# Licensed under a 3-clause BSD style license - see PYFITS.rst import sys import mmap import warnings import numpy as np from .base import DELAYED, _ValidHDU, ExtensionHDU, BITPIX2DTYPE, DTYPE2BITPIX from astropy.io.fits.header import Header from astropy.io.fits.util import (_is_pseudo_integer, _pseudo_zero, _is_int, _is_dask_array) from astropy.io.fits.verify import VerifyWarning from astropy.utils import isiterable, lazyproperty __all__ = ["Section", "PrimaryHDU", "ImageHDU"] class _ImageBaseHDU(_ValidHDU): """FITS image HDU base class. Attributes ---------- header image header data image data """ standard_keyword_comments = { 'SIMPLE': 'conforms to FITS standard', 'XTENSION': 'Image extension', 'BITPIX': 'array data type', 'NAXIS': 'number of array dimensions', 'GROUPS': 'has groups', 'PCOUNT': 'number of parameters', 'GCOUNT': 'number of groups' } 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 # Check again for BITPIX/BSCALE/BZERO in case they changed when the # data was assigned. This can happen, for example, if the input # data is an unsigned int numpy array. self._bitpix = self._header.get('BITPIX') # Do not provide default values for BZERO and BSCALE here because # the keywords will have been deleted in the header if appropriate # after scaling. We do not want to put them back in if they # should not be there. self._bzero = self._header.get('BZERO') self._bscale = self._header.get('BSCALE') # Handle case where there was no BZERO/BSCALE in the initial header # but there should be a BSCALE/BZERO now that the data has been set. if not bzero_in_header: self._orig_bzero = self._bzero if not bscale_in_header: self._orig_bscale = self._bscale @classmethod def match_header(cls, header): """ _ImageBaseHDU is sort of an abstract class for HDUs containing image data (as opposed to table data) and should never be used directly. """ raise NotImplementedError @property def is_image(self): return True @property def section(self): """ Access a section of the image array without loading the entire array into memory. The :class:`Section` object returned by this attribute is not meant to be used directly by itself. Rather, slices of the section return the appropriate slice of the data, and loads *only* that section into memory. Sections are mostly obsoleted by memmap support, but should still be used to deal with very large scaled images. See the :ref:`astropy:data-sections` section of the Astropy documentation for more details. """ return Section(self) @property def shape(self): """ Shape of the image array--should be equivalent to ``self.data.shape``. """ # Determine from the values read from the header return tuple(reversed(self._axes)) @property def header(self): return self._header @header.setter def header(self, header): self._header = header self._modified = True self.update_header() @lazyproperty def data(self): """ Image/array data as a `~numpy.ndarray`. Please remember that the order of axes on an Numpy array are opposite of the order specified in the FITS file. For example for a 2D image the "rows" or y-axis are the first dimension, and the "columns" or x-axis are the second dimension. If the data is scaled using the BZERO and BSCALE parameters, this attribute returns the data scaled to its physical values unless the file was opened with ``do_not_scale_image_data=True``. """ if len(self._axes) < 1: return data = self._get_scaled_image_data(self._data_offset, self.shape) self._update_header_scale_info(data.dtype) return data @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_integer(self.__dict__['data'].dtype) else: self._data_replaced = True was_unsigned = False if (data is not None and not isinstance(data, np.ndarray) and not _is_dask_array(data)): # 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)) self.__dict__['data'] = data self._modified = True if self.data is None: self._axes = [] else: # Set new values of bitpix, bzero, and bscale now, but wait to # revise original values until header is updated. self._bitpix = DTYPE2BITPIX[data.dtype.name] self._bscale = 1 self._bzero = 0 self._blank = None self._axes = list(data.shape) self._axes.reverse() # Update the header, including adding BZERO/BSCALE if new data is # unsigned. Does not change the values of self._bitpix, # self._orig_bitpix, etc. self.update_header() if (data is not None and was_unsigned): self._update_header_scale_info(data.dtype) # Keep _orig_bitpix as it was until header update is done, then # set it, to allow easier handling of the case of unsigned # integer data being converted to something else. Setting these here # is needed only for the case do_not_scale_image_data=True when # setting the data to unsigned int. # If necessary during initialization, i.e. if BSCALE and BZERO were # not in the header but the data was unsigned, the attributes below # will be update in __init__. self._orig_bitpix = self._bitpix self._orig_bscale = self._bscale self._orig_bzero = self._bzero # returning the data signals to lazyproperty that we've already handled # setting self.__dict__['data'] return data def update_header(self): """ Update the header keywords to agree with the data. """ if not (self._modified or self._header._modified or (self._has_data and self.shape != self.data.shape)): # Not likely that anything needs updating return old_naxis = self._header.get('NAXIS', 0) if 'BITPIX' not in self._header: bitpix_comment = self.standard_keyword_comments['BITPIX'] else: bitpix_comment = self._header.comments['BITPIX'] # Update the BITPIX keyword and ensure it's in the correct # location in the header self._header.set('BITPIX', self._bitpix, bitpix_comment, after=0) # If the data's shape has changed (this may have happened without our # noticing either via a direct update to the data.shape attribute) we # need to update the internal self._axes if self._has_data and self.shape != self.data.shape: self._axes = list(self.data.shape) self._axes.reverse() # Update the NAXIS keyword and ensure it's in the correct location in # the header if 'NAXIS' in self._header: naxis_comment = self._header.comments['NAXIS'] else: naxis_comment = self.standard_keyword_comments['NAXIS'] self._header.set('NAXIS', len(self._axes), naxis_comment, after='BITPIX') # TODO: This routine is repeated in several different classes--it # should probably be made available as a method on all standard HDU # types # add NAXISi if it does not exist for idx, axis in enumerate(self._axes): naxisn = 'NAXIS' + str(idx + 1) if naxisn in self._header: self._header[naxisn] = axis else: if (idx == 0): after = 'NAXIS' else: after = 'NAXIS' + str(idx) self._header.set(naxisn, axis, after=after) # delete extra NAXISi's for idx in range(len(self._axes) + 1, old_naxis + 1): try: del self._header['NAXIS' + str(idx)] except KeyError: pass if 'BLANK' in self._header: self._blank = self._header['BLANK'] # Add BSCALE/BZERO to header if data is unsigned int. self._update_pseudo_int_scale_keywords() self._modified = False def _update_header_scale_info(self, dtype=None): """ Delete BSCALE/BZERO from header if necessary. """ # Note that _dtype_for_bitpix determines the dtype based on the # "original" values of bitpix, bscale, and bzero, stored in # self._orig_bitpix, etc. It contains the logic for determining which # special cases of BZERO/BSCALE, if any, are auto-detected as following # the FITS unsigned int convention. # Added original_was_unsigned with the intent of facilitating the # special case of do_not_scale_image_data=True and uint=True # eventually. # FIXME: unused, maybe it should be useful? # if self._dtype_for_bitpix() is not None: # original_was_unsigned = self._dtype_for_bitpix().kind == 'u' # else: # original_was_unsigned = False if (self._do_not_scale_image_data or (self._orig_bzero == 0 and self._orig_bscale == 1)): return if dtype is None: dtype = self._dtype_for_bitpix() if (dtype is not None and dtype.kind == 'u' and (self._scale_back or self._scale_back is None)): # Data is pseudo-unsigned integers, and the scale_back option # was not explicitly set to False, so preserve all the scale # factors return for keyword in ['BSCALE', 'BZERO']: try: del self._header[keyword] # Since _update_header_scale_info can, currently, be called # *after* _prewriteto(), replace these with blank cards so # the header size doesn't change self._header.append() except KeyError: pass if dtype is None: dtype = self._dtype_for_bitpix() if dtype is not None: self._header['BITPIX'] = DTYPE2BITPIX[dtype.name] self._bzero = 0 self._bscale = 1 self._bitpix = self._header['BITPIX'] self._blank = self._header.pop('BLANK', None) def scale(self, type=None, option='old', bscale=None, bzero=None): """ Scale image data by using ``BSCALE``/``BZERO``. Call to this method will scale `data` and update the keywords of ``BSCALE`` and ``BZERO`` in the HDU's header. This method should only be used right before writing to the output file, as the data will be scaled and is therefore not very usable after the call. Parameters ---------- type : str, optional destination data type, use a string representing a numpy dtype name, (e.g. ``'uint8'``, ``'int16'``, ``'float32'`` etc.). If is `None`, use the current data type. option : str, optional How to scale the data: ``"old"`` uses the original ``BSCALE`` and ``BZERO`` values from when the data was read/created (defaulting to 1 and 0 if they don't exist). For integer data only, ``"minmax"`` uses the minimum and maximum of the data to scale. User-specified ``bscale``/``bzero`` values always take precedence. bscale, bzero : int, optional User-specified ``BSCALE`` and ``BZERO`` values """ # Disable blank support for now self._scale_internal(type=type, option=option, bscale=bscale, bzero=bzero, blank=None) def _scale_internal(self, type=None, option='old', bscale=None, bzero=None, blank=0): """ This is an internal implementation of the `scale` method, which also supports handling BLANK properly. TODO: This is only needed for fixing #3865 without introducing any public API changes. We should support BLANK better when rescaling data, and when that is added the need for this internal interface should go away. Note: the default of ``blank=0`` merely reflects the current behavior, and is not necessarily a deliberate choice (better would be to disallow conversion of floats to ints without specifying a BLANK if there are NaN/inf values). """ if self.data is None: return # Determine the destination (numpy) data type if type is None: type = BITPIX2DTYPE[self._bitpix] _type = getattr(np, type) # Determine how to scale the data # bscale and bzero takes priority if bscale is not None and bzero is not None: _scale = bscale _zero = bzero elif bscale is not None: _scale = bscale _zero = 0 elif bzero is not None: _scale = 1 _zero = bzero elif (option == 'old' and self._orig_bscale is not None and self._orig_bzero is not None): _scale = self._orig_bscale _zero = self._orig_bzero elif option == 'minmax' and not issubclass(_type, np.floating): if _is_dask_array(self.data): min = self.data.min().compute() max = self.data.max().compute() else: min = np.minimum.reduce(self.data.flat) max = np.maximum.reduce(self.data.flat) if _type == np.uint8: # uint8 case _zero = min _scale = (max - min) / (2.0 ** 8 - 1) else: _zero = (max + min) / 2.0 # throw away -2^N nbytes = 8 * _type().itemsize _scale = (max - min) / (2.0 ** nbytes - 2) else: _scale = 1 _zero = 0 # Do the scaling if _zero != 0: if _is_dask_array(self.data): self.data = self.data - _zero else: # 0.9.6.3 to avoid out of range error for BZERO = +32768 # We have to explicitly cast _zero to prevent numpy from raising an # error when doing self.data -= zero, and we do this instead of # self.data = self.data - zero to avoid doubling memory usage. np.add(self.data, -_zero, out=self.data, casting='unsafe') self._header['BZERO'] = _zero else: try: del self._header['BZERO'] except KeyError: pass if _scale and _scale != 1: self.data = self.data / _scale self._header['BSCALE'] = _scale else: try: del self._header['BSCALE'] except KeyError: pass # Set blanks if blank is not None and issubclass(_type, np.integer): # TODO: Perhaps check that the requested BLANK value fits in the # integer type being scaled to? self.data[np.isnan(self.data)] = blank self._header['BLANK'] = blank if self.data.dtype.type != _type: self.data = np.array(np.around(self.data), dtype=_type) # Update the BITPIX Card to match the data self._bitpix = DTYPE2BITPIX[self.data.dtype.name] self._bzero = self._header.get('BZERO', 0) self._bscale = self._header.get('BSCALE', 1) self._blank = blank self._header['BITPIX'] = self._bitpix # Since the image has been manually scaled, the current # bitpix/bzero/bscale now serve as the 'original' scaling of the image, # as though the original image has been completely replaced self._orig_bitpix = self._bitpix self._orig_bzero = self._bzero self._orig_bscale = self._bscale self._orig_blank = self._blank def _verify(self, option='warn'): # update_header can fix some things that would otherwise cause # verification to fail, so do that now... self.update_header() self._verify_blank() return super()._verify(option) def _verify_blank(self): # Probably not the best place for this (it should probably happen # in _verify as well) but I want to be able to raise this warning # both when the HDU is created and when written if self._blank is None: return messages = [] # TODO: Once the FITSSchema framewhere is merged these warnings # should be handled by the schema if not _is_int(self._blank): messages.append( "Invalid value for 'BLANK' keyword in header: {!r} " "The 'BLANK' keyword must be an integer. It will be " "ignored in the meantime.".format(self._blank)) self._blank = None if not self._bitpix > 0: messages.append( "Invalid 'BLANK' keyword in header. The 'BLANK' keyword " "is only applicable to integer data, and will be ignored " "in this HDU.") self._blank = None for msg in messages: warnings.warn(msg, VerifyWarning) def _prewriteto(self, checksum=False, inplace=False): if self._scale_back: self._scale_internal(BITPIX2DTYPE[self._orig_bitpix], blank=self._orig_blank) self.update_header() if not inplace and self._data_needs_rescale: # Go ahead and load the scaled image data and update the header # with the correct post-rescaling headers _ = self.data return super()._prewriteto(checksum, inplace) def _writedata_internal(self, fileobj): size = 0 if self.data is None: return size elif _is_dask_array(self.data): return self._writeinternal_dask(fileobj) else: # Based on the system type, determine the byteorders that # would need to be swapped to get to big-endian output if sys.byteorder == 'little': swap_types = ('<', '=') else: swap_types = ('<',) # deal with unsigned integer 16, 32 and 64 data if _is_pseudo_integer(self.data.dtype): # Convert the unsigned array to signed output = np.array( self.data - _pseudo_zero(self.data.dtype), dtype=f'>i{self.data.dtype.itemsize}') should_swap = False else: output = self.data byteorder = output.dtype.str[0] should_swap = (byteorder in swap_types) if should_swap: if output.flags.writeable: output.byteswap(True) try: fileobj.writearray(output) finally: output.byteswap(True) else: # For read-only arrays, there is no way around making # a byteswapped copy of the data. fileobj.writearray(output.byteswap(False)) else: fileobj.writearray(output) size += output.size * output.itemsize return size def _writeinternal_dask(self, fileobj): if sys.byteorder == 'little': swap_types = ('<', '=') else: swap_types = ('<',) # deal with unsigned integer 16, 32 and 64 data if _is_pseudo_integer(self.data.dtype): raise NotImplementedError("This dtype isn't currently supported with dask.") else: output = self.data byteorder = output.dtype.str[0] should_swap = (byteorder in swap_types) if should_swap: from dask.utils import M # NOTE: the inplace flag to byteswap needs to be False otherwise the array is # byteswapped in place every time it is computed and this affects # the input dask array. output = output.map_blocks(M.byteswap, False).map_blocks(M.newbyteorder, "S") initial_position = fileobj.tell() n_bytes = output.nbytes # Extend the file n_bytes into the future fileobj.seek(initial_position + n_bytes - 1) fileobj.write(b'\0') fileobj.flush() if fileobj.fileobj_mode not in ('rb+', 'wb+', 'ab+'): # Use another file handle if the current one is not in # read/write mode fp = open(fileobj.name, mode='rb+') should_close = True else: fp = fileobj._file should_close = False try: outmmap = mmap.mmap(fp.fileno(), length=initial_position + n_bytes, access=mmap.ACCESS_WRITE) outarr = np.ndarray(shape=output.shape, dtype=output.dtype, offset=initial_position, buffer=outmmap) output.store(outarr, lock=True, compute=True) finally: if should_close: fp.close() outmmap.close() # On Windows closing the memmap causes the file pointer to return to 0, so # we need to go back to the end of the data (since padding may be written # after) fileobj.seek(initial_position + n_bytes) return n_bytes def _dtype_for_bitpix(self): """ Determine the dtype that the data should be converted to depending on the BITPIX value in the header, and possibly on the BSCALE value as well. Returns None if there should not be any change. """ bitpix = self._orig_bitpix # Handle possible conversion to uints if enabled if self._uint and self._orig_bscale == 1: if bitpix == 8 and self._orig_bzero == -128: return np.dtype('int8') for bits, dtype in ((16, np.dtype('uint16')), (32, np.dtype('uint32')), (64, np.dtype('uint64'))): if bitpix == bits and self._orig_bzero == 1 << (bits - 1): return dtype if bitpix > 16: # scale integers to Float64 return np.dtype('float64') elif bitpix > 0: # scale integers to Float32 return np.dtype('float32') def _convert_pseudo_integer(self, data): """ Handle "pseudo-unsigned" integers, if the user requested it. Returns the converted data array if so; otherwise returns None. In this case case, we don't need to handle BLANK to convert it to NAN, since we can't do NaNs with integers, anyway, i.e. the user is responsible for managing blanks. """ dtype = self._dtype_for_bitpix() # bool(dtype) is always False--have to explicitly compare to None; this # caused a fair amount of hair loss if dtype is not None and dtype.kind == 'u': # Convert the input raw data into an unsigned integer array and # then scale the data adjusting for the value of BZERO. Note that # we subtract the value of BZERO instead of adding because of the # way numpy converts the raw signed array into an unsigned array. bits = dtype.itemsize * 8 data = np.array(data, dtype=dtype) data -= np.uint64(1 << (bits - 1)) return data def _get_scaled_image_data(self, offset, shape): """ Internal function for reading image data from a file and apply scale factors to it. Normally this is used for the entire image, but it supports alternate offset/shape for Section support. """ code = BITPIX2DTYPE[self._orig_bitpix] raw_data = self._get_raw_data(shape, code, offset) raw_data.dtype = raw_data.dtype.newbyteorder('>') if self._do_not_scale_image_data or ( self._orig_bzero == 0 and self._orig_bscale == 1 and self._blank is None): # No further conversion of the data is necessary return raw_data try: if self._file.strict_memmap: raise ValueError("Cannot load a memory-mapped image: " "BZERO/BSCALE/BLANK header keywords present. " "Set memmap=False.") except AttributeError: # strict_memmap not set pass data = None if not (self._orig_bzero == 0 and self._orig_bscale == 1): data = self._convert_pseudo_integer(raw_data) if data is None: # In these cases, we end up with floating-point arrays and have to # apply bscale and bzero. We may have to handle BLANK and convert # to NaN in the resulting floating-point arrays. # The BLANK keyword should only be applied for integer data (this # is checked in __init__ but it can't hurt to double check here) blanks = None if self._blank is not None and self._bitpix > 0: blanks = raw_data.flat == self._blank # The size of blanks in bytes is the number of elements in # raw_data.flat. However, if we use np.where instead we will # only use 8 bytes for each index where the condition is true. # So if the number of blank items is fewer than # len(raw_data.flat) / 8, using np.where will use less memory if blanks.sum() < len(blanks) / 8: blanks = np.where(blanks) new_dtype = self._dtype_for_bitpix() if new_dtype is not None: data = np.array(raw_data, dtype=new_dtype) else: # floating point cases if self._file is not None and self._file.memmap: data = raw_data.copy() elif not raw_data.flags.writeable: # create a writeable copy if needed data = raw_data.copy() # if not memmap, use the space already in memory else: data = raw_data del raw_data if self._orig_bscale != 1: np.multiply(data, self._orig_bscale, data) if self._orig_bzero != 0: data += self._orig_bzero if self._blank: data.flat[blanks] = np.nan return data def _summary(self): """ Summarize the HDU: name, dimensions, and formats. """ class_name = self.__class__.__name__ # if data is touched, use data info. if self._data_loaded: if self.data is None: format = '' else: format = self.data.dtype.name format = format[format.rfind('.')+1:] else: if self.shape and all(self.shape): # Only show the format if all the dimensions are non-zero # if data is not touched yet, use header info. format = BITPIX2DTYPE[self._bitpix] else: format = '' if (format and not self._do_not_scale_image_data and (self._orig_bscale != 1 or self._orig_bzero != 0)): new_dtype = self._dtype_for_bitpix() if new_dtype is not None: format += f' (rescales to {new_dtype.name})' # Display shape in FITS-order shape = tuple(reversed(self.shape)) return (self.name, self.ver, class_name, len(self._header), shape, format, '') def _calculate_datasum(self): """ Calculate the value for the ``DATASUM`` card in the HDU. """ if self._has_data: # We have the data to be used. d = self.data # First handle the special case where the data is unsigned integer # 16, 32 or 64 if _is_pseudo_integer(self.data.dtype): d = np.array(self.data - _pseudo_zero(self.data.dtype), dtype=f'i{self.data.dtype.itemsize}') # Check the byte order of the data. If it is little endian we # must swap it before calculating the datasum. if d.dtype.str[0] != '>': if d.flags.writeable: byteswapped = True d = d.byteswap(True) d.dtype = d.dtype.newbyteorder('>') else: # If the data is not writeable, we just make a byteswapped # copy and don't bother changing it back after d = d.byteswap(False) d.dtype = d.dtype.newbyteorder('>') byteswapped = False else: byteswapped = False cs = self._compute_checksum(d.flatten().view(np.uint8)) # If the data was byteswapped in this method then return it to # its original little-endian order. if byteswapped and not _is_pseudo_integer(self.data.dtype): d.byteswap(True) d.dtype = d.dtype.newbyteorder('<') return cs else: # This is the case where the data has not been read from the file # yet. We can handle that in a generic manner so we do it in the # base class. The other possibility is that there is no data at # all. This can also be handled in a generic manner. return super()._calculate_datasum() class Section: """ Image section. Slices of this object load the corresponding section of an image array from the underlying FITS file on disk, and applies any BSCALE/BZERO factors. Section slices cannot be assigned to, and modifications to a section are not saved back to the underlying file. See the :ref:`astropy:data-sections` section of the Astropy documentation for more details. """ def __init__(self, hdu): self.hdu = hdu def __getitem__(self, key): if not isinstance(key, tuple): key = (key,) naxis = len(self.hdu.shape) return_scalar = (all(isinstance(k, (int, np.integer)) for k in key) and len(key) == naxis) if not any(k is Ellipsis for k in key): # We can always add a ... at the end, after making note of whether # to return a scalar. key += Ellipsis, ellipsis_count = len([k for k in key if k is Ellipsis]) if len(key) - ellipsis_count > naxis or ellipsis_count > 1: raise IndexError('too many indices for array') # Insert extra dimensions as needed. idx = next(i for i, k in enumerate(key + (Ellipsis,)) if k is Ellipsis) key = key[:idx] + (slice(None),) * (naxis - len(key) + 1) + key[idx+1:] return_0dim = (all(isinstance(k, (int, np.integer)) for k in key) and len(key) == naxis) dims = [] offset = 0 # Find all leading axes for which a single point is used. for idx in range(naxis): axis = self.hdu.shape[idx] indx = _IndexInfo(key[idx], axis) offset = offset * axis + indx.offset if not _is_int(key[idx]): dims.append(indx.npts) break is_contiguous = indx.contiguous for jdx in range(idx + 1, naxis): axis = self.hdu.shape[jdx] indx = _IndexInfo(key[jdx], axis) dims.append(indx.npts) if indx.npts == axis and indx.contiguous: # The offset needs to multiply the length of all remaining axes offset *= axis else: is_contiguous = False if is_contiguous: dims = tuple(dims) or (1,) bitpix = self.hdu._orig_bitpix offset = self.hdu._data_offset + offset * abs(bitpix) // 8 data = self.hdu._get_scaled_image_data(offset, dims) else: data = self._getdata(key) if return_scalar: data = data.item() elif return_0dim: data = data.squeeze() return data def _getdata(self, keys): for idx, (key, axis) in enumerate(zip(keys, self.hdu.shape)): if isinstance(key, slice): ks = range(*key.indices(axis)) break elif isiterable(key): # Handle both integer and boolean arrays. ks = np.arange(axis, dtype=int)[key] break # This should always break at some point if _getdata is called. data = [self[keys[:idx] + (k,) + keys[idx + 1:]] for k in ks] if any(isinstance(key, slice) or isiterable(key) for key in keys[idx + 1:]): # data contains multidimensional arrays; combine them. return np.array(data) else: # Only singleton dimensions remain; concatenate in a 1D array. return np.concatenate([np.atleast_1d(array) for array in data]) class PrimaryHDU(_ImageBaseHDU): """ FITS primary HDU class. """ _default_name = 'PRIMARY' 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 ``astropy.io.fits.hdu.base.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) # insert the keywords EXTEND if header is None: dim = self._header['NAXIS'] if dim == 0: dim = '' self._header.set('EXTEND', True, after='NAXIS' + str(dim)) @classmethod def match_header(cls, header): card = header.cards[0] # Due to problems discussed in #5808, we cannot assume the 'GROUPS' # keyword to be True/False, have to check the value return (card.keyword == 'SIMPLE' and ('GROUPS' not in header or header['GROUPS'] != True) and # noqa card.value) def update_header(self): super().update_header() # Update the position of the EXTEND keyword if it already exists if 'EXTEND' in self._header: if len(self._axes): after = 'NAXIS' + str(len(self._axes)) else: after = 'NAXIS' self._header.set('EXTEND', after=after) def _verify(self, option='warn'): errs = super()._verify(option=option) # Verify location and value of mandatory keywords. # The EXTEND keyword is only mandatory if the HDU has extensions; this # condition is checked by the HDUList object. However, if we already # have an EXTEND keyword check that its position is correct if 'EXTEND' in self._header: naxis = self._header.get('NAXIS', 0) self.req_cards('EXTEND', naxis + 3, lambda v: isinstance(v, bool), True, option, errs) return errs class ImageHDU(_ImageBaseHDU, ExtensionHDU): """ FITS image extension HDU class. """ _extension = 'IMAGE' def __init__(self, data=None, header=None, name=None, do_not_scale_image_data=False, uint=True, scale_back=None, ver=None): """ Construct an image HDU. Parameters ---------- data : array The data in the HDU. header : `~astropy.io.fits.Header` The header to be used (as a template). If ``header`` is `None`, a minimal header will be provided. name : str, optional The name of the HDU, will be the value of the keyword ``EXTNAME``. do_not_scale_image_data : bool, optional If `True`, image data is not scaled using BSCALE/BZERO values when read. (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) ver : int > 0 or None, optional The ver of the HDU, will be the value of the keyword ``EXTVER``. If not given or None, it defaults to the value of the ``EXTVER`` card of the ``header`` or 1. (default: None) """ # This __init__ currently does nothing differently from the base class, # and is only explicitly defined for the docstring. super().__init__( data=data, header=header, name=name, do_not_scale_image_data=do_not_scale_image_data, uint=uint, scale_back=scale_back, ver=ver) @classmethod def match_header(cls, header): card = header.cards[0] xtension = card.value if isinstance(xtension, str): xtension = xtension.rstrip() return card.keyword == 'XTENSION' and xtension == cls._extension def _verify(self, option='warn'): """ ImageHDU verify method. """ errs = super()._verify(option=option) naxis = self._header.get('NAXIS', 0) # PCOUNT must == 0, GCOUNT must == 1; the former is verified in # ExtensionHDU._verify, however ExtensionHDU._verify allows PCOUNT # to be >= 0, so we need to check it here self.req_cards('PCOUNT', naxis + 3, lambda v: (_is_int(v) and v == 0), 0, option, errs) return errs class _IndexInfo: def __init__(self, indx, naxis): if _is_int(indx): if 0 <= indx < naxis: self.npts = 1 self.offset = indx self.contiguous = True else: raise IndexError(f'Index {indx} out of range.') elif isinstance(indx, slice): start, stop, step = indx.indices(naxis) self.npts = (stop - start) // step self.offset = start self.contiguous = step == 1 elif isiterable(indx): self.npts = len(indx) self.offset = 0 self.contiguous = False else: raise IndexError(f'Illegal index {indx}')