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(...).

Examples

Write an `std::vector`

    #include <h5pp/h5pp.h>
    int main() {
        std::vector<double> v = {1.0, 2.0, 3.0};    // Define a vector
        h5pp::File file("somePath/someFile.h5");    // Create a file 
        file.writeDataset(v, "myStdVector");        // Write the vector into a new dataset "myStdVector"
    }

Read an `std::vector`

    #include <h5pp/h5pp.h>
    int main() {
        h5pp::File file("somePath/someFile.h5", h5pp::FileAccess::READWRITE);    // Open (or create) a file
        auto v = file.readDataset<std::vector<double>>("myStdVector");           // Read the dataset from file
    }

Find more code examples in the examples directory.

File Access

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 access	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. Appends “-#” (#=1,2,3…) to the stem of existing filename
`READWRITE`	Open with read-write access	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 access to it is implied.

To give a concrete example, the syntax works as follows

    h5pp::File file("somePath/someFile.h5", h5pp::FileAccess::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

Chunked datasets can 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::hdf5::isCompressionAvaliable();   // 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, 3); // Creates a chunked dataset with compression level 3.

or use the special member function for this task:

   file.writeDataset_compressed(myData, "science/myCompressedData", 3) // // Creates a chunked dataset with compression level 3 (default).

Debug and logging

h5pp uses spdlog to emits messages to stdout about its internal state during read/write operatios. There are 7 levels of verbosity:

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

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

    // This way...
    h5pp::File file("myDir/someFile.h5", h5pp::FileAccess::REPLACE, h5pp::LogLevel::debug); 
    // or this way
    file.setLogLevel(h5pp::LogLevel::trace);                                                                       

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 = file['double-array-dataset'][()]                                     
    
    # previously written as std::vector<std::complex<double>> in h5pp
    myComplexArray = 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.