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69 lines
2.2 KiB
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69 lines
2.2 KiB
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.. _hdf5_tut_ex2:
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Example 2 : A Green function
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----------------------------------------------
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What about more complex objects ?
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The good news is that **hdf-compliant** objects can be stored easily as well.
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We can store a Green function in an hdf5 file:
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.. runblock:: python
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from pytriqs.archive import HDFArchive
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from pytriqs.gf.local import GfImFreq
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# Define a Green function
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G = GfImFreq ( indices = [1], beta = 10, n_points = 1000)
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# Opens the file myfile.h5, in read/write mode
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R = HDFArchive('myfile.h5', 'w')
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# Store the object G under the name 'g1' and mu
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R['g1'] = G
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R['mu'] = 1.29
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del R # closing the files (optional : file is closed when the R reference is deleted)
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Of course, we can retrieve G as easily:
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.. runblock:: python
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from pytriqs.archive import HDFArchive
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from pytriqs.gf.local import GfImFreq
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R = HDFArchive('myfile.h5', 'r') # Opens the file myfile.h5 in readonly mode
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G = R['g1'] # Retrieve the object named g1 in the file as G
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# ... ok now I can work with G
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The structure of the HDF file is this time ::
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MAC:~>h5ls -r myfile.h5
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/ Group
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/g1 Group
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/g1/Data Dataset {2, 2, 1000}
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/g1/Indices Dataset {2}
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/g1/Mesh Group
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/g1/Mesh/Beta Dataset {SCALAR}
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/g1/Mesh/Statistic Dataset {SCALAR}
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/g1/Mesh/TypeGF Dataset {SCALAR}
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/g1/Mesh/array Dataset {1000}
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/g1/Name Dataset {SCALAR}
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/g1/Note Dataset {SCALAR}
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/g1/Tail Group
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/g1/Tail/Indices Dataset {2}
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/g1/Tail/OrderMax Dataset {SCALAR}
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/g1/Tail/OrderMaxMAX Dataset {SCALAR}
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/g1/Tail/OrderMinMIN Dataset {SCALAR}
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/g1/Tail/array Dataset {13, 2, 2}
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/mu Dataset {SCALAR}
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.. hint:: How does this work ?
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The Green function implements (as detailed in :ref:`HDF_Protocol`)
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* a method :func:`__reduce_to_dict__` that reduces to the Green function to a dictionary containing a mesh (Mesh), a tail (Tail), the data (Data), the indices (Indices) and so on.
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* a classmethod :func:`__factory_from_dict__` that reconstructs the Green function from this dictionary.
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