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Add option to drop filtered modes #3055

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204 changes: 197 additions & 7 deletions tests/test_plugins/test_mode_solver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,12 +10,12 @@

import tidy3d as td
import tidy3d.plugins.mode.web as msweb
from tidy3d import ScalarFieldDataArray
from tidy3d import Coords, Grid, ModeIndexDataArray, ScalarFieldDataArray, ScalarModeFieldDataArray
from tidy3d.components.data.monitor_data import ModeSolverData
from tidy3d.components.mode.derivatives import create_sfactor_b, create_sfactor_f
from tidy3d.components.mode.solver import compute_modes
from tidy3d.components.mode_spec import MODE_DATA_KEYS
from tidy3d.exceptions import DataError, SetupError
from tidy3d.exceptions import DataError, SetupError, ValidationError
from tidy3d.plugins.mode import ModeSolver
from tidy3d.plugins.mode.mode_solver import MODE_MONITOR_NAME
from tidy3d.web.core.environment import Env
Expand All @@ -38,6 +38,69 @@
SOLVER_ID = "Solver-ID"


def make_fill_fraction_mode_data():
freq = np.array([2e14])
mode_spec = td.ModeSpec(num_modes=2)
monitor = td.ModeSolverMonitor(
size=(3.0, 0.0, 3.0),
center=(0.0, 0.0, 0.0),
freqs=freq,
mode_spec=mode_spec,
name="fill_fraction",
)

grid = Grid(
boundaries=Coords(
x=np.array([-1.5, -0.5, 0.5, 1.5]),
y=np.array([-0.5, 0.5]),
z=np.array([-1.5, -0.5, 0.5, 1.5]),
)
)

coords = {
"x": np.array([-1.0, 0.0, 1.0]),
"y": np.array([0.0]),
"z": np.array([-1.0, 0.0, 1.0]),
"f": freq,
"mode_index": np.arange(2),
}
shape = (3, 1, 3, 1, 2)

ex_data = np.zeros(shape, dtype=complex)
ex_data[1, 0, 1, 0, 0] = 2.0
for ix in (0, 2):
for iz in (0, 2):
ex_data[ix, 0, iz, 0, 1] = 1.0

zero_data = np.zeros(shape, dtype=complex)

fields = {
"Ex": ScalarModeFieldDataArray(ex_data, coords=coords),
"Ey": ScalarModeFieldDataArray(np.copy(zero_data), coords=coords),
"Ez": ScalarModeFieldDataArray(np.copy(zero_data), coords=coords),
"Hx": ScalarModeFieldDataArray(np.copy(zero_data), coords=coords),
"Hy": ScalarModeFieldDataArray(np.copy(zero_data), coords=coords),
"Hz": ScalarModeFieldDataArray(np.copy(zero_data), coords=coords),
}

n_complex = ModeIndexDataArray(
np.array([[1.6 + 0.0j, 1.3 + 0.0j]]),
coords={"f": freq, "mode_index": np.arange(2)},
)

data = ModeSolverData(
monitor=monitor,
symmetry=(0, 0, 0),
symmetry_center=(0.0, 0.0, 0.0),
grid_expanded=grid,
n_complex=n_complex,
**fields,
)

bounding_box = td.Box(center=(0.0, 0.0, 0.0), size=(1.0, 2.0, 1.0))
return data, bounding_box


@pytest.fixture
def mock_remote_api(monkeypatch):
def void(*args, **kwargs):
Expand Down Expand Up @@ -1343,21 +1406,41 @@ def test_modes_filter_sort():
for key in get_args(MODE_DATA_KEYS):
print(key)
# Test ascending
sort_spec = td.ModeSortSpec(sort_key=key, sort_order="ascending", track_freq=None)
sort_kwargs = {
"sort_key": key,
"sort_order": "ascending",
"track_freq": None,
}
if key == "fill_fraction_box":
sort_kwargs["bounding_box"] = td.Box(center=PLANE.center, size=(5.0, 4.0, 5.0))
sort_spec = td.ModeSortSpec(**sort_kwargs)
modes = modes.sort_modes(sort_spec)
metric = getattr(modes, key)
assert np.all(metric.diff(dim="mode_index") >= 0)

# Test descending
sort_spec = td.ModeSortSpec(sort_key=key, sort_order="descending", track_freq=None)
sort_kwargs = {
"sort_key": key,
"sort_order": "descending",
"track_freq": None,
}
if key == "fill_fraction_box":
sort_kwargs["bounding_box"] = td.Box(center=PLANE.center, size=(5.0, 4.0, 5.0))
sort_spec = td.ModeSortSpec(**sort_kwargs)
modes = modes.sort_modes(sort_spec)
metric = getattr(modes, key)
assert np.all(metric.diff(dim="mode_index") <= 0)

# Test descending with a large reference value should be the same as ascending
sort_spec = td.ModeSortSpec(
sort_key=key, sort_order="descending", sort_reference=100, track_freq=None
)
sort_kwargs = {
"sort_key": key,
"sort_order": "descending",
"sort_reference": 100,
"track_freq": None,
}
if key == "fill_fraction_box":
sort_kwargs["bounding_box"] = td.Box(center=PLANE.center, size=(5.0, 4.0, 5.0))
sort_spec = td.ModeSortSpec(**sort_kwargs)
modes = modes.sort_modes(sort_spec)
metric = getattr(modes, key)
assert np.all(metric.diff(dim="mode_index") >= 0)
Expand Down Expand Up @@ -1503,3 +1586,110 @@ def test_sort_spec_track_freq():
assert np.allclose(modes_lowest.Ex.abs, modes_lowest_retracked.Ex.abs)
assert np.all(modes_lowest.n_eff == modes_lowest_retracked.n_eff)
assert np.all(modes_lowest.n_group == modes_lowest_retracked.n_group)


def test_mode_sort_spec_drop_modes_reduces_modes():
freqs = np.array([2e14, 4e14])
mode_spec = td.ModeSpec(num_modes=3)
monitor = td.ModeSolverMonitor(
size=(1.0, 0.0, 1.0),
center=(0.0, 0.0, 0.0),
freqs=freqs,
mode_spec=mode_spec,
name="drop_modes",
)
n_complex = ModeIndexDataArray(
np.array(
[
[1.6 + 0.6j, 1.5 + 0.2j, 1.1 + 0.5j],
[1.7 + 0.4j, 1.4 + 0.3j, 1.0 + 0.1j],
]
),
coords={"f": freqs, "mode_index": np.arange(3)},
)
data = ModeSolverData(monitor=monitor, n_complex=n_complex)

sort_spec = td.ModeSortSpec(
filter_key="n_eff",
filter_reference=1.3,
filter_order="over",
sort_key="k_eff",
sort_order="ascending",
drop_modes=True,
)

sorted_data = data.sort_modes(sort_spec)

assert sorted_data.n_eff.sizes["mode_index"] == 2
assert np.allclose(sorted_data.n_eff.isel(f=0).values, [1.5, 1.6])
assert np.allclose(sorted_data.n_eff.isel(f=1).values, [1.4, 1.7])
assert sorted_data.monitor.mode_spec.num_modes == 2
assert sorted_data.monitor.mode_spec.sort_spec.drop_modes is True


def test_mode_sort_spec_drop_modes_all_filtered():
freqs = np.array([2e14, 4e14])
mode_spec = td.ModeSpec(num_modes=3)
monitor = td.ModeSolverMonitor(
size=(1.0, 0.0, 1.0),
center=(0.0, 0.0, 0.0),
freqs=freqs,
mode_spec=mode_spec,
name="drop_all",
)
n_complex = ModeIndexDataArray(
np.array(
[
[1.1 + 0.1j, 1.05 + 0.05j, 1.0 + 0.01j],
[1.1 + 0.1j, 1.05 + 0.05j, 1.0 + 0.01j],
]
),
coords={"f": freqs, "mode_index": np.arange(3)},
)
data = ModeSolverData(monitor=monitor, n_complex=n_complex)

sort_spec = td.ModeSortSpec(
filter_key="n_eff",
filter_reference=2.0,
drop_modes=True,
)

with pytest.raises(ValidationError):
_ = data.sort_modes(sort_spec)


def test_mode_sort_spec_drop_modes_requires_filter():
with pytest.raises(pydantic.ValidationError):
td.ModeSortSpec(drop_modes=True)


def test_mode_sort_spec_fill_fraction_box_filter_drops_modes():
data, bounding_box = make_fill_fraction_mode_data()

sort_spec = td.ModeSortSpec(
filter_key="fill_fraction_box",
filter_reference=0.5,
filter_order="over",
drop_modes=True,
bounding_box=bounding_box,
)

filtered = data.sort_modes(sort_spec)

assert filtered.n_eff.sizes["mode_index"] == 1
assert filtered.monitor.mode_spec.num_modes == 1

fills = data.fill_fraction(bounding_box)
assert np.isclose(fills.isel(mode_index=0, f=0).item(), 1.0)
assert np.isclose(fills.isel(mode_index=1, f=0).item(), 0.0)


def test_mode_sort_spec_fill_fraction_box_requires_bounding_box():
with pytest.raises(pydantic.ValidationError):
td.ModeSortSpec(filter_key="fill_fraction_box")


def test_mode_data_fill_fraction_box_requires_intersection():
data, _ = make_fill_fraction_mode_data()
with pytest.raises(ValidationError):
data.fill_fraction(td.Box(center=(0.0, 2.0, 0.0), size=(1.0, 1.0, 1.0)))
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