python-tables 2.3.1-2ubuntu3 / usr / share / pyshared / tables / unimplemented.py

########################################################################
#
#       License: BSD
#       Created: January 14, 2004
#       Author:  Francesc Alted - faltet@pytables.com
#
#       $Id$
#
########################################################################

"""Here is defined the UnImplemented class.

See UnImplemented class docstring for more info.

Classes:

    UnImplemented

Misc variables:

    __version__


"""

import warnings

import numpy

from tables import hdf5Extension
from tables.utils import SizeType, lazyattr
from tables.node import Node
from tables.leaf import Leaf
from tables.attributeset import AttributeSet


__version__ = "$Revision$"



class UnImplemented(hdf5Extension.UnImplemented, Leaf):
    """
    This class represents datasets not supported by PyTables in an
    HDF5 file.

    When reading a generic HDF5 file (i.e. one that has not been
    created with PyTables, but with some other HDF5 library based
    tool), chances are that the specific combination of datatypes or
    dataspaces in some dataset might not be supported by PyTables yet.
    In such a case, this dataset will be mapped into an
    `UnImplemented` instance and the user will still be able to access
    the complete object tree of the generic HDF5 file.  The user will
    also be able to *read and write the attributes* of the dataset,
    *access some of its metadata*, and perform *certain hierarchy
    manipulation operations* like deleting or moving (but not copying)
    the node.  Of course, the user will not be able to read the actual
    data on it.

    This is an elegant way to allow users to work with generic HDF5
    files despite the fact that some of its datasets are not supported
    by PyTables.  However, if you are really interested in having full
    access to an unimplemented dataset, please get in contact with the
    developer team.

    This class does not have any public instance variables or methods,
    except those inherited from the `Leaf` class.
    """

    # Class identifier.
    _c_classId = 'UNIMPLEMENTED'

    def __init__(self, parentNode, name):
        """Create the `UnImplemented` instance."""

        # UnImplemented objects always come from opening an existing node
        # (they can not be created).
        self._v_new = False
        """Is this the first time the node has been created?"""
        self.nrows = SizeType(0)
        """The length of the first dimension of the data."""
        self.shape = (SizeType(0),)
        """The shape of the stored data."""
        self.byteorder = None
        """
        The endianness of data in memory ('big', 'little' or
        'irrelevant').
        """

        super(UnImplemented, self).__init__(parentNode, name)


    def _g_open(self):
        (self.shape, self.byteorder, objectID) = \
                     self._openUnImplemented()
        try:
            self.nrows = SizeType(self.shape[0])
        except IndexError:
            self.nrows = SizeType(0)
        return objectID


    def _g_copy(self, newParent, newName, recursive, _log=True, **kwargs):
        """
        Do nothing.

        This method does nothing, but a ``UserWarning`` is issued.
        Please note that this method *does not return a new node*, but
        ``None``.
        """

        warnings.warn(
            "UnImplemented node %r does not know how to copy itself; skipping"
            % (self._v_pathname,))
        return None  # Can you see it?


    def _f_copy(self, newparent=None, newname=None,
                overwrite=False, recursive=False, createparents=False,
                **kwargs):
        """
        Do nothing.

        This method does nothing, since `UnImplemented` nodes can not
        be copied.  However, a ``UserWarning`` is issued.  Please note
        that this method *does not return a new node*, but ``None``.
        """

        # This also does nothing but warn.
        self._g_copy(newparent, newname, recursive, **kwargs)
        return None  # Can you see it?


    def __repr__(self):
        return """%s
  NOTE: <The UnImplemented object represents a PyTables unimplemented
         dataset present in the '%s' HDF5 file.  If you want to see this
         kind of HDF5 dataset implemented in PyTables, please contact the
         developers.>
""" % (str(self), self._v_file.filename)



# Classes reported as H5G_UNKNOWN by HDF5
class Unknown(Node):
    """
    This class represents nodes reported as ``unknown`` by the
    underlying HDF5 library.

    This class does not have any public instance variables or methods,
    except those inherited from the `Node` class.
    """

    # Class identifier
    _c_classId = 'UNKNOWN'

    def __init__(self, parentNode, name):
        """Create the `Unknown` instance."""
        self._v_new = False
        super(Unknown, self).__init__(parentNode, name)

    def _g_new(self, parentNode, name, init=False):
        pass

    def _g_open(self):
        return 0

    def _g_copy(self, newParent, newName, recursive, _log=True, **kwargs):
        # Silently avoid doing copies of unknown nodes
        return None

    def _g_delete(self, parent):
        pass

    def __str__(self):
        pathname = self._v_pathname
        classname = self.__class__.__name__
        return "%s (%s)" % (pathname, classname)

    def __repr__(self):
        return """%s
  NOTE: <The Unknown object represents a node which is reported as
         unknown by the underlying HDF5 library, but that might be
         supported in more recent HDF5 versions.>
""" % (str(self))



# These are listed here for backward compatibility with PyTables 0.9.x indexes
class OldIndexArray(UnImplemented):
    _c_classId = 'IndexArray'