Fix issues where +=, -= were being used on arrays with incompatible dtypes (#2128)

* Fix issues where +=, -= were being used on arrays with incompatible dtypes

* Fix copyright year

* Add spaces to copyright years

* Fix errors, ensure arrays have correct dtype

* Properly fix errors. Definitely. I'm sure of it

Author: djkirkham

lib/iris/experimental/regrid.py

@@ -953,9 +953,15 @@ def _src_align_and_flatten(coord):
         min_sx, min_tx = np.min(sx.points), np.min(tx.points)
         if min_sx < 0 and min_tx >= 0:
             indices = np.where(sx_points < 0)
+            # Ensure += doesn't raise a TypeError
+            if not np.can_cast(modulus, sx_points.dtype):
+                sx_points = sx_points.astype(type(modulus), casting='safe')
             sx_points[indices] += modulus
         elif min_sx >= 0 and min_tx < 0:
             indices = np.where(sx_points > (modulus / 2))
+            # Ensure -= doesn't raise a TypeError
+            if not np.can_cast(modulus, sx_points.dtype):
+                sx_points = sx_points.astype(type(modulus), casting='safe')
             sx_points[indices] -= modulus
 
     # Create target grid cube x and y cell boundaries.

lib/iris/tests/unit/analysis/test_WPERCENTILE.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2015, Met Office
+# (C) British Crown Copyright 2015 - 2016, Met Office
 #
 # This file is part of Iris.
 #
@@ -141,7 +141,7 @@ def test_2d_multi(self):
         actual = WPERCENTILE.aggregate(data, axis=0, percent=percent,
                                        weights=weights)
         self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
-        expected = np.tile(np.arange(shape[-1]), percent.size)
+        expected = np.tile(np.arange(shape[-1]), percent.size).astype('f8')
         expected = expected.reshape(percent.size, shape[-1]).T
         expected[:, 1:-1] += (percent[1:-1]-25)*0.2
         expected[:, -1] += 10.
@@ -156,7 +156,7 @@ def test_masked_2d_multi(self):
         actual = WPERCENTILE.aggregate(data, axis=0, percent=percent,
                                        weights=weights)
         self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
-        expected = np.tile(np.arange(shape[-1]), percent.size)
+        expected = np.tile(np.arange(shape[-1]), percent.size).astype('f8')
         expected = expected.reshape(percent.size, shape[-1]).T
         expected[:, 1:-1] += (percent[1:-1]-25)*0.4
         expected[:, -1] += 20.