def test_effective_durations(self):
deltat = 1e-4
for stf in [
gf.HalfSinusoidSTF(duration=2.0),
gf.TriangularSTF(duration=2.0, peak_ratio=0.),
gf.TriangularSTF(duration=2.0, peak_ratio=1.),
gf.TriangularSTF(duration=2.0, peak_ratio=0.5),
gf.BoxcarSTF(duration=2.0)
]:
t, a = stf.discretize_t(deltat, 0.0)
t0 = stf.centroid_time(0.0)
edur = num.sqrt(num.sum((t - t0)**2 * a)) * 2. * num.sqrt(3.)
assert abs(edur - stf.effective_duration) < 1e-3
def test_source_to_event(self):
for S in gf.source_classes:
stf = gf.TriangularSTF(effective_duration=2.0)
s1 = S(lat=10.,
lon=20.,
depth=1000.,
north_shift=500.,
east_shift=500.,
stf=stf)
ev = s1.pyrocko_event()
s2 = S.from_pyrocko_event(ev)
if not isinstance(s1, gf.DoubleDCSource):
assert numeq([
s1.effective_lat, s1.effective_lon, s1.depth,
s1.stf.effective_duration
], [
s2.effective_lat, s2.effective_lon, s2.depth,
s2.stf.effective_duration
], 0.001)
else:
assert numeq([
s1.effective_lat, s1.effective_lon, s1.depth,
s1.stf.effective_duration
], [
s2.effective_lat, s2.effective_lon, s2.depth,
s2.stf1.effective_duration
], 0.001)
def __init__(self):
Snuffling.__init__(self)
self.stf_types = ['half sin', 'triangular', 'boxcar', 'None']
self.stf_instances = [
gf.HalfSinusoidSTF(),
gf.TriangularSTF(),
gf.BoxcarSTF(), None
]
def test_stf_triangular(self, plot=False):
from matplotlib import pyplot as plt
tref = 10.
for duration in [0., 3.]:
for peak_ratio in num.linspace(0., 1., 11):
stf = gf.TriangularSTF(duration=duration,
peak_ratio=peak_ratio,
anchor=0.)
t, a = stf.discretize_t(deltat=0.1, tref=tref)
assert numeq(stf.centroid_time(tref), tref, 1e-5)
if plot:
plt.plot(t, a)
plt.plot(t, a, 'o')
if plot:
plt.show()
def test_source_to_event(self):
def stype(x):
return x.__class__.__name__
for S in gf.source_classes:
if S is gf.CombiSource:
continue
stf = gf.TriangularSTF(effective_duration=2.0)
s1 = S(lat=10.,
lon=20.,
depth=1000.,
north_shift=500.,
east_shift=500.,
stf=stf)
ev = s1.pyrocko_event()
try:
s1_mag = s1.get_magnitude()
mess = ''
except (gf.DerivedMagnitudeError, NotImplementedError) as e:
s1_mag = 'N/A'
mess = '* %s *' % e
if ev.magnitude is not None:
ev_mag = ev.magnitude
else:
ev_mag = 'N/A'
assert ev.lat == s1.lat
assert ev.lon == s1.lon
assert ev.north_shift == s1.north_shift
assert ev.east_shift == s1.east_shift
s2 = S.from_pyrocko_event(ev)
assert ev.lat == s2.lat
assert ev.lon == s2.lon
assert ev.north_shift == s2.north_shift
assert ev.east_shift == s2.east_shift
if ev.moment_tensor:
mt_mag = ev.moment_tensor.magnitude
else:
mt_mag = 'N/A'
try:
s2_mag = s2.get_magnitude()
mess = ''
except (gf.DerivedMagnitudeError, NotImplementedError) as e:
s2_mag = 'N/A'
mess = '* %s *' % e
# print(
# stype(s1).ljust(32),
# s1_mag,
# ev.magnitude or 'N/A',
# mt_mag,
# s2_mag,
# mess)
del mess
def assert_mag_eq(mag1, mag2):
if 'N/A' not in (mag1, mag2):
num.testing.assert_approx_equal(mag1, mag2)
assert_mag_eq(s1_mag, ev_mag)
assert_mag_eq(s1_mag, mt_mag)
assert_mag_eq(s1_mag, s2_mag)
if not isinstance(s1, gf.DoubleDCSource):
assert numeq([
s1.effective_lat, s1.effective_lon, s1.depth,
s1.stf.effective_duration
], [
s2.effective_lat, s2.effective_lon, s2.depth,
s2.stf.effective_duration
], 0.001)
else:
assert numeq([
s1.effective_lat, s1.effective_lon, s1.depth,
s1.stf.effective_duration
], [
s2.effective_lat, s2.effective_lon, s2.depth,
s2.stf1.effective_duration
], 0.001)
save_dir = 'Experiment2'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
velocity_model = 'crust2_m5_hardtop_16Hz'
# create random moment tensors
print('Creating random moment tensors...')
moment_tensors = [createMT_DC(1) for i in range(n_seismograms)]
source_mechanisms = n_seismograms*['DC']
#initialize source time functions
durations = np.random.randint(1,11,n_eachstf)
stfs = [LognormalSTF(shape=2, duration=dur) for dur in durations] + \
[gf.BoxcarSTF(duration=dur) for dur in durations] + \
[gf.TriangularSTF(duration=dur) for dur in durations]
stftypes = n_eachstf*['lognorm'] + n_eachstf*['boxcar'] +n_eachstf*['triangle']
# uniform depth of 20 km / 20,000m
depths = n_seismograms*[20000]
# seismometer target and the actual computation engine
target = gf.Target(
quantity='displacement',
lat=0, lon=long,
store_id=velocity_model,
codes=('NET', 'STA', 'LOC', 'E'),
tmin=tmin, tmax=tmax)
engine = LocalEngine(store_dirs=[velocity_model])
