def mesh_from_brep(brep_file, mesh_file, project_conf, inv_par):
if project_conf.method == GenieMethod.ERT:
data = pg.DataContainerERT("input_snapped.dat", removeInvalid=False)
else:
data = pg.DataContainer("input_snapped.dat",
sensorTokens='s g',
removeInvalid=False)
el_pos = []
for i in range(len(data.sensorPositions())):
pos = data.sensorPosition(i)
pos = np.array([pos[0], pos[1], pos[2]])
el_pos.append(pos)
model = gmsh.GeometryOCC("model_name")
compound = model.import_shapes(brep_file, highestDimOnly=False)
points = [model.point(pos).tags[0] for pos in el_pos]
dist = field.distance_nodes(points)
f_distance = field.threshold(dist,
lower_bound=(inv_par.elementSize_d,
inv_par.elementSize_d),
upper_bound=(inv_par.elementSize_D,
inv_par.elementSize_H))
model.set_mesh_step_field(f_distance)
model.mesh_options.CharacteristicLengthMin = 0.1
model.mesh_options.CharacteristicLengthMax = 100
model.make_mesh([compound])
model.write_mesh(mesh_file, gmsh.MeshFormat.msh2)
def test_minmax_nodes_2d():
f_distance = field.min(10, field.max(5, field.distance_nodes(
[1, 3]))) # points: (-50, -50), (50, 50)
def ref_size(x):
dist = min(np.linalg.norm(np.array(x) - np.array([-50, -50, 0])),
np.linalg.norm(np.array(x) - np.array([50, 50, 0])))
return min(10, max(5, dist))
apply_field(f_distance, ref_size, dim=2, tolerance=0.4, max_mismatch=10)
def test_distance_nodes_2d():
"""
For some reason the mesh size is actually close to half of the distance
and naturally the error is quite high, up to 70%
"""
f_distance = field.distance_nodes([1, 3]) # points: (-50, -50), (50, 50)
def ref_size(x):
dist = min(np.linalg.norm(np.array(x) - np.array([-50, -50, 0])),
np.linalg.norm(np.array(x) - np.array([50, 50, 0])))
return max(0.5 * dist, 0.5)
apply_field(f_distance,
ref_size,
dim=2,
tolerance=0.7,
max_mismatch=10,
mesh_name="dist_2d")
def test_threshold_sigmoid_2d():
f_distance = field.threshold(field.distance_nodes([1, 3]),
lower_bound=(10, 5),
upper_bound=(30, 10),
sigmoid=True)
def thold(x, minx, miny, maxx, maxy):
scaled_X = (x - minx) / (maxx - minx)
y = 1 / (1 + np.exp(-scaled_X))
return y * (maxy - miny) + miny
def ref_size(x):
dist = min(np.linalg.norm(np.array(x) - np.array([-50, -50, 0])),
np.linalg.norm(np.array(x) - np.array([50, 50, 0])))
return thold(dist, 10, 5, 30, 10)
apply_field(f_distance,
ref_size,
dim=2,
tolerance=0.4,
max_mismatch=8,
mesh_name="thds")
def test_threshold_2d():
f_distance = field.threshold(field.distance_nodes([1, 3]),
lower_bound=(10, 5),
upper_bound=(30, 10))
def thold(x, minx, miny, maxx, maxy):
if x < minx:
return miny
elif x > maxx:
return maxy
else:
return (x - minx) / (maxx - minx) * (maxy - miny) + miny
def ref_size(x):
dist = min(np.linalg.norm(np.array(x) - np.array([-50, -50, 0])),
np.linalg.norm(np.array(x) - np.array([50, 50, 0])))
return thold(dist, 10, 5, 30, 10)
apply_field(f_distance,
ref_size,
dim=2,
tolerance=0.3,
max_mismatch=3,
mesh_name="thd")