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# FITS File Handling (`astropy.io.fits`)

## Introduction

The `astropy.io.fits` 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.

Note

If you want to read or write a single table in FITS format, the recommended method is via the High-level Unified File I/O interface. In particular see the Unified I/O FITS section. Likewise, for CCD image data with a physical unit (e.g., `electron`), see the Unified Image Data section.

## Getting Started

This section provides a quick introduction of using `astropy.io.fits`. 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.

### Reading and Updating Existing FITS Files

#### Opening a FITS File

Note

The `astropy.io.fits.util.get_testdata_filepath()` function, used in the examples here, returns file path for test data shipped with `astropy`. To work with your own data instead, please use `astropy.io.fits.open()` or Convenience Functions, which take either the relative or absolute path as string or python:path-like object.

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

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

    >>> hdul = fits.open(fits_image_filename)

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 `HDUList.info()`, which summarizes the content of the opened FITS file:

    >>> hdul.info()
    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 fits.open(fits_image_filename) as hdul:
    ...     hdul.info()
    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 `astropy.io.fits.Conf.use_memmap`.

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.

Warning

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 fits.open(uri, 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 `ImageHDU.data` 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.

See also

Obtaining subsets from cloud-hosted FITS files.

##### 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

Note

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, pkzip, lzma or Unix’s compress (LZW compression). 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 and lzma do not support the append or update access mode and LZW-compressed files do not support any writing modes (including append or update).

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 `astropy.io.fits` 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 fits.open(fits_image_filename) as hdul:
    ...    hdul.verify('fix')
    ...    data = hdul[1].data

In the above example, the call to `hdul.verify("fix")` requests that `astropy.io.fits` 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 = fits.open(fits_image_filename)
    >>> hdul[0].header['DATE']
    '01/04/99'

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]
    32768.0

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']
    'NGC121-a'
    >>> 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', ...]

Note

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 `astropy.io.fits` and, in general, should not be touched by the user. Instead one should use the related attributes of the `astropy.io.fits` 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
--- | ---
All | `SIMPLE`, `BITPIX`, `NAXIS`
`PrimaryHDU` | `EXTEND`
`ImageHDU`, `TableHDU`, `BinTableHDU` | `PCOUNT`, `GCOUNT`
`GroupsHDU` | `NAXIS1`, `GCOUNT`, `PCOUNT`, `GROUPS`
`TableHDU`, `BinTableHDU` | `TFIELDS`, `TFORM`, `TBCOL`

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 `hdu.name` 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 overwritten.

#### Working with Image Data

Note

This section describes reading and writing image data in the FITS format using the `fits` package directly. For CCD image data with a unit, you should consider using the Unified Image Data interface with the CCDData class. This provides the capability to load data, uncertainty and mask from a multi-extension FITS (MEF) file.

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)
    >>> data.dtype.name
    'int16'

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])
    348

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='>i2')

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).

Note

See more information in Image Data.

#### Working with Table Data

Note

This section describes reading and writing table data in the FITS format using the `fits` package directly. If you want to read or write a single entire table in FITS format, the you should consider using the High-level Unified File I/O interface (e.g., `QTable.read`). 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 = fits.open(fits_table_filename)
    >>> 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])
    (np.int32(1), 'abc', np.float64(3.7000000715255736), np.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 `ColDefs.info()` method from the interactive prompt:

    >>> cols.info()
    name:
        ['c1', 'c2', 'c3', 'c4']
    format:
        ['1J', '3A', '1E', '1L']
    unit:
        ['', '', '', '']
    null:
        [-2147483647, '', '', '']
    bscale:
        ['', '', 3, '']
    bzero:
        ['', '', 0.4, '']
    disp:
        ['I11', 'A3', 'G15.7', 'L6']
    start:
        ['', '', '', '']
    dim:
        ['', '', '', '']
    coord_type:
        ['', '', '', '']
    coord_unit:
        ['', '', '', '']
    coord_ref_point:
        ['', '', '', '']
    coord_ref_value:
        ['', '', '', '']
    coord_inc:
        ['', '', '', '']
    time_ref_pos:
        ['', '', '', '']

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
    ColDefs(
        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()
    np.float64(5.19999989271164)

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:

    hdul.writeto('newtable.fits')

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 fits.open('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

Note

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 `astropy.io.fits` would look like this:

    >>> from astropy.io 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)

Note

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.

Note

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

`astropy.io.fits` 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.

Warning

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 astropy.io.fits 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 astropy.io.fits import getval
    >>> # get 0th extension's keyword FILTER's value
    >>> getval(fits_image_2_filename, 'filter', ext=0)
    'Clear'

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

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 astropy.io.fits 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 astropy.io.fits 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 astropy.io.fits 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:

    >>> fits.info(fits_image_filename)
    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.

## Using `astropy.io.fits`

- FITS Headers
  - Header of an HDU
  - The Header Attribute
  - Card Images
  - CONTINUE Cards
  - HIERARCH Cards
- Image Data
  - Image Data as an Array
  - Scaled Data
  - Data Sections
- Table Data
  - Table Data as a Record Array
  - Table Operations
  - Scaled Data in Tables
  - Creating a FITS Table
  - FITS Tables with Time Columns
- Verification
  - FITS Standard
  - Verification Options
  - Verifications at Different Data Object Levels
  - Verification Using the FITS Checksum Keyword Convention
- Less Familiar Objects
  - ASCII Tables
  - Variable Length Array Tables
  - Random Access Groups
  - Compressed Image Data
- Executable Scripts
  - fitsinfo
  - fitsheader
  - fitscheck
  - fitsdiff
- Miscellaneous Features
  - Differs
- Obtaining subsets from cloud-hosted FITS files
  - Subsetting FITS files hosted on an HTTP web server
  - Subsetting FITS files hosted in Amazon S3 cloud storage
  - Using `Cutout2D` with cloud-hosted FITS files
  - Performance improvement tips for subsetting remote FITS files

## 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

- astropy.io.fits FAQ
- Header Interface Transition Guide
- astropy.io.fits History

## Performance Tips

### Use dask for lazy compute

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 astropy.io import fits
    >>> hdu = fits.PrimaryHDU(data=array)
    >>> hdu.writeto('test_dask.fits')

### Arbitrary padding end of file will degrade performance

As discussed in detail in GitHub issue 19296, arbitrary padding at the end of a FITS file might cause the parser to inefficiently search for the END card of the next header. Therefore, we recommend that astropy users to not blindly open untrusted large FITS files without independently verifying their fidelity first.

## Reference/API

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.

- Reference/API
  - File Handling and Convenience Functions
  - HDU Lists
  - Header Data Unit
  - Headers
  - Cards
  - Tables
  - Images
  - Differs
  - Verification Options
  - Tiled Compression

Footnotes

[1]

For legacy code only that already depends on PyFITS, it’s acceptable to continue using “from astropy.io import fits as pyfits”.