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https://github.com/triqs/dft_tools
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91 lines
2.9 KiB
ReStructuredText
91 lines
2.9 KiB
ReStructuredText
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.. _HDF_Protocol1:
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Solution 1. The class provides the transformation into a dict of hdf-compliant objects
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----------------------------------------------------------------------------------------------------
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Principle
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^^^^^^^^^^^^^^
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The object which can be reduced to and reconstructed from a dictionary whose keys are strings,
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and whose values are of one of the following types :
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- a scalar (int, float, complex ....)
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- a numpy array of scalars
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- an hdf-compliant object
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- a list, a tuple or a dict of any of the types above.
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A class `cls` has to implement :
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.. py:method:: __reduce_to_dict__()
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Returns a dictionary of objects implementing :ref:`hdf-compliant <HDF_Protocol>`
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or basic objects as number, strings, and arrays of numbers (any type and shape).
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:rtype: a dictionary
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.. py:classmethod:: __factory_from_dict__(cls,D) :
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A **classmethod** which reconstructs a new object of type `cls`
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from the dictionary returned by :func:`__reduce_to_dict__`.
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:param cls: the class
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:param D: the dictionary returned by __reduce_to_dict__.
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:rtype: a new object of type `cls`
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Example
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^^^^^^^^^^^^^
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A little example::
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class Example:
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def __init__(self,d,t) :
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self.d,self.t = d,t # some data
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def __reduce_to_dict__(self) :
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return {'d' : self.d, 't': self.t}
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@classmethod
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def __factory_from_dict__(cls,D) :
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return cls(D['d'],D['t'])
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# registering my class
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from pytriqs.archive.hdf_archive_schemes import register_class
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register_class (myclass)
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# Testing it :
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HDFArchive("myfile2.h5")['e'] = Example( [1,2,3], 56)
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What happens in details ?
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Let us consider an object `Ob` of class `Cls`, interacting with and :py:class:`~pytriqs.archive.hdf_archive.HDFArchive`/:py:class:`~pytriqs.archive.hdf_archive.HDFArchiveGroup` `H`.
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* **Writing** ::
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H[Name] = Ob
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* a subgroup `S` of path `Root_of_H/Name` is created.
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* Ob.__reduce_to_dict__() is called and returns a dictionary `D` of scalar/numpy arrays/hdf-compliant objects.
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* The objects in `D` are then stored `S` in a regular way (scalar/arrays) or using the same recursive procedure (other objects).
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* `S` is tagged with the attribute `HDF5_data_scheme` of `Cls`.
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* **Reading** ::
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res = H[Name]
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* The subgroup `S` of path `Root_of_H/Name` is explored. All objects are taken to build a dictionary `D` : name -> values.
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This procedure is recursive : the hdf-compliant objects in the subgroup are rebuilt.
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* An attribute `HDF5_data_scheme` is searched for `S`.
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* If it is found and it corresponds to a registered class `Cls` :
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* Cls.__factory_from_dict__(D) is called and returns a new object Obj of type Cls, which is returned as `res`.
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* Otherwise, a new :py:class:`~pytriqs.archive.hdf_archive.HDFArchiveGroup` is constructed with `S` as root, and returned as `res`.
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