def sum_timeseries():
results = []
for itarget, target in enumerate(targets):
params = store_ext.make_sum_params(
store.cstore,
source_coords_arr,
source_terms,
target.coords5[num.newaxis, :].copy(),
scheme,
interpolation,
nthreads)
for weights, irecords in params:
neach = irecords.size // dsource.times.size
delays2 = num.repeat(dsource.times, neach)
r = store_ext.store_sum(
store.cstore,
irecords,
delays2,
weights,
int(itmin[itarget]),
int(nsamples[itarget]))
results.append(r)
return results
def sum_timeseries():
results = []
for itarget, target in enumerate(targets):
params = store_ext.make_sum_params(
store.cstore,
source_coords_arr,
source_terms,
target.coords5[num.newaxis, :].copy(),
scheme,
interpolation,
nthreads)
for weights, irecords in params:
neach = irecords.size // dsource.times.size
delays2 = num.repeat(dsource.times, neach)
r = store_ext.store_sum(
store.cstore,
irecords,
delays2,
weights,
int(itmin[itarget]),
int(nsamples[itarget]))
results.append(r)
return results
def make_param_c():
return store_ext.make_sum_params(
store.cstore,
source_coords_arr,
mts_arr,
receiver_coords_arr,
'elastic10',
interpolation, nthreads)
def make_param_c():
return store_ext.make_sum_params(
store.cstore,
source_coords_arr,
mts_arr,
receiver_coords_arr,
'elastic10',
interpolation, nthreads)
def fwd_model_seperate(interpolation=interp[0]):
args = (store.cstore, dsource.coords5(), mts_arr,
static_target.coords5, 'elastic10', interpolation,
nthreads)
sum_params = store_ext.make_sum_params(*args)
out = {}
for icomp, comp in enumerate(scheme_desc):
weights, irecords = sum_params[icomp]
out[comp] = store_ext.store_sum_static(
store.cstore, irecords, delays_s, weights, pos,
ntargets**2, nthreads)
return out
def sum_timeseries():
results = []
for itarget, target in enumerate(targets):
params = store_ext.make_sum_params(
store.cstore, source_coords_arr, mts_arr,
target.coords5[num.newaxis, :].copy(), 'elastic10',
interpolation, nthreads)
for weights, irecords in params:
d = num.zeros(irecords.shape[0], dtype=num.float32)
r = store_ext.store_sum(store.cstore, irecords, d,
weights, int(itmin[itarget]),
int(nsamples[itarget]))
results.append(r)
return results
def fwd_model_seperate(interpolation=interp[0]):
args = (store.cstore, dsource.coords5(), mts_arr,
static_target.coords5, 'elastic10', interpolation,
nthreads)
sum_params = store_ext.make_sum_params(*args)
out = {}
for icomp, comp in enumerate(scheme_desc):
weights, irecords = sum_params[icomp]
out[comp] = store_ext.store_sum_static(
store.cstore, irecords, delays_s, weights,
pos, ntargets**2, nthreads)
return out
def test_sum_static(self):
from pyrocko.gf import store_ext
benchmark.show_factor = True
store = gf.Store(self.get_qseis_store_dir())
store.open()
src_length = 2 * km
src_width = 5 * km
ntargets = 1600
interp = ['nearest_neighbor', 'multilinear']
interpolation = interp[0]
source = gf.RectangularSource(lat=0.,
lon=0.,
depth=5 * km,
north_shift=0.,
east_shift=0.,
width=src_width,
length=src_length)
static_target = gf.StaticTarget(
north_shifts=5 * km + random.rand(ntargets) * 5 * km,
east_shifts=0 * km + random.rand(ntargets) * 5 * km)
targets = static_target.get_targets()
dsource = source.discretize_basesource(store, targets[0])
source_coords_arr = dsource.coords5()
mts_arr = dsource.m6s
receiver_coords_arr = num.empty((len(targets), 5))
for itarget, target in enumerate(targets):
receiver = target.receiver(store)
receiver_coords_arr[itarget, :] = \
[receiver.lat, receiver.lon, receiver.north_shift,
receiver.east_shift, receiver.depth]
def sum_target(cstore, irecords, delays_t, delays_s, weights, pos,
nthreads):
@benchmark.labeled('sum-timeseries-np%d' % nthreads)
def sum_timeseries():
nsummands = weights.size // ntargets
res = num.zeros(ntargets)
for t in range(ntargets):
sl = slice(t * nsummands, (t + 1) * nsummands)
r = store_ext.store_sum(cstore, irecords[sl], delays_t[sl],
weights[sl], pos, 1)
res[t] = r[0]
return res
@benchmark.labeled('sum-static-np%d' % nthreads)
def sum_static():
return store_ext.store_sum_static(cstore, irecords, delays_s,
weights, pos, ntargets,
nthreads)
return sum_timeseries(), sum_static()
args = (store.cstore, source_coords_arr, mts_arr, receiver_coords_arr,
'elastic10', interpolation, 0)
benchmark.clear()
for nthreads in [1, 2, 4]:
for (weights, irecords) in store_ext.make_sum_params(*args):
delays_t = num.zeros_like(weights, dtype=num.float64)
delays_s = dsource.times.astype(num.float64)
pos = 6
t, s = sum_target(store.cstore, irecords, delays_t, delays_s,
weights, pos, nthreads)
# print benchmark.__str__(header=False)
num.testing.assert_equal(t, s)
benchmark.clear()
def test_sum_static(self):
from pyrocko.gf import store_ext
benchmark.show_factor = True
store = gf.Store(self.get_qseis_store_dir())
store.open()
src_length = 2 * km
src_width = 5 * km
ntargets = 1600
interp = ['nearest_neighbor', 'multilinear']
interpolation = interp[0]
source = gf.RectangularSource(
lat=0., lon=0.,
depth=5*km, north_shift=0., east_shift=0.,
width=src_width, length=src_length)
static_target = gf.StaticTarget(
north_shifts=5*km + random.rand(ntargets) * 5*km,
east_shifts=0*km + random.rand(ntargets) * 5*km)
targets = static_target.get_targets()
dsource = source.discretize_basesource(store, targets[0])
source_coords_arr = dsource.coords5()
mts_arr = dsource.m6s
receiver_coords_arr = num.empty((len(targets), 5))
for itarget, target in enumerate(targets):
receiver = target.receiver(store)
receiver_coords_arr[itarget, :] = \
[receiver.lat, receiver.lon, receiver.north_shift,
receiver.east_shift, receiver.depth]
def sum_target(cstore, irecords, delays_t, delays_s,
weights, pos, nthreads):
@benchmark.labeled('sum-timeseries-np%d' % nthreads)
def sum_timeseries():
nsummands = weights.size // ntargets
res = num.zeros(ntargets)
for t in range(ntargets):
sl = slice(t*nsummands, (t+1) * nsummands)
r = store_ext.store_sum(
cstore, irecords[sl], delays_t[sl],
weights[sl], pos, 1)
res[t] = r[0]
return res
@benchmark.labeled('sum-static-np%d' % nthreads)
def sum_static():
return store_ext.store_sum_static(
cstore, irecords, delays_s, weights,
pos, ntargets, nthreads)
return sum_timeseries(), sum_static()
args = (store.cstore, source_coords_arr, mts_arr, receiver_coords_arr,
'elastic10', interpolation, 0)
benchmark.clear()
for nthreads in [1, 2, 4]:
for (weights, irecords) in store_ext.make_sum_params(*args):
delays_t = num.zeros_like(weights, dtype=num.float64)
delays_s = dsource.times.astype(num.float64)
pos = 6
t, s = sum_target(store.cstore, irecords, delays_t, delays_s,
weights, pos, nthreads)
# print benchmark.__str__(header=False)
num.testing.assert_equal(t, s)
benchmark.clear()