Source code transformation tool for HDF5 dataformat H5CPP header only library

Build Matrix

OS / Compiler	GCC 13	GCC 14	GCC 15	Clang 14	Clang 15	Clang 17
Ubuntu 22.04
Ubuntu 24.04
macOS
Windows

This source code transformation tool simplifies the otherwise time consuming process of generating the shim code for HDF5 Compound datatypes by building the AST of a given TU translation unit, and identifying all POD datatypes referenced from H5CPP operators/functions. The result is a seamless persistence much similar to python, java or other reflection based languages.

The following excerpt shows the mechanism, how vec variable is marked by h5::write operator. When h5cpp tool is invoked it builds the full AST of the translation unit, finds the referenced types, then in topological order generates HDF5 COMPOUND datatype descriptors. The generated file has include guards, and meant to be used with H5CPP template library. POD struct types may be arbitrary deep, embedded in POD C like arrays, and may be referenced from STL containers. Currently stl::vector is supported, but in time full support will be provided.

Installation

sudo apt install build-essential cmake
cmake -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build --parallel
sudo cmake --install build

...
std::vector<sn::example::Record> vec 
    = h5::utils::get_test_data<sn::example::Record>(20);
// mark vec  with an h5:: operator and delegate 
// the details to h5cpp compiler
h5::write(fd, "orm/partial/vector one_shot", vec );
...

// some include files with complex POD types, embedded in arbitrary name space
namespace sn {
	namespace typecheck {
		struct Record { /*the types with direct mapping to HDF5*/
			char  _char; unsigned char _uchar; short _short; unsigned short _ushort; int _int; unsigned int _uint;
			long _long; unsigned long _ulong; long long int _llong; unsigned long long _ullong;
			float _float; double _double; long double _ldouble;
			bool _bool;
			// wide characters are not supported in HDF5
			// wchar_t _wchar; char16_t _wchar16; char32_t _wchar32;
		};
	}
	namespace other {
		struct Record {                    // POD struct with nested namespace
			MyUInt                    idx; // typedef type 
			MyUInt                     aa; // typedef type 
			double            field_02[3]; // const array mapped 
			typecheck::Record field_03[4]; //
		};
	}
	namespace example {
		struct Record {                    // POD struct with nested namespace
			MyUInt                    idx; // typedef type 
			float             field_02[7]; // const array mapped 
			sn::other::Record field_03[5]; // embedded Record
			sn::other::Record field_04[5]; // must be optimized out, same as previous
			other::Record  field_05[3][8]; // array of arrays 
		};
	}
	namespace not_supported_yet {
		// NON POD: not supported in phase 1
		// C++ Class -> PODstruct -> persistence[ HDF5 | ??? ] -> PODstruct -> C++ Class 
		struct Container {
			double                            idx; // 
			std::string                  field_05; // c++ object makes it non-POD
			std::vector<example::Record> field_02; // ditto
		};
	}
	/* BEGIN IGNORED STRUCT */
	// these structs are not referenced with h5::read|h5::write|h5::create operators
	// hence compiler should ignore them.
	struct IgnoredRecord {
		signed long int   idx;
		float        field_0n;
	};
	/* END IGNORED STRUCTS */

Python virtual environment for this website

Setup

python3 -m venv .venv                 # Create a virtual env (Python 3.10+ recommended)
source .venv/bin/activate             # Activate: Linux / macOS
# On Windows use: .venv\Scripts\activate

pip install --upgrade pip             # Upgrade pip
pip install mkdocs-material           # Install MkDocs + Material theme
pip install python-frontmatter jinja2 python-dateutil pyyaml  # Extra deps

Run MkDocs locally

source .venv/bin/activate
mkdocs serve --dev-addr=127.0.0.1:9000   # Live preview at http://127.0.0.1:9000
mkdocs build -v                          # Build static site locally