Merge pull request #978 from jni/more-deprecations

More deprecation removals

TODO.txt

@@ -12,13 +12,7 @@ Version 0.11
 
 Version 0.10
 ------------
-* Remove deprecated functions in `skimage.filter.rank.*`
-* Remove deprecated parameter `epsilon` of `skimage.viewer.LineProfile`
 * Remove backwards-compatability of `skimage.measure.regionprops`
-* Change default mode of random_walker segmentation to 'cg_mg' > 'cg' > 'bf',
-  depending on which optional dependencies are available.
-* Remove deprecated `out` parameter of `skimage.morphology.binary_*`
-* Remove deprecated parameter `depth` in `skimage.segmentation.random_walker`
 * Remove deprecated logger function in `skimage/__init__.py`
 * Remove deprecated function `filter.median_filter`
 * Enable doctests of experimental `skimage.feature.brief`

doc/examples/applications/plot_rank_filters.py

@@ -142,11 +142,11 @@ the central one.
 
 """
 
-from skimage.filter.rank import bilateral_mean
+from skimage.filter.rank import mean_bilateral
 
 noisy_image = img_as_ubyte(data.camera())
 
-bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
+bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
 
 fig, ax = plt.subplots(2, 2, figsize=(10, 7))
 ax1, ax2, ax3, ax4 = ax.ravel()

doc/examples/plot_segmentations.py

@@ -51,7 +51,7 @@ and image location and is therefore closely related to quickshift. As the
 clustering method is simpler, it is very efficient. It is essential for this
 algorithm to work in Lab color space to obtain good results.  The algorithm
 quickly gained momentum and is now widely used. See [3] for details.  The
-``ratio`` parameter trades off color-similarity and proximity, as in the case
+``compactness`` parameter trades off color-similarity and proximity, as in the case
 of Quickshift, while ``n_segments`` chooses the number of centers for kmeans.
 
 .. [3] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,

skimage/filter/rank/__init__.py

@@ -7,29 +7,6 @@ from ._percentile import (autolevel_percentile, gradient_percentile,
                           pop_percentile, sum_percentile, threshold_percentile)
 from .bilateral import mean_bilateral, pop_bilateral, sum_bilateral
 
-from skimage._shared.utils import deprecated
-
-
-percentile_autolevel = deprecated('autolevel_percentile')(autolevel_percentile)
-
-percentile_gradient = deprecated('gradient_percentile')(gradient_percentile)
-
-percentile_mean = deprecated('mean_percentile')(mean_percentile)
-bilateral_mean = deprecated('mean_bilateral')(mean_bilateral)
-
-meansubtraction = deprecated('subtract_mean')(subtract_mean)
-percentile_mean_subtraction = deprecated('subtract_mean_percentile')\
-                                        (subtract_mean_percentile)
-
-morph_contr_enh = deprecated('enhance_contrast')(enhance_contrast)
-percentile_morph_contr_enh = deprecated('enhance_contrast_percentile')\
-                                       (enhance_contrast_percentile)
-
-percentile_pop = deprecated('pop_percentile')(pop_percentile)
-bilateral_pop = deprecated('pop_bilateral')(pop_bilateral)
-
-percentile_threshold = deprecated('threshold_percentile')(threshold_percentile)
-
 
 __all__ = ['autolevel',
            'autolevel_percentile',
@@ -60,14 +37,4 @@ __all__ = ['autolevel',
            'noise_filter',
            'entropy',
            'otsu',
-           'percentile',
-           # Deprecated
-           'percentile_autolevel',
-           'percentile_gradient',
-           'percentile_mean',
-           'percentile_mean_subtraction',
-           'percentile_morph_contr_enh',
-           'percentile_pop',
-           'percentile_threshold',
-           'bilateral_mean',
-           'bilateral_pop']
+           'percentile']

skimage/segmentation/random_walker_segmentation.py

@@ -190,8 +190,7 @@ def _build_laplacian(data, spacing, mask=None, beta=50,
 
 
 def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
-                  multichannel=False, return_full_prob=False, depth=1.,
-                  spacing=None):
+                  multichannel=False, return_full_prob=False, spacing=None):
     """Random walker algorithm for segmentation from markers.
 
     Random walker algorithm is implemented for gray-level or multichannel
@@ -203,8 +202,8 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
         Image to be segmented in phases. Gray-level `data` can be two- or
         three-dimensional; multichannel data can be three- or four-
         dimensional (multichannel=True) with the highest dimension denoting
-        channels. Data spacing is assumed isotropic unless depth keyword
-        argument is used.
+        channels. Data spacing is assumed isotropic unless the `spacing` 
+        keyword argument is used.
     labels : array of ints, of same shape as `data` without channels dimension
         Array of seed markers labeled with different positive integers
         for different phases. Zero-labeled pixels are unlabeled pixels.
@@ -249,13 +248,6 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
     return_full_prob : bool, default False
         If True, the probability that a pixel belongs to each of the labels
         will be returned, instead of only the most likely label.
-    depth : float, default 1. [DEPRECATED]
-        Correction for non-isotropic voxel depths in 3D volumes.
-        Default (1.) implies isotropy.  This factor is derived as follows:
-        depth = (out-of-plane voxel spacing) / (in-plane voxel spacing), where
-        in-plane voxel spacing represents the first two spatial dimensions and
-        out-of-plane voxel spacing represents the third spatial dimension.
-        `depth` is deprecated as of 0.9, in favor of `spacing`.
     spacing : iterable of floats
         Spacing between voxels in each spatial dimension. If `None`, then
         the spacing between pixels/voxels in each dimension is assumed 1.
@@ -344,10 +336,12 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
     """
 
     if mode is None:
-        mode = 'bf'
-        warnings.warn("Default mode will change in the next release from 'bf' "
-                      "to 'cg_mg' if pyamg is installed, else to 'cg' if "
-                      "SciPy was built with UMFPACK, or to 'bf' otherwise.")
+        if amg_loaded:
+            mode = 'cg_mg'
+        elif UmfpackContext is not None:
+            mode = 'cg'
+        else:
+            mode = 'bf'
 
     if UmfpackContext is None and mode == 'cg':
         warnings.warn('SciPy was built without UMFPACK. Consider rebuilding '
@@ -355,19 +349,15 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
                       'random walker functions. You may also install pyamg '
                       'and run the random walker function in cg_mg mode '
                       '(see the docstrings)')
-    if depth != 1.:
-        warnings.warn('`depth` kwarg is deprecated, and will be removed in the'
-                      ' next major release. Use `spacing` instead.')
 
     # Spacing kwarg checks
     if spacing is None:
-        spacing = (1., 1.) + (depth, )
-    elif len(spacing) == 2:
-        spacing = tuple(spacing) + (depth, )
+        spacing = (1., 1., 1.)
     elif len(spacing) == 3:
         pass
     else:
-        raise ValueError('Input argument `spacing` incorrect, see docstring.')
+        raise ValueError('Input argument `spacing` incorrect, should be an '
+                         'iterable of length 3.')
 
     # Parse input data
     if not multichannel:

skimage/segmentation/tests/test_random_walker.py

@@ -184,7 +184,7 @@ def test_multispectral_3d():
     return data, multi_labels, single_labels, labels
 
 
-def test_depth():
+def test_spacing_0():
     n = 30
     lx, ly, lz = n, n, n
     data, _ = make_3d_syntheticdata(lx, ly, lz)
@@ -202,14 +202,14 @@ def test_depth():
                  ly // 2 - small_l // 4,
                  lz // 4 - small_l // 8] = 2
 
-    # Test with `depth` kwarg
+    # Test with `spacing` kwarg
     labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
-                                 depth=0.5)
+                                 spacing=(1., 1., 0.5))
 
     assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
 
 
-def test_spacing():
+def test_spacing_1():
     n = 30
     lx, ly, lz = n, n, n
     data, _ = make_3d_syntheticdata(lx, ly, lz)

skimage/viewer/plugins/lineprofile.py

@@ -1,5 +1,4 @@
 from __future__ import division
-import warnings
 
 import numpy as np
 from skimage.util.dtype import dtype_range
@@ -22,8 +21,6 @@ class LineProfile(PlotPlugin):
     ----------
     maxdist : float
         Maximum pixel distance allowed when selecting end point of scan line.
-    epsilon : float
-        Deprecated. Use `maxdist` instead.
     limits : tuple or {None, 'image', 'dtype'}
         (minimum, maximum) intensity limits for plotted profile. The following
         special values are defined:
@@ -37,10 +34,6 @@ class LineProfile(PlotPlugin):
     def __init__(self, maxdist=10, epsilon='deprecated',
                  limits='image', **kwargs):
         super(LineProfile, self).__init__(**kwargs)
-
-        if not epsilon == 'deprecated':
-            warnings.warn("Parameter `epsilon` deprecated; use `maxdist`.")
-            maxdist = epsilon
         self.maxdist = maxdist
         self._limit_type = limits
         print(self.help())