davrot/ca_imaging_funhouse
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David Rotermund 2023-07-13 02:11:36 +02:00 committed by GitHub
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commit b0e61e13d8
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1 changed files with 31 additions and 2 deletions
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33
inspection.py
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@ -5,6 +5,7 @@ import skimage
from scipy.stats import skew
filename: str = "example_data_crop"
use_svd: bool = True
torch_device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu"
@ -18,6 +19,13 @@ print("loading done")
stored_contours = np.load("cells.npy", allow_pickle=True)
if use_svd:
data_flat = torch.flatten(
data.nan_to_num(nan=0.0).movedim(0, -1),
start_dim=0,
end_dim=1,
)
to_plot = torch.zeros(
(int(data.shape[0]), int(stored_contours.shape[0])),
device=torch_device,
@ -31,9 +39,30 @@ for id in range(0, stored_contours.shape[0]):
device=torch_device,
dtype=torch.float32,
)
if use_svd:
mask_flat = torch.flatten(
mask.unsqueeze(0).nan_to_num(nan=0.0).movedim(0, -1),
start_dim=0,
end_dim=1,
)
idx = torch.where(mask_flat > 0)[0]
temp = data_flat[idx, :].clone()
whiten_mean = torch.mean(temp, dim=-1)
temp -= whiten_mean.unsqueeze(-1)
svd_u, svd_s, _ = torch.svd_lowrank(temp, q=6)
ts = (data * mask.unsqueeze(0)).nan_to_num(nan=0.0).sum(dim=(-2, -1)) / mask.sum()
to_plot[:, id] = ts
whiten_k = (
torch.sign(svd_u[0, :]).unsqueeze(0) * svd_u / (svd_s.unsqueeze(0) + 1e-20)
)[:, 0]
temp = temp * whiten_k.unsqueeze(-1)
data_svd = temp.movedim(-1, 0).sum(dim=-1)
to_plot[:, id] = data_svd
else:
ts = (data * mask.unsqueeze(0)).nan_to_num(nan=0.0).sum(
dim=(-2, -1)
) / mask.sum()
to_plot[:, id] = ts
skew_value = skew(to_plot.cpu().numpy(), axis=0)