Merge pull request #358 from effigies/numpy1.10

MRG: stride_tricks produces unbroadcastable array

A probable bug in numpy 1.10.1 causes an error when trying to broadcast an array constructed using stride tricks. 

See : numpy/numpy#6491 for a full description.

To work round, set the stride_tricks array as read-only, so that numpy does not try and set the output to writeable - that is the step that appears to cause the error.

Author: matthew-brett

nibabel/fileslice.py

@@ -767,9 +767,15 @@ def strided_scalar(shape, scalar=0.):
     strided_arr : array
         Array of shape `shape` for which all values == `scalar`, built by
         setting all strides of `strided_arr` to 0, so the scalar is broadcast
-        out to the full array `shape`.
+        out to the full array `shape`. `strided_arr` is flagged as not
+        `writeable`.
+
+        The array is set read-only to avoid a numpy error when broadcasting -
+        see https://github.com/numpy/numpy/issues/6491
     """
     shape = tuple(shape)
     scalar = np.array(scalar)
     strides = [0] * len(shape)
-    return np.lib.stride_tricks.as_strided(scalar, shape, strides)
+    strided_scalar = np.lib.stride_tricks.as_strided(scalar, shape, strides)
+    strided_scalar.flags.writeable = False
+    return strided_scalar

nibabel/tests/test_fileslice.py

@@ -639,8 +639,14 @@ def test_strided_scalar():
         assert_equal(observed.shape, shape)
         assert_equal(observed.dtype, expected.dtype)
         assert_array_equal(observed.strides, 0)
-        observed[..., 0] = 99
-        assert_array_equal(observed, expected * 0 + 99)
+        # Strided scalars are set as not writeable
+        # This addresses a numpy 1.10 breakage of broadcasting a strided
+        # array without resizing (see GitHub PR #358)
+        assert_false(observed.flags.writeable)
+        def setval(x):
+            x[..., 0] = 99
+        # RuntimeError for numpy < 1.10
+        assert_raises((RuntimeError, ValueError), setval, observed)
     # Default scalar value is 0
     assert_array_equal(strided_scalar((2, 3, 4)), np.zeros((2, 3, 4)))