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# Source code for astropy.io.fits.fitsrec

    # Licensed under a 3-clause BSD style license - see PYFITS.rst

    import copy
    import operator
    import warnings
    import weakref

    from functools import reduce

    import numpy as np

    from numpy import char as chararray

    from .column import (ASCIITNULL, FITS2NUMPY, ASCII2NUMPY, ASCII2STR, ColDefs,
                         _AsciiColDefs, _FormatX, _FormatP, _VLF, _get_index,
                         _wrapx, _unwrapx, _makep, Delayed)
    from .util import decode_ascii, encode_ascii
    from ...extern.six import string_types
    from ...extern.six.moves import xrange
    from ...utils import lazyproperty
    from ...utils.compat import ignored
    from ...utils.exceptions import AstropyDeprecationWarning, AstropyUserWarning

    class FITS_record(object):
        """
        FITS record class.

        `FITS_record` is used to access records of the `FITS_rec` object.
        This will allow us to deal with scaled columns.  It also handles
        conversion/scaling of columns in ASCII tables.  The `FITS_record`
        class expects a `FITS_rec` object as input.
        """

        def __init__(self, input, row=0, start=None, end=None, step=None,
                     base=None, **kwargs):
            """
            Parameters
            ----------
            input : array
               The array to wrap.

            row : int, optional
               The starting logical row of the array.

            start : int, optional
               The starting column in the row associated with this object.
               Used for subsetting the columns of the `FITS_rec` object.

            end : int, optional
               The ending column in the row associated with this object.
               Used for subsetting the columns of the `FITS_rec` object.
            """

            # For backward compatibility...
            for arg in [('startColumn', 'start'), ('endColumn', 'end')]:
                if arg[0] in kwargs:
                    warnings.warn('The %s argument to FITS_record is deprecated; '
                                  'use %s instead' % arg, AstropyDeprecationWarning)
                    if arg[0] == 'startColumn':
                        start = kwargs[arg[0]]
                    elif arg[0] == 'endColumn':
                        end = kwargs[arg[0]]

            self.array = input
            self.row = row
            if base:
                width = len(base)
            else:
                width = self.array._nfields

            s = slice(start, end, step).indices(width)
            self.start, self.end, self.step = s
            self.base = base

        def __getitem__(self, key):
            if isinstance(key, string_types):
                indx = _get_index(self.array.names, key)

                if indx < self.start or indx > self.end - 1:
                    raise KeyError("Key '%s' does not exist." % key)
            elif isinstance(key, slice):
                return type(self)(self.array, self.row, key.start, key.stop,
                                  key.step, self)
            else:
                indx = self._get_index(key)

                if indx > self.array._nfields - 1:
                    raise IndexError('Index out of bounds')

            return self.array.field(indx)[self.row]

        def __setitem__(self, key, value):
            if isinstance(key, string_types):
                indx = _get_index(self.array._coldefs.names, key)

                if indx < self.start or indx > self.end - 1:
                    raise KeyError("Key '%s' does not exist." % key)
            elif isinstance(key, slice):
                for indx in xrange(slice.start, slice.stop, slice.step):
                    indx = self._get_indx(indx)
                    self.array.field(indx)[self.row] = value
            else:
                indx = self._get_index(key)
                if indx > self.array._nfields - 1:
                    raise IndexError('Index out of bounds')

            self.array.field(indx)[self.row] = value

        def __getslice__(self, start, end):
            return self[slice(start, end)]

        def __len__(self):
            return len(xrange(self.start, self.end, self.step))

        def __repr__(self):
            """
            Display a single row.
            """

            outlist = []
            for idx in xrange(len(self)):
                outlist.append(repr(self[idx]))
            return '(%s)' % ', '.join(outlist)

        def field(self, field):
            """
            Get the field data of the record.
            """

            return self.__getitem__(field)

        def setfield(self, field, value):
            """
            Set the field data of the record.
            """

            self.__setitem__(field, value)

        @lazyproperty
        def _bases(self):
            bases = [weakref.proxy(self)]
            base = self.base
            while base:
                bases.append(base)
                base = base.base
            return bases

        def _get_index(self, indx):
            indices = np.ogrid[:self.array._nfields]
            for base in reversed(self._bases):
                if base.step < 1:
                    s = slice(base.start, None, base.step)
                else:
                    s = slice(base.start, base.end, base.step)
                indices = indices[s]
            return indices[indx]

    class FITS_rec(np.recarray):
        """
        FITS record array class.

        `FITS_rec` is the data part of a table HDU's data part.  This is a layer
        over the `~numpy.recarray`, so we can deal with scaled columns.

        It inherits all of the standard methods from `numpy.ndarray`.
        """

        _record_type = FITS_record

        def __new__(subtype, input):
            """
            Construct a FITS record array from a recarray.
            """

            # input should be a record array
            if input.dtype.subdtype is None:
                self = np.recarray.__new__(subtype, input.shape, input.dtype,
                                           buf=input.data)
            else:
                self = np.recarray.__new__(subtype, input.shape, input.dtype,
                                           buf=input.data, strides=input.strides)

            self._nfields = len(self.dtype.names)
            self._converted = {}
            self._heapoffset = 0
            self._heapsize = 0
            self._coldefs = None
            self._gap = 0
            self._uint = False
            return self

        def __setstate__(self, state):
            meta = state[-1]
            column_state = state[-2]
            state = state[:-2]

            super(FITS_rec, self).__setstate__(state)

            for attr, value in zip(meta, column_state):
                setattr(self, attr, value)

        def __reduce__(self):
            """
            Return a 3-tuple for pickling a FITS_rec. Use the super-class
            functionality but then add in a tuple of FITS_rec-specific
            values that get used in __setstate__.
            """

            reconst_func, reconst_func_args, state = \
                super(FITS_rec, self).__reduce__()

            # Define FITS_rec-specific attrs that get added to state
            column_state = []
            meta = []

            for attrs in ['_converted', '_heapoffset', '_heapsize', '_nfields',
                          '_gap', '_uint', 'parnames', '_coldefs']:

                with ignored(AttributeError):
                    # _coldefs can be Delayed, and file objects cannot be
                    # picked, it needs to be deepcopied first
                    if attrs == '_coldefs':
                        column_state.append(self._coldefs.__deepcopy__(None))
                    else:
                        column_state.append(getattr(self, attrs))
                    meta.append(attrs)

            state = state + (column_state, meta)

            return reconst_func, reconst_func_args, state

        def __array_finalize__(self, obj):
            if obj is None:
                return

            if isinstance(obj, FITS_rec):
                self._converted = obj._converted
                self._heapoffset = obj._heapoffset
                self._heapsize = obj._heapsize
                self._coldefs = obj._coldefs
                self._nfields = obj._nfields
                self._gap = obj._gap
                self._uint = obj._uint
            else:
                # This will allow regular ndarrays with fields, rather than
                # just other FITS_rec objects
                self._nfields = len(obj.dtype.names)
                self._converted = {}

                self._heapoffset = getattr(obj, '_heapoffset', 0)
                self._heapsize = getattr(obj, '_heapsize', 0)

                self._coldefs = None
                self._gap = getattr(obj, '_gap', 0)
                self._uint = getattr(obj, '_uint', False)

                attrs = ['_converted', '_coldefs', '_gap']
                for attr in attrs:
                    if hasattr(obj, attr):
                        value = getattr(obj, attr, None)
                        if value is None:
                            warnings.warn('Setting attribute %s as None' % attr,
                                          AstropyUserWarning)
                        setattr(self, attr, value)

                if self._coldefs is None:
                    self._coldefs = ColDefs(self)

        @classmethod
        def from_columns(cls, columns, nrows=0, fill=False):
            """
            Given a `ColDefs` object of unknown origin, initialize a new `FITS_rec`
            object.

            .. note::

                This was originally part of the `new_table` function in the table
                module but was moved into a class method since most of its
                functionality always had more to do with initializing a `FITS_rec`
                object than anything else, and much of it also overlapped with
                ``FITS_rec._scale_back``.

            Parameters
            ----------
            columns : sequence of `Column` or a `ColDefs`
                The columns from which to create the table data.  If these
                columns have data arrays attached that data may be used in
                initializing the new table.  Otherwise the input columns
                will be used as a template for a new table with the requested
                number of rows.

            nrows : int
                Number of rows in the new table.  If the input columns have data
                associated with them, the size of the largest input column is used.
                Otherwise the default is 0.

            fill : bool
                If `True`, will fill all cells with zeros or blanks.  If
                `False`, copy the data from input, undefined cells will still
                be filled with zeros/blanks.
            """

            if not isinstance(columns, ColDefs):
                columns = ColDefs(columns)

            # read the delayed data
            for column in columns:
                arr = column.array
                if isinstance(arr, Delayed):
                    if arr.hdu.data is None:
                        column.array = None
                    else:
                        column.array = _get_recarray_field(arr.hdu.data,
                                                           arr.field)
            # Reset columns._arrays (which we may want to just do away with
            # altogether
            del columns._arrays

            # use the largest column shape as the shape of the record
            if nrows == 0:
                for arr in columns._arrays:
                    if arr is not None:
                        dim = arr.shape[0]
                    else:
                        dim = 0
                    if dim > nrows:
                        nrows = dim

            raw_data = np.empty(columns.dtype.itemsize * nrows, dtype=np.uint8)
            raw_data.fill(ord(columns._padding_byte))
            data = np.recarray(nrows, dtype=columns.dtype, buf=raw_data).view(cls)

            # Make sure the data is a listener for changes to the columns
            columns._add_listener(data)

            # Previously this assignment was made from hdu.columns, but that's a
            # bug since if a _TableBaseHDU has a FITS_rec in its .data attribute
            # the _TableBaseHDU.columns property is actually returned from
            # .data._coldefs, so this assignment was circular!  Don't make that
            # mistake again.
            # All of this is an artifact of the fragility of the FITS_rec class,
            # and that it can't just be initialized by columns...
            data._coldefs = columns

            # If fill is True we don't copy anything from the column arrays.  We're
            # just using them as a template, and returning a table filled with
            # zeros/blanks
            if fill:
                return data

            # Otherwise we have to fill the recarray with data from the input
            # columns
            for idx, column in enumerate(columns):
                # For each column in the ColDef object, determine the number of
                # rows in that column.  This will be either the number of rows in
                # the ndarray associated with the column, or the number of rows
                # given in the call to this function, which ever is smaller.  If
                # the input FILL argument is true, the number of rows is set to
                # zero so that no data is copied from the original input data.
                arr = column.array

                if arr is None:
                    array_size = 0
                else:
                    array_size = len(arr)

                n = min(array_size, nrows)

                # TODO: At least *some* of this logic is mostly redundant with the
                # _convert_foo methods in this class; see if we can eliminate some
                # of that duplication.

                if not n:
                    # The input column had an empty array, so just use the fill
                    # value
                    continue

                field = _get_recarray_field(data, idx)
                name = column.name
                fitsformat = column.format
                recformat = fitsformat.recformat

                outarr = field[:n]
                inarr = arr[:n]

                if isinstance(recformat, _FormatX):
                    # Data is a bit array
                    if inarr.shape[-1] == recformat.repeat:
                        _wrapx(inarr, outarr, recformat.repeat)
                        continue
                elif isinstance(recformat, _FormatP):
                    data._converted[name] = _makep(inarr, field, recformat,
                                                   nrows=nrows)
                    continue
                # TODO: Find a better way of determining that the column is meant
                # to be FITS L formatted
                elif recformat[-2:] == FITS2NUMPY['L'] and inarr.dtype == bool:
                    # column is boolean
                    # The raw data field should be filled with either 'T' or 'F'
                    # (not 0).  Use 'F' as a default
                    field[:] = ord('F')
                    # Also save the original boolean array in data._converted so
                    # that it doesn't have to be re-converted
                    data._converted[name] = np.zeros(field.shape, dtype=bool)
                    data._converted[name][:n] = inarr
                    # TODO: Maybe this step isn't necessary at all if _scale_back
                    # will handle it?
                    inarr = np.where(inarr == False, ord('F'), ord('T'))
                elif (columns[idx]._physical_values and
                        columns[idx]._pseudo_unsigned_ints):
                    # Temporary hack...
                    bzero = column.bzero
                    data._converted[name] = np.zeros(field.shape,
                                                     dtype=inarr.dtype)
                    data._converted[name][:n] = inarr
                    if n < nrows:
                        # Pre-scale rows below the input data
                        field[n:] = -bzero

                    inarr = inarr - bzero
                elif isinstance(columns, _AsciiColDefs):
                    # Regardless whether the format is character or numeric, if the
                    # input array contains characters then it's already in the raw
                    # format for ASCII tables
                    if fitsformat._pseudo_logical:
                        # Hack to support converting from 8-bit T/F characters
                        # Normally the column array is a chararray of 1 character
                        # strings, but we need to view it as a normal ndarray of
                        # 8-bit ints to fill it with ASCII codes for 'T' and 'F'
                        outarr = field.view(np.uint8, np.ndarray)[:n]
                    elif arr.dtype.kind not in ('S', 'U'):
                        # Set up views of numeric columns with the appropriate
                        # numeric dtype
                        # Fill with the appropriate blanks for the column format
                        data._converted[name] = np.zeros(nrows, dtype=arr.dtype)
                        outarr = data._converted[name][:n]

                    outarr[:] = inarr
                    continue

                if inarr.shape != outarr.shape:
                    if (inarr.dtype.kind == outarr.dtype.kind and
                            inarr.dtype.kind in ('U', 'S') and
                            inarr.dtype != outarr.dtype):

                        inarr_rowsize = inarr[0].size
                        inarr = inarr.flatten().view(outarr.dtype)

                    # This is a special case to handle input arrays with
                    # non-trivial TDIMn.
                    # By design each row of the outarray is 1-D, while each row of
                    # the input array may be n-D
                    if outarr.ndim > 1:
                        # The normal case where the first dimension is the rows
                        inarr_rowsize = inarr[0].size
                        inarr = inarr.reshape((n, inarr_rowsize))
                        outarr[:, :inarr_rowsize] = inarr
                    else:
                        # Special case for strings where the out array only has one
                        # dimension (the second dimension is rolled up into the
                        # strings
                        outarr[:n] = inarr.ravel()
                else:
                    outarr[:] = inarr

            # Now replace the original column array references with the new
            # fields
            # This is required to prevent the issue reported in
            # https://github.com/spacetelescope/PyFITS/issues/99
            for idx in range(len(columns)):
                columns._arrays[idx] = data.field(idx)

            return data

        def __repr__(self):
            # Force use of the normal ndarray repr (rather than the new
            # one added for recarray in Numpy 1.10) for backwards compat
            return np.ndarray.__repr__(self)

        def __getitem__(self, key):
            if isinstance(key, string_types):
                return self.field(key)
            elif isinstance(key, (slice, np.ndarray, tuple, list)):
                # Have to view as a recarray then back as a FITS_rec, otherwise the
                # circular reference fix/hack in FITS_rec.field() won't preserve
                # the slice
                subtype = type(self)
                out = self.view(np.recarray).__getitem__(key).view(subtype)
                out._coldefs = ColDefs(self._coldefs)
                arrays = []
                out._converted = {}
                for idx, name in enumerate(self._coldefs.names):
                    #
                    # Store the new arrays for the _coldefs object
                    #
                    arrays.append(self._coldefs._arrays[idx][key])

                    # touch all fields to expand the original ._converted dict
                    # so the sliced FITS_rec will view the same scaled columns as
                    # the original
                    dummy = self.field(idx)
                    if name in self._converted:
                        out._converted[name] = \
                            np.ndarray.__getitem__(self._converted[name], key)
                del dummy

                out._coldefs._arrays = arrays
                return out

            # if not a slice, do this because Record has no __getstate__.
            # also more efficient.
            else:
                if isinstance(key, int) and key >= len(self):
                    raise IndexError("Index out of bounds")

                newrecord = self._record_type(self, key)
                return newrecord

        def __setitem__(self, key, value):
            if isinstance(key, string_types):
                self[key][:] = value
                return

            if isinstance(key, slice):
                end = min(len(self), key.stop or len(self))
                end = max(0, end)
                start = max(0, key.start or 0)
                end = min(end, start + len(value))

                for idx in xrange(start, end):
                    self.__setitem__(idx, value[idx - start])
                return

            if isinstance(value, FITS_record):
                for idx in range(self._nfields):
                    self.field(self.names[idx])[key] = value.field(self.names[idx])
            elif isinstance(value, (tuple, list, np.void)):
                if self._nfields == len(value):
                    for idx in range(self._nfields):
                        self.field(idx)[key] = value[idx]
                else:
                    raise ValueError('Input tuple or list required to have %s '
                                     'elements.' % self._nfields)
            else:
                raise TypeError('Assignment requires a FITS_record, tuple, or '
                                'list as input.')

        def __getslice__(self, start, end):
            return self[slice(start, end)]

        def __setslice__(self, start, end, value):
            self[slice(start, end)] = value

        def copy(self, order='C'):
            """
            The Numpy documentation lies; `numpy.ndarray.copy` is not equivalent to
            `numpy.copy`.  Differences include that it re-views the copied array as
            self's ndarray subclass, as though it were taking a slice; this means
            ``__array_finalize__`` is called and the copy shares all the array
            attributes (including ``._converted``!).  So we need to make a deep
            copy of all those attributes so that the two arrays truly do not share
            any data.
            """

            new = super(FITS_rec, self).copy(order=order)

            new.__dict__ = copy.deepcopy(self.__dict__)
            return new

        @property
        def columns(self):
            """
            A user-visible accessor for the coldefs.

            See https://aeon.stsci.edu/ssb/trac/pyfits/ticket/44
            """

            return self._coldefs

        @property
        def names(self):
            """List of column names."""

            if hasattr(self, '_coldefs') and self._coldefs is not None:
                return self._coldefs.names
            else:
                return list(self.dtype.names)

        @property
        def formats(self):
            """List of column FITS foramts."""

            return self._coldefs.formats

        @property
        def _raw_itemsize(self):
            """
            Returns the size of row items that would be written to the raw FITS
            file, taking into account the possibility of unicode columns being
            compactified.

            Currently for internal use only.
            """

            if _has_unicode_fields(self):
                total_itemsize = 0
                for field in self.dtype.fields.values():
                    itemsize = field[0].itemsize
                    if field[0].kind == 'U':
                        itemsize = itemsize // 4
                    total_itemsize += itemsize
                return total_itemsize
            else:
                # Just return the normal itemsize
                return self.itemsize

        def field(self, key):
            """
            A view of a `Column`'s data as an array.
            """

            # NOTE: The *column* index may not be the same as the field index in
            # the recarray, if the column is a phantom column
            column = self.columns[key]
            name = column.name
            format = column.format

            if format.dtype.itemsize == 0:
                warnings.warn(
                    'Field %r has a repeat count of 0 in its format code, '
                    'indicating an empty field.' % key)
                return np.array([], dtype=format.dtype)

            # If field's base is a FITS_rec, we can run into trouble because it
            # contains a reference to the ._coldefs object of the original data;
            # this can lead to a circular reference; see ticket #49
            base = self
            while (isinstance(base, FITS_rec) and
                    isinstance(base.base, np.recarray)):
                base = base.base
            # base could still be a FITS_rec in some cases, so take care to
            # use rec.recarray.field to avoid a potential infinite
            # recursion
            field = _get_recarray_field(base, name)

            if name not in self._converted:
                recformat = format.recformat
                # TODO: If we're now passing the column to these subroutines, do we
                # really need to pass them the recformat?
                if isinstance(recformat, _FormatP):
                    # for P format
                    converted = self._convert_p(column, field, recformat)
                else:
                    # Handle all other column data types which are fixed-width
                    # fields
                    converted = self._convert_other(column, field, recformat)

                self._converted[name] = converted
                return converted

            return self._converted[name]

        def _update_column_attribute_changed(self, column, idx, attr, old_value,
                                             new_value):
            """
            Update how the data is formatted depending on changes to column
            attributes initiated by the user through the `Column` interface.

            Dispatches column attribute change notifications to individual methods
            for each attribute ``_update_column_<attr>``
            """

            method_name = '_update_column_{0}'.format(attr)
            if hasattr(self, method_name):
                # Right now this is so we can be lazy and not implement updaters
                # for every attribute yet--some we may not need at all, TBD
                getattr(self, method_name)(column, idx, old_value, new_value)

        def _update_column_name(self, column, idx, old_name, name):
            """Update the dtype field names when a column name is changed."""

            dtype = self.dtype
            # Updating the names on the dtype should suffice
            dtype.names = dtype.names[:idx] + (name,) + dtype.names[idx + 1:]

        def _convert_x(self, field, recformat):
            """Convert a raw table column to a bit array as specified by the
            FITS X format.
            """

            dummy = np.zeros(self.shape + (recformat.repeat,), dtype=np.bool_)
            _unwrapx(field, dummy, recformat.repeat)
            return dummy

        def _convert_p(self, column, field, recformat):
            """Convert a raw table column of FITS P or Q format descriptors
            to a VLA column with the array data returned from the heap.
            """

            dummy = _VLF([None] * len(self), dtype=recformat.dtype)
            raw_data = self._get_raw_data()

            if raw_data is None:
                raise IOError(
                    "Could not find heap data for the %r variable-length "
                    "array column." % column.name)

            for idx in range(len(self)):
                offset = field[idx, 1] + self._heapoffset
                count = field[idx, 0]

                if recformat.dtype == 'a':
                    dt = np.dtype(recformat.dtype + str(1))
                    arr_len = count * dt.itemsize
                    da = raw_data[offset:offset + arr_len].view(dt)
                    da = np.char.array(da.view(dtype=dt), itemsize=count)
                    dummy[idx] = decode_ascii(da)
                else:
                    dt = np.dtype(recformat.dtype)
                    arr_len = count * dt.itemsize
                    dummy[idx] = raw_data[offset:offset + arr_len].view(dt)
                    dummy[idx].dtype = dummy[idx].dtype.newbyteorder('>')
                    # Each array in the field may now require additional
                    # scaling depending on the other scaling parameters
                    # TODO: The same scaling parameters apply to every
                    # array in the column so this is currently very slow; we
                    # really only need to check once whether any scaling will
                    # be necessary and skip this step if not
                    # TODO: Test that this works for X format; I don't think
                    # that it does--the recformat variable only applies to the P
                    # format not the X format
                    dummy[idx] = self._convert_other(column, dummy[idx],
                                                     recformat)

            return dummy

        def _convert_ascii(self, column, field):
            """
            Special handling for ASCII table columns to convert columns containing
            numeric types to actual numeric arrays from the string representation.
            """

            format = column.format
            recformat = ASCII2NUMPY[format[0]]
            # if the string = TNULL, return ASCIITNULL
            nullval = str(column.null).strip().encode('ascii')
            if len(nullval) > format.width:
                nullval = nullval[:format.width]

            # Before using .replace make sure that any trailing bytes in each
            # column are filled with spaces, and *not*, say, nulls; this causes
            # functions like replace to potentially leave gibberish bytes in the
            # array buffer.
            dummy = np.char.ljust(field, format.width)
            dummy = np.char.replace(dummy, encode_ascii('D'), encode_ascii('E'))
            null_fill = encode_ascii(str(ASCIITNULL).rjust(format.width))
            dummy = np.where(np.char.strip(dummy) == nullval, null_fill, dummy)

            try:
                dummy = np.array(dummy, dtype=recformat)
            except ValueError as exc:
                indx = self._coldefs.names.index(column.name)
                raise ValueError(
                    '%s; the header may be missing the necessary TNULL%d '
                    'keyword or the table contains invalid data' %
                    (exc, indx + 1))

            return dummy

        def _convert_other(self, column, field, recformat):
            """Perform conversions on any other fixed-width column data types.

            This may not perform any conversion at all if it's not necessary, in
            which case the original column array is returned.
            """

            if isinstance(recformat, _FormatX):
                # special handling for the X format
                return self._convert_x(field, recformat)

            (_str, _bool, _number, _scale, _zero, bscale, bzero, dim) = \
                self._get_scale_factors(column)

            indx = self._coldefs.names.index(column.name)

            # ASCII table, convert strings to numbers
            # TODO:
            # For now, check that these are ASCII columns by checking the coldefs
            # type; in the future all columns (for binary tables, ASCII tables, or
            # otherwise) should "know" what type they are already and how to handle
            # converting their data from FITS format to native format and vice
            # versa...
            if not _str and isinstance(self._coldefs, _AsciiColDefs):
                field = self._convert_ascii(column, field)

            # Test that the dimensions given in dim are sensible; otherwise
            # display a warning and ignore them
            if dim:
                # See if the dimensions already match, if not, make sure the
                # number items will fit in the specified dimensions
                if field.ndim > 1:
                    actual_shape = field.shape[1:]
                    if _str:
                        actual_shape = actual_shape + (field.itemsize,)
                else:
                    actual_shape = field.shape[0]

                if dim == actual_shape:
                    # The array already has the correct dimensions, so we
                    # ignore dim and don't convert
                    dim = None
                else:
                    nitems = reduce(operator.mul, dim)
                    if _str:
                        actual_nitems = field.itemsize
                    elif len(field.shape) == 1:  # No repeat count in TFORMn, equivalent to 1
                        actual_nitems = 1
                    else:
                        actual_nitems = field.shape[1]
                    if nitems > actual_nitems:
                        warnings.warn(
                            'TDIM%d value %s does not fit with the size of '
                            'the array items (%d).  TDIM%d will be ignored.'
                            % (indx + 1, self._coldefs.dims[indx],
                               actual_nitems, indx + 1))
                        dim = None

            # further conversion for both ASCII and binary tables
            # For now we've made columns responsible for *knowing* whether their
            # data has been scaled, but we make the FITS_rec class responsible for
            # actually doing the scaling
            # TODO: This also needs to be fixed in the effort to make Columns
            # responsible for scaling their arrays to/from FITS native values
            if not column.ascii and column.format.p_format:
                format_code = column.format.p_format
            else:
                # TODO: Rather than having this if/else it might be nice if the
                # ColumnFormat class had an attribute guaranteed to give the format
                # of actual values in a column regardless of whether the true
                # format is something like P or Q
                format_code = column.format.format

            if (_number and (_scale or _zero) and not column._physical_values):
                # This is to handle pseudo unsigned ints in table columns
                # TODO: For now this only really works correctly for binary tables
                # Should it work for ASCII tables as well?
                if self._uint:
                    if bzero == 2**15 and format_code == 'I':
                        field = np.array(field, dtype=np.uint16)
                    elif bzero == 2**31 and format_code == 'J':
                        field = np.array(field, dtype=np.uint32)
                    elif bzero == 2**63 and format_code == 'K':
                        field = np.array(field, dtype=np.uint64)
                        bzero64 = np.uint64(2 ** 63)
                    else:
                        field = np.array(field, dtype=np.float64)
                else:
                    field = np.array(field, dtype=np.float64)

                if _scale:
                    np.multiply(field, bscale, field)
                if _zero:
                    if self._uint and format_code == 'K':
                        # There is a chance of overflow, so be careful
                        test_overflow = field.copy()
                        try:
                            test_overflow += bzero64
                        except OverflowError:
                            warnings.warn(
                                "Overflow detected while applying TZERO{0:d}. "
                                "Returning unscaled data.".format(indx + 1))
                        else:
                            field = test_overflow
                    else:
                        field += bzero
            elif _bool and field.dtype != bool:
                field = np.equal(field, ord('T'))
            elif _str:
                with ignored(UnicodeDecodeError):
                    field = decode_ascii(field)

            if dim:
                # Apply the new field item dimensions
                nitems = reduce(operator.mul, dim)
                if field.ndim > 1:
                    field = field[:, :nitems]
                if _str:
                    fmt = field.dtype.char
                    dtype = ('|%s%d' % (fmt, dim[-1]), dim[:-1])
                    field.dtype = dtype
                else:
                    field.shape = (field.shape[0],) + dim

            return field

        def _get_heap_data(self):
            """
            Returns a pointer into the table's raw data to its heap (if present).

            This is returned as a numpy byte array.
            """

            if self._heapsize:
                raw_data = self._get_raw_data().view(np.ubyte)
                heap_end = self._heapoffset + self._heapsize
                return raw_data[self._heapoffset:heap_end]
            else:
                return np.array([], dtype=np.ubyte)

        def _get_raw_data(self):
            """
            Returns the base array of self that "raw data array" that is the
            array in the format that it was first read from a file before it was
            sliced or viewed as a different type in any way.

            This is determined by walking through the bases until finding one that
            has at least the same number of bytes as self, plus the heapsize.  This
            may be the immediate .base but is not always.  This is used primarily
            for variable-length array support which needs to be able to find the
            heap (the raw data *may* be larger than nbytes + heapsize if it
            contains a gap or padding).

            May return ``None`` if no array resembling the "raw data" according to
            the stated criteria can be found.
            """

            raw_data_bytes = self.nbytes + self._heapsize
            base = self
            while hasattr(base, 'base') and base.base is not None:
                base = base.base
                if hasattr(base, 'nbytes') and base.nbytes >= raw_data_bytes:
                    return base

        def _get_scale_factors(self, column):
            """Get all the scaling flags and factors for one column."""

            # TODO: Maybe this should be a method/property on Column?  Or maybe
            # it's not really needed at all...
            _str = column.format.format == 'A'
            _bool = column.format.format == 'L'

            _number = not (_bool or _str)
            bscale = column.bscale
            bzero = column.bzero

            _scale = bscale not in ('', None, 1)
            _zero = bzero not in ('', None, 0)

            # ensure bscale/bzero are numbers
            if not _scale:
                bscale = 1
            if not _zero:
                bzero = 0

            # column._dims gives a tuple, rather than column.dim which returns the
            # original string format code from the FITS header...
            dim = column._dims

            return (_str, _bool, _number, _scale, _zero, bscale, bzero, dim)

        def _scale_back(self, update_heap_pointers=True):
            """
            Update the parent array, using the (latest) scaled array.

            If ``update_heap_pointers`` is `False`, this will leave all the heap
            pointers in P/Q columns as they are verbatim--it only makes sense to do
            this if there is already data on the heap and it can be guaranteed that
            that data has not been modified, and there is not new data to add to
            the heap.  Currently this is only used as an optimization for
            CompImageHDU that does its own handling of the heap.
            """

            # Running total for the new heap size
            heapsize = 0

            for indx, name in enumerate(self.dtype.names):
                column = self._coldefs[indx]
                recformat = column.format.recformat
                field = _get_recarray_field(self, indx)

                # add the location offset of the heap area for each
                # variable length column
                if isinstance(recformat, _FormatP):
                    # Irritatingly, this can return a different dtype than just
                    # doing np.dtype(recformat.dtype); but this returns the results
                    # that we want.  For example if recformat.dtype is 'a' we want
                    # an array of characters.
                    dtype = np.array([], dtype=recformat.dtype).dtype

                    if update_heap_pointers and name in self._converted:
                        # The VLA has potentially been updated, so we need to
                        # update the array descriptors
                        field[:] = 0  # reset
                        npts = [len(arr) for arr in self._converted[name]]

                        field[:len(npts), 0] = npts
                        field[1:, 1] = (np.add.accumulate(field[:-1, 0]) *
                                        dtype.itemsize)
                        field[:, 1][:] += heapsize

                    heapsize += field[:, 0].sum() * dtype.itemsize
                    # Even if this VLA has not been read or updated, we need to
                    # include the size of its constituent arrays in the heap size
                    # total

                if name not in self._converted:
                    continue

                if isinstance(recformat, _FormatX):
                    _wrapx(self._converted[name], field, recformat.repeat)
                    continue

                _str, _bool, _number, _scale, _zero, bscale, bzero, _ = \
                    self._get_scale_factors(column)

                # conversion for both ASCII and binary tables
                if _number or _str:
                    if _number and (_scale or _zero) and column._physical_values:
                        dummy = self._converted[name].copy()
                        if _zero:
                            dummy -= bzero
                        if _scale:
                            dummy /= bscale
                        # This will set the raw values in the recarray back to
                        # their non-physical storage values, so the column should
                        # be mark is not scaled
                        column._physical_values = False
                    elif _str:
                        dummy = self._converted[name]
                    elif isinstance(self._coldefs, _AsciiColDefs):
                        dummy = self._converted[name]
                    else:
                        continue

                    # ASCII table, convert numbers to strings
                    if isinstance(self._coldefs, _AsciiColDefs):
                        self._scale_back_ascii(indx, dummy, field)
                    # binary table string column
                    elif isinstance(field, chararray.chararray):
                        self._scale_back_strings(indx, dummy, field)
                    # all other binary table columns
                    else:
                        if len(field) and isinstance(field[0], np.integer):
                            dummy = np.around(dummy)

                        if field.shape == dummy.shape:
                            field[:] = dummy
                        else:
                            # Reshaping the data is necessary in cases where the
                            # TDIMn keyword was used to shape a column's entries
                            # into arrays
                            field[:] = dummy.ravel().view(field.dtype)

                    del dummy

                # ASCII table does not have Boolean type
                elif _bool:
                    field[:] = np.choose(self._converted[name],
                                         (np.array([ord('F')], dtype=np.int8)[0],
                                          np.array([ord('T')], dtype=np.int8)[0]))

            # Store the updated heapsize
            self._heapsize = heapsize

        def _scale_back_strings(self, col_idx, input_field, output_field):
            # There are a few possibilities this has to be able to handle properly
            # The input_field, which comes from the _converted column is of dtype
            # 'Sn' (where n in string length) on Python 2--this is maintain the
            # existing user expectation of not being returned Python 2-style
            # unicode strings.  One Python 3 the array in _converted is of dtype
            # 'Un' so that elements read out of the array are normal Python 3 str
            # objects (i.e. unicode strings)
            #
            # At the other end the *output_field* may also be of type 'S' or of
            # type 'U'.  It will *usually* be of type 'S' (regardless of Python
            # version) because when reading an existing FITS table the raw data is
            # just ASCII strings, and represented in Numpy as an S array.
            # However, when a user creates a new table from scratch, they *might*
            # pass in a column containing unicode strings (dtype 'U'), especially
            # on Python 3 where this will be the default.  Therefore the
            # output_field of the raw array is actually a unicode array.  But we
            # still want to make sure the data is encodable as ASCII.  Later when
            # we write out the array we use, in the dtype 'U' case, a different
            # write routine that writes row by row and encodes any 'U' columns to
            # ASCII.

            # If the output_field is non-ASCII we will worry about ASCII encoding
            # later when writing; otherwise we can do it right here
            if input_field.dtype.kind == 'U' and output_field.dtype.kind == 'S':
                try:
                    _ascii_encode(input_field, out=output_field)
                except _UnicodeArrayEncodeError as exc:
                    raise ValueError(
                        "Could not save column '{0}': Contains characters that "
                        "cannot be encoded as ASCII as required by FITS, starting "
                        "at the index {1!r} of the column, and the index {2} of "
                        "the string at that location.".format(
                            self._coldefs.names[col_idx],
                            exc.index[0] if len(exc.index) == 1 else exc.index,
                            exc.start))
            else:
                # Otherwise go ahead and do a direct copy into--if both are type
                # 'U' we'll handle encoding later
                input_field = input_field.flatten().view(output_field.dtype)
                output_field.flat[:] = input_field

            # Ensure that blanks at the end of each string are
            # converted to nulls instead of spaces, see Trac #15
            # and #111
            _rstrip_inplace(output_field)

        def _scale_back_ascii(self, col_idx, input_field, output_field):
            """
            Convert internal array values back to ASCII table representation.

            The ``input_field`` is the internal representation of the values, and
            the ``output_field`` is the character array representing the ASCII
            output that will be written.
            """

            starts = self._coldefs.starts[:]
            spans = self._coldefs.spans
            format = self._coldefs.formats[col_idx]

            # The the index of the "end" column of the record, beyond
            # which we can't write
            end = super(FITS_rec, self).field(-1).itemsize
            starts.append(end + starts[-1])

            if col_idx > 0:
                lead = starts[col_idx] - starts[col_idx - 1] - spans[col_idx - 1]
            else:
                lead = 0

            if lead < 0:
                warnings.warn('Column %r starting point overlaps the previous '
                              'column.' % (col_idx + 1))

            trail = starts[col_idx + 1] - starts[col_idx] - spans[col_idx]

            if trail < 0:
                warnings.warn('Column %r ending point overlaps the next '
                              'column.' % (col_idx + 1))

            # TODO: It would be nice if these string column formatting
            # details were left to a specialized class, as is the case
            # with FormatX and FormatP
            if 'A' in format:
                _pc = '%-'
            else:
                _pc = '%'

            fmt = ''.join([_pc, format[1:], ASCII2STR[format[0]],
                           (' ' * trail)])

            # Even if the format precision is 0, we should output a decimal point
            # as long as there is space to do so--not including a decimal point in
            # a float value is discouraged by the FITS Standard
            trailing_decimal = (format.precision == 0 and
                                format.format in ('F', 'E', 'D'))

            # not using numarray.strings's num2char because the
            # result is not allowed to expand (as C/Python does).
            for jdx, value in enumerate(input_field):
                value = fmt % value
                if len(value) > starts[col_idx + 1] - starts[col_idx]:
                    raise ValueError(
                        "Value %r does not fit into the output's itemsize of "
                        "%s." % (value, spans[col_idx]))

                if trailing_decimal and value[0] == ' ':
                    # We have some extra space in the field for the trailing
                    # decimal point
                    value = value[1:] + '.'

                output_field[jdx] = value

            # Replace exponent separator in floating point numbers
            if 'D' in format:
                output_field.replace(encode_ascii('E'), encode_ascii('D'))

    def _get_recarray_field(array, key):
        """
        Compatibility function for using the recarray base class's field method.
        This incorporates the legacy functionality of returning string arrays as
        Numeric-style chararray objects.
        """

        # Numpy >= 1.10.dev recarray no longer returns chararrays for strings
        # This is currently needed for backwards-compatibility and for
        # automatic truncation of trailing whitespace
        field = np.recarray.field(array, key)
        if (field.dtype.char in ('S', 'U') and
                not isinstance(field, chararray.chararray)):
            field = field.view(chararray.chararray)
        return field

    def _rstrip_inplace(array, chars=None):
        """
        Performs an in-place rstrip operation on string arrays.
        This is necessary since the built-in `np.char.rstrip` in Numpy does not
        perform an in-place calculation.  This can be removed if ever
        https://github.com/numpy/numpy/issues/6303 is implemented (however, for
        the purposes of this module the only in-place vectorized string functions
        we need are rstrip and encode).
        """

        for item in np.nditer(array, flags=['zerosize_ok'],
                                     op_flags=['readwrite']):
            item[...] = item.item().rstrip(chars)

    class _UnicodeArrayEncodeError(UnicodeEncodeError):
        def __init__(self, encoding, object_, start, end, reason, index):
            super(_UnicodeArrayEncodeError, self).__init__(encoding, object_,
                    start, end, reason)
            self.index = index

    def _ascii_encode(inarray, out=None):
        """
        Takes a unicode array and fills the output string array with the ASCII
        encodings (if possible) of the elements of the input array.  The two arrays
        must be the same size (though not necessarily the same shape).

        This is like an inplace version of `np.char.encode` though simpler since
        it's only limited to ASCII, and hence the size of each character is
        guaranteed to be 1 byte.

        If any strings are non-ASCII an UnicodeArrayEncodeError is raised--this is
        just a `UnicodeEncodeError` with an additional attribute for the index of
        the item that couldn't be encoded.
        """

        out_dtype = np.dtype(('S{0}'.format(inarray.dtype.itemsize // 4),
                             inarray.dtype.shape))
        if out is not None:
            out = out.view(out_dtype)

        op_dtypes = [inarray.dtype, out_dtype]
        op_flags = [['readonly'], ['writeonly', 'allocate']]
        it = np.nditer([inarray, out], op_dtypes=op_dtypes,
                       op_flags=op_flags, flags=['zerosize_ok'])

        try:
            for initem, outitem in it:
                outitem[...] = initem.item().encode('ascii')
        except UnicodeEncodeError as exc:
            index = np.unravel_index(it.iterindex, inarray.shape)
            raise _UnicodeArrayEncodeError(*(exc.args + (index,)))

        return it.operands[1]

    def _has_unicode_fields(array):
        """
        Returns True if any fields in a structured array have Unicode dtype.
        """

        dtypes = (d[0] for d in array.dtype.fields.values())
        return any(d.kind == 'U' for d in dtypes)