########################################################################
#
# License: BSD
# Created: October 10, 2002
# Author: Francesc Altet - faltet@carabos.com
#
# $Id: array.py 3334 2007-11-22 16:56:15Z faltet $
#
########################################################################
"""Here is defined the Array class.
See Array class docstring for more info.
Classes:
Array
ImageArray
Functions:
Misc variables:
__version__
"""
import types, warnings, sys
import numpy
from tables import hdf5Extension
from tables.utilsExtension import lrange
from tables.filters import Filters
from tables.flavor import flavor_of, array_as_internal, internal_to_flavor
from tables.utils import is_idx, convertToNPAtom2
from tables.atom import split_type
from tables.leaf import Leaf
__version__ = "$Revision: 3334 $"
# default version for ARRAY objects
#obversion = "1.0" # initial version
#obversion = "2.0" # Added an optional EXTDIM attribute
#obversion = "2.1" # Added support for complex datatypes
#obversion = "2.2" # This adds support for time datatypes.
obversion = "2.3" # This adds support for enumerated datatypes.
class Array(hdf5Extension.Array, Leaf):
"""
This class represents homogeneous datasets in an HDF5 file.
This class provides methods to write or read data to or from array
objects in the file. This class does not allow you to enlarge the
datasets on disk; use the `EArray` class if you want enlargeable
dataset support or compression features, or `CArray` if you just
want compression.
An interesting property of the `Array` class is that it remembers
the *flavor* of the object that has been saved so that if you
saved, for example, a ``list``, you will get a ``list`` during
readings afterwards; if you saved a NumPy array, you will get a
NumPy object, and so forth.
Note that this class inherits all the public attributes and
methods that `Leaf` already provides. However, as `Array`
instances have no internal I/O buffers, it is not necessary to use
the ``flush()`` method they inherit from `Leaf` in order to save
their internal state to disk. When a writing method call returns,
all the data is already on disk.
Public instance variables
-------------------------
atom
An `Atom` instance representing the *type* and *shape* of the
atomic objects to be saved.
rowsize
The size of the rows in dimensions orthogonal to ``maindim``.
nrow
On iterators, this is the index of the current row.
Public methods
--------------
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 an object of the current flavor.
Special methods
---------------
The following methods automatically trigger actions when an
`Array` instance is accessed in a special way
(e.g. ``array[2:3,...,::2]`` will be equivalent to a call to
``array.__getitem__((slice(2, 3, None), Ellipsis, slice(None,
None, 2)))``).
__getitem__(key)
Get a row, a range of rows or a slice from the array.
__iter__()
Iterate over the rows of the array.
__setitem__(key, value)
Set a row, a range of rows or a slice in the array.
"""
# Class identifier.
_c_classId = 'ARRAY'
# Properties
# ~~~~~~~~~~
def _getnrows(self):
if self.shape == ():
return 1 # scalar case
else:
return self.shape[self.maindim]
nrows = property(
_getnrows, None, None,
"The length of the main dimension of the array.")
def _getrowsize(self):
maindim = self.maindim
rowsize = self.atom.itemsize
for i, dim in enumerate(self.shape):
if i != maindim:
rowsize *= dim
return rowsize
rowsize = property(
_getrowsize, None, None,
"The size of the rows in dimensions orthogonal to maindim.")
# Other methods
# ~~~~~~~~~~~~~
def __init__(self, parentNode, name,
object=None, title="",
byteorder=None, _log=True):
"""
Create an `Array` instance.
`object`
The array or scalar to be saved. Accepted types are NumPy
arrays and scalars, ``numarray`` arrays and string arrays,
Numeric arrays and scalars, as well as native Python
sequences and scalars, provided that values are regular
(i.e. they are not like ``[[1,2],2]``) and homogeneous
(i.e. all the elements are of the same type).
`title`
A description for this node (it sets the ``TITLE`` HDF5
attribute on disk).
`byteorder`
The byteorder of the data *on disk*, specified as 'little'
or 'big'. If this is not specified, the byteorder is that
of the given `object`.
"""
self._v_version = None
"""The object version of this array."""
self._v_new = new = object is not None
"""Is this the first time the node has been created?"""
self._v_new_title = title
"""New title for this node."""
self._object = object
"""
The object to be stored in the array. It can be any of
``numpy``, ``numarray``, ``numeric``, list, tuple, string,
integer of floating point types, provided that they are
regular (i.e. they are not like ``[[1, 2], 2]``).
"""
self._v_convert = True
"""Whether the ``Array`` object must be converted or not."""
# 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 = None
"""
An `Atom` instance representing the *type* and *shape* of the
atomic objects to be saved.
"""
self.shape = None
"""The shape of the stored array."""
self.nrow = None
"""On iterators, this is the index of the current row."""
self.extdim = -1 # ordinary arrays are not enlargeable
"""The index of the enlargeable dimension."""
# Ordinary arrays have no filters: leaf is created with default ones.
super(Array, self).__init__(parentNode, name, new, Filters(),
byteorder, _log)
def _g_create(self):
"""Save a new array in file."""
self._v_version = obversion
try:
# `Leaf._g_postInitHook()` should be setting the flavor on disk.
self._flavor = flavor = flavor_of(self._object)
nparr = array_as_internal(self._object, flavor)
except: #XXX
# Problems converting data. Close the node and re-raise exception.
#print "Problems converting input object:", str(self._object)
self.close(flush=0)
raise
# Raise an error in case of unsupported object
if nparr.dtype.kind in ['V', 'U', 'O']: # in void, unicode, object
raise TypeError, \
"Array objects cannot currently deal with void, unicode or object arrays"
# Decrease the number of references to the object
self._object = None
# The shape of this array
self.shape = nparr.shape
# Fix the byteorder of data
nparr = self._g_fix_byteorder_data(nparr, nparr.dtype.byteorder)
# Create the array on-disk
try:
# ``self._v_objectID`` needs to be set because would be
# needed for setting attributes in some descendants later
# on
(self._v_objectID, self.atom) = self._createArray(
nparr, self._v_new_title)
except: #XXX
# Problems creating the Array on disk. Close node and re-raise.
self.close(flush=0)
raise
# Compute the optimal buffer size
chunkshape = self._calc_chunkshape(
self.nrows, self.rowsize, self.atom.itemsize)
self.nrowsinbuf = self._calc_nrowsinbuf(
chunkshape, self.rowsize, self.atom.itemsize)
# Arrays don't have chunkshapes (so, set it to None)
self._v_chunkshape = None
return self._v_objectID
def _g_open(self):
"""Get the metadata info for an array in file."""
(oid, self.atom, self.shape, self._v_chunkshape) = self._openArray()
# Compute the optimal buffer size
if not self._v_chunkshape: # non-chunked case
# Compute a sensible chunkshape
chunkshape = self._calc_chunkshape(
self.nrows, self.rowsize, self.atom.itemsize)
else:
chunkshape = self._v_chunkshape
self.nrowsinbuf = self._calc_nrowsinbuf(
chunkshape, self.rowsize, self.atom.itemsize)
return oid
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 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. The
returned rows are taken from the *main dimension*.
If a range is not supplied, *all the rows* in the array are
iterated upon --you can also use the `Array.__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 `Array.read()`.
Example of use::
result = [row for row in arrayInstance.iterrows(step=4)]
"""
try:
(self._start, self._stop, self._step) = \
self._processRangeRead(start, stop, step)
except IndexError:
# If problems with indexes, silently return the null tuple
return ()
self._initLoop()
return self
def __iter__(self):
"""
Iterate over the rows of the array.
This is equivalent to calling `Array.iterrows()` with default
arguments, i.e. it iterates over *all the rows* in the array.
Example of use::
result = [row[2] for row in array]
Which is equivalent to::
result = [row[2] for row in array.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 = self._start - self._step # row number
def next(self):
"""
Get the next element of the array during an iteration.
The element is returned as an object 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
# Protection for reading more elements than needed
if self._stopb > self._stop:
self._stopb = self._stop
listarr = self._read(self._startb, self._stopb, self._step)
# Swap the axes to easy the return of elements
if self.extdim > 0:
listarr = listarr.swapaxes(self.extdim, 0)
self.listarr = internal_to_flavor(listarr, self.flavor)
self._row = -1
self._startb = self._stopb
self._row += 1
self.nrow += self._step
self._nrowsread += self._step
# Fixes bug #968132
#if self.listarr.shape:
if self.shape:
return self.listarr[self._row]
else:
return self.listarr # Scalar case
def _interpret_indexing(self, keys):
"""Internal routine used by __getitem__ and __setitem__"""
maxlen = len(self.shape)
shape = (maxlen,)
startl = numpy.empty(shape=shape, dtype=numpy.int64)
stopl = numpy.empty(shape=shape, dtype=numpy.int64)
stepl = numpy.empty(shape=shape, dtype=numpy.int64)
stop_None = numpy.zeros(shape=shape, dtype=numpy.int64)
if not isinstance(keys, tuple):
keys = (keys,)
nkeys = len(keys)
dim = 0
# Here is some problem when dealing with [...,...] params
# but this is a bit weird way to pass parameters anyway
for key in keys:
ellipsis = 0 # Sentinel
if isinstance(key, types.EllipsisType):
ellipsis = 1
for diml in xrange(dim, len(self.shape) - (nkeys - dim) + 1):
startl[dim] = 0
stopl[dim] = self.shape[diml]
stepl[dim] = 1
dim += 1
elif dim >= maxlen:
raise IndexError, "Too many indices for object '%s'" % \
self._v_pathname
elif is_idx(key):
# Protection for index out of range
if key >= self.shape[dim]:
raise IndexError, "Index out of range"
if key < 0:
# To support negative values (Fixes bug #968149)
key += self.shape[dim]
start, stop, step = self._processRange(
key, key+1, 1, dim=dim )
stop_None[dim] = 1
elif isinstance(key, slice):
start, stop, step = self._processRange(
key.start, key.stop, key.step, dim=dim )
else:
raise TypeError, "Non-valid index or slice: %s" % \
key
if not ellipsis:
startl[dim] = start
stopl[dim] = stop
stepl[dim] = step
dim += 1
# Complete the other dimensions, if needed
if dim < len(self.shape):
for diml in xrange(dim, len(self.shape)):
startl[dim] = 0
stopl[dim] = self.shape[diml]
stepl[dim] = 1
dim += 1
# Compute the shape for the container properly. Fixes #1288792
shape = []
for dim in xrange(len(self.shape)):
# The negative division operates differently with python scalars
# and numpy scalars (which are similar to C conventions). See:
# http://www.python.org/doc/faq/programming.html#why-does-22-10-return-3
# and
# http://www.peterbe.com/Integer-division-in-programming-languages
# for more info on this issue.
# I've finally decided to rely on the len(xrange) function.
# F. Altet 2006-09-25
# Switch to `lrange` to allow long ranges (see #99).
#new_dim = ((stopl[dim] - startl[dim] - 1) / stepl[dim]) + 1
new_dim = lrange(startl[dim], stopl[dim], stepl[dim]).length
if not (new_dim == 1 and stop_None[dim]):
#if not stop_None[dim]:
# Append dimension
shape.append(new_dim)
return startl, stopl, stepl, shape
def __getitem__(self, key):
"""
Get a row, a range of rows or a slice from the array.
The set of tokens allowed for the `key` is the same as that
for extended slicing in Python (including the ``Ellipsis`` or
``...`` token). The result is an object of the current
flavor; its shape depends on the kind of slice used as `key`
and the shape of the array itself.
Example of use::
array1 = array[4] # array1.shape == array.shape[1:]
array2 = array[4:1000:2] # len(array2.shape) == len(array.shape)
array3 = array[::2, 1:4, :]
array4 = array[1, ..., ::2, 1:4, 4:] # general slice selection
"""
startl, stopl, stepl, shape = self._interpret_indexing(key)
arr = self._readSlice(startl, stopl, stepl, shape)
if not self._v_convert:
return arr
return internal_to_flavor(arr, self.flavor)
def _checkShape(self, nparr, slice_shape):
"Test that nparr shape is consistent with underlying object."
if nparr.shape != slice_shape:
# Create an array compliant with the specified shape
narr = numpy.empty(shape=slice_shape, dtype=self.atom.dtype)
# Assign the value to it
try:
narr[...] = nparr
except Exception, exc: #XXX
raise ValueError, \
"""value parameter '%s' cannot be converted into an array object
compliant with %s: '%r' The error was: <%s>""" % \
(nparr, self.__class__.__name__, self, exc)
return narr
return nparr
def __setitem__(self, key, value):
"""
Set a row, a range of rows or a slice 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` (the value is broadcast when needed). If the
`key` is a slice, the row slice determined by it is set to
`value` (as usual, if the slice to be updated exceeds the
actual shape of the array, only the values in the existing
range are updated).
If the `value` is a multidimensional object, then its shape
must be compatible with the shape determined by the `key`,
otherwise, a ``ValueError`` will be raised.
Example of use::
a1[0] = 333 # assign an integer to a Integer Array row
a2[0] = 'b' # assign a string to a string Array row
a3[1:4] = 5 # broadcast 5 to slice 1:4
a4[1:4:2] = 'xXx' # broadcast 'xXx' to slice 1:4:2
# General slice update (a5.shape = (4,3,2,8,5,10).
a5[1, ..., ::2, 1:4, 4:] = arange(1728, shape=(4,3,2,4,3,6))
"""
startl, stopl, stepl, shape = self._interpret_indexing(key)
countl = ((stopl - startl - 1) / stepl) + 1
# Create an array compliant with the specified slice
nparr = convertToNPAtom2(value, self.atom)
# Check whether it has a consistent shape with underlying object
nparr = self._checkShape(nparr, tuple(shape))
if nparr.size:
self._modify(startl, stepl, countl, nparr)
# Accessor for the _readArray method in superclass
def _readSlice(self, startl, stopl, stepl, shape):
# Create the container for the slice
arr = numpy.empty(dtype=self.atom.dtype, shape=shape)
# Protection against reading empty arrays
if 0 not in shape:
# Arrays that have non-zero dimensionality
self._g_readSlice(startl, stopl, stepl, arr)
# For zero-shaped arrays, return the scalar
if arr.shape == ():
arr = arr[()]
return arr
def _read(self, start, stop, step):
"""Read the array from disk without slice or flavor processing."""
#rowstoread = ((stop - start - 1) / step) + 1
rowstoread = lrange(start, stop, step).length
shape = list(self.shape)
if shape:
shape[self.maindim] = rowstoread
arr = numpy.empty(dtype=self.atom.dtype, shape=shape)
# Protection against reading empty arrays
if 0 not in shape:
# Arrays that have non-zero dimensionality
self._readArray(start, stop, step, arr)
return arr
def read(self, start=None, stop=None, step=None):
"""
Get data in the array as an object of the current flavor.
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)
arr = self._read(start, stop, step)
return internal_to_flavor(arr, self.flavor)
def _g_copyWithStats(self, group, name, start, stop, step,
title, filters, _log):
"Private part of Leaf.copy() for each kind of leaf"
# Get the slice of the array
# (non-buffered version)
if self.shape:
arr = self[start:stop:step]
else:
arr = self[()]
# Build the new Array object
object = Array(group, name, arr, title=title, _log=_log)
nbytes = self.atom.itemsize
for i in self.shape:
nbytes*=i
return (object, nbytes)
def __repr__(self):
"""This provides more metainfo in addition to standard __str__"""
return """%s
atom := %r
maindim := %r
flavor := %r
byteorder := %r
chunkshape := %r""" % (self, self.atom, self.maindim,
self.flavor, self.byteorder,
self.chunkshape)
class ImageArray(Array):
"""
Array containing an image.
This class has no additional behaviour or functionality compared
to that of an ordinary array. It simply enables the user to open
an ``IMAGE`` HDF5 node as a normal `Array` node in PyTables.
"""
# Class identifier.
_c_classId = 'IMAGE'