Image Data#

In this chapter, we will discuss the data component in an image HDU.

Image Data as an Array#

A FITS primary HDU or an image extension HDU may contain image data. The following discussions apply to both of these HDU classes. For most cases in astropy, it is a numpy array, having the shape specified by the NAXIS keywords and the data type specified by the BITPIX keyword — unless the data is scaled, in which case see the next section. Here is a quick cross reference between allowed BITPIX values in FITS images and the numpy data types:

BITPIX    Numpy Data Type
8         numpy.uint8 (note it is UNsigned integer)
16        numpy.int16
32        numpy.int32
64        numpy.int64
-32       numpy.float32
-64       numpy.float64

To recap, in numpy the arrays are 0-indexed and the axes are ordered from slow to fast. So, if a FITS image has NAXIS1=300 and NAXIS2=400, the numpy array of its data will have the shape of (400, 300).


Here is a summary of reading and updating image data values:

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

>>> with as hdul:  # open a FITS file
...     data = hdul[1].data  # assume the first extension is an image
>>> print(data[1, 4])   # get the pixel value at x=5, y=2
>>> # get values of the subsection from x=11 to 20, y=31 to 40 (inclusive)
>>> data[30:40, 10:20]
array([[314, 314, 313, 312, 313, 313, 313, 313, 313, 312],
       [314, 314, 312, 313, 313, 311, 313, 312, 312, 314],
       [314, 315, 313, 313, 313, 313, 315, 312, 314, 312],
       [314, 313, 313, 314, 311, 313, 313, 313, 313, 313],
       [313, 314, 312, 314, 312, 314, 314, 315, 313, 313],
       [312, 311, 311, 312, 312, 312, 312, 313, 311, 312],
       [314, 314, 314, 314, 312, 313, 314, 314, 314, 311],
       [314, 313, 312, 313, 313, 314, 312, 312, 311, 314],
       [313, 313, 313, 314, 313, 313, 315, 313, 312, 313],
       [314, 313, 313, 314, 313, 312, 312, 314, 310, 314]], dtype=int16)
>>> data[1,4] = 999  # update a pixel value
>>> data[30:40, 10:20] = 0  # update values of a subsection
>>> data[3] = data[2]    # copy the 3rd row to the 4th row

Here are some more complicated examples by using the concept of the “mask array.” The first example is to change all negative pixel values in data to zero. The second one is to take logarithm of the pixel values which are positive:

>>> data[data < 0] = 0
>>> import numpy as np
>>> data[data > 0] = np.log(data[data > 0])

These examples show the concise nature of numpy array operations.

Scaled Data#

Sometimes an image is scaled; that is, the data stored in the file is not the image’s physical (true) values, but linearly transformed according to the equation:

physical value = BSCALE * (storage value) + BZERO

BSCALE and BZERO are stored as keywords of the same names in the header of the same HDU. The most common use of a scaled image is to store unsigned 16-bit integer data because the FITS standard does not allow it. In this case, the stored data is signed 16-bit integer (BITPIX=16) with BZERO=32768 (\(2^{15}\)), BSCALE=1.

Reading Scaled Image Data#

Images are scaled only when either of the BSCALE/BZERO keywords are present in the header and either of their values is not the default value (BSCALE=1, BZERO=0).

For unscaled data, the data attribute of an HDU in astropy is a numpy array of the same data type specified by the BITPIX keyword. For a scaled image, the .data attribute will be the physical data (i.e., already transformed from the storage data and may not be the same data type as prescribed in BITPIX). This means an extra step of copying is needed and thus the corresponding memory requirement. This also means that the advantage of memory mapping is reduced for scaled data.

For floating point storage data, the scaled data will have the same data type. For integer data type, the scaled data will always be single precision floating point (numpy.float32).


Here is an example of what happens to scaled data, before and after the data is touched:

>>> fits_scaledimage_filename = fits.util.get_testdata_filepath('scale.fits')

>>> hdul =
>>> hdu = hdul[0]
>>> hdu.header['bitpix']
>>> hdu.header['bzero']
>>>[0, 0]  # once data is touched, it is scaled  
>>> hdu.header['bitpix']  # BITPIX is also updated
>>> # BZERO and BSCALE are removed after the scaling
>>> hdu.header['bzero']
Traceback (most recent call last):
KeyError: "Keyword 'BZERO' not found."


An important caveat to be aware of when dealing with scaled data in astropy, is that when accessing the data via the .data attribute, the data is automatically scaled with the BZERO and BSCALE parameters. If the file was opened in “update” mode, it will be saved with the rescaled data. This surprising behavior is a compromise to err on the side of not losing data: if some floating point calculations were made on the data, rescaling it when saving could result in a loss of information.

To prevent this automatic scaling, open the file with the do_not_scale_image_data=True argument to This is especially useful for updating some header values, while ensuring that the data is not modified.

You may also manually reapply scale parameters by using hdu.scale() (see below). Alternately, you may open files with the scale_back=True argument. This assures that the original scaling is preserved when saving even when the physical values are updated. In other words, it reapplies the scaling to the new physical values upon saving.

Writing Scaled Image Data#

With the extra processing and memory requirement, we discourage the use of scaled data as much as possible. However, astropy does provide ways to write scaled data with the scale method.


To write scaled data with the scale method:

>>> # scale the data to Int16 with user specified bscale/bzero
>>> hdu.scale('int16', bzero=32768)
>>> # scale the data to Int32 with the min/max of the data range, emits
>>> # RuntimeWarning: overflow encountered in short_scalars
>>> hdu.scale('int32', 'minmax')  
>>> # scale the data, using the original BSCALE/BZERO, emits
>>> # RuntimeWarning: invalid value encountered in add
>>> hdu.scale('int32', 'old')  
>>> hdul.close()

The first example above shows how to store an unsigned short integer array.

Caution must be exercised when using the scale() method. The data attribute of an image HDU, after the scale() call, will become the storage values, not the physical values. So, only call scale() just before writing out to FITS files (i.e., calls of writeto(), flush(), or close()). No further use of the data should be exercised. Here is an example of what happens to the data attribute after the scale() call:

>>> hdu = fits.PrimaryHDU(np.array([0., 1, 2, 3]))
>>> print(  
[0. 1. 2. 3.]
>>> hdu.scale('int16', bzero=32768)
>>> print(  # now the data has storage values
[-32768 -32767 -32766 -32765]
>>> hdu.writeto('new.fits')

Data Sections#

When a FITS image HDU’s data is accessed, either the whole data is copied into memory (in cases of NOT using memory mapping or if the data is scaled) or a virtual memory space equivalent to the data size is allocated (in the case of memory mapping of non-scaled data). If there are several very large image HDUs being accessed at the same time, the system may run out of memory.

If a user does not need the entire image(s) at the same time (e.g., processing the images(s) ten rows at a time), the section attribute of an HDU can be used to alleviate such memory problems.

With astropy’s improved support for memory-mapping, the sections feature is not as necessary as it used to be for handling large images stored in local files. However, it remains very useful in the following circumstances:

  • If the image’s data is scaled with non-trivial BSCALE/BZERO values, accessing the data in sections may still be necessary under the current implementation.

  • Memory mapping is insufficient for loading images larger than 2 to 4 GB on a 32-bit system — in such cases it may be necessary to use sections.

  • Memory mapping does not work for accessing remote FITS files. In this case sections may be your only option. See Obtaining subsets from cloud-hosted FITS files.

In addition, for compressed FITS files, CompImageHDU.section can be used to access and decompress only parts of the data, and can provide a significant speedup. Note however that accessing data using CompImageHDU.section will always load tiles one at a time from disk, and therefore when accessing a large fraction of the data (or slicing it in a way that would cause most tiles to be loaded) you may obtain better performance by using


Here is an example of getting the median image from three input images of the size 5000x5000.

hdul1 ='file1.fits')
hdul2 ='file2.fits')
hdul3 ='file3.fits')
output = np.zeros((5000, 5000))
for i in range(50):
    j = i * 100
    k = j + 100
    x1 = hdul1[0].section[j:k,:]
    x2 = hdul2[0].section[j:k,:]
    x3 = hdul3[0].section[j:k,:]
    output[j:k, :] = np.median([x1, x2, x3], axis=0)

Data in each section does not need to be contiguous for memory savings to be possible. astropy will do its best to join together discontiguous sections of the array while reading as little as possible into main memory.

Sections cannot currently be assigned. Any modifications made to a data section are not saved back to the original file.