/usr/lib/python2.7/dist-packages/tables/earray.py is in python-tables 3.1.1-3.
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########################################################################
#
# License: BSD
# Created: December 15, 2003
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
########################################################################
"""Here is defined the EArray class."""
import numpy
from tables.utils import convert_to_np_atom2, SizeType
from tables.carray import CArray
from tables._past import previous_api, previous_api_property
# default version for EARRAY objects
# obversion = "1.0" # initial version
# obversion = "1.1" # support for complex datatypes
# obversion = "1.2" # This adds support for time datatypes.
# obversion = "1.3" # This adds support for enumerated datatypes.
obversion = "1.4" # Numeric and numarray flavors are gone.
class EArray(CArray):
"""This class represents extendable, homogeneous datasets in an HDF5 file.
The main difference between an EArray and a CArray (see
:ref:`CArrayClassDescr`), from which it inherits, is that the former
can be enlarged along one of its dimensions, the *enlargeable
dimension*. That means that the :attr:`Leaf.extdim` attribute (see
:class:`Leaf`) of any EArray instance will always be non-negative.
Multiple enlargeable dimensions might be supported in the future.
New rows can be added to the end of an enlargeable array by using the
:meth:`EArray.append` method.
Parameters
----------
parentnode
The parent :class:`Group` object.
.. versionchanged:: 3.0
Renamed from *parentNode* to *parentnode*.
name : str
The name of this node in its parent group.
atom
An `Atom` instance representing the *type* and *shape*
of the atomic objects to be saved.
shape
The shape of the new array. One (and only one) of
the shape dimensions *must* be 0. The dimension being 0
means that the resulting `EArray` object can be extended
along it. Multiple enlargeable dimensions are not supported
right now.
title
A description for this node (it sets the ``TITLE``
HDF5 attribute on disk).
filters
An instance of the `Filters` class that provides information
about the desired I/O filters to be applied during the life
of this object.
expectedrows
A user estimate about the number of row elements that will
be added to the growable dimension in the `EArray` node.
If not provided, the default value is ``EXPECTED_ROWS_EARRAY``
(see ``tables/parameters.py``). If you plan to create either
a much smaller or a much bigger `EArray` try providing a guess;
this will optimize the HDF5 B-Tree creation and management
process time and the amount of memory used.
chunkshape
The shape of the data chunk to be read or written in a single
HDF5 I/O operation. Filters are applied to those chunks of data.
The dimensionality of `chunkshape` must be the same as that of
`shape` (beware: no dimension should be 0 this time!).
If ``None``, a sensible value is calculated based on the
`expectedrows` parameter (which is recommended).
byteorder
The byteorder of the data *on disk*, specified as 'little' or
'big'. If this is not specified, the byteorder is that of the
platform.
Examples
--------
See below a small example of the use of the `EArray` class. The
code is available in ``examples/earray1.py``::
import tables
import numpy
fileh = tables.open_file('earray1.h5', mode='w')
a = tables.StringAtom(itemsize=8)
# Use ``a`` as the object type for the enlargeable array.
array_c = fileh.create_earray(fileh.root, 'array_c', a, (0,),
\"Chars\")
array_c.append(numpy.array(['a'*2, 'b'*4], dtype='S8'))
array_c.append(numpy.array(['a'*6, 'b'*8, 'c'*10], dtype='S8'))
# Read the string ``EArray`` we have created on disk.
for s in array_c:
print('array_c[%s] => %r' % (array_c.nrow, s))
# Close the file.
fileh.close()
The output for the previous script is something like::
array_c[0] => 'aa'
array_c[1] => 'bbbb'
array_c[2] => 'aaaaaa'
array_c[3] => 'bbbbbbbb'
array_c[4] => 'cccccccc'
"""
# Class identifier.
_c_classid = 'EARRAY'
_c_classId = previous_api_property('_c_classid')
# Special methods
# ~~~~~~~~~~~~~~~
def __init__(self, parentnode, name,
atom=None, shape=None, title="",
filters=None, expectedrows=None,
chunkshape=None, byteorder=None,
_log=True):
# Specific of EArray
if expectedrows is None:
expectedrows = parentnode._v_file.params['EXPECTED_ROWS_EARRAY']
self._v_expectedrows = expectedrows
"""The expected number of rows to be stored in the array."""
# Call the parent (CArray) init code
super(EArray, self).__init__(parentnode, name, atom, shape, title,
filters, chunkshape, byteorder, _log)
# Public and private methods
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
def _g_create(self):
"""Create a new array in file (specific part)."""
# Pre-conditions and extdim computation
zerodims = numpy.sum(numpy.array(self.shape) == 0)
if zerodims > 0:
if zerodims == 1:
self.extdim = list(self.shape).index(0)
else:
raise NotImplementedError(
"Multiple enlargeable (0-)dimensions are not "
"supported.")
else:
raise ValueError(
"When creating EArrays, you need to set one of "
"the dimensions of the Atom instance to zero.")
# Finish the common part of the creation process
return self._g_create_common(self._v_expectedrows)
def _check_shape_append(self, nparr):
"Test that nparr shape is consistent with underlying EArray."
# The arrays conforms self expandibility?
myrank = len(self.shape)
narank = len(nparr.shape) - len(self.atom.shape)
if myrank != narank:
raise ValueError(("the ranks of the appended object (%d) and the "
"``%s`` EArray (%d) differ")
% (narank, self._v_pathname, myrank))
for i in range(myrank):
if i != self.extdim and self.shape[i] != nparr.shape[i]:
raise ValueError(("the shapes of the appended object and the "
"``%s`` EArray differ in non-enlargeable "
"dimension %d") % (self._v_pathname, i))
_checkShapeAppend = previous_api(_check_shape_append)
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
The sequence must have the same type as the array; otherwise a
TypeError is raised. In the same way, the dimensions of the
sequence must conform to the shape of the array, that is, all
dimensions must match, with the exception of the enlargeable
dimension, which can be of any length (even 0!). If the shape
of the sequence is invalid, a ValueError is raised.
"""
self._g_check_open()
self._v_file._check_writable()
# Convert the sequence into a NumPy object
nparr = convert_to_np_atom2(sequence, self.atom)
# Check if it has a consistent shape with underlying EArray
self._check_shape_append(nparr)
# If the size of the nparr is zero, don't do anything else
if nparr.size > 0:
self._append(nparr)
def _g_copy_with_stats(self, group, name, start, stop, step,
title, filters, chunkshape, _log, **kwargs):
"""Private part of Leaf.copy() for each kind of leaf."""
(start, stop, step) = self._process_range_read(start, stop, step)
# Build the new EArray object
maindim = self.maindim
shape = list(self.shape)
shape[maindim] = 0
# The number of final rows
nrows = len(xrange(start, stop, step))
# Build the new EArray object
object = EArray(
group, name, atom=self.atom, shape=shape, title=title,
filters=filters, expectedrows=nrows, chunkshape=chunkshape,
_log=_log)
# Now, fill the new earray with values from source
nrowsinbuf = self.nrowsinbuf
# The slices parameter for self.__getitem__
slices = [slice(0, dim, 1) for dim in self.shape]
# This is a hack to prevent doing unnecessary conversions
# when copying buffers
self._v_convert = False
# Start the copy itself
for start2 in xrange(start, stop, step * nrowsinbuf):
# Save the records on disk
stop2 = start2 + step * nrowsinbuf
if stop2 > stop:
stop2 = stop
# Set the proper slice in the extensible dimension
slices[maindim] = slice(start2, stop2, step)
object._append(self.__getitem__(tuple(slices)))
# Active the conversion again (default)
self._v_convert = True
nbytes = numpy.prod(self.shape, dtype=SizeType) * self.atom.itemsize
return (object, nbytes)
_g_copyWithStats = previous_api(_g_copy_with_stats)
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