h5cpp is a modern C++ wrapper for HDF5's C-API.

HDF5 is a powerful binary format. There is virtually nothing that cannot be stored in an HDF5 file. You can either use the C-API or one of the wrappers for a scripting language (for Python there is for instance h5py or pytables). To keep their interfaces simple, many of these wrappers do not provide the full functionality HDF5 has to offer. If you want to use all features available, you must use the C-API, which is, however, quite unwieldy and not conducive to modern C++ idioms.

h5cpp provides a domain-agnostic and easy-to-use modern C++ interface to the full functionality of HDF5.

We currently support:

• Linux
• Windows
• macOS

Although this and Steven Varga's h5cpp project share the same name they are entirely unrelated.

You may bring in h5cpp as a conan package or build and install the library manually as described below.

In either case, you can bring it into your program by adding something like this in your CMakeLists.txt file:

find_package(h5cpp REQUIRED)
.
.
.
add_executable(some_target some_code.cpp)
target_link_libraries(some_target h5cpp)

and adding the following:

#include <h5cpp/hdf5.hpp>

to your your source file. Here is a small example of how to make use of the library in code:

using namespace hdf5;

// create a file
file::File f = file::create("writing_vector.h5",file::AccessFlags::Truncate);

// create a group
node::Group root_group = f.root();
node::Group my_group = root_group.create_group("my_group");

// create a dataset
using data_type = std::vector<int>;
data_type data{1,2,3,4};
node::Dataset dataset = my_group.create_dataset("data",
                                                datatype::create<data_type>(),
                                                dataspace::create(data));

// write to dataset
dataset.write(data);

For more, please see the full API documentation.

The minimum requirements for building the library are:

• a C++ compiler, gcc>=4.9 should do well
• the HDF5 C library (>=1.8.13 would do but >=1.10.0 is prefered)
• cmake >= 3.10
• either the boost libraries or a compiler with std::filesystem or std::experimental::filesystem (and specify H5CPP_WITH_BOOST=OFF to CMake)
• sphinx, breathe (with python) and doxygen for the documentation build
• catch2 to build the unit tests

The external library dependencies can be acquired and built using Conan. Conan can be installed with PyPI:

pip install conan

Then run the following to configure required Conan repositories:

conan remote add bincrafters https://bincrafters.jfrog.io/artifactory/api/conan/public-conan

and that's it, CMake will handle the rest!

Alternatively you can manually install the dependencies to typical system locations. In this case please disable Conan by using the -DH5CPP_CONAN=DISABLE option when you run CMake.

Building the library is standard cmake & make fare, out of source. For example, in linux shell, you would do the following:

git clone https://github.com/ess-dmsc/h5cpp.git
cd h5cpp
mkdir build
cd build
cmake ..
make

In any case you should run the tests after the build.

$ make test

To install the library to system, you would follow this up with:

sudo make install

If you do not wish to install h5cpp to your system folders you can slightly modify the above steps. When building the library, invoke CMake with the following option:

cmake -DCMAKE_INSTALL_PREFIX=/home/user1/some/path ..

and accordingly, when building the client program:

cmake -Dh5cpp_DIR=/home/user1/some/path/lib/cmake/h5cpp-0.5.2 path/to/your/source

where version number may vary.

For OSX and Windows instructions, as well as instructions for building tests and documentation, see the online documentation.