########################################################################
#
# License: BSD
# Created: November 12, 2003
# Author: Francesc Alted - faltet@pytables.com
#
# $Id: vlarray.py 3905 2008-11-13 12:22:18Z faltet $
#
########################################################################
"""Here is defined the VLArray class
See VLArray class docstring for more info.
Classes:
VLArray
Functions:
Misc variables:
__version__
"""
import sys
import warnings
import numpy
from tables import hdf5Extension
from tables.utilsExtension import lrange
from tables.utils import convertToNPAtom,convertToNPAtom2,idx2long,\
correct_byteorder, SizeType
from tables.atom import (
ObjectAtom, VLStringAtom, VLUnicodeAtom, EnumAtom, Atom, split_type )
from tables.flavor import internal_to_flavor
from tables.leaf import Leaf,calc_chunksize
__version__ = "$Revision: 3905 $"
# default version for VLARRAY objects
#obversion = "1.0" # initial version
#obversion = "1.0" # add support for complex datatypes
#obversion = "1.1" # This adds support for time datatypes.
#obversion = "1.2" # This adds support for enumerated datatypes.
obversion = "1.3" # Introduced 'PSEUDOATOM' attribute.
class VLArray(hdf5Extension.VLArray, Leaf):
"""
This class represents variable length (ragged) arrays in an HDF5 file.
Instances of this class represent array objects in the object tree
with the property that their rows can have a *variable* number of
homogeneous elements, called *atoms*. Like `Table` datasets,
variable length arrays can have only one dimension, and the
elements (atoms) of their rows can be fully multidimensional.
`VLArray` objects do also support compression.
When reading a range of rows from a `VLArray`, you will *always*
get a Python list of objects of the current flavor (each of them
for a row), which may have different lengths.
This class provides methods to write or read data to or from
variable length array objects in the file. Note that it also
inherits all the public attributes and methods that `Leaf` already
provides.
Public instance variables
-------------------------
atom
An `Atom` instance representing the *type* and *shape* of the
atomic objects to be saved. You may use a *pseudo-atom* for
storing a serialized object or variable length string per row.
flavor
The type of data object read from this leaf.
Please note that when reading several rows of `VLArray` data,
the flavor only applies to the *components* of the returned
Python list, not to the list itself.
nrow
On iterators, this is the index of the current row.
extdim
The index of the enlargeable dimension (always 0 for vlarrays).
Public methods
--------------
append(sequence)
Add a ``sequence`` of data to the end of the dataset.
getEnum()
Get the enumerated type associated with this array.
iterrows([start][, stop][, step])
Iterate over the rows of the array.
next()
Get the next element of the array during an iteration.
read([start][, stop][, step])
Get data in the array as a list of objects of the current
flavor.
Special methods
---------------
The following methods automatically trigger actions when a
`VLArray` instance is accessed in a special way
(e.g. ``vlarray[2:5]`` will be equivalent to a call to
``vlarray.__getitem__(slice(2, 5, None))``).
__getitem__(key)
Get a row or a range of rows from the array.
__iter__()
Iterate over the rows of the array.
__setitem__(key, value)
Set a row in the array.
Example of use
--------------
See below a small example of the use of the `VLArray` class. The
code is available in ``examples/vlarray1.py``::
import tables
from numpy import *
# Create a VLArray:
fileh = tables.openFile('vlarray1.h5', mode='w')
vlarray = fileh.createVLArray(fileh.root, 'vlarray1',
tables.Int32Atom(shape=()),
\"ragged array of ints\",
filters=tables.Filters(1))
# Append some (variable length) rows:
vlarray.append(array([5, 6]))
vlarray.append(array([5, 6, 7]))
vlarray.append([5, 6, 9, 8])
# Now, read it through an iterator:
print '-->', vlarray.title
for x in vlarray:
print '%s[%d]--> %s' % (vlarray.name, vlarray.nrow, x)
# Now, do the same with native Python strings.
vlarray2 = fileh.createVLArray(fileh.root, 'vlarray2',
tables.StringAtom(itemsize=2),
\"ragged array of strings\",
filters=tables.Filters(1))
vlarray2.flavor = 'python'
# Append some (variable length) rows:
print '-->', vlarray2.title
vlarray2.append(['5', '66'])
vlarray2.append(['5', '6', '77'])
vlarray2.append(['5', '6', '9', '88'])
# Now, read it through an iterator:
for x in vlarray2:
print '%s[%d]--> %s' % (vlarray2.name, vlarray2.nrow, x)
# Close the file.
fileh.close()
The output for the previous script is something like::
--> ragged array of ints
vlarray1[0]--> [5 6]
vlarray1[1]--> [5 6 7]
vlarray1[2]--> [5 6 9 8]
--> ragged array of strings
vlarray2[0]--> ['5', '66']
vlarray2[1]--> ['5', '6', '77']
vlarray2[2]--> ['5', '6', '9', '88']
"""
# Class identifier.
_c_classId = 'VLARRAY'
# Properties
# ~~~~~~~~~~
shape = property(
lambda self: (self.nrows,), None, None,
"The shape of the stored array.")
# Other methods
# ~~~~~~~~~~~~~
def __init__( self, parentNode, name,
atom=None, title="",
filters=None, expectedsizeinMB=1.0,
chunkshape=None, byteorder=None,
_log=True ):
"""
Create a `VLArray` instance.
`atom`
An `Atom` instance representing the *type* and *shape* of
the atomic objects to be saved.
`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.
`expectedsizeinMB`
An user estimate about the size (in MB) in the final
`VLArray` object. If not provided, the default value is 1
MB. If you plan to create either a much smaller or a much
bigger `VLArray` 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 1. If ``None``, a sensible value is calculated (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.
"""
self._v_version = None
"""The object version of this array."""
self._v_new = new = atom is not None
"""Is this the first time the node has been created?"""
self._v_new_title = title
"""New title for this node."""
self._v_new_filters = filters
"""New filter properties for this array."""
self._v_expectedsizeinMB = expectedsizeinMB
"""The expected size of the array in MiB."""
self._v_chunkshape = None
"""Private storage for the `chunkshape` property of Leaf."""
# Miscellaneous iteration rubbish.
self._start = None
"""Starting row for the current iteration."""
self._stop = None
"""Stopping row for the current iteration."""
self._step = None
"""Step size for the current iteration."""
self._nrowsread = None
"""Number of rows read up to the current state of iteration."""
self._startb = None
"""Starting row for current buffer."""
self._stopb = None
"""Stopping row for current buffer. """
self._row = None
"""Current row in iterators (sentinel)."""
self._init = False
"""Whether we are in the middle of an iteration or not (sentinel)."""
self.listarr = None
"""Current buffer in iterators."""
# Documented (*public*) attributes.
self.atom = atom
"""
An `Atom` instance representing the shape and type of the
atomic objects to be saved.
"""
self.nrow = None
"""On iterators, this is the index of the current row."""
self.nrows = None
"""The total number of rows."""
self.extdim = 0 # VLArray only have one dimension currently
"""The index of the enlargeable dimension (always 0 for vlarrays)."""
# Check the chunkshape parameter
if new and chunkshape is not None:
if isinstance(chunkshape, (int, numpy.integer, long)):
chunkshape = (chunkshape,)
try:
chunkshape = tuple(chunkshape)
except TypeError:
raise TypeError(
"`chunkshape` parameter must be an integer or sequence "
"and you passed a %s" % type(chunkshape) )
if len(chunkshape) != 1:
raise ValueError( "`chunkshape` rank (length) must be 1: %r"
% (chunkshape,) )
self._v_chunkshape = tuple(SizeType(s) for s in chunkshape)
super(VLArray, self).__init__(parentNode, name, new, filters,
byteorder, _log)
def _g_postInitHook(self):
super(VLArray, self)._g_postInitHook()
self.nrowsinbuf = 100 # maybe enough for most applications
# This is too specific for moving it into Leaf
def _calc_chunkshape(self, expectedsizeinMB):
"""Calculate the size for the HDF5 chunk."""
chunksize = calc_chunksize(expectedsizeinMB)
# For computing the chunkshape for HDF5 VL types, we have to
# choose the itemsize of the *each* element of the atom and
# not the size of the entire atom. I don't know why this
# should be like this, perhaps I should report this to the
# HDF5 list.
# F. Alted 2006-11-23
#elemsize = self.atom.atomsize()
elemsize = self._basesize
# Set the chunkshape
chunkshape = chunksize // elemsize
# Safeguard against itemsizes being extremely large
if chunkshape == 0:
chunkshape = 1
return (SizeType(chunkshape),)
def _g_create(self):
"""Create a variable length array (ragged array)."""
atom = self.atom
self._v_version = obversion
# Check for zero dims in atom shape (not allowed in VLArrays)
zerodims = numpy.sum(numpy.array(atom.shape) == 0)
if zerodims > 0:
raise ValueError, \
"""When creating VLArrays, none of the dimensions of the Atom instance can
be zero."""
if not hasattr(atom, 'size'): # it is a pseudo-atom
self._atomicdtype = atom.base.dtype
self._atomicsize = atom.base.size
self._basesize = atom.base.itemsize
else:
self._atomicdtype = atom.dtype
self._atomicsize = atom.size
self._basesize = atom.itemsize
self._atomictype = atom.type
self._atomicshape = atom.shape
# Compute the optimal chunkshape, if needed
if self._v_chunkshape is None:
self._v_chunkshape = self._calc_chunkshape(
self._v_expectedsizeinMB)
self.nrows = SizeType(0) # No rows at creation time
# Correct the byteorder if needed
if self.byteorder is None:
self.byteorder = correct_byteorder(atom.type, sys.byteorder)
# After creating the vlarray, ``self._v_objectID`` needs to be
# set because it is needed for setting attributes afterwards.
self._v_objectID = self._createArray(self._v_new_title)
# Add an attribute in case we have a pseudo-atom so that we
# can retrieve the proper class after a re-opening operation.
if not hasattr(atom, 'size'): # it is a pseudo-atom
self.attrs.PSEUDOATOM = atom.kind
return self._v_objectID
def _g_open(self):
"""Get the metadata info for an array in file."""
self._v_objectID, self.nrows, self._v_chunkshape, atom = \
self._openArray()
# Check if the atom can be a PseudoAtom
if "PSEUDOATOM" in self.attrs:
kind = self.attrs.PSEUDOATOM
if kind == 'vlstring':
atom = VLStringAtom()
elif kind == 'vlunicode':
atom = VLUnicodeAtom()
elif kind == 'object':
atom = ObjectAtom()
else:
raise ValueError(
"pseudo-atom name ``%s`` not known." % kind)
elif self._v_file.format_version[:1] == "1":
flavor1x = self.attrs.FLAVOR
if flavor1x == "VLString":
atom = VLStringAtom()
elif flavor1x == "Object":
atom = ObjectAtom()
self.atom = atom
return self._v_objectID
def _getnobjects(self, nparr):
"Return the number of objects in a NumPy array."
# Check for zero dimensionality array
zerodims = numpy.sum(numpy.array(nparr.shape) == 0)
if zerodims > 0:
# No objects to be added
return 0
shape = nparr.shape
atom_shape = self.atom.shape
shapelen = len(nparr.shape)
if isinstance(atom_shape, tuple):
atomshapelen = len(self.atom.shape)
else:
atom_shape = (self.atom.shape,)
atomshapelen = 1
diflen = shapelen - atomshapelen
if shape == atom_shape:
nobjects = 1
elif (diflen == 1 and shape[diflen:] == atom_shape):
# Check if the leading dimensions are all ones
#if shape[:diflen-1] == (1,)*(diflen-1):
# nobjects = shape[diflen-1]
# shape = shape[diflen:]
# It's better to accept only inputs with the exact dimensionality
# i.e. a dimensionality only 1 element larger than atom
nobjects = shape[0]
shape = shape[1:]
elif atom_shape == (1,) and shapelen == 1:
# Case where shape = (N,) and shape_atom = 1 or (1,)
nobjects = shape[0]
else:
raise ValueError, \
"""The object '%s' is composed of elements with shape '%s', which is not compatible with the atom shape ('%s').""" % \
(nparr, shape, atom_shape)
return nobjects
def getEnum(self):
"""
Get the enumerated type associated with this array.
If this array is of an enumerated type, the corresponding `Enum`
instance is returned. If it is not of an enumerated type, a
``TypeError`` is raised.
"""
if self.atom.kind != 'enum':
raise TypeError("array ``%s`` is not of an enumerated type"
% self._v_pathname)
return self.atom.enum
def append(self, sequence):
"""
Add a `sequence` of data to the end of the dataset.
This method appends the objects in the `sequence` to a *single
row* in this array. The type and shape of individual objects
must be compliant with the atoms in the array. In the case of
serialized objects and variable length strings, the object or
string to append is itself the `sequence`.
"""
self._v_file._checkWritable()
# Prepare the sequence to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
sequence = atom.toarray(sequence)
statom = atom.base
else:
try: # fastest check in most cases
len(sequence)
except TypeError:
raise TypeError("argument is not a sequence")
statom = atom
if len(sequence) > 0:
# The sequence needs to be copied to make the operation safe
# to in-place conversion.
nparr = convertToNPAtom2(sequence, statom)
nobjects = self._getnobjects(nparr)
else:
nobjects = 0
nparr = None
self._append(nparr, nobjects)
self.nrows += 1
def iterrows(self, start=None, stop=None, step=None):
"""
Iterate over the rows of the array.
This method returns an iterator yielding an object of the
current flavor for each selected row in the array.
If a range is not supplied, *all the rows* in the array are
iterated upon --you can also use the `VLArray.__iter__()`
special method for that purpose. If you only want to iterate
over a given *range of rows* in the array, you may use the
`start`, `stop` and `step` parameters, which have the same
meaning as in `VLArray.read()`.
Example of use::
for row in vlarray.iterrows(step=4):
print '%s[%d]--> %s' % (vlarray.name, vlarray.nrow, row)
"""
(self._start, self._stop, self._step) = \
self._processRangeRead(start, stop, step)
self._initLoop()
return self
def __iter__(self):
"""
Iterate over the rows of the array.
This is equivalent to calling `VLArray.iterrows()` with default
arguments, i.e. it iterates over *all the rows* in the array.
Example of use::
result = [row for row in vlarray]
Which is equivalent to::
result = [row for row in vlarray.iterrows()]
"""
if not self._init:
# If the iterator is called directly, assign default variables
self._start = 0
self._stop = self.nrows
self._step = 1
# and initialize the loop
self._initLoop()
return self
def _initLoop(self):
"Initialization for the __iter__ iterator"
self._nrowsread = self._start
self._startb = self._start
self._row = -1 # Sentinel
self._init = True # Sentinel
self.nrow = SizeType(self._start - self._step) # row number
def next(self):
"""
Get the next element of the array during an iteration.
The element is returned as a list of objects of the current
flavor.
"""
if self._nrowsread >= self._stop:
self._init = False
raise StopIteration # end of iteration
else:
# Read a chunk of rows
if self._row+1 >= self.nrowsinbuf or self._row < 0:
self._stopb = self._startb+self._step*self.nrowsinbuf
self.listarr = self.read(self._startb, self._stopb, self._step)
self._row = -1
self._startb = self._stopb
self._row += 1
self.nrow += self._step
self._nrowsread += self._step
return self.listarr[self._row]
def __getitem__(self, key):
"""
Get a row or a range of rows from the array.
If `key` argument is an integer, the corresponding array row
is returned as an object of the current flavor. If `key` is a
slice, the range of rows determined by it is returned as a
list of objects of the current flavor.
Example of use::
a_row = vlarray[4]
a_list = vlarray[4:1000:2]
"""
if type(key) in (int,long) or isinstance(key, numpy.integer):
if key >= self.nrows:
raise IndexError, "Index out of range"
if key < 0:
# To support negative values
key += self.nrows
return self.read(key)[0]
elif isinstance(key, slice):
start, stop, step = self._processRange(
key.start, key.stop, key.step)
return self.read(start, stop, step)
else:
raise IndexError, "Non-valid index or slice: %s" % \
key
def __setitem__(self, keys, value):
"""
Set a row in the array.
It takes different actions depending on the type of the `key`
parameter: if it is an integer, the corresponding array row is
set to `value`. If `key` is a tuple, the first element refers
to the row to be modified, and the second element to the range
within the row to be updated with the `value` (so it can be an
integer or a slice).
The type and shape of the `value` must be compatible with the
type and shape determined by the `key`, otherwise, a
``TypeError`` or a ``ValueError`` will be raised.
.. Note:: When updating the rows of a `VLArray` object which
uses a pseudo-atom, there is a problem: you can only update
values with *exactly* the same size in bytes than the
original row. This is very difficult to meet with
``object`` pseudo-atoms, because ``cPickle`` applied on a
Python object does not guarantee to return the same number
of bytes than over another object, even if they are of the
same class. This effectively limits the kinds of objects
than can be updated in variable-length arrays.
Example of use::
vlarray[0] = vlarray[0] * 2 + 3
vlarray[99, 3:] = arange(96) * 2 + 3
# Negative values for start and stop (but not step) are supported.
vlarray[99, -99:-89:2] = vlarray[5] * 2 + 3
"""
self._v_file._checkWritable()
if not isinstance(keys, tuple):
keys = (keys, None)
if len(keys) > 2:
raise IndexError, "You cannot specify more than two dimensions"
nrow, rng = keys
# Process the first index
if not (type(nrow) in (int,long) or isinstance(nrow, numpy.integer)):
raise IndexError, "The first dimension only can be an integer"
if nrow >= self.nrows:
raise IndexError, "First index out of range"
if nrow < 0:
# To support negative values
nrow += self.nrows
# Process the second index
if type(rng) in (int,long) or isinstance(rng, numpy.integer):
start = rng; stop = start+1; step = 1
elif isinstance(rng, slice):
start, stop, step = rng.start, rng.stop, rng.step
elif rng is None:
start, stop, step = None, None, None
else:
raise IndexError, "Non-valid second index or slice: %s" % rng
object = value
# Prepare the object to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
object = atom.toarray(object)
statom = atom.base
else:
statom = atom
value = convertToNPAtom(object, statom)
nobjects = self._getnobjects(value)
# Get the previous value
nrow = idx2long(nrow) # To convert any possible numpy scalar value
nparr = self._readArray(nrow, nrow+1, 1)[0]
nobjects = len(nparr)
if len(value) > nobjects:
raise ValueError, \
"Length of value (%s) is larger than number of elements in row (%s)" % \
(len(value), nobjects)
# Assign the value to it
# The values can be numpy scalars. Convert them before building the slice.
if start is not None: start = idx2long(start)
if stop is not None: stop = idx2long(stop)
if step is not None: step = idx2long(step)
try:
nparr[slice(start, stop, step)] = value
except Exception, exc: #XXX
raise ValueError, \
"Value parameter:\n'%r'\ncannot be converted into an array object compliant vlarray[%s] row: \n'%r'\nThe error was: <%s>" % \
(value, keys, nparr[slice(start, stop, step)], exc)
if nparr.size > 0:
self._modify(nrow, nparr, nobjects)
# Accessor for the _readArray method in superclass
def read(self, start=None, stop=None, step=1):
"""
Get data in the array as a list of objects of the current
flavor.
Please note that, as the lengths of the different rows are
variable, the returned value is a *Python list* (not an array
of the current flavor), with as many entries as specified rows
in the range parameters.
The `start`, `stop` and `step` parameters can be used to
select only a *range of rows* in the array. Their meanings
are the same as in the built-in ``range()`` Python function,
except that negative values of `step` are not allowed yet.
Moreover, if only `start` is specified, then `stop` will be
set to ``start+1``. If you do not specify neither `start` nor
`stop`, then *all the rows* in the array are selected.
"""
start, stop, step = self._processRangeRead(start, stop, step)
if start == stop:
listarr = []
else:
listarr = self._readArray(start, stop, step)
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
outlistarr = [atom.fromarray(arr) for arr in listarr]
else:
# Convert the list to the right flavor
flavor = self.flavor
outlistarr = [internal_to_flavor(arr, flavor) for arr in listarr]
return outlistarr
def _g_copyWithStats(self, group, name, start, stop, step,
title, filters, chunkshape, _log, **kwargs):
"Private part of Leaf.copy() for each kind of leaf"
# Build the new VLArray object
object = VLArray(
group, name, self.atom, title=title, filters=filters,
expectedsizeinMB=self._v_expectedsizeinMB, chunkshape=chunkshape,
_log=_log)
# Now, fill the new vlarray with values from the old one
# This is not buffered because we cannot forsee the length
# of each record. So, the safest would be a copy row by row.
# In the future, some analysis can be done in order to buffer
# the copy process.
nrowsinbuf = 1
(start, stop, step) = self._processRangeRead(start, stop, step)
# Optimized version (no conversions, no type and shape checks, etc...)
nrowscopied = SizeType(0)
nbytes = 0
if not hasattr(self.atom, 'size'): # it is a pseudo-atom
atomsize = self.atom.base.size
else:
atomsize = self.atom.size
for start2 in lrange(start, stop, step*nrowsinbuf):
# Save the records on disk
stop2 = start2+step*nrowsinbuf
if stop2 > stop:
stop2 = stop
nparr = self._readArray(start=start2, stop=stop2, step=step)[0]
nobjects = nparr.shape[0]
object._append(nparr, nobjects)
nbytes += nobjects*atomsize
nrowscopied +=1
object.nrows = nrowscopied
return (object, nbytes)
def __repr__(self):
"""This provides more metainfo in addition to standard __str__"""
return """%s
atom = %r
byteorder = %r
nrows = %s
flavor = %r""" % (self, self.atom, self.byteorder, self.nrows,
self.flavor)
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