Merge 77b8b79 into 1d5fcae

ccdproc/ccdproc.py

@@ -416,7 +416,7 @@ def gain_correct(ccd, gain, gain_unit=None):
 
 
 @log_to_metadata
-def flat_correct(ccd, flat):
+def flat_correct(ccd, flat, min_value=None):
     """Correct the image for flatfielding
 
        Parameters
@@ -427,13 +427,22 @@ def flat_correct(ccd, flat):
        flat : CCDData object
           Flatfield to apply to the data
 
+       min_value : None or float 
+          Minimum value for flat field.  The value can either be None and no 
+          minimum value is applied to the flat or specified by a float which 
+          will replace all values in the flat by the min_value.
+
        {log}
 
        Returns
        -------
        ccd :  CCDData object
           CCDData object with flat corrected
     """
+    #Use the min_value to replace any values in the flat
+    if min_value is not None:
+       flat.data[flat.data < min_value] = min_value
+
     # normalize the flat
     flat.data = flat.data / flat.data.mean()
     if flat.uncertainty is not None:

ccdproc/combiner.py

@@ -155,8 +155,8 @@ def sigma_clipping(self, low_thresh=3, high_thresh=3,
         #check for negative numbers in low_thresh
 
         #setup baseline values
-        baseline = func(self.data_arr)
-        dev = dev_func(self.data_arr)
+        baseline = func(self.data_arr, axis=0)
+        dev = dev_func(self.data_arr, axis=0)
         #reject values
         if low_thresh is not None:
             if low_thresh < 0:

ccdproc/tests/test_ccdproc.py

@@ -301,11 +301,29 @@ def test_subtract_dark_fails(ccd_data):
 @pytest.mark.data_scale(10)
 def test_flat_correct(ccd_data):
     size = ccd_data.shape[0]
+    orig_mean = ccd_data.data.mean()
+    # create the flat
+    data = 2 * np.ones((size, size))
+    flat = CCDData(data, meta=fits.header.Header(), unit=ccd_data.unit)
+    flat_data = flat_correct(ccd_data, flat)
+
+    #check that the flat was normalized
+    assert flat_data.data.mean() == ccd_data.data.mean()
+    assert (ccd_data.data / flat_data.data == flat.data).all()
 
+# test for flat correction with min_value
+@pytest.mark.data_scale(10)
+def test_flat_correct_min_value(ccd_data):
+    size = ccd_data.shape[0]
+    orig_mean = ccd_data.data.mean()
     # create the flat
     data = 2 * np.ones((size, size))
     flat = CCDData(data, meta=fits.header.Header(), unit=ccd_data.unit)
-    ccd_data = flat_correct(ccd_data, flat)
+    flat_data = flat_correct(ccd_data, flat, min_value = 4)
+
+    #check that the flat was normalized
+    assert flat_data.data.mean() == ccd_data.data.mean()
+    assert (ccd_data.data / flat_data.data == flat.data).all()
 
 
 # test for variance and for flat correction

ccdproc/tests/test_combiner.py

@@ -136,6 +136,24 @@ def test_combiner_sigmaclip_high():
                      dev_func=mad)
     assert c.data_arr[5].mask.all()
 
+def test_combiner_sigmaclip_single_pix():
+    ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),
+                CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
+                CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
+                CCDData(np.zeros((10, 10)) - 10, unit=u.adu),
+                CCDData(np.zeros((10, 10)) + 10, unit=u.adu),
+                CCDData(np.zeros((10, 10)) - 10, unit=u.adu)]
+    c = Combiner(ccd_list)
+    #add a single pixel in another array to check that 
+    #that one gets rejected
+    c.data_arr[0,5,5] = 0
+    c.data_arr[1,5,5] = -5
+    c.data_arr[2,5,5] = 5
+    c.data_arr[3,5,5] = -5
+    c.data_arr[4,5,5] = 25
+    c.sigma_clipping(high_thresh=3, low_thresh=None, func=np.median,
+                     dev_func=mad)
+    assert c.data_arr.mask[4,5,5] == True
 
 def test_combiner_sigmaclip_low():
     ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),