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# Source code for astropy.nddata.ccddata

    # Licensed under a 3-clause BSD style license - see LICENSE.rst
    """This module implements the base CCDData class."""

    import itertools

    import numpy as np

    from astropy import log
    from astropy import units as u
    from astropy.io import fits, registry
    from astropy.utils.decorators import sharedmethod
    from astropy.wcs import WCS

    from .compat import NDDataArray
    from .flag_collection import FlagCollection
    from .nduncertainty import (
        InverseVariance,
        NDUncertainty,
        StdDevUncertainty,
        VarianceUncertainty,
    )

    __all__ = ["CCDData", "fits_ccddata_reader", "fits_ccddata_writer"]

    _known_uncertainties = (StdDevUncertainty, VarianceUncertainty, InverseVariance)
    _unc_name_to_cls = {cls.__name__: cls for cls in _known_uncertainties}
    _unc_cls_to_name = {cls: cls.__name__ for cls in _known_uncertainties}

    # Global value which can turn on/off the unit requirements when creating a
    # CCDData. Should be used with care because several functions actually break
    # if the unit is None!
    _config_ccd_requires_unit = True

    def _arithmetic(op):
        """Decorator factory which temporarily disables the need for a unit when
        creating a new CCDData instance. The final result must have a unit.

        Parameters
        ----------
        op : function
            The function to apply. Supported are:

            - ``np.add``
            - ``np.subtract``
            - ``np.multiply``
            - ``np.true_divide``

        Notes
        -----
        Should only be used on CCDData ``add``, ``subtract``, ``divide`` or
        ``multiply`` because only these methods from NDArithmeticMixin are
        overwritten.
        """

        def decorator(func):
            def inner(self, operand, operand2=None, **kwargs):
                global _config_ccd_requires_unit
                _config_ccd_requires_unit = False
                result = self._prepare_then_do_arithmetic(op, operand, operand2, **kwargs)
                # Wrap it again as CCDData so it checks the final unit.
                _config_ccd_requires_unit = True
                return result.__class__(result)

            inner.__doc__ = f"See `astropy.nddata.NDArithmeticMixin.{func.__name__}`."
            return sharedmethod(inner)

        return decorator

    def _uncertainty_unit_equivalent_to_parent(uncertainty_type, unit, parent_unit):
        if uncertainty_type is StdDevUncertainty:
            return unit == parent_unit
        elif uncertainty_type is VarianceUncertainty:
            return unit == (parent_unit**2)
        elif uncertainty_type is InverseVariance:
            return unit == (1 / (parent_unit**2))
        raise ValueError(f"unsupported uncertainty type: {uncertainty_type}")

    class CCDData(NDDataArray):
        """A class describing basic CCD data.

        The CCDData class is based on the NDData object and includes a data array,
        uncertainty frame, mask frame, flag frame, meta data, units, and WCS
        information for a single CCD image.

        Parameters
        ----------
        data : `~astropy.nddata.CCDData`-like or array-like
            The actual data contained in this `~astropy.nddata.CCDData` object.
            Note that the data will always be saved by *reference*, so you should
            make a copy of the ``data`` before passing it in if that's the desired
            behavior.

        uncertainty : `~astropy.nddata.StdDevUncertainty`, \
                `~astropy.nddata.VarianceUncertainty`, \
                `~astropy.nddata.InverseVariance`, `~numpy.ndarray` or \
                None, optional
            Uncertainties on the data. If the uncertainty is a `~numpy.ndarray`, it
            it assumed to be, and stored as, a `~astropy.nddata.StdDevUncertainty`.
            Default is ``None``.

        mask : `~numpy.ndarray` or None, optional
            Mask for the data, given as a boolean Numpy array with a shape
            matching that of the data. The values must be `False` where
            the data is *valid* and `True` when it is not (like Numpy
            masked arrays). If ``data`` is a numpy masked array, providing
            ``mask`` here will causes the mask from the masked array to be
            ignored.
            Default is ``None``.

        flags : `~numpy.ndarray` or `~astropy.nddata.FlagCollection` or None, \
                optional
            Flags giving information about each pixel. These can be specified
            either as a Numpy array of any type with a shape matching that of the
            data, or as a `~astropy.nddata.FlagCollection` instance which has a
            shape matching that of the data.
            Default is ``None``.

        wcs : `~astropy.wcs.WCS` or None, optional
            WCS-object containing the world coordinate system for the data.
            Default is ``None``.

        meta : dict-like object or None, optional
            Metadata for this object. "Metadata" here means all information that
            is included with this object but not part of any other attribute
            of this particular object, e.g. creation date, unique identifier,
            simulation parameters, exposure time, telescope name, etc.

        unit : `~astropy.units.Unit` or str, optional
            The units of the data.
            Default is ``None``.

            .. warning::

                If the unit is ``None`` or not otherwise specified it will raise a
                ``ValueError``

        psf : `~numpy.ndarray` or None, optional
            Image representation of the PSF at the center of this image. In order
            for convolution to be flux-preserving, this should generally be
            normalized to sum to unity.

        Raises
        ------
        ValueError
            If the ``uncertainty`` or ``mask`` inputs cannot be broadcast (e.g.,
            match shape) onto ``data``.

        Methods
        -------
        read(\\*args, \\**kwargs)
            ``Classmethod`` to create an CCDData instance based on a ``FITS`` file.
            This method uses :func:`fits_ccddata_reader` with the provided
            parameters.
        write(\\*args, \\**kwargs)
            Writes the contents of the CCDData instance into a new ``FITS`` file.
            This method uses :func:`fits_ccddata_writer` with the provided
            parameters.

        Attributes
        ----------
        known_invalid_fits_unit_strings
            A dictionary that maps commonly-used fits unit name strings that are
            technically invalid to the correct valid unit type (or unit string).
            This is primarily for variant names like "ELECTRONS/S" which are not
            formally valid, but are unambiguous and frequently enough encountered
            that it is convenient to map them to the correct unit.

        Notes
        -----
        `~astropy.nddata.CCDData` objects can be easily converted to a regular
         Numpy array using `numpy.asarray`.

        For example::

            >>> from astropy.nddata import CCDData
            >>> import numpy as np
            >>> x = CCDData([1,2,3], unit='adu')
            >>> np.asarray(x)
            array([1, 2, 3])

        This is useful, for example, when plotting a 2D image using
        matplotlib.

            >>> from astropy.nddata import CCDData
            >>> from matplotlib import pyplot as plt   # doctest: +SKIP
            >>> x = CCDData([[1,2,3], [4,5,6]], unit='adu')
            >>> plt.imshow(x)   # doctest: +SKIP

        """

        def __init__(self, *args, **kwd):
            if "meta" not in kwd:
                kwd["meta"] = kwd.pop("header", None)
            if "header" in kwd:
                raise ValueError("can't have both header and meta.")

            super().__init__(*args, **kwd)
            if self._wcs is not None:
                llwcs = self._wcs.low_level_wcs
                if not isinstance(llwcs, WCS):
                    raise TypeError("the wcs must be a WCS instance.")
                self._wcs = llwcs

            # Check if a unit is set. This can be temporarily disabled by the
            # _CCDDataUnit contextmanager.
            if _config_ccd_requires_unit and self.unit is None:
                raise ValueError("a unit for CCDData must be specified.")

        def _slice_wcs(self, item):
            """
            Override the WCS slicing behaviour so that the wcs attribute continues
            to be an `astropy.wcs.WCS`.
            """
            if self.wcs is None:
                return None

            try:
                return self.wcs[item]
            except Exception as err:
                self._handle_wcs_slicing_error(err, item)

        @property
        def data(self):
            return self._data

        @data.setter
        def data(self, value):
            self._data = value

        @property
        def wcs(self):
            return self._wcs

        @wcs.setter
        def wcs(self, value):
            if value is not None and not isinstance(value, WCS):
                raise TypeError("the wcs must be a WCS instance.")
            self._wcs = value

        @property
        def unit(self):
            return self._unit

        @unit.setter
        def unit(self, value):
            self._unit = u.Unit(value)

        @property
        def psf(self):
            return self._psf

        @psf.setter
        def psf(self, value):
            if value is not None and not isinstance(value, np.ndarray):
                raise TypeError("The psf must be a numpy array.")
            self._psf = value

        @property
        def header(self):
            return self._meta

        @header.setter
        def header(self, value):
            self.meta = value

        @property
        def uncertainty(self):
            return self._uncertainty

        @uncertainty.setter
        def uncertainty(self, value):
            if value is not None:
                if isinstance(value, NDUncertainty):
                    if getattr(value, "_parent_nddata", None) is not None:
                        value = value.__class__(value, copy=False)
                    self._uncertainty = value
                elif isinstance(value, np.ndarray):
                    if value.shape != self.shape:
                        raise ValueError("uncertainty must have same shape as data.")
                    self._uncertainty = StdDevUncertainty(value)
                    log.info(
                        "array provided for uncertainty; assuming it is a "
                        "StdDevUncertainty."
                    )
                else:
                    raise TypeError(
                        "uncertainty must be an instance of a "
                        "NDUncertainty object or a numpy array."
                    )
                self._uncertainty.parent_nddata = self
            else:
                self._uncertainty = value

        def to_hdu(
            self,
            hdu_mask="MASK",
            hdu_uncertainty="UNCERT",
            hdu_flags=None,
            wcs_relax=True,
            key_uncertainty_type="UTYPE",
            as_image_hdu=False,
            hdu_psf="PSFIMAGE",
        ):
            """Creates an HDUList object from a CCDData object.

            Parameters
            ----------
            hdu_mask, hdu_uncertainty, hdu_flags, hdu_psf : str or None, optional
                If it is a string append this attribute to the HDUList as
                `~astropy.io.fits.ImageHDU` with the string as extension name.
                For flags stored as a `~astropy.nddata.FlagCollection`, each flag
                array will be saved as a separate ImageHDU with extension names
                of the form ``hdu_flags + '_' + flag_name``.
                If ``None`` this attribute is not appended.
                Default is ``'MASK'`` for mask, ``'UNCERT'`` for uncertainty,
                ``'PSFIMAGE'`` for psf, and `None` for flags.

            wcs_relax : bool
                Value of the ``relax`` parameter to use in converting the WCS to a
                FITS header using `~astropy.wcs.WCS.to_header`. The common
                ``CTYPE`` ``RA---TAN-SIP`` and ``DEC--TAN-SIP`` requires
                ``relax=True`` for the ``-SIP`` part of the ``CTYPE`` to be
                preserved.

            key_uncertainty_type : str, optional
                The header key name for the class name of the uncertainty (if any)
                that is used to store the uncertainty type in the uncertainty hdu.
                Default is ``UTYPE``.

                .. versionadded:: 3.1

            as_image_hdu : bool
                If this option is `True`, the first item of the returned
                `~astropy.io.fits.HDUList` is a `~astropy.io.fits.ImageHDU`, instead
                of the default `~astropy.io.fits.PrimaryHDU`.

            Raises
            ------
            ValueError
                - If ``self.mask`` is set but not a `~numpy.ndarray`.
                - If ``self.uncertainty`` is set but not a astropy uncertainty
                  type.
                - If ``self.uncertainty`` is set but has another unit then
                  ``self.data``.
                - If ``self.flags`` is set but not a `~numpy.ndarray` or
                  `~astropy.nddata.FlagCollection`.

            Returns
            -------
            hdulist : `~astropy.io.fits.HDUList`
            """
            if isinstance(self.header, fits.Header):
                # Copy here so that we can modify the HDU header by adding WCS
                # information without changing the header of the CCDData object.
                header = self.header.copy()
            else:
                # Because _insert_in_metadata_fits_safe is written as a method
                # we need to create a dummy CCDData instance to hold the FITS
                # header we are constructing. This probably indicates that
                # _insert_in_metadata_fits_safe should be rewritten in a more
                # sensible way...
                dummy_ccd = CCDData([1], meta=fits.Header(), unit="adu")
                for k, v in self.header.items():
                    dummy_ccd._insert_in_metadata_fits_safe(k, v)
                header = dummy_ccd.header
            if self.unit is not u.dimensionless_unscaled:
                header["bunit"] = self.unit.to_string()
            if self.wcs:
                # Simply extending the FITS header with the WCS can lead to
                # duplicates of the WCS keywords; iterating over the WCS
                # header should be safer.
                #
                # Turns out if I had read the io.fits.Header.extend docs more
                # carefully, I would have realized that the keywords exist to
                # avoid duplicates and preserve, as much as possible, the
                # structure of the commentary cards.
                #
                # Note that until astropy/astropy#3967 is closed, the extend
                # will fail if there are comment cards in the WCS header but
                # not header.
                wcs_header = self.wcs.to_header(relax=wcs_relax)
                header.extend(wcs_header, useblanks=False, update=True)

            if as_image_hdu:
                hdus = [fits.ImageHDU(self.data, header)]
            else:
                hdus = [fits.PrimaryHDU(self.data, header)]

            if hdu_mask and self.mask is not None:
                # Always assuming that the mask is a np.ndarray (check that it has
                # a 'shape').
                if not hasattr(self.mask, "shape"):
                    raise ValueError("only a numpy.ndarray mask can be saved.")

                # Convert boolean mask to uint since io.fits cannot handle bool.
                hduMask = fits.ImageHDU(self.mask.astype(np.uint8), name=hdu_mask)
                hdus.append(hduMask)

            if hdu_uncertainty and self.uncertainty is not None:
                # We need to save some kind of information which uncertainty was
                # used so that loading the HDUList can infer the uncertainty type.
                # No idea how this can be done so only allow StdDevUncertainty.
                uncertainty_cls = self.uncertainty.__class__
                if uncertainty_cls not in _known_uncertainties:
                    raise ValueError(
                        f"only uncertainties of type {_known_uncertainties} can be saved."
                    )
                uncertainty_name = _unc_cls_to_name[uncertainty_cls]

                hdr_uncertainty = fits.Header()
                hdr_uncertainty[key_uncertainty_type] = uncertainty_name

                # Assuming uncertainty is an StdDevUncertainty save just the array
                # this might be problematic if the Uncertainty has a unit differing
                # from the data so abort for different units. This is important for
                # astropy > 1.2
                if hasattr(self.uncertainty, "unit") and self.uncertainty.unit is not None:
                    if not _uncertainty_unit_equivalent_to_parent(
                        uncertainty_cls, self.uncertainty.unit, self.unit
                    ):
                        raise ValueError(
                            "saving uncertainties with a unit that is not "
                            "equivalent to the unit from the data unit is not "
                            "supported."
                        )

                hduUncert = fits.ImageHDU(
                    self.uncertainty.array, hdr_uncertainty, name=hdu_uncertainty
                )
                hdus.append(hduUncert)

            if hdu_flags and self.flags is not None:
                # Flags can be either a numpy array or a FlagCollection
                if isinstance(self.flags, np.ndarray):
                    # Simple numpy array of flags - save as a single ImageHDU
                    hduFlags = fits.ImageHDU(self.flags, name=hdu_flags)
                    hdus.append(hduFlags)
                elif isinstance(self.flags, FlagCollection):
                    # FlagCollection is a dict of flag arrays - save each as
                    # separate ImageHDU. Use hdu_flags as a prefix for the
                    # extension names
                    for flag_name, flag_array in self.flags.items():
                        # Create extension name by combining prefix with flag
                        # name
                        if flag_array.dtype == bool:
                            flag_data = flag_array.astype(np.uint8)
                        else:
                            flag_data = flag_array
                        ext_name = f"{hdu_flags}_{flag_name}"
                        hduFlag = fits.ImageHDU(flag_data, name=ext_name)
                        hdus.append(hduFlag)
                else:
                    raise ValueError(
                        "flags must be a numpy.ndarray or FlagCollection instance."
                    )

            if hdu_psf and self.psf is not None:
                # The PSF is an image, so write it as a separate ImageHDU.
                hdu_psf = fits.ImageHDU(self.psf, name=hdu_psf)
                hdus.append(hdu_psf)

            return fits.HDUList(hdus)

        def copy(self):
            """
            Return a copy of the CCDData object.
            """
            return self.__class__(self, copy=True)

        add = _arithmetic(np.add)(NDDataArray.add)
        subtract = _arithmetic(np.subtract)(NDDataArray.subtract)
        multiply = _arithmetic(np.multiply)(NDDataArray.multiply)
        divide = _arithmetic(np.true_divide)(NDDataArray.divide)

        def _insert_in_metadata_fits_safe(self, key, value):
            """
            Insert key/value pair into metadata in a way that FITS can serialize.

            Parameters
            ----------
            key : str
                Key to be inserted in dictionary.

            value : str or None
                Value to be inserted.

            Notes
            -----
            This addresses a shortcoming of the FITS standard. There are length
            restrictions on both the ``key`` (8 characters) and ``value`` (72
            characters) in the FITS standard. There is a convention for handling
            long keywords and a convention for handling long values, but the
            two conventions cannot be used at the same time.

            This addresses that case by checking the length of the ``key`` and
            ``value`` and, if necessary, shortening the key.
            """
            if len(key) > 8 and len(value) > 72:
                short_name = key[:8]
                self.meta[f"HIERARCH {key.upper()}"] = (
                    short_name,
                    f"Shortened name for {key}",
                )
                self.meta[short_name] = value
            else:
                self.meta[key] = value

        # A dictionary mapping "known" invalid fits unit
        known_invalid_fits_unit_strings = {
            "ELECTRONS/S": u.electron / u.s,
            "ELECTRONS": u.electron,
            "electrons": u.electron,
        }

    # These need to be importable by the tests...
    _KEEP_THESE_KEYWORDS_IN_HEADER = ["JD-OBS", "MJD-OBS", "DATE-OBS"]
    _PCs = {"PC1_1", "PC1_2", "PC2_1", "PC2_2"}
    _CDs = {"CD1_1", "CD1_2", "CD2_1", "CD2_2"}

    def _generate_wcs_and_update_header(hdr):
        """
        Generate a WCS object from a header and remove the WCS-specific
        keywords from the header.

        Parameters
        ----------
        hdr : astropy.io.fits.header or other dict-like

        Returns
        -------
        new_header, wcs
        """
        # Try constructing a WCS object.
        try:
            wcs = WCS(hdr)
        except Exception as exc:
            # Normally WCS only raises Warnings and doesn't fail but in rare
            # cases (malformed header) it could fail...
            log.info(
                "An exception happened while extracting WCS information from "
                f"the Header.\n{type(exc).__name__}: {str(exc)}"
            )
            return hdr, None
        # Test for success by checking to see if the wcs ctype has a non-empty
        # value, return None for wcs if ctype is empty.
        if not wcs.wcs.ctype[0]:
            return (hdr, None)

        new_hdr = hdr.copy()
        # If the keywords below are in the header they are also added to WCS.
        # It seems like they should *not* be removed from the header, though.

        wcs_header = wcs.to_header(relax=True)
        for k in wcs_header:
            if k not in _KEEP_THESE_KEYWORDS_IN_HEADER:
                new_hdr.remove(k, ignore_missing=True)

        # Check that this does not result in an inconsistent header WCS if the WCS
        # is converted back to a header.

        if (_PCs & set(wcs_header)) and (_CDs & set(new_hdr)):
            # The PCi_j representation is used by the astropy.wcs object,
            # so CDi_j keywords were not removed from new_hdr. Remove them now.
            for cd in _CDs:
                new_hdr.remove(cd, ignore_missing=True)

        # The other case -- CD in the header produced by astropy.wcs -- should
        # never happen based on [1], which computes the matrix in PC form.
        # [1]: https://github.com/astropy/astropy/blob/1cf277926d3598dd672dd528504767c37531e8c9/cextern/wcslib/C/wcshdr.c#L596
        #
        # The test test_ccddata.test_wcs_keyword_removal_for_wcs_test_files() does
        # check for the possibility that both PC and CD are present in the result
        # so if the implementation of to_header changes in wcslib in the future
        # then the tests should catch it, and then this code will need to be
        # updated.

        # We need to check for any SIP coefficients that got left behind if the
        # header has SIP.
        if wcs.sip is not None:
            keyword = "{}_{}_{}"
            polynomials = ["A", "B", "AP", "BP"]
            for poly in polynomials:
                order = wcs.sip.__getattribute__(f"{poly.lower()}_order")
                for i, j in itertools.product(range(order), repeat=2):
                    new_hdr.remove(keyword.format(poly, i, j), ignore_missing=True)

        return (new_hdr, wcs)

    def fits_ccddata_reader(
        filename,
        hdu=0,
        unit=None,
        hdu_uncertainty="UNCERT",
        hdu_mask="MASK",
        hdu_flags=None,
        key_uncertainty_type="UTYPE",
        hdu_psf="PSFIMAGE",
        **kwd,
    ):
        """
        Generate a CCDData object from a FITS file.

        Parameters
        ----------
        filename : str
            Name of fits file.

        hdu : int, str, tuple of (str, int), optional
            Index or other identifier of the Header Data Unit of the FITS
            file from which CCDData should be initialized. If zero and
            no data in the primary HDU, it will search for the first
            extension HDU with data. The header will be added to the primary HDU.
            Default is ``0``.

        unit : `~astropy.units.Unit`, optional
            Units of the image data. If this argument is provided and there is a
            unit for the image in the FITS header (the keyword ``BUNIT`` is used
            as the unit, if present), this argument is used for the unit.
            Default is ``None``.

        hdu_uncertainty : str or None, optional
            FITS extension from which the uncertainty should be initialized. If the
            extension does not exist the uncertainty of the CCDData is ``None``.
            Default is ``'UNCERT'``.

        hdu_mask : str or None, optional
            FITS extension from which the mask should be initialized. If the
            extension does not exist the mask of the CCDData is ``None``.
            Default is ``'MASK'``.

        hdu_flags : str or None, optional
            FITS extension name (or prefix) from which flags should be
            initialized. If a single extension with this exact name exists, flags
            will be loaded as a `~numpy.ndarray`. If multiple extensions exist
            with names starting with this prefix (e.g., ``'FLAGS_*'``), they will
            be loaded into a `~astropy.nddata.FlagCollection` where the flag
            names are derived by removing the prefix and underscore.
            If no matching extension exists, the flags of the CCDData is ``None``.
            Default is ``None``.

        key_uncertainty_type : str, optional
            The header key name where the class name of the uncertainty  is stored
            in the hdu of the uncertainty (if any).
            Default is ``UTYPE``.

            .. versionadded:: 3.1

        hdu_psf : str or None, optional
            FITS extension from which the psf image should be initialized. If the
            extension does not exist the psf of the CCDData is `None`.

        kwd :
            Any additional keyword parameters are passed through to the FITS reader
            in :mod:`astropy.io.fits`; see Notes for additional discussion.

        Notes
        -----
        FITS files that contained scaled data (e.g. unsigned integer images) will
        be scaled and the keywords used to manage scaled data in
        :mod:`astropy.io.fits` are disabled.
        """
        unsupport_open_keywords = {
            "do_not_scale_image_data": "Image data must be scaled.",
            "scale_back": "Scale information is not preserved.",
        }
        for key, msg in unsupport_open_keywords.items():
            if key in kwd:
                prefix = f"unsupported keyword: {key}."
                raise TypeError(f"{prefix} {msg}")
        with fits.open(filename, **kwd) as hdus:
            hdr = hdus[hdu].header

            if hdu_uncertainty is not None and hdu_uncertainty in hdus:
                unc_hdu = hdus[hdu_uncertainty]
                stored_unc_name = unc_hdu.header.get(key_uncertainty_type, "None")
                # For compatibility reasons the default is standard deviation
                # uncertainty because files could have been created before the
                # uncertainty type was stored in the header.
                unc_type = _unc_name_to_cls.get(stored_unc_name, StdDevUncertainty)
                uncertainty = unc_type(unc_hdu.data)
            else:
                uncertainty = None

            if hdu_mask is not None and hdu_mask in hdus:
                # Mask is saved as uint but we want it to be boolean.
                mask = hdus[hdu_mask].data.astype(np.bool_)
            else:
                mask = None

            if hdu_flags is not None:
                # Check if there's a simple flags extension or multiple flag
                # extensions (FlagCollection)
                # First, look for extensions that start with hdu_flags prefix
                flag_extensions = [
                    hdu.name for hdu in hdus if hdu.name.startswith(f"{hdu_flags}_")
                ]

                if hdu_flags in hdus and not flag_extensions:
                    # Simple case: single flags array
                    flags = hdus[hdu_flags].data
                elif flag_extensions:
                    # Multiple flag extensions - reconstruct FlagCollection
                    # Get the shape from the primary data
                    data_shape = hdus[hdu].data.shape
                    flags = FlagCollection(shape=data_shape)

                    for ext_name in flag_extensions:
                        # Remove the prefix and underscore to get flag name
                        flag_name = ext_name[len(hdu_flags) + 1 :]
                        flags[flag_name] = hdus[ext_name].data
                else:
                    flags = None
            else:
                flags = None

            if hdu_psf is not None and hdu_psf in hdus:
                psf = hdus[hdu_psf].data
            else:
                psf = None

            # search for the first instance with data if
            # the primary header is empty.
            if hdu == 0 and hdus[hdu].data is None:
                for i in range(len(hdus)):
                    if (
                        hdus.info(hdu)[i][3] == "ImageHDU"
                        and hdus.fileinfo(i)["datSpan"] > 0
                    ):
                        hdu = i
                        comb_hdr = hdus[hdu].header.copy()
                        # Add header values from the primary header that aren't
                        # present in the extension header.
                        comb_hdr.extend(hdr, unique=True)
                        hdr = comb_hdr
                        log.info(f"first HDU with data is extension {hdu}.")
                        break

            if "bunit" in hdr:
                fits_unit_string = hdr["bunit"]
                # patch to handle FITS files using ADU for the unit instead of the
                # standard version of 'adu'
                if fits_unit_string.strip().lower() == "adu":
                    fits_unit_string = fits_unit_string.lower()
            else:
                fits_unit_string = None

            if fits_unit_string:
                if unit is None:
                    # Convert the BUNIT header keyword to a unit and if that's not
                    # possible raise a meaningful error message.
                    try:
                        kifus = CCDData.known_invalid_fits_unit_strings
                        if fits_unit_string in kifus:
                            fits_unit_string = kifus[fits_unit_string]
                        fits_unit_string = u.Unit(fits_unit_string)
                    except ValueError:
                        raise ValueError(
                            f"The Header value for the key BUNIT ({fits_unit_string}) "
                            "cannot be interpreted as valid unit. To successfully read the "
                            "file as CCDData you can pass in a valid `unit` "
                            "argument explicitly or change the header of the FITS "
                            "file before reading it."
                        )
                else:
                    if str(unit) != str(fits_unit_string):
                        log.info(
                            f"using the unit {unit} passed to the FITS reader instead "
                            f"of the unit {fits_unit_string} in the FITS file."
                        )

            use_unit = unit or fits_unit_string
            hdr, wcs = _generate_wcs_and_update_header(hdr)

            return CCDData(
                hdus[hdu].data,
                meta=hdr,
                unit=use_unit,
                mask=mask,
                uncertainty=uncertainty,
                wcs=wcs,
                flags=flags,
                psf=psf,
            )

    def fits_ccddata_writer(
        ccd_data,
        filename,
        hdu_mask="MASK",
        hdu_uncertainty="UNCERT",
        hdu_flags=None,
        key_uncertainty_type="UTYPE",
        as_image_hdu=False,
        hdu_psf="PSFIMAGE",
        **kwd,
    ):
        """
        Write CCDData object to FITS file.

        Parameters
        ----------
        ccd_data : CCDData
            Object to write.

        filename : str
            Name of file.

        hdu_mask, hdu_uncertainty, hdu_flags, hdu_psf : str or None, optional
            If it is a string append this attribute to the HDUList as
            `~astropy.io.fits.ImageHDU` with the string as extension name.
            For flags stored as a `~astropy.nddata.FlagCollection`, each flag
            array will be saved as a separate ImageHDU with extension names
            of the form ``hdu_flags + '_' + flag_name``.
            If ``None`` this attribute is not appended.
            Default is ``'MASK'`` for mask, ``'UNCERT'`` for uncertainty,
            ``'PSFIMAGE'`` for psf, and `None` for flags.

        key_uncertainty_type : str, optional
            The header key name for the class name of the uncertainty (if any)
            that is used to store the uncertainty type in the uncertainty hdu.
            Default is ``UTYPE``.

            .. versionadded:: 3.1

        as_image_hdu : bool
            If this option is `True`, the first item of the returned
            `~astropy.io.fits.HDUList` is a `~astropy.io.fits.ImageHDU`, instead of
            the default `~astropy.io.fits.PrimaryHDU`.

        kwd :
            All additional keywords are passed to :py:mod:`astropy.io.fits`

        Raises
        ------
        ValueError
            - If ``self.mask`` is set but not a `~numpy.ndarray`.
            - If ``self.uncertainty`` is set but not a
              `~astropy.nddata.StdDevUncertainty`.
            - If ``self.uncertainty`` is set but has another unit then
              ``self.data``.
            - If ``self.flags`` is set but not a `~numpy.ndarray` or
              `~astropy.nddata.FlagCollection`.
        """
        hdu = ccd_data.to_hdu(
            hdu_mask=hdu_mask,
            hdu_uncertainty=hdu_uncertainty,
            key_uncertainty_type=key_uncertainty_type,
            hdu_flags=hdu_flags,
            as_image_hdu=as_image_hdu,
            hdu_psf=hdu_psf,
        )
        if as_image_hdu:
            hdu.insert(0, fits.PrimaryHDU())
        hdu.writeto(filename, **kwd)

    with registry.delay_doc_updates(CCDData):
        registry.register_reader("fits", CCDData, fits_ccddata_reader)
        registry.register_writer("fits", CCDData, fits_ccddata_writer)
        registry.register_identifier("fits", CCDData, fits.connect.is_fits)