def test_optimise(filename, benchmark):
# type: (str) -> None
"""Optimise the file at `filename`."""
print("Optimizing file '%s'..." % filename)
# Set up ToPy:
t = topy.Topology()
t.load_tpd_file(filename)
t.set_top_params()
benchmark.pedantic(topy.optimise, args=(t,), rounds=1, iterations=1)
def test_optimise(filename, benchmark):
# тип: (str) -> None
# Оптимизация файла по адресу "filename"
print("Optimizing file '%s'..." % filename)
# Настройка топологии:
t = topy.Topology()
t.load_tpd_file(filename)
t.set_top_params()
benchmark.pedantic(topy.optimise, args=(t,), rounds=1, iterations=1)
def solve_fea(self, config):
'''
Solves the ToPy FEA problem specified in TPD config
'''
t = topy.Topology()
t.load_config_dict(config)
t.set_top_params()
t.update_desvars_oc()
t.fea()
t.sens_analysis()
disp = t.d.reshape((self.domainSz[0]+1, self.domainSz[1]+1, 2, 1))
self.dispX = disp[:, :, 0, :] # TODO: change displacement size
self.dispY = disp[:, :, 1, :]
self.comp = t.qkq.T
self.comp = self.comp[:, :, np.newaxis]
def main(dir_to_save, num_samples):
if not os.path.exists(dir_to_save):
os.mkdir(dir_to_save)
print('Directory "{}" created'.format(dir_to_save))
print('Generating the dataset...')
samples_done = 0
while samples_done < num_samples:
try:
topology = topy.Topology(config=random_config())
topology.set_top_params()
sample = optimize(topology)
path = os.path.join(dir_to_save, str(samples_done))
np.savez_compressed(path, sample)
samples_done += 1
except BaseException: # For the incorrect constraints
pass
print('\rDone: {}/{}'.format(samples_done, num_samples), end='')
sys.stdout.flush()
def load_topy(self, filename):
t = topy.Topology()
t.load_tpd_file(filename)
return t
def run_through_optimisations(tpd_file, print_all_images=False):
# Set up ToPy:
t = topy.Topology()
t.load_tpd_file(tpd_file)
t.set_top_params()
# Optimising function:
def optimise(print_all_images=False):
t.fea()
t.sens_analysis()
t.filter_sens_sigmund()
t.update_desvars_oc()
# Added if to only print the end result
if print_all_images == True or t.itercount == t.numiter:
# Below this line we print and create images:
if t.nelz:
topy.create_3d_geom(t.desvars, prefix=t.probname, \
iternum=t.itercount, time='none')
else:
topy.create_2d_imag(t.desvars, prefix=t.probname, \
iternum=t.itercount, time='none')
print '%4i | %3.6e | %3.3f | %3.4e | %3.3f | %3.3f | %1.3f | %3.3f '\
% (t.itercount, t.objfval, t.desvars.sum()/(t.nelx * t.nely * nelz), \
t.change, t.p, t.q, t.eta.mean(), t.svtfrac)
# Build a list of average etas:
etas_avg.append(t.eta.mean())
# Create empty list for later use:
etas_avg = []
# Create (plot) initial design domain:
if t.nelz:
#create_3d_geom(t.desvars, prefix=t.probname, iternum=0, time='none')
nelz = t.nelz
else:
#create_2d_imag(t.desvars, prefix=t.probname, iternum=0, time='none')
nelz = 1 # else we divide by zero
# Start optimisation runs, create rest of design domains:
print '%5s | %11s | %5s | %10s | %5s | %5s | %7s | %5s ' % ('Iter', \
'Obj. func. ', 'Vol. ', 'Change ', 'P_FAC', 'Q_FAC', 'Ave ETA', 'S-V frac.')
print ' ----:|-------------:|------:|-----------:|------:|------:|--------:|---------:'
ti = time()
# Try CHG_STOP criteria, if not defined (error), use NUM_ITER for iterations:
try:
while t.change > t.chgstop:
optimise(print_all_images)
except AttributeError:
for i in range(t.numiter):
optimise(print_all_images)
te = time()
# Print solid-void ratio info:
print '\n- Solid plus void to total elements fraction = `%3.5f`' % (t.svtfrac)
# Print iteration info:
print '\n- `', t.itercount,'` iterations took `%3.3f minutes` (%3.3f min/iter or %3.3f sec/iter)'\
%((te - ti) / 60, (te - ti) / 60 / t.itercount, (te - ti) / t.itercount)
print '\n- Average of all ETA\'s = `%3.3f` (average of all a\'s = %3.3f)' \
% (array(etas_avg).mean(), 1/array(etas_avg).mean() - 1)
return t
# voxel center global coordinates
x = x_v + x_c[i, j]
y = y_v + y_c[i, j]
# cell center coordinates
xx = x_c[i, j] + cellSz[0] / 2
yy = y_c[i, j] + cellSz[1] / 2
holes[hh] = [xx, yy]
hh += 1
# calculate distance field
cell = np.sqrt((x - xx)**2 + (y - yy)**2) - p
field[i * numVoxels[0]:(i + 1) * numVoxels[0],
j * numVoxels[1]:(j + 1) * numVoxels[1]] += cell
config = tpd.minimal_tpd_dict()
config['PROB_NAME'] = 'sph_lat_test'
config['NUM_ELEM_X'] = domainSz[0]
config['NUM_ELEM_Y'] = domainSz[1]
config['VOL_FRAC'] = np.count_nonzero(field >= 0) * 1. / field.size
config['NUM_ITER'] = 2
config['PASV_ELEM'] = tpd.passive_elems(field >= 0)
config['FXTR_NODE_Y'] = '1|81'
config['FXTR_NODE_X'] = '1|81'
config['LOAD_NODE_Y'] = '12961'
config['LOAD_VALU_Y'] = '-1'
t = topy.Topology()
t.load_config_dict(config)
t.set_top_params()
topy.optimise(t)
def optimise(fname):
# Set up ToPy:
t = topy.Topology()
t.load_tpd_file(fname)
t.set_top_params()
topy.optimise(t)