def LoadFromFile(self, filename):
""" Loads an old run from a file
Parameters
----------
filename : string
Name of the file that everything will be saved to
Returns
-------
None
"""
data = np.load(filename, allow_pickle=True).item()
from FEM import FEM
self.fem = FEM()
self.fem.Load(data['fem'])
if data['update']['type'] == 'OC':
self.update = Update.OCUpdateScheme(0.2, 0.5, 0.5 * np.ones(self.fem.nElem),
np.zeros(self.fem.nElem), np.ones(self.fem.nElem))
elif data['update']['type'] == 'MMA':
self.update = Update.MMA(0.5 * np.ones(self.fem.nElem), 1,
np.zeros(self.fem.nElem), np.ones(self.fem.nElem))
self.update.Load(data['update'])
self.Filter = data['opt']['Filter']
try:
self.R = data['opt']['R']
except:
pass
import Functions as Funcs
for objective in data['opt']['objectives']:
self.AddFunction(getattr(Funcs, objective['function']),
objective['weight'], objective['min'],
objective['max'], 'objective')
for constraint in data['opt']['constraints']:
self.AddFunction(getattr(Funcs, constraint['function']),
constraint['constraint'], constraint['min'],
constraint['max'], 'constraint')
def LoadPetsc(self, folder, appendix=None, Endian='=', update='MMA'):
""" Create PyOpt structure from PETSc code results
Parameters
----------
folder : str
folder containing all of the Petsc results
appendix : str
Appendix for result values to restart from, if none picks highest penalty
Endian : char
Indicates byte ordering ('=':default, '<':little Endian, '>':big Endian)
update : str
Which updte scheme to use (MMA or OC)
Returns
-------
None
"""
from os import listdir
from os.path import sep
from PetscBinaryIO import PetscBinaryRead
import Functions_Timing as Funcs
# Load FEM data
from FEM import FEM
self.fem = FEM()
self.fem.LoadPetsc(folder, Endian=Endian)
# Load update data
if update == 'OC':
self.update = Update.OCUpdateScheme(0.2, 0.5, 0.5 * np.ones(self.fem.nElem),
np.zeros(self.fem.nElem), np.ones(self.fem.nElem))
elif update == 'MMA':
self.update = Update.MMA(0.5 * np.ones(self.fem.nElem), 1,
np.zeros(self.fem.nElem), np.ones(self.fem.nElem))
self.update.LoadPetsc(folder, appendix=appendix, Endian=Endian)
# Load filter matrics
self.Filter = PetscBinaryRead(folder + sep + "Filter.bin")
try:
self.R = PetscBinaryRead(folder + sep + "Max_Filter.bin")
edge = PetscBinaryRead(folder + sep + "Void_Edge_Volume.bin")
self.R = self.R.tocoo()
self.R = sparse.csr_matrix((np.concatenate([self.R.data, edge]),
(np.concatenate([self.R.row, np.arange(self.R.shape[0])]),
np.concatenate([self.R.col, self.R.shape[0]*np.ones(self.R.shape[0], dtype=int)]))))
except:
self.R = sparse.dia_matrix((np.ones(self.fem.nElem), np.zeros(self.fem.nElem)))
# Set up functions and material properties
inputFile = [file for file in listdir(folder) if '_Input' in file][0]
active = False
name = None
fType = None
value = None
minimum = None
maximum = None
E0, Nu0, Density = None, None, None
with open(folder + sep + inputFile, 'r') as fh:
for line in fh:
line = line.strip()
if line[:3] == 'E0:':
E0 = float(line.split(':')[-1])
elif line[:4] == 'Nu0:':
Nu0 = float(line.split(':')[-1])
elif line[:8] == 'Density:':
Density = float(line.split(':')[-1])
elif '[Functions]' in line:
active = True
elif '[/Functions]' in line:
active = False
elif active:
if line in ['Compliance', 'Stability', 'Frequencey', 'Volume']:
name = line
elif line in ['Objective', 'Constraint']:
fType = line
elif 'Values:' in line:
value = [float(val) for val in line.split(':')[-1].split(',')][0]
elif 'Range:' in line:
minimum, maximum = [float(val) for val in line.split(':')[-1].split(',')]
if name is not None and fType is not None and value is not None and minimum is not None:
self.AddFunction(getattr(Funcs, name), value, minimum, maximum, fType)
name = None
fType = None
value = None
minimum = None
maximum = None
if self.fem.nDof == 2:
self.fem.SetMaterial(Material.PlaneStressElastic(E0, Nu0))
else:
self.fem.SetMaterial(Material.Elastic3D(E0, Nu0))