FITS File Handling (


The package provides access to FITS files. FITS (Flexible Image Transport System) is a portable file standard widely used in the astronomy community to store images and tables. This subpackage was originally developed as PyFITS.

Getting Started#

This section provides a quick introduction of using The goal is to demonstrate the package’s basic features without getting into too much detail. If you are a first time user or have never used astropy or PyFITS, this is where you should start. See also the FAQ for answers to common questions and issues.


If you want to read or write a single table in FITS format, the recommended method is via the high-level Unified File Read/Write Interface. In particular see the Unified I/O FITS section.

Reading and Updating Existing FITS Files#

Opening a FITS File#


The function, used in the examples here, is for accessing data shipped with astropy. To work with your own data instead, please use, which takes either the relative or absolute path.

Once the package is loaded using the standard convention [1], we can open an existing FITS file:

>>> from import fits
>>> fits_image_filename = fits.util.get_testdata_filepath('test0.fits')

>>> hdul =

The open() function has several optional arguments which will be discussed in a later chapter. The default mode, as in the above example, is “readonly”. The open function returns an object called an HDUList which is a list-like collection of HDU objects. An HDU (Header Data Unit) is the highest level component of the FITS file structure, consisting of a header and (typically) a data array or table.

After the above open call, hdul[0] is the primary HDU, hdul[1] is the first extension HDU, etc. (if there are any extensions), and so on. It should be noted that astropy uses zero-based indexing when referring to HDUs and header cards, though the FITS standard (which was designed with Fortran in mind) uses one-based indexing.

The HDUList has a useful method, which summarizes the content of the opened FITS file:

Filename: ...test0.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU     138   ()
  1  SCI           1 ImageHDU        61   (40, 40)   int16
  2  SCI           2 ImageHDU        61   (40, 40)   int16
  3  SCI           3 ImageHDU        61   (40, 40)   int16
  4  SCI           4 ImageHDU        61   (40, 40)   int16

After you are done with the opened file, close it with the HDUList.close() method:

>>> hdul.close()

You can avoid closing the file manually by using open() as context manager:

>>> with as hdul:
Filename: ...test0.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU     138   ()
  1  SCI           1 ImageHDU        61   (40, 40)   int16
  2  SCI           2 ImageHDU        61   (40, 40)   int16
  3  SCI           3 ImageHDU        61   (40, 40)   int16
  4  SCI           4 ImageHDU        61   (40, 40)   int16

After exiting the with scope the file will be closed automatically. That is (generally) the preferred way to open a file in Python, because it will close the file even if an exception happens.

If the file is opened with lazy_load_hdus=False, all of the headers will still be accessible after the HDUList is closed. The headers and data may or may not be accessible depending on whether the data are touched and if they are memory-mapped; see later chapters for detail.

Working with large files#

The open() function supports a memmap=True argument that allows the array data of each HDU to be accessed with mmap, rather than being read into memory all at once. This is particularly useful for working with very large arrays that cannot fit entirely into physical memory. Here memmap=True by default, and this value is obtained from the configuration item

This has minimal impact on smaller files as well, though some operations, such as reading the array data sequentially, may incur some additional overhead. On 32-bit systems, arrays larger than 2 to 3 GB cannot be mmap’d (which is fine, because by that point you are likely to run out of physical memory anyways), but 64-bit systems are much less limited in this respect.


When opening a file with memmap=True, because of how mmap works this means that when the HDU data is accessed (i.e., hdul[0].data) another handle to the FITS file is opened by mmap. This means that even after calling hdul.close() the mmap still holds an open handle to the data so that it can still be accessed by unwary programs that were built with the assumption that the .data attribute has all of the data in-memory.

In order to force the mmap to close, either wait for the containing HDUList object to go out of scope, or manually call del hdul[0].data. (This works so long as there are no other references held to the data array.)

Working with remote and cloud-hosted files#

The open() function supports a use_fsspec argument which allows file paths to be opened using fsspec. The fsspec package supports a range of remote and distributed storage backends such as Amazon and Google Cloud Storage. For example, you can access a Hubble Space Telescope image located in the Hubble’s public Amazon S3 bucket as follows:

>>> # Location of a large Hubble archive image in Amazon S3 (213 MB)
>>> uri = "s3://stpubdata/hst/public/j8pu/j8pu0y010/j8pu0y010_drc.fits"
>>> # Extract a 10-by-20 pixel cutout image
>>> with, use_fsspec=True, fsspec_kwargs={"anon": True}) as hdul:  
...    cutout = hdul[1].section[10:20, 30:50]

Note that the example above obtains a cutout image using the ImageHDU.section attribute rather than the traditional attribute. The use of .section ensures that only the necessary parts of the FITS image are transferred from the server, rather than downloading the entire data array. This trick can significantly speed up your code if you require small subsets of large FITS files located on slow (remote) storage systems. See Obtaining subsets from cloud-hosted FITS files for additional information on working with remote FITS files in this way.

Unsigned integers#

Due to the FITS format’s Fortran origins, FITS does not natively support unsigned integer data in images or tables. However, there is a common convention to store unsigned integers as signed integers, along with a shift instruction (a BZERO keyword with value 2 ** (BITPIX - 1)) to shift up all signed integers to unsigned integers. For example, when writing the value 0 as an unsigned 32-bit integer, it is stored in the FITS file as -32768, along with the header keyword BZERO = 32768.

astropy recognizes and applies this convention by default, so that all data that looks like it should be interpreted as unsigned integers is automatically converted (this applies to both images and tables).

Even with uint=False, the BZERO shift is still applied, but the returned array is of “float64” type. To disable scaling/shifting entirely, use do_not_scale_image_data=True (see Why is an image containing integer data being converted unexpectedly to floats? in the FAQ for more details).

Working with compressed files#


Files that use compressed HDUs within the FITS file are discussed in Compressed Image Data.

The open() function will seamlessly open FITS files that have been compressed with gzip, bzip2 or pkzip. Note that in this context we are talking about a FITS file that has been compressed with one of these utilities (e.g., a .fits.gz file).

There are some limitations when working with compressed files. For example, with Zip files that contain multiple compressed files, only the first file will be accessible. Also bzip2 does not support the append or update access modes.

When writing a file (e.g., with the writeto() function), compression will be determined based on the filename extension given, or the compression used in a pre-existing file that is being written to.

Working with non-standard files#

When reads a FITS file which does not conform to the FITS standard it will try to make an educated interpretation of non-compliant fields. This may not always succeed and may trigger warnings when accessing headers or exceptions when writing to file. Verification of fields written to an output file can be controlled with the output_verify parameter of open(). Files opened for reading can be verified and fixed with method HDUList.verify. This method is typically invoked after opening the file but before accessing any headers or data:

>>> with as hdul:
...    hdul.verify('fix')
...    data = hdul[1].data

In the above example, the call to hdul.verify("fix") requests that fix non-compliant fields and print informative messages. Other options in addition to "fix" are described under FITS Verification

See also

FITS Verification.

Working with FITS Headers#

As mentioned earlier, each element of an HDUList is an HDU object with .header and .data attributes, which can be used to access the header and data portions of the HDU.

For those unfamiliar with FITS headers, they consist of a list of 80 byte “cards”, where a card contains a keyword, a value, and a comment. The keyword and comment must both be strings, whereas the value can be a string or an integer, floating point number, complex number, or True/False. Keywords are usually unique within a header, except in a few special cases.

The header attribute is a Header instance, another astropy object. To get the value associated with a header keyword, do (à la Python dicts):

>>> hdul =
>>> hdul[0].header['DATE']

to get the value of the keyword “DATE”, which is a string ‘01/04/99’.

Although keyword names are always in upper case inside the FITS file, specifying a keyword name with astropy is case-insensitive for the user’s convenience. If the specified keyword name does not exist, it will raise a KeyError exception.

We can also get the keyword value by indexing (à la Python lists):

>>> hdul[0].header[7]

This example returns the eighth (like Python lists, it is 0-indexed) keyword’s value — a float — 32768.0.

Similarly, it is possible to update a keyword’s value in astropy, either through keyword name or index:

>>> hdr = hdul[0].header
>>> hdr['targname'] = 'NGC121-a'
>>> hdr[27] = 99

Please note however that almost all application code should update header values via their keyword name and not via their positional index. This is because most FITS keywords may appear at any position in the header.

It is also possible to update both the value and comment associated with a keyword by assigning them as a tuple:

>>> hdr = hdul[0].header
>>> hdr['targname'] = ('NGC121-a', 'the observation target')
>>> hdr['targname']
>>> hdr.comments['targname']
'the observation target'

Like a dict, you may also use the above syntax to add a new keyword/value pair (and optionally a comment as well). In this case the new card is appended to the end of the header (unless it is a commentary keyword such as COMMENT or HISTORY, in which case it is appended after the last card with that keyword).

Another way to either update an existing card or append a new one is to use the Header.set() method:

>>> hdr.set('observer', 'Edwin Hubble')

Comment or history records are added like normal cards, though in their case a new card is always created, rather than updating an existing HISTORY or COMMENT card:

>>> hdr['history'] = 'I updated this file 2/26/09'
>>> hdr['comment'] = 'Edwin Hubble really knew his stuff'
>>> hdr['comment'] = 'I like using HST observations'
>>> hdr['history']
I updated this file 2/26/09
>>> hdr['comment']
Edwin Hubble really knew his stuff
I like using HST observations

Note: Be careful not to confuse COMMENT cards with the comment value for normal cards.

To update existing COMMENT or HISTORY cards, reference them by index:

>>> hdr['history'][0] = 'I updated this file on 2/27/09'
>>> hdr['history']
I updated this file on 2/27/09
>>> hdr['comment'][1] = 'I like using JWST observations'
>>> hdr['comment']
Edwin Hubble really knew his stuff
I like using JWST observations

To see the entire header as it appears in the FITS file (with the END card and padding stripped), enter the header object by itself, or print(repr(hdr)):

>>> hdr  
SIMPLE  =                    T / file does conform to FITS standard
BITPIX  =                   16 / number of bits per data pixel
NAXIS   =                    0 / number of data axes
>>> print(repr(hdr))  
SIMPLE  =                    T / file does conform to FITS standard
BITPIX  =                   16 / number of bits per data pixel
NAXIS   =                    0 / number of data axes

Entering only print(hdr) will also work, but may not be very legible on most displays, as this displays the header as it is written in the FITS file itself, which means there are no line breaks between cards. This is a common source of confusion for new users.

It is also possible to view a slice of the header:

>>> hdr[:2]
SIMPLE  =                    T / file does conform to FITS standard
BITPIX  =                   16 / number of bits per data pixel

Only the first two cards are shown above.

To get a list of all keywords, use the Header.keys() method just as you would with a dict:

>>> list(hdr.keys())  
['SIMPLE', 'BITPIX', 'NAXIS', ...]


See also Convenience Functions.

Structural Keywords#

FITS keywords mix up both metadata and critical information about the file structure that is needed to parse the file. These structural keywords are managed internally by and, in general, should not be touched by the user. Instead one should use the related attributes of the classes (see examples below).

The specific set of structural keywords used by the FITS standard varies with HDU type. The following table lists which keywords are associated with each HDU type:

Structural Keywords#

HDU Type

Structural Keywords





ImageHDU, TableHDU, BinTableHDU




TableHDU, BinTableHDU


There are many other reserved keywords, for instance for the data scaling, or for table’s column attributes, as described in the FITS Standard. Most of these are accessible via attributes of the Column or HDU objects, for instance to set EXTNAME, or hdu.ver for EXTVER. Structural keywords are checked and/or updated as a consequence of common operations. For example, when:

  1. Setting the data. The NAXIS* keywords are set from the data shape (.data.shape), and BITPIX from the data type (.data.dtype).

  2. Setting the header. Its keywords are updated based on the data properties (as above).

  3. Writing a file. All the necessary keywords are deleted, updated or added to the header.

  4. Calling an HDU’s verify method (e.g., PrimaryHDU.verify()). Some keywords can be fixed automatically.

In these cases any hand-written values users might assign to those keywords will be overwrittten.

Working with Image Data#

If an HDU’s data is an image, the data attribute of the HDU object will return a numpy ndarray object. Refer to the numpy documentation for details on manipulating these numerical arrays:

>>> data = hdul[1].data

Here, data points to the data object in the second HDU (the first HDU, hdul[0], being the primary HDU) which corresponds to the ‘SCI’ extension. Alternatively, you can access the extension by its extension name (specified in the EXTNAME keyword):

>>> data = hdul['SCI'].data

If there is more than one extension with the same EXTNAME, the EXTVER value needs to be specified along with the EXTNAME as a tuple; for example:

>>> data = hdul['sci',2].data

Note that the EXTNAME is also case-insensitive.

The returned numpy object has many attributes and methods for a user to get information about the array, for example:

>>> data.shape
(40, 40)

Since image data is a numpy object, we can slice it, view it, and perform mathematical operations on it. To see the pixel value at x=5, y=2:

>>> print(data[1, 4])

Note that, like C (and unlike Fortran), Python is 0-indexed and the indices have the slowest axis first and fastest changing axis last; that is, for a 2D image, the fast axis (X-axis) which corresponds to the FITS NAXIS1 keyword, is the second index. Similarly, the 1-indexed subsection of x=11 to 20 (inclusive) and y=31 to 40 (inclusive) would be given in Python as:

>>> data[30:40, 10:20]
array([[350, 349, 349, 348, 349, 348, 349, 347, 350, 348],
       [348, 348, 348, 349, 348, 349, 347, 348, 348, 349],
       [348, 348, 347, 349, 348, 348, 349, 349, 349, 349],
       [349, 348, 349, 349, 350, 349, 349, 347, 348, 348],
       [348, 348, 348, 348, 349, 348, 350, 349, 348, 349],
       [348, 347, 349, 349, 350, 348, 349, 348, 349, 347],
       [347, 348, 347, 348, 349, 349, 350, 349, 348, 348],
       [349, 349, 350, 348, 350, 347, 349, 349, 349, 348],
       [349, 348, 348, 348, 348, 348, 349, 347, 349, 348],
       [349, 349, 349, 348, 350, 349, 349, 350, 348, 350]], dtype=int16)

To update the value of a pixel or a subsection:

>>> data[30:40, 10:20] = data[1, 4] = 999

This example changes the values of both the pixel [1, 4] and the subsection [30:40, 10:20] to the new value of 999. See the Numpy documentation for more details on Python-style array indexing and slicing.

The next example of array manipulation is to convert the image data from counts to flux:

>>> photflam = hdul[1].header['photflam']
>>> exptime = hdr['exptime']
>>> data = data * photflam / exptime
>>> hdul.close()

Note that performing an operation like this on an entire image requires holding the entire image in memory. This example performs the multiplication in-place so that no copies are made, but the original image must first be able to fit in main memory. For most observations this should not be an issue on modern personal computers.

If at this point you want to preserve all of the changes you made and write it to a new file, you can use the HDUList.writeto() method (see below).


See more information in Image Data.

Working with Table Data#


This section describes reading and writing table data in the FITS format using the fits package directly. For some cases, however, the high-level Unified File Read/Write Interface (using or will often suffice and is somewhat more convenient to use. See the Unified I/O FITS section for details.

Like images, the data portion of a FITS table extension is in the .data attribute:

>>> fits_table_filename = fits.util.get_testdata_filepath('tb.fits')
>>> hdul =
>>> data = hdul[1].data # assuming the first extension is a table
>>> hdul.close()

If you are familiar with numpy recarray (record array) objects, you will find the table data is basically a record array with some extra properties. But familiarity with record arrays is not a prerequisite for this guide.

To see the first row of the table:

>>> print(data[0])
(1, 'abc', 3.7000000715255736, False)

Each row in the table is a FITS_record object which looks like a (Python) tuple containing elements of heterogeneous data types. In this example: an integer, a string, a floating point number, and a Boolean value. So the table data are just an array of such records. More commonly, a user is likely to access the data in a column-wise way. This is accomplished by using the field() method. To get the first column (or “field” in NumPy parlance — it is used here interchangeably with “column”) of the table, use:

>>> data.field(0)
array([1, 2]...)

A numpy object with the data type of the specified field is returned.

Like header keywords, a column can be referred either by index, as above, or by name:

>>> data.field('c1')
array([1, 2]...)

When accessing a column by name, dict-like access is also possible (and even preferable):

>>> data['c1']
array([1, 2]...)

In most cases it is preferable to access columns by their name, as the column name is entirely independent of its physical order in the table. As with header keywords, column names are case-insensitive.

But how do we know what columns we have in a table? First, we will introduce another attribute of the table HDU: the columns attribute:

>>> cols = hdul[1].columns

This attribute is a ColDefs (column definitions) object. If we use the method from the interactive prompt:

    ['c1', 'c2', 'c3', 'c4']
    ['1J', '3A', '1E', '1L']
    ['', '', '', '']
    [-2147483647, '', '', '']
    ['', '', 3, '']
    ['', '', 0.4, '']
    ['I11', 'A3', 'G15.7', 'L6']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']
    ['', '', '', '']

it will show the attributes of all columns in the table, such as their names, formats, bscales, bzeros, etc. A similar output that will display the column names and their formats can be printed from within a script with:

>>> hdul[1].columns
    name = 'c1'; format = '1J'; null = -2147483647; disp = 'I11'
    name = 'c2'; format = '3A'; disp = 'A3'
    name = 'c3'; format = '1E'; bscale = 3; bzero = 0.4; disp = 'G15.7'
    name = 'c4'; format = '1L'; disp = 'L6'

We can also get these properties individually; for example:

>>> cols.names
['c1', 'c2', 'c3', 'c4']

returns a (Python) list of field names.

Since each field is a numpy object, we will have the entire arsenal of numpy tools to use. We can reassign (update) the values:

>>> data['c4'][:] = 0

take the mean of a column:

>>> data['c3'].mean()  

and so on.

Save File Changes#

As mentioned earlier, after a user opened a file, made a few changes to either header or data, the user can use HDUList.writeto() to save the changes. This takes the version of headers and data in memory and writes them to a new FITS file on disk. Subsequent operations can be performed to the data in memory and written out to yet another different file, all without recopying the original data to (more) memory:


will write the current content of hdulist to a new disk file newfile.fits. If a file was opened with the update mode, the HDUList.flush() method can also be used to write all of the changes made since open(), back to the original file. The close() method will do the same for a FITS file opened with update mode:

with'original.fits', mode='update') as hdul:
    # Change something in hdul.
    hdul.flush()  # changes are written back to original.fits

# closing the file will also flush any changes and prevent further writing

Creating a New FITS File#

Creating a New Image File#

So far we have demonstrated how to read and update an existing FITS file. But how about creating a new FITS file from scratch? Such tasks are very convenient in astropy for an image HDU. We will first demonstrate how to create a FITS file consisting of only the primary HDU with image data.

First, we create a numpy object for the data part:

>>> import numpy as np
>>> data = np.arange(100.0) # a simple sequence of floats from 0.0 to 99.0

Next, we create a PrimaryHDU object to encapsulate the data:

>>> hdu = fits.PrimaryHDU(data=data)

We then create an HDUList to contain the newly created primary HDU, and write to a new file:

>>> hdul = fits.HDUList([hdu])
>>> hdul.writeto('new1.fits')

That is it! In fact, astropy even provides a shortcut for the last two lines to accomplish the same behavior:

>>> hdu.writeto('new2.fits')

This will write a single HDU to a FITS file without having to manually encapsulate it in an HDUList object first.

Creating a New Table File#


If you want to create a binary FITS table with no other HDUs, you can use Table instead and then write to FITS. This is less complicated than “lower-level” FITS interface:

>>> from astropy.table import Table
>>> t = Table([[1, 2], [4, 5], [7, 8]], names=('a', 'b', 'c'))
>>> t.write('table1.fits', format='fits')

The equivalent code using would look like this:

>>> from import fits
>>> import numpy as np
>>> c1 = fits.Column(name='a', array=np.array([1, 2]), format='K')
>>> c2 = fits.Column(name='b', array=np.array([4, 5]), format='K')
>>> c3 = fits.Column(name='c', array=np.array([7, 8]), format='K')
>>> t = fits.BinTableHDU.from_columns([c1, c2, c3])
>>> t.writeto('table2.fits')

To create a table HDU is a little more involved than an image HDU, because a table’s structure needs more information. First of all, tables can only be an extension HDU, not a primary. There are two kinds of FITS table extensions: ASCII and binary. We will use binary table examples here.

To create a table from scratch, we need to define columns first, by constructing the Column objects and their data. Suppose we have two columns, the first containing strings, and the second containing floating point numbers:

>>> import numpy as np
>>> a1 = np.array(['NGC1001', 'NGC1002', 'NGC1003'])
>>> a2 = np.array([11.1, 12.3, 15.2])
>>> col1 = fits.Column(name='target', format='20A', array=a1)
>>> col2 = fits.Column(name='V_mag', format='E', array=a2)


It is not necessary to create a Column object explicitly if the data is stored in a structured array.

Next, create a ColDefs (column-definitions) object for all columns:

>>> cols = fits.ColDefs([col1, col2])

Now, create a new binary table HDU object by using the BinTableHDU.from_columns() function:

>>> hdu = fits.BinTableHDU.from_columns(cols)

This function returns (in this case) a BinTableHDU.

The data structure used to represent FITS tables is called a FITS_rec and is derived from the numpy.recarray interface. When creating a new table HDU the individual column arrays will be assembled into a single FITS_rec array.

You can create a BinTableHDU more concisely without creating intermediate variables for the individual columns and without manually creating a ColDefs object:

>>> hdu = fits.BinTableHDU.from_columns(
...     [fits.Column(name='target', format='20A', array=a1),
...      fits.Column(name='V_mag', format='E', array=a2)])

Now you may write this new table HDU directly to a FITS file like so:

>>> hdu.writeto('table3.fits')

This shortcut will automatically create a minimal primary HDU with no data and prepend it to the table HDU to create a valid FITS file. If you require additional data or header keywords in the primary HDU you may still create a PrimaryHDU object and build up the FITS file manually using an HDUList, as described in the next section.

Creating a Multi-Extension FITS (MEF) file#

In the previous examples we created files with a single meaningful extension (a PrimaryHDU or BinTableHDU). To create a file with multiple extensions we need to create extension HDUs and append them to an HDUList.

First, we create some data for Image extensions and we place the data into separate PrimaryHDU and ImageHDU objects:

>>> import numpy as np
>>> primary_hdu = fits.PrimaryHDU(data=np.ones((3, 3)))
>>> image_hdu = fits.ImageHDU(data=np.ones((100, 100)), name="MYIMAGE")
>>> image_hdu2 = fits.ImageHDU(data=np.ones((10, 10, 10)), name="MYCUBE")

A multi-extension FITS file is not constrained to be only imaging or table data, we can mix them. To show this we’ll use the example from the previous section to make a BinTableHDU:

>>> c1 = fits.Column(name='a', array=np.array([1, 2]), format='K')
>>> c2 = fits.Column(name='b', array=np.array([4, 5]), format='K')
>>> c3 = fits.Column(name='c', array=np.array([7, 8]), format='K')
>>> table_hdu = fits.BinTableHDU.from_columns([c1, c2, c3])

Now when we create the HDUList we list all extensions we want to include:

>>> hdul = fits.HDUList([primary_hdu, image_hdu, table_hdu])

Because HDUList acts like a list we can also append, for example, an ImageHDU to an already existing HDUList:

>>> hdul.append(image_hdu2)

Multi-extension HDUList are treated just like those with only a PrimaryHDU, so to save the file use HDUList.writeto() as shown above.


The FITS standard enforces all files to have exactly one PrimaryHDU that is the first HDU present in the file. This standard is enforced during the call to HDUList.writeto() and an error will be raised if it is not met. See the output_verify option in HDUList.writeto() for ways to fix or ignore these warnings.

In the previous example the PrimaryHDU contained actual data. In some cases it is desirable to have a minimal PrimaryHDU with only basic header information. To do this, first create a new Header object to encapsulate any keywords you want to include in the primary HDU, then as before create a PrimaryHDU:

>>> hdr = fits.Header()
>>> hdr['OBSERVER'] = 'Edwin Hubble'
>>> hdr['COMMENT'] = "Here's some commentary about this FITS file."
>>> empty_primary = fits.PrimaryHDU(header=hdr)

When we create a new primary HDU with a custom header as in the above example, this will automatically include any additional header keywords that are required by the FITS format (keywords such as SIMPLE and NAXIS for example). In general, users should not have to manually manage such keywords, and should only create and modify observation-specific informational keywords.

We then create an HDUList containing both the primary HDU and any other HDUs want:

>>> hdul = fits.HDUList([empty_primary, image_hdu2, table_hdu])

Convenience Functions# also provides several high-level (“convenience”) functions. Such a convenience function is a “canned” operation to achieve one task. By using these “convenience” functions, a user does not have to worry about opening or closing a file; all of the housekeeping is done implicitly.


These functions are useful for interactive Python sessions and less complex analysis scripts, but should not be used for application code, as they are highly inefficient. For example, each call to getval() requires re-parsing the entire FITS file. Code that makes repeated use of these functions should instead open the file with open() and access the data structures directly.

The first of these functions is getheader(), to get the header of an HDU. Here are several examples of getting the header. Only the file name is required for this function. The rest of the arguments are optional and flexible to specify which HDU the user wants to access:

>>> from import getheader
>>> hdr = getheader(fits_image_filename)  # get default HDU (=0), i.e. primary HDU's header
>>> hdr = getheader(fits_image_filename, ext=0)  # get primary HDU's header
>>> hdr = getheader(fits_image_filename, ext=2)  # the second extension
>>> hdr = getheader(fits_image_filename, extname='sci')  # the first HDU with EXTNAME='SCI'
>>> hdr = getheader(fits_image_filename, extname='sci', extver=2)  # HDU with EXTNAME='SCI' and EXTVER=2
>>> hdr = getheader(fits_image_filename, ext=('sci', 2))  # use a tuple to do the same

Ambiguous specifications will raise an exception:

>>> getheader(fits_image_filename, ext=('sci', 1), extname='err', extver=2)
Traceback (most recent call last):
TypeError: Redundant/conflicting extension arguments(s): ...

After you get the header, you can access the information in it, such as getting and modifying a keyword value:

>>> fits_image_2_filename = fits.util.get_testdata_filepath('o4sp040b0_raw.fits')
>>> hdr = getheader(fits_image_2_filename, ext=0)  # get primary hdu's header
>>> filter = hdr['filter']                         # get the value of the keyword "filter'
>>> val = hdr[10]                                  # get the 11th keyword's value
>>> hdr['filter'] = 'FW555'                        # change the keyword value

For the header keywords, the header is like a dictionary, as well as a list. The user can access the keywords either by name or by numeric index, as explained earlier in this chapter.

If a user only needs to read one keyword, the getval() function can further simplify to just one call, instead of two as shown in the above examples:

>>> from import getval
>>> # get 0th extension's keyword FILTER's value
>>> getval(fits_image_2_filename, 'filter', ext=0)

>>> # get the 2nd sci extension's 11th keyword's value
>>> getval(fits_image_2_filename, 10, extname='sci', extver=2)

To edit a single header value in the header for extension 0, use the setval() function. For example, to change the value of the “filter” keyword:

>>> fits.setval(fits_image_2_filename, "filter", value="FW555")  

This can also be used to create a new keyword-value pair (“card” in FITS lingo):

>>> fits.setval(fits_image_2_filename, "ANEWKEY", value="some value")  

The function getdata() gets the data of an HDU. Similar to getheader(), it only requires the input FITS file name while the extension is specified through the optional arguments. It does have one extra optional argument header. If header is set to True, this function will return both data and header, otherwise only data is returned:

>>> from import getdata
>>> # get 3rd sci extension's data:
>>> data = getdata(fits_image_filename, extname='sci', extver=3)
>>> # get 1st extension's data AND header:
>>> data, hdr = getdata(fits_image_filename, ext=1, header=True)

The functions introduced above are for reading. The next few functions demonstrate convenience functions for writing:

>>> fits.writeto('out.fits', data, hdr)

The writeto() function uses the provided data and an optional header to write to an output FITS file.

>>> fits.append('out.fits', data, hdr)

The append() function will use the provided data and the optional header to append to an existing FITS file. If the specified output file does not exist, it will create one.

from import update
update(filename, dat, hdr, 'sci')         # update the 'sci' extension
update(filename, dat, 3)                  # update the 3rd extension
update(filename, dat, hdr, 3)             # update the 3rd extension
update(filename, dat, 'sci', 2)           # update the 2nd SCI extension
update(filename, dat, 3, header=hdr)      # update the 3rd extension
update(filename, dat, header=hdr, ext=5)  # update the 5th extension

The update() function will update the specified extension with the input data/header. The third argument can be the header associated with the data. If the third argument is not a header, it (and other positional arguments) are assumed to be the extension specification(s). Header and extension specs can also be keyword arguments.

The printdiff() function will print a difference report of two FITS files, including headers and data. The first two arguments must be two FITS filenames or FITS file objects with matching data types (i.e., if using strings to specify filenames, both inputs must be strings). The third argument is an optional extension specification, with the same call format of getheader() and getdata(). In addition you can add any keywords accepted by the FITSDiff class.

from import printdiff
# get a difference report of ext 2 of inA and inB
printdiff('inA.fits', 'inB.fits', ext=2)
# ignore HISTORY and COMMENT keywords
printdiff('inA.fits', 'inB.fits', ignore_keywords=('HISTORY','COMMENT')

Finally, the info() function will print out information of the specified FITS file:

Filename: ...test0.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
  0  PRIMARY       1 PrimaryHDU     138   ()
  1  SCI           1 ImageHDU        61   (40, 40)   int16
  2  SCI           2 ImageHDU        61   (40, 40)   int16
  3  SCI           3 ImageHDU        61   (40, 40)   int16
  4  SCI           4 ImageHDU        61   (40, 40)   int16

This is one of the most useful convenience functions for getting an overview of what a given file contains without looking at any of the details.


Command-Line Utilities#

For convenience, several of astropy’s sub-packages install utility programs on your system which allow common tasks to be performed without having to open a Python interpreter. These utilities include:

  • fitsheader: prints the headers of a FITS file.

  • fitscheck: verifies and optionally rewrites the CHECKSUM and DATASUM keywords of a FITS file.

  • fitsdiff: compares two FITS files and reports the differences.

  • Scripts: converts FITS images to bitmaps, including scaling and stretching.

  • wcslint: checks the WCS keywords in a FITS file for compliance against the standards.

Other Information#

Performance Tips#

It is possible to set the data array for PrimaryHDU and ImageHDU to a dask array. If this is written to disk, the dask array will be computed as it is being written, which will avoid using excessive memory:

>>> import dask.array as da
>>> array = da.random.random((1000, 1000))
>>> from import fits
>>> hdu = fits.PrimaryHDU(data=array)
>>> hdu.writeto('test_dask.fits')


A package for reading and writing FITS files and manipulating their contents.

A module for reading and writing Flexible Image Transport System (FITS) files. This file format was endorsed by the International Astronomical Union in 1999 and mandated by NASA as the standard format for storing high energy astrophysics data. For details of the FITS standard, see the NASA/Science Office of Standards and Technology publication, NOST 100-2.0.