Set up directory for use in ImageFileCollection unit tests

ccdproc/image_collection.py

@@ -18,6 +18,7 @@
 from astropy.utils.exceptions import AstropyUserWarning
 
 from .ccddata import fits_ccddata_reader, _recognized_fits_file_extensions
+from .tests.pytest_fixtures import directory_for_testing
 
 logger = logging.getLogger(__name__)
 
@@ -754,7 +755,8 @@ def _fits_files_in_directory(self, extensions=None,
                 files.extend(fnmatch.filter(all_files, '*' + extension))
         else:
             for infile in all_files:
-                with open(infile, 'rb') as fp:
+                inpath = path.join(self.location, infile)
+                with open(inpath, 'rb') as fp:
                     # Hmm, first argument to is_fits is not actually used in
                     # that function. *shrug*
                     if fits.connect.is_fits('just some junk', infile, fp):
@@ -970,3 +972,14 @@ def ccds(self, ccd_kwargs=None, **kwd):
         return self._generator('ccd', ccd_kwargs=ccd_kwargs, **kwd)
     ccds.__doc__ = _generator.__doc__.format(
         name='CCDData', default_scaling='True', return_type='astropy.nddata.CCDData')
+
+
+def sample_directory_with_files():
+    """
+    Returns the path to the small sample directory used
+    in the tests of ``ImageFileCollection``. Primarily intended
+    for use in the doctests.
+    """
+
+    n_test, tmpdir = directory_for_testing()
+    return tmpdir

ccdproc/tests/data/expected_ifc_file_properties.csv

@@ -1,4 +1,4 @@
-file,simple,bitpix,naxis,naxis1,extend,bscale,bzero,imagetyp,filter,exptime
+file,simple,bitpix,naxis,naxis1,extend,bscale,bzero,imagetyp,filter,exposure
 filter_no_object_light.fit,True,16,1,100,True,1,32768,LIGHT,R,1.0
 filter_object_light.fit,True,16,1,100,True,1,32768,LIGHT,R,1.0
 filter_object_light.fit.gz,True,16,1,100,True,1,32768,LIGHT,R,1.0

ccdproc/tests/pytest_fixtures.py

@@ -77,12 +77,11 @@ def _make_file_for_testing(file_name='', **kwd):
     hdu.writeto(file_name)
 
 
-@pytest.fixture
-def triage_setup(request):
+def directory_for_testing():
     """
     Set up directory with these contents:
 
-    One file with imagetyp BIAS. It has an the keyword EXPTIME in
+    One file with imagetyp BIAS. It has an the keyword EXPOSURE in
     the header, but no others beyond IMAGETYP and the bare minimum
     created with the FITS file.
 
@@ -95,9 +94,9 @@ def triage_setup(request):
     files.
 
     + One file for each compression type, currently .gz and .fz.
-    + ALL of the files will have the keyword EXPTIME
+    + ALL of the files will have the keyword EXPOSURE
       in the header.
-    + Only ONE of them will have the value EXPTIME=15.0.
+    + Only ONE of them will have the value EXPOSURE=15.0.
     + All of the files EXCEPT ONE will have the keyword
       FILTER with the value 'R'.
     + NONE of the files have the keyword OBJECT
@@ -127,20 +126,20 @@ def triage_setup(request):
 
     _make_file_for_testing(file_name='no_filter_no_object_bias.fit',
                            imagetyp='BIAS',
-                           exptime=0.0)
+                           EXPOSURE=0.0)
 
     _make_file_for_testing(file_name='no_filter_no_object_light.fit',
                            imagetyp='LIGHT',
-                           exptime=1.0)
+                           EXPOSURE=1.0)
 
     _make_file_for_testing(file_name='filter_no_object_light.fit',
                            imagetyp='LIGHT',
-                           exptime=1.0,
+                           EXPOSURE=1.0,
                            filter='R')
 
     _make_file_for_testing(file_name='filter_object_light.fit',
                            imagetyp='LIGHT',
-                           exptime=1.0,
+                           EXPOSURE=1.0,
                            filter='R')
 
     with open('filter_object_light.fit', 'rb') as f_in:
@@ -151,11 +150,20 @@ def triage_setup(request):
 
     _make_file_for_testing(file_name='test.fits.fz',
                            imagetyp='LIGHT',
-                           exptime=15.0,
+                           EXPOSURE=15.0,
                            filter='R')
 
+    os.chdir(original_dir)
+
+    return n_test, test_dir
+
+
+@pytest.fixture
+def triage_setup(request):
+
+    n_test, test_dir = directory_for_testing()
+
     def teardown():
-        os.chdir(original_dir)
         try:
             rmtree(test_dir)
         except OSError:

ccdproc/tests/test_image_collection.py

@@ -145,7 +145,7 @@ def test_filter_fz_files(self, triage_setup):
         fn = 'test.fits.fz'
         ic = ImageFileCollection(location=triage_setup.test_dir, filenames=fn)
         # Get a subset of files with a specific header value
-        filtered = ic.files_filtered(exptime=15.0)
+        filtered = ic.files_filtered(exposure=15.0)
         assert len(filtered) == 1
 
     def test_filtered_files_have_proper_path(self, triage_setup):
@@ -471,7 +471,7 @@ def test_fits_summary_when_keywords_are_not_subset(self, triage_setup):
         but the latter is not a subset of the former.
         """
         ic = ImageFileCollection(triage_setup.test_dir,
-                                 keywords=['imagetyp', 'exptime'])
+                                 keywords=['imagetyp', 'exposure'])
         n_files = len(ic.files)
         files_missing_this_key = ic.files_filtered(imagetyp='*',
                                                    monkeys=None)
@@ -899,7 +899,7 @@ def test_image_collection_with_no_location(self, triage_setup):
             assert ic.summary['naxis1'].dtype == np.array([5]).dtype
 
             # and the default float dtype
-            assert ic.summary['exptime'].dtype == np.array([5.0]).dtype
+            assert ic.summary['exposure'].dtype == np.array([5.0]).dtype
 
             expected_heads = (actual['imagetyp'] == 'LIGHT').sum()
 
@@ -925,9 +925,11 @@ def test_force_detect_fits_files_finds_fits_files(self, triage_setup):
         # Making a copy of *every* file means we can just double the expected
         # number of files as part of the tests.
         path = Path(triage_setup.test_dir)
+
         for idx, p in enumerate(path.iterdir()):
             new_name = 'no_extension{}'.format(idx)
-            (path / new_name).write_bytes(p.read_bytes())
+            new_path = path / new_name
+            new_path.write_bytes(p.read_bytes())
 
         ic = ImageFileCollection(location=str(path),
                                  find_fits_by_reading=True)
@@ -944,7 +946,7 @@ def test_force_detect_fits_files_finds_fits_files(self, triage_setup):
 
         # Only one file in the original set of test files has exposure time
         # 15, so there should be two now.
-        assert len(ic.files_filtered(exptime=15.0)) == 2
+        assert len(ic.files_filtered(exposure=15.0)) == 2
 
         # Try one of the generators
         expected_heads = (2 * triage_setup.n_test['light'] -

docs/image_management.rst

@@ -23,13 +23,15 @@ list of FITS keywords you want the
 example initialization looks like::
 
     >>> from ccdproc import ImageFileCollection
+    >>> from ccdproc.image_collection import sample_directory_with_files
     >>> keys = ['imagetyp', 'object', 'filter', 'exposure']
-    >>> ic1 = ImageFileCollection('.', keywords=keys) # only keep track of keys
+    >>> 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('.', keywords='*')
+    >>> 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.
@@ -38,13 +40,13 @@ 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('.', find_fits_by_reading=True)
+    >> 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('.', glob_include='1d_*',
+    >>> ic_all = ImageFileCollection(dir, glob_include='1d_*',
     ...                              glob_exclude='*bad*')
 
 Alternatively, you can create the collection with an explicit list of file names::
@@ -69,16 +71,16 @@ 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'] == 'I') & (ic1.summary['exposure'] < 60)  # doctest: +SKIP
-    >>> my_files = ic1.summary['file'][matches]  # doctest: +SKIP
+    >>> 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='I', exposure=30)  # doctest: +SKIP
+    >>> my_files = ic1.files_filtered(filter='R', exposure=15)
 
 The optional arguments to ``files_filtered`` are used to filter the list of
 files.
@@ -109,7 +111,7 @@ 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
 :class:`~astropy.table.Table`::
 
-    >>> ic1.sort(['object', 'filter'])  # doctest: +SKIP
+    >>> ic1.sort(['exposure', 'imagetyp'])
 
 Iterating over hdus, headers, data, or ccds
 -------------------------------------------
@@ -121,9 +123,10 @@ 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='I', exposure=30):  # doctest: +SKIP
+    >>> for hdu in ic1.hdus(imagetyp='LiGhT', filter='R', exposure=15):
     ...     hdu.header['exposure']
     ...     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
@@ -135,7 +138,7 @@ 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='I', exposure=30):  # doctest: +SKIP
+    ...                            imagetyp='LiGhT', filter='R', exposure=15):
     ...    hdu.header['meansub'] = True
     ...    hdu.data = hdu.data - hdu.data.mean()
     ...    hdu.writeto(fname + '.new')
@@ -155,7 +158,7 @@ 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='I', exposure=30):  # doctest: +SKIP
+    ...                     imagetyp='LiGhT', filter='R', exposure=15):
     ...    hdu.header['meansub'] = True
     ...    hdu.data = hdu.data - hdu.data.mean()
 
@@ -183,7 +186,7 @@ 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='I', exposure=30):  # doctest: +SKIP
+    ...                     imagetyp='LiGhT', filter='R', exposure=15): # doctest: +SKIP
     ...    hdu.header['meansub'] = True
     ...    hdu.data = hdu.data - hdu.data.mean()