Simplifying Hyperslab Reads with H5CPP

The Problem (Bert Bril, Apr 26, 2018)

Bert was trying to read a single column from a massive 3D dataset:

short data[251];
hsize_t count[3] = {1, 1, 251};
hsize_t offset[3] = {0, 0, 0};
hsize_t stride[3] = {1, 1, 1};

filedataset.selectHyperslab(H5S_SELECT_SET, count, offset, stride);
H5::DataSpace input = filedataset.getSpace();
H5::DataSpace output(1, &nrtoread);
dataset_->read(data, H5::PredType::STD_I16LE, output, input);

But weirdly, the data read back had two out of every three values missing: 3786 0 0 3555 0 0 3820 … Even simpler 1D reads using a 3D hyperslab weren’t behaving as expected—and Bert couldn’t figure out why.

Steven Varga to the Rescue (Apr 26, 2018)

Steven’s suggestion? Toss the boilerplate and go with H5CPP—a clean, MIT-licensed, header-only C++ template library for HDF5 that makes multidimensional, Armadillo-style reads so much simpler:

“Reading a cube into an arma::cube is one-step—see the Armadillo example—and use arma::Cube instead of arma::Mat.” H5CPP is well-documented, functional, with examples, and integrates with major linear-algebra libraries. — Steve

H5CPP lets you sidestep manual DataSpace manipulations by offering high-level read() calls that feel like native matrix slicing.

Why This Helps

Approach	Complexity	Behavior Clarity	Extensibility
Manual Hyperslab (HDF5 C++)	High	Error-prone and subtle	Hard to debug
H5CPP (template-based)	Low	Clear intent and results	Easy integration with arma, eigen, etc.

What to Do Next

Try H5CPP—especially for reading cubes, columns, or slices with Armadillo support.
Skip the complex hyperslab boilerplate and reach for a higher-level read() that feels like:

arma::cube C;
h5::read(ds, C); // simplified multi-dimensional read

If you’re still seeing missing elements, compare dimension ordering or endian assumptions—but often H5CPP's clean interface surfaces logic over noise.