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add spaces for travis (pep8)

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François Boulogne
2013-06-20 23:12:16 +02:00
parent 84bff08601
commit a9f962695e
1 changed files with 42 additions and 42 deletions
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skimage/morphology/tests/test_watershed.py
+42 -42
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@@ -296,27 +296,27 @@ class TestWatershed(unittest.TestCase):
def test_watershed07(self):
"A regression test of a competitive case that failed"
data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,255,255,204,153,103,103,153,204,255,255,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
data = np.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, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 153, 103, 103, 153, 204, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 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, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]])
mask = (data != 255)
markers = np.zeros(data.shape, int)
markers[6, 7] = 1
@@ -332,27 +332,27 @@ class TestWatershed(unittest.TestCase):
def test_watershed08(self):
"The border pixels + an edge are all the same value"
data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,255,255,204,153,141,141,153,204,255,255,255,255,255],
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
data = np.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, 255, 255, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255],
[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 153, 141, 141, 153, 204, 255, 255, 255, 255, 255],
[255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255],
[255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255],
[255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255],
[255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255],
[255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255],
[255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255],
[255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255],
[255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 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, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]])
mask = (data != 255)
markers = np.zeros(data.shape, int)
markers[6, 7] = 1