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test_trial_space.py
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import copy
import pytest
import numpy as np
import romtools as rt
import romtools.trial_space.utils as utils
#@pytest.mark.mpi_skip
#def test_list_snapshots_to_array():
# snapshots = np.random.normal(size=(15,7))
# snapshot_data = PythonSnapshotData(snapshots)
# matrix = snapshot_data.getSnapshotsAsArray()
# assert matrix.shape[0] == 15
# assert matrix.shape[1] == 7
@pytest.mark.mpi_skip
def test_dictionary_trial_space():
snapshots = np.random.normal(size=(3, 8, 6))
# default test
my_shifter = utils.NoOpShifter()
my_splitter = utils.NoOpSplitter()
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.DictionaryTrialSpace(snapshots,
my_shifter,
my_splitter,
my_orthogonalizer)
assert np.allclose(my_trial_space.get_basis().flatten(),
snapshots.flatten())
assert np.allclose(my_trial_space.get_shift_vector(), 0)
assert np.allclose(my_trial_space.get_dimension(), 6)
# test with a shift
my_shifter = utils.AverageShifter()
my_splitter = utils.NoOpSplitter()
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.DictionaryTrialSpace(snapshots,
my_shifter,
my_splitter,
my_orthogonalizer)
assert np.allclose(my_trial_space.get_basis().flatten(),
(snapshots - np.mean(snapshots, axis=2)[:, :, None]).flatten())
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 6)
# test with a shift and splitting
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.DictionaryTrialSpace(snapshots,
my_shifter,
my_splitter,
my_orthogonalizer)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
# test with a shift, splitting, and orthogonalization
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
my_orthogonalizer = utils.EuclideanL2Orthogonalizer()
my_trial_space = rt.DictionaryTrialSpace(snapshots,
my_shifter,
my_splitter,
my_orthogonalizer)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
basis = my_trial_space.get_basis()
basis = utils.tensor_to_matrix(basis)
assert np.allclose(basis.transpose() @ basis, np.eye(12))
@pytest.mark.mpi_skip
def test_trial_space_from_pod():
snapshots = np.random.normal(size=(3, 8, 6))
my_truncater = utils.NoOpTruncater()
my_shifter = utils.NoOpShifter()
my_splitter = utils.NoOpSplitter()
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromPOD(snapshots,
my_truncater,
my_shifter,
my_splitter,
my_orthogonalizer)
# truth trial space
snapshotMatrix = utils.tensor_to_matrix(snapshots)
u, s, v = np.linalg.svd(snapshotMatrix, full_matrices=False)
basis_tensor = my_trial_space.get_basis()
assert np.allclose(u.reshape(basis_tensor.shape), basis_tensor)
assert np.allclose(6, my_trial_space.get_dimension())
assert np.allclose(0, my_trial_space.get_shift_vector())
# test with a shift
my_shifter = utils.AverageShifter()
my_splitter = utils.NoOpSplitter()
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromPOD(snapshots,
my_truncater,
my_shifter,
my_splitter,
my_orthogonalizer)
u, s, v = np.linalg.svd(snapshotMatrix - np.mean(snapshotMatrix, axis=1)[:, None], full_matrices=False)
basis_tensor = my_trial_space.get_basis()
assert np.allclose(u.reshape(basis_tensor.shape), basis_tensor)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 6)
# test with a shift and splitting
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromPOD(snapshots,
my_truncater,
my_shifter,
my_splitter,
my_orthogonalizer)
u, s, v = np.linalg.svd(my_splitter(snapshotMatrix - np.mean(snapshotMatrix, axis=1)[:, None]), full_matrices=False)
basis_tensor = my_trial_space.get_basis()
assert np.allclose(u.reshape(basis_tensor.shape), basis_tensor)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
# test with a shift, splitting, and orthogonalization
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
weighting = np.abs(np.random.normal(size=24))
my_orthogonalizer = utils.EuclideanVectorWeightedL2Orthogonalizer(weighting)
my_trial_space = rt.TrialSpaceFromPOD(snapshots,
my_truncater,
my_shifter,
my_splitter,
my_orthogonalizer)
u, s, v = np.linalg.svd(my_splitter(snapshotMatrix - np.mean(snapshotMatrix, axis=1)[:, None]), full_matrices=False)
u = my_orthogonalizer(u)
basis_tensor = my_trial_space.get_basis()
assert np.allclose(u.reshape(basis_tensor.shape), basis_tensor)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
@pytest.mark.mpi_skip
def test_trial_space_from_scaled_pod():
snapshots = np.random.normal(size=(3, 8, 6))
my_truncater = utils.NoOpTruncater()
my_shifter = utils.NoOpShifter()
my_scaler = utils.VariableScaler('max_abs')
my_splitter = utils.NoOpSplitter()
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromScaledPOD(copy.deepcopy(snapshots),
my_truncater,
my_shifter,
my_scaler,
my_splitter,
my_orthogonalizer)
scaled_snapshots = my_scaler.pre_scaling(snapshots)
snapshotMatrix = utils.tensor_to_matrix(scaled_snapshots)
u, s, v = np.linalg.svd(snapshotMatrix, full_matrices=False)
basis_tensor = my_trial_space.get_basis()
u = u.reshape(basis_tensor.shape)
u = my_scaler.post_scaling(u)
assert np.allclose(u, basis_tensor), print(u, my_trial_space.get_basis())
assert np.allclose(6, my_trial_space.get_dimension())
assert np.allclose(0, my_trial_space.get_shift_vector())
# test with a shift
snapshots = np.random.normal(size=(3, 8, 6))
my_shifter = utils.AverageShifter()
my_splitter = utils.NoOpSplitter()
my_scaler = utils.VariableScaler('max_abs')
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromScaledPOD(snapshots,
my_truncater,
my_shifter,
my_scaler,
my_splitter,
my_orthogonalizer)
shifted_snapshots, shift_vector = my_shifter(snapshots)
my_scaler = utils.VariableScaler('max_abs')
scaled_shifted_snapshots = my_scaler.pre_scaling(shifted_snapshots)
snapshot_matrix = utils.tensor_to_matrix(scaled_shifted_snapshots)
u, s, v = np.linalg.svd(snapshot_matrix, full_matrices=False)
basis_tensor = my_trial_space.get_basis()
u = u.reshape(basis_tensor.shape)
u = my_scaler.post_scaling(u)
assert np.allclose(basis_tensor, u)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 6)
# test with a shift and splitting
snapshots = np.random.normal(size=(3, 8, 6))
my_scaler = utils.VariableScaler('max_abs')
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
my_orthogonalizer = utils.NoOpOrthogonalizer()
my_trial_space = rt.TrialSpaceFromScaledPOD(snapshots,
my_truncater,
my_shifter,
my_scaler,
my_splitter,
my_orthogonalizer)
shifted_snapshots, _ = my_shifter(snapshots)
my_scaler = utils.VariableScaler('max_abs')
scaled_shifted_snapshots = my_scaler.pre_scaling(shifted_snapshots)
snapshot_matrix = utils.tensor_to_matrix(scaled_shifted_snapshots)
snapshot_matrix = my_splitter(snapshot_matrix)
u, s, v = np.linalg.svd(snapshot_matrix, full_matrices=False)
basis_tensor = my_trial_space.get_basis()
u = u.reshape(basis_tensor.shape)
u = my_scaler.post_scaling(u)
assert np.allclose(basis_tensor, u)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
# test with a shift, splitting, and orthogonalization
snapshots = np.random.normal(size=(3, 8, 6))
my_scaler = utils.VariableScaler('max_abs')
my_shifter = utils.AverageShifter()
my_splitter = utils.BlockSplitter([[0], [1, 2]], 3)
weighting = np.abs(np.random.normal(size=24))
my_orthogonalizer = utils.EuclideanVectorWeightedL2Orthogonalizer(weighting)
my_trial_space = rt.TrialSpaceFromScaledPOD(snapshots,
my_truncater,
my_shifter,
my_scaler,
my_splitter,
my_orthogonalizer)
shifted_snapshots, shift_vector = my_shifter(snapshots)
my_scaler = utils.VariableScaler('max_abs')
scaled_shifted_snapshots = my_scaler.pre_scaling(shifted_snapshots)
snapshot_matrix = utils.tensor_to_matrix(scaled_shifted_snapshots)
snapshot_matrix = my_splitter(snapshot_matrix)
u, s, v = np.linalg.svd(snapshot_matrix, full_matrices=False)
ushp = u.shape
basis_tensor = my_trial_space.get_basis()
u = u.reshape(basis_tensor.shape)
u = my_scaler.post_scaling(u)
u = my_orthogonalizer(u.reshape(ushp))
u = u.reshape(basis_tensor.shape)
assert np.allclose(basis_tensor, u)
assert np.allclose(my_trial_space.get_shift_vector(),
np.mean(snapshots, axis=2))
assert np.allclose(my_trial_space.get_dimension(), 12)
if __name__ == "__main__":
test_dictionary_trial_space()
test_trial_space_from_pod()
test_trial_space_from_scaled_pod()