Create easily interoperable representations of Python objects in HDF5 files. The aim of this module is to provide both

1. convenient Python object persistence
2. compatibility with non-Python applications

Point 2 is useful, for example, if results from numerical calculations should be easily transferable for example to a non-Python visualization program, such as Octave. Having a serialized object format that is directly readable saves some hassle in writing custom data dumping routines for each object.

Of course, if your data does not fit into memory, you still need to use full features of PyTables. But, you can still use hdf5pickle for other parts of the data.

This module implements dump and load methods analogous to those in Python's pickle module. The programming interface corresponds to pickle protocol 2, although the data is not serialized but saved in HDF5 files. Additional methods, dump_many and load_many, are provided for loading multiple objects at once, to preserve references.

warning

Although this module passes all relevant pickle unit tests from Python2.4 plus additional tests, it is still in early stages of development.

The layout of a python object saved to a HDF5 node is described below. The notation is roughly:

type-of-hdf5-node [(array shape), array type)] = what's in it
   .attribute-of-node = what's in it
   child-node

The structure of a node corresponding to a Python object varies, depending on the type of the Python object.

• Basic types (None, bool, int, float, complex):

  array [(1,), int/float] = NONE/BOOL/INT/FLOAT/COMPLEX
      .pickletype         = PICKLE_TYPE

• Basic stream types (long, str, unicode). Longs and unicodes are converted to strings (pickle.encode_long, utf-8), and strings are converted to arrays of unsigned 8-bit integers:

  array [(n,), uint8] = DATA
      .pickletype    = LONG/STR/UNICODE
      .empty     = 1 #if len(DATA) == 0

  bug

  At present strings are not stored as HDF5 strings, as PyTables appears to chop them off at '\x00' characters.

• dicts:

  group
      .pickletype  = DICT
  
      #for KEY, VALUE in DICT:
       #if KEY is a string and a valid python variable name
      KEY          = node for VALUE
       #else
      SURROGATE    = node for VALUE
      __/SURROGATE = node for KEY
       #end if
      #end for

• instances:

  group
      .pickletype         = INST/REDUCE
  
      #if through __reduce__ / new-style class
      .has_reduce_content = 1 if state present
      __/args             = arguments for class.__new__ or func
      __/func             = creation func
      __/cls              = creation class
      #else
      __/args             = arguments for class.__init__
      __/cls              = creation class
      #endif
  
      #if state is dict
      insert entries of dict here as in dict
      #else
      __/content          = node for content
      #endif

• globals (classes, etc):

  array [as for strings] = GLOBAL/EXT4 data locator, as in pickle
      .pickletype        = GLOBAL/EXT4

• reference to an object elsewhere:

  group
      .pickletype        = REF
      .target            = abs. path to the referred object in this file