Prevent instantiation of PrimaryHDU and ImageHDU with a scalar

[saimn]

Fix #10022.

[MSeifert04]

I assume the failure is unrelated? #10039

However I would want to wait until the full tests run over this before merging.

[bsipocz]

I wanted to go ahead with the merge as the 4.0.x branch is not affected by the pytest issue, but then saw this related error locally.

@saimn - please have a look, and also rebase.

======================================================= FAILURES =======================================================
___________________________________ TestImageFunctions.test_hdu_creation_with_scalar ___________________________________

self = <astropy.io.fits.tests.test_image.TestImageFunctions object at 0x123dda0a0>

    def test_hdu_creation_with_scalar(self):
        msg = r'data object array\(1\) should have at least one dimension'
        with pytest.raises(TypeError, match=msg):
            fits.ImageHDU(data=1)
        with pytest.raises(TypeError, match=msg):
>           fits.PrimaryHDU(data=2.0)

astropy/io/fits/tests/test_image.py:1093: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <astropy.io.fits.hdu.image.PrimaryHDU object at 0x123dda070>, data = 2.0, header = None
do_not_scale_image_data = False, ignore_blank = False, uint = True, scale_back = None

    def __init__(self, data=None, header=None, do_not_scale_image_data=False,
                 ignore_blank=False,
                 uint=True, scale_back=None):
        """
        Construct a primary HDU.
    
        Parameters
        ----------
        data : array or DELAYED, optional
            The data in the HDU.
    
        header : `~astropy.io.fits.Header`, optional
            The header to be used (as a template).  If ``header`` is `None`, a
            minimal header will be provided.
    
        do_not_scale_image_data : bool, optional
            If `True`, image data is not scaled using BSCALE/BZERO values
            when read. (default: False)
    
        ignore_blank : bool, optional
            If `True`, the BLANK header keyword will be ignored if present.
            Otherwise, pixels equal to this value will be replaced with
            NaNs. (default: False)
    
        uint : bool, optional
            Interpret signed integer data where ``BZERO`` is the
            central value and ``BSCALE == 1`` as unsigned integer
            data.  For example, ``int16`` data with ``BZERO = 32768``
            and ``BSCALE = 1`` would be treated as ``uint16`` data.
            (default: True)
    
        scale_back : bool, optional
            If `True`, when saving changes to a file that contained scaled
            image data, restore the data to the original type and reapply the
            original BSCALE/BZERO values.  This could lead to loss of accuracy
            if scaling back to integer values after performing floating point
            operations on the data.  Pseudo-unsigned integers are automatically
            rescaled unless scale_back is explicitly set to `False`.
            (default: None)
        """
    
>       super().__init__(
            data=data, header=header,
            do_not_scale_image_data=do_not_scale_image_data, uint=uint,
            ignore_blank=ignore_blank,
            scale_back=scale_back)

astropy/io/fits/hdu/image.py:989: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <astropy.io.fits.hdu.image.PrimaryHDU object at 0x123dda070>, data = 2.0
header = SIMPLE  =                    T / conforms to FITS standard                      
BITPIX  =                    8 / arra...a type                                
NAXIS   =                    0 / number of array dimensions                     
do_not_scale_image_data = False, uint = True, scale_back = None, ignore_blank = False, kwargs = {}
GroupsHDU = <class 'astropy.io.fits.hdu.groups.GroupsHDU'>, c0 = ('SIMPLE', True, 'conforms to FITS standard')
cards = [('SIMPLE', True, 'conforms to FITS standard'), ('BITPIX', 8, 'array data type'), ('NAXIS', 0, 'number of array dimensions')]
bzero_in_header = False, bscale_in_header = False

    def __init__(self, data=None, header=None, do_not_scale_image_data=False,
                 uint=True, scale_back=False, ignore_blank=False, **kwargs):
    
        from .groups import GroupsHDU
    
        super().__init__(data=data, header=header)
    
        if data is DELAYED:
            # Presumably if data is DELAYED then this HDU is coming from an
            # open file, and was not created in memory
            if header is None:
                # this should never happen
                raise ValueError('No header to setup HDU.')
        else:
            # TODO: Some of this card manipulation should go into the
            # PrimaryHDU and GroupsHDU subclasses
            # construct a list of cards of minimal header
            if isinstance(self, ExtensionHDU):
                c0 = ('XTENSION', 'IMAGE',
                      self.standard_keyword_comments['XTENSION'])
            else:
                c0 = ('SIMPLE', True, self.standard_keyword_comments['SIMPLE'])
            cards = [
                c0,
                ('BITPIX', 8, self.standard_keyword_comments['BITPIX']),
                ('NAXIS', 0, self.standard_keyword_comments['NAXIS'])]
    
            if isinstance(self, GroupsHDU):
                cards.append(('GROUPS', True,
                             self.standard_keyword_comments['GROUPS']))
    
            if isinstance(self, (ExtensionHDU, GroupsHDU)):
                cards.append(('PCOUNT', 0,
                              self.standard_keyword_comments['PCOUNT']))
                cards.append(('GCOUNT', 1,
                              self.standard_keyword_comments['GCOUNT']))
    
            if header is not None:
                orig = header.copy()
                header = Header(cards)
                header.extend(orig, strip=True, update=True, end=True)
            else:
                header = Header(cards)
    
            self._header = header
    
        self._do_not_scale_image_data = do_not_scale_image_data
    
        self._uint = uint
        self._scale_back = scale_back
    
        # Keep track of whether BZERO/BSCALE were set from the header so that
        # values for self._orig_bzero and self._orig_bscale can be set
        # properly, if necessary, once the data has been set.
        bzero_in_header = 'BZERO' in self._header
        bscale_in_header = 'BSCALE' in self._header
        self._bzero = self._header.get('BZERO', 0)
        self._bscale = self._header.get('BSCALE', 1)
    
        # Save off other important values from the header needed to interpret
        # the image data
        self._axes = [self._header.get('NAXIS' + str(axis + 1), 0)
                      for axis in range(self._header.get('NAXIS', 0))]
    
        # Not supplying a default for BITPIX makes sense because BITPIX
        # is either in the header or should be determined from the dtype of
        # the data (which occurs when the data is set).
        self._bitpix = self._header.get('BITPIX')
        self._gcount = self._header.get('GCOUNT', 1)
        self._pcount = self._header.get('PCOUNT', 0)
        self._blank = None if ignore_blank else self._header.get('BLANK')
        self._verify_blank()
    
        self._orig_bitpix = self._bitpix
        self._orig_blank = self._header.get('BLANK')
    
        # These get set again below, but need to be set to sensible defaults
        # here.
        self._orig_bzero = self._bzero
        self._orig_bscale = self._bscale
    
        # Set the name attribute if it was provided (if this is an ImageHDU
        # this will result in setting the EXTNAME keyword of the header as
        # well)
        if 'name' in kwargs and kwargs['name']:
            self.name = kwargs['name']
        if 'ver' in kwargs and kwargs['ver']:
            self.ver = kwargs['ver']
    
        # Set to True if the data or header is replaced, indicating that
        # update_header should be called
        self._modified = False
    
        if data is DELAYED:
            if (not do_not_scale_image_data and
                    (self._bscale != 1 or self._bzero != 0)):
                # This indicates that when the data is accessed or written out
                # to a new file it will need to be rescaled
                self._data_needs_rescale = True
            return
        else:
            # Setting data will update the header and set _bitpix, _bzero,
            # and _bscale to the appropriate BITPIX for the data, and always
            # sets _bzero=0 and _bscale=1.
>           self.data = data

astropy/io/fits/hdu/image.py:142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <astropy.utils.decorators.lazyproperty object at 0x1211e67c0>
obj = <astropy.io.fits.hdu.image.PrimaryHDU object at 0x123dda070>, val = 2.0

    def __set__(self, obj, val):
        obj_dict = obj.__dict__
        if self.fset:
>           ret = self.fset(obj, val)

astropy/utils/decorators.py:759: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <astropy.io.fits.hdu.image.PrimaryHDU object at 0x123dda070>, data = array(2.)

    @data.setter
    def data(self, data):
        if 'data' in self.__dict__ and self.__dict__['data'] is not None:
            if self.__dict__['data'] is data:
                return
            else:
                self._data_replaced = True
            was_unsigned = _is_pseudo_unsigned(self.__dict__['data'].dtype)
        else:
            self._data_replaced = True
            was_unsigned = False
    
        if data is not None and not isinstance(data, np.ndarray):
            # Try to coerce the data into a numpy array--this will work, on
            # some level, for most objects
            try:
                data = np.array(data)
            except Exception:
                raise TypeError('data object {!r} could not be coerced into an '
                                'ndarray'.format(data))
    
            if data.shape == ():
>               raise TypeError('data object {!r} should have at least one '
                                'dimension'.format(data))
E               TypeError: data object array(2.) should have at least one dimension

astropy/io/fits/hdu/image.py:257: TypeError

During handling of the above exception, another exception occurred:

self = <astropy.io.fits.tests.test_image.TestImageFunctions object at 0x123dda0a0>

    def test_hdu_creation_with_scalar(self):
        msg = r'data object array\(1\) should have at least one dimension'
        with pytest.raises(TypeError, match=msg):
            fits.ImageHDU(data=1)
        with pytest.raises(TypeError, match=msg):
>           fits.PrimaryHDU(data=2.0)
E           AssertionError: Pattern 'data object array\\(1\\) should have at least one dimension' not found in 'data object array(2.) should have at least one dimension'

astropy/io/fits/tests/test_image.py:1093: AssertionError

[saimn]

Fixed and rebased (but still failing because of the pytest issue).

[bsipocz]

Thanks @saimn!