[triqs/h5] The HDF5 formatΒΆ

In TRIQS, the main data storage format is HDF5 (Hierarchical Data Format v5).

HDF5 (“Hierarchical Data Format”) is a portable scientific data format. It is used to store data such as the Green’s functions produced during a simulation either in c++ or in python. Irrespective of the language used to produce it, the data stored in an HDF5 archive can be loaded in Python or c++, or even dumped to a text file for a quick glimpse. The HDF5 format also allows for data compression.


Using HDF5 format has several advantages :

  • Most basic objects of TRIQS, like Green function, are hdf-compliant.
  • TRIQS provides a simple and intuitive interface HDFArchive to manipulate them.
  • HDF5 is standard, well maintained and widely used.
  • HDF5 is portable from various machines (32-bits, 64-bits, various OSs, etc)
  • HDF5 can be read and written in many langages (python, C/C++, F90, etc), beyond TRIQS. One is not tied to a particular program.
  • Simple operations to explore and manipulate the tree are provided by simple unix shell commands (e.g. h5ls, h5diff).
  • It is a binary format, hence it is compact and has compression options.
  • It is to a large extent auto-documented: the structure of the data speaks for itself.


The keys of dictionaries written to hdf5 files in Python are currently assumed to be strings. Undesirable behaviour may occur for other dictionaries with non-string keys! Should you need support for more general dictionaries, please contact us.

An hdf5 file can be thought of as a file-system. As such, its structure is that of a tree, where:

  • Leaves of the tree are basic types: scalars (int, long, double, string) and rectangular arrays of these scalars (any dimension: 1,2,3,4...).
  • Subtrees (branches) are called groups
  • Groups and leaves have a name, so an element of the tree has naturally a path: e.g. /group1/subgroup2/leaf1 and so on.
  • Any path (groups, leaves) can be optionally tagged with an attribute, in addition to their name, typically a string (or any scalar)

Any data with a tree structure with arrays or scalar leaves can be naturally stored in hdf5 files.

To be more precise, we call hereafter a data hdf-compliant iif it can be reversibly transformed into

  • a tree structure with scalar/arrays leaves.
  • or a dictionary keys -> values, where keys are strings (field names) and values are scalars, arrays or any other hdf-compliant data.

Due to the recursive nature of trees, the two definitions are equivalent. An hdf-compliant data can be naturally saved in a subgroup of an HDF5 file by adding (cf example below) new leaves for all scalar and arrays and new subgroup for other hdf-compliant data.