ny = 100
x_grid = 1. - np.cos(np.linspace(0, np.pi, nx))
y_grid = np.linspace(0, 1., ny)**2
Y, X = np.meshgrid(y_grid, x_grid)
# Create random snapshot data
num_vecs = 100
snapshots = [
mr.VecHandlePickle('%s/vec%d.pkl' % (out_dir, i))
for i in mr.range(num_vecs)
]
if parallel.is_rank_zero():
for i, snap in enumerate(snapshots):
snap.put(np.sin(X * i) + np.cos(Y * i))
parallel.barrier()
# Calculate DMD modes and save them to pickle files
weighted_IP = mr.InnerProductTrapz(x_grid, y_grid)
my_DMD = mr.DMDHandles(inner_product=weighted_IP)
my_DMD.compute_decomp(snapshots)
my_DMD.put_decomp('%s/eigvals.txt' % out_dir,
'%s/R_low_order_eigvecs.txt' % out_dir,
'%s/L_low_order_eigvecs.txt' % out_dir,
'%s/correlation_array_eigvals.txt' % out_dir,
'%s/correlation_array_eigvecs.txt' % out_dir)
mode_indices = [1, 4, 5, 0, 10]
modes = [
mr.VecHandlePickle('%s/mode%d.pkl' % (out_dir, i)) for i in mode_indices
]
my_DMD.compute_exact_modes(mode_indices, modes)
def __init__(self, grids, data_array):
self.grids = grids
self.data_array = data_array
self.weighted_ip = mr.InnerProductTrapz(*self.grids)
