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h5pp

h5pp is a C++17 wrapper for HDF5 with focus on simplicity.

In just a few lines of code, h5pp lets users read and write to HDF5 files, a portable binary format. h5pp supports common data types and common containers, such as std::vector. In particular, h5pp makes it easy to read and write Eigen matrices and tensors.

Latest release

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.

The goal of h5pp is to make HDF5 simple to use in the following sense:

  • Users should be able to read/write common C++ data-types in a single line of code.

  • Users should not need prior knowledge of HDF5 for simple tasks.

  • Sensible defaults should let simple tasks stay simple, e.g., specifying storage layout, chunk dimensions or compression.

  • Advanced tasks should stay possible, e.g. MPI parallelism.

  • Logs and error messages should be meaningful to beginners.

  • Installation should be simple and scalable with opt-in automation.

Features

  • Header-only C++17 template library

  • High-level front-end to the C API of HDF5

  • Support for common data types:

    • short,int,long, long long (+ unsigned versions), float, double, long double

      • any of the above in C-style arrays

      • any of the above in std::complex<> form

      • any of the above in POD-structs with x,y or x,y,z data members. In h5pp these go by the name Scalar2 and Scalar3. These work well together with types such as double2 or float3 found in CUDA.

    • std::string and char arrays.

    • Any container such as std::vector with .data() member for accessing a contiguous buffer (without conversion to/from row major).

    • Eigen types such as Matrix, Array and Tensor, with automatic conversion to/from row major storage layout.

    • Support for user-defined compound HDF5 types

    • Support for creating HDF5 tables from user-defined compound HDF5 types.

  • Modern CMake installation providing targets for simple linking to your projects.

  • Installation with package managers: conan, conda (and apt using .deb installation file)

  • (Opt-in) Automatically find or download dependencies using either the Conan package manager or “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 with 10 doubles
        std::vector<double> v (10, 3.14);
        
        // 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

Unless specified, h5pp will automatically decide the best storage layout for each dataset. The possible layouts are

  • 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 specifically.

    • Compression, disabled by default, and only available if HDF5 was built with zlib enabled.

    • Overwrite with different size (note that the file size never decreases, for instance after overwriting with a smaller dataset).

To specify the layout, 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(9);            // 0 to 9: 0 to disable compression, 9 for maximum compression.
    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).

Load data into Python

HDF5 data is easy to load into Python. Loading integer and floating point data is straightforward. compound data is almost as simple. HDF5 does not support complex types specifically, but h5ppenables this through compound HDF5 types. 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')
    
    # Originally written as std::vector<double> in h5pp
    myDoubleArray = np.asarray(file['double-array-dataset'])                                     
    
    # Originally 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

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 bintray repo

  • (Debian only) Download the latest release and install with apt: sudo apt install ./h5pp_<version>_amd64.deb

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

  • ghc::filesystem: This drop-in replacement for std::filesystem is downloaded and installed automatically when needed, but only if H5PP_DOWNLOAD_METHOD=<fetch|conan>.

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 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.

Option 3: Install with Conan

Make sure to install and configure Conan first. Then, either use the cmake-conan integration by passing -DH5PP_DOWNLOAD_METHOD=conan as an argument to CMake (see below) or use Conan directly, for instance by running the following command:

$ conan install h5pp/1.8.0@davidace/stable --profile default

This is by far the simplest method and will also make sure to install dependencies HDF5, Eigen3 and spdlog.

Opt-in automatic dependency installation with CMake

The CMake flag H5PP_DOWNLOAD_METHOD controls the automated behavior for finding or installing dependencies. It can take one of three valid strings:

| Option | Description | | —- | —- | | none (default) | No handling of dependencies and linking is left to the user | | find | Use CMake’s find_package to find dependencies pre-installed on your system | | fetch (!) | Use CMake-only features to download and install dependencies automatically. Disregards pre-installed dependencies on your system | | native | Deprecated. Use fetch | | find-or-fetch | Start with find and then go to fetch if not found | | conan (!!) | Use the Conan package manager to download and install dependencies automatically. Disregards pre-installed dependencies 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 build 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_DOWNLOAD_METHOD | none | Download method for dependencies, select none, find, fetch, find-or-fetch or conan. Fetch downloads and builds from sources | | 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_DOWNLOAD_METHOD=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 |

Linking

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).