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initial_cell_estimate.py

@@ -66,6 +66,7 @@ while int(master_mask.sum()) > 0:
             ).astype(dtype=np.float32)
         else:
             coords = contour.astype(dtype=np.float32)
+
         # Make a mask out of the polygon
         mask = skimage.draw.polygon2mask(scale.shape, coords)
         assert mask is not None

inspection.py

@@ -0,0 +1,54 @@
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import skimage
+from scipy.stats import skew
+
+filename: str = "example_data_crop"
+
+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)
+
+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,
+    )
+
+    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)
+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()