def cp_t3(self):
# ------------------------------------------------------------------------------
# test 6 triangles (standard pattern)
# 7 nodes
# 12 creaselines
# 3 * 7 - 12 constrains needed
# ------------------------------------------------------------------------------
cp = CreasePattern()
cp.nodes = [[0, 0, 0], [1, 0, 0], [1, 1, 0], [-1, 1, 0], [-1, 0, 0], [-1, -1, 0], [1, -1, 0]]
cp.crease_lines = [
[0, 1],
[1, 2],
[2, 0],
[2, 3],
[3, 0],
[3, 4],
[4, 0],
[4, 5],
[5, 0],
[5, 6],
[6, 0],
[6, 1],
]
cp.constraints = [[0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [4, 1], [2, 0], [6, 0], [4, 2]]
# lift node 0 in z-axes
cp.constraint_values = [0, 0.5, 0, 0, 0, 0, 0, 0, 0]
X = np.zeros((cp.n_dofs,), dtype=float)
print "initial lengths\n", cp.c_lengths
print "initial vectors\n", cp.c_vectors
print "initial R\n", cp.get_R(X)
print "initial dR\n", cp.get_dR(X)
print "constrained dofs\n", cp.cnstr_dofs
print "free dofs\n", cp.free_dofs
# Newton-Raphson iteration
MAX_ITER = 150
TOLERANCE = 1e-10
n_steps = 8
cv = np.copy(cp.constraint_values)
for k in range(n_steps):
print "step", k
cp.constraint_values = (k + 1.0) / float(n_steps) * cv
for i in range(MAX_ITER):
dR = cp.get_dR(X)[:, cp.free_dofs]
print "dR", dR.shape
R = cp.get_R(X)
if np.linalg.norm(R) < TOLERANCE:
print "==== converged in ", i, "iterations ===="
break
dX = np.linalg.solve(dR, -R)
X[cp.free_dofs] += dX
cp.set_next_node(X)
print "========== results =============="
print "solution X\n", X
print "final positions\n", cp.get_new_nodes(X)
print "final vectors\n", cp.get_new_vectors(X)
print "final lengths\n", cp.get_new_lengths(X)
# initialise View
from crease_pattern_view import CreasePatternView
my_model = CreasePatternView(data=cp)
print my_model.data.iteration_nodes.shape
my_model.configure_traits()
# X[1] = 0.01
# X[0] = 0.01
print 'initial lengths\n', cp.c_lengths
print 'initial vectors\n', cp.c_vectors
print 'initial R\n', cp.get_R(X)
print 'initial dR\n', cp.get_dR(X)
X = cp.solve_ff(X)
print '========== results =============='
print 'solution X\n', X
print 'final positions\n', cp.get_new_nodes(X)
print 'final vectors\n', cp.get_new_vectors(X)
print 'final lengths\n', cp.get_new_lengths(X)
return cp
if __name__ == '__main__':
# cp = moving_truss_cp_circle(n_steps = 10, dx = -1.99)
# cp = moving_truss_cp_ff_cnstr(n_steps = 40)
cp = triangle_cp_cnstr(n_steps = 40)
# initialise View
my_model = CreasePatternView(data = cp)
my_model.configure_traits()
[1, 3]],
F=[[0, 1, 3],
[1, 2, 3]])
init = Initialization(cp=cp)
init.U_0[5] = 0.05
lift = Lifting(source=init, n_steps=10)
print 'initial vector', lift.U_0
# lift.TS = [[r_ , s_, 0.01 + t_ * (0.5)]]
lift.CS = [[z_ - 4 * 0.4 * t_ * x_ * (1 - x_ / 3)]]
lift.GP = [[4, 0]]
lift.LP = [[5, 4],
[6, 4]]
lift.cf_lst = [(CF(Rf=lift.CS[0][0]), [1])]
lift.cnstr_lhs = [[(0, 0, 1.0)],
[(0, 1, 1.0)],
[(0, 2, 1.0)],
[(3, 0, 1.0)],
[(3, 2, 1.0)],
[(2, 2, 1.0)],
[(5, 0, 1.0)],
[(6, 0, 1.0)]]
lift.cnstr_rhs[0] = 0.9
print lift.U_1
#
v = CreasePatternView(reshaping_history=init)
v.configure_traits()
def rhombcp():
cp = CreasePattern()
cp.nodes = [[ 0, 0, 0 ],
[ 2, 0, 0 ],
[ 4, 0, 0 ],
[ 0, 1, 0 ],
[ 1, 1, 0 ],
[ 3, 1, 0 ],
[ 4, 1, 0]]
cp.crease_lines = [[ 0, 1 ],
[ 1, 2 ],
[ 0, 3 ],
[ 2, 6 ],
[ 0, 4 ],
[ 1, 4 ],
[ 3, 4 ],
[ 5, 4 ],
[ 1, 5 ],
[ 2, 5 ],
[ 6, 5 ],
]
cp.constraints = [[3, 2],
[3, 1],
[4, 1],
[4, 2],
[5, 1],
[5, 2],
[6, 0],
[6, 1],
[6, 2],
[0, 2]]
# lift node 0 in z-axes
cp.constraint_values = np.zeros((10,), dtype = float)
cp.constraint_values[3] = 0.4
cp.constraint_values[5] = 0.4
cp.constraint_values[8] = 0.0
cp.constraint_values[9] = 0.0
X = np.zeros((cp.n_dofs,), dtype = float)
X[3] = 0.1
X[5] = 0.1
print 'initial lengths\n', cp.c_lengths
print 'initial vectors\n', cp.c_vectors
print 'initial R\n', cp.get_R(X)
print 'initial dR\n', cp.get_dR(X)
print 'constrained dofs\n', cp.cnstr_dofs
print 'free dofs\n', cp.free_dofs
# Newton-Raphson iteration
MAX_ITER = 150
TOLERANCE = 1e-10
n_steps = 3
cv = np.copy(cp.constraint_values)
for k in range(n_steps):
print 'step', k
#cp.set_next_node(X)
cp.constraint_values = (k + 1.) / float(n_steps) * cv
i = 0
while i in range(MAX_ITER):
dR = cp.get_dR(X)[:, cp.free_dofs ]
print 'dR', dR.shape
R = cp.get_R(X)
if np.linalg.norm(R) < TOLERANCE:
print '==== converged in ', i, 'iterations ===='
cp.set_next_node(X)
break
dX = np.linalg.solve(dR, -R)
X[ cp.free_dofs ] += dX
i += 1
else:
raise ValueError
break
print '========== results =============='
print 'solution X\n', X
print 'final positions\n', cp.get_new_nodes(X)
print 'final vectors\n', cp.get_new_vectors(X)
print 'final lengths\n', cp.get_new_lengths(X)
# initialise View
my_model = CreasePatternView(data = cp)
my_model.configure_traits()
def show(self):
from crease_pattern_view import \
CreasePatternView
cpv = CreasePatternView()
cpv.data = self
cpv.configure_traits()
[3, 0, 0],
[3, 1, 0]],
L=[[0, 1], [0, 2], [2, 3], [1, 3], [0, 3],
[2, 3], [2, 4], [4, 5], [3, 5], [2, 5],
[4, 5], [4, 6], [6, 7], [5, 7], [4, 7],
],
F=[[0, 1, 2], [1, 2, 3],
[2, 3, 4], [3, 4, 5],
[4, 5, 6], [5, 6, 7]
]
)
init = Initialization(cp=cp)
init.t_arr
init.u_t[-1]
fold = Folding(source=init, n_steps=1,
acc=1e-6, MAX_ITER=500,
)
fold.u_t[-1]
oc = OptCritPotentialEnergy(reshaping=init)
u = np.zeros_like(cp.X)
print 'f', oc.get_f(u)
print 'f_du', oc.get_f_du(u)
cpw = CreasePatternView(root=init)
cpw.configure_traits()
