_each_event(cc_i_ev_vs_rota, catalog, rot_angles, st,
loc, dir_ro)
median_a, mean_a, std_a, switched, n_ev =\
get_m_angle_switched(cc_i_ev_vs_rota, catalog, st, ccmin)
dict_ev_angle = get_m_angle_all(cc_i_ev_vs_rota, catalog, st, ccmin)
return median_a, mean_a, std_a, switched, dict_ev_angle, n_ev
def _off_test_synthetic(self):
from pyrocko import gf
km = 1000.
nstations = 10
edepth = 5 * km
store_id = 'crust2_d0'
swin = 2.
lwin = 9. * swin
ks = 1.0
kl = 1.0
kd = 3.0
engine = gf.get_engine()
snorths = (num.random.random(nstations) - 1.0) * 50 * km
seasts = (num.random.random(nstations) - 1.0) * 50 * km
targets = []
for istation, (snorths, seasts) in enumerate(zip(snorths, seasts)):
targets.append(
gf.Target(quantity='displacement',
codes=('', 's%03i' % istation, '', 'Z'),
north_shift=float(snorths),
east_shift=float(seasts),
store_id=store_id,
interpolation='multilinear'))
source = gf.DCSource(north_shift=50 * km,
east_shift=50 * km,
depth=edepth)
store = engine.get_store(store_id)
response = engine.process(source, targets)
trs = []
station_traces = defaultdict(list)
station_targets = defaultdict(list)
for source, target, tr in response.iter_results():
tp = store.t('any_P', source, target)
t = tp - 5 * tr.deltat + num.arange(11) * tr.deltat
if False:
gauss = trace.Trace(tmin=t[0],
deltat=tr.deltat,
ydata=num.exp(-((t - tp)**2) /
((2 * tr.deltat)**2)))
tr.ydata[:] = 0.0
tr.add(gauss)
trs.append(tr)
station_traces[target.codes[:3]].append(tr)
station_targets[target.codes[:3]].append(target)
station_stalta_traces = {}
for nsl, traces in station_traces.items():
etr = None
for tr in traces:
sqr_tr = tr.copy(data=False)
sqr_tr.ydata = tr.ydata**2
if etr is None:
etr = sqr_tr
else:
etr += sqr_tr
autopick.recursive_stalta(swin, lwin, ks, kl, kd, etr)
etr.set_codes(channel='C')
station_stalta_traces[nsl] = etr
trace.snuffle(trs + list(station_stalta_traces.values()))
deltat = trs[0].deltat
nnorth = 50
neast = 50
size = 200 * km
north = num.linspace(-size, size, nnorth)
north2 = num.repeat(north, neast)
east = num.linspace(-size, size, neast)
east2 = num.tile(east, nnorth)
depth = 5 * km
def tcal(target, i):
try:
return store.t(
'any_P',
gf.Location(north_shift=north2[i],
east_shift=east2[i],
depth=depth), target)
except gf.OutOfBounds:
return 0.0
nsls = sorted(station_stalta_traces.keys())
tts = num.fromiter((tcal(station_targets[nsl][0], i)
for i in range(nnorth * neast) for nsl in nsls),
dtype=num.float)
arrays = [
station_stalta_traces[nsl].ydata.astype(num.float) for nsl in nsls
]
offsets = num.array([
int(round(station_stalta_traces[nsl].tmin / deltat))
for nsl in nsls
],
dtype=num.int32)
shifts = -num.array([int(round(tt / deltat)) for tt in tts],
dtype=num.int32).reshape(nnorth * neast, nstations)
weights = num.ones((nnorth * neast, nstations))
print(shifts[25 * neast + 25] * deltat)
print(offsets.dtype, shifts.dtype, weights.dtype)
print('stack start')
mat, ioff = parstack(arrays, offsets, shifts, weights, 1)
print('stack stop')
mat = num.reshape(mat, (nnorth, neast))
from matplotlib import pyplot as plt
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1, aspect=1.0)
axes.contourf(east / km, north / km, mat)
axes.plot(
g(targets, 'east_shift') / km,
g(targets, 'north_shift') / km, '^')
axes.plot(source.east_shift / km, source.north_shift / km, 'o')
plt.show()
def snuffle(self):
'''Open *snuffler* with requested traces.'''
trace.snuffle(self.traces_list())
def snuffle(self):
trace.snuffle(self.seismograms)
def detect(first512):
s = first512[:12]
if len(s) != 12:
return False
tag = SudsStructtag.unpack(s)
if tag.sync != 'S' \
or tag.machine != '6' \
or tag.struct_type < 0 \
or tag.struct_type > max_struct_type:
return False
return True
if __name__ == '__main__':
util.setup_logging('pyrocko.suds')
trs = list(iload(sys.argv[1], 'rb'))
stations = load_stations(sys.argv[1])
for station in stations:
print station
trace.snuffle(trs, stations=stations)
def test_against_kiwi(self):
engine = gf.get_engine()
store_id = 'chile_70km_crust'
try:
store = engine.get_store(store_id)
except gf.NoSuchStore:
logger.warn('GF Store %s not available - skipping test' % store_id)
return
base_source = gf.RectangularSource(
depth=15*km,
strike=0.,
dip=90.,
rake=0.,
magnitude=4.5,
nucleation_x=-1.,
length=10*km,
width=0*km,
stf=gf.BoxcarSTF(duration=1.0))
base_event = base_source.pyrocko_event()
channels = 'NEZ'
nstations = 10
stations = []
targets = []
for istation in xrange(nstations):
dist = rand(40.*km, 900*km)
azi = rand(-180., 180.)
north_shift = dist * math.cos(azi*d2r)
east_shift = dist * math.sin(azi*d2r)
lat, lon = od.ne_to_latlon(0., 0., north_shift, east_shift)
sta = 'S%02i' % istation
station = model.Station(
'', sta, '',
lat=lat,
lon=lon)
station.set_channels_by_name('N', 'E', 'Z')
stations.append(station)
for cha in channels:
target = gf.Target(
codes=station.nsl() + (cha,),
lat=lat,
lon=lon,
quantity='displacement',
interpolation='multilinear',
optimization='enable',
store_id=store_id)
targets.append(target)
from tunguska import glue
nsources = 10
# nprocs_max = multiprocessing.cpu_count()
nprocs = 1
try:
seis = glue.start_seismosizer(
gfdb_path=op.join(store.store_dir, 'db'),
event=base_event,
stations=stations,
hosts=['localhost']*nprocs,
balance_method='123321',
effective_dt=0.5,
verbose=False)
ksource = to_kiwi_source(base_source)
seis.set_source(ksource)
recs = seis.get_receivers_snapshot(('syn',), (), 'plain')
trs = []
for rec in recs:
for tr in rec.get_traces():
tr.set_codes(channel=transchan[tr.channel])
trs.append(tr)
trs2 = engine.process(base_source, targets).pyrocko_traces()
trace.snuffle(trs + trs2)
seis.set_synthetic_reference()
for sourcetype in ['point', 'rect']:
sources = []
for isource in xrange(nsources):
m = pmt.MomentTensor.random_dc()
strike, dip, rake = map(float, m.both_strike_dip_rake()[0])
if sourcetype == 'point':
source = gf.RectangularSource(
north_shift=rand(-20.*km, 20*km),
east_shift=rand(-20.*km, 20*km),
depth=rand(10*km, 20*km),
nucleation_x=0.0,
nucleation_y=0.0,
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0),
stf=gf.BoxcarSTF(duration=1.0))
elif sourcetype == 'rect':
source = gf.RectangularSource(
north_shift=rand(-20.*km, 20*km),
east_shift=rand(-20.*km, 20*km),
depth=rand(10*km, 20*km),
length=10*km,
width=5*km,
nucleation_x=-1.,
nucleation_y=0,
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0),
stf=gf.BoxcarSTF(duration=1.0))
else:
assert False
sources.append(source)
for temperature in ['cold', 'hot']:
t0 = time.time()
resp = engine.process(sources, targets, nprocs=nprocs)
t1 = time.time()
if temperature == 'hot':
dur_pyrocko = t1 - t0
del resp
ksources = map(to_kiwi_source, sources)
for temperature in ['cold', 'hot']:
t0 = time.time()
seis.make_misfits_for_sources(
ksources, show_progress=False)
t1 = time.time()
if temperature == 'hot':
dur_kiwi = t1 - t0
print 'pyrocko %-5s %5.2fs %5.1fx' % (
sourcetype, dur_pyrocko, 1.0)
print 'kiwi %-5s %5.2fs %5.1fx' % (
sourcetype, dur_kiwi, dur_pyrocko/dur_kiwi)
finally:
seis.close()
del seis
vs = 2000.
tlen = x[0] / vs * 2.
deltat = 0.001
n = int(num.round(tlen / deltat))
out_x = num.zeros(n)
out_y = num.zeros(n)
out_z = num.zeros(n)
import pylab as lab
tau = 0.01
t = num.arange(1000) * deltat
lab.plot(t, num.exp(-t**2/tau**2))
# lab.show()
add_seismogram(
vp, vs, 1.0, 1.0, 1.0, x, f, m6, 'displacement', deltat, 0.0,
out_x, out_y, out_z, Gauss(tau))
trs = []
for out, comp in zip([out_x, out_y, out_z], 'NED'):
tr = trace.Trace(
'', 'Naja!', '', comp, deltat=deltat, tmin=0.0, ydata=out)
trs.append(tr)
trace.snuffle(trs)
