def test_draw_tube_spheres():
"""
Test if the function maps all the points within the radius of a segment line (defined by 2 given points) to 1, otherwise 0
The output array should have the same size of input image and binary values
Distance between a point and the segment line:
<= radius (if the point has value 1)
> radius (if the point has value 0)
"""
ngl_session = NeuroglancerSession(url=url, url_segments=url_seg)
img, _, _ = ngl_session.pull_vertex_list(2, [4], expand=True)
shape = img.shape
vertex0 = [
np.random.randint(shape[0] / 2),
np.random.randint(shape[1]),
np.random.randint(shape[2]),
]
vertex1 = [
np.random.randint(shape[0] / 2, shape[0]),
np.random.randint(shape[1]),
np.random.randint(shape[2]),
]
radius = np.random.randint(1, 4)
labels = tube_seg.draw_tube_from_spheres(img, vertex0, vertex1, radius)
line = draw.line_nd(vertex0, vertex1, endpoint=True)
coords = np.where(labels < 1)
d_bg = max(shape)
for pt in np.array(coords).T:
distance_min = min(np.sum((np.array(line).T - pt)**2, axis=1))
d_bg = min(distance_min, d_bg)
coords = np.where(labels > 0)
d_tube = 0
for pt in np.array(coords).T:
distance_min = min(np.sum((np.array(line).T - pt)**2, axis=1))
d_tube = max(distance_min, d_tube)
"""
Verify:
I. the size of output array
II. if the output is binary-valued
III. minimum distance between 0-valued points and the segment line is greater than radius
IV. maximum distance between 1-valued points and the segment line is less than or equal to radius
"""
assert labels.shape == shape
assert np.unique(labels).all() in [0, 1]
assert d_bg > radius**2
assert d_tube <= radius**2
def test_tubes_seg():
"""
Test if the function maps all the points within the radius of polyline (defined by given vertices) to 1, otherwise 0
The output array should have the same size of input image and binary values
Distance between a point and the polyline:
<= radius (if the point has value 1)
> radius (if the point has value 0)
"""
ngl_session = NeuroglancerSession(url=url, url_segments=url_seg)
img, _, _ = ngl_session.pull_vertex_list(2, [4], expand=True)
shape = img.shape
vertices = np.random.randint(min(shape), size=(4, 3))
radius = np.random.randint(1, 4)
labels = tube_seg.tubes_seg(img, vertices, radius)
point = np.empty((3, 0), dtype=int)
for i in range(3):
lines = draw.line_nd(vertices[i], vertices[i + 1], endpoint=True)
point = np.concatenate((point, np.array(lines)), axis=1)
coords = np.where(labels < 1)
d_bg = max(shape)
for pt in np.array(coords).T:
distance_min = min(np.sum((point.T - pt)**2, axis=1))
d_bg = min(distance_min, d_bg)
coords = np.where(labels > 0)
d_tube = 0
for pt in np.array(coords).T:
distance_min = min(np.sum((point.T - pt)**2, axis=1))
d_tube = max(distance_min, d_tube)
"""
Verify:
I. the size of output array
II. if the output is binary-valued
III. minimum distance between 0-valued points and the polyline is greater than radius
IV. maximum distance between 1-valued points and the polyline is less than or equal to radius
"""
assert labels.shape == shape
assert np.unique(labels).all() in [0, 1]
assert d_bg > radius**2
assert d_tube <= radius**2
def test_draw_sphere():
"""
Test if the function maps all the points located within the given radius of the given center to 1, otherwise 0
The output array should have the same size of input image and binary values
Distance between a point and the given center:
<= radius (if the point has value 1)
> radius (if the point has value 0)
"""
ngl_session = NeuroglancerSession(url=url, url_segments=url_seg)
img, _, _ = ngl_session.pull_vertex_list(2, [4], expand=True)
shape = img.shape
center = [
np.random.randint(shape[0]),
np.random.randint(shape[1]),
np.random.randint(shape[2]),
]
radius = np.random.randint(1, 4)
sphere = tube_seg.draw_sphere(shape, center, radius)
coords = np.where(sphere < 1)
d_bg = min(np.sum((np.array(coords).T - center)**2, axis=1))
coords = np.where(sphere > 0)
d_s = max(np.sum((np.array(coords).T - center)**2, axis=1))
"""
Verify:
I. the size of output array
II. if the output is binary-valued
III. minimum distance between 0-valued points and the center is greater than radius
IV. maximum distance between 1-valued points and the center is less than or equal to radius
"""
assert sphere.shape == shape
assert np.unique(sphere).all() in [0, 1]
assert d_bg > radius**2
assert d_s <= radius**2
def test_pull_v_list():
url = "s3://mouse-light-viz/precomputed_volumes/brain1"
ngl = NeuroglancerSession(url, mip=4, url_segments=url + "_segments")
img, bounds, voxel = ngl.pull_vertex_list(2, np.arange(10))
assert True
G = swc.df_to_graph(df_voxel=df_voxel)
G_s3 = swc.df_to_graph(df_voxel=df_voxel_s3)
# convert directed graph into list of paths
paths = swc.graph_to_paths(G)
paths_s3 = swc.graph_to_paths(G_s3)
# create a subset of the dataframe
url = "s3://mouse-light-viz/precomputed_volumes/brain1_lowres"
mip = 1
ngl = NeuroglancerSession(url, mip=mip, url_segments=url + "_segments")
buffer = [10, 10, 10]
subneuron_df = df_s3[0:5]
vertex_list = subneuron_df["sample"].array
img, bounds, vox_in_img_list = ngl.pull_vertex_list(2,
vertex_list,
buffer=buffer,
expand=True)
df_s3_subset = swc.generate_df_subset(df_s3[0:5], vox_in_img_list)
def test_read_s3_dataframe():
"""test if output is correct type (pd.DataFrame)"""
try:
assert isinstance(df_s3, pd.DataFrame)
except:
print("s3 still buggin")
def test_read_swc_dataframe():
"""test if output is correct type (pd.DataFrame)"""
assert isinstance(df, pd.DataFrame)