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use a dictionary to keep track of parametric model parameters. Test mappings on cyl meshes, parametric casing and layer model

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This commit is contained in:
Lindsey Heagy
2016-06-25 16:51:18 -07:00
parent 14f0d90f99
commit c75e3d0246
2 changed files with 478 additions and 273 deletions
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tests/base/test_maps.py
+15 -2
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@@ -5,8 +5,10 @@ from scipy.sparse.linalg import dsolve
TOL = 1e-14
MAPS_TO_TEST_2D = ["CircleMap", "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull","FullMap","Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
MAPS_TO_TEST_3D = [ "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull","FullMap","Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
MAPS_TO_TEST_2D = ["CircleMap", "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
MAPS_TO_TEST_3D = [ "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer", "ParametrizedBlockInLayer"]
MAPS_TO_TEST_CYL = [ "ComplexMap", "ExpMap", "IdentityMap", "SurjectVertical1D", "Weighting", "SurjectFull", "FullMap", "Vertical1DMap", "ParametrizedLayer"]
class MapTests(unittest.TestCase):
@@ -17,6 +19,8 @@ class MapTests(unittest.TestCase):
self.mesh2 = Mesh.TensorMesh([a, b], x0=np.array([3, 5]))
self.mesh3 = Mesh.TensorMesh([a, b, [3,4]], x0=np.array([3, 5, 2]))
self.mesh22 = Mesh.TensorMesh([b, a], x0=np.array([3, 5]))
self.meshCyl = Mesh.CylMesh([10.,1.,10.], x0='00C')
print self.meshCyl._meshType
def test_transforms2D(self):
for M in MAPS_TO_TEST_2D:
@@ -28,6 +32,15 @@ class MapTests(unittest.TestCase):
maps = getattr(Maps, M)(self.mesh3)
self.assertTrue(maps.test())
def test_transformsCyl(self):
for M in MAPS_TO_TEST_CYL:
maps = getattr(Maps, M)(self.meshCyl)
self.assertTrue(maps.test())
def test_ParametricCasingAndLayer(self):
mapping = Maps.ParametrizedCasingAndLayer(self.meshCyl)
m = np.r_[-2., 1., 6., 2., -0.1, 0.2, 0.5, 0.2, -0.3, 0.1]
self.assertTrue(mapping.test(m))
def test_transforms_logMap_reciprocalMap(self):
# Note that log/reciprocal maps can be kinda finicky, so we are being explicit about the random seed.