def have_store(store_id):
engine = gf.get_engine()
try:
engine.get_store(store_id)
return True
except gf.NoSuchStore:
return False
def have_store(store_id):
engine = gf.get_engine()
try:
engine.get_store(store_id)
return True
except gf.NoSuchStore:
return False
def have_store(store_id):
engine = gf.get_engine()
try:
engine.get_store(store_id)
return True, ''
except gf.NoSuchStore:
return False, 'GF store "%s" not available' % store_id
def test_scenario_combinations(self):
generator = scenario.ScenarioGenerator(
seed=20,
center_lat=42.6,
center_lon=13.3,
radius=60*km,
target_generators=[
targets.WaveformGenerator(
store_id=ScenarioTestCase.store_id,
station_generator=targets.RandomStationGenerator(
avoid_water=False),
noise_generator=targets.waveform.WhiteNoiseGenerator(),
seismogram_quantity='velocity'),
targets.InSARGenerator(
resolution=(20, 20),
noise_generator=targets.insar.AtmosphericNoiseGenerator(
amplitude=1e-5)),
targets.GNSSCampaignGenerator(
station_generator=targets.RandomStationGenerator(
avoid_water=False,
channels=None))
],
source_generator=scenario.DCSourceGenerator(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=10*km,
depth_min=1*km,
depth_max=10*km,
magnitude_min=3.0,
magnitude_max=4.5,
strike=120.,
dip=45.,
rake=90.,
perturbation_angle_std=15.,
nevents=3)
)
engine = gf.get_engine()
generator.init_modelling(engine)
for src in scenario.sources.AVAILABLE_SOURCES:
generator.source_generator = src(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=1*km,
depth_min=1.5*km,
depth_max=5*km,
magnitude_min=3.0,
magnitude_max=4.5)
generator.source_generator.update_hierarchy(generator)
generator.get_stations()
generator.get_waveforms()
generator.get_insar_scenes()
generator.get_gnss_campaigns()
def test_scenario_map(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('plot', generator)
s = collection.get_scenario('plot')
s.get_map()
def test_scenario_map(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('plot', generator)
s = collection.get_scenario('plot')
s.get_map()
def test_scenario_insar(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('insar', generator)
s = collection.get_scenario('insar')
s.ensure_insar_scenes()
def test_scenario_insar(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('insar', generator)
s = collection.get_scenario('insar')
s.ensure_insar_scenes()
def test_scenario_gnss(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('gnss', generator)
s = collection.get_scenario('gnss')
assert len(s.get_gnss_campaigns()) == 1
def test_scenario_gnss(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = self.generator
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('gnss', generator)
s = collection.get_scenario('gnss')
assert len(s.get_gnss_campaigns()) == 1
def test_scenario_map(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = scenario.ScenarioGenerator(
seed=20,
center_lat=42.6,
center_lon=13.3,
radius=60*km,
target_generators=[
targets.WaveformGenerator(
store_id=ScenarioTestCase.store_id,
station_generator=targets.RandomStationGenerator(
avoid_water=False),
noise_generator=targets.waveform.WhiteNoiseGenerator(),
seismogram_quantity='velocity'),
targets.InSARGenerator(
resolution=(20, 20),
noise_generator=targets.insar.AtmosphericNoiseGenerator(
amplitude=1e-5)),
targets.GNSSCampaignGenerator(
station_generator=targets.RandomStationGenerator(
avoid_water=False,
channels=None))
],
source_generator=scenario.DCSourceGenerator(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=10*km,
depth_min=1*km,
depth_max=10*km,
magnitude_min=3.0,
magnitude_max=4.5,
strike=120.,
dip=45.,
rake=90.,
perturbation_angle_std=15.,
nevents=3)
)
engine = gf.get_engine()
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('plot', generator)
s = collection.get_scenario('plot')
s.get_map()
def test_scenario_combinations(self):
import copy
generator = copy.deepcopy(self.generator)
engine = gf.get_engine()
generator.init_modelling(engine)
for src in scenario.sources.AVAILABLE_SOURCES:
generator.source_generator = src(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=1*km,
depth_min=1.5*km,
depth_max=5*km,
magnitude_min=3.0,
magnitude_max=4.5)
generator.source_generator.update_hierarchy(generator)
generator.get_stations()
generator.get_waveforms()
generator.get_insar_scenes()
generator.get_gnss_campaigns()
def test_scenario_combinations(self):
import copy
generator = copy.deepcopy(self.generator)
engine = gf.get_engine()
generator.init_modelling(engine)
for src in scenario.sources.AVAILABLE_SOURCES:
generator.source_generator = src(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=1*km,
depth_min=1.5*km,
depth_max=5*km,
magnitude_min=3.0,
magnitude_max=4.5)
generator.source_generator.update_hierarchy(generator)
generator.get_stations()
generator.get_waveforms()
generator.get_insar_scenes()
generator.get_gnss_campaigns()
def test_scenario_gnss(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = scenario.ScenarioGenerator(
seed=20,
center_lat=42.6,
center_lon=13.3,
radius=60*km,
target_generators=[
targets.GNSSCampaignGenerator(
station_generator=targets.RandomStationGenerator(
avoid_water=False,
channels=None))
],
source_generator=scenario.DCSourceGenerator(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=10*km,
depth_min=1*km,
depth_max=10*km,
magnitude_min=3.0,
magnitude_max=4.5,
strike=120.,
dip=45.,
rake=90.,
perturbation_angle_std=15.,
nevents=3)
)
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('gnss', generator)
s = collection.get_scenario('gnss')
assert len(s.get_gnss_campaigns()) == 1
def test_scenario_insar(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
generator = scenario.ScenarioGenerator(
seed=20,
center_lat=42.6,
center_lon=13.3,
radius=60*km,
target_generators=[
targets.InSARGenerator(
resolution=(20, 20),
noise_generator=targets.insar.AtmosphericNoiseGenerator(
amplitude=1e-5))
],
source_generator=scenario.DCSourceGenerator(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=10*km,
depth_min=1*km,
depth_max=10*km,
magnitude_min=3.0,
magnitude_max=4.5,
strike=120.,
dip=45.,
rake=90.,
perturbation_angle_std=15.,
nevents=3)
)
engine = gf.get_engine()
generator.init_modelling(engine)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('insar', generator)
s = collection.get_scenario('insar')
s.ensure_data()
def test_scenario_waveforms(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
vmin = 2500.
generator = self.generator
def twin(source):
tmin = source.time
tmax = source.time + 100*km / vmin
return tmin, tmax
engine = gf.get_engine()
generator.init_modelling(engine)
ref_sources = generator.get_sources()
ref_trs_list = []
for source in ref_sources:
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
ref_trs_list.append(trs)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('one', generator)
with self.assertRaises(scenario.ScenarioError):
collection.add_scenario('one', generator)
assert len(collection.list_scenarios()) == 1
assert collection.list_scenarios()[0].scenario_id == 'one'
s = collection.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
collection2 = scenario.ScenarioCollection(tempdir, engine)
assert len(collection2.list_scenarios()) == 1
assert collection2.list_scenarios()[0].scenario_id == 'one'
s = collection2.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
tmin, tmax = s.get_time_range()
s.ensure_waveforms(tmin, tmax)
p = s.get_waveform_pile()
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
tmin, tmax = twin(source)
trs = p.all(tmin=tmin, tmax=tmax, include_last=False)
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
def test_regional(self):
engine = gf.get_engine()
store_id = 'crust2_mf'
try:
engine.get_store(store_id)
except gf.NoSuchStore:
logger.warn('GF Store %s not available - skipping test' % store_id)
return
nsources = 10
nstations = 10
print 'cache source channels par wallclock seismograms_per_second'
nprocs_max = multiprocessing.cpu_count()
for sourcetype, channels in [
['point', 'Z'],
['point', 'NEZ'],
['rect', 'Z'],
['rect', 'NEZ']]:
for nprocs in [1, 2, 4, 8, 16, 32]:
if nprocs > nprocs_max:
continue
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.DCSource(
north_shift=rand(-20.*km, 20*km),
east_shift=rand(-20.*km, 20*km),
depth=rand(10*km, 20*km),
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0))
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=0.,
nucleation_y=-1.,
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0))
else:
assert False
sources.append(source)
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)
for cha in channels:
target = gf.Target(
codes=('', 'S%04i' % istation, '', cha),
north_shift=north_shift,
east_shift=east_shift,
quantity='displacement',
interpolation='multilinear',
optimization='enable',
store_id=store_id)
targets.append(target)
ntraces = len(targets) * len(sources)
for temperature in ['cold', 'hot']:
t0 = time.time()
resp = engine.process(sources, targets, nprocs=nprocs)
# print resp.stats
t1 = time.time()
duration = t1 - t0
sps = ntraces / duration
if temperature == 'hot':
if nprocs == 1:
sps_ref = sps
print '%-5s %-6s %-8s %3i %9.3f %12.1f %12.1f' % (
temperature, sourcetype, channels, nprocs, t1-t0,
sps, sps/sps_ref)
del resp
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
def test_scenario_waveforms(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
vmin = 2500.
generator = self.generator
def twin(source):
tmin = source.time
tmax = source.time + 100*km / vmin
return tmin, tmax
engine = gf.get_engine()
generator.init_modelling(engine)
ref_sources = generator.get_sources()
ref_trs_list = []
for source in ref_sources:
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
ref_trs_list.append(trs)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('one', generator)
with self.assertRaises(scenario.ScenarioError):
collection.add_scenario('one', generator)
assert len(collection.list_scenarios()) == 1
assert collection.list_scenarios()[0].scenario_id == 'one'
s = collection.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
collection2 = scenario.ScenarioCollection(tempdir, engine)
assert len(collection2.list_scenarios()) == 1
assert collection2.list_scenarios()[0].scenario_id == 'one'
s = collection2.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
tmin, tmax = s.get_time_range()
s.ensure_waveforms(tmin, tmax)
p = s.get_waveform_pile()
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
tmin, tmax = twin(source)
trs = p.all(tmin=tmin, tmax=tmax, include_last=False)
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_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()
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)
if __name__ == '__main__':
engine = gf.get_engine()
store_id = 'crust2_m5_hardtop_8Hz_fine'
magnitude_min, magnitude_max = 3., 7.
moment_tensor = None # random DC
# moment_tensor = pmt.MomentTensor.from_values([strike, dip, rake])
stress_drop_min, stress_drop_max = 1.0e6, 10.0e6
rupture_velocity_min, rupture_velocity_max = 2500.*0.9, 3600.*0.9
depth_min, depth_max = 1.*km, 30.*km
distance_min, distance_max = 100*km, 100*km
measure_ML = wmeasure.AmplitudeMeasure(
timing_tmin=gf.Timing('vel:8'),
timing_tmax=gf.Timing('vel:2'),
fmin=None,
fmax=None,
response=wmeasure.response_wa,
def test_scenario_waveforms(self):
tempdir = mkdtemp(prefix='pyrocko-scenario')
self.tempdirs.append(tempdir)
vmin = 2500.
generator = scenario.ScenarioGenerator(
seed=20,
center_lat=42.6,
center_lon=13.3,
radius=60*km,
target_generators=[
targets.WaveformGenerator(
store_id=ScenarioTestCase.store_id,
station_generator=targets.RandomStationGenerator(
nstations=5,
avoid_water=False),
noise_generator=targets.waveform.WhiteNoiseGenerator(),
seismogram_quantity='velocity'),
],
source_generator=scenario.DCSourceGenerator(
time_min=util.str_to_time('2017-01-01 00:00:00'),
time_max=util.str_to_time('2017-01-01 02:00:00'),
radius=10*km,
depth_min=1*km,
depth_max=10*km,
magnitude_min=3.0,
magnitude_max=4.5,
strike=120.,
dip=45.,
rake=90.,
perturbation_angle_std=15.,
nevents=3)
)
def twin(source):
tmin = source.time
tmax = source.time + 100*km / vmin
return tmin, tmax
engine = gf.get_engine()
generator.init_modelling(engine)
ref_sources = generator.get_sources()
ref_trs_list = []
for source in ref_sources:
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
ref_trs_list.append(trs)
collection = scenario.ScenarioCollection(tempdir, engine)
collection.add_scenario('one', generator)
with self.assertRaises(scenario.ScenarioError):
collection.add_scenario('one', generator)
assert len(collection.list_scenarios()) == 1
assert collection.list_scenarios()[0].scenario_id == 'one'
s = collection.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
collection2 = scenario.ScenarioCollection(tempdir, engine)
assert len(collection2.list_scenarios()) == 1
assert collection2.list_scenarios()[0].scenario_id == 'one'
s = collection2.get_scenario('one')
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
trs = generator.get_waveforms(*twin(source))
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
tmin, tmax = s.get_time_range()
s.ensure_data(tmin, tmax)
p = s.get_waveform_pile()
for ref_trs, source in zip(
ref_trs_list,
s.get_generator().get_sources()):
tmin, tmax = twin(source)
trs = p.all(tmin=tmin, tmax=tmax, include_last=False)
trs.sort(key=lambda tr: tr.nslc_id)
self.assert_traces_almost_equal(trs, ref_trs)
def get_engine():
return gf.get_engine(store_superdirs=['gf_stores'])
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 get_engine():
return gf.get_engine(store_superdirs=['gf_stores'])