Image Management¶

Working with a directory of images¶

For the sake of argument all of the examples below assume you are working in a directory that contains FITS images.

The class ImageFileCollection is meant to make working with a directory of FITS images easier by allowing you select the files you act on based on the values of FITS keywords in their headers or based on Unix shell-style filename matching.

It is initialized with the name of a directory containing FITS images and a list of FITS keywords you want the ImageFileCollection to be aware of. An example initialization looks like:

>>> from ccdproc import ImageFileCollection
>>> from ccdproc.utils.sample_directory import sample_directory_with_files
>>> keys = ['imagetyp', 'object', 'filter', 'exposure']
>>> dir = sample_directory_with_files()
>>> ic1 = ImageFileCollection(dir, keywords=keys) # only keep track of keys


You can use the wildcard * in place of a list to indicate you want the collection to use all keywords in the headers:

>>> ic_all = ImageFileCollection(dir, keywords='*')


Normally identification of FITS files is done by looking at the file extension and including all files with the correct extension.

If the files are not compressed (e.g. not gzipped) then you can force the image collection to open each file and check from its contents whether it is FITS by using the find_fits_by_reading argument:

>> ic_from_content = ImageFileCollection(dir, find_fits_by_reading=True)


You can indicate filename patterns to include or exclude using Unix shell-style expressions. For example, to include all filenames that begin with 1d_ but not ones that include the word bad, you could do:

>>> ic_all = ImageFileCollection(dir, glob_include='1d_*',


Alternatively, you can create the collection with an explicit list of file names:

>>> ic_names = ImageFileCollection(filenames=['a.fits', '/some/path/b.fits.gz'])


Most of the useful interaction with the image collection is via its .summary property, a Table of the value of each keyword for each file in the collection:

>>> ic1.summary.colnames
['file', 'imagetyp', 'object', 'filter', 'exposure']
>>> ic_all.summary.colnames
# long list of keyword names omitted


Note that the name of the file is automatically added to the table as a column named file.

Selecting files¶

Selecting the files that match a set of criteria, for example all images in the I band with exposure time less than 60 seconds you could do:

>>> matches = (ic1.summary['filter'] == 'R') & (ic1.summary['exposure'] < 15)
>>> my_files = ic1.summary['file'][matches]


The column file is added automatically when the image collection is created.

For more simple selection, when you just want files whose keywords exactly match particular values, say all I band images with exposure time of 30 seconds, there is a convenience method .files_filtered:

>>> my_files = ic1.files_filtered(filter='R', exposure=15)


The optional arguments to files_filtered are used to filter the list of files.

Python regular expression patterns can also be used as the value if the regex_match flag is set. For example, to find all of the images whose object is in the Kelt exoplanet survey, you might do:

>>> my_files = ic1.files_filtered(regex_match=True, object='kelt.*')


To get all of the images that have image type BIAS or LIGHT you can also use a regular expression pattern:

>>> my_files = ic1.files_filtered(regex_match=True,
...                               imagetyp='bias|light')


Note that regular expression is different, and much more flexible than, file name matching (or “globbing”) at the command line. The Python documentation on the re module is useful for learning about regular expressions.

Finally, a new ImageFileCollection can be created with by providing a list of keywords. The example below makes a new collection containing the files whose imagetyp is BIAS or LIGHT:

>>> new_ic = ic1.filter(regex_match=True,
...                     imagetyp='bias|light')


Sorting files¶

Sometimes it is useful to bring the files into a specific order, e.g. if you make a plot for each object you probably want all images of the same object next to each other. To do this, the images in a collection can be sorted with the sort method using the fits header keys in the same way you would sort a Table:

>>> ic1.sort(['exposure', 'imagetyp'])


Iterating over hdus, headers, data, or ccds¶

Four methods are provided for iterating over the images in the collection, optionally filtered by keyword values.

For example, to iterate over all of the I band images with exposure of 30 seconds, performing some basic operation on the data (very contrived example):

>>> for hdu in ic1.hdus(imagetyp='LiGhT', filter='R', exposure=15):
...     new_data = hdu.data - hdu.data.mean()
15.0


Note that the names of the arguments to hdus here are the names of FITS keywords in the collection and the values are the values of those keywords you want to select. Note also that string comparisons are not case sensitive.

The other iterators are headers, data, and ccds.

All of them have the option to also provide the file name in addition to the hdu (or header or data):

>>> for hdu, fname in ic1.hdus(return_fname=True,
...                            imagetyp='LiGhT', filter='R', exposure=15):
...    hdu.header['meansub'] = True
...    hdu.data = hdu.data - hdu.data.mean()
...    hdu.writeto(fname + '.new')


That last use case, doing something to several files and saving them somewhere afterwards, is common enough that the iterators provide arguments to automate it.

Automatic saving from the iterators¶

There are three ways of triggering automatic saving.

1. One is with the argument save_with_name; it adds the value of the argument to the file name between the original base name and extension. The example below has (almost) the same effect of the example above, subtracting the mean from each image and saving to a new file:

>>> for hdu in ic1.hdus(save_with_name='_new',
...                     imagetyp='LiGhT', filter='R', exposure=15):
...    hdu.header['meansub'] = True
...    hdu.data = hdu.data - hdu.data.mean()


It saves, in the location of the image collection, a new FITS file with the mean subtracted from the data, with _new added to the name; as an example, if one of the files iterated over was intput001.fit then a new file, in the same directory, called input001_new.fit would be created.

2. You can also provide the directory to which you want to save the files with save_location; note that you do not need to actually do anything to the hdu (or header or data) to cause the copy to be made. The example below copies all of the I band images with 30 second exposure from the original location to other_dir:

>>> for hdu in ic1.hdus(save_location='other_dir',
...                     imagetyp='LiGhT', filter='I', exposure=30):
...     pass


This option can be combined with the previous one to also give the files a new name.

3. Finally, if you want to live dangerously, you can overwrite the files in the same location with the overwrite argument; use it carefully because it preserves no backup. The example below replaces each of the I band images with 30 second exposure with a file that has had the mean subtracted:

>>> for hdu in ic1.hdus(overwrite=True,
...                     imagetyp='LiGhT', filter='R', exposure=15):
...    hdu.header['meansub'] = True
...    hdu.data = hdu.data - hdu.data.mean()


Note

This functionality is not currently available on Windows.