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# Source code for astropy.io.fits.hdu.compressed._tiled_compression

    """
    This module contains low level helper functions for compressing and
    decompressing buffer for the Tiled Table Compression algorithms as specified in
    the FITS 4 standard.
    """

    import sys
    import warnings
    from math import prod

    import numpy as np

    from astropy.io.fits.hdu.base import BITPIX2DTYPE
    from astropy.utils.exceptions import AstropyUserWarning

    from ._codecs import PLIO1, Gzip1, Gzip2, HCompress1, NoCompress, Rice1
    from ._quantization import DITHER_METHODS, QuantizationFailedException, Quantize
    from .utils import _data_shape, _iter_array_tiles, _tile_shape

    ALGORITHMS = {
        "GZIP_1": Gzip1,
        "GZIP_2": Gzip2,
        "RICE_1": Rice1,
        "RICE_ONE": Rice1,
        "PLIO_1": PLIO1,
        "HCOMPRESS_1": HCompress1,
        "NOCOMPRESS": NoCompress,
    }

    DEFAULT_ZBLANK = -2147483648

    __all__ = [
        "compress_image_data",
        "decompress_image_data_section",
    ]

    def _decompress_tile(buf, *, algorithm: str, **settings):
        """
        Decompress the buffer of a tile using the given compression algorithm.

        Parameters
        ----------
        buf
            The compressed buffer to be decompressed.
        algorithm
            A supported decompression algorithm.
        settings
            Any parameters for the given compression algorithm
        """
        return ALGORITHMS[algorithm](**settings).decode(buf)

    def _compress_tile(buf, *, algorithm: str, **settings):
        """
        Compress the buffer of a tile using the given compression algorithm.

        Parameters
        ----------
        buf
            The decompressed buffer to be compressed.
        algorithm
            A supported compression algorithm.
        settings
            Any parameters for the given compression algorithm
        """
        return ALGORITHMS[algorithm](**settings).encode(buf)

    def _header_to_settings(header):
        """
        Extract the settings which are constant given a header
        """
        settings = {}
        compression_type = header["ZCMPTYPE"]
        if compression_type == "GZIP_2":
            settings["itemsize"] = abs(header["ZBITPIX"]) // 8
        elif compression_type in ("RICE_1", "RICE_ONE"):
            settings["blocksize"] = _get_compression_setting(header, "BLOCKSIZE", 32)
            settings["bytepix"] = _get_compression_setting(header, "BYTEPIX", 4)
        elif compression_type == "HCOMPRESS_1":
            settings["bytepix"] = 8
            settings["scale"] = int(_get_compression_setting(header, "SCALE", 0))
            settings["smooth"] = _get_compression_setting(header, "SMOOTH", 0)

        return settings

    def _update_tile_settings(settings, compression_type, actual_tile_shape):
        """
        Update the settings with tile-specific settings
        """
        if compression_type in ("PLIO_1", "RICE_1", "RICE_ONE"):
            # We have to calculate the tilesize from the shape of the tile not the
            # header, so that it's correct for edge tiles etc.
            settings["tilesize"] = prod(actual_tile_shape)
        elif compression_type == "HCOMPRESS_1":
            # HCOMPRESS requires 2D tiles, so to find the shape of the 2D tile we
            # need to ignore all length 1 tile dimensions
            # Also cfitsio expects the tile shape in C order
            shape_2d = tuple(nd for nd in actual_tile_shape if nd != 1)
            if len(shape_2d) != 2:
                raise ValueError(f"HCOMPRESS expects two dimensional tiles, got {shape_2d}")
            settings["nx"] = shape_2d[0]
            settings["ny"] = shape_2d[1]

        return settings

    def _finalize_array(tile_buffer, *, bitpix, tile_shape, algorithm, lossless):
        """
        Convert a buffer to an array.

        This is a helper function which takes a raw buffer (as output by .decode)
        and translates it into a numpy array with the correct dtype, endianness and
        shape.
        """
        tile_size = prod(tile_shape)

        if algorithm.startswith("GZIP") or algorithm == "NOCOMPRESS":
            # This algorithm is taken from fitsio
            # https://github.com/astropy/astropy/blob/a8cb1668d4835562b89c0d0b3448ac72ca44db63/cextern/cfitsio/lib/imcompress.c#L6345-L6388
            tile_bytesize = len(tile_buffer)
            if tile_bytesize == tile_size * 2:
                dtype = ">i2"
            elif tile_bytesize == tile_size * 4:
                if bitpix < 0 and lossless:
                    dtype = ">f4"
                else:
                    dtype = ">i4"
            elif tile_bytesize == tile_size * 8:
                if bitpix < 0 and lossless:
                    dtype = ">f8"
                else:
                    dtype = ">i8"
            else:
                # Just return the raw bytes
                dtype = ">u1"
            tile_data = np.asarray(tile_buffer).view(dtype).reshape(tile_shape)
        else:
            # For RICE_1 compression the tiles that are on the edge can end up
            # being padded, so we truncate excess values
            if algorithm in ("RICE_1", "RICE_ONE", "PLIO_1") and tile_size < len(
                tile_buffer
            ):
                tile_buffer = tile_buffer[:tile_size]

            if tile_buffer.data.format == "b":
                # NOTE: this feels like a Numpy bug - need to investigate
                tile_data = np.asarray(tile_buffer, dtype=np.uint8).reshape(tile_shape)
            else:
                tile_data = np.asarray(tile_buffer).reshape(tile_shape)

        return tile_data

    def _check_compressed_header(header):
        # NOTE: this could potentially be moved up into CompImageHDU, e.g. in a
        # _verify method.

        # Check for overflows which might cause issues when calling C code

        for kw in ["ZNAXIS", "ZVAL1", "ZVAL2", "ZBLANK", "BLANK"]:
            if kw in header:
                if header[kw] > 0 and header[kw] > np.iinfo(np.intc).max:
                    raise OverflowError(f"{kw} value {header[kw]} is too large")

        for i in range(1, header["ZNAXIS"] + 1):
            for kw_name in ["ZNAXIS", "ZTILE"]:
                kw = f"{kw_name}{i}"
                if kw in header:
                    if header[kw] > 0 and header[kw] > np.iinfo(np.int32).max:
                        raise OverflowError(f"{kw} value {header[kw]} is too large")

        for i in range(1, header["NAXIS"] + 1):
            kw = f"NAXIS{i}"
            if kw in header:
                if header[kw] > 0 and header[kw] > np.iinfo(np.int64).max:
                    raise OverflowError(f"{kw} value {header[kw]} is too large")

        for kw in ["TNULL1", "PCOUNT", "THEAP"]:
            if kw in header:
                if header[kw] > 0 and header[kw] > np.iinfo(np.int64).max:
                    raise OverflowError(f"{kw} value {header[kw]} is too large")

        for kw in ["ZVAL3"]:
            if kw in header:
                # float() below to avoid a NEP 50 warning about a cast to float32 inf.
                if header[kw] > float(np.finfo(np.float32).max):
                    raise OverflowError(f"{kw} value {header[kw]} is too large")

        # Validate data types

        for kw in ["ZSCALE", "ZZERO", "TZERO1", "TSCAL1"]:
            if kw in header:
                if not np.isreal(header[kw]):
                    raise TypeError(f"{kw} should be floating-point")

        for kw in ["TTYPE1", "TFORM1", "ZCMPTYPE", "ZNAME1", "ZQUANTIZ"]:
            if kw in header:
                if not isinstance(header[kw], str):
                    raise TypeError(f"{kw} should be a string")

        for kw in ["ZDITHER0"]:
            if kw in header:
                if not np.isreal(header[kw]) or not float(header[kw]).is_integer():
                    raise TypeError(f"{kw} should be an integer")

        for suffix in range(1, header["TFIELDS"] + 1):
            # Only validate COMPRESSED_DATA and GZIP_COMPRESSED_DATA columns.
            # UNCOMPRESSED_DATA columns can have floating-point formats (PE, PD, etc.)
            if header.get(f"TTYPE{suffix}", "") in (
                "COMPRESSED_DATA",
                "GZIP_COMPRESSED_DATA",
            ):
                for valid in ["PB", "PI", "PJ", "QB", "QI", "QJ"]:
                    if header[f"TFORM{suffix}"].startswith((valid, f"1{valid}")):
                        break
                else:
                    raise RuntimeError(f"Invalid TFORM{suffix}: {header[f'TFORM{suffix}']}")

        # Check values

        for kw in ["TFIELDS", "PCOUNT"] + [
            f"NAXIS{idx + 1}" for idx in range(header["NAXIS"])
        ]:
            if kw in header:
                if header[kw] < 0:
                    raise ValueError(f"{kw} should not be negative.")

        for kw in ["ZNAXIS", "TFIELDS"]:
            if kw in header:
                if header[kw] < 0 or header[kw] > 999:
                    raise ValueError(f"{kw} should be in the range 0 to 999")

        if header["ZBITPIX"] not in [8, 16, 32, 64, -32, -64]:
            raise ValueError(f"Invalid value for BITPIX: {header['ZBITPIX']}")

        if header["ZCMPTYPE"] not in ALGORITHMS:
            raise ValueError(f"Unrecognized compression type: {header['ZCMPTYPE']}")

        # Check that certain keys are present

        header["ZNAXIS"]
        header["ZBITPIX"]

    def _get_compression_setting(header, name, default):
        # Settings for the various compression algorithms are stored in pairs of
        # keywords called ZNAME? and ZVAL? - a given compression setting could be
        # in any ZNAME? so we need to check through all the possible ZNAMEs which
        # one matches the required setting.

        for i in range(1, 1000):
            if f"ZNAME{i}" not in header:
                break
            if header[f"ZNAME{i}"].lower() == name.lower():
                return header[f"ZVAL{i}"]

        return default

    def _column_dtype(compressed_coldefs, column_name):
        tform = compressed_coldefs[column_name].format.removeprefix("1")
        if tform[1] == "B":
            dtype = np.uint8
        elif tform[1] == "I":
            dtype = ">i2"
        elif tform[1] == "J":
            dtype = ">i4"
        elif tform[1] == "E":
            dtype = ">f4"
        elif tform[1] == "D":
            dtype = ">f8"
        return np.dtype(dtype)

    def _get_data_from_heap(hdu, size, offset, dtype, heap_cache=None):
        if heap_cache is None:
            return hdu._get_raw_data(size, dtype, hdu._data_offset + hdu._theap + offset)
        else:
            itemsize = dtype.itemsize
            data = heap_cache[offset : offset + size * itemsize]
            if itemsize > 1:
                return data.view(dtype)
            else:
                return data

    def decompress_image_data_section(
        compressed_data,
        compression_type,
        compressed_header,
        bintable,
        image_header,
        first_tile_index,
        last_tile_index,
    ):
        """
        Decompress the data in a `~astropy.io.fits.CompImageHDU`.

        Parameters
        ----------
        compressed_data : `~astropy.io.fits.FITS_rec`
            The compressed data
        compression_type : str
            The compression algorithm
        compressed_header : `~astropy.io.fits.Header`
            The header of the compressed binary table
        bintable : `~astropy.io.fits.BinTableHDU`
            The binary table HDU, used to access the raw heap data
        first_tile_index : iterable
            The indices of the first tile to decompress along each dimension
        last_tile_index : iterable
            The indices of the last tile to decompress along each dimension

        Returns
        -------
        data : `numpy.ndarray`
            The decompressed data array.
        """
        compressed_coldefs = compressed_data._coldefs

        _check_compressed_header(compressed_header)

        tile_shape = _tile_shape(compressed_header)
        data_shape = _data_shape(compressed_header)

        first_array_index = first_tile_index * tile_shape
        last_array_index = (last_tile_index + 1) * tile_shape

        last_array_index = np.minimum(data_shape, last_array_index)

        buffer_shape = tuple((last_array_index - first_array_index).astype(int))

        image_data = np.empty(
            buffer_shape, dtype=BITPIX2DTYPE[compressed_header["ZBITPIX"]]
        )

        quantized = "ZSCALE" in compressed_data.dtype.names

        if image_data.size == 0:
            return image_data

        settings = _header_to_settings(compressed_header)
        zbitpix = compressed_header["ZBITPIX"]
        dither_method = DITHER_METHODS[compressed_header.get("ZQUANTIZ", "NO_DITHER")]
        dither_seed = compressed_header.get("ZDITHER0", 0)

        # NOTE: in the following and below we convert the column to a Numpy array
        # for performance reasons, as accessing rows from a FITS_rec column is
        # otherwise slow.
        compressed_data_column = np.array(compressed_data["COMPRESSED_DATA"])
        compressed_data_dtype = _column_dtype(compressed_coldefs, "COMPRESSED_DATA")

        if "ZBLANK" in compressed_coldefs.dtype.names:
            zblank_column = np.array(compressed_data["ZBLANK"])
        else:
            zblank_column = None

        if "ZSCALE" in compressed_coldefs.dtype.names:
            zscale_column = np.array(compressed_data["ZSCALE"])
        else:
            zscale_column = None

        if "ZZERO" in compressed_coldefs.dtype.names:
            zzero_column = np.array(compressed_data["ZZERO"])
        else:
            zzero_column = None

        zblank_header = compressed_header.get("ZBLANK", image_header.get("BLANK", None))

        gzip_compressed_data_column = None
        gzip_compressed_data_dtype = None
        uncompressed_data_column = None
        uncompressed_data_dtype = None

        # If all the data is requested, read in all the heap.
        if tuple(buffer_shape) == tuple(data_shape):
            heap_cache = bintable._get_raw_data(
                compressed_header["PCOUNT"],
                np.uint8,
                bintable._data_offset + bintable._theap,
            )
        else:
            heap_cache = None

        for row_index, tile_slices in _iter_array_tiles(
            data_shape, tile_shape, first_tile_index, last_tile_index
        ):
            # For tiles near the edge, the tile shape from the header might not be
            # correct so we have to pass the shape manually.
            actual_tile_shape = image_data[tile_slices].shape

            settings = _update_tile_settings(settings, compression_type, actual_tile_shape)

            if compressed_data_column[row_index][0] == 0:
                # When quantizing floating point data, sometimes the data will not
                # quantize efficiently. In these cases the raw floating point data can
                # be losslessly GZIP compressed and stored in the `GZIP_COMPRESSED_DATA`
                # column, or stored uncompressed in the `UNCOMPRESSED_DATA` column.

                if "GZIP_COMPRESSED_DATA" in compressed_coldefs.dtype.names:
                    if gzip_compressed_data_column is None:
                        gzip_compressed_data_column = np.array(
                            compressed_data["GZIP_COMPRESSED_DATA"]
                        )
                        gzip_compressed_data_dtype = _column_dtype(
                            compressed_coldefs, "GZIP_COMPRESSED_DATA"
                        )

                    cdata = _get_data_from_heap(
                        bintable,
                        *gzip_compressed_data_column[row_index],
                        gzip_compressed_data_dtype,
                        heap_cache=heap_cache,
                    )

                    tile_buffer = _decompress_tile(cdata, algorithm="GZIP_1")

                    tile_data = _finalize_array(
                        tile_buffer,
                        bitpix=zbitpix,
                        tile_shape=actual_tile_shape,
                        algorithm="GZIP_1",
                        lossless=True,
                    )

                elif "UNCOMPRESSED_DATA" in compressed_coldefs.dtype.names:
                    if uncompressed_data_column is None:
                        uncompressed_data_column = np.array(
                            compressed_data["UNCOMPRESSED_DATA"]
                        )
                        uncompressed_data_dtype = _column_dtype(
                            compressed_coldefs, "UNCOMPRESSED_DATA"
                        )

                    tile_data = _get_data_from_heap(
                        bintable,
                        *uncompressed_data_column[row_index],
                        uncompressed_data_dtype,
                        heap_cache=heap_cache,
                    )

                    # Data is already uncompressed, just reshape it
                    tile_data = tile_data.reshape(actual_tile_shape)

                else:
                    raise ValueError(
                        "COMPRESSED_DATA column has zero length but neither "
                        "GZIP_COMPRESSED_DATA nor UNCOMPRESSED_DATA column exists"
                    )

            else:
                cdata = _get_data_from_heap(
                    bintable,
                    *compressed_data_column[row_index],
                    compressed_data_dtype,
                    heap_cache=heap_cache,
                )

                if compression_type == "GZIP_2":
                    # Decompress with GZIP_1 just to find the total number of
                    # elements in the uncompressed data.
                    # TODO: find a way to avoid doing this for all tiles
                    tile_data = np.asarray(_decompress_tile(cdata, algorithm="GZIP_1"))
                    settings["itemsize"] = tile_data.size // int(prod(actual_tile_shape))

                tile_buffer = _decompress_tile(
                    cdata, algorithm=compression_type, **settings
                )

                tile_data = _finalize_array(
                    tile_buffer,
                    bitpix=zbitpix,
                    tile_shape=actual_tile_shape,
                    algorithm=compression_type,
                    lossless=not quantized,
                )

                if zblank_column is None:
                    zblank = zblank_header
                else:
                    zblank = zblank_column[row_index]

                if zblank is not None:
                    blank_mask = tile_data == zblank

                if quantized:
                    q = Quantize(
                        row=(row_index + dither_seed) if dither_method != -1 else 0,
                        dither_method=dither_method,
                        quantize_level=None,
                        bitpix=zbitpix,
                    )
                    tile_data = np.asarray(
                        q.decode_quantized(
                            tile_data, zscale_column[row_index], zzero_column[row_index]
                        )
                    ).reshape(actual_tile_shape)

                if zblank is not None:
                    if not tile_data.flags.writeable:
                        tile_data = tile_data.copy()
                    tile_data[blank_mask] = zblank_header if zbitpix > 0 else np.nan

            image_data[tile_slices] = tile_data

        return image_data

    def compress_image_data(
        image_data,
        compression_type,
        compressed_header,
        compressed_coldefs,
    ):
        """
        Compress the data in a `~astropy.io.fits.CompImageHDU`.

        The input HDU is expected to have a uncompressed numpy array as it's
        ``.data`` attribute.

        Parameters
        ----------
        image_data : `~numpy.ndarray`
            The image data to compress
        compression_type : str
            The compression algorithm
        compressed_header : `~astropy.io.fits.Header`
            The header of the compressed binary table
        compressed_coldefs : `~astropy.io.fits.ColDefs`
            The ColDefs object for the compressed binary table

        Returns
        -------
        nbytes : `int`
            The number of bytes of the heap.
        heap : `bytes`
            The bytes of the FITS table heap.
        """
        if not isinstance(image_data, np.ndarray):
            raise TypeError("Image data must be a numpy.ndarray")

        # PLIO_1 encodes only non-negative integers, but astropy stores unsigned
        # FITS data via the BZERO=2**(N-1) convention which produces negative
        # values for the lower half of the range. Reject unsigned multi-byte
        # input to avoid corrupted writes.
        if (
            compression_type == "PLIO_1"
            and image_data.dtype.kind == "u"
            and image_data.dtype.itemsize >= 2
        ):
            raise ValueError(
                "PLIO_1 compression does not support unsigned integers larger than 8 bits"
            )

        if (
            compression_type in ("RICE_1", "PLIO_1", "HCOMPRESS_1")
            and image_data.dtype.kind in ("i", "u")
            and image_data.dtype.itemsize == 8
        ):
            # numpy's same_value check does not byteswap before comparing values,
            # so non-native source data silently truncates instead of raising.
            # Convert source to native byte order first.
            native_source = image_data.astype(
                image_data.dtype.newbyteorder("="), copy=False
            )
            new_dt = f"{image_data.dtype.kind}4"
            try:
                image_data = native_source.astype(new_dt, casting="same_value")
                compressed_header["ZBITPIX"] = 32
                if image_data.dtype.kind == "u":
                    # The input image header carries BZERO=2**63 for the original
                    # uint64 storage; after converting to uint32 the offset must
                    # be 2**31 so the FITS reader reconstructs the right values.
                    compressed_header["BZERO"] = 2**31
                warnings.warn(
                    f"{compression_type} compression doesn't support 64 integers, "
                    "data has been converted to 32 bits",
                    AstropyUserWarning,
                )
            except ValueError:
                raise ValueError(
                    f"{compression_type} compression doesn't support 64 integers, "
                    "but data cannot be converted to 32 bits without overflow",
                )

        _check_compressed_header(compressed_header)

        # TODO: This implementation is memory inefficient as it generates all the
        # compressed bytes before forming them into the heap, leading to 2x the
        # potential memory usage. Directly storing the compressed bytes into an
        # expanding heap would fix this.

        tile_shape = _tile_shape(compressed_header)
        data_shape = _data_shape(compressed_header)

        compressed_bytes = []
        gzip_fallback = []
        scales = []
        zeros = []
        zblank = None

        noisebit = _get_compression_setting(compressed_header, "noisebit", 0)

        settings = _header_to_settings(compressed_header)

        for irow, tile_slices in _iter_array_tiles(data_shape, tile_shape):
            tile_data = image_data[tile_slices]

            if tile_data.dtype.kind == "u":
                if tile_data.dtype.itemsize == 8:
                    # Subtract 2**63 in wrap-around uint64 arithmetic, then
                    # reinterpret the bits as int64. This is equivalent to flipping
                    # the high bit and matches the FITS BZERO=2**63 convention.
                    tile_data = (tile_data - np.uint64(2**63)).view(np.int64)
                elif tile_data.dtype.itemsize == 4:
                    tile_data = (tile_data.astype(np.int64) - 2**31).astype(np.int32)
                elif tile_data.dtype.itemsize == 2:
                    tile_data = (tile_data.astype(np.int32) - 2**15).astype(np.int16)
            elif tile_data.dtype.kind == "i" and tile_data.dtype.itemsize == 1:
                # FITS BITPIX=8 storage is unsigned, so int8 input is recorded via
                # BZERO=-128 and the stored bytes must be shifted by +128. Without
                # this the BZERO=-128 applied on read offsets every value by 128.
                tile_data = (tile_data.astype(np.int16) + 128).astype(np.uint8)

            settings = _update_tile_settings(settings, compression_type, tile_data.shape)

            quantize = "ZSCALE" in compressed_coldefs.dtype.names

            if tile_data.dtype.kind == "f" and quantize:
                dither_method = DITHER_METHODS[
                    compressed_header.get("ZQUANTIZ", "NO_DITHER")
                ]
                dither_seed = compressed_header.get("ZDITHER0", 0)
                q = Quantize(
                    row=(irow + dither_seed) if dither_method != -1 else 0,
                    dither_method=dither_method,
                    quantize_level=noisebit,
                    bitpix=compressed_header["ZBITPIX"],
                )
                original_shape = tile_data.shape

                # If there are any NaN values in the data, we should reset them to
                # a value that will not affect the quantization (an already existing
                # data value in the array) and we can then reset this after quantization
                # to ZBLANK and set the appropriate header keyword
                nan_mask = np.isnan(tile_data)
                any_nan = np.any(nan_mask)
                if any_nan:
                    # Note that we need to copy here to avoid modifying the input array.
                    tile_data = tile_data.copy()
                    if np.all(nan_mask):
                        tile_data[nan_mask] = 0
                    else:
                        tile_data[nan_mask] = np.nanmin(tile_data)

                try:
                    tile_data, scale, zero = q.encode_quantized(tile_data)
                except QuantizationFailedException:
                    if any_nan:
                        # reset NaN values since we will losslessly compress.
                        tile_data[nan_mask] = np.nan

                    scales.append(0)
                    zeros.append(0)
                    gzip_fallback.append(True)

                else:
                    tile_data = np.asarray(tile_data).reshape(original_shape)

                    if any_nan:
                        if not tile_data.flags.writeable:
                            tile_data = tile_data.copy()
                        # For now, we just use the default ZBLANK value and assume
                        # this is the same for all tiles. We could generalize this
                        # to allow different ZBLANK values (for example if the data
                        # includes this value by chance) and to allow different values
                        # per tile, which is allowed by the FITS standard.
                        tile_data[nan_mask] = DEFAULT_ZBLANK
                        zblank = DEFAULT_ZBLANK

                    scales.append(scale)
                    zeros.append(zero)
                    gzip_fallback.append(False)

            else:
                scales.append(0)
                zeros.append(0)
                gzip_fallback.append(False)

            if gzip_fallback[-1]:
                cbytes = _compress_tile(tile_data, algorithm="GZIP_1")
            else:
                cbytes = _compress_tile(tile_data, algorithm=compression_type, **settings)
            compressed_bytes.append(cbytes)

        if zblank is not None:
            compressed_header["ZBLANK"] = zblank

        table = np.zeros(
            len(compressed_bytes), dtype=compressed_coldefs.dtype.newbyteorder(">")
        )

        if "ZSCALE" in table.dtype.names:
            table["ZSCALE"] = np.array(scales)
            table["ZZERO"] = np.array(zeros)

        for irow, cbytes in enumerate(compressed_bytes):
            table["COMPRESSED_DATA"][irow, 0] = len(cbytes)

        table["COMPRESSED_DATA"][:1, 1] = 0
        table["COMPRESSED_DATA"][1:, 1] = np.cumsum(table["COMPRESSED_DATA"][:-1, 0])

        for irow in range(len(compressed_bytes)):
            if gzip_fallback[irow]:
                table["GZIP_COMPRESSED_DATA"][irow] = table["COMPRESSED_DATA"][irow]
                table["COMPRESSED_DATA"][irow] = 0

        # For PLIO_1, the size of each heap element is a factor of two lower than
        # the real size - not clear if this is deliberate or bug somewhere.
        if compression_type == "PLIO_1":
            table["COMPRESSED_DATA"][:, 0] //= 2

        # For PLIO_1, it looks like the compressed data is always stored big endian
        if compression_type == "PLIO_1":
            for irow in range(len(compressed_bytes)):
                if not gzip_fallback[irow]:
                    array = np.frombuffer(compressed_bytes[irow], dtype="i2")
                    if array.dtype.byteorder == "<" or (
                        array.dtype.byteorder == "=" and sys.byteorder == "little"
                    ):
                        compressed_bytes[irow] = array.astype(">i2", copy=False).tobytes()

        compressed_bytes = b"".join(compressed_bytes)

        table_bytes = table.tobytes()

        return table.tobytes() + compressed_bytes