This file is indexed.

/usr/lib/python2.7/dist-packages/tables/atom.py is in python-tables 3.2.2-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
# -*- 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 cPickle

import numpy

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

from tables._past import previous_api

# 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, (int, numpy.integer, long)):
        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(object):
    """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

    """

    # Register data for all subclasses.
    __metaclass__ = MetaAtom

    # 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)

        """
        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)
        # 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
    # ~~~~~~~~~~
    size = property(
        lambda self: self.dtype.itemsize,
        None, None, "Total size in bytes of the atom.")
    recarrtype = property(
        lambda self: str(self.dtype.shape) + self.dtype.base.str[1:],
        None, None, "String type to be used in numpy.rec.array().")
    ndim = property(
        lambda self: len(self.shape), None, None,
        """The number of dimensions of the atom.

        .. versionadded:: 2.4""")

    # 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.

        """

        return dict((arg, getattr(self, arg))
                    for arg in inspect.getargspec(self.__init__)[0]
                    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'
    itemsize = property(
        lambda self: self.dtype.base.itemsize,
        None, None, "Size in bytes of a sigle item in the atom.")
    type = 'string'
    _defvalue = b''

    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)


def _generate_integral_classes():
    """Generate all integral classes."""

    for baseclass in [IntAtom, UIntAtom]:
        for itemsize in [1, 2, 4, 8]:
            newclass = _create_numeric_class(baseclass, itemsize)
            yield newclass


def _generate_floating_classes():
    """Generate all floating classes."""

    itemsizes = [4, 8]

    # numpy >= 1.6
    if hasattr(numpy, 'float16'):
        itemsizes.insert(0, 2)
    if hasattr(numpy, 'float96'):
        itemsizes.append(12)
    if hasattr(numpy, 'float128'):
        itemsizes.append(16)

    for itemsize in itemsizes:
        newclass = _create_numeric_class(FloatAtom, itemsize)
        yield newclass


# Create all numeric atom classes.
#for _classgen in [_generate_integral_classes, _generate_floating_classes]:
#    for _newclass in _classgen():
#        exec('%s = _newclass' % _newclass.__name__)
#del _classgen, _newclass

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'
    itemsize = property(
        lambda self: self.dtype.base.itemsize,
        None, None, "Size in bytes of a sigle item in the atom.")
    _deftype = 'complex128'
    _defvalue = 0j
    _isizes = [8, 16]

    # 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):
    """Reminds the user to stop using the old complex atom names."""

    __metaclass__ = type  # do not register anything about this class

    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
    # ~~~~~~~~~~
    itemsize = property(
        lambda self: self.dtype.base.itemsize,
        None, None, "Size in bytes of a sigle item in the atom.")

    # 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")

    _checkBase = previous_api(_checkbase)

    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))

# 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.

    Like StringAtom, this class does not make assumptions on the encoding of
    the string, and raw bytes are stored as is.  Unicode strings are supported
    as long as no character is out of the ASCII set; otherwise, you will need
    to *explicitly* convert them to strings before you can save them.  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 strings.  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 not isinstance(object_, basestring):
            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()

    if sys.version_info[0] > 2 or sys.maxunicode <= 0xffff:
        # 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 not isinstance(object_, basestring):
                raise TypeError("object is not a string: %r" % (object_,))
            ustr = unicode(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 not isinstance(object_, basestring):
            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 cPickle.dumps(object_, cPickle.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 cPickle.loads(array.tostring())