/usr/lib/python2.7/dist-packages/tables/description.py is in python-tables 3.1.1-0ubuntu1.
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 | # -*- coding: utf-8 -*-
########################################################################
#
# License: BSD
# Created: September 21, 2002
# Author: Francesc Alted
#
# $Id$
#
########################################################################
"""Classes for describing columns for ``Table`` objects."""
# Imports
# =======
from __future__ import print_function
import sys
import copy
import warnings
import numpy
from tables import atom
from tables.path import check_name_validity
from tables._past import previous_api, previous_api_property
# Public variables
# ================
__docformat__ = 'reStructuredText'
"""The format of documentation strings in this module."""
# Private functions
# =================
def same_position(oldmethod):
"""Decorate `oldmethod` to also compare the `_v_pos` attribute."""
def newmethod(self, other):
try:
other._v_pos
except AttributeError:
return False # not a column definition
return self._v_pos == other._v_pos and oldmethod(self, other)
newmethod.__name__ = oldmethod.__name__
newmethod.__doc__ = oldmethod.__doc__
return newmethod
# Column classes
# ==============
class Col(atom.Atom):
"""Defines a non-nested column.
Col instances are used as a means to declare the different properties of a
non-nested column in a table or nested column. Col classes are descendants
of their equivalent Atom classes (see :ref:`AtomClassDescr`), but their
instances have an additional _v_pos attribute that is used to decide the
position of the column inside its parent table or nested column (see the
IsDescription class in :ref:`IsDescriptionClassDescr` for more information
on column positions).
In the same fashion as Atom, you should use a particular Col descendant
class whenever you know the exact type you will need when writing your
code. Otherwise, you may use one of the Col.from_*() factory methods.
Each factory method inherited from the Atom class is available with the
same signature, plus an additional pos parameter (placed in last position)
which defaults to None and that may take an integer value. This parameter
might be used to specify the position of the column in the table.
Besides, there are the next additional factory methods, available only for
Col objects.
The following parameters are available for most Col-derived constructors.
Parameters
----------
itemsize : int
For types with a non-fixed size, this sets the size in bytes of
individual items in the column.
shape : tuple
Sets the shape of the column. An integer shape of N is equivalent to
the tuple (N,).
dflt
Sets the default value for the column.
pos : int
Sets the position of column in table. If unspecified, the position
will be randomly selected.
"""
# Avoid mangling atom class data.
__metaclass__ = type
_class_from_prefix = {} # filled as column classes are created
"""Maps column prefixes to column classes."""
# Class methods
# ~~~~~~~~~~~~~
@classmethod
def prefix(class_):
"""Return the column class prefix."""
cname = class_.__name__
return cname[:cname.rfind('Col')]
@classmethod
def from_atom(class_, atom, pos=None):
"""Create a Col definition from a PyTables atom.
An optional position may be specified as the pos argument.
"""
prefix = atom.prefix()
kwargs = atom._get_init_args()
colclass = class_._class_from_prefix[prefix]
return colclass(pos=pos, **kwargs)
@classmethod
def from_sctype(class_, sctype, shape=(), dflt=None, pos=None):
"""Create a `Col` definition from a NumPy scalar type `sctype`.
Optional shape, default value and position may be specified as
the `shape`, `dflt` and `pos` arguments, respectively.
Information in the `sctype` not represented in a `Col` is
ignored.
"""
newatom = atom.Atom.from_sctype(sctype, shape, dflt)
return class_.from_atom(newatom, pos=pos)
@classmethod
def from_dtype(class_, dtype, dflt=None, pos=None):
"""Create a `Col` definition from a NumPy `dtype`.
Optional default value and position may be specified as the
`dflt` and `pos` arguments, respectively. The `dtype` must have
a byte order which is irrelevant or compatible with that of the
system. Information in the `dtype` not represented in a `Col`
is ignored.
"""
newatom = atom.Atom.from_dtype(dtype, dflt)
return class_.from_atom(newatom, pos=pos)
@classmethod
def from_type(class_, type, shape=(), dflt=None, pos=None):
"""Create a `Col` definition from a PyTables `type`.
Optional shape, default value and position may be specified as
the `shape`, `dflt` and `pos` arguments, respectively.
"""
newatom = atom.Atom.from_type(type, shape, dflt)
return class_.from_atom(newatom, pos=pos)
@classmethod
def from_kind(class_, kind, itemsize=None, shape=(), dflt=None, pos=None):
"""Create a `Col` definition from a PyTables `kind`.
Optional item size, shape, default value and position may be
specified as the `itemsize`, `shape`, `dflt` and `pos`
arguments, respectively. Bear in mind that not all columns
support a default item size.
"""
newatom = atom.Atom.from_kind(kind, itemsize, shape, dflt)
return class_.from_atom(newatom, pos=pos)
@classmethod
def _subclass_from_prefix(class_, prefix):
"""Get a column subclass for the given `prefix`."""
cname = '%sCol' % prefix
class_from_prefix = class_._class_from_prefix
if cname in class_from_prefix:
return class_from_prefix[cname]
atombase = getattr(atom, '%sAtom' % prefix)
class NewCol(class_, atombase):
"""Defines a non-nested column of a particular type.
The constructor accepts the same arguments as the equivalent
`Atom` class, plus an additional ``pos`` argument for
position information, which is assigned to the `_v_pos`
attribute.
"""
def __init__(self, *args, **kwargs):
pos = kwargs.pop('pos', None)
class_from_prefix = self._class_from_prefix
atombase.__init__(self, *args, **kwargs)
# The constructor of an abstract atom may have changed
# the class of `self` to something different of `NewCol`
# and `atombase` (that's why the prefix map is saved).
if self.__class__ is not NewCol:
colclass = class_from_prefix[self.prefix()]
self.__class__ = colclass
self._v_pos = pos
__eq__ = same_position(atombase.__eq__)
_is_equal_to_atom = same_position(atombase._is_equal_to_atom)
# XXX: API incompatible change for PyTables 3 line
# Overriding __eq__ blocks inheritance of __hash__ in 3.x
# def __hash__(self):
# return hash((self._v_pos, self.atombase))
if prefix == 'Enum':
_is_equal_to_enumatom = same_position(
atombase._is_equal_to_enumatom)
NewCol.__name__ = cname
class_from_prefix[prefix] = NewCol
return NewCol
# Special methods
# ~~~~~~~~~~~~~~~
def __repr__(self):
# Reuse the atom representation.
atomrepr = super(Col, self).__repr__()
lpar = atomrepr.index('(')
rpar = atomrepr.rindex(')')
atomargs = atomrepr[lpar + 1:rpar]
classname = self.__class__.__name__
return '%s(%s, pos=%s)' % (classname, atomargs, self._v_pos)
# Private methods
# ~~~~~~~~~~~~~~~
def _get_init_args(self):
"""Get a dictionary of instance constructor arguments."""
kwargs = dict((arg, getattr(self, arg)) for arg in ('shape', 'dflt'))
kwargs['pos'] = getattr(self, '_v_pos', None)
return kwargs
def _generate_col_classes():
"""Generate all column classes."""
# Abstract classes are not in the class map.
cprefixes = ['Int', 'UInt', 'Float', 'Time']
for (kind, kdata) in atom.atom_map.iteritems():
if hasattr(kdata, 'kind'): # atom class: non-fixed item size
atomclass = kdata
cprefixes.append(atomclass.prefix())
else: # dictionary: fixed item size
for atomclass in kdata.itervalues():
cprefixes.append(atomclass.prefix())
# Bottom-level complex classes are not in the type map, of course.
# We still want the user to get the compatibility warning, though.
cprefixes.extend(['Complex32', 'Complex64', 'Complex128'])
if hasattr(atom, 'Complex192Atom'):
cprefixes.append('Complex192')
if hasattr(atom, 'Complex256Atom'):
cprefixes.append('Complex256')
for cprefix in cprefixes:
newclass = Col._subclass_from_prefix(cprefix)
yield newclass
# Create all column classes.
for _newclass in _generate_col_classes():
exec('%s = _newclass' % _newclass.__name__)
del _newclass
# Table description classes
# =========================
class Description(object):
"""This class represents descriptions of the structure of tables.
An instance of this class is automatically bound to Table (see
:ref:`TableClassDescr`) objects when they are created. It provides a
browseable representation of the structure of the table, made of non-nested
(Col - see :ref:`ColClassDescr`) and nested (Description) columns.
Column definitions under a description can be accessed as attributes of it
(*natural naming*). For instance, if table.description is a Description
instance with a column named col1 under it, the later can be accessed as
table.description.col1. If col1 is nested and contains a col2 column, this
can be accessed as table.description.col1.col2. Because of natural naming,
the names of members start with special prefixes, like in the Group class
(see :ref:`GroupClassDescr`).
.. rubric:: Description attributes
.. attribute:: _v_colobjects
A dictionary mapping the names of the columns hanging
directly from the associated table or nested column to their
respective descriptions (Col - see :ref:`ColClassDescr` or
Description - see :ref:`DescriptionClassDescr` instances).
.. versionchanged:: 3.0
The *_v_colObjects* attobute has been renamed into
*_v_colobjects*.
.. attribute:: _v_dflts
A dictionary mapping the names of non-nested columns
hanging directly from the associated table or nested column
to their respective default values.
.. attribute:: _v_dtype
The NumPy type which reflects the structure of this
table or nested column. You can use this as the
dtype argument of NumPy array factories.
.. attribute:: _v_dtypes
A dictionary mapping the names of non-nested columns
hanging directly from the associated table or nested column
to their respective NumPy types.
.. attribute:: _v_is_nested
Whether the associated table or nested column contains
further nested columns or not.
.. attribute:: _v_itemsize
The size in bytes of an item in this table or nested column.
.. attribute:: _v_name
The name of this description group. The name of the
root group is '/'.
.. attribute:: _v_names
A list of the names of the columns hanging directly
from the associated table or nested column. The order of the
names matches the order of their respective columns in the
containing table.
.. attribute:: _v_nested_descr
A nested list of pairs of (name, format) tuples for all the columns
under this table or nested column. You can use this as the dtype and
descr arguments of NumPy array factories.
.. versionchanged:: 3.0
The *_v_nestedDescr* attribute has been renamed into
*_v_nested_descr*.
.. attribute:: _v_nested_formats
A nested list of the NumPy string formats (and shapes) of all the
columns under this table or nested column. You can use this as the
formats argument of NumPy array factories.
.. versionchanged:: 3.0
The *_v_nestedFormats* attribute has been renamed into
*_v_nested_formats*.
.. attribute:: _v_nestedlvl
The level of the associated table or nested column in the nested
datatype.
.. attribute:: _v_nested_names
A nested list of the names of all the columns under this table or
nested column. You can use this as the names argument of NumPy array
factories.
.. versionchanged:: 3.0
The *_v_nestedNames* attribute has been renamed into
*_v_nested_names*.
.. attribute:: _v_pathname
Pathname of the table or nested column.
.. attribute:: _v_pathnames
A list of the pathnames of all the columns under this table or nested
column (in preorder). If it does not contain nested columns, this is
exactly the same as the :attr:`Description._v_names` attribute.
.. attribute:: _v_types
A dictionary mapping the names of non-nested columns hanging directly
from the associated table or nested column to their respective PyTables
types.
"""
_v_colObjects = previous_api_property('_v_colobjects')
_v_nestedFormats = previous_api_property('_v_nested_formats')
_v_nestedNames = previous_api_property('_v_nested_names')
_v_nestedDesct = previous_api_property('_v_nested_descr')
def __init__(self, classdict, nestedlvl=-1, validate=True):
if not classdict:
raise ValueError("cannot create an empty data type")
# Do a shallow copy of classdict just in case this is going to
# be shared by other instances
newdict = self.__dict__
newdict["_v_name"] = "/" # The name for root descriptor
newdict["_v_names"] = []
newdict["_v_dtypes"] = {}
newdict["_v_types"] = {}
newdict["_v_dflts"] = {}
newdict["_v_colobjects"] = {}
newdict["_v_is_nested"] = False
nestedFormats = []
nestedDType = []
if not hasattr(newdict, "_v_nestedlvl"):
newdict["_v_nestedlvl"] = nestedlvl + 1
cols_with_pos = [] # colum (position, name) pairs
cols_no_pos = [] # just column names
# Check for special variables and convert column descriptions
for (name, descr) in classdict.iteritems():
if name.startswith('_v_'):
if name in newdict:
# print("Warning!")
# special methods &c: copy to newdict, warn about conflicts
warnings.warn("Can't set attr %r in description class %r"
% (name, self))
else:
# print("Special variable!-->", name, classdict[name])
newdict[name] = descr
continue # This variable is not needed anymore
columns = None
if (type(descr) == type(IsDescription) and
issubclass(descr, IsDescription)):
# print("Nested object (type I)-->", name)
columns = descr().columns
elif (type(descr.__class__) == type(IsDescription) and
issubclass(descr.__class__, IsDescription)):
# print("Nested object (type II)-->", name)
columns = descr.columns
elif isinstance(descr, dict):
# print("Nested object (type III)-->", name)
columns = descr
else:
# print("Nested object (type IV)-->", name)
descr = copy.copy(descr)
# The copies above and below ensure that the structures
# provided by the user will remain unchanged even if we
# tamper with the values of ``_v_pos`` here.
if columns is not None:
descr = Description(copy.copy(columns), self._v_nestedlvl)
classdict[name] = descr
pos = getattr(descr, '_v_pos', None)
if pos is None:
cols_no_pos.append(name)
else:
cols_with_pos.append((pos, name))
# Sort field names:
#
# 1. Fields with explicit positions, according to their
# positions (and their names if coincident).
# 2. Fields with no position, in alfabetical order.
cols_with_pos.sort()
cols_no_pos.sort()
keys = [name for (pos, name) in cols_with_pos] + cols_no_pos
pos = 0
# Get properties for compound types
for k in keys:
if validate:
# Check for key name validity
check_name_validity(k)
# Class variables
object = classdict[k]
newdict[k] = object # To allow natural naming
if not (isinstance(object, Col) or
isinstance(object, Description)):
raise TypeError('Passing an incorrect value to a table column.'
' Expected a Col (or subclass) instance and '
'got: "%s". Please make use of the Col(), or '
'descendant, constructor to properly '
'initialize columns.' % object)
object._v_pos = pos # Set the position of this object
object._v_parent = self # The parent description
pos += 1
newdict['_v_colobjects'][k] = object
newdict['_v_names'].append(k)
object.__dict__['_v_name'] = k
if not isinstance(k, str):
# numpy only accepts "str" for field names
if sys.version_info[0] < 3:
# Python 2.x: unicode --> str
kk = k.encode() # use the default encoding
else:
# Python 3.x: bytes --> str (unicode)
kk = k.decode()
else:
kk = k
if isinstance(object, Col):
dtype = object.dtype
newdict['_v_dtypes'][k] = dtype
newdict['_v_types'][k] = object.type
newdict['_v_dflts'][k] = object.dflt
nestedFormats.append(object.recarrtype)
baserecarrtype = dtype.base.str[1:]
nestedDType.append((kk, baserecarrtype, dtype.shape))
else: # A description
nestedFormats.append(object._v_nested_formats)
nestedDType.append((kk, object._v_dtype))
# Assign the format list to _v_nested_formats
newdict['_v_nested_formats'] = nestedFormats
newdict['_v_dtype'] = numpy.dtype(nestedDType)
# _v_itemsize is derived from the _v_dtype that already computes this
newdict['_v_itemsize'] = newdict['_v_dtype'].itemsize
if self._v_nestedlvl == 0:
# Get recursively nested _v_nested_names and _v_nested_descr attrs
self._g_set_nested_names_descr()
# Get pathnames for nested groups
self._g_set_path_names()
# Check the _v_byteorder has been used an issue an Error
if hasattr(self, "_v_byteorder"):
raise ValueError(
"Using a ``_v_byteorder`` in the description is obsolete. "
"Use the byteorder parameter in the constructor instead.")
def _g_set_nested_names_descr(self):
"""Computes the nested names and descriptions for nested datatypes."""
names = self._v_names
fmts = self._v_nested_formats
self._v_nested_names = names[:] # Important to do a copy!
self._v_nested_descr = [(names[i], fmts[i]) for i in range(len(names))]
for i in range(len(names)):
name = names[i]
new_object = self._v_colobjects[name]
if isinstance(new_object, Description):
new_object._g_set_nested_names_descr()
# replace the column nested name by a correct tuple
self._v_nested_names[i] = (name, new_object._v_nested_names)
self._v_nested_descr[i] = (name, new_object._v_nested_descr)
# set the _v_is_nested flag
self._v_is_nested = True
_g_setNestedNamesDescr = previous_api(_g_set_nested_names_descr)
def _g_set_path_names(self):
"""Compute the pathnames for arbitrary nested descriptions.
This method sets the ``_v_pathname`` and ``_v_pathnames``
attributes of all the elements (both descriptions and columns)
in this nested description.
"""
def get_cols_in_order(description):
return [description._v_colobjects[colname]
for colname in description._v_names]
def join_paths(path1, path2):
if not path1:
return path2
return '%s/%s' % (path1, path2)
# The top of the stack always has a nested description
# and a list of its child columns
# (be they nested ``Description`` or non-nested ``Col`` objects).
# In the end, the list contains only a list of column paths
# under this one.
#
# For instance, given this top of the stack::
#
# (<Description X>, [<Column A>, <Column B>])
#
# After computing the rest of the stack, the top is::
#
# (<Description X>, ['a', 'a/m', 'a/n', ... , 'b', ...])
stack = []
# We start by pushing the top-level description
# and its child columns.
self._v_pathname = ''
stack.append((self, get_cols_in_order(self)))
while stack:
desc, cols = stack.pop()
head = cols[0]
# What's the first child in the list?
if isinstance(head, Description):
# A nested description. We remove it from the list and
# push it with its child columns. This will be the next
# handled description.
head._v_pathname = join_paths(desc._v_pathname, head._v_name)
stack.append((desc, cols[1:])) # alter the top
stack.append((head, get_cols_in_order(head))) # new top
elif isinstance(head, Col):
# A non-nested column. We simply remove it from the
# list and append its name to it.
head._v_pathname = join_paths(desc._v_pathname, head._v_name)
cols.append(head._v_name) # alter the top
stack.append((desc, cols[1:])) # alter the top
else:
# Since paths and names are appended *to the end* of
# children lists, a string signals that no more children
# remain to be processed, so we are done with the
# description at the top of the stack.
assert isinstance(head, basestring)
# Assign the computed set of descendent column paths.
desc._v_pathnames = cols
if len(stack) > 0:
# Compute the paths with respect to the parent node
# (including the path of the current description)
# and append them to its list.
descName = desc._v_name
colPaths = [join_paths(descName, path) for path in cols]
colPaths.insert(0, descName)
parentCols = stack[-1][1]
parentCols.extend(colPaths)
# (Nothing is pushed, we are done with this description.)
_g_setPathNames = previous_api(_g_set_path_names)
def _f_walk(self, type='All'):
"""Iterate over nested columns.
If type is 'All' (the default), all column description objects (Col and
Description instances) are yielded in top-to-bottom order (preorder).
If type is 'Col' or 'Description', only column descriptions of that
type are yielded.
"""
if type not in ["All", "Col", "Description"]:
raise ValueError("""\
type can only take the parameters 'All', 'Col' or 'Description'.""")
stack = [self]
while stack:
object = stack.pop(0) # pop at the front so as to ensure the order
if type in ["All", "Description"]:
yield object # yield description
names = object._v_names
for i in range(len(names)):
new_object = object._v_colobjects[names[i]]
if isinstance(new_object, Description):
stack.append(new_object)
else:
if type in ["All", "Col"]:
yield new_object # yield column
def __repr__(self):
"""Gives a detailed Description column representation."""
rep = ['%s\"%s\": %r' %
(" " * self._v_nestedlvl, k, self._v_colobjects[k])
for k in self._v_names]
return '{\n %s}' % (',\n '.join(rep))
def __str__(self):
"""Gives a brief Description representation."""
return 'Description(%s)' % self._v_nested_descr
class MetaIsDescription(type):
"""Helper metaclass to return the class variables as a dictionary."""
def __new__(cls, classname, bases, classdict):
"""Return a new class with a "columns" attribute filled."""
newdict = {"columns": {}, }
if '__doc__' in classdict:
newdict['__doc__'] = classdict['__doc__']
for b in bases:
if "columns" in b.__dict__:
newdict["columns"].update(b.__dict__["columns"])
for k in classdict:
# if not (k.startswith('__') or k.startswith('_v_')):
# We let pass _v_ variables to configure class behaviour
if not (k.startswith('__')):
newdict["columns"][k] = classdict[k]
# Return a new class with the "columns" attribute filled
return type.__new__(cls, classname, bases, newdict)
metaIsDescription = previous_api(MetaIsDescription)
class IsDescription(object):
"""Description of the structure of a table or nested column.
This class is designed to be used as an easy, yet meaningful way to
describe the structure of new Table (see :ref:`TableClassDescr`) datasets
or nested columns through the definition of *derived classes*. In order to
define such a class, you must declare it as descendant of IsDescription,
with as many attributes as columns you want in your table. The name of each
attribute will become the name of a column, and its value will hold a
description of it.
Ordinary columns can be described using instances of the Col class (see
:ref:`ColClassDescr`). Nested columns can be described by using classes
derived from IsDescription, instances of it, or name-description
dictionaries. Derived classes can be declared in place (in which case the
column takes the name of the class) or referenced by name.
Nested columns can have a _v_pos special attribute which sets the
*relative* position of the column among sibling columns *also having
explicit positions*. The pos constructor argument of Col instances is used
for the same purpose. Columns with no explicit position will be placed
afterwards in alphanumeric order.
Once you have created a description object, you can pass it to the Table
constructor, where all the information it contains will be used to define
the table structure.
.. rubric:: IsDescription attributes
.. attribute:: _v_pos
Sets the position of a possible nested column description among its
sibling columns. This attribute can be specified *when declaring*
an IsDescription subclass to complement its *metadata*.
.. attribute:: columns
Maps the name of each column in the description to its own descriptive
object. This attribute is *automatically created* when an IsDescription
subclass is declared. Please note that declared columns can no longer
be accessed as normal class variables after its creation.
"""
__metaclass__ = MetaIsDescription
def descr_from_dtype(dtype_):
"""Get a description instance and byteorder from a (nested) NumPy dtype."""
fields = {}
fbyteorder = '|'
for name in dtype_.names:
dtype, pos = dtype_.fields[name][:2]
kind = dtype.base.kind
byteorder = dtype.base.byteorder
if byteorder in '><=':
if fbyteorder not in ['|', byteorder]:
raise NotImplementedError(
"structured arrays with mixed byteorders "
"are not supported yet, sorry")
fbyteorder = byteorder
# Non-nested column
if kind in 'biufSc':
col = Col.from_dtype(dtype, pos=pos)
# Nested column
elif kind == 'V' and dtype.shape in [(), (1,)]:
col, _ = descr_from_dtype(dtype)
col._v_pos = pos
else:
raise NotImplementedError(
"structured arrays with columns with type description ``%s`` "
"are not supported yet, sorry" % dtype)
fields[name] = col
return Description(fields), fbyteorder
def dtype_from_descr(descr, byteorder=None):
"""Get a (nested) NumPy dtype from a description instance and byteorder.
The descr parameter can be a Description or IsDescription
instance, sub-class of IsDescription or a dictionary.
"""
if isinstance(descr, dict):
descr = Description(descr)
elif (type(descr) == type(IsDescription)
and issubclass(descr, IsDescription)):
descr = Description(descr().columns)
elif isinstance(descr, IsDescription):
descr = Description(descr.columns)
elif not isinstance(descr, Description):
raise ValueError('invalid description: %r' % descr)
dtype_ = descr._v_dtype
if byteorder and byteorder != '|':
dtype_ = dtype_.newbyteorder(byteorder)
return dtype_
if __name__ == "__main__":
"""Test code."""
class Info(IsDescription):
_v_pos = 2
Name = UInt32Col()
Value = Float64Col()
class Test(IsDescription):
"""A description that has several columns."""
x = Col.from_type("int32", 2, 0, pos=0)
y = Col.from_kind('float', dflt=1, shape=(2, 3))
z = UInt8Col(dflt=1)
color = StringCol(2, dflt=" ")
# color = UInt32Col(2)
Info = Info()
class info(IsDescription):
_v_pos = 1
name = UInt32Col()
value = Float64Col(pos=0)
y2 = Col.from_kind('float', dflt=1, shape=(2, 3), pos=1)
z2 = UInt8Col(dflt=1)
class info2(IsDescription):
y3 = Col.from_kind('float', dflt=1, shape=(2, 3))
z3 = UInt8Col(dflt=1)
name = UInt32Col()
value = Float64Col()
class info3(IsDescription):
name = UInt32Col()
value = Float64Col()
y4 = Col.from_kind('float', dflt=1, shape=(2, 3))
z4 = UInt8Col(dflt=1)
# class Info(IsDescription):
# _v_pos = 2
# Name = StringCol(itemsize=2)
# Value = ComplexCol(itemsize=16)
# class Test(IsDescription):
# """A description that has several columns"""
# x = Col.from_type("int32", 2, 0, pos=0)
# y = Col.from_kind('float', dflt=1, shape=(2,3))
# z = UInt8Col(dflt=1)
# color = StringCol(2, dflt=" ")
# Info = Info()
# class info(IsDescription):
# _v_pos = 1
# name = StringCol(itemsize=2)
# value = ComplexCol(itemsize=16, pos=0)
# y2 = Col.from_kind('float', dflt=1, shape=(2,3), pos=1)
# z2 = UInt8Col(dflt=1)
# class info2(IsDescription):
# y3 = Col.from_kind('float', dflt=1, shape=(2,3))
# z3 = UInt8Col(dflt=1)
# name = StringCol(itemsize=2)
# value = ComplexCol(itemsize=16)
# class info3(IsDescription):
# name = StringCol(itemsize=2)
# value = ComplexCol(itemsize=16)
# y4 = Col.from_kind('float', dflt=1, shape=(2,3))
# z4 = UInt8Col(dflt=1)
# example cases of class Test
klass = Test()
# klass = Info()
desc = Description(klass.columns)
print("Description representation (short) ==>", desc)
print("Description representation (long) ==>", repr(desc))
print("Column names ==>", desc._v_names)
print("Column x ==>", desc.x)
print("Column Info ==>", desc.Info)
print("Column Info.value ==>", desc.Info.Value)
print("Nested column names ==>", desc._v_nested_names)
print("Defaults ==>", desc._v_dflts)
print("Nested Formats ==>", desc._v_nested_formats)
print("Nested Descriptions ==>", desc._v_nested_descr)
print("Nested Descriptions (info) ==>", desc.info._v_nested_descr)
print("Total size ==>", desc._v_dtype.itemsize)
# check _f_walk
for object in desc._f_walk():
if isinstance(object, Description):
print("******begin object*************", end=' ')
print("name -->", object._v_name)
# print("name -->", object._v_dtype.name)
# print("object childs-->", object._v_names)
# print("object nested childs-->", object._v_nested_names)
print("totalsize-->", object._v_dtype.itemsize)
else:
# pass
print("leaf -->", object._v_name, object.dtype)
class testDescParent(IsDescription):
c = Int32Col()
class testDesc(testDescParent):
pass
assert 'c' in testDesc.columns
## Local Variables:
## mode: python
## py-indent-offset: 4
## tab-width: 4
## fill-column: 72
## End:
|