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