Example 1: Storing basic data types

The simplest way to interact with HDF5 files is to use the TRIQS HDFArchive class, which represents the tree structure of the file in a way similar to a dictionary.

Let us start with a very simple example [file]:

from h5 import *
import numpy
R = HDFArchive('myfile.h5', 'w') # Opens the file myfile.h5, in read/write mode
R['mu'] = 1.29
R.create_group('S')
S= R['S']
S['a'] = "a string"
S['b'] = numpy.array([1,2,3])
del R,S # closing the files (optional: 
#file is closed when the references to R and subgroup are deleted)

Run this and say

MyComputer:~>h5ls -r  myfile.h5
/                        Group
/S                       Group
/S/a                     Dataset {SCALAR}
/S/b                     Dataset {3}
/mu                      Dataset {SCALAR}

This shows the tree structure of the file. We see that :

• mu is stored at the root /

• S is a subgroup, containing a and b.

• For each leaf, the type (scalar or array) is given.

To dump the content of the file use, for example, the following: (see the HDF5 documentation for more information)

MyComputer:~>h5dump myfile.h5
HDF5 "myfile.h5" {
GROUP "/" {
  GROUP "S" {
     DATASET "a" {
        DATATYPE  H5T_STRING {
              STRSIZE H5T_VARIABLE;
              STRPAD H5T_STR_NULLTERM;
              CSET H5T_CSET_ASCII;
              CTYPE H5T_C_S1;
           }
        DATASPACE  SCALAR
        DATA {
        (0): "a string"
        }
     }
     DATASET "b" {
        DATATYPE  H5T_STD_I32LE
        DATASPACE  SIMPLE { ( 3 ) / ( 3 ) }
        DATA {
        (0): 1, 2, 3
        }
     }
  }
  DATASET "mu" {
     DATATYPE  H5T_IEEE_F64LE
     DATASPACE  SCALAR
     DATA {
     (0): 1.29
     }
  }
}
}