.. currentmodule:: astropy.io.fits 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: .. parsed-literal:: **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). Examples -------- .. EXAMPLE START Image Data as an Array in astropy.io.fits Here is a summary of reading and updating image data values:: >>> from astropy.io import fits >>> fits_image_filename = fits.util.get_testdata_filepath('test0.fits') >>> with fits.open(fits_image_filename) 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=2 313 >>> # 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. .. EXAMPLE END 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: .. parsed-literal:: 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 (:math:`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``). Example ^^^^^^^ .. EXAMPLE START Reading Scaled Image Data with astropy.io.fits 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 = fits.open(fits_scaledimage_filename) >>> hdu = hdul[0] >>> hdu.header['bitpix'] 16 >>> hdu.header['bzero'] 1500.0 >>> hdu.data[0, 0] # once data is touched, it is scaled # doctest: +FLOAT_CMP 557.7563 >>> hdu.data.dtype.name 'float32' >>> hdu.header['bitpix'] # BITPIX is also updated -32 >>> # BZERO and BSCALE are removed after the scaling >>> hdu.header['bzero'] Traceback (most recent call last): ... KeyError: "Keyword 'BZERO' not found." .. warning:: 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 ``fits.open()``. 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. .. EXAMPLE END 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 `~ImageHDU.scale` method. Examples ^^^^^^^^ .. EXAMPLE START Writing Scaled Image Data in astropy.io.fits To write scaled data with the `~ImageHDU.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') # doctest: +SKIP >>> # scale the data, using the original BSCALE/BZERO, emits >>> # RuntimeWarning: invalid value encountered in add >>> hdu.scale('int32', 'old') # doctest: +SKIP >>> hdul.close() The first example above shows how to store an unsigned short integer array. Caution must be exercised when using the :meth:`~ImageHDU.scale` method. The :attr:`~ImageHDU.data` attribute of an image HDU, after the :meth:`~ImageHDU.scale` call, will become the storage values, not the physical values. So, only call :meth:`~ImageHDU.scale` just before writing out to FITS files (i.e., calls of :meth:`~HDUList.writeto`, :meth:`~HDUList.flush`, or :meth:`~HDUList.close`). No further use of the data should be exercised. Here is an example of what happens to the :attr:`~ImageHDU.data` attribute after the :meth:`~ImageHDU.scale` call:: >>> hdu = fits.PrimaryHDU(np.array([0., 1, 2, 3])) >>> print(hdu.data) # doctest: +FLOAT_CMP [0. 1. 2. 3.] >>> hdu.scale('int16', bzero=32768) >>> print(hdu.data) # now the data has storage values [-32768 -32767 -32766 -32765] >>> hdu.writeto('new.fits') .. EXAMPLE END .. _data-sections: Data Sections ============= When a FITS image HDU's :attr:`~ImageHDU.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 :attr:`~ImageHDU.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 very large images. However, 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. Memmap is also 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. Example ------- .. EXAMPLE START Data Sections in astropy.io.fits Here is an example of getting the median image from three input images of the size 5000x5000. .. code:: python hdul1 = fits.open('file1.fits') hdul2 = fits.open('file2.fits') hdul3 = fits.open('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 :attr:`~ImageHDU.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. .. EXAMPLE END