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76 lines
1.9 KiB
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76 lines
1.9 KiB
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.. _hdf5_tut_ex1:
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Example 1 : A basic example
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--------------------------------
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The simplest way to interact with HDF5 files is to use the TRIQS HDFArchive class, which
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represents the tree structure of the file in a way similar to a dictionary.
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Let us start with a very simple example :download:`[file] <./tut_ex1.py>`:
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.. runblock:: python
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from pytriqs.archive import *
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import numpy
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R = HDFArchive('myfile.h5', 'w') # Opens the file myfile.h5, in read/write mode
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R['mu'] = 1.29
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R.create_group('S')
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S= R['S']
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S['a'] = "a string"
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S['b'] = numpy.array([1,2,3])
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del R,S # closing the files (optional : file is closed when the references to R and subgroup are deleted)
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Run this and say ::
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MyComputer:~>h5ls -r myfile.h5
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/ Group
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/S Group
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/S/a Dataset {SCALAR}
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/S/b Dataset {3}
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/mu Dataset {SCALAR}
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This show the tree structure of the file. We see that :
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* `mu` is stored at the root `/`
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* `S` is a subgroup, containing `a` and `b`.
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* For each leaf, the type (scalar or array) is given.
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To dump the content of the file use, for example, the following: (see the HDF5 documentation for more information) ::
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MyComputer:~>h5dump myfile.h5
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HDF5 "myfile.h5" {
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GROUP "/" {
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GROUP "S" {
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DATASET "a" {
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DATATYPE H5T_STRING {
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STRSIZE H5T_VARIABLE;
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STRPAD H5T_STR_NULLTERM;
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CSET H5T_CSET_ASCII;
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CTYPE H5T_C_S1;
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}
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DATASPACE SCALAR
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DATA {
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(0): "a string"
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}
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}
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DATASET "b" {
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DATATYPE H5T_STD_I32LE
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DATASPACE SIMPLE { ( 3 ) / ( 3 ) }
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DATA {
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(0): 1, 2, 3
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}
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}
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}
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DATASET "mu" {
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DATATYPE H5T_IEEE_F64LE
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DATASPACE SCALAR
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DATA {
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(0): 1.29
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}
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}
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}
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}
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