def test_smoothing():
"""test smoothing on different grids"""
for grid in [
CartesianGrid([[-2, 3]], 4),
UnitGrid(7, periodic=False),
UnitGrid(7, periodic=True),
]:
f1 = ScalarField.random_uniform(grid)
sigma = 0.5 + np.random.random()
# this assumes that the grid periodicity is the same for all axes
mode = "wrap" if grid.periodic[0] else "reflect"
s = sigma / grid.typical_discretization
expected = ndimage.gaussian_filter(f1.data, sigma=s, mode=mode)
out = f1.smooth(sigma)
np.testing.assert_allclose(out.data, expected)
out.data = 0 # reset data
f1.smooth(sigma, out=out).data
np.testing.assert_allclose(out.data, expected)
# test one simple higher order smoothing
tf = Tensor2Field.random_uniform(grid)
assert tf.data.shape == tf.smooth(1).data.shape
# test in-place smoothing
g = UnitGrid([8, 8])
f1 = ScalarField.random_normal(g)
f2 = f1.smooth(3)
f1.smooth(3, out=f1)
np.testing.assert_allclose(f1.data, f2.data)
def test_storing_collection(tmp_path):
""" test methods specific to FieldCollections in memory storage """
grid = UnitGrid([2, 2])
f1 = ScalarField.random_uniform(grid, 0.1, 0.4, label="a")
f2 = VectorField.random_uniform(grid, 0.1, 0.4, label="b")
f3 = Tensor2Field.random_uniform(grid, 0.1, 0.4, label="c")
fc = FieldCollection([f1, f2, f3])
storage_classes = {"MemoryStorage": MemoryStorage}
if module_available("h5py"):
file_path = tmp_path / "test_storage_write.hdf5"
storage_classes["FileStorage"] = functools.partial(
FileStorage, file_path)
for storage_cls in storage_classes.values():
# store some data
storage = storage_cls()
storage.start_writing(fc)
storage.append(fc, 0)
storage.append(fc, 1)
storage.end_writing()
assert storage.has_collection
assert storage.extract_field(0)[0] == f1
assert storage.extract_field(1)[0] == f2
assert storage.extract_field(2)[0] == f3
assert storage.extract_field(0)[0].label == "a"
assert storage.extract_field(0,
label="new label")[0].label == "new label"
assert storage.extract_field(0)[0].label == "a" # do not alter label
assert storage.extract_field("a")[0] == f1
assert storage.extract_field("b")[0] == f2
assert storage.extract_field("c")[0] == f3
with pytest.raises(ValueError):
storage.extract_field("nonsense")
def test_simple_plotting(example_grid):
"""test simple plotting of various fields on various grids"""
vf = VectorField.random_uniform(example_grid)
tf = Tensor2Field.random_uniform(example_grid)
sf = tf[0, 0] # test extraction of fields
fc = FieldCollection([sf, vf])
for f in [sf, vf, tf, fc]:
f.plot(action="close")
f.plot(kind="line", action="close")
if example_grid.dim >= 2:
f.plot(kind="image", action="close")
if isinstance(f, VectorField) and example_grid.dim == 2:
f.plot(kind="quiver", action="close")
f.plot(kind="streamplot", action="close")
def test_writing_images(tmp_path):
"""test writing and reading files"""
from matplotlib.pyplot import imread
grid = UnitGrid([4, 4])
s = ScalarField.random_uniform(grid, label="scalar")
v = VectorField.random_uniform(grid, label="vector")
t = Tensor2Field.random_uniform(grid, label="tensor")
path = tmp_path / "test_writing_images.png"
for f in [s, v, t]:
f.to_file(path)
# try reading the file
with path.open("br") as fp:
imread(fp)
def test_hdf_input_output(tmp_path):
"""test writing and reading files"""
grid = UnitGrid([4, 4])
s = ScalarField.random_uniform(grid, label="scalar")
v = VectorField.random_uniform(grid, label="vector")
t = Tensor2Field.random_uniform(grid, label="tensor")
col = FieldCollection([s, v, t], label="collection")
path = tmp_path / "test_hdf_input_output.hdf5"
for f in [s, v, t, col]:
f.to_file(path)
f2 = FieldBase.from_file(path)
assert f == f2
assert f.label == f2.label
assert isinstance(str(f), str)
assert isinstance(repr(f), str)
def test_dot_product():
"""test dot products between vectors and tensors"""
g = UnitGrid([3, 2])
vf = VectorField.random_normal(g)
tf = Tensor2Field.random_normal(g)
v_dot = vf.make_dot_operator()
t_dot = tf.make_dot_operator()
expected = np.einsum("i...,i...->...", vf.data, vf.data)
np.testing.assert_allclose((vf @ vf).data, expected)
np.testing.assert_allclose(v_dot(vf.data, vf.data), expected)
expected = np.einsum("i...,i...->...", vf.data, tf.data)
np.testing.assert_allclose((vf @ tf).data, expected)
np.testing.assert_allclose(v_dot(vf.data, tf.data), expected)
expected = np.einsum("ji...,i...->j...", tf.data, vf.data)
np.testing.assert_allclose((tf @ vf).data, expected)
np.testing.assert_allclose(t_dot(tf.data, vf.data), expected)
expected = np.einsum("ij...,jk...->ik...", tf.data, tf.data)
np.testing.assert_allclose((tf @ tf).data, expected)
np.testing.assert_allclose(t_dot(tf.data, tf.data), expected)
