def evaluate_fields(self, cid, xi, fields):
num_fields = len(fields)
X = numpy.zeros((xi.shape[0], num_fields))
for i, field in enumerate(fields):
Phi = interpolator.weights(self.EFn[cid], xi, deriv=field[1:])
X[:, i] = numpy.dot(Phi, self.P[self.EMap[cid][field[0]]])
return X
def evaluate_fields(self, cid, xi, fields):
num_fields = len(fields)
X = numpy.zeros((xi.shape[0], num_fields))
for i, field in enumerate(fields):
Phi = interpolator.weights(self.EFn[cid], xi, deriv=field[1:])
X[:, i] = numpy.dot(Phi, self.P[self.EMap[cid][field[0]]])
return X
def evaluate(self, cid, xi, deriv=None):
num_fields = len(self.EMap[cid])
X = numpy.zeros((xi.shape[0], num_fields))
Phi = interpolator.weights(self.EFn[cid], xi, deriv=deriv)
for i in range(num_fields):
X[:, i] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
return X
def evaluates(self, cids, xi, deriv=None, X=None):
num_fields = len(self.EMap[cids[0]])
if X==None:
X = numpy.zeros((len(cids) * xi.shape[0], num_fields))
Phi = interpolator.weights(self.EFn[cids[0]], xi, deriv=deriv)
Nxi = xi.shape[0]
ind = 0
for cid in cids:
for i in range(num_fields):
X[ind:ind+Nxi, i] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
ind += Nxi
return X
def evaluates(self, cids, xi, deriv=None, X=None):
num_fields = len(self.EMap[cids[0]])
if X==None:
X = numpy.zeros((len(cids) * xi.shape[0], num_fields))
Phi = interpolator.weights(self.EFn[cids[0]], xi, deriv=deriv)
Nxi = xi.shape[0]
ind = 0
for cid in cids:
for i in range(num_fields):
X[ind:ind+Nxi, i] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
ind += Nxi
return X
def update_dependent_nodes(self):
# update dependent nodes
for dn in self.DNMap:
cid = dn[0]
xi_cids = dn[1]
dn_cids = dn[2]
shape = dn[3]
scale = dn[4]
num_fields = len(self.EMap[cid])
if num_fields == 1:
xi = numpy.array([self.P[xi_cids]]).T
else:
xi = numpy.array([self.P[xi_cids]])
if len(shape) == 1:
Phi = interpolator.weights(self.EFn[cid], xi)
for i in range(num_fields):
self.P[dn_cids[i]] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
elif len(shape) == 2:
components = shape[1]
Phi = [interpolator.weights(self.EFn[cid], xi)]
if components == 2:
Phi.append(interpolator.weights(self.EFn[cid], xi, deriv=[1]))
elif components == 4:
Phi.append(interpolator.weights(self.EFn[cid], xi, deriv=[1, 0]))
Phi.append(interpolator.weights(self.EFn[cid], xi, deriv=[0, 1]))
Phi.append(interpolator.weights(self.EFn[cid], xi, deriv=[1, 1]))
comp_idx = 0
if scale is None:
scale = numpy.ones((shape[1]))
for i in range(num_fields):
for j, phi in enumerate(Phi):
self.P[dn_cids[comp_idx]] = scale[j] * numpy.dot(phi, self.P[self.EMap[cid][i]])
comp_idx += 1
def update_dependent_nodes(self):
# update dependent nodes
for dn in self.DNMap:
cid = dn[0]
xi_cids = dn[1]
dn_cids = dn[2]
num_fields = len(self.EMap[cid])
if num_fields == 1:
xi = numpy.array([self.P[xi_cids]]).T
else:
xi = numpy.array([self.P[xi_cids]])
Phi = interpolator.weights(self.EFn[cid], xi)
for i in range(num_fields):
self.P[dn_cids[i]] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
def update_dependent_nodes(self):
# update dependent nodes
for dn in self.DNMap:
cid = dn[0]
xi_cids = dn[1]
dn_cids = dn[2]
num_fields = len(self.EMap[cid])
if num_fields == 1:
xi = numpy.array([self.P[xi_cids]]).T
else:
xi = numpy.array([self.P[xi_cids]])
Phi = interpolator.weights(self.EFn[cid], xi)
for i in range(num_fields):
self.P[dn_cids[i]] = numpy.dot(Phi, self.P[self.EMap[cid][i]])
def weights(self, cid, xi, deriv=None):
return interpolator.weights(self.EFn[cid], xi, deriv=deriv)
def weights(self, cid, xi, deriv=None):
return interpolator.weights(self.EFn[cid], xi, deriv=deriv)
