def center_disp(args):
inp_file, tumor_eleset, odb_file, breast_part_start, step, breast_part = args
abaqus = manipulate_abaqus_file.ReadAbaqusInput(inp_file)
node_indices = abaqus.get_node_from_elemset(tumor_eleset, breast_part_start)
disp = read_lastframe(odb_file, step, breast_part)
disp_eleset = []
for index in node_indices:
disp_eleset.append(disp[index - 1])
return np.mean(np.array(disp_eleset), axis=0)
def find_centers(inp_file, eleset, odb_reader, step_name, part, part_start):
abaqus = manipulate_abaqus_file.ReadAbaqusInput(inp_file)
elements = abaqus.read_elset_or_nset(eleset)
element_to_node = abaqus.read_element(part_start)
node_indices = []
for elem in elements:
node_indices.append(element_to_node[elem - 1])
nodes_unique = np.unique(np.array(node_indices))
positions_across_frame = []
for label in nodes_unique:
start, end = odb_reader.read_start_end_pos(step_name, label, part)
positions_across_frame.append(np.array(start) - np.array(end))
pdb.set_trace()
def main(args):
mode = args.mode
if args.type == 'breast':
disp_folders = glob.glob(
'F:/Research/FEA simulation for NN/train_patient_specific/disps/*')
elif args.type == 'tumor':
disp_folders = glob.glob(
'F:/Research/FEA simulation for NN/train_patient_specific/tumor/*')
elif mode == 'connet_to_unity_weight':
triangles = read_surface()
breast_point_num = triangles.shape[0]
tumor_point_num = 1
else:
return
if args.type == 'breast' or args.type == 'tumor':
ratios = []
disp_filenames = []
for disp_folder in disp_folders:
ratio = float(os.path.basename(disp_folder).split('_')[2])
temp_file = glob.glob(disp_folder + '/*')
disp_filenames += temp_file
ratios += ([ratio / 100] * len(temp_file))
col_num = 3
directory = 'F:/Research/FEA simulation for NN/stl/Abaqus_outputs/weight/'
inp_files_dirs = [
name for name in os.listdir(directory)
if os.path.isdir(os.path.join(directory, name))
]
inp_files = []
for direc in inp_files_dirs:
inp_files += glob.glob(os.path.join(directory, direc) + '/*inp')
#inp_files = glob.glob('F:/Research/FEA simulation for NN/stl/Abaqus_outputs/weight/glandular_fat_10_90/*inp')
if mode == 'train':
disp_filenames_and_ratios = list(zip(disp_filenames, ratios))
random.shuffle(disp_filenames_and_ratios)
disp_filenames, ratios = zip(*disp_filenames_and_ratios)
num_files = len(disp_filenames)
if num_files == 0:
return
if args.type == 'breast':
triangles = read_surface()
position = read(disp_filenames[0])
point_num = triangles.shape[0]
if args.type == 'tumor':
point_num = 1
nn_input = np.zeros([len(disp_filenames), 1, col_num + 1])
nn_output = np.zeros([len(disp_filenames), point_num * col_num])
if args.type == 'breast':
model = breast_model(point_num, col_num)
if args.type == 'tumor':
model = tumor_model(point_num, col_num)
gravity = '** Name: GRAVITY-1 Type: Gravity\n'
for idx, file in enumerate(disp_filenames):
name = os.path.basename(file)
file_ratio = os.path.basename(
os.path.normpath(os.path.dirname(file)))
temp_file = os.path.join(directory, file_ratio)
temp_file = os.path.join(temp_file, name.split('.')[0] + '.inp')
if temp_file not in inp_files:
print(temp_file)
if temp_file in inp_files:
abaqus = manipulate_abaqus_file.ReadAbaqusInput(temp_file)
_, direction = abaqus.read_gravity(gravity)
# print(idx)
nn_input[idx, 0:1, :] = direction + [ratios[idx]]
temp_pos = read(file)
if args.type == 'breast':
disp = np.array(get_surface_disp(triangles, temp_pos))
if args.type == 'tumor':
disp = temp_pos
nn_output[idx, :] = np.reshape(disp, point_num * col_num)
pdb.set_trace()
if args.pretrained_file != 'None':
model.load_weights(args.pretrained_file)
checkpoint = args.pretrained_file
else:
checkpoint = args.checkpoint
checkpoint = ModelCheckpoint(checkpoint,
monitor='loss',
verbose=1,
save_best_only=True)
callbacks_list = [checkpoint]
model.fit(nn_input,
nn_output,
epochs=10000,
shuffle=True,
validation_split=0.2,
callbacks=callbacks_list,
verbose=1)
elif mode == 'connet_to_unity_weight':
all_checkpoint = args.pretrained_file
breast_ck = all_checkpoint.split(',')[0]
tumor_ck = all_checkpoint.split(',')[1]
breastmodel = breast_model(breast_point_num, col_num)
tumormodel = tumor_model(tumor_point_num, col_num)
breastmodel.load_weights(breast_ck)
tumormodel.load_weights(tumor_ck)
flag = 'weight'
abaqus = manipulate_abaqus_file.ReadAbaqusInput(inp_files[0])
tumor_center = abaqus.elem_set_center('*Elset, elset=TUMOR_FROMSKIN\n',
'*Part, name=PART-1\n')
connect_to_unity(tumormodel, breastmodel, tumor_center, flag)
def __init__(self, file_name, part):
self.hand_part = part
self.abaqus = manipulate_abaqus_file.ReadAbaqusInput(file_name)
def set_assembly(self, file_name, vec, start_line):
abaqus = manipulate_abaqus_file.ReadAbaqusInput(file_name)
abaqus.set_assembly(start_line, vec)
boundary_arg = [
boundary, time_period, direction, surface_point, base, distance
]
hand.set_boundary(boundary_arg)
contact_ps = tetrahedral_mesh.contact_points(closest_point_index)
r_input.delete_elset_or_nset(nset)
r_input.add_to_elset_or_nset(nset, contact_ps)
hand.write_output('Jobs/' + str(index) + '.inp')
index += 1
if scenario == 'gravity':
breast_info = GetInfoFromBreast(element, node)
inp_file = 'Weight Jobs/reference_pos.inp'
gravity = '** Name: GRAVITY-1 Type: Gravity\n'
output_folder = 'F:/Research/FEA simulation for NN/stl/Abaqus_outputs/weight'
abaqus = manipulate_abaqus_file.ReadAbaqusInput(inp_file)
angle_interval = 5 # 5 degree
circle_angle = 360
angles = np.linspace(start=0,
stop=circle_angle,
num=circle_angle / angle_interval + 1)
gravity_magnitude = 9810
glandular_fat_ratio = 0.33
glandular_density = 1020e-12
fat_density = 910e-12
glandular_densityline = '*Material, name=GLANDULAR\n'
fat_densityline = '*Material, name=FAT\n'
glandular_elasticity = 120e-6 # c1 value in the paper
fat_elasticity = 80e-6
for angle in list(angles):
x = math.cos(math.radians(angle))