Images#

`ImageHDU`#

class astropy.io.fits.ImageHDU(data=None, header=None, name=None, do_not_scale_image_data=False, uint=True, scale_back=None, ver=None)[source]#

Bases: _ImageBaseHDU, ExtensionHDU

FITS image extension HDU class.

Construct an image HDU.

Parameters:

dataarray: The data in the HDU.
headerHeader: The header to be used (as a template). If header is None, a minimal header will be provided.
namestr, optional: The name of the HDU, will be the value of the keyword EXTNAME.
do_not_scale_image_databool, optional: If True, image data is not scaled using BSCALE/BZERO values when read. (default: False)
uintbool, 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_backbool, 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)
verint > 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)

add_checksum(when=None, override_datasum=False, checksum_keyword='CHECKSUM', datasum_keyword='DATASUM')#

Add the CHECKSUM and DATASUM cards to this HDU with the values set to the checksum calculated for the HDU and the data respectively. The addition of the DATASUM card may be overridden.

Parameters:

whenstr, optional: comment string for the cards; by default the comments will represent the time when the checksum was calculated
override_datasumbool, optional: add the CHECKSUM card only
checksum_keywordstr, optional: The name of the header keyword to store the checksum value in; this is typically ‘CHECKSUM’ per convention, but there exist use cases in which a different keyword should be used
datasum_keywordstr, optional: See checksum_keyword

Notes

For testing purposes, first call add_datasum with a when argument, then call add_checksum with a when argument and override_datasum set to True. This will provide consistent comments for both cards and enable the generation of a CHECKSUM card with a consistent value.

add_datasum(when=None, datasum_keyword='DATASUM')#

Add the DATASUM card to this HDU with the value set to the checksum calculated for the data.

Parameters:

whenstr, optional: Comment string for the card that by default represents the time when the checksum was calculated
datasum_keywordstr, optional: The name of the header keyword to store the datasum value in; this is typically ‘DATASUM’ per convention, but there exist use cases in which a different keyword should be used

Returns:

checksumint: The calculated datasum

Notes

For testing purposes, provide a when argument to enable the comment value in the card to remain consistent. This will enable the generation of a CHECKSUM card with a consistent value.

copy()#: Make a copy of the HDU, both header and data are copied.

property data#

Image/array data as a 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.

filebytes()#: Calculates and returns the number of bytes that this HDU will write to a file.

fileinfo()#

Returns a dictionary detailing information about the locations of this HDU within any associated file. The values are only valid after a read or write of the associated file with no intervening changes to the HDUList.

Returns:

dict or None

The dictionary details information about the locations of this HDU within an associated file. Returns None when the HDU is not associated with a file.

Dictionary contents:

Key	Value
file	File object associated with the HDU
filemode	Mode in which the file was opened (readonly, copyonwrite, update, append, ostream)
hdrLoc	Starting byte location of header in file
datLoc	Starting byte location of data block in file
datSpan	Data size including padding

classmethod fromstring(data, checksum=False, ignore_missing_end=False, **kwargs)#

Creates a new HDU object of the appropriate type from a string containing the HDU’s entire header and, optionally, its data.

Note: When creating a new HDU from a string without a backing file object, the data of that HDU may be read-only. It depends on whether the underlying string was an immutable Python str/bytes object, or some kind of read-write memory buffer such as a memoryview.

Parameters:

datastr, bytes, memoryview, ndarray: A byte string containing the HDU’s header and data.
checksumbool, optional: Check the HDU’s checksum and/or datasum.
ignore_missing_endbool, optional: Ignore a missing end card in the header data. Note that without the end card the end of the header may be ambiguous and resulted in a corrupt HDU. In this case the assumption is that the first 2880 block that does not begin with valid FITS header data is the beginning of the data.
**kwargsoptional: May consist of additional keyword arguments specific to an HDU type–these correspond to keywords recognized by the constructors of different HDU classes such as PrimaryHDU, ImageHDU, or BinTableHDU. Any unrecognized keyword arguments are simply ignored.

classmethod match_header(header)[source]#: _ImageBaseHDU is sort of an abstract class for HDUs containing image data (as opposed to table data) and should never be used directly.

classmethod readfrom(fileobj, checksum=False, ignore_missing_end=False, **kwargs)#

Read the HDU from a file. Normally an HDU should be opened with open() which reads the entire HDU list in a FITS file. But this method is still provided for symmetry with writeto().

Parameters:

fileobjfile-like object: Input FITS file. The file’s seek pointer is assumed to be at the beginning of the HDU.
checksumbool: If True, verifies that both DATASUM and CHECKSUM card values (when present in the HDU header) match the header and data of all HDU’s in the file.
ignore_missing_endbool: Do not issue an exception when opening a file that is missing an END card in the last header.

req_cards(keyword, pos, test, fix_value, option, errlist)#

Check the existence, location, and value of a required Card.

Parameters:

keywordstr: The keyword to validate
posint, callable(): If an int, this specifies the exact location this card should have in the header. Remember that Python is zero-indexed, so this means pos=0 requires the card to be the first card in the header. If given a callable, it should take one argument–the actual position of the keyword–and return True or False. This can be used for custom evaluation. For example if pos=lambda idx: idx > 10 this will check that the keyword’s index is greater than 10.
testcallable(): This should be a callable (generally a function) that is passed the value of the given keyword and returns True or False. This can be used to validate the value associated with the given keyword.
fix_valuestr, int, float, complex, bool, None: A valid value for a FITS keyword to use if the given test fails to replace an invalid value. In other words, this provides a default value to use as a replacement if the keyword’s current value is invalid. If None, there is no replacement value and the keyword is unfixable.
optionstr: Output verification option. Must be one of "fix", "silentfix", "ignore", "warn", or "exception". May also be any combination of "fix" or "silentfix" with "+ignore", +warn, or +exception" (e.g. ``"fix+warn"). See Verification Options for more info.
errlistlist: A list of validation errors already found in the FITS file; this is used primarily for the validation system to collect errors across multiple HDUs and multiple calls to req_cards.

Notes

If pos=None, the card can be anywhere in the header. If the card does not exist, the new card will have the fix_value as its value when created. Also check the card’s value by using the test argument.

run_option(option='warn', err_text='', fix_text='Fixed.', fix=None, fixable=True)#: Execute the verification with selected option.

scale(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:

typestr, 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.
optionstr, 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, bzeroint, optional: User-specified BSCALE and BZERO values

property section#

Access a section of the image array without loading the entire array into memory. The 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 useful for retrieving a small subset of data from a remote file that has been opened with the use_fsspec=True parameter. For example, you can use this feature to download a small cutout from a large FITS image hosted in the Amazon S3 cloud (see the Working with remote and cloud-hosted files section of the Astropy documentation for more details.)

For local files, sections are mostly obsoleted by memmap support, but should still be used to deal with very large scaled images.

Note that sections cannot currently be written to. Moreover, any in-memory updates to the image’s .data property may not be reflected in the slices obtained via .section. See the Data Sections section of the documentation for more details.

property shape#: Shape of the image array–should be equivalent to self.data.shape.

property size#: Size (in bytes) of the data portion of the HDU.

update_header()#: Update the header keywords to agree with the data.

verify(option='warn')#

Verify all values in the instance.

Parameters:

optionstr: Output verification option. Must be one of "fix", "silentfix", "ignore", "warn", or "exception". May also be any combination of "fix" or "silentfix" with "+ignore", "+warn", or "+exception" (e.g. "fix+warn"). See Verification Options for more info.

verify_checksum()#

Verify that the value in the CHECKSUM keyword matches the value calculated for the current HDU CHECKSUM.

Returns:

validint

0 - failure
1 - success
2 - no CHECKSUM keyword present

verify_datasum()#

Verify that the value in the DATASUM keyword matches the value calculated for the DATASUM of the current HDU data.

Returns:

validint

0 - failure
1 - success
2 - no DATASUM keyword present

writeto(name, output_verify='exception', overwrite=False, checksum=False)#: Works similarly to the normal writeto(), but prepends a default PrimaryHDU are required by extension HDUs (which cannot stand on their own).

`CompImageHDU`#

class astropy.io.fits.CompImageHDU(data=None, header=None, name=None, compression_type='RICE_1', tile_shape=None, hcomp_scale=0, hcomp_smooth=0, quantize_level=16.0, quantize_method=-1, dither_seed=0, do_not_scale_image_data=False, uint=False, scale_back=False, tile_size=None)[source]#

Bases: BinTableHDU

Compressed Image HDU class.

Parameters:

dataarray, optional: Uncompressed image data
headerHeader, optional: Header to be associated with the image; when reading the HDU from a file (data=DELAYED), the header read from the file
namestr, 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_typestr, optional: Compression algorithm: one of 'RICE_1', 'RICE_ONE', 'PLIO_1', 'GZIP_1', 'GZIP_2', 'HCOMPRESS_1', 'NOCOMPRESS'
tile_shapetuple, optional: Compression tile shape, which should be specified using the default Numpy convention for array shapes (C order). The default is to treat each row of image as a tile.
hcomp_scalefloat, optional: HCOMPRESS scale parameter
hcomp_smoothfloat, optional: HCOMPRESS smooth parameter
quantize_levelfloat, optional: Floating point quantization level; see note below
quantize_methodint, optional: Floating point quantization dithering method; can be either NO_DITHER (-1; default), SUBTRACTIVE_DITHER_1 (1), or SUBTRACTIVE_DITHER_2 (2); see note below
dither_seedint, 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:

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.

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. 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_shape parameter may be provided as a list of tile sizes, one for each dimension in the image. For example a tile_shape 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.

add_checksum(when=None, override_datasum=False, checksum_keyword='CHECKSUM', datasum_keyword='DATASUM')#

Add the CHECKSUM and DATASUM cards to this HDU with the values set to the checksum calculated for the HDU and the data respectively. The addition of the DATASUM card may be overridden.

Parameters:

whenstr, optional: comment string for the cards; by default the comments will represent the time when the checksum was calculated
override_datasumbool, optional: add the CHECKSUM card only
checksum_keywordstr, optional: The name of the header keyword to store the checksum value in; this is typically ‘CHECKSUM’ per convention, but there exist use cases in which a different keyword should be used
datasum_keywordstr, optional: See checksum_keyword

Notes

add_datasum(when=None, datasum_keyword='DATASUM')#

Add the DATASUM card to this HDU with the value set to the checksum calculated for the data.

Parameters:

whenstr, optional: Comment string for the card that by default represents the time when the checksum was calculated
datasum_keywordstr, optional: The name of the header keyword to store the datasum value in; this is typically ‘DATASUM’ per convention, but there exist use cases in which a different keyword should be used

Returns:

checksumint: The calculated datasum

Notes

For testing purposes, provide a when argument to enable the comment value in the card to remain consistent. This will enable the generation of a CHECKSUM card with a consistent value.

property columns#: The ColDefs objects describing the columns in this table.

property compression_type#: The name of the compression algorithm.

copy()#: Make a copy of the table HDU, both header and data are copied.

property data#

The decompressed data array.

Note that accessing this will cause all the tiles to be loaded, decompressed, and combined into a single data array. If you do not need to access the whole array, consider instead using the section property.

dump(datafile=None, cdfile=None, hfile=None, overwrite=False)#

Dump the table HDU to a file in ASCII format. The table may be dumped in three separate files, one containing column definitions, one containing header parameters, and one for table data.

Parameters:

datafilepath-like object or file-like object, optional: Output data file. The default is the root name of the fits file associated with this HDU appended with the extension .txt.
cdfilepath-like object or file-like object, optional: Output column definitions file. The default is None, no column definitions output is produced.
hfilepath-like object or file-like object, optional: Output header parameters file. The default is None, no header parameters output is produced.
overwritebool, optional: If True, overwrite the output file if it exists. Raises an OSError if False and the output file exists. Default is False.

Notes

The primary use for the dump method is to allow viewing and editing the table data and parameters in a standard text editor. The load method can be used to create a new table from the three plain text (ASCII) files.

datafile: Each line of the data file represents one row of table data. The data is output one column at a time in column order. If a column contains an array, each element of the column array in the current row is output before moving on to the next column. Each row ends with a new line.

Integer data is output right-justified in a 21-character field followed by a blank. Floating point data is output right justified using ‘g’ format in a 21-character field with 15 digits of precision, followed by a blank. String data that does not contain whitespace is output left-justified in a field whose width matches the width specified in the TFORM header parameter for the column, followed by a blank. When the string data contains whitespace characters, the string is enclosed in quotation marks (""). For the last data element in a row, the trailing blank in the field is replaced by a new line character.

For column data containing variable length arrays (‘P’ format), the array data is preceded by the string 'VLA_Length= ' and the integer length of the array for that row, left-justified in a 21-character field, followed by a blank.

Note

This format does not support variable length arrays using the (‘Q’ format) due to difficult to overcome ambiguities. What this means is that this file format cannot support VLA columns in tables stored in files that are over 2 GB in size.

For column data representing a bit field (‘X’ format), each bit value in the field is output right-justified in a 21-character field as 1 (for true) or 0 (for false).
cdfile: Each line of the column definitions file provides the definitions for one column in the table. The line is broken up into 8, sixteen-character fields. The first field provides the column name (TTYPEn). The second field provides the column format (TFORMn). The third field provides the display format (TDISPn). The fourth field provides the physical units (TUNITn). The fifth field provides the dimensions for a multidimensional array (TDIMn). The sixth field provides the value that signifies an undefined value (TNULLn). The seventh field provides the scale factor (TSCALn). The eighth field provides the offset value (TZEROn). A field value of "" is used to represent the case where no value is provided.
hfile: Each line of the header parameters file provides the definition of a single HDU header card as represented by the card image.

filebytes()#: Calculates and returns the number of bytes that this HDU will write to a file.

fileinfo()#

Returns:

dict or None

The dictionary details information about the locations of this HDU within an associated file. Returns None when the HDU is not associated with a file.

Dictionary contents:

Key	Value
file	File object associated with the HDU
filemode	Mode in which the file was opened (readonly, copyonwrite, update, append, ostream)
hdrLoc	Starting byte location of header in file
datLoc	Starting byte location of data block in file
datSpan	Data size including padding

classmethod from_columns(columns, header=None, nrows=0, fill=False, character_as_bytes=False, **kwargs)#

Given either a ColDefs object, a sequence of Column objects, or another table HDU or table data (a FITS_rec or multi-field numpy.ndarray or numpy.recarray object, return a new table HDU of the class this method was called on using the column definition from the input.

`Section`#

class astropy.io.fits.Section(hdu)[source]#

Bases: object

Class enabling subsets of ImageHDU data to be loaded lazily via slicing.

Slices of this object load the corresponding section of an image array from the underlying FITS file, 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 Data Sections section of the Astropy documentation for more details.

`CompImageSection`#

class astropy.io.fits.CompImageSection(hdu)[source]#

Bases: object

Class enabling subsets of CompImageHDU data to be loaded lazily via slicing.

Slices of this object load the corresponding section of an image array from the underlying FITS file, 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 Data Sections section of the Astropy documentation for more details.

Images#

ImageHDU#

CompImageHDU#

Section#

CompImageSection#

`ImageHDU`#

`CompImageHDU`#

`Section`#

`CompImageSection`#