106 lines
3 KiB
Python
106 lines
3 KiB
Python
import numpy as np
|
|
import torch
|
|
import matplotlib.pyplot as plt
|
|
import skimage
|
|
from scipy.stats import skew
|
|
|
|
filename: str = "example_data_crop"
|
|
use_svd: bool = True
|
|
show_movie: bool = True
|
|
|
|
from Anime import Anime
|
|
|
|
torch_device: torch.device = torch.device(
|
|
"cuda:0" if torch.cuda.is_available() else "cpu"
|
|
)
|
|
|
|
print("Load data")
|
|
input = np.load(filename + str("_decorrelated.npy"))
|
|
data = torch.tensor(input, device=torch_device)
|
|
del input
|
|
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,
|
|
dtype=torch.float32,
|
|
)
|
|
for id in range(0, stored_contours.shape[0]):
|
|
mask = torch.tensor(
|
|
skimage.draw.polygon2mask(
|
|
(int(data.shape[1]), int(data.shape[2])), stored_contours[id]
|
|
),
|
|
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)
|
|
|
|
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
|
|
|
|
with torch.no_grad():
|
|
if show_movie:
|
|
print("Calculate movie")
|
|
# Clean tensor
|
|
data *= 0.0
|
|
for id in range(0, stored_contours.shape[0]):
|
|
mask = torch.tensor(
|
|
skimage.draw.polygon2mask(
|
|
(int(data.shape[1]), int(data.shape[2])), stored_contours[id]
|
|
),
|
|
device=torch_device,
|
|
dtype=torch.float32,
|
|
)
|
|
# * 1.0 - mask: otherwise the overlapping outlines look bad
|
|
# Yes... reshape and indices would be faster...
|
|
data *= 1.0 - mask.unsqueeze(0)
|
|
data += mask.unsqueeze(0) * to_plot[:, id].unsqueeze(1).unsqueeze(2)
|
|
|
|
ani = Anime()
|
|
ani.show(data)
|
|
|
|
skew_value = skew(to_plot.cpu().numpy(), axis=0)
|
|
skew_idx = np.flip(skew_value.argsort())
|
|
skew_value = skew_value[skew_idx]
|
|
|
|
to_plot_np = to_plot.cpu().numpy()
|
|
to_plot_np = to_plot_np[:, skew_idx]
|
|
|
|
plt.plot(to_plot[:, 0:5].cpu())
|
|
plt.show()
|
|
|
|
block_size: int = 8
|
|
# print(to_plot.shape[1] // block_size)
|
|
for i in range(0, 4 * 8):
|
|
plt.subplot(8, 4, i + 1)
|
|
plt.plot(to_plot[:, i * block_size : (i + 1) * block_size].cpu())
|
|
plt.show()
|