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 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
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
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 _sum(self, irecords, delays, weights, itmin, nsamples, decimate,
implementation, optimization):
if not self._f_index:
self.open()
if optimization == 'enable':
irecords, delays, weights = self._optimize(
irecords, delays, weights)
else:
assert optimization == 'disable'
if implementation == 'c' and decimate == 1:
if delays.size != 0:
itoffset = int(num.floor(num.min(delays)/self._deltat))
else:
itoffset = 0
if nsamples is None:
nsamples = -1
if itmin is None:
itmin = 0
else:
itmin -= itoffset
try:
tr = GFTrace(*store_ext.store_sum(
self.cstore, irecords.astype(num.uint64),
(delays - itoffset*self._deltat).astype(num.float32),
weights.astype(num.float32),
int(itmin), int(nsamples)))
tr.itmin += itoffset
return tr
except store_ext.StoreExtError, e:
raise StoreError(str(e))
