dft_tools/doc/reference/python/data_analysis/hdf5/tut_ex3.rst

.. _hdf5_tut_ex3:

Example 3 : Use the dictionary interface of the archive
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The archive is like a dictionary, persistent on disk.
`[for Python afficionados, it is similar to a shelve, but in a portable format]`.
Therefore, one can iterate on its elements.

Let us imagine that you have stored 5 Green functions in an hdf5 file.
For example, we can create such a file as :download:`[file] <./tut_ex3.py>`:

.. runblock:: python

   from pytriqs.gf.local import GfReFreq
   from pytriqs.gf.local.descriptors import SemiCircular
   from pytriqs.archive import HDFArchive
   import numpy

   R = HDFArchive('myfile.h5', 'w') 
   for D in range(1,10,2) :
       g = GfReFreq(indices = [0], window = (-2.00,2.00), name = "D=%s"%D)
       g <<=  SemiCircular(half_bandwidth = 0.1*D)
       R[g.name]= g


Imagine that we want to plot those functions :download:`[file] <./tut_ex3b.py>`:

.. literalinclude:: tut_ex3b.py
   :lines: 1-13

This produces the following plot (scaled semi-circular density of states).

.. image:: tut_ex3b.png
   :width: 750
   :align: center

Various pythonic constructs can be used in combinaison with HDFArchive, e.g. 
in order to plot only a few curves from a list ::
  
 keylist = ['D=1', 'D=3']

 for g in ( R[x] for x in keylist) : # use an iterator
    oplot( (- 1/pi * g).imag, "-o", name = 'g' )
   
or if we want to add the names ::

 for n,g in ( (x,R[x]) for x in keylist) : # use an iterator
    oplot( (- 1/pi * g).imag, "-o", name = n )
 
The advantage of using an iterator is that the object is only retrieved from disk when needed.