Merge 6448e11 into 070360a

ccdproc/core.py

@@ -1306,7 +1306,8 @@ def cosmicray_lacosmic(ccd, sigclip=4.5, sigfrac=0.3,
                        satlevel=65535.0, pssl=0.0, niter=4,
                        sepmed=True, cleantype='meanmask', fsmode='median',
                        psfmodel='gauss', psffwhm=2.5, psfsize=7,
-                       psfk=None, psfbeta=4.765, verbose=False):
+                       psfk=None, psfbeta=4.765, verbose=False,
+                       gain_apply=True):
     r"""
     Identify cosmic rays through the L.A. Cosmic technique. The L.A. Cosmic
     technique identifies cosmic rays by identifying pixels based on a variation
@@ -1342,6 +1343,10 @@ def cosmicray_lacosmic(ccd, sigclip=4.5, sigfrac=0.3,
         Gain of the image (electrons / ADU). We always need to work in
         electrons for cosmic ray detection. Default: 1.0
 
+    gain_apply : bool, optional
+        If ``True``, return gain-corrected data, otherwise do not gain-correct
+        the data. Default is ``True`` to preserve backwards compatibility.
+
     readnoise : float, optional
         Read noise of the image (electrons). Used to generate the noise model
         of the image. Default: 6.5.
@@ -1472,6 +1477,8 @@ def cosmicray_lacosmic(ccd, sigclip=4.5, sigfrac=0.3,
             psfsize=psfsize, psfk=psfk, psfbeta=psfbeta,
             verbose=verbose)
 
+        if not gain_apply and gain != 1.0:
+            cleanarr = cleanarr / gain
         return cleanarr, crmask
 
     elif isinstance(ccd, CCDData):
@@ -1486,6 +1493,9 @@ def cosmicray_lacosmic(ccd, sigclip=4.5, sigfrac=0.3,
 
         # create the new ccd data object
         nccd = ccd.copy()
+        if not gain_apply and gain != 1.0:
+            cleanarr = cleanarr / gain
+
         nccd.data = cleanarr
         if nccd.mask is None:
             nccd.mask = crmask

ccdproc/tests/test_cosmicray.py

@@ -64,6 +64,42 @@ def test_cosmicray_lacosmic_check_data():
         cosmicray_lacosmic(10, noise)
 
 
+@pytest.mark.parametrize('array_input', [True, False])
+@pytest.mark.parametrize('gain_correct_data', [True, False])
+def test_cosmicray_gain_correct(array_input, gain_correct_data):
+    # Add regression check for #705 and for the new gain_correct
+    # argument.
+    # The issue is that cosmicray_lacosmic gain-corrects the
+    # data and returns that gain corrected data. That is not the
+    # intent...
+    ccd_data = ccd_data_func(data_scale=DATA_SCALE)
+    threshold = 5
+    add_cosmicrays(ccd_data, DATA_SCALE, threshold, ncrays=NCRAYS)
+    noise = DATA_SCALE * np.ones_like(ccd_data.data)
+    ccd_data.uncertainty = noise
+    # This may need units at some point.
+    gain = 2.0
+    if array_input:
+        new_data, cr_mask = cosmicray_lacosmic(ccd_data.data,
+                                               gain=gain,
+                                               gain_apply=gain_correct_data)
+    else:
+        new_ccd = cosmicray_lacosmic(ccd_data,
+                                     gain=gain,
+                                     gain_apply=gain_correct_data)
+        new_data = new_ccd.data
+        cr_mask = new_ccd.mask
+    # Fill masked locations with 0 since there is no simple relationship
+    # between the original value and the corrected value.
+    orig_data = np.ma.array(ccd_data.data, mask=cr_mask).filled(0)
+    new_data = np.ma.array(new_data, mask=cr_mask).filled(0)
+    if gain_correct_data:
+        gain_for_test = gain
+    else:
+        gain_for_test = 1.0
+    np.testing.assert_allclose(gain_for_test * orig_data, new_data)
+
+
 def test_cosmicray_median_check_data():
     with pytest.raises(TypeError):
         ndata, crarr = cosmicray_median(10, thresh=5, mbox=11,