tests structuring elements

tests/test_covariance.py

@@ -17,9 +17,9 @@ class TestFonctionCov(unittest.TestCase):
     def test_alongAxis(self):
         im = tifffile.imread( "tools/data/snow/snow.tif" )
         c = cov.alongAxis( im, range(4) )
-        # c shoud be [[86833030. 74757580. 53643410. 35916468.]   # axis 0
-        #             [86833030. 76282920. 56910720. 39792388.]   # axis 1
-        #             [86833030. 76410500. 57040076. 39938920.]]  # axis 2
+        # c should be [[86833030. 74757580. 53643410. 35916468.]   # axis 0
+        #              [86833030. 76282920. 56910720. 39792388.]   # axis 1
+        #              [86833030. 76410500. 57040076. 39938920.]]  # axis 2
         self.assertAlmostEqual(sum(c[:,3])/c[0,0], 1.3318408137585245, places=5)
 
     def test_subPixel(self):

tests/test_mesh.py

+# -*- coding: utf-8 -*-
+
+from __future__ import print_function
+
+import unittest
+import os
+import tifffile
+import numpy
+
+import spam.mesh.structured as smesh
+
+class testAll(unittest.TestCase):
+
+    def tearDown(self):
+        try:
+            pass
+        except OSError:
+            pass
+
+    # tests structured meshes
+    
+    def test_structuringElement(self):
+        a=smesh.structuringElement(radius=5,order=numpy.inf,margin=2)
+        b=smesh.structuringElement(radius=7,order=2,margin=0)
+        c=smesh.structuringElement(radius=8,order=1,margin=8)
+        self.assertEqual(int(a.sum()+len(a)+b.sum()+len(b)+c.sum()+len(c)), 3646)
+
+if __name__ == '__main__':
+        unittest.main()

tools/mesh/structured.py

@@ -463,11 +463,11 @@ def structuringElement(radius=1, order=numpy.inf, margin=0, dim=3):
             The structural element
     """
 
-    tb = tuple([2*radius+2*border+1 for _ in range(dim)])
+    tb = tuple([2*radius+2*margin+1 for _ in range(dim)])
     ts = tuple([2*radius+1 for _ in range(dim)])
     c = numpy.abs(numpy.indices(ts) - radius)
     d = numpy.zeros(tb)
-    s = [slice(border, border+2*radius+1) for _ in range(dim)]
+    s = [slice(margin, margin+2*radius+1) for _ in range(dim)]
     d[s] = numpy.power(numpy.sum(numpy.power(c, order), axis=0),1.0/float(order)) <= radius
     return d.astype('<u1')