def _get_fold_task(self):
# Link the crease factory it with the constraint client
fat = self.factory_task
gu_constant_length = GuConstantLength()
nx2 = 1
ny2 = 2
dof_constraints = fix(fat.N_grid[nx2 - 1, ny2], [1]) \
+ fix(fat.N_grid[nx2, ny2], [0, 1, 2]) \
+ fix(fat.N_grid[nx2, (ny2 - 1, ny2 + 1)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi * 0.4
diag_psi_mask = np.ones_like(fat.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_psi_constraints = [([(i, 1.0)], 0)
for i in fat.L_d_grid[diag_psi_mask].flatten()]
gu_psi_constraints = \
GuPsiConstraints(forming_task=fat,
psi_constraints=diag_psi_constraints +
[([(fat.L_h_grid[nx2, ny2], 1.0)],
lambda t: psi_max * t),
])
sim_config = SimulationConfig(goal_function_type='none',
gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints},
acc=1e-8, MAX_ITER=200)
sim_task = SimulationTask(previous_task=fat,
config=sim_config,
n_steps=20)
cp = sim_task.formed_object
cp.u[fat.N_grid[::2, :].flatten(), 2] = -0.1
cp.u[fat.N_grid[0, ::2].flatten(), 2] = -0.2
return sim_task
def _get_config(self):
# Configure simulation
gu_constant_length = GuConstantLength()
dof_constraints = fix(
[0], [0, 1, 2]) + fix([1], [1, 2]) + fix([5], [2]) + \
fix([3], [0], lambda t: math.cos(math.pi * t) - 1)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
return SimulationConfig(goal_function_type='none',
gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints},
acc=1e-5, MAX_ITER=1000)
def _get_config(self):
# Configure simulation
gu_constant_length = GuConstantLength()
dof_constraints = fix(
[0], [0, 1, 2]) + fix([1], [1, 2]) + fix([5], [2]) + \
fix([3], [0], lambda t: math.cos(math.pi * t) - 1)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
return SimulationConfig(goal_function_type='none',
gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints},
acc=1e-5, MAX_ITER=1000)
def _get_load_task(self):
self.fold_task.x_1
fat = self.factory_task
fixed_corner_n = fat.N_grid[(0, 0, -1, -1), (1, -2, 1, -2)].flatten()
fixed_n = fat.N_grid[(0, 0), (1, -2)].flatten()
slide_z = fix(fat.N_grid[-1, -2], [2], lambda t: -0.8 * self.L_x * t)
slide_x = fix(fat.N_grid[-1, -2], [0], lambda t: 0.1 * self.L_y * t)
loaded_n = fat.N_grid[self.n_x / 2, self.n_y / 2]
fixed_nodes_xyz = fix(fixed_n, (0, 2)) + slide_z
#fixed_nodes_xyz = fix(fixed_corner_n, (0, 1, 2))
dof_constraints = fixed_nodes_xyz
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
gu_constant_length = GuConstantLength()
diag_psi_mask = np.ones_like(fat.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_lines = fat.L_d_grid[diag_psi_mask].flatten()
diag_psi_constraints = [([(i, 1.0)], 0) for i in diag_lines]
gu_psi_constraints = \
GuPsiConstraints(forming_task=fat,
psi_constraints=diag_psi_constraints)
sim_config = SimulationConfig(
goal_function_type='total potential energy',
gu={
'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints
},
acc=1e-5,
MAX_ITER=1000,
debug_level=0)
def FN(F):
return lambda t: t * F
P = 0.0 * 30.5 * self.load_factor
F_ext_list = [(loaded_n, 2, FN(-P))]
fu_tot_poteng = FuPotEngTotal(kappa=np.array([1.0]),
F_ext_list=F_ext_list,
thickness=1,
exclude_lines=diag_lines)
sim_config._fu = fu_tot_poteng
st = SimulationTask(previous_task=self.fold_task,
config=sim_config,
n_steps=self.n_load_steps)
fu_tot_poteng.forming_task = st
cp = st.formed_object
cp.x_0 = self.fold_task.x_1
cp.u[:, :] = 0.0
return st
def _get_load_task(self):
self.fold_task.x_1
fat = self.factory_task
fixed_corner_n = fat.N_grid[(0, 0, -1, -1), (1, -2, 1, -2)].flatten()
fixed_n = fat.N_grid[(0, 0), (1, -2)].flatten()
slide_z = fix(fat.N_grid[-1, -2], [2], lambda t: -0.8 * self.L_x * t)
slide_x = fix(fat.N_grid[-1, -2], [0], lambda t: 0.1 * self.L_y * t)
loaded_n = fat.N_grid[self.n_x / 2, self.n_y / 2]
fixed_nodes_xyz = fix(fixed_n, (0, 2)) + slide_z
#fixed_nodes_xyz = fix(fixed_corner_n, (0, 1, 2))
dof_constraints = fixed_nodes_xyz
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
gu_constant_length = GuConstantLength()
diag_psi_mask = np.ones_like(fat.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_lines = fat.L_d_grid[diag_psi_mask].flatten()
diag_psi_constraints = [([(i, 1.0)], 0)
for i in diag_lines]
gu_psi_constraints = \
GuPsiConstraints(forming_task=fat,
psi_constraints=diag_psi_constraints)
sim_config = SimulationConfig(
goal_function_type='total potential energy',
gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints
},
acc=1e-5, MAX_ITER=1000,
debug_level=0
)
def FN(F): return lambda t: t * F
P = 0.0 * 30.5 * self.load_factor
F_ext_list = [(loaded_n, 2, FN(-P))]
fu_tot_poteng = FuPotEngTotal(kappa=np.array([1.0]),
F_ext_list=F_ext_list,
thickness=1,
exclude_lines=diag_lines
)
sim_config._fu = fu_tot_poteng
st = SimulationTask(previous_task=self.fold_task,
config=sim_config, n_steps=self.n_load_steps)
fu_tot_poteng.forming_task = st
cp = st.formed_object
cp.x_0 = self.fold_task.x_1
cp.u[:, :] = 0.0
return st
def _get_fold_task(self):
# Link the crease factory it with the constraint client
fat = self.factory_task
gu_constant_length = GuConstantLength()
nx2 = 1
ny2 = 2
dof_constraints = fix(fat.N_grid[nx2 - 1, ny2], [1]) \
+ fix(fat.N_grid[nx2, ny2], [0, 1, 2]) \
+ fix(fat.N_grid[nx2, (ny2 - 1, ny2 + 1)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi * 0.4
diag_psi_mask = np.ones_like(fat.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_psi_constraints = [([(i, 1.0)], 0)
for i in fat.L_d_grid[diag_psi_mask].flatten()]
gu_psi_constraints = \
GuPsiConstraints(forming_task=fat,
psi_constraints=diag_psi_constraints +
[([(fat.L_h_grid[nx2, ny2], 1.0)],
lambda t: psi_max * t),
])
sim_config = SimulationConfig(goal_function_type='none',
gu={
'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints
},
acc=1e-8,
MAX_ITER=200)
sim_task = SimulationTask(previous_task=fat,
config=sim_config,
n_steps=20)
cp = sim_task.formed_object
cp.u[fat.N_grid[::2, :].flatten(), 2] = -0.1
cp.u[fat.N_grid[0, ::2].flatten(), 2] = -0.2
return sim_task
if __name__ == '__main__':
cpf = create_cp_factory()
cp = cpf.formed_object
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix(cpf.N_grid[0, 1], [1]) \
+ fix(cpf.N_grid[1, 1], [0, 1, 2]) \
+ fix(cpf.N_grid[1, (0, -1)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi / 4.0
diag_psi_constraints = [([(i, 1.0)], 0) for i in cpf.L_d_grid.flatten()]
gu_psi_constraints = \
GuPsiConstraints(forming_task=cpf,
psi_constraints=diag_psi_constraints +
[([(cpf.L_h_grid[1, 1], 1.0)],
lambda t: -psi_max * t),
])
sim_config = SimulationConfig(goal_function_type='none',
gu={
'cl': gu_constant_length,
cpf = create_cp_factory()
cp = cpf.formed_object
if False:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
nx2 = 1
ny2 = 2
dof_constraints = fix(cpf.N_grid[nx2 - 1, ny2], [1]) \
+ fix(cpf.N_grid[nx2, ny2], [0, 1, 2]) \
+ fix(cpf.N_grid[nx2, (ny2 - 1, ny2 + 1)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi * 0.498
diag_psi_mask = np.ones_like(cpf.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_psi_constraints = [([(i, 1.0)], 0)
for i in cpf.L_d_grid[diag_psi_mask].flatten()]
print('psi constraints', diag_psi_constraints)
print('controlled line', cpf.L_h_grid[nx2, ny2])
gu_psi_constraints = \
GuPsiConstraints(forming_task=cpf,
psi_constraints=diag_psi_constraints +
[([(cpf.L_h_grid[nx2, ny2], 1.0)],
lambda t: psi_max * t),
# end
return cp_factory
if __name__ == '__main__':
import mayavi.mlab as m
import pylab as p
cp_factory_task = create_cp_factory()
cp = cp_factory_task.formed_object
ax = p.axes()
cp.plot_mpl(ax)
p.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix([0], [0, 1, 2]) + fix([1], [1, 2]) + fix([4], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi / 4.0
gu_psi_constraints = \
GuPsiConstraints(forming_task=cp_factory_task,
psi_constraints=[([(5, 1.0)], lambda t: psi_max * t),
([(7, 1.0)], 0),
([(8, 1.0)], 0),
])
sim_config = SimulationConfig(gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints},
acc=1e-5, MAX_ITER=10)
sim_step = SimulationStep(forming_task=cp_factory_task,
config=sim_config)
# end
return cp_factory
if __name__ == '__main__':
import mayavi.mlab as m
import pylab as p
cp_factory_task = create_cp_factory()
cp = cp_factory_task.formed_object
ax = p.axes()
cp.plot_mpl(ax)
p.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix([0], [0, 1, 2]) + fix([1], [1, 2]) + fix([4], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi / 4.0
gu_psi_constraints = \
GuPsiConstraints(forming_task=cp_factory_task,
psi_constraints=[([(5, 1.0)], lambda t: psi_max * t),
([(7, 1.0)], 0),
([(8, 1.0)], 0),
])
sim_config = SimulationConfig(gu={
'cl': gu_constant_length,
'dofs': gu_dof_constraints,
'psi': gu_psi_constraints
},
acc=1e-5,
cp_factory = CustomCPFactory(formed_object=cp)
# end
return cp_factory
if __name__ == '__main__':
import mayavi.mlab as m
cp_factory = create_cp_factory()
cp = cp_factory.formed_object
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix([0], [0, 1, 2]) + fix([1], [1, 2]) + fix([3], [2]) + \
fix([5], 2, lambda t: 0.5)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
sim_config = SimulationConfig(gu={
'cl': gu_constant_length,
'dofs': gu_dof_constraints
},
acc=1e-5,
MAX_ITER=10)
sim_step = SimulationStep(forming_task=cp_factory, config=sim_config)
sim_step._solve_nr()
m.figure(bgcolor=(1.0, 1.0, 1.0), fgcolor=(0.6, 0.6, 0.6))
cp.plot_mlab(m, nodes=True, lines=True)
def run_sim():
cp_factory, L_selection = create_cp_factory()
cp = cp_factory.formed_object
if False:
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
return
# Configure simulation
gu_constant_length = GuConstantLength()
dof_constraints = \
fix([0], [0, 1, 2]) +\
fix([1], [1, 2]) +\
fix([5], [2]) +\
fix([3], [0], lambda t: t * -1.9599)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
sim_config = SimulationConfig(gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints},
acc=1e-5, MAX_ITER=1000)
sim_step = SimulationStep(forming_task=cp_factory,
config=sim_config)
if False:
# Configure rendering visualization operators
eftlabels_viz3d = FacetsWithTextViz3D(
label='EFT labels', vis3d=cp)
efttitle_viz3d = FacetsWithImageViz3D(
label='EFT title', vis3d=cp,
F_ref=[0, 2, 4, 6, 8],
N_ref=[0, 3, 15, 12, 7],
F_covered=[[0, 9], [2, 11], [4, 13], [6, 15], [8, 17]],
atimes=[90.0, 180.0, -90, 0, 135],
im_files=['eft_01.png', 'eft_01.png',
'eft_01.png', 'eft_01.png', 'eft_01.png'],
im_widths=[2, 2, 2, 2, 2],
im_offsets=[[0, 0, 0.004], [0, 0, 0.004],
[0, 0, 0.004], [0, 0, 0.004],
[-.295, -.295, 0.004]],
)
else:
fname_eft_front = 'eft_areas_standard_02.png'
fname_eft_back = 'eft_mission_standard_trans_EF01.png'
fheight = 0.011
front_offset = 0.006
tube_radius = 0.01
plane_offsets = [0.005, -0.010]
edge_len = 2.0
x_offset = 1.0
c45 = np.cos(np.pi / 4)
shift_x = x_offset + edge_len / c45 - edge_len
shift_l = shift_x * c45
print 'shift_l', shift_l
F_ref = [0, 2, 4, 6, 8, 16, 1, 12, 14, ]
N_ref = [0, 3, 15, 12, 7, 4, 1, 11, 13]
F_covered = [[0, 9], [2, 11], [4, 13], [6, 15],
[8, 17], [16, 7], [1, 10], [12, 3], [14, 5]]
atimes = [90.0, 180.0, -90, 0, 45, 90, 90.0, -90.0, 0.0]
im_widths = [4, 4, 4, 4, 4, 4, 4, 4, 4]
imfiles_front = [
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
]
imfiles_back = [
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
]
im_front_offsets = [
[0, 0, front_offset],
[0, 0, front_offset],
[0, 0, front_offset],
[0, 0, front_offset],
[-shift_l, -shift_l, front_offset],
[0, -1, front_offset],
[-2, 0, front_offset],
[0, -1, front_offset],
[0, -1, front_offset]
]
im_back_offsets = [
[0, 0, -fheight],
[0, 0, -fheight],
[0, 0, -fheight],
[0, 0, -fheight],
[-shift_l, -shift_l, -fheight],
[0, -1, -fheight],
[-2, 0, -fheight],
[0, -1, -fheight],
[0, -1, -fheight]
]
efttitle_back_viz3d = FacetsWithImageViz3D(
label='EFT back', vis3d=cp,
F_ref=F_ref, # + F_ref,
N_ref=N_ref, # + N_ref,
F_covered=F_covered, # + F_covered,
atimes=atimes, # + atimes,
im_files=imfiles_back, # imfiles_front +
im_widths=im_widths, # + im_widths,
im_offsets=im_back_offsets, # im_front_offsets +
)
efttitle_front_viz3d = FacetsWithImageViz3D(
label='EFT front', vis3d=cp,
F_ref=F_ref,
N_ref=N_ref,
F_covered=F_covered,
atimes=atimes,
im_files=imfiles_front,
im_widths=im_widths,
im_offsets=im_front_offsets
)
eftlogo_normals = CreasePatternNormalsViz3D(
label='EFT normals', vis3d=cp)
eftlogo_bases = CreasePatternBasesViz3D(
label='EFT bases', vis3d=cp)
eft_thick_viz3d = CreasePatternThickViz3D(
label='EFT thick', vis3d=cp,
plane_offsets=plane_offsets)
cp.viz3d['cp'].set(tube_radius=tube_radius)
# Configure scene
ftv = FTV()
ftv.add(cp.viz3d['cp'])
ftv.add(eft_thick_viz3d)
ftv.add(efttitle_front_viz3d)
ftv.add(efttitle_back_viz3d)
# ftv.add(eftlabels_viz3d)
# ftv.add(eftlogo_normals)
# ftv.add(eftlogo_bases)
m = ftv.mlab
m.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
fig = m.gcf()
ftv.plot()
# oricreate_mlab_label(m)
a, e, d, f = m.view()
e = 135 # 10
a = 270 # -45 # 120
f = sim_step.forming_task.formed_object.center
m.view(a, e, d, f)
show = False
if show:
m.show()
n_u = 30
# Control the folding by a fold angle
phi_range = np.linspace(0.01, np.pi - 0.01, n_u)
dramaturgy = False
animation = True
def circular_folding(phi_range):
bot_e = 160
top_e = 10
if dramaturgy:
phi_unfolded = phi_range[0] * np.ones_like(phi_range)
phi_folded = phi_range[-1] * np.ones_like(phi_range)
phi_range_u = np.hstack(
[phi_range, phi_folded, phi_range[::-1], phi_unfolded])
else:
phi_range_u = phi_range
u_range = np.cos(phi_range_u) - 1
phi_range_e = np.linspace(0.01, np.pi - 0.01, n_u * 4)
azimuth_step = 360.0 / 4 / n_u
elevation_range = np.cos(
2 * phi_range_e)**2 * top_e + np.sin(2 * phi_range_e)**2 * bot_e
time_range = slice(None)
return u_range, elevation_range, azimuth_step, time_range
def eft_folding(phi_range):
n_u12 = 30
n_c = 60
n_u23 = 75
phi_12_range = damped_range(np.pi * 0.3, 0.012, n_u12)
phi_c = np.ones((n_c,), dtype='float_') * 0.01
phi_23_range = damped_range(0.01, np.pi - 0.005, n_u23)
phi_range_u = np.hstack([phi_12_range, phi_c, phi_23_range])
u_range = np.cos(phi_range_u) - 1
conf1 = 230, 50, 52, 1.14
confc = 230, 50, 20, 1.14
conf2 = 100, 160, 0, 1.1
conf3 = 270, 179, 0., 0.5
azimuth_range = np.hstack([damped_range(conf1[0], confc[0], n_u12),
damped_range(confc[0], conf2[0], n_c),
damped_range(conf2[0], conf3[0], n_u23)])
elevation_range = np.hstack([damped_range(conf1[1], confc[1], n_u12),
damped_range(confc[1], conf2[1], n_c),
damped_range(conf2[1], conf3[1], n_u23)])
roll_range = np.hstack([damped_range(conf1[2], confc[2], n_u12),
damped_range(confc[2], conf2[2], n_c),
damped_range(conf2[2], conf3[2], n_u23)])
d_range = np.hstack([damped_range(conf1[3], confc[3], n_u12) * d,
damped_range(confc[3], conf2[3], n_c) * d,
damped_range(conf2[3], conf3[3], n_u23) * d])
time_range = slice(None)
#time_range = np.array([-1], dtype=int)
return u_range, elevation_range, azimuth_range, roll_range, time_range, d_range
def eft_unfolding(phi_range):
n_u1 = 30
n_u2 = 60
n_u3 = 60
phi_u1 = np.pi - 0.012
phi_u2 = np.pi * 0.86
phi_u3 = 0.012
phi_1 = damped_range(phi_u1, phi_u2, n_u1)
phi_2 = np.ones((n_u2,), dtype='float_') * phi_u2
phi_3 = damped_range(phi_u2, phi_u3, n_u3)
phi_range_u = np.hstack([phi_1, phi_2, phi_3])
u_range = np.cos(phi_range_u) - 1
conf1 = 270, 179, 0., 0.5
conf2 = 230, 140, 60., 0.6
conf3 = 150, 70, 70., 0.64
conf4 = 45, 1, 314., 1.14
azimuth_range = np.hstack([damped_range(conf1[0], conf2[0], n_u1),
damped_range(conf2[0], conf3[0], n_u2),
damped_range(conf3[0], conf4[0], n_u3)])
elevation_range = np.hstack([damped_range(conf1[1], conf2[1], n_u1),
damped_range(conf2[1], conf3[1], n_u2),
damped_range(conf3[1], conf4[1], n_u3)])
roll_range = np.hstack([damped_range(conf1[2], conf2[2], n_u1),
damped_range(conf2[2], conf3[2], n_u2),
damped_range(conf3[2], conf4[2], n_u3)])
d_range = np.hstack([damped_range(conf1[3], conf2[3], n_u1) * d,
damped_range(conf2[3], conf3[3], n_u2) * d,
damped_range(conf3[3], conf4[3], n_u3) * d,
])
time_range = slice(None)
#time_range = np.array([0], dtype=int)
return u_range, elevation_range, azimuth_range, roll_range, time_range, d_range
# u_range, elevation_range, azimuth_range, roll_range, time_range, d_range = eft_folding(
# phi_range)
u_range, elevation_range, azimuth_range, roll_range, time_range, d_range = eft_unfolding(
phi_range)
tdir = tempfile.mkdtemp()
fname_list = []
for i, (u, e, a, r, d) in enumerate(zip(u_range[time_range],
elevation_range[time_range],
azimuth_range[time_range],
roll_range[time_range],
d_range[time_range])):
gu_dof_constraints.dof_constraints[-1][-1] = u
sim_step._solve_nr()
f = sim_step.forming_task.formed_object.center
print 'perspective - before', ftv.mlab.view()
print 'roll', ftv.mlab.roll()
print 'a,e,d,f', a, e, d, f
ftv.mlab.view(a, e, d, f)
ftv.mlab.roll(r)
print 'perspective', ftv.mlab.view()
print 'roll', ftv.mlab.roll()
ftv.update(force=True)
fname = os.path.join(tdir, 'eftlogo%03d.jpg' % i)
fname_list.append(fname)
ftv.mlab.savefig(fname, magnification=2.2)
if animation:
animation_file = os.path.join(tdir, 'anim01.gif')
images = string.join(fname_list, ' ')
destination = animation_file
import platform
if platform.system() == 'Linux':
os.system(
'convert -delay 8 -loop 1 ' + images + ' ' + destination)
# os.system('png2swf -o%s ' % destination + images)
else:
raise NotImplementedError(
'film production available only on linux')
print 'animation saved in', destination
ftv.show()
if __name__ == '__main__':
cpf = create_cp_factory()
cp = cpf.formed_object
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix(cpf.N_grid[0, 1], [1]) \
+ fix(cpf.N_grid[1, 1], [0, 1, 2]) \
+ fix(cpf.N_grid[1, (0, 2)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi * 0.49
diag_psi_constraints = [([(i, 1.0)], 0) for i in cpf.L_d_grid.flatten()]
gu_psi_constraints = \
GuPsiConstraints(forming_task=cpf,
psi_constraints=diag_psi_constraints +
[([(cpf.L_h_grid[1, 1], 1.0)],
lambda t: -psi_max * t),
])
sim_config = SimulationConfig(goal_function_type='none',
gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints,
cpf = create_cp_factory()
cp = cpf.formed_object
if False:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
nx2 = 1
ny2 = 2
dof_constraints = fix(cpf.N_grid[nx2 - 1, ny2], [1]) \
+ fix(cpf.N_grid[nx2, ny2], [0, 1, 2]) \
+ fix(cpf.N_grid[nx2, (ny2 - 1, ny2 + 1)], [2])
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
psi_max = np.pi * 0.498
diag_psi_mask = np.ones_like(cpf.L_d_grid, dtype=np.bool_)
diag_psi_mask[1:-1, 1:-1] = False
diag_psi_constraints = [([(i, 1.0)], 0)
for i in cpf.L_d_grid[diag_psi_mask].flatten()]
print 'psi constraints', diag_psi_constraints
print 'controlled line', cpf.L_h_grid[nx2, ny2]
gu_psi_constraints = \
GuPsiConstraints(forming_task=cpf,
psi_constraints=diag_psi_constraints +
[([(cpf.L_h_grid[nx2, ny2], 1.0)],
lambda t: psi_max * t),
cp.u[5, 2] = 0.01
cp_factory = CustomCPFactory(formed_object=cp)
# end
return cp_factory
if __name__ == '__main__':
import mayavi.mlab as m
cp_factory = create_cp_factory()
cp = cp_factory.formed_object
# Link the crease factory it with the constraint client
gu_constant_length = GuConstantLength()
dof_constraints = fix([0], [0, 1, 2]) + fix([1], [1, 2]) + fix([3], [2]) + \
fix([5], 2, lambda t: 0.5)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
sim_config = SimulationConfig(gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints},
acc=1e-5, MAX_ITER=10)
sim_step = SimulationStep(forming_task=cp_factory,
config=sim_config)
sim_step._solve_nr()
m.figure(bgcolor=(1.0, 1.0, 1.0), fgcolor=(0.6, 0.6, 0.6))
cp.plot_mlab(m, nodes=True, lines=True)
m.show()
def run_sim():
cp_factory, L_selection = create_cp_factory()
cp = cp_factory.formed_object
if False:
fig, ax = plt.subplots()
cp.plot_mpl(ax, facets=True)
plt.tight_layout()
plt.show()
return
# Configure simulation
gu_constant_length = GuConstantLength()
dof_constraints = \
fix([0], [0, 1, 2]) +\
fix([1], [1, 2]) +\
fix([5], [2]) +\
fix([3], [0], lambda t: t * -1.9599)
gu_dof_constraints = GuDofConstraints(dof_constraints=dof_constraints)
sim_config = SimulationConfig(gu={'cl': gu_constant_length,
'dofs': gu_dof_constraints},
acc=1e-5, MAX_ITER=1000)
sim_step = SimulationStep(forming_task=cp_factory,
config=sim_config)
if False:
# Configure rendering visualization operators
eftlabels_viz3d = FacetsWithTextViz3D(
label='EFT labels', vis3d=cp)
efttitle_viz3d = FacetsWithImageViz3D(
label='EFT title', vis3d=cp,
F_ref=[0, 2, 4, 6, 8],
N_ref=[0, 3, 15, 12, 7],
F_covered=[[0, 9], [2, 11], [4, 13], [6, 15], [8, 17]],
atimes=[90.0, 180.0, -90, 0, 135],
im_files=['eft_01.png', 'eft_01.png',
'eft_01.png', 'eft_01.png', 'eft_01.png'],
im_widths=[2, 2, 2, 2, 2],
im_offsets=[[0, 0, 0.004], [0, 0, 0.004],
[0, 0, 0.004], [0, 0, 0.004],
[-.295, -.295, 0.004]],
)
else:
fname_eft_front = 'eft_areas_standard_02.png'
fname_eft_back = 'eft_mission_standard_trans_EF01.png'
fheight = 0.011
front_offset = 0.006
tube_radius = 0.01
plane_offsets = [0.005, -0.010]
edge_len = 2.0
x_offset = 1.0
c45 = np.cos(np.pi / 4)
shift_x = x_offset + edge_len / c45 - edge_len
shift_l = shift_x * c45
print('shift_l', shift_l)
F_ref = [0, 2, 4, 6, 8, 16, 1, 12, 14, ]
N_ref = [0, 3, 15, 12, 7, 4, 1, 11, 13]
F_covered = [[0, 9], [2, 11], [4, 13], [6, 15],
[8, 17], [16, 7], [1, 10], [12, 3], [14, 5]]
atimes = [90.0, 180.0, -90, 0, 45, 90, 90.0, -90.0, 0.0]
im_widths = [4, 4, 4, 4, 4, 4, 4, 4, 4]
imfiles_front = [
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
fname_eft_front,
]
imfiles_back = [
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
fname_eft_back,
]
im_front_offsets = [
[0, 0, front_offset],
[0, 0, front_offset],
[0, 0, front_offset],
[0, 0, front_offset],
[-shift_l, -shift_l, front_offset],
[0, -1, front_offset],
[-2, 0, front_offset],
[0, -1, front_offset],
[0, -1, front_offset]
]
im_back_offsets = [
[0, 0, -fheight],
[0, 0, -fheight],
[0, 0, -fheight],
[0, 0, -fheight],
[-shift_l, -shift_l, -fheight],
[0, -1, -fheight],
[-2, 0, -fheight],
[0, -1, -fheight],
[0, -1, -fheight]
]
efttitle_back_viz3d = FacetsWithImageViz3D(
label='EFT back', vis3d=cp,
F_ref=F_ref, # + F_ref,
N_ref=N_ref, # + N_ref,
F_covered=F_covered, # + F_covered,
atimes=atimes, # + atimes,
im_files=imfiles_back, # imfiles_front +
im_widths=im_widths, # + im_widths,
im_offsets=im_back_offsets, # im_front_offsets +
)
efttitle_front_viz3d = FacetsWithImageViz3D(
label='EFT front', vis3d=cp,
F_ref=F_ref,
N_ref=N_ref,
F_covered=F_covered,
atimes=atimes,
im_files=imfiles_front,
im_widths=im_widths,
im_offsets=im_front_offsets
)
eftlogo_normals = CreasePatternNormalsViz3D(
label='EFT normals', vis3d=cp)
eftlogo_bases = CreasePatternBasesViz3D(
label='EFT bases', vis3d=cp)
eft_thick_viz3d = CreasePatternThickViz3D(
label='EFT thick', vis3d=cp,
plane_offsets=plane_offsets)
cp.viz3d['cp'].set(tube_radius=tube_radius)
# Configure scene
ftv = FTV()
ftv.add(cp.viz3d['cp'])
ftv.add(eft_thick_viz3d)
ftv.add(efttitle_front_viz3d)
ftv.add(efttitle_back_viz3d)
# ftv.add(eftlabels_viz3d)
# ftv.add(eftlogo_normals)
# ftv.add(eftlogo_bases)
m = ftv.mlab
m.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
fig = m.gcf()
ftv.plot()
# oricreate_mlab_label(m)
a, e, d, f = m.view()
e = 135 # 10
a = 270 # -45 # 120
f = sim_step.forming_task.formed_object.center
m.view(a, e, d, f)
show = False
if show:
m.show()
n_u = 30
# Control the folding by a fold angle
phi_range = np.linspace(0.01, np.pi - 0.01, n_u)
dramaturgy = False
animation = True
def circular_folding(phi_range):
bot_e = 160
top_e = 10
if dramaturgy:
phi_unfolded = phi_range[0] * np.ones_like(phi_range)
phi_folded = phi_range[-1] * np.ones_like(phi_range)
phi_range_u = np.hstack(
[phi_range, phi_folded, phi_range[::-1], phi_unfolded])
else:
phi_range_u = phi_range
u_range = np.cos(phi_range_u) - 1
phi_range_e = np.linspace(0.01, np.pi - 0.01, n_u * 4)
azimuth_step = 360.0 / 4 / n_u
elevation_range = np.cos(
2 * phi_range_e)**2 * top_e + np.sin(2 * phi_range_e)**2 * bot_e
time_range = slice(None)
return u_range, elevation_range, azimuth_step, time_range
def eft_folding(phi_range):
n_u12 = 30
n_c = 60
n_u23 = 75
phi_12_range = damped_range(np.pi * 0.3, 0.012, n_u12)
phi_c = np.ones((n_c,), dtype='float_') * 0.01
phi_23_range = damped_range(0.01, np.pi - 0.005, n_u23)
phi_range_u = np.hstack([phi_12_range, phi_c, phi_23_range])
u_range = np.cos(phi_range_u) - 1
conf1 = 230, 50, 52, 1.14
confc = 230, 50, 20, 1.14
conf2 = 100, 160, 0, 1.1
conf3 = 270, 179, 0., 0.5
azimuth_range = np.hstack([damped_range(conf1[0], confc[0], n_u12),
damped_range(confc[0], conf2[0], n_c),
damped_range(conf2[0], conf3[0], n_u23)])
elevation_range = np.hstack([damped_range(conf1[1], confc[1], n_u12),
damped_range(confc[1], conf2[1], n_c),
damped_range(conf2[1], conf3[1], n_u23)])
roll_range = np.hstack([damped_range(conf1[2], confc[2], n_u12),
damped_range(confc[2], conf2[2], n_c),
damped_range(conf2[2], conf3[2], n_u23)])
d_range = np.hstack([damped_range(conf1[3], confc[3], n_u12) * d,
damped_range(confc[3], conf2[3], n_c) * d,
damped_range(conf2[3], conf3[3], n_u23) * d])
time_range = slice(None)
#time_range = np.array([-1], dtype=int)
return u_range, elevation_range, azimuth_range, roll_range, time_range, d_range
def eft_unfolding(phi_range):
n_u1 = 30
n_u2 = 60
n_u3 = 60
phi_u1 = np.pi - 0.012
phi_u2 = np.pi * 0.86
phi_u3 = 0.012
phi_1 = damped_range(phi_u1, phi_u2, n_u1)
phi_2 = np.ones((n_u2,), dtype='float_') * phi_u2
phi_3 = damped_range(phi_u2, phi_u3, n_u3)
phi_range_u = np.hstack([phi_1, phi_2, phi_3])
u_range = np.cos(phi_range_u) - 1
conf1 = 270, 179, 0., 0.5
conf2 = 230, 140, 60., 0.6
conf3 = 150, 70, 70., 0.64
conf4 = 45, 1, 314., 1.14
azimuth_range = np.hstack([damped_range(conf1[0], conf2[0], n_u1),
damped_range(conf2[0], conf3[0], n_u2),
damped_range(conf3[0], conf4[0], n_u3)])
elevation_range = np.hstack([damped_range(conf1[1], conf2[1], n_u1),
damped_range(conf2[1], conf3[1], n_u2),
damped_range(conf3[1], conf4[1], n_u3)])
roll_range = np.hstack([damped_range(conf1[2], conf2[2], n_u1),
damped_range(conf2[2], conf3[2], n_u2),
damped_range(conf3[2], conf4[2], n_u3)])
d_range = np.hstack([damped_range(conf1[3], conf2[3], n_u1) * d,
damped_range(conf2[3], conf3[3], n_u2) * d,
damped_range(conf3[3], conf4[3], n_u3) * d,
])
time_range = slice(None)
#time_range = np.array([0], dtype=int)
return u_range, elevation_range, azimuth_range, roll_range, time_range, d_range
# u_range, elevation_range, azimuth_range, roll_range, time_range, d_range = eft_folding(
# phi_range)
u_range, elevation_range, azimuth_range, roll_range, time_range, d_range = eft_unfolding(
phi_range)
tdir = tempfile.mkdtemp()
fname_list = []
for i, (u, e, a, r, d) in enumerate(zip(u_range[time_range],
elevation_range[time_range],
azimuth_range[time_range],
roll_range[time_range],
d_range[time_range])):
gu_dof_constraints.dof_constraints[-1][-1] = u
sim_step._solve_nr()
f = sim_step.forming_task.formed_object.center
print('perspective - before', ftv.mlab.view())
print('roll', ftv.mlab.roll())
print('a,e,d,f', a, e, d, f)
ftv.mlab.view(a, e, d, f)
ftv.mlab.roll(r)
print('perspective', ftv.mlab.view())
print('roll', ftv.mlab.roll())
ftv.update(force=True)
fname = os.path.join(tdir, 'eftlogo%03d.jpg' % i)
fname_list.append(fname)
ftv.mlab.savefig(fname, magnification=2.2)
if animation:
animation_file = os.path.join(tdir, 'anim01.gif')
images = string.join(fname_list, ' ')
destination = animation_file
import platform
if platform.system() == 'Linux':
os.system(
'convert -delay 8 -loop 1 ' + images + ' ' + destination)
# os.system('png2swf -o%s ' % destination + images)
else:
raise NotImplementedError(
'film production available only on linux')
print('animation saved in', destination)
ftv.show()