@pytest.mark.parametrize("dtype", ["float64"])
def test_make_hermitian(ctx_factory, grid_shape, proc_shape, dtype):
if proc_shape != (1, 1, 1):
pytest.skip("test make_hermitian only on one rank")
kshape = (grid_shape[0], grid_shape[1], grid_shape[2]//2 + 1)
data = np.random.rand(*kshape) + 1j * np.random.rand(*kshape)
from pystella.fourier.rayleigh import make_hermitian
data = make_hermitian(data)
assert is_hermitian(data), "data is not hermitian"
if __name__ == "__main__":
from common import parser
parser.set_defaults(grid_shape=(32,)*3)
args = parser.parse_args()
if args.proc_shape == (1, 1, 1):
test_make_hermitian(
None, grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype,
)
for random in [True, False]:
test_generate_WKB(
None, grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing, random=random
)
test_generate(
None, grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing, random=random
max_amp = np.max(spectrum)
first_zero = np.argmax(spectrum[1:] < 1e-30 * max_amp)
first_mode_zeroed[key].append(first_zero)
for k, errs in errors.items():
errs = np.array(errs)
iters = np.arange(1, errs.shape[0] + 1)
assert (errs[10:, 0] * iters[10:] / errs[0, 0] < 1.).all(), \
"relaxation not converging at least linearly for " \
f"{grid_shape=}, {h=}, {proc_shape=}"
first_mode_zeroed = mpi.bcast(first_mode_zeroed, root=0)
for k, x in first_mode_zeroed.items():
x = np.array(list(x))[2:]
assert (x[1:] <= x[:-1]).all() and np.min(x) < np.max(x) / 5, \
f"relaxation not smoothing error {grid_shape=}, {h=}, {proc_shape=}"
if __name__ == "__main__":
from common import parser
parser.set_defaults(grid_shape=(128, ) * 3)
args = parser.parse_args()
test_relax(None,
grid_shape=args.grid_shape,
proc_shape=args.proc_shape,
h=args.h,
dtype=args.dtype,
timing=args.timing,
Solver=NewtonIterator)