# Source code for tables.atom

    # -*- coding: utf-8 -*-

    ########################################################################
    #
    # License: BSD
    # Created: December 16, 2004
    # Author: Ivan Vilata i Balaguer - ivan at selidor dot net
    #
    # $Id$
    #
    ########################################################################

    """Atom classes for describing dataset contents."""

    # Imports
    # =======
    import re
    import sys
    import inspect
    import warnings

    import numpy

    from .utils import SizeType
    from .misc.enum import Enum

    import pickle

    from .exceptions import FlavorWarning

    # Public variables
    # ================
    __docformat__ = 'reStructuredText'
    """The format of documentation strings in this module."""

    all_types = set()  # filled as atom classes are created
    """Set of all PyTables types."""

    atom_map = {}  # filled as atom classes are created
    """Maps atom kinds to item sizes and atom classes.

    If there is a fixed set of possible item sizes for a given kind, the
    kind maps to another mapping from item size in bytes to atom class.
    Otherwise, the kind maps directly to the atom class.
    """

    deftype_from_kind = {}  # filled as atom classes are created
    """Maps atom kinds to their default atom type (if any)."""

    # Public functions
    # ================
    _type_re = re.compile(r'^([a-z]+)([0-9]*)$')

    def split_type(type):
        """Split a PyTables type into a PyTables kind and an item size.

        Returns a tuple of (kind, itemsize). If no item size is present in the type
        (in the form of a precision), the returned item size is None::

            >>> split_type('int32')
            ('int', 4)
            >>> split_type('string')
            ('string', None)
            >>> split_type('int20')
            Traceback (most recent call last):
            ...
            ValueError: precision must be a multiple of 8: 20
            >>> split_type('foo bar')
            Traceback (most recent call last):
            ...
            ValueError: malformed type: 'foo bar'

        """

        match = _type_re.match(type)
        if not match:
            raise ValueError("malformed type: %r" % type)
        kind, precision = match.groups()
        itemsize = None
        if precision:
            precision = int(precision)
            itemsize, remainder = divmod(precision, 8)
            if remainder:  # 0 could be a valid item size
                raise ValueError("precision must be a multiple of 8: %d"
                                 % precision)
        return (kind, itemsize)

    # Private functions
    # =================
    def _invalid_itemsize_error(kind, itemsize, itemsizes):
        isizes = sorted(itemsizes)
        return ValueError("invalid item size for kind ``%s``: %r; "
                          "it must be one of ``%r``"
                          % (kind, itemsize, isizes))

    def _abstract_atom_init(deftype, defvalue):
        """Return a constructor for an abstract `Atom` class."""

        defitemsize = split_type(deftype)[1]

        def __init__(self, itemsize=defitemsize, shape=(), dflt=defvalue):
            assert self.kind in atom_map
            try:
                atomclass = atom_map[self.kind][itemsize]
            except KeyError:
                raise _invalid_itemsize_error(self.kind, itemsize,
                                              atom_map[self.kind])
            self.__class__ = atomclass
            atomclass.__init__(self, shape, dflt)
        return __init__

    def _normalize_shape(shape):
        """Check that the `shape` is safe to be used and return it as a tuple."""

        if isinstance(shape, (numpy.integer, int)):
            if shape < 1:
                raise ValueError("shape value must be greater than 0: %d"
                                 % shape)
            shape = (shape,)  # N is a shorthand for (N,)
        try:
            shape = tuple(shape)
        except TypeError:
            raise TypeError("shape must be an integer or sequence: %r"
                            % (shape,))

        # XXX Get from HDF5 library if possible.
        # HDF5 does not support ranks greater than 32
        if len(shape) > 32:
            raise ValueError(
                "shapes with rank > 32 are not supported: %r" % (shape,))

        return tuple(SizeType(s) for s in shape)

    def _normalize_default(value, dtype):
        """Return `value` as a valid default of NumPy type `dtype`."""

        # Create NumPy objects as defaults
        # This is better in order to serialize them as attributes
        if value is None:
            value = 0
        basedtype = dtype.base
        try:
            default = numpy.array(value, dtype=basedtype)
        except ValueError:
            array = numpy.array(value)
            if array.shape != basedtype.shape:
                raise
            # Maybe nested dtype with "scalar" value.
            default = numpy.array(value, dtype=basedtype.base)
        # 0-dim arrays will be representented as NumPy scalars
        # (PyTables attribute convention)
        if default.shape == ():
            default = default[()]
        return default

    def _cmp_dispatcher(other_method_name):
        """Dispatch comparisons to a method of the *other* object.

        Returns a new *rich comparison* method which dispatches calls to
        the method `other_method_name` of the *other* object.  If there is
        no such method in the object, ``False`` is returned.

        This is part of the implementation of a double dispatch pattern.
        """

        def dispatched_cmp(self, other):
            try:
                other_method = getattr(other, other_method_name)
            except AttributeError:
                return False
            return other_method(self)
        return dispatched_cmp

    # Helper classes
    # ==============
    class MetaAtom(type):
        """Atom metaclass.

        This metaclass ensures that data about atom classes gets inserted
        into the suitable registries.

        """

        def __init__(class_, name, bases, dict_):
            super(MetaAtom, class_).__init__(name, bases, dict_)

            kind = dict_.get('kind')
            itemsize = dict_.get('itemsize')
            type_ = dict_.get('type')
            deftype = dict_.get('_deftype')

            if kind and deftype:
                deftype_from_kind[kind] = deftype

            if type_:
                all_types.add(type_)

            if kind and itemsize and not hasattr(itemsize, '__int__'):
                # Atom classes with a non-fixed item size do have an
                # ``itemsize``, but it's not a number (e.g. property).
                atom_map[kind] = class_
                return

            if kind:  # first definition of kind, make new entry
                atom_map[kind] = {}

            if itemsize and hasattr(itemsize, '__int__'):  # fixed
                kind = class_.kind  # maybe from superclasses
                atom_map[kind][int(itemsize)] = class_

    # Atom classes
    # ============
    class Atom(metaclass=MetaAtom):
        """Defines the type of atomic cells stored in a dataset.

        The meaning of *atomic* is that individual elements of a cell can
        not be extracted directly by indexing (i.e.  __getitem__()) the
        dataset; e.g. if a dataset has shape (2, 2) and its atoms have
        shape (3,), to get the third element of the cell at (1, 0) one
        should use dataset[1,0][2] instead of dataset[1,0,2].

        The Atom class is meant to declare the different properties of the
        *base element* (also known as *atom*) of CArray, EArray and
        VLArray datasets, although they are also used to describe the base
        elements of Array datasets. Atoms have the property that their
        length is always the same.  However, you can grow datasets along
        the extensible dimension in the case of EArray or put a variable
        number of them on a VLArray row. Moreover, they are not restricted
        to scalar values, and they can be *fully multidimensional
        objects*.

        Parameters
        ----------
        itemsize : int
            For types with a non-fixed size, this sets the size in
            bytes of individual items in the atom.
        shape : tuple
            Sets the shape of the atom. An integer shape of
            N is equivalent to the tuple (N,).
        dflt
            Sets the default value for the atom.

        The following are the public methods and attributes of the Atom class.

        Notes
        -----
        A series of descendant classes are offered in order to make the
        use of these element descriptions easier. You should use a
        particular Atom descendant class whenever you know the exact type
        you will need when writing your code. Otherwise, you may use one
        of the Atom.from_*() factory Methods.

        .. rubric:: Atom attributes

        .. attribute:: dflt

            The default value of the atom.

            If the user does not supply a value for an element while
            filling a dataset, this default value will be written to disk.
            If the user supplies a scalar value for a multidimensional
            atom, this value is automatically *broadcast* to all the items
            in the atom cell. If dflt is not supplied, an appropriate zero
            value (or *null* string) will be chosen by default.  Please
            note that default values are kept internally as NumPy objects.

        .. attribute:: dtype

            The NumPy dtype that most closely matches this atom.

        .. attribute:: itemsize

            Size in bytes of a single item in the atom.
            Specially useful for atoms of the string kind.

        .. attribute:: kind

            The PyTables kind of the atom (a string).

        .. attribute:: shape

            The shape of the atom (a tuple for scalar atoms).

        .. attribute:: type

            The PyTables type of the atom (a string).

            Atoms can be compared with atoms and other objects for
            strict (in)equality without having to compare individual
            attributes::

                >>> atom1 = StringAtom(itemsize=10)  # same as ``atom2``
                >>> atom2 = Atom.from_kind('string', 10)  # same as ``atom1``
                >>> atom3 = IntAtom()
                >>> atom1 == 'foo'
                False
                >>> atom1 == atom2
                True
                >>> atom2 != atom1
                False
                >>> atom1 == atom3
                False
                >>> atom3 != atom2
                True

        """

        # Class methods
        # ~~~~~~~~~~~~~
        @classmethod
        def prefix(class_):
            """Return the atom class prefix."""
            cname = class_.__name__
            return cname[:cname.rfind('Atom')]

        @classmethod
        def from_sctype(class_, sctype, shape=(), dflt=None):
            """Create an Atom from a NumPy scalar type sctype.

            Optional shape and default value may be specified as the
            shape and dflt
            arguments, respectively. Information in the
            sctype not represented in an Atom is ignored::

                >>> import numpy
                >>> Atom.from_sctype(numpy.int16, shape=(2, 2))
                Int16Atom(shape=(2, 2), dflt=0)
                >>> Atom.from_sctype('S5', dflt='hello')
                Traceback (most recent call last):
                ...
                ValueError: unknown NumPy scalar type: 'S5'
                >>> Atom.from_sctype('Float64')
                Float64Atom(shape=(), dflt=0.0)

            """
            if (not isinstance(sctype, type)
               or not issubclass(sctype, numpy.generic)):
                if sctype not in numpy.sctypeDict:
                    raise ValueError("unknown NumPy scalar type: %r" % (sctype,))
                sctype = numpy.sctypeDict[sctype]
            return class_.from_dtype(numpy.dtype((sctype, shape)), dflt)

        @classmethod
        def from_dtype(class_, dtype, dflt=None):
            """Create an Atom from a NumPy dtype.

            An optional default value may be specified as the dflt
            argument. Information in the dtype not represented in an Atom is
            ignored::

                >>> import numpy
                >>> Atom.from_dtype(numpy.dtype((numpy.int16, (2, 2))))
                Int16Atom(shape=(2, 2), dflt=0)
                >>> Atom.from_dtype(numpy.dtype('float64'))
                Float64Atom(shape=(), dflt=0.0)

            Note: for easier use in Python 3, where all strings lead to the
            Unicode dtype, this dtype will also generate a StringAtom. Since
            this is only viable for strings that are castable as ascii, a
            warning is issued.

                >>> Atom.from_dtype(numpy.dtype('U20')) # doctest: +SKIP
                Atom.py:392: FlavorWarning: support for unicode type is very limited,
                    and only works for strings that can be cast as ascii
                StringAtom(itemsize=20, shape=(), dflt=b'')

            """
            basedtype = dtype.base
            if basedtype.names:
                raise ValueError("compound data types are not supported: %r"
                                 % dtype)
            if basedtype.shape != ():
                raise ValueError("nested data types are not supported: %r"
                                 % dtype)
            if basedtype.kind == 'S':  # can not reuse something like 'string80'
                itemsize = basedtype.itemsize
                return class_.from_kind('string', itemsize, dtype.shape, dflt)
            elif basedtype.kind == 'U':
                # workaround for unicode type (standard string type in Python 3)
                warnings.warn("support for unicode type is very limited, "
                              "and only works for strings that can be cast as ascii", FlavorWarning)
                itemsize = basedtype.itemsize // 4
                assert str(itemsize) in basedtype.str, "something went wrong in handling unicode."
                return class_.from_kind('string', itemsize, dtype.shape, dflt)
            # Most NumPy types have direct correspondence with PyTables types.
            return class_.from_type(basedtype.name, dtype.shape, dflt)

        @classmethod
        def from_type(class_, type, shape=(), dflt=None):
            """Create an Atom from a PyTables type.

            Optional shape and default value may be specified as the
            shape and dflt arguments, respectively::

                >>> Atom.from_type('bool')
                BoolAtom(shape=(), dflt=False)
                >>> Atom.from_type('int16', shape=(2, 2))
                Int16Atom(shape=(2, 2), dflt=0)
                >>> Atom.from_type('string40', dflt='hello')
                Traceback (most recent call last):
                ...
                ValueError: unknown type: 'string40'
                >>> Atom.from_type('Float64')
                Traceback (most recent call last):
                ...
                ValueError: unknown type: 'Float64'

            """

            if type not in all_types:
                raise ValueError("unknown type: %r" % (type,))
            kind, itemsize = split_type(type)
            return class_.from_kind(kind, itemsize, shape, dflt)

        @classmethod
        def from_kind(class_, kind, itemsize=None, shape=(), dflt=None):
            """Create an Atom from a PyTables kind.

            Optional item size, shape and default value may be
            specified as the itemsize, shape and dflt
            arguments, respectively. Bear in mind that not all atoms support
            a default item size::

                >>> Atom.from_kind('int', itemsize=2, shape=(2, 2))
                Int16Atom(shape=(2, 2), dflt=0)
                >>> Atom.from_kind('int', shape=(2, 2))
                Int32Atom(shape=(2, 2), dflt=0)
                >>> Atom.from_kind('int', shape=1)
                Int32Atom(shape=(1,), dflt=0)
                >>> Atom.from_kind('string', dflt=b'hello')
                Traceback (most recent call last):
                ...
                ValueError: no default item size for kind ``string``
                >>> Atom.from_kind('Float')
                Traceback (most recent call last):
                ...
                ValueError: unknown kind: 'Float'

            Moreover, some kinds with atypical constructor signatures
            are not supported; you need to use the proper
            constructor::

                >>> Atom.from_kind('enum') #doctest: +ELLIPSIS
                Traceback (most recent call last):
                ...
                ValueError: the ``enum`` kind is not supported...

            """

            kwargs = {'shape': shape}
            if kind not in atom_map:
                raise ValueError("unknown kind: %r" % (kind,))
            # This incompatibility detection may get out-of-date and is
            # too hard-wired, but I couldn't come up with something
            # smarter.  -- Ivan (2007-02-08)
            if kind in ['enum']:
                raise ValueError("the ``%s`` kind is not supported; "
                                 "please use the appropriate constructor"
                                 % kind)
            # If no `itemsize` is given, try to get the default type of the
            # kind (which has a fixed item size).
            if itemsize is None:
                if kind not in deftype_from_kind:
                    raise ValueError("no default item size for kind ``%s``"
                                     % kind)
                type_ = deftype_from_kind[kind]
                kind, itemsize = split_type(type_)
            kdata = atom_map[kind]
            # Look up the class and set a possible item size.
            if hasattr(kdata, 'kind'):  # atom class: non-fixed item size
                atomclass = kdata
                kwargs['itemsize'] = itemsize
            else:  # dictionary: fixed item size
                if itemsize not in kdata:
                    raise _invalid_itemsize_error(kind, itemsize, kdata)
                atomclass = kdata[itemsize]
            # Only set a `dflt` argument if given (`None` may not be understood).
            if dflt is not None:
                kwargs['dflt'] = dflt

            return atomclass(**kwargs)

        # Properties
        # ~~~~~~~~~~
        @property
        def size(self):
            """Total size in bytes of the atom."""
            return self.dtype.itemsize

        @property
        def recarrtype(self):
            """String type to be used in numpy.rec.array()."""
            return str(self.dtype.shape) + self.dtype.base.str[1:]

        @property
        def ndim(self):
            """The number of dimensions of the atom.

            .. versionadded:: 2.4"""
            return len(self.shape)

        # Special methods
        # ~~~~~~~~~~~~~~~
        def __init__(self, nptype, shape, dflt):
            if not hasattr(self, 'type'):
                raise NotImplementedError("``%s`` is an abstract class; "
                                          "please use one of its subclasses"
                                          % self.__class__.__name__)
            self.shape = shape = _normalize_shape(shape)
            """The shape of the atom (a tuple for scalar atoms)."""
            # Curiously enough, NumPy isn't generally able to accept NumPy
            # integers in a shape. ;(
            npshape = tuple(int(s) for s in shape)
            self.dtype = dtype = numpy.dtype((nptype, npshape))
            """The NumPy dtype that most closely matches this atom."""
            self.dflt = _normalize_default(dflt, dtype)
            """The default value of the atom.

            If the user does not supply a value for an element while
            filling a dataset, this default value will be written to
            disk. If the user supplies a scalar value for a
            multidimensional atom, this value is automatically *broadcast*
            to all the items in the atom cell. If dflt is not supplied, an
            appropriate zero value (or *null* string) will be chosen by
            default.  Please note that default values are kept internally
            as NumPy objects."""

        def __repr__(self):
            args = 'shape=%s, dflt=%r' % (self.shape, self.dflt)
            if not hasattr(self.__class__.itemsize, '__int__'):  # non-fixed
                args = 'itemsize=%s, %s' % (self.itemsize, args)
            return '%s(%s)' % (self.__class__.__name__, args)

        __eq__ = _cmp_dispatcher('_is_equal_to_atom')

        def __ne__(self, other):
            return not self.__eq__(other)

        # XXX: API incompatible change for PyTables 3 line
        # Overriding __eq__ blocks inheritance of __hash__ in 3.x
        # def __hash__(self):
        #    return hash((self.__class__, self.type, self.shape, self.itemsize,
        #                 self.dflt))

        # Public methods
        # ~~~~~~~~~~~~~~
        def copy(self, **override):
            """Get a copy of the atom, possibly overriding some arguments.

            Constructor arguments to be overridden must be passed as
            keyword arguments::

                >>> atom1 = Int32Atom(shape=12)
                >>> atom2 = atom1.copy()
                >>> print(atom1)
                Int32Atom(shape=(12,), dflt=0)
                >>> print(atom2)
                Int32Atom(shape=(12,), dflt=0)
                >>> atom1 is atom2
                False
                >>> atom3 = atom1.copy(shape=(2, 2))
                >>> print(atom3)
                Int32Atom(shape=(2, 2), dflt=0)
                >>> atom1.copy(foobar=42)
                Traceback (most recent call last):
                ...
                TypeError: __init__() got an unexpected keyword argument 'foobar'

            """
            newargs = self._get_init_args()
            newargs.update(override)
            return self.__class__(**newargs)

        # Private methods
        # ~~~~~~~~~~~~~~~
        def _get_init_args(self):
            """Get a dictionary of instance constructor arguments.

            This implementation works on classes which use the same names
            for both constructor arguments and instance attributes.

            """

            # @COMPATIBILITY: inspect.getargspec has been deprecated since
            #                 Python 3.5
            try:
                # inspect.signature is new in Python 3.5
                signature = inspect.signature(self.__init__)
            except AttributeError:
                args = inspect.getargspec(self.__init__)[0]
            else:
                parameters = signature.parameters
                args = [arg for arg, p in parameters.items()
                    if p.kind is p.POSITIONAL_OR_KEYWORD]

            return dict((arg, getattr(self, arg)) for arg in args if arg != 'self')

        def _is_equal_to_atom(self, atom):
            """Is this object equal to the given `atom`?"""

            return (self.type == atom.type and self.shape == atom.shape
                    and self.itemsize == atom.itemsize
                    and numpy.all(self.dflt == atom.dflt))

    class StringAtom(Atom):
        """Defines an atom of type string.

        The item size is the *maximum* length in characters of strings.

        """

        kind = 'string'
        type = 'string'
        _defvalue = b''

        @property
        def itemsize(self):
            "Size in bytes of a sigle item in the atom."
            return self.dtype.base.itemsize

        def __init__(self, itemsize, shape=(), dflt=_defvalue):
            if not hasattr(itemsize, '__int__') or int(itemsize) < 0:
                raise ValueError("invalid item size for kind ``%s``: %r; "
                                 "it must be a positive integer"
                                 % ('string', itemsize))
            Atom.__init__(self, 'S%d' % itemsize, shape, dflt)

    class BoolAtom(Atom):
        """Defines an atom of type bool."""

        kind = 'bool'
        itemsize = 1
        type = 'bool'
        _deftype = 'bool8'
        _defvalue = False

        def __init__(self, shape=(), dflt=_defvalue):
            Atom.__init__(self, self.type, shape, dflt)

    class IntAtom(Atom):
        """Defines an atom of a signed integral type (int kind)."""

        kind = 'int'
        signed = True
        _deftype = 'int32'
        _defvalue = 0
        __init__ = _abstract_atom_init(_deftype, _defvalue)

    class UIntAtom(Atom):
        """Defines an atom of an unsigned integral type (uint kind)."""

        kind = 'uint'
        signed = False
        _deftype = 'uint32'
        _defvalue = 0
        __init__ = _abstract_atom_init(_deftype, _defvalue)

    class FloatAtom(Atom):
        """Defines an atom of a floating point type (float kind)."""

        kind = 'float'
        _deftype = 'float64'
        _defvalue = 0.0
        __init__ = _abstract_atom_init(_deftype, _defvalue)

    def _create_numeric_class(baseclass, itemsize):
        """Create a numeric atom class with the given `baseclass` and an
        `itemsize`."""

        prefix = '%s%d' % (baseclass.prefix(), itemsize * 8)
        type_ = prefix.lower()
        classdict = {'itemsize': itemsize, 'type': type_,
                     '__doc__': "Defines an atom of type ``%s``." % type_}

        def __init__(self, shape=(), dflt=baseclass._defvalue):
            Atom.__init__(self, self.type, shape, dflt)
        classdict['__init__'] = __init__
        return type('%sAtom' % prefix, (baseclass,), classdict)

    Int8Atom = _create_numeric_class(IntAtom, 1)
    Int16Atom = _create_numeric_class(IntAtom, 2)
    Int32Atom = _create_numeric_class(IntAtom, 4)
    Int64Atom = _create_numeric_class(IntAtom, 8)
    UInt8Atom = _create_numeric_class(UIntAtom, 1)
    UInt16Atom = _create_numeric_class(UIntAtom, 2)
    UInt32Atom = _create_numeric_class(UIntAtom, 4)
    UInt64Atom = _create_numeric_class(UIntAtom, 8)

    if hasattr(numpy, 'float16'):
        Float16Atom = _create_numeric_class(FloatAtom, 2)
    Float32Atom = _create_numeric_class(FloatAtom, 4)
    Float64Atom = _create_numeric_class(FloatAtom, 8)
    if hasattr(numpy, 'float96'):
        Float96Atom = _create_numeric_class(FloatAtom, 12)
    if hasattr(numpy, 'float128'):
        Float128Atom = _create_numeric_class(FloatAtom, 16)

    class ComplexAtom(Atom):
        """Defines an atom of kind complex.

        Allowed item sizes are 8 (single precision) and 16 (double precision). This
        class must be used instead of more concrete ones to avoid confusions with
        numarray-like precision specifications used in PyTables 1.X.

        """

        # This definition is a little more complex (no pun intended)
        # because, although the complex kind is a normal numerical one,
        # the usage of bottom-level classes is artificially forbidden.
        # Everything will be back to normality when people has stopped
        # using the old bottom-level complex classes.

        kind = 'complex'
        _deftype = 'complex128'
        _defvalue = 0j
        _isizes = [8, 16]

        @property
        def itemsize(self):
             "Size in bytes of a sigle item in the atom."
             return self.dtype.base.itemsize

        # Only instances have a `type` attribute, so complex types must be
        # registered by hand.
        all_types.add('complex64')
        all_types.add('complex128')
        if hasattr(numpy, 'complex192'):
            all_types.add('complex192')
            _isizes.append(24)
        if hasattr(numpy, 'complex256'):
            all_types.add('complex256')
            _isizes.append(32)

        def __init__(self, itemsize, shape=(), dflt=_defvalue):
            if itemsize not in self._isizes:
                raise _invalid_itemsize_error('complex', itemsize, self._isizes)
            self.type = '%s%d' % (self.kind, itemsize * 8)
            Atom.__init__(self, self.type, shape, dflt)

    class _ComplexErrorAtom(ComplexAtom, metaclass=type):
        """Reminds the user to stop using the old complex atom names."""

        def __init__(self, shape=(), dflt=ComplexAtom._defvalue):
            raise TypeError(
                "to avoid confusions with PyTables 1.X complex atom names, "
                "please use ``ComplexAtom(itemsize=N)``, "
                "where N=8 for single precision complex atoms, "
                "and N=16 for double precision complex atoms")
    Complex32Atom = Complex64Atom = Complex128Atom = _ComplexErrorAtom
    if hasattr(numpy, 'complex192'):
        Complex192Atom = _ComplexErrorAtom
    if hasattr(numpy, 'complex256'):
        Complex256Atom = _ComplexErrorAtom

    class TimeAtom(Atom):
        """Defines an atom of time type (time kind).

        There are two distinct supported types of time: a 32 bit integer value and
        a 64 bit floating point value. Both of them reflect the number of seconds
        since the Unix epoch. This atom has the property of being stored using the
        HDF5 time datatypes.

        """

        kind = 'time'
        _deftype = 'time32'
        _defvalue = 0
        __init__ = _abstract_atom_init(_deftype, _defvalue)

    class Time32Atom(TimeAtom):
        """Defines an atom of type time32."""

        itemsize = 4
        type = 'time32'
        _defvalue = 0

        def __init__(self, shape=(), dflt=_defvalue):
            Atom.__init__(self, 'int32', shape, dflt)

    class Time64Atom(TimeAtom):
        """Defines an atom of type time64."""

        itemsize = 8
        type = 'time64'
        _defvalue = 0.0

        def __init__(self, shape=(), dflt=_defvalue):
            Atom.__init__(self, 'float64', shape, dflt)

    class EnumAtom(Atom):
        """Description of an atom of an enumerated type.

        Instances of this class describe the atom type used to store enumerated
        values. Those values belong to an enumerated type, defined by the first
        argument (enum) in the constructor of the atom, which accepts the same
        kinds of arguments as the Enum class (see :ref:`EnumClassDescr`).  The
        enumerated type is stored in the enum attribute of the atom.

        A default value must be specified as the second argument (dflt) in the
        constructor; it must be the *name* (a string) of one of the enumerated
        values in the enumerated type. When the atom is created, the corresponding
        concrete value is broadcast and stored in the dflt attribute (setting
        different default values for items in a multidimensional atom is not
        supported yet). If the name does not match any value in the enumerated
        type, a KeyError is raised.

        Another atom must be specified as the base argument in order to determine
        the base type used for storing the values of enumerated values in memory
        and disk. This *storage atom* is kept in the base attribute of the created
        atom. As a shorthand, you may specify a PyTables type instead of the
        storage atom, implying that this has a scalar shape.

        The storage atom should be able to represent each and every concrete value
        in the enumeration. If it is not, a TypeError is raised. The default value
        of the storage atom is ignored.

        The type attribute of enumerated atoms is always enum.

        Enumerated atoms also support comparisons with other objects::

            >>> enum = ['T0', 'T1', 'T2']
            >>> atom1 = EnumAtom(enum, 'T0', 'int8')  # same as ``atom2``
            >>> atom2 = EnumAtom(enum, 'T0', Int8Atom())  # same as ``atom1``
            >>> atom3 = EnumAtom(enum, 'T0', 'int16')
            >>> atom4 = Int8Atom()
            >>> atom1 == enum
            False
            >>> atom1 == atom2
            True
            >>> atom2 != atom1
            False
            >>> atom1 == atom3
            False
            >>> atom1 == atom4
            False
            >>> atom4 != atom1
            True

        Examples
        --------

        The next C enum construction::

            enum myEnum {
                T0,
                T1,
                T2
            };

        would correspond to the following PyTables
        declaration::

            >>> my_enum_atom = EnumAtom(['T0', 'T1', 'T2'], 'T0', 'int32')

        Please note the dflt argument with a value of 'T0'. Since the concrete
        value matching T0 is unknown right now (we have not used explicit concrete
        values), using the name is the only option left for defining a default
        value for the atom.

        The chosen representation of values for this enumerated atom uses unsigned
        32-bit integers, which surely wastes quite a lot of memory. Another size
        could be selected by using the base argument (this time with a full-blown
        storage atom)::

            >>> my_enum_atom = EnumAtom(['T0', 'T1', 'T2'], 'T0', UInt8Atom())

        You can also define multidimensional arrays for data elements::

            >>> my_enum_atom = EnumAtom(
            ...    ['T0', 'T1', 'T2'], 'T0', base='uint32', shape=(3,2))

        for 3x2 arrays of uint32.

        """

        # Registering this class in the class map may be a little wrong,
        # since the ``Atom.from_kind()`` method fails miserably with
        # enumerations, as they don't support an ``itemsize`` argument.
        # However, resetting ``__metaclass__`` to ``type`` doesn't seem to
        # work and I don't feel like creating a subclass of ``MetaAtom``.

        kind = 'enum'
        type = 'enum'

        # Properties
        # ~~~~~~~~~~
        @property
        def itemsize(self):
            """Size in bytes of a single item in the atom."""
            return self.dtype.base.itemsize

        # Private methods
        # ~~~~~~~~~~~~~~~
        def _checkbase(self, base):
            """Check the `base` storage atom."""

            if base.kind == 'enum':
                raise TypeError("can not use an enumerated atom "
                                "as a storage atom: %r" % base)

            # Check whether the storage atom can represent concrete values
            # in the enumeration...
            basedtype = base.dtype
            pyvalues = [value for (name, value) in self.enum]
            try:
                npgenvalues = numpy.array(pyvalues)
            except ValueError:
                raise TypeError("concrete values are not uniformly-shaped")
            try:
                npvalues = numpy.array(npgenvalues, dtype=basedtype.base)
            except ValueError:
                raise TypeError("storage atom type is incompatible with "
                                "concrete values in the enumeration")
            if npvalues.shape[1:] != basedtype.shape:
                raise TypeError("storage atom shape does not match that of "
                                "concrete values in the enumeration")
            if npvalues.tolist() != npgenvalues.tolist():
                raise TypeError("storage atom type lacks precision for "
                                "concrete values in the enumeration")

            # ...with some implementation limitations.
            if not npvalues.dtype.kind in ['i', 'u']:
                raise NotImplementedError("only integer concrete values "
                                          "are supported for the moment, sorry")
            if len(npvalues.shape) > 1:
                raise NotImplementedError("only scalar concrete values "
                                          "are supported for the moment, sorry")

        def _get_init_args(self):
            """Get a dictionary of instance constructor arguments."""

            return dict(enum=self.enum, dflt=self._defname,
                        base=self.base, shape=self.shape)

        def _is_equal_to_atom(self, atom):
            """Is this object equal to the given `atom`?"""

            return False

        def _is_equal_to_enumatom(self, enumatom):
            """Is this object equal to the given `enumatom`?"""

            return (self.enum == enumatom.enum and self.shape == enumatom.shape
                    and numpy.all(self.dflt == enumatom.dflt)
                    and self.base == enumatom.base)

        # Special methods
        # ~~~~~~~~~~~~~~~
        def __init__(self, enum, dflt, base, shape=()):
            if not isinstance(enum, Enum):
                enum = Enum(enum)
            self.enum = enum

            if isinstance(base, str):
                base = Atom.from_type(base)
            self._checkbase(base)
            self.base = base

            default = enum[dflt]  # check default value
            self._defname = dflt  # kept for representation purposes

            # These are kept to ease dumping this particular
            # representation of the enumeration to storage.
            names, values = [], []
            for (name, value) in enum:
                names.append(name)
                values.append(value)
            basedtype = self.base.dtype

            self._names = names
            self._values = numpy.array(values, dtype=basedtype.base)

            Atom.__init__(self, basedtype, shape, default)

        def __repr__(self):
            return ('EnumAtom(enum=%r, dflt=%r, base=%r, shape=%r)'
                    % (self.enum, self._defname, self.base, self.shape))

        __eq__ = _cmp_dispatcher('_is_equal_to_enumatom')

        # XXX: API incompatible change for PyTables 3 line
        # Overriding __eq__ blocks inheritance of __hash__ in 3.x
        # def __hash__(self):
        #    return hash((self.__class__, self.enum, self.shape, self.dflt,
        #                 self.base))

    class ReferenceAtom(Atom):
        """Defines an atom of type object to read references.
        This atom is read-only.
        """

        kind = 'reference'
        type = 'object'
        _deftype = 'NoneType'
        _defvalue = None

        # Properties
        # ~~~~~~~~~~
        @property
        def itemsize(self):
            """Size in bytes of a single item in the atom."""
            return self.dtype.base.itemsize

        def __init__(self, shape=()):
            Atom.__init__(self, self.type, shape, self._defvalue)

        def __repr__(self):
            return 'ReferenceAtom(shape=%s)' % (self.shape,)

    # Pseudo-atom classes
    # ===================
    #
    # Now, there come three special classes, `ObjectAtom`, `VLStringAtom`
    # and `VLUnicodeAtom`, that actually do not descend from `Atom`, but
    # which goal is so similar that they should be described here.
    # Pseudo-atoms can only be used with `VLArray` datasets, and they do
    # not support multidimensional values, nor multiple values per row.
    #
    # They can be recognised because they also have ``kind``, ``type`` and
    # ``shape`` attributes, but no ``size``, ``itemsize`` or ``dflt``
    # ones.  Instead, they have a ``base`` atom which defines the elements
    # used for storage.
    #
    # See ``examples/vlarray1.py`` and ``examples/vlarray2.py`` for
    # further examples on `VLArray` datasets, including object
    # serialization and string management.

    class PseudoAtom(object):
        """Pseudo-atoms can only be used in ``VLArray`` nodes.

        They can be recognised because they also have `kind`, `type` and
        `shape` attributes, but no `size`, `itemsize` or `dflt` ones.
        Instead, they have a `base` atom which defines the elements used
        for storage.
        """

        def __repr__(self):
            return '%s()' % self.__class__.__name__

        def toarray(self, object_):
            """Convert an `object_` into an array of base atoms."""

            raise NotImplementedError

        def fromarray(self, array):
            """Convert an `array` of base atoms into an object."""

            raise NotImplementedError

    class _BufferedAtom(PseudoAtom):
        """Pseudo-atom which stores data as a buffer (flat array of uints)."""

        shape = ()

        def toarray(self, object_):
            buffer_ = self._tobuffer(object_)
            array = numpy.ndarray(buffer=buffer_, dtype=self.base.dtype,
                                  shape=len(buffer_))
            return array

        def _tobuffer(self, object_):
            """Convert an `object_` into a buffer."""

            raise NotImplementedError

    class VLStringAtom(_BufferedAtom):
        """Defines an atom of type ``vlstring``.

        This class describes a *row* of the VLArray class, rather than an atom. It
        differs from the StringAtom class in that you can only add *one instance of
        it to one specific row*, i.e. the :meth:`VLArray.append` method only
        accepts one object when the base atom is of this type.

        This class stores bytestrings. It does not make assumptions on the
        encoding of the string, and raw bytes are stored as is. To store a string
        you will need to *explicitly* convert it to a bytestring before you can
        save them::

            >>> s = 'A unicode string: hbar = \u210f'
            >>> bytestring = s.encode('utf-8')
            >>> VLArray.append(bytestring) # doctest: +SKIP

        For full Unicode support, using VLUnicodeAtom (see :ref:`VLUnicodeAtom`) is
        recommended.

        Variable-length string atoms do not accept parameters and they cause the
        reads of rows to always return Python bytestrings.  You can regard vlstring
        atoms as an easy way to save generic variable length strings.

        """

        kind = 'vlstring'
        type = 'vlstring'
        base = UInt8Atom()

        def _tobuffer(self, object_):
            if isinstance(object_, str):
                warnings.warn("Storing non bytestrings in VLStringAtom is "
                              "deprecated.", DeprecationWarning)
            elif not isinstance(object_, bytes):
                raise TypeError("object is not a string: %r" % (object_,))
            return numpy.string_(object_)

        def fromarray(self, array):
            return array.tostring()

    class VLUnicodeAtom(_BufferedAtom):
        """Defines an atom of type vlunicode.

        This class describes a *row* of the VLArray class, rather than an atom.  It
        is very similar to VLStringAtom (see :ref:`VLStringAtom`), but it stores
        Unicode strings (using 32-bit characters a la UCS-4, so all strings of the
        same length also take up the same space).

        This class does not make assumptions on the encoding of plain input
        strings.  Plain strings are supported as long as no character is out of the
        ASCII set; otherwise, you will need to *explicitly* convert them to Unicode
        before you can save them.

        Variable-length Unicode atoms do not accept parameters and they cause the
        reads of rows to always return Python Unicode strings.  You can regard
        vlunicode atoms as an easy way to save variable length Unicode strings.

        """

        kind = 'vlunicode'
        type = 'vlunicode'
        base = UInt32Atom()

        # numpy.unicode_ no more implements the buffer interface in Python 3
        #
        # When the Python build is UCS-2, we need to promote the
        # Unicode string to UCS-4.  We *must* use a 0-d array since
        # NumPy scalars inherit the UCS-2 encoding from Python (see
        # NumPy ticket #525).  Since ``_tobuffer()`` can't return an
        # array, we must override ``toarray()`` itself.
        def toarray(self, object_):
            if isinstance(object_, bytes):
                warnings.warn("Storing bytestrings in VLUnicodeAtom is "
                              "deprecated.", DeprecationWarning)
            elif not isinstance(object_, str):
                raise TypeError("object is not a string: %r" % (object_,))
            ustr = str(object_)
            uarr = numpy.array(ustr, dtype='U')
            return numpy.ndarray(
                buffer=uarr, dtype=self.base.dtype, shape=len(ustr))

        def _tobuffer(self, object_):
            # This works (and is used) only with UCS-4 builds of Python,
            # where the width of the internal representation of a
            # character matches that of the base atoms.
            if isinstance(object_, bytes):
                warnings.warn("Storing bytestrings in VLUnicodeAtom is "
                              "deprecated.", DeprecationWarning)
            elif not isinstance(object_, str):
                raise TypeError("object is not a string: %r" % (object_,))
            return numpy.unicode_(object_)

        def fromarray(self, array):
            length = len(array)
            if length == 0:
                return u''  # ``array.view('U0')`` raises a `TypeError`
            return array.view('U%d' % length).item()

    class ObjectAtom(_BufferedAtom):
        """Defines an atom of type object.

        This class is meant to fit *any* kind of Python object in a row of a
        VLArray dataset by using pickle behind the scenes. Due to the fact that
        you can not foresee how long will be the output of the pickle
        serialization (i.e. the atom already has a *variable* length), you can only
        fit *one object per row*. However, you can still group several objects in a
        single tuple or list and pass it to the :meth:`VLArray.append` method.

        Object atoms do not accept parameters and they cause the reads of rows to
        always return Python objects. You can regard object atoms as an easy way to
        save an arbitrary number of generic Python objects in a VLArray dataset.

        """

        kind = 'object'
        type = 'object'
        base = UInt8Atom()

        def _tobuffer(self, object_):
            return pickle.dumps(object_, pickle.HIGHEST_PROTOCOL)

        def fromarray(self, array):
            # We have to check for an empty array because of a possible
            # bug in HDF5 which makes it claim that a dataset has one
            # record when in fact it is empty.
            if array.size == 0:
                return None
            return pickle.loads(array.tostring())