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dft_tools/doc/reference/plotting_protocols/hdf5/tut_ex1.rst
tayral edd1ff4529 Restructuring documentation.
A first general restructuration of the doc according to the pattern [tour|tutorial|reference].
In the reference part, objects are documented per topic.
In each topic, [definition|c++|python|hdf5] (not yet implemented)
2014-10-18 12:21:08 +01:00

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.. _hdf5_tut_ex1:
Example 1: A basic example
--------------------------------
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 :download:`[file] <./tut_ex1.py>`:
.. runblock:: python
from pytriqs.archive 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 show 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
}
}
}
}