def fit_NURBS(As, B1s, B2s, Cs, regularPoints, vertices, quads, fine_vertices,
parameters, fairnessWeight):
print "### Preprocessing ###"
# Throw away any datapoints which are NaN or outside the parameter range [0,1]
# since these cause trouble. (+ Scale the resulting ones so that max and min
# param values are 1 and 0 repspectively in both u and v)
[parameters, fine_vertices] = scaleAwayParameters(parameters,
fine_vertices)
# [As, B1s, B2s, Cs] = sortAB1B2VIndices(As, B1s, B2s, C) # not needed with new sorting of points
print "Done."
print "Calculating coefs."
coefs = createGlobalControlMeshCoefs(parameters, quads, As, B1s, B2s, Cs,
regularPoints)
print "Done."
print "Calculating fair coefs."
fair_coefs = createFairnessControlMeshCoefs(quads, As, B1s, B2s, Cs,
regularPoints)
print "fair_coefs.shape = " + str(fair_coefs.shape)
print "Done."
print "Sparsifying..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices...."
joined_verts = sp.vstack(
[scipy.array(fine_vertices),
scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack(
[sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Solving least-squares problem..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
#print "Writing files..."
#write_matrix_to_csv(vertices,'vertices.csv')
#write_matrix_to_asc(vertices,'vertices.asc')
#write_matrix_to_asc(fine_vertices,'vertices_fine.asc')
#print "Done."
print "Calculating NURBS control points..."
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(
quads, As, B1s, B2s, Cs, regularPoints, vertices)
print "Done."
return NURBSMatrix, NURBSIndices
def fit_NURBS(As, B1s, B2s, Cs, regularPoints, vertices, quads, fine_vertices, parameters, fairnessWeight):
print "### Preprocessing ###"
# Throw away any datapoints which are NaN or outside the parameter range [0,1]
# since these cause trouble. (+ Scale the resulting ones so that max and min
# param values are 1 and 0 repspectively in both u and v)
[parameters, fine_vertices] = scaleAwayParameters(parameters, fine_vertices)
# [As, B1s, B2s, Cs] = sortAB1B2VIndices(As, B1s, B2s, C) # not needed with new sorting of points
print "Done."
print "Calculating coefs."
coefs = createGlobalControlMeshCoefs(parameters, quads, As, B1s, B2s, Cs, regularPoints)
print "Done."
print "Calculating fair coefs."
fair_coefs = createFairnessControlMeshCoefs(quads, As, B1s, B2s, Cs, regularPoints)
print "fair_coefs.shape = "+str(fair_coefs.shape)
print "Done."
print "Sparsifying..."
sparse_coefs = sp.csr_matrix(coefs)
sparse_fair_coefs = sp.csr_matrix(fair_coefs)
print "Done."
print "Concatenating matrices...."
joined_verts = sp.vstack([scipy.array(fine_vertices), scipy.zeros([fair_coefs.shape[0], 3])]).todense()
joined_coefs = sp.vstack([sparse_coefs, fairnessWeight * sparse_fair_coefs])
print "Done."
print "Solving least-squares problem..."
vertices = solve_least_squares_problem(joined_coefs, joined_verts)
print "Done."
print "Writing files..."
write_matrix_to_csv(vertices,'vertices.csv')
write_matrix_to_asc(vertices,'vertices.asc')
write_matrix_to_asc(fine_vertices,'vertices_fine.asc')
print "Done."
print "Calculating NURBS control points..."
NURBSMatrix, NURBSIndices = createNURBSMatricesAllraised(quads,As,B1s,B2s,Cs,regularPoints,vertices)
print "Done."
return NURBSMatrix, NURBSIndices
A = mat_contents["A"] B1 = mat_contents["B1"] B2 = mat_contents["B2"] C = mat_contents["C"] regularPoints= mat_contents["regularPoints"] print "Done." print "### Preprocessing ###" # Throw away any datapoints which are NaN or outside the parameter range [0,1] # since these cause trouble. (+ Scale the resulting ones so that max and min # param values are 1 and 0 repspectively in both u and v) print "Preprocessing of input data..." [parameters, fine_vertices] = scaleAwayParameters(parameters, fine_vertices) [newA, newB1, newB2, newC] = sortAB1B2VIndices(A, B1, B2, C) print "Done." print "Calculating coefs." coefs = createGlobalControlMeshCoefs(parameters, quads, newA, newB1, newB2, newC, regularPoints) print "Done." print "Calculating fair coefs." fair_coefs = createFairnessControlMeshCoefs(quads, newA, newB1, newB2, newC, regularPoints) print "fair_coefs.shape = "+str(fair_coefs.shape) print "Done." print "Sparsify..." sparse_coefs = sp.csr_matrix(coefs) sparse_fair_coefs = sp.csr_matrix(fair_coefs) print "Done."
mat_contents = sio.loadmat('Data/Cantilever_try/cantilever.mat')
A = mat_contents["A"]
B1 = mat_contents["B1"]
B2 = mat_contents["B2"]
C = mat_contents["C"]
regularPoints = mat_contents["regularPoints"]
print "Done."
print "### Preprocessing ###"
# Throw away any datapoints which are NaN or outside the parameter range [0,1]
# since these cause trouble. (+ Scale the resulting ones so that max and min
# param values are 1 and 0 repspectively in both u and v)
print "Preprocessing of input data..."
[parameters, fine_vertices] = scaleAwayParameters(parameters, fine_vertices)
[newA, newB1, newB2, newC] = sortAB1B2VIndices(A, B1, B2, C)
print "Done."
print "Calculating coefs."
coefs = createGlobalControlMeshCoefs(parameters, quads, newA, newB1, newB2,
newC, regularPoints)
print "Done."
print "Calculating fair coefs."
fair_coefs = createFairnessControlMeshCoefs(quads, newA, newB1, newB2, newC,
regularPoints)
print "fair_coefs.shape = " + str(fair_coefs.shape)
print "Done."
print "Sparsify..."
sparse_coefs = sp.csr_matrix(coefs)