Status Resolver::resolveCached(
const vector<Address>& addresses,
vector<ExpandedNodeId>& expandedNodeIds,
vector<Status>& statuses,
Mask& expandedNodeIdMask,
Mask& relativePathMask)
{
logger_->debug("Resolving the addresses by querying the cache");
// declare the return status
Status ret(statuscodes::Good);
// declare the number of addresses
size_t noOfAddresses = addresses.size();
// resize the masks
expandedNodeIdMask.resize(noOfAddresses);
relativePathMask.resize(noOfAddresses);
// loop through the addresses
for (size_t i=0; i<noOfAddresses; i++)
{
logger_->debug("Trying to find address %d in the cache", i);
if (database_->addressCache.find(addresses[i], expandedNodeIds[i]))
{
statuses[i] = statuscodes::Good;
logger_->debug("Address %d was already cached", i);
}
else
{
if (addresses[i].isExpandedNodeId())
{
expandedNodeIdMask.set(i);
logger_->debug("Address %d is an ExpandedNodeId not found in the cache", i);
}
else if (addresses[i].isRelativePath())
{
relativePathMask.set(i);
logger_->debug("Address %d is a RelativePath not found in the cache", i);
}
else
{
ret = EmptyAddressError();
logger_->error("Address %d cannot be resolved since it's an empty address", i);
break;
}
}
}
return ret;
}
void bi::InputNetCDFBuffer::readMask0(const VarType type,
Mask<ON_HOST>& mask) {
typedef temp_host_matrix<real>::type temp_matrix_type;
mask.resize(m.getNumVars(type), false);
Var* var;
int r;
long start, len;
/* sparse masks */
for (r = 0; r < int(recDims.size()); ++r) {
if (timeVars[r] < 0) {
BOOST_AUTO(range, modelVars.equal_range(r));
BOOST_AUTO(iter, range.first);
BOOST_AUTO(end, range.second);
start = 0;
len = nc_inq_dimlen(ncid, recDims[r]);
temp_matrix_type C(iter->second->getNumDims(), len);
readCoords(coordVars[r], start, len, C);
for (; iter != end; ++iter) {
var = iter->second;
if (var->getType() == type) {
mask.addSparseMask(var->getId(), C.size2());
serialiseCoords(var, C, mask.getIndices(var->getId()));
}
}
}
}
/* dense masks */
r = -1; // for those vars not associated with a record dimension
BOOST_AUTO(range, modelVars.equal_range(r));
BOOST_AUTO(iter, range.first);
BOOST_AUTO(end, range.second);
for (; iter != end; ++iter) {
var = iter->second;
if (var->getType() == type) {
mask.addDenseMask(var->getId(), var->getSize());
}
}
}
void bi::InputNetCDFBuffer::readMask(const size_t k, const VarType type,
Mask<ON_HOST>& mask) {
typedef temp_host_matrix<real>::type temp_matrix_type;
mask.resize(m.getNumVars(type), false);
Var* var;
int r;
long start, len;
for (r = 0; r < int(recDims.size()); ++r) {
if (timeVars[r] >= 0) {
start = recStarts[k][r];
len = recLens[k][r];
if (len > 0) {
BOOST_AUTO(range, modelVars.equal_range(r));
BOOST_AUTO(iter, range.first);
BOOST_AUTO(end, range.second);
if (coordVars[r] >= 0) {
/* sparse mask */
temp_matrix_type C(iter->second->getNumDims(), len);
readCoords(coordVars[r], start, len, C);
for (; iter != end; ++iter) {
var = iter->second;
if (var->getType() == type) {
mask.addSparseMask(var->getId(), len);
serialiseCoords(var, C, mask.getIndices(var->getId()));
}
}
} else {
/* dense mask */
for (; iter != end; ++iter) {
var = iter->second;
if (var->getType() == type) {
mask.addDenseMask(var->getId(), var->getSize());
}
}
}
}
}
}
}
