def main():
results_root = pjoin(mm.rootdir(), "experiments", "parameter_sweeps", "odblock-eigs")
for _rdir in os.listdir(results_root):
results_dir = pjoin(results_root, _rdir)
data = mm.parse_run(results_dir)
if data is None:
continue
print("\nrefinement level", data['refinement'])
print("disable-ms?", data['disable-ms'])
first_m_block = 2
nblock = 5
# Just first Jacobian for now
with warnings.catch_warnings():
A = omat.import_blocks(pjoin(results_dir, "J_3_1_block"), nblock)
# Get only LLG blocks
A = omat.sub_matrix(A, first_m_block)
# Get only offdiagonal blocks
A = omat.off_diagonal_blocks(A)
evs = []
non_sym = []
for block in A.flat:
if block.nnz > 0:
ev, evec = sp.sparse.linalg.eigs(block)
evs.append(ev)
TT = block - block.T
nnz = len(filter(lambda x: x> 1e-8, TT.data))
non_sym.append(nnz)
# if nnz > 0:
# print(sp.mean(block.data))
# print(TT)
# print("overall abs", map(maxabs, evs))
# print("max real part", map(maxreal, evs))
print("max imaginary part", max(map(maximag, evs)))
print("max nnz in B - B^T", max(non_sym))
return 0
Z = sp.where(abs(Z.todense()) > precision, 1.0, 0.0)
fig, axes = plt.subplots(1, 1)
axes.imshow(Z.T, interpolation='nearest', cmap=binary)
return fig
def print_block_shapes(blocks):
for i in range(0, blocks.shape[0]):
for j in range(0, blocks.shape[1]):
print(i, j, blocks[i, j].shape)
# Load matrices
# first Jacobian of first time step
blocks = omat.import_blocks(pjoin(results_dir, "J_1_1_block"), 7)
# Get blocks of interest
F = omat.mergeblocks(blocks[2:5, 2:5])
A = blocks[0, 0].tocsr()
D = omat.mergeblocks(sp.array(blocks[0, 2:5], ndmin=2))
B = omat.mergeblocks(sp.array([[blocks[2,0]], [blocks[3,0]], [blocks[4,0]]]))
# Some info on shapes
print("F", F.shape)
print("A", A.shape)
print("B", B.shape)
print("D", D.shape)
# Calculate the schur complement
Ainv = sp.sparse.linalg.inv(A.tocsc())
