def test_zoom():
s = AnatomicalSpace("asl", resolution=(1, 1, 1))
t = AnatomicalSpace("asl", resolution=(1, 1, 2))
m = np.array(
[
[[1, 9, 15, 17, 20, 25], [1, 9, 15, 17, 20, 25]],
[[2, 18, 30, 34, 40, 50], [2, 18, 30, 34, 40, 50]],
]
).astype(np.float)
assert np.allclose(
s.map_stack_to(t, m),
np.array(
[
[[1.0, 16.20454545, 25.0], [1.0, 16.20454545, 25.0]],
[[2.0, 32.40909091, 50.0], [2.0, 32.40909091, 50.0]],
]
),
)
assert np.allclose(
s.map_stack_to(t, m, interp_order=1),
np.array(
[
[[1.0, 16.0, 25.0], [1.0, 16.0, 25.0]],
[[2.0, 32.0, 50.0], [2.0, 32.0, 50.0]],
]
),
)
def test_stack_copy(copy_flag):
source_stack = np.random.rand(3, 4, 2)
source_space = AnatomicalSpace("asl")
pre_copy = source_stack.copy()
mapped_stack = source_space.map_stack_to(
"lsp", source_stack, copy=copy_flag
)
mapped_stack[0, 1, 1] = 1
assert np.allclose(source_stack, pre_copy) == copy_flag
def test_stack_flips(src_o, tgt_o, src_shape, tgt_shape):
source_stack = create_label_array(src_o, src_shape)
target_stack = create_label_array(tgt_o, tgt_shape)
source_space = AnatomicalSpace(src_o)
# Test both mapping to AnatomicalSpace obj and origin with decorator:
for target_space in [tgt_o, AnatomicalSpace(tgt_o)]:
# Check all corners of the mapped stack:
mapped_stack = source_space.map_stack_to(target_space, source_stack)
for indexes in itertools.product([0, -1], repeat=3):
assert set(mapped_stack[indexes]) == set(target_stack[indexes])
def test_points_mapping():
points = np.array([[0, 0, 0], [10, 10, 10], [1000, 1000, 1000]])
source_space = AnatomicalSpace(
"psl", shape=(1000, 1000, 1000), resolution=(1, 1, 1)
)
target_space = AnatomicalSpace(
"asl", shape=(100, 100, 100), resolution=(10, 10, 10)
)
mapped_points = source_space.map_points_to(target_space, points)
assert np.allclose(
mapped_points,
np.array([[100.0, 0.0, 0.0], [99.0, 1.0, 1.0], [0.0, 100.0, 100.0]]),
)
def test_shape_decorator(valid_origins):
correct_shape = (20, 30, 10)
origin = "asl"
source_space = AnatomicalSpace(origin, correct_shape)
target_space = AnatomicalSpace(origin, correct_shape)
correct_mat = source_space.transformation_matrix_to(target_space)
# Check if we can overwrite none or different orientations:
for args in [(origin, None), (origin, correct_shape[::-1])]:
new_shape_mat = AnatomicalSpace(*args).transformation_matrix_to(
target_space
)
assert np.allclose(correct_mat, new_shape_mat)
def create_label_array(origin, half_shape):
"""Create stack with string labels marking regions for testing.
Parameters
----------
origin : str
Valid origin for a AnatomicalSpace obj.
half_shape : tuple
Half shape of the stack on each axis.
Returns
-------
np.array of chars
array with elements describing an anatomical location (e.g. "als", "pir)
"""
space = AnatomicalSpace(origin)
arrays = []
for lims, hs in zip(space.axes_description, half_shape):
arrays.append(
np.array(
[
lims[0],
]
* hs
+ [
lims[1],
]
* hs
)
)
x, y, z = np.meshgrid(*arrays, indexing="ij")
return char_add(char_add(x, y), z)
def test_labels_iterations(orig, lab, sect, normals):
space = AnatomicalSpace(orig)
assert space.axis_labels == lab
assert space.sections == sect
assert space.plane_normals == normals
assert space.index_pairs == ((1, 2), (0, 2), (0, 1))
def test_comparison():
assert AnatomicalSpace("asl") == AnatomicalSpace("asl")
assert AnatomicalSpace("asl") != AnatomicalSpace("als")
for k in ["offset", "shape", "resolution"]:
assert AnatomicalSpace("asl", **{k: [1, 2, 3]}) == AnatomicalSpace(
"asl", **{k: [1, 2, 3]}
)
assert AnatomicalSpace("asl", **{k: [1, 2, 3]}) != AnatomicalSpace(
"asl", **{k: [4, 5, 3]}
)
def test_point_transform(src_o, tgt_o, src_shape, tgt_shape):
# Create an array with descriptive space labels:
source_stack = create_label_array(src_o, src_shape)
# Create spaces objects:
source_space = AnatomicalSpace(src_o, shape=[s * 2 for s in src_shape])
# Test both mapping to AnatomicalSpace obj and origin with decorator:
for target_space in [tgt_o, AnatomicalSpace(tgt_o)]:
# Define grid of points sampling 4 points per axis:
grid_positions = [[1, s - 1, s + 1, s * 2 - 1] for s in src_shape]
source_pts = np.array(list(itertools.product(*grid_positions)))
# Map points and stack to target space:
mapped_pts = source_space.map_points_to(target_space, source_pts)
mapped_stack = source_space.map_stack_to(target_space, source_stack)
# Check that point coordinates keep the same values:
for p_source, p_mapped in zip(source_pts, mapped_pts):
p_s, p_m = [tuple(p.astype(np.int)) for p in [p_source, p_mapped]]
assert source_stack[p_s] == mapped_stack[p_m]
def __init__(self, path):
self.root_dir = Path(path)
self.metadata = read_json(self.root_dir / METADATA_FILENAME)
# Load structures list:
structures_list = read_json(self.root_dir / STRUCTURES_FILENAME)
self.structures_list = structures_list # keep to generate tree and dataframe views when necessary
# Add entry for file paths:
for struct in structures_list:
struct["mesh_filename"] = (
self.root_dir / MESHES_DIRNAME / "{}.obj".format(struct["id"])
)
self.structures = StructuresDict(structures_list)
# Instantiate SpaceConvention object describing the current atlas:
self.space = AnatomicalSpace(
origin=self.orientation,
shape=self.shape,
resolution=self.resolution,
)
self._reference = None
try:
self.additional_references = AdditionalRefDict(
references_list=self.metadata["additional_references"],
data_path=self.root_dir,
)
except KeyError:
warnings.warn(
"This atlas seems to be outdated as no additional_references list "
"is found in metadata!"
)
self._annotation = None
self._hemispheres = None
self._lookup = None
def test_origin_types(origin):
space = AnatomicalSpace(origin)
assert space.axes_description == ("rl", "si", "ap")
def test_init_failures(origin, errortype):
with pytest.raises(errortype) as error:
_ = AnatomicalSpace(origin)
assert str(errortype).split("'")[1] in str(error)
def test_stack_map_offset(s_dict, t_dict, f_in, result):
s = AnatomicalSpace(**s_dict)
t = AnatomicalSpace(**t_dict)
assert np.allclose(t.map_stack_to(s, np.ones((2, 2, 2)), **f_in), result)
def test_spaceconvention():
with pytest.deprecated_call():
assert SpaceConvention("asl") == AnatomicalSpace("asl")
def test_point_transform_fail():
s = AnatomicalSpace("asl")
with pytest.raises(TypeError) as error:
s.map_points_to("psl", np.array([[0, 1, 2], [0, 1, 2]]))
assert "The source space should have a shape" in str(error)
def test_iteration():
assert list(AnatomicalSpace("asl")) == ["a", "s", "l"]
def test_properties(o, axs_order, origin):
space = AnatomicalSpace(o)
assert space.axes_order == axs_order
assert space.origin == origin
assert space.origin_string == o
def test_print():
print(AnatomicalSpace("asl"))
if not datafile.exists():
raise ValueError(
"Before running this example you need to download the data for gene expression of the line brn3c:GFP from https://fishatlas.neuro.mpg.de/lines/"
)
# 1. load the data
print("Loading data")
data = imio.load.load_any(datafile)
# 2. aligned the data to the scene's atlas' axes
print("Transforming data")
scene = Scene(atlas_name="mpin_zfish_1um")
source_space = AnatomicalSpace(
"ira"
) # for more info: https://docs.brainglobe.info/bg-space/usage
target_space = scene.atlas.space
transformed_stack = source_space.map_stack_to(target_space, data)
# 3. create a Volume vedo actor and smooth
print("Creating volume")
vol = Volume(transformed_stack, origin=scene.root.origin()).medianSmooth()
# 4. Extract a surface mesh from the volume actor
print("Extracting surface")
SHIFT = [-20, 15, 30] # fine tune mesh position
mesh = (
vol.isosurface(threshold=20).c(blue_grey).decimate().clean().addPos(*SHIFT)
)
