Made tests more robust.

wafo/transform/test/test_models.py

@@ -1,40 +1,45 @@
 from wafo.transform.models import TrHermite, TrOchi, TrLinear
 import numpy as np
+from numpy.testing import assert_array_almost_equal
+
 def test_trhermite():
-    
+
-    std = 7./4
+    std = 7. / 4
     g = TrHermite(sigma=std, ysigma=std)
-    assert(np.abs(g.dist2gauss()- 0.88230868748851554)<1e-7)
+    assert(np.abs(g.dist2gauss() - 0.88230868748851554) < 1e-7)
-    
+
-    assert( g.mean == 0.0)
+    assert(g.mean == 0.0)
     assert(g.sigma == 1.75)
-    vals = g.dat2gauss([0,1,2,3])
+    vals = g.dat2gauss([0, 1, 2, 3])
-    true_vals = np.array([ 0.04654321,  1.03176393,  1.98871279,  2.91930895])
+    true_vals = np.array([0.04654321, 1.03176393, 1.98871279, 2.91930895])
-    assert((np.abs(vals-true_vals)<1e-7).all())
+    assert((np.abs(vals - true_vals) < 1e-7).all())
-    
+
+
 def test_trochi():
-    
+
-    std = 7./4
+    std = 7. / 4
     g = TrOchi(sigma=std, ysigma=std)
-    assert(g.dist2gauss()== 1.4106988010566603)
+    assert_array_almost_equal(g.dist2gauss(), 1.4106988010566603)
-    assert(g.mean== 0.0)
+    assert_array_almost_equal(g.mean, 0.0)
-    assert(g.sigma==1.75)
+    assert_array_almost_equal(g.sigma, 1.75)
-    vals = g.dat2gauss([0,1,2,3])
+    vals = g.dat2gauss([0, 1, 2, 3])
-    true_vals = np.array([  6.21927960e-04,   9.90237621e-01,   1.96075606e+00,
+    true_vals = np.array([6.21927960e-04, 9.90237621e-01, 1.96075606e+00,
-             2.91254576e+00])
+                          2.91254576e+00])
-    assert((np.abs(vals-true_vals)<1e-7).all())
+    assert_array_almost_equal(vals, true_vals)
-    
+    # assert((np.abs(vals - true_vals) < 1e-7).all())
+
+
 def test_trlinear():
-    
+
-    std = 7./4
+    std = 7. / 4
     g = TrLinear(sigma=std, ysigma=std)
     assert(g.dist2gauss() == 0.0)
-    assert(g.mean ==  0.0)
+    assert(g.mean == 0.0)
-    assert(g.sigma==  1.75)
+    assert(g.sigma == 1.75)
-    vals = g.dat2gauss([0,1,2,3])
+    vals = g.dat2gauss([0, 1, 2, 3])
-    true_vals = np.array([ 0.,  1.,  2.,  3.])
+    true_vals = np.array([0., 1., 2., 3.])
-    assert((np.abs(vals-true_vals)<1e-7).all())
+    assert((np.abs(vals - true_vals) < 1e-7).all())
-    
+
-if __name__=='__main__':
+if __name__ == '__main__':
     import nose
-    nose.run()
+    nose.run()