Source code for tables.array

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#
# License: BSD
# Created: October 10, 2002
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
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"""Here is defined the Array class."""

import operator
import sys
import numpy

from . import hdf5extension
from .filters import Filters
from .flavor import flavor_of, array_as_internal, internal_to_flavor
from .leaf import Leaf
from .utils import (is_idx, convert_to_np_atom2, SizeType, lazyattr,
                    byteorders, quantize)



# 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.
obversion = "2.4"    # Numeric and numarray flavors are gone.


[docs]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 neither to enlarge nor compress the datasets on disk; use the EArray class (see :ref:`EArrayClassDescr`) if you want enlargeable dataset support or compression features, or CArray (see :ref:`CArrayClassDescr`) 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 (see :ref:`LeafClassDescr`) 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. 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. obj The array or scalar to be saved. Accepted types are NumPy 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). .. versionchanged:: 3.0 Renamed form *object* into *obj*. 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`. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 """ # 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 # ~~~~~~~~~~ @property def nrows(self): """The number of rows in the array.""" if self.shape == (): return SizeType(1) # scalar case else: return self.shape[self.maindim] @property def rowsize(self): """The size of the rows in bytes in dimensions orthogonal to *maindim*.""" maindim = self.maindim rowsize = self.atom.size for i, dim in enumerate(self.shape): if i != maindim: rowsize *= dim return rowsize @property def size_in_memory(self): """The size of this array's data in bytes when it is fully loaded into memory.""" return self.nrows * self.rowsize # Other methods # ~~~~~~~~~~~~~ def __init__(self, parentnode, name, obj=None, title="", byteorder=None, _log=True, _atom=None, track_times=True): self._v_version = None """The object version of this array.""" self._v_new = new = obj is not None """Is this the first time the node has been created?""" self._v_new_title = title """New title for this node.""" self._obj = obj """The object to be stored in the array. It can be any of numpy, list, tuple, string, integer of floating point types, provided that they are regular (i.e. they are not like ``[[1, 2], 2]``). .. versionchanged:: 3.0 Renamed form *_object* into *_obj*. """ 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 (see :ref:`AtomClassDescr`) 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().__init__(parentnode, name, new, Filters(), byteorder, _log, track_times) def _g_create(self): """Save a new array in file.""" self._v_version = obversion try: # `Leaf._g_post_init_hook()` should be setting the flavor on disk. self._flavor = flavor = flavor_of(self._obj) nparr = array_as_internal(self._obj, 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._obj = 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._create_array( 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 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._open_array() self.nrowsinbuf = self._calc_nrowsinbuf() return oid
[docs] def get_enum(self): """Get the enumerated type associated with this array. If this array is of an enumerated type, the corresponding Enum instance (see :ref:`EnumClassDescr`) 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
[docs] 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 :meth:`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. Examples -------- :: result = [row for row in arrayInstance.iterrows(step=4)] .. versionchanged:: 3.0 If the *start* parameter is provided and *stop* is None then the array is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned. """ try: (self._start, self._stop, self._step) = self._process_range( start, stop, step) except IndexError: # If problems with indexes, silently return the null tuple return () self._init_loop() return self
[docs] def __iter__(self): """Iterate over the rows of the array. This is equivalent to calling :meth:`Array.iterrows` with default arguments, i.e. it iterates over *all the rows* in the array. Examples -------- :: 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._init_loop() return self
def _init_loop(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
[docs] def __next__(self): """Get the next element of the array during an iteration. The element is returned as an object of the current flavor. """ # this could probably be sped up for long iterations by reusing the # listarr buffer if self._nrowsread >= self._stop: self._init = False self.listarr = None # fixes issue #308 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, type(Ellipsis)): ellipsis = 1 for diml in range(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): key = operator.index(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._process_range( key, key + 1, 1, dim=dim) stop_None[dim] = 1 elif isinstance(key, slice): start, stop, step = self._process_range( 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 range(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 range(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). # use xrange, since it supports large integers as of Python 2.6 # see github #181 new_dim = len(range(startl[dim], stopl[dim], stepl[dim])) if not (new_dim == 1 and stop_None[dim]): shape.append(new_dim) return startl, stopl, stepl, shape def _fancy_selection(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 = int(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) This is for for use with the hyperslab selection routines. """ start, stop, step = exp.start, exp.stop, exp.step if start is None: start = 0 else: start = int(start) if stop is None: stop = length else: stop = int(stop) if step is None: step = 1 else: step = int(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 _fancy_selection 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 else: if (not isinstance(exp[0], (int, numpy.integer)) or (isinstance(exp[0], numpy.ndarray) and not numpy.issubdtype(exp[0].dtype, numpy.integer))): raise TypeError("Only integer coordinates allowed.") 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 (neworder.shape != (len(exp),) or numpy.sum( numpy.abs( neworder - numpy.arange(len(exp)))) != 0): 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 range(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
[docs] 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. Examples -------- :: 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 """ self._g_check_open() try: # First, try with a regular selection startl, stopl, stepl, shape = self._interpret_indexing(key) arr = self._read_slice(startl, stopl, stepl, shape) except TypeError: # Then, try with a point-wise selection try: coords = self._point_selection(key) arr = self._read_coords(coords) except TypeError: # Finally, try with a fancy selection selection, reorder, shape = self._fancy_selection(key) arr = self._read_selection(selection, reorder, shape) if self.flavor == "numpy" or not self._v_convert: return arr return internal_to_flavor(arr, self.flavor)
[docs] 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. Examples -------- :: 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 """ self._g_check_open() # Create an array compliant with the specified slice nparr = convert_to_np_atom2(value, self.atom) if nparr.size == 0: return # truncate data if least_significant_digit filter is set # TODO: add the least_significant_digit attribute to the array on disk if (self.filters.least_significant_digit is not None and not numpy.issubdtype(nparr.dtype, numpy.signedinteger)): nparr = quantize(nparr, self.filters.least_significant_digit) try: startl, stopl, stepl, shape = self._interpret_indexing(key) self._write_slice(startl, stopl, stepl, shape, nparr) except TypeError: # Then, try with a point-wise selection try: coords = self._point_selection(key) self._write_coords(coords, nparr) except TypeError: selection, reorder, shape = self._fancy_selection(key) self._write_selection(selection, reorder, shape, nparr)
def _check_shape(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 + self.atom.dtype.shape): # Create an array compliant with the specified shape narr = numpy.empty(shape=slice_shape, dtype=self.atom.dtype) # Assign the value to it. It will raise a ValueError exception # if the objects cannot be broadcast to a single shape. narr[...] = nparr return narr else: return nparr def _read_slice(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_read_slice(startl, stopl, stepl, nparr) # For zero-shaped arrays, return the scalar if nparr.shape == (): nparr = nparr[()] return nparr def _read_coords(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_read_coords(coords, nparr) # For zero-shaped arrays, return the scalar if nparr.shape == (): nparr = nparr[()] return nparr def _read_selection(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_read_selection(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 `_write_selection()` method. nparr = nparr[tuple(k)].copy() return nparr def _write_slice(self, startl, stopl, stepl, shape, nparr): """Write `nparr` in a slice based on `startl`, `stopl` and `stepl`.""" nparr = self._check_shape(nparr, tuple(shape)) countl = ((stopl - startl - 1) // stepl) + 1 self._g_write_slice(startl, stepl, countl, nparr) def _write_coords(self, coords, nparr): """Write `nparr` values in points defined by `coords` coordinates.""" if len(coords) > 0: nparr = self._check_shape(nparr, (len(coords),)) self._g_write_coords(coords, nparr) def _write_selection(self, selection, reorder, shape, nparr): """Write `nparr` in `selection`. Reorder if necessary. """ nparr = self._check_shape(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[tuple(k)].copy() self._g_write_selection(selection, nparr) def _read(self, start, stop, step, out=None): """Read the array from disk without slice or flavor processing.""" nrowstoread = len(range(start, stop, step)) shape = list(self.shape) if shape: shape[self.maindim] = nrowstoread if out is None: arr = numpy.empty(dtype=self.atom.dtype, shape=shape) else: bytes_required = self.rowsize * nrowstoread # if buffer is too small, it will segfault if bytes_required != out.nbytes: raise ValueError(('output array size invalid, got {} bytes, ' 'need {} bytes').format(out.nbytes, bytes_required)) if not out.flags['C_CONTIGUOUS']: raise ValueError('output array not C contiguous') arr = out # Protection against reading empty arrays if 0 not in shape: # Arrays that have non-zero dimensionality self._read_array(start, stop, step, arr) # data is always read in the system byteorder # if the out array's byteorder is different, do a byteswap if (out is not None and byteorders[arr.dtype.byteorder] != sys.byteorder): arr.byteswap(True) return arr
[docs] def read(self, start=None, stop=None, step=None, out=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. The out parameter may be used to specify a NumPy array to receive the output data. Note that the array must have the same size as the data selected with the other parameters. Note that the array's datatype is not checked and no type casting is performed, so if it does not match the datatype on disk, the output will not be correct. Also, this parameter is only valid when the array's flavor is set to 'numpy'. Otherwise, a TypeError will be raised. When data is read from disk in NumPy format, the output will be in the current system's byteorder, regardless of how it is stored on disk. The exception is when an output buffer is supplied, in which case the output will be in the byteorder of that output buffer. .. versionchanged:: 3.0 Added the *out* parameter. """ self._g_check_open() if out is not None and self.flavor != 'numpy': msg = ("Optional 'out' argument may only be supplied if array " "flavor is 'numpy', currently is {}").format(self.flavor) raise TypeError(msg) (start, stop, step) = self._process_range_read(start, stop, step) arr = self._read(start, stop, step, out) return internal_to_flavor(arr, self.flavor)
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.""" # Compute the correct indices. (start, stop, step) = self._process_range_read(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 of trac. 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 """{} atom := {!r} maindim := {!r} flavor := {!r} byteorder := {!r} chunkshape := {!r}""".format(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'