From fce9de633dac18b2598d301ded9ce3c5f39a6f5f Mon Sep 17 00:00:00 2001
From: Stefan van der Walt <stefan@sun.ac.za>
Date: Wed, 8 Feb 2012 02:09:33 -0800
Subject: [PATCH] ENH: Promote as_windows to a utility function.

---
 skimage/measure/_structural_similarity.py     | 42 ++-----------------
 .../tests/test_structural_similarity.py       | 13 +-----
 skimage/util/__init__.py                      |  1 +
 skimage/util/shape.py                         | 40 ++++++++++++++++++
 4 files changed, 46 insertions(+), 50 deletions(-)

diff --git a/skimage/measure/_structural_similarity.py b/skimage/measure/_structural_similarity.py
index ab2098f2..1837322a 100644
--- a/skimage/measure/_structural_similarity.py
+++ b/skimage/measure/_structural_similarity.py
@@ -3,43 +3,9 @@ from __future__ import division
 __all__ = ['structural_similarity']
 
 import numpy as np
-from numpy.lib import stride_tricks
 
 from ..util.dtype import dtype_range
-
-def _as_windows(X, win_size=7, flatten_first_axis=True):
-    """Re-stride an array to simulate a sliding window.
-
-    Parameters
-    ----------
-    X : 2D-ndarray
-        Input image.
-
-    Returns
-    -------
-    window : (N, M, win_size, win_size) ndarray
-        Sliding windows.
-
-    """
-    if not X.ndim == 2:
-        raise ValueError('Input images must be 2-dimensional.')
-
-    X = np.ascontiguousarray(X)
-    r, c = X.shape
-
-    strides = X.strides
-    row_jump, el_jump = strides
-    half_width = (win_size // 2)
-
-    new_strides = (row_jump, el_jump, row_jump, el_jump)
-    new_rows = r - 2 * half_width
-    new_cols = c - 2 * half_width
-    new_shape = (new_rows, new_cols, win_size, win_size)
-
-    windows = stride_tricks.as_strided(X, shape=new_shape, strides=new_strides)
-
-    return windows
-
+from ..util.shape import as_windows
 
 def structural_similarity(X, Y, win_size=7,
                           gradient=False, dynamic_range=None):
@@ -88,8 +54,8 @@ def structural_similarity(X, Y, win_size=7,
         dmin, dmax = dtype_range[X.dtype.type]
         dynamic_range = dmax - dmin
 
-    XW = _as_windows(X, win_size=win_size)
-    YW = _as_windows(Y, win_size=win_size)
+    XW = as_windows(X, win_size=win_size)
+    YW = as_windows(Y, win_size=win_size)
 
     NS = len(XW)
     NP = win_size * win_size
@@ -128,7 +94,7 @@ def structural_similarity(X, Y, win_size=7,
             )
 
         grad = np.zeros_like(X, dtype=float)
-        OW = _as_windows(grad, win_size=win_size)
+        OW = as_windows(grad, win_size=win_size)
 
         OW += local_grad
         grad /= NS
diff --git a/skimage/measure/tests/test_structural_similarity.py b/skimage/measure/tests/test_structural_similarity.py
index e68420b9..ec5486fb 100644
--- a/skimage/measure/tests/test_structural_similarity.py
+++ b/skimage/measure/tests/test_structural_similarity.py
@@ -1,8 +1,7 @@
 import numpy as np
 from numpy.testing import assert_equal
 
-from skimage.measure._structural_similarity import \
-     structural_similarity as ssim, _as_windows
+from skimage.measure import structural_similarity as ssim
 import scipy.optimize as opt
 
 def test_ssim_patch_range():
@@ -13,16 +12,6 @@ def test_ssim_patch_range():
     assert(ssim(X, Y, win_size=N) < 0.1)
     assert_equal(ssim(X, X, win_size=N), 1)
 
-def test_as_windows():
-    X = np.arange(100).reshape((10, 10))
-    W = _as_windows(X, win_size=7)
-    assert_equal(W.shape[:2], (4, 4))
-
-    W = _as_windows(X, win_size=3)
-    assert_equal(W[0, 0], [[0, 1, 2],
-                           [10, 11, 12],
-                           [20, 21, 22]])
-
 def test_ssim_image():
     N = 100
     X = (np.random.random((N, N)) * 255).astype(np.uint8)
diff --git a/skimage/util/__init__.py b/skimage/util/__init__.py
index 980e3880..8daaa60d 100644
--- a/skimage/util/__init__.py
+++ b/skimage/util/__init__.py
@@ -1 +1,2 @@
 from .dtype import *
+from .shape import *
diff --git a/skimage/util/shape.py b/skimage/util/shape.py
index 0126d2e3..0b82be1d 100644
--- a/skimage/util/shape.py
+++ b/skimage/util/shape.py
@@ -230,3 +230,43 @@ def view_as_windows(arr_in, window_shape):
     arr_out = as_strided(arr_in, shape=new_shape, strides=new_strides)
 
     return arr_out
+=======
+import numpy as np
+from numpy.lib import stride_tricks
+
+__all__ = ['as_windows']
+
+def as_windows(X, win_size=7):
+    """Re-stride an array to simulate a sliding window.
+
+    Parameters
+    ----------
+    X : 2D-ndarray
+        Input image.
+    win_size : int
+        Size of the sliding window.
+
+    Returns
+    -------
+    window : (N, M, win_size, win_size) ndarray
+        Sliding windows.
+
+    """
+    if not X.ndim == 2:
+        raise ValueError('Input images must be 2-dimensional.')
+
+    X = np.ascontiguousarray(X)
+    r, c = X.shape
+
+    strides = X.strides
+    row_jump, el_jump = strides
+    half_width = (win_size // 2)
+
+    new_strides = (row_jump, el_jump, row_jump, el_jump)
+    new_rows = r - win_size + 1
+    new_cols = c - win_size + 1
+    new_shape = (new_rows, new_cols, win_size, win_size)
+
+    windows = stride_tricks.as_strided(X, shape=new_shape, strides=new_strides)
+
+    return windows