/astropy/io/fits/hdu/compressed.py

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

import ctypes
import gc
import math
import re
import time
import warnings

import numpy as np

from .base import DELAYED, ExtensionHDU
from .image import _ImageBaseHDU, ImageHDU
from .table import BinTableHDU
from ..card import Card
from ..column import Column, ColDefs, TDEF_RE
from ..column import KEYWORD_NAMES as TABLE_KEYWORD_NAMES
from ..fitsrec import FITS_rec
from ..header import Header
from ..util import (_is_pseudo_unsigned, _unsigned_zero, _is_int,
                    _get_array_mmap)

from ....extern.six import string_types, iteritems
from ....utils import lazyproperty, deprecated
from ....utils.compat import ignored
from ....utils.exceptions import (AstropyPendingDeprecationWarning,
                                  AstropyUserWarning)

try:
    from .. import compression
    COMPRESSION_SUPPORTED = COMPRESSION_ENABLED = True
except ImportError:
    COMPRESSION_SUPPORTED = COMPRESSION_ENABLED = False


# Quantization dithering method constants; these are right out of fitsio.h
NO_DITHER = -1
SUBTRACTIVE_DITHER_1 = 1
SUBTRACTIVE_DITHER_2 = 2
QUANTIZE_METHOD_NAMES = {
    NO_DITHER: 'NO_DITHER',
    SUBTRACTIVE_DITHER_1: 'SUBTRACTIVE_DITHER_1',
    SUBTRACTIVE_DITHER_2: 'SUBTRACTIVE_DITHER_2'
}
DITHER_SEED_CLOCK = 0
DITHER_SEED_CHECKSUM = -1


# Default compression parameter values
DEFAULT_COMPRESSION_TYPE = 'RICE_1'
DEFAULT_QUANTIZE_LEVEL = 16.
DEFAULT_QUANTIZE_METHOD = NO_DITHER
DEFAULT_DITHER_SEED = DITHER_SEED_CLOCK
DEFAULT_HCOMP_SCALE = 0
DEFAULT_HCOMP_SMOOTH = 0
DEFAULT_BLOCK_SIZE = 32
DEFAULT_BYTE_PIX = 4

CMTYPE_ALIASES = {}

# CFITSIO version-specific features
if COMPRESSION_SUPPORTED:
    try:
        CFITSIO_SUPPORTS_GZIPDATA = compression.CFITSIO_VERSION >= 3.28
        CFITSIO_SUPPORTS_Q_FORMAT = compression.CFITSIO_VERSION >= 3.35
        if compression.CFITSIO_VERSION >= 3.35:
            CMTYPE_ALIASES['RICE_ONE'] = 'RICE_1'
    except AttributeError:
        # This generally shouldn't happen unless running setup.py in an
        # environment where an old build of pyfits exists
        CFITSIO_SUPPORTS_GZIPDATA = True
        CFITSIO_SUPPORTS_Q_FORMAT = True


COMPRESSION_KEYWORDS = set(['ZIMAGE', 'ZCMPTYPE', 'ZBITPIX', 'ZNAXIS',
                            'ZMASKCMP', 'ZSIMPLE', 'ZTENSION', 'ZEXTEND'])


class CompImageHeader(Header):
    """
    Header object for compressed image HDUs designed to keep the compression
    header and the underlying image header properly synchronized.

    This essentially wraps the image header, so that all values are read from
    and written to the image header.  However, updates to the image header will
    also update the table header where appropriate.
    """

    # TODO: The difficulty of implementing this screams a need to rewrite this
    # module

    _keyword_remaps = {
        'SIMPLE': 'ZSIMPLE', 'XTENSION': 'ZTENSION', 'BITPIX': 'ZBITPIX',
        'NAXIS': 'ZNAXIS', 'EXTEND': 'ZEXTEND', 'BLOCKED': 'ZBLOCKED',
        'PCOUNT': 'ZPCOUNT', 'GCOUNT': 'ZGCOUNT', 'CHECKSUM': 'ZHECKSUM',
        'DATASUM': 'ZDATASUM'
    }

    _zdef_re = re.compile(r'(?P<label>^[Zz][a-zA-Z]*)(?P<num>[1-9][0-9 ]*$)?')
    _compression_keywords = set(_keyword_remaps.values()).union(
        ['ZIMAGE', 'ZCMPTYPE', 'ZMASKCMP', 'ZQUANTIZ', 'ZDITHER0'])
    _indexed_compression_keywords = set(['ZNAXIS', 'ZTILE', 'ZNAME', 'ZVAL'])
    # TODO: Once it place it should be possible to manage some of this through
    # the schema system, but it's not quite ready for that yet.  Also it still
    # makes more sense to change CompImageHDU to subclass ImageHDU :/

    def __init__(self, table_header, image_header=None):
        if image_header is None:
            image_header = Header()
        self._cards = image_header._cards
        self._keyword_indices = image_header._keyword_indices
        self._rvkc_indices = image_header._rvkc_indices
        self._modified = image_header._modified
        self._table_header = table_header

    # We need to override and Header methods that can modify the header, and
    # ensure that they sync with the underlying _table_header

    def __setitem__(self, key, value):
        # This isn't pretty, but if the `key` is either an int or a tuple we
        # need to figure out what keyword name that maps to before doing
        # anything else; these checks will be repeated later in the
        # super().__setitem__ call but I don't see another way around it
        # without some major refactoring
        if self._set_slice(key, value, self):
            return

        if isinstance(key, int):
            keyword, index = self._keyword_from_index(key)
        elif isinstance(key, tuple):
            keyword, index = key
        else:
            # We don't want to specify and index otherwise, because that will
            # break the behavior for new keywords and for commentary keywords
            keyword, index = key, None

        if self._is_reserved_keyword(keyword):
            return

        super(CompImageHeader, self).__setitem__(key, value)


        if index is not None:
            remapped_keyword = self._remap_keyword(keyword)
            self._table_header[remapped_keyword, index] = value
        # Else this will pass through to ._update

    def __delitem__(self, key):
        if isinstance(key, slice) or self._haswildcard(key):
            # If given a slice pass that on to the superclass and bail out
            # early; we only want to make updates to _table_header when given
            # a key specifying a single keyword
            return super(CompImageHeader, self).__delitem__(key)

        if isinstance(key, int):
            keyword, index = self._keyword_from_index(key)
        elif isinstance(key, tuple):
            keyword, index = key
        else:
            keyword, index = key, None

        if key not in self:
            raise KeyError("Keyword %r not found." % key)

        super(CompImageHeader, self).__delitem__(key)

        remapped_keyword = self._remap_keyword(keyword)

        if remapped_keyword in self._table_header:
            if index is not None:
                del self._table_header[(remapped_keyword, index)]
            else:
                del self._table_header[remapped_keyword]

    def append(self, card=None, useblanks=True, bottom=False, end=False):
        # This logic unfortunately needs to be duplicated from the base class
        # in order to determine the keyword
        if isinstance(card, string_types):
            card = Card(card)
        elif isinstance(card, tuple):
            card = Card(*card)
        elif card is None:
            card = Card()
        elif not isinstance(card, Card):
            raise ValueError(
                'The value appended to a Header must be either a keyword or '
                '(keyword, value, [comment]) tuple; got: %r' % card)

        if self._is_reserved_keyword(card.keyword):
            return

        super(CompImageHeader, self).append(card=card, useblanks=useblanks,
                                            bottom=bottom, end=end)

        remapped_keyword = self._remap_keyword(card.keyword)
        card = Card(remapped_keyword, card.value, card.comment)
        self._table_header.append(card=card, useblanks=useblanks,
                                  bottom=bottom, end=end)

    def insert(self, key, card, useblanks=True, after=False):
        if isinstance(key, int):
            # Determine condition to pass through to append
            if after:
                if key == -1:
                    key = len(self._cards)
                else:
                    key += 1

            if key >= len(self._cards):
                self.append(card, end=True)
                return

        if isinstance(card, string_types):
            card = Card(card)
        elif isinstance(card, tuple):
            card = Card(*card)
        elif not isinstance(card, Card):
            raise ValueError(
                'The value inserted into a Header must be either a keyword or '
                '(keyword, value, [comment]) tuple; got: %r' % card)

        if self._is_reserved_keyword(card.keyword):
            return

        # Now the tricky part is to determine where to insert in the table
        # header.  If given a numerical index we need to map that to the
        # corresponding index in the table header.  Although rare, there may be
        # cases where there is no mapping in which case we just try the same
        # index
        # NOTE: It is crucial that remapped_index in particular is figured out
        # before the image header is modified
        remapped_index = self._remap_index(key)
        remapped_keyword = self._remap_keyword(card.keyword)

        super(CompImageHeader, self).insert(key, card, useblanks=useblanks,
                                            after=after)

        card = Card(remapped_keyword, card.value, card.comment)

        self._table_header.insert(remapped_index, card, useblanks=useblanks,
                                  after=after)

    def _update(self, card):
        keyword = card[0]

        if self._is_reserved_keyword(keyword):
            return

        super(CompImageHeader, self)._update(card)

        if keyword in Card._commentary_keywords:
            # Otherwise this will result in a duplicate insertion
            return

        remapped_keyword = self._remap_keyword(keyword)
        self._table_header._update((remapped_keyword,) + card[1:])

    # Last piece needed (I think) for synchronizing with the real header
    # This one is tricky since _relativeinsert calls insert
    def _relativeinsert(self, card, before=None, after=None, replace=False):
        keyword = card[0]

        if self._is_reserved_keyword(keyword):
            return

        # Now we have to figure out how to remap 'before' and 'after'
        if before is None:
            if isinstance(after, int):
                remapped_after = self._remap_index(after)
            else:
                remapped_after = self._remap_keyword(after)
            remapped_before = None
        else:
            if isinstance(before, int):
                remapped_before = self._remap_index(before)
            else:
                remapped_before = self._remap_keyword(before)
            remapped_after = None

        super(CompImageHeader, self)._relativeinsert(card, before=before,
                                                     after=after,
                                                     replace=replace)

        remapped_keyword = self._remap_keyword(keyword)

        card = Card(remapped_keyword, card[1], card[2])
        self._table_header._relativeinsert(card, before=remapped_before,
                                           after=remapped_after,
                                           replace=replace)

    @classmethod
    def _is_reserved_keyword(cls, keyword, warn=True):
        msg = ('Keyword %r is reserved for use by the FITS Tiled Image '
               'Convention and will not be stored in the header for the '
               'image being compressed.' % keyword)

        if keyword == 'TFIELDS':
            if warn:
                warnings.warn(msg)
            return True

        m = TDEF_RE.match(keyword)

        if m and m.group('label').upper() in TABLE_KEYWORD_NAMES:
            if warn:
                warnings.warn(msg)
            return True

        m = cls._zdef_re.match(keyword)

        if m:
            label = m.group('label').upper()
            num = m.group('num')
            if num is not None and label in cls._indexed_compression_keywords:
                if warn:
                    warnings.warn(msg)
                return True
            elif label in cls._compression_keywords:
                if warn:
                    warnings.warn(msg)
                return True

        return False

    @classmethod
    def _remap_keyword(cls, keyword):
        # Given a keyword that one might set on an image, remap that keyword to
        # the name used for it in the COMPRESSED HDU header
        # This is mostly just a lookup in _keyword_remaps, but needs handling
        # for NAXISn keywords

        is_naxisn = False
        if keyword[:5] == 'NAXIS':
            with ignored(ValueError):
                index = int(keyword[5:])
                is_naxisn = index > 0

        if is_naxisn:
            return 'ZNAXIS%d' % index

        # If the keyword does not need to be remapped then just return the
        # original keyword
        return cls._keyword_remaps.get(keyword, keyword)

    def _remap_index(self, idx):
        # Given an integer index into this header, map that to the index in the
        # table header for the same card.  If the card doesn't exist in the
        # table header (generally should *not* be the case) this will just
        # return the same index
        # This *does* also accept a keyword or (keyword, repeat) tuple and
        # obtains the associated numerical index with self._cardindex
        if not isinstance(idx, int):
            idx = self._cardindex(idx)

        keyword, repeat = self._keyword_from_index(idx)
        remapped_insert_keyword = self._remap_keyword(keyword)

        with ignored(IndexError, KeyError):
            idx = self._table_header._cardindex((remapped_insert_keyword,
                                                 repeat))

        return idx


# TODO: Fix this class so that it doesn't actually inherit from BinTableHDU,
# but instead has an internal BinTableHDU reference
class CompImageHDU(BinTableHDU):
    """
    Compressed Image HDU class.
    """

    # Maps deprecated keyword arguments to __init__ to their new names
    DEPRECATED_KWARGS = {
        'compressionType': 'compression_type', 'tileSize': 'tile_size',
        'hcompScale': 'hcomp_scale', 'hcompSmooth': 'hcomp_smooth',
        'quantizeLevel': 'quantize_level'
    }

    _manages_own_heap = True
    """
    The calls to CFITSIO lay out the heap data in memory, and we write it out
    the same way CFITSIO organizes it.  In principle this would break if a user
    manually changes the underlying compressed data by hand, but there is no
    reason they would want to do that (and if they do that's their
    responsibility).
    """

    def __init__(self, data=None, header=None, name=None,
                 compression_type=DEFAULT_COMPRESSION_TYPE,
                 tile_size=None,
                 hcomp_scale=DEFAULT_HCOMP_SCALE,
                 hcomp_smooth=DEFAULT_HCOMP_SMOOTH,
                 quantize_level=DEFAULT_QUANTIZE_LEVEL,
                 quantize_method=DEFAULT_QUANTIZE_METHOD,
                 dither_seed=DEFAULT_DITHER_SEED,
                 do_not_scale_image_data=False,
                 uint=False, scale_back=False, **kwargs):
        """
        Parameters
        ----------
        data : array, optional
            Uncompressed image data

        header : Header instance, optional
            Header to be associated with the image; when reading the HDU from a
            file (data=DELAYED), the header read from the file

        name : str, optional
            The ``EXTNAME`` value; if this value is `None`, then the name from
            the input image header will be used; if there is no name in the
            input image header then the default name ``COMPRESSED_IMAGE`` is
            used.

        compression_type : str, optional
            Compression algorithm: one of
            ``'RICE_1'``, ``'RICE_ONE'``, ``'PLIO_1'``, ``'GZIP_1'``,
            ``'GZIP_2'``, ``'HCOMPRESS_1'``

        tile_size : int, optional
            Compression tile sizes.  Default treats each row of image as a
            tile.

        hcomp_scale : float, optional
            HCOMPRESS scale parameter

        hcomp_smooth : float, optional
            HCOMPRESS smooth parameter

        quantize_level : float, optional
            Floating point quantization level; see note below

        quantize_method : int, optional
            Floating point quantization dithering method; can be either
            ``NO_DITHER`` (-1), ``SUBTRACTIVE_DITHER_1`` (1; default), or
            ``SUBTRACTIVE_DITHER_2`` (2); see note below

        dither_seed : int, optional
            Random seed to use for dithering; can be either an integer in the
            range 1 to 1000 (inclusive), ``DITHER_SEED_CLOCK`` (0; default), or
            ``DITHER_SEED_CHECKSUM`` (-1); see note below

        Notes
        -----
        The astropy.io.fits package supports 2 methods of image compression:

            1) The entire FITS file may be externally compressed with the gzip
               or pkzip utility programs, producing a ``*.gz`` or ``*.zip``
               file, respectively.  When reading compressed files of this type,
               Astropy first uncompresses the entire file into a temporary file
               before performing the requested read operations.  The
               astropy.io.fits package does not support writing to these types
               of compressed files.  This type of compression is supported in
               the ``_File`` class, not in the `CompImageHDU` class.  The file
               compression type is recognized by the ``.gz`` or ``.zip`` file
               name extension.

            2) The `CompImageHDU` class supports the FITS tiled image
               compression convention in which the image is subdivided into a
               grid of rectangular tiles, and each tile of pixels is
               individually compressed.  The details of this FITS compression
               convention are described at the `FITS Support Office web site
               <http://fits.gsfc.nasa.gov/registry/tilecompression.html>`_.
               Basically, the compressed image tiles are stored in rows of a
               variable length array column in a FITS binary table.  The
               astropy.io.fits recognizes that this binary table extension
               contains an image and treats it as if it were an image
               extension.  Under this tile-compression format, FITS header
               keywords remain uncompressed.  At this time, Astropy does not
               support the ability to extract and uncompress sections of the
               image without having to uncompress the entire image.

        The astropy.io.fits package supports 3 general-purpose compression
        algorithms plus one other special-purpose compression technique that is
        designed for data masks with positive integer pixel values.  The 3
        general purpose algorithms are GZIP, Rice, and HCOMPRESS, and the
        special-purpose technique is the IRAF pixel list compression technique
        (PLIO).  The ``compression_type`` parameter defines the compression
        algorithm to be used.

        The FITS image can be subdivided into any desired rectangular grid of
        compression tiles.  With the GZIP, Rice, and PLIO algorithms, the
        default is to take each row of the image as a tile.  The HCOMPRESS
        algorithm is inherently 2-dimensional in nature, so the default in this
        case is to take 16 rows of the image per tile.  In most cases, it makes
        little difference what tiling pattern is used, so the default tiles are
        usually adequate.  In the case of very small images, it could be more
        efficient to compress the whole image as a single tile.  Note that the
        image dimensions are not required to be an integer multiple of the tile
        dimensions; if not, then the tiles at the edges of the image will be
        smaller than the other tiles.  The ``tile_size`` parameter may be
        provided as a list of tile sizes, one for each dimension in the image.
        For example a ``tile_size`` value of ``[100,100]`` would divide a 300 X
        300 image into 9 100 X 100 tiles.

        The 4 supported image compression algorithms are all 'lossless' when
        applied to integer FITS images; the pixel values are preserved exactly
        with no loss of information during the compression and uncompression
        process.  In addition, the HCOMPRESS algorithm supports a 'lossy'
        compression mode that will produce larger amount of image compression.
        This is achieved by specifying a non-zero value for the ``hcomp_scale``
        parameter.  Since the amount of compression that is achieved depends
        directly on the RMS noise in the image, it is usually more convenient
        to specify the ``hcomp_scale`` factor relative to the RMS noise.
        Setting ``hcomp_scale = 2.5`` means use a scale factor that is 2.5
        times the calculated RMS noise in the image tile.  In some cases it may
        be desirable to specify the exact scaling to be used, instead of
        specifying it relative to the calculated noise value.  This may be done
        by specifying the negative of the desired scale value (typically in the
        range -2 to -100).

        Very high compression factors (of 100 or more) can be achieved by using
        large ``hcomp_scale`` values, however, this can produce undesirable
        'blocky' artifacts in the compressed image.  A variation of the
        HCOMPRESS algorithm (called HSCOMPRESS) can be used in this case to
        apply a small amount of smoothing of the image when it is uncompressed
        to help cover up these artifacts.  This smoothing is purely cosmetic
        and does not cause any significant change to the image pixel values.
        Setting the ``hcomp_smooth`` parameter to 1 will engage the smoothing
        algorithm.

        Floating point FITS images (which have ``BITPIX`` = -32 or -64) usually
        contain too much 'noise' in the least significant bits of the mantissa
        of the pixel values to be effectively compressed with any lossless
        algorithm.  Consequently, floating point images are first quantized
        into scaled integer pixel values (and thus throwing away much of the
        noise) before being compressed with the specified algorithm (either
        GZIP, RICE, or HCOMPRESS).  This technique produces much higher
        compression factors than simply using the GZIP utility to externally
        compress the whole FITS file, but it also means that the original
        floating point value pixel values are not exactly preserved.  When done
        properly, this integer scaling technique will only discard the
        insignificant noise while still preserving all the real information in
        the image.  The amount of precision that is retained in the pixel
        values is controlled by the ``quantize_level`` parameter.  Larger
        values will result in compressed images whose pixels more closely match
        the floating point pixel values, but at the same time the amount of
        compression that is achieved will be reduced.  Users should experiment
        with different values for this parameter to determine the optimal value
        that preserves all the useful information in the image, without
        needlessly preserving all the 'noise' which will hurt the compression
        efficiency.

        The default value for the ``quantize_level`` scale factor is 16, which
        means that scaled integer pixel values will be quantized such that the
        difference between adjacent integer values will be 1/16th of the noise
        level in the image background.  An optimized algorithm is used to
        accurately estimate the noise in the image.  As an example, if the RMS
        noise in the background pixels of an image = 32.0, then the spacing
        between adjacent scaled integer pixel values will equal 2.0 by default.
        Note that the RMS noise is independently calculated for each tile of
        the image, so the resulting integer scaling factor may fluctuate
        slightly for each tile.  In some cases, it may be desirable to specify
        the exact quantization level to be used, instead of specifying it
        relative to the calculated noise value.  This may be done by specifying
        the negative of desired quantization level for the value of
        ``quantize_level``.  In the previous example, one could specify
        ``quantize_level = -2.0`` so that the quantized integer levels differ
        by 2.0.  Larger negative values for ``quantize_level`` means that the
        levels are more coarsely-spaced, and will produce higher compression
        factors.

        The quantization algorithm can also apply one of two random dithering
        methods in order to reduce bias in the measured intensity of background
        regions.  The default method, specified with the constant
        ``SUBTRACTIVE_DITHER_1`` adds dithering to the zero-point of the
        quantization array itself rather than adding noise to the actual image.
        The random noise is added on a pixel-by-pixel basis, so in order
        restore each pixel from its integer value to its floating point value
        it is necessary to replay the same sequence of random numbers for each
        pixel (see below).  The other method, ``SUBTRACTIVE_DITHER_2``, is
        exactly like the first except that before dithering any pixel with a
        floating point value of ``0.0`` is replaced with the special integer
        value ``-2147483647``.  When the image is uncompressed, pixels with
        this value are restored back to ``0.0`` exactly.  Finally, a value of
        ``NO_DITHER`` disables dithering entirely.

        As mentioned above, when using the subtractive dithering algorithm it
        is necessary to be able to generate a (pseudo-)random sequence of noise
        for each pixel, and replay that same sequence upon decompressing.  To
        facilitate this, a random seed between 1 and 10000 (inclusive) is used
        to seed a random number generator, and that seed is stored in the
        ``ZDITHER0`` keyword in the header of the compressed HDU.  In order to
        use that seed to generate the same sequence of random numbers the same
        random number generator must be used at compression and decompression
        time; for that reason the tiled image convention provides an
        implementation of a very simple pseudo-random number generator.  The
        seed itself can be provided in one of three ways, controllable by the
        ``dither_seed`` argument:  It may be specified manually, or it may be
        generated arbitrarily based on the system's clock
        (``DITHER_SEED_CLOCK``) or based on a checksum of the pixels in the
        image's first tile (``DITHER_SEED_CHECKSUM``).  The clock-based method
        is the default, and is sufficient to ensure that the value is
        reasonably "arbitrary" and that the same seed is unlikely to be
        generated sequentially.  The checksum method, on the other hand,
        ensures that the same seed is used every time for a specific image.
        This is particularly useful for software testing as it ensures that the
        same image will always use the same seed.
        """

        if not COMPRESSION_SUPPORTED:
            # TODO: Raise a more specific Exception type
            raise Exception('The astropy.io.fits.compression module is not '
                            'available.  Creation of compressed image HDUs is '
                            'disabled.')

        compression_type = CMTYPE_ALIASES.get(compression_type, compression_type)

        # Handle deprecated keyword arguments
        compression_opts = {}
        for oldarg, newarg in self.DEPRECATED_KWARGS.items():
            if oldarg in kwargs:
                warnings.warn('Keyword argument %s to %s is pending '
                              'deprecation; use %s instead' %
                              (oldarg, self.__class__.__name__, newarg),
                              AstropyPendingDeprecationWarning)
                compression_opts[newarg] = kwargs[oldarg]
                del kwargs[oldarg]
            else:
                compression_opts[newarg] = locals()[newarg]
        # Include newer compression options that don't required backwards
        # compatibility with deprecated spellings
        compression_opts['quantize_method'] = quantize_method
        compression_opts['dither_seed'] = dither_seed

        if data is DELAYED:
            # Reading the HDU from a file
            super(CompImageHDU, self).__init__(data=data, header=header)
        else:
            # Create at least a skeleton HDU that matches the input
            # header and data (if any were input)
            super(CompImageHDU, self).__init__(data=None, header=header)

            # Store the input image data
            self.data = data

            # Update the table header (_header) to the compressed
            # image format and to match the input data (if any);
            # Create the image header (_image_header) from the input
            # image header (if any) and ensure it matches the input
            # data; Create the initially empty table data array to
            # hold the compressed data.
            self._update_header_data(header, name, **compression_opts)

        # TODO: A lot of this should be passed on to an internal image HDU o
        # something like that, see ticket #88
        self._do_not_scale_image_data = do_not_scale_image_data
        self._uint = uint
        self._scale_back = scale_back

        self._axes = [self._header.get('ZNAXIS' + str(axis + 1), 0)
                      for axis in range(self._header.get('ZNAXIS', 0))]

        # store any scale factors from the table header
        if do_not_scale_image_data:
            self._bzero = 0
            self._bscale = 1
        else:
            self._bzero = self._header.get('BZERO', 0)
            self._bscale = self._header.get('BSCALE', 1)
        self._bitpix = self._header['ZBITPIX']

        self._orig_bzero = self._bzero
        self._orig_bscale = self._bscale
        self._orig_bitpix = self._bitpix

    @classmethod
    def match_header(cls, header):
        card = header.cards[0]
        if card.keyword != 'XTENSION':
            return False

        xtension = card.value
        if isinstance(xtension, string_types):
            xtension = xtension.rstrip()

        if xtension not in ('BINTABLE', 'A3DTABLE'):
            return False

        if 'ZIMAGE' not in header or header['ZIMAGE'] != True:
            return False

        if COMPRESSION_SUPPORTED and COMPRESSION_ENABLED:
            return True
        elif not COMPRESSION_SUPPORTED:
            warnings.warn('Failure matching header to a compressed image '
                          'HDU: The compression module is not available.\n'
                          'The HDU will be treated as a Binary Table HDU.',
                          AstropyUserWarning)
            return False
        else:
            # Compression is supported but disabled; just pass silently (#92)
            return False

    def _update_header_data(self, image_header,
                            name=None,
                            compression_type=None,
                            tile_size=None,
                            hcomp_scale=None,
                            hcomp_smooth=None,
                            quantize_level=None,
                            quantize_method=None,
                            dither_seed=None):
        """
        Update the table header (`_header`) to the compressed
        image format and to match the input data (if any).  Create
        the image header (`_image_header`) from the input image
        header (if any) and ensure it matches the input
        data. Create the initially-empty table data array to hold
        the compressed data.

        This method is mainly called internally, but a user may wish to
        call this method after assigning new data to the `CompImageHDU`
        object that is of a different type.

        Parameters
        ----------
        image_header : Header instance
            header to be associated with the image

        name : str, optional
            the ``EXTNAME`` value; if this value is `None`, then the name from
            the input image header will be used; if there is no name in the
            input image header then the default name 'COMPRESSED_IMAGE' is used

        compression_type : str, optional
            compression algorithm 'RICE_1', 'PLIO_1', 'GZIP_1', 'HCOMPRESS_1';
            if this value is `None`, use value already in the header; if no
            value already in the header, use 'RICE_1'

        tile_size : sequence of int, optional
            compression tile sizes as a list; if this value is `None`, use
            value already in the header; if no value already in the header,
            treat each row of image as a tile

        hcomp_scale : float, optional
            HCOMPRESS scale parameter; if this value is `None`, use the value
            already in the header; if no value already in the header, use 1

        hcomp_smooth : float, optional
            HCOMPRESS smooth parameter; if this value is `None`, use the value
            already in the header; if no value already in the header, use 0

        quantize_level : float, optional
            floating point quantization level; if this value is `None`, use the
            value already in the header; if no value already in header, use 16

        quantize_method : int, optional
            floating point quantization dithering method; can be either
            NO_DITHER (-1), SUBTRACTIVE_DITHER_1 (1; default), or
            SUBTRACTIVE_DITHER_2 (2)

        dither_seed : int, optional
            random seed to use for dithering; can be either an integer in the
            range 1 to 1000 (inclusive), DITHER_SEED_CLOCK (0; default), or
            DITHER_SEED_CHECKSUM (-1)
        """

        image_hdu = ImageHDU(data=self.data, header=self._header)
        self._image_header = CompImageHeader(self._header, image_hdu.header)
        self._axes = image_hdu._axes
        del image_hdu

        # Determine based on the size of the input data whether to use the Q
        # column format to store compressed data or the P format.
        # The Q format is used only if the uncompressed data is larger than
        # 4 GB.  This is not a perfect heuristic, as one can contrive an input
        # array which, when compressed, the entire binary table representing
        # the compressed data is larger than 4GB.  That said, this is the same
        # heuristic used by CFITSIO, so this should give consistent results.
        # And the cases where this heuristic is insufficient are extreme and
        # almost entirely contrived corner cases, so it will do for now
        if self._has_data:
            huge_hdu = self.data.nbytes > 2 ** 32

            if huge_hdu and not CFITSIO_SUPPORTS_Q_FORMAT:
                raise IOError(
                    "Astropy cannot compress images greater than 4 GB in size "
                    "(%s is %s bytes) without CFITSIO >= 3.35" %
                    ((self.name, self.ver), self.data.nbytes))
        else:
            huge_hdu = False

        # Update the extension name in the table header
        if not name and not 'EXTNAME' in self._header:
            name = 'COMPRESSED_IMAGE'

        if name:
            self._header.set('EXTNAME', name,
                             'name of this binary table extension',
                             after='TFIELDS')
            self.name = name
        else:
            self.name = self._header['EXTNAME']

        # Set the compression type in the table header.
        if compression_type:
            if compression_type not in ['RICE_1', 'GZIP_1', 'PLIO_1',
                                        'HCOMPRESS_1']:
                warnings.warn('Unknown compression type provided.  Default '
                              '(%s) compression used.' %
                              DEFAULT_COMPRESSION_TYPE, AstropyUserWarning)
                compression_type = DEFAULT_COMPRESSION_TYPE

            self._header.set('ZCMPTYPE', compression_type,
                             'compression algorithm', after='TFIELDS')
        else:
            compression_type = self._header.get('ZCMPTYPE',
                                                DEFAULT_COMPRESSION_TYPE)
            compression_type = CMTYPE_ALIASES.get(compression_type,
                                                  compression_type)

        # If the input image header had BSCALE/BZERO cards, then insert
        # them in the table header.

        if image_header:
            bzero = image_header.get('BZERO', 0.0)
            bscale = image_header.get('BSCALE', 1.0)
            after_keyword = 'EXTNAME'

            if bscale != 1.0:
                self._header.set('BSCALE', bscale, after=after_keyword)
                after_keyword = 'BSCALE'

            if bzero != 0.0:
                self._header.set('BZERO', bzero, after=after_keyword)

            bitpix_comment = image_header.comments['BITPIX']
            naxis_comment = image_header.comments['NAXIS']
        else:
            bitpix_comment = 'data type of original image'
            naxis_comment = 'dimension of original image'

        # Set the label for the first column in the table

        self._header.set('TTYPE1', 'COMPRESSED_DATA', 'label for field 1',
                         after='TFIELDS')

        # Set the data format for the first column.  It is dependent
        # on the requested compression type.

        if compression_type == 'PLIO_1':
            tform1 = '1QI' if huge_hdu else '1PI'
        else:
            tform1 = '1QB' if huge_hdu else '1PB'

        self._header.set('TFORM1', tform1,
                         'data format of field: variable length array',
                         after='TTYPE1')

        # Create the first column for the table.  This column holds the
        # compressed data.
        col1 = Column(name=self._header['TTYPE1'], format=tform1)

        # Create the additional columns required for floating point
        # data and calculate the width of the output table.

        zbitpix = self._image_header['BITPIX']

        if zbitpix < 0 and quantize_level != 0.0:
            # floating point image has 'COMPRESSED_DATA',
            # 'UNCOMPRESSED_DATA', 'ZSCALE', and 'ZZERO' columns (unless using
            # lossless compression, per CFITSIO)
            ncols = 4

            # CFITSIO 3.28 and up automatically use the GZIP_COMPRESSED_DATA
            # store floating point data that couldn't be quantized, instead
            # of the UNCOMPRESSED_DATA column.  There's no way to control
            # this behavior so the only way to determine which behavior will
            # be employed is via the CFITSIO version

            if CFITSIO_SUPPORTS_GZIPDATA:
                ttype2 = 'GZIP_COMPRESSED_DATA'
                # The required format for the GZIP_COMPRESSED_DATA is actually
                # missing from the standard docs, but CFITSIO suggests it
                # should be 1PB, which is logical.
                tform2 = '1QB' if huge_hdu else '1PB'
            else:
                # Q format is not supported for UNCOMPRESSED_DATA columns.
                ttype2 = 'UNCOMPRESSED_DATA'
                if zbitpix == 8:
                    tform2 = '1QB' if huge_hdu else '1PB'
                elif zbitpix == 16:
                    tform2 = '1QI' if huge_hdu else '1PI'
                elif zbitpix == 32:
                    tform2 = '1QJ' if huge_hdu else '1PJ'
                elif zbitpix == -32:
                    tform2 = '1QE' if huge_hdu else '1PE'
                else:
                    tform2 = '1QD' if huge_hdu else '1PD'

            # Set up the second column for the table that will hold any
            # uncompressable data.
            self._header.set('TTYPE2', ttype2, 'label for field 2',
                             after='TFORM1')

            self._header.set('TFORM2', tform2,
                             'data format of field: variable length array',
                             after='TTYPE2')

            col2 = Column(name=ttype2, format=tform2)

            # Set up the third column for the table that will hold
            # the scale values for quantized data.
            self._header.set('TTYPE3', 'ZSCALE', 'label for field 3',
                             after='TFORM2')
            self._header.set('TFORM3', '1D',
                             'data format of field: 8-byte DOUBLE',
                             after='TTYPE3')
            col3 = Column(name=self._header['TTYPE3'],
                          format=self._header['TFORM3'])

            # Set up the fourth column for the table that will hold
            # the zero values for the quantized data.
            self._header.set('TTYPE4', 'ZZERO', 'label for field 4',
                             after='TFORM3')
            self._header.set('TFORM4', '1D',
                             'data format of field: 8-byte DOUBLE',
                             after='TTYPE4')
            after = 'TFORM4'
            col4 = Column(name=self._header['TTYPE4'],
                          format=self._header['TFORM4'])

            # Create the ColDefs object for the table
            cols = ColDefs([col1, col2, col3, col4])
        else:
            # default table has just one 'COMPRESSED_DATA' column
            ncols = 1
            after = 'TFORM1'

            # remove any header cards for the additional columns that
            # may be left over from the previous data
            to_remove = ['TTYPE2', 'TFORM2', 'TTYPE3', 'TFORM3', 'TTYPE4',
                         'TFORM4']

            for k in to_remove:
                try:
                    del self._header[k]
                except KeyError:
                    pass

            # Create the ColDefs object for the table
            cols = ColDefs([col1])

        # Update the table header with the width of the table, the
        # number of fields in the table, the indicator for a compressed
        # image HDU, the data type of the image data and the number of
        # dimensions in the image data array.
        self._header.set('NAXIS1', cols.dtype.itemsize,
                         'width of table in bytes')
        self._header.set('TFIELDS', ncols, 'number of fields in each row')
        self._header.set('ZIMAGE', True, 'extension contains compressed image',
                         after=after)
        self._header.set('ZBITPIX', zbitpix,
                         bitpix_comment, after='ZIMAGE')
        self._header.set('ZNAXIS', self._image_header['NAXIS'], naxis_comment,
                         after='ZBITPIX')

        # Strip the table header of all the ZNAZISn and ZTILEn keywords
        # that may be left over from the previous data

        idx = 1
        while True:
            try:
                del self._header['ZNAXIS' + str(idx)]
                del self._header['ZTILE' + str(idx)]
                idx += 1
            except KeyError:
                break

        # Verify that any input tile size parameter is the appropriate
        # size to match the HDU's data.

        naxis = self._image_header['NAXIS']

        if not tile_size:
            tile_size = []
        elif len(tile_size) != naxis:
            warnings.warn('Provided tile size not appropriate for the data.  '
                          'Default tile size will be used.', AstropyUserWarning)
            tile_size = []

        # Set default tile dimensions for HCOMPRESS_1

        if compression_type == 'HCOMPRESS_1':
            if (self._image_header['NAXIS1'] < 4 or
                    self._image_header['NAXIS2'] < 4):
                raise ValueError('Hcompress minimum image dimension is '
                                 '4 pixels')
            elif tile_size:
                if tile_size[0] < 4 or tile_size[1] < 4:
                    # user specified tile size is too small
                    raise ValueError('Hcompress minimum tile dimension is '
                                     '4 pixels')
                major_dims = len([ts for ts in tile_size if ts > 1])
                if major_dims > 2:
                    raise ValueError(
                        'HCOMPRESS can only support 2-dimensional tile sizes.'
                        'All but two of the tile_size dimensions must be set '
                        'to 1.')

            if tile_size and (tile_size[0] == 0 and tile_size[1] == 0):
                # compress the whole image as a single tile
                tile_size[0] = self._image_header['NAXIS1']
                tile_size[1] = self._image_header['NAXIS2']

                for i in range(2, naxis):
                    # set all higher tile dimensions = 1
                    tile_size[i] = 1
            elif not tile_size:
                # The Hcompress algorithm is inherently 2D in nature, so the
                # row by row tiling that is used for other compression
                # algorithms is not appropriate.  If the image has less than 30
                # rows, then the entire image will be compressed as a single
                # tile.  Otherwise the tiles will consist of 16 rows of the
                # image.  This keeps the tiles to a reasonable size, and it
                # also includes enough rows to allow good compression
                # efficiency.  It the last tile of the image happens to contain
                # less than 4 rows, then find another tile size with between 14
                # and 30 rows (preferably even), so that the last tile has at
                # least 4 rows.

                # 1st tile dimension is the row length of the image
                tile_size.append(self._image_header['NAXIS1'])

                if self._image_header['NAXIS2'] <= 30:
                    tile_size.append(self._image_header['NAXIS1'])
                else:
                    # look for another good tile dimension
                    naxis2 = self._image_header['NAXIS2']
                    for dim in [16, 24, 20, 30, 28, 26, 22, 18, 14]:
                        if naxis2 % dim == 0 or naxis2 % dim > 3:
                            tile_size.append(dim)
                            break
                    else:
                        tile_size.append(17)

                for i in range(2, naxis):
                    # set all higher tile dimensions = 1
                    tile_size.append(1)

            # check if requested tile size causes the last tile to have
            # less than 4 pixels

            remain = self._image_header['NAXIS1'] % tile_size[0]  # 1st dimen

            if remain > 0 and remain < 4:
                tile_size[0] += 1  # try increasing tile size by 1

                remain = self._image_header['NAXIS1'] % tile_size[0]

                if remain > 0 and remain < 4:
                    raise ValueError('Last tile along 1st dimension has '
                                     'less than 4 pixels')

            remain = self._image_header['NAXIS2'] % tile_size[1]  # 2nd dimen

            if remain > 0 and remain < 4:
                tile_size[1] += 1  # try increasing tile size by 1

                remain = self._image_header['NAXIS2'] % tile_size[1]

                if remain > 0 and remain < 4:
                    raise ValueError('Last tile along 2nd dimension has '
                                     'less than 4 pixels')

        # Set up locations for writing the next cards in the header.
        last_znaxis = 'ZNAXIS'

        if self._image_header['NAXIS'] > 0:
            after1 = 'ZNAXIS1'
        else:
            after1 = 'ZNAXIS'

        # Calculate the number of rows in the output table and
        # write the ZNAXISn and ZTILEn cards to the table header.
        nrows = 0

        for idx, axis in enumerate(self._axes):
            naxis = 'NAXIS' + str(idx + 1)
            znaxis = 'ZNAXIS' + str(idx + 1)
            ztile = 'ZTILE' + str(idx + 1)

            if tile_size and len(tile_size) >= idx + 1:
                ts = tile_size[idx]
            else:
                if not ztile in self._header:
                    # Default tile size
                    if not idx:
                        ts = self._image_header['NAXIS1']
                    else:
                        ts = 1
                else:
                    ts = self._header[ztile]
                tile_size.append(ts)

            if not nrows:
                nrows = (axis - 1) // ts + 1
            else:
                nrows *= ((axis - 1) // ts + 1)

            if image_header and naxis in image_header:
                self._header.set(znaxis, axis, image_header.comments[naxis],
                                 after=last_znaxis)
            else:
                self._header.set(znaxis, axis,
                                 'length of original image axis',
                                 after=last_znaxis)

            self._header.set(ztile, ts, 'size of tiles to be compressed',
                             after=after1)
            last_znaxis = znaxis
            after1 = ztile

        # Set the NAXIS2 header card in the table hdu to the number of
        # rows in the table.
        self._header.set('NAXIS2', nrows, 'number of rows in table')

        self.columns = cols

        # Set the compression parameters in the table header.

        # First, setup the values to be used for the compression parameters
        # in case none were passed in.  This will be either the value
        # already in the table header for that parameter or the default
        # value.
        idx = 1

        while True:
            zname = 'ZNAME' + str(idx)
            if zname not in self._header:
                break
            zval = 'ZVAL' + str(idx)
            if