/usr/lib/python3/dist-packages/tables/leaf.py is in python3-tables 3.4.2-4.
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 | # -*- coding: utf-8 -*-
########################################################################
#
# License: BSD
# Created: October 14, 2002
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
########################################################################
"""Here is defined the Leaf class."""
from __future__ import absolute_import
import warnings
import math
import numpy
from .flavor import (check_flavor, internal_flavor,
alias_map as flavor_alias_map)
from .node import Node
from .filters import Filters
from .utils import byteorders, lazyattr, SizeType
from .exceptions import PerformanceWarning
from . import utilsextension
import six
from six.moves import range
def csformula(expected_mb):
"""Return the fitted chunksize for expected_mb."""
# For a basesize of 8 KB, this will return:
# 8 KB for datasets <= 1 MB
# 1 MB for datasets >= 10 TB
basesize = 8 * 1024 # 8 KB is a good minimum
return basesize * int(2**math.log10(expected_mb))
def limit_es(expected_mb):
"""Protection against creating too small or too large chunks."""
if expected_mb < 1: # < 1 MB
expected_mb = 1
elif expected_mb > 10**7: # > 10 TB
expected_mb = 10**7
return expected_mb
def calc_chunksize(expected_mb):
"""Compute the optimum HDF5 chunksize for I/O purposes.
Rational: HDF5 takes the data in bunches of chunksize length to
write the on disk. A BTree in memory is used to map structures on
disk. The more chunks that are allocated for a dataset the larger
the B-tree. Large B-trees take memory and causes file storage
overhead as well as more disk I/O and higher contention for the meta
data cache. You have to balance between memory and I/O overhead
(small B-trees) and time to access to data (big B-trees).
The tuning of the chunksize parameter affects the performance and
the memory consumed. This is based on my own experiments and, as
always, your mileage may vary.
"""
expected_mb = limit_es(expected_mb)
zone = int(math.log10(expected_mb))
expected_mb = 10**zone
chunksize = csformula(expected_mb)
# XXX: Multiply by 8 seems optimal for sequential access
return chunksize * 8
@six.python_2_unicode_compatible
class Leaf(Node):
"""Abstract base class for all PyTables leaves.
A leaf is a node (see the Node class in :class:`Node`) which hangs from a
group (see the Group class in :class:`Group`) but, unlike a group, it can
not have any further children below it (i.e. it is an end node).
This definition includes all nodes which contain actual data (datasets
handled by the Table - see :ref:`TableClassDescr`, Array -
see :ref:`ArrayClassDescr`, CArray - see :ref:`CArrayClassDescr`, EArray -
see :ref:`EArrayClassDescr`, and VLArray - see :ref:`VLArrayClassDescr`
classes) and unsupported nodes (the UnImplemented
class - :ref:`UnImplementedClassDescr`) these classes do in fact inherit
from Leaf.
.. rubric:: Leaf attributes
These instance variables are provided in addition to those in Node
(see :ref:`NodeClassDescr`):
.. attribute:: byteorder
The byte ordering of the leaf data *on disk*. It will be either
``little`` or ``big``.
.. attribute:: dtype
The NumPy dtype that most closely matches this leaf type.
.. attribute:: extdim
The index of the enlargeable dimension (-1 if none).
.. attribute:: nrows
The length of the main dimension of the leaf data.
.. attribute:: nrowsinbuf
The number of rows that fit in internal input buffers.
You can change this to fine-tune the speed or memory
requirements of your application.
.. attribute:: shape
The shape of data in the leaf.
"""
# Properties
# ~~~~~~~~~~
# Node property aliases
# `````````````````````
# These are a little hard to override, but so are properties.
attrs = Node._v_attrs
"""The associated AttributeSet instance - see :ref:`AttributeSetClassDescr`
(This is an easier-to-write alias of :attr:`Node._v_attrs`."""
title = Node._v_title
"""A description for this node
(This is an easier-to-write alias of :attr:`Node._v_title`)."""
# Read-only node property aliases
# ```````````````````````````````
@property
def name(self):
"""The name of this node in its parent group (This is an easier-to-write alias of :attr:`Node._v_name`)."""
return self._v_name
@property
def chunkshape(self):
"""The HDF5 chunk size for chunked leaves (a tuple).
This is read-only because you cannot change the chunk size of a
leaf once it has been created.
"""
return getattr(self, '_v_chunkshape', None)
@property
def object_id(self):
"""A node identifier, which may change from run to run.
(This is an easier-to-write alias of :attr:`Node._v_objectid`).
.. versionchanged:: 3.0
The *objectID* property has been renamed into *object_id*.
"""
return self._v_objectid
@property
def ndim(self):
"""The number of dimensions of the leaf data.
.. versionadded: 2.4"""
return len(self.shape)
# Lazy read-only attributes
# `````````````````````````
@lazyattr
def filters(self):
"""Filter properties for this leaf.
See Also
--------
Filters
"""
return Filters._from_leaf(self)
# Other properties
# ````````````````
@property
def maindim(self):
"""The dimension along which iterators work.
Its value is 0 (i.e. the first dimension) when the dataset is not
extendable, and self.extdim (where available) for extendable ones.
"""
if self.extdim < 0:
return 0 # choose the first dimension
return self.extdim
@property
def flavor(self):
"""The type of data object read from this leaf.
It can be any of 'numpy' or 'python'.
You can (and are encouraged to) use this property to get, set
and delete the FLAVOR HDF5 attribute of the leaf. When the leaf
has no such attribute, the default flavor is used..
"""
return self._flavor
@flavor.setter
def flavor(self, flavor):
self._v_file._check_writable()
check_flavor(flavor)
self._v_attrs.FLAVOR = self._flavor = flavor # logs the change
@flavor.deleter
def flavor(self):
del self._v_attrs.FLAVOR
self._flavor = internal_flavor
@property
def size_on_disk(self):
"""
The size of this leaf's data in bytes as it is stored on disk. If the
data is compressed, this shows the compressed size. In the case of
uncompressed, chunked data, this may be slightly larger than the amount
of data, due to partially filled chunks.
"""
return self._get_storage_size()
# Special methods
# ~~~~~~~~~~~~~~~
def __init__(self, parentnode, name,
new=False, filters=None,
byteorder=None, _log=True):
self._v_new = new
"""Is this the first time the node has been created?"""
self.nrowsinbuf = None
"""
The number of rows that fits in internal input buffers.
You can change this to fine-tune the speed or memory
requirements of your application.
"""
self._flavor = None
"""Private storage for the `flavor` property."""
if new:
# Get filter properties from parent group if not given.
if filters is None:
filters = parentnode._v_filters
self.__dict__['filters'] = filters # bypass the property
if byteorder not in (None, 'little', 'big'):
raise ValueError(
"the byteorder can only take 'little' or 'big' values "
"and you passed: %s" % byteorder)
self.byteorder = byteorder
"""The byte ordering of the leaf data *on disk*."""
# Existing filters need not be read since `filters`
# is a lazy property that automatically handles their loading.
super(Leaf, self).__init__(parentnode, name, _log)
def __len__(self):
"""Return the length of the main dimension of the leaf data.
Please note that this may raise an OverflowError on 32-bit platforms
for datasets having more than 2**31-1 rows. This is a limitation of
Python that you can work around by using the nrows or shape attributes.
"""
return self.nrows
def __str__(self):
"""The string representation for this object is its pathname in the
HDF5 object tree plus some additional metainfo."""
# Get this class name
classname = self.__class__.__name__
# The title
title = self._v_title
# The filters
filters = ""
if self.filters.fletcher32:
filters += ", fletcher32"
if self.filters.complevel:
if self.filters.shuffle:
filters += ", shuffle"
if self.filters.bitshuffle:
filters += ", bitshuffle"
filters += ", %s(%s)" % (self.filters.complib,
self.filters.complevel)
return "%s (%s%s%s) %r" % \
(self._v_pathname, classname, self.shape, filters, title)
# Private methods
# ~~~~~~~~~~~~~~~
def _g_post_init_hook(self):
"""Code to be run after node creation and before creation logging.
This method gets or sets the flavor of the leaf.
"""
super(Leaf, self)._g_post_init_hook()
if self._v_new: # set flavor of new node
if self._flavor is None:
self._flavor = internal_flavor
else: # flavor set at creation time, do not log
if self._v_file.params['PYTABLES_SYS_ATTRS']:
self._v_attrs._g__setattr('FLAVOR', self._flavor)
else: # get flavor of existing node (if any)
if self._v_file.params['PYTABLES_SYS_ATTRS']:
flavor = getattr(self._v_attrs, 'FLAVOR', internal_flavor)
self._flavor = flavor_alias_map.get(flavor, flavor)
else:
self._flavor = internal_flavor
def _calc_chunkshape(self, expectedrows, rowsize, itemsize):
"""Calculate the shape for the HDF5 chunk."""
# In case of a scalar shape, return the unit chunksize
if self.shape == ():
return (SizeType(1),)
# Compute the chunksize
MB = 1024 * 1024
expected_mb = (expectedrows * rowsize) // MB
chunksize = calc_chunksize(expected_mb)
maindim = self.maindim
# Compute the chunknitems
chunknitems = chunksize // itemsize
# Safeguard against itemsizes being extremely large
if chunknitems == 0:
chunknitems = 1
chunkshape = list(self.shape)
# Check whether trimming the main dimension is enough
chunkshape[maindim] = 1
newchunknitems = numpy.prod(chunkshape, dtype=SizeType)
if newchunknitems <= chunknitems:
chunkshape[maindim] = chunknitems // newchunknitems
else:
# No, so start trimming other dimensions as well
for j in range(len(chunkshape)):
# Check whether trimming this dimension is enough
chunkshape[j] = 1
newchunknitems = numpy.prod(chunkshape, dtype=SizeType)
if newchunknitems <= chunknitems:
chunkshape[j] = chunknitems // newchunknitems
break
else:
# Ops, we ran out of the loop without a break
# Set the last dimension to chunknitems
chunkshape[-1] = chunknitems
return tuple(SizeType(s) for s in chunkshape)
def _calc_nrowsinbuf(self):
"""Calculate the number of rows that fits on a PyTables buffer."""
params = self._v_file.params
# Compute the nrowsinbuf
rowsize = self.rowsize
buffersize = params['IO_BUFFER_SIZE']
if rowsize != 0:
nrowsinbuf = buffersize // rowsize
else:
nrowsinbuf = 1
# Safeguard against row sizes being extremely large
if nrowsinbuf == 0:
nrowsinbuf = 1
# If rowsize is too large, issue a Performance warning
maxrowsize = params['BUFFER_TIMES'] * buffersize
if rowsize > maxrowsize:
warnings.warn("""\
The Leaf ``%s`` is exceeding the maximum recommended rowsize (%d bytes);
be ready to see PyTables asking for *lots* of memory and possibly slow
I/O. You may want to reduce the rowsize by trimming the value of
dimensions that are orthogonal (and preferably close) to the *main*
dimension of this leave. Alternatively, in case you have specified a
very small/large chunksize, you may want to increase/decrease it."""
% (self._v_pathname, maxrowsize),
PerformanceWarning)
return nrowsinbuf
# This method is appropriate for calls to __getitem__ methods
def _process_range(self, start, stop, step, dim=None, warn_negstep=True):
if dim is None:
nrows = self.nrows # self.shape[self.maindim]
else:
nrows = self.shape[dim]
if warn_negstep and step and step < 0:
raise ValueError("slice step cannot be negative")
#if start is not None: start = long(start)
#if stop is not None: stop = long(stop)
#if step is not None: step = long(step)
return slice(start, stop, step).indices(int(nrows))
# This method is appropriate for calls to read() methods
def _process_range_read(self, start, stop, step, warn_negstep=True):
nrows = self.nrows
if start is not None and stop is None and step is None:
# Protection against start greater than available records
# nrows == 0 is a special case for empty objects
if nrows > 0 and start >= nrows:
raise IndexError("start of range (%s) is greater than "
"number of rows (%s)" % (start, nrows))
step = 1
if start == -1: # corner case
stop = nrows
else:
stop = start + 1
# Finally, get the correct values (over the main dimension)
start, stop, step = self._process_range(start, stop, step,
warn_negstep=warn_negstep)
return (start, stop, step)
def _g_copy(self, newparent, newname, recursive, _log=True, **kwargs):
# Compute default arguments.
start = kwargs.pop('start', None)
stop = kwargs.pop('stop', None)
step = kwargs.pop('step', None)
title = kwargs.pop('title', self._v_title)
filters = kwargs.pop('filters', self.filters)
chunkshape = kwargs.pop('chunkshape', self.chunkshape)
copyuserattrs = kwargs.pop('copyuserattrs', True)
stats = kwargs.pop('stats', None)
if chunkshape == 'keep':
chunkshape = self.chunkshape # Keep the original chunkshape
elif chunkshape == 'auto':
chunkshape = None # Will recompute chunkshape
# Fix arguments with explicit None values for backwards compatibility.
if title is None:
title = self._v_title
if filters is None:
filters = self.filters
# Create a copy of the object.
(new_node, bytes) = self._g_copy_with_stats(
newparent, newname, start, stop, step,
title, filters, chunkshape, _log, **kwargs)
# Copy user attributes if requested (or the flavor at least).
if copyuserattrs:
self._v_attrs._g_copy(new_node._v_attrs, copyclass=True)
elif 'FLAVOR' in self._v_attrs:
if self._v_file.params['PYTABLES_SYS_ATTRS']:
new_node._v_attrs._g__setattr('FLAVOR', self._flavor)
new_node._flavor = self._flavor # update cached value
# Update statistics if needed.
if stats is not None:
stats['leaves'] += 1
stats['bytes'] += bytes
return new_node
def _g_fix_byteorder_data(self, data, dbyteorder):
"Fix the byteorder of data passed in constructors."
dbyteorder = byteorders[dbyteorder]
# If self.byteorder has not been passed as an argument of
# the constructor, then set it to the same value of data.
if self.byteorder is None:
self.byteorder = dbyteorder
# Do an additional in-place byteswap of data if the in-memory
# byteorder doesn't match that of the on-disk. This is the only
# place that we have to do the conversion manually. In all the
# other cases, it will be HDF5 the responsible of doing the
# byteswap properly.
if dbyteorder in ['little', 'big']:
if dbyteorder != self.byteorder:
# if data is not writeable, do a copy first
if not data.flags.writeable:
data = data.copy()
data.byteswap(True)
else:
# Fix the byteorder again, no matter which byteorder have
# specified the user in the constructor.
self.byteorder = "irrelevant"
return data
def _point_selection(self, key):
"""Perform a point-wise selection.
`key` can be any of the following items:
* A boolean array with the same shape than self. Those positions
with True values will signal the coordinates to be returned.
* A numpy array (or list or tuple) with the point coordinates.
This has to be a two-dimensional array of size len(self.shape)
by num_elements containing a list of of zero-based values
specifying the coordinates in the dataset of the selected
elements. The order of the element coordinates in the array
specifies the order in which the array elements are iterated
through when I/O is performed. Duplicate coordinate locations
are not checked for.
Return the coordinates array. If this is not possible, raise a
`TypeError` so that the next selection method can be tried out.
This is useful for whatever `Leaf` instance implementing a
point-wise selection.
"""
if type(key) in (list, tuple):
if isinstance(key, tuple) and len(key) > len(self.shape):
raise IndexError("Invalid index or slice: %r" % (key,))
# Try to convert key to a numpy array. If not possible,
# a TypeError will be issued (to be catched later on).
try:
key = numpy.array(key)
except ValueError:
raise TypeError("Invalid index or slice: %r" % (key,))
elif not isinstance(key, numpy.ndarray):
raise TypeError("Invalid index or slice: %r" % (key,))
# Protection against empty keys
if len(key) == 0:
return numpy.array([], dtype="i8")
if key.dtype.kind == 'b':
if not key.shape == self.shape:
raise IndexError(
"Boolean indexing array has incompatible shape")
# Get the True coordinates (64-bit indices!)
coords = numpy.asarray(key.nonzero(), dtype='i8')
coords = numpy.transpose(coords)
elif key.dtype.kind == 'i' or key.dtype.kind == 'u':
if len(key.shape) > 2:
raise IndexError(
"Coordinate indexing array has incompatible shape")
elif len(key.shape) == 2:
if key.shape[0] != len(self.shape):
raise IndexError(
"Coordinate indexing array has incompatible shape")
coords = numpy.asarray(key, dtype="i8")
coords = numpy.transpose(coords)
else:
# For 1-dimensional datasets
coords = numpy.asarray(key, dtype="i8")
# handle negative indices
idx = coords < 0
coords[idx] = (coords + self.shape)[idx]
# bounds check
if numpy.any(coords < 0) or numpy.any(coords >= self.shape):
raise IndexError("Index out of bounds")
else:
raise TypeError("Only integer coordinates allowed.")
# We absolutely need a contiguous array
if not coords.flags.contiguous:
coords = coords.copy()
return coords
# Public methods
# ~~~~~~~~~~~~~~
# Tree manipulation
# `````````````````
def remove(self):
"""Remove this node from the hierarchy.
This method has the behavior described
in :meth:`Node._f_remove`. Please note that there is no recursive flag
since leaves do not have child nodes.
"""
self._f_remove(False)
def rename(self, newname):
"""Rename this node in place.
This method has the behavior described in :meth:`Node._f_rename()`.
"""
self._f_rename(newname)
def move(self, newparent=None, newname=None,
overwrite=False, createparents=False):
"""Move or rename this node.
This method has the behavior described in :meth:`Node._f_move`
"""
self._f_move(newparent, newname, overwrite, createparents)
def copy(self, newparent=None, newname=None,
overwrite=False, createparents=False, **kwargs):
"""Copy this node and return the new one.
This method has the behavior described in :meth:`Node._f_copy`. Please
note that there is no recursive flag since leaves do not have child
nodes.
.. warning::
Note that unknown parameters passed to this method will be
ignored, so may want to double check the spelling of these
(i.e. if you write them incorrectly, they will most probably
be ignored).
Parameters
----------
title
The new title for the destination. If omitted or None, the original
title is used.
filters : Filters
Specifying this parameter overrides the original filter properties
in the source node. If specified, it must be an instance of the
Filters class (see :ref:`FiltersClassDescr`). The default is to
copy the filter properties from the source node.
copyuserattrs
You can prevent the user attributes from being copied by setting
this parameter to False. The default is to copy them.
start, stop, step : int
Specify the range of rows to be copied; the default is to copy all
the rows.
stats
This argument may be used to collect statistics on the copy
process. When used, it should be a dictionary with keys 'groups',
'leaves' and 'bytes' having a numeric value. Their values will be
incremented to reflect the number of groups, leaves and bytes,
respectively, that have been copied during the operation.
chunkshape
The chunkshape of the new leaf. It supports a couple of special
values. A value of keep means that the chunkshape will be the same
than original leaf (this is the default). A value of auto means
that a new shape will be computed automatically in order to ensure
best performance when accessing the dataset through the main
dimension. Any other value should be an integer or a tuple
matching the dimensions of the leaf.
"""
return self._f_copy(
newparent, newname, overwrite, createparents, **kwargs)
def truncate(self, size):
"""Truncate the main dimension to be size rows.
If the main dimension previously was larger than this size, the extra
data is lost. If the main dimension previously was shorter, it is
extended, and the extended part is filled with the default values.
The truncation operation can only be applied to *enlargeable* datasets,
else a TypeError will be raised.
"""
# A non-enlargeable arrays (Array, CArray) cannot be truncated
if self.extdim < 0:
raise TypeError("non-enlargeable datasets cannot be truncated")
self._g_truncate(size)
def isvisible(self):
"""Is this node visible?
This method has the behavior described in :meth:`Node._f_isvisible()`.
"""
return self._f_isvisible()
# Attribute handling
# ``````````````````
def get_attr(self, name):
"""Get a PyTables attribute from this node.
This method has the behavior described in :meth:`Node._f_getattr`.
"""
return self._f_getattr(name)
def set_attr(self, name, value):
"""Set a PyTables attribute for this node.
This method has the behavior described in :meth:`Node._f_setattr()`.
"""
self._f_setattr(name, value)
def del_attr(self, name):
"""Delete a PyTables attribute from this node.
This method has the behavior described in :meth:`Node_f_delAttr`.
"""
self._f_delattr(name)
# Data handling
# `````````````
def flush(self):
"""Flush pending data to disk.
Saves whatever remaining buffered data to disk. It also releases
I/O buffers, so if you are filling many datasets in the same
PyTables session, please call flush() extensively so as to help
PyTables to keep memory requirements low.
"""
self._g_flush()
def _f_close(self, flush=True):
"""Close this node in the tree.
This method has the behavior described in :meth:`Node._f_close`.
Besides that, the optional argument flush tells whether to flush
pending data to disk or not before closing.
"""
if not self._v_isopen:
return # the node is already closed or not initialized
# Only do a flush in case the leaf has an IO buffer. The
# internal buffers of HDF5 will be flushed afterwards during the
# self._g_close() call. Avoiding an unnecessary flush()
# operation accelerates the closing for the unbuffered leaves.
if flush and hasattr(self, "_v_iobuf"):
self.flush()
# Close the dataset and release resources
self._g_close()
# Close myself as a node.
super(Leaf, self)._f_close()
def close(self, flush=True):
"""Close this node in the tree.
This method is completely equivalent to :meth:`Leaf._f_close`.
"""
self._f_close(flush)
## Local Variables:
## mode: python
## py-indent-offset: 4
## tab-width: 4
## fill-column: 72
## End:
|