def test_insert_polar():
"""test the `insert` method for polar systems"""
grid = PolarSymGrid(3, 5)
f = ScalarField(grid)
g = f.copy()
a = np.random.random()
for r in np.linspace(0, 3, 8).reshape(8, 1):
f.data = g.data = 0
f.insert(r, a)
assert f.integral == pytest.approx(a)
grid.make_inserter_compiled()(g.data, r, a)
np.testing.assert_array_almost_equal(f.data, g.data)
def test_localization_polar():
"""tests simple droplets localization in polar grid"""
radius = np.random.uniform(2, 3)
width = np.random.uniform(0.5, 1.5)
d1 = DiffuseDroplet((0, 0), radius, interface_width=width)
grid_radius = 6 + 2 * np.random.random()
grid = PolarSymGrid(grid_radius, 16)
field = d1.get_phase_field(grid)
emulsion = image_analysis.locate_droplets(field, refine=True)
assert len(emulsion) == 1
d2 = emulsion[0]
np.testing.assert_almost_equal(d1.position, d2.position, decimal=5)
assert d1.radius == pytest.approx(d2.radius, rel=1e-5)
assert d1.interface_width == pytest.approx(d2.interface_width, rel=1e-5)
emulsion = image_analysis.locate_droplets(ScalarField(grid))
assert len(emulsion) == 0
c = f.grid.cell_coords[2, 2]
np.testing.assert_allclose(intp(c, f._data_full), f.data[..., 2, 2])
with pytest.raises((ValueError, IndexError)):
intp(np.array([100, -100]))
res = f.make_interpolator(backend="numba", fill=45)(np.array([100, -100]))
np.testing.assert_almost_equal(res, np.full(f.data_shape, 45))
@pytest.mark.slow
@pytest.mark.parametrize(
"grid",
[
UnitGrid((6, )),
PolarSymGrid(6, 4),
SphericalSymGrid(7, 4),
CylindricalSymGrid(6, (0, 8), (7, 8)),
],
)
def test_interpolation_to_cartesian(grid):
"""test whether data is interpolated correctly to Cartesian grid"""
dim = grid.dim
vf = VectorField(grid, 2)
sf = vf[0] # test extraction of fields
fc = FieldCollection([sf, vf])
# subset
grid_cart = UnitGrid([4] * dim)
for f in [sf, fc]:
res = f.interpolate_to_grid(grid_cart)
c = f.grid.cell_coords[2, 2]
np.testing.assert_allclose(intp(c, f._data_full), f.data[..., 2, 2])
with pytest.raises((ValueError, IndexError)):
intp(np.array([100, -100]))
res = f.make_interpolator(backend="numba", fill=45)(np.array([100, -100]))
np.testing.assert_almost_equal(res, np.full(f.data_shape, 45))
@pytest.mark.slow
@pytest.mark.parametrize(
"grid",
[
UnitGrid((6,)),
PolarSymGrid(6, 4),
SphericalSymGrid(7, 4),
CylindricalSymGrid(6, (0, 8), (7, 8)),
],
)
def test_interpolation_to_cartesian(grid):
"""test whether data is interpolated correctly to Cartesian grid"""
dim = grid.dim
vf = VectorField(grid, 2)
sf = vf[0] # test extraction of fields
fc = FieldCollection([sf, vf])
# subset
grid_cart = UnitGrid([4] * dim)
for f in [sf, fc]:
res = f.interpolate_to_grid(grid_cart)