def get_mask(inp, span=5):
instance_id, instance_num = make_label(inp)
obs_centroids = {}
mask = np.zeros((inp.shape[0], inp.shape[1]))
for i in range(instance_num):
x, y = np.where(instance_id == i + 1)
min_x = np.min(x) - span
min_y = np.min(y) - span
max_x = np.max(x) + span
max_y = np.max(y) + span
cx = int(np.mean(x))
cy = int(np.mean(y))
obs_centroids[i + 1] = [cx, cy]
mask[min_x:max_x, min_y:max_y] = 1
return mask
def toydata(tmp_path):
r"""Produces toy dataset"""
# pylint: disable=too-many-locals
num_genes = 10
num_metagenes = 3
probs = 0.1
H, W = [100] * 2
spot_size = 10
gridy, gridx = np.meshgrid(np.linspace(0.0, H - 1, H),
np.linspace(0.0, W - 1, W))
yoffset, xoffset = (distr.Normal(0.0, 0.2).sample([2, num_metagenes
]).cpu().numpy())
activity = (np.cos(gridy[..., None] / 100 - 0.5 + yoffset[None, None])**2 *
np.cos(gridx[..., None] / 100 - 0.5 + xoffset[None, None])**2)
activity = torch.as_tensor(activity, dtype=torch.float32)
metagene_profiles = (distr.Normal(0.0,
1.0).expand([num_genes, num_metagenes
]).sample().exp())
label = np.zeros(activity.shape[:2]).astype(np.uint8)
counts = [torch.zeros(num_genes)]
for i, (y, x) in enumerate(
it.product(
(np.linspace(0.0, 1, H // spot_size)[1:-1] * H).astype(int),
(np.linspace(0.0, 1, W // spot_size)[1:-1] * W).astype(int),
),
1,
):
spot_activity = torch.zeros(num_metagenes)
for dy, dx in [(dx, dy) for dx, dy in ((dy - spot_size // 2,
dx - spot_size // 2)
for dy in range(spot_size)
for dx in range(spot_size))
if dy**2 + dx**2 < spot_size**2 / 4]:
label[y + dy, x + dx] = i
spot_activity += activity[y + dy, x + dx]
rate = spot_activity @ metagene_profiles.t()
counts.append(distr.NegativeBinomial(rate, probs).sample())
image = 255 * ((activity - activity.min()) /
(activity.max() - activity.min()))
image = image.round().byte().cpu().numpy()
counts = torch.stack(counts)
counts = pd.DataFrame(
counts.cpu().numpy(),
index=pd.Index(list(range(counts.shape[0]))),
columns=[f"g{i + 1}" for i in range(counts.shape[1])],
)
annotation1 = np.arange(100) // 10 % 2 == 1
annotation1 = annotation1[:, None] & annotation1[None]
annotation1, _ = make_label(annotation1)
annotation2 = 1 + (annotation1 == 0).astype(np.uint8)
filepath = tmp_path / "data.h5"
write_data(
counts,
image,
label,
type_label="ST",
annotation={
"annotation1": (
annotation1,
{x: str(x)
for x in np.unique(annotation1) if x != 0},
),
"annotation2": (annotation2, {
1: "false",
2: "true"
}),
},
auto_rotate=True,
path=str(filepath),
)
design = pd.DataFrame({"ID": 1}, index=["toydata"]).astype("category")
slide = Slide(data=STSlide(str(filepath)), iterator=FullSlideIterator)
data = Data(slides={"toydata": slide}, design=design)
dataset = Dataset(data)
dataloader = make_dataloader(dataset)
return dataloader