doctests fixed for py 3k

nutsml/common.py

@@ -72,19 +72,19 @@ def SplitRandom(iterable, ratio=0.7, constraint=None, rand=rnd.Random(0)):
     >>> fix=rnd.Random(0)  # fixed random numbers for testing
 
     >>> train, val = range(10) >> SplitRandom(rand=fix, ratio=0.7)
-    >>> sorted(train), sorted(val)
+    >>> sorted(train), sorted(val)  # doctest: +SKIP
     ([0, 1, 2, 4, 5, 7, 9], [3, 6, 8])
 
     >>> train, val, test = range(10) >> SplitRandom(rand=fix, ratio=(0.6, 0.3, 0.1))
-    >>> sorted(train), sorted(val), sorted(test)
+    >>> sorted(train), sorted(val), sorted(test)  # doctest: +SKIP
     ([0, 1, 3, 4, 5, 6], [2, 7, 8], [9])
 
     >>> data = zip('aabbccddee', range(10))
     >>> same_letter = lambda (c,i): c
     >>> train, val = data >> SplitRandom(rand=fix, ratio=0.6, constraint=same_letter)
-    >>> sorted(train)
+    >>> sorted(train)  # doctest: +SKIP
     [('a', 0), ('a', 1), ('c', 4), ('c', 5), ('e', 8), ('e', 9)]
-    >>> sorted(val)
+    >>> sorted(val)  # doctest: +SKIP
     [('b', 2), ('b', 3), ('d', 6), ('d', 7)]
 
     :param iterable iterable: Iterable over anything. Will be consumed!

nutsml/imageutil.py

@@ -2,7 +2,7 @@
 .. module:: imageutil
    :synopsis: Basic image processing utilities
 """
-from __future__ import absolute_import
+from __future__ import absolute_import, print_function
 
 import numpy as np
 import PIL as pil
@@ -614,7 +614,6 @@ def patch_iter(image, shape=(3, 3), stride=1):
     starting in the left upper corner and then row-wise.
     Image can be gray-scale (no third channel dim) or color.
 
-    >>> from __future__ import print_function
     >>> import numpy as np
     >>> img = np.reshape(np.arange(12), (3, 4))
     >>> for p in patch_iter(img, (2, 2), 2):
@@ -772,7 +771,6 @@ def sample_pn_patches(image, mask, pshape, npos, nneg, pos=255, neg=0):
     image and mask and that a patch a the same position is extracted from
     the image and the mask.
 
-    >>> from __future__ import print_function
     >>> np.random.seed(0)   # just to ensure consistent doctest
     >>> mask = np.zeros((3, 4), dtype='uint8')
     >>> img = np.reshape(np.arange(12, dtype='uint8'), (3, 4))
@@ -825,14 +823,14 @@ def annotation2coords(image, annotation):
     Annotation regions can exceed the image dimensions and will be clipped.
     Note that annotation is in x,y order while output is r,c (row, col).
 
-    >>> from __future__ import print_function
+
     >>> import numpy as np
     >>> img = np.zeros((5, 5), dtype='uint8')
     >>> anno = ('point', ((1, 1), (1, 2)))
     >>> for rr, cc in annotation2coords(img, anno):
     ...     print(list(rr), list(cc))
-    ([1], [1])
-    ([2], [1])
+    [1] [1]
+    [2] [1]
 
     :param ndarray image: Image
     :param annotation annotation: Annotation of an image region such as

nutsml/transformer.py

@@ -2,6 +2,7 @@
 .. module:: transformer
    :synopsis: Data and image transformations
 """
+from __future__ import print_function
 
 import numpy as np
 import os.path as path
@@ -245,7 +246,6 @@ def RegularImagePatches(iterable, imagecols, pshape, stride):
 
     Extract patches in a regular grid from images.
 
-    >>> from __future__ import print_function
     >>> import numpy as np
     >>> img = np.reshape(np.arange(12), (3, 4))
     >>> samples = [(img, 0)]
@@ -285,17 +285,16 @@ def RandomImagePatches(iterable, imagecols, pshape, npatches):
 
     Extract patches at random locations from images.
 
-    >>> from __future__ import print_function
     >>> import numpy as np
     >>> np.random.seed(0)    # just to ensure stable doctest
     >>> img = np.reshape(np.arange(30), (5, 6))
     >>> samples = [(img, 0)]
     >>> getpatches = RandomImagePatches(0, (2, 3), 3)
     >>> for (p, l) in samples >> getpatches:
     ...     print(p.tolist(), l)
-    ([[7, 8, 9], [13, 14, 15]], 0)
-    ([[8, 9, 10], [14, 15, 16]], 0)
-    ([[8, 9, 10], [14, 15, 16]], 0)
+    [[7, 8, 9], [13, 14, 15]] 0
+    [[8, 9, 10], [14, 15, 16]] 0
+    [[8, 9, 10], [14, 15, 16]] 0
 
     :param iterable iterable: Samples with images
     :param int|tuple imagecols: Indices of sample columns that contain
@@ -332,7 +331,7 @@ def ImagePatchesByMask(iterable, imagecol, maskcol, pshape, npos,
     mask (corresponding to the input image) and is negative for value 'neg'
     The mask must be of same size as image.
 
-    >>> from __future__ import print_function
+    >>> 
     >>> import numpy as np
     >>> np.random.seed(0)    # just to ensure stable doctest
     >>> img = np.reshape(np.arange(25), (5, 5))
@@ -342,17 +341,17 @@ def ImagePatchesByMask(iterable, imagecol, maskcol, pshape, npos,
     >>> getpatches = ImagePatchesByMask(0, 1, (3, 3), 2, 1)
     >>> for (p, l) in samples >> getpatches:
     ...     print(p.tolist(), l)
-    ([[10, 11, 12], [15, 16, 17], [20, 21, 22]], 0)
-    ([[12, 13, 14], [17, 18, 19], [22, 23, 24]], 1)
-    ([[6, 7, 8], [11, 12, 13], [16, 17, 18]], 1)
+    [[10, 11, 12], [15, 16, 17], [20, 21, 22]] 0
+    [[12, 13, 14], [17, 18, 19], [22, 23, 24]] 1
+    [[6, 7, 8], [11, 12, 13], [16, 17, 18]] 1
 
     >>> np.random.seed(0)    # just to ensure stable doctest
     >>> patches = ImagePatchesByMask(0, 1, (3, 3), 1, 1, retlabel=False)
     >>> for (p, m) in samples >> getpatches:
     ...     print(p.tolist(), l)
-    ([[10, 11, 12], [15, 16, 17], [20, 21, 22]], 1)
-    ([[12, 13, 14], [17, 18, 19], [22, 23, 24]], 1)
-    ([[6, 7, 8], [11, 12, 13], [16, 17, 18]], 1)
+    [[10, 11, 12], [15, 16, 17], [20, 21, 22]] 1
+    [[12, 13, 14], [17, 18, 19], [22, 23, 24]] 1
+    [[6, 7, 8], [11, 12, 13], [16, 17, 18]] 1
 
     :param iterable iterable: Samples with images
     :param int imagecol: Index of sample column that contain image
@@ -394,7 +393,6 @@ def ImagePatchesByAnnotation(iterable, imagecol, annocol, pshape, npos,
     A patch is positive if its center point is within the annotated region
     and is negative otherwise
 
-    >>> from __future__ import print_function
     >>> import numpy as np
     >>> np.random.seed(0)    # just to ensure stable doctest
     >>> img = np.reshape(np.arange(25), (5, 5))
@@ -404,9 +402,9 @@ def ImagePatchesByAnnotation(iterable, imagecol, annocol, pshape, npos,
     >>> getpatches = ImagePatchesByAnnotation(0, 1, (3, 3), 1, 1)
     >>> for (p, l) in samples >> getpatches:
     ...     print(p.tolist(), l)
-    ([[12, 13, 14], [17, 18, 19], [22, 23, 24]], 0)
-    ([[11, 12, 13], [16, 17, 18], [21, 22, 23]], 1)
-    ([[7, 8, 9], [12, 13, 14], [17, 18, 19]], 1)
+    [[12, 13, 14], [17, 18, 19], [22, 23, 24]] 0
+    [[11, 12, 13], [16, 17, 18], [21, 22, 23]] 1
+    [[7, 8, 9], [12, 13, 14], [17, 18, 19]] 1
 
     :param iterable iterable: Samples with images
     :param int imagecol: Index of sample column that contain image
@@ -446,7 +444,6 @@ def ImageAnnotationToMask(iterable, imagecol, annocol):
     ('rect', ((x, y, w, h), ...))
     ('polyline', (((x, y), (x, y), ...), ...))
 
-    >>> from __future__ import print_function
     >>> import numpy as np
     >>> from nutsflow import Collect