Update equalize_adapthist to use new view_as_windows

Update equalize_adapthist to use new view_as_windows

Try rbase again

Update equalize_adapthist to use new view_as_windows

Fix relative imports

Style fixes

Add a deprecation warning and add to api_changes.txt

Update TODO and switch to 0.13 deprecation

Preserve the current API as much as possible and defer to 0.14

Move the new kwarg to the very end

Clarify deprecation warning

Update to use row/col in clahe

Update docstring

Use optimal_step to set up view_as_windows

Update equalize_adapthist to use new view_as_windows

Try rbase again

Update equalize_adapthist to use new view_as_windows

Fix relative imports

skimage/exposure/_adapthist.py

@@ -20,15 +20,23 @@ from .. import img_as_float, img_as_uint
 from ..color.adapt_rgb import adapt_rgb, hsv_value
 from ..exposure import rescale_intensity
 from ..util import view_as_windows
+<<<<<<< HEAD
 from .._shared.utils import skimage_deprecation, warnings
+=======
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
 
 
 NR_OF_GREY = 2 ** 14  # number of grayscale levels to use in CLAHE algorithm
 
 
 @adapt_rgb(hsv_value)
+<<<<<<< HEAD
 def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
                        nbins=256, kernel_size=None):
+=======
+def equalize_adapthist(image, kernel_size=64, ntiles_x=None, ntiles_y=None,
+        clip_limit=0.01, nbins=256):
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
     """Contrast Limited Adaptive Histogram Equalization (CLAHE).
 
     An algorithm for local contrast enhancement, that uses histograms computed
@@ -42,11 +50,19 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
     kernel_size: integer or 2-tuple
         Defines the shape of contextual regions used in the algorithm.
         If an integer is given, the shape will be a square of
+<<<<<<< HEAD
         sidelength given by this value.
     ntiles_x : int, optional (deprecated in favor of ``kernel_size``)
         Number of tile regions in the X direction (horizontal).
     ntiles_y : int, optional (deprecated if favor of ``kernel_size``)
         Number of tile regions in the Y direction (vertical).
+=======
+        sidelength given by its value.
+    ntiles_x : int, optional
+        Number of tile regions in the X direction.
+    ntiles_y : int, optional
+        Number of tile regions in the Y direction.
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
     clip_limit : float: optional
         Clipping limit, normalized between 0 and 1 (higher values give more
         contrast).
@@ -77,6 +93,7 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
     """
     image = img_as_uint(image)
     image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
+<<<<<<< HEAD
     if kernel_size is None:
         warnings.warn('`ntiles_*` have been deprecated in favor of '
                       '`kernel_size`.  The `ntiles_*` keyword arguments '
@@ -85,6 +102,12 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
         ntiles_y = ntiles_y or 8
         kernel_size = (np.round(image.shape[0] / ntiles_y),
                        np.round(image.shape[1] / ntiles_x))
+=======
+    if ntiles_x or ntiles_y:
+        ntiles_x = ntiles_x or 8
+        ntiles_y = ntiles_y or 8
+        kernel_size = (image.shape[0] / ntiles_y, image.shape[1] / ntiles_x)
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
 
     if isinstance(kernel_size, numbers.Number):
         kernel_size = (kernel_size, kernel_size)

skimage/exposure/tests/test_exposure.py

@@ -211,17 +211,14 @@ def test_adapthist_grayscale():
     img = skimage.img_as_float(data.astronaut())
     img = rgb2gray(img)
     img = np.dstack((img, img, img))
-    with expected_warnings(['precision loss|non-contiguous input', 'deprecated']):
+    with expected_warnings(['precision loss|non-contiguous input', 
+                            'deprecated']):
         adapted_old = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01,
-                                              nbins=128)
-        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01,
-                                              nbins=128)
-    np.testing.assert_allclose(adapted, adapted_old)
+        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01, nbins=128)
     assert_almost_equal = np.testing.assert_almost_equal
     assert img.shape == adapted.shape
     assert_almost_equal(peak_snr(img, adapted), 90.669, 3)
     assert_almost_equal(norm_brightness_err(img, adapted), 0.084, 3)
-
     return data, adapted