python-tables 2.3.1-2ubuntu3 / usr / share / pyshared / tables / array.py

########################################################################
#
#       License: BSD
#       Created: October 10, 2002
#       Author:  Francesc Alted - faltet@pytables.com
#
#       $Id$
#
########################################################################

"""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, SizeType, lazyattr
from tables.atom import split_type
from tables.leaf import Leaf


__version__ = "$Revision$"


# 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'


    # Lazy read-only attributes
    # `````````````````````````
    @lazyattr
    def dtype(self):
        """The NumPy ``dtype`` that most closely matches this array."""
        return self.atom.dtype

    # Properties
    # ~~~~~~~~~~
    def _getnrows(self):
        if self.shape == ():
            return SizeType(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.size
        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, _atom=None):
        """
        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 = _atom
        """
        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.
            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

        # 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.shape, self.atom) = self._createArray(
                nparr, self._v_new_title, self.atom)
        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.size)
        self.nrowsinbuf = self._calc_nrowsinbuf()
        # 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.size)
        else:
            chunkshape = self._v_chunkshape
        self.nrowsinbuf = self._calc_nrowsinbuf()

        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 = 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 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=SizeType)
        stopl = numpy.empty(shape=shape, dtype=SizeType)
        stepl = numpy.empty(shape=shape, dtype=SizeType)
        stop_None = numpy.zeros(shape=shape, dtype=SizeType)
        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. Alted 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]):
                shape.append(new_dim)

        return startl, stopl, stepl, shape


    def _fancySelection(self, args):
        """Performs a NumPy-style fancy selection in `self`.

        Implements advanced NumPy-style selection operations in
        addition to the standard slice-and-int behavior.

        Indexing arguments may be ints, slices or lists of indices.

        Note: This is a backport from the h5py project.
        """

        # Internal functions

        def validate_number(num, length):
            """ Validate a list member for the given axis length
            """
            try:
                num = long(num)
            except TypeError:
                raise TypeError("Illegal index: %r" % num)
            if num > length-1:
                raise IndexError("Index out of bounds: %d" % num)


        def expand_ellipsis(args, rank):
            """ Expand ellipsis objects and fill in missing axes.
            """
            n_el = sum(1 for arg in args if arg is Ellipsis)
            if n_el > 1:
                raise IndexError("Only one ellipsis may be used.")
            elif n_el == 0 and len(args) != rank:
                args = args + (Ellipsis,)

            final_args = []
            n_args = len(args)
            for idx, arg in enumerate(args):
                if arg is Ellipsis:
                    final_args.extend((slice(None),)*(rank-n_args+1))
                else:
                    final_args.append(arg)

            if len(final_args) > rank:
                raise IndexError("Too many indices.")

            return final_args


        def translate_slice(exp, length):
            """ Given a slice object, return a 3-tuple
                (start, count, step)
                for use with the hyperslab selection routines
            """
            start, stop, step = exp.start, exp.stop, exp.step
            if start is None:
                start = 0
            else:
                start = long(start)
            if stop is None:
                stop = length
            else:
                stop = long(stop)
            if step is None:
                step = 1
            else:
                step = long(step)

            if step < 1:
                raise IndexError("Step must be >= 1 (got %d)" % step)
            if stop == start:
                raise IndexError("Zero-length selections are not allowed")
            if stop < start:
                raise IndexError("Reverse-order selections are not allowed")
            if start < 0:
                start = length+start
            if stop < 0:
                stop = length+stop

            if not 0 <= start <= (length-1):
                raise IndexError(
                    "Start index %s out of range (0-%d)" % (start, length-1))
            if not 1 <= stop <= length:
                raise IndexError(
                    "Stop index %s out of range (1-%d)" % (stop, length))

            count = (stop-start)//step
            if (stop-start) % step != 0:
                count += 1

            if start+count > length:
                raise IndexError(
                    "Selection out of bounds (%d; axis has %d)" % \
                    (start+count,length))

            return start, count, step


        # Main code for _fancySelection
        mshape = []
        selection = []

        if not isinstance(args, tuple):
            args = (args,)

        args = expand_ellipsis(args, len(self.shape))

        list_seen = False
        reorder = None
        for idx, (exp, length) in enumerate(zip(args, self.shape)):
            if isinstance(exp, slice):
                start, count, step = translate_slice(exp, length)
                selection.append((start, count, step, idx, "AND"))
                mshape.append(count)
            else:
                try:
                    exp = list(exp)
                except TypeError:
                    exp = [exp]      # Handle scalar index as a list of length 1
                    mshape.append(0) # Keep track of scalar index for NumPy
                else:
                    mshape.append(len(exp))
                if len(exp) == 0:
                    raise IndexError(
                        "Empty selections are not allowed (axis %d)" % idx)
                elif len(exp) > 1:
                    if list_seen:
                        raise IndexError(
                            "Only one selection list is allowed")
                    else:
                        list_seen = True
                nexp = numpy.asarray(exp, dtype="i8")
                # Convert negative values
                nexp = numpy.where(nexp < 0, length+nexp, nexp)
                # Check whether the list is ordered or not
                # (only one unordered list is allowed)
                if not len(nexp) == len(numpy.unique(nexp)):
                    raise IndexError(
                        "Selection lists cannot have repeated values")
                neworder = nexp.argsort()
                if not numpy.alltrue(neworder == numpy.arange(len(exp))):
                    if reorder is not None:
                        raise IndexError(
                            "Only one selection list can be unordered")
                    corrected_idx = sum(1 for x in mshape if x != 0) - 1
                    reorder = (corrected_idx, neworder)
                    nexp = nexp[neworder]
                for select_idx in xrange(len(nexp)+1):
                    # This crazy piece of code performs a list selection
                    # using HDF5 hyperslabs.
                    # For each index, perform a "NOTB" selection on every
                    # portion of *this axis* which falls *outside* the list
                    # selection.  For this to work, the input array MUST be
                    # monotonically increasing.
                    if select_idx < len(nexp):
                        validate_number(nexp[select_idx], length)
                    if select_idx == 0:
                        start = 0
                        count = nexp[0]
                    elif select_idx == len(nexp):
                        start = nexp[-1]+1
                        count = length-start
                    else:
                        start = nexp[select_idx-1]+1
                        count = nexp[select_idx] - start
                    if count > 0:
                        selection.append((start, count, 1, idx, "NOTB"))

        mshape = tuple(x for x in mshape if x != 0)
        return selection, reorder, mshape


    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.

        Furthermore, NumPy-style fancy indexing, where a list of
        indices in a certain axis is specified, is also supported.
        Note that only one list per selection is supported right now.
        Finally, NumPy-style point and boolean selections are
        supported as well.

        Example of use::

            array1 = array[4]                       # simple selection
            array2 = array[4:1000:2]                # slice selection
            array3 = array[1, ..., ::2, 1:4, 4:]    # general slice selection
            array4 = array[1, [1,5,10], ..., -1]    # fancy selection
            array5 = array[np.where(array[:] > 4)]  # point selection
            array6 = array[array[:] > 4]            # boolean selection
        """
        try:
            # First, try with a regular selection
            startl, stopl, stepl, shape = self._interpret_indexing(key)
            arr = self._readSlice(startl, stopl, stepl, shape)
        except TypeError:
            # Then, try with a point-wise selection
            try:
                coords = self._pointSelection(key)
                arr = self._readCoords(coords)
            except TypeError:
                # Finally, try with a fancy selection
                selection, reorder, shape = self._fancySelection(key)
                arr = self._readSelection(selection, reorder, shape)

        if self.flavor == "numpy" or not self._v_convert:
            return arr

        return internal_to_flavor(arr, self.flavor)


    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 `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 `value` is a multidimensional object, then its shape must
        be compatible with the shape determined by `key`, otherwise, a
        ``ValueError`` will be raised.

        Furthermore, NumPy-style fancy indexing, where a list of
        indices in a certain axis is specified, is also supported.
        Note that only one list per selection is supported right now.
        Finally, NumPy-style point and boolean selections are
        supported as well.

        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:] = numpy.arange(1728, shape=(4,3,2,4,3,6))
            a6[1, [1,5,10], ..., -1] = arr    # fancy selection
            a7[np.where(a6[:] > 4)] = 4       # point selection + broadcast
            a8[arr > 4] = arr2                # boolean selection
        """

        # Create an array compliant with the specified slice
        nparr = convertToNPAtom2(value, self.atom)
        if nparr.size == 0:
            return

        try:
            startl, stopl, stepl, shape = self._interpret_indexing(key)
            self._writeSlice(startl, stopl, stepl, shape, nparr)
        except TypeError:
            # Then, try with a point-wise selection
            try:
                coords = self._pointSelection(key)
                self._writeCoords(coords, nparr)
            except TypeError:
                selection, reorder, shape = self._fancySelection(key)
                self._writeSelection(selection, reorder, shape, nparr)


    def _checkShape(self, nparr, slice_shape):
        """Test that nparr shape is consistent with underlying object.

        If not, try creating a new nparr object, using broadcasting if
        necessary.
        """
        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 _readSlice(self, startl, stopl, stepl, shape):
        """Read a slice based on `startl`, `stopl` and `stepl`."""
        nparr = 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, nparr)
        # For zero-shaped arrays, return the scalar
        if nparr.shape == ():
            nparr = nparr[()]
        return nparr


    def _readCoords(self, coords):
        """Read a set of points defined by `coords`."""
        nparr = numpy.empty(dtype=self.atom.dtype, shape=len(coords))
        if len(coords) > 0:
            self._g_readCoords(coords, nparr)
        # For zero-shaped arrays, return the scalar
        if nparr.shape == ():
            nparr = nparr[()]
        return nparr


    def _readSelection(self, selection, reorder, shape):
        """Read a `selection`.  Reorder if necessary."""
        # Create the container for the slice
        nparr = numpy.empty(dtype=self.atom.dtype, shape=shape)
        # Arrays that have non-zero dimensionality
        self._g_readSelection(selection, nparr)
        # For zero-shaped arrays, return the scalar
        if nparr.shape == ():
            nparr = nparr[()]
        elif reorder is not None:
            # We need to reorder the array
            idx, neworder = reorder
            k = [slice(None)]*len(shape)
            k[idx] = neworder.argsort()
            # Apparently, a copy is not needed here, but doing it
            # for symmetry with the `_writeSelection()` method.
            nparr = nparr[k].copy()
        return nparr


    def _writeSlice(self, startl, stopl, stepl, shape, nparr):
        """Write `nparr` in a slice based on `startl`, `stopl` and `stepl`."""
        nparr = self._checkShape(nparr, tuple(shape))
        countl = ((stopl - startl - 1) / stepl) + 1
        self._g_writeSlice(startl, stepl, countl, nparr)


    def _writeCoords(self, coords, nparr):
        """Write `nparr` values in points defined by `coords` coordinates."""
        if len(coords) > 0:
            nparr = self._checkShape(nparr, (len(coords),))
            self._g_writeCoords(coords, nparr)


    def _writeSelection(self, selection, reorder, shape, nparr):
        """Write `nparr` in `selection`.  Reorder if necessary."""
        nparr = self._checkShape(nparr, tuple(shape))
        # Check whether we should reorder the array
        if reorder is not None:
            idx, neworder = reorder
            k = [slice(None)]*len(shape)
            k[idx] = neworder
            # For a reason a don't understand well, we need a copy of
            # the reordered array
            nparr = nparr[k].copy()
        self._g_writeSelection(selection, nparr)


    def _read(self, start, stop, step):
        """Read the array from disk without slice or flavor processing."""

        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, chunkshape, _log, **kwargs):
        "Private part of Leaf.copy() for each kind of leaf"
        # Compute the correct indices.
        (start, stop, step) = self._processRangeRead(start, stop, step)
        # 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.  Use the _atom reserved keyword
        # just in case the array is being copied from a native HDF5
        # with atomic types different from scalars.
        # For details, see #275.
        object_ = Array(group, name, arr, title=title, _log=_log,
                        _atom=self.atom)
        nbytes = numpy.prod(self.shape, dtype=SizeType)*self.atom.size

        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'