network = network_from_bmesh(draw_plane(Lx=1, Ly=1, dx=ds, dy=ds))
vertices = list(network.vertices())
edges = list(network.edges())
n = len(vertices)
m = len(edges)
Xt = zeros((n, 3))
network.set_vertices_attributes(vertices, {'B': [0, 0, 1], 'P': [0, 0, 1 / n]})
for c, key in enumerate(vertices):
x, y, z = network.vertex_coordinates(key)
if (x == 1) and (y == 1):
network.set_vertex_attribute(key, 'B', [0, 0, 0])
zt = 1 - 0.5 * (x**2 + y**2)
Xt[c, :] = [x, y, zt]
network.set_edges_attributes(edges, {'E': 1, 'A': 1, 'ct': 't'})
clear_layers([0])
# Run optimisation
tol = 0.001 / n
bounds = [[0.7 * ds, 1.3 * ds]] * m
fopt, uopt = de_solver(fn,
bounds,
population=20,
iterations=10**4,
args=(tol, Xt, edges),
limit=0.002)
# Update Network and plot
for c, uv in enumerate(edges):
bpy.context.collection.objects.link(object)
if layer:
set_objects_layer(objects=[object], layer=layer)
return object
# ==============================================================================
# Main
# ==============================================================================
if __name__ == '__main__':
from compas_blender.utilities import clear_layers
from compas_blender.utilities import set_objects_show_names
clear_layers(layers=['Collection 1', 'Collection 2'])
n = 10
points = [{
'pos': [0, 0, i],
'radius': 0.2,
'name': 'pt{0}'.format(i)
} for i in range(n)]
lines = [{
'start': [1, 1, i],
'end': [1, 0, i],
'radius': 0.1,
'color': [1, 0, 1]
} for i in range(n)]
cyls = [{
from math import pi
__author__ = ['Andrew Liew <*****@*****.**>']
__copyright__ = 'Copyright 2018, BLOCK Research Group - ETH Zurich'
__license__ = 'MIT License'
__email__ = '*****@*****.**'
# Structure
mdl = Structure(name='beam_simple', path='/home/al/temp/')
# Clear
clear_layers(layers=[0, 1, 2, 3])
# Lines
L = 1.0
m = 100
x = [i * L / m for i in range(m + 1)]
vertices = [[xi, 0, 0] for xi in x]
edges = [[i, i + 1] for i in range(m)]
bmesh = xdraw_mesh(name='beam', vertices=vertices, edges=edges, layer=0)
# Points
n = 5
xdraw_spheres([{'pos': [0, 0, 0], 'layer': 1, 'radius': 0.01},
{'pos': [L, 0, 0], 'layer': 2, 'radius': 0.01}])
ind = closest_points_points(X, Xt, distances=False)
points = vstack([X, Xt[ind, :]])
n = X.shape[0]
lines = [[i, i + n] for i in range(n)]
bmesh = draw_bmesh('norms', vertices=points, edges=lines, layer=19)
return X
def fn(dofs, *args):
network, Xt, div, factor, tol, steps, ds = args
X = update(dofs, network, Xt, div, factor, tol, steps, ds, refresh=0, bmesh=0, plot=0)
ind = closest_points_points(X, Xt, distances=False)
return 1000 * mean(normrow(X - Xt[ind, :]))
clear_layers([19])
# Geometry input
L = 0.88
m = 20
n = m + 1
E = 5 * 10**9
I = 2 * 10**(-11)
A = 0.005**2
# Solver input
div = 10
factor = 1.0
tol = 0.01
bpy.context.collection.objects.link(object)
if layer:
set_objects_layer(objects=[object], layer=layer)
return object
# ==============================================================================
# Main
# ==============================================================================
if __name__ == '__main__':
from compas_blender.utilities import clear_layers
from compas_blender.utilities import set_objects_show_names
clear_layers(layers=['Collection'])
n = 10
points = [{
'pos': [0, 0, i],
'radius': 0.2,
'name': 'pt{0}'.format(i)
} for i in range(n)]
lines = [{
'start': [1, 1, i],
'end': [1, 0, i],
'radius': 0.1,
'color': [1, 0, 1]
} for i in range(n)]
cyls = [{