def test_init(self):
Xd = numpy.array([numpy.linspace(0, 10, 4), numpy.linspace(4, 6, 4)])
fit = fitter.Fit()
fit.set_data('mydata', Xd)
d = fit.data['mydata']
self.assertEqual(d.id, 'mydata')
npt.assert_almost_equal(d.values, Xd)
def test_solve(self):
fit = fitter.Fit()
fit.bind_element_point(9, [0.3], 'datacloud', 0, weight=2)
fit.bind_element_point(9, [0.8], 'datacloud', 1, weight=2)
fit.update_from_mesh(self.mesh)
Xd = numpy.array([[0.3, 0.15], [0.8, 0.4]])
fit.set_data('datacloud', Xd)
mesh, err = fit.solve(self.mesh)
npt.assert_almost_equal(mesh.get_nodes(), [[0, 0], [1.0, 0.5]])
def test_bind_element_point(self):
fit = fitter.Fit()
fit.bind_element_point(2, [0.1, 0.3], 'datacloud', weight=2)
pt = fit.points[0]
self.assertEqual(pt.eid, 2)
self.assertEqual(pt.xi, [0.1, 0.3])
self.assertEqual(pt.data, 'datacloud')
self.assertEqual(pt.bind_weight, 2)
self.assertEqual(pt.param_ids, None)
self.assertEqual(pt.param_weights, None)
def test_get_data(self):
fit = fitter.Fit()
fit.use_sparse = False
fit.bind_element_point(9, [0.3], 'datacloud', 1, weight=2)
fit.bind_element_point(9, [0.8], 'datacloud', 0, weight=2)
fit.update_from_mesh(self.mesh)
Xd = numpy.array([[0.1, 0.2], [0.3, 0.4]])
fit.set_data('datacloud', Xd)
Xr = fit.get_data(self.mesh)
npt.assert_almost_equal(Xr, [0.3, 0.4, 0.1, 0.2])
def test_update_from_mesh_node_vals_sparse(self):
fit = fitter.Fit()
fit.use_sparse = True
fit.bind_node_value(1, 1, 0, 'datacloud', 1, weight=2)
fit.bind_node_value(2, 0, 0, 'x0', weight=3)
fit.update_from_mesh(self.mesh)
self.assertEqual(fit.num_dof, 2)
self.assertEqual(fit.num_rows, 2)
npt.assert_almost_equal(fit.A.toarray(), [[2, 0], [0, 3]])
self.assertEqual(fit.data_map, [[0, 1], [1, 0]])
Xd = numpy.array([[0.1, 0.2], [0.3, 0.4]])
fit.set_data('datacloud', Xd)
fit.set_data('x0', 2.7)
Xr = fit.get_data(self.mesh)
npt.assert_almost_equal(Xr, [0.4, 2.7])
def fitting(mesh):
x = scipy.linspace(0, 2 * scipy.pi, 200)
y = 0.8 * scipy.cos(x)
Xd = scipy.array([x, y]).T
mesh.nodes[1].fix([True, False])
mesh.nodes[7].fix([True, False])
mesh._core.generate_fixed_index()
fit = fitter.Fit('m2dc')
res = 20
fit.X = scipy.zeros((2 * res, 2))
fit.Xi = scipy.array([scipy.linspace(0, 1, res)]).T
mesh = fit.optimize(mesh, Xd)
plotting_fit(mesh, Xd)
def test_update_from_mesh_elem_pts(self):
fit = fitter.Fit()
fit.use_sparse = False
fit.bind_element_point(9, [0.3], 'datacloud', 0, weight=2)
fit.bind_element_point(9, [0.7], 'datacloud', 1, weight=1)
fit.update_from_mesh(self.mesh)
self.assertEqual(fit.num_dof, 4)
self.assertEqual(fit.num_rows, 4)
pt = fit.points[0]
self.assertEqual(pt.param_ids, [[0, 2], [1, 3]])
npt.assert_almost_equal(pt.param_weights, [0.7, 0.3])
npt.assert_almost_equal(fit.A, [[1.4, 0, 0.6, 0], [0, 1.4, 0, 0.6],
[0.3, 0, 0.7, 0], [0, 0.3, 0, 0.7]])
self.assertEqual(fit.data_map, [[0, 0], [0, 1], [1, 0], [1, 1]])
Xd = numpy.array([[0.1, 0.2], [0.3, 0.4]])
fit.set_data('datacloud', Xd)
npt.assert_almost_equal(fit.data['datacloud'].values, Xd)
Xr = fit.get_data(self.mesh)
npt.assert_almost_equal(Xr, [0.1, 0.2, 0.3, 0.4])
def test_bind_subset_of_fields(self):
mesh = mesher.Mesh()
mesh.add_stdnode(1, [0, 0, 0])
mesh.add_stdnode(2, [2, 0.9, 2.4])
mesh.add_element('el1', ['L1'], [1, 2])
mesh.generate()
fit = fitter.Fit()
fit.bind_element_point('el1', [0.3], 'x3', weight=2, fields=[0])
fit.bind_element_point('el1', [0.3],
'datacloud',
weight=2,
fields=[1, 2])
fit.bind_element_point('el1', [0.8],
'datacloud',
weight=2,
fields=[2, 1])
fit.bind_element_point('el1', [0.9], 'x9', weight=2, fields=[0])
fit.update_from_mesh(mesh)
npt.assert_almost_equal(
fit.A.toarray(),
[[1.4, 0., 0., 0.6, 0., 0.], [0., 1.4, 0., 0., 0.6, 0.],
[0., 0., 1.4, 0., 0., 0.6], [0., 0., 0.4, 0., 0., 1.6],
[0., 0.4, 0., 0., 1.6, 0.], [0.2, 0., 0., 1.8, 0., 0.]])
Xd = numpy.array([[0, 0, 0], [0.5, 0.3, 1.1], [1.7, 0.8, 2.1],
[1, 0.5, 1.5]])
fit.set_data('datacloud', Xd)
fit.set_data('x3', 0.6)
fit.set_data('x9', 1.8)
fit.generate_fast_data()
for data in fit.data:
data.update_point_data(mesh._core.P[fit.param_ids])
b = fit.get_data(mesh)
npt.assert_almost_equal(b, [0.6, 0.3, 1.1, 2.1, 0.8, 1.8])
mesh, err = fit.solve(mesh)
npt.assert_almost_equal(mesh.get_nodes(),
[[0, 0, 0.5], [2.0, 1.0, 2.5]])
def test_init(self):
fit = fitter.Fit()
self.assertEqual(fit.use_sparse, True)
def test_set_data(self):
fit = fitter.Fit()
Xd = numpy.array([[0.1, 0.2], [0.3, 0.4]])
fit.set_data('datacloud', Xd)
npt.assert_almost_equal(fit.data['datacloud'].values, Xd)
def generate():
x = scipy.linspace(0, 2 * scipy.pi, 7)
y = scipy.cos(x)
#~ y = scipy.zeros(x.shape[0])
X = scipy.array([x, y]).T
# Start Generate Mesh
mesh = mesher.Mesh() # Instantiate a mesh
# Add nodes
for ind, x in enumerate(X):
mesh.add_stdnode(ind + 1, x)
# Add two quadratic elements each having 3 nodes
mesh.add_element(1, ['L3'], [1, 2, 3, 4])
mesh.add_element(2, ['L3'], [4, 5, 6, 7])
fit = fitter.Fit()
Xi = scipy.linspace(0, 1, 30)
did = 0
for elem in mesh.elements:
for xi in Xi:
if elem.id == 1:
weight = 1 + xi**4 * 100
else:
weight = 1 + (1-xi)**4 * 100
#~ weight = 1
#~ fit.bind_element_point(elem.id, [xi], 'mydata', data_index=did, weight=weight)
fit.bind_element_point(elem.id, [xi], 'mydata', weight=weight)
did += 1
Xi = scipy.linspace(0, 1, 100)
Xd = mesh.evaluate(1, Xi)
Xd = scipy.append(Xd, mesh.evaluate(2, Xi), axis=0)
Xd[:,1] = scipy.cos(Xd[:,0])
fit.set_data('mydata', Xd)
XdA = scipy.array([[0, 0.], [scipy.pi, -1]])
XdB = scipy.array([[2 * scipy.pi, 0.8]])
fit.set_data('nodeA', XdA)
fit.set_data('nodeB', XdB)
fit.bind_node_point(1, 'nodeA', data_index=0, weight=10, param=None)
#~ fit.bind_node_point(4, 'nodeA', data_index=1, weight=10)
fit.bind_node_point(7, 'nodeB', data_index=0, weight=10, param=0)
fit.bind_node_point(7, 'nodeB', data_index=0, weight=10, param=1)
fit.update_from_mesh(mesh)
fit.invert_matrix()
mesh = fit.solve(mesh, niter=100, output=True)
#~ mesh.generate(True)
pylab.figure(1)
pylab.clf()
pylab.ion()
pylab.plot(Xd[:,0], Xd[:,1], ',r')
Xn = mesh.get_nodes()
pylab.plot(Xn[:,0], Xn[:,1], 'o')
Xl = mesh.get_lines(res=32)
for xl in Xl:
pylab.plot(xl[:,0], xl[:,1], 'b')
pylab.ioff()
pylab.axis([-0.3, 6.5, -1.2, 1.2])
pylab.draw()
pylab.show()
return mesh, fit