This chapter provides a quick introduction of using PyFITS. The goal is to demonstrate PyFITS’s basic features without getting into too much detail. If you are a first time user or an occasional PyFITS user, using only the most basic functionality, this is where you should start. Otherwise, it is safe to skip this chapter.
After installing numpy and PyFITS, start Python and load the PyFITS library. Note that the module name is all lower case.
>>> import pyfits
Once the PyFITS module is loaded, we can open an existing FITS file:
>>> hdulist = pyfits.open('input.fits')
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 method returns a PyFITS object called an HDUList which is a Python-like list, consisting of HDU objects. An HDU (Header Data Unit) is the highest level component of the FITS file structure. So, after the above open call, hdulist is the primary HDU, hdulist, if any, is the first extension HDU, etc. It should be noted that PyFITS is using 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.
>>> hdulist.info() Filename: test1.fits No. Name Type Cards Dimensions Format 0 PRIMARY PrimaryHDU 220 () int16 1 SCI ImageHDU 61 (800, 800) float32 2 SCI ImageHDU 61 (800, 800) float32 3 SCI ImageHDU 61 (800, 800) float32 4 SCI ImageHDU 61 (800, 800) float32
After you are done with the opened file, close it with the HDUList.close() method:
The headers will still be accessible after the HDUlist is closed. The 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.
The pyfits.open() function supports a memmap=True argument that cause 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.
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 2-3 GB cannot be mmap’d (which is fine, because by that point you’re likely to run out of physical memory anyways), but 64-bit systems are much less limited in this respect.
As mentioned earlier, each element of an HDUList is an HDU object with attributes of header and data, which can be used to access the header keywords and the data.
For those unfamiliar with FITS headers, they consist of a list of “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, float, or complex number. Keywords are usually unique within a header, except in a few special cases.
The header attribute is a Header instance, another PyFITS object. To get the value associated with a header keyword, simply do (a la Python dicts):
>>> hdulist.header['targname'] 'NGC121'
to get the value of the keyword targname, which is a string ‘NGC121’.
Although keyword names are always in upper case inside the FITS file, specifying a keyword name with PyFITS 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 (a la Python lists):
>>> hdulist.header 96
This example returns the 28th (like Python lists, it is 0-indexed) keyword’s value–an integer–96.
Similarly, it is easy to update a keyword’s value in PyFITS, either through keyword name or index:
>>> prihdr = hdulist.header >>> prihdr['targname'] = 'NGC121-a' >>> prihdr = 99
It is also possible to update both the value and comment associated with a keyword by assigning them as a tuple:
>>> prihdr = hdulist.header >>> prihdr['targname'] = ('NGC121-a', 'the observation target') >>> prihdr['targname'] 'NGC121-a' >>> prihdr.comments['targname'] 'the observation target'
Like a dict, one 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’s 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:
>>> prihdr.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:
>>> prihdr['history'] = 'I updated this file 2/26/09' >>> prihdr['comment'] = 'Edwin Hubble really knew his stuff' >>> prihdr['comment'] = 'I like using HST observations' >>> prihdr['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 updating existing COMMENT or HISTORY cards, reference them by index:
>>> prihdr['history'] = 'I updated this file on 2/26/09' >>> prihdr['history'] I updated this file on 2/26/09
To see the entire header as it appears in the FITS file (with the END card and padding stripped), simply enter the header object by itself, or print repr(header):
>>> header SIMPLE = T / file does conform to FITS standard BITPIX = 16 / number of bits per data pixel NAXIS = 0 / number of data axes ...all cards are shown... >>> print repr(header) ...identical...
It’s also possible to view a slice of the header:
>>> header[: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:
>>> prihdr.keys() ['SIMPLE', 'BITPIX', 'NAXIS', ...]
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.
>>> scidata = hdulist.data
Here, scidata points to the data object in the second HDU (the first HDU, hdulist, being the primary HDU) in hdulist, which corresponds to the ‘SCI’ extension. Alternatively, you can access the extension by its extension name (specified in the EXTNAME keyword):
>>> scidata = hdulist['SCI'].data
If there is more than one extension with the same EXTNAME, EXTVER’s value needs to be specified as the second argument, e.g.:
>>> scidata = hdulist['sci',2].data
The returned numpy object has many attributes and methods for a user to get information about the array, e. g.:
>>> scidata.shape (800, 800) >>> scidata.dtype.name 'float32'
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 scidata[1,4]
Note that, like C (and unlike FORTRAN), Python is 0-indexed and the indices have the slowest axis first and fast axis last, i.e. for a 2-D image, the fast axis (X-axis) which corresponds to the FITS NAXIS1 keyword, is the second index. Similarly, the sub-section of x=11 to 20 (inclusive) and y=31 to 40 (inclusive) is:
>>> scidata[30:40, 10:20]
To update the value of a pixel or a sub-section:
>>> scidata[30:40,10:20] = scidata[1,4] = 999
This example changes the values of both the pixel [1,4] and the sub-section [30:40,10:20] to the new value of 999.
The next example of array manipulation is to convert the image data from counts to flux:
>>> photflam = hdulist.header['photflam'] >>> exptime = prihdr['exptime'] >>> scidata \*= photflam / exptime
This example performs the math on the array in-place, thereby keeping the memory usage to a minimum.
If at this point you want to preserve all the changes you made and write it to a new file, you can use the HDUList.writeto() method (see below).
If you are familiar with the record array in numpy, 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.
Like images, the data portion of a FITS table extension is in the .data attribute:
>>> hdulist = pyfits.open('table.fits') >>> tbdata = hdulist.data # assuming the first extension is a table
To see the first row of the table:
>>> print tbdata (1, 'abc', 3.7000002861022949, 0)
Each row in the table is a FITS_rec 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) of the table, use:
>>> tbdata.field(0) array([1, 2])
A numpy object with the data type of the specified field is returned.
Like header keywords, a field can be referred either by index, as above, or by name:
>>> tbdata.field('id') array([1, 2])
But how do we know what field names we’ve got? First, let’s introduce another attribute of the table HDU: the .columns attribute:
>>> cols = hdulist.columns
>>> cols.info() name: ['c1', 'c2', 'c3', 'c4'] format: ['1J', '3A', '1E', '1L'] unit: ['', '', '', ''] null: [-2147483647, '', '', ''] bscale: ['', '', 3, ''] bzero: ['', '', 0.40000000000000002, ''] disp: ['I11', 'A3', 'G15.7', 'L6'] start: ['', '', '', ''] dim: ['', '', '', '']
it will show all its attributes, such as names, formats, bscales, bzeros, etc. We can also get these properties individually, e.g.:
>>> cols.names ['ID', 'name', 'mag', 'flag']
returns a (Python) list of field names.
Since each field is a numpy object, we’ll have the entire arsenal of numpy tools to use. We can reassign (update) the values:
>>> tbdata.field('flag')[:] = 0
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 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.
>>> f = pyfits.open('original.fits', mode='update') ... # making changes in data and/or header >>> f.flush() # changes are written back to original.fits
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 task is very easy in PyFITS for an image HDU. We’ll first demonstrate how to create a FITS file consisting only the primary HDU with image data.
First, we create a numpy object for the data part:
>>> import numpy as np >>> n = np.arange(100) # a simple sequence from 0 to 99
Next, we create a PrimaryHDU object to encapsulate the data:
>>> hdu = pyfits.PrimaryHDU(n)
We then create a HDUList to contain the newly created primary HDU, and write to a new file:
>>> hdulist = pyfits.HDUList([hdu]) >>> hdulist.writeto('new.fits')
That’s it! In fact, PyFITS even provides a short cut for the last two lines to accomplish the same behavior:
To create a table HDU is a little more involved than 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’ll 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 pyfits >>> import numpy as np >>> a1 = np.array(['NGC1001', 'NGC1002', 'NGC1003']) >>> a2 = np.array([11.1, 12.3, 15.2]) >>> col1 = pyfits.Column(name='target', format='20A', array=a1) >>> col2 = pyfits.Column(name='V_mag', format='E', array=a2)
Next, create a ColDefs (column-definitions) object for all columns:
>>> cols = pyfits.ColDefs([col1, col2])
Now, create a new binary table HDU object by using the PyFITS function new_table():
>>> tbhdu = pyfits.new_table(cols)
This function returns (in this case) a BinTableHDU.
Of course, you can do this more concisely:
>>> tbhdu = pyfits.new_table(pyfits.ColDefs([pyfits.Column(name='target', ... format='20A', ... array=a1), ... pyfits.Column(name='V_mag', ... format='E', ... array=a2)] ... ))
As before, we create a PrimaryHDU object to encapsulate the data:
>>> hdu = pyfits.PrimaryHDU(n)
We then create a HDUList containing both the primary HDU and the newly created table extension, and write to a new file:
>>> thdulist = pyfits.HDUList([hdu, tbhdu]) >>> thdulist.writeto('table.fits')
If this will be the only extension of the new FITS file and you only have a minimal primary HDU with no data, PyFITS again provides a short cut:
Alternatively, you can append it to the hdulist we have already created from the image file section:
So far, we have covered the most basic features of PyFITS. In the following chapters we’ll show more advanced examples and explain options in each class and method.
PyFITS also provides several high level (“convenience”) functions. Such a convenience function is a “canned” operation to achieve one simple task. By using these “convenience” functions, a user does not have to worry about opening or closing a file, all the housekeeping is done implicitly.
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 get:
>>> from pyfits import getheader >>> getheader('in.fits') # get default HDU (=0), i.e. primary HDU's header >>> getheader('in.fits', 0) # get primary HDU's header >>> getheader('in.fits', 2) # the second extension # the HDU with EXTNAME='sci' (if there is only 1) >>> getheader('in.fits', 'sci') # the HDU with EXTNAME='sci' and EXTVER=2 >>> getheader('in.fits', 'sci', 2) >>> getheader('in.fits', ('sci', 2)) # use a tuple to do the same >>> getheader('in.fits', ext=2) # the second extension # the 'sci' extension, if there is only 1 >>> getheader('in.fits', extname='sci') # the HDU with EXTNAME='sci' and EXTVER=2 >>> getheader('in.fits', extname='sci', extver=2) # ambiguous specifications will raise an exception, DON'T DO IT!! >>> getheader('in.fits', ext=('sci',1), extname='err', extver=2)
After you get the header, you can access the information in it, such as getting and modifying a keyword value:
>>> from pyfits import getheader >>> hdr = getheader('in.fits', 1) # get first extension's header >>> filter = hdr['filter'] # get the value of the keyword "filter' >>> val = hdr # 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 pyfits import getval >>> flt = getval('in.fits', 'filter', 1) # get 1st extension's keyword # FILTER's value >>> val = getval('in.fits', 10, 'sci', 2) # get the 2nd sci extension's # 11th keyword's 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 pyfits import getdata >>> dat = getdata('in.fits', 'sci', 3) # get 3rd sci extension's data # get 1st extension's data and header >>> data, hdr = getdata('in.fits', 1, header=True)
The functions introduced above are for reading. The next few functions demonstrate convenience functions for writing:
>>> pyfits.writeto('out.fits', data, header)
The writeto() function uses the provided data and an optional header to write to an output FITS file.
>>> pyfits.append('out.fits', data, header)
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 pyfits import update >>> update(file, dat, hdr, 'sci') # update the 'sci' extension >>> update(file, dat, 3) # update the 3rd extension >>> update(file, dat, hdr, 3) # update the 3rd extension >>> update(file, dat, 'sci', 2) # update the 2nd SCI extension >>> update(file, dat, 3, header=hdr) # update the 3rd extension >>> update(file, dat, header=hdr, ext=5) # update the 5th extension
The update() function will update the specified extension with the input data/header. The 3rd argument can be the header associated with the data. If the 3rd 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.
Finally, the info() function will print out information of the specified FITS file:
>>> pyfits.info('test0.fits') Filename: test0.fits No. Name Type Cards Dimensions Format 0 PRIMARY PrimaryHDU 138 () Int16 1 SCI ImageHDU 61 (400, 400) Int16 2 SCI ImageHDU 61 (400, 400) Int16 3 SCI ImageHDU 61 (400, 400) Int16 4 SCI ImageHDU 61 (400, 400) Int16