Merge 93c87e3 into 5de14c7

AUTHORS.rst

@@ -27,6 +27,7 @@ Alphabetical list of contributors
 * Hans Moritz Günther (@hamogu)
 * Forrest Gasdia (@EP-Guy)
 * Nathan Heidt (@heidtha)
+* Michael Hlabathe (@hlabathems)
 * Elias Holte (@Sondanaa)
 * Anthony Horton (@AnthonyHorton)
 * Jennifer Karr (@JenniferKarr)

CHANGES.rst

@@ -19,6 +19,8 @@ Other Changes and Additions
 
 - Added support for .bz2, .Z and .zip file formats in ``ImageFileCollection``.
 
+- Modified weights function to also accept 1D array in ``Combiner``. [#634, #670]
+
 Bug Fixes
 ^^^^^^^^^
 - Function ``median_combine`` now correctly calculates the uncertainty for

ccdproc/combiner.py

@@ -124,14 +124,15 @@ def weights(self):
         return self._weights
 
     @weights.setter
-    def weights(self, value):
+    def weights(self, value, axis=0):
         if value is not None:
             if isinstance(value, np.ndarray):
-                if value.shape == self.data_arr.data.shape:
-                    self._weights = value
-                else:
-                    raise ValueError(
-                        "dimensions of weights do not match data.")
+                if value.shape != self.data_arr.data.shape:
+                    if value.ndim != 1:
+                        raise ValueError("1D weights expected when shapes of the data and weights differ.")
+                    if value.shape[0] != self.data_arr.data.shape[axis]:
+                        raise ValueError("Length of weights not compatible with specified axis.")
+                self._weights = value
             else:
                 raise TypeError("weights must be a numpy.ndarray.")
         else:

ccdproc/tests/test_combiner.py

@@ -101,6 +101,17 @@ def test_weights_shape(ccd_data):
         c.weights = ccd_data.data
 
 
+def test_1Dweights():
+    ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),
+                CCDData(np.zeros((10, 10)) - 1000, unit=u.adu),
+                CCDData(np.zeros((10, 10)) + 1000, unit=u.adu)]
+
+    c = Combiner(ccd_list)
+    c.weights = np.array([1, 5, 10])
+    ccd = c.average_combine()
+    np.testing.assert_almost_equal(ccd.data, 312.5)
+
+
 # test the min-max rejection
 def test_combiner_minmax():
     ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),