From 271ea14c0e0f5454171e3d738c6529b3f911e0e3 Mon Sep 17 00:00:00 2001 From: odebeir Date: Sun, 4 Nov 2012 15:49:54 +0100 Subject: [PATCH] remove trivial examples from perentile_rank --- skimage/filter/rank/percentile_rank.pyx | 228 ------------------------ 1 file changed, 228 deletions(-) diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 3817e1cd..90e24892 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -71,34 +71,6 @@ def percentile_autolevel(image, selem, out=None, mask=None, shift_x=False, shift local autolevel : uint8 array or uint16 array depending on input image The result of the local autolevel. - Examples - -------- - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_autolevel(ima8, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 0, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_autolevel(ima16, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 0, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -136,35 +108,7 @@ def percentile_gradient(image, selem, out=None, mask=None, shift_x=False, shift_ local percentile_gradient : uint8 array or uint16 array depending on input image The result of the local percentile_gradient. - Examples - -------- - - >>> # Local gradient - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_gradient(ima8, square(3), p0=0.,p1=1.) - array([[255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_gradient(ima16, square(3), p0=0.,p1=1.) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) """ @@ -202,35 +146,6 @@ def percentile_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=Fa local mean : uint8 array or uint16 array depending on input image The result of the local mean. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_mean(ima8, square(3),p0=0.,p1=1.) - array([[ 63, 85, 127, 85, 63], - [ 85, 113, 170, 113, 85], - [127, 170, 255, 170, 127], - [ 85, 113, 170, 113, 85], - [ 63, 85, 127, 85, 63]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_mean(ima16, square(3),p0=0.,p1=1.) - array([[1023, 1365, 2047, 1365, 1023], - [1365, 1820, 2730, 1820, 1365], - [2047, 2730, 4095, 2730, 2047], - [1365, 1820, 2730, 1820, 1365], - [1023, 1365, 2047, 1365, 1023]], dtype=uint16) """ @@ -268,35 +183,7 @@ def percentile_mean_substraction(image, selem, out=None, mask=None, shift_x=Fals local mean_substraction : uint8 array or uint16 array depending on input image The result of the local mean_substraction. - Examples - -------- - - >>> # Local mean_substraction - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_mean_substraction(ima8, square(3), p0=0.,p1=1.) - array([[ 95, 84, 63, 84, 95], - [ 84, 198, 169, 198, 84], - [ 63, 169, 127, 169, 63], - [ 84, 198, 169, 198, 84], - [ 95, 84, 63, 84, 95]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_mean_substraction(ima16, square(3), p0=0.,p1=1.) - array([[1536, 1365, 1024, 1365, 1536], - [1365, 3185, 2730, 3185, 1365], - [1024, 2730, 2048, 2730, 1024], - [1365, 3185, 2730, 3185, 1365], - [1536, 1365, 1024, 1365, 1536]], dtype=uint16) """ @@ -334,35 +221,7 @@ def percentile_morph_contr_enh(image, selem, out=None, mask=None, shift_x=False, local morph_contr_enh : uint8 array or uint16 array depending on input image The result of the local morph_contr_enh. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_morph_contr_enh(ima8, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 255, 255, 255, 0], - [ 0, 0, 0, 0, 0]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_morph_contr_enh(ima16, square(3), p0=0.,p1=1.) - array([[ 0, 0, 0, 0, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 4095, 4095, 4095, 0], - [ 0, 0, 0, 0, 0]], dtype=uint16) """ @@ -400,35 +259,6 @@ def percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, local percentile : uint8 array or uint16 array depending on input image The result of the local percentile. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 128*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile(ima8, square(3), p0=0.,p1=1.) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile(ima16, square(3), p0=0.,p1=1.) - array([[0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0], - [0, 0, 0, 0, 0]], dtype=uint16) """ @@ -467,35 +297,7 @@ def percentile_pop(image, selem, out=None, mask=None, shift_x=False, shift_y=Fal local pop : uint8 array or uint16 array depending on input image The result of the local pop. - Examples - -------- - - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_pop(ima8, square(3), p0=0.,p1=1.) - array([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]], dtype=uint8) - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_pop(ima16, square(3), p0=0.,p1=1.) - array([[4, 6, 6, 6, 4], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [6, 9, 9, 9, 6], - [4, 6, 6, 6, 4]], dtype=uint16) """ @@ -533,37 +335,7 @@ def percentile_threshold(image, selem, out=None, mask=None, shift_x=False, shift local threshold : uint8 array or uint16 array depending on input image The result of the local threshold. - Examples - -------- - >>> # Local mean - >>> from skimage.morphology import square - >>> import skimage.filter.rank as rank - >>> ima8 = 255*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint8) - >>> rank.percentile_threshold(ima8, square(3), p0=0.,p1=1.) - array([[255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255], - [255, 255, 255, 255, 255]], dtype=uint8) - - >>> ima16 = 4095*np.array([[0, 0, 0, 0, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 1, 1, 1, 0], - ... [0, 0, 0, 0, 0]], dtype=np.uint16) - >>> rank.percentile_threshold(ima16, square(3), p0=0.,p1=1.) - array([[4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095], - [4095, 4095, 4095, 4095, 4095]], dtype=uint16) - - """ return _apply(