Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Polar components on tensors #295

Merged
merged 1 commit into from
Jul 18, 2024
Merged

Polar components on tensors #295

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
68 changes: 30 additions & 38 deletions dedalus/core/basis.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5974,28 +5974,24 @@ class PolarAzimuthalComponent(operators.AzimuthalComponent):
basis_type = IntervalBasis

def subproblem_matrix(self, subproblem):
# I'm not sure how to generalize this to higher order tensors, since we do
# not have spin_weights for the S1 basis.
matrix = np.array([[1,0]])
operand = self.args[0]
input_dim = len(operand.tensorsig)
output_dim = len(self.tensorsig)
matrix = []
for output in range(2**output_dim):
Copy link

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

you can vectorise this part with numpy instead of using two for loops

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

How would you suggest that we vectorize this?

index_out = np.unravel_index(output, [2]*output_dim)
matrix_row = []
for input in range(2**input_dim):
index_in = np.unravel_index(input, [2]*input_dim)
if tuple(index_in[:self.index] + index_in[self.index+1:]) == index_out and index_in[self.index] == 0:
matrix_row.append(1)
else:
matrix_row.append(0)
matrix.append(matrix_row)
matrix = np.array(matrix)
if self.dtype == np.float64:
# Block-diag for sin/cos parts for real dtype
matrix = sparse.kron(matrix, sparse.eye(2))

# operand = self.args[0]
# basis = self.domain.get_basis(self.coordsys)
# S_in = basis.spin_weights(operand.tensorsig)
# S_out = basis.spin_weights(self.tensorsig)
#
# matrix = []
# for spinindex_out, spintotal_out in np.ndenumerate(S_out):
# matrix_row = []
# for spinindex_in, spintotal_in in np.ndenumerate(S_in):
# if tuple(spinindex_in[:self.index] + spinindex_in[self.index+1:]) == spinindex_out and spinindex_in[self.index] == 2:
# matrix_row.append( 1 )
# else:
# matrix_row.append( 0 )
# matrix.append(matrix_row)
# matrix = np.array(matrix)
return matrix

def operate(self, out):
Expand All @@ -6012,28 +6008,24 @@ class PolarRadialComponent(operators.RadialComponent):
basis_type = IntervalBasis

def subproblem_matrix(self, subproblem):
# I'm not sure how to generalize this to higher order tensors, since we do
# not have spin_weights for the S1 basis.
matrix = np.array([[0,1]])
operand = self.args[0]
input_dim = len(operand.tensorsig)
output_dim = len(self.tensorsig)
matrix = []
for output in range(2**output_dim):
index_out = np.unravel_index(output, [2]*output_dim)
matrix_row = []
for input in range(2**input_dim):
index_in = np.unravel_index(input, [2]*input_dim)
if tuple(index_in[:self.index] + index_in[self.index+1:]) == index_out and index_in[self.index] == 1:
matrix_row.append(1)
else:
matrix_row.append(0)
matrix.append(matrix_row)
matrix = np.array(matrix)
if self.dtype == np.float64:
# Block-diag for sin/cos parts for real dtype
matrix = sparse.kron(matrix, sparse.eye(2))

# operand = self.args[0]
# basis = self.domain.get_basis(self.coordsys)
# S_in = basis.spin_weights(operand.tensorsig)
# S_out = basis.spin_weights(self.tensorsig)
#
# matrix = []
# for spinindex_out, spintotal_out in np.ndenumerate(S_out):
# matrix_row = []
# for spinindex_in, spintotal_in in np.ndenumerate(S_in):
# if tuple(spinindex_in[:self.index] + spinindex_in[self.index+1:]) == spinindex_out and spinindex_in[self.index] == 2:
# matrix_row.append( 1 )
# else:
# matrix_row.append( 0 )
# matrix.append(matrix_row)
# matrix = np.array(matrix)
return matrix

def operate(self, out):
Expand Down