Merge pull request #971 from stefanv/bug/views_are_copies

Do not allow `view_as_*` to return copies (closes #866)

skimage/_shared/_warnings.py

@@ -0,0 +1,61 @@
+__all__ = ['all_warnings']
+
+from contextlib import contextmanager
+import sys
+import warnings
+import inspect
+
+
+@contextmanager
+def all_warnings():
+    """
+    Context for use in testing to ensure that all warnings are raised.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def foo():
+    ...     warnings.warn(RuntimeWarning("bar"))
+
+    We raise the warning once, while the warning filter is set to "once".
+    Hereafter, the warning is invisible, even with custom filters:
+
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('once')
+    ...     foo()
+
+    We can now run ``foo()`` without a warning being raised:
+
+    >>> from numpy.testing import assert_warns
+    >>> foo()
+
+    To catch the warning, we call in the help of ``all_warnings``:
+
+    >>> with all_warnings():
+    ...     assert_warns(RuntimeWarning, foo)
+    """
+
+    # Whenever a warning is triggered, Python adds a __warningregistry__
+    # member to the *calling* module.  The exercize here is to find
+    # and eradicate all those breadcrumbs that were left lying around.
+    #
+    # We proceed by first searching all parent calling frames and explicitly
+    # clearing their warning registries (necessary for the doctests above to
+    # pass).  Then, we search for all submodules of skimage and clear theirs
+    # as well (necessary for the skimage test suite to pass).
+
+    frame = inspect.currentframe()
+    if frame:
+        for f in inspect.getouterframes(frame):
+            f[0].f_locals['__warningregistry__'] = {}
+    del frame
+
+    for mod in list(sys.modules.values()):
+        try:
+            mod.__warningregistry__.clear()
+        except AttributeError:
+            pass
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        yield w

skimage/_shared/utils.py

@@ -4,8 +4,9 @@ import sys
 
 import six
 
+from ._warnings import all_warnings
 
-__all__ = ['deprecated', 'get_bound_method_class']
+__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings']
 
 
 class skimage_deprecation(Warning):

skimage/util/shape.py

@@ -2,6 +2,7 @@ __all__ = ['view_as_blocks', 'view_as_windows']
 
 import numpy as np
 from numpy.lib.stride_tricks import as_strided
+from warnings import warn
 
 
 def view_as_blocks(arr_in, block_shape):
@@ -11,17 +12,17 @@ def view_as_blocks(arr_in, block_shape):
 
     Parameters
     ----------
-    arr_in: ndarray
-        The n-dimensional input array.
-
-    block_shape: tuple
+    arr_in : ndarray
+        N-d input array.
+    block_shape : tuple
         The shape of the block. Each dimension must divide evenly into the
         corresponding dimensions of `arr_in`.
 
     Returns
     -------
-    arr_out: ndarray
-        Block view of the input array.
+    arr_out : ndarray
+        Block view of the input array.  If `arr_in` is non-contiguous, a copy
+        is made.
 
     Examples
     --------
@@ -70,8 +71,6 @@ def view_as_blocks(arr_in, block_shape):
            [[[76, 77],
              [82, 83]]]])
     """
-
-    # -- basic checks on arguments
     if not isinstance(block_shape, tuple):
         raise TypeError('block needs to be a tuple')
 
@@ -88,6 +87,11 @@ def view_as_blocks(arr_in, block_shape):
         raise ValueError("'block_shape' is not compatible with 'arr_in'")
 
     # -- restride the array to build the block view
+
+    if not arr_in.flags.contiguous:
+        warn(RuntimeWarning("Cannot provide views on a non-contiguous input "
+                            "array without copying."))
+
     arr_in = np.ascontiguousarray(arr_in)
 
     new_shape = tuple(arr_shape / block_shape) + tuple(block_shape)
@@ -106,9 +110,9 @@ def view_as_windows(arr_in, window_shape, step=1):
 
     Parameters
     ----------
-    arr_in: ndarray
-        The n-dimensional input array.
-    window_shape: tuple
+    arr_in : ndarray
+        N-d input array.
+    window_shape : tuple
         Defines the shape of the elementary n-dimensional orthotope
         (better know as hyperrectangle [1]_) of the rolling window view.
     step : int, optional
@@ -118,8 +122,9 @@ def view_as_windows(arr_in, window_shape, step=1):
 
     Returns
     -------
-    arr_out: ndarray
-        (rolling) window view of the input array.
+    arr_out : ndarray
+        (rolling) window view of the input array.   If `arr_in` is
+        non-contiguous, a copy is made.
 
     Notes
     -----
@@ -228,6 +233,10 @@ def view_as_windows(arr_in, window_shape, step=1):
         raise ValueError("`window_shape` is too small")
 
     # -- build rolling window view
+    if not arr_in.flags.contiguous:
+        warn(RuntimeWarning("Cannot provide views on a non-contiguous input "
+                            "array without copying."))
+
     arr_in = np.ascontiguousarray(arr_in)
 
     new_shape = tuple((arr_shape - window_shape) // step + 1) + \

skimage/util/tests/test_shape.py

@@ -1,7 +1,9 @@
 import numpy as np
 from nose.tools import raises
-from numpy.testing import assert_equal
+from numpy.testing import assert_equal, assert_warns
+
 from skimage.util.shape import view_as_blocks, view_as_windows
+from skimage._shared.utils import all_warnings
 
 
 @raises(TypeError)
@@ -131,7 +133,7 @@ def test_view_as_windows_2D():
                                 [17, 18, 19]]]]))
 
 
-def test_view_as_windows_With_skip():
+def test_view_as_windows_with_skip():
     A = np.arange(20).reshape((5, 4))
     B = view_as_windows(A, (2, 2), step=2)
     assert_equal(B, [[[[0, 1],
@@ -147,5 +149,14 @@ def test_view_as_windows_With_skip():
     assert_equal(C.shape, (1, 1, 2, 2))
 
 
+def test_views_non_contiguous():
+    A = np.arange(16).reshape((4, 4))
+    A = A[::2, :]
+
+    with all_warnings():
+        assert_warns(RuntimeWarning, view_as_blocks, A, (2, 2))
+        assert_warns(RuntimeWarning, view_as_windows, A, (2, 2))
+
+
 if __name__ == '__main__':
     np.testing.run_module_suite()