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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 contextlib import suppress
    from functools import reduce

    import numpy as np
    from numpy import char as chararray

    from astropy.utils import lazyproperty

    from .column import (
        _VLF,
        ASCII2NUMPY,
        ASCII2STR,
        ASCIITNULL,
        FITS2NUMPY,
        ColDefs,
        Delayed,
        _AsciiColDefs,
        _FormatP,
        _FormatX,
        _get_index,
        _makep,
        _unwrapx,
        _wrapx,
    )
    from .util import _rstrip_inplace, decode_ascii, encode_ascii

    class FITS_record:
        """
        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.
            """
            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, str):
                index = _get_index(self.array.names, key)

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

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

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

        def __setitem__(self, key, value):
            if isinstance(key, str):
                index = _get_index(self.array.names, key)

                if index < self.start or index > self.end - 1:
                    raise KeyError(f"Key '{key}' does not exist.")
            elif isinstance(key, slice):
                start, stop, step = key.indices(self.array._nfields)
                for i, val in zip(range(start, stop, step), value, strict=True):
                    self.array.field(self._get_index(i))[self.row] = val
                return
            else:
                index = self._get_index(key)
                if index > self.array._nfields - 1:
                    raise IndexError("Index out of bounds")

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

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

        def __repr__(self):
            """
            Display a single row.
            """
            outlist = []
            for idx in range(len(self)):
                outlist.append(repr(self[idx]))
            return f"({', '.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, index):
            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[index]

    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
        _character_as_bytes = False
        _load_variable_length_data = True

        def __new__(cls, 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__(cls, input.shape, input.dtype, buf=input.data)
            else:
                self = np.recarray.__new__(
                    cls, input.shape, input.dtype, buf=input.data, strides=input.strides
                )

            self._init()
            if self.dtype.fields:
                self._nfields = len(self.dtype.fields)

            return self

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

            super().__setstate__(state)

            self._col_weakrefs = weakref.WeakSet()

            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().__reduce__()

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

            for attrs in [
                "_converted",
                "_heapoffset",
                "_heapsize",
                "_tbsize",
                "_nfields",
                "_gap",
                "_uint",
                "parnames",
                "_coldefs",
            ]:
                with suppress(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._character_as_bytes = obj._character_as_bytes

            if isinstance(obj, FITS_rec) and obj.dtype == self.dtype:
                self._converted = obj._converted
                self._heapoffset = obj._heapoffset
                self._heapsize = obj._heapsize
                self._tbsize = obj._tbsize
                self._col_weakrefs = obj._col_weakrefs
                self._coldefs = obj._coldefs
                self._nfields = obj._nfields
                self._gap = obj._gap
                self._uint = obj._uint
            elif self.dtype.fields is not None:
                # This will allow regular ndarrays with fields, rather than
                # just other FITS_rec objects
                self._nfields = len(self.dtype.fields)
                self._converted = {}

                self._heapoffset = getattr(obj, "_heapoffset", 0)
                self._heapsize = getattr(obj, "_heapsize", 0)
                self._tbsize = getattr(obj, "_tbsize", 0)

                self._gap = getattr(obj, "_gap", 0)
                self._uint = getattr(obj, "_uint", False)
                self._col_weakrefs = weakref.WeakSet()
                self._coldefs = ColDefs(self)

                # Work around chicken-egg problem.  Column.array relies on the
                # _coldefs attribute to set up ref back to parent FITS_rec; however
                # in the above line the self._coldefs has not been assigned yet so
                # this fails.  This patches that up...
                for col in self._coldefs:
                    del col.array
                    col._parent_fits_rec = weakref.ref(self)
            else:
                self._init()

        def _init(self):
            """Initializes internal attributes specific to FITS-isms."""
            self._nfields = 0
            self._converted = {}
            self._heapoffset = 0
            self._heapsize = 0
            self._tbsize = 0
            self._col_weakrefs = weakref.WeakSet()
            self._coldefs = None
            self._gap = 0
            self._uint = False

        @classmethod
        def from_columns(cls, columns, nrows=0, fill=False, character_as_bytes=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
                    nrows = max(dim, nrows)

            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)
            data._character_as_bytes = character_as_bytes

            # 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:
                    # The input column had an empty array, so just use the fill
                    # value
                    continue

                n = min(len(arr), 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.

                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._cache_field(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
                    converted = np.zeros(field.shape, dtype=bool)
                    converted[:n] = inarr
                    data._cache_field(name, converted)
                    # TODO: Maybe this step isn't necessary at all if _scale_back
                    # will handle it?
                    inarr = np.where(inarr == np.False_, ord("F"), ord("T"))
                elif column._physical_values and column._pseudo_unsigned_ints:
                    # Temporary hack...
                    bzero = column.bzero
                    converted = np.zeros(field.shape, dtype=inarr.dtype)
                    converted[:n] = inarr
                    data._cache_field(name, converted)
                    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._cache_field(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):
            # recarray.__repr__ hard-codes the name of the class, so we overwrite.
            # The following is mostly a straight copy except for the name change
            # and for treating using str to typeset integer -- the latter to fix
            # the case where the integer columns are scaled (see gh-17583). Also,
            # removed a branch for "if the user is playing strange game with dtypes".
            #
            # FIXME: recarray removes the "numpy.record" mention in the dtype repr,
            # we could do the same in a future version

            repr_dtype = self.dtype
            # if repr_dtype.type is np.record:
            #     repr_dtype = np.dtype((np.void, repr_dtype))
            prefix = "FITS_rec("
            fmt = "FITS_rec(%s,%sdtype=%s)"
            # get data/shape string. logic taken from numeric.array_repr
            if self.size > 0 or self.shape == (0,):
                lst = np.array2string(
                    self, separator=", ", prefix=prefix, suffix=",", formatter=dict(int=str)
                )
            else:
                # show zero-length shape unless it is (0,)
                lst = "[], shape=%s" % (repr(self.shape),)  # noqa: UP031
            lf = "\n" + " " * len(prefix)
            return fmt % (lst, lf, repr_dtype)

        def __getattribute__(self, attr):
            # First, see if ndarray has this attr, and return it if so. Note that
            # this means a field with the same name as an ndarray attr cannot be
            # accessed by attribute, this is Numpy's default behavior.
            # We avoid using np.recarray.__getattribute__ here because after doing
            # this check it would access the columns without doing the conversions
            # that we need (with .field, see below).
            try:
                return object.__getattribute__(self, attr)
            except AttributeError:
                pass

            # attr might still be a fieldname.  If we have column definitions,
            # we should access this via .field, as the data may have to be scaled.
            if self._coldefs is not None and attr in self.columns.names:
                return self.field(attr)

            # If not, just let the usual np.recarray override deal with it.
            return super().__getattribute__(attr)

        def __getitem__(self, key):
            if self._coldefs is None:
                return super().__getitem__(key)

            if isinstance(key, str):
                return self.field(key)

            # 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.
            out = self.view(np.recarray)[key]
            if type(out) is not np.recarray:
                # Oops, we got a single element rather than a view. In that case,
                # return a Record, which has no __getstate__ and is more efficient.
                return self._record_type(self, key)

            # We got a view; change it back to our class, and add stuff
            out = out.view(type(self))
            out._heapoffset = self._heapoffset
            out._heapsize = self._heapsize
            out._tbsize = self._tbsize
            out._gap = self._gap
            out._uint = self._uint
            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])

                # Ensure that the sliced FITS_rec will view the same scaled
                # columns as the original; this is one of the few cases where
                # it is not necessary to use _cache_field()
                if name in self._converted:
                    dummy = self._converted[name]
                    field = np.ndarray.__getitem__(dummy, key)
                    out._converted[name] = field

            out._coldefs._arrays = arrays
            return out

        def __setitem__(self, key, value):
            if self._coldefs is None:
                return super().__setitem__(key, value)

            if isinstance(key, str):
                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 range(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(
                        f"Input tuple or list required to have {self._nfields} elements."
                    )
            else:
                raise TypeError(
                    "Assignment requires a FITS_record, tuple, or list as input."
                )

        def _ipython_key_completions_(self):
            return self.names

        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().copy(order=order)

            new.__dict__ = copy.deepcopy(self.__dict__)
            new._col_weakrefs = weakref.WeakSet()
            return new

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

        @property
        def _coldefs(self):
            # This used to be a normal internal attribute, but it was changed to a
            # property as a quick and transparent way to work around the reference
            # leak bug fixed in https://github.com/astropy/astropy/pull/4539
            #
            # See the long comment in the Column.array property for more details
            # on this.  But in short, FITS_rec now has a ._col_weakrefs attribute
            # which is a WeakSet of weakrefs to each Column in _coldefs.
            #
            # So whenever ._coldefs is set we also add each Column in the ColDefs
            # to the weakrefs set.  This is an easy way to find out if a Column has
            # any references to it external to the FITS_rec (i.e. a user assigned a
            # column to a variable).  If the column is still in _col_weakrefs then
            # there are other references to it external to this FITS_rec.  We use
            # that information in __del__ to save off copies of the array data
            # for those columns to their Column.array property before our memory
            # is freed.
            return self.__dict__.get("_coldefs")

        @_coldefs.setter
        def _coldefs(self, cols):
            self.__dict__["_coldefs"] = cols
            if isinstance(cols, ColDefs):
                for col in cols.columns:
                    self._col_weakrefs.add(col)

        @_coldefs.deleter
        def _coldefs(self):
            try:
                del self.__dict__["_coldefs"]
            except KeyError as exc:
                raise AttributeError(exc.args[0])

        def __del__(self):
            try:
                del self._coldefs
                if self.dtype.fields is not None:
                    for col in self._col_weakrefs:
                        if col.array is not None:
                            col.array = col.array.copy()

            # See issues #4690 and #4912
            except (AttributeError, TypeError):  # pragma: no cover
                pass

        @property
        def names(self):
            """List of column names."""
            if self.dtype.fields:
                return list(self.dtype.names)
            elif getattr(self, "_coldefs", None) is not None:
                return self._coldefs.names
            else:
                return None

        @property
        def formats(self):
            """List of column FITS formats."""
            if getattr(self, "_coldefs", None) is not None:
                return self._coldefs.formats

            return None

        @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 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) and self._load_variable_length_data:
                    # 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)

                # Note: Never assign values directly into the self._converted dict;
                # always go through self._cache_field; this way self._converted is
                # only used to store arrays that are not already direct views of
                # our own data.
                self._cache_field(name, converted)
                return converted

            return self._converted[name]

        def _cache_field(self, name, field):
            """
            Do not store fields in _converted if one of its bases is self,
            or if it has a common base with self.

            This results in a reference cycle that cannot be broken since
            ndarrays do not participate in cyclic garbage collection.
            """
            base = field
            while True:
                self_base = self
                while True:
                    if self_base is base:
                        return

                    if getattr(self_base, "base", None) is not None:
                        self_base = self_base.base
                    else:
                        break

                if getattr(base, "base", None) is not None:
                    base = base.base
                else:
                    break

            self._converted[name] = field

        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 = f"_update_column_{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.
            """
            if column.dim:
                vla_shape = tuple(
                    reversed(tuple(map(int, column.dim.strip("()").split(","))))
                )
            dummy = _VLF([None] * len(self), dtype=recformat.dtype)
            raw_data = self._get_raw_data()

            if raw_data is None:
                raise OSError(
                    f"Could not find heap data for the {column.name!r} variable-length "
                    "array column."
                )

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

                if recformat.dtype == "S":
                    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)
                    if column.dim and len(vla_shape) > 1:
                        # The VLA is reshaped consistently with TDIM instructions
                        if vla_shape[0] == 1:
                            dummy[idx] = dummy[idx].reshape(1, len(dummy[idx]))
                        else:
                            vla_dim = vla_shape[1:]
                            vla_first = int(len(dummy[idx]) / np.prod(vla_dim))
                            dummy[idx] = dummy[idx].reshape((vla_first,) + vla_dim)

                    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 = getattr(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))

            # Convert all fields equal to the TNULL value (nullval) to empty fields.
            # TODO: These fields really should be converted to NaN or something else undefined.
            # Currently they are converted to empty fields, which are then set to zero.
            dummy = np.where(np.char.strip(dummy) == nullval, null_fill, dummy)

            # always replace empty fields, see https://github.com/astropy/astropy/pull/5394
            if nullval != b"":
                dummy = np.where(np.char.strip(dummy) == b"", null_fill, dummy)

            try:
                dummy = np.array(dummy, dtype=recformat)
            except ValueError as exc:
                raise ValueError(
                    f"{exc}; the header may be missing the necessary "
                    f"TNULL{self.names.index(column.name) + 1} keyword or the table "
                    "contains invalid data"
                )

            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)

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

            index = self.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 and not isinstance(recformat, _FormatP):
                        warnings.warn(
                            f"TDIM{index + 1} value {self._coldefs[index].dims:d} does not "
                            f"fit with the size of the array items ({actual_nitems:d}).  "
                            f"TDIM{index + 1:d} will be ignored."
                        )
                        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(
                                f"Overflow detected while applying TZERO{index + 1:d}. "
                                "Returning unscaled data."
                            )
                        else:
                            field = test_overflow
                    else:
                        field += bzero

                # mark the column as scaled
                column._physical_values = True

            elif _bool and field.dtype != bool:
                field = np.equal(field, ord("T"))
            elif _str:
                if not self._character_as_bytes:
                    with suppress(UnicodeDecodeError):
                        field = decode_ascii(field)

            if dim and not isinstance(recformat, _FormatP):
                # 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 = (f"|{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.
            """
            raw_data = self._get_raw_data()
            if self._heapsize and raw_data is not None:
                # Read the heap from disk
                raw_data = raw_data.view(np.ubyte)
                heap_end = self._heapoffset + self._heapsize
                return raw_data[self._heapoffset : heap_end]
            else:
                # Data is only in memory so create the heap data, one column
                # at a time, in the order that the data pointers appear in the
                # column (regardless if that data pointer has a different,
                # previous heap offset listed)
                data = []
                for idx in range(self._nfields):
                    # data should already be byteswapped from the caller
                    # using _binary_table_byte_swap
                    if not isinstance(self.columns._recformats[idx], _FormatP):
                        continue

                    for row in self.field(idx):
                        if len(row) > 0:
                            data.append(row.view(type=np.ndarray, dtype=np.ubyte))

                if data:
                    return np.concatenate(data)
                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._tbsize + self._heapsize
            base = self
            while hasattr(base, "base") and base.base is not None:
                base = base.base
                # Variable-length-arrays: should take into account the case of
                # empty arrays
                if hasattr(base, "_heapoffset"):
                    if hasattr(base, "nbytes") and base.nbytes > raw_data_bytes:
                        return base
                # non variable-length-arrays
                else:
                    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 index, name in enumerate(self.dtype.names):
                column = self._coldefs[index]
                recformat = column.format.recformat
                raw_field = _get_recarray_field(self, index)

                # 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 'S' 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
                        raw_field[:] = 0  # reset
                        npts = [np.prod(arr.shape) for arr in self._converted[name]]

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

                    heapsize += raw_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 isinstance(recformat, _FormatX) and name in self._converted:
                    _wrapx(self._converted[name], raw_field, recformat.repeat)
                    continue

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

                field = self._converted.get(name, raw_field)

                # conversion for both ASCII and binary tables
                if _number or _str:
                    if _number and (_scale or _zero) and column._physical_values:
                        dummy = field.copy()
                        if _zero:
                            # Cast before subtracting to avoid overflow problems.
                            dummy -= np.array(bzero).astype(dummy.dtype, casting="unsafe")
                        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 or isinstance(self._coldefs, _AsciiColDefs):
                        dummy = field
                    else:
                        continue

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

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

                    del dummy

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

            # 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
            # 'Un' so that elements read out of the array are normal 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' 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').  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(
                        f"Could not save column '{self._coldefs[col_idx].name}': "
                        "Contains characters that cannot be encoded as ASCII as required "
                        "by FITS, starting at the index "
                        f"{exc.index[0] if len(exc.index) == 1 else exc.index!r} of the "
                        f"column, and the index {exc.start} of the string at that location."
                    )
            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[col_idx].format

            # The index of the "end" column of the record, beyond
            # which we can't write
            end = super().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(
                    f"Column {col_idx + 1} starting point overlaps the previous column."
                )

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

            if trail < 0:
                warnings.warn(
                    f"Column {col_idx + 1} ending point overlaps the next column."
                )

            # 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.format(value)
                if len(value) > starts[col_idx + 1] - starts[col_idx]:
                    raise ValueError(
                        f"Value {value!r} does not fit into the output's itemsize of "
                        f"{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[:] = output_field.replace(b"E", b"D")

        def tolist(self):
            # Override .tolist to take care of special case of VLF

            column_lists = [self[name].tolist() for name in self.columns.names]

            return [list(row) for row in zip(*column_lists)]

    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

    class _UnicodeArrayEncodeError(UnicodeEncodeError):
        def __init__(self, encoding, object_, start, end, reason, index):
            super().__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((f"S{inarray.dtype.itemsize // 4}", inarray.dtype.shape))
        if out is not None:
            out = out.view(out_dtype)
        if inarray.size == 0:
            return out

        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)