h5pp

h5pp is a high-level C++17 wrapper for the HDF5 C library.

With simplicity in mind, h5pp lets users store common C++ data types into portable binary HDF5 files. In particular, h5pp makes it easy to read and write Eigen matrices and tensors.

Latest release

Go to quickstart to see install examples

Go to examples to learn how to use h5pp

Introduction
Features
Usage
Installation

Introduction

HDF5 is a popular format for portable binary storage of large datasets. With bindings to languages such as Python, Julia, Matlab and many others, it is straightforward to export, import and analyze data in a collaborative setting.

In C/C++ using HDF5 directly is not straightforward. Beginners are met with a steep learning curve to the vast API of HDF5. There are many C/C++ libraries already that simplify the user experience, but as a matter of opinion, things could be even simpler.

h5pp makes HDF5 simple in the following sense:

Read and write common C++ types in a single line of code.
No prior knowledge of HDF5 is required.
Default settings let simple tasks stay simple, e.g., storage layout, chunking and compression.
Advanced tasks remain possible, e.g. MPI parallelism.
Meaningful logs and error messages even for beginners.
Simple installation with modular dependencies and opt-in automation.

Features

Header-only C++17 template library
High-level front-end to the C API of HDF5
Support for common C++ types such as:
- numeric types short,int,long, long long (+ unsigned versions), float, double, long double
- std::complex<> with any of the types above
- CUDA-style POD-structs with x,y or x,y,z members as atomic type, such as float3 or double2. These work with any of the types above. In h5pp these go by the name Scalar2<> and Scalar3<>.
- Contiguous containers with a .data() member, such as std::vector<>
- std::string, char arrays, and std::vector<std::string>
- C-style arrays or pointer-to-buffers
Support for Eigen types such as Eigen::Matrix<>, Eigen::Array<> and Eigen::Tensor<>, with automatic conversion to/from row-major storage
Support for user-defined compound HDF5 types (see example)
Support for HDF5 tables (with user-defined compound HDF5 types for entries)
Modern installation of h5pp and its dependencies. Choose:
- Installation with package managers: conan, conda or apt (.deb installation file)
- CMake installation providing targets for linking to your projects. (Opt-in) Automatically find or download dependencies with "CMake-only" methods.
Multi-platform: Linux, Windows, OSX. (Developed under Linux)

Usage

Using h5pp is intended to be simple. After initializing a file, most of the work can be achieved using just two member functions .writeDataset(...) and .readDataset(...).

Example: Writing an `std::vector`

    #include <h5pp/h5pp.h>
    
    int main() {
        
        // Initialize a file
        h5pp::File file("myDir/someFile.h5");
    
        // Initialize a vector doubles
        std::vector<double> v = {1.0, 2.0, 3.0};
        
        // Write the vector to file.
        // Inside the file, the data will be stored in a dataset named "myStdVector"
        file.writeDataset(v, "myStdVector");
        return 0;
    }

Find more code examples in the examples directory or in the Wiki.

File permissions

h5pp offers more flags for file access permissions than HDF5. The new flags are primarily intended to prevent accidental loss of data, but also to clarify intent and avoid mutually exclusive options.

The flags are listed in the order of increasing "danger" that they pose to previously existing files.

Flag	File exists	No file exists	Comment
`READONLY`	Open with read-only permission	Throw error	Never writes to disk, fails if the file is not found
`COLLISION_FAIL`	Throw error	Create new file	Never deletes existing files and fails if it already exists
`RENAME` (default)	Create renamed file	Create new file	Never deletes existing files. Invents a new filename to avoid collision by appending "-#" (#=1,2,3...) to the stem of the filename
`READWRITE`	Open with read-write permission	Create new file	Never deletes existing files, but is allowed to open/modify
`BACKUP`	Rename existing file and create new	Create new file	Avoids collision by backing up the existing file, appending ".bak_#" (#=1,2,3...) to the filename
`REPLACE`	Truncate (overwrite)	Create new file	Deletes the existing file and creates a new one in place

When a new file is created, the intermediate directories are always created automatically.
When a new file is created, READWRITE permission to it is implied.

To give a concrete example, the syntax works as follows

    h5pp::File file("myDir/someFile.h5", h5pp::FilePermission::REPLACE);

Storage Layout

HDF5 offers three storage layouts:

H5D_COMPACT: For scalar or small datasets which can fit in the metadata header. Default on datasets smaller than 32 KB.
H5D_CONTIGUOUS: For medium size datasets. Default on datasets smaller than 512 KB.
H5D_CHUNKED: For large datasets. Default on datasets larger than 512 KB. This layout has some additional features:
- Chunking, portioning of the data to improve IO performance by caching more efficiently. Chunk dimensions are calculated by h5pp if not given by the user.
- Compression, disabled by default, and only available if HDF5 was built with zlib enabled.
- Resize datasets. Note that the file size never decreases, for instance after overwriting with a smaller dataset.

h5pp can automatically determine the storage layout for each new dataset. To specify the layout manually, pass it as a third argument when writing a new dataset, for instance:

    file.writeDataset(myData, "science/myChunkedData", H5D_CHUNKED);      // Creates a chunked dataset

Compression

Extendable (or chunked) datasets can also be compressed if HDF5 was built with zlib support. Use these functions to set or check the compression level:

    file.setCompressionLevel(3);            // 0 to 9: 0 to disable compression, 9 for maximum compression. Recommended 2 to 5
    file.getCompressionLevel();             // Gets the current compression level
    h5pp::checkIfCompressionIsAvailable();  // True if your installation of HDF5 has zlib support

or pass a temporary compression level as the fifth argument when writing a dataset:

    file.writeDataset(myData, "science/myCompressedData", H5D_CHUNKED, std::nullopt, 8); // Creates a chunked dataset with compression level 8.

Debug and logging

Spdlog can be used to emit debugging information efficiently. The amount of console output (verbosity) can be set to any level between 0 and 5:

0: trace (highest verbosity)
1: debug
2: info (default)
3: warn
4: error
5: critical (lowest verbosity)

Set the level when constructing a h5pp::File or by calling the function .setLogLevel(int):

    int logLevel = 0; // Highest verbosity
    // This way...
    h5pp::File file("myDir/someFile.h5", h5pp::FilePermission::REPLACE, logLevel); 
    // or this way
    file.setLogLevel(logLevel);

NOTE: Logging works the same with or without Spdlog enabled. When Spdlog is not found, a hand-crafted logger is used in its place to give identical output but without any performance considerations (implemented with STL lists, strings and streams).

Tips

NEW: h5du

List the size of objects inside an HDF5 file with h5du.

View data

Try HDF Compass or HDFView. Both are available in Ubuntu's package repository.

Load data into Python

HDF5 data is easy to load into Python using h5py. Loading integer and floating point data is straightforward. Complex data is almost as simple, so let's use that as an example.

HDF5 does not support complex types natively, but h5ppenables this by using a custom compound HDF5 type with real and imag fields. Here is a python example which uses h5py to load 1D arrays from an HDF5 file generated with h5pp:

    import h5py
    import numpy as np
    file  = h5py.File('myFile.h5', 'r')
    
    # previously written as std::vector<double> in h5pp
    myDoubleArray = np.asarray(file['double-array-dataset'])                                     
    
    # previously written as std::vector<std::complex<double>> in h5pp
    myComplexArray = np.asarray(file['complex-double-array-dataset']).view(dtype=np.complex128)

Notice the cast to dtype=np.complex128 which interprets each element of the array as two doubles, i.e. the real and imaginary parts are 2 * 64 = 128 bits.

Installation

Requirements

C++17 capable compiler. GCC version >= 7 or Clang version >= 7.0
CMake version >= 3.12
HDF5 library, version >= 1.8

Optional dependencies:

Eigen: Write Eigen matrices and tensors directly. Tested with version >= 3.3.4
spdlog: Enables logging for debug purposes. Tested with version >= 1.3.1
- fmt: Formatting library used in spdlog.
ghc::filesystem: This drop-in replacement for std::filesystem is downloaded and installed automatically when needed, but only if H5PP_PACKAGE_MANAGER=find-or-cmake, cmake or conan

Obtaining `h5pp`

There are currently 4 ways to obtain h5pp:

git clone https://github.com/DavidAce/h5pp.git and install (see below)
From conda: conda install -c davidace h5pp
From conan-center
(Ubuntu/Debian only) Download the latest release and install with apt: sudo apt install ./h5pp_<version>_amd64.deb

Install methods

For full working examples see the directory quickstart. Find a summary below.

Option 1: Copy the headers

Copy the files under h5pp/source/include and add #include<h5pp/h5pp.h>. Make sure to compile with -std=c++17 -lstdc++fs and link the dependencies hdf5, Eigen3 and spdlog. The actual linking is a non-trivial step, see linking below.

Option 2: Install with Conan (Recommended)

Make sure to install and configure Conan first. E.g. add the line compiler.cppstd=17 under [settings] in your conan profile ~/.conan/profile/default. Then run the following command:

$ conan install h5pp/1.8.5@ --build=missing

The flag --build=missing lets conan install dependencies such as HDF5, Eigen3 and spdlog.

After this step, use h5pp like any other conan package. For more information refer to the conan docs or have a look at quickstart.

Option 3: Install with CMake

Build the library just as any CMake project. For instance, from the project's root in command-line:

    mkdir build
    cd build
    cmake -DCMAKE_INSTALL_PREFIX=<install-dir> ../
    make
    make install

Headers will be installed under <install-dir>/include and config files under <install-dir>/share/h5pp/cmake. These config files allow you to usefind_package(h5pp) in your own projects, which in turn defines the target h5pp::h5pp with everything you need to link h5pp correctly (including dependencies, if you so choose). If not set, CMAKE_INSTALL_PREFIX defaults to ${CMAKE_BINARY_DIR}/install, where ${CMAKE_BINARY_DIR} is the directory you are building from.

Opt-in automatic dependency installation with CMake

The CMake flag H5PP_PACKAGE_MANAGER controls the automated behavior for finding or installing dependencies. It can take one of these strings:

Option	Description
`find` (default)	Use CMake's `find_package` to find dependencies pre-installed on your system
`cmake` (!)	Use CMake-only features to download and install dependencies automatically. Disregards pre-installed dependencies on your system
`find-or-cmake`	Start with `find` and then go to `cmake` if not found
`conan` (!!)	Use the Conan package manager to download and install dependencies automatically. Disregards libraries elsewhere on your system

There are several variables you can pass to CMake to guide find_package calls, see CMake build options below.

(!) Dependencies are installed into CMAKE_INSTALL_PREFIX. Pass the CMake variable H5PP_DEPS_IN_SUBDIR to install into separate directories under CMAKE_INSTALL_PREFIX/<libname>.

(!!) Conan is guided by conanfile.txt found in this project's root directory. This method requires conan to be installed prior (for instance through pip, conda, apt, etc). To let CMake find conan you have three options:

Add Conan install (or bin) directory to the environment variable PATH.
Export Conan install (or bin) directory in the environment variable CONAN_PREFIX, i.e. from command line: export CONAN_PREFIX=<path-to-conan>
Give the variable CONAN_PREFIX directly to CMake, i.e. from command line: cmake -DCONAN_PREFIX:PATH=<path-to-conan> ...

CMake options

The cmake step above takes several options, cmake [-DOPTIONS=var] ../ :

Var	Default	Description
`CMAKE_INSTALL_PREFIX`	`${CMAKE_BINARY_DIR}/install`	Specify `h5pp` install directory
`BUILD_SHARED_LIBS`	`OFF`	Link dependencies with static or shared libraries
`H5PP_ENABLE_TESTS`	`OFF`	Build tests (recommended!)
`H5PP_BUILD_EXAMPLES`	`OFF`	Build example programs
`H5PP_PACKAGE_MANAGER`	`find`	Download method for dependencies, select, `find`, `cmake`, `find-or-cmake` or `conan`
`H5PP_PRINT_INFO`	`OFF`	Use h5pp with add_subdirectory()
`H5PP_IS_SUBPROJECT`	`OFF`	Print extra CMake info about the host and generated targets during configure
`H5PP_ENABLE_EIGEN3`	`OFF`	Enables Eigen3 linear algebra library support
`H5PP_ENABLE_SPDLOG`	`OFF`	Enables Spdlog support for logging `h5pp` internal info to stdout
`H5PP_DEPS_IN_SUBDIR`	`OFF`	Appends `<libname>` to install location of dependencies, i.e. `CMAKE_INSTALL_PREFIX/<libname>`. This allows simple removal
`H5PP_PREFER_CONDA_LIBS`	`OFF`	Prioritize finding dependencies `hdf5`, `Eigen3` and `spdlog` installed through conda. No effect when `H5PP_PACKAGE_MANAGER=conan`

The following variables can be set to help guide CMake's find_package to your pre-installed software (no defaults):

Var	Path to
`Eigen3_DIR`	Eigen3Config.cmake
`Eigen3_ROOT`	Eigen3 install directory
`EIGEN3_INCLUDE_DIR`	Eigen3 include directory
`spdlog_DIR`	spdlogConfig.cmake
`spdlog_ROOT`	Spdlog install directory
`HDF5_DIR`	HDF5Config.cmake
`HDF5_ROOT`	HDF5 install directory
`CONAN_PREFIX`	conan install directory

Link to your project

Link using CMake targets (easy)

h5pp is easily imported into your project using CMake's find_package. Just point it to the h5pp install directory. When found, targets are made available to compile and link to dependencies correctly. A minimal CMakeLists.txt to use h5pp would look like:

    cmake_minimum_required(VERSION 3.12)
    project(myProject)
    add_executable(myExecutable main.cpp)
    find_package(h5pp PATHS <path-to-h5pp-install-dir> REQUIRED) # If h5pp is installed through conda the path may be $ENV{CONDA_PREFIX}
    target_link_libraries(myExecutable PRIVATE h5pp::h5pp)

Targets explained

h5pp::h5pp is the main target including "everything" and should normally be the only target that you need -- headers,flags and (if enabled) the found/downloaded dependencies.
h5pp::headers links the h5pp headers only.
h5pp::deps collects library targets to link all the dependencies that were found/downloaded when h5pp was built. These can of course be used independently.
- If H5PP_PACKAGE_MANAGER==find|cmake|find-or-cmake the targets are Eigen3::Eigen, spdlog::spdlog and hdf5::all,
- If H5PP_PACKAGE_MANAGER==conan the targets are CONAN_PKG::eigen, CONAN_PKG::spdlog and CONAN_PKG::HDF5.
- If H5PP_PACKAGE_MANAGER==none then h5pp::deps is empty.
h5pp::flags sets compile and linker flags to enable C++17 and std::filesystem library, i.e. -std=c++17 and -lstdc++fs. On MSVC it sets /permissive- to enable logical and/or in C++.

Link manually (not as easy)

From the command-line you can of course link using linker flags such as -std=c++17 -lstdc++fs -leigen3 -lspdlog -lhdf5_hl -lhdf5 provided these flags make sense on your system. You could also use CMake's find_package(...) mechanism. A minimal CMakeLists.txt could be:

    cmake_minimum_required(VERSION 3.12)
    project(myProject)
    
    add_executable(myExecutable main.cpp)
    target_include_directories(myExecutable PRIVATE <path-to-h5pp-headers>)
    # Setup h5pp
    target_compile_features(myExecutable PRIVATE cxx_std_17)
    target_link_libraries(myExecutable PRIVATE  stdc++fs)
    
    # Possibly use find_package() here

    # Link dependencies (this is the tricky part)
    target_include_directories(myExecutable PRIVATE <path-to-Eigen3-include-dir>) 
    target_include_directories(myExecutable PRIVATE <path-to-spdlog-include-dir>) 
    target_include_directories(myExecutable PRIVATE <path-to-hdf5-include-dir>) 
    # Link dependencies (this is the difficult part). Note that you only need the C libs for HDF5.
    target_link_libraries(myExecutable PRIVATE hdf5_hl hdf5 rt dl m z pthread) # Possibly more libs, such as aec, dependending on your HDF5 installation

The difficult part is linking to HDF5 libraries and its dependencies.

Use the custom FindHDF5.cmake bundled with `h5pp`

When installing h5pp, finding HDF5 and setting up the CMake target hdf5::all for linking is handled by a custom module for finding HDF5, defined in cmake/FindHDF5.cmake. This module wraps the default FindHDF5.cmake which comes with CMake and uses the same call signature, but fixes some annoyances with naming conventions in different versions of CMake and HDF5 executables. It reads hints passed through CMake flags to find HDF5 somewhere on your system (e.g. installed via conda,apt, brew, Easybuild,etc) and defines a CMake target hdf5::all with everything you need to link correctly. Most importantly, it avoids injecting shared versions of libraries (dl, zlib, szip, aec) during static builds on older platforms. You can use the custom module too. Add the path pointing to FindHDF5.cmake to the variable CMAKE_MODULE_PATH from within your own project, e.g.:

    list(APPEND CMAKE_MODULE_PATH path/to/h5pp/cmake/FindHDF5.cmake)
    find_package(HDF5 1.10 COMPONENTS C HL REQUIRED)
    if(TARGET hdf5::all)
            target_link_libraries(myExecutable PRIVATE hdf5::all)
    endif()

These are variables that can be used to guide the custom FindHDF5.cmake module:

Var	Where	Description
`CMAKE_MODULE_PATH`	CMake	List of directories where `CMake` should search for find-modules
`CMAKE_PREFIX_PATH`	CMake	List of directories where `find_package` should look for dependencies
`HDF5_ROOT`	CMake/ENV	Path to HDF5 root install directory
`HDF5_FIND_VERBOSE`	CMake	Prints more information about the search for HDF5. See also `HDF5_FIND_DEBUG` in the original module
`EBROOTHDF5`	ENV	Variable defined by Easybuild with `module load HDF5`

Uninstall

The target uninstall is defined by h5pp which removes installed headers and dependencies using their respective install manifests. From the build directory, run the following in the command-line to uninstall:

    cmake --build .  --target uninstall

To-do

In no particular order

Expand documentation. Perhaps a doxygen/sphinx webpage
Expand testing for more edge-cases in
- filesystem permissions
- user-defined types
- tables
Expose more of the C-API:
- Support for packed user-defined types. Read more: H5TPack
- True support for parallel read/write with MPI
Support row-major <-> col-major transformation for types other than Eigen3 matrices and tensors. For instance, when raw pointers are passed together with dimension initializer list {x,y,z..}. (Although, this can be done by wrapping the data in an Eigen Map object).

sbreuils / h5pp Goto Github PK

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